Revert "[Performance] Move apply_w8a8_block_fp8_linear to an op class… (#25607)

Signed-off-by: Tyler Michael Smith <tlrmchlsmth@gmail.com>

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -17,7 +17,7 @@ from weight_shapes import WEIGHT_SHAPES
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    w8a8_triton_block_scaled_mm,
+    w8a8_block_fp8_matmul,
 )
 from vllm.utils import FlexibleArgumentParser, cdiv
 
@@ -158,7 +158,7 @@ def bench_fp8(
         "cutlass_fp8_fp8_fp16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
             a, b, scale_a, scale_b, torch.float16, bias.to(dtype=torch.float16)
         ),
-        "triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_triton_block_scaled_mm(
+        "triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_block_fp8_matmul(
             a_cont, b.t(), block_scale_a, block_scale_b.t(), (128, 128)
         ),
         "cutlass_fp8_fp8_fp16_scaled_mm_blockwise": lambda: ops.cutlass_scaled_mm(

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -9,7 +9,7 @@ import torch
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     per_token_group_quant_fp8,
-    w8a8_triton_block_scaled_mm,
+    w8a8_block_fp8_matmul,
 )
 from vllm.triton_utils import triton
 from vllm.utils.deep_gemm import (
@@ -63,7 +63,7 @@ def benchmark_shape(m: int,
 
     # === vLLM Triton Implementation ===
     def vllm_triton_gemm():
-        return w8a8_triton_block_scaled_mm(A_vllm,
+        return w8a8_block_fp8_matmul(A_vllm,
                                      B_vllm,
                                      A_scale_vllm,
                                      B_scale_vllm,

tests/kernels/quantization/test_block_fp8.py

@@ -11,7 +11,7 @@ from tests.kernels.quant_utils import (native_per_token_group_quant_fp8,
                                        native_w8a8_block_matmul)
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    cutlass_scaled_mm, per_token_group_quant_fp8, w8a8_triton_block_scaled_mm)
+    cutlass_scaled_mm, per_token_group_quant_fp8, w8a8_block_fp8_matmul)
 from vllm.platforms import current_platform
 from vllm.utils import has_deep_gemm
 from vllm.utils.deep_gemm import (fp8_gemm_nt,
@@ -91,8 +91,7 @@ def test_w8a8_block_fp8_matmul(M, N, K, block_size, out_dtype, seed):
 
     ref_out = native_w8a8_block_matmul(A_fp8, B_fp8, As, Bs, block_size,
                                        out_dtype)
-    out = w8a8_triton_block_scaled_mm(A_fp8, B_fp8, As, Bs, block_size,
-                                      out_dtype)
+    out = w8a8_block_fp8_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
 
     rel_diff = (torch.mean(
         torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))) /

tests/kernels/quantization/test_fp8_quant_group.py

@@ -20,11 +20,9 @@ from vllm.platforms import current_platform
         (8, 513, 64),  # Non-divisible (native only)
     ])
 @pytest.mark.parametrize("seed", [42])
-@pytest.mark.parametrize("use_ue8m0", [True, False])
 @torch.inference_mode()
 def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int,
-                                      group_size: int, seed: int,
-                                      use_ue8m0: bool) -> None:
+                                      group_size: int, seed: int) -> None:
     """Test QuantFP8 group quantization with various configurations.
 
     Tests both CUDA and native implementations, column-major scales,
@@ -40,8 +38,7 @@ def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int,
     group_shape = GroupShape(1, group_size)
     quant_op = QuantFP8(static=False,
                         group_shape=group_shape,
-                        column_major_scales=False,
-                        use_ue8m0=use_ue8m0)
+                        column_major_scales=False)
 
     # 1. Test native implementation (always available)
     x_quant_native, scales_native = quant_op.forward_native(x.clone())
@@ -51,15 +48,9 @@ def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int,
     # 2. Test column-major scales configuration
     quant_op_col = QuantFP8(static=False,
                             group_shape=group_shape,
-                            column_major_scales=True,
-                            use_ue8m0=use_ue8m0)
+                            column_major_scales=True)
     _, scales_col = quant_op_col.forward_native(x.clone())
-    assert scales_col.shape == (batch_size, expected_num_groups)
-    assert scales_col.stride(0) == 1
-    assert scales_col.stride(1) == batch_size
-
-    # Test column-major scales consistency
-    assert torch.allclose(scales_col, scales_native, rtol=1e-9, atol=1e-8)
+    assert scales_col.shape == (expected_num_groups, batch_size)
 
     # 3. Test CUDA implementation (only for divisible dimensions)
     if is_divisible:
@@ -77,9 +68,8 @@ def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int,
 
 
 @pytest.mark.parametrize("seed", [42])
-@pytest.mark.parametrize("use_ue8m0", [True, False])
 @torch.inference_mode()
-def test_quantfp8_group_multidimensional(seed: int, use_ue8m0: bool) -> None:
+def test_quantfp8_group_multidimensional(seed: int) -> None:
     current_platform.seed_everything(seed)
 
     group_size = 64
@@ -92,8 +82,7 @@ def test_quantfp8_group_multidimensional(seed: int, use_ue8m0: bool) -> None:
     group_shape = GroupShape(1, group_size)
     quant_op = QuantFP8(static=False,
                         group_shape=group_shape,
-                        column_major_scales=False,
-                        use_ue8m0=use_ue8m0)
+                        column_major_scales=False)
 
     x_quant, scales = quant_op.forward_native(x_3d.clone())
     assert x_quant.shape == x_3d.shape
@@ -102,8 +91,7 @@ def test_quantfp8_group_multidimensional(seed: int, use_ue8m0: bool) -> None:
     # Test column_major_scales with multi-dim
     quant_op_col = QuantFP8(static=False,
                             group_shape=group_shape,
-                            column_major_scales=True,
-                            use_ue8m0=use_ue8m0)
+                            column_major_scales=True)
     _, scales_col = quant_op_col.forward_native(x_3d.clone())
     assert scales_col.shape == (batch1, hidden_dim // group_size, batch2)
 

tests/model_executor/test_enabled_custom_ops.py

@@ -17,6 +17,8 @@ from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
 from vllm.model_executor.layers.layernorm import (RMSNorm,
                                                   dispatch_rocm_rmsnorm_func,
                                                   fused_add_rms_norm, rms_norm)
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    cutlass_scaled_mm, dispatch_w8a8_blockscale_func, w8a8_block_fp8_matmul)
 from vllm.platforms import current_platform
 
 RMS_NORM_SUPPORTED_DTYPES = [torch.float16, torch.bfloat16]
@@ -109,6 +111,34 @@ def test_enabled_ops_invalid(env: str):
             RMSNorm(1024).enabled()
 
 
+@pytest.mark.skipif(
+    not current_platform.is_rocm() or not current_platform.is_fp8_fnuz(),
+    reason="AITER is a feature exclusive for ROCm and FP8_FNUZ")
+@pytest.mark.parametrize("use_cutlass", [True, False])
+@pytest.mark.parametrize("use_rocm_aiter", ["0", "1"])
+@pytest.mark.parametrize("use_rocm_aiter_gemm_w8a8_blockscale", ["0", "1"])
+def test_w8a8_blockscale_dispatch(use_cutlass: bool, use_rocm_aiter: str,
+                                  use_rocm_aiter_gemm_w8a8_blockscale: str,
+                                  monkeypatch):
+
+    monkeypatch.setenv("VLLM_ROCM_USE_AITER", use_rocm_aiter)
+    monkeypatch.setenv("VLLM_ROCM_USE_AITER_LINEAR",
+                       use_rocm_aiter_gemm_w8a8_blockscale)
+
+    use_aiter_and_is_supported = (bool(int(use_rocm_aiter)) and bool(
+        int(use_rocm_aiter_gemm_w8a8_blockscale)))
+    block_scale_func = dispatch_w8a8_blockscale_func(
+        use_cutlass, use_aiter_and_is_supported=use_aiter_and_is_supported)
+    if use_cutlass:
+        assert block_scale_func == cutlass_scaled_mm
+    elif current_platform.is_rocm() and int(use_rocm_aiter) and int(
+            use_rocm_aiter_gemm_w8a8_blockscale):
+        assert block_scale_func == (
+            torch.ops.vllm.rocm_aiter_gemm_w8a8_blockscale)
+    else:
+        assert block_scale_func == w8a8_block_fp8_matmul
+
+
 @pytest.mark.parametrize("use_rocm_aiter", ["0", "1"])
 def test_topk_dispatch(use_rocm_aiter: str, monkeypatch):
     monkeypatch.setenv("VLLM_ROCM_USE_AITER", use_rocm_aiter)

tests/quantization/test_compressed_tensors.py

@@ -18,9 +18,6 @@ from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tenso
     CompressedTensorsW4A16Fp4, CompressedTensorsW4A16Sparse24,
     CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8,
     CompressedTensorsW8A16Fp8, CompressedTensorsWNA16)
-from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
-from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    W8A8BlockFp8LinearOp)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     cutlass_fp4_supported)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
@@ -745,35 +742,3 @@ def test_compressed_tensors_transforms_perplexity(vllm_runner, model, prompt,
         perplexity = llm.generate_prompt_perplexity([prompt])[0]
         print(perplexity)
         assert perplexity <= exp_perplexity
-
-
-def test_compressed_tensors_fp8_block_enabled(vllm_runner):
-    model_path = "RedHatAI/Qwen3-0.6B-FP8-BLOCK"
-    with vllm_runner(model_path) as llm:
-
-        fp8_dtype = current_platform.fp8_dtype()
-
-        def check_model(model):
-            layer = model.model.layers[0]
-
-            qkv_proj = layer.self_attn.qkv_proj
-            assert isinstance(qkv_proj.quant_method,
-                              CompressedTensorsLinearMethod)
-            assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Fp8)
-            assert isinstance(qkv_proj.scheme.w8a8_block_fp8_linear,
-                              W8A8BlockFp8LinearOp)
-
-            assert qkv_proj.weight.dtype is fp8_dtype
-            assert qkv_proj.weight_scale.dtype is torch.float32
-            assert len(qkv_proj.weight.shape) == 2
-            assert len(qkv_proj.weight_scale.shape) == 2
-
-            input_quant_op = \
-                qkv_proj.scheme.w8a8_block_fp8_linear.input_quant_op
-            assert isinstance(input_quant_op, QuantFP8)
-            assert input_quant_op._forward_method == input_quant_op.forward_cuda
-
-        llm.apply_model(check_model)
-
-        output = llm.generate_greedy("Hello my name is", max_tokens=20)
-        assert output

vllm/config/__init__.py

@@ -545,23 +545,6 @@ class VllmConfig:
                     # local attention.
                     self.scheduler_config.disable_hybrid_kv_cache_manager = True
 
-        def has_blocked_weights():
-            if self.quant_config is not None:
-                if hasattr(self.quant_config, "weight_block_size"):
-                    return self.quant_config.weight_block_size is not None
-                elif hasattr(self.quant_config, "has_blocked_weights"):
-                    return self.quant_config.has_blocked_weights()
-            return False
-
-        # Enable quant_fp8 CUDA ops (TODO disable in follow up)
-        # On H100 the CUDA kernel is faster than
-        # native implementation
-        # https://github.com/vllm-project/vllm/issues/25094
-        if has_blocked_weights():
-            custom_ops = self.compilation_config.custom_ops
-            if "none" not in custom_ops and "-quant_fp8" not in custom_ops:
-                custom_ops.append("+quant_fp8")
-
     def update_sizes_for_sequence_parallelism(self,
                                               possible_sizes: list) -> list:
         # remove the sizes that not multiple of tp_size when

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

@@ -644,14 +644,6 @@ class CompressedTensorsConfig(QuantizationConfig):
         # If no matches, return None
         return None
 
-    def has_blocked_weights(self) -> bool:
-        for scheme in self.target_scheme_map.values():
-            weight_quant = scheme.get("weights")
-            if (weight_quant is not None
-                    and weight_quant.strategy == QuantizationStrategy.BLOCK):
-                return True
-        return False
-
     @staticmethod
     def supports_cutlass_24(
             weight_quant: Optional[QuantizationArgs],

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

@@ -11,7 +11,7 @@ from torch.nn import Parameter
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    W8A8BlockFp8LinearOp, check_aiter_fp8_linear_support,
+    apply_fp8_block_linear, check_aiter_fp8_linear_support,
     create_fp8_input_scale, create_fp8_scale_parameter,
     create_fp8_weight_parameter, maybe_post_process_fp8_weight_block,
     process_fp8_weight_block_strategy, process_fp8_weight_channel_strategy,
@@ -41,30 +41,16 @@ class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
         self.strategy = weight_quant.strategy
         self.out_dtype = torch.get_default_dtype()
         self.is_static_input_scheme = is_static_input_scheme
-
-        self.weight_block_size = self.weight_quant.block_structure
-        if self.weight_block_size is not None:
-            self.act_q_group_shape = GroupShape(1, self.weight_block_size[0])
-        else:
         self.act_q_group_shape = GroupShape.PER_TENSOR \
             if is_static_input_scheme else GroupShape.PER_TOKEN
-
-        self.cutlass_block_fp8_supported = cutlass_block_fp8_supported()
-        self.use_aiter_and_is_supported = check_aiter_fp8_linear_support()
-
-        if self.weight_block_size is not None:
-            assert not self.is_static_input_scheme
-            self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp(
-                weight_group_shape=GroupShape(*self.weight_block_size),
-                act_quant_group_shape=self.act_q_group_shape,
-                cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
-                use_aiter_and_is_supported=self.use_aiter_and_is_supported,
-            )
-        else:
         self.fp8_linear = Fp8LinearOp(
             act_quant_static=self.is_static_input_scheme,
             act_quant_group_shape=self.act_q_group_shape)
 
+        self.weight_block_size = self.weight_quant.block_structure
+        self.cutlass_block_fp8_supported = cutlass_block_fp8_supported()
+        self.use_aiter_and_is_supported = check_aiter_fp8_linear_support()
+
     @classmethod
     def get_min_capability(cls) -> int:
         # lovelace and up
@@ -155,14 +141,13 @@ class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
                       x: torch.Tensor,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
 
-        if self.weight_block_size is not None:
-            return self.w8a8_block_fp8_linear.apply(
+        if layer.weight_block_size is not None:
+            return apply_fp8_block_linear(
+                layer,
                 input=x,
-                weight=layer.weight,
-                weight_scale=layer.weight_scale,
-                input_scale=layer.input_scale,
                 bias=bias,
-            )
+                cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
+                use_aiter_and_is_supported=self.use_aiter_and_is_supported)
 
         return self.fp8_linear.apply(input=x,
                                      weight=layer.weight,

vllm/model_executor/layers/quantization/deepgemm.py

@@ -42,7 +42,7 @@ def prepare_block_fp8_matmul_inputs(
     return M, N, K, C
 
 
-def w8a8_deepgemm_block_scaled_mm(
+def w8a8_block_fp8_matmul_deepgemm(
     A: torch.Tensor,
     B: torch.Tensor,
     As: torch.Tensor,
@@ -58,7 +58,7 @@ def w8a8_deepgemm_block_scaled_mm(
     return C
 
 
-def w8a8_deepgemm_block_scaled_mm_fake(
+def w8a8_block_fp8_matmul_deepgemm_fake(
     A: torch.Tensor,
     B: torch.Tensor,
     As: torch.Tensor,
@@ -72,7 +72,7 @@ def w8a8_deepgemm_block_scaled_mm_fake(
 
 
 direct_register_custom_op(
-    op_name="w8a8_deepgemm_block_scaled_mm",
-    op_func=w8a8_deepgemm_block_scaled_mm,
-    fake_impl=w8a8_deepgemm_block_scaled_mm_fake,
+    op_name="w8a8_block_fp8_matmul_deepgemm",
+    op_func=w8a8_block_fp8_matmul_deepgemm,
+    fake_impl=w8a8_block_fp8_matmul_deepgemm_fake,
 )

vllm/model_executor/layers/quantization/fp8.py

@@ -33,7 +33,7 @@ from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
     register_moe_scaling_factors, rotate_flashinfer_fp8_moe_weights,
     select_cutlass_fp8_gemm_impl, swap_w13_to_w31)
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    W8A8BlockFp8LinearOp, check_aiter_fp8_linear_support,
+    apply_fp8_block_linear, check_aiter_fp8_linear_support,
     create_fp8_input_scale, create_fp8_scale_parameter,
     create_fp8_weight_parameter, expert_weight_is_col_major,
     maybe_post_process_fp8_weight_block, process_fp8_weight_block_strategy,
@@ -242,25 +242,12 @@ class Fp8LinearMethod(LinearMethodBase):
         self.weight_block_size = self.quant_config.weight_block_size
         self.block_quant = self.weight_block_size is not None
         self.act_q_static = self.quant_config.activation_scheme == "static"
-        if self.weight_block_size:
-            self.act_q_group_shape = GroupShape(1, self.weight_block_size[0])
-        else:
         # Use per-token quantization for better perf if dynamic and cutlass
         if not self.act_q_static and cutlass_fp8_supported():
             self.act_q_group_shape = GroupShape.PER_TOKEN
         else:
             self.act_q_group_shape = GroupShape.PER_TENSOR
 
-        if self.block_quant:
-            assert not self.act_q_static
-            assert self.weight_block_size is not None
-            self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp(
-                weight_group_shape=GroupShape(*self.weight_block_size),
-                act_quant_group_shape=self.act_q_group_shape,
-                cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
-                use_aiter_and_is_supported=self.use_aiter_and_is_supported,
-            )
-        else:
         self.fp8_linear = Fp8LinearOp(
             act_quant_static=self.act_q_static,
             act_quant_group_shape=self.act_q_group_shape)
@@ -412,15 +399,12 @@ class Fp8LinearMethod(LinearMethodBase):
                 bias=bias)
 
         if self.block_quant:
-            assert self.weight_block_size is not None
-
-            return self.w8a8_block_fp8_linear.apply(
+            return apply_fp8_block_linear(
+                layer,
                 input=x,
-                weight=layer.weight,
-                weight_scale=layer.weight_scale,
-                input_scale=layer.input_scale,
                 bias=bias,
-            )
+                cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
+                use_aiter_and_is_supported=self.use_aiter_and_is_supported)
 
         return self.fp8_linear.apply(input=x,
                                      weight=layer.weight,

vllm/model_executor/layers/quantization/input_quant_fp8.py

@@ -27,14 +27,11 @@ class QuantFP8(CustomOp):
     This CustomOp supports both static and dynamic quantization.
     """
 
-    def __init__(
-            self,
+    def __init__(self,
                  static: bool,
                  group_shape: GroupShape,
                  num_token_padding: Optional[int] = None,
-            column_major_scales: bool = False,
-            use_ue8m0: Optional[bool] = None,  # for Torch compile
-    ):
+                 column_major_scales: bool = False):
         """
         :param static: static or dynamic quantization
         :param group_shape: quantization group shape (PER_TOKEN, PER_TENSOR,
@@ -49,7 +46,6 @@ class QuantFP8(CustomOp):
         self.group_shape = group_shape
         self.num_token_padding = num_token_padding
         self.column_major_scales = column_major_scales
-        self.use_ue8m0 = use_ue8m0
 
         self.is_group_quant = group_shape.is_per_group()
         if self.is_group_quant:
@@ -74,8 +70,7 @@ class QuantFP8(CustomOp):
                 x,
                 group_size=self.group_size,
                 column_major_scales=self.column_major_scales,
-                dtype=_FP8_DTYPE,
-                use_ue8m0=self.use_ue8m0)
+                dtype=_FP8_DTYPE)
 
         assert (scale is not None) == self.static
         assert scale_ub is None or (not self.static and self.group_shape
@@ -142,10 +137,7 @@ class QuantFP8(CustomOp):
 
         x_grouped = x.view(-1, num_groups, self.group_size)
         absmax = x_grouped.abs().max(dim=-1, keepdim=True)[0].float()
-        scales_raw = absmax / _FP8_MAX
-        if self.use_ue8m0:
-            scales_raw = torch.exp2(torch.ceil(torch.log2(scales_raw)))
-        scales = (scales_raw).clamp(min=_FP8_MIN_SCALING_FACTOR)
+        scales = (absmax / _FP8_MAX).clamp(min=_FP8_MIN_SCALING_FACTOR)
 
         x_scaled = x_grouped / scales
         x_quant = x_scaled.clamp(_FP8_MIN, _FP8_MAX).to(_FP8_DTYPE)
@@ -159,6 +151,6 @@ class QuantFP8(CustomOp):
         scales = scales.reshape(orig_shape[:-1] + (num_groups, ))
 
         if self.column_major_scales:
-            scales = scales.transpose(-2, -1).contiguous().transpose(-1, -2)
+            scales = scales.transpose(-2, -1).contiguous()
 
         return x_quant, scales

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

@@ -13,9 +13,8 @@ import torch
 import vllm.envs as envs
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
-from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    GroupShape, group_broadcast)
+    group_broadcast)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     CUTLASS_BLOCK_FP8_SUPPORTED)
 from vllm.model_executor.parameter import (BlockQuantScaleParameter,
@@ -25,7 +24,6 @@ from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils import direct_register_custom_op
 from vllm.utils.deep_gemm import (is_deep_gemm_e8m0_used,
-                                  is_deep_gemm_supported,
                                   should_use_deepgemm_for_fp8_linear)
 
 logger = init_logger(__name__)
@@ -37,8 +35,6 @@ def is_fp8(x: Union[torch.dtype, torch.Tensor]) -> bool:
     return x == torch.float8_e4m3fn or x == torch.float8_e4m3fnuz
 
 
-# We need to pass in the is_hopper flag as argument because the function
-# current_platform.is_device_capability() is not supported by Torch compiler.
 def cutlass_scaled_mm(
     A: torch.Tensor,
     B: torch.Tensor,
@@ -46,17 +42,15 @@ def cutlass_scaled_mm(
     Bs: torch.Tensor,
     block_size: list[int],
     output_dtype: torch.dtype = torch.float16,
-    is_hopper: Optional[bool] = None,
 ) -> torch.Tensor:
-    if is_hopper is None:
-        is_hopper = current_platform.is_device_capability(90)
     return ops.cutlass_scaled_mm(
         A,
         B.T,
         out_dtype=output_dtype,
         scale_a=As,
         # SM90 block FP8 requires row-major scale_b, which we do ahead of time
-        scale_b=Bs if block_size is not None and is_hopper else Bs.T)
+        scale_b=Bs if block_size is not None
+        and current_platform.is_device_capability(90) else Bs.T)
 
 
 def rocm_aiter_gemm_w8a8_blockscale_impl(
@@ -102,190 +96,122 @@ if current_platform.is_rocm():
         aiter_per1x128_quant = get_hip_quant(rocm_aiter.QuantType.per_1x128)
 
 
-# TODO we should be able to change the type of block_size to GroupShape
-# after we resolve GroupShape compilation issue
-# https://github.com/vllm-project/vllm/issues/25270
-def _w8a8_triton_block_scaled_mm_func(
-    qx: torch.Tensor,
-    weight: torch.Tensor,
-    x_scale: torch.Tensor,
-    weight_scale: torch.Tensor,
-    block_size: list[int],
-    output_dtype: torch.dtype,
-) -> torch.Tensor:
-    return w8a8_triton_block_scaled_mm(qx, weight, x_scale, weight_scale,
-                                       block_size, output_dtype)
-
-
-def _w8a8_triton_block_scaled_mm_fake(
-    qx: torch.Tensor,
-    weight: torch.Tensor,
-    x_scale: torch.Tensor,
-    weight_scale: torch.Tensor,
-    block_size: list[int],
-    output_dtype: torch.dtype,
-) -> torch.Tensor:
-    return torch.empty((qx.size(0), weight.size(0)),
-                       dtype=output_dtype,
-                       device=qx.device)
-
-
-# Note: the check can be removed when CPU torch > 2.7
-if not current_platform.is_cpu():
-    direct_register_custom_op(
-        "w8a8_triton_block_scaled_mm_func",
-        _w8a8_triton_block_scaled_mm_func,
-        fake_impl=_w8a8_triton_block_scaled_mm_fake,
-        dispatch_key="CUDA",
-    )
+def dispatch_w8a8_blockscale_func(
+    use_cutlass: bool, use_aiter_and_is_supported: bool
+) -> Callable[[
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        list[int],
+        torch.dtype,
+], torch.Tensor]:
+    if use_cutlass:
+        return cutlass_scaled_mm
+    if (use_aiter_and_is_supported):
+        return torch.ops.vllm.rocm_aiter_gemm_w8a8_blockscale
+    return w8a8_block_fp8_matmul
 
 
 # TODO fix ROCm->Triton custom path:
 #  https://github.com/vllm-project/vllm/issues/14397
-class W8A8BlockFp8LinearOp:
-    """
-    This class executes a Blocked FP8 linear layer using cutlass if supported
-    and torch.scaled_mm otherwise.
-    """
-
-    def __init__(
-        self,
-        weight_group_shape: GroupShape,
-        act_quant_group_shape: GroupShape,
-        cutlass_block_fp8_supported: bool = CUTLASS_BLOCK_FP8_SUPPORTED,
-        use_aiter_and_is_supported: bool = False,
-    ):
-        self.weight_group_shape = weight_group_shape
-        self.act_quant_group_shape = act_quant_group_shape
-        self.is_deep_gemm_supported = is_deep_gemm_supported()
-        self.is_hopper = current_platform.is_device_capability(90)
-
-        # Get the correct blockscale mul and input quant operations.
-        # We can't use _dispatch_w8a8_blockscale_op to figure out if we want
-        # to use deepgemm because we don't know the shape of weights (and
-        # whether deepgemm supports it) at the init time.
-        self.w8a8_blockscale_op, self.input_quant_op = \
-         self._dispatch_w8a8_blockscale_op(
-            cutlass_block_fp8_supported, use_aiter_and_is_supported)
-        self.deepgemm_input_quant_op = (QuantFP8(
-            False,
-            self.act_quant_group_shape,
-            column_major_scales=True,
-            use_ue8m0=is_deep_gemm_e8m0_used()) if self.is_deep_gemm_supported
-                                        else None)
-
-    def apply(
-        self,
+def apply_w8a8_block_fp8_linear(
     input: torch.Tensor,
     weight: torch.Tensor,
+    block_size: list[int],
     weight_scale: torch.Tensor,
     input_scale: Optional[torch.Tensor] = None,
     bias: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
+    cutlass_block_fp8_supported: bool = CUTLASS_BLOCK_FP8_SUPPORTED,
+    use_aiter_and_is_supported: bool = False,
+) -> torch.Tensor:
     assert input_scale is None
     # View input as 2D matrix for fp8 methods
     input_2d = input.view(-1, input.shape[-1])
     output_shape = [*input.shape[:-1], weight.shape[0]]
     output_dtype = input.dtype
 
-        if should_use_deepgemm_for_fp8_linear(output_dtype, weight,
-                                              self.is_deep_gemm_supported):
-            output = self._run_deepgemm(input, weight, weight_scale)
-            if bias is not None:
-                output = output + bias
-            return output.to(dtype=input.dtype).view(*output_shape)
+    if should_use_deepgemm_for_fp8_linear(output_dtype, weight):
 
-        output = self.w8a8_blockscale_op(input_2d, weight, weight_scale)
-        if bias is not None:
-            output = output + bias
-        return output.to(dtype=input.dtype).view(*output_shape)
+        input_2d = input.view(-1, input.shape[-1])
+        output_shape = [*input.shape[:-1], weight.shape[0]]
+
+        q_input, x_scale = per_token_group_quant_fp8(
+            input_2d,
+            block_size[1],
+            column_major_scales=True,
+        )
 
-    def _run_deepgemm(
-        self,
-        input_2d: torch.Tensor,
-        weight: torch.Tensor,
-        weight_scale: torch.Tensor,
-    ) -> torch.Tensor:
         # ensure DeepGEMM-backed custom op is registered before use
         import vllm.model_executor.layers.quantization.deepgemm  # noqa: F401
 
-        assert self.deepgemm_input_quant_op is not None
-        q_input, x_scale = self.deepgemm_input_quant_op(input_2d)
-        return torch.ops.vllm.w8a8_deepgemm_block_scaled_mm(
+        output = torch.ops.vllm.w8a8_block_fp8_matmul_deepgemm(
             q_input,
             weight,
             x_scale,
             weight_scale,
-            self.weight_group_shape,
-            output_dtype=input_2d.dtype)
+            block_size,
+            output_dtype=output_dtype)
+        if bias is not None:
+            output += bias
+        return output.to(dtype=output_dtype).view(*output_shape)
 
-    def _run_cutlass(
-        self,
-        input_2d: torch.Tensor,
-        weight: torch.Tensor,
-        weight_scale: torch.Tensor,
-    ) -> torch.Tensor:
-        assert self.input_quant_op is not None
-        if self.is_hopper:
-            # We pad unconditionally (even if shape is already divisible by 4)
-            # to support dynamic shape for input_2d.shape[0] in torch.compile
-            x = torch.nn.functional.pad(input_2d,
-                                        (0, 0, 0, -input_2d.shape[0] % 4))
+    w8a8_blockscale_func = dispatch_w8a8_blockscale_func(
+        cutlass_block_fp8_supported, use_aiter_and_is_supported)
+    if cutlass_block_fp8_supported:
+        num_pad = 0
+        if current_platform.is_device_capability(90):
+            # pad first dimension to be divisible by 4 due to
+            # cutlass blockwise gemm limitation for hopper
+            num_pad = 4 - (input_2d.shape[0] % 4)
+            if num_pad > 0:
+                input_2d = torch.nn.functional.pad(input_2d,
+                                                   (0, 0, 0, num_pad),
+                                                   "constant", 0)
+        q_input, x_scale = per_token_group_quant_fp8(input_2d,
+                                                     block_size[1],
+                                                     column_major_scales=True)
+        output = w8a8_blockscale_func(q_input, weight, x_scale, weight_scale,
+                                      block_size, input.dtype)
+        if num_pad > 0:
+            output = output[:-num_pad]
     else:
-            x = input_2d
-
-        q_input, x_scale = self.input_quant_op(x)
-        output = cutlass_scaled_mm(q_input, weight, x_scale, weight_scale,
-                                   list(self.weight_group_shape),
-                                   input_2d.dtype, self.is_hopper)
-        output = output[0:input_2d.shape[0], ...]
-        return output
-
-    def _run_aiter(
-        self,
-        input_2d: torch.Tensor,
-        weight: torch.Tensor,
-        weight_scale: torch.Tensor,
-    ) -> torch.Tensor:
-        assert self.act_quant_group_shape == GroupShape(1, 128)
+        if use_aiter_and_is_supported:
             q_input, x_scale = aiter_per1x128_quant(
                 input_2d.contiguous(), quant_dtype=rocm_aiter.dtypes.fp8)
-        return torch.ops.vllm.rocm_aiter_gemm_w8a8_blockscale(
-            q_input, weight, x_scale, weight_scale, self.weight_group_shape,
-            input_2d.dtype)
+        else:
+            q_input, x_scale = per_token_group_quant_fp8(
+                input_2d, block_size[1], column_major_scales=False)
 
-    def _run_triton(
-        self,
-        input_2d: torch.Tensor,
+        output = w8a8_blockscale_func(q_input, weight, x_scale, weight_scale,
+                                      block_size, input.dtype)
+
+    if bias is not None:
+        output = output + bias
+    return output.to(dtype=input.dtype).view(*output_shape)
+
+
+def apply_w8a8_block_fp8_linear_fake(
+    input: torch.Tensor,
     weight: torch.Tensor,
+    block_size: list[int],
     weight_scale: torch.Tensor,
-    ) -> torch.Tensor:
-        assert self.input_quant_op is not None
-        q_input, x_scale = self.input_quant_op(input_2d)
-        return torch.ops.vllm.w8a8_triton_block_scaled_mm_func(
-            q_input, weight, x_scale, weight_scale, self.weight_group_shape,
-            input_2d.dtype)
-
-    def _dispatch_w8a8_blockscale_op(
-        self,
-        use_cutlass: bool,
-        use_aiter_and_is_supported: bool,
-    ) -> tuple[Callable[[
-            torch.Tensor,
-            torch.Tensor,
-            torch.Tensor,
-    ], torch.Tensor], Optional[QuantFP8]]:
-        if use_cutlass:
-            return self._run_cutlass, (QuantFP8(False,
-                                                self.act_quant_group_shape,
-                                                column_major_scales=True,
-                                                use_ue8m0=False))
-        if use_aiter_and_is_supported:
-            return self._run_aiter, None
-        return self._run_triton, (QuantFP8(False,
-                                           self.act_quant_group_shape,
-                                           column_major_scales=False,
-                                           use_ue8m0=False))
+    input_scale: Optional[torch.Tensor] = None,
+    bias: Optional[torch.Tensor] = None,
+    cutlass_block_fp8_supported: bool = CUTLASS_BLOCK_FP8_SUPPORTED,
+    use_aiter_and_is_supported: bool = False,
+) -> torch.Tensor:
+    output_shape = [*input.shape[:-1], weight.shape[0]]
+    return torch.empty(output_shape, dtype=input.dtype, device=input.device)
+
+
+if not current_platform.is_cpu():
+    direct_register_custom_op(
+        op_name="apply_w8a8_block_fp8_linear",
+        op_func=apply_w8a8_block_fp8_linear,
+        mutates_args=[],
+        fake_impl=apply_w8a8_block_fp8_linear_fake,
+    )
 
 
 def input_to_float8(
@@ -537,7 +463,7 @@ def per_token_group_quant_fp8(
 
 
 @triton.jit
-def _w8a8_triton_block_scaled_mm(
+def _w8a8_block_fp8_matmul(
     # Pointers to inputs and output
     A,
     B,
@@ -662,7 +588,7 @@ def get_w8a8_block_fp8_configs(N: int, K: int, block_n: int,
     return None
 
 
-def w8a8_triton_block_scaled_mm(
+def w8a8_block_fp8_matmul(
     A: torch.Tensor,
     B: torch.Tensor,
     As: torch.Tensor,
@@ -722,7 +648,7 @@ def w8a8_triton_block_scaled_mm(
         return (triton.cdiv(M, META["BLOCK_SIZE_M"]) *
                 triton.cdiv(N, META["BLOCK_SIZE_N"]), )
 
-    _w8a8_triton_block_scaled_mm[grid](
+    _w8a8_block_fp8_matmul[grid](
         A,
         B,
         C,
@@ -1005,6 +931,25 @@ def maybe_post_process_fp8_weight_block(layer: torch.nn.Module,
             layer.weight_scale.data.T.contiguous(), requires_grad=False)
 
 
+def apply_fp8_block_linear(layer: torch.nn.Module, input: torch.Tensor,
+                           bias: Optional[torch.Tensor],
+                           cutlass_block_fp8_supported: bool,
+                           use_aiter_and_is_supported: bool) -> torch.Tensor:
+    """Apply block-wise FP8 linear operation."""
+    assert layer.weight_block_size is not None
+
+    return torch.ops.vllm.apply_w8a8_block_fp8_linear(
+        input=input,
+        weight=layer.weight,
+        block_size=layer.weight_block_size,
+        weight_scale=layer.weight_scale,
+        input_scale=layer.input_scale,
+        bias=bias,
+        cutlass_block_fp8_supported=cutlass_block_fp8_supported,
+        use_aiter_and_is_supported=use_aiter_and_is_supported,
+    )
+
+
 def expert_weight_is_col_major(x: torch.Tensor) -> bool:
     assert x.dim() == 3
     b, m, n = x.shape

vllm/utils/deep_gemm.py

@@ -9,7 +9,7 @@ from __future__ import annotations
 import functools
 import importlib
 import os
-from typing import Any, Callable, NoReturn, Optional
+from typing import Any, Callable, NoReturn
 
 import torch
 
@@ -184,13 +184,9 @@ 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,
-        supports_deep_gemm: Optional[bool] = None):
-    if supports_deep_gemm is None:
-        supports_deep_gemm = is_deep_gemm_supported()
-    return (supports_deep_gemm and output_dtype == torch.bfloat16
+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)