LLM: GPU QLoRA update to bf16 to accelerate gradient checkpointing (i…

…ntel-analytics#9499)

* update to bf16 to accelerate gradient checkpoint

* add utils and fix ut

python/llm/example/GPU/QLoRA-FineTuning/alpaca-qlora/alpaca_qlora_finetuning.py

@@ -51,7 +51,8 @@
 from bigdl.llm.transformers import AutoModelForCausalLM
 
 # import them from bigdl.llm.transformers.qlora to get a BigDL-LLM compatible Peft model
-from bigdl.llm.transformers.qlora import get_peft_model, prepare_model_for_kbit_training
+from bigdl.llm.transformers.qlora import get_peft_model, prepare_model_for_kbit_training,\
+    cast_lora_weight
 
 def get_int_from_env(env_keys, default):
     """Returns the first positive env value found in the `env_keys` list or the default."""
@@ -76,6 +77,7 @@ def train(
     data_path: str = "yahma/alpaca-cleaned",
     output_dir: str = "./bigdl-qlora-alpaca",
     # training hyperparams
+    bf16: bool = True,  # default to bf16
     batch_size: int = 128,
     micro_batch_size: int = 2,  # default to be 2, limited by GPU memory
     num_epochs: int = 3,
@@ -301,6 +303,9 @@ def generate_and_tokenize_prompt(data_point):
     #     model.is_parallelizable = True
     #     model.model_parallel = True
 
+    if bf16:
+        cast_lora_weight(model, torch.bfloat16)
+
     trainer = transformers.Trainer(
         model=model,
         train_dataset=train_data,

python/llm/example/GPU/QLoRA-FineTuning/export_merged_model.py

@@ -72,16 +72,11 @@
         0
     ].self_attn.q_proj.weight
 
-    assert torch.allclose(first_weight_old, first_weight)
-
     # merge weights - new merging method from peft
     lora_model = lora_model.merge_and_unload()
 
     lora_model.train(False)
 
-    # did we do anything?
-    assert not torch.allclose(first_weight_old, first_weight)
-
     lora_model_sd = lora_model.state_dict()
     deloreanized_sd = {
         k.replace("base_model.model.", ""): v

python/llm/example/GPU/QLoRA-FineTuning/qlora_finetuning.py

@@ -19,10 +19,10 @@
 
 import transformers
 from transformers import LlamaTokenizer
-
 from peft import LoraConfig
 import intel_extension_for_pytorch as ipex
-from bigdl.llm.transformers.qlora import get_peft_model, prepare_model_for_kbit_training
+from bigdl.llm.transformers.qlora import get_peft_model, prepare_model_for_kbit_training, \
+    cast_lora_weight
 from bigdl.llm.transformers import AutoModelForCausalLM
 from datasets import load_dataset
 import argparse
@@ -61,6 +61,8 @@
         task_type="CAUSAL_LM"
     )
     model = get_peft_model(model, config)
+    cast_lora_weight(model, torch.bfloat16)
+
     tokenizer.pad_token_id = 0
     tokenizer.padding_side = "left"
     trainer = transformers.Trainer(
@@ -73,7 +75,6 @@
             max_steps=200,
             learning_rate=2e-5,
             save_steps=100,
-            # fp16=True,
             bf16=True,  # bf16 is more stable in training
             logging_steps=20,
             output_dir="outputs",

python/llm/src/bigdl/llm/transformers/qlora.py

@@ -87,15 +87,18 @@ def __init__(
 
     def forward(self, x: torch.Tensor):
         autocast_dtype = get_autocast_dtype(x)
-        if autocast_dtype is not None:
+        if x.device.type == "xpu":
+            # force to use bf16 on gpu
+            x = x.to(torch.bfloat16)
+        elif autocast_dtype is not None:
             x = x.to(autocast_dtype)
         result = super().forward(x)
 
         if self.disable_adapters or self.active_adapter not in self.lora_A.keys():
             return result
         elif self.r[self.active_adapter] > 0:
             result = result.clone()
-            if autocast_dtype is None:
+            if autocast_dtype is None and x.device.type == "cpu":
                 expected_dtype = result.dtype
                 x = x.to(self.lora_A[self.active_adapter].weight.dtype)
                 output = (
@@ -357,3 +360,15 @@ def _setup_devices(self) -> "torch.device":
 # patch transformer for xpu DDP traing
 from transformers import TrainingArguments
 TrainingArguments._setup_devices = _setup_devices
+
+
+def cast_lora_weight(model, dtype=torch.bfloat16):
+    for name, module in model.named_modules():
+        if isinstance(module, LoraLayer):
+            module = module.to(dtype)
+        if 'norm' in name:
+            module = module.to(torch.float32)
+        if 'lm_head' in name or 'embed_tokens' in name:
+            if hasattr(module, 'weight'):
+                if module.weight.dtype == torch.float32:
+                    module = module.to(dtype)