【PIR API adaptor No.75-76】Migrate some ops into pir (#59627)

longranger2 · web-flow · commit 8f598d9fba34 · 2023-12-27T16:27:57.000+08:00
diff --git a/python/paddle/nn/functional/common.py b/python/paddle/nn/functional/common.py
@@ -2401,7 +2401,7 @@ def _is_list_or_turple_(data):
             "of 2 or 4 integers"
         )
 
-    if in_dynamic_mode():
+    if in_dynamic_or_pir_mode():
         out = _C_ops.fold(
             x, output_sizes, kernel_sizes, strides, paddings, dilations
         )
diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py
@@ -3176,7 +3176,7 @@ def sigmoid_focal_loss(
                 )
             )
 
-    if in_dynamic_mode():
+    if in_dynamic_or_pir_mode():
         place = _current_expected_place()
         one = _C_ops.full(logit.shape, 1.0, logit.dtype, place)
 
@@ -3193,7 +3193,10 @@ def sigmoid_focal_loss(
             ),
         )
 
-        alpha = base.dygraph.base.to_variable([alpha], dtype=loss.dtype)
+        if in_dynamic_mode():
+            alpha = base.dygraph.base.to_variable([alpha], dtype=loss.dtype)
+        else:
+            alpha = paddle.to_tensor(alpha, dtype=loss.dtype)
         alpha_t = _C_ops.add(
             _C_ops.multiply(alpha, label),
             _C_ops.multiply(
@@ -3202,7 +3205,8 @@ def sigmoid_focal_loss(
         )
         loss = _C_ops.multiply(alpha_t, loss)
 
-        gamma = base.dygraph.base.to_variable([gamma], dtype=loss.dtype)
+        if in_dynamic_mode():
+            gamma = base.dygraph.base.to_variable([gamma], dtype=loss.dtype)
         gamma_t = _C_ops.pow(_C_ops.subtract(one, p_t), gamma)
         loss = _C_ops.multiply(gamma_t, loss)
 
diff --git a/test/legacy_test/test_fold_op.py b/test/legacy_test/test_fold_op.py
@@ -133,10 +133,10 @@ def setUp(self):
         self.set_data()
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_pir=True)
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Y')
+        self.check_grad(['X'], 'Y', check_pir=True)
 
 
 class TestFold_Complex64(TestFoldOp):
diff --git a/test/legacy_test/test_sigmoid_focal_loss.py b/test/legacy_test/test_sigmoid_focal_loss.py
@@ -18,6 +18,7 @@
 
 import paddle
 from paddle import base
+from paddle.pir_utils import test_with_pir_api
 
 
 def call_sfl_functional(
@@ -119,6 +120,7 @@ def calc_sigmoid_focal_loss(
 
 
 class TestSigmoidFocalLoss(unittest.TestCase):
+    @test_with_pir_api
     def test_SigmoidFocalLoss(self):
         logit_np = np.random.uniform(0.1, 0.8, size=(2, 3, 4, 10)).astype(
             np.float64

Original file line number	Diff line number	Diff line change
`@@ -2401,7 +2401,7 @@ def _is_list_or_turple_(data):`
`2401`	`2401`	`"of 2 or 4 integers"`
`2402`	`2402`	`)`
`2403`	`2403`
`2404`		`- if in_dynamic_mode():`
	`2404`	`+ if in_dynamic_or_pir_mode():`
`2405`	`2405`	`out = _C_ops.fold(`
`2406`	`2406`	`x, output_sizes, kernel_sizes, strides, paddings, dilations`
`2407`	`2407`	`)`
Original file line number	Diff line number	Diff line change
`@@ -3176,7 +3176,7 @@ def sigmoid_focal_loss(`
`3176`	`3176`	`)`
`3177`	`3177`	`)`
`3178`	`3178`
`3179`		`- if in_dynamic_mode():`
	`3179`	`+ if in_dynamic_or_pir_mode():`
`3180`	`3180`	`place = _current_expected_place()`
`3181`	`3181`	`one = _C_ops.full(logit.shape, 1.0, logit.dtype, place)`
`3182`	`3182`
`@@ -3193,7 +3193,10 @@ def sigmoid_focal_loss(`
`3193`	`3193`	`),`
`3194`	`3194`	`)`
`3195`	`3195`
`3196`		`- alpha = base.dygraph.base.to_variable([alpha], dtype=loss.dtype)`
	`3196`	`+ if in_dynamic_mode():`
	`3197`	`+ alpha = base.dygraph.base.to_variable([alpha], dtype=loss.dtype)`
	`3198`	`+ else:`
	`3199`	`+ alpha = paddle.to_tensor(alpha, dtype=loss.dtype)`
`3197`	`3200`	`alpha_t = _C_ops.add(`
`3198`	`3201`	`_C_ops.multiply(alpha, label),`
`3199`	`3202`	`_C_ops.multiply(`
`@@ -3202,7 +3205,8 @@ def sigmoid_focal_loss(`
`3202`	`3205`	`)`
`3203`	`3206`	`loss = _C_ops.multiply(alpha_t, loss)`
`3204`	`3207`
`3205`		`- gamma = base.dygraph.base.to_variable([gamma], dtype=loss.dtype)`
	`3208`	`+ if in_dynamic_mode():`
	`3209`	`+ gamma = base.dygraph.base.to_variable([gamma], dtype=loss.dtype)`
`3206`	`3210`	`gamma_t = _C_ops.pow(_C_ops.subtract(one, p_t), gamma)`
`3207`	`3211`	`loss = _C_ops.multiply(gamma_t, loss)`
`3208`	`3212`