BruceLoveDecimalandYueqian Lin
3a28882946
Signed-off-by: liuqihao <liuqihao970610@gmail.com> Signed-off-by: Yueqian Lin <linyueqian@outlook.com> Co-authored-by: Yueqian Lin <linyueqian@outlook.com>
287 lines
13 KiB
Python
287 lines
13 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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"""The AuK codec's decode fast paths keep the reference numerics.
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Five paths are checked against the plain eager decode: the Snake activation
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with precomputed exponent caches, the per-channel FIR filter cache, the
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per-length CUDA graph tier, the torch.compile bucket tier (both fall back to
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eager off CUDA) and the tiled decode of long clips.
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"""
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import pytest
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import torch
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from vllm_omni.diffusion.models.auk.auk_vae import AuKVAE, LowPass, SnakeBeta, Upsample
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from vllm_omni.diffusion.models.auk.vae_cudagraph import AuKVAEDecodeGraph, plan_tiles
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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def _small_vae() -> AuKVAE:
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torch.manual_seed(3)
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# Six halvings of the initial width must leave at least two channels.
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vae = AuKVAE(
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upsample_initial_channel=128,
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downsample_channels=(4, 4, 8, 8, 8, 16, 16),
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latent_dim=8,
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).eval()
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# Give the activations non-trivial parameters so the paths are exercised.
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with torch.no_grad():
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for module in vae.modules():
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if isinstance(module, SnakeBeta):
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module.alpha.normal_(0.0, 0.3)
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module.beta.normal_(0.0, 0.3)
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vae.remove_weight_norm()
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return vae
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def _reference_snake(module: SnakeBeta, x: torch.Tensor) -> torch.Tensor:
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"""The formula as the reference implementation writes it."""
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alpha = module.alpha.unsqueeze(0).unsqueeze(-1)
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beta = module.beta.unsqueeze(0).unsqueeze(-1)
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if module.alpha_logscale:
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alpha = torch.exp(alpha)
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beta = torch.exp(beta)
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return x + (1.0 / (beta + 1e-9)) * torch.sin(x * alpha).pow(2)
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@torch.inference_mode()
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def test_window_padding_decodes_to_raw_zero() -> None:
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"""Bucket padding is the normalized latent whose denormalized value is zero."""
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vae = _small_vae()
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with torch.no_grad():
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vae.global_mean.normal_()
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vae.global_log_std.uniform_(0.5, 2.0)
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pad = AuKVAEDecodeGraph(vae)._pad_for(torch.device("cpu"))
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raw = pad * torch.sqrt(vae.global_log_std) + vae.global_mean
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torch.testing.assert_close(raw, torch.zeros_like(raw), atol=1e-6, rtol=0.0)
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@torch.inference_mode()
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def test_eager_snake_matches_the_reference_formula() -> None:
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module = SnakeBeta(6, alpha_logscale=True)
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with torch.no_grad():
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module.alpha.normal_()
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module.beta.normal_()
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module.precompute_exp_cache()
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x = torch.randn(2, 6, 17)
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assert torch.equal(module(x), _reference_snake(module, x))
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@torch.inference_mode()
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def test_filter_cache_leaves_the_filters_and_output_unchanged() -> None:
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lowpass = LowPass(cutoff=0.25, half_width=0.3, stride=2, kernel_size=12, causal=True)
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upsample = Upsample(ratio=2, kernel_size=12)
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x = torch.randn(1, 5, 40)
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for module in (lowpass, upsample):
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module.cache_filters = False
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plain = module(x)
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module.cache_filters = True
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cached = module(x)
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assert torch.equal(cached, plain)
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assert module._expanded is not None and module._expanded.shape == (5, 1, 12)
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assert module._expanded.is_contiguous()
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# A different channel count rebuilds the cache rather than reusing it.
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module(torch.randn(1, 3, 40))
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assert module._expanded.shape == (3, 1, 12)
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@torch.inference_mode()
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def test_decode_fast_paths_reproduce_the_plain_decode() -> None:
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vae = _small_vae()
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latents = torch.randn(1, 6, vae.latent_dim)
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vae.set_decode_fast_paths(cached_filters=False)
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plain = vae.decode(latents)
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assert plain.shape == (1, 6 * vae.hop_size)
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vae.set_decode_fast_paths(cached_filters=True)
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fast = vae.decode(latents)
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assert torch.equal(fast, plain)
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assert all(module._cached for module in vae.modules() if isinstance(module, SnakeBeta))
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@torch.inference_mode()
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def test_graph_wrapper_falls_back_to_eager_off_cuda(mocker) -> None:
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vae = _small_vae()
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wrapper = AuKVAEDecodeGraph(vae, frame_alignment=64, compile_shapes=(8,))
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capture_spy = mocker.spy(wrapper, "_capture")
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compile_spy = mocker.spy(torch, "compile")
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latents = torch.randn(1, 6, vae.latent_dim)
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wrapper.warmup(torch.device("cpu"))
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assert torch.equal(wrapper(latents), vae.decode(latents))
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assert wrapper.last_mode == "eager"
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capture_spy.assert_not_called()
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compile_spy.assert_not_called()
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assert not wrapper._cache and not wrapper._compiled
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def test_compiled_bucket_is_the_smallest_captured_one_that_fits() -> None:
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wrapper = AuKVAEDecodeGraph(_small_vae(), compile_shapes=(16, 8, 8))
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assert wrapper.compile_shapes == [8, 16]
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# Nothing captured yet: every length goes to the per-length graph tier.
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assert wrapper.compiled_bucket(6) is None
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wrapper._compiled = {8: object(), 16: object()} # type: ignore[dict-item]
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assert [wrapper.compiled_bucket(frames) for frames in (6, 8, 9, 16, 17)] == [8, 8, 16, 16, None]
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# A bucket whose capture failed is skipped, not padded to.
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wrapper._compiled = {16: object()} # type: ignore[dict-item]
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assert wrapper.compiled_bucket(6) == 16
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def test_plan_tiles_covers_the_clip_once_with_full_context() -> None:
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assert plan_tiles(40, 64, 10, 3) == [(0, 40, 0, 40)]
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windows = plan_tiles(1000, 512, 53, 9)
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assert windows == [(0, 512, 0, 503), (450, 512, 503, 953), (488, 512, 953, 1000)]
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# Every emitted frame sits at least the context away from a window edge
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# that is not the clip's own edge, and the emitted ranges tile the clip.
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emitted = 0
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for start, width, emit_start, emit_end in windows:
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assert emit_start == emitted and start + width <= 1000
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assert start == 0 or emit_start - start >= 53
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assert start + width == 1000 or start + width - emit_end >= 9
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emitted = emit_end
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assert emitted == 1000
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# With the compiled buckets known, the last window shrinks to the smallest
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# one that holds the remainder plus the left context: 600 frames cost a
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# 512 and a 256 window instead of two 512s.
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assert plan_tiles(600, 512, 53, 9, sizes=(128, 256, 512)) == [(0, 512, 0, 503), (344, 256, 503, 600)]
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assert plan_tiles(1000, 512, 53, 9, sizes=(128, 256, 512))[-1] == (872, 128, 953, 1000)
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with pytest.raises(ValueError, match="must exceed"):
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plan_tiles(100, 60, 53, 9)
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@torch.inference_mode()
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def test_decode_context_bounds_the_measured_receptive_field() -> None:
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vae = _small_vae()
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left, right = vae.decode_context_frames()
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# The production geometry: causal stack, conv_pre and the upsamplers look ahead a little.
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assert (left, right) == (53, 9)
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frames, start, end = 240, 70, 190
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latents = torch.randn(1, frames, vae.latent_dim)
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whole = vae.decode(latents)[:, start * vae.hop_size : end * vae.hop_size]
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window = vae.decode(latents[:, start:end])
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per_frame = (window - whole).abs().reshape(-1, vae.hop_size).amax(dim=1)
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differing = (per_frame > 1e-5).nonzero().flatten().tolist()
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# Only frames within the context of a fake edge may differ, and some do:
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# the bound is tight enough that the halo is not wasted.
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assert differing and all(index < left or end - start - index <= right for index in differing)
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@torch.inference_mode()
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def test_tiled_decode_matches_the_whole_decode() -> None:
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vae = _small_vae()
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# Off the accelerator every window decodes eagerly, so this isolates the stitching.
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wrapper = AuKVAEDecodeGraph(vae, compile_shapes=(64,), tile_frames=64)
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assert wrapper.tile_frames == 64 and wrapper.context_frames == (53, 9)
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for frames in (64, 65, 200, 331):
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latents = torch.randn(1, frames, vae.latent_dim)
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whole = vae.decode(latents)
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tiled = wrapper(latents)
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assert tiled.shape == whole.shape
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torch.testing.assert_close(tiled, whole, atol=1e-5, rtol=0.0)
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assert wrapper.last_mode == ("eager" if frames == 64 else "tiled")
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# The streaming form yields the same audio in order.
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latents = torch.randn(1, 200, vae.latent_dim)
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pieces = [(start, chunk.clone()) for start, chunk in wrapper.decode_tiles(latents)]
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# With a 64-frame tile and a 62-frame halo each interior tile adds two frames.
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assert [start for start, _ in pieces] == [emit for _, _, emit, _ in plan_tiles(200, 64, 53, 9)]
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assert [start for start, _ in pieces][:3] == [0, 55, 57]
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torch.testing.assert_close(
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torch.cat([chunk for _, chunk in pieces], dim=1), vae.decode(latents), atol=1e-5, rtol=0.0
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)
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# tile_frames=0 turns tiling off; a tile smaller than the context is refused.
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assert AuKVAEDecodeGraph(vae, tile_frames=0).tile_frames == 0
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with pytest.raises(ValueError, match="must exceed"):
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AuKVAEDecodeGraph(vae, tile_frames=60)
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@pytest.mark.cuda
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA graph replay requires CUDA")
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@torch.inference_mode()
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def test_graph_replay_matches_eager_per_length() -> None:
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vae = _small_vae().to("cuda")
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wrapper = AuKVAEDecodeGraph(vae, max_graphs=2)
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pools = []
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for frames in (6, 9, 6, 12, 6):
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latents = torch.randn(1, frames, vae.latent_dim, device="cuda")
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eager = vae.decode(latents)
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replay = wrapper(latents)
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# Exact-length graphs replay the very same kernels: bit-identical,
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# including after the generation was retired.
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assert torch.equal(replay, eager), frames
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pools.append(wrapper._plain_pool)
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# The third distinct length found the cache full, so the whole generation
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# (6 and 9) was retired together with its pool rather than one graph at a
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# time; 12 and the re-captured 6 share the new pool.
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assert list(wrapper._cache) == [12, 6]
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assert pools[0] is pools[2] and pools[3] is pools[4] and pools[2] is not pools[3]
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@pytest.mark.cuda
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA graph replay requires CUDA")
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@torch.inference_mode()
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def test_bucketed_graph_only_disturbs_the_tail() -> None:
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vae = _small_vae().to("cuda")
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wrapper = AuKVAEDecodeGraph(vae, frame_alignment=8)
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latents = torch.randn(1, 6, vae.latent_dim, device="cuda")
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eager = vae.decode(latents)
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replay = wrapper(latents)
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assert replay.shape == eager.shape and list(wrapper._cache) == [8]
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# The padding decodes to raw zero, which is what the eager conv_pre pads
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# with, but it still leaks in through the alias-free upsamplers, whose
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# lookahead accumulates through the stack, so a bucketed replay is close
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# to but not identical with the eager decode. That is why frame_alignment
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# defaults to 1.
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assert torch.isfinite(replay).all()
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assert not torch.equal(replay, eager)
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torch.testing.assert_close(replay, eager, atol=0.1, rtol=0.0)
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@pytest.mark.cuda
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="torch.compile + CUDA graph capture requires CUDA")
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@torch.inference_mode()
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def test_compiled_buckets_replay_within_fusion_tolerance_and_leave_longer_clips_to_plain_graphs() -> None:
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vae = _small_vae().to("cuda")
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wrapper = AuKVAEDecodeGraph(vae, compile_shapes=(8,))
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wrapper.warmup(torch.device("cuda"))
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assert list(wrapper._compiled) == [8]
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exact = torch.randn(1, 8, vae.latent_dim, device="cuda")
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eager = vae.decode(exact)
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replay = wrapper(exact)
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assert wrapper.last_mode == "compiled"
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assert replay.shape == eager.shape
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# Same formula, different fusion order: not bit-identical, but close.
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torch.testing.assert_close(replay, eager, atol=1e-4, rtol=0.0)
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short = torch.randn(1, 5, vae.latent_dim, device="cuda")
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padded = wrapper(short)
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assert wrapper.last_mode == "compiled" and padded.shape == (1, 5 * vae.hop_size)
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torch.testing.assert_close(padded, vae.decode(short), atol=0.1, rtol=0.0)
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# An 8-frame bucket cannot hold the decoder context, so tiling is off and
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# a longer clip gets its own plain graph.
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assert wrapper.tile_frames == 0
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long = torch.randn(1, 12, vae.latent_dim, device="cuda")
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assert torch.equal(wrapper(long), vae.decode(long))
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assert wrapper.last_mode == "graph" and list(wrapper._cache) == [12]
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@pytest.mark.cuda
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="torch.compile + CUDA graph capture requires CUDA")
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@torch.inference_mode()
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def test_tiles_replay_the_compiled_bucket_for_long_clips() -> None:
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vae = _small_vae().to("cuda")
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wrapper = AuKVAEDecodeGraph(vae, compile_shapes=(72,))
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wrapper.warmup(torch.device("cuda"))
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assert wrapper.tile_frames == 72 and list(wrapper._compiled) == [72]
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latents = torch.randn(1, 150, vae.latent_dim, device="cuda")
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tiled = wrapper(latents)
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assert wrapper.last_mode == "tiled" and not wrapper._cache
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assert tiled.shape == (1, 150 * vae.hop_size)
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torch.testing.assert_close(tiled, vae.decode(latents), atol=1e-4, rtol=0.0)
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