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96
intel_extension_for_transformers/llm/runtime/graph/__init__.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,96 @@ | ||
| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # Copyright (c) 2023 Intel Corporation | ||
| # | ||
| # 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. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| import os | ||
| from transformers import AutoConfig | ||
| from intel_extension_for_transformers.llm.runtime.graph.scripts.convert_model import convert_model | ||
|
|
||
| model_maps = {"gpt_neox": "gptneox", "RefinedWebModel": "falcon"} | ||
|
|
||
| class Model: | ||
| def __init__(self): | ||
| self.module = None | ||
| self.model = None | ||
| self.model_type = None | ||
| self.bin_file = None | ||
|
|
||
| def __import_package(self, model_name): | ||
| if self.module: | ||
| return | ||
| if model_name == "gptj": | ||
| import intel_extension_for_transformers.llm.runtime.graph.gptj_cpp as cpp_model | ||
| elif model_name == "falcon": | ||
| import intel_extension_for_transformers.llm.runtime.graph.falcon_cpp as cpp_model | ||
| elif model_name == "gptneox": | ||
| import intel_extension_for_transformers.llm.runtime.graph.gptneox_cpp as cpp_model | ||
| elif model_name == "dolly": | ||
| import intel_extension_for_transformers.llm.runtime.graph.dolly_cpp as cpp_model | ||
| elif model_name == "llama" or model_name == "llama2": | ||
| import intel_extension_for_transformers.llm.runtime.graph.llama_cpp as cpp_model | ||
| elif model_name == "mpt": | ||
| import intel_extension_for_transformers.llm.runtime.graph.mpt_cpp as cpp_model | ||
| elif model_name == "starcoder": | ||
| import intel_extension_for_transformers.llm.runtime.graph.starcoder_cpp as cpp_model | ||
| elif model_name == "opt": | ||
| import intel_extension_for_transformers.llm.runtime.graph.opt_cpp as cpp_model | ||
| elif model_name == "bloom": | ||
| import intel_extension_for_transformers.llm.runtime.graph.bloom_cpp as cpp_model | ||
| elif model_name == "chatglm2": | ||
| import intel_extension_for_transformers.llm.runtime.graph.chatglm2_cpp as cpp_model | ||
| else: | ||
| raise TypeError("Unspported model type {}!".format(model_name)) | ||
| self.module = cpp_model | ||
|
|
||
| def init(self, model_name, **kwargs): | ||
| config = AutoConfig.from_pretrained(model_name) | ||
| model_type = model_maps.get(config.model_type, config.model_type) | ||
| self.__import_package(model_type) | ||
|
|
||
| # 1. convert model | ||
| fp32_bin = "ne_{}_f32.bin".format(model_type) | ||
| convert_model(model_name, fp32_bin, "f32") | ||
|
|
||
| # 2. quant model | ||
| quant_bin = "ne_{}_q.bin".format(model_type) | ||
| self.module.Model.quant_model(model_path = fp32_bin, out_path = quant_bin, **kwargs) | ||
|
|
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| self.model_type = model_type | ||
| self.bin_file = quant_bin | ||
|
|
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| # clean | ||
| os.remove(fp32_bin) | ||
|
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|
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| def init_from_bin(self, model_name, model_path, **kwargs): | ||
| self.__import_package(model_name) | ||
| self.model = self.module.Model() | ||
| self.model.init_model(model_path, **kwargs) | ||
|
|
||
| def quant_model(self, model_name, model_path, out_path, **kwargs): | ||
| self.__import_package(model_name) | ||
| self.module.Model.quant_model(model_path = model_path, | ||
| out_path = out_path, **kwargs) | ||
|
|
||
| def generate(self, prompt, streamer = None, sentence_mode = True, **kwargs): | ||
| # TODO support streamer | ||
| if self.model is None: | ||
| self.init_from_bin(self.model_type, self.bin_file, **kwargs) | ||
|
|
||
| out = self.model.generate(prompt = prompt, sentence_mode = sentence_mode) | ||
| return out | ||
|
|
||
| def is_token_end(self): | ||
| return self.model.is_token_end() |
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