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vllm/model_executor/layers/quantization/quark/quark_moe.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any, Callable, Optional, Union
+
+import torch
+
+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, FusedMoEConfig,
+                                                  FusedMoEMethodBase,
+                                                  FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEQuantConfig, fp8_w8a8_moe_quant_config,
+    mxfp4_w4a4_moe_quant_config)
+from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+    is_rocm_aiter_moe_enabled)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
+    prepare_moe_fp8_layer_for_marlin)
+from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
+    OCP_MX_BLOCK_SIZE)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape)
+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.platforms import current_platform
+from vllm.scalar_type import scalar_types
+
+logger = init_logger(__name__)
+
+__all__ = [
+    "QuarkMoEMethod", "QuarkW8A8Fp8MoEMethod", "QuarkW4A4MXFp4MoEMethod"
+]
+
+
+class QuarkMoEMethod(FusedMoEMethodBase):
+
+    def __init__(self, moe: FusedMoEConfig):
+        super().__init__(moe)
+
+    @staticmethod
+    def get_moe_method(
+            quant_config: "QuarkConfig",  # type: ignore # noqa E501 # noqa F821
+            module: torch.nn.Module,
+            layer_name: str) -> "QuarkMoEMethod":
+        layer_quant_config = quant_config._find_matched_config(
+            layer_name, module)
+
+        if (layer_quant_config.get("output_tensors")
+                or layer_quant_config.get("bias")):
+            raise NotImplementedError("Currently, Quark models with "
+                                      "output_tensors and bias "
+                                      "quantized are not supported")
+        weight_config = layer_quant_config.get("weight")
+        input_config = layer_quant_config.get("input_tensors")
+
+        if quant_config._is_fp8_w8a8(weight_config, input_config):
+            return QuarkW8A8Fp8MoEMethod(weight_config, input_config,
+                                         module.moe_config)
+        elif quant_config._is_mx_fp4(weight_config, input_config):
+            return QuarkW4A4MXFp4MoEMethod(weight_config, input_config,
+                                           module.moe_config)
+        else:
+            raise RuntimeError("Unsupported FusedMoe scheme")
+
+
+class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
+
+    def __init__(
+        self,
+        weight_config: dict[str, Any],
+        input_config: dict[str, Any],
+        moe: FusedMoEConfig,
+    ):
+        super().__init__(moe)
+        self.weight_quant = weight_config
+        self.input_quant = input_config
+
+        self.weight_qscheme = self.weight_quant.get("qscheme")
+        self.input_qscheme = self.input_quant.get("qscheme")
+        per_tensor = (self.weight_qscheme == "per_tensor"
+                      and self.input_qscheme == "per_tensor")
+        per_channel = (self.weight_qscheme == "per_channel"
+                       and self.input_qscheme == "per_channel")
+        self.act_quant_group_shape = GroupShape.PER_TOKEN \
+            if per_channel else GroupShape.PER_TENSOR
+        if not (per_tensor or per_channel):
+            raise ValueError(
+                "For FP8 Fused MoE layers, only per-tensor and per-channel "
+                "scales for weights and activations are supported. Found "
+                f"{self.weight_qscheme}, {self.input_qscheme}")  # noqa E501
+
+        self.static_input_scales = not self.input_quant.get("is_dynamic")
+        if self.static_input_scales and per_channel:
+            raise ValueError(
+                "For FP8 Fused MoE layer, we require either per tensor or "
+                "channelwise, dynamic per token quantization.")
+
+        # 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 current_platform.is_rocm():
+            self.use_marlin = False
+
+        self.rocm_aiter_moe_enabled = is_rocm_aiter_moe_enabled()
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        layer.intermediate_size_per_partition = intermediate_size_per_partition
+        layer.hidden_size = hidden_size
+        layer.num_experts = num_experts
+        layer.orig_dtype = params_dtype
+        layer.weight_block_size = None
+        params_dtype = torch.float8_e4m3fn
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            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_per_partition,
+            dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        if self.weight_qscheme == "per_tensor":
+            # Allocate 2 scales for w1 and w3 respectively.
+            # They are 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 PER-TENSOR quantization for FusedMoE.weight_loader.
+            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)
+        elif self.weight_qscheme == "per_channel":
+            # quark's scale is 1 dim.
+            w13_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                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, hidden_size, dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w2_weight_scale", w2_weight_scale)
+            # Add PER-CHANNEL quantization for FusedMoE.weight_loader.
+            extra_weight_attrs.update(
+                {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.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)):
+                logger.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 current_platform.is_fp8_fnuz():
+            # 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)
+
+        # For per-tensor case, Fp8 moe kernel needs single weight scale
+        # for w13 per expert. Use max then dequant and requant each expert.
+        if self.weight_qscheme == "per_tensor":
+            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.local_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)
+        # quark's scale is 1 dim.
+        elif self.weight_qscheme == "per_channel":
+            if self.act_quant_group_shape == GroupShape.PER_TOKEN:
+                w13_weight_scale = layer.w13_weight_scale.unsqueeze(-1)
+                layer.w13_weight_scale = torch.nn.Parameter(
+                    w13_weight_scale, requires_grad=False)
+                w2_weight_scale = layer.w2_weight_scale.unsqueeze(-1)
+                layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
+                                                           requires_grad=False)
+        # Property to determine if AITER is used
+        if self.rocm_aiter_moe_enabled:
+            from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa E501
+                rocm_aiter_fused_experts, shuffle_weights)
+
+            # reshaping weights is required for aiter moe kernel.
+            shuffled_w13, shuffled_w2 = shuffle_weights(
+                layer.w13_weight.data, layer.w2_weight.data)
+
+            layer.w13_weight = torch.nn.Parameter(shuffled_w13,
+                                                  requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(shuffled_w2,
+                                                 requires_grad=False)
+
+            self.rocm_aiter_fused_experts_func = rocm_aiter_fused_experts
+        elif self.use_marlin:
+
+            prepare_moe_fp8_layer_for_marlin(layer, False)
+            # Activations not quantized for marlin.
+            del layer.w13_input_scale
+            del layer.w2_input_scale
+            self.fused_experts_func = None
+        else:
+            from vllm.model_executor.layers.fused_moe import fused_experts
+            self.fused_experts_func = fused_experts
+
+    def get_fused_moe_quant_config(
+            self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
+        return fp8_w8a8_moe_quant_config(
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+            per_act_token_quant=self.weight_qscheme == "per_channel",
+        )
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        assert self.fused_experts is None
+
+        if enable_eplb:
+            raise NotImplementedError(
+                "EPLB not supported for `QuarkW8A8Fp8MoEMethod` yet.")
+
+        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,
+            scoring_func=scoring_func,
+            routed_scaling_factor=routed_scaling_factor,
+            e_score_correction_bias=e_score_correction_bias,
+            indices_type=self.topk_indices_dtype)
+
+        if self.rocm_aiter_moe_enabled:
+            return self.rocm_aiter_fused_experts_func(
+                hidden_states=x,
+                w1=layer.w13_weight,
+                w2=layer.w2_weight,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                activation=activation,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                quant_config=self.moe_quant_config,
+                expert_map=expert_map)
+        if self.use_marlin:
+            assert activation == "silu", (
+                f"{activation} not supported for Marlin MoE.")
+            return torch.ops.vllm.fused_marlin_moe(
+                x,
+                layer.w13_weight,
+                layer.w2_weight,
+                None,
+                None,
+                layer.w13_weight_scale,
+                layer.w2_weight_scale,
+                router_logits,
+                topk_weights,
+                topk_ids,
+                quant_type_id=scalar_types.float8_e4m3fn.id,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                global_num_experts=global_num_experts,
+                expert_map=expert_map)
+
+        assert self.fused_experts_func is not None
+
+        return self.fused_experts_func(
+            hidden_states=x,
+            w1=layer.w13_weight,
+            w2=layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            activation=activation,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            quant_config=self.moe_quant_config)
+
+
+class QuarkW4A4MXFp4MoEMethod(QuarkMoEMethod):
+
+    def __init__(
+        self,
+        weight_config: dict[str, Any],
+        input_config: dict[str, Any],
+        moe: FusedMoEConfig,
+    ):
+        super().__init__(moe)
+        self.weight_quant = weight_config
+        self.input_quant = input_config
+
+        weight_qscheme = self.weight_quant.get("qscheme")
+        input_qscheme = self.input_quant.get("qscheme")
+        if not (weight_qscheme == "per_group"
+                and input_qscheme == "per_group"):
+            raise ValueError(
+                "For MX(FP4) Fused MoE layers, only per-group scales "
+                "for weights and activations are supported. Found "
+                f"{weight_qscheme}, {input_qscheme}")  # noqa E501
+
+        self.static_input_scales = not self.input_quant.get("is_dynamic")
+
+        if self.static_input_scales:
+            raise NotImplementedError(
+                "QuarkW4A4MXFp4MoEMethod with static input scales is currently "
+                "not implemented. Please open an issue.")
+
+        if not current_platform.supports_mx():
+            self.emulate = True
+            logger.warning_once(
+                "The current platform does not support native MXFP4 "
+                "computation. Simulated weight dequantization and activation "
+                "QDQ (quantize and dequantize) will be used, with the linear "
+                "layers computed in high precision.")
+        else:
+            self.emulate = True
+            logger.warning_once(
+                "The current platform supports native MXFP4 "
+                "computation, but kernels are not yet integrated in vLLM. "
+                "Simulated weight dequantization and activation "
+                "QDQ (quantize and dequantize) will be used, with the linear "
+                "layers computed in high precision.")
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        # 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.BLOCK.value})
+
+        params_dtype = torch.uint8
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            hidden_size // 2,
+            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_per_partition // 2,
+            dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        w13_weight_scale = torch.nn.Parameter(
+            torch.ones(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_size // OCP_MX_BLOCK_SIZE,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        w2_weight_scale = torch.nn.Parameter(
+            torch.ones(
+                num_experts,
+                hidden_size,
+                intermediate_size_per_partition // OCP_MX_BLOCK_SIZE,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+
+    def get_fused_moe_quant_config(
+            self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
+        return mxfp4_w4a4_moe_quant_config(
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=None,
+            a2_scale=None,
+            block_shape=None,
+        )
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        assert self.fused_experts is None
+
+        if enable_eplb:
+            raise NotImplementedError(
+                "EPLB not supported for `QuarkW4A4MXFp4MoEMethod` yet.")
+
+        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,
+            scoring_func=scoring_func,
+            routed_scaling_factor=routed_scaling_factor,
+            e_score_correction_bias=e_score_correction_bias,
+            indices_type=self.topk_indices_dtype)
+
+        out = fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            activation=activation,
+            global_num_experts=global_num_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            expert_map=expert_map,
+            quant_config=self.moe_quant_config,
+        )
+        return out