support lora target auto find

src/llmtuner/tuner/core/utils.py

@@ -0,0 +1,72 @@
+import torch
+from typing import TYPE_CHECKING, List, Optional
+
+from llmtuner.extras.constants import LAYERNORM_NAMES
+
+if TYPE_CHECKING:
+    from transformers.modeling_utils import PreTrainedModel
+
+
+def find_all_linear_modules(
+    model: "PreTrainedModel",
+    quantization_bit: Optional[int] = None,
+    output_layer_name: Optional[str] = "lm_head"
+) -> List[str]:
+    if quantization_bit is not None:
+        import bitsandbytes as bnb
+        linear_cls = bnb.nn.Linear4bit if quantization_bit == 4 else bnb.nn.Linear8bitLt
+    else:
+        linear_cls = torch.nn.Linear
+
+    module_names = set()
+    for name, module in model.named_modules():
+        if isinstance(module, linear_cls):
+            module_names.add(name.split(".")[-1])
+
+    if output_layer_name in module_names:
+        module_names.pop(output_layer_name)
+
+    return list(module_names)
+
+
+def prepare_model_for_training(
+    model: "PreTrainedModel",
+    finetuning_type: str,
+    output_layer_name: Optional[str] = "lm_head",
+    use_gradient_checkpointing: Optional[bool] = True,
+    layer_norm_names: Optional[List[str]] = LAYERNORM_NAMES
+) -> "PreTrainedModel":
+    r"""
+    Includes:
+        (1) cast the layernorm in fp32
+        (2) make output embedding layer require grads
+        (3) upcast the lm_head to fp32
+    Inspired by: https://github.com/huggingface/peft/blob/v0.2.0/src/peft/utils/other.py#L33
+    """
+    for name, param in model.named_parameters():
+        if param.ndim == 1 and any(layer_norm_name in name for layer_norm_name in layer_norm_names):
+            param.data = param.data.to(torch.float32)
+
+    if use_gradient_checkpointing:
+        if hasattr(model, "enable_input_require_grads"):
+            model.enable_input_require_grads()
+        else:
+            def make_inputs_require_grad(module, input, output):
+                output.requires_grad_(True)
+            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        model.gradient_checkpointing_enable()
+        model.config.use_cache = False # turn off when gradient checkpointing is enabled
+
+    if finetuning_type != "full" and hasattr(model, output_layer_name):
+        output_layer: torch.nn.Linear = getattr(model, output_layer_name)
+        input_dtype = output_layer.weight.dtype
+
+        class CastOutputToFloat(torch.nn.Sequential):
+
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                return super().forward(x.to(input_dtype)).to(torch.float32)
+
+        setattr(model, output_layer_name, CastOutputToFloat(output_layer))
+
+    return model