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legacy_backward.yaml
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legacy_backward.yaml
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- backward_op : 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
- backward_op : 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
backward : abs_double_grad
- backward_op : 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_op : 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
composite : add_grad(x, y, out_grad, axis)
backward : add_double_grad
inplace : (out_grad -> x_grad)
- backward_op : 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_op : affine_grid_grad
forward : affine_grid (Tensor input, IntArray outputShape, bool align_corners=true) -> Tensor(output)
args : (Tensor input, Tensor output_grad, IntArray outputShape, 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]
no_need_buffer : input
- backward_op : amax_grad
forward: amax (Tensor x, int64_t[] axis={}, bool keepdim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] axis={}, bool keepdim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : amax_grad
- backward_op : amin_grad
forward: amin (Tensor x, int64_t[] axis={}, bool keepdim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] axis={}, bool keepdim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : amin_grad
- backward_op : assign_grad
forward : assign (Tensor x) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
invoke : assign(out_grad)
- backward_op : 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_op : 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) -> 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)
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, grad_x_grad, grad_scale_grad, grad_bias_grad
inplace : (grad_out -> grad_out_grad)
- backward_op : batch_norm_grad
forward : batch_norm (Tensor x, Tensor mean, Tensor variance, Tensor scale, Tensor bias, bool is_test, float momentum, float epsilon, str data_layout, bool use_global_stats, bool trainable_statistics) -> 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)
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_op : 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_op : bicubic_interp_grad
forward : bicubic_interp (Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_layout, int out_d, int out_h, int out_w, float[] scale, str interp_method, bool align_corners, int align_mode) -> Tensor(output)
args : (Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, Tensor output_grad, str data_layout, int out_d, int out_h, int out_w, float[] scale, str interp_method, bool align_corners, int align_mode)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
optional: out_size, size_tensor, scale_tensor
kernel :
func : bicubic_interp_grad
data_type : output_grad
- backward_op : bilinear_interp_grad
forward : bilinear_interp (Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_layout, int out_d, int out_h, int out_w, float[] scale, str interp_method, bool align_corners, int align_mode) -> Tensor(output)
args : (Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, Tensor output_grad, str data_layout, int out_d, int out_h, int out_w, float[] scale, str interp_method, bool align_corners, int align_mode)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
optional: out_size, size_tensor, scale_tensor
kernel :
func : bilinear_interp_grad
data_type : output_grad
- backward_op : 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_op : cast_grad
forward : cast (Tensor x, DataType dtype) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
invoke : cast (out_grad, x.dtype())
composite: cast_grad(x, out_grad)
no_need_buffer : x
- backward_op : channel_shuffle_grad
forward : channel_shuffle (Tensor x, int groups, str data_format="NCHW") -> Tensor(out)
args : (Tensor out_grad, int groups, str data_format="NCHW")
output : Tensor(x_grad)
infer_meta :
func : ChannelShuffleGradInferMeta
kernel :
func : channel_shuffle_grad
- backward_op : 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)
invoke : concat(grad_x_grad, axis)
- backward_op : 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_op : conv2d_grad
forward : conv2d (Tensor input, Tensor filter, int[] strides, int[] paddings, str padding_algorithm, int[] dilations, int groups, str data_format) -> Tensor(out)
args : (Tensor input, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, str padding_algorithm, int[] dilations, int groups, str data_format)
output : Tensor(input_grad), Tensor(filter_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [input, filter]
kernel :
func : conv2d_grad
backward : conv2d_grad_grad
- backward_op : conv2d_grad_grad
forward : conv2d_grad (Tensor input, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, str padding_algorithm, int[] dilations, int groups, str data_format) -> 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 padding_algorithm, int[] dilations, int groups, str data_format)
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
optional : grad_input_grad, grad_filter_grad
- backward_op : conv2d_transpose_double_grad
forward : conv2d_transpose_grad(Tensor x, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, int[] output_padding, IntArray 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, IntArray 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
- backward_op : conv2d_transpose_grad
forward : conv2d_transpose(Tensor x, Tensor filter, int[] strides, int[] paddings, int[] output_padding, IntArray 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, IntArray output_size, str padding_algorithm, int groups, int[] dilations, str data_format)
output : Tensor(x_grad), Tensor(filter_grad)
infer_meta :
func : Conv2dTransposeGradInferMeta
kernel :
func : conv2d_transpose_grad
backward : conv2d_transpose_double_grad
- backward_op : conv3d_double_grad
forward : conv3d_grad (Tensor input, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, str padding_algorithm, int groups, int[] dilations, str data_format) -> 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 padding_algorithm, int groups, int[] dilations, str data_format)
output : Tensor(input_grad), Tensor(filter_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param: [input, filter, grad_out]
kernel :
func : conv3d_double_grad
optional : grad_input_grad, grad_filter_grad
- backward_op : conv3d_grad
forward : conv3d (Tensor input, Tensor filter, int[] strides, int[] paddings, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(out)
args : (Tensor input, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, str padding_algorithm, int groups, int[] dilations, str data_format)
output : Tensor(input_grad), Tensor(filter_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [input, filter]
kernel :
func : conv3d_grad
backward : conv3d_double_grad
- backward_op : 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
- backward_op : 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_op : 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_op : cumsum_grad
forward : cumsum(Tensor x, Scalar axis, bool flatten, bool exclusive, bool reverse) -> Tensor(out)
args : (Tensor x, Tensor out_grad, Scalar axis, bool flatten, bool exclusive, bool reverse)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : cumsum_grad
data_type: x
composite: cumsum_grad(x, out_grad, axis, flatten, exclusive, reverse, x_grad)
- backward_op : 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_op : depthwise_conv2d_double_grad
forward : depthwise_conv2d_grad (Tensor input, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, str padding_algorithm, int groups, int[] dilations, str data_format) -> 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 padding_algorithm, int groups, int[] dilations, str data_format)
output : Tensor(input_grad), Tensor(filter_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param: [input, filter, grad_out]
kernel :
func : depthwise_conv2d_double_grad
optional : grad_input_grad, grad_filter_grad
- backward_op : depthwise_conv2d_grad
forward : depthwise_conv2d (Tensor input, Tensor filter, int[] strides, int[] paddings, str padding_algorithm, int groups, int[] dilations, str data_format) -> Tensor(out)
args : (Tensor input, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, str padding_algorithm, int groups, int[] dilations, str data_format)
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, padding_algorithm, groups, dilations, data_format]
backward : depthwise_conv2d_double_grad
- backward_op : depthwise_conv2d_transpose_grad
forward : depthwise_conv2d_transpose(Tensor x, Tensor filter, int[] strides, int[] paddings, int[] output_padding, IntArray 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, IntArray output_size, str padding_algorithm, int groups, int[] dilations, str data_format)
output : Tensor(x_grad), Tensor(filter_grad)
infer_meta :
func : Conv2dTransposeGradInferMeta
kernel :
func : depthwise_conv2d_transpose_grad
- backward_op : 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_op : 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
composite : divide_grad(x, y, out, out_grad, axis)
backward : divide_double_grad
- backward_op : dropout_grad
forward : dropout (Tensor x, Tensor seed_tensor, Scalar p, bool is_test, str mode, int seed, bool fix_seed) -> Tensor(out), Tensor(mask)
args : (Tensor mask, Tensor out_grad, Scalar p, bool is_test, str mode)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out_grad]
kernel :
func : dropout_grad
- backward_op : 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_op : 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_op : 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_op : 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)
no_need_buffer : weight
- backward_op : 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_op : 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)
invoke : expand(grad_x_grad, shape)
- backward_op : 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
composite: expand_grad(x, out_grad, shape, x_grad_p)
- backward_op : exponential__grad
forward : exponential_ (Tensor x, float lam) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
invoke : zeros_like(out_grad)
- backward_op : 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_op : 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_op : fmax_grad
forward : fmax(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 : fmax_grad
- backward_op : fmin_grad
forward : fmin(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param: [x, y]
kernel :
func : fmin_grad
- backward_op : 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_op : 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
composite : gather_grad(x, index, out_grad, axis, overwrite)
no_need_buffer : x
- backward_op : 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_op : hardswish_grad
forward : hardswish (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad, float threshold = 6.0, float scale = 6.0, float offset = 3.0)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : hardswish_grad
inplace : (out_grad -> x_grad)
- backward_op : heaviside_grad
forward : heaviside (Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : heaviside_grad
- backward_op : hsigmoid_loss_grad
forward : hsigmoid_loss (Tensor x, Tensor label, Tensor w, Tensor bias, Tensor path, Tensor code, int num_classes, bool remote_prefetch, bool is_sparse) -> Tensor(out), Tensor(pre_out), Tensor(w_out)
args : (Tensor x, Tensor w, Tensor label, Tensor path, Tensor code, Tensor bias, Tensor pre_out, Tensor out_grad, int num_classes, bool remote_prefetch, bool is_sparse)
output : Tensor(x_grad), Tensor(w_grad), Tensor(bias_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param : [x ,w, bias]
optional: path, code, bias
kernel :
func : hsigmoid_loss_grad
- backward_op : huber_loss_grad
forward : huber_loss (Tensor input, Tensor label, float delta) -> Tensor(out), Tensor(residual)
args : (Tensor residual, Tensor out_grad, float delta)
output : Tensor(input_grad), Tensor(label_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [residual, residual]
kernel :
func : huber_loss_grad
- backward_op : index_add_grad
forward : index_add(Tensor x, Tensor index, Tensor add_value, int axis) -> Tensor(out)
args : (Tensor index, Tensor add_value, Tensor out_grad, int axis)
output : Tensor(x_grad), Tensor(add_value_grad)
infer_meta :
func : IndexAddGradInferMeta
kernel :
func : index_add_grad
data_type : out_grad
inplace : (out_grad -> x_grad)
- backward_op : instance_norm_double_grad
forward : instance_norm_grad(Tensor x, Tensor fwd_scale, Tensor saved_mean, Tensor saved_variance, Tensor grad_y, float epsilon) -> Tensor(grad_x), Tensor(grad_scale), Tensor(grad_bias)
args : (Tensor x, Tensor fwd_scale, Tensor saved_mean, Tensor saved_variance, Tensor grad_y, Tensor grad_x_grad, Tensor grad_scale_grad, Tensor grad_bias_grad, float epsilon)
output : Tensor(x_grad), Tensor(fwd_scale_grad), Tensor(grad_y_grad)
infer_meta :
func : InstanceNormDoubleGradInferMeta
kernel :
func : instance_norm_double_grad
data_type : x
optional : fwd_scale, grad_x_grad, grad_scale_grad, grad_bias_grad
- backward_op : instance_norm_grad
forward : instance_norm(Tensor x, Tensor scale, Tensor bias, float epsilon) -> Tensor(y), Tensor(saved_mean), Tensor(saved_variance)
args : (Tensor x, Tensor scale, Tensor saved_mean, Tensor saved_variance, Tensor y_grad, float epsilon)
output : Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad)
infer_meta :
func : InstanceNormGradInferMeta
kernel :
func : instance_norm_grad
data_type : x
optional : scale
backward : instance_norm_double_grad
- backward_op : kldiv_loss_grad
forward : kldiv_loss(Tensor x, Tensor label, str reduction) -> Tensor(out)
args : (Tensor x, Tensor label, Tensor out_grad, str reduction)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : kldiv_loss_grad
no_need_buffer : x
- backward_op : layer_norm_grad
forward : layer_norm (Tensor x, Tensor scale, Tensor bias, float epsilon, int begin_norm_axis) -> Tensor(out), Tensor(mean), Tensor(variance)
args : (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, Tensor out_grad, float epsilon, int begin_norm_axis)
output : Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad)
infer_meta :
func : LayerNormGradInferMeta
param : [x, scale, bias]
kernel :
func : layer_norm_grad
data_type : out_grad
no_need_buffer : bias
optional : scale, bias
- backward_op : linear_interp_grad
forward : linear_interp (Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_layout, int out_d, int out_h, int out_w, float[] scale, str interp_method, bool align_corners, int align_mode) -> Tensor(output)
args : (Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, Tensor output_grad, str data_layout, int out_d, int out_h, int out_w, float[] scale, str interp_method, bool align_corners, int align_mode)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
optional: out_size, size_tensor, scale_tensor
kernel :
func : linear_interp_grad
data_type : output_grad
- backward_op : log_softmax_grad
forward : log_softmax(Tensor x, int axis) -> Tensor(out)
args : (Tensor out, Tensor out_grad, int axis)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [out]
kernel :
func : log_softmax_grad
- backward_op : logcumsumexp_grad
forward : logcumsumexp(Tensor x, int axis, bool flatten, bool exclusive, bool reverse) -> Tensor(out)
infer_meta :
func : UnchangedInferMeta
param : [x]
args : (Tensor x, Tensor out, Tensor out_grad, int axis, bool flatten, bool exclusive, bool reverse)
output : Tensor(x_grad)
kernel :
func : logcumsumexp_grad
- backward_op : logsumexp_grad
forward : logsumexp(Tensor x, int64_t[] axis, bool keepdim, bool reduce_all) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] axis, bool keepdim, bool reduce_all)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : logsumexp_grad
- backward_op : lu_grad
forward : lu (Tensor x, bool pivot) -> Tensor(out), Tensor(pivots), Tensor(infos)
args : (Tensor x, Tensor out, Tensor pivots, Tensor out_grad, bool pivot)
output : Tensor(x_grad)
infer_meta :
func : LUGradInferMeta
kernel :
func : lu_grad
- backward_op : margin_cross_entropy_grad
forward : margin_cross_entropy (Tensor logits, Tensor label, bool return_softmax, int ring_id, int rank, int nranks, float margin1, float margin2, float margin3, float scale) -> Tensor(softmax), Tensor(loss)
args : (Tensor logits, Tensor label, Tensor softmax, Tensor loss_grad, bool return_softmax, int ring_id, int rank, int nranks, float margin1, float margin2, float margin3, float scale)
output : Tensor(logits_grad)
infer_meta :
func : MarginCrossEntropyGradInferMeta
kernel :
func : margin_cross_entropy_grad
data_type : softmax
inplace : (softmax -> logits_grad)
- backward_op : matmul_double_grad
forward : matmul_grad (Tensor x, Tensor y, Tensor grad_out, bool transpose_x=false, bool transpose_y=false) -> Tensor(grad_x), Tensor(grad_y)
args : (Tensor x, Tensor y, Tensor grad_out, Tensor grad_x_grad, Tensor grad_y_grad, bool transpose_x=false, bool transpose_y=false)
output : Tensor(x_grad), Tensor(y_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param : [x, y, grad_out]
kernel :
func : matmul_double_grad
backward : matmul_triple_grad
optional : grad_x_grad, grad_y_grad
- backward_op : matmul_grad
forward : matmul (Tensor x, Tensor y, bool transpose_x=false, bool transpose_y=false) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad, bool transpose_x=false, bool transpose_y=false)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : matmul_grad
backward : matmul_double_grad
- backward_op : matmul_triple_grad
forward : matmul_double_grad (Tensor x, Tensor y, Tensor fwd_grad_out, Tensor fwd_grad_grad_x, Tensor fwd_grad_grad_y, bool transpose_x=false, bool transpose_y=false) -> Tensor(grad_x), Tensor(grad_y), Tensor(grad_grad_out)
args : (Tensor x, Tensor y, Tensor fwd_grad_out, Tensor fwd_grad_grad_x, Tensor fwd_grad_grad_y, Tensor grad_x_grad, Tensor grad_y_grad, Tensor grad_grad_out_grad, bool transpose_x=false, bool transpose_y=false)
output : Tensor(x_grad), Tensor(y_grad), Tensor(fwd_grad_out_grad), Tensor(fwd_grad_grad_x_grad), Tensor(fwd_grad_grad_y_grad)
infer_meta :
func : GeneralQuinaryGradInferMeta
param : [x, y, fwd_grad_out, fwd_grad_grad_x, fwd_grad_grad_y]
kernel :
func : matmul_triple_grad
optional : fwd_grad_grad_x, fwd_grad_grad_y, grad_x_grad, grad_y_grad, grad_grad_out_grad
- backward_op : max_grad
forward: max (Tensor x, IntArray axis={}, bool keepdim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, IntArray axis={}, bool keepdim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : max_grad
- backward_op : max_pool2d_with_index_grad
forward : max_pool2d_with_index(Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool global_pooling, bool adaptive) -> Tensor(out), Tensor(mask)
args : (Tensor x, Tensor mask, Tensor out_grad, int[] kernel_size, int[] strides, int[] paddings, bool global_pooling, bool adaptive)
output : Tensor(x_grad)
infer_meta :
func : MaxPoolWithIndexGradInferMeta
kernel :
func : max_pool2d_with_index_grad
- backward_op : max_pool3d_with_index_grad
forward : max_pool3d_with_index(Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool global_pooling, bool adaptive) -> Tensor(out), Tensor(mask)
args : (Tensor x, Tensor mask, Tensor out_grad, int[] kernel_size, int[] strides, int[] paddings, bool global_pooling, bool adaptive)
output : Tensor(x_grad)
infer_meta :
func : MaxPoolWithIndexGradInferMeta
kernel :
func : max_pool3d_with_index_grad
- backward_op : maximum_grad
forward : maximum(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 : maximum_grad
- backward_op : mean_all_grad
forward : mean_all(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : mean_all_grad
- backward_op : mean_double_grad
forward: mean_grad (Tensor x, Tensor grad_out, IntArray axis={}, bool keepdim=false, bool reduce_all = false) -> Tensor(grad_x)
args : (Tensor grad_x_grad, IntArray axis={}, bool keepdim=false)
output : Tensor(grad_out_grad)
invoke : mean(grad_x_grad, axis, keepdim)
- backward_op : mean_grad
forward: mean (Tensor x, IntArray axis={}, bool keepdim=false) -> Tensor(out)
args : (Tensor x, Tensor out_grad, IntArray axis={}, bool keepdim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : mean_grad
backward : mean_double_grad
no_need_buffer : x
- backward_op : min_grad
forward: min (Tensor x, IntArray axis={}, bool keepdim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, IntArray axis={}, bool keepdim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : min_grad
- backward_op : minimum_grad
forward : minimum(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 : minimum_grad
- backward_op : mish_grad
forward : mish (Tensor x, float threshold) -> Tensor(out)
args : (Tensor x, Tensor out_grad, float threshold)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : mish_grad
inplace : (out_grad -> x_grad)
- backward_op : multiply_double_grad
forward : multiply_grad (Tensor x, Tensor y, Tensor grad_out, int axis = -1) -> Tensor(grad_x), Tensor(grad_y)
args : (Tensor x, Tensor y, Tensor grad_out, Tensor grad_x_grad, Tensor grad_y_grad, int axis = -1)
output : Tensor(x_grad), Tensor(y_grad), Tensor(grad_out_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param : [x, y, grad_out]
kernel :
func : multiply_double_grad
optional : grad_x_grad, grad_y_grad
backward : multiply_triple_grad
inplace : (grad_x_grad -> grad_out_grad)
- backward_op : multiply_grad
forward : multiply (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 : multiply_grad
composite: multiply_grad(x, y, out_grad, axis, x_grad, y_grad)
backward : multiply_double_grad
- backward_op : multiply_triple_grad
forward : multiply_double_grad (Tensor x, Tensor y, Tensor fwd_grad_out, Tensor fwd_grad_grad_x, Tensor fwd_grad_grad_y, int aixs = -1) -> Tensor(grad_x), Tensor(grad_y), Tensor(grad_grad_out)
args : (Tensor x, Tensor y, Tensor fwd_grad_out, Tensor fwd_grad_grad_x, Tensor fwd_grad_grad_y, Tensor grad_x_grad, Tensor grad_y_grad, Tensor grad_grad_out_grad, int axis = -1)
output : Tensor(x_grad), Tensor(y_grad), Tensor(fwd_grad_out_grad), Tensor(fwd_grad_grad_x_grad), Tensor(fwd_grad_grad_y_grad)
infer_meta :
func : GeneralQuinaryGradInferMeta
param : [x, y, fwd_grad_out, fwd_grad_grad_x, fwd_grad_grad_y]
kernel :
func : multiply_triple_grad
optional : fwd_grad_grad_x, fwd_grad_grad_y, grad_x_grad, grad_y_grad, grad_grad_out_grad
- backward_op : nearest_interp_grad
forward : nearest_interp (Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_layout, int out_d, int out_h, int out_w, float[] scale, str interp_method, bool align_corners, int align_mode) -> Tensor(output)
args : (Tensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, Tensor output_grad, str data_layout, int out_d, int out_h, int out_w, float[] scale, str interp_method, bool align_corners, int align_mode)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
optional: out_size, size_tensor, scale_tensor
kernel :
func : nearest_interp_grad
data_type : output_grad
- backward_op : norm_grad
forward : norm (Tensor x, int axis, float epsilon, bool is_test) -> Tensor(out), Tensor(norm)
args : (Tensor x, Tensor norm, Tensor out_grad, int axis, float epsilon, bool is_test)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : norm_grad
- backward_op : p_norm_grad
forward : p_norm(Tensor x, float porder, int axis, float epsilon, bool keepdim, bool asvector=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, float porder, int axis, float epsilon, bool keepdim, bool asvector)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : p_norm_grad
- backward_op : pad3d_double_grad
forward : pad3d_grad(Tensor x, Tensor grad_out, IntArray paddings, str mode, float pad_value, str data_format) -> Tensor(grad_x)
args : (Tensor grad_x_grad, IntArray paddings, str mode, float pad_value, str data_format)
output : Tensor(grad_out_grad)
infer_meta :
func : Pad3dInferMeta
kernel :
func : pad3d
- backward_op : pad3d_grad
forward : pad3d(Tensor x, IntArray paddings, str mode, float pad_value, str data_format) -> Tensor(out)
args : (Tensor x, Tensor out_grad, IntArray paddings, str mode, float pad_value, str data_format)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : pad3d_grad
no_need_buffer : x
backward : pad3d_double_grad
- backward_op : pad_double_grad
forward : pad_grad(Tensor x, Tensor grad_out, int[] paddings, Scalar pad_value) -> Tensor(grad_x)
args : (Tensor grad_x_grad, int[] paddings, Scalar pad_value)
output : Tensor(grad_out_grad)
infer_meta :
func : PadInferMeta
kernel :
func : pad
- backward_op : pad_grad
forward : pad(Tensor x, int[] paddings, Scalar pad_value) -> Tensor(out)
args : (Tensor x, Tensor out_grad, int[] paddings, Scalar pad_value)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : pad_grad
param: [out_grad, paddings, pad_value]
no_need_buffer : x
backward : pad_double_grad
- backward_op : pool2d_double_grad
forward : pool2d_grad(Tensor x, Tensor out, Tensor grad_out, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(grad_x)
args : (Tensor x, Tensor grad_x_grad, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(grad_out_grad)
infer_meta :
func : Pool2DInferMeta
param : [grad_x_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
kernel :
func : pool2d_double_grad
param : [grad_x_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
no_need_buffer : x
- backward_op : pool2d_grad
forward : pool2d(Tensor x, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : pool2d_grad
param : [x, out, out_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
backward : pool2d_double_grad
- backward_op : pool3d_grad
forward : pool3d(Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : pool3d_grad
param : [x, out, out_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
- backward_op : prelu_grad
forward : prelu(Tensor x, Tensor alpha, str data_format, str mode) -> Tensor(out)
args : (Tensor x, Tensor alpha, Tensor out_grad, str data_format, str mode)
output : Tensor(x_grad), Tensor(alpha_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param: [x, alpha]
kernel :