# 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 SupportedOperators class which is a collection of all supported operators and parameter checks. from collections import defaultdict import numpy as np from .data_type import BaseType from .data_type import DataType from .numeric_util import is_integer from .operation import get_slice_offsets from .operation import Op from .operation import Padding from .tensor import check_quantized_tens_scaling_equal from .tflite_mapping import BUILTIN_OPERATOR_UNKNOWN from .tflite_mapping import optype_to_builtintype # Custom decorator function to allow formatting docstrings containing "{}" def docstring_format_args(args): def docstring(func): func.__doc__ = func.__doc__.format(*args) return func return docstring def _list_formatter(arg): # Order and join into a string representation return ", ".join(sorted(map(str, arg))) 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 SupportedOperators: # 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 ) 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 = Op.op_set(Op.is_relu_op) 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,)) | concat_ops | split_ops shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV,)) 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) # Supported consumers supported_pad_consumers = convolution_ops | depthwise_convolution_ops | pooling_ops def __init__(self): # Setup the generic constraints. Note: the order matters self.generic_constraints = [] self.generic_constraints.append(SupportedOperators.constraint_tens_no_dynamic) self.generic_constraints.append(SupportedOperators.constraint_tens_defined_shape) self.generic_constraints.append(SupportedOperators.constraint_tens_output_scalar) self.generic_constraints.append(SupportedOperators.constraint_tens_input_scalar) self.generic_constraints.append(SupportedOperators.constraint_tens_shape_size) self.generic_constraints.append(SupportedOperators.constraint_tens_dtype) self.generic_constraints.append(SupportedOperators.constraint_tens_int32_ops) self.generic_constraints.append(SupportedOperators.constraint_tens_dimension) self.generic_constraints.append(SupportedOperators.constraint_tens_quant_none_check) self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale) self.generic_constraints.append(SupportedOperators.constraint_tens_quant_per_axis) self.generic_constraints.append(SupportedOperators.constraint_faf) self.generic_constraints.append(SupportedOperators.constraint_faf_type) self.generic_constraints.append(SupportedOperators.constraint_quant_scale_inf) # Setup specific constraints. Note: the order matters self.specific_constraints = defaultdict(list) # Conv-like checks: for op_type in SupportedOperators.convolution_like_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_range) self.specific_constraints[op_type].append(SupportedOperators.constraint_dilation_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_dilation_range) self.specific_constraints[op_type].append(SupportedOperators.constraint_dilated_height_range) self.specific_constraints[op_type].append(SupportedOperators.constraint_dilated_product_range) self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_const) self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_limit) self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_40bit) self.specific_constraints[op_type].append(SupportedOperators.constraint_batch_size) # Depthwise Conv specific checks: for op_type in SupportedOperators.depthwise_convolution_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_depth_multiplier) # Transpose Conv specific checks: for op_type in SupportedOperators.transpose_convolution_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_stride) self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_same) self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_valid) # Pooling checks: for op_type in SupportedOperators.pooling_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_batch_size) self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_range) # AVG pooling specific checks: for op_type in SupportedOperators.avg_pooling_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_range) self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_height_range_valid_pad) self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_product_range_valid_pad) # MAX pooling specific checks: for op_type in SupportedOperators.max_pooling_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_height_range) self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_product_range) # Resizing specific checks: for op_type in SupportedOperators.resizing_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_resize) # Vector Product specific checks: for op_type in SupportedOperators.fc_vector_products: self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_const) self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_40bit) # Concat specific checks: for op_type in (Op.Concat, Op.ConcatTFLite): self.specific_constraints[op_type].append(SupportedOperators.constraint_axis_exists) self.specific_constraints[op_type].append(SupportedOperators.constraint_axis_valid) self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_dimensionality) self.specific_constraints[op_type].append(SupportedOperators.constraint_valid_dimensions) # Element-wise checks: for op_type in SupportedOperators.elem_wise_main_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_elemwise_batch_size) self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_either_shapes) # Unary specific checks: for op_type in SupportedOperators.unary_elem_wise_main_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) # Binary Min/Max specific checks: for op_type in SupportedOperators.binary_elem_wise_min_max_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_quantization_parameters) self.specific_constraints[op_type].append(SupportedOperators.constraint_broadcast_shapes) # Binary Add/Mul/Sub specific checks: for op_type in SupportedOperators.binary_elem_wise_add_mul_sub: self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_inputs_types) self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_signed) self.specific_constraints[op_type].append(SupportedOperators.constraint_unsigned_valid) self.specific_constraints[op_type].append(SupportedOperators.constraint_broadcast_shapes) # Binary Shift specific checks: for op_type in SupportedOperators.binary_elem_wise_shift_ops: self.specific_constraints[op_type].append(SupportedOperators.constraint_inputs_int32) self.specific_constraints[op_type].append(SupportedOperators.constraint_broadcast_shapes) # SHL specific checks: self.specific_constraints[Op.SHL].append(SupportedOperators.constraint_output_int32) # CLZ specific checks: self.specific_constraints[Op.CLZ].append(SupportedOperators.constraint_output_int32) # Softmax specific checks: self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_matching_shapes) self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_matching_in_out_types) self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_beta_value_range) # SplitV specific checks: self.specific_constraints[Op.SplitV].append(SupportedOperators.constraint_splitv_inferred) # StridedSlice specific checks: self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_input_count) self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_inputs_const) self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_stride_values) self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_ellipsis_mask) self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_axis_masks) self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_slice_ranges) # LeakyRelu specific checks: self.specific_constraints[Op.LeakyRelu].append(SupportedOperators.constraint_alpha_valid) # FullyConnected specific checks: self.specific_constraints[Op.FullyConnected].append(SupportedOperators.constraint_fc_output_2d) self.specific_constraints[Op.FullyConnected].append(SupportedOperators.constraint_keep_dim_ifm_ofm) # Pad specific checks: self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_matching_in_out_types) self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_matching_quantization_parameters) self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_input_count) self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_shape) self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_padding_dimensions) self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_type) self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_constant) self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_ofm) self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_size) # HardSwish specific checks: self.specific_constraints[Op.HardSwish].append(SupportedOperators.constraint_input_8bit) self.specific_constraints[Op.HardSwish].append(SupportedOperators.constraint_matching_in_out_types) def is_operator_supported(self, op): ext_type = optype_to_builtintype(op.type) if op.type not in SupportedOperators.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 @staticmethod def constraint_tens_no_dynamic(op): "Input(s) and Output tensors must not be dynamic" valid = True extra = [] tensors = [tens for tens in op.inputs + op.outputs if tens] for tens in tensors: if (tens.shape == []) and (tens.values is None): valid = False extra.append(tens.name) extra = ", ".join(extra) return valid, f"Op has dynamic tensor(s): {extra}" @staticmethod def constraint_tens_defined_shape(op): "Input(s) and Output tensors must have a defined shape" valid = True extra = [] tensors = [tens for tens in op.inputs + op.outputs if tens] for tens in tensors: if not tens.has_fully_defined_shape(): valid = False extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") return valid, ", ".join(extra) @staticmethod def constraint_tens_output_scalar(op): "Output tensors cannot be scalar" ofm = op.ofm valid = ofm.shape != [] return valid, f"Output Tensor '{ofm.name}' is scalar" @classmethod @docstring_format_args([_optype_formatter(shapeless_input_ops)]) def constraint_tens_input_scalar(cls, op): "Scalar Input tensors are only valid for op type: {}" valid = True extra = [] tensors = [tens for tens in op.inputs if tens] for tens in tensors: if (tens.shape == []) and (op.type not in cls.shapeless_input_ops): valid = False extra.append(tens.name) extra = ", ".join(extra) return valid, f"Op has scalar input tensor(s): {extra}" @staticmethod def constraint_tens_shape_size(op): "Input(s) and Output tensors must not be greater than 4D" valid = True extra = [] tensors = [tens for tens in op.inputs + op.outputs if tens] for tens in tensors: if len(tens.shape) > 4: valid = False extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") return valid, ", ".join(extra) @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) @staticmethod def constraint_tens_quant_none_check(op): "Input(s), Output and Weight tensors must have quantization parameters" valid = True extra = [] tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] for tens in tensors: if tens.quantization is None: valid = False extra.append(tens.name) extra = ", ".join(extra) return valid, f"Op has tensors with missing quantization parameters: {extra}" @staticmethod def constraint_tens_quant_scale(op): "Input(s), Output and Weight tensors with quantization scales must be finite" valid = True extra = [] tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] for tens in tensors: if (tens.quantization.scale_f32 is not None) and np.isinf(tens.quantization.scale_f32).any(): valid = False extra.append(f"Tensor '{tens.name}' has quantization scale: {tens.quantization.scale_f32}") 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) @staticmethod def constraint_fc_output_2d(op): "The output tensor(s) must have 2D shape" valid = True extra = [] for tens in op.outputs: if len(tens.shape) != 2: valid = False extra.append(f"Tensor '{tens.name}' is {len(tens.shape)}D") return valid, ", ".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 @staticmethod def constraint_stride_type(op): "Stride values for both width and height must be integer types" w, h = op.get_kernel_stride() valid = is_integer(w) and is_integer(h) return valid, f"Op has stride WxH as: {repr(w)}x{repr(h)}" @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}" @staticmethod def constraint_dilation_type(op): "Dilation factor values for both width and height must be integer types" w, h = op.get_kernel_dilation() valid = is_integer(w) and is_integer(h) return valid, f"Op has dilation factor WxH as: {repr(w)}x{repr(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.quant_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.quant_values is not None: valid = all(len(bin(quant_value)[2:]) <= 40 for quant_value in bias.quant_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_quant_scale_inf(op): "Input and Output tensors must have quantization scales that fit within float32 precision" if op.ofm is not None and op.ofm.is_quantized(): ofm_scale = op.ofm.quantization.scale_f32 if ofm_scale < np.finfo(np.float32).tiny: return ( False, f"The quantization scale of the output tensor is {ofm_scale}, " + f"minimum supported is: {np.finfo(np.float32).tiny}", ) if op.ifm is not None and op.ifm.is_quantized(): ifm_scale = op.ifm.quantization.scale_f32 if np.isinf(ifm_scale / ofm_scale): return ( False, f"IFM scale divided by OFM scale is infinite, ifm_scale={ifm_scale} ofm_scale={ofm_scale}", ) return True, "Op's quantization is ok" @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" @staticmethod def constraint_matching_in_out_types(op): "IFM and OFM data types must match" ifm_dtype = op.ifm.dtype ofm_dtype = op.ofm.dtype valid = ifm_dtype == ofm_dtype return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" @staticmethod def constraint_beta_value_range(op): "Beta value needs to be positive" beta = op.attrs.get("beta", 1.0) valid = beta >= 0 return valid, f"Op has beta={beta}" @staticmethod def constraint_filter_type(op): "Kernel filter values for both width and height must be integer types" w = op.kernel.width h = op.kernel.height valid = is_integer(w) and is_integer(h) return valid, f"Op has kernel filter WxH as: {repr(w)}x{repr(h)}" @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 SupportedOperators.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 SupportedOperators.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 2x IFM -1, if align_corners is True OFM W and H must be 2x 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: upscaled_shape = np.array(ifm_shape[1:3]) out_shape = np.array(ofm_shape[1:3]) while (upscaled_shape < out_shape).all(): upscaled_shape *= 2 if align_corners: upscaled_shape -= 1 # Valid if OFM is 2x IFM (-1 for align corners) if np.array_equal(out_shape, upscaled_shape): valid = True break return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and align_corners={align_corners}" @staticmethod def constraint_matching_shapes(op): "IFM and OFM shapes must match" ifm_shape = op.ifm.shape ofm_shape = op.ofm.shape valid = ifm_shape == ofm_shape return valid, f"Op has ifm_shape={ifm_shape} and ofm_shape={ofm_shape}" @staticmethod def constraint_splitv_inferred(op): "Only one size is allowed to be inferred" sizes = op.inputs[1].values valid = np.count_nonzero(sizes == -1) <= 1 return valid, f"Op has multiple inferred sizes (-1): {sizes}" @staticmethod def constraint_axis_exists(op): "Axis attribute must exist" axis = op.attrs.get("axis") valid = axis is not None return valid, f"Op has axis={axis}" @staticmethod def constraint_axis_valid(op): "Axis attribute must be in the range [0, )" dims = len(op.ofm.shape) axis = op.attrs["axis"] axis += dims if axis < 0 else 0 valid = 0 <= axis < dims return valid, f"Op has ofm_dimensions={dims} and axis attribute is: {axis}" @staticmethod def constraint_matching_dimensionality(op): "All Input dimensionalities must match OFM dimensionality" valid = True extra = [] ofm_dim = len(op.ofm.shape) tensors = [tens for tens in op.inputs if tens] for tens in tensors: dim = len(tens.shape) if dim != ofm_dim: valid = False extra.append(f"Tensor '{tens.name}' has dimension: {dim}") extra = ", ".join(extra) return valid, f"Op has ofm_dimension={ofm_dim} and the list of mismatching inputs are: {extra}" @staticmethod def constraint_valid_dimensions(op): "All Input dimensions must match OFM dimension in all axes except the one defined by the axis attribute" valid = True extra = [] ofm_shape = op.ofm.shape ofm_dim = len(ofm_shape) axis = op.attrs["axis"] axis += ofm_dim if axis < 0 else 0 tensors = [tens for tens in op.inputs if tens] for tens in tensors: if any(tens.shape[dim] != ofm_shape[dim] for dim in range(ofm_dim) if dim != axis): valid = False extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") extra = ", ".join(extra) return valid, f"Op has axis={axis}, ofm_shape={ofm_shape} and the list of mismatching inputs are: {extra}" @staticmethod def constraint_stridedslice_input_count(op): "Exactly 4 Input tensors are required" inputs = len(op.inputs) valid = inputs == 4 return valid, f"Op has {inputs} inputs" @staticmethod def constraint_pad_input_count(op): "Number of input tensors must be exactly 2" inputs = len(op.inputs) valid = inputs == 2 return valid, f"Op has {inputs} inputs" @staticmethod def constraint_pad_shape(op): "The padding tensor must have the shape [4,2]" valid = op.inputs[1].shape == [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[0, :]) + sum(pad_tensor[-1, :]) == 0 return valid, f"First dimension padding: {pad_tensor[0,:]}, last dimension padding: {pad_tensor[-1,:]}" @staticmethod def constraint_pad_constant(op): "The padding tensor must be constant" pad_tensor = op.inputs[1].values valid = pad_tensor is not None return valid, f"Op has non-constant padding tensor: {op.inputs[1].values}" @classmethod @docstring_format_args([_optype_formatter(supported_pad_consumers)]) def constraint_pad_ofm(cls, op): "Must be followed by one of the following operator types: {}" consumers = op.ofm.consumers() unsupported_consumers = [ cons.type for cons in consumers if cons is not None if cons.type not in cls.supported_pad_consumers or cons.attrs["padding"] != Padding.VALID ] + [None for cons in consumers if cons is None] none_string = ", ".join(["NoneType" for cons in consumers if cons is None]) valid = len(unsupported_consumers) == 0 return valid, f"PAD operator is followed by: {_optype_formatter(unsupported_consumers)+none_string}" @staticmethod 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 @staticmethod def constraint_pad_size(op): "Padding must be at most kernel size divided by 2" if SupportedOperators.constraint_pad_ofm(op)[0]: padding = op.inputs[1].values # 4x2 tensor, first dimension is N, H, W, C top, left, bottom, right = (padding[1][0], padding[2][0], padding[1][1], padding[2][1]) for cons in op.ofm.consumers(): if cons is not None: # Note: pre-order graph traversal removes inputs of operators that are in traversal, # which makes it impossible to calculate kernel size, hence use cached _kernel for those operators k = cons.kernel if cons.inputs else cons._kernel k_w, k_h = k.dilated_wh() if cons.type.is_avgpool_op(): # For average pool, padding works different on the NPU; more restrictions apply for name, pad, k_size in ( ("Left", left, k_w), ("Right", right, k_w), ("Top", top, k_h), ("Bottom", bottom, k_h), ): if pad not in (0, k_size // 2): return False, f"{name} padding is {pad}, only 0 or {k_size // 2} are supported" else: if left > k_w // 2: return False, f"Left padding is {left}, kernel width is {k_w}" if right > k_w // 2: return False, f"Right padding is {right}, kernel width is {k_w}" if top > k_h // 2: return False, f"Top padding is {top}, kernel height is {k_h}" if bottom > k_h // 2: return False, f"Bottom padding is {bottom}, kernel height is {k_h}" if not SupportedOperators.__leading_pad_ok(top, k.stride.y, k_h): return False, f"Top padding is {top}, must be {k_h // 2} or multiple of {k.stride.y}" if not SupportedOperators.__leading_pad_ok(left, k.stride.x, k_w): return False, f"Left padding is {left}, must be {k_w // 2} or multiple of {k.stride.x}" return True, "Pad size is ok" @staticmethod def constraint_stridedslice_inputs_const(op): "Begin, End and Stride Input tensors must be constant" valid = True extra = [] _, begin, end, strides = op.inputs if begin.values is None: valid = False extra.append(f"Begin tensor '{begin.name}'") if end.values is None: valid = False extra.append(f"End tensor '{end.name}'") if strides.values is None: valid = False extra.append(f"Stride tensor '{strides.name}'") extra = ", ".join(extra) return valid, f"Op has non-constant tensors: {extra}" @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_ellipsis_mask(op): "ellipsis_mask must be 0" ellipsis = op.attrs["ellipsis_mask"] valid = ellipsis == 0 return valid, f"Op has ellipsis mask as: {ellipsis}" @staticmethod def constraint_axis_masks(op): "new_axis_mask and shrink_axis_mask cannot both be set" new_axis = op.attrs["new_axis_mask"] shrink_axis = op.attrs["shrink_axis_mask"] valid = (new_axis == 0) or (shrink_axis == 0) return valid, f"Op has new_axis_mask={new_axis} and shrink_axis_mask={shrink_axis}" @staticmethod def constraint_slice_ranges(op): "Slice 'end' values must be greater than 'begin' values" ifm, begin, end, _ = op.inputs # Calculate offset begin/end offset_begin = get_slice_offsets(ifm.shape, begin, op.attrs["begin_mask"], is_begin=True) offset_end = get_slice_offsets(ifm.shape, end, op.attrs["end_mask"], is_begin=False) # Check "end - begin" doesn't result in any zero or negative elements valid = all((e - b) > 0 for b, e in zip(offset_begin, offset_end)) return valid, f"Op has begin_values={begin.values} and end_values={end.values}" @staticmethod def constraint_matching_inputs_types(op): "Both Input data types must match" ifm_dtype = op.ifm.dtype ifm2_dtype = op.ifm2.dtype valid = ifm_dtype == ifm2_dtype return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}" @staticmethod def constraint_matching_signed(op): "For IFM that are signed, OFM must also be signed" valid = True ifm_dtype = op.ifm.dtype ofm_dtype = op.ofm.dtype if ifm_dtype.type & BaseType.Signed: valid = bool(ofm_dtype.type & BaseType.Signed) return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" @staticmethod def constraint_unsigned_valid(op): "For IFM that are unsigned, OFM must either be the same type or int32" valid = True ifm_dtype = op.ifm.dtype ofm_dtype = op.ofm.dtype if ifm_dtype.type & BaseType.Unsigned: valid = (ifm_dtype == ofm_dtype) or (ofm_dtype == DataType.int32) return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" @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_input_8bit(op): "IFM must be int8 or uint8" ifm_dtype = op.ifm.dtype valid = (ifm_dtype == DataType.int8) or (ifm_dtype == DataType.uint8) return valid, f"Op has ifm_dtype={ifm_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_matching_either_shapes(op): "At least one Input's shape must match the OFM's shape" ifm_shape = op.ifm.shape ifm2_shape = op.ifm2.shape if op.ifm2 else None ofm_shape = op.ofm.shape valid = (ifm_shape == ofm_shape) or (ifm2_shape == ofm_shape) return valid, f"Op has ifm_shape={ifm_shape}, ifm2_shape={ifm2_shape} and ofm_shape={ofm_shape}" @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}" @staticmethod def constraint_alpha_valid(op): "Alpha must not be negative" alpha = op.attrs["alpha"] valid = alpha >= 0 return valid, f"Op has alpha={alpha}" @staticmethod def constraint_keep_dim_ifm_ofm(op): "The IFM and OFM must have the same number of dimensions if keep_num_dims is set to true" valid = True if op.attrs.get("keep_num_dims"): valid = len(op.ifm.shape) == len(op.ofm.shape) return valid, f"Op has ifm shape={op.ifm.shape} and ofm shape={op.ofm.shape}"