Use tempfile to save model in InferencePassTest (#54038)

test/ir/inference/inference_pass_test.py

@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import random
+import tempfile
 import unittest
 
 import numpy as np
@@ -41,7 +43,10 @@ class InferencePassTest(unittest.TestCase):
         self.dynamic_shape_params = None
         self.enable_lite = False
         self.lite_parameters = None
-        self.path = "./inference_pass/" + self.__class__.__name__ + "/"
+        self.temp_dir = tempfile.TemporaryDirectory()
+        self.path = os.path.join(
+            self.temp_dir.name, 'inference_pass', self.__class__.__name__
+        )
         np.random.seed(1)
         random.seed(1)
 

test/ir/inference/test_trt_activation_pass.py

@@ -53,8 +53,9 @@ class TensorRTSubgraphPassActivationTest(InferencePassTest):
     def test_check_output(self):
         if core.is_compiled_with_cuda():
             use_gpu = True
-            if os.path.exists(self.path + "_opt_cache"):
-                shutil.rmtree(self.path + "_opt_cache")
+            opt_path = os.path.join(self.path, '_opt_cache')
+            if os.path.exists(opt_path):
+                shutil.rmtree(opt_path)
             if (
                 self.trt_parameters.precision
                 == AnalysisConfig.Precision.Float32

test/ir/inference/test_trt_elementwise_op.py

@@ -53,8 +53,9 @@ class TensorRTSubgraphPassElementwiseBroadcastTest(InferencePassTest):
         return paddle.tensor.math.add(x=data1, y=data2)
 
     def test_check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu)

test/ir/inference/test_trt_instance_norm_op.py

@@ -55,8 +55,9 @@ class TRTInstanceNormTest(InferencePassTest):
         self.fetch_list = [out]
 
     def check_output(self, remove_cache=False):
-        if remove_cache and os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if remove_cache and os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             atol = 1e-5

test/ir/inference/test_trt_pool3d_op.py

@@ -84,8 +84,9 @@ class TensorRTPool3dTest(InferencePassTest):
             self.fetch_list = [pool_out]
 
     def check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             if self.precision == AnalysisConfig.Precision.Float32:
@@ -200,8 +201,9 @@ class TensorRTAdaptiveAvgPool3DTest(InferencePassTest):
             self.fetch_list = [pool_out]
 
     def check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu)
@@ -300,8 +302,9 @@ class TensorRTAdaptiveMaxPool3DTest(InferencePassTest):
             self.fetch_list = [pool_out]
 
     def check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu)

test/ir/inference/test_trt_pool_op.py

@@ -86,8 +86,9 @@ class TensorRTPoolTest(InferencePassTest):
             self.fetch_list = [out]
 
     def check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             if self.precision == AnalysisConfig.Precision.Float32:

test/ir/inference/test_trt_skip_layernorm_fuse_pass.py

@@ -60,8 +60,9 @@ class SkipLayernormFusePassTest0(InferencePassTest):
         return paddle.add(data1, data2)
 
     def test_check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu, atol=0.01, rtol=0.00001)
@@ -107,8 +108,9 @@ class SkipLayernormFusePassTest1(InferencePassTest):
         return paddle.add(data1, data2)
 
     def test_check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu, atol=0.01, rtol=0.00001)
@@ -154,8 +156,9 @@ class SkipLayernormFusePassTest2(InferencePassTest):
         return paddle.add(data1, data2)
 
     def test_check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu, atol=0.1, rtol=0.00001)
@@ -201,8 +204,9 @@ class SkipLayernormFusePassTest3(InferencePassTest):
         return paddle.add(data1, data2)
 
     def test_check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu, atol=0.1, rtol=0.00001)

test/ir/inference/test_trt_subgraph_pass.py

@@ -128,8 +128,9 @@ class TensorRTSubgraphPassSplitSerializeTest(InferencePassTest):
     def test_check_output(self):
         if paddle.is_compiled_with_cuda():
             use_gpu = True
-            if os.path.exists(self.path + "_opt_cache"):
-                shutil.rmtree(self.path + "_opt_cache")
+            opt_path = os.path.join(self.path, '_opt_cache')
+            if os.path.exists(opt_path):
+                shutil.rmtree(opt_path)
             self.check_output_with_option(use_gpu)
             self.assertTrue(
                 PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
@@ -164,8 +165,9 @@ class TensorRTSubgraphPassDynamicSplitFp16SerializeTest(InferencePassTest):
     def test_check_output(self):
         if paddle.is_compiled_with_cuda():
             use_gpu = True
-            if os.path.exists(self.path + "_opt_cache"):
-                shutil.rmtree(self.path + "_opt_cache")
+            opt_path = os.path.join(self.path, '_opt_cache')
+            if os.path.exists(opt_path):
+                shutil.rmtree(opt_path)
             self.check_output_with_option(use_gpu, 1e-3)
             self.assertTrue(
                 PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
@@ -313,8 +315,9 @@ class TensorRTSubgraphPassLayerNormDynamicTest(InferencePassTest):
         self.serialize = True
 
     def test_check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if paddle.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu)
@@ -332,8 +335,9 @@ class TensorRTSubgraphPassLayerNormDynamicFP16Test(
         self.serialize = True
 
     def test_check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if paddle.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu, atol=0.01, rtol=0.01)
@@ -406,8 +410,9 @@ class TensorRTSubgraphPassElementwiseSerializeTest(
         )
 
     def test_check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         super().test_check_output()
 
 
@@ -444,8 +449,9 @@ class TensorRTSubgraphPassElementwiseBroadcastDynamicTest(InferencePassTest):
         return paddle.add(x=data1, y=data2)
 
     def test_check_output(self):
-        if os.path.exists(self.path + "_opt_cache"):
-            shutil.rmtree(self.path + "_opt_cache")
+        opt_path = os.path.join(self.path, '_opt_cache')
+        if os.path.exists(opt_path):
+            shutil.rmtree(opt_path)
         if paddle.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu)