Upload modeling_rope_utils.py

modeling_rope_utils.py

@@ -0,0 +1,944 @@
+# 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 available.
+                *   `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
+    ignore_keys_at_rope_validation = set()
+
+    def convert_rope_params_to_dict(self, **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. Priority for these parameters is:
+        # 1. Values in `rope_parameters` dict (where they should be after standardization)
+        # 2. Values in `kwargs` (i.e. it's in config.json but not MyConfig.__init__'s args)
+        # 3. Values in the config's attributes (i.e. it's in MyConfig.__init__'s args)
+        # 4. Default values (i.e. not present at all but other RoPE parameters are present)
+        rope_theta = kwargs.pop("rope_theta", getattr(self, "rope_theta", self.default_theta))
+        self.rope_parameters.setdefault("rope_theta", rope_theta)
+
+        partial_rotary_factor = kwargs.get("partial_rotary_factor", getattr(self, "partial_rotary_factor", None))
+        if partial_rotary_factor is not None:
+            self.rope_parameters.setdefault("partial_rotary_factor", partial_rotary_factor)
+            self.ignore_keys_at_rope_validation = self.ignore_keys_at_rope_validation | {"partial_rotary_factor"}
+
+        self.standardize_rope_params()
+        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"):
+        """
+        Validate the RoPE config arguments, given a `"PreTrainedConfig"` object
+        """
+        # Don't validate if no rope_parameters found (`None`) or if it's an empty dict
+        # Note that validation runs every time a new config is created, even if config is non-RoPE
+        rope_parameters_dict = getattr(self, "rope_parameters", None)
+        if not rope_parameters_dict:
+            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=self.ignore_keys_at_rope_validation)
+            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()