vllm/model_executor/layers/rotary_embedding.py

# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.33.2/src/transformers/models/llama/modeling_llama.py
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
"""Rotary Positional Embeddings."""
import math
from typing import Any, Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from vllm._C import ops


def _rotate_neox(x: torch.Tensor) -> torch.Tensor:
    x1 = x[..., :x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2:]
    return torch.cat((-x2, x1), dim=-1)


def _rotate_gptj(x: torch.Tensor) -> torch.Tensor:
    x1 = x[..., ::2]
    x2 = x[..., 1::2]
    x = torch.stack((-x2, x1), dim=-1)
    return x.flatten(-2)


class RotaryEmbedding(nn.Module):
    """Original rotary positional embedding."""

    def __init__(
        self,
        head_size: int,
        rotary_dim: int,
        max_position_embeddings: int,
        base: int,
        is_neox_style: bool,
    ) -> None:
        super().__init__()
        self.head_size = head_size
        self.rotary_dim = rotary_dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        self.is_neox_style = is_neox_style

        cache = self._compute_cos_sin_cache()
        cache = cache.to(torch.get_default_dtype())
        self.register_buffer("cos_sin_cache", cache, persistent=False)

    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
        """Compute the inverse frequency."""
        # NOTE(woosuk): The HF implementation uses `torch.arange(...).float()`.
        # However, we use `torch.arange(..., dtype=torch.float)` instead to
        # avoid numerical issues with large base values (e.g., 10000000).
        # This may cause a slight numerical difference between the HF
        # implementation and ours.
        # NOTE(woosuk): To exactly match the HF implementation, we need to
        # use CPU to compute the cache and then move it to GPU. However, we
        # create the cache on GPU for faster initialization. This may cause
        # a slight numerical difference between the HF implementation and ours.
        inv_freq = 1.0 / (base**(torch.arange(
            0, self.rotary_dim, 2, dtype=torch.float, device="cuda") /
                                 self.rotary_dim))
        return inv_freq

    def _compute_cos_sin_cache(self) -> torch.Tensor:
        """Compute the cos and sin cache."""
        inv_freq = self._compute_inv_freq(self.base)
        t = torch.arange(self.max_position_embeddings,
                         dtype=torch.float,
                         device="cuda")

        freqs = torch.einsum("i,j -> ij", t, inv_freq)
        cos = freqs.cos()
        sin = freqs.sin()
        cache = torch.cat((cos, sin), dim=-1)
        return cache

    def _forward(
        self,
        positions: torch.Tensor,
        query: torch.Tensor,
        key: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """PyTorch-native implementation equivalent to forward()."""
        query = query.view(*query.shape[:-1], -1, self.head_size)
        key = key.view(*key.shape[:-1], -1, self.head_size)

        query_rot = query[..., :self.rotary_dim]
        key_rot = key[..., :self.rotary_dim]
        if self.rotary_dim < self.head_size:
            query_pass = query[..., self.rotary_dim:]
            key_pass = key[..., self.rotary_dim:]

        cos_sin = self.cos_sin_cache[positions]
        cos, sin = cos_sin.chunk(2, dim=-1)
        if self.is_neox_style:
            # NOTE(woosuk): Here we assume that the positions tensor has the
            # shape [batch_size, seq_len].
            cos = cos.repeat(1, 1, 2).unsqueeze(-2)
            sin = sin.repeat(1, 1, 2).unsqueeze(-2)
        else:
            cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2)
            sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2)

        rotate_fn = _rotate_neox if self.is_neox_style else _rotate_gptj
        query_rot = query_rot * cos + rotate_fn(query_rot) * sin
        key_rot = key_rot * cos + rotate_fn(key_rot) * sin

        if self.rotary_dim < self.head_size:
            query = torch.cat((query_rot, query_pass), dim=-1)
            key = torch.cat((key_rot, key_pass), dim=-1)
        else:
            query = query_rot
            key = key_rot
        query = query.flatten(-2)
        key = key.flatten(-2)
        return query, key

    def forward(
        self,
        positions: torch.Tensor,
        query: torch.Tensor,
        key: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # ops.rotary_embedding() is an in-place operation that
        # updates the query and key tensors.
        ops.rotary_embedding(positions, query, key, self.head_size,
                             self.cos_sin_cache, self.is_neox_style)
        return query, key


class LinearScalingRotaryEmbedding(RotaryEmbedding):
    """RotaryEmbedding extended with linear scaling.

    Credits to the Reddit user /u/kaiokendev
    """

    def __init__(
        self,
        head_size: int,
        rotary_dim: int,
        max_position_embeddings: int,
        base: int,
        is_neox_style: bool,
        scaling_factor: float,
    ) -> None:
        self.scaling_factor = scaling_factor
        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
                         is_neox_style)

    def _compute_cos_sin_cache(self) -> torch.Tensor:
        inv_freq = self._compute_inv_freq(self.base)
        # NOTE(woosuk): self.max_position_embeddings is the original
        # maximum length before applying the rope scaling.
        # Thus, the maximum length after applying the rope scaling is
        # self.max_position_embeddings * self.scaling_factor.
        max_len = self.max_position_embeddings * self.scaling_factor
        t = torch.arange(max_len, dtype=torch.float, device="cuda")
        t = t / self.scaling_factor

        freqs = torch.einsum("i,j -> ij", t, inv_freq)
        cos = freqs.cos()
        sin = freqs.sin()
        cache = torch.cat((cos, sin), dim=-1)
        return cache


class DynamicNTKScalingRotaryEmbedding(RotaryEmbedding):
    """RotaryEmbedding extended with Dynamic NTK scaling.

    Credits to the Reddit users /u/bloc97 and /u/emozilla
    """

    def __init__(
        self,
        head_size: int,
        rotary_dim: int,
        max_position_embeddings: int,
        base: int,
        is_neox_style: bool,
        scaling_factor: float,
    ) -> None:
        self.scaling_factor = scaling_factor
        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
                         is_neox_style)

    def _compute_cos_sin_cache(self) -> torch.Tensor:
        # NOTE(woosuk): self.max_position_embeddings is the original
        # maximum length before applying the rope scaling.
        # Thus, the maximum length after applying the rope scaling is
        # self.max_position_embeddings * self.scaling_factor.
        max_len = self.max_position_embeddings * self.scaling_factor
        base = self.base * (
            (self.scaling_factor * max_len / self.max_position_embeddings) -
            (self.scaling_factor - 1))**(self.rotary_dim /
                                         (self.rotary_dim - 2))
        inv_freq = self._compute_inv_freq(base)
        t = torch.arange(max_len, dtype=torch.float, device="cuda")

        freqs = torch.einsum("i,j -> ij", t, inv_freq)
        cos = freqs.cos()
        sin = freqs.sin()
        cache = torch.cat((cos, sin), dim=-1)
        return cache


# Inverse dim formula to find dim based on number of rotations
def _yarn_find_correction_dim(num_rotations: int,
                              dim: int,
                              base: float = 10000,
                              max_position_embeddings: int = 2048) -> float:
    return (dim * math.log(max_position_embeddings /
                           (num_rotations * 2 * math.pi))) / (2 *
                                                              math.log(base))


# Find dim range bounds based on rotations
def _yarn_find_correction_range(low_rot: int,
                                high_rot: int,
                                dim: int,
                                base: float = 10000,
                                max_position_embeddings: int = 2048) -> int:
    low = math.floor(
        _yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings))
    high = math.ceil(
        _yarn_find_correction_dim(high_rot, dim, base,
                                  max_position_embeddings))
    return max(low, 0), min(high, dim - 1)  # Clamp values just in case


def _yarn_linear_ramp_mask(low: float, high: float, dim: int,
                           dtype: torch.dtype,
                           device: torch.device) -> torch.Tensor:
    if low == high:
        high += 0.001  # Prevent singularity

    linear_func = (torch.arange(dim, dtype=dtype, device=device) -
                   low) / (high - low)
    ramp_func = torch.clamp(linear_func, 0, 1)
    return ramp_func


def _yarn_get_mscale(scale: float = 1) -> float:
    if scale <= 1:
        return 1.0
    return 0.1 * math.log(scale) + 1.0


class YaRNScalingRotaryEmbedding(RotaryEmbedding):
    """RotaryEmbedding extended with YaRN method.

    Credits to Peng et al. github.com/jquesnelle/yarn
    """

    def __init__(
        self,
        head_size: int,
        rotary_dim: int,
        max_position_embeddings: int,
        base: int,
        is_neox_style: bool,
        scaling_factor: float,
        *,
        extrapolation_factor: float = 1,
        attn_factor: float = 1,
        beta_fast: float = 32,
        beta_slow: float = 1,
    ) -> None:
        self.scaling_factor = scaling_factor
        self.extrapolation_factor = extrapolation_factor
        self.attn_factor = attn_factor
        self.beta_fast = beta_fast
        self.beta_slow = beta_slow
        # Get n-d magnitude scaling corrected for interpolation
        self.mscale = float(
            _yarn_get_mscale(self.scaling_factor) * attn_factor)
        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
                         is_neox_style)

    def _compute_inv_freq(self, scaling_factor: float) -> torch.Tensor:
        pos_freqs = self.base**(torch.arange(
            0, self.rotary_dim, 2, dtype=torch.float, device="cuda") /
                                self.rotary_dim)
        inv_freq_extrapolation = 1.0 / pos_freqs
        inv_freq_interpolation = 1.0 / (scaling_factor * pos_freqs)

        low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow,
                                                self.rotary_dim, self.base,
                                                self.max_position_embeddings)
        # Get n-d rotational scaling corrected for extrapolation
        inv_freq_mask = (1 - _yarn_linear_ramp_mask(
            low, high, self.rotary_dim // 2, dtype=torch.float,
            device="cuda")) * self.extrapolation_factor
        inv_freq = inv_freq_interpolation * (
            1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
        return inv_freq

    def _compute_cos_sin_cache(self) -> torch.Tensor:
        inv_freq = self._compute_inv_freq(self.scaling_factor)
        t = torch.arange(self.max_position_embeddings * self.scaling_factor,
                         device="cuda",
                         dtype=torch.float32)
        freqs = torch.einsum("i,j -> ij", t, inv_freq)
        cos = (freqs.cos() * self.mscale)
        sin = (freqs.sin() * self.mscale)
        cache = torch.cat((cos, sin), dim=-1)
        return cache


_ROPE_DICT: Dict[Tuple, RotaryEmbedding] = {}


def get_rope(
    head_size: int,
    rotary_dim: int,
    max_position: int,
    base: int,
    is_neox_style: bool = True,
    rope_scaling: Optional[Dict[str, Any]] = None,
) -> RotaryEmbedding:
    key = (head_size, rotary_dim, max_position, base, is_neox_style,
           tuple(rope_scaling.items()) if rope_scaling is not None else None)
    if key in _ROPE_DICT:
        return _ROPE_DICT[key]

    if rope_scaling is None:
        rotary_emb = RotaryEmbedding(head_size, rotary_dim, max_position, base,
                                     is_neox_style)
    else:
        scaling_type = rope_scaling["type"]
        scaling_factor = rope_scaling["factor"]
        if scaling_type == "linear":
            rotary_emb = LinearScalingRotaryEmbedding(head_size, rotary_dim,
                                                      max_position, base,
                                                      is_neox_style,
                                                      scaling_factor)
        elif scaling_type == "dynamic":
            rotary_emb = DynamicNTKScalingRotaryEmbedding(
                head_size, rotary_dim, max_position, base, is_neox_style,
                scaling_factor)
        elif scaling_type == "yarn":
            original_max_position = rope_scaling[
                "original_max_position_embeddings"]
            assert max_position == original_max_position * scaling_factor
            extra_kwargs = {
                k: v
                for k, v in rope_scaling.items()
                if k in ("extrapolation_factor", "attn_factor", "beta_fast",
                         "beta_slow")
            }
            rotary_emb = YaRNScalingRotaryEmbedding(head_size, rotary_dim,
                                                    original_max_position,
                                                    base, is_neox_style,
                                                    scaling_factor,
                                                    **extra_kwargs)
        else:
            raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
    _ROPE_DICT[key] = rotary_emb
    return rotary_emb