更新示例llama脚本

Llama_pretraining.sh → Llama2_7b.sh

@@ -8,45 +8,56 @@ export OMP_NUM_THREADS=1
 export NCCL_P2P_LEVEL=PXB # SYS
 
 #export HIP_ALLOC_INITIALIZE=0
-#export GPU_MAX_HW_QUEUES=20 # sglang空泡
+# export GPU_MAX_HW_QUEUES=10
 
 export NCCL_ALGO=Ring
 export NCCL_NCHANNELS_PER_PEER=16
-export NCCL_MIN_NCHANNELS=20
+export NCCL_MIN_NCHANNELS=32 # 20
+export NCCL_MAX_NCHANNELS=32 # 20
 export NCCL_IB_TIMEOUT=22
 export CUDA_DEVICE_MAX_CONNECTIONS=1
 
-export NCCL_IB_HCA=mlx5_1,mlx5_2
-# export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,,mlx5_4,,mlx5_5,,mlx5_6,,mlx5_7
-export NCCL_NET_GDR_LEVEL=SYS
-export NCCL_NET_GDR_READ=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_NET_GDR_LEVEL=7
+export NCCL_NET_GDR_READ=1
+export RCCL_SDMA_COPY_ENABLE=0
+export NCCL_TOPO_FILE="/public/home/wangxj/Projects/rccl-test/rccl-tests-0204/topo-input.xml"
+# export NCCL_TOPO_FILE="/workspace/rccl-test/rccl-tests-0204/topo-input.xml"
 export GLOG_minloglevel=3 # 打印error级别的nccl日志
-
-# export TORCH_COMPILE_DEBUG=1 # 查看编译后的图
-
 source /opt/dtk/env.sh
 # 导入hipblaslt库
 # export LD_LIBRARY_PATH=/data/hipblaslt-install-0904/lib:$LD_LIBRARY_PATH 
+# export LD_LIBRARY_PATH=/data/hipblaslt-install-dtk-25.04-0212/lib:$LD_LIBRARY_PATH 
+export LD_LIBRARY_PATH=/public/home/wangxj/Downloads/hipblaslt-install-dtk-25.04-0212/lib:$LD_LIBRARY_PATH 
+
 # 更新rocblas
 # export LD_LIBRARY_PATH=/data/rocblas-install_qwen1211/lib:$LD_LIBRARY_PATH
 # export LD_LIBRARY_PATH=/data/rocblas-install_qwen1228/lib:$LD_LIBRARY_PATH
-# export LD_LIBRARY_PATH=/data/rocblas-install_0107_trans/lib:$LD_LIBRARY_PATH
+# export LD_LIBRARY_PATH=/public/home/wangxj/Downloads/rocblas-install-0224/lib:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=/public/home/wangxj/Downloads/rocblas-install-0227/lib:$LD_LIBRARY_PATH
+# export LD_LIBRARY_PATH=/data/rocblas-install-0118-bf16/lib:$LD_LIBRARY_PATH
 
 # torch控制多流转单流
-# export ALLREDUCE_STREAM_WITH_COMPUTE=1
+export ALLREDUCE_STREAM_WITH_COMPUTE=1
+export SENDRECV_STREAM_WITH_COMPUTE=1 
 
 # prof采集添加同步, 避免卡顿
 # export GPU_FLUSH_ON_EXECUTION=1
 # export HIP_DIRECT_DISPATCH=0
 
 # 采集rocblas size
-export ROCBLAS_LAYER=3
+# export ROCBLAS_LAYER=3
 # 采集 fa size
 # export FLASH_ATTENTION_PRINT_PARAM=1
 
-CHECKPOINT_PATH=./tmp_7b #$1 #<Specify path>
+#增加编译缓存
+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/nemo_pretrain/oscar-1GB/oscar-1GB-llama_text_document" 
+DATA_PATH="/public/home/wangxj/Downloads/datasets/oscar-1GB-head/oscar-1GB_head-llama2_text_document" #<Specify path and file prefix>_text_document
+# DATA_PATH="/data/datasets/oscar-1GB-head/oscar-1GB_head-llama2_text_document" #<Specify path and file prefix>_text_document
 
 GPT_MODEL_ARGS=(
     --num-layers 32
@@ -56,34 +67,22 @@ GPT_MODEL_ARGS=(
     --max-position-embeddings 4096
 
     --normalization RMSNorm 
-    --position-embedding-type rope
+    --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
 )
 
-# GPT_MODEL_ARGS=(
-#     --num-layers 40
-#     --hidden-size 5120
-#     --ffn-hidden-size 13824 
-#     --num-attention-heads 40
-#     --max-position-embeddings 4096
-
-#     --normalization RMSNorm 
-#     --position-embedding-type rope
-#     --untie-embeddings-and-output-weights # 分开处理embed和输出权重, 增加灵活性
-# )
-
-# export NVTE_FLASH_ATTN=1 # 走cutlass
-export NVTE_FLASH_ATTN_TRITON=1 # 走triton_fa
+export NVTE_FLASH_ATTN=1 # 走cutlass
+# export NVTE_FLASH_ATTN_TRITON=1 # 走triton_fa
 # --transformer-impl transformer_engine # 走core用这两组参数
     # --use-mcore-models
     # --transformer-impl local # 走legacy用这两组参数
     # --use-legacy-models 
 TRAINING_ARGS=(
-    --transformer-impl local # 走legacy用这两组参数
-    --use-legacy-models 
+    --transformer-impl transformer_engine # 走core用这两组参数
+    --use-mcore-models
     --micro-batch-size 1
-    --global-batch-size 64 #240 #60 #512 #64
-    --train-iters 10
+    --global-batch-size 256 #256 #240 #60 #512 #64
+    --train-iters 50
     --weight-decay 0.1 
     --adam-beta1 0.9 
     --adam-beta2 0.95 
@@ -96,7 +95,7 @@ TRAINING_ARGS=(
     --disable-bias-linear
     --attention-dropout 0
     --hidden-dropout 0
-    --no-gradient-accumulation-fusion # 开启后精度不对, apex更新后可以开启
+    # --no-gradient-accumulation-fusion
     --swiglu
     --lr 3.0e-5 
     --lr-decay-style cosine 
@@ -109,16 +108,26 @@ TRAINING_ARGS=(
     # --recompute-method block
     --overlap-grad-reduce # 重叠ddp grad reduce
     # --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, 优化项未适配
-    --use-flash-attn-cutlass
+    # --tp-comm-overlap-rs-dgrad # reduce-scatter和dgrad gemm重叠
+    --use-flash-attn
 )
+# 使用torch fa的环境变量
+# export TORCHINDUCTOR_COORDINATE_DESCENT_TUNING=1
+# export TORCHINDUCTOR_BENCHMARK_FUSION=1
+# export TORCHINDUCTOR_BENCHMARK_MULTI_TEMPLATES=1
+# export TORCHINDUCTOR_MAX_AUTOTUNE=1
+# export TORCHINDUCTOR_CACHE_DIR=./cache
 # --use-flash-attn-cutlass # cutlass fa
 # --use-flash-attn-triton # triton fa
+# --use-flash-attn-torch # torch fa
 
 MODEL_PARALLEL_ARGS=(
     --sequence-parallel
-	--tensor-model-parallel-size 2
+	--tensor-model-parallel-size 1
 	--pipeline-model-parallel-size 2
+  # --num-layers-per-virtual-pipeline-stage 4
+  # --microbatch-group-size-per-virtual-pipeline-stage 1
+  # --no-overlap-p2p-communication # 开启后
 )
 
 DATA_ARGS=(
@@ -126,7 +135,8 @@ 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
+    # --tokenizer-model /data/model_weights/llama2_7b_hf/tokenizer.model
 )
 
 EVAL_AND_LOGGING_ARGS=(
@@ -134,12 +144,20 @@ EVAL_AND_LOGGING_ARGS=(
     --log-throughput
     --save-interval 1000 
     --eval-interval 1000 
-    --save $CHECKPOINT_PATH 
-    --load $CHECKPOINT_PATH 
+    --save $SAVE_PATH 
+    --load $SAVE_PATH 
     --eval-iters 10
     --tensorboard-dir $TENSORBOARD_LOGS_PATH 
 )
 
+# FINETUNE_ARGS=(
+#     # --finetune
+#     # --pretrained-checkpoint $CHECKPOINT_PATH
+#     --load $CHECKPOINT_PATH
+#     --no-load-optim
+#     --no-load-rng
+# )
+
 PROFILE_ARGS=(
     --profile
     --profile-step-start 4
@@ -153,7 +171,7 @@ RANK=$OMPI_COMM_WORLD_RANK
 LOCAL_RANK=$OMPI_COMM_WORLD_LOCAL_RANK
 WORLD_SIZE=$OMPI_COMM_WORLD_SIZE
 DIST_URL=${1}
-DIST_PORT=34567
+DIST_PORT=34577
 
 DISTRIBUTED_ARGS=(
     --rank ${RANK}
@@ -162,6 +180,13 @@ DISTRIBUTED_ARGS=(
     --dist-url tcp://${DIST_URL}:${DIST_PORT}
 )
 
+# torchrun --nproc_per_node 8 pretrain_gpt.py \
+#     ${GPT_MODEL_ARGS[@]} \
+#     ${TRAINING_ARGS[@]} \
+#     ${MODEL_PARALLEL_ARGS[@]} \
+#     ${DATA_ARGS[@]} \
+#     ${EVAL_AND_LOGGING_ARGS[@]}
+
 APP="python -u pretrain_gpt.py \
         ${GPT_MODEL_ARGS[@]} \
         ${TRAINING_ARGS[@]} \
@@ -174,48 +199,49 @@ APP="python -u pretrain_gpt.py \
 # 开启profile
 # ${PROFILE_ARGS[@]} \
 
+# export HIP_VISIBLE_DEVICES=0,7 #  # 4,5,6,7 #,
 export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 #  # 4,5,6,7 #,
 # export CUDA_VISIBLE_DEVICES=4,5,6,7 # 0,1,2,3,
-${APP}
-# case ${LOCAL_RANK} in
-# [0])
-# #   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-#   ${APP}
-#   # numactl --cpunodebind=0 --membind=0 ${APP}
-#   ;;
-# [1])
-# #   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-#   ${APP}
-#   # numactl --cpunodebind=0 --membind=0 ${APP}
-#   ;;
-# [2])
-# #   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-#   ${APP}
-#   # numactl --cpunodebind=0 --membind=0 ${APP}
-#   ;;
-# [3])
-# #   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-#   ${APP}
-#   # numactl --cpunodebind=0 --membind=0 ${APP}
-#   ;;
-# [4])
-#   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-#   ${APP}
-#   # numactl --cpunodebind=0 --membind=0 ${APP}
-#   ;;
-# [5])
-#   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-#   ${APP}
-#   # numactl --cpunodebind=0 --membind=0 ${APP}
-#   ;;
-# [6])
-#   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-#   ${APP}
-#   # numactl --cpunodebind=0 --membind=0 ${APP}
-#   ;;
-# [7])
-#   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
 # ${APP}
-#   # numactl --cpunodebind=0 --membind=0 ${APP}
-#   ;;
-# esac
+case ${LOCAL_RANK} in
+[0])
+  export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+  # hipprof --hip-trace --trace-off numactl --cpunodebind=0 --membind=0 ${APP}
+  numactl --cpunodebind=0 --membind=0 ${APP}
+  ;;
+[1])
+  export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+  # hipprof --hip-trace --trace-off numactl --cpunodebind=0 --membind=0 ${APP}
+  numactl --cpunodebind=1 --membind=1 ${APP}
+  ;;
+[2])
+  export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+  # hipprof --hip-trace --trace-off numactl --cpunodebind=0 --membind=0 ${APP}
+  numactl --cpunodebind=2 --membind=2 ${APP}
+  ;;
+[3])
+  export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+  numactl --cpunodebind=3 --membind=3 ${APP}
+  # hipprof --hip-trace --trace-off numactl --cpunodebind=0 --membind=0 ${APP}
+  ;;
+[4])
+  export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+  numactl --cpunodebind=4 --membind=4 ${APP}
+  # hipprof --hip-trace --trace-off numactl --cpunodebind=0 --membind=0 ${APP}
+  ;;
+[5])
+  export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+  numactl --cpunodebind=5 --membind=5 ${APP}
+  # hipprof --hip-trace --trace-off numactl --cpunodebind=0 --membind=0 ${APP}
+  ;;
+[6])
+  export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+  numactl --cpunodebind=6 --membind=6 ${APP}
+  # hipprof --hip-trace --trace-off numactl --cpunodebind=0 --membind=0 ${APP}
+  ;;
+[7])
+  export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+  numactl --cpunodebind=7 --membind=7 ${APP}
+  # hipprof --hip-trace --trace-off numactl --cpunodebind=0 --membind=0 ${APP}
+  ;;
+esac
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