clean repeated get_cuda_version - part

test/ir/inference/test_float8_gemm.py

@@ -12,12 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
-import re
 import shutil
 import unittest
 
 import numpy as np
+from op_test import get_cuda_version
 
 import paddle
 from paddle.base import core
@@ -28,18 +27,6 @@
 E5M2_MAX_POS = 57344.0
 
 
-def get_cuda_version():
-    result = os.popen("nvcc --version").read()
-    regex = r'release (\S+),'
-    match = re.search(regex, result)
-    if match:
-        num = str(match.group(1))
-        integer, decimal = num.split('.')
-        return int(integer) * 1000 + int(float(decimal) * 10)
-    else:
-        return -1
-
-
 def check_fp8_support() -> bool:
     """Return if fp8 support is available"""
     gpu_arch = (

test/ir/pir/fused_pass/test_fused_flash_attn_pass.py

@@ -12,11 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
-import re
 import unittest
 
 import numpy as np
+from op_test import get_cuda_version
 from pass_test import PassTest
 
 import paddle
@@ -26,18 +25,6 @@
 paddle.enable_static()
 
 
-def get_cuda_version():
-    result = os.popen("nvcc --version").read()
-    regex = r'release (\S+),'
-    match = re.search(regex, result)
-    if match:
-        num = str(match.group(1))
-        integer, decimal = num.split('.')
-        return int(integer) * 1000 + int(float(decimal) * 10)
-    else:
-        return -1
-
-
 is_sm_supported = (
     core.is_compiled_with_cuda()
     and paddle.device.cuda.get_device_capability()[0] >= 8

test/ir/pir/fused_pass/test_fused_weight_only_linear_pass.py

@@ -12,8 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
-import re
 import unittest
 
 import numpy as np
@@ -26,18 +24,6 @@
 np.random.seed(2013)
 
 
-def get_cuda_version():
-    result = os.popen("nvcc --version").read()
-    regex = r'release (\S+),'
-    match = re.search(regex, result)
-    if match:
-        num = str(match.group(1))
-        integer, decimal = num.split('.')
-        return int(integer) * 1000 + int(float(decimal) * 10)
-    else:
-        return -1
-
-
 @unittest.skipIf(
     not core.is_compiled_with_cuda(),
     "weight_only_linear requires compiled with CUDA",

test/ir/pir/test_convert_mea_2_fa_pass.py

@@ -12,12 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
-import re
 import unittest
 
 import numpy as np
 from fused_pass.pass_test import PassTest
+from op_test import get_cuda_version
 
 import paddle
 from paddle.base import core
@@ -28,18 +27,6 @@
 paddle.enable_static()
 
 
-def get_cuda_version():
-    result = os.popen("nvcc --version").read()
-    regex = r'release (\S+),'
-    match = re.search(regex, result)
-    if match:
-        num = str(match.group(1))
-        integer, decimal = num.split('.')
-        return int(integer) * 1000 + int(float(decimal) * 10)
-    else:
-        return -1
-
-
 is_sm8x = (
     core.is_compiled_with_cuda()
     and paddle.device.cuda.get_device_capability()[0] == 8

test/legacy_test/test_bfloat16_embedding.py

@@ -15,7 +15,6 @@
 
 import numpy as np
 from op_test import is_custom_device
-from test_sparse_attention_op import get_cuda_version
 
 import paddle
 import paddle.nn.functional as F
@@ -61,7 +60,7 @@ def gen_random(self):
     def test_main(self):
         if (
             not (paddle.is_compiled_with_cuda() or is_custom_device())
-            or get_cuda_version() < 11000
+            or paddle.is_compiled_with_rocm()
         ):
             return
 

test/legacy_test/test_fused_gate_attention_op.py

@@ -27,7 +27,6 @@
     get_device_place,
     is_custom_device,
 )
-from test_sparse_attention_op import get_cuda_version
 
 import paddle
 import paddle.incubate.nn.functional as F
@@ -411,7 +410,7 @@ def config(self):
 
 @unittest.skipIf(
     not core.is_compiled_with_cuda()
-    or get_cuda_version() < 11000
+    or paddle.is_compiled_with_rocm()
     or paddle.device.cuda.get_device_capability()[0] < 8,
     "core is not compiled with CUDA and cuda version need larger than or equal to 11.3",
 )

test/legacy_test/test_matmul_int8_op.py

@@ -14,8 +14,7 @@
 import unittest
 
 import numpy as np
-from op_test import is_custom_device
-from test_sparse_attention_op import get_cuda_version
+from op_test import get_cuda_version, is_custom_device
 
 import paddle
 from paddle.base import core

test/legacy_test/test_sparse_matmul_op.py

@@ -67,8 +67,8 @@ def check_result(self, x_shape, y_shape, format):
 
     @unittest.skipIf(
         not (paddle.is_compiled_with_cuda() or is_custom_device())
-        or get_cuda_version() < 11000,
-        "only support cuda>=11.0",
+        or paddle.is_compiled_with_rocm(),
+        "only support cuda",
     )
     def test_matmul_2d(self):
         self.check_result([16, 12], [12, 10], 'coo')
@@ -125,17 +125,17 @@ def check_result(self, x_shape, y_shape, format):
 
     @unittest.skipIf(
         not (paddle.is_compiled_with_cuda() or is_custom_device())
-        or get_cuda_version() < 11000,
-        "only support cuda>=11.0",
+        or paddle.is_compiled_with_rocm(),
+        "only support cuda",
     )
     def test_matmul_2d(self):
         self.check_result([16, 12], [12, 10], 'coo')
         self.check_result([16, 12], [12, 10], 'csr')
 
     @unittest.skipIf(
         not (paddle.is_compiled_with_cuda() or is_custom_device())
-        or get_cuda_version() < 11000,
-        "only support cuda>=11.0",
+        or paddle.is_compiled_with_rocm(),
+        "only support cuda",
     )
     def test_matmul_3d(self):
         self.check_result([8, 16, 12], [8, 12, 10], 'coo')
@@ -206,17 +206,17 @@ def check_result(self, x_shape, y_shape, format):
 
     @unittest.skipIf(
         not (paddle.is_compiled_with_cuda() or is_custom_device())
-        or get_cuda_version() < 11000,
-        "only support cuda>=11.0",
+        or paddle.is_compiled_with_rocm(),
+        "only support cuda",
     )
     def test_matmul_2d(self):
         self.check_result([16, 12], [12, 10], 'coo')
         self.check_result([16, 12], [12, 10], 'csr')
 
     @unittest.skipIf(
         not (paddle.is_compiled_with_cuda() or is_custom_device())
-        or get_cuda_version() < 11000,
-        "only support cuda>=11.0",
+        or paddle.is_compiled_with_rocm(),
+        "only support cuda",
     )
     def test_matmul_3d(self):
         self.check_result([8, 16, 12], [8, 12, 10], 'coo')
@@ -367,8 +367,8 @@ def check_result(self, x_shape, y_shape):
 
     @unittest.skipIf(
         not (paddle.is_compiled_with_cuda() or is_custom_device())
-        or get_cuda_version() < 11000,
-        "only support cuda>=11.0",
+        or paddle.is_compiled_with_rocm(),
+        "only support cuda",
     )
     def test_matmul_2d(self):
         if in_pir_mode():

test/legacy_test/test_sparse_pca_lowrank.py

@@ -11,9 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import os
 import random
-import re
 import unittest
 
 import numpy as np
@@ -22,18 +20,6 @@
 import paddle
 
 
-def get_cuda_version():
-    result = os.popen("nvcc --version").read()
-    regex = r'release (\S+),'
-    match = re.search(regex, result)
-    if match:
-        num = str(match.group(1))
-        integer, decimal = num.split('.')
-        return int(integer) * 1000 + int(float(decimal) * 10)
-    else:
-        return -1
-
-
 class TestSparsePcaLowrankAPI(unittest.TestCase):
     def transpose(self, x):
         shape = x.shape
@@ -91,8 +77,8 @@ def run_subtest(self, guess_rank, matrix_size, batches, pca, **options):
 
     @unittest.skipIf(
         not (paddle.is_compiled_with_cuda() or is_custom_device())
-        or get_cuda_version() < 11000,
-        "only support cuda>=11.0",
+        or paddle.is_compiled_with_rocm(),
+        "only support cuda",
     )
     def test_sparse(self):
         pca_lowrank = paddle.sparse.pca_lowrank