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modeling_internvl_chat.py
CHANGED
@@ -34,6 +34,7 @@ def version_cmp(v1, v2, op='eq'):
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class InternVLChatModel(PreTrainedModel):
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config_class = InternVLChatConfig
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main_input_name = 'pixel_values'
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_supports_flash_attn_2 = True
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_no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer']
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@@ -100,7 +101,7 @@ class InternVLChatModel(PreTrainedModel):
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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image_flags = image_flags.squeeze(-1)
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input_embeds = self.language_model.get_input_embeddings()(input_ids)
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vit_embeds = self.extract_feature(pixel_values)
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vit_embeds = vit_embeds[image_flags == 1]
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@@ -233,8 +234,8 @@ class InternVLChatModel(PreTrainedModel):
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tokenizer.padding_side = 'left'
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model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
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input_ids = model_inputs['input_ids'].
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attention_mask = model_inputs['attention_mask'].
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eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
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generation_config['eos_token_id'] = eos_token_id
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generation_output = self.generate(
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@@ -282,8 +283,8 @@ class InternVLChatModel(PreTrainedModel):
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query = query.replace('<image>', image_tokens, 1)
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model_inputs = tokenizer(query, return_tensors='pt')
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input_ids = model_inputs['input_ids'].
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attention_mask = model_inputs['attention_mask'].
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generation_config['eos_token_id'] = eos_token_id
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generation_output = self.generate(
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pixel_values=pixel_values,
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class InternVLChatModel(PreTrainedModel):
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config_class = InternVLChatConfig
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main_input_name = 'pixel_values'
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+
base_model_prefix = 'language_model'
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_supports_flash_attn_2 = True
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_no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer']
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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image_flags = image_flags.squeeze(-1)
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input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()
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vit_embeds = self.extract_feature(pixel_values)
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vit_embeds = vit_embeds[image_flags == 1]
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tokenizer.padding_side = 'left'
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model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
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input_ids = model_inputs['input_ids'].to(self.device)
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attention_mask = model_inputs['attention_mask'].to(self.device)
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eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
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generation_config['eos_token_id'] = eos_token_id
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generation_output = self.generate(
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query = query.replace('<image>', image_tokens, 1)
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model_inputs = tokenizer(query, return_tensors='pt')
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input_ids = model_inputs['input_ids'].to(self.device)
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attention_mask = model_inputs['attention_mask'].to(self.device)
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generation_config['eos_token_id'] = eos_token_id
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generation_output = self.generate(
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pixel_values=pixel_values,
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