# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/aclgraph_utils.py
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright (c) 2025 Huawei Technologies 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.
# This file is a part of the vllm-ascend project.
#
from typing import Any

import torch
import torch.nn as nn
from vllm.config import VllmConfig
from vllm.config.compilation import CUDAGraphMode
from vllm.forward_context import get_forward_context, set_forward_context
from vllm.logger import logger
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.v1.worker.gpu.block_table import BlockTables
from vllm.v1.worker.gpu.cudagraph_utils import BatchExecutionDescriptor, ModelCudaGraphManager
from vllm.v1.worker.gpu.input_batch import InputBuffers
from vllm.v1.worker.gpu.model_states.interface import ModelState
from vllm.v1.worker.utils import AttentionGroup

from vllm_ascend.ascend_forward_context import _EXTRA_CTX
from vllm_ascend.compilation.acl_graph import set_graph_params, update_full_graph_params


class ModelAclGraphManager(ModelCudaGraphManager):
    """ACL Model Cuda Graph Manager for Ascend NPUs."""

    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
        cudagraph_mode: CUDAGraphMode,
        decode_query_len: int,
        model_runner: Any,
    ):
        super().__init__(
            vllm_config,
            device,
            cudagraph_mode,
            decode_query_len,
        )
        # set model runner attribute, so we can access attributes model runner
        # when call `run_fullgraph` method in CudaGraphManager,
        # then we don't need to # copy `execute_model` method in `NPUModelRunner` class.
        self.model_runner = model_runner
        # capture_sizes sorts in ascending order.
        self.capture_sizes = sorted(self.compilation_config.cudagraph_capture_sizes)
        # vllm-ascend need to update graph params of attention backend.
        # so we need to set graph params before capture full graph.
        if super().needs_capture():
            set_graph_params(self.capture_sizes)

    def run_fullgraph(self, desc: BatchExecutionDescriptor) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:
        """Override run_fullgraph to update full graph params in run_fullgraph."""
        num_tokens = desc.num_tokens
        logger.info_once(f"run_fullgraph with num_tokens={num_tokens}")
        ret = super().run_fullgraph(desc)

        positions = self.model_runner.input_buffers.positions[:num_tokens]
        # refer to vllm.v1.worker.gpu.dp_utils.sync_cudagraph_and_dp_padding to
        # calculate num_tokens_across_dp.
        num_tokens_across_dp = torch.full([self.model_runner.dp_size], num_tokens, device=self.device)
        with set_forward_context(
            self.model_runner.input_batch.attn_metadata,
            self.vllm_config,
            num_tokens=num_tokens,
            cudagraph_runtime_mode=desc.cg_mode,
            num_tokens_across_dp=num_tokens_across_dp,
            batch_descriptor=None,  # Full graph model don't need batch_descriptor
            slot_mapping=self.model_runner.input_batch.slot_mappings,
        ):
            forward_context = get_forward_context()
            update_full_graph_params(
                # FIXME(Ronald1995): support hybrid attn backend
                list(self.model_runner.attn_backends.values())[0],
                self.model_runner.update_stream,
                forward_context,
                num_tokens,
                self.vllm_config,
                self.model_runner.speculative_config,
                positions.shape[0],
            )
        return ret

    def capture(
        self,
        model: nn.Module,
        model_state: ModelState,
        input_buffers: InputBuffers,
        block_tables: BlockTables,
        attn_groups: list[list[AttentionGroup]],
        kv_cache_config: KVCacheConfig,
        has_lora: bool = False,
        use_aux_hidden_state_outputs: bool = False,
        progress_bar_desc: str = "Capturing CUDA graphs",
    ) -> None:
        """Capture CUDA graphs for model forward pass."""
        model = ModelWithContext(model)
        return super().capture(
            model,
            model_state,
            input_buffers,
            block_tables,
            attn_groups,
            kv_cache_config,
            has_lora,
            use_aux_hidden_state_outputs,
            progress_bar_desc,
        )


class ModelWithContext(nn.Module):
    """Define a wrapper model to inject forward context.
    so we can inherit vllm's CudaGraphManager._capture_full_graph.
    """

    def __init__(self, original_model):
        super().__init__()
        self.original_model = original_model

    def forward(self, *args, **kwargs):
        # In warmup phase, capturing=False by default.
        # when capturing, we need to set capturing=True in forward context.
        if torch.npu.is_current_stream_capturing():
            _EXTRA_CTX.capturing = True

        return self.original_model(*args, **kwargs)