add e2e tests
Former-commit-id: 0156a37450604641c4f5f9756ad84324698fc88c
This commit is contained in:
hiyouga
@@ -23,12 +23,133 @@ from typing_extensions import Self
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@dataclass
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class ModelArguments:
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class QuantizationArguments:
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r"""
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Arguments pertaining to the quantization method.
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"""
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quantization_method: Literal["bitsandbytes", "hqq", "eetq"] = field(
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default="bitsandbytes",
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metadata={"help": "Quantization method to use for on-the-fly quantization."},
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)
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quantization_bit: Optional[int] = field(
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default=None,
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metadata={"help": "The number of bits to quantize the model using on-the-fly quantization."},
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)
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quantization_type: Literal["fp4", "nf4"] = field(
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default="nf4",
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metadata={"help": "Quantization data type to use in bitsandbytes int4 training."},
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)
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double_quantization: bool = field(
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default=True,
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metadata={"help": "Whether or not to use double quantization in bitsandbytes int4 training."},
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)
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quantization_device_map: Optional[Literal["auto"]] = field(
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default=None,
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metadata={"help": "Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0."},
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)
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@dataclass
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class ProcessorArguments:
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r"""
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Arguments pertaining to the image processor.
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"""
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image_resolution: int = field(
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default=512,
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metadata={"help": "Keeps the height or width of image below this resolution."},
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)
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video_resolution: int = field(
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default=128,
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metadata={"help": "Keeps the height or width of video below this resolution."},
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)
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video_fps: float = field(
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default=2.0,
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metadata={"help": "The frames to sample per second for video inputs."},
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)
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video_maxlen: int = field(
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default=64,
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metadata={"help": "The maximum number of sampled frames for video inputs."},
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)
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@dataclass
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class ExportArguments:
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r"""
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Arguments pertaining to the model export.
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"""
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export_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Path to the directory to save the exported model."},
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)
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export_size: int = field(
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default=1,
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metadata={"help": "The file shard size (in GB) of the exported model."},
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)
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export_device: Literal["cpu", "auto"] = field(
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default="cpu",
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metadata={"help": "The device used in model export, use `auto` to accelerate exporting."},
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)
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export_quantization_bit: Optional[int] = field(
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default=None,
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metadata={"help": "The number of bits to quantize the exported model."},
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)
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export_quantization_dataset: Optional[str] = field(
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default=None,
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metadata={"help": "Path to the dataset or dataset name to use in quantizing the exported model."},
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)
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export_quantization_nsamples: int = field(
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default=128,
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metadata={"help": "The number of samples used for quantization."},
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)
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export_quantization_maxlen: int = field(
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default=1024,
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metadata={"help": "The maximum length of the model inputs used for quantization."},
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)
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export_legacy_format: bool = field(
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default=False,
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metadata={"help": "Whether or not to save the `.bin` files instead of `.safetensors`."},
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)
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export_hub_model_id: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the repository if push the model to the Hugging Face hub."},
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)
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@dataclass
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class VllmArguments:
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r"""
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Arguments pertaining to the vLLM worker.
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"""
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vllm_maxlen: int = field(
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default=2048,
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metadata={"help": "Maximum sequence (prompt + response) length of the vLLM engine."},
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)
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vllm_gpu_util: float = field(
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default=0.9,
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metadata={"help": "The fraction of GPU memory in (0,1) to be used for the vLLM engine."},
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)
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vllm_enforce_eager: bool = field(
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default=False,
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metadata={"help": "Whether or not to disable CUDA graph in the vLLM engine."},
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)
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vllm_max_lora_rank: int = field(
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default=32,
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metadata={"help": "Maximum rank of all LoRAs in the vLLM engine."},
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)
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@dataclass
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class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments, VllmArguments):
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r"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune or infer.
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"""
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model_name_or_path: str = field(
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": "Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models."
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},
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@@ -74,26 +195,6 @@ class ModelArguments:
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default=True,
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metadata={"help": "Whether or not to use memory-efficient model loading."},
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)
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quantization_method: Literal["bitsandbytes", "hqq", "eetq"] = field(
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default="bitsandbytes",
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metadata={"help": "Quantization method to use for on-the-fly quantization."},
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)
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quantization_bit: Optional[int] = field(
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default=None,
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metadata={"help": "The number of bits to quantize the model using bitsandbytes."},
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)
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quantization_type: Literal["fp4", "nf4"] = field(
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default="nf4",
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metadata={"help": "Quantization data type to use in int4 training."},
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)
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double_quantization: bool = field(
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default=True,
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metadata={"help": "Whether or not to use double quantization in int4 training."},
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)
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quantization_device_map: Optional[Literal["auto"]] = field(
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default=None,
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metadata={"help": "Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0."},
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)
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rope_scaling: Optional[Literal["linear", "dynamic"]] = field(
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default=None,
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metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
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@@ -138,34 +239,10 @@ class ModelArguments:
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default=False,
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metadata={"help": "Whether or not to randomly initialize the model weights."},
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)
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image_resolution: int = field(
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default=512,
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metadata={"help": "Keeps the height or width of image below this resolution."},
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)
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video_fps: float = field(
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default=2.0,
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metadata={"help": "The frames to sample per second for video training."},
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)
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infer_backend: Literal["huggingface", "vllm"] = field(
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default="huggingface",
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metadata={"help": "Backend engine used at inference."},
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)
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vllm_maxlen: int = field(
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default=2048,
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metadata={"help": "Maximum sequence (prompt + response) length of the vLLM engine."},
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)
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vllm_gpu_util: float = field(
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default=0.9,
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metadata={"help": "The fraction of GPU memory in (0,1) to be used for the vLLM engine."},
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)
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vllm_enforce_eager: bool = field(
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default=False,
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metadata={"help": "Whether or not to disable CUDA graph in the vLLM engine."},
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)
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vllm_max_lora_rank: int = field(
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default=32,
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metadata={"help": "Maximum rank of all LoRAs in the vLLM engine."},
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)
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offload_folder: str = field(
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default="offload",
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metadata={"help": "Path to offload model weights."},
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@@ -186,42 +263,6 @@ class ModelArguments:
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default=None,
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metadata={"help": "Auth token to log in with ModelScope Hub."},
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)
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export_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Path to the directory to save the exported model."},
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)
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export_size: int = field(
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default=1,
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metadata={"help": "The file shard size (in GB) of the exported model."},
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)
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export_device: Literal["cpu", "auto"] = field(
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default="cpu",
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metadata={"help": "The device used in model export, use `auto` to accelerate exporting."},
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)
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export_quantization_bit: Optional[int] = field(
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default=None,
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metadata={"help": "The number of bits to quantize the exported model."},
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)
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export_quantization_dataset: Optional[str] = field(
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default=None,
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metadata={"help": "Path to the dataset or dataset name to use in quantizing the exported model."},
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)
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export_quantization_nsamples: int = field(
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default=128,
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metadata={"help": "The number of samples used for quantization."},
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)
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export_quantization_maxlen: int = field(
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default=1024,
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metadata={"help": "The maximum length of the model inputs used for quantization."},
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)
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export_legacy_format: bool = field(
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default=False,
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metadata={"help": "Whether or not to save the `.bin` files instead of `.safetensors`."},
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)
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export_hub_model_id: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the repository if push the model to the Hugging Face hub."},
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)
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print_param_status: bool = field(
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default=False,
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metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
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@@ -248,6 +289,9 @@ class ModelArguments:
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)
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def __post_init__(self):
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if self.model_name_or_path is None:
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raise ValueError("Please provide `model_name_or_path`.")
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if self.split_special_tokens and self.use_fast_tokenizer:
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raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")
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