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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| from audiocraft.modules.rope import RotaryEmbedding | |
| from audiocraft.modules.transformer import StreamingTransformer, set_efficient_attention_backend | |
| def test_rope(): | |
| set_efficient_attention_backend('torch') | |
| B, T, H, C = 8, 75, 16, 128 | |
| rope = RotaryEmbedding(dim=C) | |
| xq = torch.rand((B, T, H, C)) | |
| xk = torch.rand((B, T, H, C)) | |
| xq_out, xk_out = rope.rotate_qk(xq, xk, start=7) | |
| assert list(xq_out.shape) == [B, T, H, C] | |
| assert list(xk_out.shape) == [B, T, H, C] | |
| def test_rope_io_dtypes(): | |
| set_efficient_attention_backend('torch') | |
| B, T, H, C = 8, 75, 16, 128 | |
| rope_32 = RotaryEmbedding(dim=C, dtype=torch.float32) | |
| rope_64 = RotaryEmbedding(dim=C, dtype=torch.float64) | |
| # Test bfloat16 inputs w/ both 32 and 64 precision rope. | |
| xq_16 = torch.rand((B, T, H, C)).to(torch.bfloat16) | |
| xk_16 = torch.rand((B, T, H, C)).to(torch.bfloat16) | |
| xq_out, xk_out = rope_32.rotate_qk(xq_16, xk_16) | |
| assert xq_out.dtype == torch.bfloat16 | |
| xq_out, xk_out = rope_64.rotate_qk(xq_16, xk_16) | |
| assert xq_out.dtype == torch.bfloat16 | |
| # Test float32 inputs w/ both 32 and 64 precision rope. | |
| xq_32 = torch.rand((B, T, H, C)).to(torch.float32) | |
| xk_32 = torch.rand((B, T, H, C)).to(torch.float32) | |
| xq_out, xk_out = rope_32.rotate_qk(xq_32, xk_32) | |
| assert xq_out.dtype == torch.float32 | |
| xq_out, xk_out = rope_64.rotate_qk(xq_32, xk_32) | |
| assert xq_out.dtype == torch.float32 | |
| def test_transformer_with_rope(): | |
| set_efficient_attention_backend('torch') | |
| torch.manual_seed(1234) | |
| for pos in ['rope', 'sin_rope']: | |
| tr = StreamingTransformer( | |
| 16, 4, 2, custom=True, dropout=0., layer_scale=0.1, | |
| positional_embedding=pos) | |
| tr.eval() | |
| steps = 12 | |
| x = torch.randn(3, steps, 16) | |
| out = tr(x) | |
| assert list(out.shape) == list(x.shape) | |
| def test_rope_streaming(): | |
| set_efficient_attention_backend('torch') | |
| torch.manual_seed(1234) | |
| tr = StreamingTransformer( | |
| 16, 4, 2, causal=True, dropout=0., | |
| custom=True, positional_embedding='rope') | |
| tr.eval() | |
| steps = 12 | |
| x = torch.randn(3, steps, 16) | |
| ref = tr(x) | |
| with tr.streaming(): | |
| outs = [] | |
| frame_sizes = [1] * steps | |
| for frame_size in frame_sizes: | |
| frame = x[:, :frame_size] | |
| x = x[:, frame_size:] | |
| outs.append(tr(frame)) | |
| out = torch.cat(outs, dim=1) | |
| assert list(out.shape) == [3, steps, 16] | |
| delta = torch.norm(out - ref) / torch.norm(out) | |
| assert delta < 1e-6, delta | |
| def test_rope_streaming_past_context(): | |
| set_efficient_attention_backend('torch') | |
| torch.manual_seed(1234) | |
| for context in [None, 10]: | |
| tr = StreamingTransformer( | |
| 16, 4, 1 if context else 2, | |
| causal=True, past_context=context, custom=True, | |
| dropout=0., positional_embedding='rope') | |
| tr.eval() | |
| steps = 20 | |
| x = torch.randn(3, steps, 16) | |
| ref = tr(x) | |
| with tr.streaming(): | |
| outs = [] | |
| frame_sizes = [1] * steps | |
| for frame_size in frame_sizes: | |
| frame = x[:, :frame_size] | |
| x = x[:, frame_size:] | |
| outs.append(tr(frame)) | |
| out = torch.cat(outs, dim=1) | |
| assert list(out.shape) == [3, steps, 16] | |
| delta = torch.norm(out - ref) / torch.norm(out) | |
| assert delta < 1e-6, delta | |
| def test_rope_memory_efficient(): | |
| set_efficient_attention_backend('torch') | |
| torch.manual_seed(1234) | |
| tr = StreamingTransformer( | |
| 16, 4, 2, custom=True, dropout=0., layer_scale=0.1, | |
| positional_embedding='rope') | |
| tr_mem_efficient = StreamingTransformer( | |
| 16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1, | |
| positional_embedding='rope') | |
| tr_mem_efficient.load_state_dict(tr.state_dict()) | |
| tr.eval() | |
| steps = 12 | |
| x = torch.randn(3, steps, 16) | |
| with torch.no_grad(): | |
| y = tr(x) | |
| y2 = tr_mem_efficient(x) | |
| # Check at float precision b/c this is the rope default. | |
| assert torch.allclose(y, y2, atol=1e-7), (y - y2).norm() | |
| def test_rope_with_xpos(): | |
| set_efficient_attention_backend('torch') | |
| B, T, H, C = 8, 75, 16, 128 | |
| rope = RotaryEmbedding(dim=C, xpos=True) | |
| xq = torch.rand((B, T, H, C)) | |
| xk = torch.rand((B, T, H, C)) | |
| xq_out, xk_out = rope.rotate_qk(xq, xk, start=7) | |
| assert list(xq_out.shape) == [B, T, H, C] | |
| assert list(xk_out.shape) == [B, T, H, C] | |
| def test_positional_scale(): | |
| set_efficient_attention_backend('torch') | |
| B, T, H, C = 8, 75, 16, 128 | |
| rope = RotaryEmbedding(dim=C, xpos=True, scale=0.0) | |
| xq = torch.rand((B, T, H, C)) | |
| xk = torch.rand((B, T, H, C)) | |
| xq_out, xk_out = rope.rotate_qk(xq, xk, start=7) | |
| assert torch.allclose(xq, xq_out) | |
| assert torch.allclose(xk, xk_out) | |