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sparse_backward.yaml
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/
sparse_backward.yaml
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- backward_op : abs_grad
forward : tanh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : abs_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
abs_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : 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_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
acos_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : 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_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
acosh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : add_grad
forward : add(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 : add_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
add_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr},
add_coo_dense_grad{sparse_coo, dense, sparse_coo -> sparse_coo, dense}
- backward_op : addmm_grad
forward : addmm(Tensor input, Tensor x, Tensor y, float alpha=1.0, float beta=1.0) -> Tensor(out)
args : (Tensor input, Tensor x, Tensor y, Tensor out_grad, float alpha=1.0, float beta=1.0)
output : Tensor(input_grad), Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralTernaryGradInferMeta
param : [input, x, y]
kernel :
func : addmm_csr_dense_grad {dense, sparse_csr, dense, dense -> dense, sparse_csr, dense},
addmm_csr_csr_grad {sparse_csr, sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr, sparse_csr},
addmm_coo_dense_grad {dense, sparse_coo, dense, dense -> dense, sparse_coo, dense},
addmm_coo_coo_grad {sparse_coo, sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo, sparse_coo}
- backward_op : 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_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
asin_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : 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_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
asinh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : 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_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
atan_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : 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_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
atanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : 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_coo_grad {sparse_coo, dense, dense, dense, dense, dense, dense, dense, sparse_coo -> sparse_coo, dense, dense}
data_type : out_grad
optional : mean_out, variance_out, reserve_space
- backward_op : cast_grad
forward : cast(Tensor x, DataType index_dtype, DataType value_dtype) -> Tensor(out)
args : (Tensor x, Tensor out_grad, DataType value_dtype)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : cast_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
cast_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
data_type : out_grad
- backward_op : conv3d_grad
forward : conv3d (Tensor x, Tensor kernel, int[] paddings, int[] dilations, int[] strides, int groups, bool subm, str key) -> Tensor(out), Tensor(rulebook), Tensor(counter)
args : (Tensor x, Tensor kernel, Tensor out, Tensor rulebook, Tensor counter, Tensor out_grad, int[] paddings, int[] dilations, int[] strides, int groups, bool subm, str key)
output : Tensor(x_grad), Tensor(kernel_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, kernel]
kernel :
func : conv3d_coo_grad{sparse_coo, dense, sparse_coo, dense, dense, sparse_coo -> sparse_coo, dense}
- backward_op : divide_grad
forward : divide(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : divide_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
divide_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_op : divide_scalar_grad
forward : divide_scalar (Tensor x, float scalar) -> Tensor(out)
args : (Tensor out_grad, float scalar)
output : Tensor(x_grad)
invoke : divide_scalar(out_grad, scalar)
- backward_op : 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_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
expm1_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : leaky_relu_grad
forward : leaky_relu(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 : leaky_relu_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
leaky_relu_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : log1p_grad
forward : log1p(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : log1p_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
log1p_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : masked_matmul_grad
forward : masked_matmul(Tensor x, Tensor y, Tensor mask) -> 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 : masked_matmul_csr_grad{dense, dense, sparse_csr -> dense, dense}
- backward_op : matmul_grad
forward : matmul(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 : matmul_csr_dense_grad {sparse_csr, dense, dense -> sparse_csr, dense},
matmul_csr_csr_grad {sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr},
matmul_coo_dense_grad {sparse_coo, dense, dense -> sparse_coo, dense},
matmul_coo_coo_grad {sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo}
- backward_op : maxpool_grad
forward : maxpool(Tensor x, int[] kernel_sizes, int[] paddings, int[] dilations, int[] strides) -> Tensor(out), Tensor(rulebook), Tensor(counter)
args : (Tensor x, Tensor rulebook, Tensor counter, Tensor out, Tensor out_grad, int[] kernel_sizes)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : maxpool_coo_grad {sparse_coo, dense, dense, sparse_coo, sparse_coo -> sparse_coo}
- backward_op : multiply_grad
forward : multiply(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 : multiply_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
multiply_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_op : mv_grad
forward : mv(Tensor x, Tensor vec) -> Tensor(out)
args : (Tensor x, Tensor vec, Tensor out_grad)
output : Tensor(x_grad), Tensor(vec_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, vec]
kernel :
func : mv_coo_grad{sparse_coo, dense, dense -> sparse_coo, dense},
mv_csr_grad{sparse_csr, dense, dense -> sparse_csr, dense}
- backward_op : pow_grad
forward : pow(Tensor x, float factor) -> Tensor(out)
args : (Tensor x, Tensor out_grad, float factor)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : pow_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
pow_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : relu6_grad
forward : relu6(Tensor x, float threshold) -> Tensor(out)
args : (Tensor out, Tensor out_grad, float threshold)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out]
kernel :
func : relu6_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
relu6_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : relu_grad
forward : relu(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out]
kernel :
func : relu_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
relu_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : reshape_grad
forward : reshape(Tensor x, IntArray shape) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : reshape_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
reshape_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : scale_grad
forward : scale(Tensor x, float scale, float bias, bool bias_after_scale) -> Tensor(out)
args : (Tensor out_grad, float scale)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out_grad]
invoke : scale(out_grad, scale, 0.0, true)
- backward_op : sin_grad
forward : sin(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : sin_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
sin_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : sinh_grad
forward : sinh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : sinh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
sinh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : softmax_grad
forward : softmax(Tensor x, int axis=-1) -> Tensor(out)
args : (Tensor out, Tensor out_grad, int axis)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out]
kernel :
func : softmax_csr_grad{sparse_csr, sparse_csr -> sparse_csr}
- backward_op : sparse_coo_tensor_grad
forward : sparse_coo_tensor(Tensor values, Tensor indices, IntArray dense_shape) -> Tensor(out)
args : (Tensor indices, Tensor out_grad)
output : Tensor(values_grad)
infer_meta :
func : UnchangedInferMeta
param: [out_grad]
kernel :
func : sparse_coo_tensor_grad{dense, sparse_coo -> dense}
- backward_op : sqrt_grad
forward : sqrt(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out]
kernel :
func : sqrt_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
sqrt_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : square_grad
forward : square(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : square_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
square_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : subtract_grad
forward : subtract(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 : subtract_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
subtract_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_op : sync_batch_norm_grad
forward : sync_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 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 : sync_batch_norm_coo_grad{sparse_coo, dense, dense, dense, dense, dense, sparse_coo -> sparse_coo, dense, dense}
data_type : out_grad
optional : reserve_space
- backward_op : tan_grad
forward : tan(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : tan_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
tan_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : tanh_grad
forward : tanh(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out]
kernel :
func : tanh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
tanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_op : to_dense_grad
forward : to_dense(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : coo_to_dense_grad{sparse_coo, dense -> sparse_coo}
- backward_op : to_sparse_coo_grad
forward : to_sparse_coo(Tensor x, int64_t sparse_dim) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
kernel :
func : coo_to_dense { sparse_coo -> dense }
- backward_op : transpose_grad
forward : transpose(Tensor x, int[] perm) -> Tensor(out)
args : (Tensor out_grad, int[] perm)
output : Tensor(x_grad)
infer_meta :
func : TransposeGradInferMeta
param : [out_grad, perm]
kernel :
func : transpose_coo_grad {sparse_coo -> sparse_coo},
transpose_csr_grad {sparse_csr -> sparse_csr}
- backward_op : values_grad
forward : values_coo(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : values_coo_grad{sparse_coo, dense-> sparse_coo}
- backward_op: fused_attention_grad
forward : fused_attention_csr(Tensor query, Tensor key, Tensor value, Tensor sparse_mask, Tensor key_padding_mask, Tensor attn_mask) -> Tensor(out), Tensor(softmax)
args: (Tensor query, Tensor key, Tensor value, Tensor softmax, Tensor out_grad)
output : Tensor(query_grad), Tensor(key_grad), Tensor(value_grad)
infer_meta :
func : sparse::FusedAttentionGradInferMeta
kernel :
func : fused_attention_csr_grad{dense, dense, dense, sparse_csr, dense -> dense, dense, dense}
layout : softmax
data_type: query