Merge pull request #6524 from hiyouga/hiyouga/upd_scripts

[misc] update scripts

scripts/llama_pro.py

@@ -24,7 +24,7 @@
 import torch
 from safetensors.torch import save_file
 from tqdm import tqdm
-from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, PreTrainedModel
 from transformers.modeling_utils import (
     SAFE_WEIGHTS_INDEX_NAME,
     SAFE_WEIGHTS_NAME,
@@ -35,7 +35,7 @@
 
 
 if TYPE_CHECKING:
-    from transformers import PretrainedConfig, PreTrainedModel
+    from transformers import PretrainedConfig
 
 
 def change_name(name: str, old_index: int, new_index: int) -> str:
@@ -61,17 +61,18 @@ def block_expansion(
     tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
     tokenizer.save_pretrained(output_dir)
 
-    config: "PretrainedConfig" = AutoConfig.from_pretrained(model_name_or_path)  # load the original one
+    config = AutoConfig.from_pretrained(model_name_or_path)  # load the original one
     if save_safetensors:
         setattr(config, "tie_word_embeddings", False)  # safetensors does not allow shared weights
 
-    model: "PreTrainedModel" = AutoModelForCausalLM.from_pretrained(
+    model = AutoModelForCausalLM.from_pretrained(
         model_name_or_path,
         config=config,
         torch_dtype="auto",
         trust_remote_code=True,
         low_cpu_mem_usage=True,
     )
+    assert isinstance(model, PreTrainedModel)  # type hint
     state_dict = model.state_dict()
 
     if num_layers % num_expand != 0:
@@ -85,7 +86,7 @@ def block_expansion(
             if f".{i:d}." in key:
                 output_state_dict[change_name(key, i, layer_cnt)] = value
 
-        print(f"Add layer {layer_cnt} copied from layer {i}")
+        print(f"Add layer {layer_cnt} copied from layer {i}.")
         layer_cnt += 1
         if (i + 1) % split == 0:
             for key, value in state_dict.items():
@@ -95,7 +96,7 @@ def block_expansion(
                     else:
                         output_state_dict[change_name(key, i, layer_cnt)] = torch.clone(value)
 
-            print(f"Add layer {layer_cnt} expanded from layer {i}")
+            print(f"Add layer {layer_cnt} expanded from layer {i}.")
             layer_cnt += 1
 
     for key, value in state_dict.items():
@@ -112,12 +113,13 @@ def block_expansion(
             torch.save(shard, os.path.join(output_dir, shard_file))
 
     if index is None:
-        print(f"Model weights saved in {os.path.join(output_dir, weights_name)}")
+        print(f"Model weights saved in {os.path.join(output_dir, weights_name)}.")
     else:
         index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
         with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
             json.dump(index, f, indent=2, sort_keys=True)
-        print(f"Model weights saved in {output_dir}")
+
+        print(f"Model weights saved in {output_dir}.")
 
     print("- Fine-tune this model with:")
     print(f"model_name_or_path: {output_dir}")

scripts/stat_utils/cal_lr.py

@@ -41,7 +41,7 @@ def calculate_lr(
     dataset: str = "alpaca_en_demo",
     dataset_dir: str = "data",
     template: str = "default",
-    cutoff_len: int = 1024,  # i.e. maximum input length during training
+    cutoff_len: int = 2048,  # i.e. maximum input length during training
     is_mistral_or_gemma: bool = False,  # mistral and gemma models opt for a smaller learning rate,
     packing: bool = False,
 ):
@@ -59,6 +59,7 @@ def calculate_lr(
             template=template,
             cutoff_len=cutoff_len,
             packing=packing,
+            preprocessing_num_workers=16,
             output_dir="dummy_dir",
             overwrite_cache=True,
             do_train=True,
@@ -79,7 +80,7 @@ def calculate_lr(
 
     dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
     valid_tokens, total_tokens = 0, 0
-    for batch in tqdm(dataloader):
+    for batch in tqdm(dataloader, desc="Collecting valid tokens"):
         valid_tokens += torch.sum(batch["labels"] != IGNORE_INDEX).item()
         total_tokens += torch.numel(batch["labels"])
 

scripts/stat_utils/cal_ppl.py

@@ -63,7 +63,7 @@ def calculate_ppl(
     dataset: str = "alpaca_en_demo",
     dataset_dir: str = "data",
     template: str = "default",
-    cutoff_len: int = 1024,
+    cutoff_len: int = 2048,
     max_samples: Optional[int] = None,
     train_on_prompt: bool = False,
 ):
@@ -82,6 +82,7 @@ def calculate_ppl(
             cutoff_len=cutoff_len,
             max_samples=max_samples,
             train_on_prompt=train_on_prompt,
+            preprocessing_num_workers=16,
             output_dir="dummy_dir",
             overwrite_cache=True,
             do_train=True,
@@ -111,7 +112,7 @@ def calculate_ppl(
     perplexities = []
     batch: Dict[str, "torch.Tensor"]
     with torch.no_grad():
-        for batch in tqdm(dataloader):
+        for batch in tqdm(dataloader, desc="Computing perplexities"):
             batch = batch.to(model.device)
             outputs = model(**batch)
             shift_logits: "torch.Tensor" = outputs["logits"][..., :-1, :]

scripts/stat_utils/length_cdf.py

@@ -42,6 +42,7 @@ def length_cdf(
             dataset_dir=dataset_dir,
             template=template,
             cutoff_len=1_000_000,
+            preprocessing_num_workers=16,
             output_dir="dummy_dir",
             overwrite_cache=True,
             do_train=True,
@@ -52,7 +53,7 @@ def length_cdf(
     trainset = get_dataset(template, model_args, data_args, training_args, "sft", **tokenizer_module)["train_dataset"]
     total_num = len(trainset)
     length_dict = defaultdict(int)
-    for sample in tqdm(trainset["input_ids"]):
+    for sample in tqdm(trainset["input_ids"], desc="Collecting lengths"):
         length_dict[len(sample) // interval * interval] += 1
 
     length_tuples = list(length_dict.items())

scripts/vllm_infer.py

@@ -64,6 +64,7 @@ def vllm_infer(
             template=template,
             cutoff_len=cutoff_len,
             max_samples=max_samples,
+            preprocessing_num_workers=16,
             vllm_config=vllm_config,
             temperature=temperature,
             top_p=top_p,