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Arm backend: Update Vela to 4.4.0 python package #14062

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Arm backend: Update Vela to 4.4.0 python package #14062

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2 changes: 1 addition & 1 deletion backends/arm/requirements-arm-ethos-u.txt
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Expand Up @@ -3,4 +3,4 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

ethos-u-vela @ git+https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela@d37febc1715edf0d236c2ff555739a8a9aadcf9a
ethos-u-vela == 4.4.0
11 changes: 4 additions & 7 deletions backends/arm/test/ops/test_logical.py
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Expand Up @@ -86,9 +86,6 @@ def forward(self, tensor: torch.Tensor):
#################


xfails = {"rand_rank4": "MLBEDSW-11031: Output diff on u85 bool transpose."}


@common.parametrize("test_data", And().test_data)
def test_logical_and_tosa_FP(test_data: input_t2):
pipeline = TosaPipelineFP[input_t2](
Expand Down Expand Up @@ -132,7 +129,7 @@ def test_logical_and_u55_INT_not_delegated(test_data: input_t2):
pipeline.run()


@common.parametrize("test_data", And().test_data, xfails=xfails)
@common.parametrize("test_data", And().test_data)
@common.XfailIfNoCorstone320
def test_logical_and_u85_INT(test_data: input_t2):
pipeline = EthosU85PipelineINT[input_t2](
Expand Down Expand Up @@ -226,7 +223,7 @@ def test_logical_xor_u55_INT_not_delegated(test_data: input_t2):
pipeline.run()


@common.parametrize("test_data", Xor().test_data, xfails=xfails)
@common.parametrize("test_data", Xor().test_data)
@common.XfailIfNoCorstone320
def test_logical_xor_u85_INT(test_data: input_t2):
pipeline = EthosU85PipelineINT[input_t2](
Expand Down Expand Up @@ -320,7 +317,7 @@ def test_logical_or_u55_INT_not_delegated(test_data: input_t2):
pipeline.run()


@common.parametrize("test_data", Or().test_data, xfails=xfails)
@common.parametrize("test_data", Or().test_data)
@common.XfailIfNoCorstone320
def test_logical_or_u85_INT(test_data: input_t2):
pipeline = EthosU85PipelineINT[input_t2](
Expand Down Expand Up @@ -414,7 +411,7 @@ def test_logical_not_u55_INT_not_delegated(test_data: input_t2):
pipeline.run()


@common.parametrize("test_data", Not().test_data, xfails=xfails)
@common.parametrize("test_data", Not().test_data)
@common.XfailIfNoCorstone320
def test_logical_not_u85_INT(test_data: input_t2):
pipeline = EthosU85PipelineINT[input_t2](
Expand Down
8 changes: 1 addition & 7 deletions backends/arm/test/ops/test_logsoftmax.py
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Expand Up @@ -64,13 +64,7 @@ def test_log_softmax_tosa_INT(test_data):
pipeline.run()


@common.parametrize(
"test_data",
LogSoftmax.test_data,
xfails={
"randn_neg_dim": "MLBEDSW-11032: ILLEGAL_OFM_BASE error: Base addresses must be aligned to brick depth on u55."
},
)
@common.parametrize("test_data", LogSoftmax.test_data)
@common.XfailIfNoCorstone300()
def test_log_softmax_u55_INT(test_data):
data, dim = test_data()
Expand Down
8 changes: 1 addition & 7 deletions backends/arm/test/ops/test_softmax.py
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Expand Up @@ -61,13 +61,7 @@ def test_softmax_tosa_INT(test_data):
pipeline.run()


@common.parametrize(
"test_data",
Softmax.test_data,
{
"randn_neg_dim": "MLBEDSW-11032: ILLEGAL_OFM_BASE error: Base addresses must be aligned to brick depth on u55."
},
)
@common.parametrize("test_data", Softmax.test_data)
@common.XfailIfNoCorstone300
def test_softmax_u55_INT(test_data):
data, dim = test_data()
Expand Down
9 changes: 1 addition & 8 deletions backends/arm/test/passes/test_rescale_pass.py
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Expand Up @@ -172,14 +172,7 @@ def test_quantized_rescale_tosa_bi(test_data: tuple[torch.Tensor, torch.Tensor])
pipeline.run()


u55_xfails = {
"ones": "MLBEDSW-11032: ILLEGAL_OFM_BASE error: Base addresses must be aligned to brick depth on u55.",
"randn_ones": "MLBEDSW-11032: ILLEGAL_OFM_BASE error: Base addresses must be aligned to brick depth on u55.",
"randn_large": "MLBEDSW-11032: ILLEGAL_OFM_BASE error: Base addresses must be aligned to brick depth on u55.",
}


@common.parametrize("test_data", RescaleNetwork.test_data, xfails=u55_xfails)
@common.parametrize("test_data", RescaleNetwork.test_data)
@common.XfailIfNoCorstone300
def test_quantized_rescale_u55(test_data: tuple[torch.Tensor, torch.Tensor]):
"""Tests a model with many ops that requires rescales. As more ops are quantized to int32 and
Expand Down
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