support unsloth

Former-commit-id: b857f00234b90b785d82ca7cdb29af3d948b1a7b

src/llmtuner/model/loader.py

@@ -4,10 +4,10 @@ from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.utils.versions import require_version
 from trl import AutoModelForCausalLMWithValueHead
 
-import llmtuner.model.patcher as patcher
 from llmtuner.extras.logging import get_logger
-from llmtuner.extras.misc import count_parameters, try_download_model_from_ms
+from llmtuner.extras.misc import count_parameters, get_current_device, try_download_model_from_ms
 from llmtuner.model.adapter import init_adapter
+from llmtuner.model.patcher import patch_config, patch_tokenizer, patch_model, patch_valuehead_model
 from llmtuner.model.utils import (
     load_valuehead_params, prepare_model_for_training, resize_embedding_layer, register_autoclass
 )
@@ -24,7 +24,7 @@ require_version("transformers>=4.36.2", "To fix: pip install transformers>=4.36.
 require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
 require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
 require_version("peft>=0.7.0", "To fix: pip install peft>=0.7.0")
-require_version("trl==0.7.4", "To fix: pip install trl==0.7.4")
+require_version("trl>=0.7.6", "To fix: pip install trl>=0.7.6")
 
 
 def load_model_and_tokenizer(
@@ -52,26 +52,48 @@ def load_model_and_tokenizer(
         model_args.model_name_or_path,
         use_fast=model_args.use_fast_tokenizer,
         split_special_tokens=model_args.split_special_tokens,
-        padding_side="right", # training with left-padded tensors in fp16 precision may cause overflow
+        padding_side="right",
         **config_kwargs
     )
+    patch_tokenizer(tokenizer)
+
     config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
+    patch_config(config, tokenizer, model_args, config_kwargs, is_trainable)
 
-    patcher.patch_tokenizer(tokenizer)
-    patcher.patch_config(config, model_args)
-    patcher.configure_rope(config, model_args, is_trainable)
-    patcher.configure_flashattn(config_kwargs, model_args)
-    patcher.configure_longlora(config, model_args, is_trainable)
-    patcher.configure_quantization(config, config_kwargs, tokenizer, model_args, finetuning_args)
+    model = None
+    if is_trainable and model_args.use_unsloth:
+        require_version("unsloth==2023.12", "Follow the instructions at: https://github.com/unslothai/unsloth")
+        from unsloth import FastLlamaModel, FastMistralModel # type: ignore
+        unsloth_kwargs = {
+            "model_name": model_args.model_name_or_path,
+            "max_seq_length": model_args.model_max_length,
+            "load_in_4bit": model_args.quantization_bit == 4,
+            "token": model_args.hf_hub_token,
+            "device_map": get_current_device(),
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+        if getattr(config, "model_type", None) == "llama":
+            model, _ = FastLlamaModel.from_pretrained(**unsloth_kwargs)
+        elif getattr(config, "model_type", None) == "mistral":
+            model, _ = FastMistralModel.from_pretrained(**unsloth_kwargs)
+        else:
+            logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
+            model_args.use_unsloth = False
+
+        if model_args.adapter_name_or_path:
+            model_args.adapter_name_or_path = None
+            logger.warning("Unsloth does not support loading adapters.")
+
+    if model is None:
+        model = AutoModelForCausalLM.from_pretrained(
+            model_args.model_name_or_path,
+            config=config,
+            low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()),
+            **config_kwargs
+        )
 
-    model = AutoModelForCausalLM.from_pretrained(
-        model_args.model_name_or_path,
-        config=config,
-        low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()),
-        **config_kwargs
-    )
     model = model.to(model_args.compute_dtype) if not getattr(model, "quantization_method", None) else model
-    patcher.patch_model(model)
+    patch_model(model)
     register_autoclass(config, model, tokenizer)
     if not is_deepspeed_zero3_enabled():
         resize_embedding_layer(model, tokenizer)
@@ -81,7 +103,7 @@ def load_model_and_tokenizer(
 
     if add_valuehead:
         model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
-        patcher.patch_valuehead_model(model)
+        patch_valuehead_model(model)
 
         if model_args.adapter_name_or_path is not None:
             vhead_path = model_args.adapter_name_or_path[-1]
@@ -94,7 +116,7 @@ def load_model_and_tokenizer(
             logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
 
     if not is_trainable:
-        model.requires_grad_(False) # fix all model params
+        model.requires_grad_(False)
         model.eval()
     else:
         model.train()