mirror of
https://github.com/open-compass/opencompass.git
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213 lines
7.3 KiB
Python
213 lines
7.3 KiB
Python
# Adopted from https://github.com/haotian-liu/LLaVA. Below is the original copyright:
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# Copyright 2023 Haotian Liu
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import torch
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import logging
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import logging.handlers
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import transformers
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from opencompass.models.ola.constants import LOGDIR
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server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
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moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN."
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handler = None
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def build_logger(logger_name, logger_filename):
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global handler
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formatter = logging.Formatter(
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fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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# Set the format of root handlers
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if not logging.getLogger().handlers:
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logging.basicConfig(level=logging.INFO)
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logging.getLogger().handlers[0].setFormatter(formatter)
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# Redirect stdout and stderr to loggers
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stdout_logger = logging.getLogger("stdout")
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stdout_logger.setLevel(logging.INFO)
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sl = StreamToLogger(stdout_logger, logging.INFO)
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sys.stdout = sl
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stderr_logger = logging.getLogger("stderr")
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stderr_logger.setLevel(logging.ERROR)
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sl = StreamToLogger(stderr_logger, logging.ERROR)
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sys.stderr = sl
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# Get logger
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logger = logging.getLogger(logger_name)
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logger.setLevel(logging.INFO)
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# Add a file handler for all loggers
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if handler is None:
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os.makedirs(LOGDIR, exist_ok=True)
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filename = os.path.join(LOGDIR, logger_filename)
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handler = logging.handlers.TimedRotatingFileHandler(
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filename, when='D', utc=True, encoding='UTF-8')
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handler.setFormatter(formatter)
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for name, item in logging.root.manager.loggerDict.items():
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if isinstance(item, logging.Logger):
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item.addHandler(handler)
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return logger
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class StreamToLogger(object):
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"""
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Fake file-like stream object that redirects writes to a logger instance.
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"""
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def __init__(self, logger, log_level=logging.INFO):
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self.terminal = sys.stdout
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self.logger = logger
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self.log_level = log_level
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self.linebuf = ''
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def __getattr__(self, attr):
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return getattr(self.terminal, attr)
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def write(self, buf):
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temp_linebuf = self.linebuf + buf
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self.linebuf = ''
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for line in temp_linebuf.splitlines(True):
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# From the io.TextIOWrapper docs:
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# On output, if newline is None, any '\n' characters written
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# are translated to the system default line separator.
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# By default sys.stdout.write() expects '\n' newlines and then
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# translates them so this is still cross platform.
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if line[-1] == '\n':
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self.logger.log(self.log_level, line.rstrip())
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else:
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self.linebuf += line
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def flush(self):
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if self.linebuf != '':
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self.logger.log(self.log_level, self.linebuf.rstrip())
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self.linebuf = ''
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def maybe_zero_3(param, ignore_status=False, name=None):
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from deepspeed import zero
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from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
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if hasattr(param, "ds_id"):
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if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
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if not ignore_status:
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logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}")
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with zero.GatheredParameters([param]):
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param = param.data.detach().cpu().clone()
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else:
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param = param.detach().cpu().clone()
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return param
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# Borrowed from peft.utils.get_peft_model_state_dict
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def get_peft_state_maybe_zero_3(named_params, bias):
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if bias == "none":
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to_return = {k: t for k, t in named_params if "lora_" in k}
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elif bias == "all":
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to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
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elif bias == "lora_only":
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to_return = {}
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maybe_lora_bias = {}
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lora_bias_names = set()
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for k, t in named_params:
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if "lora_" in k:
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to_return[k] = t
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bias_name = k.split("lora_")[0] + "bias"
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lora_bias_names.add(bias_name)
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elif "bias" in k:
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maybe_lora_bias[k] = t
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for k, t in maybe_lora_bias:
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if bias_name in lora_bias_names:
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to_return[bias_name] = t
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else:
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raise NotImplementedError
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to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()}
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return to_return
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def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True):
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to_return = {k: t for k, t in named_params if "lora_" not in k}
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if require_grad_only:
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to_return = {k: t for k, t in to_return.items() if t.requires_grad}
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to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
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return to_return
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def get_speech_projector_state_maybe_zero_3(named_params, keys_to_match):
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to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
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to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
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return to_return
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def lengths_to_padding_mask(lens):
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bsz, max_lens = lens.size(0), torch.max(lens).item()
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mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
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mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
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return mask
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def lengths_to_mask(lens):
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return ~lengths_to_padding_mask(lens)
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def disable_torch_init():
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"""
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Disable the redundant torch default initialization to accelerate model creation.
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"""
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import torch
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setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
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setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
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def get_model_name_from_path(model_path):
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model_path = model_path.strip("/")
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model_paths = model_path.split("/")
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if model_paths[-1].startswith('checkpoint-'):
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return model_paths[-2] + "_" + model_paths[-1]
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else:
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return model_paths[-1]
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def violates_moderation(text):
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"""
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Check whether the text violates OpenAI moderation API.
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"""
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url = "https://api.openai.com/v1/moderations"
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headers = {"Content-Type": "application/json",
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"Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]}
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text = text.replace("\n", "")
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data = "{" + '"input": ' + f'"{text}"' + "}"
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data = data.encode("utf-8")
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try:
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ret = requests.post(url, headers=headers, data=data, timeout=5)
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flagged = ret.json()["results"][0]["flagged"]
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except requests.exceptions.RequestException as e:
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flagged = False
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except KeyError as e:
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flagged = False
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return flagged
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def pretty_print_semaphore(semaphore):
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if semaphore is None:
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return "None"
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return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})" |