[Perf][fp8] Use CustomOp abstraction for fp8 quant for better perf (#19830)

Signed-off-by: Luka Govedic <lgovedic@redhat.com>
Co-authored-by: mgoin <mgoin64@gmail.com>

Author: ProExpertProg

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import itertools
+from typing import Callable
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.config import CompilationConfig, VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
+from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
+from vllm.triton_utils import triton
+
+
+# TODO(luka): use standalone_compile utility
+def with_dyn_arg(fn: Callable, arg_index: int, dim_index: int):
+    def inner(*args):
+        torch._dynamo.mark_dynamic(args[arg_index], dim_index)
+        return fn(*args)
+
+    return inner
+
+
+torch._dynamo.config.recompile_limit = 8888
+compilation_config = CompilationConfig(custom_ops=["none"])
+with set_current_vllm_config(VllmConfig(compilation_config=compilation_config)):
+    torch_per_token_quant_fp8 = torch.compile(
+        QuantFP8(False, GroupShape.PER_TOKEN),
+        fullgraph=True,
+        dynamic=False,  # recompile for different shapes
+    )
+
+    # First dim is explicitly dynamic to simulate vLLM usage
+    torch_per_token_quant_fp8 = with_dyn_arg(torch_per_token_quant_fp8, 0, 0)
+
+
+def cuda_per_token_quant_fp8(
+    input: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return ops.scaled_fp8_quant(input)
+
+
+def calculate_diff(batch_size: int, seq_len: int):
+    """Calculate difference between Triton and CUDA implementations."""
+    device = torch.device("cuda")
+    x = torch.rand((batch_size * seq_len, 4096), dtype=torch.float16, device=device)
+
+    torch_out, torch_scale = torch_per_token_quant_fp8(x)
+    cuda_out, cuda_scale = cuda_per_token_quant_fp8(x)
+
+    if torch.allclose(
+        cuda_out.to(torch.float32), torch_out.to(torch.float32), rtol=1e-3, atol=1e-5
+    ) and torch.allclose(cuda_scale, torch_scale, rtol=1e-3, atol=1e-5):
+        print("✅ All implementations match")
+    else:
+        print("❌ Implementations differ")
+
+
+batch_size_range = [1, 16, 32, 64, 128]
+seq_len_range = [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]
+
+configs = list(itertools.product(batch_size_range, seq_len_range))
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size", "seq_len"],
+        x_vals=configs,
+        line_arg="provider",
+        line_vals=["torch", "cuda"],
+        line_names=["Torch", "CUDA"],
+        styles=[("blue", "-"), ("green", "-")],
+        ylabel="us",
+        plot_name="per-token-dynamic-quant-fp8-performance",
+        args={},
+    )
+)
+def benchmark_quantization(batch_size, seq_len, provider):
+    dtype = torch.float16
+    device = torch.device("cuda")
+
+    x = torch.randn(batch_size * seq_len, 4096, device=device, dtype=dtype)
+
+    quantiles = [0.5, 0.2, 0.8]
+
+    if provider == "torch":
+        fn = lambda: torch_per_token_quant_fp8(x.clone())
+    elif provider == "cuda":
+        fn = lambda: cuda_per_token_quant_fp8(x.clone())
+
+    ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(fn, quantiles=quantiles)
+
+    return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+
+if __name__ == "__main__":
+    calculate_diff(batch_size=4, seq_len=4096)
+    benchmark_quantization.run(print_data=True)

tests/compile/test_fusion.py

@@ -44,7 +44,9 @@ def __init__(self, hidden_size: int, eps: float, static: bool,
         ]
         self.fp8_linear = Fp8LinearOp(
             cutlass_fp8_supported=cutlass_fp8_enabled,
-            use_per_token_if_dynamic=True)
+            act_quant_static=static,
+            act_quant_group_shape=group_shape,
+        )
 
     def forward(self, x):
         resid = torch.sqrt(x)
@@ -91,9 +93,10 @@ def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static,
     maybe_create_device_identity()  # needed for certain non-cutlass fp8 paths
 
     vllm_config = VllmConfig(compilation_config=CompilationConfig(
-        level=CompilationLevel.PIECEWISE, custom_ops=["+rms_norm"]))
-    vllm_config.compilation_config.pass_config = \
-        PassConfig(enable_fusion=True, enable_noop=True)
+        level=CompilationLevel.PIECEWISE,
+        custom_ops=["+rms_norm", "+quant_fp8"],
+        pass_config=PassConfig(enable_fusion=True, enable_noop=True),
+    ))
     with vllm.config.set_current_vllm_config(vllm_config):
         # Reshape pass is needed for the fusion pass to work
         noop_pass = NoOpEliminationPass(vllm_config)

tests/compile/test_fusion_attn.py

@@ -50,6 +50,7 @@ def test_attention_fusion(example_prompts, monkeypatch, model: str,
         # DYNAMO_ONCE does not properly propagate shapes.
         level=CompilationLevel.DYNAMO_AS_IS,
         backend="tests.compile.test_fusion_attn.backend_unfused",
+        custom_ops=["+quant_fp8"],
     )
     vllm_config = VllmConfig(compilation_config=compile_config)
     backend_unfused = TestBackend(NoOpEliminationPass(vllm_config))
@@ -73,6 +74,7 @@ def test_attention_fusion(example_prompts, monkeypatch, model: str,
         # DYNAMO_ONCE does not properly propagate shapes.
         level=CompilationLevel.DYNAMO_AS_IS,
         backend="tests.compile.test_fusion_attn.backend",
+        custom_ops=["+quant_fp8"],
     )
     vllm_config = VllmConfig(compilation_config=compile_config)
 

tests/compile/test_silu_mul_quant_fusion.py

@@ -4,33 +4,56 @@
 import torch
 
 import vllm.envs as envs
-from vllm._custom_ops import scaled_fp8_quant
 from vllm.compilation.activation_quant_fusion import ActivationQuantFusionPass
 from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe
+from vllm.compilation.noop_elimination import NoOpEliminationPass
 from vllm.config import CompilationConfig, PassConfig, VllmConfig
 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)
+from vllm.platforms import current_platform
 
 from .backend import TestBackend
 
 
 class TestModel(torch.nn.Module):
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, hidden_size: int, cutlass_fp8_enabled: bool, *args,
+                 **kwargs):
         super().__init__(*args, **kwargs)
         self.silu_and_mul = SiluAndMul()
+        self.wscale = torch.rand(1, dtype=torch.float32)
         self.scale = torch.rand(1, dtype=torch.float32)
 
+        self.w = (torch.rand(
+            hidden_size,
+            hidden_size).to(dtype=current_platform.fp8_dtype()).t())
+
+        self.fp8_linear = Fp8LinearOp(
+            cutlass_fp8_supported=cutlass_fp8_enabled,
+            act_quant_static=True,
+            act_quant_group_shape=GroupShape.PER_TENSOR,
+        )
+
     def forward(self, x):
         y = self.silu_and_mul(x)
-        x2 = scaled_fp8_quant(y, self.scale)
+        x2 = self.fp8_linear.apply(y,
+                                   self.w,
+                                   self.wscale,
+                                   input_scale=self.wscale)
         return x2
 
 
 @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.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):
+def test_fusion_silu_and_mul_quant(num_tokens, hidden_size,
+                                   cutlass_fp8_enabled):
     torch.set_default_device("cuda")
     torch.set_default_dtype(torch.float16)
 
@@ -40,11 +63,11 @@ def test_fusion_silu_and_mul_quant(num_tokens, hidden_size):
         pass_config=PassConfig(enable_fusion=True, enable_noop=True))
     fusion_pass = ActivationQuantFusionPass(config)
 
-    backend = TestBackend(fusion_pass)
-    model = TestModel()
+    backend = TestBackend(NoOpEliminationPass(config), fusion_pass)
+    model = TestModel(hidden_size, cutlass_fp8_enabled)
 
     # First dimension dynamic
-    x = torch.rand(num_tokens, hidden_size)
+    x = torch.rand(num_tokens, hidden_size * 2)
     torch._dynamo.mark_dynamic(x, 0)
 
     result = model(x)

vllm/attention/backends/abstract.py

@@ -9,6 +9,8 @@
 
 import torch
 
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape)
 from vllm.multimodal import MultiModalPlaceholderMap
 
 if TYPE_CHECKING:
@@ -289,7 +291,7 @@ def forward(
         raise NotImplementedError
 
     def fused_output_quant_supported(self, dtype: torch.dtype, static: bool,
-                                     group_shape: tuple[int, int]):
+                                     group_shape: GroupShape):
         """
         Does this attention implementation support fused output quantization.
         This is used by the AttnFusionPass to only fuse output quantization
@@ -298,7 +300,7 @@ def fused_output_quant_supported(self, dtype: torch.dtype, static: bool,
         TODO(luka) merge parameters into QuantDescriptor
         :param dtype: quantized dtype
         :param static: static or dynamic quantization
-        :param group_shape: quant group shape. (-1, -1) for per-tensor.
+        :param group_shape: quant group shape.
         :return: is fusion supported for this type of quantization
         """
         return False

vllm/attention/backends/rocm_flash_attn.py

@@ -19,6 +19,8 @@
                                            PagedAttentionMetadata)
 from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape)
 from vllm.platforms import current_platform
 from vllm.platforms.rocm import use_rocm_custom_paged_attention
 
@@ -598,10 +600,10 @@ def repeat_kv(self, x: torch.Tensor, n_rep: int) -> torch.Tensor:
                                                     head_dim))
 
     def fused_output_quant_supported(self, dtype: torch.dtype, static: bool,
-                                     group_shape: tuple[int, int]):
+                                     group_shape: GroupShape):
         if self.use_triton_flash_attn:
             return dtype == current_platform.fp8_dtype(
-            ) and static and group_shape == (-1, -1)  # per-tensor
+            ) and static and group_shape == GroupShape.PER_TENSOR
 
         # Only supported in the Triton backend
         return False

vllm/compilation/fusion.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Callable, ClassVar, NamedTuple, Optional
+from typing import Callable, NamedTuple, Optional
 
 import torch
 import torch._inductor.pattern_matcher as pm
@@ -11,6 +11,8 @@
 
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape)
 from vllm.platforms import current_platform
 
 from .fx_utils import find_getitem_maybe
@@ -33,27 +35,6 @@ def empty_fp32(*args, **kwargs):
 RMS_ADD_OP = torch.ops._C.fused_add_rms_norm.default
 
 
-# Use proxy as NamedTuple direct subclasses cannot have static members
-class _GroupShape(NamedTuple):
-    row: int
-    col: int
-
-
-class GroupShape(_GroupShape):
-    """
-    This class describes the quantization group shape.
-    It includes static members for common shapes (per-tensor, per-token).
-    """
-
-    # Aliases for common quantization group shapes
-    PER_TENSOR: ClassVar['GroupShape']
-    PER_TOKEN: ClassVar['GroupShape']
-
-
-GroupShape.PER_TENSOR = GroupShape(-1, -1)
-GroupShape.PER_TOKEN = GroupShape(1, -1)
-
-
 class QuantKey(NamedTuple):
     """
     Named tuple for identifying the type of quantization.

vllm/model_executor/layers/fused_moe/utils.py

@@ -111,6 +111,8 @@ def _fp8_quantize(
     is provided, the output will be blocked.
     """
     if block_shape is None:
+        # TODO(luka): use QuantFP8 custom op
+        #  https://github.com/vllm-project/vllm/issues/20711
         A, A_scale = ops.scaled_fp8_quant(
             A, A_scale, use_per_token_if_dynamic=per_act_token)
     else:

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_24.py

@@ -15,6 +15,9 @@
                                                QKVParallelLinear)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     convert_to_channelwise, sparse_cutlass_supported)
 from vllm.model_executor.parameter import (BasevLLMParameter,
@@ -24,6 +27,8 @@
 
 __all__ = ["CompressedTensors24"]
 
+from vllm.platforms import current_platform
+
 
 class CompressedTensors24(CompressedTensorsScheme):
 
@@ -45,6 +50,12 @@ def __init__(
             and self.model_compressor.sparsity_config.format
             == CompressionFormat.sparse_24_bitmask.value)
 
+        if quantized and input_quant is not None and \
+                self._get_quant_dtype() == current_platform.fp8_dtype():
+            static = not input_quant.dynamic
+            g_shape = GroupShape.PER_TENSOR if static else GroupShape.PER_TOKEN
+            self.quant_fp8 = QuantFP8(static, g_shape)
+
     @classmethod
     def get_min_capability(cls) -> int:
         # Only cutlass 3.x kernels are implemented so far
@@ -232,21 +243,15 @@ def apply_weights(
         :return: The output tensor of the layer
         """
         if self.quantized:
-            scale = None
-            if hasattr(layer, "input_scale"):
-                scale = layer.input_scale
+            scale = getattr(layer, 'input_scale', None)
 
             if self.weights_dtype == torch.int8:
                 ops_output = ops.scaled_int8_quant(x, scale=scale)
                 q_input = ops_output[0]
                 input_scale = ops_output[1]
             else:
                 assert self.weights_dtype == torch.float8_e4m3fn
-                if scale is not None:
-                    q_input, input_scale = ops.scaled_fp8_quant(x, scale=scale)
-                else:
-                    q_input, input_scale = ops.scaled_fp8_quant(
-                        x, use_per_token_if_dynamic=True)
+                q_input, input_scale = self.quant_fp8(x, scale=scale)
 
         else:
             # Not quantized, nothing to do with the input_scales, use as is
@@ -269,7 +274,10 @@ def apply_weights(
     def _get_params_dtype(self, params_dtype: torch.dtype) -> torch.dtype:
         if not self.quantized:
             return params_dtype
+        return self._get_quant_dtype()
 
+    def _get_quant_dtype(self) -> torch.dtype:
+        assert self.quantized
         assert self.weight_quant is not None
         assert self.input_quant is not None