Fix temp memory allocation issue in torch.topk operations (#12810)

Summary: Fixes issue #8700 

changed the temp_allocator_ from a
MemoryAllocator with null buffer to a MallocMemoryAllocator that can
 dynamically allocate memory as needed.

Test plan: To verify the fix, a new end-to-end test suite has been added
(`test/end2end/test_temp_allocator_fix.py`). This suite includes tests
for `torch.topk` both with and without the `out` parameter, as well as a
test with a larger input to ensure the allocator can handle more
significant memory requirements. These tests now pass with the
implemented fix. I will now write the PR description.

Author: ZihangFang

extension/pybindings/pybindings.cpp

@@ -358,7 +358,7 @@ class Module final {
 
     MallocMemoryAllocator runtime_allocator_;
 
-    MemoryAllocator temp_allocator_{MemoryAllocator(0, nullptr)};
+    MallocMemoryAllocator temp_allocator_{};
 
     std::vector<std::vector<uint8_t>> non_const_buffers_;
 
@@ -1061,7 +1061,7 @@ class ProgramMemory {
 
   MallocMemoryAllocator runtime_allocator_;
 
-  MemoryAllocator temp_allocator_{MemoryAllocator(0, nullptr)};
+  MallocMemoryAllocator temp_allocator_{};
 
   std::vector<std::vector<uint8_t>> non_const_buffers_;
 

test/end2end/test_temp_allocator_fix.py

@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+"""
+Test to verify the fix for temp memory allocation issue in torch.topk operations.
+
+This test specifically checks that the MallocMemoryAllocator fix in pybindings.cpp
+resolves the "Memory allocation failed" error when executing operations that
+require temporary memory allocation.
+"""
+
+import os
+import tempfile
+from pathlib import Path
+
+import torch
+from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
+from executorch.exir import EdgeCompileConfig, to_edge_transform_and_lower
+from executorch.runtime import Runtime, Verification
+from torch.export import export
+
+
+class TopKModel(torch.nn.Module):
+    """Model that uses torch.topk operation which requires temp memory allocation."""
+
+    def __init__(self, k=3) -> None:
+        super().__init__()
+        self.k = k
+
+    def forward(self, x) -> "tuple[torch.Tensor, torch.Tensor]":
+        # This operation requires temporary memory allocation
+        top_values, top_indices = torch.topk(x, self.k)
+        return top_values, top_indices
+
+
+class TopKModelWithOut(torch.nn.Module):
+    """Model that uses torch.topk with out parameter which also requires temp memory."""
+
+    def __init__(self, k=3) -> None:
+        super().__init__()
+        self.k = k
+
+    def forward(self, x) -> "tuple[torch.Tensor, torch.Tensor]":
+        top_values = torch.ones(x.shape[0], self.k, dtype=torch.float32)
+        top_indices = torch.ones(x.shape[0], self.k, dtype=torch.long)
+        torch.topk(x.contiguous(), self.k, out=(top_values, top_indices))
+        return top_values, top_indices
+
+
+def test_topk_without_out_parameter():
+    """Test torch.topk without out parameter."""
+    print("Testing torch.topk without out parameter...")
+
+    model = TopKModel(k=5)
+    example_input = (torch.randn(3, 100),)
+
+    # Export and compile the model
+    with torch.no_grad():
+        aten_dialect = export(model, example_input)
+
+        backend_dialect = to_edge_transform_and_lower(
+            aten_dialect,
+            compile_config=EdgeCompileConfig(_check_ir_validity=False),
+            partitioner=[XnnpackPartitioner()],
+        )
+
+        executorch_dialect = backend_dialect.to_executorch()
+
+        # Save to temporary file
+        with tempfile.NamedTemporaryFile(suffix=".pte", delete=False) as f:
+            temp_path = f.name
+
+        try:
+            executorch_dialect.save(temp_path)
+
+            # Load and execute with ExecuTorch runtime
+            et_runtime = Runtime.get()
+            program = et_runtime.load_program(
+                Path(temp_path),
+                verification=Verification.Minimal,
+            )
+
+            forward = program.load_method("forward")
+            outputs = forward.execute(example_input)
+
+            print(
+                f"✓ Successfully executed topk model: {example_input[0].shape} -> {outputs[0].shape}"
+            )
+            return True
+
+        finally:
+            # Clean up temporary file
+            if os.path.exists(temp_path):
+                os.unlink(temp_path)
+
+
+def test_topk_with_out_parameter():
+    """Test torch.topk with out parameter (original failing case)."""
+    print("Testing torch.topk with out parameter...")
+
+    model = TopKModelWithOut(k=3)
+    example_input = (torch.randn(2, 256),)
+
+    # Export and compile the model
+    with torch.no_grad():
+        aten_dialect = export(model, example_input)
+
+        backend_dialect = to_edge_transform_and_lower(
+            aten_dialect,
+            compile_config=EdgeCompileConfig(_check_ir_validity=False),
+            partitioner=[XnnpackPartitioner()],
+        )
+
+        executorch_dialect = backend_dialect.to_executorch()
+
+        # Save to temporary file
+        with tempfile.NamedTemporaryFile(suffix=".pte", delete=False) as f:
+            temp_path = f.name
+
+        try:
+            executorch_dialect.save(temp_path)
+
+            # Load and execute with ExecuTorch runtime
+            et_runtime = Runtime.get()
+            program = et_runtime.load_program(
+                Path(temp_path),
+                verification=Verification.Minimal,
+            )
+
+            forward = program.load_method("forward")
+            outputs = forward.execute(example_input)
+
+            print(
+                f"✓ Successfully executed topk model with out parameter: {example_input[0].shape} -> {outputs[0].shape}"
+            )
+            return True
+
+        finally:
+            # Clean up temporary file
+            if os.path.exists(temp_path):
+                os.unlink(temp_path)
+
+
+def test_larger_topk_operation():
+    """Test larger topk operation that would require more temporary memory."""
+    print("Testing larger topk operation...")
+
+    model = TopKModel(k=50)
+    example_input = (torch.randn(5, 1000),)
+
+    # Export and compile the model
+    with torch.no_grad():
+        aten_dialect = export(model, example_input)
+
+        backend_dialect = to_edge_transform_and_lower(
+            aten_dialect,
+            compile_config=EdgeCompileConfig(_check_ir_validity=False),
+            partitioner=[XnnpackPartitioner()],
+        )
+
+        executorch_dialect = backend_dialect.to_executorch()
+
+        # Save to temporary file
+        with tempfile.NamedTemporaryFile(suffix=".pte", delete=False) as f:
+            temp_path = f.name
+
+        try:
+            executorch_dialect.save(temp_path)
+
+            # Load and execute with ExecuTorch runtime
+            et_runtime = Runtime.get()
+            program = et_runtime.load_program(
+                Path(temp_path),
+                verification=Verification.Minimal,
+            )
+
+            forward = program.load_method("forward")
+            outputs = forward.execute(example_input)
+
+            print(
+                f"✓ Successfully executed large topk model: {example_input[0].shape} -> {outputs[0].shape}"
+            )
+            return True
+
+        finally:
+            # Clean up temporary file
+            if os.path.exists(temp_path):
+                os.unlink(temp_path)
+
+
+def main():
+    """Run all tests to verify the temp memory allocation fix."""
+    print("Testing temp memory allocation fix for torch.topk operations")
+    print("=" * 60)
+
+    tests = [
+        test_topk_without_out_parameter,
+        test_topk_with_out_parameter,
+        test_larger_topk_operation,
+    ]
+
+    passed = 0
+    failed = 0
+
+    for test in tests:
+        try:
+            if test():
+                passed += 1
+            else:
+                failed += 1
+        except Exception as e:
+            print(f"✗ Test {test.__name__} failed with exception: {e}")
+            failed += 1
+
+    print("\n" + "=" * 60)
+    print(f"Test Results: {passed} passed, {failed} failed")
+
+    if failed == 0:
+        print(
+            "✓ All tests passed! The temp memory allocation fix is working correctly."
+        )
+        return True
+    else:
+        print("✗ Some tests failed. The fix may not be working correctly.")
+        return False
+
+
+if __name__ == "__main__":
+    success = main()
+    exit(0 if success else 1)