# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 csv import json from pathlib import Path import numpy as np import torch from transformers import PreTrainedTokenizerFast import tensorrt_llm from tensorrt_llm.quantization import QuantMode from tensorrt_llm.runtime import ModelConfig, SamplingConfig from build import get_engine_name # isort:skip def parse_arguments(): parser = argparse.ArgumentParser() parser.add_argument('--max_output_len', type=int, required=True) parser.add_argument('--log_level', type=str, default='error') parser.add_argument('--engine_dir', type=str, default='falcon_outputs') parser.add_argument('--tokenizer_dir', type=str, default="tiiuae/falcon-rw-1b", help="Tokenizer path or name.") parser.add_argument('--input_text', type=str, default='Born in north-east France, Soyer trained as a') parser.add_argument( '--input_tokens', dest='input_file', type=str, help= 'CSV or Numpy file containing tokenized input. Alternative to text input.', default=None) 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('--num_beams', type=int, help="Use beam search if num_beams >1", default=1) parser.add_argument('--debug', action='store_true') return parser.parse_args() def read_config(config_path: Path): with config_path.open('r') as f: config = json.load(f) builder_config = config['builder_config'] dtype = builder_config['precision'] tp_size = builder_config['tensor_parallel'] pp_size = builder_config['pipeline_parallel'] world_size = tp_size * pp_size assert world_size == tensorrt_llm.mpi_world_size(), \ f'Engine world size ({world_size}) != Runtime world size '\ f'({tensorrt_llm.mpi_world_size()})' num_heads = builder_config['num_heads'] // tp_size num_kv_heads = builder_config.get('num_kv_heads', num_heads) num_kv_heads = (num_kv_heads + tp_size - 1) // tp_size hidden_size = builder_config['hidden_size'] // tp_size vocab_size = builder_config['vocab_size'] num_layers = builder_config['num_layers'] quant_mode = QuantMode(builder_config['quant_mode']) plugin_config = config['plugin_config'] use_gpt_attention_plugin = plugin_config['gpt_attention_plugin'] paged_kv_cache = plugin_config['paged_kv_cache'] tokens_per_block = plugin_config['tokens_per_block'] remove_input_padding = plugin_config['remove_input_padding'] use_custom_all_reduce = plugin_config.get('use_custom_all_reduce', False) model_config = ModelConfig(num_heads=num_heads, num_kv_heads=num_kv_heads, hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, gpt_attention_plugin=use_gpt_attention_plugin, paged_kv_cache=paged_kv_cache, tokens_per_block=tokens_per_block, remove_input_padding=remove_input_padding, quant_mode=quant_mode, dtype=dtype, use_custom_all_reduce=use_custom_all_reduce) return model_config, tp_size, pp_size, world_size, dtype def parse_input(input_text: str, input_file: str, tokenizer, pad_id: int, remove_input_padding: bool): input_tokens = [] if input_file is None: input_tokens.append( tokenizer.encode(input_text, add_special_tokens=False)) 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_tokens.append(np.array(line, dtype='int32')) elif input_file.endswith('.npy'): inputs = np.load(input_file) for row in inputs: row = row[row != pad_id] input_tokens.append(row) else: print('Input file format not supported.') raise SystemExit input_ids = None input_lengths = torch.tensor([len(x) for x in input_tokens], dtype=torch.int32, device='cuda') if remove_input_padding: input_ids = np.concatenate(input_tokens) input_ids = torch.tensor(input_ids, dtype=torch.int32, device='cuda').unsqueeze(0) else: input_ids = torch.nested.to_padded_tensor( torch.nested.nested_tensor(input_tokens, dtype=torch.int32), pad_id).cuda() return input_ids, input_lengths def print_output(output_ids, input_lengths, max_output_len, tokenizer, output_csv, output_npy): num_beams = output_ids.size(1) if output_csv is None and output_npy is None: for b in range(input_lengths.size(0)): inputs = output_ids[b][0][:input_lengths[b]].tolist() input_text = tokenizer.decode(inputs) print(f'Input: \"{input_text}\"') for beam in range(num_beams): output_begin = input_lengths[b] output_end = input_lengths[b] + max_output_len outputs = output_ids[b][beam][output_begin:output_end].tolist() output_text = tokenizer.decode(outputs) print(f'Output: \"{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) def main(): args = parse_arguments() tensorrt_llm.logger.set_level(args.log_level) engine_dir = Path(args.engine_dir) model_config, tp_size, pp_size, world_size, dtype = read_config( engine_dir / 'config.json') runtime_rank = tensorrt_llm.mpi_rank() runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank, tp_size=tp_size, pp_size=pp_size) torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node) engine_name = get_engine_name('falcon', dtype, tp_size, pp_size, runtime_rank) serialize_path = engine_dir / engine_name tokenizer = PreTrainedTokenizerFast.from_pretrained(args.tokenizer_dir) if tokenizer.pad_token_id is None: tokenizer.pad_token_id = tokenizer.eos_token_id input_ids, input_lengths = parse_input(args.input_text, args.input_file, tokenizer, tokenizer.eos_token_id, model_config.remove_input_padding) sampling_config = SamplingConfig(end_id=tokenizer.eos_token_id, pad_id=tokenizer.pad_token_id, num_beams=args.num_beams) with open(serialize_path, 'rb') as f: engine_buffer = f.read() decoder = tensorrt_llm.runtime.GenerationSession(model_config, engine_buffer, runtime_mapping, debug_mode=args.debug) decoder.setup(input_ids.size(0), max_context_length=input_ids.size(1), max_new_tokens=args.max_output_len, beam_width=args.num_beams) output_ids = decoder.decode(input_ids, input_lengths, sampling_config) torch.cuda.synchronize() if runtime_rank == 0: print_output(output_ids, input_lengths, args.max_output_len, tokenizer, args.output_csv, args.output_npy) if __name__ == '__main__': main()