# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import ast import csv from pathlib import Path import numpy as np import torch from utils import (DEFAULT_HF_MODEL_DIRS, DEFAULT_PROMPT_TEMPLATES, load_tokenizer, read_model_name, throttle_generator) import tensorrt_llm import tensorrt_llm.profiler from tensorrt_llm.logger import logger from tensorrt_llm.runtime import PYTHON_BINDINGS, ModelRunner if PYTHON_BINDINGS: from tensorrt_llm.runtime import ModelRunnerCpp def parse_arguments(args=None): parser = argparse.ArgumentParser() parser.add_argument('--max_output_len', type=int, required=True) parser.add_argument( '--max_attention_window_size', type=int, default=None, help= 'The attention window size that controls the sliding window attention / cyclic kv cache behavior' ) parser.add_argument('--sink_token_length', type=int, default=None, help='The sink token length.') parser.add_argument('--log_level', type=str, default='warning') parser.add_argument('--engine_dir', type=str, default='engine_outputs') parser.add_argument('--use_py_session', default=False, action='store_true', help="Whether or not to use Python runtime session") parser.add_argument( '--input_text', type=str, nargs='+', default=["Born in north-east France, Soyer trained as a"]) parser.add_argument( '--no_prompt_template', dest='use_prompt_template', default=True, action='store_false', help= "Whether or not to use default prompt template to wrap the input text.") parser.add_argument( '--input_file', type=str, help= 'CSV or Numpy file containing tokenized input. Alternative to text input.', default=None) parser.add_argument('--max_input_length', type=int, default=923) parser.add_argument('--output_csv', type=str, help='CSV file where the tokenized output is stored.', default=None) parser.add_argument('--output_npy', type=str, help='Numpy file where the tokenized output is stored.', default=None) parser.add_argument( '--output_logits_npy', type=str, help= 'Numpy file where the generation logits are stored. Use only when num_beams==1', default=None) parser.add_argument('--output_log_probs_npy', type=str, help='Numpy file where the log_probs are stored', default=None) parser.add_argument('--output_cum_log_probs_npy', type=str, help='Numpy file where the cum_log_probs are stored', default=None) parser.add_argument('--tokenizer_dir', help="HF tokenizer config path", default='gpt2') parser.add_argument( '--tokenizer_type', help= 'Specify that argument when providing a .model file as the tokenizer_dir. ' 'It allows AutoTokenizer to instantiate the correct tokenizer type.') parser.add_argument('--vocab_file', help="Used for sentencepiece tokenizers") parser.add_argument('--num_beams', type=int, help="Use beam search if num_beams > 1", default=1) parser.add_argument('--temperature', type=float, default=1.0) parser.add_argument('--top_k', type=int, default=1) parser.add_argument('--top_p', type=float, default=0.0) parser.add_argument('--length_penalty', type=float, default=1.0) parser.add_argument('--repetition_penalty', type=float, default=1.0) parser.add_argument('--presence_penalty', type=float, default=0.0) parser.add_argument('--frequency_penalty', type=float, default=0.0) parser.add_argument('--early_stopping', type=int, help='Use early stopping if num_beams > 1' '1 for early-stopping, 0 for non-early-stopping' 'other values for stopping by length', default=1) parser.add_argument('--debug_mode', default=False, action='store_true', help="Whether or not to turn on the debug mode") parser.add_argument('--no_add_special_tokens', dest='add_special_tokens', default=True, action='store_false', help="Whether or not to add special tokens") parser.add_argument('--streaming', default=False, action='store_true') parser.add_argument('--streaming_interval', type=int, help="How often to return tokens when streaming.", default=5) parser.add_argument( '--prompt_table_path', type=str, help="Path to .npy file, exported by nemo_prompt_convert.py") parser.add_argument( '--prompt_tasks', help="Comma-separated list of tasks for prompt tuning, e.g., 0,3,1,0") parser.add_argument('--lora_dir', type=str, default=None, nargs="+", help="The directory of LoRA weights") parser.add_argument( '--lora_task_uids', type=str, default=None, nargs="+", help="The list of LoRA task uids; use -1 to disable the LoRA module") parser.add_argument('--lora_ckpt_source', type=str, default="hf", choices=["hf", "nemo"], help="The source of lora checkpoint.") parser.add_argument( '--num_prepend_vtokens', nargs="+", type=int, help="Number of (default) virtual tokens to prepend to each sentence." " For example, '--num_prepend_vtokens=10' will prepend the tokens" " [vocab_size, vocab_size + 1, ..., vocab_size + 9] to the sentence.") parser.add_argument( '--run_profiling', default=False, action='store_true', help="Run several 10 iterations to profile the inference latencies.") parser.add_argument( '--medusa_choices', type=str, default=None, help="Medusa choice to use, if not none, will use Medusa decoding." " E.g.: [[0, 0, 0, 0], [0, 1, 0], [1, 0], [1, 1]] for 9 medusa tokens." ) return parser.parse_args(args=args) def parse_input(tokenizer, input_text=None, prompt_template=None, input_file=None, add_special_tokens=True, max_input_length=923, pad_id=None, num_prepend_vtokens=[], model_name=None, model_version=None): if pad_id is None: pad_id = tokenizer.pad_token_id batch_input_ids = [] if input_file is None: for curr_text in input_text: if prompt_template is not None: curr_text = prompt_template.format(input_text=curr_text) input_ids = tokenizer.encode(curr_text, add_special_tokens=add_special_tokens, truncation=True, max_length=max_input_length) batch_input_ids.append(input_ids) else: if input_file.endswith('.csv'): with open(input_file, 'r') as csv_file: csv_reader = csv.reader(csv_file, delimiter=',') for line in csv_reader: input_ids = np.array(line, dtype='int32') batch_input_ids.append(input_ids[-max_input_length:]) elif input_file.endswith('.npy'): inputs = np.load(input_file) for row in inputs: input_ids = row[row != pad_id] batch_input_ids.append(input_ids[-max_input_length:]) elif input_file.endswith('.txt'): with open(input_file, 'r', encoding='utf-8', errors='replace') as txt_file: input_text = txt_file.readlines() batch_input_ids = tokenizer( input_text, add_special_tokens=add_special_tokens, truncation=True, max_length=max_input_length)["input_ids"] else: print('Input file format not supported.') raise SystemExit if num_prepend_vtokens: assert len(num_prepend_vtokens) == len(batch_input_ids) base_vocab_size = tokenizer.vocab_size - len( tokenizer.special_tokens_map.get('additional_special_tokens', [])) for i, length in enumerate(num_prepend_vtokens): batch_input_ids[i] = list( range(base_vocab_size, base_vocab_size + length)) + batch_input_ids[i] if model_name == 'ChatGLMForCausalLM' and model_version == 'glm': for ids in batch_input_ids: ids.append(tokenizer.sop_token_id) batch_input_ids = [ torch.tensor(x, dtype=torch.int32) for x in batch_input_ids ] return batch_input_ids def print_output(tokenizer, output_ids, input_lengths, sequence_lengths, output_csv=None, output_npy=None, context_logits=None, generation_logits=None, cum_log_probs=None, log_probs=None, output_logits_npy=None, output_cum_log_probs_npy=None, output_log_probs_npy=None): batch_size, num_beams, _ = output_ids.size() if output_csv is None and output_npy is None: for batch_idx in range(batch_size): inputs = output_ids[batch_idx][0][:input_lengths[batch_idx]].tolist( ) input_text = tokenizer.decode(inputs) print(f'Input [Text {batch_idx}]: \"{input_text}\"') for beam in range(num_beams): 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 = tokenizer.decode(outputs) print( f'Output [Text {batch_idx} Beam {beam}]: \"{output_text}\"') output_ids = output_ids.reshape((-1, output_ids.size(2))) if output_csv is not None: output_file = Path(output_csv) output_file.parent.mkdir(exist_ok=True, parents=True) outputs = output_ids.tolist() with open(output_file, 'w') as csv_file: writer = csv.writer(csv_file, delimiter=',') writer.writerows(outputs) if output_npy is not None: output_file = Path(output_npy) output_file.parent.mkdir(exist_ok=True, parents=True) outputs = np.array(output_ids.cpu().contiguous(), dtype='int32') np.save(output_file, outputs) # Save context logits if context_logits is not None and output_logits_npy is not None: context_logits = torch.cat(context_logits, axis=0) vocab_size_padded = context_logits.shape[-1] context_logits = context_logits.reshape([1, -1, vocab_size_padded]) output_context_logits_npy = output_logits_npy.split( '.npy')[0] + "_context" output_context_logits_file = Path(output_context_logits_npy) context_outputs = np.array( context_logits.squeeze(0).cpu().contiguous(), dtype='float32') # [promptLengthSum, vocabSize] np.save(output_context_logits_file, context_outputs) # Save generation logits if generation_logits is not None and output_logits_npy is not None and num_beams == 1: output_generation_logits_npy = output_logits_npy.split( '.npy')[0] + "_generation" output_generation_logits_file = Path(output_generation_logits_npy) generation_outputs = np.array(generation_logits.cpu().contiguous(), dtype='float32') np.save(output_generation_logits_file, generation_outputs) # Save cum log probs if cum_log_probs is not None and output_cum_log_probs_npy is not None: cum_log_probs_file = Path(output_cum_log_probs_npy) cum_log_probs_outputs = np.array(cum_log_probs.cpu().contiguous(), dtype='float32') np.save(cum_log_probs_file, cum_log_probs_outputs) # Save cum log probs if log_probs is not None and output_log_probs_npy is not None: log_probs_file = Path(output_log_probs_npy) log_probs_outputs = np.array(log_probs.cpu().contiguous(), dtype='float32') np.save(log_probs_file, log_probs_outputs) def main(args): runtime_rank = tensorrt_llm.mpi_rank() logger.set_level(args.log_level) model_name, model_version = read_model_name(args.engine_dir) if args.tokenizer_dir is None: logger.warning( "tokenizer_dir is not specified. Try to infer from model_name, but this may be incorrect." ) args.tokenizer_dir = DEFAULT_HF_MODEL_DIRS[model_name] tokenizer, pad_id, end_id = load_tokenizer( tokenizer_dir=args.tokenizer_dir, vocab_file=args.vocab_file, model_name=model_name, model_version=model_version, tokenizer_type=args.tokenizer_type, ) # # An example to stop generation when the model generate " London" on first sentence, " eventually became" on second sentence # stop_words_list = [[" London"], ["eventually became"]] # stop_words_list = tensorrt_llm.runtime.to_word_list_format(stop_words_list, tokenizer) # stop_words_list = torch.Tensor(stop_words_list).to(torch.int32).to("cuda").contiguous() stop_words_list = None # # An example to prevent generating " chef" on first sentence, " eventually" and " chef before" on second sentence # bad_words_list = [[" chef"], [" eventually, chef before"]] # bad_words_list = tensorrt_llm.runtime.to_word_list_format(bad_words_list, tokenizer) # bad_words_list = torch.Tensor(bad_words_list).to(torch.int32).to("cuda").contiguous() bad_words_list = None prompt_template = None if args.use_prompt_template and model_name in DEFAULT_PROMPT_TEMPLATES: prompt_template = DEFAULT_PROMPT_TEMPLATES[model_name] batch_input_ids = parse_input(tokenizer=tokenizer, input_text=args.input_text, prompt_template=prompt_template, input_file=args.input_file, add_special_tokens=args.add_special_tokens, max_input_length=args.max_input_length, pad_id=pad_id, num_prepend_vtokens=args.num_prepend_vtokens, model_name=model_name, model_version=model_version) input_lengths = [x.size(0) for x in batch_input_ids] if not PYTHON_BINDINGS and not args.use_py_session: logger.warning( "Python bindings of C++ session is unavailable, fallback to Python session." ) args.use_py_session = True if args.debug_mode and not args.use_py_session: logger.warning( "Debug mode is not supported in C++ session for now, fallback to Python session." ) args.use_py_session = True runner_cls = ModelRunner if args.use_py_session else ModelRunnerCpp runner_kwargs = dict(engine_dir=args.engine_dir, lora_dir=args.lora_dir, rank=runtime_rank, debug_mode=args.debug_mode, lora_ckpt_source=args.lora_ckpt_source) if args.medusa_choices is not None: args.medusa_choices = ast.literal_eval(args.medusa_choices) assert args.use_py_session, "Medusa is only supported by py_session" assert args.temperature == 1.0, "Medusa should use temperature == 1.0" assert args.num_beams == 1, "Medusa should use num_beams == 1" runner_kwargs.update(medusa_choices=args.medusa_choices) if not args.use_py_session: runner_kwargs.update( max_batch_size=len(batch_input_ids), max_input_len=max(input_lengths), max_output_len=args.max_output_len, max_beam_width=args.num_beams, max_attention_window_size=args.max_attention_window_size, sink_token_length=args.sink_token_length, ) runner = runner_cls.from_dir(**runner_kwargs) with torch.no_grad(): outputs = runner.generate( batch_input_ids, max_new_tokens=args.max_output_len, max_attention_window_size=args.max_attention_window_size, sink_token_length=args.sink_token_length, end_id=end_id, pad_id=pad_id, temperature=args.temperature, top_k=args.top_k, top_p=args.top_p, num_beams=args.num_beams, length_penalty=args.length_penalty, early_stopping=args.early_stopping, repetition_penalty=args.repetition_penalty, presence_penalty=args.presence_penalty, frequency_penalty=args.frequency_penalty, stop_words_list=stop_words_list, bad_words_list=bad_words_list, output_cum_log_probs=(args.output_cum_log_probs_npy != None), output_log_probs=(args.output_log_probs_npy != None), lora_uids=args.lora_task_uids, prompt_table=args.prompt_table_path, prompt_tasks=args.prompt_tasks, streaming=args.streaming, output_sequence_lengths=True, return_dict=True, medusa_choices=args.medusa_choices) torch.cuda.synchronize() if args.streaming: for curr_outputs in throttle_generator(outputs, args.streaming_interval): if runtime_rank == 0: output_ids = curr_outputs['output_ids'] sequence_lengths = curr_outputs['sequence_lengths'] cum_log_probs = None log_probs = None if args.output_cum_log_probs_npy != None: cum_log_probs = outputs['cum_log_probs'] if args.output_log_probs_npy != None: log_probs = outputs['log_probs'] print_output( tokenizer, output_ids, input_lengths, sequence_lengths, output_csv=args.output_csv, output_npy=args.output_npy, cum_log_probs=cum_log_probs, log_probs=log_probs, output_cum_log_probs_npy=args.output_cum_log_probs_npy, output_log_probs_npy=args.output_log_probs_npy) else: if runtime_rank == 0: output_ids = outputs['output_ids'] sequence_lengths = outputs['sequence_lengths'] context_logits = None generation_logits = None cum_log_probs = None log_probs = None if runner.gather_context_logits: context_logits = outputs['context_logits'] if runner.gather_generation_logits: generation_logits = outputs['generation_logits'] if args.output_cum_log_probs_npy != None: cum_log_probs = outputs['cum_log_probs'] if args.output_log_probs_npy != None: log_probs = outputs['log_probs'] print_output(tokenizer, output_ids, input_lengths, sequence_lengths, output_csv=args.output_csv, output_npy=args.output_npy, context_logits=context_logits, generation_logits=generation_logits, output_logits_npy=args.output_logits_npy, cum_log_probs=cum_log_probs, log_probs=log_probs, output_cum_log_probs_npy=args.output_cum_log_probs_npy, output_log_probs_npy=args.output_log_probs_npy) if args.run_profiling: ite = 10 # warmup for _ in range(ite): with torch.no_grad(): outputs = runner.generate( batch_input_ids, max_new_tokens=args.max_output_len, max_attention_window_size=args.max_attention_window_size, end_id=end_id, pad_id=pad_id, temperature=args.temperature, top_k=args.top_k, top_p=args.top_p, num_beams=args.num_beams, length_penalty=args.length_penalty, early_stopping=args.early_stopping, repetition_penalty=args.repetition_penalty, presence_penalty=args.presence_penalty, frequency_penalty=args.frequency_penalty, stop_words_list=stop_words_list, bad_words_list=bad_words_list, lora_uids=args.lora_task_uids, prompt_table=args.prompt_table_path, prompt_tasks=args.prompt_tasks, streaming=args.streaming, output_sequence_lengths=True, return_dict=True) torch.cuda.synchronize() tensorrt_llm.profiler.start("tmp") for _ in range(ite): with torch.no_grad(): outputs = runner.generate( batch_input_ids, max_new_tokens=args.max_output_len, max_attention_window_size=args.max_attention_window_size, end_id=end_id, pad_id=pad_id, temperature=args.temperature, top_k=args.top_k, top_p=args.top_p, num_beams=args.num_beams, length_penalty=args.length_penalty, early_stopping=args.early_stopping, repetition_penalty=args.repetition_penalty, presence_penalty=args.presence_penalty, frequency_penalty=args.frequency_penalty, stop_words_list=stop_words_list, bad_words_list=bad_words_list, lora_uids=args.lora_task_uids, prompt_table=args.prompt_table_path, prompt_tasks=args.prompt_tasks, streaming=args.streaming, output_sequence_lengths=True, return_dict=True) torch.cuda.synchronize() tensorrt_llm.profiler.stop("tmp") print( f"batch_size: {len(batch_input_ids)}, avg latency of {ite} iterations: : {tensorrt_llm.profiler.elapsed_time_in_sec('tmp') / ite} sec" ) if __name__ == '__main__': args = parse_arguments() main(args)