aleozlx commented on code in PR #87:
URL: https://github.com/apache/tvm-ffi/pull/87#discussion_r2403507578
##########
tests/python/test_load_inline.py:
##########
@@ -318,3 +318,34 @@ def test_load_inline_both() -> None:
y_cuda = torch.empty_like(x_cuda)
mod.add_one_cuda(x_cuda, y_cuda)
torch.testing.assert_close(x_cuda + 1, y_cuda)
+
+
[email protected](
+ torch is None or not torch.cuda.is_available(), reason="Requires torch and
CUDA"
+)
+def test_cuda_memory_alloc_noleak() -> None:
+ assert torch is not None
+ mod = tvm_ffi.cpp.load_inline(
+ name="hello",
+ cuda_sources=r"""
+ #include <tvm/ffi/function.h>
+ #include <tvm/ffi/container/tensor.h>
+
+ namespace ffi = tvm::ffi;
+
+ ffi::Tensor return_tensor(tvm::ffi::TensorView x) {
+ ffi::Tensor y = ffi::Tensor::FromDLPackAlloc(
+ TVMFFIEnvGetTensorAllocator(), x.shape(), x.dtype(),
x->device);
+ return y;
+ }
+ """,
+ functions=["return_tensor"],
+ )
+ x = torch.arange(1024 * 1024, dtype=torch.float32, device="cuda")
+ current_allocated = torch.cuda.memory_allocated()
+ repeat = 8
+ for i in range(repeat):
+ mod.return_tensor(x)
+ diff = torch.cuda.memory_allocated() - current_allocated
+ # memory should not grow as we loop over
+ assert diff <= 1024**2 * 8
Review Comment:
hi i have a naive question: why is the threshold 8MiB?
if return_tensor(x) is created and triggers decref immediately as no var is
holding the reference to the returned obj, my naive understanding is that diff
= 0, then if so i interpret 8MiB here as some safety margin, but not related to
the x size nor loop iteration count, correct?
thanks
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