Rename DefaultBackend to CompositeExplicitAutograd (#54470)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54470
```
git grep -l 'DefaultBackend' | xargs sed -i 's/DefaultBackend/CompositeExplicitAutograd/g'
```
Plus a quick fixup in native/README.md
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D27253240
Pulled By: ezyang
fbshipit-source-id: 964df951ea8b52fa72937f3cc66aeaf49a702e6f
diff --git a/BUILD.bazel b/BUILD.bazel
index 00e726a..8f4c26d 100644
--- a/BUILD.bazel
+++ b/BUILD.bazel
@@ -132,7 +132,7 @@
"aten/src/ATen/RegisterSparseCPU.cpp",
"aten/src/ATen/RegisterCompositeImplicitAutograd.cpp",
"aten/src/ATen/RegisterMeta.cpp",
- "aten/src/ATen/RegisterDefaultBackend.cpp",
+ "aten/src/ATen/RegisterCompositeExplicitAutograd.cpp",
"aten/src/ATen/RegisterSchema.cpp",
"aten/src/ATen/CPUFunctions.h",
"aten/src/ATen/CUDAFunctions.h",
diff --git a/aten/src/ATen/core/dispatch/OperatorEntry.cpp b/aten/src/ATen/core/dispatch/OperatorEntry.cpp
index a136009..bdc315e 100644
--- a/aten/src/ATen/core/dispatch/OperatorEntry.cpp
+++ b/aten/src/ATen/core/dispatch/OperatorEntry.cpp
@@ -183,16 +183,16 @@
// For any dispatch key, it'll pick a kernel using the following order:
// (1) Use kernel if it's directly registered to this key
// (2) Handle runtime keys that have kernels available from alias keys
- // (2.1) Use kernel from DispatchKey::DefaultBackend if available.
+ // (2.1) Use kernel from DispatchKey::CompositeExplicitAutograd if available.
// This is used to register a kernel that works for all backend in inference. But it requires
// separate registration for Autograd keys to support training.
// (2.2) Use kernel from DispatchKey::CompositeImplicitAutograd if available.
// For autograd keys, we only use kernel from CompositeImplicitAutograd when there's no direct registration
- // to its corresponding backend key or DefaultBackend. See Note [DefaultBackend and CompositeImplicitAutograd].
+ // to its corresponding backend key or CompositeExplicitAutograd. See Note [CompositeExplicitAutograd and CompositeImplicitAutograd].
// For AutogradOther, we eagerly return ambiguousAutogradOtherKernel_ if there's registration to any of
// its backends and ask backend extender to request a decicated Autograd key for the backend.
// See Note [Ambiguity in AutogradOther kernel] for more details.
- // A DefaultBackend kernel prevents CompositeImplicitAutograd kernel being used for Autograd keys, but it doesn't
+ // A CompositeExplicitAutograd kernel prevents CompositeImplicitAutograd kernel being used for Autograd keys, but it doesn't
// cause confusion for AutogradOther. It's pretty straightforward to use Autograd (if available)
// in this case.
// (2.3) Use kernel from DispatchKey::Autograd if available
@@ -201,11 +201,11 @@
// backend key. See Note [Refresh Runtime Autograd entries in dispatchTable_]
// (3) Use fallthrough kernel that are registered as fallback.
// Alias Key Precedence:
- // DefaultBackend > CompositeImplicitAutograd > Autograd
- // Note [DefaultBackend and CompositeImplicitAutograd]
- // When there're registrations to both DefaultBackend & CompositeImplicitAutograd & Autograd, from (2.2) we know DefaultBackend
+ // CompositeExplicitAutograd > CompositeImplicitAutograd > Autograd
+ // Note [CompositeExplicitAutograd and CompositeImplicitAutograd]
+ // When there're registrations to both CompositeExplicitAutograd & CompositeImplicitAutograd & Autograd, from (2.2) we know CompositeExplicitAutograd
// and Autograd kernels will be picked up and CompositeImplicitAutograd is overriden.
- // This is fine and in practice DefaultBackend and CompositeImplicitAutograd shouldn't co-exist for an op.
+ // This is fine and in practice CompositeExplicitAutograd and CompositeImplicitAutograd shouldn't co-exist for an op.
// TODO: Update alias key precedence after we add new alias keys AutogradDispatchCPUOrCUDA .
// 1. Operator registration
@@ -213,21 +213,21 @@
return {*direct_registration.value(), "kernel"};
}
- // 2.1 Use DefaultBackend kernel if available.
+ // 2.1 Use CompositeExplicitAutograd kernel if available.
// See Note [Undefined in dispatchTable_] for the special handling for Undefined.
- if (dispatch_key == DispatchKey::Undefined || isIncludedInAlias(dispatch_key, DispatchKey::DefaultBackend)) {
- if (auto default_backend_registration = getKernelForDispatchKey(DispatchKey::DefaultBackend)) {
+ if (dispatch_key == DispatchKey::Undefined || isIncludedInAlias(dispatch_key, DispatchKey::CompositeExplicitAutograd)) {
+ if (auto default_backend_registration = getKernelForDispatchKey(DispatchKey::CompositeExplicitAutograd)) {
return {*default_backend_registration.value(), "default backend kernel"};
}
}
- // Note when there's direct registration to DefaultBackend, this code path will only be hit by
+ // Note when there's direct registration to CompositeExplicitAutograd, this code path will only be hit by
// non backend keys (e.g AutogradXXX, Batched etc) due to (2.1).
bool has_backend_kernel =
- hasKernelForAnyDispatchKey(getBackendKeySetFromAutograd(dispatch_key).add(DispatchKey::DefaultBackend));
+ hasKernelForAnyDispatchKey(getBackendKeySetFromAutograd(dispatch_key).add(DispatchKey::CompositeExplicitAutograd));
// 2.2. Use CompositeImplicitAutograd kernel if available. For autograd keys, we only use kernel from CompositeImplicitAutograd
- // when there's no direct registration to its corresponding backend key or DefaultBackend.
+ // when there's no direct registration to its corresponding backend key or CompositeExplicitAutograd.
// For AutogradOther, we return ambiguousAutogradOtherKernel_ if there's registration
// to any of its backends.
// See Note [Undefined in dispatchTable_] for the special handling for Undefined.
@@ -286,9 +286,9 @@
for (auto k : c10::getRuntimeDispatchKeySet(dispatch_key)) {
updateDispatchTableEntry_(dispatcher, k);
}
- // Registration to DefaultBackend and CompositeImplicitAutograd should be populated to Undefined.
+ // Registration to CompositeExplicitAutograd and CompositeImplicitAutograd should be populated to Undefined.
// We cannot do this above since Undefined cannot be represented in DispatchKeySet.
- if (dispatch_key == DispatchKey::CompositeImplicitAutograd || dispatch_key == DispatchKey::DefaultBackend) {
+ if (dispatch_key == DispatchKey::CompositeImplicitAutograd || dispatch_key == DispatchKey::CompositeExplicitAutograd) {
updateDispatchTableEntry_(dispatcher, DispatchKey::Undefined);
}
// Note [Refresh Runtime Autograd entries in dispatchTable_]
@@ -318,7 +318,7 @@
// no dispatch keys are available we just slide into the undefined handler which would then raise
// the error message.
// In the old world of catchAll, the only way to "register" a kernel to Undefined is by registering it to
- // catchAll. After catchAllKernel_ is removed, Undefined now can get a kernel from either DefaultBackend
+ // catchAll. After catchAllKernel_ is removed, Undefined now can get a kernel from either CompositeExplicitAutograd
// or CompositeImplicitAutograd alias key so that we don't break the support. Ideally isIncludedInAlias(Undefined, CompositeImplicitAutograd)
// should return true, it returns false because Undefined cannot be represented in a DispatchKeySet.
for (uint8_t iter = 0; iter != static_cast<uint8_t>(DispatchKey::NumDispatchKeys); ++iter) {
diff --git a/aten/src/ATen/core/op_registration/op_registration_test.cpp b/aten/src/ATen/core/op_registration/op_registration_test.cpp
index bb296a3..2cc3d7e 100644
--- a/aten/src/ATen/core/op_registration/op_registration_test.cpp
+++ b/aten/src/ATen/core/op_registration/op_registration_test.cpp
@@ -1502,10 +1502,10 @@
}
}
-TEST(NewOperatorRegistrationTest, dispatchWithDefaultBackendKernel) {
+TEST(NewOperatorRegistrationTest, dispatchWithCompositeExplicitAutogradKernel) {
bool called = false;
auto m = MAKE_TORCH_LIBRARY(test);
- m.def("fn", torch::dispatch(c10::DispatchKey::DefaultBackend, [&](const Tensor& x) { called = true; return x; }));
+ m.def("fn", torch::dispatch(c10::DispatchKey::CompositeExplicitAutograd, [&](const Tensor& x) { called = true; return x; }));
auto op = Dispatcher::singleton().findSchema({"test::fn", ""});
ASSERT_TRUE(op.has_value());
@@ -1550,11 +1550,11 @@
}
}
-TEST(NewOperatorRegistrationTest, dispatchWithDefaultBackendAndCompositeImplicitAutogradKernel) {
+TEST(NewOperatorRegistrationTest, dispatchWithCompositeExplicitAutogradAndCompositeImplicitAutogradKernel) {
bool backend_called = false;
bool math_called = false;
auto m = MAKE_TORCH_LIBRARY(test);
- m.def("fn", torch::dispatch(c10::DispatchKey::DefaultBackend, [&](const Tensor& x) { backend_called = true; return x; }));
+ m.def("fn", torch::dispatch(c10::DispatchKey::CompositeExplicitAutograd, [&](const Tensor& x) { backend_called = true; return x; }));
m.impl("fn", c10::DispatchKey::CompositeImplicitAutograd, [&](const Tensor& x) { math_called = true; return x; });
auto op = Dispatcher::singleton().findSchema({"test::fn", ""});
@@ -1606,11 +1606,11 @@
}
}
-TEST(NewOperatorRegistrationTest, BackendOverridesDefaultBackendKernel) {
+TEST(NewOperatorRegistrationTest, BackendOverridesCompositeExplicitAutogradKernel) {
bool default_called = false;
bool backend_called = false;
auto m = MAKE_TORCH_LIBRARY(test);
- m.def("fn", torch::dispatch(c10::DispatchKey::DefaultBackend, [&](const Tensor& x) { default_called = true; return x; }));
+ m.def("fn", torch::dispatch(c10::DispatchKey::CompositeExplicitAutograd, [&](const Tensor& x) { default_called = true; return x; }));
m.impl("fn", c10::DispatchKey::CPU, [&](const Tensor& x) { backend_called = true; return x; });
auto op = Dispatcher::singleton().findSchema({"test::fn", ""});
diff --git a/aten/src/ATen/native/README.md b/aten/src/ATen/native/README.md
index 3bde885..0fc35b5 100644
--- a/aten/src/ATen/native/README.md
+++ b/aten/src/ATen/native/README.md
@@ -485,7 +485,7 @@
- Yes, but you still want to provide a numerically stable gradient formula instead of using autograd, write
```
dispatch:
- DefaultBackend: kernel
+ CompositeExplicitAutograd: kernel
```
You're done. This op will be called in inference for all backends.
@@ -505,7 +505,7 @@
For `sign` and `sign_`, write
```
dispatch:
- DefaultBackend: kernel
+ CompositeExplicitAutograd: kernel
```
You're done. This op will be called in inference for all backends.
@@ -529,8 +529,8 @@
Note that in native_functions.yaml you can mix using backend keywords and alias keywords above for one op:
- direct registration to backend always has higher precendence than alias
- - DO NOT provide multiple alias keywords to the same op: alias keywords have precedence `DefaultBackend > CompositeImplicitAutograd`,
- e.g. adding both `CompositeImplicitAutograd` and `DefaultBackend` kernels for one op will completely ignore `CompositeImplicitAutograd` kernel for
+ - DO NOT provide multiple alias keywords to the same op: alias keywords have precedence `CompositeExplicitAutograd > CompositeImplicitAutograd`,
+ e.g. adding both `CompositeImplicitAutograd` and `CompositeExplicitAutograd` kernels for one op will completely ignore `CompositeImplicitAutograd` kernel for
both inference and training. Thus this will trigger an error when native_functions.yaml is parsed.
diff --git a/aten/src/ATen/native/UpSampleNearest3d.cpp b/aten/src/ATen/native/UpSampleNearest3d.cpp
index 2ea356e..df7cffb 100644
--- a/aten/src/ATen/native/UpSampleNearest3d.cpp
+++ b/aten/src/ATen/native/UpSampleNearest3d.cpp
@@ -91,7 +91,7 @@
return at::upsample_nearest3d(input, osize, scale_d, scale_h, scale_w);
}
-// when structured kernels can handle QuantizedCPU, update these overloads to be DefaultBackend
+// when structured kernels can handle QuantizedCPU, update these overloads to be CompositeExplicitAutograd
Tensor upsample_nearest3d_backward_cpu(
const Tensor& grad_output,
c10::optional<IntArrayRef> output_size,
diff --git a/aten/src/ATen/native/cuda/UpSampleNearest3d.cu b/aten/src/ATen/native/cuda/UpSampleNearest3d.cu
index b08fd57..9330433 100644
--- a/aten/src/ATen/native/cuda/UpSampleNearest3d.cu
+++ b/aten/src/ATen/native/cuda/UpSampleNearest3d.cu
@@ -289,7 +289,7 @@
return at::upsample_nearest3d(input, osize, scale_d, scale_h, scale_w);
}
-// when structured kernels can handle QuantizedCPU, update these overloads to be DefaultBackend
+// when structured kernels can handle QuantizedCPU, update these overloads to be CompositeExplicitAutograd
Tensor upsample_nearest3d_backward_cuda(
const Tensor& grad_output,
c10::optional<IntArrayRef> output_size,
diff --git a/aten/src/ATen/native/native_functions.yaml b/aten/src/ATen/native/native_functions.yaml
index 3c19908..32b7161 100644
--- a/aten/src/ATen/native/native_functions.yaml
+++ b/aten/src/ATen/native/native_functions.yaml
@@ -92,7 +92,7 @@
- func: _fw_primal(Tensor(a) self, int level) -> Tensor(a)
variants: method
dispatch:
- DefaultBackend: _fw_primal
+ CompositeExplicitAutograd: _fw_primal
- func: _make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a)
variants: function
@@ -199,12 +199,12 @@
- func: abs(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: abs
+ CompositeExplicitAutograd: abs
- func: abs_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: abs_
+ CompositeExplicitAutograd: abs_
- func: abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -267,12 +267,12 @@
- func: sgn(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: sgn
+ CompositeExplicitAutograd: sgn
- func: sgn_(Tensor(a!) self) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: sgn_
+ CompositeExplicitAutograd: sgn_
- func: sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -294,7 +294,7 @@
- func: _conj(Tensor self) -> Tensor
variants: function
dispatch:
- DefaultBackend: _conj
+ CompositeExplicitAutograd: _conj
- func: acos(Tensor self) -> Tensor
variants: function, method
@@ -304,7 +304,7 @@
- func: acos_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: acos_
+ CompositeExplicitAutograd: acos_
- func: acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -368,12 +368,12 @@
- func: add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: add
+ CompositeExplicitAutograd: add
- func: add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: add_
+ CompositeExplicitAutograd: add_
- func: addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function, method
@@ -403,7 +403,7 @@
- func: addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: addr_
+ CompositeExplicitAutograd: addr_
- func: addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -413,7 +413,7 @@
- func: affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor
variants: function
dispatch:
- DefaultBackend: affine_grid_generator
+ CompositeExplicitAutograd: affine_grid_generator
- func: affine_grid_generator_backward(Tensor grad, int[] size, bool align_corners) -> Tensor
variants: function
@@ -498,7 +498,7 @@
- func: acosh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: acosh_
+ CompositeExplicitAutograd: acosh_
- func: acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -521,7 +521,7 @@
- func: asinh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: asinh_
+ CompositeExplicitAutograd: asinh_
- func: asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -544,7 +544,7 @@
- func: atanh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: atanh_
+ CompositeExplicitAutograd: atanh_
- func: atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -571,7 +571,7 @@
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: as_strided_
+ CompositeExplicitAutograd: as_strided_
- func: asin(Tensor self) -> Tensor
variants: function, method
@@ -607,7 +607,7 @@
- func: atan_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: atan_
+ CompositeExplicitAutograd: atan_
- func: atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -680,7 +680,7 @@
- func: bernoulli(Tensor self, *, Generator? generator=None) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: bernoulli
+ CompositeExplicitAutograd: bernoulli
- func: bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
variants: function
@@ -738,7 +738,7 @@
- func: binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor
variants: function
dispatch:
- DefaultBackend: binary_cross_entropy_with_logits
+ CompositeExplicitAutograd: binary_cross_entropy_with_logits
- func: binary_cross_entropy_with_logits_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor
variants: function
@@ -863,11 +863,11 @@
- func: cat(Tensor[] tensors, int dim=0) -> Tensor
dispatch:
- DefaultBackend: cat
+ CompositeExplicitAutograd: cat
- func: cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: cat_out
+ CompositeExplicitAutograd: cat_out
- func: cat.names(Tensor[] tensors, Dimname dim) -> Tensor
@@ -879,12 +879,12 @@
- func: ceil(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: ceil
+ CompositeExplicitAutograd: ceil
- func: ceil_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: ceil_
+ CompositeExplicitAutograd: ceil_
- func: ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -919,7 +919,7 @@
- func: clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: clamp_
+ CompositeExplicitAutograd: clamp_
- func: clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -928,12 +928,12 @@
- func: clamp_max(Tensor self, Scalar max) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: clamp_max
+ CompositeExplicitAutograd: clamp_max
- func: clamp_max_(Tensor(a!) self, Scalar max) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: clamp_max_
+ CompositeExplicitAutograd: clamp_max_
- func: clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -942,12 +942,12 @@
- func: clamp_min(Tensor self, Scalar min) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: clamp_min
+ CompositeExplicitAutograd: clamp_min
- func: clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: clamp_min_
+ CompositeExplicitAutograd: clamp_min_
- func: clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -969,7 +969,7 @@
- func: complex(Tensor real, Tensor imag) -> Tensor
variants: function
dispatch:
- DefaultBackend: complex
+ CompositeExplicitAutograd: complex
- func: complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -978,7 +978,7 @@
- func: polar(Tensor abs, Tensor angle) -> Tensor
variants: function
dispatch:
- DefaultBackend: polar
+ CompositeExplicitAutograd: polar
- func: polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -987,7 +987,7 @@
- func: constant_pad_nd(Tensor self, int[] pad, Scalar value=0) -> Tensor
variants: function
dispatch:
- DefaultBackend: constant_pad_nd
+ CompositeExplicitAutograd: constant_pad_nd
- func: contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a)
variants: method
@@ -997,11 +997,11 @@
- func: convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensor
dispatch:
- DefaultBackend: convolution_overrideable
+ CompositeExplicitAutograd: convolution_overrideable
- func: convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
dispatch:
- DefaultBackend: convolution_backward_overrideable
+ CompositeExplicitAutograd: convolution_backward_overrideable
- func: _convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor
@@ -1031,7 +1031,7 @@
- func: conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor
dispatch:
- DefaultBackend: conv_tbc
+ CompositeExplicitAutograd: conv_tbc
- func: conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor)
@@ -1046,7 +1046,7 @@
variants: method
device_guard: False
dispatch:
- DefaultBackend: copy_
+ CompositeExplicitAutograd: copy_
- func: _copy_from(Tensor self, Tensor dst, bool non_blocking=False) -> Tensor
dispatch: {}
@@ -1059,7 +1059,7 @@
- func: cos_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: cos_
+ CompositeExplicitAutograd: cos_
- func: cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -1073,7 +1073,7 @@
- func: cosh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: cosh_
+ CompositeExplicitAutograd: cosh_
- func: cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -1089,7 +1089,7 @@
- func: count_nonzero(Tensor self, int? dim=None) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: count_nonzero
+ CompositeExplicitAutograd: count_nonzero
- func: cudnn_affine_grid_generator(Tensor theta, int N, int C, int H, int W) -> Tensor grid
dispatch:
@@ -1179,11 +1179,11 @@
- func: cummax(Tensor self, int dim) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
- DefaultBackend: cummax
+ CompositeExplicitAutograd: cummax
- func: cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
- DefaultBackend: cummax_out
+ CompositeExplicitAutograd: cummax_out
- func: cummax.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)
variants: function, method
@@ -1200,11 +1200,11 @@
- func: cummin(Tensor self, int dim) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
- DefaultBackend: cummin
+ CompositeExplicitAutograd: cummin
- func: cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
- DefaultBackend: cummin_out
+ CompositeExplicitAutograd: cummin_out
- func: cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)
variants: function, method
@@ -1225,16 +1225,16 @@
- func: cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: cumprod
+ CompositeExplicitAutograd: cumprod
- func: cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: cumprod_
+ CompositeExplicitAutograd: cumprod_
- func: cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: cumprod_out
+ CompositeExplicitAutograd: cumprod_out
- func: cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
@@ -1251,16 +1251,16 @@
- func: cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: cumsum
+ CompositeExplicitAutograd: cumsum
- func: cumsum_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: cumsum_
+ CompositeExplicitAutograd: cumsum_
- func: cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: cumsum_out
+ CompositeExplicitAutograd: cumsum_out
- func: cumsum.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
@@ -1294,7 +1294,7 @@
- func: diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a)
variants: function, method
dispatch:
- DefaultBackend: diagonal
+ CompositeExplicitAutograd: diagonal
- func: diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a)
variants: function, method
@@ -1349,22 +1349,22 @@
- func: div.Scalar(Tensor self, Scalar other) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: div
+ CompositeExplicitAutograd: div
- func: div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: div_
+ CompositeExplicitAutograd: div_
- func: div.Scalar_mode(Tensor self, Scalar other, *, str rounding_mode) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: div
+ CompositeExplicitAutograd: div
- func: div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str rounding_mode) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: div_
+ CompositeExplicitAutograd: div_
# divide, alias for div
- func: divide.Tensor(Tensor self, Tensor other) -> Tensor
@@ -1418,7 +1418,7 @@
- func: dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: dot_out
+ CompositeExplicitAutograd: dot_out
- func: vdot(Tensor self, Tensor other) -> Tensor
variants: function, method
@@ -1428,13 +1428,13 @@
- func: vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: vdot_out
+ CompositeExplicitAutograd: vdot_out
- func: einsum(str equation, Tensor[] tensors) -> Tensor
- func: embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor
dispatch:
- DefaultBackend: embedding
+ CompositeExplicitAutograd: embedding
- func: embedding_backward(Tensor grad, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor
@@ -1571,7 +1571,7 @@
- func: erf_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: erf_
+ CompositeExplicitAutograd: erf_
- func: erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -1585,7 +1585,7 @@
- func: erfc_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: erfc_
+ CompositeExplicitAutograd: erfc_
- func: erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -1599,7 +1599,7 @@
- func: exp_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: exp_
+ CompositeExplicitAutograd: exp_
- func: exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -1613,7 +1613,7 @@
- func: exp2_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: exp2_
+ CompositeExplicitAutograd: exp2_
- func: exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -1627,7 +1627,7 @@
- func: expm1_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: expm1_
+ CompositeExplicitAutograd: expm1_
- func: expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -1637,7 +1637,7 @@
variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too.
device_guard: False
dispatch:
- DefaultBackend: expand
+ CompositeExplicitAutograd: expand
- func: expand_as(Tensor(a) self, Tensor other) -> Tensor(a)
variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too.
@@ -1690,12 +1690,12 @@
- func: floor(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: floor
+ CompositeExplicitAutograd: floor
- func: floor_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: floor_
+ CompositeExplicitAutograd: floor_
- func: floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -1727,12 +1727,12 @@
- func: frac(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: frac
+ CompositeExplicitAutograd: frac
- func: frac_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: frac_
+ CompositeExplicitAutograd: frac_
- func: frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -1805,7 +1805,7 @@
# See NOTE [ grid_sample CPU fallback ]
- func: _grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
dispatch:
- DefaultBackend: _grid_sampler_2d_cpu_fallback
+ CompositeExplicitAutograd: _grid_sampler_2d_cpu_fallback
- func: _grid_sampler_2d_cpu_fallback_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> (Tensor, Tensor)
@@ -1919,7 +1919,7 @@
- func: index_copy_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: index_copy_
+ CompositeExplicitAutograd: index_copy_
- func: index_copy(Tensor self, int dim, Tensor index, Tensor source) -> Tensor
variants: function, method
@@ -1933,7 +1933,7 @@
- func: index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: index_put_
+ CompositeExplicitAutograd: index_put_
# NB: The following functions are declared in aten/src/ATen/templates/TensorBody.h and defined in aten/src/ATen/TensorIndexing.cpp:
# - Tensor & Tensor::index_put_(ArrayRef<TensorIndex> indices, Tensor const & rhs)
# - Tensor & Tensor::index_put_(ArrayRef<TensorIndex> indices, Scalar v)
@@ -1954,11 +1954,11 @@
- func: inverse(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: inverse
+ CompositeExplicitAutograd: inverse
- func: inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: inverse_out
+ CompositeExplicitAutograd: inverse_out
- func: _inverse_helper(Tensor self) -> Tensor
variants: function
@@ -2008,7 +2008,7 @@
- func: kl_div(Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor
dispatch:
- DefaultBackend: kl_div
+ CompositeExplicitAutograd: kl_div
- func: kl_div_backward(Tensor grad_output, Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor
dispatch:
@@ -2023,7 +2023,7 @@
- func: kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
- DefaultBackend: kthvalue
+ CompositeExplicitAutograd: kthvalue
- func: kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
@@ -2051,16 +2051,16 @@
- func: nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: nan_to_num
+ CompositeExplicitAutograd: nan_to_num
- func: nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: nan_to_num_
+ CompositeExplicitAutograd: nan_to_num_
- func: nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: nan_to_num_out
+ CompositeExplicitAutograd: nan_to_num_out
- func: linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor
python_module: nn
@@ -2125,7 +2125,7 @@
- func: log_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: log_
+ CompositeExplicitAutograd: log_
- func: log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -2139,7 +2139,7 @@
- func: log10_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: log10_
+ CompositeExplicitAutograd: log10_
- func: log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -2170,7 +2170,7 @@
- func: log2_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: log2_
+ CompositeExplicitAutograd: log2_
- func: log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -2183,7 +2183,7 @@
- func: logaddexp(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: logaddexp
+ CompositeExplicitAutograd: logaddexp
- func: logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -2192,7 +2192,7 @@
- func: logaddexp2(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: logaddexp2
+ CompositeExplicitAutograd: logaddexp2
- func: xlogy.Tensor(Tensor self, Tensor other) -> Tensor
variants: function, method
@@ -2239,7 +2239,7 @@
- func: logdet(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: logdet
+ CompositeExplicitAutograd: logdet
- func: logspace(Scalar start, Scalar end, int? steps=None, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -2279,11 +2279,11 @@
- func: logcumsumexp(Tensor self, int dim) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: logcumsumexp
+ CompositeExplicitAutograd: logcumsumexp
- func: logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: logcumsumexp_out
+ CompositeExplicitAutograd: logcumsumexp_out
- func: logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor
variants: function, method
@@ -2293,11 +2293,11 @@
- func: logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: logsumexp
+ CompositeExplicitAutograd: logsumexp
- func: logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: logsumexp_out
+ CompositeExplicitAutograd: logsumexp_out
- func: logsumexp.names(Tensor self, Dimname[1] dim, bool keepdim=False) -> Tensor
variants: function, method
@@ -2368,7 +2368,7 @@
- func: amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: amax
+ CompositeExplicitAutograd: amax
- func: amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -2440,7 +2440,7 @@
- func: median.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
- DefaultBackend: median
+ CompositeExplicitAutograd: median
- func: median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
@@ -2461,7 +2461,7 @@
- func: nanmedian.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
- DefaultBackend: nanmedian
+ CompositeExplicitAutograd: nanmedian
- func: nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
@@ -2490,7 +2490,7 @@
- func: amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: amin
+ CompositeExplicitAutograd: amin
- func: amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -2498,7 +2498,7 @@
- func: mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensor
dispatch:
- DefaultBackend: mkldnn_convolution
+ CompositeExplicitAutograd: mkldnn_convolution
- func: mkldnn_convolution_backward_input(int[] self_size, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool bias_defined) -> Tensor
@@ -2506,7 +2506,7 @@
- func: mkldnn_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
- DefaultBackend: mkldnn_convolution_backward
+ CompositeExplicitAutograd: mkldnn_convolution_backward
- func: miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor)
dispatch:
@@ -2610,7 +2610,7 @@
- func: mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
- DefaultBackend: mode_out
+ CompositeExplicitAutograd: mode_out
- func: mode.dimname(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
@@ -2644,12 +2644,12 @@
- func: mul.Scalar(Tensor self, Scalar other) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: mul
+ CompositeExplicitAutograd: mul
- func: mul_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: mul_
+ CompositeExplicitAutograd: mul_
# multiply, alias for mul
- func: multiply.Tensor(Tensor self, Tensor other) -> Tensor
@@ -2674,24 +2674,24 @@
- func: mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: mv_out
+ CompositeExplicitAutograd: mv_out
- func: mvlgamma(Tensor self, int p) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: mvlgamma
+ CompositeExplicitAutograd: mvlgamma
- func: mvlgamma_(Tensor(a!) self, int p) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: mvlgamma_
+ CompositeExplicitAutograd: mvlgamma_
- func: narrow_copy(Tensor self, int dim, int start, int length) -> Tensor
variants: function, method
dispatch:
CPU: narrow_copy_dense_cpu
SparseCPU, SparseCUDA: narrow_copy_sparse
- DefaultBackend: narrow_copy_dense
+ CompositeExplicitAutograd: narrow_copy_dense
- func: narrow_copy.out(Tensor self, int dim, int start, int length, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -2764,7 +2764,7 @@
- func: _nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, int[2] padding, int[2] stride=1) -> Tensor
variants: function
dispatch:
- DefaultBackend: _nnpack_spatial_convolution
+ CompositeExplicitAutograd: _nnpack_spatial_convolution
- func: _nnpack_spatial_convolution_backward(Tensor input, Tensor grad_output, Tensor weight, int[2] padding, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
variants: function
@@ -2793,7 +2793,7 @@
- func: _euclidean_dist(Tensor x1, Tensor x2) -> Tensor
dispatch:
- DefaultBackend: _euclidean_dist
+ CompositeExplicitAutograd: _euclidean_dist
- func: _cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor
dispatch:
@@ -2819,7 +2819,7 @@
- func: permute(Tensor(a) self, int[] dims) -> Tensor(a)
variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too.
dispatch:
- DefaultBackend: permute
+ CompositeExplicitAutograd: permute
- func: movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)
variants: function, method
@@ -2868,30 +2868,30 @@
- func: rad2deg(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: rad2deg
+ CompositeExplicitAutograd: rad2deg
- func: rad2deg_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: rad2deg_
+ CompositeExplicitAutograd: rad2deg_
- func: rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: rad2deg_out
+ CompositeExplicitAutograd: rad2deg_out
- func: deg2rad(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: deg2rad
+ CompositeExplicitAutograd: deg2rad
- func: deg2rad_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: deg2rad_
+ CompositeExplicitAutograd: deg2rad_
- func: deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: deg2rad_out
+ CompositeExplicitAutograd: deg2rad_out
- func: scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -2999,7 +2999,7 @@
- func: reciprocal_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: reciprocal_
+ CompositeExplicitAutograd: reciprocal_
- func: reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -3033,7 +3033,7 @@
- func: repeat(Tensor self, int[] repeats) -> Tensor
variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too.
dispatch:
- DefaultBackend: repeat
+ CompositeExplicitAutograd: repeat
- func: repeat_interleave.Tensor(Tensor repeats) -> Tensor
variants: function
@@ -3063,12 +3063,12 @@
- func: round(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: round
+ CompositeExplicitAutograd: round
- func: round_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: round_
+ CompositeExplicitAutograd: round_
- func: round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -3146,7 +3146,7 @@
- func: rsqrt_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: rsqrt_
+ CompositeExplicitAutograd: rsqrt_
- func: rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -3160,7 +3160,7 @@
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: select
+ CompositeExplicitAutograd: select
- func: select_backward(Tensor grad, int[] input_sizes, int dim, int index) -> Tensor
variants: function
@@ -3172,21 +3172,21 @@
- func: celu(Tensor self, Scalar alpha=1.0) -> Tensor
dispatch:
- DefaultBackend: celu
+ CompositeExplicitAutograd: celu
- func: celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!)
dispatch:
- DefaultBackend: celu_
+ CompositeExplicitAutograd: celu_
- func: silu(Tensor self) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: silu
+ CompositeExplicitAutograd: silu
- func: silu_(Tensor(a!) self) -> Tensor(a!)
python_module: nn
dispatch:
- DefaultBackend: silu_
+ CompositeExplicitAutograd: silu_
- func: silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
@@ -3266,7 +3266,7 @@
- func: sinh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: sinh_
+ CompositeExplicitAutograd: sinh_
- func: sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -3286,7 +3286,7 @@
- func: detach(Tensor(a) self) -> Tensor(a)
variants: function, method
dispatch:
- DefaultBackend: detach
+ CompositeExplicitAutograd: detach
# Like `detach()`, but modifies this `Variable` in-place. This method may
# only be called on non-view `Variable`s. You can use `is_view()` to check
@@ -3294,7 +3294,7 @@
- func: detach_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: detach_
+ CompositeExplicitAutograd: detach_
- func: size.int(Tensor self, int dim) -> int
variants: function
@@ -3309,7 +3309,7 @@
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: slice
+ CompositeExplicitAutograd: slice
- func: slice_backward(Tensor grad, int[] input_sizes, int dim, int start, int end, int step) -> Tensor
variants: function
@@ -3318,7 +3318,7 @@
- func: slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet)
variants: function, method
dispatch:
- DefaultBackend: slogdet
+ CompositeExplicitAutograd: slogdet
- func: smm(Tensor self, Tensor mat2) -> Tensor
variants: function, method
@@ -3345,37 +3345,37 @@
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: unsafe_split
+ CompositeExplicitAutograd: unsafe_split
- func: split.Tensor(Tensor(a) self, int split_size, int dim=0) -> Tensor(a)[]
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: split
+ CompositeExplicitAutograd: split
- func: unsafe_split_with_sizes(Tensor self, int[] split_sizes, int dim=0) -> Tensor[]
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: unsafe_split_with_sizes
+ CompositeExplicitAutograd: unsafe_split_with_sizes
- func: split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -> Tensor(a)[]
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: split_with_sizes
+ CompositeExplicitAutograd: split_with_sizes
- func: squeeze(Tensor(a) self) -> Tensor(a)
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: squeeze
+ CompositeExplicitAutograd: squeeze
- func: squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: squeeze
+ CompositeExplicitAutograd: squeeze
- func: squeeze.dimname(Tensor(a) self, Dimname dim) -> Tensor(a)
variants: function, method
@@ -3385,13 +3385,13 @@
variants: method
device_guard: False
dispatch:
- DefaultBackend: squeeze_
+ CompositeExplicitAutograd: squeeze_
- func: squeeze_.dim(Tensor(a!) self, int dim) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
- DefaultBackend: squeeze_
+ CompositeExplicitAutograd: squeeze_
- func: squeeze_.dimname(Tensor(a!) self, Dimname dim) -> Tensor(a!)
variants: method
@@ -3409,21 +3409,21 @@
- func: stack(Tensor[] tensors, int dim=0) -> Tensor
dispatch:
- DefaultBackend: stack
+ CompositeExplicitAutograd: stack
- func: stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
- DefaultBackend: stack_out
+ CompositeExplicitAutograd: stack_out
- func: _stack(Tensor[] tensors, int dim=0) -> Tensor
dispatch: # match the backends supported by _cat
CPU: _stack_cpu
- DefaultBackend: _stack
+ CompositeExplicitAutograd: _stack
- func: _stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch: # match the backends supported by _cat_out
CPU: _stack_out_cpu
- DefaultBackend: _stack_out
+ CompositeExplicitAutograd: _stack_out
- func: hstack(Tensor[] tensors) -> Tensor
@@ -3574,13 +3574,13 @@
device_guard: False
variants: function, method
dispatch:
- DefaultBackend: t
+ CompositeExplicitAutograd: t
- func: t_(Tensor(a!) self) -> Tensor(a!)
device_guard: False
variants: method
dispatch:
- DefaultBackend: t_
+ CompositeExplicitAutograd: t_
- func: tan(Tensor self) -> Tensor
variants: function, method
@@ -3590,7 +3590,7 @@
- func: tan_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: tan_
+ CompositeExplicitAutograd: tan_
- func: tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -3605,7 +3605,7 @@
- func: tanh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: tanh_
+ CompositeExplicitAutograd: tanh_
- func: tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -3652,7 +3652,7 @@
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: transpose
+ CompositeExplicitAutograd: transpose
- func: transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a)
variants: function, method
@@ -3667,7 +3667,7 @@
variants: method
device_guard: False
dispatch:
- DefaultBackend: transpose_
+ CompositeExplicitAutograd: transpose_
- func: _mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)
device_guard: False
@@ -3701,7 +3701,7 @@
- func: rot90(Tensor self, int k=1, int[] dims=[0,1]) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: rot90
+ CompositeExplicitAutograd: rot90
- func: trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor
@@ -3709,19 +3709,19 @@
- func: _trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor
dispatch:
- DefaultBackend: _trilinear
+ CompositeExplicitAutograd: _trilinear
- func: triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, float margin=1.0, float p=2, float eps=1e-06, bool swap=False, int reduction=Mean) -> Tensor
- func: trunc(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: trunc
+ CompositeExplicitAutograd: trunc
- func: trunc_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: trunc_
+ CompositeExplicitAutograd: trunc_
- func: trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -3778,19 +3778,19 @@
- func: _unsafe_view(Tensor self, int[] size) -> Tensor
dispatch:
- DefaultBackend: _unsafe_view
+ CompositeExplicitAutograd: _unsafe_view
- func: unsqueeze(Tensor(a) self, int dim) -> Tensor(a)
variants: function, method
device_guard: False
dispatch:
- DefaultBackend: unsqueeze
+ CompositeExplicitAutograd: unsqueeze
- func: unsqueeze_(Tensor(a!) self, int dim) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
- DefaultBackend: unsqueeze_
+ CompositeExplicitAutograd: unsqueeze_
- func: vander(Tensor x, int? N=None, bool increasing=False) -> Tensor
@@ -3937,7 +3937,7 @@
- func: _sparse_sum.dim(Tensor self, int[1] dim) -> Tensor
dispatch:
- DefaultBackend: _sparse_sum
+ CompositeExplicitAutograd: _sparse_sum
- func: _sparse_sum.dim_dtype(Tensor self, int[1] dim, *, ScalarType dtype) -> Tensor
@@ -4020,11 +4020,11 @@
- func: frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent)
variants: method, function
dispatch:
- DefaultBackend: frexp
+ CompositeExplicitAutograd: frexp
- func: frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent)
dispatch:
- DefaultBackend: frexp_out
+ CompositeExplicitAutograd: frexp_out
- func: frobenius_norm(Tensor self) -> Tensor
variants: function
@@ -4058,7 +4058,7 @@
- func: resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: resize_as_
+ CompositeExplicitAutograd: resize_as_
- func: zero_(Tensor(a!) self) -> Tensor(a!)
variants: method, function
@@ -4090,12 +4090,12 @@
- func: sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: sub
+ CompositeExplicitAutograd: sub
- func: sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: sub_
+ CompositeExplicitAutograd: sub_
# subtract, alias for sub
- func: subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
@@ -4133,13 +4133,13 @@
- func: rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
variants: function
dispatch:
- DefaultBackend: rsub
+ CompositeExplicitAutograd: rsub
# Functionally the same as addmm, but we give it a different derivative formula
# that doesn't propagate gradients to non-present entries on sparse.
- func: _sparse_addmm(Tensor self, Tensor sparse, Tensor dense, *, Scalar beta=1, Scalar alpha=1) -> Tensor
dispatch:
- DefaultBackend: _sparse_addmm
+ CompositeExplicitAutograd: _sparse_addmm
- func: addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -4418,7 +4418,7 @@
- func: unbind.int(Tensor(a) self, int dim=0) -> Tensor(a)[]
variants: function, method
dispatch:
- DefaultBackend: unbind
+ CompositeExplicitAutograd: unbind
- func: unbind.Dimname(Tensor(a) self, Dimname dim) -> Tensor(a)[]
variants: function, method
@@ -4700,7 +4700,7 @@
# PackedSequence utilities
- func: _pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor)
dispatch:
- DefaultBackend: _pack_padded_sequence
+ CompositeExplicitAutograd: _pack_padded_sequence
- func: _pack_padded_sequence_backward(Tensor grad, int[] input_size, Tensor batch_sizes, bool batch_first) -> Tensor
@@ -4784,7 +4784,7 @@
variants: method
device_guard: False
dispatch:
- DefaultBackend: view_dtype
+ CompositeExplicitAutograd: view_dtype
- func: put_(Tensor(a!) self, Tensor index, Tensor source, bool accumulate=False) -> Tensor(a!)
variants: method
@@ -4879,12 +4879,12 @@
- func: eq_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: eq_
+ CompositeExplicitAutograd: eq_
- func: eq_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: eq_
+ CompositeExplicitAutograd: eq_
- func: bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
@@ -5108,7 +5108,7 @@
- func: addcdiv_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: addcdiv_
+ CompositeExplicitAutograd: addcdiv_
- func: random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!)
variants: method
@@ -5160,7 +5160,7 @@
- func: diag(Tensor self, int diagonal=0) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: diag
+ CompositeExplicitAutograd: diag
- func: diag_backward(Tensor grad, int[] input_sizes, int diagonal) -> Tensor
variants: function
@@ -5183,7 +5183,7 @@
- func: triu(Tensor self, int diagonal=0) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: triu
+ CompositeExplicitAutograd: triu
- func: tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -5193,7 +5193,7 @@
- func: tril(Tensor self, int diagonal=0) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: tril
+ CompositeExplicitAutograd: tril
- func: tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
@@ -5240,12 +5240,12 @@
- func: ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: ne_
+ CompositeExplicitAutograd: ne_
- func: ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: ne_
+ CompositeExplicitAutograd: ne_
# not_equal, alias for torch.ne
- func: not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
@@ -5311,12 +5311,12 @@
- func: ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: ge_
+ CompositeExplicitAutograd: ge_
- func: ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: ge_
+ CompositeExplicitAutograd: ge_
# greater_equal, alias for torch.ge
- func: greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
@@ -5360,12 +5360,12 @@
- func: le_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: le_
+ CompositeExplicitAutograd: le_
- func: le_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: le_
+ CompositeExplicitAutograd: le_
# less_equal, alias for torch.le
- func: less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
@@ -5409,12 +5409,12 @@
- func: gt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: gt_
+ CompositeExplicitAutograd: gt_
- func: gt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: gt_
+ CompositeExplicitAutograd: gt_
# greater, alias for torch.gt
- func: greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
@@ -5458,12 +5458,12 @@
- func: lt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: lt_
+ CompositeExplicitAutograd: lt_
- func: lt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: lt_
+ CompositeExplicitAutograd: lt_
# less, alias for torch.lt
- func: less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
@@ -5586,12 +5586,12 @@
- func: addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: addcmul
+ CompositeExplicitAutograd: addcmul
- func: addcmul_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: addcmul_
+ CompositeExplicitAutograd: addcmul_
- func: addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -5600,7 +5600,7 @@
- func: addcdiv(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: addcdiv
+ CompositeExplicitAutograd: addcdiv
- func: cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensor
python_module: nn
@@ -5629,12 +5629,12 @@
- func: symeig.e(Tensor self, bool eigenvectors=False, bool upper=True, *, Tensor(a!) e, Tensor(b!) V) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
- DefaultBackend: symeig_out
+ CompositeExplicitAutograd: symeig_out
- func: symeig(Tensor self, bool eigenvectors=False, bool upper=True) -> (Tensor eigenvalues, Tensor eigenvectors)
variants: method, function
dispatch:
- DefaultBackend: symeig
+ CompositeExplicitAutograd: symeig
- func: _symeig_helper(Tensor self, bool eigenvectors, bool upper) -> (Tensor, Tensor)
variants: function
@@ -5645,12 +5645,12 @@
- func: eig.e(Tensor self, bool eigenvectors=False, *, Tensor(a!) e, Tensor(b!) v) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
- DefaultBackend: eig_out
+ CompositeExplicitAutograd: eig_out
- func: eig(Tensor self, bool eigenvectors=False) -> (Tensor eigenvalues, Tensor eigenvectors)
variants: method, function
dispatch:
- DefaultBackend: eig
+ CompositeExplicitAutograd: eig
- func: svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -5685,12 +5685,12 @@
- func: cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
- DefaultBackend: cholesky_out
+ CompositeExplicitAutograd: cholesky_out
- func: cholesky(Tensor self, bool upper=False) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: cholesky
+ CompositeExplicitAutograd: cholesky
- func: _cholesky_helper(Tensor self, bool upper) -> Tensor
variants: function
@@ -5701,12 +5701,12 @@
- func: cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
- DefaultBackend: cholesky_solve_out
+ CompositeExplicitAutograd: cholesky_solve_out
- func: cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: cholesky_solve
+ CompositeExplicitAutograd: cholesky_solve
- func: _cholesky_solve_helper(Tensor self, Tensor A, bool upper) -> Tensor
variants: function
@@ -5717,12 +5717,12 @@
- func: solve(Tensor self, Tensor A) -> (Tensor solution, Tensor LU)
variants: function, method
dispatch:
- DefaultBackend: solve
+ CompositeExplicitAutograd: solve
- func: solve.solution(Tensor self, Tensor A, *, Tensor(a!) solution, Tensor(b!) lu) -> (Tensor(a!) solution, Tensor(b!) LU)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
- DefaultBackend: solve_out
+ CompositeExplicitAutograd: solve_out
- func: _solve_helper(Tensor self, Tensor A) -> (Tensor, Tensor)
variants: function
@@ -5782,12 +5782,12 @@
- func: lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
- DefaultBackend: lu_solve_out
+ CompositeExplicitAutograd: lu_solve_out
- func: lu_solve(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: lu_solve
+ CompositeExplicitAutograd: lu_solve
- func: _lu_solve_helper(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor
variants: function
@@ -5838,7 +5838,7 @@
- func: polygamma(int n, Tensor self) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: polygamma
+ CompositeExplicitAutograd: polygamma
- func: erfinv(Tensor self) -> Tensor
variants: method, function
@@ -5858,12 +5858,12 @@
- func: i0(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: i0
+ CompositeExplicitAutograd: i0
- func: i0_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
- DefaultBackend: i0_
+ CompositeExplicitAutograd: i0_
- func: i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -5873,12 +5873,12 @@
- func: sign(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: sign
+ CompositeExplicitAutograd: sign
- func: sign_(Tensor(a!) self) -> Tensor(a!)
variants: method
dispatch:
- DefaultBackend: sign_
+ CompositeExplicitAutograd: sign_
- func: sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -5895,7 +5895,7 @@
- func: dist(Tensor self, Tensor other, Scalar p=2) -> Tensor
variants: method, function
dispatch:
- DefaultBackend: dist
+ CompositeExplicitAutograd: dist
- func: atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -5972,7 +5972,7 @@
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
variants: method
dispatch:
- DefaultBackend: hypot_
+ CompositeExplicitAutograd: hypot_
- func: igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -6019,7 +6019,7 @@
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
variants: method
dispatch:
- DefaultBackend: nextafter_
+ CompositeExplicitAutograd: nextafter_
- func: remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
@@ -6336,7 +6336,7 @@
- func: alias(Tensor(a) self) -> Tensor(a)
variants: method, function
dispatch:
- DefaultBackend: alias
+ CompositeExplicitAutograd: alias
- func: _index_copy_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!)
dispatch:
@@ -7013,12 +7013,12 @@
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
python_module: nn
dispatch:
- DefaultBackend: l1_loss_out
+ CompositeExplicitAutograd: l1_loss_out
- func: l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: l1_loss
+ CompositeExplicitAutograd: l1_loss
- func: l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -7029,7 +7029,7 @@
- func: l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: l1_loss_backward
+ CompositeExplicitAutograd: l1_loss_backward
- func: multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -7181,7 +7181,7 @@
- func: smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: smooth_l1_loss_backward
+ CompositeExplicitAutograd: smooth_l1_loss_backward
- func: huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
@@ -7201,29 +7201,29 @@
- func: huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: huber_loss_backward
+ CompositeExplicitAutograd: huber_loss_backward
- func: soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
python_module: nn
dispatch:
- DefaultBackend: soft_margin_loss_out
+ CompositeExplicitAutograd: soft_margin_loss_out
- func: soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: soft_margin_loss
+ CompositeExplicitAutograd: soft_margin_loss
- func: soft_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
python_module: nn
dispatch:
- DefaultBackend: soft_margin_loss_backward_out
+ CompositeExplicitAutograd: soft_margin_loss_backward_out
- func: soft_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: soft_margin_loss_backward
+ CompositeExplicitAutograd: soft_margin_loss_backward
- func: elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -7244,7 +7244,7 @@
- func: elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!)
python_module: nn
dispatch:
- DefaultBackend: elu_
+ CompositeExplicitAutograd: elu_
- func: glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -7418,7 +7418,7 @@
- func: rrelu_with_noise_backward(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: rrelu_with_noise_backward
+ CompositeExplicitAutograd: rrelu_with_noise_backward
- func: rrelu_with_noise_(Tensor(a!) self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!)
python_module: nn
@@ -7943,62 +7943,62 @@
- func: upsample_linear1d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_linear1d
+ CompositeExplicitAutograd: upsample_linear1d
- func: upsample_linear1d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_linear1d_backward
+ CompositeExplicitAutograd: upsample_linear1d_backward
- func: upsample_bilinear2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_bilinear2d
+ CompositeExplicitAutograd: upsample_bilinear2d
- func: upsample_bilinear2d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_bilinear2d_backward
+ CompositeExplicitAutograd: upsample_bilinear2d_backward
- func: upsample_trilinear3d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_trilinear3d
+ CompositeExplicitAutograd: upsample_trilinear3d
- func: upsample_trilinear3d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_trilinear3d_backward
+ CompositeExplicitAutograd: upsample_trilinear3d_backward
- func: upsample_bicubic2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_bicubic2d
+ CompositeExplicitAutograd: upsample_bicubic2d
- func: upsample_bicubic2d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_bicubic2d_backward
+ CompositeExplicitAutograd: upsample_bicubic2d_backward
- func: upsample_nearest1d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_nearest1d
+ CompositeExplicitAutograd: upsample_nearest1d
- func: upsample_nearest1d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_nearest1d_backward
+ CompositeExplicitAutograd: upsample_nearest1d_backward
- func: upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_nearest2d
+ CompositeExplicitAutograd: upsample_nearest2d
- func: upsample_nearest2d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
- DefaultBackend: upsample_nearest2d_backward
+ CompositeExplicitAutograd: upsample_nearest2d_backward
- func: upsample_nearest3d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> Tensor
python_module: nn
@@ -8517,7 +8517,7 @@
python_module: special
variants: function
dispatch:
- DefaultBackend: special_entr
+ CompositeExplicitAutograd: special_entr
- func: special_entr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
@@ -8733,14 +8733,14 @@
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_cholesky
+ CompositeExplicitAutograd: linalg_cholesky
- func: linalg_cholesky.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_cholesky_out
+ CompositeExplicitAutograd: linalg_cholesky_out
# torch.linalg.det, alias for torch.det
- func: linalg_det(Tensor self) -> Tensor
@@ -8750,13 +8750,13 @@
- func: det(Tensor self) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: det
+ CompositeExplicitAutograd: det
- func: linalg_lstsq(Tensor self, Tensor b, float? cond=None, *, str? driver=None) -> (Tensor solution, Tensor residuals, Tensor rank, Tensor singular_values)
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_lstsq
+ CompositeExplicitAutograd: linalg_lstsq
- func: _lstsq_helper(Tensor a, Tensor b, float cond, str? driver_name) -> (Tensor, Tensor, Tensor)
variants: function
@@ -8785,25 +8785,25 @@
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_eigh
+ CompositeExplicitAutograd: linalg_eigh
- func: linalg_eigh.eigvals(Tensor self, str UPLO="L", *, Tensor(a!) eigvals, Tensor(b!) eigvecs) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
python_module: linalg
dispatch:
- DefaultBackend: linalg_eigh_out
+ CompositeExplicitAutograd: linalg_eigh_out
- func: linalg_eigvalsh(Tensor self, str UPLO="L") -> Tensor
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_eigvalsh
+ CompositeExplicitAutograd: linalg_eigvalsh
- func: linalg_eigvalsh.out(Tensor self, str UPLO='L', *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
python_module: linalg
dispatch:
- DefaultBackend: linalg_eigvalsh_out
+ CompositeExplicitAutograd: linalg_eigvalsh_out
- func: linalg_householder_product(Tensor input, Tensor tau) -> Tensor
python_module: linalg
@@ -8826,14 +8826,14 @@
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_inv
+ CompositeExplicitAutograd: linalg_inv
- func: linalg_inv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
variants: function
dispatch:
- DefaultBackend: linalg_inv_out
+ CompositeExplicitAutograd: linalg_inv_out
- func: inner(Tensor self, Tensor other) -> Tensor
variants: function, method
@@ -8851,12 +8851,12 @@
- func: ger(Tensor self, Tensor vec2) -> Tensor
variants: function, method
dispatch:
- DefaultBackend: ger
+ CompositeExplicitAutograd: ger
- func: ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
- DefaultBackend: ger_out
+ CompositeExplicitAutograd: ger_out
- func: linalg_norm(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
python_module: linalg
@@ -8880,12 +8880,12 @@
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_vector_norm
+ CompositeExplicitAutograd: linalg_vector_norm
- func: linalg_vector_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
dispatch:
- DefaultBackend: linalg_vector_norm_out
+ CompositeExplicitAutograd: linalg_vector_norm_out
- func: linalg_svd.U(Tensor self, bool full_matrices=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -8941,13 +8941,13 @@
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_solve
+ CompositeExplicitAutograd: linalg_solve
- func: linalg_solve.out(Tensor input, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
- DefaultBackend: linalg_solve_out
+ CompositeExplicitAutograd: linalg_solve_out
- func: linalg_tensorinv(Tensor self, int ind=2) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
@@ -8972,14 +8972,14 @@
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_qr
+ CompositeExplicitAutograd: linalg_qr
- func: linalg_qr.out(Tensor self, str mode='reduced', *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
python_module: linalg
variants: function
dispatch:
- DefaultBackend: linalg_qr_out
+ CompositeExplicitAutograd: linalg_qr_out
- func: _linalg_qr_helper(Tensor self, str mode) -> (Tensor, Tensor)
variants: function
diff --git a/c10/core/DispatchKey.cpp b/c10/core/DispatchKey.cpp
index 12ec486..cbc94dd 100644
--- a/c10/core/DispatchKey.cpp
+++ b/c10/core/DispatchKey.cpp
@@ -112,8 +112,8 @@
case DispatchKey::CompositeImplicitAutograd:
return "CompositeImplicitAutograd";
- case DispatchKey::DefaultBackend:
- return "DefaultBackend";
+ case DispatchKey::CompositeExplicitAutograd:
+ return "CompositeExplicitAutograd";
case DispatchKey::TESTING_ONLY_GenericWrapper:
return "TESTING_ONLY_GenericWrapper";
diff --git a/c10/core/DispatchKey.h b/c10/core/DispatchKey.h
index c16c72b..b67ada0 100644
--- a/c10/core/DispatchKey.h
+++ b/c10/core/DispatchKey.h
@@ -271,18 +271,19 @@
// See Note [Alias Dispatch Key : Autograd]
Autograd,
CompositeImplicitAutograd, // registered at build/aten/src/ATen/RegisterCompositeImplicitAutograd.cpp
- DefaultBackend, // registered at
- // build/aten/src/ATen/RegisterDefaultBackend.cpp
+ CompositeExplicitAutograd, // registered at
+ // build/aten/src/ATen/RegisterCompositeExplicitAutograd.cpp
// Define an alias key to represent end of alias dispatch keys.
// If you add new alias keys after Autograd, please also update it here.
- EndOfAliasKeys = DefaultBackend, //
+ EndOfAliasKeys = CompositeExplicitAutograd, //
// ~~~~~~~~~~~~~~~~~~~~~~~~~ BC ALIASES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ //
// The aliases exist for backwards compatibility reasons, they shouldn't
// be used
CPUTensorId = CPU,
CUDATensorId = CUDA,
+ DefaultBackend = CompositeExplicitAutograd,
PrivateUse1_PreAutograd = AutogradPrivateUse1,
PrivateUse2_PreAutograd = AutogradPrivateUse2,
PrivateUse3_PreAutograd = AutogradPrivateUse3,
diff --git a/c10/core/DispatchKeySet.cpp b/c10/core/DispatchKeySet.cpp
index 2b9ea17..2a29b96 100644
--- a/c10/core/DispatchKeySet.cpp
+++ b/c10/core/DispatchKeySet.cpp
@@ -3,7 +3,7 @@
namespace c10 {
// backend_dispatch_keyset should include all runtime backend keys.
-// Alias key DispatchKey::DefaultBackend maps to backend_dispatch_keyset
+// Alias key DispatchKey::CompositeExplicitAutograd maps to backend_dispatch_keyset
// NestedTensor has been explicitly removed due to incompatibility with some
// kernels, such as structured kernels, that use the DefaultBackend key.
constexpr DispatchKeySet backend_dispatch_keyset = autogradother_backends |
@@ -33,7 +33,7 @@
return autograd_dispatch_keyset;
case DispatchKey::CompositeImplicitAutograd:
return math_dispatch_keyset;
- case DispatchKey::DefaultBackend:
+ case DispatchKey::CompositeExplicitAutograd:
return backend_dispatch_keyset;
default:
return DispatchKeySet(t);
diff --git a/test/test_dispatch.py b/test/test_dispatch.py
index 8f8ecdb..aa39ce9 100644
--- a/test/test_dispatch.py
+++ b/test/test_dispatch.py
@@ -562,8 +562,8 @@
lambda m: m.def_("foo(Tensor x) -> Tensor"),
# m.impl("foo", torch::kCPU, [](const Tensor & x) { return x })
lambda m: m.impl_t_t("foo", "CPU", debug="fn_cpu"),
- # m.impl("foo", torch::kDefaultBackend, [](const Tensor & x) { return x })
- lambda m: m.impl_t_t("foo", "DefaultBackend", debug="fn_defaultbackend"),
+ # m.impl("foo", torch::kCompositeExplicitAutograd, [](const Tensor & x) { return x })
+ lambda m: m.impl_t_t("foo", "CompositeExplicitAutograd", debug="fn_defaultbackend"),
])
state, table = result.state, result.table
self.assertExpectedInline(state, '''\
@@ -572,7 +572,7 @@
debug: registered at /dev/null:0
alias analysis kind: FROM_SCHEMA
CPU: fn_cpu :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
-DefaultBackend[alias]: fn_defaultbackend :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
+CompositeExplicitAutograd[alias]: fn_defaultbackend :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
''')
# computed dispatch table is too big, so we only check on a few entries we're interested in.
@@ -597,8 +597,8 @@
lambda m: m.impl_t_t("foo", "CPU", debug="fn_cpu"),
# m.impl("foo", torch::kAutograd, [](const Tensor & x) { return x })
lambda m: m.impl_t_t("foo", "Autograd", debug="fn_autograd"),
- # m.impl("foo", torch::kDefaultBackend, [](const Tensor & x) { return x })
- lambda m: m.impl_t_t("foo", "DefaultBackend", debug="fn_defaultbackend"),
+ # m.impl("foo", torch::kCompositeExplicitAutograd, [](const Tensor & x) { return x })
+ lambda m: m.impl_t_t("foo", "CompositeExplicitAutograd", debug="fn_defaultbackend"),
])
state, table = result.state, result.table
self.assertExpectedInline(state, '''\
@@ -608,7 +608,7 @@
alias analysis kind: FROM_SCHEMA
CPU: fn_cpu :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
Autograd[alias]: fn_autograd :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
-DefaultBackend[alias]: fn_defaultbackend :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
+CompositeExplicitAutograd[alias]: fn_defaultbackend :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
''')
# computed dispatch table is too big, so we only check on a few entries we're interested in.
@@ -636,8 +636,8 @@
lambda m: m.impl_t_t("foo", "Autograd", debug="fn_autograd"),
# m.impl("foo", torch::kCompositeImplicitAutograd, [](const Tensor & x) { return x })
lambda m: m.impl_t_t("foo", "CompositeImplicitAutograd", debug="fn_math"),
- # m.impl("foo", torch::kDefaultBackend, [](const Tensor & x) { return x })
- lambda m: m.impl_t_t("foo", "DefaultBackend", debug="fn_defaultbackend"),
+ # m.impl("foo", torch::kCompositeExplicitAutograd, [](const Tensor & x) { return x })
+ lambda m: m.impl_t_t("foo", "CompositeExplicitAutograd", debug="fn_defaultbackend"),
])
state, table = result.state, result.table
self.assertExpectedInline(state, '''\
@@ -648,7 +648,7 @@
CPU: fn_cpu :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
Autograd[alias]: fn_autograd :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
CompositeImplicitAutograd[alias]: fn_math :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
-DefaultBackend[alias]: fn_defaultbackend :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
+CompositeExplicitAutograd[alias]: fn_defaultbackend :: (Tensor _0) -> (Tensor _0) [ boxed unboxed ]
''')
# computed dispatch table is too big, so we only check on a few entries we're interested in.
@@ -809,7 +809,7 @@
def test_defaultbackend_autogradcpu(self):
dispatcher = PythonDispatcher()
- dispatcher.register(["CPU", "XLA", "DefaultBackend", "AutogradCPU"])
+ dispatcher.register(["CPU", "XLA", "CompositeExplicitAutograd", "AutogradCPU"])
self.assertExpectedInline(
dispatcher.dispatchTable(),
'''\
@@ -819,7 +819,7 @@
---------------------------
CPU fn_CPU [kernel]
XLA fn_XLA [kernel]
-QuantizedCPU fn_DefaultBackend [default backend kernel]
+QuantizedCPU fn_CompositeExplicitAutograd [default backend kernel]
AutogradOther fallthrough [backend fallback]
AutogradCPU fn_AutogradCPU [kernel]
AutogradXLA fallthrough [backend fallback]
@@ -836,7 +836,7 @@
CPU fn_CPU
XLA fn_XLA
AutogradCPU fn_AutogradCPU
-DefaultBackend[alias] fn_DefaultBackend
+CompositeExplicitAutograd[alias] fn_CompositeExplicitAutograd
'''
)
@@ -883,8 +883,8 @@
with self.assertRaisesRegex(
RuntimeError,
- r"Registration to both CompositeImplicitAutograd and DefaultBackend is not allowed"):
- dispatcher.register(["DefaultBackend", "CompositeImplicitAutograd"])
+ r"Registration to both CompositeImplicitAutograd and CompositeExplicitAutograd is not allowed"):
+ dispatcher.register(["CompositeExplicitAutograd", "CompositeImplicitAutograd"])
if __name__ == '__main__':
diff --git a/tools/autograd/gen_variable_type.py b/tools/autograd/gen_variable_type.py
index 29bc567..505cca9 100644
--- a/tools/autograd/gen_variable_type.py
+++ b/tools/autograd/gen_variable_type.py
@@ -325,7 +325,7 @@
if name in MANUAL_AUTOGRAD_AND_TRACER or (fn.info and fn.info.has_derivatives):
msg = (f'There\'s a formula for {name}(or its functional variant) in derivatives.yaml. '
f'It\'s required to add a dispatch section for it with explicit supported backends e.g CPU/CUDA '
- f'or DefaultBackend in native_functions.yaml. Please see '
+ f'or CompositeExplicitAutograd in native_functions.yaml. Please see '
f'https://github.com/pytorch/pytorch/tree/master/aten/src/ATen/native#choosing-the-right-dispatch-keyword '
f'for instructions to choose the right dispatch keyword.')
assert f.is_abstract, msg
diff --git a/tools/codegen/dest/register_dispatch_key.py b/tools/codegen/dest/register_dispatch_key.py
index 2dca455..7a5de26 100644
--- a/tools/codegen/dest/register_dispatch_key.py
+++ b/tools/codegen/dest/register_dispatch_key.py
@@ -67,9 +67,9 @@
assert self.dispatch_key not in g.out.dispatch, \
"Do not explicitly specify Meta dispatch key on structured " \
"functions, they will be automatically generated for you"
- elif self.dispatch_key == DispatchKey.DefaultBackend:
+ elif self.dispatch_key == DispatchKey.CompositeExplicitAutograd:
assert self.dispatch_key not in g.out.dispatch, \
- "Do not explicitly specify DefaultBackend dispatch key on structured " \
+ "Do not explicitly specify CompositeExplicitAutograd dispatch key on structured " \
"functions, they will be automatically generated for you"
elif not is_structured_dispatch_key(self.dispatch_key):
return list(mapMaybe(self.gen_unstructured, g.functions()))
@@ -233,7 +233,7 @@
"""
def gen_class_set_output_body(self, k: SchemaKind) -> str:
- if self.dispatch_key in [DispatchKey.CUDA, DispatchKey.DefaultBackend]:
+ if self.dispatch_key in [DispatchKey.CUDA, DispatchKey.CompositeExplicitAutograd]:
maybe_set_guard = """
auto current_device = guard_.current_device();
if (C10_UNLIKELY(current_device.has_value())) {
@@ -264,7 +264,7 @@
elif self.dispatch_key == DispatchKey.CUDA:
empty_impl = "at::native::empty_cuda"
empty_strided_impl = "at::native::empty_strided_cuda"
- elif self.dispatch_key == DispatchKey.DefaultBackend:
+ elif self.dispatch_key == DispatchKey.CompositeExplicitAutograd:
empty_impl = "at::empty"
empty_strided_impl = "at::empty_strided"
else:
@@ -337,7 +337,7 @@
guard_field = 'c10::hip::OptionalHIPGuardMasqueradingAsCUDA guard_;'
else:
guard_field = 'c10::cuda::OptionalCUDAGuard guard_;'
- elif self.dispatch_key == DispatchKey.DefaultBackend:
+ elif self.dispatch_key == DispatchKey.CompositeExplicitAutograd:
guard_field = 'c10::OptionalDeviceGuard guard_;'
else:
guard_field = ''
@@ -362,7 +362,7 @@
return None
# TODO: Now, there is something interesting going on here. In the code below,
- # we generate DefaultBackend implementations of functional and inplace
+ # we generate CompositeExplicitAutograd implementations of functional and inplace
# based on the out implementation. But in fact, out is definable by
# functional too (just not very efficiently), and this is honestly the
# MORE likely situation for a backend implementor. How do we pick?
@@ -372,7 +372,7 @@
# someone to implement one or the other. We'd have to do a little bit
# of work to not register one of these "weak" definitions unless there
# is a strong definition somewhere in the DAG! So it's not implemented yet.
- if self.dispatch_key == DispatchKey.DefaultBackend and f.func.kind() is SchemaKind.out:
+ if self.dispatch_key == DispatchKey.CompositeExplicitAutograd and f.func.kind() is SchemaKind.out:
# Never generate a default implementation for out, that's what you
# have to define as a backend implementor
return None
@@ -421,7 +421,7 @@
if self.dispatch_key is DispatchKey.Meta:
class_name = f"structured_{meta.name(self.g)}_meta_{k.name}"
parent_class = f"at::meta::{meta.name(self.g)}"
- elif self.dispatch_key is DispatchKey.DefaultBackend:
+ elif self.dispatch_key is DispatchKey.CompositeExplicitAutograd:
# TODO: dedup this branch
class_name = f"structured_{meta.name(self.g)}_default_backend_{k.name}"
parent_class = f"at::meta::{meta.name(self.g)}"
@@ -464,7 +464,7 @@
# With the expanded context, do the impl call (if not a meta
# function)
- if self.dispatch_key == DispatchKey.DefaultBackend:
+ if self.dispatch_key == DispatchKey.CompositeExplicitAutograd:
# TODO: https://github.com/pytorch/pytorch/issues/53023
out_sig_group = CppSignatureGroup.from_native_function(
self.g.out, method=False, fallback_binding=f.manual_cpp_binding)
diff --git a/tools/codegen/gen.py b/tools/codegen/gen.py
index 61b9f0e..8859e66 100644
--- a/tools/codegen/gen.py
+++ b/tools/codegen/gen.py
@@ -124,7 +124,7 @@
return ''
return f"""
#include <ATen/{backend}Functions.h>
-#include <ATen/DefaultBackendFunctions.h>
+#include <ATen/CompositeExplicitAutogradFunctions.h>
#include <ATen/CompositeImplicitAutogradFunctions.h>
"""
@@ -147,7 +147,7 @@
# migrate math/default_backend ops to use structured delegate.
return f'return at::{backend.lower()}::{name}({exprs_str});'
- for dispatch_key in (backend, DispatchKey.DefaultBackend, DispatchKey.CompositeImplicitAutograd):
+ for dispatch_key in (backend, DispatchKey.CompositeExplicitAutograd, DispatchKey.CompositeImplicitAutograd):
if dispatch_key in f.dispatch:
return f'return at::{dispatch_key.lower()}::{name}({exprs_str});'
@@ -863,7 +863,7 @@
DispatchKey.QuantizedCPU,
DispatchKey.QuantizedCUDA,
DispatchKey.CompositeImplicitAutograd,
- DispatchKey.DefaultBackend,
+ DispatchKey.CompositeExplicitAutograd,
# Meta is a magic key: it is automatically generated for structured
# kernels
DispatchKey.Meta,
@@ -874,7 +874,7 @@
DispatchKey.CPU,
DispatchKey.CUDA,
DispatchKey.CompositeImplicitAutograd,
- DispatchKey.DefaultBackend,
+ DispatchKey.CompositeExplicitAutograd,
}
if options.backend_whitelist:
dispatch_keys = [k for k in dispatch_keys if is_generic_dispatch_key(k) or str(k) in options.backend_whitelist]
diff --git a/tools/codegen/model.py b/tools/codegen/model.py
index 9dbf7eb..e739424 100644
--- a/tools/codegen/model.py
+++ b/tools/codegen/model.py
@@ -103,8 +103,8 @@
NumDispatchKeys = auto()
Autograd = auto()
CompositeImplicitAutograd = auto()
- DefaultBackend = auto()
- EndOfAliasKeys = DefaultBackend
+ CompositeExplicitAutograd = auto()
+ EndOfAliasKeys = CompositeExplicitAutograd
CPUTensorId = CPU
CUDATensorId = CUDA
@@ -134,7 +134,7 @@
# Dispatch keys that "support all backends". These codegen slightly differently
# then backend specific keys.
def is_generic_dispatch_key(dk: DispatchKey) -> bool:
- return dk in {DispatchKey.DefaultBackend, DispatchKey.CompositeImplicitAutograd}
+ return dk in {DispatchKey.CompositeExplicitAutograd, DispatchKey.CompositeImplicitAutograd}
# CUDA specific dispatch keys
def is_cuda_dispatch_key(dk: DispatchKey) -> bool:
@@ -347,10 +347,10 @@
elif not structured and structured_delegate is None:
dispatch[DispatchKey.CompositeImplicitAutograd] = cpp.name(func)
- assert not (DispatchKey.DefaultBackend in dispatch and DispatchKey.CompositeImplicitAutograd in dispatch), \
- "cannot specify both DefaultBackend and CompositeImplicitAutograd on a single kernel; each " \
+ assert not (DispatchKey.CompositeExplicitAutograd in dispatch and DispatchKey.CompositeImplicitAutograd in dispatch), \
+ "cannot specify both CompositeExplicitAutograd and CompositeImplicitAutograd on a single kernel; each " \
"strictly subsumes the other. If you wanted to provide an explicit autograd " \
- "implementation, specify DefaultBackend; otherwise specify CompositeImplicitAutograd only"
+ "implementation, specify CompositeExplicitAutograd; otherwise specify CompositeImplicitAutograd only"
e.pop('__line__')
assert not e, f"leftover entries: {e}"
diff --git a/torch/_python_dispatcher.py b/torch/_python_dispatcher.py
index 9ed1b88..d13b0aa 100644
--- a/torch/_python_dispatcher.py
+++ b/torch/_python_dispatcher.py
@@ -22,16 +22,18 @@
kernel defined in pytorch core library. Backend owner is responsible for registering both
inference & autograd kernels in their extensions(e.g. torch-xla) for the operators they support.
E.g. XLA, XPU, MLC
-- DefaultBackend: alias key mapped to inference kernels of all backends like CPU, CUDA, XLA etc.
+- CompositeExplicitAutograd: alias key mapped to inference kernels of all backends like CPU, CUDA, XLA etc.
Kernels registered to this key MUST work for inference for all backends.
- Autograd: alias key mapped to autograd of all backends like AutogradCPU, AutogradXLA, AutogradOther.
Kernels registered to this key MUST work for autograd for all backends.
-- CompositeImplicitAutograd: alias key CompositeImplicitAutograd = DefaultBackend + Autograd
+- CompositeImplicitAutograd: alias key CompositeImplicitAutograd = CompositeExplicitAutograd + Autograd
Kernels registered to this key MUST work for both inference + autograd for all backends.
-Note we only allow registrations to alias keys inside pytorch core library. E.g you shouldn't register
-a CompositeImplicitAutograd or DefaultBackend kernel from torch-xla extension, instead you should upstream the kernel into
-pytorch/pytorch repo so that it's available for all backends and continuously tested even without the extension.
+Note we only allow registrations to alias keys inside pytorch core library. E.g
+you shouldn't register a CompositeImplicitAutograd or CompositeExplicitAutograd
+kernel from torch-xla extension, instead you should upstream the kernel into
+pytorch/pytorch repo so that it's available for all backends and continuously
+tested even without the extension.
Usage:
dispatcher = PythonDispatcher()
@@ -55,7 +57,7 @@
"XLA", "AutogradXLA",
]
alias_keys = [
- "DefaultBackend",
+ "CompositeExplicitAutograd",
"Autograd",
"CompositeImplicitAutograd",
]
@@ -85,8 +87,8 @@
if len(set(dispatchKeys)) != len(dispatchKeys):
raise RuntimeError(f"Overriden is not allowed but found duplicates in {dispatchKeys}.")
# We currently forbid this in codegen instead of C++ dispatcher.
- if 'CompositeImplicitAutograd' in dispatchKeys and 'DefaultBackend' in dispatchKeys:
- raise RuntimeError("Registration to both CompositeImplicitAutograd and DefaultBackend is not allowed.")
+ if 'CompositeImplicitAutograd' in dispatchKeys and 'CompositeExplicitAutograd' in dispatchKeys:
+ raise RuntimeError("Registration to both CompositeImplicitAutograd and CompositeExplicitAutograd is not allowed.")
for key in dispatchKeys:
if key not in self.supported_keys:
raise RuntimeError(f"{key} is not supported, please select a dispatch key in {self.supported_keys}.")
diff --git a/torch/csrc/autograd/VariableTypeManual.cpp b/torch/csrc/autograd/VariableTypeManual.cpp
index 90e055f..6414313 100644
--- a/torch/csrc/autograd/VariableTypeManual.cpp
+++ b/torch/csrc/autograd/VariableTypeManual.cpp
@@ -373,14 +373,14 @@
// Ops in the following registration list are registered as
// (1) CompositeImplicitAutograd kernels
// (2) Autograd kernels
-// (3) DefaultBackend kernels and additionally Autograd kernels
+// (3) CompositeExplicitAutograd kernels and additionally Autograd kernels
// The reason for (3) is that ops that also use dispatch (e.g. register CPU/CUDA/QuantizedCPU
// kernels) will skip picking up CompositeImplicitAutograd kernels for Autograd, so we register them to both
-// DefaultBackend and Autograd instead. See
+// CompositeExplicitAutograd and Autograd instead. See
// https://github.com/pytorch/pytorch/tree/master/aten/src/ATen/native#choosing-the-right-dispatch-keyword
// for more details.
// Invariant:
-// - Ops registered to CompositeImplicitAutograd or DefaultBackend below must match `MANUAL_BACKEND` set in tools/autograd/gen_variable_type.py.
+// - Ops registered to CompositeImplicitAutograd or CompositeExplicitAutograd below must match `MANUAL_BACKEND` set in tools/autograd/gen_variable_type.py.
// and they have manual_kernel_registration=True in native_functions.yaml.
// - Ops registered to DispatchKey::Autograd below must be included in `MANUAL_AUTOGRAD` in tools/autograd/gen_variable_type.py
@@ -393,9 +393,9 @@
m.impl("_fw_primal", torch::dispatch(DispatchKey::Autograd, TORCH_FN(VariableType::_fw_primal)));
}
-TORCH_LIBRARY_IMPL(aten, DefaultBackend, m) {
- m.impl("_backward", torch::dispatch(DispatchKey::DefaultBackend, TORCH_FN(VariableType::_backward)));
- m.impl("requires_grad_", torch::dispatch(DispatchKey::DefaultBackend, TORCH_FN(VariableType::requires_grad_)));
+TORCH_LIBRARY_IMPL(aten, CompositeExplicitAutograd, m) {
+ m.impl("_backward", torch::dispatch(DispatchKey::CompositeExplicitAutograd, TORCH_FN(VariableType::_backward)));
+ m.impl("requires_grad_", torch::dispatch(DispatchKey::CompositeExplicitAutograd, TORCH_FN(VariableType::requires_grad_)));
}
TORCH_LIBRARY_IMPL(aten, CompositeImplicitAutograd, m) {
diff --git a/torch/csrc/utils/python_dispatch.cpp b/torch/csrc/utils/python_dispatch.cpp
index 2ff386d..995c87c 100644
--- a/torch/csrc/utils/python_dispatch.cpp
+++ b/torch/csrc/utils/python_dispatch.cpp
@@ -34,7 +34,7 @@
{"QuantizedCPU", c10::DispatchKey::QuantizedCPU},
{"CompositeImplicitAutograd", c10::DispatchKey::CompositeImplicitAutograd},
{"Autograd", c10::DispatchKey::Autograd},
- {"DefaultBackend", c10::DispatchKey::DefaultBackend},
+ {"CompositeExplicitAutograd", c10::DispatchKey::CompositeExplicitAutograd},
{"AutogradCPU", c10::DispatchKey::AutogradCPU},
{"", c10::DispatchKey::Undefined},
};
