################################################################################
# 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.
#
################################################################################

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# Copyright 2025 The ZhipuAI Team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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 GLM-4.5 model compatible with HuggingFace weights."""
import typing
from collections.abc import Callable, Iterable
from typing import Optional, Union

import torch
import torch_br
from torch import nn
from transformers.models.glm4_moe import Glm4MoeConfig

import vllm
import vllm.model_executor.models.glm4_moe
from vllm.config import CacheConfig, get_current_vllm_config
from vllm.distributed import (get_ep_group, get_pp_group,
                              get_tensor_model_parallel_world_size)
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import ReplicatedLinear
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.models.glm4_moe import (
    Glm4MoeAttention, Glm4MoeDecoderLayer, get_spec_layer_idx_from_weight_name)
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)
from .supa_module import MergedGateUpMLPSiluL2


class Glm4MoE(nn.Module):

    def __init__(
        self,
        config: Glm4MoeConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        enable_eplb: bool = False,
    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.routed_scaling_factor = config.routed_scaling_factor

        self.ep_group = get_ep_group().device_group
        self.ep_rank = self.ep_group.rank()
        self.ep_size = self.ep_group.size()
        self.n_routed_experts: int = config.n_routed_experts
        self.n_shared_experts: int = config.n_shared_experts

        if config.hidden_act != "silu":
            raise ValueError(f"Unsupported activation: {config.hidden_act}. "
                             "Only silu is supported for now.")
        self.gate = ReplicatedLinear(config.hidden_size,
                                     config.n_routed_experts,
                                     bias=False,
                                     quant_config=None,
                                     params_dtype=torch.float32,
                                     prefix=f"{prefix}.gate")
        self.gate.e_score_correction_bias = nn.Parameter(
            torch.empty(config.n_routed_experts, dtype=torch.float32))

        # Load balancing settings.
        vllm_config = get_current_vllm_config()
        eplb_config = vllm_config.parallel_config.eplb_config
        self.enable_eplb = enable_eplb

        self.n_redundant_experts = eplb_config.num_redundant_experts
        self.n_logical_experts = self.n_routed_experts
        self.n_physical_experts = (self.n_logical_experts +
                                   self.n_redundant_experts)
        self.n_local_physical_experts = self.n_physical_experts // self.ep_size

        self.physical_expert_start = (self.ep_rank *
                                      self.n_local_physical_experts)
        self.physical_expert_end = (self.physical_expert_start +
                                    self.n_local_physical_experts)

        self.experts = FusedMoE(
            num_experts=config.n_routed_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            reduce_results=False,
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            use_grouped_topk=True,
            num_expert_group=config.n_group,
            topk_group=config.topk_group,
            prefix=f"{prefix}.experts",
            scoring_func="sigmoid",
            # we do scaling outside, set factor to 1.0 to avoid double mul
            routed_scaling_factor=1.0,
            e_score_correction_bias=self.gate.e_score_correction_bias,
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.n_redundant_experts)

        if config.n_shared_experts is not None:
            intermediate_size = (config.moe_intermediate_size *
                                 config.n_shared_experts)
            self.shared_experts = MergedGateUpMLPSiluL2(
                hidden_size=config.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                # reduce_results=self.experts.must_reduce_shared_expert_outputs(
                # ),
                prefix=f"{prefix}.shared_experts",
            )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_shape = hidden_states.shape
        assert self.n_shared_experts is not None, 'n_shared_experts must be set'
        # NOTE: gate has been fused with shared_experts, no more single gate call
        # and we packed router weights, shared_experts weights and down weights in a tuple
        tuple_router_shared_expert_weight = (
            self.gate.weight, self.shared_experts.gate_up_proj.weight,
            self.shared_experts.down_proj.weight)
        hidden_states = hidden_states.view(-1, orig_shape[-1])

        final_hidden_states = self.experts(
            hidden_states=hidden_states,
            router_logits=tuple_router_shared_expert_weight)

        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 = (
                self.experts.maybe_all_reduce_tensor_model_parallel(
                    final_hidden_states))
        return final_hidden_states.view(orig_shape)


vllm.model_executor.models.glm4_moe.Glm4MoE = Glm4MoE


def Glm4MoeAttention_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 seq_len <= decode_seql:
        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 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
            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,
                rotary_dim=self.rotary_emb.rotary_dim,
                bias=self.qkv_proj.bias,
                scales=qkv_scales)
            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
    else:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = torch_br.br_fused_split_rms_rope_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,
            positions,
            rotary_dim=self.rotary_emb.rotary_dim)
        if hasattr(attn_metadata, 'do_cache'):
            attn_metadata.do_cache = True
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output


vllm.model_executor.models.glm4_moe.Glm4MoeAttention.forward = Glm4MoeAttention_forward


def Glm4MoeDecoderLayer__init__(
    self,
    config: Glm4MoeConfig,
    cache_config: Optional[CacheConfig] = None,
    quant_config: Optional[QuantizationConfig] = None,
    prefix: str = "",
    enable_eplb: bool = False,
) -> None:
    super(Glm4MoeDecoderLayer, self).__init__()
    self.hidden_size = config.hidden_size
    rope_theta = getattr(config, "rope_theta", 10000)
    rope_scaling = getattr(config, "rope_scaling", None)
    max_position_embeddings = getattr(config, "max_position_embeddings",
                                      131072)
    # DecoderLayers are created with `make_layers` which passes the prefix
    # with the layer's index.
    layer_idx = int(prefix.split(sep='.')[-1])
    self.layer_idx = layer_idx

    self.self_attn = Glm4MoeAttention(
        config=config,
        hidden_size=self.hidden_size,
        num_heads=config.num_attention_heads,
        num_kv_heads=config.num_key_value_heads,
        rope_theta=rope_theta,
        rope_scaling=rope_scaling,
        max_position_embeddings=max_position_embeddings,
        head_dim=config.head_dim,
        rms_norm_eps=config.rms_norm_eps,
        qkv_bias=config.attention_bias,
        cache_config=cache_config,
        quant_config=quant_config,
        prefix=f"{prefix}.self_attn",
        use_qk_norm=config.use_qk_norm,
    )

    if (config.n_routed_experts is not None
            and layer_idx >= config.first_k_dense_replace):
        self.mlp = Glm4MoE(
            config=config,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
            enable_eplb=enable_eplb,
        )
    else:
        self.mlp = MergedGateUpMLPSiluL2(
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp")

    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)
    self.routed_scaling_factor = config.routed_scaling_factor


vllm.model_executor.models.glm4_moe.Glm4MoeDecoderLayer.__init__ = Glm4MoeDecoderLayer__init__


def Glm4MoeModel_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"]
        residual = residual.unsqueeze(0)  # NOTE: SUPA wants 3D input

    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 hidden_states,
            "residual":
            residual.squeeze(0) if residual is not None else residual
        })

    hidden_states, _ = self.norm(hidden_states, residual)
    return hidden_states.squeeze(0)


vllm.model_executor.models.glm4_moe.Glm4MoeModel.forward = Glm4MoeModel_forward


def Glm4MoeModel_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_dict = dict(self.named_parameters())
    loaded_params: set[str] = set()
    expert_params_mapping = self.get_expert_mapping()
    for name, loaded_weight in weights:
        spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
        if spec_layer is not None:
            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
            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)
            # weight layout infer
            if name.find("norm.weight") != -1 or name.find(
                    "e_score_correction_bias") != -1:
                param.data = param.data.to(torch.float32)
            torch.supa.empty_cache()
            break
        else:
            is_expert_weight = False
            for mapping in expert_params_mapping:
                param_name, weight_name, expert_id, shard_id = mapping
                if weight_name not in name:
                    continue

                # Anyway, this is an expert weight and should not be
                # attempted to load as other weights later
                is_expert_weight = True

                # Do not modify `name` since the loop may continue here
                # Instead, create a new variable
                name_mapped = name.replace(weight_name, param_name)

                if is_pp_missing_parameter(name_mapped, self):
                    continue

                if name_mapped not in params_dict:
                    continue
                param = params_dict[name_mapped]
                # We should ask the weight loader to return success or not
                # here since otherwise we may skip experts with other
                # available replicas.
                weight_loader = typing.cast(Callable[..., bool],
                                            param.weight_loader)
                success = weight_loader(param,
                                        loaded_weight,
                                        name_mapped,
                                        shard_id=shard_id,
                                        expert_id=expert_id,
                                        return_success=True)
                # weight layout infer
                if name.find("norm.weight") != -1 or name.find(
                        "e_score_correction_bias") != -1:
                    param.data = param.data.to(torch.float32)
                torch.supa.empty_cache()
                if success:
                    name = name_mapped
                    break
            else:
                if is_expert_weight:
                    # We've checked that this is an expert weight
                    # However it's not mapped locally to this rank
                    # So we simply skip it
                    continue

                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue

                # Remapping the name of FP8 kv-scale.
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue

                if is_pp_missing_parameter(name, self):
                    continue

                if name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
                # weight layout infer
                if name.find("norm.weight") != -1 or name.find(
                        "e_score_correction_bias") != -1:
                    param.data = param.data.to(torch.float32)
                if name.find("gate.weight") != -1:
                    param.data = param.data.to(torch.bfloat16)
                torch.supa.empty_cache()
        loaded_params.add(name)

    return loaded_params


vllm.model_executor.models.glm4_moe.Glm4MoeModel.load_weights = Glm4MoeModel_load_weights