wehub-resource-sync
913 lines
27 KiB
Python
913 lines
27 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""Tests for PyLayer of Dynamic-to-Static.
|
|
Only test simple cases here."""
|
|
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
from dygraph_to_static_utils import enable_to_static_guard
|
|
|
|
sys.path.append(
|
|
str(Path(__file__).absolute().parent.parent.joinpath("legacy_test"))
|
|
)
|
|
|
|
import os
|
|
import tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from test_jit_save_load import train
|
|
|
|
import paddle
|
|
from paddle.autograd.py_layer import PyLayer
|
|
from paddle.jit.dy2static.utils import ENV_ENABLE_CINN_IN_DY2ST
|
|
|
|
SEED = 2023
|
|
np.random.seed(SEED)
|
|
ENV_ENABLE_CINN_IN_DY2ST.set(False)
|
|
|
|
|
|
def compare_result(dygraph_res, static_res, rtol=1e-5, atol=0):
|
|
np.testing.assert_allclose(
|
|
dygraph_res.detach().numpy(),
|
|
static_res.detach().numpy(),
|
|
rtol=rtol,
|
|
atol=atol,
|
|
err_msg=f'dygraph result is {dygraph_res}\nstatic_result is {static_res}',
|
|
)
|
|
|
|
|
|
class scaled_layer_1(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x):
|
|
y = x * 3
|
|
return y
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy):
|
|
dx = paddle.sin(dy)
|
|
return dx
|
|
|
|
|
|
class scaled_layer_2(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x1, x2):
|
|
y = 3 * x1 + x2 / 5
|
|
return y
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy):
|
|
dx1 = paddle.sin(dy)
|
|
dx2 = paddle.cos(dy)
|
|
return dx1, dx2
|
|
|
|
|
|
class cus_tanh_1(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x):
|
|
y = paddle.tanh(x)
|
|
ctx.save_for_backward(y)
|
|
return y
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy):
|
|
(y,) = ctx.saved_tensor()
|
|
grad = dy * (1 - paddle.square(y))
|
|
return grad
|
|
|
|
|
|
class cus_tanh_2(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x, func1, func2=paddle.square):
|
|
ctx.func = func2
|
|
y = func1(x)
|
|
ctx.save_for_backward(y)
|
|
return y
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy):
|
|
(y,) = ctx.saved_tensor()
|
|
grad = dy * (1 - ctx.func(y))
|
|
return grad
|
|
|
|
|
|
class cus_tanh_3(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x1, x2, func1, func2=paddle.square):
|
|
y1 = func1(x1)
|
|
y2 = func1(x2)
|
|
ctx.save_for_backward(y1, y2)
|
|
return 1, None, y1, y2, ''
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy1, dy2):
|
|
y1, y2 = ctx.saved_tensor()
|
|
re1 = dy1 * (1 - paddle.square(y1))
|
|
re2 = dy2 * (1 - paddle.square(y2))
|
|
return re1, None
|
|
|
|
|
|
def user_defined_tanh(x):
|
|
y = paddle.tanh(x)
|
|
return y
|
|
|
|
|
|
def user_defined_square(x):
|
|
y = paddle.square(x)
|
|
return y
|
|
|
|
|
|
class cus_tanh_4(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x, func, name="cus_tanh_4"):
|
|
ctx.func = func
|
|
y = user_defined_tanh(x)
|
|
ctx.save_for_backward(y)
|
|
return y
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy):
|
|
(y,) = ctx.saved_tensor()
|
|
grad = dy * (1 - ctx.func(y))
|
|
return grad
|
|
|
|
|
|
class cus_tanh_5(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x1, x2, func1, func2=paddle.square):
|
|
ctx.func = func2
|
|
y1 = func1(x1)
|
|
y2 = func1(x2)
|
|
ctx.save_for_backward(y1, y2)
|
|
return 1, None, y1, y2, ''
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy1, dy2):
|
|
y1, y2 = ctx.saved_tensor()
|
|
re1 = dy1 * (1 - ctx.func(y1))
|
|
re2 = dy2 * (1 - paddle.square(y2))
|
|
return re1, re2
|
|
|
|
|
|
class cus_sigmoid(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x, func1, func2):
|
|
ctx.func = func2
|
|
y = 1 / (1 + func1(-x))
|
|
ctx.save_for_backward(x)
|
|
return y
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy):
|
|
(x,) = ctx.saved_tensor()
|
|
grad = dy * ctx.func(x) * (1 - ctx.func(x))
|
|
return grad
|
|
|
|
|
|
class nested_layer(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x1, x2):
|
|
y = cus_tanh_1.apply(x1)
|
|
ctx.save_for_backward(y)
|
|
ret = y + x2
|
|
return ret
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy):
|
|
(y,) = ctx.saved_tensor()
|
|
grad1 = scaled_layer_1.apply(dy)
|
|
grad2 = dy - paddle.square(y)
|
|
return grad1, grad2
|
|
|
|
|
|
class SimpleNet_1(paddle.nn.Layer):
|
|
def __init__(self, in_size, out_size):
|
|
super().__init__()
|
|
self.linear = paddle.nn.Linear(in_size, out_size)
|
|
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def forward(self, data):
|
|
hidden = self.linear(data)
|
|
z = cus_tanh_1.apply(hidden)
|
|
return z
|
|
|
|
|
|
class SimpleNet_2(paddle.nn.Layer):
|
|
def __init__(self, in_size, out_size):
|
|
super().__init__()
|
|
self.linear = paddle.nn.Linear(in_size, out_size)
|
|
|
|
def forward(self, x):
|
|
y = self.linear(x)
|
|
out = cus_tanh_2.apply(y, func1=paddle.tanh)
|
|
return out
|
|
|
|
|
|
class SimpleNet_3(paddle.nn.Layer):
|
|
def __init__(self, in_size, out_size):
|
|
super().__init__()
|
|
self.linear = paddle.nn.Linear(in_size, out_size)
|
|
|
|
def forward(self, x):
|
|
y = self.linear(x)
|
|
out = cus_sigmoid.apply(
|
|
y, func1=paddle.exp, func2=paddle.nn.functional.sigmoid
|
|
)
|
|
return out
|
|
|
|
|
|
class SimpleNetInplace(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def forward(self, data):
|
|
data = data**2
|
|
z = paddle.tanh(data)
|
|
z = cus_tanh_1.apply(z)
|
|
return z
|
|
|
|
|
|
class SimplePyLayerNet(paddle.nn.Layer):
|
|
def __init__(self, in_size, out_size):
|
|
super().__init__()
|
|
self.linear = paddle.nn.Linear(in_size, out_size)
|
|
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def forward(self, x):
|
|
y = self.linear(x)
|
|
out = cus_tanh_2.apply(y, func1=paddle.tanh)
|
|
out = paddle.mean(out)
|
|
return out
|
|
|
|
|
|
class SimplePyLayerNetMultiIn(paddle.nn.Layer):
|
|
def __init__(self, in_size, out_size):
|
|
super().__init__()
|
|
self.linear1 = paddle.nn.Linear(in_size, out_size)
|
|
self.linear2 = paddle.nn.Linear(in_size, out_size)
|
|
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def forward(self, x1, x2):
|
|
y1 = self.linear1(x1)
|
|
y2 = self.linear1(x2)
|
|
out = cus_tanh_2.apply(y1, paddle.tanh)
|
|
out = out + y2
|
|
out = paddle.mean(out)
|
|
return out
|
|
|
|
|
|
class SimplePyLayerNetStopGrad(paddle.nn.Layer):
|
|
def __init__(self, in_size, out_size):
|
|
super().__init__()
|
|
self.linear = paddle.nn.Linear(in_size, out_size)
|
|
|
|
def forward(self, x):
|
|
y = self.linear(x)
|
|
y.stop_gradient = True
|
|
out = cus_tanh_2.apply(y, func1=paddle.tanh)
|
|
return out
|
|
|
|
|
|
class TestPyLayerBase(unittest.TestCase):
|
|
def setUp(self):
|
|
self.place = "cpu"
|
|
if paddle.is_compiled_with_cuda():
|
|
self.place = "gpu"
|
|
if paddle.is_compiled_with_xpu():
|
|
self.place = "xpu"
|
|
self.to_static: bool = False
|
|
|
|
def _run(self, *input_args, **input_kwargs):
|
|
assert getattr(self, "dygraph_func", None), (
|
|
"Please setting `self.dygraph_func` before calling `self._run`"
|
|
)
|
|
|
|
with enable_to_static_guard(self.to_static):
|
|
paddle.set_device(self.place)
|
|
result = self.dygraph_func(*input_args, **input_kwargs)
|
|
result.mean().backward()
|
|
return result
|
|
|
|
def _run_dygraph(self, *args, **kwargs):
|
|
self.to_static = False
|
|
return self._run(*args, **kwargs)
|
|
|
|
def _run_static(self, *args, **kwargs):
|
|
self.to_static = True
|
|
fn = self._run
|
|
return fn(*args, **kwargs)
|
|
|
|
# TODO(MarioLulab): In the future, this will be supported: not only `paddle.Tensor`
|
|
# but also non-Tensor objects will be included in the argument list.
|
|
def _run_and_compare(self, *args, **kwargs):
|
|
# Step1. Clone args and kwargs to avoid dygraph and static overwriting with each other
|
|
dygraph_inp_args = []
|
|
static_inp_args = []
|
|
for v in args:
|
|
assert isinstance(v, paddle.Tensor), (
|
|
f"Only Support `paddle.Tensor` now, but got {type(v)}"
|
|
)
|
|
stop_gradient = v.stop_gradient
|
|
# detach from the compute graph to turn `dygraph_inp_args` and `static_inp_args` into leaf nodes
|
|
v = v.detach()
|
|
dygraph_inp_args.append(v.clone())
|
|
static_inp_args.append(v.clone())
|
|
if not stop_gradient:
|
|
dygraph_inp_args[-1].stop_gradient = False
|
|
static_inp_args[-1].stop_gradient = False
|
|
|
|
dygraph_inp_kwargs = {}
|
|
static_inp_kwargs = {}
|
|
for k, v in kwargs.items():
|
|
stop_gradient = v.stop_gradient
|
|
assert isinstance(v, paddle.Tensor), (
|
|
"Only Support `paddle.Tensor` now"
|
|
)
|
|
# detach from the compute graph to turn `dygraph_inp_kwargs` and `static_inp_kwargs` into leaf nodes
|
|
v = v.detach()
|
|
dygraph_inp_kwargs[k] = v.clone()
|
|
static_inp_kwargs[k] = v.clone()
|
|
if not stop_gradient:
|
|
dygraph_inp_kwargs[k].stop_gradient = False
|
|
static_inp_kwargs[k].stop_gradient = False
|
|
|
|
# Step2. Run the dygraph and the static separately
|
|
dygraph_res = self._run_dygraph(*dygraph_inp_args, **dygraph_inp_kwargs)
|
|
static_res = self._run_static(*static_inp_args, **static_inp_kwargs)
|
|
|
|
# Step3. Compare forward result between dygraph and static
|
|
if not isinstance(dygraph_res, tuple):
|
|
dygraph_res = (dygraph_res,)
|
|
if not isinstance(static_res, tuple):
|
|
static_res = (static_res,)
|
|
for d, s in zip(dygraph_res, static_res):
|
|
compare_result(d, s)
|
|
|
|
# Step4. Compare grad between dygraph and static
|
|
for i in range(len(dygraph_inp_args)):
|
|
self.assertEqual(
|
|
dygraph_inp_args[i].stop_gradient,
|
|
static_inp_args[i].stop_gradient,
|
|
)
|
|
if dygraph_inp_args[i].stop_gradient:
|
|
continue
|
|
|
|
compare_result(dygraph_inp_args[i].grad, static_inp_args[i].grad)
|
|
|
|
for key in dygraph_inp_kwargs.keys():
|
|
self.assertEqual(
|
|
dygraph_inp_kwargs[key].stop_gradient,
|
|
static_inp_kwargs[key].stop_gradient,
|
|
)
|
|
if dygraph_inp_kwargs[key].stop_gradient:
|
|
continue
|
|
|
|
compare_result(
|
|
dygraph_inp_kwargs[key].grad, static_inp_kwargs[key].grad
|
|
)
|
|
|
|
|
|
class TestPyLayerWithoutContext(TestPyLayerBase):
|
|
def test_single_in_single_out(self):
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def test_func(x):
|
|
y = scaled_layer_1.apply(x)
|
|
return y
|
|
|
|
self.dygraph_func = test_func
|
|
|
|
input1 = paddle.randn([2, 3]).astype("float32")
|
|
input1.stop_gradient = False
|
|
|
|
self._run_and_compare(input1)
|
|
|
|
def test_multi_in_single_out(self):
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def test_func(x1, x2):
|
|
y = scaled_layer_2.apply(x1, x2)
|
|
return y
|
|
|
|
self.dygraph_func = test_func
|
|
|
|
input1 = paddle.randn([2, 3]).astype("float32")
|
|
input2 = paddle.randn([2, 3]).astype("float32")
|
|
input1.stop_gradient = False
|
|
input2.stop_gradient = False
|
|
|
|
self._run_and_compare(input1, input2)
|
|
|
|
|
|
class TestPyLayerWithContext(TestPyLayerBase):
|
|
def test_single_in_single_out(self):
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def test_func(x):
|
|
y = cus_tanh_1.apply(x)
|
|
return y
|
|
|
|
self.dygraph_func = test_func
|
|
|
|
input1 = paddle.randn([2, 3]).astype("float32")
|
|
input1.stop_gradient = False
|
|
|
|
self._run_and_compare(input1)
|
|
|
|
def test_nested_pylayer(self):
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def test_func(x1, x2):
|
|
y = nested_layer.apply(x1, x2)
|
|
return y
|
|
|
|
self.dygraph_func = test_func
|
|
|
|
input1 = paddle.randn([2, 3]).astype("float32")
|
|
input2 = paddle.randn([2, 3]).astype("float32")
|
|
input1.stop_gradient = False
|
|
input2.stop_gradient = False
|
|
|
|
self._run_and_compare(input1, input2)
|
|
|
|
def test_apply_kwargs_pylayer(self):
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def test_func(x1, x2):
|
|
y = scaled_layer_2.apply(x1=x2, x2=x1)
|
|
return y
|
|
|
|
self.dygraph_func = test_func
|
|
|
|
input1 = paddle.randn([2, 3]).astype("float32")
|
|
input2 = paddle.randn([2, 3]).astype("float32")
|
|
input1.stop_gradient = False
|
|
input2.stop_gradient = False
|
|
|
|
self._run_and_compare(input1, input2)
|
|
|
|
def test_non_variable_inputs(self):
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def test_func(x):
|
|
y = cus_tanh_2.apply(x, func1=paddle.tanh)
|
|
return y
|
|
|
|
self.dygraph_func = test_func
|
|
|
|
input1 = paddle.randn([2, 3]).astype("float32")
|
|
input1.stop_gradient = False
|
|
|
|
self._run_and_compare(input1)
|
|
|
|
def test_simple_pylayer_return_none_with_no_grad(self):
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def test_func(input1, input2):
|
|
z = cus_tanh_3.apply(input1, input2, paddle.tanh, paddle.square)
|
|
z = z[2] + z[3]
|
|
return z
|
|
|
|
self.dygraph_func = test_func
|
|
|
|
input1 = paddle.randn([2, 3]).astype("float32")
|
|
input2 = paddle.randn([2, 3]).astype("float32")
|
|
input1.stop_gradient = False
|
|
input2.stop_gradient = True
|
|
|
|
self._run_and_compare(input1, input2)
|
|
|
|
def test_simple_pylayer_return_none(self):
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def test_func(input1, input2):
|
|
z = cus_tanh_5.apply(input1, input2, paddle.tanh, paddle.square)
|
|
z = z[2] + z[3]
|
|
return z
|
|
|
|
self.dygraph_func = test_func
|
|
|
|
input1 = paddle.randn([2, 3]).astype("float32")
|
|
input2 = paddle.randn([2, 3]).astype("float32")
|
|
input1.stop_gradient = False
|
|
input2.stop_gradient = False
|
|
|
|
self._run_and_compare(input1, input2)
|
|
|
|
def test_non_variable_inputs_and_userdefined_call(self):
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def test_func(input1):
|
|
y = cus_tanh_4.apply(
|
|
input1, func=user_defined_square, name="cus_tanh_test"
|
|
)
|
|
return y
|
|
|
|
self.dygraph_func = test_func
|
|
|
|
input1 = paddle.randn([2, 3]).astype("float32")
|
|
input1.stop_gradient = False
|
|
|
|
self._run_and_compare(input1)
|
|
|
|
|
|
class TestPyLayerInsideNet(TestPyLayerBase):
|
|
def test_single_in_single_out(self):
|
|
simple_net = SimpleNet_1(in_size=4, out_size=8)
|
|
self.dygraph_func = simple_net
|
|
|
|
input1 = paddle.randn([3, 4]).astype("float32")
|
|
input1.stop_gradient = False
|
|
|
|
self._run_and_compare(input1)
|
|
|
|
def test_inplace(self):
|
|
simple_net = SimpleNetInplace()
|
|
self.dygraph_func = simple_net
|
|
|
|
input1 = paddle.randn([3, 4]).astype("float32")
|
|
input1.stop_gradient = False
|
|
|
|
self._run_and_compare(input1)
|
|
|
|
def test_non_variable_args_pylayernet(self):
|
|
simple_net = SimplePyLayerNet(in_size=4, out_size=8)
|
|
self.dygraph_func = simple_net
|
|
|
|
input1 = paddle.randn([3, 4]).astype("float32")
|
|
input1.stop_gradient = False
|
|
|
|
self._run_and_compare(input1)
|
|
|
|
def test_pylayer_net_with_no_grad(self):
|
|
simple_net = SimplePyLayerNetMultiIn(in_size=4, out_size=8)
|
|
self.dygraph_func = simple_net
|
|
|
|
input1 = paddle.randn([3, 4]).astype("float32")
|
|
input2 = paddle.randn([3, 4]).astype("float32")
|
|
input1.stop_gradient = False
|
|
input2.stop_gradient = True
|
|
|
|
self._run_and_compare(input1, input2)
|
|
|
|
|
|
class PyLayerTrainHelper(unittest.TestCase):
|
|
def setUp(self):
|
|
self.place = "cpu"
|
|
if paddle.is_compiled_with_cuda():
|
|
self.place = "gpu"
|
|
if paddle.is_compiled_with_xpu():
|
|
self.place = "xpu"
|
|
|
|
def _run_train(
|
|
self, to_static: bool, layer_builder, build_strategy=None, in_pir=True
|
|
):
|
|
"""
|
|
Tests model decorated by `dygraph_to_static_output` in static graph mode. For users, the model is defined in dygraph mode and trained in static graph mode.
|
|
"""
|
|
paddle.set_device(self.place)
|
|
np.random.seed(SEED)
|
|
paddle.seed(SEED)
|
|
paddle.framework.random._manual_program_seed(SEED)
|
|
|
|
net = layer_builder()
|
|
|
|
if to_static:
|
|
net = paddle.jit.to_static(
|
|
net, build_strategy=build_strategy, full_graph=True
|
|
)
|
|
|
|
_, _, avg_loss = train(net)
|
|
|
|
return avg_loss.numpy()
|
|
|
|
|
|
class TestTrainingPyLayer(PyLayerTrainHelper):
|
|
def test_tanh_pylayer(self):
|
|
build_layer = lambda: SimpleNet_2(784, 20)
|
|
|
|
legacy_static_loss = self._run_train(
|
|
to_static=True, in_pir=False, layer_builder=build_layer
|
|
)
|
|
pir_static_loss = self._run_train(
|
|
to_static=True, in_pir=True, layer_builder=build_layer
|
|
)
|
|
dygraph_loss = self._run_train(
|
|
to_static=False, layer_builder=build_layer
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
legacy_static_loss,
|
|
dygraph_loss,
|
|
rtol=1e-05,
|
|
err_msg=f'legacy_static_loss: {legacy_static_loss} \n dygraph_loss: {dygraph_loss}',
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
pir_static_loss,
|
|
dygraph_loss,
|
|
rtol=1e-05,
|
|
err_msg=f'pir_static_loss: {pir_static_loss} \n dygraph_loss: {dygraph_loss}',
|
|
)
|
|
|
|
def test_sigmoid_pylayer(self):
|
|
build_layer = lambda: SimpleNet_3(784, 20)
|
|
|
|
legacy_static_loss = self._run_train(
|
|
to_static=True, in_pir=False, layer_builder=build_layer
|
|
)
|
|
pir_static_loss = self._run_train(
|
|
to_static=True, in_pir=True, layer_builder=build_layer
|
|
)
|
|
dygraph_loss = self._run_train(
|
|
to_static=False, layer_builder=build_layer
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
legacy_static_loss,
|
|
dygraph_loss,
|
|
rtol=1e-05,
|
|
err_msg=f'legacy_static_loss: {legacy_static_loss} \n dygraph_loss: {dygraph_loss}',
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
pir_static_loss,
|
|
dygraph_loss,
|
|
rtol=1e-05,
|
|
err_msg=f'pir_static_loss: {pir_static_loss} \n dygraph_loss: {dygraph_loss}',
|
|
)
|
|
|
|
def test_pylayer_net_no_grad(self):
|
|
build_layer = lambda: SimplePyLayerNetStopGrad(784, 20)
|
|
|
|
legacy_static_loss = self._run_train(
|
|
to_static=True, in_pir=False, layer_builder=build_layer
|
|
)
|
|
pir_static_loss = self._run_train(
|
|
to_static=True, in_pir=True, layer_builder=build_layer
|
|
)
|
|
dygraph_loss = self._run_train(
|
|
to_static=False, layer_builder=build_layer
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
legacy_static_loss,
|
|
dygraph_loss,
|
|
rtol=1e-05,
|
|
err_msg=f'legacy_static_loss: {legacy_static_loss} \n dygraph_loss: {dygraph_loss}',
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
pir_static_loss,
|
|
dygraph_loss,
|
|
rtol=1e-05,
|
|
err_msg=f'pir_static_loss: {pir_static_loss} \n dygraph_loss: {dygraph_loss}',
|
|
)
|
|
|
|
|
|
class TestPyLayerJitSaveLoad(unittest.TestCase):
|
|
def setUp(self):
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
self.model_path = os.path.join(
|
|
self.temp_dir.name, "test_pylayer/jit_save_model"
|
|
)
|
|
# enable dygraph mode
|
|
paddle.base.enable_dygraph()
|
|
# config seed
|
|
paddle.seed(SEED)
|
|
paddle.framework.random._manual_program_seed(SEED)
|
|
|
|
def tearDown(self):
|
|
self.temp_dir.cleanup()
|
|
|
|
def train_and_save_model(self, model_path=None):
|
|
layer = SimpleNet_1(784, 20)
|
|
example_inputs, layer, _ = train(layer)
|
|
final_model_path = model_path if model_path else self.model_path
|
|
orig_input_types = [type(x) for x in example_inputs]
|
|
paddle.jit.save(
|
|
layer=layer, path=final_model_path, input_spec=example_inputs
|
|
)
|
|
new_input_types = [type(x) for x in example_inputs]
|
|
self.assertEqual(orig_input_types, new_input_types)
|
|
return layer
|
|
|
|
def test_save_load(self):
|
|
# train and save model
|
|
train_layer = self.train_and_save_model()
|
|
# load model
|
|
loaded_layer = paddle.jit.load(self.model_path)
|
|
self.load_and_inference(train_layer, loaded_layer)
|
|
|
|
def load_and_inference(self, train_layer, infer_layer):
|
|
train_layer.eval()
|
|
infer_layer.eval()
|
|
# inference & compare
|
|
x = paddle.to_tensor(np.random.random((1, 784)).astype('float32'))
|
|
train_layer_result = train_layer(x).numpy()
|
|
infer_layer_result = infer_layer(x).numpy()
|
|
|
|
np.testing.assert_array_equal(train_layer_result, infer_layer_result)
|
|
|
|
|
|
class PyLayerWrongUsage(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x):
|
|
ctx.x = x
|
|
x1 = paddle.tanh(x)
|
|
return x1
|
|
|
|
@staticmethod
|
|
def backward(ctx, grad):
|
|
x = ctx.x
|
|
x_grad = grad * (1 - paddle.square(x))
|
|
return x_grad
|
|
|
|
|
|
class PyLayerWrongUsageWrapper(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.layer = PyLayerWrongUsage()
|
|
|
|
def forward(self, x):
|
|
return PyLayerWrongUsage.apply(x)
|
|
|
|
|
|
class TestPyLayerWrongUsage(unittest.TestCase):
|
|
def test_wrong_usage(self):
|
|
layer = PyLayerWrongUsageWrapper()
|
|
static_layer = paddle.jit.to_static(layer, full_graph=True)
|
|
x = paddle.to_tensor(np.random.random((1, 784)).astype('float32'))
|
|
with self.assertRaisesRegex(
|
|
AttributeError,
|
|
r"`ctx.x = tensor` is not allowed in static mode, please use `ctx.save_for_backward\(tensor\)` instead.",
|
|
):
|
|
static_layer(x)
|
|
|
|
|
|
class NestedStructurePyLayer(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x, y):
|
|
ctx.save_for_backward(x, y)
|
|
x1 = paddle.tanh(x[0])
|
|
y1 = paddle.tanh(x[1])
|
|
z1 = paddle.tanh(y)
|
|
return [x1, y1, z1]
|
|
|
|
@staticmethod
|
|
def backward(ctx, *grad1):
|
|
x0, x1 = ctx.saved_tensor()
|
|
x_grad = grad1[0] * (1 - paddle.square(x0[0]))
|
|
y_grad = grad1[1] * (1 - paddle.square(x0[1]))
|
|
z_grad = grad1[2] * (1 - paddle.square(x1))
|
|
|
|
return [x_grad, y_grad], z_grad
|
|
|
|
|
|
class NestedStructurePyLayerModel(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.w0 = self.create_parameter(shape=[42, 42])
|
|
self.w1 = self.create_parameter(shape=[42, 42])
|
|
self.w2 = self.create_parameter(shape=[42, 42])
|
|
|
|
def forward(self, x):
|
|
y1 = paddle.matmul(x, self.w0)
|
|
y2 = paddle.matmul(x, self.w1)
|
|
y3 = paddle.matmul(x, self.w2)
|
|
|
|
z = NestedStructurePyLayer.apply([y1, y2], y3)
|
|
return z[0] + z[1] + z[2]
|
|
|
|
|
|
class TestNestedStructurePyLayer(unittest.TestCase):
|
|
def test_nested_structure(self):
|
|
input = paddle.randn([2, 42]).astype("float32")
|
|
input.stop_gradient = False
|
|
|
|
model = NestedStructurePyLayerModel()
|
|
dygraph_res = model(input)
|
|
dygraph_res.backward()
|
|
dygraph_input_grads = [
|
|
paddle.assign(input.grad),
|
|
paddle.assign(model.w0.grad),
|
|
paddle.assign(model.w1.grad),
|
|
paddle.assign(model.w2.grad),
|
|
]
|
|
input.clear_grad()
|
|
model.w0.clear_grad()
|
|
model.w1.clear_grad()
|
|
model.w2.clear_grad()
|
|
|
|
static_model = paddle.jit.to_static(model, full_graph=True)
|
|
static_res = static_model(input)
|
|
static_res.backward()
|
|
static_input_grads = [
|
|
paddle.assign(input.grad),
|
|
paddle.assign(model.w0.grad),
|
|
paddle.assign(model.w1.grad),
|
|
paddle.assign(model.w2.grad),
|
|
]
|
|
input.clear_grad()
|
|
model.w0.clear_grad()
|
|
model.w1.clear_grad()
|
|
model.w2.clear_grad()
|
|
for i, (dygraph_grad, static_grad) in enumerate(
|
|
zip(dygraph_input_grads, static_input_grads)
|
|
):
|
|
np.testing.assert_allclose(
|
|
dygraph_grad.numpy(),
|
|
static_grad.numpy(),
|
|
rtol=1e-5,
|
|
atol=0,
|
|
err_msg=f"dygraph_grad[{i}]: {dygraph_grad} \n static_grad[{i}]: {static_grad}",
|
|
)
|
|
|
|
|
|
class NestedStructureWithNonePyLayer(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x, y):
|
|
ctx.save_for_backward(x, y)
|
|
x1 = paddle.tanh(x[0])
|
|
y1 = paddle.tanh(x[1])
|
|
z1 = paddle.tanh(y)
|
|
return [x1, y1, z1]
|
|
|
|
@staticmethod
|
|
def backward(ctx, *grad1):
|
|
x0, x1 = ctx.saved_tensor()
|
|
x_grad = grad1[0] * (1 - paddle.square(x0[0]))
|
|
z_grad = grad1[2] * (1 - paddle.square(x1))
|
|
|
|
return [x_grad, None], z_grad
|
|
|
|
|
|
class NestedStructureWithNonePyLayerModel(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.w0 = self.create_parameter(shape=[42, 42])
|
|
self.w1 = self.create_parameter(shape=[42, 42])
|
|
self.w2 = self.create_parameter(shape=[42, 42])
|
|
|
|
def forward(self, x):
|
|
y1 = paddle.matmul(x, self.w0)
|
|
y2 = paddle.matmul(x, self.w1)
|
|
y2.stop_gradient = True
|
|
y3 = paddle.matmul(x, self.w2)
|
|
|
|
z = NestedStructurePyLayer.apply([y1, y2], y3)
|
|
return z[0] + z[1] + z[2]
|
|
|
|
|
|
class TestNestedStructureWithNonePyLayer(unittest.TestCase):
|
|
def test_nested_structure(self):
|
|
input = paddle.randn([2, 42]).astype("float32")
|
|
input.stop_gradient = False
|
|
|
|
model = NestedStructurePyLayerModel()
|
|
dygraph_res = model(input)
|
|
dygraph_res.backward()
|
|
dygraph_input_grads = [
|
|
paddle.assign(input.grad),
|
|
paddle.assign(model.w0.grad),
|
|
paddle.assign(model.w1.grad),
|
|
paddle.assign(model.w2.grad),
|
|
]
|
|
input.clear_grad()
|
|
model.w0.clear_grad()
|
|
model.w1.clear_grad()
|
|
model.w2.clear_grad()
|
|
|
|
static_model = paddle.jit.to_static(model, full_graph=True)
|
|
static_res = static_model(input)
|
|
static_res.backward()
|
|
static_input_grads = [
|
|
paddle.assign(input.grad),
|
|
paddle.assign(model.w0.grad),
|
|
paddle.assign(model.w1.grad),
|
|
paddle.assign(model.w2.grad),
|
|
]
|
|
input.clear_grad()
|
|
model.w0.clear_grad()
|
|
model.w1.clear_grad()
|
|
model.w2.clear_grad()
|
|
for i, (dygraph_grad, static_grad) in enumerate(
|
|
zip(dygraph_input_grads, static_input_grads)
|
|
):
|
|
np.testing.assert_allclose(
|
|
dygraph_grad.numpy(),
|
|
static_grad.numpy(),
|
|
rtol=1e-5,
|
|
atol=0,
|
|
err_msg=f"dygraph_grad[{i}]: {dygraph_grad} \n static_grad[{i}]: {static_grad}",
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|