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support val set in streaming mode

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Former-commit-id: faed15b58ed00b1e09bb091e7eee48f5ef7c508b
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hiyouga
2023-08-09 23:00:26 +08:00
parent 972bfa700a
commit 467d571206
10 changed files with 58 additions and 50 deletions
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52
src/llmtuner/tuner/core/parser.py
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@@ -67,33 +67,33 @@ def get_train_args(
# Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
data_args.init_for_training()
assert general_args.stage == "sft" or (not training_args.predict_with_generate), \
"`predict_with_generate` cannot be set as True at PT, RM and PPO stages."
if general_args.stage != "sft" and training_args.predict_with_generate:
raise ValueError("`predict_with_generate` cannot be set as True at PT, RM and PPO stages.")
assert not (training_args.do_train and training_args.predict_with_generate), \
"`predict_with_generate` cannot be set as True while training."
if training_args.do_train and training_args.predict_with_generate:
raise ValueError("`predict_with_generate` cannot be set as True while training.")
assert general_args.stage != "sft" or (not training_args.do_predict) or training_args.predict_with_generate, \
"Please enable `predict_with_generate` to save model predictions."
if general_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
raise ValueError("Please enable `predict_with_generate` to save model predictions.")
assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \
"Quantization is only compatible with the LoRA method."
if training_args.max_steps == -1 and data_args.streaming:
raise ValueError("Please specify `max_steps` in streaming mode.")
assert not (training_args.max_steps == -1 and data_args.streaming), \
"Please specify `max_steps` in streaming mode."
if general_args.stage == "ppo" and data_args.streaming:
raise ValueError("Streaming mode does not suppport PPO training currently.")
assert training_args.evaluation_strategy == "no" or (not data_args.streaming), \
"Streaming mode does not support evaluation currently."
if data_args.val_size > 1e-6 and data_args.val_size < 1 and data_args.streaming:
raise ValueError("Streaming mode should have an integer val size.")
assert not (general_args.stage == "ppo" and data_args.streaming), \
"Streaming mode does not suppport PPO training currently."
if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
raise ValueError("Quantization is only compatible with the LoRA method.")
if model_args.checkpoint_dir is not None:
if finetuning_args.finetuning_type != "lora":
assert len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
else:
assert model_args.quantization_bit is None or len(model_args.checkpoint_dir) == 1, \
"Quantized model only accepts a single checkpoint."
if len(model_args.checkpoint_dir) != 1:
raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
raise ValueError("Quantized model only accepts a single checkpoint.")
if model_args.quantization_bit is not None and (not training_args.do_train):
logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
@@ -113,10 +113,6 @@ def get_train_args(
logger.warning("`max_samples` is incompatible with `streaming`. Disabling max_samples.")
data_args.max_samples = None
if data_args.dev_ratio > 1e-6 and data_args.streaming:
logger.warning("`dev_ratio` is incompatible with `streaming`. Disabling development set.")
data_args.dev_ratio = 0
training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
if model_args.quantization_bit is not None:
@@ -145,14 +141,14 @@ def get_infer_args(
) -> Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]:
model_args, data_args, finetuning_args, generating_args = parse_infer_args(args)
assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \
"Quantization is only compatible with the LoRA method."
if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
raise ValueError("Quantization is only compatible with the LoRA method.")
if model_args.checkpoint_dir is not None:
if finetuning_args.finetuning_type != "lora":
assert len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
else:
assert model_args.quantization_bit is None or len(model_args.checkpoint_dir) == 1, \
"Quantized model only accepts a single checkpoint."
if len(model_args.checkpoint_dir) != 1:
raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
raise ValueError("Quantized model only accepts a single checkpoint.")
return model_args, data_args, finetuning_args, generating_args