remove maybe_create_device_identity

Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>

tests/compile/test_fusion.py

@@ -40,9 +40,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
     ScaleDesc,
 )
-from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
-    maybe_create_device_identity,
-)
 from vllm.platforms import current_platform
 
 from ..utils import TestFP8Layer
@@ -191,7 +188,6 @@ def test_fusion_rmsnorm_quant(
     torch.set_default_device("cuda")
     torch.set_default_dtype(dtype)
     torch.manual_seed(1)
-    maybe_create_device_identity()  # needed for certain non-cutlass fp8 paths
 
     custom_ops = []
     if enable_rms_norm_custom_op:

tests/compile/test_silu_mul_quant_fusion.py

@@ -28,9 +28,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     kFp8StaticTensorSym,
     kNvfp4Quant,
 )
-from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
-    maybe_create_device_identity,
-)
 from vllm.platforms import current_platform
 
 from ..utils import TestFP8Layer, override_cutlass_fp8_supported
@@ -157,7 +154,6 @@ def test_fusion_silu_and_mul_quant(
 
     torch.set_default_device("cuda")
     torch.set_default_dtype(dtype)
-    maybe_create_device_identity()
 
     x = torch.rand(num_tokens, hidden_size * 2)
 

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py

@@ -34,7 +34,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 )
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     cutlass_block_fp8_supported,
-    maybe_create_device_identity,
 )
 from vllm.model_executor.parameter import (
     BlockQuantScaleParameter,
@@ -108,8 +107,6 @@ class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
         weight_loader: Callable,
         **kwargs,
     ):
-        maybe_create_device_identity()
-
         output_size_per_partition = sum(output_partition_sizes)
         layer.logical_widths = output_partition_sizes
         layer.weight_block_size = None

vllm/model_executor/layers/quantization/fbgemm_fp8.py

@@ -31,7 +31,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     kFp8StaticTokenSym,
 )
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
-    maybe_create_device_identity,
     normalize_e4m3fn_to_e4m3fnuz,
 )
 from vllm.model_executor.parameter import (
@@ -112,7 +111,6 @@ class FBGEMMFp8LinearMethod(LinearMethodBase):
         params_dtype: torch.dtype,
         **extra_weight_attrs,
     ):
-        maybe_create_device_identity()
         weight_loader = extra_weight_attrs.get("weight_loader")
         del input_size, output_size
         output_size_per_partition = sum(output_partition_sizes)

vllm/model_executor/layers/quantization/fp8.py

@@ -86,7 +86,6 @@ from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d,
     cutlass_block_fp8_supported,
     cutlass_fp8_supported,
-    maybe_create_device_identity,
     normalize_e4m3fn_to_e4m3fnuz,
     per_tensor_dequantize,
 )
@@ -416,8 +415,6 @@ class Fp8LinearMethod(LinearMethodBase):
         params_dtype: torch.dtype,
         **extra_weight_attrs,
     ):
-        maybe_create_device_identity()
-
         output_size_per_partition = sum(output_partition_sizes)
         weight_loader = extra_weight_attrs.get("weight_loader")
         layer.logical_widths = output_partition_sizes

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

@@ -14,17 +14,6 @@ from .ScaledMMLinearKernel import (
     FP8ScaledMMLinearLayerConfig,
 )
 
-# Input scaling factors are no longer optional in _scaled_mm starting
-# from pytorch 2.5. Allocating a dummy tensor to pass as input_scale
-TORCH_DEVICE_IDENTITY = None
-
-
-def maybe_create_device_identity():
-    # Allocate dummy ones tensor for torch._scaled_mm
-    global TORCH_DEVICE_IDENTITY
-    if TORCH_DEVICE_IDENTITY is None:
-        TORCH_DEVICE_IDENTITY = torch.ones(1, dtype=torch.float32)
-
 
 def torch_per_tensor_w8a8_scaled_mm(
     *,
@@ -57,8 +46,7 @@ def torch_row_wise_w8a8_scaled_mm(
     bias: torch.Tensor,
     output_shape: list,
 ) -> torch.Tensor:
-    # Note: Callers of this function should check USE_ROWWISE_TORCH_SCALED_MM
+    #  Note:
-    #  when using it.
     #  For now it has only been validated on ROCm platform.
     #  fp8 rowwise scaling in torch._scaled_mm is introduced in
     #  https://github.com/pytorch/pytorch/pull/144432 using
@@ -106,14 +94,18 @@ def torch_channelwise_w8a8_scaled_mm(
     # For the scaled_mm fallback case, we break this down, since it
     # does not support s_w being a vector.
 
+    # Input scaling factors are no longer optional in _scaled_mm starting
+    # from pytorch 2.5. Allocating a dummy tensor to pass as scales
+    dummy_tensor = torch.ones(1, dtype=torch.float32, device=A.device)
+
     # GEMM
     # This computes C = (X * W).
     # Output in fp32 to allow subsequent ops to happen in-place
     output = torch._scaled_mm(
         A,
         B,
-        scale_a=TORCH_DEVICE_IDENTITY,
+        scale_a=dummy_tensor,
-        scale_b=TORCH_DEVICE_IDENTITY,
+        scale_b=dummy_tensor,
         out_dtype=torch.float32,
     )
     # A fix for discrepancy in scaled_mm which returns tuple
@@ -214,19 +206,11 @@ class RowWiseTorchScaledMMLinearKernel(TorchScaledMMLinearKernel):
 class ChannelWiseTorchScaledMMLinearKernel(TorchScaledMMLinearKernel):
     @classmethod
     def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
-        is_static = c.activation_quant_key.scale.static
-
         per_tensor_activation_scales = (
             c.activation_quant_key.scale.group_shape.is_per_tensor()
         )
         per_tensor_weight_scales = c.weight_quant_key.scale.group_shape.is_per_tensor()
 
-        if not is_static:
-            return (
-                False,
-                "ChannelWiseTorchScaledMMLinearKernel requires static scales",
-            )
-
         if per_tensor_activation_scales and per_tensor_weight_scales:
             return (
                 False,

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

@@ -3,25 +3,10 @@
 
 
 import torch
-from packaging import version
 
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
 
-# Input scaling factors are no longer optional in _scaled_mm starting
-# from pytorch 2.5. Allocating a dummy tensor to pass as input_scale
-TORCH_DEVICE_IDENTITY = None
-
-# The condition to determine if it is on a platform that supports
-# torch._scaled_mm rowwise feature.
-# The condition is determined once as the operations
-# are time-consuming.
-USE_ROWWISE_TORCH_SCALED_MM = (
-    current_platform.is_rocm()
-    and version.parse(torch.__version__) >= version.parse("2.7")
-    and current_platform.has_device_capability(94)
-)
-
 
 def sparse_cutlass_supported() -> bool:
     if not current_platform.is_cuda():
@@ -129,13 +114,6 @@ def requantize_with_max_scale(
     return max_w_scale, weight
 
 
-def maybe_create_device_identity():
-    # Allocate dummy ones tensor for torch._scaled_mm
-    global TORCH_DEVICE_IDENTITY
-    if TORCH_DEVICE_IDENTITY is None:
-        TORCH_DEVICE_IDENTITY = torch.ones(1, dtype=torch.float32)
-
-
 def normalize_e4m3fn_to_e4m3fnuz(
     weight: torch.Tensor,
     weight_scale: torch.Tensor,