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Diffstat (limited to 'ethosu/vela/tensor.py')
| -rw-r--r-- | ethosu/vela/tensor.py | 629 |
1 files changed, 629 insertions, 0 deletions
diff --git a/ethosu/vela/tensor.py b/ethosu/vela/tensor.py new file mode 100644 index 00000000..46040a46 --- /dev/null +++ b/ethosu/vela/tensor.py @@ -0,0 +1,629 @@ +# 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 +from . import numeric_util +import numpy as np +from . import data_type +import uuid +from .range_set import MemoryRangeSet +from .numeric_util import round_up_divide + + +class MemArea(enum.IntFlag): + Unknown = 0 + Sram = 1 + Dram = 2 + OnChipFlash = 3 + OffChipFlash = 4 + Size = OffChipFlash + 1 + + def display_name(self): + return ("Unknown", "SRAM", "DRAM", "On-chip Flash", "Off-chip Flash", "Size")[self.value] + + def identifier_name(self): + return ("unknown", "sram", "dram", "on_chip_flash", "off_chip_flash", "size")[self.value] + + def all(): + return (MemArea.Sram, MemArea.Dram, MemArea.OnChipFlash, MemArea.OffChipFlash) + + def __str__(self): + return self.name + + +class TensorPurpose(enum.IntFlag): + Unknown = 0 + Weights = 1 + FeatureMap = 2 + Scratch = 3 + Size = 4 + + def display_name(self): + return ("Unknown", "Weights", "FeatureMap", "Scratch", "Size")[self.value] + + def identifier_name(self): + return ("unknown", "weights", "feature_map", "scratch", "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 + + +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 + + +class Tensor: + __slots__ = ( + "shape", + "storage_shape", + "bandwidth_shape", + "dtype", + "name", + "ops", + "consumer_list", + "values", + "quant_values", + "compressed_values", + "mem_area", + "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", + "storage_rounding_quantum", + "brick_size", + "address", + "quantization", + "weight_compressed_offsets", + "element_size_bytes", + "reshaped", + "block_traversal", + "offset", + "cpu_tensor", + "npu_tensor", + "equivalence_id", + ) + 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 = [] + # Below attributes are only set if a tensor has been cloned, + # either from Cpu -> Npu or vice versa. Needed for offline allocation + self.cpu_tensor = None # reference to the corresponding Cpu tensor + self.npu_tensor = None # reference to the corresponding Npu tensor + + self.values = None + self.quant_values = None + self.compressed_values = None + self.mem_area = MemArea.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 + self.weight_compression_config = None + self.weight_compressed_offsets = [] + self.storage_rounding_quantum = (1, 1, 1, 1) + self.brick_size = (1, 1, 1, 1) + self.address = 0 # start address of tensor. will be filled in by tensor allocator + self.element_size_bytes = 0 + + # quantization parameters + self.quantization = None + + self.reshaped = False + self.block_traversal = TensorBlockTraversal.Default + + 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.equivalence_id = self.equivalence_id + + res.values = self.values + res.quant_values = self.quant_values + res.compressed_values = self.compressed_values + res.mem_area = self.mem_area + res.format = self.format + res.purpose = self.purpose + res.sub_purpose = self.sub_purpose + res.alignment = self.alignment + res.weight_transpose_depthwise = self.weight_transpose_depthwise + + res.storage_compression_scale = self.storage_compression_scale + res.bandwidth_compression_scale = self.bandwidth_compression_scale + res.compression_scale_for_worst_weight_stream = self.compression_scale_for_worst_weight_stream + res.weight_compression_scales = self.weight_compression_scales + res.storage_rounding_quantum = self.storage_rounding_quantum + res.brick_size = self.brick_size + res.address = 0 + + if self.quantization is not None: + res.quantization = self.quantization.clone() + else: + res.quantization = None + + return res + + def clone_into_fast_storage(self, arch): + res = self.clone(suffix="_fast_storage") + res.mem_area = arch.fast_storage_mem_area + return res + + 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:] + if self.format == TensorFormat.NHCWB16: + self.storage_rounding_quantum = self.storage_rounding_quantum[:-1] + ( + int(self.storage_rounding_quantum[-1] / self.dtype.size_in_bytes()), + ) + 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): + raw_size = self.storage_elements() * 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_size_for_sub_purpose(self, 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 + else: + 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): + shp = list(self.storage_shape) + if sub_purpose == TensorSubPurpose.DoubleBuffer: + assert len(shp) >= 2 + shp[-1] = min(shp[-1], param_a * 2) + elif 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 = int(16 / self.element_size()) + 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 + channel_divisor = int(16 / self.element_size()) + strides[4] = stride + strides[3] = channel_divisor # STRIDE_X + strides[1] = strides[3] * augmented_shape[2] # STRIDE_C + strides[2] = augmented_shape[2] * augmented_shape[3] # 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 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 = 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 len(self.ops) == 1 and self.ops[0].type == "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 = 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 __str__(self): + return "<nng.Tensor '%s' shape=%s dtype=%s>" % (self.name, self.shape, self.dtype) + + __repr__ = __str__ |