access2rohit commented on a change in pull request #16409: added support for
large tensors for Dropout operator and tests to verify support for more
operators
URL: https://github.com/apache/incubator-mxnet/pull/16409#discussion_r336235196
##########
File path: tests/nightly/test_large_array.py
##########
@@ -1199,6 +1199,243 @@ def test_full():
assert a[-1][-1] == 3
+def test_astype():
+ x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+ y = x.astype('int32')
+ assert y.dtype == np.int32
+ assert y[-1][-1] == SMALL_Y-1
+
+
+def test_cast():
+ x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
+ y = nd.cast(x, np.int32)
+ assert y.dtype == np.int32
+ assert y[-1][-1] == SMALL_Y-1
+
+
+def test_repeat():
+ x = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X//2)
+ y = nd.repeat(x, repeats=2, axis = 1)
+ assert y.shape == (SMALL_Y, LARGE_X)
+ assert y[0][1] == 0
+ assert y[-1][-1] == SMALL_Y-1
+ x = create_2d_tensor(rows=SMALL_Y//2, columns=LARGE_X)
+ y = nd.repeat(x, repeats=2, axis = 0)
+ assert y.shape == (SMALL_Y, LARGE_X)
+ assert y[0][1] == 0
+ assert y[-1][0] == SMALL_Y//2-1
+
+
+def create_input_for_rounding_ops():
+ # Creates an vector with values (-LARGE_X/2 .... -2, -1, 0, 1, 2, .... ,
LARGE_X/2-1)
+ # then divides each element by 2 i.e (-LARGE_X/4 .... -1, -0.5, 0, 0.5, 1,
.... , LARGE_X/4-1)
+ # and finally broadcasts to
+ inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64).reshape(1,
LARGE_X)
+ inp = inp/2
+ inp = nd.broadcast_to(inp, (SMALL_Y, LARGE_X))
+ return inp
+
+
+def assert_correctness_of_rounding_ops(output, mid, expected_vals):
+ # checks verifies 5 values at the middle positions of the input vector
+ # i.e mid-2, mid-1, mid, mid+1, mid+2
+ output_idx_to_inspect = [mid-2, mid-1, mid, mid+1, mid+2]
+ for i in range(len(output_idx_to_inspect)):
+ assert output[1][output_idx_to_inspect[i]] == expected_vals[i]
+
+
+def test_rounding_ops():
+ x = create_input_for_rounding_ops()
+
+ def check_ceil():
+ y = nd.ceil(x)
+ # expected ouput for middle 5 values after applying ceil()
+ expected_output = [-1, 0, 0, 1, 1]
+ assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+ def check_fix():
+ y = nd.fix(x)
+ # expected ouput for middle 5 values after applying fix()
+ expected_output = [-1, 0, 0, 0, 1]
+ assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+ def check_floor():
+ y = nd.floor(x)
+ # expected ouput for middle 5 values after applying floor()
+ expected_output = [-1, -1, 0, 0, 1]
+ assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+ def check_rint():
+ y = nd.rint(x)
+ # expected ouput for middle 5 values after applying rint()
+ expected_output = [-1, -1, 0, 0, 1]
+ assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+ def check_round():
+ y = nd.round(x)
+ # expected ouput for middle 5 values after applying round()
+ expected_output = [-1, -1, 0, 1, 1]
+ assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+ def check_trunc():
+ y = nd.trunc(x)
+ # expected ouput for middle 5 values after applying trunc()
+ expected_output = [-1, 0, 0, 0, 1]
+ assert_correctness_of_rounding_ops(y, LARGE_X//2, expected_output)
+
+ check_ceil()
+ check_fix()
+ check_floor()
+ check_rint()
+ check_round()
+ check_trunc()
+
+
+def create_input_for_trigonometric_ops(vals):
+ # Creates large vector input of size(LARGE_X*10, SMALL_Y/10) from vals
using tile operator
+ inp = nd.array(vals).reshape(1, 5)
+ inp = nd.broadcast_to(inp, (LARGE_X*10, SMALL_Y//10))
+ return inp
+
+
+def assert_correctness_of_trigonometric_ops(output, expected_vals):
+ # checks verifies 5 values at positions(0, 1, -3, -2, -1) of the input
vector
+ output_idx_to_inspect = [0, 1, -3, -2, -1]
+ for i in range(len(output_idx_to_inspect)):
+ assert
np.abs(output[1][output_idx_to_inspect[i]].asnumpy()-expected_vals[i]) <= 1e-3
+
+
+def test_trigonometric_ops():
+ def check_arcsin():
+ x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+ y = nd.arcsin(x)
+ # expected ouput for indices=(0, 1, -3, -2, -1) after applying arcsin()
+ expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+ assert_correctness_of_trigonometric_ops(y, expected_output)
+
+ def check_arccos():
+ x = create_input_for_trigonometric_ops([-1, -.707, 0, .707, 1])
+ y = nd.arccos(x)
+ # expected ouput for indices=(0, 1, -3, -2, -1) after applying arccos()
+ expected_output = [np.pi, 3*np.pi/4, np.pi/2, np.pi/4, 0]
+ assert_correctness_of_trigonometric_ops(y, expected_output)
+
+ def check_arctan():
+ x = create_input_for_trigonometric_ops([-np.Inf, -1, 0, 1, np.Inf])
+ y = nd.arctan(x)
+ # expected ouput for indices=(0, 1, -3, -2, -1) after applying arctan()
+ expected_output = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]
+ assert_correctness_of_trigonometric_ops(y, expected_output)
+
+ def check_sin():
+ x = create_input_for_trigonometric_ops([-np.pi/2, -np.pi/4, 0,
np.pi/4, np.pi/2])
+ y = nd.sin(x)
+ # expected ouput for indices=(0, 1, -3, -2, -1) after applying sin()
+ expected_output = [-1, -.707, 0, .707, 1]
+ assert_correctness_of_trigonometric_ops(y, expected_output)
+
+ def check_cos():
+ x = create_input_for_trigonometric_ops([0, np.pi/4, np.pi/2,
3*np.pi/4, np.pi])
+ y = nd.cos(x)
+ # expected ouput for indices=(0, 1, -3, -2, -1) after applying cos()
+ expected_output = [1, .707, 0, -.707, -1]
+ assert_correctness_of_trigonometric_ops(y, expected_output)
+
+ def check_tan():
+ x = create_input_for_trigonometric_ops([-np.pi/6, -np.pi/4, 0,
np.pi/4, np.pi/6])
+ y = nd.tan(x)
+ # expected ouput for indices=(0, 1, -3, -2, -1) after applying tan()
+ expected_output = [-.577, -1, 0, 1, .577]
+ assert_correctness_of_trigonometric_ops(y, expected_output)
+
+ def check_radians():
+ x = create_input_for_trigonometric_ops([0, 90, 180, 270, 360])
+ y = nd.radians(x)
+ # expected ouput for indices=(0, 1, -3, -2, -1) after applying
radians()
+ expected_output = [0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi]
+ assert_correctness_of_trigonometric_ops(y, expected_output)
+
+ def check_degrees():
+ x = create_input_for_trigonometric_ops([0, np.pi/2, np.pi, 3*np.pi/2,
2*np.pi])
+ y = nd.degrees(x)
+ # expected ouput for indices=(0, 1, -3, -2, -1) after applying
degrees()
+ expected_output = [0, 90, 180, 270, 360]
+ assert_correctness_of_trigonometric_ops(y, expected_output)
+
+ check_arcsin()
+ check_arccos()
+ check_arctan()
+ check_sin()
+ check_cos()
+ check_tan()
+ check_radians()
+ check_degrees()
+
+
+def test_L2Normalization():
+ x = nd.ones((2, LARGE_X*2))
+ x[0] = 3
+ x[1] = 4
+ # Channel Mode
+ z = x.reshape(1, 2, LARGE_X*2)
+ y = nd.L2Normalization(z, mode='channel')
+ assert y[0][0][0] == 0.6
+ assert y[0][0][-1] == 0.6
+ assert y[0][1][0] == 0.8
+ assert y[0][1][-1] == 0.8
+ # Instance Mode
+ z = x.T
+ y = nd.L2Normalization(z, mode='instance')
+ assert y[0][0] == 0.6
+ assert y[0][1] == 0.8
+ assert y[-1][0] == 0.6
+ assert y[-1][1] == 0.8
+ # Spatial Mode
+ z = z.reshape(1, 200000000, 2)
+ y = nd.L2Normalization(z, mode='spatial')
+ assert y[0][0][0] == 0.6
+ assert y[0][0][1] == 0.8
+ assert y[0][-1][0] == 0.6
+ assert y[0][-1][1] == 0.8
+
+
+def test_instance_norm():
+ dtype = np.float32
+ forward_check_eps = 1E-3
+ axis = -1
+ eps = 1E-5
+ in_shape = (LARGE_X, 1, SMALL_Y)
+ ctx = mx.cpu()
+
+ def npy_instance_norm(data, gamma, beta, axis, eps=1E-5):
+ if axis < 0:
+ axis += data.ndim
+ broadcast_shape = [1 for _ in range(data.ndim)]
+ broadcast_shape[axis] = data.shape[axis]
+ mean = data.mean(axis=axis, keepdims=True).astype(dtype)
+ var = data.var(axis=axis, keepdims=True).astype(dtype)
+ std = np.sqrt(var + dtype(eps)).astype(dtype)
+ out = gamma * (data - mean) / std + \
+ beta
+ return out
+ data = np.random.normal(0, 1, in_shape).astype(dtype)
+ gamma = np.random.normal(0, 1, (1,)).astype(dtype)
+ beta = np.random.normal(0, 1, (1,)).astype(dtype)
+ data_s = mx.symbol.Variable('data')
+ gamma_s = mx.symbol.Variable('gamma')
+ beta_s = mx.symbol.Variable('beta')
+ out_s = mx.symbol.InstanceNorm(data=data_s, gamma=gamma_s, beta=beta_s,
+ eps=eps)
+ exe = out_s.simple_bind(ctx, data=in_shape)
Review comment:
Done
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