ethosu/vela/tensor.py

# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
# 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
#
# 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.
# Description:
# Internal representation of a Neural Network Tensor.
import copy
import enum
import uuid
from collections import defaultdict
from enum import auto
from functools import lru_cache
from functools import total_ordering
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
from uuid import UUID

import numpy as np

from . import numeric_util
from .data_type import BaseType
from .data_type import DataType
from .errors import UnsupportedFeatureError
from .errors import VelaError
from .ethos_u55_regs.ethos_u55_regs import resampling_mode
from .numeric_util import full_shape
from .operation import Op
from .operation import Operation
from .shape4d import Shape4D

Shape = List


class MemType(enum.IntFlag):
    Unknown = 0
    Permanent_NPU = 1
    Permanent_CPU = 2
    Scratch = 3
    Scratch_fast = 4
    Size = Scratch_fast + 1

    def display_name(self) -> str:
        return ("Unknown", "Permanent_NPU", "Permanent_CPU", "Scratch", "Scratch_fast", "Size")[self.value]

    def identifier_name(self) -> str:
        return ("unknown", "permanent_npu", "permanent_cpu", "scratch", "scratch_fast", "size")[self.value]

    @staticmethod
    def all():
        return (MemType.Permanent_NPU, MemType.Permanent_CPU, MemType.Scratch, MemType.Scratch_fast)

    def __str__(self):
        return self.name


class BandwidthDirection(enum.IntEnum):
    Read = 0
    Write = auto()
    Size = auto()

    def display_name(self):
        return self.name

    def identifier_name(self):
        return self.name.lower()

    @staticmethod
    def all():
        return (BandwidthDirection.Read, BandwidthDirection.Write)


class MemArea(enum.IntFlag):
    Unknown = 0
    Sram = 1
    Dram = 2
    OnChipFlash = 3
    OffChipFlash = 4
    Shram = 5  # for LUT
    Size = Shram + 1

    def display_name(self) -> str:
        return ("Unknown", "SRAM", "DRAM", "On-chip Flash", "Off-chip Flash", "SHRAM", "Size")[self.value]

    def identifier_name(self) -> str:
        return ("unknown", "sram", "dram", "on_chip_flash", "off_chip_flash", "shram", "size")[self.value]

    @staticmethod
    def all():
        return (MemArea.Sram, MemArea.Dram, MemArea.OnChipFlash, MemArea.OffChipFlash, MemArea.Shram)

    def __str__(self):
        return self.name


class TensorPurpose(enum.IntFlag):
    Unknown = 0
    Weights = 1
    FeatureMap = 2
    Scratch = 3
    ScratchFast = 4
    LUT = 5
    FSBias = 6
    Size = 7

    def display_name(self) -> str:
        return ("Unknown", "Weights", "FeatureMap", "Scratch", "ScratchFast", "LUT", "FastStorageBias", "Size")[
            self.value
        ]

    def identifier_name(self) -> str:
        return ("unknown", "weights", "feature_map", "scratch", "scratch_fast", "lut", "fast_storage_bias", "size")[
            self.value
        ]

    @staticmethod
    def all():
        return (TensorPurpose.Weights, TensorPurpose.FeatureMap, TensorPurpose.FSBias)


class TensorSubPurpose(enum.Enum):
    Standard = 0
    DoubleBuffer = 1
    RollingBufferX = 2
    RollingBufferY = 3
    RollingBufferXY = 4

    def display_name(self) -> str:
        return ("Standard", "Double Buffer", "Rolling Buffer X", "Rolling Buffer Y", "Rolling Buffer XY")[self.value]

    def identifier_name(self) -> str:
        return ("standard", "double_buffer", "rolling_buffer_x", "rolling_buffer_y", "rolling_buffer_xy")[self.value]

    @staticmethod
    def all():
        return (
            TensorSubPurpose.Standard,
            TensorSubPurpose.DoubleBuffer,
            TensorSubPurpose.RollingBufferX,
            TensorSubPurpose.RollingBufferY,
            TensorSubPurpose.RollingBufferXY,
        )


class TensorFormat(enum.Flag):
    Unknown = 0
    WeightsCompressed = 1
    NHWC = 2
    NHCWB16 = 3

    def __str__(self):
        return self.name


class TensorBlockTraversal(enum.Enum):
    Default = 0
    DepthWise = 1
    DepthFirst = 2
    PartKernelFirst = 3


def shape_num_elements(shp: Shape) -> Optional[int]:
    elems = 1
    if shp is None:
        return None
    for d in shp:
        if d is None:
            return None
        elems *= d
    return elems


def shape_fully_defined(shp: Shape) -> bool:
    if shp is None:
        return False
    for d in shp:
        if d is None:
            return False
    return True


def shape_round_to_quantum(shp: Shape, quantum: Tuple) -> Shape:
    new_shp = list(shp)

    # Traverse backwards using length of shape since there may be more rounding quantums than shape elements
    for i in range(-1, -len(shp) - 1, -1):
        if new_shp[i] is not None:
            new_shp[i] = numeric_util.round_up(new_shp[i], quantum[i])
    return new_shp


@lru_cache(maxsize=None)
def create_equivalence_id(key) -> UUID:
    # Generates equivalence_id based on the given key.
    return uuid.uuid4()


class QuantizationParameters:
    __slots__ = "min", "max", "num_bits", "narrow_range", "scale_f32", "zero_point", "quant_min", "quant_max"

    def __init__(
        self,
        min: Union[float, np.ndarray, None] = None,
        max: Union[float, np.ndarray, None] = None,
        num_bits=None,
        narrow_range=None,
    ):
        self.min = min
        self.max = max

        self.num_bits = num_bits
        self.narrow_range = narrow_range

        self.scale_f32: Union[float, np.ndarray, None] = None
        self.zero_point: Union[int, np.ndarray, None] = None
        self.quant_min: Optional[float] = None
        self.quant_max: Optional[float] = None

    def __str__(self):
        return "<nng.QuantizationParameters min=%s max=%s, num_bits=%s, scale=%s, zero_point=%s>" % (
            self.min,
            self.max,
            self.num_bits,
            self.scale_f32,
            self.zero_point,
        )

    __repr__ = __str__

    def clone(self) -> "QuantizationParameters":
        res = QuantizationParameters()
        res.min = self.min
        res.max = self.max

        res.num_bits = self.num_bits
        res.narrow_range = self.narrow_range

        res.scale_f32 = self.scale_f32
        res.zero_point = self.zero_point
        res.quant_min = self.quant_min
        res.quant_max = self.quant_max
        return res

    def dequantize(self, values):
        if self.zero_point.size == 1 and self.scale_f32.size == 1:
            # same scale is used for all values
            res = (values.astype(np.float64) - self.zero_point) * self.scale_f32
        else:
            # a different scale is used for different sets of values
            values_as_float = values.astype(np.float64)

            # this is not compatible with the format of depthwise weights,
            # where input is at index 3 (Output, Kh, Kw, Input)
            # return the quantized values
            return np.ndarray((values_as_float.shape))

        return res

    def is_scaling_equal(self, other: Optional["QuantizationParameters"]) -> bool:
        # quantisation parameter scaling is not equal if 'other' is None because
        # it implies that the tensor it belongs to is not quantised. otherwise,
        # it depends upon whether the scale and zero point are equal

        if not isinstance(other, QuantizationParameters):
            return False

        return self.scale_f32 == other.scale_f32 and self.zero_point == other.zero_point

    def is_valid(self) -> bool:
        # quantisation parameters are consider valid if they have a scale and zero point

        return None not in (self.scale_f32, self.zero_point)

    def is_per_axis(self) -> bool:
        """Returns True if either the scale, zero point, minimum or maximum values are arrays"""
        for attr in ("scale_f32", "zero_point", "min", "max"):
            if isinstance(getattr(self, attr), np.ndarray):
                return True
        return False


def create_const_tensor(
    name: str,
    shape: Shape,
    dtype: DataType,
    values: np.ndarray,
    value_dtype: np.dtype = None,
    purpose: TensorPurpose = TensorPurpose.Unknown,
    quantization: QuantizationParameters = None,
):
    # Tensor
    const_tensor = Tensor(shape, dtype, name + "_0")
    const_tensor.purpose = purpose
    const_tensor.quantization = quantization
    const_tensor.values = np.array(values, dtype=value_dtype)
    const_tensor.quant_values = np.frombuffer(const_tensor.values.tobytes(), dtype=np.uint8)
    # Operator
    const_op = Operation(Op.Const, name)
    const_op.set_output_tensor(const_tensor)
    const_op.set_ifm_ofm_shapes()
    return const_tensor


def create_reshape_tensor(tens, shape, ifm_reshape=True):
    if shape == tens.shape:
        return tens
    # Tensors
    name = tens.name + "_reshape"
    reshape_ifm = tens
    reshape_ofm = tens.clone("_reshaped")
    reshape_ofm.set_all_shapes(shape)
    if not ifm_reshape:
        reshape_ifm, reshape_ofm = reshape_ofm, reshape_ifm
    # Operator
    reshape_op = Operation(Op.Reshape, name)
    reshape_op.attrs["new_shape"] = shape
    reshape_op.add_input_tensor(reshape_ifm)
    reshape_op.add_input_tensor(create_const_tensor(name + "_shape", [1], DataType.int32, shape))
    reshape_op.set_output_tensor(reshape_ofm)
    reshape_op.set_ifm_ofm_shapes()
    return reshape_ofm if ifm_reshape else reshape_ifm


# class that keeps track of all tensor addresses in the different memory types
class TensorAddressMap:
    address_map: Dict = defaultdict(dict)  # dict (tens.equivalence_id -> dict (mem_type -> address))

    @classmethod
    def get_address_for_tens(cls, tens_id: UUID, mem_type: MemType) -> int:
        return cls.address_map[tens_id].get(mem_type)

    @classmethod
    def set_address_for_tens(cls, tens_id: UUID, mem_type: MemType, address: int):
        # Check previous address if there is one
        previous_address = cls.address_map[tens_id].get(mem_type)
        if address is not None and previous_address is not None:
            assert previous_address == address, "Two different addresses cannot be assigned to the same tensor."

        # Set tensor's address for memory type
        cls.address_map[tens_id][mem_type] = address


@total_ordering
class Tensor:
    __slots__ = (
        "shape",
        "storage_shape",
        "bandwidth_shape",
        "dtype",
        "name",
        "ops",
        "consumer_list",
        "values",
        "quant_values",
        "compressed_values",
        "compressed_values_substream_offsets",
        "mem_area",
        "mem_type",
        "format",
        "purpose",
        "sub_purpose",
        "alignment",
        "weight_transpose_depthwise",
        "storage_compression_scale",
        "bandwidth_compression_scale",
        "compression_scale_for_worst_weight_stream",
        "weight_compression_scales",
        "weight_compression_config",
        "value_id",
        "storage_rounding_quantum",
        "brick_size",
        "quantization",
        "weight_compressed_offsets",
        "element_size_bytes",
        "block_traversal",
        "equivalence_id",
        "resampling_mode",
        "avoid_NHCWB16",
    )
    AllocationQuantum = 16

    def __init__(self, shape: Shape, dtype: DataType, name: str):
        self.shape = shape
        self.storage_shape = shape
        self.bandwidth_shape = shape
        self.dtype = dtype
        self.name = name
        self.equivalence_id: UUID = uuid.uuid4()

        self.ops: List[Operation] = []
        self.consumer_list: List[Operation] = []

        self.values: Optional[np.ndarray] = None
        self.quant_values: Optional[np.ndarray] = None
        self.compressed_values: Optional[np.ndarray] = None
        self.compressed_values_substream_offsets: Optional[List] = None
        self.mem_area: MemArea = MemArea.Unknown
        self.mem_type: MemType = MemType.Unknown
        self.format: TensorFormat = TensorFormat.Unknown
        self.purpose: TensorPurpose = TensorPurpose.Unknown
        self.sub_purpose: TensorSubPurpose = TensorSubPurpose.Standard
        self.alignment: int = Tensor.AllocationQuantum
        self.weight_transpose_depthwise: bool = False

        self.storage_compression_scale: float = 1.0
        self.bandwidth_compression_scale: float = 1.0
        self.compression_scale_for_worst_weight_stream: float = 1.0
        self.weight_compression_scales: Optional[np.ndarray] = None
        # if two tensors have the same weight_compression_config, then they have the same compressed values
        self.weight_compression_config = None
        # if two tensors have the same value_id, then they have the same values
        self.value_id: UUID = uuid.uuid4()
        self.weight_compressed_offsets: List = []
        self.storage_rounding_quantum: Tuple = (1, 1, 1, 1)
        self.brick_size: Tuple = (1, 1, 1, 1)
        self.element_size_bytes: int = 0

        # quantization parameters
        self.quantization: Optional[QuantizationParameters] = None
        self.block_traversal: TensorBlockTraversal = TensorBlockTraversal.Default
        self.resampling_mode: resampling_mode = resampling_mode.NONE

        self.avoid_NHCWB16: bool = False

    @property
    def address(self) -> int:
        return TensorAddressMap.get_address_for_tens(self.equivalence_id, self.mem_type)

    @address.setter
    def address(self, address: int):
        TensorAddressMap.set_address_for_tens(self.equivalence_id, self.mem_type, address)

    def element_size(self) -> int:
        if self.element_size_bytes == 0:
            return self.dtype.size_in_bits() / 8
        return self.element_size_bytes

    # Returns a copy, renamed to self.name + suffix
    # The references to Operators will be empty when returned
    # Depending on set_unique, the copy is shallow, or deep
    # For set_unique==True, a new equivalence_id will be set
    def clone(self, suffix="_clone", set_unique: bool = False) -> "Tensor":
        if set_unique:
            res = copy.deepcopy(self)
            res.equivalence_id = uuid.uuid4()
        else:
            res = copy.copy(self)
            res.storage_shape = list(self.storage_shape)
            res.bandwidth_shape = list(self.bandwidth_shape)
            if self.quantization is not None:
                res.quantization = self.quantization.clone()

        res.name = res.name + suffix
        res.ops = []
        res.consumer_list = []

        return res

    def clone_into_fast_storage(self, arch) -> "Tensor":
        res = self.clone(suffix="_fast_storage")
        res.mem_area = arch.fast_storage_mem_area
        res.mem_type = MemType.Scratch_fast
        return res

    def copy_compressed_weight_info(self, src_tens: "Tensor"):
        # Copies compressed values + all related weight compression info from the given tensor
        self.equivalence_id = src_tens.equivalence_id
        self.compressed_values = src_tens.compressed_values
        self.compressed_values_substream_offsets = src_tens.compressed_values_substream_offsets
        self.storage_shape = src_tens.storage_shape
        self.brick_size = src_tens.brick_size
        self.weight_compression_scales = src_tens.weight_compression_scales
        self.weight_compressed_offsets = src_tens.weight_compressed_offsets
        self.weight_transpose_depthwise = src_tens.weight_transpose_depthwise
        self.compression_scale_for_worst_weight_stream = src_tens.compression_scale_for_worst_weight_stream
        self.storage_compression_scale = src_tens.storage_compression_scale
        self.bandwidth_compression_scale = src_tens.bandwidth_compression_scale
        self.block_traversal = src_tens.block_traversal
        self.weight_compression_config = src_tens.weight_compression_config
        self.value_id = src_tens.value_id

    def set_format(self, fmt: TensorFormat, arch):
        self.format = fmt
        shape_len = 0
        try:
            shape_len = len(self.shape)
        except TypeError:
            pass

        if shape_len > 4:
            return
        self.storage_rounding_quantum = arch.storage_rounding_quantums[self.format]
        self.storage_rounding_quantum = tuple(self.storage_rounding_quantum[-shape_len:])
        self.brick_size = arch.brick_sizes[self.format]
        self.brick_size = tuple(self.brick_size[-shape_len:])
        if self.shape is None:
            return

        self.bandwidth_shape = shape_round_to_quantum(self.shape, self.brick_size)
        self.storage_shape = shape_round_to_quantum(self.shape, self.storage_rounding_quantum)

        if fmt == TensorFormat.WeightsCompressed:
            compression_ratio = 5 / 8
            self.storage_compression_scale = compression_ratio
            self.bandwidth_compression_scale = compression_ratio
            self.compression_scale_for_worst_weight_stream = compression_ratio

    def storage_elements(self) -> int:
        elems = shape_num_elements(self.storage_shape)
        if elems is None:
            return 0
        return elems

    def elements(self) -> int:
        elems = shape_num_elements(self.shape)
        if elems is None:
            return 0
        return elems

    def has_fully_defined_shape(self) -> bool:
        return shape_fully_defined(self.shape)

    def storage_size(self, scale: float = 1.0) -> int:
        raw_size = self.storage_elements() * self.element_size() * scale
        if raw_size == 0:
            raw_size = 1  # force it to take up space
        rounded_size = numeric_util.round_up(numeric_util.round_up_to_int(raw_size), self.alignment)
        return rounded_size

    def storage_size_for_sub_purpose(
        self, arch, sub_purpose: TensorSubPurpose, param_a: Optional[int] = None, param_b: Optional[int] = None
    ) -> int:
        alt_shape = self.storage_shape_for_sub_purpose(sub_purpose, param_a, param_b)
        elems = shape_num_elements(alt_shape)
        if elems is None:
            return 0
        if sub_purpose == TensorSubPurpose.DoubleBuffer:
            raw_size = (
                elems
                * self.element_size()
                * self.compression_scale_for_worst_weight_stream
                * arch.weight_estimation_scaling
            )
        else:
            # Rolling buffers are used for intermediate data in ifm streaming
            # These will all use the NHCWB16 format, and need to be aligned to 16 in the C-dimension
            if alt_shape[-1] % 16 != 0:
                nhcwb16_shape = alt_shape[0:-1] + [numeric_util.round_up(alt_shape[-1], 16)]
                elems = shape_num_elements(nhcwb16_shape)

            raw_size = elems * self.element_size() * self.storage_compression_scale
        rounded_size = numeric_util.round_up(numeric_util.round_up_to_int(raw_size), self.alignment)
        return rounded_size

    def storage_shape_for_sub_purpose(
        self, sub_purpose: TensorSubPurpose, param_a: Optional[int], param_b: Optional[int]
    ) -> Shape:
        if sub_purpose == TensorSubPurpose.DoubleBuffer:
            shp = list(self.shape)
            assert len(shp) >= 2
            assert param_a is not None
            shp[-1] = min(shp[-1], param_a * 2)
        else:
            shp = list(self.storage_shape)
            if sub_purpose == TensorSubPurpose.RollingBufferX:
                assert len(shp) == 4
                assert param_a is not None
                shp[0] = 1
                shp[2] = min(shp[2], param_a)
            elif sub_purpose == TensorSubPurpose.RollingBufferY:
                assert len(shp) == 4
                assert param_a is not None
                shp[0] = 1
                shp[1] = min(shp[1], param_a)
            elif sub_purpose == TensorSubPurpose.RollingBufferXY:
                assert len(shp) == 4
                assert param_a is not None
                assert param_b is not None
                shp[0] = 1
                shp[2] = min(shp[2], param_a)
                shp[1] = min(shp[1], param_b)
            elif sub_purpose == TensorSubPurpose.Standard:
                pass
            else:
                assert 0, "did not expect new sub purpose %s" % (sub_purpose,)

        return shp

    def set_new_sub_purpose(self, sub_purpose: TensorSubPurpose, param_a=None, param_b=None):
        self.storage_shape = self.storage_shape_for_sub_purpose(sub_purpose, param_a, param_b)
        self.sub_purpose = sub_purpose
        if sub_purpose == TensorSubPurpose.DoubleBuffer:
            self.storage_compression_scale = self.compression_scale_for_worst_weight_stream

    def bandwidth(self) -> float:
        elems = shape_num_elements(self.bandwidth_shape)
        if elems is None:
            return 0
        return elems * self.element_size() * self.bandwidth_compression_scale

    def consumers(self) -> List[Operation]:
        return self.consumer_list

    def addresses_for_rolling_buffer(self, start_coord: Shape, end_coord: Shape, fm_shape: Shape4D) -> Tuple:
        # returns ( box_height0, box_height1, box_width, [address_tl, address_tr, address_bl, address_br] )

        if self.storage_shape == []:
            return (
                1,
                1,
                1,
                [self.address_for_coordinate(start_coord, shape=fm_shape.as_list()), None, None, None],
            )

        storage_shape_4D = full_shape(4, self.storage_shape, 1)
        crossing_y = numeric_util.round_up(start_coord[1] + 1, storage_shape_4D[1])
        crossing_x = numeric_util.round_up(start_coord[2] + 1, storage_shape_4D[2])

        crossing_y = min(crossing_y, end_coord[1])
        crossing_x = min(crossing_x, end_coord[2])

        box_height0 = crossing_y - start_coord[1]
        box_width = crossing_x - start_coord[2]

        addresses: List = [None] * 4
        addresses[0] = self.address_for_coordinate(start_coord, shape=fm_shape.as_list())

        if end_coord[2] > crossing_x:
            addresses[1] = self.address_for_coordinate(
                [start_coord[0], start_coord[1], crossing_x, start_coord[3]], shape=fm_shape.as_list()
            )
            raise UnsupportedFeatureError("Striping in vertical direction is not supported")
        if end_coord[1] > crossing_y:
            addresses[2] = self.address_for_coordinate(
                [start_coord[0], crossing_y, start_coord[2], start_coord[3]], shape=fm_shape.as_list()
            )
        if end_coord[1] > crossing_y and end_coord[2] > crossing_x:
            addresses[3] = self.address_for_coordinate(
                [start_coord[0], crossing_y, crossing_x, start_coord[3]], shape=fm_shape.as_list()
            )

        return box_height0, box_height0, box_width, addresses

    def address_for_coordinate(self, coord: Shape, is_top_box: bool = False, shape: Shape = None) -> int:
        if shape is None:
            shape = self.shape
        offset = self.address_offset_for_coordinate(coord, shape, is_top_box)
        assert offset is not None
        return self.address + offset

    def get_strides_and_coord(self, coord: Optional[Shape] = None) -> Tuple[Optional[Shape], Optional[Shape]]:
        if coord is None:
            coord = [0] * len(self.storage_shape)

        augmented_coord = coord
        augmented_shape = self.storage_shape
        while len(augmented_shape) < 4:
            augmented_shape = [1] + augmented_shape

        while len(augmented_coord) < 4:
            augmented_coord = [0] + augmented_coord

        assert len(augmented_coord) == len(augmented_shape)

        if self.format == TensorFormat.NHWC:
            augmented_shape = [augmented_shape[0], augmented_shape[3]] + augmented_shape[1:3] + [1]
            augmented_coord = [augmented_coord[0], augmented_coord[3]] + augmented_coord[1:3] + [0]

        elif self.format == TensorFormat.NHCWB16:
            channel_divisor = 16
            augmented_shape = augmented_shape[0:4] + [1]
            augmented_coord = (
                [augmented_coord[0], augmented_coord[3] // channel_divisor]
                + augmented_coord[1:3]
                + [augmented_coord[3] % channel_divisor]
            )

            if augmented_shape[1] == 0:
                augmented_shape[1] = 1

        else:
            assert self.format in (TensorFormat.Unknown, TensorFormat.WeightsCompressed)
            return None, None

        strides: List = [0] * len(augmented_shape)
        stride = self.element_size() * self.storage_compression_scale

        if self.format != TensorFormat.NHCWB16:
            stride_order = [4, 1, 3, 2, 0]
            for i in stride_order:
                strides[i] = stride
                stride *= augmented_shape[i]
        else:
            assert len(strides) == 5
            strides[4] = stride
            strides[3] = 16 * stride  # STRIDE_X
            strides[1] = strides[3] * augmented_shape[2]  # STRIDE_C
            strides[2] = augmented_shape[2] * augmented_shape[3] * stride  # STRIDE_Y
            strides[0] = strides[2] * augmented_shape[1]  # STRIDE_N

        return strides, augmented_coord

    def get_strides(self) -> Shape:
        strides, _ = self.get_strides_and_coord()
        assert strides is not None
        return strides

    def needs_dma(self) -> bool:
        return len(self.ops) == 1 and self.ops[0].type == Op.DMA

    def get_dma_src_tensor(self) -> "Optional[Tensor]":
        # For weight tensors that need DMA: returns the source tensor in Flash, else None
        # Note: for DMA ops, Pass.weight_tensor is referring to the SRAM weight tensor
        return self.ops[0].inputs[0] if self.needs_dma() else None

    def find_npu_op(self) -> Optional[Operation]:
        # Returns the NPU operator that uses this tensor, excluding DMA operators.
        for op in self.consumers():
            if op.type == Op.DMA:
                return op.outputs[0].find_npu_op()
            if op.run_on_npu:
                return op
        return None

    def compressed_stream_index_from_coord(self, coord: Shape) -> int:
        assert self.format == TensorFormat.WeightsCompressed
        assert self.compressed_values is not None
        assert len(self.compressed_values) > 0
        assert len(self.compressed_values) + 1 == len(self.weight_compressed_offsets)

        depth = coord[-1]
        brick_depth = self.brick_size[-1]
        # Clamp position at final element index
        if depth > self.shape[-1]:
            depth = self.shape[-1]

        # Always round up to next boundary
        index = numeric_util.round_up_divide(depth, brick_depth)

        # Check boundaries on all but last weight set (which may be shorter
        # than the brick we divided it up into)
        if index < len(self.weight_compressed_offsets) - 1:
            # There are no half-way points in the weights
            if (depth % brick_depth) != 0:
                raise UnsupportedFeatureError("Offset into weights must be aligned to a brick")

        return index

    def size_of_compressed_stream(self, index: int) -> int:
        assert self.compressed_values is not None
        assert 0 <= index < len(self.compressed_values)
        return len(self.compressed_values[index])

    def is_last_index_in_compressed_stream(self, index: int) -> bool:
        assert self.compressed_values is not None
        assert 0 <= index < len(self.compressed_values)
        return index == len(self.compressed_values) - 1

    def address_offset_for_coordinate(self, orig_coord: Shape, shape: Shape, is_top_box: bool = False) -> Optional[int]:
        address_offset = 0
        coord = orig_coord

        coord = coord[-len(self.storage_shape) :]

        if self.sub_purpose == TensorSubPurpose.Standard:
            for idx, c in enumerate(orig_coord):
                if is_top_box:
                    assert c > 0 and c <= shape[idx]
                else:
                    assert c >= 0 and c < shape[idx]

        if self.format == TensorFormat.WeightsCompressed:
            if len(self.weight_compressed_offsets) == 0:
                return 0

            if self.needs_dma() and self.sub_purpose == TensorSubPurpose.DoubleBuffer:
                depth = orig_coord[-1]
                brick_depth = self.brick_size[-1]
                # Clamp position at final element index
                if depth > self.shape[-1]:
                    depth = self.shape[-1]

                # Always round up to next boundary
                index = numeric_util.round_up_divide(depth, brick_depth)
                index = index % 2
                assert self.compressed_values is not None

                if len(self.compressed_values) <= 2:
                    if is_top_box and index == 0:
                        for cv in self.compressed_values:
                            address_offset += len(cv)
                    else:
                        address_offset = index * len(self.compressed_values[0])
                else:
                    if is_top_box and index == 0:
                        address_offset = self.storage_shape[-1]
                    else:
                        address_offset = index * (self.storage_shape[-1] // 2)
            else:
                index = self.compressed_stream_index_from_coord(orig_coord)
                assert index < len(self.weight_compressed_offsets)
                address_offset = self.weight_compressed_offsets[index]
        else:
            if is_top_box:
                coord = [c - 1 for c in coord]

            # handle wraparound for partial buffers. make sure to do this after subtracting top box:
            coord = [c % self.storage_shape[idx] for idx, c in enumerate(coord)]

            strides, augmented_coord = self.get_strides_and_coord(coord)
            if strides is None:
                return None

            if is_top_box:
                address_offset += 1 * strides[-1]  # one element

            address_offset += np.dot(augmented_coord, strides)

        assert address_offset >= 0
        assert address_offset <= self.storage_size()
        return address_offset

    def is_allocated_in_tensor_arena(self, scratch_tensor_mem_area: MemArea) -> bool:
        return (self.mem_area == scratch_tensor_mem_area) and (self.mem_type in (MemType.Scratch, MemType.Scratch_fast))

    def equivalent(self, tens: "Tensor") -> bool:
        return self.equivalence_id == tens.equivalence_id

    def set_all_shapes(self, shape: Shape):
        self.shape = shape
        self.storage_shape = shape
        self.bandwidth_shape = shape

    def get_full_shape(self) -> Shape:
        d = len(self.shape)
        if d in (1, 3):
            return full_shape(4, self.shape, 1)
        elif d == 2:
            return [self.shape[0], 1, 1, self.shape[1]]
        else:
            return self.shape.copy()

    def is_quantized(self) -> bool:
        # a tensor is quantized if it has an integral type and it contains valid quantization params

        if not isinstance(self.quantization, QuantizationParameters):
            return False

        return (self.dtype.type & BaseType.Int) != 0 and self.quantization.is_valid()

    def __lt__(self, other: "Tensor") -> bool:
        return self.equivalence_id < other.equivalence_id

    def __str__(self):
        return "<nng.Tensor '%s' shape=%s dtype=%s>" % (self.name, self.shape, self.dtype)

    __repr__ = __str__

    def error(self, msg):
        """
        Raises a VelaError exception for errors encountered when parsing a Tensor

        :param self: Tensor object that resulted in the error
        :param msg: str object that contains a description of the specific error encountered
        """

        def _print_operators(ops):
            lines = []
            for idx, op in enumerate(ops):
                op_type = getattr(op, "type", "Not an Operation")
                op_id = getattr(op, "op_index", "-")
                lines.append(f"        {idx} = {op_type} ({op_id})")
            return lines

        lines = [f"Invalid {self.name} tensor. {msg}"]

        lines += ["    Driving operators:"]
        lines += _print_operators(self.ops)

        lines += ["    Consuming operators:"]
        lines += _print_operators(self.consumer_list)

        raise VelaError("\n".join(lines))


def check_quantized_tens_scaling_equal(tens_a: Tensor, tens_b: Tensor) -> bool:
    # checks that the scaling of two quantized tensors are equal

    return tens_a.is_quantized() and tens_b.is_quantized() and tens_a.quantization.is_scaling_equal(tens_b.quantization)