diff --git a/test/TritonGPU/amd/optimize-lds-usage.mlir b/test/TritonGPU/amd/optimize-lds-usage.mlir
index 0ec2ad5382ec..5cd34aab27d2 100644
--- a/test/TritonGPU/amd/optimize-lds-usage.mlir
+++ b/test/TritonGPU/amd/optimize-lds-usage.mlir
@@ -118,3 +118,22 @@ module attributes {"triton_gpu.num-warps" = 8 : i32, "triton_gpu.threads-per-war
     tt.return
   }
 }
+
+// -----
+
+// Checks that optimization do not crash on 1d tensor
+// CHECK-LABEL: convert_1d
+// CHECK: triton_gpu.local_alloc
+// CHECK-NEXT: triton_gpu.convert_layout
+// CHECK-NEXT: triton_gpu.local_load
+#blocked = #triton_gpu.blocked<{sizePerThread = [1], threadsPerWarp = [64], warpsPerCTA = [4], order = [0]}>
+#mma = #triton_gpu.amd_mfma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [4, 1], instrShape = [32, 32], isTransposed = true}>
+#shared = #triton_gpu.shared<{vec = 4, perPhase = 1, maxPhase = 16, order = [0, 1], hasLeadingOffset = false}>
+module attributes {"triton_gpu.num-warps" = 4 : i32, "triton_gpu.threads-per-warp" = 64 : i32} {
+  tt.func public @convert_1d(%arg0: tensor<128xf32, #triton_gpu.slice<{dim = 0, parent = #mma}>>, %arg1: tensor<128x128xf32, #mma>) attributes {noinline = false} {
+    %alloc = triton_gpu.local_alloc %arg1 : (tensor<128x128xf32, #mma>) -> !tt.memdesc<128x128xf32, #shared, #triton_gpu.shared_memory>
+    %1 = triton_gpu.convert_layout %arg0 : tensor<128xf32, #triton_gpu.slice<{dim = 0, parent = #mma}>> -> tensor<128xf32, #blocked>
+    %load = triton_gpu.local_load %alloc : !tt.memdesc<128x128xf32, #shared, #triton_gpu.shared_memory> -> tensor<128x128xf32, #mma>
+    tt.return
+  }
+}
diff --git a/third_party/amd/lib/TritonAMDGPUToLLVM/OptimizeLDSUsage.cpp b/third_party/amd/lib/TritonAMDGPUToLLVM/OptimizeLDSUsage.cpp
index db3223f119da..4a0a7fed22b0 100644
--- a/third_party/amd/lib/TritonAMDGPUToLLVM/OptimizeLDSUsage.cpp
+++ b/third_party/amd/lib/TritonAMDGPUToLLVM/OptimizeLDSUsage.cpp
@@ -96,7 +96,8 @@ class OptimizeAMDLDSUsage
     auto dstEnc = dstType.getEncoding();
 
     auto ctx = srcEnc.getContext();
-    auto rank = srcType.getShape().size();
+    auto rank = srcType.getRank();
+
     unsigned numWarps = triton::gpu::getNumWarpsPerCTA(srcEnc);
     auto warpSize = triton::gpu::getWarpSize(srcEnc);
 
@@ -109,11 +110,20 @@ class OptimizeAMDLDSUsage
     // Create a list of temporary layouts
     SmallVector<unsigned> elemsPerThread(rank, 1);
     SmallVector<unsigned> threadsPerWarp(rank, 1);
-    threadsPerWarp[rank - 1] = warpSize / 8;
-    threadsPerWarp[rank - 2] = warpSize / threadsPerWarp[rank - 1];
+
+    // Special case for rank == 1
+    if (rank == 1) {
+      threadsPerWarp[0] = warpSize;
+    } else {
+      assert(rank > 1);
+      threadsPerWarp[rank - 1] = warpSize / 8;
+      threadsPerWarp[rank - 2] = warpSize / threadsPerWarp[rank - 1];
+    }
+
     auto layoutCTA = triton::gpu::getCTALayout(srcEnc);
     auto order = triton::gpu::getOrder(srcEnc);
     SmallVector<unsigned> dummyWarpsPerCTA(rank, 1);
+
     auto baseFallbackLayout = triton::gpu::BlockedEncodingAttr::get(
         ctx, elemsPerThread, threadsPerWarp, dummyWarpsPerCTA, order,
         layoutCTA);
diff --git a/third_party/amd/lib/TritonAMDGPUToLLVM/OptimizeLDSUtility.cpp b/third_party/amd/lib/TritonAMDGPUToLLVM/OptimizeLDSUtility.cpp
index dfa8e06e247c..fb0bfb656ef4 100644
--- a/third_party/amd/lib/TritonAMDGPUToLLVM/OptimizeLDSUtility.cpp
+++ b/third_party/amd/lib/TritonAMDGPUToLLVM/OptimizeLDSUtility.cpp
@@ -68,9 +68,13 @@ Attribute createTmpLayout(Attribute layout, ArrayRef<unsigned> warpsPerCTA) {
         ctx, src.getOpIdx(), createTmpLayout(src.getParent(), warpsPerCTA),
         src.getKWidth());
   }
-  if (auto src = dyn_cast<triton::gpu::SliceEncodingAttr>(layout))
+  if (auto src = dyn_cast<triton::gpu::SliceEncodingAttr>(layout)) {
+    // TODO: think of a way to construct slice layouts based on warpsPerCTA
+    // argument
+    auto parentWarpsPerCTA = triton::gpu::getWarpsPerCTA(src.getParent());
     return triton::gpu::SliceEncodingAttr::get(
-        ctx, src.getDim(), createTmpLayout(src.getParent(), warpsPerCTA));
+        ctx, src.getDim(), createTmpLayout(src.getParent(), parentWarpsPerCTA));
+  }
   assert("Encountered unsupported layout");
   return Attribute();
 }
diff --git a/third_party/nvidia/backend/driver.py b/third_party/nvidia/backend/driver.py
index 38ce62b0c2a2..e0727a74e10d 100644
--- a/third_party/nvidia/backend/driver.py
+++ b/third_party/nvidia/backend/driver.py
@@ -208,6 +208,15 @@ def format_of(ty):
 static void _launch(int gridX, int gridY, int gridZ, int num_warps, int num_ctas, int clusterDimX, int clusterDimY, int clusterDimZ, int shared_memory, CUstream stream, CUfunction function{', ' + arg_decls if len(arg_decls) > 0 else ''}) {{
   void *params[] = {{ {', '.join(f"&arg{i}" for i in params)} }};
   if (gridX*gridY*gridZ > 0) {{
+    CUcontext pctx;
+    CUDA_CHECK(cuCtxGetCurrent(&pctx));
+    if (!pctx) {{
+      // Ensure device context.
+      CUdevice device;
+      CUDA_CHECK(cuDeviceGet(&device, 0));
+      CUDA_CHECK(cuDevicePrimaryCtxRetain(&pctx, device));
+      CUDA_CHECK(cuCtxSetCurrent(pctx));
+    }}
     if (num_ctas == 1) {{
       CUDA_CHECK(cuLaunchKernel(function, gridX, gridY, gridZ, 32*num_warps, 1, 1, shared_memory, stream, params, 0));
     }} else {{