1917bbe626
* fix bf16 related issue * fix * update based on comment & add arc lora script * update readme * update based on comment * update based on comment * update * force to bf16 * fix style * move check input dtype into function * update convert * meet code review * meet code review * update merged model to support new training_mode api * fix typo
119 lines
4.6 KiB
Python
119 lines
4.6 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This file is adapted from https://github.com/tloen/alpaca-lora/blob/main/export_hf_checkpoint.py
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#
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# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import torch
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from transformers import LlamaTokenizer # noqa: F402
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from bigdl.llm.transformers.qlora import PeftModel, LoraConfig
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from bigdl.llm.transformers import AutoModelForCausalLM
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from bigdl.llm.transformers.low_bit_linear import get_block_size
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import argparse
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import tempfile
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import shutil
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-hf",
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help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--adapter_path', type=str,)
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parser.add_argument('--output_path', type=str,)
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args = parser.parse_args()
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base_model = model_path = args.repo_id_or_model_path
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adapter_path = args.adapter_path
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tokenizer = LlamaTokenizer.from_pretrained(base_model)
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lora_config = LoraConfig.from_json_file(os.path.join(adapter_path, "adapter_config.json"))
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training_mode = lora_config.get("training_mode", "qlora")
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qa_lora = training_mode == "qalora"
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temp_dir = None
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if qa_lora:
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# Convert the qa-lora adapter to the correct shapes
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# The default 4-bit format for qa_lora is sym_int4
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block_size = get_block_size("sym_int4")
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temp_dir = tempfile.TemporaryDirectory()
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tmpdirname = os.path.join(temp_dir.name, "adapter")
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try:
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shutil.copytree(adapter_path, tmpdirname)
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except Exception as e:
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print(f"Failed to copy adapter dir, error: {e}")
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mid_lora_path = os.path.join(tmpdirname, "adapter_model.bin")
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adapter_path = os.path.join(adapter_path, "adapter_model.bin")
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lora = torch.load(adapter_path, map_location='cpu')
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# Get lora_a names
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tmp_keys = [key for key in lora.keys() if 'lora_A' in key]
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for tmp_key in tmp_keys:
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lora_a = lora[tmp_key] / block_size
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lora[tmp_key] = torch.repeat_interleave(lora_a, block_size, dim=1)
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torch.save(lora, mid_lora_path)
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adapter_path = tmpdirname
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try:
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base_model = AutoModelForCausalLM.from_pretrained(
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base_model,
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# load_in_low_bit="nf4", # should load the orignal model
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torch_dtype=torch.float16,
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device_map={"": "cpu"},
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)
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lora_model = PeftModel.from_pretrained(
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base_model,
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adapter_path,
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device_map={"": "cpu"},
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torch_dtype=torch.float16,
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)
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# merge weights - new merging method from peft
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lora_model = lora_model.merge_and_unload()
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lora_model.train(False)
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lora_model_sd = lora_model.state_dict()
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deloreanized_sd = {
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k.replace("base_model.model.", ""): v
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for k, v in lora_model_sd.items()
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if "lora" not in k
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}
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base_model.save_pretrained(args.output_path, state_dict=deloreanized_sd)
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tokenizer.save_pretrained(args.output_path)
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except Exception as e:
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print(f"Failed to merge the adapter, error: {e}.")
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finally:
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if qa_lora and temp_dir:
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temp_dir.cleanup()
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