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Merge branch 'add_0d_output_support_for_det' of github.com:GGBond8488…
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…/Paddle into add_0d_output_support_for_det
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@GGBond8488
GGBond8488 committed Apr 24, 2023
2 parents fd10479 + 6269092 commit 55b8046
Showing 1 changed file with 85 additions and 75 deletions.
160 changes: 85 additions & 75 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 @@ -2441,13 +2441,12 @@ def test_linalg_norm(self):
# 1D input, p = fro ,axis = None, using reduceInferMeta
x_1 = paddle.arange(24, dtype="float32") - 12
x_1.stop_gradient = False
# using frobenius_norm, depends on reduce inferMeta support 0d output
# out_1 = paddle.linalg.norm(x_1)
# out_1.retain_grads()
# out_1.backward()
out_1 = paddle.linalg.norm(x_1)
out_1.retain_grads()
out_1.backward()

# self.assertEqual(out_1.shape, [])
# self.assertTrue(x_1.grad.shape, [24])
self.assertEqual(out_1.shape, [])
self.assertTrue(x_1.grad.shape, [24])

# 1D input, p = 1 ,axis = None,
# using p_nrom, as_vector = True
Expand Down Expand Up @@ -2483,45 +2482,46 @@ def test_linalg_norm(self):
self.assertEqual(x_2_fro.grad.shape, [24])

# 2D input, p = 1, axis = [0, 1]
# using p_matrix_norm ,depends on abs, pow, sum
# using p_matrix_norm ,depends on paddle.sum
# x_3 = x_2.reshape([4, 6])
# x_3.stop_gradient = False
# out_3 = paddle.linalg.norm(x_3, p = 1, axis=[0,1])
# out_3.retain_grads()
# out_3.backward()

# self.assertEqual(out_3.shape, [])
# self.assertEqual(x_3.grad.shape, [4, 6])

# 2D input, p = 1, axis = None
# using p_matrix_norm, depends on paddle.sum
# x_4 = x_2.reshape([4, 6])
# x_4.stop_gradient = False
# out_4 = paddle.linalg.norm(x_4)
# out_4.retain_grads()
# out_4.backward()

# self.assertEqual(out_4.shape, [])
# self.assertEqual(x_4.grad.shape, [4, 6])

# 2D input, p = inf, axis = None
# using p_matrix_norm, depends on paddle.max, paddle.min
# x_5 = x_2.reshape([4, 6])
# out_5 = paddle.linalg.norm(x_5)
# 2D input, p = inf, axis = [0, 1]
# using p_matrix_norm, depends on paddle.sum
# x_5 = paddle.arange(24, dtype="float32").reshape([4, 6])
# x_5.stop_gradient = False
# out_5 = paddle.linalg.norm(x_5, p=2, axis = [0, 1])
# out_5.retain_grads()
# out_5.backward()

# self.assertEqual(out_5.shape, [])
# self.assertEqual(x_5.grad.shape, [4, 6])

# 2D input, p = -inf, axis = [0, 1]
# using inf_norm, depends on paddle.max, paddle.min, paddle.abs
# x_6 = x_2.reshape([4, 6])
# out_6 = paddle.linalg.norm(x_6, p = -float("inf"), axis = [0, 1])
# out_6.retain_grads()
# out_6.backward()
# using inf_norm
x_6 = paddle.arange(24, dtype="float32").reshape([4, 6])
x_6.stop_gradient = False
out_6 = paddle.linalg.norm(x_6, p=-float("inf"), axis=[0, 1])
out_6.retain_grads()
out_6.backward()

# self.assertEqual(out_6.shape, [])
# self.assertEqual(x_6.grad.shape, [4, 6])
self.assertEqual(out_6.shape, [])
self.assertEqual(x_6.grad.shape, [4, 6])

def test_cov(self):
xt = paddle.randn((3, 4))
Expand Down Expand Up @@ -2572,45 +2572,51 @@ def test_dist(self):
self.assertEqual(x.grad.shape, [2, 2])
self.assertEqual(y.grad.shape, [2, 2])

def test_cond(self):
pass
# def assert_shape(out):
# self.assertEqual(out.shape, [])
def test_linalg_cond(self):
def assert_shape(out):
self.assertEqual(out.shape, [])

# x = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
# x.stop_gradient = False
# p = 2 : use paddle.sum, paddle.max, paddle.min
# out = paddle.linalg.cond(x)
# x1 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
# x1.stop_gradient = False
# p = 2 : use paddle.sum
# out = paddle.linalg.cond(x1)
# assert_shape(out)

# p = fro : use paddle.sum
# out_fro = paddle.linalg.cond(x, p='fro')
# x2 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
# x2.stop_gradient = False
# out_fro = paddle.linalg.cond(x2, p='fro')
# assert_shape(out_fro)

# p = nuc : use paddle.sum, paddle.max, paddle.min
# out_nuc = paddle.linalg.cond(x, p='nuc')
# p = nuc : use paddle.sum
# x3 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
# x3.stop_gradient = False
# out_nuc = paddle.linalg.cond(x3, p='nuc')
# assert_shape(out_nuc)

# p in (-1, 1) : use paddle.sum, paddle.max, paddle.min
# out_1 = paddle.linalg.cond(x, p=1)
# p in (-1, 1) : use paddle.sum
# x4 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
# x4.stop_gradient = False
# out_1 = paddle.linalg.cond(x4, p=1)
# assert_shape(out_1)
# out_minus_1 = paddle.linalg.cond(x, p=-1)
# x5 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
# x5.stop_gradient = False
# out_minus_1 = paddle.linalg.cond(x5, p=-1)
# assert_shape(out_minus_1)

# p in (-2, 2) :use paddle.max, paddle.min
# out_2 = paddle.linalg.cond(x, p=2)
# p in (-2, 2) depends on paddle.sum
# x6 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
# x6.stop_gradient = False
# out_2 = paddle.linalg.cond(x6, p=2)
# assert_shape(out_2)
# out_minus_2 = paddle.linalg.cond(x, p=-2)
# assert_shape(out_minus_2)

# p in (-inf, inf):use paddle.sum, paddle.max, paddle.min
# out_inf = paddle.linalg.cond(x, p=float("inf"))
# p in (-inf, inf):use paddle.sum
# x8 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
# x8.stop_gradient = False
# out_inf = paddle.linalg.cond(x8, p=float("inf"))
# assert_shape(out_inf)
# out_minus_inf = paddle.linalg.cond(x, p=-float("inf"))
# assert_shape(out_minus_inf)
# out_minus_inf.backward()
# self.assertTrue(x.grad.shape, [3, 3])

# depends on paddle.sum
# a = paddle.randn([2, 4, 4])
# a.stop_gradient = False
# a_cond_fro = paddle.linalg.cond(a, p='fro')
Expand Down Expand Up @@ -4622,13 +4628,14 @@ def test_linalg_norm(self):
# 1D input, p = fro ,axis = None, using reduceInferMeta
x_1 = paddle.arange(24, dtype="float32") - 12
x_1.stop_gradient = False
# using frobenius_norm, depends on reduce inferMeta support 0d output
# out_1 = paddle.linalg.norm(x_1)
# out_1.retain_grads()
# out_1.backward()
out_1 = paddle.linalg.norm(x_1)
paddle.static.append_backward(out_1)

# self.assertEqual(out_1.shape, [])
# self.assertTrue(x_1.grad.shape, [24])
prog = paddle.static.default_main_program()

res = self.exe.run(prog, fetch_list=[out_1, x_1.grad_name])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, (24,))

# 1D input, p = 1 ,axis = None,
# using p_nrom, as_vector = True
Expand Down Expand Up @@ -4667,45 +4674,48 @@ def test_linalg_norm(self):
self.assertEqual(res[1].shape, (24,))

# 2D input, p = 1, axis = [0, 1]
# using p_matrix_norm ,depends on abs, pow, sum
# using p_matrix_norm ,depends on paddle.sum
# x_3 = x_2.reshape([4, 6])
# x_3.stop_gradient = False
# out_3 = paddle.linalg.norm(x_3, p = 1, axis=[0,1])
# out_3.retain_grads()
# out_3.backward()
# paddle.static.append_backward(out_3.sum())

# self.assertEqual(out_3.shape, [])
# self.assertEqual(x_3.grad.shape, [4, 6])
# prog = paddle.static.default_main_program()
# res = self.exe.run(prog, fetch_list=[out_3, x_3.grad_name])
# self.assertEqual(res[0].shape, ())
# self.assertEqual(res[1].shape, (4, 6))

# 2D input, p = 1, axis = None
# using p_matrix_norm, depends on paddle.sum
# x_4 = x_2.reshape([4, 6])
# out_4 = paddle.linalg.norm(x_4)
# out_4.retain_grads()
# out_4.backward()
# paddle.static.append_backward(out_4.sum())

# self.assertEqual(out_4.shape, [])
# self.assertEqual(x_4.grad.shape, [4, 6])
# prog = paddle.static.default_main_program()
# res = self.exe.run(prog, fetch_list=[out_4, x_4.grad_name])

# self.assertEqual(res[0].shape, ())
# self.assertEqual(res[1].shape, (4, 6))

# 2D input, p = inf, axis = None
# using p_matrix_norm, depends on paddle.max, paddle.min
# x_5 = x_2.reshape([4, 6])
# out_5 = paddle.linalg.norm(x_5)
# out_5.retain_grads()
# out_5.backward()
x_5 = x_2.reshape([4, 6])
out_5 = paddle.linalg.norm(x_5)
paddle.static.append_backward(out_5.sum())
prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out_5, x_5.grad_name])

# self.assertEqual(out_5.shape, [])
# self.assertEqual(x_5.grad.shape, [4, 6])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, (4, 6))

# 2D input, p = -inf, axis = [0, 1]
# using inf_norm, depends on paddle.max, paddle.min, paddle.abs
# x_6 = x_2.reshape([4, 6])
# out_6 = paddle.linalg.norm(x_6, p = -float("inf"), axis = [0, 1])
# out_6.retain_grads()
# out_6.backward()
x_6 = x_2.reshape([4, 6])
out_6 = paddle.linalg.norm(x_6, p=-float("inf"), axis=[0, 1])
paddle.static.append_backward(out_6.sum())
prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out_6, x_6.grad_name])

# self.assertEqual(out_6.shape, [])
# self.assertEqual(x_6.grad.shape, [4, 6])
self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, (4, 6))

@prog_scope()
def test_cov(self):
Expand Down Expand Up @@ -4752,9 +4762,9 @@ def test_dist(self):
np.testing.assert_array_equal(res[0], np.array(2).astype(np.float32))

@prog_scope()
def test_cond(self):
def test_linalg_cond(self):
pass
# use paddle.sum, paddle.max, paddle.min
# use paddle.sum
# x = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
# x.stop_gradient = False
# out = paddle.linalg.cond(x)
Expand Down

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