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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:
# The TFLiteSupportedOperators class which is a collection of all TFLite supported operators and parameter checks.
from collections import defaultdict
import numpy as np
from .data_type import DataType
from .numeric_util import full_shape
from .operation import Op
from .operation import Padding
from .supported_operators_util import docstring_format_args
from .supported_operators_util import list_formatter
from .tensor import check_quantized_tens_scaling_equal
from .tflite_mapping import BUILTIN_OPERATOR_UNKNOWN
from .tflite_mapping import optype_to_builtintype
def _optype_formatter(op_list):
# Convert internal op types to external names
output = map(optype_to_builtintype, op_list)
# Remove UNKNOWNs
output = (x for x in output if x is not BUILTIN_OPERATOR_UNKNOWN)
return list_formatter(output)
class TFLiteSupportedOperators:
# Categorised lists of supported operators
npu_pre_ops = set(
(
Op.SplitSliceRead,
Op.Shape,
)
)
convolution_ops = set(
(
Op.Conv2DBias,
Op.Conv2D,
Op.QuantizedConv2D,
)
)
depthwise_convolution_ops = set((Op.DepthwiseConv2DBias,))
transpose_convolution_ops = set((Op.Conv2DBackpropInput,))
convolution_like_ops = convolution_ops | depthwise_convolution_ops | transpose_convolution_ops
max_pooling_ops = Op.op_set(Op.is_maxpool_op)
avg_pooling_ops = Op.op_set(Op.is_avgpool_op)
pooling_ops = set((Op.ReduceSum,)) | max_pooling_ops | avg_pooling_ops
resizing_ops = Op.op_set(Op.is_resize_op)
fc_vector_products = set(
(
Op.QuantizedMatMul,
Op.MatMul,
Op.FullyConnected,
)
)
mac_main_ops = (
# RNN/LSTM/GRU
set((Op.BlockLSTM,))
# conv/depthwiseconv/transposeconv
| convolution_like_ops
# pooling
| pooling_ops
# resizing/upscaling
| resizing_ops
# FC layers
| fc_vector_products
# Mean (converts to depthwise conv)
| set((Op.Mean,))
# ArgMax (converts to depthwise conv and maxpool)
| set((Op.ArgMax,))
)
unary_elem_wise_main_ops = Op.op_set(Op.is_unary_elementwise_op)
binary_elem_wise_min_max_ops = set(
(
Op.Minimum,
Op.Maximum,
)
)
binary_elem_wise_shift_ops = set(
(
Op.SHL,
Op.SHR,
)
)
binary_elem_wise_add_mul_sub = set(
(
Op.Add,
Op.Mul,
Op.Sub,
)
)
binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops
pad_ops = set((Op.Pad,))
supported_int32_tensor_ops = (
set((Op.ReduceSum, Op.CLZ, Op.Shape, Op.ArgMax)) | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
)
relu_ops = set(
(
Op.Relu,
Op.Relu6,
Op.ReluN1To1,
Op.Clip,
)
)
activation_ops = relu_ops | set(
(
Op.Tanh,
Op.Sigmoid,
Op.Softmax,
Op.HardSwish,
Op.LeakyRelu,
Op.Prelu,
)
)
npu_post_ops = (
# activation functions
activation_ops
# concatenation write direction
| set((Op.ConcatSliceWrite,))
# Quantization
| set((Op.Quantize,))
)
split_ops = set(
(
Op.Split,
Op.SplitV,
Op.StridedSlice,
Op.Slice,
Op.UnpackReshaped,
Op.Unpack,
)
)
concat_ops = set(
(
Op.Concat,
Op.ConcatTFLite,
Op.PackReshaped,
Op.Pack,
)
)
memory_only_ops = (
set(
(
Op.Reshape,
Op.QuantizedReshape,
Op.Squeeze,
Op.ExpandDims,
)
)
| concat_ops
| split_ops
)
per_axis_quant_ops = convolution_like_ops # per-axis/channel quantization only currently supported for conv ops
supported_fused_activations = relu_ops | set(
(
Op.Tanh,
Op.Sigmoid,
Op.LUT,
)
)
supported_operators = npu_pre_ops | mac_main_ops | elem_wise_main_ops | pad_ops | npu_post_ops | memory_only_ops
# Supported data types
supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32))
supported_faf_dtypes = set((DataType.uint8, DataType.int8, DataType.int16))
supported_bias_dtypes = set((DataType.int32, DataType.int64))
supported_pad_dtypes = set((DataType.int32, DataType.int64))
# Defined ranges for allowed values:
tens_dim_range = (1, 65535)
stride_range = (1, 3)
dilated_height_range = (1, 64)
dilated_product_range = (1, 64 * 64)
weights_limit = 127 * 65536
filter_range = (1, 8)
filter_height_range = (1, 256)
filter_product_range = (1, 256 * 256)
mean_kernel_product = 64 * 64
mean_kernel_product_avgpool = 256 * 256
def __init__(self):
# Setup the generic constraints. Note: the order matters
self.generic_constraints = []
self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_dtype)
self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_int32_ops)
self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_dimension)
self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_quant_per_axis)
self.generic_constraints.append(TFLiteSupportedOperators.constraint_batch_size)
self.generic_constraints.append(TFLiteSupportedOperators.constraint_faf)
self.generic_constraints.append(TFLiteSupportedOperators.constraint_faf_type)
# Setup generic constraint exceptions
self.generic_constraints_exceptions = defaultdict(list)
self.generic_constraints_exceptions[Op.FullyConnected].append(TFLiteSupportedOperators.constraint_batch_size)
self.generic_constraints_exceptions[Op.Softmax].append(TFLiteSupportedOperators.constraint_batch_size)
self.generic_constraints_exceptions[Op.Reshape].append(TFLiteSupportedOperators.constraint_batch_size)
self.generic_constraints_exceptions[Op.Shape].append(TFLiteSupportedOperators.constraint_batch_size)
self.generic_constraints_exceptions[Op.Squeeze].append(TFLiteSupportedOperators.constraint_batch_size)
for op_type in TFLiteSupportedOperators.split_ops - set((Op.UnpackReshaped,)):
self.generic_constraints_exceptions[op_type].append(TFLiteSupportedOperators.constraint_batch_size)
# Setup specific constraints. Note: the order matters
self.specific_constraints = defaultdict(list)
# Conv specific ops:
for op_type in TFLiteSupportedOperators.convolution_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_conv_stride)
# Conv-like checks:
for op_type in TFLiteSupportedOperators.convolution_like_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_dilated_height_range)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_dilated_product_range)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_type)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_const)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_limit)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_shape)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_type)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_40bit)
# Transpose Conv specific checks:
for op_type in TFLiteSupportedOperators.transpose_convolution_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_tconv_stride)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_tconv_same)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_tconv_valid)
# Depthwise Conv specific checks:
for op_type in TFLiteSupportedOperators.depthwise_convolution_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_depthwise_conv_stride)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_depth_multiplier)
# Pooling checks:
for op_type in TFLiteSupportedOperators.pooling_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_stride_range)
# AVG pooling specific checks:
for op_type in TFLiteSupportedOperators.avg_pooling_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_range)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_height_range_valid_pad)
self.specific_constraints[op_type].append(
TFLiteSupportedOperators.constraint_filter_product_range_valid_pad
)
# MAX pooling specific checks:
for op_type in TFLiteSupportedOperators.max_pooling_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_height_range)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_product_range)
# Resizing specific checks:
for op_type in TFLiteSupportedOperators.resizing_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_resize)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_resize_size)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_resize_attrs)
# Resize Bilinear specific checks:
self.specific_constraints[Op.ResizeBilinear].append(
TFLiteSupportedOperators.constraint_resizebi_half_pixel_centers_dims
)
# Vector Product specific checks:
for op_type in TFLiteSupportedOperators.fc_vector_products:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_type)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_const)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_shape)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_type)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_40bit)
# Element-wise checks
# Binary Min/Max specific checks:
for op_type in TFLiteSupportedOperators.binary_elem_wise_min_max_ops:
self.specific_constraints[op_type].append(
TFLiteSupportedOperators.constraint_matching_quantization_parameters
)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_broadcast_shapes)
# Binary Add/Mul/Sub specific checks:
for op_type in TFLiteSupportedOperators.binary_elem_wise_add_mul_sub:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_broadcast_shapes)
# Binary Shift specific checks:
for op_type in TFLiteSupportedOperators.binary_elem_wise_shift_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_inputs_int32)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_broadcast_shapes)
# SHL specific checks:
self.specific_constraints[Op.SHL].append(TFLiteSupportedOperators.constraint_output_int32)
# CLZ specific checks:
self.specific_constraints[Op.CLZ].append(TFLiteSupportedOperators.constraint_output_int32)
# StridedSlice specific checks:
self.specific_constraints[Op.StridedSlice].append(
TFLiteSupportedOperators.constraint_stridedslice_stride_values
)
# Pad specific checks:
self.specific_constraints[Op.Pad].append(TFLiteSupportedOperators.constraint_pad_shape)
self.specific_constraints[Op.Pad].append(TFLiteSupportedOperators.constraint_padding_dimensions)
self.specific_constraints[Op.Pad].append(TFLiteSupportedOperators.constraint_pad_type)
# Mean specific checks:
self.specific_constraints[Op.Mean].append(TFLiteSupportedOperators.constraint_mean_height_width_product_avgpool)
self.specific_constraints[Op.Mean].append(TFLiteSupportedOperators.constraint_mean_height_width_product)
self.specific_constraints[Op.Mean].append(TFLiteSupportedOperators.constraint_mean_height_single_axis)
# Reshape specific checks:
self.specific_constraints[Op.Reshape].append(TFLiteSupportedOperators.constraint_reshape_shape_constant)
# ArgMax specific checks:
self.specific_constraints[Op.ArgMax].append(TFLiteSupportedOperators.constraint_argmax_axis)
self.specific_constraints[Op.ArgMax].append(TFLiteSupportedOperators.constraint_argmax_depth)
def is_operator_supported(self, op):
ext_type = optype_to_builtintype(op.type)
if op.type not in TFLiteSupportedOperators.supported_operators:
if op.type not in (Op.Placeholder, Op.SubgraphInput, Op.Const):
print(f"Info: {ext_type} '{op.name}' is a CPU only op")
return False
op_exceptions = self.generic_constraints_exceptions[op.type]
generic_constraints = [constraint for constraint in self.generic_constraints if constraint not in op_exceptions]
for constraint in generic_constraints + self.specific_constraints[op.type]:
valid, extra = constraint(op)
if not valid:
print(f"Warning: {ext_type} '{op.name}' is not supported on the NPU. Placing on CPU instead")
print(f" - {constraint.__doc__}")
if extra:
print(f" {extra}")
return False
return True
@classmethod
@docstring_format_args([list_formatter(supported_op_dtypes)])
def constraint_tens_dtype(cls, op):
"Tensors must be of type: {}"
valid = True
extra = []
tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
if not tensors:
tensors = [tens for tens in op.inputs if tens]
for tens in tensors:
if tens.dtype not in cls.supported_op_dtypes:
valid = False
extra.append(f"Tensor '{tens.name}' has data type: {tens.dtype}")
return valid, ", ".join(extra)
@classmethod
@docstring_format_args([_optype_formatter(supported_int32_tensor_ops)])
def constraint_tens_int32_ops(cls, op):
"Tensors which are int32 are only valid when op type is: {}"
valid = True
extra = []
tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
if not tensors:
tensors = [tens for tens in op.inputs if tens]
for tens in tensors:
if (tens.dtype == DataType.int32) and (op.type not in cls.supported_int32_tensor_ops):
valid = False
extra.append(tens.name)
extra = ", ".join(extra)
return valid, f"Op has int32 tensor(s): {extra}"
@classmethod
@docstring_format_args(tens_dim_range)
def constraint_tens_dimension(cls, op):
"Tensor dimensions must be in the range [{}, {}]"
tens_min, tens_max = cls.tens_dim_range
valid = True
extra = []
tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
if not tensors:
tensors = [tens for tens in op.inputs if tens]
for tens in tensors:
if not all(tens_min <= dim <= tens_max for dim in tens.shape):
valid = False
extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}")
return valid, ", ".join(extra)
@classmethod
@docstring_format_args([_optype_formatter(per_axis_quant_ops)])
def constraint_tens_quant_per_axis(cls, op):
"Per-axis quantization is only supported for the following op types: {}"
valid = True
extra = []
if op.type not in cls.per_axis_quant_ops:
tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
for tens in tensors:
if tens.quantization and tens.quantization.is_per_axis():
valid = False
extra.append(tens.name)
return valid, "The following tensor(s) have per-axis quantization parameters: " + ", ".join(extra)
@classmethod
@docstring_format_args([_optype_formatter(supported_fused_activations)])
def constraint_faf(cls, op):
"The fused activation function (if present) must be one of type: {}"
if op.activation is None:
res = True, "Op has no fused activation function"
else:
faf = op.activation.op_type
valid = faf in cls.supported_fused_activations
res = valid, f"Op has its fused activation function as: {faf}"
return res
@classmethod
@docstring_format_args([list_formatter(supported_faf_dtypes)])
def constraint_faf_type(cls, op):
"If a fused activation function is present, the Output tensor must be one of type: {}"
if op.activation is None:
res = True, "Op has no fused activation function"
else:
valid = op.ofm.dtype in cls.supported_faf_dtypes
ext_type = optype_to_builtintype(op.activation.op_type)
res = valid, f"Op has fused activation function {ext_type}, and Output tensor data type: {op.ofm.dtype}"
return res
@classmethod
@docstring_format_args(stride_range)
def constraint_stride_range(cls, op):
"Stride values for both width and height must be in the range [{}, {}]"
w, h = op.get_kernel_stride()
stride_min, stride_max = cls.stride_range
valid = (stride_min <= w <= stride_max) and (stride_min <= h <= stride_max)
return valid, f"Op has stride WxH as: {w}x{h}"
@classmethod
@docstring_format_args(dilated_height_range)
def constraint_dilated_height_range(cls, op):
"Dilated kernel height must be in the range [{}, {}]"
h = op.kernel.area_height()
dilated_height_min, dilated_height_max = cls.dilated_height_range
valid = dilated_height_min <= h <= dilated_height_max
return valid, f"Op has dilated kernel height as: {h}"
@classmethod
@docstring_format_args(dilated_product_range)
def constraint_dilated_product_range(cls, op):
"Product of dilated kernel width and height must be in the range [{}, {}]"
product = op.kernel.area_width() * op.kernel.area_height()
dilated_product_min, dilated_product_max = cls.dilated_product_range
valid = dilated_product_min <= product <= dilated_product_max
return valid, f"Op has product of dilated kernel width and height as: {product}"
@staticmethod
def constraint_weights_type(op):
"Weight tensor must be 8-bit"
weights = op.weights
valid = weights.element_size() == 1
return valid, f"Tensor '{weights.name}' is {int(weights.element_size() * 8)}-bit"
@staticmethod
def constraint_weights_const(op):
"Weight tensor must be constant"
weights = op.weights
valid = weights.values is not None
return valid, f"Tensor '{weights.name}' has non-constant values"
@classmethod
@docstring_format_args([weights_limit])
def constraint_weights_limit(cls, op):
"The sum of the weights cannot exceed {}"
weights = op.weights
values = weights.values.astype(np.int64) - weights.quantization.zero_point
limit = np.amax(np.sum(np.absolute(values), axis=(0, 1, 2)))
valid = limit <= cls.weights_limit
return valid, f"Tensor '{weights.name}' has the sum of weights: {limit}"
@staticmethod
def constraint_bias_shape(op):
"Optional Bias tensor must be of shape: 1D"
bias = op.bias
if bias:
valid = len(bias.shape) == 1
return valid, f"Tensor '{bias.name}' has shape: {bias.shape}"
return True, "Op has no bias tensor"
@classmethod
@docstring_format_args([list_formatter(supported_bias_dtypes)])
def constraint_bias_type(cls, op):
"Optional Bias tensor must be of type: {}"
bias = op.bias
if bias:
valid = bias.dtype in cls.supported_bias_dtypes
return valid, f"Tensor '{bias.name}' has data type: {bias.dtype}"
return True, "Op has no bias tensor"
@staticmethod
def constraint_bias_40bit(op):
"Optional Bias tensor values must fit within 40-bits"
bias = op.bias
if bias and bias.dtype == DataType.int64 and bias.values is not None:
valid = all(len(bin(value)[2:]) <= 40 for value in bias.values)
return valid, f"Tensor '{bias.name}' has values larger than 40-bits"
return True, "Op has no bias tensor, or it fits in 40-bit"
@staticmethod
def constraint_batch_size(op):
"IFM Tensor batch size must be 1"
valid = True
extra = []
for tens in (op.ifm, op.ifm2):
if tens is not None:
batch_size = full_shape(4, tens.shape, 1)[0]
if batch_size != 1:
valid = False
extra.append(f"Tensor '{tens.name}' has batch size: {batch_size}")
extra = "\n ".join(extra)
return valid, extra
@staticmethod
def constraint_depth_multiplier(op):
"For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier"
depth_multiplier = op.attrs.get("depth_multiplier", 1)
if depth_multiplier > 1:
ifm_channels = op.ifm.shape[3]
ofm_channels = op.ofm.shape[3]
valid = (ifm_channels == 1) and (ofm_channels == depth_multiplier)
extra = (
f"Op has ifm_channels={ifm_channels}, ofm_channels={ofm_channels}"
f" and depth_multiplier={depth_multiplier}"
)
return valid, extra
return True, "Op has depth_multiplier=1"
@staticmethod
def constraint_conv_stride(op):
"Stride values for height must be between 1 and 3 and for width between 1 and 4"
w, h = op.get_kernel_stride()
stride_min_w_h = 1
stride_max_w = 4
stride_max_h = 3
valid = (stride_min_w_h <= w <= stride_max_w) and (stride_min_w_h <= h <= stride_max_h)
return valid, f"Op has stride WxH as: {w}x{h}"
@staticmethod
def constraint_depthwise_conv_stride(op):
"Stride values for both width and height must be between 1 and 3"
w, h = op.get_kernel_stride()
stride_min, stride_max = 1, 3
valid = (stride_min <= w <= stride_max) and (stride_min <= h <= stride_max)
return valid, f"Op has stride WxH as: {w}x{h}"
@staticmethod
def constraint_tconv_stride(op):
"Stride values for both width and height must be 2"
w = op.kernel.stride.x
h = op.kernel.stride.y
valid = (w == 2) and (h == 2)
return valid, f"Op has stride WxH as: {w}x{h}"
@staticmethod
def constraint_tconv_same(op):
"SAME padding: OFM dimensions must equal IFM dimensions multiplied by stride"
if op.attrs["padding"] == Padding.SAME:
w = op.kernel.stride.x
h = op.kernel.stride.y
ifm_shape = op.ifm.shape
ofm_shape = op.ofm.shape
valid = (ofm_shape[1] == (ifm_shape[1] * h)) and (ofm_shape[2] == (ifm_shape[2] * w))
return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and stride WxH as {w}x{h}"
return True, "Op has padding=VALID"
@staticmethod
def constraint_tconv_valid(op):
"""VALID padding: OFM dimensions must equal IFM dimensions multiplied by stride,
minus difference between kernel size and stride"""
if op.attrs["padding"] == Padding.VALID:
s_w = op.kernel.stride.x
s_h = op.kernel.stride.y
k_w = op.kernel.width
k_h = op.kernel.height
ifm_shape = op.ifm.shape
ofm_shape = op.ofm.shape
height_check = ofm_shape[1] == (ifm_shape[1] * s_h + max(k_h - s_h, 0))
width_check = ofm_shape[2] == (ifm_shape[2] * s_w + max(k_w - s_w, 0))
valid = height_check and width_check
extra = (
f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape},"
f" stride WxH as {s_w}x{s_h} and kernel WxH as {k_w}x{k_h}"
)
return valid, extra
return True, "Op has padding=SAME"
@classmethod
@docstring_format_args(filter_range)
def constraint_filter_range(cls, op):
"Kernel filter values for both width and height must be in the range [{}, {}]"
if op.attrs["padding"] == Padding.SAME:
w = op.kernel.width
h = op.kernel.height
filter_min, filter_max = cls.filter_range
valid = (filter_min <= w <= filter_max) and (filter_min <= h <= filter_max)
return valid, f"Op has kernel filter WxH as: {w}x{h}"
return True, "Op has padding=VALID"
@classmethod
@docstring_format_args(filter_height_range)
def constraint_filter_height_range(cls, op):
"Kernel filter height must be in the range [{}, {}]"
h = op.kernel.height
filter_height_min, filter_height_max = cls.filter_height_range
valid = filter_height_min <= h <= filter_height_max
return valid, f"Op has kernel filter height as: {h}"
@classmethod
@docstring_format_args(filter_product_range)
def constraint_filter_product_range(cls, op):
"Product of kernel filter width and height must be in the range [{}, {}]"
product = op.kernel.elements_wh()
filter_product_min, filter_product_max = cls.filter_product_range
valid = filter_product_min <= product <= filter_product_max
return valid, f"Op has product of kernel filter width and height as: {product}"
@staticmethod
@docstring_format_args(filter_height_range)
def constraint_filter_height_range_valid_pad(op):
"VALID padding: Kernel filter height must be in the range [{}, {}]"
if op.attrs["padding"] == Padding.VALID:
return TFLiteSupportedOperators.constraint_filter_height_range(op)
return True, "Op has padding=SAME"
@staticmethod
@docstring_format_args(filter_product_range)
def constraint_filter_product_range_valid_pad(op):
"VALID padding: Product of kernel filter width and height must be in the range [{}, {}]"
if op.attrs["padding"] == Padding.VALID:
return TFLiteSupportedOperators.constraint_filter_product_range(op)
return True, "Op has padding=SAME"
@staticmethod
def constraint_resize(op):
"""The width and height of the IFM and OFM must match one of the following criteria:
IFM W and H must both be 1
IFM must match OFM
W and H scaling must be equal and OFM W-1 and H-1 must be 2x/4x/8x IFM W-1 and H-1, if align_corners is True
W and H scaling must be equal and OFM W and H must be 2x/4x/8x IFM W and H, if align_corners is False"""
# Easier to start with False condition as very few cases result in a supported resize
valid = False
ifm_shape = op.ifm.shape
ifm_shape_h = ifm_shape[1]
ifm_shape_w = ifm_shape[2]
ofm_shape = op.ofm.shape
ofm_shape_h = ofm_shape[1]
ofm_shape_w = ofm_shape[2]
align_corners = op.attrs.get("align_corners", False)
if len(ifm_shape) == 4:
# Valid if IFM W and H are both 1, or IFM and OFM shape are the same
if ((ifm_shape_h == 1) and (ifm_shape_w == 1)) or (ifm_shape == ofm_shape):
valid = True
else:
# Valid if OFM is 2/4/8x IFM (-1 for align corners)
if align_corners:
h_upscale_factor = (ofm_shape_h - 1) / (ifm_shape_h - 1)
w_upscale_factor = (ofm_shape_w - 1) / (ifm_shape_w - 1)
else:
h_upscale_factor = ofm_shape_h / ifm_shape_h
w_upscale_factor = ofm_shape_w / ifm_shape_w
# could use either height or width. save as int because it is more usable later in graph optimiser
op.attrs["upscale_factor"] = int(h_upscale_factor)
valid = h_upscale_factor == w_upscale_factor and h_upscale_factor in (2.0, 4.0, 8.0)
return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and align_corners={align_corners}"
@staticmethod
def constraint_resize_size(op):
"The size tensor must match the output tensor shape"
valid = False
ofm_shape = op.ofm.shape
size_h, size_w = None, None
# check that the size tensor (the second input) exists, is not none, and has the correct values
if len(op.inputs) == 2 and op.inputs[1] is not None and len(op.inputs[1].values) == 2:
size_h, size_w = op.inputs[1].values
# check size and output size match
if size_h == ofm_shape[1] and size_w == ofm_shape[2]:
valid = True
return valid, f"Op has size={size_h}x{size_w} and ofm_shape={ofm_shape}."
@staticmethod
def constraint_resize_attrs(op):
"Both align_corners and half_pixel_centers can't be True"
valid = True
align_corners = op.attrs.get("align_corners", False)
half_pixel_centers = op.attrs.get("half_pixel_centers", False)
if align_corners and half_pixel_centers:
valid = False
return valid, "Op has both align_corners and half_pixel_centers set to True."
@staticmethod
def constraint_resizebi_half_pixel_centers_dims(op):
"""Half_pixel_centers for resize bilinear requires that OFM W and H is 2x IFM W and H"""
half_pixel_centers = op.attrs.get("half_pixel_centers", False)
if not half_pixel_centers:
valid = True
elif len(op.ifm.shape) >= 3:
ifm_h, ifm_w = op.ifm.shape[-3:-1]
ofm_h, ofm_w = op.ofm.shape[-3:-1]
valid = ofm_h / ifm_h == 2 and ofm_w / ifm_w == 2
else:
# Unexpected IFM shape
valid = False
return (
valid,
f"Op has ifm_shape={op.ifm.shape}, ofm_shape={op.ofm.shape} and half_pixel_centers={half_pixel_centers}",
)
@staticmethod
def constraint_pad_shape(op):
"The padding tensor must have the shape [3,2] or [4,2]"
valid = op.inputs[1].shape in ([3, 2], [4, 2])
return valid, f"The pad tensor has the shape: {op.inputs[1].shape}"
@classmethod
@docstring_format_args([list_formatter(supported_pad_dtypes)])
def constraint_pad_type(cls, op):
"Pad tensor must be of type: {}"
pad_tensor = op.inputs[1]
valid = pad_tensor.dtype in cls.supported_pad_dtypes
return valid, f"Tensor '{pad_tensor.name}' has data type: {pad_tensor.dtype}"
@staticmethod
def constraint_padding_dimensions(op):
"The pad tensor can only pad width and height"
pad_tensor = op.inputs[1].values
valid = sum(pad_tensor[-1, :]) == 0
if valid and len(pad_tensor) > 3:
valid = sum(pad_tensor[0, :]) == 0
return valid, f"First dimension padding: {pad_tensor[0,:]}, last dimension padding: {pad_tensor[-1,:]}"
@staticmethod
def constraint_stridedslice_stride_values(op):
"All Strides values must be 1"
strides = op.inputs[3]
valid = all(stride == 1 for stride in strides.values)
return valid, f"Op has strides values {strides.values}"
@staticmethod
def constraint_inputs_int32(op):
"Both Input data types must be int32"
ifm_dtype = op.ifm.dtype
ifm2_dtype = op.ifm2.dtype
valid = (ifm_dtype == DataType.int32) and (ifm2_dtype == DataType.int32)
return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}"
@staticmethod
def constraint_output_int32(op):
"OFM must be int32"
ofm_dtype = op.ofm.dtype
valid = ofm_dtype == DataType.int32
return valid, f"Op has ofm_dtype={ofm_dtype}"
@staticmethod
def constraint_matching_quantization_parameters(op):
"Both Input quantization parameters must match OFM quantization parameters"
valid = True
extra = []
if not check_quantized_tens_scaling_equal(op.ofm, op.ifm):
valid = False
extra.append(op.ifm.name)
if op.ifm2 is not None and not check_quantized_tens_scaling_equal(op.ofm, op.ifm2):
valid = False
extra.append(op.ifm2.name)
extra = ", ".join(extra)
return valid, f"Op has tensors with different quantization parameters to the OFM '{op.ofm.name}': {extra}"
@staticmethod
def constraint_broadcast_shapes(op):
"Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2"
ifm_shape = op.ifm.shape
ifm2_shape = op.ifm2.shape if op.ifm2 else None
ofm_shape = op.ofm.shape
valid = True
if ifm_shape is not None and ifm2_shape is not None:
# align trailing dimensions
size = min(len(ifm_shape), len(ifm2_shape))
for i, i2, o in zip(ifm_shape[-size:], ifm2_shape[-size:], ofm_shape[-size:]):
mi = max(i, i2)
# Input dimensions should match or one should be of dimension 1
# Output dimension should match the largest input dimension, together
# with constraint_match_either_shapes ensures broadcast from only one input
if not (i == i2 or i == 1 or i2 == 1) or o != mi:
valid = False
break
return valid, f"Op has ifm_shape={ifm_shape} and ifm2_shape={ifm2_shape}"
@classmethod
@docstring_format_args([mean_kernel_product_avgpool])
def constraint_mean_height_width_product_avgpool(cls, op):
"""Product of height and width must be no greater than {}"""
shape = op.inputs[0].shape
hi = 0 if len(shape) < 4 else 1
h, w = shape[hi : hi + 2]
max_prod = cls.mean_kernel_product_avgpool
return h * w <= max_prod, f"Product of height and width is {h * w}"
@classmethod
@docstring_format_args([mean_kernel_product])
def constraint_mean_height_width_product(cls, op):
"""Product of height and width must be no greater than {} when:
IFM and OFM have different scale or zero point; or
'keep_dims' is True"""
ifmq, ofmq = op.ifm.quantization, op.ofm.quantization
keep_dims = op.attrs.get("keep_dims")
# doesn't apply, size is checked by constraint_mean_height_width_product_avgpool
if not keep_dims and ifmq.scale_f32 == ofmq.scale_f32 and ifmq.zero_point == ofmq.zero_point:
return True, ""
shape = op.inputs[0].shape
hi = 0 if len(shape) < 4 else 1
h, w = shape[hi : hi + 2]
max_prod = cls.mean_kernel_product
return h * w <= max_prod, f"Product of height and width is {h * w}"
@classmethod
@docstring_format_args([filter_height_range[1], dilated_height_range[1]])
def constraint_mean_height_single_axis(cls, op):
"""For single axis averages across the height dimension:
IFM height must be no greater than {} if the IFM and OFM scale and zero point match; otherwise
IFM height must be no greater than {} if the IFM and OFM scale or zero point do not match"""
inp, axis = op.inputs
if axis.shape == [] or axis.shape[0] == 1: # single axis
axis = int(axis.values) if len(axis.shape) == 0 else int(axis.values[0])
else:
# Multiple axes
return True, ""
shape = inp.shape
if len(shape) < 3:
# No height dimension present in IFM
return True, ""
if axis != len(shape) - 3:
# Not averaging across the height dimension
return True, ""
h = shape[axis]
ifm, ofm = op.get_ifm_ofm()
if check_quantized_tens_scaling_equal(ifm, ofm):
return h <= cls.filter_height_range[1], f"Height is {h}, IFM and OFM quantizations match"
else:
return h <= cls.dilated_height_range[1], f"Height is {h}, IFM and OFM quantizations do not match"
@staticmethod
def constraint_reshape_shape_constant(op):
"Shape must be constant"
valid = True
extra = []
reshape_tens = op.inputs[1]
if reshape_tens is not None:
# constant inputs have either no driving operator or a const one
# create a list of non-constant inputs
if not (len(reshape_tens.ops) == 0 or reshape_tens.ops[0].type == Op.Const):
valid = False
extra.append(reshape_tens.name)
extra = ", ".join(extra)
return valid, f"Op has non-const input(s): {extra}"
@staticmethod
def constraint_argmax_axis(op):
"Operation must be performed along the depth axis"
inp_dims = len(op.inputs[0].shape)
axis = op.inputs[1].values
return (
axis in (inp_dims - 1, -1),
f"Axis is {axis} and number of input dimensions is {inp_dims}",
)
@staticmethod
def constraint_argmax_depth(op):
"IFM depth must be no greater than 127"
ifm_depth = op.inputs[0].shape[-1]
return ifm_depth <= 127, f"IFM depth is {ifm_depth}"
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