mirror of
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111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import torch
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import sys
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import warnings
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from pilot.singleton import Singleton
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from typing import Optional
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from pilot.model.compression import compress_module
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from pilot.model.adapter import get_llm_model_adapter
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from pilot.utils import get_gpu_memory
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from pilot.model.llm.monkey_patch import replace_llama_attn_with_non_inplace_operations
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def raise_warning_for_incompatible_cpu_offloading_configuration(
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device: str, load_8bit: bool, cpu_offloading: bool
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):
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if cpu_offloading:
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if not load_8bit:
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warnings.warn(
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"The cpu-offloading feature can only be used while also using 8-bit-quantization.\n"
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"Use '--load-8bit' to enable 8-bit-quantization\n"
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"Continuing without cpu-offloading enabled\n"
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)
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return False
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if not "linux" in sys.platform:
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warnings.warn(
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"CPU-offloading is only supported on linux-systems due to the limited compatability with the bitsandbytes-package\n"
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"Continuing without cpu-offloading enabled\n"
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)
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return False
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if device != "cuda":
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warnings.warn(
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"CPU-offloading is only enabled when using CUDA-devices\n"
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"Continuing without cpu-offloading enabled\n"
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)
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return False
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return cpu_offloading
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class ModelLoader(metaclass=Singleton):
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"""Model loader is a class for model load
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Args: model_path
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TODO: multi model support.
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"""
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kwargs = {}
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def __init__(self,
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model_path) -> None:
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.model_path = model_path
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self.kwargs = {
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"torch_dtype": torch.float16,
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"device_map": "auto",
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}
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# TODO multi gpu support
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def loader(self, num_gpus, load_8bit=False, debug=False, cpu_offloading=False, max_gpu_memory: Optional[str]=None):
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if self.device == "cpu":
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kwargs = {"torch_dtype": torch.float32}
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elif self.device == "cuda":
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kwargs = {"torch_dtype": torch.float16}
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num_gpus = int(num_gpus)
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if num_gpus != 1:
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kwargs["device_map"] = "auto"
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if max_gpu_memory is None:
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kwargs["device_map"] = "sequential"
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available_gpu_memory = get_gpu_memory(num_gpus)
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kwargs["max_memory"] = {
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i: str(int(available_gpu_memory[i] * 0.85)) + "GiB"
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for i in range(num_gpus)
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}
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else:
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kwargs["max_memory"] = {i: max_gpu_memory for i in range(num_gpus)}
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elif self.device == "mps":
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kwargs = kwargs = {"torch_dtype": torch.float16}
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replace_llama_attn_with_non_inplace_operations()
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else:
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raise ValueError(f"Invalid device: {self.device}")
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# TODO when cpu loading, need use quantization config
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llm_adapter = get_llm_model_adapter(self.model_path)
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model, tokenizer = llm_adapter.loader(self.model_path, kwargs)
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if load_8bit:
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if num_gpus != 1:
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warnings.warn(
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"8-bit quantization is not supported for multi-gpu inference"
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)
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else:
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compress_module(model, self.device)
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if (self.device == "cuda" and num_gpus == 1 and not cpu_offloading) or self.device == "mps":
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model.to(self.device)
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if debug:
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print(model)
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return model, tokenizer
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