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e6585ddb45
Signed-off-by: elvischenv <219235043+elvischenv@users.noreply.github.com> Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>
76 lines
2.9 KiB
Python
76 lines
2.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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import torch
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from tests.kernels.quantization.nvfp4_utils import (FLOAT4_E2M1_MAX,
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FLOAT8_E4M3_MAX,
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dequantize_nvfp4_to_dtype)
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from vllm._custom_ops import scaled_fp4_quant
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.platforms import current_platform
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if not current_platform.has_device_capability(100):
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pytest.skip(reason="Nvfp4 Requires compute capability of 10 or above.",
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allow_module_level=True)
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FP4_DTYPE = torch.uint8
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FP8_DTYPE = current_platform.fp8_dtype()
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DTYPES = [torch.float16, torch.bfloat16]
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SHAPES = [(128, 256), (128, 128), (256, 256), (256, 128)]
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BLOCK_SIZE = 16
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("shape", SHAPES)
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@torch.inference_mode()
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def test_silu_mul_nvfp4_quant(
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dtype: torch.dtype,
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shape: tuple[int, int],
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) -> None:
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current_platform.seed_everything(42)
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device = 'cuda:0'
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torch.set_default_device(device)
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x = torch.randn(shape, dtype=dtype)
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# ref op
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ref_output = SiluAndMul().forward_native(x)
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ref_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) /
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torch.abs(ref_output).max().to(torch.float32))
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ref_output_quant, ref_block_scale = scaled_fp4_quant(
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ref_output, ref_global_scale)
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# fused op
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fused_output_quant = torch.empty_like(ref_output_quant)
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fused_block_scale = torch.empty_like(ref_block_scale)
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torch.ops._C.silu_and_mul_nvfp4_quant(fused_output_quant,
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fused_block_scale, x,
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ref_global_scale)
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# check dtype
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assert ref_output_quant.dtype == FP4_DTYPE
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assert fused_output_quant.dtype == FP4_DTYPE
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assert ref_output_quant.shape == fused_output_quant.shape
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assert ref_block_scale.dtype == FP8_DTYPE
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assert fused_block_scale.dtype == FP8_DTYPE
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assert ref_block_scale.shape == fused_block_scale.shape
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# check dequantized output
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ref_output_dequant = dequantize_nvfp4_to_dtype(ref_output_quant,
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ref_block_scale,
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ref_global_scale, dtype,
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device)
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fused_output_dequant = dequantize_nvfp4_to_dtype(fused_output_quant,
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fused_block_scale,
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ref_global_scale, dtype,
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device)
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atol, rtol = 3e-1, 3e-1
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torch.testing.assert_close(ref_output_dequant,
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fused_output_dequant,
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atol=atol,
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rtol=rtol)
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