添加reproduce功能, 开启后可实现训练loss完全复现, 少量降低训练性能

dcu_megatron/adaptor/megatron_adaptor.py

@@ -201,6 +201,7 @@ class CoreAdaptation(MegatronAdaptationABC):
         from ..training.initialize import _initialize_distributed
         from ..training.initialize import _compile_dependencies
         from ..training.training import train
+        from ..training.initialize import _set_random_seed
 
         MegatronAdaptation.register('megatron.training.tokenizer.tokenizer.build_tokenizer',
                                     build_tokenizer)
@@ -208,6 +209,8 @@ class CoreAdaptation(MegatronAdaptationABC):
                                     _initialize_distributed)
         MegatronAdaptation.register('megatron.training.initialize._compile_dependencies',
                                     _compile_dependencies)
+        MegatronAdaptation.register('megatron.training.initialize._set_random_seed',
+                                    _set_random_seed)
 
         # traing.train
         MegatronAdaptation.register('megatron.training.training.train',

dcu_megatron/training/arguments.py

@@ -56,6 +56,7 @@ def parse_args(extra_args_provider=None, ignore_unknown_args=False):
     parser = _add_training_args(parser)
     parser = _add_extra_training_args(parser)
     parser = _add_initialization_args(parser)
+    parser = _add_extra_initialization_args(parser)
     parser = _add_learning_rate_args(parser)
     parser = _add_checkpointing_args(parser)
     parser = _add_mixed_precision_args(parser)
@@ -63,6 +64,7 @@ def parse_args(extra_args_provider=None, ignore_unknown_args=False):
     parser = _add_extra_distributed_args(parser)
     parser = _add_validation_args(parser)
     parser = _add_data_args(parser)
+    parser = _add_extra_data_args(parser)
     parser = _add_tokenizer_args(parser)
     parser = _add_extra_tokenizer_args(parser)
     parser = _add_autoresume_args(parser)
@@ -140,7 +142,24 @@ def _add_extra_training_args(parser):
 
     return parser
 
+def _add_extra_initialization_args(parser):
+    group = parser.add_argument_group(title='extra initialization args')
+    group.add_argument('--reproduce', action='store_true',
+                       help='reproduce train loss, need set --seed > 0.')
+    
+    return parser
+
+def _add_extra_data_args(parser):
+    # 删除原参数
+    remove_original_params(parser, ["num_workers"])
 
+    # 重定义参数
+    group = parser.add_argument_group(title='extra data args')
+    group.add_argument('--num-workers', type=int, default=0,
+                       help="Dataloader number of workers.")
+
+    return parser
+    
 def _add_extra_tokenizer_args(parser):
     # 删除原参数
     remove_original_params(parser, ["tokenizer_type"])

dcu_megatron/training/initialize.py

 """Megatron initialization."""
+import random
 import time
+import numpy as np
 import torch
 from datetime import timedelta
 
 from megatron.training import get_args
-from megatron.core import mpu
+from megatron.core import mpu, tensor_parallel
 
 
 def _initialize_distributed(get_embedding_ranks, get_position_embedding_ranks):
@@ -79,6 +81,32 @@ def _initialize_distributed(get_embedding_ranks, get_position_embedding_ranks):
                     f"{mpu.get_pipeline_model_parallel_world_size()}"
                 )
 
+def _set_random_seed(seed_, data_parallel_random_init=False, te_rng_tracker=False, inference_rng_tracker=False):
+    """Set random seed for reproducability."""
+    args = get_args() 
+    if seed_ is not None and seed_ > 0:
+        # Ensure that different pipeline MP stages get different seeds.
+        seed = seed_ + (100 * mpu.get_pipeline_model_parallel_rank())
+        # Ensure different data parallel ranks get different seeds
+        if data_parallel_random_init:
+            seed = seed + (10 * mpu.get_data_parallel_rank())
+        # 设置cpu随机种子
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        if torch.cuda.device_count() > 0:
+            # 设置gpu随机种子
+            tensor_parallel.model_parallel_cuda_manual_seed(seed, te_rng_tracker, inference_rng_tracker)
+        if args.reproduce:
+            assert (args.attention_dropout > 0) is False, f"To utilize the reproduction function, args.attention_dropout = {args.attention_dropout} must be set to 0."
+            assert (args.hidden_dropout > 0) is False, f"To utilize the reproduction function, args.hidden_dropout = {args.hidden_dropout} must be set to 0."
+            torch.backends.cudnn.deterministic = True # 设置cudnn后端为确定性算法
+            torch.backends.cudnn.benchmark = False # 固定卷积算法
+            torch.use_deterministic_algorithms(True) # 使用torch的deterministic算子 避免不确定性
+            
+
+    else:
+        raise ValueError("Seed ({}) should be a positive integer.".format(seed_))
 
 def _compile_dependencies():
 

examples/llama/Llama2_7b.sh

 #!/bin/bash
 # set -eux
 
-for para in $*
+for para in $@
 do
     if [[ $para == --profiling* ]];then
         profiling=${para#*=}
+    elif [[ $para == --reproduce ]];then
+        export MIOPEN_DEBUG_CONVOLUTION_DETERMINISTIC=1  # miopen 确定算法打开
+        export ROCBLAS_ATOMICS_MOD=0                     # rocblas 关闭原子操作
+        # 关闭miopen中的atomic操作算法, 只保留gemm算法
+        export MIOPEN_DEBUG_CONV_FFT=0
+        export MIOPEN_DEBUG_CONV_DIRECT=0
+        export MIOPEN_DEBUG_CONV_GEMM=1
+        export MIOPEN_DEBUG_CONV_WINOGRAD=0
+        export MIOPEN_DEBUG_CONV_IMPLICIT_GEMM=0
     fi
 done
 
@@ -31,15 +40,15 @@ export NCCL_NET_GDR_LEVEL=7
 export NCCL_NET_GDR_READ=1
 export RCCL_SDMA_COPY_ENABLE=0
 export NCCL_IB_HCA=mlx5_2:1,mlx5_3:1,mlx5_4:1,mlx5_5:1,mlx5_6:1,mlx5_7:1,mlx5_8:1,mlx5_9:1
-export NCCL_TOPO_FILE="/workspace/rccl-test/rccl-tests-0204/topo-input.xml"
+export NCCL_TOPO_FILE="/public/home/wangxj/Projects/rccl-test/rccl-tests-0204/topo-input.xml"
 export GLOG_minloglevel=3 # 打印error级别的nccl日志
 source /opt/dtk/env.sh
 
 # hipblaslt库
-export LD_LIBRARY_PATH=/data/blas/hipblaslt-install-dtk-25.04-0212/lib:$LD_LIBRARY_PATH 
+export LD_LIBRARY_PATH=/public/home/wangxj/Downloads/blas/hipblaslt-install0507-1/lib:$LD_LIBRARY_PATH 
 
 # rocblas
-export LD_LIBRARY_PATH=/data/blas/rocblas-install-0331-release/lib:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=/public/home/wangxj/Downloads/blas/rocblas-install-0331-release/lib:$LD_LIBRARY_PATH
 
 # torch控制多流转单流
 export ALLREDUCE_STREAM_WITH_COMPUTE=1
@@ -51,7 +60,7 @@ export cache_size_limit=64
 # CHECKPOINT_PATH=./Llama-2-7b-hf-to-meg-tp1-pp2 #CHECKPOINT_PATH=./tmp_7b # 
 SAVE_PATH=./tmp_7b
 TENSORBOARD_LOGS_PATH=./tmp_7b  #$2 #<Specify path>
-DATA_PATH="/data/datasets/oscar-1GB/oscar-1GB-llama2_text_document" #<Specify path and file prefix>_text_document
+DATA_PATH="/public/home/wangxj/Downloads/datasets/oscar-1GB/oscar-1GB-llama2_text_document" #<Specify path and file prefix>_text_document
 
 GPT_MODEL_ARGS=(
     --num-layers 32
@@ -60,7 +69,7 @@ GPT_MODEL_ARGS=(
     --num-attention-heads 32
     --max-position-embeddings 4096
 
-    --normalization RMSNorm # LightopRMSNorm
+    --normalization LightopRMSNorm # RMSNorm
     --position-embedding-type rope # none # 
     --untie-embeddings-and-output-weights # 分开处理embed和输出权重, 增加灵活性
 )
@@ -104,6 +113,8 @@ TRAINING_ARGS=(
     # --tp-comm-overlap # tensor parallel comm和gemm重叠, 优化项未适配
     # --tp-comm-overlap-rs-dgrad # reduce-scatter和dgrad gemm重叠
     --use-flash-attn
+    # --reproduce
+    --num-workers 2
 )
 # 使用torch fa的环境变量
 # export TORCHINDUCTOR_COORDINATE_DESCENT_TUNING=1
@@ -130,7 +141,7 @@ DATA_ARGS=(
     --seq-length 4096 #4096
     --split 949,50,1
     --tokenizer-type Llama2Tokenizer
-    --tokenizer-model /data/model_weights/llama2_7b_hf/tokenizer.model
+    --tokenizer-model /public/home/wangxj/Downloads/model_weights/llama2_7b_hf/tokenizer.model
 )
 
 EVAL_AND_LOGGING_ARGS=(