MLBEDSW-2688: Improved LUT support
- Support for more than one 256-byte LUT in SHRAM
- No DMA is performed for a LUT that is already located in SHRAM
- Added MemArea.Shram, used for LUT, to avoid false address collision
asserts during SRAM tensor allocation
- Added read access to LUT in memory access calculation
Change-Id: If4d1eded5ed029d253f4f5efb2d80495fc3eac99
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/test/test_lut.py b/ethosu/vela/test/test_lut.py
new file mode 100644
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+++ b/ethosu/vela/test/test_lut.py
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+# 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 LUT support
+import numpy as np
+
+from ethosu.vela import insert_dma
+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.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):
+ values = list(range(256))
+ lut_tensor = create_const_tensor(
+ op.name + "_lut", [1, 1, 1, 256], DataType.int8, values, np.uint8, TensorPurpose.LUT
+ )
+ lut_tensor.equivalence_id = lut.create_equivalence_id(key)
+ op.set_activation_lut(lut_tensor)
+
+
+def set_1K_lut(op, key):
+ values = list(range(256))
+ lut_tensor = create_const_tensor(
+ op.name + "_lut", [1, 1, 1, 256], DataType.int32, values, np.uint32, TensorPurpose.LUT
+ )
+ lut_tensor.equivalence_id = lut.create_equivalence_id(key)
+ op.set_activation_lut(lut_tensor)
+
+
+def set_2K_lut(op, key):
+ values = list(range(512))
+ lut_tensor = create_const_tensor(
+ op.name + "_lut", [1, 1, 1, 512], DataType.int32, values, np.uint32, TensorPurpose.LUT
+ )
+ lut_tensor.equivalence_id = lut.create_equivalence_id(key)
+ op.set_activation_lut(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)
+ nng = insert_dma.insert_dma_commands(nng, arch, False)
+ assert verify_graph_health(nng)
+ 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 intermediate in ps.intermediates:
+ if intermediate.needs_dma():
+ cmd_list.append(DMA(ps, intermediate.get_dma_src_tensor(), intermediate, None))
+ sg.high_level_command_stream = cmd_list
+ return sg
+
+
+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("AddAct", "op0", shape, shape, shape)
+ set_256_lut(op0, "lut0")
+ # u8 LUT op, should lead to DMA
+ op1 = testutil.create_elemwise_op("AddAct", "op1", shape, shape, shape)
+ set_256_lut(op1, "lut1")
+ # u8 LUT op with different LUT, should lead to DMA
+ op2 = testutil.create_elemwise_op("AddAct", "op2", shape, shape, shape)
+ set_256_lut(op2, "lut2")
+ # u8 LUT op with same LUT as in op1, should not lead to DMA
+ op3 = testutil.create_elemwise_op("AddAct", "op3", shape, shape, shape)
+ set_256_lut(op3, "lut1")
+ # u8 LUT op with same LUT as in op2, should not lead to DMA
+ op4 = testutil.create_elemwise_op("AddAct", "op4", shape, shape, shape)
+ set_256_lut(op4, "lut2")
+ # 2K LUT op, should lead to DMA, and will overwrite all previous LUTs in SHRAM
+ op5_2K = testutil.create_elemwise_op("AddAct", "op5", shape, shape, shape)
+ set_2K_lut(op5_2K, "lut5")
+ # Another 2K LUT op, should lead to DMA, and will overwrite the previous LUT in SHRAM
+ op6_2K = testutil.create_elemwise_op("AddAct", "op6", shape, shape, shape)
+ set_2K_lut(op6_2K, "lut6")
+ # u8 LUT op with same LUT as in op1, should lead to DMA
+ op7 = testutil.create_elemwise_op("AddAct", "op7", shape, shape, shape)
+ set_256_lut(op7, "lut1")
+
+ 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]
+ for (cmd, op) in zip(cmd_list, expected_dma_ops):
+ assert cmd.in_tensor == op.activation_lut
+ # 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("AddAct", "op0", shape, shape, shape)
+ set_256_lut(op0, "lut0")
+ # u8 LUT op, should lead to DMA
+ op1 = testutil.create_elemwise_op("AddAct", "op1", shape, shape, shape)
+ set_256_lut(op1, "lut1")
+ # 1K LUT op with different LUT, should lead to DMA
+ op2_1K = testutil.create_elemwise_op("AddAct", "op2", shape, shape, shape)
+ set_1K_lut(op2_1K, "lut2")
+ # u8 LUT op with same LUT as in op1, should not lead to DMA
+ op3 = testutil.create_elemwise_op("AddAct", "op3", shape, shape, shape)
+ set_256_lut(op3, "lut1")
+ # 1K LUT op with same LUT as in op2, should not lead to DMA
+ op4_1K = testutil.create_elemwise_op("AddAct", "op4", shape, shape, shape)
+ set_1K_lut(op4_1K, "lut2")
+ # 1K LUT op, should lead to DMA, and will overwrite lut2
+ op5_2K = testutil.create_elemwise_op("AddAct", "op5", shape, shape, shape)
+ set_1K_lut(op5_2K, "lut5")
+ # u8 LUT op, lut0 should still be present, should not lead to DMA
+ op6 = testutil.create_elemwise_op("AddAct", "op6", shape, shape, shape)
+ set_256_lut(op6, "lut0")
+ # 1K LUT op with same LUT as in op2, should lead to DMA
+ op7 = testutil.create_elemwise_op("AddAct", "op7", shape, shape, shape)
+ set_1K_lut(op7, "lut2")
+
+ 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
+ # 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
+ # 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