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legacy_backward.yaml
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/
legacy_backward.yaml
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- backward_api : abs_double_grad
forward : abs_grad (Tensor x, Tensor grad_out) -> Tensor(grad_x)
args : (Tensor x, Tensor grad_x_grad)
output : Tensor(grad_out_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : abs_double_grad
data_transform:
skip_transform : grad_x_grad
- backward_api : abs_grad
forward : abs (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : abs_grad
data_transform:
skip_transform : out_grad
backward : abs_double_grad
- backward_api : acos_grad
forward : acos (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : acos_grad
inplace : (out_grad -> x_grad)
- backward_api : acosh_grad
forward : acosh (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : acosh_grad
inplace : (out_grad -> x_grad)
- backward_api : add_double_grad
forward : add_grad (Tensor x, Tensor y, Tensor grad_out, int axis = -1) -> Tensor(grad_x), Tensor(grad_y)
args : (Tensor y, Tensor grad_out, Tensor grad_x_grad, Tensor grad_y_grad, int axis = -1)
output : Tensor(grad_out_grad)
infer_meta :
func : UnchangedInferMeta
param : [grad_out]
kernel :
func : add_double_grad
optional : grad_x_grad, grad_y_grad
backward : add_triple_grad
inplace : (grad_x_grad -> grad_out_grad)
- backward_api : add_grad
forward : add (Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad, int axis = -1)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : add_grad
no_need_buffer : x, y
backward : add_double_grad
inplace : (out_grad -> x_grad)
- backward_api : add_triple_grad
forward : add_double_grad (Tensor y, Tensor grad_out, Tensor grad_grad_x, Tensor grad_grad_y, int axis = -1) -> Tensor(grad_grad_out)
args : (Tensor grad_grad_x, Tensor grad_grad_y, Tensor grad_grad_out_grad, int axis = -1)
output : Tensor(grad_grad_x_grad), Tensor(grad_grad_y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [grad_grad_x, grad_grad_y]
kernel :
func : add_triple_grad
inplace : (grad_grad_out_grad -> grad_grad_x_grad)
- backward_api : addmm_grad
forward : addmm (Tensor input, Tensor x, Tensor y, float alpha, float beta) -> Tensor(out)
args : (Tensor input, Tensor x, Tensor y, Tensor out_grad, float alpha, float beta)
output : Tensor(input_grad), Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param : [input, x, y]
kernel :
func : addmm_grad
- backward_api : affine_grid_grad
forward : affine_grid (Tensor input, IntArray outputShape, bool use_cudnn=true, bool align_corners=true) -> Tensor(output)
args : (Tensor output_grad, IntArray outputShape, bool use_cudnn=true, bool align_corners=true)
output : Tensor(input_grad)
infer_meta :
func : AffineGridGradInferMeta
param : [output_grad, outputShape, align_corners]
kernel :
func : affine_grid_grad
param : [output_grad, outputShape, align_corners]
use_gpudnn: use_cudnn
- backward_api : amax_grad
forward: amax (Tensor x, int64_t[] dims={}, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims={}, bool keep_dim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : amax_grad
- backward_api : amin_grad
forward: amin (Tensor x, int64_t[] dims={}, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims={}, bool keep_dim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : amin_grad
- backward_api : angle_grad
forward : angle (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : angle_grad
data_transform:
skip_transform : out_grad
- backward_api : argsort_grad
forward : argsort (Tensor x, int axis, bool descending) -> Tensor(out), Tensor(indices)
args : (Tensor indices, Tensor x, Tensor out_grad, int axis, bool descending)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : argsort_grad
data_type : out_grad
no_need_buffer : x
- backward_api : as_complex_grad
forward : as_complex (Tensor x) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
invoke : as_real(out_grad)
- backward_api : as_real_grad
forward : as_real (Tensor x) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
invoke : as_complex(out_grad)
- backward_api : asin_grad
forward : asin (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : asin_grad
inplace : (out_grad -> x_grad)
- backward_api : asinh_grad
forward : asinh (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : asinh_grad
inplace : (out_grad -> x_grad)
- backward_api : assign_double_grad
forward : assign_grad (Tensor grad_out) -> Tensor(grad_x)
args : (Tensor grad_x_grad)
output : Tensor(grad_out_grad)
infer_meta :
func : UnchangedInferMeta
kernel :
func : assign
backward: assign_triple_grad
inplace : (grad_x_grad -> grad_out_grad)
- backward_api : assign_grad
forward : assign (Tensor x) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
kernel :
func : assign
backward: assign_double_grad
inplace : (out_grad -> x_grad)
- backward_api : assign_out__grad
forward : assign_out_ (Tensor x, Tensor output) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
kernel :
func : assign
inplace : (out_grad -> x_grad)
- backward_api : assign_triple_grad
forward : assign_double_grad (Tensor grad_out) -> Tensor(grad_x)
args : (Tensor grad_x_grad)
output : Tensor(grad_out_grad)
infer_meta :
func : UnchangedInferMeta
kernel :
func : assign
inplace : (grad_x_grad -> grad_out_grad)
- backward_api : atan_grad
forward : atan (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : atan_grad
inplace : (out_grad -> x_grad)
- backward_api : atanh_grad
forward : atanh (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : atanh_grad
inplace : (out_grad -> x_grad)
- backward_api : batch_norm_double_grad
forward : batch_norm_grad (Tensor x, Tensor scale, Tensor bias, Tensor out_mean, Tensor out_variance, Tensor saved_mean, Tensor saved_variance, Tensor reserve_space, Tensor grad_out, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu) -> Tensor(grad_x), Tensor(grad_scale), Tensor(grad_bias)
args : (Tensor x, Tensor scale, Tensor out_mean, Tensor out_variance, Tensor saved_mean, Tensor saved_variance, Tensor grad_out, Tensor grad_x_grad, Tensor grad_scale_grad, Tensor grad_bias_grad, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
output : Tensor(x_grad), Tensor(scale_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param : [x, scale, x]
kernel :
func : batch_norm_grad_grad
data_type : x
optional : out_mean, out_variance
inplace : (grad_out -> grad_out_grad)
- backward_api : batch_norm_grad
forward : batch_norm (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu) -> Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
args : (Tensor x, Tensor scale, Tensor bias, Tensor mean_out, Tensor variance_out, Tensor saved_mean, Tensor saved_variance, Tensor reserve_space, Tensor out_grad, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
output : Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param : [x, scale, bias]
kernel :
func : batch_norm_grad
data_type : out_grad
optional : mean_out, variance_out, reserve_space
backward : batch_norm_double_grad
- backward_api : bce_loss_grad
forward : bce_loss (Tensor input, Tensor label) -> Tensor(out)
args : (Tensor input, Tensor label, Tensor out_grad)
output : Tensor(input_grad)
infer_meta :
func : UnchangedInferMeta
param : [input]
kernel :
func : bce_loss_grad
inplace : (out_grad -> input_grad)
- backward_api : bilinear_tensor_product_grad
forward : bilinear_tensor_product (Tensor x, Tensor y, Tensor weight, Tensor bias) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor weight, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad), Tensor(weight_grad), Tensor(bias_grad)
infer_meta :
func : BilinearTensorProductGradInferMeta
kernel :
func : bilinear_tensor_product_grad
- backward_api : bmm_grad
forward : bmm (Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : BmmGradInferMeta
kernel :
func : bmm_grad
- backward_api : brelu_grad
forward : brelu (Tensor x, float t_min, float t_max) -> Tensor(out)
args : (Tensor x, Tensor out_grad, float t_min, float t_max)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : brelu_grad
inplace : (out_grad -> x_grad)
- backward_api : broadcast_tensors_grad
forward : broadcast_tensors (Tensor[] x) -> Tensor[](out)
args : (Tensor[] x, Tensor[] out_grad)
output : Tensor[](x_grad)
infer_meta :
func : UnchangedMultiInferMeta
param : [x]
kernel :
func : broadcast_tensors_grad
param : [out_grad]
no_need_buffer : x
- backward_api : cast_grad
forward : cast (Tensor x, DataType out_dtype) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : cast_grad
data_type : out_grad
no_need_buffer : x
- backward_api : ceil_grad
forward : ceil(Tensor x) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [out_grad]
kernel :
func : ceil_grad
inplace : (out_grad -> x_grad)
- backward_api : celu_double_grad
forward : celu_grad(Tensor x, Tensor grad_out, float alpha) -> Tensor(grad_x)
args : (Tensor x, Tensor grad_out, Tensor grad_x_grad, float alpha)
output : Tensor(x_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, x]
kernel :
func : celu_double_grad
inplace : (grad_x_grad -> grad_out_grad)
- backward_api : celu_grad
forward : celu(Tensor x, float alpha) -> Tensor(out)
args : (Tensor x, Tensor out_grad, float alpha)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : celu_grad
backward : celu_double_grad
inplace : (out_grad -> x_grad)
- backward_api : clip_double_grad
forward : clip_grad (Tensor x, Tensor grad_out, Scalar min = 0., Scalar max = 0.) -> Tensor(grad_x)
args : (Tensor x, Tensor grad_x_grad, Scalar min = 0., Scalar max = 0.)
output : Tensor(grad_out_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : clip_grad
- backward_api : clip_grad
forward : clip (Tensor x, Scalar min, Scalar max) -> Tensor(out)
args : (Tensor x, Tensor out_grad, Scalar min = 0., Scalar max = 0.)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : clip_grad
backward : clip_double_grad
inplace : (out_grad -> x_grad)
- backward_api : complex_grad
forward : complex (Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : ComplexGradInferMeta
kernel :
func : complex_grad
data_type : x
- backward_api : concat_double_grad
forward : concat_grad (Tensor[] x, Tensor grad_out, Scalar axis) -> Tensor[](grad_x)
args : (Tensor[] grad_x_grad, Scalar axis = 0)
output : Tensor(grad_out_grad)
infer_meta :
func : ConcatInferMeta
param : [grad_x_grad, axis]
kernel :
func : concat
- backward_api : concat_grad
forward : concat (Tensor[] x, Scalar axis) -> Tensor(out)
args : (Tensor[] x, Tensor out_grad, Scalar axis = 0)
output : Tensor[](x_grad){x.size()}
infer_meta :
func : UnchangedMultiInferMeta
param : [x]
kernel :
func : concat_grad
no_need_buffer : x
backward : concat_double_grad
- backward_api : conj_grad
forward : conj (Tensor x) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [out_grad]
kernel :
func : conj
- backward_api : conv2d_grad
forward : conv2d (Tensor input, Tensor filter, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) -> Tensor(out)
args : (Tensor input, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search)
output : Tensor(input_grad), Tensor(filter_grad)
invoke : conv2d_grad_impl(input, filter, out_grad, strides, paddings, paddding_algorithm, groups, dilations, data_format, use_addto, workspace_size_MB, exhaustive_search, input_grad, filter_grad)
backward : conv2d_grad_grad
- backward_api : conv2d_grad_grad
forward : conv2d_grad (Tensor input, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) -> Tensor(grad_input), Tensor(grad_filter)
args : (Tensor input, Tensor filter, Tensor grad_out, Tensor grad_input_grad, Tensor grad_filter_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search)
output : Tensor(input_grad), Tensor(filter_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param: [input, filter, grad_out]
kernel :
func : conv2d_grad_grad
use_gpudnn : true
optional : grad_input_grad, grad_filter_grad
- backward_api : conv2d_transpose_double_grad
forward : conv2d_transpose_grad(Tensor x, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(grad_x), Tensor(grad_filter)
args : (Tensor x, Tensor filter, Tensor grad_out, Tensor grad_x_grad, Tensor grad_filter_grad, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format)
output : Tensor(x_grad), Tensor(filter_grad), Tensor(grad_out_grad)
infer_meta :
func : Conv2dTransposeDoubleGradInferMeta
kernel :
func : conv2d_transpose_grad_grad
use_gpudnn : true
- backward_api : conv2d_transpose_grad
forward : conv2d_transpose(Tensor x, Tensor filter, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(out)
args : (Tensor x, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format)
output : Tensor(x_grad), Tensor(filter_grad)
infer_meta :
func : ConvTransposeGradInferMeta
kernel :
func : conv2d_transpose_grad
use_gpudnn : true
backward : conv2d_transpose_double_grad
- backward_api : conv3d_grad
forward : conv3d (Tensor input, Tensor filter, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) -> Tensor(out)
args : (Tensor input, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search)
output : Tensor(input_grad), Tensor(filter_grad)
invoke : conv3d_grad_impl(input, filter, out_grad, strides, paddings, paddding_algorithm, groups, dilations, data_format, use_addto, workspace_size_MB, exhaustive_search, input_grad, filter_grad)
backward : conv3d_grad_grad
- backward_api : conv3d_grad_grad
forward : conv3d_grad (Tensor input, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) -> Tensor(grad_input), Tensor(grad_filter)
args : (Tensor input, Tensor filter, Tensor grad_out, Tensor grad_input_grad, Tensor grad_filter_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search)
output : Tensor(input_grad), Tensor(filter_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param: [input, filter, grad_out]
kernel :
func : conv3d_grad_grad
use_gpudnn : true
optional : grad_input_grad, grad_filter_grad
- backward_api : conv3d_transpose_grad
forward : conv3d_transpose(Tensor x, Tensor filter, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(out)
args : (Tensor x, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format)
output : Tensor(x_grad), Tensor(filter_grad)
infer_meta :
func : ConvTransposeGradInferMeta
kernel :
func : conv3d_transpose_grad
use_gpudnn : true
- backward_api : cos_grad
forward : cos (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : cos_grad
inplace : (out_grad -> x_grad)
- backward_api : cosh_grad
forward : cosh (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : cosh_grad
inplace : (out_grad -> x_grad)
- backward_api : crop_tensor_grad
forward : crop_tensor (Tensor x, IntArray shape, IntArray offsets) -> Tensor(out)
args : (Tensor x, Tensor out_grad, IntArray offsets)
output : Tensor(x_grad)
infer_meta :
func : CropTensorGradInferMeta
kernel :
func : crop_tensor_grad
data_type : x
- backward_api : cross_entropy_with_softmax_grad
forward : cross_entropy_with_softmax (Tensor input, Tensor label, bool soft_label, bool use_softmax, bool numeric_stable_mode, int ignore_index, int axis) -> Tensor(softmax), Tensor(loss)
args : (Tensor label, Tensor softmax, Tensor loss_grad, bool soft_label, bool use_softmax, bool numeric_stable_mode, int ignore_index, int axis)
output : Tensor(input_grad)
infer_meta :
func : CrossEntropyWithSoftmaxGradInferMeta
kernel :
func : cross_entropy_with_softmax_grad
data_type : softmax
inplace : (softmax -> input_grad)
- backward_api : cumprod_grad
forward : cumprod (Tensor x, int dim) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int dim)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : cumprod_grad
- backward_api : cumsum_grad
forward : cumsum(Tensor x, int axis, bool flatten, bool exclusive, bool reverse) -> Tensor(out)
infer_meta :
func : UnchangedInferMeta
param : [x]
args : (Tensor out_grad, int axis, bool flatten, bool exclusive, bool reverse)
output : Tensor(x_grad)
invoke : cumsum(out_grad, axis, flatten, exclusive, !reverse)
- backward_api : deformable_conv_grad
forward : deformable_conv(Tensor x, Tensor offset, Tensor filter, Tensor mask, int[] strides, int[] paddings, int[] dilations, int deformable_groups, int groups, int im2col_step) -> Tensor(out)
args : (Tensor x, Tensor offset, Tensor filter, Tensor mask, Tensor out_grad, int[] strides, int[] paddings, int[] dilations, int deformable_groups, int groups, int im2col_step)
output : Tensor(x_grad), Tensor(offset_grad), Tensor(filter_grad), Tensor(mask_grad)
infer_meta :
func : DeformableConvGradInferMeta
kernel :
func : deformable_conv_grad
data_type : x
optional : mask
- backward_api : depthwise_conv2d_grad
forward : depthwise_conv2d (Tensor input, Tensor filter, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search, bool fuse_relu, bool use_gpudnn) -> Tensor(out)
args : (Tensor input, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search, bool fuse_relu, bool use_gpudnn)
output : Tensor(input_grad), Tensor(filter_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [input, filter]
kernel :
func : depthwise_conv2d_grad
param : [input, filter, out_grad, strides, paddings, paddding_algorithm, groups, dilations, data_format, use_addto, workspace_size_MB, exhaustive_search, fuse_relu]
use_gpudnn : use_gpudnn
backward : depthwise_conv2d_grad_grad
- backward_api : depthwise_conv2d_grad_grad
forward : depthwise_conv2d_grad (Tensor input, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search, bool fuse_relu, bool use_gpudnn) -> Tensor(grad_input), Tensor(grad_filter)
args : (Tensor input, Tensor filter, Tensor grad_out, Tensor grad_input_grad, Tensor grad_filter_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search, bool fuse_relu)
output : Tensor(input_grad), Tensor(filter_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param: [input, filter, grad_out]
kernel :
func : depthwise_conv2d_grad_grad
optional : grad_input_grad, grad_filter_grad
- backward_api : depthwise_conv2d_transpose_grad
forward : depthwise_conv2d_transpose(Tensor x, Tensor filter, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(out)
args : (Tensor x, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, int[] output_padding, int[] output_size, str padding_algorithm, int groups, int[] dilations, str data_format)
output : Tensor(x_grad), Tensor(filter_grad)
infer_meta :
func : ConvTransposeGradInferMeta
kernel :
func : depthwise_conv2d_transpose_grad
- backward_api : det_grad
forward : det (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : determinant_grad
- backward_api : divide_double_grad
forward : divide_grad (Tensor x, Tensor y, Tensor out, Tensor grad_out, int axis = -1) -> Tensor(grad_x), Tensor(grad_y)
args : (Tensor y, Tensor out, Tensor grad_x, Tensor grad_x_grad, Tensor grad_y_grad, int axis = -1)
output : Tensor(y_grad), Tensor(out_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param : [y, grad_x, grad_x]
kernel :
func : divide_double_grad
data_type : out
optional : grad_x_grad, grad_y_grad
inplace : (grad_x_grad -> grad_out_grad)
- backward_api : divide_grad
forward : divide (Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out, Tensor out_grad, int axis = -1)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : divide_grad
backward : divide_double_grad
- backward_api : dropout_grad
forward : dropout (Tensor x, Tensor seed_tensor, float p, bool is_test, str mode, int seed, bool fix_seed) -> Tensor(out), Tensor(mask)
args : (Tensor mask, Tensor out_grad, float p, bool is_test, str mode)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out_grad]
kernel :
func : dropout_grad
- backward_api : eig_grad
forward : eig (Tensor x) -> Tensor(out_w), Tensor(out_v)
args : (Tensor out_w, Tensor out_v, Tensor out_w_grad, Tensor out_v_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out_v]
kernel :
func : eig_grad
data_type : out_v
data_transform:
skip_transform : out_w, out_w_grad
- backward_api : eigh_grad
forward : eigh (Tensor x, str uplo) -> Tensor(out_w), Tensor(out_v)
args : (Tensor out_w, Tensor out_v, Tensor out_w_grad, Tensor out_v_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out_v]
kernel :
func : eigh_grad
data_type : out_v
data_transform:
skip_transform : out_w, out_w_grad
- backward_api : eigvalsh_grad
forward : eigvalsh (Tensor x, str uplo, bool is_test) -> Tensor(eigenvalues), Tensor(eigenvectors)
args : (Tensor eigenvectors, Tensor eigenvalues_grad, str uplo, bool is_test)
output : Tensor(x_grad)
infer_meta :
func : EigvalshGradInferMeta
kernel :
func : eigvalsh_grad
data_type : eigenvectors
data_transform :
skip_transform : eigenvalues_grad
- backward_api : einsum_grad
forward : einsum (Tensor[] x, str equation) -> Tensor(out), Tensor[](inner_cache), Tensor[](x_shape)
args : (Tensor[] x_shape, Tensor[] inner_cache, Tensor out_grad, str equation)
output : Tensor[](x_grad){x.size()}
infer_meta :
func : UnchangedMultiInferMeta
param : [x_shape]
kernel :
func : einsum_grad
- backward_api : elementwise_pow_grad
forward : elementwise_pow(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad, int axis=-1)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param: [x, y]
kernel :
func : elementwise_pow_grad
- backward_api : elu_double_grad
forward : elu_grad (Tensor x, Tensor out, Tensor grad_out, float alpha)-> Tensor(grad_x)
args : (Tensor x, Tensor grad_out, Tensor grad_x_grad, float alpha)
output : Tensor(x_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, x]
kernel :
func : elu_double_grad
inplace : (grad_x_grad -> grad_out_grad)
- backward_api : elu_grad
forward : elu (Tensor x, float alpha) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, float alpha)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : elu_grad
backward : elu_double_grad
inplace : (out_grad -> x_grad)
- backward_api : embedding_grad
forward : embedding (Tensor x, Tensor weight, int64_t padding_idx=-1, bool sparse=false) -> Tensor(out)
args : (Tensor x, Tensor weight, Tensor out_grad, int64_t padding_idx=-1, bool sparse=false)
output : Tensor(weight_grad)
invoke : embedding_grad_impl(x, weight, out_grad, padding_idx, sparse, weight_grad)
- backward_api : exp_grad
forward : exp (Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out]
kernel :
func : exp_grad
inplace : (out_grad -> x_grad)
- backward_api : expand_as_grad
forward : expand_as (Tensor x, Tensor y, int[] target_shape) -> Tensor(out)
args : (Tensor x, Tensor out_grad, int[] target_shape)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : expand_as_grad
no_need_buffer : x
- backward_api : expand_double_grad
forward : expand_grad (Tensor x, Tensor grad_out, IntArray shape) -> Tensor(grad_x)
args : (Tensor grad_x_grad, IntArray shape)
output : Tensor(grad_out_grad)
infer_meta :
func : ExpandInferMeta
kernel :
func : expand
- backward_api : expand_grad
forward : expand (Tensor x, IntArray shape) -> Tensor(out)
args : (Tensor x, Tensor out_grad, IntArray shape)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : expand_grad
no_need_buffer : x
backward : expand_double_grad
- backward_api : expm1_grad
forward : expm1 (Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out]
kernel :
func : expm1_grad
inplace : (out_grad -> x_grad)
- backward_api : exponential__grad
forward : exponential_ (Tensor x, float lambda) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
invoke : zeros_like(out_grad, DataType::UNDEFINED, {})
- backward_api : fill_diagonal_grad
forward : fill_diagonal (Tensor x, float value, int offset, bool wrap) -> Tensor(out)
args : (Tensor out_grad, float value, int offset, bool wrap)
output : Tensor(x_grad)
infer_meta :
func : FillDiagonalGradInferMeta
kernel :
func : fill_diagonal_grad
- backward_api : fill_diagonal_tensor_grad
forward : fill_diagonal_tensor (Tensor x, Tensor y, int64_t offset, int dim1, int dim2) -> Tensor(out)
args : (Tensor out_grad, int64_t offset, int dim1, int dim2)
output : Tensor(x_grad)
infer_meta :
func : FillDiagonalTensorGradInferMeta
kernel :
func : fill_diagonal_tensor_grad
inplace : (out_grad -> x_grad)
- backward_api : fill_grad
forward : fill (Tensor x, Scalar value) -> Tensor(out)
args : (Tensor out_grad, Scalar value)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out_grad]
kernel :
func : fill_grad
inplace : (out_grad -> x_grad)
- backward_api : flatten_grad
forward : flatten(Tensor x, int start_axis, int stop_axis) -> Tensor(out), Tensor(xshape)
args : (Tensor xshape, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : KernelWithXShapeInferMeta
param : [xshape]
kernel :
func : flatten_grad
data_type: out_grad
backend: out_grad
layout: out_grad
inplace : (out_grad -> x_grad)
- backward_api : flip_grad
forward : flip (Tensor x, int[] axis) -> Tensor(out)
args : (Tensor out_grad, int[] axis)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [out_grad]
kernel :
func : flip
- backward_api : floor_grad
forward : floor(Tensor x) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [out_grad]
kernel :
func : floor_grad
inplace : (out_grad -> x_grad)
- backward_api : fmax_grad
forward : fmax(Tensor x, Tensor y, int axis) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad, int axis)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param: [x, y]
kernel :
func : fmax_grad
- backward_api : fmin_grad
forward : fmin(Tensor x, Tensor y, int axis) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad, int axis)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param: [x, y]
kernel :
func : fmin_grad
- backward_api : frame_grad
forward : frame(Tensor x, int frame_length, int hop_length, int axis) -> Tensor(out)
args : (Tensor x, Tensor out_grad, int frame_length, int hop_length, int axis)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : frame_grad
- backward_api : frobenius_norm_grad
forward : frobenius_norm(Tensor x, int64_t[] axis, bool keep_dim, bool reduce_all) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] axis, bool keep_dim, bool reduce_all)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : frobenius_norm_grad
- backward_api : gather_grad
forward : gather(Tensor x, Tensor index, Scalar axis=0) -> Tensor(out)
args : (Tensor x, Tensor index, Tensor out_grad, Scalar axis=0, bool overwrite=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
data_type: x
func : gather_grad
no_need_buffer : x
- backward_api : gather_nd_grad
forward : gather_nd (Tensor x, Tensor index) -> Tensor(out)
args : (Tensor x, Tensor index, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : gather_nd_grad
no_need_buffer : x
- backward_api : gelu_grad
forward : gelu(Tensor x, bool approximate) -> Tensor(out)
args : (Tensor x, Tensor out_grad, bool approximate)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : gelu_grad
- backward_api : graph_send_recv_grad
forward : graph_send_recv (Tensor x, Tensor src_index, Tensor dst_index, str reduce_op = "SUM", IntArray out_size = {0}) -> Tensor(out), Tensor(dst_count)
args : (Tensor x, Tensor src_index, Tensor dst_index, Tensor out, Tensor dst_count, Tensor out_grad, str reduce_op = "SUM")
output : Tensor(x_grad)
infer_meta :
func : GeneralUnaryGradInferMeta
param : [x]
kernel :
func : graph_send_recv_grad
data_type : out_grad
optional: out, dst_count
- backward_api : graph_send_ue_recv_grad
forward : graph_send_ue_recv (Tensor x, Tensor y, Tensor src_index, Tensor dst_index, str message_op, str reduce_op, IntArray out_size) -> Tensor(out), Tensor(dst_count)
args : (Tensor x, Tensor y, Tensor src_index, Tensor dst_index, Tensor out, Tensor dst_count, Tensor out_grad, str message_op, str reduce_op)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : graph_send_ue_recv_grad
data_type : out_grad
optional: out, dst_count
# grid sample
- backward_api : grid_sample_grad
forward : grid_sample (Tensor x, Tensor grid, str mode, str padding_mode, bool align_corners) -> Tensor(out)
args : (Tensor x, Tensor grid, Tensor out_grad, str mode, str padding_mode, bool align_corners)
output : Tensor(x_grad), Tensor(grid_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, grid]
kernel :
func : grid_sample_grad
data_type : x
- backward_api : group_norm_grad
forward : group_norm (Tensor x, Tensor scale, Tensor bias, float epsilon, int groups, str data_layout) -> Tensor(y), Tensor(mean), Tensor(variance)
args : (Tensor x, Tensor scale, Tensor bias, Tensor y, Tensor mean, Tensor variance, Tensor y_grad, float epsilon, int groups, str data_layout)
output : Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param : [y, scale, bias]
kernel :
func : group_norm_grad
data_type : y_grad
optional: scale, bias
inplace : (y_grad -> x_grad)
- backward_api : gumbel_softmax_grad
forward : gumbel_softmax (Tensor x, float temperature, bool hard, int axis) -> Tensor(out)
args : (Tensor out, Tensor out_grad, int axis)
output : Tensor(x_grad)
infer_meta :
func : GumbelSoftmaxGradInferMeta
param : [out, out_grad, axis]
kernel :
func : gumbel_softmax_grad