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modules.cohere.cohere_configuration

CohereConfig

Bases: EasyDeLPretrainedConfig

Source code in src/python/easydel/modules/cohere/cohere_configuration.py 
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class CohereConfig(EasyDeLPretrainedConfig):
    model_type: str = "cohere"

    def __init__(
            self,
            vocab_size=256000,
            hidden_size=8192,
            intermediate_size=22528,
            logit_scale=0.0625,
            num_hidden_layers=40,
            num_attention_heads=64,
            num_key_value_heads=None,
            hidden_act="silu",
            max_position_embeddings=8192,
            initializer_range=0.02,
            layer_norm_eps=1e-5,
            use_cache=True,
            pad_token_id=0,
            bos_token_id=5,
            eos_token_id=255001,
            tie_word_embeddings=True,
            rope_theta=10000.0,
            attention_bias=False,
            attention_dropout=0.0,
            use_qk_norm: bool = False,
            gradient_checkpointing: str = "nothing_saveable",
            bits: Optional[int] = None,
            **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.logit_scale = logit_scale
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.use_qk_norm = use_qk_norm
        # 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.layer_norm_eps = layer_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.gradient_checkpointing = gradient_checkpointing
        self.bits = bits
        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,
        )

    def get_partition_rules(self, fully_sharded_data_parallel: bool = True):
        """
        The get_partition_rules function is used to define the partitioning scheme for a model.
        It returns a list of tuples, where each tuple contains two elements:
            1) A regex string that matches the name of one or more parameters in the model.
            2) A PartitionScheme object that defines how those parameters should be partitioned across devices.

        :param fully_sharded_data_parallel: bool: Determine whether to partition the model fully or not
        :return: A list of tuples

        """
        return (

            ("model/embed_tokens/embedding", PartitionSpec("tp", ("fsdp", "sp"))),

            ("attn/(q_proj|k_proj|v_proj)/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
            ("attn/o_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),

            ("linear/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
            ("linear_1/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
            ("linear_v/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
            ("gate/kernel", PartitionSpec(("fsdp", "sp"))),

            ("post_attn_norm/kernel", PartitionSpec(None)),
            ("pre_attn_norm/kernel", PartitionSpec(None)),
            ("pre_moe_norm/kernel", PartitionSpec(None)),
            ("post_moe_norm/kernel", PartitionSpec(None)),

            ("model/norm/kernel", PartitionSpec(None)),
            ("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
            (".*", PartitionSpec(None)),
        ) if not fully_sharded_data_parallel else (

            ("model/embed_tokens/embedding", PartitionSpec(("fsdp", "sp"))),

            ("attn/(q_proj|k_proj|v_proj)/kernel", PartitionSpec(("fsdp", "sp"))),
            ("attn/o_proj/kernel", PartitionSpec(("fsdp", "sp"))),

            ("linear/kernel", PartitionSpec(("fsdp", "sp"))),
            ("linear_1/kernel", PartitionSpec(("fsdp", "sp"))),
            ("linear_v/kernel", PartitionSpec(("fsdp", "sp"))),
            ("gate/kernel", PartitionSpec(("fsdp", "sp"))),

            ("post_attn_norm/kernel", PartitionSpec(("fsdp", "sp"))),
            ("pre_attn_norm/kernel", PartitionSpec(("fsdp", "sp"))),
            ("pre_moe_norm/kernel", PartitionSpec(("fsdp", "sp"))),
            ("post_moe_norm/kernel", PartitionSpec(("fsdp", "sp"))),

            ("model/norm/kernel", PartitionSpec(("fsdp", "sp"))),
            ("lm_head/kernel", PartitionSpec(("fsdp", "sp"))),
            (".*", PartitionSpec(("fsdp", "sp"))),
        )

    def add_jax_args(
            self,
            tie_word_embeddings: bool = False,
            gradient_checkpointing: str = "nothing_saveable",
            bits: Optional[int] = None,
            **kwargs,
    ):
        """
        The add_jax_args function adds the following arguments to the Transformer class:

        :param self: Refer to the current object
        :param tie_word_embeddings: bool: Tie the word embeddings to the decoder
        :param gradient_checkpointing: str: Control the amount of memory used by jax
        :param bits: Optional[int]: Determine the number of bits used in the quantization
        """
        self.tie_word_embeddings = tie_word_embeddings
        self.gradient_checkpointing = gradient_checkpointing
        self.bits = bits

    @staticmethod
    def get_weight_decay_exclusions():
        return tuple()

    @staticmethod
    def rng_keys():
        return 'params', 'dropout'

add_jax_args(tie_word_embeddings=False, gradient_checkpointing='nothing_saveable', bits=None, **kwargs)

The add_jax_args function adds the following arguments to the Transformer class:

Parameters:

Name Type Description Default
self

Refer to the current object

 required
tie_word_embeddings bool

bool: Tie the word embeddings to the decoder

 False
gradient_checkpointing str

str: Control the amount of memory used by jax

 'nothing_saveable'
bits Optional[int]

Optional[int]: Determine the number of bits used in the quantization

 None
Source code in src/python/easydel/modules/cohere/cohere_configuration.py 
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def add_jax_args(
        self,
        tie_word_embeddings: bool = False,
        gradient_checkpointing: str = "nothing_saveable",
        bits: Optional[int] = None,
        **kwargs,
):
    """
    The add_jax_args function adds the following arguments to the Transformer class:

    :param self: Refer to the current object
    :param tie_word_embeddings: bool: Tie the word embeddings to the decoder
    :param gradient_checkpointing: str: Control the amount of memory used by jax
    :param bits: Optional[int]: Determine the number of bits used in the quantization
    """
    self.tie_word_embeddings = tie_word_embeddings
    self.gradient_checkpointing = gradient_checkpointing
    self.bits = bits

get_partition_rules(fully_sharded_data_parallel=True)

The get_partition_rules function is used to define the partitioning scheme for a model. It returns a list of tuples, where each tuple contains two elements: 1) A regex string that matches the name of one or more parameters in the model. 2) A PartitionScheme object that defines how those parameters should be partitioned across devices.

Parameters:

Name Type Description Default
fully_sharded_data_parallel bool

bool: Determine whether to partition the model fully or not

 True

Returns:

Type Description

A list of tuples
Source code in src/python/easydel/modules/cohere/cohere_configuration.py 
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def get_partition_rules(self, fully_sharded_data_parallel: bool = True):
    """
    The get_partition_rules function is used to define the partitioning scheme for a model.
    It returns a list of tuples, where each tuple contains two elements:
        1) A regex string that matches the name of one or more parameters in the model.
        2) A PartitionScheme object that defines how those parameters should be partitioned across devices.

    :param fully_sharded_data_parallel: bool: Determine whether to partition the model fully or not
    :return: A list of tuples

    """
    return (

        ("model/embed_tokens/embedding", PartitionSpec("tp", ("fsdp", "sp"))),

        ("attn/(q_proj|k_proj|v_proj)/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
        ("attn/o_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),

        ("linear/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
        ("linear_1/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
        ("linear_v/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
        ("gate/kernel", PartitionSpec(("fsdp", "sp"))),

        ("post_attn_norm/kernel", PartitionSpec(None)),
        ("pre_attn_norm/kernel", PartitionSpec(None)),
        ("pre_moe_norm/kernel", PartitionSpec(None)),
        ("post_moe_norm/kernel", PartitionSpec(None)),

        ("model/norm/kernel", PartitionSpec(None)),
        ("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
        (".*", PartitionSpec(None)),
    ) if not fully_sharded_data_parallel else (

        ("model/embed_tokens/embedding", PartitionSpec(("fsdp", "sp"))),

        ("attn/(q_proj|k_proj|v_proj)/kernel", PartitionSpec(("fsdp", "sp"))),
        ("attn/o_proj/kernel", PartitionSpec(("fsdp", "sp"))),

        ("linear/kernel", PartitionSpec(("fsdp", "sp"))),
        ("linear_1/kernel", PartitionSpec(("fsdp", "sp"))),
        ("linear_v/kernel", PartitionSpec(("fsdp", "sp"))),
        ("gate/kernel", PartitionSpec(("fsdp", "sp"))),

        ("post_attn_norm/kernel", PartitionSpec(("fsdp", "sp"))),
        ("pre_attn_norm/kernel", PartitionSpec(("fsdp", "sp"))),
        ("pre_moe_norm/kernel", PartitionSpec(("fsdp", "sp"))),
        ("post_moe_norm/kernel", PartitionSpec(("fsdp", "sp"))),

        ("model/norm/kernel", PartitionSpec(("fsdp", "sp"))),
        ("lm_head/kernel", PartitionSpec(("fsdp", "sp"))),
        (".*", PartitionSpec(("fsdp", "sp"))),
    )