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review.mlplatform.org / ml / ethos-u / ethos-u-vela / 6986a079020ab6344c9191aa67af13beeb475593 / . / ethosu / vela / tflite_graph_optimiser.py
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# 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:
# Early optimisation of a TensorFlow Lite based network graph, using the rewrite_graph module
# to do the traversal of the graph.
import math
import uuid
import numpy as np
from . import fp_math
from . import rewrite_graph
from . import scaling
from .api import NpuRoundingMode
from .data_type import BaseType
from .data_type import DataType
from .debug_database import DebugDatabase
from .errors import UnsupportedFeatureError
from .ethos_u55_regs.ethos_u55_regs import resampling_mode
from .graph_optimiser_util import bypass_memory_only_ops
from .graph_optimiser_util import calc_explicit_padding
from .graph_optimiser_util import convert_depthwise_to_conv
from .graph_optimiser_util import convert_to_lut
from .graph_optimiser_util import memory_only_ops
from .graph_optimiser_util import move_splitsliceread_to_consumer
from .graph_optimiser_util import needed_total_padding
from .graph_optimiser_util import set_ifm_ofm_op_shapes
from .graph_optimiser_util import set_tensor_equivalence
from .numeric_util import clamp_sigmoid
from .numeric_util import round_away_zero
from .operation import create_activation_function
from .operation import ExplicitScaling
from .operation import NpuBlockType
from .operation import Op
from .operation import Operation
from .operation import Padding
from .operation_util import create_add_nop
from .operation_util import create_avgpool_nop
from .operation_util import create_depthwise_maxpool
from .operation_util import get_pad_values_from_input
from .scaling import quantise_scale
from .shape4d import Shape4D
from .softmax import SoftMax
from .tensor import check_quantized_tens_scaling_equal
from .tensor import create_const_tensor
from .tensor import create_equivalence_id
from .tensor import QuantizationParameters
from .tensor import Tensor
from .tensor import TensorPurpose
from .tflite_mapping import optype_to_builtintype
passthrough_nodes = (Op.Identity,)
def create_avg_pool_for_concat(concat_op, name, ifm, ifm_shape: Shape4D, write_offset: Shape4D):
"""Creates an average pool for the given concat op/input feature map"""
ofm = concat_op.ofm
avgpool_op = create_avgpool_nop(name)
avgpool_op.inputs = [ifm]
avgpool_op.outputs = [ofm]
avgpool_op.write_offset = write_offset
avgpool_op.write_shape = ifm_shape
ofm.ops.append(avgpool_op)
avgpool_op.ifm_shapes.append(ifm_shape)
avgpool_op.ofm_shapes.append(concat_op.ofm_shapes[0])
avgpool_op.memory_function = Op.ConcatSliceWrite
DebugDatabase.add_optimised(concat_op, avgpool_op)
return avgpool_op
def remove_passthrough_tensor(tens, arch, nng):
if len(tens.ops) == 1 and tens.ops[0].type in passthrough_nodes:
assert len(tens.ops[0].inputs) == 1
tens = tens.ops[0].inputs[0]
return tens
def rewrite_concat_ops(op, arch):
if not op.run_on_npu or not op.type.is_concat_op():
return
axis_4D = 0
ofm = op.ofm
ofm.ops = []
offset = 0
unfuse_activation_function(op)
if op.type == Op.Pack:
# Pack is also referred to as Stack
axis = int(op.attrs["axis"])
if axis < 0: # Convert to positive axis
axis = len(op.inputs[0].shape) + 1 + axis
desired_shape = op.inputs[0].shape[:axis] + [1] + op.inputs[0].shape[axis:]
axis_4D = axis + (4 - len(desired_shape))
for idx, inp in enumerate(op.inputs):
op.ifm_shapes[idx] = Shape4D(desired_shape)
op.type = Op.PackReshaped
inputs, axis = op.get_concat_inputs_axis()
for idx, inp in enumerate(inputs):
if op.type != Op.PackReshaped:
op.ifm_shapes[idx] = Shape4D(inp.shape)
if axis >= 0:
axis_4D = axis + (4 - len(inp.shape))
else:
axis_4D = axis
write_offset = [0, 0, 0, 0]
write_offset[axis_4D] = offset
concat_end = offset + op.ifm_shapes[idx][axis_4D]
create_avg_pool_for_concat(
op, op.name + str(idx) + "_avgpool", inp, op.ifm_shapes[idx], Shape4D.from_list(write_offset)
)
offset = concat_end
assert ofm.shape[axis] == offset
return op
def rewrite_split_ops(tens, arch, nng):
if len(tens.ops) == 1 and tens.ops[0].type.is_split_op() and tens.ops[0].type != Op.Unpack:
split_op = tens.ops[0]
# Not supported so leave it and run on CPU
if not split_op.run_on_npu:
return tens
inp, outputs, axis, offset_start, offset_end = split_op.get_split_inputs_axis()
tens.ops = []
new_op = Operation(Op.SplitSliceRead, split_op.name)
new_op.inputs = [inp]
ofm_shape_idx = 0
if None in (offset_end, offset_start):
read_shape = None
else:
# the read shape is relative to each start offset
read_shape = [oe - os for oe, os in zip(offset_end, offset_start)]
# For Split the offset cannot be extracted from the tensor so it has to
# be calculated from the index of the output tensor
if axis is not None:
# Get the start and end of the split
offset_start = [0] * 4
axis_4D_list = split_op.attrs.get("split_axis_4D", None) # Present for UnpackReshaped and some StridedSlice
for idx, out in enumerate(outputs):
if axis_4D_list is not None:
axis_4D = axis_4D_list[idx]
else:
split_op.ofm_shapes[idx] = Shape4D(out.shape)
if axis >= 0:
axis_4D = axis + (4 - len(out.shape))
else:
axis_4D = axis
if out == tens:
ofm_shape_idx = idx
read_shape = split_op.ofm_shapes[idx]
break
offset_start[axis_4D] += split_op.ofm_shapes[idx][axis_4D]
new_op.read_offsets[0] = Shape4D.from_list(offset_start, 0)
new_op.read_shapes[0] = read_shape
new_op.run_on_npu = True
new_op.set_output_tensor(tens)
new_op.ifm_shapes.append(Shape4D(inp.shape))
new_op.ofm_shapes.append(split_op.ofm_shapes[ofm_shape_idx])
DebugDatabase.add_optimised(split_op, new_op)
return tens
def remove_SplitSliceRead(op, arch):
if op.type == Op.SplitSliceRead:
# Check if it is possible to put the SplitSliceRead on the tensor consumer, or if an avgpool need to be inserted
if (
len(op.ofm.consumer_list) == 1
and op.ofm.consumer_list[0] is not None
and op.ofm.consumer_list[0].run_on_npu
and op.ofm.consumer_list[0].type not in memory_only_ops
and op.ofm_shapes[0] == Shape4D.from_list(op.ofm.shape)
):
# SplitSliceRead can be performed by tensor consumer
cons_op = op.ofm.consumer_list[0]
move_splitsliceread_to_consumer(op, cons_op)
else:
avgpool_op = create_avgpool_nop(op.name + "_avgpool")
avgpool_op.add_input_tensor(op.ifm)
avgpool_op.outputs = [op.ofm]
op.ofm.ops.remove(op)
op.ofm.ops.append(avgpool_op)
avgpool_op.ifm_shapes.append(op.ifm_shapes[0])
avgpool_op.ofm_shapes.append(op.ofm_shapes[0])
avgpool_op.read_offsets[0] = op.read_offsets[0]
avgpool_op.read_shapes[0] = op.read_shapes[0]
op.ifm.consumer_list.remove(op)
DebugDatabase.add_optimised(op, avgpool_op)
def calc_padding_and_skirt(padding_type, kernel, input_shape, explicit_padding):
k_w, k_h = kernel.dilated_wh()
s_x, s_y = kernel.stride
ypad = needed_total_padding(int(input_shape.height), int(s_y), int(k_h))
xpad = needed_total_padding(int(input_shape.width), int(s_x), int(k_w))
if padding_type == Padding.SAME:
left_pad = (xpad + 0) // 2
right_pad = (xpad + 1) // 2
top_pad = (ypad + 0) // 2
bottom_pad = (ypad + 1) // 2
elif padding_type == Padding.VALID:
left_pad = 0
right_pad = 0
top_pad = 0
bottom_pad = 0
elif padding_type == Padding.EXPLICIT:
# Padding is specified in a PAD operator which has been bypassed.
top, left, bottom, right = explicit_padding
top_pad, bottom_pad = calc_explicit_padding(int(input_shape.height), int(s_y), int(k_h), int(top), int(bottom))
left_pad, right_pad = calc_explicit_padding(int(input_shape.width), int(s_x), int(k_w), int(left), int(right))
elif padding_type == Padding.TILE:
# The values in the explicit padding only represent the "direction" in which to pad
top_pad, left_pad, bottom_pad, right_pad = explicit_padding
else:
raise UnsupportedFeatureError(f"Unsupported padding = {padding_type} for padding calculation")
padding = (top_pad, left_pad, bottom_pad, right_pad)
skirt = (top_pad, left_pad, ypad - top_pad, xpad - left_pad)
return padding, skirt
def calc_upscaled_padding_and_skirt(padding_type, kernel_size, stride, input_shape, upscaling_factor):
kernel_height, kernel_width = kernel_size[0], kernel_size[1]
if padding_type == Padding.SAME:
ypad = needed_total_padding(int(input_shape.height) * upscaling_factor, int(stride[1]), int(kernel_height))
xpad = needed_total_padding(int(input_shape.width) * upscaling_factor, int(stride[2]), int(kernel_width))
right_pad = max(((xpad + 1) // upscaling_factor) - 1, 0)
bottom_pad = max(((ypad + 1) // upscaling_factor) - 1, 0)
left_pad = max(kernel_width - 1 - right_pad, 0)
top_pad = max(kernel_height - 1 - bottom_pad, 0)
elif padding_type == Padding.VALID:
right_pad = max(kernel_width - 2, 0)
bottom_pad = max(kernel_height - 2, 0)
left_pad = kernel_width - 1
top_pad = kernel_height - 1
else:
raise UnsupportedFeatureError(f"Unsupported padding = {padding_type} for up-scaled padding calculation")
padding = (top_pad, left_pad, bottom_pad, right_pad)
skirt = padding
return padding, skirt
def fixup_conv2d_backprop(op, arch, nng):
if op.type == Op.Conv2DBackpropInput:
# flip the inputs
op.inputs[0], op.inputs[2] = op.inputs[2], op.inputs[0]
op.type = Op.Conv2DBackpropInputSwitchedBias
op.ifm_resampling_mode = resampling_mode.TRANSPOSE
# Update strides
op.attrs.update({"stride_w": 1, "stride_h": 1, "strides": (1, 1, 1, 1)})
DebugDatabase.add_optimised(op, op)
return op
# Convert the op to an elementwise add
def convert_resize_1x1_to_add(op):
op.type = Op.Add # original_type will stay as Op.ResizeBilinear or Op.ResizeNearestNeighbor
op.name = op.name + "_add"
# Create an input tensor filled with zeros
name = op.inputs[1].name + "_add"
dtype = op.inputs[0].dtype
shape = op.ofm_shapes[0].as_list()
values = np.zeros(shape, dtype.as_numpy_type())
quantization = QuantizationParameters(0.0, 255.0)
quantization.scale_f32 = 1.0
quantization.zero_point = 0
op.inputs[1] = op.inputs[0]
op.set_input_tensor(create_const_tensor(name, shape, dtype, values, quantization=quantization), 0)
op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, op)
return op
# Convert ResizeNearestNeightbor with align corners to a depthwise convolution. The IFM will already have been upscaled
# apart from the final x2 scaling which will be done as part of this operation. The kernel contains a single coefficient
# to select the appropriate nearest neighbor value
def convert_resizenn_ac_to_depthwise_conv(op, upscale_factor):
ifm = op.ifm
ofm = op.ofm
output_depth = ofm.shape[-1]
dw_op_attrs = {
"padding": Padding.VALID,
"stride_h": 1,
"stride_w": 1,
"strides": (1, 1, 1, 1),
"depth_multiplier": 1,
"channel_multiplier": 1,
"dilation_h_factor": 1,
"dilation_w_factor": 1,
"dilation": (1, 1, 1, 1),
}
# change resizebilinear to depthwise
op.type = Op.DepthwiseConv2DBias
op.attrs.update(dw_op_attrs)
op.set_input_tensor(ifm, 0) # ifm tensor index
op.activation = None
# add input resample to resize by x2
op.ifm_resampling_mode = resampling_mode.NEAREST
# don't care about the rounding mode as it is nearest neighbor
# setup weight tensor
weight_quant = QuantizationParameters()
weight_quant.scale_f32 = 1.0 # no scaling as only a single non-zero coeff to select the desired value
weight_quant.zero_point = 0
weight_quant.quant_dim = 0
ofm_dtype = ofm.dtype
if ofm_dtype.type == BaseType.UnsignedInt:
weight_quant.quant_min = 0
weight_quant.quant_max = (1 << ofm_dtype.bits) - 1
else:
weight_quant.quant_min = -(1 << (ofm_dtype.bits - 1))
weight_quant.quant_max = (1 << (ofm_dtype.bits - 1)) - 1
weight_shape = [upscale_factor, upscale_factor, output_depth, output_depth] # HWIO
# the single non-zero coefficient used to select the desired value needs to be placed in the 'centre value', which
# is calculated by finding the 'centre position' ('*' in the diagram below) and then choosing the 'value' that is
# below-and-right (i.e. next) to it (D).
# 0---1---2
# | A | B |
# 1---*---+
# | C | D |
# 2---+---+
weight_values = [0] * (upscale_factor * upscale_factor)
centre_coeff = (upscale_factor // 2) * upscale_factor + (upscale_factor // 2)
weight_values[centre_coeff] = 1
# add weight tensor, this will discard the size tensor of the resize op
op.set_input_tensor(
create_const_tensor(
"weights",
weight_shape,
ofm_dtype,
np.array(weight_values).reshape(weight_shape),
quantization=weight_quant,
),
1, # inputs tensor weight index
)
# setup bias tensor by assign None and then call the fix-up function to create a suitable tensor.
# need to append the bias tensor as resize ops only have 2 inputs
assert len(op.inputs) == 2
op.inputs.append(None)
fixup_bias_tensors(op, None, None, DataType.int32)
# finally update the shape incase we've change the tensor shapes or connections
op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, op)
return op
# Convert ResizeBilinear/NearestNeighbor to a number of 1x1 average pools with nearest neighbor x2 upscaling and one
# final average pool with a kernel size that depends upon the resize ops upscaling factor (x2, x4 or x8). The maximum
# upscale factor is limited to x8 because of the limit 8x8 kernel size limit for average pool with padding.
def convert_resize_to_upscale_and_average_pool(op):
pre_op = op
outputs = op.outputs
dtype = op.ifm.dtype
op.attrs.update({"strides": (1, 1, 1, 1), "ksize": (1, 1, 1, 1)})
op.attrs["padding"] = Padding.SAME # doesn't really matter as the kernel is 1x1
op.ifm_resampling_mode = resampling_mode.NEAREST
upscaled_shape = np.array(op.ifm_shapes[0].get_hw_as_list())
# Get upscale factor that was calculated in the supported operators check
upscale_factor = op.attrs["upscale_factor"]
# Calculate how many times 2x2 upscaling needs to be performed
# Force the result of round to be an integer. This is because the behaviour of rounding numpy.float64 values changed
# between different versions of numpy. This consistency ensures that the kernel dimensions are kept integral
n = int(np.log2(upscale_factor))
# Perform x2 upscaling n-1 times
scaled_op = pre_op
for count in range(n - 1):
if count > 0:
scaled_op = op.clone(f"_{count}")
scaled_op.inputs[0] = pre_op.outputs[0]
# Nearest neighbor x2 upscaling
upscaled_shape = upscaled_shape * 2
shape = op.ofm_shapes[0].as_list()
shape[1:3] = upscaled_shape
out_tens = Tensor(shape, dtype, f"{op.outputs[0].name}_{count}")
out_tens.quantization = op.outputs[0].quantization.clone()
scaled_op.set_output_tensor(out_tens)
pre_op = scaled_op
scaled_op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, scaled_op)
# Last x2 upscaling
if n > 1:
scaled_op = op.clone(f"_{n-1}")
scaled_op.inputs[0] = pre_op.outputs[0]
if scaled_op.original_type == Op.ResizeBilinear:
if scaled_op.attrs["align_corners"]:
# no padding
scaled_op.attrs["padding"] = Padding.VALID
else:
# padding to the right and bottom (limits average pool to 8x8 kernel)
scaled_op.attrs["padding"] = Padding.EXPLICIT
scaled_op.attrs["explicit_padding"] = [0, 0, upscale_factor - 1, upscale_factor - 1]
# kernal size dependent on the upscaling factor
scaled_op.attrs.update({"ksize": (1, upscale_factor, upscale_factor, 1)})
else: # Op.ResizeNearestNeighbor
if scaled_op.attrs["align_corners"]:
# use depthwise conv to select the correct value
scaled_op = convert_resizenn_ac_to_depthwise_conv(scaled_op, upscale_factor)
else:
# Keep 1x1 kernel and average pool, this applies both when
# half-pixel-centers is True and False. Calculations are the
# same in the reference.
pass
scaled_op.outputs = outputs
scaled_op.outputs[0].ops = [scaled_op]
scaled_op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, scaled_op)
return op
def convert_argmax_to_depthwise_conv_and_max_pool(op, arch, nng):
"""
Convert ArgMax to DWConv2D->MaxPool->DWConv2D, see details below.
Example:
arr = [4, [00000100,
6, = 00000110, # <-- This is the largest value, so we're expecting argmax(arr) = 1
5] 00000101]
Use 16-bit precision and shift all values 7 bits to the left:
Shifted_arr = [0000001000000000,
0000001100000000,
0000001010000000]
Add "c - index of channel" to each channel:
Shifted_arr_plus_reverse_idx = [0000001000000010, (+2)
0000001100000001, (+1)
0000001010000000] (+0)
The index is reversed since ArgMax selects the lowest index if maximum value is found at two index. The index will
act as a tie-breaker between channels with equal values and since we want the smallest channel index to be chosen
we reverse the index before the maxpool and then subtract the index from the number of channel after the maxpool to
get the correct index.
Find the maximum value in the array:
val = max(shifted_arr_plus_reverse_idx) = 0000001100000001
Subtract the value from the number of channels:
shifted_arr_plus_idx = (c-1) - val = 2 - 1 = 1
Extract the 7 lowest bits using a LUT to cut off the 9 most significant bits:
idx = LUT(val) = 0000000000000001 = 1
"""
if op.type == Op.ArgMax:
ifm, ofm = op.inputs[0], op.outputs[0]
identity_quant = QuantizationParameters()
identity_quant.zero_point = 0
identity_quant.scale_f32 = 1.0
if ofm.quantization is None:
ofm.quantization = identity_quant
# Add last dimension to ofm shape
ofm.shape += [1]
ofm.ops = []
# Create 1x1 Depthwise convolution with 2**7 weights for each channel to convert precision to 16 bit and shift
# all values 7 bits to the left
# Set necessary depthwise attributes
dw_op_attrs = {
"padding": Padding.VALID,
"stride_h": 1,
"stride_w": 1,
"strides": (1, 1, 1, 1),
"depth_multiplier": 1,
"channel_multiplier": 1,
"dilation_h_factor": 1,
"dilation_w_factor": 1,
"dilation": (1, 1, 1, 1),
"explicit_padding": None,
}
op.name = "depthwise_conv_SHL_7"
op.type = Op.DepthwiseConv2DBias
op.attrs.update(dw_op_attrs)
n, h, w, c = ifm.shape
shape = [1, 1, 1, c]
kernel = np.dstack([2**7] * c)
op.inputs = []
op.add_input_tensor(ifm)
op.add_input_tensor(
create_const_tensor(
"weights",
shape,
DataType.uint8,
np.array(kernel).reshape(shape),
quantization=identity_quant,
),
)
# Let the bias for each channel be the "reverse" index of the channel it is in, ie c - channel_idx
reverse_idxs = list(reversed(range(c)))
bias_tensor = create_const_tensor(op.name + "_bias", [c], DataType.int64, reverse_idxs)
op.add_input_tensor(bias_tensor)
intermediate_tens = Tensor([n, h, w, c], DataType.int16, "int16_and_shifted_7_bits_left")
intermediate_tens.quantization = ifm.quantization
op.set_output_tensor(intermediate_tens)
op.set_ifm_ofm_shapes()
orig_ifm_shape = op.ifm_shapes[0]
DebugDatabase.add_optimised(op, op)
# To extract 7 least significant bits and swap reverse index back to real index using a LUT activation, we set
# the base value to c-1 and slope to -128. The 16-bit LUT uses a table of 32-bit values where the top 16 bits
# represent the slope and bottom 16 bits the base which are used to interpolate the activation value.
slope = (-128 & 0xFFFF) << 16 # Top 16 bits of 32 bit LUT table value
base = c - 1 # Bottom 16 bits of the LUT table value
lut_tensor = create_const_tensor(
"maxpool_LUT_extract_7_LSB",
[1, 1, 1, 512],
DataType.uint32,
[slope + base] * 512,
TensorPurpose.LUT,
)
# Split large feature maps into smaller chunks since the Depthwise Maxpool height dimension can overflow due to
# flattening the ifm to (H*W)xCx1
max_height = 2**16 // orig_ifm_shape.width
num_full_height_ops = orig_ifm_shape.height // max_height
last_op_height = orig_ifm_shape.height - max_height * num_full_height_ops
op_heights = [max_height] * num_full_height_ops
if last_op_height > 0:
op_heights.append(last_op_height)
# Create maxpool output tensor which is reshaped to 1x(H*W)x1x1. The product H*W might be larger than the
# maximum allowed height, but that's handled by reading and writing the data in chunks
maxpool_ofm = Tensor([1, orig_ifm_shape.height * orig_ifm_shape.width, 1, 1], DataType.int16, "argmax_maxpool")
maxpool_ofm.quantization = identity_quant
for op_idx, op_height in enumerate(op_heights):
maxpool_op = create_depthwise_maxpool(
f"dw_maxpool_{op_idx}", intermediate_tens, orig_ifm_shape, identity_quant
)
maxpool_op.outputs = [maxpool_ofm]
maxpool_ofm.ops.append(maxpool_op)
maxpool_op.ofm_shapes = [Shape4D(maxpool_ofm.shape)]
maxpool_op.set_activation_lut(lut_tensor)
# Set read and write shapes/offsets to read/write chunks of the IFM/OFM
maxpool_op.read_shapes[0] = Shape4D([1, op_height * orig_ifm_shape.width, orig_ifm_shape.depth, 1])
maxpool_op.read_offsets[0] = Shape4D([0, sum(op_heights[:op_idx]) * orig_ifm_shape.width, 0, 0])
maxpool_op.write_shape = Shape4D([1, op_height * orig_ifm_shape.width, 1, 1])
maxpool_op.write_offset = Shape4D([0, sum(op_heights[:op_idx]) * orig_ifm_shape.width, 0, 0])
DebugDatabase.add_optimised(op, maxpool_op)
# Convert output to OFM dtype and reshape back to original OFM shape with 1x1 DWConv
dw_conv = Operation(Op.DepthwiseConv2DBias, f"depthwise_conv_convert_to_32bit_{op_idx}")
dw_conv.attrs.update(dw_op_attrs)
dw_conv.inputs = [maxpool_op.ofm]
dw_conv.add_input_tensor(
create_const_tensor(
"weights",
[1, 1, 1, 1],
DataType.uint8,
np.array([1]).reshape([1, 1, 1, 1]),
quantization=identity_quant,
),
)
dw_conv.add_input_tensor(create_const_tensor(dw_conv.name + "_bias", [1], DataType.int64, [0]))
ofm.ops.append(dw_conv)
dw_conv.outputs = [ofm]
dw_conv.ifm_shapes.append(Shape4D([1, orig_ifm_shape.height, orig_ifm_shape.width, 1]))
dw_conv.ofm_shapes.append(Shape4D(ofm.shape))
DebugDatabase.add_optimised(op, dw_conv)
return op
def convert_resizebilinear_to_depthwise_convolutions(op, half_pixel_centers=True):
def _compute_interpolation_values(index, input_size, output_size):
scale = input_size / output_size
scaled_value = (index + 0.5 * half_pixel_centers) * scale - 0.5 * half_pixel_centers
lower_bound = max(np.floor(scaled_value), 0)
return scaled_value, lower_bound
def _compute_kernels(input_height, input_width, output_height, output_width):
kernels = []
for y in (1, 2):
for x in (1, 2):
sv_h, lb_h = _compute_interpolation_values(y, input_height, output_height)
sv_w, lb_w = _compute_interpolation_values(x, input_width, output_width)
# Interpolation values calculated for (x, y) = ([1, 2], [1, 2]) will always generalize to the whole
# input for upscale = 2 and input sizes >= 2x2 and be in the correct order for going left-to-right,
# top-to-bottom - same as the depthwise convolution strides across each tile
kernel = np.zeros((2, 2))
kernel[1, 1] = (1 - (sv_h - lb_h)) * (1 - (sv_w - lb_w))
kernel[0, 1] = (sv_h - lb_h) * (1 - (sv_w - lb_w))
kernel[1, 0] = (1 - (sv_h - lb_h)) * (sv_w - lb_w)
kernel[0, 0] = (sv_h - lb_h) * (sv_w - lb_w)
kernel *= 16
kernels.append(kernel)
return kernels
def _build_convolutions(op, kernels):
dw_op_attrs = {
"padding": Padding.TILE,
"stride_h": 1,
"stride_w": 1,
"strides": (1, 1, 1, 1),
"depth_multiplier": 1,
"channel_multiplier": 1,
"dilation_h_factor": 1,
"dilation_w_factor": 1,
"dilation": (1, 1, 1, 1),
}
ifm = op.ifm
ofm = op.ofm
ofm.ops = []
elem_size = 2 if ofm.dtype == DataType.int16 else 1
n, h, w, c = ifm.shape
_, _, ow, _ = ofm.shape
intermediate_tens = Tensor(ifm.shape, ifm.dtype, "intermediate_tens")
intermediate_tens.quantization = op.outputs[0].quantization.clone()
avgpool_op = op
avgpool_op.name = "rb_init_avgpool"
avgpool_op.type = Op.AvgPool
avgpool_op.attrs["padding"] = Padding.VALID
avgpool_op.attrs["stride_w"] = 1
avgpool_op.attrs["stride_h"] = 1
avgpool_op.attrs["filter_width"] = 1
avgpool_op.attrs["filter_height"] = 1
avgpool_op.attrs["strides"] = [1, 1, 1, 1]
avgpool_op.attrs["ksize"] = [1, 1, 1, 1]
avgpool_op.add_input_tensor(ifm)
avgpool_op.set_output_tensor(intermediate_tens)
avgpool_op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, op)
dw_conv = Operation(Op.DepthwiseConv2DBias, "depthwise_conv")
dw_conv._original_type = Op.ResizeBilinear
dw_conv.write_shape = Shape4D(n, h, w, c)
dw_conv.write_offset = Shape4D(0, 0, 0, 0)
# Set the output rounding mode. Resize bilinear requires rounding away from zero. Therefore, we need to
# adjust the accumulated value by a "small" amount before applying natural rounding. The "small" amount
# should be big enough to cause a x.5 to be rounded correctly but small enough not to cause smaller
# values to be incorrectly rounded
ofm.quantization.next_after = True
dw_conv.rounding_mode = NpuRoundingMode.NATURAL
# Double height and width stride to write the output of each of the four depthwise convolutions below
# interleaved with each other when combined with OFM tile base offsets.
dw_conv.ofm_stride_multiplier = [1, 2, 2] # C/H/W
# Choose tile padding direction - pad by 1 with edge values in two direction.
# For example, TL (top left) will pad top and left in H/W-plane in all channels.
directions = [[1, 1, 0, 0], [1, 0, 0, 1], [0, 1, 1, 0], [0, 0, 1, 1]] # TL, TR, BL, BR
for i in (0, 1):
for j in (0, 1):
index = i * 2 + j
dw_conv.name = f"depthwise_conv_{index}"
dw_op_attrs["explicit_padding"] = directions[index]
dw_conv.attrs.update(dw_op_attrs)
# This will offset the start of the write by modifying the Tile 0 base address
dw_conv.tile_base_offsets_ofm[0] = (i * ow + j) * c * elem_size
ofm.ops.append(dw_conv)
dw_conv.outputs = [ofm]
kernel = kernels[index]
shape = [2, 2, 1, c]
kernel = np.dstack([kernel] * c)
quant = QuantizationParameters()
quant.zero_point = 0
quant.scale_f32 = 1.0 / 16
dw_conv.inputs = []
dw_conv.add_input_tensor(intermediate_tens)
dw_conv.add_input_tensor(
create_const_tensor(
"weights",
shape,
intermediate_tens.dtype,
np.array(kernel).reshape(shape),
quantization=quant,
),
)
# setup bias tensor by assign None and then call the fix-up function to create a suitable tensor.
# need to append the bias tensor as resize ops only have 2 inputs
assert len(dw_conv.inputs) == 2
dw_conv.inputs.append(None)
fixup_bias_tensors(dw_conv, None, None, dtype=DataType.int32)
dw_conv.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, dw_conv)
dw_conv = dw_conv.clone(f"_{index}")
return op
_, input_height, input_width, _ = op.ifm.shape
_, output_height, output_width, _ = op.ofm.shape
kernels = _compute_kernels(input_height, input_width, output_height, output_width)
op = _build_convolutions(op, kernels)
return op
def fixup_resize(op, arch, nng):
if op.type.is_resize_op() and op.run_on_npu:
if op.ifm_shapes[0] == op.ofm_shapes[0]:
# Bypass the resize op which is essentially a NOP
op.inputs = op.inputs[:1]
op.type = Op.Identity
elif op.ifm_shapes[0].height == 1 and op.ifm_shapes[0].width == 1:
convert_resize_1x1_to_add(op)
elif op.type == Op.ResizeBilinear and op.attrs.get("half_pixel_centers", False):
convert_resizebilinear_to_depthwise_convolutions(op)
else:
convert_resize_to_upscale_and_average_pool(op)
return op
def convert_nop_split_to_identity(op, arch, nng):
if op.type == Op.Split and op.attrs.get("num_splits") == 1:
# the list comprehension should return a list with a single tensor
# if it shouldn't, remove_passthrough_tensor will fail appropriately
op.inputs = [i for i in op.inputs if i.shape == op.outputs[0].shape]
op.type = Op.Identity
return op
def rewrite_fully_connected_input(op: Operation, arch, nng):
if op.type == Op.FullyConnected:
new_shape = op.ifm.get_shape_as_2d(op.weights.shape[-2])
assert new_shape is not None, "Tensor can not be reshaped to 2D"
op.ifm_shapes[0] = new_shape
if op.ifm_shapes[0].batch > 1 and op.ofm_shapes[0].batch == 1:
# If IFM is batching then also make sure OFM is batching
h, w = op.ofm_shapes[0].height, op.ofm_shapes[0].width
op.ofm_shapes[0] = Shape4D([h * w, 1, 1, op.ofm_shapes[0].depth])
return op
def convert_batched_fc_shape(op, arch, nng):
if op.type == Op.FullyConnected:
# Check if the first dimension indicates batching
if op.ifm_shapes[0].batch > 1:
batching_split = {4: (2, 2), 8: (2, 4), 16: (4, 4)}
n = op.ifm_shapes[0].batch
h, w = batching_split.get(n, (1, n))
op.ifm_shapes[0] = Shape4D([1, h, w, op.ifm_shapes[0].depth])
# Reshape Weights to be 4D. IO becomes HWIO
weight_tensor = op.inputs[1]
weight_tensor.values = np.expand_dims(np.expand_dims(weight_tensor.values, axis=0), axis=0)
weight_tensor.set_all_shapes(list(weight_tensor.values.shape))
n = op.ofm_shapes[0].batch
h, w = batching_split.get(n, (1, n))
op.ofm_shapes[0] = Shape4D([1, h, w, op.ofm_shapes[0].depth])
return op
def unfuse_activation_function(op):
if op.type == Op.ConcatTFLite and op.run_on_npu and op.activation is not None:
act_op = Operation(op.activation.op_type, op.name + op.activation.op_type.name)
op.activation = None
out_tens = op.outputs[0]
intermediate_tens = out_tens.clone("_act_intermediate")
act_op.set_output_tensor(out_tens)
act_op.add_input_tensor(intermediate_tens)
op.set_output_tensor(intermediate_tens)
act_op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, act_op)
def rewrite_stridedslice_output(op, arch, nng):
if not op.run_on_npu or op.type != Op.StridedSlice:
return op
new_axis_mask = op.attrs["new_axis_mask"]
shrink_axis_mask = op.attrs["shrink_axis_mask"]
if shrink_axis_mask == 0 and new_axis_mask == 0:
return op
axis_4D = [0] * len(op.outputs)
for idx, out_tens in enumerate(op.outputs):
output_shape = list(out_tens.shape)
if shrink_axis_mask != 0:
n = 0
axis = 0
while shrink_axis_mask:
prev_mask = shrink_axis_mask
n += 1
shrink_axis_mask &= shrink_axis_mask - 1
axis = int(math.log2(prev_mask - shrink_axis_mask))
output_shape = output_shape[:axis] + [1] + output_shape[axis:]
assert len(out_tens.shape) == (len(op.inputs[0].shape) - n)
op.attrs["shrink_axis_mask"] = 0
if axis >= 0:
axis_4D[idx] = axis + (4 - len(output_shape))
else:
axis_4D[idx] = axis
op.ofm_shapes[idx] = Shape4D(output_shape)
elif new_axis_mask != 0:
n = 0
axis = 0
while new_axis_mask:
prev_mask = new_axis_mask
n += 1
new_axis_mask &= new_axis_mask - 1
axis = int(math.log2(prev_mask - new_axis_mask))
output_shape = output_shape[:axis] + output_shape[(axis + 1) :]
new_axis_mask >>= 1
assert len(out_tens.shape) == (len(op.inputs[0].shape) + n)
op.attrs["new_axis_mask"] = 0
if axis >= 0:
axis_4D[idx] = axis + (4 - len(output_shape))
else:
axis_4D[idx] = axis
op.ofm_shapes[idx] = Shape4D(output_shape)
op.attrs["split_axis_4D"] = axis_4D
return op
def rewrite_unpack_output(op, arch, nng):
tens = op.outputs[0]
if op.run_on_npu and op.type == Op.Unpack:
# Unpack is also referred to as Unstack
axis = int(op.attrs["axis"])
if axis < 0: # Convert to positive axis
axis = len(op.inputs[0].shape) + 1 + axis
op.type = Op.UnpackReshaped
desired_output_shape = tens.shape[:axis] + [1] + tens.shape[axis:]
axis_4D = axis + (4 - len(desired_output_shape))
op.attrs["split_axis_4D"] = [axis_4D] * len(op.outputs)
for idx, out_tens in enumerate(op.outputs):
op.ofm_shapes[idx] = Shape4D(desired_output_shape)
return op
def add_padding_fields(op, arch, nng):
if op.run_on_npu:
if "padding" in op.attrs:
input_shape = op.ifm_shapes[0]
output_shape = op.ofm_shapes[0]
if op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op():
kernel_size = op.inputs[1].shape[:2]
elif op.type.is_pool_op() or op.type.npu_block_type == NpuBlockType.ReduceSum:
kernel_size = op.attrs["ksize"][1:3]
else:
raise UnsupportedFeatureError(f"Unknown operation that uses padding: {optype_to_builtintype(op.type)}")
if op.type == Op.Conv2DBackpropInputSwitchedBias:
upscaling_factor = output_shape.height // input_shape.height
padding, skirt = calc_upscaled_padding_and_skirt(
op.attrs["padding"], kernel_size, op.attrs["strides"], input_shape, upscaling_factor
)
else:
padding, skirt = calc_padding_and_skirt(
op.attrs["padding"],
op.kernel,
input_shape,
op.attrs.get("explicit_padding"),
)
op.attrs["explicit_padding"] = padding
op.attrs["skirt"] = skirt
return op
def reorder_depthwise_weights(op, arch, nng):
if op.type.is_depthwise_conv2d_op():
weight_tensor = op.inputs[1]
weight_tensor.values = np.transpose(weight_tensor.values, (0, 1, 3, 2))
weight_tensor.set_all_shapes(list(weight_tensor.values.shape))
weight_tensor.weight_transpose_depthwise = True
return op
def fixup_strided_conv(op, arch, nng):
if op.type != Op.Conv2DBias:
return op
stride_x, stride_y = op.get_kernel_stride()
weight_tensor = op.weights
ifm_shape = op.ifm_shapes[0]
# Do not optimize if op is not the first in the network and stride is
# supported by the hardware
if op.op_index != 0 and stride_x < 4:
return op
op.ifm.needs_linear_format = True
if (
(stride_x == 2 or stride_x == 4)
and ifm_shape.depth <= 4
and ifm_shape.width % 2 == 0
and weight_tensor is not None
and weight_tensor.shape[1] >= 2
):
k_w, _ = op.get_kernel_size()
curr_padding_x = needed_total_padding(ifm_shape.width, stride_x, k_w)
optimised_padding_x = needed_total_padding(ifm_shape.width // stride_x, 1, (k_w + 1) // stride_x)
padding_type = op.attrs.get("padding", None)
# If padding is enabled, check if current padding matches optimised padding
if not padding_type or (padding_type != Padding.VALID and curr_padding_x != optimised_padding_x):
# Horizontal padding would become different after optimisation; this would not work
return op
# IFM
op.ifm_shapes[0] = Shape4D(
[ifm_shape.batch, ifm_shape.height, ifm_shape.width // stride_x, ifm_shape.depth * stride_x]
)
# Weights
weight_shape = weight_tensor.shape
if weight_shape[1] % 2 != 0:
weight_shape[1] = weight_shape[1] + 1
padded_array = np.zeros(weight_shape)
for i in range(weight_shape[0]):
padded_array[i] = np.vstack(
[
weight_tensor.values[i],
np.full((1, weight_shape[2], weight_shape[3]), weight_tensor.quantization.zero_point),
]
)
weight_tensor.values = padded_array
# Change weight shape based on stride_x
weight_shape[1] //= stride_x
weight_shape[2] *= stride_x
weight_tensor.values = np.reshape(weight_tensor.values, weight_shape)
weight_tensor.set_all_shapes(weight_shape)
# If multiple copies of the weights are used, we could avoid
# them having the same address by changing the value_id
weight_tensor.value_id = uuid.uuid4()
# Strides
stride_x = 1
op.attrs.update({"stride_w": stride_x, "stride_h": stride_y, "strides": (1, stride_y, stride_x, 1)})
return op
def convert_conv_to_fc(op, arch, nng):
# Conv 1x1 can be equivalent to Fully Connected.
# By representing certain convs as fully connected layers, Vela can better determine wether or not to use
# caching/double buffering for the weights.
# (Weights dont need to be reloaded for convs when IFM H and W are 1)
if op.type == Op.Conv2DBias:
h = op.ifm_shapes[0].height
w = op.ifm_shapes[0].width
kh, kw, _, _ = op.inputs[1].shape
if h == 1 and w == 1 and kh == 1 and kw == 1:
# Overwrite this op as a Fully Connected Op
op.name += "_fc"
op.type = Op.FullyConnected
op.attrs = {
"weights_format": 0,
}
# Reshape Weights to be 2D. HWIO becomes just IO (as H and W are 1, they can just be dropped)
weight_tensor = op.inputs[1]
weight_tensor.values = weight_tensor.values.squeeze(axis=(0, 1))
weight_tensor.set_all_shapes(list(weight_tensor.values.shape))
DebugDatabase.add_optimised(op, op)
return op
def fixup_relus_with_differing_ifm_ofm_scaling(op, arch, nng):
if op.run_on_npu and op.type.is_relu_op():
ifm = op.inputs[0]
ofm = op.outputs[0]
# Relu with differing IFM and OFM scaling cannot be fused with another primary op
# and requires its own to be inserted
if not check_quantized_tens_scaling_equal(ifm, ofm):
# Override this op with its own primary op (avgpool)
relu_fused_op = create_avgpool_nop(op.name + "_avgpool")
# And fuse the original activation function to it
relu_fused_op.activation = create_activation_function(op.type)
# Add explicit rescaling
rescale = ifm.quantization.scale_f32 / ofm.quantization.scale_f32
multiplier, shift = scaling.quantise_scale(rescale)
relu_fused_op.explicit_scaling = ExplicitScaling(False, [shift], [multiplier])
# Tidy up and assign the ifm and ofm to the new op
ifm.consumer_list.remove(op)
relu_fused_op.add_input_tensor(ifm)
relu_fused_op.set_output_tensor(ofm)
relu_fused_op.set_ifm_ofm_shapes()
op = relu_fused_op
return op
def convert_softmax(op, arch, nng):
if op.type == Op.Softmax and op.run_on_npu:
softmax = SoftMax(op)
op = softmax.get_graph()
return op
def convert_prelu(op, arch, nng):
if op.type == Op.Prelu:
ifm, alpha, ofm = op.get_ifm_ifm2_ofm()
if None in (ifm, alpha, ofm):
return op
if alpha.values is not None:
# If const alpha check for possible optimisations
alpha_zp = alpha.quantization.zero_point
alpha_scale = alpha.quantization.scale_f32
# If all alpha values are the same the PReLU can be converted to LeakyRelu
alpha_min = (alpha.values.min().astype(int) - alpha_zp) * alpha_scale
alpha_max = (alpha.values.max().astype(int) - alpha_zp) * alpha_scale
if alpha_min == alpha_max:
# or even a Relu
if alpha_min == 0:
new_op = Op.Relu
else:
new_op = Op.LeakyRelu
op.attrs["alpha"] = alpha_min
# setup alpha_scaling for bit exact result
ifm_scale = ifm.quantization.scale_f32
ofm_scale = ofm.quantization.scale_f32
alpha_scale, alpha_shift = scaling.elementwise_mul_scale(ifm_scale, alpha_scale, ofm_scale)
op.attrs["alpha_scaling"] = (alpha.values.min() - alpha_zp, alpha_scale, alpha_shift)
# Change op type
op.type = new_op
op.name = op.name.replace("Prelu", new_op.name)
del op.inputs[1] # Remove alpha tensor
return op
elif alpha_max < 1:
# If alpha_max is less than 1 convert PReLU to Max(alpha * IFM, identity * IFM)
# Multiply with alpha tensor
mul_alpha = Operation(Op.Mul, op.name + "_mul_alpha")
mul_alpha.add_input_tensor(ifm)
mul_alpha.add_input_tensor(alpha)
fm_alpha = ofm.clone(op.name + "_alpha", set_unique=True)
mul_alpha.set_output_tensor(fm_alpha)
mul_alpha.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, mul_alpha)
if check_quantized_tens_scaling_equal(ifm, ofm):
# No scaling is needed
fm_id = ifm
else:
# Add multiplication with identity
mul_identity = Operation(Op.Mul, op.name + "_mul_identity")
mul_identity.add_input_tensor(ifm)
# Create const tensor containing identity as scalar
quantization = ifm.quantization.clone()
quantization.scale_f32 = np.float32(1)
quantization.zero_point = 0
one = create_const_tensor("one_const", [], ifm.dtype, [1], quantization=quantization)
mul_identity.add_input_tensor(one)
# Make sure that fm_id is allocated to a different address than fm_alpha
fm_id = ofm.clone(op.name + "_id", set_unique=True)
mul_identity.set_output_tensor(fm_id)
mul_identity.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, mul_identity)
# Combine scaled and alpha multiplied values
max_op = Operation(Op.Maximum, op.name + "_max")
max_op.add_input_tensor(fm_alpha)
max_op.add_input_tensor(fm_id)
max_op.set_output_tensor(ofm)
max_op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, max_op)
ifm.consumer_list.remove(op)
return max_op
# Catch all PReLU conversion for the cases that could not be optimised above
no_scale_quant = ifm.quantization.clone()
no_scale_quant.scale_f32 = None
no_scale_quant.zero_point = 0
zero = create_const_tensor("zero_const", [], ifm.dtype, [0], quantization=no_scale_quant)
# Select values < 0
min_op = Operation(Op.Minimum, op.name + "_min")
min_op.add_input_tensor(ifm)
min_op.add_input_tensor(zero)
fm_negative = ifm.clone(op.name + "_negative", set_unique=True)
min_op.set_output_tensor(fm_negative)
min_op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, min_op)
# and multiply with alpha tensor
mul_alpha = Operation(Op.Mul, op.name + "_mul_alpha")
mul_alpha.add_input_tensor(fm_negative)
mul_alpha.add_input_tensor(alpha)
fm_alpha = ofm.clone(op.name + "_negative_alpha", set_unique=True)
mul_alpha.set_output_tensor(fm_alpha)
mul_alpha.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, mul_alpha)
# Select (and scale) values > 0
relu_op = Operation(Op.Relu, op.name + "_relu")
relu_op.add_input_tensor(ifm)
fm_scaled = ofm.clone(op.name + "_positive_scaled", set_unique=True)
relu_op.set_output_tensor(fm_scaled)
relu_op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, relu_op)
# Add scaled and alpha multiplied values (without scaling)
add_op = Operation(Op.Add, op.name + "_add")
add_op.explicit_scaling = ExplicitScaling(False, shift=[0], multiplier=[1]) # No scaling
add_op.add_input_tensor(fm_alpha)
add_op.add_input_tensor(fm_scaled)
add_op.set_output_tensor(ofm)
add_op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, add_op)
ifm.consumer_list.remove(op)
op = add_op
return op
def convert_mul_max_to_abs_or_lrelu(op, arch, nng):
r"""Whenever there is a subgraph with this topology:
Input X For X = -1 or X > 0
| \ / This subgraph can be replaced with either
| Mul an Abs (if X = -1) or a LeakyReLU (if X > 0)
| /
Max
"""
if op.type == Op.Maximum:
# finds the Mul input(s) to the Max
muls = [i for i in op.inputs if i.ops[0].type == Op.Mul]
if len(muls) == 1:
mul = muls[0].ops[0]
elif len(muls) == 2:
# In the case both inputs are Muls, find the one with the same input as the Max
mul_ifms = [m for m in muls if len(set(op.inputs + m.ops[0].inputs)) == 1]
if len(mul_ifms):
mul = mul_ifms[0].ops[0]
else:
# Not using same input
return op
else:
# No Mul inputs
return op
# make sure the Mul doesn't have any other consumers
mul_ofm = mul.outputs[0]
if len(mul_ofm.consumers()) != 1:
return op
# make sure the Mul doesn't have a fused activation function
if mul.activation:
return op
ifm, ofm = op.get_ifm_ofm()
if ifm is None or ofm is None:
return op
if ifm.dtype not in (DataType.uint8, DataType.int8) or ifm.dtype != ofm.dtype:
return op
if not check_quantized_tens_scaling_equal(ifm, ofm) or not check_quantized_tens_scaling_equal(ifm, mul_ofm):
# rewrite to LeakyRelu currently only makes sense if the quantization is identical
return op
# finds the branched input that goes to both the Max and the Mul
shared = set(op.inputs) & set(mul.inputs)
if len(shared) == 1:
shared_in = shared.pop()
# find the constant scalar input to the Mul
const_tens = (set(mul.inputs) - {shared_in}).pop()
# check that it is a scalar
if const_tens.shape != []:
return op
const = const_tens.ops[0]
# check that it is a constant
if const.type != Op.Const:
return op
# Remove the Mul from the shared input's consumers
shared_in.consumer_list.remove(mul)
else:
return op
val = const.outputs[0].values
if val >= 0:
new_op = Op.LeakyRelu
op.attrs["alpha"] = val
# to produce bit exact results, the alpha is not enough;
# save additional scaling info in attr "alpha_scale", to be used as input
# to the LUT construction
alpha_scalar = const_tens.values - const_tens.quantization.zero_point
mul_ifm_scale = np.double(ifm.quantization.scale_f32)
mul_ifm2_scale = np.double(const_tens.quantization.scale_f32)
mul_ofm_scale = np.double(mul_ofm.quantization.scale_f32)
alpha_scale, alpha_shift = scaling.elementwise_mul_scale(mul_ifm_scale, mul_ifm2_scale, mul_ofm_scale)
op.attrs["alpha_scaling"] = (alpha_scalar, alpha_scale, alpha_shift)
elif val == -1:
new_op = Op.Abs
else:
return op
op.type = new_op
op.name = op.name.replace("Maximum", new_op.name)
op.outputs[0].name = op.outputs[0].name.replace("Maximum", new_op.name)
op.inputs = [shared_in]
op.set_ifm_ofm_shapes()
# Record optimisation in debug database
DebugDatabase.add_optimised(op, op)
return op
def convert_hardswish_to_lut(op, arch, nng):
if op.type == Op.HardSwish:
ifm, ofm = op.get_ifm_ofm()
# Generate the LUT
ifm_scale = np.double(ifm.quantization.scale_f32)
ofm_scale = np.double(ofm.quantization.scale_f32)
zp_in = ifm.quantization.zero_point
zp_out = ofm.quantization.zero_point
ifm_scale_hires = (1 / 128) * ifm_scale
relu_multiplier = np.double(3 / 32768)
out_scale, out_shift = scaling.quantise_scale(ifm_scale_hires / ofm_scale)
relu_scale, relu_shift = scaling.quantise_scale(ifm_scale_hires / relu_multiplier)
# Use 16bit scale
out_scale_16 = fp_math.downscale_multiplier_int32_to_int16(out_scale)
relu_scale_16 = fp_math.downscale_multiplier_int32_to_int16(relu_scale)
values = []
ix = range(256) if ifm.dtype == DataType.uint8 else range(-128, 128)
quantized_min = min(ix)
quantized_max = max(ix)
for x in ix:
input_value = x - zp_in
input_value_hires = input_value * 128
# Compute the input value on essentially the output scale, not shifted yet
input_value_preshift = fp_math.saturating_rounding_mul16(input_value_hires, out_scale_16)
# Compute the "relu-ish multiplier". This matches the code in TensorFlow Lite Micro kernel
relu_value = np.int16(input_value_hires)
if relu_shift < 31:
relu_value = fp_math.shift_left16(relu_value, 30 - relu_shift)
relu_value = fp_math.saturating_rounding_mul16(relu_value, relu_scale_16)
if relu_shift < 31:
relu_value = fp_math.shift_left16(relu_value, 1)
if relu_shift > 31:
relu_value = fp_math.rounding_divide_by_pot(relu_value, relu_shift - 31)
# Rescaled the value into a 16bit fixedpoint relu_value in [-1, 1]
# Now convert that to a 16bit fixedpoint value in [0, 1]
relu_value = (relu_value + (1 << 15)) >> 1
lut_result = fp_math.saturating_mul16(relu_value, input_value_preshift)
shift = 31 - out_shift
shift = -shift if shift < 0 else 0
# Finally apply the output shift
lut_result = fp_math.rounding_divide_by_pot(lut_result, shift) + zp_out
lut_result = min(quantized_max, max(quantized_min, lut_result))
values.append(lut_result)
return convert_to_lut(op, values, "hardswish")
return op
def convert_lrelu_to_mul_max(op, arch):
# Converts LeakyRelu to Max(alpha * IFM, identity * IFM)
# (the opposite of convert_mul_max_to_abs_or_lrelu)
ifm, ofm = op.get_ifm_ofm()
if ifm is None or ofm is None:
return op
alpha = np.float32(op.attrs["alpha"])
use_mul_max = 0 < alpha < 1
is_converted_prelu = "alpha_scaling" in op.attrs
if use_mul_max:
mul_ifm = ifm
new_op = Op.Maximum
else:
# Need to use a different approach for alpha < 0 or alpha > 1
no_scale_quant = ifm.quantization.clone()
no_scale_quant.scale_f32 = None
no_scale_quant.zero_point = 0
zero = create_const_tensor("zero_const", [], ifm.dtype, [0], quantization=no_scale_quant)
# Select values < 0
min_op = Operation(Op.Minimum, op.name + "_min")
min_op.add_input_tensor(ifm)
min_op.add_input_tensor(zero)
mul_ifm = ifm.clone(op.name + "_negative", set_unique=True)
if alpha < 0 and not is_converted_prelu:
# For negative alpha that is not from a converted PReLU we need to use
# int32 Mul below to perform the (negative) alpha scaling
mul_ifm.dtype = DataType.int32
min_op.set_output_tensor(mul_ifm)
min_op.set_ifm_ofm_shapes()
new_op = Op.Add
op.explicit_scaling = ExplicitScaling(False, shift=[0], multiplier=[1]) # No scaling
DebugDatabase.add_optimised(op, min_op)
# Add multiplication with alpha
mul_alpha = Operation(Op.Mul, op.name + "_mul_alpha")
mul_alpha.add_input_tensor(mul_ifm)
# Create const tensor containing alpha as scalar
quantization = ifm.quantization.clone()
quantization.min = 0
quantization.max = alpha * (quantization.quant_max - quantization.quant_min)
quantization.zero_point = 0
alpha_dtype = mul_ifm.dtype
if is_converted_prelu:
# The LeakyRelu was the result from convert_prelu and the scaling is provided
scalar, alpha_scale, alpha_shift = op.attrs["alpha_scaling"]
mul_alpha.explicit_scaling = ExplicitScaling(False, [alpha_shift], [alpha_scale])
elif alpha == 0 or np.isinf(1 / alpha):
# Handling of alpha near or at zero
quantization.scale_f32 = np.float32(1)
scalar = 0
else:
quantization.scale_f32 = alpha
if alpha_dtype == DataType.int32:
# When the datatype is int32 (alpha negative) we need to do the scaling with the multiplication
scalar, _ = scaling.elementwise_mul_scale(ifm.quantization.scale_f32, alpha, ofm.quantization.scale_f32)
else:
scalar = 1
alpha_tens = create_const_tensor(op.name + "_alpha_scalar", [1], alpha_dtype, [scalar], quantization=quantization)
mul_alpha.add_input_tensor(alpha_tens)
fm_alpha = ofm.clone(op.name + "_alpha", set_unique=True)
mul_alpha.set_output_tensor(fm_alpha)
mul_alpha.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, mul_alpha)
if not use_mul_max:
relu_op = Operation(Op.Relu, op.name + "_relu")
relu_op.add_input_tensor(ifm)
fm_id = ofm.clone(op.name + "_positive_scaled", set_unique=True)
relu_op.set_output_tensor(fm_id)
relu_op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, relu_op)
elif check_quantized_tens_scaling_equal(ifm, ofm):
# No identity multiplication is needed
fm_id = ifm
else:
# Add multiplication with identity
mul_identity = Operation(Op.Mul, op.name + "_mul_identity")
mul_identity.add_input_tensor(ifm)
# Create const tensor containing identity as scalar
quantization = ifm.quantization.clone()
quantization.min = 0
quantization.max = quantization.quant_max - quantization.quant_min
quantization.scale_f32 = np.float32(1)
quantization.zero_point = 0
identity_tens = create_const_tensor(op.name + "_id_scalar", [], ifm.dtype, [1], quantization=quantization)
mul_identity.add_input_tensor(identity_tens)
# Make sure that fm_id is allocated to a different address than fm_alpha
fm_id = ofm.clone(op.name + "_id", set_unique=True)
mul_identity.set_output_tensor(fm_id)
mul_identity.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, mul_identity)
# Convert LeakyRelu to Max, add the results of the multiplication(s) as inputs
op.type = new_op
op.name = op.name.replace("LeakyRelu", new_op.name)
op.inputs = []
ifm.consumer_list.remove(op)
op.add_input_tensor(fm_alpha)
op.add_input_tensor(fm_id)
op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, op)
return op
def convert_to_lut8(op, fn, fn_name):
# Converts op to a no-op + int8/uint8 LUT which is generated with the given function.
# fn is a function(real) -> real
ifm, ofm = op.get_ifm_ofm()
if ifm.dtype not in (DataType.uint8, DataType.int8) or ifm.dtype != ofm.dtype:
return op
# Generate the LUT
ifm_scale = np.double(ifm.quantization.scale_f32)
ofm_scale = np.double(ofm.quantization.scale_f32)
zp_in = ifm.quantization.zero_point
zp_out = ofm.quantization.zero_point
values = []
ix = range(256) if ifm.dtype == DataType.uint8 else range(-128, 128)
quantized_min = min(ix)
quantized_max = max(ix)
for x in ix:
x_real = ifm_scale * (x - zp_in)
y_real = fn(x_real)
lut_result = round_away_zero(zp_out + y_real / ofm_scale)
lut_result = min(quantized_max, max(quantized_min, lut_result))
values.append(lut_result)
return convert_to_lut(op, values, fn_name)
def convert_lrelu_to_lut(op, arch):
ifm, ofm = op.get_ifm_ofm()
# Generate the LUT
alpha = op.attrs["alpha"]
ifm_scale = np.double(ifm.quantization.scale_f32)
ofm_scale = np.double(ofm.quantization.scale_f32)
zp_in = ifm.quantization.zero_point
zp_out = ofm.quantization.zero_point
identity_scale, identity_shift = scaling.elementwise_mul_scale(ifm_scale, 1, ofm_scale)
alpha_scalar = 1
alpha_scale, alpha_shift = scaling.elementwise_mul_scale(ifm_scale, alpha, ofm_scale)
if "alpha_scaling" in op.attrs:
# The LeakyRelu was the result from convert_mul_max_to_abs_or_lrelu
alpha_scalar, alpha_scale, alpha_shift = op.attrs["alpha_scaling"]
values = []
ix = range(256) if ifm.dtype == DataType.uint8 else range(-128, 128)
quantized_min = min(ix)
quantized_max = max(ix)
for x in ix:
if x < zp_in:
lut_result = zp_out + fp_math.multiply_by_quantized_multiplier(
alpha_scalar * (x - zp_in), alpha_scale, alpha_shift
)
else:
lut_result = zp_out + fp_math.multiply_by_quantized_multiplier(x - zp_in, identity_scale, identity_shift)
lut_result = min(quantized_max, max(quantized_min, lut_result))
values.append(lut_result)
return convert_to_lut(op, values, "lrelu")
def convert_lrelu(op, arch, nng):
# Converts LeakyRelu to a LUT based solution if possible, otherwise a mul + max
if op.type != Op.LeakyRelu:
return op
ifm, ofm = op.get_ifm_ofm()
if ifm is None or ofm is None:
return op
alpha = op.attrs["alpha"]
if alpha == 0:
# When alpha is 0 the opertion can be converted to a ReLU
op.type = Op.Relu
op.name = op.name.replace("LeakyRelu", op.type.name)
return op
if ifm.dtype in (DataType.uint8, DataType.int8) and ifm.dtype == ofm.dtype:
# use LUT for int8/uint8
return convert_lrelu_to_lut(op, arch)
if check_quantized_tens_scaling_equal(ifm, ofm) and ifm.dtype == ofm.dtype == DataType.int16 and alpha > 0:
# use LeakyRelu unmodified for int16 with equal input/output scaling and positive alpha
return op
return convert_lrelu_to_mul_max(op, arch)
def convert_tanh_sigmoid_to_lut(op, arch, nng):
# Converts int8/uint8 Sigmoid and Tanh to a LUT based solution
if op.type == Op.Sigmoid:
return convert_to_lut8(op, clamp_sigmoid, "sigmoid")
elif op.type == Op.Tanh:
return convert_to_lut8(op, math.tanh, "tanh")
return op
def fuse_activation_function_with_prev(op, arch, nng):
# if op is a no-op: attempts to move the activation function to the preceding op
if not op.attrs.get("is_nop", False) or op.activation is None:
return op
ifm, ofm = op.get_ifm_ofm()
if ifm is None or ofm is None:
return op
# finds the input(s) to the operation
prev_op = ifm.ops[0]
# Note: the below checks on prev_op require that a first optimize pass on the full graph has been performed
fuse = (
prev_op.run_on_npu
and prev_op.type.npu_block_type != NpuBlockType.Default
and len(ifm.ops) == 1
and len(prev_op.outputs[0].consumers()) == 1
and prev_op.activation is None
)
if op.activation_lut is not None and arch.shram_reserved_unused_banks == 0:
# TODO: if SHRAM LUT space is shared with SHRAM ACC (32, 64 MAC),
# LUT currently only works correctly for elementwise ops
fuse = False
if not fuse:
return op
# Move the fused activation function + corresponding info to prev_op
prev_op.activation = op.activation
prev_op.forced_output_quantization = op.forced_output_quantization
if op.activation_lut is not None:
prev_op.set_activation_lut(op.activation_lut)
# Bypass op
prev_op.set_output_tensor(ofm)
DebugDatabase.add_optimised(prev_op, prev_op)
return op
def _leading_pad_ok(leading_pad, stride, kernel_size):
# If kernel size // 2 > stride, then (left, top) padding must be a multiple of stride,
# otherwise replacing PAD by hardware padding would iterate the wrong IFM rows/columns
max_size = kernel_size // 2
return leading_pad == max_size or max_size <= stride or leading_pad % stride == 0
def replace_pad_by_hw_pad(op: Operation, arch, nng):
"""
Tries to completely remove a PAD operator by using hardware padding.
E.g. a PAD operation that pads 1, followed by a CONV with VALID padding and kernel size 3
is rewritten such that the PAD is removed, and the CONV uses SAME padding.
Converts tens1 -> PAD -> tens2 -> CONV to tens1 -> CONV
if both operations can be run on the NPU.
This is the most efficient way to implement PAD, but cannot be done for all pad sizes.
"""
if (
(op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op() or op.type.is_avgpool_op())
and op.type not in (Op.Conv2DBackpropInput, Op.Conv2DBackpropInputSwitchedBias)
and op.run_on_npu
and op.attrs["padding"] == Padding.VALID
):
pad_op = op.ifm.ops[0]
if pad_op.type != Op.Pad or not pad_op.run_on_npu:
return op
if pad_op.ifm.dtype != pad_op.ofm.dtype or not check_quantized_tens_scaling_equal(pad_op.ofm, pad_op.ifm):
return op
top, left, bottom, right = get_pad_values_from_input(pad_op.inputs[1].values)
k = op.kernel
k_w, k_h = k.dilated_wh()
# Check if the PAD operator can be replaced by hardware padding
if left > k_w // 2 or right > k_w // 2 or top > k_h // 2 or bottom > k_h // 2:
# Too much padding, it would require hardware padding to actually insert zeros
return op
if not _leading_pad_ok(top, k.stride.y, k_h) or not _leading_pad_ok(left, k.stride.x, k_w):
return op
if op.type.is_avgpool_op():
# For average pool, hardware padding can only be used if padding is 0 or kernel size / 2
for pad, k_size in (
(left, k_w),
(right, k_w),
(top, k_h),
(bottom, k_h),
):
if pad not in (0, k_size // 2):
return op
# Average pool is converted to depthwise, because NPU average pool + same padding
# has a special implementation that is different from PAD followed by average pool with
# valid padding.
k_w, k_h = op.kernel.width, op.kernel.height
ifm = op.ifm
# Remember other inputs
other_inputs = op.inputs[1:]
# Create a weight tensor, all weights are set to 1/(kernel width * kernel height)
quantization = QuantizationParameters(0.0, 255.0)
quantization.scale_f32 = 1.0 / (k_w * k_h)
quantization.zero_point = 0
shape = [k_h, k_w, 1, op.ofm.shape[-1]]
weights = np.full(shape, 1)
weight_tens = create_const_tensor(
op.name + "_weights",
shape,
op.ifm.dtype,
weights,
purpose=TensorPurpose.Weights,
quantization=quantization,
)
weight_tens.values = weights
op.type = Op.DepthwiseConv2DBias
op.inputs = []
op.add_input_tensor(ifm)
op.add_input_tensor(weight_tens)
# Add bias tensor, all biases set to 0
op.inputs.append(None)
fixup_bias_tensors(op, arch, nng, DataType.int32)
# Add other inputs
op.inputs.extend(other_inputs)
op.rounding_mode = NpuRoundingMode.NATURAL
# Bypass the PAD operator
op.set_input_tensor(pad_op.ifm, 0)
# Adjust the padding attributes of the convolution operator
op.attrs["padding"] = Padding.EXPLICIT
op.attrs["explicit_padding"] = (top, left, bottom, right)
op.set_ifm_ofm_shapes()
DebugDatabase.add_optimised(op, op)
return op
def convert_pad(op: Operation, arch, nng):
"""
Rewrites PAD operator to an average pool that copies the IFM to the OFM
+ up to 4 average pool operators that fill the OFM with zeros at the borders.
This is done as fall-back for the PAD operators that remain after replace_pad_by_hw_pad
"""
if op.type != Op.Pad or not op.run_on_npu:
return op
top, left, bottom, right = get_pad_values_from_input(op.inputs[1].values)
ifm = op.ifm
assert ifm is not None
ifm_shape = op.ifm_shapes[0]
ofm = op.ofm
assert ofm is not None
ofm.ops = []
ofm_shape = op.ofm_shapes[0]
# Average pool op that copies IFM to the right place inside the OFM
shp0 = Shape4D(0, 0, 0, 0)
shp_top = shp0.with_height(top)
avgpool_op = create_avg_pool_for_concat(op, op.name + "_main", ifm, ifm_shape, shp_top.with_width(left))
avgpool_op.activation = op.activation
quant = ofm.quantization
pad_value = quant.zero_point
# Add operations that fill the borders of the OFM
if top > 0:
shape = Shape4D(1, top, ofm_shape.width, ofm_shape.depth)
zero_tens = create_const_tensor(
op.name + "_top", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], quantization=quant
)
# If top/bottom or left/right are equal, the const tensors can be allocated to the same address
zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values))
create_avg_pool_for_concat(op, op.name + "_top", zero_tens, shape, shp0)
if bottom > 0:
shape = Shape4D(1, bottom, ofm_shape.width, ofm_shape.depth)
zero_tens = create_const_tensor(
op.name + "_bottom",
shape.as_list(),
ofm.dtype,
shape.elements() * [pad_value],
quantization=quant,
)
zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values))
create_avg_pool_for_concat(
op, op.name + "_bottom", zero_tens, shape, shp0.with_height(ofm_shape.height - bottom)
)
if left > 0:
shape = Shape4D(1, ifm_shape.height, left, ofm_shape.depth)
zero_tens = create_const_tensor(
op.name + "_left", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], quantization=quant
)
zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values))
create_avg_pool_for_concat(op, op.name + "_left", zero_tens, shape, shp_top)
if right > 0:
shape = Shape4D(1, ifm_shape.height, right, ofm_shape.depth)
zero_tens = create_const_tensor(
op.name + "_right", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], quantization=quant
)
zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values))
create_avg_pool_for_concat(
op, op.name + "_right", zero_tens, shape, shp_top.with_width(ofm_shape.width - right)
)
op.type = Op.ConcatTFLite
return avgpool_op
def fixup_bias_tensors(op, arch, nng, dtype=None):
if op.type.needs_bias() and op.bias is None:
# Op has no bias, add bias tensor filled with zeros
nr_biases = op.inputs[1].shape[-1]
bias_values = [0] * nr_biases
# The DataType of the bias tensor can be explicitly provided or deduced from the ifm
# DataType. Default is int32 bias for 8-bit ifms and int64 for int16 ifms.
# For int16 the selected bias DataType will have an impact on the scaling
# used when encoding the scales and biases later. The default mode will match the
# refence with reduced scaling for int64 bias.
# This means that in cases (in the graph optimiser) where DepthwiseConv2DBias
# is used to emulate average pool int32 bias should be selected for full precision
# int16 scaling.
if dtype is None:
dtype = DataType.int64 if op.ifm.dtype == DataType.int16 else DataType.int32
bias_tensor = create_const_tensor(op.name + "_bias", [nr_biases], dtype, bias_values)
op.set_input_tensor(bias_tensor, op.type.info.indices.biases[0])
return op
def detect_asymmetric_weights(op):
# Check all ops (cpu and npu)
if op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op():
if op.ifm.dtype in (DataType.int8, DataType.int16):
if not np.all(op.weights.quantization.zero_point == 0):
print(f"Warning: Op {op.type} '{op.name}' has asymmetric weights.", end=" ")
return True
return False
def fixup_asymmetric_weights(op, arch, nng):
if detect_asymmetric_weights(op):
if op.run_on_npu:
print("Zero points have been adjusted.")
op.weights.quantization.zero_point *= 0
return op
def check_asymmetric_weights(op, arch, nng):
# This function can modify the run_on_npu flag which causes an operator to be placed on the CPU. It is usually only
# set by the supported operator checks. Therefore, it should be run immediately after those checks to avoid the
# possibility of other graph optimiser functions modify the operator (that is later run on the CPU)
if detect_asymmetric_weights(op):
if op.run_on_npu:
print("To run the operator on Ethos-U use the option --force-symmetric-int-weights")
op.run_on_npu = False
return op
def fixup_or_check_asymmetric_weights(force_symmetric_int_weights):
if force_symmetric_int_weights:
return fixup_asymmetric_weights
else:
return check_asymmetric_weights
def convert_mean_to_depthwise_conv_or_avgpool(op, arch, nng):
if op.type == Op.Mean and op.run_on_npu:
inp, axis = op.inputs
shape = inp.shape
ofm_shape = op.ofm.shape
dims = len(shape)
dims_ofm = len(ofm_shape)
# Height and width axes have different index depending on dimensions
if axis.shape == [] or axis.shape[0] == 1: # single axis
axis = int(axis.values) if len(axis.shape) == 0 else int(axis.values[0])
if dims in (2, 3):
if axis == 0:
h, w = shape[axis], 1
else:
h, w = 1, shape[axis]
else:
if axis == 1:
h, w = shape[axis], 1
else:
h, w = 1, shape[axis]
else: # multiple axes
axis = sorted(axis.values)
h, w = [shape[i] for i in axis]
# Set necessary depthwise attributes
op.attrs.update(
{
"padding": Padding.VALID,
"stride_h": 1,
"stride_w": 1,
"strides": (1, 1, 1, 1),
"depth_multiplier": 1,
"channel_multiplier": 1,
"dilation_h_factor": 1,
"dilation_w_factor": 1,
"dilation": (1, 1, 1, 1),
}
)
# Change op type
op.type = Op.DepthwiseConv2DBias
# Set IFM/OFM shapes after changing op type
op.set_ifm_ofm_shapes()
weight_scale, bias = 1, 0
ofmq, ifmq = op.ofm.quantization, inp.quantization
if ifmq.is_scaling_equal(ofmq):
# Here we can just use a simple AvgPool with truncating rounding,
# as we're emulating simple integer division.
op.rounding_mode = NpuRoundingMode.TRUNCATE
op.type = Op.AvgPool
op.attrs.update({"ksize": (1, h, w, 1), "filter_height": h, "filter_width": w})
else:
op.rounding_mode = NpuRoundingMode.NATURAL
weight_scale = 1 / (h * w)
# Input zero point is adjusted after mean calculation, so we emulate that with a bias
bias = -ifmq.zero_point * h * w
fiq = ifmq.clone()
fiq.zero_point = 0
op.forced_input_quantization = fiq
# Change dimensions to 4
def extend_dims(dim, in_shape):
if dim < 4:
in_shape = [1] + in_shape
if dim == 2:
in_shape += [1]
return in_shape
if dims < 4 or dims_ofm < 4:
# Fix the ofm dimension when keep_dims is false
# e.g. IFM=1xHxWxC axis=2 OFM=1xHxC, the ofm_shape should be 1xHx1xC, not 1x1xHxC
if isinstance(axis, int) and dims_ofm + 1 == dims:
ofm_shape.insert(axis, 1)
elif isinstance(axis, list) and (dims_ofm + len(axis) == dims):
for i in axis:
ofm_shape.insert(i, 1)
shape = extend_dims(dims, shape)
dims_ofm = len(ofm_shape)
ofm_shape = extend_dims(dims_ofm, ofm_shape)
op.set_ifm_ofm_shapes()
# If height is greater than max kernel height, reshape from HxW to 1x(HxW)
weight_shape = None
if (h > 64 and op.type == Op.DepthwiseConv2DBias) or (h > 256 and op.type == Op.AvgPool):
# This can only happen and be done for multiple axes, and
# h * w <= 256 for DepthwiseConv2DBias
# h * w <= 4096 for AvgPool
# which is checked in supported ops
shape = [shape[0], 1, h * w, shape[3]]
op.ifm_shapes[0] = Shape4D(shape)
weight_shape = [1, h * w, shape[3], shape[0]]
if h > 256 and op.type == Op.AvgPool:
op.attrs.update({"ksize": (1, 1, h * w, 1), "filter_height": 1, "filter_width": h * w})
# If the AvgPool version is used, we don't need to do anything else
if op.type == Op.AvgPool:
DebugDatabase.add_optimised(op, op)
return op
# Make unit weight tensor quantization
weight_quant = ifmq.clone()
weight_quant.min = 0
weight_quant.max = 255
weight_quant.scale_f32 = weight_scale
weight_quant.zero_point = 0
if weight_shape is None:
# Set weight shape to [H,W,C,B]
weight_shape = [h, w, shape[3], shape[0]]
# Add unit weight tensor
op.set_input_tensor(
create_const_tensor(
"weights",
weight_shape,
inp.dtype,
np.ones(weight_shape),
quantization=weight_quant,
),
1,
)
op.weights.values = np.reshape(op.inputs[1].values, weight_shape)
# Add bias tensor
bias_shape = [shape[-1]]
op.inputs.append(create_const_tensor("bias", bias_shape, DataType.int32, np.ones(bias_shape) * bias))
DebugDatabase.add_optimised(op, op)
return op
def optimise_quantize(op: Operation, arch, nng):
if op.type == Op.Quantize and op.run_on_npu:
ifm, ofm = op.get_ifm_ofm()
input_values = ifm.values
# Guard clause - input not const or no values to quantize
if ifm.ops[0].type != Op.Const or input_values is None:
return op
# Singular val in numpy array, convert to indexable array
if input_values.ndim == 0:
input_values = np.array([input_values])
# requantized int8 to int8 or int16 to int16
if ifm.dtype == ofm.dtype == DataType.int8 or ifm.dtype == ofm.dtype == DataType.int16:
# scale needs to use double precision to match TFLite reference kernel
effective_scale = np.float64(ifm.quantization.scale_f32) / np.float64(ofm.quantization.scale_f32)
effective_multiplier, effective_shift = quantise_scale(effective_scale)
requantized_vals = []
for val in input_values.flatten():
input_val = val - ifm.quantization.zero_point
ofm_val = fp_math.multiply_by_quantized_multiplier(input_val, effective_multiplier, effective_shift)
ofm_val += ofm.quantization.zero_point
clamped_ofm_value = max(min(ofm_val, ofm.quantization.quant_max), ofm.quantization.quant_min)
requantized_vals.append(clamped_ofm_value)
ofm.values = np.array(requantized_vals, ofm.dtype.as_numpy_type())
ofm.values.shape = input_values.shape
# Case: Float input - quantize to int
elif ifm.dtype.type == BaseType.Float:
quantized_vals = []
for val in input_values:
# Derive quantized value
quant_val = (val / ofm.quantization.scale_f32) + ofm.quantization.zero_point
clamped_quantized_val = np.clip(quant_val, ofm.quantization.quant_min, ofm.quantization.quant_max)
quantized_vals.append(clamped_quantized_val)
# Pass the statically calculated quant val to output tensor
ofm.values = np.array(quantized_vals, ofm.dtype.as_numpy_type())
# Unsupported data type
else:
return op
# Make quantize op const and disconnect from parent node
# Remove reference of the current quant op from the parent tensor's consumer list
ifm.consumer_list = [consumer for consumer in ifm.consumer_list if consumer.op_index != op.op_index]
# Clear any references to parent node
op.inputs = []
# Convert this quantize op to const
op.type = Op.Const
return op
def convert_shape_op_to_constant_tensor(op: Operation, arch, nng):
"""Static optimisation for SHAPE operator output value known at compile time"""
# Disconnect SHAPE operator from its parent and transform SHAPE OP into constant
if op.type == Op.Shape and op.run_on_npu:
ifm, ofm = op.get_ifm_ofm()
if len(ifm.shape) != ofm.shape[0]:
return op
# Remove reference of the current shape op from the parent tensor's consumer list
ifm.consumer_list = [consumer for consumer in ifm.consumer_list if consumer.op_index != op.op_index]
# Clear any references to parent node
op.inputs = []
# Convert this SHAPE op to const
op.type = Op.Const
# Add size calculation to shape output tensors
ofm.values = np.array(ifm.shape)
return op
def fixup_dilation_gt2(op, arch, nng):
assert op.run_on_npu
if op.type == Op.Conv2DBias or op.type == Op.DepthwiseConv2DBias:
dilation_w, dilation_h = op.get_kernel_dilation()
# if dilation in either axis is greater than that supported by the hardware then we must manually dilate the
# kernel
if dilation_w > 2 or dilation_h > 2:
kernel_w, kernel_h = op.get_kernel_size()
kernel_ic = op.weights.shape[-2]
kernel_oc = op.weights.shape[-1]
# if the dilation is a multiple of 2 then the hardware dialtion can be enabled to provide that multiple
# of 2. this allows the kernel size to be reduced (via the scaled dilation) by half in that dimension.
# odd = 1, even = 2
hw_dilation_h = 1 if (dilation_h & 1) else 2
hw_dilation_w = 1 if (dilation_w & 1) else 2
scale_dilation_h = dilation_h // hw_dilation_h
scale_dilation_w = dilation_w // hw_dilation_w
# create new empty kernel (HWIO format)
new_kernel_h = (kernel_h - 1) * scale_dilation_h + 1
new_kernel_w = (kernel_w - 1) * scale_dilation_w + 1
new_kernel_shape = [new_kernel_h, new_kernel_w, kernel_ic, kernel_oc]
new_kernel_values = np.zeros(new_kernel_shape, dtype=op.weights.values.dtype)
# copy the original kernel values into the new sparse kernel
for h in range(0, kernel_h):
for w in range(0, kernel_w):
new_h = h * scale_dilation_h
new_w = w * scale_dilation_w
new_kernel_values[new_h, new_w, :, :] = op.weights.values[h, w, :, :]
# update the weight tensor with the new dilated kernel
op.weights.shape = new_kernel_shape
op.weights.values = new_kernel_values
# enable(=2) / disable(=1) hardware dilation
op.attrs["dilation"] = (1, hw_dilation_h, hw_dilation_w, 1) # nhwc format
op.attrs["dilation_h_factor"] = hw_dilation_h
op.attrs["dilation_w_factor"] = hw_dilation_w
return op
def supported_operator_check(op, arch, nng):
op.run_on_npu = arch.tflite_supported_operators.is_operator_supported(op)
return op
def tflite_optimise_graph(nng, arch, force_symmetric_int_weights):
# Compile time static optimisations
optimisation_list = [
optimise_quantize,
convert_shape_op_to_constant_tensor,
fixup_or_check_asymmetric_weights(force_symmetric_int_weights),
]
for idx, sg in enumerate(nng.subgraphs):
nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
nng,
sg,
arch,
[],
optimisation_list,
rewrite_unsupported=False,
)
# Pre-processing step
pre_process_list = [supported_operator_check, set_ifm_ofm_op_shapes]
for idx, sg in enumerate(nng.subgraphs):
nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
nng,
sg,
arch,
[],
pre_process_list,
rewrite_unsupported=False,
)
# Handle Concat Ops
for idx, sg in enumerate(nng.subgraphs):
rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [rewrite_concat_ops])
sg.refresh_after_modification()
# Handle Split Ops
for idx, sg in enumerate(nng.subgraphs):
nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
nng,
sg,
arch,
[],
[rewrite_unpack_output, rewrite_stridedslice_output, convert_nop_split_to_identity],
rewrite_unsupported=False,
)
for idx, sg in enumerate(nng.subgraphs):
nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
nng,
sg,
arch,
[rewrite_split_ops],
[],
rewrite_unsupported=False,
)
# Bypass or rewrite memory only operators
for idx, sg in enumerate(nng.subgraphs):
nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
nng,
sg,
arch,
[],
[bypass_memory_only_ops],
rewrite_unsupported=False,
)
# Rewrite of operators
op_rewrite_list = [
set_tensor_equivalence,
convert_mean_to_depthwise_conv_or_avgpool,
convert_depthwise_to_conv,
convert_conv_to_fc,
convert_softmax,
convert_prelu,
convert_mul_max_to_abs_or_lrelu,
convert_lrelu,
fixup_strided_conv,
convert_hardswish_to_lut,
rewrite_fully_connected_input,
convert_batched_fc_shape,
fixup_conv2d_backprop,
fixup_relus_with_differing_ifm_ofm_scaling,
reorder_depthwise_weights,
convert_argmax_to_depthwise_conv_and_max_pool,
fixup_resize,
fixup_bias_tensors,
fixup_asymmetric_weights,
convert_tanh_sigmoid_to_lut,
replace_pad_by_hw_pad,
fixup_dilation_gt2,
]
for idx, sg in enumerate(nng.subgraphs):
nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
nng,
sg,
arch,
[],
op_rewrite_list,
rewrite_unsupported=False,
)
for idx, sg in enumerate(nng.subgraphs):
# remove passthrough tensors and attempt further optimizations
nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
nng,
sg,
arch,
[remove_passthrough_tensor],
[fuse_activation_function_with_prev, convert_pad, add_padding_fields],
)
# Removal of SplitSliceRead, need to be done after optimisation has been performed,
# since ifm/ofm_shapes are of importance to this function
for sg in nng.subgraphs:
rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_SplitSliceRead])
sg.refresh_after_modification()
# Make sure that const optimisations on subgraph outputs are handled correctly
for sg in nng.subgraphs:
for ofm in sg.output_tensors:
if ofm.is_const and ofm.ops[0].type_changed:
# Subgraph output cannot be const - insert a memory copy
op = ofm.ops[0]
ofm_clone = ofm.clone()
ofm_clone.values = ofm.values
ofm.values = None
zero = create_const_tensor("zero", [1], ofm.dtype, [0], quantization=ofm.quantization)
memcpy = create_add_nop(f"{ofm.name}_copy")
memcpy.add_input_tensor(ofm_clone)
memcpy.add_input_tensor(zero)
memcpy.set_output_tensor(ofm)
memcpy.set_ifm_ofm_shapes()
op.set_output_tensor(ofm_clone)
DebugDatabase.add_optimised(op, memcpy)
return nng