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remove xpu eager guard tests #48786

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Dec 9, 2022
Merged

remove xpu eager guard tests #48786

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92 changes: 45 additions & 47 deletions python/paddle/fluid/tests/unittests/xpu/parallel_dygraph_gradient_check_in_eager_mode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,6 @@
import paddle
import paddle.distributed as dist
import paddle.fluid as fluid
from paddle.fluid.framework import _test_eager_guard
from paddle.nn import Linear

paddle.seed(1024)
Expand Down Expand Up @@ -69,58 +68,57 @@ def forward(self, x):
class TestDistTraning(unittest.TestCase):
def test_multiple_xpus(self):
self.trainer_id = dist.get_rank()
with _test_eager_guard():
self.pg = dist.init_parallel_env()
self.pg = dist.init_parallel_env()

model_a = SimpleNet(self.trainer_id)
model_b = SimpleNet(self.trainer_id)
model_a = SimpleNet(self.trainer_id)
model_b = SimpleNet(self.trainer_id)

state_dict = model_a.state_dict()
model_b.set_state_dict(state_dict)
state_dict = model_a.state_dict()
model_b.set_state_dict(state_dict)

model_a = paddle.DataParallel(
model_a, find_unused_parameters=True, group=self.pg
model_a = paddle.DataParallel(
model_a, find_unused_parameters=True, group=self.pg
)
model_b = paddle.DataParallel(
model_b, find_unused_parameters=True, group=self.pg
)

ones_input = paddle.ones(shape=(batch, in_dim))
ones_input.stop_gradient = True

w1_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')
w2_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')

for step_id in range(5):
random_input = paddle.rand(shape=(batch, in_dim))
random_input.stop_gradient = True

if step_id % 2 == 0:
out_a = model_a(random_input)
out_b = model_b(random_input)
else:
out_a = model_a(ones_input)
out_b = model_b(ones_input)

out_a.sum().backward()
out_b.sum().backward()

self.check_gradient(model_a.parameters())
self.check_gradient(model_b.parameters())

# test acc gradient
w1_grad_sum = self.check_acc(
model_a._layers.w1.grad,
w1_grad_sum,
model_b._layers.w1.grad,
)
model_b = paddle.DataParallel(
model_b, find_unused_parameters=True, group=self.pg
w2_grad_sum = self.check_acc(
model_a._layers.w2.grad,
w2_grad_sum,
model_b._layers.w2.grad,
)

ones_input = paddle.ones(shape=(batch, in_dim))
ones_input.stop_gradient = True

w1_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')
w2_grad_sum = np.zeros((in_dim, out_dim), dtype='float32')

for step_id in range(5):
random_input = paddle.rand(shape=(batch, in_dim))
random_input.stop_gradient = True

if step_id % 2 == 0:
out_a = model_a(random_input)
out_b = model_b(random_input)
else:
out_a = model_a(ones_input)
out_b = model_b(ones_input)

out_a.sum().backward()
out_b.sum().backward()

self.check_gradient(model_a.parameters())
self.check_gradient(model_b.parameters())

# test acc gradient
w1_grad_sum = self.check_acc(
model_a._layers.w1.grad,
w1_grad_sum,
model_b._layers.w1.grad,
)
w2_grad_sum = self.check_acc(
model_a._layers.w2.grad,
w2_grad_sum,
model_b._layers.w2.grad,
)

model_a.clear_gradients()
model_a.clear_gradients()

def check_acc(self, grad, grad_sum, acc_grad):
if grad is not None:
Expand Down
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