[dygraph]remove legacy code : _in_eager_mode_ and _in_eager_without_dygraph_check() (#53761)

* remove _in_eager_mode_

* remove _in_eager_mode_

python/paddle/autograd/backward_mode.py

@@ -107,10 +107,7 @@ def backward(tensors, grad_tensors=None, retain_graph=False):
                     each_tensor, (paddle.Tensor, core.eager.Tensor)
                 ), "The argument 'grad_tensors' of paddle.autograd.backward is invalid, it can be 'None', 'paddle.Tensor' or 'list[None/paddle.Tensor]'."
     else:
-        if framework.global_var._in_eager_mode_:
         grad_tensors = []
-        else:
-            grad_tensors = [None] * len(tensors)
 
     if len(grad_tensors) > 0:
         assert len(tensors) == len(
@@ -119,9 +116,4 @@ def backward(tensors, grad_tensors=None, retain_graph=False):
 
     assert isinstance(retain_graph, bool), "retain_graph must be True or False"
 
-    if framework.global_var._in_eager_mode_:
     core.eager.run_backward(tensors, grad_tensors, retain_graph)
-    else:
-        core.dygraph_run_backward(
-            tensors, grad_tensors, retain_graph, framework._dygraph_tracer()
-        )

python/paddle/fluid/data_feeder.py

@@ -24,7 +24,6 @@ from .framework import (
     default_main_program,
     _current_expected_place,
     _non_static_mode,
-    _in_eager_without_dygraph_check,
 )
 from .framework import _cpu_num, _cuda_ids
 

python/paddle/fluid/dygraph/base.py

@@ -26,10 +26,7 @@ from .tracer import Tracer
 import logging
 from ..data_feeder import convert_dtype
 import warnings
-from ..framework import (
-    _get_paddle_place,
-    _in_eager_without_dygraph_check,
-)
+from ..framework import _get_paddle_place
 import paddle
 import warnings
 
@@ -796,14 +793,9 @@ def grad(
                 var, core.eager.Tensor
             ), "no_grad_vars can only contains Tensor"
     else:
-        if _in_eager_without_dygraph_check():
         raise AssertionError(
             "no_grad_vars must be None, Tensor or list/tuple/set of Tensors"
         )
-        else:
-            raise AssertionError(
-                "no_grad_vars must be None, Variable or list/tuple/set of Variables"
-            )
 
     assert isinstance(create_graph, bool), "create_graph must be True or False"
 
@@ -819,7 +811,6 @@ def grad(
     assert isinstance(only_inputs, bool), "only_inputs must be True or False"
     assert only_inputs, "only_inputs=False is not supported yet"
 
-    if _in_eager_without_dygraph_check():
     return core.eager.run_partial_grad(
         outputs,
         inputs,
@@ -830,20 +821,6 @@ def grad(
         allow_unused,
         no_grad_vars,
     )
-    else:
-        place = core.Place()
-        place.set_place(framework._current_expected_place())
-        return core.dygraph_partial_grad(
-            inputs,
-            outputs,
-            grad_outputs,
-            no_grad_vars,
-            place,
-            create_graph,
-            retain_graph,
-            allow_unused,
-            only_inputs,
-        )
 
 
 @framework.dygraph_only

python/paddle/fluid/dygraph/math_op_patch.py

@@ -71,31 +71,6 @@ def monkey_patch_math_tensor():
     The difference is, in dygraph mode, use auto-generated op functions for better performance.
     """
 
-    @no_grad
-    def create_tensor(value, dtype, shape):
-        if framework.global_var._in_eager_mode_:
-            out = _C_ops.full(
-                shape, value, dtype, framework._current_expected_place()
-            )
-        else:
-            out = framework_create_tensor(dtype=dtype)
-            out = _legacy_C_ops.fill_constant(
-                out,
-                'dtype',
-                dtype,
-                'shape',
-                shape,
-                'value',
-                value,
-                'force_cpu',
-                False,
-            )
-        out.stop_gradient = True
-        return out
-
-    def create_scalar(value, dtype):
-        return create_tensor(value, dtype, shape=[])
-
     def astype(self, dtype):
         """
 
@@ -191,253 +166,6 @@ def monkey_patch_math_tensor():
         out = _C_ops.transpose(var, perm)
         return out
 
-    def _scalar_add_(var, value):
-        return _scalar_elementwise_op_(var, 1.0, value)
-
-    def _scalar_sub_(var, value):
-        return _scalar_elementwise_op_(var, 1.0, -value)
-
-    def _scalar_rsub_(var, value):
-        return _scalar_elementwise_op_(var, -1.0, value)
-
-    def _scalar_mul_(var, value):
-        return _scalar_elementwise_op_(var, value, 0.0)
-
-    def _scalar_div_(var, value):
-        return _scalar_elementwise_op_(var, 1.0 / value, 0.0)
-
-    # for binary operator such as elementwise, compare
-    def _binary_creator_(
-        method_name,
-        op_type,
-        reverse=False,
-        scalar_method=None,
-        call_final_api=False,
-    ):
-        def __impl__(self, other_var):
-            # 1. scalar exists cases
-            # we need combine the tensor.dtype and scalar.dtype, cast correct object
-            if isinstance(other_var, float):
-                # in all cases(+, -, *, /, **, //, %), we need cast tensor.dtype to float
-                if self.dtype in _supported_int_dtype_:
-                    self = astype(self, 'float32')
-                # here use `scale` replace `elementwise` to get better performance
-                # but only +, -, *, / can use this method
-                if scalar_method is not None:
-                    return scalar_method(self, other_var)
-            elif isinstance(other_var, int):
-                # in all cases(+, -, *, /, **, //, %), we can cast it to float
-                # because the output tensor.dtype depend on the type of input tensor
-                other_var = float(other_var)
-                # division is a special case
-                # NOTE(chenweihang): because we cast tensor to float32 instead float64,
-                # the division result can only guarantee the numerical accuracy of 6 digits
-                # after the decimal point. The result of numpy calculation is of float64 type,
-                # so the calculation result here and the calculation result of numpy are
-                # different after 6 decimal point. If necessary, we can also use float64 here.
-                # torch's behavior here is consistent with ours
-                if (
-                    op_type == "divide" or op_type == "elementwise_div"
-                ) and self.dtype in _supported_int_dtype_:
-                    self = astype(self, 'float32')
-                # here use `scale` replace `elementwise` to get better performance
-                # but only +, -, *, / can use this method
-                if scalar_method is not None:
-                    return scalar_method(self, other_var)
-            else:
-                # do nothing
-                pass
-
-            # 2. create Tensor for scalar
-            lhs_dtype = self.dtype
-            other_var_should_be = core.eager.Tensor
-            if not isinstance(other_var, other_var_should_be):
-                if isinstance(other_var, complex):
-                    import paddle
-
-                    other_var = paddle.to_tensor(other_var, dtype='complex64')
-                else:
-                    if reverse:
-                        other_var = create_tensor(
-                            other_var, dtype=lhs_dtype, shape=self.shape
-                        )
-                    else:
-                        # add fill_op
-                        other_var = create_scalar(
-                            value=other_var, dtype=lhs_dtype
-                        )
-
-            # 3. promote types or unify right var type to left var
-            rhs_dtype = other_var.dtype
-            if lhs_dtype != rhs_dtype:
-                if method_name in _supported_promote_complex_types_ and (
-                    lhs_dtype in _complex_dtypes or rhs_dtype in _complex_dtypes
-                ):
-                    # only when lhs_dtype or rhs_dtype is complex type,
-                    # the dtype will promote, in other cases, directly
-                    # use lhs_dtype, this is consistent will original rule
-                    promote_dtype = core._promote_types_if_complex_exists(
-                        lhs_dtype, rhs_dtype
-                    )
-                    self = (
-                        self
-                        if lhs_dtype == promote_dtype
-                        else astype(self, promote_dtype)
-                    )
-                    other_var = (
-                        other_var
-                        if rhs_dtype == promote_dtype
-                        else astype(other_var, promote_dtype)
-                    )
-                else:
-                    warnings.warn(
-                        'The dtype of left and right variables are not the same, left dtype is {}, but right dtype is {}, the right dtype will convert to {}'.format(
-                            lhs_dtype, rhs_dtype, lhs_dtype
-                        )
-                    )
-                    other_var = astype(other_var, lhs_dtype)
-
-            if reverse:
-                tmp = self
-                self = other_var
-                other_var = tmp
-
-            if (
-                op_type == "divide" or op_type == "elementwise_div"
-            ) and self.dtype in _supported_int_dtype_:
-                self = astype(self, 'float32')
-                other_var = astype(other_var, 'float32')
-
-            # 4. calculation
-            axis = -1
-            if in_dygraph_mode():
-                math_op = getattr(_C_ops, op_type)
-            else:
-                math_op = getattr(_legacy_C_ops, op_type)
-            if call_final_api:
-                if op_type == "matmul":
-                    return math_op(self, other_var, False, False)
-                if op_type == "pow":
-                    if isinstance(other_var, core.eager.Tensor):
-                        return _C_ops.elementwise_pow(self, other_var)
-                    else:
-                        return _C_ops.elementwise_pow(self, other_var)
-                return math_op(self, other_var, -1)
-            return math_op(self, other_var, 'axis', axis)
-
-        if call_final_api:
-            comment = ""
-        else:
-            comment = OpProtoHolder.instance().get_op_proto(op_type).comment
-
-        __impl__.__doc__ = """
-        {0}
-        Args:
-            other_var(Tensor|float|int): right hand Tensor
-
-        Returns:
-            Tensor
-        """.format(
-            comment
-        )
-        __impl__.__name__ = method_name
-        return __impl__
-
-    tensor_methods = [
-        ('__neg__', _neg_),
-        ('__float__', _float_),
-        ('__long__', _long_),
-        ('__int__', _int_),
-        ('__len__', _len_),
-        ('__index__', _index_),
-        ('astype', astype),
-        ('dim', lambda x: len(x.shape)),
-        ('ndimension', lambda x: len(x.shape)),
-        ('ndim', _ndim_),
-        ('size', _size_),
-        ('T', _T_),
-        (
-            '__add__',
-            _binary_creator_('__add__', 'elementwise_add', False, _scalar_add_),
-        ),
-        #  a+b == b+a. Do not need to reverse explicitly
-        (
-            '__radd__',
-            _binary_creator_(
-                '__radd__', 'elementwise_add', False, _scalar_add_
-            ),
-        ),
-        (
-            '__sub__',
-            _binary_creator_('__sub__', 'elementwise_sub', False, _scalar_sub_),
-        ),
-        (
-            '__rsub__',
-            _binary_creator_(
-                '__rsub__', 'elementwise_sub', True, _scalar_rsub_
-            ),
-        ),
-        (
-            '__mul__',
-            _binary_creator_('__mul__', 'elementwise_mul', False, _scalar_mul_),
-        ),
-        ## a*b == b*a. Do not need to reverse explicitly
-        (
-            '__rmul__',
-            _binary_creator_(
-                '__rmul__', 'elementwise_mul', False, _scalar_mul_
-            ),
-        ),
-        (
-            '__div__',
-            _binary_creator_('__div__', 'elementwise_div', False, _scalar_div_),
-        ),
-        (
-            '__truediv__',
-            _binary_creator_(
-                '__truediv__', 'elementwise_div', False, _scalar_div_
-            ),
-        ),
-        (
-            '__rdiv__',
-            _binary_creator_('__rdiv__', 'elementwise_div', True, None),
-        ),
-        (
-            '__rtruediv__',
-            _binary_creator_('rtruediv__', 'elementwise_div', True, None),
-        ),
-        (
-            '__pow__',
-            _binary_creator_('__pow__', 'elementwise_pow', False, None),
-        ),
-        (
-            '__rpow__',
-            _binary_creator_('__rpow__', 'elementwise_pow', True, None),
-        ),
-        (
-            '__floordiv__',
-            _binary_creator_(
-                '__floordiv__', 'elementwise_floordiv', False, None
-            ),
-        ),
-        (
-            '__mod__',
-            _binary_creator_('__mod__', 'elementwise_mod', False, None),
-        ),
-        (
-            '__matmul__',
-            _binary_creator_('__matmul__', "matmul_v2", False, None),
-        ),
-        ## for logical compare
-        ('__eq__', _binary_creator_('__eq__', 'equal', False, None)),
-        ('__ne__', _binary_creator_('__ne__', 'not_equal', False, None)),
-        ('__lt__', _binary_creator_('__lt__', 'less_than', False, None)),
-        ('__le__', _binary_creator_('__le__', 'less_equal', False, None)),
-        ('__gt__', _binary_creator_('__gt__', 'greater_than', False, None)),
-        ('__ge__', _binary_creator_('__ge__', 'greater_equal', False, None)),
-        ('__array_ufunc__', None),
-    ]
-
     eager_methods = [
         ('__neg__', _neg_),
         ('__float__', _float_),
@@ -486,7 +214,6 @@ def monkey_patch_math_tensor():
     local_tensor = core.eager.Tensor
 
     if not local_already_patch:
-        if framework.global_var._in_eager_mode_:
         for method_name in eager_cpp_level_patch:
             method_impl = getattr(local_tensor, method_name, None)
             if method_impl:
@@ -496,12 +223,6 @@ def monkey_patch_math_tensor():
             method_name = method[0]
             method_impl = method[1]
             setattr(local_tensor, method_name, method_impl)
-
-        else:
-            for method in tensor_methods:
-                method_name = method[0]
-                method_impl = method[1]
-                setattr(local_tensor, method_name, method_impl)
     else:
         import paddle.tensor
 

python/paddle/fluid/dygraph/tensor_patch_methods.py

@@ -54,11 +54,7 @@ class TensorHookRemoveHelper:
     """
 
     def __init__(self, tensor, hook_id):
-        self._tensor = (
-            tensor
-            if framework.global_var._in_eager_mode_
-            else weakref.ref(tensor)
-        )
+        self._tensor = tensor
         self._hook_id = hook_id
 
     def remove(self):
@@ -68,11 +64,7 @@ class TensorHookRemoveHelper:
         Returns:
             bool: Return True if removed successfully
         """
-        tensor = (
-            self._tensor
-            if framework.global_var._in_eager_mode_
-            else self._tensor()
-        )
+        tensor = self._tensor
         if tensor is not None:
             res = tensor._remove_grad_hook(self._hook_id)
             if res is True:
@@ -285,21 +277,16 @@ def monkey_patch_tensor():
                 )
                 record_event.begin()
             if grad_tensor is not None:
-                if framework.global_var._in_eager_mode_:
                 assert isinstance(
                     grad_tensor, core.eager.Tensor
                 ), "The type of grad_tensor must be paddle.Tensor"
-                else:
-                    assert isinstance(
-                        grad_tensor, paddle.Tensor
-                    ), "The type of grad_tensor must be paddle.Tensor"
+
                 assert (
                     grad_tensor.shape == self.shape
                 ), "Tensor shape not match, Tensor of grad_tensor [ {} ] with shape {} mismatch Tensor [ {} ] with shape {}".format(
                     grad_tensor.name, grad_tensor.shape, self.name, self.shape
                 )
 
-            if framework.global_var._in_eager_mode_:
             if grad_tensor is None:
                 grad_tensor = []
             else:
@@ -307,15 +294,9 @@ def monkey_patch_tensor():
             if _grad_scalar:
                 # When using amp with Fleet DistributedStrategy, we do loss scaling implicitly.
                 self = _grad_scalar.scale(self)
-            if framework.global_var._in_eager_mode_:
+
             core.eager.run_backward([self], grad_tensor, retain_graph)
-            else:
-                core.dygraph_run_backward(
-                    [self],
-                    [grad_tensor],
-                    retain_graph,
-                    framework._dygraph_tracer(),
-                )
+
             if in_profiler_mode():
                 record_event.end()
         else:
@@ -352,31 +333,11 @@ def monkey_patch_tensor():
                 # [500.]
 
         """
-        if framework.global_var._in_eager_mode_:
         if self.grad is None:
             return None
         if self.grad.is_selected_rows():
             return (np.array(self.grad), np.array(self.grad.rows()))
         return np.array(self.grad)
-        else:
-            if self._grad_ivar() is None:
-                return None
-
-            new_ivar = self._grad_ivar()
-            # TODO(qili93): temporary for ascned npu performance to be removed along with npu_identity op
-            if (
-                _global_flags()['FLAGS_npu_storage_format']
-                and 'npu' in get_all_custom_device_type()
-            ):
-                new_ivar = paddle.incubate._npu_identity(x=new_ivar, format=-1)
-            new_ivar = new_ivar._copy_to(core.CPUPlace(), True)
-            if self._grad_ivar().type == core.VarDesc.VarType.SELECTED_ROWS:
-                return (
-                    np.array(new_ivar.value().get_selected_rows().get_tensor()),
-                    np.array(new_ivar.value().get_selected_rows().rows()),
-                )
-            else:
-                return np.array(new_ivar.value().get_tensor())
 
     @framework.dygraph_only
     def register_hook(self, hook):
@@ -705,13 +666,8 @@ def monkey_patch_tensor():
         assert (
             numel == 1
         ), "When Variable is used as the condition of if/while , Variable can only contain one element."
-        if framework.global_var._in_eager_mode_:
         assert self._is_initialized(), "tensor not initialized"
         return bool(np.array(self) > 0)
-        else:
-            tensor = self.value().get_tensor()
-            assert tensor._is_initialized(), "tensor not initialized"
-            return bool(np.array(tensor) > 0)
 
     def __bool__(self):
         return self.__nonzero__()
@@ -830,11 +786,7 @@ def monkey_patch_tensor():
             return _setitem_impl_(self, item, value)
 
         else:
-            if framework.global_var._in_eager_mode_:
             return self.__setitem_eager_tensor__(item, value)
-            else:
-                # Call c++ func __setitem_varbase__ to speedup.
-                return self.__setitem_varbase__(item, value)
 
     @framework.dygraph_only
     def _set_grad_ivar(self, value):
@@ -1000,7 +952,7 @@ def monkey_patch_tensor():
     def __hash__(self):
         return hash(id(self))
 
-    if framework.global_var._in_eager_mode_ and not hasattr(core, "eager"):
+    if not hasattr(core, "eager"):
         return
 
     for method_name, method in (

python/paddle/fluid/framework.py

@@ -83,7 +83,6 @@ class GlobalThreadLocal(threading.local):
         self._in_declarative_mode_ = False
         self._functional_dygraph_context_manager = None
         self._dygraph_tracer_ = _dygraph_tracer_
-        self._in_eager_mode_ = True
 
     def __str__(self):
         strings = []
@@ -95,7 +94,6 @@ class GlobalThreadLocal(threading.local):
             + str(self._functional_dygraph_context_manager)
         )
         strings.append("_dygraph_tracer_:" + str(self._dygraph_tracer_))
-        strings.append("_in_eager_mode_:" + str(self._in_eager_mode_))
         return "\n".join(strings)
 
     def __setattr__(self, name, val):
@@ -180,10 +178,6 @@ extra_op_attrs = {
 # to make sure in most case, we find new dygraph mode first with only one if statement.
 
 
-def _in_eager_without_dygraph_check():
-    return global_var._in_eager_mode_
-
-
 # FIXME(dev): We haven't fully verified eager mode on XPU et.al but
 # only GPU/CPU. Remove this after we improve this feature.
 _is_first_import_ = True
@@ -216,9 +210,7 @@ def in_dygraph_mode():
             print(paddle.in_dynamic_mode())  # True, Now we are in dynamic mode
 
     """
-    return (
-        global_var._dygraph_tracer_ is not None
-    ) and global_var._in_eager_mode_
+    return global_var._dygraph_tracer_ is not None
 
 
 def _non_static_mode():

python/paddle/fluid/layer_helper_base.py

@@ -22,7 +22,6 @@ from .framework import (
     default_startup_program,
     _non_static_mode,
     _current_expected_place,
-    _in_eager_without_dygraph_check,
 )
 from . import unique_name
 from .param_attr import ParamAttr, WeightNormParamAttr

python/paddle/fluid/reader.py

@@ -29,7 +29,6 @@ from .framework import (
     _non_static_mode,
     cpu_places,
     _current_expected_place,
-    _in_eager_without_dygraph_check,
 )
 from .executor import global_scope
 from .data_feeder import DataFeeder, BatchedTensorProvider
@@ -663,12 +662,9 @@ class DygraphGeneratorLoader(DataLoaderBase):
 
     def __next__(self):
         try:
-            if _in_eager_without_dygraph_check():
             return core.eager.read_next_tensor_list(
                 self._reader.read_next_list()[0]
             )
-            else:
-                return self._reader.read_next_var_list()
         except StopIteration:
             self._reset()
             raise

python/paddle/fluid/tests/unittests/test_run_program_op.py

@@ -21,7 +21,6 @@ import paddle
 from paddle import _legacy_C_ops, fluid
 from paddle.fluid import core, framework
 from paddle.fluid.dygraph.base import switch_to_static_graph
-from paddle.fluid.framework import global_var
 
 paddle.enable_static()
 
@@ -207,16 +206,7 @@ class RunProgramOpTest(unittest.TestCase):
         for name in self.output_names['Out']:
             outputs['Out'].append(create_var_base(False, name))
 
-        if global_var._in_eager_mode_:
         outputs['OutScope'] = [core.Scope()]
-        else:
-            outputs['OutScope'] = framework._create_tensor(
-                type=core.VarDesc.VarType.STEP_SCOPES,
-                name="program_out_scope",
-                persistable=True,
-            )
-            inner_scope = core.Scope()
-            outputs['OutScope'].value().set_scope(inner_scope)
 
         outputs['DOut'] = [create_var_base(False, "Fake_var")]
         return outputs

python/paddle/optimizer/optimizer.py

@@ -24,7 +24,6 @@ from paddle.fluid import core
 from paddle.fluid.framework import (
     Variable,
     _current_expected_place,
-    _in_eager_without_dygraph_check,
     default_main_program,
     device_guard,
     in_dygraph_mode,
@@ -736,9 +735,7 @@ class Optimizer:
             name=var_name,
             persistable=True,
             dtype=dtype or param.dtype,
-            type=core.VarDesc.VarType.LOD_TENSOR
-            if framework._in_eager_without_dygraph_check()
-            else (param.type if type is None else type),
+            type=core.VarDesc.VarType.LOD_TENSOR,
             shape=shape,
             belong_to_optimizer=True,
         )
@@ -1380,11 +1377,8 @@ class Optimizer:
                     if not p.stop_gradient:
                         param_list.append(p)
 
-        if _in_eager_without_dygraph_check():
         for p in param_list:
             p.clear_gradient(set_to_zero)
-        else:
-            core.clear_gradients(param_list, set_to_zero)
 
     @imperative_base.no_grad()
     def minimize(

python/paddle/tensor/creation.py

@@ -30,11 +30,7 @@ from ..fluid.data_feeder import (
     convert_dtype,
     convert_float_to_uint16,
 )
-from ..fluid.framework import (
-    Variable,
-    _in_eager_without_dygraph_check,
-    device_guard,
-)
+from ..fluid.framework import Variable, device_guard
 from ..fluid.param_attr import ParamAttr
 from ..framework import (
     LayerHelper,
@@ -625,7 +621,7 @@ def _to_tensor_non_static(data, dtype=None, place=None, stop_gradient=True):
     if dtype:
         data = _handle_np_dtype(data, dtype)
 
-    if _in_eager_without_dygraph_check() and isinstance(data, np.ndarray):
+    if isinstance(data, np.ndarray):
         return core.eager.Tensor(
             value=data,
             place=place,

test/legacy_test/test_model.py

@@ -776,7 +776,7 @@ class TestModelFunction(unittest.TestCase):
         paddle.summary(nlp_net, (1, 1, 2))
 
     def test_static_flops(self):
-        if paddle.fluid.framework._in_eager_without_dygraph_check():
+        if True:
             return
         paddle.disable_static()
         net = models.__dict__['mobilenet_v2'](pretrained=False)