[Dy2St][NO.2] pir dy2st unittest fix test_bert - Part 1 (#60164)

gouzil · web-flow · commit 7c0c2e831878 · 2023-12-20T10:02:17.000+08:00
diff --git a/test/dygraph_to_static/test_bert.py b/test/dygraph_to_static/test_bert.py
@@ -23,16 +23,21 @@
 from dygraph_to_static_utils import (
     Dy2StTestBase,
     enable_to_static_guard,
+    test_legacy_and_pt_and_pir,
     test_sot_only,
 )
 from predictor_utils import PredictorTools
 
 import paddle
 from paddle import base
 from paddle.base import core
+from paddle.base.framework import unique_name
+from paddle.framework import use_pir_api
 from paddle.jit.translated_layer import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
 
-place = base.CUDAPlace(0) if base.is_compiled_with_cuda() else base.CPUPlace()
+place = (
+    paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() else paddle.CPUPlace()
+)
 SEED = 2020
 STEP_NUM = 10
 PRINT_STEP = 2
@@ -95,7 +100,7 @@ def tearDown(self):
         self.temp_dir.cleanup()
 
     def train(self, bert_config, data_reader, to_static):
-        with base.dygraph.guard(place):
+        with unique_name.guard():
             base.default_main_program().random_seed = SEED
             base.default_startup_program().random_seed = SEED
 
@@ -158,7 +163,9 @@ def train(self, bert_config, data_reader, to_static):
                 step_idx += 1
                 if step_idx == STEP_NUM:
                     if to_static:
-                        paddle.jit.save(bert, self.model_save_prefix)
+                        # TODO(pir-save-load): Fix this after we support save/load in PIR
+                        if not use_pir_api():
+                            paddle.jit.save(bert, self.model_save_prefix)
                     else:
                         paddle.save(
                             bert.state_dict(),
@@ -172,8 +179,7 @@ def train_dygraph(self, bert_config, data_reader):
             return self.train(bert_config, data_reader, False)
 
     def train_static(self, bert_config, data_reader):
-        with enable_to_static_guard(True):
-            return self.train(bert_config, data_reader, True)
+        return self.train(bert_config, data_reader, True)
 
     def predict_static(self, data):
         paddle.enable_static()
@@ -195,11 +201,12 @@ def predict_static(self, data):
             fetch_list=fetch_targets,
         )
 
+        paddle.disable_static()
         return pred_res
 
     def predict_dygraph(self, bert_config, data):
         with enable_to_static_guard(False):
-            with base.dygraph.guard(place):
+            with unique_name.guard():
                 bert = PretrainModelLayer(
                     config=bert_config, weight_sharing=False, use_fp16=False
                 )
@@ -210,7 +217,7 @@ def predict_dygraph(self, bert_config, data):
                 bert.set_dict(model_dict)
                 bert.eval()
 
-                input_vars = [base.dygraph.to_variable(x) for x in data]
+                input_vars = [paddle.to_tensor(x) for x in data]
                 (
                     src_ids,
                     pos_ids,
@@ -234,31 +241,30 @@ def predict_dygraph(self, bert_config, data):
                 return pred_res
 
     def predict_dygraph_jit(self, data):
-        with base.dygraph.guard(place):
-            bert = paddle.jit.load(self.model_save_prefix)
-            bert.eval()
-
-            (
-                src_ids,
-                pos_ids,
-                sent_ids,
-                input_mask,
-                mask_label,
-                mask_pos,
-                labels,
-            ) = data
-            pred_res = bert(
-                src_ids,
-                pos_ids,
-                sent_ids,
-                input_mask,
-                mask_label,
-                mask_pos,
-                labels,
-            )
-            pred_res = [var.numpy() for var in pred_res]
+        bert = paddle.jit.load(self.model_save_prefix)
+        bert.eval()
+
+        (
+            src_ids,
+            pos_ids,
+            sent_ids,
+            input_mask,
+            mask_label,
+            mask_pos,
+            labels,
+        ) = data
+        pred_res = bert(
+            src_ids,
+            pos_ids,
+            sent_ids,
+            input_mask,
+            mask_label,
+            mask_pos,
+            labels,
+        )
+        pred_res = [var.numpy() for var in pred_res]
 
-            return pred_res
+        return pred_res
 
     def predict_analysis_inference(self, data):
         output = PredictorTools(
@@ -267,6 +273,7 @@ def predict_analysis_inference(self, data):
         out = output()
         return out
 
+    @test_legacy_and_pt_and_pir
     def test_train(self):
         static_loss, static_ppl = self.train_static(
             self.bert_config, self.data_reader
@@ -280,6 +287,7 @@ def test_train(self):
         self.verify_predict()
 
     @test_sot_only
+    @test_legacy_and_pt_and_pir
     def test_train_composite(self):
         core._set_prim_backward_enabled(True)
         # core._add_skip_comp_ops("layer_norm")
@@ -297,43 +305,45 @@ def test_train_composite(self):
     def verify_predict(self):
         for data in self.data_reader.data_generator()():
             dygraph_pred_res = self.predict_dygraph(self.bert_config, data)
-            static_pred_res = self.predict_static(data)
-            dygraph_jit_pred_res = self.predict_dygraph_jit(data)
-            predictor_pred_res = self.predict_analysis_inference(data)
-
-            for dy_res, st_res, dy_jit_res, predictor_res in zip(
-                dygraph_pred_res,
-                static_pred_res,
-                dygraph_jit_pred_res,
-                predictor_pred_res,
-            ):
-                np.testing.assert_allclose(
-                    st_res,
-                    dy_res,
-                    rtol=1e-05,
-                    err_msg='dygraph_res: {},\n static_res: {}'.format(
-                        dy_res[~np.isclose(st_res, dy_res)],
-                        st_res[~np.isclose(st_res, dy_res)],
-                    ),
-                )
-                np.testing.assert_allclose(
-                    st_res,
-                    dy_jit_res,
-                    rtol=1e-05,
-                    err_msg='dygraph_jit_res: {},\n static_res: {}'.format(
-                        dy_jit_res[~np.isclose(st_res, dy_jit_res)],
-                        st_res[~np.isclose(st_res, dy_jit_res)],
-                    ),
-                )
-                np.testing.assert_allclose(
-                    st_res,
-                    predictor_res,
-                    rtol=1e-05,
-                    err_msg='dygraph_jit_res: {},\n static_res: {}'.format(
-                        predictor_res[~np.isclose(st_res, predictor_res)],
-                        st_res[~np.isclose(st_res, predictor_res)],
-                    ),
-                )
+            # TODO(pir-save-load): Fix this after we support save/load in PIR
+            if not use_pir_api():
+                static_pred_res = self.predict_static(data)
+                dygraph_jit_pred_res = self.predict_dygraph_jit(data)
+                predictor_pred_res = self.predict_analysis_inference(data)
+
+                for dy_res, st_res, dy_jit_res, predictor_res in zip(
+                    dygraph_pred_res,
+                    static_pred_res,
+                    dygraph_jit_pred_res,
+                    predictor_pred_res,
+                ):
+                    np.testing.assert_allclose(
+                        st_res,
+                        dy_res,
+                        rtol=1e-05,
+                        err_msg='dygraph_res: {},\n static_res: {}'.format(
+                            dy_res[~np.isclose(st_res, dy_res)],
+                            st_res[~np.isclose(st_res, dy_res)],
+                        ),
+                    )
+                    np.testing.assert_allclose(
+                        st_res,
+                        dy_jit_res,
+                        rtol=1e-05,
+                        err_msg='dygraph_jit_res: {},\n static_res: {}'.format(
+                            dy_jit_res[~np.isclose(st_res, dy_jit_res)],
+                            st_res[~np.isclose(st_res, dy_jit_res)],
+                        ),
+                    )
+                    np.testing.assert_allclose(
+                        st_res,
+                        predictor_res,
+                        rtol=1e-05,
+                        err_msg='dygraph_jit_res: {},\n static_res: {}'.format(
+                            predictor_res[~np.isclose(st_res, predictor_res)],
+                            st_res[~np.isclose(st_res, predictor_res)],
+                        ),
+                    )
             break
 
 
