feat(nn4k): add huggingface decode only model local sft feature (#1) (#…

…109)

Co-authored-by: xionghuaidong <[email protected]>

.licenserc.yaml

@@ -32,6 +32,7 @@ header:
     - '**/*.schema'
     - '**/*.rule'
     - '**/*.json'
+    - '**/*.json5'
     - '**/*.in'
     - '**/META-INF/services/*'
     - '**/*.conf'

python/nn4k/nn4k/consts/__init__.py

@@ -22,8 +22,7 @@
 NN_EXECUTOR_KEY = "nn_executor"
 NN_EXECUTOR_TEXT = "NN executor"
 
-NN_DEVICE_KEY = "device"
-NN_TRUST_REMOTE_CODE_KEY = "trust_remote_code"
+NN_DEVICE_KEY = "nn_device"
 
 NN_OPENAI_MODEL_NAME_KEY = NN_NAME_KEY
 NN_OPENAI_MODEL_NAME_TEXT = "openai model name"

python/nn4k/nn4k/executor/__init__.py

@@ -9,4 +9,4 @@
 # is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 # or implied.
 
-from nn4k.executor.base import NNExecutor, LLMExecutor
+from nn4k.executor.base import NNExecutor, LLMExecutor, NNModelArgs, NNAdapterModelArgs

python/nn4k/nn4k/executor/base.py

@@ -10,7 +10,8 @@
 # or implied.
 
 from abc import ABC, abstractmethod
-from typing import Union
+from dataclasses import dataclass, field
+from typing import Optional, Union
 
 
 class NNExecutor(ABC):
@@ -145,7 +146,23 @@ def from_config(cls, nn_config: Union[str, dict]) -> "NNExecutor":
         raise RuntimeError(message)
 
 
-class LLMExecutor(NNExecutor):
+class LLMExecutor(NNExecutor, ABC):
+    """
+    Base Executor for LLM.
+    """
+
+    @classmethod
+    def from_config(cls, nn_config: Union[str, dict]) -> "LLMExecutor":
+        """
+        Implement distribution logic for LLM, since we only support Huggingface Decode Only models for now,
+        it is directly point to HFDecodeOnlyExecutor. Will use the hub management functions later on.
+        """
+        from nn4k.executor.huggingface.hf_decode_only_executor import (
+            HFDecodeOnlyExecutor,
+        )
+
+        return HFDecodeOnlyExecutor.from_config(nn_config)
+
     def execute_sft(self, args=None, callbacks=None, **kwargs):
         """
         The entry point of SFT execution in a certain pod.
@@ -159,3 +176,75 @@ def execute_rl_tuning(self, args=None, callbacks=None, **kwargs):
         raise NotImplementedError(
             f"{self.__class__.__name__} does not support RL-Tuning."
         )
+
+
+@dataclass
+class NNModelArgs:
+    """
+    Base NN4K-supported model definition and load related args.
+    """
+
+    nn_name: Optional[str] = field(
+        default=None,
+        metadata={"help": ("NN4K model name")},
+    )
+    nn_version: Optional[str] = field(
+        default="default",
+        metadata={"help": ("NN4K model version, by default is 'default'")},
+    )
+    nn_model_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "model path dir, could be delivered by user or get managed in Hub."
+            )
+        },
+    )
+    nn_device: Optional[str] = field(
+        default="auto", metadata={"help": ("device to use to load model")}
+    )
+
+    def __post_init__(self):
+        assert (
+            self.nn_name is not None or self.nn_model_path is not None
+        ), "either nn_name or nn_model_path has to be provided"
+
+
+@dataclass
+class NNAdapterModelArgs(NNModelArgs):
+    """
+    One should use this args dataclass to enable adapter models.
+    """
+
+    adapter_name: str = field(
+        default=None,
+        metadata={
+            "help": "adapter name. Should be provided if you want to sft or load a adapter model."
+        },
+    )
+    adapter_version: str = field(
+        default="auto",
+        metadata={
+            "help": "adapter is designed to get managed by versions, by default is 'latest'"
+        },
+    )
+    adapter_type: str = field(
+        default="lora", metadata={"help": "adapter type, lora by default."}
+    )
+    adapter_path: str = field(
+        default=None,
+        metadata={
+            "help": "adapter weight and config path, could be delivered by user or get managed in Hub."
+        },
+    )
+    adapter_config: Optional[dict] = field(
+        default=None,
+        metadata={
+            "help": "Only necessary if you want to init a new adapter model and train from scratch or resume"
+            "from a checkpoint (in this case, should be the same as the previous adapter_config)."
+            "Values are the same as peft config init args."
+        },
+    )
+
+    def __post_init__(self):
+        super().__post_init__()

python/nn4k/nn4k/executor/huggingface/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2023 OpenSPG Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied.
+
+from nn4k.executor.huggingface.base.hf_llm_executor import HFLLMExecutor
+from nn4k.executor.huggingface.base.hf_args import HFModelArgs, HFSftArgs

python/nn4k/nn4k/executor/huggingface/base/__init__.py

@@ -0,0 +1,10 @@
+# Copyright 2023 OpenSPG Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied.