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[Relax] Fix wrong memory planning when only lower bound was provided #18663

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Merged
tlopex merged 1 commit into from
Jan 16, 2026
Merged

[Relax] Fix wrong memory planning when only lower bound was provided #18663

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11 changes: 7 additions & 4 deletions src/relax/transform/static_plan_block_memory.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -437,15 +437,18 @@ void SetTIRVarRangeConstraints(Function func, arith::Analyzer* ana,
auto it_upper = var_upper_bound_attr.find(tir_var->name_hint);
auto it_lower = var_lower_bound_attr.find(tir_var->name_hint);

if (it_upper != var_upper_bound_attr.end() || it_lower != var_lower_bound_attr.end()) {
// Only bind the variable to a range if an upper bound is explicitly provided.
// Without an upper bound, memory planning cannot determine the required storage size,
// so we skip binding and let the variable remain unbounded.
if (it_upper != var_upper_bound_attr.end()) {
int64_t lower = (it_lower != var_lower_bound_attr.end()) ? it_lower->second->value : 0;
int64_t upper = (it_upper != var_upper_bound_attr.end())
? it_upper->second->value
: std::numeric_limits<int64_t>::max();
int64_t upper = it_upper->second->value;
tvm::Range range = tvm::Range::FromMinExtent(
tvm::IntImm(DataType::Int(64), lower), tvm::IntImm(DataType::Int(64), upper - lower + 1));
ana->Bind(tir_var, range);
dom_map->Set(tir_var, arith::IntSet::FromRange(range));
} else if (it_lower != var_lower_bound_attr.end() && it_lower->second->value >= 0) {
ana->MarkGlobalNonNegValue(tir_var);
} else if (non_negative_var_attr.count(tir_var->name_hint)) {
ana->MarkGlobalNonNegValue(tir_var);
}
Expand Down
263 changes: 239 additions & 24 deletions tests/python/relax/test_transform_static_plan_block_memory.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1018,6 +1018,245 @@ def main(x: R.Tensor((2, "n"), dtype="float32")) -> R.Tensor(("2 * n + 2",), dty
tvm.ir.assert_structural_equal(mod, Expected)


def test_lower_bound_only():
# fmt: off
@tvm.script.ir_module
class Module:
@T.prim_func
def add(rxplaceholder: T.handle, rxplaceholder_1: T.handle, T_add: T.handle):
T.evaluate(0)

@T.prim_func
def reshape(rxplaceholder: T.handle, T_reshape: T.handle):
T.evaluate(0)

@T.prim_func
def relu(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def log(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def exp(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def pad(rxplaceholder: T.handle, PadInput: T.handle):
T.evaluate(0)

@R.function
def main(x: R.Tensor((2, "n"), dtype="float32")) -> R.Tensor(("2 * n + 2",), dtype="float32"):
R.func_attr({"tir_var_lower_bound": {"n": 2}, "relax.force_pure": True})
n = T.int64()
cls = Module
alloc: R.Tensor((2, n), dtype="float32") = R.builtin.alloc_tensor(R.shape([2, n]), dtype="float32", runtime_device_index=0)
_: R.Tuple() = cls.exp(x, alloc)
lv: R.Tensor((2, n), dtype="float32") = alloc
lv1: R.Tensor((2 * n,), dtype="float32") = R.reshape(lv, (2 * n,))
alloc1: R.Tensor((2 * n,), dtype="float32") = R.builtin.alloc_tensor(R.shape([2 * n]), dtype="float32", runtime_device_index=0)
_1: R.Tuple() = cls.relu(lv1, alloc1)
lv2: R.Tensor((2 * n,), dtype="float32") = alloc1
alloc2: R.Tensor((2 * n,), dtype="float32") = R.builtin.alloc_tensor(R.shape([2 * n]), dtype="float32", runtime_device_index=0)
_2: R.Tuple() = cls.add(lv2, R.const(1, "float32"), alloc2)
lv3: R.Tensor((2 * n,), dtype="float32") = alloc2
alloc3: R.Tensor((2 * n + 2,), dtype="float32") = R.builtin.alloc_tensor(R.shape([2 * n + 2]), dtype="float32", runtime_device_index=0)
_3: R.Tuple() = cls.pad(lv3, alloc3)
lv4: R.Tensor((2 * n + 2,), dtype="float32") = alloc3
alloc4: R.Tensor((2 * n + 2,), dtype="float32") = R.builtin.alloc_tensor(R.shape([10]), dtype="float32", runtime_device_index=0)
_4: R.Tuple() = cls.log(lv4, alloc4)
gv: R.Tensor((2 * n + 2,), dtype="float32") = alloc4
return gv

@I.ir_module
class Expected:
@T.prim_func
def add(rxplaceholder: T.handle, rxplaceholder_1: T.handle, T_add: T.handle):
T.evaluate(0)

@T.prim_func
def exp(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def log(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def pad(rxplaceholder: T.handle, PadInput: T.handle):
T.evaluate(0)

@T.prim_func
def relu(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def reshape(rxplaceholder: T.handle, T_reshape: T.handle):
T.evaluate(0)

@R.function
def main(x: R.Tensor((2, "n"), dtype="float32")) -> R.Tensor(("2 * n + 2",), dtype="float32"):
n = T.int64()
R.func_attr({"tir_var_lower_bound": {"n": 2}, "relax.force_pure": True})
cls = Expected
storage: R.Object = R.memory.alloc_storage(R.shape([8 * n]), R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc: R.Tensor((2, n), dtype="float32") = R.memory.alloc_tensor(storage, R.prim_value(0), R.shape([2, n]), R.dtype("float32"), R.prim_value(0))
_: R.Tuple = cls.exp(x, alloc)
lv: R.Tensor((2, n), dtype="float32") = alloc
lv1: R.Tensor((2 * n,), dtype="float32") = R.reshape(lv, R.shape([2 * n]))
storage1: R.Object = R.memory.alloc_storage(R.shape([4 * (2 * n)]), R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc1: R.Tensor((2 * n,), dtype="float32") = R.memory.alloc_tensor(storage1, R.prim_value(0), R.shape([2 * n]), R.dtype("float32"))
_1: R.Tuple = cls.relu(lv1, alloc1)
lv2: R.Tensor((2 * n,), dtype="float32") = alloc1
alloc2: R.Tensor((2 * n,), dtype="float32") = R.memory.alloc_tensor(storage, R.prim_value(0), R.shape([2 * n]), R.dtype("float32"))
_2: R.Tuple = cls.add(lv2, R.const(1, "float32"), alloc2)
lv3: R.Tensor((2 * n,), dtype="float32") = alloc2
storage2: R.Object = R.memory.alloc_storage(R.shape([4 * (2 * n + 2)]), R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc3: R.Tensor((2 * n + 2,), dtype="float32") = R.memory.alloc_tensor(storage2, R.prim_value(0), R.shape([2 * n + 2]), R.dtype("float32"), R.prim_value(0))
_3: R.Tuple = cls.pad(lv3, alloc3)
lv4: R.Tensor((2 * n + 2,), dtype="float32") = alloc3
alloc4: R.Tensor((2 * n + 2,), dtype="float32") = R.builtin.alloc_tensor(R.shape([10]), R.dtype("float32"), R.prim_value(0))
_4: R.Tuple = cls.log(lv4, alloc4)
gv: R.Tensor((2 * n + 2,), dtype="float32") = alloc4
return gv
# fmt: on

mod = relax.transform.StaticPlanBlockMemory()(Module)
tvm.ir.assert_structural_equal(mod, Expected)

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def test_upper_and_lower_bounds():
# fmt: off
@tvm.script.ir_module
class Module:
@T.prim_func
def add(rxplaceholder: T.handle, rxplaceholder_1: T.handle, T_add: T.handle):
T.evaluate(0)

@T.prim_func
def reshape(rxplaceholder: T.handle, T_reshape: T.handle):
T.evaluate(0)

@T.prim_func
def relu(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def log(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def exp(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def pad(rxplaceholder: T.handle, PadInput: T.handle):
T.evaluate(0)

@R.function
def main(x: R.Tensor((2, "n"), dtype="float32")) -> R.Tensor(("2 * n + 2",), dtype="float32"):
R.func_attr({"tir_var_upper_bound": {"n": 4}, "tir_var_lower_bound": {"n": 2}, "relax.force_pure": True})
n = T.int64()
cls = Module
alloc: R.Tensor((2, n), dtype="float32") = R.builtin.alloc_tensor(R.shape([2, n]), dtype="float32", runtime_device_index=0)
_: R.Tuple() = cls.exp(x, alloc)
lv: R.Tensor((2, n), dtype="float32") = alloc
lv1: R.Tensor((2 * n,), dtype="float32") = R.reshape(lv, (2 * n,))
alloc1: R.Tensor((2 * n,), dtype="float32") = R.builtin.alloc_tensor(R.shape([2 * n]), dtype="float32", runtime_device_index=0)
_1: R.Tuple() = cls.relu(lv1, alloc1)
lv2: R.Tensor((2 * n,), dtype="float32") = alloc1
alloc2: R.Tensor((2 * n,), dtype="float32") = R.builtin.alloc_tensor(R.shape([2 * n]), dtype="float32", runtime_device_index=0)
_2: R.Tuple() = cls.add(lv2, R.const(1, "float32"), alloc2)
lv3: R.Tensor((2 * n,), dtype="float32") = alloc2
alloc3: R.Tensor((2 * n + 2,), dtype="float32") = R.builtin.alloc_tensor(R.shape([2 * n + 2]), dtype="float32", runtime_device_index=0)
_3: R.Tuple() = cls.pad(lv3, alloc3)
lv4: R.Tensor((2 * n + 2,), dtype="float32") = alloc3
alloc4: R.Tensor((2 * n + 2,), dtype="float32") = R.builtin.alloc_tensor(R.shape([10]), dtype="float32", runtime_device_index=0)
_4: R.Tuple() = cls.log(lv4, alloc4)
gv: R.Tensor((2 * n + 2,), dtype="float32") = alloc4
return gv

@I.ir_module
class Expected:
@T.prim_func
def add(rxplaceholder: T.handle, rxplaceholder_1: T.handle, T_add: T.handle):
T.evaluate(0)

@T.prim_func
def exp(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def log(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def pad(rxplaceholder: T.handle, PadInput: T.handle):
T.evaluate(0)

@T.prim_func
def relu(rxplaceholder: T.handle, compute: T.handle):
T.evaluate(0)

@T.prim_func
def reshape(rxplaceholder: T.handle, T_reshape: T.handle):
T.evaluate(0)

@R.function
def main(x: R.Tensor((2, "n"), dtype="float32")) -> R.Tensor(("2 * n + 2",), dtype="float32"):
n = T.int64()
R.func_attr({"tir_var_upper_bound": {"n": 4}, "tir_var_lower_bound": {"n": 2}, "relax.force_pure": True})
cls = Expected
storage: R.Object = R.memory.alloc_storage(R.shape([32]), R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc: R.Tensor((2, n), dtype="float32") = R.memory.alloc_tensor(storage, R.prim_value(0), R.shape([2, n]), R.dtype("float32"))
_: R.Tuple = cls.exp(x, alloc)
lv: R.Tensor((2, n), dtype="float32") = alloc
lv1: R.Tensor((2 * n,), dtype="float32") = R.reshape(lv, R.shape([2 * n]))
storage1: R.Object = R.memory.alloc_storage(R.shape([40]), R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc1: R.Tensor((2 * n,), dtype="float32") = R.memory.alloc_tensor(storage1, R.prim_value(0), R.shape([2 * n]), R.dtype("float32"))
_1: R.Tuple = cls.relu(lv1, alloc1)
lv2: R.Tensor((2 * n,), dtype="float32") = alloc1
alloc2: R.Tensor((2 * n,), dtype="float32") = R.memory.alloc_tensor(storage, R.prim_value(0), R.shape([2 * n]), R.dtype("float32"))
_2: R.Tuple = cls.add(lv2, R.const(1, "float32"), alloc2)
lv3: R.Tensor((2 * n,), dtype="float32") = alloc2
alloc3: R.Tensor((2 * n + 2,), dtype="float32") = R.memory.alloc_tensor(storage1, R.prim_value(0), R.shape([2 * n + 2]), R.dtype("float32"))
_3: R.Tuple = cls.pad(lv3, alloc3)
lv4: R.Tensor((2 * n + 2,), dtype="float32") = alloc3
alloc4: R.Tensor((2 * n + 2,), dtype="float32") = R.builtin.alloc_tensor(R.shape([10]), R.dtype("float32"), R.prim_value(0))
_4: R.Tuple = cls.log(lv4, alloc4)
gv: R.Tensor((2 * n + 2,), dtype="float32") = alloc4
return gv
# fmt: on

mod = relax.transform.StaticPlanBlockMemory()(Module)
tvm.ir.assert_structural_equal(mod, Expected)


def test_invalid_tir_var_upper_bound():
@tvm.script.ir_module
class Module:
@R.function
def main(x: R.Tensor((2, "n"), dtype="float32")):
R.func_attr({"tir_var_upper_bound": {"n": [4]}, "relax.force_pure": True})
return x

with pytest.raises((TVMError, TypeError)):
relax.transform.StaticPlanBlockMemory()(Module)


def test_invalid_tir_var_lower_bound():
@tvm.script.ir_module
class Module:
@R.function
def main(x: R.Tensor((2, "n"), dtype="float32")):
R.func_attr({"tir_var_lower_bound": {"n": [4]}, "relax.force_pure": True})
return x

with pytest.raises((TVMError, TypeError)):
relax.transform.StaticPlanBlockMemory()(Module)


def test_tir_var_decreasing_monotone():
# fmt: off
@I.ir_module
Expand Down Expand Up @@ -1335,30 +1574,6 @@ def func2(x: R.Tensor((10,), dtype="float32")) -> R.Tensor((10,), dtype="float32
tvm.ir.assert_structural_equal(mod, Expected)


def test_invalid_tir_var_upper_bound():
@tvm.script.ir_module
class Module:
@R.function
def main(x: R.Tensor((2, "n"), dtype="float32")):
R.func_attr({"tir_var_upper_bound": {"n": [4]}, "relax.force_pure": True})
return x

with pytest.raises((TVMError, TypeError)):
relax.transform.StaticPlanBlockMemory()(Module)


def test_invalid_tir_var_lower_bound():
@tvm.script.ir_module
class Module:
@R.function
def main(x: R.Tensor((2, "n"), dtype="float32")):
R.func_attr({"tir_var_lower_bound": {"n": [4]}, "relax.force_pure": True})
return x

with pytest.raises((TVMError, TypeError)):
relax.transform.StaticPlanBlockMemory()(Module)


def test_add():
@I.ir_module
class Module:
Expand Down
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