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[Relax] Memory planning for "partially dynamic" shapes #16466

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Merged
merged 1 commit into from
Jan 25, 2024
Merged

[Relax] Memory planning for "partially dynamic" shapes #16466

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12 changes: 8 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 @@ -828,15 +828,19 @@ class StorageAllocationRewriter : public ExprMutator {
const auto* shape = sinfo->shape.as<ShapeExprNode>();
ICHECK_NOTNULL(shape);
Array<PrimExpr> upper_bounded_shape = GetUpperBoundShape(shape->values, &ana_);
if (!IsStaticShape(shape->values) && IsStaticShape(upper_bounded_shape)) {
if (!IsStaticShape(shape->values)) {
ICHECK(!sinfo->IsUnknownDtype());
ICHECK_EQ(sinfo->dtype, Downcast<DataTypeImm>(call->args[1])->value);
StorageToken token(upper_bounded_shape, sinfo->dtype);
PrimExpr bytes = upper_bounded_shape[0];
for (int i = 1; i < static_cast<int>(upper_bounded_shape.size()); ++i) {
bytes *= upper_bounded_shape[i];
}
bytes *= sinfo->dtype.bytes() * sinfo->dtype.lanes();
Call alloc_storage(mem_alloc_storage,
{/*size=*/ShapeExpr({tvm::IntImm(DataType::Int(64), token->bytes)}),
{/*size=*/ShapeExpr({bytes}),
/*virtual_device_index=*/Downcast<PrimValue>(call->args[2]),
/*storage_scope=*/StringImm("global"), //
/*dtype=*/DataTypeImm(token->dtype)});
/*dtype=*/DataTypeImm(sinfo->dtype)});
Var storage = builder_->Emit(alloc_storage, "storage");
return Call(mem_alloc_tensor, {storage, //
/*offset=*/PrimValue::Int64(0),
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64 changes: 64 additions & 0 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 @@ -1171,6 +1171,70 @@ def main(s: R.Shape(["n"])) -> R.Tensor(("n",), dtype="float32"):
tvm.ir.assert_structural_equal(mod, Expected)


def test_call_tir_dyn_plan_partially_dynamic():
# fmt: off
@I.ir_module
class Module:
@T.prim_func
def tir_full(var_full: T.handle, n: T.int64, m: T.int64):
T.evaluate(0)

@T.prim_func
def tir_exp(var_rxplaceholder: T.handle, var_compute: T.handle):
T.evaluate(0)

@R.function
def main(s: R.Shape(["n", "m"])) -> R.Tensor(("n", "m"), dtype="float32"):
n = T.int64()
m = T.int64()
R.func_attr({"tir_var_upper_bound": {"n": 20}, "relax.force_pure": True, "relax.memory_plan_dynamic_func_output": True})
cls = Module
alloc: R.Tensor((n, m), dtype="float32") = R.builtin.alloc_tensor(R.shape([n, m]), R.dtype("float32"), R.prim_value(0))
_: R.Tuple = R.vm.call_tir_dyn(cls.tir_full, (alloc, R.shape([n, m])))
full: R.Tensor((n, m), dtype="float32") = alloc
alloc1: R.Tensor((n, m), dtype="float32") = R.builtin.alloc_tensor(R.shape([n, m]), R.dtype("float32"), R.prim_value(0))
_1: R.Tuple = cls.tir_exp(full, alloc1)
lv2: R.Tensor((n, m), dtype="float32") = alloc1
alloc2: R.Tensor((n, m), dtype="float32") = R.builtin.alloc_tensor(R.shape([n, m]), R.dtype("float32"), R.prim_value(0))
_2: R.Tuple = cls.tir_exp(lv2, alloc2)
lv3: R.Tensor((n, m), dtype="float32") = alloc2
return lv3

@I.ir_module
class Expected:
@T.prim_func
def tir_full(var_full: T.handle, n: T.int64, m: T.int64):
T.evaluate(0)

@T.prim_func
def tir_exp(var_rxplaceholder: T.handle, var_compute: T.handle):
T.evaluate(0)

@R.function
def main(s: R.Shape(["n", "m"])) -> R.Tensor(("n", "m"), dtype="float32"):
n = T.int64()
m = T.int64()
R.func_attr({"relax.force_pure": True, "tir_var_upper_bound": {"n": 20}})
cls = Expected
storage: R.Object = R.memory.alloc_storage(R.shape([20 * m * 4]), R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc: R.Tensor((n, m), dtype="float32") = R.memory.alloc_tensor(storage, R.prim_value(0), R.shape([n, m]), R.dtype("float32"))
_: R.Tuple = R.vm.call_tir_dyn(cls.tir_full, (alloc, R.shape([n, m])))
full: R.Tensor((n, m), dtype="float32") = alloc
storage1: R.Object = R.memory.alloc_storage(R.shape([20 * m * 4]), R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc1: R.Tensor((n, m), dtype="float32") = R.memory.alloc_tensor(storage1, R.prim_value(0), R.shape([n, m]), R.dtype("float32"))
_1: R.Tuple = cls.tir_exp(full, alloc1)
lv2: R.Tensor((n, m), dtype="float32") = alloc1
storage2: R.Object = R.memory.alloc_storage(R.shape([20 * m * 4]), R.prim_value(0), R.str("global"), R.dtype("float32"))
alloc2: R.Tensor((n, m), dtype="float32") = R.memory.alloc_tensor(storage2, R.prim_value(0), R.shape([n, m]), R.dtype("float32"))
_2: R.Tuple = cls.tir_exp(lv2, alloc2)
lv3: R.Tensor((n, m), dtype="float32") = alloc2
return lv3
# fmt: on

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


def test_function_independence():
# fmt: off
@tvm.script.ir_module
Expand Down