def _trace(layer,
inputs,
feed_prefix='feed_',
fetch_prefix='fetch_',
tmp_prefix='t_'):
assert isinstance(layer, Layer)
if not isinstance(inputs, (list, tuple)):
inputs = [inputs]
tracer = _dygraph_tracer()._get_program_desc_tracer()
var_list = extract_vars(inputs)
with program_desc_tracing_guard(True):
original_outputs = layer(*inputs)
if not isinstance(original_outputs, (list, tuple)):
outputs = [original_outputs]
else:
outputs = original_outputs
out_vars = [var for var in outputs]
program_desc, feed_names, fetch_names, parameters = tracer.create_program_desc(
var_list, feed_prefix, out_vars, fetch_prefix, tmp_prefix)
tracer.reset()
with _dygraph_guard(None):
program = create_program_from_desc(program_desc)
return original_outputs, program, feed_names, fetch_names, parameters
def guard(place=None):
train = framework.Program()
startup = framework.Program()
tracer = Tracer(train.current_block().desc)
if place is None:
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with framework.program_guard(train, startup):
with framework.unique_name.guard():
with framework._dygraph_guard(tracer):
with framework._dygraph_place_guard(place):
yield
def dygraph2program(layer,
inputs,
feed_prefix='feed_',
fetch_prefix='fetch_',
tmp_prefix='t_',
extract_inputs_fn=None,
extract_outputs_fn=None,
dtypes=None):
print(type(layer))
assert isinstance(layer, Layer)
extract_inputs_fn = extract_inputs_fn if extract_inputs_fn is not None else extract_vars
extract_outputs_fn = extract_outputs_fn if extract_outputs_fn is not None else extract_vars
if os.environ.get("FLAGS_enable_eager_mode") == "1":
return _dy2prog(layer, inputs, feed_prefix, fetch_prefix, tmp_prefix,
extract_inputs_fn, extract_outputs_fn, dtypes)
tracer = _dygraph_tracer()._get_program_desc_tracer()
with program_desc_tracing_guard(True):
if _is_shape(inputs):
shapes = [inputs]
inputs = _create_tensors(shapes, dtypes=dtypes)
input_var_list = inputs
elif _is_shapes(inputs):
inputs = _create_tensors(inputs, dtypes=dtypes)
input_var_list = inputs
else:
inputs = to_variables(inputs)
input_var_list = extract_inputs_fn(inputs)
original_outputs = layer(*inputs)
# 'original_outputs' may be dict, so we should convert it to list of varibles.
# And should not create new varibles in 'extract_vars'.
out_var_list = extract_outputs_fn(original_outputs)
program_desc, feed_names, fetch_names, parameters = tracer.create_program_desc(
input_var_list, feed_prefix, out_var_list, fetch_prefix,
tmp_prefix)
tracer.reset()
with _dygraph_guard(None):
program = Program()
program.desc = program_desc
program.blocks = [Block(program, 0)]
program._sync_with_cpp()
return program
def guard(place=None):
"""
:api_attr: imperative
This context will create a dygraph context for dygraph to run, using python ``with`` statement.
Parameters:
place(fluid.CPUPlace| fluid.CUDAPlace|str, optional): Place to execute dygraph.
If None, the running place will be determined according to the way of paddle compilation.
If ``place`` is string, It can be ``cpu``, ``gpu:x`` and ``xpu:x``, where ``x`` is the
index of the GPUs or XPUs. Default: None
return:
None
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
with fluid.dygraph.guard():
inp = np.ones([3, 1024], dtype='float32')
t = fluid.dygraph.base.to_variable(inp)
linear1 = fluid.Linear(1024, 4, bias_attr=False)
linear2 = fluid.Linear(4, 4)
ret = linear1(t)
dy_ret = linear2(ret)
"""
train = framework.Program()
startup = framework.Program()
tracer = Tracer()
VarBase = core.VarBase
if place is not None:
expected_place = _get_paddle_place(place)
else:
expected_place = framework._current_expected_place()
with framework.program_guard(train, startup):
with framework.unique_name.guard():
with framework._dygraph_guard(tracer):
with framework._dygraph_place_guard(expected_place):
yield
def guard(place=None):
"""
:api_attr: imperative
This context will create a dygraph context for dygraph to run, using python ``with`` statement.
Parameters:
place(fluid.CPUPlace or fluid.CUDAPlace, optional): Place to execute dygraph.
If None, the running place will be determined according to the way of paddle compilation. Default: None
return:
None
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
with fluid.dygraph.guard():
inp = np.ones([3, 1024], dtype='float32')
t = fluid.dygraph.base.to_variable(inp)
linear1 = fluid.Linear(1024, 4, bias_attr=False)
linear2 = fluid.Linear(4, 4)
ret = linear1(t)
dy_ret = linear2(ret)
"""
train = framework.Program()
startup = framework.Program()
tracer = Tracer()
VarBase = core.VarBase
if place is None:
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
tracer._expected_place = place
with framework.program_guard(train, startup):
with framework.unique_name.guard():
with framework._dygraph_guard(tracer):
with framework._dygraph_place_guard(place):
yield
def guard(place=None):
"""
This context will create a dygraph context for dygraph to run
Args:
place(fluid.CPUPlace|fluid.CUDAPlace|None): Place to run
return:
None
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
with fluid.dygraph.guard():
inp = np.ones([3, 32, 32], dtype='float32')
t = fluid.dygraph.base.to_variable(inp)
fc1 = fluid.FC('fc1', size=4, bias_attr=False, num_flatten_dims=1)
fc2 = fluid.FC('fc2', size=4)
ret = fc1(t)
dy_ret = fc2(ret)
"""
train = framework.Program()
startup = framework.Program()
tracer = Tracer()
if place is None:
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with framework.program_guard(train, startup):
with framework.unique_name.guard():
with framework._dygraph_guard(tracer):
with framework._dygraph_place_guard(place):
yield
def __impl__(*args, **kwargs):
with framework._dygraph_guard(None):
return func(*args, **kwargs)
def is_equal_program(prog1, prog2):
with _dygraph_guard(None):
return _is_equal_program(prog1, prog2)
