| # SPDX-FileCopyrightText: Copyright 2020-2021, 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: |
| # Unit tests for LUT support |
| import random |
| |
| from ethosu.vela import lut |
| from ethosu.vela import mark_tensors |
| from ethosu.vela import pass_packing |
| from ethosu.vela.data_type import DataType |
| from ethosu.vela.high_level_command_stream import DMA |
| from ethosu.vela.nn_graph import Graph |
| from ethosu.vela.operation import Op |
| from ethosu.vela.rewrite_graph import rewrite_graph_pre_order |
| from ethosu.vela.rewrite_graph import verify_graph_health |
| from ethosu.vela.tensor import create_const_tensor |
| from ethosu.vela.tensor import TensorPurpose |
| from ethosu.vela.test import testutil |
| |
| |
| def set_256_lut(op, key, arch): |
| random.seed(key) |
| values = random.choices(range(256), k=256) |
| lut_tensor = create_const_tensor(op.name + "_lut", [1, 1, 1, 256], DataType.uint8, values, TensorPurpose.LUT) |
| scratch_lut_tensor = lut_tensor.clone_into_shram(arch) |
| op.set_activation_lut(scratch_lut_tensor) |
| |
| |
| def set_1K_lut(op, key, arch): |
| random.seed(key) |
| values = random.choices(range(256), k=256) |
| lut_tensor = create_const_tensor(op.name + "_lut", [1, 1, 1, 256], DataType.int32, values, TensorPurpose.LUT) |
| scratch_lut_tensor = lut_tensor.clone_into_shram(arch) |
| op.set_activation_lut(scratch_lut_tensor) |
| |
| |
| def set_2K_lut(op, key, arch): |
| random.seed(key) |
| values = random.choices(range(512), k=512) |
| lut_tensor = create_const_tensor(op.name + "_lut", [1, 1, 1, 512], DataType.int32, values, TensorPurpose.LUT) |
| scratch_lut_tensor = lut_tensor.clone_into_shram(arch) |
| op.set_activation_lut(scratch_lut_tensor) |
| |
| |
| def process(arch, op_list): |
| # Returns subgraph with given operations |
| nng = Graph() |
| sg = testutil.create_subgraph(op_list) |
| nng.subgraphs.append(sg) |
| assert verify_graph_health(nng) |
| nng = mark_tensors.mark_tensor_purpose(nng, arch, False) |
| assert verify_graph_health(nng) |
| rewrite_graph_pre_order(nng, sg, arch, [], []) |
| pass_packing.pack_into_passes(nng, arch, False) |
| assert verify_graph_health(nng) |
| # Create a DMA instruction for every op |
| cmd_list = [] |
| for ps in sg.passes: |
| for input_tens in ps.inputs: |
| if input_tens.src_tensor: |
| cmd_list.append(DMA(ps, input_tens.src_tensor, input_tens, None)) |
| |
| sg.high_level_command_stream = cmd_list |
| return sg |
| |
| |
| def filter_lut_cmds(cmd_list): |
| lut_cmd_list = [] |
| for cmd in cmd_list: |
| if "lut" in cmd.in_tensor.name: |
| lut_cmd_list.append(cmd) |
| return lut_cmd_list |
| |
| |
| def test_optimize_high_level_cmd_stream_2K(): |
| # Tests lut.optimize_high_level_cmd_stream, blending 256 byte and 2K luts |
| arch = testutil.create_arch() |
| shape = [1, 1, 1, 1] |
| # u8 LUT op, should lead to DMA |
| op0 = testutil.create_elemwise_op(Op.Add, "op0", shape, shape, shape) |
| set_256_lut(op0, "lut0", arch) |
| # u8 LUT op, should lead to DMA |
| op1 = testutil.create_elemwise_op(Op.Add, "op1", shape, shape, shape) |
| set_256_lut(op1, "lut1", arch) |
| # u8 LUT op with different LUT, should lead to DMA |
| op2 = testutil.create_elemwise_op(Op.Add, "op2", shape, shape, shape) |
| set_256_lut(op2, "lut2", arch) |
| # u8 LUT op with same LUT as in op1, should not lead to DMA |
| op3 = testutil.create_elemwise_op(Op.Add, "op3", shape, shape, shape) |
| set_256_lut(op3, "lut1", arch) |
| # u8 LUT op with same LUT as in op2, should not lead to DMA |
| op4 = testutil.create_elemwise_op(Op.Add, "op4", shape, shape, shape) |
| set_256_lut(op4, "lut2", arch) |
| # 2K LUT op, should lead to DMA, and will overwrite all previous LUTs in SHRAM |
| op5_2K = testutil.create_elemwise_op(Op.Add, "op5", shape, shape, shape) |
| set_2K_lut(op5_2K, "lut5", arch) |
| # Another 2K LUT op, should lead to DMA, and will overwrite the previous LUT in SHRAM |
| op6_2K = testutil.create_elemwise_op(Op.Add, "op6", shape, shape, shape) |
| set_2K_lut(op6_2K, "lut6", arch) |
| # u8 LUT op with same LUT as in op1, should lead to DMA |
| op7 = testutil.create_elemwise_op(Op.Add, "op7", shape, shape, shape) |
| set_256_lut(op7, "lut1", arch) |
| |
| op_list = [op0, op1, op2, op3, op4, op5_2K, op6_2K, op7] |
| sg = process(arch, op_list) |
| orig_cmd_list = sg.high_level_command_stream |
| sg.high_level_command_stream = orig_cmd_list |
| lut.optimize_high_level_cmd_stream(sg, arch) |
| cmd_list = sg.high_level_command_stream |
| # Check that only the needed DMA commands are left |
| expected_dma_ops = [op0, op1, op2, op5_2K, op6_2K, op7] |
| |
| cmd_list = filter_lut_cmds(cmd_list) |
| orig_cmd_list = filter_lut_cmds(orig_cmd_list) |
| |
| for (cmd, op) in zip(cmd_list, expected_dma_ops): |
| assert cmd.in_tensor == op.activation_lut.src_tensor |
| # Check that lut0, lut1 and lut2 in op0, op1, op2 are stored on different addresses |
| assert orig_cmd_list[0].out_tensor.address != orig_cmd_list[1].out_tensor.address |
| assert orig_cmd_list[0].out_tensor.address != orig_cmd_list[2].out_tensor.address |
| assert orig_cmd_list[1].out_tensor.address != orig_cmd_list[2].out_tensor.address |
| # Check that lut1 in op1 and op3 have same address |
| assert orig_cmd_list[1].out_tensor.address == orig_cmd_list[3].out_tensor.address |
| # Check that lut2 in op2 and op4 have same address |
| assert orig_cmd_list[2].out_tensor.address == orig_cmd_list[4].out_tensor.address |
| # Check that lut-s for 16 bit (op5 and op6) are stored on same address |
| assert orig_cmd_list[5].out_tensor.address == orig_cmd_list[6].out_tensor.address |
| |
| |
| def test_optimize_high_level_cmd_stream_1K(): |
| # Tests lut.optimize_high_level_cmd_stream, blending 256 and 1K luts |
| arch = testutil.create_arch() |
| shape = [1, 1, 1, 1] |
| # u8 LUT op, should lead to DMA |
| op0 = testutil.create_elemwise_op(Op.Add, "op0", shape, shape, shape) |
| set_256_lut(op0, "lut0", arch) |
| # u8 LUT op, should lead to DMA |
| op1 = testutil.create_elemwise_op(Op.Add, "op1", shape, shape, shape) |
| set_256_lut(op1, "lut1", arch) |
| # 1K LUT op with different LUT, should lead to DMA |
| op2_1K = testutil.create_elemwise_op(Op.Add, "op2", shape, shape, shape) |
| set_1K_lut(op2_1K, "lut2", arch) |
| # u8 LUT op with same LUT as in op1, should not lead to DMA |
| op3 = testutil.create_elemwise_op(Op.Add, "op3", shape, shape, shape) |
| set_256_lut(op3, "lut1", arch) |
| # 1K LUT op with same LUT as in op2, should not lead to DMA |
| op4_1K = testutil.create_elemwise_op(Op.Add, "op4", shape, shape, shape) |
| set_1K_lut(op4_1K, "lut2", arch) |
| # 1K LUT op, should lead to DMA, and will overwrite lut2 |
| op5_2K = testutil.create_elemwise_op(Op.Add, "op5", shape, shape, shape) |
| set_1K_lut(op5_2K, "lut5", arch) |
| # u8 LUT op, lut0 should still be present, should not lead to DMA |
| op6 = testutil.create_elemwise_op(Op.Add, "op6", shape, shape, shape) |
| set_256_lut(op6, "lut0", arch) |
| # 1K LUT op with same LUT as in op2, should lead to DMA |
| op7 = testutil.create_elemwise_op(Op.Add, "op7", shape, shape, shape) |
| set_1K_lut(op7, "lut2", arch) |
| |
| op_list = [op0, op1, op2_1K, op3, op4_1K, op5_2K, op6, op7] |
| sg = process(arch, op_list) |
| orig_cmd_list = sg.high_level_command_stream |
| sg.high_level_command_stream = orig_cmd_list |
| lut.optimize_high_level_cmd_stream(sg, arch) |
| cmd_list = sg.high_level_command_stream |
| |
| cmd_list = filter_lut_cmds(cmd_list) |
| orig_cmd_list = filter_lut_cmds(orig_cmd_list) |
| |
| # Check that only the needed DMA commands are left |
| expected_dma_ops = [op0, op1, op2_1K, op5_2K, op7] |
| for (cmd, op) in zip(cmd_list, expected_dma_ops): |
| assert cmd.in_tensor == op.activation_lut.src_tensor |
| # Check that lut0, lut1 and lut2 in op0, op1, op2 are stored on different addresses |
| assert orig_cmd_list[0].out_tensor.address != orig_cmd_list[1].out_tensor.address |
| assert orig_cmd_list[0].out_tensor.address != orig_cmd_list[2].out_tensor.address |
| assert orig_cmd_list[1].out_tensor.address != orig_cmd_list[2].out_tensor.address |
| # Check that lut1 in op1 and op3 have same address |
| assert orig_cmd_list[1].out_tensor.address == orig_cmd_list[3].out_tensor.address |
| # Check that lut2 in op2 and op4 and op7 have same address |
| assert orig_cmd_list[2].out_tensor.address == orig_cmd_list[4].out_tensor.address |
| assert orig_cmd_list[2].out_tensor.address == orig_cmd_list[7].out_tensor.address |