#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM 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.
# This file is a part of the vllm-ascend project.
# Adapted from vllm-project/vllm/vllm/worker/worker.py
#

import atexit
import math
from contextlib import contextmanager, nullcontext
from threading import Lock
from typing import TYPE_CHECKING, List, Tuple

import torch
import torchair  # type: ignore[import]  # noqa: F401
from packaging.version import InvalidVersion, Version
from torch_npu.npu.streams import Event
from vllm.logger import logger

import vllm_ascend.envs as envs

try:
    # Recent release of torchair has moved these ops to `.scope`.
    from torchair.scope import npu_stream_switch as _npu_stream_switch
    from torchair.scope import npu_wait_tensor as _npu_wait_tensor
except ImportError:
    from torchair.ops import NpuStreamSwitch as _npu_stream_switch
    from torchair.ops import npu_wait_tensor as _npu_wait_tensor

if TYPE_CHECKING:
    from vllm.config import VllmConfig
else:
    VllmConfig = None

# NOTE: Currently, we can only capture 1920 graphs at most,
# due to the limitation of ACL graph. This number is bounded by
# the number of streams, which is 2048, we save 128 streams
# as a buffer.
# Maximum number of graphs that can be captured by ACL Graph
MAX_CAPTURE_SIZE = 1920

ASCEND_QUATIZATION_METHOD = "ascend"


def try_register_lib(lib_name: str, lib_info: str = ""):
    import importlib
    import importlib.util
    try:
        module_spec = importlib.util.find_spec(lib_name)
        if module_spec is not None:
            importlib.import_module(lib_name)
            if lib_info:
                logger.info(lib_info)
    except Exception:
        pass


def find_hccl_library() -> str:
    """
    We either use the library file specified by the `HCCL_SO_PATH`
    environment variable, or we find the library file brought by PyTorch.
    After importing `torch`, `libhccl.so` can be
    found by `ctypes` automatically.
    """
    so_file = envs.HCCL_SO_PATH

    # manually load the hccl library
    if so_file:
        logger.info("Found hccl from environment variable HCCL_SO_PATH=%s",
                    so_file)
    else:
        if torch.version.cann is not None:
            so_file = "libhccl.so"
        else:
            raise ValueError("HCCL only supports Ascend NPU backends.")
        logger.info("Found hccl from library %s", so_file)
    return so_file


_current_stream = None


def current_stream() -> torch.npu.Stream:
    """
    replace `torch.npu.current_stream()` with `vllm.utils.current_stream()`.
    it turns out that `torch.npu.current_stream()` is quite expensive,
    as it will construct a new stream object at each call.
    here we patch `torch.npu.set_stream` to keep track of the current stream
    directly, so that we can avoid calling `torch.npu.current_stream()`.

    """
    global _current_stream
    if _current_stream is None:
        # when this function is called before any stream is set,
        # we return the default stream.
        _current_stream = torch.npu.current_stream()
    return _current_stream


def adapt_patch(is_global_patch: bool = False):
    if is_global_patch:
        from vllm_ascend.patch import platform  # noqa: F401
    else:
        from vllm_ascend.patch import worker  # noqa: F401


def vllm_version_is(target_vllm_version: str):
    if envs.VLLM_VERSION is not None:
        vllm_version = envs.VLLM_VERSION
    else:
        import vllm
        vllm_version = vllm.__version__
    try:
        return Version(vllm_version) == Version(target_vllm_version)
    except InvalidVersion:
        raise ValueError(
            f"Invalid vllm version {vllm_version} found. A dev version of vllm "
            "is installed probably. Set the environment variable VLLM_VERSION "
            "to control it by hand. And please make sure the value follows the "
            "format of x.y.z.")


def update_aclgraph_sizes(vllm_config: VllmConfig) -> None:
    """Update ACL graph capture sizes based on hardware limitations"""
    # Store original configuration and temporarily clear it
    compilation_config = vllm_config.compilation_config
    original_sizes, compilation_config.cudagraph_capture_sizes = \
        compilation_config.cudagraph_capture_sizes, None

    # Calculate parallel configuration factor
    num_hidden_layers = vllm_config.model_config.hf_config.num_hidden_layers
    parallel_config = vllm_config.parallel_config

    # TODO: Find out whether we need to take into account the pp_size
    parallel_factor = 1 + sum(size > 1 for size in [
        parallel_config.data_parallel_size_local,
        parallel_config.tensor_parallel_size,
        parallel_config.expert_parallel_size,
        parallel_config.expert_tensor_parallel_size,
    ])

    # Calculate maximum supported batch sizes considering model architecture
    max_num_batch_sizes = math.floor(MAX_CAPTURE_SIZE /
                                     (num_hidden_layers + 1) / parallel_factor)
    logger.info("Calculated maximum supported batch sizes for ACL graph: %s",
                max_num_batch_sizes)

    # If original sizes exceed maximum, sample a representative subset
    if max_num_batch_sizes < len(original_sizes):
        # Sample uniformly from original sizes
        step = (len(original_sizes) - 1) / (max_num_batch_sizes - 1)
        indices = [round(i * step) for i in range(max_num_batch_sizes)]

        # Ensure first and last elements are preserved
        indices[0], indices[-1] = 0, len(original_sizes) - 1

        sampled_sizes = [original_sizes[i] for i in indices]
        compilation_config.init_with_cudagraph_sizes(sampled_sizes)

        logger.info(
            "Adjusted ACL graph batch sizes for %s model (layers: %d): %d → %d sizes",
            vllm_config.model_config.architectures[0],
            num_hidden_layers,
            len(original_sizes),
            len(compilation_config.
                cudagraph_capture_sizes  # type: ignore[arg-type]
                ))
    else:
        # No adjustment needed
        compilation_config.cudagraph_capture_sizes = original_sizes
        logger.info(
            "No adjustment needed for ACL graph batch sizes: %s model (layers: %d) with %d sizes",
            vllm_config.model_config.architectures[0], num_hidden_layers,
            len(original_sizes))


def dispose_tensor(x: torch.Tensor):
    x.set_(torch.empty((0, ), device=x.device, dtype=x.dtype))


class ProfileExecuteDuration:
    _instance = None
    _observations: List[Tuple[str, Event, Event]] = []
    _lock = Lock()

    def __new__(cls):
        with cls._lock:
            if cls._instance is None:
                cls._instance = super().__new__(cls)
                atexit.register(cls._instance.destroy)
            return cls._instance

    def destroy(self):
        with self._lock:
            self._observations.clear()

    @contextmanager
    def capture_async(self, duration_tag: str):
        if not envs.VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE:
            yield
            return

        observe_start = Event(enable_timing=True)
        observe_start.record()
        try:
            yield
        finally:
            observe_end = Event(enable_timing=True)
            observe_end.record()
            with self._lock:
                self._observations.append(
                    (duration_tag, observe_start, observe_end))

    def pop_captured_sync(self) -> dict:
        """Pop and synchronize all events in the observation list"""
        durations: dict[str, float] = {}
        if not envs.VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE:
            return durations

        while self._observations:
            with self._lock:
                tag, observe_start, observe_end = self._observations.pop()
            observe_end.synchronize()
            durations[tag] = observe_start.elapsed_time(observe_end)

        return durations


def npu_stream_switch(tag: str, priority: int, *, enabled: bool = True):
    return _npu_stream_switch(tag, priority) if enabled else nullcontext()


def npu_wait_tensor(self: torch.Tensor,
                    dependency: torch.Tensor,
                    *,
                    enabled: bool = True):
    return _npu_wait_tensor(self, dependency) if enabled else self