chore: import upstream snapshot with attribution

python/paddle/tensorrt/impls/logic.py

@@ -0,0 +1,100 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 numpy as np
+import tensorrt as trt
+
+from paddle.tensorrt.converter_utils import (
+    add_elementwise_layer,
+    set_layer_name,
+    unary_op_converter,
+)
+from paddle.tensorrt.register import converter_registry
+
+logic_type_map = {
+    "pd_op.greater_than": trt.ElementWiseOperation.GREATER,
+    "pd_op.less_than": trt.ElementWiseOperation.LESS,
+    "pd_op.equal": trt.ElementWiseOperation.EQUAL,
+    "pd_op.bitwise_and": trt.ElementWiseOperation.AND,
+    "pd_op.bitwise_or": trt.ElementWiseOperation.OR,
+    "pd_op.logical_xor": trt.ElementWiseOperation.XOR,
+    "pd_op.logical_or": trt.ElementWiseOperation.OR,
+    "pd_op.logical_or_": trt.ElementWiseOperation.OR,
+    "pd_op.logical_and": trt.ElementWiseOperation.AND,
+}
+
+
+@converter_registry.register("pd_op.greater_than")
+@converter_registry.register("pd_op.less_than")
+@converter_registry.register("pd_op.equal")
+@converter_registry.register("pd_op.bitwise_and")
+@converter_registry.register("pd_op.bitwise_or")
+@converter_registry.register("pd_op.logical_xor")
+@converter_registry.register("pd_op.logical_or")
+@converter_registry.register("pd_op.logical_or_")
+@converter_registry.register("pd_op.logical_and")
+def logic_converter(network, paddle_op, inputs):
+    layer_output = add_elementwise_layer(
+        network, paddle_op, inputs, logic_type_map[paddle_op.name()]
+    )
+    return layer_output
+
+
+@converter_registry.register("pd_op.not_equal")
+def not_equal_converter(network, paddle_op, inputs):
+    layer_output = add_elementwise_layer(
+        network, paddle_op, inputs, trt.ElementWiseOperation.EQUAL
+    )
+    not_layer = network.add_unary(layer_output, trt.UnaryOperation.NOT)
+    set_layer_name(not_layer, paddle_op)
+    layer_output = not_layer.get_output(0)
+    return layer_output
+
+
+@converter_registry.register("pd_op.bitwise_not")
+def bitwise_not_converter(network, paddle_op, inputs):
+    input_tensor = inputs[0]
+    if input_tensor.dtype == trt.bool:
+        bitwise_not_layer = network.add_unary(
+            input_tensor, trt.UnaryOperation.NOT
+        )
+        set_layer_name(bitwise_not_layer, paddle_op)
+        layer_output = bitwise_not_layer.get_output(0)
+    else:
+        neg_one_tensor_dims = trt.Dims([1] * len(input_tensor.shape))
+        neg_one_value = np.array([-1], dtype=np.int32)
+        neg_one_weights = trt.Weights(neg_one_value)
+        neg_one_tensor = network.add_constant(
+            neg_one_tensor_dims, neg_one_weights
+        )
+        set_layer_name(neg_one_tensor, paddle_op)
+        neg_one_tensor = neg_one_tensor.get_output(0)
+        mul_neg_one = network.add_elementwise(
+            input_tensor, neg_one_tensor, trt.ElementWiseOperation.PROD
+        )
+        set_layer_name(mul_neg_one, paddle_op)
+        mul_neg_one = mul_neg_one.get_output(0)
+        layer_output = network.add_elementwise(
+            mul_neg_one, neg_one_tensor, trt.ElementWiseOperation.SUM
+        )
+        set_layer_name(layer_output, paddle_op)
+        layer_output = layer_output.get_output(0)
+    return layer_output
+
+
+@converter_registry.register("pd_op.logical_not")
+@converter_registry.register("pd_op.logical_not_")
+def logic_not_converter(network, paddle_op, inputs):
+    layer_output = unary_op_converter(network, paddle_op, inputs)
+    return layer_output