mindformers.models.glm2.ChatGLM2ForConditionalGeneration

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class mindformers.models.glm2.ChatGLM2ForConditionalGeneration(config: ChatGLM2Config, **kwargs)[source]

Provide ChatGLM2 training loss or logits through network.

Parameters

  • config (ChatGLM2Config) – The config of ChatGLM2Model.

  • kwargs (dict, optional) – A variable number of keyword parameters reserved for the keyword parameters to be expanded.

Inputs:

  • input_ids (Tensor, optional) - A tokenized input tensor, which is of int32 integer type and has a shape of (batch, seq_length). Default: None .

  • labels (Tensor, optional) - A tokenized label tensor, which is of int32 integer type and has a shape of (batch, seq_length). Default: None .

  • input_position (Tensor, optional) - The current position, used in predict. Default: None .

  • position_ids (Tensor, optional) - Keep the parameter unused. Default: None .

  • attention_mask (Tensor, optional) - Keep the parameter unused. Default: None .

  • input_embeds (Tensor, optional) - Keep the parameter unused. Default: None .

  • init_reset (Tensor, optional) - A bool tensor with shape [1], used to clear previous key-value pairs in incremental inference. Default: None .

  • batch_valid_length (Tensor, optional) - In incremental inference, a tensor used for calculating the index of the previous step. It is of int32 type and has a shape of [batch_size]. Default: None .

  • prefix_key_values (Tensor, optional) - A set of additional key-value pairs added before the regular key-value pairs. These prefix key-value pairs can be used to capture long-term dependencies or provide prior knowledge, thereby helping the model better understand and generate sequences. Default: None .

  • block_tables (Tensor, optional) - Store the mapping table for each sequence. Default: None .

  • slot_mapping (Tensor, optional) - Store the physical slot index of the sequence cache. Default: None .

  • batch_index (Tensor, optional) - Keep the parameter unused. Default: None .

  • zactivate_len (Tensor, optional) - Keep the parameter unused. Default: None .

Outputs:

output(Tensor), including an on-line loss value or a logical value, a sequence of predictive text, an input mask.

Examples

>>> from mindformers.models.glm2 import ChatGLM2Config, ChatGLM2ForConditionalGeneration
>>> config = ChatGLM2Config(batch_size=2)
>>> network = ChatGLM2ForConditionalGeneration(config=config)
>>> type(network)
<class 'mindformers.models.glm2.glm2.ChatGLM2ForConditionalGeneration'>
>>> from mindformers import ChatGLM2ForConditionalGeneration
>>> network = ChatGLM2ForConditionalGeneration.from_pretrained('glm3_6b')
>>> type(network)
<class 'mindformers.models.glm2.glm2.ChatGLM2ForConditionalGeneration'>