Louis Verhaard | 0b8268a | 2020-08-05 16:11:29 +0200 | [diff] [blame^] | 1 | # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. |
| 2 | # |
| 3 | # SPDX-License-Identifier: Apache-2.0 |
| 4 | # |
| 5 | # Licensed under the Apache License, Version 2.0 (the License); you may |
| 6 | # not use this file except in compliance with the License. |
| 7 | # You may obtain a copy of the License at |
| 8 | # |
| 9 | # www.apache.org/licenses/LICENSE-2.0 |
| 10 | # |
| 11 | # Unless required by applicable law or agreed to in writing, software |
| 12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| 13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | # See the License for the specific language governing permissions and |
| 15 | # limitations under the License. |
| 16 | # Description: |
| 17 | # Utilities used in vela unit tests |
| 18 | import numpy as np |
| 19 | |
| 20 | from ethosu.vela import architecture_features |
| 21 | from ethosu.vela.data_type import DataType |
| 22 | from ethosu.vela.nn_graph import Subgraph |
| 23 | from ethosu.vela.operation import NpuBlockType |
| 24 | from ethosu.vela.operation import Operation |
| 25 | from ethosu.vela.tensor import create_const_tensor |
| 26 | from ethosu.vela.tensor import MemArea |
| 27 | from ethosu.vela.tensor import Tensor |
| 28 | |
| 29 | |
| 30 | def create_arch(): |
| 31 | return architecture_features.ArchitectureFeatures( |
| 32 | vela_config=None, |
| 33 | system_config=None, |
| 34 | accelerator_config=architecture_features.Accelerator.Ethos_U55_128.value, |
| 35 | permanent_storage=MemArea.OnChipFlash, |
| 36 | override_block_config=None, |
| 37 | block_config_limit=None, |
| 38 | global_memory_clock_scale=1.0, |
| 39 | max_blockdep=0, |
| 40 | softmax_support=True, |
| 41 | ) |
| 42 | |
| 43 | |
| 44 | def create_elemwise_op(type, name, ifm_shape, ifm2_shape, ofm_shape, datatype=DataType.uint8): |
| 45 | # Creates elementwise operation with constant IFM/IFM2 |
| 46 | if datatype.size_in_bytes() == 1: |
| 47 | np_type = np.uint8 |
| 48 | elif datatype.size_in_bytes() == 2: |
| 49 | np_type = np.int16 |
| 50 | else: |
| 51 | np_type = np.int32 |
| 52 | op = Operation(type, name) |
| 53 | op.add_input_tensor(create_const_tensor(name + "_ifm", ifm_shape, datatype, np.zeros(ifm_shape), np_type)) |
| 54 | op.add_input_tensor(create_const_tensor(name + "_ifm2", ifm2_shape, datatype, np.zeros(ifm2_shape), np_type)) |
| 55 | ofm = Tensor(ofm_shape, datatype, name + "_ofm") |
| 56 | op.set_output_tensor(ofm) |
| 57 | op.attrs["npu_block_type"] = NpuBlockType.ElementWise |
| 58 | return op |
| 59 | |
| 60 | |
| 61 | def create_subgraph(op_list): |
| 62 | # Creates subgraph using the given list of operations |
| 63 | sg = Subgraph() |
| 64 | all_inputs = set(tens for op in op_list for tens in op.inputs) |
| 65 | # Reversing, so that the resulting subgraph has same order as op_list |
| 66 | for op in op_list[::-1]: |
| 67 | for tens in op.outputs: |
| 68 | if tens not in all_inputs and tens not in sg.output_tensors: |
| 69 | sg.output_tensors.append(tens) |
| 70 | return sg |