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| author | Jacob Bohlin <jacob.bohlin@arm.com> | 2020-09-11 10:04:15 +0200 |
|---|---|---|
| committer | patrik.gustavsson <patrik.gustavsson@arm.com> | 2020-09-17 08:18:50 +0000 |
| commit | 1a66697b80a527af6d6dd1ed235199264696767e (patch) | |
| tree | 447f19903eedb0ed163348769da28267ccf3bf47 /ethosu/vela/test/test_lut.py | |
| parent | 1356c2ab034738bcf51822de18911cc499fa2e8e (diff) | |
| download | ethos-u-vela-1a66697b80a527af6d6dd1ed235199264696767e.tar.gz | |
MLBEDSW-2809: Redo the Tensor addressing
Added a static class TensorAddressMap that stores all Tensor addresses
based on their equivalence_id. Made the "address" field into a property
which getter and setter looks up/sets the tensor's address in
TensorAddressMap.
This makes the references to cpu_tensor/npu_tensor obsolete and they
have been removed.
Addition to scheduler: avoid SRAM spilling if an op has consumers in
other subgraphs.
Minor rework in LUTState; it will now assign a unique equivalence_id to
the SHRAM lut tensor to avoid issues with addressing. The equivalent
checks in LUTState now compares the values of the LUT instead of the the
equivalence_id.
Updated LUT unit tests accordingly.
Signed-off-by: Jacob Bohlin <jacob.bohlin@arm.com>
Change-Id: I41de5a8a4e5f07b77d6544d8d4034b754993e503
Diffstat (limited to 'ethosu/vela/test/test_lut.py')
| -rw-r--r-- | ethosu/vela/test/test_lut.py | 14 |
1 files changed, 8 insertions, 6 deletions
diff --git a/ethosu/vela/test/test_lut.py b/ethosu/vela/test/test_lut.py index 3dda1793..ee1a40fe 100644 --- a/ethosu/vela/test/test_lut.py +++ b/ethosu/vela/test/test_lut.py @@ -15,6 +15,8 @@ # limitations under the License. # Description: # Unit tests for LUT support +import random + import numpy as np from ethosu.vela import insert_dma @@ -31,29 +33,29 @@ from ethosu.vela.test import testutil def set_256_lut(op, key): - values = list(range(256)) + random.seed(key) + values = random.choices(range(256), k=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)) + 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, 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)) + 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, np.uint32, TensorPurpose.LUT ) - lut_tensor.equivalence_id = lut.create_equivalence_id(key) op.set_activation_lut(lut_tensor) |