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Diffstat (limited to 'ethosu/vela/tflite_model_semantic.py')
-rw-r--r-- | ethosu/vela/tflite_model_semantic.py | 527 |
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diff --git a/ethosu/vela/tflite_model_semantic.py b/ethosu/vela/tflite_model_semantic.py new file mode 100644 index 00000000..c8b373a3 --- /dev/null +++ b/ethosu/vela/tflite_model_semantic.py @@ -0,0 +1,527 @@ +# Copyright (C) 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 TFLiteSemantic class which is a collection of TensorFlow lite model semantic 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 .supported_operators_util import docstring_format_args +from .supported_operators_util import list_formatter +from .tflite_mapping import BUILTIN_OPERATOR_UNKNOWN +from .tflite_mapping import optype_to_builtintype + + +def _optype_formatter(op_list): + # Convert internal op types to external names + output = map(optype_to_builtintype, op_list) + # Remove UNKNOWNs + output = (x for x in output if x is not BUILTIN_OPERATOR_UNKNOWN) + return list_formatter(output) + + +class TFLiteSemantic: + # Categorised lists of operators + 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 + 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 + shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV, Op.Mean)) + + def __init__(self): + # Setup the generic constraints. Note: the order matters + self.generic_constraints = [] + self.generic_constraints.append(TFLiteSemantic.constraint_tens_no_dynamic) + self.generic_constraints.append(TFLiteSemantic.constraint_tens_defined_shape) + self.generic_constraints.append(TFLiteSemantic.constraint_tens_output_scalar) + self.generic_constraints.append(TFLiteSemantic.constraint_tens_input_scalar) + self.generic_constraints.append(TFLiteSemantic.constraint_tens_shape_size) + + self.generic_constraints.append(TFLiteSemantic.constraint_tens_quant_none_check) + self.generic_constraints.append(TFLiteSemantic.constraint_tens_quant_scale) + self.generic_constraints.append(TFLiteSemantic.constraint_quant_scale_inf) + + # Setup specific constraints. Note: the order matters + self.specific_constraints = defaultdict(list) + + # Conv-like checks: + for op_type in TFLiteSemantic.convolution_like_ops: + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_stride_type) + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_dilation_type) + + # Pooling checks: + for op_type in TFLiteSemantic.pooling_ops: + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_stride_type) + # AVG pooling specific checks: + for op_type in TFLiteSemantic.avg_pooling_ops: + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types) + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_filter_type) + # MAX pooling specific checks: + for op_type in TFLiteSemantic.max_pooling_ops: + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types) + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_filter_type) + + # Concat specific checks: + for op_type in (Op.Concat, Op.ConcatTFLite): + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_axis_exists) + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_axis_valid) + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_dimensionality) + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_valid_dimensions) + + # Element-wise checks: + for op_type in TFLiteSemantic.elem_wise_main_ops: + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_either_shapes) + # Unary specific checks: + for op_type in TFLiteSemantic.unary_elem_wise_main_ops: + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types) + # Binary Min/Max specific checks: + for op_type in TFLiteSemantic.binary_elem_wise_min_max_ops: + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_types) + # Binary Add/Mul/Sub specific checks: + for op_type in TFLiteSemantic.binary_elem_wise_add_mul_sub: + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_inputs_types) + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_signed) + self.specific_constraints[op_type].append(TFLiteSemantic.constraint_unsigned_valid) + + # Softmax specific checks: + self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_matching_shapes) + self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_matching_in_out_types) + self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_beta_value_range) + + # SplitV specific checks: + self.specific_constraints[Op.SplitV].append(TFLiteSemantic.constraint_splitv_inferred) + + # StridedSlice specific checks: + self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_stridedslice_input_count) + self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_stridedslice_inputs_const) + self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_ellipsis_mask) + self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_axis_masks) + self.specific_constraints[Op.StridedSlice].append(TFLiteSemantic.constraint_slice_ranges) + + # LeakyRelu specific checks: + self.specific_constraints[Op.LeakyRelu].append(TFLiteSemantic.constraint_alpha_valid) + + # FullyConnected specific checks: + self.specific_constraints[Op.FullyConnected].append(TFLiteSemantic.constraint_fc_output_2d) + self.specific_constraints[Op.FullyConnected].append(TFLiteSemantic.constraint_keep_dim_ifm_ofm) + + # Pad specific checks: + self.specific_constraints[Op.Pad].append(TFLiteSemantic.constraint_pad_input_count) + self.specific_constraints[Op.Pad].append(TFLiteSemantic.constraint_pad_constant) + + # HardSwish specific checks: + self.specific_constraints[Op.HardSwish].append(TFLiteSemantic.constraint_input_8bit) + self.specific_constraints[Op.HardSwish].append(TFLiteSemantic.constraint_matching_in_out_types) + # Mean specific checks: + self.specific_constraints[Op.Mean].append(TFLiteSemantic.constraint_input_8bit) + self.specific_constraints[Op.Mean].append(TFLiteSemantic.constraint_mean_input_dims) + self.specific_constraints[Op.Mean].append(TFLiteSemantic.constraint_mean_axis) + + def is_operator_semantic_valid(self, op): + ext_type = optype_to_builtintype(op.type) + + if op.type in (Op.Placeholder, Op.SubgraphInput, Op.Const): + return True + + for constraint in self.generic_constraints + self.specific_constraints[op.type]: + valid, extra = constraint(op) + if not valid: + print( + f"Warning: unsupported TensorFlow Lite semantics for {ext_type} '{op.name}'. 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) + + @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) + + @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) + + @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)}" + + @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)}" + + @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_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)}" + + @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, <ofm_dimensions>)" + 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_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}" + + @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_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_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_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_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}" + + @staticmethod + def constraint_mean_input_dims(op): + "Input tensor must be at least 2D" + dims = len(op.inputs[0].shape) + return 2 <= dims <= 4, f"Input is {dims}D" + + @staticmethod + def constraint_mean_axis(op): + "Axis indices must correspond to height and width axes" + dims = len(op.inputs[0].shape) + axis = int(op.inputs[1].values) if op.inputs[1].shape == [] else list(op.inputs[1].values) + if dims == 2 or dims == 3: + valid = axis in (0, 1, [0], [1], [0, 1], [1, 0]) + elif dims == 4: + valid = axis in (1, 2, [1], [2], [1, 2], [2, 1]) + return valid, f"Axis is {axis}" + + +def tflite_semantic_checker(nng): + semantic_checker = TFLiteSemantic() + for sg in nng.subgraphs: + for op in sg.get_all_ops(): + op.run_on_npu = semantic_checker.is_operator_semantic_valid(op) + return nng |