import json from pathlib import Path from typing import Optional import logging logging.basicConfig(level = logging.INFO) import numpy as np import torch from transformers import AutoTokenizer import tensorrt_llm from tensorrt_llm.logger import logger from tensorrt_llm.runtime import PYTHON_BINDINGS, ModelRunner if PYTHON_BINDINGS: from tensorrt_llm.runtime import ModelRunnerCpp def read_model_name(engine_dir: str): engine_version = tensorrt_llm.runtime.engine.get_engine_version(engine_dir) with open(Path(engine_dir) / "config.json", 'r') as f: config = json.load(f) if engine_version is None: return config['builder_config']['name'] return config['pretrained_config']['architecture'] def throttle_generator(generator, stream_interval): for i, out in enumerate(generator): if not i % stream_interval: yield out if i % stream_interval: yield out def load_tokenizer(tokenizer_dir: Optional[str] = None, vocab_file: Optional[str] = None, model_name: str = 'gpt', tokenizer_type: Optional[str] = None): if vocab_file is None: use_fast = True if tokenizer_type is not None and tokenizer_type == "llama": use_fast = False # Should set both padding_side and truncation_side to be 'left' tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir, legacy=False, padding_side='left', truncation_side='left', trust_remote_code=True, tokenizer_type=tokenizer_type, use_fast=use_fast) else: # For gpt-next, directly load from tokenizer.model assert model_name == 'gpt' tokenizer = T5Tokenizer(vocab_file=vocab_file, padding_side='left', truncation_side='left') if model_name == 'qwen': with open(Path(tokenizer_dir) / "generation_config.json") as f: gen_config = json.load(f) chat_format = gen_config['chat_format'] if chat_format == 'raw': pad_id = gen_config['pad_token_id'] end_id = gen_config['eos_token_id'] elif chat_format == 'chatml': pad_id = tokenizer.im_end_id end_id = tokenizer.im_end_id else: raise Exception(f"unknown chat format: {chat_format}") elif model_name == 'glm_10b': pad_id = tokenizer.pad_token_id end_id = tokenizer.eop_token_id else: if tokenizer.pad_token_id is None: tokenizer.pad_token_id = tokenizer.eos_token_id pad_id = tokenizer.pad_token_id end_id = tokenizer.eos_token_id return tokenizer, pad_id, end_id class MistralTensorRTLLM: def __init__(self): pass def initialize_model(self, engine_dir, tokenizer_dir): self.log_level = 'error' self.runtime_rank = tensorrt_llm.mpi_rank() logger.set_level(self.log_level) model_name = read_model_name(engine_dir) self.tokenizer, self.pad_id, self.end_id = load_tokenizer( tokenizer_dir=tokenizer_dir, vocab_file=None, model_name=model_name, tokenizer_type=None, ) self.prompt_template = None self.runner_cls = ModelRunner self.runner_kwargs = dict(engine_dir=engine_dir, lora_dir=None, rank=self.runtime_rank, debug_mode=False, lora_ckpt_source='hf') self.runner = self.runner_cls.from_dir(**self.runner_kwargs) self.last_prompt = None self.last_output = None def parse_input( self, input_text=None, add_special_tokens=True, max_input_length=923, pad_id=None, ): if self.pad_id is None: self.pad_id = self.tokenizer.pad_token_id batch_input_ids = [] for curr_text in input_text: if self.prompt_template is not None: curr_text = self.prompt_template.format(input_text=curr_text) input_ids = self.tokenizer.encode( curr_text, add_special_tokens=add_special_tokens, truncation=True, max_length=max_input_length ) batch_input_ids.append(input_ids) batch_input_ids = [ torch.tensor(x, dtype=torch.int32) for x in batch_input_ids ] return batch_input_ids def decode_tokens( self, output_ids, input_lengths, sequence_lengths, transcription_queue ): batch_size, num_beams, _ = output_ids.size() for batch_idx in range(batch_size): if transcription_queue.qsize() != 0: return None inputs = output_ids[batch_idx][0][:input_lengths[batch_idx]].tolist() input_text = self.tokenizer.decode(inputs) output = [] for beam in range(num_beams): if transcription_queue.qsize() != 0: return None output_begin = input_lengths[batch_idx] output_end = sequence_lengths[batch_idx][beam] outputs = output_ids[batch_idx][beam][ output_begin:output_end].tolist() output_text = self.tokenizer.decode(outputs) logging.info(f"[LLM] output: {output_text}") output.append(output_text) return output def format_prompt_qa(self, prompt): return f"Instruct: {prompt}\nOutput:" def format_prompt_chat(self, prompt): return f"Alice: {prompt}\nBob:" def run( self, model_path, tokenizer_path, transcription_queue=None, llm_queue=None, audio_queue=None, input_text=None, max_output_len=40, max_attention_window_size=4096, num_beams=1, streaming=False, streaming_interval=4, debug=False, ): self.initialize_model( model_path, tokenizer_path, ) logging.info("[LLM] loaded: True") while True: # Get the last transcription output from the queue transcription_output = transcription_queue.get() if transcription_queue.qsize() != 0: logging.info("[LLM] interrupted by transcription queue!!!!!!!!!!!!!!!!!!!!!!!!") continue prompt = transcription_output['prompt'].strip() input_text=[self.format_prompt_qa(prompt)] # if prompt is same but EOS is True, we need that to send outputs to websockets if self.last_prompt == prompt: if self.last_output is not None and transcription_output["eos"]: self.eos = transcription_output["eos"] llm_queue.put({"uid": transcription_output["uid"], "llm_output": self.last_output, "eos": self.eos}) audio_queue.put({"llm_output": self.last_output, "eos": self.eos}) continue self.eos = transcription_output["eos"] logging.info(f"[LLM INFO:] WhisperLive prompt: {prompt}, eos: {self.eos}") batch_input_ids = self.parse_input( input_text=input_text, add_special_tokens=True, max_input_length=923, pad_id=None, ) input_lengths = [x.size(0) for x in batch_input_ids] with torch.no_grad(): outputs = self.runner.generate( batch_input_ids, max_new_tokens=max_output_len, max_attention_window_size=max_attention_window_size, end_id=self.end_id, pad_id=self.pad_id, temperature=1.0, top_k=1, top_p=0.0, num_beams=num_beams, length_penalty=1.0, repetition_penalty=1.0, stop_words_list=None, bad_words_list=None, lora_uids=None, prompt_table_path=None, prompt_tasks=None, streaming=streaming, output_sequence_lengths=True, return_dict=True) torch.cuda.synchronize() if streaming: for curr_outputs in throttle_generator(outputs, streaming_interval): output_ids = curr_outputs['output_ids'] sequence_lengths = curr_outputs['sequence_lengths'] output = self.decode_tokens( output_ids, input_lengths, sequence_lengths, transcription_queue ) if output is None: break # Interrupted by transcription queue if output is None: logging.info(f"[LLM] interrupted by transcription queue!!!!!!!!!!!!!!!!!!!!!!!!") continue else: output_ids = outputs['output_ids'] sequence_lengths = outputs['sequence_lengths'] context_logits = None generation_logits = None if self.runner.gather_context_logits: context_logits = outputs['context_logits'] if self.runner.gather_generation_logits: generation_logits = outputs['generation_logits'] output = self.decode_tokens( output_ids, input_lengths, sequence_lengths, transcription_queue ) # if self.eos: if output is not None: self.last_output = output self.last_prompt = prompt llm_queue.put({"uid": transcription_output["uid"], "llm_output": output, "eos": self.eos}) audio_queue.put({"llm_output": output, "eos": self.eos}) if self.eos: self.last_prompt = None self.last_output = None if __name__=="__main__": llm = MistralTensorRTLLM() llm.initialize_model( "/root/TensorRT-LLM/examples/llama/tmp/mistral/7B/trt_engines/fp16/1-gpu", "teknium/OpenHermes-2.5-Mistral-7B", ) logging.info("intialized") for i in range(1): output = llm( ["Born in north-east France, Soyer trained as a"], streaming=True ) logging.info(output)