| # SPDX-FileCopyrightText: Copyright 2020-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> |
| # |
| # 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 |
| |
| import numpy as np |
| |
| 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 .reader_util import clone_and_reshape_tensor |
| from .shape4d import Shape4D |
| from .tensor import create_const_tensor |
| from .tensor import create_equivalence_id |
| 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] # [top, left, bottom, right] |
| op.run_on_npu = True |
| return op |
| |
| |
| def create_add_nop(name: str) -> Operation: |
| op = Operation(Op.Add, name) |
| op.run_on_npu = True |
| return op |
| |
| |
| def create_memcpy( |
| name: str, |
| ifm: Tensor, |
| ofm: Tensor, |
| ) -> Operation: |
| op = Operation(Op.Memcpy, name) |
| op.run_on_npu = True |
| op.add_input_tensor(ifm) |
| op.set_output_tensor(ofm) |
| op.set_ifm_ofm_shapes() |
| return op |
| |
| |
| def create_pad_nop(name: str) -> Operation: |
| op = Operation(Op.Pad, name) |
| op.run_on_npu = True |
| return op |
| |
| |
| def create_cast_op( |
| name: str, |
| ifm: Tensor, |
| ofm: Tensor, |
| ) -> Operation: |
| op = Operation(Op.DepthwiseConv2DBias, name) |
| op_attrs = { |
| "padding": Padding.VALID, |
| "stride_h": 1, |
| "stride_w": 1, |
| "strides": (1, 1, 1, 1), |
| "depth_multiplier": 1, |
| "channel_multiplier": 1, |
| "dilation_h_factor": 1, |
| "dilation_w_factor": 1, |
| "dilation": (1, 1, 1, 1), |
| "explicit_padding": None, |
| } |
| op.attrs.update(op_attrs) |
| op.add_input_tensor(ifm) |
| |
| c = ifm.shape[-1] |
| |
| # Weigth shape is in format [h, w, b, c] for DepthwiseConv2D |
| shape = [1, 1, 1, c] |
| kernel = np.dstack([1] * c) |
| identity_quant = QuantizationParameters(scale_f32=1.0, zero_point=0) |
| op.add_input_tensor( |
| create_const_tensor( |
| op.name + "_weights", |
| shape, |
| DataType.uint8, |
| np.array(kernel).reshape(shape), |
| quantization=identity_quant, |
| ), |
| ) |
| # Set flag to indicate that weights are already in correct order |
| # and prevent that they are transposed in reorder_depthwise_weights |
| op.inputs[1].weight_transpose_depthwise = True |
| bias_values = [0] * c |
| dtype = DataType.int64 if op.ifm.dtype == DataType.int16 else DataType.int32 |
| op.add_input_tensor(create_const_tensor(op.name + "_bias", [c], dtype, bias_values)) |
| op.set_output_tensor(ofm) |
| op.set_ifm_ofm_shapes() |
| |
| return op |
| |
| |
| def create_fused_activation(op_type: Op, name: str, ifm: Tensor, quantization: QuantizationParameters) -> Operation: |
| assert op_type.is_activation_op() |
| op = create_avgpool_nop(name) |
| op.activation = ActivationFunction(op_type) |
| ofm = Tensor(ifm.shape, ifm.dtype, f"{op.name}_tens0") |
| ofm.quantization = quantization |
| op.add_input_tensor(ifm) |
| op.set_output_tensor(ofm) |
| op.set_ifm_ofm_shapes() |
| return op |
| |
| |
| def create_fullyconnected( |
| name: str, |
| ifm: Tensor, |
| weights: Tensor, |
| bias: Optional[Tensor], |
| quantization: QuantizationParameters, |
| vela_weight_order: bool = True, |
| ) -> Operation: |
| # Reshape weights if needed |
| if not vela_weight_order: |
| weights = clone_and_reshape_tensor(weights, (1, 0), False) |
| |
| n_ofm = weights.shape[-1] |
| |
| # Setup bias if needed |
| if not bias: |
| bias_values = [0] * n_ofm |
| dtype = DataType.int64 if ifm.dtype == DataType.int16 else DataType.int32 |
| bias = create_const_tensor(f"{name}_bias", [n_ofm], dtype, bias_values) |
| # Set equivalence_id based on values to avoid placing duplicate data in flash |
| bias.equivalence_id = create_equivalence_id(tuple(bias_values)) |
| bias.value_id = bias.equivalence_id |
| |
| # Setup ofm |
| ofm = Tensor([ifm.shape[0], n_ofm], ifm.dtype, f"{name}_tens0") |
| ofm.quantization = quantization |
| |
| # Create op and add tensors |
| op = Operation(Op.FullyConnected, name) |
| op.add_input_tensor(ifm) |
| op.add_input_tensor(weights) |
| op.add_input_tensor(bias) |
| op.set_output_tensor(ofm) |
| op.set_ifm_ofm_shapes() |
| return op |
| |
| |
| def create_depthwise_maxpool( |
| name: str, |
| ifm: Tensor, |
| inp_shape: Shape4D, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| ) -> Operation: |
| op = Operation(Op.MaxPool, name) |
| height = inp_shape.height * inp_shape.width |
| width = inp_shape.depth |
| ifm_shape = Shape4D([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 = [ifm] |
| ofm = Tensor([1, height, 1, 1], ifm.dtype, op.name + "_tens0") |
| ofm.quantization = quantization |
| op.set_output_tensor(ofm) |
| op.ifm_shapes.append(ifm_shape) |
| op.ofm_shapes.append(Shape4D(ofm.shape)) |
| 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, |
| ifm_shape: Optional[Shape4D] = None, |
| ifm2_shape: Optional[Shape4D] = None, |
| ) -> Operation: |
| return create_binary_elementwise( |
| Op.Add, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape |
| ) |
| |
| |
| def create_clz( |
| name: str, |
| ifm: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ifm_shape: Optional[Shape4D] = None, |
| ) -> Operation: |
| return create_unary_elementwise(Op.CLZ, name, ifm, quantization, activation, dtype, attrs, ifm_shape) |
| |
| |
| def create_mul( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ifm_shape: Optional[Shape4D] = None, |
| ifm2_shape: Optional[Shape4D] = None, |
| ) -> Operation: |
| return create_binary_elementwise( |
| Op.Mul, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape |
| ) |
| |
| |
| def create_shl( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ifm_shape: Optional[Shape4D] = None, |
| ifm2_shape: Optional[Shape4D] = None, |
| ) -> Operation: |
| return create_binary_elementwise( |
| Op.SHL, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape |
| ) |
| |
| |
| def create_shr( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ifm_shape: Optional[Shape4D] = None, |
| ifm2_shape: Optional[Shape4D] = None, |
| ) -> Operation: |
| return create_binary_elementwise( |
| Op.SHR, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape |
| ) |
| |
| |
| def create_sub( |
| name: str, |
| ifm: Tensor, |
| ifm2: Tensor, |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ifm_shape: Optional[Shape4D] = None, |
| ifm2_shape: Optional[Shape4D] = None, |
| ) -> Operation: |
| return create_binary_elementwise( |
| Op.Sub, name, ifm, ifm2, quantization, activation, dtype, attrs, ifm_shape, ifm2_shape |
| ) |
| |
| |
| 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, |
| ifm_shape: Optional[Shape4D] = None, |
| ) -> Operation: |
| return create_binary_elementwise(op_type, name, ifm, None, quantization, activation, dtype, attrs, ifm_shape, None) |
| |
| |
| def create_binary_elementwise( |
| op_type: Op, |
| name: str, |
| ifm: Tensor, |
| ifm2: Optional[Tensor], |
| quantization: QuantizationParameters, |
| activation: Optional[ActivationFunction] = None, |
| dtype: Optional[DataType] = None, |
| attrs: Optional[dict] = None, |
| ifm_shape: Optional[Shape4D] = None, |
| ifm2_shape: Optional[Shape4D] = None, |
| ) -> Operation: |
| if ifm_shape is None: |
| ifm_shape = Shape4D(ifm.shape) |
| op = Operation(op_type, name) |
| op.add_input_tensor(ifm) |
| op.ifm_shapes.append(ifm_shape) |
| if ifm2: |
| op.add_input_tensor(ifm2) |
| if ifm2_shape is None: |
| ifm2_shape = Shape4D(ifm2.shape) |
| op.ifm_shapes.append(ifm2_shape) |
| op.activation = activation |
| if not dtype: |
| dtype = ifm.dtype |
| if attrs: |
| op.attrs.update(attrs) |
| |
| if ifm2 is None: |
| ofm_shape = ifm_shape |
| else: |
| in_shape = None if ifm.shape == [] else ifm_shape |
| in2_shape = None if ifm2.shape == [] else ifm2_shape |
| ofm_shape = ifm_shape if ifm_ifm2_correct_order(in_shape, in2_shape) else ifm2_shape |
| |
| ofm = Tensor(ofm_shape.as_list(), dtype, f"{op.name}_tens0") |
| ofm.quantization = quantization |
| op.set_output_tensor(ofm) |
| op.ofm_shapes.append(ofm_shape) |
| return op |
| |
| |
| def get_pad_values_from_input(padding) -> Tuple: |
| """Returns top, left, bottom, right padding from input values in a Pad input tensor""" |
| return (padding[-3][0], padding[-2][0], padding[-3][1], padding[-2][1]) |