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Use GraphBuilder in unit tests for ops removal. #11010

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May 22, 2025
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Use GraphBuilder in unit tests for ops removal. #11010

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264 changes: 99 additions & 165 deletions backends/cadence/aot/tests/test_remove_ops_passes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,6 @@
import executorch.backends.cadence.aot.ops_registrations # noqa
import torch
import torch.nn as nn
import torch.nn.functional as F
from executorch.backends.cadence.aot import compiler
from executorch.backends.cadence.aot.compiler import export_to_edge
from executorch.backends.cadence.aot.fuse_ops import FuseQuantDequantToRequantizePass
from executorch.backends.cadence.aot.graph_builder import GraphBuilder
Expand Down Expand Up @@ -53,16 +51,15 @@ class TestRemoveOpsPasses(unittest.TestCase):
)
@torch.no_grad()
def test_remove_to_ops(self, shape: Tuple[int]):
class M(torch.nn.Module):
def forward(self, x: torch.Tensor):
return exir_ops.edge.aten.to(x, dtype=torch.float32)

model = M()
x = torch.randn(shape)
graph_module = export_to_edge(model, (x,)).exported_program().graph_module
p = RemoveToOpsPass()

graph_after_passes = cast(PassResult, p(graph_module)).graph_module
builder = GraphBuilder()
x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
x = builder.call_operator(
op=exir_ops.edge.aten.to.dtype,
args=(x, torch.float32),
)
builder.output([x])
original = builder.get_graph_module()
graph_after_passes = cast(PassResult, RemoveToOpsPass()(original)).graph_module

self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.to.dtype),
Expand All @@ -83,31 +80,24 @@ def forward(self, x: torch.Tensor):
)
@torch.no_grad()
def test_remove_nop_add_op_pass(self, shape: Tuple[int]):
class FullX(torch.nn.Module):
def forward(self, t: torch.Tensor):
return torch.add(torch.full(shape, 0), t)

class FullY(torch.nn.Module):
def forward(self, t: torch.Tensor):
return torch.add(t, torch.full(shape, 0))

model = FullX()
t = torch.full(shape, 3)
graph_module = export_to_edge(model, (t,)).exported_program().graph_module

p = RemoveNopAddOpPass()

graph_after_passes = cast(PassResult, p(graph_module)).graph_module
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.add.Tensor),
0,
)

model = FullY()
graph_module = export_to_edge(model, (t,)).exported_program().graph_module

graph_after_passes = cast(PassResult, p(graph_module)).graph_module

builder = GraphBuilder()
x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
zeros = builder.call_operator(
op=exir_ops.edge.aten.full.default, args=(shape, 0)
)
left_add = builder.call_operator(
op=exir_ops.edge.aten.add.Tensor,
args=(zeros, x),
)
right_add = builder.call_operator(
op=exir_ops.edge.aten.add.Tensor,
args=(left_add, zeros),
)
builder.output([right_add])
original = builder.get_graph_module()
graph_after_passes = cast(
PassResult, RemoveNopAddOpPass()(original)
).graph_module
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.add.Tensor),
0,
Expand All @@ -122,31 +112,24 @@ def forward(self, t: torch.Tensor):
)
@torch.no_grad()
def test_remove_nop_mul_op_pass(self, shape: Tuple[int]):
class FullX(torch.nn.Module):
def forward(self, t: torch.Tensor):
return torch.mul(torch.full(shape, 0), t)

class FullY(torch.nn.Module):
def forward(self, t: torch.Tensor):
return torch.mul(t, torch.full(shape, 0))

model = FullX()
t = torch.full(shape, 3)
graph_module = export_to_edge(model, (t,)).exported_program().graph_module

p = RemoveNopMulOpPass()

graph_after_passes = cast(PassResult, p(graph_module)).graph_module
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.mul.Tensor),
0,
)

model = FullY()
graph_module = export_to_edge(model, (t,)).exported_program().graph_module

graph_after_passes = cast(PassResult, p(graph_module)).graph_module

builder = GraphBuilder()
x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
zeros = builder.call_operator(
op=exir_ops.edge.aten.full.default, args=(shape, 0)
)
left_mul = builder.call_operator(
op=exir_ops.edge.aten.mul.Tensor,
args=(zeros, x),
)
right_mul = builder.call_operator(
op=exir_ops.edge.aten.mul.Tensor,
args=(left_mul, zeros),
)
builder.output([right_mul])
original = builder.get_graph_module()
graph_after_passes = cast(
PassResult, RemoveNopMulOpPass()(original)
).graph_module
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.mul.Tensor),
0,
Expand All @@ -159,18 +142,16 @@ def forward(self, t: torch.Tensor):
)
@torch.no_grad()
def test_remove_alias_copy(self, shape: Tuple[int]):
class M(torch.nn.Module):
def forward(self, x: torch.Tensor):
return exir_ops.edge.aten.alias_copy(x)

model = M()
x = torch.randn(shape)
graph_module = export_to_edge(model, (x,)).exported_program().graph_module

p = RemoveAliasCopyOpPass()

graph_after_passes = cast(PassResult, p(graph_module)).graph_module

builder = GraphBuilder()
x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
alias = builder.call_operator(
op=exir_ops.edge.aten.alias_copy.default, args=(x,)
)
builder.output([alias])
original = builder.get_graph_module()
graph_after_passes = cast(
PassResult, RemoveAliasCopyOpPass()(original)
).graph_module
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.alias_copy.default),
0,
Expand All @@ -183,19 +164,16 @@ def forward(self, x: torch.Tensor):
)
@torch.no_grad()
def test_remove_detach_copy(self, shape: Tuple[int]):
# aten::detach is converted to aten::alias_copy after functionalization & decomposition.
class M(torch.nn.Module):
def forward(self, x: torch.Tensor):
return exir_ops.edge.aten.detach_copy(x)

model = M()
x = torch.randn(shape)
graph_module = export_to_edge(model, (x,)).exported_program().graph_module

p = RemoveDetachCopyPass()

graph_after_passes = cast(PassResult, p(graph_module)).graph_module

builder = GraphBuilder()
x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
detach = builder.call_operator(
op=exir_ops.edge.aten.detach_copy.default, args=(x,)
)
builder.output([detach])
original = builder.get_graph_module()
graph_after_passes = cast(
PassResult, RemoveDetachCopyPass()(original)
).graph_module
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.detach_copy.default),
0,
Expand All @@ -210,95 +188,51 @@ def forward(self, x: torch.Tensor):
def test_remove_zero_sized_constant_pad_nd(
self, shape: Tuple[int], padding: Tuple[int]
):
# F.pad is converted to aten::constant_pad_nd after functionalization & decomposition.
class Padding(torch.nn.Module):
def __init__(self):
super().__init__()
self.padding = padding

def forward(self, x: torch.Tensor):
return F.pad(x, self.padding)

model = Padding()
x = torch.randn(shape)
graph_module = export_to_edge(model, (x,)).exported_program().graph_module

p = RemoveZeroSizedConstantPadNd()

graph_after_passes = cast(PassResult, p(graph_module)).graph_module

builder = GraphBuilder()
x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
pad = builder.call_operator(
op=exir_ops.edge.aten.constant_pad_nd.default, args=(x, padding)
)
builder.output([pad])
original = builder.get_graph_module()
graph_after_passes = cast(
PassResult, RemoveZeroSizedConstantPadNd()(original)
).graph_module
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.constant_pad_nd.default),
0,
)

def test_remove_expand(self):
class Expand(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.expand_copy(x, [2, 3, 5])

x = torch.ones(2, 3, 5)
p = RemoveNopExpandOpPass()
graph_module = export_to_edge(Expand(), (x,)).exported_program().graph_module
graph_module = p(graph_module).graph_module
# Assert that expand op is optimized away, since it is a nop
builder = GraphBuilder()
x = builder.placeholder("x", torch.randn([2, 3, 5], dtype=torch.float32))
expand = builder.call_operator(
op=exir_ops.edge.aten.expand_copy.default, args=(x, [2, 3, 5])
)
builder.output([expand])
original = builder.get_graph_module()
graph_after_passes = cast(
PassResult, RemoveNopExpandOpPass()(original)
).graph_module
self.assertEqual(
count_node(graph_module, exir_ops.edge.aten.expand_copy.default), 0
count_node(graph_after_passes, exir_ops.edge.aten.expand_copy.default), 0
)

def test_remove_zero_arg_cat(self):
class Cat(torch.nn.Module):
def forward(self, x, y):
return torch.ops.aten.cat((x, y), 0)

x = torch.ones(1, 0, 3, 5)
y = torch.ones(2, 0, 3, 5)
graph_module = (
compiler.export_to_cadence(Cat(), (x, y)).exported_program().graph_module
)
# Assert that cat op is optimized away, since it concatenates
# two zero-sized tensors
self.assertEqual(count_node(graph_module, exir_ops.edge.aten.cat.default), 0)

def test_remove_single_arg_cat(self):
class Cat(torch.nn.Module):
def forward(self, x, y):
z = torch.ones(0, 5)
# z is an empty tensor, and concatenation of x with z will
# be x. So we can safely eliminate the following cat op.
x1 = torch.ops.aten.cat((x, z))
x2 = torch.add(x1, 2.4, 3.1)
y1 = torch.add(y, 1, 2)
return torch.add(x2, y1)

x = torch.ones(3, 5)
y = torch.ones(3, 5)
graph_module = export_to_edge(Cat(), (x, y)).exported_program().graph_module
new_graph_module = RemoveZeroSizedCatArgsPass()(graph_module).graph_module
new_graph_module.graph.eliminate_dead_code()
# Assert that x1 is optimized away
self.assertEqual(count_node(new_graph_module, torch.ops.aten.cat.out), 0)

def test_remove_zero_sized_cat(self):
class Cat(torch.nn.Module):
def __init__(self, dim: int):
super().__init__()
self.dim = dim

def forward(self, tensors):
return torch.cat(tensors, self.dim)

shapes, dim, dtype, _max = [(1, 0, 3), (2, 0, 3)], 0, torch.float32, 127

in_tensors = [(torch.rand(shape) * _max).to(dtype=dtype) for shape in shapes]

model = Cat(dim)
graph_module = (
export_to_edge(model, (in_tensors,)).exported_program().graph_module
builder = GraphBuilder()
x = builder.placeholder("x", torch.randn([1, 0, 3, 5], dtype=torch.float32))
y = builder.placeholder("y", torch.randn([2, 0, 3, 5], dtype=torch.float32))
concat = builder.call_operator(
op=exir_ops.edge.aten.cat.default, args=([x, y], 0)
)
builder.output([concat])
original = builder.get_graph_module()
graph_after_passes = cast(
PassResult, RemoveZeroSizedCatArgsPass()(original)
).graph_module
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.cat.default), 0
)
new_graph_module = RemoveZeroSizedCatArgsPass()(graph_module).graph_module
new_graph_module.graph.eliminate_dead_code()
self.assertEqual(count_node(new_graph_module, torch.ops.aten.cat.out), 0)

def test_remove_clone(self):
class Clone(torch.nn.Module):
Expand Down
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