[Migrate Fluid] Migrate Decoder, BeamSearchDecoder (#48754)

python/paddle/fluid/tests/unittests/test_rnn_decode_api.py

@@ -151,23 +151,15 @@ class Decoder:
 
         if self.decoding_strategy == "beam_search":
             beam_size = kwargs.get("beam_size", 4)
-            encoder_output = (
-                layers.BeamSearchDecoder.tile_beam_merge_with_batch(
+            encoder_output = BeamSearchDecoder.tile_beam_merge_with_batch(
                 encoder_output, beam_size
             )
-            )
-            encoder_padding_mask = (
-                layers.BeamSearchDecoder.tile_beam_merge_with_batch(
+            encoder_padding_mask = BeamSearchDecoder.tile_beam_merge_with_batch(
                 encoder_padding_mask, beam_size
             )
-            )
-            decoder = layers.BeamSearchDecoder(
+            decoder = BeamSearchDecoder(
                 cell=self.decoder_cell, output_fn=output_layer, **kwargs
             )
-        else:
-            decoder = layers.BasicDecoder(
-                self.decoder_cell, helper, output_fn=output_layer
-            )
 
         (
             decoder_output,
@@ -535,130 +527,6 @@ class TestDynamicDecode(unittest.TestCase):
         )
         self.exe = Executor(place)
 
-    def test_mle_train(self):
-        paddle.enable_static()
-        self.model_hparams["decoding_strategy"] = "train_greedy"
-        agent = SeqPGAgent(
-            model_cls=Seq2SeqModel,
-            alg_cls=MLE,
-            model_hparams=self.model_hparams,
-            alg_hparams={"lr": 0.001},
-            executor=self.exe,
-            main_program=fluid.Program(),
-            startup_program=fluid.Program(),
-            seed=123,
-        )
-        self.exe.run(agent.startup_program)
-        for iter_idx in range(self.iter_num):
-            reward, cost = agent.learn(
-                {
-                    "src": self.data["src"][
-                        iter_idx
-                        * self.batch_size : (iter_idx + 1)
-                        * self.batch_size,
-                        :,
-                    ],
-                    "src_sequence_length": self.data["src_sequence_length"][
-                        iter_idx
-                        * self.batch_size : (iter_idx + 1)
-                        * self.batch_size
-                    ],
-                    "trg": self.data["trg"][
-                        iter_idx
-                        * self.batch_size : (iter_idx + 1)
-                        * self.batch_size,
-                        :,
-                    ],
-                    "trg_sequence_length": self.data["trg_sequence_length"][
-                        iter_idx
-                        * self.batch_size : (iter_idx + 1)
-                        * self.batch_size
-                    ],
-                    "label": self.data["label"][
-                        iter_idx
-                        * self.batch_size : (iter_idx + 1)
-                        * self.batch_size
-                    ],
-                },
-                fetch_list=[agent.cost, agent.cost],
-            )
-            print(
-                "iter_idx: %d, reward: %f, cost: %f"
-                % (iter_idx, reward.mean(), cost)
-            )
-
-    def test_greedy_train(self):
-        paddle.enable_static()
-        self.model_hparams["decoding_strategy"] = "infer_greedy"
-        agent = SeqPGAgent(
-            model_cls=Seq2SeqModel,
-            alg_cls=PolicyGradient,
-            model_hparams=self.model_hparams,
-            alg_hparams={"lr": 0.001},
-            executor=self.exe,
-            main_program=fluid.Program(),
-            startup_program=fluid.Program(),
-            seed=123,
-        )
-        self.exe.run(agent.startup_program)
-        for iter_idx in range(self.iter_num):
-            reward, cost = agent.learn(
-                {
-                    "src": self.data["src"][
-                        iter_idx
-                        * self.batch_size : (iter_idx + 1)
-                        * self.batch_size,
-                        :,
-                    ],
-                    "src_sequence_length": self.data["src_sequence_length"][
-                        iter_idx
-                        * self.batch_size : (iter_idx + 1)
-                        * self.batch_size
-                    ],
-                },
-                fetch_list=[agent.reward, agent.cost],
-            )
-            print(
-                "iter_idx: %d, reward: %f, cost: %f"
-                % (iter_idx, reward.mean(), cost)
-            )
-
-    def test_sample_train(self):
-        paddle.enable_static()
-        self.model_hparams["decoding_strategy"] = "infer_sample"
-        agent = SeqPGAgent(
-            model_cls=Seq2SeqModel,
-            alg_cls=PolicyGradient,
-            model_hparams=self.model_hparams,
-            alg_hparams={"lr": 0.001},
-            executor=self.exe,
-            main_program=fluid.Program(),
-            startup_program=fluid.Program(),
-            seed=123,
-        )
-        self.exe.run(agent.startup_program)
-        for iter_idx in range(self.iter_num):
-            reward, cost = agent.learn(
-                {
-                    "src": self.data["src"][
-                        iter_idx
-                        * self.batch_size : (iter_idx + 1)
-                        * self.batch_size,
-                        :,
-                    ],
-                    "src_sequence_length": self.data["src_sequence_length"][
-                        iter_idx
-                        * self.batch_size : (iter_idx + 1)
-                        * self.batch_size
-                    ],
-                },
-                fetch_list=[agent.reward, agent.cost],
-            )
-            print(
-                "iter_idx: %d, reward: %f, cost: %f"
-                % (iter_idx, reward.mean(), cost)
-            )
-
     def test_beam_search_infer(self):
         paddle.set_default_dtype("float32")
         paddle.enable_static()
@@ -693,19 +561,6 @@ class TestDynamicDecode(unittest.TestCase):
                 fetch_list=[output],
             )[0]
 
-    def func_dynamic_basic_decoder(self):
-        paddle.disable_static()
-        src = paddle.to_tensor(np.random.randint(8, size=(8, 4)))
-        src_length = paddle.to_tensor(np.random.randint(8, size=(8)))
-        model = Seq2SeqModel(**self.model_hparams)
-        probs, samples, sample_length = model(src, src_length)
-        paddle.enable_static()
-
-    def test_dynamic_basic_decoder(self):
-        with _test_eager_guard():
-            self.func_dynamic_basic_decoder()
-        self.func_dynamic_basic_decoder()
-
 
 class ModuleApiTest(unittest.TestCase):
     @classmethod