【SCU】【Paddle TensorRT No.24】Add pd_op.minimum converter (#69736)

PolaKuma · web-flow · commit df36407baae1 · 2024-12-18T14:53:37.000+08:00
* add_minimum

* fix codestyle

* Update math.py

* fix

* fix

* fix

* fix

* add_fp16

* Update test_converter_math.py
diff --git a/python/paddle/tensorrt/impls/math.py b/python/paddle/tensorrt/impls/math.py
@@ -299,6 +299,14 @@ def sqrt_converter(network, paddle_op, inputs):
     return layer.get_output(0)
 
 
+@converter_registry.register("pd_op.minimum", trt_version="8.x")
+def minimum_converter(network, paddle_op, inputs):
+    min_layer = add_elementwise_layer(
+        network, paddle_op, inputs, trt.ElementWiseOperation.MIN
+    )
+    return min_layer
+
+
 @converter_registry.register("pd_op.maximum", trt_version="8.x")
 def maximum_converter(network, paddle_op, inputs):
     max_layer = add_elementwise_layer(
diff --git a/test/tensorrt/test_converter_math.py b/test/tensorrt/test_converter_math.py
@@ -404,11 +404,14 @@ def setUp(self):
         self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]}
         self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]}
 
-    def test_trt_result(self):
+    def test_trt_result_fp16(self):
+        self.check_trt_result(precision_mode="fp16")
+
+    def test_trt_result_fp32(self):
         self.check_trt_result()
 
 
-class TestMinimumBroadcastTRTPattern(TensorRTBaseTest):
+class TestMaximumBroadcastTRTPattern(TensorRTBaseTest):
     def setUp(self):
         self.python_api = paddle.maximum
         self.api_args = {
@@ -419,11 +422,14 @@ def setUp(self):
         self.min_shape = {"x": [1, 3, 4], "y": [4]}
         self.max_shape = {"x": [5, 3, 4], "y": [4]}
 
-    def test_trt_result(self):
+    def test_trt_result_fp16(self):
+        self.check_trt_result(precision_mode="fp16")
+
+    def test_trt_result_fp32(self):
         self.check_trt_result()
 
 
-class TestMinimumIntTRTPattern(TensorRTBaseTest):
+class TestMaximumIntTRTPattern(TensorRTBaseTest):
     def setUp(self):
         self.python_api = paddle.maximum
         self.api_args = {
@@ -446,7 +452,76 @@ def setUp(self):
         self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]}
         self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]}
 
-    def test_trt_result(self):
+    def test_trt_result_fp16(self):
+        self.check_trt_result(precision_mode="fp16")
+
+    def test_trt_result_fp32(self):
+        self.check_trt_result()
+
+
+class TestMinimumTRTPattern(TensorRTBaseTest):
+    def setUp(self):
+        self.python_api = paddle.minimum
+        self.api_args = {
+            "x": np.random.randn(2, 3, 4).astype("float32"),
+            "y": np.random.randn(2, 3, 4).astype("float32"),
+        }
+        self.program_config = {"feed_list": ["x", "y"]}
+        self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]}
+        self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]}
+
+    def test_trt_result_fp16(self):
+        self.check_trt_result(precision_mode="fp16")
+
+    def test_trt_result_fp32(self):
+        self.check_trt_result()
+
+
+class TestMinimumBroadcastTRTPattern(TensorRTBaseTest):
+    def setUp(self):
+        self.python_api = paddle.minimum
+        self.api_args = {
+            "x": np.random.randn(2, 3, 4).astype("float32"),
+            "y": np.random.randn(4).astype("float32"),
+        }
+        self.program_config = {"feed_list": ["x", "y"]}
+        self.min_shape = {"x": [1, 3, 4], "y": [4]}
+        self.max_shape = {"x": [5, 3, 4], "y": [4]}
+
+    def test_trt_result_fp16(self):
+        self.check_trt_result(precision_mode="fp16")
+
+    def test_trt_result_fp32(self):
+        self.check_trt_result()
+
+
+class TestMinimumIntTRTPattern(TensorRTBaseTest):
+    def setUp(self):
+        self.python_api = paddle.minimum
+        self.api_args = {
+            "x": np.random.randint(
+                low=1, high=100, size=(2, 3, 4), dtype="int64"
+            ),
+            "y": np.random.randint(
+                low=1, high=100, size=(2, 3, 4), dtype="int64"
+            ),
+        }
+        self.dynamic_shape_data = {
+            "x": lambda shape: np.random.randint(
+                1, 100, size=shape, dtype="int64"
+            ),
+            "y": lambda shape: np.random.randint(
+                1, 100, size=shape, dtype="int64"
+            ),
+        }
+        self.program_config = {"feed_list": ["x", "y"]}
+        self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]}
+        self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]}
+
+    def test_trt_result_fp16(self):
+        self.check_trt_result(precision_mode="fp16")
+
+    def test_trt_result_fp32(self):
         self.check_trt_result()
 
 
