diff --git a/docs/api/paddle/autograd/PyLayerContext_cn.rst b/docs/api/paddle/autograd/PyLayerContext_cn.rst index 4d8c6ddbef5..1ce8d77241c 100644 --- a/docs/api/paddle/autograd/PyLayerContext_cn.rst +++ b/docs/api/paddle/autograd/PyLayerContext_cn.rst @@ -11,25 +11,7 @@ PyLayerContext 代码示例 :::::::::::: -.. code-block:: python - - import paddle - from paddle.autograd import PyLayer - - class cus_tanh(PyLayer): - @staticmethod - def forward(ctx, x): - # ctx is a object of PyLayerContext. - y = paddle.tanh(x) - ctx.save_for_backward(y) - return y - - @staticmethod - def backward(ctx, dy): - # ctx is a object of PyLayerContext. - y, = ctx.saved_tensor() - grad = dy * (1 - paddle.square(y)) - return grad +COPY-FROM: paddle.autograd.PyLayerContext 方法 @@ -53,26 +35,7 @@ None **代码示例** -.. code-block:: python - - import paddle - from paddle.autograd import PyLayer - - class cus_tanh(PyLayer): - @staticmethod - def forward(ctx, x): - # ctx is a context object that store some objects for backward. - y = paddle.tanh(x) - # Pass tensors to backward. - ctx.save_for_backward(y) - return y - - @staticmethod - def backward(ctx, dy): - # Get the tensors passed by forward. - y, = ctx.saved_tensor() - grad = dy * (1 - paddle.square(y)) - return grad +COPY-FROM: paddle.autograd.PyLayerContext.save_for_backward saved_tensor() @@ -87,26 +50,7 @@ saved_tensor() **代码示例** -.. code-block:: python - - import paddle - from paddle.autograd import PyLayer - - class cus_tanh(PyLayer): - @staticmethod - def forward(ctx, x): - # ctx is a context object that store some objects for backward. - y = paddle.tanh(x) - # Pass tensors to backward. - ctx.save_for_backward(y) - return y - - @staticmethod - def backward(ctx, dy): - # Get the tensors passed by forward. - y, = ctx.saved_tensor() - grad = dy * (1 - paddle.square(y)) - return grad +COPY-FROM: paddle.autograd.PyLayerContext.saved_tensor mark_not_inplace(self, *tensors) @@ -129,33 +73,7 @@ None **代码示例** -.. code-block:: python - - import paddle - - class Exp(paddle.autograd.PyLayer): - @staticmethod - def forward(ctx, x): - ctx.mark_not_inplace(x) - return x - - @staticmethod - def backward(ctx, grad_output): - out = grad_output.exp() - return out - - x = paddle.randn((1, 1)) - x.stop_gradient = False - attn_layers = [] - for idx in range(0, 2): - attn_layers.append(Exp()) - - for step in range(0, 2): - a = x - for j in range(0,2): - a = attn_layers[j].apply(x) - a.backward() - +COPY-FROM: paddle.autograd.PyLayerContext.mark_not_inplace mark_non_differentiable(self, *tensors) ''''''''' @@ -179,32 +97,7 @@ None **代码示例** -.. code-block:: python - - import os - os.environ['FLAGS_enable_eager_mode'] = '1' - import paddle - from paddle.autograd import PyLayer - import numpy as np - - class Tanh(PyLayer): - @staticmethod - def forward(ctx, x): - a = x + x - b = x + x + x - ctx.mark_non_differentiable(a) - return a, b - - @staticmethod - def backward(ctx, grad_a, grad_b): - assert np.equal(grad_a.numpy(), paddle.zeros([1]).numpy()) - assert np.equal(grad_b.numpy(), paddle.ones([1], dtype="float64").numpy()) - return grad_b - - x = paddle.ones([1], dtype="float64") - x.stop_gradient = False - a, b = Tanh.apply(x) - b.sum().backward() +COPY-FROM: paddle.autograd.PyLayerContext.mark_non_differentiable set_materialize_grads(self, value) ''''''''' @@ -227,39 +120,4 @@ None **代码示例** -.. code-block:: python - - import os - os.environ['FLAGS_enable_eager_mode'] = '1' - import paddle - from paddle.autograd import PyLayer - import numpy as np - - class Tanh(PyLayer): - @staticmethod - def forward(ctx, x): - return x+x+x, x+x - - @staticmethod - def backward(ctx, grad, grad2): - assert np.equal(grad2.numpy(), paddle.zeros([1]).numpy()) - return grad - - class Tanh2(PyLayer): - @staticmethod - def forward(ctx, x): - ctx.set_materialize_grads(False) - return x+x+x, x+x - - @staticmethod - def backward(ctx, grad, grad2): - assert grad2==None - return grad - - x = paddle.ones([1], dtype="float64") - x.stop_gradient = False - Tanh.apply(x)[0].backward() - - x2 = paddle.ones([1], dtype="float64") - x2.stop_gradient = False - Tanh2.apply(x2)[0].backward() +COPY-FROM: paddle.autograd.PyLayerContext.set_materialize_grads diff --git a/docs/api/paddle/autograd/PyLayer_cn.rst b/docs/api/paddle/autograd/PyLayer_cn.rst index f8267ef0b6b..4c4ca717d39 100644 --- a/docs/api/paddle/autograd/PyLayer_cn.rst +++ b/docs/api/paddle/autograd/PyLayer_cn.rst @@ -19,38 +19,7 @@ Paddle 通过创建 ``PyLayer`` 子类的方式实现 Python 端自定义算子 代码示例 :::::::::::: -.. code-block:: python - - import paddle - from paddle.autograd import PyLayer - - # Inherit from PyLayer - class cus_tanh(PyLayer): - @staticmethod - def forward(ctx, x, func1, func2=paddle.square): - # ctx is a context object that store some objects for backward. - ctx.func = func2 - y = func1(x) - # Pass tensors to backward. - ctx.save_for_backward(y) - return y - - @staticmethod - # forward has only one output, so there is only one gradient in the input of backward. - def backward(ctx, dy): - # Get the tensors passed by forward. - y, = ctx.saved_tensor() - grad = dy * (1 - ctx.func(y)) - # forward has only one input, so only one gradient tensor is returned. - return grad - - - data = paddle.randn([2, 3], dtype="float64") - data.stop_gradient = False - z = cus_tanh.apply(data, func1=paddle.tanh) - z.mean().backward() - - print(data.grad) +COPY-FROM: paddle.autograd.PyLayer 方法 @@ -71,25 +40,7 @@ Tensor 或至少包含一个 Tensor 的 list/tuple **代码示例** -.. code-block:: python - - import paddle - from paddle.autograd import PyLayer - - class cus_tanh(PyLayer): - @staticmethod - def forward(ctx, x): - y = paddle.tanh(x) - # Pass tensors to backward. - ctx.save_for_backward(y) - return y - - @staticmethod - def backward(ctx, dy): - # Get the tensors passed by forward. - y, = ctx.saved_tensor() - grad = dy * (1 - paddle.square(y)) - return grad +COPY-FROM: paddle.autograd.PyLayer.forward backward(ctx, *args, **kwargs) @@ -108,25 +59,7 @@ backward(ctx, *args, **kwargs) **代码示例** -.. code-block:: python - - import paddle - from paddle.autograd import PyLayer - - class cus_tanh(PyLayer): - @staticmethod - def forward(ctx, x): - y = paddle.tanh(x) - # Pass tensors to backward. - ctx.save_for_backward(y) - return y - - @staticmethod - def backward(ctx, dy): - # Get the tensors passed by forward. - y, = ctx.saved_tensor() - grad = dy * (1 - paddle.square(y)) - return grad +COPY-FROM: paddle.autograd.PyLayer.backward apply(cls, *args, **kwargs) @@ -145,29 +78,4 @@ Tensor 或至少包含一个 Tensor 的 list/tuple **代码示例** -.. code-block:: python - - import paddle - from paddle.autograd import PyLayer - - class cus_tanh(PyLayer): - @staticmethod - def forward(ctx, x, func1, func2=paddle.square): - ctx.func = func2 - y = func1(x) - # Pass tensors to backward. - ctx.save_for_backward(y) - return y - - @staticmethod - def backward(ctx, dy): - # Get the tensors passed by forward. - y, = ctx.saved_tensor() - grad = dy * (1 - ctx.func(y)) - return grad - - - data = paddle.randn([2, 3], dtype="float64") - data.stop_gradient = False - # run custom Layer. - z = cus_tanh.apply(data, func1=paddle.tanh) +COPY-FROM: paddle.autograd.PyLayer diff --git a/docs/api/paddle/autograd/saved_tensors_hooks_cn.rst b/docs/api/paddle/autograd/saved_tensors_hooks_cn.rst index 8e2332e5bf9..452596554e1 100644 --- a/docs/api/paddle/autograd/saved_tensors_hooks_cn.rst +++ b/docs/api/paddle/autograd/saved_tensors_hooks_cn.rst @@ -21,57 +21,4 @@ saved_tensors_hooks 用于动态图，注册一对 pack / unpack hook，用于 代码示例 :::::::::::: -.. code-block:: python - - # Example1 - import paddle - - def pack_hook(x): - print("Packing", x) - return x.numpy() - - def unpack_hook(x): - print("UnPacking", x) - return paddle.to_tensor(x) - - a = paddle.ones([3,3]) - b = paddle.ones([3,3]) * 2 - a.stop_gradient = False - b.stop_gradient = False - with paddle.autograd.saved_tensors_hooks(pack_hook, unpack_hook): - y = paddle.multiply(a, b) - y.sum().backward() - - # Example2 - import paddle - from paddle.autograd import PyLayer - - class cus_tanh(PyLayer): - @staticmethod - def forward(ctx, a, b): - y = paddle.multiply(a, b) - ctx.save_for_backward(a, b) - return y - - @staticmethod - def backward(ctx, dy): - a,b = ctx.saved_tensor() - grad_a = dy * a - grad_b = dy * b - return grad_a, grad_b - - def pack_hook(x): - print("Packing", x) - return x.numpy() - - def unpack_hook(x): - print("UnPacking", x) - return paddle.to_tensor(x) - - a = paddle.ones([3,3]) - b = paddle.ones([3,3]) * 2 - a.stop_gradient = False - b.stop_gradient = False - with paddle.autograd.saved_tensors_hooks(pack_hook, unpack_hook): - y = cus_tanh.apply(a, b) - y.sum().backward() +COPY-FROM: paddle.autograd.saved_tensors_hooks