Jee Jee LiandYiliu Dong
559d6710bf
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com> Signed-off-by: qianlihuang <91178480+qianlihuang@users.noreply.github.com> Co-authored-by: Yiliu Dong <91178480+qianlihuang@users.noreply.github.com>
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Tests for fp32_router_gemm kernel: activation×weight→fp32, H=3072, E=256.
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Correctness baseline: torch.matmul in float64.
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"""
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import pytest
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import torch
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from vllm._custom_ops import fp32_router_gemm
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NUM_EXPERTS = 256
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HIDDEN_DIM = 3072
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# Absolute tolerance for fp32 kernel vs float64 reference
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ATOL_FP32 = 2e-4
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ATOL_BF16 = 2e-2 # bf16 activation has lower precision
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def _requires_sm90():
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if not torch.cuda.is_available():
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pytest.skip("CUDA not available")
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major, minor = torch.cuda.get_device_capability()
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if major * 10 + minor < 90:
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pytest.skip(f"fp32_router_gemm requires SM90+, got SM{major}{minor}")
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def _ref(mat_a: torch.Tensor, mat_b: torch.Tensor) -> torch.Tensor:
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"""Reference: F.linear in float32 on GPU."""
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return torch.nn.functional.linear(mat_a.float(), mat_b.float())
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@pytest.mark.parametrize("num_tokens", [1, 2, 4, 8, 16, 32])
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def test_fp32_activation(num_tokens: int):
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"""fp32 activation → fp32 output should match reference closely."""
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_requires_sm90()
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torch.manual_seed(42)
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device = torch.device("cuda")
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mat_a = torch.randn(num_tokens, HIDDEN_DIM, dtype=torch.float32, device=device)
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mat_b = torch.randn(NUM_EXPERTS, HIDDEN_DIM, dtype=torch.float32, device=device)
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out = fp32_router_gemm(mat_a, mat_b)
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ref = _ref(mat_a, mat_b)
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assert out.shape == (num_tokens, NUM_EXPERTS)
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assert out.dtype == torch.float32
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torch.testing.assert_close(out, ref, atol=ATOL_FP32, rtol=0)
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@pytest.mark.parametrize("num_tokens", [1, 2, 4, 8, 16, 32])
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def test_bf16_activation(num_tokens: int):
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"""bf16 activation → fp32 output should match reference within bf16 error."""
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_requires_sm90()
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torch.manual_seed(42)
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device = torch.device("cuda")
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mat_a_bf16 = torch.randn(
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num_tokens, HIDDEN_DIM, dtype=torch.bfloat16, device=device
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)
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mat_b = torch.randn(NUM_EXPERTS, HIDDEN_DIM, dtype=torch.float32, device=device)
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out = fp32_router_gemm(mat_a_bf16, mat_b)
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ref = _ref(mat_a_bf16, mat_b).to(device)
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assert out.shape == (num_tokens, NUM_EXPERTS)
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assert out.dtype == torch.float32
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torch.testing.assert_close(out, ref, atol=ATOL_BF16, rtol=0)
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def test_output_shape_and_dtype():
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"""Basic shape and dtype checks."""
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_requires_sm90()
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device = torch.device("cuda")
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mat_a = torch.randn(4, HIDDEN_DIM, dtype=torch.float32, device=device)
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mat_b = torch.randn(NUM_EXPERTS, HIDDEN_DIM, dtype=torch.float32, device=device)
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out = fp32_router_gemm(mat_a, mat_b)
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assert out.shape == (4, NUM_EXPERTS)
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assert out.dtype == torch.float32
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assert out.device.type == "cuda"
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