Use name for output variables instead of out in JIT (#17386)

Summary:
This adds 88 matches.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17386

Differential Revision: D14179139

Pulled By: cpuhrsch

fbshipit-source-id: 2c3263b8e4d084db84791e53290e8c8b1b7aecd5

diff --git a/aten/src/ATen/native/README.md b/aten/src/ATen/native/README.md
index f518b31..612c943 100644 (file)
--- a/aten/src/ATen/native/README.md
+++ b/aten/src/ATen/native/README.md
@@ -112,6 +112,12 @@ argument name you provide, and a rename of an argument name is considered a BC-b
 change (e.g., you will probably need to update `tools/autograd/derivatives.yaml` at
 least). For more details please see the section on `variants`.
 
+As a convention we use 'out' to indicate an output argument. This aligns with the
+Python bindings. Even if a function might not be used in the Python bindings, we
+still advise to follow this convention. Check the generated code when making a change
+to make sure you're not breaking the API when renaming an argument name of an
+existing function.
+
 TODO: Do argument names affect Python keyword arguments?
 
 **Defaults.** Any suffix of arguments can have a default value defined;

diff --git a/aten/src/ATen/native/native_functions.yaml b/aten/src/ATen/native/native_functions.yaml
index 5a7536c..11d50a0 100644 (file)
--- a/aten/src/ATen/native/native_functions.yaml
+++ b/aten/src/ATen/native/native_functions.yaml
@@ -3881,17 +3881,20 @@
 - func: pow(Scalar self, Tensor exponent) -> Tensor
   matches_jit_signature: True
 
-- func: normal(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) output) -> Tensor(a!)
+- func: normal(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
 
 - func: normal(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor
   matches_jit_signature: True
 
-- func: normal(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) output) -> Tensor(a!)
+- func: normal(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
 
 - func: normal(float mean, Tensor std, *, Generator? generator=None) -> Tensor
   matches_jit_signature: True
 
-- func: normal(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) output) -> Tensor(a!)
+- func: normal(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
 
 - func: normal(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor
   matches_jit_signature: True
@@ -3900,14 +3903,16 @@
   matches_jit_signature: True
   variants: method, function
 
-- func: _dirichlet_grad(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) output) -> Tensor(a!)
+- func: _dirichlet_grad(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
 
 - func: _dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor
   matches_jit_signature: True
 
 ## NN wrappers
 
-- func: binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) output) -> Tensor(a!)
+- func: binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor
@@ -3915,13 +3920,15 @@
   python_module: nn
 
 - func: binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor weight, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor weight, int reduction) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: mse_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) output) -> Tensor(a!)
+- func: mse_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
@@ -3929,13 +3936,15 @@
   python_module: nn
 
 - func: mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: l1_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) output) -> Tensor(a!)
+- func: l1_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
@@ -3943,13 +3952,15 @@
   python_module: nn
 
 - func: l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) output) -> Tensor(a!)
+- func: multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensor
@@ -3957,13 +3968,15 @@
   python_module: nn
 
 - func: multi_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor weight, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: multi_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor weight, int reduction) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: multilabel_margin_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) output) -> Tensor(a!)
+- func: multilabel_margin_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: multilabel_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
@@ -3978,13 +3991,15 @@
   python_module: nn
 
 - func: multilabel_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: multilabel_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) output) -> Tensor(a!)
+- func: nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensor
@@ -3999,13 +4014,15 @@
   python_module: nn
 
 - func: nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) output) -> Tensor(a!)
+- func: nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensor
@@ -4020,13 +4037,15 @@
   python_module: nn
 
 - func: nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: smooth_l1_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) output) -> Tensor(a!)
+- func: smooth_l1_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: smooth_l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
@@ -4034,13 +4053,15 @@
   python_module: nn
 
 - func: smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: soft_margin_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) output) -> Tensor(a!)
+- func: soft_margin_loss(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
@@ -4048,13 +4069,15 @@
   python_module: nn
 
 - func: soft_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: soft_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) output) -> Tensor(a!)
+- func: elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor
@@ -4062,6 +4085,7 @@
   python_module: nn
 
 - func: elu_backward(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: elu_backward(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, Tensor output) -> Tensor
@@ -4072,7 +4096,8 @@
   matches_jit_signature: True
   python_module: nn
 
-- func: glu(Tensor self, int dim=-1, *, Tensor(a!) output) -> Tensor(a!)
+- func: glu(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: glu(Tensor self, int dim=-1) -> Tensor
@@ -4080,13 +4105,15 @@
   python_module: nn
 
 - func: glu_backward(Tensor grad_output, Tensor self, int dim, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) output) -> Tensor(a!)
+- func: hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor
@@ -4094,6 +4121,7 @@
   python_module: nn
 
 - func: hardtanh_backward(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: hardtanh_backward(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val) -> Tensor
@@ -4104,7 +4132,8 @@
   matches_jit_signature: True
   python_module: nn
 
-- func: leaky_relu(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) output) -> Tensor(a!)
+- func: leaky_relu(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor
@@ -4112,6 +4141,7 @@
   python_module: nn
 
 - func: leaky_relu_backward(Tensor grad_output, Tensor self, Scalar negative_slope, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: leaky_relu_backward(Tensor grad_output, Tensor self, Scalar negative_slope) -> Tensor
@@ -4122,7 +4152,8 @@
   matches_jit_signature: True
   python_module: nn
 
-- func: log_sigmoid(Tensor self, *, Tensor(a!) output) -> Tensor(a!)
+- func: log_sigmoid(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: log_sigmoid(Tensor self) -> Tensor
@@ -4137,13 +4168,14 @@
   python_module: nn
 
 - func: log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: rrelu_with_noise(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) output) -> Tensor(a!)
+- func: rrelu_with_noise(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
   matches_jit_signature: False # TODO: The default value of upper and some Caffe2 builds will trigger the assert.
   python_module: nn
 
@@ -4152,6 +4184,7 @@
   python_module: nn
 
 - func: rrelu_with_noise_backward(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: rrelu_with_noise_backward(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training) -> Tensor
@@ -4162,7 +4195,8 @@
   matches_jit_signature: False # TODO: The default value of upper and some Caffe2 builds will trigger the assert.
   python_module: nn
 
-- func: softplus(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) output) -> Tensor(a!)
+- func: softplus(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: softplus(Tensor self, Scalar beta=1, Scalar threshold=20) -> Tensor
@@ -4170,13 +4204,15 @@
   python_module: nn
 
 - func: softplus_backward(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: softplus_backward(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, Tensor output) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: softshrink(Tensor self, Scalar lambd=0.5, *, Tensor(a!) output) -> Tensor(a!)
+- func: softshrink(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: softshrink(Tensor self, Scalar lambd=0.5) -> Tensor
@@ -4184,13 +4220,15 @@
   python_module: nn
 
 - func: softshrink_backward(Tensor grad_output, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: softshrink_backward(Tensor grad_output, Tensor self, Scalar lambd) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: adaptive_avg_pool2d(Tensor self, int[2] output_size, *, Tensor(a!) output) -> Tensor(a!)
+- func: adaptive_avg_pool2d(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: adaptive_avg_pool2d_out_cpu
@@ -4213,7 +4251,8 @@
     CPU: adaptive_avg_pool2d_backward_cpu
     CUDA: adaptive_avg_pool2d_backward_cuda
 
-- func: adaptive_avg_pool3d(Tensor self, int[3] output_size, *, Tensor(a!) output) -> Tensor(a!)
+- func: adaptive_avg_pool3d(Tensor self, int[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: adaptive_avg_pool3d(Tensor self, int[3] output_size) -> Tensor
@@ -4221,6 +4260,7 @@
   python_module: nn
 
 - func: adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor
@@ -4237,6 +4277,7 @@
   python_module: nn
 
 - func: adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor
@@ -4253,13 +4294,15 @@
   python_module: nn
 
 - func: adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, *, Tensor(a!) output) -> Tensor(a!)
+- func: avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True) -> Tensor
@@ -4267,13 +4310,15 @@
   python_module: nn
 
 - func: avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, *, Tensor(a!) output) -> Tensor(a!)
+- func: avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True) -> Tensor
@@ -4281,6 +4326,7 @@
   python_module: nn
 
 - func: avg_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: avg_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad) -> Tensor
@@ -4303,6 +4349,7 @@
     CUDA: fractional_max_pool2d_cuda
 
 - func: fractional_max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: fractional_max_pool2d_backward_out_cpu
@@ -4331,6 +4378,7 @@
     CUDA: fractional_max_pool3d_cuda
 
 - func: fractional_max_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: fractional_max_pool3d_backward_out_cpu
@@ -4353,6 +4401,7 @@
   python_module: nn
 
 - func: max_pool2d_with_indices_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: max_pool2d_with_indices_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices) -> Tensor
@@ -4369,13 +4418,15 @@
   python_module: nn
 
 - func: max_pool3d_with_indices_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: max_pool3d_with_indices_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: max_unpool2d(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) output) -> Tensor(a!)
+- func: max_unpool2d(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: max_unpool2d(Tensor self, Tensor indices, int[2] output_size) -> Tensor
@@ -4383,13 +4434,15 @@
   python_module: nn
 
 - func: max_unpool2d_backward(Tensor grad_output, Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: max_unpool2d_backward(Tensor grad_output, Tensor self, Tensor indices, int[2] output_size) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) output) -> Tensor(a!)
+- func: max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor
@@ -4397,13 +4450,15 @@
   python_module: nn
 
 - func: max_unpool3d_backward(Tensor grad_output, Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: max_unpool3d_backward(Tensor grad_output, Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: reflection_pad1d(Tensor self, int[2] padding, *, Tensor(a!) output) -> Tensor(a!)
+- func: reflection_pad1d(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: reflection_pad1d_out_cpu
@@ -4417,6 +4472,7 @@
     CUDA: reflection_pad1d_cuda
 
 - func: reflection_pad1d_backward(Tensor grad_output, Tensor self, int[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: reflection_pad1d_backward_out_cpu
@@ -4429,7 +4485,8 @@
     CPU: reflection_pad1d_backward_cpu
     CUDA: reflection_pad1d_backward_cuda
 
-- func: reflection_pad2d(Tensor self, int[4] padding, *, Tensor(a!) output) -> Tensor(a!)
+- func: reflection_pad2d(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: reflection_pad2d_out_cpu
@@ -4443,6 +4500,7 @@
     CUDA: reflection_pad2d_cuda
 
 - func: reflection_pad2d_backward(Tensor grad_output, Tensor self, int[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: reflection_pad2d_backward_out_cpu
@@ -4455,7 +4513,8 @@
     CPU: reflection_pad2d_backward_cpu
     CUDA: reflection_pad2d_backward_cuda
 
-- func: replication_pad1d(Tensor self, int[2] padding, *, Tensor(a!) output) -> Tensor(a!)
+- func: replication_pad1d(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: replication_pad1d_out_cpu
@@ -4469,6 +4528,7 @@
     CUDA: replication_pad1d_cuda
 
 - func: replication_pad1d_backward(Tensor grad_output, Tensor self, int[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: replication_pad1d_backward_out_cpu
@@ -4481,7 +4541,8 @@
     CPU: replication_pad1d_backward_cpu
     CUDA: replication_pad1d_backward_cuda
 
-- func: replication_pad2d(Tensor self, int[4] padding, *, Tensor(a!) output) -> Tensor(a!)
+- func: replication_pad2d(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: replication_pad2d_out_cpu
@@ -4495,6 +4556,7 @@
     CUDA: replication_pad2d_cuda
 
 - func: replication_pad2d_backward(Tensor grad_output, Tensor self, int[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: replication_pad2d_backward_out_cpu
@@ -4507,7 +4569,8 @@
     CPU: replication_pad2d_backward_cpu
     CUDA: replication_pad2d_backward_cuda
 
-- func: replication_pad3d(Tensor self, int[6] padding, *, Tensor(a!) output) -> Tensor(a!)
+- func: replication_pad3d(Tensor self, int[6] padding, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: replication_pad3d_out_cpu
@@ -4521,6 +4584,7 @@
     CUDA: replication_pad3d_cuda
 
 - func: replication_pad3d_backward(Tensor grad_output, Tensor self, int[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
   dispatch:
     CPU: replication_pad3d_backward_out_cpu
@@ -4533,7 +4597,8 @@
     CPU: replication_pad3d_backward_cpu
     CUDA: replication_pad3d_backward_cuda
 
-- func: upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, *, Tensor(a!) output) -> Tensor(a!)
+- func: upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_linear1d(Tensor self, int[1] output_size, bool align_corners) -> Tensor
@@ -4541,13 +4606,15 @@
   python_module: nn
 
 - func: upsample_linear1d_backward(Tensor grad_output, int[1] output_size, int[3] input_size, bool align_corners, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_linear1d_backward(Tensor grad_output, int[1] output_size, int[3] input_size, bool align_corners) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) output) -> Tensor(a!)
+- func: upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners) -> Tensor
@@ -4555,13 +4622,15 @@
   python_module: nn
 
 - func: upsample_bilinear2d_backward(Tensor grad_output, int[2] output_size, int[4] input_size, bool align_corners, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_bilinear2d_backward(Tensor grad_output, int[2] output_size, int[4] input_size, bool align_corners) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: upsample_bicubic2d(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) output) -> Tensor(a!)
+- func: upsample_bicubic2d(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_bicubic2d(Tensor self, int[2] output_size, bool align_corners) -> Tensor
@@ -4569,13 +4638,15 @@
   python_module: nn
 
 - func: upsample_bicubic2d_backward(Tensor grad_output, int[2] output_size, int[4] input_size, bool align_corners, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_bicubic2d_backward(Tensor grad_output, int[2] output_size, int[4] input_size, bool align_corners) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, *, Tensor(a!) output) -> Tensor(a!)
+- func: upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners) -> Tensor
@@ -4583,13 +4654,15 @@
   python_module: nn
 
 - func: upsample_trilinear3d_backward(Tensor grad_output, int[3] output_size, int[5] input_size, bool align_corners, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_trilinear3d_backward(Tensor grad_output, int[3] output_size, int[5] input_size, bool align_corners) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: upsample_nearest1d(Tensor self, int[1] output_size, *, Tensor(a!) output) -> Tensor(a!)
+- func: upsample_nearest1d(Tensor self, int[1] output_size, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_nearest1d(Tensor self, int[1] output_size) -> Tensor
@@ -4597,13 +4670,15 @@
   python_module: nn
 
 - func: upsample_nearest1d_backward(Tensor grad_output, int[1] output_size, int[3] input_size, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_nearest1d_backward(Tensor grad_output, int[1] output_size, int[3] input_size) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: upsample_nearest2d(Tensor self, int[2] output_size, *, Tensor(a!) output) -> Tensor(a!)
+- func: upsample_nearest2d(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_nearest2d(Tensor self, int[2] output_size) -> Tensor
@@ -4611,13 +4686,15 @@
   python_module: nn
 
 - func: upsample_nearest2d_backward(Tensor grad_output, int[2] output_size, int[4] input_size, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_nearest2d_backward(Tensor grad_output, int[2] output_size, int[4] input_size) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: upsample_nearest3d(Tensor self, int[3] output_size, *, Tensor(a!) output) -> Tensor(a!)
+- func: upsample_nearest3d(Tensor self, int[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_nearest3d(Tensor self, int[3] output_size) -> Tensor
@@ -4625,6 +4702,7 @@
   python_module: nn
 
 - func: upsample_nearest3d_backward(Tensor grad_output, int[3] output_size, int[5] input_size, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: upsample_nearest3d_backward(Tensor grad_output, int[3] output_size, int[5] input_size) -> Tensor
@@ -4632,6 +4710,7 @@
   python_module: nn
 
 - func: sigmoid_backward(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: sigmoid_backward(Tensor grad_output, Tensor output) -> Tensor
@@ -4639,13 +4718,15 @@
   python_module: nn
 
 - func: tanh_backward(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: tanh_backward(Tensor grad_output, Tensor output) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: thnn_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) output) -> Tensor(a!)
+- func: thnn_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: thnn_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1) -> Tensor
@@ -4666,7 +4747,8 @@
   matches_jit_signature: True
   python_module: nn
 
-- func: thnn_conv_transpose3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1, *, Tensor(a!) output) -> Tensor(a!)
+- func: thnn_conv_transpose3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: thnn_conv_transpose3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1) -> Tensor
@@ -4687,7 +4769,8 @@
   matches_jit_signature: True
   python_module: nn
 
-- func: thnn_conv2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, *, Tensor(a!) output) -> Tensor(a!)
+- func: thnn_conv2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: thnn_conv2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0) -> Tensor
@@ -4708,14 +4791,16 @@
   matches_jit_signature: True
   python_module: nn
 
-- func: thnn_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, *, Tensor(a!) output) -> Tensor(a!)
+- func: thnn_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: thnn_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensor
   matches_jit_signature: True
   python_module: nn
 
-- func: thnn_conv_depthwise2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation, *, Tensor(a!) output) -> Tensor(a!)
+- func: thnn_conv_depthwise2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: thnn_conv_depthwise2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation) -> Tensor
@@ -4729,7 +4814,8 @@
   matches_jit_signature: True
   python_module: nn
 
-- func: thnn_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, *, Tensor(a!) output) -> Tensor(a!)
+- func: thnn_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: thnn_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0) -> Tensor
@@ -4750,7 +4836,8 @@
   matches_jit_signature: True
   python_module: nn
 
-- func: thnn_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, *, Tensor(a!) output) -> Tensor(a!)
+- func: thnn_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: thnn_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensor
@@ -4771,7 +4858,8 @@
   matches_jit_signature: True
   python_module: nn
 
-- func: thnn_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, *, Tensor(a!) output) -> Tensor(a!)
+- func: thnn_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
+  matches_jit_signature: True
   python_module: nn
 
 - func: thnn_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensor

diff --git a/tools/autograd/gen_python_functions.py b/tools/autograd/gen_python_functions.py
index 91bc537..7f9ebad 100644 (file)
--- a/tools/autograd/gen_python_functions.py
+++ b/tools/autograd/gen_python_functions.py
@@ -891,7 +891,14 @@ def get_python_signature(declaration, include_out):
             typename = 'TensorList[{}]'.format(len(typenames))
         else:
             typename = typenames[0]
-        py_formal_args.append(typename + ' out=None')
+        if len(output_args) == 1:
+            # The nn module bindings are often not exposed to the user directly
+            # but via torch.nn modules and functionals.
+            py_formal_args.append(typename + ' ' + output_args[0]['name'] + '=None')
+        else:
+            # NB: For more than 1 output args the type name is a TensorList
+            # and as such we don't (yet) need to consider the naming.
+            py_formal_args.append(typename + ' out=None')
 
     # we could put this in the loop above but we want to ensure both type dispatched args
     # and python binding arguments are after the out argument; this matches the case

diff --git a/tools/jit/gen_jit_dispatch.py b/tools/jit/gen_jit_dispatch.py
index cfc979b..a47ca47 100644 (file)
--- a/tools/jit/gen_jit_dispatch.py
+++ b/tools/jit/gen_jit_dispatch.py
@@ -449,7 +449,7 @@ def match_signature(decl, constructed_string, should_match_schema):
 
 def signature(decl, should_match_schema=True):
     def format_arg(arg):
-        name = arg['name'] if not arg.get('output') else 'out'
+        name = arg['name']
         typ = jit_type_of(arg)
         decl = '{} {}'.format(typ, name)
         if 'default' in arg: