fef18b60bc
Refactor E2E CI to make it clear and faster
1. remove some uesless e2e test
2. remove some uesless function
3. Make sure all test runs with VLLMRunner to avoid oom error
4. Make sure all ops test end with torch.empty_cache to avoid oom error
5. run the test one by one to avoid resource limit error
- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
import gc
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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_shrink_cpu_impl(x: torch.Tensor, w: torch.Tensor,
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indices: torch.Tensor, y: torch.tensor,
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scaling: float) -> 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[:, :] += z * scaling
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return y
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@torch.inference_mode()
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def test_bgmv_shrink():
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B = 1
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x = torch.randn([B, 128], dtype=torch.float16)
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w = torch.randn([64, 16, 128], dtype=torch.float16)
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indices = torch.zeros([B], dtype=torch.int64)
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y = torch.zeros([B, 16])
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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 = bgmv_shrink_cpu_impl(x, w, indices, y, 0.5)
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torch.ops._C.bgmv_shrink(x_npu, w_npu, indices_npu, y_npu, 0.5)
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# Compare the results.
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torch.testing.assert_close(y_npu.cpu(),
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y,
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atol=DEFAULT_ATOL,
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rtol=DEFAULT_RTOL)
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gc.collect()
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torch.npu.empty_cache()
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torch.npu.reset_peak_memory_stats()
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