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utils.py
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utils.py
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# 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 json
from pathlib import Path
from typing import Optional
from transformers import AutoTokenizer, LlamaTokenizer, T5Tokenizer
from tensorrt_llm.bindings import GptJsonConfig
from tensorrt_llm.builder import get_engine_version
DEFAULT_HF_MODEL_DIRS = {
'BaichuanForCausalLM': 'baichuan-inc/Baichuan-13B-Chat',
'BaiChuanForCausalLM': 'baichuan-inc/Baichuan-13B-Chat',
'BloomForCausalLM': 'bigscience/bloom-560m',
'GLMModel': 'THUDM/glm-10b',
'ChatGLMModel': 'THUDM/chatglm3-6b',
'ChatGLMForCausalLM': 'THUDM/chatglm3-6b',
'RWForCausalLM': 'tiiuae/falcon-rw-1b',
'FalconForCausalLM': 'tiiuae/falcon-rw-1b',
'GPT2LMHeadModel': 'gpt2',
'GPT2LMHeadCustomModel': 'gpt2',
'Starcoder2ForCausalLM': 'bigcode/starcoder2-3b',
'GPTForCausalLM': 'gpt2',
'GPTJForCausalLM': 'EleutherAI/gpt-j-6b',
'GPTNeoXForCausalLM': 'EleutherAI/gpt-neox-20b',
'InternLMForCausalLM': 'internlm/internlm-chat-7b',
'InternLM2ForCausalLM': 'internlm/internlm2-chat-7b',
'LlamaForCausalLM': 'meta-llama/Llama-2-7b-hf',
'MPTForCausalLM': 'mosaicml/mpt-7b',
'PhiForCausalLM': 'microsoft/phi-2',
'OPTForCausalLM': 'facebook/opt-350m',
'QWenLMHeadModel': 'Qwen/Qwen-7B',
'QWenForCausalLM': 'Qwen/Qwen-7B',
'Qwen2ForCausalLM': 'Qwen/Qwen1.5-7B',
'Qwen2MoeForCausalLM': 'Qwen/Qwen1.5-MoE-A2.7B',
'RecurrentGemmaForCausalLM': 'google/recurrentgemma-2b',
}
INTERNLM_META_INSTRUCTION = """You are an AI assistant whose name is InternLM (书生·浦语).
- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.
"""
QWEN_PROMPT_TEMPLATE = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{input_text}<|im_end|>\n<|im_start|>assistant\n"
DEFAULT_PROMPT_TEMPLATES = {
'InternLMForCausalLM': "<|User|>:{input_text}<eoh>\n<|Bot|>:",
'InternLM2ForCausalLM': "<|im_start|>system\n" + INTERNLM_META_INSTRUCTION +
"<|im_end|>\n<|im_start|>user\n{input_text}<|im_end|>\n<|im_start|>assistant\n",
'QWenLMHeadModel': QWEN_PROMPT_TEMPLATE,
'QWenForCausalLM': QWEN_PROMPT_TEMPLATE,
'Qwen2ForCausalLM': QWEN_PROMPT_TEMPLATE,
'Qwen2MoeForCausalLM': QWEN_PROMPT_TEMPLATE,
}
def supports_inflight_batching(engine_dir):
config_path = Path(engine_dir) / "config.json"
json_config = GptJsonConfig.parse_file(config_path)
model_config = json_config.model_config
return model_config.supports_inflight_batching
def read_decoder_start_token_id(engine_dir):
with open(Path(engine_dir) / "config.json", 'r') as f:
config = json.load(f)
return config['pretrained_config']['decoder_start_token_id']
def read_model_name(engine_dir: str):
engine_version = 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'], None
model_arch = config['pretrained_config']['architecture']
model_version = None
if 'GLM' in model_arch:
model_version = config['pretrained_config']['chatglm_version']
if 'qwen' in model_arch.lower():
model_version = config['pretrained_config']['qwen_type']
return model_arch, model_version
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 = 'GPTForCausalLM',
model_version: Optional[str] = None,
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)
elif model_name == 'GemmaForCausalLM' or model_name == 'RecurrentGemmaForCausalLM':
from transformers import GemmaTokenizer
# Initialize tokenizer from vocab file.
tokenizer = GemmaTokenizer(vocab_file=vocab_file,
padding_side='left',
truncation_side='left',
legacy=False)
elif model_name == 'Grok1ModelForCausalLM':
tokenizer = LlamaTokenizer(vocab_file=vocab_file,
padding_side='left',
truncation_side='left',
legacy=False,
use_fast=False)
else:
# For gpt-next, directly load from tokenizer.model
tokenizer = T5Tokenizer(vocab_file=vocab_file,
padding_side='left',
truncation_side='left',
legacy=False)
if 'qwen' in model_name.lower() and model_version == 'qwen':
with open(Path(tokenizer_dir) / "generation_config.json") as f:
gen_config = json.load(f)
pad_id = gen_config['pad_token_id']
end_id = gen_config['eos_token_id']
elif 'GLM' in model_name and model_version == 'glm':
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
def add_common_args(parser):
# sampling arguments
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('--beam_search_diversity_rate', type=float, default=0.0)
parser.add_argument('--random_seed', type=int, default=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(
'--end_id',
default=None,
type=int,
help="Override tokenizer end_id to stop on given end_id token.")
parser.add_argument(
'--stop_words',
default=None,
type=str,
nargs="+",
action='append',
help=
'Set stop words for a batch. Successive invocations of --stop_words set stop words for other batches.'
' E.g.: --stop_words " London" " chef" --stop_words "eventually became" "was not"',
)
parser.add_argument(
'--bad_words',
default=None,
type=str,
nargs="+",
action='append',
help=
'Set bad words for a batch. Successive invocations of --bad_words set bad words for other batches.'
' E.g.: --bad_words " London" " chef" --bad_words "eventually became" "was not"',
)
parser.add_argument('--no_repeat_ngram_size', type=int, default=None)
# common runtime arguments
parser.add_argument('--sink_token_length',
type=int,
default=None,
help='The sink token length.')
parser.add_argument(
'--max_attention_window_size',
type=int,
default=None,
nargs="+",
help=
'The attention window size that controls the sliding window attention / cyclic kv cache behavior'
)
parser.add_argument(
'--multi_block_mode',
action='store_true',
help=
"Distribute the work across multiple CUDA thread-blocks on the GPU for masked MHA kernel."
)
parser.add_argument('--enable_context_fmha_fp32_acc',
action='store_true',
help="Enable FMHA runner FP32 accumulation.")
parser.add_argument('--log_level', type=str, default='info')
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('--use_py_session',
default=False,
action='store_true',
help="Whether or not to use Python runtime session")
parser.add_argument('--debug_mode',
default=False,
action='store_true',
help="Whether or not to turn on the debug mode")
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_ckpt_source',
type=str,
default="hf",
choices=["hf", "nemo"],
help="The source of lora checkpoint.")
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(
'--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(
'--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."
)
parser.add_argument(
'--lookahead_config',
type=str,
default=None,
help="lookahead config to use, if not none, will use lookahead decoding."
" E.g.: [5, 6, 7] for [max_window_size, max_ngram_size, max_verification_set_size]."
)
# model arguments
parser.add_argument('--engine_dir', type=str, default='engine_outputs')
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('--no_add_special_tokens',
dest='add_special_tokens',
default=True,
action='store_false',
help="Whether or not to add special tokens")
parser.add_argument('--hf_model_dir', '--model_dir', type=str, default=None)
parser.add_argument(
'--tokenizer_dir',
default=None,
help='tokenizer path; defaults to hf_model_dir if left unspecified')
# memory argument
parser.add_argument(
'--gpu_weights_percent',
default=1,
type=float,
help=
'Specify the percentage of weights that reside on GPU instead of CPU and streaming load during runtime.',
)
parser.add_argument(
'--max_tokens_in_paged_kv_cache',
default=None,
type=int,
help=
'Specify the maximum number of tokens in a kv cache page (only available with cpp session).',
)
parser.add_argument(
'--kv_cache_enable_block_reuse',
action='store_true',
help=
'Enables block reuse in kv cache (only available with cpp session).',
)
parser.add_argument(
'--kv_cache_free_gpu_memory_fraction',
default=0.9,
type=float,
help='Specify the free gpu memory fraction.',
)
parser.add_argument(
'--enable_chunked_context',
action='store_true',
help='Enables chunked context (only available with cpp session).',
)
# hf model argument (if use hf model)
parser.add_argument(
'--hf_data_type',
'--data_type',
type=str,
choices=['fp32', 'fp16', 'bf16', 'float32', 'float16', 'bfloat16'],
default='fp16',
help="The data type for hf model.")
parser.add_argument(
'--hf_device_map_auto',
action='store_true',
help="Use device map 'auto' to load a pretrained HF model. This may "
"help to test a large model that cannot fit into a singlue GPU.")
parser.add_argument(
"--return_all_generated_tokens",
default=False,
action="store_true",
help="This option changes the token output only for streaming. "
"If not specified, return only generated tokens at each step. "
"If specified, return the full beams/outputs at each step. "
"It is automatically enabled for num_beams>1 (only available with cpp session). "
"WARNING: using this option may increase network usage significantly (quadratically w.r.t output length)."
)
return parser