Mu GuanLinandHongsheng Liu
5e4daaf197
Signed-off-by: mglyn <1203789601@qq.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
784 lines
31 KiB
Python
784 lines
31 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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"""Unit tests for LTX video VAE tiling and distributed decode behavior."""
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from types import SimpleNamespace
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from typing import Any
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import pytest
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import torch
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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def test_ltx_base_vocoder_keeps_native_dtype(monkeypatch):
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import vllm_omni.diffusion.models.ltx2.ltx2_runtime as ltx_runtime
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class FakeBaseVocoder(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.input_dtype = None
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def forward(self, value):
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self.input_dtype = value.dtype
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return value
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monkeypatch.setattr(
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ltx_runtime.torch,
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"autocast",
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lambda **_kwargs: (_ for _ in ()).throw(AssertionError("base vocoder must not use autocast")),
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)
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vocoder = FakeBaseVocoder()
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output = ltx_runtime._run_ltx_vocoder(vocoder, torch.ones(1, dtype=torch.bfloat16))
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assert vocoder.input_dtype == torch.bfloat16
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assert output.dtype == torch.bfloat16
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class TestLTXDiffusionDecoder:
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@pytest.mark.parametrize("mode", ["spatial_shard_height", "spatial_shard_width"])
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def test_distributed_diffusion_decoder_rejects_non_tile_parallel_modes(self, mode):
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from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder_distributed import (
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DistributedLTX2VideoDiffusionDecoderModel,
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)
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model = object.__new__(DistributedLTX2VideoDiffusionDecoderModel)
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torch.nn.Module.__init__(model)
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model.distributed_executor = SimpleNamespace(set_parallel_size=lambda *_args, **_kwargs: None)
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with pytest.raises(ValueError, match="only supports vae_parallel_mode='tile'"):
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model.set_parallel_size(2, mode=mode)
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def test_distributed_diffusion_decoder_accepts_tile_parallel_mode(self):
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from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder_distributed import (
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DistributedLTX2VideoDiffusionDecoderModel,
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)
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calls = []
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model = object.__new__(DistributedLTX2VideoDiffusionDecoderModel)
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torch.nn.Module.__init__(model)
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model.distributed_executor = SimpleNamespace(
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set_parallel_size=lambda parallel_size, mode: calls.append((parallel_size, mode))
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)
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model.set_parallel_size(4, mode="tile")
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assert calls == [(4, "tile")]
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def test_short_clip_keeps_stage5_temporal_context_then_crops_output(self):
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from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder import (
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LTX2VideoDiffusionDecoder3d,
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LTX2VideoDiffusionDecoderModel,
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)
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class IdentityAttention:
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def build_block_mask(self, _hidden_states):
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return None
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class IdentityBlock(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.attn = IdentityAttention()
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def forward(self, hidden_states, _block_mask):
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return hidden_states
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class IdentityUpsample(torch.nn.Module):
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def forward(self, hidden_states, *, drop_leading_frame):
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assert drop_leading_frame
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return hidden_states
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decoder = object.__new__(LTX2VideoDiffusionDecoder3d)
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torch.nn.Module.__init__(decoder)
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decoder.det_stages = torch.nn.ModuleList([torch.nn.ModuleList([IdentityBlock()])])
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decoder.upsamples = torch.nn.ModuleList([IdentityUpsample()])
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decoder.trailing_pad_latent_frames = 2
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decoder.temporal_compression_ratio = 8
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decoder.stage5_kernel = (11, 3, 3)
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# 17 frames contain one real frame followed by 16 propagated ghost
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# frames. The reference keeps 11 for stage-5 NATTEN instead of
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# cropping the tensor immediately back to one.
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context = decoder.forward_stage_4(torch.zeros(1, 17, 3, 3, 4))
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assert context.shape == (1, 11, 3, 3, 4)
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class FakeDecoder(torch.nn.Module):
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def forward(self, z, *, generator, num_inference_steps):
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assert generator is None
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assert num_inference_steps is None
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return torch.zeros(z.shape[0], 3, 11, z.shape[3] * 2, z.shape[4] * 2)
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model = object.__new__(LTX2VideoDiffusionDecoderModel)
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torch.nn.Module.__init__(model)
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model.decoder = FakeDecoder()
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model.use_tiling = False
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model.tile_sample_min_num_frames = 80
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model.tile_sample_min_height = 768
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model.tile_sample_min_width = 768
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model.temporal_compression_ratio = 8
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model.spatial_compression_ratio = 2
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output = model.decode(torch.zeros(1, 4, 1, 2, 3), generator=None, return_dict=False)[0]
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assert output.shape == (1, 3, 1, 4, 6)
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@pytest.mark.parametrize(
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("model_version", "extras", "expected"),
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[
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("2.5", {}, True),
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("2.5", {"ltx2_use_conv_vae": True}, False),
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("2.3", {}, False),
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],
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)
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def test_ltx25_diffusion_decoder_is_default_with_conv_vae_opt_in(self, model_version, extras, expected):
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from vllm_omni.diffusion.models.ltx2.ltx2_components import _ltx2_use_diffusion_decoder
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assert _ltx2_use_diffusion_decoder(SimpleNamespace(extras=extras), model_version) is expected
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def test_conv_vae_opt_in_rejects_non_boolean_model_extra(self):
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from vllm_omni.diffusion.models.ltx2.ltx2_components import _ltx2_use_diffusion_decoder
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with pytest.raises(TypeError, match="ltx2_use_conv_vae"):
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_ltx2_use_diffusion_decoder(SimpleNamespace(extras={"ltx2_use_conv_vae": "true"}), "2.5")
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def test_native_diffusion_decoder_conversion_splits_qkv_and_folds_gates(self):
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from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder import (
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convert_ltx25_native_diffusion_decoder_state_dict,
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)
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qkv_weight = torch.arange(24, dtype=torch.float32).reshape(6, 4)
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qkv_bias = torch.arange(6, dtype=torch.float32)
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projection_weight = torch.arange(4, dtype=torch.float32).reshape(2, 2)
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projection_bias = torch.ones(2)
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gate = torch.tensor([2.0, 3.0])
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mean = torch.tensor([0.1, 0.2])
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std = torch.tensor([0.3, 0.4])
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native = {
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"encoder.ignored": torch.ones(1),
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"per_channel_statistics.mean-of-means": mean,
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"vae.per_channel_statistics.std-of-means": std,
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"decoder.type_emb": torch.ones(2),
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"decoder.coarse_head.weight": torch.ones(1),
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"decoder.det_stages.0.0.attn.qkv.weight": qkv_weight,
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"decoder.det_stages.0.0.attn.qkv.bias": qkv_bias,
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"decoder.det_stages.0.0.attn.q_norm.weight": torch.tensor([5.0, 6.0]),
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"decoder.det_stages.0.0.attn.k_norm.weight": torch.tensor([7.0, 8.0]),
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"decoder.diff_blocks.0.attn.proj.weight": projection_weight,
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"decoder.diff_blocks.0.attn.proj.bias": projection_bias,
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"decoder.diff_blocks.0.gate_msa": gate,
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"vae.decoder.t_embedder.mlp.0.weight": torch.ones(2, 2),
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}
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converted = convert_ltx25_native_diffusion_decoder_state_dict(native)
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torch.testing.assert_close(converted["latents_mean"], mean)
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torch.testing.assert_close(converted["latents_std"], std)
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torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_q.weight"], qkv_weight[:2])
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torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_k.weight"], qkv_weight[2:4])
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torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_v.weight"], qkv_weight[4:])
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torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_q.bias"], qkv_bias[:2])
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torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_k.bias"], qkv_bias[2:4])
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torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.to_v.bias"], qkv_bias[4:])
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torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.norm_q.weight"], torch.tensor([5.0, 6.0]))
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torch.testing.assert_close(converted["decoder.det_stages.0.0.attn.norm_k.weight"], torch.tensor([7.0, 8.0]))
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torch.testing.assert_close(
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converted["decoder.diff_blocks.0.attn.to_out.0.weight"], gate.unsqueeze(1) * projection_weight
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)
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torch.testing.assert_close(converted["decoder.diff_blocks.0.attn.to_out.0.bias"], gate * projection_bias)
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assert "decoder.t_embedder.timestep_embedder.linear_1.weight" in converted
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assert not any("type_emb" in key or "coarse" in key or "gate_msa" in key for key in converted)
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def test_native_diffusion_decoder_conversion_rejects_invalid_fused_qkv(self):
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from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder import (
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convert_ltx25_native_diffusion_decoder_state_dict,
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)
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with pytest.raises(ValueError, match="not divisible by 3"):
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convert_ltx25_native_diffusion_decoder_state_dict(
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{"decoder.det_stages.0.0.attn.qkv.weight": torch.ones(5, 2)}
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)
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def test_decode_uses_diffusion_decoder_without_conv_vae_conditioning(self):
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from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
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seen = {}
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class ConvVae:
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dtype = torch.float32
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config = SimpleNamespace(timestep_conditioning=True)
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def decode(self, *_args, **_kwargs):
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raise AssertionError("the convolutional VAE decoder must not run")
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class DiffusionDecoder:
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dtype = torch.float32
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def decode(self, latents, *, generator, return_dict):
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seen["latents"] = latents
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seen["generator"] = generator
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seen["return_dict"] = return_dict
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return (latents + 1,)
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class AudioVae:
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dtype = torch.float32
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def decode(self, audio_latents, *, return_dict):
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assert return_dict is False
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return (audio_latents + 2,)
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class VideoProcessor:
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def postprocess_video(self, video, *, output_type):
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seen["output_type"] = output_type
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return video
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pipe = object.__new__(LTX2Pipeline)
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torch.nn.Module.__init__(pipe)
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pipe.use_diffusion_decoder = True
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pipe.vae = ConvVae()
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pipe.diffusion_decoder = DiffusionDecoder()
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pipe.audio_vae = AudioVae()
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pipe.vocoder = torch.nn.Identity()
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pipe.video_processor = VideoProcessor()
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generator = torch.Generator().manual_seed(123)
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latents = torch.ones(1, 1)
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output = pipe._decode_output(
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latents=latents,
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audio_latents=torch.ones(1, 1),
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output_type="pt",
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connector_prompt_embeds=torch.ones(1, 1),
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generator=generator,
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device=torch.device("cpu"),
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decode_timestep=0.7,
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decode_noise_scale=0.5,
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prompt_batch_size=1,
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)
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torch.testing.assert_close(seen["latents"], latents)
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assert seen["generator"] is generator
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assert seen["return_dict"] is False
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assert seen["output_type"] == "pt"
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torch.testing.assert_close(output.output[0], latents + 1)
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torch.testing.assert_close(output.output[1], torch.full((1, 1), 3.0))
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def test_diffusion_decode_denormalizes_with_decoder_statistics(self):
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from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
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pipe = object.__new__(LTX2Pipeline)
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torch.nn.Module.__init__(pipe)
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pipe.use_diffusion_decoder = True
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pipe.transformer_spatial_patch_size = 1
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pipe.transformer_temporal_patch_size = 1
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pipe.vae = SimpleNamespace(
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latents_mean=torch.full((2,), 100.0),
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latents_std=torch.full((2,), 10.0),
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config=SimpleNamespace(scaling_factor=1.0),
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)
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pipe.diffusion_decoder = SimpleNamespace(
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latents_mean=torch.tensor([10.0, 20.0]),
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latents_std=torch.tensor([2.0, 4.0]),
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config=SimpleNamespace(scaling_factor=2.0),
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)
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pipe.audio_vae = SimpleNamespace(
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latents_mean=torch.tensor(0.0),
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latents_std=torch.tensor(1.0),
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)
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forward_ctx = SimpleNamespace(
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latent_num_frames=1,
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latent_height=1,
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latent_width=1,
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original_audio_num_frames=1,
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latent_mel_bins=2,
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)
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video, audio = pipe._unpack_and_denormalize_stage(
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forward_ctx,
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torch.tensor([[[1.0, 2.0]]]),
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torch.zeros(1, 1, 2),
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)
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torch.testing.assert_close(video.flatten(), torch.tensor([11.0, 24.0]))
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torch.testing.assert_close(audio, torch.zeros(1, 1, 1, 2))
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def test_non_output_rank_does_not_enter_diffusion_decoder(self, monkeypatch):
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import vllm_omni.diffusion.models.ltx2.ltx2_runtime as ltx_runtime
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from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
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class ConvVae:
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config = SimpleNamespace(timestep_conditioning=False)
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def is_distributed_enabled(self):
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raise AssertionError("DiffVAE decode must not inspect ConvVAE collectives")
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class DiffusionDecoder:
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dtype = torch.float32
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def decode(self, *_args, **_kwargs):
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raise AssertionError("only the output rank may run DiffVAE decode")
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monkeypatch.setattr(ltx_runtime.torch.distributed, "is_initialized", lambda: True)
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monkeypatch.setattr(ltx_runtime.torch.distributed, "get_rank", lambda: 1)
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pipe = object.__new__(LTX2Pipeline)
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torch.nn.Module.__init__(pipe)
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pipe.use_diffusion_decoder = True
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pipe.vae = ConvVae()
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pipe.diffusion_decoder = DiffusionDecoder()
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output = pipe._decode_output(
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latents=torch.ones(1, 1),
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audio_latents=torch.ones(1, 1),
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output_type="pt",
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connector_prompt_embeds=torch.ones(1, 1),
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generator=None,
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device=torch.device("cpu"),
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decode_timestep=0.0,
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decode_noise_scale=None,
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prompt_batch_size=1,
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)
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assert output.output[0].numel() == 0
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assert output.output[1].numel() == 0
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def test_non_output_rank_enters_distributed_diffusion_decoder(self, monkeypatch):
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import vllm_omni.diffusion.models.ltx2.ltx2_runtime as ltx_runtime
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from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
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class ConvVae:
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config = SimpleNamespace(timestep_conditioning=False)
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def is_distributed_enabled(self):
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raise AssertionError("DiffVAE decode must query the active decoder")
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class DiffusionDecoder:
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dtype = torch.float32
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def __init__(self):
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self.decode_calls = 0
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def is_distributed_enabled(self):
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return True
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def decode(self, *_args, **_kwargs):
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self.decode_calls += 1
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return (torch.empty(0, 3, 0, 0, 0),)
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monkeypatch.setattr(ltx_runtime.torch.distributed, "is_initialized", lambda: True)
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monkeypatch.setattr(ltx_runtime.torch.distributed, "get_rank", lambda: 1)
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pipe = object.__new__(LTX2Pipeline)
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torch.nn.Module.__init__(pipe)
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pipe.use_diffusion_decoder = True
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pipe.vae = ConvVae()
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pipe.diffusion_decoder = DiffusionDecoder()
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output = pipe._decode_output(
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latents=torch.ones(1, 1),
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audio_latents=torch.ones(1, 1),
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output_type="pt",
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connector_prompt_embeds=torch.ones(1, 1),
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generator=None,
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device=torch.device("cpu"),
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decode_timestep=0.0,
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decode_noise_scale=None,
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prompt_batch_size=1,
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)
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assert pipe.diffusion_decoder.decode_calls == 1
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assert output.output[0].numel() == 0
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assert output.output[1].numel() == 0
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def test_diffusion_decoder_patch_parallel_size_one_uses_native_tiling(self, monkeypatch):
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from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder import LTX2VideoDiffusionDecoderModel
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from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder_distributed import (
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DistributedLTX2VideoDiffusionDecoderModel,
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)
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expected = torch.ones(1, 3, 1, 2, 2)
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seen = {}
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def native_tiled_decode(self, z, generator=None, num_inference_steps=None):
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seen["z"] = z
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seen["generator"] = generator
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seen["num_inference_steps"] = num_inference_steps
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return expected
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monkeypatch.setattr(LTX2VideoDiffusionDecoderModel, "tiled_decode", native_tiled_decode)
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model = object.__new__(DistributedLTX2VideoDiffusionDecoderModel)
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torch.nn.Module.__init__(model)
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model.is_distributed_enabled = lambda: False
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generator = torch.Generator().manual_seed(17)
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z = torch.zeros(1, 1, 1, 1, 1)
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result = DistributedLTX2VideoDiffusionDecoderModel.tiled_decode(
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model,
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z,
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generator=generator,
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num_inference_steps=1,
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)
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assert result is expected
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assert seen["z"] is z
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assert seen["generator"] is generator
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assert seen["num_inference_steps"] == 1
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def test_distributed_diffusion_tiles_preserve_serial_noise_order(self, monkeypatch):
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from diffusers.utils.torch_utils import randn_tensor
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from vllm_omni.diffusion.models.ltx2.ltx2_diffusion_decoder_distributed import (
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DistributedLTX2VideoDiffusionDecoderModel,
|
|
)
|
|
|
|
model = DistributedLTX2VideoDiffusionDecoderModel(
|
|
out_channels=1,
|
|
latent_channels=1,
|
|
patch_size=1,
|
|
decoder_head_dim=8,
|
|
decoder_stage_channels=(8, 8, 8, 8, 8),
|
|
decoder_stage_depths=(1, 1, 1, 1, 1),
|
|
decoder_stage_kernels=((1, 1, 1),) * 4,
|
|
decoder_upsample_strides=((1, 1, 1),) * 4,
|
|
decoder_upsample_channel_reductions=(1, 1, 1, 1),
|
|
decoder_stage5_kernel=(1, 1, 1),
|
|
spatial_compression_ratio=1,
|
|
temporal_compression_ratio=1,
|
|
)
|
|
model.tile_sample_min_num_frames = 2
|
|
model.tile_sample_stride_num_frames = 1
|
|
model.tile_sample_min_height = 2
|
|
model.tile_sample_stride_height = 1
|
|
model.tile_sample_min_width = 2
|
|
model.tile_sample_stride_width = 1
|
|
model.distributed_executor = SimpleNamespace(group=None)
|
|
model.decoder.forward_stages_1_to_3 = lambda z: z.permute(0, 2, 3, 4, 1)
|
|
monkeypatch.setattr(torch.distributed, "broadcast", lambda *_args, **_kwargs: None)
|
|
|
|
z = torch.zeros(1, 1, 2, 3, 3)
|
|
distributed_generator = torch.Generator().manual_seed(123)
|
|
reference_generator = torch.Generator().manual_seed(123)
|
|
tasks, grid_spec = model._distributed_tile_split(z, distributed_generator, num_inference_steps=1)
|
|
|
|
assert grid_spec.grid_shape == (1, 3, 3)
|
|
assert grid_spec.split_dims == (2, 3, 4)
|
|
for task in tasks:
|
|
tile_shape = model._tiled_pixel_shape_from_features(
|
|
task.tensor,
|
|
drop_leading_frame=task.drop_leading_frame,
|
|
crop_trailing_ghost=task.crop_trailing_ghost,
|
|
)
|
|
expected = randn_tensor(tile_shape, generator=reference_generator, device=z.device, dtype=z.dtype)
|
|
actual = randn_tensor(tile_shape, generator=task.noise_generator, device=z.device, dtype=z.dtype)
|
|
torch.testing.assert_close(actual, expected)
|
|
|
|
assert torch.equal(distributed_generator.get_state(), reference_generator.get_state())
|
|
|
|
|
|
class TestLTXOutputRank:
|
|
@pytest.mark.parametrize(
|
|
("distributed_vae_state", "expected_decode_calls"),
|
|
[(False, 0), (True, 1), (RuntimeError("unavailable"), None)],
|
|
)
|
|
def test_non_output_rank_only_enters_collective_vae_decode(
|
|
self,
|
|
monkeypatch,
|
|
distributed_vae_state,
|
|
expected_decode_calls,
|
|
):
|
|
import vllm_omni.diffusion.models.ltx2.ltx2_runtime as ltx_runtime
|
|
from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
|
|
|
|
class FakeVae:
|
|
dtype = torch.float32
|
|
config = SimpleNamespace(timestep_conditioning=False)
|
|
|
|
def __init__(self):
|
|
self.decode_calls = 0
|
|
|
|
def is_distributed_enabled(self):
|
|
if isinstance(distributed_vae_state, Exception):
|
|
raise distributed_vae_state
|
|
return distributed_vae_state
|
|
|
|
def decode(self, *_args, **_kwargs):
|
|
self.decode_calls += 1
|
|
return (torch.ones(1, 1),)
|
|
|
|
monkeypatch.setattr(ltx_runtime.torch.distributed, "is_initialized", lambda: True)
|
|
monkeypatch.setattr(ltx_runtime.torch.distributed, "get_rank", lambda: 1)
|
|
pipe = object.__new__(LTX2Pipeline)
|
|
torch.nn.Module.__init__(pipe)
|
|
pipe.vae = FakeVae()
|
|
|
|
decode_kwargs = {
|
|
"latents": torch.ones(1, 1),
|
|
"audio_latents": torch.ones(1, 1),
|
|
"output_type": "np",
|
|
"connector_prompt_embeds": torch.ones(1, 1),
|
|
"generator": None,
|
|
"device": torch.device("cpu"),
|
|
"decode_timestep": 0.0,
|
|
"decode_noise_scale": None,
|
|
"prompt_batch_size": 1,
|
|
}
|
|
if isinstance(distributed_vae_state, Exception):
|
|
with pytest.raises(RuntimeError, match="unavailable"):
|
|
pipe._decode_output(**decode_kwargs)
|
|
assert pipe.vae.decode_calls == 0
|
|
return
|
|
|
|
output = pipe._decode_output(**decode_kwargs)
|
|
|
|
assert pipe.vae.decode_calls == expected_decode_calls
|
|
assert output.output[0].numel() == 0
|
|
assert output.output[1].numel() == 0
|
|
|
|
|
|
class TestLTX23VaeDistributedDecode:
|
|
"""Test LTX-2.3 distributed VAE helpers without loading weights."""
|
|
|
|
def test_ltx23_video_vae_is_distributed_tile_only_class(self):
|
|
from vllm_omni.diffusion.distributed.autoencoders.autoencoder_kl_ltx2 import (
|
|
DistributedAutoencoderKLLTX2Video,
|
|
)
|
|
from vllm_omni.diffusion.distributed.autoencoders.distributed_vae_executor import DistributedVaeMixin
|
|
|
|
assert issubclass(DistributedAutoencoderKLLTX2Video, DistributedVaeMixin)
|
|
assert not hasattr(DistributedAutoencoderKLLTX2Video, "patch_split")
|
|
|
|
def test_ltx23_vae_executor_gathers_known_tile_shapes_and_returns_empty_on_non_rank0(self):
|
|
from vllm_omni.diffusion.distributed.autoencoders.autoencoder_kl_ltx2 import LTX2VaeExecutor
|
|
from vllm_omni.diffusion.distributed.autoencoders.distributed_vae_executor import (
|
|
DistributedOperator,
|
|
GridSpec,
|
|
TileTask,
|
|
)
|
|
|
|
z = torch.zeros(1, 1, 1, 1, 1)
|
|
tile_output_shapes = {
|
|
0: (1, 1, 1, 2, 2),
|
|
1: (1, 1, 1, 2, 1),
|
|
2: (1, 1, 1, 1, 2),
|
|
3: (1, 1, 1, 1, 1),
|
|
}
|
|
tasks = [
|
|
TileTask(0, (0, 0), z, workload=4),
|
|
TileTask(1, (0, 1), z, workload=2),
|
|
TileTask(2, (1, 0), z, workload=2),
|
|
TileTask(3, (1, 1), z, workload=1),
|
|
]
|
|
grid_spec = GridSpec(
|
|
split_dims=(3, 4),
|
|
grid_shape=(2, 2),
|
|
tile_spec={
|
|
"max_tile_output_shape": (1, 1, 1, 2, 2),
|
|
"tile_output_shapes": tile_output_shapes,
|
|
},
|
|
output_dtype=torch.float32,
|
|
)
|
|
seen: dict[str, Any] = {}
|
|
|
|
def exec_tile(task):
|
|
return torch.full(tile_output_shapes[task.tile_id], float(task.tile_id + 1))
|
|
|
|
def merge_tiles(coord_tensor_map, passed_grid_spec):
|
|
seen["merged_shapes"] = {coord: tuple(tile.shape) for coord, tile in coord_tensor_map.items()}
|
|
assert passed_grid_spec is grid_spec
|
|
return torch.stack(
|
|
[
|
|
coord_tensor_map[(0, 0)].flatten()[0],
|
|
coord_tensor_map[(0, 1)].flatten()[0],
|
|
coord_tensor_map[(1, 0)].flatten()[0],
|
|
coord_tensor_map[(1, 1)].flatten()[0],
|
|
]
|
|
)
|
|
|
|
operator = DistributedOperator(split=lambda _z: (tasks, grid_spec), exec=exec_tile, merge=merge_tiles)
|
|
|
|
rank0_executor = object.__new__(LTX2VaeExecutor)
|
|
rank0_executor.parallel_size = 2
|
|
rank0_executor.world_size = 2
|
|
rank0_executor.rank = 0
|
|
|
|
def gather_rank0(local_tile_tensor):
|
|
assigned = rank0_executor._balance_tasks(tasks, 2)
|
|
rank1_results = [(task.tile_id, exec_tile(task)) for task in assigned[1]]
|
|
rank1_tile_tensor = rank0_executor._pack_local_tiles_without_meta(
|
|
rank1_results,
|
|
list(local_tile_tensor.shape),
|
|
z.device,
|
|
torch.float32,
|
|
)
|
|
seen["rank0_gather_shape"] = tuple(local_tile_tensor.shape)
|
|
return [local_tile_tensor, rank1_tile_tensor]
|
|
|
|
def fail_final_sync(*_args, **_kwargs):
|
|
raise AssertionError("broadcast_result=False should not sync the final result")
|
|
|
|
rank0_executor.gather_tensors = gather_rank0
|
|
rank0_executor._sync_final_result = fail_final_sync
|
|
|
|
rank0_result = rank0_executor.execute(z, operator, broadcast_result=False)
|
|
|
|
torch.testing.assert_close(rank0_result, torch.tensor([1.0, 2.0, 3.0, 4.0]))
|
|
assert seen["rank0_gather_shape"] == (2, 1, 1, 1, 2, 2)
|
|
assert seen["merged_shapes"] == {
|
|
(0, 0): (1, 1, 1, 2, 2),
|
|
(0, 1): (1, 1, 1, 2, 1),
|
|
(1, 0): (1, 1, 1, 1, 2),
|
|
(1, 1): (1, 1, 1, 1, 1),
|
|
}
|
|
|
|
non_rank0_executor = object.__new__(LTX2VaeExecutor)
|
|
non_rank0_executor.parallel_size = 2
|
|
non_rank0_executor.world_size = 2
|
|
non_rank0_executor.rank = 1
|
|
|
|
def gather_rank1(local_tile_tensor):
|
|
seen["rank1_gather_shape"] = tuple(local_tile_tensor.shape)
|
|
return None
|
|
|
|
def fail_non_rank0_merge(*_args, **_kwargs):
|
|
raise AssertionError("non-rank0 should not merge gathered tiles")
|
|
|
|
non_rank0_executor.gather_tensors = gather_rank1
|
|
non_rank0_executor._sync_final_result = fail_final_sync
|
|
|
|
empty_result = non_rank0_executor.execute(
|
|
z,
|
|
DistributedOperator(
|
|
split=lambda _z: (tasks, grid_spec),
|
|
exec=exec_tile,
|
|
merge=fail_non_rank0_merge,
|
|
),
|
|
broadcast_result=False,
|
|
)
|
|
|
|
assert tuple(empty_result.shape) == (0,)
|
|
assert seen["rank1_gather_shape"] == (2, 1, 1, 1, 2, 2)
|
|
|
|
|
|
class TestLTX23VaeTiling:
|
|
"""Test LTX-2.3 video VAE tile helpers without loading weights."""
|
|
|
|
def test_ltx23_video_vae_tile_split_uses_native_ltx23_tile_geometry(self):
|
|
from vllm_omni.diffusion.distributed.autoencoders.autoencoder_kl_ltx2 import (
|
|
DistributedAutoencoderKLLTX2Video,
|
|
)
|
|
|
|
vae = SimpleNamespace(
|
|
spatial_compression_ratio=32,
|
|
tile_sample_min_height=512,
|
|
tile_sample_min_width=512,
|
|
tile_sample_stride_height=448,
|
|
tile_sample_stride_width=448,
|
|
temporal_compression_ratio=8,
|
|
dtype=torch.float32,
|
|
)
|
|
|
|
z = torch.zeros(1, 2, 5, 16, 24)
|
|
tasks, grid_spec = DistributedAutoencoderKLLTX2Video.tile_split(vae, z)
|
|
|
|
assert grid_spec.grid_shape == (2, 2)
|
|
assert grid_spec.split_dims == (3, 4)
|
|
assert grid_spec.tile_spec["sample_height"] == 512
|
|
assert grid_spec.tile_spec["sample_width"] == 768
|
|
assert grid_spec.tile_spec["blend_height"] == 64
|
|
assert grid_spec.tile_spec["blend_width"] == 64
|
|
assert grid_spec.tile_spec["max_tile_output_shape"] == (1, 3, 33, 512, 512)
|
|
assert grid_spec.tile_spec["tile_output_shapes"] == {
|
|
0: (1, 3, 33, 512, 512),
|
|
1: (1, 3, 33, 512, 320),
|
|
2: (1, 3, 33, 64, 512),
|
|
3: (1, 3, 33, 64, 320),
|
|
}
|
|
assert [task.grid_coord for task in tasks] == [(0, 0), (0, 1), (1, 0), (1, 1)]
|
|
assert [tuple(task.tensor.shape) for task in tasks] == [
|
|
(1, 2, 5, 16, 16),
|
|
(1, 2, 5, 16, 10),
|
|
(1, 2, 5, 2, 16),
|
|
(1, 2, 5, 2, 10),
|
|
]
|
|
assert [task.workload for task in tasks] == [5 * 16 * 16, 5 * 16 * 10, 5 * 2 * 16, 5 * 2 * 10]
|
|
|
|
def test_ltx23_video_vae_tile_merge_blends_and_crops_like_tiled_decode(self):
|
|
from vllm_omni.diffusion.distributed.autoencoders.autoencoder_kl_ltx2 import (
|
|
DistributedAutoencoderKLLTX2Video,
|
|
)
|
|
from vllm_omni.diffusion.distributed.autoencoders.distributed_vae_executor import GridSpec
|
|
|
|
class FakeVae:
|
|
def __init__(self):
|
|
self.blend_calls = []
|
|
|
|
def clear_cache(self):
|
|
pass
|
|
|
|
def blend_v(self, _previous, current, blend_height):
|
|
self.blend_calls.append(("v", blend_height))
|
|
return current
|
|
|
|
def blend_h(self, _previous, current, blend_width):
|
|
self.blend_calls.append(("h", blend_width))
|
|
return current
|
|
|
|
fake_vae = FakeVae()
|
|
grid_spec = GridSpec(
|
|
split_dims=(3, 4),
|
|
grid_shape=(2, 2),
|
|
tile_spec={
|
|
"sample_height": 10,
|
|
"sample_width": 10,
|
|
"blend_height": 1,
|
|
"blend_width": 2,
|
|
"tile_sample_stride_height": 5,
|
|
"tile_sample_stride_width": 5,
|
|
},
|
|
)
|
|
tiles = {
|
|
(0, 0): torch.full((1, 3, 2, 6, 6), 1.0),
|
|
(0, 1): torch.full((1, 3, 2, 6, 6), 2.0),
|
|
(1, 0): torch.full((1, 3, 2, 6, 6), 3.0),
|
|
(1, 1): torch.full((1, 3, 2, 6, 6), 4.0),
|
|
}
|
|
|
|
merged = DistributedAutoencoderKLLTX2Video.tile_merge(fake_vae, tiles, grid_spec)
|
|
|
|
assert merged.shape == (1, 3, 2, 10, 10)
|
|
assert fake_vae.blend_calls == [("h", 2), ("v", 1), ("v", 1), ("h", 2)]
|
|
torch.testing.assert_close(merged[:, :, :, :5, :5], torch.ones(1, 3, 2, 5, 5))
|
|
torch.testing.assert_close(merged[:, :, :, :5, 5:], torch.full((1, 3, 2, 5, 5), 2.0))
|
|
torch.testing.assert_close(merged[:, :, :, 5:, :5], torch.full((1, 3, 2, 5, 5), 3.0))
|
|
torch.testing.assert_close(merged[:, :, :, 5:, 5:], torch.full((1, 3, 2, 5, 5), 4.0))
|
|
|
|
def test_ltx23_video_vae_tiled_decode_dispatches_to_tile_operator(self):
|
|
from vllm_omni.diffusion.distributed.autoencoders import autoencoder_kl_ltx2
|
|
|
|
z = torch.zeros(1, 2, 1, 16, 24)
|
|
expected = torch.ones(1, 3, 1, 512, 768)
|
|
seen = {}
|
|
|
|
class FakeExecutor:
|
|
def execute(self, tensor, operator, broadcast_result=True):
|
|
seen["tensor"] = tensor
|
|
seen["operator"] = operator
|
|
seen["broadcast_result"] = broadcast_result
|
|
return expected
|
|
|
|
vae = SimpleNamespace(distributed_executor=FakeExecutor(), is_distributed_enabled=lambda: True)
|
|
vae.tile_split = autoencoder_kl_ltx2.DistributedAutoencoderKLLTX2Video.tile_split.__get__(vae)
|
|
vae.tile_exec = autoencoder_kl_ltx2.DistributedAutoencoderKLLTX2Video.tile_exec.__get__(vae)
|
|
vae.tile_merge = autoencoder_kl_ltx2.DistributedAutoencoderKLLTX2Video.tile_merge.__get__(vae)
|
|
|
|
output = autoencoder_kl_ltx2.DistributedAutoencoderKLLTX2Video.tiled_decode(
|
|
vae,
|
|
z,
|
|
temb=torch.tensor(0.5),
|
|
return_dict=False,
|
|
)
|
|
|
|
assert len(output) == 1
|
|
assert output[0] is expected
|
|
assert seen["tensor"] is z
|
|
assert seen["broadcast_result"] is False
|
|
assert seen["operator"].split.__name__ == "tile_split"
|
|
assert seen["operator"].merge.__name__ == "tile_merge"
|