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未验证 提交 d4571470 编写于 作者: W wanghuancoder 提交者： GitHub
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Del old dygraph varbase (#52236) 

* delete old dygraph op test
上级 b8850521
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Showing with 310 addition and 3699 deletion
python/paddle/__init__.py
浏览文件 @ d4571470
......@@ -56,11 +56,7 @@ from .framework.dtype import bool # noqa: F401
from .framework.dtype import complex64 # noqa: F401
from .framework.dtype import complex128 # noqa: F401
if fluid.framework.global_var._in_eager_mode_:
Tensor = framework.core.eager.Tensor
else:
from .framework import VarBase as Tensor # noqa: F401
Tensor = framework.core.eager.Tensor # noqa: F401
Tensor.__qualname__ = 'Tensor' # noqa: F401
import paddle.distributed # noqa: F401
import paddle.sysconfig # noqa: F401
......
python/paddle/amp/grad_scaler.py
浏览文件 @ d4571470
......@@ -179,7 +179,7 @@ class AmpScaler:
scaled.backward()
scaler.minimize(optimizer, scaled)
"""
check_type(var, "var", core.VarBase, 'AmpScaler.scale()')
check_type(var, "var", core.eager.Tensor, 'AmpScaler.scale()')
if not self._enable:
return var
......
python/paddle/distributed/auto_parallel/engine.py
浏览文件 @ d4571470
......@@ -266,7 +266,7 @@ class Engine:
specs.append(spec)
else:
specs.append(spec.batch(batch_size))
elif isinstance(item, (Variable, core.VarBase, core.eager.Tensor)):
elif isinstance(item, (Variable, core.eager.Tensor)):
spec = InputSpec.from_tensor(item, name)
_adjust_item_spec(num_shards, spec)
if batch_size is None:
......
python/paddle/distributed/fleet/recompute/recompute.py
浏览文件 @ d4571470
......@@ -31,7 +31,7 @@ __all__ = []
def detach_variable(inputs):
out = []
for inp in inputs:
if not isinstance(inp, (core.eager.Tensor, core.VarBase)):
if not isinstance(inp, core.eager.Tensor):
out.append(inp)
continue
......@@ -172,7 +172,7 @@ class RecomputeFunction(PyLayer):
detached_inputs = detach_variable(tuple(inputs))
outputs = ctx.run_function(*detached_inputs, **ctx.kwargs)
if isinstance(outputs, (core.VarBase, core.eager.Tensor)):
if isinstance(outputs, core.eager.Tensor):
outputs = (outputs,)
assert len(outputs) == len(args)
......@@ -185,7 +185,7 @@ class RecomputeFunction(PyLayer):
backward_inputs_with_grad = []
for i in range(len(outputs)):
if (
isinstance(outputs[i], (core.VarBase, core.eager.Tensor))
isinstance(outputs[i], core.eager.Tensor)
and not outputs[i].stop_gradient
):
forward_outputs_with_grad.append(outputs[i])
......@@ -206,13 +206,13 @@ class RecomputeFunction(PyLayer):
grads = tuple(
inp._grad_ivar()
for inp in detached_inputs
if isinstance(inp, (core.VarBase, core.eager.Tensor))
if isinstance(inp, core.eager.Tensor)
)
else:
grads = [
inp._grad_ivar()
for inp in detached_inputs
if isinstance(inp, (core.VarBase, core.eager.Tensor))
if isinstance(inp, core.eager.Tensor)
]
return grads
......
python/paddle/distributed/fleet/recompute/recompute_hybrid.py
浏览文件 @ d4571470
......@@ -220,7 +220,7 @@ class _HPRecomputeFunction(PyLayer):
detached_inputs = detach_variable(tuple(inputs))
outputs = ctx.run_function(*detached_inputs, **ctx.kwargs)
if isinstance(outputs, (core.VarBase, core.eager.Tensor)):
if isinstance(outputs, core.eager.Tensor):
outputs = (outputs,)
assert len(outputs) == len(args)
......@@ -229,7 +229,7 @@ class _HPRecomputeFunction(PyLayer):
for i in range(len(outputs)):
if (
isinstance(outputs[i], (core.VarBase, core.eager.Tensor))
isinstance(outputs[i], core.eager.Tensor)
and not outputs[i].stop_gradient
):
forward_outputs_with_grad.append(outputs[i])
......@@ -245,7 +245,7 @@ class _HPRecomputeFunction(PyLayer):
grads = tuple(
inp._grad_ivar()
for inp in detached_inputs
if isinstance(inp, (core.VarBase, core.eager.Tensor))
if isinstance(inp, core.eager.Tensor)
)
return grads
......
python/paddle/distributed/fleet/utils/hybrid_parallel_util.py
浏览文件 @ d4571470
......@@ -156,7 +156,7 @@ def broadcast_input_data(hcg, *inputs, **kwargs):
place = eval(f"paddle.{dev.upper()}Place")(dev_idx)
for v in inputs:
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
with framework.no_grad():
if in_dygraph_mode() and not eval(f"v.place.is_{dev}_place")():
v_gpu = v._copy_to(place, True)
......@@ -167,7 +167,7 @@ def broadcast_input_data(hcg, *inputs, **kwargs):
logger.warning("it doesn't support data type {}".format(type(v)))
for k, v in kwargs.items():
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
with framework.no_grad():
if in_dygraph_mode() and not eval(f"v.place.is_{dev}_place")():
v_gpu = v._copy_to(place, True)
......
python/paddle/distributed/parallel.py
浏览文件 @ d4571470
......@@ -44,7 +44,7 @@ from paddle.distributed.fleet.base.private_helper_function import ( # noqa: F40
from paddle.distributed.fleet.launch_utils import check_backend
# (TODO: GhostScreaming) It will be removed later.
from paddle.framework import ParamBase, _set_expected_place
from paddle.framework import _set_expected_place
from paddle.framework import base as imperative_base
from paddle.framework import core, in_dygraph_mode, to_variable
from paddle.nn.layer import layers
......@@ -158,14 +158,14 @@ def sync_params_buffers(
):
model_vars = []
for _, param in model._obtain_parameters_buffers().items():
if not isinstance(param, (core.VarBase, core.eager.Tensor)):
if not isinstance(param, core.eager.Tensor):
raise TypeError(
"The data type of '%s' must be Varbase or eager.Tensor"
% param.name
)
# is_distributed param not need to sync when in mp mode
if isinstance(param, (ParamBase, core.eager.Tensor)):
if isinstance(param, core.eager.Tensor):
if is_model_parallel:
if hasattr(param, "is_distributed") and param.is_distributed:
continue
......@@ -379,9 +379,7 @@ class DataParallel(layers.Layer):
self.find_unused_parameters = find_unused_parameters
self.grad_need_sync = True
self.group = group
self.var_dtype = (
core.eager.Tensor if in_dygraph_mode() else core.VarBase
)
self.var_dtype = core.eager.Tensor
# NOTE(chenweihang): The ParallelStrategy here is not strictly a strategy.
# It just stores some environment variables, which can be constructed by
......@@ -491,7 +489,7 @@ class DataParallel(layers.Layer):
)
def _find_varbase(self, obj):
var_type = core.eager.Tensor if in_dygraph_mode() else core.VarBase
var_type = core.eager.Tensor
if isinstance(obj, var_type):
return [obj]
if isinstance(obj, (list, tuple)):
......
python/paddle/fluid/data_feeder.py
浏览文件 @ d4571470
......@@ -116,32 +116,22 @@ def check_type(input, input_name, expected_type, op_name, extra_message=''):
return
# NOTE: `in_declarative_mode` is used to determined whether this op is called under
# @to_static in transformation from dygrah to static layer. We add VarBase in
# expected_type to skip checking because varBase may be created and used in unusual way.
# @to_static in transformation from dygrah to static layer. We add Tensor in
# expected_type to skip checking because Tensor may be created and used in unusual way.
from .dygraph.base import in_declarative_mode
# Need a better design to be fix this.
if in_declarative_mode():
if not isinstance(expected_type, tuple):
expected_type = (expected_type,)
expected_type += (core.VarBase,)
if _in_eager_without_dygraph_check():
expected_type += (core.eager.Tensor,)
elif isinstance(input, core.VarBase):
expected_type += (core.eager.Tensor,)
elif isinstance(input, core.eager.Tensor):
raise TypeError(
"Please use `with fluid.dygraph.guard()` as context or `fluid.enable_dygraph()` to switch to imperative mode firstly. "
"Because received '{}' in {} is a imperative Variable.".format(
input_name, op_name
)
)
elif hasattr(core, "eager"):
if isinstance(input, core.eager.Tensor):
raise TypeError(
"Please use `with fluid.dygraph.guard()` as context or `fluid.enable_dygraph()` to switch to imperative mode firstly. "
"Because received '{}' in {} is a imperative Variable.".format(
input_name, op_name
)
)
if not isinstance(input, expected_type):
raise TypeError(
"The type of '%s' in %s must be %s, but received %s. %s"
......
python/paddle/fluid/dygraph/base.py
浏览文件 @ d4571470
......@@ -127,20 +127,18 @@ def _convert_into_variable(tensor):
"""
Convert Varbase into Variable.
"""
if isinstance(tensor, (core.eager.Tensor, core.VarBase)):
if isinstance(tensor, core.eager.Tensor):
# Check whether has been created before.
new_var = tensor.block._find_var_recursive(tensor.name)
if new_var is not None:
assert isinstance(new_var, framework.Variable)
# Convert ParamBase into Parameter with same attributes in dy2stat.
elif isinstance(
tensor, (framework.EagerParamBase, framework.ParamBase)
):
# Convert EagerParamBase into Parameter with same attributes in dy2stat.
elif isinstance(tensor, framework.EagerParamBase):
new_var = tensor._to_static_var(to_parameter=True)
else:
# Note(Aurelius84): Convert VarBase in self._buffers into Variable with
# Note(Aurelius84): Convert Tensor in self._buffers into Variable with
# same attributes and set persistable=True to allow saving this var.
# Because users can create a VarBase in `__init__` like a
# Because users can create a Tensor in `__init__` like a
# `mask` Tensor or `hidden_0` in RNN layers, which is equivalent to a Parameter
# and necessary for inferring. It will be pruned if it's not necessary for inferring.
......@@ -587,7 +585,6 @@ def guard(place=None):
train = framework.Program()
startup = framework.Program()
tracer = Tracer()
VarBase = core.VarBase
if place is not None:
expected_place = _get_paddle_place(place)
......@@ -757,24 +754,14 @@ def grad(
if isinstance(in_out_list, (list, tuple)):
assert len(in_out_list) > 0, "{} cannot be empty".format(name)
for each_var in in_out_list:
if _in_eager_without_dygraph_check():
assert isinstance(
each_var, core.eager.Tensor
), "Elements of {} must be Tensor".format(name)
else:
assert isinstance(
each_var, core.VarBase
), "Elements of {} must be Variable".format(name)
assert isinstance(
each_var, core.eager.Tensor
), "Elements of {} must be Tensor".format(name)
return in_out_list
else:
if _in_eager_without_dygraph_check():
assert isinstance(
in_out_list, core.eager.Tensor
), "{} must be Tensor or list of Tensor".format(name)
else:
assert isinstance(
in_out_list, core.VarBase
), "{} must be Variable or list of Variable".format(name)
assert isinstance(
in_out_list, core.eager.Tensor
), "{} must be Tensor or list of Tensor".format(name)
return [in_out_list]
outputs = check_in_out(outputs, 'outputs')
......@@ -786,14 +773,9 @@ def grad(
for each_var in grad_outputs:
if each_var is not None:
if _in_eager_without_dygraph_check():
assert isinstance(
each_var, core.eager.Tensor
), "grad_outputs must be None, a Variable or a list containing None or Variables"
else:
assert isinstance(
each_var, core.VarBase
), "grad_outputs must be None, a Variable or a list containing None or Variables"
assert isinstance(
each_var, core.eager.Tensor
), "grad_outputs must be None, a Variable or a list containing None or Variables"
else:
grad_outputs = []
......@@ -804,21 +786,16 @@ def grad(
if no_grad_vars is None:
no_grad_vars = []
elif isinstance(no_grad_vars, (core.VarBase, core.eager.Tensor)):
elif isinstance(no_grad_vars, core.eager.Tensor):
no_grad_vars = [no_grad_vars]
elif isinstance(no_grad_vars, core.eager.Tensor):
no_grad_vars = [no_grad_vars]
elif isinstance(no_grad_vars, (list, tuple, set)):
no_grad_vars = list(no_grad_vars)
for var in no_grad_vars:
if _in_eager_without_dygraph_check():
assert isinstance(
var, core.eager.Tensor
), "no_grad_vars can only contains Tensor"
else:
assert isinstance(
var, core.VarBase
), "no_grad_vars can only contains Variable"
assert isinstance(
var, core.eager.Tensor
), "no_grad_vars can only contains Tensor"
else:
if _in_eager_without_dygraph_check():
raise AssertionError(
......@@ -932,7 +909,6 @@ def to_variable(value, name=None, zero_copy=None, dtype=None):
tuple,
np.ndarray,
core.eager.Tensor,
core.VarBase,
framework.Variable,
core.Tensor,
core.LoDTensor,
......@@ -942,10 +918,10 @@ def to_variable(value, name=None, zero_copy=None, dtype=None):
"The type of 'value' in fluid.dygraph.to_variable must be %s, but received %s."
% (support_type, type(value))
)
if isinstance(value, (core.eager.Tensor, core.VarBase, framework.Variable)):
if isinstance(value, (core.eager.Tensor, framework.Variable)):
return value
elif isinstance(value, (core.Tensor, core.LoDTensor)):
return core.VarBase(value)
return core.eager.Tensor(value)
else:
if isinstance(
framework._current_expected_place(), framework.core.CPUPlace
......@@ -974,21 +950,11 @@ def to_variable(value, name=None, zero_copy=None, dtype=None):
if value.dtype != dtype:
value = value.astype(dtype)
if _in_eager_without_dygraph_check():
return core.eager.Tensor(
value,
framework._current_expected_place(),
False,
zero_copy,
name if name else None,
True,
)
else:
py_var = core.VarBase(
value=value,
place=framework._current_expected_place(),
persistable=False,
zero_copy=zero_copy,
name=name if name else '',
)
return py_var
return core.eager.Tensor(
value,
framework._current_expected_place(),
False,
zero_copy,
name if name else None,
True,
)
python/paddle/fluid/dygraph/math_op_patch.py
浏览文件 @ d4571470
......@@ -62,7 +62,6 @@ _complex_dtypes = [
core.VarDesc.VarType.COMPLEX128,
]
_already_patch_varbase = False
_already_patch_eager_tensor = False
......@@ -251,10 +250,7 @@ def monkey_patch_math_varbase():
# 2. create varbase for scalar
lhs_dtype = self.dtype
if framework.global_var._in_eager_mode_:
other_var_should_be = core.eager.Tensor
else:
other_var_should_be = core.VarBase
other_var_should_be = core.eager.Tensor
if not isinstance(other_var, other_var_should_be):
if isinstance(other_var, complex):
import paddle
......@@ -483,17 +479,11 @@ def monkey_patch_math_varbase():
'__ne__',
]
global _already_patch_varbase
global _already_patch_eager_tensor
if framework.global_var._in_eager_mode_:
local_already_patch = _already_patch_eager_tensor
_already_patch_eager_tensor = True
local_tensor = core.eager.Tensor
else:
local_already_patch = _already_patch_varbase
_already_patch_varbase = True
local_tensor = core.VarBase
local_already_patch = _already_patch_eager_tensor
_already_patch_eager_tensor = True
local_tensor = core.eager.Tensor
if not local_already_patch:
if framework.global_var._in_eager_mode_:
......
python/paddle/fluid/dygraph/varbase_patch_methods.py
浏览文件 @ d4571470
......@@ -26,7 +26,6 @@ from .. import unique_name
from ..framework import (
Variable,
Parameter,
ParamBase,
_getitem_impl_,
_setitem_impl_,
EagerParamBase,
......@@ -94,12 +93,12 @@ def monkey_patch_varbase():
**Notes**:
**This API is ONLY available in Dygraph mode**
Transform a VarBase into static Variable with same attributes. It's a low level interface used
Transform a Tensor into static Variable with same attributes. It's a low level interface used
in dy2static and shall not be called directly.
Args:
to_parameter (bool): It takes effect only if the input a VarBase. If set True,
the VarBase will be converted into framework.Parameters. Otherwise, it will
to_parameter (bool): It takes effect only if the input a Tensor. If set True,
the Tensor will be converted into framework.Parameters. Otherwise, it will
be converted into framework.Variable. Default False.
Examples:
......@@ -120,7 +119,7 @@ def monkey_patch_varbase():
# It will fail. So, for propery that different between dynamic and static graph, should not getattr(self, attr, None).
attr_not_need_keys = ['grad', 'T', 'place', '_place_str']
param_keys = ['stop_gradient', 'trainable']
if isinstance(self, (ParamBase, EagerParamBase)):
if isinstance(self, EagerParamBase):
attr_kwargs = self.__dict__.copy()
for key in param_keys:
attr_kwargs[key] = getattr(self, key)
......@@ -144,7 +143,7 @@ def monkey_patch_varbase():
attr_kwargs.update(kwargs)
if to_parameter or isinstance(self, (ParamBase, EagerParamBase)):
if to_parameter or isinstance(self, EagerParamBase):
del attr_kwargs['persistable']
# NOTE(Aurelius84): All parameters should be placed into global block.
attr_kwargs['block'] = attr_kwargs['block'].program.global_block()
......@@ -183,13 +182,10 @@ def monkey_patch_varbase():
out = linear(t) # call with different weight
"""
if framework.global_var._in_eager_mode_:
base_tensor = core.eager.Tensor
else:
base_tensor = core.VarBase
base_tensor = core.eager.Tensor
assert isinstance(
value, (np.ndarray, base_tensor, dict, str)
), "Variable set_value function, arguments type only support Variable, numpy, VarBase, dict, string."
), "Variable set_value function, arguments type only support Variable, numpy, Tensor, dict, string."
if isinstance(value, (dict, str)):
assert len(self) == len(
......@@ -219,8 +215,7 @@ def monkey_patch_varbase():
self.name, self.dtype, dtype
)
# NOTE(wuweilong): self could be VarBase or Tensor, the subsequent behavior are defined in different files
# if self is VarBase, method value() return Variable that bindded in imperative.cc, get_tensor() bindded in pybind.cc
# NOTE(wuweilong): self could be Tensor, the subsequent behavior are defined in different files
# if self is Tensor, method value() return self that defined in this file, get_tensor() defined in eager_method.cc
# this Interface behavior will be unifed in the future.
self.value().get_tensor().set(
......@@ -665,7 +660,7 @@ def monkey_patch_varbase():
def __str__(self):
"""
Convert a VarBase object to a readable string.
Convert a Tensor object to a readable string.
Returns(str): A readable string.
......@@ -680,14 +675,9 @@ def monkey_patch_varbase():
# [[0.30574632, 0.55739117, 0.30902600, 0.39413780, 0.44830436],
# [0.79010487, 0.53972793, 0.09495186, 0.44267157, 0.72112119]])
"""
if framework.global_var._in_eager_mode_:
from paddle.tensor.to_string import tensor_to_string
return tensor_to_string(self)
else:
from paddle.tensor.to_string import to_string
from paddle.tensor.to_string import tensor_to_string
return to_string(self)
return tensor_to_string(self)
def __deepcopy__(self, memo):
"""
......@@ -714,10 +704,7 @@ def monkey_patch_varbase():
raise RuntimeError(
"Only Leaf Tensor support the deepcopy at the moment, non-Leaf Tensors contains graph information that does't support deepcopy"
)
if framework.global_var._in_eager_mode_:
new_varbase = core.eager.Tensor()
else:
new_varbase = core.VarBase()
new_varbase = core.eager.Tensor()
new_varbase.name = self.name + unique_name.generate("_deepcopy")
memo[id(self)] = new_varbase
new_varbase.copy_(self, True)
......@@ -1055,26 +1042,19 @@ def monkey_patch_varbase():
("to_dense", to_dense),
("to_sparse_coo", to_sparse_coo),
):
if framework.global_var._in_eager_mode_:
setattr(core.eager.Tensor, method_name, method)
else:
setattr(core.VarBase, method_name, method)
if framework.global_var._in_eager_mode_:
setattr(core.eager.Tensor, "_set_grad_ivar", _set_grad_ivar)
setattr(core.eager.Tensor, "value", value)
setattr(core.eager.Tensor, "cpu", cpu)
setattr(core.eager.Tensor, "cuda", cuda)
setattr(core.eager.Tensor, "pin_memory", pin_memory)
setattr(core.eager.Tensor, "_slice", _slice)
setattr(core.eager.Tensor, "_numel", _numel)
setattr(core.eager.Tensor, "_uva", _uva)
setattr(core.eager.Tensor, "_clear_data", _clear_data)
setattr(core.eager.Tensor, "__hash__", __hash__)
setattr(core.eager.Tensor, "_use_gpudnn", _use_gpudnn)
else:
setattr(core.VarBase, "__name__", "Tensor")
setattr(core.VarBase, "grad", grad)
setattr(core.eager.Tensor, method_name, method)
setattr(core.eager.Tensor, "_set_grad_ivar", _set_grad_ivar)
setattr(core.eager.Tensor, "value", value)
setattr(core.eager.Tensor, "cpu", cpu)
setattr(core.eager.Tensor, "cuda", cuda)
setattr(core.eager.Tensor, "pin_memory", pin_memory)
setattr(core.eager.Tensor, "_slice", _slice)
setattr(core.eager.Tensor, "_numel", _numel)
setattr(core.eager.Tensor, "_uva", _uva)
setattr(core.eager.Tensor, "_clear_data", _clear_data)
setattr(core.eager.Tensor, "__hash__", __hash__)
setattr(core.eager.Tensor, "_use_gpudnn", _use_gpudnn)
global _already_patch_repr
if not _already_patch_repr:
......
python/paddle/fluid/framework.py
浏览文件 @ d4571470
......@@ -192,9 +192,9 @@ extra_op_attrs = {
# to make sure in most case, we find new dygraph mode first with only one if statement.
def _update_monkey_methods(is_eager):
def _update_monkey_methods():
"""
Update monkey methods of VarBase or eager.Tensor while
Update monkey methods of Tensor or eager.Tensor while
switching eager mode and legacy mode.
"""
from paddle import _C_ops, _legacy_C_ops
......@@ -204,46 +204,23 @@ def _update_monkey_methods(is_eager):
global _already_patch_eager_tensor
global _already_patch_varbase
assert isinstance(is_eager, bool)
# switch into eager mode
if is_eager:
if not _already_patch_eager_tensor:
monkey_patch_varbase()
monkey_patch_math_varbase()
if not _already_patch_eager_tensor:
monkey_patch_varbase()
monkey_patch_math_varbase()
_already_patch_eager_tensor = True
# switch back into legacy mode
else:
if not _already_patch_varbase:
monkey_patch_varbase()
monkey_patch_math_varbase()
_already_patch_varbase = True
_already_patch_eager_tensor = True
# switch Paddle.Tensor bind type
_switch_tensor_bind_type(is_eager)
_switch_tensor_bind_type()
def _switch_tensor_bind_type(is_eager):
def _switch_tensor_bind_type():
import paddle
if is_eager:
paddle.Tensor = core.eager.Tensor
else:
paddle.Tensor = core.VarBase
paddle.Tensor = core.eager.Tensor
paddle.Tensor.__qualname__ = 'Tensor'
def _enable_legacy_dygraph():
global_var._in_eager_mode_ = False
_update_monkey_methods(is_eager=False)
def _disable_legacy_dygraph():
global_var._in_eager_mode_ = True
_update_monkey_methods(is_eager=True)
def _in_eager_without_dygraph_check():
return global_var._in_eager_mode_
......@@ -253,36 +230,6 @@ def _in_eager_without_dygraph_check():
_is_first_import_ = True
def _fallback_legacy_dygraph():
global _is_first_import_
need_fallback = False
# Only enable eager on CPU/GPU/XPU
is_not_support = (
core.is_compiled_with_npu()
or core.is_compiled_with_ipu()
or core.is_compiled_with_mlu()
)
if global_var._in_eager_mode_ and is_not_support:
# switch into legacy dygraph mode
warnings.warn(
"We will fallback into legacy dygraph on NPU/XPU/MLU/IPU/ROCM devices. Because we only support new eager dygraph mode on CPU/GPU currently. "
)
global_var._in_eager_mode_ = False
if not _is_first_import_:
_enable_legacy_dygraph()
need_fallback = True
need_fallback = False
_is_first_import_ = False
return need_fallback
# switch into legacy mode if need while import paddle
_fallback_legacy_dygraph()
def in_dygraph_mode():
"""
......@@ -319,19 +266,6 @@ def _non_static_mode():
return global_var._dygraph_tracer_ is not None
@signature_safe_contextmanager
def _test_eager_guard(place=None):
# FIXME(dev): We haven't fully verified eager mode on NPU et.al but
# only GPU/CPU/XPU. Remove this after we improve this feature.
already_fallback = _fallback_legacy_dygraph()
if not already_fallback:
_disable_legacy_dygraph()
try:
yield
finally:
pass
global_ipu_index = -1
global_ipu_stage = -1
ipu_index_attr_name = 'ipu_index'
......@@ -634,11 +568,11 @@ def _set_pipeline_stage(stage):
# NOTE(zhiqiu): This decorator is used for the APIs of Variable which is only
# used to make Variable and VarBase has same interfaces, like numpy. Since VarBase is not exposed in our
# official docments, logically, we want to keep VarBase and logically consistent. While, actually,
# used to make Variable and Tensor has same interfaces, like numpy. Since Tensor is not exposed in our
# official docments, logically, we want to keep Tensor and logically consistent. While, actually,
# in our implementation, there some APIs not supported, like numpy, because Variable contains the desc.
# So, those APIs are listed under class Variable to generate docs only.
# TODO(zhiqiu): We should make VarBase consistent with Variable in future, for example, by inheritting
# TODO(zhiqiu): We should make Tensor consistent with Variable in future, for example, by inheritting
# same base class.
def _fake_interface_only_(func):
def __impl__(*args, **kwargs):
......@@ -759,23 +693,6 @@ def _set_expected_place(place):
_set_dygraph_tracer_expected_place(place)
# TODO(zhiqiu): remove this function.
def _var_base_to_np(var_base):
"""
convert VarBase tp numpy
Args:
var_base(VarBase) : the VarBase to convert
Returns (np.ndarray): the np.ndarray contain the value of VarBase
"""
warnings.warn(
"paddle.fluid.framework._var_base_to_np is deprecated, please use var_base.numpy() instead of _var_base_to_np(var_base)."
)
return var_base.numpy(False)
def _cpu_num():
if "CPU_NUM" not in os.environ.keys():
if multiprocessing.cpu_count() > 1:
......@@ -1392,24 +1309,15 @@ def _varbase_creator(
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if global_var._in_eager_mode_:
eager_tensor = core.eager.Tensor(
dtype if dtype else core.VarDesc.VarType.FP32,
list(shape) if shape else [],
name,
type if type else core.VarDesc.VarType.LOD_TENSOR,
True if persistable else False,
)
eager_tensor.retain_grads()
return eager_tensor
else:
return core.VarBase(
dtype if dtype else core.VarDesc.VarType.FP32,
list(shape) if shape else [],
name,
type if type else core.VarDesc.VarType.LOD_TENSOR,
True if persistable else False,
)
eager_tensor = core.eager.Tensor(
dtype if dtype else core.VarDesc.VarType.FP32,
list(shape) if shape else [],
name,
type if type else core.VarDesc.VarType.LOD_TENSOR,
True if persistable else False,
)
eager_tensor.retain_grads()
return eager_tensor
def _all_is_type(vals, expected_type):
......@@ -3141,7 +3049,7 @@ class Operator:
in_arg_names.append(arg)
elif isinstance(arg, bytes):
in_arg_names.append(arg.decode())
elif isinstance(arg, (Variable, core.VarBase)):
elif isinstance(arg, (Variable, core.eager.Tensor)):
in_arg_names.append(arg.name)
else:
raise TypeError(
......@@ -4274,7 +4182,7 @@ class Block:
op_desc = self.desc.append_op()
inputs = kwargs.get("inputs", None)
outputs = kwargs.get("outputs", None)
# NOTE(Aurelius84): In case of @to_static, all VarBase(s) should
# NOTE(Aurelius84): In case of @to_static, all Tensor(s) should
# be converted into Variable(s) with same name and block location.
# This is ONE and ONLY logic of type transformation of dy2static.
ignore_ops = {
......@@ -7205,155 +7113,7 @@ class Parameter(Variable, metaclass=ParameterMetaClass):
__repr__ = __str__
class ParamBase(core.VarBase):
"""
ParamBase is derived from Tensor( Which is the concept in Dygraph Mode).
A ParamBase is a persistable Tensor, and will be updated by optimizers
after each iteration.
The training of a neural network is essentially the updating of
its ParamBase.
Relative to a general Tensor, a ParamBase has several its own
member variables:
Args:
trainable(bool): True if the ParamBase need to be updated after
iterations.
optimize_attr(map): ParamBase attributes related with optimizing.
Currently, it only contains 'learning_rate'.
Default: {'learning_rate': 1.0}
regularizer(WeightDecayRegularizer): The Regularizer which will
be applied on the ParamBase. Default: None
do_model_average(bool): True if the model average strategy will
be applied on this ParamBase.
need_clip (bool): Whether the parameter gradient need to be cliped
in optimizer. Default is True.
"""
@dygraph_only
def __init__(self, shape, dtype, **kwargs):
if shape is None:
raise ValueError("The shape of Parameter should not be None")
if dtype is None:
raise ValueError("The dtype of Parameter should not be None")
for each in shape:
if each < 0:
raise ValueError(
"Each dimension of shape for Parameter must be greater than 0, but received %s"
% list(shape)
)
if dtype is not None:
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
name = kwargs.get('name', unique_name.generate('_param_base'))
super().__init__(
dtype if dtype else core.VarDesc.VarType.FP32,
list(shape) if shape else [],
name,
core.VarDesc.VarType.LOD_TENSOR,
True,
)
trainable = kwargs.get('trainable', True)
self.stop_gradient = not trainable
self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})
self.regularizer = kwargs.get('regularizer', None)
self.do_model_average = kwargs.get('do_model_average', None)
self.need_clip = kwargs.get('need_clip', True)
self.is_distributed = kwargs.get('is_distributed', False)
# self.block = default_main_program().global_block()
@property
def trainable(self):
return not self.stop_gradient
@trainable.setter
def trainable(self, trainable):
if isinstance(trainable, bool):
self.stop_gradient = not trainable
else:
raise ValueError(
"The type of trainable MUST be bool, but the type is ",
type(trainable),
)
def __str__(self):
"""
Convert a ParamBase object to a readable string.
Returns(str): A readable string.
Examples:
.. code-block:: python
import paddle
linear = paddle.nn.Linear(3, 3)
print(linear.weight)
# Parameter containing:
# Tensor(shape=[3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
# [[ 0.48948765, 0.05829060, -0.25524026],
# [-0.70368278, 0.52986908, -0.68742192],
# [-0.54217887, 0.48439729, 0.34082305]])
"""
return "Parameter containing:\n{tensor}".format(
tensor=super().__str__()
)
def __deepcopy__(self, memo):
"""
Deep copy parameter, it will always performs Tensor copy.
Examples:
.. code-block:: python
import paddle
import copy
linear = paddle.nn.Linear(1, 3)
linear_copy = copy.deepcopy(linear)
print(linear.weight)
# Parameter containing:
# Tensor(shape=[1, 3], dtype=float32, place=CPUPlace, stop_gradient=False,
# [[-0.30929261, -0.90929240, -1.07851017]])
print(linear_copy.weight)
# Parameter containing:
# Tensor(shape=[1, 3], dtype=float32, place=CPUPlace, stop_gradient=False,
# [[-0.30929261, -0.90929240, -1.07851017]])
"""
state = copy.deepcopy(self.__dict__, memo)
state["name"] = self.name + unique_name.generate("_deepcopy")
new_param = ParamBase(self.shape, self.dtype, **state)
memo[id(self)] = new_param
new_param.copy_(self, True)
return new_param
def _copy_to(self, device, blocking):
state = copy.deepcopy(self.__dict__)
new_param = ParamBase(self.shape, self.dtype, **state)
core.varbase_copy(self, new_param, device, blocking)
return new_param
__repr__ = __str__
if hasattr(core, "eager"):
_core_eager_eagertensor = core.eager.Tensor
else:
_core_eager_eagertensor = object
class EagerParamBase(_core_eager_eagertensor):
class EagerParamBase(core.eager.Tensor):
"""
EagerParamBase is derived from Tensor( Which is the concept in Eager-Dygraph Mode).
A EagerParamBase is a persistable Tensor, and will be updated by optimizers
......
python/paddle/fluid/layer_helper_base.py
浏览文件 @ d4571470
......@@ -88,25 +88,15 @@ class LayerHelperBase:
"""
if isinstance(value, np.ndarray):
if _in_eager_without_dygraph_check():
return core.eager.Tensor(
value,
_current_expected_place(),
False,
False,
name if name else None,
True,
)
else:
py_var = core.VarBase(
value=value,
name=name if name else '',
persistable=False,
place=_current_expected_place(),
zero_copy=False,
)
return py_var
elif isinstance(value, (core.VarBase, Variable, core.eager.Tensor)):
return core.eager.Tensor(
value,
_current_expected_place(),
False,
False,
name if name else None,
True,
)
elif isinstance(value, (Variable, core.eager.Tensor)):
return value
else:
raise TypeError(
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ d4571470
......@@ -1053,7 +1053,7 @@ def assign_skip_lod_tensor_array(input, output):
return True
return False
if not isinstance(input, (Variable, core.VarBase)):
if not isinstance(input, (Variable, core.eager.Tensor)):
if isinstance(output, Variable) and isinstance(
input, support_ret_buildin_type
):
......
python/paddle/fluid/optimizer.py
浏览文件 @ d4571470
......@@ -309,7 +309,7 @@ class Optimizer:
self._learning_rate.step_num = global_step[0]
else:
raise RuntimeError(
"Type not supprt, value in state dict must be [VarBase, Variable, numpy], the type is ",
"Type not supprt, value in state dict must be [Tensor, Variable, numpy], the type is ",
type(global_step),
)
......@@ -320,7 +320,7 @@ class Optimizer:
load_para = state_dict[param.name]
if isinstance(load_para, Variable):
load_para_np = load_para.numpy()
elif isinstance(load_para, core.VarBase):
elif isinstance(load_para, core.eager.Tensor):
load_para_np = load_para.numpy()
elif isinstance(load_para, np.ndarray):
load_para_np = load_para
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_len.py
浏览文件 @ d4571470
......@@ -62,7 +62,7 @@ class TestLen(unittest.TestCase):
else:
out = self.func(self.x_data)
if isinstance(out, fluid.core.VarBase):
if isinstance(out, fluid.core.eager.Tensor):
out = out.numpy()
return out
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py
浏览文件 @ d4571470
......@@ -320,7 +320,7 @@ class TestTransformWhileLoop(unittest.TestCase):
def _run(self, to_static):
with fluid.dygraph.guard(self.place):
# Set the input of dyfunc to VarBase
# Set the input of dyfunc to Tensor
tensor_x = fluid.dygraph.to_variable(self.x, zero_copy=False)
if to_static:
ret = paddle.jit.to_static(self.dyfunc)(tensor_x)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_partial_program.py
浏览文件 @ d4571470
......@@ -116,9 +116,7 @@ class TestWithNestedOutput(unittest.TestCase):
self.assertTrue(len(dygraph_res) == len(static_res))
for dy_var, st_var in zip(dygraph_res, static_res):
if isinstance(
dy_var, (fluid.core.VarBase, fluid.core.eager.Tensor)
):
if isinstance(dy_var, fluid.core.eager.Tensor):
np.testing.assert_allclose(
dy_var.numpy(), st_var.numpy(), rtol=1e-05
)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_return.py
浏览文件 @ d4571470
......@@ -282,7 +282,7 @@ class TestReturnBase(unittest.TestCase):
res = self.dygraph_func(self.input)
if isinstance(res, (tuple, list)):
return tuple(r.numpy() for r in res)
elif isinstance(res, (core.VarBase, core.eager.Tensor)):
elif isinstance(res, core.eager.Tensor):
return res.numpy()
return res
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_save_inference_model.py
浏览文件 @ d4571470
......@@ -161,13 +161,13 @@ class TestPartialProgramRaiseError(unittest.TestCase):
concrete_program.parameters = params[0]
# TypeError: Type of self._params should be list or tuple,
# but received <class 'paddle.fluid.framework.ParamBase'>.
# but received <class 'paddle.fluid.framework.EagerParamBase'>.
with self.assertRaises(TypeError):
partial_program_from(concrete_program)
params[0] = "linear.w.0"
concrete_program.parameters = params
# TypeError: Type of self._params[0] should be framework.ParamBase,
# TypeError: Type of self._params[0] should be framework.EagerParamBase,
# but received <type 'str'>.
with self.assertRaises(TypeError):
partial_program_from(concrete_program)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_typehint.py
浏览文件 @ d4571470
......@@ -53,7 +53,7 @@ class TestTransformWhileLoop(unittest.TestCase):
def _run(self, to_static):
with fluid.dygraph.guard(self.place):
# Set the input of dyfunc to VarBase
# Set the input of dyfunc to Tensor
tensor_x = fluid.dygraph.to_variable(self.x, zero_copy=False)
if to_static:
ret = paddle.jit.to_static(self.dyfunc)(tensor_x)
......
python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_bf16_mkldnn_op.py
浏览文件 @ d4571470
......@@ -21,7 +21,7 @@ from paddle import enable_static
from paddle.fluid import core
sys.path.append("..")
from op_test import OpTest, OpTestTool, convert_float_to_uint16
from eager_op_test import OpTest, OpTestTool, convert_float_to_uint16
from test_pool2d_op import (
TestPool2D_Op_Mixin,
adaptive_end_index,
......
python/paddle/fluid/tests/unittests/test_base_layer.py
浏览文件 @ d4571470
......@@ -19,7 +19,7 @@ import numpy as np
import paddle
from paddle import fluid
from paddle.fluid.dygraph import to_variable
from paddle.fluid.framework import EagerParamBase, ParamBase, in_dygraph_mode
from paddle.fluid.framework import EagerParamBase
class L1(paddle.nn.Layer):
......@@ -184,14 +184,9 @@ class TestBuffer(unittest.TestCase):
with self.assertRaisesRegex(
TypeError, "buffer should be a Paddle.Tensor"
):
if in_dygraph_mode():
net.register_buffer(
"buffer_name", EagerParamBase([2, 2], 'float32')
)
else:
net.register_buffer(
"buffer_name", ParamBase([2, 2], 'float32')
)
net.register_buffer(
"buffer_name", EagerParamBase([2, 2], 'float32')
)
with self.assertRaisesRegex(
KeyError, "name of buffer can not contain"
......@@ -208,10 +203,7 @@ class TestBuffer(unittest.TestCase):
net.register_buffer("attr_name", var)
del net.attr_name
if in_dygraph_mode():
net.attr_name = EagerParamBase([2, 2], 'float32')
else:
net.attr_name = ParamBase([2, 2], 'float32')
net.attr_name = EagerParamBase([2, 2], 'float32')
with self.assertRaisesRegex(KeyError, "already exists"):
net.register_buffer("attr_name", var)
......@@ -278,11 +270,8 @@ class TestBuffer(unittest.TestCase):
self.assertEqual(len(net.buffers()), 1)
self.assertEqual(len(net.state_dict()), 0)
# Re-assign a ParamBase will remove the buffer.
if in_dygraph_mode():
net.buffer_name = EagerParamBase([2, 2], 'float32')
else:
net.buffer_name = ParamBase([2, 2], 'float32')
# Re-assign a EagerParamBase will remove the buffer.
net.buffer_name = EagerParamBase([2, 2], 'float32')
self.assertEqual(len(net.buffers()), 0)
self.assertEqual(len(net.state_dict()), 1)
......@@ -403,12 +392,9 @@ class TestLayerTo(unittest.TestCase):
paddle.fluid.core.VarDesc.VarType.FP64,
)
for p in self.linear.parameters():
if in_dygraph_mode():
self.assertTrue(
isinstance(p, paddle.fluid.framework.EagerParamBase)
)
else:
self.assertTrue(isinstance(p, paddle.fluid.framework.ParamBase))
self.assertTrue(
isinstance(p, paddle.fluid.framework.EagerParamBase)
)
if paddle.fluid.is_compiled_with_cuda():
self.linear.to(device=paddle.CUDAPlace(0))
......@@ -435,14 +421,9 @@ class TestLayerTo(unittest.TestCase):
self.linear.weight._grad_ivar().place.gpu_device_id(), 0
)
for p in self.linear.parameters():
if in_dygraph_mode():
self.assertTrue(
isinstance(p, paddle.fluid.framework.EagerParamBase)
)
else:
self.assertTrue(
isinstance(p, paddle.fluid.framework.ParamBase)
)
self.assertTrue(
isinstance(p, paddle.fluid.framework.EagerParamBase)
)
self.linear.to(device=paddle.CPUPlace())
self.assertTrue(self.linear.weight.place.is_cpu_place())
......@@ -489,12 +470,9 @@ class TestLayerTo(unittest.TestCase):
paddle.fluid.core.VarDesc.VarType.FP64,
)
for p in self.linear.parameters():
if in_dygraph_mode():
self.assertTrue(
isinstance(p, paddle.fluid.framework.EagerParamBase)
)
else:
self.assertTrue(isinstance(p, paddle.fluid.framework.ParamBase))
self.assertTrue(
isinstance(p, paddle.fluid.framework.EagerParamBase)
)
def func_test_to_api_numpy_dtype(self):
self.linear.to(dtype=np.float64)
......@@ -527,12 +505,9 @@ class TestLayerTo(unittest.TestCase):
paddle.fluid.core.VarDesc.VarType.FP64,
)
for p in self.linear.parameters():
if in_dygraph_mode():
self.assertTrue(
isinstance(p, paddle.fluid.framework.EagerParamBase)
)
else:
self.assertTrue(isinstance(p, paddle.fluid.framework.ParamBase))
self.assertTrue(
isinstance(p, paddle.fluid.framework.EagerParamBase)
)
def func_test_to_api_none_buffer(self):
model = paddle.nn.Linear(2, 4)
......
python/paddle/fluid/tests/unittests/test_conv2d_op_depthwise_conv.py
浏览文件 @ d4571470
......@@ -30,7 +30,7 @@ from test_conv2d_op import (
)
from paddle.fluid import core
from paddle.fluid.tests.unittests.op_test import get_numeric_gradient
from paddle.fluid.tests.unittests.eager_op_test import get_numeric_gradient
from paddle.fluid.tests.unittests.testsuite import create_op
# ----------------TestDepthwiseConv -----
......@@ -416,14 +416,14 @@ def create_test_fp16_class(parent, grad_check=True):
place = core.CUDAPlace(0)
if core.is_float16_supported(place) and grad_check:
self.check_grad_with_place(
place, ['Input'], 'Output', no_grad_set=set(['Filter'])
place, ['Input'], 'Output', no_grad_set={'Filter'}
)
def test_check_grad_no_input(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place) and grad_check:
self.check_grad_with_place(
place, ['Filter'], 'Output', no_grad_set=set(['Input'])
place, ['Filter'], 'Output', no_grad_set={'Input'}
)
cls_name = "{0}_{1}".format(parent.__name__, "FP16OP")
......@@ -464,7 +464,7 @@ def create_test_bf16_class(parent, atol=1e-2):
place,
['Input'],
'Output',
no_grad_set=set(['Filter']),
no_grad_set={'Filter'},
user_defined_grads=[numeric_grads],
)
......@@ -475,7 +475,7 @@ def create_test_bf16_class(parent, atol=1e-2):
place,
['Filter'],
'Output',
no_grad_set=set(['Input']),
no_grad_set={'Input'},
user_defined_grads=[numeric_grads],
)
......@@ -503,14 +503,14 @@ def create_test_channel_last_fp16_class(parent, grad_check=True):
place = core.CUDAPlace(0)
if core.is_float16_supported(place) and grad_check:
self.check_grad_with_place(
place, ['Input'], 'Output', no_grad_set=set(['Filter'])
place, ['Input'], 'Output', no_grad_set={'Filter'}
)
def test_check_grad_no_input(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place) and grad_check:
self.check_grad_with_place(
place, ['Filter'], 'Output', no_grad_set=set(['Input'])
place, ['Filter'], 'Output', no_grad_set={'Input'}
)
def init_data_format(self):
......
python/paddle/fluid/tests/unittests/test_eager_run_program.py
浏览文件 @ d4571470
......@@ -44,7 +44,7 @@ def _append_backward_desc(main_program, outs):
# def _set_grad_type(params, train_program):
# # NOTE: if user set sparse gradient mode, the param's gradient
# # will be SelectedRows, not LoDTensor. But tracer will just
# # set param grad VarBase by forward VarBase(LoDTensor)
# # set param grad Tensor by forward Tensor(LoDTensor)
# # If we don't change grad_var type here, RunProgramOp need
# # transform SelectedRows to LoDTensor forcibly, it may not
# # be user wanted result.
......
python/paddle/fluid/tests/unittests/test_einsum_v2.py
浏览文件 @ d4571470
......@@ -600,7 +600,7 @@ class TestSimpleUndiagonal2(unittest.TestCase):
class TestSimpleComplexGrad(unittest.TestCase):
"""
EinsumOp support complex grad. but op_test don't support numeric grad for complex dtype.
EinsumOp support complex grad. but eager_op_test don't support numeric grad for complex dtype.
"""
def test_shape(self):
......
python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py
浏览文件 @ d4571470
......@@ -321,7 +321,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
self.opti_dict = adam.state_dict()
self.base_opti = {}
for k, v in self.opti_dict.items():
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
self.base_opti[v.name] = v.numpy()
self.assertTrue(np.sum(np.abs(v.numpy())) != 0)
else:
......@@ -423,7 +423,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
opti_dict = adam.state_dict()
# set to zero
for k, v in opti_dict.items():
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
np_t = v.numpy()
var = v.value().get_tensor()
var.set(np.zeros_like(np_t), place)
......@@ -440,7 +440,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
opti_dict = adam.state_dict()
for k, v in opti_dict.items():
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
np.testing.assert_array_equal(
v.numpy(), self.base_opti[v.name]
)
......@@ -545,7 +545,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
opti_dict = adam.state_dict()
# set to zero
for k, v in opti_dict.items():
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
np_t = v.numpy()
var = v.value().get_tensor()
var.set(np.zeros_like(np_t), place)
......@@ -558,7 +558,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
adam.set_state_dict(self.opti_dict)
opti_dict = adam.state_dict()
for k, v in opti_dict.items():
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
np.testing.assert_array_equal(
v.numpy(), self.base_opti[v.name]
)
......@@ -664,7 +664,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
np_opti_dict = {}
# set to zero
for k, v in opti_dict.items():
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
np_t = v.numpy()
np_opti_dict[v.name] = np_t
var = v.value().get_tensor()
......@@ -680,7 +680,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
opti_dict = adam.state_dict()
for k, v in opti_dict.items():
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
np.testing.assert_array_equal(
v.numpy(), self.base_opti[v.name]
)
......@@ -953,7 +953,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
np_state_dict = {}
for k, v in self.opti_dict.items():
if isinstance(v, (core.VarBase, core.eager.Tensor)):
if isinstance(v, core.eager.Tensor):
np_opti_dict[v.name] = v.numpy()
else:
np_opti_dict[k] = v
......
python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_dataset.py
浏览文件 @ d4571470
......@@ -82,12 +82,8 @@ class TestTensorDataset(unittest.TestCase):
assert len(label) == 1
assert input.shape == [1, 3, 4]
assert label.shape == [1, 1]
assert isinstance(
input, (fluid.core.VarBase, fluid.core.eager.Tensor)
)
assert isinstance(
label, (fluid.core.VarBase, fluid.core.eager.Tensor)
)
assert isinstance(input, fluid.core.eager.Tensor)
assert isinstance(label, fluid.core.eager.Tensor)
assert np.allclose(input.numpy(), input_np[i])
assert np.allclose(label.numpy(), label_np[i])
......@@ -184,12 +180,8 @@ class TestSubsetDataset(unittest.TestCase):
assert len(label) == 1
assert input.shape == [1, 3, 4]
assert label.shape == [1, 1]
assert isinstance(
input, (fluid.core.VarBase, fluid.core.eager.Tensor)
)
assert isinstance(
label, (fluid.core.VarBase, fluid.core.eager.Tensor)
)
assert isinstance(input, fluid.core.eager.Tensor)
assert isinstance(label, fluid.core.eager.Tensor)
elements_list = []
for _, (input, label) in enumerate(dataloader()):
......@@ -285,12 +277,8 @@ class TestNumpyMixTensorDataset(TestTensorDataset):
assert len(label) == 1
assert input.shape == [1, IMAGE_SIZE]
assert label.shape == [1, 1]
assert isinstance(
input, (fluid.core.VarBase, fluid.core.eager.Tensor)
)
assert isinstance(
label, (fluid.core.VarBase, fluid.core.eager.Tensor)
)
assert isinstance(input, fluid.core.eager.Tensor)
assert isinstance(label, fluid.core.eager.Tensor)
class ComplextDataset(Dataset):
......@@ -385,9 +373,7 @@ class TestSingleFieldDataset(unittest.TestCase):
)
for i, data in enumerate(dataloader()):
assert isinstance(
data, (fluid.core.VarBase, fluid.core.eager.Tensor)
)
assert isinstance(data, fluid.core.eager.Tensor)
assert data.shape == [2, 2, 3]
def test_main(self):
......
python/paddle/fluid/tests/unittests/test_nonzero_api.py
浏览文件 @ d4571470
......@@ -20,7 +20,7 @@ from eager_op_test import OpTest
import paddle
from paddle import fluid
from paddle.fluid import Program, program_guard
from paddle.fluid.tests.unittests.op_test import convert_float_to_uint16
from paddle.fluid.tests.unittests.eager_op_test import convert_float_to_uint16
def call_nonzero(x):
......
python/paddle/fluid/tests/unittests/test_paddle_save_load.py
浏览文件 @ d4571470
......@@ -360,7 +360,7 @@ class TestSaveLoadAny(unittest.TestCase):
self.assertTrue(
isinstance(
t_dygraph,
(paddle.fluid.core.VarBase, paddle.fluid.core.eager.Tensor),
paddle.fluid.core.eager.Tensor,
)
)
np.testing.assert_array_equal(tensor.numpy(), np_dygraph)
......@@ -788,14 +788,14 @@ class TestSaveLoadAny(unittest.TestCase):
self.assertTrue(
isinstance(
load_tensor3[0],
(fluid.core.VarBase, fluid.core.eager.Tensor),
fluid.core.eager.Tensor,
)
)
np.testing.assert_array_equal(load_tensor3[0].numpy(), obj3[0])
self.assertTrue(
isinstance(
load_tensor3[1],
(fluid.core.VarBase, fluid.core.eager.Tensor),
fluid.core.eager.Tensor,
)
)
np.testing.assert_array_equal(load_tensor3[1].numpy(), obj3[1])
......@@ -804,7 +804,7 @@ class TestSaveLoadAny(unittest.TestCase):
self.assertTrue(
isinstance(
load_tensor3[2]["state_dict"][k],
(fluid.core.VarBase, fluid.core.eager.Tensor),
fluid.core.eager.Tensor,
)
)
np.testing.assert_array_equal(
......@@ -815,7 +815,7 @@ class TestSaveLoadAny(unittest.TestCase):
self.assertTrue(
isinstance(
load_tensor3[2]["opt"][k],
(fluid.core.VarBase, fluid.core.eager.Tensor),
fluid.core.eager.Tensor,
)
)
np.testing.assert_array_equal(
......@@ -825,7 +825,7 @@ class TestSaveLoadAny(unittest.TestCase):
self.assertTrue(
isinstance(
load_tensor4[0],
(fluid.core.VarBase, fluid.core.eager.Tensor),
fluid.core.eager.Tensor,
)
)
np.testing.assert_array_equal(load_tensor4[0].numpy(), obj4[0])
......
python/paddle/fluid/tests/unittests/test_parameter.py
浏览文件 @ d4571470
......@@ -21,7 +21,7 @@ import paddle
from paddle.fluid import core
from paddle.fluid.dygraph import guard
from paddle.fluid.executor import Executor
from paddle.fluid.framework import ParamBase, Variable, default_main_program
from paddle.fluid.framework import Variable, default_main_program
paddle.enable_static()
main_program = default_main_program()
......@@ -73,9 +73,6 @@ class ParameterChecks(unittest.TestCase):
pram_copy2 = copy.deepcopy(param, memo)
self.assertEqual(id(param_copy), id(pram_copy2))
zero_dim_param = ParamBase(shape=[], dtype='float32')
self.assertEqual(zero_dim_param.shape, [])
def func_exception(self):
b = main_program.global_block()
with self.assertRaises(ValueError):
......
python/paddle/fluid/tests/unittests/test_pylayer_op.py
浏览文件 @ d4571470
......@@ -20,11 +20,6 @@ import paddle
from paddle.autograd.py_layer import PyLayer
class FakeTensor(paddle.fluid.core.VarBase):
def __init__(self):
pass
class TestPyLayer(unittest.TestCase):
def test_simple_pylayer_multiple_output(self):
class tanh(PyLayer):
......
python/paddle/fluid/tests/unittests/test_run_program_op.py
浏览文件 @ d4571470
......@@ -175,14 +175,9 @@ class RunProgramOpTest(unittest.TestCase):
def prepare_dygraph_input(self, place, return_param_list=False):
def create_var_base(is_input, name, np_value, stop_gradient):
if global_var._in_eager_mode_:
var = core.eager.Tensor(
value=np_value, name=name, place=place, zero_copy=True
)
else:
var = core.VarBase(
value=np_value, name=name, place=place, zero_copy=True
)
var = core.eager.Tensor(
value=np_value, name=name, place=place, zero_copy=True
)
var.stop_gradient = stop_gradient
return var
......
python/paddle/fluid/tests/unittests/test_trapezoid.py
浏览文件 @ d4571470
......@@ -17,7 +17,6 @@ import unittest
import numpy as np
import paddle
from paddle.fluid.framework import _test_eager_guard
class TestTrapezoidAPI(unittest.TestCase):
......@@ -61,9 +60,6 @@ class TestTrapezoidAPI(unittest.TestCase):
np.testing.assert_allclose(out, self.output, rtol=1e-05)
def test_dygraph(self):
with _test_eager_guard():
self.setUp()
self.func_dygraph()
self.setUp()
self.func_dygraph()
......@@ -267,8 +263,6 @@ class Testfp16Trapezoid(TestTrapezoidAPI):
out = self.paddle_api(y=y, x=x)
def test_fp16_dygraph(self):
with _test_eager_guard():
self.func_dygraph()
self.func_dygraph()
......
python/paddle/fluid/tests/unittests/test_var_base.py
浏览文件 @ d4571470
......@@ -1018,9 +1018,7 @@ class TestVarBase(unittest.TestCase):
def test_var_base_to_np(self):
with fluid.dygraph.guard():
var = fluid.dygraph.to_variable(self.array)
np.testing.assert_array_equal(
var.numpy(), fluid.framework._var_base_to_np(var)
)
np.testing.assert_array_equal(var.numpy(), var.numpy(False))
def test_var_base_as_np(self):
with fluid.dygraph.guard():
......@@ -1051,7 +1049,7 @@ class TestVarBase(unittest.TestCase):
def test_to_static_var(self):
with fluid.dygraph.guard():
# Convert VarBase into Variable or Parameter
# Convert Tensor into Variable or Parameter
var_base = fluid.dygraph.to_variable(self.array, name="var_base_1")
static_var = var_base._to_static_var()
self._assert_to_static(var_base, static_var)
......@@ -1060,7 +1058,7 @@ class TestVarBase(unittest.TestCase):
static_param = var_base._to_static_var(to_parameter=True)
self._assert_to_static(var_base, static_param, True)
# Convert ParamBase into Parameter
# Convert EagerParamBase into Parameter
fc = paddle.nn.Linear(
10,
20,
......@@ -1078,7 +1076,7 @@ class TestVarBase(unittest.TestCase):
if is_param:
self.assertTrue(isinstance(static_var, fluid.framework.Parameter))
self.assertTrue(static_var.persistable, True)
if isinstance(var_base, fluid.framework.ParamBase):
if isinstance(var_base, fluid.framework.EagerParamBase):
for attr in ['trainable', 'is_distributed', 'do_model_average']:
self.assertEqual(
getattr(var_base, attr), getattr(static_var, attr)
......
python/paddle/fluid/tests/unittests/xpu/test_unbind_op_xpu.py
浏览文件 @ d4571470
......@@ -27,7 +27,6 @@ from xpu.get_test_cover_info import (
import paddle
from paddle import fluid, tensor
from paddle.fluid import Program, program_guard
from paddle.fluid.framework import _test_eager_guard
paddle.enable_static()
......@@ -74,8 +73,7 @@ class XPUTestUnbindOP(XPUOpTestWrapper):
np.testing.assert_array_equal(x.grad.numpy(), np_grad)
def test_unbind_dygraph_final_state(self):
with _test_eager_guard():
self.test_unbind_dygraph()
self.test_unbind_dygraph()
class TestLayersUnbind(unittest.TestCase):
def test_layers_unbind(self):
......
python/paddle/framework/__init__.py
浏览文件 @ d4571470
......@@ -27,7 +27,6 @@ from ..fluid.core import CUDAPinnedPlace # noqa: F401
from ..fluid.core import NPUPlace # noqa: F401
from ..fluid.core import MLUPlace # noqa: F401
from ..fluid.core import CustomPlace # noqa: F401
from ..fluid.core import VarBase # noqa: F401
from ..fluid import core # noqa: F401
from ..fluid.dygraph import base, to_variable
......@@ -51,7 +50,7 @@ from ..fluid.dygraph import monkey_patch_math_varbase
from ..fluid.framework import disable_signal_handler # noqa: F401
from ..fluid.framework import get_flags # noqa: F401
from ..fluid.framework import set_flags # noqa: F401
from ..fluid.framework import Parameter, ParamBase
from ..fluid.framework import Parameter
from ..fluid.dygraph.base import enable_dygraph as disable_static # noqa: F401
from ..fluid.dygraph.base import disable_dygraph as enable_static # noqa: F401
from ..fluid.framework import _non_static_mode as in_dynamic_mode # noqa: F401
......
python/paddle/framework/io.py
浏览文件 @ d4571470
......@@ -29,7 +29,6 @@ from paddle import fluid
from paddle.fluid import core
from paddle.fluid.framework import (
EagerParamBase,
ParamBase,
Program,
Variable,
_current_expected_place,
......@@ -55,7 +54,7 @@ def _build_saved_state_dict(state_dict):
save_dict = {}
name_table = {}
for key, value in state_dict.items():
if isinstance(value, (Variable, core.VarBase, core.eager.Tensor)):
if isinstance(value, (Variable, core.eager.Tensor)):
if value.type == core.VarDesc.VarType.VOCAB:
save_dict[key] = value.value().get_map_tensor()
else:
......@@ -118,8 +117,8 @@ def _load_state_dict_from_save_inference_model(model_path, config):
def _load_state_dict_from_save_params(model_path):
# Try to load all the files in the directory in VarBase format,
# the file name is used as the name of VarBase
# Try to load all the files in the directory in Tensor format,
# the file name is used as the name of Tensor
load_var_list = []
# 1. load file names
......@@ -131,7 +130,7 @@ def _load_state_dict_from_save_params(model_path):
var_name = tmp_var_name.replace("\\", "/")
var_name_list.append(var_name)
# 2. create and load VarBase
# 2. create and load Tensor
with fluid.dygraph.guard():
for name in var_name_list:
new_var = _varbase_creator(name=name, persistable=True)
......@@ -320,17 +319,13 @@ def _pickle_save(obj, f, protocol):
def add_dispatch_table():
# This is not a good method, because the pickle module has been modified.
pickle.dispatch_table[core.VarBase] = reduce_varbase
pickle.dispatch_table[ParamBase] = reduce_varbase
pickle.dispatch_table[core.eager.Tensor] = reduce_varbase
pickle.dispatch_table[EagerParamBase] = reduce_varbase
pickle.dispatch_table[core.LoDTensor] = reduce_LoDTensor
pickle.dispatch_table.update(dispatch_table_layer)
def pop_dispatch_table():
pickle.dispatch_table.pop(core.VarBase)
pickle.dispatch_table.pop(core.LoDTensor)
pickle.dispatch_table.pop(ParamBase)
pickle.dispatch_table.pop(core.eager.Tensor)
pickle.dispatch_table.pop(EagerParamBase)
for k in dispatch_table_layer:
......@@ -349,9 +344,7 @@ def _pickle_save(obj, f, protocol):
pickler = pickle.Pickler(f, protocol)
pickler.dispatch_table = copyreg.dispatch_table.copy()
pickler.dispatch_table[core.VarBase] = reduce_varbase
pickler.dispatch_table[core.LoDTensor] = reduce_LoDTensor
pickler.dispatch_table[ParamBase] = reduce_varbase
pickler.dispatch_table[core.eager.Tensor] = reduce_varbase
pickler.dispatch_table[EagerParamBase] = reduce_varbase
pickler.dispatch_table.update(dispatch_table_layer)
......@@ -390,24 +383,21 @@ def _is_state_dict(obj):
(
paddle.nn.Layer,
Program,
core.VarBase,
core.eager.Tensor,
core.LoDTensor,
core.SelectedRows,
),
)
# If the value of a dict is a core.VarBase/LoDTensor or a dict
# that does not contain a paddle type(Layer, Program, VarBase, LoDTensor, SelectedRows),
# If the value of a dict is a core.Tensor/LoDTensor or a dict
# that does not contain a paddle type(Layer, Program, Tensor, LoDTensor, SelectedRows),
# the dict is considered to be a state_ dict.
for key, value in obj.items():
if isinstance(value, dict):
for k, v in value.items():
if _contain_x(v, condition):
return False
elif not isinstance(
value, (core.VarBase, core.eager.Tensor, core.LoDTensor)
):
elif not isinstance(value, (core.eager.Tensor, core.LoDTensor)):
return False
return True
......@@ -415,8 +405,8 @@ def _is_state_dict(obj):
def _transformed_from_varbase(obj):
# In paddle2.1 version, VarBase is saved as tuple(tensor.name, tensor.numpy()).
# When executing paddle.load, use this function to determine whether to restore to VarBase/LoDTensor.
# In paddle2.1 version, Tensor is saved as tuple(tensor.name, tensor.numpy()).
# When executing paddle.load, use this function to determine whether to restore to Tensor/LoDTensor.
if isinstance(obj, tuple) and len(obj) == 2:
name_types = str
if isinstance(obj[0], name_types) and isinstance(obj[1], np.ndarray):
......@@ -426,7 +416,7 @@ def _transformed_from_varbase(obj):
def _transformed_from_lodtensor(obj):
# In paddle2.1 version, LoDTensor is saved as np.array(tensor).
# When executing paddle.load, use this function to determine whether to restore to VarBase/LoDTensor.
# When executing paddle.load, use this function to determine whether to restore to Tensor/LoDTensor.
if isinstance(obj, np.ndarray):
return True
return False
......@@ -498,7 +488,7 @@ def _parse_every_object(obj, condition_func, convert_func):
else:
if isinstance(obj, Iterable) and not isinstance(
obj,
(str, np.ndarray, core.VarBase, core.eager.Tensor, core.LoDTensor),
(str, np.ndarray, core.eager.Tensor, core.LoDTensor),
):
raise NotImplementedError(
"The iteratable objects supported are tuple, list, dict, OrderedDict, string. But received {}.".format(
......@@ -642,7 +632,7 @@ def _save_binary_var(obj, path):
_save_lod_tensor(obj, path)
elif isinstance(obj, core.SelectedRows):
_save_selected_rows(obj, path)
elif isinstance(obj, (core.VarBase, core.eager.Tensor)):
elif isinstance(obj, core.eager.Tensor):
_save_lod_tensor(obj.value().get_tensor(), path)
else:
# Since the concept of 'Tensor' is only exposed to users, the error message can only contain tensor instead of 'LoDTensor' or 'SelectedRows'
......
python/paddle/framework/io_utils.py
浏览文件 @ d4571470
......@@ -180,7 +180,9 @@ def _load_program_scope(main=None, startup=None, scope=None):
@static_only
def _legacy_static_save(param_dict, model_path, protocol=2):
def get_tensor(var):
if isinstance(var, (core.VarBase, core.eager.Tensor, core.LoDTensor)):
if isinstance(var, core.eager.Tensor):
return np.array(var)
elif isinstance(var, core.LoDTensor):
return np.array(var)
return var
......
python/paddle/hapi/model.py
浏览文件 @ d4571470
......@@ -58,9 +58,9 @@ def to_list(value):
def to_numpy(var):
assert isinstance(
var, (Variable, fluid.core.VarBase, fluid.core.eager.Tensor)
var, (Variable, fluid.core.eager.Tensor)
), "not a variable"
if isinstance(var, (fluid.core.VarBase, fluid.core.eager.Tensor)):
if isinstance(var, fluid.core.eager.Tensor):
return np.array(var)
t = global_scope().find_var(var.name).get_tensor()
return np.array(t)
......
python/paddle/hapi/model_summary.py
浏览文件 @ d4571470
......@@ -261,7 +261,9 @@ def summary_string(model, input_size=None, dtypes=None, input=None):
depth = len(list(model.sublayers()))
def _get_shape_from_tensor(x):
if isinstance(x, (paddle.fluid.Variable, paddle.fluid.core.VarBase)):
if isinstance(
x, (paddle.fluid.Variable, paddle.fluid.core.eager.Tensor)
):
return list(x.shape)
elif isinstance(x, (list, tuple)):
return [_get_shape_from_tensor(xx) for xx in x]
......
python/paddle/jit/api.py
浏览文件 @ d4571470
......@@ -59,7 +59,6 @@ from paddle.nn import Layer
from paddle.fluid.executor import Executor, scope_guard
from paddle.fluid.framework import (
Block,
ParamBase,
Program,
Variable,
Parameter,
......@@ -390,7 +389,7 @@ class _SaveLoadConfig:
% type(input)
)
for var in spec:
if not isinstance(var, core.VarBase):
if not isinstance(var, core.eager.Tensor):
raise TypeError(
"The element in config `output_spec` list should be 'Variable', but received element's type is %s."
% type(var)
......@@ -543,7 +542,7 @@ def _get_input_var_names(inputs, input_spec):
# name is None, the input_spec only can be InputSpec
raise ValueError(name_none_error % spec)
elif spec.name not in input_var_names:
# the input_spec can be `InputSpec` or `VarBase`
# the input_spec can be `InputSpec` or `Tensor`
raise ValueError(name_no_exists_error % spec.name)
else:
result_list.append(spec.name)
......@@ -973,7 +972,7 @@ def save(layer, path, input_spec=None, **configs):
for var in paddle.utils.flatten(input_spec):
if isinstance(var, paddle.static.InputSpec):
inner_input_spec.append(var)
elif isinstance(var, (core.VarBase, core.eager.Tensor, Variable)):
elif isinstance(var, (core.eager.Tensor, Variable)):
inner_input_spec.append(
paddle.static.InputSpec.from_tensor(var)
)
......@@ -1132,7 +1131,7 @@ def save(layer, path, input_spec=None, **configs):
extra_info_dict[
'stop_gradient'
] = param_or_buffer.stop_gradient
if isinstance(param_or_buffer, (ParamBase, EagerParamBase)):
if isinstance(param_or_buffer, EagerParamBase):
extra_info_dict['trainable'] = param_or_buffer.trainable
extra_var_info[param_or_buffer.name] = extra_info_dict
......@@ -1151,7 +1150,7 @@ def save(layer, path, input_spec=None, **configs):
# NOTE(chenweihang): [ Get output variables ]
# the rule is like [ Get input variables name ]. For output var,
# we only support VarBase spec, and actually, we only need the
# we only support Tensor spec, and actually, we only need the
# var name of output, and we don't recommended to use output_spec
# print(concrete_program.main_program)
# print(concrete_program.outputs, configs.output_spec)
......
python/paddle/jit/dy2static/base_transformer.py
浏览文件 @ d4571470
......@@ -205,7 +205,7 @@ class ForNodeVisitor:
In this process, the semantics of for does not change.
Now only can parse 3 type statements (Here var is VarBase(Tensor) or python variable):
Now only can parse 3 type statements (Here var is Tensor(Tensor) or python variable):
1). for x in range(var[*]|var.numpy()[*])
2). for x in var|var.numpy()
3). for i, x enumerate(var|var.numpy())
......
python/paddle/jit/dy2static/function_spec.py
浏览文件 @ d4571470
......@@ -286,7 +286,7 @@ def _replace_value_with_input_spec(args):
if isinstance(input_var, np.ndarray):
input_var = paddle.static.InputSpec.from_numpy(input_var)
input_var.stop_gradient = True
elif isinstance(input_var, (core.VarBase, core.eager.Tensor)):
elif isinstance(input_var, core.eager.Tensor):
stop_gradient = input_var.stop_gradient
input_var = paddle.static.InputSpec.from_tensor(input_var)
input_var.stop_gradient = stop_gradient
......@@ -341,7 +341,7 @@ def convert_to_input_spec(inputs, input_spec):
# without specific InputSpec, raise warning.
if len(inputs) > len(input_spec):
for rest_input in inputs[len(input_spec) :]:
if isinstance(rest_input, (core.VarBase, np.ndarray)):
if isinstance(rest_input, (core.eager.Tensor, np.ndarray)):
logging_utils.warn(
"The inputs constain `{}` without specificing InputSpec, its shape and dtype will be treated immutable. "
"Please specific InputSpec information in `@to_static` if you expect them as mutable inputs.".format(
......
python/paddle/jit/dy2static/partial_program.py
浏览文件 @ d4571470
......@@ -59,9 +59,7 @@ class NestSequence:
def _get_var_ids(self):
var_ids = []
for idx, var in enumerate(self.__input_list):
if isinstance(
var, (framework.Variable, core.VarBase, core.eager.Tensor)
):
if isinstance(var, (framework.Variable, core.eager.Tensor)):
var_ids.append(idx)
return var_ids
......@@ -73,9 +71,7 @@ class NestSequence:
if need_check:
warning_types = set()
for var in self.__input_list:
if not isinstance(
var, (framework.Variable, core.VarBase, core.eager.Tensor)
):
if not isinstance(var, (framework.Variable, core.eager.Tensor)):
warning_types.add(type(var))
if warning_types:
logging_utils.warn(
......@@ -161,7 +157,7 @@ class PartialProgramLayer:
main_program(Program): The main program that contains ops need to be executed.
inputs(list[Variable]): The input list of the decorated function by `@to_static`.
outputs(list[Variable]): The output list of the decorated function by `@to_static`.
parameters(list[VarBase]|None): All trainable parameters included in the program. Default None.
parameters(list[Tensor]|None): All trainable parameters included in the program. Default None.
Returns:
Layer: A Layer object that run all ops internally in static graph mode.
......@@ -669,22 +665,13 @@ class PartialProgramLayer:
if "@GRAD" in name:
var_desc = block.vars[name].desc
var_base = None
if not framework.global_var._in_eager_mode_:
var_base = core.VarBase(
var_desc.dtype(),
var_desc.shape(),
var_desc.name(),
var_desc.type(),
False,
)
else:
var_base = core.eager.Tensor(
var_desc.dtype(),
var_desc.shape(),
var_desc.name(),
var_desc.type(),
False,
)
var_base = core.eager.Tensor(
var_desc.dtype(),
var_desc.shape(),
var_desc.name(),
var_desc.type(),
False,
)
double_grads.append(var_base)
return self._valid_vars(double_grads)
......@@ -866,29 +853,20 @@ class PartialProgramLayer:
assert isinstance(inputs, (tuple, list))
# Flatten inputs with nested structure into single list.
flatten_inputs = paddle.utils.flatten(inputs)
# Convert variable into VarBase and feed in training data.
# Convert variable into Tensor and feed in training data.
input_vars = []
expected_place = framework._current_expected_place()
for i, value in enumerate(flatten_inputs):
if isinstance(value, np.ndarray):
var = None
if not framework.global_var._in_eager_mode_:
var = core.VarBase(
value=value,
name=self._inputs[i].desc.name(),
persistable=False,
place=expected_place,
zero_copy=True,
)
else:
var = core.eager.Tensor(
value=value,
name=self._inputs[i].desc.name(),
persistable=False,
place=expected_place,
zero_copy=True,
)
elif isinstance(value, (core.VarBase, core.eager.Tensor)):
var = core.eager.Tensor(
value=value,
name=self._inputs[i].desc.name(),
persistable=False,
place=expected_place,
zero_copy=True,
)
elif isinstance(value, core.eager.Tensor):
# NOTE(Aurelius84): If var is on CPUPlace, it will be transformed multi times
# into CUDAPlace when it's as input of multi Ops. so we move it in advance
# to avoid this problem.
......@@ -904,7 +882,7 @@ class PartialProgramLayer:
continue
input_vars.append(var)
# mapping from name(string) -> VarBase
# mapping from name(string) -> Tensor
out_varbase_map = {}
def create_out(var_id):
......@@ -916,27 +894,18 @@ class PartialProgramLayer:
if var_desc.name() in out_varbase_map:
return out_varbase_map[var_desc.name()]
if not framework.global_var._in_eager_mode_:
var_base = core.VarBase(
var_desc.dtype(),
var_desc.shape(),
var_desc.name(),
var_desc.type(),
False,
)
else:
var_base = core.eager.Tensor(
var_desc.dtype(),
var_desc.shape(),
var_desc.name(),
var_desc.type(),
False,
)
var_base = core.eager.Tensor(
var_desc.dtype(),
var_desc.shape(),
var_desc.name(),
var_desc.type(),
False,
)
var_base.stop_gradient = var.stop_gradient
out_varbase_map[var_desc.name()] = var_base
return var_base
# Create VarBase to receive output data.
# Create Tensor to receive output data.
out_vars = list(map(create_out, self._outputs.var_ids))
return input_vars, out_vars
......@@ -947,21 +916,11 @@ class PartialProgramLayer:
inner_scope = self._get_scope(
program_id=program_id, use_scope_cache=use_scope_cache
)
if not framework.global_var._in_eager_mode_:
tmp_scope_vec = core.VarBase(
core.VarDesc.VarType.FP32,
[],
"program_out_scope",
core.VarDesc.VarType.STEP_SCOPES,
True,
)
tmp_scope_vec.value().set_scope(inner_scope)
else:
tmp_scope_vec = [inner_scope]
tmp_scope_vec = [inner_scope]
return tmp_scope_vec
def _create_cuda_graph_vec(self):
var = core.VarBase(
var = core.eager.Tensor(
core.VarDesc.VarType.FP32,
[],
"cuda_graph",
......@@ -973,7 +932,7 @@ class PartialProgramLayer:
def _restore_out(self, out_vars):
"""
Restores same nested outputs by only replacing the Variable with VarBase.
Restores same nested outputs by only replacing the Variable with Tensor.
"""
flatten_outputs = self._outputs.tolist()
......@@ -990,9 +949,7 @@ class PartialProgramLayer:
return main_program.clone(for_test=True)
def _is_no_value(self, var):
if isinstance(var, (core.VarBase, core.eager.Tensor)) and var.shape == [
1
]:
if isinstance(var, core.eager.Tensor) and var.shape == [1]:
# NOTE: .numpy() will insert MemcpySync operation, it hits performance.
if var.numpy()[0] == RETURN_NO_VALUE_MAGIC_NUM:
return True
......@@ -1002,7 +959,7 @@ class PartialProgramLayer:
"""
Removes invalid value for various-length return statement
"""
if isinstance(out_vars, (core.VarBase, core.eager.Tensor)):
if isinstance(out_vars, core.eager.Tensor):
if self._is_no_value(out_vars):
return None
return out_vars
......@@ -1029,7 +986,7 @@ class PartialProgramLayer:
def _set_grad_type(self, params, train_program):
# NOTE: if user set sparse gradient mode, the param's gradient
# will be SelectedRows, not LoDTensor. But tracer will just
# set param grad VarBase by forward VarBase(LoDTensor)
# set param grad Tensor by forward Tensor(LoDTensor)
# If we don't change grad_var type here, RunProgramOp need
# transform SelectedRows to LoDTensor forcibly, it may not
# be user wanted result.
......@@ -1057,9 +1014,9 @@ class PartialProgramLayer:
def _check_params_all_inited(self, main_program):
"""
Check all params from main program are already initialized, see details as follows:
1. all parameters in self._params should be type `framework.ParamBase` which are created in dygraph.
1. all parameters in self._params should be type `framework.EagerParamBase` which are created in dygraph.
2. all parameters from transformed program can be found in self._params.
Because they share same data with ParamBase of original dygraph.
Because they share same data with EagerParamBase of original dygraph.
"""
if not isinstance(self._params, (list, tuple)):
raise TypeError(
......@@ -1070,7 +1027,7 @@ class PartialProgramLayer:
param_and_buffer_names_set = set()
for i, var in enumerate(self._params):
# self._params constains parameters and buffers with persistable=True.
if not isinstance(var, (core.VarBase, core.eager.Tensor)):
if not isinstance(var, core.eager.Tensor):
raise TypeError(
'Type of self._params[{}] in PartialProgramLayer should be Parameter or Variable, but received {}.'.format(
i, type(var)
......
python/paddle/jit/translated_layer.py
浏览文件 @ d4571470
......@@ -648,22 +648,13 @@ def _load_persistable_vars_by_program(
orig_each_name = program_holder._suffix_varname_dict[each_var.name()]
if _is_parameter(each_var, program_holder.infer_program):
# create output varbase
if framework._in_eager_without_dygraph_check():
new_var = framework.EagerParamBase(
shape=each_var.shape(),
dtype=each_var.dtype(),
name=each_var.name(),
type=each_var.type(),
persistable=True,
)
else:
new_var = framework.ParamBase(
shape=each_var.shape(),
dtype=each_var.dtype(),
name=each_var.name(),
type=each_var.type(),
persistable=True,
)
new_var = framework.EagerParamBase(
shape=each_var.shape(),
dtype=each_var.dtype(),
name=each_var.name(),
type=each_var.type(),
persistable=True,
)
else:
new_var = framework._varbase_creator(
type=each_var.type(),
......@@ -746,24 +737,12 @@ def _load_persistable_vars(
# create output varbase
if extra_var_info[name].get('trainable', None) is not None:
# use default shape and dtype
if framework._in_eager_without_dygraph_check():
new_var = framework.EagerParamBase(
shape=[
1
], # only to pass check, this shape is not meaningful
dtype=core.VarDesc.VarType.FP32,
name=new_name,
persistable=True,
)
else:
new_var = framework.ParamBase(
shape=[
1
], # only to pass check, this shape is not meaningful
dtype=core.VarDesc.VarType.FP32,
name=new_name,
persistable=True,
)
new_var = framework.EagerParamBase(
shape=[1], # only to pass check, this shape is not meaningful
dtype=core.VarDesc.VarType.FP32,
name=new_name,
persistable=True,
)
else:
new_var = framework._varbase_creator(
name=new_name, persistable=True
......@@ -901,29 +880,20 @@ def _run_dygraph(instance, input, program_holder):
# 1. prepare inputs, outputs, attrs
input_vars = []
for i, value in enumerate(input):
if not isinstance(value, (np.ndarray, core.VarBase, core.eager.Tensor)):
if not isinstance(value, (np.ndarray, core.eager.Tensor)):
raise TypeError(
"The type of input in TranslatedLayer must be numpy array or Variable(VarBase), but received %s."
"The type of input in TranslatedLayer must be numpy array or Variable(Tensor), but received %s."
% type(value)
)
# NOTE: In order to unify the API, firstly convert the input to VarBase
# NOTE: In order to unify the API, firstly convert the input to Tensor
if isinstance(value, np.ndarray):
if framework._in_eager_without_dygraph_check():
var = core.eager.Tensor(
value=value,
name=program_holder.input_descs[i].name(),
persistable=False,
place=framework._current_expected_place(),
zero_copy=True,
)
else:
var = core.VarBase(
value=value,
name=program_holder.input_descs[i].name(),
persistable=False,
place=framework._current_expected_place(),
zero_copy=True,
)
var = core.eager.Tensor(
value=value,
name=program_holder.input_descs[i].name(),
persistable=False,
place=framework._current_expected_place(),
zero_copy=True,
)
else:
var = value
# NOTE: we changed var name here,
......@@ -950,55 +920,27 @@ def _run_dygraph(instance, input, program_holder):
output_vars = []
for var_desc in program_holder.output_descs:
if framework._in_eager_without_dygraph_check():
var = core.eager.Tensor(
dtype=var_desc.dtype(),
dims=var_desc.shape(),
name=var_desc.name(),
type=var_desc.type(),
persistable=False,
)
else:
var = core.VarBase(
var_desc.dtype(),
var_desc.shape(),
var_desc.name(),
var_desc.type(),
False,
)
var = core.eager.Tensor(
dtype=var_desc.dtype(),
dims=var_desc.shape(),
name=var_desc.name(),
type=var_desc.type(),
persistable=False,
)
output_vars.append(var)
# hold forward variables
if framework._in_eager_without_dygraph_check():
tmp_scope_vec = [program_holder.scope]
else:
tmp_scope_vec = core.VarBase(
core.VarDesc.VarType.FP32,
[],
"program_out_scope",
core.VarDesc.VarType.STEP_SCOPES,
True,
)
tmp_scope_vec.value().set_scope(program_holder.scope)
tmp_scope_vec = [program_holder.scope]
double_grad_vars = []
for var_desc in program_holder.double_grad_descs:
if framework._in_eager_without_dygraph_check():
var = core.eager.Tensor(
dtype=var_desc.dtype(),
dims=var_desc.shape(),
name=var_desc.name(),
type=var_desc.type(),
persistable=False,
)
else:
var = core.VarBase(
var_desc.dtype(),
var_desc.shape(),
var_desc.name(),
var_desc.type(),
False,
)
var = core.eager.Tensor(
dtype=var_desc.dtype(),
dims=var_desc.shape(),
name=var_desc.name(),
type=var_desc.type(),
persistable=False,
)
double_grad_vars.append(var)
# 2. run program by op
......@@ -1064,7 +1006,7 @@ def _run_dygraph(instance, input, program_holder):
# NOTE: [ why need set param's gradient type here ]
# if user set sparse gradient mode, the param's gradient
# will be SelectedRows, not LoDTensor. But tracer will just
# set param grad VarBase by forward VarBase(LoDTensor)
# set param grad Tensor by forward Tensor(LoDTensor)
# If we don't change grad_var type here, RunProgramOp need
# transform SelectedRows to LoDTensor forcibly, it may not
# be user wanted result.
......@@ -1454,20 +1396,18 @@ class TranslatedLayer(layers.Layer):
# When add parameter or buffer to Layer by follow apis,
# the variable name can't contain `.`, beccause which may cause
# AttributeError when access the newly added parameter or buffer
# in the form of `self.**.**``, but the ParamBase or BarBase
# in the form of `self.**.**``, but the EagerParamBase or BarBase
# name contains `.` originally, such as `linear_0.w_0`, so here
# need to generate new var name for each var
self._persistable_var_name_dict = {}
# the TranslatedLayer object holded var names count started from 0
with unique_name.guard():
for name, var in persistable_vars.items():
if isinstance(
var, (framework.ParamBase, framework.EagerParamBase)
):
if isinstance(var, framework.EagerParamBase):
dy_name = _generate_unique_var_name(PARAMETER_NAME_PREFIX)
self._persistable_var_name_dict[name] = dy_name
self.add_parameter(dy_name, var)
elif isinstance(var, (core.VarBase, core.eager.Tensor)):
elif isinstance(var, core.eager.Tensor):
dy_name = _generate_unique_var_name(BUFFER_NAME_PREFIX)
self._persistable_var_name_dict[name] = dy_name
self.register_buffer(dy_name, var)
......
python/paddle/nn/layer/layers.py
浏览文件 @ d4571470
此差异已折叠。
python/paddle/optimizer/optimizer.py
浏览文件 @ d4571470
......@@ -383,7 +383,7 @@ class Optimizer:
if isinstance(load_para, Variable):
load_para_np = np.array(load_para)
elif isinstance(load_para, core.VarBase):
elif isinstance(load_para, core.eager.Tensor):
load_para_np = np.array(load_para)
elif isinstance(load_para, np.ndarray):
load_para_np = load_para
......
python/paddle/static/input.py
浏览文件 @ d4571470
......@@ -195,7 +195,7 @@ class InputSpec:
print(x_spec) # InputSpec(shape=(2, 2), dtype=paddle.float32, name=x)
"""
if isinstance(tensor, (Variable, core.VarBase, core.eager.Tensor)):
if isinstance(tensor, (Variable, core.eager.Tensor)):
return cls(tensor.shape, tensor.dtype, name or tensor.name)
else:
raise ValueError(
......
python/paddle/static/nn/control_flow.py
浏览文件 @ d4571470
......@@ -368,7 +368,7 @@ def assign_skip_lod_tensor_array(input, output):
return True
return False
if not isinstance(input, (Variable, core.VarBase)):
if not isinstance(input, (Variable, core.eager.Tensor)):
if isinstance(output, Variable) and isinstance(
input, support_ret_buildin_type
):
......
python/paddle/tensor/creation.py
浏览文件 @ d4571470
此差异已折叠。
python/paddle/tensor/logic.py
浏览文件 @ d4571470
此差异已折叠。
tools/check_file_diff_approvals.sh
浏览文件 @ d4571470
此差异已折叠。
tools/count_api_without_core_ops.py
浏览文件 @ d4571470
此差异已折叠。
tools/jetson_infer_op.py
浏览文件 @ d4571470
此差异已折叠。
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