chatlearn/models/deepspeed/deepspeed_utils.py

# Copyright 2024 Alibaba Group Holding Limited. All Rights Reserved.
#
# 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.
# ==============================================================================
"""DeepSpeed utils"""

import os
import torch
import deepspeed
from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
from transformers import AutoTokenizer


DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "</s>"
DEFAULT_BOS_TOKEN = "<s>"
DEFAULT_UNK_TOKEN = "<unk>"


def get_tokenizer(pretrain_path, model=None, padding_side="left", use_fast=True):
    tokenizer = AutoTokenizer.from_pretrained(pretrain_path, trust_remote_code=True, use_fast=use_fast)
    tokenizer.padding_side = padding_side
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
        tokenizer.pad_token_id = tokenizer.eos_token_id
        if model is not None:
            model.config.pad_token_id = tokenizer.pad_token_id

    return tokenizer


def get_eval_ds_config(
    offload,
    stage=0,
    bf16=True,
):
    zero_opt_dict = {
        "stage": stage,
        "stage3_param_persistence_threshold": "auto",
        "offload_param": {
            "device": "cpu" if offload else "none",
            "pin_memory": True,
        },
    }
    return {
        "steps_per_print": 100,
        "zero_optimization": zero_opt_dict,
        "bf16": {
            "enabled": bf16,
        },
        "gradient_clipping": 1.0,
        "prescale_gradients": False,
        "wall_clock_breakdown": False,
    }

def get_train_ds_config(
    offload,
    adam_offload=True,
    stage=2,
    bf16=True,
    max_norm=1.0,
    zpg=8,
    grad_accum_dtype=None,
    disable_trace_cache=False,
):
    device = "cpu" if offload else "none"
    zero_opt_dict = {
        "stage": stage,
        "offload_param": {"device": device},
        "offload_optimizer": {
            "device": "cpu" if adam_offload else "none",
            "pin_memory": True,
        },
        "sub_group_size": "auto",
        "stage3_max_live_parameters": "auto",
        "stage3_max_reuse_distance": "auto",
        "stage3_param_persistence_threshold": "auto",
        "stage3_prefetch_bucket_size": "auto",
        "reduce_bucket_size": "auto",
        # ZeRO++
        "zero_hpz_partition_size": zpg,
        "zero_quantized_weights": False,
        "zero_quantized_gradients": False,
    }
    if disable_trace_cache:
        zero_opt_dict["stage3_prefetch_bucket_size"] = 0
        zero_opt_dict["stage3_max_live_parameters"] = 0
        zero_opt_dict["stage3_max_reuse_distance"] = 0

    return {
        "steps_per_print": 100,
        "zero_optimization": zero_opt_dict,
        "bf16": {
            "enabled": bf16,
        },
        "gradient_clipping": max_norm,
        "prescale_gradients": False,
        "wall_clock_breakdown": False,
        "data_types": {"grad_accum_dtype": grad_accum_dtype if grad_accum_dtype else "fp32"},
    }

def get_optimizer_grouped_parameters(
    model,
    weight_decay,
    no_decay_name_list=("bias", "layer_norm.weight", "layernorm.weight", "norm.weight", "ln_f.weight"),
):
    optimizer_grouped_parameters = [
        {
            "params": [
                p
                for n, p in model.named_parameters()
                if (not any(nd in n for nd in no_decay_name_list) and p.requires_grad)
            ],
            "weight_decay": weight_decay,
        },
        {
            "params": [
                p
                for n, p in model.named_parameters()
                if (any(nd in n for nd in no_decay_name_list) and p.requires_grad)
            ],
            "weight_decay": 0.0,
        },
    ]
    return optimizer_grouped_parameters

def create_optimizer(model, adam_offload, **kwargs):
    # Optimizer
    AdamOptimizer = DeepSpeedCPUAdam if adam_offload else FusedAdam
    optim_params = get_optimizer_grouped_parameters(model, kwargs["weight_decay"])
    optim = AdamOptimizer(optim_params, **kwargs)
    return optim

def save_hf_format(model, tokenizer, output_dir, sub_folder=""):
    # used to save huggingface format, so we can use it for hf.from_pretrained
    model_to_save = model.module if hasattr(model, 'module') else model
    CONFIG_NAME = "config.json"
    WEIGHTS_NAME = "pytorch_model.bin"
    output_dir = os.path.join(output_dir, sub_folder)
    os.makedirs(output_dir, exist_ok=True)
    output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
    output_config_file = os.path.join(output_dir, CONFIG_NAME)
    save_dict = model_to_save.state_dict()
    for key in list(save_dict.keys()):
        if "lora" in key:
            del save_dict[key]
    torch.save(save_dict, output_model_file)
    model_to_save.config.to_json_file(output_config_file)
    tokenizer.save_vocabulary(output_dir)

def _z3_params_to_fetch(param_list):
    return [
        p for p in param_list
        if hasattr(p, 'ds_id') and p.ds_status == ZeroParamStatus.NOT_AVAILABLE
    ]


def save_zero_three_model(model_ema, global_rank, save_dir, zero_stage=0):
    zero_stage_3 = (zero_stage == 3)
    os.makedirs(save_dir, exist_ok=True)
    WEIGHTS_NAME = "pytorch_model.bin"
    output_model_file = os.path.join(save_dir, WEIGHTS_NAME)

    model_to_save = model_ema.module if hasattr(model_ema,
                                                'module') else model_ema
    if not zero_stage_3:
        if global_rank == 0:
            torch.save(model_to_save.state_dict(), output_model_file)
    else:
        output_state_dict = {}
        for k, v in model_to_save.named_parameters():

            if hasattr(v, 'ds_id'):
                with deepspeed.zero.GatheredParameters(_z3_params_to_fetch([v
                                                                            ]),
                                                       enabled=zero_stage_3):
                    v_p = v.data.cpu()
            else:
                v_p = v.cpu()
            if global_rank == 0 and "lora" not in k:
                output_state_dict[k] = v_p
        if global_rank == 0:
            torch.save(output_state_dict, output_model_file)
        del output_state_dict