【PIR API adaptor No.248、249】 Migrate CTCLoss/RNNTLossinto pir

python/paddle/nn/functional/loss.py

@@ -1893,7 +1893,7 @@ def warpctc(
         input_length=None,
         label_length=None,
     ):
-        if in_dynamic_mode():
+        if in_dynamic_or_pir_mode():
             if input_length is None or label_length is None:
                 raise ValueError(
                     "input_length and label_length must not be None in dygraph mode!"
@@ -2017,7 +2017,7 @@ def rnnt_loss(
     def warprnnt(
         input, label, input_length, label_length, blank=0, fastemit_lambda=0.001
     ):
-        if in_dynamic_mode():
+        if in_dynamic_or_pir_mode():
             loss_out = _C_ops.warprnnt(
                 input,
                 label,

python/paddle/tensor/linalg.py

@@ -3500,13 +3500,7 @@ def lstsq(x, y, rcond=None, driver=None, name=None):
             x, y, rcond, driver
         )
         if driver == "gels":
-            if in_dynamic_mode():
-                rank = paddle.empty(shape=[0], dtype=paddle.int32)
-
-            else:
-                rank = paddle.empty(
-                    shape=[0], dtype=paddle.base.core.DataType.INT32
-                )
+            rank = paddle.empty(shape=[0], dtype="int32")
             singular_values = paddle.empty(shape=[0], dtype=x.dtype)
         elif driver == "gelsy":
             singular_values = paddle.empty(shape=[0], dtype=x.dtype)

test/legacy_test/test_warpctc_op.py

@@ -21,7 +21,7 @@
 
 import paddle
 import paddle.nn.functional as F
-from paddle.base import Program, core, program_guard
+from paddle.base import core
 
 CUDA_BLOCK_SIZE = 32
 
@@ -394,7 +394,7 @@ def setUp(self):
         }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_pir=True)
 
     def test_check_grad(self):
         self.outputs['WarpCTCGrad'] = self.gradient
@@ -404,13 +404,15 @@ def test_check_grad(self):
                 "Loss",
                 max_relative_error=0.009,
                 check_dygraph=False,
+                check_pir=True,
             )
         else:
             self.check_grad(
                 ["Logits"],
                 "Loss",
                 max_relative_error=0.007,
                 check_dygraph=False,
+                check_pir=True,
             )
 
 
@@ -516,17 +518,21 @@ def setUp(self):
         }
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_pir=True)
 
     def test_check_grad(self):
         self.outputs['WarpCTCGrad'] = self.gradient
-        self.check_grad(["Logits"], "Loss")
+        self.check_grad(["Logits"], "Loss", check_pir=True)
 
 
 class TestWarpCTCOpError(unittest.TestCase):
     def test_errors(self):
         paddle.enable_static()
-        with program_guard(Program(), Program()):
+        main_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(
+            main_program=main_program, startup_program=startup_program
+        ):
             logits = paddle.static.data(
                 name='logits', shape=[5, 16, 6], dtype='float32'
             )
@@ -660,7 +666,7 @@ def test_class_api(self):
         np.testing.assert_allclose(loss_pd, loss_np, rtol=1e-05, atol=1)
 
     def test_eager_ctcloss(self):
-        def test_functinal_api():
+        def test_functional_api():
             self.batch_size = 4
             self.num_classes = CUDA_BLOCK_SIZE + 2
             self.logits_length = np.array([4, 1, 3, 3], dtype=np.int64)
@@ -730,7 +736,7 @@ def test_functinal_api():
                 loss_pd_sum, loss_np_sum, rtol=1e-05, atol=1
             )
 
-        test_functinal_api()
+        test_functional_api()
 
 
 if __name__ == "__main__":

test/legacy_test/test_warprnnt_op.py

@@ -246,7 +246,7 @@ def test_check_grad(self):
 class TestWarpRNNTFP64Op(TestWarpRNNTOp):
     def test_check_output(self):
         self.acts.astype(np.float64)
-        self.check_output()
+        self.check_output(check_pir=True)
 
     def test_check_grad(self):
         self.acts.astype(np.float64)