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modules.qwen2.qwen_configuration

Qwen2Config

Bases: EasyDeLPretrainedConfig

Source code in src/python/easydel/modules/qwen2/qwen_configuration.py 
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class Qwen2Config(EasyDeLPretrainedConfig):
    model_type: str = "qwen2"

    def __init__(
            self,
            vocab_size=151936,
            hidden_size=4096,
            intermediate_size=22016,
            num_hidden_layers=32,
            num_attention_heads=32,
            num_key_value_heads=32,
            hidden_act="silu",
            max_position_embeddings=32768,
            initializer_range=0.02,
            rms_norm_eps=1e-6,
            use_cache=True,
            tie_word_embeddings=False,
            rope_theta=10000.0,
            use_sliding_window=False,
            sliding_window=4096,
            max_window_layers=28,
            attention_dropout=0.0,
            resid_pdrop: float = 0.0,
            embd_pdrop: float = 0.0,
            gradient_checkpointing: str = "nothing_saveable",
            fcm_min_ratio: float = 0.0,
            fcm_max_ratio: float = 0.0,
            use_scan_mlp: bool = False,
            scan_mlp_chunk_size: int = 1024,
            number_rep_kv: int = 1,
            bits: Optional[int] = None,
            scan_layers: bool = True,
            rope_scaling: Optional[Mapping[str, str | float]] = None,
            **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
        self.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window
        self.max_window_layers = max_window_layers

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

        self.rope_scaling = rope_scaling
        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.scan_layers = scan_layers
        self.embd_pdrop = embd_pdrop
        self.number_rep_kv = number_rep_kv
        self.resid_pdrop = resid_pdrop
        self.attention_dropout = attention_dropout
        self.tie_word_embeddings = tie_word_embeddings
        self.gradient_checkpointing = gradient_checkpointing
        self.fcm_min_ratio = fcm_min_ratio
        self.fcm_max_ratio = fcm_max_ratio
        self.use_scan_mlp = use_scan_mlp
        self.scan_mlp_chunk_size = scan_mlp_chunk_size
        self.bits = bits
        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            use_scan_mlp=use_scan_mlp,
            scan_mlp_chunk_size=scan_mlp_chunk_size,
            bits=bits,
            **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"))),

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

            ("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
            ("mlp/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
            ("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),

            ("input_layernorm/kernel", PartitionSpec(None)),
            ("post_attention_layernorm/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("tp", ("fsdp", "sp"))),

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

            ("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"))),
            ("mlp/down_proj/kernel", PartitionSpec(("fsdp", "sp"))),
            ("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"))),

            ("input_layernorm/kernel", PartitionSpec(None)),
            ("post_attention_layernorm/kernel", PartitionSpec(None)),

            ("model/norm/kernel", PartitionSpec(None)),
            ("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
            (".*", PartitionSpec(("fsdp", "sp"))),
        )

    def add_jax_args(
            self,
            resid_pdrop: float = 0.0,
            embd_pdrop: float = 0.0,
            attention_dropout: float = 0.0,
            tie_word_embeddings: bool = False,
            gradient_checkpointing: str = "nothing_saveable",
            fcm_min_ratio: float = 0.0,
            fcm_max_ratio: float = 0.0,
            use_scan_mlp: bool = False,
            scan_mlp_chunk_size: int = 1024,
            number_rep_kv: int = 1,
            bits: Optional[int] = None,
            rope_theta: float = 10000.,
            hidden_act: str = "silu",
            scan_layers: bool = True,
            rope_scaling: Optional[Mapping[str, str | float]] = None,
            **kwargs,
    ):
        """
        The add_jax_args function adds the following arguments to the Transformer class:

        :param self: Refer to the current object
        :param resid_pdrop: float: Set the dropout rate for residual connections
        :param embd_pdrop: float: Set the probability of dropping an embedding
        :param attention_dropout: float: Set the probability of dropping out the attention layer
        :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 fcm_min_ratio: float: Control the minimum ratio of the number of chunks to be used in flash-based computation
        :param fcm_max_ratio: float: Set the maximum ratio of the number of input tokens to output tokens
        :param use_scan_mlp: bool: Determine whether to use the scan_mlp function or not
        :param scan_mlp_chunk_size: int: Set the chunk size for scan_mlp
        :param number_rep_kv: int: Determine how many times the key and value vectors are repeated
        :param bits: Optional[int]: Determine the number of bits used in the quantization
        :param rope_theta: float : rope_theta for compute rope
        :param hidden_act: str : hidden_act for mlp
        :param scan_layers: bool: Determine whether to use scan layers or not
        :return: The following:

        """
        self.scan_layers = scan_layers
        self.embd_pdrop = embd_pdrop
        self.number_rep_kv = number_rep_kv
        self.resid_pdrop = resid_pdrop
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.attention_dropout = attention_dropout
        self.hidden_act = hidden_act
        self.tie_word_embeddings = tie_word_embeddings
        self.gradient_checkpointing = gradient_checkpointing
        self.fcm_min_ratio = fcm_min_ratio
        self.fcm_max_ratio = fcm_max_ratio

        self.use_scan_mlp = use_scan_mlp
        self.scan_mlp_chunk_size = scan_mlp_chunk_size
        self.bits = bits

    @staticmethod
    def get_weight_decay_exclusions():
        return tuple()

    @staticmethod
    def rng_keys():
        return "params", "dropout", "fcm"

add_jax_args(resid_pdrop=0.0, embd_pdrop=0.0, attention_dropout=0.0, tie_word_embeddings=False, gradient_checkpointing='nothing_saveable', fcm_min_ratio=0.0, fcm_max_ratio=0.0, use_scan_mlp=False, scan_mlp_chunk_size=1024, number_rep_kv=1, bits=None, rope_theta=10000.0, hidden_act='silu', scan_layers=True, rope_scaling=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
resid_pdrop float

float: Set the dropout rate for residual connections

 0.0
embd_pdrop float

float: Set the probability of dropping an embedding

 0.0
attention_dropout float

float: Set the probability of dropping out the attention layer

 0.0
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'
fcm_min_ratio float

float: Control the minimum ratio of the number of chunks to be used in flash-based computation

 0.0
fcm_max_ratio float

float: Set the maximum ratio of the number of input tokens to output tokens

 0.0
use_scan_mlp bool

bool: Determine whether to use the scan_mlp function or not

 False
scan_mlp_chunk_size int

int: Set the chunk size for scan_mlp

 1024
number_rep_kv int

int: Determine how many times the key and value vectors are repeated

 1
bits Optional[int]

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

 None
rope_theta float

float : rope_theta for compute rope

 10000.0
hidden_act str

str : hidden_act for mlp

 'silu'
scan_layers bool

bool: Determine whether to use scan layers or not

 True

Returns:

Type Description

The following:
Source code in src/python/easydel/modules/qwen2/qwen_configuration.py 
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def add_jax_args(
        self,
        resid_pdrop: float = 0.0,
        embd_pdrop: float = 0.0,
        attention_dropout: float = 0.0,
        tie_word_embeddings: bool = False,
        gradient_checkpointing: str = "nothing_saveable",
        fcm_min_ratio: float = 0.0,
        fcm_max_ratio: float = 0.0,
        use_scan_mlp: bool = False,
        scan_mlp_chunk_size: int = 1024,
        number_rep_kv: int = 1,
        bits: Optional[int] = None,
        rope_theta: float = 10000.,
        hidden_act: str = "silu",
        scan_layers: bool = True,
        rope_scaling: Optional[Mapping[str, str | float]] = None,
        **kwargs,
):
    """
    The add_jax_args function adds the following arguments to the Transformer class:

    :param self: Refer to the current object
    :param resid_pdrop: float: Set the dropout rate for residual connections
    :param embd_pdrop: float: Set the probability of dropping an embedding
    :param attention_dropout: float: Set the probability of dropping out the attention layer
    :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 fcm_min_ratio: float: Control the minimum ratio of the number of chunks to be used in flash-based computation
    :param fcm_max_ratio: float: Set the maximum ratio of the number of input tokens to output tokens
    :param use_scan_mlp: bool: Determine whether to use the scan_mlp function or not
    :param scan_mlp_chunk_size: int: Set the chunk size for scan_mlp
    :param number_rep_kv: int: Determine how many times the key and value vectors are repeated
    :param bits: Optional[int]: Determine the number of bits used in the quantization
    :param rope_theta: float : rope_theta for compute rope
    :param hidden_act: str : hidden_act for mlp
    :param scan_layers: bool: Determine whether to use scan layers or not
    :return: The following:

    """
    self.scan_layers = scan_layers
    self.embd_pdrop = embd_pdrop
    self.number_rep_kv = number_rep_kv
    self.resid_pdrop = resid_pdrop
    self.rope_theta = rope_theta
    self.rope_scaling = rope_scaling
    self.attention_dropout = attention_dropout
    self.hidden_act = hidden_act
    self.tie_word_embeddings = tie_word_embeddings
    self.gradient_checkpointing = gradient_checkpointing
    self.fcm_min_ratio = fcm_min_ratio
    self.fcm_max_ratio = fcm_max_ratio

    self.use_scan_mlp = use_scan_mlp
    self.scan_mlp_chunk_size = scan_mlp_chunk_size
    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/qwen2/qwen_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"))),

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

        ("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
        ("mlp/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
        ("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),

        ("input_layernorm/kernel", PartitionSpec(None)),
        ("post_attention_layernorm/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("tp", ("fsdp", "sp"))),

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

        ("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"))),
        ("mlp/down_proj/kernel", PartitionSpec(("fsdp", "sp"))),
        ("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"))),

        ("input_layernorm/kernel", PartitionSpec(None)),
        ("post_attention_layernorm/kernel", PartitionSpec(None)),

        ("model/norm/kernel", PartitionSpec(None)),
        ("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
        (".*", PartitionSpec(("fsdp", "sp"))),
    )