# Copyright (C) 2020-2021 Arm Limited or its affiliates. All rights reserved. # # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the License); you may # not use this file except in compliance with the License. # You may obtain a copy of the License at # # www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an AS IS BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Description: # 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 .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,)) 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 = set((Op.ResizeBilinear,)) 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,)) ) 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, ) ) | 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)) 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) dilation_range = (1, 2) 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_int8 = 16 * 16 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_faf) self.generic_constraints.append(TFLiteSupportedOperators.constraint_faf_type) # Setup specific constraints. Note: the order matters self.specific_constraints = defaultdict(list) # Conv-like checks: for op_type in TFLiteSupportedOperators.convolution_like_ops: self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_stride_range) self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_dilation_range) 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_type) self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_40bit) self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_batch_size) # Depthwise Conv specific checks: for op_type in TFLiteSupportedOperators.depthwise_convolution_ops: self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_depth_multiplier) # 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) # Pooling checks: for op_type in TFLiteSupportedOperators.pooling_ops: self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_batch_size) 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_bilinear_resize_attrs) # 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_type) self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_40bit) # Element-wise checks: for op_type in TFLiteSupportedOperators.elem_wise_main_ops: self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_elemwise_batch_size) # 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_batch_size) 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_width_product_int8) 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) 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 for constraint in self.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.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(dilation_range) def constraint_dilation_range(cls, op): "Dilation factor values for both width and height must be in the range [{}, {}]" w, h = op.get_kernel_dilation() dilation_min, dilation_max = cls.dilation_range valid = (dilation_min <= w <= dilation_max) and (dilation_min <= h <= dilation_max) return valid, f"Op has dilation factor 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}" @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" ifm = op.ifm valid = ifm.shape[0] == 1 return valid, f"Tensor '{ifm.name}' has batch size: {ifm.shape[0]}" @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_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 OFM W and H must be equal and 2/4/8x IFM -1, if align_corners is True OFM W and H must be equal and 2/4/8x IFM, 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 ofm_shape = op.ofm.shape 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[1] == 1) and (ifm_shape[2] == 1)) or (ifm_shape == ofm_shape): valid = True else: # Valid if OFM is 2/4/8x IFM (-1 for align corners) w_upscale_factor = (ofm_shape[1] + 1) / ifm_shape[1] if align_corners else ofm_shape[1] / ifm_shape[1] h_upscale_factor = (ofm_shape[2] + 1) / ifm_shape[2] if align_corners else ofm_shape[2] / ifm_shape[2] valid = w_upscale_factor == h_upscale_factor and w_upscale_factor in [2, 4, 8] return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and align_corners={align_corners}" @staticmethod def constraint_bilinear_resize_attrs(op): "half_pixel_centers are not supported" valid = True if op.attrs.get("half_pixel_centers"): valid = False return valid, f"Op has half_pixel_centers set to {not valid}." @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_elemwise_batch_size(op): "Batch size must be 1 for Input tensors with more than 2 dimensions" valid = True extra = [] for tens in (op.ifm, op.ifm2): # Unary ops have ifm2 as None if tens is not None: if (len(tens.shape) > 2) and (tens.shape[0] != 1): valid = False extra.append(tens.name) extra = ", ".join(extra) return valid, f"Op has invalid input tensors: {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([mean_kernel_product_int8]) def constraint_mean_height_width_product_int8(cls, op): """Product of IFM height and width must be no greater than {} when: The IFM shape has 4 dimensions; and The axis indices specify reduction across 2 dimensions; and The axis indices correspond to the width and height dimensions of the IFM; and 'keep_dims' is True; and IFM datatype is int8""" shape = op.ifm.shape axis = int(op.inputs[1].values) if op.inputs[1].shape == [] else list(op.inputs[1].values) # doesn't apply, size is checked by constraint_mean_height_width_product_avgpool # and constraint_mean_height_width_product if ( len(shape) != 4 or op.ifm.dtype != DataType.int8 or not op.attrs.get("keep_dims") or axis not in ([1, 2], [2, 1]) ): return True, "" h = shape[-3] w = shape[-2] max_prod = cls.mean_kernel_product_int8 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}"