Fix samples after driver refactoring. (#9391)

Fixing this failed build from last night: https://buildkite.com/iree/iree-samples/builds/371. Here's a build on this PR: https://buildkite.com/iree/iree-samples/builds/372 (fixed one more test after triggering that) * Fix CMake target names for web samples * Enable HAL drivers / executable loaders for web samples * Update driver names in dynamic_shapes and variables_and_state samples (`dylib`, `vmvx` -> `local-task`), in both scripts and Colab notebooks * Add `.to_host()` to Colab notebooks that print results
iree-org · Jun 8, 2022 · 74d741e · 74d741e
1 parent 718e04e
commit 74d741e
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Showing 11 changed files with 151 additions and 134 deletions.
diff --git a/experimental/web/sample_dynamic/CMakeLists.txt b/experimental/web/sample_dynamic/CMakeLists.txt
@@ -30,7 +30,7 @@ target_compile_options(${_NAME} PRIVATE ${IREE_DEFAULT_COPTS})
 target_link_libraries(${_NAME}
   iree_runtime_runtime
   iree_hal_local_loaders_system_library_loader
-  iree_hal_local_sync_driver
+  iree_hal_drivers_local_sync_sync_driver
 )
 
 target_link_options(${_NAME} PRIVATE

diff --git a/experimental/web/sample_dynamic/build_sample.sh b/experimental/web/sample_dynamic/build_sample.sh
@@ -76,6 +76,7 @@ emcmake "${CMAKE_BIN?}" -G Ninja .. \
   -DIREE_HOST_BINARY_ROOT=${INSTALL_ROOT} \
   -DIREE_BUILD_EXPERIMENTAL_WEB_SAMPLES=ON \
   -DIREE_HAL_DRIVER_DEFAULTS=OFF \
+  -DIREE_HAL_DRIVER_LOCAL_SYNC=ON \
   -DIREE_BUILD_COMPILER=OFF \
   -DIREE_BUILD_TESTS=OFF
 

diff --git a/experimental/web/sample_static/CMakeLists.txt b/experimental/web/sample_static/CMakeLists.txt
@@ -46,7 +46,7 @@ target_link_libraries(${_NAME}
   ${_MNIST_OBJECT_NAME}
   iree_runtime_runtime
   iree_hal_local_loaders_static_library_loader
-  iree_hal_local_sync_driver
+  iree_hal_drivers_local_sync_sync_driver
 )
 
 target_link_options(${_NAME} PRIVATE
@@ -90,7 +90,7 @@ target_link_libraries(${_NAME}
   ${_MNIST_OBJECT_NAME}
   iree_runtime_runtime
   iree_hal_local_loaders_static_library_loader
-  iree_hal_local_task_driver
+  iree_hal_drivers_local_task_task_driver
   iree_task_api
 )
 

diff --git a/experimental/web/sample_static/build_sample.sh b/experimental/web/sample_static/build_sample.sh
@@ -81,6 +81,8 @@ emcmake "${CMAKE_BIN?}" -G Ninja .. \
   -DIREE_HOST_BINARY_ROOT=${INSTALL_ROOT} \
   -DIREE_BUILD_EXPERIMENTAL_WEB_SAMPLES=ON \
   -DIREE_HAL_DRIVER_DEFAULTS=OFF \
+  -DIREE_HAL_DRIVER_LOCAL_SYNC=ON \
+  -DIREE_HAL_DRIVER_LOCAL_TASK=ON \
   -DIREE_BUILD_COMPILER=OFF \
   -DIREE_BUILD_TESTS=OFF
 

diff --git a/samples/colab/low_level_invoke_function.ipynb b/samples/colab/low_level_invoke_function.ipynb
@@ -63,7 +63,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "b3810038-9b88-48b8-d3dc-1d8c37300ac8"
+        "outputId": "ec9dbc63-c510-41d8-98fb-54a2151b4815"
       },
       "source": [
         "!python -m pip install iree-compiler iree-runtime -f https://github.com/google/iree/releases"
@@ -74,17 +74,18 @@
           "output_type": "stream",
           "name": "stdout",
           "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Looking in links: https://github.com/google/iree/releases\n",
             "Collecting iree-compiler\n",
-            "  Downloading https://github.com/google/iree/releases/download/candidate-20220514.138/iree_compiler-20220514.138-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.0 MB)\n",
-            "\u001b[K     |████████████████████████████████| 49.0 MB 8.9 kB/s \n",
+            "  Downloading https://github.com/google/iree/releases/download/candidate-20220608.163/iree_compiler-20220608.163-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.5 MB)\n",
+            "\u001b[K     |████████████████████████████████| 49.5 MB 151 kB/s \n",
             "\u001b[?25hCollecting iree-runtime\n",
-            "  Downloading https://github.com/google/iree/releases/download/candidate-20220514.138/iree_runtime-20220514.138-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)\n",
-            "\u001b[K     |████████████████████████████████| 2.2 MB 40.2 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from iree-compiler) (1.21.6)\n",
-            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from iree-compiler) (3.13)\n",
+            "  Downloading https://github.com/google/iree/releases/download/candidate-20220608.163/iree_runtime-20220608.163-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
+            "\u001b[K     |████████████████████████████████| 2.1 MB 53.6 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from iree-compiler) (3.13)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from iree-compiler) (1.21.6)\n",
             "Installing collected packages: iree-runtime, iree-compiler\n",
-            "Successfully installed iree-compiler-20220514.138 iree-runtime-20220514.138\n"
+            "Successfully installed iree-compiler-20220608.163 iree-runtime-20220608.163\n"
           ]
         }
       ]
@@ -119,6 +120,7 @@
         "  }\n",
         "\"\"\"\n",
         "\n",
+        "# Compile using the vmvx (reference) target:\n",
         "compiled_flatbuffer = compile_str(SIMPLE_MUL_ASM, target_backends=[\"vmvx\"])\n",
         "vm_module = ireert.VmModule.from_flatbuffer(compiled_flatbuffer)"
       ],
@@ -132,12 +134,12 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "933b2530-b3f9-4dbf-bdfd-8d222843e2cf"
+        "outputId": "25552fbd-cc24-4365-8cac-d166750a0b56"
       },
       "source": [
         "# Register the module with a runtime context.\n",
-        "# Use the CPU interpreter (which has the most implementation done):\n",
-        "config = ireert.Config(\"vmvx\")\n",
+        "# Use the \"local-task\" CPU driver, which can load the vmvx executable:\n",
+        "config = ireert.Config(\"local-task\")\n",
         "ctx = ireert.SystemContext(config=config)\n",
         "ctx.add_vm_module(vm_module)\n",
         "\n",

diff --git a/samples/dynamic_shapes/README.md b/samples/dynamic_shapes/README.md
@@ -82,10 +82,9 @@ them.
 
     ```
     ../iree-build/tools/iree-compile \
-        --iree-mlir-to-vm-bytecode-module \
-        --iree-hal-target-backends=dylib-llvm-aot \
+        --iree-hal-target-backends=cpu \
         --iree-input-type=mhlo \
-        dynamic_shapes.mlir -o dynamic_shapes_dylib.vmfb
+        dynamic_shapes.mlir -o dynamic_shapes_cpu.vmfb
     ```
 
 4. Build the `iree_samples_dynamic_shapes` CMake target
@@ -98,5 +97,5 @@ them.
 
    ```
    ../iree-build/samples/dynamic_shapes/dynamic-shapes \
-       /path/to/dynamic_shapes_dylib.vmfb dylib
+       /path/to/dynamic_shapes_cpu.vmfb local-task
    ```
diff --git a/samples/dynamic_shapes/dynamic_shapes.ipynb b/samples/dynamic_shapes/dynamic_shapes.ipynb
@@ -62,7 +62,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "bfab242f-9bb2-44bd-de62-24fd6df58be4"
+        "outputId": "8926d9c6-8236-4757-c465-4bedf4e06432"
       },
       "source": [
         "#@title General setup\n",
@@ -153,7 +153,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "3nSXZiZ_X8-P",
-        "outputId": "f193b051-1935-4fcc-ba4a-8a5d7a2f3c51"
+        "outputId": "5f26e6c7-b296-42ad-fd87-f92dba13ac62"
       },
       "source": [
         "#@title Import the TensorFlow program into IREE as MLIR\n",
@@ -184,44 +184,44 @@
           "text": [
             "Dynamic Shapes MLIR:\n",
             "```\n",
-            "\"builtin.module\"() ( {\n",
-            "  \"builtin.func\"() ( {\n",
-            "  ^bb0(%arg0: !iree_input.buffer_view):  // no predecessors\n",
+            "\"builtin.module\"() ({\n",
+            "  \"func.func\"() ({\n",
+            "  ^bb0(%arg0: !iree_input.buffer_view):\n",
             "    %0 = \"iree_input.cast.buffer_view_to_tensor\"(%arg0) : (!iree_input.buffer_view) -> tensor<?xi32>\n",
-            "    %1 = \"std.call\"(%0) {callee = @__inference_add_one_70} : (tensor<?xi32>) -> tensor<?xi32>\n",
+            "    %1 = \"func.call\"(%0) {callee = @__inference_add_one_70} : (tensor<?xi32>) -> tensor<?xi32>\n",
             "    %2 = \"iree_input.cast.tensor_to_buffer_view\"(%1) : (tensor<?xi32>) -> !iree_input.buffer_view\n",
-            "    \"std.return\"(%2) : (!iree_input.buffer_view) -> ()\n",
-            "  }) {iree.abi = \"{\\22a\\22:[[\\22ndarray\\22,\\22i32\\22,1,null]],\\22r\\22:[[\\22ndarray\\22,\\22i32\\22,1,null]],\\22v\\22:1}\", sym_name = \"add_one\", type = (!iree_input.buffer_view) -> !iree_input.buffer_view} : () -> ()\n",
-            "  \"builtin.func\"() ( {\n",
-            "  ^bb0(%arg0: tensor<?xi32>):  // no predecessors\n",
+            "    \"func.return\"(%2) : (!iree_input.buffer_view) -> ()\n",
+            "  }) {function_type = (!iree_input.buffer_view) -> !iree_input.buffer_view, iree.abi = \"{\\22a\\22:[[\\22ndarray\\22,\\22i32\\22,1,null]],\\22r\\22:[[\\22ndarray\\22,\\22i32\\22,1,null]],\\22v\\22:1}\", sym_name = \"add_one\"} : () -> ()\n",
+            "  \"func.func\"() ({\n",
+            "  ^bb0(%arg0: tensor<?xi32>):\n",
             "    %0 = \"mhlo.constant\"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>\n",
             "    %1 = \"chlo.broadcast_add\"(%arg0, %0) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<?xi32>, tensor<i32>) -> tensor<?xi32>\n",
-            "    \"std.return\"(%1) : (tensor<?xi32>) -> ()\n",
-            "  }) {arg_attrs = [{tf._user_specified_name = \"values\"}], sym_name = \"__inference_add_one_70\", sym_visibility = \"private\", tf._construction_context = \"kEagerRuntime\", tf._input_shapes = [#tf_type.shape<?>], type = (tensor<?xi32>) -> tensor<?xi32>} : () -> ()\n",
-            "  \"builtin.func\"() ( {\n",
-            "  ^bb0(%arg0: !iree_input.buffer_view):  // no predecessors\n",
+            "    \"func.return\"(%1) : (tensor<?xi32>) -> ()\n",
+            "  }) {arg_attrs = [{tf._user_specified_name = \"values\"}], function_type = (tensor<?xi32>) -> tensor<?xi32>, sym_name = \"__inference_add_one_70\", sym_visibility = \"private\", tf._construction_context = \"kEagerRuntime\", tf._input_shapes = [#tf_type.shape<?>]} : () -> ()\n",
+            "  \"func.func\"() ({\n",
+            "  ^bb0(%arg0: !iree_input.buffer_view):\n",
             "    %0 = \"mhlo.constant\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n",
             "    %1 = \"iree_input.cast.buffer_view_to_tensor\"(%arg0) : (!iree_input.buffer_view) -> tensor<?xi32>\n",
-            "    %2 = \"mhlo.reduce\"(%1, %0) ( {\n",
-            "    ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>):  // no predecessors\n",
+            "    %2 = \"mhlo.reduce\"(%1, %0) ({\n",
+            "    ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>):\n",
             "      %4 = \"mhlo.add\"(%arg1, %arg2) : (tensor<i32>, tensor<i32>) -> tensor<i32>\n",
             "      \"mhlo.return\"(%4) : (tensor<i32>) -> ()\n",
             "    }) {dimensions = dense<0> : tensor<1xi64>} : (tensor<?xi32>, tensor<i32>) -> tensor<i32>\n",
             "    %3 = \"iree_input.cast.tensor_to_buffer_view\"(%2) : (tensor<i32>) -> !iree_input.buffer_view\n",
-            "    \"std.return\"(%3) : (!iree_input.buffer_view) -> ()\n",
-            "  }) {iree.abi = \"{\\22a\\22:[[\\22ndarray\\22,\\22i32\\22,1,null]],\\22r\\22:[[\\22ndarray\\22,\\22i32\\22,0]],\\22v\\22:1}\", sym_name = \"reduce_sum_1d\", type = (!iree_input.buffer_view) -> !iree_input.buffer_view} : () -> ()\n",
-            "  \"builtin.func\"() ( {\n",
-            "  ^bb0(%arg0: !iree_input.buffer_view):  // no predecessors\n",
+            "    \"func.return\"(%3) : (!iree_input.buffer_view) -> ()\n",
+            "  }) {function_type = (!iree_input.buffer_view) -> !iree_input.buffer_view, iree.abi = \"{\\22a\\22:[[\\22ndarray\\22,\\22i32\\22,1,null]],\\22r\\22:[[\\22ndarray\\22,\\22i32\\22,0]],\\22v\\22:1}\", sym_name = \"reduce_sum_1d\"} : () -> ()\n",
+            "  \"func.func\"() ({\n",
+            "  ^bb0(%arg0: !iree_input.buffer_view):\n",
             "    %0 = \"mhlo.constant\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n",
             "    %1 = \"iree_input.cast.buffer_view_to_tensor\"(%arg0) : (!iree_input.buffer_view) -> tensor<?x3xi32>\n",
-            "    %2 = \"mhlo.reduce\"(%1, %0) ( {\n",
-            "    ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>):  // no predecessors\n",
+            "    %2 = \"mhlo.reduce\"(%1, %0) ({\n",
+            "    ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>):\n",
             "      %4 = \"mhlo.add\"(%arg1, %arg2) : (tensor<i32>, tensor<i32>) -> tensor<i32>\n",
             "      \"mhlo.return\"(%4) : (tensor<i32>) -> ()\n",
             "    }) {dimensions = dense<0> : tensor<1xi64>} : (tensor<?x3xi32>, tensor<i32>) -> tensor<3xi32>\n",
             "    %3 = \"iree_input.cast.tensor_to_buffer_view\"(%2) : (tensor<3xi32>) -> !iree_input.buffer_view\n",
-            "    \"std.return\"(%3) : (!iree_input.buffer_view) -> ()\n",
-            "  }) {iree.abi = \"{\\22a\\22:[[\\22ndarray\\22,\\22i32\\22,2,null,3]],\\22r\\22:[[\\22ndarray\\22,\\22i32\\22,1,3]],\\22v\\22:1}\", sym_name = \"reduce_sum_2d\", type = (!iree_input.buffer_view) -> !iree_input.buffer_view} : () -> ()\n",
+            "    \"func.return\"(%3) : (!iree_input.buffer_view) -> ()\n",
+            "  }) {function_type = (!iree_input.buffer_view) -> !iree_input.buffer_view, iree.abi = \"{\\22a\\22:[[\\22ndarray\\22,\\22i32\\22,2,null,3]],\\22r\\22:[[\\22ndarray\\22,\\22i32\\22,1,3]],\\22v\\22:1}\", sym_name = \"reduce_sum_2d\"} : () -> ()\n",
             "}) : () -> ()\n",
             "\n",
             "```\n",
@@ -264,24 +264,38 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "GF0dzDsbaP2w"
+        "id": "GF0dzDsbaP2w",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5cf5fdbd-1d04-41ee-ab4a-07a94cfa39d0"
       },
       "source": [
         "#@title Compile the imported MLIR further into an IREE VM bytecode module\n",
         "\n",
         "from iree.compiler import compile_str\n",
         "\n",
-        "# Note: we'll use the dylib-llvm-aot backend since it has the best support\n",
+        "# Note: we'll use the cpu (LLVM) backend since it has the best support\n",
         "# for dynamic shapes among our compiler targets.\n",
         "\n",
-        "flatbuffer_blob = compile_str(compiler_module, target_backends=[\"dylib-llvm-aot\"], input_type=\"mhlo\")\n",
+        "flatbuffer_blob = compile_str(compiler_module, target_backends=[\"cpu\"], input_type=\"mhlo\")\n",
         "\n",
-        "# Note: the dylib-llvm-aot target produces platform-specific code. Since you\n",
-        "# may need to recompile it yourself using the appropriate\n",
-        "# `-iree-llvm-target-triple` flag, we skip saving it to disk and downloading it."
+        "# Save the compiled program to disk.\n",
+        "flatbuffer_path = os.path.join(ARTIFACTS_DIR, \"dynamic_shapes_cpu.vmfb\")\n",
+        "with open(flatbuffer_path, \"wb\") as output_file:\n",
+        "  output_file.write(flatbuffer_blob)\n",
+        "print(f\"Wrote compiled program to path '{flatbuffer_path}'\")"
       ],
-      "execution_count": 7,
-      "outputs": []
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Wrote compiled program to path '/tmp/iree/colab_artifacts/dynamic_shapes_cpu.vmfb'\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",
@@ -306,7 +320,7 @@
         "from iree import runtime as ireert\n",
         "\n",
         "vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)\n",
-        "config = ireert.Config(\"dylib\")\n",
+        "config = ireert.Config(\"local-task\")\n",
         "ctx = ireert.SystemContext(config=config)\n",
         "ctx.add_vm_module(vm_module)"
       ],
@@ -320,20 +334,20 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "CQffg1iQatkb",
-        "outputId": "dab42bd6-0291-41fa-d664-3739fbfa1908"
+        "outputId": "25820d2f-2b4a-4bec-aca4-e4f2ec38e6c4"
       },
       "source": [
         "import numpy as np\n",
         "\n",
         "# Our @tf.functions are accessible by name on the module named 'module'\n",
         "dynamic_shapes_program = ctx.modules.module\n",
         "\n",
-        "print(dynamic_shapes_program.reduce_sum_1d(np.array([1, 10, 100], dtype=np.int32)))\n",
-        "print(dynamic_shapes_program.reduce_sum_2d(np.array([[1, 2, 3], [10, 20, 30]], dtype=np.int32)))\n",
-        "print(dynamic_shapes_program.reduce_sum_2d(np.array([[1, 2, 3], [10, 20, 30], [100, 200, 300]], dtype=np.int32)))\n",
-        "print(dynamic_shapes_program.add_one(np.array([1, 10, 100], dtype=np.int32)))"
+        "print(dynamic_shapes_program.reduce_sum_1d(np.array([1, 10, 100], dtype=np.int32)).to_host())\n",
+        "print(dynamic_shapes_program.reduce_sum_2d(np.array([[1, 2, 3], [10, 20, 30]], dtype=np.int32)).to_host())\n",
+        "print(dynamic_shapes_program.reduce_sum_2d(np.array([[1, 2, 3], [10, 20, 30], [100, 200, 300]], dtype=np.int32)).to_host())\n",
+        "print(dynamic_shapes_program.add_one(np.array([1, 10, 100], dtype=np.int32)).to_host())"
       ],
-      "execution_count": 10,
+      "execution_count": 12,
       "outputs": [
         {
           "output_type": "stream",
@@ -360,10 +374,10 @@
       "cell_type": "code",
       "metadata": {
         "id": "bUaNUkS2ohRj",
-        "outputId": "0382c67c-72a9-4310-a3a4-550708c515aa",
+        "outputId": "f33c7864-1bf2-43f1-83f4-bcf83b61cbbd",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 69
+          "height": 86
         }
       },
       "source": [
@@ -380,20 +394,24 @@
         "except ImportError:\n",
         "  print(\"Missing google_colab Python package, can't download files\")"
       ],
-      "execution_count": 11,
+      "execution_count": 14,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "Zipping '/tmp/iree/colab_artifacts' to '/tmp/dynamic_shapes_colab_artifacts.zip' for download...\n",
-            "  adding: dynamic_shapes.mlir (deflated 80%)\n",
+            "updating: dynamic_shapes.mlir (deflated 80%)\n",
+            "  adding: dynamic_shapes_cpu.vmfb (deflated 63%)\n",
             "Downloading the artifacts zip file...\n"
           ]
         },
         {
           "output_type": "display_data",
           "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
             "application/javascript": [
               "\n",
               "    async function download(id, filename, size) {\n",
@@ -436,21 +454,18 @@
               "      div.remove();\n",
               "    }\n",
               "  "
-            ],
-            "text/plain": [
-              "<IPython.core.display.Javascript object>"
             ]
           },
           "metadata": {}
         },
         {
           "output_type": "display_data",
           "data": {
-            "application/javascript": [
-              "download(\"download_f673a666-f490-4fc5-865b-7d6da53b46a9\", \"dynamic_shapes_colab_artifacts.zip\", 831)"
-            ],
             "text/plain": [
               "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_66541154-4c92-458c-b27d-4adb2370f0db\", \"dynamic_shapes_colab_artifacts.zip\", 5923)"
             ]
           },
           "metadata": {}

diff --git a/samples/dynamic_shapes/test.sh b/samples/dynamic_shapes/test.sh
@@ -27,14 +27,13 @@ cmake --build ${BUILD_DIR} --target iree-compile -- -k 0
 
 # 3. Compile `dynamic_shapes.mlir` using `iree-compile`.
 ${BUILD_DIR}/tools/iree-compile \
-  --iree-mlir-to-vm-bytecode-module \
-  --iree-hal-target-backends=dylib-llvm-aot \
+  --iree-hal-target-backends=cpu \
   --iree-input-type=mhlo \
-  ${ARTIFACTS_DIR}/dynamic_shapes.mlir -o ${ARTIFACTS_DIR}/dynamic_shapes_dylib.vmfb
+  ${ARTIFACTS_DIR}/dynamic_shapes.mlir -o ${ARTIFACTS_DIR}/dynamic_shapes_cpu.vmfb
 
 # 4. Build the `iree_samples_dynamic_shapes` CMake target.
 cmake --build ${BUILD_DIR} --target iree_samples_dynamic_shapes -- -k 0
 
 # 5. Run the sample binary.
 ${BUILD_DIR}/samples/dynamic_shapes/dynamic-shapes \
-  ${ARTIFACTS_DIR}/dynamic_shapes_dylib.vmfb dylib
+  ${ARTIFACTS_DIR}/dynamic_shapes_cpu.vmfb local-task