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whisky-12SigureMo
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[xdoctest][task 171-180] reformat example code with google style in audio/* (PaddlePaddle#57111)
* [Doctest]fix No.171-180, test=docs_preview * [Doctest]fix No.171-180, test=docs_preview * fix python/paddle/amp/auto_cast.py * fix python/paddle/amp/debugging.py * fix python/paddle/amp/grad_scaler.py * fix python/paddle/device/__init__.py * fix python/paddle/profiler/utils.py * fix python/paddle/sparse/binary.py * fix python/paddle/sparse/creation.py * fix ci error; * remove prompt before output * skip some output --------- Co-authored-by: SigureMo <[email protected]>
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Diff for: ‎python/paddle/amp/auto_cast.py

+105-78
Original file line numberDiff line numberDiff line change
@@ -298,21 +298,27 @@ def amp_guard(
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Examples:
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.. code-block:: python
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import numpy as np
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import paddle
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.. code-block:: python
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data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
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conv2d = paddle.nn.Conv2D(3, 2, 3)
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data = paddle.to_tensor(data)
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with paddle.amp.amp_guard():
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conv = conv2d(data)
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print(conv.dtype) # FP16
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with paddle.amp.amp_guard(enable=False):
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conv = conv2d(data)
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print(conv.dtype) # FP32
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>>> # doctest: +REQUIRES(env:GPU)
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>>> import paddle
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>>> data = paddle.uniform([10, 3, 32, 32], paddle.float32, -1, 1)
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>>> conv2d = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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>>> conv2d = paddle.amp.amp_decorate(models=conv2d, level='O2')
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>>> with paddle.amp.amp_guard():
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... conv = conv2d(data)
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... print(conv.dtype)
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>>> # doctest: +SKIP("This has diff in xdoctest env")
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paddle.float16
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>>> # doctest: -SKIP
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...
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>>> with paddle.amp.amp_guard(enable=False):
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... conv = conv2d(data)
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... print(conv.dtype)
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>>> # doctest: +SKIP("This has diff in xdoctest env")
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paddle.float32
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>>> # doctest: -SKIP
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"""
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amp_state = locals()
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global _g_amp_state_
@@ -515,50 +521,53 @@ def amp_decorate(
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Examples:
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.. code-block:: python
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.. code-block:: python
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# required: gpu
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# Demo1: single model and optimizer:
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import paddle
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>>> # doctest: +REQUIRES(env:GPU)
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>>> # Demo1: single model and optimizer:
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>>> import paddle
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>>> paddle.device.set_device('gpu')
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model = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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optimizer = paddle.optimizer.SGD(parameters=model.parameters())
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>>> model = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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>>> optimizer = paddle.optimizer.SGD(parameters=model.parameters())
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model, optimizer = paddle.amp.amp_decorate(models=model, optimizers=optimizer, level='O2')
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>>> model, optimizer = paddle.amp.amp_decorate(models=model, optimizers=optimizer, level='O2')
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data = paddle.rand([10, 3, 32, 32])
536+
>>> data = paddle.rand([10, 3, 32, 32])
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with paddle.amp.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
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output = model(data)
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print(output.dtype) # FP16
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>>> with paddle.amp.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
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... output = model(data)
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... print(output.dtype)
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paddle.float16
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# required: gpu
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# Demo2: multi models and optimizers:
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model2 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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optimizer2 = paddle.optimizer.Adam(parameters=model2.parameters())
543+
>>> # Demo2: multi models and optimizers:
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>>> model2 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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>>> optimizer2 = paddle.optimizer.Adam(parameters=model2.parameters())
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models, optimizers = paddle.amp.amp_decorate(models=[model, model2], optimizers=[optimizer, optimizer2], level='O2')
547+
>>> models, optimizers = paddle.amp.amp_decorate(models=[model, model2], optimizers=[optimizer, optimizer2], level='O2')
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data = paddle.rand([10, 3, 32, 32])
549+
>>> data = paddle.rand([10, 3, 32, 32])
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with paddle.amp.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
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output = models[0](data)
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output2 = models[1](data)
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print(output.dtype) # FP16
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print(output2.dtype) # FP16
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>>> with paddle.amp.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
552+
... output = models[0](data)
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... output2 = models[1](data)
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... print(output.dtype)
555+
... print(output2.dtype)
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paddle.float16
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paddle.float16
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# required: gpu
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# Demo3: optimizers is None:
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model3 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
553-
optimizer3 = paddle.optimizer.Adam(parameters=model2.parameters())
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>>> # Demo3: optimizers is None:
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>>> model3 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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>>> optimizer3 = paddle.optimizer.Adam(parameters=model2.parameters())
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model = paddle.amp.amp_decorate(models=model3, level='O2')
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>>> model = paddle.amp.amp_decorate(models=model3, level='O2')
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data = paddle.rand([10, 3, 32, 32])
565+
>>> data = paddle.rand([10, 3, 32, 32])
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with paddle.amp.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
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output = model(data)
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print(output.dtype) # FP16
567+
>>> with paddle.amp.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
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... output = model(data)
569+
... print(output.dtype)
570+
paddle.float16
562571
"""
563572
if not (level in ['O1', 'O2']):
564573
raise ValueError(
@@ -717,34 +726,50 @@ def auto_cast(
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Examples:
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.. code-block:: python
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import paddle
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conv2d = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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data = paddle.rand([10, 3, 32, 32])
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with paddle.amp.auto_cast():
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conv = conv2d(data)
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print(conv.dtype) # paddle.float16
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with paddle.amp.auto_cast(enable=False):
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conv = conv2d(data)
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print(conv.dtype) # paddle.float32
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.. code-block:: python
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with paddle.amp.auto_cast(custom_black_list={'conv2d'}):
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conv = conv2d(data)
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print(conv.dtype) # paddle.float32
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>>> # doctest: +REQUIRES(env:GPU)
732+
>>> import paddle
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a = paddle.rand([2,3])
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b = paddle.rand([2,3])
741-
with paddle.amp.auto_cast(custom_white_list={'elementwise_add'}):
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c = a + b
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print(c.dtype) # paddle.float16
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>>> conv2d = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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>>> data = paddle.rand([10, 3, 32, 32])
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with paddle.amp.auto_cast(custom_white_list={'elementwise_add'}, level='O2'):
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d = a + b
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print(d.dtype) # paddle.float16
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>>> with paddle.amp.auto_cast():
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... conv = conv2d(data)
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... print(conv.dtype)
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>>> # doctest: +SKIP("This has diff in xdoctest env")
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paddle.float16
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>>> # doctest: -SKIP
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744+
>>> with paddle.amp.auto_cast(enable=False):
745+
... conv = conv2d(data)
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... print(conv.dtype)
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>>> # doctest: +SKIP("This has diff in xdoctest env")
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paddle.float32
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>>> # doctest: -SKIP
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>>> with paddle.amp.auto_cast(custom_black_list={'conv2d'}):
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... conv = conv2d(data)
753+
... print(conv.dtype)
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>>> # doctest: +SKIP("This has diff in xdoctest env")
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paddle.float32
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>>> # doctest: -SKIP
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>>> a = paddle.rand([2, 3])
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>>> b = paddle.rand([2, 3])
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>>> with paddle.amp.auto_cast(custom_white_list={'elementwise_add'}):
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... c = a + b
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... print(c.dtype)
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>>> # doctest: +SKIP("This has diff in xdoctest env")
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paddle.float16
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>>> # doctest: -SKIP
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>>> with paddle.amp.auto_cast(custom_white_list={'elementwise_add'}, level='O2'):
768+
... d = a + b
769+
... print(d.dtype)
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>>> # doctest: +SKIP("This has diff in xdoctest env")
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paddle.float16
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>>> # doctest: -SKIP
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"""
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return amp_guard(
@@ -800,9 +825,9 @@ def decorate(
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>>> with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
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... output = model(data)
803-
... assert output.dtype == paddle.float16
828+
... print(output.dtype)
829+
paddle.float16
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>>> # doctest: +REQUIRES(env:GPU)
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>>> # Demo2: multi models and optimizers:
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>>> model2 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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>>> optimizer2 = paddle.optimizer.Adam(parameters=model2.parameters())
@@ -812,12 +837,13 @@ def decorate(
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>>> data = paddle.rand([10, 3, 32, 32])
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>>> with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
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... output = models[0](data)
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... output2 = models[1](data)
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... assert output.dtype == paddle.float16
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... assert output2.dtype == paddle.float16
840+
... output = models[0](data)
841+
... output2 = models[1](data)
842+
... print(output.dtype)
843+
... print(output2.dtype)
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paddle.float16
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paddle.float16
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>>> # doctest: +REQUIRES(env:GPU)
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>>> # Demo3: optimizers is None:
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>>> model3 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
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>>> optimizer3 = paddle.optimizer.Adam(parameters=model3.parameters())
@@ -827,8 +853,9 @@ def decorate(
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>>> data = paddle.rand([10, 3, 32, 32])
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>>> with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
830-
... output = model(data)
831-
... assert output.dtype == paddle.float16
856+
... output = model(data)
857+
... print(output.dtype)
858+
paddle.float16
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"""
834861
return amp_decorate(

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