support loading local models

applications/DeepSpeed-Chat/dschat/utils/ds_utils.py

@@ -22,7 +22,10 @@ def get_train_ds_config(offload,
                         enable_tensorboard=False,
                         enable_mixed_precision_lora=False,
                         tb_path="",
-                        tb_name=""):
+                        tb_name="",
+                        offload_optimizer_config=None,
+                        offload_param_config=None,
+                        aio_config=None):
 
     device = "cpu" if offload else "none"
     if dtype == "fp16":
@@ -45,12 +48,16 @@ def get_train_ds_config(offload,
         "stage3_prefetch_bucket_size": 3e7,
         "memory_efficient_linear": False
     }
+    if offload_optimizer_config:
+        zero_opt_dict["offload_optimizer"].update(offload_optimizer_config)
+    if offload_param_config:
+        zero_opt_dict["offload_param"].update(offload_param_config)
     if enable_mixed_precision_lora:
         zero_opt_dict["zero_quantized_nontrainable_weights"] = True
         if dist.get_world_size() != get_accelerator().device_count():
             zero_opt_dict["zero_hpz_partition_size"] = get_accelerator(
             ).device_count()
-    return {
+    config = {
         "train_batch_size": GLOBAL_BATCH_SIZE,
         "train_micro_batch_size_per_gpu": MICRO_BATCH_SIZE,
         "steps_per_print": 10,
@@ -73,6 +80,9 @@ def get_train_ds_config(offload,
             "job_name": f"{tb_name}_tensorboard"
         }
     }
+    if aio_config:
+        config["aio"] = aio_config
+    return config
 
 
 def get_eval_ds_config(offload, dtype, stage=0):

applications/DeepSpeed-Chat/dschat/utils/utils.py

@@ -74,8 +74,7 @@ def get(self):
 
 def get_tokenizer(model_name_or_path, fast_tokenizer=True):
     if "llama" in model_name_or_path:
-        from transformers.models.llama import LlamaTokenizer
-        tokenizer = LlamaTokenizer.from_pretrained(
+        tokenizer = AutoTokenizer.from_pretrained(
             model_name_or_path, fast_tokenizer=fast_tokenizer)
         if tokenizer.pad_token is None:
             # assert tokenizer.eos_token is not None
@@ -94,16 +93,13 @@ def get_tokenizer(model_name_or_path, fast_tokenizer=True):
 def load_hf_tokenizer(model_name_or_path,
                       fast_tokenizer=True,
                       add_special_tokens=None):
-    if os.path.exists(model_name_or_path):
-        # Locally tokenizer loading has some issue, so we need to force download
-        model_json = os.path.join(model_name_or_path, "config.json")
-        if os.path.exists(model_json):
-            model_json_file = json.load(open(model_json))
-            model_name = model_json_file.get("_name_or_path",
-                                             model_name_or_path)
-            tokenizer = get_tokenizer(model_name,
-                                      fast_tokenizer=fast_tokenizer)
+    # Support loading from local path directly
+    if os.path.exists(model_name_or_path) and os.path.isdir(model_name_or_path):
+        # Directly load tokenizer from local path
+        tokenizer = get_tokenizer(model_name_or_path,
+                                  fast_tokenizer=fast_tokenizer)
     else:
+        # Load from HuggingFace Hub or use original logic
         tokenizer = get_tokenizer(model_name_or_path,
                                   fast_tokenizer=fast_tokenizer)
 

applications/DeepSpeed-Chat/training/step1_supervised_finetuning/main.py

@@ -5,6 +5,8 @@
 # DeepSpeed Team
 import argparse
 import math
+import os
+from pprint import pformat
 
 import torch
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
@@ -29,6 +31,18 @@
 from dschat.utils.perf import print_throughput
 
 
+def str2bool(value):
+    if isinstance(value, bool):
+        return value
+    lowered = value.lower()
+    if lowered in ("yes", "true", "t", "1"):
+        return True
+    if lowered in ("no", "false", "f", "0"):
+        return False
+    raise argparse.ArgumentTypeError(
+        f"Boolean value expected, got `{value}`.")
+
+
 def parse_args():
     parser = argparse.ArgumentParser(
         description=
@@ -145,6 +159,80 @@ def parse_args():
     parser.add_argument('--offload',
                         action='store_true',
                         help='Enable ZeRO Offload techniques.')
+    parser.add_argument('--offload_optimizer_device',
+                        type=str,
+                        choices=['cpu', 'nvme'],
+                        default=None,
+                        help='Device to use for ZeRO optimizer state offload.')
+    parser.add_argument('--offload_optimizer_nvme_path',
+                        type=str,
+                        default=None,
+                        help='NVMe path used when offloading optimizer states to nvme.')
+    parser.add_argument('--offload_optimizer_pin_memory',
+                        type=str2bool,
+                        default=None,
+                        help='Whether to pin optimizer offload memory (true|false).')
+    parser.add_argument('--offload_optimizer_ratio',
+                        type=float,
+                        default=None,
+                        help='Ratio of optimizer state to keep on device when offloading.')
+    parser.add_argument('--offload_optimizer_buffer_count',
+                        type=int,
+                        default=None,
+                        help='Number of optimizer offload buffers.')
+    parser.add_argument('--offload_optimizer_fast_init',
+                        type=str2bool,
+                        default=None,
+                        help='Use fast init for optimizer offload buffers (true|false).')
+    parser.add_argument('--offload_param_device',
+                        type=str,
+                        choices=['cpu', 'nvme'],
+                        default=None,
+                        help='Device to use for ZeRO parameter offload.')
+    parser.add_argument('--offload_param_nvme_path',
+                        type=str,
+                        default=None,
+                        help='NVMe path used when offloading parameters to nvme.')
+    parser.add_argument('--offload_param_pin_memory',
+                        type=str2bool,
+                        default=None,
+                        help='Whether to pin parameter offload memory (true|false).')
+    parser.add_argument('--offload_param_buffer_size',
+                        type=int,
+                        default=None,
+                        help='Parameter offload buffer size (number of elements). Increase if embedding layer is larger than the default.')
+    parser.add_argument('--offload_param_buffer_count',
+                        type=int,
+                        default=None,
+                        help='Number of parameter offload buffers.')
+    parser.add_argument('--offload_param_max_in_cpu',
+                        type=float,
+                        default=None,
+                        help='Maximum number of parameters to keep in CPU memory during offload.')
+    parser.add_argument('--aio_block_size',
+                        type=int,
+                        default=1048576,
+                        help='AIO block size for NVMe offload (bytes).')
+    parser.add_argument('--aio_queue_depth',
+                        type=int,
+                        default=8,
+                        help='AIO queue depth for NVMe offload.')
+    parser.add_argument('--aio_intra_op_parallelism',
+                        type=int,
+                        default=1,
+                        help='AIO intra_op_parallelism for NVMe offload.')
+    parser.add_argument('--aio_single_submit',
+                        type=str2bool,
+                        default=False,
+                        help='AIO single_submit flag.')
+    parser.add_argument('--aio_overlap_events',
+                        type=str2bool,
+                        default=True,
+                        help='AIO overlap_events flag.')
+    parser.add_argument('--aio_use_gds',
+                        type=str2bool,
+                        default=False,
+                        help='AIO use_gds flag.')
     parser.add_argument('--dtype',
                         type=str,
                         default='fp16',
@@ -222,18 +310,91 @@ def main():
 
     args.global_rank = torch.distributed.get_rank()
 
+    # 根据 local_rank 动态设置 NVMe 路径
+    # 如果命令行参数中已经指定了路径，则使用命令行参数；否则根据 local_rank 设置
+    local_rank = args.local_rank if args.local_rank != -1 else 0
+
+    # 支持通过环境变量配置 NVMe 路径列表（用冒号分隔）
+    # 例如：export NVME_PATHS="/mnt/deepspeed_nvme0:/mnt/deepspeed_nvme1"
+    nvme_paths_env = os.environ.get('NVME_PATHS', '')
+    if nvme_paths_env:
+        nvme_paths = [path.strip() for path in nvme_paths_env.split(':') if path.strip()]
+        if local_rank < len(nvme_paths):
+            default_nvme_path = nvme_paths[local_rank]
+        else:
+            default_nvme_path = nvme_paths[0] if nvme_paths else None
+    else:
+        # 默认映射：GPU 0 -> /mnt/deepspeed_nvme0, GPU 1 -> /mnt/deepspeed_nvme1, 以此类推
+        default_nvme_path = f"/mnt/deepspeed_nvme{local_rank}"
+
+    # 如果命令行参数中没有指定 optimizer nvme_path，则使用根据 local_rank 确定的路径
+    if args.offload_optimizer_nvme_path is None and default_nvme_path:
+        args.offload_optimizer_nvme_path = default_nvme_path
+        print_rank_0(f"Rank {args.global_rank} (local_rank {local_rank}) using optimizer NVMe path: {args.offload_optimizer_nvme_path}", args.global_rank)
+
+    # 如果命令行参数中没有指定 param nvme_path，则使用根据 local_rank 确定的路径
+    if args.offload_param_nvme_path is None and default_nvme_path:
+        args.offload_param_nvme_path = default_nvme_path
+        print_rank_0(f"Rank {args.global_rank} (local_rank {local_rank}) using param NVMe path: {args.offload_param_nvme_path}", args.global_rank)
+
+    offload_optimizer_overrides = {
+        "device": args.offload_optimizer_device,
+        "nvme_path": args.offload_optimizer_nvme_path,
+        "pin_memory": args.offload_optimizer_pin_memory,
+        "ratio": args.offload_optimizer_ratio,
+        "buffer_count": args.offload_optimizer_buffer_count,
+        "fast_init": args.offload_optimizer_fast_init
+    }
+    offload_optimizer_overrides = {
+        key: value
+        for key, value in offload_optimizer_overrides.items()
+        if value is not None
+    }
+    offload_param_overrides = {
+        "device": args.offload_param_device,
+        "nvme_path": args.offload_param_nvme_path,
+        "pin_memory": args.offload_param_pin_memory,
+        "buffer_size": args.offload_param_buffer_size,
+        "buffer_count": args.offload_param_buffer_count,
+        "max_in_cpu": args.offload_param_max_in_cpu
+    }
+    offload_param_overrides = {
+        key: value
+        for key, value in offload_param_overrides.items()
+        if value is not None
+    }
+    aio_config = {
+        "block_size": args.aio_block_size,
+        "queue_depth": args.aio_queue_depth,
+        "intra_op_parallelism": args.aio_intra_op_parallelism,
+        "single_submit": args.aio_single_submit,
+        "overlap_events": args.aio_overlap_events,
+        "use_gds": args.aio_use_gds,
+    }
     ds_config = get_train_ds_config(offload=args.offload,
                                     dtype=args.dtype,
                                     stage=args.zero_stage,
                                     enable_tensorboard=args.enable_tensorboard,
                                     tb_path=args.tensorboard_path,
-                                    tb_name="step1_model")
+                                    tb_name="step1_model",
+                                    offload_optimizer_config=(
+                                        offload_optimizer_overrides
+                                        if offload_optimizer_overrides else None),
+                                    offload_param_config=(
+                                        offload_param_overrides
+                                        if offload_param_overrides else None),
+                                    aio_config=aio_config)
     ds_config[
         'train_micro_batch_size_per_gpu'] = args.per_device_train_batch_size
     ds_config[
         'train_batch_size'] = args.per_device_train_batch_size * torch.distributed.get_world_size(
         ) * args.gradient_accumulation_steps
 
+
+    # It seems that ds_config is completed here, so we print configuration here
+    print_rank_0("***** DeepSpeed User Provided config *****", args.global_rank)
+    print_rank_0(pformat(ds_config), args.global_rank)
+
     # If passed along, set the training seed now.
     set_random_seed(args.seed)
 
@@ -245,6 +406,9 @@ def main():
                                   fast_tokenizer=True,
                                   add_special_tokens=additional_special_tokens)
 
+    print_rank_0("***** Tokenizer *****", args.global_rank)
+    print_rank_0(tokenizer, args.global_rank)
+
     model = create_hf_model(AutoModelForCausalLM,
                             args.model_name_or_path,
                             tokenizer,
@@ -264,6 +428,10 @@ def main():
             model = only_optimize_lora_parameters(model)
             model = make_model_gradient_checkpointing_compatible(model)
 
+    # Print full model architecture (rank 0 only to avoid log spam)
+    print_rank_0("***** Model architecture *****", args.global_rank)
+    print_rank_0(model, args.global_rank)
+
     # Prepare the data
     train_phase = 1
     train_dataset, eval_dataset = create_prompt_dataset(
@@ -319,6 +487,7 @@ def evaluation(model, eval_dataloader):
         model, args.weight_decay, args.lora_learning_rate)
 
     AdamOptimizer = DeepSpeedCPUAdam if args.offload else FusedAdam
+    print_rank_0(f"offload: {args.offload}", args.global_rank)
     optimizer = AdamOptimizer(optimizer_grouped_parameters,
                               lr=args.learning_rate,
                               betas=(0.9, 0.95))
@@ -348,8 +517,9 @@ def evaluation(model, eval_dataloader):
     print_rank_0(
         f"***** Evaluating perplexity, Epoch {0}/{args.num_train_epochs} *****",
         args.global_rank)
-    perplexity, eval_loss = evaluation(model, eval_dataloader)
-    print_rank_0(f"ppl: {perplexity}, loss: {eval_loss}", args.global_rank)
+    print_rank_0("Jump Evaluation", args.global_rank)
+    # perplexity, eval_loss = evaluation(model, eval_dataloader)
+    # print_rank_0(f"ppl: {perplexity}, loss: {eval_loss}", args.global_rank)
 
     for epoch in range(args.num_train_epochs):
         print_rank_0(
@@ -372,6 +542,11 @@ def evaluation(model, eval_dataloader):
             if torch.distributed.get_rank() == 0:
                 print_throughput(model.model, args, end - start,
                                  args.global_rank)
+
+            # return for debugging
+            if step > 5:
+                return 0
+
 
         # Evaluate perplexity on the validation set.
         print_rank_0(

applications/DeepSpeed-Chat/training/step1_supervised_finetuning/training_scripts/llama2/run_70b_nonsys.sh

@@ -0,0 +1,71 @@
+#!/bin/bash
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+source /home/ckr/miniconda3/etc/profile.d/conda.sh
+conda activate ds
+
+export http_proxy=http://127.0.0.1:7890
+export https_proxy=http://127.0.0.1:7890
+
+# DeepSpeed Team
+OUTPUT=$1
+ZERO_STAGE=$2
+if [ "$OUTPUT" == "" ]; then
+    OUTPUT=./output_result/test_output
+fi
+if [ "$ZERO_STAGE" == "" ]; then
+    ZERO_STAGE=3
+fi
+mkdir -p $OUTPUT
+
+# 设置 Hugging Face token（从环境变量读取，如果未设置则使用默认值）
+# 建议在 ~/.bashrc 或 ~/.zshrc 中设置：export HF_TOKEN="your_token_here"
+if [ -z "$HF_TOKEN" ]; then
+    echo "Warning: HF_TOKEN environment variable is not set. Please set it before running this script."
+    echo "You can set it by: export HF_TOKEN='your_token_here'"
+fi
+export HUGGING_FACE_HUB_TOKEN="${HF_TOKEN:-}"
+
+# 可选：如果网络连接有问题，可以设置镜像（取消下面的注释）
+export HF_ENDPOINT="https://hf-mirror.com"
+
+#    --offload_param_nvme_path /mnt/raid0 \
+CUDA_VISIBLE_DEVICES=0,1 deepspeed  --master_port=29603 main.py \
+    --aio_block_size=16777216 \
+    --offload \
+    --offload_optimizer_device nvme \
+    --offload_optimizer_nvme_path /mnt/raid0 \
+    --offload_optimizer_pin_memory true \
+    --offload_optimizer_ratio 0.3 \
+    --offload_optimizer_buffer_count 8 \
+    --offload_optimizer_fast_init false \
+    --offload_param_device nvme \
+    --offload_param_pin_memory true \
+    --offload_param_buffer_size 360349696 \
+    --offload_param_buffer_count 32 \
+    --offload_param_max_in_cpu 0 \
+    --aio_use_gds true \
+    --dtype bf16 \
+    --data_path Dahoas/rm-static \
+    --data_split 2,4,4 \
+    --model_name_or_path /home/ckr/LoRA/models/70B \
+    --per_device_train_batch_size 8 \
+    --per_device_eval_batch_size 8 \
+    --max_seq_len 512 \
+    --learning_rate 9.65e-6 \
+    --weight_decay 0. \
+    --num_train_epochs 4  \
+    --gradient_accumulation_steps 1 \
+    --lr_scheduler_type cosine \
+    --num_warmup_steps 0 \
+    --seed 1234 \
+    --gradient_checkpointing \
+    --zero_stage $ZERO_STAGE \
+    --deepspeed \
+    --lora_dim 128 \
+    --lora_module_name "layers." \
+    --data_output_path ./data \
+    --output_dir $OUTPUT \
+   &> $OUTPUT/training.log
+