Jonas Ohlsson | 45e653d | 2021-07-26 16:13:12 +0200 | [diff] [blame] | 1 | # Copyright (C) 2021 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. |
| 16 | # Description: |
| 17 | # The TFLiteSemantic class which is a collection of TensorFlow lite model semantic checks. |
| 18 | from collections import defaultdict |
| 19 | |
| 20 | import numpy as np |
| 21 | |
| 22 | from .data_type import BaseType |
| 23 | from .data_type import DataType |
| 24 | from .numeric_util import is_integer |
| 25 | from .operation import get_slice_offsets |
| 26 | from .operation import Op |
| 27 | from .supported_operators_util import docstring_format_args |
| 28 | from .supported_operators_util import list_formatter |
| 29 | from .tflite_mapping import BUILTIN_OPERATOR_UNKNOWN |
| 30 | from .tflite_mapping import optype_to_builtintype |
| 31 | |
| 32 | |
| 33 | def _optype_formatter(op_list): |
| 34 | # Convert internal op types to external names |
| 35 | output = map(optype_to_builtintype, op_list) |
| 36 | # Remove UNKNOWNs |
| 37 | output = (x for x in output if x is not BUILTIN_OPERATOR_UNKNOWN) |
| 38 | return list_formatter(output) |
| 39 | |
| 40 | |
| 41 | class TFLiteSemantic: |
| 42 | # Categorised lists of operators |
| 43 | convolution_ops = set((Op.Conv2DBias, Op.Conv2D, Op.QuantizedConv2D,)) |
| 44 | depthwise_convolution_ops = set((Op.DepthwiseConv2DBias,)) |
| 45 | transpose_convolution_ops = set((Op.Conv2DBackpropInput,)) |
| 46 | convolution_like_ops = convolution_ops | depthwise_convolution_ops | transpose_convolution_ops |
| 47 | max_pooling_ops = Op.op_set(Op.is_maxpool_op) |
| 48 | avg_pooling_ops = Op.op_set(Op.is_avgpool_op) |
| 49 | pooling_ops = set((Op.ReduceSum,)) | max_pooling_ops | avg_pooling_ops |
| 50 | unary_elem_wise_main_ops = Op.op_set(Op.is_unary_elementwise_op) |
| 51 | binary_elem_wise_min_max_ops = set((Op.Minimum, Op.Maximum,)) |
| 52 | binary_elem_wise_shift_ops = set((Op.SHL, Op.SHR,)) |
| 53 | binary_elem_wise_add_mul_sub = set((Op.Add, Op.Mul, Op.Sub,)) |
| 54 | binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops |
| 55 | elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops |
| 56 | shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV, Op.Mean)) |
| 57 | |
| 58 | def __init__(self): |
| 59 | # Setup the generic constraints. Note: the order matters |
| 60 | self.generic_constraints = [] |
| 61 | self.generic_constraints.append(TFLiteSemantic.constraint_tens_no_dynamic) |
| 62 | self.generic_constraints.append(TFLiteSemantic.constraint_tens_defined_shape) |
| 63 | self.generic_constraints.append(TFLiteSemantic.constraint_tens_output_scalar) |
| 64 | self.generic_constraints.append(TFLiteSemantic.constraint_tens_input_scalar) |
| 65 | self.generic_constraints.append(TFLiteSemantic.constraint_tens_shape_size) |
| 66 | |
| 67 | self.generic_constraints.append(TFLiteSemantic.constraint_tens_quant_none_check) |
| 68 | self.generic_constraints.append(TFLiteSemantic.constraint_tens_quant_scale) |
| 69 | self.generic_constraints.append(TFLiteSemantic.constraint_quant_scale_inf) |
| 70 | |
| 71 | # Setup specific constraints. Note: the order matters |
| 72 | self.specific_constraints = defaultdict(list) |
| 73 | |
| 74 | # Conv-like checks: |
| 75 | for op_type in TFLiteSemantic.convolution_like_ops: |
| 76 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_stride_type) |
| 77 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_dilation_type) |
| 78 | |
| 79 | # Pooling checks: |
| 80 | for op_type in TFLiteSemantic.pooling_ops: |
| 81 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_stride_type) |
| 82 | # AVG pooling specific checks: |
| 83 | for op_type in TFLiteSemantic.avg_pooling_ops: |
| 84 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types) |
| 85 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_filter_type) |
| 86 | # MAX pooling specific checks: |
| 87 | for op_type in TFLiteSemantic.max_pooling_ops: |
| 88 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types) |
| 89 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_filter_type) |
| 90 | |
| 91 | # Concat specific checks: |
| 92 | for op_type in (Op.Concat, Op.ConcatTFLite): |
| 93 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_axis_exists) |
| 94 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_axis_valid) |
| 95 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_dimensionality) |
| 96 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_valid_dimensions) |
| 97 | |
| 98 | # Element-wise checks: |
| 99 | for op_type in TFLiteSemantic.elem_wise_main_ops: |
| 100 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_either_shapes) |
| 101 | # Unary specific checks: |
| 102 | for op_type in TFLiteSemantic.unary_elem_wise_main_ops: |
| 103 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types) |
| 104 | # Binary Min/Max specific checks: |
| 105 | for op_type in TFLiteSemantic.binary_elem_wise_min_max_ops: |
| 106 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types) |
| 107 | # Binary Add/Mul/Sub specific checks: |
| 108 | for op_type in TFLiteSemantic.binary_elem_wise_add_mul_sub: |
| 109 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_inputs_types) |
| 110 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_signed) |
| 111 | self.specific_constraints[op_type].append(TFLiteSemantic.constraint_unsigned_valid) |
| 112 | |
| 113 | # Softmax specific checks: |
| 114 | self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_matching_shapes) |
| 115 | self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_matching_in_out_types) |
| 116 | self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_beta_value_range) |
| 117 | |
| 118 | # SplitV specific checks: |
| 119 | self.specific_constraints[Op.SplitV].append(TFLiteSemantic.constraint_splitv_inferred) |
| 120 | |
| 121 | # StridedSlice specific checks: |
| 122 | self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_stridedslice_input_count) |
| 123 | self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_stridedslice_inputs_const) |
| 124 | self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_ellipsis_mask) |
| 125 | self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_axis_masks) |
| 126 | self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_slice_ranges) |
| 127 | |
| 128 | # LeakyRelu specific checks: |
| 129 | self.specific_constraints[Op.LeakyRelu].append(TFLiteSemantic.constraint_alpha_valid) |
| 130 | |
| 131 | # FullyConnected specific checks: |
| 132 | self.specific_constraints[Op.FullyConnected].append(TFLiteSemantic.constraint_fc_output_2d) |
| 133 | self.specific_constraints[Op.FullyConnected].append(TFLiteSemantic.constraint_keep_dim_ifm_ofm) |
| 134 | |
| 135 | # Pad specific checks: |
| 136 | self.specific_constraints[Op.Pad].append(TFLiteSemantic.constraint_pad_input_count) |
| 137 | self.specific_constraints[Op.Pad].append(TFLiteSemantic.constraint_pad_constant) |
| 138 | |
| 139 | # HardSwish specific checks: |
| 140 | self.specific_constraints[Op.HardSwish].append(TFLiteSemantic.constraint_input_8bit) |
| 141 | self.specific_constraints[Op.HardSwish].append(TFLiteSemantic.constraint_matching_in_out_types) |
| 142 | # Mean specific checks: |
| 143 | self.specific_constraints[Op.Mean].append(TFLiteSemantic.constraint_input_8bit) |
| 144 | self.specific_constraints[Op.Mean].append(TFLiteSemantic.constraint_mean_input_dims) |
| 145 | self.specific_constraints[Op.Mean].append(TFLiteSemantic.constraint_mean_axis) |
| 146 | |
| 147 | def is_operator_semantic_valid(self, op): |
| 148 | ext_type = optype_to_builtintype(op.type) |
| 149 | |
| 150 | if op.type in (Op.Placeholder, Op.SubgraphInput, Op.Const): |
| 151 | return True |
| 152 | |
| 153 | for constraint in self.generic_constraints + self.specific_constraints[op.type]: |
| 154 | valid, extra = constraint(op) |
| 155 | if not valid: |
| 156 | print( |
| 157 | f"Warning: unsupported TensorFlow Lite semantics for {ext_type} '{op.name}'. Placing on CPU instead" |
| 158 | ) |
| 159 | print(f" - {constraint.__doc__}") |
| 160 | if extra: |
| 161 | print(f" {extra}") |
| 162 | return False |
| 163 | |
| 164 | return True |
| 165 | |
| 166 | @staticmethod |
| 167 | def constraint_tens_no_dynamic(op): |
| 168 | "Input(s) and Output tensors must not be dynamic" |
| 169 | valid = True |
| 170 | extra = [] |
| 171 | tensors = [tens for tens in op.inputs + op.outputs if tens] |
| 172 | for tens in tensors: |
| 173 | if (tens.shape == []) and (tens.values is None): |
| 174 | valid = False |
| 175 | extra.append(tens.name) |
| 176 | extra = ", ".join(extra) |
| 177 | return valid, f"Op has dynamic tensor(s): {extra}" |
| 178 | |
| 179 | @staticmethod |
| 180 | def constraint_tens_defined_shape(op): |
| 181 | "Input(s) and Output tensors must have a defined shape" |
| 182 | valid = True |
| 183 | extra = [] |
| 184 | tensors = [tens for tens in op.inputs + op.outputs if tens] |
| 185 | for tens in tensors: |
| 186 | if not tens.has_fully_defined_shape(): |
| 187 | valid = False |
| 188 | extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
| 189 | return valid, ", ".join(extra) |
| 190 | |
| 191 | @staticmethod |
| 192 | def constraint_tens_output_scalar(op): |
| 193 | "Output tensors cannot be scalar" |
| 194 | ofm = op.ofm |
| 195 | valid = ofm.shape != [] |
| 196 | return valid, f"Output Tensor '{ofm.name}' is scalar" |
| 197 | |
| 198 | @classmethod |
| 199 | @docstring_format_args([_optype_formatter(shapeless_input_ops)]) |
| 200 | def constraint_tens_input_scalar(cls, op): |
| 201 | "Scalar Input tensors are only valid for op type: {}" |
| 202 | valid = True |
| 203 | extra = [] |
| 204 | tensors = [tens for tens in op.inputs if tens] |
| 205 | for tens in tensors: |
| 206 | if (tens.shape == []) and (op.type not in cls.shapeless_input_ops): |
| 207 | valid = False |
| 208 | extra.append(tens.name) |
| 209 | extra = ", ".join(extra) |
| 210 | return valid, f"Op has scalar input tensor(s): {extra}" |
| 211 | |
| 212 | @staticmethod |
| 213 | def constraint_tens_shape_size(op): |
| 214 | "Input(s) and Output tensors must not be greater than 4D" |
| 215 | valid = True |
| 216 | extra = [] |
| 217 | tensors = [tens for tens in op.inputs + op.outputs if tens] |
| 218 | for tens in tensors: |
| 219 | if len(tens.shape) > 4: |
| 220 | valid = False |
| 221 | extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
| 222 | return valid, ", ".join(extra) |
| 223 | |
| 224 | @staticmethod |
| 225 | def constraint_tens_quant_none_check(op): |
| 226 | "Input(s), Output and Weight tensors must have quantization parameters" |
| 227 | valid = True |
| 228 | extra = [] |
| 229 | tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| 230 | for tens in tensors: |
| 231 | if tens.quantization is None: |
| 232 | valid = False |
| 233 | extra.append(tens.name) |
| 234 | extra = ", ".join(extra) |
| 235 | return valid, f"Op has tensors with missing quantization parameters: {extra}" |
| 236 | |
| 237 | @staticmethod |
| 238 | def constraint_tens_quant_scale(op): |
| 239 | "Input(s), Output and Weight tensors with quantization scales must be finite" |
| 240 | valid = True |
| 241 | extra = [] |
| 242 | tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| 243 | for tens in tensors: |
| 244 | if (tens.quantization.scale_f32 is not None) and np.isinf(tens.quantization.scale_f32).any(): |
| 245 | valid = False |
| 246 | extra.append(f"Tensor '{tens.name}' has quantization scale: {tens.quantization.scale_f32}") |
| 247 | return valid, ", ".join(extra) |
| 248 | |
| 249 | @staticmethod |
| 250 | def constraint_fc_output_2d(op): |
| 251 | "The output tensor(s) must have 2D shape" |
| 252 | valid = True |
| 253 | extra = [] |
| 254 | for tens in op.outputs: |
| 255 | if len(tens.shape) != 2: |
| 256 | valid = False |
| 257 | extra.append(f"Tensor '{tens.name}' is {len(tens.shape)}D") |
| 258 | return valid, ", ".join(extra) |
| 259 | |
| 260 | @staticmethod |
| 261 | def constraint_stride_type(op): |
| 262 | "Stride values for both width and height must be integer types" |
| 263 | w, h = op.get_kernel_stride() |
| 264 | valid = is_integer(w) and is_integer(h) |
| 265 | return valid, f"Op has stride WxH as: {repr(w)}x{repr(h)}" |
| 266 | |
| 267 | @staticmethod |
| 268 | def constraint_dilation_type(op): |
| 269 | "Dilation factor values for both width and height must be integer types" |
| 270 | w, h = op.get_kernel_dilation() |
| 271 | valid = is_integer(w) and is_integer(h) |
| 272 | return valid, f"Op has dilation factor WxH as: {repr(w)}x{repr(h)}" |
| 273 | |
| 274 | @staticmethod |
| 275 | def constraint_quant_scale_inf(op): |
| 276 | "Input and Output tensors must have quantization scales that fit within float32 precision" |
| 277 | if op.ofm is not None and op.ofm.is_quantized(): |
| 278 | ofm_scale = op.ofm.quantization.scale_f32 |
| 279 | if ofm_scale < np.finfo(np.float32).tiny: |
| 280 | return ( |
| 281 | False, |
| 282 | f"The quantization scale of the output tensor is {ofm_scale}, " |
| 283 | + f"minimum supported is: {np.finfo(np.float32).tiny}", |
| 284 | ) |
| 285 | if op.ifm is not None and op.ifm.is_quantized(): |
| 286 | ifm_scale = op.ifm.quantization.scale_f32 |
| 287 | if np.isinf(ifm_scale / ofm_scale): |
| 288 | return ( |
| 289 | False, |
| 290 | f"IFM scale divided by OFM scale is infinite, ifm_scale={ifm_scale} ofm_scale={ofm_scale}", |
| 291 | ) |
| 292 | return True, "Op's quantization is ok" |
| 293 | |
| 294 | @staticmethod |
| 295 | def constraint_matching_in_out_types(op): |
| 296 | "IFM and OFM data types must match" |
| 297 | ifm_dtype = op.ifm.dtype |
| 298 | ofm_dtype = op.ofm.dtype |
| 299 | valid = ifm_dtype == ofm_dtype |
| 300 | return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" |
| 301 | |
| 302 | @staticmethod |
| 303 | def constraint_beta_value_range(op): |
| 304 | "Beta value needs to be positive" |
| 305 | beta = op.attrs.get("beta", 1.0) |
| 306 | valid = beta >= 0 |
| 307 | return valid, f"Op has beta={beta}" |
| 308 | |
| 309 | @staticmethod |
| 310 | def constraint_filter_type(op): |
| 311 | "Kernel filter values for both width and height must be integer types" |
| 312 | w = op.kernel.width |
| 313 | h = op.kernel.height |
| 314 | valid = is_integer(w) and is_integer(h) |
| 315 | return valid, f"Op has kernel filter WxH as: {repr(w)}x{repr(h)}" |
| 316 | |
| 317 | @staticmethod |
| 318 | def constraint_matching_shapes(op): |
| 319 | "IFM and OFM shapes must match" |
| 320 | ifm_shape = op.ifm.shape |
| 321 | ofm_shape = op.ofm.shape |
| 322 | valid = ifm_shape == ofm_shape |
| 323 | return valid, f"Op has ifm_shape={ifm_shape} and ofm_shape={ofm_shape}" |
| 324 | |
| 325 | @staticmethod |
| 326 | def constraint_splitv_inferred(op): |
| 327 | "Only one size is allowed to be inferred" |
| 328 | sizes = op.inputs[1].values |
| 329 | valid = np.count_nonzero(sizes == -1) <= 1 |
| 330 | return valid, f"Op has multiple inferred sizes (-1): {sizes}" |
| 331 | |
| 332 | @staticmethod |
| 333 | def constraint_axis_exists(op): |
| 334 | "Axis attribute must exist" |
| 335 | axis = op.attrs.get("axis") |
| 336 | valid = axis is not None |
| 337 | return valid, f"Op has axis={axis}" |
| 338 | |
| 339 | @staticmethod |
| 340 | def constraint_axis_valid(op): |
| 341 | "Axis attribute must be in the range [0, <ofm_dimensions>)" |
| 342 | dims = len(op.ofm.shape) |
| 343 | axis = op.attrs["axis"] |
| 344 | axis += dims if axis < 0 else 0 |
| 345 | valid = 0 <= axis < dims |
| 346 | return valid, f"Op has ofm_dimensions={dims} and axis attribute is: {axis}" |
| 347 | |
| 348 | @staticmethod |
| 349 | def constraint_matching_dimensionality(op): |
| 350 | "All Input dimensionalities must match OFM dimensionality" |
| 351 | valid = True |
| 352 | extra = [] |
| 353 | ofm_dim = len(op.ofm.shape) |
| 354 | tensors = [tens for tens in op.inputs if tens] |
| 355 | for tens in tensors: |
| 356 | dim = len(tens.shape) |
| 357 | if dim != ofm_dim: |
| 358 | valid = False |
| 359 | extra.append(f"Tensor '{tens.name}' has dimension: {dim}") |
| 360 | extra = ", ".join(extra) |
| 361 | return valid, f"Op has ofm_dimension={ofm_dim} and the list of mismatching inputs are: {extra}" |
| 362 | |
| 363 | @staticmethod |
| 364 | def constraint_valid_dimensions(op): |
| 365 | "All Input dimensions must match OFM dimension in all axes except the one defined by the axis attribute" |
| 366 | valid = True |
| 367 | extra = [] |
| 368 | ofm_shape = op.ofm.shape |
| 369 | ofm_dim = len(ofm_shape) |
| 370 | axis = op.attrs["axis"] |
| 371 | axis += ofm_dim if axis < 0 else 0 |
| 372 | tensors = [tens for tens in op.inputs if tens] |
| 373 | for tens in tensors: |
| 374 | if any(tens.shape[dim] != ofm_shape[dim] for dim in range(ofm_dim) if dim != axis): |
| 375 | valid = False |
| 376 | extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
| 377 | extra = ", ".join(extra) |
| 378 | return valid, f"Op has axis={axis}, ofm_shape={ofm_shape} and the list of mismatching inputs are: {extra}" |
| 379 | |
| 380 | @staticmethod |
| 381 | def constraint_stridedslice_input_count(op): |
| 382 | "Exactly 4 Input tensors are required" |
| 383 | inputs = len(op.inputs) |
| 384 | valid = inputs == 4 |
| 385 | return valid, f"Op has {inputs} inputs" |
| 386 | |
| 387 | @staticmethod |
| 388 | def constraint_pad_input_count(op): |
| 389 | "Number of input tensors must be exactly 2" |
| 390 | inputs = len(op.inputs) |
| 391 | valid = inputs == 2 |
| 392 | return valid, f"Op has {inputs} inputs" |
| 393 | |
| 394 | @staticmethod |
| 395 | def constraint_pad_constant(op): |
| 396 | "The padding tensor must be constant" |
| 397 | pad_tensor = op.inputs[1].values |
| 398 | valid = pad_tensor is not None |
| 399 | return valid, f"Op has non-constant padding tensor: {op.inputs[1].values}" |
| 400 | |
| 401 | @staticmethod |
| 402 | def constraint_stridedslice_inputs_const(op): |
| 403 | "Begin, End and Stride Input tensors must be constant" |
| 404 | valid = True |
| 405 | extra = [] |
| 406 | _, begin, end, strides = op.inputs |
| 407 | if begin.values is None: |
| 408 | valid = False |
| 409 | extra.append(f"Begin tensor '{begin.name}'") |
| 410 | if end.values is None: |
| 411 | valid = False |
| 412 | extra.append(f"End tensor '{end.name}'") |
| 413 | if strides.values is None: |
| 414 | valid = False |
| 415 | extra.append(f"Stride tensor '{strides.name}'") |
| 416 | extra = ", ".join(extra) |
| 417 | return valid, f"Op has non-constant tensors: {extra}" |
| 418 | |
| 419 | @staticmethod |
| 420 | def constraint_ellipsis_mask(op): |
| 421 | "ellipsis_mask must be 0" |
| 422 | ellipsis = op.attrs["ellipsis_mask"] |
| 423 | valid = ellipsis == 0 |
| 424 | return valid, f"Op has ellipsis mask as: {ellipsis}" |
| 425 | |
| 426 | @staticmethod |
| 427 | def constraint_axis_masks(op): |
| 428 | "new_axis_mask and shrink_axis_mask cannot both be set" |
| 429 | new_axis = op.attrs["new_axis_mask"] |
| 430 | shrink_axis = op.attrs["shrink_axis_mask"] |
| 431 | valid = (new_axis == 0) or (shrink_axis == 0) |
| 432 | return valid, f"Op has new_axis_mask={new_axis} and shrink_axis_mask={shrink_axis}" |
| 433 | |
| 434 | @staticmethod |
| 435 | def constraint_slice_ranges(op): |
| 436 | "Slice 'end' values must be greater than 'begin' values" |
| 437 | ifm, begin, end, _ = op.inputs |
| 438 | # Calculate offset begin/end |
| 439 | offset_begin = get_slice_offsets(ifm.shape, begin, op.attrs["begin_mask"], is_begin=True) |
| 440 | offset_end = get_slice_offsets(ifm.shape, end, op.attrs["end_mask"], is_begin=False) |
| 441 | # Check "end - begin" doesn't result in any zero or negative elements |
| 442 | valid = all((e - b) > 0 for b, e in zip(offset_begin, offset_end)) |
| 443 | return valid, f"Op has begin_values={begin.values} and end_values={end.values}" |
| 444 | |
| 445 | @staticmethod |
| 446 | def constraint_matching_inputs_types(op): |
| 447 | "Both Input data types must match" |
| 448 | ifm_dtype = op.ifm.dtype |
| 449 | ifm2_dtype = op.ifm2.dtype |
| 450 | valid = ifm_dtype == ifm2_dtype |
| 451 | return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}" |
| 452 | |
| 453 | @staticmethod |
| 454 | def constraint_matching_signed(op): |
| 455 | "For IFM that are signed, OFM must also be signed" |
| 456 | valid = True |
| 457 | ifm_dtype = op.ifm.dtype |
| 458 | ofm_dtype = op.ofm.dtype |
| 459 | if ifm_dtype.type & BaseType.Signed: |
| 460 | valid = bool(ofm_dtype.type & BaseType.Signed) |
| 461 | return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" |
| 462 | |
| 463 | @staticmethod |
| 464 | def constraint_unsigned_valid(op): |
| 465 | "For IFM that are unsigned, OFM must either be the same type or int32" |
| 466 | valid = True |
| 467 | ifm_dtype = op.ifm.dtype |
| 468 | ofm_dtype = op.ofm.dtype |
| 469 | if ifm_dtype.type & BaseType.Unsigned: |
| 470 | valid = (ifm_dtype == ofm_dtype) or (ofm_dtype == DataType.int32) |
| 471 | return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" |
| 472 | |
| 473 | @staticmethod |
| 474 | def constraint_input_8bit(op): |
| 475 | "IFM must be int8 or uint8" |
| 476 | ifm_dtype = op.ifm.dtype |
| 477 | valid = (ifm_dtype == DataType.int8) or (ifm_dtype == DataType.uint8) |
| 478 | return valid, f"Op has ifm_dtype={ifm_dtype}" |
| 479 | |
| 480 | @staticmethod |
| 481 | def constraint_matching_either_shapes(op): |
| 482 | "At least one Input's shape must match the OFM's shape" |
| 483 | ifm_shape = op.ifm.shape |
| 484 | ifm2_shape = op.ifm2.shape if op.ifm2 else None |
| 485 | ofm_shape = op.ofm.shape |
| 486 | valid = (ifm_shape == ofm_shape) or (ifm2_shape == ofm_shape) |
| 487 | return valid, f"Op has ifm_shape={ifm_shape}, ifm2_shape={ifm2_shape} and ofm_shape={ofm_shape}" |
| 488 | |
| 489 | @staticmethod |
| 490 | def constraint_alpha_valid(op): |
| 491 | "Alpha must not be negative" |
| 492 | alpha = op.attrs["alpha"] |
| 493 | valid = alpha >= 0 |
| 494 | return valid, f"Op has alpha={alpha}" |
| 495 | |
| 496 | @staticmethod |
| 497 | def constraint_keep_dim_ifm_ofm(op): |
| 498 | "The IFM and OFM must have the same number of dimensions if keep_num_dims is set to true" |
| 499 | valid = True |
| 500 | if op.attrs.get("keep_num_dims"): |
| 501 | valid = len(op.ifm.shape) == len(op.ofm.shape) |
| 502 | return valid, f"Op has ifm shape={op.ifm.shape} and ofm shape={op.ofm.shape}" |
| 503 | |
| 504 | @staticmethod |
| 505 | def constraint_mean_input_dims(op): |
| 506 | "Input tensor must be at least 2D" |
| 507 | dims = len(op.inputs[0].shape) |
| 508 | return 2 <= dims <= 4, f"Input is {dims}D" |
| 509 | |
| 510 | @staticmethod |
| 511 | def constraint_mean_axis(op): |
| 512 | "Axis indices must correspond to height and width axes" |
| 513 | dims = len(op.inputs[0].shape) |
| 514 | axis = int(op.inputs[1].values) if op.inputs[1].shape == [] else list(op.inputs[1].values) |
| 515 | if dims == 2 or dims == 3: |
| 516 | valid = axis in (0, 1, [0], [1], [0, 1], [1, 0]) |
| 517 | elif dims == 4: |
| 518 | valid = axis in (1, 2, [1], [2], [1, 2], [2, 1]) |
| 519 | return valid, f"Axis is {axis}" |
| 520 | |
| 521 | |
| 522 | def tflite_semantic_checker(nng): |
| 523 | semantic_checker = TFLiteSemantic() |
| 524 | for sg in nng.subgraphs: |
| 525 | for op in sg.get_all_ops(): |
| 526 | op.run_on_npu = semantic_checker.is_operator_semantic_valid(op) |
| 527 | return nng |