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Diffstat (limited to 'ethosu/vela/tflite_supported_operators.py')
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diff --git a/ethosu/vela/tflite_supported_operators.py b/ethosu/vela/tflite_supported_operators.py new file mode 100644 index 00000000..cb3d5048 --- /dev/null +++ b/ethosu/vela/tflite_supported_operators.py @@ -0,0 +1,674 @@ +# 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 TFLiteSupportedOperators class which is a collection of all TFLite supported operators and parameter checks. +from collections import defaultdict + +import numpy as np + +from .data_type import DataType +from .operation import Op +from .operation import Padding +from .supported_operators_util import docstring_format_args +from .supported_operators_util import list_formatter +from .tensor import check_quantized_tens_scaling_equal +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 TFLiteSupportedOperators: + # 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 = set((Op.Relu, Op.Relu6, Op.ReluN1To1, Op.Clip,)) + 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 + 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 + + def __init__(self): + # Setup the generic constraints. Note: the order matters + self.generic_constraints = [] + self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_dtype) + self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_int32_ops) + self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_dimension) + self.generic_constraints.append(TFLiteSupportedOperators.constraint_tens_quant_per_axis) + self.generic_constraints.append(TFLiteSupportedOperators.constraint_faf) + self.generic_constraints.append(TFLiteSupportedOperators.constraint_faf_type) + + # Setup specific constraints. Note: the order matters + self.specific_constraints = defaultdict(list) + + # Conv-like checks: + for op_type in TFLiteSupportedOperators.convolution_like_ops: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_stride_range) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_dilation_range) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_dilated_height_range) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_dilated_product_range) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_type) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_const) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_limit) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_type) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_40bit) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_batch_size) + # Depthwise Conv specific checks: + for op_type in TFLiteSupportedOperators.depthwise_convolution_ops: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_depth_multiplier) + # Transpose Conv specific checks: + for op_type in TFLiteSupportedOperators.transpose_convolution_ops: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_tconv_stride) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_tconv_same) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_tconv_valid) + + # Pooling checks: + for op_type in TFLiteSupportedOperators.pooling_ops: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_batch_size) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_stride_range) + # AVG pooling specific checks: + for op_type in TFLiteSupportedOperators.avg_pooling_ops: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_range) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_height_range_valid_pad) + self.specific_constraints[op_type].append( + TFLiteSupportedOperators.constraint_filter_product_range_valid_pad + ) + # MAX pooling specific checks: + for op_type in TFLiteSupportedOperators.max_pooling_ops: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_height_range) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_product_range) + + # Resizing specific checks: + for op_type in TFLiteSupportedOperators.resizing_ops: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_resize) + + # Vector Product specific checks: + for op_type in TFLiteSupportedOperators.fc_vector_products: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_type) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_weights_const) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_type) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bias_40bit) + + # Element-wise checks: + for op_type in TFLiteSupportedOperators.elem_wise_main_ops: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_elemwise_batch_size) + # Binary Min/Max specific checks: + for op_type in TFLiteSupportedOperators.binary_elem_wise_min_max_ops: + self.specific_constraints[op_type].append( + TFLiteSupportedOperators.constraint_matching_quantization_parameters + ) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_broadcast_shapes) + # Binary Add/Mul/Sub specific checks: + for op_type in TFLiteSupportedOperators.binary_elem_wise_add_mul_sub: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_broadcast_shapes) + # Binary Shift specific checks: + for op_type in TFLiteSupportedOperators.binary_elem_wise_shift_ops: + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_inputs_int32) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_broadcast_shapes) + + # SHL specific checks: + self.specific_constraints[Op.SHL].append(TFLiteSupportedOperators.constraint_output_int32) + + # CLZ specific checks: + self.specific_constraints[Op.CLZ].append(TFLiteSupportedOperators.constraint_output_int32) + + # StridedSlice specific checks: + self.specific_constraints[Op.StridedSlice].append( + TFLiteSupportedOperators.constraint_stridedslice_stride_values + ) + + # Pad specific checks: + self.specific_constraints[Op.Pad].append(TFLiteSupportedOperators.constraint_pad_shape) + self.specific_constraints[Op.Pad].append(TFLiteSupportedOperators.constraint_padding_dimensions) + self.specific_constraints[Op.Pad].append(TFLiteSupportedOperators.constraint_pad_type) + + # Mean specific checks: + self.specific_constraints[Op.Mean].append(TFLiteSupportedOperators.constraint_mean_height_width_product_avgpool) + self.specific_constraints[Op.Mean].append(TFLiteSupportedOperators.constraint_mean_height_width_product) + self.specific_constraints[Op.Mean].append(TFLiteSupportedOperators.constraint_mean_height_width_product_int8) + + def is_operator_supported(self, op): + ext_type = optype_to_builtintype(op.type) + if op.type not in TFLiteSupportedOperators.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 + + @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) + + @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) + + @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 + + @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}" + + @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.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.values is not None: + valid = all(len(bin(quant_value)[2:]) <= 40 for quant_value in bias.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_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" + + @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 TFLiteSupportedOperators.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 TFLiteSupportedOperators.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_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_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_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_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_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}" + + @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 = int(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}" |