| # 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: |
| # Utilities used in vela unit tests |
| import numpy as np |
| |
| from ethosu.vela import architecture_features |
| from ethosu.vela.data_type import DataType |
| from ethosu.vela.nn_graph import Graph |
| from ethosu.vela.nn_graph import PassPlacement |
| from ethosu.vela.nn_graph import Subgraph |
| from ethosu.vela.operation import Op |
| from ethosu.vela.operation import Operation |
| from ethosu.vela.tensor import create_const_tensor |
| from ethosu.vela.tensor import QuantizationParameters |
| from ethosu.vela.tensor import Tensor |
| |
| |
| def create_arch(): |
| return architecture_features.create_default_arch(architecture_features.Accelerator.Ethos_U55_128) |
| |
| |
| def default_quant_params(): |
| qp = QuantizationParameters() |
| qp.scale_f32 = np.float32(1) |
| qp.zero_point = 0 |
| return qp |
| |
| |
| def create_elemwise_op( |
| op_type, |
| name, |
| ifm_shape, |
| ifm2_shape, |
| ofm_shape, |
| datatype=DataType.uint8, |
| ifm_quant=default_quant_params(), |
| ifm2_quant=default_quant_params(), |
| ofm_quant=default_quant_params(), |
| ): |
| # Creates elementwise operation with constant IFM/IFM2 |
| if datatype.size_in_bytes() == 1: |
| np_type = np.uint8 |
| elif datatype.size_in_bytes() == 2: |
| np_type = np.int16 |
| else: |
| np_type = np.int32 |
| op = Operation(op_type, name) |
| op.add_input_tensor( |
| create_const_tensor(name + "_ifm", ifm_shape, datatype, np.zeros(ifm_shape), np_type, quantization=ifm_quant) |
| ) |
| if ifm2_shape is not None: |
| op.add_input_tensor( |
| create_const_tensor( |
| name + "_ifm2", ifm2_shape, datatype, np.zeros(ifm2_shape), np_type, quantization=ifm2_quant |
| ) |
| ) |
| ofm = Tensor(ofm_shape, datatype, name + "_ofm") |
| ofm.quantization = ofm_quant |
| op.set_output_tensor(ofm) |
| op.set_ifm_ofm_shapes() |
| |
| return op |
| |
| |
| def create_op_with_quant_tensors( |
| op_type, ifm_shape, ofm_shape, weights_shape=None, bias_shape=None, datatype=DataType.uint8, set_ifm_ofm_shapes=True |
| ): |
| ifm = Tensor(ifm_shape, datatype, "in") |
| ifm.quantization = default_quant_params() |
| ofm = Tensor(ofm_shape, datatype, "out") |
| ofm.quantization = default_quant_params() |
| op = Operation(op_type, "op") |
| op.add_input_tensor(ifm) |
| op.set_output_tensor(ofm) |
| # Optional weight tensor |
| if weights_shape is not None: |
| if datatype.size_in_bytes() == 1: |
| np_type = np.uint8 |
| elif datatype.size_in_bytes() == 2: |
| np_type = np.int16 |
| else: |
| np_type = np.int32 |
| qp = default_quant_params() |
| if op.type is not Op.FullyConnected: |
| qp.zero_point = np.zeros(weights_shape) |
| weights = create_const_tensor( |
| "weights", weights_shape, datatype, np.zeros(weights_shape), np_type, quantization=qp |
| ) |
| op.add_input_tensor(weights) |
| # Optional bias tensor |
| if bias_shape is not None: |
| qp = default_quant_params() |
| if op.type is not Op.FullyConnected: |
| qp.zero_point = np.zeros(bias_shape) |
| bias = create_const_tensor("bias", bias_shape, DataType.int32, np.zeros(bias_shape), np.int32, quantization=qp) |
| op.add_input_tensor(bias) |
| |
| if set_ifm_ofm_shapes: |
| op.set_ifm_ofm_shapes() |
| |
| return op |
| |
| |
| def create_op(op_type, inputs, output, attrs=None, set_ifm_ofm_shapes=True): |
| op = Operation(op_type, output.name + "_op") |
| for input in inputs: |
| op.add_input_tensor(input) |
| op.set_output_tensor(output) |
| if attrs is not None: |
| op.attrs = attrs |
| if set_ifm_ofm_shapes: |
| op.set_ifm_ofm_shapes() |
| return op |
| |
| |
| def create_subgraph(op_list): |
| # Creates subgraph using the given list of operations |
| sg = Subgraph() |
| sg.placement = PassPlacement.Npu |
| all_inputs = set(tens for op in op_list for tens in op.inputs) |
| # Reversing, so that the resulting subgraph has same order as op_list |
| for op in op_list[::-1]: |
| for tens in op.outputs: |
| if tens not in all_inputs and tens not in sg.output_tensors: |
| sg.output_tensors.append(tens) |
| return sg |
| |
| |
| def create_graph(op_list): |
| # Creates subgraph using the given list of operations |
| nng = Graph() |
| sg = create_subgraph(op_list) |
| nng.subgraphs.append(sg) |
| return nng |