MaciejBalaNVand汪志鹏
97d6c693a6
Signed-off-by: Maciej Bala <mbala@nvidia.com> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
201 lines
8.8 KiB
Python
201 lines
8.8 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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"""Compare full and pre-sharded Cosmos loading with real two-rank FSDP."""
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from contextlib import ExitStack
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from datetime import timedelta
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from pathlib import Path
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from types import SimpleNamespace
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from unittest.mock import patch
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import pytest
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import torch
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import torch.distributed as dist
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import torch.nn.functional as F
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from torch import nn
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from tests.helpers.mark import hardware_test
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pytestmark = [pytest.mark.diffusion, pytest.mark.parallel, pytest.mark.core_model, pytest.mark.gpu]
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class _SDPAAttention(nn.Module):
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"""Use deterministic native attention while testing loading and FSDP."""
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def __init__(self, *, causal, softmax_scale, **kwargs):
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super().__init__()
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self.causal = causal
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self.scale = softmax_scale
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def forward(self, query, key, value):
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return F.scaled_dot_product_attention(
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query.transpose(1, 2),
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key.transpose(1, 2),
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value.transpose(1, 2),
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is_causal=self.causal,
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scale=self.scale,
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).transpose(1, 2)
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def _load_cosmos_worker(rank, rendezvous, checkpoint_dir, edge):
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from tests.model_executor.helpers import bootstrap_vllm_layer_custom_op_modules
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bootstrap_vllm_layer_custom_op_modules()
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from safetensors.torch import save_file
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from vllm.config import DeviceConfig, VllmConfig, set_current_vllm_config
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from vllm.config.load import LoadConfig
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from vllm.model_executor import parameter
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from vllm.model_executor.layers import linear
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from vllm.utils.torch_utils import set_default_torch_dtype
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from vllm_omni.diffusion.distributed import hsdp
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from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
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from vllm_omni.diffusion.models.cosmos3 import transformer_cosmos3, transformer_cosmos3_edge
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from vllm_omni.platforms import current_omni_platform
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device = torch.device("cuda", rank)
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current_omni_platform.set_device(device)
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dist.init_process_group("nccl", init_method=rendezvous, rank=rank, world_size=2, timeout=timedelta(seconds=120))
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try:
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with ExitStack() as stack:
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stack.enter_context(set_current_vllm_config(VllmConfig(device_config=DeviceConfig(device="cuda"))))
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stack.enter_context(set_default_torch_dtype(torch.bfloat16))
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# HSDP spans both workers, while every vLLM linear uses TP=1.
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for module in (linear, parameter):
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stack.enter_context(patch.object(module, "get_tensor_model_parallel_rank", return_value=0))
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stack.enter_context(patch.object(module, "get_tensor_model_parallel_world_size", return_value=1))
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for module in (transformer_cosmos3, transformer_cosmos3_edge):
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stack.enter_context(patch.object(module, "get_tensor_model_parallel_world_size", return_value=1))
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stack.enter_context(patch.object(module, "FrameworkAttention", _SDPAAttention))
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stack.enter_context(patch.object(transformer_cosmos3, "_get_ulysses_state", return_value=(1, 0, None)))
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stack.enter_context(
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patch.object(hsdp, "get_world_group", return_value=SimpleNamespace(world_size=2, rank_in_group=rank))
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)
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model_cls = (
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transformer_cosmos3_edge.Cosmos3EdgeVFMTransformer
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if edge
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else transformer_cosmos3.Cosmos3VFMTransformer
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)
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tf_config = dict(
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hidden_size=16,
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num_hidden_layers=1,
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num_attention_heads=2,
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num_key_value_heads=2,
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head_dim=8,
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intermediate_size=32,
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vocab_size=32,
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latent_patch_size=2,
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latent_channel=48,
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temporal_compression_factor=4,
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rope_scaling={"mrope_section": [2, 1, 1]},
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)
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if edge:
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tf_config.update(
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backbone_type=transformer_cosmos3_edge.COSMOS3_EDGE_BACKBONE_TYPE,
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qk_norm_for_text=False,
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use_und_k_norm_for_gen=True,
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)
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config = SimpleNamespace(
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tf_model_config=tf_config,
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dtype=torch.bfloat16,
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quantization_config=None,
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lora_path=None,
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num_weight_load_threads=1,
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hsdp_weight_load_strategy="full",
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parallel_config=SimpleNamespace(
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use_hsdp=True, hsdp_replicate_size=1, hsdp_shard_size=2, tensor_parallel_size=1
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),
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)
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class Pipeline(nn.Module):
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def __init__(self):
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super().__init__()
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self.transformer = model_cls(config)
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self.transformer.register_buffer("scratch", torch.tensor([7.0]), persistent=False)
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self.vae = nn.Linear(2, 2)
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self.weights_sources = [
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DiffusersPipelineLoader.ComponentSource(checkpoint_dir, None, None, "transformer.", False)
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]
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def load_weights(self, weights):
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state = dict(weights)
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self.transformer.load_state_dict(
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{name.removeprefix("transformer."): t for name, t in state.items()}
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)
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self.transformer.post_load_weights()
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self.transformer.eval()
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self.transformer.validate_loaded_weights(set(state))
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return set(state)
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full = Pipeline()
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if rank == 0:
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torch.manual_seed(42)
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with torch.no_grad():
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for name, tensor in full.transformer.named_parameters():
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tensor.copy_(torch.randn_like(tensor) * 0.02)
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if "norm" in name:
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tensor.fill_(1)
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save_file(full.transformer.state_dict(), str(Path(checkpoint_dir) / "model.safetensors"))
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dist.barrier()
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def load(model):
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loader = DiffusersPipelineLoader(LoadConfig(), config)
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with patch.object(loader, "_init_from_load_format", return_value=model):
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return loader._load_model_with_hsdp(device)
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full = load(full)
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config.hsdp_weight_load_strategy = "pre_sharded"
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pre_sharded = load(Pipeline())
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full_parameters = dict(full.transformer.named_parameters())
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from torch.distributed.tensor import DTensor
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for name, actual in pre_sharded.transformer.named_parameters():
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expected = full_parameters[name]
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if name.startswith("time_embedder."):
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assert not isinstance(actual, DTensor)
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assert actual.dtype == torch.float32
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else:
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assert isinstance(actual, DTensor)
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actual = actual.full_tensor()
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expected = expected.full_tensor()
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torch.testing.assert_close(actual, expected, rtol=0, atol=0)
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assert pre_sharded.vae.weight.device == device
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torch.testing.assert_close(pre_sharded.transformer.scratch, full.transformer.scratch)
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assert all(t.device.type != "meta" for t in pre_sharded.transformer.buffers())
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inputs = dict(
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hidden_states=torch.linspace(-1, 1, 48 * 4, device=device).reshape(1, 48, 1, 2, 2),
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timestep=torch.tensor([1.0], device=device),
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text_ids=torch.tensor([[1, 2]], device=device),
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text_mask=torch.ones(1, 2, dtype=torch.long, device=device),
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video_shape=(1, 2, 2),
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fps=24.0,
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)
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with torch.no_grad():
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# Also exercise the cached UND path on the second denoising step.
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for step in (1.0, 0.5):
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inputs["timestep"].fill_(step)
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expected = full.transformer(**inputs)
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actual = pre_sharded.transformer(**inputs)
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assert torch.isfinite(actual).all()
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torch.testing.assert_close(actual, expected, rtol=0, atol=0)
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finally:
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dist.destroy_process_group()
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@pytest.mark.parametrize("edge", [False, True], ids=["cosmos3", "cosmos3_edge"])
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@hardware_test(res={"cuda": "L4"}, num_cards=2)
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@pytest.mark.skipif(
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not torch.cuda.is_available() or torch.accelerator.device_count() < 2 or not dist.is_nccl_available(),
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reason="requires two CUDA GPUs and NCCL",
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)
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def test_cosmos3_pre_sharded_matches_full_hsdp(tmp_path, edge):
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torch.multiprocessing.spawn(
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_load_cosmos_worker,
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args=(f"file://{tmp_path / 'rendezvous'}", str(tmp_path), edge),
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nprocs=2,
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join=True,
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)
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