| # 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: |
| # Unit tests for graph_optimiser |
| import numpy as np |
| |
| from ethosu.vela.data_type import DataType |
| from ethosu.vela.graph_optimiser import convert_batched_fc_shape |
| from ethosu.vela.graph_optimiser import optimise_pad |
| from ethosu.vela.nn_graph import Graph |
| from ethosu.vela.operation import Op |
| from ethosu.vela.operation import Padding |
| from ethosu.vela.tensor import create_const_tensor |
| from ethosu.vela.tensor import Shape4D |
| from ethosu.vela.tensor import Tensor |
| from ethosu.vela.test import testutil |
| |
| |
| def test_convert_batched_fc(): |
| """Tests shape conversion of batched fully connected""" |
| shape = [4, 8] |
| ifm = create_const_tensor("test_in", shape, np.uint8, np.zeros(shape)) |
| weights = create_const_tensor("weight_in", shape, np.uint8, np.zeros(shape)) |
| ofm = Tensor(ifm.shape, np.uint8, "test_out") |
| op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm) |
| |
| ifm.consumer_list.append(op) |
| |
| op.ifm_shapes.append(Shape4D([4, 1, 1, 8])) |
| op.ofm_shapes.append(Shape4D([4, 1, 1, 8])) |
| |
| prev_op = op.clone() |
| prev_op.ifm_shapes = op.ifm_shapes |
| prev_op.ofm_shapes = op.ofm_shapes |
| |
| conv_op = convert_batched_fc_shape(op, None, None) |
| |
| assert conv_op.ifm != prev_op.ifm |
| assert conv_op.ofm != prev_op.ofm |
| assert conv_op.type == Op.FullyConnected |
| assert len(conv_op.ifm.shape) == 4 |
| assert conv_op.ifm.shape == conv_op.ofm.shape |
| assert conv_op.ifm.ops[0].type == Op.Reshape |
| |
| shape = [1, 8] |
| ifm.shape = shape |
| weights.shape = shape |
| ofm.shape = shape |
| op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm) |
| ifm.consumer_list.append(op) |
| |
| op.ifm_shapes.append([1, 1, 1, 8]) |
| op.ofm_shapes.append([1, 1, 1, 8]) |
| |
| prev_op = op.clone() |
| prev_op.ifm_shapes = op.ifm_shapes |
| prev_op.ofm_shapes = op.ofm_shapes |
| |
| conv_op = convert_batched_fc_shape(op, None, None) |
| |
| assert conv_op.ifm == prev_op.ifm |
| assert conv_op.ofm == prev_op.ofm |
| assert conv_op.type == Op.FullyConnected |
| assert len(conv_op.ifm.shape) == 2 |
| assert conv_op.ifm.shape == conv_op.ofm.shape |
| |
| |
| def test_optimise_pad(): |
| """ |
| Tests that the PAD operator is bypassed when followed by a convolution operator, |
| and that the padding of the convolution operation is correctly updated |
| """ |
| # Create Pad operation followed by Conv2D |
| quant = testutil.default_quant_params() |
| in_tens = Tensor([1, 76, 75, 64], DataType.uint8, "input") |
| in_tens.quantization = quant |
| pad_input = create_const_tensor("pad_input", [4, 2], DataType.int32, [[0, 0], [2, 1], [1, 1], [0, 0]]) |
| temp_tens = Tensor([1, 79, 77, 64], DataType.uint8, "pad_out") |
| temp_tens.quantization = quant.clone() |
| out_tens = Tensor([1, 76, 75, 64], DataType.uint8, "output") |
| out_tens.quantization = quant.clone() |
| weight_tens = Tensor([5, 3, 64, 64], DataType.uint8, "weights") |
| weight_tens.values = np.zeros(weight_tens.shape) |
| weight_tens.quant_values = np.zeros(weight_tens.shape, np.uint8) |
| weight_tens.quantization = quant.clone() |
| |
| bias_tens = Tensor([64], DataType.int32, "biases") |
| pad_op = testutil.create_op(Op.Pad, [in_tens, pad_input], temp_tens) |
| attrs = {"padding": Padding.VALID, "stride_w": 2, "stride_h": 2, "dilation_w_factor": 1, "dilation_h_factor": 1} |
| attrs["strides"] = (1, attrs["stride_h"], attrs["stride_w"], 1) |
| pad_op.run_on_npu = True |
| conv2d_op = testutil.create_op(Op.Conv2D, [temp_tens, weight_tens, bias_tens], out_tens, attrs) |
| conv2d_op.run_on_npu = True |
| nng = Graph() |
| sg = testutil.create_subgraph([pad_op, conv2d_op]) |
| nng.subgraphs.append(sg) |
| arch = testutil.create_arch() |
| |
| optimise_pad(conv2d_op, nng, arch) |
| |
| op = sg.output_tensors[0].ops[0] |
| assert op.type == Op.Conv2D |
| assert op.attrs["padding"] == Padding.EXPLICIT |
| assert op.attrs["explicit_padding"] == (2, 1, 1, 1) |
| assert op.ifm.shape == [1, 76, 75, 64] |
| assert pad_op not in op.ifm.ops |