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Copy pathrun_train.sh
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172 lines (155 loc) · 9.43 KB
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MAX_STEP=150
RES_FOLDER='amd_res'
DATA_FOLDER='/root/workspace/datasets/Imagenet_DiT_feat'
# CUDA_VISIBLE_DEVICES=6,7 accelerate launch --multi_gpu --num_processes 2 \
# --mixed_precision no train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision no \
# --exp-name bs256_2gpu_mi250_fp32
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision fp16 train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision fp16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi250_fp16 \
# --use_fa
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi308_bf16_baseline
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi308_bf16_with_fa \
# --use_fa
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi308_bf16_baseline \
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi308_bf16_baseline_and_fa\
# --use_fa
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi308_bf16_baseline_and_fa_compile \
# --use_fa \
# --compile
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi308_bf16_baseline_and_fa_compile_gemm_tuning \
# --use_fa \
# --compile \
# --gemm-tuning
DISABLE_ADDMM_HIP_LT=0 TORCH_BLAS_PREFER_HIPBLASLT=1 ROCBLAS_USE_HIPBLASLT=1 accelerate launch --multi_gpu --num_processes 8 \
--mixed_precision bf16 train.py --model DiT-XL/2 \
--feature-path ${DATA_FOLDER} \
--max-train-steps ${MAX_STEP} \
--results-dir ${RES_FOLDER} \
--mixed-precision bf16 \
--dummydata \
--image-size 256 \
--global-batch-size 256\
--exp-name bs256_8gpu_mi308_bf16_with_fa_gemm_tuning_use_hipblaslt_only \
--use_fa \
--gemm-tuning \
--compile
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train_profiling.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi308_bf16_with_fa_profiling_gemm_tuning \
# --use_fa \
# --gemm_tuning
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train_profiling.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi308_bf16_with_fa_profiling \
# --use_fa\
# --compile
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train_profiling.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 256 \
# --global-batch-size 256\
# --exp-name bs256_8gpu_mi308_bf16_with_fa_profiling \
# --use_fa \
# --compile
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision fp16 train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision fp16 \
# --dummydata \
# --image-size 512 \
# --exp-name bs512_8gpu_mi250_fp16
# accelerate launch --multi_gpu --num_processes 8 \
# --mixed_precision bf16 train.py --model DiT-XL/2 \
# --feature-path ${DATA_FOLDER} \
# --max-train-steps ${MAX_STEP} \
# --results-dir ${RES_FOLDER} \
# --mixed-precision bf16 \
# --dummydata \
# --image-size 512 \
# --exp-name bs512_8gpu_mi250_bf16