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[Kernel] [Helion] [4/N] Add silu_mul_fp8 Helion kernel (#33373)

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Signed-off-by: Yanan Cao <gmagogsfm@gmail.com>
This commit is contained in:
Yanan Cao
2026-02-12 18:13:12 -08:00
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parent 4453ba8d9e
commit 96161fe978
7 changed files with 1002 additions and 4 deletions
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tests/kernels/helion/test_silu_mul_fp8.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
import torch.nn.functional as F
from vllm.utils.import_utils import has_helion
if not has_helion():
pytest.skip(
"Helion is not installed. Install with: pip install vllm[helion]",
allow_module_level=True,
)
from vllm.kernels.helion.config_manager import ConfigManager
from vllm.kernels.helion.ops.silu_mul_fp8 import (
pick_silu_mul_fp8_config,
silu_mul_fp8,
silu_mul_fp8_baseline,
)
def skip_if_platform_unsupported():
try:
from vllm.kernels.helion.utils import get_canonical_gpu_name
if not torch.cuda.is_available():
pytest.skip("CUDA not available")
platform = get_canonical_gpu_name()
try:
config_manager = ConfigManager.get_instance()
except RuntimeError:
config_manager = ConfigManager()
configs = config_manager.get_platform_configs("silu_mul_fp8", platform)
if len(configs) == 0:
pytest.skip("Current GPU platform not supported for silu_mul_fp8 kernel")
except (ImportError, RuntimeError, KeyError):
pytest.skip("Error detecting platform support for silu_mul_fp8 kernel")
@pytest.fixture(autouse=True)
def reset_config_manager_singleton():
ConfigManager.reset_instance()
ConfigManager()
yield
ConfigManager.reset_instance()
class TestSiluMulFp8ConfigPicker:
def test_config_picker_exact_match(self):
config_keys = [
"intermediate_2048_batchsize_256",
"intermediate_4096_batchsize_256",
]
input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
args = (input_tensor, scale)
selected_key = pick_silu_mul_fp8_config(args, config_keys)
assert selected_key == "intermediate_2048_batchsize_256"
def test_config_picker_closest_match(self):
config_keys = [
"intermediate_2048_batchsize_256",
"intermediate_4096_batchsize_256",
]
# Use 7000 (intermediate_size=3500) which is closer to 4096 than 2048
input_tensor = torch.randn(32, 7000, dtype=torch.bfloat16, device="cuda")
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
args = (input_tensor, scale)
selected_key = pick_silu_mul_fp8_config(args, config_keys)
assert selected_key == "intermediate_4096_batchsize_256"
def test_config_picker_fallback_to_default(self):
config_keys = ["default", "some_other_key"]
input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
args = (input_tensor, scale)
selected_key = pick_silu_mul_fp8_config(args, config_keys)
assert selected_key == "default"
def test_config_picker_no_configs(self):
config_keys: list[str] = []
input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
args = (input_tensor, scale)
selected_key = pick_silu_mul_fp8_config(args, config_keys)
assert selected_key is None
@pytest.mark.parametrize("intermediate_size", [2048, 4096, 5120])
def test_config_picker_different_sizes(self, intermediate_size):
config_keys = [
"intermediate_2048_batchsize_256",
"intermediate_4096_batchsize_256",
"intermediate_5120_batchsize_256",
]
input_tensor = torch.randn(
32, 2 * intermediate_size, dtype=torch.bfloat16, device="cuda"
)
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
args = (input_tensor, scale)
selected_key = pick_silu_mul_fp8_config(args, config_keys)
expected_key = f"intermediate_{intermediate_size}_batchsize_256"
assert selected_key == expected_key
class TestSiluMulFp8Correctness:
@pytest.mark.parametrize("batch_size", [1, 8, 32, 128])
@pytest.mark.parametrize("intermediate_size", [2048, 3000, 3500, 4096, 5000])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
def test_silu_mul_fp8_correctness(self, batch_size, intermediate_size, dtype):
skip_if_platform_unsupported()
input_size = 2 * intermediate_size
input_tensor = torch.randn(batch_size, input_size, dtype=dtype, device="cuda")
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
reference_output = silu_mul_fp8_baseline(input_tensor, scale)
helion_output = silu_mul_fp8(input_tensor, scale)
assert helion_output.shape == reference_output.shape
assert helion_output.dtype == torch.float8_e4m3fn
assert reference_output.dtype == torch.float8_e4m3fn
ref_f32 = reference_output.to(torch.float32)
helion_f32 = helion_output.to(torch.float32)
# FP8 E4M3 has limited precision. Values near quantization boundaries
# can round differently due to intermediate precision differences.
torch.testing.assert_close(
helion_f32,
ref_f32,
atol=0.05,
rtol=0.05,
msg=f"Mismatch at batch={batch_size}, size={intermediate_size}",
)
def test_silu_mul_fp8_shape_inference(self):
skip_if_platform_unsupported()
batch_size, input_size = 32, 8192
intermediate_size = input_size // 2
input_tensor = torch.randn(
batch_size, input_size, dtype=torch.bfloat16, device="cuda"
)
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
output = silu_mul_fp8(input_tensor, scale)
expected_shape = (batch_size, intermediate_size)
assert output.shape == expected_shape
assert output.dtype == torch.float8_e4m3fn
def test_silu_mul_fp8_scale_variations(self):
skip_if_platform_unsupported()
batch_size, input_size = 16, 4096
input_tensor = torch.randn(
batch_size, input_size, dtype=torch.bfloat16, device="cuda"
)
scales = [0.1, 0.5, 1.0, 2.0, 10.0]
for scale_val in scales:
scale = torch.tensor([scale_val], dtype=torch.float32, device="cuda")
reference_output = silu_mul_fp8_baseline(input_tensor, scale)
helion_output = silu_mul_fp8(input_tensor, scale)
ref_f32 = reference_output.to(torch.float32)
helion_f32 = helion_output.to(torch.float32)
torch.testing.assert_close(
helion_f32,
ref_f32,
atol=0.05,
rtol=0.05,
msg=f"Mismatch for scale={scale_val}",
)
@pytest.mark.parametrize(
"shape",
[
(1, 4096),
(16, 4096),
(128, 4096),
(1024, 4096),
(1, 8192),
(16, 8192),
(128, 8192),
],
)
def test_silu_mul_fp8_various_shapes(self, shape):
skip_if_platform_unsupported()
input_tensor = torch.randn(*shape, dtype=torch.bfloat16, device="cuda")
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
reference_output = silu_mul_fp8_baseline(input_tensor, scale)
helion_output = silu_mul_fp8(input_tensor, scale)
assert helion_output.shape == reference_output.shape
ref_f32 = reference_output.to(torch.float32)
helion_f32 = helion_output.to(torch.float32)
torch.testing.assert_close(
helion_f32, ref_f32, atol=0.05, rtol=0.05, msg=f"Mismatch for shape={shape}"
)
def silu_mul_fp8_pytorch(input: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
"""Pure PyTorch reference using F.silu.
This matches vLLM's SiluAndMul.forward_native exactly:
F.silu(x[..., :d]) * x[..., d:]
"""
d = input.shape[-1] // 2
result = F.silu(input[..., :d]) * input[..., d:]
return (result.to(torch.float32) / scale).to(torch.float8_e4m3fn)
class TestSiluMulFp8PytorchReference:
"""Tests comparing Helion kernel against pure PyTorch implementation.
Uses tighter tolerance since both use PyTorch's FP8 conversion
(same rounding mode), unlike the vLLM C++ baseline which uses
NVIDIA's hardware FP8 conversion with different rounding.
"""
@pytest.mark.parametrize("batch_size", [1, 8, 32, 128, 256])
@pytest.mark.parametrize("intermediate_size", [1024, 2048, 4096])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
def test_silu_mul_fp8_vs_pytorch(self, batch_size, intermediate_size, dtype):
skip_if_platform_unsupported()
input_tensor = torch.randn(
batch_size, 2 * intermediate_size, dtype=dtype, device="cuda"
)
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
pytorch_output = silu_mul_fp8_pytorch(input_tensor, scale)
helion_output = silu_mul_fp8(input_tensor, scale)
assert helion_output.shape == pytorch_output.shape
assert helion_output.dtype == torch.float8_e4m3fn
pytorch_f32 = pytorch_output.to(torch.float32)
helion_f32 = helion_output.to(torch.float32)
# Tolerance accounts for FP8 quantization boundary effects
torch.testing.assert_close(
helion_f32,
pytorch_f32,
atol=0.05,
rtol=0.05,
msg=(
f"Mismatch at batch={batch_size}, size={intermediate_size}, "
f"dtype={dtype}"
),
)
@pytest.mark.parametrize(
"shape",
[
(1, 2, 4096), # 3D input
(2, 4, 2048), # 3D input
(1, 1, 1, 8192), # 4D input
],
)
def test_silu_mul_fp8_multidim_vs_pytorch(self, shape):
skip_if_platform_unsupported()
input_tensor = torch.randn(*shape, dtype=torch.bfloat16, device="cuda")
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
pytorch_output = silu_mul_fp8_pytorch(input_tensor, scale)
helion_output = silu_mul_fp8(input_tensor, scale)
assert helion_output.shape == pytorch_output.shape
pytorch_f32 = pytorch_output.to(torch.float32)
helion_f32 = helion_output.to(torch.float32)
torch.testing.assert_close(
helion_f32,
pytorch_f32,
atol=0.05,
rtol=0.05,
msg=f"Mismatch for shape={shape}",
)
class TestSiluMulFp8Integration:
def test_kernel_registration_integration(self):
from vllm.kernels.helion.register import get_registered_kernels
registered_kernels = get_registered_kernels()
assert "silu_mul_fp8" in registered_kernels
kernel_wrapper = registered_kernels["silu_mul_fp8"]
assert kernel_wrapper.op_name == "silu_mul_fp8"
assert kernel_wrapper._config_picker is not None
def test_fake_impl_functionality(self):
skip_if_platform_unsupported()
from vllm.kernels.helion.register import get_registered_kernels
input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
scale = torch.tensor([0.5], dtype=torch.float32, device="cuda")
registered_kernels = get_registered_kernels()
kernel_wrapper = registered_kernels["silu_mul_fp8"]
fake_impl = kernel_wrapper._fake_impl
fake_output = fake_impl(input_tensor, scale)
expected_shape = (32, 2048)
assert fake_output.shape == expected_shape
assert fake_output.dtype == torch.float8_e4m3fn
assert fake_output.device == input_tensor.device