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95 lines
4.7 KiB
Python
95 lines
4.7 KiB
Python
# coding=utf-8
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# Copyright (c) 2025, HUAWEI CORPORATION. All rights reserved.
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from mindspeed_rl.config_cls.base_config import BaseConfig
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class GenerateConfig(BaseConfig):
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"""
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Generate configuration class.
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Initialize model configuration from the provided config dictionary.
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All instance attributes are initialized using the dictionary keys.
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:param config_dict: Dictionary containing the configuration parameters.
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If None, default values will be used for all attributes.
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data_parallel_size: data parallel size for rollout (default: None)
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tokenizer_name_or_path: Path or name of the tokenizer. Default is "/path/to/tokenizer".
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trust_remote_code: Whether to trust remote code (e.g., for custom tokenizers). Default is True.
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infer_tensor_parallel_size: Tensor parallel size during inference. Default is 8.
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infer_pipeline_parallel_size: Pipeline parallel size during inference. Default is 1.
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infer_expert_parallel_size: Expert parallel size during inference. Default is 1.
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max_num_seqs: Maximum number of sequences to process simultaneously. Default is 256.
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max_model_len: Maximum model length (in tokens). Default is 2048.
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dtype: Data type for model weights. Default is "bfloat16".
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gpu_memory_utilization: GPU memory utilization factor. Default is 0.5.
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enforce_eager: Whether to always use eager-mode PyTorch. If True, we will disable ACL graph and always execute the model in eager mode.
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If False, we will use ACL graph and eager execution in hybrid for maximal performance and flexibility.
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sampling_config: Configuration for text generation sampling. Default values are set for various sampling parameters.
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- num_completions: The number of independent completions to generate for each input prompt. Default is 1.
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- logprobs: The number of top tokens to return log probabilities for. Default is 1.
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- max_tokens: The maximum number of tokens to generate in the output. Default is 128.
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- best_of: The number of candidate completions to generate internally before returning the best one. Default is 2.
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- top_p: The cumulative probability threshold for nucleus sampling. Default is 1.0.
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- top_k: The number of highest - probability tokens to consider for sampling. Default is 50.
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- min_p: The minimum probability threshold for token selection. Default is 0.0.
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- temperature: Controls the randomness of predictions by scaling the logits before applying softmax. Default is 0.2.
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- detokenize: Whether to convert the generated tokens back into a human - readable string. Default is False.
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"""
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def __init__(self, config_dict):
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self.data_parallel_size = None
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# 设置 tokenizer 的名称或路径，默认指向一个示例路径，可根据实际情况修改
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self.tokenizer_name_or_path = "/path/to/tokenizer"
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# 是否信任远程代码，例如用于自定义 tokenizer，默认为 True
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self.trust_remote_code = True
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# 推理时的张量并行大小，默认为 8
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self.infer_tensor_parallel_size = 8
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# 推理时的流水线并行大小，默认为 1
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self.infer_pipeline_parallel_size = 1
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# 推理时的专家并行大小，默认为 1
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self.infer_expert_parallel_size = 1
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# 最大可处理的序列数量，默认为 1
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self.max_num_seqs = 1
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# 模型的最大长度（以 token 为单位），默认为 2048
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self.max_model_len = 2048
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self.max_num_batched_tokens = 2048
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# 模型权重的数据类型，默认为 bfloat16
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self.dtype = "bfloat16"
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# GPU 内存的利用率，默认为 0.5
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self.gpu_memory_utilization = 0.5
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self.offload_train_optimizer = False
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self.offload_train_grad = False
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self.offload_train_param = False
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self.enable_prefix_caching = False
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self.num_scheduler_steps = 1
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self.enforce_eager = False
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# 采样配置的默认值，用于生成文本时的采样策略设置
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self.sampling_config = {
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"logprobs": 1, # 返回的 top token 的对数概率数量
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"max_tokens": 128, # 生成输出的最大 token 数量
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"top_p": 1.0, # 核采样的累积概率阈值
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"top_k": 50, # 采样时考虑的最高概率 token 的数量
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"min_p": 0.0, # token 选择的最小概率阈值
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"temperature": 0.2, # 控制预测随机性的温度参数
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"detokenize": False, # 是否将生成的 token 转换回可读字符串
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"seed": None # 随机种子
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}
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# 如果提供了配置字典，则更新默认值
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if config_dict is not None:
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self.update(config_dict)
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