#
# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
# Author: Bao Qian
# Email: baoqian@baidu.com
# This file is a part of the vllm-kunlun project.
#
# 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.
import os
import sys
from abc import abstractmethod
from contextlib import contextmanager
from types import CodeType
from typing import Callable, Optional

import torch

import vllm.envs as envs
from vllm.logger import init_logger

logger = init_logger(__name__)


class TorchCompileWrapperWithCustomDispatcher:
    """
    A wrapper class for torch.compile, with a custom dispatch logic.
    Subclasses should:
    1. Implement the forward method
    2. Implement the dispatch logic in the __call__ method
        It can use `self.compiled_codes` to access the compiled bytecode,
        and `with self.dispatch_to_code(index):` to dispatch to
        the compiled code.
    3. Implement the `__init__` method to determine how to call
        `torch.compile` over the forward method.
    """

    def __init__(self,
                 compiled_callable: Optional[Callable] = None,
                 compilation_level: int = 0):
        from vllm.config import get_current_vllm_config
        vllm_config = get_current_vllm_config()
        self.vllm_config = vllm_config
        if compiled_callable is None:
            # default compilation settings
            # compiling the forward method

            backend = vllm_config.compilation_config.init_backend(vllm_config)
            options = None
            if isinstance(backend, str) and backend == "inductor":
                options = get_current_vllm_config(
                ).compilation_config.inductor_compile_config

            compiled_callable = torch.compile(
                self.forward,
                fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
                backend=backend,
                options=options)

        self.compiled_callable = compiled_callable
        self.original_code_object = self.__class__.forward.__code__
        self.compiled_codes: list[CodeType] = []
        torch._dynamo.convert_frame.register_bytecode_hook(self.bytecode_hook)

        # read the env var to determine whether to use the custom dispatcher
        # subclasses can use this to switch between the custom dispatcher
        # and the default Dynamo guard mechanism.
        from vllm.config import CompilationLevel
        self.use_custom_dispatcher: bool = \
            compilation_level >= CompilationLevel.DYNAMO_ONCE

    def __call__(self, *args, **kwargs):
        """Implement the dispatch logic here, beyond the torch.compile level.
        NOTE: this function can have additional arguments beyond the forward
         method, for directly dispatching to the compiled code.
        """
        return self.compiled_callable(*args, **kwargs)

    @abstractmethod
    def forward(self, *args, **kwargs):
        ...

    def bytecode_hook(self, old_code: CodeType, new_code: CodeType):
        """Hook to save the compiled bytecode for direct execution."""
        if old_code is not self.original_code_object:
            return
        # code borrowed from https://github.com/thuml/depyf/blob/f4ad79fadee27ea113b4c75202db1eb1a11c0dbc/depyf/explain/enable_debugging.py#L25
        frame = sys._getframe()
        while frame and frame.f_back:
            frame = frame.f_back
            code_name = frame.f_code.co_name
            file_name = frame.f_code.co_filename.split(os.path.sep)[-1]
            if code_name == "_compile" and file_name == "convert_frame.py":
                break
        frame = frame.f_locals["frame"]
        assert frame.f_code == old_code

        if frame.f_locals["self"] is not self:
            return

        self.compiled_codes.append(new_code)
        local_cache_dir = self.vllm_config.compilation_config.local_cache_dir
        if isinstance(local_cache_dir, str):
            decompiled_file = os.path.join(local_cache_dir,
                                           "transformed_code.py")
            if not os.path.exists(decompiled_file):
                try:
                    # usually the decompilation will succeed for most models,
                    # as we guarantee a full-graph compilation in Dynamo.
                    # but there's no 100% guarantee, since decompliation is
                    # not a reversible process.
                    import depyf
                    src = depyf.decompile(new_code)
                    with open(decompiled_file, "w") as f:
                        f.write(src)

                    logger.debug("Dynamo transformed code saved to %s",
                                 decompiled_file)
                except Exception:
                    pass


    @contextmanager
    def dispatch_to_code(self, index: int):
        """Context manager to dispatch to the compiled code.
        Why does this work? Because Dynamo guarantees that the compiled
        bytecode has exactly the same arguments, cell variables, and free
        variables as the original code. Therefore we can directly switch
        the code object in the function and call it.

        See https://dev-discuss.pytorch.org/t/what-is-the-relationship-requirement-among-original-bytecode-transformed-bytecode-and-bytecode-returned-by-hooks-in-dynamo/1693/7 for more details.
        """ # noqa
        self.__class__.forward.__code__ = self.compiled_codes[index]
        yield
        self.__class__.forward.__code__ = self.original_code_object