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NXP backend: Remove broadcasting restrictions #20412

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15 changes: 5 additions & 10 deletions backends/nxp/backend/ir/converter/node_converters/ops_converters/add_tensor_converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@

import torch

from executorch.backends.nxp.backend.data_format import NXP_NODE_FORMAT
from executorch.backends.nxp.backend.ir.converter.node_converter import (
CustomDelegationOptions,
NodeConverter,
Expand All @@ -29,14 +28,6 @@ def _is_supported_on_target(
if not NodeConverter.at_least_one_input_shape_matches_the_output_shape(node):
return False

# If one input is in channel first and ranks of input tensors are not equal, we need to add Transposes
# Transpose is currently not supported for new flow
if any(
input_node.meta[NXP_NODE_FORMAT].is_channels_first()
for input_node in node.all_input_nodes
) and NodeConverter._node_inputs_ranks_not_equal(node):
return False

supported_types = [torch.int8, torch.uint8]
if not NodeConverter.uses_quantization_type_for_io(
node, supported_types, [0, 1], [0]
Expand Down Expand Up @@ -68,4 +59,8 @@ def convert(self, node: Node):
t_op = self._create_tflite_op_with_io_tensors(node)
t_op.builtin_options = add_options.Add()

self.builder.append_operators([t_op])
# Create additional ops in case of shape broadcasting
additional_ops = self.builder.ensure_correct_broadcasting(
t_op, t_op.tmp_outputs[0]
)
self.builder.append_operators(additional_ops + [t_op])
15 changes: 5 additions & 10 deletions backends/nxp/backend/ir/converter/node_converters/ops_converters/mul_tensor_converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
# LICENSE file in the root directory of this source tree.

import torch
from executorch.backends.nxp.backend.data_format import NXP_NODE_FORMAT
from executorch.backends.nxp.backend.ir.converter.node_converter import (
CustomDelegationOptions,
NodeConverter,
Expand All @@ -28,14 +27,6 @@ def _is_supported_on_target(
if not NodeConverter.at_least_one_input_shape_matches_the_output_shape(node):
return False

# If one input is in channel first and ranks of input tensors are not equal, we need to add Transposes
# Transpose is currently not supported for new flow
if any(
input_node.meta[NXP_NODE_FORMAT].is_channels_first()
for input_node in node.all_input_nodes
) and NodeConverter._node_inputs_ranks_not_equal(node):
return False

supported_types = [torch.int8, torch.uint8]
if not NodeConverter.uses_quantization_type_for_io(
node, supported_types, [0, 1], [0]
Expand Down Expand Up @@ -64,4 +55,8 @@ def convert(self, node: Node):
t_op = self._create_tflite_op_with_io_tensors(node)
t_op.builtin_options = mul_options.Mul()

self.builder.append_operators([t_op])
# Create additional ops in case of shape broadcasting
additional_ops = self.builder.ensure_correct_broadcasting(
t_op, t_op.tmp_outputs[0]
)
self.builder.append_operators(additional_ops + [t_op])
16 changes: 6 additions & 10 deletions backends/nxp/backend/ir/converter/node_converters/ops_converters/sub_tensor_converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@

import torch

from executorch.backends.nxp.backend.data_format import NXP_NODE_FORMAT
from executorch.backends.nxp.backend.ir.converter.node_converter import (
CustomDelegationOptions,
NodeConverter,
Expand All @@ -29,14 +28,6 @@ def _is_supported_on_target(
if not NodeConverter.at_least_one_input_shape_matches_the_output_shape(node):
return False

# If one input is in channel first and ranks of input tensors are not equal, we need to add Transposes
# Transpose is currently not supported for new flow
if any(
input_node.meta[NXP_NODE_FORMAT].is_channels_first()
for input_node in node.all_input_nodes
) and NodeConverter._node_inputs_ranks_not_equal(node):
return False

supported_types = [torch.int8, torch.uint8]
if not NodeConverter.uses_quantization_type_for_io(
node, supported_types, [0, 1], [0]
Expand Down Expand Up @@ -72,4 +63,9 @@ def convert(self, node: Node):
t_op = self._create_tflite_op_with_io_tensors(node)

t_op.builtin_options = sub_options.Sub()
self.builder.append_operators([t_op])

# Create additional ops in case of shape broadcasting
additional_ops = self.builder.ensure_correct_broadcasting(
t_op, t_op.tmp_outputs[0]
)
self.builder.append_operators(additional_ops + [t_op])
52 changes: 29 additions & 23 deletions backends/nxp/tests/ir/converter/node_converter/test_add_tensor_converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,6 +104,10 @@ def test__basic_nsys_inference_qat(self, mocker, request):
pytest.param(
[ModelInputSpec((4,)), ModelInputSpec((4, 4))], id="2 inputs 1D + 2D."
),
pytest.param(
[ModelInputSpec((1, 4, 8, 8)), ModelInputSpec((8, 8))],
id="2 inputs 4D + 2D.",
),
],
)
def test__broadcast(self, mocker, request, input_spec):
Expand Down Expand Up @@ -193,9 +197,33 @@ def test__w_conv(self, mocker, request, x_input_shape):
[ModelInputSpec((1, 4, 1, 67)), ModelInputSpec((1, 8, 5, 67))],
id="2 inputs 4D + 4D same width.",
),
pytest.param(
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 1))],
id="2 inputs 4D + 2D ones tensor.",
),
pytest.param(
[ModelInputSpec((1, 4, 8, 8)), ModelInputSpec((8, 8))],
id="2 inputs 4D + 2D both dims 8.",
),
pytest.param(
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 5))],
id="2 inputs 4D + 2D one dim 5.",
),
pytest.param(
[ModelInputSpec((1, 4, 12, 10)), ModelInputSpec((8, 1, 10))],
id="2 inputs 4D + 3D channels dim 1.",
),
pytest.param(
[ModelInputSpec((1, 4, 4, 10)), ModelInputSpec((1, 4, 1))],
id="2 inputs 4D + 3D channels dim 4.",
),
pytest.param(
[ModelInputSpec((1, 4, 25, 18)), ModelInputSpec((4, 1, 8, 25, 18))],
id="2 inputs 4D + 5D.",
),
],
)
def test__w_conv_broadcast(self, mocker, request, input_spec):
def test__broadcast_w_conv(self, mocker, request, input_spec):
model = AddTensorConvModule()

graph_verifier = DetailedGraphVerifier(
Expand All @@ -215,25 +243,3 @@ def test__w_conv_broadcast(self, mocker, request, input_spec):
comparator,
remove_quant_io_ops=True,
)

@pytest.mark.parametrize(
"input_spec",
[
pytest.param(
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 5))],
id="2 inputs 4D + 2D.",
),
pytest.param(
[ModelInputSpec((1, 4, 4, 10)), ModelInputSpec((1, 4, 1))],
id="2 inputs 4D + 3D.",
),
],
)
def test__w_conv_unsupported(self, input_spec):
model = AddTensorConvModule()

delegated_ep = to_quantized_edge_program(model, input_spec).exported_program()

# Make sure the `add.Tensor` was NOT delegated.
assert graph_contains_any_of_ops(delegated_ep.graph, [ExecutorchDelegateCall])
assert graph_contains_any_of_ops(delegated_ep.graph, [AddTensor])
60 changes: 50 additions & 10 deletions backends/nxp/tests/ir/converter/node_converter/test_mul_tensor_converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,12 +9,16 @@
import pytest
import torch

from executorch.backends.nxp.tests.dataset_creator import RandomDatasetCreator
from executorch.backends.nxp.tests.executorch_pipeline import (
ModelInputSpec,
to_quantized_edge_program,
)
from executorch.backends.nxp.tests.executors import graph_contains_any_of_ops
from executorch.backends.nxp.tests.graph_verifier import DetailedGraphVerifier
from executorch.backends.nxp.tests.model_output_comparator import (
AllCloseOutputComparator,
)
from executorch.backends.nxp.tests.models import MulTensorConvModule, MulTensorModule
from executorch.backends.nxp.tests.nsys_testing import lower_run_compare
from executorch.backends.nxp.tests.ops_aliases import (
Expand All @@ -39,6 +43,7 @@ class TestMulTensor:
pytest.param((6, 8), id="2D."),
pytest.param((1, 4, 8), id="3D."),
pytest.param((1, 4, 8, 8), id="4D."),
pytest.param((1, 4, 9, 11, 4), id="5D."),
],
)
def test__basic_nsys_inference(self, mocker, request, x_input_shape):
Expand Down Expand Up @@ -92,7 +97,7 @@ def test__basic_nsys_inference_qat(self, mocker, request, x_input_shape):
),
],
)
def test__correct_broadcast(self, input_spec, mocker, request):
def test__broadcast(self, input_spec, mocker, request):
model = MulTensorModule()
graph_verifier = DetailedGraphVerifier(
mocker, expected_delegated_ops={MulTensor: 1}, expected_non_delegated_ops={}
Expand All @@ -116,7 +121,7 @@ def test__correct_broadcast(self, input_spec, mocker, request):
),
],
)
def test__incorrect_broadcast(self, input_spec):
def test__broadcast_unsupported(self, input_spec):
# Broadcast where at least one of the inputs is not equal to output is not supported
model = MulTensorModule()

Expand Down Expand Up @@ -159,21 +164,56 @@ def test__w_conv(self, mocker, request, x_input_shape):
@pytest.mark.parametrize(
"input_spec",
[
pytest.param(
[ModelInputSpec((1, 4, 7, 1)), ModelInputSpec((1, 8, 1, 1))],
id="2 inputs 4D + 4D.",
),
pytest.param(
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 8, 5, 1))],
id="2 inputs 4D + 4D same height.",
),
pytest.param(
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 1))],
id="2 inputs 4D + 2D ones tensor.",
),
pytest.param(
[ModelInputSpec((1, 4, 8, 8)), ModelInputSpec((8, 8))],
id="2 inputs 4D + 2D both dims 8.",
),
pytest.param(
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 5))],
id="2 inputs 4D + 2D.",
id="2 inputs 4D + 2D one dim 5.",
),
pytest.param(
[ModelInputSpec((1, 4, 12, 10)), ModelInputSpec((8, 1, 10))],
id="2 inputs 4D + 3D channels dim 1.",
),
pytest.param(
[ModelInputSpec((1, 4, 4, 10)), ModelInputSpec((1, 4, 1))],
id="2 inputs 4D + 3D.",
id="2 inputs 4D + 3D channels dim 4.",
),
pytest.param(
[ModelInputSpec((1, 4, 25, 18)), ModelInputSpec((4, 1, 8, 25, 18))],
id="2 inputs 4D + 5D.",
),
],
)
def test__w_conv_unsupported(self, input_spec):
def test__broadcast_w_conv(self, mocker, request, input_spec):
model = MulTensorConvModule()
graph_verifier = DetailedGraphVerifier(
mocker,
expected_delegated_ops={MulTensor: 1, Convolution: 1},
expected_non_delegated_ops={},
)
dataset_creator = RandomDatasetCreator(low=-1.0, high=1.0)
comparator = AllCloseOutputComparator(atol=1)

delegated_ep = to_quantized_edge_program(model, input_spec).exported_program()

# Make sure the `mul.Tensor` was NOT delegated.
assert graph_contains_any_of_ops(delegated_ep.graph, [ExecutorchDelegateCall])
assert graph_contains_any_of_ops(delegated_ep.graph, [MulTensor])
lower_run_compare(
model,
input_spec,
graph_verifier,
request,
dataset_creator,
comparator,
remove_quant_io_ops=True,
)
48 changes: 25 additions & 23 deletions backends/nxp/tests/ir/converter/node_converter/test_sub_tensor_converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -193,9 +193,33 @@ def test__w_conv(self, mocker, request, x_input_shape):
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 8, 5, 1))],
id="2 inputs 4D + 4D same height.",
),
pytest.param(
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 1))],
id="2 inputs 4D + 2D ones tensor.",
),
pytest.param(
[ModelInputSpec((1, 4, 8, 8)), ModelInputSpec((8, 8))],
id="2 inputs 4D + 2D both dims 8.",
),
pytest.param(
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 5))],
id="2 inputs 4D + 2D one dim 5.",
),
pytest.param(
[ModelInputSpec((1, 4, 12, 10)), ModelInputSpec((8, 1, 10))],
id="2 inputs 4D + 3D channels dim 1.",
),
pytest.param(
[ModelInputSpec((1, 4, 4, 10)), ModelInputSpec((1, 4, 1))],
id="2 inputs 4D + 3D channels dim 4.",
),
pytest.param(
[ModelInputSpec((1, 4, 25, 18)), ModelInputSpec((4, 1, 8, 25, 18))],
id="2 inputs 4D + 5D.",
),
],
)
def test__w_conv_broadcast(self, mocker, request, input_spec):
def test__broadcast_w_conv(self, mocker, request, input_spec):
model = SubTensorConvModule()
graph_verifier = DetailedGraphVerifier(
mocker,
Expand All @@ -214,25 +238,3 @@ def test__w_conv_broadcast(self, mocker, request, input_spec):
comparator,
remove_quant_io_ops=True,
)

@pytest.mark.parametrize(
"input_spec",
[
pytest.param(
[ModelInputSpec((1, 4, 5, 5)), ModelInputSpec((1, 5))],
id="2 inputs 4D + 2D.",
),
pytest.param(
[ModelInputSpec((1, 4, 4, 10)), ModelInputSpec((1, 4, 1))],
id="2 inputs 4D + 3D.",
),
],
)
def test__w_conv_unsupported(self, input_spec):
model = SubTensorConvModule()

delegated_ep = to_quantized_edge_program(model, input_spec).exported_program()

# Make sure the `sub.Tensor` was NOT delegated.
assert graph_contains_any_of_ops(delegated_ep.graph, [ExecutorchDelegateCall])
assert graph_contains_any_of_ops(delegated_ep.graph, [SubTensor])
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