text-generation-webui/modules/tensorrt_llm.py

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2024-06-24 07:30:03 +02:00
from pathlib import Path
import tensorrt_llm
import torch
from tensorrt_llm.runtime import ModelRunner, ModelRunnerCpp
from modules import shared
from modules.logging_colors import logger
from modules.text_generation import (
get_max_prompt_length,
get_reply_from_output_ids
)
class TensorRTLLMModel:
def __init__(self):
pass
@classmethod
def from_pretrained(self, path_to_model):
path_to_model = Path(f'{shared.args.model_dir}') / Path(path_to_model)
runtime_rank = tensorrt_llm.mpi_rank()
# Define model settings
runner_kwargs = dict(
engine_dir=str(path_to_model),
lora_dir=None,
rank=runtime_rank,
debug_mode=False,
lora_ckpt_source="hf",
)
if shared.args.cpp_runner:
logger.info("TensorRT-LLM: Using \"ModelRunnerCpp\"")
runner_kwargs.update(
max_batch_size=1,
max_input_len=shared.args.max_seq_len - 512,
max_output_len=512,
max_beam_width=1,
max_attention_window_size=None,
sink_token_length=None,
)
else:
logger.info("TensorRT-LLM: Using \"ModelRunner\"")
# Load the model
runner_cls = ModelRunnerCpp if shared.args.cpp_runner else ModelRunner
runner = runner_cls.from_dir(**runner_kwargs)
result = self()
result.model = runner
result.runtime_rank = runtime_rank
return result
def generate_with_streaming(self, prompt, state):
batch_input_ids = []
input_ids = shared.tokenizer.encode(
prompt,
add_special_tokens=True,
truncation=False,
)
input_ids = torch.tensor(input_ids, dtype=torch.int32)
input_ids = input_ids[-get_max_prompt_length(state):] # Apply truncation_length
batch_input_ids.append(input_ids)
if shared.args.cpp_runner:
max_new_tokens = min(512, state['max_new_tokens'])
elif state['auto_max_new_tokens']:
max_new_tokens = state['truncation_length'] - input_ids.shape[-1]
else:
max_new_tokens = state['max_new_tokens']
with torch.no_grad():
generator = self.model.generate(
batch_input_ids,
max_new_tokens=max_new_tokens,
max_attention_window_size=None,
sink_token_length=None,
end_id=shared.tokenizer.eos_token_id if not state['ban_eos_token'] else -1,
pad_id=shared.tokenizer.pad_token_id or shared.tokenizer.eos_token_id,
temperature=state['temperature'],
top_k=state['top_k'],
top_p=state['top_p'],
num_beams=1,
length_penalty=1.0,
repetition_penalty=state['repetition_penalty'],
presence_penalty=state['presence_penalty'],
frequency_penalty=state['frequency_penalty'],
stop_words_list=None,
bad_words_list=None,
lora_uids=None,
prompt_table_path=None,
prompt_tasks=None,
streaming=not shared.args.cpp_runner,
output_sequence_lengths=True,
return_dict=True,
medusa_choices=None
)
torch.cuda.synchronize()
cumulative_reply = ''
starting_from = batch_input_ids[0].shape[-1]
if shared.args.cpp_runner:
sequence_length = generator['sequence_lengths'][0].item()
output_ids = generator['output_ids'][0][0][:sequence_length].tolist()
cumulative_reply += get_reply_from_output_ids(output_ids, state, starting_from=starting_from)
starting_from = sequence_length
yield cumulative_reply
else:
for curr_outputs in generator:
if shared.stop_everything:
break
sequence_length = curr_outputs['sequence_lengths'][0].item()
output_ids = curr_outputs['output_ids'][0][0][:sequence_length].tolist()
cumulative_reply += get_reply_from_output_ids(output_ids, state, starting_from=starting_from)
starting_from = sequence_length
yield cumulative_reply
def generate(self, prompt, state):
output = ''
for output in self.generate_with_streaming(prompt, state):
pass
return output