| # Copyright (C) 2020 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 .data_type import BaseType |
| |
| |
| class SupportedOperators: |
| def __init__(self): |
| # Categorised lists of supported operators |
| self.npu_pre_ops = set(("QuantizedResizeBilinear", "SplitSliceRead")) |
| self.convolution_ops = set(("Conv2DBiasAct", "Conv2D", "QuantizedConv2D", "Conv2DBackpropInputSwitched")) |
| self.depthwise_convolution_ops = set( |
| ("DepthwiseConv2dBiasAct", "DepthwiseConv2dNative", "QuantizedDepthwiseConv2D") |
| ) |
| self.max_pooling_ops = set(("QuantizedMaxPool", "MaxPool", "MaxPoolAct")) |
| self.avg_pooling_ops = set(("QuantizedAvgPool", "AvgPool", "AvgPoolAct")) |
| self.pooling_ops = self.max_pooling_ops | self.avg_pooling_ops |
| self.fc_vector_products = set(("QuantizedMatMul", "MatMul", "FullyConnectedAct")) |
| self.mac_main_ops = ( |
| # convolutions |
| self.convolution_ops |
| # depth-wise convolutions |
| | self.depthwise_convolution_ops |
| # pooling |
| | self.pooling_ops |
| # FC layers |
| | self.fc_vector_products |
| # RNN/LSTM/GRU |
| | set(("BlockLSTM")) |
| ) |
| self.elem_wise_main_ops = set( |
| ( |
| # element-wise |
| "AddAct", |
| "MulAct", |
| "SubAct", |
| "QuantizedAdd", |
| "QuantizedSub", |
| "QuantizedMul", |
| "Mul", |
| "Add", |
| "Sub", |
| "Minimum", |
| "Maximum", |
| ) |
| ) |
| self.activation_ops = set( |
| ("QuantizedRelu", "QuantizedRelu1", "QuantizedRelu6", "Relu", "Relu6", "ReluN1To1", "Sigmoid", "Tanh") |
| ) |
| self.npu_post_ops = ( |
| # activation functions |
| self.activation_ops |
| # concatenation write direction |
| | set(("ConcatSliceWrite")) |
| # bias add and batch norm |
| | set(("QuantizedBiasAdd", "Requantize", "QuantizedBatchNorm", "BiasAdd", "FusedBatchNorm")) |
| ) |
| self.split_ops = set(("Split", "StridedSlice", "Slice", "UnpackReshaped", "Unpack")) |
| self.concat_ops = set(("Concat", "ConcatV2", "QuantizedConcat", "ConcatTFLite", "PackReshaped", "Pack")) |
| self.memory_only_ops = ( |
| set(("Squeeze", "Reshape", "QuantizedReshape", "ExpandDims")) | self.concat_ops | self.split_ops |
| ) |
| self.supported_fused_activations = set(("Relu", "Relu6", "ReluN1To1", "Tanh", "Sigmoid")) |
| self.supported_operators = ( |
| self.npu_pre_ops | self.mac_main_ops | self.elem_wise_main_ops | self.npu_post_ops | self.memory_only_ops |
| ) |
| # Setup supported operator restriction checkers |
| self.supported_operator_restrictions = {} |
| self.supported_operator_restrictions.update( |
| {op: self.check_convolution_restrictions for op in self.convolution_ops} |
| ) |
| self.supported_operator_restrictions.update( |
| {op: self.check_depthwise_convolution_restrictions for op in self.depthwise_convolution_ops} |
| ) |
| self.supported_operator_restrictions.update({op: self.check_pooling_restrictions for op in self.pooling_ops}) |
| self.supported_operator_restrictions.update( |
| {op: self.check_vector_product_restrictions for op in self.fc_vector_products} |
| ) |
| self.supported_operator_restrictions.update( |
| {op: self.check_element_wise_restrictions for op in self.elem_wise_main_ops} |
| ) |
| self.supported_operator_restrictions.update( |
| {op: self.check_memory_only_restrictions for op in self.memory_only_ops} |
| ) |
| |
| def is_operator_supported(self, op): |
| if op.type not in self.supported_operators: |
| return False |
| if not self.check_generic_restrictions(op): |
| return False |
| if op.type in self.supported_operator_restrictions: |
| return self.supported_operator_restrictions[op.type](op) |
| return True |
| |
| def check_generic_restrictions(self, op): |
| # check fully defined shapes |
| for t in op.inputs + op.outputs: |
| if not t.has_fully_defined_shape(): |
| print("Warning:", op, "has inputs/outputs of undefined shape, placing on CPU") |
| return False |
| |
| # check data type |
| tensors = [t for t in op.get_ifm_ifm2_weights_ofm() if t is not None] |
| if not tensors: |
| tensors = op.inputs |
| for t in tensors: |
| if not (t.dtype.type & BaseType.Int): |
| return False |
| if t.element_size() > 2 and op.type != "Requantize": |
| return False |
| # check size |
| if any(dim > 65536 for dim in t.shape): |
| return False |
| |
| # check fused activations |
| if ( |
| "fused_activation_function" in op.attrs |
| and op.attrs["fused_activation_function"] is not None |
| and op.attrs["fused_activation_function"] not in self.supported_fused_activations |
| ): |
| return False |
| return True |
| |
| def check_convolution_restrictions(self, op): |
| # check stride |
| if op.attrs["stride_w"] > 2 or op.attrs["stride_h"] > 2: |
| return False |
| |
| # check dilation |
| dilation_w_factor = op.attrs.get("dilation_w_factor", 1) |
| dilation_h_factor = op.attrs.get("dilation_h_factor", 1) |
| if dilation_w_factor > 2 or dilation_h_factor > 2: |
| return False |
| |
| # check data type |
| ifm_tensor, _, weight_tensor, _ = op.get_ifm_ifm2_weights_ofm() |
| if weight_tensor.element_size() > 1: |
| return False |
| |
| # check kernel size |
| dilated_weight_w = weight_tensor.shape[0] + (weight_tensor.shape[0] - 1) * (dilation_w_factor - 1) |
| dilated_weight_h = weight_tensor.shape[1] + (weight_tensor.shape[1] - 1) * (dilation_h_factor - 1) |
| if ( |
| dilated_weight_w > 64 |
| or dilated_weight_h > 64 |
| or dilated_weight_w * dilated_weight_h * weight_tensor.shape[2] > 127 * 65536 |
| ): |
| return False |
| |
| # check batch size |
| if ifm_tensor.shape[0] != 1: |
| return False |
| return True |
| |
| def check_depthwise_convolution_restrictions(self, op): |
| # check depth |
| ifm_tensor, _, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm() |
| if op.attrs["depth_multiplier"] > 1 and not ( |
| (ifm_tensor.shape[3] == 1) and (ofm_tensor.shape[3] == op.attrs["depth_multiplier"]) |
| ): |
| return False |
| return self.check_convolution_restrictions(op) |
| |
| def check_pooling_restrictions(self, op): |
| # check stride |
| if op.attrs["stride_w"] > 2 or op.attrs["stride_h"] > 2: |
| return False |
| |
| # check data type |
| ifm_tensor, _, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm() |
| if ifm_tensor.dtype != ofm_tensor.dtype: |
| return False |
| |
| # check batch size |
| if ifm_tensor.shape[0] != 1: |
| return False |
| |
| if op.type in self.avg_pooling_ops: |
| # check kernel size |
| if op.attrs["padding"] == b"SAME" and (op.attrs["filter_width"] > 8 or op.attrs["filter_height"] > 8): |
| return False |
| if op.attrs["padding"] == b"VALID" and (op.attrs["filter_width"] > 256 or op.attrs["filter_height"] > 256): |
| return False |
| |
| if op.type in self.max_pooling_ops: |
| # check data type |
| if not ifm_tensor.dtype == ofm_tensor.dtype: |
| return False |
| # check kernel size |
| if op.attrs["filter_width"] > 256 or op.attrs["filter_height"] > 256: # any padding |
| return False |
| return True |
| |
| def check_vector_product_restrictions(self, op): |
| # check data type |
| ifm_tensor, _, weight_tensor, _ = op.get_ifm_ifm2_weights_ofm() |
| if weight_tensor.element_size() > 1: |
| return False |
| |
| return True |
| |
| def check_element_wise_restrictions(self, op): |
| # check data type |
| ifm_tensor, ifm2_tensor, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm() |
| if op.type in ("Minimum", "Maximum") and ifm_tensor.dtype != ofm_tensor.dtype: |
| return False |
| |
| # check batch size |
| if (len(ifm_tensor.shape) > 2 and ifm_tensor.shape[0] != 1) or ( |
| len(ifm2_tensor.shape) > 2 and ifm2_tensor.shape[0] != 1 |
| ): |
| return False |
| |
| # check scalar size |
| if (hasattr(ifm_tensor.values, "__len__") and len(ifm_tensor.values) > 1) or ( |
| hasattr(ifm2_tensor.values, "__len__") and len(ifm2_tensor.values) > 1 |
| ): |
| return False |
| return True |
| |
| def check_memory_only_restrictions(self, op): |
| # check stride size |
| if op.type == "StridedSlice": |
| if len(op.inputs) > 3 and any(stride != 1 for stride in op.inputs[3].values): |
| return False |
| return True |