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Remove line numbers from expected files #1061

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Merged
merged 1 commit into from
Oct 31, 2025
Merged

Remove line numbers from expected files #1061

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6 changes: 5 additions & 1 deletion helion/_testing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -829,7 +829,11 @@ def replacer(match: re.Match[str]) -> str:
newline = match.group("newline")
return f"{indent}# src[{prefix}{suffix}]: {text}{newline}"

return pattern.sub(replacer, code)
# Normalize structured src comments
code = pattern.sub(replacer, code)

# Normalize file line refs: foo.py:123 -> foo.py:N
return re.sub(r"(\b[^:\s]+\.py):(\d+)\b", r"\1:N", code)

@classmethod
def normalize_code(cls, code: str) -> str:
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38 changes: 19 additions & 19 deletions test/test_reductions.expected
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Original file line number Diff line number Diff line change
Expand Up @@ -515,18 +515,18 @@ def layer_norm_fwd_nonpow2(x: torch.Tensor, weight: torch.Tensor, bias: torch.Te
--- assertExpectedJournal(TestReductions.test_mean)
def reduce_kernel(x: torch.Tensor, fn: Callable[[torch.Tensor], torch.Tensor], out_dtype=torch.float32):
n, _m =
# Call: SequenceType((LiteralType(512), LiteralType(512))) SourceOrigin(location=<SourceLocation test_reductions.py:51>)
# Call: SequenceType((LiteralType(512), LiteralType(512))) SourceOrigin(location=<SourceLocation test_reductions.py:N>)
# Attribute: TensorAttributeType AttributeOrigin(value=ArgumentOrigin(name='x'), key='size')
# Name: TensorType([512, 512], torch.float32) ArgumentOrigin(name='x')
x.size()
out =
# Call: TensorType([512], torch.float32) SourceOrigin(location=<SourceLocation test_reductions.py:52>)
# Call: TensorType([512], torch.float32) SourceOrigin(location=<SourceLocation test_reductions.py:N>)
# Attribute: CallableType(_VariableFunctionsClass.empty) AttributeOrigin(value=GlobalOrigin(name='torch'), key='empty')
# Name: PythonModuleType(torch) GlobalOrigin(name='torch')
torch.empty(
# List: SequenceType([LiteralType(512)]) SourceOrigin(location=<SourceLocation test_reductions.py:53>)
# List: SequenceType([LiteralType(512)]) SourceOrigin(location=<SourceLocation test_reductions.py:N>)
[
# Name: LiteralType(512) GetItemOrigin(value=SourceOrigin(location=<SourceLocation test_reductions.py:51>), key=0)
# Name: LiteralType(512) GetItemOrigin(value=SourceOrigin(location=<SourceLocation test_reductions.py:N>), key=0)
n], dtype=
# Name: LiteralType(torch.float32) ArgumentOrigin(name='out_dtype')
out_dtype, device=
Expand All @@ -536,38 +536,38 @@ x.device)
# For: loop_type=GRID

for tile_n in
# Call: IterType(TileIndexType(0)) SourceOrigin(location=<SourceLocation test_reductions.py:57>)
# Call: IterType(TileIndexType(0)) SourceOrigin(location=<SourceLocation test_reductions.py:N>)
# Attribute: CallableType(tile) AttributeOrigin(value=GlobalOrigin(name='hl'), key='tile')
# Name: PythonModuleType(helion.language) GlobalOrigin(name='hl')
hl.tile(
# Name: LiteralType(512) GetItemOrigin(value=SourceOrigin(location=<SourceLocation test_reductions.py:51>), key=0)
# Name: LiteralType(512) GetItemOrigin(value=SourceOrigin(location=<SourceLocation test_reductions.py:N>), key=0)
n):

# Subscript: TensorType([block_size_0], torch.float32) DeviceOrigin(location=<SourceLocation test_reductions.py:58>)
# Name: TensorType([512], torch.float32) SourceOrigin(location=<SourceLocation test_reductions.py:52>)
# Subscript: TensorType([block_size_0], torch.float32) DeviceOrigin(location=<SourceLocation test_reductions.py:N>)
# Name: TensorType([512], torch.float32) SourceOrigin(location=<SourceLocation test_reductions.py:N>)
out[
# Name: TileIndexType(0) SourceOrigin(location=<SourceLocation test_reductions.py:57>)
# Name: TileIndexType(0) SourceOrigin(location=<SourceLocation test_reductions.py:N>)
tile_n] =
# Call: TensorType([block_size_0], torch.float32) DeviceOrigin(location=<SourceLocation test_reductions.py:58>)
# Call: TensorType([block_size_0], torch.float32) DeviceOrigin(location=<SourceLocation test_reductions.py:N>)
# Name: CallableType(_VariableFunctionsClass.mean) ArgumentOrigin(name='fn')
fn(
# Subscript: TensorType([block_size_0, rdim_1], torch.float32) DeviceOrigin(location=<SourceLocation test_reductions.py:58>)
# Subscript: TensorType([block_size_0, rdim_1], torch.float32) DeviceOrigin(location=<SourceLocation test_reductions.py:N>)
# Name: TensorType([512, 512], torch.float32) ArgumentOrigin(name='x')
x[
# Name: TileIndexType(0) SourceOrigin(location=<SourceLocation test_reductions.py:57>)
# Name: TileIndexType(0) SourceOrigin(location=<SourceLocation test_reductions.py:N>)
tile_n,
# Slice: SliceType(LiteralType(None):LiteralType(None):LiteralType(None)) DeviceOrigin(location=<SourceLocation test_reductions.py:58>)
# Slice: SliceType(LiteralType(None):LiteralType(None):LiteralType(None)) DeviceOrigin(location=<SourceLocation test_reductions.py:N>)
:], dim=
# UnaryOp: LiteralType(-1) DeviceOrigin(location=<SourceLocation test_reductions.py:58>)
# UnaryOp: LiteralType(-1) DeviceOrigin(location=<SourceLocation test_reductions.py:N>)
-
# Constant: LiteralType(1) DeviceOrigin(location=<SourceLocation test_reductions.py:58>)
# Constant: LiteralType(1) DeviceOrigin(location=<SourceLocation test_reductions.py:N>)
1)
return
# Name: TensorType([512], torch.float32) SourceOrigin(location=<SourceLocation test_reductions.py:52>)
# Name: TensorType([512], torch.float32) SourceOrigin(location=<SourceLocation test_reductions.py:N>)
out

def root_graph_0():
# File: .../test_reductions.py:58 in reduce_kernel, code: out[tile_n] = fn(x[tile_n, :], dim=-1)
# File: .../test_reductions.py:N in reduce_kernel, code: out[tile_n] = fn(x[tile_n, :], dim=-1)
x: "f32[512, 512]" = helion_language__tracing_ops__host_tensor('x')
block_size_0: "Sym(u0)" = helion_language__tracing_ops__get_symnode('block_size_0')
load: "f32[u0, u1]" = helion_language_memory_ops_load(x, [block_size_0, slice(None, None, None)], None, None); x = None
Expand All @@ -578,15 +578,15 @@ def root_graph_0():
return None

def reduction_loop_1():
# File: .../test_reductions.py:58 in reduce_kernel, code: out[tile_n] = fn(x[tile_n, :], dim=-1)
# File: .../test_reductions.py:N in reduce_kernel, code: out[tile_n] = fn(x[tile_n, :], dim=-1)
x: "f32[512, 512]" = helion_language__tracing_ops__host_tensor('x')
block_size_0: "Sym(u0)" = helion_language__tracing_ops__get_symnode('block_size_0')
load: "f32[u0, u1]" = helion_language_memory_ops_load(x, [block_size_0, slice(None, None, None)], None, None); x = block_size_0 = None
mean_extra: "f32[u0]" = helion_language__tracing_ops__inductor_lowering_extra([load]); load = None
return [mean_extra]

def root_graph_2():
# File: .../test_reductions.py:58 in reduce_kernel, code: out[tile_n] = fn(x[tile_n, :], dim=-1)
# File: .../test_reductions.py:N in reduce_kernel, code: out[tile_n] = fn(x[tile_n, :], dim=-1)
block_size_0: "Sym(u0)" = helion_language__tracing_ops__get_symnode('block_size_0')
_get_symnode = helion_language__tracing_ops__get_symnode('rdim1')
_for_loop = helion_language__tracing_ops__for_loop(1, [0], [_get_symnode], []); _get_symnode = None
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