Fix 3 unittest errors (#43532)

* Fix test_fuse_resnet_unit failure

* Fix test_imperative_auto_mixed_precision failure

* Fix sparse_attention_op error

* Fix sparse_attention_op error

paddle/fluid/operators/sparse_attention_op.cu

@@ -378,7 +378,7 @@ void DotSdd(const platform::CUDADeviceContext& ctx, const Tensor* a,
                                          const_cast<T*>(b_data), gpu_type,
                                          CUSPARSE_ORDER_ROW);
   // Create sparse matrix C in CSR format
-  int c_nnz = c_columns->dims()[1];
+  int c_nnz = c_columns->numel();
   platform::dynload::cusparseCreateCsr(
       &mat_c, num_rows, num_rows, c_nnz, const_cast<int*>(c_offset_data),
       const_cast<int*>(c_columns_data), c_value_data, CUSPARSE_INDEX_32I,
@@ -427,7 +427,7 @@ void DotDsd(const platform::CUDADeviceContext& ctx, const Tensor* a_offset,
   platform::dynload::cusparseCreate(&handle);
 
   // Create sparse matrix A in CSR format
-  int a_nnz = a_columns->dims()[1];
+  int a_nnz = a_columns->numel();
   platform::dynload::cusparseCreateCsr(
       &mat_a, num_rows, num_rows, a_nnz, const_cast<int*>(a_offset_data),
       const_cast<int*>(a_columns_data), const_cast<T*>(a_value_data),
@@ -600,7 +600,7 @@ class SparseAttentionGradCUDAKernel : public framework::OpKernel<T> {
                                &dvalue_lists[i], M, N, true, false);
 
       // dSoftmax = dOut * transpose(Value)
-      int nnz_num = columns.dims()[0];
+      int nnz_num = columns_lists[i].numel();
       Tensor dsoftmax;
       dsoftmax.Resize({nnz_num});
       dsoftmax.mutable_data<T>(ctx.GetPlace());

python/paddle/fluid/tests/unittests/ir/test_fuse_resnet_unit.py

@@ -25,8 +25,10 @@ np.random.seed(0)
 
 
 @unittest.skipIf(not paddle.is_compiled_with_cuda()
-                 or paddle.get_cudnn_version() < 8000,
-                 "only support with cuda and cudnn version is at least 8.0.")
+                 or paddle.get_cudnn_version() < 8000
+                 or paddle.device.cuda.get_device_capability()[0] < 7,
+                 "only support with cuda and cudnn version is at least 8.0 "
+                 "and device's compute capability is at least 7.0")
 class TestFuseResNetUnit(unittest.TestCase):
 
     def test_fuse_resenet_unit(self):

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

@@ -15,6 +15,7 @@
 import unittest
 import paddle
 import paddle.fluid as fluid
+import paddle.fluid.core as core
 import numpy as np
 import six
 import cv2
@@ -1304,6 +1305,10 @@ class TestLayerNormFp16(unittest.TestCase):
         func_isinstance()
 
 
+@unittest.skipIf(
+    paddle.is_compiled_with_cuda()
+    and not core.is_bfloat16_supported(core.CUDAPlace(0)),
+    "skip bf16 test if cuda is in use but bf16 is not supported by gpu arch.")
 class TestBf16(unittest.TestCase):
     '''
     test amp for BF16 
@@ -1323,15 +1328,13 @@ class TestBf16(unittest.TestCase):
     def test_bf16(self):
 
         def func_isinstance():
-            if fluid.core.is_compiled_with_cuda(
-            ) and fluid.core.is_bfloat16_supported(paddle.CUDAPlace(0)):
-                out_fp32 = self.train(enable_amp=False)
-                out_bf16_O1 = self.train(enable_amp=True, amp_level='O1')
-                out_bf16_O2 = self.train(enable_amp=True, amp_level='O2')
-                self.assertTrue(
-                    np.allclose(out_fp32, out_bf16_O1, rtol=1.e-3, atol=1.e-1))
-                self.assertTrue(
-                    np.allclose(out_fp32, out_bf16_O2, rtol=1.e-3, atol=1.e-1))
+            out_fp32 = self.train(enable_amp=False)
+            out_bf16_O1 = self.train(enable_amp=True, amp_level='O1')
+            out_bf16_O2 = self.train(enable_amp=True, amp_level='O2')
+            self.assertTrue(
+                np.allclose(out_fp32, out_bf16_O1, rtol=1.e-3, atol=1.e-1))
+            self.assertTrue(
+                np.allclose(out_fp32, out_bf16_O2, rtol=1.e-3, atol=1.e-1))
 
         with _test_eager_guard():
             func_isinstance()