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[Fix][Relax] Lower bool prod as logical all #19557

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[Fix][Relax] Lower bool prod as logical all #19557

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18 changes: 13 additions & 5 deletions src/tirx/op/op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -994,11 +994,19 @@ PrimExpr min(PrimExpr source, ffi::Array<IterVar> rdom, ffi::Array<PrimExpr> ini
}

PrimExpr prod(PrimExpr source, ffi::Array<IterVar> rdom, ffi::Array<PrimExpr> init, Span span) {
Var x("x", source.dtype(), span), y("y", source.dtype(), span);
PrimExpr result = tirx::Mul(x, y, span);
PrimExpr identity_element = make_const(source.dtype(), 1, span);
tirx::CommReducer combiner = tirx::CommReducer({x}, {y}, {result}, {identity_element}, span);
return tirx::Reduce(combiner, {source}, rdom, make_const(DataType::Bool(), true), 0, init, span);
if (source.dtype().is_bool()) {
// Bool product (prod) has the same truth table as logical AND. Reuse all() to
// avoid lowering bool prod through Mul, which LLVM codegen does not support.
return all(source, rdom, init, span);
} else {
// For non-bool types, we lower prod through Mul.
Var x("x", source.dtype(), span), y("y", source.dtype(), span);
PrimExpr result = tirx::Mul(x, y, span);
PrimExpr identity_element = make_const(source.dtype(), 1, span);
tirx::CommReducer combiner = tirx::CommReducer({x}, {y}, {result}, {identity_element}, span);
return tirx::Reduce(combiner, {source}, rdom, make_const(DataType::Bool(), true), 0, init,
span);
}
}

// fmod
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18 changes: 11 additions & 7 deletions tests/python/relax/test_frontend_from_exported_program.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7724,24 +7724,28 @@ def main(
verify_model(VarCorrection0(), example_args, {}, Expected0)


def test_prod():
@pytest.mark.parametrize(
"torch_dtype,relax_dtype",
[(torch.float32, "float32"), (torch.bool, "bool")],
)
def test_prod(torch_dtype, relax_dtype):
class Prod(Module):
def forward(self, x):
return torch.prod(x)
return torch.prod(x, dtype=torch_dtype)

@tvm.script.ir_module
class Expected:
@R.function
def main(
x: R.Tensor((5, 3), dtype="float32"),
) -> R.Tuple(R.Tensor((), dtype="float32")):
x: R.Tensor((5, 3), dtype=relax_dtype),
) -> R.Tuple(R.Tensor((), dtype=relax_dtype)):
with R.dataflow():
lv: R.Tensor((), dtype="float32") = R.prod(x, axis=None, keepdims=False)
gv: R.Tuple(R.Tensor((), dtype="float32")) = (lv,)
lv: R.Tensor((), dtype=relax_dtype) = R.prod(x, axis=None, keepdims=False)
gv: R.Tuple(R.Tensor((), dtype=relax_dtype)) = (lv,)
R.output(gv)
return gv

example_args = (torch.randn(5, 3, dtype=torch.float32),)
example_args = (torch.ones(5, 3, dtype=torch_dtype),)
verify_model(Prod(), example_args, {}, Expected)


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18 changes: 11 additions & 7 deletions tests/python/relax/test_frontend_from_fx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6231,24 +6231,28 @@ def main(
verify_model(Var(), [([5, 3], "float32")], {}, Expected)


def test_prod():
@pytest.mark.parametrize(
"torch_dtype,relax_dtype",
[(torch.float32, "float32"), (torch.bool, "bool")],
)
def test_prod(torch_dtype, relax_dtype):
class Prod(Module):
def forward(self, x):
return torch.prod(x)
return torch.prod(x, dtype=torch_dtype)

@tvm.script.ir_module
class Expected:
@R.function
def main(
inp_0: R.Tensor((5, 3), dtype="float32"),
) -> R.Tensor((), dtype="float32"):
inp_0: R.Tensor((5, 3), dtype=relax_dtype),
) -> R.Tensor((), dtype=relax_dtype):
with R.dataflow():
lv: R.Tensor((), dtype="float32") = R.prod(inp_0, axis=None, keepdims=False)
gv: R.Tensor((), dtype="float32") = lv
lv: R.Tensor((), dtype=relax_dtype) = R.prod(inp_0, axis=None, keepdims=False)
gv: R.Tensor((), dtype=relax_dtype) = lv
R.output(gv)
return gv

verify_model(Prod(), [([5, 3], "float32")], {}, Expected)
verify_model(Prod(), [([5, 3], relax_dtype)], {}, Expected)


def test_cumprod():
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2 changes: 1 addition & 1 deletion tests/python/relax/test_frontend_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4924,7 +4924,7 @@ def test_nms_max_output_boxes_per_class_zero(with_explicit_max: bool):
check_correctness(model, inputs=inputs, opset=11)

tvm_out = run_in_tvm(model, inputs=inputs, opset=11)
tvm_selected = tvm_out[0].numpy() if isinstance(tvm_out, (list, tuple)) else tvm_out.numpy()
tvm_selected = tvm_out[0].numpy() if isinstance(tvm_out, list | tuple) else tvm_out.numpy()
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@gemini-code-assist gemini-code-assist Bot May 13, 2026

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high

The use of the pipe operator (|) for type unions in isinstance is a Python 3.10+ feature (PEP 604). TVM currently maintains compatibility with older Python versions (e.g., 3.8 and 3.9), and this change will cause a TypeError in those environments. Please revert to the compatible tuple syntax: isinstance(tvm_out, (list, tuple)). Additionally, this change appears to be an unrelated drive-by modification.

Suggested change
tvm_selected = tvm_out[0].numpy() if isinstance(tvm_out, list | tuple) else tvm_out.numpy()
tvm_selected = tvm_out[0].numpy() if isinstance(tvm_out, (list, tuple)) else tvm_out.numpy()

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@ConvolutedDog ConvolutedDog May 13, 2026

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Since TVM now requires Python 3.10+, this compatibility issue is no longer relevan.

assert tvm_selected.shape == (0, 3)


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33 changes: 33 additions & 0 deletions tests/python/relax/test_transform_legalize_ops_search_statistical.py
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Expand Up @@ -557,6 +557,39 @@ def prod(rxplaceholder: T.Buffer((T.int64(2), T.int64(3), T.int64(4), T.int64(5)
tvm.ir.assert_structural_equal(mod, Expected)


def test_prod_bool():
# fmt: off
@tvm.script.ir_module
class Prod:
@R.function
def main(x: R.Tensor((2, 3, 4, 5), "bool")) -> R.Tensor((1, 1, 1, 1), "bool"):
gv: R.Tensor((1, 1, 1, 1), "bool") = R.prod(x, keepdims=True)
return gv

@tvm.script.ir_module
class Expected:
@R.function
def main(x: R.Tensor((2, 3, 4, 5), "bool")) -> R.Tensor((1, 1, 1, 1), "bool"):
gv = R.call_tir(Expected.prod, (x,), R.Tensor((1, 1, 1, 1), dtype="bool"))
return gv

@T.prim_func(private=True)
def prod(rxplaceholder: T.Buffer((T.int64(2), T.int64(3), T.int64(4), T.int64(5)), "bool"), rxplaceholder_red: T.Buffer((T.int64(1), T.int64(1), T.int64(1), T.int64(1)), "bool")):
T.func_attr({"tirx.noalias": True})
for i0, i1, i2, i3, i4, i5, i6, i7 in T.grid(T.int64(1), T.int64(1), T.int64(1), T.int64(1), T.int64(2), T.int64(3), T.int64(4), T.int64(5)):
with T.sblock("rxplaceholder_red"):
ax0, ax1, ax2, ax3, k0, k1, k2, k3 = T.axis.remap("SSSSRRRR", [i0, i1, i2, i3, i4, i5, i6, i7])
T.reads(rxplaceholder[k0, k1, k2, k3])
T.writes(rxplaceholder_red[ax0, ax1, ax2, ax3])
with T.init():
rxplaceholder_red[ax0, ax1, ax2, ax3] = T.bool(1)
rxplaceholder_red[ax0, ax1, ax2, ax3] = rxplaceholder_red[ax0, ax1, ax2, ax3] and rxplaceholder[k0, k1, k2, k3]
# fmt: on

mod = LegalizeOps()(Prod)
tvm.ir.assert_structural_equal(mod, Expected)


def test_prod_symbolic():
# fmt: off
@tvm.script.ir_module
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