Supervised Fine-tuning for HugginFace pretrained weight. (deepspeedai#318)

* alpaca hf weight finetune

clean up

update

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arg fix

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clean up

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refine weight converter

don't cat when dim=0

format

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* add finetune script

* add condition for no padded token case

* add reference

---------

Co-authored-by: Conglong Li <[email protected]>

Author: inkcherry

examples_deepspeed/finetune_hf_llama/README.md

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+## Example of Finetuning LLAMA-7B from Hugging Face Weights
+
+### Dataset
+You can access the dataset from [here](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json).
+
+### Pre-trained Weights
+The pre-trained weights can be found at [Hugging Face - LLAMA-7B](https://huggingface.co/huggyllama/llama-7b).
+
+### Usage:
+
+#### 1. Converting Hugging Face Model Weights to Megatron-Deepspeed Model
+```bash
+bash examples_deepspeed/finetune_hf_llama/finetune_llama.sh convert
+```
+This command writes the Hugging Face model weights into the Megatron-Deepspeed model and saves it. You can adjust the parallel configuration in the script.
+
+#### 2. Fine-tuning Process
+```bash
+bash examples_deepspeed/finetune_hf_llama/finetune_llama.sh
+```
+Execute this command to initiate the finetuning process. The task originates from [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git).
+
+
+

examples_deepspeed/finetune_hf_llama/ds_config.json

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+{
+  "train_batch_size" : 256,
+  "train_micro_batch_size_per_gpu": 16,
+  "steps_per_print": 100,
+  "zero_optimization": {
+    "stage": 0
+  },
+  "bf16": {
+    "enabled": true
+  }
+}

examples_deepspeed/finetune_hf_llama/finetune_llama.sh

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+DS_CONFIG=./examples_deepspeed/finetune_hf_llama/ds_config.json
+DATASET_PATH=./alpaca_data.json
+# dataset link: https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json
+
+HF_LLAMA_PATH=/data/llama-7b/
+# weights link: https://huggingface.co/huggyllama/llama-7b
+
+MICRO_BATCH_SIZE=16
+GLOBAL_BATCH_SIZE=256
+TP=2
+PP=2
+# require to align with weight dimensions
+HIDDEN_SIZE=4096
+FFN_HIDDEN_SIZE=11008
+NUM_LAYERS=32
+NUM_HEADS=32
+SEQ_LENGTH=512
+######################################
+
+MEGA_DS_LLAMA_PATH=./"llama-7b-mega-ds-T${TP}P${PP}"
+
+# Below configuration required for llama model as per llama paper
+# --no-query-key-layer-scaling \
+# --attention-dropout 0 \
+# --hidden-dropout 0 \
+# --use-rotary-position-embeddings \
+# --untie-embeddings-and-output-weights \
+# --swiglu \
+# --normalization rmsnorm \
+# --disable-bias-linear \
+######################################
+cat <<EOT > $DS_CONFIG
+{
+  "train_batch_size" : $GLOBAL_BATCH_SIZE,
+  "train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE,
+  "steps_per_print": 100,
+  "zero_optimization": {
+    "stage": 0
+  },
+  "bf16": {
+    "enabled": true
+  }
+}
+EOT
+
+
+covert_args="deepspeed tools/hf2megads_weight_converter.py \
+--hf-ckpt-num-shards 2 \
+--origin-hf-ckpt-dir $HF_LLAMA_PATH \
+--save $MEGA_DS_LLAMA_PATH"
+
+finetune_args="deepspeed finetune_llama.py \
+--load $MEGA_DS_LLAMA_PATH"
+
+comm_args="--tensor-model-parallel-size $TP \
+--pipeline-model-parallel-size $PP \
+--lr-warmup-iters 2000 \
+--weight-decay 0.1 \
+--clip-grad 1 \
+--num-layers $NUM_LAYERS \
+--hidden-size $HIDDEN_SIZE \
+--num-attention-heads $NUM_HEADS \
+--ffn-hidden-size $FFN_HIDDEN_SIZE \
+--attention-dropout 0 \
+--hidden-dropout 0 \
+--no-query-key-layer-scaling \
+--disable-bias-linear \
+--normalization rmsnorm \
+--use-rotary-position-embeddings \
+--untie-embeddings-and-output-weights \
+--swiglu \
+--seq-length $SEQ_LENGTH \
+--max-position-embeddings $SEQ_LENGTH \
+--micro-batch-size $MICRO_BATCH_SIZE \
+--global-batch-size $GLOBAL_BATCH_SIZE \
+--train-iters 3500 \
+--lr 2e-5 \
+--tensorboard-dir tensorboard_output \
+--lr-decay-iters 320000 \
+--lr-decay-style cosine \
+--log-interval 1 \
+--eval-iters 100 \
+--eval-interval 100 \
+--data-path $DATASET_PATH \
+--save-interval 1500 \
+--split 100,0,0 \
+--bf16 \
+--zero-stage 0 \
+--tokenizer-type HFTokenizer \
+--tokenizer-model $HF_LLAMA_PATH \
+--deepspeed_config ./examples_deepspeed/finetune_hf_llama/ds_config.json \
+--deepspeed \
+--distributed-backend nccl \
+--num-workers 0 \
+--no-masked-softmax-fusion \
+--no-bias-gelu-fusion \
+--no-bias-dropout-fusion \
+--no-gradient-accumulation-fusion \
+--repeated-dataloader"
+
+if [ "$1" = "convert" ]; then
+    task_args="$covert_args"
+else
+    task_args="$finetune_args"
+fi
+
+full_cmd="$task_args $comm_args"
+
+eval "$full_cmd"
+