540336edc9
### What this PR does / why we need it?
Add two custom kernels(bgmv_shrink and bgmv expand) to solve the
performance of LoRA
### Does this PR introduce _any_ user-facing change?
no user-facing change
### How was this patch tested?
we add Unit Test file to test the custom ascendc kernel. See
vllm-ascend/tests/e2e/singlecard/ops/test_bgmv_expand.py and
vllm-ascend/tests/e2e/singlecard/ops/test_bgmv_expand.py
Based on the actual test of the QWen2.5 7B model using vllm-ascend
version v0.9.2.rc1, the TTFT, TPOT and throughput have increased by
about 70%.
- vLLM version: v0.9.2
- vLLM main:
40d86ee412
---------
Signed-off-by: taoxudonghaha <justsheldon@163.com>
42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import torch
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from vllm_ascend.utils import enable_custom_op
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enable_custom_op()
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DEFAULT_ATOL = 1e-3
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DEFAULT_RTOL = 1e-3
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def bgmv_expand_cpu_impl(x: torch.Tensor, w: torch.Tensor,
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indices: torch.Tensor, y: torch.tensor,
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slice_offset: int, slice_size: int) -> torch.Tensor:
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W = w[indices, :, :].transpose(-1, -2).to(torch.float32)
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z = torch.bmm(x.unsqueeze(1).to(torch.float32), W).squeeze()
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y[:, slice_offset:slice_offset + slice_size] += z
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return y
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@torch.inference_mode()
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def test_bgmv_expand() -> None:
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B = 1
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x = torch.randn([B, 16], dtype=torch.float)
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w = torch.randn([64, 128, 16], dtype=torch.float16)
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indices = torch.zeros([B], dtype=torch.int64)
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y = torch.randn([B, 128 * 3], dtype=torch.float16)
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x_npu = x.npu()
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w_npu = w.npu()
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indices_npu = indices.npu()
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y_npu = y.npu()
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y_out = bgmv_expand_cpu_impl(x, w, indices, y, 0, 128)
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y_out_npu = torch.ops._C.bgmv_expand(x_npu, w_npu, indices_npu, y_npu, 0,
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128)
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# Compare the results.
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torch.testing.assert_close(y_out_npu.cpu(),
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y_out,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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