mirror of https://github.com/hpcaitech/ColossalAI
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
import math
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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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import triton
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import triton.language as tl
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from colossalai.kernel.triton.token_attention_kernel import token_attn_fwd_2
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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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def torch_attn(V, P, bs, seqlen, num_head, head_dim):
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V = V.view(bs, seqlen, num_head, head_dim).transpose(1, 2)
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P = P.reshape(num_head, bs, 1, seqlen).transpose(0, 1)
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attn_out = torch.matmul(P, V)
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return attn_out
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@pytest.mark.skipif(not TRITON_CUDA_SUPPORT or not HAS_TRITON,
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reason="triton requires cuda version to be higher than 11.4")
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def test_token_attn_2():
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import time
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batch_size, seq_len, head_num, head_dim = 17, 1025, 12, 128
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dtype = torch.float16
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V = torch.empty((batch_size * seq_len, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=10)
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Prob = torch.empty(
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(head_num, batch_size * seq_len), dtype=dtype,
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device="cuda").normal_(mean=0.4, std=0.2).reshape(head_num, batch_size,
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seq_len).softmax(-1).reshape(head_num, batch_size * seq_len)
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attn_out = torch.empty((batch_size, head_num, head_dim), dtype=dtype, device="cuda")
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kv_cache_start_loc = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
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kv_cache_seq_len = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
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kv_cache_loc = torch.zeros((batch_size, seq_len), dtype=torch.int32, device="cuda")
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for i in range(batch_size):
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kv_cache_start_loc[i] = i * seq_len
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kv_cache_seq_len[i] = seq_len
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kv_cache_loc[i] = i * seq_len + torch.arange(0, seq_len, dtype=torch.int32, device="cuda")
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token_attn_fwd_2(Prob, V, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, seq_len)
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torch_out = torch_attn(V, Prob, batch_size, seq_len, head_num, head_dim).squeeze()
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o = attn_out
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print("max ", torch.max(torch.abs(torch_out - o)))
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print("mean ", torch.mean(torch.abs(torch_out - o)))
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assert torch.allclose(torch_out, o, atol=1e-2, rtol=0)
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if __name__ == "__main__":
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test_token_attn_2()
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