[PIR] add python api for if

paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc

@@ -215,8 +215,36 @@ void IfInstruction::CopyBranchOutput(const std::vector<std::string>& var_names,
 }
 
 void IfInstruction::Run() {
-  DeviceContext().Wait();
-  if (cond_var_->Get<phi::DenseTensor>().data<bool>()[0]) {
+  bool cond = true;
+  if (cond_var_->IsType<phi::DenseTensor>()) {
+    auto& cond_tensor = cond_var_->Get<phi::DenseTensor>();
+    if (paddle::platform::is_cpu_place(cond_tensor.place())) {
+      cond = cond_tensor.data<bool>()[0];
+    } else {
+      // when platform::is_gpu_place(cond.place()) or
+      // platform::is_xpu_place(cond.place()) is true
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) || \
+    defined(PADDLE_WITH_XPU) || defined(PADDLE_WITH_CUSTOM_DEVICE)
+      DeviceContext().Wait();
+      phi::DenseTensor cpu_cond;
+      paddle::framework::TensorCopySync(
+          cond_tensor, platform::CPUPlace(), &cpu_cond);
+      cond = cpu_cond.data<bool>()[0];
+#else
+      PADDLE_THROW(paddle::platform::errors::PreconditionNotMet(
+          "This version of PaddlePaddle does NOT support GPU/XPU but got "
+          "GPU/XPU tensor Cond in WhileOp. Please compile WITH_GPU or "
+          "WITH_XPU option."));
+#endif
+    }
+  } else if (cond_var_->IsType<VariableRefArray>()) {
+    auto& cond_array = cond_var_->Get<VariableRefArray>();
+    cond = std::all_of(
+        cond_array.begin(), cond_array.end(), [](const Variable* t) {
+          return t->Get<phi::DenseTensor>().numel() != 0;
+        });
+  }
+  if (cond) {
     true_branch_inter_->Run({}, false);
     CopyBranchOutput(true_branch_outputs_, true_branch_inter_);
   } else {

paddle/fluid/pir/dialect/operator/ir/api_builder.h

@@ -43,7 +43,7 @@ class ApiBuilder {
   void SetParameter(const std::string& name,
                     std::unique_ptr<pir::Parameter>&& parameter);
 
-  std::shared_ptr<pir::Builder> GetBuilder() { return builder_; }
+  const std::shared_ptr<pir::Builder>& GetBuilder() const { return builder_; }
 
   const pir::InsertionPoint& GetCurrentInsertionPoint() const {
     return builder_->insertion_point();

paddle/fluid/pir/dialect/operator/ir/control_flow_op.cc

@@ -217,8 +217,14 @@ void IfOp::VerifyRegion() {
       1u,
       phi::errors::PreconditionNotMet("The size %d of true_region must be 1.",
                                       (*this)->region(0).size()));
-  if ((*this)->region(0).front().size() > 0) {
-    auto &true_last_op = (*this)->region(0).front().back();
+  if ((*this)->num_results() != 0) {
+    auto &true_block = (*this)->region(0).front();
+    PADDLE_ENFORCE_GT(
+        true_block.size(),
+        0u,
+        phi::errors::PreconditionNotMet(
+            "The true block must have at least one op yield op."));
+    auto &true_last_op = true_block.back();
     PADDLE_ENFORCE_EQ(true,
                       true_last_op.isa<pir::YieldOp>(),
                       phi::errors::PreconditionNotMet(
@@ -228,15 +234,19 @@ void IfOp::VerifyRegion() {
                       phi::errors::PreconditionNotMet(
                           "The size of last of true block op's input must be "
                           "equal to IfOp's outputs num."));
-  }
-  VLOG(4) << "Start Verifying false branch.";
-  PADDLE_ENFORCE_EQ(
-      (*this)->region(1).size(),
-      1u,
-      phi::errors::PreconditionNotMet("The size %d of false_region must be 1.",
-                                      (*this)->region(0).size()));
-  if ((*this)->region(1).front().size() > 0) {
-    auto &false_last_op = (*this)->region(1).front().back();
+    VLOG(4) << "Start Verifying false branch.";
+    PADDLE_ENFORCE_EQ((*this)->region(1).size(),
+                      1u,
+                      phi::errors::PreconditionNotMet(
+                          "The size %d of false_region must be 1.",
+                          (*this)->region(0).size()));
+    auto &false_block = (*this)->region(1).front();
+    PADDLE_ENFORCE_GT(
+        false_block.size(),
+        0u,
+        phi::errors::PreconditionNotMet(
+            "The false block must have at least one op yield op."));
+    auto &false_last_op = false_block.back();
     PADDLE_ENFORCE_EQ(true,
                       false_last_op.isa<pir::YieldOp>(),
                       phi::errors::PreconditionNotMet(

paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc

@@ -1124,27 +1124,6 @@ void HandleForIfOp(
           "[%d]'s input of [%s] op MUST in map pair", 0, op_item->name()));
   auto new_cond = map_value_pair->at(old_cond);
 
-  // NOTE(zhangbo): IfOp's input cond should be a cpu type.
-  AllocatedDenseTensorType new_cond_type =
-      new_cond.type().dyn_cast<AllocatedDenseTensorType>();
-  if (new_cond_type) {
-    if (new_cond_type.place().GetType() == phi::AllocationType::GPU) {
-      auto out_type = AllocatedDenseTensorType::get(
-          ctx, phi::CPUPlace(), old_cond.type().dyn_cast<DenseTensorType>());
-      phi::KernelKey kernel_key(
-          phi::Backend::GPU, phi::DataLayout::ALL_LAYOUT, phi::DataType::BOOL);
-      new_cond = AddPlaceTransferOp(new_cond,
-                                    out_type,
-                                    new_cond_type.place(),
-                                    phi::CPUPlace(),
-                                    kernel_key,
-                                    block);
-    }
-  } else {
-    PADDLE_THROW(
-        phi::errors::Unimplemented("IfOp onlu support DenseTensorType"));
-  }
-
   // Create IfOp and insert to kernel dialect program
   pir::Builder builder(ctx, block);
   auto old_ifop = op_item->dyn_cast<IfOp>();

paddle/fluid/pybind/control_flow_api.cc

@@ -44,6 +44,7 @@ using paddle::pybind::PyIfOp;
 using paddle::pybind::PyWhileOp;
 using pir::Block;
 using pir::Builder;
+using pir::CombineOp;
 using pir::Operation;
 using pir::Program;
 using pir::Region;
@@ -60,13 +61,19 @@ void BindIfOp(py::module* m) {
     return PyIfOp(ApiBuilder::Instance().GetBuilder()->Build<IfOp>(
         cond, std::vector<Type>{}));
   });
+  m->def("build_if_op", [](const std::vector<Value>& cond) {
+    auto& builder = ApiBuilder::Instance().GetBuilder();
+    auto new_cond = builder->Build<CombineOp>(cond).out();
+    return PyIfOp(builder->Build<IfOp>(new_cond, std::vector<Type>{}));
+  });
   py::class_<PyIfOp> if_op(*m, "IfOp", R"DOC(
     The PyIfOp is a encapsulation of IfOp. Compared with ifOp, it provides an additional 'update_output' interface.
     The 'update_output' interface will construct a new IfOp operation to replace its underlying IfOp. In the process, the original
     IfOp will be destroyed. In order to avoid the risk of memory used in python side, We encapsulate PyIfOp to python api.
   )DOC");
   if_op.def("true_block", &PyIfOp::true_block, return_value_policy::reference)
       .def("false_block", &PyIfOp::false_block, return_value_policy::reference)
+      .def("cond", &PyIfOp::cond)
       .def("update_output", &PyIfOp::UpdateOutput)
       .def("as_operation", &PyIfOp::operation, return_value_policy::reference)
       .def("results", [](PyIfOp& self) -> py::list {

python/paddle/base/layer_helper.py

@@ -22,6 +22,7 @@
     Parameter,
     dtype_is_floating,
     in_dygraph_mode,
+    in_pir_mode,
 )
 from .layer_helper_base import LayerHelperBase
 from .param_attr import ParamAttr
@@ -132,6 +133,8 @@ def append_bias_op(self, input_var, dim_start=1, dim_end=None):
         b = self.create_parameter(
             attr=bias_attr, shape=size, dtype=input_var.dtype, is_bias=True
         )
+        if in_pir_mode():
+            return input_var + b
         tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
         self.append_op(
             type='elementwise_add',

python/paddle/base/layer_helper_base.py

@@ -19,6 +19,7 @@
 import paddle
 
 from . import core, unique_name
+from .data_feeder import convert_dtype
 from .framework import (
     Variable,
     _current_expected_place,
@@ -359,6 +360,8 @@ def create_parameter(
         # set global dtype
         if not dtype:
             dtype = self.__dtype
+        if isinstance(dtype, core.DataType):
+            dtype = convert_dtype(dtype)
         if is_bias:
             suffix = 'b'
             default_initializer = (

python/paddle/static/nn/common.py

@@ -27,6 +27,7 @@
     default_main_program,
     in_dygraph_mode,
     in_dynamic_or_pir_mode,
+    in_pir_mode,
     name_scope,
     program_guard,
     static_only,
@@ -191,10 +192,17 @@ def fc_base(
         name=None,
     ):
         helper = LayerHelper("fc", **locals())
-        check_type(input, 'input', (list, tuple, Variable), 'fc')
+        check_type(
+            input, 'input', (list, tuple, Variable, paddle.pir.Value), 'fc'
+        )
         if isinstance(input, (list, tuple)):
             for i, input_x in enumerate(input):
-                check_type(input_x, 'input[' + str(i) + ']', Variable, 'fc')
+                check_type(
+                    input_x,
+                    'input[' + str(i) + ']',
+                    (Variable, paddle.pir.Value),
+                    'fc',
+                )
         dtype = helper.input_dtype()
         check_dtype(
             dtype, 'input', ['float16', 'uint16', 'float32', 'float64'], 'fc'
@@ -210,17 +218,25 @@ def fc_base(
             w = helper.create_parameter(
                 attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False
             )
-            tmp = helper.create_variable_for_type_inference(dtype)
-            helper.append_op(
-                type="mul",
-                inputs={"X": input_var, "Y": w},
-                outputs={"Out": tmp},
-                attrs={"x_num_col_dims": num_flatten_dims, "y_num_col_dims": 1},
-            )
+            if in_pir_mode():
+                tmp = paddle.matmul(input_var, w)
+            else:
+                tmp = helper.create_variable_for_type_inference(dtype)
+                helper.append_op(
+                    type="mul",
+                    inputs={"X": input_var, "Y": w},
+                    outputs={"Out": tmp},
+                    attrs={
+                        "x_num_col_dims": num_flatten_dims,
+                        "y_num_col_dims": 1,
+                    },
+                )
             mul_results.append(tmp)
 
         if len(mul_results) == 1:
             pre_bias = mul_results[0]
+        elif in_pir_mode():
+            pre_bias = paddle.add_n(mul_results)
         else:
             pre_bias = helper.create_variable_for_type_inference(dtype)
             helper.append_op(

python/paddle/static/nn/control_flow.py

@@ -175,6 +175,51 @@ def __exit__(self, exc_type, exc_val, exc_tb):
         return super().__exit__(exc_type, exc_val, exc_tb)
 
 
+class If:
+    '''
+    **If**
+
+    If is an operator that bind two blocks (true_block and false_block) to a specific condition,
+    According to the condition, the corresponding block will be executed.
+
+    Args:
+        cond (Value): A value whose data type is bool controlling which block is executed.
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+            >>> from paddle.static.nn.control_flow import ConditionalBlock
+
+            >>> label = paddle.rand([1])
+            >>> limit = paddle.ones([1]) * 0.5
+            >>> cond = paddle.less_than(x=label, y=limit)
+            >>> if_op = If(cond)
+            >>> with if_op.true_block():
+            ...     pass
+            >>> with if_op.false_block():
+            ...     pass
+    '''
+
+    def __init__(self, cond):
+        if not isinstance(cond, list):
+            check_variable_and_dtype(cond, 'cond', ['bool'], 'static.nn.If')
+            if reduce(lambda a, b: a * b, cond.shape, 1) != 1:
+                raise TypeError(
+                    "condition expected shape as [1], but given shape as {}.".format(
+                        list(cond.shape)
+                    )
+                )
+        self.if_op = build_if_op(cond)
+        self.cond_var = self.if_op.cond()
+
+    def true_block(self):
+        return self.if_op.true_block()
+
+    def false_block(self):
+        return self.if_op.false_block()
+
+
 class ConditionalBlock:
     '''
     **ConditionalBlock**
@@ -208,13 +253,23 @@ class ConditionalBlock:
     '''
 
     def __init__(self, inputs, is_scalar_condition=False, name=None):
-        for each_input in inputs:
-            check_type(each_input, "input", Variable, "ConditionalBlock")
         self.inputs = inputs
+        if in_pir_mode():
+            if is_scalar_condition and len(inputs) != 1:
+                raise TypeError(
+                    "For ConditionalBlock Api,  Only support one input while is_scalar_condition is True"
+                )
+            return
+        else:
+            for each_input in inputs:
+                check_type(each_input, "input", Variable, "ConditionalBlock")
+
         self.is_scalar_condition = is_scalar_condition
         self.helper = LayerHelper('conditional_block', name=name)
 
     def block(self):
+        if in_pir_mode():
+            return If(self.inputs).true_block()
         return ConditionalBlockGuard(self)
 
     def complete(self):
@@ -1244,9 +1299,9 @@ def cond(pred, true_fn=None, false_fn=None, name=None, return_names=None):
         return None
     true_output = None
     false_output = None
+    check_variable_and_dtype(pred, "pred", ['bool'], "base.layers.cond")
+    check_type(name, "name", (str, type(None)), "base.layers.cond")
     if in_pir_mode():
-        check_variable_and_dtype(pred, "pred", ['bool'], "base.layers.cond")
-        check_type(name, "name", (str, type(None)), "base.layers.cond")
         if_op = build_if_op(pred)
         if true_fn is not None:
             if not callable(true_fn):
@@ -1267,8 +1322,6 @@ def cond(pred, true_fn=None, false_fn=None, name=None, return_names=None):
             with if_op.false_block():
                 false_output = false_fn()
     else:
-        check_variable_and_dtype(pred, "pred", ['bool'], "base.layers.cond")
-        check_type(name, "name", (str, type(None)), "base.layers.cond")
         helper = LayerHelper('cond', **locals())
         copy_to_parent_func = lambda var: copy_var_to_parent_block(var, helper)
         if true_fn is not None:

test/dygraph_to_static/test_loop.py

@@ -19,7 +19,6 @@
 import numpy as np
 from dygraph_to_static_utils import (
     Dy2StTestBase,
-    test_legacy_and_pt_and_pir,
 )
 
 import paddle
@@ -252,7 +251,6 @@ def setUp(self):
 
         self.nested_for_loop_func = nested_for_loop_dyfunc
 
-    @test_legacy_and_pt_and_pir
     def test_loop_vars(self):
         for i in range(len(self.loop_funcs)):
             func = self.loop_funcs[i]
@@ -268,7 +266,6 @@ def test_loop_vars(self):
                     self.assertEqual(loop_var_names, self.loop_var_names[i])
                     self.assertEqual(create_var_names, self.create_var_names[i])
 
-    @test_legacy_and_pt_and_pir
     def test_nested_loop_vars(self):
         func = self.nested_for_loop_func
         test_func = inspect.getsource(func)

test/legacy_test/test_conditional_block.py

@@ -31,7 +31,10 @@ def test_forward(self):
             data = paddle.static.data(name='X', shape=[-1, 1], dtype='float32')
             data.stop_gradient = False
             cond = ConditionalBlock(inputs=[data])
-            out = paddle.tensor.create_tensor(dtype='float32')
+            out = paddle.tensor.fill_constant(
+                [10, 10], dtype='float32', value=0.0
+            )
+            out.stop_gradient = False
             with cond.block():
                 hidden = paddle.static.nn.fc(x=data, size=10)
                 paddle.assign(hidden, out)
@@ -43,15 +46,13 @@ def test_forward(self):
             x = np.random.random(size=(10, 1)).astype('float32')
 
             outs = exe.run(main_program, feed={'X': x}, fetch_list=[out])[0]
-            print(outs)
             loss = paddle.mean(out)
             append_backward(loss=loss)
             outs = exe.run(
                 main_program,
                 feed={'X': x},
                 fetch_list=[main_program.block(0).var(data.name + "@GRAD")],
             )[0]
-            print(outs)
 
 
 class TestConditionalBlockOpInferShape(unittest.TestCase):