[Chat] fix readme

applications/ColossalChat/coati/dataset/tokenization_utils.py

@@ -169,7 +169,7 @@ def tokenize_prompt(
         template.messages = template.messages[:-1]
 
     # Prepare data
-    prompt = template.get_prompt(length=len(template.messages) - 1, add_generation_prompt=True)
+    prompt = template.get_prompt(length=len(template.messages), add_generation_prompt=True)
     tokenized = tokenizer([prompt], add_special_tokens=False)["input_ids"][0]
 
     if tokenizer.bos_token_id is not None:

applications/ColossalChat/coati/models/utils.py

@@ -138,6 +138,7 @@ def disable_dropout(model: torch.nn.Module):
     Returns:
         None
     """
-    for module in model.modules():
-        if isinstance(module, torch.nn.Dropout):
-            module.p = 0.0
+    if model is not None:
+        for module in model.modules():
+            if isinstance(module, torch.nn.Dropout):
+                module.p = 0.0

applications/ColossalChat/examples/README.md

@@ -462,26 +462,24 @@ Stage1 is supervised instructs fine-tuning (SFT). This step is a crucial part of
 
 
 #### Step 1: Data Collection
-The first step in Stage 1 is to collect a dataset of human demonstrations of the following format.
+The first step in Stage 1 is to collect a dataset of human demonstrations of the following JSONL format.
 
 
 ```json
-[
-    {"messages":
-      [
-        {
-          "from": "user",
-          "content": "what are some pranks with a pen i can do?"
-        },
-        {
-          "from": "assistant",
-          "content": "Are you looking for practical joke ideas?"
-        },
-        ...
-      ]
+{"messages":
+  [
+    {
+      "from": "user",
+      "content": "what are some pranks with a pen i can do?"
+    },
+    {
+      "from": "assistant",
+      "content": "Are you looking for practical joke ideas?"
     },
     ...
-]
+  ]
+},
+...
 ```
 
 

applications/ColossalChat/examples/inference/inference.py

@@ -151,7 +151,6 @@ def main(args):
         chat_io.prompt_for_output("assistant")
 
         prompt = conv.get_prompt(add_generation_prompt=True)
-        print(prompt + "<end_of_prompt>")
         input_ids = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)["input_ids"].to(
             torch.cuda.current_device()
         )

applications/ColossalChat/examples/training_scripts/train_ppo.py

@@ -502,7 +502,7 @@ def train(args):
     parser.add_argument("--disable_loss_mask", default=False, action="store_true")
     parser.add_argument("--max_length", type=int, default=2048)
     parser.add_argument("--max_seq_len", type=int, default=256)
-    parser.add_argument("--log_dir", default="logs", type=str)
+    parser.add_argument("--log_dir", default=None, type=str)
     parser.add_argument("--use_wandb", default=False, action="store_true")
     parser.add_argument("--grad_checkpoint", default=False, action="store_true")
     parser.add_argument("--use_flash_attn", default=False, action="store_true")

applications/ColossalChat/requirements.txt

@@ -2,7 +2,7 @@ transformers==4.39.3
 tqdm
 datasets==2.14.7
 loralib
-colossalai==0.4.0
+colossalai>=0.4.0
 torch>=2.1.0
 langchain
 tokenizers

applications/ColossalChat/tests/test_train.sh

@@ -15,7 +15,7 @@ set_n_least_used_CUDA_VISIBLE_DEVICES() {
     echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"
 }
 
-set_n_least_used_CUDA_VISIBLE_DEVICES 4
+set_n_least_used_CUDA_VISIBLE_DEVICES 2
 
 set -xu
 
@@ -119,11 +119,11 @@ for lora_rank in ${LORA_RANK[@]}; do
                 lora_config=""
             fi
             if [[ $plugin == "3d" ]]; then
-                tp='4'
+                tp='2'
                 bs='8'
             fi
             if [[ $plugin == "tp_zero2" ]]; then
-                tp='4'
+                tp='2'
                 bs='8'
                 zero_stage='2'
                 plugin='3d'
@@ -136,21 +136,21 @@ for lora_rank in ${LORA_RANK[@]}; do
             fi
             if [[ $plugin == "pp" ]]; then
                 bs='8'
-                pp='4'
+                pp='2'
                 plugin='3d'
             fi
             if [[ $plugin == "sp_split_gather" ]]; then
                 enable_sequence_parallelism='--enable_sequence_parallelism'
                 sp_mode='split_gather'
-                tp='4'
+                tp='2'
                 sp='1'
                 bs='8'
                 plugin='3d'
             fi
             if [[ $plugin == "sp_ring" ]]; then
                 enable_sequence_parallelism='--enable_sequence_parallelism'
                 sp_mode='ring'
-                tp='4'
+                tp='2'
                 sp='1'
                 bs='8'
                 plugin='3d'
@@ -159,7 +159,7 @@ for lora_rank in ${LORA_RANK[@]}; do
                 enable_sequence_parallelism='--enable_sequence_parallelism'
                 sp_mode='all_to_all'
                 tp='1'
-                sp='4'
+                sp='2'
                 bs='8'
                 plugin='3d'
             fi
@@ -175,7 +175,7 @@ for lora_rank in ${LORA_RANK[@]}; do
                 for split in $(seq -f "%05g" 0 0); do
                     dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_sft/arrow/part-$split")
                 done
-                colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_sft.py \
+                colossalai run --nproc_per_node 2 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_sft.py \
                     --pretrain $pretrain \
                     --tokenizer_dir $tokenizer_dir \
                     --dataset ${dataset[@]} \
@@ -242,7 +242,7 @@ for lora_rank in ${LORA_RANK[@]}; do
                 lora_config=""
             fi
             if [[ $plugin == "3d" ]]; then
-                tp='4'
+                tp='2'
                 bs='8'
             fi
             grad_accu='2'
@@ -256,7 +256,7 @@ for lora_rank in ${LORA_RANK[@]}; do
                 for split in $(seq -f "%05g" 0 0); do
                     dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_preference/arrow/part-$split")
                 done
-                colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_rm.py \
+                colossalai run --nproc_per_node 2 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_rm.py \
                     --pretrain $pretrain \
                     --tokenizer_dir $tokenizer_dir \
                     --dataset ${dataset[@]} \
@@ -325,7 +325,7 @@ for lora_rank in ${LORA_RANK[@]}; do
                 lora_config=""
             fi
             if [[ $plugin == "3d" ]]; then
-                tp='4'
+                tp='2'
                 bs='16'
                 ebs='32'
             fi
@@ -350,7 +350,7 @@ for lora_rank in ${LORA_RANK[@]}; do
                 for split in $(seq -f "%05g" 0 0); do
                     ptx_dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_sft/arrow/part-$split")
                 done
-                colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_ppo.py \
+                colossalai run --nproc_per_node 2 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_ppo.py \
                     --pretrain $pretrain \
                     --rm_pretrain $pretrain \
                     --tokenizer_dir $tokenizer_dir \
@@ -417,7 +417,7 @@ for lora_rank in ${LORA_RANK[@]}; do
             tp='1'
             bs='2'
             if [[ $plugin == "3d" ]]; then
-                tp='4'
+                tp='2'
                 bs='8'
             fi
             if [[ $plugin == "zero2" ]]; then
@@ -442,7 +442,7 @@ for lora_rank in ${LORA_RANK[@]}; do
                 for split in $(seq -f "%05g" 0 0); do
                     dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_preference/arrow/part-$split")
                 done
-                colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_dpo.py \
+                colossalai run --nproc_per_node 2 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_dpo.py \
                     --pretrain $pretrain \
                     --tokenizer_dir $tokenizer_dir \
                     --dataset ${dataset[@]} \
@@ -500,7 +500,7 @@ for lora_rank in ${LORA_RANK[@]}; do
             tp='1'
             bs='2'
             if [[ $plugin == "3d" ]]; then
-                tp='4'
+                tp='2'
                 bs='8'
             fi
             if [[ $plugin == "zero2" ]]; then
@@ -525,7 +525,7 @@ for lora_rank in ${LORA_RANK[@]}; do
                 for split in $(seq -f "%05g" 0 0); do
                     dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_preference/arrow/part-$split")
                 done
-                colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_orpo.py \
+                colossalai run --nproc_per_node 2 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_orpo.py \
                     --pretrain $pretrain \
                     --tokenizer_dir $tokenizer_dir \
                     --dataset ${dataset[@]} \
@@ -583,7 +583,7 @@ for lora_rank in ${LORA_RANK[@]}; do
             tp='1'
             bs='2'
             if [[ $plugin == "3d" ]]; then
-                tp='4'
+                tp='2'
                 bs='8'
             fi
             if [[ $plugin == "zero2" ]]; then
@@ -608,7 +608,7 @@ for lora_rank in ${LORA_RANK[@]}; do
                 for split in $(seq -f "%05g" 0 0); do
                     dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_kto/arrow/part-$split")
                 done
-                colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_kto.py \
+                colossalai run --nproc_per_node 2 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_kto.py \
                     --pretrain $pretrain \
                     --tokenizer_dir $tokenizer_dir \
                     --dataset ${dataset[@]} \