| # Copyright (C) 2020-2021 Arm Limited or its affiliates. All rights reserved. |
| # |
| # SPDX-License-Identifier: Apache-2.0 |
| # |
| # Licensed under the Apache License, Version 2.0 (the License); you may |
| # not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # Description: |
| # The SupportedOperators class which is a collection of all supported operators and parameter checks. |
| from collections import defaultdict |
| |
| import numpy as np |
| |
| from .data_type import BaseType |
| from .data_type import DataType |
| from .numeric_util import is_integer |
| from .operation import get_slice_offsets |
| from .operation import Op |
| from .operation import Padding |
| from .tensor import check_quantized_tens_scaling_equal |
| from .tflite_mapping import BUILTIN_OPERATOR_UNKNOWN |
| from .tflite_mapping import optype_to_builtintype |
| |
| |
| # Custom decorator function to allow formatting docstrings containing "{}" |
| def docstring_format_args(args): |
| def docstring(func): |
| func.__doc__ = func.__doc__.format(*args) |
| return func |
| |
| return docstring |
| |
| |
| def _list_formatter(arg): |
| # Order and join into a string representation |
| return ", ".join(sorted(map(str, arg))) |
| |
| |
| def _optype_formatter(op_list): |
| # Convert internal op types to external names |
| output = map(optype_to_builtintype, op_list) |
| # Remove UNKNOWNs |
| output = (x for x in output if x is not BUILTIN_OPERATOR_UNKNOWN) |
| return _list_formatter(output) |
| |
| |
| class SupportedOperators: |
| # Categorised lists of supported operators |
| npu_pre_ops = set((Op.SplitSliceRead,)) |
| convolution_ops = set((Op.Conv2DBias, Op.Conv2D, Op.QuantizedConv2D,)) |
| depthwise_convolution_ops = set((Op.DepthwiseConv2DBias,)) |
| transpose_convolution_ops = set((Op.Conv2DBackpropInput,)) |
| convolution_like_ops = convolution_ops | depthwise_convolution_ops | transpose_convolution_ops |
| max_pooling_ops = Op.op_set(Op.is_maxpool_op) |
| avg_pooling_ops = Op.op_set(Op.is_avgpool_op) |
| pooling_ops = set((Op.ReduceSum,)) | max_pooling_ops | avg_pooling_ops |
| resizing_ops = set((Op.ResizeBilinear,)) |
| fc_vector_products = set((Op.QuantizedMatMul, Op.MatMul, Op.FullyConnected,)) |
| mac_main_ops = ( |
| # RNN/LSTM/GRU |
| set((Op.BlockLSTM,)) |
| # conv/depthwiseconv/transposeconv |
| | convolution_like_ops |
| # pooling |
| | pooling_ops |
| # resizing/upscaling |
| | resizing_ops |
| # FC layers |
| | fc_vector_products |
| # Mean (converts to depthwise conv) |
| | set((Op.Mean,)) |
| ) |
| unary_elem_wise_main_ops = Op.op_set(Op.is_unary_elementwise_op) |
| binary_elem_wise_min_max_ops = set((Op.Minimum, Op.Maximum,)) |
| binary_elem_wise_shift_ops = set((Op.SHL, Op.SHR,)) |
| binary_elem_wise_add_mul_sub = set((Op.Add, Op.Mul, Op.Sub,)) |
| binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops |
| elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops |
| pad_ops = set((Op.Pad,)) |
| supported_int32_tensor_ops = ( |
| set((Op.ReduceSum, Op.CLZ,)) | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops |
| ) |
| relu_ops = Op.op_set(Op.is_relu_op) |
| activation_ops = relu_ops | set((Op.Tanh, Op.Sigmoid, Op.Softmax, Op.HardSwish)) |
| npu_post_ops = ( |
| # activation functions |
| activation_ops |
| # concatenation write direction |
| | set((Op.ConcatSliceWrite,)) |
| # Quantization |
| | set((Op.Quantize,)) |
| ) |
| split_ops = set((Op.Split, Op.SplitV, Op.StridedSlice, Op.Slice, Op.UnpackReshaped, Op.Unpack,)) |
| concat_ops = set((Op.Concat, Op.ConcatTFLite, Op.PackReshaped, Op.Pack,)) |
| memory_only_ops = set((Op.Reshape, Op.QuantizedReshape,)) | concat_ops | split_ops |
| shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV, Op.Mean)) |
| per_axis_quant_ops = convolution_like_ops # per-axis/channel quantization only currently supported for conv ops |
| supported_fused_activations = relu_ops | set((Op.Tanh, Op.Sigmoid, Op.LUT,)) |
| supported_operators = npu_pre_ops | mac_main_ops | elem_wise_main_ops | pad_ops | npu_post_ops | memory_only_ops |
| # Supported data types |
| supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32)) |
| supported_faf_dtypes = set((DataType.uint8, DataType.int8, DataType.int16)) |
| supported_bias_dtypes = set((DataType.int32, DataType.int64)) |
| supported_pad_dtypes = set((DataType.int32, DataType.int64)) |
| # Defined ranges for allowed values: |
| tens_dim_range = (1, 65535) |
| stride_range = (1, 3) |
| dilation_range = (1, 2) |
| dilated_height_range = (1, 64) |
| dilated_product_range = (1, 64 * 64) |
| weights_limit = 127 * 65536 |
| filter_range = (1, 8) |
| filter_height_range = (1, 256) |
| filter_product_range = (1, 256 * 256) |
| mean_kernel_product = 64 * 64 |
| mean_kernel_product_int8 = 16 * 16 |
| mean_kernel_product_avgpool = 256 * 256 |
| # Supported consumers |
| supported_pad_consumers = convolution_ops | depthwise_convolution_ops | pooling_ops |
| |
| def __init__(self): |
| # Setup the generic constraints. Note: the order matters |
| self.generic_constraints = [] |
| self.generic_constraints.append(SupportedOperators.constraint_tens_no_dynamic) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_defined_shape) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_output_scalar) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_input_scalar) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_shape_size) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_dtype) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_int32_ops) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_dimension) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_quant_none_check) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale) |
| self.generic_constraints.append(SupportedOperators.constraint_tens_quant_per_axis) |
| self.generic_constraints.append(SupportedOperators.constraint_faf) |
| self.generic_constraints.append(SupportedOperators.constraint_faf_type) |
| self.generic_constraints.append(SupportedOperators.constraint_quant_scale_inf) |
| |
| # Setup specific constraints. Note: the order matters |
| self.specific_constraints = defaultdict(list) |
| |
| # Conv-like checks: |
| for op_type in SupportedOperators.convolution_like_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_type) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_range) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_dilation_type) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_dilation_range) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_dilated_height_range) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_dilated_product_range) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_type) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_const) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_limit) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_type) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_40bit) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_batch_size) |
| # Depthwise Conv specific checks: |
| for op_type in SupportedOperators.depthwise_convolution_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_depth_multiplier) |
| # Transpose Conv specific checks: |
| for op_type in SupportedOperators.transpose_convolution_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_stride) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_same) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_tconv_valid) |
| |
| # Pooling checks: |
| for op_type in SupportedOperators.pooling_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_batch_size) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_type) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_stride_range) |
| # AVG pooling specific checks: |
| for op_type in SupportedOperators.avg_pooling_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_type) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_range) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_height_range_valid_pad) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_product_range_valid_pad) |
| # MAX pooling specific checks: |
| for op_type in SupportedOperators.max_pooling_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_type) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_height_range) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_product_range) |
| |
| # Resizing specific checks: |
| for op_type in SupportedOperators.resizing_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_resize) |
| |
| # Vector Product specific checks: |
| for op_type in SupportedOperators.fc_vector_products: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_type) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_weights_const) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_type) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_bias_40bit) |
| |
| # Concat specific checks: |
| for op_type in (Op.Concat, Op.ConcatTFLite): |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_axis_exists) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_axis_valid) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_dimensionality) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_valid_dimensions) |
| |
| # Element-wise checks: |
| for op_type in SupportedOperators.elem_wise_main_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_elemwise_batch_size) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_either_shapes) |
| # Unary specific checks: |
| for op_type in SupportedOperators.unary_elem_wise_main_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) |
| # Binary Min/Max specific checks: |
| for op_type in SupportedOperators.binary_elem_wise_min_max_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_in_out_types) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_quantization_parameters) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_broadcast_shapes) |
| # Binary Add/Mul/Sub specific checks: |
| for op_type in SupportedOperators.binary_elem_wise_add_mul_sub: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_inputs_types) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_matching_signed) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_unsigned_valid) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_broadcast_shapes) |
| # Binary Shift specific checks: |
| for op_type in SupportedOperators.binary_elem_wise_shift_ops: |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_inputs_int32) |
| self.specific_constraints[op_type].append(SupportedOperators.constraint_broadcast_shapes) |
| |
| # SHL specific checks: |
| self.specific_constraints[Op.SHL].append(SupportedOperators.constraint_output_int32) |
| |
| # CLZ specific checks: |
| self.specific_constraints[Op.CLZ].append(SupportedOperators.constraint_output_int32) |
| |
| # Softmax specific checks: |
| self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_matching_shapes) |
| self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_matching_in_out_types) |
| self.specific_constraints[Op.Softmax].append(SupportedOperators.constraint_beta_value_range) |
| |
| # SplitV specific checks: |
| self.specific_constraints[Op.SplitV].append(SupportedOperators.constraint_splitv_inferred) |
| |
| # StridedSlice specific checks: |
| self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_input_count) |
| self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_inputs_const) |
| self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_stridedslice_stride_values) |
| self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_ellipsis_mask) |
| self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_axis_masks) |
| self.specific_constraints[Op.StridedSlice].append(SupportedOperators.constraint_slice_ranges) |
| |
| # LeakyRelu specific checks: |
| self.specific_constraints[Op.LeakyRelu].append(SupportedOperators.constraint_alpha_valid) |
| |
| # FullyConnected specific checks: |
| self.specific_constraints[Op.FullyConnected].append(SupportedOperators.constraint_fc_output_2d) |
| self.specific_constraints[Op.FullyConnected].append(SupportedOperators.constraint_keep_dim_ifm_ofm) |
| |
| # Pad specific checks: |
| self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_input_count) |
| self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_shape) |
| self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_padding_dimensions) |
| self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_type) |
| self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_constant) |
| |
| # HardSwish specific checks: |
| self.specific_constraints[Op.HardSwish].append(SupportedOperators.constraint_input_8bit) |
| self.specific_constraints[Op.HardSwish].append(SupportedOperators.constraint_matching_in_out_types) |
| # Mean specific checks: |
| self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_input_8bit) |
| self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_input_dims) |
| self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_axis) |
| self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_height_width_product_avgpool) |
| self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_height_width_product) |
| self.specific_constraints[Op.Mean].append(SupportedOperators.constraint_mean_height_width_product_int8) |
| |
| def is_operator_supported(self, op): |
| ext_type = optype_to_builtintype(op.type) |
| if op.type not in SupportedOperators.supported_operators: |
| if op.type not in (Op.Placeholder, Op.SubgraphInput, Op.Const): |
| print(f"Info: {ext_type} '{op.name}' is a CPU only op") |
| return False |
| |
| for constraint in self.generic_constraints + self.specific_constraints[op.type]: |
| valid, extra = constraint(op) |
| if not valid: |
| print(f"Warning: {ext_type} '{op.name}' is not supported on the NPU. Placing on CPU instead") |
| print(f" - {constraint.__doc__}") |
| if extra: |
| print(f" {extra}") |
| return False |
| |
| return True |
| |
| @staticmethod |
| def constraint_tens_no_dynamic(op): |
| "Input(s) and Output tensors must not be dynamic" |
| valid = True |
| extra = [] |
| tensors = [tens for tens in op.inputs + op.outputs if tens] |
| for tens in tensors: |
| if (tens.shape == []) and (tens.values is None): |
| valid = False |
| extra.append(tens.name) |
| extra = ", ".join(extra) |
| return valid, f"Op has dynamic tensor(s): {extra}" |
| |
| @staticmethod |
| def constraint_tens_defined_shape(op): |
| "Input(s) and Output tensors must have a defined shape" |
| valid = True |
| extra = [] |
| tensors = [tens for tens in op.inputs + op.outputs if tens] |
| for tens in tensors: |
| if not tens.has_fully_defined_shape(): |
| valid = False |
| extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
| return valid, ", ".join(extra) |
| |
| @staticmethod |
| def constraint_tens_output_scalar(op): |
| "Output tensors cannot be scalar" |
| ofm = op.ofm |
| valid = ofm.shape != [] |
| return valid, f"Output Tensor '{ofm.name}' is scalar" |
| |
| @classmethod |
| @docstring_format_args([_optype_formatter(shapeless_input_ops)]) |
| def constraint_tens_input_scalar(cls, op): |
| "Scalar Input tensors are only valid for op type: {}" |
| valid = True |
| extra = [] |
| tensors = [tens for tens in op.inputs if tens] |
| for tens in tensors: |
| if (tens.shape == []) and (op.type not in cls.shapeless_input_ops): |
| valid = False |
| extra.append(tens.name) |
| extra = ", ".join(extra) |
| return valid, f"Op has scalar input tensor(s): {extra}" |
| |
| @staticmethod |
| def constraint_tens_shape_size(op): |
| "Input(s) and Output tensors must not be greater than 4D" |
| valid = True |
| extra = [] |
| tensors = [tens for tens in op.inputs + op.outputs if tens] |
| for tens in tensors: |
| if len(tens.shape) > 4: |
| valid = False |
| extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
| return valid, ", ".join(extra) |
| |
| @classmethod |
| @docstring_format_args([_list_formatter(supported_op_dtypes)]) |
| def constraint_tens_dtype(cls, op): |
| "Tensors must be of type: {}" |
| valid = True |
| extra = [] |
| tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| if not tensors: |
| tensors = [tens for tens in op.inputs if tens] |
| for tens in tensors: |
| if tens.dtype not in cls.supported_op_dtypes: |
| valid = False |
| extra.append(f"Tensor '{tens.name}' has data type: {tens.dtype}") |
| return valid, ", ".join(extra) |
| |
| @classmethod |
| @docstring_format_args([_optype_formatter(supported_int32_tensor_ops)]) |
| def constraint_tens_int32_ops(cls, op): |
| "Tensors which are int32 are only valid when op type is: {}" |
| valid = True |
| extra = [] |
| tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| if not tensors: |
| tensors = [tens for tens in op.inputs if tens] |
| for tens in tensors: |
| if (tens.dtype == DataType.int32) and (op.type not in cls.supported_int32_tensor_ops): |
| valid = False |
| extra.append(tens.name) |
| extra = ", ".join(extra) |
| return valid, f"Op has int32 tensor(s): {extra}" |
| |
| @classmethod |
| @docstring_format_args(tens_dim_range) |
| def constraint_tens_dimension(cls, op): |
| "Tensor dimensions must be in the range [{}, {}]" |
| tens_min, tens_max = cls.tens_dim_range |
| valid = True |
| extra = [] |
| tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| if not tensors: |
| tensors = [tens for tens in op.inputs if tens] |
| for tens in tensors: |
| if not all(tens_min <= dim <= tens_max for dim in tens.shape): |
| valid = False |
| extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") |
| return valid, ", ".join(extra) |
| |
| @staticmethod |
| def constraint_tens_quant_none_check(op): |
| "Input(s), Output and Weight tensors must have quantization parameters" |
| valid = True |
| extra = [] |
| tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| for tens in tensors: |
| if tens.quantization is None: |
| valid = False |
| extra.append(tens.name) |
| extra = ", ".join(extra) |
| return valid, f"Op has tensors with missing quantization parameters: {extra}" |
| |
| @staticmethod |
| def constraint_tens_quant_scale(op): |
| "Input(s), Output and Weight tensors with quantization scales must be finite" |
| valid = True |
| extra = [] |
| tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| for tens in tensors: |
| if (tens.quantization.scale_f32 is not None) and np.isinf(tens.quantization.scale_f32).any(): |
| valid = False |
| extra.append(f"Tensor '{tens.name}' has quantization scale: {tens.quantization.scale_f32}") |
| return valid, ", ".join(extra) |
| |
| @classmethod |
| @docstring_format_args([_optype_formatter(per_axis_quant_ops)]) |
| def constraint_tens_quant_per_axis(cls, op): |
| "Per-axis quantization is only supported for the following op types: {}" |
| valid = True |
| extra = [] |
| if op.type not in cls.per_axis_quant_ops: |
| tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] |
| for tens in tensors: |
| if tens.quantization.is_per_axis(): |
| valid = False |
| extra.append(tens.name) |
| return valid, "The following tensor(s) have per-axis quantization parameters: " + ", ".join(extra) |
| |
| @staticmethod |
| def constraint_fc_output_2d(op): |
| "The output tensor(s) must have 2D shape" |
| valid = True |
| extra = [] |
| for tens in op.outputs: |
| if len(tens.shape) != 2: |
| valid = False |
| extra.append(f"Tensor '{tens.name}' is {len(tens.shape)}D") |
| return valid, ", ".join(extra) |
| |
| @classmethod |
| @docstring_format_args([_optype_formatter(supported_fused_activations)]) |
| def constraint_faf(cls, op): |
| "The fused activation function (if present) must be one of type: {}" |
| if op.activation is None: |
| res = True, "Op has no fused activation function" |
| else: |
| faf = op.activation.op_type |
| valid = faf in cls.supported_fused_activations |
| res = valid, f"Op has its fused activation function as: {faf}" |
| return res |
| |
| @classmethod |
| @docstring_format_args([_list_formatter(supported_faf_dtypes)]) |
| def constraint_faf_type(cls, op): |
| "If a fused activation function is present, the Output tensor must be one of type: {}" |
| if op.activation is None: |
| res = True, "Op has no fused activation function" |
| else: |
| valid = op.ofm.dtype in cls.supported_faf_dtypes |
| ext_type = optype_to_builtintype(op.activation.op_type) |
| res = valid, f"Op has fused activation function {ext_type}, and Output tensor data type: {op.ofm.dtype}" |
| return res |
| |
| @staticmethod |
| def constraint_stride_type(op): |
| "Stride values for both width and height must be integer types" |
| w, h = op.get_kernel_stride() |
| valid = is_integer(w) and is_integer(h) |
| return valid, f"Op has stride WxH as: {repr(w)}x{repr(h)}" |
| |
| @classmethod |
| @docstring_format_args(stride_range) |
| def constraint_stride_range(cls, op): |
| "Stride values for both width and height must be in the range [{}, {}]" |
| w, h = op.get_kernel_stride() |
| stride_min, stride_max = cls.stride_range |
| valid = (stride_min <= w <= stride_max) and (stride_min <= h <= stride_max) |
| return valid, f"Op has stride WxH as: {w}x{h}" |
| |
| @staticmethod |
| def constraint_dilation_type(op): |
| "Dilation factor values for both width and height must be integer types" |
| w, h = op.get_kernel_dilation() |
| valid = is_integer(w) and is_integer(h) |
| return valid, f"Op has dilation factor WxH as: {repr(w)}x{repr(h)}" |
| |
| @classmethod |
| @docstring_format_args(dilation_range) |
| def constraint_dilation_range(cls, op): |
| "Dilation factor values for both width and height must be in the range [{}, {}]" |
| w, h = op.get_kernel_dilation() |
| dilation_min, dilation_max = cls.dilation_range |
| valid = (dilation_min <= w <= dilation_max) and (dilation_min <= h <= dilation_max) |
| return valid, f"Op has dilation factor WxH as: {w}x{h}" |
| |
| @classmethod |
| @docstring_format_args(dilated_height_range) |
| def constraint_dilated_height_range(cls, op): |
| "Dilated kernel height must be in the range [{}, {}]" |
| h = op.kernel.area_height() |
| dilated_height_min, dilated_height_max = cls.dilated_height_range |
| valid = dilated_height_min <= h <= dilated_height_max |
| return valid, f"Op has dilated kernel height as: {h}" |
| |
| @classmethod |
| @docstring_format_args(dilated_product_range) |
| def constraint_dilated_product_range(cls, op): |
| "Product of dilated kernel width and height must be in the range [{}, {}]" |
| product = op.kernel.area_width() * op.kernel.area_height() |
| dilated_product_min, dilated_product_max = cls.dilated_product_range |
| valid = dilated_product_min <= product <= dilated_product_max |
| return valid, f"Op has product of dilated kernel width and height as: {product}" |
| |
| @staticmethod |
| def constraint_weights_type(op): |
| "Weight tensor must be 8-bit" |
| weights = op.weights |
| valid = weights.element_size() == 1 |
| return valid, f"Tensor '{weights.name}' is {int(weights.element_size() * 8)}-bit" |
| |
| @staticmethod |
| def constraint_weights_const(op): |
| "Weight tensor must be constant" |
| weights = op.weights |
| valid = weights.values is not None |
| return valid, f"Tensor '{weights.name}' has non-constant values" |
| |
| @classmethod |
| @docstring_format_args([weights_limit]) |
| def constraint_weights_limit(cls, op): |
| "The sum of the weights cannot exceed {}" |
| weights = op.weights |
| values = weights.quant_values.astype(np.int64) - weights.quantization.zero_point |
| limit = np.amax(np.sum(np.absolute(values), axis=(0, 1, 2))) |
| valid = limit <= cls.weights_limit |
| return valid, f"Tensor '{weights.name}' has the sum of weights: {limit}" |
| |
| @classmethod |
| @docstring_format_args([_list_formatter(supported_bias_dtypes)]) |
| def constraint_bias_type(cls, op): |
| "Optional Bias tensor must be of type: {}" |
| bias = op.bias |
| if bias: |
| valid = bias.dtype in cls.supported_bias_dtypes |
| return valid, f"Tensor '{bias.name}' has data type: {bias.dtype}" |
| return True, "Op has no bias tensor" |
| |
| @staticmethod |
| def constraint_bias_40bit(op): |
| "Optional Bias tensor values must fit within 40-bits" |
| bias = op.bias |
| if bias and bias.dtype == DataType.int64 and bias.quant_values is not None: |
| valid = all(len(bin(quant_value)[2:]) <= 40 for quant_value in bias.quant_values) |
| return valid, f"Tensor '{bias.name}' has values larger than 40-bits" |
| return True, "Op has no bias tensor, or it fits in 40-bit" |
| |
| @staticmethod |
| def constraint_batch_size(op): |
| "IFM Tensor batch size must be 1" |
| ifm = op.ifm |
| valid = ifm.shape[0] == 1 |
| return valid, f"Tensor '{ifm.name}' has batch size: {ifm.shape[0]}" |
| |
| @staticmethod |
| def constraint_quant_scale_inf(op): |
| "Input and Output tensors must have quantization scales that fit within float32 precision" |
| if op.ofm is not None and op.ofm.is_quantized(): |
| ofm_scale = op.ofm.quantization.scale_f32 |
| if ofm_scale < np.finfo(np.float32).tiny: |
| return ( |
| False, |
| f"The quantization scale of the output tensor is {ofm_scale}, " |
| + f"minimum supported is: {np.finfo(np.float32).tiny}", |
| ) |
| if op.ifm is not None and op.ifm.is_quantized(): |
| ifm_scale = op.ifm.quantization.scale_f32 |
| if np.isinf(ifm_scale / ofm_scale): |
| return ( |
| False, |
| f"IFM scale divided by OFM scale is infinite, ifm_scale={ifm_scale} ofm_scale={ofm_scale}", |
| ) |
| return True, "Op's quantization is ok" |
| |
| @staticmethod |
| def constraint_depth_multiplier(op): |
| "For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier" |
| depth_multiplier = op.attrs.get("depth_multiplier", 1) |
| if depth_multiplier > 1: |
| ifm_channels = op.ifm.shape[3] |
| ofm_channels = op.ofm.shape[3] |
| valid = (ifm_channels == 1) and (ofm_channels == depth_multiplier) |
| extra = ( |
| f"Op has ifm_channels={ifm_channels}, ofm_channels={ofm_channels}" |
| f" and depth_multiplier={depth_multiplier}" |
| ) |
| return valid, extra |
| return True, "Op has depth_multiplier=1" |
| |
| @staticmethod |
| def constraint_tconv_stride(op): |
| "Stride values for both width and height must be 2" |
| w = op.kernel.stride.x |
| h = op.kernel.stride.y |
| valid = (w == 2) and (h == 2) |
| return valid, f"Op has stride WxH as: {w}x{h}" |
| |
| @staticmethod |
| def constraint_tconv_same(op): |
| "SAME padding: OFM dimensions must equal IFM dimensions multiplied by stride" |
| if op.attrs["padding"] == Padding.SAME: |
| w = op.kernel.stride.x |
| h = op.kernel.stride.y |
| ifm_shape = op.ifm.shape |
| ofm_shape = op.ofm.shape |
| valid = (ofm_shape[1] == (ifm_shape[1] * h)) and (ofm_shape[2] == (ifm_shape[2] * w)) |
| return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and stride WxH as {w}x{h}" |
| return True, "Op has padding=VALID" |
| |
| @staticmethod |
| def constraint_tconv_valid(op): |
| """VALID padding: OFM dimensions must equal IFM dimensions multiplied by stride, |
| minus difference between kernel size and stride""" |
| if op.attrs["padding"] == Padding.VALID: |
| s_w = op.kernel.stride.x |
| s_h = op.kernel.stride.y |
| k_w = op.kernel.width |
| k_h = op.kernel.height |
| ifm_shape = op.ifm.shape |
| ofm_shape = op.ofm.shape |
| height_check = ofm_shape[1] == (ifm_shape[1] * s_h + max(k_h - s_h, 0)) |
| width_check = ofm_shape[2] == (ifm_shape[2] * s_w + max(k_w - s_w, 0)) |
| valid = height_check and width_check |
| extra = ( |
| f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape}," |
| f" stride WxH as {s_w}x{s_h} and kernel WxH as {k_w}x{k_h}" |
| ) |
| return valid, extra |
| return True, "Op has padding=SAME" |
| |
| @staticmethod |
| def constraint_matching_in_out_types(op): |
| "IFM and OFM data types must match" |
| ifm_dtype = op.ifm.dtype |
| ofm_dtype = op.ofm.dtype |
| valid = ifm_dtype == ofm_dtype |
| return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" |
| |
| @staticmethod |
| def constraint_beta_value_range(op): |
| "Beta value needs to be positive" |
| beta = op.attrs.get("beta", 1.0) |
| valid = beta >= 0 |
| return valid, f"Op has beta={beta}" |
| |
| @staticmethod |
| def constraint_filter_type(op): |
| "Kernel filter values for both width and height must be integer types" |
| w = op.kernel.width |
| h = op.kernel.height |
| valid = is_integer(w) and is_integer(h) |
| return valid, f"Op has kernel filter WxH as: {repr(w)}x{repr(h)}" |
| |
| @classmethod |
| @docstring_format_args(filter_range) |
| def constraint_filter_range(cls, op): |
| "Kernel filter values for both width and height must be in the range [{}, {}]" |
| if op.attrs["padding"] == Padding.SAME: |
| w = op.kernel.width |
| h = op.kernel.height |
| filter_min, filter_max = cls.filter_range |
| valid = (filter_min <= w <= filter_max) and (filter_min <= h <= filter_max) |
| return valid, f"Op has kernel filter WxH as: {w}x{h}" |
| return True, "Op has padding=VALID" |
| |
| @classmethod |
| @docstring_format_args(filter_height_range) |
| def constraint_filter_height_range(cls, op): |
| "Kernel filter height must be in the range [{}, {}]" |
| h = op.kernel.height |
| filter_height_min, filter_height_max = cls.filter_height_range |
| valid = filter_height_min <= h <= filter_height_max |
| return valid, f"Op has kernel filter height as: {h}" |
| |
| @classmethod |
| @docstring_format_args(filter_product_range) |
| def constraint_filter_product_range(cls, op): |
| "Product of kernel filter width and height must be in the range [{}, {}]" |
| product = op.kernel.elements_wh() |
| filter_product_min, filter_product_max = cls.filter_product_range |
| valid = filter_product_min <= product <= filter_product_max |
| return valid, f"Op has product of kernel filter width and height as: {product}" |
| |
| @staticmethod |
| @docstring_format_args(filter_height_range) |
| def constraint_filter_height_range_valid_pad(op): |
| "VALID padding: Kernel filter height must be in the range [{}, {}]" |
| if op.attrs["padding"] == Padding.VALID: |
| return SupportedOperators.constraint_filter_height_range(op) |
| return True, "Op has padding=SAME" |
| |
| @staticmethod |
| @docstring_format_args(filter_product_range) |
| def constraint_filter_product_range_valid_pad(op): |
| "VALID padding: Product of kernel filter width and height must be in the range [{}, {}]" |
| if op.attrs["padding"] == Padding.VALID: |
| return SupportedOperators.constraint_filter_product_range(op) |
| return True, "Op has padding=SAME" |
| |
| @staticmethod |
| def constraint_resize(op): |
| """The width and height of the IFM and OFM must match one of the following criteria: |
| IFM W and H must both be 1 |
| IFM must match OFM |
| OFM W and H must be 2x IFM -1, if align_corners is True |
| OFM W and H must be 2x IFM, if align_corners is False""" |
| # Easier to start with False condition as very few cases result in a supported resize |
| valid = False |
| ifm_shape = op.ifm.shape |
| ofm_shape = op.ofm.shape |
| align_corners = op.attrs.get("align_corners", False) |
| if len(ifm_shape) == 4: |
| # Valid if IFM W and H are both 1, or IFM and OFM shape are the same |
| if ((ifm_shape[1] == 1) and (ifm_shape[2] == 1)) or (ifm_shape == ofm_shape): |
| valid = True |
| else: |
| upscaled_shape = np.array(ifm_shape[1:3]) |
| out_shape = np.array(ofm_shape[1:3]) |
| while (upscaled_shape < out_shape).all(): |
| upscaled_shape *= 2 |
| if align_corners: |
| upscaled_shape -= 1 |
| # Valid if OFM is 2x IFM (-1 for align corners) |
| if np.array_equal(out_shape, upscaled_shape): |
| valid = True |
| break |
| return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and align_corners={align_corners}" |
| |
| @staticmethod |
| def constraint_matching_shapes(op): |
| "IFM and OFM shapes must match" |
| ifm_shape = op.ifm.shape |
| ofm_shape = op.ofm.shape |
| valid = ifm_shape == ofm_shape |
| return valid, f"Op has ifm_shape={ifm_shape} and ofm_shape={ofm_shape}" |
| |
| @staticmethod |
| def constraint_splitv_inferred(op): |
| "Only one size is allowed to be inferred" |
| sizes = op.inputs[1].values |
| valid = np.count_nonzero(sizes == -1) <= 1 |
| return valid, f"Op has multiple inferred sizes (-1): {sizes}" |
| |
| @staticmethod |
| def constraint_axis_exists(op): |
| "Axis attribute must exist" |
| axis = op.attrs.get("axis") |
| valid = axis is not None |
| return valid, f"Op has axis={axis}" |
| |
| @staticmethod |
| def constraint_axis_valid(op): |
| "Axis attribute must be in the range [0, <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_shape(op): |
| "The padding tensor must have the shape [3,2] or [4,2]" |
| valid = op.inputs[1].shape in ([3, 2], [4, 2]) |
| return valid, f"The pad tensor has the shape: {op.inputs[1].shape}" |
| |
| @classmethod |
| @docstring_format_args([_list_formatter(supported_pad_dtypes)]) |
| def constraint_pad_type(cls, op): |
| "Pad tensor must be of type: {}" |
| pad_tensor = op.inputs[1] |
| valid = pad_tensor.dtype in cls.supported_pad_dtypes |
| return valid, f"Tensor '{pad_tensor.name}' has data type: {pad_tensor.dtype}" |
| |
| @staticmethod |
| def constraint_padding_dimensions(op): |
| "The pad tensor can only pad width and height" |
| pad_tensor = op.inputs[1].values |
| |
| valid = sum(pad_tensor[-1, :]) == 0 |
| if valid and len(pad_tensor) > 3: |
| valid = sum(pad_tensor[0, :]) == 0 |
| return valid, f"First dimension padding: {pad_tensor[0,:]}, last dimension padding: {pad_tensor[-1,:]}" |
| |
| @staticmethod |
| def constraint_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_stridedslice_stride_values(op): |
| "All Strides values must be 1" |
| strides = op.inputs[3] |
| valid = all(stride == 1 for stride in strides.values) |
| return valid, f"Op has strides values {strides.values}" |
| |
| @staticmethod |
| def constraint_ellipsis_mask(op): |
| "ellipsis_mask must be 0" |
| ellipsis = op.attrs["ellipsis_mask"] |
| valid = ellipsis == 0 |
| return valid, f"Op has ellipsis mask as: {ellipsis}" |
| |
| @staticmethod |
| def constraint_axis_masks(op): |
| "new_axis_mask and shrink_axis_mask cannot both be set" |
| new_axis = op.attrs["new_axis_mask"] |
| shrink_axis = op.attrs["shrink_axis_mask"] |
| valid = (new_axis == 0) or (shrink_axis == 0) |
| return valid, f"Op has new_axis_mask={new_axis} and shrink_axis_mask={shrink_axis}" |
| |
| @staticmethod |
| def constraint_slice_ranges(op): |
| "Slice 'end' values must be greater than 'begin' values" |
| ifm, begin, end, _ = op.inputs |
| # Calculate offset begin/end |
| offset_begin = get_slice_offsets(ifm.shape, begin, op.attrs["begin_mask"], is_begin=True) |
| offset_end = get_slice_offsets(ifm.shape, end, op.attrs["end_mask"], is_begin=False) |
| # Check "end - begin" doesn't result in any zero or negative elements |
| valid = all((e - b) > 0 for b, e in zip(offset_begin, offset_end)) |
| return valid, f"Op has begin_values={begin.values} and end_values={end.values}" |
| |
| @staticmethod |
| def constraint_matching_inputs_types(op): |
| "Both Input data types must match" |
| ifm_dtype = op.ifm.dtype |
| ifm2_dtype = op.ifm2.dtype |
| valid = ifm_dtype == ifm2_dtype |
| return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}" |
| |
| @staticmethod |
| def constraint_matching_signed(op): |
| "For IFM that are signed, OFM must also be signed" |
| valid = True |
| ifm_dtype = op.ifm.dtype |
| ofm_dtype = op.ofm.dtype |
| if ifm_dtype.type & BaseType.Signed: |
| valid = bool(ofm_dtype.type & BaseType.Signed) |
| return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" |
| |
| @staticmethod |
| def constraint_unsigned_valid(op): |
| "For IFM that are unsigned, OFM must either be the same type or int32" |
| valid = True |
| ifm_dtype = op.ifm.dtype |
| ofm_dtype = op.ofm.dtype |
| if ifm_dtype.type & BaseType.Unsigned: |
| valid = (ifm_dtype == ofm_dtype) or (ofm_dtype == DataType.int32) |
| return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}" |
| |
| @staticmethod |
| def constraint_inputs_int32(op): |
| "Both Input data types must be int32" |
| ifm_dtype = op.ifm.dtype |
| ifm2_dtype = op.ifm2.dtype |
| valid = (ifm_dtype == DataType.int32) and (ifm2_dtype == DataType.int32) |
| return valid, f"Op has ifm_dtype={ifm_dtype} and ifm2_dtype={ifm2_dtype}" |
| |
| @staticmethod |
| def constraint_output_int32(op): |
| "OFM must be int32" |
| ofm_dtype = op.ofm.dtype |
| valid = ofm_dtype == DataType.int32 |
| return valid, f"Op has ofm_dtype={ofm_dtype}" |
| |
| @staticmethod |
| def constraint_input_8bit(op): |
| "IFM must be int8 or uint8" |
| ifm_dtype = op.ifm.dtype |
| valid = (ifm_dtype == DataType.int8) or (ifm_dtype == DataType.uint8) |
| return valid, f"Op has ifm_dtype={ifm_dtype}" |
| |
| @staticmethod |
| def constraint_matching_quantization_parameters(op): |
| "Both Input quantization parameters must match OFM quantization parameters" |
| valid = True |
| extra = [] |
| if not check_quantized_tens_scaling_equal(op.ofm, op.ifm): |
| valid = False |
| extra.append(op.ifm.name) |
| if op.ifm2 is not None and not check_quantized_tens_scaling_equal(op.ofm, op.ifm2): |
| valid = False |
| extra.append(op.ifm2.name) |
| extra = ", ".join(extra) |
| return valid, f"Op has tensors with different quantization parameters to the OFM '{op.ofm.name}': {extra}" |
| |
| @staticmethod |
| def constraint_elemwise_batch_size(op): |
| "Batch size must be 1 for Input tensors with more than 2 dimensions" |
| valid = True |
| extra = [] |
| for tens in (op.ifm, op.ifm2): |
| # Unary ops have ifm2 as None |
| if tens is not None: |
| if (len(tens.shape) > 2) and (tens.shape[0] != 1): |
| valid = False |
| extra.append(tens.name) |
| extra = ", ".join(extra) |
| return valid, f"Op has invalid input tensors: {extra}" |
| |
| @staticmethod |
| def constraint_matching_either_shapes(op): |
| "At least one Input's shape must match the OFM's shape" |
| ifm_shape = op.ifm.shape |
| ifm2_shape = op.ifm2.shape if op.ifm2 else None |
| ofm_shape = op.ofm.shape |
| valid = (ifm_shape == ofm_shape) or (ifm2_shape == ofm_shape) |
| return valid, f"Op has ifm_shape={ifm_shape}, ifm2_shape={ifm2_shape} and ofm_shape={ofm_shape}" |
| |
| @staticmethod |
| def constraint_broadcast_shapes(op): |
| "Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2" |
| ifm_shape = op.ifm.shape |
| ifm2_shape = op.ifm2.shape if op.ifm2 else None |
| ofm_shape = op.ofm.shape |
| valid = True |
| if ifm_shape is not None and ifm2_shape is not None: |
| # align trailing dimensions |
| size = min(len(ifm_shape), len(ifm2_shape)) |
| for i, i2, o in zip(ifm_shape[-size:], ifm2_shape[-size:], ofm_shape[-size:]): |
| mi = max(i, i2) |
| # Input dimensions should match or one should be of dimension 1 |
| # Output dimension should match the largest input dimension, together |
| # with constraint_match_either_shapes ensures broadcast from only one input |
| if not (i == i2 or i == 1 or i2 == 1) or o != mi: |
| valid = False |
| break |
| |
| return valid, f"Op has ifm_shape={ifm_shape} and ifm2_shape={ifm2_shape}" |
| |
| @staticmethod |
| def constraint_alpha_valid(op): |
| "Alpha must not be negative" |
| alpha = op.attrs["alpha"] |
| valid = alpha >= 0 |
| return valid, f"Op has alpha={alpha}" |
| |
| @staticmethod |
| def constraint_keep_dim_ifm_ofm(op): |
| "The IFM and OFM must have the same number of dimensions if keep_num_dims is set to true" |
| valid = True |
| if op.attrs.get("keep_num_dims"): |
| valid = len(op.ifm.shape) == len(op.ofm.shape) |
| return valid, f"Op has ifm shape={op.ifm.shape} and ofm shape={op.ofm.shape}" |
| |
| @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 = 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], [1, 0]) |
| elif dims == 4: |
| valid = axis in (1, 2, [1, 2], [2, 1]) |
| return valid, f"Axis is {axis}" |
| |
| @classmethod |
| @docstring_format_args([mean_kernel_product_avgpool]) |
| def constraint_mean_height_width_product_avgpool(cls, op): |
| """Product of height and width can be at most {}""" |
| shape = op.inputs[0].shape |
| hi = 0 if len(shape) < 4 else 1 |
| h, w = shape[hi : hi + 2] |
| max_prod = cls.mean_kernel_product_avgpool |
| return h * w <= max_prod, f"Product of height and width is {h * w}" |
| |
| @classmethod |
| @docstring_format_args([mean_kernel_product]) |
| def constraint_mean_height_width_product(cls, op): |
| """Product of height and width can be at most {} when IFM and OFM have different scale or zero point, |
| or keep_dims is True""" |
| ifmq, ofmq = op.ifm.quantization, op.ofm.quantization |
| keep_dims = op.attrs.get("keep_dims") |
| # doesn't apply, size is checked by constraint_mean_height_width_product_avgpool |
| if not keep_dims and ifmq.scale_f32 == ofmq.scale_f32 and ifmq.zero_point == ofmq.zero_point: |
| return True, "" |
| shape = op.inputs[0].shape |
| hi = 0 if len(shape) < 4 else 1 |
| h, w = shape[hi : hi + 2] |
| max_prod = cls.mean_kernel_product |
| return h * w <= max_prod, f"Product of height and width is {h * w}" |
| |
| @classmethod |
| @docstring_format_args([mean_kernel_product_int8]) |
| def constraint_mean_height_width_product_int8(cls, op): |
| """Product of IFM height and width can be at most {} when the following are true: |
| IFM dimensions are 4, |
| Axis indices are 1 and 2, |
| keep_dims is set to True and |
| IFM datatype is int8""" |
| shape = op.ifm.shape |
| axis = op.inputs[1].values if op.inputs[1].shape == [] else list(op.inputs[1].values) |
| # doesn't apply, size is checked by constraint_mean_height_width_product_avgpool |
| # and constraint_mean_height_width_product |
| if ( |
| len(shape) != 4 |
| or op.ifm.dtype != DataType.int8 |
| or not op.attrs.get("keep_dims") |
| or axis not in ([1, 2], [2, 1]) |
| ): |
| return True, "" |
| hi = 0 if len(shape) < 4 else 1 |
| h, w = shape[hi : hi + 2] |
| max_prod = cls.mean_kernel_product_int8 |
| return h * w <= max_prod, f"Product of height and width is {h * w}" |