[Feature] Enable DeepGEMM Linear on B200; 1.5% E2E throughput improvement (#23351)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -19,8 +19,9 @@ from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     CUTLASS_BLOCK_FP8_SUPPORTED)
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
-from vllm.utils import cdiv, direct_register_custom_op, has_deep_gemm
-from vllm.utils.deep_gemm import is_blackwell_deep_gemm_e8m0_used
+from vllm.utils import cdiv, direct_register_custom_op
+from vllm.utils.deep_gemm import (is_blackwell_deep_gemm_e8m0_used,
+                                  should_use_deepgemm_for_fp8_linear)
 
 logger = init_logger(__name__)
 
@@ -108,19 +109,6 @@ def dispatch_w8a8_blockscale_func(
     return w8a8_block_fp8_matmul
 
 
-def should_use_deepgemm(output_dtype: torch.dtype, weight: torch.Tensor):
-    """
-    Check if DeepGEMM should be used based on the output dtype and weight shape.
-    DeepGEMM is only supported for bfloat16 output dtype and weights with shape
-    divisible by 128.
-    """
-
-    return (current_platform.is_cuda()
-            and current_platform.is_device_capability(90) and has_deep_gemm()
-            and envs.VLLM_USE_DEEP_GEMM and output_dtype == torch.bfloat16
-            and weight.shape[0] % 128 == 0 and weight.shape[1] % 128 == 0)
-
-
 # TODO fix ROCm->Triton custom path:
 #  https://github.com/vllm-project/vllm/issues/14397
 def apply_w8a8_block_fp8_linear(
@@ -139,7 +127,7 @@ def apply_w8a8_block_fp8_linear(
     output_shape = [*input.shape[:-1], weight.shape[0]]
     output_dtype = input.dtype
 
-    if should_use_deepgemm(output_dtype, weight):
+    if should_use_deepgemm_for_fp8_linear(output_dtype, weight):
 
         input_2d = input.view(-1, input.shape[-1])
         output_shape = [*input.shape[:-1], weight.shape[0]]
@@ -150,7 +138,9 @@ def apply_w8a8_block_fp8_linear(
             column_major_scales=True,
         )
 
+        # ensure DeepGEMM-backed custom op is registered before use
         import vllm.model_executor.layers.quantization.deepgemm  # noqa: F401
+
         output = torch.ops.vllm.w8a8_block_fp8_matmul_deepgemm(
             q_input,
             weight,

vllm/utils/deep_gemm.py

@@ -202,6 +202,12 @@ def calc_diff(x: torch.Tensor, y: torch.Tensor):
     return 1 - sim
 
 
+def should_use_deepgemm_for_fp8_linear(output_dtype: torch.dtype,
+                                       weight: torch.Tensor):
+    return (is_deep_gemm_supported() and output_dtype == torch.bfloat16
+            and weight.shape[0] % 128 == 0 and weight.shape[1] % 128 == 0)
+
+
 __all__ = [
     "calc_diff",
     "fp8_gemm_nt",
@@ -210,4 +216,5 @@ __all__ = [
     "per_block_cast_to_fp8",
     "is_blackwell_deep_gemm_e8m0_used",
     "is_deep_gemm_supported",
+    "should_use_deepgemm_for_fp8_linear",
 ]