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"""PyTorch LLaMA model.""" |
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import json |
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from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union |
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import torch |
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import torch.utils.checkpoint |
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from transformers.generation.configuration_utils import GenerationConfig |
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from transformers.generation.logits_process import LogitsProcessorList |
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from transformers.generation.stopping_criteria import StoppingCriteriaList |
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from transformers.generation.utils import ( |
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GenerateBeamDecoderOnlyOutput, |
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GenerateBeamEncoderDecoderOutput, |
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GenerateDecoderOnlyOutput, |
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GenerateEncoderDecoderOutput |
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) |
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from transformers.models.llama.modeling_llama import LlamaForCausalLM |
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from transformers.utils import logging |
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if TYPE_CHECKING: |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.generation.streamers import BaseStreamer |
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logger = logging.get_logger(__name__) |
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GenerateNonBeamOutput = Union[GenerateDecoderOnlyOutput, GenerateEncoderDecoderOutput] |
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GenerateBeamOutput = Union[GenerateBeamDecoderOnlyOutput, GenerateBeamEncoderDecoderOutput] |
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GenerateOutput = Union[GenerateNonBeamOutput, GenerateBeamOutput] |
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class FunctionaryForCausalLM(LlamaForCausalLM): |
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def generate_tool_use( |
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self, |
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inputs: Optional[torch.Tensor] = None, |
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generation_config: Optional[GenerationConfig] = None, |
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logits_processor: Optional[LogitsProcessorList] = None, |
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stopping_criteria: Optional[StoppingCriteriaList] = None, |
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prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, |
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synced_gpus: Optional[bool] = None, |
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assistant_model: Optional["PreTrainedModel"] = None, |
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streamer: Optional["BaseStreamer"] = None, |
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negative_prompt_ids: Optional[torch.Tensor] = None, |
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negative_prompt_attention_mask: Optional[torch.Tensor] = None, |
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**kwargs, |
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) -> Union[GenerateOutput, torch.LongTensor]: |
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tokenizer = kwargs.pop("tokenizer", None) |
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results = self.generate( |
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inputs=inputs, |
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generation_config=generation_config, |
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logits_processor=logits_processor, |
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stopping_criteria=stopping_criteria, |
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prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, |
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synced_gpus=synced_gpus, |
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assistant_model=assistant_model, |
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streamer=streamer, |
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negative_prompt_ids=negative_prompt_ids, |
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negative_prompt_attention_mask=negative_prompt_attention_mask, |
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**kwargs, |
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) |
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input_ids = kwargs.pop("input_ids") |
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function_call_token = "<|reserved_special_token_249|>" |
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correct_results = [] |
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for input_id, result in zip(input_ids, results): |
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final_output_json = {"role": "assistant", "content": None, "tool_calls": None} |
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tool_calls = [] |
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raw_output_str = tokenizer.decode(result[len(input_id):].cpu()) |
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has_text = False if raw_output_str.startswith(function_call_token) else True |
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chunks = raw_output_str.split(function_call_token) |
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for i, chunk in enumerate(chunks): |
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if len(chunk) == 0: |
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continue |
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chunk = chunk.replace(tokenizer.pad_token, "") |
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if i == 0 and has_text is not False: |
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final_output_json["content"] = chunk.strip[:-len("<|eot_id|>")] if chunk.endswith("<|eot_id|>") else chunk |
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else: |
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tool_calls.append( |
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{ |
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"name": chunk[: chunk.index("\n{")], |
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"arguments": chunk[chunk.index("\n{") + 1: -len("<|eot_id|>")] if chunk.endswith("<|eot_id|>") else chunk[chunk.index("\n{") + 1:] |
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} |
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) |
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if len(tool_calls) > 0: |
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final_output_json["tool_calls"] = tool_calls |
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final_output_str = json.dumps(final_output_json, indent=4) |
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final_output_ids = tokenizer(final_output_str, add_special_tokens=False)["input_ids"] |
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correct_results.append( |
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torch.cat( |
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(result[:len(input_id)].cpu(), torch.tensor(final_output_ids)) |
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) |
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) |
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max_len = max([tensor.shape[0] for tensor in correct_results]) |
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correct_results = [ |
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torch.nn.functional.pad( |
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correct_result, (0, max_len - correct_result.shape[0]), value=tokenizer.eos_token_id |
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) for correct_result in correct_results |
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] |
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correct_results = torch.stack(correct_results) |
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return correct_results |
