run.py

# 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
import os
from pathlib import Path
from typing import List, Optional

import numpy as np
import torch
from utils import (DEFAULT_HF_MODEL_DIRS, DEFAULT_PROMPT_TEMPLATES,
                   add_common_args, get_beam_width_array, load_tokenizer,
                   prepare_enc_dec_inputs, read_model_name,
                   supports_inflight_batching, 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

from prompt_lookup.run_dtm_pld import run_dtm_pld


def parse_arguments(args=None):
    # see `add_common_args` for extended list of arguments
    parser = argparse.ArgumentParser()
    parser.add_argument('--max_input_length', type=int, default=923)
    parser.add_argument('--max_output_len', type=int, required=True)
    parser.add_argument(
        '--draft_engine_dir',
        type=str,
        default=None,
        help='Path to engine of draft model in Draft-Target-Model mode.')
    parser.add_argument(
        '--input_text',
        type=str,
        nargs='+',
        default=["Born in north-east France, Soyer trained as a"])
    parser.add_argument(
        '--input_file',
        type=str,
        help=
        'CSV or Numpy file containing tokenized input. Alternative to text input.',
        default=None)
    parser.add_argument('--multimodal_input_file',
                        type=str,
                        help='Path to multimodal input file.')
    parser.add_argument(
        '--input_token_extra_ids',
        type=int,
        nargs='+',
        help=
        'Input token extra ids for using p-tuning and KV Cache reuse together (only available with cpp session).',
        default=None)
    parser.add_argument(
        '--input_token_extra_ids_file',
        type=str,
        help=
        'CSV or Numpy file containing input token extra ids file. Alternative to text input (only available with cpp session).',
        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('--output_generation_logits',
                        default=False,
                        action='store_true',
                        help="Enable gathering generation logits.")
    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(
        '--run_profiling',
        default=False,
        action='store_true',
        help="Run several 10 iterations to profile the inference latencies.")

    parser = add_common_args(parser)

    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:
        if 'whisper' in model_name.lower():
            batch_input_ids.append(tokenizer.prefix_tokens)
        else:
            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
        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 input_file is None and 'GLM' in model_name 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
    ]

    logger.debug(f"Input token ids (batch_size = {len(batch_input_ids)}):")
    for i, input_ids in enumerate(batch_input_ids):
        logger.debug(f"Request {i}: {input_ids.tolist()}")

    return batch_input_ids


def parse_input_token_extra_ids(prompt_table_path, kv_cache_enable_block_reuse,
                                input_token_extra_ids,
                                input_token_extra_ids_file, max_input_length):
    batch_extra_ids = None
    if prompt_table_path and kv_cache_enable_block_reuse:
        assert input_token_extra_ids or input_token_extra_ids_file, \
            "Input token extra ids must be provided when p-tuning and KV Cache reuse are both enabled"
        batch_extra_ids = []
        if input_token_extra_ids_file:
            if input_token_extra_ids_file.endswith('.csv'):
                with open(input_token_extra_ids_file, 'r') as csv_file:
                    csv_reader = csv.reader(csv_file, delimiter=',')
                    for line in csv_reader:
                        extra_ids = [int(num) for num in line]
                        batch_extra_ids.append(extra_ids[-max_input_length:])
            elif input_token_extra_ids_file.endswith('.npy'):
                inputs = np.load(input_token_extra_ids_file)
                for extra_ids in inputs:
                    batch_extra_ids.append(extra_ids[-max_input_length:])
            else:
                print('Input file format not supported.')
                raise SystemExit
        else:
            batch_extra_ids.append(input_token_extra_ids)
    return batch_extra_ids


def print_output(tokenizer,
                 output_ids: torch.Tensor,
                 input_lengths: List[int],
                 sequence_lengths: torch.Tensor,
                 output_csv: Optional[str] = None,
                 output_npy: Optional[str] = None,
                 context_logits: Optional[torch.Tensor] = None,
                 generation_logits: Optional[torch.Tensor] = None,
                 cum_log_probs: Optional[torch.Tensor] = None,
                 log_probs: Optional[torch.Tensor] = None,
                 output_logits_npy: Optional[str] = None,
                 output_cum_log_probs_npy: Optional[str] = None,
                 output_log_probs_npy: Optional[str] = None):
    num_output_sents, num_beams, _ = output_ids.size()
    batch_size = len(input_lengths)
    num_return_sequences = num_output_sents // batch_size

    if output_csv is None and output_npy is None and tokenizer is not None:
        for i in range(batch_size * num_return_sequences):
            batch_idx = i // num_return_sequences
            seq_idx = i % num_return_sequences
            inputs = output_ids[i][0][:input_lengths[batch_idx]].tolist()
            input_text = tokenizer.decode(inputs)
            if seq_idx == 0:
                print(f'Input [Text {batch_idx}]: \"{input_text}\"')

            for beam in range(num_beams):
                output_begin = input_lengths[batch_idx]
                output_end = sequence_lengths[i][beam]
                outputs = output_ids[i][beam][output_begin:output_end].tolist()
                output_text = tokenizer.decode(outputs)
                index_str = (f'Text {batch_idx} Seq {seq_idx} Beam {beam}'
                             if num_return_sequences > 1 else
                             f'Text {batch_idx} Beam {beam}')
                print(f'Output [{index_str}]: \"{output_text}\"')
                logger.debug(str(outputs))

    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)

    # different handling if encoder-decoder models
    is_enc_dec = {'encoder', 'decoder'}.issubset({
        name
        for name in os.listdir(args.engine_dir)
        if os.path.isdir(os.path.join(args.engine_dir, name))
    })
    if is_enc_dec:
        logger.warning(
            "This path is an encoder-decoder model. Using different handling.")
        assert not args.use_py_session, "Encoder-decoder models don't have a unified python runtime, please use its own examples/models/core/enc_dec/run.py instead."

    model_name, model_version = read_model_name(
        args.engine_dir if not is_enc_dec else os.path.
        join(args.engine_dir, 'encoder'))

    if args.tokenizer_dir is None and model_name in DEFAULT_HF_MODEL_DIRS:
        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,
    )

    if args.end_id:
        end_id = args.end_id

    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)

    stop_words_list = None
    if args.stop_words:
        stop_words_list = tensorrt_llm.runtime.decode_words_list(
            args.stop_words, tokenizer)
    if model_version == 'glm4':  # add default stop token ids for GLM-4
        glm4_stop_ids = [[151329], [151336], [151338]]
        if stop_words_list is None:
            stop_words_list = [glm4_stop_ids] * len(batch_input_ids)
        else:
            for req_stop_words_list in stop_words_list:
                req_stop_words_list.extend(glm4_stop_ids)

    bad_words_list = None
    if args.bad_words:
        bad_words_list = tensorrt_llm.runtime.decode_words_list(
            args.bad_words, tokenizer)

    if is_enc_dec:
        encoder_input_ids, encoder_input_features, encoder_output_lengths, decoder_input_ids = prepare_enc_dec_inputs(
            batch_input_ids, model_name, args.engine_dir,
            args.multimodal_input_file)

    input_token_extra_ids = parse_input_token_extra_ids(
        args.prompt_table_path, args.kv_cache_enable_block_reuse,
        args.input_token_extra_ids, args.input_token_extra_ids_file,
        args.max_input_length)

    input_lengths = [x.size(0) for x in decoder_input_ids
                     ] if is_enc_dec else [x.size(0) for x in batch_input_ids]

    encoder_input_lengths = [
        x.size(0) for x in (encoder_input_features or encoder_input_ids)
    ] if is_enc_dec else None

    if args.beam_width_array is not None:
        logger.info("Enable Variable-Beam-Width-Search (VBWS)")
        assert not args.use_py_session, "`--use_py_session` is not supported in VBWS."
        args.beam_width_array, args.num_beams = get_beam_width_array(
            args.beam_width_array)

    if not args.use_py_session and not supports_inflight_batching(
            os.path.join(args.engine_dir, "decoder") if is_enc_dec else args.
            engine_dir):
        logger.warning(
            "The given engine does not support in-flight batching, fallback to python session"
        )
        args.use_py_session = True

    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
    if args.return_all_generated_tokens and args.use_py_session:
        raise ValueError(
            "Returning all the generated tokens at each step is not supported in the Python session, use C++ session instead."
        )
    if (not args.return_all_generated_tokens) and args.streaming and (
            args.num_beams > 1):
        logger.warning(
            "Setting return_all_generated_tokens to True since streaming AND beam search are done simultaneously. "
            "Returning the full beams at each streaming step is needed because beam search + streaming can change previous outputs. "
            "WARNING: using this option may increase network usage significantly (quadratically w.r.t output length)."
        )
        args.return_all_generated_tokens = True

    logger.info(f"Using {'Python' if args.use_py_session else 'C++'} session")

    if args.draft_target_model_config is not None or args.prompt_lookup_config is not None:
        # Speculative-Decoding of Draft-Target-Model (DTM) and Prompt-Lookup-Decoding (PLD)
        # If the parameters of `runner_kwargs` and `runner.generate()` in the "else" branch change, the same change should be done for `examples/prompt_lookup/run_dtm_pld.py`
        assert args.kv_cache_enable_block_reuse, "`--kv_cache_enable_block_reuse` must be specified in speculative decoding."
        assert not args.use_py_session, "`--use_py_session` is not supported in Speculative decoding."
        assert not is_enc_dec, "Encoder-Decoder model is not supported in Speculative decoding."
        assert args.num_beams == 1, "`--num_beams>1` is not supported in Speculative decoding."

        outputs = run_dtm_pld(batch_input_ids, args, runtime_rank, end_id,
                              pad_id, stop_words_list, bad_words_list,
                              len(tokenizer))
        if not args.streaming:  # Unpack runner from the return value in No-Streaming mode
            outputs, runner = list(outputs)[0]

    else:  # Normal run
        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,
            gpu_weights_percent=args.gpu_weights_percent,
            max_output_len=args.max_output_len,
            enable_context_fmha_fp32_acc=args.enable_context_fmha_fp32_acc)
        if args.medusa_choices is not None:
            args.medusa_choices = ast.literal_eval(args.medusa_choices)
            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 args.eagle_choices is not None or args.eagle_posterior_threshold is not None or args.eagle_use_dynamic_tree:
            assert args.num_beams == 1, "Eagle should use num_beams == 1"
            assert not args.use_py_session, "Eagle does not support py session"
            if args.eagle_choices is not None and not args.eagle_use_dynamic_tree:
                args.eagle_choices = ast.literal_eval(args.eagle_choices)
                runner_kwargs.update(eagle_choices=args.eagle_choices)
            if args.eagle_posterior_threshold is not None:
                runner_kwargs.update(
                    eagle_posterior_threshold=args.eagle_posterior_threshold)
            if args.eagle_use_dynamic_tree:
                runner_kwargs.update(
                    eagle_use_dynamic_tree=args.eagle_use_dynamic_tree)
                assert args.eagle_dynamic_tree_max_top_k is not None and args.eagle_dynamic_tree_max_top_k > 0
                runner_kwargs.update(eagle_dynamic_tree_max_top_k=args.
                                     eagle_dynamic_tree_max_top_k)
        if args.lookahead_config is not None:
            args.lookahead_config = ast.literal_eval(args.lookahead_config)
            assert len(
                args.lookahead_config
            ) == 3, "Lookahead needs [max_window_size, max_ngram_size, max_verification_set_size]"
            runner_kwargs.update(lookahead_config=args.lookahead_config)
        if not args.use_py_session:
            runner_kwargs.update(
                is_enc_dec=is_enc_dec,
                max_batch_size=len(batch_input_ids),
                max_input_len=max(
                    encoder_input_lengths if is_enc_dec else input_lengths),
                max_beam_width=args.num_beams,
                max_attention_window_size=args.max_attention_window_size,
                sink_token_length=args.sink_token_length,
                max_tokens_in_paged_kv_cache=args.max_tokens_in_paged_kv_cache,
                kv_cache_enable_block_reuse=args.kv_cache_enable_block_reuse,
                kv_cache_free_gpu_memory_fraction=args.
                kv_cache_free_gpu_memory_fraction,
                cross_kv_cache_fraction=args.cross_kv_cache_fraction
                if is_enc_dec else None,
                enable_chunked_context=args.enable_chunked_context,
                multi_block_mode=args.multi_block_mode,
                cuda_graph_mode=args.cuda_graph_mode,
                gather_generation_logits=args.output_generation_logits,
                use_variable_beam_width_search=(args.beam_width_array
                                                is not None),
            )
        runner = runner_cls.from_dir(**runner_kwargs)

        with torch.no_grad():
            outputs = runner.generate(
                batch_input_ids=decoder_input_ids
                if is_enc_dec else batch_input_ids,
                encoder_input_ids=encoder_input_ids if is_enc_dec else None,
                encoder_input_features=encoder_input_features
                if is_enc_dec else None,
                encoder_output_lengths=encoder_output_lengths
                if is_enc_dec else None,
                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,
                num_return_sequences=args.num_return_sequences,
                length_penalty=args.length_penalty,
                early_stopping=args.early_stopping,
                beam_width_array=args.beam_width_array,
                repetition_penalty=args.repetition_penalty,
                presence_penalty=args.presence_penalty,
                frequency_penalty=args.frequency_penalty,
                min_p=args.min_p,
                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),
                random_seed=args.random_seed,
                lora_uids=args.lora_task_uids,
                prompt_table=args.prompt_table_path,
                prompt_tasks=args.prompt_tasks,
                streaming=args.streaming,
                output_sequence_lengths=True,
                output_generation_logits=args.output_generation_logits,
                no_repeat_ngram_size=args.no_repeat_ngram_size,
                return_dict=True,
                medusa_choices=args.medusa_choices,
                eagle_choices=args.eagle_choices,
                return_all_generated_tokens=args.return_all_generated_tokens,
                input_token_extra_ids=input_token_extra_ids,
                language_adapter_uids=args.language_task_uids)
            torch.cuda.synchronize()

    # Receive output, print to screen or save to file
    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 is not None:
                    cum_log_probs = curr_outputs['cum_log_probs']
                if args.output_log_probs_npy is not None:
                    log_probs = curr_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 or args.output_generation_logits:
                generation_logits = outputs['generation_logits']
            if args.output_cum_log_probs_npy is not None:
                cum_log_probs = outputs['cum_log_probs']
            if args.output_log_probs_npy is not 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)

    # Profiling
    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,
                    beam_width_array=args.beam_width_array,
                    repetition_penalty=args.repetition_penalty,
                    presence_penalty=args.presence_penalty,
                    frequency_penalty=args.frequency_penalty,
                    min_p=args.min_p,
                    stop_words_list=stop_words_list,
                    bad_words_list=bad_words_list,
                    output_cum_log_probs=(args.output_cum_log_probs_npy
                                          is not None),
                    output_log_probs=(args.output_log_probs_npy is not None),
                    random_seed=args.random_seed,
                    lora_uids=args.lora_task_uids,
                    lookahead_config=args.lookahead_config,
                    prompt_table=args.prompt_table_path,
                    prompt_tasks=args.prompt_tasks,
                    streaming=args.streaming,
                    output_sequence_lengths=True,
                    return_dict=True,
                    return_all_generated_tokens=args.
                    return_all_generated_tokens,
                    input_token_extra_ids=input_token_extra_ids)
                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,
                    beam_width_array=args.beam_width_array,
                    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),
                    random_seed=args.random_seed,
                    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,
                    return_all_generated_tokens=args.
                    return_all_generated_tokens,
                    input_token_extra_ids=input_token_extra_ids)
                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)