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

# Copyright 2025 The vLLM team.
# Copyright 2025 The Qwen Team.
# Copyright 2025 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 Qwen3-VL-MoE model compatible with HuggingFace weights."""

import typing
from collections.abc import Callable, Iterable
from itertools import islice

import torch
from transformers.models.qwen3_vl_moe.configuration_qwen3_vl_moe import (
    Qwen3VLMoeConfig,
)

from vllm.compilation.decorators import support_torch_compile
from vllm.config import VllmConfig
from vllm.distributed import get_pp_group
from vllm.logger import init_logger
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.sequence import IntermediateTensors
from vllm.tokenizers.registry import cached_tokenizer_from_config

from .interfaces import MixtureOfExperts
from .qwen3_moe import (
    Qwen3MoeForCausalLM,
    Qwen3MoeModel,
    Qwen3MoeSparseMoeBlock,
)
from .qwen3_vl import (
    Qwen3_VisionTransformer,
    Qwen3VLDummyInputsBuilder,
    Qwen3VLForConditionalGeneration,
    Qwen3VLMultiModalProcessor,
    Qwen3VLProcessingInfo,
)
from .utils import is_pp_missing_parameter, maybe_prefix

logger = init_logger(__name__)


class Qwen3VLMoeProcessingInfo(Qwen3VLProcessingInfo):
    def get_hf_config(self):
        return self.ctx.get_hf_config(Qwen3VLMoeConfig)


@support_torch_compile(
    dynamic_arg_dims={
        "input_ids": 0,
        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
        # otherwise (seq_len, ).
        "positions": -1,
        "intermediate_tensors": 0,
        "inputs_embeds": 0,
        # the same shape as input_embeds
        "deepstack_input_embeds": 0,
    }
)
class Qwen3MoeLLMModel(Qwen3MoeModel):
    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        deepstack_input_embeds: IntermediateTensors | None = None,
    ) -> torch.Tensor | IntermediateTensors:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.embed_input_ids(input_ids)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        aux_hidden_states = []
        for layer_idx, layer in islice(
            enumerate(self.layers), self.start_layer, self.end_layer
        ):
            if layer_idx in self.aux_hidden_state_layers:
                aux_hidden_states.append(hidden_states + residual)

            hidden_states, residual = layer(
                positions,
                hidden_states,
                residual,
            )

            if deepstack_input_embeds is not None and layer_idx in range(
                0, len(deepstack_input_embeds)
            ):
                hidden_states = (
                    hidden_states
                    + deepstack_input_embeds[f"deepstack_input_embeds_{layer_idx}"]
                )

        if not get_pp_group().is_last_rank:
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
        hidden_states, _ = self.norm(hidden_states, residual)

        if len(aux_hidden_states) > 0:
            return hidden_states, aux_hidden_states
        return hidden_states

    def load_fused_expert_weights(
        self,
        name: str,
        params_dict: dict,
        loaded_weight: torch.Tensor,
        shard_id: str,
        num_experts: int,
    ) -> bool:
        param = params_dict[name]
        weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
        loaded_local_expert = False
        for expert_id in range(num_experts):
            curr_expert_weight = loaded_weight[expert_id]
            success = weight_loader(
                param,
                curr_expert_weight,
                name,
                shard_id,
                expert_id,
                return_success=True,
            )
            if success:
                loaded_local_expert = True

        return loaded_local_expert

    def 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),
        ]
        # Skip loading extra parameters for GPTQ/modelopt models.
        ignore_suffixes = (
            ".bias",
            "_bias",
            ".weight_scale",
            "_weight_scale",
            ".input_scale",
            "_input_scale",
        )
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        expert_params_mapping = self.get_expert_mapping()
        is_fused_expert = False
        fused_expert_params_mapping = [
            ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
            ("experts.w2_weight", "experts.down_proj", 0, "w2"),
        ]
        num_experts = self.config.num_experts
        for name, loaded_weight in weights:
            if "scale" in name or "zero_point" in name:
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if "experts.gate_up_proj" in name or "experts.down_proj" in name:
                    is_fused_expert = True
                    expert_params_mapping = fused_expert_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 parameters for GPTQ/modelopt models.
                if name.endswith(ignore_suffixes) and name not in params_dict:
                    continue
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue
                if name.endswith("scale"):
                    # Remapping the name of FP8 kv-scale.
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        continue
                if name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                if weight_loader == default_weight_loader:
                    weight_loader(param, loaded_weight)
                else:
                    weight_loader(param, loaded_weight, shard_id)
                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
                    name_mapped = name.replace(weight_name, param_name)
                    if is_pp_missing_parameter(name_mapped, self):
                        continue
                    if is_fused_expert:
                        loaded_weight = loaded_weight.transpose(-1, -2)  # no bias
                        if "experts.gate_up_proj" in name:
                            loaded_weight = loaded_weight.chunk(2, dim=-2)
                            success_w1 = self.load_fused_expert_weights(
                                name_mapped,
                                params_dict,
                                loaded_weight[0],
                                "w1",
                                num_experts,
                            )
                            success_w3 = self.load_fused_expert_weights(
                                name_mapped,
                                params_dict,
                                loaded_weight[1],
                                "w3",
                                num_experts,
                            )
                            success = success_w1 and success_w3
                        else:
                            # down_proj
                            success = self.load_fused_expert_weights(
                                name_mapped,
                                params_dict,
                                loaded_weight,
                                shard_id,
                                num_experts,
                            )
                    else:
                        # Skip loading extra parameters for GPTQ/modelopt models
                        if (
                            name_mapped.endswith(ignore_suffixes)
                            and 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,
                        )
                    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 parameters for GPTQ/modelopt models.
                    if name.endswith(ignore_suffixes) 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 = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class Qwen3MoeLLMForCausalLM(Qwen3MoeForCausalLM):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super(Qwen3MoeForCausalLM, self).__init__()
        self.config = vllm_config.model_config.hf_config
        self.quant_config = vllm_config.quant_config
        self.model = Qwen3MoeLLMModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        self.lm_head = ParallelLMHead(
            self.config.vocab_size,
            self.config.hidden_size,
            quant_config=self.quant_config,
            prefix=maybe_prefix(prefix, "lm_head"),
        )
        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight
        self.logits_processor = LogitsProcessor(self.config.vocab_size)
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )


class Qwen3VLMoeMixtureOfExperts(MixtureOfExperts):
    def update_physical_experts_metadata(
        self,
        num_physical_experts: int,
        num_local_physical_experts: int,
    ) -> None:
        assert self.num_local_physical_experts == num_local_physical_experts
        self.num_physical_experts = num_physical_experts
        self.num_local_physical_experts = num_local_physical_experts
        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
        for layer in self.language_model.model.layers:
            if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
                moe = layer.mlp
                moe.n_local_physical_experts = num_local_physical_experts
                moe.n_physical_experts = num_physical_experts
                moe.n_redundant_experts = self.num_redundant_experts
                moe.experts.update_expert_map()

    def set_moe_parameters(self):
        self.expert_weights = []

        self.moe_layers = []
        example_moe = None
        for layer in self.language_model.model.layers:
            if hasattr(layer, "mlp") and isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
                example_moe = layer.mlp
                self.moe_layers.append(layer.mlp.experts)

        if example_moe is None:
            raise RuntimeError("No Qwen3Moe layer found in the language_model.")

        # Set MoE hyperparameters
        self.num_moe_layers = len(self.moe_layers)
        self.num_expert_groups = 1
        self.num_shared_experts = 0
        self.num_logical_experts = example_moe.n_logical_experts
        self.num_physical_experts = example_moe.n_physical_experts
        self.num_local_physical_experts = example_moe.n_local_physical_experts
        self.num_routed_experts = example_moe.n_routed_experts
        self.num_redundant_experts = example_moe.n_redundant_experts


@MULTIMODAL_REGISTRY.register_processor(
    Qwen3VLMultiModalProcessor,
    info=Qwen3VLMoeProcessingInfo,
    dummy_inputs=Qwen3VLDummyInputsBuilder,
)
class Qwen3VLMoeForConditionalGeneration(
    Qwen3VLForConditionalGeneration, Qwen3VLMoeMixtureOfExperts
):
    is_3d_moe_weight: bool = True
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super(Qwen3VLForConditionalGeneration, self).__init__()
        config: Qwen3VLMoeConfig = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config

        self.config = config
        self._tokenizer = cached_tokenizer_from_config(vllm_config.model_config)
        self.multimodal_config = multimodal_config
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
        self.video_pruning_rate = multimodal_config.video_pruning_rate
        self.is_multimodal_pruning_enabled = (
            multimodal_config.is_multimodal_pruning_enabled()
        )

        self.use_deepstack = hasattr(config.vision_config, "deepstack_visual_indexes")
        self.deepstack_num_level = (
            len(config.vision_config.deepstack_visual_indexes)
            if self.use_deepstack
            else 0
        )
        self.visual_dim = config.vision_config.out_hidden_size
        self.multiscale_dim = self.visual_dim * self.deepstack_num_level

        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.visual = Qwen3_VisionTransformer(
                config.vision_config,
                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "visual"),
            )

            # register buffer for deepstack
            if self.use_deepstack:
                self.deepstack_input_embeds = [
                    torch.zeros(
                        vllm_config.scheduler_config.max_num_batched_tokens,
                        config.text_config.hidden_size,
                    )
                    for _ in range(self.deepstack_num_level)
                ]

        with self._mark_language_model(vllm_config):
            self.language_model = Qwen3MoeLLMForCausalLM(
                vllm_config=vllm_config.with_hf_config(config.text_config),
                prefix=maybe_prefix(prefix, "language_model"),
            )

        if not get_pp_group().is_first_rank and hasattr(
            config.vision_config, "deepstack_visual_indexes"
        ):
            assert self.language_model.start_layer >= len(
                config.vision_config.deepstack_visual_indexes
            ), (
                "start_layer should be greater than or equal to "
                "len(deepstack_visual_indexes)"
            )

        # Whether to include the gate_up_proj mapping is determined by
        # the language model.
        self.packed_modules_mapping = (
            self.packed_modules_mapping | self.language_model.packed_modules_mapping
        )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

        # Set MoE hyperparameters
        self.set_moe_parameters()
