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@@ -112,7 +112,7 @@ deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.jso
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  --dataset_path corpora/cluecorpussmall_lm_seq128_dataset.pt \
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  --vocab_path models/google_zh_vocab.txt \
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  --config_path models/gpt2/xlarge_config.json \
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- --output_model_path models/cluecorpussmall_gpt2_xlarge_seq128 \
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  --world_size 8 --batch_size 64 \
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  --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
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  --deepspeed_checkpoint_activations --deepspeed_checkpoint_layers_num 24
@@ -121,8 +121,8 @@ deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.jso
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  Before stage2, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
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  ```
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- python3 models/cluecorpussmall_gpt2_xlarge_seq128/zero_to_fp32.py models/cluecorpussmall_gpt2_xlarge_seq128/ \
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- models/cluecorpussmall_gpt2_xlarge_seq128.bin
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  ```
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  Stage2:
@@ -139,8 +139,8 @@ deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.jso
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  --dataset_path corpora/cluecorpussmall_lm_seq1024_dataset.pt \
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  --vocab_path models/google_zh_vocab.txt \
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  --config_path models/gpt2/xlarge_config.json \
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- --pretrained_model_path models/cluecorpussmall_gpt2_xlarge_seq128.bin \
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- --output_model_path models/cluecorpussmall_gpt2_xlarge_seq1024_stage2 \
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  --world_size 8 --batch_size 16 --learning_rate 5e-5 \
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  --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
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  --deepspeed_checkpoint_activations --deepspeed_checkpoint_layers_num 6
@@ -149,14 +149,14 @@ deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.jso
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  Then, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
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  ```
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- python3 models/cluecorpussmall_gpt2_xlarge_seq1024_stage2/zero_to_fp32.py models/cluecorpussmall_gpt2_xlarge_seq1024_stage2/ \
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- models/cluecorpussmall_gpt2_xlarge_seq1024_stage2.bin
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  ```
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  Finally, we convert the pre-trained model into Huggingface's format:
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  ```
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- python3 scripts/convert_gpt2_from_tencentpretrain_to_huggingface.py --input_model_path models/cluecorpussmall_gpt2_xlarge_seq1024_stage2.bin \
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  --output_model_path pytorch_model.bin \
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  --layers_num 48
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  ```
 
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  --dataset_path corpora/cluecorpussmall_lm_seq128_dataset.pt \
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  --vocab_path models/google_zh_vocab.txt \
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  --config_path models/gpt2/xlarge_config.json \
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+ --output_model_path models/cluecorpussmall_gpt2_xlarge_seq128_model \
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  --world_size 8 --batch_size 64 \
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  --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
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  --deepspeed_checkpoint_activations --deepspeed_checkpoint_layers_num 24
 
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  Before stage2, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
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  ```
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+ python3 models/cluecorpussmall_gpt2_xlarge_seq128_model/zero_to_fp32.py models/cluecorpussmall_gpt2_xlarge_seq128_model/ \
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+ models/cluecorpussmall_gpt2_xlarge_seq128_model.bin
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  ```
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  Stage2:
 
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  --dataset_path corpora/cluecorpussmall_lm_seq1024_dataset.pt \
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  --vocab_path models/google_zh_vocab.txt \
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  --config_path models/gpt2/xlarge_config.json \
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+ --pretrained_model_path models/cluecorpussmall_gpt2_xlarge_seq128_model.bin \
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+ --output_model_path models/cluecorpussmall_gpt2_xlarge_seq1024_model \
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  --world_size 8 --batch_size 16 --learning_rate 5e-5 \
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  --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
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  --deepspeed_checkpoint_activations --deepspeed_checkpoint_layers_num 6
 
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  Then, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
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  ```
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+ python3 models/cluecorpussmall_gpt2_xlarge_seq1024_model/zero_to_fp32.py models/cluecorpussmall_gpt2_xlarge_seq1024_model/ \
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+ models/cluecorpussmall_gpt2_xlarge_seq1024_model.bin
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  ```
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  Finally, we convert the pre-trained model into Huggingface's format:
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  ```
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+ python3 scripts/convert_gpt2_from_tencentpretrain_to_huggingface.py --input_model_path models/cluecorpussmall_gpt2_xlarge_seq1024_model.bin \
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  --output_model_path pytorch_model.bin \
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  --layers_num 48
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  ```