# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# top-level folder for each specific model found within the models/ directory at
# the top-level of this source tree.

import math
from typing import Optional, Tuple, Union

# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
import torch


def apply_scaling(freqs: torch.Tensor):
    # Values obtained from grid search
    scale_factor = 8
    low_freq_factor = 1
    high_freq_factor = 4
    old_context_len = 8192  # original llama3 length

    low_freq_wavelen = old_context_len / low_freq_factor
    high_freq_wavelen = old_context_len / high_freq_factor
    new_freqs = []
    for freq in freqs:
        wavelen = 2 * math.pi / freq
        if wavelen < high_freq_wavelen:
            new_freqs.append(freq)
        elif wavelen > low_freq_wavelen:
            new_freqs.append(freq / scale_factor)
        else:
            assert low_freq_wavelen != high_freq_wavelen
            smooth = (old_context_len / wavelen - low_freq_factor) / (
                high_freq_factor - low_freq_factor
            )
            new_freqs.append((1 - smooth) * freq / scale_factor + smooth * freq)
    return torch.tensor(new_freqs, dtype=freqs.dtype, device=freqs.device)


def precompute_freqs_cis(
    dim: int,
    end: int,
    theta: float = 10000.0,
    use_scaled: bool = False,
    device: str = "cuda:0",
):
    freqs = 1.0 / (
        theta ** (torch.arange(0, dim, 2, device=device)[: (dim // 2)].float() / dim)
    )
    t = torch.arange(end, device=freqs.device, dtype=torch.float32)
    if use_scaled:
        freqs = apply_scaling(freqs)
    freqs = torch.outer(t, freqs)
    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
    return freqs_cis


def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
    ndim = x.ndim
    assert 0 <= 1 < ndim
    assert freqs_cis.shape == (x.shape[1], x.shape[-1])
    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
    return freqs_cis.view(*shape)


def apply_rotary_emb(
    xq: torch.Tensor,
    xk: torch.Tensor,
    freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
    return xq_out.type_as(xq), xk_out.type_as(xk)


def apply_rotary_pos_emb(q, k, cos, sin):
    cos = cos.unsqueeze(1)
    sin = sin.unsqueeze(1)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed.to(q.dtype), k_embed.to(k.dtype)


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


def generate_cos_sin_f32_cache(
    max_seq_len, head_dim, theta=1e4, use_scaled: bool = False, device: str = "cuda:0"
):
    position = torch.arange(max_seq_len, device=device, dtype=torch.float32).unsqueeze(
        1
    )
    freqs = 1.0 / (
        theta
        ** (torch.arange(0, head_dim, 2, device=device, dtype=torch.float32) / head_dim)
    )
    freqs = torch.cat([freqs, freqs], dim=-1).contiguous()
    if use_scaled:
        freqs = apply_scaling(freqs)
    args = position * freqs
    sin_cache = torch.sin(args)
    cos_cache = torch.cos(args)
    return cos_cache, sin_cache


# The following code is from the vLLM's implementation of RoPE.
# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/rotary_embedding.py


class RotaryEmbedding(torch.nn.Module):
    def __init__(
        self,
        head_size: int,
        rotary_dim: int,
        max_position_embeddings: int,
        base: int,
        is_neox_style: bool,
        dtype: torch.dtype,
        device: str = "cuda:0",
    ) -> 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
        self.dtype = dtype
        self.device = device
        cache = self._compute_cos_sin_cache()
        self.cos_sin_cache: torch.Tensor
        self.register_buffer("cos_sin_cache", cache, persistent=False)

    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
        inv_freq = 1.0 / (
            base
            ** (
                torch.arange(
                    0, self.rotary_dim, 2, dtype=torch.float, device=self.device
                )
                / 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=self.device
        )

        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 _apply_rotary_emb(
        self,
        x: torch.Tensor,
        cos: torch.Tensor,
        sin: torch.Tensor,
        is_neox_style: bool,
    ) -> torch.Tensor:
        """
        Args:
            x: [num_tokens, num_heads, head_size]
            cos: [num_tokens, head_size // 2]
            sin: [num_tokens, head_size // 2]
            is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
                positional embeddings.
        """
        cos = cos.unsqueeze(-2).to(x.dtype)
        sin = sin.unsqueeze(-2).to(x.dtype)
        if is_neox_style:
            x1, x2 = torch.chunk(x, 2, dim=-1)
        else:
            x1 = x[..., ::2]
            x2 = x[..., 1::2]
        o1 = x1 * cos - x2 * sin
        o2 = x2 * cos + x1 * sin
        if is_neox_style:
            return torch.cat((o1, o2), dim=-1)
        else:
            return torch.stack((o1, o2), dim=-1).flatten(-2)

    def forward_native(
        self,
        positions: torch.Tensor,
        query: torch.Tensor,
        key: torch.Tensor,
        offsets: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """A PyTorch-native implementation of forward()."""
        if offsets is not None:
            positions = positions + offsets

        positions = positions.flatten()
        num_tokens = positions.shape[0]
        cos_sin = self.cos_sin_cache.index_select(0, positions)

        # Note: the is different from the vLLM's implementation,
        # We added float32 conversion because float32 is required for the rotary embedding to work correctly for long contexts
        query = query.to(torch.float32)
        key = key.to(torch.float32)

        cos, sin = cos_sin.chunk(2, dim=-1)

        query_shape = query.shape
        query = query.view(num_tokens, -1, self.head_size)
        query_rot = query[..., : self.rotary_dim]
        query_pass = query[..., self.rotary_dim :]
        query_rot = self._apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
        query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)

        key_shape = key.shape
        key = key.view(num_tokens, -1, self.head_size)
        key_rot = key[..., : self.rotary_dim]
        key_pass = key[..., self.rotary_dim :]
        key_rot = self._apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)
        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)

        query = query.to(self.dtype)
        key = key.to(self.dtype)
        return query, key