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

import torch
import torch.distributed

import vllm.envs as envs
from vllm.config import VllmConfig
from vllm.distributed import get_world_group
from vllm.logger import init_logger
from vllm.model_executor import set_random_seed
from vllm.platforms import current_platform
from vllm.v1.worker.gpu_worker import Worker, init_worker_distributed_environment
from vllm.v1.worker.xpu_model_runner import XPUModelRunner

logger = init_logger(__name__)


class XPUWorker(Worker):
    """A XPU worker class."""

    def __init__(
        self,
        vllm_config: VllmConfig,
        local_rank: int,
        rank: int,
        distributed_init_method: str,
        is_driver_worker: bool = False,
    ):
        super().__init__(
            vllm_config, local_rank, rank, distributed_init_method, is_driver_worker
        )
        device_config = self.device_config
        assert device_config.device_type == "xpu"
        assert current_platform.is_xpu()

        # Torch profiler. Enabled and configured through env vars:
        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
        if envs.VLLM_TORCH_PROFILER_DIR:
            torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
            worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
            logger.info(
                "Profiling enabled. Traces will be saved to: %s",
                torch_profiler_trace_dir,
            )
            logger.debug(
                "Profiler config: record_shapes=%s,"
                "profile_memory=%s,with_stack=%s,with_flops=%s",
                envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
                envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
                envs.VLLM_TORCH_PROFILER_WITH_STACK,
                envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
            )
            self.profiler = torch.profiler.profile(
                activities=[
                    torch.profiler.ProfilerActivity.CPU,
                    torch.profiler.ProfilerActivity.XPU,
                ],
                record_shapes=envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
                profile_memory=envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
                with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
                with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
                on_trace_ready=torch.profiler.tensorboard_trace_handler(
                    torch_profiler_trace_dir, worker_name=worker_name, use_gzip=True
                ),
            )
        else:
            self.profiler = None

    # we provide this function due to `torch.xpu.mem_get_info()` doesn't
    # return correct free_gpu_memory on intel client GPU. We need to
    # calculate/estiamte it.
    def xpu_get_mem_info(self):
        if current_platform.is_data_center_gpu():
            return torch.xpu.mem_get_info()
        else:
            _, total_gpu_memory = torch.xpu.mem_get_info()
            # FIXME: memory_allocated() doesn't count non-torch allocations,
            # and we don't have any API to get it. so we mark it as 128MB.
            used_memory = torch.xpu.memory_allocated()
            non_torch_allocations = 128 * 1024 * 1024
            free_gpu_memory = total_gpu_memory - (used_memory + non_torch_allocations)
            return free_gpu_memory, total_gpu_memory

    @torch.inference_mode()
    def determine_available_memory(self) -> int:
        """Profiles the peak memory usage of the model to determine how many
        KV blocks may be allocated without OOMs.
        The engine will first conduct a profiling of the existing memory usage.
        Then, it calculates the maximum possible number of GPU and CPU blocks
        that can be allocated with the remaining free memory.
        .. tip::
            You may limit the usage of GPU memory
            by adjusting the `gpu_memory_utilization` parameter.
        """
        # Profile the memory usage of the model and get the maximum number of
        # cache blocks that can be allocated with the remaining free memory.
        torch.xpu.empty_cache()
        torch.xpu.reset_peak_memory_stats()

        free_gpu_memory, total_gpu_memory = torch.xpu.mem_get_info()
        current_allocated_bytes = torch.xpu.memory_allocated()
        msg = (
            "Before memory profiling run, "
            f"total GPU memory: {total_gpu_memory / 1024**2:.2f} MB, "
            f"model load takes {current_allocated_bytes / 1024**2:.2f} MB, "
            f"free gpu memory is {free_gpu_memory / 1024**2:.2f} MB."
        )
        logger.info(msg)
        # Execute a forward pass with dummy inputs to profile the memory usage
        # of the model.
        self.model_runner.profile_run()

        free_gpu_memory, _ = self.xpu_get_mem_info()
        # NOTE(woosuk): Here we assume that the other processes using the same
        # GPU did not change their memory usage during the profiling.
        assert self.init_gpu_memory > free_gpu_memory, (
            "Error in memory profiling. "
            f"Initial free memory {self.init_gpu_memory}, current free memory"
            f" {free_gpu_memory}. This happens when the GPU memory was "
            "not properly cleaned up before initializing the vLLM instance."
        )

        # Get the peak memory allocation recorded by torch
        peak_memory = torch.xpu.memory_stats()["allocated_bytes.all.peak"]

        torch.xpu.empty_cache()
        torch_allocated_bytes = torch.xpu.memory_stats()["allocated_bytes.all.current"]
        total_allocated_bytes = self.xpu_get_mem_info()[1] - self.xpu_get_mem_info()[0]

        non_torch_allocations = total_allocated_bytes - torch_allocated_bytes
        if non_torch_allocations > 0:
            peak_memory += non_torch_allocations
        available_kv_cache_memory = (
            total_gpu_memory * self.cache_config.gpu_memory_utilization - peak_memory
        )

        msg = (
            "After memory profiling run, "
            f"peak memory usage is {peak_memory / 1024**2:.2f} MB,"
            f"torch mem is {torch_allocated_bytes / 1024**2:.2f} MB, "
            f"non-torch mem is {non_torch_allocations / 1024**2:.2f} MB, "
            f"free gpu memory is {free_gpu_memory / 1024**2:.2f} MB."
        )
        logger.info(msg)

        return int(available_kv_cache_memory)

    def init_device(self):
        if self.device_config.device.type == "xpu" and current_platform.is_xpu():
            self.device = torch.device(f"xpu:{self.local_rank}")
            current_platform.set_device(self.device)
            current_platform.check_if_supports_dtype(self.model_config.dtype)
            torch.xpu.empty_cache()
            self.init_gpu_memory = torch.xpu.get_device_properties(
                self.local_rank
            ).total_memory
        else:
            raise RuntimeError(f"Not support device type: {self.device_config.device}")

        ENV_CCL_ZE_IPC_EXCHANGE = os.getenv("CCL_ZE_IPC_EXCHANGE", "pidfd")
        ENV_CCL_ATL_TRANSPORT = os.getenv("CCL_ATL_TRANSPORT", "ofi")
        ENV_LOCAL_WORLD_SIZE = os.getenv(
            "LOCAL_WORLD_SIZE", str(self.parallel_config.world_size)
        )
        os.environ["CCL_ZE_IPC_EXCHANGE"] = ENV_CCL_ZE_IPC_EXCHANGE
        os.environ["CCL_ATL_TRANSPORT"] = ENV_CCL_ATL_TRANSPORT
        os.environ["LOCAL_WORLD_SIZE"] = ENV_LOCAL_WORLD_SIZE
        os.environ["LOCAL_RANK"] = str(self.local_rank)

        init_worker_distributed_environment(
            self.vllm_config,
            self.rank,
            self.distributed_init_method,
            self.local_rank,
            current_platform.dist_backend,
        )

        # global all_reduce needed for overall oneccl warm up
        torch.distributed.all_reduce(
            torch.zeros(1).xpu(), group=get_world_group().device_group
        )

        # Set random seed.
        set_random_seed(self.model_config.seed)

        # Construct the model runner
        self.model_runner = XPUModelRunner(  # type: ignore
            self.vllm_config, self.device
        )
