chore: import upstream snapshot with attribution

2026-07-13 12:40:42 +08:00
commit e25996e7db
15472 changed files with 3536181 additions and 0 deletions
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+# 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 unittest
+
+import numpy as np
+from get_program import (
+    get_bert_program,
+)
+
+from paddle.tensorrt.export import (
+    Input,
+    TensorRTConfig,
+    convert_to_trt,
+)
+from paddle.tensorrt.util import (
+    predict_program,
+)
+
+
+class TestConverterBert(unittest.TestCase):
+    def test_paddle_to_tensorrt_conversion_bert(self):
+        # Step1: get program and init fake inputs
+        program, scope, param_dict = get_bert_program()
+
+        # Set input
+        input_config = Input(
+            min_input_shape=(1, 100),
+            optim_input_shape=(4, 1000),
+            max_input_shape=(8, 1000),
+        )
+        input_config.input_data_type = 'int64'
+        input_min_data, _, input_max_data = input_config.generate_input_data()
+
+        # Create a TensorRTConfig with inputs as a required field.
+        trt_config = TensorRTConfig(inputs=[input_config])
+        trt_config.disable_ops = "pd_op.dropout"
+        trt_config.disable_passes = [
+            'constant_folding_pass',
+            'dead_code_elimination_pass',
+        ]
+
+        # Step1.1: get original results(for tests only)
+        output_var = program.global_block().ops[-1].result(0)
+
+        output_expected = predict_program(
+            program, {"input_ids": input_min_data}, [output_var]
+        )
+        # get tensorrt_engine_op(converted_program)
+        program_with_trt = convert_to_trt(program, trt_config, scope)
+        output_var = program_with_trt.global_block().ops[-1].result(0)
+
+        # run inference(converted_program)
+        output_converted = predict_program(
+            program_with_trt,
+            {"input_ids": input_min_data},
+            [output_var],
+        )
+
+        # # Check that the results are close to each other within a tolerance of 1e-2
+        np.testing.assert_allclose(
+            output_expected[0],
+            output_converted[0],
+            rtol=1e-2,
+            atol=1e-2,
+            err_msg="Outputs are not within the 1e-2 tolerance",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()