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Diffstat (limited to 'ethosu/vela/operation_util.py')
-rw-r--r-- | ethosu/vela/operation_util.py | 192 |
1 files changed, 192 insertions, 0 deletions
diff --git a/ethosu/vela/operation_util.py b/ethosu/vela/operation_util.py new file mode 100644 index 00000000..2fc7622c --- /dev/null +++ b/ethosu/vela/operation_util.py @@ -0,0 +1,192 @@ +# 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 .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 .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"] = b"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"] = b"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) + 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"] = b"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) + 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_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) + return op |