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199 lines
10 KiB
199 lines
10 KiB
4 days ago
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# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch Phi-MoE model."""
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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class PhimoeConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PhimoeModel`]. It is used to instantiate a Phi-moe
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the
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[microsoft/Phi-3.5-MoE-instruct](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct).
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32064):
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Vocabulary size of the Phimoe model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`PhimoeModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 6400):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
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The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention
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allows sequence of up to 4096*32 tokens.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
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The id of the padding token.
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bos_token_id (`int`, *optional*, defaults to 1):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 2):
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The id of the "end-of-sequence" token.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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sliding_window (`int`, *optional*):
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Sliding window attention window size. If not specified, will default to `262144`.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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num_experts_per_tok (`int`, *optional*, defaults to 2):
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The number of experts to root per-token, can be also interpreted as the `top-p` routing
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parameter
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num_local_experts (`int`, *optional*, defaults to 16):
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Number of experts per Sparse MLP layer.
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output_router_logits (`bool`, *optional*, defaults to `False`):
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Whether or not the router logits should be returned by the model. Enabling this will also
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allow the model to output the auxiliary loss. See [here]() for more details
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router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
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The aux loss factor for the total loss.
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router_jitter_noise (`float`, *optional*, defaults to 0.01):
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Amount of noise to add to the router.
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input_jitter_noise (`float`, *optional*, defaults to 0.0): Input jitter noise
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attention_bias (`bool`, *optional*, defaults to `False`): Attention bias
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lm_head_bias (`bool`, *optional*, defaults to `False`): LM head bias
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Example:
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```python
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>>> from transformers import PhimoeModel, PhimoeConfig
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>>> # Initializing a Phi-3 style configuration
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>>> configuration = PhimoeConfig.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
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>>> # Initializing a model from the configuration
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>>> model = PhimoeModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "phimoe"
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keys_to_ignore_at_inference = ["past_key_values"]
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default_theta = 1000000.0
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def __init__(
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self,
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vocab_size: int | None = 32064,
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hidden_size: int | None = 4096,
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intermediate_size: int | None = 6400,
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num_hidden_layers: int | None = 32,
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num_attention_heads: int | None = 32,
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num_key_value_heads: int | None = 8,
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hidden_act: str | None = "silu",
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max_position_embeddings: int | None = 4096 * 32,
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initializer_range: float | None = 0.02,
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rms_norm_eps: int | None = 1e-5,
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use_cache: bool | None = True,
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pad_token_id: int | None = None,
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bos_token_id: int | None = 1,
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eos_token_id: int | None = 2,
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tie_word_embeddings: int | None = False,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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sliding_window: int | None = None,
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attention_dropout: float | None = 0.0,
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num_experts_per_tok: int | None = 2,
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num_local_experts: int | None = 16,
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output_router_logits: bool | None = False,
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router_aux_loss_coef: float | None = 0.001,
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router_jitter_noise: float | None = 0.01,
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input_jitter_noise: float | None = 0.0,
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attention_bias: bool | None = False,
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lm_head_bias: bool | None = False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.sliding_window = sliding_window
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self.attention_bias = attention_bias
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self.lm_head_bias = lm_head_bias
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.attention_dropout = attention_dropout
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self.num_experts_per_tok = num_experts_per_tok
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self.num_local_experts = num_local_experts
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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self.router_jitter_noise = router_jitter_noise
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self.input_jitter_noise = input_jitter_noise
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self.rope_parameters = rope_parameters
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self.tie_word_embeddings = tie_word_embeddings
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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super().__init__(**kwargs)
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def validate_rope(self, ignore_keys=None):
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"""
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Validate the `rope_parameters` configuration.
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"""
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super().validate_rope(ignore_keys=ignore_keys)
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# Run model-specific rope validation
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if self.rope_parameters["rope_type"] != "default":
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if "original_max_position_embeddings" in self.rope_parameters:
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self.original_max_position_embeddings = self.rope_parameters["original_max_position_embeddings"]
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rope_parameters_short_mscale = self.rope_parameters.get("short_mscale", None)
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rope_parameters_long_mscale = self.rope_parameters.get("long_mscale", None)
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if not isinstance(rope_parameters_short_mscale, (int, float)):
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raise TypeError(
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f"`rope_parameters`'s short_mscale field must be a number, got {rope_parameters_short_mscale}"
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)
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if not isinstance(rope_parameters_long_mscale, (int, float)):
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raise TypeError(
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f"`rope_parameters`'s long_mscale field must be a number, got {rope_parameters_long_mscale}"
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)
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__all__ = ["PhimoeConfig"]
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