def test_nobroadcast_fp16():
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
np.random.seed(42)
x1_np = np.random.rand(10, 20).astype(np.float16)
x2_np = np.random.rand(10, 20).astype(np.float16)
output_ms = P.Minimum()(Tensor(x1_np), Tensor(x2_np))
output_np = np.minimum(x1_np, x2_np)
assert np.allclose(output_ms.asnumpy(), output_np)
output_ms = P.Maximum()(Tensor(x1_np), Tensor(x2_np))
output_np = np.maximum(x1_np, x2_np)
assert np.allclose(output_ms.asnumpy(), output_np)
output_ms = P.Greater()(Tensor(x1_np), Tensor(x2_np))
output_np = x1_np > x2_np
assert np.allclose(output_ms.asnumpy(), output_np)
output_ms = P.Less()(Tensor(x1_np), Tensor(x2_np))
output_np = x1_np < x2_np
assert np.allclose(output_ms.asnumpy(), output_np)
output_ms = P.Pow()(Tensor(x1_np), Tensor(x2_np))
output_np = np.power(x1_np, x2_np)
assert np.allclose(output_ms.asnumpy(), output_np)
output_ms = P.RealDiv()(Tensor(x1_np), Tensor(x2_np))
output_np = x1_np / x2_np
assert np.allclose(output_ms.asnumpy(), output_np)
output_ms = P.Mul()(Tensor(x1_np), Tensor(x2_np))
output_np = x1_np * x2_np
assert np.allclose(output_ms.asnumpy(), output_np)
output_ms = P.Sub()(Tensor(x1_np), Tensor(x2_np))
output_np = x1_np - x2_np
assert np.allclose(output_ms.asnumpy(), output_np)
output_ms = P.DivNoNan()(Tensor(x1_np), Tensor(x2_np))
output_np = x1_np / x2_np
assert np.allclose(output_ms.asnumpy(), output_np)
x2_np_zero = np.zeros_like(x2_np)
output_ms = P.DivNoNan()(Tensor(x1_np), Tensor(x2_np_zero))
assert np.allclose(output_ms.asnumpy(), x2_np_zero)
output_ms = P.Mod()(Tensor(x1_np), Tensor(x2_np))
output_np = np.fmod(x1_np, x2_np)
assert np.allclose(output_ms.asnumpy(), output_np)
output_ms = P.FloorMod()(Tensor(x1_np), Tensor(x2_np))
output_np = np.mod(x1_np, x2_np)
assert np.allclose(output_ms.asnumpy(), output_np)
'skip': ['backward']}),
('RealDiv', {
'block': P.RealDiv(),
'desc_inputs': [[4], Tensor(np.ones(4).astype(np.float32))],
'desc_bprop': [[4]]}),
('RealDiv_1', {
'block': P.RealDiv(),
'desc_inputs': [[512, 1024], [512, 1024]],
'desc_bprop': [[512, 1024]]}),
('FloorDiv', {
'block': P.FloorDiv(),
'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16)),
Tensor(np.random.rand(4).astype(np.float16))],
'skip': ['backward']}),
('FloorMod', {
'block': P.FloorMod(),
'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16)),
Tensor(np.random.rand(4).astype(np.float16))],
'skip': ['backward']}),
('identity', {
'block': ops.functional.identity,
'desc_inputs': [[2, 2]],
'skip': ['backward']}),
('MatMul_1', {
'block': P.MatMul(transpose_a=False, transpose_b=False),
'desc_inputs': [[1024, 160], [160, 1024]],
'desc_bprop': [[1024, 1024]]}),
('MatMul_2', {
'block': P.MatMul(transpose_a=True, transpose_b=True),
'desc_inputs': [[160, 1024], [1024, 160]],
'desc_bprop': [[1024, 1024]]}),
# input two tensors, their shapes do not match
('FloorDiv2', {
'block': (P.FloorDiv(), {'exception': ValueError, 'error_keywords': ['FloorDiv']}),
'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input x is Tensor(int32), not Tensor(float)
('Floor1', {
'block': (P.Floor(),
{'exception': TypeError, 'error_keywords': ['Floor']}),
'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32))],
'skip': ['backward']}),
# one input is scalar, and another is Tensor(float32)
('FloorMod0', {
'block': (P.FloorMod(), {'exception': TypeError, 'error_keywords': ['FloorMod']}),
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input two tensors, but element types are not same
('FloorMod1', {
'block': (P.FloorMod(), {'exception': TypeError, 'error_keywords': ['FloorMod']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input two tensors, their shapes do not match
('FFloorMod2', {
'block': (P.FloorMod(), {'exception': ValueError, 'error_keywords': ['FloorMod']}),
'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input x is Tensor(int32), not Tensor(float)
('Acosh1', {
'skip': ['backward']
}),
# input x is Tensor(int32), not Tensor(float)
('Floor1', {
'block': (P.Floor(), {
'exception': TypeError,
'error_keywords': ['Floor']
}),
'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.int32))],
'skip': ['backward']
}),
# input two tensors, their shapes do not match
('FFloorMod2', {
'block': (P.FloorMod(), {
'exception': ValueError,
'error_keywords': ['FloorMod']
}),
'desc_inputs': [
Tensor(np.ones([3, 5]).astype(np.float32)),
Tensor(np.ones([3, 4]).astype(np.float32))
],
'skip': ['backward']
}),
# input x is Tensor(int32), not Tensor(float)
('Acosh1', {
'block': (P.Acosh(), {
'exception': TypeError,
'error_keywords': ['Acosh']
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul = P.MatMul().shard(strategy1)
self.floormod = P.FloorMod().shard(strategy2)
'block': P.RealDiv(),
'desc_inputs': [[512, 1024], [512, 1024]],
'desc_bprop': [[512, 1024]]
}),
('FloorDiv', {
'block':
P.FloorDiv(),
'desc_inputs': [
Tensor(np.random.rand(4).astype(np.float16)),
Tensor(np.random.rand(4).astype(np.float16))
],
'skip': ['backward']
}),
('FloorMod', {
'block':
P.FloorMod(),
'desc_inputs': [
Tensor(np.random.rand(4).astype(np.float16)),
Tensor(np.random.rand(4).astype(np.float16))
],
'skip': ['backward']
}),
('identity', {
'block': ops.functional.identity,
'desc_inputs': [[2, 2]],
'skip': ['backward']
}),
('MatMul_1', {
'block': P.MatMul(transpose_a=False, transpose_b=False),
'desc_inputs': [[1024, 160], [160, 1024]],
'desc_bprop': [[1024, 1024]]