[Quant] Support MXFP4 W4A16 for compressed-tensors dense models (#31926)

Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: Michael Goin <mgoin64@gmail.com>

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

@@ -43,6 +43,7 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsW4A8Fp8,
     CompressedTensorsW4A8Int,
     CompressedTensorsW4A16Fp4,
+    CompressedTensorsW4A16Mxfp4,
     CompressedTensorsW4A16Sparse24,
     CompressedTensorsW8A8Fp8,
     CompressedTensorsW8A8Int8,
@@ -350,6 +351,25 @@ class CompressedTensorsConfig(QuantizationConfig):
             and is_symmetric
         )
 
+    @staticmethod
+    def _is_mxfp4(quant_args: QuantizationArgs) -> bool:
+        if quant_args is None:
+            return False
+
+        is_group_quant = quant_args.strategy == QuantizationStrategy.GROUP.value
+        is_symmetric = quant_args.symmetric
+        is_group_size_32 = quant_args.group_size == 32
+        is_float_type = quant_args.type == QuantizationType.FLOAT
+        is_4_bits = quant_args.num_bits == 4
+
+        return (
+            is_group_quant
+            and is_float_type
+            and is_4_bits
+            and is_group_size_32
+            and is_symmetric
+        )
+
     @staticmethod
     def _is_static_tensor_w8a8(
         weight_quant: QuantizationArgs, input_quant: QuantizationArgs
@@ -550,6 +570,9 @@ class CompressedTensorsConfig(QuantizationConfig):
         if self._is_nvfp4_format(weight_quant) and input_quant is None:
             return CompressedTensorsW4A16Fp4()
 
+        if self._is_mxfp4(weight_quant):
+            return CompressedTensorsW4A16Mxfp4()
+
         if self._is_fp8_w4a8_sm90(weight_quant, input_quant):
             return CompressedTensorsW4A8Fp8(
                 num_bits=weight_quant.num_bits,

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

@@ -9,6 +9,7 @@ from .compressed_tensors_w4a16_24 import (
     W4A16SPARSE24_SUPPORTED_BITS,
     CompressedTensorsW4A16Sparse24,
 )
+from .compressed_tensors_w4a16_mxfp4 import CompressedTensorsW4A16Mxfp4
 from .compressed_tensors_w4a16_nvfp4 import CompressedTensorsW4A16Fp4
 from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8
 from .compressed_tensors_w8a8_int8 import CompressedTensorsW8A8Int8
@@ -29,6 +30,7 @@ __all__ = [
     "W4A16SPARSE24_SUPPORTED_BITS",
     "CompressedTensors24",
     "CompressedTensorsW4A16Fp4",
+    "CompressedTensorsW4A16Mxfp4",
     "CompressedTensorsW4A4Fp4",
     "CompressedTensorsW4A8Int",
     "CompressedTensorsW4A8Fp8",

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_mxfp4.py

@@ -0,0 +1,106 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme,
+)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
+    apply_fp4_marlin_linear,
+    prepare_fp4_layer_for_marlin,
+)
+from vllm.model_executor.parameter import (
+    GroupQuantScaleParameter,
+    ModelWeightParameter,
+)
+
+__all__ = ["CompressedTensorsW4A16Mxfp4"]
+
+
+class CompressedTensorsW4A16Mxfp4(CompressedTensorsScheme):
+    """
+    Compressed tensors scheme for MXFP4 weight-only quantization.
+
+    Supports models quantized with the compressed-tensors mxfp4-pack-quantized
+    format.
+
+    MXFP4 format:
+    - 4-bit float weights (E2M1) packed into uint8
+    - Per-group E8M0 scales with group_size=32
+    - No global scale (unlike NVFP4)
+    """
+
+    def __init__(self):
+        self.group_size = 32
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    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
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.params_dtype = params_dtype
+
+        # Packed FP4 weights (2 values per byte)
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                output_size_per_partition,
+                input_size_per_partition // 2,
+                dtype=torch.uint8,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight_packed", weight)
+
+        # Per-group E8M0 scales
+        weight_scale = GroupQuantScaleParameter(
+            data=torch.empty(
+                output_size_per_partition,
+                input_size_per_partition // self.group_size,
+                dtype=torch.uint8,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight_scale", weight_scale)
+
+    def process_weights_after_loading(self, layer) -> None:
+        # Rename weight_packed to weight that marlin expects
+        layer.weight = Parameter(layer.weight_packed.data, requires_grad=False)
+        del layer.weight_packed
+
+        prepare_fp4_layer_for_marlin(layer)
+
+    def apply_weights(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        return apply_fp4_marlin_linear(
+            input=x,
+            weight=layer.weight,
+            weight_scale=layer.weight_scale,
+            weight_scale_2=None,
+            workspace=layer.workspace,
+            size_n=layer.output_size_per_partition,
+            size_k=layer.input_size_per_partition,
+            bias=bias,
+        )