[Misc] Update fbgemmfp8 to use vLLMParameters (vllm-project#7972)

Co-authored-by: Michael Goin <michael@neuralmagic.com>

vllm/model_executor/layers/linear.py

@@ -26,7 +26,7 @@
     "CompressedTensorsLinearMethod", "AWQMarlinLinearMethod",
     "AWQLinearMethod", "GPTQMarlinLinearMethod", "Fp8LinearMethod",
     "MarlinLinearMethod", "QQQLinearMethod", "GPTQMarlin24LinearMethod",
-    "TPUInt8LinearMethod", "GPTQLinearMethod"
+    "TPUInt8LinearMethod", "GPTQLinearMethod", "FBGEMMFp8LinearMethod"
 ]
 
 

vllm/model_executor/layers/quantization/fbgemm_fp8.py

@@ -15,8 +15,9 @@
 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, create_per_channel_scale_param)
-from vllm.model_executor.utils import set_weight_attrs
+    apply_fp8_linear)
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           ModelWeightParameter)
 from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
@@ -85,6 +86,7 @@ def create_weights(
         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)
 
@@ -95,20 +97,21 @@ def create_weights(
         layer.orig_dtype = params_dtype
 
         # WEIGHT
-        weight = Parameter(torch.empty(output_size_per_partition,
-                                       input_size_per_partition,
-                                       dtype=torch.float8_e4m3fn),
-                           requires_grad=False)
+        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)
-        set_weight_attrs(weight, {
-            "input_dim": 1,
-            "output_dim": 0,
-            **extra_weight_attrs,
-        })
 
         # WEIGHT SCALE
-        weight_scale = create_per_channel_scale_param(output_partition_sizes,
-                                                      **extra_weight_attrs)
+        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
@@ -118,6 +121,11 @@ def create_weights(
         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
         layer.weight = Parameter(weight.t(), requires_grad=False)
 

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

@@ -1,10 +1,8 @@
 from typing import List, Optional, Tuple, Union
 
 import torch
-from torch.nn import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.utils import is_hip
 
@@ -38,31 +36,6 @@ def all_close_1d(x: torch.Tensor) -> bool:
     return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0]))
 
 
-def create_per_tensor_scale_param(
-    output_partition_sizes: List[int],
-    **extra_weight_attrs,
-) -> Parameter:
-    scale = Parameter(torch.empty(len(output_partition_sizes),
-                                  dtype=torch.float32),
-                      requires_grad=False)
-    scale[:] = torch.finfo(torch.float32).min
-    set_weight_attrs(scale, {
-        "needs_scalar_to_array": True,
-        **extra_weight_attrs
-    })
-    return scale
-
-
-def create_per_channel_scale_param(output_partition_sizes: List[int],
-                                   **extra_weight_attrs) -> Parameter:
-    scale = Parameter(torch.empty((sum(output_partition_sizes), 1),
-                                  dtype=torch.float32),
-                      requires_grad=False)
-    scale[:] = torch.finfo(torch.float32).min
-    set_weight_attrs(scale, {"output_dim": 0, **extra_weight_attrs})
-    return scale
-
-
 def convert_to_channelwise(
         weight_scale: torch.Tensor,
         logical_widths: List[int]) -> Tuple[torch.Tensor, torch.Tensor]: