[PIR] mark_mask_of_dropout_as_intermediate

paddle/fluid/framework/new_executor/pir_adaptor/pir_adaptor_util.cc

@@ -425,7 +425,7 @@ void HandleForSpecialOp(pir::Operation* op,
         if (place.GetType() == phi::AllocationType::UNDEFINED) {
           place = phi::CPUPlace();
         }
-        if (phi::product(dim) >= 0) {
+        if (!common::contain_unknown_dim(dim)) {
           phi::DenseTensorMeta meta(dtype.data(), dim);
           t->set_meta(meta);
           auto* dev_ctx = platform::DeviceContextPool::Instance().Get(place);

paddle/fluid/ir_adaptor/translator/op_translator.cc

@@ -1983,7 +1983,7 @@ struct SelectInputOpTranscriber : public OpTranscriber {
         undefine_value.defining_op()->set_attribute(
             "dtype",
             dialect::DataTypeAttribute::get(
-                ctx, PirTypeToPhiDType(undefined_var_type.dtype())));
+                ctx, dialect::TransToPhiDataType(undefined_var_type.dtype())));
         auto& attribute_translator = AttributeTranslator::instance();
         undefine_value.defining_op()->set_attribute(
             "shape",

paddle/fluid/ir_adaptor/translator/utils.cc

@@ -105,36 +105,5 @@ std::vector<std::string> CheckUnregisteredOperation(
   return unregistered_ops;
 }
 
-phi::DataType PirTypeToPhiDType(pir::Type type) {
-  if (type.isa<pir::UInt8Type>()) {
-    return phi::DataType::UINT8;
-  } else if (type.isa<pir::Int8Type>()) {
-    return phi::DataType::INT8;
-  } else if (type.isa<pir::Int16Type>()) {
-    return phi::DataType::INT16;
-  } else if (type.isa<pir::Int32Type>()) {
-    return phi::DataType::INT32;
-  } else if (type.isa<pir::Int64Type>()) {
-    return phi::DataType::INT64;
-  } else if (type.isa<pir::Float32Type>()) {
-    return phi::DataType::FLOAT32;
-  } else if (type.isa<pir::Float64Type>()) {
-    return phi::DataType::FLOAT64;
-  } else if (type.isa<pir::BoolType>()) {
-    return phi::DataType::BOOL;
-  } else if (type.isa<pir::Float16Type>()) {
-    return phi::DataType::FLOAT16;
-  } else if (type.isa<pir::BFloat16Type>()) {
-    return phi::DataType::BFLOAT16;
-  } else if (type.isa<pir::Complex64Type>()) {
-    return phi::DataType::COMPLEX64;
-  } else if (type.isa<pir::Complex128Type>()) {
-    return phi::DataType::COMPLEX128;
-  } else {
-    PADDLE_THROW(phi::errors::Unimplemented(
-        "Unsupported pirType `%s` when casting it into phi::DataType.", type));
-  }
-}
-
 }  // namespace translator
 }  // namespace paddle

paddle/fluid/ir_adaptor/translator/utils.h

@@ -100,7 +100,5 @@ inline DataType VarTypeToDataType(
   }
 }
 
-phi::DataType PirTypeToPhiDType(pir::Type type);
-
 }  // namespace translator
 }  // namespace paddle

paddle/fluid/pir/dialect/operator/ir/ops.yaml

@@ -371,7 +371,8 @@
   kernel :
     func : dropout
     data_type : x
-  optional : seed_tensor, mask
+  optional : seed_tensor
+  intermediate : mask
   backward : dropout_grad
 
 - op : einsum

paddle/phi/api/yaml/legacy_ops.yaml

@@ -334,6 +334,7 @@
     func : dropout
     data_type : x
   optional : seed_tensor
+  intermediate : mask
   backward : dropout_grad
 
 - op : einsum

python/paddle/nn/functional/common.py

@@ -1137,7 +1137,7 @@ def dropout(
             if default_main_program().random_seed != 0:
                 seed = default_main_program().random_seed
 
-            out, mask = _C_ops.dropout(
+            out = _C_ops.dropout(
                 x,
                 None,
                 p,

test/legacy_test/test_dropout_op.py

@@ -20,7 +20,7 @@
 from utils import static_guard
 
 import paddle
-from paddle import _C_ops, base, static
+from paddle import base, static
 from paddle.autograd.ir_backward import grad
 from paddle.base import Program, Scope, core, program_guard
 from paddle.base.executor import scope_guard
@@ -79,6 +79,9 @@ def setUp(self):
             'Out': self.inputs['X'],
             'Mask': np.ones((32, 64)).astype('uint8'),
         }
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
         # Because prim op compare res with dygraph
         # when p = 0 dropout api return x,in dygraph mode x_grad = out_grad,
         # but in static mode x_grad = []
@@ -108,6 +111,9 @@ def setUp(self):
         # when p = 0 dropout api return x,in dygraph mode x_grad = out_grad,
         # but in static mode x_grad = []
         self.enable_check_static_comp = False
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
 
 class TestDropoutOpInput1d(OpTest):
@@ -122,6 +128,9 @@ def setUp(self):
             'Out': self.inputs['X'],
             'Mask': np.ones(2000).astype('uint8'),
         }
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
         # Because prim op compare res with dygraph
         # when p = 0 dropout api return x,in dygraph mode x_grad = out_grad,
         # but in static mode x_grad = []
@@ -147,6 +156,9 @@ def setUp(self):
             'Out': np.zeros((32, 64)).astype('float32'),
             'Mask': np.zeros((32, 64)).astype('uint8'),
         }
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
 
 class TestDropoutOp2_ZeroDim(TestDropoutOp2):
@@ -161,6 +173,9 @@ def setUp(self):
             'Out': np.zeros(()).astype('float32'),
             'Mask': np.zeros(()).astype('uint8'),
         }
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
 
 class TestDropoutOp3(TestDropoutOp):
@@ -179,6 +194,9 @@ def setUp(self):
         # when p = 0 dropout api return x,in dygraph mode x_grad = out_grad,
         # but in static mode x_grad = []
         self.enable_check_static_comp = False
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
 
 @skip_check_grad_ci(reason="For inference, check_grad is not required.")
@@ -193,6 +211,9 @@ def setUp(self):
         self.outputs = {
             'Out': self.inputs['X'] * (1.0 - self.attrs['dropout_prob'])
         }
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
     def test_check_output(self):
         self.check_output(check_prim=True, check_prim_pir=True, check_pir=True)
@@ -210,6 +231,9 @@ def setUp(self):
         self.outputs = {
             'Out': self.inputs['X'] * (1.0 - self.attrs['dropout_prob'])
         }
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
     def test_check_output(self):
         self.check_output(check_prim=True, check_prim_pir=True, check_pir=True)
@@ -232,6 +256,9 @@ def setUp(self):
             'Out': np.zeros((32, 64)).astype('float32'),
             'Mask': np.zeros((32, 64)).astype('uint8'),
         }
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
 
 class TestDropoutOp7(TestDropoutOp):
@@ -255,6 +282,9 @@ def setUp(self):
         # when p = 0 dropout api return x,in dygraph mode x_grad = out_grad,
         # but in static mode x_grad = []
         self.enable_check_static_comp = False
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
 
 @skip_check_grad_ci(reason="For inference, check_grad is not required.")
@@ -272,6 +302,9 @@ def setUp(self):
             'dropout_implementation': 'upscale_in_train',
         }
         self.outputs = {'Out': self.inputs['X']}
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
     def test_check_output(self):
         self.check_output(check_prim=True, check_prim_pir=True, check_pir=True)
@@ -291,6 +324,9 @@ def setUp(self):
             'dropout_implementation': 'upscale_in_train',
         }
         self.outputs = {'Out': self.inputs['X']}
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
     def test_check_output(self):
         self.check_output(check_prim=True, check_prim_pir=True, check_pir=True)
@@ -313,6 +349,9 @@ def setUp(self):
             'Out': self.inputs['X'],
             'Mask': np.ones((32, 64)).astype('uint8'),
         }
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
         # Because prim op compare res with dygraph
         # when p = 0 dropout api return x,in dygraph mode x_grad = out_grad,
         # but in static mode x_grad = []
@@ -355,6 +394,9 @@ def setUp(self):
             'is_test': True,
         }
         self.outputs = {'Out': out}
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
     def init_test_case(self):
         self.input_size = [32, 64]
@@ -404,6 +446,9 @@ def setUp(self):
             ),
             'Mask': np.zeros((32, 64)).astype('uint8'),
         }
+        self.python_out_sig = [
+            "Out"
+        ]  # python out sig is customized output signature.
 
     def test_check_output(self):
         self.check_output(check_prim=True, check_prim_pir=True, check_pir=True)
@@ -1328,55 +1373,6 @@ def cal_grad_upscale_train(self, mask, prob):
     def cal_grad_downscale_in_infer(self, mask):
         return mask.astype("float32")
 
-    def test_backward_downscale_in_infer(self):
-        for place in self.places:
-            with base.dygraph.guard(place):
-                input = paddle.uniform([40, 40], dtype="float32")
-                input.stop_gradient = False
-                out, mask = _C_ops.dropout(
-                    input, None, 0.5, False, "downgrade_in_infer", 0, False
-                )
-                out.backward()
-
-                np.testing.assert_array_equal(
-                    input.gradient(),
-                    self.cal_grad_downscale_in_infer(mask.numpy()),
-                )
-
-    def test_backward_upscale_train(self):
-        for place in self.places:
-            with base.dygraph.guard(place):
-                prob = 0.5
-                input = paddle.uniform([40, 40], dtype="float32")
-                input.stop_gradient = False
-                out, mask = _C_ops.dropout(
-                    input, None, 0.5, False, "upscale_in_train", 0, False
-                )
-                out.backward()
-
-                np.testing.assert_allclose(
-                    input.gradient(),
-                    self.cal_grad_upscale_train(mask.numpy(), prob),
-                    rtol=1e-05,
-                )
-
-    def test_backward_upscale_train_2(self):
-        for place in self.places:
-            with base.dygraph.guard(place):
-                prob = 0.3
-                input = paddle.uniform([40, 40], dtype="float32")
-                input.stop_gradient = False
-                out, mask = _C_ops.dropout(
-                    input, None, 0.3, False, "upscale_in_train", 0, False
-                )
-                out.backward()
-
-                np.testing.assert_allclose(
-                    input.gradient(),
-                    self.cal_grad_upscale_train(mask.numpy(), prob),
-                    rtol=1e-05,
-                )
-
 
 class TestDropOutWithProbTensor(unittest.TestCase):
     def setUp(self):