| # 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: |
| # Utility functions for creating Network Operations. |
| from typing import Optional |
| from typing import Tuple |
| |
| from .data_type import DataType |
| from .high_level_command_to_npu_op import ifm_ifm2_correct_order |
| from .operation import ActivationFunction |
| from .operation import Op |
| from .operation import Operation |
| from .operation import Padding |
| from .tensor import create_reshape_tensor |
| from .tensor import QuantizationParameters |
| from .tensor import Tensor |
| |
| |
| def create_avgpool_nop(name: str) -> Operation: |
| op = Operation(Op.AvgPool, name) |
| op.attrs["padding"] = Padding.VALID |
| op.attrs["stride_w"] = 1 |
| op.attrs["stride_h"] = 1 |
| op.attrs["filter_width"] = 1 |
| op.attrs["filter_height"] = 1 |
| op.attrs["strides"] = [1, 1, 1, 1] |
| op.attrs["ksize"] = [1, 1, 1, 1] |
| op.attrs["skirt"] = [0, 0, 0, 0] |
| op.attrs["explicit_padding"] = [0, 0, 0, 0] |
| return op |
| |
| |
| def create_depthwise_maxpool( |
| name: str, ifm: Tensor, quantization: QuantizationParameters, activation: Optional[ActivationFunction] = None |
| ) -> Operation: |
| op = Operation(Op.MaxPool, name) |
| height = ifm.shape[1] * ifm.shape[2] |
| width = ifm.shape[3] |
| ifm_shape = [1, height, width, 1] |
| op.attrs["padding"] = Padding.VALID |
| op.attrs["stride_w"] = 1 |
| op.attrs["stride_h"] = 1 |
| op.attrs["filter_width"] = width |
| op.attrs["filter_height"] = 1 |
| op.attrs["strides"] = [1, op.attrs["stride_h"], op.attrs["stride_w"], 1] |
| op.attrs["ksize"] = [1, op.attrs["filter_height"], op.attrs["filter_width"], 1] |
| op.activation = activation |
| op.inputs = [create_reshape_tensor(ifm, ifm_shape)] |
| ofm = Tensor([1, height, 1, 1], ifm.dtype, op.name + "_tens0") |
| ofm.quantization = quantization |
| op.set_output_tensor(ofm) |
| op.set_ifm_ofm_shapes() |
| return op |
| |
| |
| def create_reduce_sum( |
| name: str, ifm: Tensor, quantization: QuantizationParameters, activation: Optional[ActivationFunction] = None |
| ) -> Operation: |
| op = Operation(Op.ReduceSum, name) |
| op.attrs["padding"] = Padding.VALID |
| op.attrs["stride_w"] = 1 |
| op.attrs["stride_h"] = 1 |
| op.attrs["filter_width"] = 1 |
| op.attrs["filter_height"] = 1 |
| op.attrs["strides"] = [1, op.attrs["stride_h"], op.attrs["stride_w"], 1] |
| op.attrs["ksize"] = [1, op.attrs["filter_height"], op.attrs["filter_width"], 1] |
| op.add_input_tensor(ifm) |
| op.activation = activation |
| ofm_shape = [1, ifm.shape[1], ifm.shape[2], 1] |
| sum_of_exp = Tensor(ofm_shape, DataType.int32, op.name + "_tens0") |
| sum_of_exp.quantization = quantization |
| op.set_output_tensor(sum_of_exp) |
| op.set_ifm_ofm_shapes() |
| return op |
| |
| |
| def create_add( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ) -> Operation: |
| return create_binary_elementwise(Op.Add, name, ifm, ifm2, quantization, activation, dtype, attrs) |
| |
| |
| def create_rescale_add( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| rescale: Tuple[int, int], |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ) -> Operation: |
| op = create_binary_elementwise(Op.RescaleAdd, name, ifm, ifm2, quantization, activation, dtype, attrs) |
| op.rescale = rescale |
| return op |
| |
| |
| def create_clz( |
| name: str, |
| ifm: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ) -> Operation: |
| return create_unary_elementwise(Op.CLZ, name, ifm, quantization, activation, dtype, attrs) |
| |
| |
| def create_mul( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ) -> Operation: |
| return create_binary_elementwise(Op.Mul, name, ifm, ifm2, quantization, activation, dtype, attrs) |
| |
| |
| def create_shl( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ) -> Operation: |
| return create_binary_elementwise(Op.SHL, name, ifm, ifm2, quantization, activation, dtype, attrs) |
| |
| |
| def create_shr( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ) -> Operation: |
| return create_binary_elementwise(Op.SHR, name, ifm, ifm2, quantization, activation, dtype, attrs) |
| |
| |
| def create_sub( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ) -> Operation: |
| return create_binary_elementwise(Op.Sub, name, ifm, ifm2, quantization, activation, dtype, attrs) |
| |
| |
| def create_unary_elementwise( |
| op_type: Op, |
| name: str, |
| ifm: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ) -> Operation: |
| return create_binary_elementwise(op_type, name, ifm, None, quantization, activation, dtype, attrs) |
| |
| |
| def create_binary_elementwise( |
| op_type: Op, |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ) -> Operation: |
| op = Operation(op_type, name) |
| op.add_input_tensor(ifm) |
| if ifm2: |
| op.add_input_tensor(ifm2) |
| op.activation = activation |
| if not dtype: |
| dtype = ifm.dtype |
| if attrs: |
| op.attrs.update(attrs) |
| ofm_shape = ifm.shape if ifm2 is None or ifm_ifm2_correct_order(ifm.shape, ifm2.shape) else ifm2.shape |
| ofm = Tensor(ofm_shape, dtype, f"{op.name}_tens0") |
| ofm.quantization = quantization |
| op.set_output_tensor(ofm) |
| op.set_ifm_ofm_shapes() |
| return op |