# Adapted from
# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/mixtral.py#L1
"""Inference-only Mixtral model."""
from typing import Iterable, Optional, Tuple

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from transformers import MixtralConfig
from vllm import _custom_ops as ops
from vllm.config import CacheConfig
from vllm.distributed import (
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_reduce,
)
from vllm.model_executor.layers.fused_moe import fused_moe
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
from vllm.model_executor.layers.quantization.fp8 import Fp8Config
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.utils import set_weight_attrs
from vllm.utils import print_warning_once


from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.managers.controller.model_runner import InputMetadata



class MixtralMoE(nn.Module):
    """A tensor-parallel MoE implementation for Mixtral that shards each expert
    across all ranks.

    Each expert's weights are sharded across all ranks and a fused MoE
    kernel is used for the forward pass, and finally we reduce the outputs
    across ranks.
    """

    def __init__(
        self,
        num_experts: int,
        top_k: int,
        hidden_size: int,
        intermediate_size: int,
        params_dtype: Optional[torch.dtype] = None,
        tp_size: Optional[int] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.tp_size = tp_size or get_tensor_model_parallel_world_size()
        self.num_total_experts = num_experts
        self.top_k = top_k
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size // self.tp_size
        self.quant_config = quant_config

        # FIXME(pcmoritz): Make this more general to support different
        # quantization schemes
        self.use_fp8 = isinstance(quant_config, Fp8Config)

        if params_dtype is None:
            params_dtype = torch.get_default_dtype()
        self.params_dtype = params_dtype

        # Gate always runs at half / full precision for now.
        self.gate = ReplicatedLinear(self.hidden_size,
                                     self.num_total_experts,
                                     bias=False,
                                     params_dtype=self.params_dtype,
                                     quant_config=None)

        if self.use_fp8 and self.quant_config.is_checkpoint_fp8_serialized:
            params_dtype = torch.float8_e4m3fn

        self.w13_weight = nn.Parameter(
            torch.empty(self.num_total_experts,
                        2 * self.intermediate_size,
                        self.hidden_size,
                        dtype=params_dtype))
        self.w2_weight = nn.Parameter(
            torch.empty(self.num_total_experts,
                        self.hidden_size,
                        self.intermediate_size,
                        dtype=params_dtype))

        set_weight_attrs(self.w13_weight, {
            "weight_loader": self.weight_loader,
        })
        set_weight_attrs(self.w2_weight, {
            "weight_loader": self.weight_loader,
        })

        # Used for fp8.
        self.w13_scale = None
        self.w2_scale = None
        self.a13_scale = None
        self.a2_scale = None

        if self.use_fp8:
            # WEIGHT_SCALE (for fp8)
            self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts,
                                                     dtype=torch.float32),
                                          requires_grad=False)
            self.w2_scale = nn.Parameter(torch.ones(self.num_total_experts,
                                                    dtype=torch.float32),
                                         requires_grad=False)

            # If loading fp8 checkpoint, pass the weight loaders.
            # If loading an fp16 checkpoint, do not (we will quantize in
            #   process_weights_after_loading()
            if quant_config.is_checkpoint_fp8_serialized:
                set_weight_attrs(self.w13_scale, {
                    "weight_loader": self.weight_loader,
                })
                set_weight_attrs(self.w2_scale, {
                    "weight_loader": self.weight_loader,
                })

            # ACT_SCALE (for fp8)
            if quant_config.activation_scheme == "static":
                if not quant_config.is_checkpoint_fp8_serialized:
                    raise ValueError(
                        "Found static activation scheme for checkpoint that "
                        "was not serialized fp8.")
                self.a13_scale = nn.Parameter(torch.zeros(
                    self.num_total_experts, dtype=torch.float32),
                                              requires_grad=False)
                self.a2_scale = nn.Parameter(torch.zeros(
                    self.num_total_experts, dtype=torch.float32),
                                             requires_grad=False)

                set_weight_attrs(self.a13_scale, {
                    "weight_loader": self.weight_loader,
                })
                set_weight_attrs(self.a2_scale, {
                    "weight_loader": self.weight_loader,
                })

    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
                      weight_name: str, expert_id: int):
        tp_rank = get_tensor_model_parallel_rank()
        param_data = param.data
        shard_size = self.intermediate_size
        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
        if weight_name.endswith("w1.weight"):
            param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
        if weight_name.endswith("w3.weight"):
            param_data[expert_id,
                       shard_size:2 * shard_size, :] = loaded_weight[shard, :]
        if weight_name.endswith("w2.weight"):
            param_data[expert_id, :, :] = loaded_weight[:, shard]
        if "act_scale" in weight_name or "weight_scale" in weight_name:
            param_data[expert_id] = loaded_weight

    def process_weights_after_loading(self):
        # Fp8 is the only case where we need to process after loading.
        if not self.use_fp8:
            return

        # If checkpoint is fp16, quantize here.
        if not self.quant_config.is_checkpoint_fp8_serialized:
            w13_weight = torch.empty_like(self.w13_weight.data,
                                          dtype=torch.float8_e4m3fn)
            w2_weight = torch.empty_like(self.w2_weight.data,
                                         dtype=torch.float8_e4m3fn)
            for expert in range(self.num_total_experts):
                w13_weight[expert, :, :], self.w13_scale[
                    expert] = ops.scaled_fp8_quant(
                        self.w13_weight.data[expert, :, :])
                w2_weight[expert, :, :], self.w2_scale[
                    expert] = ops.scaled_fp8_quant(
                        self.w2_weight.data[expert, :, :])
            self.w13_weight = nn.Parameter(w13_weight, requires_grad=False)
            self.w2_weight = nn.Parameter(w2_weight, requires_grad=False)

        # If checkpoint is fp8 + static, cleanup act_scales.
        #   Since state_dict has an act_scale per expert but our kernels
        #   are passed one act_scale shared across all experts.
        elif self.quant_config.activation_scheme == "static":
            if self.a13_scale is None or self.a2_scale is None:
                raise ValueError(
                    "QuantConfig has static quantization, but found "
                    "activation scales are None.")

            if (not all_close_1d(self.a13_scale)
                    or not all_close_1d(self.a2_scale)):
                print_warning_once(
                    "Found act_scales that are not equal for fp8 MoE layer. "
                    "Using the maximum across experts for each layer. ")

            self.a13_scale = nn.Parameter(self.a13_scale.max(),
                                          requires_grad=False)
            self.a2_scale = nn.Parameter(self.a2_scale.max(),
                                         requires_grad=False)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        num_tokens, hidden_size = hidden_states.shape
        hidden_states = hidden_states.view(-1, self.hidden_size)
        # router_logits: (num_tokens, n_experts)
        router_logits, _ = self.gate(hidden_states)
        final_hidden_states = fused_moe(hidden_states,
                                        self.w13_weight,
                                        self.w2_weight,
                                        router_logits,
                                        self.top_k,
                                        renormalize=True,
                                        inplace=True,
                                        use_fp8=self.use_fp8,
                                        w1_scale=self.w13_scale,
                                        w2_scale=self.w2_scale,
                                        a1_scale=self.a13_scale,
                                        a2_scale=self.a2_scale)

        if self.tp_size > 1:
            final_hidden_states = tensor_model_parallel_all_reduce(
                final_hidden_states)

        return final_hidden_states.view(num_tokens, hidden_size)


class MixtralAttention(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        layer_id: int = 0,
        max_position: int = 4096 * 32,
        rope_theta: float = 10000,
        quant_config: Optional[QuantizationConfig] = None,
        sliding_window: Optional[int] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.rope_theta = rope_theta
        self.sliding_window = sliding_window

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config,
        )
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position,
            base=int(self.rope_theta),
            is_neox_style=True,
        )
        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            layer_id=layer_id,
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        input_metadata: InputMetadata,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v, input_metadata)
        output, _ = self.o_proj(attn_output)
        return output


class MixtralDecoderLayer(nn.Module):
    def __init__(
        self,
        config: MixtralConfig,
        layer_id: int = 0,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        # Requires transformers > 4.32.0
        rope_theta = getattr(config, "rope_theta", 10000)
        self.self_attn = MixtralAttention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            max_position=config.max_position_embeddings,
            num_kv_heads=config.num_key_value_heads,
            layer_id=layer_id,
            rope_theta=rope_theta,
            sliding_window=config.sliding_window,
            quant_config=quant_config,
        )
        self.block_sparse_moe = MixtralMoE(
            num_experts=config.num_local_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
            quant_config=quant_config)
        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        input_metadata: InputMetadata,
        residual: Optional[torch.Tensor],
    ) -> torch.Tensor:
        # Self Attention
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(hidden_states, residual)
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            input_metadata=input_metadata,
        )

        # Fully Connected
        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
        hidden_states = self.block_sparse_moe(hidden_states)
        return hidden_states, residual


class MixtralModel(nn.Module):
    def __init__(
        self,
        config: MixtralConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
        # config.num_hidden_layers=16
        self.layers = nn.ModuleList(
            [
                MixtralDecoderLayer(config, i, quant_config=quant_config)
                for i in range(config.num_hidden_layers)
            ]
        )
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        input_metadata: InputMetadata,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        if input_embeds is None:
            hidden_states = self.embed_tokens(input_ids)
        else:
            hidden_states = input_embeds
        residual = None
        for i in range(len(self.layers)):
            layer = self.layers[i]
            hidden_states, residual = layer(
                positions, hidden_states, input_metadata, residual
            )
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class MixtralForCausalLM(nn.Module):
    def __init__(
        self,
        config: MixtralConfig,
        quant_config: Optional[QuantizationConfig] = None,
        cache_config: Optional[CacheConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.quant_config = quant_config
        self.model = MixtralModel(config, quant_config=quant_config)
        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
        self.logits_processor = LogitsProcessor(config)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        input_metadata: InputMetadata,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
        return self.logits_processor(
            input_ids, hidden_states, self.lm_head.weight, input_metadata
        )

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]

        expert_params_mapping = [
            # These are the weight scales for the experts
            # (param_name, weight_name, expert_id)
            ("w13_scale" if weight_name in ["w1", "w3"] else "w2_scale",
             f"experts.{expert_id}.{weight_name}.weight_scale", expert_id)
            for expert_id in range(self.config.num_local_experts)
            for weight_name in ["w1", "w2", "w3"]
        ] + [
            # These are the weights for the experts
            # (param_name, weight_name, expert_id)
            ("w13_weight" if weight_name in ["w1", "w3"] else "w2_weight",
             f"experts.{expert_id}.{weight_name}.weight", expert_id)
            for expert_id in range(self.config.num_local_experts)
            for weight_name in ["w1", "w2", "w3"]
        ] + [
            # These are the activation scales for the experts
            # (param_name, weight_name, expert_id)
            ("a13_scale" if weight_name in ["w1", "w3"] else "a2_scale",
             f"experts.{expert_id}.{weight_name}.act_scale", expert_id)
            for expert_id in range(self.config.num_local_experts)
            for weight_name in ["w1", "w2", "w3"]
        ]

        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for param_name, weight_name, expert_id in expert_params_mapping:
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(param,
                                  loaded_weight,
                                  weight_name,
                                  expert_id=expert_id)
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    param = params_dict[name]
                    weight_loader = getattr(param, "weight_loader",
                                            default_weight_loader)
                    weight_loader(param, loaded_weight)


def all_close_1d(x: torch.Tensor) -> bool:
    assert len(x.shape) == 1
    return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0]))


EntryClass = MixtralForCausalLM
