| # 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. |
| import numpy as np |
| |
| from .data_type import BaseType |
| from .data_type import DataType |
| |
| |
| class SupportedOperators: |
| def __init__(self): |
| # Categorised lists of supported operators |
| self.npu_pre_ops = set(("QuantizedResizeBilinear", "SplitSliceRead",)) |
| self.convolution_ops = set(("Conv2DBiasAct", "Conv2D", "QuantizedConv2D",)) |
| self.depthwise_convolution_ops = set( |
| ("DepthwiseConv2dBiasAct", "DepthwiseConv2dNative", "QuantizedDepthwiseConv2D,") |
| ) |
| self.transpose_convolution_ops = set(("Conv2DBackpropInput",)) |
| self.max_pooling_ops = set(("QuantizedMaxPool", "MaxPool", "MaxPoolAct",)) |
| self.avg_pooling_ops = set(("QuantizedAvgPool", "AvgPool", "AvgPoolAct",)) |
| self.pooling_ops = set(("ReduceSum",)) | self.max_pooling_ops | self.avg_pooling_ops |
| self.resizing_ops = set(("ResizeBilinear",)) |
| self.fc_vector_products = set(("QuantizedMatMul", "MatMul", "FullyConnectedAct",)) |
| self.mac_main_ops = ( |
| # convolutions |
| self.convolution_ops |
| # depth-wise convolutions |
| | self.depthwise_convolution_ops |
| # transpose convolutions |
| | self.transpose_convolution_ops |
| # pooling |
| | self.pooling_ops |
| # resizing/upscaling |
| | self.resizing_ops |
| # FC layers |
| | self.fc_vector_products |
| # RNN/LSTM/GRU |
| | set(("BlockLSTM",)) |
| ) |
| self.unary_elem_wise_main_ops = set(("LeakyRelu", "Abs", "CLZ",)) |
| self.binary_elem_wise_min_max_ops = set(("Minimum", "Maximum",)) |
| self.binary_elem_wise_shift_ops = set(("SHL", "SHR",)) |
| self.binary_elem_wise_add_mul_sub = set( |
| ("AddAct", "MulAct", "SubAct", "QuantizedAdd", "QuantizedSub", "QuantizedMul", "Mul", "Add", "Sub",) |
| ) |
| self.binary_elem_wise_main_ops = ( |
| self.binary_elem_wise_min_max_ops | self.binary_elem_wise_add_mul_sub | self.binary_elem_wise_shift_ops |
| ) |
| self.elem_wise_main_ops = self.binary_elem_wise_main_ops | self.unary_elem_wise_main_ops |
| self.activation_ops = set( |
| ( |
| "QuantizedRelu", |
| "QuantizedRelu1", |
| "QuantizedRelu6", |
| "Relu", |
| "Relu6", |
| "ReluN1To1", |
| "Sigmoid", |
| "Tanh", |
| "Softmax", |
| ) |
| ) |
| 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",)) |
| # Quantization |
| | set(("Quantize",)) |
| ) |
| self.split_ops = set(("Split", "SplitV", "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.shapeless_input_ops = self.binary_elem_wise_main_ops | set(("Split", "SplitV",)) |
| self.supported_fused_activations = set(("Relu", "Relu6", "ReluN1To1", "Tanh", "Sigmoid", "LUT",)) |
| 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_transpose_convolution_restrictions for op in self.transpose_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_resize_restrictions for op in self.resizing_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} |
| ) |
| self.supported_operator_restrictions.update( |
| {op: self.check_quantization_restrictions_binary_elem_wise for op in self.binary_elem_wise_min_max_ops} |
| ) |
| self.supported_operator_restrictions.update({op: self.check_activation_ops for op in self.activation_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: |
| if not t: |
| continue |
| if not t.has_fully_defined_shape(): |
| print("Warning:", op.type, "has input(s) of undefined shape, placing on CPU") |
| return False |
| if t.shape == [] and op.type not in self.shapeless_input_ops: |
| print( |
| "Warning:", |
| op.type, |
| "has input(s) of shape [].", |
| "Scalar input or broadcasting is not supported for this operator,", |
| "placing on CPU", |
| ) |
| return False |
| for t in op.outputs: |
| if not t.has_fully_defined_shape(): |
| print("Warning:", op.type, "has output(s) of undefined shape, placing on CPU") |
| return False |
| if t.shape == []: |
| print( |
| "Warning:", |
| op.type, |
| "has output(s) of shape [].", |
| "Scalar input or broadcasting is not supported for this operator,", |
| "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 |
| not in set(("Requantize", "ReduceSum", "CLZ",)) |
| | self.binary_elem_wise_add_mul_sub |
| | self.binary_elem_wise_shift_ops |
| ): |
| 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"] > 3 or op.attrs["stride_h"] > 3: |
| 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, bias_tensor, _ = op.get_ifm_ifm2_weights_biases_ofm() |
| if weight_tensor.element_size() > 1: |
| return False |
| |
| if not self.check_bias_restrictions(bias_tensor): |
| return False |
| |
| # check kernel size [HWIO] |
| dilated_weight_w = weight_tensor.shape[1] + (weight_tensor.shape[1] - 1) * (dilation_w_factor - 1) |
| dilated_weight_h = weight_tensor.shape[0] + (weight_tensor.shape[0] - 1) * (dilation_h_factor - 1) |
| |
| if dilated_weight_w > 64 or dilated_weight_h > 64: |
| return False |
| |
| # check weight sums over [HWI] |
| zero_point = weight_tensor.quantization.zero_point |
| quant_weights = weight_tensor.quant_values.astype(np.int64) |
| weights = quant_weights - zero_point |
| totals = np.sum(np.absolute(weights), axis=(0, 1, 2)) |
| |
| if np.amax(totals) > 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_transpose_convolution_restrictions(self, op): |
| # check stride |
| stride_h, stride_w = op.attrs["stride_h"], op.attrs["stride_w"] |
| if stride_h != stride_w != 2: |
| return False |
| |
| # check output dimensions |
| ifm_tensor, weight_tensor, _, ofm_tensor = op.get_ifm_weights_biases_ofm() |
| ifm_h, ifm_w = ifm_tensor.shape[1], ifm_tensor.shape[2] |
| ofm_h, ofm_w = ofm_tensor.shape[1], ofm_tensor.shape[2] |
| if op.attrs["padding"] == b"SAME": |
| if (ofm_h != ifm_h * stride_h) or (ofm_w != ifm_w * stride_w): |
| return False |
| elif op.attrs["padding"] == b"VALID": |
| kernel_h, kernel_w = weight_tensor.shape[0], weight_tensor.shape[1] |
| if (ofm_h != (ifm_h) * stride_h + max(kernel_h - stride_h, 0)) or ( |
| ofm_w != (ifm_w) * stride_w + max(kernel_w - stride_w, 0) |
| ): |
| return False |
| |
| return self.check_convolution_restrictions(op) |
| |
| def check_pooling_restrictions(self, op): |
| # check stride |
| if op.attrs["stride_w"] > 3 or op.attrs["stride_h"] > 3: |
| return False |
| |
| # check data type |
| ifm_tensor, _, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm() |
| if ifm_tensor.dtype != ofm_tensor.dtype: |
| if op.type != "ReduceSum": |
| return False |
| # TODO: else check ReduceSum restrictions. |
| |
| # 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"] * op.attrs["filter_height"] > 256 * 256 or op.attrs["filter_height"] > 256 |
| ): |
| return False |
| |
| if op.type in self.max_pooling_ops: |
| # check kernel size (any padding) |
| if op.attrs["filter_width"] * op.attrs["filter_height"] > 256 * 256 or op.attrs["filter_height"] > 256: |
| return False |
| return True |
| |
| def check_resize_restrictions(self, op): |
| # check unsupported upscaling factor |
| if op.type == "ResizeBilinear": |
| if op.inputs[0].shape[1] == 1 and op.inputs[0].shape[2] == 1: |
| return True |
| if op.inputs[0].shape == op.outputs[0].shape: |
| return True |
| upscaled_shape = np.array(op.inputs[0].shape[1:3]) |
| out_shape = np.array(op.outputs[0].shape[1:3]) |
| while (upscaled_shape < out_shape).all(): |
| upscaled_shape *= 2 |
| if op.attrs["align_corners"]: |
| upscaled_shape -= 1 |
| if np.array_equal(out_shape, upscaled_shape): |
| return True |
| return False |
| |
| def check_vector_product_restrictions(self, op): |
| # check data type |
| _, _, weight_tensor, bias_tensor, _ = op.get_ifm_ifm2_weights_biases_ofm() |
| if weight_tensor.element_size() > 1: |
| return False |
| |
| if not self.check_bias_restrictions(bias_tensor): |
| 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() |
| # input and output datatype must match for these operators |
| if ( |
| op.type in self.binary_elem_wise_min_max_ops | self.unary_elem_wise_main_ops |
| and ifm_tensor.dtype != ofm_tensor.dtype |
| ): |
| return False |
| if op.type in self.binary_elem_wise_add_mul_sub: |
| # both inputs must have same type |
| if ifm_tensor.dtype != ifm2_tensor.dtype: |
| return False |
| # signed input check |
| if ifm_tensor.dtype.type & BaseType.Signed: |
| # output must be signed |
| if ofm_tensor.dtype.type & BaseType.Unsigned: |
| return False |
| # and 8, 16 or 32-bit |
| if ofm_tensor.element_size() not in (1, 2, 4): |
| return False |
| # unsigned input check, output must be same type or int32 |
| if ifm_tensor.dtype.type & BaseType.Unsigned and not ( |
| ifm_tensor.dtype == ofm_tensor.dtype or ofm_tensor.dtype == DataType.int32 |
| ): |
| return False |
| elif op.type in self.binary_elem_wise_shift_ops | set(("CLZ")): |
| if ifm_tensor.dtype != DataType.int32 or ifm2_tensor.dtype != DataType.int32: |
| return False |
| if op.type in ("CLZ", "SHL") and ofm_tensor.dtype != DataType.int32: |
| return False |
| |
| # check batch size |
| if len(ifm_tensor.shape) > 2 and ifm_tensor.shape[0] != 1: |
| return False |
| if op.type in self.binary_elem_wise_main_ops: # if op type is unary, ifm2_tensor is None |
| if len(ifm2_tensor.shape) > 2 and ifm2_tensor.shape[0] != 1: |
| return False |
| |
| # negative alpha values are not supported |
| if op.type == "LeakyRelu" and op.attrs["alpha"] < 0: |
| return False |
| |
| return True |
| |
| def check_memory_only_restrictions(self, op): |
| if op.type == "StridedSlice": |
| # check stride size |
| if len(op.inputs) > 3 and any(stride != 1 for stride in op.inputs[3].values): |
| return False |
| # check "end - begin" doesnt result in any zero or negative elements |
| if any((end - begin) <= 0 for begin, end in zip(op.inputs[1].values, op.inputs[2].values)): |
| return False |
| # check ellipsis_mask |
| if op.attrs["ellipsis_mask"] != 0: |
| return False |
| # check if both new_axis_mask and shrink_axis_mask have bit set |
| if op.attrs["new_axis_mask"] != 0 and op.attrs["shrink_axis_mask"] != 0: |
| return False |
| return True |
| |
| def check_quantization_restrictions_binary_elem_wise(self, op): |
| # makes sure IFM1, IFM2 and OFM quantization are equal for binary ops |
| assert len(op.inputs) >= 2 and len(op.outputs) == 1 |
| |
| if ( |
| op.inputs[0].quantization is None |
| or not op.inputs[0].quantization.is_scaling_equal(op.inputs[1].quantization) |
| or not op.inputs[0].quantization.is_scaling_equal(op.outputs[0].quantization) |
| ): |
| print( |
| "Warning: Input/output tensors with different quantization is unsupported for the", op.type, "operator" |
| ) |
| return False |
| |
| return True |
| |
| def check_activation_ops(self, op): |
| if op.type == "Softmax": |
| ifm_tensor = op.inputs[0] |
| ofm_tensor = op.outputs[0] |
| |
| # check data type |
| if ifm_tensor.dtype != ofm_tensor.dtype: |
| return False |
| |
| if ifm_tensor.dtype not in (DataType.uint8, DataType.int8, DataType.int16): |
| return False |
| |
| # check batch size |
| if len(ifm_tensor.shape) in (2, 4) and ifm_tensor.shape[0] != 1: |
| return False |
| |
| return True |
| |
| def check_bias_restrictions(self, bias_tensor): |
| # check data type |
| if bias_tensor.dtype not in (DataType.int32, DataType.int64): |
| return False |
| |
| # check if values fits in 40-bit |
| if bias_tensor.dtype == DataType.int64: |
| for value in bias_tensor.values: |
| if not (-(1 << 39) <= value < (1 << 39)): |
| return False |
| |
| return True |