| # SPDX-FileCopyrightText: Copyright 2020-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> |
| # |
| # 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 .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 |
| Virtual = 7 |
| Size = 8 |
| |
| 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", |
| "next_after", |
| "scale_f32", |
| "zero_point", |
| "quant_min", |
| "quant_max", |
| "quant_dim", |
| ) |
| |
| 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 |
| |
| # Use the 'next after' float value of scale_f32 when converting to scale and shift. It can be combined with |
| # natural rounding to perform rounding away from zero. This only affects the ofm scale and bias tensor, it has |
| # no affect on global scaling i.e. the ofm_scale register |
| self.next_after = False |
| 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 |
| self.quant_dim: Optional[int] = None |
| |
| def __str__(self): |
| return ( |
| f"<nng.QuantizationParameters min={self.min}, max={self.max}, num_bits={self.num_bits}, " |
| f"scale={self.scale_f32}, zero_point={self.zero_point}, next={self.next_after}>" |
| ) |
| |
| __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.next_after = self.next_after |
| res.scale_f32 = self.scale_f32 |
| res.zero_point = self.zero_point |
| res.quant_min = self.quant_min |
| res.quant_max = self.quant_max |
| res.quant_dim = self.quant_dim |
| return res |
| |
| def dequantize(self, values) -> np.ndarray: |
| return np.subtract(values, self.zero_point) * self.scale_f32 |
| |
| def is_scaling_equal(self, other: Optional["QuantizationParameters"]) -> bool: |
| """ |
| Returns True if the scale and zero point of self and other are equal. If other is None then the scaling is |
| not considered equal because the tensor is assumed to not be quantised and False will be returned |
| """ |
| |
| 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: |
| """Return True if the quantisation parameters have a scale and zero point""" |
| |
| return self.scale_f32 is not None and self.zero_point is not None |
| |
| def is_per_axis(self) -> bool: |
| """Returns True if either the scale, zero point, minimum or maximum values have more than one value""" |
| |
| for attr in ("scale_f32", "zero_point", "min", "max"): |
| if np.size(getattr(self, attr)) > 1: |
| return True |
| return False |
| |
| |
| def create_virtual_tensor( |
| name: str, |
| ): |
| virtual_tensor = Tensor([], DataType.int8, name) |
| virtual_tensor.purpose = TensorPurpose.Virtual |
| return virtual_tensor |
| |
| |
| def create_const_tensor( |
| name: str, |
| shape: Shape, |
| dtype: DataType, # datatype of the tensor |
| values: Optional[Union[np.ndarray, list]], # list-like data of some type, or scalar (skip mypy), or None |
| purpose: TensorPurpose = TensorPurpose.Unknown, |
| quantization: Optional[QuantizationParameters] = None, |
| ): |
| assert isinstance(dtype, DataType) |
| |
| # Tensor |
| const_tensor = Tensor(shape, dtype, name + "_0") |
| const_tensor.purpose = purpose |
| const_tensor.quantization = quantization |
| |
| # if the tensor datatype does not match that of the values then np.array() will perform a cast operation. this can |
| # result in undefined behaviour if casting from a numpy float to a numpy unsigned integer. therefore, we need to |
| # avoid this undefined behaviour by converting the numpy floats to python floats as these give the desired behaviour |
| # when casting to unsigned integers |
| if ( |
| values is not None |
| and shape != [] # values are not a scalar |
| and isinstance(values[0], np.floating) |
| and dtype.type == BaseType.Unsigned |
| ): |
| values = [float(v) for v in values] |
| |
| const_tensor.values = np.array(values, dtype=dtype.as_numpy_type()) |
| # Operator |
| const_op = Operation(Op.Const, name) |
| const_op.set_output_tensor(const_tensor) |
| const_op.set_ifm_ofm_shapes() |
| return const_tensor |
| |
| |
| # 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", |
| "_original_shape", |
| "storage_shape", |
| "bandwidth_shape", |
| "dtype", |
| "name", |
| "is_variable", |
| "pre_buffer", |
| "ops", |
| "consumer_list", |
| "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", |
| "src_tensor", |
| "needs_linear_format", |
| "ifm_write_protected", |
| ) |
| AllocationQuantum = 16 |
| |
| def __init__(self, shape: Shape, dtype: DataType, name: str): |
| self.shape = shape |
| self._original_shape = shape |
| self.storage_shape = shape |
| self.bandwidth_shape = shape |
| self.dtype = dtype |
| self.name = name |
| self.is_variable = False |
| self.pre_buffer = False |
| self.equivalence_id: UUID = uuid.uuid4() |
| |
| self.ops: List[Operation] = [] |
| self.consumer_list: List[Operation] = [] |
| |
| self.values: Optional[np.ndarray] = None # elements are of type self.dtype |
| 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.needs_linear_format = True |
| self.ifm_write_protected = False |
| |
| # Reference to parent-tensor if this tensor is a clone |
| self.src_tensor: Optional[Tensor] = None |
| |
| @property |
| def original_shape(self): |
| return self._original_shape |
| |
| @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) |
| |
| @property |
| def is_standard_fm(self) -> bool: |
| return self.sub_purpose == TensorSubPurpose.Standard and self.purpose == TensorPurpose.FeatureMap |
| |
| @property |
| def is_const(self) -> bool: |
| return self.ops != [] and self.ops[0].type == Op.Const |
| |
| @property |
| def is_scalar(self) -> bool: |
| return self.shape == [] and self.elements() == 1 |
| |
| def is_broadcast(self, ofm) -> bool: |
| return self.shape != ofm.shape |
| |
| 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": |
| res = copy.copy(self) |
| if set_unique: |
| res.equivalence_id = uuid.uuid4() |
| 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_shram(self, arch) -> "Tensor": |
| res = self.clone(suffix="_shram") |
| res.mem_area = MemArea.Shram |
| res.src_tensor = self |
| return res |
| |
| def as_1D(self): |
| self.shape = [np.prod(self.shape)] |
| if self.values is not None: |
| self.values = self.values.reshape(self.shape) |
| |
| def transpose(self, reorder): |
| self.shape = [self.shape[idx] for idx in reorder] |
| self._original_shape = [self._original_shape[idx] for idx in reorder] |
| if self.values is not None: |
| self.values = self.values.transpose(reorder) |
| |
| 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 |
| assert not (self.needs_linear_format and fmt == TensorFormat.NHCWB16) |
| 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_shape(self, op_storage_shape: Shape) -> int: |
| elems = shape_num_elements(op_storage_shape) |
| elems = elems if elems else 0 |
| raw_size = elems * self.element_size() |
| 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_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 = full_shape(4, self.storage_shape, 1) |
| 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 get_4D_storage_shape_for_shape(self, op_shape4D: Shape4D) -> Shape4D: |
| rounding_quantum = full_shape(4, list(self.storage_rounding_quantum), 1) |
| return Shape4D(shape_round_to_quantum(op_shape4D.as_list(), rounding_quantum)) |
| |
| def addresses_for_rolling_buffer( |
| self, start_coord: Shape, end_coord: Shape, strides: List[int], op_shape4D: 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, strides, op_shape4D), 0, 0, 0], |
| ) |
| |
| if self.is_standard_fm: |
| storage_shape_4D = self.get_4D_storage_shape_for_shape(op_shape4D) |
| else: |
| storage_shape_4D = Shape4D(self.storage_shape) |
| |
| crossing_y = numeric_util.round_up(start_coord[1] + 1, storage_shape_4D.height) |
| crossing_x = numeric_util.round_up(start_coord[2] + 1, storage_shape_4D.width) |
| |
| 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 = [0] * 4 |
| addresses[0] = self.address_for_coordinate(start_coord, strides, op_shape4D) |
| |
| if end_coord[2] > crossing_x: |
| addresses[1] = self.address_for_coordinate( |
| [start_coord[0], start_coord[1], crossing_x, start_coord[3]], strides, op_shape4D |
| ) |
| 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]], strides, op_shape4D |
| ) |
| 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]], strides, op_shape4D |
| ) |
| |
| return box_height0, box_height0, box_width, addresses |
| |
| def get_strides(self, shape4D: Optional[Shape4D]) -> List[int]: |
| |
| augmented_shape = self.get_augmented_shape(shape4D) |
| assert len(augmented_shape) == 5 |
| 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: |
| 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 |
| |
| def get_augmented_shape(self, shape4D: Optional[Shape4D] = None) -> Optional[Shape]: |
| |
| if shape4D and self.is_standard_fm: |
| augmented_shape = self.get_4D_storage_shape_for_shape(shape4D).as_list() |
| else: |
| augmented_shape = full_shape(4, self.storage_shape, 1) |
| |
| if self.format == TensorFormat.NHWC: |
| augmented_shape = [augmented_shape[0], augmented_shape[3]] + augmented_shape[1:3] + [1] |
| |
| elif self.format == TensorFormat.NHCWB16: |
| augmented_shape = augmented_shape[0:4] + [1] |
| |
| if augmented_shape[1] == 0: |
| augmented_shape[1] = 1 |
| |
| else: |
| assert self.format in (TensorFormat.Unknown, TensorFormat.WeightsCompressed) |
| return None |
| |
| return augmented_shape |
| |
| def get_augmented_coord(self, coord: Optional[Shape] = None) -> Optional[Shape]: |
| if coord is None: |
| coord = [0] * min(len(self.storage_shape), 4) |
| |
| missing_len = 4 - len(coord) |
| augmented_coord = ([0] * missing_len) + coord |
| |
| if self.format == TensorFormat.NHWC: |
| augmented_coord = [augmented_coord[0], augmented_coord[3]] + augmented_coord[1:3] + [0] |
| |
| elif self.format == TensorFormat.NHCWB16: |
| channel_divisor = 16 |
| augmented_coord = ( |
| [augmented_coord[0], augmented_coord[3] // channel_divisor] |
| + augmented_coord[1:3] |
| + [augmented_coord[3] % channel_divisor] |
| ) |
| else: |
| assert self.format in (TensorFormat.Unknown, TensorFormat.WeightsCompressed) |
| return None |
| |
| return augmented_coord |
| |
| def find_npu_op(self) -> Optional[Operation]: |
| # Returns the NPU operator that uses this tensor |
| for op in self.consumers(): |
| 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_for_coordinate( |
| self, |
| orig_coord: Shape, |
| strides: Optional[List[int]] = None, |
| op_shape4D: Optional[Shape4D] = None, |
| is_top_box: bool = False, |
| ) -> Optional[int]: |
| |
| address_offset = 0 |
| assert self.purpose != TensorPurpose.Weights |
| |
| # Strides may be passed as an argument, for example when creating feature maps as the strides may be modified |
| # by the "ofm_stride_multiplier" operation attribute. If not, they are calculated here. |
| if not strides: |
| strides = self.get_strides(op_shape4D) |
| |
| coord = orig_coord |
| if is_top_box: |
| coord = [c - 1 for c in orig_coord] |
| address_offset += 1 * strides[-1] # one element |
| |
| if self.sub_purpose == TensorSubPurpose.Standard: |
| shape = op_shape4D.as_list() if op_shape4D else self.shape |
| for _coord, _shape in zip(coord, shape): |
| assert _coord >= 0 and _coord < _shape |
| |
| if op_shape4D and self.is_standard_fm: |
| storage_shape = self.get_4D_storage_shape_for_shape(op_shape4D).as_list() |
| storage_size = self.storage_size_for_shape(storage_shape) |
| else: |
| storage_shape = self.storage_shape |
| coord = coord[-len(storage_shape) :] |
| storage_size = self.storage_size() |
| |
| # Handle wraparound for partial buffers. Make sure to do this after subtracting top box |
| coord = [_coord % _shape for _coord, _shape in zip(coord, storage_shape)] |
| |
| augmented_coord = self.get_augmented_coord(coord) |
| assert augmented_coord is not None |
| |
| address_offset += np.dot(augmented_coord, strides) |
| |
| assert address_offset >= 0 and address_offset <= storage_size |
| return self.address + 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 get_scalar(self): |
| """ |
| return: Unquantized or dequantized scalar value |
| rtype: self.dtype (if unquantized) or float (if dequantized) |
| """ |
| assert self.values.size == 1, "get_scalar called on non-scalar tensor" |
| if self.is_quantized(): |
| return self.quantization.dequantize(self.values).item(0) |
| else: |
| return self.values.item(0) |
| |
| def get_shape_as_2d(self, dimension_2_size: int) -> Optional[Shape4D]: |
| |
| elms = self.elements() |
| dimension_1_size = elms // dimension_2_size |
| # Checks if the reduction works and shape is not 1D |
| is_reducible = dimension_1_size * dimension_2_size == elms and not (len(self.shape) == 1) |
| |
| new_shape = None |
| if is_reducible: |
| new_shape = Shape4D([dimension_1_size, 1, 1, dimension_2_size]) |
| |
| return new_shape |
| |
| 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) |