def _test_batch_matmul_backward(test_case, device):
input1 = flow.Tensor(
[
[
[
-0.0036776792258024216, 1.9946473836898804,
-0.423959881067276
],
[
1.0892143249511719, 0.04005361348390579,
-0.27883127331733704
],
],
[
[
-0.970306396484375, 0.017771577462553978,
0.019596196711063385
],
[
0.27402883768081665, -0.8192587494850159,
-0.3135920464992523
],
],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
input2 = flow.Tensor(
[
[
[1.118346929550171, -0.930071234703064],
[1.1238232851028442, 1.373764157295227],
[0.17178462445735931, -1.1010534763336182],
],
[
[0.6694859862327576, 0.9250285029411316],
[-1.0835869312286377, 0.4192655086517334],
[1.2616937160491943, 0.33809131383895874],
],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
of_out = flow.matmul(input1, input2)
of_out = of_out.sum()
of_out.backward()
np_grad = [
[
[0.18827569484710693, 2.4975874423980713, -0.9292688369750977],
[0.18827569484710693, 2.4975874423980713, -0.9292688369750977],
],
[
[1.5945144891738892, -0.6643214225769043, 1.5997850894927979],
[1.5945144891738892, -0.6643214225769043, 1.5997850894927979],
],
]
test_case.assertTrue(
np.allclose(input1.grad.numpy(), np_grad, atol=1e-05, rtol=1e-05))
def _test_matmul_backward_y_grad(test_case, device):
input1 = flow.Tensor(
[
[-1.8604081869125366, -2.0019688606262207],
[1.0511547327041626, -2.263841390609741],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=False,
)
input2 = flow.Tensor(
[
[-0.13973912596702576, 0.8478717803955078],
[-0.2144828885793686, -1.7145386934280396],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
of_out = flow.matmul(input1, input2)
of_out = of_out.sum()
of_out.backward()
print(input2.grad.numpy().tolist())
np_grad = [
[-0.809253454208374, -0.809253454208374],
[-4.265810012817383, -4.265810012817383],
]
test_case.assertTrue(
np.allclose(input2.grad.numpy(), np_grad, atol=1e-05, rtol=1e-05))
def _test_matmul_backward_x_grad(test_case, device):
input1 = flow.Tensor(
[
[-1.8604081869125366, -2.0019688606262207],
[1.0511547327041626, -2.263841390609741],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
input2 = flow.Tensor(
[
[-0.13973912596702576, 0.8478717803955078],
[-0.2144828885793686, -1.7145386934280396],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=False,
)
of_out = flow.matmul(input1, input2)
of_out = of_out.sum()
of_out.backward()
np_grad = [
[0.7081326246261597, -1.9290215969085693],
[0.7081326246261597, -1.9290215969085693],
]
test_case.assertTrue(
np.allclose(input1.grad.numpy(), np_grad, atol=1e-05, rtol=1e-05))
def test_broadcast_matmul(test_case):
input1 = flow.Tensor(np.random.randn(3, 4, 5), dtype=flow.float32)
input2 = flow.Tensor(np.random.randn(5, 6), dtype=flow.float32)
of_out = flow.matmul(input1, input2)
np_out = np.matmul(input1.numpy(), input2.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5))
test_case.assertTrue(of_out.numpy().shape, np_out.shape)
def _test_batch_matmul(test_case, device):
input1 = flow.Tensor(np.random.randn(10, 3, 4),
dtype=flow.float32,
device=flow.device(device))
input2 = flow.Tensor(np.random.randn(10, 4, 5),
dtype=flow.float32,
device=flow.device(device))
of_out = flow.matmul(input1, input2)
np_out = np.matmul(input1.numpy(), input2.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5))
def _test_broadcast_matmul_backward(test_case, device):
input1 = flow.Tensor(
[
[
[0.5893293023109436, -0.0376124233007431, 0.7791574001312256],
[1.1614371538162231, 0.009700910188257694, 0.7281601428985596],
],
[
[
-0.27213698625564575, 0.7058051824569702,
-0.4643424451351166
],
[
2.2279646396636963, 0.05870082601904869,
-0.18335142731666565
],
],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
input2 = flow.Tensor(
[
[0.25825661420822144, -0.4875393807888031],
[-0.040459781885147095, -0.3713535666465759],
[-1.633512258529663, -2.0034799575805664],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
of_out = flow.matmul(input1, input2)
of_out = of_out.sum()
of_out.backward()
np_grad = [
[
[-0.22928276658058167, -0.411813348531723, -3.6369922161102295],
[-0.22928276658058167, -0.411813348531723, -3.6369922161102295],
],
[
[-0.22928276658058167, -0.411813348531723, -3.6369922161102295],
[-0.22928276658058167, -0.411813348531723, -3.6369922161102295],
],
]
test_case.assertTrue(
np.allclose(input1.grad.numpy(), np_grad, atol=1e-05, rtol=1e-05))
def _test_matmul_backward(test_case, device):
input1 = flow.Tensor(
[
[
-0.36023932695388794,
0.5571867227554321,
-1.4987696409225464,
-0.9674592018127441,
0.021076146513223648,
2.9180469512939453,
],
[
-0.29169487953186035,
0.2978641390800476,
0.8198832273483276,
-0.3385652005672455,
-2.9260432720184326,
0.22528153657913208,
],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
input2 = flow.Tensor(
[
[
-0.5270200371742249,
-0.4325239062309265,
-0.33396217226982117,
1.2983192205429077,
-0.463693231344223,
],
[
1.893467903137207,
-1.0874812602996826,
0.7068315744400024,
-0.23532593250274658,
-0.011510828509926796,
],
[
-0.5477776527404785,
-0.0381619855761528,
0.03451986983418465,
-0.8248650431632996,
-1.8885509967803955,
],
[
-1.0034432411193848,
0.5428839921951294,
-0.7785694599151611,
-0.4489346146583557,
1.780846118927002,
],
[
0.9378347396850586,
-0.38816362619400024,
0.8186876177787781,
-0.9630932807922363,
-0.11487948149442673,
],
[
-0.12073716521263123,
2.181835174560547,
0.5511962175369263,
-1.294308066368103,
-0.7765272855758667,
],
],
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
of_out = flow.matmul(input1, input2)
of_out = of_out.sum()
of_out.backward()
np_grad = [
[
-0.45888009667396545,
1.2659813165664673,
-3.264835834503174,
0.09278273582458496,
0.2903860807418823,
0.5414588451385498,
],
[
-0.45888009667396545,
1.2659813165664673,
-3.264835834503174,
0.09278273582458496,
0.2903860807418823,
0.5414588451385498,
],
]
test_case.assertTrue(
np.allclose(input1.grad.numpy(), np_grad, atol=1e-05, rtol=1e-05))
