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modeling_internlm2.py

@@ -39,18 +39,6 @@ try:
     from transformers.generation.streamers import BaseStreamer
 except:  # noqa # pylint: disable=bare-except
     BaseStreamer = None
-from typing import Any, List, Optional, Tuple, Union
-import torch.distributed as dist
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers.generation.logits_process import LogitsProcessorList
-from transformers.generation.stopping_criteria import StoppingCriteriaList
-from transformers.generation.streamers import BaseStreamer
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import ModelOutput, logging
-from transformers.generation.utils import GreedySearchOutput, validate_stopping_criteria, GreedySearchDecoderOnlyOutput, GreedySearchEncoderDecoderOutput
 
 from .configuration_internlm2 import InternLM2Config
 
@@ -1094,13 +1082,16 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
             output = (logits,) + outputs[1:]
             return (loss,) + output if loss is not None else output
 
-        return CausalLMOutputWithPast(
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        output = CausalLMOutputWithPast(
             loss=loss,
             logits=logits,
             past_key_values=outputs.past_key_values,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+        output['logits'] = output['logits'].to(device)
+        return output
 
     def prepare_inputs_for_generation(
         self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
@@ -1284,174 +1275,6 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
 
         return consumer()
 
-    def greedy_search(
-        self,
-        input_ids: torch.LongTensor,
-        logits_processor: Optional[LogitsProcessorList] = None,
-        stopping_criteria: Optional[StoppingCriteriaList] = None,
-        max_length: Optional[int] = None,
-        pad_token_id: Optional[int] = None,
-        eos_token_id: Optional[Union[int, List[int]]] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_scores: Optional[bool] = None,
-        return_dict_in_generate: Optional[bool] = None,
-        synced_gpus: bool = False,
-        streamer: Optional["BaseStreamer"] = None,
-        **model_kwargs,
-    ) -> Union[GreedySearchOutput, torch.LongTensor]:
-                # init values
-        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
-        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-        if max_length is not None:
-            warnings.warn(
-                "`max_length` is deprecated in this function, use"
-                " `stopping_criteria=StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])` instead.",
-                UserWarning,
-            )
-            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
-        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
-        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
-        if isinstance(eos_token_id, int):
-            eos_token_id = [eos_token_id]
-        eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
-        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
-        output_attentions = (
-            output_attentions if output_attentions is not None else self.generation_config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
-        )
-        return_dict_in_generate = (
-            return_dict_in_generate
-            if return_dict_in_generate is not None
-            else self.generation_config.return_dict_in_generate
-        )
-
-        # init attention / hidden states / scores tuples
-        scores = () if (return_dict_in_generate and output_scores) else None
-        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
-        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
-        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
-
-        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
-        if return_dict_in_generate and self.config.is_encoder_decoder:
-            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
-            encoder_hidden_states = (
-                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
-            )
-
-        # keep track of which sequences are already finished
-        unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
-
-        this_peer_finished = False  # used by synced_gpus only
-        while True:
-            if synced_gpus:
-                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
-                # The following logic allows an early break if all peers finished generating their sequence
-                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
-                # send 0.0 if we finished, 1.0 otherwise
-                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
-                # did all peers finish? the reduced sum will be 0.0 then
-                if this_peer_finished_flag.item() == 0.0:
-                    break
-
-            # prepare model inputs
-            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
-
-            # forward pass to get next token
-            outputs = self(
-                **model_inputs,
-                return_dict=True,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-            )
-
-            if synced_gpus and this_peer_finished:
-                continue  # don't waste resources running the code we don't need
-
-            next_token_logits = outputs.logits[:, -1, :]
-
-            # pre-process distribution
-            next_tokens_scores = logits_processor(input_ids, next_token_logits)
-
-            # Store scores, attentions and hidden_states when required
-            if return_dict_in_generate:
-                if output_scores:
-                    scores += (next_tokens_scores,)
-                if output_attentions:
-                    decoder_attentions += (
-                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
-                    )
-                    if self.config.is_encoder_decoder:
-                        cross_attentions += (outputs.cross_attentions,)
-
-                if output_hidden_states:
-                    decoder_hidden_states += (
-                        (outputs.decoder_hidden_states,)
-                        if self.config.is_encoder_decoder
-                        else (outputs.hidden_states,)
-                    )
-
-            # argmax
-            next_tokens = torch.argmax(next_tokens_scores, dim=-1).to(device=input_ids.device)
-            # finished sentences should have their next token be a padding token
-            if eos_token_id is not None:
-                if pad_token_id is None:
-                    raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
-                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
-
-            # update generated ids, model inputs, and length for next step
-            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
-            if streamer is not None:
-                streamer.put(next_tokens.cpu())
-            model_kwargs = self._update_model_kwargs_for_generation(
-                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
-            )
-
-            # if eos_token was found in one sentence, set sentence to finished
-            if eos_token_id_tensor is not None:
-                unfinished_sequences = unfinished_sequences.mul(
-                    next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
-                )
-
-                # stop when each sentence is finished
-                if unfinished_sequences.max() == 0:
-                    this_peer_finished = True
-
-            # stop if we exceed the maximum length
-            if stopping_criteria(input_ids, scores):
-                this_peer_finished = True
-
-            if this_peer_finished and not synced_gpus:
-                break
-
-        if streamer is not None:
-            streamer.end()
-
-        if return_dict_in_generate:
-            if self.config.is_encoder_decoder:
-                return GreedySearchEncoderDecoderOutput(
-                    sequences=input_ids,
-                    scores=scores,
-                    encoder_attentions=encoder_attentions,
-                    encoder_hidden_states=encoder_hidden_states,
-                    decoder_attentions=decoder_attentions,
-                    cross_attentions=cross_attentions,
-                    decoder_hidden_states=decoder_hidden_states,
-                    past_key_values=model_kwargs.get("past_key_values"),
-                )
-            else:
-                return GreedySearchDecoderOnlyOutput(
-                    sequences=input_ids,
-                    scores=scores,
-                    attentions=decoder_attentions,
-                    hidden_states=decoder_hidden_states,
-                    past_key_values=model_kwargs.get("past_key_values"),
-                )
-        else:
-            return input_ids
-
 
 # Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
 @add_start_docstrings(

modeling_internvl_chat.py

@@ -313,8 +313,8 @@ class InternVLChatModel(PreTrainedModel):
         if return_history:
             return response, history
         else:
-            # query_to_print = query.replace(image_tokens, '<image>')
-            # print(query_to_print, response)
+            query_to_print = query.replace(image_tokens, '<image>')
+            print(query_to_print, response)
             return response
         return response