Squashed commit of the following:

commit d6e136246b0dfc824c8111827cc6a0f166d3e2ea
Author: Mason Remy <masonr@microsoft.com>
Date:   Sat Mar 25 02:32:07 2023 +0000

    Merged PR 3181: Fix bug with reinterpret_cast of partially-dynamic array

    Fix bug with reinterpret_cast of partially-dynamic array

commit a4e8fa8874191c5c58475727937cfab4951427fc
Author: Mason Remy <masonr@microsoft.com>
Date:   Fri Mar 24 22:19:01 2023 +0000

    Merged PR 3180: Enable getting a memref shape from a memref_cast result

    Enable getting a memref shape from a memref_cast result

commit f3df546c84fd1c86d93c357811d43e34e35ae215
Author: Lisa Ong <onglisa@microsoft.com>
Date:   Fri Mar 24 17:28:30 2023 +0000

    Merged PR 3179: Fix vulkan-specific smoke test break

    Missing an import for test_vulkan_gpu_matmul(). This test code path is only exercised when vulkan is installed.

    ```
            format = self.PACKAGE_FORMAT if "VULKAN_SDK" in os.environ else Package.Format.HAT_STATIC
            with verifiers.VerifyPackage(self, "test_vulkan_gpu_matmul", TEST_PACKAGE_DIR):
                package.build(
                    name="test_vulkan_gpu_matmul", format=format, mode=self.PACKAGE_MODE, output_dir=TEST_PACKAGE_DIR
                )
    ```

accera/ir/src/IRUtil.cpp

@@ -1304,6 +1304,13 @@ namespace util
             return shape;
         }
 
+        // TODO : more generalized dynamic shape detection
+        // If the memref is a memref_cast, then check the memref_cast source operand for a shape because the memref_cast does not change the shape
+        if (auto memrefCastOp = memref.getDefiningOp<ir::value::MemRefCastOp>())
+        {
+            memref = memrefCastOp.source();
+        }
+
         // Currently this utility only supports dynamic memrefs that are alloc ops with shape args or
         // function arguments with dimension size handles which are also function arguments
         if (auto allocOp = memref.getDefiningOp<ir::value::AllocOp>())

accera/python/accera/test/dsl_tests.py

@@ -753,6 +753,35 @@ def _():
                 _quiet=False
             )
 
+    def test_reinterpret_cast_partially_dynamic_shape(self) -> None:
+        from accera import create_dimensions
+        test_name = "test_reinterpret_cast_partially_dynamic_shape"
+        package = Package()
+
+        M, N = create_dimensions()
+        A = Array(role=Role.INPUT, element_type=ScalarType.int32, shape=(5, M, N))
+        B = Array(role=Role.INPUT_OUTPUT, element_type=ScalarType.float32, shape=(5, M, N))
+
+        nest = Nest((5, M, N))
+        indices = nest.get_indices()
+
+        @nest.iteration_logic
+        def _():
+            float_A = A._reinterpret_cast(ScalarType.float32)
+            B[indices] = float_A[indices]
+
+        package.add(nest, args=(M, N, A, B), base_name=test_name)
+
+        output_dir = pathlib.Path(TEST_PACKAGE_DIR) / test_name
+        with verifiers.VerifyPackage(self, test_name, output_dir):
+            package.build(
+                test_name,
+                format=TEST_FORMAT,
+                mode=Package.Mode.RELEASE,
+                output_dir=output_dir,
+                _quiet=False
+            )
+
     def test_subarray(self) -> None:
         package = Package()
 

accera/python/accera/test/smoke_tests.py

@@ -51,7 +51,7 @@
 
 from accera._lang_python import _MemoryLayout
 from accera._lang_python._lang import Array as NativeArray
-from accera._lang_python._lang import Dimension, _MemorySpace, _MMAShape, _If
+from accera._lang_python._lang import Dimension, _MemorySpace, _MMAShape, _If, as_index
 from accera._lang_python._lang._gpu import Barrier
 from accera.samples import MatrixMultiplication
 from accera.Targets import KNOWN_DEVICES

accera/transforms/src/value/ValueToLLVMLoweringPass.cpp

@@ -647,7 +647,10 @@ struct ValueMemRefCastOpLowering : public ValueLLVMOpConversionPattern<MemRefCas
                 }
                 else
                 {
-                    size = rewriter.create<LLVM::MulOp>(loc, size, rewriter.create<memref::DimOp>(loc, op.getViewSource(), s.index()));
+                    mlir::Value dimSize = rewriter.create<memref::DimOp>(loc, op.getViewSource(), s.index());
+                    auto castOp = rewriter.create<mlir::UnrealizedConversionCastOp>(loc, llvmIndexType, dimSize);
+                    mlir::Value intDimSize = castOp.getResult(0);
+                    size = rewriter.create<LLVM::MulOp>(loc, size, intDimSize);
                 }
             }
             targetMemrefDesc.setSize(rewriter, loc, 0, size);