Have _embedding_bag_dense_backward match JIT signature. (#19428)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19428
ghimport-source-id: 037efa3df95efc1fbff631826351d1698a3c49ec
Differential Revision: D15003379
Pulled By: gchanan
fbshipit-source-id: f8e82800171f632e28535e416283d858156068ec
diff --git a/aten/src/ATen/native/native_functions.yaml b/aten/src/ATen/native/native_functions.yaml
index 8a07de1..cc2b0fd 100644
--- a/aten/src/ATen/native/native_functions.yaml
+++ b/aten/src/ATen/native/native_functions.yaml
@@ -669,8 +669,7 @@
- func: _embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, int num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights) -> Tensor
-- func: _embedding_bag_dense_backward(Tensor grad, IndexTensor indices, IndexTensor offsets, IndexTensor offset2bag, IndexTensor bag_size, IndexTensor maximum_indices, int num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights) -> Tensor
- matches_jit_signature: False
+- func: _embedding_bag_dense_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, int num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights) -> Tensor
dispatch:
CPU: _embedding_bag_dense_backward_cpu
CUDA: _embedding_bag_dense_backward_cuda
diff --git a/tools/autograd/derivatives.yaml b/tools/autograd/derivatives.yaml
index 9845551..4be3090 100644
--- a/tools/autograd/derivatives.yaml
+++ b/tools/autograd/derivatives.yaml
@@ -957,6 +957,13 @@
weight: _embedding_bag_backward(grad, indices, offsets, result1, result2, result3, weight.size(0), scale_grad_by_freq, mode, sparse, per_sample_weights)
per_sample_weights: _embedding_bag_per_sample_weights_backward(grad, weight, indices, result1, mode)
+- name: _embedding_bag_dense_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, Tensor per_sample_weights)
+ indices: non_differentiable
+ offsets: non_differentiable
+ offset2bag: non_differentiable
+ bag_size: non_differentiable
+ maximum_indices: non_differentiable
+
- name: embedding_renorm_(Tensor self, Tensor indices, double max_norm, double norm_type)
indices: non_differentiable
self: not_implemented("embedding_renorm")
