51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import pytest
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import torch
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from vllm.model_executor.layers.fused_moe import fused_moe
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from vllm.model_executor.layers.activation import SiluAndMul
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def torch_moe(a, w1, w2, topk_weight, topk_ids):
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B, D = a.shape
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a = a.view(B, -1, D).repeat(1, topk_ids.shape[1], 1).reshape(-1, D)
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out = torch.zeros(B * topk_ids.shape[1],
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w2.shape[1],
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dtype=a.dtype,
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device=a.device)
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topk_ids = topk_ids.view(-1)
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topk_weight = topk_weight.view(-1)
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for i in range(w1.shape[0]):
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mask = topk_ids == i
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if mask.sum():
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out[mask] = SiluAndMul()(
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a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(0, 1)
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return (out.view(B, -1, w2.shape[1]) *
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topk_weight.view(B, -1, 1)).sum(dim=1)
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@pytest.mark.parametrize("m", [512, 222, 33, 1])
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@pytest.mark.parametrize("n", [2048, 256, 1024])
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@pytest.mark.parametrize("k", [128, 511, 1024])
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@pytest.mark.parametrize("e", [8, 64])
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@pytest.mark.parametrize("topk", [2, 6])
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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def test_fused_moe(
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m: int,
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n: int,
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k: int,
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e: int,
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topk: int,
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dtype: torch.dtype,
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):
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a = torch.randn((m, k), device='cuda', dtype=dtype) / 10
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w1 = torch.randn((e, 2 * n, k), device='cuda', dtype=dtype) / 10
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w2 = torch.randn((e, k, n), device='cuda', dtype=dtype) / 10
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score = torch.randn((m, e), device='cuda', dtype=dtype)
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score = torch.softmax(score, dim=-1)
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topk_weight, topk_ids = torch.topk(score, topk)
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triton_output = fused_moe(a, w1, w2, topk_weight, topk_ids, False)
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torch_output = torch_moe(a, w1, w2, topk_weight, topk_ids)
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assert torch.allclose(triton_output, torch_output, atol=1e-2, rtol=0)
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