# Copyright © 2023-2024 Apple Inc.

import math
from dataclasses import dataclass
from typing import Any, Dict, Optional

import mlx.core as mx
import mlx.nn as nn
from mlx.nn.layers.distributed import shard_inplace, shard_linear, sum_gradients

from .activations import swiglu
from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
from .pipeline import PipelineMixin
from .switch_layers import SwitchGLU


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str = "deepseek_v2"
    vocab_size: int = 102400
    hidden_size: int = 4096
    intermediate_size: int = 11008
    moe_intermediate_size: int = 1407
    num_hidden_layers: int = 30
    num_attention_heads: int = 32
    num_key_value_heads: int = 32
    n_shared_experts: Optional[int] = None
    n_routed_experts: Optional[int] = None
    routed_scaling_factor: float = 1.0
    kv_lora_rank: int = 512
    q_lora_rank: int = 1536
    qk_rope_head_dim: int = 64
    v_head_dim: int = 128
    qk_nope_head_dim: int = 128
    topk_method: str = "gready"
    n_group: Optional[int] = None
    topk_group: Optional[int] = None
    num_experts_per_tok: Optional[int] = None
    moe_layer_freq: int = 1
    first_k_dense_replace: int = 0
    max_position_embeddings: int = 2048
    rms_norm_eps: float = 1e-6
    rope_theta: float = 10000.0
    rope_scaling: Dict = None
    attention_bias: bool = False


def yarn_find_correction_dim(
    num_rotations, dim, base=10000, max_position_embeddings=2048
):
    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
        2 * math.log(base)
    )


def yarn_find_correction_range(
    low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
):
    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)


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


def yarn_linear_ramp_mask(min_val, max_val, dim):
    if min_val == max_val:
        max_val += 0.001  # Prevent singularity

    linear_func = (mx.arange(dim, dtype=mx.float32) - min_val) / (max_val - min_val)
    return mx.clip(linear_func, 0, 1)


class DeepseekV2YarnRotaryEmbedding(nn.Module):
    def __init__(
        self,
        dim,
        max_position_embeddings=2048,
        base=10000,
        scaling_factor=1.0,
        original_max_position_embeddings=4096,
        beta_fast=32,
        beta_slow=1,
        mscale=1,
        mscale_all_dim=0,
    ):
        super().__init__()
        self.mscale = yarn_get_mscale(scaling_factor, mscale) / yarn_get_mscale(
            scaling_factor, mscale_all_dim
        )
        freq_extra = base ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim)
        freq_inter = scaling_factor * base ** (
            mx.arange(0, dim, 2, dtype=mx.float32) / dim
        )
        low, high = yarn_find_correction_range(
            beta_fast,
            beta_slow,
            dim,
            base,
            original_max_position_embeddings,
        )
        freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2)
        self._freqs = (freq_inter * freq_extra) / (
            freq_inter * freq_mask + freq_extra * (1 - freq_mask)
        )

    def __call__(self, x, offset=0):
        if self.mscale != 1.0:
            x = self.mscale * x
        return mx.fast.rope(
            x,
            x.shape[-1],
            traditional=True,
            base=None,
            scale=1.0,
            offset=offset,
            freqs=self._freqs,
        )


class DeepseekV2Attention(nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.q_lora_rank = config.q_lora_rank
        self.qk_rope_head_dim = config.qk_rope_head_dim
        self.kv_lora_rank = config.kv_lora_rank
        self.v_head_dim = config.v_head_dim
        self.qk_nope_head_dim = config.qk_nope_head_dim
        self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim

        self.scale = self.q_head_dim**-0.5

        if self.q_lora_rank is None:
            self.q_proj = nn.Linear(
                self.hidden_size, self.num_heads * self.q_head_dim, bias=False
            )
        else:
            self.q_a_proj = nn.Linear(
                self.hidden_size, self.q_lora_rank, bias=config.attention_bias
            )
            self.q_a_layernorm = nn.RMSNorm(self.q_lora_rank, eps=1e-6)
            self.q_b_proj = nn.Linear(
                self.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
            )

        self.kv_a_proj_with_mqa = nn.Linear(
            self.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=config.attention_bias,
        )
        self.kv_a_layernorm = nn.RMSNorm(self.kv_lora_rank, eps=1e-6)
        self.kv_b_proj = nn.Linear(
            self.kv_lora_rank,
            self.num_heads
            * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
            bias=False,
        )

        self.o_proj = nn.Linear(
            self.num_heads * self.v_head_dim,
            self.hidden_size,
            bias=config.attention_bias,
        )

        mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
        scaling_factor = self.config.rope_scaling["factor"]
        if mscale_all_dim:
            mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
            self.scale = self.scale * mscale * mscale

        rope_kwargs = {
            key: self.config.rope_scaling[key]
            for key in [
                "original_max_position_embeddings",
                "beta_fast",
                "beta_slow",
                "mscale",
                "mscale_all_dim",
            ]
            if key in self.config.rope_scaling
        }
        self.rope = DeepseekV2YarnRotaryEmbedding(
            dim=self.qk_rope_head_dim,
            max_position_embeddings=self.max_position_embeddings,
            scaling_factor=scaling_factor,
            base=self.rope_theta,
            **rope_kwargs,
        )

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Any] = None,
    ) -> mx.array:
        B, L, D = x.shape

        if self.q_lora_rank is None:
            q = self.q_proj(x)
        else:
            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(x)))

        q = q.reshape(B, L, self.num_heads, self.q_head_dim).transpose(0, 2, 1, 3)
        q_nope, q_pe = mx.split(q, [self.qk_nope_head_dim], axis=-1)
        compressed_kv = self.kv_a_proj_with_mqa(x)
        compressed_kv, k_pe = mx.split(compressed_kv, [self.kv_lora_rank], axis=-1)
        k_pe = k_pe.reshape(B, L, 1, self.qk_rope_head_dim).transpose(0, 2, 1, 3)
        kv = self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
        kv = kv.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3)

        k_nope, values = mx.split(kv, [self.qk_nope_head_dim], axis=-1)

        if cache is not None:
            q_pe = self.rope(q_pe, cache.offset)
            k_pe = self.rope(k_pe, cache.offset)
            k_pe = mx.repeat(k_pe, self.num_heads, axis=1)
            keys, values = cache.update_and_fetch(
                mx.concatenate([k_nope, k_pe], axis=-1), values
            )
        else:
            q_pe = self.rope(q_pe)
            k_pe = self.rope(k_pe)
            k_pe = mx.repeat(k_pe, self.num_heads, axis=1)
            keys = mx.concatenate([k_nope, k_pe], axis=-1)

        queries = mx.concatenate([q_nope, q_pe], axis=-1)

        output = scaled_dot_product_attention(
            queries, keys, values, cache=cache, scale=self.scale, mask=mask
        )
        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
        return self.o_proj(output)


class DeepseekV2MLP(nn.Module):
    def __init__(
        self, config: ModelArgs, hidden_size: int = None, intermediate_size: int = None
    ):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
        self.intermediate_size = (
            config.intermediate_size if intermediate_size is None else intermediate_size
        )

        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)

    def __call__(self, x):
        down_proj = self.down_proj(swiglu(self.gate_proj(x), self.up_proj(x)))
        return down_proj


class MoEGate(nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.n_routed_experts = config.n_routed_experts
        self.routed_scaling_factor = config.routed_scaling_factor
        self.topk_method = config.topk_method
        self.n_group = config.n_group
        self.topk_group = config.topk_group
        self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))

    def __call__(self, x):
        gates = x @ self.weight.T

        scores = mx.softmax(gates, axis=-1, precise=True)

        if self.topk_method == "group_limited_greedy":
            bsz, seq_len = x.shape[:2]
            scores = scores.reshape(bsz, seq_len, self.n_group, -1)
            group_scores = scores.max(axis=-1, keepdims=True)
            k = self.n_group - self.topk_group
            group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-2)[..., :k, :]
            scores = mx.put_along_axis(
                scores, group_idx, mx.array(0.0, scores.dtype), axis=-2
            )
            scores = scores.reshape(bsz, seq_len, -1)

        k = self.top_k
        inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k]
        scores = mx.take_along_axis(scores, inds, axis=-1)
        scores = scores * self.routed_scaling_factor

        return inds, scores


class DeepseekV2MoE(nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.config = config
        self.num_experts_per_tok = config.num_experts_per_tok
        self.switch_mlp = SwitchGLU(
            config.hidden_size, config.moe_intermediate_size, config.n_routed_experts
        )

        self.gate = MoEGate(config)
        if config.n_shared_experts is not None:
            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
            self.shared_experts = DeepseekV2MLP(
                config=config, intermediate_size=intermediate_size
            )

        self.sharding_group = None

    def __call__(self, x):
        if self.sharding_group is not None:
            x = sum_gradients(self.sharding_group)(x)

        inds, scores = self.gate(x)
        y = self.switch_mlp(x, inds)
        y = (y * scores[..., None]).sum(axis=-2)
        if self.config.n_shared_experts is not None:
            y = y + self.shared_experts(x)

        if self.sharding_group is not None:
            y = mx.distributed.all_sum(y, group=self.sharding_group)

        return y


class DeepseekV2DecoderLayer(nn.Module):
    def __init__(self, config: ModelArgs, layer_idx: int):
        super().__init__()
        self.self_attn = DeepseekV2Attention(config)
        self.mlp = (
            DeepseekV2MoE(config)
            if (
                config.n_routed_experts is not None
                and layer_idx >= config.first_k_dense_replace
                and layer_idx % config.moe_layer_freq == 0
            )
            else DeepseekV2MLP(config)
        )
        self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = nn.RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Any] = None,
    ) -> mx.array:
        r = self.self_attn(self.input_layernorm(x), mask, cache)
        h = x + r
        r = self.mlp(self.post_attention_layernorm(h))
        out = h + r
        return out


class DeepseekV2Model(PipelineMixin, nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.vocab_size = config.vocab_size
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = [
            DeepseekV2DecoderLayer(config, idx)
            for idx in range(config.num_hidden_layers)
        ]
        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def __call__(
        self,
        x: mx.array,
        cache: Optional[Any] = None,
    ) -> mx.array:
        h = self.embed_tokens(x)

        pipeline_rank = self.pipeline_rank
        pipeline_size = self.pipeline_size

        if cache is None:
            cache = [None] * len(self.pipeline_layers)
        mask = create_attention_mask(h, cache[0])

        # Receive from the previous process in the pipeline
        if pipeline_rank < pipeline_size - 1:
            h = mx.distributed.recv_like(h, (pipeline_rank + 1))

        for l, c in zip(self.pipeline_layers, cache):
            h = l(h, mask, cache=c)

        # Send to the next process in the pipeline
        if pipeline_rank != 0:
            h = mx.distributed.send(h, (pipeline_rank - 1) % pipeline_size)
            if cache[-1] is not None:
                cache[-1].keys = mx.depends(cache[-1].keys, h)

        # Broadcast h while keeping it in the graph
        if pipeline_size > 1:
            h = mx.distributed.all_gather(h)[: h.shape[0]]

        return self.norm(h)


class Model(nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.args = config
        self.model_type = config.model_type
        self.model = DeepseekV2Model(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

    def __call__(
        self,
        inputs: mx.array,
        cache: Optional[Any] = None,
    ):
        out = self.model(inputs, cache)
        return self.lm_head(out)

    def sanitize(self, weights):
        for l in range(self.args.num_hidden_layers):
            prefix = f"model.layers.{l}"
            for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]:
                for k in ["weight", "scales", "biases"]:
                    if f"{prefix}.mlp.experts.0.{m}.{k}" in weights:
                        to_join = [
                            weights.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}")
                            for e in range(self.args.n_routed_experts)
                        ]
                        weights[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join)
        return weights

    def shard(self, group: Optional[mx.distributed.Group] = None):
        group = group or mx.distributed.init()
        N = group.size()
        for layer in self.model.layers:
            # Shard the self attention
            if layer.self_attn.q_lora_rank is None:
                layer.self_attn.q_proj = shard_linear(
                    layer.self_attn.q_proj, "all-to-sharded", group=group
                )
            else:
                layer.self_attn.q_b_proj = shard_linear(
                    layer.self_attn.q_b_proj, "all-to-sharded", group=group
                )
            layer.self_attn.kv_b_proj = shard_linear(
                layer.self_attn.kv_b_proj, "all-to-sharded", group=group
            )
            layer.self_attn.o_proj = shard_linear(
                layer.self_attn.o_proj, "sharded-to-all", group=group
            )
            layer.self_attn.num_heads //= N

            # Shard the MLP
            if isinstance(layer.mlp, DeepseekV2MLP):
                layer.mlp.gate_proj = shard_linear(
                    layer.mlp.gate_proj, "all-to-sharded", group=group
                )
                layer.mlp.down_proj = shard_linear(
                    layer.mlp.down_proj, "sharded-to-all", group=group
                )
                layer.mlp.up_proj = shard_linear(
                    layer.mlp.up_proj, "all-to-sharded", group=group
                )

            # Shard the MoE. Shard in place since the MoE should be responsible
            # for aggregating the results.
            else:
                layer.mlp.sharding_group = group
                shard_inplace(
                    layer.mlp.shared_experts.gate_proj, "all-to-sharded", group=group
                )
                shard_inplace(
                    layer.mlp.shared_experts.down_proj, "sharded-to-all", group=group
                )
                shard_inplace(
                    layer.mlp.shared_experts.up_proj, "all-to-sharded", group=group
                )
                shard_inplace(
                    layer.mlp.switch_mlp.gate_proj, "all-to-sharded", group=group
                )
                shard_inplace(
                    layer.mlp.switch_mlp.down_proj, "sharded-to-all", group=group
                )
                shard_inplace(
                    layer.mlp.switch_mlp.up_proj, "all-to-sharded", group=group
                )

    @property
    def layers(self):
        return self.model.pipeline_layers
