init src 0.9.2

vllm/model_executor/layers/quantization/kernels/scaled_mm/ScaledMMLinearKernel.py

@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+
+
+@dataclass
+class ScaledMMLinearLayerConfig:
+    is_channelwise: bool
+    is_static_input_scheme: bool
+    input_symmetric: bool
+
+
+class ScaledMMLinearKernel(ABC):
+
+    @classmethod
+    @abstractmethod
+    def get_min_capability(cls) -> int:
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def can_implement(
+            cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, Optional[str]]:
+        raise NotImplementedError
+
+    def __init__(self, c: ScaledMMLinearLayerConfig, w_q_param_name: str,
+                 w_s_param_name: str, i_s_param_name: str,
+                 i_zp_param_name: str, azp_adj_param_name: str) -> 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.i_s_name = i_s_param_name
+        self.i_zp_name = i_zp_param_name
+        self.azp_adj_name = azp_adj_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 _get_weight_params(
+            self, layer: torch.nn.Module) -> tuple[
+                torch.Tensor,  # weight
+                torch.Tensor,  # weight_scale
+                Optional[torch.Tensor],  # input_scale, 
+                Optional[torch.Tensor],  # input_zp
+                Optional[torch.Tensor],  # azp_adj
+            ]:
+        return (
+            getattr(layer, self.w_q_name),
+            getattr(layer, self.w_s_name),
+            getattr(layer, self.i_s_name),
+            getattr(layer, self.i_zp_name),
+            getattr(layer, self.azp_adj_name),
+        )

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

@@ -0,0 +1,87 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+from typing import Optional
+
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.aiter import (
+    AiterScaledMMLinearKernel)
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.cutlass import (
+    CutlassScaledMMLinearKernel)
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import (  # noqa: E501
+    ScaledMMLinearKernel, ScaledMMLinearLayerConfig)
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.triton import (
+    TritonScaledMMLinearKernel)
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.xla import (
+    XLAScaledMMLinearKernel)
+from vllm.platforms import PlatformEnum, current_platform
+
+# in priority/performance order (when available)
+_POSSIBLE_KERNELS: dict[PlatformEnum, list[type[ScaledMMLinearKernel]]] = {
+    PlatformEnum.CPU: [CutlassScaledMMLinearKernel],
+    PlatformEnum.CUDA: [CutlassScaledMMLinearKernel],
+    PlatformEnum.ROCM: [AiterScaledMMLinearKernel, TritonScaledMMLinearKernel],
+    PlatformEnum.TPU: [XLAScaledMMLinearKernel],
+}
+
+
+def choose_scaled_mm_linear_kernel(
+        config: ScaledMMLinearLayerConfig,
+        compute_capability: Optional[int] = None
+) -> type[ScaledMMLinearKernel]:
+    """
+    Choose an ScaledMMLinearKernel that can implement the given config for the 
+    given compute capability. Attempts to choose the best kernel in terms of 
+    performance.
+
+    Args:
+        config (ScaledMMLinearLayerConfig): 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[ScaledMMLinearKernel]: Chosen kernel.
+    """
+
+    if compute_capability is None:
+        _cc = current_platform.get_device_capability()
+        if _cc is not None:
+            compute_capability = _cc[0] * 10 + _cc[1]
+
+    failure_reasons = []
+    for kernel in _POSSIBLE_KERNELS[current_platform._enum]:
+        if kernel.__name__ in os.environ.get("VLLM_DISABLED_KERNELS", "")\
+            .split(","):
+            failure_reasons.append(
+                f' {kernel.__name__} disabled by environment variable')
+            continue
+
+        # If the current platform uses compute_capability,
+        # make sure the kernel supports the compute cability.
+        if compute_capability is not None:
+            kernel_min_capability = kernel.get_min_capability()
+            if (kernel_min_capability is not None
+                    and kernel_min_capability > compute_capability):
+                failure_reasons.append(
+                    f"{kernel.__name__} requires capability "
+                    f"{kernel_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 "\
+        "ScaledMM linear layer. Reasons: \n"
+        + '\n'.join(failure_reasons))

vllm/model_executor/layers/quantization/kernels/scaled_mm/aiter.py

@@ -0,0 +1,120 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+
+import vllm.envs as envs
+from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
+
+from .cutlass import CutlassScaledMMLinearKernel
+from .ScaledMMLinearKernel import ScaledMMLinearLayerConfig
+
+
+class AiterScaledMMLinearKernel(CutlassScaledMMLinearKernel):
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 90
+
+    @classmethod
+    def can_implement(
+            cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, Optional[str]]:
+        if not current_platform.is_rocm():
+            return (
+                False,
+                "AiterScaledMMLinearKernel requires `aiter` which is not " +
+                "currently supported on non-ROCm platform.")
+
+        try:
+            import aiter  # noqa: F401 # deliberately attempt to import aiter
+        except Exception:
+            return (
+                False,
+                "AiterScaledMMLinearKernel requires `aiter` which is not " +
+                "installed on ROCm.")
+        # Check if rocm_aiter_gemm_w8a8_scaled_mm is enabled
+        if not (
+            envs.VLLM_ROCM_USE_AITER_LINEAR \
+            and envs.VLLM_ROCM_USE_AITER
+        ):
+            return (False, "AiterScaledMMLinearKernel is disabled. " +
+                    "Enable by setting `VLLM_ROCM_USE_AITER=1` " +
+                    "and `VLLM_ROCM_USE_AITER_LINEAR=1`. " +
+                    "`VLLM_ROCM_USE_AITER_LINEAR` default is True.")
+
+        if not c.input_symmetric:
+            return (False,
+                    "AiterScaledMMLinearKernel only supports symmetric " +
+                    "quantization.")
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        super().process_weights_after_loading(layer)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        """
+        `AiterScaledMMLinearKernel` implements a fused version of
+            `output = torch.mm((scale_a * a), (scale_b * b)).to(out_dtype)`
+        where scale_a * a and scale_b * b are implemented using numpy-style
+        broadcasting.
+        Currently only support per-tensor-per-tensor GEMM
+        and per-token-per-channel GEMM through AITER
+        w8a8 scaled gemm. `AiterScaledMMLinearKernel` also does not support
+        ATIER block scaled GEMM and mix-precision GEMM.
+        """
+        w_q, w_s, i_s, i_zp, azp_adj = self._get_weight_params(layer)
+
+        # ops.scaled_int8_quant supports both dynamic and static quant:
+        # * dynamic, i_s is None and x_s computed from x.
+        # * static, i_s is scalar and x_s is i_s.
+        symmetric = azp_adj is None
+        assert symmetric, ("AiterScaledMMLinearKernel only supports"
+                           " symmetric quantization.")
+        x_q, x_s, x_zp = ops.scaled_int8_quant(x,
+                                               i_s,
+                                               i_zp,
+                                               symmetric=symmetric)
+
+        assert x_zp is None, ("AiterScaledMMLinearKernel only supports"
+                              " symmetric quantization.")
+        out_dtype = x.dtype
+
+        assert (w_q.shape[0] % 16 == 0 and w_q.shape[1] % 16 == 0)
+        assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
+        assert bias is None or bias.shape[0] == w_q.shape[
+            1] and bias.dtype == out_dtype
+
+        m = x_q.shape[0]  # a
+        n = w_q.shape[1]  # b
+
+        per_tensor_scale_a = (x_s.numel() == 1)
+        per_tensor_scale_b = (w_s.numel() == 1)
+        per_token_scale_a = (x_s.numel() == m)
+        per_channel_scale_b = (w_s.numel() == n)
+
+        # @TODO:
+        # Maybe broadcast the per-tensor-scale into per-channel-scale
+        # if one of the scale is a per-channel-scale.
+        # For now, it only supports:
+        # - per-tensor-per-tensor a8w8 scaled GEMM, and
+        # - per-token-per-channel a8w8 scaled GEMM
+        assert ((per_tensor_scale_a and per_tensor_scale_b)
+                or (per_token_scale_a and per_channel_scale_b)), (
+                    "Currently only support per-tensor-per-tensor GEMM " +
+                    " and per-token-per-channel GEMM through AITER"
+                    " w8a8 scaled gemm. `AiterScaledMMLinearKernel` " +
+                    "does not support AITER block scaled GEMM.")
+
+        from aiter import gemm_a8w8_CK
+
+        # gemm_a8w8_CK(a, b, scale_a, scale_b, bias) expects
+        # a to be [M, K]
+        # b to be [N, K]
+        # CutlassScaledMMLinearKernel prepare weight `w_q` in [K, N] format
+        return gemm_a8w8_CK(x_q, w_q.t(), x_s, w_s, bias).to(out_dtype)

vllm/model_executor/layers/quantization/kernels/scaled_mm/cutlass.py

@@ -0,0 +1,144 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.utils import replace_parameter
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    convert_to_channelwise)
+from vllm.platforms import current_platform
+
+from .ScaledMMLinearKernel import (ScaledMMLinearKernel,
+                                   ScaledMMLinearLayerConfig)
+
+from lmslim.layers.gemm.int8_utils import per_token_quant_int8
+
+
+class CutlassScaledMMLinearKernel(ScaledMMLinearKernel):
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 75
+
+    @classmethod
+    def can_implement(
+            cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, Optional[str]]:
+
+        if (not current_platform.is_cuda() and not current_platform.is_cpu()):
+            return False, "CutlassScaledMM requires running on CUDA or CPU."
+
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # WEIGHT
+        # Cutlass kernels need transposed weight.
+        weight = getattr(layer, self.w_q_name)
+        replace_parameter(
+            layer, self.w_q_name,
+            torch.nn.Parameter(weight.t().data, 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(layer.logical_widths) > 1
+        weight_scale = getattr(layer, self.w_s_name)
+        if is_fused_module and not self.config.is_channelwise:
+            weight_scale = convert_to_channelwise(weight_scale,
+                                                  layer.logical_widths)
+        replace_parameter(
+            layer, self.w_s_name,
+            torch.nn.Parameter(weight_scale.data, requires_grad=False))
+
+        # INPUT SCALE
+        if self.config.is_static_input_scheme:
+            input_scale = getattr(layer, self.i_s_name)
+
+            if self.config.input_symmetric:
+                replace_parameter(
+                    layer, self.i_s_name,
+                    torch.nn.Parameter(input_scale.max(), requires_grad=False))
+                setattr(layer, self.i_zp_name, None)
+            else:
+                input_zero_point = getattr(layer, self.i_zp_name)
+
+                # reconstruct the ranges
+                int8_traits = torch.iinfo(torch.int8)
+                azps = input_zero_point.to(dtype=torch.int32)
+                range_max = (input_scale * (int8_traits.max - azps)).max()
+                range_min = (input_scale * (int8_traits.min - azps)).min()
+
+                scale = (range_max - range_min) / (int8_traits.max -
+                                                   int8_traits.min)
+                replace_parameter(
+                    layer, self.i_s_name,
+                    torch.nn.Parameter(scale, requires_grad=False))
+
+                # AZP loaded as int8 but used as int32
+                azp = (int8_traits.min -
+                       range_min / scale).to(dtype=torch.int32)
+                replace_parameter(layer, self.i_zp_name,
+                                  torch.nn.Parameter(azp, requires_grad=False))
+
+        else:
+            setattr(layer, self.i_s_name, None)
+            setattr(layer, self.i_zp_name, 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.config.input_symmetric:
+            weight = getattr(layer, self.w_q_name)
+            azp_adj = weight.sum(dim=0, keepdim=True, dtype=torch.int32)
+            if self.config.is_static_input_scheme:
+                # cutlass_w8a8 requires azp to be folded into azp_adj
+                # in the per-tensor case
+                azp_adj = getattr(layer, self.i_zp_name) * azp_adj
+            setattr(layer, self.azp_adj_name,
+                    torch.nn.Parameter(azp_adj, requires_grad=False))
+        else:
+            setattr(layer, self.azp_adj_name, None)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        w_q, w_s, i_s, i_zp, azp_adj = self._get_weight_params(layer)
+
+        # ops.scaled_int8_quant supports both dynamic and static quant:
+        # * dynamic, i_s is None and x_s computed from x.
+        # * static, i_s is scalar and x_s is i_s.
+        symmetric = azp_adj is None
+        if i_s is None and i_zp is None and symmetric is True:
+            x_q, x_s=per_token_quant_int8(x)
+            x_zp =None
+
+        else:
+            x_q, x_s, x_zp = ops.scaled_int8_quant(x.contiguous(),
+                                                i_s,
+                                                i_zp,
+                                                symmetric=symmetric)
+
+        if x_zp is not None:
+            # Currently, static is always per-tensor and dynamic is per-token
+            static = i_zp is not None
+            azp = None if static else x_zp
+            return ops.cutlass_scaled_mm_azp(x_q,
+                                             w_q,
+                                             scale_a=x_s,
+                                             scale_b=w_s,
+                                             out_dtype=x.dtype,
+                                             azp_adj=azp_adj,
+                                             azp=azp,
+                                             bias=bias)
+        return ops.cutlass_scaled_mm(x_q,
+                                     w_q,
+                                     scale_a=x_s,
+                                     scale_b=w_s,
+                                     out_dtype=x.dtype,
+                                     bias=bias)

vllm/model_executor/layers/quantization/kernels/scaled_mm/triton.py

@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+
+from vllm.platforms import current_platform
+
+from .cutlass import CutlassScaledMMLinearKernel
+from .ScaledMMLinearKernel import ScaledMMLinearLayerConfig
+
+
+class TritonScaledMMLinearKernel(CutlassScaledMMLinearKernel):
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 75
+
+    @classmethod
+    def can_implement(
+            cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, Optional[str]]:
+        if current_platform.is_cpu():
+            return (
+                False,
+                "TritonScaledMMLinearKernel requires Triton which is not " +
+                "currently supported on CPU.")
+        if not c.input_symmetric:
+            return (False,
+                    "TritonScaledMMLinearKernel only supports symmetric " +
+                    "quantization.")
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        super().process_weights_after_loading(layer)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return super().apply_weights(layer, x, bias)

vllm/model_executor/layers/quantization/kernels/scaled_mm/xla.py

@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import warnings
+from typing import Optional
+
+import torch
+from functorch.experimental.control_flow import cond  # noqa: F401
+
+from vllm.model_executor.layers.quantization.utils import replace_parameter
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    convert_to_channelwise)
+from vllm.platforms import current_platform
+
+from .ScaledMMLinearKernel import (ScaledMMLinearKernel,
+                                   ScaledMMLinearLayerConfig)
+
+
+class XLAScaledMMLinearKernel(ScaledMMLinearKernel):
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        raise NotImplementedError(
+            "TPU platform does have a concept of compute capability, "
+            "this method should not be called.")
+
+    @classmethod
+    def can_implement(
+            cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, Optional[str]]:
+
+        if not current_platform.is_tpu():
+            return False, "ScaledMMXLA requires running on TPU."
+
+        if c.is_static_input_scheme:
+            return False, "ScaledMMXLA requires dynamic activation scales."
+
+        if not c.input_symmetric:
+            return False, "ScaledMMXLA requires symmetric activation scales."
+
+        if not c.is_channelwise:
+            return False, "ScaledMMXLA requires channelwise weight scales"
+
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # WEIGHT
+        # [out, in] (different than cutlass_scaled_mm)
+        weight = getattr(layer, self.w_q_name)
+        replace_parameter(layer, self.w_q_name,
+                          torch.nn.Parameter(weight.data, requires_grad=False))
+
+        # WEIGHT SCALE
+        # XLA 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(layer.logical_widths) > 1
+        weight_scale = getattr(layer, self.w_s_name)
+        if is_fused_module and not self.config.is_channelwise:
+            weight_scale = convert_to_channelwise(weight_scale,
+                                                  layer.logical_widths)
+
+        # [out_channel,] (different than cutlass_scaled_mm)
+        weight_scale = weight_scale.squeeze(-1)
+        replace_parameter(
+            layer, self.w_s_name,
+            torch.nn.Parameter(weight_scale.data, requires_grad=False))
+
+        # Only support symmetric dynamic activation quantization.
+        setattr(layer, self.i_s_name, None)
+        setattr(layer, self.i_zp_name, None)
+        setattr(layer, self.azp_adj_name, None)
+
+        # Filter warning for cond usage in apply_weights. It is okay
+        # to specialize the graph since bias is not dynamic.
+        warnings.filterwarnings(
+            "ignore",
+            message=
+            "Pred is a Python constant. When used with torch.cond, it specializes on one of the branches."  # noqa: E501
+        )
+
+    def no_add_bias(self, x: torch.Tensor, bias: Optional[torch.Tensor]):
+        return x
+
+    def add_bias(self, x: torch.Tensor, bias: Optional[torch.Tensor]):
+        return x + bias
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        w_q, w_s, _, _, _ = self._get_weight_params(layer)
+
+        import torch_xla.experimental.xla_quantized_matmul  # noqa: F401
+        out = torch.ops.xla.quantized_matmul(x,
+                                             w_q,
+                                             w_s,
+                                             zero_point=None,
+                                             block_size=-1,
+                                             int4_weight=False,
+                                             quantize_activation=True)
+        # `quantized_matmul` output is fp32, cast it down to bf16 for perf
+        out = out.to(x.dtype)
+        # Explicitly capture control flow to make dynamo happy.
+        # https://pytorch.org/docs/main/generated/exportdb/index.html#cond-branch-class-method # noqa: E501
+        return cond(bias is None, self.no_add_bias, self.add_bias, [out, bias])