| 2.5.0 | 2.6.0 |
ops.Xlogy(input [Tensor, numbers.Number, bool],
other [Tensor, numbers.Number, bool])
|
ops.Xlogy(input [Tensor],
other [Tensor])
|
| 2.5.0 | 2.6.0 |
>>> import numpy as np >>> from mindspore import Tensor, jit >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(mode="PIJit") ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
>>> import numpy as np >>> from mindspore import Tensor, jit >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(capture_mode="bytecode") ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
| 2.5.0 | 2.6.0 |
>>> import numpy as np >>> from mindspore import Tensor, jit, JitConfig >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(jit_config=JitConfig(jit_level="O0")) ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
>>> import numpy as np >>> from mindspore import Tensor, jit >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(jit_level="O0") ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
| 2.5.0 | 2.6.0 |
>>> import numpy as np >>> from mindspore import Tensor, jit >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
>>> import numpy as np >>> from mindspore import Tensor, jit >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(dynamic=1) ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
| 2.5.0 | 2.6.0 |
>>> import numpy as np >>> from mindspore import Tensor, jit, JitConfig >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(jit_config=JitConfig(jit_syntax_level="STRICT")) ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
>>> import numpy as np >>> from mindspore import Tensor, jit >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(fullgraph=True) ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
| 2.5.0 | 2.6.0 |
>>> import numpy as np >>> from mindspore import Tensor, jit >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
>>> import numpy as np >>> from mindspore import Tensor, jit >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(backend="ms_backend") ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
| 2.5.0 | 2.6.0 |
>>> import numpy as np >>> from mindspore import Tensor, jit, JitConfig >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(jit_config=JitConfig(infer_boost="on")) ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
>>> import numpy as np >>> from mindspore import Tensor, jit >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> @jit(infer_boost="on") ... def tensor_add_with_dec(x, y): ... z = x + y ... return z ... >>> out = tensor_add_with_dec(x, y) |
| 2.5.0 | 2.6.0 |
>>> from mindspore.profiler import tensor_board_trace_handler |
>>> from mindspore.profiler import tensorboard_trace_handler |
| 2.5.0 | 2.6.0 |
>>> import mindspore as ms
>>> ms.set_context(
... ascend_config = {"exception_dump": "2"}
... )
|
>>> import mindspore as ms
>>> ms.device_context.ascend.op_debug.aclinit_config(
... {"dump": {"dump_scene": "lite_exception"}}
... )
|
| 2.5.0 | 2.6.0 |
>>> import mindspore as ms >>> tensor = ms.Tensor([1,1,1], dtype=ms.float32) >>> print(tensor) [1. 1. 1.] |
>>> import mindspore as ms
>>> tensor = ms.Tensor([1,1,1], dtype=ms.float32)
>>> print(tensor)
Tensor(shape=[3],
dtype=Float32,
value= [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00])
|