Update the batch size used in test_resnet50_with_cinn.py. (#37013)

* Update the batch size used in test_resnet50_with_cinn.py.
* Enable more debug info.

cmake/external/cinn.cmake

@@ -27,7 +27,7 @@ add_definitions(-w)
 include(ExternalProject)
 set(CINN_SOURCE_DIR ${THIRD_PARTY_PATH}/CINN)
 # TODO(zhhsplendid): Modify git tag after we have release tag
-set(CINN_GIT_TAG 2122413fc74f4020ff4397b54488a793529d581b)
+set(CINN_GIT_TAG develop)
 set(CINN_OPTIONAL_ARGS -DPY_VERSION=${PY_VERSION} -DWITH_CUDA=${WITH_GPU} -DWITH_CUDNN=${WITH_GPU} -DPUBLISH_LIBS=ON -DWITH_TESTING=ON)
 set(CINN_BUILD_COMMAND $(MAKE) cinnapi -j)
 ExternalProject_Add(

paddle/fluid/framework/paddle2cinn/cinn_compiler.cc

@@ -67,7 +67,7 @@ const CinnCompiledObject& CinnCompiler::Compile(
     const Graph& graph,
     const std::map<std::string, const LoDTensor*>& input_tensors,
     const Target& target) {
-  VLOG(4) << "-- The graph to be compiled is:\n" << VizGraph(graph);
+  VLOG(1) << "-- The graph to be compiled is:\n" << VizGraph(graph);
   CinnCacheKey cur_key(graph, input_tensors, target.arch_str());
   bool exist = false;
   {
@@ -195,7 +195,7 @@ std::unique_ptr<CinnCompiledObject> CinnCompiler::CompileGraph(
   ProgramPass::Apply(&frontend_program, target, {"Decomposer"});
   auto cinn_graph = std::make_shared<::cinn::hlir::framework::Graph>(
       frontend_program, target);
-  VLOG(4) << "-- The " << compiled_num << "-th compilation ("
+  VLOG(1) << "-- The " << compiled_num << "-th compilation ("
           << target.arch_str() << "), and its related graph:\n"
           << cinn_graph->Visualize();
   ApplyPass(cinn_graph.get(), "OpFusion");

python/paddle/fluid/tests/unittests/test_resnet50_with_cinn.py

@@ -40,8 +40,8 @@ def set_cinn_flag(val):
 class TestResnet50Accuracy(unittest.TestCase):
     def reader(self, limit):
         for _ in range(limit):
-            yield np.random.randint(0, 256, size=[1, 3, 224, 224]).astype('float32'), \
-                  np.random.randint(0, 1000, size=[1]).astype('int64')
+            yield np.random.randint(0, 256, size=[32, 3, 224, 224]).astype('float32'), \
+                  np.random.randint(0, 1000, size=[32]).astype('int64')
 
     def generate_random_data(self, loop_num=10):
         feed = []
@@ -54,8 +54,8 @@ class TestResnet50Accuracy(unittest.TestCase):
     def build_program(self, main_program, startup_program):
         with paddle.static.program_guard(main_program, startup_program):
             image = paddle.static.data(
-                name='image', shape=[1, 3, 224, 224], dtype='float32')
-            label = paddle.static.data(name='label', shape=[1], dtype='int64')
+                name='image', shape=[32, 3, 224, 224], dtype='float32')
+            label = paddle.static.data(name='label', shape=[32], dtype='int64')
 
             model = paddle.vision.models.resnet50()
             prediction = model(image)
@@ -80,7 +80,7 @@ class TestResnet50Accuracy(unittest.TestCase):
         loss = self.build_program(main_program, startup_program)
         exe = paddle.static.Executor(place)
 
-        parallel_exec = paddle.static.CompiledProgram(
+        compiled_prog = paddle.static.CompiledProgram(
             main_program).with_data_parallel(loss_name=loss.name)
         loss_vals = []
         scope = paddle.static.Scope()
@@ -88,7 +88,7 @@ class TestResnet50Accuracy(unittest.TestCase):
         with paddle.static.scope_guard(scope):
             exe.run(startup_program)
             for step in range(iters):
-                loss_v = exe.run(parallel_exec,
+                loss_v = exe.run(compiled_prog,
                                  feed=feed[step],
                                  fetch_list=[loss],
                                  return_numpy=True)