################################################################################
# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
# 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.
#
################################################################################

from collections.abc import Iterable
from typing import Optional, Union

import torch
import torch_br
from fastcore.basics import patch_to

import vllm
from vllm.distributed import get_pp_group, tensor_model_parallel_all_reduce
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.qwen3_moe import Qwen3MoeModel
from vllm.model_executor.models.utils import is_pp_missing_parameter
from vllm.sequence import IntermediateTensors
from vllm_br.v1.attention.backends.attention_v1 import (
    SUPAFlashAttentionMetadata)

logger = init_logger(__name__)


@patch_to(vllm.model_executor.models.qwen3_moe.Qwen3MoeSparseMoeBlock)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
    # NOTE: hidden_states can have either 1D or 2D shape.
    orig_shape = hidden_states.shape
    if len(hidden_states.shape) == 3:
        hidden_states = hidden_states.squeeze(0)
    final_hidden_states = self.experts(hidden_states=hidden_states,
                                       router_logits=(self.gate.weight, None,
                                                      None))
    if hasattr(final_hidden_states, 'all_reduced'):
        # NOTE: this flag indicates that the final_hidden_states has been reduced in fused_moe
        delattr(final_hidden_states, 'all_reduced')
    elif self.tp_size > 1:
        final_hidden_states = tensor_model_parallel_all_reduce(
            final_hidden_states)
    return final_hidden_states.view(orig_shape)


@patch_to(vllm.model_executor.models.qwen3_moe.Qwen3MoeAttention)
def forward(
    self,
    positions: torch.Tensor,
    hidden_states: torch.Tensor,
) -> torch.Tensor:
    forward_context: ForwardContext = get_forward_context()
    attn_metadata: SUPAFlashAttentionMetadata = forward_context.attn_metadata
    if attn_metadata is None:
        ## for dummy run
        return hidden_states

    seq_len = hidden_states.shape[-2]
    decode_seql = 512

    if isinstance(attn_metadata, dict):
        attn_metadata = attn_metadata[self.attn.layer_name]
    kv_cache = self.attn.kv_cache[forward_context.virtual_engine]
    if kv_cache is not None:
        if seq_len <= decode_seql:
            if hasattr(self.qkv_proj, "qweight"):
                qkv_weight = self.qkv_proj.qweight.data
                qkv_scales = self.qkv_proj.scales.data
            elif hasattr(self.qkv_proj, "weight_packed"):
                qkv_weight = self.qkv_proj.weight_packed.data
                qkv_scales = self.qkv_proj.weight_scale.data
            else:
                qkv_weight = self.qkv_proj.weight
                qkv_scales = None
            if isinstance(self.rotary_emb, MRotaryEmbedding):
                assert len(
                    self.rotary_emb.mrope_section
                ) == 3 and self.rotary_emb.mrope_section[
                    1] == self.rotary_emb.mrope_section[
                        2], "current only support mrope_section width and height are equal!"
                q, k, v = torch_br.br_qwen3_vl_prefix_attn_infer(
                    hidden_states,
                    qkv_weight, [self.q_size, self.kv_size, self.kv_size],
                    self.head_dim,
                    self.q_norm.variance_epsilon,
                    self.q_norm.weight,
                    self.k_norm.weight,
                    self.rotary_emb.cos_sin_cache,
                    kv_cache,
                    positions,
                    attn_metadata.slot_mapping,
                    self.rotary_emb.mrope_section[1],
                    bias=self.qkv_proj.bias,
                    scales=qkv_scales)
            else:
                q, k, v = torch_br.br_qwen3_prefix_attn_infer(
                    hidden_states,
                    qkv_weight, [self.q_size, self.kv_size, self.kv_size],
                    self.head_dim,
                    self.q_norm.variance_epsilon,
                    self.q_norm.weight,
                    self.k_norm.weight,
                    self.rotary_emb.sin_cache,
                    self.rotary_emb.cos_cache,
                    kv_cache,
                    positions,
                    attn_metadata.slot_mapping,
                    bias=self.qkv_proj.bias,
                    scales=qkv_scales)
        else:
            qkv, _ = self.qkv_proj(hidden_states)
            if isinstance(self.rotary_emb, MRotaryEmbedding):
                assert len(
                    self.rotary_emb.mrope_section
                ) == 3 and self.rotary_emb.mrope_section[
                    1] == self.rotary_emb.mrope_section[
                        2], "current only support mrope_section width and height are equal!"
                q, k, v = torch_br.br_fused_rms_mrope_kvstore_infer(
                    qkv, [self.q_size, self.kv_size, self.kv_size],
                    self.head_dim, self.q_norm.variance_epsilon,
                    self.q_norm.weight, self.k_norm.weight,
                    self.rotary_emb.cos_sin_cache, kv_cache, positions,
                    attn_metadata.slot_mapping, attn_metadata.block_table,
                    attn_metadata.query_start_loc, attn_metadata.context_lens,
                    self.rotary_emb.mrope_section[1])
            else:
                q, k, v = torch_br.br_fused_rms_rope_kvstore_infer(
                    qkv, [self.q_size, self.kv_size, self.kv_size],
                    self.head_dim, self.q_norm.variance_epsilon,
                    self.q_norm.weight, self.k_norm.weight,
                    self.rotary_emb.sin_cache, self.rotary_emb.cos_cache,
                    kv_cache, positions, attn_metadata.slot_mapping,
                    attn_metadata.block_table, attn_metadata.query_start_loc,
                    attn_metadata.context_lens)
        if hasattr(attn_metadata, 'do_cache'):
            attn_metadata.do_cache = False
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output
    else:
        return hidden_states


def model_forward(
    self,
    input_ids: torch.Tensor,
    positions: torch.Tensor,
    intermediate_tensors: Optional[IntermediateTensors] = None,
    inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
    if get_pp_group().is_first_rank:
        if inputs_embeds is not None:
            hidden_states = inputs_embeds
        else:
            hidden_states = self.get_input_embeddings(input_ids)
        residual = None
    else:
        assert intermediate_tensors is not None
        hidden_states = intermediate_tensors["hidden_states"]
        residual = intermediate_tensors["residual"]

    if len(hidden_states.shape) == 2:
        hidden_states = hidden_states.unsqueeze(0)
    for i in range(self.start_layer, self.end_layer):
        layer = self.layers[i]
        hidden_states, residual = layer(positions, hidden_states, residual)
    if not get_pp_group().is_last_rank:
        return IntermediateTensors({
            "hidden_states":
            hidden_states.squeeze(0) if hidden_states is not None else None,
            "residual":
            residual.squeeze(0) if residual is not None else None
        })
    hidden_states, _ = self.norm(hidden_states, residual)

    # NOTE: convert back to 2D
    hidden_states = hidden_states.squeeze()
    if hidden_states.dim() == 1:
        hidden_states = hidden_states.unsqueeze(0)

    return hidden_states


Qwen3MoeModel.forward = model_forward


def Qwen3MoeModel_load_weights(
        self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
    stacked_params_mapping = [
        # (param_name, shard_name, shard_id)
        ("qkv_proj", "q_proj", "q"),
        ("qkv_proj", "k_proj", "k"),
        ("qkv_proj", "v_proj", "v"),
        ("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)
    expert_params_mapping = FusedMoE.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.num_experts)

    params_dict = dict(self.named_parameters())
    loaded_params: set[str] = set()
    for name, loaded_weight in weights:
        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:
                continue
            name = name.replace(weight_name, param_name)
            # Skip loading extra bias for GPTQ models.
            if ((name.endswith(".bias") or name.endswith("_bias"))
                    and name not in params_dict):
                continue
            # Skip layers on other devices.
            if is_pp_missing_parameter(name, self):
                continue
            if 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)
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue
                # Skip loading extra bias for GPTQ models.
                if ((name.endswith(".bias") or name.endswith("_bias"))
                        and name not in params_dict):
                    continue
                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") or name.endswith("_bias"))
                        and name not in params_dict):
                    continue
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue
                # Remapping the name of FP8 kv-scale.
                if name.endswith("kv_scale"):
                    remapped_kv_scale_name = name.replace(
                        ".kv_scale", ".attn.kv_scale")
                    if remapped_kv_scale_name not in params_dict:
                        logger.warning_once(
                            "Found kv scale in the checkpoint (e.g. %s), but not found the expected name in the model (e.g. %s). kv-scale is not loaded.",  # noqa: E501
                            name,
                            remapped_kv_scale_name,
                        )
                        continue
                    else:
                        name = remapped_kv_scale_name
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
                if name.find("norm.weight") != -1:
                    param.data = param.data.to(torch.float32)
        loaded_params.add(name)
    return loaded_params


Qwen3MoeModel.load_weights = Qwen3MoeModel_load_weights