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#!/usr/bin/env python
# coding=utf-8
"""The Inferencer class simplifies the process of model inferencing."""
import os
import torch
import wandb
import deepspeed
import sys
import numpy as np
import datetime
import json
from transformers import AutoConfig
import torch.distributed as dist
from lmflow.args import DatasetArguments
from lmflow.datasets.dataset import Dataset
from lmflow.pipeline.base_pipeline import BasePipeline
from lmflow.models.hf_decoder_model import HFDecoderModel
from lmflow.utils.data_utils import set_random_seed, batchlize, answer_extraction
os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warnings about parallelism in tokenizers
def rstrip_partial_utf8(string):
return string.replace("\ufffd", "")
class Inferencer(BasePipeline):
"""
Initializes the `Inferencer` class with given arguments.
Parameters
------------
model_args : ModelArguments object.
Contains the arguments required to load the model.
data_args : DatasetArguments object.
Contains the arguments required to load the dataset.
inferencer_args : InferencerArguments object.
Contains the arguments required to perform inference.
"""
def __init__(self, model_args, data_args, inferencer_args):
self.data_args = data_args
self.inferencer_args = inferencer_args
self.model_args = model_args
set_random_seed(self.inferencer_args.random_seed)
self.local_rank = int(os.getenv("LOCAL_RANK", "0"))
self.world_size = int(os.getenv("WORLD_SIZE", "1"))
if inferencer_args.device == "gpu":
torch.cuda.set_device(self.local_rank) # NOTE: cpu-only machine will have error
deepspeed.init_distributed()
else:
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "15000"
dist.init_process_group(
"gloo", rank=self.local_rank, world_size=self.world_size
)
self.config = AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
try:
self.model_hidden_size = self.config.hidden_size
except:
print("Error in setting hidden size, use the default size 1024")
self.model_hidden_size = 1024 # gpt2 seems do not have hidden_size in config
def create_dataloader(self, dataset: Dataset):
data_dict = dataset.to_dict()
inputs = [ instance["text"] for instance in data_dict["instances"] ]
dataset_size = len(inputs)
dataset_buf = []
for idx in range(dataset_size):
dataset_buf.append({
"input": inputs[idx],
"input_idx": idx
})
dataloader = batchlize(
dataset_buf,
batch_size=1,
random_shuffle=False,
)
return dataloader, dataset_size
def inference(
self,
model,
dataset: Dataset,
max_new_tokens: int=100,
temperature: float=0.0,
prompt_structure: str='{input}',
):
"""
Perform inference for a model
Parameters
------------
model : TunableModel object.
TunableModel to perform inference
dataset : Dataset object.
Returns:
output_dataset: Dataset object.
"""
if dataset.get_type() != "text_only":
raise NotImplementedError(
'input dataset should have type "text_only"'
)
dataloader, data_size = self.create_dataloader(dataset)
# The output dataset
output_dict = {
"type": "text_only",
"instances": [
]
}
for batch_index, batch in enumerate(dataloader):
current_batch = batch[0] # batch size is 1
input = prompt_structure.format(input=current_batch['input'])
if self.inferencer_args.device == "gpu":
inputs = model.encode(input, return_tensors="pt").to(device=self.local_rank)
elif self.inferencer_args.device == "cpu":
inputs = model.encode(input, return_tensors="pt").to(device='cpu')
else:
raise NotImplementedError(
f"device \"{self.inferencer_args.device}\" is not supported"
)
outputs = model.inference(
inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
repetition_penalty=1.0,
)
text_out = model.decode(outputs[0], skip_special_tokens=True)
# only return the generation, trucating the input
prompt_length = len(model.decode(inputs[0], skip_special_tokens=True,))
text_out = text_out[prompt_length:]
output_dict["instances"].append({ "text": text_out })
output_dataset = Dataset(DatasetArguments(dataset_path = None))
output_dataset = output_dataset.from_dict(output_dict)
return output_dataset
def stream_inference(self, context, model, max_new_tokens, token_per_step, temperature, end_string, input_dataset):
response = ""
history = []
if "ChatGLMModel" in self.config.architectures:
for response, history in model.get_backend_model().stream_chat(model.get_tokenizer(), context, history=history):
response = rstrip_partial_utf8(response)
yield response, False
else:
for _ in range(0, max_new_tokens // token_per_step):
output_dataset = self.inference(
model=model,
dataset=input_dataset,
max_new_tokens=token_per_step,
temperature=temperature,
)
new_append_text = output_dataset.to_dict()["instances"][0]["text"]
new_append_text = rstrip_partial_utf8(new_append_text)
response += new_append_text
input_dict = input_dataset.to_dict()
input_dict["instances"][0]["text"] += new_append_text
input_dataset = input_dataset.from_dict(input_dict)
flag_break = False
try:
index = response.index(end_string)
flag_break = True
except ValueError:
response += end_string
index = response.index(end_string)
response = response[:index]
yield response, flag_break
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