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Extend permute removal through singleton and sink views (#22549) #22549

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Extend permute removal through singleton and sink views (#22549) #22549

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115 changes: 112 additions & 3 deletions backends/arm/test/passes/test_remove_permutes_around_elementwise_tosa_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,7 +152,7 @@ def test_remove_permutes_around_rescale_tosa_INT() -> None:
assert _count_nodes(result.graph_module, RESCALE_TARGET) == 1


def test_sink_view_preserves_layout_through_rescale_to_broadcast_tosa_INT() -> None:
def test_terminal_sink_view_broadcast_is_optimized_tosa_INT() -> None:
graph = torch.fx.Graph()
x = graph.placeholder("x")
x.meta["val"] = torch.randn(1, 4, 1, 1)
Expand Down Expand Up @@ -181,11 +181,120 @@ def test_sink_view_preserves_layout_through_rescale_to_broadcast_tosa_INT() -> N
graph_module
)

assert not result.modified
assert _count_nodes(result.graph_module, PERMUTE_TARGET) == 1
assert result.modified
assert _count_nodes(result.graph_module, PERMUTE_TARGET) == 0
assert sub.args == (direct, rescale)


def test_remove_auth_se_layout_region_tosa_INT() -> None:
graph = torch.fx.Graph()
x = graph.placeholder("x")
x.meta["val"] = torch.randn(1, 8, 4)
gate_source = graph.placeholder("gate_source")
gate_source.meta["val"] = torch.randn(1, 1, 1, 4)
skip = graph.placeholder("skip")
skip.meta["val"] = torch.randn(1, 8, 4)

layout_in = graph.create_node("call_function", PERMUTE_TARGET, args=(x, [0, 2, 1]))
layout_in.meta["val"] = torch.randn(1, 4, 8)
clamp = graph.create_node(
"call_function", exir_ops.edge.aten.clamp.default, args=(layout_in, 0, None)
)
clamp.meta["val"] = torch.randn(1, 4, 8)

pool_view = graph.create_node(
"call_function", VIEW_TARGET, args=(clamp, [1, 4, 8, 1])
)
pool_view.meta["val"] = torch.randn(1, 4, 8, 1)
pool_layout = graph.create_node(
"call_function", PERMUTE_TARGET, args=(pool_view, [0, 2, 3, 1])
)
pool_layout.meta["val"] = torch.randn(1, 8, 1, 4)

gate = graph.create_node(
"call_function", VIEW_TARGET, args=(gate_source, [1, 4, 1])
)
gate.meta["val"] = torch.randn(1, 4, 1)
gated = graph.create_node("call_function", MUL_TARGET, args=(clamp, gate))
gated.meta["val"] = torch.randn(1, 4, 8)

skip_layout = graph.create_node(
"call_function", PERMUTE_TARGET, args=(skip, [0, 2, 1])
)
skip_layout.meta["val"] = torch.randn(1, 4, 8)
residual = graph.create_node("call_function", ADD_TARGET, args=(gated, skip_layout))
residual.meta["val"] = torch.randn(1, 4, 8)
layout_out = graph.create_node(
"call_function", PERMUTE_TARGET, args=(residual, [0, 2, 1])
)
layout_out.meta["val"] = torch.randn(1, 8, 4)
graph.output((pool_layout, layout_out))

graph_module = torch.fx.GraphModule({}, graph)
inputs = (
torch.randn(1, 8, 4),
torch.randn(1, 1, 1, 4),
torch.randn(1, 8, 4),
)
expected = graph_module(*inputs)

with TosaLoweringContext(TOSA_INT_SPEC):
result = RemovePermutesAroundElementwiseTosaOps(_fake_exported_program()).call(
graph_module
)

assert result.modified
assert _count_nodes(result.graph_module, PERMUTE_TARGET) == 0
assert pool_view.args[1] == [1, 8, 1, 4]
assert gate.args[1] == [1, 1, 4]
actual = result.graph_module(*inputs)
torch.testing.assert_close(actual, expected)


def test_remove_auth_sap_amax_layout_region_tosa_INT() -> None:
graph = torch.fx.Graph()
x = graph.placeholder("x")
x.meta["val"] = torch.randn(1, 8, 4)

layout_in = graph.create_node("call_function", PERMUTE_TARGET, args=(x, [0, 2, 1]))
layout_in.meta["val"] = torch.randn(1, 4, 8)
clamp = graph.create_node(
"call_function", exir_ops.edge.aten.clamp.default, args=(layout_in, 0, None)
)
clamp.meta["val"] = torch.randn(1, 4, 8)

conv_layout = graph.create_node(
"call_function", PERMUTE_TARGET, args=(clamp, [0, 2, 1])
)
conv_layout.meta["val"] = torch.randn(1, 8, 4)
maximum = graph.create_node(
"call_function", exir_ops.edge.aten.amax.default, args=(clamp, 2, True)
)
maximum.meta["val"] = torch.randn(1, 4, 1)
centered = graph.create_node("call_function", SUB_TARGET, args=(clamp, maximum))
centered.meta["val"] = torch.randn(1, 4, 8)
stats_layout = graph.create_node(
"call_function", PERMUTE_TARGET, args=(centered, [0, 2, 1])
)
stats_layout.meta["val"] = torch.randn(1, 8, 4)
graph.output((conv_layout, stats_layout))

graph_module = torch.fx.GraphModule({}, graph)
inputs = (torch.randn(1, 8, 4),)
expected = graph_module(*inputs)

with TosaLoweringContext(TOSA_INT_SPEC):
result = RemovePermutesAroundElementwiseTosaOps(_fake_exported_program()).call(
graph_module
)

assert result.modified
assert _count_nodes(result.graph_module, PERMUTE_TARGET) == 0
assert maximum.args[1] == 1
actual = result.graph_module(*inputs)
torch.testing.assert_close(actual, expected)


def test_remove_permutes_around_gelu_with_folded_scalar_constants_tosa_FP() -> None:
graph = torch.fx.Graph()
x = graph.placeholder("x")
Expand Down
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