[PIR]Migrate CrossEntropyLoss into pir (#58519)

python/paddle/nn/functional/common.py

@@ -2050,7 +2050,7 @@ def label_smooth(label, prior_dist=None, epsilon=0.1, name=None):
     if epsilon > 1.0 or epsilon < 0.0:
         raise ValueError("The value of epsilon must be between 0 and 1.")
 
-    if in_dynamic_mode():
+    if in_dynamic_or_pir_mode():
         return _C_ops.label_smooth(label, prior_dist, float(epsilon))
 
     check_variable_and_dtype(

python/paddle/nn/functional/input.py

@@ -17,7 +17,7 @@
 from ...base.data_feeder import check_variable_and_dtype
 from ...base.layer_helper import LayerHelper
 from ...common_ops_import import Variable
-from ...framework import in_dynamic_mode, in_dynamic_or_pir_mode
+from ...framework import in_dynamic_or_pir_mode
 
 __all__ = []
 
@@ -89,7 +89,7 @@ def one_hot(x, num_classes, name=None):
 
     """
 
-    if in_dynamic_mode():
+    if in_dynamic_or_pir_mode():
         return _C_ops.one_hot(x, num_classes)
     else:
         check_variable_and_dtype(x, 'input', ['int32', 'int64'], 'one_hot_v2')

python/paddle/nn/functional/loss.py

@@ -22,7 +22,7 @@
 from paddle.utils import deprecated
 
 from ...base.data_feeder import check_variable_and_dtype
-from ...base.framework import _current_expected_place
+from ...base.framework import _current_expected_place, in_pir_mode
 from ...base.layer_helper import LayerHelper
 from ...common_ops_import import Variable
 from ...tensor.manipulation import reshape
@@ -2935,24 +2935,31 @@ def cross_entropy(
             ['uint8', 'int8', 'int16', 'int32', 'int64', 'float32', 'float64'],
             'softmax_cross_entropy',
         )
-        attrs = {
-            'soft_label': soft_label,
-            'ignore_index': ignore_index,
-            'numeric_stable_mode': True,
-            'axis': axis,
-            'use_softmax': use_softmax,
-        }
-        helper = LayerHelper('softmax_with_cross_entropy', **locals())
-        softmax = helper.create_variable_for_type_inference(dtype=input.dtype)
-        out = helper.create_variable_for_type_inference(dtype=input.dtype)
+        if in_pir_mode():
+            softmax, out = _C_ops.cross_entropy_with_softmax(
+                input, label, soft_label, use_softmax, True, ignore_index, axis
+            )
+        else:
+            attrs = {
+                'soft_label': soft_label,
+                'ignore_index': ignore_index,
+                'numeric_stable_mode': True,
+                'axis': axis,
+                'use_softmax': use_softmax,
+            }
+            helper = LayerHelper('softmax_with_cross_entropy', **locals())
+            softmax = helper.create_variable_for_type_inference(
+                dtype=input.dtype
+            )
+            out = helper.create_variable_for_type_inference(dtype=input.dtype)
 
-        outputs = {'Softmax': softmax, 'Loss': out}
-        helper.append_op(
-            type='softmax_with_cross_entropy',
-            inputs={'Logits': input, 'Label': label},
-            outputs=outputs,
-            attrs=attrs,
-        )
+            outputs = {'Softmax': softmax, 'Loss': out}
+            helper.append_op(
+                type='softmax_with_cross_entropy',
+                inputs={'Logits': input, 'Label': label},
+                outputs=outputs,
+                attrs=attrs,
+            )
 
         if weight is not None:
             check_variable_and_dtype(
@@ -3036,19 +3043,21 @@ def cross_entropy(
                 if weight is None:
                     mask = paddle.cast(mask, dtype=out_sum.dtype)
                     count = paddle.sum(mask, name=name)
-                    ret = out_sum / (count + (count == 0.0))
+                    ret = out_sum / (count + paddle.equal(count, 0.0))
                 else:
                     mask = paddle.cast(mask, weight_gather_reshape.dtype)
                     weight_ignored = paddle.multiply(
                         mask, weight_gather_reshape
                     )
                     weight_sum = paddle.sum(weight_ignored, name=name)
-                    ret = out_sum / (weight_sum + (weight_sum == 0.0))
+                    ret = out_sum / (weight_sum + paddle.equal(weight_sum, 0.0))
                 return ret
             elif weight is not None:
                 out_sum = paddle.sum(out, name=name)
                 total_weight = paddle.sum(weight_gather_reshape)
-                return out_sum / (total_weight + (total_weight == 0.0))
+                return out_sum / (
+                    total_weight + paddle.equal(total_weight, 0.0)
+                )
             else:
                 return paddle.mean(out, name=name)
 

test/legacy_test/test_cross_entropy_loss.py

@@ -21,6 +21,7 @@
 import paddle
 from paddle import base
 from paddle.base import Program, program_guard
+from paddle.pir_utils import test_with_pir_api
 
 
 def label_smooth(label, C, epsilon, is_onehot=True):
@@ -272,6 +273,7 @@ def test_softmax_with_cross_entropy(self):
 
     # soft_label test start
     # soft_label test 1
+    @test_with_pir_api
     def test_cross_entropy_loss_soft_1d(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -360,6 +362,7 @@ def test_cross_entropy_loss_soft_1d(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # soft_label test 2
+    @test_with_pir_api
     def test_cross_entropy_loss_soft_1d_weight(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -460,6 +463,7 @@ def test_cross_entropy_loss_soft_1d_weight(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # soft_label test 3
+    @test_with_pir_api
     def test_cross_entropy_loss_soft_1d_mean(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -544,6 +548,7 @@ def test_cross_entropy_loss_soft_1d_mean(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # soft_label test 4
+    @test_with_pir_api
     def test_cross_entropy_loss_soft_1d_weight_mean(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -634,6 +639,7 @@ def test_cross_entropy_loss_soft_1d_weight_mean(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # soft_label test 5
+    @test_with_pir_api
     def test_cross_entropy_loss_soft_2d(self):
         def inner_cross_entropy_loss_soft_2d(soft_label):
             self.numeric_stable_mode = False
@@ -739,6 +745,7 @@ def inner_cross_entropy_loss_soft_2d(soft_label):
         inner_cross_entropy_loss_soft_2d(False)
 
     # soft_label test 6
+    @test_with_pir_api
     def test_cross_entropy_loss_soft_2d_weight_mean(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -840,6 +847,7 @@ def test_cross_entropy_loss_soft_2d_weight_mean(self):
     # soft_label test end
 
     # label_smoothing test 1
+    @test_with_pir_api
     def test_cross_entropy_loss_onehot_label_smoothing_1d(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -937,6 +945,7 @@ def test_cross_entropy_loss_onehot_label_smoothing_1d(self):
         paddle.enable_static()
 
     # label_smoothing test 2
+    @test_with_pir_api
     def test_cross_entropy_loss_onehot_label_smoothing_1d_weight_mean(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -1036,6 +1045,7 @@ def test_cross_entropy_loss_onehot_label_smoothing_1d_weight_mean(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # label_smoothing test 3
+    @test_with_pir_api
     def test_cross_entropy_loss_onehot_label_smoothing_2d(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -1143,6 +1153,7 @@ def test_cross_entropy_loss_onehot_label_smoothing_2d(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # label_smoothing test 4
+    @test_with_pir_api
     def test_cross_entropy_loss_onehot_label_smoothing_2d_weight_mean(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -1253,6 +1264,7 @@ def test_cross_entropy_loss_onehot_label_smoothing_2d_weight_mean(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # label_smoothing test 5
+    @test_with_pir_api
     def test_cross_entropy_loss_integer_label_smoothing_1d(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -1350,6 +1362,7 @@ def test_cross_entropy_loss_integer_label_smoothing_1d(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # label_smoothing test 6
+    @test_with_pir_api
     def test_cross_entropy_loss_integer_label_smoothing_1d_weight_mean(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -1452,6 +1465,7 @@ def test_cross_entropy_loss_integer_label_smoothing_1d_weight_mean(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # label_smoothing test 7
+    @test_with_pir_api
     def test_cross_entropy_loss_integer_label_smoothing_2d(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -1557,6 +1571,7 @@ def test_cross_entropy_loss_integer_label_smoothing_2d(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # label_smoothing test 8
+    @test_with_pir_api
     def test_cross_entropy_loss_integer_label_smoothing_2d_weight_mean(self):
         self.numeric_stable_mode = False
         self.soft_label = True
@@ -1667,7 +1682,7 @@ def test_cross_entropy_loss_integer_label_smoothing_2d_weight_mean(self):
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
     # label_smoothing test end
-
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_with_mean_ignore(self):
         input_np = np.random.random([2, 4]).astype(self.dtype)
         label_np = np.random.randint(0, 4, size=(2)).astype(np.int64)
@@ -1714,6 +1729,7 @@ def test_cross_entropy_loss_1d_with_mean_ignore(self):
         np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_with_mean_ignore_negative(self):
         N = 100
         C = 200
@@ -1763,6 +1779,7 @@ def test_cross_entropy_loss_1d_with_mean_ignore_negative(self):
         np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_with_weight_mean_ignore(self):
         N = 100
         C = 200
@@ -1846,6 +1863,7 @@ def test_cross_entropy_loss_1d_with_weight_mean_ignore_exceedlabel(self):
 
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_with_weight_mean(self):
         input_np = np.random.random([2, 4]).astype(self.dtype)
         label_np = np.random.randint(0, 4, size=(2)).astype(np.int64)
@@ -1901,6 +1919,7 @@ def test_cross_entropy_loss_1d_with_weight_mean(self):
         np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_with_weight_sum(self):
         input_np = np.random.random([100, 200]).astype(self.dtype)  # N,C
         label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  # N,1
@@ -1954,6 +1973,7 @@ def test_cross_entropy_loss_1d_with_weight_sum(self):
         np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_with_weight_none(self):
         input_np = np.random.random([100, 200]).astype(self.dtype)  # N,C
         label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  # N,1
@@ -2011,6 +2031,7 @@ def test_cross_entropy_loss_1d_with_weight_none(self):
         np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_with_weight_none_func(self):
         input_np = np.random.random([100, 200]).astype(self.dtype)  # N,C
         label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  # N
@@ -2064,6 +2085,7 @@ def test_cross_entropy_loss_1d_with_weight_none_func(self):
         np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_mean(self):
         input_np = np.random.random([100, 200]).astype(self.dtype)  # N,C
         label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  # N,1
@@ -2102,6 +2124,7 @@ def test_cross_entropy_loss_1d_mean(self):
         np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_sum(self):
         input_np = np.random.random([100, 200]).astype(self.dtype)  # N,C
         label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  # N,1
@@ -2144,6 +2167,7 @@ def test_cross_entropy_loss_1d_sum(self):
         np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_1d_none(self):
         input_np = np.random.random([100, 200]).astype(self.dtype)  # N,C
         label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  # N,1
@@ -2188,6 +2212,7 @@ def test_cross_entropy_loss_1d_none(self):
         np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_2d_with_weight_none(self):
         input_np = np.random.random(size=(2, 2, 2, 3)).astype(
             self.dtype
@@ -2250,6 +2275,7 @@ def test_cross_entropy_loss_2d_with_weight_none(self):
         np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_2d_with_weight_axis_change_mean(self):
         input_np = np.random.random(size=(2, 3, 2, 2)).astype(
             self.dtype
@@ -2341,6 +2367,7 @@ def test_cross_entropy_loss_2d_with_weight_mean_ignore_exceedlabel(self):
         )[0]
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_2d_with_weight_mean(self):
         input_np = np.random.random(size=(2, 2, 2, 3)).astype(
             self.dtype
@@ -2400,6 +2427,7 @@ def test_cross_entropy_loss_2d_with_weight_mean(self):
         np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_2d_with_weight_sum(self):
         input_np = np.random.random(size=(2, 2, 2, 3)).astype(
             self.dtype
@@ -2460,6 +2488,7 @@ def test_cross_entropy_loss_2d_with_weight_sum(self):
         np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_2d_none(self):
         input_np = np.random.random(size=(2, 2, 2, 3)).astype(
             self.dtype
@@ -2513,6 +2542,7 @@ def test_cross_entropy_loss_2d_none(self):
         np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_2d_mean(self):
         input_np = np.random.random(size=(2, 2, 2, 3)).astype(
             self.dtype
@@ -2567,6 +2597,7 @@ def test_cross_entropy_loss_2d_mean(self):
         np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
         np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)
 
+    @test_with_pir_api
     def test_cross_entropy_loss_2d_sum(self):
         input_np = np.random.random(size=(2, 2, 2, 3)).astype(
             self.dtype