webapp/seed/Lib/site-packages/transformers/modeling_rope_utils.py

# 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.

import math
import warnings
from functools import wraps
from typing import TYPE_CHECKING, Optional, TypedDict

from .utils import is_torch_available, logging


logger = logging.get_logger(__name__)


if is_torch_available():
    import torch

if TYPE_CHECKING:
    from .configuration_utils import PreTrainedConfig


def dynamic_rope_update(rope_forward):
    """
    Decorator function to update the RoPE parameters in the forward pass, if the model is using a dynamic RoPE
    (i.e. a RoPE implementation that may recompute its frequencies in the forward pass).

    Args:
        rope_forward (Callable):
            The forward pass of the RoPE implementation.

    Returns:
        The decorated forward pass.
    """

    def longrope_frequency_update(self, position_ids, device, layer_type=None):
        """Longrope uses long factor if sequence is larger than original pretraining length, short otherwise."""
        seq_len = torch.max(position_ids) + 1

        if layer_type is None:
            rope_type = self.rope_type
            original_inv_freq = self.original_inv_freq
            prefix = ""
            original_max_position_embeddings = self.config.rope_parameters["original_max_position_embeddings"]
        else:
            rope_type = self.rope_type[layer_type]
            original_inv_freq = getattr(self, f"{layer_type}_original_inv_freq")
            prefix = f"{layer_type}_"
            original_max_position_embeddings = self.config.rope_parameters[layer_type][
                "original_max_position_embeddings"
            ]

        if seq_len > original_max_position_embeddings:
            if not hasattr(self, f"{layer_type}_long_inv_freq"):
                rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type]
                long_inv_freq, _ = rope_init_fn(
                    self.config,
                    device,
                    seq_len=original_max_position_embeddings + 1,
                    layer_type=layer_type,
                )
            self.register_buffer(f"{prefix}inv_freq", long_inv_freq, persistent=False)
            setattr(self, f"{prefix}long_inv_freq", long_inv_freq)
        else:
            # This .to() is needed if the model has been moved to a device after being initialized (because
            # the buffer is automatically moved, but not the original copy)
            original_inv_freq = original_inv_freq.to(device)
            self.register_buffer(f"{prefix}inv_freq", original_inv_freq, persistent=False)
            setattr(self, f"{prefix}original_inv_freq", original_inv_freq)

    def dynamic_frequency_update(self, position_ids, device, layer_type=None):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        seq_len = torch.max(position_ids) + 1
        if layer_type is None:
            rope_type = self.rope_type
            max_seq_len_cached = self.max_seq_len_cached
            original_inv_freq = self.original_inv_freq
            prefix = ""
        else:
            rope_type = self.rope_type[layer_type]
            max_seq_len_cached = getattr(self, f"{layer_type}_max_seq_len_cached", self.max_seq_len_cached)
            original_inv_freq = getattr(self, f"{layer_type}_original_inv_freq")
            prefix = f"{layer_type}_"

        if seq_len > max_seq_len_cached:  # growth
            rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type]
            inv_freq, self.attention_scaling = rope_init_fn(
                self.config,
                device,
                seq_len=seq_len,
                layer_type=layer_type,
            )
            # TODO joao: may break with compilation
            self.register_buffer(f"{prefix}inv_freq", inv_freq, persistent=False)
            setattr(self, f"{layer_type}_max_seq_len_cached", seq_len)

        if seq_len < self.original_max_seq_len and max_seq_len_cached > self.original_max_seq_len:  # reset
            # This .to() is needed if the model has been moved to a device after being initialized (because
            # the buffer is automatically moved, but not the original copy)
            original_inv_freq = original_inv_freq.to(device)
            self.register_buffer(f"{prefix}inv_freq", original_inv_freq, persistent=False)
            setattr(self, f"{prefix}original_inv_freq", original_inv_freq)
            setattr(self, f"{layer_type}_max_seq_len_cached", self.original_max_seq_len)

    @wraps(rope_forward)
    def wrapper(self, x, position_ids, layer_type=None):
        rope_type = self.rope_type if layer_type is None else self.rope_type[layer_type]
        kwargs = {"layer_type": layer_type} if layer_type is not None else {}
        if "dynamic" in rope_type:
            dynamic_frequency_update(self, position_ids, device=x.device, **kwargs)
        elif rope_type == "longrope":
            longrope_frequency_update(self, position_ids, device=x.device, **kwargs)
        return rope_forward(self, x, position_ids, **kwargs)

    return wrapper


def _compute_linear_scaling_rope_parameters(
    config: Optional["PreTrainedConfig"] = None,
    device: Optional["torch.device"] = None,
    seq_len: int | None = None,
    layer_type: str | None = None,
) -> tuple["torch.Tensor", float]:
    """
    Computes the inverse frequencies with linear scaling. Credits to the Reddit user /u/kaiokendev
    Args:
        config ([`~transformers."PreTrainedConfig"`]):
            The model configuration. This function assumes that the config will provide at least the following
            properties:

            *   rope_theta (`float`): The base wavelength from which the inverse frequencies will be derived.
            *   hidden_size (`int`): The numerator when deriving a head_dim, if not provided directly.
            *   num_attention_heads (`int`): The denominator when deriving a head_dim, if not provided directly.

            Additionally, this function will make use of the following properties if they are found in the config:

            *   head_dim (`int`, *optional*): The size of the key-value heads in the model. If None, this value will be
                derived as hidden_size // num_attention_heads.
            *   partial_rotary_factor (`float`, *optional*): If less than 1.0, inverse frequencies will be returned for
                the first fraction of the head_dim. Defaults to 1.0.
        device (`torch.device`):
            The device to use for initialization of the inverse frequencies.
        seq_len (`int`, *optional*):
            The current sequence length. Unused for this type of RoPE.

    Returns:
        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
    """
    # For backward compatibility standardize the `rope_parameters_dict` if it uses old format
    config.standardize_rope_params()
    rope_parameters_dict = config.rope_parameters[layer_type] if layer_type is not None else config.rope_parameters
    factor = rope_parameters_dict["factor"]

    # Gets the default RoPE parameters
    base = rope_parameters_dict["rope_theta"]
    partial_rotary_factor = rope_parameters_dict.get("partial_rotary_factor", 1.0)
    head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
    dim = int(head_dim * partial_rotary_factor)
    attention_factor = 1.0  # Unused in this type of RoPE

    # Compute the inverse frequencies
    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim))

    # Then applies linear scaling to the frequencies.
    # NOTE: originally, scaling was applied to the position_ids. However, we get `embs = inv_freq @ position_ids`, so
    # applying scaling to the inverse frequencies is equivalent.
    inv_freq /= factor
    return inv_freq, attention_factor


def _compute_dynamic_ntk_parameters(
    config: Optional["PreTrainedConfig"] = None,
    device: Optional["torch.device"] = None,
    seq_len: int | None = None,
    layer_type: str | None = None,
) -> tuple["torch.Tensor", float]:
    """
    Computes the inverse frequencies with NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla

    Args:
        config ([`~transformers."PreTrainedConfig"`]):
            The model configuration. This function assumes that the config will provide at least the following
            properties:

            *   rope_theta (`float`): The base wavelength from which the inverse frequencies will be derived.
            *   hidden_size (`int`): The numerator when deriving a head_dim, if not provided directly.
            *   num_attention_heads (`int`): The denominator when deriving a head_dim, if not provided directly.
            *   max_position_embeddings (`int`): The default sequence length used to update the dynamic RoPE at
                inference time
            *   rope_parameters (`dict[str, float]`): The standard RoPE scaling parameters, from which `factor`
                will be accessed. The value of `factor` is used to determine the new base frequency, along with the
                current sequence length (seq_len), the maximum positional embeddings (max_position_embeddings), and the
                computed dimensionality (dim) of the rotary embeddings. If seq_len <= max_position_embeddings, this
                factor has no effect. If seq_len <= max_position_embeddings, this factor effectively stretches the
                context window using an exponent derived from `dim`.

            Additionally, this function will make use of the following properties if they are found in the config:

            *   head_dim (`int`, *optional*): The size of the key-value heads in the model. If None, this value will be
                derived as hidden_size // num_attention_heads.
            *   partial_rotary_factor (`float`, *optional*): If less than 1.0, inverse frequencies will be returned for
                the first fraction of the head_dim. Defaults to 1.0.
        device (`torch.device`):
            The device to use for initialization of the inverse frequencies.
        seq_len (`int`, *optional*):
            The current sequence length, used to update the dynamic RoPE at inference time. If `None` or shorter than
            max_position_embeddings, this value will be overridden by max_position_embeddings.

    Returns:
        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
    """
    # For backward compatibility standardize the `rope_parameters_dict` if it uses old format
    config.standardize_rope_params()
    rope_parameters_dict = config.rope_parameters[layer_type] if layer_type is not None else config.rope_parameters

    base = rope_parameters_dict["rope_theta"]
    partial_rotary_factor = rope_parameters_dict.get("partial_rotary_factor", 1.0)
    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
    dim = int(head_dim * partial_rotary_factor)
    factor = rope_parameters_dict["factor"]
    attention_factor = 1.0  # Unused in this type of RoPE

    # seq_len: default to max_position_embeddings, e.g. at init time
    if seq_len is None:
        seq_len = config.max_position_embeddings
    elif isinstance(seq_len, torch.Tensor):
        seq_len = torch.maximum(
            seq_len,
            torch.tensor(config.max_position_embeddings, dtype=seq_len.dtype, device=seq_len.device),
        )
    else:
        seq_len = max(seq_len, config.max_position_embeddings)

    # Compute the inverse frequencies
    base = base * ((factor * seq_len / config.max_position_embeddings) - (factor - 1)) ** (dim / (dim - 2))
    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim))
    return inv_freq, attention_factor


def _compute_yarn_parameters(
    config: "PreTrainedConfig",
    device: Optional["torch.device"] = None,
    seq_len: int | None = None,
    layer_type: str | None = None,
) -> tuple["torch.Tensor", float]:
    """
    Computes the inverse frequencies with NTK scaling. Please refer to the
    [original paper](https://huggingface.co/papers/2309.00071)

    Args:
        config ([`~transformers."PreTrainedConfig"`]):
            The model configuration. This function assumes that the config will provide at least the following
            properties:

            *   rope_theta (`float`): The base wavelength from which the inverse frequencies will be derived.
            *   hidden_size (`int`): The numerator when deriving a head_dim, if not provided directly.
            *   num_attention_heads (`int`): The denominator when deriving a head_dim, if not provided directly.
            *   max_position_embeddings (`int`): The maximum length of the positional embeddings.
            *   rope_parameters (`dict[str, float | int]`): The standard RoPE scaling parameters, from which the following
                keys will be accessed:
                *   `attention_factor` (`float`, *optional*): The scaling factor to be applied to the computed cos/sin.
                    If None, the value is inferred from `factor`, `mscale`, and `mscale_all_dim` as avaialble.
                *   `beta_fast` (`float`, *optional*, defaults to 32): Parameter to set the boundary for extrapolation
                    (only) in the linear ramp function.
                *   `beta_slow` (`float`, *optional*, defaults to 1): Parameter to set the boundary for interpolation
                    (only) in the linear ramp function.
                *   `factor` (`float`, *optional*): The scaling factor applied when interpolating the position IDs to
                    extend the possible context length. Additionally, if `attention_factor` is None, the log of this
                    value is used to compute a value for `attention_factor`, possibly in conjunciton with `mscale` and
                    `mscale_all_dim`, if provided.
                *   `mscale` (`float`, *optional*): If `attention_factor` is None and both `mscale` and
                    `mscale_all_dim` are provided, `mscale` acts scalar augmenting `log(factor)` when computing the
                    numerator for the inferred value of `attention_factor`. If not provided, `attention_factor` will be
                    calculated based on `factor` only.
                *   `mscale_all_dim` (`float`, *optional*): If `attention_factor` is None and both `mscale` and
                    `mscale_all_dim` are provided, `mscale_all_dim` acts scalar augmenting `log(factor)` when computing
                    the denominator for the inferred value of `attention_factor`. If not provided, `attention_factor`
                    will be calculated based on `factor` only.
                *   `original_max_position_embeddings` (`int`): The original max position embeddings used during pretraining.
                *   `truncate` (`bool`, *optional*): Whether to truncate the correction range.

            Additionally, this function will make use of the following properties if they are found in the config:

            *   head_dim (`int`, *optional*): The size of the key-value heads in the model. If None, this value will be
                derived as hidden_size // num_attention_heads.
            *   partial_rotary_factor (`float`, *optional*, defaults to 1.0): If less than 1.0, inverse frequencies
                will be returned for the first fraction of the head_dim.
        device (`torch.device`):
            The device to use for initialization of the inverse frequencies.
        seq_len (`int`, *optional*):
            The current sequence length. Unused for this type of RoPE.

    Returns:
        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
        post-processing scaling factor applied to the computed cos/sin.
    """
    # For backward compatibility standardize the `rope_parameters_dict` if it uses old format
    config.standardize_rope_params()
    rope_parameters_dict = config.rope_parameters[layer_type] if layer_type is not None else config.rope_parameters

    base = rope_parameters_dict["rope_theta"]
    partial_rotary_factor = rope_parameters_dict.get("partial_rotary_factor", 1.0)
    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
    dim = int(head_dim * partial_rotary_factor)

    factor = rope_parameters_dict["factor"]
    attention_factor = rope_parameters_dict.get("attention_factor")
    mscale = rope_parameters_dict.get("mscale")
    mscale_all_dim = rope_parameters_dict.get("mscale_all_dim")
    original_max_position_embeddings = rope_parameters_dict["original_max_position_embeddings"]

    # NOTE: DeekSeek-V3 (and potentially other models) have `original_max_position_embeddings` field
    # containing the pretrained value. They use the ratio between `max_position_embeddings` and this value
    # to compute the default attention scaling factor, instead of using `factor`.
    if factor is None:
        factor = config.max_position_embeddings / original_max_position_embeddings

    def get_mscale(scale, mscale=1):
        if scale <= 1:
            return 1.0
        return 0.1 * mscale * math.log(scale) + 1.0

    # Sets the attention factor as suggested in the paper
    if attention_factor is None:
        if mscale and mscale_all_dim:
            attention_factor = float(get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dim))
        else:
            attention_factor = get_mscale(factor)

    # Optional config options
    # beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
    beta_fast = rope_parameters_dict.get("beta_fast") or 32
    beta_slow = rope_parameters_dict.get("beta_slow") or 1

    # Compute the inverse frequencies
    def find_correction_dim(num_rotations, dim, base, max_position_embeddings):
        """Inverse dimension formula to find the dimension based on the number of rotations"""
        return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))

    def find_correction_range(low_rot, high_rot, dim, base, max_position_embeddings, truncate):
        """Find dimension range bounds based on rotations"""
        low = find_correction_dim(low_rot, dim, base, max_position_embeddings)
        high = find_correction_dim(high_rot, dim, base, max_position_embeddings)
        if truncate:
            low = math.floor(low)
            high = math.ceil(high)
        return max(low, 0), min(high, dim - 1)

    def linear_ramp_factor(min, max, dim):
        if min == max:
            max += 0.001  # Prevent singularity

        linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
        ramp_func = torch.clamp(linear_func, 0, 1)
        return ramp_func

    # Note on variable naming: "interpolation" comes from the original technique, where we interpolate the position IDs
    # to expand the possible context length. In other words, interpolation = apply scaling factor.
    pos_freqs = base ** (torch.arange(0, dim, 2).to(device=device, dtype=torch.float) / dim)
    inv_freq_extrapolation = 1.0 / pos_freqs
    inv_freq_interpolation = 1.0 / (factor * pos_freqs)

    truncate = config.rope_parameters.get("truncate", True)
    low, high = find_correction_range(beta_fast, beta_slow, dim, base, original_max_position_embeddings, truncate)

    # Get n-dimensional rotational scaling corrected for extrapolation
    inv_freq_extrapolation_factor = 1 - linear_ramp_factor(low, high, dim // 2).to(device=device, dtype=torch.float)
    inv_freq = (
        inv_freq_interpolation * (1 - inv_freq_extrapolation_factor)
        + inv_freq_extrapolation * inv_freq_extrapolation_factor
    )
    return inv_freq, attention_factor


def _compute_longrope_parameters(
    config: "PreTrainedConfig",
    device: Optional["torch.device"] = None,
    seq_len: int | None = None,
    layer_type: str | None = None,
) -> tuple["torch.Tensor", float]:
    """
    Computes the inverse frequencies with LongRoPE scaling. Please refer to the
    [original implementation](https://github.com/microsoft/LongRoPE)

    Args:
        config ([`~transformers."PreTrainedConfig"`]):
            The model configuration. This function assumes that the config will provide at least the following
            properties:

            *   rope_theta (`float`): The base wavelength from which the inverse frequencies will be derived.
            *   hidden_size (`int`): The numerator when deriving a head_dim, if not provided directly.
            *   num_attention_heads (`int`): The denominator when deriving a head_dim, if not provided directly.
            *   max_position_embeddings (`int`): The maximum length of the positional embeddings.
            *   original_max_position_embeddings (`int`, *optional*): The original max position embeddings used during
                pretraining. If not provided, defaults to `max_position_embeddings`.
            *   rope_parameters (`dict[str, float]`): The standard RoPE scaling parameters, from which the following keys
                will be accessed:
                *   `attention_factor` (`float`, *optional*): The scaling factor to be applied on the attention
                    computation. If unspecified, it defaults to value recommended by the implementation, inferred from
                    the value of `factor`.
                *   `factor` (`float`, *optional*): The scaling factor to apply to the RoPE embeddings. If both
                    `max_position_embeddings` and `original_max_position_embeddings` are provided, this value will be
                    overridden s the ratio between those values.
                *   `long_factor` (`float`, *optional*): The scale factor applied when computing the inverse
                    frequencies if `seq_len` is provided and greater than `original_max_position_embeddings`.
                *   `short_factor` (`float`, *optional*): The scale factor applied when computing the inverse
                    frequencies if `seq_len` is None or less-than-or-equal-to `original_max_position_embeddings`.

            Additionally, this function will make use of the following properties if they are found in the config:

            *   head_dim (`int`, *optional*): The size of the key-value heads in the model. If None, this value will be
                derived as hidden_size // num_attention_heads.
            *   partial_rotary_factor (`float`, *optional*, defaults to 1.0): If less than 1.0, inverse frequencies
                will be returned for the first fraction of the head_dim.
        device (`torch.device`):
            The device to use for initialization of the inverse frequencies.
        seq_len (`int`, *optional*):
            The current sequence length.

    Returns:
        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
        post-processing scaling factor applied to the computed cos/sin.
    """
    # For backward compatibility standardize the `rope_parameters_dict` if it uses old format
    config.standardize_rope_params()
    rope_parameters_dict = config.rope_parameters[layer_type] if layer_type is not None else config.rope_parameters

    base = rope_parameters_dict["rope_theta"]
    partial_rotary_factor = rope_parameters_dict.get("partial_rotary_factor", 1.0)
    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
    dim = int(head_dim * partial_rotary_factor)

    long_factor = rope_parameters_dict["long_factor"]
    short_factor = rope_parameters_dict["short_factor"]
    factor = rope_parameters_dict.get("factor")
    attention_factor = rope_parameters_dict.get("attention_factor")
    original_max_position_embeddings = rope_parameters_dict["original_max_position_embeddings"]

    # NOTE: Phi3 (and potentially other models) modify `max_position_embeddings` and have a
    # `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
    # values to compute the default attention scaling factor, instead of using `factor`.
    if factor is None:
        factor = config.max_position_embeddings / original_max_position_embeddings

    # Sets the attention factor as suggested in the paper
    if attention_factor is None:
        if factor <= 1.0:
            attention_factor = 1.0
        else:
            attention_factor = math.sqrt(1 + math.log(factor) / math.log(original_max_position_embeddings))

    # Compute the inverse frequencies -- scaled based on the target sequence length
    if seq_len and seq_len > original_max_position_embeddings:
        ext_factors = torch.tensor(long_factor, dtype=torch.float32, device=device)
    else:
        ext_factors = torch.tensor(short_factor, dtype=torch.float32, device=device)
    inv_freq_shape = torch.arange(0, dim, 2, dtype=torch.int64, device=device).float() / dim
    inv_freq = 1.0 / (ext_factors * base**inv_freq_shape)

    return inv_freq, attention_factor


def _compute_llama3_parameters(
    config: "PreTrainedConfig",
    device: Optional["torch.device"] = None,
    seq_len: int | None = None,
    layer_type: str | None = None,
) -> tuple["torch.Tensor", float]:
    """
    Computes the inverse frequencies for llama 3.1.

    Args:
        config ([`~transformers."PreTrainedConfig"`]):
            The model configuration. This function assumes that the config will provide at least the following
            properties:

            *   rope_theta (`float`): The base wavelength from which the inverse frequencies will be derived.
            *   hidden_size (`int`): The numerator when deriving a head_dim, if not provided directly.
            *   num_attention_heads (`int`): The denominator when deriving a head_dim, if not provided directly.
            *   rope_parameters (`dict[str, float | int]`): The standard RoPE scaling parameters, from which the following
                keys will be accessed:
                *   `factor` (`float`, *optional*): The scaling factor applied to the inverse frequencies when 1) the
                    wavelength is greater than `low_freq_wavelen` prior to smoothing, and 2) to all inverse frequencies
                    during smoothing.
                *   `high_freq_factor` (`float`): The scale factor used to compute `high_freq_wavelen` and
                    the value for the denominator of the smoothing factor prior to the `low_freq_factor` shift.
                *   `low_freq_factor` (`float`): The scale factor used to compute `low_freq_wavelen` and
                    the shift applied to the numerator and denominator of the smoothing factor.
                    frequencies if `seq_len` is None or less-than-or-equal-to `original_max_position_embeddings`.
                *   `original_max_position_embeddings` (`int`): The original max position embeddings used
                    during pretraining. If not provided, the function falls back to `max_position_embeddings`.

            Additionally, this function will make use of the following properties if they are found in the config:

            *   head_dim (`int`, *optional*): The size of the key-value heads in the model. If None, this value will be
                derived as hidden_size // num_attention_heads.
            *   partial_rotary_factor (`float`, *optional*): If less than 1.0, inverse frequencies will be returned for
                the first fraction of the head_dim. Defaults to 1.0.
        device (`torch.device`):
            The device to use for initialization of the inverse frequencies.
        seq_len (`int`, *optional*):
            The current sequence length. Unused for this type of RoPE.
    Returns:
        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
        post-processing scaling factor applied to the computed cos/sin.
    """
    # For backward compatibility standardize the `rope_parameters_dict` if it uses old format
    config.standardize_rope_params()
    rope_parameters_dict = config.rope_parameters[layer_type] if layer_type is not None else config.rope_parameters

    # Gets the default RoPE parameters
    base = rope_parameters_dict["rope_theta"]
    partial_rotary_factor = rope_parameters_dict.get("partial_rotary_factor", 1.0)
    head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
    dim = int(head_dim * partial_rotary_factor)
    attention_factor = 1.0  # Unused in this type of RoPE

    # Compute the inverse frequencies
    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim))

    factor = rope_parameters_dict["factor"]  # `8` in the original implementation
    low_freq_factor = rope_parameters_dict["low_freq_factor"]  # `1` in the original implementation
    high_freq_factor = rope_parameters_dict["high_freq_factor"]  # `4` in the original implementation
    old_context_len = rope_parameters_dict["original_max_position_embeddings"]  # `8192` in the original implementation

    low_freq_wavelen = old_context_len / low_freq_factor
    high_freq_wavelen = old_context_len / high_freq_factor

    wavelen = 2 * math.pi / inv_freq
    # wavelen < high_freq_wavelen: do nothing
    # wavelen > low_freq_wavelen: divide by factor
    inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
    # otherwise: interpolate between the two, using a smooth factor
    smooth_factor = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
    smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama / factor + smooth_factor * inv_freq_llama
    is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen)
    inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)

    return inv_freq_llama, attention_factor


# This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
# from the model config. You can append new {'rope_type': callable} pairs to this rope_parameters to enable custom RoPE
# parameterizations, as long as the callable has the same signature.
ROPE_INIT_FUNCTIONS = {
    "linear": _compute_linear_scaling_rope_parameters,
    "dynamic": _compute_dynamic_ntk_parameters,
    "yarn": _compute_yarn_parameters,
    "longrope": _compute_longrope_parameters,
    "llama3": _compute_llama3_parameters,
}


class RopeParameters(TypedDict, total=False):
    """
    Args:
        rope_theta (`float`):
            The base period of the RoPE embeddings.
        rope_type (`str`, *optional*, defaults to "default"):
            The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
            'llama3'], with 'default' being the original RoPE implementation.
        partial_rotary_factor (`float`, *optional*):
            The percentage of the query and key head embedding on which RoPE will be applied.
        factor (`float`, *optional*):
            Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
            most scaling types, a `factor` of x will enable the model to handle sequences of length x *
            original maximum pre-trained length.
        original_max_position_embeddings (`int`, *optional*):
            Used with 'yarn', 'longrope' and 'llama3'. The original max position embeddings used during
            pretraining.
        attention_factor (`float`, *optional*):
            Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
            computation. If unspecified, it defaults to value recommended by the implementation, using the
            `factor` field to infer the suggested value.
        beta_fast (`float`, *optional*):
            Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
            ramp function. If unspecified, it defaults to 32.
        beta_slow (`float`, *optional*):
            Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
            ramp function. If unspecified, it defaults to 1.
        short_factor (`list[float]`, *optional*):
            Only used with 'longrope'. The scaling factor to be applied to short contexts (<
            `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
            size divided by the number of attention heads divided by 2
        long_factor (`list[float]`, *optional*):
            Only used with 'longrope'. The scaling factor to be applied to long contexts (<
            `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
            size divided by the number of attention heads divided by 2
        low_freq_factor (`float`, *optional*):
            Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
        high_freq_factor (`float`, *optional*):
            Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
    """

    rope_theta: float
    rope_type: str | None
    partial_rotary_factor: float | None
    factor: float | None
    original_max_position_embeddings: int | None
    attention_factor: float | None
    beta_fast: float | None
    beta_slow: float | None
    short_factor: list[float] | None
    long_factor: list[float] | None
    low_freq_factor: float | None
    high_freq_factor: float | None


class RotaryEmbeddingConfigMixin:
    """
    A Mixin containing the functionality to standardize and validate RoPE parameters.
    """

    default_theta = 10_000.0

    def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation: set | None = None, **kwargs):
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or self.rope_parameters
        self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {}

        # Standardize and validate the correctness of rotary position embeddings parameters
        self.rope_parameters.setdefault("rope_theta", kwargs.pop("rope_theta", self.default_theta))

        if "partial_rotary_factor" in kwargs:
            self.rope_parameters.setdefault("partial_rotary_factor", kwargs["partial_rotary_factor"])
            ignore_keys_at_rope_validation = {"partial_rotary_factor"}

        self.standardize_rope_params()
        self.validate_rope(ignore_keys=ignore_keys_at_rope_validation)
        return kwargs

    def standardize_rope_params(self):
        """
        Helper to standardize the config's rope params field by ensuring the params are defined for each
        later type. For old model the fn will duplicate a single rope param in each layer type (backward compatibility)
        """
        # Move `rope_theta` and `partial_rotary_factor` to the `rope_parameters`, if not there yet
        rope_theta = getattr(self, "rope_theta", None)
        partial_rotary_factor = getattr(self, "partial_rotary_factor", None)
        rope_parameters = getattr(self, "rope_parameters", None) or {}
        layer_types = getattr(self, "layer_types", None)

        # Case 0: no RoPE params defined
        if not (rope_parameters or rope_theta):
            # partial_rotary_factor without rope_theta is invalid, so we don't check for it here
            logger.warning("`standardize_rope_params` was called but no RoPE parameters were found.")
            return
        # Case 1: RoPE param keys do not intersect with possible `layer_types` -> one global dict
        elif layer_types is None or rope_parameters == {} or not set(rope_parameters.keys()).issubset(layer_types):
            rope_parameters.setdefault("rope_type", rope_parameters.get("type", "default"))
            rope_parameters.setdefault("rope_theta", rope_theta)
            if partial_rotary_factor is not None:
                rope_parameters["partial_rotary_factor"] = partial_rotary_factor

            # Move pretraining-time maximum length to rope parameter dict for RoPE types with scaling
            if rope_parameters["rope_type"] in ["llama3", "yarn", "longrope"]:
                if hasattr(self, "original_max_position_embeddings"):
                    # NOTE: Phi3 (and potentially other models) save `original_max_position_embeddings` field
                    # containing the pretrained value outside rope parameters. This is an exception case where we
                    # give priority to `self.original_max_position_embeddings
                    self.rope_parameters["original_max_position_embeddings"] = self.original_max_position_embeddings
                else:
                    self.rope_parameters.setdefault("original_max_position_embeddings", self.max_position_embeddings)

        # Case 2: different RoPE for each layer -> several params as nested dict
        else:
            for layer_type in set(layer_types):
                rope_parameters[layer_type].setdefault("rope_type", rope_parameters[layer_type].get("type", "default"))
                rope_parameters[layer_type].setdefault("rope_theta", rope_theta)
                if partial_rotary_factor is not None:
                    rope_parameters[layer_type]["partial_rotary_factor"] = partial_rotary_factor

                if rope_parameters[layer_type]["rope_type"] in ["llama3", "yarn", "longrope"]:
                    self.rope_parameters[layer_type].setdefault(
                        "original_max_position_embeddings", self.max_position_embeddings
                    )

        self.rope_parameters = rope_parameters

    def validate_rope(self: "PreTrainedConfig", ignore_keys: set | None = None):
        """
        Validate the RoPE config arguments, given a `"PreTrainedConfig"` object
        """
        rope_parameters_dict = self.rope_parameters
        if rope_parameters_dict is None:
            return

        if getattr(self, "layer_types", None) is not None and set(rope_parameters_dict.keys()).issubset(
            self.layer_types
        ):
            pass
        else:
            rope_parameters_dict = {"full_attention": rope_parameters_dict}

        for rope_parameters in rope_parameters_dict.values():
            rope_type = rope_parameters.get("rope_type", rope_parameters.get("type", "default"))
            validation_fn = getattr(self, f"_validate_{rope_type}_rope_parameters", None)
            rope_parameters["rope_type"] = rope_type

            if validation_fn is not None:
                validation_fn(rope_parameters, ignore_keys=ignore_keys)
            else:
                logger.warning(
                    f"Missing validation function in 'RotaryEmbeddingConfigMixin' for 'rope_type'='{rope_type}'"
                )

    def _validate_default_rope_parameters(self, rope_parameters: dict, ignore_keys: set | None = None):
        required_keys = {"rope_type", "rope_theta"}
        received_keys = set(rope_parameters.keys())
        rope_type = rope_parameters["rope_type"]
        self._check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)

    def _validate_linear_rope_parameters(self, rope_parameters: dict, ignore_keys: set | None = None):
        required_keys = {"rope_type", "factor", "rope_theta"}
        received_keys = set(rope_parameters.keys())
        rope_type = rope_parameters["rope_type"]
        self._check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)

        factor = rope_parameters["factor"]
        if factor is None or not isinstance(factor, float) or factor < 1.0:
            logger.warning(f"`rope_parameters`'s factor field must be a float >= 1, got {factor}")

    def _validate_dynamic_rope_parameters(self, rope_parameters: dict, ignore_keys: set | None = None):
        required_keys = {"rope_type", "factor"}
        received_keys = set(rope_parameters.keys())
        rope_type = rope_parameters["rope_type"]
        self._check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)

        factor = rope_parameters["factor"]
        if factor is None or not isinstance(factor, float) or factor < 1.0:
            logger.warning(f"`rope_parameters`'s factor field must be a float >= 1, got {factor}")

    def _validate_yarn_rope_parameters(self, rope_parameters: dict, ignore_keys: set | None = None):
        required_keys = {"rope_type", "factor", "rope_theta", "original_max_position_embeddings"}
        optional_keys = {
            "attention_factor",
            "beta_fast",
            "beta_slow",
            "mscale",
            "mscale_all_dim",
            "truncate",
        }
        received_keys = set(rope_parameters.keys())
        rope_type = rope_parameters["rope_type"]
        self._check_received_keys(rope_type, received_keys, required_keys, optional_keys, ignore_keys=ignore_keys)

        factor = rope_parameters["factor"]
        if factor is None or not isinstance(factor, float) or factor < 1.0:
            logger.warning(f"`rope_parameters`'s factor field must be a float >= 1, got {factor}")

        attention_factor = rope_parameters.get("attention_factor")
        if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0):
            logger.warning(
                f"`rope_parameters`'s attention_factor field must be a float greater than 0, got {attention_factor}"
            )
        beta_fast = rope_parameters.get("beta_fast")
        if beta_fast is not None and not isinstance(beta_fast, float):
            logger.warning(f"`rope_parameters`'s beta_fast field must be a float, got {beta_fast}")
        beta_slow = rope_parameters.get("beta_slow")
        if beta_slow is not None and not isinstance(beta_slow, float):
            logger.warning(f"`rope_parameters`'s beta_slow field must be a float, got {beta_slow}")

        if (beta_fast or 32) < (beta_slow or 1):
            logger.warning(
                f"`rope_parameters`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
                f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
            )

        # Double-check: `factor` should be the ratio between the pre-yarn and post-yarn context lengths.
        # NOTE: we might get `implicit_factor == 1` if config's `original_max_position_embeddings` was
        # inferred from `max_position_embeddings` during standardization
        original_max_position_embeddings = self.rope_parameters["original_max_position_embeddings"]
        implicit_factor = self.max_position_embeddings / original_max_position_embeddings
        if implicit_factor != factor and implicit_factor != 1:
            logger.warning_once(
                f"The explicitly set RoPE scaling factor (config.rope_parameters['factor'] = {factor}) does not match "
                "the ratio implicitly set by other parameters (implicit factor = "
                "post-yarn context length / pre-yarn context length = "
                "config.max_position_embeddings / config.rope_parameters['original_max_position_embeddings'] = "
                f"{implicit_factor}). Using the explicit factor ({factor}) in YaRN. This may cause unexpected "
                "behaviour in model usage, please correct the 'original_max_position_embeddings' fields in the model config."
            )

    def _validate_longrope_rope_parameters(self, rope_parameters: dict, ignore_keys: set | None = None):
        required_keys = {"rope_type", "short_factor", "long_factor", "rope_theta", "original_max_position_embeddings"}
        optional_keys = {"attention_factor", "factor"}
        received_keys = set(rope_parameters.keys())
        rope_type = rope_parameters["rope_type"]
        self._check_received_keys(rope_type, received_keys, required_keys, optional_keys, ignore_keys=ignore_keys)

        partial_rotary_factor = rope_parameters.get("partial_rotary_factor", 1.0)
        head_dim = getattr(self, "head_dim", self.hidden_size // self.num_attention_heads)
        dim = int(head_dim * partial_rotary_factor)

        short_factor = rope_parameters.get("short_factor")
        if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
            logger.warning(f"`rope_parameters`'s short_factor field must be a list of numbers, got {short_factor}")
        if len(short_factor) != dim // 2:
            logger.warning(
                f"`rope_parameters`'s short_factor field must have length {dim // 2}, got {len(short_factor)}"
            )

        long_factor = rope_parameters.get("long_factor")
        if not isinstance(long_factor, list) and all(isinstance(x, (int, float)) for x in long_factor):
            logger.warning(f"`rope_parameters`'s long_factor field must be a list of numbers, got {long_factor}")
        if len(long_factor) != dim // 2:
            logger.warning(
                f"`rope_parameters`'s long_factor field must have length {dim // 2}, got {len(long_factor)}"
            )

        factor = rope_parameters.get("factor")
        original_max_position_embeddings = rope_parameters["original_max_position_embeddings"]

        # Handle Phi3 divergence: we prefer the use of `attention_factor` and/or `factor` over
        # `original_max_position_embeddings` to compute internal variables. The latter is undesirable
        if factor is None and original_max_position_embeddings is not None:
            logger.warning_once(
                "This model config has set a `rope_parameters['original_max_position_embeddings']` field, to be used together with "
                "`max_position_embeddings` to determine a scaling factor. Please set the `factor` field of `rope_parameters`"
                "with this ratio instead -- we recommend the use of this field over `original_max_position_embeddings`, "
                "as it is compatible with most model architectures."
            )
        elif factor is None and original_max_position_embeddings is None:
            logger.warning("Missing required keys in `rope_parameters`: 'factor'")
        elif not isinstance(factor, float) or factor < 1.0:
            logger.warning(f"`rope_parameters`'s factor field must be a float >= 1, got {factor}")

        attention_factor = rope_parameters.get("attention_factor")
        if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0.0):
            logger.warning(
                f"`rope_parameters`'s attention_factor field must be a float greater than 0, got {attention_factor}"
            )

    def _validate_llama3_rope_parameters(self, rope_parameters: dict, ignore_keys: set | None = None):
        required_keys = {
            "rope_type",
            "factor",
            "original_max_position_embeddings",
            "low_freq_factor",
            "high_freq_factor",
            "rope_theta",
        }
        rope_type = rope_parameters["rope_type"]
        received_keys = set(rope_parameters.keys())
        self._check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)

        factor = rope_parameters["factor"]
        if factor is None or not isinstance(factor, float) or factor < 1.0:
            logger.warning(f"`rope_parameters`'s factor field must be a float >= 1, got {factor}")

        low_freq_factor = rope_parameters["low_freq_factor"]
        high_freq_factor = rope_parameters["high_freq_factor"]
        if low_freq_factor is None or not isinstance(low_freq_factor, float):
            logger.warning(f"`rope_parameters`'s low_freq_factor field must be a float, got {low_freq_factor}")
        if high_freq_factor is None or not isinstance(high_freq_factor, float):
            logger.warning(f"`rope_parameters`'s high_freq_factor field must be a float, got {high_freq_factor}")
        if high_freq_factor <= low_freq_factor:
            logger.warning(
                "`rope_parameters`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
                f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
            )

        original_max_position_embeddings = rope_parameters["original_max_position_embeddings"]
        if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
            logger.warning(
                "`rope_parameters`'s original_max_position_embeddings field must be an integer, got "
                f"{original_max_position_embeddings}"
            )
        if original_max_position_embeddings >= self.max_position_embeddings:
            logger.warning(
                "`rope_parameters`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
                f"{original_max_position_embeddings} and max_position_embeddings={self.max_position_embeddings}"
            )

    @staticmethod
    def _check_received_keys(
        rope_type: str,
        received_keys: set,
        required_keys: set,
        optional_keys: set | None = None,
        ignore_keys: set | None = None,
    ):
        """Compare the received keys in `config.rope_parameters` against the expected and optional keys"""
        # BC: "rope_type" was originally "type" -- let's check for "rope_type" when "type" is present
        if "type" in received_keys:
            received_keys -= {"type"}
            required_keys.add("rope_type")

        optional_keys = optional_keys or set()
        if "partial_rotary_factor" not in optional_keys:
            optional_keys.add("partial_rotary_factor")

        # Some models need to store model-specific keys, and we don't want to throw warning at them
        if ignore_keys is not None:
            received_keys -= ignore_keys

        missing_keys = required_keys - received_keys
        if missing_keys:
            raise KeyError(f"Missing required keys in `rope_parameters` for 'rope_type'='{rope_type}': {missing_keys}")

        unused_keys = received_keys - required_keys - optional_keys
        if unused_keys:
            logger.warning(f"Unrecognized keys in `rope_parameters` for 'rope_type'='{rope_type}': {unused_keys}")


def rope_config_validation(config: RotaryEmbeddingConfigMixin, ignore_keys: set | None = None):
    """
    This is a deprecated function.
    It has been kept for backward compatibility with custom code models.
    """
    warnings.warn(
        "`rope_config_validation` is deprecated and has been removed. "
        "Its functionality has been moved to RotaryEmbeddingConfigMixin.validate_rope method. "
        "PreTrainedConfig inherits this class, so please call self.validate_rope() instead. "
        "Also, make sure to use the new rope_parameters syntax. "
        "You can call self.standardize_rope_params() in the meantime.",
        FutureWarning,
    )
    config.standardize_rope_params()
    config.validate_rope(ignore_keys=ignore_keys)