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support llama pro #2338 , add rslora

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Former-commit-id: 40d659b7f30dd5a004703c176ec1f22dc864e505
This commit is contained in:
hiyouga
2024-02-15 02:27:36 +08:00
parent b403f8d8a8
commit 596b6828cb
24 changed files with 438 additions and 203 deletions
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51
src/llmtuner/model/adapter.py
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@@ -1,8 +1,7 @@
import inspect
from typing import TYPE_CHECKING
import torch
from peft import LoraConfig, PeftModel, TaskType, get_peft_model
from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
from transformers.integrations import is_deepspeed_zero3_enabled
from ..extras.logging import get_logger
@@ -47,12 +46,22 @@ def init_adapter(
if not num_layers:
raise ValueError("Current model does not support freeze tuning.")
if finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
trainable_layer_ids = [num_layers - k - 1 for k in range(finetuning_args.num_layer_trainable)]
else: # fine-tuning the first n layers if num_layer_trainable < 0
trainable_layer_ids = [k for k in range(-finetuning_args.num_layer_trainable)] # noqa: C416
if finetuning_args.use_llama_pro:
if num_layers % finetuning_args.num_layer_trainable != 0:
raise ValueError(
"`num_layers` {} should be divisible by `num_layer_trainable` {}.".format(
num_layers, finetuning_args.num_layer_trainable
)
)
freeze_modules = set()
stride = num_layers // finetuning_args.num_layer_trainable
trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
elif finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
trainable_layer_ids = range(num_layers - finetuning_args.num_layer_trainable, num_layers)
else: # fine-tuning the first n layers if num_layer_trainable < 0
trainable_layer_ids = range(-finetuning_args.num_layer_trainable)
freeze_modules = {"all"}
for name, _ in model.named_modules():
if "0." in name:
freeze_modules.add(name.split("0.")[-1].split(".")[0])
@@ -65,13 +74,13 @@ def init_adapter(
)
for idx in trainable_layer_ids:
trainable_layers.append("{:d}.{}".format(idx, module_name))
trainable_layers.append(".{:d}.{}".format(idx, module_name if module_name != "all" else ""))
for name, param in model.named_parameters():
if not any(trainable_layer in name for trainable_layer in trainable_layers):
param.requires_grad_(False)
else:
if any(trainable_layer in name for trainable_layer in trainable_layers):
param.data = param.data.to(torch.float32)
else:
param.requires_grad_(False)
if finetuning_args.finetuning_type == "lora":
logger.info("Fine-tuning method: LoRA")
@@ -94,7 +103,7 @@ def init_adapter(
adapter_to_merge = model_args.adapter_name_or_path
for adapter in adapter_to_merge:
model = PeftModel.from_pretrained(model, adapter)
model: "LoraModel" = PeftModel.from_pretrained(model, adapter)
model = model.merge_and_unload()
if len(adapter_to_merge) > 0:
@@ -114,22 +123,14 @@ def init_adapter(
"target_modules": target_modules,
"lora_alpha": finetuning_args.lora_alpha,
"lora_dropout": finetuning_args.lora_dropout,
"use_rslora": finetuning_args.use_rslora,
}
if model_args.use_unsloth:
from unsloth import FastLlamaModel, FastMistralModel # type: ignore
from unsloth import FastLanguageModel # type: ignore
unsloth_peft_kwargs = {"model": model, "max_seq_length": model_args.model_max_length}
if "loftq_config" in inspect.signature(FastLlamaModel.get_peft_model).parameters:
unsloth_peft_kwargs["loftq_config"] = {}
if getattr(model.config, "model_type", None) == "llama":
model = FastLlamaModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)
elif getattr(model.config, "model_type", None) == "mistral":
model = FastMistralModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)
else:
raise NotImplementedError
model = FastLanguageModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)
else:
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
@@ -142,7 +143,7 @@ def init_adapter(
for param in filter(lambda p: p.requires_grad, model.parameters()):
param.data = param.data.to(torch.bfloat16 if finetuning_args.lora_bf16_mode else torch.float32)
if model_args.adapter_name_or_path is not None:
logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
if model_args.adapter_name_or_path is not None:
logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
return model