| # 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 enum |
| import uuid |
| from collections import defaultdict |
| from functools import lru_cache |
| |
| import numpy as np |
| |
| from . import numeric_util |
| from .data_type import BaseType |
| from .data_type import DataType |
| from .ethos_u55_regs.ethos_u55_regs import resampling_mode |
| from .operation import Op |
| from .operation import Operation |
| from .range_set import MemoryRangeSet |
| |
| |
| 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): |
| return ("Unknown", "Permanent_NPU", "Permanent_CPU", "Scratch", "Scratch_fast", "Size")[self.value] |
| |
| def identifier_name(self): |
| return ("unknown", "permanent_npu", "permanent_cpu", "scratch", "scratch_fast", "size")[self.value] |
| |
| def all(): |
| return (MemType.Permanent_NPU, MemType.Permanent_CPU, MemType.Scratch, MemType.Scratch_fast) |
| |
| def __str__(self): |
| return self.name |
| |
| |
| 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): |
| return ("Unknown", "SRAM", "DRAM", "On-chip Flash", "Off-chip Flash", "SHRAM", "Size")[self.value] |
| |
| def identifier_name(self): |
| return ("unknown", "sram", "dram", "on_chip_flash", "off_chip_flash", "shram", "size")[self.value] |
| |
| 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 |
| LUT = 4 |
| Size = 5 |
| |
| def display_name(self): |
| return ("Unknown", "Weights", "FeatureMap", "Scratch", "LUT", "Size")[self.value] |
| |
| def identifier_name(self): |
| return ("unknown", "weights", "feature_map", "scratch", "lut", "size")[self.value] |
| |
| def all(): |
| return (TensorPurpose.Weights, TensorPurpose.FeatureMap) |
| |
| |
| class TensorSubPurpose(enum.Enum): |
| Standard = 0 |
| DoubleBuffer = 1 |
| RollingBufferX = 2 |
| RollingBufferY = 3 |
| RollingBufferXY = 4 |
| |
| def display_name(self): |
| return ("Standard", "Double Buffer", "Rolling Buffer X", "Rolling Buffer Y", "Rolling Buffer XY")[self.value] |
| |
| def identifier_name(self): |
| return ("standard", "double_buffer", "rolling_buffer_x", "rolling_buffer_y", "rolling_buffer_xy")[self.value] |
| |
| 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): |
| 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): |
| if shp is None: |
| return False |
| for d in shp: |
| if d is None: |
| return False |
| return True |
| |
| |
| def shape_round_to_quantum(shp, quantum): |
| 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): |
| # 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=None, max=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 = None |
| self.zero_point = None |
| self.quant_min = None |
| self.quant_max = 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): |
| 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)) |
| |
| shape = values_as_float.shape[0] |
| assert self.zero_point.size == self.scale_f32.size == shape |
| res = np.ndarray(values_as_float.shape) |
| for i in range(shape): |
| res[i] = (values_as_float[i] - self.zero_point[i]) * self.scale_f32[i] |
| |
| return res |
| |
| def is_scaling_equal(self, other): |
| # 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 other is None: |
| return False |
| |
| assert isinstance(other, QuantizationParameters) |
| |
| return self.scale_f32 == other.scale_f32 and self.zero_point == other.zero_point |
| |
| def is_valid(self): |
| # quantisation parameters are consider valid if they have a scale and zero point |
| |
| return None not in (self.scale_f32, self.zero_point) |
| |
| |
| def create_const_tensor(name, shape, dtype, values, value_dtype=None, purpose=TensorPurpose.Unknown, quantization=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) |
| 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) |
| 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 = defaultdict(dict) # dict (tens.equivalence_id -> dict (mem_type -> address)) |
| |
| @classmethod |
| def get_address_for_tens(cls, tens_id, mem_type): |
| return cls.address_map[tens_id].get(mem_type) |
| |
| @classmethod |
| def set_address_for_tens(cls, tens_id, mem_type, address): |
| # 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 |
| |
| |
| 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, dtype, name): |
| self.shape = shape |
| self.storage_shape = shape |
| self.bandwidth_shape = shape |
| self.dtype = dtype |
| self.name = name |
| self.equivalence_id = uuid.uuid4() |
| |
| self.ops = [] |
| self.consumer_list = [] |
| |
| self.values = None |
| self.quant_values = None |
| self.compressed_values = None |
| self.compressed_values_substream_offsets = None |
| self.mem_area = MemArea.Unknown |
| self.mem_type = MemType.Unknown |
| self.format = TensorFormat.Unknown |
| self.purpose = TensorPurpose.Unknown |
| self.sub_purpose = TensorSubPurpose.Standard |
| self.alignment = Tensor.AllocationQuantum |
| self.weight_transpose_depthwise = False |
| |
| self.storage_compression_scale = 1.0 |
| self.bandwidth_compression_scale = 1.0 |
| self.compression_scale_for_worst_weight_stream = 1.0 |
| self.weight_compression_scales = 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.uuid4() |
| self.weight_compressed_offsets = [] |
| self.storage_rounding_quantum = (1, 1, 1, 1) |
| self.brick_size = (1, 1, 1, 1) |
| self.element_size_bytes = 0 |
| |
| # quantization parameters |
| self.quantization = None |
| self.block_traversal = TensorBlockTraversal.Default |
| self.resampling_mode = resampling_mode.NONE |
| |
| self.avoid_NHCWB16 = False |
| |
| @property |
| def address(self): |
| return TensorAddressMap.get_address_for_tens(self.equivalence_id, self.mem_type) |
| |
| @address.setter |
| def address(self, address): |
| TensorAddressMap.set_address_for_tens(self.equivalence_id, self.mem_type, address) |
| |
| def element_size(self): |
| if self.element_size_bytes == 0: |
| return self.dtype.size_in_bits() / 8 |
| return self.element_size_bytes |
| |
| def clone(self, suffix="_clone"): |
| res = Tensor(self.shape, self.dtype, self.name + suffix) |
| res.storage_shape = list(self.storage_shape) |
| res.bandwidth_shape = list(self.bandwidth_shape) |
| |
| res.ops = [] |
| res.consumer_list = [] |
| |
| res.values = self.values |
| res.quant_values = self.quant_values |
| res.mem_area = self.mem_area |
| res.mem_type = self.mem_type |
| res.format = self.format |
| res.purpose = self.purpose |
| res.sub_purpose = self.sub_purpose |
| res.alignment = self.alignment |
| res.bandwidth_compression_scale = self.bandwidth_compression_scale |
| res.storage_rounding_quantum = self.storage_rounding_quantum |
| |
| if self.quantization is not None: |
| res.quantization = self.quantization.clone() |
| else: |
| res.quantization = None |
| |
| res.resampling_mode = self.resampling_mode |
| |
| res.copy_compressed_weight_info(self) |
| res.avoid_NHCWB16 = self.avoid_NHCWB16 |
| return res |
| |
| def clone_into_fast_storage(self, arch): |
| 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): |
| # 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.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, arch): |
| self.format = fmt |
| shape_len = 0 |
| try: |
| shape_len = len(self.shape) |
| except TypeError: |
| pass |
| |
| self.storage_rounding_quantum = arch.storage_rounding_quantums[self.format] |
| self.storage_rounding_quantum = self.storage_rounding_quantum[-shape_len:] |
| self.brick_size = arch.brick_sizes[self.format] |
| self.brick_size = 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): |
| elems = shape_num_elements(self.storage_shape) |
| if elems is None: |
| return 0 |
| return elems |
| |
| def elements(self): |
| elems = shape_num_elements(self.shape) |
| if elems is None: |
| return 0 |
| return elems |
| |
| def has_fully_defined_shape(self): |
| return shape_fully_defined(self.shape) |
| |
| def storage_size(self, scale=1.0): |
| 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, param_a=None, param_b=None): |
| 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, param_a, param_b): |
| if sub_purpose == TensorSubPurpose.DoubleBuffer: |
| shp = list(self.shape) |
| assert len(shp) >= 2 |
| shp[-1] = min(shp[-1], param_a * 2) |
| else: |
| shp = list(self.storage_shape) |
| if sub_purpose == TensorSubPurpose.RollingBufferX: |
| assert len(shp) == 4 |
| shp[0] = 1 |
| shp[2] = min(shp[2], param_a) |
| elif sub_purpose == TensorSubPurpose.RollingBufferY: |
| assert len(shp) == 4 |
| shp[0] = 1 |
| shp[1] = min(shp[1], param_a) |
| elif sub_purpose == TensorSubPurpose.RollingBufferXY: |
| assert len(shp) == 4 |
| 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, 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): |
| 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): |
| return self.consumer_list |
| |
| def get_address_ranges_for_coordinates(self, start_coord, end_coord): |
| if self.sub_purpose in set( |
| (TensorSubPurpose.RollingBufferX, TensorSubPurpose.RollingBufferY, TensorSubPurpose.RollingBufferXY) |
| ): |
| # build dummy coordinates that cover the entire buffer |
| start_coord = [0] * len(start_coord) |
| end_coord = [min(self.storage_shape[i], self.shape[i]) for i in range(len(end_coord))] |
| |
| start = self.address_for_coordinate(start_coord, is_top_box=False) |
| end = self.address_for_coordinate(end_coord, is_top_box=True) |
| return MemoryRangeSet(self.mem_area, start, end) |
| |
| def addresses_for_rolling_buffer(self, start_coord, end_coord): |
| # returns ( box_height0, box_height1, box_width, [address_tl, address_tr, address_bl, address_br] ) |
| |
| if len(start_coord) < 4: |
| box_height0 = 1 |
| box_width = 1 |
| |
| if len(start_coord) >= 2: |
| box_width = end_coord[-2] - start_coord[-2] |
| |
| return box_height0, box_height0, box_width, [self.address_for_coordinate(start_coord), None, None, None] |
| |
| crossing_y = numeric_util.round_up(start_coord[1] + 1, self.storage_shape[1]) |
| crossing_x = numeric_util.round_up(start_coord[2] + 1, self.storage_shape[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 = [None] * 4 |
| addresses[0] = self.address_for_coordinate(start_coord) |
| |
| if end_coord[2] > crossing_x: |
| addresses[1] = self.address_for_coordinate([start_coord[0], start_coord[1], crossing_x, start_coord[3]]) |
| raise Exception("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]]) |
| 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]]) |
| |
| return box_height0, box_height0, box_width, addresses |
| |
| def address_for_coordinate(self, coord, is_top_box=False): |
| return self.address + self.address_offset_for_coordinate(coord, is_top_box) |
| |
| def get_strides_and_coord(self, coord=None): |
| 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] |
| stride_order = [4, 1, 3, 2, 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 set((TensorFormat.Unknown, TensorFormat.WeightsCompressed)) |
| return None, None |
| |
| strides = [0] * len(augmented_shape) |
| stride = self.element_size() * self.storage_compression_scale |
| |
| if self.format != TensorFormat.NHCWB16: |
| 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): |
| strides, _ = self.get_strides_and_coord() |
| |
| return strides |
| |
| def needs_dma(self): |
| return len(self.ops) == 1 and self.ops[0].type == Op.DMA |
| |
| def get_dma_src_tensor(self): |
| # 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): |
| # 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): |
| assert self.format == TensorFormat.WeightsCompressed |
| 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 Exception("Offset into weights must be aligned to a brick") |
| |
| return index |
| |
| def size_of_compressed_stream(self, index): |
| assert 0 <= index < len(self.compressed_values) |
| return len(self.compressed_values[index]) |
| |
| def is_last_index_in_compressed_stream(self, index): |
| assert 0 <= index < len(self.compressed_values) |
| return index == len(self.compressed_values) - 1 |
| |
| def address_offset_for_coordinate(self, orig_coord, is_top_box=False): |
| address_offset = 0 |
| coord = orig_coord |
| |
| coord = coord[-len(self.storage_shape) :] |
| |
| if self.sub_purpose == TensorSubPurpose.Standard: |
| for idx, c in enumerate(coord): |
| if is_top_box: |
| assert c > 0 and c <= self.shape[idx] |
| else: |
| assert c >= 0 and c < self.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 |
| |
| 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): |
| if self.mem_area == scratch_tensor_mem_area and (self.mem_type in set((MemType.Scratch, MemType.Scratch_fast))): |
| return True |
| return False |
| |
| def equivalent(self, tens): |
| return self.equivalence_id == tens.equivalence_id |
| |
| def set_all_shapes(self, shape): |
| self.shape = shape |
| self.storage_shape = shape |
| self.bandwidth_shape = shape |
| |
| def get_full_shape(self): |
| d = len(self.shape) |
| if d in (1, 3): |
| return numeric_util.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): |
| # a tensor is quantized if it has an integral type and it contains valid quantization params |
| |
| if (self.dtype.type & BaseType.Int) == 0 or self.quantization is None: |
| return False |
| |
| assert isinstance(self.quantization, QuantizationParameters) |
| assert self.quantization.is_valid() |
| |
| return True |
| |
| def __str__(self): |
| return "<nng.Tensor '%s' shape=%s dtype=%s>" % (self.name, self.shape, self.dtype) |
| |
| __repr__ = __str__ |
| |
| |
| def check_tens_quantized(tens): |
| # checks that a tensor is quantized |
| |
| return isinstance(tens, Tensor) and tens.is_quantized() |
| |
| |
| def check_quantized_tens_scaling_equal(tens_a, tens_b): |
| # checks that the scaling of two quantized tensors are equal |
| |
| assert check_tens_quantized(tens_a) |
| assert check_tens_quantized(tens_b) |
| |
| return tens_a.quantization.is_scaling_equal(tens_b.quantization) |