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"""Falcon-H1 model configuration"""


from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

from sglang.srt.configs.mamba_utils import Mamba2CacheParams, Mamba2StateShape
from sglang.srt.layers.dp_attention import get_tensor_model_parallel_world_size

logger = logging.get_logger(__name__)


class FalconH1Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`FalconH1Model`]. It is used to instantiate a
    FalconH1Model model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with defaults taken from [ibm-fms/FalconH1-9.8b-2.2T-hf](https://huggingface.co/ibm-fms/FalconH1-9.8b-2.2T-hf).
    The FalconH1Model is a hybrid [mamba2](https://github.com/state-spaces/mamba) architecture with SwiGLU.
    The checkpoints are  jointly trained by IBM, Princeton, and UIUC.
    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.
    Args:
        vocab_size (`int`, *optional*, defaults to 128000):
            Vocabulary size of the FalconH1 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`FalconH1Model`]
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
            model has a output word embedding layer.
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 14336):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 8):
            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 `8`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        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-05):
            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`.
        num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
            Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an
            integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the
            logits of the last prompt token are needed for generation. For long sequences, the logits for the entire
            sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint
            significantly.
        pad_token_id (`int`, *optional*, defaults to 0):
            The id of the padding token.
        bos_token_id (`int`, *optional*, defaults to 1):
            The id of the "beginning-of-sequence" token.
        eos_token_id (`int`, *optional*, defaults to 2):
            The id of the "end-of-sequence" token.
        max_position_embeddings (`int`, *optional*, defaults to 8192):
            Max cached sequence length for the model
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        mamba_d_ssm (`int`, *optional*, defaults to 1024):
            The dimension of the SSM state space latents.
        mamba_n_heads (`int`, *optional*, defaults to 128):
            The number of mamba heads used in the v2 implementation.
        mamba_d_head (`int`, *optional*, defaults to `"auto"`):
            Head embedding dimension size
        mamba_n_groups (`int`, *optional*, defaults to 1):
            The number of the mamba groups used in the v2 implementation.
        mamba_d_state (`int`, *optional*, defaults to 256):
            The dimension the mamba state space latents
        mamba_d_conv (`int`, *optional*, defaults to 4):
            The size of the mamba convolution kernel
        mamba_expand (`int`, *optional*, defaults to 2):
            Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
        mamba_chunk_size (`int`, *optional*, defaults to 256):
            The chunks in which to break the sequence when doing prefill/training
        mamba_conv_bias (`bool`, *optional*, defaults to `True`):
            Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
        mamba_proj_bias (`bool`, *optional*, defaults to `False`):
            Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block
        mamba_norm_before_gate (`bool`, *optional*, defaults to `True`):
            Whether to use RMSNorm before the gate in the Mamba block
        mamba_rms_norm (`bool`, *optional*, defaults to `False`):
            Whether to use RMSNorm instead of LayerNorm in the Mamba block
        projectors_bias (`bool`, *optional*, defaults to `False`):
            Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the attention block
        rope_theta (`float`, *optional*, defaults to 100000.0):
            The theta value used for the RoPE embeddings.
        rope_scaling (`float`, *optional*):
            The scaling value used for the RoPE embeddings. If `None`, no scaling is applied.
        lm_head_multiplier (`float`, *optional*, defaults to 1.0):
            The multiplier for the LM head. This is used to scale the output of the LM head.
        embedding_multiplier (`float`, *optional*, defaults to 1.0):
            The multiplier for the embedding layer. This is used to scale the output of the embedding layer.
        mlp_multipliers (`list[float]`, *optional*):
            The multipliers for the MLP layers. This is used to scale the output of the MLP layers. The first value is
            the multiplier of gate layer, the second value is the multiplier of the down_proj layer.
        key_multiplier (`float`, *optional*):
            The multiplier for the key layer. This is used to scale the output of the key layer.
        attention_out_multiplier (`float`, *optional*):
            The multiplier for the attention output layer. This is used to scale the output of the attention output
        attention_in_multiplier (`float`, *optional*):
            The multiplier for the attention input layer. This is used to scale the output of the attention input layer.
        ssm_multipliers (`list[float]`, *optional*):
            The multipliers for the SSM layers. This is used to scale the output of the SSM layers.
        ssm_in_multiplier (`float`, *optional*):
            The multiplier for the SSM input layer. This is used to scale the output of the SSM input layer.
        ssm_out_multiplier (`float`, *optional*):
            The multiplier for the SSM output layer. This is used to scale the output of the SSM output layer.
    """

    model_type = "falcon_h1"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=128000,
        tie_word_embeddings=False,
        hidden_size=4096,
        intermediate_size=14336,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=8,
        hidden_act="silu",
        initializer_range=0.02,
        rms_norm_eps=1e-5,
        use_cache=True,
        num_logits_to_keep=1,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        max_position_embeddings=8192,
        attention_dropout=0.0,
        mamba_d_ssm=1024,
        mamba_n_heads=128,
        mamba_d_head="auto",
        mamba_n_groups=1,
        mamba_d_state=256,
        mamba_d_conv=4,
        mamba_expand=2,
        mamba_chunk_size=256,
        mamba_conv_bias=True,
        mamba_proj_bias=False,
        mamba_norm_before_gate=True,
        mamba_rms_norm=False,
        projectors_bias=False,
        rope_theta=100000.0,
        rope_scaling=None,
        lm_head_multiplier=1.0,
        embedding_multiplier=1.0,
        mlp_multipliers=None,
        key_multiplier=None,
        attention_out_multiplier=None,
        attention_in_multiplier=None,
        ssm_multipliers=None,
        ssm_in_multiplier=None,
        ssm_out_multiplier=None,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        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.max_position_embeddings = max_position_embeddings
        self.attention_dropout = attention_dropout
        self.attention_bias = False
        self.mlp_bias = False

        # 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.num_logits_to_keep = num_logits_to_keep

        self.rope_theta = rope_theta
        self.rope_scaling = None
        self.rope_scaling = rope_scaling
        self.projectors_bias = projectors_bias
        self.mamba_intermediate = mamba_intermediate = (
            mamba_expand * hidden_size if mamba_d_ssm is None else mamba_d_ssm
        )

        if mamba_intermediate % mamba_n_heads != 0:
            raise ValueError("mamba_n_heads must divide mamba_expand * hidden_size")

        # for the mamba_v2, must satisfy the following
        if mamba_d_head == "auto":
            mamba_d_head = mamba_intermediate // mamba_n_heads

        if mamba_d_head * mamba_n_heads != mamba_intermediate:
            raise ValueError(
                "The dimensions for the Mamba head state do not match the model intermediate_size"
            )

        self.mamba_d_ssm = mamba_d_ssm
        self.mamba_n_heads = mamba_n_heads
        self.mamba_d_head = mamba_d_head
        self.mamba_n_groups = mamba_n_groups
        self.mamba_d_state = mamba_d_state
        self.mamba_d_conv = mamba_d_conv
        self.mamba_expand = mamba_expand
        self.mamba_chunk_size = mamba_chunk_size
        self.mamba_conv_bias = mamba_conv_bias
        self.mamba_proj_bias = mamba_proj_bias

        self.mamba_norm_before_gate = mamba_norm_before_gate
        self.mamba_rms_norm = mamba_rms_norm

        self.lm_head_multiplier = lm_head_multiplier
        self.embedding_multiplier = embedding_multiplier

        if mlp_multipliers is not None:
            self.mlp_multipliers = mlp_multipliers
        else:
            self.mlp_multipliers = [1.0, 1.0]

        if attention_out_multiplier is not None:
            self.attention_out_multiplier = attention_out_multiplier
        else:
            self.attention_out_multiplier = 1.0

        if attention_in_multiplier is not None:
            self.attention_in_multiplier = attention_in_multiplier
        else:
            self.attention_in_multiplier = 1.0

        if key_multiplier is not None:
            self.key_multiplier = key_multiplier
        else:
            self.key_multiplier = 1.0

        if ssm_multipliers is not None:
            self.ssm_multipliers = ssm_multipliers
        else:
            self.ssm_multipliers = [1.0, 1.0, 1.0, 1.0, 1.0]

        if ssm_in_multiplier is not None:
            self.ssm_in_multiplier = ssm_in_multiplier
        else:
            self.ssm_in_multiplier = 1.0

        if ssm_out_multiplier is not None:
            self.ssm_out_multiplier = ssm_out_multiplier
        else:
            self.ssm_out_multiplier = 1.0

        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,
        )

    @property
    def layers_block_type(self):
        return ["falcon_h1" for i in range(self.num_hidden_layers)]

    @property
    def full_attention_layer_ids(self):
        # For Falcon-H1, we do have attention on all layers
        return range(self.num_hidden_layers)

    @property
    def linear_layer_ids(self):
        # For Falcon-H1, we do have mamba on all layers
        return range(self.num_hidden_layers)

    @property
    def mamba2_cache_params(self):
        shape = Mamba2StateShape.create(
            tp_world_size=get_tensor_model_parallel_world_size(),
            intermediate_size=self.mamba_intermediate,
            n_groups=self.mamba_n_groups,
            num_heads=self.mamba_n_heads,
            head_dim=self.mamba_d_head,
            state_size=self.mamba_d_state,
            conv_kernel=self.mamba_d_conv,
        )
        return Mamba2CacheParams(shape=shape, layers=self.linear_layer_ids)
