mirror of https://github.com/hpcaitech/ColossalAI
57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
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import pytest
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import torch
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from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding, apply_rotary_pos_emb
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from colossalai.kernel.triton import rotary_embedding
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def torch_rotary_emb(x, cos, sin):
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seq_len, h, dim = x.shape
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x0 = x[:, :, 0 : dim // 2]
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x1 = x[:, :, dim // 2 : dim]
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cos = cos.view((seq_len, 1, dim // 2))
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sin = sin.view((seq_len, 1, dim // 2))
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o0 = x0 * cos - x1 * sin
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o1 = x0 * sin + x1 * cos
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return torch.cat((o0, o1), dim=-1)
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@pytest.mark.parametrize("BATCH_SIZE", [4])
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@pytest.mark.parametrize("SEQ_LEN", [64])
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@pytest.mark.parametrize("H", [32])
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@pytest.mark.parametrize("D", [64])
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@pytest.mark.parametrize("dtype", [torch.float32])
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def test_rotary_emb(BATCH_SIZE, SEQ_LEN, H, D, dtype):
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TOTAL_TOKENS = BATCH_SIZE * SEQ_LEN
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# our crafted op equals to Transformers
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x0 = torch.randn(TOTAL_TOKENS, SEQ_LEN, D)
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x1 = torch.randn(TOTAL_TOKENS, SEQ_LEN, D)
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emb = LlamaRotaryEmbedding(D)
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cos, sin = emb(x0, TOTAL_TOKENS)
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cos_2 = cos[:, :32]
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sin_2 = sin[:, :32]
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position_ids = torch.arange(TOTAL_TOKENS)
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embd_x0, _ = apply_rotary_pos_emb(x0, x1, cos, sin, position_ids)
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embd_stimulated_x = torch_rotary_emb(x0, cos_2, sin_2)
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assert torch.allclose(embd_x0, embd_stimulated_x)
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# create data
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q_shape = (TOTAL_TOKENS, H, D)
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q = -2.3 + 0.5 * torch.randn(q_shape, dtype=dtype, device="cuda")
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k_shape = (TOTAL_TOKENS, H, D)
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k = -2.3 + 0.5 * torch.randn(k_shape, dtype=dtype, device="cuda")
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cos_shape = (TOTAL_TOKENS, D // 2)
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cos = -1.2 + 0.5 * torch.randn(cos_shape, dtype=dtype, device="cuda")
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sin = -2.0 + 0.5 * torch.randn(cos_shape, dtype=dtype, device="cuda")
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q_ref = torch_rotary_emb(q, cos, sin)
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k_ref = torch_rotary_emb(k, cos, sin)
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rotary_embedding(q, k, cos, sin)
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assert torch.allclose(q, q_ref, atol=1e-4, rtol=1e-4)
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assert torch.allclose(k, k_ref, atol=1e-4, rtol=1e-4)
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if __name__ == "__main__":
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test_rotary_emb(4, 64, 32, 64, torch.float32)
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