test5=[rand(3, 4), 0.0, rand(3, 5), rand(5, 4), 0.6],
test6=[rand(3, 4), 0.0, rand(3, 5), rand(5, 4), -1.0],
test7=[rand(3, 4), -1.0, rand(3, 5), rand(5, 4), 0.0],
test8=[rand(3, 4), -1.0, rand(3, 5), rand(5, 4), 1.1],
test9=[rand(3, 4), -1.0, rand(3, 5), rand(5, 4), -1.1],
# test10=[rand(0, 4), -1.0, rand(0, 5), rand(5, 4), 0.0],
# test11=[rand(3, 0), -1.0, rand(3, 5), rand(5, 0), 1.1],
# test12=[rand(3, 4), -1.0, rand(3, 0), rand(0, 4), -1.1],
# test13=[rand(0, 0), -1.0, rand(0, 0), rand(0, 0), -1.1],
)
)
GpuGemmBatchTester = makeTester(
'GpuGemmBatchTester',
op=lambda z, alpha, x, y, beta: alpha * batched_dot(x, y) + beta * z,
gpu_op=gpugemmbatch_no_inplace,
cases=dict(
("test_b%im%ik%in%i" % (b, m, k, n),
[rand(b, m, n), rand(), rand(b, m, k), rand(b, k, n), rand()])
for b, m, k, n in itertools.combinations([2, 3, 5, 7, 11, 13], 4)))
class TestGpuSger(TestGer):
def setUp(self):
self.mode = mode_with_gpu
dtype = self.dtype = 'float32' # optimization isn't dtype-dependent
self.A = tensor.tensor(dtype=dtype, broadcastable=(False, False))
self.a = tensor.tensor(dtype=dtype, broadcastable=())
self.x = tensor.tensor(dtype=dtype, broadcastable=(False,))
self.y = tensor.tensor(dtype=dtype, broadcastable=(False,))
rand(5, 4), -1.0],
test7=[rand(3, 4), -1.0, rand(3, 5),
rand(5, 4), 0.0],
test8=[rand(3, 4), -1.0, rand(3, 5),
rand(5, 4), 1.1],
test9=[rand(3, 4), -1.0,
rand(3, 5), rand(5, 4), -1.1],
# test10=[rand(0, 4), -1.0, rand(0, 5), rand(5, 4), 0.0],
# test11=[rand(3, 0), -1.0, rand(3, 5), rand(5, 0), 1.1],
# test12=[rand(3, 4), -1.0, rand(3, 0), rand(0, 4), -1.1],
# test13=[rand(0, 0), -1.0, rand(0, 0), rand(0, 0), -1.1],
))
GpuGemmBatchTester = makeTester(
'GpuGemmBatchTester',
op=lambda z, alpha, x, y, beta: alpha * batched_dot(x, y) + beta * z,
gpu_op=gpugemmbatch_no_inplace,
cases=dict(
("test_b%im%ik%in%i" % (b, m, k, n),
[rand(b, m, n),
rand(), rand(b, m, k),
rand(b, k, n),
rand()])
for b, m, k, n in itertools.combinations([2, 3, 5, 7, 11, 13], 4)))
class TestGpuSger(TestGer):
def setUp(self):
self.mode = mode_with_gpu
dtype = self.dtype = 'float32' # optimization isn't dtype-dependent
self.A = tensor.tensor(dtype=dtype, broadcastable=(False, False))
