RishAI-Base-v2 / configuration_rish_ai.py

Initial upload of RishAI-Base-v2: Sparse MoE multilingual model

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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from transformers.configuration_utils import PretrainedConfig


class RishAIConfig(PretrainedConfig):
    r"""
    Configuration class for RishAI models.
    Args:
        vocab_size (`int`, *optional*, defaults to 100352):
            Vocabulary size of the RishAI model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`RishAIModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
            `num_attention_heads`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 4096):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*, defaults to 100277):
            Padding token id.
        bos_token_id (`int`, *optional*):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 100257):
            End of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 500000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
            these scaling strategies behave:
            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
            experimental feature, subject to breaking API changes in future versions.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        num_experts_per_tok (`int`, *optional*, defaults to 5):
            Number of selected experts.
        num_experts (`int`, *optional*, defaults to 7):
            Number of routed experts.
        output_router_logits (`bool`, *optional*, defaults to `False`):
            Whether or not the router logits should be returned by the model. Enabling this will also
            allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
        router_aux_loss_coef (`float`, *optional*, defaults to 0.01):
            The aux loss factor for the total loss.
        norm_topk_prob (`bool`, *optional*, defaults to `False`):
            Whether to normalize the topk probabilities.
    Example:
    ```python
    >>> from transformers import RishAIConfig, RishAIModel
    >>> # Initializing a RishAI rish_ai style configuration
    >>> configuration = RishAIConfig()
    >>> # Initializing a model from the RishAI style configuration
    >>> model = RishAIModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """

    model_type = "rish_ai"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=100352,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=None,
        hidden_act="silu",
        max_position_embeddings=4096,
        initializer_range=0.02,
        rms_norm_eps=1e-06,
        use_cache=True,
        pad_token_id=100277,
        bos_token_id=None,
        eos_token_id=100257,
        tie_word_embeddings=False,
        rope_theta=500000.0,
        rope_scaling=None,
        attention_bias=False,
        attention_dropout=0.0,
        num_experts_per_tok=5,
        num_experts=7,
        output_router_logits=False,
        router_aux_loss_coef=0.01,
        norm_topk_prob=False,
        **kwargs,
    ):
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.num_experts_per_tok = num_experts_per_tok
        self.num_experts = num_experts
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef
        self.norm_topk_prob = norm_topk_prob

        # Validate the correctness of rotary position embeddings parameters
        # BC: if there is a 'type' field, move it to 'rope_type'.
        if self.rope_scaling is not None and "type" in self.rope_scaling:
            self.rope_scaling["rope_type"] = self.rope_scaling["type"]

	# Copyright 2024 The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	from transformers.configuration_utils import PretrainedConfig


	class RishAIConfig(PretrainedConfig):
	r"""
	Configuration class for RishAI models.

	Args:
	vocab_size (`int`, optional, defaults to 100352):
	Vocabulary size of the RishAI model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`RishAIModel`]
	hidden_size (`int`, optional, defaults to 4096):
	Dimension of the hidden representations.
	intermediate_size (`int`, optional, defaults to 11008):
	Dimension of the MLP representations.
	num_hidden_layers (`int`, optional, defaults to 32):
	Number of hidden layers in the Transformer decoder.
	num_attention_heads (`int`, optional, defaults to 32):
	Number of attention heads for each attention layer in the Transformer decoder.
	num_key_value_heads (`int`, optional):
	This is the number of key_value heads that should be used to implement Grouped Query Attention. If
	`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
	`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
	converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
	by meanpooling all the original heads within that group. For more details, check out [this
	paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
	`num_attention_heads`.
	hidden_act (`str` or `function`, optional, defaults to `"silu"`):
	The non-linear activation function (function or string) in the decoder.
	max_position_embeddings (`int`, optional, defaults to 4096):
	The maximum sequence length that this model might ever be used with.
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	rms_norm_eps (`float`, optional, defaults to 1e-06):
	The epsilon used by the rms normalization layers.
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models). Only
	relevant if `config.is_decoder=True`.
	pad_token_id (`int`, optional, defaults to 100277):
	Padding token id.
	bos_token_id (`int`, optional):
	Beginning of stream token id.
	eos_token_id (`int`, optional, defaults to 100257):
	End of stream token id.
	tie_word_embeddings (`bool`, optional, defaults to `False`):
	Whether to tie weight embeddings
	rope_theta (`float`, optional, defaults to 500000.0):
	The base period of the RoPE embeddings.
	rope_scaling (`Dict`, optional):
	Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
	strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
	`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
	`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
	these scaling strategies behave:
	https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
	experimental feature, subject to breaking API changes in future versions.
	attention_bias (`bool`, defaults to `False`, optional, defaults to `False`):
	Whether to use a bias in the query, key, value and output projection layers during self-attention.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.
	num_experts_per_tok (`int`, optional, defaults to 5):
	Number of selected experts.
	num_experts (`int`, optional, defaults to 7):
	Number of routed experts.
	output_router_logits (`bool`, optional, defaults to `False`):
	Whether or not the router logits should be returned by the model. Enabling this will also
	allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
	router_aux_loss_coef (`float`, optional, defaults to 0.01):
	The aux loss factor for the total loss.
	norm_topk_prob (`bool`, optional, defaults to `False`):
	Whether to normalize the topk probabilities.

	Example:
	```python
	>>> from transformers import RishAIConfig, RishAIModel

	>>> # Initializing a RishAI rish_ai style configuration
	>>> configuration = RishAIConfig()

	>>> # Initializing a model from the RishAI style configuration
	>>> model = RishAIModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```
	"""

	model_type = "rish_ai"
	keys_to_ignore_at_inference = ["past_key_values"]

	def __init__(
	self,
	vocab_size=100352,
	hidden_size=4096,
	intermediate_size=11008,
	num_hidden_layers=32,
	num_attention_heads=32,
	num_key_value_heads=None,
	hidden_act="silu",
	max_position_embeddings=4096,
	initializer_range=0.02,
	rms_norm_eps=1e-06,
	use_cache=True,
	pad_token_id=100277,
	bos_token_id=None,
	eos_token_id=100257,
	tie_word_embeddings=False,
	rope_theta=500000.0,
	rope_scaling=None,
	attention_bias=False,
	attention_dropout=0.0,
	num_experts_per_tok=5,
	num_experts=7,
	output_router_logits=False,
	router_aux_loss_coef=0.01,
	norm_topk_prob=False,
	**kwargs,
	):
	super().__init__(
	pad_token_id=pad_token_id,
	bos_token_id=bos_token_id,
	eos_token_id=eos_token_id,
	tie_word_embeddings=tie_word_embeddings,
	**kwargs,
	)
	self.vocab_size = vocab_size
	self.max_position_embeddings = max_position_embeddings
	self.hidden_size = hidden_size
	self.intermediate_size = intermediate_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads

	# for backward compatibility
	if num_key_value_heads is None:
	num_key_value_heads = num_attention_heads

	self.num_key_value_heads = num_key_value_heads
	self.hidden_act = hidden_act
	self.initializer_range = initializer_range
	self.rms_norm_eps = rms_norm_eps
	self.use_cache = use_cache
	self.rope_theta = rope_theta
	self.rope_scaling = rope_scaling
	self.attention_bias = attention_bias
	self.attention_dropout = attention_dropout
	self.num_experts_per_tok = num_experts_per_tok
	self.num_experts = num_experts
	self.output_router_logits = output_router_logits
	self.router_aux_loss_coef = router_aux_loss_coef
	self.norm_topk_prob = norm_topk_prob

	# Validate the correctness of rotary position embeddings parameters
	# BC: if there is a 'type' field, move it to 'rope_type'.
	if self.rope_scaling is not None and "type" in self.rope_scaling:
	self.rope_scaling["rope_type"] = self.rope_scaling["type"]