[MLBEDSW-3690] Refactor Softmax
Move operator generation code to common functions.
Signed-off-by: Fredrik Svedberg <fredrik.svedberg@arm.com>
Change-Id: I02e185fd793a96ae435fa7d235c9d1e97f388a03
diff --git a/ethosu/vela/operation_util.py b/ethosu/vela/operation_util.py
new file mode 100644
index 0000000..2fc7622
--- /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