[ROCM] added a cudnn switch of conv2d for rocm platform (#31836) (#31932)

paddle/fluid/platform/flags.cc

@@ -564,3 +564,15 @@ DEFINE_string(tracer_mkldnn_ops_on, "",
  */
 DEFINE_string(tracer_mkldnn_ops_off, "",
               "List of OneDNN operation types to be turned off");
+
+/**
+ * CUDNN related FLAG
+ * Name: conv2d_disable_cudnn
+ * Since Version:
+ * Value Range: bool, default=false
+ * Example:
+ * Note: Disable cudnn in conv2d.
+ */
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+DEFINE_bool(conv2d_disable_cudnn, false, "Disable cudnn in conv2d");
+#endif

paddle/fluid/pybind/global_value_getter_setter.cc

@@ -72,6 +72,7 @@ DECLARE_uint64(conv_workspace_size_limit);
 DECLARE_bool(cudnn_batchnorm_spatial_persistent);
 DECLARE_bool(cudnn_deterministic);
 DECLARE_bool(cudnn_exhaustive_search);
+DECLARE_bool(conv2d_disable_cudnn);
 // data processing
 DECLARE_bool(enable_cublas_tensor_op_math);
 // device management
@@ -367,7 +368,8 @@ static void RegisterGlobalVarGetterSetter() {
       FLAGS_fraction_of_cuda_pinned_memory_to_use,
       FLAGS_fraction_of_gpu_memory_to_use, FLAGS_initial_gpu_memory_in_mb,
       FLAGS_reallocate_gpu_memory_in_mb, FLAGS_enable_cublas_tensor_op_math,
-      FLAGS_selected_gpus, FLAGS_sync_nccl_allreduce);
+      FLAGS_selected_gpus, FLAGS_sync_nccl_allreduce,
+      FLAGS_conv2d_disable_cudnn);
 #endif
 #ifdef PADDLE_WITH_XPU
   REGISTER_PUBLIC_GLOBAL_VAR(FLAGS_selected_xpus);

python/paddle/fluid/__init__.py

@@ -230,6 +230,7 @@ def __bootstrap__():
             'gpu_allocator_retry_time',
             'local_exe_sub_scope_limit',
             'gpu_memory_limit_mb',
+            'conv2d_disable_cudnn',
         ]
     core.init_gflags(["--tryfromenv=" + ",".join(read_env_flags)])
     core.init_glog(sys.argv[0])

python/paddle/fluid/layers/nn.py

@@ -1603,6 +1603,10 @@ def conv2d(input,
 
     pre_bias = helper.create_variable_for_type_inference(dtype)
 
+    if (core.is_compiled_with_cuda() and paddle.fluid.get_flags(
+            "FLAGS_conv2d_disable_cudnn")["FLAGS_conv2d_disable_cudnn"]):
+        use_cudnn = False
+
     helper.append_op(
         type=l_type,
         inputs={

python/paddle/fluid/tests/unittests/test_conv2d_op.py

@@ -1465,5 +1465,41 @@ class TestConv2DAPI_Error(unittest.TestCase):
         self.assertRaises(ValueError, run_7)
 
 
+# --------- test environment variable ------
+@unittest.skipIf(
+    not (core.is_compiled_with_cuda() or core.is_compiled_with_rocm()),
+    "core is not compiled with CUDA or ROCM")
+class TestConv2DEnviron(unittest.TestCase):
+    def run_conv2d_api(self):
+        inputs = fluid.layers.data(
+            shape=[2, 3, 5, 5],
+            append_batch_size=False,
+            name="inputs",
+            dtype="float32")
+        fluid.layers.conv2d(
+            input=inputs,
+            num_filters=4,
+            filter_size=[3, 3],
+            stride=[1, 1],
+            padding=0,
+            dilation=[1, 1],
+            groups=1,
+            data_format="NCHW")
+
+        x_var = paddle.uniform((2, 3, 5, 5), dtype="float32", min=-1., max=1.)
+        conv = paddle.nn.Conv2D(
+            in_channels=3,
+            out_channels=4,
+            kernel_size=(3, 3),
+            data_format="NCHW")
+        y_var = conv(x_var)
+
+    def test_environ(self):
+        fluid.set_flags({'FLAGS_conv2d_disable_cudnn': False})
+        self.run_conv2d_api()
+        fluid.set_flags({'FLAGS_conv2d_disable_cudnn': True})
+        self.run_conv2d_api()
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/nn/layer/conv.py

@@ -25,6 +25,7 @@ __all__ = [
 
 import numpy as np
 
+from ...fluid import get_flags
 from ...fluid import core
 from ...device import get_cudnn_version
 from ...fluid.dygraph import layers
@@ -644,6 +645,10 @@ class Conv2D(_ConvNd):
             bias_attr=bias_attr,
             data_format=data_format)
 
+        if (core.is_compiled_with_cuda() and get_flags(
+                "FLAGS_conv2d_disable_cudnn")["FLAGS_conv2d_disable_cudnn"]):
+            self._use_cudnn = False
+
     def forward(self, x):
         if self._padding_mode != 'zeros':
             x = F.pad(x,