定义一个名为 grok_1_model 的 LanguageModelConfig 对象，该对象包含有关模型配置的详细信息，例如词汇表大小、序列长度、嵌入层初始化比例、输出和嵌入层的乘数比例等。模型的架构是一个 TransformerConfig 对象，其中包括了嵌入大小、扩展因子、键大小、头数量、层数、注意力输出乘数等参数。

2、定义并初始化推理运行器

2.1、创建一个 InferenceRunner 对象(用于运行模型推理)

InferenceRunner 接受一个 ModelRunner 对象作为参数，该对象包含了模型配置、批处理大小、检查点路径等信息。InferenceRunner 还需要指定一些其他参数，如名称、加载路径、分词器路径、本地和跨主机配置等。

2.2、调用 inference_runner.initialize() 方法初始化推理运行器。

2.3、调用 inference_runner.run() 方法运行模型推理并获取生成器。

3、模型生成

定义一个输入字符串 inp，然后使用 sample_from_model 函数从生成器中获取一个样本，并将其打印出来。

全部代码


# Copyright 2024 X.AI Corp.
#
# 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 logging
 
from model import LanguageModelConfig, TransformerConfig, QuantizedWeight8bit as QW8Bit
from runners import InferenceRunner, ModelRunner, sample_from_model
 
 
CKPT_PATH = "./checkpoints/"
 
 
def main():
    grok_1_model = LanguageModelConfig(
        vocab_size=128 * 1024,
        pad_token=0,
        eos_token=2,
        sequence_len=8192,
        embedding_init_scale=1.0,
        output_multiplier_scale=0.5773502691896257,
        embedding_multiplier_scale=78.38367176906169,
        model=TransformerConfig(
            emb_size=48 * 128,
            widening_factor=8,
            key_size=128,
            num_q_heads=48,
            num_kv_heads=8,
            num_layers=64,
            attn_output_multiplier=0.08838834764831845,
            shard_activations=True,
            # MoE.
            num_experts=8,
            num_selected_experts=2,
            # Activation sharding.
            data_axis="data",
            model_axis="model",
        ),
    )
    inference_runner = InferenceRunner(
        pad_sizes=(1024,),
        runner=ModelRunner(
            model=grok_1_model,
            bs_per_device=0.125,
            checkpoint_path=CKPT_PATH,
        ),
        name="local",
        load=CKPT_PATH,
        tokenizer_path="./tokenizer.model",
        local_mesh_config=(1, 8),
        between_hosts_config=(1, 1),
    )
    inference_runner.initialize()
    gen = inference_runner.run()
 
    inp = "The answer to life the universe and everything is of course"
    print(f"Output for prompt: {inp}", sample_from_model(gen, inp, max_len=100, temperature=0.01))
 
 
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    main()

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