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fix: demo/fastapi_app.py with mps device.
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yangmeng
parent
877c778c0e
commit
1b69d7f99b
@ -12,9 +12,9 @@ app = FastAPI()
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# Device and dtype configuration
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def get_device_and_dtype():
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if torch.cuda.is_available():
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return 'cuda', torch.bfloat16
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return 'cuda', torch.float32
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elif torch.backends.mps.is_available():
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return 'mps', torch.float16
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return 'mps', torch.float32
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return 'cpu', torch.float32
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device, dtype = get_device_and_dtype()
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@ -35,11 +35,17 @@ tokenizer = vl_chat_processor.tokenizer
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@torch.inference_mode()
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def multimodal_understanding(image_data, question, seed, top_p, temperature):
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torch.cuda.empty_cache() if device == 'cuda' else None
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# Clear CUDA cache if using CUDA
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if device == 'cuda':
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torch.cuda.empty_cache()
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# set seed
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torch.manual_seed(seed)
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np.random.seed(seed)
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if device == 'cuda':
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torch.cuda.manual_seed(seed)
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elif device == 'mps':
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torch.mps.manual_seed(seed)
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conversation = [
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{
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@ -94,36 +100,39 @@ def generate(input_ids,
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cfg_weight: float = 5,
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image_token_num_per_image: int = 576,
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patch_size: int = 16):
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torch.cuda.empty_cache() if device == 'cuda' else None
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tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).to(device)
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for i in range(parallel_size * 2):
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tokens[i, :] = input_ids
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if i % 2 != 0:
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tokens[i, 1:-1] = vl_chat_processor.pad_id
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inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens)
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generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).to(device)
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try:
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torch.cuda.empty_cache() if device == 'cuda' else None
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tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).to(device)
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for i in range(parallel_size * 2):
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tokens[i, :] = input_ids
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if i % 2 != 0:
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tokens[i, 1:-1] = vl_chat_processor.pad_id
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inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens)
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generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).to(device)
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pkv = None
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for i in range(image_token_num_per_image):
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outputs = vl_gpt.language_model.model(inputs_embeds=inputs_embeds, use_cache=True, past_key_values=pkv)
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pkv = outputs.past_key_values
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hidden_states = outputs.last_hidden_state
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logits = vl_gpt.gen_head(hidden_states[:, -1, :])
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logit_cond = logits[0::2, :]
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logit_uncond = logits[1::2, :]
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logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
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probs = torch.softmax(logits / temperature, dim=-1)
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next_token = torch.multinomial(probs, num_samples=1)
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generated_tokens[:, i] = next_token.squeeze(dim=-1)
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next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
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img_embeds = vl_gpt.prepare_gen_img_embeds(next_token)
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inputs_embeds = img_embeds.unsqueeze(dim=1)
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patches = vl_gpt.gen_vision_model.decode_code(
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generated_tokens.to(dtype=torch.int),
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shape=[parallel_size, 8, width // patch_size, height // patch_size]
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)
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pkv = None
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for i in range(image_token_num_per_image):
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outputs = vl_gpt.language_model.model(inputs_embeds=inputs_embeds, use_cache=True, past_key_values=pkv)
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pkv = outputs.past_key_values
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hidden_states = outputs.last_hidden_state
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logits = vl_gpt.gen_head(hidden_states[:, -1, :])
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logit_cond = logits[0::2, :]
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logit_uncond = logits[1::2, :]
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logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
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probs = torch.softmax(logits / temperature, dim=-1)
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next_token = torch.multinomial(probs, num_samples=1)
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generated_tokens[:, i] = next_token.squeeze(dim=-1)
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next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
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img_embeds = vl_gpt.prepare_gen_img_embeds(next_token)
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inputs_embeds = img_embeds.unsqueeze(dim=1)
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patches = vl_gpt.gen_vision_model.decode_code(
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generated_tokens.to(dtype=torch.int),
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shape=[parallel_size, 8, width // patch_size, height // patch_size]
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)
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return generated_tokens.to(dtype=torch.int), patches
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return generated_tokens.to(dtype=torch.int), patches
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except Exception as e:
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raise Exception(f"Error in generate function: {str(e)}")
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def unpack(dec, width, height, parallel_size=5):
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@ -138,29 +147,37 @@ def unpack(dec, width, height, parallel_size=5):
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@torch.inference_mode()
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def generate_image(prompt, seed, guidance):
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torch.cuda.empty_cache() if device == 'cuda' else None
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seed = seed if seed is not None else 12345
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torch.manual_seed(seed)
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if device == 'cuda':
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torch.cuda.manual_seed(seed)
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np.random.seed(seed)
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width = 384
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height = 384
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parallel_size = 5
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try:
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# Clear CUDA cache if using CUDA
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if device == 'cuda':
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torch.cuda.empty_cache()
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# Set the seed for reproducible results
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if seed is not None:
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torch.manual_seed(seed)
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if device == 'cuda':
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torch.cuda.manual_seed(seed)
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elif device == 'mps':
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torch.mps.manual_seed(seed)
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np.random.seed(seed)
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width = 384
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height = 384
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parallel_size = 5
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with torch.no_grad():
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messages = [{'role': 'User', 'content': prompt}, {'role': 'Assistant', 'content': ''}]
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text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
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conversations=messages,
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sft_format=vl_chat_processor.sft_format,
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system_prompt=''
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)
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text = text + vl_chat_processor.image_start_tag
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input_ids = torch.LongTensor(tokenizer.encode(text))
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_, patches = generate(input_ids, width // 16 * 16, height // 16 * 16, cfg_weight=guidance, parallel_size=parallel_size)
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images = unpack(patches, width // 16 * 16, height // 16 * 16)
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with torch.no_grad():
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messages = [{'role': 'User', 'content': prompt}, {'role': 'Assistant', 'content': ''}]
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text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
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conversations=messages,
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sft_format=vl_chat_processor.sft_format,
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system_prompt=''
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)
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text = text + vl_chat_processor.image_start_tag
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input_ids = torch.LongTensor(tokenizer.encode(text))
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_, patches = generate(input_ids, width // 16 * 16, height // 16 * 16, cfg_weight=guidance, parallel_size=parallel_size)
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images = unpack(patches, width // 16 * 16, height // 16 * 16)
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return [Image.fromarray(images[i]).resize((1024, 1024), Image.LANCZOS) for i in range(parallel_size)]
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return [Image.fromarray(images[i]).resize((1024, 1024), Image.LANCZOS) for i in range(parallel_size)]
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except Exception as e:
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raise Exception(f"Error in generate_image function: {str(e)}")
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@app.post("/generate_images/")
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