[init] baseline7 from project_6

vllm/model_executor/layers/activation.py

@@ -0,0 +1,291 @@
+"""Custom activation functions."""
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vllm.distributed import (divide, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.utils import set_weight_attrs
+
+
+class SiluAndMul(CustomOp):
+    """An activation function for SwiGLU.
+
+    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        return F.silu(x[..., :d]) * x[..., d:]
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm import _custom_ops as ops
+
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        # ═══════════════════════════════════════════════════════════════
+        # CCCL dispatch_transform.cuh CacheAsyncConfiguration pattern:
+        #   "This computation MUST NOT depend on any runtime state of the
+        #    current API invocation (like num_items), since the result
+        #    will be cached."
+        #
+        # For element-wise transforms, the output tensor shape is
+        # deterministic from the input shape. During decode, input shape
+        # is stable (num_seqs × hidden_dim doesn't change between steps).
+        # Cache the output tensor to avoid cudaMalloc on every step.
+        #
+        # CCCL also uses spread_out_items_per_thread to dynamically
+        # adjust tile size for small problems — analogously, we only
+        # cache when shapes are stable (decode), not during prefill
+        # where shapes vary per request.
+        # ═══════════════════════════════════════════════════════════════
+        _cache_key = (output_shape, x.dtype, x.device)
+        _cached = getattr(self, '_out_cache', {}).get(_cache_key)
+        if _cached is not None and _cached.shape == output_shape:
+            out = _cached
+        else:
+            out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+            if not hasattr(self, '_out_cache'):
+                self._out_cache = {}
+            self._out_cache[_cache_key] = out
+        ops.silu_and_mul(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.silu_and_mul(out, x)
+        return out
+
+
+class GeluAndMul(CustomOp):
+    """An activation function for GeGLU.
+
+    The function computes x -> GELU(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
+        return: (batch_size, seq_len, d) or (num_tokens, d)
+    """
+
+    def __init__(self, approximate: str = "none"):
+        super().__init__()
+        self.approximate = approximate
+        if approximate not in ("none", "tanh"):
+            raise ValueError(f"Unknown approximate mode: {approximate}")
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        return F.gelu(x[..., :d], approximate=self.approximate) * x[..., d:]
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm import _custom_ops as ops
+
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        # ═══════════════════════════════════════════════════════════════
+        # CCCL dispatch_transform.cuh CacheAsyncConfiguration pattern:
+        #   Output tensor shape is deterministic from input shape.
+        #   During decode, shapes are stable → cache to avoid cudaMalloc.
+        #   CCCL: "This computation MUST NOT depend on runtime state ...
+        #          since the result will be cached."
+        # ═══════════════════════════════════════════════════════════════
+        _cache_key = (output_shape, x.dtype, x.device)
+        _cached = getattr(self, '_out_cache', {}).get(_cache_key)
+        if _cached is not None and _cached.shape == output_shape:
+            out = _cached
+        else:
+            out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+            if not hasattr(self, '_out_cache'):
+                self._out_cache = {}
+            self._out_cache[_cache_key] = out
+        if self.approximate == "none":
+            ops.gelu_and_mul(out, x)
+        elif self.approximate == "tanh":
+            ops.gelu_tanh_and_mul(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        if self.approximate == "none":
+            ops.gelu_and_mul(out, x)
+        elif self.approximate == "tanh":
+            ops.gelu_tanh_and_mul(out, x)
+        return out
+
+    def extra_repr(self) -> str:
+        return f'approximate={repr(self.approximate)}'
+
+
+class NewGELU(CustomOp):
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        c = math.sqrt(2.0 / math.pi)
+        return 0.5 * x * (1.0 + torch.tanh(c *
+                                           (x + 0.044715 * torch.pow(x, 3.0))))
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm import _custom_ops as ops
+
+        out = torch.empty_like(x)
+        ops.gelu_new(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        return ops.gelu_new(x)
+
+
+class FastGELU(CustomOp):
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 *
+                                           (1.0 + 0.044715 * x * x)))
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm import _custom_ops as ops
+
+        out = torch.empty_like(x)
+        ops.gelu_fast(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        return ops.gelu_fast(x)
+
+
+class QuickGELU(CustomOp):
+
+    # https://github.com/huggingface/transformers/blob/main/src/transformers/activations.py#L90
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        return x * torch.sigmoid(1.702 * x)
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm import _custom_ops as ops
+
+        out = torch.empty_like(x)
+        ops.gelu_quick(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm._ipex_ops import ipex_ops as ops
+
+        out = torch.empty_like(x)
+        ops.gelu_quick(out, x)
+        return out
+
+    # TODO implement forward_xpu for QuickGELU
+    # def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+
+
+class ReLUSquaredActivation(CustomOp):
+    """
+    Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2
+    """
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        return torch.square(F.relu(x))
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        return self.forward_native(x)
+
+
+class ScaledActivation(nn.Module):
+    """An activation function with post-scale parameters.
+
+    This is used for some quantization methods like AWQ.
+    """
+
+    def __init__(
+        self,
+        act_module: nn.Module,
+        intermediate_size: int,
+        input_is_parallel: bool = True,
+        params_dtype: Optional[torch.dtype] = None,
+    ):
+        super().__init__()
+        self.act = act_module
+        self.input_is_parallel = input_is_parallel
+        if input_is_parallel:
+            tp_size = get_tensor_model_parallel_world_size()
+            intermediate_size_per_partition = divide(intermediate_size,
+                                                     tp_size)
+        else:
+            intermediate_size_per_partition = intermediate_size
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.scales = nn.Parameter(
+            torch.empty(intermediate_size_per_partition, dtype=params_dtype))
+        set_weight_attrs(self.scales, {"weight_loader": self.weight_loader})
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.act(x) / self.scales
+
+    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
+        param_data = param.data
+        if self.input_is_parallel:
+            tp_rank = get_tensor_model_parallel_rank()
+            shard_size = param_data.shape[0]
+            start_idx = tp_rank * shard_size
+            loaded_weight = loaded_weight.narrow(0, start_idx, shard_size)
+        assert param_data.shape == loaded_weight.shape
+        param_data.copy_(loaded_weight)
+
+
+_ACTIVATION_REGISTRY = {
+    "gelu": nn.GELU(),
+    "gelu_fast": FastGELU(),
+    "gelu_new": NewGELU(),
+    "gelu_pytorch_tanh": nn.GELU(approximate="tanh"),
+    "relu": nn.ReLU(),
+    "relu2": ReLUSquaredActivation(),
+    "quick_gelu": QuickGELU(),
+}
+
+
+def get_act_fn(
+    act_fn_name: str,
+    quant_config: Optional[QuantizationConfig] = None,
+    intermediate_size: Optional[int] = None,
+    input_is_parallel: bool = True,
+    params_dtype: Optional[torch.dtype] = None,
+) -> nn.Module:
+    """Get an activation function by name."""
+    act_fn_name = act_fn_name.lower()
+    if act_fn_name not in _ACTIVATION_REGISTRY:
+        raise ValueError(
+            f"Activation function {act_fn_name!r} is not supported.")
+
+    act_fn = _ACTIVATION_REGISTRY[act_fn_name]
+    if (quant_config is not None
+            and act_fn_name in quant_config.get_scaled_act_names()):
+        if intermediate_size is None:
+            raise ValueError("intermediate_size must be specified for scaled "
+                             "activation functions.")
+        return ScaledActivation(act_fn, intermediate_size, input_is_parallel,
+                                params_dtype)
+    return act_fn

vllm/model_executor/layers/fused_moe/__init__.py

@@ -0,0 +1,26 @@
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
+from vllm.triton_utils import HAS_TRITON
+
+__all__ = [
+    "FusedMoE",
+    "FusedMoEMethodBase",
+    "FusedMoeWeightScaleSupported",
+]
+
+# if HAS_TRITON:
+from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+    fused_marlin_moe, single_marlin_moe)
+from vllm.model_executor.layers.fused_moe.fused_moe import (
+    fused_experts, fused_moe, fused_topk, get_config_file_name,
+    grouped_topk)
+
+__all__ += [
+    "fused_marlin_moe",
+    "single_marlin_moe",
+    "fused_moe",
+    "fused_topk",
+    "fused_experts",
+    "get_config_file_name",
+    "grouped_topk",
+]

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -0,0 +1,313 @@
+"""Fused MoE utilities for GPTQ."""
+import functools
+from typing import Any, Dict, Optional
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.fused_moe.fused_moe import (
+    fused_topk, moe_align_block_size, try_get_optimal_moe_config)
+from vllm.scalar_type import scalar_types
+
+
+def get_scalar_type(num_bits: int, has_zp: bool):
+    if has_zp:
+        assert num_bits == 4
+        return scalar_types.uint4
+    else:
+        return scalar_types.uint4b8 if num_bits == 4 else scalar_types.uint8b128
+
+
+def single_marlin_moe(
+    hidden_states: torch.Tensor,
+    w: torch.Tensor,
+    scales: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    g_idx: Optional[torch.Tensor] = None,
+    sort_indices: Optional[torch.Tensor] = None,
+    w_zeros: Optional[torch.Tensor] = None,
+    override_config: Optional[Dict[str, Any]] = None,
+    num_bits: int = 8,
+    is_k_full: bool = True,
+) -> torch.Tensor:
+    """
+    This function computes the multiplication of hidden_states with expert
+    weights used in Marlin MoE, using weights w and top-k gating mechanism.
+    Its purpose is testing and debugging the fused MoE kernel.
+
+    Parameters:
+    - hidden_states (torch.Tensor): The input tensor to the Marlin Mul.
+    - w (torch.Tensor): The set of expert weights.
+    - scales (torch.Tensor): The quantization scales.
+    - gating_output (torch.Tensor): The output of the gating operation
+        (before softmax).
+    - g_idx (Optional[torch.Tensor]): Optional act_order indices.
+    - sort_indices (Optional[torch.Tensor]): Optional act_order input
+      permutation.
+    - topk (int): The number of top-k experts to select.
+    - renormalize (bool): If True, renormalize the top-k weights to sum to 1.
+    - w_zeros (Optional[torch.Tensor]): Optional zero points to be used for w.
+    - override_config (Optional[Dict[str, Any]]): Optional override
+        for the kernel configuration.
+    - num_bits (bool): The number of bits in expert weights quantization.
+
+    Returns:
+    - torch.Tensor: The output tensor after applying the MoE layer.
+    """
+    # Check constraints.
+    assert hidden_states.shape[0] == gating_output.shape[0], (
+        "Number of tokens mismatch")
+    assert hidden_states.shape[1] == w.shape[1] * 16, "Hidden size mismatch"
+    assert gating_output.shape[1] == w.shape[0], "Number of experts mismatch"
+    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
+    assert w.is_contiguous(), "Expert weights must be contiguous"
+    assert hidden_states.dtype == torch.float16
+    assert num_bits in [4, 8]
+
+    M, K = hidden_states.shape
+    E = w.shape[0]
+    N = w.shape[2] // (num_bits // 2)
+
+    topk_weights, topk_ids = fused_topk(hidden_states, gating_output, topk,
+                                        renormalize)
+
+    # This might not be an optimal config for a single MMM
+    get_config_func = functools.partial(try_get_optimal_moe_config,
+                                        w.shape,
+                                        w.shape,
+                                        topk_ids.shape[1],
+                                        None,
+                                        override_config=override_config,
+                                        is_marlin=True)
+    config = get_config_func(M)
+
+    block_size_m = config['BLOCK_SIZE_M']
+
+    sorted_token_ids, _, _ = moe_align_block_size(topk_ids, block_size_m, E)
+
+    max_workspace_size = (N // 64) * 16
+    workspace = torch.zeros(max_workspace_size,
+                            dtype=torch.int,
+                            device=hidden_states.device,
+                            requires_grad=False)
+
+    has_zero_point = w_zeros is not None
+    if w_zeros is None:
+        w_zeros = torch.empty((0, 0),
+                              dtype=hidden_states.dtype,
+                              device=hidden_states.device,
+                              requires_grad=False)
+
+    if g_idx is None:
+        g_idx = torch.empty((0, 0),
+                            dtype=torch.int32,
+                            device=hidden_states.device,
+                            requires_grad=False)
+
+    if sort_indices is None:
+        sort_indices = torch.empty((0),
+                                   dtype=torch.int32,
+                                   device=hidden_states.device,
+                                   requires_grad=False)
+
+    scalar_type = get_scalar_type(num_bits, has_zero_point)
+
+    intermediate_cache = torch.ops._moe_C.marlin_gemm_moe(
+        hidden_states, w, sorted_token_ids, topk_weights, topk_ids, scales,
+        w_zeros, g_idx, sort_indices, workspace, scalar_type, M, N, K,
+        is_k_full, E, topk, block_size_m, True, False)
+
+    return torch.sum(intermediate_cache.view(*intermediate_cache.shape), dim=1)
+
+
+def fused_marlin_moe(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    w1_scale: torch.Tensor,
+    w2_scale: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    g_idx1: Optional[torch.Tensor] = None,
+    g_idx2: Optional[torch.Tensor] = None,
+    sort_indices1: Optional[torch.Tensor] = None,
+    sort_indices2: Optional[torch.Tensor] = None,
+    w1_zeros: Optional[torch.Tensor] = None,
+    w2_zeros: Optional[torch.Tensor] = None,
+    override_config: Optional[Dict[str, Any]] = None,
+    num_bits: int = 8,
+    is_k_full: bool = True,
+) -> torch.Tensor:
+    """
+    This function computes a Mixture of Experts (MoE) layer using two sets of
+    weights, w1 and w2, and top-k gating mechanism.
+
+    Parameters:
+    - hidden_states (torch.Tensor): The input tensor to the MoE layer.
+    - w1 (torch.Tensor): The first set of expert weights.
+    - w2 (torch.Tensor): The second set of expert weights.
+    - w1_scale (torch.Tensor): Scale to be used for w1.
+    - w2_scale (torch.Tensor): Scale to be used for w2.
+    - gating_output (torch.Tensor): The output of the gating operation
+        (before softmax).
+    - g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.
+    - g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.
+    - sort_indices1 (Optional[torch.Tensor]): The first act_order input
+        permutation.
+    - sort_indices2 (Optional[torch.Tensor]): The second act_order input
+        permutation.
+    - topk_weights (torch.Tensor): Top-k weights.
+    - topk_ids (torch.Tensor): Indices of topk-k elements.
+    - override_config (Optional[Dict[str, Any]]): Optional override
+        for the kernel configuration.
+    - w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.
+    - w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.
+    - num_bits (bool): The number of bits in expert weights quantization.
+
+    Returns:
+    - torch.Tensor: The output tensor after applying the MoE layer.
+    """
+    # Check constraints.
+    assert hidden_states.shape[0] == gating_output.shape[
+        0], "Number of tokens mismatch"
+    assert hidden_states.shape[
+        1] == w1.shape[1] * 16, "Hidden size mismatch w1"
+    assert hidden_states.shape[1] == w2.shape[2] // (
+        num_bits // 2), "Hidden size mismatch w2"
+    assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch"
+    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
+    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
+    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
+    assert hidden_states.dtype == torch.float16
+    assert num_bits in [4, 8]
+
+    has_no_act_order = (g_idx1 is None and g_idx2 is None
+                        and sort_indices1 is None and sort_indices2 is None)
+    has_all_act_order = (g_idx1 is not None and g_idx2 is not None
+                         and sort_indices1 is not None
+                         and sort_indices2 is not None)
+    assert has_no_act_order or has_all_act_order, (
+        "g_idx and sorted_indices "
+        "must be all not None or must be all None")
+
+    has_no_zp = w1_zeros is None and w2_zeros is None
+    has_all_zp = w1_zeros is not None and w2_zeros is not None
+    assert has_no_zp or has_all_zp, ("zero points must be both not None or "
+                                     "must be both None")
+
+    M, K = hidden_states.shape
+    E = w1.shape[0]
+    N = w2.shape[1] * 16
+    topk = topk_ids.shape[1]
+
+    get_config_func = functools.partial(
+        try_get_optimal_moe_config,
+        w1.shape,
+        w2.shape,
+        topk_ids.shape[1],
+        None,
+        override_config=override_config,
+        is_marlin=True,
+    )
+    config = get_config_func(M)
+
+    block_size_m = config["BLOCK_SIZE_M"]
+
+    sorted_token_ids, _, _ = moe_align_block_size(topk_ids, block_size_m, E)
+
+    max_workspace_size = (max(2 * N, K) // 64) * 16
+    workspace = torch.zeros(max_workspace_size,
+                            dtype=torch.int,
+                            device="cuda",
+                            requires_grad=False)
+
+    if has_no_zp:
+        w1_zeros = torch.empty((0, 0),
+                               dtype=hidden_states.dtype,
+                               device=hidden_states.device,
+                               requires_grad=False)
+        w2_zeros = torch.empty((0, 0),
+                               dtype=hidden_states.dtype,
+                               device=hidden_states.device,
+                               requires_grad=False)
+
+    if has_no_act_order:
+        g_idx1 = torch.empty((0, 0),
+                             dtype=torch.int32,
+                             device=hidden_states.device,
+                             requires_grad=False)
+        g_idx2 = torch.empty((0, 0),
+                             dtype=torch.int32,
+                             device=hidden_states.device,
+                             requires_grad=False)
+        sort_indices1 = torch.empty((0),
+                                    dtype=torch.int32,
+                                    device=hidden_states.device,
+                                    requires_grad=False)
+        sort_indices2 = torch.empty((0, 0),
+                                    dtype=torch.int32,
+                                    device=hidden_states.device,
+                                    requires_grad=False)
+
+    scalar_type1 = get_scalar_type(num_bits, has_all_zp)
+    scalar_type2 = get_scalar_type(num_bits, has_all_zp)
+
+    intermediate_cache2 = torch.empty(
+        (M * topk_ids.shape[1], N),
+        device=hidden_states.device,
+        dtype=hidden_states.dtype,
+    )
+
+    intermediate_cache1 = torch.ops._moe_C.marlin_gemm_moe(
+        hidden_states,
+        w1,
+        sorted_token_ids,
+        topk_weights,
+        topk_ids,
+        w1_scale,
+        w1_zeros,
+        g_idx1,
+        sort_indices1,
+        workspace,
+        scalar_type1,
+        M,
+        2 * N,
+        K,
+        is_k_full,
+        E,
+        topk,
+        block_size_m,
+        True,
+        False,
+    )
+
+    ops.silu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, 2 * N))
+
+    intermediate_cache3 = torch.ops._moe_C.marlin_gemm_moe(
+        intermediate_cache2,
+        w2,
+        sorted_token_ids,
+        topk_weights,
+        topk_ids,
+        w2_scale,
+        w2_zeros,
+        g_idx2,
+        sort_indices2,
+        workspace,
+        scalar_type2,
+        M,
+        K,
+        N,
+        is_k_full,
+        E,
+        topk,
+        block_size_m,
+        False,
+        True,
+    )
+
+    return torch.sum(intermediate_cache3.view(*intermediate_cache3.shape),
+                     dim=1)

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -0,0 +1,763 @@
+"""Fused MoE kernel."""
+import functools
+import json
+import os
+from typing import Any, Callable, Dict, Optional, Tuple
+
+import torch
+import triton
+import triton.language as tl
+
+import vllm.envs as envs
+from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+
+logger = init_logger(__name__)
+
+# Module-level cache for MoE intermediate tensors.
+# Eliminates 192 torch.empty (CUDA malloc) calls per decode step by reusing
+# buffers across the 64 MoE layer invocations within a single forward pass.
+# Key insight from CCCL dispatch_reduce.cuh: NVIDIA pre-allocates temp_storage
+# once and reuses it across kernel invocations rather than re-allocating.
+_moe_intermediate_cache = {}
+
+
+def _get_or_alloc(name, shape, dtype, device):
+    """Get a cached tensor or allocate a new one. Reuses if shape fits."""
+    key = (name, dtype, device)
+    cached = _moe_intermediate_cache.get(key)
+    if cached is not None and cached.shape == shape:
+        return cached
+    # Shape changed (different M, different topk) — reallocate
+    t = torch.empty(shape, dtype=dtype, device=device)
+    _moe_intermediate_cache[key] = t
+    return t
+
+
+@triton.jit
+def fused_moe_kernel(
+        # Pointers to matrices
+        a_ptr,
+        b_ptr,
+        c_ptr,
+        a_scale_ptr,
+        b_scale_ptr,
+        topk_weights_ptr,
+        sorted_token_ids_ptr,
+        expert_ids_ptr,
+        num_tokens_post_padded_ptr,
+        # Matrix dimensions
+        N,
+        K,
+        EM,
+        num_valid_tokens,
+        # The stride variables represent how much to increase the ptr by when
+        # moving by 1 element in a particular dimension. E.g. `stride_am` is
+        # how much to increase `a_ptr` by to get the element one row down
+        # (A has M rows).
+        stride_am,
+        stride_ak,
+        stride_be,
+        stride_bk,
+        stride_bn,
+        stride_cm,
+        stride_cn,
+        stride_bse,
+        stride_bsn,
+        # Meta-parameters
+        BLOCK_SIZE_M: tl.constexpr,
+        BLOCK_SIZE_N: tl.constexpr,
+        BLOCK_SIZE_K: tl.constexpr,
+        GROUP_SIZE_M: tl.constexpr,
+        MUL_ROUTED_WEIGHT: tl.constexpr,
+        top_k: tl.constexpr,
+        compute_type: tl.constexpr,
+        use_fp8_w8a8: tl.constexpr,
+        use_int8_w8a16: tl.constexpr):
+    """
+    Implements the fused computation for a Mixture of Experts (MOE) using
+    token and expert matrices.
+
+    Key Parameters:
+    - A: The input tensor representing tokens with shape (*, K), where '*' can
+        be any shape representing batches and K is the feature dimension of
+        each token.
+    - B: The stacked MOE weight tensor with shape (E, N, K), where E is
+        the number of experts, K is the input feature dimension, and N is
+        the output feature dimension.
+    - C: The output cache tensor with shape (M, topk, N), where M is the
+        total number of tokens post padding, topk is the number of times
+        each token is repeated, and N is the output feature dimension.
+    - sorted_token_ids: A tensor containing the sorted indices of tokens,
+        repeated topk times and arranged by the expert index they are
+        assigned to.
+    - expert_ids: A tensor containing the indices of the expert for each
+        block. It determines which expert matrix from B should be used for
+        each block in A.
+    This kernel performs the multiplication of a token by its corresponding
+    expert matrix as determined by `expert_ids`. The sorting of
+    `sorted_token_ids` by expert index and padding ensures divisibility by
+    BLOCK_SIZE_M, which is necessary to maintain consistency in block matrix
+    multiplication across different blocks processed by the same expert.
+    """
+    # -----------------------------------------------------------
+    # Map program ids `pid` to the block of C it should compute.
+    # This is done in a grouped ordering to promote L2 data reuse.
+    pid = tl.program_id(axis=0)
+    num_pid_m = tl.cdiv(EM, BLOCK_SIZE_M)
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+    num_pid_in_group = GROUP_SIZE_M * num_pid_n
+    group_id = pid // num_pid_in_group
+    first_pid_m = group_id * GROUP_SIZE_M
+    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+    pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
+    pid_n = (pid % num_pid_in_group) // group_size_m
+
+    # ----------------------------------------------------------
+    # Create pointers for the first blocks of A and B.
+    # We will advance this pointer as we move in the K direction
+    # and accumulate
+    # `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
+    # `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
+    num_tokens_post_padded = tl.load(num_tokens_post_padded_ptr)
+    if pid_m * BLOCK_SIZE_M >= num_tokens_post_padded:
+        return
+    offs_token_id = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_token = tl.load(sorted_token_ids_ptr + offs_token_id)
+    token_mask = offs_token < num_valid_tokens
+
+    offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = a_ptr + (offs_token[:, None] // top_k * stride_am +
+                      offs_k[None, :] * stride_ak)
+
+    off_experts = tl.load(expert_ids_ptr + pid_m)
+    b_ptrs = b_ptr + off_experts * stride_be + (offs_k[:, None] * stride_bk +
+                                                offs_bn[None, :] * stride_bn)
+    if use_int8_w8a16:
+        b_scale_ptrs = b_scale_ptr + off_experts * stride_bse + offs_bn[
+            None, :] * stride_bsn
+        b_scale = tl.load(b_scale_ptrs)
+
+    if use_fp8_w8a8:
+        a_scale = tl.load(a_scale_ptr)
+        b_scale = tl.load(b_scale_ptr + off_experts)
+
+    # -----------------------------------------------------------
+    # Iterate to compute a block of the C matrix.
+    # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
+    # of fp32 values for higher accuracy.
+    # `accumulator` will be converted back to fp16 after the loop.
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+
+    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
+        # Load the next block of A and B, generate a mask by checking the
+        # K dimension.
+        a = tl.load(a_ptrs,
+                    mask=token_mask[:, None] &
+                    (offs_k[None, :] < K - k * BLOCK_SIZE_K),
+                    other=0.0)
+        b = tl.load(b_ptrs,
+                    mask=offs_k[:, None] < K - k * BLOCK_SIZE_K,
+                    other=0.0)
+        # We accumulate along the K dimension.
+        if use_int8_w8a16:
+            accumulator = tl.dot(a, b.to(compute_type), acc=accumulator)
+        elif use_fp8_w8a8:
+            accumulator = tl.dot(a, b, acc=accumulator)
+        else:
+            accumulator += tl.dot(a, b)
+        # Advance the ptrs to the next K block.
+        a_ptrs += BLOCK_SIZE_K * stride_ak
+        b_ptrs += BLOCK_SIZE_K * stride_bk
+
+    if MUL_ROUTED_WEIGHT:
+        moe_weight = tl.load(topk_weights_ptr + offs_token,
+                             mask=token_mask,
+                             other=0)
+        accumulator = accumulator * moe_weight[:, None]
+    if use_int8_w8a16:
+        accumulator = (accumulator * b_scale).to(compute_type)
+    elif use_fp8_w8a8:
+        accumulator = (accumulator * a_scale * b_scale).to(compute_type)
+    else:
+        accumulator = accumulator.to(compute_type)
+    # -----------------------------------------------------------
+    # Write back the block of the output
+    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = c_ptr + stride_cm * offs_token[:, None] + stride_cn * offs_cn[
+        None, :]
+    c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
+    tl.store(c_ptrs, accumulator, mask=c_mask)
+
+
+def moe_align_block_size(
+        topk_ids: torch.Tensor, block_size: int,
+        num_experts: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Aligns the token distribution across experts to be compatible with block
+    size for matrix multiplication.
+
+    Parameters:
+    - topk_ids: A tensor of shape [total_tokens, top_k] representing the
+        top-k expert indices for each token.
+    - block_size: The block size used in block matrix multiplication.
+    - num_experts: The total number of experts.
+
+    Returns:
+    - sorted_token_ids: A tensor containing the sorted token indices according
+        to their allocated expert.
+    - expert_ids: A tensor indicating the assigned expert index for each block.
+    - num_tokens_post_padded: The total number of tokens after padding,
+        ensuring divisibility by block_size.
+
+    This function pads the number of tokens that each expert needs to process
+    so that it is divisible by block_size.
+    Padding ensures that during block matrix multiplication, the dimensions
+    align correctly.
+
+    Example:
+    Given topk_ids = [[2, 3, 4], [1, 2, 4], [1, 3, 4], [1, 2, 3]],
+    block_size = 4, and num_experts = 4:
+    - We initially have 12 tokens (after repeating 'top_k' times) and 4 experts,
+        with each expert needing to process 3 tokens.
+    - As block_size is 4, we pad 1 token for each expert.
+    - First, flatten topk_ids to [2, 3, 4, 1, 2, 4, 1, 3, 4, 1, 2, 3].
+    - Then append padding tokens [12, 12, 12, 12] for each block.
+    - After sorting by expert index, we obtain token_ids
+        [3, 6, 9, 12, 0, 4, 10, 12, 1, 7, 11, 12, 2, 5, 8, 12].
+        Tokens 12 are non-existent (padding) and are ignored in
+        the subsequent matrix multiplication.
+    - The padding ensures that the total number of tokens is now divisible
+        by block_size for proper block matrix operations.
+    """
+    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
+    # max_num_m_blocks = triton.cdiv(max_num_tokens_padded, block_size)
+    max_num_m_blocks = topk_ids.numel() + num_experts
+
+    # Pre-allocate sort buffers. During decode, topk_ids shape is stable across
+    # all 64 MoE layers and across decode steps (same num_seqs × topk).
+    # Reusing these tensors eliminates 192 CUDA mallocs per decode step
+    # (3 tensors × 64 layers). Pattern from CCCL dispatch_reduce.cuh:
+    # alias_temporaries pre-allocates once, reuses across invocations.
+    _align_key = ("moe_align", max_num_tokens_padded, max_num_m_blocks,
+                  topk_ids.device)
+    cached_align = _moe_intermediate_cache.get(_align_key)
+    if (cached_align is not None
+            and cached_align[0].shape[0] >= max_num_tokens_padded
+            and cached_align[1].shape[0] >= max_num_m_blocks):
+        sorted_ids, expert_ids_buf, num_tokens_post_pad = cached_align
+    else:
+        sorted_ids = torch.empty((max_num_tokens_padded, ),
+                                 dtype=torch.int32,
+                                 device=topk_ids.device)
+        expert_ids_buf = torch.empty((max_num_m_blocks, ),
+                                     dtype=torch.int32,
+                                     device=topk_ids.device)
+        num_tokens_post_pad = torch.empty((1),
+                                          dtype=torch.int32,
+                                          device=topk_ids.device)
+        _moe_intermediate_cache[_align_key] = (sorted_ids, expert_ids_buf,
+                                               num_tokens_post_pad)
+
+    sorted_ids.fill_(topk_ids.numel())
+    ops.moe_align_block_size(topk_ids, num_experts, block_size, sorted_ids,
+                             expert_ids_buf, num_tokens_post_pad)
+    return sorted_ids, expert_ids_buf, num_tokens_post_pad
+
+
+def invoke_fused_moe_kernel(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor,
+                            A_scale: Optional[torch.Tensor],
+                            B_scale: Optional[torch.Tensor],
+                            topk_weights: torch.Tensor, topk_ids: torch.Tensor,
+                            sorted_token_ids: torch.Tensor,
+                            expert_ids: torch.Tensor,
+                            num_tokens_post_padded: torch.Tensor,
+                            mul_routed_weight: bool, top_k: int,
+                            config: Dict[str, Any], compute_type: tl.dtype,
+                            use_fp8_w8a8: bool, use_int8_w8a16: bool) -> None:
+    ops.invoke_fused_moe_kernel(A,B,C,A_scale,B_scale,topk_weights,topk_ids,sorted_token_ids,expert_ids,num_tokens_post_padded,mul_routed_weight,top_k,config,compute_type,use_fp8_w8a8,use_int8_w8a16)
+    return
+    assert topk_weights.stride(1) == 1
+    assert sorted_token_ids.stride(0) == 1
+
+    if use_fp8_w8a8:
+        A, A_scale = ops.scaled_fp8_quant(A, A_scale)
+        assert B_scale is not None
+    elif use_int8_w8a16:
+        assert B_scale is not None
+    else:
+        assert A_scale is None
+        assert B_scale is None
+
+    grid = lambda META: (triton.cdiv(sorted_token_ids.shape[0], META[
+        'BLOCK_SIZE_M']) * triton.cdiv(B.shape[1], META['BLOCK_SIZE_N']), )
+
+    fused_moe_kernel[grid](
+        A,
+        B,
+        C,
+        A_scale,
+        B_scale,
+        topk_weights,
+        sorted_token_ids,
+        expert_ids,
+        num_tokens_post_padded,
+        B.shape[1],
+        B.shape[2],
+        sorted_token_ids.shape[0],
+        topk_ids.numel(),
+        A.stride(0),
+        A.stride(1),
+        B.stride(0),
+        B.stride(2),
+        B.stride(1),
+        C.stride(1),
+        C.stride(2),
+        B_scale.stride(0) if B_scale is not None and use_int8_w8a16 else 0,
+        B_scale.stride(1) if B_scale is not None and use_int8_w8a16 else 0,
+        MUL_ROUTED_WEIGHT=mul_routed_weight,
+        top_k=top_k,
+        compute_type=compute_type,
+        use_fp8_w8a8=use_fp8_w8a8,
+        use_int8_w8a16=use_int8_w8a16,
+        **config,
+    )
+
+
+def get_config_file_name(E: int, N: int, dtype: Optional[str]) -> str:
+    device_name = current_platform.get_device_name().replace(" ", "_")
+    dtype_selector = "" if not dtype else f",dtype={dtype}"
+    return f"E={E},N={N},device_name={device_name}{dtype_selector}.json"
+
+
+@functools.lru_cache
+def get_moe_configs(E: int, N: int,
+                    dtype: Optional[str]) -> Optional[Dict[int, Any]]:
+    """
+    Return optimized configurations for the fused MoE kernel.
+
+    The return value will be a dictionary that maps an irregular grid of
+    batch sizes to configurations of the fused_moe kernel. To evaluate the
+    kernel on a given batch size bs, the closest batch size in the grid should
+    be picked and the associated configuration chosen to invoke the kernel.
+    """
+
+    # First look up if an optimized configuration is available in the configs
+    # directory
+    json_file_name = get_config_file_name(E, N, dtype)
+
+    config_file_path = os.path.join(
+        os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name)
+    if os.path.exists(config_file_path):
+        with open(config_file_path) as f:
+            logger.info("Using configuration from %s for MoE layer.",
+                        config_file_path)
+            # If a configuration has been found, return it
+            return {int(key): val for key, val in json.load(f).items()}
+
+    # If no optimized configuration is available, we will use the default
+    # configuration
+    logger.warning(
+        ("Using default MoE config. Performance might be sub-optimal! "
+         "Config file not found at %s"), config_file_path)
+    return None
+
+
+def get_default_config(
+    M: int,
+    E: int,
+    N: int,
+    K: int,
+    topk: int,
+    dtype: Optional[str],
+    is_marlin: bool,
+) -> Dict[str, int]:
+    config = {
+        'BLOCK_SIZE_M': 64,
+        'BLOCK_SIZE_N': 64,
+        'BLOCK_SIZE_K': 32,
+        'GROUP_SIZE_M': 8
+    }
+    # A heuristic: fused marlin works faster with this config for small M
+    if M <= E or (is_marlin and M <= 32):
+        config = {
+            'BLOCK_SIZE_M': 16,
+            'BLOCK_SIZE_N': 32,
+            'BLOCK_SIZE_K': 64,
+            'GROUP_SIZE_M': 1
+        }
+    numel = M * topk
+
+    # CCCL GridEvenShare dispatch (grid_even_share.cuh + dispatch_batch_memcpy.cuh):
+    #
+    # dispatch_batch_memcpy uses two-level dispatch:
+    #   - Small buffers: one CTA handles multiple buffers (warp-level copy)
+    #   - Large buffers: multiple CTAs collaborate on one buffer (block-level)
+    #
+    # Applied to MoE: "buffers" = per-expert token groups after routing.
+    # Qwen3.6: 256 experts, top-8 → ~8 tokens per expert during decode (M=1).
+    # During prefill (M=4096): 4096×8/256 = 128 tokens per expert average.
+    #
+    # CCCL GridEvenShare formula:
+    #   max_blocks = sm_count × subscription_factor = 16 × 5 = 80
+    #   optimal_block_m = ceil(numel / max_blocks)
+    #   block_m = clamp(round_up(optimal_block_m, 16), 16, 256)
+    #
+    # ixformer only reads BLOCK_SIZE_M for token padding alignment.
+    # Smaller BLOCK_SIZE_M = less wasted padding, more tiles.
+    # Larger BLOCK_SIZE_M = fewer tiles, less launch overhead.
+    _BI100_MAX_BLOCKS = 80  # 16 SMs × 5 subscription (CCCL default)
+
+    if numel <= 16:
+        config['BLOCK_SIZE_M'] = 16
+    elif numel <= _BI100_MAX_BLOCKS * 16:
+        # Small problem: want ~1 tile per expert-group
+        # BLOCK_SIZE_M = 16 gives numel/16 tiles, enough to fill SMs
+        config['BLOCK_SIZE_M'] = 16
+    elif numel <= _BI100_MAX_BLOCKS * 64:
+        # Medium: target ~80 tiles for full SM saturation
+        # ceil(numel / 80) ≈ 64 → use 64
+        config['BLOCK_SIZE_M'] = 64
+    elif numel <= _BI100_MAX_BLOCKS * 128:
+        config['BLOCK_SIZE_M'] = 128
+    else:
+        # Large prefill: 256 to amortize launch overhead
+        # CCCL dispatch_batch_memcpy MultiBlockBatchMemcpyKernel:
+        # large buffers use TILE_SIZE = BLOCK_THREADS × ITEMS_PER_THREAD
+        # with do-while loop over tiles. Same pattern: large BLOCK_SIZE_M
+        # means each CTA does more work per iteration.
+        config['BLOCK_SIZE_M'] = 256
+    return config
+
+
+def try_get_optimal_moe_config(
+    w1_shape: Tuple[int, ...],
+    w2_shape: Tuple[int, ...],
+    top_k: int,
+    dtype: Optional[str],
+    M: int,
+    override_config: Optional[Dict[str, Any]] = None,
+    is_marlin: bool = False,
+):
+    if override_config:
+        config = override_config
+    else:
+        # First try to load optimal config from the file
+        E, _, N = w2_shape
+        # configs = get_moe_configs(E, N, dtype)
+        configs = None
+
+        if configs:
+            # If an optimal configuration map has been found, look up the
+            # optimal config
+            config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
+        else:
+            # Else use the default config
+            config = get_default_config(M, E, N, w1_shape[2], top_k, dtype,
+                                        is_marlin)
+    return config
+
+
+def fused_topk(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+):
+    assert hidden_states.shape[0] == gating_output.shape[0], (
+        "Number of tokens mismatch")
+
+    M, _ = hidden_states.shape
+
+    topk_weights = torch.empty(M,
+                               topk,
+                               dtype=torch.float32,
+                               device=hidden_states.device)
+    topk_ids = torch.empty(M,
+                           topk,
+                           dtype=torch.int32,
+                           device=hidden_states.device)
+    token_expert_indicies = torch.empty(M,
+                                        topk,
+                                        dtype=torch.int32,
+                                        device=hidden_states.device)
+
+    ops.topk_softmax(
+        topk_weights,
+        topk_ids,
+        token_expert_indicies,
+        gating_output.float(),  # TODO(woosuk): Optimize this.
+    )
+    del token_expert_indicies  # Not used. Will be used in the future.
+
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    return topk_weights, topk_ids
+
+
+# This is used by the Deepseek-V2 model
+def grouped_topk(hidden_states: torch.Tensor,
+                 gating_output: torch.Tensor,
+                 topk: int,
+                 renormalize: bool,
+                 num_expert_group: int = 0,
+                 topk_group: int = 0):
+
+    assert hidden_states.shape[0] == gating_output.shape[0], (
+        "Number of tokens mismatch")
+
+    scores = torch.softmax(gating_output, dim=-1)
+    num_token = scores.shape[0]
+    group_scores = scores.view(num_token, num_expert_group,
+                               -1).max(dim=-1).values  # [n, n_group]
+    group_idx = torch.topk(group_scores, k=topk_group, dim=-1,
+                           sorted=False)[1]  # [n, top_k_group]
+    group_mask = torch.zeros_like(group_scores)  # [n, n_group]
+    group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
+    score_mask = group_mask.unsqueeze(-1).expand(
+        num_token, num_expert_group,
+        scores.shape[-1] // num_expert_group).reshape(num_token, -1)  # [n, e]
+    tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
+    topk_weights, topk_ids = torch.topk(tmp_scores,
+                                        k=topk,
+                                        dim=-1,
+                                        sorted=False)
+
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+
+
+def get_config_dtype_str(dtype: torch.dtype,
+                         use_int8_w8a16: Optional[bool] = False,
+                         use_fp8_w8a8: Optional[bool] = False):
+    if use_fp8_w8a8:
+        return "fp8_w8a8"
+    elif use_int8_w8a16:
+        return "int8_w8a16"
+    elif dtype == torch.float:
+        # avoiding cases where kernel fails when float32 MoE
+        # use fp16/bfloat16 configs
+        return "float32"
+    return None
+
+
+def fused_experts(hidden_states: torch.Tensor,
+                  w1: torch.Tensor,
+                  w2: torch.Tensor,
+                  topk_weights: torch.Tensor,
+                  topk_ids: torch.Tensor,
+                  inplace: bool = False,
+                  override_config: Optional[Dict[str, Any]] = None,
+                  use_fp8_w8a8: bool = False,
+                  use_int8_w8a16: bool = False,
+                  w1_scale: Optional[torch.Tensor] = None,
+                  w2_scale: Optional[torch.Tensor] = None,
+                  a1_scale: Optional[torch.Tensor] = None,
+                  a2_scale: Optional[torch.Tensor] = None):
+    if use_fp8_w8a8 or use_int8_w8a16:
+        raise NotImplementedError("fused_experts has not implemented fp8_w8a8 and int8_w8a16 yet.")
+    if a1_scale is not None or a2_scale is not None:
+        raise NotImplementedError("fused_experts has not implemented static_w8a8 yet.")
+
+    # Check constraints.
+    assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
+    assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
+    assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
+    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
+    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
+    assert hidden_states.dtype in [
+        torch.float32, torch.float16, torch.bfloat16
+    ]
+
+    num_tokens, _ = hidden_states.shape
+    E, N, _ = w1.shape
+    # We execute the fused_moe kernel in chunks to circumvent this issue:
+    # https://github.com/vllm-project/vllm/issues/5938
+    CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
+    M = min(num_tokens, CHUNK_SIZE)
+    config_dtype = get_config_dtype_str(use_fp8_w8a8=use_fp8_w8a8,
+                                        use_int8_w8a16=use_int8_w8a16,
+                                        dtype=hidden_states.dtype)
+
+    get_config_func = functools.partial(
+        try_get_optimal_moe_config,
+        w1.shape,
+        w2.shape,
+        topk_ids.shape[1],
+        config_dtype,
+        override_config=override_config,
+    )
+
+    config = get_config_func(M)
+
+    intermediate_cache1 = _get_or_alloc(
+        'moe_c1', (M, topk_ids.shape[1], N),
+        hidden_states.dtype, hidden_states.device)
+    intermediate_cache2 = _get_or_alloc(
+        'moe_c2', (M * topk_ids.shape[1], N // 2),
+        hidden_states.dtype, hidden_states.device)
+    intermediate_cache3 = _get_or_alloc(
+        'moe_c3', (M, topk_ids.shape[1], w2.shape[1]),
+        hidden_states.dtype, hidden_states.device)
+
+    compute_type = (tl.bfloat16
+                    if hidden_states.dtype == torch.bfloat16 else tl.float16)
+
+    if inplace:
+        out_hidden_states = hidden_states
+    else:
+        out_hidden_states = torch.empty_like(hidden_states)
+
+    for chunk in range((num_tokens // CHUNK_SIZE) + 1):
+        begin_chunk_idx, end_chunk_idx = (chunk * CHUNK_SIZE,
+                                          min((chunk + 1) * CHUNK_SIZE,
+                                              num_tokens))
+        curr_hidden_states = hidden_states[begin_chunk_idx:end_chunk_idx]
+        tokens_in_chunk, _ = curr_hidden_states.shape
+
+        if tokens_in_chunk == 0:
+            break
+
+        if tokens_in_chunk < CHUNK_SIZE and chunk > 0:
+            # Adjust the intermediate cache size and config for the last
+            # chunk. Note that in most cases we only have one chunk
+            # so the cache size and config are already set correctly and
+            # do not need to be adjusted.
+            intermediate_cache1 = intermediate_cache1[:tokens_in_chunk]
+            intermediate_cache2 = intermediate_cache2[:tokens_in_chunk]
+            intermediate_cache3 = intermediate_cache3[:tokens_in_chunk]
+            config = get_config_func(tokens_in_chunk)
+
+        curr_topk_ids = topk_ids[begin_chunk_idx:end_chunk_idx]
+        curr_topk_weights = topk_weights[begin_chunk_idx:end_chunk_idx]
+
+        sorted_token_ids, expert_ids, num_tokens_post_padded = (
+            moe_align_block_size(curr_topk_ids, config['BLOCK_SIZE_M'], E))
+
+        invoke_fused_moe_kernel(curr_hidden_states,
+                                w1,
+                                intermediate_cache1,
+                                a1_scale,
+                                w1_scale,
+                                curr_topk_weights,
+                                curr_topk_ids,
+                                sorted_token_ids,
+                                expert_ids,
+                                num_tokens_post_padded,
+                                False,
+                                topk_ids.shape[1],
+                                config,
+                                compute_type=compute_type,
+                                use_fp8_w8a8=use_fp8_w8a8,
+                                use_int8_w8a16=use_int8_w8a16)
+
+        ops.silu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, N))
+
+        invoke_fused_moe_kernel(intermediate_cache2,
+                                w2,
+                                intermediate_cache3,
+                                a2_scale,
+                                w2_scale,
+                                curr_topk_weights,
+                                curr_topk_ids,
+                                sorted_token_ids,
+                                expert_ids,
+                                num_tokens_post_padded,
+                                True,
+                                1,
+                                config,
+                                compute_type=compute_type,
+                                use_fp8_w8a8=use_fp8_w8a8,
+                                use_int8_w8a16=use_int8_w8a16)
+
+        torch.sum(intermediate_cache3.view(*intermediate_cache3.shape),
+                  dim=1,
+                  out=out_hidden_states[begin_chunk_idx:end_chunk_idx])
+    return out_hidden_states
+
+
+def fused_moe(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    inplace: bool = False,
+    override_config: Optional[Dict[str, Any]] = None,
+    use_grouped_topk: bool = False,
+    num_expert_group: Optional[int] = None,
+    topk_group: Optional[int] = None,
+    custom_routing_function: Optional[Callable] = None,
+    use_fp8_w8a8: bool = False,
+    use_int8_w8a16: bool = False,
+    w1_scale: Optional[torch.Tensor] = None,
+    w2_scale: Optional[torch.Tensor] = None,
+    a1_scale: Optional[torch.Tensor] = None,
+    a2_scale: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """
+    This function computes a Mixture of Experts (MoE) layer using two sets of
+    weights, w1 and w2, and top-k gating mechanism.
+
+    Parameters:
+    - hidden_states (torch.Tensor): The input tensor to the MoE layer.
+    - w1 (torch.Tensor): The first set of expert weights.
+    - w2 (torch.Tensor): The second set of expert weights.
+    - gating_output (torch.Tensor): The output of the gating operation
+        (before softmax).
+    - topk (int): The number of top-k experts to select.
+    - renormalize (bool): If True, renormalize the top-k weights to sum to 1.
+    - inplace (bool): If True, perform the operation in-place.
+        Defaults to False.
+    - override_config (Optional[Dict[str, Any]]): Optional override
+        for the kernel configuration.
+    - num_expert_group: Optional[int]: additional parameter for grouped_topk
+    - topk_group: Optional[int]: additional parameter for grouped_topk
+    - use_grouped_topk: If True, use grouped_topk instead of fused_topk
+        note: Deepseekv2 model uses grouped_topk
+    - use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner
+        products for w1 and w2. Defaults to False.
+    - use_int8_w8a16 (bool): If True, use fp8 arithmetic to compute the inner
+        products for w1 and w2. Defaults to False.
+    - w1_scale (Optional[torch.Tensor]): Optional scale to be used for
+        w1.
+    - w2_scale (Optional[torch.Tensor]): Optional scale to be used for
+        w2.
+
+    Returns:
+    - torch.Tensor: The output tensor after applying the MoE layer.
+    """
+    # Check constraints.
+    assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch"
+
+    if use_grouped_topk:
+        assert num_expert_group is not None and topk_group is not None
+        topk_weights, topk_ids = grouped_topk(hidden_states, gating_output,
+                                              topk, renormalize,
+                                              num_expert_group, topk_group)
+    elif custom_routing_function is None:
+        topk_weights, topk_ids = fused_topk(hidden_states, gating_output, topk,
+                                            renormalize)
+    else:
+        topk_weights, topk_ids = custom_routing_function(
+            hidden_states, gating_output, topk, renormalize)
+
+    return fused_experts(hidden_states,
+                         w1,
+                         w2,
+                         topk_weights,
+                         topk_ids,
+                         inplace=inplace,
+                         override_config=override_config,
+                         use_fp8_w8a8=use_fp8_w8a8,
+                         use_int8_w8a16=use_int8_w8a16,
+                         w1_scale=w1_scale,
+                         w2_scale=w2_scale,
+                         a1_scale=a1_scale,
+                         a2_scale=a2_scale)

vllm/model_executor/layers/fused_moe/layer.py

@@ -0,0 +1,717 @@
+from abc import abstractmethod
+from enum import Enum
+from typing import Callable, List, Optional, Tuple
+
+import torch
+
+from vllm.distributed import (get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.logger import init_logger
+from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+# from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import CompressedTensorsConfig
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import CompressionFormat, QuantizationStrategy
+# from vllm.model_executor.layers.quantization.utils.w8a8_utils import create_per_channel_scale_param
+from vllm.model_executor.utils import set_weight_attrs
+
+logger = init_logger(__name__)
+
+
+class FusedMoeWeightScaleSupported(Enum):
+    TENSOR = "tensor"
+    CHANNEL = "channel"
+    GROUP = "group"
+
+
+class FusedMoEMethodBase(QuantizeMethodBase):
+
+    @abstractmethod
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply(self, layer: torch.nn.Module, x: torch.Tensor,
+              router_logits: torch.Tensor, top_k: int, renormalize: bool,
+              use_grouped_topk: bool) -> torch.Tensor:
+        raise NotImplementedError
+
+
+class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
+    """MoE method without quantization."""
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        # Fused gate_up_proj (column parallel)
+        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                    2 * intermediate_size,
+                                                    hidden_size,
+                                                    dtype=params_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        # down_proj (row parallel)
+        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                   hidden_size,
+                                                   intermediate_size,
+                                                   dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+    def apply(
+            self,
+            layer: torch.nn.Module,
+            x: torch.Tensor,
+            router_logits: torch.Tensor,
+            top_k: int,
+            renormalize: bool,
+            use_grouped_topk: bool,
+            topk_group: Optional[int] = None,
+            num_expert_group: Optional[int] = None,
+            custom_routing_function: Optional[Callable] = None
+    ) -> torch.Tensor:
+
+        return self.forward(x=x,
+                            layer=layer,
+                            router_logits=router_logits,
+                            top_k=top_k,
+                            renormalize=renormalize,
+                            use_grouped_topk=use_grouped_topk,
+                            topk_group=topk_group,
+                            num_expert_group=num_expert_group,
+                            custom_routing_function=custom_routing_function)
+
+    def forward_cuda(
+            self,
+            layer: torch.nn.Module,
+            x: torch.Tensor,
+            use_grouped_topk: bool,
+            top_k: int,
+            router_logits: torch.Tensor,
+            renormalize: bool,
+            topk_group: Optional[int] = None,
+            num_expert_group: Optional[int] = None,
+            custom_routing_function: Optional[Callable] = None
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe.fused_moe import (
+            fused_experts)
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function)
+
+        return fused_experts(hidden_states=x,
+                             w1=layer.w13_weight,
+                             w2=layer.w2_weight,
+                             topk_weights=topk_weights,
+                             topk_ids=topk_ids,
+                             inplace=True)
+
+    def forward_cpu(self, *args, **kwargs):
+        raise NotImplementedError(
+            "The CPU backend currently does not support MoE.")
+
+    def forward_tpu(
+            self,
+            layer: torch.nn.Module,
+            x: torch.Tensor,
+            use_grouped_topk: bool,
+            top_k: int,
+            router_logits: torch.Tensor,
+            renormalize: bool,
+            topk_group: Optional[int] = None,
+            num_expert_group: Optional[int] = None,
+            custom_routing_function: Optional[Callable] = None
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe.moe_pallas import fused_moe
+        assert not use_grouped_topk
+        assert num_expert_group is None
+        assert topk_group is None
+        assert custom_routing_function is None
+        return fused_moe(hidden_states=x,
+                         w1=layer.w13_weight,
+                         w2=layer.w2_weight,
+                         topk=top_k,
+                         gating_output=router_logits,
+                         renormalize=renormalize)
+
+class W8A8QuantizedFusedMoEMethod(FusedMoEMethodBase):
+    """MoE method W8A8 quantization. This class is for compressed-tensors format loading"""
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+        self.strategy = extra_weight_attrs['quant_config'].target_scheme_map['Linear']['weights'].strategy
+        self.is_static_input_scheme = not extra_weight_attrs['quant_config'].target_scheme_map['Linear']['input_activations'].dynamic
+        # assert self.is_static_input_scheme, "W8A8 int quantization only support static input activation for now"
+
+        self.quant_config = extra_weight_attrs["quant_config"]
+        self.weight_loader = extra_weight_attrs["weight_loader"]
+
+        self.logical_widths_13 = [intermediate_size * 2]
+        self.logical_widths_2 = [intermediate_size * 2]
+        # Fused gate_up_proj (column parallel)
+        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                    2 * intermediate_size,
+                                                    hidden_size,
+                                                    dtype=torch.int8),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, {
+            "input_dim": 1,
+            "output_dim": 0,
+            "weight_loader": self.weight_loader,
+        })
+        # WEIGHT SCALE
+        layer_kwargs = {"weight_loader": self.weight_loader}
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            scale = torch.nn.Parameter(torch.empty((num_experts, intermediate_size * 2, 1),
+                                        dtype=torch.float32),
+                            requires_grad=False)
+            scale[:] = torch.finfo(torch.float32).min
+            set_weight_attrs(scale, {"input_dim": 1, "output_dim": 0, **layer_kwargs})
+        else:
+            assert self.strategy == QuantizationStrategy.TENSOR
+            scale = torch.nn.Parameter(torch.empty((num_experts, 2), dtype=torch.float32),
+                                       requires_grad=False)
+            scale[:] = torch.finfo(torch.float32).min
+            set_weight_attrs(scale, {
+                "needs_scalar_to_array": True,
+                **layer_kwargs
+            })
+        set_weight_attrs(scale, {"is_int8_weight_scale": True})
+        layer.register_parameter("w13_weight_scale", scale)
+
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                  dtype=torch.float32),
+                                       requires_grad=False)
+            set_weight_attrs(scale, {
+                "needs_scalar_to_array": True,
+                **layer_kwargs
+            })
+            set_weight_attrs(scale, {"is_int8_input_scale": True})
+            layer.register_parameter("w13_input_scale", scale)
+
+
+        # down_proj (row parallel)
+        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                   hidden_size,
+                                                   intermediate_size,
+                                                   dtype=torch.int8),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+
+        set_weight_attrs(w2_weight, {
+            "input_dim": 1,
+            "output_dim": 0,
+            "weight_loader": self.weight_loader,
+        })
+
+        # WEIGHT SCALE
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            scale = torch.nn.Parameter(torch.empty((num_experts, hidden_size, 1),
+                                        dtype=torch.float32),
+                            requires_grad=False)
+            scale[:] = torch.finfo(torch.float32).min
+            set_weight_attrs(scale, {"input_dim": 0, "output_dim": 1, **layer_kwargs})
+
+        else:
+            assert self.strategy == QuantizationStrategy.TENSOR
+            scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                  dtype=torch.float32),
+                                       requires_grad=False)
+            set_weight_attrs(scale, {
+                "needs_scalar_to_array": True,
+                **layer_kwargs
+            })
+        set_weight_attrs(scale, {"is_int8_weight_scale": True})
+        layer.register_parameter("w2_weight_scale", scale)
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                  dtype=torch.float32),
+                                       requires_grad=False)
+            set_weight_attrs(scale, {
+                "needs_scalar_to_array": True,
+                **layer_kwargs
+            })
+            set_weight_attrs(scale, {"is_int8_input_scale": True})
+            layer.register_parameter("w2_input_scale", scale)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              router_logits: torch.Tensor,
+              top_k: int,
+              renormalize: bool = True,
+              use_grouped_topk: bool = False,
+              num_expert_group: Optional[int] = None,
+              topk_group: Optional[int] = None) -> torch.Tensor:
+        assert False, "fused_moe w8a8 use ixformer.contrib.vllm.layers.mixtral_decoder_layer_forward"
+        from vllm.model_executor.layers.fused_moe.fused_moe import fused_moe
+        if self.is_static_input_scheme:
+            return fused_moe(x,
+                            layer.w13_weight,
+                            layer.w2_weight,
+                            router_logits,
+                            top_k,
+                            renormalize=renormalize,
+                            inplace=True,
+                            use_grouped_topk=use_grouped_topk,
+                            num_expert_group=num_expert_group,
+                            topk_group=topk_group,
+                            use_int8_w8a8=True,
+                            w1_scale=layer.w13_weight_scale,
+                            w2_scale=layer.w2_weight_scale,
+                            a1_scale=layer.w13_input_scale,
+                            a2_scale=layer.w2_input_scale)
+        else:
+            return fused_moe(x,
+                            layer.w13_weight,
+                            layer.w2_weight,
+                            router_logits,
+                            top_k,
+                            renormalize=renormalize,
+                            inplace=True,
+                            use_grouped_topk=use_grouped_topk,
+                            num_expert_group=num_expert_group,
+                            topk_group=topk_group,
+                            use_int8_w8a8=True,
+                            w1_scale=layer.w13_weight_scale,
+                            w2_scale=layer.w2_weight_scale,
+                            a1_scale=None,
+                            a2_scale=None)
+
+class FusedMoE(torch.nn.Module):
+    """FusedMoE layer for MoE models.
+
+    This layer contains both MergedColumnParallel weights (gate_up_proj / 
+    w13) and RowParallelLinear weights (down_proj/ w2).
+
+    Note: Mixtral uses w1, w2, and w3 for gate, up, and down_proj. We
+    copy that naming convention here and handle any remapping in the
+    load_weights function in each model implementation.
+
+    Args:
+        num_experts: Number of experts in the model
+        top_k: Number of experts selected for each token
+        hidden_size: Input hidden state size of the transformer
+        intermediate_size: Intermediate size of the experts
+        params_dtype: Data type for the parameters.
+        reduce_results: Whether to all all_reduce on the output of the layer
+        renomalize: Whether to renormalize the logits in the fused_moe kernel
+        quant_config: Quantization configure.
+    """
+
+    def __init__(
+        self,
+        num_experts: int,
+        top_k: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: Optional[torch.dtype] = None,
+        reduce_results: bool = False,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        tp_size: Optional[int] = None,
+        prefix: str = "",
+        custom_routing_function: Optional[Callable] = None,
+    ):
+        super().__init__()
+
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+
+        self.tp_size = (tp_size if tp_size is not None else
+                        get_tensor_model_parallel_world_size())
+        self.top_k = top_k
+        self.num_experts = num_experts
+        self.intermediate_size_per_partition = intermediate_size // self.tp_size
+        self.reduce_results = reduce_results
+        self.renormalize = renormalize
+        self.use_grouped_topk = use_grouped_topk
+        if self.use_grouped_topk:
+            assert num_expert_group is not None and topk_group is not None
+        self.num_expert_group = num_expert_group
+        self.topk_group = topk_group
+        self.custom_routing_function = custom_routing_function
+
+        from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import CompressedTensorsConfig
+        if quant_config is None:
+            self.quant_method: Optional[QuantizeMethodBase] = (
+                UnquantizedFusedMoEMethod())
+        elif (isinstance(quant_config, CompressedTensorsConfig)
+         and quant_config.quant_format == CompressionFormat.int_quantized.value
+         and quant_config.target_scheme_map['Linear']['input_activations'].num_bits == 8
+         and quant_config.target_scheme_map['Linear']['weights'].num_bits == 8):
+            self.quant_method: Optional[QuantizeMethodBase] = (
+                W8A8QuantizedFusedMoEMethod())
+        else:
+            self.quant_method = quant_config.get_quant_method(self, prefix)
+        assert self.quant_method is not None
+
+        self.quant_method.create_weights(
+            layer=self,
+            num_experts=num_experts,
+            hidden_size=hidden_size,
+            intermediate_size=self.intermediate_size_per_partition,
+            params_dtype=params_dtype,
+            weight_loader=self.weight_loader,
+            quant_config=quant_config)
+
+    def _load_per_tensor_weight_scale(self, shard_id: str,
+                                      param: torch.nn.Parameter,
+                                      loaded_weight: torch.Tensor,
+                                      expert_id: int):
+        param_data = param.data
+        # for per tensor weight quantization
+        if shard_id in ("w1", "w3"):
+            # We have to keep the weight scales of w1 and w3 because
+            # we need to re-quantize w1/w3 weights after weight loading.
+            idx = 0 if shard_id == "w1" else 1
+            param_data[expert_id][idx] = loaded_weight
+        # If we are in the row parallel case (down_proj)
+        elif shard_id == "w2":
+            param_data[expert_id] = loaded_weight
+
+    def _load_model_weight_or_group_weight_scale(self, shard_dim: int,
+                                                 expert_data: torch.Tensor,
+                                                 shard_id: str,
+                                                 loaded_weight: torch.tensor,
+                                                 tp_rank: int):
+        # Load grouped weight scales for group quantization
+        # or model weights
+        if shard_id == "w2":
+            self._load_w2(shard_id=shard_id,
+                          shard_dim=shard_dim,
+                          loaded_weight=loaded_weight,
+                          expert_data=expert_data,
+                          tp_rank=tp_rank)
+        elif shard_id in ("w1", "w3"):
+            self._load_w13(shard_id=shard_id,
+                           shard_dim=shard_dim,
+                           loaded_weight=loaded_weight,
+                           expert_data=expert_data,
+                           tp_rank=tp_rank)
+
+    def _load_per_channel_weight_scale(self, expert_data: torch.Tensor,
+                                       shard_dim: int, shard_id: str,
+                                       loaded_weight: torch.tensor,
+                                       tp_rank: int):
+        # for per channel weight quantization
+        if shard_id == "w2":
+            expert_data.copy_(loaded_weight)
+        elif shard_id in ("w1", "w3"):
+            self._load_w13(shard_id=shard_id,
+                           shard_dim=shard_dim,
+                           loaded_weight=loaded_weight,
+                           expert_data=expert_data,
+                           tp_rank=tp_rank)
+
+    def _load_w13(self, expert_data: torch.Tensor, shard_dim: int,
+                  shard_id: str, loaded_weight: torch.tensor, tp_rank: int):
+
+        # Index the loaded weight for tp sharding.
+        # gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
+        shard_size = expert_data.shape[shard_dim] // 2
+        loaded_weight = loaded_weight.narrow(shard_dim, shard_size * tp_rank,
+                                             shard_size)
+        # Narrow parameter and load.
+        # w1, gate_proj: Load into first logical weight of w13.
+        if shard_id == "w1":
+            expert_data = expert_data.narrow(shard_dim, 0, shard_size)
+        # w3, up_proj: Load into second logical weight of w13.
+        else:
+            assert shard_id == "w3"
+            expert_data = expert_data.narrow(shard_dim, shard_size, shard_size)
+        expert_data.copy_(loaded_weight)
+
+    def _load_w2(self, expert_data: torch.Tensor, shard_dim: int,
+                 shard_id: str, loaded_weight: torch.tensor, tp_rank: int):
+
+        # Index the loaded weight for tp sharding.
+        # down_proj: "RowParallel" so tp sharding on input_dim
+        # Narrow parameter and load.
+        shard_size = expert_data.shape[shard_dim]
+        loaded_weight = loaded_weight.narrow(shard_dim, shard_size * tp_rank,
+                                             shard_size)
+        # w2, down_proj: Load into only logical weight of w2.
+        expert_data.copy_(loaded_weight)
+
+    def _load_single_value(self, param: torch.nn.Parameter,
+                           loaded_weight: torch.Tensor, expert_id: int):
+        param_data = param.data
+
+        # Input scales can be loaded directly and should be equal.
+        param_data[expert_id] = loaded_weight
+
+    def _load_g_idx(self, shard_id: str, expert_data: torch.Tensor,
+                    shard_dim: int, loaded_weight: torch.tensor, tp_rank: int):
+
+        if shard_id == "w2":
+            self._load_w2(shard_id=shard_id,
+                          shard_dim=shard_dim,
+                          loaded_weight=loaded_weight,
+                          expert_data=expert_data,
+                          tp_rank=tp_rank)
+        else:
+            assert shard_id in ("w1", "w3")
+            expert_data.copy_(loaded_weight)
+
+    def weight_loader(self, param: torch.nn.Parameter,
+                      loaded_weight: torch.Tensor, weight_name: str,
+                      shard_id: str, expert_id: int) -> None:
+
+        # compressed-tensors checkpoints with packed weights are stored flipped
+        # TODO (mgoin): check self.quant_method.quant_config.quant_format
+        # against known CompressionFormat enum values that have this quality
+        loaded_weight = loaded_weight.t().contiguous() if (
+            self.quant_method.__class__.__name__
+            == "CompressedTensorsWNA16MoEMethod") else loaded_weight
+
+        if shard_id not in ("w1", "w2", "w3"):
+            raise ValueError(f"shard_id must be ['w1','w2','w3'] but "
+                             f"got {shard_id}.")
+
+        # Special case for fp8 scales.
+        if getattr(param, "is_fp8_scale", False):
+            self._load_fp8_scale(param.data, loaded_weight, weight_name,
+                                 shard_id, expert_id)
+            return
+        elif getattr(param, "is_int8_input_scale", False):
+            self._load_int8_input_scale(param.data, loaded_weight,
+                                 shard_id, expert_id)
+            return
+
+        WEIGHT_SCALE_SUPPORTED = [
+            e.value for e in FusedMoeWeightScaleSupported
+        ]
+        # Fetch the dim to shard the parameter/loaded weight
+        # based on the shard id. This will be whatever
+        # dimension intermediate_size is used.
+        SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}
+
+        expert_data = param.data[expert_id]
+        tp_rank = get_tensor_model_parallel_rank()
+
+        # is_transposed: if the dim to shard the weight
+        # should be flipped. Required by GPTQ, compressed-tensors
+        # should be whatever dimension intermediate_size is
+        is_transposed = getattr(param, "is_transposed", False)
+        shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
+        if is_transposed:
+            shard_dim = ~shard_dim
+
+        # Case input scale: input_scale loading is only supported for fp8
+        if "input_scale" in weight_name:
+            # this is needed for compressed-tensors only
+            loaded_weight = loaded_weight.to(param.data.device)
+
+            if param.data[expert_id] != 1 and (param.data[expert_id] -
+                                               loaded_weight).abs() > 1e-5:
+                raise ValueError(
+                    "input_scales of w1 and w3 of a layer "
+                    f"must be equal. But got {param.data[expert_id]} "
+                    f"vs. {loaded_weight}")
+
+            self._load_single_value(param=param,
+                                    loaded_weight=loaded_weight,
+                                    expert_id=expert_id)
+            return
+
+        # Case g_idx
+        if "g_idx" in weight_name:
+            self._load_g_idx(shard_dim=0,
+                             shard_id=shard_id,
+                             loaded_weight=loaded_weight,
+                             expert_data=expert_data,
+                             tp_rank=tp_rank)
+            return
+
+        # Case weight scales and zero_points
+        if ("scale" in weight_name or "zero" in weight_name):
+            # load the weight scales and zp based on the quantization scheme
+            # supported weight scales/zp can be found in
+            # FusedMoeWeightScaleSupported
+            # TODO @dsikka: once hardened, refactor to use vLLM Parameters
+            # specific to each case
+            quant_method = getattr(param, "quant_method", FusedMoeWeightScaleSupported.CHANNEL.value)
+            if quant_method == FusedMoeWeightScaleSupported.CHANNEL.value:
+                self._load_per_channel_weight_scale(
+                    shard_id=shard_id,
+                    shard_dim=shard_dim,
+                    loaded_weight=loaded_weight,
+                    expert_data=expert_data,
+                    tp_rank=tp_rank)
+            elif quant_method == FusedMoeWeightScaleSupported.GROUP.value:
+                self._load_model_weight_or_group_weight_scale(
+                    shard_id=shard_id,
+                    shard_dim=shard_dim,
+                    loaded_weight=loaded_weight,
+                    expert_data=expert_data,
+                    tp_rank=tp_rank)
+            elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value:
+                self._load_per_tensor_weight_scale(shard_id=shard_id,
+                                                   param=param,
+                                                   loaded_weight=loaded_weight,
+                                                   expert_id=expert_id)
+            else:
+                raise ValueError(
+                    f"quant method must be one of {WEIGHT_SCALE_SUPPORTED}")
+            return
+
+        # Case weight_shape
+        if "weight_shape" in weight_name:
+            # only required by compressed-tensors
+            self._load_single_value(param=param,
+                                    loaded_weight=loaded_weight,
+                                    expert_id=expert_id)
+            return
+
+        # Case model weights
+        if "weight" in weight_name:
+            self._load_model_weight_or_group_weight_scale(
+                shard_id=shard_id,
+                shard_dim=shard_dim,
+                loaded_weight=loaded_weight,
+                expert_data=expert_data,
+                tp_rank=tp_rank)
+            return
+
+    @staticmethod
+    def select_experts(hidden_states: torch.Tensor,
+                       router_logits: torch.Tensor,
+                       top_k: int,
+                       use_grouped_topk: bool,
+                       renormalize: bool,
+                       topk_group: Optional[int] = None,
+                       num_expert_group: Optional[int] = None,
+                       custom_routing_function: Optional[Callable] = None):
+        from vllm.model_executor.layers.fused_moe.fused_moe import (
+            fused_topk, grouped_topk)
+
+        # DeekSeekv2 uses grouped_top_k
+        if use_grouped_topk:
+            assert topk_group is not None
+            assert num_expert_group is not None
+            topk_weights, topk_ids = grouped_topk(
+                hidden_states=hidden_states,
+                gating_output=router_logits,
+                topk=top_k,
+                renormalize=renormalize,
+                num_expert_group=num_expert_group,
+                topk_group=topk_group)
+        elif custom_routing_function is None:
+            topk_weights, topk_ids = fused_topk(hidden_states=hidden_states,
+                                                gating_output=router_logits,
+                                                topk=top_k,
+                                                renormalize=renormalize)
+        else:
+            topk_weights, topk_ids = custom_routing_function(
+                hidden_states=hidden_states,
+                gating_output=router_logits,
+                topk=top_k,
+                renormalize=renormalize)
+
+        return topk_weights, topk_ids
+
+    def forward(self, hidden_states: torch.Tensor,
+                router_logits: torch.Tensor):
+        assert self.quant_method is not None
+
+        # Matrix multiply.
+        final_hidden_states = self.quant_method.apply(
+            layer=self,
+            x=hidden_states,
+            router_logits=router_logits,
+            top_k=self.top_k,
+            renormalize=self.renormalize,
+            use_grouped_topk=self.use_grouped_topk,
+            topk_group=self.topk_group,
+            num_expert_group=self.num_expert_group,
+            custom_routing_function=self.custom_routing_function)
+
+        if self.reduce_results and self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
+
+        return final_hidden_states
+
+    @classmethod
+    def make_expert_params_mapping(
+            cls, ckpt_gate_proj_name: str, ckpt_down_proj_name: str,
+            ckpt_up_proj_name: str,
+            num_experts: int) -> List[Tuple[str, str, int, str]]:
+
+        return [
+            # (param_name, weight_name, expert_id, shard_id)
+            ("experts.w13_" if weight_name
+             in [ckpt_gate_proj_name, ckpt_up_proj_name] else "experts.w2_",
+             f"experts.{expert_id}.{weight_name}.", expert_id, shard_id)
+            for expert_id in range(num_experts) for shard_id, weight_name in [
+                ("w1", ckpt_gate_proj_name),
+                ("w2", ckpt_down_proj_name),
+                ("w3", ckpt_up_proj_name),
+            ]
+        ]
+
+    def _load_fp8_scale(self, param: torch.nn.Parameter,
+                        loaded_weight: torch.Tensor, weight_name: str,
+                        shard_id: str, expert_id: int) -> None:
+        param_data = param.data
+
+        # Input scales can be loaded directly and should be equal.
+        if "input_scale" in weight_name:
+            if param_data[expert_id] != 1 and (param_data[expert_id] -
+                                               loaded_weight).abs() > 1e-5:
+                raise ValueError(
+                    "input_scales of w1 and w3 of a layer "
+                    f"must be equal. But got {param_data[expert_id]} "
+                    f"vs. {loaded_weight}")
+            param_data[expert_id] = loaded_weight
+        # Weight scales
+        elif "weight_scale" in weight_name:
+            # If we are in merged column case (gate_up_proj)
+            if shard_id in ("w1", "w3"):
+                # We have to keep the weight scales of w1 and w3 because
+                # we need to re-quantize w1/w3 weights after weight loading.
+                idx = 0 if shard_id == "w1" else 1
+                param_data[expert_id][idx] = loaded_weight
+            # If we are in the row parallel case (down_proj)
+            else:
+                param_data[expert_id] = loaded_weight
+
+    def _load_int8_input_scale(self, param: torch.nn.Parameter,
+                               loaded_weight: torch.Tensor,
+                               shard_id: str, expert_id: int) -> None:
+        param_data = param.data
+        # Input scales can be loaded directly and should be equal.
+        if param_data[expert_id] != 1 and (param_data[expert_id].to(loaded_weight.device) -
+                                            loaded_weight).abs() > 1e-5:
+            raise ValueError(
+                "input_scales of w1 and w3 of a layer "
+                f"must be equal. But got {param_data[expert_id]} "
+                f"vs. {loaded_weight}")
+        param_data[expert_id] = loaded_weight

vllm/model_executor/layers/fused_moe/moe_pallas.py

@@ -0,0 +1,62 @@
+import torch
+import torch.nn.functional as F
+from torch_xla.experimental.custom_kernel import _histogram
+
+
+def fused_moe(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+) -> torch.Tensor:
+    """
+    Args:
+        hidden_states: [*, hidden_size]
+        w1: [num_experts, intermediate_size * 2, hidden_size]
+        w2: [num_experts, hidden_size, intermediate_size]
+        gating_output: [*, num_experts]
+    """
+    orig_shape = hidden_states.shape
+    hidden_size = hidden_states.shape[-1]
+    num_tokens = hidden_states.shape[:-1].numel()
+    num_experts = w1.shape[0]
+    intermediate_size = w2.shape[-1]
+    device = hidden_states.device
+    dtype = hidden_states.dtype
+    assert (num_tokens * topk) % 16 == 0, (
+        "The Pallas GMM kernel requires num_tokens * topk to be a multiple of "
+        f"16 but got {num_tokens * topk}")
+
+    hidden_states = hidden_states.view(num_tokens, hidden_size)
+    gating_output = gating_output.view(num_tokens, num_experts)
+    topk_weights = gating_output.softmax(dim=-1, dtype=torch.float)
+    topk_weights, topk_indices = topk_weights.topk(topk, dim=-1)
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+    topk_weights = topk_weights.to(dtype)
+
+    topk_indices = topk_indices.flatten()
+    topk_argsort_indices = topk_indices.argsort()
+    topk_argsort_revert_indices = topk_argsort_indices.argsort()
+    token_indices = torch.arange(num_tokens,
+                                 device=device).repeat_interleave(topk)
+    token_indices = token_indices[topk_argsort_indices]
+    group_sizes = _histogram(topk_indices.to(torch.int32), 0, num_experts - 1)
+
+    # NOTE(woosuk): The GMM Pallas kernel requires a different weight layout
+    # from HF Transformers.
+    w1 = w1.transpose(1, 2)
+    w2 = w2.transpose(1, 2)
+
+    x = hidden_states[token_indices]
+    x = torch.ops.xla.gmm(x, w1, group_sizes)
+    x = F.silu(x[..., :intermediate_size]) * x[..., intermediate_size:]
+    x = torch.ops.xla.gmm(x, w2, group_sizes)
+    x = x[topk_argsort_revert_indices].reshape(-1, topk, hidden_size)
+
+    x = x * topk_weights.unsqueeze_(dim=-1)
+    x = x.sum(dim=-2)
+    x = x.reshape(orig_shape)
+    return x

vllm/model_executor/layers/layernorm.py

@@ -0,0 +1,208 @@
+"""Custom normalization layers."""
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from vllm.model_executor.custom_op import CustomOp
+
+
+class RMSNorm(CustomOp):
+    """Root mean square normalization.
+
+    Computes x -> w * x / sqrt(E[x^2] + eps) where w is the learned weight.
+    Refer to https://arxiv.org/abs/1910.07467
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        eps: float = 1e-6,
+        var_hidden_size: Optional[int] = None,
+    ) -> None:
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.variance_epsilon = eps
+        self.variance_size_override = (None if var_hidden_size == hidden_size
+                                       else var_hidden_size)
+
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+
+    def forward_native(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """PyTorch-native implementation equivalent to forward()."""
+        orig_dtype = x.dtype
+        x = x.to(torch.float32)
+        if residual is not None:
+            x = x + residual.to(torch.float32)
+            residual = x.to(orig_dtype)
+
+        hidden_size = x.shape[-1]
+        if hidden_size != self.hidden_size:
+            raise ValueError("Expected hidden_size to be "
+                             f"{self.hidden_size}, but found: {hidden_size}")
+
+        if self.variance_size_override is None:
+            x_var = x
+        else:
+            if hidden_size < self.variance_size_override:
+                raise ValueError(
+                    "Expected hidden_size to be at least "
+                    f"{self.variance_size_override}, but found: {hidden_size}")
+
+            x_var = x[:, :, :self.variance_size_override]
+
+        variance = x_var.pow(2).mean(dim=-1, keepdim=True)
+
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        x = x.to(orig_dtype) * self.weight
+        if residual is None:
+            return x
+        else:
+            return x, residual
+
+    def forward_cuda(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+        residual_alpha: Optional[float] = 1.0,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        if self.variance_size_override is not None:
+            return self.forward_native(x, residual)
+
+        from vllm import _custom_ops as ops
+
+        if residual is not None:
+            ops.fused_add_rms_norm(
+                x,
+                residual,
+                self.weight.data,
+                self.variance_epsilon,
+                residual_alpha,
+            )
+            return x, residual
+        # ═══════════════════════════════════════════════════════════════
+        # CCCL dispatch_transform.cuh CacheAsyncConfiguration pattern:
+        #   Element-wise transforms have deterministic output shapes.
+        #   During decode, input shape is stable (num_seqs × hidden_dim).
+        #   Cache the output tensor to avoid cudaMalloc on every step.
+        #
+        #   CCCL: "This computation MUST NOT depend on runtime state ...
+        #          since the result will be cached."
+        #
+        #   RMSNorm is called 64× per forward pass (Qwen3.6 has 64 layers).
+        #   Each call was doing torch.empty_like → cudaMalloc.
+        #   With caching: 64 cudaMalloc calls → 0 per decode step.
+        # ═══════════════════════════════════════════════════════════════
+        _cache_key = (x.shape, x.dtype, x.device)
+        _cached = getattr(self, '_out_cache', {}).get(_cache_key)
+        if _cached is not None and _cached.shape == x.shape:
+            out = _cached
+        else:
+            out = torch.empty_like(x)
+            if not hasattr(self, '_out_cache'):
+                self._out_cache = {}
+            self._out_cache[_cache_key] = out
+        ops.rms_norm(
+            out,
+            x,
+            self.weight.data,
+            self.variance_epsilon,
+        )
+        return out
+
+    def forward_xpu(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        if self.variance_size_override is not None:
+            return self.forward_native(x, residual)
+
+        from vllm._ipex_ops import ipex_ops as ops
+
+        if residual is not None:
+            ops.fused_add_rms_norm(
+                x,
+                residual,
+                self.weight.data,
+                self.variance_epsilon,
+            )
+            return x, residual
+        return ops.rms_norm(
+            x,
+            self.weight.data,
+            self.variance_epsilon,
+        )
+
+    def extra_repr(self) -> str:
+        s = f"hidden_size={self.weight.data.size(0)}"
+        s += f", eps={self.variance_epsilon}"
+        return s
+
+
+class GemmaRMSNorm(CustomOp):
+    """RMS normalization for Gemma.
+
+    Two differences from the above RMSNorm:
+        1. x * (1 + w) instead of x * w.
+        2. (x * w).to(orig_dtype) instead of x.to(orig_dtype) * w.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        eps: float = 1e-6,
+    ) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.zeros(hidden_size))
+        self.variance_epsilon = eps
+
+    @staticmethod
+    def forward_static(
+        weight: torch.Tensor,
+        variance_epsilon: float,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """PyTorch-native implementation equivalent to forward()."""
+        orig_dtype = x.dtype
+        if residual is not None:
+            x = x + residual
+            residual = x
+
+        x = x.float()
+        variance = x.pow(2).mean(dim=-1, keepdim=True)
+        x = x * torch.rsqrt(variance + variance_epsilon)
+        # Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16)
+        # See https://github.com/huggingface/transformers/pull/29402
+        x = x * (1.0 + weight.float())
+        x = x.to(orig_dtype)
+        return x if residual is None else (x, residual)
+
+    def forward_native(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """PyTorch-native implementation equivalent to forward()."""
+        return self.forward_static(self.weight.data, self.variance_epsilon, x,
+                                   residual)
+
+    def forward_cuda(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        # if torch.compiler.is_compiling():
+        #     return self.forward_native(x, residual)
+
+        # if not getattr(self, "_is_compiled", False):
+        #     self.forward_static = torch.compile(  # type: ignore
+        #         self.forward_static)
+        #     self._is_compiled = True
+        return self.forward_native(x, residual)

vllm/model_executor/layers/logits_processor.py

@@ -0,0 +1,156 @@
+"""A layer that compute logits from hidden_stats."""
+import inspect
+from typing import Optional
+
+import torch
+import torch.nn as nn
+
+from vllm.distributed import (tensor_model_parallel_all_gather,
+                              tensor_model_parallel_gather)
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.platforms import current_platform
+
+
+class LogitsProcessor(nn.Module):
+    """Process logits and apply logits processors from sampling metadata.
+
+    This layer does the following:
+    1. Gather logits from model hidden_states.
+    2. Scale logits if needed.
+    3. Apply logits processors (if any).
+    """
+
+    def __init__(self,
+                 vocab_size: int,
+                 org_vocab_size: Optional[int] = None,
+                 scale: float = 1.0,
+                 logits_as_input: bool = False,
+                 soft_cap: Optional[float] = None) -> None:
+        """
+        Args:
+            scale: A scaling factor to apply to the logits.
+        """
+        super().__init__()
+        self.scale = scale
+        self.vocab_size = vocab_size
+        # Whether the input is logits (default is hidden states).
+        self.logits_as_input = logits_as_input
+        # original vocabulary size (without LoRA).
+        self.org_vocab_size = org_vocab_size or vocab_size
+        # Soft cap the logits. Used in Gemma 2.
+        self.soft_cap = soft_cap
+        # Whether to use gather or all-gather to gather the logits.
+        self.use_gather = not current_platform.is_tpu()
+
+    def forward(
+        self,
+        lm_head: VocabParallelEmbedding,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+        embedding_bias: Optional[torch.Tensor] = None,
+    ) -> Optional[torch.Tensor]:
+        if self.logits_as_input:
+            logits = hidden_states
+        else:
+            hidden_states = _prune_hidden_states(hidden_states,
+                                                 sampling_metadata)
+
+            # Get the logits for the next tokens.
+            if hidden_states.shape[0] > 0:
+                logits = self._get_logits(hidden_states, lm_head, embedding_bias)
+            else:
+                logits = torch.empty([0, lm_head.weight.shape[0]], device=hidden_states.device, dtype=hidden_states.dtype)
+        if logits is not None:
+            if self.soft_cap is not None:
+                logits = logits / self.soft_cap
+                logits = torch.tanh(logits)
+                logits = logits * self.soft_cap
+
+            if self.scale != 1.0:
+                logits *= self.scale
+
+            # Apply logits processors (if any).
+            logits = _apply_logits_processors(logits, sampling_metadata)
+
+        return logits
+
+    def _get_logits(
+        self,
+        hidden_states: torch.Tensor,
+        lm_head: VocabParallelEmbedding,
+        embedding_bias: Optional[torch.Tensor],
+    ) -> Optional[torch.Tensor]:
+        # Get the logits for the next tokens.
+        logits = lm_head.linear_method.apply(lm_head,
+                                             hidden_states,
+                                             bias=embedding_bias)
+        if self.use_gather:
+            # None may be returned for rank > 0
+            logits = tensor_model_parallel_gather(logits)
+        else:
+            # Gather is not supported for some devices such as TPUs.
+            # Use all-gather instead.
+            # NOTE(woosuk): Here, the outputs of every device should not be None
+            # because XLA requires strict SPMD among all devices. Every device
+            # should execute the same operations after gathering the logits.
+            logits = tensor_model_parallel_all_gather(logits)
+        # Remove paddings in vocab (if any).
+        if logits is not None:
+            logits = logits[..., :self.org_vocab_size]
+        return logits
+
+    def extra_repr(self) -> str:
+        s = f"vocab_size={self.vocab_size}"
+        s += f", forg_vocab_size={self.org_vocab_size}"
+        s += f", scale={self.scale}, logits_as_input={self.logits_as_input}"
+        return s
+
+
+def _prune_hidden_states(
+    hidden_states: torch.Tensor,
+    sampling_metadata: SamplingMetadata,
+) -> torch.Tensor:
+    return hidden_states.index_select(0,
+                                      sampling_metadata.selected_token_indices)
+
+
+def _apply_logits_processors(
+    logits: torch.Tensor,
+    sampling_metadata: SamplingMetadata,
+) -> torch.Tensor:
+    found_logits_processors = False
+    logits_processed = 0
+    for seq_group in sampling_metadata.seq_groups:
+        seq_ids = seq_group.seq_ids
+        sampling_params = seq_group.sampling_params
+        logits_processors = sampling_params.logits_processors
+        if logits_processors:
+            found_logits_processors = True
+
+            for seq_id, logits_row_idx in zip(seq_ids,
+                                              seq_group.sample_indices):
+                logits_row = logits[logits_row_idx]
+                past_tokens_ids = seq_group.seq_data[seq_id].output_token_ids
+                prompt_tokens_ids = seq_group.seq_data[seq_id].prompt_token_ids
+
+                for logits_processor in logits_processors:
+                    parameters = inspect.signature(logits_processor).parameters
+                    if len(parameters) == 3:
+                        logits_row = logits_processor(prompt_tokens_ids,
+                                                      past_tokens_ids,
+                                                      logits_row)
+                    else:
+                        logits_row = logits_processor(past_tokens_ids,
+                                                      logits_row)
+
+                logits[logits_row_idx] = logits_row
+
+        logits_processed += len(seq_group.sample_indices) + len(
+            seq_group.prompt_logprob_indices)
+
+    if found_logits_processors:
+        # verifies that no rows in logits were missed unexpectedly
+        assert logits_processed == logits.shape[0]
+    return logits

vllm/model_executor/layers/mamba/ops/causal_conv1d.py

@@ -0,0 +1,89 @@
+# Copyright (c) 2024, Tri Dao.
+# Adapted from https://github.com/Dao-AILab/causal-conv1d/blob/main/causal_conv1d/causal_conv1d_interface.py
+
+from typing import Optional
+
+import torch
+
+from vllm import _custom_ops as ops
+
+
+def causal_conv1d_fn(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: Optional[torch.Tensor] = None,
+    query_start_loc: Optional[torch.Tensor] = None,
+    cache_indices: Optional[torch.Tensor] = None,
+    has_initial_state: Optional[torch.Tensor] = None,
+    conv_states: Optional[torch.Tensor] = None,
+    activation: Optional[str] = "silu",
+):
+    """
+    x: (batch, dim, seqlen) or (dim,cu_seq_len) for varlen
+        sequences are concatenated from left to right for varlen
+    weight: (dim, width)
+    bias: (dim,)
+    query_start_loc: (batch + 1) int32
+        The cumulative sequence lengths of the sequences in
+        the batch, used to index into sequence. prepended by 0.
+        for example: query_start_loc = torch.Tensor([0,10,16,17]), 
+        x.shape=(dim,17)
+    cache_indices: (batch)  int32
+        indicates the corresponding state index, 
+        like so: conv_state = conv_states[cache_indices[batch_id]]
+    has_initial_state: (batch) bool
+        indicates whether should the kernel take the current state as initial 
+        state for the calculations
+    conv_states: (...,dim,width - 1) itype
+        updated inplace if provided
+    activation: either None or "silu" or "swish"
+
+    out: (batch, dim, seqlen)
+    """
+    if activation not in [None, "silu", "swish"]:
+        raise NotImplementedError("activation must be None, silu, or swish")
+    if x.stride(-1) != 1:
+        x = x.contiguous()
+    bias = bias.contiguous() if bias is not None else None
+
+    out = ops.causal_conv1d_fwd(x, weight, bias, conv_states, query_start_loc,
+                                cache_indices, has_initial_state, activation
+                                in ["silu", "swish"])
+    return out
+
+
+def causal_conv1d_update(x: torch.Tensor,
+                         conv_state: torch.Tensor,
+                         weight: torch.Tensor,
+                         bias: Optional[torch.Tensor] = None,
+                         activation: Optional[str] = None,
+                         cache_seqlens: Optional[torch.Tensor] = None,
+                         conv_state_indices: Optional[torch.Tensor] = None):
+    """
+    x: (batch, dim) or (batch, dim, seqlen)
+    conv_state: (batch, dim, state_len), where state_len >= width - 1
+    weight: (dim, width)
+    bias: (dim,)
+    cache_seqlens: (batch,), dtype int32.
+        If not None, the conv_state is treated as a circular buffer.
+        The conv_state will be updated by copying x to the conv_state 
+        starting at the index
+        @cache_seqlens % state_len.
+    conv_state_indices: (batch,), dtype int32
+        If not None, the conv_state is a larger tensor along the batch dim, 
+        and we are selecting the batch coords specified by conv_state_indices.
+        Useful for a continuous batching scenario.
+
+    out: (batch, dim) or (batch, dim, seqlen)
+    """
+    if activation not in [None, "silu", "swish"]:
+        raise NotImplementedError("activation must be None, silu, or swish")
+    activation_val = activation in ["silu", "swish"]
+    unsqueeze = x.dim() == 2
+    if unsqueeze:
+        x = x.unsqueeze(-1)
+    out = ops.causal_conv1d_update(x, conv_state, weight, bias, activation_val,
+                                   cache_seqlens, conv_state_indices)
+    if unsqueeze:
+        out = out.squeeze(-1)
+    return out

vllm/model_executor/layers/mamba/ops/mamba_ssm.py

@@ -0,0 +1,395 @@
+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Adapted from https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/triton/selective_state_update.py
+
+from typing import Tuple
+
+import torch
+import triton
+import triton.language as tl
+from packaging import version
+
+from vllm import _custom_ops as ops
+
+TRITON3 = version.parse(triton.__version__) >= version.parse("3.0.0")
+
+if TRITON3:
+
+    @triton.jit
+    def softplus(dt):
+        dt = tl.where(dt <= 20.0, tl.math.log(tl.math.exp(dt) + 1), dt)
+        return dt
+else:
+
+    @triton.jit
+    def softplus(dt):
+        dt = tl.where(dt <= 20.0, tl.math.log1p(tl.exp(dt)), dt)
+        return dt
+
+
+@triton.heuristics(
+    {"HAS_DT_BIAS": lambda args: args["dt_bias_ptr"] is not None})
+@triton.heuristics({"HAS_D": lambda args: args["D_ptr"] is not None})
+@triton.heuristics({"HAS_Z": lambda args: args["z_ptr"] is not None})
+@triton.heuristics({
+    "HAS_STATE_BATCH_INDICES":
+    lambda args: args["state_batch_indices_ptr"] is not None
+})
+@triton.heuristics(
+    {"BLOCK_SIZE_DSTATE": lambda args: triton.next_power_of_2(args["dstate"])})
+@triton.jit
+def _selective_scan_update_kernel(
+    # Pointers to matrices
+    state_ptr,
+    x_ptr,
+    dt_ptr,
+    dt_bias_ptr,
+    A_ptr,
+    B_ptr,
+    C_ptr,
+    D_ptr,
+    z_ptr,
+    out_ptr,
+    state_batch_indices_ptr,
+    # Matrix dimensions
+    batch,
+    nheads,
+    dim,
+    dstate,
+    nheads_ngroups_ratio,
+    # Strides
+    stride_state_batch,
+    stride_state_head,
+    stride_state_dim,
+    stride_state_dstate,
+    stride_x_batch,
+    stride_x_head,
+    stride_x_dim,
+    stride_dt_batch,
+    stride_dt_head,
+    stride_dt_dim,
+    stride_dt_bias_head,
+    stride_dt_bias_dim,
+    stride_A_head,
+    stride_A_dim,
+    stride_A_dstate,
+    stride_B_batch,
+    stride_B_group,
+    stride_B_dstate,
+    stride_C_batch,
+    stride_C_group,
+    stride_C_dstate,
+    stride_D_head,
+    stride_D_dim,
+    stride_z_batch,
+    stride_z_head,
+    stride_z_dim,
+    stride_out_batch,
+    stride_out_head,
+    stride_out_dim,
+    # Meta-parameters
+    DT_SOFTPLUS: tl.constexpr,
+    TIE_HDIM: tl.constexpr,
+    BLOCK_SIZE_M: tl.constexpr,
+    HAS_DT_BIAS: tl.constexpr,
+    HAS_D: tl.constexpr,
+    HAS_Z: tl.constexpr,
+    HAS_STATE_BATCH_INDICES: tl.constexpr,
+    BLOCK_SIZE_DSTATE: tl.constexpr,
+):
+    pid_m = tl.program_id(axis=0)
+    pid_b = tl.program_id(axis=1)
+    pid_h = tl.program_id(axis=2)
+
+    # If HAS_STATE_BATCH_INDICES is true, then the ssm state's batch coordinate
+    # is taken from the state_batch_indices_ptr Otherwise, the state coordinate
+    # is the same as the batch id.
+    if HAS_STATE_BATCH_INDICES:
+        state_batch_indices_ptr += pid_b
+        state_batch_idx = tl.load(state_batch_indices_ptr)
+        state_ptr += (state_batch_idx * stride_state_batch +
+                      pid_h * stride_state_head)
+    else:
+        state_ptr += pid_b * stride_state_batch + pid_h * stride_state_head
+
+    x_ptr += pid_b * stride_x_batch + pid_h * stride_x_head
+    dt_ptr += pid_b * stride_dt_batch + pid_h * stride_dt_head
+    if HAS_DT_BIAS:
+        dt_bias_ptr += pid_h * stride_dt_bias_head
+    A_ptr += pid_h * stride_A_head
+    B_ptr += pid_b * stride_B_batch + (pid_h //
+                                       nheads_ngroups_ratio) * stride_B_group
+    C_ptr += pid_b * stride_C_batch + (pid_h //
+                                       nheads_ngroups_ratio) * stride_C_group
+    if HAS_Z:
+        z_ptr += pid_b * stride_z_batch + pid_h * stride_z_head
+    out_ptr += pid_b * stride_out_batch + pid_h * stride_out_head
+
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = tl.arange(0, BLOCK_SIZE_DSTATE)
+    state_ptrs = state_ptr + (offs_m[:, None] * stride_state_dim +
+                              offs_n[None, :] * stride_state_dstate)
+    x_ptrs = x_ptr + offs_m * stride_x_dim
+    dt_ptrs = dt_ptr + offs_m * stride_dt_dim
+    if HAS_DT_BIAS:
+        dt_bias_ptrs = dt_bias_ptr + offs_m * stride_dt_bias_dim
+    if HAS_D:
+        D_ptr += pid_h * stride_D_head
+    A_ptrs = A_ptr + (offs_m[:, None] * stride_A_dim +
+                      offs_n[None, :] * stride_A_dstate)
+    B_ptrs = B_ptr + offs_n * stride_B_dstate
+    C_ptrs = C_ptr + offs_n * stride_C_dstate
+    if HAS_D:
+        D_ptrs = D_ptr + offs_m * stride_D_dim
+    if HAS_Z:
+        z_ptrs = z_ptr + offs_m * stride_z_dim
+    out_ptrs = out_ptr + offs_m * stride_out_dim
+
+    state = tl.load(state_ptrs,
+                    mask=(offs_m[:, None] < dim) & (offs_n[None, :] < dstate),
+                    other=0.0)
+    x = tl.load(x_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+    if not TIE_HDIM:
+        dt = tl.load(dt_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+        if HAS_DT_BIAS:
+            dt += tl.load(dt_bias_ptrs, mask=offs_m < dim,
+                          other=0.0).to(tl.float32)
+        if DT_SOFTPLUS:
+            dt = softplus(dt)
+        A = tl.load(A_ptrs,
+                    mask=(offs_m[:, None] < dim) & (offs_n[None, :] < dstate),
+                    other=0.0).to(tl.float32)
+        dA = tl.exp(A * dt[:, None])
+    else:
+        dt = tl.load(dt_ptr).to(tl.float32)
+        if HAS_DT_BIAS:
+            dt += tl.load(dt_bias_ptr).to(tl.float32)
+        if DT_SOFTPLUS:
+            dt = softplus(dt)
+        A = tl.load(A_ptr).to(tl.float32)
+        dA = tl.exp(A * dt)  # scalar, not a matrix
+
+    B = tl.load(B_ptrs, mask=offs_n < dstate, other=0.0).to(tl.float32)
+    C = tl.load(C_ptrs, mask=offs_n < dstate, other=0.0).to(tl.float32)
+    if HAS_D:
+        D = tl.load(D_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+    if HAS_Z:
+        z = tl.load(z_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
+
+    dB = B[None, :] * dt[:, None] if not TIE_HDIM else B * dt
+    state = state * dA + dB * x[:, None]
+    tl.store(state_ptrs,
+             state,
+             mask=(offs_m[:, None] < dim) & (offs_n[None, :] < dstate))
+    out = tl.sum(state * C[None, :], axis=1)
+    if HAS_D:
+        out += x * D
+    if HAS_Z:
+        out *= z * tl.sigmoid(z)
+    tl.store(out_ptrs, out, mask=offs_m < dim)
+
+
+def selective_state_update(state,
+                           x,
+                           dt,
+                           A,
+                           B,
+                           C,
+                           D=None,
+                           z=None,
+                           dt_bias=None,
+                           dt_softplus=False,
+                           state_batch_indices=None):
+    """
+    Argument:
+        state: (batch, dim, dstate) or (batch, nheads, dim, dstate)
+        x: (batch, dim) or (batch, nheads, dim)
+        dt: (batch, dim) or (batch, nheads, dim)
+        A: (dim, dstate) or (nheads, dim, dstate)
+        B: (batch, dstate) or (batch, ngroups, dstate)
+        C: (batch, dstate) or (batch, ngroups, dstate)
+        D: (dim,) or (nheads, dim)
+        z: (batch, dim) or (batch, nheads, dim)
+        dt_bias: (dim,) or (nheads, dim)
+    Return:
+        out: (batch, dim) or (batch, nheads, dim)
+    """
+    has_heads = state.dim() > 3
+    if state.dim() == 3:
+        state = state.unsqueeze(1)
+    if x.dim() == 2:
+        x = x.unsqueeze(1)
+    if dt.dim() == 2:
+        dt = dt.unsqueeze(1)
+    if A.dim() == 2:
+        A = A.unsqueeze(0)
+    if B.dim() == 2:
+        B = B.unsqueeze(1)
+    if C.dim() == 2:
+        C = C.unsqueeze(1)
+    if D is not None and D.dim() == 1:
+        D = D.unsqueeze(0)
+    if z is not None and z.dim() == 2:
+        z = z.unsqueeze(1)
+    if dt_bias is not None and dt_bias.dim() == 1:
+        dt_bias = dt_bias.unsqueeze(0)
+
+    _, nheads, dim, dstate = state.shape
+    batch = x.shape[0]
+
+    assert x.shape == (batch, nheads, dim)
+    assert dt.shape == x.shape
+    assert A.shape == (nheads, dim, dstate)
+    ngroups = B.shape[1]
+    assert nheads % ngroups == 0, "nheads must be divisible by ngroups"
+    assert B.shape == (batch, ngroups, dstate)
+    assert C.shape == B.shape
+    if D is not None:
+        assert D.shape == (nheads, dim)
+    if z is not None:
+        assert z.shape == x.shape
+    if dt_bias is not None:
+        assert dt_bias.shape == (nheads, dim)
+    if state_batch_indices is not None:
+        assert state_batch_indices.shape == (batch, )
+    out = torch.empty_like(x)
+    grid = lambda META: (triton.cdiv(dim, META['BLOCK_SIZE_M']), batch, nheads)
+    z_strides = ((z.stride(0), z.stride(1), z.stride(2)) if z is not None else
+                 (0, 0, 0))
+    # We don't want autotune since it will overwrite the state
+    # We instead tune by hand.
+    BLOCK_SIZE_M, num_warps = ((32, 4) if dstate <= 16 else
+                               ((16, 4) if dstate <= 32 else
+                                ((8, 4) if dstate <= 64 else
+                                 ((4, 4) if dstate <= 128 else ((4, 8))))))
+    tie_hdim = A.stride(-1) == 0 and A.stride(-2) == 0 and dt.stride(
+        -1) == 0 and dt_bias.stride(-1) == 0
+    with torch.cuda.device(x.device.index):
+        _selective_scan_update_kernel[grid](
+            state,
+            x,
+            dt,
+            dt_bias,
+            A,
+            B,
+            C,
+            D,
+            z,
+            out,
+            state_batch_indices,
+            batch,
+            nheads,
+            dim,
+            dstate,
+            nheads // ngroups,
+            state.stride(0),
+            state.stride(1),
+            state.stride(2),
+            state.stride(3),
+            x.stride(0),
+            x.stride(1),
+            x.stride(2),
+            dt.stride(0),
+            dt.stride(1),
+            dt.stride(2),
+            *(dt_bias.stride(0),
+              dt_bias.stride(1)) if dt_bias is not None else 0,
+            A.stride(0),
+            A.stride(1),
+            A.stride(2),
+            B.stride(0),
+            B.stride(1),
+            B.stride(2),
+            C.stride(0),
+            C.stride(1),
+            C.stride(2),
+            *(D.stride(0), D.stride(1)) if D is not None else 0,
+            z_strides[0],
+            z_strides[1],
+            z_strides[2],
+            out.stride(0),
+            out.stride(1),
+            out.stride(2),
+            dt_softplus,
+            tie_hdim,
+            BLOCK_SIZE_M,
+            num_warps=num_warps,
+        )
+    if not has_heads:
+        out = out.squeeze(1)
+    return out
+
+
+def selective_scan_fn(
+        u,
+        ssm_states,
+        delta,
+        A,
+        B,
+        C,
+        D=None,
+        z=None,
+        delta_bias=None,
+        delta_softplus=False,
+        query_start_loc=None,
+        cache_indices=None,
+        has_initial_state=None) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    u: (dim, total_length) for varlen or (batch, dim, seqlen) 
+    delta: (dim, total_length) for varlen or (batch, dim, seqlen)
+    A: (dim, dstate) 
+    B: (ngroups, dstate, total_length) for varlen or 
+                                        (batch,ngroups,dstate,seqlen)
+    C: (ngroups, dstate, total_length) for varlen or 
+                                        (batch,ngroups,dstate,seqlen)
+    D: (dim,) 
+    z: (dim, total_length) for varlen or (batch, dim, seqlen) 
+    dt_bias: (dim,) or (dim)
+    query_start_loc: (batch + 1) int32
+        The cumulative sequence lengths of the sequences in
+        the batch, used to index into sequence. prepended with 0.
+        for example: query_start_loc = torch.Tensor([0,10,16,17]), 
+        x.shape=(dim,17)
+    cache_indices: (batch) int32
+        A tensor with each cell is a correspondent 
+        input and output ssm_state index
+    has_initial_state: (batch) bool
+        A tensor populated with ones and zeros, 
+        indicate if the ssm_state at the corresponding index should be 
+        used as initial state. Not providing argument assumes 
+        there's no initial state
+
+    returns
+        output: (dim, total_length) for varlen or (batch, dim, seqlen) 
+                supports inplace replacement
+        last_state has shape (batch, dim, dstate). 
+                supports inplace replacement if ssm_state was provided
+    """
+    if u.stride(-1) != 1:
+        u = u.contiguous()
+    if delta.stride(-1) != 1:
+        delta = delta.contiguous()
+    if D is not None:
+        D = D.contiguous()
+    if B.stride(-1) != 1:
+        B = B.contiguous()
+    if C.stride(-1) != 1:
+        C = C.contiguous()
+    if z is not None and z.stride(-1) != 1:
+        z = z.contiguous()
+    if B.dim() == 3 and query_start_loc is None:
+        B = B.unsqueeze(1)
+    if B.dim() == 2 and query_start_loc is not None:
+        B = B.unsqueeze(0)
+    if C.dim() == 3 and query_start_loc is None:
+        C = C.unsqueeze(1)
+    if C.dim() == 2 and query_start_loc is not None:
+        C = C.unsqueeze(0)
+
+    ops.selective_scan_fwd(u, delta, A, B, C, D, z, delta_bias, delta_softplus,
+                           query_start_loc, cache_indices, has_initial_state,
+                           ssm_states)
+
+    if z is None:
+        return delta  # output written inplace to delta
+    else:
+        return z  # output written inplace to z

vllm/model_executor/layers/pooler.py

@@ -0,0 +1,63 @@
+from enum import IntEnum
+
+import torch
+import torch.nn as nn
+
+from vllm.model_executor.pooling_metadata import (PoolingMetadata,
+                                                  PoolingTensors)
+from vllm.sequence import EmbeddingSequenceGroupOutput, PoolerOutput
+
+
+class PoolingType(IntEnum):
+    """Enumeration for different types of pooling methods."""
+    LAST = 0
+    ALL = 1
+
+
+class Pooler(nn.Module):
+    """A layer that pools specific information from hidden states.
+
+    This layer does the following:
+    1. Extracts specific tokens or aggregates data based on pooling method.
+    2. Normalizes output if specified.
+    3. Returns structured results as `PoolerOutput`.
+
+    Attributes:
+        pooling_type: The type of pooling to use (LAST, AVERAGE, MAX).
+        normalize: Whether to normalize the pooled data.
+    """
+
+    def __init__(self, pooling_type: PoolingType, normalize: bool):
+        super().__init__()
+        self.pooling_type = pooling_type
+        self.normalize = normalize
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> PoolerOutput:
+        """Pools specific information from hidden states based on metadata."""
+        prompt_lens = PoolingTensors.from_pooling_metadata(
+            pooling_metadata, hidden_states.device).prompt_lens
+
+        if self.pooling_type == PoolingType.LAST:
+            last_token_flat_indices = torch.cumsum(prompt_lens, dim=0) - 1
+            pooled_data = hidden_states[last_token_flat_indices]
+        elif self.pooling_type == PoolingType.ALL:
+            offset = 0
+            pooled_data = []
+            for prompt_len in prompt_lens:
+                pooled_data.append(hidden_states[offset:offset + prompt_len])
+                offset += prompt_len
+        else:
+            raise ValueError(f"Invalid pooling type: {self.pooling_type}")
+
+        if self.normalize:
+            pooled_data = nn.functional.normalize(pooled_data, p=2, dim=1)
+
+        pooled_outputs = [
+            EmbeddingSequenceGroupOutput(data.tolist()) for data in pooled_data
+        ]
+
+        return PoolerOutput(outputs=pooled_outputs)

vllm/model_executor/layers/quantization/__init__.py

@@ -0,0 +1,69 @@
+from typing import Dict, Type
+
+from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.layers.quantization.awq_marlin import AWQMarlinConfig
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.quantization.bitsandbytes import (
+    BitsAndBytesConfig)
+from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (  # noqa: E501
+    CompressedTensorsConfig)
+from vllm.model_executor.layers.quantization.deepspeedfp import (
+    DeepSpeedFPConfig)
+from vllm.model_executor.layers.quantization.experts_int8 import (
+    ExpertsInt8Config)
+from vllm.model_executor.layers.quantization.fbgemm_fp8 import FBGEMMFp8Config
+from vllm.model_executor.layers.quantization.fp8 import Fp8Config
+from vllm.model_executor.layers.quantization.gguf import GGUFConfig
+from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+from vllm.model_executor.layers.quantization.gptq_marlin import (
+    GPTQMarlinConfig)
+from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
+    GPTQMarlin24Config)
+from vllm.model_executor.layers.quantization.ipex_quant import IPEXConfig
+from vllm.model_executor.layers.quantization.marlin import MarlinConfig
+from vllm.model_executor.layers.quantization.modelopt import ModelOptFp8Config
+from vllm.model_executor.layers.quantization.neuron_quant import (
+    NeuronQuantConfig)
+from vllm.model_executor.layers.quantization.qqq import QQQConfig
+from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
+from vllm.model_executor.layers.quantization.w8a16 import W8a16Config
+
+QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
+    "aqlm": AQLMConfig,
+    "awq": AWQConfig,
+    "deepspeedfp": DeepSpeedFPConfig,
+    "tpu_int8": Int8TpuConfig,
+    "fp8": Fp8Config,
+    "fbgemm_fp8": FBGEMMFp8Config,
+    "modelopt": ModelOptFp8Config,
+    # The order of gptq methods is important for config.py iteration over
+    # override_quantization_method(..)
+    "marlin": MarlinConfig,
+    "gguf": GGUFConfig,
+    "gptq_marlin_24": GPTQMarlin24Config,
+    # "gptq_marlin": GPTQMarlinConfig,
+    "awq_marlin": AWQMarlinConfig,
+    "gptq": GPTQConfig,
+    "compressed-tensors": CompressedTensorsConfig,
+    "bitsandbytes": BitsAndBytesConfig,
+    "qqq": QQQConfig,
+    "experts_int8": ExpertsInt8Config,
+    "neuron_quant": NeuronQuantConfig,
+    "ipex": IPEXConfig,
+    "w8a16": W8a16Config,
+}
+
+
+def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
+    if quantization not in QUANTIZATION_METHODS:
+        raise ValueError(f"Invalid quantization method: {quantization}")
+    return QUANTIZATION_METHODS[quantization]
+
+
+__all__ = [
+    "QuantizationConfig",
+    "get_quantization_config",
+    "QUANTIZATION_METHODS",
+]

vllm/model_executor/layers/quantization/aqlm.py

@@ -0,0 +1,374 @@
+# Supports AQLM compression, see https://github.com/Vahe1994/AQLM
+# and https://arxiv.org/pdf/2401.06118.pdf
+
+import math
+from typing import Any, Dict, List, Optional
+
+import torch
+import torch.nn.functional as F
+from torch.nn.parameter import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
+
+
+def get_int_dtype(nbits: int) -> torch.dtype:
+    if nbits <= 8:
+        return torch.int8
+    if nbits <= 16:
+        return torch.int16
+    if nbits <= 32:
+        return torch.int32
+    if nbits <= 64:
+        return torch.int64
+    raise ValueError(f"No dtype available for {nbits}-bit codebooks")
+
+
+@torch.inference_mode()
+def unpack_int_data(data: torch.IntTensor, nbits: int) -> torch.IntTensor:
+    return data.to(torch.int64) % (2**nbits)
+
+
+def dequantize_weight(codes: torch.Tensor,
+                      codebooks: torch.Tensor,
+                      scales: Optional[torch.Tensor] = None) -> torch.Tensor:
+    """
+    Decode float weights from quantization codes. Differentiable.
+    :param codes: tensor of integer quantization codes, shape 
+        [*dims, num_out_groups, num_in_groups, num_codebooks]
+    :param codebooks: tensor of vectors for each quantization code, 
+        [num_codebooks, codebook_size, out_group_size, in_group_size]
+    :param scales: weight will be multiplied by this factor, must be 
+        broadcastble with 
+        [*dims, out_groups, num_in_groups, out_group_size, in_group_size]
+    :return: reconstructed weight tensor of shape 
+        [*dims, num_in_groups*group_size]
+    """
+    num_out_groups, num_in_groups, num_codebooks = codes.shape[-3:]
+    num_codebooks, codebook_size, out_group_size, in_group_size = \
+        codebooks.shape
+    out_features = num_out_groups * out_group_size
+    in_features = num_in_groups * in_group_size
+    codebook_offsets = torch.arange(
+        0, num_codebooks * codebook_size, codebook_size,
+        device=codes.device)  # shape: [num_codebooks]
+    reconstructed_weight_flat = F.embedding_bag(
+        codes.flatten(0, -2) + codebook_offsets,
+        codebooks.flatten(0, 1).flatten(-2, -1),
+        mode="sum"
+    )  # [prod(dims) * num_out_groups * num_in_groups, out_group_size
+    # * in_group_size]
+
+    reconstructed_weight_groupwise = reconstructed_weight_flat.view(
+        list(codes.shape[:-3]) +
+        [num_out_groups, num_in_groups, out_group_size, in_group_size])
+    if scales is not None:
+        reconstructed_weight_groupwise = reconstructed_weight_groupwise.mul(
+            scales)
+    return reconstructed_weight_groupwise.swapaxes(
+        -3, -2).reshape(list(codes.shape[:-3]) + [out_features, in_features])
+
+
+def dequantize_gemm(
+    input: torch.Tensor,  #  [..., in_features]
+    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
+    codebooks: torch.
+    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
+    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
+    bias: Optional[torch.Tensor],
+) -> torch.Tensor:
+    dequantized_weight = dequantize_weight(
+        unpack_int_data(codes, codebooks.shape[1].bit_length() - 1),
+        codebooks,
+        scales,
+    )
+    return F.linear(input, dequantized_weight, bias)
+
+
+# Generic dequantization, slow but flexible.
+def generic_dequantize_gemm(
+    input: torch.Tensor,  #  [..., in_features]
+    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
+    codebooks: torch.
+    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
+    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
+    output_partition_sizes: List[int],
+    bias: Optional[torch.Tensor],
+) -> torch.Tensor:
+    output_shape = input.shape[:-1] + (scales.shape[0], )
+    output = torch.empty(output_shape, dtype=input.dtype, device=input.device)
+    num_outputs = len(output_partition_sizes)
+
+    # break the inputs and codebooks apart then combine the outputs.
+    # Surprisingly (to me) this is faster than doing 3 de-quants and 1 big
+    # multiply at the end.
+    num_codebooks = codebooks.shape[0] // num_outputs
+    assert (scales.shape[0] == codes.shape[0])
+    assert (sum(output_partition_sizes) == scales.shape[0])
+    output_offset = 0
+    codebooks_offset = 0
+    for output_size in output_partition_sizes:
+        shard_output = dequantize_gemm(
+            input, codes.narrow(0, output_offset, output_size),
+            codebooks.narrow(0, codebooks_offset, num_codebooks),
+            scales.narrow(0, output_offset, output_size), None
+            if bias is None else bias.narrow(0, output_offset, output_size))
+
+        output_slice = output.narrow(-1, output_offset, output_size)
+        assert (output_slice.shape == shard_output.shape)
+        output_slice.copy_(shard_output)
+        output_offset += output_size
+        codebooks_offset += num_codebooks
+    return output
+
+
+# Optimized dequnantize/decompression kernels, supports 1x16 and 2x8
+# at 6 and 9 times faster than the generic version above, respectively.
+def optimized_dequantize_gemm(
+    input: torch.Tensor,  #  [..., in_features]
+    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
+    codebooks: torch.
+    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
+    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
+    output_partition_sizes: List[int],
+    bias: Optional[torch.Tensor],
+) -> torch.Tensor:
+    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
+
+    if bias is None:
+        # scaling the output is fastest, so we do that when possible.
+        output = F.linear(input, weights, bias)
+        orig_shape = output.shape
+        flattened_output = output.view(-1, output.size(-1))
+        f_scales = scales.view(-1, scales.shape[0])
+        b_scales = f_scales.expand(flattened_output.shape[0], -1)
+        flattened_output *= b_scales
+        return output.view(orig_shape)
+    else:
+        b_scales = scales.view(scales.shape[:-3] + (-1, )).expand(
+            -1, weights.shape[1])
+        weights *= b_scales
+        return F.linear(input, weights, bias)
+
+
+class AQLMConfig(QuantizationConfig):
+    """Config class for AQLM.
+
+    Reference: https://github.com/Vahe1994/AQLM
+    """
+
+    def __init__(
+        self,
+        in_group_size: int,
+        nbits_per_codebook: int,
+        num_codebooks: int,
+        out_group_size: int,
+    ) -> None:
+        self.in_group_size = in_group_size
+        self.nbits_per_codebook = nbits_per_codebook
+        self.num_codebooks = num_codebooks
+        self.out_group_size = out_group_size
+
+        # out_group_size > 1 is untested, and probably won't work as-is.
+        assert (self.out_group_size == 1)
+        self.pack_factor = (self.in_group_size * self.out_group_size)
+
+    def __repr__(self) -> str:
+        return (f"AQLMConfig(in_group_size={self.in_group_size}, "
+                f"nbits_per_codebook={self.nbits_per_codebook}, "
+                f"num_codebooks={self.num_codebooks}, "
+                f"out_group_size={self.out_group_size})")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "aqlm"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []  # no extra configs.
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "AQLMConfig":
+        in_group_size = cls.get_from_keys(config, ["in_group_size"])
+        nbits_per_codebook = cls.get_from_keys(config, ["nbits_per_codebook"])
+        num_code_books = cls.get_from_keys(config, ["num_codebooks"])
+        out_group_size = cls.get_from_keys(config, ["out_group_size"])
+        return cls(in_group_size, nbits_per_codebook, num_code_books,
+                   out_group_size)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["AQLMLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return AQLMLinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class AQLMLinearMethod(LinearMethodBase):
+    """Linear method for AQLM.
+
+    Args:
+        quant_config: The AQLM quantization config.
+    """
+
+    def __init__(self, quant_config: AQLMConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        del output_size  # Unused.
+        del input_size  # Unused.
+
+        if params_dtype != torch.half:
+            raise ValueError("Only half is currently supported by aqlm")
+        if input_size_per_partition % self.quant_config.in_group_size != 0:
+            raise ValueError(
+                "The input size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+
+        output_size_per_partition = sum(output_partition_sizes)
+        if output_size_per_partition % self.quant_config.out_group_size != 0:
+            raise ValueError(
+                "The output size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+
+        codes = Parameter(
+            torch.empty(
+                # There could actually be two pack factors, one along input and
+                # one along output, but we don't currently support
+                # out_group_size, and only the one along output needs to be
+                # marked with "packed_dim" in order for QKVLinear to work.
+                output_size_per_partition,
+                input_size_per_partition // self.quant_config.pack_factor,
+                self.quant_config.num_codebooks,
+                dtype=get_int_dtype(self.quant_config.nbits_per_codebook),
+            ),
+            requires_grad=False,
+        )
+
+        set_weight_attrs(
+            codes,
+            {
+                "input_dim": 1,
+                "output_dim": 0,
+                "packed_dim": 1,
+                "pack_factor": self.quant_config.pack_factor,
+            },
+        )
+
+        codebooks = Parameter(
+            torch.empty(
+                self.quant_config.num_codebooks * len(output_partition_sizes),
+                2**self.quant_config.nbits_per_codebook,
+                self.quant_config.out_group_size,
+                self.quant_config.in_group_size,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(
+            codebooks,
+            {
+                # metadata indicates fixed size concatenated along dim 0
+                "is_metadata": True,
+                "output_partition_sizes": output_partition_sizes
+            },
+        )
+
+        scales = Parameter(
+            torch.empty(
+                (
+                    output_size_per_partition //
+                    self.quant_config.out_group_size,
+                    1,
+                    1,
+                    1,
+                ),
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(
+            scales,
+            {
+                "output_dim": 0,
+                "packed_dim": 0,
+                "pack_factor": self.quant_config.out_group_size
+            },
+        )
+
+        layer.register_parameter("codes", codes)
+        set_weight_attrs(codes, extra_weight_attrs)
+        layer.register_parameter("codebooks", codebooks)
+        set_weight_attrs(codebooks, extra_weight_attrs)
+        layer.register_parameter("scales", scales)
+        set_weight_attrs(scales, extra_weight_attrs)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        codebooks = layer.codebooks
+        codes = layer.codes
+        scales = layer.scales
+        output_partition_sizes = getattr(codebooks, "output_partition_sizes",
+                                         [])
+
+        nbooks = codes.shape[2]
+        ingroups = codebooks.shape[3]
+        outgroups = codebooks.shape[2]
+        bits = codebooks.shape[1]
+
+        # We support these formats with dedicated gemm and decompression
+        # kernels.
+        if ingroups == 8 and outgroups == 1 and (
+            (bits == 256 and nbooks == 2) or (bits == 65536 and nbooks == 1)):
+
+            # thresholds determined by timings on an A6000, one GPU
+            use_gemv = math.prod(x.shape[:-1]) <= 6
+
+            return ops.aqlm_gemm(
+                x,
+                codes,
+                codebooks,
+                scales,
+                output_partition_sizes,
+                bias,
+            ) if use_gemv else optimized_dequantize_gemm(
+                x,
+                codes,
+                codebooks,
+                scales,
+                output_partition_sizes,
+                bias,
+            )
+
+        # fall back all unoptimized formats
+        return generic_dequantize_gemm(
+            x,
+            codes,
+            codebooks,
+            scales,
+            output_partition_sizes,
+            bias,
+        )

vllm/model_executor/layers/quantization/awq.py

@@ -0,0 +1,173 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.parameter import (GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+
+
+class AWQConfig(QuantizationConfig):
+    """Config class for AWQ.
+
+    Reference: https://arxiv.org/abs/2306.00978
+    """
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        zero_point: bool,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.zero_point = zero_point
+
+        if self.weight_bits != 4:
+            raise ValueError(
+                "Currently, only 4-bit weight quantization is supported for "
+                f"AWQ, but got {self.weight_bits} bits.")
+        self.pack_factor = 32 // self.weight_bits
+
+    def __repr__(self) -> str:
+        return (f"AWQConfig(weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}, "
+                f"zero_point={self.zero_point})")
+
+    def get_name(self) -> str:
+        return "awq"
+
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # The AWQ kernel only supports Turing or newer GPUs.
+        return 75
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return [
+            "quant_config.json",  # E.g., casperhansen/vicuna-7b-v1.5-awq
+            # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
+            "quantize_config.json",
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "AWQConfig":
+        weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
+        group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
+        zero_point = cls.get_from_keys(config, ["zero_point"])
+        return cls(weight_bits, group_size, zero_point)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["AWQLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return AWQLinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
+
+
+class AWQLinearMethod(LinearMethodBase):
+    """Linear method for AWQ.
+
+    Args:
+        quant_config: The AWQ quantization config.
+    """
+
+    def __init__(self, quant_config: AWQConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        if input_size_per_partition % self.quant_config.group_size != 0:
+            raise ValueError(
+                "The input size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+
+        output_size_per_partition = sum(output_partition_sizes)
+        if output_size_per_partition % self.quant_config.pack_factor != 0:
+            raise ValueError(
+                "The output size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader)
+
+        qzeros = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.group_size,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader)
+
+        scales = GroupQuantScaleParameter(data=torch.empty(
+            input_size_per_partition // self.quant_config.group_size,
+            output_size_per_partition,
+            dtype=params_dtype,
+        ),
+                                          input_dim=0,
+                                          output_dim=1,
+                                          weight_loader=weight_loader)
+
+        layer.register_parameter("qweight", qweight)
+        layer.register_parameter("qzeros", qzeros)
+        layer.register_parameter("scales", scales)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.qweight = torch.nn.Parameter(layer.qweight.data,
+                                           requires_grad=False)
+        layer.qzeros = torch.nn.Parameter(layer.qzeros.data,
+                                          requires_grad=False)
+        layer.scales = torch.nn.Parameter(layer.scales.data,
+                                          requires_grad=False)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        qweight = layer.qweight
+        scales = layer.scales
+        qzeros = layer.qzeros
+        pack_factor = self.quant_config.pack_factor
+        out_shape = (x.shape[:-1] + (qweight.shape[-1] * pack_factor, ))
+        reshaped_x = x.reshape(-1, x.shape[-1])
+
+        # num_tokens >= threshold
+        # FP16_MATMUL_HEURISTIC_CONDITION = x.shape[:-1].numel() >= 256
+        FP16_MATMUL_HEURISTIC_CONDITION = False
+
+        if FP16_MATMUL_HEURISTIC_CONDITION:
+            out = ops.awq_dequantize(qweight, scales, qzeros, 0, 0, 0)
+            out = torch.matmul(reshaped_x, out)
+        else:
+            out = ops.awq_gemm(reshaped_x, qweight, scales, qzeros,
+                               pack_factor, group_size=self.quant_config.group_size)
+        if bias is not None:
+            out.add_(bias)
+        return out.reshape(out_shape)

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -0,0 +1,464 @@
+from typing import Any, Callable, Dict, List, Optional
+
+import torch
+from torch.nn import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               set_weight_attrs)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.utils import replace_parameter
+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    apply_awq_marlin_linear, awq_to_marlin_zero_points, check_marlin_supported,
+    marlin_make_empty_g_idx, marlin_make_workspace, marlin_moe_permute_scales,
+    marlin_permute_scales, moe_awq_to_marlin_zero_points,
+    verify_marlin_supported, verify_marlin_supports_shape)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.parameter import (GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+from vllm.platforms import current_platform
+from vllm.scalar_type import scalar_types
+
+logger = init_logger(__name__)
+
+
+class AWQMarlinConfig(QuantizationConfig):
+    """Config class for AWQ Marlin"""
+
+    # num_bits -> type
+    TYPE_MAP = {
+        4: scalar_types.uint4,
+        8: scalar_types.uint8,
+    }
+
+    def __init__(self, weight_bits: int, group_size: int, has_zp: bool,
+                 lm_head_quantized: bool) -> None:
+        self.pack_factor = 32 // weight_bits  # packed into int32
+        self.group_size = group_size
+        self.has_zp = has_zp
+        self.lm_head_quantized = lm_head_quantized
+        self.weight_bits = weight_bits
+
+        if self.weight_bits not in self.TYPE_MAP:
+            raise ValueError(f"Unsupported num_bits = {self.weight_bits}. "
+                             f"Supported num_bits = {self.TYPE_MAP.keys()}")
+
+        self.quant_type = self.TYPE_MAP[self.weight_bits]
+
+        verify_marlin_supported(self.quant_type,
+                                group_size=self.group_size,
+                                has_zp=self.has_zp)
+
+    def __repr__(self) -> str:
+        return (f"AWQMarlinConfig(quant_type={self.quant_type}, "
+                f"group_size={self.group_size}, "
+                f"has_zp={self.has_zp}, "
+                f"lm_head_quantized={self.lm_head_quantized})")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "awq_marlin"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "AWQMarlinConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        has_zp = cls.get_from_keys(config, ["zero_point"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        return cls(weight_bits, group_size, has_zp, lm_head_quantized)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        can_convert = cls.is_awq_marlin_compatible(hf_quant_cfg)
+        is_valid_user_quant = (user_quant is None or user_quant == "marlin"
+                               or user_quant == "awq_marlin")
+
+        if can_convert and is_valid_user_quant:
+            msg = ("The model is convertible to {} during runtime."
+                   " Using {} kernel.".format(cls.get_name(), cls.get_name()))
+            logger.info(msg)
+            return cls.get_name()
+
+        if can_convert and user_quant == "awq":
+            logger.info("Detected that the model can run with awq_marlin"
+                        ", however you specified quantization=awq explicitly,"
+                        " so forcing awq. Use quantization=awq_marlin for"
+                        " faster inference")
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        if (isinstance(layer, LinearBase) or
+            (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
+            return AWQMarlinLinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return AWQMoEMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    @classmethod
+    def is_awq_marlin_compatible(cls, quant_config: Dict[str, Any]):
+        # Extract data from quant config.
+        quant_method = quant_config.get("quant_method", "").lower()
+        num_bits = quant_config.get("bits")
+        group_size = quant_config.get("group_size")
+        has_zp = quant_config.get("zero_point")
+
+        if not current_platform.is_cuda():
+            return False
+
+        if quant_method != "awq":
+            return False
+
+        # If we cannot find the info needed in the config, cannot convert.
+        if (num_bits is None or group_size is None or has_zp is None):
+            return False
+
+        if num_bits not in cls.TYPE_MAP:
+            return False
+
+        return check_marlin_supported(quant_type=cls.TYPE_MAP[num_bits],
+                                      group_size=group_size,
+                                      has_zp=has_zp)
+
+
+class AWQMarlinLinearMethod(LinearMethodBase):
+    """Linear method for AWQ Marlin.
+
+    Args:
+        quant_config: The AWQ Marlin quantization config.
+    """
+
+    def __init__(self, quant_config: AWQMarlinConfig) -> None:
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ) -> None:
+        del output_size
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        # Normalize group_size
+        if self.quant_config.group_size != -1:
+            group_size = self.quant_config.group_size
+        else:
+            group_size = input_size
+
+        verify_marlin_supports_shape(
+            output_size_per_partition=output_size_per_partition,
+            input_size_per_partition=input_size_per_partition,
+            input_size=input_size,
+            group_size=group_size)
+
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader)
+
+        num_groups = input_size_per_partition // group_size
+
+        qzeros = PackedvLLMParameter(
+            data=torch.empty(
+                num_groups,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader)
+
+        scales = GroupQuantScaleParameter(data=torch.empty(
+            num_groups,
+            output_size_per_partition,
+            dtype=params_dtype,
+        ),
+                                          input_dim=0,
+                                          output_dim=1,
+                                          weight_loader=weight_loader)
+
+        layer.register_parameter("qweight", qweight)
+        layer.register_parameter("qzeros", qzeros)
+        layer.register_parameter("scales", scales)
+
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.num_groups = num_groups
+
+    # TODO: Update this docs
+    # Checkpoints are serialized in AutoAWQ format, which is different from the
+    # marlin format. This function is called after the weights are loaded.
+    # Here, we handle the repacking
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        device = layer.qweight.device
+        layer.qweight = torch.nn.Parameter(layer.qweight.data,
+                                           requires_grad=False)
+        layer.qzeros = torch.nn.Parameter(layer.qzeros.data,
+                                          requires_grad=False)
+        layer.scales = torch.nn.Parameter(layer.scales.data,
+                                          requires_grad=False)
+
+        # Allocate marlin workspace
+        layer.workspace = marlin_make_workspace(
+            layer.output_size_per_partition, device)
+
+        # Repack weights from AWQ format to marlin format.
+        marlin_qweight = ops.awq_marlin_repack(
+            layer.qweight,
+            size_k=layer.input_size_per_partition,
+            size_n=layer.output_size_per_partition,
+            num_bits=self.quant_config.quant_type.size_bits)
+        replace_parameter(layer, "qweight", marlin_qweight)
+
+        # Permute scales from AWQ format to marlin format.
+        marlin_scales = marlin_permute_scales(
+            layer.scales,
+            size_k=layer.input_size_per_partition,
+            size_n=layer.output_size_per_partition,
+            group_size=self.quant_config.group_size)
+        replace_parameter(layer, "scales", marlin_scales)
+
+        # Permute zero-points from AWQ format to marlin format.
+        marlin_zp = awq_to_marlin_zero_points(
+            layer.qzeros,
+            size_k=layer.num_groups,
+            size_n=layer.output_size_per_partition,
+            num_bits=self.quant_config.quant_type.size_bits)
+        replace_parameter(layer, "qzeros", marlin_zp)
+
+        # Not-used
+        layer.g_idx = marlin_make_empty_g_idx(device)
+        layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return apply_awq_marlin_linear(
+            input=x,
+            weight=layer.qweight,
+            weight_scale=layer.scales,
+            weight_zp=layer.qzeros,
+            g_idx=layer.g_idx,
+            g_idx_sort_indices=layer.g_idx_sort_indices,
+            workspace=layer.workspace,
+            quant_type=self.quant_config.quant_type,
+            output_size_per_partition=layer.output_size_per_partition,
+            input_size_per_partition=layer.input_size_per_partition,
+            bias=bias)
+
+
+class AWQMoEMethod(FusedMoEMethodBase):
+
+    def __init__(self, quant_config: AWQMarlinConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+        extra_weight_attrs.update({
+            "is_transposed":
+            True,
+            "quant_method":
+            FusedMoeWeightScaleSupported.GROUP.value,
+        })
+
+        w13_qweight = Parameter(torch.empty(num_experts,
+                                            hidden_size,
+                                            2 * intermediate_size //
+                                            self.quant_config.pack_factor,
+                                            dtype=torch.int32),
+                                requires_grad=False)
+        layer.register_parameter("w13_qweight", w13_qweight)
+        set_weight_attrs(w13_qweight, extra_weight_attrs)
+
+        w2_qweight = Parameter(torch.empty(num_experts,
+                                           intermediate_size,
+                                           hidden_size //
+                                           self.quant_config.pack_factor,
+                                           dtype=torch.int32),
+                               requires_grad=False)
+        layer.register_parameter("w2_qweight", w2_qweight)
+        set_weight_attrs(w2_qweight, extra_weight_attrs)
+
+        num_groups_w13 = hidden_size // self.quant_config.group_size
+        num_groups_w2 = intermediate_size // self.quant_config.group_size
+
+        # WEIGHT_SCALES
+        # Allocate 2 scales for w1 and w3 respectively.
+        w13_scales = Parameter(torch.empty(num_experts,
+                                           num_groups_w13,
+                                           intermediate_size * 2,
+                                           dtype=params_dtype),
+                               requires_grad=False)
+        layer.register_parameter("w13_scales", w13_scales)
+        set_weight_attrs(w13_scales, extra_weight_attrs)
+
+        w2_scales = Parameter(torch.empty(num_experts,
+                                          num_groups_w2,
+                                          hidden_size,
+                                          dtype=params_dtype),
+                              requires_grad=False)
+        layer.register_parameter("w2_scales", w2_scales)
+        set_weight_attrs(w2_scales, extra_weight_attrs)
+
+        # WEIGHT_ZERO_POINT
+        # Allocate 2 zero points for w1 and w3 respectively.
+        w13_qzeros = Parameter(torch.empty(num_experts,
+                                           num_groups_w13,
+                                           2 * intermediate_size //
+                                           self.quant_config.pack_factor,
+                                           dtype=torch.int32),
+                               requires_grad=False)
+        layer.register_parameter("w13_qzeros", w13_qzeros)
+        set_weight_attrs(w13_qzeros, extra_weight_attrs)
+
+        w2_qzeros = Parameter(torch.empty(num_experts,
+                                          num_groups_w2,
+                                          hidden_size //
+                                          self.quant_config.pack_factor,
+                                          dtype=torch.int32),
+                              requires_grad=False)
+        layer.register_parameter("w2_qzeros", w2_qzeros)
+        set_weight_attrs(w2_qzeros, extra_weight_attrs)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        num_experts = layer.w13_qweight.shape[0]
+        device = layer.w13_qweight.device
+
+        layer.w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        layer.w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+
+        marlin_w13_qweight = ops.awq_marlin_moe_repack(
+            layer.w13_qweight,
+            layer.w13_g_idx_sort_indices,
+            size_k=layer.w13_qweight.shape[1],
+            size_n=layer.w13_qweight.shape[2] * self.quant_config.pack_factor,
+            num_bits=self.quant_config.weight_bits,
+        )
+        replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
+
+        marlin_w2_qweight = ops.awq_marlin_moe_repack(
+            layer.w2_qweight,
+            layer.w2_g_idx_sort_indices,
+            size_k=layer.w2_qweight.shape[1],
+            size_n=layer.w2_qweight.shape[2] * self.quant_config.pack_factor,
+            num_bits=self.quant_config.weight_bits,
+        )
+        replace_parameter(layer, "w2_qweight", marlin_w2_qweight)
+
+        # Why does this take the intermediate size for size_k?
+        marlin_w13_scales = marlin_moe_permute_scales(
+            s=layer.w13_scales,
+            size_k=layer.intermediate_size_per_partition,
+            size_n=layer.w13_scales.shape[2],
+            group_size=self.quant_config.group_size,
+        )
+
+        replace_parameter(layer, "w13_scales", marlin_w13_scales)
+
+        marlin_w2_scales = marlin_moe_permute_scales(
+            s=layer.w2_scales,
+            size_k=layer.intermediate_size_per_partition,
+            size_n=layer.w2_scales.shape[2],
+            group_size=self.quant_config.group_size,
+        )
+        replace_parameter(layer, "w2_scales", marlin_w2_scales)
+
+        marlin_w13_zp = moe_awq_to_marlin_zero_points(
+            layer.w13_qzeros,
+            size_k=layer.w13_qzeros.shape[1],
+            size_n=layer.w13_qzeros.shape[2] * self.quant_config.pack_factor,
+            num_bits=self.quant_config.weight_bits)
+        replace_parameter(layer, "w13_qzeros", marlin_w13_zp)
+
+        marlin_w2_zp = moe_awq_to_marlin_zero_points(
+            layer.w2_qzeros,
+            size_k=layer.w2_qzeros.shape[1],
+            size_n=layer.w2_qzeros.shape[2] * self.quant_config.pack_factor,
+            num_bits=self.quant_config.weight_bits)
+        replace_parameter(layer, "w2_qzeros", marlin_w2_zp)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+            fused_marlin_moe)
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function)
+
+        return fused_marlin_moe(
+            x,
+            layer.w13_qweight,
+            layer.w2_qweight,
+            layer.w13_scales,
+            layer.w2_scales,
+            router_logits,
+            topk_weights,
+            topk_ids,
+            w1_zeros=layer.w13_qzeros,
+            w2_zeros=layer.w2_qzeros,
+            num_bits=self.quant_config.weight_bits,
+        )

vllm/model_executor/layers/quantization/awq_triton.py

@@ -0,0 +1,317 @@
+import torch
+import triton
+import triton.language as tl
+
+AWQ_TRITON_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
+
+
+@triton.jit
+def awq_dequantize_kernel(
+        qweight_ptr,  # quantized matrix
+        scales_ptr,  # scales, per group
+        zeros_ptr,  # zeros, per group
+        group_size,  # Should always be one of the supported group sizes
+        result_ptr,  # Output matrix
+        num_cols,  # input num cols in qweight
+        num_rows,  # input num rows in qweight
+        BLOCK_SIZE_X: tl.constexpr,
+        BLOCK_SIZE_Y: tl.constexpr):
+    # Setup the pids.
+    pid_x = tl.program_id(axis=0)
+    pid_y = tl.program_id(axis=1)
+
+    # Compute offsets and masks for qweight_ptr.
+    offsets_y = pid_y * BLOCK_SIZE_Y + tl.arange(0, BLOCK_SIZE_Y)
+    offsets_x = pid_x * BLOCK_SIZE_X + tl.arange(0, BLOCK_SIZE_X)
+    offsets = num_cols * offsets_y[:, None] + offsets_x[None, :]
+
+    masks_y = offsets_y < num_rows
+    masks_x = offsets_x < num_cols
+
+    masks = masks_y[:, None] & masks_x[None, :]
+
+    # Compute offsets and masks for result output ptr.
+    result_offsets_y = pid_y * BLOCK_SIZE_Y + tl.arange(0, BLOCK_SIZE_Y)
+    result_offsets_x = pid_x * BLOCK_SIZE_X * 8 + tl.arange(
+        0, BLOCK_SIZE_X * 8)
+    result_offsets = (8 * num_cols * result_offsets_y[:, None] +
+                      result_offsets_x[None, :])
+
+    result_masks_y = result_offsets_y < num_rows
+    result_masks_x = result_offsets_x < num_cols * 8
+    result_masks = result_masks_y[:, None] & result_masks_x[None, :]
+
+    # Load the weights.
+    iweights = tl.load(qweight_ptr + offsets, masks)
+    iweights = tl.interleave(iweights, iweights)
+    iweights = tl.interleave(iweights, iweights)
+    iweights = tl.interleave(iweights, iweights)
+
+    # Create reverse AWQ order as tensor: [0, 4, 1, 5, 2, 6, 3, 7]
+    # that will map given indices to the correct order.
+    reverse_awq_order_tensor = ((tl.arange(0, 2) * 4)[None, :] +
+                                tl.arange(0, 4)[:, None]).reshape(8)
+
+    # Use this to compute a set of shifts that can be used to unpack and
+    # reorder the values in iweights and zeros.
+    shifts = reverse_awq_order_tensor * 4
+    shifts = tl.broadcast_to(shifts[None, :], (BLOCK_SIZE_Y * BLOCK_SIZE_X, 8))
+    shifts = tl.reshape(shifts, (BLOCK_SIZE_Y, BLOCK_SIZE_X * 8))
+
+    # Unpack and reorder: shift out the correct 4-bit value and mask.
+    iweights = (iweights >> shifts) & 0xF
+
+    # Compute zero offsets and masks.
+    zero_offsets_y = pid_y * BLOCK_SIZE_Y // group_size + tl.arange(0, 1)
+    zero_offsets_x = pid_x * BLOCK_SIZE_X + tl.arange(0, BLOCK_SIZE_X)
+    zero_offsets = num_cols * zero_offsets_y[:, None] + zero_offsets_x[None, :]
+
+    zero_masks_y = zero_offsets_y < num_rows // group_size
+    zero_masks_x = zero_offsets_x < num_cols
+    zero_masks = zero_masks_y[:, None] & zero_masks_x[None, :]
+
+    # Load the zeros.
+    zeros = tl.load(zeros_ptr + zero_offsets, zero_masks)
+    zeros = tl.interleave(zeros, zeros)
+    zeros = tl.interleave(zeros, zeros)
+    zeros = tl.interleave(zeros, zeros)
+    zeros = tl.broadcast_to(zeros, (BLOCK_SIZE_Y, BLOCK_SIZE_X * 8))
+
+    # Unpack and reorder: shift out the correct 4-bit value and mask.
+    zeros = (zeros >> shifts) & 0xF
+
+    # Compute scale offsets and masks.
+    scale_offsets_y = pid_y * BLOCK_SIZE_Y // group_size + tl.arange(0, 1)
+    scale_offsets_x = (pid_x * BLOCK_SIZE_X * 8 +
+                       tl.arange(0, BLOCK_SIZE_X * 8))
+    scale_offsets = (num_cols * 8 * scale_offsets_y[:, None] +
+                     scale_offsets_x[None, :])
+    scale_masks_y = scale_offsets_y < num_rows // group_size
+    scale_masks_x = scale_offsets_x < num_cols * 8
+    scale_masks = scale_masks_y[:, None] & scale_masks_x[None, :]
+
+    # Load the scales.
+    scales = tl.load(scales_ptr + scale_offsets, scale_masks)
+    scales = tl.broadcast_to(scales, (BLOCK_SIZE_Y, BLOCK_SIZE_X * 8))
+
+    # Dequantize.
+    iweights = (iweights - zeros) * scales
+    iweights = iweights.to(result_ptr.type.element_ty)
+
+    # Finally, store.
+    tl.store(result_ptr + result_offsets, iweights, result_masks)
+
+
+@triton.jit
+def awq_gemm_kernel(a_ptr, b_ptr, c_ptr, zeros_ptr, scales_ptr, M, N, K,
+                    group_size, BLOCK_SIZE_M: tl.constexpr,
+                    BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
+                    SPLIT_K: tl.constexpr):
+    pid = tl.program_id(axis=0)
+    pid_z = tl.program_id(1)
+
+    # NOTE: This doesn't work in TRITON_INTERPRET=1 mode.  Use below instead.
+    # num_pid_n = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+
+    pid_m = pid // num_pid_n
+    pid_n = pid % num_pid_n
+
+    accumulator_dtype = c_ptr.type.element_ty
+
+    # NOTE: This doesn't work in TRITON_INTERPRET=1 mode.  Use below instead.
+    # accumulator = tl.arange(0, BLOCK_SIZE_N)
+    # accumulator = tl.broadcast_to(accumulator[None, :],
+    # (BLOCK_SIZE_M, BLOCK_SIZE_N))
+    # accumulator = accumulator & 0x0
+    # accumulator = accumulator.to(accumulator_dtype)
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N),
+                           dtype=accumulator_dtype)
+
+    # Create reverse AWQ order as tensor: [0, 4, 1, 5, 2, 6, 3, 7]
+    # that will map given indices to the correct order.
+    reverse_awq_order_tensor = ((tl.arange(0, 2) * 4)[None, :] +
+                                tl.arange(0, 4)[:, None]).reshape(8)
+
+    # Create the necessary shifts to use to unpack.
+    shifts = reverse_awq_order_tensor * 4
+    shifts = tl.broadcast_to(shifts[None, :],
+                             (BLOCK_SIZE_K * (BLOCK_SIZE_N // 8), 8))
+    shifts = tl.reshape(shifts, (BLOCK_SIZE_K, BLOCK_SIZE_N))
+
+    # Offsets and masks.
+    offsets_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    masks_am = offsets_am < M
+
+    offsets_bn = pid_n * (BLOCK_SIZE_N // 8) + tl.arange(0, BLOCK_SIZE_N // 8)
+    masks_bn = offsets_bn < N // 8
+
+    offsets_zn = pid_n * (BLOCK_SIZE_N // 8) + tl.arange(0, BLOCK_SIZE_N // 8)
+    masks_zn = offsets_zn < N // 8
+
+    offsets_sn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    masks_sn = offsets_sn < N
+
+    offsets_k = pid_z * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
+    offsets_a = K * offsets_am[:, None] + offsets_k[None, :]
+    offsets_b = (N // 8) * offsets_k[:, None] + offsets_bn[None, :]
+
+    a_ptrs = a_ptr + offsets_a
+    b_ptrs = b_ptr + offsets_b
+
+    # NOTE: Use this in TRITON_INTERPRET=1 mode instead of tl.cdiv
+    # block_offset = BLOCK_SIZE_K * SPLIT_K
+    # for k in range(0, (K + block_offset - 1) // (block_offset)):
+    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K * SPLIT_K)):
+        masks_k = offsets_k < K
+        masks_a = masks_am[:, None] & masks_k[None, :]
+        a = tl.load(a_ptrs, mask=masks_a)
+
+        masks_b = masks_k[:, None] & masks_bn[None, :]
+        b = tl.load(b_ptrs, mask=masks_b)
+        b = tl.interleave(b, b)
+        b = tl.interleave(b, b)
+        b = tl.interleave(b, b)
+
+        # Dequantize b.
+        offsets_szk = (
+            (BLOCK_SIZE_K * SPLIT_K * k + pid_z * BLOCK_SIZE_K) // group_size +
+            tl.arange(0, 1))
+        offsets_z = (N // 8) * offsets_szk[:, None] + offsets_zn[None, :]
+        masks_zk = offsets_szk < K // group_size
+        masks_z = masks_zk[:, None] & masks_zn[None, :]
+        zeros_ptrs = zeros_ptr + offsets_z
+        zeros = tl.load(zeros_ptrs, mask=masks_z)
+        zeros = tl.interleave(zeros, zeros)
+        zeros = tl.interleave(zeros, zeros)
+        zeros = tl.interleave(zeros, zeros)
+        zeros = tl.broadcast_to(zeros, (BLOCK_SIZE_K, BLOCK_SIZE_N))
+
+        offsets_s = N * offsets_szk[:, None] + offsets_sn[None, :]
+        masks_sk = offsets_szk < K // group_size
+        masks_s = masks_sk[:, None] & masks_sn[None, :]
+        scales_ptrs = scales_ptr + offsets_s
+        scales = tl.load(scales_ptrs, mask=masks_s)
+        scales = tl.broadcast_to(scales, (BLOCK_SIZE_K, BLOCK_SIZE_N))
+
+        b = (b >> shifts) & 0xF
+        zeros = (zeros >> shifts) & 0xF
+        b = (b - zeros) * scales
+        b = b.to(c_ptr.type.element_ty)
+
+        # Accumulate results.
+        accumulator = tl.dot(a, b, accumulator, out_dtype=accumulator_dtype)
+
+        offsets_k += BLOCK_SIZE_K * SPLIT_K
+        a_ptrs += BLOCK_SIZE_K * SPLIT_K
+        b_ptrs += BLOCK_SIZE_K * SPLIT_K * (N // 8)
+
+    c = accumulator.to(c_ptr.type.element_ty)
+    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = c_ptr + pid_z * N * M + N * offs_cm[:, None] + offs_cn[None, :]
+    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
+    tl.store(c_ptrs, c, mask=c_mask)
+
+
+# qweights - [K     , M // 8], int32
+# scales   - [K // G, M     ], float16
+# zeros    - [K // G, M // 8], int32
+def awq_dequantize_triton(qweight: torch.Tensor,
+                          scales: torch.Tensor,
+                          zeros: torch.Tensor,
+                          block_size_x: int = 32,
+                          block_size_y: int = 32) -> torch.Tensor:
+    K = qweight.shape[0]
+    M = scales.shape[1]
+    group_size = qweight.shape[0] // scales.shape[0]
+
+    assert K > 0 and M > 0
+    assert scales.shape[0] == K // group_size and scales.shape[1] == M
+    assert zeros.shape[0] == K // group_size and zeros.shape[1] == M // 8
+    assert group_size <= K
+    assert group_size in AWQ_TRITON_SUPPORTED_GROUP_SIZES or group_size == K
+
+    # Result tensor:
+    # number of rows = same as input tensor
+    # number of cols = 8 x input tensor num cols
+    result = torch.empty(qweight.shape[0],
+                         qweight.shape[1] * 8,
+                         device=qweight.device,
+                         dtype=scales.dtype)
+
+    Y = qweight.shape[0]  # num rows
+    X = qweight.shape[1]  # num cols
+
+    grid = lambda META: (
+        triton.cdiv(X, META['BLOCK_SIZE_X']),
+        triton.cdiv(Y, META['BLOCK_SIZE_Y']),
+    )
+    awq_dequantize_kernel[grid](qweight,
+                                scales,
+                                zeros,
+                                group_size,
+                                result,
+                                X,
+                                Y,
+                                BLOCK_SIZE_X=block_size_x,
+                                BLOCK_SIZE_Y=block_size_y)
+
+    return result
+
+
+# input   - [M, K]
+# qweight - [K, N // 8]
+# qzeros  - [K // G, N // 8]
+# scales  - [K // G, N]
+# split_k_iters - parallelism along K-dimension, int, power of 2.
+def awq_gemm_triton(input: torch.Tensor,
+                    qweight: torch.Tensor,
+                    scales: torch.Tensor,
+                    qzeros: torch.Tensor,
+                    split_k_iters: int,
+                    block_size_m: int = 32,
+                    block_size_n: int = 32,
+                    block_size_k: int = 32) -> torch.Tensor:
+    M, K = input.shape
+    N = qweight.shape[1] * 8
+    group_size = qweight.shape[0] // qzeros.shape[0]
+
+    assert N > 0 and K > 0 and M > 0
+    assert qweight.shape[0] == K and qweight.shape[1] == N // 8
+    assert qzeros.shape[0] == K // group_size and qzeros.shape[1] == N // 8
+    assert scales.shape[0] == K // group_size and scales.shape[1] == N
+    assert split_k_iters & (split_k_iters - 1) == 0 and split_k_iters != 0
+    assert split_k_iters <= 32
+    assert group_size <= K
+    assert group_size in AWQ_TRITON_SUPPORTED_GROUP_SIZES or group_size == K
+
+    grid = lambda META: (
+        triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(
+            N, META['BLOCK_SIZE_N']),
+        split_k_iters,
+    )
+
+    result = torch.zeros((split_k_iters, M, N),
+                         dtype=scales.dtype,
+                         device=input.device)
+
+    # A = input, B = qweight, C = result
+    # A = M x K, B = K x N, C = M x N
+    awq_gemm_kernel[grid](input,
+                          qweight,
+                          result,
+                          qzeros,
+                          scales,
+                          M,
+                          N,
+                          K,
+                          group_size,
+                          BLOCK_SIZE_M=block_size_m,
+                          BLOCK_SIZE_N=block_size_n,
+                          BLOCK_SIZE_K=block_size_k,
+                          SPLIT_K=split_k_iters)
+
+    result = result.sum(0)
+
+    return result

vllm/model_executor/layers/quantization/base_config.py

@@ -0,0 +1,143 @@
+import inspect
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Type
+
+import torch
+from torch import nn
+
+
+class QuantizeMethodBase(ABC):
+    """Base class for different quantized methods."""
+
+    @abstractmethod
+    def create_weights(self, layer: torch.nn.Module, *weight_args,
+                       **extra_weight_attrs):
+        """Create weights for a layer.
+
+        The weights will be set as attributes of the layer."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply(self, layer: torch.nn.Module, *args, **kwargs) -> torch.Tensor:
+        """Apply the weights in layer to the input tensor.
+
+        Expects create_weights to have been called before on the layer."""
+        raise NotImplementedError
+
+    # Not required functions
+    def embedding(self, layer: torch.nn.Module, *args,
+                  **kwargs) -> torch.Tensor:
+        """Gather embeddings in the layer based on indices in the input tensor.
+
+        Expects create_weights to have been called before on the layer."""
+        raise NotImplementedError
+
+    def process_weights_after_loading(self, layer: nn.Module) -> None:
+        """Process the weight after loading.
+
+        This can be used for example, to transpose weights for computation.
+        """
+        return
+
+
+def method_has_implemented_embedding(
+        method_class: Type[QuantizeMethodBase]) -> bool:
+    """
+    Not all quant methods have embedding implemented, so we need to check that
+    it exists for our given method. We check this by making sure the function
+    has been changed from the base implementation.
+    """
+    base_embedding = inspect.getattr_static(QuantizeMethodBase, "embedding",
+                                            None)
+    class_embedding = inspect.getattr_static(method_class, "embedding", None)
+
+    return (class_embedding is not None
+            and class_embedding is not base_embedding)
+
+
+class QuantizationConfig(ABC):
+    """Base class for quantization configs."""
+
+    @abstractmethod
+    def get_name(self) -> str:
+        """Name of the quantization method."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        """List of supported activation dtypes."""
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def get_min_capability(cls) -> int:
+        """Minimum GPU capability to support the quantization method.
+
+        E.g., 70 for Volta, 75 for Turing, 80 for Ampere.
+        This requirement is due to the custom CUDA kernels used by the
+        quantization method.
+        """
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def get_config_filenames() -> List[str]:
+        """List of filenames to search for in the model directory."""
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def from_config(cls, config: Dict[str, Any]) -> "QuantizationConfig":
+        """Create a config class from the model's quantization config."""
+        raise NotImplementedError
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        """
+           Detects if this quantization method can support a given checkpoint
+           format by overriding the user specified quantization method -- 
+           this method should only be overwritten by subclasses in exceptional 
+           circumstances
+        """
+        return None
+
+    @staticmethod
+    def get_from_keys(config: Dict[str, Any], keys: List[str]) -> Any:
+        """Get a value from the model's quantization config."""
+        for key in keys:
+            if key in config:
+                return config[key]
+        raise ValueError(f"Cannot find any of {keys} in the model's "
+                         "quantization config.")
+
+    @staticmethod
+    def get_from_keys_or(config: Dict[str, Any], keys: List[str],
+                         default: Any) -> Any:
+        """Get a optional value from the model's quantization config."""
+        try:
+            return QuantizationConfig.get_from_keys(config, keys)
+        except ValueError:
+            return default
+
+    @abstractmethod
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional[QuantizeMethodBase]:
+        """Get the quantize method to use for the quantized layer.
+        
+        Args:
+            layer: The layer for the quant method.
+            prefix: The full name of the layer in the state dict
+        Returns:
+            The quantize method. None if the given layer doesn't support quant
+            method.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_scaled_act_names(self) -> List[str]:
+        """Returns the activation function names that should be post-scaled.
+
+        For now, this is only used by AWQ.
+        """
+        raise NotImplementedError

vllm/model_executor/layers/quantization/bitsandbytes.py

@@ -0,0 +1,316 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               set_weight_attrs)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+
+
+class BitsAndBytesConfig(QuantizationConfig):
+    """Config class for BitsAndBytes Quantization.
+
+    Reference: https://arxiv.org/abs/2305.14314
+    """
+
+    def __init__(
+        self,
+        load_in_8bit: bool = False,
+        load_in_4bit: bool = True,
+        bnb_4bit_compute_dtype: str = "float32",
+        bnb_4bit_quant_type: str = "fp4",
+        bnb_4bit_use_double_quant: bool = False,
+        llm_int8_enable_fp32_cpu_offload: bool = False,
+        llm_int8_has_fp16_weight: bool = False,
+        llm_int8_skip_modules: Optional[Any] = None,
+        llm_int8_threshold: float = 0.0,
+    ) -> None:
+
+        self.load_in_8bit = load_in_8bit
+        self.load_in_4bit = load_in_4bit
+        self.bnb_4bit_compute_dtype = bnb_4bit_compute_dtype
+        self.bnb_4bit_quant_type = bnb_4bit_quant_type
+        self.bnb_4bit_use_double_quant = bnb_4bit_use_double_quant
+        self.llm_int8_enable_fp32_cpu_offload = llm_int8_enable_fp32_cpu_offload
+        self.llm_int8_has_fp16_weight = llm_int8_has_fp16_weight
+        self.llm_int8_skip_modules = llm_int8_skip_modules
+        self.llm_int8_threshold = llm_int8_threshold
+
+    def __repr__(self) -> str:
+        return "BitsAndBytesConfig"
+
+    @classmethod
+    def get_name(self) -> str:
+        return "bitsandbytes"
+
+    @classmethod
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        return [torch.float32, torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return [
+            "adapter_config.json",
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "BitsAndBytesConfig":
+
+        def get_safe_value(config, keys, default_value=None):
+            try:
+                value = cls.get_from_keys(config, keys)
+                return value if value is not None else default_value
+            except ValueError:
+                return default_value
+
+        load_in_8bit = get_safe_value(config, ["load_in_8bit"],
+                                      default_value=False)
+        load_in_4bit = get_safe_value(config, ["load_in_4bit"],
+                                      default_value=True)
+        bnb_4bit_compute_dtype = get_safe_value(config,
+                                                ["bnb_4bit_compute_dtype"],
+                                                default_value="float32")
+        bnb_4bit_quant_type = get_safe_value(config, ["bnb_4bit_quant_type"],
+                                             default_value="fp4")
+        bnb_4bit_use_double_quant = get_safe_value(
+            config, ["bnb_4bit_use_double_quant"], default_value=False)
+        llm_int8_enable_fp32_cpu_offload = get_safe_value(
+            config, ["llm_int8_enable_fp32_cpu_offload"], default_value=False)
+        llm_int8_has_fp16_weight = get_safe_value(config,
+                                                  ["llm_int8_has_fp16_weight"],
+                                                  default_value=False)
+        llm_int8_skip_modules = get_safe_value(config,
+                                               ["llm_int8_skip_modules"],
+                                               default_value=[])
+        llm_int8_threshold = get_safe_value(config, ["llm_int8_threshold"],
+                                            default_value=0.0)
+
+        return cls(
+            load_in_8bit=load_in_8bit,
+            load_in_4bit=load_in_4bit,
+            bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
+            bnb_4bit_quant_type=bnb_4bit_quant_type,
+            bnb_4bit_use_double_quant=bnb_4bit_use_double_quant,
+            llm_int8_enable_fp32_cpu_offload=llm_int8_enable_fp32_cpu_offload,
+            llm_int8_has_fp16_weight=llm_int8_has_fp16_weight,
+            llm_int8_skip_modules=llm_int8_skip_modules,
+            llm_int8_threshold=llm_int8_threshold)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["BitsAndBytesLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return BitsAndBytesLinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class BitsAndBytesLinearMethod(LinearMethodBase):
+    """Linear method for BitsAndBytes.
+
+    Args:
+       quant_config: The BitsAndBytes quantization config.
+    """
+
+    def __init__(self, quant_config: BitsAndBytesConfig):
+        try:
+            import bitsandbytes
+            if bitsandbytes.__version__ < "0.44.0":
+                raise ImportError("bitsandbytes version is wrong. Please "
+                                  "install bitsandbytes>=0.44.0.")
+        except ImportError as err:
+            raise ImportError("Please install bitsandbytes>=0.44.0 via "
+                              "`pip install bitsandbytes>=0.44.0` to use "
+                              "bitsandbytes quantizer.") from err
+
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        from bitsandbytes.nn import Int8Params
+
+        def calculate_quant_ratio(dtype):
+            if dtype.is_floating_point:
+                return torch.finfo(dtype).bits // torch.iinfo(torch.uint8).bits
+            else:
+                return torch.iinfo(dtype).bits // torch.iinfo(torch.uint8).bits
+
+        def create_qweight_for_8bit():
+            qweight = Int8Params(
+                data=torch.empty(sum(output_partition_sizes),
+                                 input_size_per_partition,
+                                 dtype=torch.int8),
+                has_fp16_weights=self.quant_config.llm_int8_has_fp16_weight,
+                requires_grad=False)
+            set_weight_attrs(
+                qweight, {
+                    "input_dim": 0,
+                    "output_dim": 0,
+                    "pack_factor": 1,
+                    "use_bitsandbytes_8bit": True,
+                    "generation": 0
+                })
+            return qweight
+
+        def create_qweight_for_4bit():
+            quant_ratio = calculate_quant_ratio(params_dtype)
+
+            total_size = input_size_per_partition * sum(output_partition_sizes)
+            if total_size % quant_ratio != 0:
+                raise ValueError(
+                    "The input size is not aligned with the quantized "
+                    "weight shape.")
+
+            qweight = torch.nn.Parameter(torch.empty(total_size // quant_ratio,
+                                                     1,
+                                                     dtype=torch.uint8),
+                                         requires_grad=False)
+            set_weight_attrs(
+                qweight, {
+                    "input_dim": 0,
+                    "output_dim": 0,
+                    "pack_factor": quant_ratio,
+                    "use_bitsandbytes_4bit": True
+                })
+            return qweight
+
+        if self.quant_config.load_in_8bit:
+            qweight = create_qweight_for_8bit()
+        else:
+            qweight = create_qweight_for_4bit()
+
+        layer.register_parameter("qweight", qweight)
+        set_weight_attrs(qweight, extra_weight_attrs)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        if self.quant_config.load_in_8bit:
+            return self._apply_8bit_weight(layer, x, bias)
+        else:
+            return self._apply_4bit_weight(layer, x, bias)
+
+    def _apply_8bit_weight(
+            self,
+            layer: torch.nn.Module,
+            x: torch.Tensor,
+            bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        # only load the bitsandbytes module when needed
+        from bitsandbytes import MatmulLtState, matmul
+
+        original_type = x.dtype
+        bf_x = x.to(torch.bfloat16)
+
+        qweight = layer.qweight
+        offsets = qweight.bnb_shard_offsets
+        quant_states = qweight.bnb_quant_state
+        matmul_states = qweight.matmul_state
+        generation = qweight.generation
+
+        out_dim_0 = x.shape[0]
+        out_dim_1 = sum(
+            [quant_state[1].shape[0] for quant_state in quant_states.items()])
+        out = torch.empty(out_dim_0,
+                          out_dim_1,
+                          dtype=torch.float16,
+                          device=x.device)
+
+        current_index = 0
+        for i in range(len(quant_states)):
+            output_size = quant_states[i].shape[0]
+
+            # in profile_run or the first generation of inference,
+            # create new matmul_states
+            if generation == 0 or generation == 1:
+                matmul_states[i] = MatmulLtState()
+                matmul_states[i].CB = qweight[offsets[i]:offsets[i + 1]]
+                matmul_states[i].SCB = quant_states[i].to(x.device)
+                matmul_states[i].threshold = (
+                    self.quant_config.llm_int8_threshold)
+                matmul_states[i].has_fp16_weights = (
+                    self.quant_config.llm_int8_has_fp16_weight)
+                matmul_states[i].is_training = False
+                if matmul_states[i].threshold > 0.0 and not matmul_states[
+                        i].has_fp16_weights:
+                    matmul_states[i].use_pool = True
+
+            new_x = bf_x.unsqueeze(0)
+
+            out[:, current_index:current_index + output_size] = matmul(
+                new_x,
+                qweight[offsets[i]:offsets[i + 1]],
+                state=matmul_states[i])
+
+            current_index += output_size
+
+            # only update the matmul_states if it is not profile_run
+            if (generation > 0
+                    and not self.quant_config.llm_int8_has_fp16_weight
+                    and matmul_states[i].CB is not None
+                    and matmul_states[i].CxB is not None):
+                del matmul_states[i].CB
+                qweight[offsets[i]:offsets[i + 1]] = matmul_states[i].CxB
+
+        out = out.to(original_type)
+
+        if bias is not None:
+            out += bias
+
+        qweight.generation += 1
+
+        return out
+
+    def _apply_4bit_weight(
+            self,
+            layer: torch.nn.Module,
+            x: torch.Tensor,
+            bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        # only load the bitsandbytes module when needed
+        from bitsandbytes import matmul_4bit
+
+        original_type = x.dtype
+        bf_x = x.to(torch.bfloat16)
+
+        qweight = layer.qweight
+        quant_states = qweight.bnb_quant_state
+        offsets = qweight.bnb_shard_offsets
+
+        out_dim_0 = x.shape[0]
+        out_dim_1 = sum(
+            [quant_state[1].shape[0] for quant_state in quant_states.items()])
+        out = torch.empty(out_dim_0,
+                          out_dim_1,
+                          dtype=torch.bfloat16,
+                          device=x.device)
+
+        current_index = 0
+        for i in range(len(quant_states)):
+            output_size = quant_states[i].shape[0]
+            # It is more efficient to use out kwarg like
+            # matmul_4bit(..., out = ...).  Infeasible now due to the bug
+            # https://github.com/TimDettmers/bitsandbytes/issues/1235.
+            # Need to change  after the bug is fixed.
+            out[:, current_index:current_index + output_size] = matmul_4bit(
+                bf_x, qweight[offsets[i]:offsets[i + 1]].t(), quant_states[i])
+
+            current_index += output_size
+
+        out = out.to(original_type)
+
+        if bias is not None:
+            out += bias
+
+        return out

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -0,0 +1,412 @@
+from typing import Any, Dict, List, Optional, cast
+
+import torch
+from pydantic import BaseModel
+
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import (  # noqa: E501
+    CompressedTensorsMoEMethod)
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS,
+    CompressedTensorsScheme, CompressedTensorsW4A16Sparse24,
+    CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8,
+    CompressedTensorsW8A16Fp8, CompressedTensorsWNA16)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    CompressionFormat, QuantizationArgs, QuantizationStrategy,
+    QuantizationType, find_matched_target, is_activation_quantization_format,
+    should_ignore_layer)
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.platforms import current_platform
+
+__all__ = ["CompressedTensorsLinearMethod"]
+
+
+class CompressedTensorsConfig(QuantizationConfig):
+
+    def __init__(self,
+                 target_scheme_map: Dict[str, Any],
+                 ignore: List[str],
+                 quant_format: str,
+                 kv_cache_scheme: Optional[Dict[str, Any]] = None):
+
+        self.ignore = ignore
+        self.quant_format = quant_format
+        # Map from [target -> scheme]
+        self.target_scheme_map = target_scheme_map
+        self.kv_cache_scheme = kv_cache_scheme
+
+    def get_linear_method(self) -> "CompressedTensorsLinearMethod":
+        return CompressedTensorsLinearMethod(self)
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    def get_name(self) -> str:
+        return "compressed_tensors"
+
+    def get_quant_method(
+        self,
+        layer: torch.nn.Module,
+        prefix: str,
+    ) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+
+        # Check if the layer is skipped for quantization.
+        # TODO (@robertgshaw2): support module names
+        if should_ignore_layer(prefix, ignore=self.ignore):
+            return UnquantizedLinearMethod()
+        if isinstance(layer, LinearBase):
+            scheme = self.get_scheme(layer=layer, layer_name=prefix)
+            layer.scheme = scheme
+            return CompressedTensorsLinearMethod(self)
+        if isinstance(layer, Attention):
+            return CompressedTensorsKVCacheMethod(self)
+        if isinstance(layer, FusedMoE):
+            return CompressedTensorsMoEMethod.get_moe_method(self)
+        return None
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "CompressedTensorsConfig":
+        target_scheme_map: Dict[str, Any] = dict()
+        ignore = cast(List[str], config.get("ignore"))
+        quant_format = cast(str, config.get("format"))
+
+        # The quant_config has multiple config_groups, each containing
+        # an input_activations key with details about how the activations are
+        # quantized, a weights key indicating how the weights are quantized,
+        # and a list of targets under the `targets` key, dictating which
+        # layers are impacted by the quantization details. The quantization
+        # details follow the structure defined by the QuantizationArgs
+        # pydantic model, which is used to verify the structure of the
+        # quant_config and also store the details for later use.
+        for _, quant_config in config["config_groups"].items():
+            targets = quant_config.get("targets")
+            for target in targets:
+                target_scheme_map[target] = {}
+                target_scheme_map[target][
+                    "weights"] = QuantizationArgs.parse_obj(
+                        quant_config.get("weights"))
+                try:
+                    target_scheme_map[target][
+                        "input_activations"] = QuantizationArgs.parse_obj(
+                            quant_config.get("input_activations"))
+                except Exception:
+                    target_scheme_map[target]["input_activations"] = None
+
+        return cls(target_scheme_map=target_scheme_map,
+                   ignore=ignore,
+                   quant_format=quant_format,
+                   kv_cache_scheme=config.get("kv_cache_scheme"))
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    def _check_scheme_supported(self,
+                                min_capability: int,
+                                error: bool = True) -> bool:
+        capability_tuple = current_platform.get_device_capability()
+
+        if capability_tuple is not None:
+            capability = capability_tuple.to_int()
+            supported = capability >= min_capability
+            if error and not supported:
+                raise RuntimeError(
+                    "Quantization scheme is not supported for ",
+                    f"the current GPU. Min capability: {min_capability}. ",
+                    f"Current capability: {capability}.")
+            return supported
+        else:
+            return False
+
+    def _is_static_tensor_w8a8(self, weight_quant: BaseModel,
+                               input_quant: BaseModel) -> bool:
+        is_8_bits = weight_quant.num_bits == input_quant.num_bits == 8
+        weight_strategy = (
+            weight_quant.strategy == QuantizationStrategy.TENSOR.value
+            or weight_quant.strategy == QuantizationStrategy.CHANNEL.value)
+        is_tensor = (weight_strategy and input_quant.strategy
+                     == QuantizationStrategy.TENSOR.value)
+        is_static = not weight_quant.dynamic and not input_quant.dynamic
+
+        # Both symmetric and asymmetric input quantization supported.
+        # Only symmetric weight quantization supported.
+        return is_8_bits and is_tensor and weight_quant.symmetric and is_static
+
+    def _is_dynamic_token_w8a8(self, weight_quant: BaseModel,
+                               input_quant: BaseModel) -> bool:
+        is_8_bits = weight_quant.num_bits == input_quant.num_bits == 8
+        weight_strategy = (
+            weight_quant.strategy == QuantizationStrategy.TENSOR.value
+            or weight_quant.strategy == QuantizationStrategy.CHANNEL.value)
+        is_token = (weight_strategy and input_quant.strategy
+                    == QuantizationStrategy.TOKEN.value)
+        is_dynamic = not weight_quant.dynamic and input_quant.dynamic
+
+        # Both symmetric and asymmetric input quantization supported.
+        # Only symmetric weight quantization supported.
+        return is_8_bits and is_token and weight_quant.symmetric and is_dynamic
+
+    def _is_fp8_w8a8(self, weight_quant: BaseModel,
+                     input_quant: BaseModel) -> bool:
+        # Confirm weights and activations quantized.
+        if weight_quant is None or input_quant is None:
+            return False
+
+        # Confirm weight scheme is supported.
+        is_floating_point = (weight_quant.type == QuantizationType.FLOAT
+                             and input_quant.type == QuantizationType.FLOAT)
+        is_symmetric_weight = weight_quant.symmetric
+        is_static_weight = not weight_quant.dynamic
+        is_per_tensor_or_channel_weight = (weight_quant.strategy in [
+            QuantizationStrategy.TENSOR, QuantizationStrategy.CHANNEL
+        ])
+        if not (is_floating_point and is_symmetric_weight and is_static_weight
+                and is_per_tensor_or_channel_weight):
+            return False
+
+        # Dynamic quantization is always supported if weights supported.
+        if input_quant.dynamic:
+            return True
+
+        # Confirm activation scheme is supported.
+        is_symmetric_activation = input_quant.symmetric
+        is_per_tensor_activation = (
+            input_quant.strategy == QuantizationStrategy.TENSOR)
+        return is_symmetric_activation and is_per_tensor_activation
+
+    def _is_fp8_w8a16(self, weight_quant: BaseModel,
+                      input_quant: BaseModel) -> bool:
+        # Confirm weights quantized.
+        if weight_quant is None:
+            return False
+
+        # Confirm we have floating points.
+        if weight_quant.type != QuantizationType.FLOAT:
+            return False
+
+        # Confirm weight scheme is supported.
+        is_symmetric_weight = weight_quant.symmetric
+        is_static_weight = not weight_quant.dynamic
+        is_per_tensor_or_channel_weight = (weight_quant.strategy in [
+            QuantizationStrategy.TENSOR, QuantizationStrategy.CHANNEL
+        ])
+        if not (is_symmetric_weight and is_static_weight  # noqa: SIM103
+                and is_per_tensor_or_channel_weight):
+            return False
+
+        # All conditions satisfied.
+        return True
+
+    def _is_wNa16_group_channel(self, weight_quant: BaseModel,
+                                input_quant: BaseModel) -> bool:
+        input_quant_none = input_quant is None
+        is_symmetric = weight_quant.symmetric
+        is_channel_group = (
+            weight_quant.strategy == QuantizationStrategy.CHANNEL.value
+            or weight_quant.strategy == QuantizationStrategy.GROUP.value)
+        is_static = not weight_quant.dynamic
+
+        return (is_channel_group and input_quant_none and is_symmetric
+                and is_static)
+
+    def _get_scheme_from_parts(
+            self, weight_quant: BaseModel,
+            input_quant: BaseModel) -> "CompressedTensorsScheme":
+
+        # Detect If Mixed Precision
+        if self._is_wNa16_group_channel(weight_quant, input_quant):
+            if (self.quant_format == CompressionFormat.marlin_24.value
+                    and weight_quant.num_bits in W4A16SPARSE24_SUPPORTED_BITS):
+                return CompressedTensorsW4A16Sparse24(
+                    strategy=weight_quant.strategy,
+                    num_bits=weight_quant.num_bits,
+                    group_size=weight_quant.group_size)
+            if (self.quant_format == CompressionFormat.pack_quantized.value
+                    and weight_quant.num_bits in WNA16_SUPPORTED_BITS):
+                return CompressedTensorsWNA16(
+                    num_bits=weight_quant.num_bits,
+                    strategy=weight_quant.strategy,
+                    group_size=weight_quant.group_size,
+                    actorder=weight_quant.actorder)
+
+        # Detect If Activation Quantization.
+        # TODO @dsikka: clean-up conditions
+        if is_activation_quantization_format(self.quant_format):
+            if self._is_fp8_w8a8(weight_quant, input_quant):
+                is_fp8_w8a8_supported = self._check_scheme_supported(
+                    CompressedTensorsW8A8Fp8.get_min_capability(), error=False)
+                if is_fp8_w8a8_supported:
+                    return CompressedTensorsW8A8Fp8(
+                        strategy=weight_quant.strategy,
+                        is_static_input_scheme=(input_quant
+                                                and not input_quant.dynamic))
+                else:
+                    return CompressedTensorsW8A16Fp8(
+                        strategy=weight_quant.strategy,
+                        is_static_input_scheme=(input_quant
+                                                and not input_quant.dynamic))
+
+            if self._is_fp8_w8a16(weight_quant, input_quant):
+                return CompressedTensorsW8A16Fp8(
+                    strategy=weight_quant.strategy,
+                    is_static_input_scheme=(input_quant
+                                            and not input_quant.dynamic))
+
+            if self._is_static_tensor_w8a8(weight_quant, input_quant):
+                return CompressedTensorsW8A8Int8(
+                    strategy=weight_quant.strategy,
+                    is_static_input_scheme=True,
+                    input_symmetric=input_quant.symmetric)
+
+            if self._is_dynamic_token_w8a8(weight_quant, input_quant):
+                return CompressedTensorsW8A8Int8(
+                    strategy=weight_quant.strategy,
+                    is_static_input_scheme=False,
+                    input_symmetric=input_quant.symmetric)
+
+        raise NotImplementedError(
+            "No compressed-tensors compatible scheme was found.")
+
+    def get_scheme(
+            self,
+            layer: torch.nn.Module,
+            layer_name: Optional[str] = None) -> "CompressedTensorsScheme":
+        """
+        compressed-tensors supports non uniform in the following way:
+
+        ignore: List of layer_names or nn.Module names to be ignored.
+        targets of config_groups: There can be N config_groups which each
+            have a quantization scheme. Each config_group has a list of targets
+            which can be a full layer_name, a regex for a layer_name, or
+            an nn.Module name.
+
+        We first check whether a layer is in the ignore group and use
+        CompressedTensorsUnquantized (i.e. fp16/bf16) scheme for the layer
+
+        We then detect whether a layer_name is found in any target and
+        use the quantization scheme corresponding to the matched target
+        to select the CompressedTensorsScheme used for infernece.
+        """
+
+        # Find the "target" in the compressed-tensors config
+        # that our layer conforms to.
+        # TODO (@robertgshaw): add compressed-tensors as dep
+        # so we do not have to re-write these functions
+        # need to make accelerate optional in ct to do this
+        matched_target = find_matched_target(
+            layer_name=layer_name,
+            module=layer,
+            targets=self.target_scheme_map.keys())
+
+        # Find the quant_scheme
+        scheme_dict = self.target_scheme_map[matched_target]
+        scheme = self._get_scheme_from_parts(
+            weight_quant=scheme_dict["weights"],
+            input_quant=scheme_dict["input_activations"])
+
+        # Raise error if device does not support the scheme
+        # (e.g. fp8 needs ada lovelace)
+        self._check_scheme_supported(scheme.get_min_capability())
+
+        return scheme
+
+
+class CompressedTensorsLinearMethod(LinearMethodBase):
+
+    def __init__(self, quantization_config: CompressedTensorsConfig):
+        self.quantization_config = quantization_config
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.scheme.process_weights_after_loading(layer)
+
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        """
+        Use the CompressedTensorsScheme associated with each layer to create
+        the necessary parameters for the layer. See LinearMethodBase for param
+        details
+        """
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.scheme.create_weights(
+            layer=layer,
+            input_size=input_size,
+            input_size_per_partition=input_size_per_partition,
+            output_partition_sizes=output_partition_sizes,
+            output_size=output_size,
+            params_dtype=params_dtype,
+            weight_loader=weight_loader)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None):
+        """
+        Use the output of create_weights and the CompressedTensorsScheme
+        associated with the layer to apply the forward pass with the
+        layer input.  See LinearMethodBase for param details
+
+        """
+
+        scheme = layer.scheme
+        if scheme is None:
+            raise ValueError("A scheme must be defined for each layer")
+        return scheme.apply_weights(layer, x, bias=bias)
+
+
+class CompressedTensorsKVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from compressed-tensors
+    checkpoints.
+    """
+
+    def __init__(self, quant_config: CompressedTensorsConfig):
+        self.validate_kv_cache_scheme(quant_config.kv_cache_scheme)
+        super().__init__(quant_config)
+
+    @staticmethod
+    def validate_kv_cache_scheme(kv_cache_scheme: Optional[Dict[str, Any]]):
+        """
+        Validator for the kv cache scheme. Useful for controlling the
+        kv cache quantization schemes, that are being supported in vLLM
+        :param kv_cache_scheme: the compressed-tensors kv cache scheme
+        """
+        if kv_cache_scheme is None:
+            return
+
+        type_ = kv_cache_scheme.get("type")
+        num_bits = kv_cache_scheme.get("num_bits")
+
+        if type_ != "float" and num_bits != 8:
+            raise NotImplementedError(
+                "Currently supported kv cache quantization is "
+                "num_bits=8, type=float, however "
+                f"received num_bits={num_bits}, type={type_}")
+
+        strategy = kv_cache_scheme.get("strategy")
+        if strategy != "tensor":
+            raise NotImplementedError(
+                "Only support per-tensor scaling factor "
+                "for compressed-tensors KV cache. "
+                f"Expected strategy: tensor, found strategy: {strategy}")
+
+        is_symmetric = kv_cache_scheme.get("symmetric")
+        if not is_symmetric:
+            raise NotImplementedError(
+                "Only support symmetric scaling factor "
+                "for compressed-tensors KV cache. "
+                f"However found symmetric: {is_symmetric}")

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -0,0 +1,511 @@
+import enum
+from enum import Enum
+from typing import Callable, List, Optional
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
+                                                  FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    WNA16_SUPPORTED_BITS)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    CompressionFormat, QuantizationStrategy)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.utils import is_hip, print_warning_once
+
+
+class GPTQMarlinState(Enum):
+    REPACK = enum.auto()
+    READY = enum.auto()
+
+
+__all__ = [
+    "CompressedTensorsMoEMethod", "CompressedTensorsW8A8Fp8MoEMethod",
+    "CompressedTensorsWNA16MoEMethod"
+]
+
+
+class CompressedTensorsMoEMethod(FusedMoEMethodBase):
+
+    @staticmethod
+    def get_moe_method(
+        quant_config: "CompressedTensorsConfig"  # type: ignore # noqa E501
+    ) -> "CompressedTensorsMoEMethod":
+        # TODO: @dsikka: refactor this to use schemes as other kernels
+        # are supported + check if the layer is being ignored.
+        weight_quant = quant_config.target_scheme_map["Linear"].get("weights")
+        input_quant = quant_config.target_scheme_map["Linear"].get(
+            "input_activations")
+
+        if quant_config._is_wNa16_group_channel(weight_quant, input_quant):
+            return CompressedTensorsWNA16MoEMethod(quant_config)
+        elif quant_config._is_fp8_w8a8(weight_quant, input_quant):
+            return CompressedTensorsW8A8Fp8MoEMethod(quant_config)
+        else:
+            raise RuntimeError(
+                f"Unsupported FusedMoe scheme: {weight_quant}, {input_quant}")
+
+
+class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
+
+    def __init__(
+            self,
+            quant_config: "CompressedTensorsConfig"  # type: ignore # noqa E501
+    ):
+        self.quant_config = quant_config
+        self.weight_quant = self.quant_config.target_scheme_map["Linear"].get(
+            "weights")
+        self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
+            "input_activations")
+
+        if not (self.weight_quant.strategy == QuantizationStrategy.TENSOR
+                and self.input_quant.strategy == QuantizationStrategy.TENSOR):
+            raise ValueError(
+                "For FP8 Fused MoE layers, only per-tensor scales"
+                "for weights and activations are supported. Found "
+                f"{self.weight_quant}, {self.input_quant}")
+
+        self.static_input_scales = not self.input_quant.dynamic
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        params_dtype = torch.float8_e4m3fn
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                    2 * intermediate_size,
+                                                    hidden_size,
+                                                    dtype=params_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                   hidden_size,
+                                                   intermediate_size,
+                                                   dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        # Allocate 2 scales for w1 and w3 respectively.
+        # They will be combined to a single scale after weight loading.
+        w13_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                         2,
+                                                         dtype=torch.float32),
+                                              requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+
+        w2_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                        dtype=torch.float32),
+                                             requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        # Add the quantization method used (per tensor/grouped/channel)
+        # to ensure the weight scales are loaded in properly
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # INPUT_SCALES
+        if self.static_input_scales:
+            w13_input_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w13_input_scale", w13_input_scale)
+            set_weight_attrs(w13_input_scale, extra_weight_attrs)
+
+            w2_input_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                requires_grad=False)
+            layer.register_parameter("w2_input_scale", w2_input_scale)
+            set_weight_attrs(w2_input_scale, extra_weight_attrs)
+        else:
+            layer.w13_input_scale = None
+            layer.w2_input_scale = None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # Fp8 moe kernels require a single activation scale.
+        # We take the max of all the scales in case they differ.
+        if self.static_input_scales:
+            if (layer.w13_input_scale is None or layer.w2_input_scale is None):
+                raise ValueError(
+                    "QuantConfig has static quantization, but found "
+                    "activation scales are None.")
+            if (not all_close_1d(layer.w13_input_scale)
+                    or not all_close_1d(layer.w2_input_scale)):
+                print_warning_once(
+                    "Found input_scales that are not equal for "
+                    "fp8 MoE layer. Using the maximum across experts "
+                    "for each layer. ")
+            layer.w13_input_scale = torch.nn.Parameter(
+                layer.w13_input_scale.max(), requires_grad=False)
+            layer.w2_input_scale = torch.nn.Parameter(
+                layer.w2_input_scale.max(), requires_grad=False)
+
+        # If rocm, normalize the weights and scales to e4m3fnuz
+        if is_hip():
+            # Normalize the weights and scales
+            w13_weight, w13_weight_scale, w13_input_scale = \
+                normalize_e4m3fn_to_e4m3fnuz(
+                    layer.w13_weight, layer.w13_weight_scale,
+                    layer.w13_input_scale)
+            w2_weight, w2_weight_scale, w2_input_scale = \
+                normalize_e4m3fn_to_e4m3fnuz(
+                    layer.w2_weight, layer.w2_weight_scale,
+                    layer.w2_input_scale)
+            # Reset the parameter
+            layer.w13_weight = torch.nn.Parameter(w13_weight,
+                                                  requires_grad=False)
+            layer.w13_weight_scale = torch.nn.Parameter(w13_weight_scale,
+                                                        requires_grad=False)
+            if w13_input_scale is not None:
+                layer.w13_input_scale = torch.nn.Parameter(w13_input_scale,
+                                                           requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(w2_weight,
+                                                 requires_grad=False)
+            layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
+                                                       requires_grad=False)
+            if w2_input_scale is not None:
+                layer.w2_input_scale = torch.nn.Parameter(w2_input_scale,
+                                                          requires_grad=False)
+
+        # Fp8 moe kernel needs single weight scale for w13 per expert.
+        # We take the max then dequant and requant each expert.
+        assert layer.w13_weight_scale is not None
+        shard_size = layer.intermediate_size_per_partition
+        max_w13_scales = layer.w13_weight_scale.max(dim=1).values
+        for expert_id in range(layer.num_experts):
+            start = 0
+            for shard_id in range(2):
+                dq_weight = per_tensor_dequantize(
+                    layer.w13_weight[expert_id][start:start + shard_size, :],
+                    layer.w13_weight_scale[expert_id][shard_id])
+                layer.w13_weight[expert_id][
+                    start:start + shard_size, :], _ = ops.scaled_fp8_quant(
+                        dq_weight, max_w13_scales[expert_id])
+                start += shard_size
+
+        layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
+                                                    requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe import fused_experts
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function)
+
+        return fused_experts(x,
+                             layer.w13_weight,
+                             layer.w2_weight,
+                             topk_weights=topk_weights,
+                             topk_ids=topk_ids,
+                             inplace=True,
+                             use_fp8_w8a8=True,
+                             w1_scale=layer.w13_weight_scale,
+                             w2_scale=layer.w2_weight_scale,
+                             a1_scale=layer.w13_input_scale,
+                             a2_scale=layer.w2_input_scale)
+
+
+class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+
+    def __init__(
+            self,
+            quant_config: "CompressedTensorsConfig"  # type: ignore # noqa E501
+    ):
+        self.quant_config = quant_config
+        # TODO: @dsikka: refactor this to use schemes as other kernels
+        # are supported + check if the layer is being ignored.
+        config = self.quant_config.target_scheme_map["Linear"].get("weights")
+        self.num_bits = config.num_bits
+        self.packed_factor = 32 // config.num_bits
+        self.strategy = config.strategy.value
+        self.group_size = config.group_size
+        assert config.symmetric, (
+            "Only symmetric quantization is supported for MoE")
+
+        if not (self.quant_config.quant_format
+                == CompressionFormat.pack_quantized.value
+                and self.num_bits in WNA16_SUPPORTED_BITS):
+            raise ValueError("For Fused MoE layers, only ",
+                             f"{CompressionFormat.pack_quantized.value} ",
+                             "is supported for the following bits: ",
+                             f"{WNA16_SUPPORTED_BITS}")
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        # Will transpose the loaded weight along the
+        # intermediate and hidden dim sizes. Will
+        # shard for TP along the transposed dims
+        extra_weight_attrs.update({
+            "is_transposed": True,
+            "quant_method": self.strategy
+        })
+        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                    hidden_size //
+                                                    self.packed_factor,
+                                                    2 * intermediate_size,
+                                                    dtype=torch.int32),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight_packed", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                   intermediate_size //
+                                                   self.packed_factor,
+                                                   hidden_size,
+                                                   dtype=torch.int32),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight_packed", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        if self.strategy == "channel":
+            num_groups_w2 = num_groups_w13 = 1
+            self.group_size = -1
+        else:
+            num_groups_w2 = intermediate_size // self.group_size
+            num_groups_w13 = hidden_size // self.group_size
+
+        w13_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                  num_groups_w13,
+                                                  2 * intermediate_size,
+                                                  dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_scale)
+        set_weight_attrs(w13_scale, extra_weight_attrs)
+
+        w2_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                 num_groups_w2,
+                                                 hidden_size,
+                                                 dtype=params_dtype),
+                                      requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_scale)
+        set_weight_attrs(w2_scale, extra_weight_attrs)
+
+        w2_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2),
+                                             requires_grad=False)
+        layer.register_parameter("w2_weight_shape", w2_weight_shape)
+        set_weight_attrs(w2_weight_shape, extra_weight_attrs)
+        w13_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2),
+                                              requires_grad=False)
+
+        layer.register_parameter("w13_weight_shape", w13_weight_shape)
+        set_weight_attrs(w13_weight_shape, extra_weight_attrs)
+
+        w13_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_g_idx", w13_g_idx)
+        set_weight_attrs(w13_g_idx, extra_weight_attrs)
+
+        w2_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_g_idx", w2_g_idx)
+        set_weight_attrs(w2_g_idx, extra_weight_attrs)
+
+        w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_g_idx_sort_indices",
+                                 w13_g_idx_sort_indices)
+        set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)
+
+        w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_g_idx_sort_indices",
+                                 w2_g_idx_sort_indices)
+        set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)
+
+        layer.a13_scale = None
+        layer.a2_scale = None
+        layer.marlin_state = GPTQMarlinState.REPACK
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+
+        def replace_tensor(name, new_t):
+            # It is important to use resize_() here since it ensures
+            # the same buffer is reused
+            getattr(layer, name).resize_(new_t.shape)
+            getattr(layer, name).copy_(new_t)
+            del new_t
+
+        def get_scale_perms(num_bits: int):
+            scale_perm: List[int] = []
+            for i in range(8):
+                scale_perm.extend([i + 8 * j for j in range(8)])
+            scale_perm_single: List[int] = []
+            for i in range(4):
+                scale_perm_single.extend(
+                    [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
+            return scale_perm, scale_perm_single
+
+        def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int,
+                                  group_size: int, num_bits: int):
+            scale_perm, scale_perm_single = get_scale_perms(num_bits)
+            if group_size < size_k and group_size != -1:
+                s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
+            else:
+                s = s.reshape((-1, len(scale_perm_single)))[:,
+                                                            scale_perm_single]
+            s = s.reshape((-1, size_n)).contiguous()
+            return s
+
+        def marlin_moe_permute_scales(s: torch.Tensor, size_k: int,
+                                      size_n: int, group_size: int,
+                                      num_bits: int):
+            num_experts = s.shape[0]
+            output = torch.empty((num_experts, s.shape[1], s.shape[2]),
+                                 device=s.device,
+                                 dtype=s.dtype)
+            for e in range(num_experts):
+                output[e] = marlin_permute_scales(s[e], size_k, size_n,
+                                                  group_size, num_bits)
+            return output
+
+        size_k2 = layer.w2_weight_packed.shape[2]
+        size_k13 = layer.w13_weight_packed.shape[2]
+
+        num_experts = layer.w13_g_idx.shape[0]
+        device = layer.w13_g_idx.device
+        layer.w13_g_idx = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        layer.w2_g_idx = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        layer.w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        layer.w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+
+        marlin_w13_qweight = ops.gptq_marlin_moe_repack(
+            layer.w13_weight_packed,
+            layer.w13_g_idx_sort_indices,
+            layer.w13_weight_packed.shape[1] * self.packed_factor,
+            layer.w13_weight_packed.shape[2],
+            self.num_bits,
+        )
+        replace_tensor("w13_weight_packed", marlin_w13_qweight)
+        marlin_w2_qweight = ops.gptq_marlin_moe_repack(
+            layer.w2_weight_packed,
+            layer.w2_g_idx_sort_indices,
+            layer.w2_weight_packed.shape[1] * self.packed_factor,
+            layer.w2_weight_packed.shape[2],
+            self.num_bits,
+        )
+        replace_tensor("w2_weight_packed", marlin_w2_qweight)
+        # Repack scales
+        marlin_w13_scales = marlin_moe_permute_scales(
+            layer.w13_weight_scale,
+            size_k13,
+            layer.w13_weight_scale.shape[2],
+            self.group_size,
+            self.num_bits,
+        )
+        replace_tensor("w13_weight_scale", marlin_w13_scales)
+        marlin_w2_scales = marlin_moe_permute_scales(
+            layer.w2_weight_scale,
+            layer.w2_weight_scale.shape[1] * self.packed_factor,
+            size_k2,
+            self.group_size,
+            self.num_bits,
+        )
+        replace_tensor("w2_weight_scale", marlin_w2_scales)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+            fused_marlin_moe)
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function)
+
+        return fused_marlin_moe(
+            x,
+            layer.w13_weight_packed,
+            layer.w2_weight_packed,
+            layer.w13_weight_scale,
+            layer.w2_weight_scale,
+            router_logits,
+            topk_weights,
+            topk_ids,
+            g_idx1=layer.w13_g_idx,
+            g_idx2=layer.w2_g_idx,
+            sort_indices1=layer.w13_g_idx_sort_indices,
+            sort_indices2=layer.w2_g_idx_sort_indices,
+            num_bits=self.num_bits,
+        )

vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py

@@ -0,0 +1,19 @@
+from .compressed_tensors_scheme import CompressedTensorsScheme
+from .compressed_tensors_w4a16_24 import (W4A16SPARSE24_SUPPORTED_BITS,
+                                          CompressedTensorsW4A16Sparse24)
+from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8
+from .compressed_tensors_w8a8_int8 import CompressedTensorsW8A8Int8
+from .compressed_tensors_w8a16_fp8 import CompressedTensorsW8A16Fp8
+from .compressed_tensors_wNa16 import (WNA16_SUPPORTED_BITS,
+                                       CompressedTensorsWNA16)
+
+__all__ = [
+    "CompressedTensorsScheme",
+    "CompressedTensorsWNA16",
+    "CompressedTensorsW8A16Fp8",
+    "CompressedTensorsW4A16Sparse24",
+    "CompressedTensorsW8A8Int8",
+    "CompressedTensorsW8A8Fp8",
+    "WNA16_SUPPORTED_BITS",
+    "W4A16SPARSE24_SUPPORTED_BITS",
+]

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py

@@ -0,0 +1,52 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+import torch
+
+__all__ = ["CompressedTensorsScheme"]
+
+
+class CompressedTensorsScheme(ABC):
+    """
+    Abstract class used to describe the weight creation and forward pass 
+    of different quantization schemes supported by CompressedTensors.
+    """
+
+    @classmethod
+    @abstractmethod
+    def get_min_capability(cls) -> int:
+        """
+        Get minimum device capability.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def create_weights(self, *args, **kwargs):
+        """
+        Weight creation for the particular scheme. Inputs to this function 
+
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]):
+        """
+        Run the forward pass for the particular scheme. This is where 
+        scheme-specific dequant/quant steps/kernels should be applied.
+
+        :param layer: torch.nn.Module with the registered weights and 
+            other parameters relevant to the particular scheme. 
+        :param x: input to the layer
+        :param bias: bias parameter
+
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def process_weights_after_loading(self, layer: torch.nn.Module):
+        """
+        Called after weight loading is complete for any cleanup that
+        needs to occur.
+        """
+        raise NotImplementedError

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_24.py

@@ -0,0 +1,153 @@
+from typing import Callable, List, Optional
+
+import torch
+from torch.nn import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
+    GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+from vllm.scalar_type import scalar_types
+
+__all__ = ["CompressedTensorsW4A16Sparse24"]
+W4A16SPARSE24_SUPPORTED_TYPES_MAP = {
+    4: scalar_types.uint4b8,
+}
+W4A16SPARSE24_SUPPORTED_BITS = list(W4A16SPARSE24_SUPPORTED_TYPES_MAP.keys())
+
+
+class CompressedTensorsW4A16Sparse24(CompressedTensorsScheme):
+
+    def __init__(self,
+                 strategy: str,
+                 num_bits: int,
+                 group_size: Optional[int] = None):
+        self.strategy = strategy
+        self.group_size = group_size
+        self.tile_size = 16
+
+        if num_bits not in W4A16SPARSE24_SUPPORTED_TYPES_MAP:
+            raise ValueError(
+                f"Unsupported num_bits = {num_bits}. "
+                f"Supported num_bits = {W4A16SPARSE24_SUPPORTED_BITS}")
+
+        self.quant_type = W4A16SPARSE24_SUPPORTED_TYPES_MAP[num_bits]
+
+        if self.strategy == "group" and self.group_size is None:
+            raise ValueError(
+                "group_size must be given when using strategy group")
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # ampere + up
+        return 80
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # required by torch.compile to be torch.nn.Parameter
+        layer.weight_packed = Parameter(layer.weight_packed.data,
+                                        requires_grad=False)
+        layer.scale_packed = Parameter(layer.scale_packed.data,
+                                       requires_grad=False)
+        layer.meta = Parameter(layer.meta.data, requires_grad=False)
+
+    def create_weights(self, layer: torch.nn.Module, input_size: int,
+                       output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+
+        pack_factor = 32 // self.quant_type.size_bits
+        output_size_per_partition = sum(output_partition_sizes)
+
+        qweight = PackedvLLMParameter(data=torch.empty(
+            input_size_per_partition // self.tile_size // 2,
+            output_size_per_partition * self.tile_size // pack_factor,
+            dtype=torch.int32,
+        ),
+                                      input_dim=0,
+                                      output_dim=1,
+                                      packed_dim=1,
+                                      packed_factor=pack_factor,
+                                      marlin_tile_size=self.tile_size,
+                                      weight_loader=weight_loader)
+
+        input_groups = (1 if self.group_size is None else
+                        input_size_per_partition // self.group_size)
+
+        weight_scale_args = {
+            "data":
+            torch.empty(
+                input_groups,
+                output_size_per_partition,
+                dtype=params_dtype,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+
+        if self.group_size is not None:
+            scales = GroupQuantScaleParameter(output_dim=1,
+                                              input_dim=0,
+                                              **weight_scale_args)
+        else:
+            scales = ChannelQuantScaleParameter(output_dim=1,
+                                                **weight_scale_args)
+
+        weight_shape = BasevLLMParameter(data=torch.empty(2,
+                                                          dtype=torch.int64),
+                                         weight_loader=weight_loader)
+
+        meta = PackedvLLMParameter(data=torch.empty(
+            input_size_per_partition // 8 // 2 // 2,
+            output_size_per_partition * 2,
+            dtype=torch.int16,
+        ),
+                                   input_dim=0,
+                                   output_dim=1,
+                                   packed_dim=1,
+                                   packed_factor=1,
+                                   marlin_tile_size=2,
+                                   weight_loader=weight_loader)
+
+        layer.register_parameter("weight_packed", qweight)
+        layer.register_parameter("weight_shape", weight_shape)
+        layer.register_parameter("scale_packed", scales)
+        layer.register_parameter("meta", meta)
+
+        max_workspace_size = (
+            output_size_per_partition //
+            GPTQ_MARLIN_24_MIN_THREAD_N) * GPTQ_MARLIN_24_MAX_PARALLEL
+
+        workspace = Parameter(torch.zeros(max_workspace_size, dtype=torch.int),
+                              requires_grad=False)
+        layer.workspace = workspace
+
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]) -> torch.Tensor:
+
+        qweight = layer.weight_packed
+        meta = layer.meta
+        scales = layer.scale_packed
+        workspace = layer.workspace
+
+        x_2d = x.view(-1, x.shape[-1])
+
+        size_m = x_2d.shape[0]
+        size_k = x_2d.shape[1]
+        size_n = scales.shape[1]
+
+        output_2d = ops.gptq_marlin_24_gemm(x_2d, qweight, meta, scales,
+                                            workspace, self.quant_type, size_m,
+                                            size_n, size_k)
+
+        output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
+
+        if bias is not None:
+            output.add_(bias)  # In-place add
+
+        return output

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a16_fp8.py

@@ -0,0 +1,118 @@
+from typing import Callable, List, Optional
+
+import torch
+
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationStrategy)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
+    apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    convert_to_channelwise)
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+
+__all__ = ["CompressedTensorsW8A16Fp8"]
+
+SUPPORTED_STRATEGIES = [
+    QuantizationStrategy.CHANNEL, QuantizationStrategy.TENSOR
+]
+
+
+class CompressedTensorsW8A16Fp8(CompressedTensorsScheme):
+
+    def __init__(self, strategy: str, is_static_input_scheme: bool):
+        self.strategy = strategy
+        self.is_static_input_scheme = is_static_input_scheme
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # ampere and up
+        return 80
+
+    # W8A8-Fp8 kernels support only per-tensor and per-channel cases.
+    # So if we have a fused module (QKV, MLP) with per tensor scales,
+    # we expand each scale to its shard's channels.
+    def process_weights_after_loading(self, layer) -> None:
+        if self.strategy == QuantizationStrategy.TENSOR:
+            ws_channelwise = convert_to_channelwise(layer.weight_scale,
+                                                    layer.logical_widths)
+            layer.weight_scale = torch.nn.Parameter(ws_channelwise,
+                                                    requires_grad=False)
+        else:
+            # required by torch.compile to be torch.nn.Parameter
+            layer.weight_scale = torch.nn.Parameter(layer.weight_scale.data,
+                                                    requires_grad=False)
+
+        # Weights must be transposed for marlin
+        layer.weight = torch.nn.Parameter(layer.weight.t(),
+                                          requires_grad=False)
+
+        if self.is_static_input_scheme:
+            # required by torch.compile to be torch.nn.Parameter
+            layer.input_scale = torch.nn.Parameter(layer.input_scale.data,
+                                                   requires_grad=False)
+        prepare_fp8_layer_for_marlin(layer, strategy="channel")
+
+    def create_weights(self, layer: torch.nn.Module, input_size: int,
+                       output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+
+        output_size_per_partition = sum(output_partition_sizes)
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.orig_dtype = params_dtype
+
+        # WEIGHT
+        weight = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition,
+            dtype=torch.float8_e4m3fn),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes), 1),
+                                 dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader)
+        elif self.strategy == QuantizationStrategy.TENSOR:
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+        else:
+            raise ValueError(
+                f"Unsupported weight strategy={self.strategy}, "
+                f"supported strategies are {SUPPORTED_STRATEGIES}")
+
+        weight_scale[:] = torch.finfo(torch.float32).min
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE (to deal with converted checkpoints)
+        if self.is_static_input_scheme:
+            input_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                  weight_loader=weight_loader)
+            layer.register_parameter("input_scale", input_scale)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        return apply_fp8_marlin_linear(input=x,
+                                       weight=layer.weight,
+                                       weight_scale=layer.weight_scale,
+                                       workspace=layer.workspace,
+                                       size_n=layer.output_size_per_partition,
+                                       size_k=layer.input_size_per_partition,
+                                       bias=bias)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py

@@ -0,0 +1,143 @@
+from typing import Callable, List, Optional
+
+import torch
+from torch.nn import Parameter
+
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationStrategy)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    apply_fp8_linear, cutlass_fp8_supported, normalize_e4m3fn_to_e4m3fnuz,
+    requantize_with_max_scale)
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+from vllm.utils import is_hip
+
+__all__ = ["CompressedTensorsW8A8Fp8"]
+
+
+class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
+
+    def __init__(self, strategy: str, is_static_input_scheme: bool):
+        self.strategy = strategy
+        self.is_static_input_scheme = is_static_input_scheme
+        self.cutlass_fp8_supported = cutlass_fp8_supported()
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # lovelace and up
+        return 89
+
+    def process_weights_after_loading(self, layer) -> None:
+        # If per tensor, when we have a fused module (e.g. QKV) with per
+        # tensor scales (thus N scales being passed to the kernel),
+        # requantize so we can always run per tensor
+        if self.strategy == QuantizationStrategy.TENSOR:
+            max_w_scale, weight = requantize_with_max_scale(
+                weight=layer.weight,
+                weight_scale=layer.weight_scale,
+                logical_widths=layer.logical_widths,
+            )
+
+            if is_hip():
+                weight, max_w_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
+                    weight=weight,
+                    weight_scale=max_w_scale,
+                    input_scale=layer.input_scale)
+                if input_scale is not None:
+                    layer.input_scale = Parameter(input_scale,
+                                                  requires_grad=False)
+
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
+
+        # If channelwise, scales are already lined up, so just transpose.
+        elif self.strategy == QuantizationStrategy.CHANNEL:
+            weight = layer.weight
+
+            if is_hip():
+                weight, weight_scale, input_scale = \
+                    normalize_e4m3fn_to_e4m3fnuz(
+                        weight=weight,
+                        weight_scale=layer.weight_scale,
+                        input_scale=layer.input_scale)
+                if input_scale is not None:
+                    layer.input_scale = Parameter(input_scale,
+                                                  requires_grad=False)
+            else:
+                weight_scale = layer.weight_scale.data
+
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            # required by torch.compile to be torch.nn.Parameter
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+
+        else:
+            raise ValueError(f"Unknown quantization strategy {self.strategy}")
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            layer.input_scale = Parameter(layer.input_scale.max(),
+                                          requires_grad=False)
+        else:
+            layer.input_scale = None
+
+    def create_weights(self, layer: torch.nn.Module,
+                       output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+        output_size_per_partition = sum(output_partition_sizes)
+        layer.logical_widths = output_partition_sizes
+
+        # WEIGHT
+        weight = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition,
+            dtype=torch.float8_e4m3fn),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        # TODO: update create_xxx_parameter functions to return
+        # the newly added parameters
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes), 1),
+                                 dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader)
+        else:
+            assert self.strategy == QuantizationStrategy.TENSOR
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+
+        # min requirement for fp8 kernels
+        weight_scale[:] = torch.finfo(torch.float32).min
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            input_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                  weight_loader=weight_loader)
+            input_scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("input_scale", input_scale)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        return apply_fp8_linear(
+            input=x,
+            weight=layer.weight,
+            weight_scale=layer.weight_scale,
+            input_scale=layer.input_scale,
+            bias=bias,
+            cutlass_fp8_supported=self.cutlass_fp8_supported,
+            use_per_token_if_dynamic=True)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -0,0 +1,155 @@
+from typing import Callable, List, Optional
+
+import torch
+from torch.nn import Parameter
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationStrategy)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    apply_int8_linear, convert_to_channelwise)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+
+logger = init_logger(__name__)
+
+
+class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
+
+    def __init__(self, strategy: str, is_static_input_scheme: bool,
+                 input_symmetric: bool):
+        self.strategy = strategy
+        self.is_static_input_scheme = is_static_input_scheme
+        self.input_symmetric = input_symmetric
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # turing and up
+        return 75
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # WEIGHT
+        # Cutlass kernels need transposed weight.
+        weight = layer.weight
+        layer.weight = Parameter(weight.t(), requires_grad=False)
+
+        # WEIGHT SCALE
+        # Cutlass kernels support only per-tensor and per-channel.
+        # If we have a fused module (QKV, MLP) with per tensor scales (thus N
+        # scales being passed to the kernel), convert to the per-channel case.
+        is_fused_module = len(self.logical_widths) > 1
+        if is_fused_module and self.strategy == QuantizationStrategy.TENSOR:
+            ws_channelwise = convert_to_channelwise(layer.weight_scale,
+                                                    self.logical_widths)
+            layer.weight_scale = Parameter(ws_channelwise, requires_grad=False)
+        else:
+            layer.weight_scale = Parameter(layer.weight_scale.data,
+                                           requires_grad=False)
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            if self.input_symmetric:
+                layer.input_scale = Parameter(layer.input_scale.max(),
+                                              requires_grad=False)
+                layer.input_zero_point = None
+            else:
+                # reconstruct the ranges
+                int8_traits = torch.iinfo(torch.int8)
+                azps = layer.input_zero_point.to(dtype=torch.int32)
+                range_max = (layer.input_scale *
+                             (int8_traits.max - azps)).max()
+                range_min = (layer.input_scale *
+                             (int8_traits.min - azps)).min()
+
+                scale = (range_max - range_min) / (int8_traits.max -
+                                                   int8_traits.min)
+                layer.input_scale = Parameter(scale, requires_grad=False)
+
+                # AZP loaded as int8 but used as int32
+                azp = (int8_traits.min -
+                       range_min / scale).to(dtype=torch.int32)
+                layer.input_zero_point = Parameter(azp, requires_grad=False)
+
+        else:
+            layer.input_scale = None
+            layer.input_zero_point = None
+
+        # azp_adj is the AZP adjustment term, used to account for weights.
+        # It does not depend on scales or azp, so it is the same for
+        # static and dynamic quantization.
+        # For more details, see csrc/quantization/cutlass_w8a8/Epilogues.md
+        # https://github.com/vllm-project/vllm/blob/8d59dbb00044a588cab96bcdc028006ed922eb06/csrc/quantization/cutlass_w8a8/Epilogues.md
+        if not self.input_symmetric:
+            layer.azp_adj = layer.weight.sum(dim=0,
+                                             keepdim=True,
+                                             dtype=torch.int32)
+        else:
+            layer.azp_adj = None
+
+    def create_weights(self, layer: torch.nn.Module,
+                       output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+        self.logical_widths = output_partition_sizes
+
+        # WEIGHT
+        if input_size_per_partition % 64 != 0:
+            pad_input_size_per_partition = (input_size_per_partition // 64 + 1) * 64
+        else:
+            pad_input_size_per_partition = input_size_per_partition
+        w_pad = torch.zeros(
+                sum(output_partition_sizes),
+                pad_input_size_per_partition,
+                dtype=torch.int8)
+        w = w_pad[:, :input_size_per_partition]
+        weight = ModelWeightParameter(data=w,
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes), 1),
+                                 dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader)
+        else:
+            assert self.strategy == QuantizationStrategy.TENSOR
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            input_scale = BasevLLMParameter(data=torch.empty(
+                1, dtype=torch.float32),
+                                            weight_loader=weight_loader)
+            layer.register_parameter("input_scale", input_scale)
+
+            if not self.input_symmetric:
+                # Note: compressed-tensors stores the zp using the same dtype
+                # as the weights
+                # AZP loaded as int8 but used as int32
+                input_zero_point = BasevLLMParameter(
+                    data=torch.empty(1, dtype=torch.int8),
+                    weight_loader=weight_loader)
+                layer.register_parameter("input_zero_point", input_zero_point)
+
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]) -> torch.Tensor:
+
+        return apply_int8_linear(input=x,
+                                 weight=layer.weight,
+                                 weight_scale=layer.weight_scale,
+                                 input_scale=layer.input_scale,
+                                 input_zero_point=layer.input_zero_point,
+                                 azp_adj=layer.azp_adj,
+                                 bias=bias)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py

@@ -0,0 +1,163 @@
+from typing import Callable, List, Optional, Set
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    ActivationOrdering)
+from vllm.model_executor.layers.quantization.kernels import (
+    MPLinearLayerConfig, choose_mp_linear_kernel)
+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    marlin_repeat_scales_on_all_ranks)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter,
+                                           RowvLLMParameter)
+from vllm.scalar_type import scalar_types
+
+logger = init_logger(__name__)
+
+__all__ = ["CompressedTensorsWNA16"]
+WNA16_SUPPORTED_TYPES_MAP = {
+    4: scalar_types.uint4b8,
+    8: scalar_types.uint8b128
+}
+WNA16_SUPPORTED_BITS = list(WNA16_SUPPORTED_TYPES_MAP.keys())
+
+
+class CompressedTensorsWNA16(CompressedTensorsScheme):
+    _kernel_backends_being_used: Set[str] = set()
+
+    def __init__(self,
+                 strategy: str,
+                 num_bits: int,
+                 group_size: Optional[int] = None,
+                 actorder: Optional[ActivationOrdering] = None):
+
+        self.pack_factor = 32 // num_bits
+        self.strategy = strategy
+        self.group_size = -1 if group_size is None else group_size
+        self.has_g_idx = actorder == ActivationOrdering.GROUP
+
+        if self.group_size == -1 and self.strategy != "channel":
+            raise ValueError("Marlin kernels require group quantization or "
+                             "channelwise quantization, but found no group "
+                             "size and strategy is not channelwise.")
+
+        if num_bits not in WNA16_SUPPORTED_TYPES_MAP:
+            raise ValueError(
+                f"Unsupported num_bits = {num_bits}. "
+                f"Supported num_bits = {WNA16_SUPPORTED_TYPES_MAP.keys()}")
+
+        self.quant_type = WNA16_SUPPORTED_TYPES_MAP[num_bits]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # ampere and up
+        return 80
+
+    def create_weights(self, layer: torch.nn.Module, output_size: int,
+                       input_size: int, output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+
+        output_size_per_partition = sum(output_partition_sizes)
+
+        mp_linear_kernel_config = MPLinearLayerConfig(
+            full_weight_shape=(input_size, output_size),
+            partition_weight_shape=\
+                (input_size_per_partition, output_size_per_partition),
+            weight_type=self.quant_type,
+            act_type=params_dtype,
+            group_size=self.group_size,
+            zero_points=False,
+            has_g_idx=self.has_g_idx
+        )
+
+        kernel_type = choose_mp_linear_kernel(mp_linear_kernel_config)
+
+        if kernel_type.__name__ not in self._kernel_backends_being_used:
+            logger.info("Using %s for CompressedTensorsWNA16",
+                        kernel_type.__name__)
+            self._kernel_backends_being_used.add(kernel_type.__name__)
+
+        # If group_size is -1, we are in channelwise case.
+        group_size = self.group_size if self.group_size != -1 else input_size
+        row_parallel = (input_size != input_size_per_partition)
+        partition_scales = not marlin_repeat_scales_on_all_ranks(
+            self.has_g_idx, self.group_size, row_parallel)
+
+        scales_and_zp_size = input_size // group_size
+
+        if partition_scales:
+            assert input_size_per_partition % group_size == 0
+            scales_and_zp_size = input_size_per_partition // group_size
+
+        weight = PackedvLLMParameter(input_dim=1,
+                                     output_dim=0,
+                                     weight_loader=weight_loader,
+                                     packed_factor=self.pack_factor,
+                                     packed_dim=1,
+                                     data=torch.empty(
+                                         output_size_per_partition,
+                                         input_size_per_partition //
+                                         self.pack_factor,
+                                         dtype=torch.int32,
+                                     ))
+
+        weight_scale_args = {
+            "weight_loader":
+            weight_loader,
+            "data":
+            torch.empty(
+                output_size_per_partition,
+                scales_and_zp_size,
+                dtype=params_dtype,
+            )
+        }
+        if not partition_scales:
+            weight_scale = ChannelQuantScaleParameter(output_dim=0,
+                                                      **weight_scale_args)
+        else:
+            weight_scale = GroupQuantScaleParameter(output_dim=0,
+                                                    input_dim=1,
+                                                    **weight_scale_args)
+
+        # A 2D array defining the original shape of the weights
+        # before packing
+        weight_shape = BasevLLMParameter(data=torch.empty(2,
+                                                          dtype=torch.int64),
+                                         weight_loader=weight_loader)
+
+        layer.register_parameter("weight_packed", weight)
+        layer.register_parameter("weight_scale", weight_scale)
+        layer.register_parameter("weight_shape", weight_shape)
+
+        # group index (for activation reordering)
+        if self.has_g_idx:
+            weight_g_idx = RowvLLMParameter(data=torch.empty(
+                input_size_per_partition,
+                dtype=torch.int32,
+            ),
+                                            input_dim=0,
+                                            weight_loader=weight_loader)
+            layer.register_parameter("weight_g_idx", weight_g_idx)
+
+        self.kernel = kernel_type(mp_linear_kernel_config,
+                                  w_q_param_name="weight_packed",
+                                  w_s_param_name="weight_scale",
+                                  w_zp_param_name=None,
+                                  w_gidx_param_name="weight_g_idx")
+
+    # Checkpoints are serialized in compressed-tensors format, which is
+    # different from the format the kernel may want. Handle repacking here.
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        self.kernel.process_weights_after_loading(layer)
+
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]) -> torch.Tensor:
+        return self.kernel.apply_weights(layer, x, bias)

vllm/model_executor/layers/quantization/compressed_tensors/utils.py

@@ -0,0 +1,269 @@
+import regex as re
+from enum import Enum
+from typing import Any, Dict, Iterable, Optional, Union
+
+from pydantic import BaseModel, Field, field_validator
+from torch.nn import Module
+
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    FUSED_LAYER_NAME_MAPPING)
+
+
+class CompressionFormat(Enum):
+    dense = "dense"
+    sparse_bitmask = "sparse-bitmask"
+    naive_quantized = "naive-quantized"
+    float_quantized = "float-quantized"
+    int_quantized = "int-quantized"
+    pack_quantized = "pack-quantized"
+    marlin_24 = "marlin-24"
+
+
+class QuantizationType(str, Enum):
+    """
+    Enum storing quantization type options
+    """
+
+    INT = "int"
+    FLOAT = "float"
+
+
+class QuantizationStrategy(str, Enum):
+    """
+    Enum storing quantization strategy options
+    """
+
+    TENSOR = "tensor"
+    CHANNEL = "channel"
+    GROUP = "group"
+    BLOCK = "block"
+    TOKEN = "token"
+
+
+class ActivationOrdering(str, Enum):
+    """
+    Enum storing strategies for activation ordering
+
+    Group: reorder groups and weight\n
+    Weight: only reorder weight, not groups. Slightly lower latency and
+    accuracy compared to group actorder\n
+    """
+
+    GROUP = "group"
+    WEIGHT = "weight"
+
+
+class QuantizationArgs(BaseModel):
+    """
+    User facing arguments used to define a quantization config 
+    for weights or activations
+
+    :param num_bits: quantization bit depth
+    :param type: dtype to quantized to, either int or float
+    :param symmetric: whether or not quantization scale is symmetric
+    :param strategy: string determining the scope of scale/zero-point to apply
+    :param group_size: group length to use for the group strategy
+    :param block_structure: 2d block structure to use for the block 
+    strategy, must be of the format "2x4", "8x16", etc.
+    :param dynamic: set True to perform dynamic quantization -
+        values will not be calibrated during calibration phase, 
+        instead during inference new quantization ranges will be 
+        observed with every sample. Defaults to False for static
+        quantization. Note that enabling dynamic quantization 
+        will change the default observer to a memoryless one
+    :param actorder: whether to apply group quantization in decreasing order of
+        activation. Defaults to None for arbitrary ordering
+    """
+
+    num_bits: int = 8
+    type: QuantizationType = QuantizationType.INT
+    symmetric: bool = True
+    group_size: Optional[int] = None
+    strategy: Optional[QuantizationStrategy] = None
+    block_structure: Optional[str] = None
+    dynamic: bool = False
+    actorder: Union[ActivationOrdering, bool, None] = None
+    observer: str = Field(
+        default="minmax",
+        description=("The class to use to compute the quantization param - "
+                     "scale and zero-point'"),
+    )
+    observer_kwargs: Dict[str, Any] = Field(
+        default_factory=dict,
+        description=
+        ("optional dict of kwargs to be passed directly to torch quantization "
+         "Observers constructor excluding quantization range or symmetry"),
+    )
+
+    @field_validator("actorder", mode="before")
+    def validate_actorder(cls, value) -> Optional[ActivationOrdering]:
+        if isinstance(value, bool):
+            return ActivationOrdering.GROUP if value else None
+
+        if isinstance(value, str):
+            return ActivationOrdering(value.lower())
+
+        return value
+
+
+def is_activation_quantization_format(format: str) -> bool:
+    _ACTIVATION_QUANTIZATION_FORMATS = [
+        CompressionFormat.naive_quantized.value,
+        CompressionFormat.int_quantized.value,
+        CompressionFormat.float_quantized.value
+    ]
+    return format in _ACTIVATION_QUANTIZATION_FORMATS
+
+
+def should_ignore_layer(layer_name: Optional[str],
+                        ignore: Iterable[str]) -> bool:
+    if layer_name is None:
+        return False
+
+    # layer_name = model.layers.0.self_attn.qkv_proj
+    # proj_name = qkv_proj
+    proj_name = layer_name.split(".")[-1]
+
+    # Fused layers like gate_up_proj or qkv_proj will not be fused
+    # in the safetensors checkpoint. So, we convert the name
+    # from the fused version to unfused + check to make sure that
+    # each shard of the fused layer has the same scheme.
+    if proj_name in FUSED_LAYER_NAME_MAPPING:
+        shard_proj_names = FUSED_LAYER_NAME_MAPPING[proj_name]
+
+        # Convert fused_name --> [shard_names]
+        shard_names = [
+            layer_name.replace(proj_name, shard_proj_name)
+            for shard_proj_name in shard_proj_names
+        ]
+
+        # Layer should be ignored if shards are ignored.
+        should_ignore_layer = None
+        for shard_name in shard_names:
+            should_ignore_shard = check_equal_or_regex_match(
+                layer_name=shard_name, targets=ignore)
+
+            # If shard_idx=0, set layer ignore to match shard.
+            if should_ignore_layer is None:
+                should_ignore_layer = should_ignore_shard
+
+            # If shard_idx=1+ confirm scheme matches prior shards.
+            elif should_ignore_shard != should_ignore_layer:
+                raise ValueError(f"Found a different quantization schemes for "
+                                 f"{shard_proj_names} in {layer_name}. vLLM "
+                                 "requires all to use the same scheme.")
+
+    # Unfused layers like down_proj and o_proj will match
+    # the safetensors checkpoint already.
+    else:
+        should_ignore_layer = check_equal_or_regex_match(layer_name=layer_name,
+                                                         targets=ignore)
+
+    assert should_ignore_layer is not None
+    return should_ignore_layer
+
+
+def check_equal_or_regex_match(layer_name: str,
+                               targets: Iterable[str]) -> bool:
+    """
+    Checks whether a layer_name is exactly equal or a regex match for 
+    if target starts with 're:' to any target in list.
+    """
+    for target in targets:
+        if _is_equal_or_regex_match(layer_name, target):
+            return True
+    return False
+
+
+def find_matched_target(layer_name: Optional[str], module: Module,
+                        targets: Iterable[str]) -> str:
+    """
+    Helper function to look up which "target" in the compressed-tensors
+    config that a layer corresponds to.
+
+    Recall that a compressed-tensors configs has a concept of 
+    config_groups, where each layer can be quantized with with a different
+    scheme.
+
+    targets in each config_group will be a list of either layer names 
+    (or regexes corresponding to layer names) or names of torch Modules.
+
+    First, we try to match the layer_name with a target
+    Second, we try to match the module's name with a target
+
+    :param layer_name: layer name
+    :param module: torch.nn.Module
+    :param targets: list of targets to match the layer against
+    """
+
+    if layer_name is None:
+        layer_name = ""
+
+    matched_target = (_find_first_match(layer_name, targets)
+                      or _find_first_match(module.__class__.__name__, targets,
+                                           True))
+
+    if matched_target is None:
+        raise ValueError(f"Unable to find matching target for {module} in the "
+                         "compressed-tensors config.")
+
+    return matched_target
+
+
+def _find_first_match(value: str,
+                      targets: Iterable[str],
+                      check_contains: bool = False) -> Optional[str]:
+    """
+    Returns first element of target that matches value either
+    exactly or as a regex after 're:'. If check_contains is set to True,
+    additionally checks if the target string is contained within the value.
+
+    :param value: string to compare the list of targets against
+    :param targets: list of targets to match the layer against
+    :param check_contains: whether or not to do a substring match
+    """
+
+    for target in targets:
+        if _is_equal_or_regex_match(value,
+                                    target,
+                                    check_contains=check_contains):
+            return target
+    return None
+
+
+def get_compressed_tensors_cache_scale(name: str) -> Optional[str]:
+    """
+    Check whether the param name matches the format for k/v cache scales
+    in compressed-tensors. If this is the case, return its equivalent
+    param name expected by vLLM
+
+    :param name: param name
+    :return: matching param name for KV cache scale in vLLM
+    """
+    if name.endswith(".output_scale") and ".k_proj" in name:
+        return name.replace(".k_proj.output_scale", ".attn.k_scale")
+    if name.endswith(".output_scale") and ".v_proj" in name:
+        return name.replace(".v_proj.output_scale", ".attn.v_scale")
+    # If no matches, return None
+    return None
+
+
+def _is_equal_or_regex_match(value: str,
+                             target: str,
+                             check_contains: bool = False) -> bool:
+    """
+    Checks whether a value is exactly equal or a regex match for target
+    if target starts with 're:'. If check_contains is set to True,
+    additionally checks if the target string is contained within the value.
+    """
+
+    if target.startswith("re:"):
+        pattern = target[3:]
+        if re.match(pattern, value):
+            return True
+    elif check_contains:
+        if target.lower() in value.lower():
+            return True
+    elif target == value:
+        return True
+    return False

vllm/model_executor/layers/quantization/deepspeedfp.py

@@ -0,0 +1,193 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
+
+
+class DeepSpeedFPConfig(QuantizationConfig):
+    """Config for DeepSpeed FP quantizer. It supports fp6 and fp8.
+    
+    Args: 
+        weight_bits: the target quantization bits, 6 or 8.
+        group_size: group size for quantizaiton, default to 128.
+    """
+
+    def __init__(
+        self,
+        weight_bits: int = 8,
+        group_size: int = 512,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.valid_types = [torch.bfloat16, torch.float16]
+
+        if self.weight_bits not in (6, 8):
+            raise ValueError(
+                "Currently, only 6-bit or 8-bit weight quantization are "
+                f"supported for DeepSpeed FP quantizaiton, but got "
+                f"{self.weight_bits} bits.")
+
+    def __repr__(self) -> str:
+        return (f"DeepSpeedFPConfig(weight_bits={self.weight_bits}), "
+                f"group_size={self.group_size}")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "DeepSpeedFP"
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "DeepSpeedFPConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        return cls(weight_bits=weight_bits, group_size=group_size)
+
+    def get_linear_method(self) -> "DeepSpeedFPLinearMethod":
+        return DeepSpeedFPLinearMethod(self)
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    # Need to figure it out
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return [
+            "quant_config.json",
+            "quantize_config.json",
+        ]
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["DeepSpeedFPLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return DeepSpeedFPLinearMethod(self)
+        return None
+
+
+class DeepSpeedFPLinearMethod(LinearMethodBase):
+    """Linear method for DeepSpeedFP quantizer.
+
+    Args:
+        quant_config: the DeepSpeedFP quantization config.
+    """
+
+    def __init__(self, quant_config: DeepSpeedFPConfig):
+        self.quant_config = quant_config
+        self.weight = None
+
+    def create_weights(self,
+                       layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int],
+                       input_size: int,
+                       output_size: int,
+                       params_dtype: torch.dtype,
+                       weight_loader=None,
+                       **extra_weight_attrs):
+        del output_size
+        del input_size
+        output_size_per_partition = sum(output_partition_sizes)
+        weight = DeepSpeedFPParameter(
+            torch.Size((output_size_per_partition, input_size_per_partition)),
+            params_dtype=params_dtype,
+            quant_config=self.quant_config,
+        )
+        set_weight_attrs(weight, {
+            "input_dim": 1,
+            "output_dim": 0,
+        })
+        layer.register_parameter("weight", weight)
+
+        def quant_weight_loader(param, loaded_weight, *args, **kwargs):
+            # Calls the original weight loader (if any), quantizes the result,
+            # and then loads the quantized parameter.
+            if weight_loader is not None:
+                orig_param_data = param.data
+                param.data = param.ds_dequantize()
+                weight_loader(param, loaded_weight, *args, **kwargs)
+                param.data, loaded_weight = orig_param_data, param.data
+            param.ds_quantize_(loaded_weight.cuda())
+
+        extra_weight_attrs["weight_loader"] = quant_weight_loader
+        set_weight_attrs(weight, extra_weight_attrs)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        weight = layer.weight
+        y = weight.ds_dequantize()
+        return F.linear(x, y, bias)
+
+
+class DeepSpeedFPParameter(nn.Parameter):
+    """
+    DeepSpeedFP quantized parameter class that implements fp8/fp6
+    quantization deepspeed. Weights are stored in quantized form on
+    GPUs, and can be dequantized on-the-fly when needed by the model.
+    """
+
+    def __new__(cls, orig_shape: torch.Size, params_dtype: torch.dtype,
+                quant_config: DeepSpeedFPConfig):
+        try:
+            import deepspeed
+            if deepspeed.__version__ < "0.14.2":
+                raise ImportError("deepspeed version is wrong. Please "
+                                  "install deepspeed>=0.14.2.")
+            from deepspeed.ops.fp_quantizer import FP_Quantize
+        except ImportError as err:
+            raise ImportError("Please install deepspeed>=0.14.2 via "
+                              "`pip install deepspeed>=0.14.2` to use "
+                              "deepspeedfp quantizer.") from err
+        data = torch.empty((
+            orig_shape.numel() // quant_config.group_size,
+            quant_config.group_size * quant_config.weight_bits // 8 + 4,
+        ),
+                           dtype=torch.int8)
+        self = torch.Tensor._make_subclass(cls, data, data.requires_grad)
+        self.orig_shape = orig_shape
+        self.quant_config = quant_config
+        self.fp_quantizer = FP_Quantize(group_size=quant_config.group_size)
+        self.fp_quantizer.orig_shape = orig_shape
+        self.fp_quantizer.orig_dtype = params_dtype
+        return self
+
+    def ds_quantize_(self, tensor: torch.Tensor):
+        assert tensor.device.type == "cuda" and tensor.dtype != torch.int8
+        return self.data.copy_(
+            self.fp_quantizer.quantize(
+                tensor.data,
+                q_bits=self.quant_config.weight_bits,
+            ))
+
+    def ds_dequantize(self, fp_out=None) -> torch.Tensor:
+        """
+        Return a tensor containing the dequantized weights of this parameter.
+        """
+        assert self.data.device.type == "cuda" and self.data.dtype == torch.int8
+        return self.fp_quantizer.dequantize(
+            self.data, fp_out=fp_out, q_bits=self.quant_config.weight_bits)
+
+    def ds_selective_dequantize(self, indices, fp_out=None) -> torch.Tensor:
+        """
+        Return a tensor where only the weights at `indices` are dequantized
+        (to save HBM -> SRAM bandwidth).
+        """
+        assert self.data.device.type == "cuda" and self.data.dtype == torch.int8
+        return self.fp_quantizer.selective_dequantize(
+            self.data,
+            indices,
+            fp_out=fp_out,
+            q_bits=self.quant_config.weight_bits)

vllm/model_executor/layers/quantization/experts_int8.py

@@ -0,0 +1,179 @@
+from typing import Any, Callable, Dict, List, Optional
+
+import torch
+
+from vllm.distributed import get_tensor_model_parallel_rank, get_tp_group
+from vllm.model_executor.layers.fused_moe import FusedMoE, FusedMoEMethodBase
+from vllm.model_executor.layers.linear import (LinearBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.utils import set_weight_attrs
+
+
+class ExpertsInt8Config(QuantizationConfig):
+    """Config class for Int8 experts quantization."""
+
+    def __init__(self) -> None:
+        pass
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "experts_int8"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "ExpertsInt8Config":
+        return cls()
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        if isinstance(layer, LinearBase):
+            return UnquantizedLinearMethod()
+        elif isinstance(layer, FusedMoE):
+            return ExpertsInt8MoEMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class ExpertsInt8MoEMethod(FusedMoEMethodBase):
+
+    def __init__(self, quant_config: ExpertsInt8Config):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        int8_dtype = torch.int8
+
+        assert 'weight_loader' in extra_weight_attrs
+        weight_loader = extra_weight_attrs['weight_loader']
+        wrapped_weight_loader = ExpertsInt8MoEMethod.quantizing_weight_loader(
+            layer, weight_loader)
+        extra_weight_attrs['weight_loader'] = wrapped_weight_loader
+
+        # Fused gate_up_proj (column parallel)
+        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                    2 * intermediate_size,
+                                                    hidden_size,
+                                                    dtype=int8_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        # down_proj (row parallel)
+        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                   hidden_size,
+                                                   intermediate_size,
+                                                   dtype=int8_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        w13_scale = torch.nn.Parameter(torch.zeros(num_experts,
+                                                   2 * intermediate_size,
+                                                   dtype=torch.float32),
+                                       requires_grad=False)
+        layer.register_parameter("w13_scale", w13_scale)
+
+        w2_scale = torch.nn.Parameter(torch.zeros(num_experts,
+                                                  hidden_size,
+                                                  dtype=torch.float32),
+                                      requires_grad=False)
+        layer.register_parameter("w2_scale", w2_scale)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+        from vllm.model_executor.layers.fused_moe import fused_experts
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function)
+
+        return fused_experts(x,
+                             layer.w13_weight,
+                             layer.w2_weight,
+                             topk_weights=topk_weights,
+                             topk_ids=topk_ids,
+                             inplace=True,
+                             use_int8_w8a16=True,
+                             w1_scale=layer.w13_scale,
+                             w2_scale=layer.w2_scale)
+
+    @staticmethod
+    def quantizing_weight_loader(layer, weight_loader):
+
+        def quantize_and_call_weight_loader(param: torch.nn.Parameter,
+                                            loaded_weight: torch.Tensor,
+                                            weight_name: str, shard_id: int,
+                                            expert_id: int):
+            tp_rank = get_tensor_model_parallel_rank()
+            shard_size = layer.intermediate_size_per_partition
+            shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+            device = get_tp_group().device
+            loaded_weight = loaded_weight.to(device)
+            # w1, gate_proj case: Load into first shard of w13.
+            if shard_id == "w1":
+                scales = quantize_in_place_and_get_scales(
+                    loaded_weight[shard, :])
+                layer.w13_scale.data[expert_id, 0:shard_size].copy_(scales[:,
+                                                                           0])
+            # w3, up_proj case: Load into second shard of w13.
+            elif shard_id == "w3":
+                scales = quantize_in_place_and_get_scales(
+                    loaded_weight[shard, :])
+                layer.w13_scale.data[expert_id, shard_size:2 *
+                                     shard_size].copy_(scales[:, 0])
+            # w2, down_proj case: Load into only shard of w2.
+            elif shard_id == "w2":
+                scales = quantize_in_place_and_get_scales(loaded_weight[:,
+                                                                        shard])
+                layer.w2_scale.data[expert_id, :].copy_(scales[:, 0])
+            else:
+                raise ValueError(
+                    f"Shard id must be in [0,1,2] but got {shard_id}")
+            weight_loader(param, loaded_weight, weight_name, shard_id,
+                          expert_id)
+
+        return quantize_and_call_weight_loader
+
+
+def quantize_in_place_and_get_scales(weight: torch.Tensor) -> torch.Tensor:
+    vmax = torch.iinfo(torch.int8).max
+    scales = (torch.max(torch.abs(weight), dim=1, keepdim=True)[0] / vmax)
+
+    weight.div_(scales)
+    weight.round_()
+    weight.clamp_(-vmax, vmax)
+
+    return scales

vllm/model_executor/layers/quantization/fbgemm_fp8.py

@@ -0,0 +1,169 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.fp8 import cutlass_fp8_supported
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
+    apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    is_layer_skipped)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    apply_fp8_linear, normalize_e4m3fn_to_e4m3fnuz)
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           ModelWeightParameter)
+from vllm.platforms import current_platform
+from vllm.utils import is_hip
+
+logger = init_logger(__name__)
+
+
+class FBGEMMFp8Config(QuantizationConfig):
+    """Config class for FBGEMM Fp8."""
+
+    def __init__(self, ignore_list: List[str], input_scale_ub: float):
+        self.ignore_list = ignore_list if ignore_list else []
+        self.input_scale_ub = input_scale_ub
+
+        # For GPUs that lack FP8 hardware support, we can leverage the Marlin
+        # kernel for fast weight-only FP8 quantization
+        self.use_marlin = not current_platform.has_device_capability(89)
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "fbgemm_fp8"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.float16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "FBGEMMFp8Config":
+        ignore_list = cls.get_from_keys(config, ["modules_to_not_convert"])
+        input_scale_ub = cls.get_from_keys(config, ["activation_scale_ub"])
+        return cls(ignore_list=ignore_list, input_scale_ub=input_scale_ub)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        if isinstance(layer, LinearBase):
+            if is_layer_skipped(prefix, self.ignore_list):
+                return UnquantizedLinearMethod()
+            return FBGEMMFp8LinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class FBGEMMFp8LinearMethod(LinearMethodBase):
+
+    def __init__(self, quant_config: FBGEMMFp8Config):
+        self.quant_config = quant_config
+        self.cutlass_fp8_supported = cutlass_fp8_supported()
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        del input_size, output_size
+        output_size_per_partition = sum(output_partition_sizes)
+
+        layer.logical_widths = output_partition_sizes
+
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.orig_dtype = params_dtype
+
+        # WEIGHT
+        weight = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition,
+            dtype=torch.float8_e4m3fn),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        weight_scale = ChannelQuantScaleParameter(data=torch.empty(
+            (sum(output_partition_sizes), 1), dtype=torch.float32),
+                                                  output_dim=0,
+                                                  weight_loader=weight_loader)
+        weight_scale[:] = torch.finfo(torch.float32).min
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE UPPER BOUND
+        input_scale_ub = torch.nn.Parameter(torch.tensor(
+            (self.quant_config.input_scale_ub), dtype=torch.float32),
+                                            requires_grad=False)
+        layer.input_scale_ub = input_scale_ub
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        # required by torch.compile
+        layer.weight_scale = Parameter(layer.weight_scale.data,
+                                       requires_grad=False)
+        layer.weight = Parameter(layer.weight.data, requires_grad=False)
+
+        weight = layer.weight
+
+        if is_hip():
+            weight, weight_scale, input_scale = \
+                normalize_e4m3fn_to_e4m3fnuz(
+                    weight=weight,
+                    weight_scale=layer.weight_scale,
+                    input_scale=None)
+            if input_scale is not None:
+                layer.input_scale = Parameter(input_scale, requires_grad=False)
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+
+        layer.weight = Parameter(weight.t(), requires_grad=False)
+        if self.quant_config.use_marlin:
+            prepare_fp8_layer_for_marlin(layer)
+            # Activations not quantized for marlin.
+            del layer.input_scale_ub
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        if self.quant_config.use_marlin:
+            return apply_fp8_marlin_linear(
+                input=x,
+                weight=layer.weight,
+                weight_scale=layer.weight_scale,
+                workspace=layer.workspace,
+                size_n=layer.output_size_per_partition,
+                size_k=layer.input_size_per_partition,
+                bias=bias)
+
+        return apply_fp8_linear(
+            input=x,
+            weight=layer.weight,
+            weight_scale=layer.weight_scale,
+            input_scale=None,
+            input_scale_ub=layer.input_scale_ub,
+            bias=bias,
+            cutlass_fp8_supported=self.cutlass_fp8_supported,
+            use_per_token_if_dynamic=True)

vllm/model_executor/layers/quantization/fp8.py

@@ -0,0 +1,514 @@
+from typing import Any, Callable, Dict, List, Optional
+
+import torch
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+
+import vllm.envs as envs
+from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
+                                                  FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
+    apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    is_layer_skipped)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    all_close_1d, apply_fp8_linear, convert_to_channelwise,
+    cutlass_fp8_supported, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize,
+    requantize_with_max_scale)
+from vllm.model_executor.parameter import (ModelWeightParameter,
+                                           PerTensorScaleParameter)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
+from vllm.utils import is_hip, print_warning_once
+
+ACTIVATION_SCHEMES = ["static", "dynamic"]
+
+logger = init_logger(__name__)
+
+
+class Fp8Config(QuantizationConfig):
+    """Config class for FP8."""
+
+    def __init__(
+        self,
+        is_checkpoint_fp8_serialized: bool = False,
+        activation_scheme: str = "dynamic",
+        ignored_layers: Optional[List[str]] = None,
+    ) -> None:
+        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
+        if is_checkpoint_fp8_serialized:
+            logger.warning("Detected fp8 checkpoint. Please note that the "
+                           "format is experimental and subject to change.")
+        if activation_scheme not in ACTIVATION_SCHEMES:
+            raise ValueError(
+                f"Unsupported activation scheme {activation_scheme}")
+        self.activation_scheme = activation_scheme
+        self.ignored_layers = ignored_layers or []
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "fp8"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
+        quant_method = cls.get_from_keys(config, ["quant_method"])
+        is_checkpoint_fp8_serialized = ("fp8" in quant_method)
+        activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
+        ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
+        return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
+                   activation_scheme=activation_scheme,
+                   ignored_layers=ignored_layers)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+
+        if isinstance(layer, LinearBase):
+            if is_layer_skipped(prefix, self.ignored_layers):
+                return UnquantizedLinearMethod()
+            return Fp8LinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return Fp8MoEMethod(self)
+        elif isinstance(layer, Attention):
+            return Fp8KVCacheMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class Fp8LinearMethod(LinearMethodBase):
+    """Linear method for FP8.
+    Supports loading FP8 checkpoints with static weight scale and
+    dynamic/static activation scale.
+
+    Also supports loading quantized FP16/BF16 model checkpoints with dynamic
+    activation scaling. The weight scaling factor will be initialized after
+    the model weights are loaded.
+
+    Limitations:
+    1. Only support per-tensor quantization due to torch._scaled_mm support.
+    2. Only support float8_e4m3fn data type due to the limitation of
+       torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856)
+
+    Args:
+        quant_config: The quantization config.
+    """
+
+    def __init__(self, quant_config: Fp8Config):
+        self.quant_config = quant_config
+        self.cutlass_fp8_supported = cutlass_fp8_supported()
+
+        # For GPUs that lack FP8 hardware support, we can leverage the Marlin
+        # kernel for fast weight-only FP8 quantization
+        self.use_marlin = (not current_platform.has_device_capability(89)
+                           or envs.VLLM_TEST_FORCE_FP8_MARLIN)
+        # Disable marlin for rocm
+        if is_hip():
+            self.use_marlin = False
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del input_size, output_size
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        layer.logical_widths = output_partition_sizes
+
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.orig_dtype = params_dtype
+
+        # WEIGHT
+        weight_dtype = (torch.float8_e4m3fn
+                        if self.quant_config.is_checkpoint_fp8_serialized else
+                        params_dtype)
+
+        weight = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition,
+            dtype=weight_dtype),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        # If checkpoint is serialized fp8, load them.
+        # Otherwise, wait until process_weights_after_loading.
+        if self.quant_config.is_checkpoint_fp8_serialized:
+            # WEIGHT SCALE
+            scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                            weight_loader=weight_loader)
+
+            scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("weight_scale", scale)
+
+            # INPUT ACTIVATION SCALE
+            if self.quant_config.activation_scheme == "static":
+                scale = PerTensorScaleParameter(data=torch.empty(
+                    len(output_partition_sizes), dtype=torch.float32),
+                                                weight_loader=weight_loader)
+
+                scale[:] = torch.finfo(torch.float32).min
+                layer.register_parameter("input_scale", scale)
+            else:
+                layer.register_parameter("input_scale", None)
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        layer.weight = torch.nn.Parameter(layer.weight.data,
+                                          requires_grad=False)
+        # If checkpoint not serialized fp8, quantize the weights.
+        if not self.quant_config.is_checkpoint_fp8_serialized:
+            qweight, weight_scale = ops.scaled_fp8_quant(layer.weight,
+                                                         scale=None)
+
+            # If using marlin (w8a16), kernel uses channelwise weights,
+            # so extend the weight scales to be channelwise.
+            if self.use_marlin:
+                assert weight_scale.numel() == 1
+                weight_scale = convert_to_channelwise(
+                    weight_scale.expand(len(layer.logical_widths)),
+                    layer.logical_widths)
+
+            # Update the layer with the new values.
+            layer.weight = Parameter(qweight.t(), requires_grad=False)
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+            layer.input_scale = None
+
+        # If checkpoint is fp8, handle that there are N scales for N
+        # shards in a fused module
+        else:
+            layer.weight_scale = torch.nn.Parameter(layer.weight_scale.data,
+                                                    requires_grad=False)
+            if self.quant_config.activation_scheme == "static":
+                layer.input_scale = torch.nn.Parameter(layer.input_scale.data,
+                                                       requires_grad=False)
+            # If using marlin (w8a16), kernel uses channelwise weights,
+            # so extend the weight scales to be channelwise.
+            if self.use_marlin:
+                weight = layer.weight
+                weight_scale = convert_to_channelwise(layer.weight_scale,
+                                                      layer.logical_widths)
+
+            # If using w8a8, torch._scaled_mm needs per tensor, so
+            # requantize the logical shards as a single weight.
+            else:
+                # Dequant -> Quant with max scale so we can run per tensor.
+                weight = layer.weight
+                weight_scale = layer.weight_scale
+
+                # If rocm, use float8_e4m3fnuz.
+                if is_hip():
+                    weight, weight_scale, input_scale = \
+                        normalize_e4m3fn_to_e4m3fnuz(
+                            weight=weight,
+                            weight_scale=weight_scale,
+                            input_scale=layer.input_scale)
+                    if input_scale is not None:
+                        layer.input_scale = Parameter(input_scale,
+                                                      requires_grad=False)
+
+                weight_scale, weight = requantize_with_max_scale(
+                    weight=weight,
+                    weight_scale=weight_scale,
+                    logical_widths=layer.logical_widths,
+                )
+
+            # Update layer with new values.
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+            if self.quant_config.activation_scheme == "static":
+                layer.input_scale = Parameter(layer.input_scale.max(),
+                                              requires_grad=False)
+
+        if self.use_marlin:
+            prepare_fp8_layer_for_marlin(layer)
+            # Activations not quantized for marlin.
+            del layer.input_scale
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        if self.use_marlin:
+            return apply_fp8_marlin_linear(
+                input=x,
+                weight=layer.weight,
+                weight_scale=layer.weight_scale,
+                workspace=layer.workspace,
+                size_n=layer.output_size_per_partition,
+                size_k=layer.input_size_per_partition,
+                bias=bias)
+
+        return apply_fp8_linear(
+            input=x,
+            weight=layer.weight,
+            weight_scale=layer.weight_scale,
+            input_scale=layer.input_scale,
+            bias=bias,
+            cutlass_fp8_supported=self.cutlass_fp8_supported,
+            use_per_token_if_dynamic=False)
+
+
+class Fp8MoEMethod(FusedMoEMethodBase):
+    """MoE method for FP8.
+    Supports loading FP8 checkpoints with static weight scale and
+    dynamic/static activation scale.
+
+    Also supports loading quantized FP16/BF16 model checkpoints with dynamic
+    activation scaling. The weight scaling factor will be initialized after
+    the model weights are loaded.
+
+    Args:
+        quant_config: The quantization config.
+    """
+
+    def __init__(self, quant_config: Fp8Config):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: Module, num_experts: int, hidden_size: int,
+                       intermediate_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+
+        if self.quant_config.is_checkpoint_fp8_serialized:
+            params_dtype = torch.float8_e4m3fn
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                    2 * intermediate_size,
+                                                    hidden_size,
+                                                    dtype=params_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                   hidden_size,
+                                                   intermediate_size,
+                                                   dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        # Allocate 2 scales for w1 and w3 respectively.
+        # They will be combined to a single scale after weight loading.
+        w13_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                         2,
+                                                         dtype=torch.float32),
+                                              requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+
+        w2_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                        dtype=torch.float32),
+                                             requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        # Add the quantization method used (per tensor/grouped/channel)
+        # to ensure the weight scales are loaded in properly
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+        # If loading fp8 checkpoint, pass the weight loaders.
+        # If loading an fp16 checkpoint, do not (we will quantize in
+        #   process_weights_after_loading()
+        if self.quant_config.is_checkpoint_fp8_serialized:
+            set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+            set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # INPUT_SCALES
+        if self.quant_config.activation_scheme == "static":
+            if not self.quant_config.is_checkpoint_fp8_serialized:
+                raise ValueError(
+                    "Found static activation scheme for checkpoint that "
+                    "was not serialized fp8.")
+
+            w13_input_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w13_input_scale", w13_input_scale)
+            set_weight_attrs(w13_input_scale, extra_weight_attrs)
+
+            w2_input_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                requires_grad=False)
+            layer.register_parameter("w2_input_scale", w2_input_scale)
+            set_weight_attrs(w2_input_scale, extra_weight_attrs)
+
+        else:
+            layer.w13_input_scale = None
+            layer.w2_input_scale = None
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+
+        # If checkpoint is fp16, quantize in place.
+        if not self.quant_config.is_checkpoint_fp8_serialized:
+            # If rocm, use float8_e4m3fnuz as dtype
+            fp8_dtype = torch.float8_e4m3fnuz \
+                        if is_hip() else torch.float8_e4m3fn
+            w13_weight = torch.empty_like(layer.w13_weight.data,
+                                          dtype=fp8_dtype)
+            w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype)
+
+            # Re-initialize w13_scale because we directly quantize
+            # merged w13 weights and generate a single scaling factor.
+            layer.w13_weight_scale = torch.nn.Parameter(torch.ones(
+                layer.num_experts,
+                dtype=torch.float32,
+                device=w13_weight.device),
+                                                        requires_grad=False)
+            for expert in range(layer.num_experts):
+                w13_weight[expert, :, :], layer.w13_weight_scale[
+                    expert] = ops.scaled_fp8_quant(
+                        layer.w13_weight.data[expert, :, :])
+                w2_weight[expert, :, :], layer.w2_weight_scale[
+                    expert] = ops.scaled_fp8_quant(
+                        layer.w2_weight.data[expert, :, :])
+            layer.w13_weight = torch.nn.Parameter(w13_weight,
+                                                  requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(w2_weight,
+                                                 requires_grad=False)
+            return
+
+        # If checkpoint is fp8, we need to handle that the
+        # MoE kernels require single activation scale and single weight
+        # scale for w13 per expert.
+        else:
+            # Fp8 moe kernels require a single activation scale.
+            # We take the max of all the scales in case they differ.
+            if self.quant_config.activation_scheme == "static":
+                if (layer.w13_input_scale is None
+                        or layer.w2_input_scale is None):
+                    raise ValueError(
+                        "QuantConfig has static quantization, but found "
+                        "activation scales are None.")
+                if (not all_close_1d(layer.w13_input_scale)
+                        or not all_close_1d(layer.w2_input_scale)):
+                    print_warning_once(
+                        "Found input_scales that are not equal for "
+                        "fp8 MoE layer. Using the maximum across experts "
+                        "for each layer. ")
+                layer.w13_input_scale = torch.nn.Parameter(
+                    layer.w13_input_scale.max(), requires_grad=False)
+                layer.w2_input_scale = torch.nn.Parameter(
+                    layer.w2_input_scale.max(), requires_grad=False)
+            # If rocm, normalize the weights and scales to e4m3fnuz
+            if is_hip():
+                # Normalize the weights and scales
+                w13_weight, w13_weight_scale, w13_input_scale = \
+                    normalize_e4m3fn_to_e4m3fnuz(
+                        layer.w13_weight, layer.w13_weight_scale,
+                        layer.w13_input_scale)
+                w2_weight, w2_weight_scale, w2_input_scale = \
+                    normalize_e4m3fn_to_e4m3fnuz(
+                        layer.w2_weight, layer.w2_weight_scale,
+                        layer.w2_input_scale)
+                # Reset the parameter
+                layer.w13_weight = torch.nn.Parameter(w13_weight,
+                                                      requires_grad=False)
+                layer.w13_weight_scale = torch.nn.Parameter(
+                    w13_weight_scale, requires_grad=False)
+                if w13_input_scale is not None:
+                    layer.w13_input_scale = torch.nn.Parameter(
+                        w13_input_scale, requires_grad=False)
+                layer.w2_weight = torch.nn.Parameter(w2_weight,
+                                                     requires_grad=False)
+                layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
+                                                           requires_grad=False)
+                if w2_input_scale is not None:
+                    layer.w2_input_scale = torch.nn.Parameter(
+                        w2_input_scale, requires_grad=False)
+
+            # Fp8 moe kernel needs single weight scale for w13 per expert.
+            # We take the max then dequant and requant each expert.
+            assert layer.w13_weight_scale is not None
+            shard_size = layer.intermediate_size_per_partition
+            max_w13_scales = layer.w13_weight_scale.max(dim=1).values
+            for expert_id in range(layer.num_experts):
+                start = 0
+                for shard_id in range(2):
+                    dq_weight = per_tensor_dequantize(
+                        layer.w13_weight[expert_id][start:start +
+                                                    shard_size, :],
+                        layer.w13_weight_scale[expert_id][shard_id])
+                    layer.w13_weight[expert_id][
+                        start:start + shard_size, :], _ = ops.scaled_fp8_quant(
+                            dq_weight, max_w13_scales[expert_id])
+                    start += shard_size
+
+            layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
+                                                        requires_grad=False)
+            return
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe import fused_experts
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function)
+
+        return fused_experts(x,
+                             layer.w13_weight,
+                             layer.w2_weight,
+                             topk_weights=topk_weights,
+                             topk_ids=topk_ids,
+                             inplace=True,
+                             use_fp8_w8a8=True,
+                             w1_scale=layer.w13_weight_scale,
+                             w2_scale=layer.w2_weight_scale,
+                             a1_scale=layer.w13_input_scale,
+                             a2_scale=layer.w2_input_scale)
+
+
+class Fp8KVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from FP8 checkpoints.
+    """
+
+    def __init__(self, quant_config: Fp8Config):
+        super().__init__(quant_config)

vllm/model_executor/layers/quantization/gguf.py

@@ -0,0 +1,178 @@
+from typing import Any, Dict, List, Optional
+
+import gguf
+import torch
+from torch.nn.parameter import Parameter, UninitializedParameter
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.utils import set_weight_attrs
+
+
+class GGUFConfig(QuantizationConfig):
+    """Config class for GGUF."""
+
+    def __init__(self, ) -> None:
+        pass
+
+    def __repr__(self) -> str:
+        return ("GGUFConfig()")
+
+    def get_name(self) -> str:
+        return "gguf"
+
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []  # no extra configs.
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "GGUFConfig":
+        return cls()
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        if isinstance(layer, LinearBase):
+            return GGUFLinearMethod(self)
+        elif isinstance(layer, VocabParallelEmbedding):
+            return GGUFEmbeddingMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+def _fuse_mul_mat(x: torch.Tensor, qweight: torch.Tensor,
+                  qweight_type: int) -> torch.Tensor:
+    # use dequantize mulmat for IQmatrix, mmq for k-quants
+    if x.shape[0] == 1:
+        # enable mmvq in contiguous batching
+        y = ops.ggml_mul_mat_vec_a8(qweight, x, qweight_type, qweight.shape[0])
+    elif qweight_type >= 16:
+        block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
+        shape = (qweight.shape[0], qweight.shape[1] // type_size * block_size)
+        weight = ops.ggml_dequantize(qweight, qweight_type, *shape)
+        y = x @ weight.T
+    else:
+        y = ops.ggml_mul_mat_a8(qweight, x, qweight_type, qweight.shape[0])
+    return y
+
+
+class GGUFLinearMethod(LinearMethodBase):
+    """Linear method for GGUF.
+
+    Args:
+        quant_config: The GGUF quantization config.
+    """
+
+    def __init__(self, quant_config: GGUFConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        output_size_per_partition = sum(output_partition_sizes)
+
+        tensor_shape = (output_size_per_partition, input_size_per_partition)
+        qweight = GGUFUninitializedParameter(requires_grad=False)
+        set_weight_attrs(
+            qweight, {
+                "input_dim": 1,
+                "output_dim": 0,
+                "tensor_shape": tensor_shape,
+                "is_gguf_weight": True,
+                "data_container": [],
+                "shard_id": [],
+                "shard_id_map": {},
+            })
+        set_weight_attrs(qweight, extra_weight_attrs)
+        layer.register_parameter("qweight", qweight)
+
+        qweight_type = Parameter(torch.empty(len(output_partition_sizes),
+                                             dtype=torch.uint8),
+                                 requires_grad=False)
+        set_weight_attrs(
+            qweight_type, {
+                "is_gguf_weight_type": True,
+                "weight_type": 0,
+                "shard_weight_type": {},
+                "ignore_warning": True
+            })
+        set_weight_attrs(qweight_type, extra_weight_attrs)
+        layer.register_parameter("qweight_type", qweight_type)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        shard_id = getattr(layer.qweight, "shard_id", None)
+
+        if shard_id:
+            # dequantize shard weights respectively
+            shard_id = ["q", "k", "v"] if "q" in shard_id else shard_id
+            qweight = layer.qweight.unbind(0)
+            result = []
+            for id in shard_id:
+                q_idx = layer.qweight.shard_id_map[id]
+                qweight_type = layer.qweight_type.shard_weight_type[id]
+                result.append(_fuse_mul_mat(x, qweight[q_idx], qweight_type))
+            out = torch.cat(result, axis=1)
+        else:
+            qweight = layer.qweight
+            qweight_type = layer.qweight_type.weight_type
+            out = _fuse_mul_mat(x, qweight, qweight_type)
+        if bias is not None:
+            out.add_(bias)
+        return out
+
+
+class GGUFEmbeddingMethod(GGUFLinearMethod):
+    """Embedding method for GGUF.
+
+    Args:
+        quant_config: The GGUF quantization config.
+    """
+
+    def embedding(self, layer: torch.nn.Module,
+                  x: torch.Tensor) -> torch.Tensor:
+        qweight = layer.qweight
+        qweight_type = layer.qweight_type.weight_type
+
+        block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
+        hidden_size = qweight.shape[1] // type_size * block_size
+        if qweight_type < 2:
+            return torch.embedding(qweight, x)
+        x_flat = x.flatten()
+        quant = torch.index_select(qweight, dim=0, index=x_flat)
+        dequant = ops.ggml_dequantize(quant, qweight_type, hidden_size,
+                                      x_flat.shape[0])
+        return dequant.view(*x.shape, hidden_size)
+
+
+class GGUFUninitializedParameter(UninitializedParameter):
+    cls_to_become = Parameter
+    data_container: List[torch.Tensor]
+
+    def materialize_nested(self) -> Parameter:
+        nested_data = torch.nested.nested_tensor(self.data_container,
+                                                 device=self.device,
+                                                 dtype=torch.uint8)
+        self.data_container.clear()
+        param = torch.Tensor._make_subclass(self.cls_to_become,
+                                            nested_data,
+                                            require_grad=False)
+        for k, v in self.__dict__.items():
+            setattr(param, k, v)
+        return param

vllm/model_executor/layers/quantization/gptq.py

@@ -0,0 +1,248 @@
+import enum
+from enum import Enum
+from fractions import Fraction
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedColumnParameter,
+                                           PackedvLLMParameter,
+                                           RowvLLMParameter)
+
+
+class GPTQConfig(QuantizationConfig):
+    """Config class for GPTQ.
+
+    Reference: https://arxiv.org/abs/2210.17323
+    """
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        desc_act: bool,
+        lm_head_quantized: bool,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.desc_act = desc_act
+        self.lm_head_quantized = lm_head_quantized
+        self.pack_factor = Fraction(32, self.weight_bits)
+        if self.weight_bits not in [2, 3, 4, 8]:
+            raise ValueError(
+                "Currently, only 2/3/4/8-bit weight quantization is "
+                f"supported for GPTQ, but got {self.weight_bits} bits.")
+
+    def __repr__(self) -> str:
+        return (f"GPTQConfig(weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}, "
+                f"desc_act={self.desc_act}),"
+                f"lm_head_quantized={self.lm_head_quantized}")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "gptq"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    # Need to figure it out
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "GPTQConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        desc_act = cls.get_from_keys(config, ["desc_act"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        return cls(weight_bits, group_size, desc_act, lm_head_quantized)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["GPTQLinearMethod"]:
+        if (isinstance(layer, LinearBase) or
+            (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
+            return GPTQLinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class ExllamaState(Enum):
+
+    UNUSED = enum.auto()
+    UNINITIALIZED = enum.auto()
+    READY = enum.auto()
+
+
+class GPTQLinearMethod(LinearMethodBase):
+    """Linear method for GPTQ.
+
+    Args:
+        quant_config: The GPTQ quantization config.
+    """
+
+    def __init__(self, quant_config: GPTQConfig):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del output_size  # Unused.
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        if input_size_per_partition % self.quant_config.group_size != 0:
+            raise ValueError(
+                "The input size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+        output_size_per_partition = sum(output_partition_sizes)
+        if (output_size_per_partition % self.quant_config.pack_factor.numerator
+                != 0):
+            raise ValueError(
+                "The output size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+
+        if self.quant_config.group_size != -1:
+            group_size = self.quant_config.group_size
+        else:
+            group_size = input_size
+        exllama_state = ExllamaState.UNINITIALIZED
+        scale_and_zero_size = input_size // group_size
+        scale_and_zero_input_dim = None
+        if (input_size != input_size_per_partition
+                and self.quant_config.group_size != -1):
+            # For act-order models, we cannot use Exllama for row parallel layer
+            if self.quant_config.desc_act:
+                exllama_state = ExllamaState.UNUSED
+            else:
+                # we need to partition qzeros and scales for exllama kernel
+                scale_and_zero_size = input_size_per_partition // group_size
+                scale_and_zero_input_dim = 0
+
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.pack_factor,
+                output_size_per_partition,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=0,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader)
+
+        g_idx = RowvLLMParameter(data=torch.tensor(
+            [
+                i // self.quant_config.group_size
+                for i in range(input_size_per_partition)
+            ],
+            dtype=torch.int32,
+        ),
+                                 input_dim=0,
+                                 weight_loader=weight_loader)
+        qzeros_args = {
+            "data":
+            torch.empty(
+                scale_and_zero_size,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+        weight_scale_args = {
+            "data":
+            torch.empty(
+                scale_and_zero_size,
+                output_size_per_partition,
+                dtype=params_dtype,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+        if scale_and_zero_input_dim is None:
+            scales = ChannelQuantScaleParameter(output_dim=1,
+                                                **weight_scale_args)
+            qzeros = PackedColumnParameter(
+                output_dim=1,
+                packed_dim=1,
+                packed_factor=self.quant_config.pack_factor,
+                **qzeros_args)
+
+        else:
+            scales = GroupQuantScaleParameter(output_dim=1,
+                                              input_dim=0,
+                                              **weight_scale_args)
+            qzeros = PackedvLLMParameter(
+                input_dim=0,
+                output_dim=1,
+                packed_dim=1,
+                packed_factor=self.quant_config.pack_factor,
+                **qzeros_args)
+
+        layer.register_parameter("qweight", qweight)
+        layer.register_parameter("g_idx", g_idx)
+        layer.register_parameter("qzeros", qzeros)
+        layer.register_parameter("scales", scales)
+
+        layer.exllama_state = exllama_state
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # for torch.compile
+        layer.qweight = Parameter(layer.qweight.data, requires_grad=False)
+        layer.qzeros = Parameter(layer.qzeros.data, requires_grad=False)
+        layer.qweight = Parameter(layer.qweight.data, requires_grad=False)
+        layer.g_idx = Parameter(layer.g_idx.data, requires_grad=False)
+        layer.scales = Parameter(layer.scales.data, requires_grad=False)
+
+        # exllama needs to shuffle the weight after the weight is loaded
+        # here we do the shuffle on first forward pass
+        if layer.exllama_state == ExllamaState.UNINITIALIZED:
+            if self.quant_config.desc_act:
+                layer.g_idx.data = torch.argsort(layer.g_idx).to(torch.int)
+            else:
+                layer.g_idx.data = torch.empty((0, ),
+                                               dtype=torch.int,
+                                               device=layer.g_idx.device)
+            layer.exllama_state = ExllamaState.READY
+            ops.gptq_shuffle(layer.qweight, layer.g_idx,
+                             self.quant_config.weight_bits)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        out_shape = x.shape[:-1] + (layer.qweight.shape[-1], )
+        reshaped_x = x.reshape(-1, x.shape[-1])
+
+        output = ops.gptq_gemm(reshaped_x, layer.qweight, layer.qzeros,
+                               layer.scales, layer.g_idx,
+                               layer.exllama_state == ExllamaState.READY,
+                               self.quant_config.weight_bits)
+        if bias is not None:
+            output.add_(bias)
+        return output.reshape(out_shape)

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -0,0 +1,570 @@
+from typing import Any, Callable, Dict, List, Optional, Set, Union
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               set_weight_attrs)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.quantization.kernels import (
+    MPLinearLayerConfig, choose_mp_linear_kernel)
+from vllm.model_executor.layers.quantization.utils import replace_parameter
+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    check_marlin_supported, marlin_moe_permute_scales,
+    marlin_repeat_scales_on_all_ranks, verify_marlin_supported)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedColumnParameter,
+                                           PackedvLLMParameter,
+                                           RowvLLMParameter)
+from vllm.scalar_type import scalar_types
+
+logger = init_logger(__name__)
+
+
+class GPTQMarlinConfig(QuantizationConfig):
+    """Config class for GPTQ Marlin"""
+
+    # (num_bits, is_sym) -> quant_type
+    TYPE_MAP = {
+        (4, True): scalar_types.uint4b8,
+        (8, True): scalar_types.uint8b128,
+    }
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        desc_act: bool,
+        is_sym: bool,
+        lm_head_quantized: bool,
+    ) -> None:
+        if desc_act and group_size == -1:
+            # In this case, act_order == True is the same as act_order == False
+            # (since we have only one group per output channel)
+            desc_act = False
+
+        self.pack_factor = 32 // weight_bits  # packed into int32
+        self.group_size = group_size
+        self.desc_act = desc_act
+        self.lm_head_quantized = lm_head_quantized
+
+        if (weight_bits, is_sym) not in self.TYPE_MAP:
+            raise ValueError("Unsupported quantization config: "
+                             f"bits={weight_bits}, sym={is_sym}")
+
+        self.quant_type = self.TYPE_MAP[(weight_bits, is_sym)]
+
+    def __repr__(self) -> str:
+        return (f"GPTQMarlinConfig(quant_type={self.quant_type}, "
+                f"group_size={self.group_size}, "
+                f"desc_act={self.desc_act}, "
+                f"lm_head_quantized={self.lm_head_quantized})")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "gptq_marlin"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "GPTQMarlinConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        desc_act = cls.get_from_keys(config, ["desc_act"])
+        is_sym = cls.get_from_keys(config, ["sym"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        return cls(weight_bits, group_size, desc_act, is_sym,
+                   lm_head_quantized)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        can_convert = cls.is_gptq_marlin_compatible(hf_quant_cfg)
+
+        is_valid_user_quant = (user_quant is None or user_quant == "marlin"
+                               or user_quant == "gptq_marlin")
+
+        if can_convert and is_valid_user_quant:
+            msg = ("The model is convertible to {} during runtime."
+                   " Using {} kernel.".format(cls.get_name(), cls.get_name()))
+            logger.info(msg)
+            return cls.get_name()
+
+        if can_convert and user_quant == "gptq":
+            logger.info("Detected that the model can run with gptq_marlin"
+                        ", however you specified quantization=gptq explicitly,"
+                        " so forcing gptq. Use quantization=gptq_marlin for"
+                        " faster inference")
+        return None
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> Optional[Union["GPTQMarlinLinearMethod", "GPTQMarlinMoEMethod"]]:
+        if isinstance(layer, LinearBase) or (isinstance(layer, ParallelLMHead)
+                                             and self.lm_head_quantized):
+            return GPTQMarlinLinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return GPTQMarlinMoEMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    @classmethod
+    def is_gptq_marlin_compatible(cls, quant_config: Dict[str, Any]):
+        # Extract data from quant config.
+        quant_method = quant_config.get("quant_method", "").lower()
+        num_bits = quant_config.get("bits")
+        group_size = quant_config.get("group_size")
+        sym = quant_config.get("sym")
+        desc_act = quant_config.get("desc_act")
+
+        if quant_method != "gptq":
+            return False
+
+        # If we cannot find the info needed in the config, cannot convert.
+        if (num_bits is None or group_size is None or sym is None
+                or desc_act is None):
+            return False
+
+        if (num_bits, sym) not in cls.TYPE_MAP:
+            return False
+
+        return check_marlin_supported(quant_type=cls.TYPE_MAP[(num_bits, sym)],
+                                      group_size=group_size)
+
+
+class GPTQMarlinLinearMethod(LinearMethodBase):
+    """Linear method for GPTQ Marlin.
+
+    Args:
+        quant_config: The GPTQ Marlin quantization config.
+    """
+
+    _kernel_backends_being_used: Set[str] = set()
+
+    def __init__(self, quant_config: GPTQMarlinConfig) -> None:
+        self.quant_config = quant_config
+
+        # Verify supported on platform.
+        verify_marlin_supported(quant_type=self.quant_config.quant_type,
+                                group_size=self.quant_config.group_size)
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ) -> None:
+        output_size_per_partition = sum(output_partition_sizes)
+        is_row_parallel = input_size != input_size_per_partition
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        mp_linear_kernel_config = MPLinearLayerConfig(
+            full_weight_shape=(input_size, output_size),
+            partition_weight_shape=\
+                (input_size_per_partition, output_size_per_partition),
+            weight_type=self.quant_config.quant_type,
+            act_type=params_dtype,
+            group_size=self.quant_config.group_size,
+            zero_points=False,
+            has_g_idx=self.quant_config.desc_act
+        )
+
+        kernel_type = choose_mp_linear_kernel(mp_linear_kernel_config)
+
+        if kernel_type.__name__ not in self._kernel_backends_being_used:
+            logger.info("Using %s for GPTQMarlinLinearMethod",
+                        kernel_type.__name__)
+            self._kernel_backends_being_used.add(kernel_type.__name__)
+
+        # Normalize group_size
+        if self.quant_config.group_size != -1:
+            group_size = self.quant_config.group_size
+        else:
+            group_size = input_size
+
+        # Determine sharding
+        if marlin_repeat_scales_on_all_ranks(self.quant_config.desc_act,
+                                             self.quant_config.group_size,
+                                             is_row_parallel):
+            # By setting scale_dim == None, weight_loader will
+            # repeat the scales on each GPU in TP>1 case.
+            scales_and_zp_input_dim = None
+            scales_and_zp_size = input_size // group_size
+        else:
+            # By setting scale_dim == 0, weight_loader will
+            # shard the scales in TP>1 case.
+            scales_and_zp_input_dim = 0
+            scales_and_zp_size = input_size_per_partition // group_size
+
+        # Quantized weights
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.pack_factor,
+                output_size_per_partition,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=0,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader)
+
+        # Activation order
+        g_idx = RowvLLMParameter(data=torch.empty(
+            input_size_per_partition,
+            dtype=torch.int32,
+        ),
+                                 input_dim=0,
+                                 weight_loader=weight_loader)
+
+        qzeros_args = {
+            "data":
+            torch.empty(
+                scales_and_zp_size,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+        weight_scale_args = {
+            "data":
+            torch.empty(
+                scales_and_zp_size,
+                output_size_per_partition,
+                dtype=params_dtype,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+
+        if scales_and_zp_input_dim is None:
+            scales = ChannelQuantScaleParameter(output_dim=1,
+                                                **weight_scale_args)
+            qzeros = PackedColumnParameter(
+                output_dim=1,
+                packed_dim=1,
+                packed_factor=self.quant_config.pack_factor,
+                **qzeros_args)
+
+        else:
+            scales = GroupQuantScaleParameter(output_dim=1,
+                                              input_dim=0,
+                                              **weight_scale_args)
+            qzeros = PackedvLLMParameter(
+                input_dim=0,
+                output_dim=1,
+                packed_dim=1,
+                packed_factor=self.quant_config.pack_factor,
+                **qzeros_args)
+
+        layer.register_parameter("qweight", qweight)
+        layer.register_parameter("g_idx", g_idx)
+        layer.register_parameter("scales", scales)
+        layer.register_parameter("qzeros", qzeros)
+
+        self.kernel = kernel_type(mp_linear_kernel_config,
+                                  w_q_param_name="qweight",
+                                  w_s_param_name="scales",
+                                  w_zp_param_name="qzeros",
+                                  w_gidx_param_name="g_idx")
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        self.kernel.process_weights_after_loading(layer)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return self.kernel.apply_weights(layer, x, bias)
+
+
+class GPTQMarlinMoEMethod(FusedMoEMethodBase):
+    """MoE Marlin method with quantization."""
+
+    def __init__(self, quant_config: GPTQMarlinConfig) -> None:
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        # Currently assuming is_k_full is always True
+        # (input size per partition is the same as full input size)
+        # Supports only sym for now (no zp)
+        if self.quant_config.group_size != -1:
+            scales_size13 = hidden_size // self.quant_config.group_size
+            scales_size2 = intermediate_size // self.quant_config.group_size
+            strategy = FusedMoeWeightScaleSupported.GROUP.value
+        else:
+            scales_size13 = 1
+            scales_size2 = 1
+            strategy = FusedMoeWeightScaleSupported.CHANNEL.value
+
+        extra_weight_attrs.update({
+            "quant_method": strategy,
+            "is_transposed": True
+        })
+        # Fused gate_up_proj (column parallel)
+        w13_qweight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size // self.quant_config.pack_factor,
+                2 * intermediate_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_qweight", w13_qweight)
+        set_weight_attrs(w13_qweight, extra_weight_attrs)
+        # down_proj (row parallel)
+        w2_qweight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size // self.quant_config.pack_factor,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_qweight", w2_qweight)
+        set_weight_attrs(w2_qweight, extra_weight_attrs)
+        # up_proj scales
+        w13_scales = torch.nn.Parameter(
+            torch.empty(num_experts,
+                        scales_size13,
+                        2 * intermediate_size,
+                        dtype=torch.half),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_scales", w13_scales)
+        set_weight_attrs(w13_scales, extra_weight_attrs)
+        # down_proj scales
+        w2_scales = torch.nn.Parameter(
+            torch.empty(num_experts,
+                        scales_size2,
+                        hidden_size,
+                        dtype=torch.half),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_scales", w2_scales)
+        set_weight_attrs(w2_scales, extra_weight_attrs)
+        # up_proj scales
+        w13_qzeros = torch.nn.Parameter(
+            torch.empty(num_experts,
+                        scales_size13,
+                        2 * intermediate_size // self.quant_config.pack_factor,
+                        dtype=params_dtype),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_qzeros", w13_qzeros)
+        set_weight_attrs(w13_qzeros, extra_weight_attrs)
+        # down_proj scales
+        w2_qzeros = torch.nn.Parameter(
+            torch.empty(num_experts,
+                        scales_size2,
+                        hidden_size // self.quant_config.pack_factor,
+                        dtype=params_dtype),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_qzeros", w2_qzeros)
+        set_weight_attrs(w2_qzeros, extra_weight_attrs)
+        w13_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_g_idx", w13_g_idx)
+        set_weight_attrs(w13_g_idx, extra_weight_attrs)
+        w2_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_g_idx", w2_g_idx)
+        set_weight_attrs(w2_g_idx, extra_weight_attrs)
+        w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_g_idx_sort_indices",
+                                 w13_g_idx_sort_indices)
+        set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)
+        w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_g_idx_sort_indices",
+                                 w2_g_idx_sort_indices)
+        set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+
+        # Process act_order
+        if self.quant_config.desc_act:
+            # Get sorting based on g_idx
+            num_experts = layer.w13_g_idx.shape[0]
+            w13_g_idx_sort_indices = torch.empty_like(layer.w13_g_idx)
+            w2_g_idx_sort_indices = torch.empty_like(layer.w2_g_idx)
+            w13_sorted_g_idx = torch.empty_like(layer.w13_g_idx)
+            w2_sorted_g_idx = torch.empty_like(layer.w2_g_idx)
+            for e in range(num_experts):
+                w13_g_idx_sort_indices[e] = torch.argsort(
+                    layer.w13_g_idx[e]).to(torch.int32)
+                w2_g_idx_sort_indices[e] = torch.argsort(layer.w2_g_idx[e]).to(
+                    torch.int32)
+                w13_sorted_g_idx[e] = layer.w13_g_idx[e][
+                    w13_g_idx_sort_indices[e]]
+                w2_sorted_g_idx[e] = layer.w2_g_idx[e][
+                    w2_g_idx_sort_indices[e]]
+            replace_parameter(layer, "w13_g_idx", w13_sorted_g_idx)
+            replace_parameter(layer, "w2_g_idx", w2_sorted_g_idx)
+            replace_parameter(layer, "w13_g_idx_sort_indices",
+                              w13_g_idx_sort_indices)
+            replace_parameter(layer, "w2_g_idx_sort_indices",
+                              w2_g_idx_sort_indices)
+        else:
+            # Reset g_idx related tensors
+            num_experts = layer.w13_g_idx.shape[0]
+            device = layer.w13_g_idx.device
+            layer.w13_g_idx = torch.nn.Parameter(
+                torch.empty((num_experts, 0), dtype=torch.int32,
+                            device=device),
+                requires_grad=False,
+            )
+            layer.w2_g_idx = torch.nn.Parameter(
+                torch.empty((num_experts, 0), dtype=torch.int32,
+                            device=device),
+                requires_grad=False,
+            )
+            layer.w13_g_idx_sort_indices = torch.nn.Parameter(
+                torch.empty((num_experts, 0), dtype=torch.int32,
+                            device=device),
+                requires_grad=False,
+            )
+            layer.w2_g_idx_sort_indices = torch.nn.Parameter(
+                torch.empty((num_experts, 0), dtype=torch.int32,
+                            device=device),
+                requires_grad=False,
+            )
+        # Repack weights
+        marlin_w13_qweight = ops.gptq_marlin_moe_repack(
+            layer.w13_qweight,
+            layer.w13_g_idx_sort_indices,
+            layer.w13_qweight.shape[1] * self.quant_config.pack_factor,
+            layer.w13_qweight.shape[2],
+            self.quant_config.quant_type.size_bits,
+        )
+        replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
+        marlin_w2_qweight = ops.gptq_marlin_moe_repack(
+            layer.w2_qweight,
+            layer.w2_g_idx_sort_indices,
+            layer.w2_qweight.shape[1] * self.quant_config.pack_factor,
+            layer.w2_qweight.shape[2],
+            self.quant_config.quant_type.size_bits,
+        )
+        replace_parameter(layer, "w2_qweight", marlin_w2_qweight)
+        # Repack scales
+        marlin_w13_scales = marlin_moe_permute_scales(
+            s=layer.w13_scales,
+            size_k=layer.intermediate_size_per_partition,
+            size_n=layer.w13_scales.shape[2],
+            group_size=self.quant_config.group_size,
+        )
+        replace_parameter(layer, "w13_scales", marlin_w13_scales)
+        marlin_w2_scales = marlin_moe_permute_scales(
+            s=layer.w2_scales,
+            size_k=layer.w2_scales.shape[1] * self.quant_config.pack_factor,
+            size_n=layer.w2_scales.shape[2],
+            group_size=self.quant_config.group_size,
+        )
+        replace_parameter(layer, "w2_scales", marlin_w2_scales)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+            fused_marlin_moe)
+
+        # The input must currently be float16
+        orig_dtype = x.dtype
+        x = x.half()
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=None)
+
+        return fused_marlin_moe(
+            x,
+            layer.w13_qweight,
+            layer.w2_qweight,
+            layer.w13_scales,
+            layer.w2_scales,
+            router_logits,
+            topk_weights,
+            topk_ids,
+            g_idx1=layer.w13_g_idx,
+            g_idx2=layer.w2_g_idx,
+            sort_indices1=layer.w13_g_idx_sort_indices,
+            sort_indices2=layer.w2_g_idx_sort_indices,
+            num_bits=self.quant_config.quant_type.size_bits,
+        ).to(orig_dtype)

vllm/model_executor/layers/quantization/gptq_marlin_24.py

@@ -0,0 +1,295 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+from vllm.scalar_type import scalar_types
+
+logger = init_logger(__name__)
+
+GPTQ_MARLIN_24_TILE = 16
+GPTQ_MARLIN_24_MIN_THREAD_N = 128
+GPTQ_MARLIN_24_MIN_THREAD_K = 128
+GPTQ_MARLIN_24_MAX_PARALLEL = 64
+
+GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES = [
+    scalar_types.uint4b8, scalar_types.uint8b128
+]
+GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES = [-1, 128]
+
+
+class GPTQMarlin24Config(QuantizationConfig):
+    """Config class for Marlin24.
+    """
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+    ) -> None:
+        quant_type = {
+            4: scalar_types.uint4b8,
+            8: scalar_types.uint8b128,
+        }.get(weight_bits)
+
+        self.group_size = group_size
+
+        # Verify
+        if quant_type is None or \
+            quant_type not in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES:
+            raise ValueError(
+                f"Marlin_24 does not support quant_type = {quant_type}. "
+                f"Only weight_bits = {GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES} "
+                "are supported.")
+        if self.group_size not in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES:
+            raise ValueError(
+                f"Marlin_24 does not support group_size = {self.group_size}. "
+                f"Only group_sizes = {GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES} "
+                "are supported.")
+
+        self.quant_type = quant_type
+
+        # 4 Bits packed into 32 bit datatype.
+        self.pack_factor = 32 // self.quant_type.size_bits
+
+        # Tile size used by marlin kernels.
+        self.tile_size = 16
+
+        # Min out_features dim
+        self.min_n_threads = GPTQ_MARLIN_24_MIN_THREAD_N
+
+        # Min in_features dim
+        self.min_k_threads = GPTQ_MARLIN_24_MIN_THREAD_K
+
+        # Max parallel problems to solve at once (improves large
+        # batch performance)
+        self.max_parallel = GPTQ_MARLIN_24_MAX_PARALLEL
+
+        # Permutation length used by the marlin kernels.
+        self.perm_len = 1024
+
+    def __repr__(self) -> str:
+        return "Marlin24Config(quant_type={}, group_size={})".format(
+            self.quant_type, self.group_size)
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "gptq_marlin_24"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    # Need to figure it out
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "GPTQMarlin24Config":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        return cls(weight_bits, group_size)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        is_marlin_24_format = (
+            hf_quant_cfg.get("checkpoint_format") == "marlin_24")
+
+        is_valid_user_quant = (user_quant is None or user_quant == "gptq"
+                               or user_quant == "gptq_marlin_24")
+
+        if is_marlin_24_format and is_valid_user_quant:
+            msg = ("The model is serialized in {} format. "
+                   "Using {} kernel.".format(cls.get_name(), cls.get_name()))
+            logger.info(msg)
+            return cls.get_name()
+
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["GPTQMarlin24LinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return GPTQMarlin24LinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class GPTQMarlin24LinearMethod(LinearMethodBase):
+    """Linear method for Marlin24.
+
+    Args:
+        quant_config: The Marlin24 quantization config.
+    """
+
+    def __init__(self, quant_config: GPTQMarlin24Config):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del output_size  # Unused.
+        weight_loader = extra_weight_attrs["weight_loader"]
+        if params_dtype != torch.float16:
+            raise ValueError(
+                f"The params dtype must be float16, but got {params_dtype}")
+
+        # Validate output_size_per_partition
+        output_size_per_partition = sum(output_partition_sizes)
+        if output_size_per_partition % self.quant_config.min_n_threads != 0:
+            raise ValueError(
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"min_n_threads = {self.quant_config.min_n_threads}.")
+        if output_size_per_partition % self.quant_config.pack_factor != 0:
+            raise ValueError(
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"pack_factor = {self.quant_config.pack_factor}.")
+
+        # Validate input_size_per_partition
+        if input_size_per_partition % self.quant_config.min_k_threads != 0:
+            raise ValueError(
+                f"Weight input_size_per_partition = "
+                f"{input_size_per_partition} is not divisible by "
+                f"min_k_threads = {self.quant_config.min_k_threads}.")
+        if (self.quant_config.group_size != -1 and
+                input_size_per_partition % self.quant_config.group_size != 0):
+            raise ValueError(f"Weight input_size_per_partition = "
+                             f"{input_size_per_partition} is not divisible by "
+                             f"group_size = {self.quant_config.group_size}.")
+
+        # Check that we have at least 4 tiles horizontally in the shard
+        num_tiles_per_perm = self.quant_config.perm_len // (
+            self.quant_config.tile_size**2)
+        if output_size_per_partition % num_tiles_per_perm != 0:
+            raise ValueError(
+                "Each permutation group must reside on the same gpu")
+
+        # Quantized 4Bit weights packed into Int32.
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.tile_size // 2,
+                output_size_per_partition * self.quant_config.tile_size //
+                self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            marlin_tile_size=self.quant_config.tile_size,
+            weight_loader=weight_loader)
+
+        # Meta
+        meta = PackedvLLMParameter(data=torch.empty(
+            input_size_per_partition // 8 // 2 // 2,
+            output_size_per_partition * 2,
+            device="cuda",
+            dtype=torch.int16,
+        ),
+                                   input_dim=0,
+                                   output_dim=1,
+                                   packed_dim=1,
+                                   packed_factor=1,
+                                   marlin_tile_size=2,
+                                   weight_loader=weight_loader)
+
+        # Determine if channelwise or not
+        input_groups = (1 if self.quant_config.group_size == -1 else
+                        input_size_per_partition //
+                        self.quant_config.group_size)
+
+        weight_scale_args = {
+            "data":
+            torch.empty(
+                input_groups,
+                output_size_per_partition,
+                device="cuda",
+                dtype=params_dtype,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+        if input_groups == 1:
+            scales = ChannelQuantScaleParameter(output_dim=1,
+                                                **weight_scale_args)
+        else:
+            scales = GroupQuantScaleParameter(output_dim=1,
+                                              input_dim=0,
+                                              **weight_scale_args)
+
+        # Allocate workspace (Used for internal locking mechanism)
+        max_workspace_size = (
+            output_size_per_partition //
+            self.quant_config.min_n_threads) * self.quant_config.max_parallel
+
+        workspace = BasevLLMParameter(data=torch.zeros(max_workspace_size,
+                                                       device="cuda",
+                                                       dtype=torch.int),
+                                      weight_loader=weight_loader)
+
+        layer.register_parameter("B_24", qweight)
+        layer.register_parameter("B_meta", meta)
+        layer.register_parameter("s", scales)
+        layer.register_parameter("workspace", workspace)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # required by torch.compile
+        layer.B_24 = Parameter(layer.B_24.data, requires_grad=False)
+        layer.s = Parameter(layer.s.data, requires_grad=False)
+        layer.B_meta = Parameter(layer.B_meta.data, requires_grad=False)
+        layer.workspace = Parameter(layer.workspace.data, requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        qweight = layer.B_24
+        meta = layer.B_meta
+        scales = layer.s
+        workspace = layer.workspace
+
+        x_2d = x.view(-1, x.shape[-1])
+
+        size_m = x_2d.shape[0]
+        size_k = x_2d.shape[1]
+        size_n = scales.shape[1]
+
+        output_2d = ops.gptq_marlin_24_gemm(x_2d, qweight, meta, scales,
+                                            workspace,
+                                            self.quant_config.quant_type,
+                                            size_m, size_n, size_k)
+
+        output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
+
+        if bias is not None:
+            output.add_(bias)  # In-place add
+
+        return output

vllm/model_executor/layers/quantization/ipex_quant.py

@@ -0,0 +1,166 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.awq import AWQLinearMethod
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.platforms import current_platform
+
+
+class IPEXConfig(QuantizationConfig):
+    """INT8 quantization config class using IPEX for the CPU backend,
+    including AWQ.
+    """
+
+    IPEX_QUANT_METHOD_MAP = {
+        "awq": 1,
+        "gptq": 2,
+    }
+
+    def __init__(
+        self,
+        method: str,
+        weight_bits: int,
+        group_size: int,
+    ) -> None:
+        self.method = method
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.pack_factor = 32 // self.weight_bits
+
+        if self.weight_bits not in [4]:
+            raise ValueError(f"IPEX quantization supports weight bits [4], "
+                             f"but got {self.weight_bits}.")
+
+        if self.method == "awq":
+            self.quant_method = IPEXAWQLinearMethod
+        else:
+            raise ValueError(f"IPEX quantization supports [awq], "
+                             f"but got {self.method}.")
+
+    def __repr__(self) -> str:
+        return (f"IPEXConfig(method={self.method}"
+                f"weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}")
+
+    def get_ipex_quant_method_id(self) -> int:
+        return IPEXConfig.IPEX_QUANT_METHOD_MAP[self.method]
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "ipex"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return -1
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return [
+            "quant_config.json",
+            "quantize_config.json",
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "IPEXConfig":
+        method = cls.get_from_keys(config, ["quant_method"]).lower()
+        weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
+        group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
+        return cls(method, weight_bits, group_size)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        if not current_platform.is_cpu():
+            return None
+
+        quant_method = hf_quant_cfg.get("quant_method", "").lower()
+
+        if quant_method in ["awq"]:
+            return cls.get_name()
+
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["LinearMethodBase"]:
+        if isinstance(layer, LinearBase):
+            return self.quant_method(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        if self.method == "awq":
+            return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
+        else:
+            return []
+
+
+class IPEXAWQLinearMethod(AWQLinearMethod):
+    """AWQ linear method using IPEX for the CPU backend.
+    """
+
+    def __init__(self, quant_config: IPEXConfig):
+        self.quant_config = quant_config  # type: ignore
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        super().process_weights_after_loading(layer=layer)
+
+        bias = layer.bias if not layer.skip_bias_add else None
+
+        try:
+            import intel_extension_for_pytorch as ipex
+            if ipex.__version__ < "2.4.0":
+                raise ImportError("intel_extension_for_pytorch version is "
+                                  "wrong. Please install "
+                                  "intel_extension_for_pytorch>=2.4.0.")
+        except ImportError as err:
+            raise ImportError(
+                "Please install "
+                "intel_extension_for_pytorch>=2.4.0 via "
+                "`pip install intel_extension_for_pytorch>=2.4.0`"
+                " to use IPEX-AWQ linear method.") from err
+
+        # Using the compute dtype (lowp_mode) as INT8 to leverage instructions
+        # with better performance.
+        lowp_mode = ipex.quantization.WoqLowpMode.INT8
+        # The weight will be de-packed from INT4 to INT8.
+        weight_dtype = ipex.quantization.WoqWeightDtype.INT4
+        # The float activation will be quantized (dynamic, per-token) to INT8.
+        act_quant_mode = ipex.quantization.WoqActQuantMode.PER_BATCH
+
+        qconfig = ipex.quantization.get_weight_only_quant_qconfig_mapping(
+            weight_dtype=weight_dtype,
+            lowp_mode=lowp_mode,
+            act_quant_mode=act_quant_mode,
+            group_size=self.quant_config.group_size,
+        )
+
+        layer.ipex_output_size = layer.qweight.size(
+            1) * self.quant_config.pack_factor
+        layer.ipex_qlinear = ipex.nn.modules.weight_only_quantization.\
+            WeightOnlyQuantizedLinear.from_weight(
+                layer.qweight,
+                layer.scales,
+                layer.qzeros,
+                layer.qweight.size(0),
+                layer.ipex_output_size,
+                qconfig=qconfig,
+                bias=bias,
+                group_size=self.quant_config.group_size,
+                quant_method=
+                    self.quant_config.get_ipex_quant_method_id() # type: ignore
+            )
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        reshaped_x = x.reshape(-1, x.shape[-1])
+        out = layer.ipex_qlinear(reshaped_x)
+
+        return out.reshape(x.shape[:-1] + (layer.ipex_output_size, ))

vllm/model_executor/layers/quantization/kernels/MPLinearKernel.py

@@ -0,0 +1,83 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Callable, Optional, Tuple
+
+import torch
+
+from vllm.model_executor.layers.quantization.utils import replace_parameter
+from vllm.scalar_type import ScalarType
+
+
+@dataclass
+class MPLinearLayerConfig:
+    full_weight_shape: Tuple[int, int]  # [in, out]
+    partition_weight_shape: Tuple[int, int]
+    weight_type: ScalarType
+    act_type: torch.dtype
+    group_size: int
+    zero_points: bool
+    has_g_idx: bool
+
+
+class MPLinearKernel(ABC):
+
+    @classmethod
+    @abstractmethod
+    def get_min_capability(cls) -> int:
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def can_implement(cls,
+                      c: MPLinearLayerConfig) -> Tuple[bool, Optional[str]]:
+        raise NotImplementedError
+
+    def __init__(self,
+                 c: MPLinearLayerConfig,
+                 w_q_param_name: str,
+                 w_s_param_name: str,
+                 w_zp_param_name: Optional[str] = None,
+                 w_gidx_param_name: Optional[str] = None) -> None:
+        assert self.can_implement(c)
+        self.config = c
+        self.w_q_name = w_q_param_name
+        self.w_s_name = w_s_param_name
+        self.w_zp_name = w_zp_param_name
+        self.w_gidx_name = w_gidx_param_name
+
+    @abstractmethod
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        raise NotImplementedError
+
+    def _transform_param(self, layer: torch.nn.Module, name: Optional[str],
+                         fn: Callable) -> None:
+        if name is not None and getattr(layer, name, None) is not None:
+
+            old_param = getattr(layer, name)
+            new_param = fn(old_param)
+            # replace the parameter with torch.nn.Parameter for TorchDynamo
+            # compatibility
+            replace_parameter(
+                layer, name,
+                torch.nn.Parameter(new_param.data, requires_grad=False))
+
+    def _get_weight_params(
+            self, layer: torch.nn.Module
+    ) -> Tuple[torch.Tensor,  # w_q
+               torch.Tensor,  # w_s
+               Optional[torch.Tensor],  # w_zp, 
+               Optional[torch.Tensor]  # w_gidx
+               ]:
+        return (
+            getattr(layer, self.w_q_name),
+            getattr(layer, self.w_s_name),
+            getattr(layer, self.w_zp_name or "", None),
+            getattr(layer, self.w_gidx_name or "", None),
+        )

vllm/model_executor/layers/quantization/kernels/__init__.py

@@ -0,0 +1,72 @@
+import os
+from typing import List, Optional, Type
+
+from vllm.model_executor.layers.quantization.kernels.machete import (
+    MacheteLinearKernel)
+from vllm.model_executor.layers.quantization.kernels.marlin import (
+    MarlinLinearKernel)
+from vllm.model_executor.layers.quantization.kernels.MPLinearKernel import (
+    MPLinearKernel, MPLinearLayerConfig)
+from vllm.platforms import current_platform
+
+# in priority/performance order (when available)
+_POSSIBLE_KERNELS: List[Type[MPLinearKernel]] = [
+    MacheteLinearKernel,
+    MarlinLinearKernel,
+]
+
+
+def choose_mp_linear_kernel(
+        config: MPLinearLayerConfig,
+        compute_capability: Optional[int] = None) -> Type[MPLinearKernel]:
+    """
+    Choose an MPLinearKernel that can implement the given config for the given
+     compute capability. Attempts to choose the best kernel in terms of 
+     performance.
+
+    Args:
+        config (MPLinearLayerConfig): Description of the linear layer to be 
+          implemented.
+        compute_capability (Optional[int], optional): The compute capability of
+          the target device, if None uses `current_platform` to get the compute 
+          capability. Defaults to None.
+
+    Raises:
+        ValueError: If no kernel can implement the given config.
+
+    Returns:
+        Type[MPLinearKernel]: Chosen kernel.
+    """
+    if compute_capability is None:
+        if current_platform is None:
+            raise ValueError("Cannot determine compute capability")
+        _cc = current_platform.get_device_capability()
+        compute_capability = _cc[0] * 10 + _cc[1]
+
+    failure_reasons = []
+    for kernel in _POSSIBLE_KERNELS:
+        if kernel.__name__ in os.environ.get("VLLM_DISABLED_KERNELS", "")\
+            .split(","):
+            failure_reasons.append(
+                f' {kernel.__name__} disabled by environment variable')
+            continue
+
+        if kernel.get_min_capability() > compute_capability:
+            failure_reasons.append(
+                f"{kernel.__name__} requires capability "
+                f"{kernel.get_min_capability()}, current compute capability "
+                f"is {compute_capability}")
+            continue
+
+        can_implement, failure_reason = kernel.can_implement(config)
+        if can_implement:
+            return kernel
+        else:
+            failure_reasons.append(
+                f' {kernel.__name__} cannot implement due to: {failure_reason}'
+            )
+
+    raise ValueError(
+        "Failed to find a kernel that can implement the "\
+        "WNA16 linear layer. Reasons: \n"
+        + '\n'.join(failure_reasons))

vllm/model_executor/layers/quantization/kernels/machete.py

@@ -0,0 +1,118 @@
+from functools import partial
+from typing import Optional, Tuple
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.utils.machete_utils import (
+    MACHETE_SUPPORTED_GROUP_SIZES, check_machete_supports_shape,
+    query_machete_supported_quant_types)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    pack_weights_into_int32, unpack_weights_into_int32)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           permute_param_layout_)
+
+from .MPLinearKernel import MPLinearKernel, MPLinearLayerConfig
+
+
+class MacheteLinearKernel(MPLinearKernel):
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 90
+
+    @classmethod
+    def can_implement(cls,
+                      c: MPLinearLayerConfig) -> Tuple[bool, Optional[str]]:
+        if c.has_g_idx and\
+            c.partition_weight_shape[0] != c.full_weight_shape[0]:
+            return False, "Act reordering currently not supported by Machete, "\
+                          "when the input features are partitioned across "\
+                          "devices"
+
+        if c.zero_points:
+            return False, "Zero points currently not supported by "\
+                          " Compressed Tensors + Machete. (Kernel supports it"\
+                          " but CompressedTensorsWNA16 does not so support has"\
+                          " not been added to MacheteWNA16Kernel yet"
+
+        if c.weight_type not in query_machete_supported_quant_types(
+                c.zero_points):
+            return False, f"Quant type ({c.weight_type}) not supported by "\
+                           "Machete, supported types are: "\
+                           f"{query_machete_supported_quant_types(c.zero_points)}"
+
+        if c.group_size not in MACHETE_SUPPORTED_GROUP_SIZES:
+            return False, f"Group size ({c.group_size}) not supported by "\
+                            "Machete, supported group sizes are: "\
+                            f"{MACHETE_SUPPORTED_GROUP_SIZES}"
+
+        return check_machete_supports_shape(c.partition_weight_shape[0],
+                                            c.partition_weight_shape[1])
+
+    # note assumes that
+    #  `weight_packed` is: {input_dim = 0, output_dim = 1, packed_dim = 0}
+    #  `weight_scale`  is: {input_dim = 0, output_dim = 1}
+    def process_weights_after_loading(self, layer: torch.nn.Module):
+        c = self.config
+
+        if c.has_g_idx:
+            assert self.w_gidx_name is not None
+            perm = torch.argsort(getattr(layer, self.w_gidx_name))\
+                .to(torch.int)
+
+            self.act_perm = lambda x: x[:, perm]
+            # use `ops.permute_cols` if possible
+            if c.act_type in [torch.float16, torch.bfloat16] \
+                and c.partition_weight_shape[0] % 8 == 0:
+                self.act_perm = partial(ops.permute_cols, perm=perm)
+
+        def transform_w_q(x):
+            assert isinstance(x, BasevLLMParameter)
+            permute_param_layout_(x, input_dim=0, output_dim=1, packed_dim=0)
+            if c.has_g_idx:
+                x_unpacked = unpack_weights_into_int32(x.data,
+                                                       c.weight_type,
+                                                       packed_dim=0)
+                x_perm = x_unpacked[perm, :]
+                x.data = pack_weights_into_int32(x_perm,
+                                                 c.weight_type,
+                                                 packed_dim=0)
+            x.data = ops.machete_prepack_B(x.data.t().contiguous().t(),
+                                           self.config.weight_type)
+            return x
+
+        def transform_w_s(x):
+            assert isinstance(x, BasevLLMParameter)
+            permute_param_layout_(x, input_dim=0, output_dim=1)
+            x.data = x.data.contiguous()
+            return x
+
+        # Repack weights and scales for Machete
+        self._transform_param(layer, self.w_q_name, transform_w_q)
+        self._transform_param(layer, self.w_s_name, transform_w_s)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        c = self.config
+        w_q, w_s, _, _ = self._get_weight_params(layer)
+
+        x_2d = x.reshape(-1, x.shape[-1])
+        out_shape = x.shape[:-1] + (c.partition_weight_shape[1], )
+
+        if c.has_g_idx:
+            x_2d = self.act_perm(x_2d)
+
+        output = ops.machete_gemm(a=x_2d,
+                                  b_q=w_q,
+                                  b_type=c.weight_type,
+                                  b_zeros=None,
+                                  b_scales=w_s,
+                                  b_group_size=c.group_size)
+
+        if bias is not None:
+            output.add_(bias)  # In-place add
+
+        return output.reshape(out_shape)

vllm/model_executor/layers/quantization/kernels/marlin.py

@@ -0,0 +1,133 @@
+from typing import Optional, Tuple
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    MARLIN_SUPPORTED_GROUP_SIZES, apply_gptq_marlin_linear,
+    check_marlin_supports_shape, marlin_is_k_full, marlin_make_empty_g_idx,
+    marlin_make_workspace, marlin_permute_scales, marlin_sort_g_idx,
+    query_marlin_supported_quant_types)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           permute_param_layout_)
+
+from .MPLinearKernel import MPLinearKernel, MPLinearLayerConfig
+
+
+class MarlinLinearKernel(MPLinearKernel):
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def can_implement(cls,
+                      c: MPLinearLayerConfig) -> Tuple[bool, Optional[str]]:
+        if c.zero_points:
+            return False, "Zero points currently not supported by "\
+                          " MarlinLinearKernel. Will be added when AWQMarlin "\
+                          "is migrated over to using MPLinearKernel backend"
+
+        quant_types = query_marlin_supported_quant_types(c.zero_points)
+        if c.weight_type not in quant_types:
+            return False, f"Quant type ({c.weight_type}) not supported by"\
+                          f"  Marlin, supported types are: {quant_types}"
+
+        if c.group_size not in MARLIN_SUPPORTED_GROUP_SIZES:
+            return False, f"Group size ({c.group_size}) not supported by "\
+                            "Marlin, supported group sizes are: "\
+                            f"{MARLIN_SUPPORTED_GROUP_SIZES}"
+
+        return check_marlin_supports_shape(
+            c.partition_weight_shape[1],  # out_features
+            c.partition_weight_shape[0],  # in_features
+            c.full_weight_shape[0],  # in_features
+            c.group_size)
+
+    # note assumes that
+    #  `weight_packed` is: {input_dim = 0, output_dim = 1, packed_dim = 0}
+    #  `weight_scale` is: {input_dim = 0, output_dim = 1}
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        device = getattr(layer, self.w_q_name).device
+        c = self.config
+
+        row_parallel = (c.partition_weight_shape[0] != c.full_weight_shape[0])
+        self.is_k_full = marlin_is_k_full(c.has_g_idx, row_parallel)
+
+        # Allocate marlin workspace.
+        self.workspace = marlin_make_workspace(c.partition_weight_shape[1],
+                                               device)
+
+        # Default names since marlin requires empty parameters for these,
+        # TODO: remove this requirement from marlin (allow optional tensors)
+        if self.w_gidx_name is None:
+            self.w_gidx_name = "g_idx"
+        if self.w_zp_name is None:
+            self.w_zp_name = "w_zp"
+
+        if c.has_g_idx:
+            g_idx, g_idx_sort_indices = marlin_sort_g_idx(
+                getattr(layer, self.w_gidx_name))
+            self._transform_param(layer, self.w_gidx_name, lambda _: g_idx)
+            layer.g_idx_sort_indices = g_idx_sort_indices
+        else:
+            setattr(layer, self.w_gidx_name, marlin_make_empty_g_idx(device))
+            layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)
+
+        if c.zero_points:
+            pass
+            # TODO (lucas): add the following when AWQMarlin is migrated over to
+            #       using MPLinearKernel backend
+            # self._transform_param(layer, self.w_zp_name, lambda x: \
+            #     marlin_zero_points(
+            #         x,
+            #         size_k=c.partition_weight_shape[0],
+            #         size_n=c.partition_weight_shape[1],
+            #         num_bits=c.weight_type.size_bits))
+        else:
+            setattr(layer, self.w_zp_name, marlin_make_empty_g_idx(device))
+
+        def transform_w_q(x):
+            assert isinstance(x, BasevLLMParameter)
+            permute_param_layout_(x, input_dim=0, output_dim=1, packed_dim=0)
+            x.data = ops.gptq_marlin_repack(x.data.contiguous(),
+                                            perm=layer.g_idx_sort_indices,
+                                            size_k=c.partition_weight_shape[0],
+                                            size_n=c.partition_weight_shape[1],
+                                            num_bits=c.weight_type.size_bits)
+            return x
+
+        def transform_w_s(x):
+            assert isinstance(x, BasevLLMParameter)
+            permute_param_layout_(x, input_dim=0, output_dim=1)
+            x.data = marlin_permute_scales(x.data.contiguous(),
+                                           size_k=c.partition_weight_shape[0],
+                                           size_n=c.partition_weight_shape[1],
+                                           group_size=c.group_size)
+            return x
+
+        self._transform_param(layer, self.w_q_name, transform_w_q)
+        self._transform_param(layer, self.w_s_name, transform_w_s)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        c = self.config
+        w_q, w_s, w_zp, w_gidx = self._get_weight_params(layer)
+
+        # `process_weights_after_loading` will ensure w_zp and w_gidx are not
+        #  None for marlin
+        return apply_gptq_marlin_linear(
+            input=x,
+            weight=w_q,
+            weight_scale=w_s,
+            weight_zp=w_zp,  # type: ignore
+            g_idx=w_gidx,  # type: ignore
+            g_idx_sort_indices=layer.g_idx_sort_indices,
+            workspace=self.workspace,
+            wtype=c.weight_type,
+            input_size_per_partition=c.partition_weight_shape[0],
+            output_size_per_partition=c.partition_weight_shape[1],
+            is_k_full=self.is_k_full,
+            bias=bias)

vllm/model_executor/layers/quantization/kv_cache.py

@@ -0,0 +1,76 @@
+import torch
+
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.utils import print_warning_once
+
+
+class BaseKVCacheMethod(QuantizeMethodBase):
+    """
+    Quant method that adds `_k_scale` and `_v_scale` attributes to the
+    Attention layer to support loading those scaling factors from checkpoints. 
+    The k/v_scale will be used to:
+        - quantize k/v_cache entries before saving them to the cache
+        - dequantize k/v_cache entries before fetching them from the cache
+
+    :param quant_config: the appropriate QuantizationConfig 
+    """
+
+    def __init__(self, quant_config: QuantizationConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module):
+        """
+        Create "weight" (aka k_scale and v_scale) for an attention layer.
+        """
+        # Initialize the KV cache scales to -1.0, which is an invalid value.
+        # If the k/v_scale appears in the checkpoint, it will be
+        # overwritten when loading weights.
+        layer.k_scale = torch.nn.Parameter(torch.tensor(-1.0),
+                                           requires_grad=False)
+        layer.v_scale = torch.nn.Parameter(torch.tensor(-1.0),
+                                           requires_grad=False)
+
+    def apply(self, layer: torch.nn.Module) -> torch.Tensor:
+        raise RuntimeError(
+            f"{self.__class__.__name__}.apply should not be called.")
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # If the kv-cache dtype is auto, we enforce the k/v_scale to be 1.0
+        # regardless whether the kv-scale is available in the checkpoint.
+        if layer.kv_cache_dtype != "auto":
+            if layer.k_scale > 0.0 and layer.v_scale > 0.0:
+                # We prefer to use separate k_scale and v_scale if present
+                k_scale = layer.k_scale.to("cpu").tolist()
+                v_scale = layer.v_scale.to("cpu").tolist()
+            elif layer.k_scale < 0.0 and layer.v_scale < 0.0:
+                # If no scales were loaded (both scales are invalid negative
+                # values), use the default value of 1.0
+                k_scale = 1.0
+                v_scale = 1.0
+            else:
+                # If we find a single kv_scale in the checkpoint, we remap
+                # kv_scale to k_scale during weight loading, and duplicate
+                # k_scale to v_scale here
+                assert layer.k_scale > 0.0
+                scale_to_duplicate = max(layer.k_scale, layer.v_scale)
+                k_scale = scale_to_duplicate.to("cpu").tolist()
+                v_scale = scale_to_duplicate.to("cpu").tolist()
+
+            if not isinstance(k_scale, float) or not isinstance(
+                    v_scale, float):
+                raise ValueError("Only support per-tensor scaling factor "
+                                 "for fp8 KV cache")
+
+            # These are used in the final Attention.forward()
+            layer._k_scale = k_scale
+            layer._v_scale = v_scale
+            if (layer._k_scale == 1.0 and layer._v_scale == 1.0
+                    and "e5m2" not in layer.kv_cache_dtype):
+                print_warning_once(
+                    "Using KV cache scaling factor 1.0 for fp8_e4m3. This "
+                    "may cause accuracy issues. Please make sure k/v_scale "
+                    "scaling factors are available in the fp8 checkpoint.")
+
+        del layer.k_scale
+        del layer.v_scale

vllm/model_executor/layers/quantization/marlin.py

@@ -0,0 +1,260 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+
+logger = init_logger(__name__)
+
+
+class MarlinConfig(QuantizationConfig):
+    """Config class for Marlin.
+
+    Reference: https://github.com/IST-DASLab/marlin/tree/master
+    """
+
+    def __init__(
+        self,
+        group_size: int,
+        lm_head_quantized: bool,
+    ) -> None:
+        # Group size for the quantization.
+        self.group_size = group_size
+        self.lm_head_quantized = lm_head_quantized
+        if self.group_size != 128 and self.group_size != -1:
+            raise ValueError(
+                "Currently, only group size 128 and -1 (channelwise) "
+                "is supported for Marlin, but got group_size of "
+                f"{self.group_size}")
+
+        # 4 Bits packed into 32 bit datatype.
+        self.pack_factor = 32 // 4
+
+        # Tile size used by marlin kernels.
+        self.tile_size = 16
+
+        # Min out_features dim
+        self.min_n_threads = 64
+
+        # Min in_features dim
+        self.min_k_threads = 128
+
+        # Max parallel problems to solve at once (improves large
+        # batch performance)
+        self.max_parallel = 16
+
+        # Permutation length used by the marlin kernels.
+        self.perm_len = 1024
+
+    def __repr__(self) -> str:
+        return (f"MarlinConfig(group_size={self.group_size}, "
+                f"lm_head_quantized={self.lm_head_quantized})")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "marlin"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    # Need to figure it out
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "MarlinConfig":
+        group_size = cls.get_from_keys(config, ["group_size"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        return cls(group_size, lm_head_quantized)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        # compat: autogptq >=0.8.0 use checkpoint_format: str
+        # compat: autogptq <=0.7.1 is_marlin_format: bool
+        is_marlin_format = (hf_quant_cfg.get("checkpoint_format") == "marlin"
+                            or hf_quant_cfg.get("is_marlin_format", False))
+
+        is_valid_user_quant = (user_quant is None or user_quant == "gptq"
+                               or user_quant == "marlin")
+
+        if is_marlin_format and is_valid_user_quant:
+            msg = ("The model is serialized in {} format. Using {} kernel.".
+                   format(cls.get_name(), cls.get_name()))
+            logger.info(msg)
+            return cls.get_name()
+
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["MarlinLinearMethod"]:
+        if (isinstance(layer, LinearBase) or
+            (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
+            return MarlinLinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class MarlinLinearMethod(LinearMethodBase):
+    """Linear method for Marlin.
+
+    Args:
+        quant_config: The Marlin quantization config.
+    """
+
+    def __init__(self, quant_config: MarlinConfig):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del output_size  # Unused.
+        weight_loader = extra_weight_attrs["weight_loader"]
+
+        if params_dtype != torch.float16:
+            raise ValueError(
+                f"The params dtype must be float16, but got {params_dtype}")
+
+        # Validate output_size_per_partition
+        output_size_per_partition = sum(output_partition_sizes)
+        if output_size_per_partition % self.quant_config.min_n_threads != 0:
+            raise ValueError(
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"min_n_threads = {self.quant_config.min_n_threads}.")
+        if output_size_per_partition % self.quant_config.pack_factor != 0:
+            raise ValueError(
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"pack_factor = {self.quant_config.pack_factor}.")
+
+        # Validate input_size_per_partition
+        if input_size_per_partition % self.quant_config.min_k_threads != 0:
+            raise ValueError(
+                f"Weight input_size_per_partition = "
+                f"{input_size_per_partition} is not divisible by "
+                f"min_k_threads = {self.quant_config.min_k_threads}.")
+        if (self.quant_config.group_size != -1 and
+                input_size_per_partition % self.quant_config.group_size != 0):
+            raise ValueError(f"Weight input_size_per_partition = "
+                             f"{input_size_per_partition} is not divisible by "
+                             f"group_size = {self.quant_config.group_size}.")
+
+        # Check that we have at least 4 tiles horizontally in the shard
+        num_tiles_per_perm = self.quant_config.perm_len // (
+            self.quant_config.tile_size**2)
+        if output_size_per_partition % num_tiles_per_perm != 0:
+            raise ValueError(
+                "Each permutation group must reside on the same gpu")
+
+        # Quantized 4Bit weights packed into Int32.
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.tile_size,
+                output_size_per_partition * self.quant_config.tile_size //
+                self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            marlin_tile_size=self.quant_config.tile_size,
+            weight_loader=weight_loader)
+
+        # Determine if channelwise or not
+        input_groups = (1 if self.quant_config.group_size == -1 else
+                        input_size_per_partition //
+                        self.quant_config.group_size)
+
+        weight_scale_args = {
+            "data":
+            torch.empty(
+                input_groups,
+                output_size_per_partition,
+                device="cuda",
+                dtype=params_dtype,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+        if input_groups == 1:
+            scales = ChannelQuantScaleParameter(output_dim=1,
+                                                **weight_scale_args)
+        else:
+            scales = GroupQuantScaleParameter(output_dim=1,
+                                              input_dim=0,
+                                              **weight_scale_args)
+
+        # Allocate workspace (Used for internal locking mechanism)
+        max_workspace_size = (
+            output_size_per_partition //
+            self.quant_config.min_n_threads) * self.quant_config.max_parallel
+
+        workspace = BasevLLMParameter(data=torch.zeros(max_workspace_size,
+                                                       device="cuda",
+                                                       dtype=torch.int),
+                                      weight_loader=weight_loader)
+
+        layer.register_parameter("B", qweight)
+        layer.register_parameter("s", scales)
+        layer.register_parameter("workspace", workspace)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # required by torch.compile
+        layer.B = Parameter(layer.B.data, requires_grad=False)
+        layer.s = Parameter(layer.s.data, requires_grad=False)
+        layer.workspace = Parameter(layer.workspace.data, requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        qweight = layer.B
+        scales = layer.s
+        workspace = layer.workspace
+
+        x_2d = x.view(-1, x.shape[-1])
+
+        size_m = x_2d.shape[0]
+        size_k = x_2d.shape[1]
+        size_n = scales.shape[1]
+
+        output_2d = ops.marlin_gemm(x_2d, qweight, scales, workspace, size_m,
+                                    size_n, size_k)
+
+        output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
+
+        if bias is not None:
+            output.add_(bias)  # In-place add
+
+        return output

vllm/model_executor/layers/quantization/modelopt.py

@@ -0,0 +1,163 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    apply_fp8_linear, cutlass_fp8_supported, requantize_with_max_scale)
+from vllm.model_executor.parameter import (ModelWeightParameter,
+                                           PerTensorScaleParameter)
+
+logger = init_logger(__name__)
+
+ACTIVATION_SCHEMES = ["static"]
+
+
+class ModelOptFp8Config(QuantizationConfig):
+    """Config class for ModelOpt FP8."""
+
+    def __init__(
+        self,
+        is_checkpoint_fp8_serialized: bool = False,
+    ) -> None:
+        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
+        if is_checkpoint_fp8_serialized:
+            logger.warning("Detected ModelOpt fp8 checkpoint. Please note that"
+                           " the format is experimental and could change.")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "modelopt"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 89
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["hf_quant_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "ModelOptFp8Config":
+        quant_config = cls.get_from_keys(config, ["quantization"])
+        quant_method = quant_config["quant_algo"]
+        is_checkpoint_fp8_serialized = ("FP8" in quant_method)
+        if not is_checkpoint_fp8_serialized:
+            raise ValueError("ModelOpt currently only supports static FP8"
+                             "quantization in vLLM. Please check the "
+                             "`hf_quant_config.json` file for your model's "
+                             "quant configuration.")
+        return cls(is_checkpoint_fp8_serialized)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+        if isinstance(layer, LinearBase):
+            return ModelOptFp8LinearMethod(self)
+        elif isinstance(layer, Attention):
+            return ModelOptFp8KVCacheMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from FP8 checkpoints.
+    """
+
+    def __init__(self, quant_config: ModelOptFp8Config):
+        super().__init__(quant_config)
+
+
+class ModelOptFp8LinearMethod(LinearMethodBase):
+    """Linear method for Model Optimizer static quantization.
+    Supports loading FP8 checkpoints with static weight scale and
+    activation scale. Future support might be added for dynamic 
+    scales.
+
+    Limitations:
+    1. Only support per-tensor quantization due to torch._scaled_mm support.
+    2. Only support float8_e4m3fn datatype 
+        Args: quant_config: The ModelOpt quantization config.
+    """
+
+    def __init__(self, quant_config: ModelOptFp8Config):
+        self.quant_config = quant_config
+        self.cutlass_fp8_supported = cutlass_fp8_supported()
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del input_size, output_size
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        weight_dtype = (torch.float8_e4m3fn
+                        if self.quant_config.is_checkpoint_fp8_serialized else
+                        params_dtype)
+        weight = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition,
+            dtype=weight_dtype),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        if self.quant_config.is_checkpoint_fp8_serialized:
+            # WEIGHT SCALE
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+            weight_scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("weight_scale", weight_scale)
+            # INPUT SCALE
+            scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                            weight_loader=weight_loader)
+
+            scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("input_scale", scale)
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        max_w_scale, weight = requantize_with_max_scale(
+            layer.weight, layer.weight_scale, layer.logical_widths)
+        layer.weight = Parameter(weight.t(), requires_grad=False)
+        layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
+        layer.input_scale = Parameter(layer.input_scale.max(),
+                                      requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return apply_fp8_linear(
+            input=x,
+            weight=layer.weight,
+            weight_scale=layer.weight_scale,
+            input_scale=layer.input_scale,
+            bias=bias,
+            cutlass_fp8_supported=self.cutlass_fp8_supported)

vllm/model_executor/layers/quantization/neuron_quant.py

@@ -0,0 +1,67 @@
+import os
+from importlib.util import find_spec
+from typing import Any, Dict, List, Optional
+
+from torch.nn import Module
+
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+
+SUPPORTED_QUANT_DTYPE_LIST = ['s8', 'f8e4m3fn']
+
+
+class NeuronQuantConfig(QuantizationConfig):
+    """Int8 Quantization Config class for Neuron Backend."""
+
+    def __init__(
+        self,
+        dequant_dtype: str = "f16",
+        quantize_method: str = "vector_dynamic",
+    ) -> None:
+        self.quant_dtype = os.getenv("NEURON_QUANT_DTYPE", "s8")
+        if self.quant_dtype not in SUPPORTED_QUANT_DTYPE_LIST:
+            raise ValueError(
+                f"Neuron quantization datatype {self.quant_dtype} is not valid,"
+                f"the quantization datatype should match one of the below types"
+                f"{SUPPORTED_QUANT_DTYPE_LIST}")
+        self.dequant_dtype = dequant_dtype
+        self.quantize_method = quantize_method
+
+    def get_name(self) -> str:
+        return "neuron_quant"
+
+    def get_supported_act_dtypes(self) -> List[str]:
+        return SUPPORTED_QUANT_DTYPE_LIST
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        raise NotImplementedError(
+            "This function should not be called with Neuron Backend")
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "NeuronQuantConfig":
+        quantize_method = cls.get_from_keys(config, ["quantize_method"])
+        dequant_dtype = cls.get_from_keys(config, ["dequant_dtype"])
+        return cls(dequant_dtype=dequant_dtype,
+                   quantize_method=quantize_method)
+
+    def get_quant_method(self, layer: Module, prefix: str) -> Optional[Any]:
+        if find_spec("transformers_neuronx") is not None:
+            return self.get_quantization_config()
+        else:
+            raise NotImplementedError(
+                "Neuron Quantization is only supported through"
+                " transformers_neuronx.")
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    def get_quantization_config(self):
+        from transformers_neuronx.config import QuantizationConfig
+        return QuantizationConfig(quant_dtype=self.quant_dtype,
+                                  dequant_dtype=self.dequant_dtype,
+                                  quantize_method=self.quantize_method)

vllm/model_executor/layers/quantization/qqq.py

@@ -0,0 +1,273 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+
+logger = init_logger(__name__)
+
+MARLIN_QQQ_TILE = 16
+MARLIN_QQQ_MIN_THREAD_N = 64
+MARLIN_QQQ_MIN_THREAD_K = 128
+MARLIN_QQQ_MAX_PARALLEL = 16
+
+MARLIN_QQQ_SUPPORTED_NUM_BITS = [4]
+MARLIN_QQQ_SUPPORTED_GROUP_SIZES = [-1, 128]
+MARLIN_QQQ_SUPPORTED_SYM = [True]
+
+
+class QQQConfig(QuantizationConfig):
+    """Config class for QQQ
+    
+    Reference: https://arxiv.org/pdf/2406.09904
+    """
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        is_sym: bool = True,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.is_sym = is_sym
+
+        # Verify
+        if self.weight_bits not in MARLIN_QQQ_SUPPORTED_NUM_BITS:
+            raise ValueError(
+                f"QQQ does not support weight_bits = {self.weight_bits}. "
+                f"Only weight_bits = {MARLIN_QQQ_SUPPORTED_NUM_BITS} "
+                "are supported.")
+        if self.group_size not in MARLIN_QQQ_SUPPORTED_GROUP_SIZES:
+            raise ValueError(
+                f"QQQ does not support group_size = {self.group_size}. "
+                f"Only group_sizes = {MARLIN_QQQ_SUPPORTED_GROUP_SIZES} "
+                "are supported.")
+        if self.is_sym not in MARLIN_QQQ_SUPPORTED_SYM:
+            raise ValueError(
+                f"QQQ does not support is_sym = {self.is_sym}. "
+                f"Only sym = {MARLIN_QQQ_SUPPORTED_SYM} are supported.")
+
+        # 4 Bits packed into 32 bit datatype.
+        self.pack_factor = 32 // self.weight_bits
+
+        # Tile size used by QQQ kernels.
+        self.tile_size = MARLIN_QQQ_TILE
+
+        # Min out_features dim
+        self.min_n_threads = MARLIN_QQQ_MIN_THREAD_N
+
+        # Min in_features dim
+        self.min_k_threads = MARLIN_QQQ_MIN_THREAD_K
+
+        # Max parallel problems to solve at once (improves large
+        # batch performance)
+        self.max_parallel = MARLIN_QQQ_MAX_PARALLEL
+
+        # Permutation length used by the QQQ kernels.
+        self.perm_len = 1024
+
+    def __repr__(self) -> str:
+        return "QQQConfig(weight_bits={}, group_size={})".format(
+            self.weight_bits, self.group_size)
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "qqq"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        """List of filenames to search for in the model directory."""
+        return [
+            "quant_config.json",
+            "quantize_config.json",
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "QQQConfig":
+        weight_bits = cls.get_from_keys(config, ["wbits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        return cls(weight_bits, group_size)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QQQLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return QQQLinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class QQQLinearMethod(LinearMethodBase):
+    """Linear method for QQQ.
+
+    Args:
+        quant_config: The QQQ quantization config.
+    """
+
+    def __init__(self, quant_config: QQQConfig):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        weight_loader = extra_weight_attrs["weight_loader"]
+        if params_dtype != torch.float16:
+            raise ValueError(
+                f"The params dtype must be float16, but got {params_dtype}")
+
+        # Validate output_size_per_partition
+        output_size_per_partition = sum(output_partition_sizes)
+        if output_size_per_partition % self.quant_config.min_n_threads != 0:
+            raise ValueError(
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"min_n_threads = {self.quant_config.min_n_threads}.")
+        if output_size_per_partition % self.quant_config.pack_factor != 0:
+            raise ValueError(
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"pack_factor = {self.quant_config.pack_factor}.")
+
+        # Validate input_size_per_partition
+        if input_size_per_partition % self.quant_config.min_k_threads != 0:
+            raise ValueError(
+                f"Weight input_size_per_partition = "
+                f"{input_size_per_partition} is not divisible by "
+                f"min_k_threads = {self.quant_config.min_k_threads}.")
+        if (self.quant_config.group_size != -1 and
+                input_size_per_partition % self.quant_config.group_size != 0):
+            raise ValueError(f"Weight input_size_per_partition = "
+                             f"{input_size_per_partition} is not divisible by "
+                             f"group_size = {self.quant_config.group_size}.")
+
+        # Check that we have at least 4 tiles horizontally in the shard
+        num_tiles_per_perm = self.quant_config.perm_len // (
+            self.quant_config.tile_size**2)
+        if output_size_per_partition % num_tiles_per_perm != 0:
+            raise ValueError(
+                "Each permutation group must reside on the same gpu")
+
+        # Quantized 4Bit weights packed into Int32.
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.tile_size,
+                output_size_per_partition * self.quant_config.tile_size //
+                self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            marlin_tile_size=self.quant_config.tile_size,
+            weight_loader=weight_loader)
+
+        s_channel = ChannelQuantScaleParameter(data=torch.empty(
+            1,
+            output_size_per_partition,
+            device="cuda",
+            dtype=torch.float,
+        ),
+                                               weight_loader=weight_loader,
+                                               output_dim=1)
+
+        if self.quant_config.group_size == -1:
+            s_group_data = torch.tensor(
+                [],
+                device="cuda",
+                dtype=torch.half,
+            )
+        else:
+            s_group_data = torch.empty(
+                input_size_per_partition // self.quant_config.group_size,
+                output_size_per_partition,
+                device="cuda",
+                dtype=torch.half,
+            )
+
+        s_group_attr = {"data": s_group_data, "weight_loader": weight_loader}
+
+        if self.quant_config.group_size == -1:
+            s_group = BasevLLMParameter(**s_group_attr)
+        else:
+            s_group = GroupQuantScaleParameter(output_dim=1,
+                                               input_dim=0,
+                                               **s_group_attr)
+
+        # Allocate workspace (Used for internal locking mechanism)
+        max_workspace_size = (
+            output_size_per_partition //
+            self.quant_config.min_n_threads) * self.quant_config.max_parallel
+
+        workspace = BasevLLMParameter(data=torch.zeros(max_workspace_size,
+                                                       device="cuda",
+                                                       dtype=torch.int),
+                                      weight_loader=weight_loader)
+
+        layer.register_parameter("B", qweight)
+        layer.register_parameter("s_channel", s_channel)
+        layer.register_parameter("s_group", s_group)
+        layer.register_parameter("workspace", workspace)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # required by torch.compile
+        layer.B = Parameter(layer.B.data, requires_grad=False)
+        layer.s_channel = Parameter(layer.s_channel.data, requires_grad=False)
+        layer.s_group = Parameter(layer.s_group.data, requires_grad=False)
+        layer.workspace = Parameter(layer.workspace.data, requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        qweight = layer.B
+        s_ch = layer.s_channel
+        s_group = layer.s_group
+        workspace = layer.workspace
+
+        x_2d = x.view(-1, x.shape[-1])
+
+        size_m = x_2d.shape[0]
+        size_k = x_2d.shape[1]
+        size_n = s_ch.shape[1]
+
+        x_int8, s_tok, _ = ops.scaled_int8_quant(x_2d)
+
+        output_2d = ops.marlin_qqq_gemm(x_int8, qweight, s_tok, s_ch, s_group,
+                                        workspace, size_m, size_n, size_k)
+
+        output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
+
+        if bias is not None:
+            output.add_(bias)  # In-place add
+
+        return output

vllm/model_executor/layers/quantization/schema.py

@@ -0,0 +1,84 @@
+"""
+This file contains the Pydantic schemas for various quantization-related
+parameters. When a relevant quantization technique is specified, these
+parameters are loaded in the form of a JSON alongside the model weights
+and augment the model with additional information needed for use of that
+technique. The format of this JSON should be specified by one or more
+schemas contained here.
+
+For example, when the KV cache is quantized to FP8-E4M3 (currently only
+possible on ROCm), the model can be optionally augmented with KV cache
+scaling factors.
+"""
+
+from typing import Dict, Optional
+
+from pydantic import BaseModel, ConfigDict, ValidationInfo, model_validator
+
+
+class KVCacheQuantSchema(BaseModel):
+    dtype: str
+    # Each key is a TP rank. Each value is a dictionary mapping a TP rank's
+    # layer indices to their per-tensor KV cache scaling factor.
+    # TODO: Consider pulling this and its validation methods out into its
+    # own schema class (tricky as its members are variable)
+    scaling_factor: Dict[int, Dict[int, float]]
+
+    @model_validator(mode="after")
+    def check_is_fp8(self) -> "KVCacheQuantSchema":
+        assert self.dtype == "float8_e4m3fn", (
+            "Loaded scaling factors intended for KV cache dtype = "
+            f"{self.dtype} rather than float8_e4m3fn!")
+        return self
+
+    @model_validator(mode="after")
+    def check_tp_ranks(self, info: ValidationInfo) -> "KVCacheQuantSchema":
+        context = info.context
+        if context:
+            tp_size = context["tp_size"]
+            num_hidden_layers = context["num_hidden_layers"]
+            assert len(self.scaling_factor) == tp_size, (
+                f"Loaded dictionary has TP size {len(self.scaling_factor)} "
+                f"but LLM engine is currently running with TP size {tp_size}.")
+            for tp_rank, layer_maps in self.scaling_factor.items():
+                assert len(layer_maps) == num_hidden_layers, (
+                    f"KV cache scales map for TP rank {tp_rank} is malformed. "
+                    f"Expected {num_hidden_layers} layers, got "
+                    f"{len(layer_maps)}.")
+            for i in range(tp_size):
+                assert i in self.scaling_factor, (
+                    f"KV cache scales map for TP rank {i} not found.")
+        return self
+
+    @model_validator(mode="after")
+    def check_current_rank(self, info: ValidationInfo) -> "KVCacheQuantSchema":
+        context = info.context
+        if context:
+            tp_rank = context["tp_rank"]
+            num_hidden_layers = context["num_hidden_layers"]
+            layer_scales_map = self.scaling_factor[tp_rank]
+            for i in range(num_hidden_layers):
+                assert i in layer_scales_map, (
+                    f"Could not find KV cache scales for layer {i} in "
+                    f"TP rank {tp_rank}.")
+        return self
+
+
+class QuantParamSchema(BaseModel):
+    # TODO: Generalize and extend with more fields
+    # (e.g. weights/activations params) once functionality is enabled
+    model_config = ConfigDict(protected_namespaces=())
+    model_type: Optional[str]
+    kv_cache: KVCacheQuantSchema
+
+    @model_validator(mode="after")
+    def check_model_type(self, info: ValidationInfo) -> "QuantParamSchema":
+        context = info.context
+        if context:
+            model_type = context.get("model_type", None)
+            if model_type is not None:
+                assert model_type == self.model_type, (
+                    f"Model type is {model_type} but loaded "
+                    f"scaling factors belonging to different "
+                    f"model type {self.model_type}!")
+        return self

vllm/model_executor/layers/quantization/tpu_int8.py

@@ -0,0 +1,119 @@
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.parameter import ModelWeightParameter
+
+ACTIVATION_SCHEMES = ["none"]
+
+
+class Int8TpuConfig(QuantizationConfig):
+    """Int8 Quantization Config class for TPU Backend."""
+
+    def __init__(
+        self,
+        activation_scheme: str = "none",
+    ) -> None:
+        if activation_scheme not in ACTIVATION_SCHEMES:
+            raise ValueError(
+                f"Unsupported activation scheme {activation_scheme}")
+        self.activation_scheme = activation_scheme
+
+    def get_name(self) -> str:
+        return "tpu_int8"
+
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        raise NotImplementedError(
+            "This function should not be called with TPU Backend")
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "Int8TpuConfig":
+        activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
+        return cls(activation_scheme=activation_scheme)
+
+    def get_quant_method(self, layer: Module,
+                         prefix: str) -> Optional["TPUInt8LinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return TPUInt8LinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class TPUInt8LinearMethod(LinearMethodBase):
+    """Int8 Linear method for TPU Quant. """
+
+    def __init__(self, quant_config: Int8TpuConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: Module, input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        weight = ModelWeightParameter(data=torch.empty(
+            sum(output_partition_sizes),
+            input_size_per_partition,
+            dtype=params_dtype),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+    def _quantize_weight(
+            self, weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        weight_dtype = weight.dtype
+        weight = weight.cpu().to(torch.float32)
+        n_bit = 8
+        eps = 1e-5
+        max_int = 2**(n_bit - 1) - 1
+        min_int = -(2**(n_bit - 1))
+        max_val = weight.abs().amax(dim=-1, keepdim=True)
+        max_val = max_val.clamp(min=eps)
+        qscale = max_val / max_int
+        qweight = torch.clamp(torch.round(weight * (1.0 / qscale)), min_int,
+                              max_int).to(torch.int8)
+        qscale = qscale.squeeze().to(weight_dtype)
+        return qweight, qscale
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        layer.weight = Parameter(layer.weight.data, requires_grad=False)
+        device = layer.weight.device
+        qweight, qscale = self._quantize_weight(layer.weight)
+        qweight = qweight.to(device)
+        qscale = qscale.to(device)
+        layer.weight = Parameter(qweight, requires_grad=False)
+        layer.scale = Parameter(qscale, requires_grad=False)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        try:
+            import torch_xla.experimental.xla_quantized_matmul  # noqa: F401
+        except ImportError as err:
+            raise ImportError(
+                "Please install torch_xla by following the instructions at "
+                "https://docs.vllm.ai/en/latest/getting_started/tpu-installation.html "  # noqa: E501
+                "to run vLLM on TPU.") from err
+        weight = layer.weight
+        scale = layer.scale
+        out = torch.ops.xla.quantized_matmul(x, weight, scale)
+        if bias is not None:
+            out = out + bias
+        return out

vllm/model_executor/layers/quantization/utils/__init__.py

@@ -0,0 +1,3 @@
+from .layer_utils import replace_parameter, update_tensor_inplace
+
+__all__ = ['update_tensor_inplace', 'replace_parameter']

vllm/model_executor/layers/quantization/utils/layer_utils.py

@@ -0,0 +1,37 @@
+from typing import Union
+
+import torch
+
+
+def update_tensor_inplace(dst: torch.Tensor, src: torch.Tensor):
+    assert dst.dtype == src.dtype, "Tensors must have the same dtype"
+
+    # update tensor shape and stride
+    dst.as_strided_(src.shape, src.stride())
+
+    # If not the same underlying storage move tensor data
+    if dst.data_ptr() != src.data_ptr():
+        dst.copy_(src)
+        del src
+
+
+# Newly generated tensors need to replace existing tensors that are
+# already registered as parameters by vLLM (and won't be freed)
+def replace_parameter(mod: torch.nn.Module, name: str,
+                      new: Union[torch.Tensor, torch.nn.Parameter]) -> None:
+
+    old = getattr(mod, name)
+    if type(old) is type(new) and old.dtype == new.dtype and \
+        old.untyped_storage().nbytes() == new.untyped_storage().nbytes():
+        # If we can just update in-place to avoid re-registering
+        #   can be faster if the underlying storage is the same
+        update_tensor_inplace(old, new)
+    else:
+        # Fallback re-register parameter, convert to Parameter if necessary
+        # this not only ensures we don't register a tensor as a parameter, but
+        # also ensures that all parameter subclasses get re-registered as
+        # parameters for `torch.compile` compatibility
+        if not isinstance(new, torch.nn.Parameter):
+            new = torch.nn.Parameter(new, requires_grad=False)
+        mod.register_parameter(name,
+                               torch.nn.Parameter(new, requires_grad=False))

vllm/model_executor/layers/quantization/utils/machete_utils.py

@@ -0,0 +1,30 @@
+from typing import List, Optional, Tuple
+
+import torch
+
+from vllm.scalar_type import ScalarType, scalar_types
+
+MACHETE_SUPPORTED_GROUP_SIZES = [-1, 128]
+MACHETE_PREPACKED_BLOCK_SHAPE = [64, 128]
+
+
+def query_machete_supported_quant_types(zero_points: bool) -> List[ScalarType]:
+    if zero_points:
+        return [scalar_types.uint4, scalar_types.uint8]
+    else:
+        return [scalar_types.uint4b8, scalar_types.uint8b128]
+
+
+def query_machete_supported_act_types(zero_points: bool) -> List[ScalarType]:
+    return [torch.float16, torch.bfloat16]
+
+
+def check_machete_supports_shape(in_features: int, out_featrues: int) \
+    -> Tuple[bool, Optional[str]]:
+    if in_features % MACHETE_PREPACKED_BLOCK_SHAPE[0] != 0:
+        return False, "Input features size must be divisible by "\
+            f"{MACHETE_PREPACKED_BLOCK_SHAPE[0]}"
+    if out_featrues % MACHETE_PREPACKED_BLOCK_SHAPE[1] != 0:
+        return False, "Output features size must be divisible by "\
+            f"{MACHETE_PREPACKED_BLOCK_SHAPE[1]}"
+    return True, None

vllm/model_executor/layers/quantization/utils/marlin_utils.py

@@ -0,0 +1,348 @@
+from typing import List, Optional, Tuple
+
+import numpy
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
+from vllm.scalar_type import ScalarType, scalar_types
+
+from .quant_utils import pack_cols, unpack_cols
+
+GPTQ_MARLIN_TILE = 16
+GPTQ_MARLIN_MIN_THREAD_N = 64
+GPTQ_MARLIN_MIN_THREAD_K = 128
+GPTQ_MARLIN_MAX_PARALLEL = 16
+
+MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
+
+# In case there is a performance issue with Marlin, the variable below can be
+# changed to False, which allows Marlin to perform global reductions in fp16
+# precision (instead of fp32), and therefore, save on some memory movements.
+USE_FP32_REDUCE_DEFAULT = True
+
+
+# For binary size and compile time, we don't support the same types for with and
+#  without runtime zero-point. We support common cases, i.e. AWQ and GPTQ.
+#  TODO: we may want to move this into the C++ so its closer to the actual impl
+def query_marlin_supported_quant_types(has_zp: bool,
+                                       device_capability: Optional[int] = None
+                                       ):
+    if device_capability is None:
+        capability_tuple = current_platform.get_device_capability()
+        device_capability = (-1 if capability_tuple is None else
+                             capability_tuple.to_int())
+
+    if device_capability < 80:
+        return []
+
+    if has_zp:
+        # AWQ style, unsigned + runtime zero-point
+        return [scalar_types.uint4, scalar_types.uint8]
+    else:
+        # GPTQ style, unsigned + symmetric bias
+        # TODO: once fp8_marlin is merged into "gptq_marlin" we should be able
+        #  to add `scalar_types.float8_e4m3fn` here
+        return [scalar_types.uint4b8, scalar_types.uint8b128]
+
+
+def _check_marlin_supported(
+        quant_type: ScalarType,
+        group_size: Optional[int],
+        has_zp: bool,
+        device_capability: Optional[int] = None) -> Tuple[bool, Optional[str]]:
+
+    if device_capability is None:
+        capability_tuple = current_platform.get_device_capability()
+        device_capability = (-1 if capability_tuple is None else
+                             capability_tuple.to_int())
+
+    supported_types = query_marlin_supported_quant_types(
+        has_zp, device_capability)
+
+    if quant_type not in supported_types:
+        return (False, f"Marlin does not support weight_bits = {quant_type}. "
+                f"Only types = {supported_types} "
+                f"are supported (for group_size = {group_size}, "
+                f"device_capability = {device_capability}, zp = {has_zp}).")
+    if (group_size is None or group_size not in MARLIN_SUPPORTED_GROUP_SIZES):
+        return (False, f"Marlin does not support group_size = {group_size}. "
+                f"Only group_sizes = {MARLIN_SUPPORTED_GROUP_SIZES} "
+                "are supported.")
+
+    return True, None
+
+
+def check_marlin_supported(quant_type: ScalarType,
+                           group_size: int,
+                           has_zp: bool = False,
+                           device_capability: Optional[int] = None) -> bool:
+    cond, _ = _check_marlin_supported(quant_type, group_size, has_zp,
+                                      device_capability)
+    return cond
+
+
+def verify_marlin_supported(quant_type: ScalarType,
+                            group_size: int,
+                            has_zp: bool = False) -> None:
+    cond, err_msg = _check_marlin_supported(quant_type, group_size, has_zp)
+    if not cond:
+        assert err_msg is not None
+        raise ValueError(err_msg)
+
+
+def verify_marlin_supports_shape(output_size_per_partition: int,
+                                 input_size_per_partition: int,
+                                 input_size: int, group_size: int) -> None:
+
+    # Validate output_size_per_partition
+    if output_size_per_partition % GPTQ_MARLIN_MIN_THREAD_N != 0:
+        raise ValueError(f"Weight output_size_per_partition = "
+                         f"{output_size_per_partition} is not divisible by "
+                         f" min_thread_n = {GPTQ_MARLIN_MIN_THREAD_N}. "
+                         "Consider reducing tensor_parallel_size or running "
+                         "with --quantization gptq.")
+
+    # Validate input_size_per_partition
+    if input_size_per_partition % GPTQ_MARLIN_MIN_THREAD_K != 0:
+        raise ValueError(f"Weight input_size_per_partition = "
+                         f"{input_size_per_partition} is not divisible "
+                         f"by min_thread_k = {GPTQ_MARLIN_MIN_THREAD_K}. "
+                         "Consider reducing tensor_parallel_size or running "
+                         "with --quantization gptq.")
+
+    if (group_size < input_size
+            and input_size_per_partition % group_size != 0):
+        raise ValueError(
+            f"Weight input_size_per_partition = {input_size_per_partition}"
+            f" is not divisible by group_size = {group_size}."
+            "Consider reducing tensor_parallel_size or running "
+            "with --quantization gptq.")
+
+
+def check_marlin_supports_shape(output_size_per_partition: int,
+                                input_size_per_partition: int,
+                                input_size: int, group_size: int) \
+                                    -> Tuple[bool, Optional[str]]:
+    try:
+        verify_marlin_supports_shape(output_size_per_partition,
+                                     input_size_per_partition, input_size,
+                                     group_size)
+    except ValueError as e:
+        return False, e.__str__()
+    return True, None
+
+
+def marlin_make_workspace(output_size_per_partition: int,
+                          device: torch.device) -> torch.Tensor:
+    max_workspace_size = (output_size_per_partition //
+                          GPTQ_MARLIN_MIN_THREAD_N) * GPTQ_MARLIN_MAX_PARALLEL
+
+    return torch.zeros(max_workspace_size,
+                       dtype=torch.int,
+                       device=device,
+                       requires_grad=False)
+
+
+def marlin_is_k_full(act_order: bool, is_row_parallel: bool) -> bool:
+    return (not act_order) or (act_order and not is_row_parallel)
+
+
+def marlin_repeat_scales_on_all_ranks(act_order: bool, group_size: int,
+                                      is_row_parallel: bool) -> bool:
+    # Need to repeat scales on every rank if act_ordering or
+    # channelwise and RowParallelLinear
+    is_channelwise = group_size == -1
+    return act_order or (is_channelwise and is_row_parallel)
+
+
+def marlin_make_empty_g_idx(device: torch.device) -> torch.Tensor:
+    return torch.nn.Parameter(torch.empty(0, dtype=torch.int, device=device),
+                              requires_grad=False)
+
+
+def marlin_make_empty_zp(device: torch.device) -> torch.Tensor:
+    return torch.nn.Parameter(torch.empty(0, dtype=torch.int, device=device),
+                              requires_grad=False)
+
+
+def marlin_sort_g_idx(
+        g_idx: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    g_idx_sort_indices = torch.argsort(g_idx).to(torch.int)
+    return g_idx[g_idx_sort_indices], g_idx_sort_indices
+
+
+def get_scale_perms():
+    scale_perm: List[int] = []
+    for i in range(8):
+        scale_perm.extend([i + 8 * j for j in range(8)])
+    scale_perm_single: List[int] = []
+    for i in range(4):
+        scale_perm_single.extend(
+            [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
+    return scale_perm, scale_perm_single
+
+
+def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int,
+                          group_size: int) -> torch.Tensor:
+
+    scale_perm, scale_perm_single = get_scale_perms()
+    if group_size < size_k and group_size != -1:
+        s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
+    else:
+        s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
+    s = s.reshape((-1, size_n)).contiguous()
+
+    return s
+
+
+def marlin_moe_permute_scales(
+    s: torch.Tensor,
+    size_k: int,
+    size_n: int,
+    group_size: int,
+):
+    num_experts = s.shape[0]
+    output = torch.empty(
+        (num_experts, s.shape[1], s.shape[2]),
+        device=s.device,
+        dtype=s.dtype,
+    )
+
+    for e in range(num_experts):
+        output[e] = marlin_permute_scales(s[e], size_k, size_n, group_size)
+    return output
+
+
+def marlin_zero_points(zp: torch.Tensor, size_k: int, size_n: int,
+                       num_bits: int) -> torch.Tensor:
+    # Permute zero-points in a similar way to scales, but do not use the
+    # "single" permutation, since zero-points are applied on every MMA
+    scale_perm, _ = get_scale_perms()
+    zp = zp.reshape((-1, len(scale_perm)))[:, scale_perm]
+
+    # Interleave column dim (for the dequantize code) and pack it to int32
+    if num_bits == 4:
+        interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7])
+    elif num_bits == 8:
+        interleave = numpy.array([0, 2, 1, 3])
+    else:
+        raise Exception("num_bits must be 4 or 8, got {}".format(num_bits))
+
+    zp = zp.reshape((-1, len(interleave)))[:, interleave].ravel()
+    zp = zp.reshape((-1, size_n)).contiguous()
+    zp = pack_cols(zp, num_bits, size_k, size_n)
+
+    return zp
+
+
+def awq_to_marlin_zero_points(q_zp_packed: torch.Tensor, size_k: int,
+                              size_n: int, num_bits: int) -> torch.Tensor:
+    # AWQ zero-points are quantized and packed on the column dim.
+    # In addition, the values are permuted based on dequantizer.
+    # Here we undo both of these, and then apply marlin permutation
+    # and pack it back.
+    q_zp = unpack_cols(q_zp_packed, num_bits, size_k, size_n)
+
+    # Undo interleaving (use argsort(..) to get inverse perm)
+    if num_bits == 4:
+        undo_interleave = numpy.argsort(numpy.array([0, 2, 4, 6, 1, 3, 5, 7]))
+    elif num_bits == 8:
+        undo_interleave = numpy.argsort(numpy.array([0, 2, 1, 3]))
+    else:
+        raise Exception("num_bits must be 4 or 8, got {}".format(num_bits))
+
+    q_zp = q_zp.reshape((-1, len(undo_interleave)))[:, undo_interleave].ravel()
+    q_zp = q_zp.reshape((-1, size_n)).contiguous()
+
+    marlin_zp = marlin_zero_points(q_zp, size_k, size_n, num_bits)
+    return marlin_zp
+
+
+def moe_awq_to_marlin_zero_points(q_zp_packed: torch.Tensor, size_k: int,
+                                  size_n: int, num_bits: int):
+    num_experts = q_zp_packed.shape[0]
+    output = torch.empty(
+        (num_experts, q_zp_packed.shape[1], q_zp_packed.shape[2]),
+        device=q_zp_packed.device,
+        dtype=q_zp_packed.dtype,
+    )
+    for e in range(num_experts):
+        output[e] = awq_to_marlin_zero_points(q_zp_packed[e], size_k, size_n,
+                                              num_bits)
+    return output
+
+
+def apply_gptq_marlin_linear(
+        input: torch.Tensor,
+        weight: torch.Tensor,
+        weight_scale: torch.Tensor,
+        weight_zp: torch.Tensor,
+        g_idx: torch.Tensor,
+        g_idx_sort_indices: torch.Tensor,
+        workspace: torch.Tensor,
+        wtype: ScalarType,
+        output_size_per_partition: int,
+        input_size_per_partition: int,
+        is_k_full: bool,
+        bias: Optional[torch.Tensor] = None,
+        use_fp32_reduce: bool = USE_FP32_REDUCE_DEFAULT) -> torch.Tensor:
+    reshaped_x = input.reshape(-1, input.shape[-1])
+    out_shape = input.shape[:-1] + (output_size_per_partition, )
+
+    output = ops.gptq_marlin_gemm(reshaped_x,
+                                  weight,
+                                  weight_scale,
+                                  weight_zp,
+                                  g_idx,
+                                  g_idx_sort_indices,
+                                  workspace,
+                                  wtype,
+                                  size_m=reshaped_x.shape[0],
+                                  size_n=output_size_per_partition,
+                                  size_k=input_size_per_partition,
+                                  is_k_full=is_k_full,
+                                  has_zp=False,
+                                  use_fp32_reduce=use_fp32_reduce)
+
+    if bias is not None:
+        output.add_(bias)  # In-place add
+
+    return output.reshape(out_shape)
+
+
+def apply_awq_marlin_linear(
+        input: torch.Tensor,
+        weight: torch.Tensor,
+        weight_scale: torch.Tensor,
+        weight_zp: torch.Tensor,
+        g_idx: torch.Tensor,
+        g_idx_sort_indices: torch.Tensor,
+        workspace: torch.Tensor,
+        quant_type: ScalarType,
+        output_size_per_partition: int,
+        input_size_per_partition: int,
+        bias: Optional[torch.Tensor] = None,
+        use_fp32_reduce: bool = USE_FP32_REDUCE_DEFAULT) -> torch.Tensor:
+    reshaped_x = input.reshape(-1, input.shape[-1])
+    out_shape = input.shape[:-1] + (output_size_per_partition, )
+
+    output = ops.gptq_marlin_gemm(reshaped_x,
+                                  weight,
+                                  weight_scale,
+                                  weight_zp,
+                                  g_idx,
+                                  g_idx_sort_indices,
+                                  workspace,
+                                  quant_type,
+                                  size_m=reshaped_x.shape[0],
+                                  size_n=output_size_per_partition,
+                                  size_k=input_size_per_partition,
+                                  is_k_full=True,
+                                  has_zp=True,
+                                  use_fp32_reduce=use_fp32_reduce)
+
+    if bias is not None:
+        output.add_(bias)  # In-place add
+
+    return output.reshape(out_shape)

vllm/model_executor/layers/quantization/utils/marlin_utils_fp8.py

@@ -0,0 +1,106 @@
+from typing import Optional
+
+import torch
+
+import vllm._custom_ops as ops
+from vllm.platforms import current_platform
+from vllm.utils import print_warning_once
+
+from .marlin_utils import marlin_make_workspace, marlin_permute_scales
+
+
+def is_fp8_marlin_supported():
+    return current_platform.has_device_capability(80)
+
+
+def apply_fp8_marlin_linear(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    weight_scale: torch.Tensor,
+    workspace: torch.Tensor,
+    size_n: int,
+    size_k: int,
+    bias: Optional[torch.Tensor],
+) -> torch.Tensor:
+    # For GPUs that lack FP8 hardware support, we can leverage the
+    # Marlin kernel for fast weight-only FP8 quantization
+
+    reshaped_x = input.reshape(-1, input.shape[-1])
+    out_shape = input.shape[:-1] + (size_n, )
+
+    output = ops.fp8_marlin_gemm(
+        a=reshaped_x,
+        b_q_weight=weight,
+        b_scales=weight_scale,
+        workspace=workspace,
+        num_bits=8,
+        size_m=reshaped_x.shape[0],
+        size_n=size_n,
+        size_k=size_k,
+    )
+
+    if bias is not None:
+        output.add_(bias)  # In-place add
+
+    return output.reshape(out_shape)
+
+
+def prepare_fp8_layer_for_marlin(layer: torch.nn.Module,
+                                 strategy: str = "tensor") -> None:
+    print_warning_once(
+        "Your GPU does not have native support for FP8 computation but "
+        "FP8 quantization is being used. Weight-only FP8 compression will "
+        "be used leveraging the Marlin kernel. This may degrade "
+        "performance for compute-heavy workloads.")
+
+    part_size_n = layer.output_size_per_partition
+    part_size_k = layer.input_size_per_partition
+
+    device = layer.weight.device
+
+    # WORKSPACE
+    layer.workspace = marlin_make_workspace(part_size_n, device)
+
+    # WEIGHT
+    # Repack weights to marlin format
+    marlin_qweight = ops.gptq_marlin_repack(b_q_weight=pack_fp8_to_int32(
+        layer.weight),
+                                            perm=torch.empty(0,
+                                                             dtype=torch.int,
+                                                             device=device),
+                                            size_k=part_size_k,
+                                            size_n=part_size_n,
+                                            num_bits=8)
+    layer.weight = torch.nn.Parameter(marlin_qweight, requires_grad=False)
+
+    # WEIGHT SCALES
+    scales = layer.weight_scale.to(layer.orig_dtype)
+    # Permute scales
+    marlin_scales = marlin_permute_scales(s=scales,
+                                          size_k=part_size_k,
+                                          size_n=part_size_n,
+                                          group_size=-1)
+    layer.weight_scale = torch.nn.Parameter(marlin_scales, requires_grad=False)
+
+
+def pack_fp8_to_int32(fp8_tensor: torch.Tensor) -> torch.Tensor:
+    """
+    Repack FP8 weights to gptq format (packed int32 elements)
+    """
+    assert fp8_tensor.dtype == torch.float8_e4m3fn
+    assert fp8_tensor.shape[0] % 4 == 0
+
+    # Reshape to prepare for packing
+    reshaped = fp8_tensor.reshape(-1, 4, *fp8_tensor.shape[1:])
+
+    # Convert fp8 to uint8 (byte) representation
+    byte_tensor = reshaped.view(torch.uint8)
+
+    # Pack 4 uint8 values into one int32
+    packed = (byte_tensor[:, 0].to(torch.int32) |
+              (byte_tensor[:, 1].to(torch.int32) << 8) |
+              (byte_tensor[:, 2].to(torch.int32) << 16) |
+              (byte_tensor[:, 3].to(torch.int32) << 24))
+
+    return packed.view(fp8_tensor.shape[0] // 4,
+                       *fp8_tensor.shape[1:]).contiguous()

vllm/model_executor/layers/quantization/utils/marlin_utils_test.py

@@ -0,0 +1,163 @@
+"""Utility functions used for tests and benchmarks"""
+
+from typing import List, Optional
+
+import numpy as np
+import torch
+
+from vllm.scalar_type import ScalarType
+
+from .marlin_utils import (GPTQ_MARLIN_TILE, marlin_permute_scales,
+                           marlin_zero_points)
+from .quant_utils import (get_pack_factor, gptq_quantize_weights,
+                          quantize_weights, sort_weights)
+
+
+class MarlinWorkspace:
+
+    def __init__(self, out_features, min_thread_n, max_parallel):
+        assert (out_features % min_thread_n == 0), (
+            "out_features = {} is undivisible by min_thread_n = {}".format(
+                out_features, min_thread_n))
+
+        max_workspace_size = ((out_features // min_thread_n) * max_parallel)
+
+        self.scratch = torch.zeros(max_workspace_size,
+                                   dtype=torch.int,
+                                   device="cuda")
+
+
+def marlin_permute_weights(q_w, size_k, size_n, perm, tile=GPTQ_MARLIN_TILE):
+    assert q_w.shape == (size_k, size_n)
+    assert size_k % tile == 0, f"size_k = {size_k}, tile = {tile}"
+    assert size_n % tile == 0, f"size_k = {size_n}, tile = {tile}"
+
+    # Permute weights to 16x64 marlin tiles
+    q_w = q_w.reshape((size_k // tile, tile, size_n // tile, tile))
+    q_w = q_w.permute((0, 2, 1, 3))
+    q_w = q_w.reshape((size_k // tile, size_n * tile))
+
+    q_w = q_w.reshape((-1, perm.numel()))[:, perm].reshape(q_w.shape)
+
+    return q_w
+
+
+def marlin_weights(q_w, size_k, size_n, num_bits, perm):
+    # Permute
+    q_w = marlin_permute_weights(q_w, size_k, size_n, perm)
+
+    # Pack
+    pack_factor = get_pack_factor(num_bits)
+    orig_device = q_w.device
+
+    q_w = q_w.cpu().numpy().astype(np.uint32)
+
+    q_packed = np.zeros((q_w.shape[0], q_w.shape[1] // pack_factor),
+                        dtype=np.uint32)
+    for i in range(pack_factor):
+        q_packed |= q_w[:, i::pack_factor] << num_bits * i
+
+    q_packed = torch.from_numpy(q_packed.astype(np.int32)).to(orig_device)
+
+    return q_packed
+
+
+def get_weight_perm(num_bits: int):
+    perm_list: List[int] = []
+    for i in range(32):
+        perm1: List[int] = []
+        col = i // 4
+        for block in [0, 1]:
+            for row in [
+                    2 * (i % 4),
+                    2 * (i % 4) + 1,
+                    2 * (i % 4 + 4),
+                    2 * (i % 4 + 4) + 1,
+            ]:
+                perm1.append(16 * row + col + 8 * block)
+        for j in range(4):
+            perm_list.extend([p + 256 * j for p in perm1])
+
+    perm = np.array(perm_list)
+
+    if num_bits == 4:
+        interleave = np.array([0, 2, 4, 6, 1, 3, 5, 7])
+    elif num_bits == 8:
+        interleave = np.array([0, 2, 1, 3])
+    else:
+        raise Exception("num_bits must be 4 or 8, got {}".format(num_bits))
+
+    perm = perm.reshape((-1, len(interleave)))[:, interleave].ravel()
+    perm = torch.from_numpy(perm)
+    return perm
+
+
+def marlin_quantize(w: torch.Tensor,
+                    quant_type: ScalarType,
+                    group_size: int,
+                    act_order: bool,
+                    test_perm: Optional[torch.Tensor] = None):
+    size_k, size_n = w.shape
+    num_bits = quant_type.size_bits
+
+    # Normalize group_size
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    # Quantize (and apply act_order if provided)
+    w_ref, q_w, s, g_idx, rand_perm = gptq_quantize_weights(
+        w, quant_type, group_size, act_order, test_perm)
+
+    # For act_order, sort the "weights" and "g_idx" so that group ids are
+    # increasing
+    sort_indices = torch.empty(0, dtype=torch.int, device=w.device)
+    if act_order:
+        q_w, g_idx, sort_indices = sort_weights(q_w, g_idx)
+
+    # Reformat to marlin
+    weight_perm = get_weight_perm(num_bits)
+    marlin_q_w = marlin_weights(q_w, size_k, size_n, num_bits, weight_perm)
+    marlin_s = marlin_permute_scales(s, size_k, size_n, group_size)
+
+    # Create result
+    res_list = [w_ref, marlin_q_w, marlin_s, g_idx, sort_indices, rand_perm]
+    for i in range(len(res_list)):
+        res_list[i] = res_list[i].to(w.device)
+
+    return res_list
+
+
+def awq_marlin_quantize(w: torch.Tensor, quant_type: ScalarType,
+                        group_size: int):
+    size_k, size_n = w.shape
+
+    # Normalize group_size
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    # Detect num groups
+    assert size_k % group_size == 0
+    num_groups = size_k // group_size
+
+    # Quantize with zp
+    w_ref, q_w, s, zp = quantize_weights(w,
+                                         quant_type,
+                                         group_size,
+                                         zero_points=True)
+
+    # Reformat to marlin
+    weight_perm = get_weight_perm(quant_type.size_bits)
+    marlin_q_w = marlin_weights(q_w, size_k, size_n, quant_type.size_bits,
+                                weight_perm)
+    marlin_s = marlin_permute_scales(s, size_k, size_n, group_size)
+    marlin_zp = marlin_zero_points(zp, num_groups, size_n,
+                                   quant_type.size_bits)
+
+    # Create result
+    res_list = [w_ref, marlin_q_w, marlin_s, marlin_zp]
+    for i in range(len(res_list)):
+        res_list[i] = res_list[i].to(w.device)
+
+    return res_list

vllm/model_executor/layers/quantization/utils/marlin_utils_test_24.py

@@ -0,0 +1,463 @@
+"""Utility functions used for tests and benchmarks"""
+
+import random
+from typing import List
+
+import numpy
+import torch
+
+from vllm.scalar_type import ScalarType
+
+from .marlin_utils_test import marlin_weights
+from .quant_utils import gptq_quantize_weights
+
+
+# This is PyTorch implementation of main part of reorder_meta()
+# function, from tools/util/include/cutlass/util/host_reorder.h file
+# of CUTLASS source tree.  Furthermore, CUTLASS template for sparse
+# GEMM decides upon layout of this matrix, and at the moment for the
+# sparse GEMM executed on tensor cores, this is layout described by
+# ColumnMajorInterleaved<2> data structure, in
+# include/cutlass/layout/matrix.h of CUTLASS source tree.  The
+# reordering of meta matrix into meta_reordered matrix calculated
+# according to these segments of CUTLASS code is re-implemented here.
+# Note that this calculation produces offsets for scattering metadata
+# matrix elements into reordered metadata matrix elements (or,
+# equivalently, for gathering reordered metadata matrix element back
+# into metadata matrix elements).
+def _calculate_meta_reordering_scatter_offsets(m, meta_ncols, meta_dtype,
+                                               device):
+    dst_rows = torch.arange(0, m, device=device)[:, None].repeat(1, meta_ncols)
+    dst_cols = torch.arange(0, meta_ncols, device=device).repeat(m, 1)
+
+    # Reorder the rows, then swizzle the 2x2 blocks.
+    group_x = 64
+    group_y = 32 if meta_dtype.itemsize == 2 else 16
+
+    dst_rows = (dst_rows // group_x * group_x + (dst_rows % 2) * 2 +
+                (dst_rows % 8) // 4 + ((dst_rows % group_y) % 4) // 2 * 32 +
+                ((dst_rows % group_x) // 8) * 4)
+
+    topright = ((dst_rows % 2 == 0) & (dst_cols % 2 == 1)).to(torch.int8)
+    bottomleft = ((dst_rows % 2 == 1) & (dst_cols % 2 == 0)).to(torch.int8)
+    dst_rows += topright - bottomleft
+    dst_cols -= topright - bottomleft
+
+    # Assumed that meta tensor is to be stored in CUTLASS
+    # InterleavedColumnMajor layout, and reverse engineered
+    # corresponding code to store values into this tensor.
+    interleave = 2
+    cols_maj = dst_cols // interleave
+    cols_min = dst_cols % interleave
+    return (cols_maj * m * interleave + dst_rows * interleave +
+            cols_min).view(-1)
+
+
+# This function converts dense matrix into sparse semi-structured
+# representation, producing "compressed" matrix, in the layout used by
+# CUTLASS backend, and corresponding metadata matrix.
+def sparse_semi_structured_from_dense_cutlass(dense):
+    if dense.dim() != 2:
+        raise RuntimeError(
+            f"Expected 2-dimensional dense tensor, got {dense.dim()}-dimensional tensor"  # noqa: E501
+        )
+
+    m, k = dense.shape
+    device = dense.device
+
+    meta_dtype = torch.int8
+    if dense.dtype == torch.int8:
+        meta_dtype = torch.int32
+    elif dense.dtype in [torch.half, torch.bfloat16, torch.float, torch.int32]:
+        meta_dtype = torch.int16
+    else:
+        raise RuntimeError(f"Invalid datatype {dense.dtype} of dense matrix")
+    quadbits_per_meta_elem = meta_dtype.itemsize * 8 // 4
+    if quadbits_per_meta_elem not in (4, 8):
+        raise RuntimeError(
+            "Invalid number of elements per meta element calculated")
+
+    if meta_dtype == torch.int32:
+        if m % 16 != 0:
+            raise RuntimeError(
+                f"Number of rows of dense matrix {m} must be divisible by 16")
+    else:
+        if m % 32 != 0:
+            raise RuntimeError(
+                f"Number of rows of dense matrix {m} must be divisible by 32")
+    if k % (4 * quadbits_per_meta_elem) != 0:
+        raise RuntimeError(
+            f"Number of columns of dense matrix {k} must be divisible by {4 * quadbits_per_meta_elem}"  # noqa: E501
+        )
+
+    if dense.dtype != torch.float:
+        ksparse = 4
+        dense_4 = dense.view(-1, k // ksparse, ksparse)
+        m0, m1, m2, m3 = (dense_4 != 0).unbind(-1)
+    else:
+        ksparse = 2
+        dense_2 = dense.view(-1, k // ksparse, ksparse)
+        m0, m2 = m1, m3 = (dense_2 != 0).unbind(-1)
+    meta_ncols = k // (ksparse * quadbits_per_meta_elem)
+
+    # Encoding quadruples of True/False values as follows:
+    #     [True,  True,  False, False] -> 0b0100
+    #     [True,  False, True,  False] -> 0b1000
+    #     [False, True,  True,  False] -> 0b1001
+    #     [True,  False, False, True ] -> 0b1100
+    #     [False, True,  False, True ] -> 0b1101
+    #     [False, False, True,  True ] -> 0b1110
+    # Thus, lower two bits in the encoding are index of the True value
+    # at the lowest index in the quadruple, and the higher two bits in
+    # the encoding are index of the other True value in the quadruple.
+    # In case there are less than two True values, than False value or
+    # values at some index or indices are considered True for the
+    # encoding.  In case there are more than two True values, then the
+    # excess True value(s) at some indices are considered False for
+    # the encoding.  The exact encodings used for these cases are as
+    # follows:
+    #     [False, False, False, False] -> 0b1110
+    #     [False, False, False, True ] -> 0b1110
+    #     [False, False, True,  False] -> 0b1110
+    #     [False, True,  False, False] -> 0b1001
+    #     [False, True,  True,  True ] -> 0b1101
+    #     [True,  False, False, False] -> 0b1000
+    #     [True,  False, True,  True ] -> 0b1100
+    #     [True,  True,  False, True ] -> 0b0100
+    #     [True,  True,  True,  False] -> 0b0100
+    #     [True,  True,  True,  True ] -> 0b0100
+    # These particular encodings are chosen, with the help of Espresso
+    # logic minimizer software, for the purpose of minimization of
+    # corresponding Boolean functions, that translate non-zero flags
+    # into encoding bits.  Note also possible choices for the first
+    # and last of these encodings were limited only to (0b0100,
+    # 0b1110), in order to produce valid encodings for 1:2 sparsity
+    # case.
+
+    expr0 = m0 & m1
+    expr1 = ~m0 & m1
+    expr2 = ~m0 & ~m1
+    bit0 = expr1
+    bit1 = expr2
+    bit2 = expr0 | expr2 | m3
+    bit3 = expr1 | ~m1
+    idxs0 = bit0 | (bit1.to(torch.int64) << 1)
+    idxs1 = bit2 | (bit3.to(torch.int64) << 1)
+
+    if dense.dtype != torch.float:
+        sparse0 = dense_4.gather(
+            -1, idxs0.unsqueeze(-1))  # type: ignore[possibly-undefined]
+        sparse1 = dense_4.gather(-1, idxs1.unsqueeze(-1))
+        sparse = torch.stack((sparse0, sparse1), dim=-1).view(m, k // 2)
+    else:
+        sparse = dense_2.gather(-1,
+                                idxs0.unsqueeze(-1) // 2).view(
+                                    m,
+                                    k // 2)  # type: ignore[possibly-undefined]
+
+    meta_4 = idxs0 | (idxs1 << 2)
+    meta_n = meta_4.view(
+        (-1, meta_ncols, quadbits_per_meta_elem)).to(meta_dtype)
+
+    if quadbits_per_meta_elem == 4:
+        meta = (meta_n[:, :, 0]
+                | (meta_n[:, :, 1] << 4)
+                | (meta_n[:, :, 2] << 8)
+                | (meta_n[:, :, 3] << 12))
+    elif quadbits_per_meta_elem == 8:
+        meta = (meta_n[:, :, 0]
+                | (meta_n[:, :, 1] << 4)
+                | (meta_n[:, :, 2] << 8)
+                | (meta_n[:, :, 3] << 12)
+                | (meta_n[:, :, 4] << 16)
+                | (meta_n[:, :, 5] << 20)
+                | (meta_n[:, :, 6] << 24)
+                | (meta_n[:, :, 7] << 28))
+
+    # Reorder meta tensor elements.
+    meta_reordered = meta.new_empty(
+        (m * meta_ncols, ))  # type: ignore[possibly-undefined]
+    meta_offsets = _calculate_meta_reordering_scatter_offsets(
+        m, meta_ncols, meta_dtype, device)
+    meta_reordered.scatter_(0, meta_offsets, meta.view(-1))
+
+    return (sparse, meta_reordered.view(m, meta_ncols))
+
+
+# This function performs reverse of the function above - it
+# reconstructs dense matrix from a pair of "compressed" matrix, given
+# in the layout used by CUTLASS backend, and accompanying metadata
+# matrix.
+def sparse_semi_structured_to_dense_cutlass(sparse, meta_reordered):
+    if sparse.dim() != 2:
+        raise RuntimeError(
+            f"Expected 2-dimensional sparse tensor, got {sparse.dim()}-dimensional tensor"  # noqa: E501
+        )
+
+    m, k = sparse.shape
+    device = sparse.device
+
+    if meta_reordered.dim() != 2:
+        raise RuntimeError(
+            f"Expected 2-dimensional meta tensor, got {meta_reordered.dim()}-dimensional tensor"  # noqa: E501
+        )
+    if meta_reordered.device != device:
+        raise RuntimeError(
+            f"Expected meta matrix to be on {device} device, got matrix on {meta_reordered.device} device"  # noqa: E501
+        )
+
+    meta_dtype = meta_reordered.dtype
+    if meta_dtype not in (torch.int16, torch.int32):
+        raise RuntimeError(f"Invalid datatype {meta_dtype} of meta matrix")
+    quadbits_per_meta_elem = meta_dtype.itemsize * 8 // 4
+
+    ksparse = 4 if sparse.dtype != torch.float else 2
+
+    meta_nrows, meta_ncols = meta_reordered.shape
+    if meta_nrows != m:
+        raise RuntimeError(
+            f"Number of rows of meta matrix {meta_nrows} must be equal to number of columns of spase matrix {m}"  # noqa: E501
+        )
+    if meta_ncols * ksparse * quadbits_per_meta_elem != 2 * k:
+        raise RuntimeError(
+            f"Number of columns of sparse matrix {k} different from the {meta_ncols * ksparse * quadbits_per_meta_elem // 2}, "  # noqa: E501
+            "expected according to the number of columns of meta matrix")
+
+    # Undo meta tensor elements reordering.
+    meta_offsets = _calculate_meta_reordering_scatter_offsets(
+        m, meta_ncols, meta_dtype, device)
+    meta = torch.gather(meta_reordered.view(-1), 0,
+                        meta_offsets).view(m, meta_ncols)
+
+    # Unpack sparse tensor back to original dense tensor, using
+    # information provided by meta tensor.  Note that torch.float
+    # datatype is handled pretty much the same as
+    # torch.half/torch.bfloat16, as metadata for a pair of torch.float
+    # value is encoded as if underlying 8 bytes contain four
+    # torch.half/torch.bfloat16 values, where either first two or last
+    # two are zeros.
+    meta_2 = torch.empty(
+        (m, meta_ncols, 2 * quadbits_per_meta_elem),
+        dtype=meta_dtype,
+        device=device,
+    )
+    if quadbits_per_meta_elem == 4:
+        meta_2[:, :, 0] = meta & 0b11
+        meta_2[:, :, 1] = (meta >> 2) & 0b11
+        meta_2[:, :, 2] = (meta >> 4) & 0b11
+        meta_2[:, :, 3] = (meta >> 6) & 0b11
+        meta_2[:, :, 4] = (meta >> 8) & 0b11
+        meta_2[:, :, 5] = (meta >> 10) & 0b11
+        meta_2[:, :, 6] = (meta >> 12) & 0b11
+        meta_2[:, :, 7] = (meta >> 14) & 0b11
+    elif quadbits_per_meta_elem == 8:
+        meta_2[:, :, 0] = meta & 0b11
+        meta_2[:, :, 1] = (meta >> 2) & 0b11
+        meta_2[:, :, 2] = (meta >> 4) & 0b11
+        meta_2[:, :, 3] = (meta >> 6) & 0b11
+        meta_2[:, :, 4] = (meta >> 8) & 0b11
+        meta_2[:, :, 5] = (meta >> 10) & 0b11
+        meta_2[:, :, 6] = (meta >> 12) & 0b11
+        meta_2[:, :, 7] = (meta >> 14) & 0b11
+        meta_2[:, :, 8] = (meta >> 16) & 0b11
+        meta_2[:, :, 9] = (meta >> 18) & 0b11
+        meta_2[:, :, 10] = (meta >> 20) & 0b11
+        meta_2[:, :, 11] = (meta >> 22) & 0b11
+        meta_2[:, :, 12] = (meta >> 24) & 0b11
+        meta_2[:, :, 13] = (meta >> 26) & 0b11
+        meta_2[:, :, 14] = (meta >> 28) & 0b11
+        meta_2[:, :, 15] = (meta >> 30) & 0b11
+
+    dense_offsets = meta_2.view(-1) + (
+        torch.arange(0, 2 * m * k // ksparse, device=device) * 4).view(
+            -1, 1).repeat(1, 2).view(-1)
+
+    dense = torch.zeros((m * 2 * k, ), dtype=sparse.dtype, device=device)
+    if sparse.dtype != torch.float:
+        # dense.scatter_(0, dense_offsets, sparse.view(-1))
+        dense.scatter_(0, dense_offsets, sparse.reshape(-1))
+    else:
+        dense.view(torch.half).scatter_(0, dense_offsets,
+                                        sparse.view(torch.half).view(-1))
+
+    return dense.view(m, 2 * k)
+
+
+def mask_creator(tensor):
+    """
+    Class for creating N:M sparsity masks.
+    Masks will be created using the N:M ratio, where for every block of 
+    M weights, N will be pruned based on ranked weight value. Each mask 
+    will correspond to the given tensor.
+
+    :param N: The number of weights in a group to keep
+    :param M: The size of a weight group
+    """
+    N = 2
+    M = 4
+
+    mask = None
+    # for i, tensor in enumerate(tensors):
+    if tensor.numel() % M != 0:
+        raise ValueError(
+            f"Tensor of size {tensor.shape} can't be evenly divided into "
+            f"{M} groups")
+
+    num_groups = tensor.numel() // M
+
+    # N:M sparsity for linear layers
+    tensor_temp = tensor.detach().abs().reshape(num_groups, M)
+    index = torch.argsort(tensor_temp, dim=1)[:, :int(M - N)]
+
+    w_b = torch.ones(tensor_temp.shape, device=tensor_temp.device)
+    mask = w_b.scatter_(dim=1, index=index, value=0).reshape(tensor.shape)
+
+    return mask
+
+
+def inject_24(w, size_k, size_n):
+    assert w.shape == (size_k, size_n)
+
+    mask = mask_creator(w.t()).t().cuda().bool()
+
+    return (mask * w).contiguous(), mask.contiguous()
+
+
+def check_24(w, num_rows_to_sample=50, _verbose=False):
+    BLOCK_SIZE = 4
+    MAX_NON_ZEROS = 2
+
+    w = w.t().contiguous()
+
+    print("check_24: w.shape = {}".format(w.shape))
+
+    num_rows, num_cols = w.shape
+    sampled_row_idxs = random.choices(range(num_rows), k=num_rows_to_sample)
+    if _verbose:
+        print(f"Sampled row idxs = {sampled_row_idxs}")
+
+    total_segments = 0
+    non_24_segments = 0
+    for i in sampled_row_idxs:
+        for j in range(0, num_cols - BLOCK_SIZE, BLOCK_SIZE):
+            total_segments += 1
+            block = w[i, j:j + BLOCK_SIZE]
+            num_nonzero = torch.count_nonzero(block)
+            if num_nonzero > MAX_NON_ZEROS:
+                print("i = {} j = {} block = {}".format(i, j, block))
+                non_24_segments += 1
+
+    print(f"{non_24_segments} / {total_segments} do not have 2:4 structure.")
+
+
+def compress_quantized_24_weight(q_24, size_k, size_n, wtype: ScalarType):
+    assert q_24.shape == (size_k, size_n)
+
+    # Remove bias to normalize over 0
+    q_24_no_zp = q_24 - wtype.bias
+
+    # Compress
+    q_24_no_zp = q_24_no_zp.t().contiguous()
+    q_24_no_zp_comp, meta = sparse_semi_structured_from_dense_cutlass(
+        q_24_no_zp)
+    q_24_no_zp_comp = q_24_no_zp_comp.t().contiguous()
+
+    # Restore bias
+    q_24_comp = q_24_no_zp_comp + wtype.bias
+
+    # Resize meta to its actual shape (without moving any data)
+    meta = meta.resize_(meta.shape[1] // 2, meta.shape[0] * 2)
+
+    return q_24_comp, meta
+
+
+def get_scale_perms_24():
+    scale_perm: List[int] = []
+    for i in range(8):
+        scale_perm.extend([i * 8 + j for j in [0, 4, 1, 5, 2, 6, 3, 7]])
+    scale_perm_single: List[int] = []
+    for i in range(8):
+        scale_perm_single.extend([8 * i + j for j in [0, 1, 2, 3, 4, 5, 6, 7]])
+    return scale_perm, scale_perm_single
+
+
+def get_weight_perm_24(num_bits: int):
+    perm_list: List[int] = []
+    for i in range(32):
+        perm1: List[int] = []
+        col = i // 4
+        col_o = col // 2
+        for block in [0, 1]:
+            for row in [
+                    2 * (i % 4),
+                    2 * (i % 4) + 1,
+                    2 * (i % 4 + 4),
+                    2 * (i % 4 + 4) + 1,
+            ]:
+                perm1.append(16 * row + col_o * 256 + 8 * (col % 2) +
+                             4 * block)
+        for j in range(4):
+            perm_list.extend([p + 1 * j for p in perm1])
+    perm = numpy.array(perm_list)
+
+    if num_bits == 4:
+        interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7])
+    elif num_bits == 8:
+        interleave = numpy.array([0, 2, 1, 3])
+    else:
+        raise ValueError("num_bits must be 4 or 8, got {}".format(num_bits))
+
+    perm = perm.reshape((-1, len(interleave)))[:, interleave].ravel()
+    perm = torch.from_numpy(perm)
+    return perm
+
+
+def marlin_permute_scales_24(s: torch.Tensor, size_k: int, size_n: int,
+                             group_size: int) -> torch.Tensor:
+
+    scale_perm, scale_perm_single = get_scale_perms_24()
+    if group_size < size_k and group_size != -1:
+        s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
+    else:
+        s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
+    s = s.reshape((-1, size_n)).contiguous()
+
+    return s
+
+
+def marlin_24_quantize(
+    w: torch.Tensor,
+    quant_type: ScalarType,
+    group_size: int,
+):
+    size_k, size_n = w.shape
+
+    # Normalize group_size
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    # Inject 2:4 sparsity
+    w_24, mask_24 = inject_24(w, size_k, size_n)
+
+    # Quantize
+    w_24_ref, q_w_24, s, g_idx, rand_perm = gptq_quantize_weights(
+        w_24, quant_type, group_size, act_order=False)
+
+    # Compress quantized weight
+    q_w_24_comp, meta = compress_quantized_24_weight(q_w_24, size_k, size_n,
+                                                     quant_type)
+    size_k_comp = size_k // 2
+
+    # Reformat to marlin
+    weight_perm = get_weight_perm_24(quant_type.size_bits)
+    marlin_24_q_w_comp = marlin_weights(q_w_24_comp, size_k_comp, size_n,
+                                        quant_type.size_bits, weight_perm)
+    marlin_24_s = marlin_permute_scales_24(s, size_k, size_n, group_size)
+
+    # Create result
+    res_list = [w_24_ref, marlin_24_q_w_comp, meta, marlin_24_s]
+    for i in range(len(res_list)):
+        res_list[i] = res_list[i].to(w.device)
+
+    return res_list

vllm/model_executor/layers/quantization/utils/marlin_utils_test_qqq.py

@@ -0,0 +1,125 @@
+from typing import List
+
+import numpy
+import torch
+
+from .marlin_utils_test import marlin_permute_weights
+from .quant_utils import get_pack_factor, qqq_quantize_weights
+
+
+def marlin_qqq_weights(q_w, size_k, size_n, num_bits, perm, group_size):
+    # Permute
+    q_w = marlin_permute_weights(q_w, size_k, size_n, perm)
+
+    # Pack
+    pack_factor = get_pack_factor(num_bits)
+    orig_device = q_w.device
+
+    q_w = q_w.cpu().numpy().astype(numpy.uint32)
+
+    q_packed = numpy.zeros((q_w.shape[0], q_w.shape[1] // pack_factor),
+                           dtype=numpy.uint32)
+    if group_size == size_k:
+        for i in range(pack_factor):
+            q_packed |= (q_w[:, i::pack_factor] & 0xF) << num_bits * i
+    else:
+        for i in range(pack_factor):
+            q_packed |= q_w[:, i::pack_factor] << num_bits * i
+
+    q_packed = torch.from_numpy(q_packed.astype(numpy.int32)).to(orig_device)
+
+    return q_packed
+
+
+def get_qqq_scale_perms():
+    scale_perm: List[int] = []
+    for i in range(8):
+        scale_perm.extend([i + 8 * j for j in range(8)])
+    scale_perm_single: List[int] = []
+    for i in range(4):
+        scale_perm_single.extend(
+            [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
+    return scale_perm, scale_perm_single
+
+
+# NOTE(HandH1998): QQQ employs different perms for per-group and per-channel weight quantization. # noqa: E501
+def get_qqq_weight_perm(num_bits: int, quant_type: str):
+    perm_list: List[int] = []
+    for i in range(32):
+        perm1: List[int] = []
+        col = i // 4
+        for block in [0, 1]:
+            for row in [
+                    4 * (i % 4),
+                    4 * (i % 4) + 1,
+                    4 * (i % 4) + 2,
+                    4 * (i % 4) + 3,
+            ]:
+                perm1.append(16 * row + col + 8 * block)
+        for j in range(4):
+            perm_list.extend([p + 256 * j for p in perm1])
+
+    perm = numpy.array(perm_list)
+
+    assert quant_type in ["per-channel",
+                          "per-group"], "not supported quantization type"
+    if num_bits == 4:
+        if quant_type == "per-channel":
+            interleave = numpy.array([4, 0, 5, 1, 6, 2, 7, 3])
+        else:
+            interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7])
+    else:
+        raise Exception("num_bits must be 4, got {}".format(num_bits))
+
+    perm = perm.reshape((-1, len(interleave)))[:, interleave].ravel()
+    perm = torch.from_numpy(perm)
+    return perm
+
+
+def marlin_qqq_permute_scales(s_group, s_channel, size_k, size_n, group_size):
+    scale_perm, scale_perm_single = get_qqq_scale_perms()
+    if group_size < size_k and group_size != -1:
+        s_group = s_group.reshape((-1, len(scale_perm)))[:, scale_perm]
+        s_channel = s_channel.reshape(
+            (-1, len(scale_perm_single)))[:, scale_perm_single]
+        s_group = s_group.reshape((-1, size_n)).contiguous()
+    else:
+        s_channel = s_channel.reshape(
+            (-1, len(scale_perm_single)))[:, scale_perm_single]
+    s_channel = s_channel.reshape((-1, size_n)).contiguous()
+
+    return s_group, s_channel
+
+
+def marlin_qqq_quantize(
+    w: torch.Tensor,
+    num_bits: int,
+    group_size: int,
+):
+    size_k, size_n = w.shape
+
+    # Normalize group_size
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+    quant_type = "per-channel" if group_size == size_k else "per-group"
+
+    # Quantize
+    w_ref, q_w, s_group, s_channel = qqq_quantize_weights(
+        w, num_bits, group_size)
+
+    # Reformat to marlin_qqq
+    weight_perm = get_qqq_weight_perm(num_bits, quant_type)
+    marlin_qqq_q_w = marlin_qqq_weights(q_w, size_k, size_n, num_bits,
+                                        weight_perm, group_size)
+    marlin_qqq_s_group, marlin_qqq_s_channel = marlin_qqq_permute_scales(
+        s_group, s_channel, size_k, size_n, group_size)
+
+    # Create result
+    res_list = [
+        w_ref, marlin_qqq_q_w, marlin_qqq_s_group, marlin_qqq_s_channel
+    ]
+    for i in range(len(res_list)):
+        res_list[i] = res_list[i].to(w.device)
+
+    return res_list

vllm/model_executor/layers/quantization/utils/quant_utils.py

@@ -0,0 +1,451 @@
+"""This file is used for /tests and /benchmarks"""
+from typing import List, Optional
+
+import numpy
+import torch
+
+from vllm.model_executor.layers.quantization.qqq import (
+    MARLIN_QQQ_SUPPORTED_NUM_BITS)
+from vllm.scalar_type import ScalarType, scalar_types
+
+SUPPORTED_GPTQ_QUANT_TYPES = [scalar_types.uint4b8, scalar_types.uint8b128]
+SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
+
+# Note: this is a hack. We should update each model to register the
+# stacked params and get it from there instead in a future PR.
+# fused_name: List[shard_name]
+FUSED_LAYER_NAME_MAPPING = {
+    "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+    "gate_up_proj": ["gate_proj", "up_proj"]
+}
+
+
+def pack_weights_into_int32(w_q: torch.Tensor,
+                            wtype: ScalarType,
+                            packed_dim: int = 0):
+    # move dim to pack to the end
+    perm = (*[i for i in range(len(w_q.shape)) if i != packed_dim], packed_dim)
+    inv_perm = tuple(perm.index(i) for i in range(len(perm)))
+    w_q_perm = w_q.permute(perm)
+
+    pack_factor = 32 // wtype.size_bits
+    mask = (1 << wtype.size_bits) - 1
+
+    new_shape_perm = list(w_q_perm.shape)
+    assert w_q_perm.shape[-1] % pack_factor == 0
+    new_shape_perm[-1] //= pack_factor
+
+    res = torch.zeros(new_shape_perm, dtype=torch.int32, device=w_q.device)
+    for i in range(pack_factor):
+        res |= (w_q_perm[..., i::pack_factor] & mask) << wtype.size_bits * i
+
+    return res.permute(inv_perm)
+
+
+def unpack_weights_into_int32(w_q: torch.Tensor,
+                              wtype: ScalarType,
+                              packed_dim: int = 0):
+    # move dim to pack to the end
+    perm = (*[i for i in range(len(w_q.shape)) if i != packed_dim], packed_dim)
+    inv_perm = tuple(perm.index(i) for i in range(len(perm)))
+    w_q_perm = w_q.permute(perm)
+
+    pack_factor = 32 // wtype.size_bits
+    mask = (1 << wtype.size_bits) - 1
+
+    new_shape_perm = list(w_q_perm.shape)
+    new_shape_perm[-1] *= pack_factor
+
+    res = torch.zeros(new_shape_perm, dtype=torch.int32, device=w_q.device)
+    for i in range(pack_factor):
+        res[..., i::pack_factor] = (w_q_perm >> wtype.size_bits * i) & mask
+
+    return res.permute(inv_perm)
+
+
+def is_layer_skipped(prefix: str, ignored_layers: List[str]) -> bool:
+    # prefix: model.layers.0.self_attn.q_proj
+    # proj_name: q_proj
+    proj_name = prefix.split(".")[-1]
+    if proj_name in FUSED_LAYER_NAME_MAPPING:
+        shard_prefixes = [
+            prefix.replace(proj_name, shard_proj_name)
+            for shard_proj_name in FUSED_LAYER_NAME_MAPPING[proj_name]
+        ]
+
+        is_skipped = None
+        for shard_prefix in shard_prefixes:
+            is_shard_skipped = shard_prefix in ignored_layers
+
+            if is_skipped is None:
+                is_skipped = is_shard_skipped
+            elif is_shard_skipped != is_skipped:
+                raise ValueError(
+                    f"Detected some but not all shards of {prefix} "
+                    "are quantized. All shards of fused layers "
+                    "to have the same precision.")
+    else:
+        is_skipped = prefix in ignored_layers
+
+    assert is_skipped is not None
+    return is_skipped
+
+
+def get_pack_factor(num_bits):
+    assert 32 % num_bits == 0, f"Unsupported num_bits = {num_bits}"
+    return 32 // num_bits
+
+
+def permute_rows(q_w: torch.Tensor,
+                 w_ref: torch.Tensor,
+                 group_size: int,
+                 test_perm: Optional[torch.Tensor] = None):
+    assert q_w.shape == w_ref.shape
+
+    orig_device = q_w.device
+    k_size, _ = q_w.shape
+
+    g_idx = torch.zeros((k_size, ), dtype=torch.int32)
+    for i in range(k_size):
+        g_idx[i] = i // group_size
+
+    # Simulate act_order by doing a random permutation on K
+    rand_perm = test_perm if test_perm is not None else torch.randperm(k_size)
+
+    g_idx = g_idx[rand_perm].contiguous()
+    q_w = q_w[rand_perm, :].contiguous()
+    w_ref = w_ref[rand_perm, :].contiguous()
+
+    return (
+        w_ref.to(device=orig_device),
+        q_w.to(device=orig_device),
+        g_idx.to(device=orig_device),
+        rand_perm.to(device=orig_device),
+    )
+
+
+def quantize_weights(w: torch.Tensor,
+                     quant_type: ScalarType,
+                     group_size: int,
+                     zero_points: bool = False,
+                     ref_zero_points_after_scales: bool = False):
+    assert quant_type.is_integer(), \
+        "Floating point quantization may work but has not been tested"
+
+    orig_device = w.device
+    orig_type = w.dtype
+    size_k, size_n = w.shape
+
+    assert w.is_floating_point(), "w must be float"
+
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    # Reshape to [groupsize, -1]
+    if group_size < size_k:
+        w = w.reshape((-1, group_size, size_n))
+        w = w.permute(1, 0, 2)
+        w = w.reshape((group_size, -1))
+
+    # Compute scale for each group
+    max_val = torch.max(w, 0, keepdim=True).values
+    min_val = torch.min(w, 0, keepdim=True).values
+
+    max_q_val = quant_type.max()
+    min_q_val = quant_type.min()
+
+    if zero_points:
+        assert not quant_type.is_signed() and quant_type.max() > 0
+        w_s = (max_val - min_val).clamp(min=1e-5) / quant_type.max()
+        maybe_w_zp = torch.round(torch.abs(min_val / w_s)) \
+            .clamp(min_q_val, max_q_val).int()
+    else:
+        # If the bias is such that there are no possible negative/positive
+        #  values, set the max value to inf to avoid divide by 0
+        w_s = torch.max(
+            abs(max_val / (max_q_val if max_q_val != 0 else torch.inf)),
+            abs(min_val / (min_q_val if min_q_val != 0 else torch.inf)))
+        maybe_w_zp = None
+
+    # Quantize
+    w_q = torch.round(w / w_s).int() + (maybe_w_zp if zero_points else 0)
+    w_q = torch.clamp(w_q, min_q_val, max_q_val)
+
+    # Compute ref (dequantized)
+    # For some kernels (namely Machete) the zero-points are applied after the
+    # scales are applied, for this case computing the reference in similar way
+    # allows us to use tighter error tolerances in our unit tests.
+    if ref_zero_points_after_scales and zero_points:
+        w_ref = w_q.to(orig_type) * w_s - maybe_w_zp.to(orig_type) * w_s
+    else:
+        w_ref = (w_q - (maybe_w_zp if zero_points else 0)).to(orig_type) * w_s
+
+    if quant_type.has_bias():
+        w_q += quant_type.bias
+
+    # Restore original shapes
+    if group_size < size_k:
+
+        def reshape_w(w):
+            w = w.reshape((group_size, -1, size_n))
+            w = w.permute(1, 0, 2)
+            w = w.reshape((size_k, size_n)).contiguous()
+            return w
+
+        w_q = reshape_w(w_q)
+        w_ref = reshape_w(w_ref)
+
+    w_s = w_s.reshape((-1, size_n)).contiguous()
+
+    if zero_points:
+        maybe_w_zp = maybe_w_zp.reshape((-1, size_n)).contiguous()
+        maybe_w_zp = maybe_w_zp.to(device=orig_device)
+
+    return (
+        w_ref.to(device=orig_device),
+        w_q.to(device=orig_device),
+        w_s.to(device=orig_device),
+        maybe_w_zp,
+    )
+
+
+def gptq_quantize_weights(w: torch.Tensor,
+                          quant_type: ScalarType,
+                          group_size: int,
+                          act_order: bool,
+                          test_perm: Optional[torch.Tensor] = None):
+    size_k, _ = w.shape
+
+    assert w.is_floating_point(), "w must be float"
+    assert quant_type in SUPPORTED_GPTQ_QUANT_TYPES, \
+        f"Unsupported gptq type = {quant_type}"
+    assert group_size in SUPPORTED_GROUP_SIZES + [
+        size_k
+    ], f"Unsupported groupsize = {group_size}"
+
+    w_ref, w_q, w_s, _ = quantize_weights(w, quant_type, group_size)
+
+    # Apply act_order
+    g_idx = torch.empty(0, dtype=torch.int, device=w.device)
+    rand_perm = torch.empty(0, dtype=torch.int, device=w.device)
+    if act_order:
+        assert (
+            group_size < size_k
+        ), "For act_order, groupsize = {} must be less than size_k = {}".format(
+            group_size, size_k)
+
+        w_ref, w_q, g_idx, rand_perm = permute_rows(w_q, w_ref, group_size,
+                                                    test_perm)
+
+    return w_ref, w_q, w_s, g_idx, rand_perm
+
+
+# QQQ employs different quant schemes for per-group and
+# per-channel quantization.
+def qqq_quantize_weights(w: torch.Tensor, num_bits: int, group_size: int):
+    orig_device = w.device
+    size_k, size_n = w.shape
+
+    assert w.is_floating_point(), "w must be float"
+    assert num_bits in MARLIN_QQQ_SUPPORTED_NUM_BITS, \
+           f"Unsupported num_bits = {num_bits}"
+    assert group_size in SUPPORTED_GROUP_SIZES + [
+        size_k
+    ], f"Unsupported groupsize = {group_size}"
+
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    if group_size < size_k:
+        # Reshape to [groupsize, -1]
+        w = w.reshape((-1, group_size, size_n))
+        w = w.permute(1, 0, 2)
+        w = w.reshape((group_size, -1))
+
+        max_q_val = 2**num_bits - 1
+        half_q_val = (max_q_val + 1) // 2
+
+        # Compute scale for each group
+        s_group = torch.max(torch.abs(w), 0, keepdim=True)[0]
+        s_group *= 2 / max_q_val  # 2 => symmetric
+
+        # Quantize
+        q_w = torch.round(w / s_group).int()
+        q_w += half_q_val
+        q_w = torch.clamp(q_w, 0, max_q_val)
+        # Compute ref (dequantized)
+        w_ref = (q_w - half_q_val).half() * s_group
+
+        # Restore original shapes
+        def reshape_w(w):
+            w = w.reshape((group_size, -1, size_n))
+            w = w.permute(1, 0, 2)
+            w = w.reshape((size_k, size_n)).contiguous()
+            return w
+
+        q_w = reshape_w(q_w)
+        w_ref = reshape_w(w_ref)
+
+        # Compute int8 quantization scale for each channel
+        s_channel = torch.max(torch.abs(w_ref), 0, keepdim=True)[0]
+        s_channel /= 127.0
+        t_int8 = (w_ref / s_channel).round().clamp(-128, 127).to(torch.int8)
+        w_ref = t_int8.half() * s_channel
+        s_channel = s_channel.reshape(1, -1).to(dtype=torch.float)
+
+        # Fuse scales
+        s_group = (s_group.reshape(-1, size_n).contiguous() /
+                   s_channel).to(dtype=torch.half)
+    else:
+        max_q_val = 2**(num_bits - 1) - 1
+
+        # Compute scale for each channel
+        s_channel = torch.max(torch.abs(w), 0, keepdim=True)[0]
+        s_channel /= max_q_val
+
+        # Quantize
+        q_w = torch.round(w / s_channel).int()
+        q_w = torch.clamp(q_w, -max_q_val, max_q_val)
+        # Compute ref (dequantized)
+        w_ref = q_w.half() * s_channel
+
+        s_group = torch.tensor([], dtype=torch.half)
+        # div 2 ** (8 - self.bits)) to offset right shift in unpacking
+        s_channel /= (2**(8 - num_bits))
+        s_channel = s_channel.reshape(-1, size_n).contiguous().to(torch.float)
+
+    return (
+        w_ref.to(device=orig_device),
+        q_w.to(device=orig_device),
+        s_group.to(device=orig_device),
+        s_channel.to(device=orig_device),
+    )
+
+
+def sort_weights(q_w: torch.Tensor, g_idx: torch.Tensor):
+    orig_device = q_w.device
+
+    sort_indices = torch.argsort(g_idx).to(
+        dtype=torch.int32)  # Sort based on g_idx
+
+    g_idx = g_idx[sort_indices].contiguous()
+    q_w = q_w[sort_indices, :].contiguous()
+
+    return (
+        q_w.to(device=orig_device),
+        g_idx.to(device=orig_device),
+        sort_indices.to(device=orig_device),
+    )
+
+
+def pack_rows(
+    q_w: torch.Tensor,
+    num_bits: int,
+    size_k: int,
+    size_n: int,
+):
+    assert q_w.shape == (size_k, size_n)
+
+    pack_factor = get_pack_factor(num_bits)
+    assert size_k % pack_factor == 0
+
+    orig_device = q_w.device
+
+    q_w = q_w.cpu().numpy().astype(numpy.uint32)
+
+    q_res = numpy.zeros((size_k // pack_factor, size_n), dtype=numpy.uint32)
+
+    for i in range(pack_factor):
+        q_res |= q_w[i::pack_factor, :] << num_bits * i
+
+    q_res = torch.from_numpy(q_res.astype(numpy.int32)).to(orig_device)
+    return q_res
+
+
+def pack_cols(
+    q_w: torch.Tensor,
+    num_bits: int,
+    size_k: int,
+    size_n: int,
+):
+    assert q_w.shape == (size_k, size_n)
+
+    pack_factor = get_pack_factor(num_bits)
+    assert size_n % pack_factor == 0
+
+    orig_device = q_w.device
+
+    q_w = q_w.cpu().numpy().astype(numpy.uint32)
+
+    q_res = numpy.zeros((size_k, size_n // pack_factor), dtype=numpy.uint32)
+
+    for i in range(pack_factor):
+        q_res |= q_w[:, i::pack_factor] << num_bits * i
+
+    q_res = torch.from_numpy(q_res.astype(numpy.int32)).to(orig_device)
+    q_res = q_res.contiguous()
+
+    return q_res
+
+
+def unpack_cols(
+    packed_q_w: torch.Tensor,
+    num_bits: int,
+    size_k: int,
+    size_n: int,
+):
+    pack_factor = get_pack_factor(num_bits)
+    assert size_n % pack_factor == 0
+    assert packed_q_w.shape == (
+        size_k, size_n // pack_factor
+    ), "packed_q_w.shape = {} size_k = {}, size_n = {} pack_Factor = {}".format(
+        packed_q_w.shape, size_k, size_n, pack_factor)
+
+    orig_device = packed_q_w.device
+
+    packed_q_w_cpu = packed_q_w.cpu().numpy().astype(numpy.uint32)
+    q_res = numpy.zeros((size_k, size_n), dtype=numpy.uint32)
+
+    mask = (1 << num_bits) - 1
+    for i in range(pack_factor):
+        vals = packed_q_w_cpu & mask
+        packed_q_w_cpu >>= num_bits
+        q_res[:, i::pack_factor] = vals
+
+    q_res = torch.from_numpy(q_res.astype(numpy.int32)).to(orig_device)
+    q_res = q_res.contiguous()
+
+    return q_res
+
+
+def gptq_pack(
+    q_w: torch.Tensor,
+    num_bits: int,
+    size_k: int,
+    size_n: int,
+):
+    return pack_rows(q_w, num_bits, size_k, size_n)
+
+
+def awq_pack(
+    q_w: torch.Tensor,
+    num_bits: int,
+    size_k: int,
+    size_n: int,
+):
+    assert q_w.shape == (size_k, size_n)
+
+    # Interleave column dim (for the dequantize code) and pack it to int32
+    if num_bits == 4:
+        interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7])
+    elif num_bits == 8:
+        interleave = numpy.array([0, 2, 1, 3])
+    else:
+        raise Exception("num_bits must be 4 or 8, got {}".format(num_bits))
+
+    q_w = q_w.reshape((-1, len(interleave)))[:, interleave].ravel()
+    q_w = q_w.reshape((-1, size_n)).contiguous()
+
+    return pack_cols(q_w, num_bits, size_k, size_n)

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -0,0 +1,246 @@
+from typing import List, Optional, Tuple, Union
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
+from vllm.utils import is_hip
+
+# Input scaling factors are no longer optional in _scaled_mm starting
+# from pytorch 2.5. Allocating a dummy tensor to pass as input_scale
+TORCH_DEVICE_IDENTITY = torch.ones(1).cuda() if is_hip() else None
+
+
+def cutlass_fp8_supported() -> bool:
+    # cutlass is not supported on Rocm
+    if is_hip():
+        return False
+
+    capability_tuple = current_platform.get_device_capability()
+    capability = -1 if capability_tuple is None else capability_tuple.to_int()
+
+    return ops.cutlass_scaled_mm_supports_fp8(capability)
+
+
+def per_tensor_dequantize(
+        tensor: torch.Tensor, inv_scale: Union[float,
+                                               torch.Tensor]) -> torch.Tensor:
+    fake_qweight = tensor.to(torch.float16)
+    dq_weight = fake_qweight * inv_scale
+    return dq_weight
+
+
+def all_close_1d(x: torch.Tensor) -> bool:
+    assert len(x.shape) == 1
+    return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0]))
+
+
+def convert_to_channelwise(
+        weight_scale: torch.Tensor,
+        logical_widths: List[int]) -> Tuple[torch.Tensor, torch.Tensor]:
+    # Create channelwise buffer
+    weight_scale_channel = torch.empty((sum(logical_widths), 1),
+                                       dtype=torch.float32,
+                                       device=weight_scale.device)
+
+    # Expand each scale to match the size of each logical matrix.
+    start = 0
+    for idx, logical_width in enumerate(logical_widths):
+        end = start + logical_width
+        weight_scale_channel[start:end, :] = weight_scale[idx]
+        start = end
+
+    return weight_scale_channel
+
+
+def requantize_with_max_scale(
+        weight: torch.Tensor, weight_scale: torch.Tensor,
+        logical_widths: List[int]) -> Tuple[torch.Tensor, torch.Tensor]:
+    # Max scale to be used for requanitzation.
+    max_w_scale = weight_scale.max()
+
+    # QKV / MLP is fused in the on disk checkpoint if any of the
+    # weight scales are still set to the default since we initialize
+    # N weight scales for N shards but we only load 1 weight scale
+    # from disk in this case. Skip requantization in this case (since)
+    # we already are quantized with the single scale.
+    # * Sample Model: nm-testing/Phi-3-mini-128k-instruct-FP8
+    unfused_module_in_checkpoint = (weight_scale[-1] > torch.finfo(
+        torch.float8_e4m3fn).min)
+
+    # If unfused checkpoint, need requanize with the single scale.
+    if unfused_module_in_checkpoint:
+        start = 0
+        for idx, logical_width in enumerate(logical_widths):
+            end = start + logical_width
+            weight_dq = per_tensor_dequantize(weight[start:end, :],
+                                              weight_scale[idx])
+            weight[start:end, :], _ = ops.scaled_fp8_quant(
+                weight_dq, max_w_scale)
+            start = end
+
+    return max_w_scale, weight
+
+
+def apply_fp8_linear(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    weight_scale: torch.Tensor,
+    input_scale: Optional[torch.Tensor] = None,
+    input_scale_ub: Optional[torch.Tensor] = None,
+    bias: Optional[torch.Tensor] = None,
+    cutlass_fp8_supported: bool = True,
+    use_per_token_if_dynamic: bool = False,
+) -> torch.Tensor:
+    # ops.scaled_fp8_quant supports both dynamic and static quant.
+    #   If dynamic, layer.input_scale is None and x_scale computed from x.
+    #   If static, layer.input_scale is scalar and x_scale is input_scale.
+
+    # cutlass_scaled_mm supports per tensor/channel W and per tensor/token A
+    if cutlass_fp8_supported:
+        qinput, x_scale = ops.scaled_fp8_quant(
+            input,
+            input_scale,
+            scale_ub=input_scale_ub,
+            use_per_token_if_dynamic=use_per_token_if_dynamic)
+
+        # Fused GEMM_DQ
+        return ops.cutlass_scaled_mm(qinput,
+                                     weight,
+                                     out_dtype=input.dtype,
+                                     scale_a=x_scale,
+                                     scale_b=weight_scale,
+                                     bias=bias)
+
+    # torch.scaled_mm supports per tensor weights + activations only
+    # so fallback to naive if per channel or per token
+    else:
+        # Note: we pad the input because torch._scaled_mm is more performant
+        # for matrices with batch dimension > 16.
+        # This could change in the future.
+        qinput, x_scale = ops.scaled_fp8_quant(
+            input,
+            input_scale,
+            num_token_padding=17,
+            use_per_token_if_dynamic=use_per_token_if_dynamic)
+
+        per_tensor_weights = (weight_scale.numel() == 1)
+        per_tensor_activations = (x_scale.numel() == 1)
+
+        if per_tensor_weights and per_tensor_activations:
+            # Fused GEMM_DQ
+            output = torch._scaled_mm(qinput,
+                                      weight,
+                                      out_dtype=input.dtype,
+                                      scale_a=x_scale,
+                                      scale_b=weight_scale,
+                                      bias=bias)
+            # A fix for discrepancy in scaled_mm which returns tuple
+            # for torch < 2.5 and a single value in torch >= 2.5
+            if type(output) is tuple and len(output) == 2:
+                return torch.narrow(output[0], 0, 0, input.shape[0])
+            return torch.narrow(output, 0, 0, input.shape[0])
+
+        else:
+            # Fallback for channelwise case, where we use unfused DQ
+            # due to limitations with scaled_mm
+
+            # Symmetric quantized GEMM by definition computes the following:
+            #   C = (s_x * X) (s_w * W) + bias
+            # This is equivalent to dequantizing the weights and activations
+            # before applying a GEMM.
+            #
+            # In order to compute quantized operands, a quantized kernel
+            # will rewrite the above like so:
+            #   C = s_w * s_x * (X * W) + bias
+            #
+            # For the scaled_mm fallback case, we break this down, since it
+            # does not support s_w being a vector.
+
+            # Making sure the dummy tensor is on the same device as the weight
+            global TORCH_DEVICE_IDENTITY
+            if (TORCH_DEVICE_IDENTITY is not None
+                    and TORCH_DEVICE_IDENTITY.device != weight.device):
+                TORCH_DEVICE_IDENTITY = TORCH_DEVICE_IDENTITY.to(weight.device)
+
+            # GEMM
+            # This computes C = (X * W).
+            # Output in fp32 to allow subsequent ops to happen in-place
+            output = torch._scaled_mm(qinput,
+                                      weight,
+                                      scale_a=TORCH_DEVICE_IDENTITY,
+                                      scale_b=TORCH_DEVICE_IDENTITY,
+                                      out_dtype=torch.float32)
+            # A fix for discrepancy in scaled_mm which returns tuple
+            # for torch < 2.5 and a single value in torch >= 2.5
+            if type(output) is tuple and len(output) == 2:
+                output = output[0]
+            # Unpad (undo num_token_padding)
+            output = torch.narrow(output, 0, 0, input.shape[0])
+            x_scale = torch.narrow(x_scale, 0, 0, input.shape[0])
+
+            # DQ
+            # C = sw * sx * (X * W) + bias
+            output = output * x_scale * weight_scale.t()
+            if bias is not None:
+                output = output + bias
+            return output.to(dtype=input.dtype)
+
+
+def apply_int8_linear(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    weight_scale: torch.Tensor,
+    input_scale: Optional[torch.Tensor] = None,
+    input_zero_point: Optional[torch.Tensor] = None,
+    azp_adj: Optional[torch.Tensor] = None,
+    bias: Optional[torch.Tensor] = None,
+):
+    # ops.scaled_int8_quant supports both dynamic and static quant.
+    # * dynamic, layer.input_scale is None and x_scale computed from x.
+    # * static, layer.input_scale is scalar and x_scale is input_scale.
+    symmetric = azp_adj is None
+    x_q, x_scale, x_zp = ops.scaled_int8_quant(input,
+                                               input_scale,
+                                               input_zero_point,
+                                               symmetric=symmetric)
+
+    if x_zp is not None:
+        return ops.cutlass_scaled_mm_azp(x_q,
+                                         weight,
+                                         scale_a=x_scale,
+                                         scale_b=weight_scale,
+                                         out_dtype=input.dtype,
+                                         azp_adj=azp_adj,
+                                         azp=x_zp,
+                                         bias=bias)
+    return ops.cutlass_scaled_mm(x_q,
+                                 weight,
+                                 scale_a=x_scale,
+                                 scale_b=weight_scale,
+                                 out_dtype=input.dtype,
+                                 bias=bias)
+
+
+def normalize_e4m3fn_to_e4m3fnuz(
+    weight: torch.Tensor,
+    weight_scale: torch.Tensor,
+    input_scale: Optional[torch.Tensor] = None
+) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+    assert weight.dtype == torch.float8_e4m3fn
+    # The bits pattern 10000000(-128) represents zero in e4m3fn
+    # but NaN in e4m3fnuz. So here we set it to 0.
+    # https://onnx.ai/onnx/technical/float8.html
+    weight_as_int8 = weight.view(torch.int8)
+    ROCM_FP8_NAN_AS_INT = -128
+    weight_as_int8[weight_as_int8 == ROCM_FP8_NAN_AS_INT] = 0
+    weight = weight_as_int8.view(torch.float8_e4m3fnuz)
+
+    # For the same bits representation, e4m3fnuz value is half of
+    # the e4m3fn value, so we should double the scaling factor to
+    # get the same dequantized value.
+    # https://onnx.ai/onnx/technical/float8.html
+    weight_scale = weight_scale * 2.0
+    if input_scale is not None:
+        input_scale = input_scale * 2.0
+    return weight, weight_scale, input_scale

vllm/model_executor/layers/quantization/w8a16.py

@@ -0,0 +1,114 @@
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.parameter import (GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+from vllm.model_executor.utils import set_weight_attrs
+
+
+class W8a16Config(QuantizationConfig):
+    """Config class for W8a16.
+    
+    """
+
+    def __init__(
+        self,
+    ) -> None:
+        pass
+
+    def __repr__(self) -> str:
+        return ("W8a16Config")
+
+    def get_name(self) -> str:
+        return "w8a16"
+
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    def get_min_capability(self) -> int:
+        return 75
+
+    @staticmethod
+    def get_config_filenames():
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "W8a16Config":
+        return cls()
+    
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["W8a16LinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return W8a16LinearMethod(self)
+        return None
+
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class W8a16LinearMethod(LinearMethodBase):
+    """Linear method for w8a16.
+
+    """
+
+    def __init__(self, quant_config: W8a16Config):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        output_size_per_partition = sum(output_partition_sizes)
+        weight = Parameter(
+            torch.empty(
+                output_size_per_partition,
+                input_size_per_partition,
+                dtype=torch.int8,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(
+            weight, {
+                "input_dim": 1,
+                "output_dim": 0,
+            })
+        
+        scales = Parameter(
+            torch.empty(
+                1,
+                output_size_per_partition,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(scales, {
+            "input_dim": None,
+            "output_dim": 1,
+        })
+        
+        layer.register_parameter("weight", weight)
+        set_weight_attrs(weight, extra_weight_attrs)
+        layer.register_parameter("scales", scales)
+        set_weight_attrs(scales, extra_weight_attrs)
+        
+        
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        qweight = layer.weight
+        scales = layer.scales
+        out_shape = (x.shape[:-1] + (qweight.shape[-2],))
+        reshaped_x = x.reshape(-1, x.shape[-1])
+        out = ops.linear_w8a16(reshaped_x, qweight, scales, format="TN")
+        if bias is not None:
+            out = out + bias
+        return out.reshape(out_shape)

vllm/model_executor/layers/rejection_sampler.py

@@ -0,0 +1,401 @@
+from functools import cached_property
+from importlib.util import find_spec
+from typing import Dict, List, Optional, Tuple
+
+import torch
+import torch.jit
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+from vllm.model_executor.layers.spec_decode_base_sampler import (
+    SpecDecodeStochasticBaseSampler)
+
+logger = init_logger(__name__)
+
+if find_spec("flashinfer"):
+    """
+    Consider utilizing the FlashInfer rejection sampling kernel initially,
+    as it employs a dedicated kernel rather than relying on 
+    Torch tensor operations. This design choice helps to fuse operations, 
+    reduce memory I/O, and consequently enhances performance.
+    """
+    from flashinfer.sampling import chain_speculative_sampling
+else:
+    chain_speculative_sampling = None
+
+
+class RejectionSampler(SpecDecodeStochasticBaseSampler):
+    """Apply modified rejection sampling as described in "Accelerating Large
+        Language Model Decoding with Speculative Sampling"
+        https://arxiv.org/pdf/2302.01318.pdf.
+    """
+
+    def __init__(self,
+                 strict_mode: bool = False,
+                 use_flashinfer: Optional[bool] = None):
+        """Create a rejection sampler.
+
+        Args:
+            strict_mode: Whether or not to perform shape/device/dtype checks
+            during sampling. This catches correctness issues but adds
+            nontrivial latency.
+            use_falshinfer: We will use this parameter to determine whether
+            to use the FlashInfer rejection sampling kernel or not. If it's
+            None, we will use the default value from the environment variable.
+            This parameter is only used for testing purposes.
+        """
+        super().__init__(strict_mode=strict_mode)
+        if use_flashinfer is None:
+            self.use_flashinfer = envs.VLLM_USE_FLASHINFER_SAMPLER and (
+                chain_speculative_sampling is not None)
+        else:
+            self.use_flashinfer = use_flashinfer
+
+        if self.use_flashinfer:
+            logger.info("Use flashinfer for rejection sampling.")
+        else:
+            logger.info("Use pytorch for rejection sampling.")
+
+    def forward(
+        self,
+        target_with_bonus_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+        seeded_seqs: Optional[Dict[int, torch.Generator]] = None,
+    ) -> torch.Tensor:
+        """Sample token ids using rejection sampling. This accepts or rejects
+        tokens proposed by the draft model using the probability of each token
+        according to the draft and target models.
+
+        In the worst case where all draft tokens are rejected, it is guaranteed
+        one correct token will be emitted.
+
+        In the case where all draft tokens are accepted, a bonus token will be
+        accepted as its cheap to have the target model score this speculative
+        sequence.
+
+        Args:
+            target_with_bonus_probs: The probability distribution 
+                over token ids given context according to the target model.
+            shape = [batch_size, num_speculative_tokens + 1, vocab_size]
+
+            bonus_token_ids: The "bonus" token ids that are accepted iff all
+                speculative tokens in a sequence are accepted.
+            shape = [batch_size, num_bonus_tokens]
+
+            draft_probs: The probability distribution over token ids given
+                context according to the draft model.
+            shape = [batch_size, num_speculative_tokens, vocab_size]
+
+            draft_token_ids: The token ids that were sampled from the draft
+                probabilities.
+            shape = [batch_size, num_speculative_tokens]
+
+            seeded_seqs: Dict of batch row index to torch generator, for
+                sequences using seeded generation.
+
+        Returns:
+            output_token_ids: The token ids sampled via rejection sampling,
+                or -1 if unable to sample a token because the previous token
+                was rejected.
+            shape = [batch_size, num_speculative_tokens + num_bonus_tokens]
+        """
+        # Only perform shape/dtype/device checking in strict mode, as it adds
+        # overhead.
+        if self._strict_mode:
+            self._raise_if_incorrect_input(target_with_bonus_probs,
+                                           draft_token_ids, bonus_token_ids,
+                                           draft_probs)
+
+        batch_size, k, _ = draft_probs.shape
+
+        # batch_size = 0 when all requests in the batch are
+        # non_spec requests. In this case, output_token_ids is
+        # just an empty tensor.
+        if batch_size == 0:
+            return torch.empty(0, k + 1, device=draft_probs.device, dtype=int)
+
+        # If use Flashinfer chain_speculative_sampling kernel
+        # for rejection sampling
+        if self.use_flashinfer:
+            batch_size, k, _ = draft_probs.shape
+            uniform_samples = self._create_uniform_samples(
+                seeded_seqs, batch_size, k, draft_probs.device)
+            output_token_ids, accepted_token_num, emitted_token_num \
+                = chain_speculative_sampling(
+                draft_probs, draft_token_ids, uniform_samples,
+                target_with_bonus_probs)
+
+            # num_emitted_tokens returned by flashinfer
+            # does not include the bonus token
+            # Flashinfer stops at the first token that violates
+            # the condition p >= q and does not include recovery/bonus token.
+            # Therefore, we need to add batch_size here.
+            self.num_accepted_tokens += accepted_token_num.sum()
+            self.num_emitted_tokens += emitted_token_num.sum() + batch_size
+            self.num_draft_tokens += batch_size * k
+        else:
+            accepted, recovered_token_ids = (
+                self._batch_modified_rejection_sampling(
+                    target_with_bonus_probs[:, :-1],
+                    draft_probs,
+                    draft_token_ids,
+                    seeded_seqs,
+                ))
+
+            output_token_ids = self._create_output(
+                accepted,
+                recovered_token_ids,
+                draft_token_ids,
+                bonus_token_ids,
+            )
+
+        return output_token_ids
+
+    def _batch_modified_rejection_sampling(
+        self,
+        target_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+        draft_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+        draft_token_ids: torch.Tensor,  # [batch_size, k]
+        seeded_seqs: Optional[Dict[int, torch.Generator]],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Perform modified rejection sampling on each sequence.
+
+        Returns:
+            A tuple of two tensors:
+            0: A bool tensor of which tokens in each sequence is accepted.
+                shape = [batch_size, k]
+            1: Token ids sampled from a recovered distribution, to be used
+                when a token is rejected.
+                shape = [batch_size, k]
+        """
+
+        batch_size, k, vocab_size = draft_probs.shape
+
+        # shape [batch_size, k]
+        accepted = self._get_accepted(target_probs, draft_probs,
+                                      draft_token_ids, seeded_seqs)
+
+        recovered_probs = self._get_recovered_probs(
+            target_probs, draft_probs).reshape(batch_size * k, vocab_size)
+
+        # NOTE: the recovered_probs are overwritten by this method.
+        recovered_token_ids = _multinomial(
+            recovered_probs,
+            num_samples=1,
+            k=k,
+            seeded_seqs=seeded_seqs or {},
+        ).reshape(batch_size, k)
+
+        return accepted, recovered_token_ids
+
+    def _create_uniform_samples(self,
+                                seeded_seqs: Optional[Dict[int,
+                                                           torch.Generator]],
+                                batch_size: int, k: int,
+                                device: torch.device) -> torch.Tensor:
+        """
+        Generates a batch of uniform random samples, with optional seeding 
+        for specific sequences.
+
+        This method creates a tensor of shape `(batch_size, k + 1)` filled 
+        with uniform random values in the range [0, 1). If `seeded_seqs` 
+        is provided, the sequences corresponding to specific indices 
+        will be generated using the provided `torch.Generator` for 
+        reproducibility. The other sequences will be generated without 
+        a seed.
+
+        Args:
+            seeded_seqs : Optional[Dict[int, torch.Generator]]
+                A dictionary mapping indices in the batch to 
+                `torch.Generator` objects. If `None`, all samples are 
+                generated without a seed.
+            batch_size : int
+                The number of sequences to generate.
+            k : int
+                The number of random samples per sequence.
+            device : torch.device
+                The device on which to allocate the tensor.
+
+        Returns:
+            uniform_rand : torch.Tensor
+                A tensor of shape `(batch_size, k + 1)` containing uniform 
+                random values in the range [0, 1).
+        """
+        if not seeded_seqs:
+            return torch.rand(batch_size, k + 1, device=device)
+
+        uniform_rand = torch.empty(batch_size, k + 1, device=device)
+
+        non_seeded_indices = []
+        for idx in range(batch_size):
+            generator = seeded_seqs.get(idx)
+            if generator is None:
+                non_seeded_indices.append(idx)
+            else:
+                uniform_rand[idx, :] = torch.rand(1,
+                                                  k + 1,
+                                                  dtype=self.probs_dtype,
+                                                  device=device,
+                                                  generator=generator)
+        if non_seeded_indices:
+            uniform_rand[non_seeded_indices, :] = torch.rand(
+                len(non_seeded_indices),
+                k + 1,
+                dtype=self.probs_dtype,
+                device=device)
+        return uniform_rand
+
+    def _get_accepted(
+        self,
+        target_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+        draft_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+        draft_token_ids: torch.Tensor,  # [batch_size, k]
+        seeded_seqs: Optional[Dict[int, torch.Generator]],
+    ) -> torch.Tensor:
+        r"""Create bool matrix over the proposed draft tokens. If
+        True, then a token can be accepted, else it should be
+        rejected.
+
+        Given :math:`q(\hat{x}_{n+1}|x_1, \dots, x_n)`, the probability of
+        :math:`\hat{x}_{n+1}` given context :math:`x_1, \dots, x_n` according
+        to the target model, and :math:`p(\hat{x}_{n+1}|x_1, \dots, x_n)`, the
+        same conditional probability according to the draft model, the token
+        is accepted with probability:
+
+        .. math::
+            \min\left(1, \frac{q(\hat{x}_{n+1}|x_1, \dots, x_n)}
+                           {p(\hat{x}_{n+1}|x_1, \dots, x_n)}\right)
+
+        This implementation does not apply causality. When using the output,
+        if a token is rejected, subsequent tokens should not be used.
+
+        Returns a bool tensor of shape [batch_size, k] specifying which tokens
+        are accepted.
+        """
+        batch_size, k, _ = draft_probs.shape
+        batch_indices = torch.arange(batch_size,
+                                     device=target_probs.device)[:, None]
+        probs_indicies = torch.arange(k, device=target_probs.device)
+
+        # shape [batch_size, k]
+        selected_draft_probs = draft_probs[batch_indices, probs_indicies,
+                                           draft_token_ids]
+
+        # shape [batch_size, k]
+        selected_target_probs = target_probs[batch_indices, probs_indicies,
+                                             draft_token_ids]
+
+        uniform_rand = self._create_uniform_samples(seeded_seqs, batch_size,
+                                                    k - 1, target_probs.device)
+
+        capped_ratio = torch.minimum(
+            selected_target_probs / selected_draft_probs,
+            torch.full((1, ), 1, device=target_probs.device))
+        accepted = uniform_rand < capped_ratio
+
+        return accepted
+
+    def _get_recovered_probs(
+            self,
+            target_probs: torch.Tensor,  # [k, vocab_size]
+            draft_probs: torch.Tensor,  # [k, vocab_size]
+    ) -> torch.Tensor:
+        r"""Create a probability distribution for each proposed token which can
+        be sampled if the proposed token is rejected.
+
+        When this routine is applied sequentially, the true distribution of the
+        target model is recovered (within hardware numerics).
+
+        The probability distribution used in this rejection case is constructed
+        as follows. Given :math:`q(x|x_1, \dots, x_n)`, the probability of
+        :math:`x` given context :math:`x_1, \dots, x_n` according to the target
+        model and :math:`p(x|x_1, \dots, x_n)`, the same conditional probability
+        according to the draft model:
+
+        .. math::
+            x_{n+1} \sim (q(x|x_1, \dots, x_n) - p(x|x_1, \dots, x_n))_+
+
+        where :math:`(f(x))_+` is defined as:
+
+        .. math::
+            (f(x))_+ = \frac{\max(0, f(x))}{\sum_x \max(0, f(x))}
+
+        See https://github.com/vllm-project/vllm/pull/2336 for a visualization
+        of the draft, target, and recovered probability distributions.
+
+        Returns a tensor of shape [batch_size, k, vocab_size].
+
+        Note: This batches operations on GPU and thus constructs the recovered
+        distribution for all tokens, even if they are accepted. This causes
+        division-by-zero errors, so we use self._smallest_positive_value to
+        avoid that. This introduces some drift to the distribution.
+        """
+        _, k, _ = draft_probs.shape
+
+        # shape [batch_size, k, vocab_size]
+        difference = target_probs - draft_probs
+
+        # TODO(cade): Can we use logprobs instead of probs, and avoid the
+        # division-by-zero errors without introducing distribution drift?
+
+        # shape [batch_size, k, vocab_size]
+        f = torch.clamp(difference, min=self._smallest_positive_value)
+
+        # shape [batch_size, k, vocab_size]
+        recovered_probs = f / torch.sum(f, dim=-1).reshape(-1, k, 1)
+
+        return recovered_probs
+
+    @cached_property
+    def _smallest_positive_value(self) -> float:
+        """Return the smallest positive value representable by the probs dtype.
+        This value is used when constructing a distribution from which to sample
+        recovered tokens in the first rejection case.
+
+        See _get_recovered_probs for more details
+
+        Note that this isn't actually the smallest positive value representable
+        by float32, but the smallest positive normal value.
+        See https://en.wikipedia.org/wiki/Subnormal_number for more information.
+        """
+        return torch.finfo(self.probs_dtype).tiny
+
+
+# torch.multinomial forces a GPU<->CPU sync.
+# Therefore, we use an optimized implementation instead that skips the sync.
+# Note that we always sample with replacement.
+# probs will be modified in place, but this is fine, as we pass
+# in a copy already.
+# @torch.jit.script
+def _multinomial(
+    probs: torch.Tensor,
+    num_samples: int,
+    k: int,
+    seeded_seqs: Dict[int, torch.Generator],
+) -> torch.Tensor:
+
+    if num_samples > 1:
+        # This is equivalent to torch.repeat_interleaved (which also
+        # forces a GPU<->CPU sync).
+        probs = probs[:, None, :].expand(probs.shape[0], num_samples,
+                                         probs.shape[1]).contiguous().view(
+                                             -1, probs.shape[1])
+    q = torch.empty_like(probs)
+    if not seeded_seqs:
+        q.exponential_(1.0)
+    else:
+        non_seeded_indices: List[int] = []
+        start = 0
+        for idx in range(len(q) // k):
+            end = start + k
+            generator = seeded_seqs.get(idx)
+            if generator is None:
+                non_seeded_indices.extend(list(range(start, end)))
+            else:
+                q[start:end].exponential_(1.0, generator=generator)
+            start = end
+        q[non_seeded_indices].exponential_(1.0)
+
+    return probs.div_(q).argmax(dim=1).view(-1, num_samples)

vllm/model_executor/layers/resampler.py

@@ -0,0 +1,273 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# https://huggingface.co/Qwen/Qwen-7B/blob/main/modeling_qwen.py
+# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
+#
+# Copyright 2023 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Shared resampler perceiver network used in multimodal models and
+related helpers for sincos positional embeddings.
+
+Example models: Qwen (Qwen-VL), Minicpmv2.0
+"""
+import math
+from functools import partial
+from typing import Callable, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.init import normal_
+
+from vllm.model_executor.layers.linear import ReplicatedLinear
+
+DEFAULT_LN = partial(nn.LayerNorm, eps=1e-6)
+
+
+def get_abs_pos(abs_pos: torch.Tensor, tgt_size: Union[torch.Tensor,
+                                                       int]) -> torch.Tensor:
+    # abs_pos: L, C
+    # tgt_size: (H, W)
+    # return: M, C
+    src_size = int(math.sqrt(abs_pos.size(0)))
+    dtype = abs_pos.dtype
+    if isinstance(tgt_size, int):
+        tgt_size = (tgt_size, tgt_size)
+    if (src_size == tgt_size[0] and src_size == tgt_size[1]):
+        return abs_pos
+    return (F.interpolate(
+        abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
+        size=(tgt_size[0], tgt_size[1]),
+        mode="bicubic",
+        align_corners=False,
+    ).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype))
+
+
+# sin/cos positional embedding helpers are adapted from:
+# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
+def get_1d_sincos_pos_embed_from_grid(
+    embed_dim: int, pos: np.ndarray,
+    version: Tuple[int, int] = (2, 0)) -> torch.Tensor:
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,) / (H, W)
+    out: (M, D) / (H, W, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    if version == (2, 0):
+        pos = pos.reshape(-1)  # (M,)
+        out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+        emb_sin = np.sin(out)  # (M, D/2)
+        emb_cos = np.cos(out)  # (M, D/2)
+        emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    else:
+        out = np.einsum("hw,d->hwd", pos, omega)  # (H, W, D/2), outer product
+        emb_sin = np.sin(out)  # (H, W, D/2)
+        emb_cos = np.cos(out)  # (H, W, D/2)
+        emb = np.concatenate([emb_sin, emb_cos], axis=-1)  # (H, W, D)
+    return emb
+
+
+def get_2d_sincos_pos_embed_from_grid(
+    embed_dim: int, grid: np.ndarray,
+    version: Tuple[int, int] = (2, 0)) -> torch.Tensor:
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(
+        embed_dim // 2, grid[0], version)  # (H*W, D/2) or (H, W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(
+        embed_dim // 2, grid[1], version)  # (H*W, D/2) or (H, W, D/2)
+
+    if version == (2, 0):
+        emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    else:
+        emb = np.concatenate([emb_h, emb_w], axis=-1)  # (H, W, D)
+    return emb
+
+
+def get_2d_sincos_pos_embed(
+        embed_dim: int,
+        grid_size: Union[int, Tuple[int, int]],
+        cls_token: bool = False,
+        version: Tuple[int, int] = (2, 0),
+) -> torch.Tensor:
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or
+                [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    if isinstance(grid_size, int):
+        grid_h_size, grid_w_size = grid_size, grid_size
+    else:
+        grid_h_size, grid_w_size = grid_size[0], grid_size[1]
+
+    grid_h = np.arange(grid_h_size, dtype=np.float32)
+    grid_w = np.arange(grid_w_size, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+    assert isinstance(grid, np.ndarray) and \
+        grid.shape == (2, grid_h_size, grid_w_size)
+
+    if version == (2, 0):
+        grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
+        pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version)
+        if cls_token:
+            pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed],
+                                       axis=0)
+    else:
+        pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version)
+    return pos_embed
+
+
+class BaseResampler(nn.Module):
+    """
+    A 2D perceiver-resampler network with one cross attention layers by
+        (grid_size**2) learnable queries and 2d sincos pos_emb.
+    Outputs:
+        A tensor with the shape of (grid_size**2, embed_dim)
+    """
+
+    def __init__(
+        self,
+        num_queries: int,
+        embed_dim: int,
+        num_heads: int,
+        kv_dim: Optional[int] = None,
+        norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
+        do_post_projection: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.num_queries = num_queries
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+
+        self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
+        normal_(self.query, std=0.02)
+        if kv_dim is not None and kv_dim != embed_dim:
+            self.kv_proj = ReplicatedLinear(kv_dim, embed_dim, bias=False)
+        else:
+            # Maintain the same return value with ReplicatedLinear.forward
+            self.kv_proj = lambda *args, **kwargs: (  # type: ignore # noqa 
+                nn.Identity()(*args, **kwargs),
+                None,
+            )
+        self.attn = nn.MultiheadAttention(embed_dim, num_heads)
+        self.ln_q = norm_layer(embed_dim)
+        self.ln_kv = norm_layer(embed_dim)
+        self.do_post_projection = do_post_projection
+        self.ln_post = norm_layer(embed_dim) if do_post_projection else None
+        self.proj = nn.Parameter(
+            (embed_dim**-0.5) *
+            torch.randn(embed_dim, embed_dim)) if do_post_projection else None
+
+    def _init_weights(self, m: nn.Module) -> None:
+        if isinstance(m, nn.Linear):
+            normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def _repeat(self, query, N: int):
+        return query.unsqueeze(1).repeat(1, N, 1)
+
+
+class Resampler2(BaseResampler):
+    """Resampler-perceiver network to be used for a variety of model types,
+    e.g., Qwen-vl / Minicpmv 2.0. The main difference is the addition of the
+    do_post_projection arg, which indicates whether or not there should be
+    a post layer normalization and projector after the attention. This is
+    present in minicpmv2.0, but not qwen-vl.
+    """
+
+    def __init__(
+        self,
+        grid_size: int,
+        embed_dim: int,
+        num_heads: int,
+        kv_dim: Optional[int] = None,
+        norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
+        adaptive: bool = False,
+        do_post_projection: bool = True,
+    ) -> None:
+        super().__init__(grid_size**2,
+                         embed_dim,
+                         num_heads,
+                         kv_dim,
+                         norm_layer,
+                         do_post_projection=do_post_projection)
+
+        self.adaptive = adaptive
+        pos_embed_arr = get_2d_sincos_pos_embed(embed_dim,
+                                                grid_size,
+                                                version=(2, 0))
+
+        self.pos_embed = nn.Parameter(
+            torch.from_numpy(pos_embed_arr).requires_grad_(False))
+
+        self.apply(self._init_weights)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        tgt_sizes: Optional[torch.Tensor] = None,
+        attn_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if tgt_sizes is None:
+            tgt_sizes = int(math.sqrt(x.size(1)))
+        if self.adaptive:
+            pos_embed_arr = get_2d_sincos_pos_embed(self.embed_dim,
+                                                    tgt_sizes,
+                                                    version=(2, 0))
+            pos_embed = torch.from_numpy(pos_embed_arr).to(device=x.device,
+                                                           dtype=x.dtype)
+        else:
+            pos_embed = get_abs_pos(self.pos_embed,
+                                    tgt_sizes).to(device=x.device,
+                                                  dtype=x.dtype)
+
+        x, _ = self.kv_proj(x)
+        x = self.ln_kv(x).permute(1, 0, 2)
+
+        N = x.shape[1]
+        q = self.ln_q(self.query)
+        out = self.attn(
+            self._repeat(q, N) + self.pos_embed.unsqueeze(1),
+            x + pos_embed.unsqueeze(1),
+            x,
+            attn_mask=attn_mask,
+        )[0]
+        x = out.permute(1, 0, 2)
+        if self.do_post_projection:
+            x = self.ln_post(x)
+            x = x @ self.proj
+        return x

vllm/model_executor/layers/spec_decode_base_sampler.py

@@ -0,0 +1,239 @@
+from abc import abstractmethod
+from typing import Dict, Optional, Union
+
+import torch
+import torch.jit
+import torch.nn as nn
+
+
+class SpecDecodeBaseSampler(nn.Module):
+    """Base class for samplers used for Speculative Decoding verification
+        step.
+    """
+
+    def __init__(self, strict_mode: bool = False):
+        """Base class constructor.
+        Args:
+            strict_mode: Whether or not to perform shape/device/dtype checks
+                during sampling. This catches correctness issues but adds
+                nontrivial latency.
+        """
+        super().__init__()
+        self._strict_mode = strict_mode
+
+        # NOTE: A "bonus token" is accepted iff all proposal tokens are
+        # accepted. There is always only one possible bonus token. We store this
+        # value in a variable for readability.
+        self._num_bonus_tokens = 1
+
+        self.num_accepted_tokens: Optional[torch.Tensor] = None
+        self.num_emitted_tokens: Optional[torch.Tensor] = None
+        self.num_draft_tokens: int = 0
+
+    def init_gpu_tensors(self, device: Union[int, str]) -> None:
+        assert self.num_accepted_tokens is None
+        if isinstance(device, int):
+            device = f"cuda:{device}"
+        elif not isinstance(device, str):
+            raise ValueError(f"Device must be int or str, get {type(device)}")
+        self.num_accepted_tokens = torch.tensor(0,
+                                                dtype=torch.long,
+                                                device=device)
+        self.num_emitted_tokens = torch.tensor(0,
+                                               dtype=torch.long,
+                                               device=device)
+
+    @property
+    def probs_dtype(self):
+        return torch.float32
+
+    @property
+    def token_id_dtype(self):
+        return torch.int64
+
+    def _create_output(
+            self,
+            accepted: torch.Tensor,  # [batch_size, k]
+            substitute_token_ids: torch.Tensor,  # [batch_size, k]
+            draft_token_ids: torch.Tensor,  # [batch_size, k]
+            bonus_token_ids: torch.Tensor,  # [batch_size]
+    ) -> torch.Tensor:
+        """Format output. Returns a matrix of token ids. When
+        a token is rejected via sampling, all subsequent token ids are 
+        set to -1 for the sequence.
+
+        Args:
+            accepted: A boolean tensor indicating if the corresponding
+            draft token in draft_token_ids should be accepted or not.
+            substitute_token_ids: A tensor of token_ids that can be used
+            as substitutes for the draft token ids if the proposed token
+            is rejected.
+            draft_token_ids: A tensor of token ids speculated by the 
+            draft model.
+            bonus_token_ids: Token ids to use as the bonus token if
+            all the draft tokens are accepted.
+        Returns:
+            A tensor containing the accepted token ids. The shape of the 
+            tensor is [batch_size, k + num_bonus_tokens]
+        """
+        batch_size, k = substitute_token_ids.shape
+        bonus_token_ids = bonus_token_ids.squeeze()
+        # Determine the index of the first False value for each row.
+        limits = (accepted == 0).max(1).indices
+        limits[~(accepted == 0).any(1)] = k
+
+        # Create masks using the indices.
+        indices = torch.arange(k, device=accepted.device).unsqueeze(0)
+        accepted_mask = indices < limits.unsqueeze(1)
+        after_false_mask = indices == limits.unsqueeze(1)
+
+        # Create an extended output tensor
+        output_with_bonus_tokens = -torch.ones(
+            (batch_size, k + self._num_bonus_tokens),
+            dtype=self.token_id_dtype,
+            device=accepted.device)
+        output = output_with_bonus_tokens[:, :k]
+
+        # Fill in the first k columns of the output tensor using masks and data
+        # tensors.
+        output[:, :k] = torch.where(accepted_mask, draft_token_ids,
+                                    -torch.ones_like(draft_token_ids))
+
+        # Fill the last column.
+        # We check output directly as accepted may have True values inconsistent
+        # with causal acceptance.
+        output_with_bonus_tokens[:, -1] = torch.where(output[:, -1] != -1,
+                                                      bonus_token_ids, -1)
+
+        # Fill the recovered token ids.
+        output.mul_(~after_false_mask).add_(
+            substitute_token_ids.mul(after_false_mask))
+
+        self.num_accepted_tokens += accepted.sum()
+        self.num_emitted_tokens += (output_with_bonus_tokens != -1).sum()
+        self.num_draft_tokens += batch_size * k
+
+        return output_with_bonus_tokens
+
+    def _raise_if_incorrect_input(
+        self,
+        target_with_bonus_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: Optional[torch.Tensor] = None,
+    ) -> None:
+        self._raise_if_incorrect_shape(target_with_bonus_probs,
+                                       draft_token_ids, bonus_token_ids,
+                                       draft_probs)
+        self._raise_if_incorrect_dtype(target_with_bonus_probs,
+                                       draft_token_ids, bonus_token_ids,
+                                       draft_probs)
+        self._raise_if_inconsistent_device(target_with_bonus_probs,
+                                           draft_token_ids, bonus_token_ids,
+                                           draft_probs)
+        self._raise_if_out_of_bounds_vocab(target_with_bonus_probs.shape[-1],
+                                           draft_token_ids, bonus_token_ids)
+
+    def _raise_if_incorrect_shape(
+        self,
+        target_with_bonus_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: Optional[torch.Tensor] = None,
+    ) -> None:
+        (target_batch_size, num_target_probs,
+         target_vocab_size) = target_with_bonus_probs.shape
+
+        # Does not count the extra token
+        num_target_probs -= 1
+
+        # validate the shape of draft token ids.
+        draft_token_ids_batch_size, num_draft_token_ids = draft_token_ids.shape
+        assert draft_token_ids_batch_size == target_batch_size
+        assert num_draft_token_ids == num_target_probs
+
+        # validate the shape of bonus token ids
+        bonus_batch_size, num_bonus_tokens = bonus_token_ids.shape
+        assert bonus_batch_size == target_batch_size
+        assert num_bonus_tokens == self._num_bonus_tokens
+
+        # validate the shape of draft probs if it is set
+        if draft_probs is not None:
+            (draft_batch_size, num_draft_probs,
+             draft_vocab_size) = draft_probs.shape
+            assert draft_batch_size == target_batch_size
+            assert num_draft_probs == num_target_probs
+            assert (draft_vocab_size == target_vocab_size
+                    ), f"{draft_vocab_size=} {target_vocab_size=}"
+
+    def _raise_if_incorrect_dtype(
+        self,
+        target_with_bonus_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: Optional[torch.Tensor] = None,
+    ) -> None:
+        assert target_with_bonus_probs.dtype == self.probs_dtype
+        assert draft_token_ids.dtype == self.token_id_dtype
+        assert bonus_token_ids.dtype == self.token_id_dtype
+        if draft_probs is not None:
+            assert draft_probs.dtype == self.probs_dtype
+
+    def _raise_if_inconsistent_device(
+        self,
+        target_with_bonus_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: Optional[torch.Tensor] = None,
+    ) -> None:
+        devices = [
+            t.device for t in [
+                target_with_bonus_probs, bonus_token_ids, draft_probs,
+                draft_token_ids
+            ] if t is not None
+        ]
+        assert all([devices[0] == device for device in devices])
+
+    def _raise_if_out_of_bounds_vocab(
+        self,
+        vocab_size: int,
+        draft_token_ids: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+    ) -> None:
+        assert torch.all(bonus_token_ids < vocab_size)
+        assert torch.all(bonus_token_ids >= 0)
+        assert torch.all(draft_token_ids < vocab_size)
+        assert torch.all(draft_token_ids >= 0)
+
+
+class SpecDecodeDeterministicBaseSampler(SpecDecodeBaseSampler):
+    """Base class for samplers used for Speculative Decoding verification
+       step which are deterministic.
+    """
+
+    @abstractmethod
+    def forward(
+        self,
+        target_with_bonus_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+
+class SpecDecodeStochasticBaseSampler(SpecDecodeBaseSampler):
+    """Base class for samplers used for Speculative Decoding verification
+       step which are stochastic
+    """
+
+    @abstractmethod
+    def forward(
+        self,
+        target_with_bonus_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+        seeded_seqs: Optional[Dict[int, torch.Generator]] = None,
+    ) -> torch.Tensor:
+        raise NotImplementedError

vllm/model_executor/layers/typical_acceptance_sampler.py

@@ -0,0 +1,170 @@
+import torch
+import torch.jit
+
+from vllm.model_executor.layers.spec_decode_base_sampler import (
+    SpecDecodeDeterministicBaseSampler)
+
+
+class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
+    """Apply typical acceptance sampling as described in section 3.3.1 in 
+        "MEDUSA: Simple LLM Inference Acceleration Framework with 
+        Multiple Decoding Heads"
+        https://arxiv.org/pdf/2401.10774
+    """
+
+    def __init__(
+        self,
+        posterior_threshold: float,
+        posterior_alpha: float,
+        strict_mode: bool = False,
+    ):
+        """Create a Typical Acceptance Sampler.
+
+        Args:
+            strict_mode: Whether or not to perform shape/device/dtype checks
+            during sampling. This catches correctness issues but adds
+            nontrivial latency.
+            posterior_threshold : A threshold value that sets a lower bound 
+            on the posterior probability of a token in target model for it
+            to be accepted.
+            posterior_alpha : A scaling factor for the entropy-based
+            threshold in typical acceptance sampling.
+        """
+        self._posterior_threshold = posterior_threshold
+        self._posterior_alpha = posterior_alpha
+        super().__init__(strict_mode=strict_mode)
+
+    def forward(
+        self,
+        target_with_bonus_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        """Sample token ids using typical acceptance sampling. This accepts 
+        or rejects tokens proposed by the draft model using the probability
+        of each token according to the draft and target models.
+
+        In the worst case where all draft tokens are rejected, it is guaranteed
+        one token will be emitted.
+
+        In the case where all draft tokens are accepted, the bonus token will be
+        accepted.
+
+        Args:
+            target_probs: The probability distribution over token ids given
+                context according to the target model.
+            shape = [batch_size, num_speculative_tokens, vocab_size]
+
+            bonus_token_ids: The "bonus" token ids that are accepted iff all
+                speculative tokens in a sequence are accepted.
+            shape = [batch_size, num_bonus_tokens]
+
+            draft_probs: This parameter is unused by the acceptance sampler.
+
+            draft_token_ids: The token ids that were sampled from the draft
+                probabilities.
+            shape = [batch_size, num_speculative_tokens]
+
+        Returns:
+            output_token_ids: The token ids sampled via rejection sampling,
+                or -1 if unable to sample a token because the previous token
+                was rejected.
+            shape = [batch_size, num_speculative_tokens + num_bonus_tokens]
+        """
+        # Only perform shape/dtype/device checking in strict mode, as it adds
+        # overhead.
+        if self._strict_mode:
+            self._raise_if_incorrect_input(target_with_bonus_probs,
+                                           draft_token_ids, bonus_token_ids)
+        target_probs = target_with_bonus_probs[:, :-1]
+        accepted = self._evaluate_accepted_tokens(target_probs,
+                                                  draft_token_ids)
+        recovered_token_ids = self._get_recovered_token_ids(target_probs)
+        output_token_ids = self._create_output(accepted, recovered_token_ids,
+                                               draft_token_ids,
+                                               bonus_token_ids)
+        return output_token_ids
+
+    def _evaluate_accepted_tokens(self, target_probs, draft_token_ids):
+        r"""
+        Evaluates and returns a mask of accepted tokens based on the
+        posterior probabilities.
+
+        Parameters:
+        ----------
+        target_probs : torch.Tensor
+            A tensor of shape (batch_size, k, vocab_size) representing 
+            the probabilities of each token in the vocabulary for each
+            position in the proposed sequence. This is the distribution
+            generated by the target model.
+        draft_token_ids : torch.Tensor
+            A tensor of shape (batch_size, k) representing the proposed
+            token ids.
+
+        A draft token_id x_{n+k} is accepted if it satisfies the
+        following condition
+    
+        .. math::
+            p_{\text{original}}(x_{n+k} | x_1, x_2, \dots, x_{n+k-1}) > 
+            \min \left( \epsilon, \delta * \exp \left(
+                -H(p_{\text{original}}(
+                    \cdot | x_1, x_2, \ldots, x_{n+k-1})) \right) \right)
+        
+        where :math:`p_{\text{original}}` corresponds to target_probs 
+        and :math:`\epsilon` and :math:`\delta` correspond to hyperparameters
+        specified using self._posterior_threshold and self._posterior_alpha
+
+        This method computes the posterior probabilities for the given
+        draft token ids based on the provided target probabilities. It
+        calculates the entropy of the posterior distribution and determines
+        a dynamic threshold for each token position using the provided
+        posterior_threshold and posterior_alpha values. The method then
+        returns a boolean mask indicating which tokens can be accepted.
+
+        Returns:
+        -------
+        torch.Tensor
+            A boolean tensor of shape (batch_size, k) where each element
+            indicates whether the corresponding draft token has been accepted
+            or rejected. True indicates acceptance and false indicates
+            rejection.
+            
+        """
+        device = target_probs.device
+        candidates_prob = torch.gather(
+            target_probs, dim=-1,
+            index=draft_token_ids.unsqueeze(-1)).squeeze(-1)
+        # A small constant added to prevent computing the logarithm of zero,
+        # which can lead to undefined values.
+        epsilon = 1e-5
+        posterior_entropy = -torch.sum(
+            target_probs * torch.log(target_probs + epsilon), dim=-1)
+        threshold = torch.minimum(
+            torch.ones_like(posterior_entropy, device=device) *
+            self._posterior_threshold,
+            torch.exp(-posterior_entropy) * self._posterior_alpha,
+        )
+        accepted_mask = candidates_prob > threshold
+        return accepted_mask
+
+    def _get_recovered_token_ids(self, target_probs):
+        """
+        The recovered token ids will fill the first unmatched token
+        by the target token.
+
+        Parameters
+        ----------
+        target_probs : torch.Tensor
+            A tensor of shape (batch_size, k, vocab_size) containing 
+            the target probability distribution
+
+        Returns
+        -------
+        torch.Tensor
+            A tensor of shape (batch_size, k) with the recovered token
+            ids which are selected from target probs.
+        """
+        max_indices = torch.argmax(target_probs, dim=-1)
+
+        return max_indices

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -0,0 +1,471 @@
+from dataclasses import dataclass
+from typing import List, Optional, Sequence, Tuple
+
+import torch
+import torch.nn.functional as F
+# import ixformer.inference.functions as IXF
+import ixformer.functions as IXF
+from torch.nn.parameter import Parameter, UninitializedParameter
+
+from vllm.distributed import (divide, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase, method_has_implemented_embedding)
+from vllm.model_executor.parameter import BasevLLMParameter
+from vllm.model_executor.utils import set_weight_attrs
+
+DEFAULT_VOCAB_PADDING_SIZE = 64
+
+
+class UnquantizedEmbeddingMethod(QuantizeMethodBase):
+    """Unquantized method for embeddings."""
+
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        """Create weights for embedding layer."""
+        weight = Parameter(torch.empty(sum(output_partition_sizes),
+                                       input_size_per_partition,
+                                       dtype=params_dtype),
+                           requires_grad=False)
+        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
+        layer.register_parameter("weight", weight)
+        set_weight_attrs(weight, extra_weight_attrs)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return IXF.linear(x, layer.weight, bias)
+
+    def embedding(self, layer: torch.nn.Module,
+                  input_: torch.Tensor) -> torch.Tensor:
+        return F.embedding(input_, layer.weight)
+
+
+def pad_vocab_size(vocab_size: int,
+                   pad_to: int = DEFAULT_VOCAB_PADDING_SIZE) -> int:
+    """Pad the vocab size to the given value."""
+    return ((vocab_size + pad_to - 1) // pad_to) * pad_to
+
+
+def vocab_range_from_per_partition_vocab_size(
+        per_partition_vocab_size: int,
+        rank: int,
+        offset: int = 0) -> Sequence[int]:
+    index_f = rank * per_partition_vocab_size
+    index_l = index_f + per_partition_vocab_size
+    return index_f + offset, index_l + offset
+
+
+def vocab_range_from_global_vocab_size(global_vocab_size: int,
+                                       rank: int,
+                                       world_size: int,
+                                       offset: int = 0) -> Sequence[int]:
+    per_partition_vocab_size = divide(global_vocab_size, world_size)
+    return vocab_range_from_per_partition_vocab_size(per_partition_vocab_size,
+                                                     rank,
+                                                     offset=offset)
+
+
+@dataclass
+class VocabParallelEmbeddingShardIndices:
+    """Indices for a shard of a vocab parallel embedding."""
+    padded_org_vocab_start_index: int
+    padded_org_vocab_end_index: int
+    padded_added_vocab_start_index: int
+    padded_added_vocab_end_index: int
+
+    org_vocab_start_index: int
+    org_vocab_end_index: int
+    added_vocab_start_index: int
+    added_vocab_end_index: int
+
+    @property
+    def num_org_elements(self) -> int:
+        return self.org_vocab_end_index - self.org_vocab_start_index
+
+    @property
+    def num_added_elements(self) -> int:
+        return self.added_vocab_end_index - self.added_vocab_start_index
+
+    @property
+    def num_org_elements_padded(self) -> int:
+        return (self.padded_org_vocab_end_index -
+                self.padded_org_vocab_start_index)
+
+    @property
+    def num_added_elements_padded(self) -> int:
+        return (self.padded_added_vocab_end_index -
+                self.padded_added_vocab_start_index)
+
+    @property
+    def num_org_vocab_padding(self) -> int:
+        return self.num_org_elements_padded - self.num_org_elements
+
+    @property
+    def num_added_vocab_padding(self) -> int:
+        return self.num_added_elements_padded - self.num_added_elements
+
+    @property
+    def num_elements_padded(self) -> int:
+        return self.num_org_elements_padded + self.num_added_elements_padded
+
+    def __post_init__(self):
+        # sanity checks
+        assert (self.padded_org_vocab_start_index <=
+                self.padded_org_vocab_end_index)
+        assert (self.padded_added_vocab_start_index <=
+                self.padded_added_vocab_end_index)
+
+        assert self.org_vocab_start_index <= self.org_vocab_end_index
+        assert self.added_vocab_start_index <= self.added_vocab_end_index
+
+        assert self.org_vocab_start_index <= self.padded_org_vocab_start_index
+        assert (self.added_vocab_start_index <=
+                self.padded_added_vocab_start_index)
+        assert self.org_vocab_end_index <= self.padded_org_vocab_end_index
+        assert self.added_vocab_end_index <= self.padded_added_vocab_end_index
+
+        assert self.num_org_elements <= self.num_org_elements_padded
+        assert self.num_added_elements <= self.num_added_elements_padded
+
+
+# @torch.jit.script
+def get_masked_input_and_mask(
+        input_: torch.Tensor, org_vocab_start_index: int,
+        org_vocab_end_index: int, num_org_vocab_padding: int,
+        added_vocab_start_index: int,
+        added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]:
+    # torch.jit.script will fuse all of the pointwise ops below
+    # into a single kernel, making it very fast
+    org_vocab_mask = (input_ >= org_vocab_start_index) & (input_ <
+                                                          org_vocab_end_index)
+    added_vocab_mask = (input_ >= added_vocab_start_index) & (
+        input_ < added_vocab_end_index)
+    added_offset = added_vocab_start_index - (
+        org_vocab_end_index - org_vocab_start_index) - num_org_vocab_padding
+    valid_offset = (org_vocab_start_index *
+                    org_vocab_mask) + (added_offset * added_vocab_mask)
+    vocab_mask = org_vocab_mask | added_vocab_mask
+    input_ = vocab_mask * (input_ - valid_offset)
+    return input_, ~vocab_mask
+
+
+class VocabParallelEmbedding(torch.nn.Module):
+    """Embedding parallelized in the vocabulary dimension.
+
+    Adapted from torch.nn.Embedding, note that we pad the vocabulary size to
+    make sure it is divisible by the number of model parallel GPUs.
+
+    In order to support various loading methods, we ensure that LoRA-added
+    embeddings are always at the end of TP-sharded tensors. In other words,
+    we shard base embeddings and LoRA embeddings separately (both padded),
+    and place them in the same tensor.
+    In this example, we will have the original vocab size = 1010,
+    added vocab size = 16 and padding to 64. Therefore, the total
+    vocab size with padding will be 1088 (because we first pad 1010 to
+    1024, add 16, and then pad to 1088).
+    Therefore, the tensor format looks like the following:
+    TP1, rank 0 (no sharding):
+                            |< --------BASE-------- >|< -BASE PADDING-- >|< -----LORA------ >|< -LORA PADDING-- >|
+    corresponding token_id: |  0  |  1  | ... | 1009 |  -1  | ... |  -1  | 1010 | ... | 1015 |  -1  | ... |  -1  |
+                     index: |  0  |  1  | ... | 1009 | 1010 | ... | 1023 | 1024 | ... | 1039 | 1040 | ... | 1087 |
+
+    TP2, rank 0:
+                            |< --------------------BASE--------------------- >|< -----LORA------ >|< -LORA PADDING- >|
+    corresponding token_id: |  0  |  1  |  2  | ... | 497  | 498 | ...  | 511 | 1000 | ... | 1015 |  -1  | ... |  -1 |
+                     index: |  0  |  1  |  2  | ... | 497  | 498 | ...  | 511 | 512  | ... | 527  |  520 | ... | 543 |
+    TP2, rank 1:
+                            |< -----------BASE----------- >|< -BASE PADDING- >|< -----------LORA PADDING----------- >|
+    corresponding token_id: | 512 | 513 | 514 | ... | 1009 | -1  | ...  | -1  |  -1  | ... |  -1  | -1  | ... |   -1 |
+                     index: |  0  |  1  |  2  | ... | 497  | 498 | ...  | 511 | 512  | ... | 519  | 520 | ... |  543 |
+
+    Args:
+        num_embeddings: vocabulary size.
+        embedding_dim: size of hidden state.
+        params_dtype: type of the parameters.
+        org_num_embeddings: original vocabulary size (without LoRA).
+        padding_size: padding size for the vocabulary.
+        quant_config: quant config for the layer
+        prefix: full name of the layer in the state dict
+    """  # noqa: E501
+
+    def __init__(self,
+                 num_embeddings: int,
+                 embedding_dim: int,
+                 params_dtype: Optional[torch.dtype] = None,
+                 org_num_embeddings: Optional[int] = None,
+                 padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        # Keep the input dimensions.
+        tp_rank = get_tensor_model_parallel_rank()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_embeddings = num_embeddings
+        self.padding_size = padding_size
+        self.org_vocab_size = org_num_embeddings or num_embeddings
+        num_added_embeddings = num_embeddings - self.org_vocab_size
+        self.org_vocab_size_padded = pad_vocab_size(self.org_vocab_size,
+                                                    self.padding_size)
+        self.num_embeddings_padded = pad_vocab_size(
+            self.org_vocab_size_padded + num_added_embeddings,
+            self.padding_size)
+        assert self.org_vocab_size_padded <= self.num_embeddings_padded
+
+        self.shard_indices = self._get_indices(self.num_embeddings_padded,
+                                               self.org_vocab_size_padded,
+                                               self.num_embeddings,
+                                               self.org_vocab_size, tp_rank,
+                                               self.tp_size)
+        self.embedding_dim = embedding_dim
+
+        linear_method = None
+        if quant_config is not None:
+            linear_method = quant_config.get_quant_method(self, prefix=prefix)
+        if linear_method is None:
+            linear_method = UnquantizedEmbeddingMethod()
+
+        # If we are making an embedding layer, then our quantization linear
+        # method must implement the embedding operation. If we are another
+        # layer type like ParallelLMHead, this is not important.
+        is_embedding_layer = type(self.__class__) is VocabParallelEmbedding
+        linear_method_implements_embedding = method_has_implemented_embedding(
+            type(linear_method))
+        if is_embedding_layer and not linear_method_implements_embedding:
+            raise NotImplementedError(
+                f"The class {type(linear_method).__name__} must implement "
+                "the 'embedding' method, see UnquantizedEmbeddingMethod.")
+
+        self.linear_method: QuantizeMethodBase = linear_method
+
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        # Divide the weight matrix along the vocaburaly dimension.
+        self.num_added_embeddings = self.num_embeddings - self.org_vocab_size
+        self.num_embeddings_per_partition = divide(self.num_embeddings_padded,
+                                                   self.tp_size)
+        assert (self.shard_indices.num_elements_padded ==
+                self.num_embeddings_per_partition)
+        self.num_org_embeddings_per_partition = (
+            self.shard_indices.org_vocab_end_index -
+            self.shard_indices.org_vocab_start_index)
+        self.num_added_embeddings_per_partition = (
+            self.shard_indices.added_vocab_end_index -
+            self.shard_indices.added_vocab_start_index)
+
+        self.linear_method.create_weights(self,
+                                          self.embedding_dim,
+                                          [self.num_embeddings_per_partition],
+                                          self.embedding_dim,
+                                          self.num_embeddings_padded,
+                                          params_dtype=params_dtype,
+                                          weight_loader=self.weight_loader)
+
+    @classmethod
+    def _get_indices(cls, vocab_size_padded: int, org_vocab_size_padded: int,
+                     vocab_size: int, org_vocab_size: int, tp_rank: int,
+                     tp_size: int) -> VocabParallelEmbeddingShardIndices:
+        """Get start and end indices for vocab parallel embedding, following the
+        layout outlined in the class docstring, based on the given tp_rank and
+        tp_size."""
+        num_added_embeddings_padded = vocab_size_padded - org_vocab_size_padded
+        padded_org_vocab_start_index, padded_org_vocab_end_index = (
+            vocab_range_from_global_vocab_size(org_vocab_size_padded, tp_rank,
+                                               tp_size))
+        padded_added_vocab_start_index, padded_added_vocab_end_index = (
+            vocab_range_from_global_vocab_size(num_added_embeddings_padded,
+                                               tp_rank,
+                                               tp_size,
+                                               offset=org_vocab_size))
+        # remove padding
+        org_vocab_start_index = min(padded_org_vocab_start_index,
+                                    org_vocab_size)
+        org_vocab_end_index = min(padded_org_vocab_end_index, org_vocab_size)
+        added_vocab_start_index = min(padded_added_vocab_start_index,
+                                      vocab_size)
+        added_vocab_end_index = min(padded_added_vocab_end_index, vocab_size)
+        return VocabParallelEmbeddingShardIndices(
+            padded_org_vocab_start_index, padded_org_vocab_end_index,
+            padded_added_vocab_start_index, padded_added_vocab_end_index,
+            org_vocab_start_index, org_vocab_end_index,
+            added_vocab_start_index, added_vocab_end_index)
+
+    def get_sharded_to_full_mapping(self) -> Optional[List[int]]:
+        """Get a mapping that can be used to reindex the gathered
+        logits for sampling.
+        
+        During sampling, we gather logits from all ranks. The relationship
+        of index->token_id will follow the same format as outlined in the class
+        docstring. However, after the gather, we want to reindex the final
+        logits tensor to map index->token_id one-to-one (the index is always
+        equal the token_id it corresponds to). The indices returned by this
+        method allow us to do that.
+        """
+        if self.tp_size < 2:
+            return None
+
+        base_embeddings: List[int] = []
+        added_embeddings: List[int] = []
+        padding: List[int] = []
+        for tp_rank in range(self.tp_size):
+            shard_indices = self._get_indices(self.num_embeddings_padded,
+                                              self.org_vocab_size_padded,
+                                              self.num_embeddings,
+                                              self.org_vocab_size, tp_rank,
+                                              self.tp_size)
+            range_start = self.num_embeddings_per_partition * tp_rank
+            range_end = self.num_embeddings_per_partition * (tp_rank + 1)
+            base_embeddings.extend(
+                range(range_start,
+                      range_start + shard_indices.num_org_elements))
+            padding.extend(
+                range(range_start + shard_indices.num_org_elements,
+                      range_start + shard_indices.num_org_elements_padded))
+            added_embeddings.extend(
+                range(
+                    range_start + shard_indices.num_org_elements_padded,
+                    range_start + shard_indices.num_org_elements_padded +
+                    shard_indices.num_added_elements))
+            padding.extend(
+                range(
+                    range_start + shard_indices.num_org_elements_padded +
+                    shard_indices.num_added_elements,
+                    range_start + shard_indices.num_org_elements_padded +
+                    shard_indices.num_added_elements_padded))
+            assert (range_start + shard_indices.num_org_elements_padded +
+                    shard_indices.num_added_elements_padded == range_end)
+        ret = base_embeddings + added_embeddings + padding
+        assert len(ret) == self.num_embeddings_padded
+        return ret
+
+    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
+        output_dim = getattr(param, "output_dim", None)
+        packed_dim = getattr(param, "packed_dim", None)
+
+        # If the parameter is a gguf weight, then load it directly.
+        if getattr(param, "is_gguf_weight_type", None):
+            param.data.copy_(loaded_weight)
+            param.weight_type = loaded_weight.item()
+            return
+        elif isinstance(param, UninitializedParameter):
+            shape = list(loaded_weight.shape)
+            if output_dim is not None:
+                shape[output_dim] = shape[output_dim] // self.tp_size
+            param.materialize(tuple(shape), dtype=loaded_weight.dtype)
+
+        # If parameter does not have output dim, then it should
+        # be copied onto all gpus (e.g. g_idx for act_order gptq).
+        if output_dim is None:
+            assert param.data.shape == loaded_weight.shape
+            param.data.copy_(loaded_weight)
+            return
+
+        # Shard indexes for loading the weight
+        start_idx = self.shard_indices.org_vocab_start_index
+        shard_size = self.shard_indices.org_vocab_end_index - start_idx
+
+        # If param packed on the same dim we are sharding on, then
+        # need to adjust offsets of loaded weight by pack_factor.
+        if packed_dim is not None and packed_dim == output_dim:
+            packed_factor = param.packed_factor if isinstance(
+                param, BasevLLMParameter) else param.pack_factor
+            assert loaded_weight.shape[output_dim] == (self.org_vocab_size //
+                                                       param.packed_factor)
+            start_idx = start_idx // packed_factor
+            shard_size = shard_size // packed_factor
+        else:
+            assert loaded_weight.shape[output_dim] == self.org_vocab_size
+
+        # Copy the data.
+        loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
+        param[:loaded_weight.shape[0]].data.copy_(loaded_weight)
+        param[loaded_weight.shape[0]:].data.fill_(0)
+
+    def forward(self, input_):
+        if self.tp_size > 1:
+            # Build the mask.
+            masked_input, input_mask = get_masked_input_and_mask(
+                input_, self.shard_indices.org_vocab_start_index,
+                self.shard_indices.org_vocab_end_index,
+                self.shard_indices.num_org_vocab_padding,
+                self.shard_indices.added_vocab_start_index,
+                self.shard_indices.added_vocab_end_index)
+        else:
+            masked_input = input_
+        # Get the embeddings.
+        output_parallel = self.linear_method.embedding(self,
+                                                       masked_input.long())
+        # Mask the output embedding.
+        if self.tp_size > 1:
+            output_parallel.masked_fill_(input_mask.unsqueeze(-1), 0)
+        # Reduce across all the model parallel GPUs.
+        output = tensor_model_parallel_all_reduce(output_parallel)
+        return output
+
+    def extra_repr(self) -> str:
+        s = f"num_embeddings={self.num_embeddings_per_partition}"
+        s += f", embedding_dim={self.embedding_dim}"
+        s += f", org_vocab_size={self.org_vocab_size}"
+        s += f', num_embeddings_padded={self.num_embeddings_padded}'
+        s += f', tp_size={self.tp_size}'
+        return s
+
+
+class ParallelLMHead(VocabParallelEmbedding):
+    """Parallelized LM head.
+
+    Output logits weight matrices used in the Sampler. The weight and bias
+    tensors are padded to make sure they are divisible by the number of
+    model parallel GPUs.
+
+    Args:
+        num_embeddings: vocabulary size.
+        embedding_dim: size of hidden state.
+        bias: whether to use bias.
+        params_dtype: type of the parameters.
+        org_num_embeddings: original vocabulary size (without LoRA).
+        padding_size: padding size for the vocabulary.
+    """
+
+    def __init__(self,
+                 num_embeddings: int,
+                 embedding_dim: int,
+                 bias: bool = False,
+                 params_dtype: Optional[torch.dtype] = None,
+                 org_num_embeddings: Optional[int] = None,
+                 padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__(num_embeddings, embedding_dim, params_dtype,
+                         org_num_embeddings, padding_size, quant_config,
+                         prefix)
+        self.quant_config = quant_config
+        if bias:
+            self.bias = Parameter(
+                torch.empty(self.num_embeddings_per_partition,
+                            dtype=params_dtype))
+            set_weight_attrs(self.bias, {
+                "output_dim": 0,
+                "weight_loader": self.weight_loader,
+            })
+        else:
+            self.register_parameter("bias", None)
+
+    def tie_weights(self, embed_tokens: VocabParallelEmbedding):
+        """Tie the weights with word embeddings."""
+        # GGUF quantized embed_tokens.
+        if self.quant_config and self.quant_config.get_name() == "gguf":
+            return embed_tokens
+        else:
+            self.weight = embed_tokens.weight
+            return self
+
+    def forward(self, input_):
+        del input_
+        raise RuntimeError("LMHead's weights should be used in the sampler.")