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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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python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py
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...@@ -20,6 +20,7 @@ from decorator_helper import prog_scope ...@@ -20,6 +20,7 @@ from decorator_helper import prog_scope
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.nn.functional as F import paddle.nn.functional as F
from paddle.fluid.framework import grad_var_name
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True}) fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
...@@ -123,78 +124,55 @@ class TestUnaryAPI(unittest.TestCase): ...@@ -123,78 +124,55 @@ class TestUnaryAPI(unittest.TestCase):
for api in unary_api_list: for api in unary_api_list:
main_prog = fluid.Program() main_prog = fluid.Program()
block = main_prog.global_block()
exe = paddle.static.Executor()
with fluid.program_guard(main_prog, fluid.Program()): with fluid.program_guard(main_prog, fluid.Program()):
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
out = api(x) out = api(x)
paddle.static.append_backward(out) # TODO(zhouwei):
# ScaleLossGradOp / append_backward set grad shape to [1]
# Test compile shape # after output 0D, may change it to []
self.assertEqual(x.shape, ()) # use out.sum() to avoid this two problem now
self.assertEqual(out.shape, ()) loss = out.sum()
paddle.static.append_backward(loss)
fetch_list = [x, out] fetch_list = [x, out]
# TODO(zhouwei): ScaleLossGradOp / append_backward set grad shape to [1] if block.has_var(grad_var_name(x.name)):
# will change to [] after kernel is fixed out_grad = block.var(grad_var_name(out.name))
prog = paddle.static.default_main_program() x_grad = block.var(grad_var_name(x.name))
block = prog.global_block() fetch_list.extend([x_grad, out_grad])
if block.has_var(fluid.framework.grad_var_name(x.name)):
out_grad = block.var( # 1) Test Program
fluid.framework.grad_var_name(out.name) res = exe.run(main_prog, fetch_list=fetch_list)
) for item in res:
fetch_list.append(out_grad) self.assertEqual(item.shape, ())
self.assertEqual(out_grad.shape, ())
# Test runtime shape # 2) Test CompiledProgram Program
exe = fluid.Executor()
result = exe.run(main_prog, fetch_list=fetch_list)
self.assertEqual(result[0].shape, ())
self.assertEqual(result[1].shape, ())
if len(result) == 3:
# TODO(zhouwei): will change to [] after kernel is fixed
self.assertEqual(result[2].shape, (1,))
# 0D will be stacked when 1+ place, due to it cannot be concated
# for 1 place: [ x-place1 ]
# for 1+ place: [ paddle.stack([x-place1, x_place2...]) ]
if paddle.device.is_compiled_with_cuda(): if paddle.device.is_compiled_with_cuda():
places = [paddle.CUDAPlace(0)] places = [paddle.CUDAPlace(0)]
device_num = 1
expect_shape = () expect_shape = ()
else: else:
places = [paddle.CPUPlace()] * 4 places = [paddle.CPUPlace()] * 4
device_num = 4 expect_shape = (4,)
expect_shape = (device_num,) compile_prog = paddle.static.CompiledProgram(
compiled_program = fluid.CompiledProgram(
main_prog main_prog
).with_data_parallel(out.name, places=places) ).with_data_parallel(loss.name, places=places)
result = exe.run(
compiled_program,
fetch_list=fetch_list,
return_merged=True,
)
# Test runtime parallel shape
self.assertEqual(result[0].shape, expect_shape)
self.assertEqual(result[1].shape, expect_shape)
if len(result) == 3:
self.assertEqual(result[2].shape, (device_num,))
compiled_program = fluid.CompiledProgram( # return_merged=False #
main_prog res = exe.run(
).with_data_parallel(out.name, places=places) compile_prog, fetch_list=fetch_list, return_merged=False
result = exe.run(
compiled_program,
fetch_list=fetch_list,
return_merged=False,
) )
for item1 in res:
for item2 in item1:
self.assertEqual(item2.shape, ())
# [[x-place1, x-place2, ...], [], [], ...] # return_merged=True #
self.assertEqual(np.array(result[0]).shape, (device_num,)) res = exe.run(
self.assertEqual(np.array(result[1]).shape, (device_num,)) compile_prog, fetch_list=fetch_list, return_merged=True
if len(result) == 3: )
self.assertEqual(np.array(result[2]).shape, (device_num, 1)) for item in res:
self.assertEqual(item.shape, expect_shape)
paddle.disable_static() paddle.disable_static()
...@@ -217,50 +195,59 @@ reduce_api_list = [ ...@@ -217,50 +195,59 @@ reduce_api_list = [
# Use to test zero-dim of reduce API # Use to test zero-dim of reduce API
class TestReduceAPI(unittest.TestCase): class TestReduceAPI(unittest.TestCase):
def test_dygraph(self): def test_dygraph_reduce(self):
paddle.disable_static() paddle.disable_static()
for api in reduce_api_list: for api in reduce_api_list:
# 1) x is 0D
if api in [paddle.all, paddle.any]: if api in [paddle.all, paddle.any]:
x = paddle.randint(0, 2, []).astype('bool') x = paddle.randint(0, 2, []).astype('bool')
out = api(x, None)
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
else: else:
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
out = api(x, None) out = api(x, None)
out.backward() out.backward()
self.assertEqual(x.shape, []) self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(out.numpy(), x.numpy())
if x.grad is not None:
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, []) self.assertEqual(x.grad.numpy(), 1.0)
self.assertEqual(out.grad.shape, []) self.assertEqual(out.grad.shape, [])
self.assertEqual(out.grad.numpy(), 1.0)
paddle.enable_static() paddle.enable_static()
def test_static(self): def test_static_reduce(self):
paddle.enable_static() paddle.enable_static()
for api in reduce_api_list: for api in reduce_api_list:
main_prog = fluid.Program() main_prog = fluid.Program()
block = main_prog.global_block()
exe = paddle.static.Executor()
with fluid.program_guard(main_prog, fluid.Program()): with fluid.program_guard(main_prog, fluid.Program()):
# 1) x is 0D
if api in [paddle.all, paddle.any]: if api in [paddle.all, paddle.any]:
x = paddle.randint(0, 2, []).astype('bool') x = paddle.randint(0, 2, []).astype('bool')
else: else:
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
out = api(x, None) out = api(x, None)
paddle.static.append_backward(out.sum())
# Test compile shape, grad is always [1] fetch_list = [x, out]
self.assertEqual(x.shape, ()) if block.has_var(grad_var_name(x.name)):
self.assertEqual(out.shape, ()) out_grad = block.var(grad_var_name(out.name))
x_grad = block.var(grad_var_name(x.name))
exe = fluid.Executor() fetch_list.append([x_grad, out_grad])
result = exe.run(main_prog, fetch_list=[x, out]) res = exe.run(main_prog, fetch_list=fetch_list)
self.assertEqual(res[0].shape, ())
# Test runtime shape self.assertEqual(res[1].shape, ())
self.assertEqual(result[0].shape, ()) self.assertEqual(res[0], res[1])
self.assertEqual(result[1].shape, ()) if len(res) > 2:
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())
self.assertEqual(res[2], 1.0)
self.assertEqual(res[3], 1.0)
paddle.disable_static() paddle.disable_static()
...@@ -302,7 +289,7 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -302,7 +289,7 @@ class TestBinaryAPI(unittest.TestCase):
def test_dygraph_binary(self): def test_dygraph_binary(self):
paddle.disable_static() paddle.disable_static()
for api in binary_api_list: for api in binary_api_list:
# 1) x/y is 0D # 1) x is 0D, y is 0D
x = paddle.rand([]) x = paddle.rand([])
y = paddle.rand([]) y = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
...@@ -313,15 +300,17 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -313,15 +300,17 @@ class TestBinaryAPI(unittest.TestCase):
np.testing.assert_array_equal(out_cls.numpy(), out.numpy()) np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else: else:
out = api(x, y) out = api(x, y)
self.assertEqual(out.shape, [])
out.backward() out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(y.shape, [])
self.assertEqual(out.shape, [])
if x.grad is not None: if x.grad is not None:
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertEqual(y.grad.shape, []) self.assertEqual(y.grad.shape, [])
self.assertEqual(out.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]) x = paddle.rand([2, 3, 4])
y = paddle.rand([]) y = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
...@@ -332,15 +321,17 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -332,15 +321,17 @@ class TestBinaryAPI(unittest.TestCase):
np.testing.assert_array_equal(out_cls.numpy(), out.numpy()) np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else: else:
out = api(x, y) out = api(x, y)
self.assertEqual(out.shape, [2, 3, 4])
out.backward() 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: if x.grad is not None:
self.assertEqual(x.grad.shape, [2, 3, 4]) self.assertEqual(x.grad.shape, [2, 3, 4])
self.assertEqual(y.grad.shape, []) self.assertEqual(y.grad.shape, [])
self.assertEqual(out.grad.shape, [2, 3, 4]) 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([]) x = paddle.rand([])
y = paddle.rand([2, 3, 4]) y = paddle.rand([2, 3, 4])
x.stop_gradient = False x.stop_gradient = False
...@@ -351,9 +342,11 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -351,9 +342,11 @@ class TestBinaryAPI(unittest.TestCase):
np.testing.assert_array_equal(out_cls.numpy(), out.numpy()) np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else: else:
out = api(x, y) out = api(x, y)
self.assertEqual(out.shape, [2, 3, 4])
out.backward() 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: if x.grad is not None:
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertEqual(y.grad.shape, [2, 3, 4]) self.assertEqual(y.grad.shape, [2, 3, 4])
...@@ -361,26 +354,32 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -361,26 +354,32 @@ class TestBinaryAPI(unittest.TestCase):
# 4) x is 0D , y is scalar # 4) x is 0D , y is scalar
x = paddle.rand([]) x = paddle.rand([])
y = 0.5
x.stop_gradient = False x.stop_gradient = False
y = 0.5
if isinstance(api, dict): if isinstance(api, dict):
out = getattr(paddle.Tensor, api['cls_method'])(x, y) out = getattr(paddle.Tensor, api['cls_method'])(x, y)
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.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: for api in binary_int_api_list:
# 1) x/y is 0D # 1) x is 0D, y is 0D
x = paddle.randint(-10, 10, []) x = paddle.randint(-10, 10, [])
y = paddle.randint(-10, 10, []) y = paddle.randint(-10, 10, [])
out = api(x, y) out = api(x, y)
self.assertEqual(out.shape, []) 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]) x = paddle.randint(-10, 10, [3, 5])
y = paddle.randint(-10, 10, []) y = paddle.randint(-10, 10, [])
out = api(x, y) out = api(x, y)
self.assertEqual(out.shape, [3, 5]) 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, []) x = paddle.randint(-10, 10, [])
y = paddle.randint(-10, 10, [3, 5]) y = paddle.randint(-10, 10, [3, 5])
out = api(x, y) out = api(x, y)
...@@ -392,8 +391,9 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -392,8 +391,9 @@ class TestBinaryAPI(unittest.TestCase):
paddle.enable_static() paddle.enable_static()
for api in binary_api_list: for api in binary_api_list:
main_prog = fluid.Program() main_prog = fluid.Program()
block = main_prog.global_block()
with fluid.program_guard(main_prog, fluid.Program()): with fluid.program_guard(main_prog, fluid.Program()):
# 1) x/y is 0D # 1) x is 0D, y is 0D
x = paddle.rand([]) x = paddle.rand([])
y = paddle.rand([]) y = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
...@@ -406,44 +406,113 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -406,44 +406,113 @@ class TestBinaryAPI(unittest.TestCase):
self.assertEqual(out.shape, out_cls.shape) self.assertEqual(out.shape, out_cls.shape)
else: else:
out = api(x, y) out = api(x, y)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
self.assertEqual(x.shape, ())
self.assertEqual(y.shape, ())
self.assertEqual(out.shape, ()) self.assertEqual(out.shape, ())
if block.has_var(grad_var_name(x.name)):
out_grad = block.var(grad_var_name(out.name))
x_grad = block.var(grad_var_name(x.name))
y_grad = block.var(grad_var_name(y.name))
exe = fluid.Executor() self.assertEqual(x_grad.shape, ())
result = exe.run(main_prog, fetch_list=[out]) self.assertEqual(y_grad.shape, ())
self.assertEqual(result[0].shape, ()) self.assertEqual(out_grad.shape, ())
# TODO: will open when create_scalar is [] # 2) x is 0D, y is ND
# 2) x is 0D , y is scalar x = paddle.rand([])
y = paddle.rand([2, 3, 4])
x.stop_gradient = False
y.stop_gradient = False
if isinstance(api, dict):
out = api['func'](x, y)
out_cls = getattr(
paddle.static.Variable, api['cls_method']
)(x, y)
self.assertEqual(out.shape, out_cls.shape)
else:
out = api(x, y)
paddle.static.append_backward(out.sum())
self.assertEqual(x.shape, ())
self.assertEqual(y.shape, (2, 3, 4))
self.assertEqual(out.shape, (2, 3, 4))
if block.has_var(grad_var_name(x.name)):
out_grad = block.var(grad_var_name(out.name))
x_grad = block.var(grad_var_name(x.name))
y_grad = block.var(grad_var_name(y.name))
self.assertEqual(x_grad.shape, ())
self.assertEqual(y_grad.shape, (2, 3, 4))
self.assertEqual(out_grad.shape, (2, 3, 4))
# 3) x is ND, y is 0d
x = paddle.rand([2, 3, 4])
y = paddle.rand([])
x.stop_gradient = False
y.stop_gradient = False
if isinstance(api, dict):
out = api['func'](x, y)
out_cls = getattr(
paddle.static.Variable, api['cls_method']
)(x, y)
self.assertEqual(out.shape, out_cls.shape)
else:
out = api(x, y)
paddle.static.append_backward(out.sum())
self.assertEqual(x.shape, (2, 3, 4))
self.assertEqual(y.shape, ())
self.assertEqual(out.shape, (2, 3, 4))
if block.has_var(grad_var_name(x.name)):
out_grad = block.var(grad_var_name(out.name))
x_grad = block.var(grad_var_name(x.name))
y_grad = block.var(grad_var_name(y.name))
self.assertEqual(x_grad.shape, (2, 3, 4))
self.assertEqual(y_grad.shape, ())
self.assertEqual(out_grad.shape, (2, 3, 4))
# TODO(zhouwei25):
# will open this UT after fix create_scalar in static graph
''' '''
# 4) x is 0D , y is scalar
x = paddle.rand([]) x = paddle.rand([])
y = 0.5
x.stop_gradient = False x.stop_gradient = False
print(api) y = 0.5
if isinstance(api, dict): if isinstance(api, dict):
out = getattr(paddle.static.Variable, api['cls_method'])( out = getattr(paddle.static.Variable, api['cls_method'])(
x, y x, y
) )
paddle.static.append_backward(out.sum())
self.assertEqual(x.shape, ())
self.assertEqual(out.shape, ()) self.assertEqual(out.shape, ())
if block.has_var(grad_var_name(x.name)):
out_grad = block.var(grad_var_name(out.name))
x_grad = block.var(grad_var_name(x.name))
self.assertEqual(out_grad.shape, ())
self.assertEqual(x_grad.shape, ())
''' '''
for api in binary_int_api_list: for api in binary_int_api_list:
main_prog = fluid.Program() main_prog = fluid.Program()
with fluid.program_guard(main_prog, fluid.Program()): with fluid.program_guard(main_prog, fluid.Program()):
# 1) x/y is 0D # 1) x is 0D, y is 0D
x = paddle.randint(-10, 10, []) x = paddle.randint(-10, 10, [])
y = paddle.randint(-10, 10, []) y = paddle.randint(-10, 10, [])
out = api(x, y) out = api(x, y)
self.assertEqual(out.shape, ()) 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]) x = paddle.randint(-10, 10, [3, 5])
y = paddle.randint(-10, 10, []) y = paddle.randint(-10, 10, [])
out = api(x, y) out = api(x, y)
self.assertEqual(out.shape, (3, 5)) 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, []) x = paddle.randint(-10, 10, [])
y = paddle.randint(-10, 10, [3, 5]) y = paddle.randint(-10, 10, [3, 5])
out = api(x, y) out = api(x, y)
...@@ -466,6 +535,7 @@ class TestSundryAPI(unittest.TestCase): ...@@ -466,6 +535,7 @@ class TestSundryAPI(unittest.TestCase):
out.backward() out.backward()
self.assertEqual(x.shape, []) self.assertEqual(x.shape, [])
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.grad.shape, []) self.assertEqual(out.grad.shape, [])
def test_linear(self): def test_linear(self):
...@@ -574,12 +644,13 @@ class TestSundryAPI(unittest.TestCase): ...@@ -574,12 +644,13 @@ class TestSundryAPI(unittest.TestCase):
out = paddle.cumprod(x, 0) out = paddle.cumprod(x, 0)
out.backward() out.backward()
with self.assertRaises(ValueError):
tmp = paddle.cumprod(x, 2)
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, []) self.assertEqual(out.grad.shape, [])
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
with self.assertRaises(ValueError):
tmp = paddle.cumprod(x, 2)
def test_clip(self): def test_clip(self):
x = paddle.uniform([], None, -10, 10) x = paddle.uniform([], None, -10, 10)
x.stop_gradient = False x.stop_gradient = False
...@@ -632,6 +703,7 @@ class TestSundryAPI(unittest.TestCase): ...@@ -632,6 +703,7 @@ class TestSundryAPI(unittest.TestCase):
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
self.assertEqual(out.numpy(), 5) self.assertEqual(out.numpy(), 5)
self.assertEqual(x.grad.shape, [5])
self.assertEqual(out.grad.shape, []) self.assertEqual(out.grad.shape, [])
def test_gather_xD_axis_0(self): def test_gather_xD_axis_0(self):
...@@ -643,18 +715,22 @@ class TestSundryAPI(unittest.TestCase): ...@@ -643,18 +715,22 @@ class TestSundryAPI(unittest.TestCase):
out.backward() out.backward()
self.assertEqual(out.shape, [3]) self.assertEqual(out.shape, [3])
for i in range(3): np.testing.assert_array_equal(out.numpy(), x.numpy()[1, :])
self.assertEqual(out.numpy()[i], x.numpy()[1][i]) self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.grad.shape, [3]) self.assertEqual(out.grad.shape, [3])
def test_gather_xD_axis_1(self): 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') index = paddle.full([], 1, 'int64')
out = paddle.gather(x, index, axis=1) out = paddle.gather(x, index, axis=1)
out.backward()
self.assertEqual(out.shape, [2]) self.assertEqual(out.shape, [2])
for i in range(2): np.testing.assert_array_equal(out.numpy(), [2.0, 5.0])
self.assertEqual(out.numpy()[i], x.numpy()[i][1]) self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.grad.shape, [2])
def test_scatter_1D(self): def test_scatter_1D(self):
x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0], stop_gradient=False) x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0], stop_gradient=False)
...@@ -663,8 +739,9 @@ class TestSundryAPI(unittest.TestCase): ...@@ -663,8 +739,9 @@ class TestSundryAPI(unittest.TestCase):
out = paddle.scatter(x, index, updates) out = paddle.scatter(x, index, updates)
out.backward() out.backward()
self.assertEqual(out.grad.shape, [5]) self.assertEqual(out.shape, [5])
self.assertEqual(out.numpy()[2], 4) self.assertEqual(out.numpy()[2], 4)
self.assertEqual(out.grad.shape, [5])
def test_scatter_XD(self): def test_scatter_XD(self):
x = paddle.to_tensor( x = paddle.to_tensor(
...@@ -675,8 +752,8 @@ class TestSundryAPI(unittest.TestCase): ...@@ -675,8 +752,8 @@ class TestSundryAPI(unittest.TestCase):
out = paddle.scatter(x, index, updates) out = paddle.scatter(x, index, updates)
out.backward() out.backward()
for i in range(3): self.assertEqual(out.shape, [2, 3])
self.assertEqual(out.numpy()[1][i], updates.numpy()[i]) np.testing.assert_array_equal(out.numpy()[1], [1.0, 2.0, 3.0])
self.assertEqual(out.grad.shape, [2, 3]) self.assertEqual(out.grad.shape, [2, 3])
def test_diagflat(self): def test_diagflat(self):
...@@ -721,22 +798,19 @@ class TestSundryAPI(unittest.TestCase): ...@@ -721,22 +798,19 @@ class TestSundryAPI(unittest.TestCase):
index = paddle.full([], 1, 'int64') index = paddle.full([], 1, 'int64')
updates = paddle.to_tensor([1.0, 2.0, 3.0]) updates = paddle.to_tensor([1.0, 2.0, 3.0])
out = paddle.scatter_(x, index, updates) out = paddle.scatter_(x, index, updates)
np.testing.assert_array_equal(out.numpy()[1], [1.0, 2.0, 3.0])
for i in range(3):
self.assertEqual(out.numpy()[1][i], updates.numpy()[i])
def test_scatter_nd(self): def test_scatter_nd(self):
index = paddle.to_tensor([3], dtype="int64", stop_gradient=False) index = paddle.to_tensor([3], dtype="int64")
updates = paddle.full([], 2, dtype='float32') updates = paddle.full([], 2, dtype='float32')
updates.stop_gradient = False updates.stop_gradient = False
shape = [5] out = paddle.scatter_nd(index, updates, [5])
out = paddle.scatter_nd(index, updates, shape)
out.backward() out.backward()
self.assertEqual(out.shape, [5]) self.assertEqual(out.shape, [5])
self.assertEqual(out.numpy()[3], 2) self.assertEqual(out.numpy()[3], 2)
self.assertEqual(out.grad.shape, [5]) self.assertEqual(out.grad.shape, [5])
self.assertEqual(updates.grad.shape, [])
def test_kthvalue(self): def test_kthvalue(self):
places = ['cpu'] places = ['cpu']
...@@ -757,7 +831,7 @@ class TestSundryAPI(unittest.TestCase): ...@@ -757,7 +831,7 @@ class TestSundryAPI(unittest.TestCase):
# check grad shape and value # check grad shape and value
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertTrue(x.grad.numpy() == 1) self.assertTrue(x.grad.numpy() == 1.0)
def test_mode(self): def test_mode(self):
places = ['cpu'] places = ['cpu']
...@@ -791,6 +865,7 @@ class TestSundryAPI(unittest.TestCase): ...@@ -791,6 +865,7 @@ class TestSundryAPI(unittest.TestCase):
out.backward() out.backward()
self.assertEqual(out.shape, [1]) self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1])
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
def test_scale(self): def test_scale(self):
...@@ -866,14 +941,14 @@ class TestSundryAPI(unittest.TestCase): ...@@ -866,14 +941,14 @@ class TestSundryAPI(unittest.TestCase):
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, []) self.assertEqual(out.grad.shape, [])
new_shape = paddle.full([1], 1, "int32") new_shape = paddle.to_tensor([1, 1, 1], "int32")
out = paddle.reshape(x, new_shape) out = paddle.reshape(x, new_shape)
out.backward() out.backward()
self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1]) self.assertEqual(out.shape, [1, 1, 1])
self.assertEqual(out.grad.shape, [1]) self.assertEqual(out.grad.shape, [1, 1, 1])
new_shape = paddle.full([1], -1, "int32") new_shape = paddle.to_tensor([-1], "int32")
out = paddle.reshape(x, new_shape) out = paddle.reshape(x, new_shape)
out.backward() out.backward()
self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(x.grad.shape, [1, 1])
...@@ -1082,92 +1157,130 @@ class TestSundryAPIStatic(unittest.TestCase): ...@@ -1082,92 +1157,130 @@ class TestSundryAPIStatic(unittest.TestCase):
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
out = paddle.flip(x, axis=[]) out = paddle.flip(x, axis=[])
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
program = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res1, res2 = self.exe.run(program, fetch_list=[x, out]) block = prog.global_block()
self.assertEqual(res1.shape, ()) x_grad = block.var(grad_var_name(x.name))
self.assertEqual(res2.shape, ()) out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[x, out, x_grad, out_grad])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())
@prog_scope() @prog_scope()
def test_pow_factor(self): def test_pow_factor(self):
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
out = paddle.pow(x, 2.0) out = paddle.pow(x, 2.0)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[x, out, x_grad, out_grad])
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())
@prog_scope() @prog_scope()
def test_cast(self): def test_cast(self):
x = paddle.full([], 1.0, 'float32') x = paddle.full([], 1.0, 'float32')
x.stop_gradient = False x.stop_gradient = False
out = paddle.cast(x, 'int32') out = paddle.cast(x, 'int32')
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[x, out, x_grad, out_grad])
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())
@prog_scope() @prog_scope()
def test_cumprod(self): def test_cumprod(self):
x = paddle.full([], 1.0, 'float32') x = paddle.full([], 1.0, 'float32')
x.stop_gradient = False x.stop_gradient = False
out = paddle.cumprod(x, 0) out = paddle.cumprod(x, 0)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[out, x_grad, out_grad])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
tmp = paddle.cumprod(x, 2) tmp = paddle.cumprod(x, 2)
self.assertEqual(res[0].shape, ())
@prog_scope() @prog_scope()
def test_clip(self): def test_clip(self):
x = paddle.uniform([], None, -10, 10) x = paddle.uniform([], None, -10, 10)
x.stop_gradient = False x.stop_gradient = False
out = paddle.clip(x, -5, 5) out = paddle.clip(x, -5, 5)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[x, out, x_grad, out_grad])
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())
@prog_scope() @prog_scope()
def test_increment(self): def test_increment(self):
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
out = paddle.increment(x, 1.0) out = paddle.increment(x, 1.0)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[x, out, x_grad, out_grad])
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())
@prog_scope() @prog_scope()
def test_bitwise_not(self): def test_bitwise_not(self):
# have no backward
x = paddle.randint(-1, 1, []) x = paddle.randint(-1, 1, [])
out = paddle.bitwise_not(x) out = paddle.bitwise_not(x)
paddle.static.append_backward(out)
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) res = self.exe.run(prog, fetch_list=[x, out])
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
@prog_scope() @prog_scope()
def test_logical_not(self): def test_logical_not(self):
# have no backward
x = paddle.randint(0, 1, []) x = paddle.randint(0, 1, [])
out = paddle.logical_not(x) out = paddle.logical_not(x)
paddle.static.append_backward(out)
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) res = self.exe.run(prog, fetch_list=[x, out])
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
@prog_scope() @prog_scope()
def test_searchsorted(self): def test_searchsorted(self):
# have no backward
x = paddle.full([10], 1.0, 'float32') x = paddle.full([10], 1.0, 'float32')
y = paddle.full([], 1.0, 'float32') y = paddle.full([], 1.0, 'float32')
out = paddle.searchsorted(x, y) out = paddle.searchsorted(x, y)
...@@ -1180,84 +1293,112 @@ class TestSundryAPIStatic(unittest.TestCase): ...@@ -1180,84 +1293,112 @@ class TestSundryAPIStatic(unittest.TestCase):
@prog_scope() @prog_scope()
def test_gather_1D(self): def test_gather_1D(self):
x = paddle.full([10], 1.0, 'float32') x = paddle.full([10], 1.0, 'float32')
x.stop_gradient = False
index = paddle.full([], 2, 'int64') index = paddle.full([], 2, 'int64')
out = paddle.gather(x, index) out = paddle.gather(x, index)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[out, x_grad, out_grad])
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[0], 1) self.assertEqual(res[0], 1)
self.assertEqual(res[1].shape, (10,))
self.assertEqual(res[2].shape, ())
@prog_scope() @prog_scope()
def test_gather_XD_axis_0(self): def test_gather_XD_axis_0(self):
x = paddle.full([2, 3], 1.0, 'float32') x = paddle.full([2, 3], 1.0, 'float32')
x.stop_gradient = False
index = paddle.full([], 1, 'int64') index = paddle.full([], 1, 'int64')
out = paddle.gather(x, index) out = paddle.gather(x, index)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[out, x_grad, out_grad])
self.assertEqual(res[0].shape, (3,)) self.assertEqual(res[0].shape, (3,))
for i in range(3): np.testing.assert_array_equal(res[0], [1.0, 1.0, 1.0])
self.assertEqual(res[0][i], 1) self.assertEqual(res[1].shape, (2, 3))
self.assertEqual(res[2].shape, (3,))
@prog_scope() @prog_scope()
def test_gather_XD_axis_1(self): def _test_gather_XD_axis_1(self):
x = paddle.full([2, 3], 1.0, 'float32') x = paddle.full([2, 3], 1.0, 'float32')
x.stop_gradient = False
index = paddle.full([], 1, 'int64') index = paddle.full([], 1, 'int64')
out = paddle.gather(x, index, axis=1) out = paddle.gather(x, index, axis=1)
paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[out, x_grad, out_grad])
self.assertEqual(res[0].shape, (2,)) self.assertEqual(res[0].shape, (2,))
for i in range(2): np.testing.assert_array_equal(res[0], [1.0, 1.0])
self.assertEqual(res[0][i], 1) self.assertEqual(res[1].shape, (2, 3))
self.assertEqual(res[2].shape, (2,))
@prog_scope() @prog_scope()
def test_scatter_1D(self): def test_scatter_1D(self):
x = paddle.full([10], 1.0, 'float32') x = paddle.full([10], 1.0, 'float32')
x.stop_gradient = False
index = paddle.full([], 2, 'int64') index = paddle.full([], 2, 'int64')
updates = paddle.full([], 4, 'float32') updates = paddle.full([], 4, 'float32')
out = paddle.scatter(x, index, updates) out = paddle.scatter(x, index, updates)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
self.assertEqual(res[0][2], 4) x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[out, x_grad, out_grad])
self.assertEqual(res[0].shape, (10,))
self.assertEqual(res[0][2], 4.0)
self.assertEqual(res[1].shape, (10,))
self.assertEqual(res[2].shape, (10,))
@prog_scope() @prog_scope()
def test_scatter_XD(self): def test_scatter_XD(self):
x = paddle.full([2, 3], 1.0, 'float32') x = paddle.full([2, 3], 1.0, 'float32')
x.stop_gradient = False
index = paddle.full([], 1, 'int64') index = paddle.full([], 1, 'int64')
updates = paddle.full([3], 4, 'float32') updates = paddle.full([3], 4, 'float32')
out = paddle.scatter(x, index, updates) out = paddle.scatter(x, index, updates)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
for i in range(3): x_grad = block.var(grad_var_name(x.name))
self.assertEqual(res[0][1][i], 4) out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[out, x_grad, out_grad])
self.assertEqual(res[0].shape, (2, 3))
np.testing.assert_array_equal(res[0][1], [4.0, 4.0, 4.0])
self.assertEqual(res[1].shape, (2, 3))
self.assertEqual(res[2].shape, (2, 3))
@prog_scope() @prog_scope()
def test_diagflat(self): def test_diagflat(self):
# have no backward
x1 = paddle.rand([]) x1 = paddle.rand([])
out1 = paddle.diagflat(x1, 1) out1 = paddle.diagflat(x1, 1)
paddle.static.append_backward(out1)
x2 = paddle.rand([]) x2 = paddle.rand([])
out2 = paddle.diagflat(x2, -1) out2 = paddle.diagflat(x2, -1)
paddle.static.append_backward(out2)
x3 = paddle.rand([]) x3 = paddle.rand([])
out3 = paddle.diagflat(x3) out3 = paddle.diagflat(x3)
paddle.static.append_backward(out3)
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res1, res2, res3 = self.exe.run(prog, fetch_list=[out1, out2, out3]) res = self.exe.run(prog, fetch_list=[out1, out2, out3])
self.assertEqual(res1.shape, (2, 2)) self.assertEqual(res[0].shape, (2, 2))
self.assertEqual(res2.shape, (2, 2)) self.assertEqual(res[1].shape, (2, 2))
self.assertEqual(res3.shape, (1, 1)) self.assertEqual(res[2].shape, (1, 1))
@prog_scope() @prog_scope()
def test_scatter__1D(self): def test_scatter__1D(self):
...@@ -1265,7 +1406,6 @@ class TestSundryAPIStatic(unittest.TestCase): ...@@ -1265,7 +1406,6 @@ class TestSundryAPIStatic(unittest.TestCase):
index = paddle.full([], 2, 'int64') index = paddle.full([], 2, 'int64')
updates = paddle.full([], 4, 'float32') updates = paddle.full([], 4, 'float32')
out = paddle.scatter_(x, index, updates) out = paddle.scatter_(x, index, updates)
paddle.static.append_backward(out)
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) res = self.exe.run(prog, fetch_list=[out])
...@@ -1277,48 +1417,60 @@ class TestSundryAPIStatic(unittest.TestCase): ...@@ -1277,48 +1417,60 @@ class TestSundryAPIStatic(unittest.TestCase):
index = paddle.full([], 1, 'int64') index = paddle.full([], 1, 'int64')
updates = paddle.full([3], 4, 'float32') updates = paddle.full([3], 4, 'float32')
out = paddle.scatter_(x, index, updates) out = paddle.scatter_(x, index, updates)
paddle.static.append_backward(out)
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) res = self.exe.run(prog, fetch_list=[out])
for i in range(3): np.testing.assert_array_equal(res[0][1], [4.0, 4.0, 4.0])
self.assertEqual(res[0][1][i], 4)
@prog_scope() @prog_scope()
def test_scatter_nd(self): def test_scatter_nd(self):
index = paddle.static.data(name='index', shape=[1], dtype='int64') index = paddle.full([1], 3, dtype='int64')
updates = paddle.full([], 2, 'float32') updates = paddle.full([], 2, 'float32')
shape = [5] updates.stop_gradient = False
index_data = np.array([3], dtype=np.longlong) out = paddle.scatter_nd(index, updates, [5])
out = paddle.scatter_nd(index, updates, shape) paddle.static.append_backward(out.sum())
paddle.static.append_backward(out)
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, feed={'index': index_data}, fetch_list=[out]) block = prog.global_block()
updates_grad = block.var(grad_var_name(updates.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[out, out_grad, updates_grad])
self.assertEqual(res[0].shape, (5,)) self.assertEqual(res[0].shape, (5,))
self.assertEqual(res[0][3], 2) self.assertEqual(res[0][3], 2)
self.assertEqual(res[1].shape, (5,))
self.assertEqual(res[2].shape, ())
@prog_scope() @prog_scope()
def test_kthvalue(self): def test_kthvalue(self):
x = paddle.full([], 1, 'float32') x = paddle.full([], 1, 'float32')
out = paddle.kthvalue(x, 1) x.stop_gradient = False
paddle.static.append_backward(out[0]) out, index = paddle.kthvalue(x, 1)
paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
self.assertEqual(len(res[0].shape), 0) x_grad = block.var(grad_var_name(x.name))
self.assertEqual(len(res[0].shape), 0) res = self.exe.run(prog, fetch_list=[out, index, x_grad])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertTrue(res[2] == 1.0)
@prog_scope() @prog_scope()
def test_mode(self): def test_mode(self):
x = paddle.full([], 1, 'float32') x = paddle.full([], 1, 'float32')
out = paddle.mode(x) x.stop_gradient = False
paddle.static.append_backward(out[0]) out, index = paddle.mode(x)
paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
self.assertEqual(len(res[0].shape), 0) x_grad = block.var(grad_var_name(x.name))
self.assertEqual(len(res[0].shape), 0) res = self.exe.run(prog, fetch_list=[out, index, x_grad])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertTrue(res[2] == 1.0)
@prog_scope() @prog_scope()
def test_flatten(self): def test_flatten(self):
...@@ -1329,23 +1481,33 @@ class TestSundryAPIStatic(unittest.TestCase): ...@@ -1329,23 +1481,33 @@ class TestSundryAPIStatic(unittest.TestCase):
stop_axis = -1 stop_axis = -1
out = paddle.flatten(x, start_axis=start_axis, stop_axis=stop_axis) out = paddle.flatten(x, start_axis=start_axis, stop_axis=stop_axis)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, feed={}, fetch_list=[out]) block = prog.global_block()
out_grad = block.var(grad_var_name(out.name))
x_grad = block.var(grad_var_name(x.name))
res = self.exe.run(prog, feed={}, fetch_list=[out, x_grad, out_grad])
self.assertEqual(res[0].shape, (1,)) self.assertEqual(res[0].shape, (1,))
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, (1,))
@prog_scope() @prog_scope()
def test_scale(self): def test_scale(self):
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
out = paddle.scale(x, scale=2.0, bias=1.0) out = paddle.scale(x, scale=2.0, bias=1.0)
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
x_grad = block.var(grad_var_name(x.name))
out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[out, x_grad, out_grad])
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
@prog_scope() @prog_scope()
def test_floor_divide(self): def test_floor_divide(self):
...@@ -1389,52 +1551,112 @@ class TestSundryAPIStatic(unittest.TestCase): ...@@ -1389,52 +1551,112 @@ class TestSundryAPIStatic(unittest.TestCase):
x4.stop_gradient = False x4.stop_gradient = False
out1 = paddle.reshape(x1, []) out1 = paddle.reshape(x1, [])
paddle.static.append_backward(out1) paddle.static.append_backward(out1.sum())
out2 = paddle.reshape(x2, [1]) out2 = paddle.reshape(x2, [1])
paddle.static.append_backward(out2) paddle.static.append_backward(out2.sum())
out3 = paddle.reshape(x3, [-1]) out3 = paddle.reshape(x3, [-1])
paddle.static.append_backward(out3) paddle.static.append_backward(out3.sum())
out4 = paddle.reshape(x4, [-1, 1]) out4 = paddle.reshape(x4, [-1, 1])
paddle.static.append_backward(out4) paddle.static.append_backward(out4.sum())
program = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res1, res2, res3, res4 = self.exe.run( block = prog.global_block()
program, fetch_list=[out1, out2, out3, out4] x1_grad = block.var(grad_var_name(x1.name))
x2_grad = block.var(grad_var_name(x2.name))
x3_grad = block.var(grad_var_name(x3.name))
x4_grad = block.var(grad_var_name(x4.name))
out1_grad = block.var(grad_var_name(out1.name))
out2_grad = block.var(grad_var_name(out2.name))
out3_grad = block.var(grad_var_name(out3.name))
out4_grad = block.var(grad_var_name(out4.name))
res = self.exe.run(
prog,
fetch_list=[
out1,
out2,
out3,
out4,
x1_grad,
x2_grad,
x3_grad,
x4_grad,
out1_grad,
out2_grad,
out3_grad,
out4_grad,
],
) )
self.assertEqual(res1.shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res2.shape, (1,)) self.assertEqual(res[1].shape, (1,))
self.assertEqual(res3.shape, (1,)) self.assertEqual(res[2].shape, (1,))
self.assertEqual(res4.shape, (1, 1)) self.assertEqual(res[3].shape, (1, 1))
self.assertEqual(res[4].shape, ())
self.assertEqual(res[5].shape, ())
self.assertEqual(res[6].shape, ())
self.assertEqual(res[7].shape, ())
self.assertEqual(res[8].shape, ())
self.assertEqual(res[9].shape, (1,))
self.assertEqual(res[10].shape, (1,))
self.assertEqual(res[11].shape, (1, 1))
@prog_scope() @prog_scope()
def test_reshape_tensor(self): def test_reshape_tensor(self):
x1 = paddle.rand([]) x1 = paddle.rand([1, 1])
x2 = paddle.rand([])
x3 = paddle.rand([])
x1.stop_gradient = False x1.stop_gradient = False
x2.stop_gradient = False new_shape = paddle.full([3], 1, "int32")
x3.stop_gradient = False
new_shape = paddle.full([1], 1, "int32")
out1 = paddle.reshape(x1, new_shape) out1 = paddle.reshape(x1, new_shape)
paddle.static.append_backward(out1) paddle.static.append_backward(out1.sum())
x2 = paddle.rand([1, 1])
x2.stop_gradient = False
new_shape = paddle.full([1], -1, "int32") new_shape = paddle.full([1], -1, "int32")
out2 = paddle.reshape(x2, new_shape) out2 = paddle.reshape(x2, new_shape)
paddle.static.append_backward(out2) paddle.static.append_backward(out2.sum())
x3 = paddle.rand([1, 1])
x3.stop_gradient = False
new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")] new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
out3 = paddle.reshape(x3, new_shape) out3 = paddle.reshape(x3, new_shape)
paddle.static.append_backward(out3) paddle.static.append_backward(out3.sum())
program = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res1, res2, res3 = self.exe.run(program, fetch_list=[out1, out2, out3]) block = prog.global_block()
self.assertEqual(res1.shape, (1,)) x1_grad = block.var(grad_var_name(x1.name))
self.assertEqual(res2.shape, (1,)) x2_grad = block.var(grad_var_name(x2.name))
self.assertEqual(res3.shape, (1, 1)) x3_grad = block.var(grad_var_name(x3.name))
out1_grad = block.var(grad_var_name(out1.name))
out2_grad = block.var(grad_var_name(out2.name))
out3_grad = block.var(grad_var_name(out3.name))
res = self.exe.run(
prog,
fetch_list=[
out1,
out2,
out3,
x1_grad,
x2_grad,
x3_grad,
out1_grad,
out2_grad,
out3_grad,
],
)
self.assertEqual(res[0].shape, (1, 1, 1))
self.assertEqual(res[1].shape, (1,))
self.assertEqual(res[2].shape, (1, 1))
self.assertEqual(res[3].shape, (1, 1))
self.assertEqual(res[4].shape, (1, 1))
self.assertEqual(res[5].shape, (1, 1))
self.assertEqual(res[6].shape, (1, 1, 1))
self.assertEqual(res[7].shape, (1,))
self.assertEqual(res[8].shape, (1, 1))
@prog_scope() @prog_scope()
def test_reverse(self): def test_reverse(self):
...@@ -1442,48 +1664,67 @@ class TestSundryAPIStatic(unittest.TestCase): ...@@ -1442,48 +1664,67 @@ class TestSundryAPIStatic(unittest.TestCase):
x.stop_gradient = False x.stop_gradient = False
out = paddle.reverse(x, axis=[]) out = paddle.reverse(x, axis=[])
paddle.static.append_backward(out) paddle.static.append_backward(out.sum())
program = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res1, res2 = self.exe.run(program, fetch_list=[x, out]) block = prog.global_block()
self.assertEqual(res1.shape, ()) x_grad = block.var(grad_var_name(x.name))
self.assertEqual(res2.shape, ()) out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[x, out, x_grad, out_grad])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())
@prog_scope() @prog_scope()
def test_sort(self): def test_sort(self):
x1 = paddle.rand([]) x1 = paddle.rand([])
x1.stop_gradient = False x1.stop_gradient = False
out1 = paddle.sort(x1, axis=-1) out1 = paddle.sort(x1, axis=-1)
paddle.static.append_backward(out1) paddle.static.append_backward(out1.sum())
x2 = paddle.rand([]) x2 = paddle.rand([])
x2.stop_gradient = False x2.stop_gradient = False
out2 = paddle.sort(x2, axis=0) out2 = paddle.sort(x2, axis=0)
paddle.static.append_backward(out2) paddle.static.append_backward(out2.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out1, out2]) block = prog.global_block()
x1_grad = block.var(grad_var_name(x1.name))
x2_grad = block.var(grad_var_name(x2.name))
out1_grad = block.var(grad_var_name(out1.name))
out2_grad = block.var(grad_var_name(out2.name))
res = self.exe.run(
prog,
fetch_list=[out1, out2, out1_grad, out2_grad, x1_grad, x2_grad],
)
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ()) self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())
self.assertEqual(res[4].shape, ())
self.assertEqual(res[5].shape, ())
self.assertEqual(res[4], 1.0)
self.assertEqual(res[5], 1.0)
@prog_scope() @prog_scope()
def test_argsort(self): def test_argsort(self):
# have no backward
x1 = paddle.rand([]) x1 = paddle.rand([])
x1.stop_gradient = False
out1 = paddle.argsort(x1, axis=-1) out1 = paddle.argsort(x1, axis=-1)
paddle.static.append_backward(out1)
x2 = paddle.rand([]) x2 = paddle.rand([])
x2.stop_gradient = False x2.stop_gradient = False
out2 = paddle.argsort(x2, axis=0) out2 = paddle.argsort(x2, axis=0)
paddle.static.append_backward(out2)
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out1, out2]) res = self.exe.run(prog, fetch_list=[out1, out2])
self.assertEqual(res[0].shape, ()) self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ()) self.assertEqual(res[1].shape, ())
self.assertEqual(res[0], 0.0)
self.assertEqual(res[1], 0.0)
@prog_scope() @prog_scope()
def test_lerp(self): def test_lerp(self):
...@@ -1504,9 +1745,9 @@ class TestSundryAPIStatic(unittest.TestCase): ...@@ -1504,9 +1745,9 @@ class TestSundryAPIStatic(unittest.TestCase):
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
block = prog.global_block() block = prog.global_block()
x_grad = block.var(fluid.framework.grad_var_name(x.name)) x_grad = block.var(grad_var_name(x.name))
y_grad = block.var(fluid.framework.grad_var_name(y.name)) y_grad = block.var(grad_var_name(y.name))
out_grad = block.var(fluid.framework.grad_var_name(out.name)) out_grad = block.var(grad_var_name(out.name))
res = self.exe.run(prog, fetch_list=[out, out_grad, y_grad, x_grad]) res = self.exe.run(prog, fetch_list=[out, out_grad, y_grad, x_grad])
self.assertEqual(res[0].shape, shape[3]) self.assertEqual(res[0].shape, shape[3])
...@@ -1516,21 +1757,33 @@ class TestSundryAPIStatic(unittest.TestCase): ...@@ -1516,21 +1757,33 @@ class TestSundryAPIStatic(unittest.TestCase):
@prog_scope() @prog_scope()
def test_repeat_interleave(self): def test_repeat_interleave(self):
x = paddle.full([], 1.0, 'float32') x1 = paddle.full([], 1.0, 'float32')
out = paddle.repeat_interleave(x, 2, None) x1.stop_gradient = False
paddle.static.append_backward(out) out1 = paddle.repeat_interleave(x1, 2, None)
paddle.static.append_backward(out1.sum())
prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out])
self.assertEqual(res[0].shape, (2,))
x2 = paddle.full([], 1.0, 'float32')
x2.stop_gradient = False
repeats = paddle.to_tensor([3], dtype='int32') repeats = paddle.to_tensor([3], dtype='int32')
out = paddle.repeat_interleave(x, repeats, None) out2 = paddle.repeat_interleave(x2, repeats, None)
paddle.static.append_backward(out) paddle.static.append_backward(out2.sum())
prog = paddle.static.default_main_program() prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out]) block = prog.global_block()
self.assertEqual(res[0].shape, (3,)) x1_grad = block.var(grad_var_name(x1.name))
x2_grad = block.var(grad_var_name(x2.name))
out1_grad = block.var(grad_var_name(out1.name))
out2_grad = block.var(grad_var_name(out2.name))
res = self.exe.run(
prog,
fetch_list=[out1, out2, x1_grad, x2_grad, out1_grad, out2_grad],
)
self.assertEqual(res[0].shape, (2,))
self.assertEqual(res[1].shape, (3,))
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())
self.assertEqual(res[4].shape, (2,))
self.assertEqual(res[5].shape, (3,))
# Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest. # Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest.
......
python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py
浏览文件 @ 5fd115f3
...@@ -17,9 +17,11 @@ import unittest ...@@ -17,9 +17,11 @@ import unittest
import numpy as np import numpy as np
import paddle import paddle
import paddle.fluid as fluid
import paddle.nn.functional as F import paddle.nn.functional as F
paddle.set_device('xpu') paddle.set_device('xpu')
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
unary_api_list = [ unary_api_list = [
...@@ -100,9 +102,7 @@ class TestUnaryAPI(unittest.TestCase): ...@@ -100,9 +102,7 @@ class TestUnaryAPI(unittest.TestCase):
for api in unary_api_list: for api in unary_api_list:
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
x.retain_grads()
out = api(x) out = api(x)
out.retain_grads()
out.backward() out.backward()
self.assertEqual(x.shape, []) self.assertEqual(x.shape, [])
...@@ -138,25 +138,22 @@ reduce_api_list = [ ...@@ -138,25 +138,22 @@ reduce_api_list = [
# Use to test zero-dim of reduce API # Use to test zero-dim of reduce API
class TestReduceAPI(unittest.TestCase): class TestReduceAPI(unittest.TestCase):
def test_dygraph(self): def test_dygraph_reduce(self):
paddle.disable_static() paddle.disable_static()
for api in reduce_api_list: for api in reduce_api_list:
# 1) x is 0D
if api in [paddle.all, paddle.any]: if api in [paddle.all, paddle.any]:
x = paddle.randint(0, 2, []).astype('bool') x = paddle.randint(0, 2, []).astype('bool')
out = api(x, None)
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
else: else:
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
x.retain_grads() out = api(x, None)
out = api(x, None) out.backward()
out.retain_grads()
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(x.grad.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, []) self.assertEqual(out.grad.shape, [])
paddle.enable_static() paddle.enable_static()
...@@ -196,29 +193,28 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -196,29 +193,28 @@ class TestBinaryAPI(unittest.TestCase):
def test_dygraph_binary(self): def test_dygraph_binary(self):
paddle.disable_static() paddle.disable_static()
for api in binary_api_list: for api in binary_api_list:
# 1) x/y is 0D # 1) x is 0D, y is 0D
x = paddle.rand([]) x = paddle.rand([])
y = paddle.rand([]) y = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
y.stop_gradient = False y.stop_gradient = False
x.retain_grads()
y.retain_grads()
if isinstance(api, dict): if isinstance(api, dict):
out = api['func'](x, y) out = api['func'](x, y)
out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y) out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y)
np.testing.assert_array_equal(out_cls.numpy(), out.numpy()) np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else: else:
out = api(x, y) out = api(x, y)
out.retain_grads()
self.assertEqual(out.shape, [])
out.backward() out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(y.shape, [])
self.assertEqual(out.shape, [])
if x.grad is not None: if x.grad is not None:
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertEqual(y.grad.shape, []) self.assertEqual(y.grad.shape, [])
self.assertEqual(out.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]) x = paddle.rand([2, 3, 4])
y = paddle.rand([]) y = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
...@@ -229,16 +225,17 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -229,16 +225,17 @@ class TestBinaryAPI(unittest.TestCase):
np.testing.assert_array_equal(out_cls.numpy(), out.numpy()) np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else: else:
out = api(x, y) out = api(x, y)
out.retain_grads()
self.assertEqual(out.shape, [2, 3, 4])
out.backward() 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: if x.grad is not None:
self.assertEqual(x.grad.shape, [2, 3, 4]) self.assertEqual(x.grad.shape, [2, 3, 4])
self.assertEqual(y.grad.shape, []) self.assertEqual(y.grad.shape, [])
self.assertEqual(out.grad.shape, [2, 3, 4]) 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([]) x = paddle.rand([])
y = paddle.rand([2, 3, 4]) y = paddle.rand([2, 3, 4])
x.stop_gradient = False x.stop_gradient = False
...@@ -249,10 +246,11 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -249,10 +246,11 @@ class TestBinaryAPI(unittest.TestCase):
np.testing.assert_array_equal(out_cls.numpy(), out.numpy()) np.testing.assert_array_equal(out_cls.numpy(), out.numpy())
else: else:
out = api(x, y) out = api(x, y)
out.retain_grads()
self.assertEqual(out.shape, [2, 3, 4])
out.backward() 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: if x.grad is not None:
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertEqual(y.grad.shape, [2, 3, 4]) self.assertEqual(y.grad.shape, [2, 3, 4])
...@@ -260,26 +258,32 @@ class TestBinaryAPI(unittest.TestCase): ...@@ -260,26 +258,32 @@ class TestBinaryAPI(unittest.TestCase):
# 4) x is 0D , y is scalar # 4) x is 0D , y is scalar
x = paddle.rand([]) x = paddle.rand([])
y = 0.5
x.stop_gradient = False x.stop_gradient = False
y = 0.5
if isinstance(api, dict): if isinstance(api, dict):
out = getattr(paddle.Tensor, api['cls_method'])(x, y) out = getattr(paddle.Tensor, api['cls_method'])(x, y)
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.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: for api in binary_int_api_list:
# 1) x/y is 0D # 1) x is 0D, y is 0D
x = paddle.randint(-10, 10, []) x = paddle.randint(-10, 10, [])
y = paddle.randint(-10, 10, []) y = paddle.randint(-10, 10, [])
out = api(x, y) out = api(x, y)
self.assertEqual(out.shape, []) 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]) x = paddle.randint(-10, 10, [3, 5])
y = paddle.randint(-10, 10, []) y = paddle.randint(-10, 10, [])
out = api(x, y) out = api(x, y)
self.assertEqual(out.shape, [3, 5]) 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, []) x = paddle.randint(-10, 10, [])
y = paddle.randint(-10, 10, [3, 5]) y = paddle.randint(-10, 10, [3, 5])
out = api(x, y) out = api(x, y)
...@@ -374,9 +378,7 @@ class TestSundryAPI(unittest.TestCase): ...@@ -374,9 +378,7 @@ class TestSundryAPI(unittest.TestCase):
def test_pow_factor(self): def test_pow_factor(self):
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
x.retain_grads()
out = paddle.pow(x, 2.0) out = paddle.pow(x, 2.0)
out.retain_grads()
out.backward() out.backward()
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
...@@ -386,9 +388,7 @@ class TestSundryAPI(unittest.TestCase): ...@@ -386,9 +388,7 @@ class TestSundryAPI(unittest.TestCase):
def test_cast(self): def test_cast(self):
x = paddle.full([], 1.0, 'float32') x = paddle.full([], 1.0, 'float32')
x.stop_gradient = False x.stop_gradient = False
x.retain_grads()
out = paddle.cast(x, 'int32') out = paddle.cast(x, 'int32')
out.retain_grads()
out.backward() out.backward()
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
...@@ -399,7 +399,6 @@ class TestSundryAPI(unittest.TestCase): ...@@ -399,7 +399,6 @@ class TestSundryAPI(unittest.TestCase):
x = paddle.uniform([], None, -10, 10) x = paddle.uniform([], None, -10, 10)
x.stop_gradient = False x.stop_gradient = False
out = paddle.clip(x, -5, 5) out = paddle.clip(x, -5, 5)
out.retain_grads()
out.backward() out.backward()
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
...@@ -444,11 +443,11 @@ class TestSundryAPI(unittest.TestCase): ...@@ -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) x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0], stop_gradient=False)
index = paddle.full([], 2, 'int64') index = paddle.full([], 2, 'int64')
out = paddle.gather(x, index) out = paddle.gather(x, index)
out.retain_grads()
out.backward() out.backward()
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
self.assertEqual(out.numpy(), 5) self.assertEqual(out.numpy(), 5)
self.assertEqual(x.grad.shape, [5])
self.assertEqual(out.grad.shape, []) self.assertEqual(out.grad.shape, [])
def test_gather_xD_axis_0(self): def test_gather_xD_axis_0(self):
...@@ -457,61 +456,62 @@ class TestSundryAPI(unittest.TestCase): ...@@ -457,61 +456,62 @@ class TestSundryAPI(unittest.TestCase):
) )
index = paddle.full([], 1, 'int64') index = paddle.full([], 1, 'int64')
out = paddle.gather(x, index) out = paddle.gather(x, index)
out.retain_grads()
out.backward() out.backward()
self.assertEqual(out.shape, [3]) self.assertEqual(out.shape, [3])
for i in range(3): np.testing.assert_array_equal(out.numpy(), x.numpy()[1, :])
self.assertEqual(out.numpy()[i], x.numpy()[1][i]) self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.grad.shape, [3]) self.assertEqual(out.grad.shape, [3])
def test_gather_xD_axis_1(self): 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') index = paddle.full([], 1, 'int64')
out = paddle.gather(x, index, axis=1) out = paddle.gather(x, index, axis=1)
out.backward()
self.assertEqual(out.shape, [2]) self.assertEqual(out.shape, [2])
for i in range(2): np.testing.assert_array_equal(out.numpy(), [2.0, 5.0])
self.assertEqual(out.numpy()[i], x.numpy()[i][1]) self.assertEqual(x.grad.shape, [2, 3])
self.assertEqual(out.grad.shape, [2])
def test_scatter_1D(self): 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') index = paddle.full([], 2, 'int64')
updates = paddle.full([], 4.0) updates = paddle.full([], 4.0)
out = paddle.scatter(x, index, updates) out = paddle.scatter(x, index, updates)
out.backward()
self.assertEqual(out.shape, [5])
self.assertEqual(out.numpy()[2], 4) self.assertEqual(out.numpy()[2], 4)
self.assertEqual(out.grad.shape, [5])
def test_scatter_XD(self): 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') index = paddle.full([], 1, 'int64')
updates = paddle.to_tensor([1.0, 2.0, 3.0]) updates = paddle.to_tensor([1.0, 2.0, 3.0])
out = paddle.scatter(x, index, updates) out = paddle.scatter(x, index, updates)
out.backward()
for i in range(3): self.assertEqual(out.shape, [2, 3])
self.assertEqual(out.numpy()[1][i], updates.numpy()[i]) 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): def test_diagflat(self):
x1 = paddle.rand([]) x1 = paddle.rand([])
x2 = paddle.rand([]) x2 = paddle.rand([])
x3 = paddle.rand([]) x3 = paddle.rand([])
x1.stop_gradient = False x1.stop_gradient = False
x2.stop_gradient = False x2.stop_gradient = False
x3.stop_gradient = False x3.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
x3.retain_grads()
out1 = paddle.diagflat(x1, 1) out1 = paddle.diagflat(x1, 1)
out2 = paddle.diagflat(x2, -1) out2 = paddle.diagflat(x2, -1)
out3 = paddle.diagflat(x3, 0) out3 = paddle.diagflat(x3, 0)
out1.retain_grads()
out2.retain_grads()
out3.retain_grads()
out1.backward() out1.backward()
out2.backward() out2.backward()
out3.backward() out3.backward()
...@@ -541,9 +541,7 @@ class TestSundryAPI(unittest.TestCase): ...@@ -541,9 +541,7 @@ class TestSundryAPI(unittest.TestCase):
index = paddle.full([], 1, 'int64') index = paddle.full([], 1, 'int64')
updates = paddle.to_tensor([1.0, 2.0, 3.0]) updates = paddle.to_tensor([1.0, 2.0, 3.0])
out = paddle.scatter_(x, index, updates) out = paddle.scatter_(x, index, updates)
np.testing.assert_array_equal(out.numpy()[1], [1.0, 2.0, 3.0])
for i in range(3):
self.assertEqual(out.numpy()[1][i], updates.numpy()[i])
def test_flatten(self): def test_flatten(self):
x = paddle.full([], 1, 'float32') x = paddle.full([], 1, 'float32')
...@@ -561,9 +559,7 @@ class TestSundryAPI(unittest.TestCase): ...@@ -561,9 +559,7 @@ class TestSundryAPI(unittest.TestCase):
def test_scale(self): def test_scale(self):
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
x.retain_grads()
out = paddle.scale(x, scale=2.0, bias=1.0) out = paddle.scale(x, scale=2.0, bias=1.0)
out.retain_grads()
out.backward() out.backward()
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
...@@ -598,31 +594,26 @@ class TestSundryAPI(unittest.TestCase): ...@@ -598,31 +594,26 @@ class TestSundryAPI(unittest.TestCase):
def test_reshape_list(self): def test_reshape_list(self):
x = paddle.rand([]) x = paddle.rand([])
x.stop_gradient = False x.stop_gradient = False
x.retain_grads()
out = paddle.reshape(x, []) out = paddle.reshape(x, [])
out.retain_grads()
out.backward() out.backward()
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, []) self.assertEqual(out.grad.shape, [])
out = paddle.reshape(x, [1]) out = paddle.reshape(x, [1])
out.retain_grads()
out.backward() out.backward()
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [1]) self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1]) self.assertEqual(out.grad.shape, [1])
out = paddle.reshape(x, [-1]) out = paddle.reshape(x, [-1])
out.retain_grads()
out.backward() out.backward()
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [1]) self.assertEqual(out.shape, [1])
self.assertEqual(out.grad.shape, [1]) self.assertEqual(out.grad.shape, [1])
out = paddle.reshape(x, [-1, 1]) out = paddle.reshape(x, [-1, 1])
out.retain_grads()
out.backward() out.backward()
self.assertEqual(x.grad.shape, []) self.assertEqual(x.grad.shape, [])
self.assertEqual(out.shape, [1, 1]) self.assertEqual(out.shape, [1, 1])
...@@ -631,26 +622,22 @@ class TestSundryAPI(unittest.TestCase): ...@@ -631,26 +622,22 @@ class TestSundryAPI(unittest.TestCase):
def test_reshape_tensor(self): def test_reshape_tensor(self):
x = paddle.rand([1, 1]) x = paddle.rand([1, 1])
x.stop_gradient = False x.stop_gradient = False
x.retain_grads()
out = paddle.reshape(x, []) out = paddle.reshape(x, [])
out.retain_grads()
out.backward() out.backward()
self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, []) self.assertEqual(out.shape, [])
self.assertEqual(out.grad.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 = paddle.reshape(x, new_shape)
out.retain_grads()
out.backward() out.backward()
self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1]) self.assertEqual(out.shape, [1, 1, 1])
self.assertEqual(out.grad.shape, [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 = paddle.reshape(x, new_shape)
out.retain_grads()
out.backward() out.backward()
self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1]) self.assertEqual(out.shape, [1])
...@@ -658,7 +645,6 @@ class TestSundryAPI(unittest.TestCase): ...@@ -658,7 +645,6 @@ class TestSundryAPI(unittest.TestCase):
new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")] new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
out = paddle.reshape(x, new_shape) out = paddle.reshape(x, new_shape)
out.retain_grads()
out.backward() out.backward()
self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(x.grad.shape, [1, 1])
self.assertEqual(out.shape, [1, 1]) self.assertEqual(out.shape, [1, 1])
...@@ -700,13 +686,9 @@ class TestSundryAPI(unittest.TestCase): ...@@ -700,13 +686,9 @@ class TestSundryAPI(unittest.TestCase):
x2 = paddle.rand([]) x2 = paddle.rand([])
x1.stop_gradient = False x1.stop_gradient = False
x2.stop_gradient = False x2.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
out1 = paddle.sort(x1, axis=-1) out1 = paddle.sort(x1, axis=-1)
out2 = paddle.sort(x2, axis=0) out2 = paddle.sort(x2, axis=0)
out1.retain_grads()
out2.retain_grads()
out1.backward() out1.backward()
out2.backward() out2.backward()
...@@ -727,13 +709,9 @@ class TestSundryAPI(unittest.TestCase): ...@@ -727,13 +709,9 @@ class TestSundryAPI(unittest.TestCase):
x2 = paddle.rand([]) x2 = paddle.rand([])
x1.stop_gradient = False x1.stop_gradient = False
x2.stop_gradient = False x2.stop_gradient = False
x1.retain_grads()
x2.retain_grads()
out1 = paddle.argsort(x1, axis=-1) out1 = paddle.argsort(x1, axis=-1)
out2 = paddle.argsort(x2, axis=0) out2 = paddle.argsort(x2, axis=0)
out1.retain_grads()
out2.retain_grads()
out1.backward() out1.backward()
out2.backward() out2.backward()
......
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