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|
# 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
import numpy as np
from . import numeric_util
from .ethos_u55_regs.ethos_u55_regs import resampling_mode
from .numeric_util import round_up_divide
from .range_set import MemoryRangeSet
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",
"resampling_mode",
)
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
self.resampling_mode = resampling_mode.NONE
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
res.resampling_mode = self.resampling_mode
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:]
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 = 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 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__
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