138 lines
5.9 KiB
Python
138 lines
5.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import os
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import numpy as np
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import pytest
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import torch
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from torch.distributed.tensor import DTensor
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from torch.testing import assert_close
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from transformers import AutoConfig, AutoTokenizer, UMT5EncoderModel
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from fastvideo.configs.pipelines import PipelineConfig
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from fastvideo.forward_context import set_forward_context
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from fastvideo.logger import init_logger
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from fastvideo.models.loader.component_loader import TextEncoderLoader
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from fastvideo.utils import maybe_download_model, PRECISION_TO_TYPE
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from fastvideo.fastvideo_args import FastVideoArgs
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from fastvideo.configs.models.encoders import T5Config
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logger = init_logger(__name__)
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = "29503"
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BASE_MODEL_PATH = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
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MODEL_PATH = maybe_download_model(BASE_MODEL_PATH,
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local_dir=os.path.join(
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'data', BASE_MODEL_PATH))
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TEXT_ENCODER_PATH = os.path.join(MODEL_PATH, "text_encoder")
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TOKENIZER_PATH = os.path.join(MODEL_PATH, "tokenizer")
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@pytest.mark.usefixtures("distributed_setup")
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def test_t5_encoder():
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# Initialize the two model implementations
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hf_config = AutoConfig.from_pretrained(TEXT_ENCODER_PATH)
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print(hf_config)
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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precision_str = "fp32"
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precision = PRECISION_TO_TYPE[precision_str]
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model1 = UMT5EncoderModel.from_pretrained(TEXT_ENCODER_PATH).to(
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precision).to(device).eval()
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tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)
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args = FastVideoArgs(model_path=TEXT_ENCODER_PATH,
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pipeline_config=PipelineConfig(text_encoder_configs=(T5Config(),),
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text_encoder_precisions=(precision_str,)),
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pin_cpu_memory=False)
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loader = TextEncoderLoader()
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model2 = loader.load(TEXT_ENCODER_PATH, args)
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model2 = model2.to(precision)
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model2.eval()
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# Sanity check weights between the two models
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logger.info("Comparing model weights for sanity check...")
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params1 = dict(model1.named_parameters())
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params2 = dict(model2.named_parameters())
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# Check number of parameters
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logger.info("Model1 has %s parameters", len(params1))
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logger.info("Model2 has %s parameters", len(params2))
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weight_diffs = []
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# check if embed_tokens are the same
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weights = ["encoder.block.{}.layer.0.layer_norm.weight", "encoder.block.{}.layer.0.SelfAttention.relative_attention_bias.weight", \
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"encoder.block.{}.layer.0.SelfAttention.o.weight", "encoder.block.{}.layer.1.DenseReluDense.wi_0.weight", "encoder.block.{}.layer.1.DenseReluDense.wi_1.weight",\
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"encoder.block.{}.layer.1.DenseReluDense.wo.weight", \
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"encoder.block.{}.layer.1.layer_norm.weight", "encoder.final_layer_norm.weight"]
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for idx in range(hf_config.num_hidden_layers):
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for w in weights:
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name1 = w.format(idx)
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name2 = w.format(idx)
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p1 = params1[name1]
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p2 = params2[name2]
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p2 = (p2.to_local() if isinstance(p2, DTensor) else p2).to(p1)
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assert_close(p1, p2, atol=1e-4, rtol=1e-4)
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# Test with some sample prompts
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prompts = [
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"Once upon a time", "The quick brown fox jumps over",
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"In a galaxy far, far away"
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]
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logger.info("Testing T5 encoder with sample prompts")
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with torch.no_grad():
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for prompt in prompts:
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logger.info("Testing prompt: %s", prompt)
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# Tokenize the prompt
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tokens = tokenizer(prompt,
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padding="max_length",
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max_length=512,
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truncation=True,
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return_tensors="pt").to(device)
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# Get outputs from HuggingFace implementation
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# filter out padding input_ids
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# tokens.input_ids = tokens.input_ids[tokens.attention_mask==1]
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# tokens.attention_mask = tokens.attention_mask[tokens.attention_mask==1]
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outputs1 = model1(input_ids=tokens.input_ids,
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attention_mask=tokens.attention_mask,
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output_hidden_states=True).last_hidden_state
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print("--------------------------------")
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logger.info("Testing model2")
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# Get outputs from our implementation
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with set_forward_context(current_timestep=0, attn_metadata=None):
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outputs2 = model2(
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input_ids=tokens.input_ids,
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attention_mask=tokens.attention_mask,
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).last_hidden_state
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# Compare last hidden states
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last_hidden_state1 = outputs1[tokens.attention_mask == 1]
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last_hidden_state2 = outputs2[tokens.attention_mask == 1]
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assert last_hidden_state1.shape == last_hidden_state2.shape, \
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f"Hidden state shapes don't match: {last_hidden_state1.shape} vs {last_hidden_state2.shape}"
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max_diff_hidden = torch.max(
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torch.abs(last_hidden_state1 - last_hidden_state2))
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mean_diff_hidden = torch.mean(
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torch.abs(last_hidden_state1 - last_hidden_state2))
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logger.info("Maximum difference in last hidden states: %s",
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max_diff_hidden.item())
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logger.info("Mean difference in last hidden states: %s",
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mean_diff_hidden.item())
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logger.info("Max memory allocated: %s GB", torch.cuda.max_memory_allocated() / 1024**3)
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# Check if outputs are similar (allowing for small numerical differences)
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assert mean_diff_hidden < 1e-4, \
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f"Hidden states differ significantly: mean diff = {mean_diff_hidden.item()}"
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assert max_diff_hidden < 1e-4, \
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f"Hidden states differ significantly: max diff = {max_diff_hidden.item()}" |