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use tvm assert all close
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@MarisaKirisame
MarisaKirisame committed Mar 23, 2019
1 parent cbd6a35 commit aa98c9a
Showing 1 changed file with 28 additions and 28 deletions.
56 changes: 28 additions & 28 deletions tests/python/relay/test_pass_gradient.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,8 +20,8 @@ def test_id():
ex = create_executor()
x = rand(dtype, *shape)
forward, (grad,) = ex.evaluate(back_func)(x)
np.testing.assert_allclose(forward.asnumpy(), x.asnumpy())
np.testing.assert_allclose(grad.asnumpy(), np.ones_like(x.asnumpy()))
tvm.testing.assert_allclose(forward.asnumpy(), x.asnumpy())
tvm.testing.assert_allclose(grad.asnumpy(), np.ones_like(x.asnumpy()))


def test_add():
Expand All @@ -35,8 +35,8 @@ def test_add():
ex = create_executor()
x = rand(dtype, *shape)
forward, (grad,) = ex.evaluate(back_func)(x)
np.testing.assert_allclose(forward.asnumpy(), 2 * x.asnumpy())
np.testing.assert_allclose(grad.asnumpy(), 2 * np.ones_like(x.asnumpy()))
tvm.testing.assert_allclose(forward.asnumpy(), 2 * x.asnumpy())
tvm.testing.assert_allclose(grad.asnumpy(), 2 * np.ones_like(x.asnumpy()))


def test_temp_add():
Expand All @@ -51,8 +51,8 @@ def test_temp_add():
ex = create_executor()
x = rand(dtype, *shape)
forward, (grad,) = ex.evaluate(back_func)(x)
np.testing.assert_allclose(forward.asnumpy(), 4 * x.asnumpy())
np.testing.assert_allclose(grad.asnumpy(), 4 * np.ones_like(x.asnumpy()))
tvm.testing.assert_allclose(forward.asnumpy(), 4 * x.asnumpy())
tvm.testing.assert_allclose(grad.asnumpy(), 4 * np.ones_like(x.asnumpy()))


def test_sub():
Expand All @@ -66,8 +66,8 @@ def test_sub():
ex = create_executor()
x = rand(dtype, *shape)
forward, (grad,) = ex.evaluate(back_func)(x)
np.testing.assert_allclose(forward.asnumpy(), np.zeros_like(x.asnumpy()))
np.testing.assert_allclose(grad.asnumpy(), np.zeros_like(x.asnumpy()))
tvm.testing.assert_allclose(forward.asnumpy(), np.zeros_like(x.asnumpy()))
tvm.testing.assert_allclose(grad.asnumpy(), np.zeros_like(x.asnumpy()))


def test_broadcast_add():
Expand All @@ -90,11 +90,11 @@ def test_broadcast_add():
relay.TupleType([t1, t2])]))
ex = create_executor()
forward, (grad_x, grad_y) = ex.evaluate(full_func)(x_nd, y_nd)
np.testing.assert_allclose(forward.asnumpy(), expected_forward)
np.testing.assert_allclose(grad_x.asnumpy(),
np.ones_like(expected_forward).sum(axis=2, keepdims=True))
np.testing.assert_allclose(grad_y.asnumpy(),
np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0))
tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
tvm.testing.assert_allclose(grad_x.asnumpy(),
np.ones_like(expected_forward).sum(axis=2, keepdims=True))
tvm.testing.assert_allclose(grad_y.asnumpy(),
np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0))


def test_broadcast_subtract():
Expand All @@ -117,11 +117,11 @@ def test_broadcast_subtract():
relay.TupleType([t1, t2])]))
ex = create_executor()
forward, (grad_x, grad_y) = ex.evaluate(full_func)(x_nd, y_nd)
np.testing.assert_allclose(forward.asnumpy(), expected_forward)
np.testing.assert_allclose(grad_x.asnumpy(),
np.ones_like(expected_forward).sum(axis=2, keepdims=True))
np.testing.assert_allclose(grad_y.asnumpy(),
-np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0))
tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
tvm.testing.assert_allclose(grad_x.asnumpy(),
np.ones_like(expected_forward).sum(axis=2, keepdims=True))
tvm.testing.assert_allclose(grad_y.asnumpy(),
-np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0))


def test_tuple():
Expand All @@ -147,10 +147,10 @@ def test_tuple():
expected_forward = x_np + y_np - z_np
ex = create_executor()
forward, (grad_x, grad_y, grad_z) = ex.evaluate(back_func)(x_nd, y_nd, z_nd)
np.testing.assert_allclose(forward.asnumpy(), expected_forward)
np.testing.assert_allclose(grad_x.asnumpy(), np.ones_like(grad_x.asnumpy()))
np.testing.assert_allclose(grad_y.asnumpy(), np.ones_like(grad_y.asnumpy()))
np.testing.assert_allclose(grad_z.asnumpy(), -1 * np.ones_like(grad_z.asnumpy()))
tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
tvm.testing.assert_allclose(grad_x.asnumpy(), np.ones_like(grad_x.asnumpy()))
tvm.testing.assert_allclose(grad_y.asnumpy(), np.ones_like(grad_y.asnumpy()))
tvm.testing.assert_allclose(grad_z.asnumpy(), -1 * np.ones_like(grad_z.asnumpy()))


def test_pow():
Expand All @@ -168,8 +168,8 @@ def test_pow():
i_nd = rand(dtype, *shape)
ex = create_executor(mod=mod)
forward, (grad_i,) = ex.evaluate(back_func)(i_nd)
np.testing.assert_allclose(forward.asnumpy(), 8 * i_nd.asnumpy())
np.testing.assert_allclose(grad_i.asnumpy(), 8 * np.ones_like(grad_i.asnumpy()))
tvm.testing.assert_allclose(forward.asnumpy(), 8 * i_nd.asnumpy())
tvm.testing.assert_allclose(grad_i.asnumpy(), 8 * np.ones_like(grad_i.asnumpy()))

def test_ref():
shape = (10, 10)
Expand All @@ -187,8 +187,8 @@ def test_ref():
x_nd = rand(dtype, *shape)
ex = create_executor()
forward, (grad_x,) = ex.evaluate(back_func)(x_nd)
np.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy())
np.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy()))
tvm.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy())
tvm.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy()))

def test_square_second_order():
shape = (10, 10)
Expand All @@ -205,8 +205,8 @@ def test_square_second_order():
x_nd = rand(dtype, *shape)
ex = create_executor()
forward, (grad_x,) = ex.evaluate(back_back_func)(x_nd)
np.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy())
np.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy()))
tvm.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy())
tvm.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy()))

if __name__ == "__main__":
test_id()
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