# Copyright 2023-2024 SGLang Team
# 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.
# ==============================================================================

"""Inference-only DeepSeek NextN Speculative Decoding."""
from typing import Iterable, Optional, Tuple

import torch
from torch import nn
from transformers import PretrainedConfig
from vllm import _custom_ops as ops

from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import ReplicatedLinear
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.moe.ep_moe.layer import EPMoE
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.quantization.fp8_utils import (
    block_quant_to_tensor_quant,
    normalize_e4m3fn_to_e4m3fnuz,
)
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.models.deepseek_v2 import DeepseekV2DecoderLayer, DeepseekV3ForCausalLM
from sglang.srt.utils import is_hip

is_hip_ = is_hip()


class DeepseekModelNextN(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            enable_tp=not global_server_args_dict["enable_dp_attention"],
        )

        self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.eh_proj = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)

        self.decoder = DeepseekV2DecoderLayer(
            config, 0, quant_config=quant_config, is_nextn=True
        )

        self.shared_head = nn.Module()
        self.shared_head.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        if input_embeds is None:
            hidden_states = self.embed_tokens(input_ids)
        else:
            hidden_states = input_embeds

        hidden_states = self.eh_proj(
            torch.cat(
                (
                    self.enorm(hidden_states),
                    self.hnorm(forward_batch.spec_info.hidden_states),
                ),
                dim=-1,
            )
        )

        residual = None
        hidden_states, residual = self.decoder(
            positions, hidden_states, forward_batch, residual
        )

        if not forward_batch.forward_mode.is_idle():
            hidden_states, _ = self.shared_head.norm(hidden_states, residual)
        return hidden_states


class DeepseekV3ForCausalLMNextN(DeepseekV3ForCausalLM):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        nn.Module.__init__(self)
        self.config = config
        self.quant_config = quant_config

        self.model = DeepseekModelNextN(config, quant_config)

        if global_server_args_dict["enable_dp_attention"]:
            self.lm_head = ReplicatedLinear(
                config.hidden_size,
                config.vocab_size,
                bias=False,
            )
            self.logits_processor = LogitsProcessor(config, skip_all_gather=True)
        else:
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
            )
            self.logits_processor = LogitsProcessor(config)

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        hidden_states = self.model(input_ids, positions, forward_batch)
        return self.logits_processor(
            input_ids, hidden_states, self.lm_head, forward_batch
        )

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        if hasattr(self.config, "num_nextn_predict_layers"):
            num_nextn_layers = self.config.num_nextn_predict_layers
            assert num_nextn_layers == 1, "Only 1 nextn layer is supportted"
            assert num_nextn_layers == self.config.num_hidden_layers
        else:
            raise ValueError("num_nextn_predict_layers is not in the config")

        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        MoEImpl = EPMoE if global_server_args_dict["enable_ep_moe"] else FusedMoE
        expert_params_mapping = MoEImpl.make_expert_params_mapping(
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.config.n_routed_experts,
        )

        nextn_layer_prefix = "model.layers.0"
        nextn_spec_weight_names = [
            "shared_head.norm",
            "eh_proj",
            "enorm",
            "hnorm",
        ]

        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if not name.startswith(nextn_layer_prefix):
                continue

            # Use shared head and embed weights from target model
            if "shared_head.head" in name or "embed_tokens" in name:
                continue

            is_decoder = True
            # For nextn specific weights
            for weight_name in nextn_spec_weight_names:
                if weight_name in name:
                    name = name.replace(nextn_layer_prefix, "model")
                    is_decoder = False
                    break
            # For decoder layer weights
            if is_decoder:
                name = name.replace(nextn_layer_prefix, "model.decoder")

            if "rotary_emb.inv_freq" in name:
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                # Skip non-stacked layers and experts (experts handled below).
                if weight_name not in name:
                    continue
                # We have mlp.experts[0].gate_proj in the checkpoint.
                # Since we handle the experts below in expert_params_mapping,
                # we need to skip here BEFORE we update the name, otherwise
                # name will be updated to mlp.experts[0].gate_up_proj, which
                # will then be updated below in expert_params_mapping
                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
                if ("mlp.experts." in name) and name not in params_dict:
                    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 mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = 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,
                        name,
                        shard_id=shard_id,
                        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)

        if not global_server_args_dict["disable_mla"]:
            self_attn = self.model.decoder.self_attn
            if hasattr(self_attn.kv_b_proj, "qweight"):
                # AWQ compatible
                w = ops.awq_dequantize(
                    self_attn.kv_b_proj.qweight,
                    self_attn.kv_b_proj.scales,
                    self_attn.kv_b_proj.qzeros,
                    0,
                    0,
                    0,
                ).T
            else:
                w = self_attn.kv_b_proj.weight
            # NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.
            # This may affect the accuracy of fp8 model.
            if hasattr(self.quant_config, "weight_block_size") and w.dtype in (
                torch.float8_e4m3fn,
                torch.float8_e4m3fnuz,
            ):
                weight_block_size = self.quant_config.weight_block_size
                if weight_block_size is not None:
                    assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
                    if is_hip_:
                        weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                            weight=w,
                            weight_scale=self_attn.kv_b_proj.weight_scale_inv,
                            input_scale=None,
                        )
                    else:
                        weight = w
                        weight_scale = self_attn.kv_b_proj.weight_scale_inv

                    w, scale = block_quant_to_tensor_quant(
                        weight, weight_scale, weight_block_size
                    )
                    self_attn.w_scale = scale
            w_kc, w_vc = w.unflatten(
                0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
            ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
            self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
            self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
            if (
                hasattr(self_attn.kv_b_proj, "weight_scale")
                and self_attn.w_scale is None
            ):
                self_attn.w_scale = self_attn.kv_b_proj.weight_scale
                if is_hip_:
                    self_attn.w_scale *= 2.0


EntryClass = [DeepseekV3ForCausalLMNextN]
