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import math |
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import json |
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import torch |
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from threading import Thread |
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from copy import deepcopy |
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from PIL import Image |
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from torchvision import transforms |
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from transformers import LlamaPreTrainedModel, LlamaForCausalLM, TextIteratorStreamer |
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from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer |
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from transformers import AutoProcessor |
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from .configuration_minicpm import MiniCPMVConfig |
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from .resampler import Resampler |
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IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5) |
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IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5) |
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class MiniCPMVPreTrainedModel(LlamaPreTrainedModel): |
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config_class = MiniCPMVConfig |
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class MiniCPMV(MiniCPMVPreTrainedModel): |
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def __init__(self, config): |
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super().__init__(config) |
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self.llm = LlamaForCausalLM(config) |
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self.vpm = self.init_vision_module() |
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self.vision_dim = self.vpm.embed_dim |
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self.embed_dim = self.llm.config.hidden_size |
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self.resampler = self.init_resampler(self.embed_dim, self.vision_dim) |
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self.transform = self.init_transform() |
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def init_vision_module(self): |
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model = Idefics2VisionTransformer(self.config.vision_config) |
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if self.config.drop_vision_last_layer: |
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model.encoder.layers = model.encoder.layers[:-1] |
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setattr(model, 'embed_dim', model.embeddings.embed_dim) |
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setattr(model, 'patch_size', model.embeddings.patch_size) |
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return model |
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def init_resampler(self, embed_dim, vision_dim): |
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return Resampler( |
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num_queries=self.config.query_num, |
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embed_dim=embed_dim, |
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num_heads=embed_dim // 128, |
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kv_dim=vision_dim, |
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adaptive=True |
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) |
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def init_transform(self): |
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return transforms.Compose( |
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[ |
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transforms.ToTensor(), |
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transforms.Normalize( |
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD |
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), |
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] |
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) |
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def get_input_embeddings(self): |
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return self.llm.get_input_embeddings() |
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def set_input_embeddings(self, value): |
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self.llm.embed_tokens = value |
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def get_output_embeddings(self): |
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return self.llm.lm_head |
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def set_output_embeddings(self, new_embeddings): |
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self.llm.lm_head = new_embeddings |
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def set_decoder(self, decoder): |
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self.llm = decoder |
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def get_decoder(self): |
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return self.llm |
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def get_vllm_embedding(self, data): |
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if 'vision_hidden_states' not in data: |
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dtype = self.llm.model.embed_tokens.weight.dtype |
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device = self.llm.model.embed_tokens.weight.device |
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tgt_sizes = data['tgt_sizes'] |
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pixel_values_list = data['pixel_values'] |
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vision_hidden_states = [] |
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all_pixel_values = [] |
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img_cnt = [] |
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for pixel_values in pixel_values_list: |
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img_cnt.append(len(pixel_values)) |
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all_pixel_values.extend([i.flatten(end_dim=1).permute(1, 0) for i in pixel_values]) |
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if all_pixel_values: |
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tgt_sizes = torch.vstack(tgt_sizes).type(torch.int32) |
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if self.config.batch_vision_input: |
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max_patches = torch.max(tgt_sizes[:, 0] * tgt_sizes[:, 1]) |
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all_pixel_values = torch.nn.utils.rnn.pad_sequence(all_pixel_values, batch_first=True, |
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padding_value=0.0) |
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B, L, _ = all_pixel_values.shape |
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all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L) |
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patch_attn_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool, device=device) |
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for i in range(B): |
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patch_attn_mask[i, :tgt_sizes[i][0] * tgt_sizes[i][1]] = True |
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vision_embedding = self.vpm(all_pixel_values.type(dtype), patch_attention_mask=patch_attn_mask).last_hidden_state |
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vision_embedding = self.resampler(vision_embedding, tgt_sizes) |
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else: |
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vision_embedding = [] |
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for single_tgt_size, single_pixel_values in zip(tgt_sizes, all_pixel_values): |
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single_pixel_values = single_pixel_values.unsqueeze(0) |
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B, L, _ = single_pixel_values.shape |
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single_pixel_values = single_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L) |
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single_vision_embedding = self.vpm(single_pixel_values.type(dtype)).last_hidden_state |
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single_vision_embedding = self.resampler(single_vision_embedding, single_tgt_size.unsqueeze(0)) |
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vision_embedding.append(single_vision_embedding) |
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vision_embedding = torch.vstack(vision_embedding) |
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start = 0 |
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for pixel_values in pixel_values_list: |
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img_cnt = len(pixel_values) |
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if img_cnt > 0: |
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vision_hidden_states.append(vision_embedding[start: start + img_cnt]) |
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start += img_cnt |
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else: |
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vision_hidden_states.append([]) |
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else: |
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if self.training: |
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dummy_image = torch.zeros( |
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(1, 3, 224, 224), |
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device=device, dtype=dtype |
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) |
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tgt_sizes = torch.Tensor([[(224 // self.config.patch_size), math.ceil(224 / self.config.patch_size)]]).type(torch.int32) |
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dummy_feature = self.resampler(self.vpm(dummy_image).last_hidden_state, tgt_sizes) |
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else: |
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dummy_feature = [] |
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for _ in range(len(pixel_values_list)): |
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vision_hidden_states.append(dummy_feature) |
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else: |
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vision_hidden_states = data['vision_hidden_states'] |
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if hasattr(self.llm.config, 'scale_emb'): |
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vllm_embedding = self.llm.model.embed_tokens(data['input_ids']) * self.llm.config.scale_emb |
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else: |
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vllm_embedding = self.llm.model.embed_tokens(data['input_ids']) |
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vision_hidden_states = [i.type(vllm_embedding.dtype) if isinstance( |
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i, torch.Tensor) else i for i in vision_hidden_states] |
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bs = len(data['input_ids']) |
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for i in range(bs): |
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cur_vs_hs = vision_hidden_states[i] |
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if len(cur_vs_hs) > 0: |
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cur_vllm_emb = vllm_embedding[i] |
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cur_image_bound = data['image_bound'][i] |
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if len(cur_image_bound) > 0: |
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image_indices = torch.stack( |
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[torch.arange(r[0], r[1], dtype=torch.long) for r in cur_image_bound] |
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).to(vllm_embedding.device) |
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cur_vllm_emb.scatter_(0, image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]), |
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cur_vs_hs.view(-1, cur_vs_hs.shape[-1])) |
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elif self.training: |
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cur_vllm_emb += cur_vs_hs[0].mean() * 0 |
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return vllm_embedding, vision_hidden_states |
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def forward(self, data, **kwargs): |
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vllm_embedding, vision_hidden_states = self.get_vllm_embedding(data) |
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position_ids = data["position_ids"] |
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if position_ids.dtype != torch.int64: |
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position_ids = position_ids.long() |
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return self.llm( |
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input_ids=None, |
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position_ids=position_ids, |
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inputs_embeds=vllm_embedding, |
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**kwargs |
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) |
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def _decode_text(self, result_ids, tokenizer): |
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result_text = [] |
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for result in result_ids: |
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result = result[result != 0] |
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if result[0] == tokenizer.bos_id: |
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result = result[1:] |
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if result[-1] == tokenizer.eos_id or result[-1] == tokenizer.eot_id: |
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result = result[:-1] |
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result_text.append(tokenizer.decode(result).strip()) |
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return result_text |
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def _decode(self, inputs_embeds, tokenizer, decode_text=False, **kwargs): |
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terminators = [ |
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tokenizer.eos_token_id, |
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tokenizer.convert_tokens_to_ids("<|eot_id|>") |
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] |
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output = self.llm.generate( |
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inputs_embeds=inputs_embeds, |
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pad_token_id=0, |
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eos_token_id=terminators, |
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**kwargs |
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) |
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if decode_text: |
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return self._decode_text(output, tokenizer) |
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return output |
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def _decode_stream(self, inputs_embeds, tokenizer, **kwargs): |
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terminators = [ |
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tokenizer.eos_token_id, |
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tokenizer.convert_tokens_to_ids("<|eot_id|>") |
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] |
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streamer = TextIteratorStreamer(tokenizer=tokenizer) |
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generation_kwargs = { |
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'inputs_embeds': inputs_embeds, |
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'pad_token_id': 0, |
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'eos_token_id': terminators, |
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'streamer': streamer |
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} |
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generation_kwargs.update(kwargs) |
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thread = Thread(target=self.llm.generate, kwargs=generation_kwargs) |
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thread.start() |
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return streamer |
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def generate( |
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self, |
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model_inputs, |
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tokenizer=None, |
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vision_hidden_states=None, |
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stream=False, |
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**kwargs |
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): |
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bs = len(model_inputs["input_ids"]) |
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img_list = model_inputs["pixel_values"] |
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tgt_sizes = model_inputs["tgt_sizes"] |
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if img_list is None: |
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img_list = [[] for i in range(bs)] |
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assert bs == len(img_list) |
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if vision_hidden_states is None: |
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pixel_values = [] |
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for i in range(bs): |
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img_inps = [] |
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for img in img_list[i]: |
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img_inps.append(img.to(self.device)) |
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if img_inps: |
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pixel_values.append(img_inps) |
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else: |
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pixel_values.append([]) |
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model_inputs["pixel_values"] = pixel_values |
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model_inputs['tgt_sizes'] = tgt_sizes |
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else: |
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model_inputs["vision_hidden_states"] = vision_hidden_states |
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( |
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input_embeds, |
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vision_hidden_states, |
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) = self.get_vllm_embedding(model_inputs) |
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if stream: |
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kwargs.pop("decode_text") |
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result = self._decode_stream(input_embeds, tokenizer, **kwargs) |
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else: |
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result = self._decode(input_embeds, tokenizer, **kwargs) |
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return result |
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def chat( |
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self, |
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image, |
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msgs, |
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tokenizer, |
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processor=None, |
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vision_hidden_states=None, |
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max_new_tokens=1024, |
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sampling=True, |
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max_inp_length=2048, |
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system_prompt='', |
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stream=False, |
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**kwargs |
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): |
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if processor is None: |
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processor = AutoProcessor.from_pretrained(self.config._name_or_path, trust_remote_code=True) |
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if isinstance(msgs, str): |
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msgs = json.loads(msgs) |
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copy_msgs = deepcopy(msgs) |
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assert len(msgs) > 0, "msgs is empty" |
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assert sampling or not stream, "if use stream mode, make sure sampling=True" |
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if image is not None and isinstance(copy_msgs[0]["content"], str): |
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copy_msgs[0]["content"].insert(-1, im) |
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images = [] |
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for i, msg in enumerate(copy_msgs): |
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role = msg["role"] |
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content = msg["content"] |
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assert role in ["user", "assistant"] |
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if i == 0: |
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assert role == "user", "The role of first msg should be user" |
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if isinstance(content, str): |
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content = [content] |
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cur_msgs = [] |
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for c in content: |
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if isinstance(c, Image.Image): |
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images.append(c) |
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cur_msgs.append("(<image>./</image>)") |
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elif isinstance(c, str): |
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cur_msgs.append(c) |
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msg["content"] = "\n".join(cur_msgs) |
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if system_prompt: |
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sys_msg = {'role': 'system', 'content': system_prompt} |
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copy_msgs = [sys_msg] + copy_msgs |
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prompt = processor.tokenizer.apply_chat_template(copy_msgs, tokenize=False, add_generation_prompt=True) |
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inputs = processor(prompt, images, return_tensors="pt", max_length=max_inp_length).to(self.device) |
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if sampling: |
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generation_config = { |
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"top_p": 0.8, |
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"top_k": 100, |
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"temperature": 0.7, |
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"do_sample": True, |
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"repetition_penalty": 1.05 |
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} |
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else: |
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generation_config = { |
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"num_beams": 3, |
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"repetition_penalty": 1.2, |
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} |
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generation_config.update( |
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(k, kwargs[k]) for k in generation_config.keys() & kwargs.keys() |
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) |
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with torch.inference_mode(): |
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res = self.generate( |
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inputs, |
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tokenizer=tokenizer, |
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max_new_tokens=max_new_tokens, |
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vision_hidden_states=vision_hidden_states, |
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stream=stream, |
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decode_text=True, |
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**generation_config |
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) |
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if stream: |
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def stream_gen(): |
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for text in res: |
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text = text.replace(tokenizer.eot_token, '').replace(tokenizer.eos_token, '') |
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yield text |
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return stream_gen() |
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else: |
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answer = res[0] |
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return answer |
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