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[Zero-Dim] Support output 0D for squeeze, unbind, unstack. #52843

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merged 12 commits into from
Apr 28, 2023
Merged

[Zero-Dim] Support output 0D for squeeze, unbind, unstack. #52843

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c1d0875
test=allcase
zhengqiwen1997 Apr 12, 2023
b8d7816
test=allcase
zhengqiwen1997 Apr 13, 2023
5744bf6
test=allcase
zhengqiwen1997 Apr 13, 2023
9e162b4
test=allcase
zhengqiwen1997 Apr 13, 2023
f4cef7f
test=allcase
zhengqiwen1997 Apr 17, 2023
a872938
fix test cases, test=allcase
zhengqiwen1997 Apr 23, 2023
9b35eaf
fix test cases, test=allcase
zhengqiwen1997 Apr 23, 2023
4be61b0
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
zhengqiwen1997 Apr 24, 2023
bd0aef1
modify the test_squeeze to not use Tensor type axis, test=allcase
zhengqiwen1997 Apr 24, 2023
e203149
add grad check for unbind and unstack, test=allcase
zhengqiwen1997 Apr 24, 2023
531e35d
check for squeeze axis tensor type, test=allcase
zhengqiwen1997 Apr 24, 2023
fce4037
fix bug, test=allcase
zhengqiwen1997 Apr 27, 2023
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3 changes: 3 additions & 0 deletions paddle/phi/infermeta/unary.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -3761,6 +3761,9 @@ void SqueezeInferMeta(const MetaTensor& x,
if (!config.is_runtime && axes.FromTensor()) {
// compile time infershape, set all elements to -1.
int output_size = x.dims().size() - axes.GetData().size();
if (x.dims().size() == 0 && output_size == -1) {
output_size = 0;
}
std::vector<int64_t> vec_out_dims(output_size, -1);
out->set_dims(phi::make_ddim(vec_out_dims));
} else {
Expand Down
142 changes: 142 additions & 0 deletions python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2292,6 +2292,56 @@ def test_upsample(self):
self.assertEqual(out1.shape, [2, 3, 12, 12])
self.assertEqual(input_x.grad.shape, [2, 3, 6, 6])

def test_unstack(self):
x1 = paddle.full([1], 0)
x2 = paddle.full([2], 2)
x1.retain_grads()
x2.retain_grads()
x1.stop_gradient = False
x2.stop_gradient = False

[out1] = paddle.unstack(x1, 0)
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这个API有反向，需要测一下反向。x1.grad/x2.grad

out1.retain_grads()
out1.backward()
[out2_1, out2_2] = paddle.unstack(x2, 0)
out2 = paddle.add_n([out2_1, out2_2])
out2.retain_grads()
out2.backward()

self.assertEqual(out1.shape, [])
self.assertEqual(out1.numpy(), 0)

self.assertEqual(out2_1.shape, [])
self.assertEqual(out2_1.numpy(), 2)
self.assertEqual(out2_2.shape, [])
self.assertEqual(out2_2.numpy(), 2)
self.assertEqual(x2.grad.shape, [2])

def test_unbind(self):
x1 = paddle.full([1], 0)
x2 = paddle.full([2], 2)
x1.retain_grads()
x2.retain_grads()
x1.stop_gradient = False
x2.stop_gradient = False

[out1] = paddle.unbind(x1, 0)
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这个API有反向，需要测一下反向。x1.grad/x2.grad

out1.retain_grads()
out1.backward()
[out2_1, out2_2] = paddle.unbind(x2, 0)
out2 = paddle.add_n([out2_1, out2_2])
out2.retain_grads()
out2.backward()

self.assertEqual(out1.shape, [])
self.assertEqual(out1.numpy(), 0)

self.assertEqual(out2_1.shape, [])
self.assertEqual(out2_1.numpy(), 2)
self.assertEqual(out2_2.shape, [])
self.assertEqual(out2_2.numpy(), 2)
self.assertEqual(x2.grad.shape, [2])

def test_maseked_select(self):
x = paddle.rand([])
x.stop_gradient = False
Expand All @@ -2306,6 +2356,26 @@ def test_maseked_select(self):
self.assertEqual(x.grad.shape, [])
self.assertEqual(x.grad.numpy(), 1)

def test_squeeze(self):
x1 = paddle.full([], 2)
x1.stop_gradient = False
x1.retain_grads()
out1 = paddle.squeeze(x1, axis=0)
out1.retain_grads()
out1.backward()
self.assertEqual(out1.shape, [])
self.assertEqual(x1.grad.shape, [])

x2 = paddle.full([], 3)
x3 = paddle.full([1], 0, dtype='int32')
x2.stop_gradient = False
x2.retain_grads()
out2 = paddle.squeeze(x2, axis=x3)
out2.retain_grads()
out2.backward()
self.assertEqual(out2.shape, [])
self.assertEqual(x2.grad.shape, [])

def test_unsqueeze(self):
x1 = paddle.full([], 2)
x1.stop_gradient = False
Expand Down Expand Up @@ -4073,6 +4143,50 @@ def test_upsample(self):
self.assertEqual(res1[0].shape, (2, 3, 12, 12))
self.assertEqual(res1[1].shape, (2, 3, 6, 6))

@prog_scope()
def test_unstack(self):
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测试case同静态图

x1 = paddle.full([1], 0, 'float32')
x1.stop_gradient = False
out1 = paddle.unstack(x1, 0)
out1 = paddle.add_n(out1)
paddle.static.append_backward(out1)
prog = paddle.static.default_main_program()
res = self.exe.run(prog, feed={}, fetch_list=[out1, x1.grad_name])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, (1,))

x2 = paddle.full([2], 2, 'float32')
x2.stop_gradient = False
out2 = paddle.unstack(x2, 0)
out2_sum = paddle.add_n(out2)
paddle.static.append_backward(out2_sum)
prog = paddle.static.default_main_program()
res = self.exe.run(prog, feed={}, fetch_list=[out2_sum, x2.grad_name])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, (2,))

@prog_scope()
def test_unbind(self):
x1 = paddle.full([1], 0, 'float32')
x1.stop_gradient = False
out1 = paddle.unbind(x1, 0)
out1 = paddle.add_n(out1)
paddle.static.append_backward(out1)
prog = paddle.static.default_main_program()
res = self.exe.run(prog, feed={}, fetch_list=[out1, x1.grad_name])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, (1,))

x2 = paddle.full([2], 2, 'float32')
x2.stop_gradient = False
out2 = paddle.unbind(x2, 0)
out2_sum = paddle.add_n(out2)
paddle.static.append_backward(out2_sum)
prog = paddle.static.default_main_program()
res = self.exe.run(prog, feed={}, fetch_list=[out2_sum, x2.grad_name])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, (2,))

@prog_scope()
def test_maseked_select(self):
x = paddle.rand([])
Expand All @@ -4089,6 +4203,34 @@ def test_maseked_select(self):
self.assertEqual(res[3].shape, ())
self.assertEqual(res[3], 1)

@prog_scope()
def test_squeeze(self):
x1 = paddle.full([], 2)
x1.stop_gradient = False
out1 = paddle.squeeze(x1, axis=0)
paddle.static.append_backward(out1.sum())

x2 = paddle.full([], 3)
x3 = paddle.full([], 0, dtype='int32')
x2.stop_gradient = False
out2 = paddle.squeeze(x2, axis=x3)
paddle.static.append_backward(out2.sum())

prog = paddle.static.default_main_program()
res = self.exe.run(
prog,
fetch_list=[
out1,
out2,
x1.grad_name,
x2.grad_name,
],
)
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())
self.assertEqual(res[2].shape, ())
self.assertEqual(res[3].shape, ())

@prog_scope()
def test_unsqueeze(self):
x1 = paddle.full([], 2)
Expand Down