ludwig-ai · arnavgarg1 · Sep 27, 2023 · Sep 21, 2023 · Sep 21, 2023 · Sep 21, 2023
@@ -429,8 +429,9 @@ def train(
             will contain the training statistics, TensorBoard logs, the saved
             model and the training progress files.
         :param random_seed: (int, default: `42`) a random seed that will be
-               used anywhere there is a call to a random number generator: data
-               splitting, parameter initialization and training set shuffling
+            used anywhere there is a call to a random number generator: data
+            splitting, parameter initialization and training set shuffling
+        :param kwargs: (dict, default: {}) a dictionary of optional parameters.
 
         # Return
 
@@ -645,6 +646,9 @@ def on_epoch_end(self, trainer, progress_tracker, save_path):
                     )
                     (self.model, train_trainset_stats, train_valiset_stats, train_testset_stats) = train_stats
 
+                    # For an LLM model trained with a LoRA adapter, handle merge and unload postprocessing directives.
+                    self._merge_and_unload()
+
                     # Calibrates output feature probabilities on validation set if calibration is enabled.
                     # Must be done after training, and before final model parameters are saved.
                     if self.backend.is_coordinator():
@@ -807,6 +811,21 @@ def train_online(
 
         self.model = self._online_trainer.train_online(training_dataset)
 
+    def _merge_and_unload(self) -> None:
+        """For an LLM model trained with a LoRA adapter, handle merge and unload postprocessing directives.
+
+        First, check that the model is of the "llm" type.  Then check if the "adapter" configuration section contains
+        the "postprocessor" subsection and apply the "merge_adapter_into_base_model" and "progressbar" directives.
+        """
+        if (
+            self.config_obj.model_type == "llm"
+            and self.config_obj.adapter is not None
+            and self.config_obj.adapter.postprocessor is not None
+            and self.config_obj.adapter.postprocessor.merge_adapter_into_base_model
+            and hasattr(self.model, "merge_and_unload")
+        ):
+            self.model.merge_and_unload(progressbar=self.config_obj.adapter.postprocessor.progressbar)
+
     def _tune_batch_size(self, trainer, dataset, random_seed: int = default_random_seed):
         """Sets AUTO batch-size-related parameters based on the trainer, backend type, and number of workers.
 
@@ -1643,6 +1662,9 @@ def load(
         # load model weights
         ludwig_model.load_weights(model_dir)
 
+        # The LoRA layers appear to be loaded again (perhaps due to a potential bug); hence, we merge and unload again.
+        ludwig_model._merge_and_unload()
+
         # load train set metadata
         ludwig_model.training_set_metadata = backend.broadcast_return(
             lambda: load_metadata(os.path.join(model_dir, TRAIN_SET_METADATA_FILE_NAME))

@@ -283,6 +283,8 @@
 PROMPT = "prompt"
 ADAPTER = "adapter"
 PRETRAINED_ADAPTER_WEIGHTS = "pretrained_adapter_weights"
+MERGE_ADAPTER_INTO_BASE_MODEL = "merge_adapter_into_base_model"
+PROGRESSBAR = "progressbar"
 
 # CrossEntropyLoss for LLMs
 IGNORE_INDEX_TOKEN_ID = -100

@@ -451,6 +451,21 @@ def generate(
 
         return outputs
 
+    def merge_and_unload(self, progressbar: bool = False) -> None:
+        """This method merges the LoRa layers into the base model.  This is needed if someone wants to use the base
+        model as a standalone model.  The implementation calls merge_and_unload() of the underlying LoraModel class
+        (in peft).
+
+        Args:
+            progressbar (bool): whether to show a progressbar indicating the unload and merge process
+        """
+        from peft import LoraModel
+
+        if isinstance(self.model.base_model, LoraModel):
+            self.model.base_model.merge_and_unload(progressbar=progressbar)
+        else:
+            raise ValueError("This operation requires an LLM model trained with a LoRA adapter.")
+
     def _unpack_inputs(
         self,
         inputs: Union[

@@ -25,6 +25,32 @@ def wrap(config: BaseAdapterConfig):
     return wrap
 
 
+@DeveloperAPI
+@ludwig_dataclass
+class LoraPostprocessorConfig(schema_utils.BaseMarshmallowConfig):
+    """This Dataclass is a schema for the nested postprocessing config under adapter of type "lora"."""
+
+    merge_adapter_into_base_model: bool = schema_utils.Boolean(
+        default=False,
+        description="""Instructs whether or not the fine-tuned LoRA weights are to be merged into the base LLM model so
+that the complete fine-tuned model is available to be used and/or persisted, and then reused upon loading as a single
+model (rather than having to load base and fine-tuned models separately).""",
+    )
+    progressbar: bool = schema_utils.Boolean(
+        default=False,
+        description="Instructs whether or not to show a progress bar indicating the unload and merge process.",
+    )
+
+
+@DeveloperAPI
+class LoraPostprocessorConfigField(schema_utils.DictMarshmallowField):
+    def __init__(self):
+        super().__init__(LoraPostprocessorConfig)
+
+    def _jsonschema_type_mapping(self):
+        return schema_utils.unload_jsonschema_from_marshmallow_class(LoraPostprocessorConfig, title="LoraPostprocessor")
+
+
 @DeveloperAPI
 @ludwig_dataclass
 class BaseAdapterConfig(schema_utils.BaseMarshmallowConfig, ABC):
@@ -34,6 +60,8 @@ class BaseAdapterConfig(schema_utils.BaseMarshmallowConfig, ABC):
         default=None, description="Path to pretrained weights.", allow_none=True
     )
 
+    postprocessor: LoraPostprocessorConfig = LoraPostprocessorConfigField().get_default_field()
+
     @abstractmethod
     def to_config(self, **kwargs) -> "PeftConfig":
         pass

@@ -1,5 +1,5 @@
 import os
-from typing import Dict, Tuple
+from typing import Dict, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -15,11 +15,14 @@
     EPOCHS,
     GENERATION,
     INPUT_FEATURES,
+    MERGE_ADAPTER_INTO_BASE_MODEL,
     MODEL_LLM,
     MODEL_TYPE,
     OUTPUT_FEATURES,
+    POSTPROCESSOR,
     PREPROCESSING,
     PRETRAINED_ADAPTER_WEIGHTS,
+    PROGRESSBAR,
     PROMPT,
     TRAINER,
     TYPE,
@@ -352,6 +355,84 @@ def _prepare_finetuning_test(
     return train_df, prediction_df, config
 
 
+def _finetune_strategy_requires_cuda(finetune_strategy_name: str, quantization_args: Union[dict, None]) -> bool:
+    """This method returns whether or not a given finetine_strategy requires CUDA.
+
+    For all finetune strategies, except "qlora", the decision is based just on the name of the finetine_strategy; in the
+    case of qlora, if the quantization dictionary is non-empty (i.e., contains quantization specifications), then the
+    original finetine_strategy name of "lora" is interpreted as "qlora" and used in the lookup, based on the list of
+    finetine strategies requiring CUDA.
+    """
+    cuda_only_finetune_strategy_names: list[str] = [
+        "prompt_tuning",
+        "prefix_tuning",
+        "p_tuning",
+        "qlora",
+    ]
+
+    if finetune_strategy_name == "lora" and quantization_args:
+        finetune_strategy_name = "qlora"
+
+    return finetune_strategy_name in cuda_only_finetune_strategy_names
+
+
+def _verify_lm_lora_finetuning_layers(
+    attention_layer: torch.nn.Module,
+    merge_adapter_into_base_model: bool,
+    expected_lora_in_features: int,
+    expected_lora_out_features: int,
+) -> bool:
+    """This method verifies that LoRA finetuning layers have correct types and shapes, depending on whether or not
+    the optional "model.merge_and_unload()" method (based on the "merge_adapter_into_base_model" directive) was
+    executed.
+
+    If merge_adapter_into_base_model is True, then both LoRA projection layers, V and Q, in the attention layer must
+    contain square weight matrices (with the dimensions expected_lora_in_features by expected_lora_in_features).
+    However, if merge_adapter_into_base_model is False, then the LoRA part of the attention layer must include Lora_A
+    and Lora_B children layers for each of V and Q projections, such that the product of V and Q matrices is a square
+    matrix (with the dimensions expected_lora_in_features by expected_lora_in_features) for both V and Q projections.
+    """
+    success: bool = True
+    success = success and isinstance(attention_layer.v_proj, torch.nn.Linear)
+    success = success and isinstance(attention_layer.q_proj, torch.nn.Linear)
+    if merge_adapter_into_base_model:
+        success = success and (attention_layer.v_proj.in_features, attention_layer.v_proj.out_features) == (
+            expected_lora_in_features,
+            expected_lora_out_features,
+        )
+        success = success and (attention_layer.q_proj.in_features, attention_layer.q_proj.out_features) == (
+            expected_lora_in_features,
+            expected_lora_out_features,
+        )
+        success = success and not list(attention_layer.v_proj.children())
+        success = success and not list(attention_layer.q_proj.children())
+    else:
+        v_proj_named_children: dict[str, torch.nn.Modeule] = dict(attention_layer.v_proj.named_children())
+        assert isinstance(v_proj_named_children["lora_A"]["default"], torch.nn.Linear)
+        assert (
+            v_proj_named_children["lora_A"]["default"].in_features,
+            v_proj_named_children["lora_A"]["default"].out_features,
+        ) == (expected_lora_in_features, expected_lora_out_features)
+        assert isinstance(v_proj_named_children["lora_B"]["default"], torch.nn.Linear)
+        assert (
+            v_proj_named_children["lora_B"]["default"].in_features,
+            v_proj_named_children["lora_B"]["default"].out_features,
+        ) == (expected_lora_out_features, expected_lora_in_features)
+        q_proj_named_children: dict[str, torch.nn.Modeule] = dict(attention_layer.q_proj.named_children())
+        assert isinstance(q_proj_named_children["lora_A"]["default"], torch.nn.Linear)
+        assert (
+            q_proj_named_children["lora_A"]["default"].in_features,
+            q_proj_named_children["lora_A"]["default"].out_features,
+        ) == (expected_lora_in_features, expected_lora_out_features)
+        assert isinstance(q_proj_named_children["lora_B"]["default"], torch.nn.Linear)
+        assert (
+            q_proj_named_children["lora_B"]["default"].in_features,
+            q_proj_named_children["lora_B"]["default"].out_features,
+        ) == (expected_lora_out_features, expected_lora_in_features)
+
+    return success
+
+
 # TODO(arnav): p-tuning and prefix tuning have errors when enabled that seem to stem from DDP:
 #
 # prefix tuning:
@@ -376,8 +457,12 @@ def _prepare_finetuning_test(
         (None, {}),
         ("lora", {}),
         ("lora", {"r": 4, "dropout": 0.1}),
+        ("lora", {POSTPROCESSOR: {MERGE_ADAPTER_INTO_BASE_MODEL: True, PROGRESSBAR: True}}),
+        ("lora", {POSTPROCESSOR: {MERGE_ADAPTER_INTO_BASE_MODEL: False}}),
         ("adalora", {}),
         ("adalora", {"init_r": 8, "beta1": 0.8}),
+        ("adalora", {POSTPROCESSOR: {MERGE_ADAPTER_INTO_BASE_MODEL: True, PROGRESSBAR: True}}),
+        ("adalora", {POSTPROCESSOR: {MERGE_ADAPTER_INTO_BASE_MODEL: False}}),
         ("adaption_prompt", {}),
         ("adaption_prompt", {"adapter_len": 6, "adapter_layers": 1}),
         # (
@@ -403,8 +488,12 @@ def _prepare_finetuning_test(
         "full",
         "lora-defaults",
         "lora-modified-defaults",
+        "lora_merged",
+        "lora_not_merged",
         "adalora-defaults",
         "adalora-modified-defaults",
+        "adalora_merged",
+        "adalora_not_merged",
         "adaption_prompt-defaults",
         "adaption_prompt-modified-defaults",
         # "prompt_tuning_init_random",
@@ -445,7 +534,10 @@ def test_llm_finetuning_strategies(tmpdir, csv_filename, backend, finetune_strat
     ],
 )
 def test_llm_finetuning_strategies_quantized(tmpdir, csv_filename, finetune_strategy, adapter_args, quantization):
-    if quantization and (not torch.cuda.is_available() or torch.cuda.device_count() == 0):
+    if (
+        _finetune_strategy_requires_cuda(finetune_strategy_name=finetune_strategy, quantization_args=quantization)
+        and not (torch.cuda.is_available() and torch.cuda.device_count()) > 0
+    ):
         pytest.skip("Skip: quantization requires GPU and none are available.")
 
     backend = LOCAL_BACKEND
@@ -469,6 +561,66 @@ def test_llm_finetuning_strategies_quantized(tmpdir, csv_filename, finetune_stra
     assert preds
 
 
+@pytest.mark.llm
+@pytest.mark.parametrize(
+    "backend",
+    [
+        pytest.param(LOCAL_BACKEND, id="local"),
+        # TODO: Re-enable once we can run tests on GPUs
+        # This is because fine-tuning requires Ray with the deepspeed strategy, and deepspeed
+        # only works with GPUs
+        # pytest.param(RAY_BACKEND, id="ray"),
+    ],
+)
+@pytest.mark.parametrize(
+    "merge_adapter_into_base_model,expected_lora_in_features,expected_lora_out_features",
+    [
+        pytest.param(
+            False,
+            32,
+            8,
+            id="lora_not_merged",
+        ),
+        pytest.param(
+            True,
+            32,
+            32,
+            id="lora_merged",
+        ),
+    ],
+)
+def test_llm_lora_finetuning_merge_and_unload(
+    tmpdir, csv_filename, backend, merge_adapter_into_base_model, expected_lora_in_features, expected_lora_out_features
+):
+    finetune_strategy: str = "lora"
+    adapter_args: dict = {
+        POSTPROCESSOR: {
+            MERGE_ADAPTER_INTO_BASE_MODEL: merge_adapter_into_base_model,
+        },
+    }
+    train_df, prediction_df, config = _prepare_finetuning_test(
+        csv_filename=csv_filename, finetune_strategy=finetune_strategy, backend=backend, adapter_args=adapter_args
+    )
+
+    model = LudwigModel(config)
+    model.train(dataset=train_df, output_directory=str(tmpdir), skip_save_processed_input=False)
+    assert _verify_lm_lora_finetuning_layers(
+        attention_layer=model.model.model.base_model.model.transformer.h[1].attn,
+        merge_adapter_into_base_model=merge_adapter_into_base_model,
+        expected_lora_in_features=expected_lora_in_features,
+        expected_lora_out_features=expected_lora_out_features,
+    )
+
+    # Make sure we can load the saved model and verify that the LoRA layers have expected shapes.
+    model = LudwigModel.load(os.path.join(str(tmpdir), "api_experiment_run", "model"), backend=backend)
+    assert _verify_lm_lora_finetuning_layers(
+        attention_layer=model.model.model.base_model.model.transformer.h[1].attn,
+        merge_adapter_into_base_model=merge_adapter_into_base_model,
+        expected_lora_in_features=expected_lora_in_features,
+        expected_lora_out_features=expected_lora_out_features,
+    )
+
+
 @pytest.mark.llm
 @pytest.mark.parametrize("use_adapter", [True, False], ids=["with_adapter", "without_adapter"])
 def test_llm_training_with_gradient_checkpointing(tmpdir, csv_filename, use_adapter):
@@ -628,23 +780,37 @@ def test_load_pretrained_adapter_weights(adapter):
 
 def _compare_models(model_1: torch.nn.Module, model_2: torch.nn.Module) -> bool:
     # For a full explanation of this 8-bit workaround, see https://github.com/ludwig-ai/ludwig/pull/3606
-    def filter_for_weight_format(i):
-        """Remove bitsandbytes metadata keys added on state dict creation.
 
-        8-bit quantized models that have been put on gpu will have a set of `weight_format` keys in their state dict.
-        These contain strings that are used to reshape quantized tensors, however these have no impact until the state
-        dict is loaded into a model. These keys were causing `torch.equal` to raise an exception, so we skip them in the
-        evaluation.
-        """
-        return "weight_format" not in i[0]
+    # TODO: Uncomment "filter_for_weight_format()" method definition and enable its usage once GPU tests are set up.
+    # def filter_for_weight_format(i):
+    #     """Remove bitsandbytes metadata keys added on state dict creation.
+    #
+    #     8-bit quantized models that have been put on gpu will have a set of `weight_format` keys in their state dict.
+    #     These contain strings that are used to reshape quantized tensors, however these have no impact until the state
+    #     dict is loaded into a model. These keys were causing `torch.equal` to raise an exception, so we skip them in
+    #     the evaluation.
+    #     """
+    #     return "weight_format" not in i[0]
 
-    model_1_filtered_state_dict = filter(filter_for_weight_format, model_1.state_dict().items())
-    model_2_filtered_state_dict = filter(filter_for_weight_format, model_2.state_dict().items())
+    # model_1_filtered_state_dict = filter(filter_for_weight_format, model_1.state_dict().items())
+    # model_2_filtered_state_dict = filter(filter_for_weight_format, model_2.state_dict().items())
 
     # Source: https://discuss.pytorch.org/t/check-if-models-have-same-weights/4351/6
-    for key_item_1, key_item_2 in zip(model_1_filtered_state_dict, model_2_filtered_state_dict):
+
+    if model_1.__class__.__name__ != model_2.__class__.__name__:
+        return False
+
+    if (
+        hasattr(model_1, "model")
+        and hasattr(model_2, "model")
+        and not _compare_models(model_1=model_1.model, model_2=model_2.model)
+    ):
+        return False
+
+    for key_item_1, key_item_2 in zip(model_1.state_dict().items(), model_2.state_dict().items()):
         if not torch.equal(key_item_1[1], key_item_2[1]):
             return False
+
     return True