mindformers.models.LlamaConfig

class mindformers.models.LlamaConfig(batch_size: int = 1, seq_length: int = 2048, hidden_size: int = 4096, num_layers: int = 32, num_heads: int = 32, n_kv_heads: Optional[int] = None, max_position_embedding: Optional[int] = None, intermediate_size: Optional[int] = None, vocab_size: int = 32000, multiple_of: int = 256, ffn_dim_multiplier: Optional[int] = None, rms_norm_eps: float = 1e-5, bos_token_id: int = 1, eos_token_id: int = 2, pad_token_id: int = 0, ignore_token_id: int = - 100, theta: float = 10000.0, compute_dtype: str = 'float16', layernorm_compute_type: str = 'float32', softmax_compute_type: str = 'float32', rotary_dtype: str = 'float32', param_init_type: str = 'float16', residual_dtype: str = None, embedding_init_type=None, qkv_has_bias: bool = False, qkv_concat: bool = False, attn_proj_has_bias: bool = False, parallel_config: Union[dict, TransformerOpParallelConfig] = default_transformer_config, moe_config: Union[dict, MoEConfig] = default_moe_config, use_past: bool = False, extend_method: str = 'None', scaling_factor: float = 1.0, is_dynamic: bool = False, use_rope_slice: bool = False, use_flash_attention: bool = False, use_ring_attention: bool = False, use_attn_mask_compression: bool = False, parallel_optimizer: bool = False, fine_grain_interleave: int = 1, pp_interleave_num: int = 1, offset: int = 0, init_method_std: float = 0.01, checkpoint_name_or_path: str = '', repetition_penalty: float = 1.0, max_decode_length: int = 1024, block_size: int = 16, num_blocks: int = 512, top_k: int = 5, top_p: float = 1.0, do_sample: bool = True, quant_config: dict = None, tie_word_embeddings: bool = False, llm_backend: str = '', fused_rms_norm: bool = True, input_sliced_sig: bool = False, **kwargs)

Llama config class which defines the model size.

Parameters

• batch_size (int, optional) – batch size for input data, use in predict. Default: 1 .

• seq_length (int, optional) – The sequence length of input_ids. Default: 2048 .

• vocab_size (int, optional) – Default: 32000 . Vocabulary size of the BERT model.

• hidden_size (int, optional) – Dimensionality of the encoder layers and the pooler layer. Default: 4096 .

• num_layers (int, optional) – Number of hidden layers in the Transformer encoder. Default: 32 .

• num_heads (int, optional) – Number of attention heads for each attention layer in the Transformer encoder. Default: 32 .

• multiple_of (int, optional) – Define SwiGLU hidden layer size multiples. Default: 256 .

• n_kv_heads (int, optional) – Define multi group head attention heads number. Default: None .

• ffn_dim_multiplier (int, optional) – Define ffn layer dim multiples. Default: None .

• rms_norm_eps (float, optional) – The epsilon value of the denominator. Default: 1e-5 .

• bos_token_id (int, optional) – The id of the beginning-of-sequence token. Default: 1 .

• eos_token_id (int, optional) – The id of the end-of-sequence token. Default: 2 .

• pad_token_id (int, optional) – The id of the padding token. Default: 0 .

• ignore_token_id (int, optional) – The id of the ignoring token. Default: -100 .

• compute_dtype (str, optional) – Linear layer compute dtype. Default: float16 .

• layernorm_compute_type (str, optional) – layernorm compute dtype. Default: float32 .

• softmax_compute_type (str, optional) – softmax compute dtype. Default: float32 .

• rotary_dtype (str, optional) – rope compute dtype. Default: float32 .

• param_init_type (str, optional) – parameter initial dtype. Default: float16 .

• init_method_std (float, optional) – The sigma value when using normal type to initialize Linear. Default: 0.01 .

• qkv_has_bias (bool, optional) – Whether the Query, Key, and Value projection has bias. Default: False .

• attn_proj_has_bias (bool, optional) – Whether the attn projection has bias. Default: False .

• use_past (bool, optional) – Whether the model should use the past last key/values attentions (if applicable to the model) to speed up decoding. Default: False .

• parallel_config (TransformerOpParallelConfig) – The parallel configure. Default: default_transformer_config , an instance of TransformerOpParallelConfig with default args.

• residual_dtype (str, optional) – residual compute dtype. Default: None .

• extend_method (str, optional) – The extent method of seq length of inference. Default: None .

• use_flash_attention (bool, optional) – Whether enable flash attention ops. Default: False .

• use_ring_attention (bool, optional) – Whether enable ring attention ops. Default: False .

• offset (int, optional) – Offset of transformer layer when set pipeline stage number. Default: 0 .

• checkpoint_name_or_path (str, optional) – checkpoint path or name used to load to the network. Default: None .

• repetition_penalty (float, optional) – The parameter for repetition penalty. 1.0 means no penalty. See this paper for more details. Default: 1.0 .

• max_decode_length (int, optional) – The maximum length the generated tokens can have. Corresponds to the length of the input prompt + max_new_tokens. Its effect is overridden by max_new_tokens, if also set. Default: 1024 .

• top_k (int, optional) – The number of highest probability vocabulary tokens to keep for top-k-filtering. Default: 5 .

• top_p (float, optional) – If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. Default: 1.0 .

• do_sample (bool, optional) – Whether to use sampling; use greedy decoding otherwise. Default: True .

• block_size (int, optional) – The maximum number of tokens in one block can have when using paged attention. Default: 16 .

• num_blocks (int, optional) – The maximum number of blocks when using paged attention. Default: 512 .

• tie_word_embeddings (bool, optional) – Whether to tie input and output embeddings. Default: False .

• llm_backend (str, optional) – Llm boost backend. Default: None .

• fused_rms_norm (bool, optional) – Whether or not to use the RMS_NORM of the fusion operator. Default: True .

• input_sliced_sig (bool, optional) – If input_ids and labels have been processed to equal to seq_length, input_sliced_sig should be True, if not, input_sliced_sig should be False. Default: False .

Returns

LlamaConfig, a LlamaConfig instance.

Examples

>>> from mindformers.models import LlamaConfig
>>> config = LlamaConfig(num_layers=2, seq_length=1024)
>>> print(config)
LlamaConfig {
    "batch_size": 1,
    "block_size": 16,
    "bos_token_id": 1,
    "checkpoint_name_or_path": "",
    "compute_dtype": "float16",
    "do_sample": true,
    "embedding_init_type": "float16",
    "eos_token_id": 2,
    "extend_method": "None",
    "ffn_dim_multiplier": null,
    "fine_grain_interleave": 1,
    "hidden_size": 4096,
    "ignore_token_id": -100,
    "intermediate_size": null,
    "is_dynamic": false,
    "layernorm_compute_type": "float32",
    "llm_backend": "",
    "max_decode_length": 1024,
    "max_position_embedding": 1024,
    "mindformers_version": "dev",
    "model_type": "llama",
    "multiple_of": 256,
    "n_kv_heads": null,
    "num_blocks": 512,
    "num_heads": 32,
    "num_layers": 2,
    "offset": 0,
    "pad_token_id": 0,
    "parallel_decoding_params": null,
    "parallel_optimizer": false,
    "param_init_type": "float16",
    "pp_interleave_num": 1,
    "qkv_concat": false,
    "qkv_has_bias": false,
    "quant_config": null,
    "repetition_penalty": 1.0,
    "rms_norm_eps": 1e-05,
    "rotary_dtype": "float32",
    "scaling_factor": 1.0,
    "seq_length": 1024,
    "softmax_compute_type": "float32",
    "theta": 10000.0,
    "tie_word_embeddings": false,
    "top_k": 5,
    "top_p": 1.0,
    "use_attn_mask_compression": false,
    "use_flash_attention": false,
    "use_past": false,
    "use_ring_attention": false,
    "use_rope_slice": false,
    "vocab_size": 32000
    }