import argparse import csv import json from pathlib import Path import numpy as np import torch from transformers import AutoTokenizer, T5Tokenizer import tensorrt_llm 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='gpt_outputs') 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('--tokenizer', dest='tokenizer_path', help="HF tokenizer config path", default='EleutherAI/gpt-neox-20b') 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( '--prompt_table', type=Path, help="Path to .npy file, exported by nemo_prompt_convert.py") parser.add_argument( '--tasks', help="Comma-separated list of tasks for prompt tuning: ex 0,3,1,0") return parser.parse_args() def generate( max_output_len: int, log_level: str = 'error', engine_dir: str = 'gpt_outputs', input_text: str = 'Born in north-east France, Soyer trained as a', input_file: str = None, output_csv: str = None, output_npy: str = None, tokenizer_path: str = 'gpt2', vocab_file=None, num_beams: int = 1, prompt_table: Path = None, tasks: str = None, ): tensorrt_llm.logger.set_level(log_level) engine_dir = Path(engine_dir) config_path = engine_dir / 'config.json' with open(config_path, 'r') as f: config = json.load(f) use_gpt_attention_plugin = config['plugin_config']['gpt_attention_plugin'] remove_input_padding = config['plugin_config']['remove_input_padding'] dtype = config['builder_config']['precision'] world_size = config['builder_config']['tensor_parallel'] assert world_size == tensorrt_llm.mpi_world_size(), \ f'Engine world size ({world_size}) != Runtime world size ({tensorrt_llm.mpi_world_size()})' num_heads = config['builder_config']['num_heads'] // world_size hidden_size = config['builder_config']['hidden_size'] // world_size vocab_size = config['builder_config']['vocab_size'] num_layers = config['builder_config']['num_layers'] multi_query_mode = config['builder_config']['multi_query_mode'] paged_kv_cache = config['builder_config']['paged_kv_cache'] tokens_per_block = config['builder_config']['tokens_per_block'] use_prompt_tuning = config['builder_config']['use_prompt_tuning'] runtime_rank = tensorrt_llm.mpi_rank() runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank, tp_size=world_size) torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node) if vocab_file is not None: tokenizer = T5Tokenizer(vocab_file=vocab_file) END_ID = 50256 else: tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) END_ID = tokenizer.eos_token_id model_config = ModelConfig( num_heads=num_heads, num_kv_heads=(1 if multi_query_mode else num_heads), hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, gpt_attention_plugin=use_gpt_attention_plugin, remove_input_padding=remove_input_padding, paged_kv_cache=paged_kv_cache, tokens_per_block=tokens_per_block, use_prompt_tuning=use_prompt_tuning) sampling_config = SamplingConfig(end_id=END_ID, pad_id=END_ID, num_beams=num_beams, temperature=1.0, top_k=1, top_p=1.0) engine_name = get_engine_name('mpt', dtype, world_size, runtime_rank) serialize_path = engine_dir / engine_name with open(serialize_path, 'rb') as f: engine_buffer = f.read() decoder = tensorrt_llm.runtime.GenerationSession(model_config, engine_buffer, runtime_mapping) 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 != END_ID] input_tokens.append(row) else: print('Input file format not supported.') raise SystemExit input_ids = None input_lengths = None if input_file is None: input_ids = torch.cuda.IntTensor(input_tokens) input_lengths = torch.cuda.IntTensor([input_ids.size(1)]) else: input_lengths = torch.cuda.IntTensor([len(x) for x in input_tokens]) if remove_input_padding: input_ids = np.concatenate(input_tokens) input_ids = torch.cuda.IntTensor(input_ids).unsqueeze(0) else: input_ids = torch.nested.to_padded_tensor( torch.nested.nested_tensor(input_tokens, dtype=torch.int32), END_ID).cuda() max_input_length = torch.max(input_lengths).item() decoder.setup(input_lengths.size(0), max_input_length, max_output_len) ptuning_args = [] if use_prompt_tuning: if prompt_table is not None: prompt_table = torch.from_numpy(np.load(prompt_table)) task_vocab_size = torch.tensor([prompt_table.shape[1]], dtype=torch.int32, device="cuda") prompt_table = prompt_table.view( (prompt_table.shape[0] * prompt_table.shape[1], prompt_table.shape[2])) prompt_table = prompt_table.cuda().to( dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype)) else: prompt_table = torch.empty([1, hidden_size]).cuda() task_vocab_size = torch.zeros([1]).cuda() if tasks is not None: tasks = torch.tensor([int(t) for t in tasks.split(',')], dtype=torch.int32, device="cuda") assert tasks.shape[0] == input_ids.shape[ 0], "Number of supplied tasks must match input batch size" else: tasks = torch.zeros([input_ids.size(0)]).cuda() ptuning_args = [prompt_table, tasks, task_vocab_size] output_ids = decoder.decode(input_ids, input_lengths, sampling_config, *ptuning_args) torch.cuda.synchronize() if runtime_rank == 0: if output_csv is None and output_npy is None: for b in range(input_lengths.size(0)): inputs = input_tokens[b] input_text = tokenizer.decode(inputs) print(f'Input: {input_text}') if num_beams <= 1: output_begin = max_input_length outputs = output_ids[b][0][output_begin:].tolist() output_text = tokenizer.decode(output_ids[b][0]) #outputs) print(f'Output: {output_text}') else: 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) if __name__ == '__main__': args = parse_arguments() generate(**vars(args))