Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1 | # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. |
| 2 | # |
| 3 | # SPDX-License-Identifier: Apache-2.0 |
| 4 | # |
| 5 | # Licensed under the Apache License, Version 2.0 (the License); you may |
| 6 | # not use this file except in compliance with the License. |
| 7 | # You may obtain a copy of the License at |
| 8 | # |
| 9 | # www.apache.org/licenses/LICENSE-2.0 |
| 10 | # |
| 11 | # Unless required by applicable law or agreed to in writing, software |
| 12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| 13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | # See the License for the specific language governing permissions and |
| 15 | # limitations under the License. |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 16 | # Description: |
| 17 | # The SupportedOperators class which is a collection of all supported operators and parameter checks. |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 18 | from collections import defaultdict |
| 19 | |
Charles Xu | 87c1350 | 2020-08-06 12:17:26 +0200 | [diff] [blame] | 20 | import numpy as np |
| 21 | |
Tim Hall | c30f495 | 2020-06-15 20:47:35 +0100 | [diff] [blame] | 22 | from .data_type import BaseType |
| 23 | from .data_type import DataType |
Dwight Lidman | 8359a47 | 2020-09-28 15:53:40 +0200 | [diff] [blame] | 24 | from .numeric_util import is_integer |
Louis Verhaard | fa2f92a | 2020-09-21 11:56:18 +0200 | [diff] [blame] | 25 | from .operation import get_slice_offsets |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 26 | from .operation import Op |
Tim Hall | 9358296 | 2020-09-09 21:58:15 +0100 | [diff] [blame] | 27 | from .tensor import check_quantized_tens_scaling_equal |
Louis Verhaard | fa2f92a | 2020-09-21 11:56:18 +0200 | [diff] [blame] | 28 | |
| 29 | |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 30 | # Custom decorator function to allow formatting docstrings containing "{}" |
| 31 | def docstring_format_args(args): |
| 32 | def docstring(func): |
| 33 | func.__doc__ = func.__doc__.format(*args) |
| 34 | return func |
| 35 | |
| 36 | return docstring |
| 37 | |
| 38 | |
Louis Verhaard | fa2f92a | 2020-09-21 11:56:18 +0200 | [diff] [blame] | 39 | def warn_cpu(op, msg): |
| 40 | print("Warning: {} {}, placing on CPU".format(op.type, msg)) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 41 | |
| 42 | |
| 43 | class SupportedOperators: |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 44 | # Categorised lists of supported operators |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 45 | npu_pre_ops = set((Op.SplitSliceRead,)) |
| 46 | convolution_ops = set((Op.Conv2DBias, Op.Conv2D, Op.QuantizedConv2D,)) |
| 47 | depthwise_convolution_ops = set((Op.DepthwiseConv2DBias,)) |
| 48 | transpose_convolution_ops = set((Op.Conv2DBackpropInput,)) |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 49 | convolution_like_ops = convolution_ops | depthwise_convolution_ops | transpose_convolution_ops |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 50 | max_pooling_ops = Op.op_set(Op.is_maxpool_op) |
| 51 | avg_pooling_ops = Op.op_set(Op.is_avgpool_op) |
| 52 | pooling_ops = set((Op.ReduceSum,)) | max_pooling_ops | avg_pooling_ops |
| 53 | resizing_ops = set((Op.ResizeBilinear,)) |
| 54 | fc_vector_products = set((Op.QuantizedMatMul, Op.MatMul, Op.FullyConnected,)) |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 55 | mac_main_ops = ( |
| 56 | # RNN/LSTM/GRU |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 57 | set((Op.BlockLSTM,)) |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 58 | # conv/depthwiseconv/transposeconv |
| 59 | | convolution_like_ops |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 60 | # pooling |
| 61 | | pooling_ops |
| 62 | # resizing/upscaling |
| 63 | | resizing_ops |
| 64 | # FC layers |
| 65 | | fc_vector_products |
| 66 | ) |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 67 | unary_elem_wise_main_ops = Op.op_set(Op.is_unary_elementwise_op) |
| 68 | binary_elem_wise_min_max_ops = set((Op.Minimum, Op.Maximum,)) |
| 69 | binary_elem_wise_shift_ops = set((Op.SHL, Op.SHR,)) |
| 70 | binary_elem_wise_add_mul_sub = set((Op.Add, Op.Mul, Op.Sub,)) |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 71 | binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops |
| 72 | elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 73 | supported_int32_tensor_ops = ( |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 74 | set((Op.ReduceSum, Op.CLZ,)) | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 75 | ) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 76 | relu_ops = Op.op_set(Op.is_relu_op) |
| 77 | activation_ops = relu_ops | set((Op.Tanh, Op.Sigmoid, Op.Softmax,)) |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 78 | npu_post_ops = ( |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 79 | # activation functions |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 80 | activation_ops |
| 81 | # concatenation write direction |
| 82 | | set((Op.ConcatSliceWrite,)) |
| 83 | # Quantization |
| 84 | | set((Op.Quantize,)) |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 85 | ) |
Louis Verhaard | aee5d75 | 2020-09-30 09:01:52 +0200 | [diff] [blame] | 86 | split_ops = set((Op.Split, Op.SplitV, Op.StridedSlice, Op.Slice, Op.UnpackReshaped, Op.Unpack,)) |
| 87 | concat_ops = set((Op.Concat, Op.ConcatTFLite, Op.PackReshaped, Op.Pack,)) |
| 88 | memory_only_ops = set((Op.Squeeze, Op.Reshape, Op.QuantizedReshape, Op.ExpandDims,)) | concat_ops | split_ops |
| 89 | shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV,)) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 90 | supported_fused_activations = relu_ops | set((Op.Tanh, Op.Sigmoid, Op.LUT,)) |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 91 | supported_operators = npu_pre_ops | mac_main_ops | elem_wise_main_ops | npu_post_ops | memory_only_ops |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 92 | # Supported data types |
| 93 | supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32)) |
| 94 | supported_bias_dtypes = set((DataType.int32, DataType.int64)) |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 95 | # Defined ranges for allowed values: |
| 96 | tens_dim_range = (1, 65535) |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 97 | stride_range = (1, 3) |
| 98 | dilation_range = (1, 2) |
| 99 | dilated_height_range = (1, 64) |
| 100 | dilated_product_range = (1, 64 * 64) |
| 101 | weights_limit = 127 * 65536 |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 102 | filter_range = (1, 8) |
| 103 | filter_height_range = (1, 256) |
| 104 | filter_product_range = (1, 256 * 256) |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 105 | |
Fredrik Svedberg | 880e735 | 2020-08-25 11:31:47 +0200 | [diff] [blame] | 106 | def __init__(self): |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 107 | # Setup the generic constraints. Note: the order matters |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 108 | self.generic_constraints = [] |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 109 | self.generic_constraints.append(SupportedOperators.constraint_tens_no_dynamic) |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 110 | self.generic_constraints.append(SupportedOperators.constraint_tens_defined_shape) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 111 | self.generic_constraints.append(SupportedOperators.constraint_tens_output_scalar) |
| 112 | self.generic_constraints.append(SupportedOperators.constraint_tens_input_scalar) |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 113 | self.generic_constraints.append(SupportedOperators.constraint_tens_shape_size) |
| 114 | self.generic_constraints.append(SupportedOperators.constraint_tens_dtype) |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 115 | self.generic_constraints.append(SupportedOperators.constraint_tens_int32_ops) |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 116 | self.generic_constraints.append(SupportedOperators.constraint_tens_dimension) |
Dwight Lidman | 8359a47 | 2020-09-28 15:53:40 +0200 | [diff] [blame] | 117 | self.generic_constraints.append(SupportedOperators.constraint_tens_quant_none_check) |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 118 | self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale) |
| 119 | self.generic_constraints.append(SupportedOperators.constraint_faf) |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 120 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 121 | # Setup specific constraints. Note: the order matters |
| 122 | self.specific_constraints = defaultdict(list) |
| 123 | |
| 124 | # Conv-like checks: |
| 125 | for op_type in SupportedOperators.convolution_like_ops: |
| 126 | self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_type) |
| 127 | self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_range) |
| 128 | self.specific_constraints[op_type].append(SupportedOperators.constraint_dilation_type) |
| 129 | self.specific_constraints[op_type].append(SupportedOperators.constraint_dilation_range) |
| 130 | self.specific_constraints[op_type].append(SupportedOperators.constraint_dilated_height_range) |
| 131 | self.specific_constraints[op_type].append(SupportedOperators.constraint_dilated_product_range) |
| 132 | self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_type) |
| 133 | self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_const) |
| 134 | self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_limit) |
| 135 | self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_type) |
| 136 | self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_40bit) |
| 137 | self.specific_constraints[op_type].append(SupportedOperators.constraint_batch_size) |
| 138 | # Depthwise Conv specific checks: |
| 139 | for op_type in SupportedOperators.depthwise_convolution_ops: |
| 140 | self.specific_constraints[op_type].append(SupportedOperators.constraint_depth_multiplier) |
| 141 | # Transpose Conv specific checks: |
| 142 | for op_type in SupportedOperators.transpose_convolution_ops: |
| 143 | self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_stride) |
| 144 | self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_same) |
| 145 | self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_valid) |
| 146 | |
| 147 | # Pooling checks: |
| 148 | for op_type in SupportedOperators.pooling_ops: |
| 149 | self.specific_constraints[op_type].append(SupportedOperators.constraint_batch_size) |
| 150 | self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_type) |
| 151 | self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_range) |
| 152 | # AVG pooling specific checks: |
| 153 | for op_type in SupportedOperators.avg_pooling_ops: |
| 154 | self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) |
| 155 | self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_type) |
| 156 | self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_range) |
| 157 | self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_height_range_valid_pad) |
| 158 | self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_product_range_valid_pad) |
| 159 | # MAX pooling specific checks: |
| 160 | for op_type in SupportedOperators.max_pooling_ops: |
| 161 | self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) |
| 162 | self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_type) |
| 163 | self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_height_range) |
| 164 | self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_product_range) |
| 165 | # TODO: Check ReduceSum restrictions |
| 166 | |
| 167 | # Relu specific checks: |
| 168 | for op_type in SupportedOperators.relu_ops: |
| 169 | self.specific_constraints[op_type].append(SupportedOperators.constraint_quant_scale_inf) |
| 170 | |
| 171 | # Resizing specific checks: |
| 172 | for op_type in SupportedOperators.resizing_ops: |
| 173 | self.specific_constraints[op_type].append(SupportedOperators.constraint_resize) |
| 174 | |
| 175 | # Vector Product specific checks: |
| 176 | for op_type in SupportedOperators.fc_vector_products: |
| 177 | self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_type) |
| 178 | self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_const) |
| 179 | self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_type) |
| 180 | self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_40bit) |
| 181 | |
| 182 | # Concat specific checks: |
| 183 | for op_type in (Op.Concat, Op.ConcatTFLite): |
| 184 | self.specific_constraints[op_type].append(SupportedOperators.constraint_axis_exists) |
| 185 | self.specific_constraints[op_type].append(SupportedOperators.constraint_axis_valid) |
| 186 | self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_dimensionality) |
| 187 | self.specific_constraints[op_type].append(SupportedOperators.constraint_valid_dimensions) |
| 188 | |
| 189 | # Element-wise checks: |
| 190 | for op_type in SupportedOperators.elem_wise_main_ops: |
| 191 | self.specific_constraints[op_type].append(SupportedOperators.constraint_elemwise_batch_size) |
| 192 | self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_either_shapes) |
| 193 | # Unary specific checks: |
| 194 | for op_type in SupportedOperators.unary_elem_wise_main_ops: |
| 195 | self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) |
| 196 | # Binary Min/Max specific checks: |
| 197 | for op_type in SupportedOperators.binary_elem_wise_min_max_ops: |
| 198 | self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) |
| 199 | self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_quantization_parameters) |
| 200 | # Binary Add/Mul/Sub specific checks: |
| 201 | for op_type in SupportedOperators.binary_elem_wise_add_mul_sub: |
| 202 | self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_inputs_types) |
| 203 | self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_signed) |
| 204 | self.specific_constraints[op_type].append(SupportedOperators.constraint_unsigned_valid) |
| 205 | # Binary Shift specific checks: |
| 206 | for op_type in SupportedOperators.binary_elem_wise_shift_ops: |
| 207 | self.specific_constraints[op_type].append(SupportedOperators.constraint_inputs_int32) |
| 208 | |
| 209 | # SHL specific checks: |
| 210 | self.specific_constraints[Op.SHL].append(SupportedOperators.constraint_output_int32) |
| 211 | |
| 212 | # CLZ specific checks: |
| 213 | self.specific_constraints[Op.CLZ].append(SupportedOperators.constraint_output_int32) |
| 214 | |
| 215 | # Softmax specific checks: |
| 216 | self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_matching_shapes) |
| 217 | self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_matching_in_out_types) |
| 218 | |
| 219 | # SplitV specific checks: |
| 220 | self.specific_constraints[Op.SplitV].append(SupportedOperators.constraint_splitv_inferred) |
| 221 | |
| 222 | # StridedSlice specific checks: |
| 223 | self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_input_count) |
| 224 | self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_inputs_const) |
| 225 | self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_tens_size_matches) |
| 226 | self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_stride_values) |
| 227 | self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_ellipsis_mask) |
| 228 | self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_axis_masks) |
| 229 | self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_slice_ranges) |
| 230 | |
| 231 | # LeakyRelu specific checks: |
| 232 | self.specific_constraints[Op.LeakyRelu].append(SupportedOperators.constraint_alpha_valid) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 233 | |
| 234 | def is_operator_supported(self, op): |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 235 | if op.type not in SupportedOperators.supported_operators: |
Louis Verhaard | 5f2ea2f | 2020-10-15 08:39:44 +0200 | [diff] [blame] | 236 | if op.type not in (Op.Placeholder, Op.SubgraphInput, Op.Const): |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 237 | print(f"Info: {op.type} '{op.name}' is not supported on the NPU. Placing on CPU instead") |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 238 | return False |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 239 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 240 | for constraint in self.generic_constraints + self.specific_constraints[op.type]: |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 241 | valid, extra = constraint(op) |
| 242 | if not valid: |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 243 | print(f"Warning: {op.type} '{op.name}' is not supported on the NPU. Placing on CPU instead") |
| 244 | print(f" - {constraint.__doc__}") |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 245 | if extra: |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 246 | print(f" {extra}") |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 247 | return False |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 248 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 249 | return True |
| 250 | |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 251 | @staticmethod |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 252 | def constraint_tens_no_dynamic(op): |
| 253 | "Input(s) and Output tensors must not be dynamic" |
| 254 | valid = True |
| 255 | extra = [] |
| 256 | tensors = [tens for tens in op.inputs + op.outputs if tens] |
| 257 | for tens in tensors: |
| 258 | if (tens.shape == []) and (tens.values is None): |
| 259 | valid = False |
| 260 | extra.append(tens.name) |
| 261 | extra = ", ".join(extra) |
| 262 | return valid, f"Op has dynamic tensor(s): {extra}" |
| 263 | |
| 264 | @staticmethod |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 265 | def constraint_tens_defined_shape(op): |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 266 | "Input(s) and Output tensors must have a defined shape" |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 267 | valid = True |
| 268 | extra = [] |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 269 | tensors = [tens for tens in op.inputs + op.outputs if tens] |
| 270 | for tens in tensors: |
| 271 | if not tens.has_fully_defined_shape(): |
| 272 | valid = False |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 273 | extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 274 | return valid, ", ".join(extra) |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 275 | |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 276 | @staticmethod |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 277 | def constraint_tens_output_scalar(op): |
| 278 | "Output tensors cannot be scalar" |
| 279 | ofm = op.ofm |
| 280 | valid = ofm.shape != [] |
| 281 | return valid, f"Output Tensor '{ofm.name}' is scalar" |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 282 | |
| 283 | @classmethod |
| 284 | @docstring_format_args([shapeless_input_ops]) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 285 | def constraint_tens_input_scalar(cls, op): |
| 286 | "Scalar Input tensors are only valid for op type: {}" |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 287 | valid = True |
| 288 | extra = [] |
| 289 | tensors = [tens for tens in op.inputs if tens] |
| 290 | for tens in tensors: |
| 291 | if (tens.shape == []) and (op.type not in cls.shapeless_input_ops): |
| 292 | valid = False |
| 293 | extra.append(tens.name) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 294 | extra = ", ".join(extra) |
| 295 | return valid, f"Op has scalar input tensor(s): {extra}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 296 | |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 297 | @staticmethod |
| 298 | def constraint_tens_shape_size(op): |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 299 | "Input(s) and Output tensors must not be greater than 4D" |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 300 | valid = True |
| 301 | extra = [] |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 302 | tensors = [tens for tens in op.inputs + op.outputs if tens] |
| 303 | for tens in tensors: |
| 304 | if len(tens.shape) > 4: |
| 305 | valid = False |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 306 | extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 307 | return valid, ", ".join(extra) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 308 | |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 309 | @classmethod |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 310 | @docstring_format_args([supported_op_dtypes]) |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 311 | def constraint_tens_dtype(cls, op): |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 312 | "Tensors must be of type: {}" |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 313 | valid = True |
| 314 | extra = [] |
| 315 | tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 316 | if not tensors: |
| 317 | tensors = [tens for tens in op.inputs if tens] |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 318 | for tens in tensors: |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 319 | if tens.dtype not in cls.supported_op_dtypes: |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 320 | valid = False |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 321 | extra.append(f"Tensor '{tens.name}' has data type: {tens.dtype}") |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 322 | return valid, ", ".join(extra) |
| 323 | |
| 324 | @classmethod |
| 325 | @docstring_format_args([supported_int32_tensor_ops]) |
| 326 | def constraint_tens_int32_ops(cls, op): |
| 327 | "Tensors which are int32 are only valid when op type is: {}" |
| 328 | valid = True |
| 329 | extra = [] |
| 330 | tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 331 | if not tensors: |
| 332 | tensors = [tens for tens in op.inputs if tens] |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 333 | for tens in tensors: |
| 334 | if (tens.dtype == DataType.int32) and (op.type not in cls.supported_int32_tensor_ops): |
| 335 | valid = False |
| 336 | extra.append(tens.name) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 337 | extra = ", ".join(extra) |
| 338 | return valid, f"Op has int32 tensor(s): {extra}" |
Andreas Nevalainen | eadb166 | 2020-09-01 15:36:26 +0200 | [diff] [blame] | 339 | |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 340 | @classmethod |
| 341 | @docstring_format_args(tens_dim_range) |
| 342 | def constraint_tens_dimension(cls, op): |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 343 | "Tensor dimensions must be in the range [{}, {}]" |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 344 | tens_min, tens_max = cls.tens_dim_range |
| 345 | valid = True |
| 346 | extra = [] |
| 347 | tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 348 | if not tensors: |
| 349 | tensors = [tens for tens in op.inputs if tens] |
Michael McGeagh | 37ded34 | 2020-10-01 15:37:44 +0100 | [diff] [blame] | 350 | for tens in tensors: |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 351 | if not all(tens_min <= dim <= tens_max for dim in tens.shape): |
| 352 | valid = False |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 353 | extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 354 | return valid, ", ".join(extra) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 355 | |
Dwight Lidman | 8359a47 | 2020-09-28 15:53:40 +0200 | [diff] [blame] | 356 | @staticmethod |
| 357 | def constraint_tens_quant_none_check(op): |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 358 | "Input(s), Output and Weight tensors must have quantization parameters" |
Dwight Lidman | 8359a47 | 2020-09-28 15:53:40 +0200 | [diff] [blame] | 359 | valid = True |
| 360 | extra = [] |
| 361 | tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| 362 | for tens in tensors: |
| 363 | if tens.quantization is None: |
| 364 | valid = False |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 365 | extra.append(tens.name) |
| 366 | extra = ", ".join(extra) |
| 367 | return valid, f"Op has tensors with missing quantization parameters: {extra}" |
Dwight Lidman | 8359a47 | 2020-09-28 15:53:40 +0200 | [diff] [blame] | 368 | |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 369 | @staticmethod |
| 370 | def constraint_tens_quant_scale(op): |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 371 | "Input(s), Output and Weight tensors with quantization scales must be finite" |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 372 | valid = True |
| 373 | extra = [] |
| 374 | tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| 375 | for tens in tensors: |
| 376 | if (tens.quantization.scale_f32 is not None) and np.isinf(tens.quantization.scale_f32).any(): |
| 377 | valid = False |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 378 | extra.append(f"Tensor '{tens.name}' has quantization scale: {tens.quantization.scale_f32}") |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 379 | return valid, ", ".join(extra) |
| 380 | |
| 381 | @classmethod |
| 382 | @docstring_format_args([supported_fused_activations]) |
| 383 | def constraint_faf(cls, op): |
| 384 | "The fused activation function (if present) must be one of type: {}" |
| 385 | faf = op.activation |
| 386 | valid = (faf is None) or (faf in cls.supported_fused_activations) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 387 | return valid, f"Op has its fused activation function as: {faf}" |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 388 | |
| 389 | @staticmethod |
| 390 | def constraint_stride_type(op): |
| 391 | "Stride values for both width and height must be integer types" |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 392 | w, h = op.get_kernel_stride() |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 393 | valid = is_integer(w) and is_integer(h) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 394 | return valid, f"Op has stride WxH as: {repr(w)}x{repr(h)}" |
Michael McGeagh | 184b250 | 2020-10-09 17:19:52 +0100 | [diff] [blame] | 395 | |
Michael McGeagh | 1eeea51 | 2020-09-30 14:23:09 +0100 | [diff] [blame] | 396 | @classmethod |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 397 | @docstring_format_args(stride_range) |
| 398 | def constraint_stride_range(cls, op): |
| 399 | "Stride values for both width and height must be in the range [{}, {}]" |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 400 | w, h = op.get_kernel_stride() |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 401 | stride_min, stride_max = cls.stride_range |
| 402 | valid = (stride_min <= w <= stride_max) and (stride_min <= h <= stride_max) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 403 | return valid, f"Op has stride WxH as: {w}x{h}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 404 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 405 | @staticmethod |
| 406 | def constraint_dilation_type(op): |
| 407 | "Dilation factor values for both width and height must be integer types" |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 408 | w, h = op.get_kernel_dilation() |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 409 | valid = is_integer(w) and is_integer(h) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 410 | return valid, f"Op has dilation factor WxH as: {repr(w)}x{repr(h)}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 411 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 412 | @classmethod |
| 413 | @docstring_format_args(dilation_range) |
| 414 | def constraint_dilation_range(cls, op): |
| 415 | "Dilation factor values for both width and height must be in the range [{}, {}]" |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 416 | w, h = op.get_kernel_dilation() |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 417 | dilation_min, dilation_max = cls.dilation_range |
| 418 | valid = (dilation_min <= w <= dilation_max) and (dilation_min <= h <= dilation_max) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 419 | return valid, f"Op has dilation factor WxH as: {w}x{h}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 420 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 421 | @classmethod |
| 422 | @docstring_format_args(dilated_height_range) |
| 423 | def constraint_dilated_height_range(cls, op): |
| 424 | "Dilated kernel height must be in the range [{}, {}]" |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 425 | h = op.kernel.area_height() |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 426 | dilated_height_min, dilated_height_max = cls.dilated_height_range |
| 427 | valid = dilated_height_min <= h <= dilated_height_max |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 428 | return valid, f"Op has dilated kernel height as: {h}" |
Jacob Bohlin | 49d9212 | 2020-08-19 14:36:46 +0200 | [diff] [blame] | 429 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 430 | @classmethod |
| 431 | @docstring_format_args(dilated_product_range) |
| 432 | def constraint_dilated_product_range(cls, op): |
| 433 | "Product of dilated kernel width and height must be in the range [{}, {}]" |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 434 | product = op.kernel.area_width() * op.kernel.area_height() |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 435 | dilated_product_min, dilated_product_max = cls.dilated_product_range |
| 436 | valid = dilated_product_min <= product <= dilated_product_max |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 437 | return valid, f"Op has product of dilated kernel width and height as: {product}" |
Andreas Nevalainen | f0c59bf | 2020-08-26 10:56:23 +0200 | [diff] [blame] | 438 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 439 | @staticmethod |
| 440 | def constraint_weights_type(op): |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 441 | "Weight tensor must be 8-bit" |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 442 | weights = op.weights |
| 443 | valid = weights.element_size() == 1 |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 444 | return valid, f"Tensor '{weights.name}' is {int(weights.element_size() * 8)}-bit" |
Andreas Nevalainen | f0c59bf | 2020-08-26 10:56:23 +0200 | [diff] [blame] | 445 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 446 | @staticmethod |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 447 | def constraint_weights_const(op): |
| 448 | "Weight tensor must be constant" |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 449 | weights = op.weights |
| 450 | valid = weights.values is not None |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 451 | return valid, f"Tensor '{weights.name}' has non-constant values" |
Andreas Nevalainen | 8854dc9 | 2020-09-24 13:43:00 +0200 | [diff] [blame] | 452 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 453 | @classmethod |
| 454 | @docstring_format_args([weights_limit]) |
| 455 | def constraint_weights_limit(cls, op): |
| 456 | "The sum of the weights cannot exceed {}" |
| 457 | weights = op.weights |
| 458 | values = weights.quant_values.astype(np.int64) - weights.quantization.zero_point |
| 459 | limit = np.amax(np.sum(np.absolute(values), axis=(0, 1, 2))) |
| 460 | valid = limit <= cls.weights_limit |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 461 | return valid, f"Tensor '{weights.name}' has the sum of weights: {limit}" |
Andreas Nevalainen | f0c59bf | 2020-08-26 10:56:23 +0200 | [diff] [blame] | 462 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 463 | @classmethod |
| 464 | @docstring_format_args([supported_bias_dtypes]) |
| 465 | def constraint_bias_type(cls, op): |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 466 | "Optional Bias tensor must be of type: {}" |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 467 | bias = op.bias |
| 468 | if bias: |
| 469 | valid = bias.dtype in cls.supported_bias_dtypes |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 470 | return valid, f"Tensor '{bias.name}' has data type: {bias.dtype}" |
| 471 | return True, "Op has no bias tensor" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 472 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 473 | @staticmethod |
| 474 | def constraint_bias_40bit(op): |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 475 | "Optional Bias tensor values must fit within 40-bits" |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 476 | bias = op.bias |
| 477 | if bias and bias.dtype == DataType.int64: |
| 478 | valid = all(len(bin(quant_value)[2:]) <= 40 for quant_value in bias.quant_values) |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 479 | return valid, f"Tensor '{bias.name}' has values larger than 40-bits" |
| 480 | return True, "Op has no bias tensor, or it fits in 40-bit" |
Andreas Nevalainen | d8c032d | 2020-09-11 10:25:09 +0200 | [diff] [blame] | 481 | |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 482 | @staticmethod |
| 483 | def constraint_batch_size(op): |
| 484 | "IFM Tensor batch size must be 1" |
| 485 | ifm = op.ifm |
| 486 | valid = ifm.shape[0] == 1 |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 487 | return valid, f"Tensor '{ifm.name}' has batch size: {ifm.shape[0]}" |
| 488 | |
| 489 | @staticmethod |
| 490 | def constraint_quant_scale_inf(op): |
| 491 | "The IFM quantization scale divided by the OFM quantization scale must not be infinite" |
| 492 | ifm_scale = op.ifm.quantization.scale_f32 |
| 493 | ofm_scale = op.ofm.quantization.scale_f32 |
| 494 | valid = not np.isinf(ifm_scale / ofm_scale) |
| 495 | return valid, f"Op has infinite quantization scale. ifm_scale={ifm_scale} ofm_scale={ofm_scale}" |
| 496 | |
| 497 | @staticmethod |
| 498 | def constraint_depth_multiplier(op): |
| 499 | "For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier" |
| 500 | depth_multiplier = op.attrs.get("depth_multiplier", 1) |
| 501 | if depth_multiplier > 1: |
| 502 | ifm_channels = op.ifm.shape[3] |
| 503 | ofm_channels = op.ofm.shape[3] |
| 504 | valid = (ifm_channels == 1) and (ofm_channels == depth_multiplier) |
| 505 | extra = ( |
| 506 | f"Op has ifm_channels={ifm_channels}, ofm_channels={ofm_channels}" |
| 507 | f" and depth_multiplier={depth_multiplier}" |
| 508 | ) |
| 509 | return valid, extra |
| 510 | return True, "Op has depth_multiplier=1" |
| 511 | |
| 512 | @staticmethod |
| 513 | def constraint_tconv_stride(op): |
| 514 | "Stride values for both width and height must be 2" |
| 515 | w = op.kernel.stride.x |
| 516 | h = op.kernel.stride.y |
| 517 | valid = (w == 2) and (h == 2) |
| 518 | return valid, f"Op has stride WxH as: {w}x{h}" |
| 519 | |
| 520 | @staticmethod |
| 521 | def constraint_tconv_same(op): |
| 522 | "SAME padding: OFM dimensions must equal IFM dimensions multiplied by stride" |
| 523 | if op.attrs["padding"] == b"SAME": |
| 524 | w = op.kernel.stride.x |
| 525 | h = op.kernel.stride.y |
| 526 | ifm_shape = op.ifm.shape |
| 527 | ofm_shape = op.ofm.shape |
| 528 | valid = (ofm_shape[1] == (ifm_shape[1] * h)) and (ofm_shape[2] == (ifm_shape[2] * w)) |
| 529 | return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and stride WxH as {w}x{h}" |
| 530 | return True, "Op has padding=VALID" |
| 531 | |
| 532 | @staticmethod |
| 533 | def constraint_tconv_valid(op): |
| 534 | """VALID padding: OFM dimensions must equal IFM dimensions multiplied by stride, |
| 535 | minus difference between kernel size and stride""" |
| 536 | if op.attrs["padding"] == b"VALID": |
| 537 | s_w = op.kernel.stride.x |
| 538 | s_h = op.kernel.stride.y |
| 539 | k_w = op.kernel.width |
| 540 | k_h = op.kernel.height |
| 541 | ifm_shape = op.ifm.shape |
| 542 | ofm_shape = op.ofm.shape |
| 543 | height_check = ofm_shape[1] == (ifm_shape[1] * s_h + max(k_h - s_h, 0)) |
| 544 | width_check = ofm_shape[2] == (ifm_shape[2] * s_w + max(k_w - s_w, 0)) |
| 545 | valid = height_check and width_check |
| 546 | extra = ( |
| 547 | f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape}," |
| 548 | f" stride WxH as {s_w}x{s_h} and kernel WxH as {k_w}x{k_h}" |
| 549 | ) |
| 550 | return valid, extra |
| 551 | return True, "Op has padding=SAME" |
| 552 | |
| 553 | @staticmethod |
| 554 | def constraint_matching_in_out_types(op): |
| 555 | "IFM and OFM data types must match" |
| 556 | ifm_dtype = op.ifm.dtype |
| 557 | ofm_dtype = op.ofm.dtype |
| 558 | valid = ifm_dtype == ofm_dtype |
| 559 | return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" |
| 560 | |
| 561 | @staticmethod |
| 562 | def constraint_filter_type(op): |
| 563 | "Kernel filter values for both width and height must be integer types" |
| 564 | w = op.kernel.width |
| 565 | h = op.kernel.height |
| 566 | valid = is_integer(w) and is_integer(h) |
| 567 | return valid, f"Op has kernel filter WxH as: {repr(w)}x{repr(h)}" |
| 568 | |
| 569 | @classmethod |
| 570 | @docstring_format_args(filter_range) |
| 571 | def constraint_filter_range(cls, op): |
| 572 | "Kernel filter values for both width and height must be in the range [{}, {}]" |
| 573 | if op.attrs["padding"] == b"SAME": |
| 574 | w = op.kernel.width |
| 575 | h = op.kernel.height |
| 576 | filter_min, filter_max = cls.filter_range |
| 577 | valid = (filter_min <= w <= filter_max) and (filter_min <= h <= filter_max) |
| 578 | return valid, f"Op has kernel filter WxH as: {w}x{h}" |
| 579 | return True, "Op has padding=VALID" |
| 580 | |
| 581 | @classmethod |
| 582 | @docstring_format_args(filter_height_range) |
| 583 | def constraint_filter_height_range(cls, op): |
| 584 | "Kernel filter height must be in the range [{}, {}]" |
| 585 | h = op.kernel.height |
| 586 | filter_height_min, filter_height_max = cls.filter_height_range |
| 587 | valid = filter_height_min <= h <= filter_height_max |
| 588 | return valid, f"Op has kernel filter height as: {h}" |
| 589 | |
| 590 | @classmethod |
| 591 | @docstring_format_args(filter_product_range) |
| 592 | def constraint_filter_product_range(cls, op): |
| 593 | "Product of kernel filter width and height must be in the range [{}, {}]" |
| 594 | product = op.kernel.elements_wh() |
| 595 | filter_product_min, filter_product_max = cls.filter_product_range |
| 596 | valid = filter_product_min <= product <= filter_product_max |
| 597 | return valid, f"Op has product of kernel filter width and height as: {product}" |
| 598 | |
| 599 | @staticmethod |
| 600 | @docstring_format_args(filter_height_range) |
| 601 | def constraint_filter_height_range_valid_pad(op): |
| 602 | "VALID padding: Kernel filter height must be in the range [{}, {}]" |
| 603 | if op.attrs["padding"] == b"VALID": |
| 604 | return SupportedOperators.constraint_filter_height_range(op) |
| 605 | return True, "Op has padding=SAME" |
| 606 | |
| 607 | @staticmethod |
| 608 | @docstring_format_args(filter_product_range) |
| 609 | def constraint_filter_product_range_valid_pad(op): |
| 610 | "VALID padding: Product of kernel filter width and height must be in the range [{}, {}]" |
| 611 | if op.attrs["padding"] == b"VALID": |
| 612 | return SupportedOperators.constraint_filter_product_range(op) |
| 613 | return True, "Op has padding=SAME" |
| 614 | |
| 615 | @staticmethod |
| 616 | def constraint_resize(op): |
| 617 | """The width and height of the IFM and OFM must match one of the following criteria: |
| 618 | IFM W and H must both be 1 |
| 619 | IFM must match OFM |
| 620 | OFM W and H must be 2x IFM -1, if align_corners is True |
| 621 | OFM W and H must be 2x IFM, if align_corners is False""" |
| 622 | # Easier to start with False condition as very few cases result in a supported resize |
| 623 | valid = False |
| 624 | ifm_shape = op.ifm.shape |
| 625 | ofm_shape = op.ofm.shape |
| 626 | align_corners = op.attrs.get("align_corners", False) |
| 627 | if len(ifm_shape) == 4: |
| 628 | # Valid if IFM W and H are both 1, or IFM and OFM shape are the same |
| 629 | if ((ifm_shape[1] == 1) and (ifm_shape[2] == 1)) or (ifm_shape == ofm_shape): |
| 630 | valid = True |
| 631 | else: |
| 632 | upscaled_shape = np.array(ifm_shape[1:3]) |
| 633 | out_shape = np.array(ofm_shape[1:3]) |
| 634 | while (upscaled_shape < out_shape).all(): |
| 635 | upscaled_shape *= 2 |
| 636 | if align_corners: |
| 637 | upscaled_shape -= 1 |
| 638 | # Valid if OFM is 2x IFM (-1 for align corners) |
| 639 | if np.array_equal(out_shape, upscaled_shape): |
| 640 | valid = True |
| 641 | break |
| 642 | return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and align_corners={align_corners}" |
| 643 | |
| 644 | @staticmethod |
| 645 | def constraint_matching_shapes(op): |
| 646 | "IFM and OFM shapes must match" |
| 647 | ifm_shape = op.ifm.shape |
| 648 | ofm_shape = op.ofm.shape |
| 649 | valid = ifm_shape == ofm_shape |
| 650 | return valid, f"Op has ifm_shape={ifm_shape} and ofm_shape={ofm_shape}" |
| 651 | |
| 652 | @staticmethod |
| 653 | def constraint_splitv_inferred(op): |
| 654 | "Only one size is allowed to be inferred" |
| 655 | sizes = op.ifm2.values |
| 656 | valid = np.count_nonzero(sizes == -1) <= 1 |
| 657 | return valid, f"Op has multiple inferred sizes (-1): {sizes}" |
| 658 | |
| 659 | @staticmethod |
| 660 | def constraint_axis_exists(op): |
| 661 | "Axis attribute must exist" |
| 662 | axis = op.attrs.get("axis") |
| 663 | valid = axis is not None |
| 664 | return valid, f"Op has axis={axis}" |
| 665 | |
| 666 | @staticmethod |
| 667 | def constraint_axis_valid(op): |
| 668 | "Axis attribute must be in the range [0, <ofm_dimensions>)" |
| 669 | dims = len(op.ofm.shape) |
| 670 | axis = op.attrs["axis"] |
| 671 | axis += dims if axis < 0 else 0 |
| 672 | valid = 0 <= axis < dims |
| 673 | return valid, f"Op has ofm_dimensions={dims} and axis attribute is: {axis}" |
| 674 | |
| 675 | @staticmethod |
| 676 | def constraint_matching_dimensionality(op): |
| 677 | "All Input dimensionalities must match OFM dimensionality" |
| 678 | valid = True |
| 679 | extra = [] |
| 680 | ofm_dim = len(op.ofm.shape) |
| 681 | tensors = [tens for tens in op.inputs if tens] |
| 682 | for tens in tensors: |
| 683 | dim = len(tens.shape) |
| 684 | if dim != ofm_dim: |
| 685 | valid = False |
| 686 | extra.append(f"Tensor '{tens.name}' has dimension: {dim}") |
| 687 | extra = ", ".join(extra) |
| 688 | return valid, f"Op has ofm_dimension={ofm_dim} and the list of mismatching inputs are: {extra}" |
| 689 | |
| 690 | @staticmethod |
| 691 | def constraint_valid_dimensions(op): |
| 692 | "All Input dimensions must match OFM dimension in all axes except the one defined by the axis attribute" |
| 693 | valid = True |
| 694 | extra = [] |
| 695 | ofm_shape = op.ofm.shape |
| 696 | ofm_dim = len(ofm_shape) |
| 697 | axis = op.attrs["axis"] |
| 698 | axis += ofm_dim if axis < 0 else 0 |
| 699 | tensors = [tens for tens in op.inputs if tens] |
| 700 | for tens in tensors: |
| 701 | if any(tens.shape[dim] != ofm_shape[dim] for dim in range(ofm_dim) if dim != axis): |
| 702 | valid = False |
| 703 | extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
| 704 | extra = ", ".join(extra) |
| 705 | return valid, f"Op has axis={axis}, ofm_shape={ofm_shape} and the list of mismatching inputs are: {extra}" |
| 706 | |
| 707 | @staticmethod |
| 708 | def constraint_stridedslice_input_count(op): |
| 709 | "Exactly 4 Input tensors are required" |
| 710 | inputs = len(op.inputs) |
| 711 | valid = inputs == 4 |
| 712 | return valid, f"Op has {inputs} inputs" |
| 713 | |
| 714 | @staticmethod |
| 715 | def constraint_stridedslice_inputs_const(op): |
| 716 | "Begin, End and Stride Input tensors must be constant" |
| 717 | valid = True |
| 718 | extra = [] |
| 719 | _, begin, end, strides = op.inputs |
| 720 | if begin.values is None: |
| 721 | valid = False |
| 722 | extra.append(f"Begin tensor '{begin.name}'") |
| 723 | if end.values is None: |
| 724 | valid = False |
| 725 | extra.append(f"End tensor '{end.name}'") |
| 726 | if strides.values is None: |
| 727 | valid = False |
| 728 | extra.append(f"Stride tensor '{strides.name}'") |
| 729 | extra = ", ".join(extra) |
| 730 | return valid, f"Op has non-constant tensors: {extra}" |
| 731 | |
| 732 | @staticmethod |
| 733 | def constraint_stridedslice_tens_size_matches(op): |
| 734 | "All Input sizes must match OFM size" |
| 735 | ifm, begin, end, strides = op.inputs |
| 736 | ifm_size = len(ifm.shape) |
| 737 | ofm_size = len(op.ofm.shape) |
| 738 | begin_size = len(begin.values) |
| 739 | end_size = len(end.values) |
| 740 | strides_size = len(strides.values) |
| 741 | valid = ifm_size == ofm_size == begin_size == end_size == strides_size |
| 742 | extra = ( |
| 743 | f"Op has ofm_size={ofm_size}, ifm_size={ifm_size}," |
| 744 | f" begin_size={begin_size}, end_size={end_size} and strides_size={strides_size}" |
| 745 | ) |
Michael McGeagh | 1f951fc | 2020-10-14 09:30:02 +0100 | [diff] [blame] | 746 | return valid, extra |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 747 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 748 | @staticmethod |
| 749 | def constraint_stridedslice_stride_values(op): |
| 750 | "All Strides values must be 1" |
| 751 | strides = op.inputs[3] |
| 752 | valid = all(stride == 1 for stride in strides.values) |
| 753 | return valid, f"Op has strides values {strides.values}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 754 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 755 | @staticmethod |
| 756 | def constraint_ellipsis_mask(op): |
| 757 | "ellipsis_mask must be 0" |
| 758 | ellipsis = op.attrs["ellipsis_mask"] |
| 759 | valid = ellipsis == 0 |
| 760 | return valid, f"Op has ellipsis mask as: {ellipsis}" |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 761 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 762 | @staticmethod |
| 763 | def constraint_axis_masks(op): |
| 764 | "new_axis_mask and shrink_axis_mask cannot both be set" |
| 765 | new_axis = op.attrs["new_axis_mask"] |
| 766 | shrink_axis = op.attrs["shrink_axis_mask"] |
| 767 | valid = (new_axis == 0) or (shrink_axis == 0) |
| 768 | return valid, f"Op has new_axis_mask={new_axis} and shrink_axis_mask={shrink_axis}" |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 769 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 770 | @staticmethod |
| 771 | def constraint_slice_ranges(op): |
| 772 | "Slice 'end' values must be greater than 'begin' values" |
| 773 | ifm, begin, end, _ = op.inputs |
| 774 | # Calculate offset begin/end |
| 775 | offset_begin = get_slice_offsets(ifm.shape, begin, op.attrs["begin_mask"], is_begin=True) |
| 776 | offset_end = get_slice_offsets(ifm.shape, end, op.attrs["end_mask"], is_begin=False) |
| 777 | # Check "end - begin" doesn't result in any zero or negative elements |
| 778 | valid = all((e - b) > 0 for b, e in zip(offset_begin, offset_end)) |
| 779 | return valid, f"Op has begin_values={begin.values} and end_values={end.values}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 780 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 781 | @staticmethod |
| 782 | def constraint_matching_inputs_types(op): |
| 783 | "Both Input data types must match" |
| 784 | ifm_dtype = op.ifm.dtype |
| 785 | ifm2_dtype = op.ifm2.dtype |
| 786 | valid = ifm_dtype == ifm2_dtype |
| 787 | return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 788 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 789 | @staticmethod |
| 790 | def constraint_matching_signed(op): |
| 791 | "For IFM that are signed, OFM must also be signed" |
| 792 | valid = True |
| 793 | ifm_dtype = op.ifm.dtype |
| 794 | ofm_dtype = op.ofm.dtype |
| 795 | if ifm_dtype.type & BaseType.Signed: |
| 796 | valid = bool(ofm_dtype.type & BaseType.Signed) |
| 797 | return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 798 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 799 | @staticmethod |
| 800 | def constraint_unsigned_valid(op): |
| 801 | "For IFM that are unsigned, OFM must either be the same type or int32" |
| 802 | valid = True |
| 803 | ifm_dtype = op.ifm.dtype |
| 804 | ofm_dtype = op.ofm.dtype |
| 805 | if ifm_dtype.type & BaseType.Unsigned: |
| 806 | valid = (ifm_dtype == ofm_dtype) or (ofm_dtype == DataType.int32) |
| 807 | return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 808 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 809 | @staticmethod |
| 810 | def constraint_inputs_int32(op): |
| 811 | "Both Input data types must be int32" |
| 812 | ifm_dtype = op.ifm.dtype |
| 813 | ifm2_dtype = op.ifm2.dtype |
| 814 | valid = (ifm_dtype == DataType.int32) and (ifm2_dtype == DataType.int32) |
| 815 | return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}" |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 816 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 817 | @staticmethod |
| 818 | def constraint_output_int32(op): |
| 819 | "OFM must be int32" |
| 820 | ofm_dtype = op.ofm.dtype |
| 821 | valid = ofm_dtype == DataType.int32 |
| 822 | return valid, f"Op has ofm_dtype={ofm_dtype}" |
Dwight Lidman | 42fed94 | 2020-05-29 09:37:03 +0200 | [diff] [blame] | 823 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 824 | @staticmethod |
| 825 | def constraint_matching_quantization_parameters(op): |
| 826 | "Both Input quantization parameters must match OFM quantization parameters" |
| 827 | valid = True |
| 828 | extra = [] |
| 829 | if not check_quantized_tens_scaling_equal(op.ofm, op.ifm): |
| 830 | valid = False |
| 831 | extra.append(op.ifm.name) |
| 832 | if not check_quantized_tens_scaling_equal(op.ofm, op.ifm2): |
| 833 | valid = False |
| 834 | extra.append(op.ifm2.name) |
| 835 | extra = ", ".join(extra) |
| 836 | return valid, f"Op has tensors with different quantization parameters to the OFM '{op.ofm.name}': {extra}" |
Dwight Lidman | 8359a47 | 2020-09-28 15:53:40 +0200 | [diff] [blame] | 837 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 838 | @staticmethod |
| 839 | def constraint_elemwise_batch_size(op): |
| 840 | "Batch size must be 1 for Input tensors with more than 2 dimensions" |
| 841 | valid = True |
| 842 | extra = [] |
| 843 | for tens in (op.ifm, op.ifm2): |
| 844 | # Unary ops have ifm2 as None |
| 845 | if tens is not None: |
| 846 | if (len(tens.shape) > 2) and (tens.shape[0] != 1): |
| 847 | valid = False |
| 848 | extra.append(tens.name) |
| 849 | extra = ", ".join(extra) |
| 850 | return valid, f"Op has invalid input tensors: {extra}" |
Jacob Bohlin | 49d9212 | 2020-08-19 14:36:46 +0200 | [diff] [blame] | 851 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 852 | @staticmethod |
| 853 | def constraint_matching_either_shapes(op): |
| 854 | "At least one Input's shape must match the OFM's shape" |
| 855 | ifm_shape = op.ifm.shape |
| 856 | ifm2_shape = op.ifm2.shape if op.ifm2 else None |
| 857 | ofm_shape = op.ofm.shape |
| 858 | valid = (ifm_shape == ofm_shape) or (ifm2_shape == ofm_shape) |
| 859 | return valid, f"Op has ifm_shape={ifm_shape}, ifm2_shape={ifm2_shape} and ofm_shape={ofm_shape}" |
Andreas Nevalainen | d8c032d | 2020-09-11 10:25:09 +0200 | [diff] [blame] | 860 | |
Michael McGeagh | 65fd998 | 2020-10-20 11:49:28 +0100 | [diff] [blame^] | 861 | @staticmethod |
| 862 | def constraint_alpha_valid(op): |
| 863 | "Alpha must not be negative" |
| 864 | alpha = op.attrs["alpha"] |
| 865 | valid = alpha >= 0 |
| 866 | return valid, f"Op has alpha={alpha}" |