xc-llm-ascend/vllm_ascend/utils.py

#
# 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
#

from __future__ import annotations

import atexit
import functools
import math
import os
from contextlib import nullcontext
from enum import Enum
from functools import lru_cache
from threading import Lock
from typing import TYPE_CHECKING, Any

import regex as re
import torch
import torch_npu  # noqa: F401
from packaging.version import InvalidVersion, Version
from vllm.logger import logger
from vllm.sequence import IntermediateTensors

import vllm_ascend.envs as envs_ascend
from vllm_ascend.ascend_config import WeightPrefetchConfig, get_ascend_config

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

COMPILATION_PASS_KEY = "graph_fusion_manager"
ASCEND_QUANTIZATION_METHOD = "ascend"
COMPRESSED_TENSORS_METHOD = "compressed-tensors"
SOC_VERSION_INFERENCE_SERIES = ["Ascend310P3"]
REGISTERED_ASCEND_OPS = {}

ACL_FORMAT_FRACTAL_ND = 2
ACL_FORMAT_FRACTAL_NZ = 29

_CUSTOM_OP_ENABLED = None
_CURRENT_STREAM = None
_PREFETCH_STREAM = None
_WEIGHT_PREFETCH_METHOD = None
_GLOBAL_STREAM = None
_SHARED_EXPERTS_CALCULATION_STREAM = None
_CP_CHUNKEDPREFILL_COMM_STREAM = None
_ASCEND_CUSTOMOP_IS_REIGISTERED = False
_DEFAULT_BUFFER_SIZE = 200
_MIN_DP_BUFFER_SIZE = 50
_DYNAMIC_EPLB_BUFFER_SIZE = 100
_IS_MOE_MODEL = None
_IS_DRAFTER_MOE_MODEL = None
_IS_VL_MODEL = None
_ENABLE_SP = None
_HAS_LAYER_IDX = None
_SUBSCRIBED_COMPUTE_STREAMS = set()
_GRAPH_PRINT_STREAM = None
_GRAPH_PRINT_STREAM_LOCK = Lock()
_HAS_ROPE = None


def is_310p():
    return get_ascend_device_type() == AscendDeviceType._310P


def _print_callback_on_stream(*args):
    """Callback function to print arguments on the dedicated print stream."""
    global _GRAPH_PRINT_STREAM
    with torch_npu.npu.stream(_GRAPH_PRINT_STREAM):
        print(*args, flush=True)


def acl_graph_print(*args):
    """
    Prints arguments from within an ACL graph.

    This function is provided for developers to print debug information when encountering
    issues within an ACL graph, pretty handy for dumping input/output tensor values, or
    resolving unexpected hangs. Usage:
    ```python
    from vllm_ascend.utils import acl_graph_print

    ...
    acl_graph_print("Debug info")
    ```

    This function launches a host function on the current compute stream to print
    the given arguments. It uses a dedicated stream for printing to avoid
    interfering with computation.

    NOTE: torch.compile does not support this function, only use this in non-compiled code.
    For example, those custom ops like `unified_attention_with_output` or `moe_forward`.
    """
    global _SUBSCRIBED_COMPUTE_STREAMS
    global _GRAPH_PRINT_STREAM

    current_compute_stream = torch_npu.npu.current_stream()

    with _GRAPH_PRINT_STREAM_LOCK:
        if _GRAPH_PRINT_STREAM is None:
            _GRAPH_PRINT_STREAM = torch_npu.npu.Stream()

        if current_compute_stream not in _SUBSCRIBED_COMPUTE_STREAMS:
            # Subscribe the compute stream to allow launching host functions.
            torch_npu.npu._subscribe_report(current_compute_stream)
            _SUBSCRIBED_COMPUTE_STREAMS.add(current_compute_stream)

    torch_npu.npu._launch_host_func(current_compute_stream, _print_callback_on_stream, args)


def _unregister_print_streams_on_exit():
    """Unsubscribe all compute streams used for printing at exit."""
    global _SUBSCRIBED_COMPUTE_STREAMS
    with _GRAPH_PRINT_STREAM_LOCK:
        for stream in _SUBSCRIBED_COMPUTE_STREAMS:
            torch_npu.npu._unsubscribe_report(stream)


atexit.register(_unregister_print_streams_on_exit)


def _should_trans_nz(weight: torch.Tensor) -> bool:
    # FP32 cannot use NZ.
    if weight.dtype == torch.float32:
        return False

    # 310P always converts to NZ.
    if is_310p():
        return True

    # NZ is disabled on non-310P.
    if not envs_ascend.VLLM_ASCEND_ENABLE_NZ:
        return False

    # BF16/FP16 convert only when enable_nz == 2.
    if weight.dtype in {torch.bfloat16, torch.float16}:
        return envs_ascend.VLLM_ASCEND_ENABLE_NZ == 2

    # Quantized or other supported dtypes convert by default.
    return True


# NZ conversion policy:
# - 310P: always convert supported weights to FRACTAL_NZ
# - non-310P: follow VLLM_ASCEND_ENABLE_NZ
# - FP32: never convert
def maybe_trans_nz(weight: torch.Tensor) -> torch.Tensor:
    if not _should_trans_nz(weight):
        return weight
    return torch_npu.npu_format_cast(weight, ACL_FORMAT_FRACTAL_NZ)


def _round_up(x: int, align: int):
    # round up x to align, for example, if align is 16, x will be rounded up to 16, 32, 48, etc.
    # input: 15, 16 -> output: 16
    # input: 17, 16 -> output: 32
    # input: 30, 16 -> output: 32
    # input: 33, 16 -> output: 48
    # ...
    return (x + align - 1) // align * align


def _custom_pad(x, pad_dims):
    # pad the input tensor to the shape of pad_dims
    # input: (13, 30), pad_dims: [0, 2, 0, 3]
    # output: (16, 32)
    return torch.nn.functional.pad(x, pad_dims)


def _custom_reshape(x, target_shape):
    # reshape the input tensor to the shape of target_shape
    # input: (16, 32), target_shape: [1, 16, 2, 16]
    # output: (1, 16, 2, 16)
    return x.reshape(target_shape)


def _custom_transpose(x, dim1, dim2):
    # transpose the input tensor
    # input: (1, 16, 2, 16), dim1: 1, dim2: 2
    # output: (1, 2, 16, 16)
    return x.transpose(dim1, dim2)


def nd_to_nz_2d(in_tensor: torch.Tensor) -> torch.Tensor:
    # in_tensor: (13, 30)
    aux_dims = [1, 0, 0, 16]
    # aux_dims[1]: 16
    aux_dims[1] = _round_up(in_tensor.size(0), 16)
    # aux_dims[2]: 2
    aux_dims[2] = _round_up(in_tensor.size(1), 16) // 16

    # after: aux_dims: [1, 16, 2, 16]

    pad_dims = [0, 0, 0, 0]
    # pad_dims[1]: 2
    pad_dims[1] = _round_up(in_tensor.size(1), 16) - in_tensor.size(1)
    # pad_dims[3]: 3
    pad_dims[3] = _round_up(in_tensor.size(0), 16) - in_tensor.size(0)

    # after: pad_dims: [0, 2, 0, 3]

    # return: (1, 2, 16, 16)
    return _custom_transpose(_custom_reshape(_custom_pad(in_tensor, pad_dims), aux_dims), 1, 2).contiguous()


def nd_to_nz_spec(mask_tensor: torch.Tensor) -> torch.Tensor:
    num_tokens = mask_tensor.shape[0]
    max_seq_len = mask_tensor.shape[1]

    tokens_pad = (num_tokens + 15) // 16 * 16
    max_seq_len_pad = (max_seq_len + 15) // 16 * 16

    mask_tensor_pad = torch.zeros((1, tokens_pad, max_seq_len_pad), dtype=mask_tensor.dtype, device=mask_tensor.device)
    mask_tensor_pad[0][:num_tokens, :max_seq_len] = mask_tensor
    mask = mask_tensor_pad.reshape((1, tokens_pad, max_seq_len_pad // 16, 16)).permute(0, 2, 1, 3)
    return mask


def aligned_16(tensor: torch.Tensor):
    """Aligned tensor for 310P"""

    # Get the size of the current 0th dimension
    n = tensor.size(0)

    # Calculate the aligned size
    n_aligned = ((n + 15) // 16) * 16

    # If already aligned, return the original tensor
    if n == n_aligned:
        return tensor

    # Create a new tensor with shape (n_aligned, H, W) and fill it with zeros
    new_tensor = torch.zeros(n_aligned, *tensor.shape[1:], dtype=tensor.dtype, device=tensor.device)

    # Copy the original tensor to the first N positions of the new tensor
    new_tensor[:n] = tensor

    return new_tensor


def enable_custom_op():
    """
    Enable lazy init for vllm_ascend_C to avoid early initialization of CANN's RTS component.
    Ensure that ASCEND_RT_VISIBLE_DEVICES can be dynamically modified before torch.npu.set_device().
    """
    from vllm.model_executor.layers.batch_invariant import vllm_is_batch_invariant

    global _CUSTOM_OP_ENABLED

    if _CUSTOM_OP_ENABLED is not None:
        return _CUSTOM_OP_ENABLED

    # There are some customed operators which aren't implemented
    # with batch invariant in vllm-ascend, we need to disable them.
    # FIXME(linfeng): Currently custom op compilation and execution are partially available
    # in ASCEND950 chip, we temporarily disable all custom ops. Please refer to
    # https://github.com/vllm-project/vllm-ascend/issues/7157 for latest update about custom op.
    if vllm_is_batch_invariant() or get_ascend_device_type() == AscendDeviceType.A5:
        _CUSTOM_OP_ENABLED = False
        return _CUSTOM_OP_ENABLED

    try:
        # isort: off
        # register custom ops into torch_library here
        import vllm_ascend.vllm_ascend_C  # type: ignore  # noqa: F401

        # register the meta implementation for custom kernel if necessary
        import vllm_ascend.meta_registration  # type: ignore  # noqa: F401

        # isort: on
        _CUSTOM_OP_ENABLED = True
    except ImportError:
        _CUSTOM_OP_ENABLED = False
        logger.warning("Warning: Failed to register custom ops, all custom ops will be disabled")
    return _CUSTOM_OP_ENABLED


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_ascend.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


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 prefetch_stream() -> torch.npu.Stream:
    global _PREFETCH_STREAM
    if _PREFETCH_STREAM is None:
        # when this function is called before any stream is set,
        # we return the default stream.
        _PREFETCH_STREAM = torch_npu.npu.Stream()
    return _PREFETCH_STREAM


def set_weight_prefetch_method(weight_prefetch_config: WeightPrefetchConfig):
    global _WEIGHT_PREFETCH_METHOD
    if _WEIGHT_PREFETCH_METHOD is None:
        from vllm_ascend.ops.weight_prefetch import WeightPrefetchMethod

        _WEIGHT_PREFETCH_METHOD = WeightPrefetchMethod(weight_prefetch_config)
    return _WEIGHT_PREFETCH_METHOD


def get_weight_prefetch_method():
    return _WEIGHT_PREFETCH_METHOD


def global_stream() -> torch.npu.Stream:
    global _GLOBAL_STREAM
    if _GLOBAL_STREAM is None:
        # when this function is called before any stream is set,
        # we return the default stream.
        _GLOBAL_STREAM = torch_npu.npu.Stream()
    return _GLOBAL_STREAM


def shared_experts_calculation_stream() -> torch.npu.Stream:
    global _SHARED_EXPERTS_CALCULATION_STREAM
    if _SHARED_EXPERTS_CALCULATION_STREAM is None:
        # when this function is called before any stream is set,
        # we return the default stream.
        _SHARED_EXPERTS_CALCULATION_STREAM = torch_npu.npu.Stream()
    return _SHARED_EXPERTS_CALCULATION_STREAM


def cp_chunkedprefill_comm_stream() -> torch.npu.Stream:
    global _CP_CHUNKEDPREFILL_COMM_STREAM
    if _CP_CHUNKEDPREFILL_COMM_STREAM is None:
        _CP_CHUNKEDPREFILL_COMM_STREAM = torch_npu.npu.Stream()
    return _CP_CHUNKEDPREFILL_COMM_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


@functools.cache
def vllm_version_is(target_vllm_version: str):
    if envs_ascend.VLLM_VERSION is not None:
        vllm_version = envs_ascend.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 get_max_hidden_layers(hf_config) -> int:
    cfg_dict = hf_config.to_dict()
    layer_counts = []

    def _rec_find(d):
        if isinstance(d, dict):
            for k, v in d.items():
                if k == "num_hidden_layers" and isinstance(v, int):
                    layer_counts.append(v)
                else:
                    _rec_find(v)

    _rec_find(cfg_dict)
    if not layer_counts:
        raise ValueError("Not found num_hidden_layers in model config.")
    return max(layer_counts)


# Update cudagraph capture sizes for vllm config
def update_cudagraph_capture_sizes(vllm_config: VllmConfig, cudagraph_capture_sizes: list[int]):
    valid_max_size = cudagraph_capture_sizes[-1] if cudagraph_capture_sizes else 0
    if (
        vllm_config.compilation_config.max_cudagraph_capture_size is not None
        and vllm_config.compilation_config.max_cudagraph_capture_size != valid_max_size
    ):
        if vllm_config.compilation_config.cudagraph_capture_sizes is not None:
            raise ValueError(
                "customized max_cudagraph_capture_size"
                f"(={vllm_config.compilation_config.max_cudagraph_capture_size}) "
                "should be consistent with the max value of "
                f"cudagraph_capture_sizes(={valid_max_size})"
            )
        logger.warning(
            "Truncating max_cudagraph_capture_size to %d",
            valid_max_size,
        )

    vllm_config.compilation_config.max_cudagraph_capture_size = valid_max_size

    if vllm_config.compilation_config.cudagraph_capture_sizes is not None and len(cudagraph_capture_sizes) < len(
        vllm_config.compilation_config.cudagraph_capture_sizes
    ):
        logger.warning(
            ("cudagraph_capture_sizes specified in compilation_config %s is overridden by config %s"),
            vllm_config.compilation_config.cudagraph_capture_sizes,
            cudagraph_capture_sizes,
        )
    vllm_config.compilation_config.cudagraph_capture_sizes = cudagraph_capture_sizes
    vllm_config.compilation_config.post_init_cudagraph_sizes()


def update_aclgraph_sizes(vllm_config: VllmConfig) -> None:
    """Update ACL graph capture sizes based on hardware limitations"""
    # NOTE: Currently, we can only capture 1800 graphs at most,
    # due to the limitation of ACL graph. This number is bounded by
    # the number of streams, which is 2048, we save 248 streams
    # as a buffer.
    # Maximum number of graphs that can be captured by ACL Graph
    # TODO: Find out whether we need to solve allreduce function
    MAX_CAPTURE_SIZE = 1800

    # enable pcp or dcp will add new communication and consume additional approximately less than 100 streams
    CP_ADDITIONAL_STREAM_NUM = 100

    # 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
    if not vllm_config.model_config:
        logger.warning(
            "Got empty model config. This typically occurs when an empty vllm_config is "
            "initialized (e.g., in unit tests), where config updates are intentionally skipped."
        )

        return
    hf_config = vllm_config.model_config.hf_text_config
    if hasattr(hf_config, "num_hidden_layers"):
        num_hidden_layers = hf_config.num_hidden_layers
    else:
        num_hidden_layers = get_max_hidden_layers(hf_config)
    parallel_config = vllm_config.parallel_config

    # Calculate maximum supported batch sizes considering model architecture
    resources_per_graph = num_hidden_layers + 1
    # For suffix decoding, use the suffix path when no draft_model_config is provided.
    if (spec := vllm_config.speculative_config) and (draft := spec.draft_model_config):
        # Use get_total_num_hidden_layers() to correctly handle MTP models,
        # which store layer count in num_nextn_predict_layers or
        # mtp_num_hidden_layers (for Qwen3.5) instead of num_hidden_layers.
        resources_per_graph += draft.get_total_num_hidden_layers() + 1

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

    if os.getenv("HCCL_OP_EXPANSION_MODE") == "AIV":
        # TODO: Find out whether we need to take into account the pp_size
        parallel_factor = (
            1
            + num_comm_groups
            + int(parallel_config.enable_expert_parallel)
            + int(vllm_config.additional_config.get("multistream_overlap_shared_expert", False))
        )
        if is_moe_model(vllm_config):
            parallel_factor += parallel_config.data_parallel_size > 1
        else:
            # When AIV mode is enabled, the allreduce operator of the dense
            # layer model will occupy additional streams, which are buffered here.
            MAX_CAPTURE_SIZE = MAX_CAPTURE_SIZE - parallel_factor * resources_per_graph

        # Calculate maximum supported batch sizes considering model architecture on the A2 Hardware Device
        # Assume the following case:
        # MAX_CAPTURE_SIZE = 1920, num_hidden_layers = 48, data_parallel_size is 1, tensor_parallel_size is 4,
        # According to the formula, max_num_batch_sizes = math.floor(1920 / (48 + 1) / 2) = 19
        max_num_batch_sizes = math.floor(MAX_CAPTURE_SIZE / resources_per_graph / parallel_factor)
        logger.info("Calculated maximum supported batch sizes for ACL graph: %s", max_num_batch_sizes)
    else:
        # enable pcp or dcp will add new communication and consume additional approximately less than 100 streams
        if parallel_config.prefill_context_parallel_size > 1:
            MAX_CAPTURE_SIZE = MAX_CAPTURE_SIZE - CP_ADDITIONAL_STREAM_NUM
        if parallel_config.decode_context_parallel_size > 1:
            MAX_CAPTURE_SIZE = MAX_CAPTURE_SIZE - CP_ADDITIONAL_STREAM_NUM

        # The above describes an empirical formula applicable to the A2 hardware.
        # Under this configuration, HCCL employs the FFTS+ method for execution unfolding,
        # which adds only 1 concurrent stream without consuming collective communication execution unfolding streams.
        # On A3 hardware, HCCL defaults to the AICPU method.
        # This approach may additionally allocate up to rank_size (max 16) - 1 streams per collective communication
        # domain on the device (worst case).
        # Using the default collective communication unfolding method on A3 will lead to a significant reduction
        # in the maximum supported sizes.
        # Therefore, the calculation formula has been modified as follows:
        # Assume the following case:
        # MAX_CAPTURE_SIZE = 1920, num_hidden_layers = 48, data_parallel_size is 1, tensor_parallel_size is 4,
        # According to the formula, max_num_batch_sizes = math.floor((1920 - 1 * 40) / (48 + 1) / (1 + 1 * 2)) = 12
        max_num_batch_sizes = math.floor(
            (MAX_CAPTURE_SIZE - num_comm_groups * 40) / resources_per_graph / (1 + num_comm_groups * 2)
        )
        logger.info("Calculated maximum supported batch sizes for ACL graph: %s", max_num_batch_sizes)
        logger.warning(
            "Currently, communication is performed using FFTS+ method, which reduces "
            "the number of available streams and, as a result, limits the range of runtime "
            "shapes that can be handled. To both improve communication performance and "
            "increase the number of supported shapes, set HCCL_OP_EXPANSION_MODE=AIV."
        )

    arch_name = vllm_config.model_config.architecture

    # 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]
        update_cudagraph_capture_sizes(vllm_config, sampled_sizes)
        logger.info(
            "Adjusted ACL graph batch sizes for %s model (layers: %d): %d → %d sizes",
            arch_name,
            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",
            arch_name,
            num_hidden_layers,
            len(original_sizes),
        )


# TODO(wxy): Move to ops module
def dispose_tensor(x: torch.Tensor):
    x.set_(torch.empty((0,), device=x.device, dtype=x.dtype))


def register_ascend_customop(vllm_config: VllmConfig | None = None):
    """Register Ascend CustomOP

    NOTE: if the register branch requires model type, please use `vllm.config.get_current_vllm_config`,
    and ensure this will execute after model config is initilazed.
    """
    global _ASCEND_CUSTOMOP_IS_REIGISTERED
    if _ASCEND_CUSTOMOP_IS_REIGISTERED:
        return
    from vllm.model_executor.custom_op import CustomOp

    from vllm_ascend.ops.activation import AscendQuickGELU, AscendSiluAndMul
    from vllm_ascend.ops.conv import AscendConv3dLayer
    from vllm_ascend.ops.fused_moe.fused_moe import AscendFusedMoE, AscendSharedFusedMoE
    from vllm_ascend.ops.layernorm import AscendGemmaRMSNorm, AscendRMSNorm, AscendRMSNormGated
    from vllm_ascend.ops.linear import (
        AscendColumnParallelLinear,
        AscendMergedColumnParallelLinear,
        AscendQKVParallelLinear,
        AscendReplicatedLinear,
        AscendRowParallelLinear,
    )
    from vllm_ascend.ops.mla import AscendMultiHeadLatentAttention
    from vllm_ascend.ops.mm_encoder_attention import AscendMMEncoderAttention
    from vllm_ascend.ops.rel_pos_attention import AscendRelPosAttention
    from vllm_ascend.ops.rotary_embedding import (
        AscendApplyRotaryEmb,
        AscendDeepseekScalingRotaryEmbedding,
        AscendMRotaryEmbedding,
        AscendRotaryEmbedding,
        AscendYaRNRotaryEmbedding,
    )
    from vllm_ascend.ops.vocab_parallel_embedding import (
        AscendLogitsProcessor,
        AscendParallelLMHead,
        AscendVocabParallelEmbedding,
    )

    global REGISTERED_ASCEND_OPS
    REGISTERED_ASCEND_OPS = {
        "QuickGELU": AscendQuickGELU,
        "SiluAndMul": AscendSiluAndMul,
        "RotaryEmbedding": AscendRotaryEmbedding,
        "MRotaryEmbedding": AscendMRotaryEmbedding,
        "ColumnParallelLinear": AscendColumnParallelLinear,
        "RowParallelLinear": AscendRowParallelLinear,
        "YaRNScalingRotaryEmbedding": AscendYaRNRotaryEmbedding,
        "MergedColumnParallelLinear": AscendMergedColumnParallelLinear,
        "QKVParallelLinear": AscendQKVParallelLinear,
        "ReplicatedLinear": AscendReplicatedLinear,
        "DeepseekScalingRotaryEmbedding": AscendDeepseekScalingRotaryEmbedding,
        "VocabParallelEmbedding": AscendVocabParallelEmbedding,
        "ParallelLMHead": AscendParallelLMHead,
        "LogitsProcessor": AscendLogitsProcessor,
        "RMSNorm": AscendRMSNorm,
        "GemmaRMSNorm": AscendGemmaRMSNorm,
        "FusedMoE": AscendFusedMoE,
        "SharedFusedMoE": AscendSharedFusedMoE,
        "MultiHeadLatentAttentionWrapper": AscendMultiHeadLatentAttention,
        "MMEncoderAttention": AscendMMEncoderAttention,
        "ApplyRotaryEmb": AscendApplyRotaryEmb,
        "RMSNormGated": AscendRMSNormGated,
        "Conv3dLayer": AscendConv3dLayer,
        "RelPosAttention": AscendRelPosAttention,
    }

    # 310P: override selected ops with 310P implementations (keep minimal changes outside _310p)
    if is_310p():
        from vllm_ascend._310p.fused_moe.fused_moe import AscendFusedMoE310, AscendSharedFusedMoE310
        from vllm_ascend._310p.ops.activation import AscendSiluAndMul310
        from vllm_ascend._310p.ops.layernorm import (
            AscendGemmaRMSNorm310,
            AscendRMSNorm310,
            AscendRMSNormGated310,
        )
        from vllm_ascend._310p.ops.mm_encoder_attention import AscendMMEncoderAttention310
        from vllm_ascend._310p.ops.rotary_embedding import AscendRotaryEmbedding310
        from vllm_ascend._310p.ops.vocab_parallel_embedding import (
            AscendParallelLMHead310,
            AscendVocabParallelEmbedding310,
        )

        REGISTERED_ASCEND_OPS.update(
            {
                "SiluAndMul": AscendSiluAndMul310,
                "RotaryEmbedding": AscendRotaryEmbedding310,
                "RMSNorm": AscendRMSNorm310,
                "GemmaRMSNorm": AscendGemmaRMSNorm310,
                "RMSNormGated": AscendRMSNormGated310,
                "FusedMoE": AscendFusedMoE310,
                "SharedFusedMoE": AscendSharedFusedMoE310,
                "ParallelLMHead": AscendParallelLMHead310,
                "VocabParallelEmbedding": AscendVocabParallelEmbedding310,
                "MMEncoderAttention": AscendMMEncoderAttention310,
            }
        )

        REGISTERED_ASCEND_OPS.pop("MRotaryEmbedding", None)

    for name, op_cls in REGISTERED_ASCEND_OPS.items():
        CustomOp.register_oot(_decorated_op_cls=op_cls, name=name)

    # NOTE: Keep this at last to ensure all custom actions are registered
    _ASCEND_CUSTOMOP_IS_REIGISTERED = True


class AscendDeviceType(Enum):
    A2 = 0
    A3 = 1
    _310P = 2
    A5 = 3


_ascend_device_type = None


def _init_ascend_device_type():
    global _ascend_device_type
    from vllm_ascend import _build_info  # type: ignore

    _ascend_device_type = AscendDeviceType[_build_info.__device_type__]


def check_ascend_device_type():
    global _ascend_device_type
    if _ascend_device_type is None:
        _init_ascend_device_type()

    soc_version = torch_npu.npu.get_soc_version()
    if 220 <= soc_version <= 225:
        cur_device_type = AscendDeviceType.A2
    elif 250 <= soc_version <= 255:
        cur_device_type = AscendDeviceType.A3
    elif 200 <= soc_version <= 205:
        cur_device_type = AscendDeviceType._310P
    elif soc_version == 260:
        cur_device_type = AscendDeviceType.A5
    else:
        raise RuntimeError(f"Can not support soc_version: {soc_version}.")

    assert _ascend_device_type == cur_device_type, (
        f"Current device type: {cur_device_type} does not match the installed version's device type: "
        f"{_ascend_device_type}, please check your installation package."
    )


def get_ascend_device_type():
    global _ascend_device_type
    if _ascend_device_type is None:
        _init_ascend_device_type()
    return _ascend_device_type


def lmhead_tp_enable() -> bool:
    return get_ascend_config().finegrained_tp_config.lmhead_tensor_parallel_size > 0


def embedding_tp_enable() -> bool:
    return get_ascend_config().finegrained_tp_config.embedding_tensor_parallel_size > 0


def oproj_tp_enable() -> bool:
    return get_ascend_config().finegrained_tp_config.oproj_tensor_parallel_size > 0


def mlp_tp_enable() -> bool:
    return get_ascend_config().finegrained_tp_config.mlp_tensor_parallel_size > 0


def matmul_allreduce_enable() -> bool:
    return envs_ascend.VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE


def enable_sp_by_pass():
    return get_ascend_config().enable_sp_by_pass


def enable_sp(vllm_config=None, enable_shared_expert_dp: bool = False) -> bool:
    global _ENABLE_SP
    if _ENABLE_SP is None:
        if vllm_config is None:
            from vllm.config import get_current_vllm_config

            vllm_config = get_current_vllm_config()
        _ENABLE_SP = (
            envs_ascend.VLLM_ASCEND_ENABLE_FLASHCOMM1
            # Flash comm 1 should be enabled by env VLLM_ASCEND_ENABLE_FLASHCOMM1
            # We retain the env VLLM_ASCEND_ENABLE_FLASHCOMM here for backward compatibility.
            or bool(int(os.getenv("VLLM_ASCEND_ENABLE_FLASHCOMM", "0")))
        )

        if not _ENABLE_SP and enable_shared_expert_dp:
            _ENABLE_SP = True
            logger.info("shared_expert_dp requires enable_sp = True. has set enable_sp to True")

    return _ENABLE_SP


# TODO remove it after vllm has this func
def shared_expert_dp_enabled() -> bool:
    return get_ascend_config().enable_shared_expert_dp or enable_sp() or enable_sp_by_pass()


def prefill_context_parallel_enable() -> bool:
    return envs_ascend.VLLM_ASCEND_ENABLE_CONTEXT_PARALLEL


def is_moe_model(vllm_config: VllmConfig):
    """Checks if the model is a MoE model by config"""
    global _IS_MOE_MODEL
    if _IS_MOE_MODEL is None:
        model_configs = vllm_config.model_config.hf_text_config.to_dict()
        _IS_MOE_MODEL = _is_contain_expert(model_configs)
    return _IS_MOE_MODEL


def is_drafter_moe_model(vllm_config: VllmConfig):
    """Checks if the drafter model is a MoE model by config"""
    global _IS_DRAFTER_MOE_MODEL
    if _IS_DRAFTER_MOE_MODEL is None:
        model_configs = vllm_config.speculative_config.draft_model_config.hf_text_config.to_dict()
        _IS_DRAFTER_MOE_MODEL = _is_contain_expert(model_configs)
    return _IS_DRAFTER_MOE_MODEL


def speculative_enable_dispatch_gmm_combine_decode(vllm_config: VllmConfig) -> bool:
    """When draft contains MOE Arch and non-w8a8, disable dispatch_gmm_combine_decode."""
    if vllm_config.speculative_config is None:
        return True
    speculative_method = getattr(vllm_config.speculative_config, "method", None)
    if speculative_method in [None, "ngram", "suffix"]:
        return True
    if speculative_method in ["eagle", "eagle3"]:
        if is_drafter_moe_model(vllm_config):
            draft_model_config = vllm_config.speculative_config.draft_model_config
            hf_text_config = draft_model_config.hf_text_config
            quant_type = getattr(hf_text_config, "moe_quantize", None)
            if quant_type is None:
                quant_type = getattr(hf_text_config, "quantize", None)
            return quant_type == "w8a8_dynamic"
        else:
            return True
    if speculative_method == "mtp":
        mtp_quant_type = getattr(vllm_config.model_config.hf_text_config, "mtp_quantize", None)
        return mtp_quant_type == "w8a8_dynamic"
    return False


def _is_contain_expert(config: Any):
    if isinstance(config, dict):
        for k, v in config.items():
            if "expert" in str(k):
                return True
            if _is_contain_expert(v):
                return True
    return False


def is_vl_model(vllm_config: VllmConfig = None):
    """Checks if the model is a VL model by config.

    Uses the same criterion as vllm itself (model_config.py): a model is
    multimodal when its top-level hf_config differs from its hf_text_config
    (i.e. there is a separate vision sub-config).  The legacy key-name checks
    are kept as fallbacks for configs that override get_text_config() to return
    self (rare but possible).
    """
    global _IS_VL_MODEL
    if vllm_config is None:
        from vllm.config import get_current_vllm_config_or_none

        vllm_config = get_current_vllm_config_or_none()
    if _IS_VL_MODEL is None and vllm_config and vllm_config.model_config:
        model_config = vllm_config.model_config
        # Primary: vllm's own VL detection — hf_config is the top-level
        # (multimodal) config; hf_text_config is the language-model sub-config.
        # They are the same object for pure-text models.
        if model_config.hf_config is not model_config.hf_text_config:
            _IS_VL_MODEL = True
        else:
            # Fallback: check well-known config keys
            hf_config = model_config.hf_config.to_dict()
            if "thinker_config" in hf_config or "vision_config" in hf_config:
                _IS_VL_MODEL = True
            else:
                _IS_VL_MODEL = False
    return _IS_VL_MODEL


def has_rope(vllm_config: VllmConfig):
    """Checks if the model uses rope."""
    global _HAS_ROPE
    if _HAS_ROPE is None and vllm_config and vllm_config.model_config:
        hf_config = vllm_config.model_config.hf_text_config.to_dict()
        _HAS_ROPE = "rope_parameters" in hf_config
    return _HAS_ROPE


def weak_ref_tensor(tensor: Any) -> Any:
    """
    Create a weak reference to a tensor.
    The new tensor will share the same data as the original tensor,
    but will not keep the original tensor alive.
    """
    if isinstance(tensor, torch.Tensor):
        return torch_npu._C._weak_ref_tensor(tensor)
    else:
        return tensor


def weak_ref_tensors(
    tensors: torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor],
) -> torch.Tensor | list[Any] | tuple[Any] | Any:
    """
    Convenience function to create weak references to tensors,
    for single tensor, list of tensors or tuple of tensors.

    This function should be used in the following scenario:
    When a tensor is created during graph capture, and it's held by a method
    that's not part of the graph, we don't really need to store it, but we
    **do need** its buffer pointer. If we don't handle this, it cannot
    be garbage collected, leading to a memory leak. To avoid this,
    we should create a weak reference to the tensor.
    """
    if isinstance(tensors, torch.Tensor):
        return weak_ref_tensor(tensors)
    if isinstance(tensors, list):
        return [weak_ref_tensor(t) for t in tensors]
    if isinstance(tensors, tuple):
        return tuple(weak_ref_tensor(t) for t in tensors)
    # For IntermediateTensors used in pipeline parallelism
    if isinstance(tensors, IntermediateTensors):
        ret = IntermediateTensors({key: weak_ref_tensor(val) for key, val in tensors.tensors.items()})
        return ret
    raise ValueError("Invalid type for tensors")


def npu_stream_switch(target_stream: torch.npu.Stream, *, enabled: bool = True):
    """
    Switch to the target stream if enabled is True.
    Otherwise, do nothing.
    """
    if not enabled:
        return nullcontext()
    assert target_stream is not None
    return torch.npu.stream(target_stream)


def create_hccl_pg_options(group_name: str):
    options = torch_npu._C._distributed_c10d.ProcessGroupHCCL.Options()
    hccl_config = get_hccl_config_for_pg_options(group_name)
    if hccl_config is not None:
        options.hccl_config = hccl_config
    return options


def get_hccl_config_for_pg_options(group_name: str) -> dict | None:
    """
    Get HCCL process group options for the given communication group name.

    Args:
        group_name: Name of the communication group

    Returns:
        HCCL pg_options or None for mc2 group
    """
    # FIXME: Current mc2 operators only perform communication space partitioning
    # based on HCCL_BUFFSIZE configuration. Using pg_options with mc2 group would
    # result in memory misalignment problems.
    if group_name and "mc2" in group_name:
        return None
    hccl_config_map = {
        "dp": {"hccl_buffer_size": calculate_dp_buffer_size()},
        "dynamic_eplb": {"hccl_buffer_size": _DYNAMIC_EPLB_BUFFER_SIZE},
    }
    return hccl_config_map.get(group_name, get_default_buffer_config())


def get_default_buffer_config() -> dict:
    return {"hccl_buffer_size": _DEFAULT_BUFFER_SIZE}


def calculate_dp_buffer_size() -> int:
    """
    formula of dp buffer size:
    dp_size + 1 (flags: with_prefill)
    """
    from vllm.config import get_current_vllm_config

    vllm_config = get_current_vllm_config()
    dp_size = vllm_config.parallel_config.data_parallel_size
    int32_size = torch.iinfo(torch.int32).bits // 8
    dp_buffer_size = math.ceil((dp_size + 1) * int32_size / (1024 * 1024))
    return max(dp_buffer_size, _MIN_DP_BUFFER_SIZE)


# Currently, when in A2, setting the environment variables HCCL_INTRA_PCIE_ENABLE=1
# and HCCL_INTRA_ROCE_ENABLE=0 can reduce cross-machine communication traffic and
# significantly improve communication performance of MC2 ops dispatch/combine.
def is_hierarchical_communication_enabled():
    return (
        os.getenv("HCCL_INTRA_ROCE_ENABLE", "") == "0" and os.getenv("HCCL_INTRA_PCIE_ENABLE", "") == "1"
    ) or get_ascend_config().enable_mc2_hierarchy_comm


def has_layer_idx(model_instance: torch.nn.Module) -> bool:
    if model_instance is None:
        return False

    global _HAS_LAYER_IDX
    if _HAS_LAYER_IDX is None:
        _HAS_LAYER_IDX = hasattr(model_instance, "model") and hasattr(model_instance.model, "start_layer")
    return _HAS_LAYER_IDX


def flashcomm2_enable() -> bool:
    return envs_ascend.VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE > 0


def o_shard_enable() -> bool:
    layer_sharding = get_ascend_config().layer_sharding
    if layer_sharding is None:
        return False
    return "o_proj" in layer_sharding


def get_flashcomm2_config_and_validate(ascend_config, vllm_config):
    flashcomm2_oproj_tp_size = envs_ascend.VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE
    global_tp_size = vllm_config.parallel_config.tensor_parallel_size

    if not flashcomm2_enable():
        return 0

    logger.info(f"Enable FLASHCOMM2 with flashcomm2_oproj_tensor_parallel_size = {flashcomm2_oproj_tp_size}")

    layer_sharding = ascend_config.layer_sharding or []
    if layer_sharding:
        if layer_sharding == ["o_proj"]:
            logger.info_once("Enable FLASHCOMM2 with o_proj layer sharding for reduced memory consumption.")
        else:
            raise ValueError(
                "FLASHCOMM2 only supports 'o_proj' as the sole layer sharding configuration! "
                f"Found invalid layer_sharding: {layer_sharding}"
            )
    if not envs_ascend.VLLM_ASCEND_ENABLE_FLASHCOMM1:
        logger.warning_once(
            "It is recommended to enable FLASHCOMM1 simultaneously when starting FLASHCOMM2 for optimal performance."
        )
    if ascend_config.finegrained_tp_config.oproj_tensor_parallel_size > 0:
        raise AssertionError(
            "flashcomm2_oproj_tensor_parallel_size cannot be enabled simultaneously with oproj_tensor_parallel_size"
        )
    if global_tp_size <= flashcomm2_oproj_tp_size:
        raise AssertionError(
            f"flashcomm2_oproj_tensor_parallel_size ({flashcomm2_oproj_tp_size}) cannot exceed "
            f"global tensor parallel size ({global_tp_size})"
        )
    if global_tp_size % flashcomm2_oproj_tp_size != 0:
        raise AssertionError(
            f"Global tensor parallel size ({global_tp_size}) must be divisible by "
            f"flashcomm2_oproj_tensor_parallel_size ({flashcomm2_oproj_tp_size})"
        )
    if vllm_config.kv_transfer_config is None:
        logger.warning_once(
            "It is recommended to enable FLASHCOMM2 in P-scenario deployments, enable it in hybrid deployment "
            "may lead to decode performance degradation."
        )
    if vllm_config.kv_transfer_config is not None and vllm_config.kv_transfer_config.is_kv_consumer:
        raise AssertionError(
            "FLASHCOMM2 primarily targets P-scenario deployments, with additional support "
            "for hybrid deployment scenarios. It is not applicable in D-scenario environments."
        )

    return flashcomm2_oproj_tp_size


def get_flashcomm2_reorgnized_batch_ids(global_tp_size) -> list[list[int]]:
    # Reorganize batch_ids so that, after the all2all and reduce-scatter operation,
    # each batch_id corresponds to the rank_id within the DP domain.
    # For example, when DP = [0, 1, 2, ..., 15] and flashcomm2_oproj_tensor_parallel_size = 2,
    # the reorganized batch_ids will be [[batch0, batch8], [batch1, batch9], ..., [batch7, batch15]].
    flashcomm2_otp_size = get_ascend_config().flashcomm2_oproj_tensor_parallel_size
    num_oproj_tensor_parallel_groups: int = global_tp_size // flashcomm2_otp_size

    reorgnized_batch_ids = []
    for i in range(num_oproj_tensor_parallel_groups):
        ranks = []
        for j in range(flashcomm2_otp_size):
            rank_idx = i + j * num_oproj_tensor_parallel_groups
            ranks.append(rank_idx)
        reorgnized_batch_ids.append(ranks)

    return reorgnized_batch_ids


def refresh_block_size(vllm_config):
    """
    Refresh the block size in cache config.
    """
    cache_config = vllm_config.cache_config
    scheduler_config = vllm_config.scheduler_config
    model_config = vllm_config.model_config

    if not cache_config:
        return

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

    if not scheduler_config or not model_config:
        return

    if model_config.is_hybrid:
        # Hybrid attention+mamba models rely on the model-specific sizing
        # logic rather than the generic platform default.
        return

    if cache_config.block_size != 128:
        if cache_config.enable_prefix_caching or scheduler_config.enable_chunked_prefill:
            logger.info("Block size is set to 128 if prefix cache or chunked prefill is enabled.")
            cache_config.block_size = 128


def dispose_layer(layer: Any):
    for attr_name in dir(layer):
        attr_value = getattr(layer, attr_name)
        if isinstance(attr_value, torch.Tensor):
            dispose_tensor(attr_value)


def check_kv_extra_config(vllm_config):
    def _check(name: str, config: dict):
        tp_key = "tp_size"
        dp_key = "dp_size"
        if tp_key in config:
            config_tp = config[tp_key]
            vllm_tp = vllm_config.parallel_config.tensor_parallel_size
            if config_tp != vllm_tp:
                raise ValueError(
                    f"KV transfer '{name}' config has a conflicting tensor parallel size. "
                    f"Expected {vllm_tp}, but got {config_tp}."
                )
        if dp_key in config:
            config_dp = config[dp_key]
            vllm_dp = vllm_config.parallel_config.data_parallel_size
            if config_dp != vllm_dp:
                raise ValueError(
                    f"KV transfer '{name}' config has a conflicting data parallel size. "
                    f"Expected {vllm_dp}, but got {config_dp}."
                )

    if vllm_config.kv_transfer_config.is_kv_producer:
        _check("prefill", vllm_config.kv_transfer_config.get_from_extra_config("prefill", {}))
    if vllm_config.kv_transfer_config.is_kv_consumer:
        _check("decode", vllm_config.kv_transfer_config.get_from_extra_config("decode", {}))


def singleton(cls):
    instances = {}

    def get_instance(*args, **kwargs):
        if cls not in instances:
            instances[cls] = cls(*args, **kwargs)
        return instances[cls]

    return get_instance


# TODO: Temporarily use enable_sp to enable the dsa_cp feature of ds32.
# and subsequent updates will introduce new interfaces. --zzhx1
@lru_cache(maxsize=1)
def enable_dsa_cp() -> bool:
    from vllm.config import get_current_vllm_config

    vllm_config = get_current_vllm_config()
    is_ds_v32 = hasattr(vllm_config.model_config, "hf_text_config") and hasattr(
        vllm_config.model_config.hf_text_config, "index_topk"
    )
    return bool(is_ds_v32 and enable_sp())


@lru_cache(maxsize=1)
def enable_dsa_cp_with_layer_shard() -> bool:
    if not enable_dsa_cp():
        return False
    from vllm.config import get_current_vllm_config

    vllm_config = get_current_vllm_config()
    # because the broadcast in layer sharding needs to be overlapped with a heavy compute stream to be
    # effectively hidden, it is enabled only during the prefill stage.
    is_prefill_instance = vllm_config.kv_transfer_config is not None and vllm_config.kv_transfer_config.is_kv_producer
    return is_prefill_instance


@lru_cache(maxsize=1)
def enable_dsa_cp_with_o_proj_tp() -> bool:
    if not enable_dsa_cp():
        return False
    from vllm.config import get_current_vllm_config

    vllm_config = get_current_vllm_config()
    # if is PD mix stage, using original TP o_proj weight, and also need to
    # full gather for o_proj weight for prefill stage.
    return vllm_config.kv_transfer_config is None


def check_gdn_layer(vllm_config) -> bool:
    """
    gdn layer is marked with `linear_attention`.
    So, if `linear_attention` is detected, we think the model has gdn-attention.
    """
    if not hasattr(vllm_config, "model_config"):
        return False

    model_config = vllm_config.model_config
    if not hasattr(model_config, "hf_config"):
        return False

    hf_config = model_config.hf_config

    # Use `or []` to prevent errors when layer_types is None
    layer_types = getattr(hf_config, "layer_types", None) or []
    if "linear_attention" in layer_types:
        return True

    text_config = getattr(hf_config, "text_config", None)
    if text_config:
        text_layer_types = getattr(text_config, "layer_types", None) or []
        if "linear_attention" in text_layer_types:
            return True

    return False


def get_rope_dim(vllm_config):
    model_config = vllm_config.model_config

    if model_config.use_mla:
        rope_dim = model_config.hf_text_config.qk_rope_head_dim
    else:
        rope_dim = model_config.get_head_size()
        # For models using partial rope like Qwen3-Next.
        if hasattr(model_config.hf_text_config, "partial_rotary_factor"):
            rope_dim = int(rope_dim * model_config.hf_text_config.partial_rotary_factor)
        elif hasattr(model_config.hf_text_config, "rotary_dim"):
            rope_dim = int(model_config.hf_text_config.rotary_dim)

    return rope_dim


def calc_split_factor(num_list: list[int]):
    total = sum(num_list)
    split_factor_list = []
    for num in num_list:
        split_factor_list.append(total / num)
    return split_factor_list


# NOTE: The last two dimensions of ND are transferred to NZ
def trans_nd_to_nz(cache_tensor: torch.Tensor):
    assert len(cache_tensor.shape) >= 2
    batch = cache_tensor.shape[:-2]
    a, b = cache_tensor.shape[-2], cache_tensor.shape[-1]

    dtype = cache_tensor.dtype
    if dtype == torch.int8:
        a0, b0 = 16, 32
    else:
        a0, b0 = 16, 16

    nz_shape = list(batch) + [math.ceil(b / b0), math.ceil(a / a0), a0, b0]

    # Generate the axis order for the transpose operation.
    offset = len(cache_tensor.shape) - 2
    base = [2, 0, 1, 3]
    array_trans = [i for i in range(offset)] + [i + offset for i in base]
    # Perform shape transformation and transpose operation.
    *_, n1, m1, m0, n0 = nz_shape
    cache_tensor = cache_tensor.reshape(nz_shape[:-4] + [m1, m0, n1, n0])
    cache_tensor = cache_tensor.permute(*array_trans)
    return cache_tensor


def parse_layer_idx(prefix: str) -> int | None:
    """Extract the layer index from a module prefix string like 'model.layers.0.self_attn'."""
    match = re.search(r"layers\.(\d+)", prefix)
    return int(match.group(1)) if match else None