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PR 10072 fix + new sts

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lintfix

doc fix + code cleaned

doc update 2
pull/10073/head
danishnxt 4 years ago
parent a7ca8a4c1d
commit b0b768335e
2 changed files with 92 additions and 10 deletions
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22
mindspore/ops/composite/math_ops.py
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@ -167,11 +167,17 @@ def _validate_axes(x1_shape, x2_shape, axes):
raise ValueError(f"axes for input: {ix_input + 1} contains index: "
f"{x_value}, but range is: [{min_val}, {max_val}]")
# axis value input compatibility
# check axis value with input shape - both ways for axis valid
invalid_a = False
invalid_b = False
for i in range(len(axes[0])): # sizes already validated
if x1_shape[axes[0][i]] != x2_shape[axes[1][i]]:
raise ValueError(
"Given Axes are incompatible with given input arrays")
invalid_a = True
if x1_shape[axes[0][i]] != x2_shape[axes[1][len(axes[0])-1-i]]:
invalid_b = True
if invalid_a and invalid_b:
raise ValueError("Given Axes are incompatible with given input arrays")
@constexpr
def _calc_new_shape(shape, axes, position=0):
@ -200,16 +206,18 @@ def tensor_dot(x1, x2, axes):
Selected dims in both inputs must also match.
axes = 0 leads to outer product, and axes = 1 leads to normal matrix multiplication.
axes = 1 is the same as axes = ((0,),(1,) where length of input shape is 2 for both `a` and `b`
axes = 2 is the same as axes = ((0,1),(1,2)) where length of input shape is 3 for both `a` and `b`
axes = 0 leads to outer product
axes = 1 leads to normal matrix multiplication when inputs both 2D.
axes = 1 is the same as axes = ((1,),(0,) where both `a` and `b` are 2D.
axes = 2 is the same as axes = ((1,2),(0,1)) where both `a` and `b` are 3D.
Inputs:
- **x1** (Tensor) - First tensor in tensor_dot with datatype float16 or float32
- **x2** (Tensor) - Second tensor in tensor_dot with datatype float16 or float32
- **axes** (Union[int, tuple(int), tuple(tuple(int)), list(list(int))]) - Single value or
tuple/list of length 2 with dimensions specified for `a` and `b` each. If single value `N` passed,
automatically picks up first N dims from `a` input shape and last N dims from `b` input shape.
automatically picks up last N dims from `a` input shape and first N dims from `b` input shape in order
as axes for each respectively.
Outputs:
Tensor, the shape of the output tensor is :math:`(N + M)`. Where :math:`N` and :math:`M` are the free axes not

80
tests/st/ops/gpu/test_tensordot_op.py
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@ -188,6 +188,83 @@ def test_tensor_dot_outer():
np.testing.assert_array_almost_equal(ms_result_np, np_result)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tensor_dot_reverse_axes():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
np.random.seed(2746)
shape_x1 = (1, 2, 3)
shape_x2 = (1, 2, 3)
axes = ((1, 0), (0, 1))
x1 = np.random.random(shape_x1).astype(np.float32)
x2 = np.random.random(shape_x2).astype(np.float32)
x1_tensor = Tensor(x1, dtype=mindspore.float32)
x2_tensor = Tensor(x2, dtype=mindspore.float32)
network = NetTensorDot(axes)
ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
expected_result = np.array([[0.58561826, 0.21897982, 0.906598],
[0.19630799, 0.10731681, 0.2680981],
[0.8003185, 0.457294, 1.0721111]]).astype(np.float32)
np.testing.assert_array_almost_equal(ms_result_np, expected_result)
np.random.seed(1526)
shape_x1 = (1, 2, 3, 4, 5)
shape_x2 = (1, 2, 3)
axes = ((0, 2), (2, 0))
x1 = np.random.random(shape_x1).astype(np.float32)
x2 = np.random.random(shape_x2).astype(np.float32)
x1_tensor = Tensor(x1, dtype=mindspore.float32)
x2_tensor = Tensor(x2, dtype=mindspore.float32)
network = NetTensorDot(axes)
ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
expected_result = np.array([[[[1.106365, 0.9736746],
[0.91042125, 0.7814131],
[0.5576207, 0.327488],
[0.93404585, 0.7108171],
[1.078351, 0.87405884]],
[[1.1720579, 0.9948833],
[1.1594493, 1.0185612],
[0.7251004, 0.60322404],
[0.4724398, 0.2930961],
[0.9711088, 0.8482977]],
[[1.4110168, 1.1171235],
[0.81948525, 0.778057],
[0.7914786, 0.78767675],
[0.77509344, 0.6020987],
[0.8986199, 0.7100061]],
[[0.7270926, 0.35752398],
[0.5529937, 0.31682697],
[0.73876995, 0.48478222],
[0.96520174, 0.73099715],
[0.96569407, 0.8556314]]],
[[[1.2093457, 0.90222925],
[1.3758272, 0.8189213],
[1.2997738, 1.045748],
[1.1460838, 0.67475325],
[0.95835257, 0.67791444]],
[[0.84732395, 0.8058369],
[1.1979935, 0.57202166],
[0.2577264, 0.22021212],
[0.8855853, 0.5440637],
[0.8993537, 0.4622679]],
[[0.6797033, 0.58302796],
[0.7820443, 0.49587217],
[0.64423263, 0.5469],
[1.0270302, 0.5271675],
[1.0278721, 0.9446807]],
[[1.2069539, 1.0113767],
[0.86160654, 0.7664283],
[0.9797001, 0.7087945],
[0.47638205, 0.4660839],
[0.6920749, 0.36285543]]]]).astype(np.float32)
np.testing.assert_array_almost_equal(ms_result_np, expected_result)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
@ -218,17 +295,14 @@ def test_tensor_dot_backprop():
[2.9727, 1.4873],
[1.7910, 3.4727],
[2.4160, 1.7227]],
[[2.5547, 2.5039],
[3.4062, 2.3320],
[2.6270, 3.1543],
[2.1406, 1.7666]]])
expect_dx2 = np.array([[[2.1523, 2.9199, 0.8350],
[2.0254, 2.7734, 1.3213]],
[[2.6836, 2.4707, 1.0156],
[2.9746, 3.0254, 1.9199]],
[[1.8545, 1.7803, 1.3457],
[2.2676, 2.1797, 1.2764]]])
np.allclose(dx1, expect_dx1)

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