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未验证 提交 5fd115f3 编写于 作者: zhouweiwei2014's avatar zhouweiwei2014 提交者： GitHub
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[Zero-Dim] add static graph gradient test method for 0D Tensor input (#49755)

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Showing with 558 addition and 327 deletion
python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py
浏览文件 @ 5fd115f3
......@@ -17,9 +17,11 @@ import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.nn.functional as F
paddle.set_device('xpu')
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
unary_api_list = [
......@@ -100,9 +102,7 @@ class TestUnaryAPI(unittest.TestCase):
for api in unary_api_list:
x = paddle.rand([])
x.stop_gradient = False
x.retain_grads()
out = api(x)
out.retain_grads()
out.backward()
self.assertEqual(x.shape, [])
......@@ -138,25 +138,22 @@ reduce_api_list = [
# Use to test zero-dim of reduce API
class TestReduceAPI(unittest.TestCase):
def test_dygraph(self):
def test_dygraph_reduce(self):
paddle.disable_static()
for api in reduce_api_list:
# 1) x is 0D
if api in [paddle.all, paddle.any]:
x = paddle.randint(0, 2, []).astype('bool')
out = api(x, None)
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
else:
x = paddle.rand([])
x.stop_gradient = False
x.retain_grads()
out = api(x, None)
out.retain_grads()
out.backward()
x.stop_gradient = False
out = api(x, None)
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
paddle.enable_static()
......@@ -196,29 +193,28 @@ class TestBinaryAPI(unittest.TestCase):
def test_dygraph_binary(self):
paddle.disable_static()
for api in binary_api_list:
# 1) x/y is 0D
# 1) x is 0D, y is 0D
x = paddle.rand([])
y = paddle.rand([])
x.stop_gradient = False
y.stop_gradient = False
x.retain_grads()
y.retain_grads()
if isinstance(api, dict):
out = api['func'](x, y)
out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y)
np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else:
out = api(x, y)
out.retain_grads()
self.assertEqual(out.shape, [])
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(y.shape, [])
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(y.grad.shape, [])
self.assertEqual(out.grad.shape, [])
# 2) x is not 0D , y is 0D
# 2) x is ND, y is 0D
x = paddle.rand([2, 3, 4])
y = paddle.rand([])
x.stop_gradient = False
......@@ -229,16 +225,17 @@ class TestBinaryAPI(unittest.TestCase):
np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else:
out = api(x, y)
out.retain_grads()
self.assertEqual(out.shape, [2, 3, 4])
out.backward()
self.assertEqual(x.shape, [2, 3, 4])
self.assertEqual(y.shape, [])
self.assertEqual(out.shape, [2, 3, 4])
if x.grad is not None:
self.assertEqual(x.grad.shape, [2, 3, 4])
self.assertEqual(y.grad.shape, [])
self.assertEqual(out.grad.shape, [2, 3, 4])
# 3) x is 0D , y is not 0D
# 3) x is 0D , y is ND
x = paddle.rand([])
y = paddle.rand([2, 3, 4])
x.stop_gradient = False
......@@ -249,10 +246,11 @@ class TestBinaryAPI(unittest.TestCase):
np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else:
out = api(x, y)
out.retain_grads()
self.assertEqual(out.shape, [2, 3, 4])
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(y.shape, [2, 3, 4])
self.assertEqual(out.shape, [2, 3, 4])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(y.grad.shape, [2, 3, 4])
......@@ -260,26 +258,32 @@ class TestBinaryAPI(unittest.TestCase):
# 4) x is 0D , y is scalar
x = paddle.rand([])
y = 0.5
x.stop_gradient = False
y = 0.5
if isinstance(api, dict):
out = getattr(paddle.Tensor, api['cls_method'])(x, y)
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
if x.grad is not None:
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.grad.shape, [])
for api in binary_int_api_list:
# 1) x/y is 0D
# 1) x is 0D, y is 0D
x = paddle.randint(-10, 10, [])
y = paddle.randint(-10, 10, [])
out = api(x, y)
self.assertEqual(out.shape, [])
# 2) x is not 0D , y is 0D
# 2) x is ND, y is 0D
x = paddle.randint(-10, 10, [3, 5])
y = paddle.randint(-10, 10, [])
out = api(x, y)
self.assertEqual(out.shape, [3, 5])
# 3) x is 0D , y is not 0D
# 3) x is 0D , y is ND
x = paddle.randint(-10, 10, [])
y = paddle.randint(-10, 10, [3, 5])
out = api(x, y)
......@@ -374,9 +378,7 @@ class TestSundryAPI(unittest.TestCase):
def test_pow_factor(self):
x = paddle.rand([])
x.stop_gradient = False
x.retain_grads()
out = paddle.pow(x, 2.0)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
......@@ -386,9 +388,7 @@ class TestSundryAPI(unittest.TestCase):
def test_cast(self):
x = paddle.full([], 1.0, 'float32')
x.stop_gradient = False
x.retain_grads()
out = paddle.cast(x, 'int32')
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
......@@ -399,7 +399,6 @@ class TestSundryAPI(unittest.TestCase):
x = paddle.uniform([], None, -10, 10)
x.stop_gradient = False
out = paddle.clip(x, -5, 5)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
......@@ -444,11 +443,11 @@ class TestSundryAPI(unittest.TestCase):
x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0], stop_gradient=False)
index = paddle.full([], 2, 'int64')
out = paddle.gather(x, index)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
self.assertEqual(out.numpy(), 5)
self.assertEqual(x.grad.shape, [5])
self.assertEqual(out.grad.shape, [])
def test_gather_xD_axis_0(self):
......@@ -457,61 +456,62 @@ class TestSundryAPI(unittest.TestCase):
)
index = paddle.full([], 1, 'int64')
out = paddle.gather(x, index)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [3])
for i in range(3):
self.assertEqual(out.numpy()[i], x.numpy()[1][i])
np.testing.assert_array_equal(out.numpy(), x.numpy()[1, :])
self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.grad.shape, [3])
def test_gather_xD_axis_1(self):
x = paddle.to_tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
x = paddle.to_tensor(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], stop_gradient=False
)
index = paddle.full([], 1, 'int64')
out = paddle.gather(x, index, axis=1)
out.backward()
self.assertEqual(out.shape, [2])
for i in range(2):
self.assertEqual(out.numpy()[i], x.numpy()[i][1])
np.testing.assert_array_equal(out.numpy(), [2.0, 5.0])
self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.grad.shape, [2])
def test_scatter_1D(self):
x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0])
x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0], stop_gradient=False)
index = paddle.full([], 2, 'int64')
updates = paddle.full([], 4.0)
out = paddle.scatter(x, index, updates)
out.backward()
self.assertEqual(out.shape, [5])
self.assertEqual(out.numpy()[2], 4)
self.assertEqual(out.grad.shape, [5])
def test_scatter_XD(self):
x = paddle.to_tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
x = paddle.to_tensor(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], stop_gradient=False
)
index = paddle.full([], 1, 'int64')
updates = paddle.to_tensor([1.0, 2.0, 3.0])
out = paddle.scatter(x, index, updates)
out.backward()
for i in range(3):
self.assertEqual(out.numpy()[1][i], updates.numpy()[i])
self.assertEqual(out.shape, [2, 3])
np.testing.assert_array_equal(out.numpy()[1], [1.0, 2.0, 3.0])
self.assertEqual(out.grad.shape, [2, 3])
def test_diagflat(self):
x1 = paddle.rand([])
x2 = paddle.rand([])
x3 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
x3.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
x3.retain_grads()
out1 = paddle.diagflat(x1, 1)
out2 = paddle.diagflat(x2, -1)
out3 = paddle.diagflat(x3, 0)
out1.retain_grads()
out2.retain_grads()
out3.retain_grads()
out1.backward()
out2.backward()
out3.backward()
......@@ -541,9 +541,7 @@ class TestSundryAPI(unittest.TestCase):
index = paddle.full([], 1, 'int64')
updates = paddle.to_tensor([1.0, 2.0, 3.0])
out = paddle.scatter_(x, index, updates)
for i in range(3):
self.assertEqual(out.numpy()[1][i], updates.numpy()[i])
np.testing.assert_array_equal(out.numpy()[1], [1.0, 2.0, 3.0])
def test_flatten(self):
x = paddle.full([], 1, 'float32')
......@@ -561,9 +559,7 @@ class TestSundryAPI(unittest.TestCase):
def test_scale(self):
x = paddle.rand([])
x.stop_gradient = False
x.retain_grads()
out = paddle.scale(x, scale=2.0, bias=1.0)
out.retain_grads()
out.backward()
self.assertEqual(out.shape, [])
......@@ -598,31 +594,26 @@ class TestSundryAPI(unittest.TestCase):
def test_reshape_list(self):
x = paddle.rand([])
x.stop_gradient = False
x.retain_grads()
out = paddle.reshape(x, [])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
out = paddle.reshape(x, [1])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1])
out = paddle.reshape(x, [-1])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1])
out = paddle.reshape(x, [-1, 1])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [1, 1])
......@@ -631,26 +622,22 @@ class TestSundryAPI(unittest.TestCase):
def test_reshape_tensor(self):
x = paddle.rand([1, 1])
x.stop_gradient = False
x.retain_grads()
out = paddle.reshape(x, [])
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])
new_shape = paddle.full([], 1, "int32")
new_shape = paddle.to_tensor([1, 1, 1], "int32")
out = paddle.reshape(x, new_shape)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1])
self.assertEqual(out.shape, [1, 1, 1])
self.assertEqual(out.grad.shape, [1, 1, 1])
new_shape = paddle.full([], -1, "int32")
new_shape = paddle.to_tensor([-1], "int32")
out = paddle.reshape(x, new_shape)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1])
......@@ -658,7 +645,6 @@ class TestSundryAPI(unittest.TestCase):
new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
out = paddle.reshape(x, new_shape)
out.retain_grads()
out.backward()
self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1, 1])
......@@ -700,13 +686,9 @@ class TestSundryAPI(unittest.TestCase):
x2 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
out1 = paddle.sort(x1, axis=-1)
out2 = paddle.sort(x2, axis=0)
out1.retain_grads()
out2.retain_grads()
out1.backward()
out2.backward()
......@@ -727,13 +709,9 @@ class TestSundryAPI(unittest.TestCase):
x2 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
out1 = paddle.argsort(x1, axis=-1)
out2 = paddle.argsort(x2, axis=0)
out1.retain_grads()
out2.retain_grads()
out1.backward()
out2.backward()
......
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