| """A HuggingFace-style model configuration.""" | |
| import warnings | |
| from typing import Any, Dict, Optional, Union | |
| from transformers import PretrainedConfig | |
| attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8} | |
| ffn_config_defaults: Dict = {'ffn_type': 'mptmlp'} | |
| init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu', 'init_div_is_residual': True, 'emb_init_std': None, 'emb_init_uniform_lim': None, 'init_std': None, 'init_gain': 0.0} | |
| class MPTConfig(PretrainedConfig): | |
| model_type = 'mpt' | |
| def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: int=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, ffn_config: Dict=ffn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, fc_type: str='torch', verbose: Optional[int]=None, **kwargs: Any): | |
| """The MPT configuration class. | |
| Args: | |
| d_model (int): The size of the embedding dimension of the model. | |
| n_heads (int): The number of attention heads. | |
| n_layers (int): The number of layers in the model. | |
| expansion_ratio (int): The ratio of the up/down scale in the ffn. | |
| max_seq_len (int): The maximum sequence length of the model. | |
| vocab_size (int): The size of the vocabulary. | |
| resid_pdrop (float): The dropout probability applied to the attention output before combining with residual. | |
| emb_pdrop (float): The dropout probability for the embedding layer. | |
| learned_pos_emb (bool): Whether to use learned positional embeddings | |
| attn_config (Dict): A dictionary used to configure the model's attention module: | |
| attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention, grouped_query_attention | |
| attn_pdrop (float): The dropout probability for the attention layers. | |
| attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'. | |
| qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer. | |
| clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to | |
| this value. | |
| softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None, | |
| use the default scale of ``1/sqrt(d_keys)``. | |
| prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an | |
| extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix | |
| can attend to one another bi-directionally. Tokens outside the prefix use causal attention. | |
| attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id. | |
| When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates | |
| which sub-sequence each token belongs to. | |
| Defaults to ``False`` meaning any provided `sequence_id` will be ignored. | |
| alibi (bool): Whether to use the alibi bias instead of position embeddings. | |
| alibi_bias_max (int): The maximum value of the alibi bias. | |
| kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads. | |
| ffn_config (Dict): A dictionary used to configure the model's ffn module: | |
| ffn_type (str): type of ffn to use. Options: mptmlp, te_ln_mlp | |
| init_device (str): The device to use for parameter initialization. | |
| logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value. | |
| no_bias (bool): Whether to use bias in all layers. | |
| verbose (int): The verbosity level. 0 is silent. | |
| embedding_fraction (float): The fraction to scale the gradients of the embedding layer by. | |
| norm_type (str): choose type of norm to use | |
| use_cache (bool): Whether or not the model should return the last key/values attentions | |
| init_config (Dict): A dictionary used to configure the model initialization: | |
| init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_', | |
| 'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or | |
| 'xavier_normal_'. These mimic the parameter initialization methods in PyTorch. | |
| init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True. | |
| emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer. | |
| emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution | |
| used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``. | |
| init_std (float): The standard deviation of the normal distribution used to initialize the model, | |
| if using the baseline_ parameter initialization scheme. | |
| init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes. | |
| fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes. | |
| init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes. | |
| --- | |
| See llmfoundry.models.utils.param_init_fns.py for info on other param init config options | |
| fc_type (str): choose fc layer implementation. Options: torch and te. te layers support fp8 when using H100 GPUs. | |
| """ | |
| self.d_model = d_model | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.expansion_ratio = expansion_ratio | |
| self.max_seq_len = max_seq_len | |
| self.vocab_size = vocab_size | |
| self.resid_pdrop = resid_pdrop | |
| self.emb_pdrop = emb_pdrop | |
| self.learned_pos_emb = learned_pos_emb | |
| self.attn_config = attn_config | |
| self.ffn_config = ffn_config | |
| self.init_device = init_device | |
| self.logit_scale = logit_scale | |
| self.no_bias = no_bias | |
| self.embedding_fraction = embedding_fraction | |
| self.verbose = verbose | |
| self.norm_type = norm_type | |
| self.use_cache = use_cache | |
| self.init_config = init_config | |
| self.fc_type = fc_type | |
| if verbose is not None: | |
| warnings.warn(DeprecationWarning('verbose argument for MPTConfig is now ignored and will be removed. Use python_log_level instead.')) | |
| if 'name' in kwargs: | |
| del kwargs['name'] | |
| if 'loss_fn' in kwargs: | |
| del kwargs['loss_fn'] | |
| if self.attn_config.get('alibi', False): | |
| self.learned_pos_emb = False | |
| warnings.warn(f'alibi is turned on, setting `learned_pos_emb` to `False.`') | |
| super().__init__(**kwargs) | |
| self._validate_config() | |
| def _set_config_defaults(self, config: Dict[str, Any], config_defaults: Dict[str, Any]) -> Dict[str, Any]: | |
| for (k, v) in config_defaults.items(): | |
| if k not in config: | |
| config[k] = v | |
| return config | |
| def _validate_config(self) -> None: | |
| self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults) | |
| self.ffn_config = self._set_config_defaults(self.ffn_config, ffn_config_defaults) | |
| self.init_config = self._set_config_defaults(self.init_config, init_config_defaults) | |
| if self.d_model % self.n_heads != 0: | |
| raise ValueError('d_model must be divisible by n_heads') | |
| if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])): | |
| raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1") | |
| if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']: | |
| raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}") | |
| if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']: | |
| raise NotImplementedError('prefix_lm only implemented with torch and triton attention.') | |
| if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']: | |
| raise NotImplementedError('alibi only implemented with torch and triton attention.') | |
| if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']: | |
| raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.') | |
| if self.embedding_fraction > 1 or self.embedding_fraction <= 0: | |
| raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!') | |
| if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model': | |
| raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.") | |
| if self.init_config.get('name', None) is None: | |
| raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.") | |
| if not self.learned_pos_emb and (not self.attn_config['alibi']): | |
| warnings.warn(f'Positional information not being provided to the model using either learned_pos_emb or alibi.') | |
| if self.fc_type == 'te' or self.ffn_config['ffn_type'] == 'te_ln_mlp': | |
| try: | |
| import transformer_engine.pytorch as te | |
| del te | |
| except: | |
| raise ImportError('TransformerEngine import fail. `fc_type: te` requires TransformerEngine be installed. ' + 'The required version of transformer_engine also requires FlashAttention v1.0.6 is installed:\n' + 'pip install flash-attn==1.0.6 --no-build-isolation \n' + 'pip install git+https://github.com/NVIDIA/TransformerEngine.git@144e4888b2cdd60bd52e706d5b7a79cb9c1a7156') | |
| if self.ffn_config['ffn_type'] == 'mptmlp': | |
| self.ffn_config['fc_type'] = self.fc_type | |
| elif self.ffn_config['ffn_type'] == 'te_ln_mlp': | |
| self.ffn_config['bias'] = not self.no_bias |