Use NVFP4 Marlin for CompressedTensorsW4A16Fp4 (vllm-project#18000)

Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: Dipika <dipikasikka1@gmail.com>
Co-authored-by: Dipika <dipikasikka1@gmail.com>
Signed-off-by: Yuqi Zhang <yuqizhang@google.com>

Author: 2 people

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_nvfp4.py

@@ -2,13 +2,12 @@
 from typing import Callable, List, Optional
 
 import torch
-import torch.nn.functional as F
 from torch.nn.parameter import Parameter
 
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
-from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import (  # noqa: E501
-    dequantize_to_dtype)
+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,
                                            PerTensorScaleParameter)
@@ -31,6 +30,10 @@ def create_weights(self, layer: torch.nn.Module,
                        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
 
         # Weight
         weight = ModelWeightParameter(data=torch.empty(
@@ -60,48 +63,30 @@ def create_weights(self, layer: torch.nn.Module,
 
         layer.register_parameter("weight_scale", weight_scale)
 
-    def swizzle_blockscale(self, scale: torch.tensor):
-        assert (scale.dtype == torch.float8_e4m3fn)
-        # Pad and blockwise interleave weight_scale
-        scale_ndim = scale.ndim
-        if scale.ndim == 2:
-            scale = scale.unsqueeze(0)
-        assert scale.ndim == 3
-        B, M, K = scale.shape
-        round_up_multiple = lambda x, m: (x + m - 1) // m * m
-        M_padded = round_up_multiple(M, 128)
-        K_padded = round_up_multiple(K, 4)
-        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
-        padded_scale[:B, :M, :K] = scale
-        batches, rows, cols = padded_scale.shape
-        assert rows % 128 == 0
-        assert cols % 4 == 0
-        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
-                                            cols // 4, 4)
-        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
-        swizzled_scale = swizzled_scale.contiguous().cuda()
-        return (swizzled_scale.reshape(M, K)
-                if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))
-
     def process_weights_after_loading(self, layer) -> None:
-        layer.weight_global_scale = Parameter(
-            layer.weight_global_scale.max().to(torch.float32),
+        # Process parameters for marlin repacking
+
+        # Rename weight_packed to weight that marlin expects
+        layer.weight = Parameter(layer.weight_packed.data, requires_grad=False)
+        del layer.weight_packed
+        # Rename weight_global_scale to weight_scale_2 that marlin expects
+        # Note: ct stores the inverse of what is expected by the marlin kernel
+        layer.weight_scale_2 = Parameter(
+            1 / layer.weight_global_scale.max().to(torch.float32),
             requires_grad=False)
-        # Note: a post weight loading step but not required for the emulation
-        swizzled_weight_scale = self.swizzle_blockscale(layer.weight_scale)
-        layer.weight_scale_swizzled = Parameter(swizzled_weight_scale,
-                                                requires_grad=False)
+        del layer.weight_global_scale
+
+        prepare_fp4_layer_for_marlin(layer)
 
     def apply_weights(self,
                       layer: torch.nn.Module,
                       x: torch.Tensor,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-
-        w_fp4 = layer.weight_packed.data
-        w_global_scale = layer.weight_global_scale
-        w_blockscale = layer.weight_scale_swizzled.data
-        w_dq = dequantize_to_dtype(w_fp4, w_blockscale, w_global_scale,
-                                   x.dtype, x.device, self.group_size)
-        out = F.linear(x, w_dq)
-        del w_dq, w_fp4, w_global_scale, w_blockscale
-        return out
+        return apply_fp4_marlin_linear(input=x,
+                                       weight=layer.weight,
+                                       weight_scale=layer.weight_scale,
+                                       weight_scale_2=layer.weight_scale_2,
+                                       workspace=layer.workspace,
+                                       size_n=layer.output_size_per_partition,
+                                       size_k=layer.input_size_per_partition,
+                                       bias=bias)