vllm-ascend/vllm_ascend/platform.py

#
# 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.
#

import gc
import os
from datetime import timedelta
from typing import TYPE_CHECKING, Optional, Tuple

import torch
import vllm.envs as envs_vllm
from torch.distributed import ProcessGroup
from torch.distributed.distributed_c10d import PrefixStore
from vllm.logger import logger
from vllm.platforms import Platform, PlatformEnum

from vllm_ascend.ascend_config import (check_ascend_config, get_ascend_config,
                                       init_ascend_config)
from vllm_ascend.torchair.utils import (check_torchair_cache_exist,
                                        delete_torchair_cache_file)
from vllm_ascend.utils import (ASCEND_QUANTIZATION_METHOD, is_310p,
                               update_aclgraph_sizes, vllm_version_is)

if TYPE_CHECKING:
    from vllm.config import ModelConfig, VllmConfig
    from vllm.utils import FlexibleArgumentParser
else:
    ModelConfig = None
    VllmConfig = None
    FlexibleArgumentParser = None


class NPUPlatform(Platform):

    _enum = PlatformEnum.OOT
    device_name: str = "npu"
    device_type: str = "npu"
    simple_compile_backend: str = "eager"  # Disable torch.compile()
    ray_device_key: str = "NPU"
    device_control_env_var: str = "ASCEND_RT_VISIBLE_DEVICES"
    dispatch_key: str = "PrivateUse1"

    supported_quantization: list[str] = [ASCEND_QUANTIZATION_METHOD]

    def is_sleep_mode_available(self) -> bool:
        return True

    @classmethod
    def pre_register_and_update(cls,
                                parser: Optional[FlexibleArgumentParser] = None
                                ) -> None:
        # Adapt the global patch here.
        from vllm_ascend.utils import adapt_patch
        adapt_patch(is_global_patch=True)

        # For online serving, "ascend" quantization method is not a choice natively,
        # so we need to add "ascend" quantization method to quantization methods list
        # and the user can enable quantization using "vllm serve --quantization ascend".
        if parser is not None:
            quant_action = parser._option_string_actions.get('--quantization')
            if quant_action and hasattr(quant_action,
                                        'choices') and quant_action.choices:
                if ASCEND_QUANTIZATION_METHOD not in quant_action.choices:
                    quant_action.choices.append(ASCEND_QUANTIZATION_METHOD)

        from vllm_ascend.quantization.quant_config import \
            AscendQuantConfig  # noqa: F401

    @classmethod
    def get_device_capability(cls, device_id: int = 0):
        return None

    @classmethod
    def get_device_name(cls, device_id: int = 0) -> str:
        return torch.npu.get_device_name(device_id)

    @classmethod
    def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
        return True

    @classmethod
    def inference_mode(cls):
        return torch.inference_mode()

    @classmethod
    def set_device(cls, device: torch.device):
        torch.npu.set_device(device)

    @classmethod
    def empty_cache(cls):
        torch.npu.empty_cache()

    @classmethod
    def synchronize(cls):
        torch.npu.synchronize()

    @classmethod
    def mem_get_info(cls) -> Tuple[int, int]:
        return torch.npu.mem_get_info()

    @classmethod
    def clear_npu_memory(cls):
        gc.collect()
        torch.npu.empty_cache()
        torch.npu.reset_peak_memory_stats()

    @classmethod
    def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
        if not envs_vllm.VLLM_USE_V1:
            raise ValueError("vLLM Ascend does not support V0 engine.")
        # initialize ascend config from vllm additional_config
        ascend_config = init_ascend_config(vllm_config)

        from vllm.config import CompilationLevel  # noqa: E402
        compilation_config = vllm_config.compilation_config
        model_config = vllm_config.model_config
        parallel_config = vllm_config.parallel_config
        cache_config = vllm_config.cache_config
        scheduler_config = vllm_config.scheduler_config
        ascend_scheduler_config = ascend_config.ascend_scheduler_config
        if vllm_version_is("0.10.2"):
            structured_outputs_config = vllm_config.decoding_config
        else:
            structured_outputs_config = vllm_config.structured_outputs_config

        if (model_config is not None and not model_config.use_mla
                and not scheduler_config.async_scheduling):
            logger.info(
                "Non-MLA LLMs forcibly disable the chunked prefill feature,"
                "as the performance of operators supporting this feature "
                "functionality is currently suboptimal.")
            if not model_config.is_multimodal_model and \
                structured_outputs_config.backend == "auto" and \
                not getattr(scheduler_config, "scheduler_delay_factor", 0) > 0 and \
                not scheduler_config.send_delta_data and \
                scheduler_config.policy == "fcfs":
                ascend_scheduler_config.enabled = True
                chunked_prefill_enabled_in_ascend_scheduler = getattr(
                    ascend_scheduler_config, "enable_chunked_prefill", False)
                if chunked_prefill_enabled_in_ascend_scheduler:
                    logger.warning(
                        "Chunked prefill feature is enabled in ascend_scheduler,"
                        "but note that the operator supporting this feature "
                        "would lead to performance degradation.")
                # In this situation, max_num_batched_tokens would have been rewritten.
                # So we must make sure max_num_batched_tokens is not smaller than max_model_len.
                if (scheduler_config.max_num_batched_tokens
                        < scheduler_config.max_model_len
                        and not chunked_prefill_enabled_in_ascend_scheduler):
                    scheduler_config.max_num_batched_tokens = scheduler_config.max_model_len

        kv_cache_dtype = vllm_config.additional_config.get(
            "kv_cache_dtype", None)
        if kv_cache_dtype is not None:
            vllm_config.cache_config.cache_dtype = kv_cache_dtype
        if model_config is None:
            logger.warning("Model config is missing. This may indicate "
                           "that we are running a test case")
            enforce_eager = False
        else:
            enforce_eager = getattr(model_config, "enforce_eager", False)

        check_ascend_config(vllm_config, enforce_eager)
        from vllm.config.compilation import CUDAGraphMode
        if enforce_eager:
            logger.info("Compilation disabled, using eager mode by default")
            compilation_config.level = CompilationLevel.NO_COMPILATION

        compilation_config.cudagraph_num_of_warmups = 1

        if compilation_config.level not in [
                CompilationLevel.NO_COMPILATION, CompilationLevel.PIECEWISE
        ]:
            logger.warning(
                "NPU does not support %s compilation level. Setting CUDAGraphMode to NONE",
                compilation_config.level)
            compilation_config.cudagraph_mode = CUDAGraphMode.NONE

        # set CUDAGraphMode to None when torchair is enabled, no mather what compilation_config.level is.
        if ascend_config.torchair_graph_config.enabled:
            logger.info(
                "Torchair compilation enabled on NPU. Setting CUDAGraphMode to NONE"
            )
            compilation_config.cudagraph_mode = CUDAGraphMode.NONE
            # Note: We delete the torchair cache folder here to prevent runtime issues caused by dimension
            # mismatches or configuration inconsistencies when users reuse cached computation graphs. Though
            # this will increase graph compilation duration, it significantly enhances robustness and decreases
            # graph launching time during inference.
            if check_torchair_cache_exist(
            ) and not ascend_config.torchair_graph_config.use_cached_kv_cache_bytes:
                logger.warning(
                    "Torchair cache folder is deleted here to prevent runtime issues caused by dimension "
                    "mismatches or configuration inconsistencies when users reuse cached computation graphs. "
                    "In order to decrease torchair graph compilation time, users can enable both use_cached_graph "
                    "and use_cached_kv_cache_bytes in torchair_graph_config.")
                delete_torchair_cache_file()

        # set cudaprah sizes before extending `compilation_config.splitting_ops`
        vllm_config._set_cudagraph_sizes()

        # TODO: Full graph is fully supported later, and the default value will be set to full graph.
        if not vllm_version_is("0.10.2"):
            if compilation_config.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE:
                compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE

        if compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
            compilation_config.level = CompilationLevel.NO_COMPILATION
        # TODO: Currently MLA does not support FULL_DECODE_ONLY, remove the second condition
        # after MLA being supported
        elif compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE or (
                compilation_config.cudagraph_mode
                == CUDAGraphMode.FULL_DECODE_ONLY and model_config is not None
                and model_config.use_mla):
            logger.info(
                "PIECEWISE compilation enabled on NPU. use_inductor not supported - "
                "using only ACL Graph mode")
            assert compilation_config.level == CompilationLevel.PIECEWISE, \
                "When enabling piecewise aclgraph, please make sure compilation_config.level == CompilationLevel.PIECEWISE and compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE"
            compilation_config.set_splitting_ops_for_v1()
            compilation_config.use_inductor = False
            compilation_config.splitting_ops.extend([
                "vllm.unified_ascend_attention_with_output", "vllm.mla_forward"
            ])
            update_aclgraph_sizes(vllm_config)
        elif compilation_config.cudagraph_mode == CUDAGraphMode.FULL_DECODE_ONLY:
            logger.info(
                "FULL_DECODE_ONLY compilation enabled on NPU. use_inductor not supported - "
                "using only ACL Graph mode")
            compilation_config.use_inductor = False
            warning_message = """\033[91m
            **********************************************************************************
            * WARNING: You have enabled the *full graph* feature.
            * This is an early experimental stage and may involve various unknown issues.
            * A known problem is that capturing too many batch sizes can lead to OOM
            * (Out of Memory) errors or inference hangs. If you encounter such issues,
            * consider reducing `gpu_memory_utilization` or manually specifying a smaller
            * batch size for graph capture.
            * For more details, please refer to:
            * https://docs.vllm.ai/en/stable/configuration/conserving_memory.html#reduce-cuda-graphs
            **********************************************************************************\033[0m
            """
            logger.warning(warning_message)
        else:
            logger.info(
                "%s cudagraph_mode is not support on NPU. falling back to NONE",
                compilation_config.cudagraph_mode)
            compilation_config.cudagraph_mode = CUDAGraphMode.NONE
            compilation_config.level = CompilationLevel.NO_COMPILATION

        if parallel_config and parallel_config.worker_cls == "auto":
            # TODO: this is a tricky way to disable `use_sequence_parallel_moe` in vllm.
            os.environ["VLLM_ALL2ALL_BACKEND"] = "flashinfer_all2allv"
            if ascend_config.torchair_graph_config.enabled or ascend_config.enable_shared_expert_dp:
                parallel_config.worker_cls = "vllm_ascend.torchair.torchair_worker.NPUTorchairWorker"
            else:
                parallel_config.worker_cls = "vllm_ascend.worker.worker_v1.NPUWorker"

        if cache_config:
            if cache_config.block_size is None:
                cache_config.block_size = 128

            if cache_config.enable_prefix_caching and cache_config.block_size != 128:
                logger.warning(
                    "If prefix caching is enabled, block size must be set to 128."
                )
                cache_config.block_size = 128

        # Activate custom ops for v1, except on 310P
        if not is_310p():
            compilation_config.custom_ops = ["all"]

        # If ascend_scheduler_config is enabled,
        # extents original scheduler_config to use AscendScheduler.
        if ascend_config.ascend_scheduler_config.enabled:
            from vllm_ascend.core.schedule_config import AscendSchedulerConfig
            ascend_scheduler_config = AscendSchedulerConfig.initialize_from_config(
                vllm_config.scheduler_config,
                ascend_config.ascend_scheduler_config)
            vllm_config.scheduler_config = ascend_scheduler_config

    @classmethod
    def get_attn_backend_cls(cls,
                             selected_backend,
                             head_size,
                             dtype,
                             kv_cache_dtype,
                             block_size,
                             use_v1,
                             use_mla,
                             use_sfa,
                             has_sink=False):
        if not use_v1:
            raise ValueError("vLLM Ascend does not support V0 engine.")

        ascend_config = get_ascend_config()

        if use_mla and ascend_config.enable_shared_expert_dp:
            if use_mla and not use_sfa:
                return "vllm_ascend.torchair.torchair_mla.AscendMLATorchairBackend"
            if use_mla and use_sfa:
                return "vllm_ascend.torchair.torchair_sfa.AscendSFATorchairBackend"

        use_torchair = ascend_config.torchair_graph_config.enabled
        # choose attention backend based on use_mla and use_torchair
        backend_map = {
            (True, False, True):
            "vllm_ascend.torchair.torchair_mla.AscendMLATorchairBackend",
            (True, False, False):
            "vllm_ascend.attention.mla_v1.AscendMLABackend",
            (False, False, True):
            "vllm_ascend.torchair.torchair_attention.AscendAttentionTorchairBackend",
            (False, False, False):
            "vllm_ascend.attention.attention_v1.AscendAttentionBackend",
            (True, True, False):
            "vllm_ascend.attention.sfa_v1.AscendSFABackend",
            (True, True, True):
            "vllm_ascend.torchair.torchair_sfa.AscendSFATorchairBackend",
        }
        return backend_map[(use_mla, use_sfa, use_torchair)]

    @classmethod
    def get_punica_wrapper(cls) -> str:
        return "vllm_ascend.lora.punica_npu.PunicaWrapperNPU"

    @classmethod
    def get_current_memory_usage(cls,
                                 device: Optional[torch.types.Device] = None
                                 ) -> float:
        torch.npu.reset_peak_memory_stats(device)
        return torch.npu.max_memory_allocated(device)

    @classmethod
    def get_device_communicator_cls(cls) -> str:
        return "vllm_ascend.distributed.communicator.NPUCommunicator"

    @classmethod
    def is_pin_memory_available(cls):
        return True

    @classmethod
    def supports_v1(cls, model_config: ModelConfig) -> bool:
        """Returns whether the current platform can support v1 for the supplied
        model configuration.
        """
        return True

    @classmethod
    def get_static_graph_wrapper_cls(cls) -> str:
        """
        Get piecewise backend class for piecewise graph.
        """
        return "vllm_ascend.compilation.acl_graph.ACLGraphWrapper"  # noqa

    @classmethod
    def stateless_init_device_torch_dist_pg(
        cls,
        backend: str,
        prefix_store: PrefixStore,
        group_rank: int,
        group_size: int,
        timeout: timedelta,
    ) -> ProcessGroup:
        from torch.distributed import is_hccl_available
        from torch_npu._C._distributed_c10d import ProcessGroupHCCL

        assert is_hccl_available()

        pg: ProcessGroup = ProcessGroup(
            prefix_store,
            group_rank,
            group_size,
        )

        backend_options = ProcessGroupHCCL.Options()
        backend_options._timeout = timeout

        backend_class = ProcessGroupHCCL(prefix_store, group_rank, group_size,
                                         backend_options)
        device = torch.device("npu")
        # TODO(Yizhou): Like we mentioned above, _set_default_backend is not
        # implemented in the 2.5.1 version of PyTorch. But we need to set it
        # after the latest version is released.
        # pg._set_default_backend(backend_type)
        backend_class._set_sequence_number_for_group()
        backend_type = ProcessGroup.BackendType.CUSTOM

        pg._register_backend(device, backend_type, backend_class)
        return pg

    @classmethod
    def support_hybrid_kv_cache(cls) -> bool:
        return True

    @classmethod
    def support_static_graph_mode(cls) -> bool:
        return True