485 lines
19 KiB
Python
485 lines
19 KiB
Python
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# coding=utf-8
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# Copyright 2022 The OpenBMB team.
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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 inspect
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import json
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import math
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import os
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import re
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import sys
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import time
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from typing import Any
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from typing import Dict
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from typing import List
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from typing import Union
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import bmtrain as bmt
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import torch
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sys.path.insert(0, "/home/wangshuo1/code/9G-Train")
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from cpm.arguments import get_args
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from cpm.cpm9g.models import CPM9G
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from cpm.cpm9g.models import CPM9GConfig
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from cpm.cpm9g.tokenizers import CPM9GTokenizer
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from cpm.cpm9g.training_tasks import FinetuneDataset
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from cpm.utils import allgather_objects
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from cpm.utils import logger
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import shutil
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def get_tokenizer(args):
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tokenizer = CPM9GTokenizer(path=args.vocab)
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return tokenizer
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def get_model(args):
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config = CPM9GConfig.from_json_file(args.model_config)
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if args.flash == "none":
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config.use_flash_attn = False
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else:
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config.use_flash_attn = True
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if args.flash == "1d":
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config.flash_attn_mask_shape = "1d"
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else:
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config.flash_attn_mask_shape = "2d"
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if args.flash == "triton":
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config.flash_impl = "triton"
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elif args.flash == "cuda":
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config.flash_impl = "cuda"
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model = CPM9G(config)
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if args.load is not None:
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bmt.init_parameters(model)
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bmt.synchronize()
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bmt.print_rank("args.load is not None, start to load checkpoints" + args.load)
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bmt.load(model, args.load, strict=False)
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model_inspect = bmt.inspect.inspect_model(model, "*")
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bmt.print_rank(bmt.inspect.format_summary(model_inspect))
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else:
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bmt.print_rank("args.load is None, start to initialize parameters")
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bmt.init_parameters(model)
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return model
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def get_optimizer(args, model):
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if args.offload:
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optimizer = bmt.optim.AdamOffloadOptimizer(
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model.parameters(), betas=(0.9, 0.95), weight_decay=args.weight_decay
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)
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else:
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optimizer = bmt.optim.AdamOptimizer(model.parameters(), betas=(0.9, 0.95), weight_decay=args.weight_decay)
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if args.load is not None and args.load_grad:
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start = time.time()
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print(
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sum(
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[
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1
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if i.find(".opt") != -1 and i.find("-{}.rank".format(args.start_step % (args.save_iters * 5))) != -1
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else 0
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for i in os.listdir(args.save)
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]
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)
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)
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if (
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sum(
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[
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1
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if i.find(".opt") != -1 and i.find("-{}.rank".format(args.start_step % (args.save_iters * 5))) != -1
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else 0
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for i in os.listdir(args.save)
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]
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)
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== bmt.world_size()
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):
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file_name = os.path.join(
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args.save,
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args.save_name + "-{}.rank-{}.opt".format(args.start_step % (args.save_iters * 5), bmt.rank()),
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)
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print(file_name)
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if os.path.exists(file_name):
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print("start to load grad ckpt {}".format(file_name))
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states = torch.load(file_name)
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optimizer.load_state_dict(states)
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logger.info("load grad in {:.2f}s".format(time.time() - start))
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return optimizer
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class Cosine(bmt.lr_scheduler.WarmupLRScheduler):
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r"""
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After a warmup period during which learning rate increases linearly between 0 and the start_lr,
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The decay period performs :math:`\text{lr}=\text{start_lr}\times \dfrac{1+\cos \left( \pi \cdot \dfrac{\text{num_iter}-\text{warmup_iter}}{\text{end_iter}-\text{warmup_iter}}\right)}{2}`
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"""
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def get_lr_warmup(self, num_iter) -> float:
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return self.start_lr * num_iter / self.warmup_iter
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def get_lr_decay(self, num_iter) -> float:
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progress = (num_iter - self.warmup_iter) / max(1, (self.end_iter - self.warmup_iter))
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return max(self.start_lr * 0.1, self.start_lr * (0.1 + 0.45 * (1.0 + math.cos(progress * math.pi))))
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def get_learning_rate_scheduler(args, optimizer):
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if args.lr_decay_iters is None:
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args.lr_decay_iters = args.train_iters
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# lr_scheduler = bmt.lr_scheduler.Noam(
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lr_scheduler = Cosine(
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optimizer,
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start_lr=args.lr,
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warmup_iter=args.warmup_iters,
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end_iter=args.lr_decay_iters,
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num_iter=args.start_step,
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)
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return lr_scheduler
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def setup_model_and_optimizer(args):
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start = time.time()
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model = get_model(args)
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logger.info("load model in {:.2f}s".format(time.time() - start))
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start = time.time()
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tokenizer = get_tokenizer(args)
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bmt.synchronize()
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logger.info("load tokenizer in {:.2f}s".format(time.time() - start))
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start = time.time()
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optimizer = get_optimizer(args, model)
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lr_scheduler = get_learning_rate_scheduler(args, optimizer)
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bmt.synchronize()
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logger.info("load lr_scheduler in {:.2f}s".format(time.time() - start))
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return tokenizer, model, optimizer, lr_scheduler
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def initialize():
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args = get_args(finetune=True)
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# hack
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if "checkpointing" in inspect.signature(bmt.init_distributed).parameters:
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bmt.init_distributed(checkpointing=False, seed=args.seed)
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else:
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bmt.init_distributed(seed=args.seed)
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if args.save is not None:
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os.makedirs(args.save, exist_ok=True)
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# if args.load is not None:
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# if args.start_step == 0:
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# args.start_step = (int)(re.search("(\d+).pt", args.load)[1])
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return args
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def see_memory(detail=False):
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if detail:
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res = torch.cuda.memory_summary()
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else:
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res = (
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round(torch.cuda.memory_allocated() / (1024 * 1024 * 1024), 2),
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round(torch.cuda.max_memory_allocated() / (1024 * 1024 * 1024), 2),
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)
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torch.cuda.reset_peak_memory_stats()
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return res
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def add_mem_time(info, mem_usage, tim_usage):
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torch.cuda.synchronize()
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mem_usage[info] = see_memory()
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tim_usage[info] = time.time()
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return mem_usage, tim_usage
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class LossSpikeDetector:
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def __init__(self, log_path: str) -> None:
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self._last_loss: Dict[str, float] = {}
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self._last_data: List[Any] = [None]
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self._log_path = log_path
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def update_data(self, data: Any):
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self._last_data.append(data)
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if len(self._last_data) > 2:
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self._last_data = self._last_data[-2:]
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def update_loss(self, iteration: int, loss_map: Dict[str, float]):
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loss_spike_result = []
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for task, loss in loss_map.items():
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if task in self._last_loss:
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if loss > self._last_loss[task] * 3:
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# loss spike!
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loss_spike_result.append(
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{
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"prev": self._last_loss[task],
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"curr": loss,
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"task": task,
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}
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)
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self._last_loss[task] = float(loss)
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if len(loss_spike_result) > 0:
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self._write_log(iteration, self._last_data[-1], loss_spike_result)
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def _write_log(self, iteration: int, data: Any, result: List[Dict[str, Any]]):
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return
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with open(self._log_path, "a", encoding="utf-8") as fp:
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fp.write("=" * 20)
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fp.write("\nloss spike at {}\n".format(iteration))
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fp.write("{}\n".format(json.dumps(result, indent=4, ensure_ascii=False)))
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fp.write("data: \n")
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for d in data:
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fp.write("{}\n".format(json.dumps(d, indent=4, ensure_ascii=False)))
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fp.write("\n\n")
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def finetune(
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args,
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bin_file: str,
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tokenizer: CPM9GTokenizer,
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model: CPM9G,
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optimizer,
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lr_scheduler: bmt.lr_scheduler.WarmupLRScheduler,
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):
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average_time = bmt.utils.AverageRecorder()
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loss_func = bmt.loss.FusedCrossEntropy(ignore_index=-100)
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optim_manager = bmt.optim.OptimManager(loss_scale=args.loss_scale, loss_scale_steps=args.loss_scale_steps, loss_scale_factor=2, max_loss_scale=args.max_loss_scale, min_loss_scale=args.min_loss_scale,)
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optim_manager.add_optimizer(optimizer, lr_scheduler)
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if args.tensorboard is not None and bmt.rank() == 0:
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import distutils.version # noqa: F401
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from tensorboardX import SummaryWriter
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if not os.path.exists(args.tensorboard):
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os.makedirs(args.tensorboard)
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writer = SummaryWriter(log_dir=args.tensorboard)
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global_token_pass = 0.0
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global_world_size = bmt.world_size()
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for epoch in range(args.epoch):
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epoch = epoch + 1
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last_data = None
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dataloader = FinetuneDataset(
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bin_file, args.batch_size, args.max_length, tokenizer, unpad=(args.flash == "cuda"), task_name="task", drop_last=True
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)
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optim_manager.zero_grad()
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for iteration, data in enumerate(dataloader):
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iteration = iteration + 1
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skip_this_batch = False
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if data is None:
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if last_data is None:
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raise RuntimeError(
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"Dataset is too small, please use a smaller batch size or sequence length!"
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)
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data = last_data # use last data
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skip_this_batch = True
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else:
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last_data = data
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assert data["inputs"].shape[0] == args.batch_size
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input_ids = torch.from_numpy(data["inputs"]).cuda().to(torch.int32)
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input_length = torch.from_numpy(data["length"]).cuda().to(torch.int32)
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targets = torch.from_numpy(data["target"]).cuda().long()
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# bmt.print_rank(input_ids[0].tolist())
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# bmt.print_rank(targets[0].tolist())
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# bmt.print_rank(data["spans"].tolist())
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# bmt.print_rank(tokenizer.decode(input_ids[0]))
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# bmt.print_rank(tokenizer.path)
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# bmt.synchronize()
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# exit()
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task_ids = torch.from_numpy(data["task_ids"]).cuda().to(torch.int32)
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task_names = data["task_names"]
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if args.flash == "cuda":
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cu_seqlens = torch.from_numpy(data["cu_seqlens"]).cuda().to(torch.int32)
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max_seqlen = data["max_seqlen"]
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position_ids = torch.from_numpy(data["position_ids"]).cuda().to(torch.int32)
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else:
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input_context = torch.zeros_like(input_ids).cuda().bool()
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input_span = torch.from_numpy(data["spans"]).cuda().to(torch.int32)
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# ===========
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# optim_manager.zero_grad()
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# torch.cuda.empty_cache()
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mem_usage = {}
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tim_usage = {}
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mem_usage, tim_usage = add_mem_time("init", mem_usage, tim_usage)
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# ===========
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if args.flash == "cuda":
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logits, _ = model(
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input_ids,
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cu_seqlens=cu_seqlens,
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max_seqlen=max_seqlen,
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position_ids=position_ids,
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)
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else:
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logits, _ = model(
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input_ids,
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input_length,
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input_context,
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input_span,
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)
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mem_usage, tim_usage = add_mem_time("forward_1", mem_usage, tim_usage)
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loss = loss_func(logits.view(-1, logits.size(-1)), targets.view(-1))
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if skip_this_batch:
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loss = loss * 0
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mem_usage, tim_usage = add_mem_time("forward", mem_usage, tim_usage)
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# ===========
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optim_manager.backward(loss)
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mem_usage, tim_usage = add_mem_time("backward", mem_usage, tim_usage)
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# ===========
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if iteration % args.gradient_accumulation_steps == 0:
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grad_norm = optim_manager.clip_grad_norm(optimizer.param_groups, args.clip_grad, norm_type=2)
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optim_manager.step()
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mem_usage, tim_usage = add_mem_time("optim", mem_usage, tim_usage)
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optim_manager.zero_grad()
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else:
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grad_norm = None
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mem_usage, tim_usage = add_mem_time("optim", mem_usage, tim_usage)
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# ==========
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iter_time = tim_usage["optim"] - tim_usage["init"]
|
||
|
|
average_time.record(iter_time)
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
task_num = len(task_names)
|
||
|
|
targets_tmp = targets.expand(task_num, -1, -1)
|
||
|
|
task = torch.arange(task_num, dtype=torch.long, device="cuda")[:, None, None]
|
||
|
|
targets_tmp = torch.where(
|
||
|
|
task_ids == task,
|
||
|
|
targets_tmp,
|
||
|
|
torch.scalar_tensor(-100, dtype=torch.long, device="cuda"),
|
||
|
|
)
|
||
|
|
|
||
|
|
task_loss_map: Dict[str, float] = {}
|
||
|
|
if not skip_this_batch:
|
||
|
|
for i in range(task_num):
|
||
|
|
task_loss = loss_func(logits.view(-1, logits.size(-1)), targets_tmp[i, :].view(-1))
|
||
|
|
task_loss_map[task_names[i]] = task_loss.item()
|
||
|
|
gatherd_task_loss_map: List[Dict[str, float]] = allgather_objects(task_loss_map)
|
||
|
|
|
||
|
|
global_task_loss_map: Dict[str, Union[List[float], float]] = {}
|
||
|
|
for local_task_loss_map in gatherd_task_loss_map:
|
||
|
|
for task_name, task_loss in local_task_loss_map.items():
|
||
|
|
if task_name not in global_task_loss_map:
|
||
|
|
global_task_loss_map[task_name] = []
|
||
|
|
global_task_loss_map[task_name].append(task_loss)
|
||
|
|
|
||
|
|
task_loss_map = {}
|
||
|
|
for task_name in sorted(list(global_task_loss_map.keys())):
|
||
|
|
avg_loss = sum(global_task_loss_map[task_name]) / len(global_task_loss_map[task_name])
|
||
|
|
task_loss_map[task_name] = avg_loss
|
||
|
|
|
||
|
|
local_total_rate = torch.Tensor([input_length.float().mean() / args.max_length]).cuda()
|
||
|
|
local_total_rate = bmt.sum_loss(local_total_rate).item()
|
||
|
|
global_token_pass += global_world_size * local_total_rate * args.max_length * args.batch_size
|
||
|
|
avg_time = average_time.value
|
||
|
|
|
||
|
|
train_info = {
|
||
|
|
"time": tim_usage["init"],
|
||
|
|
"epoch": epoch,
|
||
|
|
"iteration": iteration,
|
||
|
|
"loss": task_loss_map[args.task_name],
|
||
|
|
"lr": lr_scheduler.current_lr,
|
||
|
|
"lr_scale": int(optim_manager.loss_scale),
|
||
|
|
"time_usage": tim_usage,
|
||
|
|
"mem_usage": mem_usage,
|
||
|
|
"avg_time": avg_time,
|
||
|
|
"token_max": local_total_rate,
|
||
|
|
"token_pass": global_token_pass,
|
||
|
|
"throughout": args.max_length * args.batch_size * local_total_rate / avg_time,
|
||
|
|
"grad_norm": 0 if grad_norm == None else grad_norm.item(),
|
||
|
|
"mask_max": ((targets >= 0).sum(-1).float().mean() / args.max_length).item(),
|
||
|
|
"num_gpus": global_world_size,
|
||
|
|
"task_loss": task_loss_map,
|
||
|
|
}
|
||
|
|
# bmt.print_rank(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
|
||
|
|
bmt.print_rank(
|
||
|
|
(
|
||
|
|
"| Epoch: {:3d} | Iter: {:6d}/{:6d} | loss: {:.4f} | lr: {:.6e} | scale: {:10.0f} | time: {:.1f} |"
|
||
|
|
+ " token/max: {:.3f} | mask/max: {:.3f} | grad_norm: {:.3f}"
|
||
|
|
).format(
|
||
|
|
epoch,
|
||
|
|
iteration,
|
||
|
|
args.train_iters,
|
||
|
|
task_loss_map[args.task_name],
|
||
|
|
lr_scheduler.current_lr,
|
||
|
|
int(optim_manager.loss_scale),
|
||
|
|
avg_time,
|
||
|
|
input_length.float().mean() / args.max_length,
|
||
|
|
(targets >= 0).sum(-1).float().mean() / args.max_length,
|
||
|
|
0 if grad_norm == None else grad_norm,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
bmt.print_rank(
|
||
|
|
"| "
|
||
|
|
+ " | ".join(["{}: {:.4f}".format(task_name, loss) for task_name, loss in task_loss_map.items()])
|
||
|
|
+ " |"
|
||
|
|
)
|
||
|
|
if iteration % args.inspect_iters == 0:
|
||
|
|
model_inspect = bmt.inspect.inspect_model(model, "*")
|
||
|
|
bmt.print_rank(bmt.inspect.format_summary(model_inspect))
|
||
|
|
train_info["model_inspect"] = model_inspect
|
||
|
|
|
||
|
|
# save_folder_name = f"{args.save}{epoch}{iteration}"
|
||
|
|
# model_fname = os.path.join(save_folder_name, f"{args.save_name}-iter-{iteration}.pt")
|
||
|
|
# os.makedirs(os.path.dirname(model_fname), exist_ok=True)
|
||
|
|
# if bmt.rank() == 0:
|
||
|
|
# shutil.copy(args.model_config, os.path.join(save_folder_name, "config.json"))
|
||
|
|
# shutil.copy(args.vocab, os.path.join(save_folder_name, "vocabs.txt"))
|
||
|
|
|
||
|
|
if args.tensorboard is not None and bmt.rank() == 0:
|
||
|
|
writer.add_scalar(f"Loss/train/{epoch}", task_loss_map[args.task_name], iteration)
|
||
|
|
writer.add_scalar(f"Optimizer/lr/{epoch}", lr_scheduler.current_lr, iteration)
|
||
|
|
writer.add_scalar(f"Optimizer/scale/{epoch}", optim_manager.loss_scale, iteration)
|
||
|
|
writer.add_scalar(f"Optimizer/grad_norm/{epoch}", 0 if grad_norm == None else grad_norm.item(), iteration)
|
||
|
|
|
||
|
|
# if iteration % 10 == 0:
|
||
|
|
# save_folder_name = f"{args.save}{epoch}"
|
||
|
|
# model_fname = os.path.join(save_folder_name, f"{args.save_name}-epoch-{epoch}.pt")
|
||
|
|
# os.makedirs(os.path.dirname(model_fname), exist_ok=True)
|
||
|
|
# bmt.save(model, model_fname)
|
||
|
|
# if bmt.rank() == 0:
|
||
|
|
# shutil.copy(args.model_config, os.path.join(save_folder_name, "config.json"))
|
||
|
|
# shutil.copy(args.vocab, os.path.join(save_folder_name, "vocabs.txt"))
|
||
|
|
|
||
|
|
save_folder_name = f"{args.save}-epoch-{epoch}"
|
||
|
|
model_fname = os.path.join(save_folder_name, f"{args.save_name}-epoch-{epoch}.pt")
|
||
|
|
os.makedirs(os.path.dirname(model_fname), exist_ok=True)
|
||
|
|
bmt.save(model, model_fname)
|
||
|
|
if bmt.rank() == 0:
|
||
|
|
shutil.copy(args.model_config, os.path.join(save_folder_name, "config.json"))
|
||
|
|
shutil.copy(args.vocab, os.path.join(save_folder_name, "vocabs.txt"))
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
args = initialize()
|
||
|
|
# To Be Specified
|
||
|
|
bin_file = args.dataset
|
||
|
|
bmt.print_rank(f"dataset: {bin_file}")
|
||
|
|
|
||
|
|
tokenizer = get_tokenizer(args)
|
||
|
|
dataloader = FinetuneDataset(
|
||
|
|
bin_file, args.batch_size, args.max_length, tokenizer, unpad=(args.flash == "cuda"), task_name="task", drop_last=True
|
||
|
|
)
|
||
|
|
bmt.print_rank(f"#batch: {len(dataloader)}")
|
||
|
|
total_steps = len(dataloader) * args.epoch
|
||
|
|
|
||
|
|
setattr(args, 'train_iters', int(total_steps))
|
||
|
|
setattr(args, 'warmup_iters', max(int(total_steps*0.02), args.warmup_iters))
|
||
|
|
bmt.print_rank(json.dumps(vars(args), indent=2, sort_keys=True))
|
||
|
|
tokenizer, model, optimizer, lr_scheduler = setup_model_and_optimizer(args)
|
||
|
|
|
||
|
|
bmt.print_rank("finish loading")
|
||
|
|
finetune(args, bin_file, tokenizer, model, optimizer, lr_scheduler)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|