168 lines
6.6 KiB
Python
168 lines
6.6 KiB
Python
from fastvideo.v1.models.encoders.clip import CLIPTextModel
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from fastvideo.v1.models.hunyuan.text_encoder import TextEncoder
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import os
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import torch
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import torch.nn as nn
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import argparse
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import numpy as np
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from transformers import CLIPTextModel as HFCLIPTextModel
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from fastvideo.v1.logger import init_logger
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from fastvideo.v1.models.hunyuan.text_encoder import load_text_encoder, load_tokenizer
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logger = init_logger(__name__)
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def initialize_identical_weights(model1, model2, seed=42):
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"""Initialize both models with identical weights using a fixed seed for reproducibility."""
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# Get all parameters from both models
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params1 = dict(model1.named_parameters())
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params2 = dict(model2.named_parameters())
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# Initialize each layer with identical values
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with torch.no_grad():
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# Initialize weights
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for name1, param1 in params1.items():
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if 'weight' in name1:
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# Set seed before each weight initialization
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torch.manual_seed(seed)
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nn.init.normal_(param1, mean=0.0, std=0.05)
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for name2, param2 in params2.items():
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if 'weight' in name2:
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# Reset seed to get same initialization
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torch.manual_seed(seed)
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nn.init.normal_(param2, mean=0.0, std=0.05)
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# Initialize biases
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for name1, param1 in params1.items():
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if 'bias' in name1:
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torch.manual_seed(seed)
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nn.init.normal_(param1, mean=0.0, std=0.05)
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param1.data = param1.data.to(torch.bfloat16)
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for name2, param2 in params2.items():
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if 'bias' in name2:
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torch.manual_seed(seed)
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nn.init.normal_(param2, mean=0.0, std=0.05)
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param2.data = param2.data.to(torch.bfloat16)
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logger.info("Both models initialized with identical weights in bfloat16")
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return model1, model2
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def setup_args():
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parser = argparse.ArgumentParser(description='CLIP Text Encoder Test')
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parser.add_argument('--model-path', type=str, default="openai/clip-vit-large-patch14",
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help='Path to the CLIP model')
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parser.add_argument('--precision', type=str, default="bfloat16",
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help='Precision to use for the model (float32, float16, bfloat16)')
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return parser.parse_args()
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def test_clip_encoder():
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args = setup_args()
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# Set fixed random seed for reproducibility
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torch.manual_seed(42)
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np.random.seed(42)
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Initialize the two model implementations
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logger.info(f"Loading models from {args.model_path}")
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# Load our implementation using the loader from text_encoder/__init__.py
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model1, _ = load_text_encoder(
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text_encoder_type="clipL",
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text_encoder_precision=args.precision,
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text_encoder_path=args.model_path,
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logger=logger,
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device=device
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)
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# Load the HuggingFace implementation directly
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model2 = HFCLIPTextModel.from_pretrained(args.model_path)
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if args.precision == "bfloat16":
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model2 = model2.to(torch.bfloat16)
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elif args.precision == "float16":
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model2 = model2.to(torch.float16)
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model2 = model2.to(device)
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model2.eval()
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# Load tokenizer
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tokenizer, _ = load_tokenizer(
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tokenizer_type="clipL",
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tokenizer_path=args.model_path,
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logger=logger
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)
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# Test with some sample prompts
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prompts = [
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"a photo of a cat",
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"a beautiful landscape with mountains",
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"an astronaut riding a horse on the moon"
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]
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logger.info("Testing CLIP text 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(f"Testing prompt: '{prompt}'")
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# Tokenize the prompt
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tokens = tokenizer(
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prompt,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_tensors="pt"
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).to(device)
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# Get embeddings from our implementation
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outputs1 = model1(
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input_ids=tokens.input_ids,
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attention_mask=tokens.attention_mask,
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output_hidden_states=True
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)
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# Get embeddings from HuggingFace implementation
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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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output_hidden_states=True
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)
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# Compare last hidden states
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last_hidden_state1 = outputs1.last_hidden_state
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last_hidden_state2 = outputs2.last_hidden_state
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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(torch.abs(last_hidden_state1 - last_hidden_state2))
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mean_diff_hidden = torch.mean(torch.abs(last_hidden_state1 - last_hidden_state2))
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logger.info(f"Maximum difference in last hidden states: {max_diff_hidden.item()}")
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logger.info(f"Mean difference in last hidden states: {mean_diff_hidden.item()}")
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# Compare pooler outputs
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pooler_output1 = outputs1.pooler_output
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pooler_output2 = outputs2.pooler_output
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assert pooler_output1.shape == pooler_output2.shape, \
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f"Pooler output shapes don't match: {pooler_output1.shape} vs {pooler_output2.shape}"
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max_diff_pooler = torch.max(torch.abs(pooler_output1 - pooler_output2))
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mean_diff_pooler = torch.mean(torch.abs(pooler_output1 - pooler_output2))
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logger.info(f"Maximum difference in pooler outputs: {max_diff_pooler.item()}")
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logger.info(f"Mean difference in pooler outputs: {mean_diff_pooler.item()}")
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# Check if outputs are similar (allowing for small numerical differences)
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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()}"
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assert max_diff_pooler < 1e-4, \
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f"Pooler outputs differ significantly: max diff = {max_diff_pooler.item()}"
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logger.info("Test passed! Both CLIP text encoder implementations produce similar outputs.")
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logger.info("Test completed successfully")
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if __name__ == "__main__":
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test_clip_encoder() |