[Kernel] Added flashinfer fp8 per-tensor gemms (#22895)

Signed-off-by: Julien Lin <jullin@nvidia.com>
Co-authored-by: Michael Goin <mgoin64@gmail.com>

.buildkite/test-pipeline.yaml

@@ -655,6 +655,7 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
     - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
     - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
+    - pytest -v -s tests/kernels/quantization/test_flashinfer_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_mxfp4_moe.py

tests/compile/test_fusion.py

@@ -15,7 +15,7 @@ from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape, QuantKey, ScaleDesc)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
-    CUTLASS_FP8_SUPPORTED, Fp8LinearOp, maybe_create_device_identity)
+    Fp8LinearOp, maybe_create_device_identity)
 from vllm.platforms import current_platform
 
 from .backend import TestBackend
@@ -26,9 +26,9 @@ FP8_DTYPE = current_platform.fp8_dtype()
 class TestModel(torch.nn.Module):
 
     def __init__(self, hidden_size: int, eps: float, static: bool,
-                 cutlass_fp8_enabled: bool, *args, **kwargs):
+                 force_fp8_e4m3fnuz: bool, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.cutlass_fp8_enabled = cutlass_fp8_enabled
+        self.force_fp8_e4m3fnuz = force_fp8_e4m3fnuz
         self.norm = [RMSNorm(hidden_size, eps) for _ in range(3)]
         self.wscale = [torch.rand(1, dtype=torch.float32) for _ in range(2)]
         group_shape = GroupShape.PER_TENSOR if static else GroupShape.PER_TOKEN
@@ -43,7 +43,7 @@ class TestModel(torch.nn.Module):
             for _ in range(2)
         ]
         self.fp8_linear = Fp8LinearOp(
-            cutlass_fp8_supported=cutlass_fp8_enabled,
+            force_fp8_e4m3fnuz=force_fp8_e4m3fnuz,
             act_quant_static=static,
             act_quant_group_shape=group_shape,
         )
@@ -81,12 +81,11 @@ class TestModel(torch.nn.Module):
 @pytest.mark.parametrize("num_tokens", [7, 256, 533, 2048, 2049])
 @pytest.mark.parametrize("eps", [1e-5, 1e-6])
 @pytest.mark.parametrize("static", [True, False])
-@pytest.mark.parametrize("cutlass_fp8_enabled",
-                         [True, False] if CUTLASS_FP8_SUPPORTED else [False])
+@pytest.mark.parametrize("force_fp8_e4m3fnuz", [True, False])
 @pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda", "rocm"],
                     reason="Only test on CUDA and ROCm")
 def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static,
-                              cutlass_fp8_enabled):
+                              force_fp8_e4m3fnuz):
     torch.set_default_device("cuda")
     torch.set_default_dtype(dtype)
     torch.manual_seed(1)
@@ -103,7 +102,7 @@ def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static,
         fusion_pass = FusionPass.instance(vllm_config)
 
         backend = TestBackend(noop_pass, fusion_pass)
-        model = TestModel(hidden_size, eps, static, cutlass_fp8_enabled)
+        model = TestModel(hidden_size, eps, static, force_fp8_e4m3fnuz)
 
         # First dimension dynamic
         x = torch.rand(num_tokens, hidden_size)

tests/compile/test_sequence_parallelism.py

@@ -104,8 +104,7 @@ class TestQuantModel(torch.nn.Module):
         # Initialize weights
         torch.nn.init.normal_(self.gate_proj, std=0.02)
 
-        self.fp8_linear = Fp8LinearOp(cutlass_fp8_supported=True,
-                                      use_per_token_if_dynamic=False)
+        self.fp8_linear = Fp8LinearOp(use_per_token_if_dynamic=False)
 
         self.scale = torch.rand(1, dtype=torch.float32)
         # Create a weight that is compatible with torch._scaled_mm,

tests/compile/test_silu_mul_quant_fusion.py

@@ -12,7 +12,7 @@ from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
-    CUTLASS_FP8_SUPPORTED, Fp8LinearOp)
+    Fp8LinearOp)
 from vllm.platforms import current_platform
 
 from .backend import TestBackend
@@ -20,7 +20,7 @@ from .backend import TestBackend
 
 class TestModel(torch.nn.Module):
 
-    def __init__(self, hidden_size: int, cutlass_fp8_enabled: bool, *args,
+    def __init__(self, hidden_size: int, force_fp8_e4m3fnuz: bool, *args,
                  **kwargs):
         super().__init__(*args, **kwargs)
         self.silu_and_mul = SiluAndMul()
@@ -32,7 +32,7 @@ class TestModel(torch.nn.Module):
             hidden_size).to(dtype=current_platform.fp8_dtype()).t())
 
         self.fp8_linear = Fp8LinearOp(
-            cutlass_fp8_supported=cutlass_fp8_enabled,
+            force_fp8_e4m3fnuz=force_fp8_e4m3fnuz,
             act_quant_static=True,
             act_quant_group_shape=GroupShape.PER_TENSOR,
         )
@@ -48,12 +48,11 @@ class TestModel(torch.nn.Module):
 
 @pytest.mark.parametrize("num_tokens", [256])
 @pytest.mark.parametrize("hidden_size", [64])
-@pytest.mark.parametrize("cutlass_fp8_enabled",
-                         [True, False] if CUTLASS_FP8_SUPPORTED else [False])
+@pytest.mark.parametrize("force_fp8_e4m3fnuz", [True, False])
 @pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda", "rocm"],
                     reason="Only test on CUDA and ROCm")
 def test_fusion_silu_and_mul_quant(num_tokens, hidden_size,
-                                   cutlass_fp8_enabled):
+                                   force_fp8_e4m3fnuz):
     torch.set_default_device("cuda")
     torch.set_default_dtype(torch.float16)
 
@@ -64,7 +63,7 @@ def test_fusion_silu_and_mul_quant(num_tokens, hidden_size,
     fusion_pass = ActivationQuantFusionPass(config)
 
     backend = TestBackend(NoOpEliminationPass(config), fusion_pass)
-    model = TestModel(hidden_size, cutlass_fp8_enabled)
+    model = TestModel(hidden_size, force_fp8_e4m3fnuz)
 
     # First dimension dynamic
     x = torch.rand(num_tokens, hidden_size * 2)

tests/kernels/quantization/test_flashinfer_scaled_mm.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
+from vllm.utils.flashinfer import flashinfer_scaled_fp8_mm
+
+if not current_platform.has_device_capability(100):
+    pytest.skip(
+        reason=
+        "Flashinfer FP8 gemms requires compute capability of 10.0 or above.",
+        allow_module_level=True,
+    )
+
+DTYPES = [torch.float16, torch.bfloat16]
+# m, n, k
+SHAPES = [(128, 128, 64), (128, 128, 128), (256, 128, 64), (128, 256, 128)]
+PAD_SHAPES = [(150, 128, 64), (128, 128, 96)]
+SHAPES.extend(PAD_SHAPES)
+
+SEEDS = [42]
+CUDA_DEVICES = ["cuda:0"]
+
+
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("shape", SHAPES)
+@pytest.mark.parametrize("use_bias", [True, False])
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("autotune", [False, True])
+@torch.inference_mode()
+def test_flashinfer_fp8_gemm(
+    dtype: torch.dtype,
+    shape: tuple[int, int, int],
+    use_bias: bool,
+    seed: int,
+    device: str,
+    autotune: bool,
+) -> None:
+    current_platform.seed_everything(seed)
+    m, n, k = shape
+    a = torch.randn((m, k), dtype=dtype, device=device)
+    b = torch.randn((n, k), dtype=dtype, device=device) / k
+
+    a_fp8, a_scale = ops.scaled_fp8_quant(a)
+    b_fp8, b_scale = ops.scaled_fp8_quant(b)
+
+    expected_out = torch.mm(
+        a_scale * a_fp8.to(dtype=torch.float32),
+        b_scale * b_fp8.to(dtype=torch.float32).t(),
+    ).to(dtype=dtype)
+
+    if use_bias:
+        bias = torch.randn((n, ), dtype=dtype, device=device)
+        expected_out = expected_out + bias
+    else:
+        bias = None
+
+    import flashinfer
+
+    with flashinfer.autotune(autotune):
+        out = flashinfer_scaled_fp8_mm(
+            a_fp8,
+            b_fp8.t(),
+            a_scale,
+            b_scale,
+            dtype,
+            bias=bias,
+        )
+
+    torch.testing.assert_close(out, expected_out, atol=1e-2, rtol=1e-2)

vllm/model_executor/layers/quantization/fp8.py

@@ -223,8 +223,7 @@ class Fp8LinearMethod(LinearMethodBase):
 
         self.fp8_linear = Fp8LinearOp(
             act_quant_static=self.act_q_static,
-            act_quant_group_shape=self.act_q_group_shape,
-            cutlass_fp8_supported=cutlass_fp8_supported())
+            act_quant_group_shape=self.act_q_group_shape)
 
     def create_weights(
         self,
@@ -376,6 +375,8 @@ class Fp8LinearMethod(LinearMethodBase):
             # Update the layer with the new values.
             layer.weight = Parameter(qweight.t(), requires_grad=False)
             layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+            # layer.input_scale is None indicates dynamic quant and scale is
+            # computed from input.
             layer.input_scale = None
 
         # If checkpoint is fp8, handle that there are N scales for N

vllm/model_executor/layers/quantization/ptpc_fp8.py

@@ -97,8 +97,8 @@ class PTPCFp8LinearMethod(Fp8LinearMethod):
         self.quant_config.is_checkpoint_fp8_serialized = False
         self.fp8_linear = Fp8LinearOp(
             act_quant_static=False,
-            cutlass_fp8_supported=False,
-            act_quant_group_shape=GroupShape.PER_TOKEN)
+            act_quant_group_shape=GroupShape.PER_TOKEN,
+            force_fp8_e4m3fnuz=True)
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         layer.weight = torch.nn.Parameter(layer.weight.data,

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

@@ -14,6 +14,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape)
 from vllm.platforms import current_platform
 from vllm.utils import direct_register_custom_op
+from vllm.utils.flashinfer import flashinfer_scaled_fp8_mm, has_flashinfer
 
 # Input scaling factors are no longer optional in _scaled_mm starting
 # from pytorch 2.5. Allocating a dummy tensor to pass as input_scale
@@ -157,6 +158,19 @@ def cutlass_w8a8_scaled_mm(*, qinput: torch.Tensor, weight: torch.Tensor,
     return output.view(*output_shape)
 
 
+def flashinfer_w8a8_scaled_mm(*, qinput: torch.Tensor, weight: torch.Tensor,
+                              out_dtype: torch.dtype, scale_a: torch.Tensor,
+                              scale_b: torch.Tensor, bias: torch.Tensor,
+                              output_shape: list, **kwargs) -> torch.Tensor:
+
+    return flashinfer_scaled_fp8_mm(qinput,
+                                    weight,
+                                    out_dtype=out_dtype,
+                                    scale_a=scale_a,
+                                    scale_b=scale_b,
+                                    bias=bias)
+
+
 def rocm_per_tensor_w8a8_scaled_mm_impl(
         qinput: torch.Tensor, weight: torch.Tensor, out_dtype: torch.dtype,
         scale_a: torch.Tensor, scale_b: torch.Tensor, bias: torch.Tensor,
@@ -231,8 +245,8 @@ def torch_per_token_w8a8_scaled_mm(*, qinput: torch.Tensor,
                                    out_dtype: torch.dtype,
                                    scale_a: torch.Tensor,
                                    scale_b: torch.Tensor, bias: torch.Tensor,
-                                   input_2d: torch.Tensor,
-                                   output_shape: list) -> torch.Tensor:
+                                   input_2d: torch.Tensor, output_shape: list,
+                                   **kwargs) -> torch.Tensor:
     # Note: Callers of this function should check USE_ROWWISE_TORCH_SCALED_MM
     #  when using it.
     #  For now it has only been validated on ROCm platform.
@@ -303,16 +317,22 @@ def torch_channelwise_w8a8_scaled_mm(*, qinput: torch.Tensor,
 
 
 def dispatch_w8a8_scaled_mm(
-        cutlass_fp8_supported: bool, per_tensor_weights: bool,
+        preferred_backend: str, per_tensor_weights: bool,
         per_tensor_activations: bool) -> Callable[..., torch.Tensor]:
 
-    # cutlass_scaled_mm supports per tensor/channel W and per tensor/token A
-    if cutlass_fp8_supported:
-        return cutlass_w8a8_scaled_mm
     if per_tensor_weights and per_tensor_activations:
-        if current_platform.is_rocm():
+        if preferred_backend == "rocm":
             return rocm_per_tensor_w8a8_scaled_mm
+        if preferred_backend == "flashinfer":
+            return flashinfer_w8a8_scaled_mm
+        if preferred_backend == "cutlass":
+            return cutlass_w8a8_scaled_mm
         return torch_per_tensor_w8a8_scaled_mm
+
+    # cutlass_scaled_mm supports per tensor/channel W and per tensor/token A
+    if preferred_backend == "cutlass" or preferred_backend == "flashinfer":
+        return cutlass_w8a8_scaled_mm
+
     # If torch.scaled_mm supports per-channel (weights) per-token (inputs)
     if not per_tensor_weights and not per_tensor_activations \
             and USE_ROWWISE_TORCH_SCALED_MM:
@@ -334,10 +354,20 @@ class Fp8LinearOp:
 
     def __init__(self,
                  act_quant_static: bool,
-                 cutlass_fp8_supported: bool = cutlass_fp8_supported(),
                  act_quant_group_shape: GroupShape = GroupShape.PER_TENSOR,
-                 pad_output: Optional[bool] = None):
-        self.cutlass_fp8_supported = cutlass_fp8_supported
+                 pad_output: Optional[bool] = None,
+                 force_fp8_e4m3fnuz: bool = False):
+        if current_platform.is_rocm():
+            self.preferred_backend = "rocm"
+        elif current_platform.is_cuda(
+        ) and not force_fp8_e4m3fnuz and cutlass_fp8_supported():
+            if has_flashinfer() and current_platform.has_device_capability(
+                    100):
+                self.preferred_backend = "flashinfer"
+            else:
+                self.preferred_backend = "cutlass"
+        else:
+            self.preferred_backend = "torch"
 
         # Note: we pad the input because torch._scaled_mm is more performant
         # for matrices with batch dimension > 16.
@@ -347,8 +377,7 @@ class Fp8LinearOp:
         if pad_output is None:
             config = get_current_vllm_config().compilation_config
             pad_output = config.level < CompilationLevel.PIECEWISE and \
-                         not cutlass_fp8_supported and \
-                         not current_platform.is_rocm()
+                         self.preferred_backend == "torch"
 
         self.output_padding = 17 if pad_output else None
         self.act_quant_static = act_quant_static
@@ -393,9 +422,9 @@ class Fp8LinearOp:
         per_tensor_activations = (x_scale.numel() == 1)
 
         # TODO(luka) do this dispatch during init (after ScaledMM refactor)
-        w8a8_scaled_mm_func = dispatch_w8a8_scaled_mm(
-            self.cutlass_fp8_supported, per_tensor_weights,
-            per_tensor_activations)
+        w8a8_scaled_mm_func = dispatch_w8a8_scaled_mm(self.preferred_backend,
+                                                      per_tensor_weights,
+                                                      per_tensor_activations)
 
         return w8a8_scaled_mm_func(qinput=qinput,
                                    weight=weight,

vllm/utils/flashinfer.py

@@ -265,6 +265,37 @@ if has_flashinfer():
                            dtype=dtype,
                            device=A.device)
 
+    @torch.library.custom_op(
+        "vllm::bmm_fp8",
+        mutates_args=[],
+        device_types="cuda",
+    )
+    def bmm_fp8(
+        A: torch.Tensor,
+        B: torch.Tensor,
+        A_scale: torch.Tensor,
+        B_scale: torch.Tensor,
+        dtype: torch.dtype,
+        backend: str,
+    ) -> torch.Tensor:
+        from flashinfer import bmm_fp8 as bmm_fp8_
+        return bmm_fp8_(A, B, A_scale, B_scale, dtype, None, backend)
+
+    @torch.library.register_fake("vllm::bmm_fp8", )
+    def bmm_fp8_fake(
+        A: torch.Tensor,
+        B: torch.Tensor,
+        A_scale: torch.Tensor,
+        B_scale: torch.Tensor,
+        dtype: torch.dtype,
+        backend: str,
+    ) -> torch.Tensor:
+        return torch.empty(A.shape[0],
+                           A.shape[1],
+                           B.shape[2],
+                           dtype=dtype,
+                           device=A.device)
+
 
 def flashinfer_scaled_fp4_mm(a: torch.Tensor, b: torch.Tensor,
                              block_scale_a: torch.Tensor,
@@ -293,6 +324,35 @@ def flashinfer_scaled_fp4_mm(a: torch.Tensor, b: torch.Tensor,
     )
 
 
+def flashinfer_scaled_fp8_mm(
+        a: torch.Tensor,
+        b: torch.Tensor,
+        scale_a: torch.Tensor,
+        scale_b: torch.Tensor,
+        out_dtype: torch.dtype,
+        bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    assert a.ndim == 2 and b.ndim == 2
+    assert a.shape[1] == b.shape[0]
+    assert scale_a.numel() == 1 and scale_b.numel() == 1
+    assert a.dtype == torch.float8_e4m3fn and b.dtype == torch.float8_e4m3fn
+    assert a.device.type == "cuda" and b.device.type == "cuda"
+    assert scale_a.dtype == torch.float32 and scale_b.dtype == torch.float32
+    assert scale_a.device.type == "cuda" and scale_b.device.type == "cuda"
+
+    output = bmm_fp8(
+        a.unsqueeze(0),
+        b.unsqueeze(0),
+        scale_a,
+        scale_b,
+        out_dtype,
+        "auto",
+    ).view(a.shape[0], b.shape[1])
+
+    if bias is not None:
+        output = output + bias
+    return output
+
+
 __all__ = [
     "has_flashinfer",
     "flashinfer_trtllm_fp8_block_scale_moe",
@@ -307,4 +367,5 @@ __all__ = [
     "supports_trtllm_attention",
     "use_trtllm_attention",
     "flashinfer_scaled_fp4_mm",
+    "flashinfer_scaled_fp8_mm",
 ]