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51 lines
1.7 KiB
51 lines
1.7 KiB
import pytest
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import torch
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from packaging import version
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try:
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pass
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from colossalai.kernel.triton import llama_context_attn_fwd
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from tests.test_infer_ops.triton.kernel_utils import torch_context_attention
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HAS_TRITON = True
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except ImportError:
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HAS_TRITON = False
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print("please install triton from https://github.com/openai/triton")
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TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.4")
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@pytest.mark.skipif(
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not TRITON_CUDA_SUPPORT or not HAS_TRITON, reason="triton requires cuda version to be higher than 11.4"
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)
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def test_llama_context_attention():
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bs = 4
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head_num = 8
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seq_len = 1024
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head_dim = 64
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query = torch.randn((bs * seq_len, head_num, head_dim), dtype=torch.float16, device="cuda")
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k = torch.randn((bs * seq_len, head_num, head_dim), dtype=torch.float16, device="cuda")
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v = torch.randn((bs * seq_len, head_num, head_dim), dtype=torch.float16, device="cuda")
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max_input_len = seq_len
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b_start = torch.zeros((bs,), device="cuda", dtype=torch.int32)
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b_len = torch.zeros((bs,), device="cuda", dtype=torch.int32)
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for i in range(bs):
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b_start[i] = i * seq_len
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b_len[i] = seq_len
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o = torch.randn((bs * seq_len, head_num, head_dim), dtype=torch.float16, device="cuda")
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llama_context_attn_fwd(query.clone(), k.clone(), v.clone(), o, b_start, b_len, max_input_len)
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torch_out = torch_context_attention(query.clone(), k.clone(), v.clone(), bs, seq_len, head_num, head_dim)
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assert torch.allclose(
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torch_out.cpu(), o.cpu(), rtol=1e-3, atol=1e-3
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), "outputs from triton and torch are not matched"
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if __name__ == "__main__":
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test_llama_context_attention()
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