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import torch | ||
import torch_xla2 | ||
import jax.numpy as jnp | ||
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env = torch_xla2.default_env() | ||
#env.config.debug_print_each_op = True | ||
#env.config.debug_accuracy_for_each_op = True | ||
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def squeeze(): | ||
with env: | ||
t1 = torch.tensor([-3.5]) | ||
r1 = t1.squeeze_(-1) | ||
print("xla | torch.squeeze :", r1) | ||
t1 = torch.tensor([-3.5]) | ||
r1 = t1.squeeze_(-1) | ||
print("native| torch.squeeze :", r1) | ||
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def nanquantile(): | ||
with env: | ||
t1 = torch.tensor([-7.0, 0.0, torch.nan]) | ||
r1 = t1.nanquantile(0.5) | ||
print("xla | torch.nanquantile(",t1,") :", r1) | ||
t1 = torch.tensor([-7.0, 0.0, torch.nan]) | ||
r1 = t1.nanquantile(0.5) | ||
print("native| torch.nanquantile(",t1,") :", r1) | ||
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def empty(): | ||
with env: | ||
#print("xla torch.full: ", torch.full((2,), 3)) | ||
start1 = torch.tensor(-2) | ||
print("xla | torch.tensor(-2) ->", start1) | ||
start2 = torch.tensor([-2]) | ||
print("xla | torch.tensor([-2]) ->", start2) | ||
emp1 = torch.empty((1,)) | ||
ret1 = emp1.copy_(start1) | ||
print("xla | torch.empty((1,)).copy_(tensor(-2)) :", ret1) | ||
emp2 = torch.empty((1,)) | ||
ret2 = emp2.copy_(start2) | ||
print("xla | torch.empty((1,)).copy_(tensor([-2])):", ret2) | ||
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#print("native torch.full: ", torch.full((2,), 3)) | ||
start1 = torch.tensor(-2) | ||
print("native| torch.tensor(-2) ->", start1) | ||
start2 = torch.tensor([-2]) | ||
print("native| torch.tensor([-2]) ->", start2) | ||
emp1 = torch.empty((1,)) | ||
ret1 = emp1.copy_(start1) | ||
print("native| torch.empty((1,)).copy_(tensor(-2)) :", ret1) | ||
emp2 = torch.empty((1,)) | ||
ret2 = emp2.copy_(start2) | ||
print("native| torch.empty((1,)).copy_(tensor([-2])):", ret2) | ||
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def casting(): | ||
t = torch.tensor([ 4.3000, 4.1510, 4.0020, 3.8531, 3.7041, 3.5551, 3.4061, 3.2571, | ||
3.1082, 2.9592, 2.8102, 2.6612, 2.5122, 2.3633, 2.2143, 2.0653, | ||
1.9163, 1.7673, 1.6184, 1.4694, 1.3204, 1.1714, 1.0224, 0.8735, | ||
0.7245, 0.5755, 0.4265, 0.2776, 0.1286, -0.0204, -0.1694, -0.3184, | ||
-0.4673, -0.6163, -0.7653, -0.9143, -1.0633, -1.2122, -1.3612, -1.5102, | ||
-1.6592, -1.8082, -1.9571, -2.1061, -2.2551, -2.4041, -2.5531, -2.7020, | ||
-2.8510, -3.0000]) | ||
with env: | ||
print("xla |", t.type(torch.int64)) | ||
print("native|", t.type(torch.int64)) | ||
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def linspace(): | ||
dtype=torch.int64 | ||
with env: | ||
print("xla | torch.linspace(): ", torch.linspace(4.9, 3, 5, dtype=dtype)) | ||
print("native| torch.linspace(): ", torch.linspace(4.9, 3, 5, dtype=dtype)) | ||
return | ||
with env: | ||
print("xla | torch.linspace(): ", torch.linspace(-2, -3, 50, dtype=dtype)) | ||
print("native| torch.linspace(): ", torch.linspace(-2, -3, 50, dtype=dtype)) | ||
with env: | ||
print("xla | torch.linspace(): ", torch.linspace(4.3, -3, 50, dtype=dtype)) | ||
print("native| torch.linspace(): ", torch.linspace(4.3, -3, 50, dtype=dtype)) | ||
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def logspace(): | ||
with env: | ||
print("xla torch.logspace: ", torch.logspace(start=-10, end=10, steps=5)) | ||
print("native torch.logspace: ", torch.logspace(start=-10, end=10, steps=5)) | ||
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def log_normal(): | ||
with env: | ||
t = torch.tensor([-0.0674, 4.8280, -7.4074, -6.6235, -3.4664, 2.4134, -0.1783, 7.1360, -0.7987, 2.3815]) | ||
print("xla |torch.log_normal: ", t.log_normal_(0, 0.25)) | ||
t = torch.tensor([-0.0674, 4.8280, -7.4074, -6.6235, -3.4664, 2.4134, -0.1783, 7.1360, -0.7987, 2.3815]) | ||
print("native |torch.log_normal: ", t.log_normal_(0, 0.25)) | ||
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def linalg_vector_norm(): | ||
with env: | ||
t = torch.tensor(-0.06738138198852539) | ||
print("xla | linalg.vector_norm()", torch.linalg.vector_norm(t, ord=0).dtype) | ||
t = torch.tensor(-0.06738138198852539) | ||
print("native| linalg.vector_norm()", torch.linalg.vector_norm(t, ord=0).dtype) | ||
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def linalg_tensorsolve(): | ||
with env: | ||
A = torch.tensor([[[-0.0674, 4.8280, -7.4074, -6.6235, -3.4664, 2.4134], | ||
[-0.1783, 7.1360, -0.7987, 2.3815, -2.7199, -1.7691], | ||
[-8.5981, -5.9605, -3.7100, 0.3334, 3.5580, 5.4002]], | ||
[[-6.1015, -3.9192, 3.2690, 7.4735, -1.8522, 6.7348], | ||
[-1.4507, 0.9523, 8.1493, -8.3490, -5.6658, -2.2785], | ||
[-3.5082, 7.7760, -5.8336, -4.1430, -6.2878, -8.4290]]]) | ||
B = torch.tensor([[-5.2537, 7.7364, 4.0160], | ||
[ 4.3621, 0.4733, -4.6142]]) | ||
print("xla | linalg.vectorsolve()", torch.linalg.tensorsolve(A, B)) | ||
A = torch.tensor([[[-0.0674, 4.8280, -7.4074, -6.6235, -3.4664, 2.4134], | ||
[-0.1783, 7.1360, -0.7987, 2.3815, -2.7199, -1.7691], | ||
[-8.5981, -5.9605, -3.7100, 0.3334, 3.5580, 5.4002]], | ||
[[-6.1015, -3.9192, 3.2690, 7.4735, -1.8522, 6.7348], | ||
[-1.4507, 0.9523, 8.1493, -8.3490, -5.6658, -2.2785], | ||
[-3.5082, 7.7760, -5.8336, -4.1430, -6.2878, -8.4290]]]) | ||
B = torch.tensor([[-5.2537, 7.7364, 4.0160], | ||
[ 4.3621, 0.4733, -4.6142]]) | ||
print("native| linalg.vectorsolve()", torch.linalg.tensorsolve(A, B)) | ||
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def test_lu(): | ||
A = torch.tensor([[ 0.0437, 0.6733, -0.7089, -0.4736, -0.3145], | ||
[ 0.2206, -0.3749, 0.8442, -0.5197, 0.2332], | ||
[-0.2896, -0.6009, -0.6085, -0.9129, -0.3178]]) | ||
print("native| lu()", torch.lu(A, pivot=True, get_infos=True)) | ||
with env: | ||
A = torch.tensor([[ 0.0437, 0.6733, -0.7089, -0.4736, -0.3145], | ||
[ 0.2206, -0.3749, 0.8442, -0.5197, 0.2332], | ||
[-0.2896, -0.6009, -0.6085, -0.9129, -0.3178]]) | ||
print("xla | lu()", torch.lu(A, pivot=True, get_infos=True)) | ||
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def test_lu_solve(): | ||
b = torch.tensor([[ 2.3815, -2.7199, -1.7691, -8.5981], | ||
[-5.9605, -3.7100, 0.3334, 3.5580], | ||
[ 5.4002, -6.1015, -3.9192, 3.2690]]) | ||
LU = torch.tensor([[-0.7679, -0.4551, 0.3539], | ||
[ 0.0390, 1.2674, 0.2928], | ||
[-0.0856, 0.2779, -1.2844]]) | ||
pivots = torch.tensor([2, 3, 3], dtype=torch.int32) | ||
print("native| lu_solve()", torch.lu_solve(b, LU, pivots)) | ||
with env: | ||
b = torch.tensor([[ 2.3815, -2.7199, -1.7691, -8.5981], | ||
[-5.9605, -3.7100, 0.3334, 3.5580], | ||
[ 5.4002, -6.1015, -3.9192, 3.2690]]) | ||
LU = torch.tensor([[-0.7679, -0.4551, 0.3539], | ||
[ 0.0390, 1.2674, 0.2928], | ||
[-0.0856, 0.2779, -1.2844]]) | ||
pivots = torch.tensor([2, 3, 3], dtype=torch.int32) | ||
print("xla | lu_solve()", torch.lu_solve(b, LU, pivots)) | ||
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def test_lu_unpack(): | ||
unpack_data=True | ||
unpack_pivots=True | ||
if False: | ||
lu = torch.tensor([[-2.7199, -1.7691, -8.5981, -5.9605, -3.7100], | ||
[ 0.0248, 4.8718, -7.1944, -6.4758, -3.3745], | ||
[-0.8873, -0.3588, -3.0746, -8.4111, -2.1212]]) | ||
pivots = torch.tensor([3, 3, 3], dtype=torch.int32) | ||
print("native| lu_unpack()", torch.lu_unpack(lu, pivots,unpack_data=unpack_data, unpack_pivots=unpack_pivots)) | ||
with env: | ||
lu = torch.tensor([[-2.7199, -1.7691, -8.5981, -5.9605, -3.7100], | ||
[ 0.0248, 4.8718, -7.1944, -6.4758, -3.3745], | ||
[-0.8873, -0.3588, -3.0746, -8.4111, -2.1212]]) | ||
pivots = torch.tensor([3, 3, 3], dtype=torch.int32) | ||
print("xla | lu_unpack()", torch.lu_unpack(lu, pivots,unpack_data=unpack_data, unpack_pivots=unpack_pivots)) | ||
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if False: | ||
lu = torch.tensor([[-8.3876, 7.9964, 6.8432, -8.9778, 1.6845], | ||
[ 0.8269, -9.9104, -2.1215, 14.8806, 6.4389], | ||
[ 0.1808, 0.2953, -4.7303, 0.6897, -7.5366], | ||
[-0.4855, -0.7570, 0.7641, 9.0972, 16.3916], | ||
[ 0.1354, 0.0746, -0.2784, 0.6465, -4.7616], | ||
[-0.9468, -0.9447, 0.7085, 0.6482, 0.6800]]) | ||
pivots=torch.tensor([5, 3, 6, 5, 6], dtype=torch.int32) | ||
print("native| lu_unpack()", torch.lu_unpack(lu, pivots,unpack_data=unpack_data, unpack_pivots=unpack_pivots)) | ||
with env: | ||
lu = torch.tensor([[-8.3876, 7.9964, 6.8432, -8.9778, 1.6845], | ||
[ 0.8269, -9.9104, -2.1215, 14.8806, 6.4389], | ||
[ 0.1808, 0.2953, -4.7303, 0.6897, -7.5366], | ||
[-0.4855, -0.7570, 0.7641, 9.0972, 16.3916], | ||
[ 0.1354, 0.0746, -0.2784, 0.6465, -4.7616], | ||
[-0.9468, -0.9447, 0.7085, 0.6482, 0.6800]]) | ||
pivots=torch.tensor([5, 3, 6, 5, 6], dtype=torch.int32) | ||
print("xla | lu_unpack()", torch.lu_unpack(lu, pivots,unpack_data=unpack_data, unpack_pivots=unpack_pivots)) | ||
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if True: | ||
lu = torch.tensor([[[ -5.3344, -2.2530, -4.3840, -3.1485, -7.3766], | ||
[ 0.3589, 2.7324, -4.2898, 0.6681, 9.0900], | ||
[ 0.1734, 0.2346, 0.9901, 2.2108, 4.8699]], | ||
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[[ 8.5252, 5.7155, 8.5447, -0.6509, -8.0849], | ||
[ -0.5005, 8.9886, 4.2181, -4.7992, -10.9431], | ||
[ -0.9880, -0.2169, 7.5312, 3.2518, -5.4951]], | ||
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[[ -8.6799, 5.6140, -7.0426, -1.9027, -3.6493], | ||
[ -0.0134, -4.0132, 3.2959, -8.1260, -0.6563], | ||
[ 0.1997, 0.7197, -9.0417, -1.5426, -0.2071]]]) | ||
pivots=torch.tensor([[1, 2, 3], | ||
[1, 2, 3], | ||
[1, 3, 3]], dtype=torch.int32) | ||
print("native| lu_unpack()", torch.lu_unpack(lu, pivots,unpack_data=unpack_data, unpack_pivots=unpack_pivots)) | ||
with env: | ||
lu = torch.tensor([[[ -5.3344, -2.2530, -4.3840, -3.1485, -7.3766], | ||
[ 0.3589, 2.7324, -4.2898, 0.6681, 9.0900], | ||
[ 0.1734, 0.2346, 0.9901, 2.2108, 4.8699]], | ||
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[[ 8.5252, 5.7155, 8.5447, -0.6509, -8.0849], | ||
[ -0.5005, 8.9886, 4.2181, -4.7992, -10.9431], | ||
[ -0.9880, -0.2169, 7.5312, 3.2518, -5.4951]], | ||
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[[ -8.6799, 5.6140, -7.0426, -1.9027, -3.6493], | ||
[ -0.0134, -4.0132, 3.2959, -8.1260, -0.6563], | ||
[ 0.1997, 0.7197, -9.0417, -1.5426, -0.2071]]]) | ||
pivots=torch.tensor([[1, 2, 3], | ||
[1, 2, 3], | ||
[1, 3, 3]], dtype=torch.int32) | ||
print("xla | lu_unpack()", torch.lu_unpack(lu, pivots,unpack_data=unpack_data, unpack_pivots=unpack_pivots)) | ||
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def pivot_to_permutation(): | ||
with env: | ||
P = torch.tensor([ | ||
[[1., 0., 0.], | ||
[0.,1.,0.], | ||
[0.,0.,1.]], | ||
[[1., 0., 0.], | ||
[0.,1.,0.], | ||
[0.,0.,1.]], | ||
[[1., 0., 0.], | ||
[0.,1.,0.], | ||
[0.,0.,1.]], | ||
]) | ||
print("debug: start permutation matrix:", P) | ||
pivots=torch.tensor([[1, 2, 3], | ||
[1, 2, 3], | ||
[1, 3, 3]], dtype=torch.int32) | ||
pivot_size = pivots.shape[-1] | ||
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row_idx = jnp.arange(3) #? row_idx = jnp.arange(m) or (pivot_size) | ||
i=1 | ||
col_idx = pivots[..., i] | ||
indices = torch.tensor(row_idx) | ||
c1 = P.gather(2, indices) | ||
c1 = P.index_select(2, indices) | ||
print("debug:", c1) | ||
return | ||
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# Apply the swaps iteratively | ||
for i in range(pivot_size): | ||
# Get the swap indices | ||
row_idx = jnp.arange(3) #? row_idx = jnp.arange(m) or (pivot_size) | ||
col_idx = pivots[..., i] | ||
print("r c :", row_idx, col_idx, i) | ||
indices = torch.tensor([[0, 0]]) | ||
c1 = P.gather(2, indices) | ||
print("debug:", c1) | ||
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#nanquantile() | ||
#squeeze() | ||
#linspace() | ||
#casting() | ||
#log_normal() | ||
#linalg_vector_norm() | ||
#linalg_tensorsolve() | ||
#test_lu() | ||
#test_lu_solve() | ||
#test_lu_unpack() | ||
pivot_to_permutation() |
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