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Merge Attention
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| import torch | |
| from torch.nn.attention.flex_attention import create_block_mask, flex_attention | |
| torch.set_default_device('cuda') | |
| q, k, v = [torch.randn(8, 8, 1024, 64, requires_grad=True) for _ in range(3)] | |
| causal_mask = create_block_mask(lambda b, h, q_idx, kv_idx: q_idx >= kv_idx, None, None, 1024, 1024) | |
| uncausal_mask = create_block_mask(lambda b, h, q_idx, kv_idx: q_idx < kv_idx, None, None, 1024, 1024) | |
| ref_out = flex_attention(q, k, v) | |
| causal_out, causal_lse = flex_attention(q, k, v, block_mask=causal_mask, return_lse=True) | |
| uncausal_out, uncausal_lse = flex_attention(q, k, v, block_mask=uncausal_mask, return_lse=True) | |
| # merge_attention(*attention(q, k1, v1), *attention(q, k2, v2)) == attention(q, cat(k1, k2), cat(v1, v2)) | |
| def merge_attention(a, lse_a, b, lse_b): | |
| max_lse = torch.maximum(lse_a, lse_b) | |
| lse_a = torch.exp(lse_a - max_lse) | |
| lse_b = torch.exp(lse_b - max_lse) | |
| out = ((a * lse_a[..., None] + b * lse_b[..., None]) / (lse_a + lse_b)[..., None]) | |
| return out | |
| merge_out = merge_attention(causal_out, causal_lse, uncausal_out, uncausal_lse) | |
| assert (ref_out - merge_out).abs().max() < 1e-5 | |
| ref_out.sum().backward() | |
| ref_q_grad = q.grad | |
| q.grad = None | |
| merge_out.sum().backward() | |
| assert (q.grad - ref_q_grad).abs().max() < 1e-5 |
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