op_compat.yaml

# All the configuration in this file are only for existing operators,
# which cannot be modified in principle. There's no need to configure
# this file for new operator.
#
# This file is used for two purposes:
# 1. Configure the mapping relationship of parameter names of operator
#    between the operators in ops.yaml and the old operators defined
#    in fluid.
# 2. Save the extra parameters in the OpMaker of operators temporarily,
#    which will be removed in the future.

# - op : rnn
#   backward : rnn_grad
#   extra :
#     attrs : [bool is_test = false]

- op : abs
  backward : abs_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false]

- op : acos
  inputs :
    x : X
  outputs :
    out : Out

- op : acosh
  inputs :
    x : X
  outputs :
    out : Out
  backward : acosh_grad
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : add (elementwise_add)
  backward : add_grad (elementwise_add_grad)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : addmm
  backward : addmm_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : affine_grid
  backward : affine_grid_grad
  extra :
    attrs : [bool use_cudnn = true]

- op : angle
  backward : angle_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false]

- op : argsort
  inputs :
    x : X
  outputs :
    out : Out
    indices : Indices

- op : as_complex
  inputs :
    x : X
  outputs :
    out : Out

- op : as_real
  inputs :
    x : X
  outputs :
    out : Out

- op : asin
  inputs :
    x : X
  outputs :
    out : Out

- op : asinh
  backward : asinh_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : atan
  inputs :
    x : X
  outputs :
    out : Out

- op : atan2
  inputs :
    {x : X1, y : X2}
  outputs :
    out : Out

- op : atanh
  backward : atanh_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : batch_norm
  backward : batch_norm_grad
  extra :
    attrs : [bool use_mkldnn = false, bool fuse_with_relu = false]

- op : bernoulli
  inputs :
    x : X
  outputs :
    out : Out

- op : bicubic_interp (bicubic_interp_v2)
  backward : bicubic_interp_grad (bicubic_interp_v2_grad)
  extra :
    attrs : [bool use_mkldnn = false]

- op : bilinear_interp (bilinear_interp_v2)
  backward : bilinear_interp_grad (bilinear_interp_v2_grad)
  extra :
    attrs : [bool use_mkldnn = false]

- op : bmm
  inputs :
    {x : X, y : Y}
  outputs :
    out : Out

- op : ceil
  backward : ceil_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : celu
  backward : celu_grad, celu_double_grad(celu_grad_grad)
  inputs :
    x : X
  outputs :
    out : Out

- op : cholesky
  inputs :
    x : X
  outputs :
    out : Out

- op : cholesky_solve
  inputs :
    {x : X, y : Y}
  outputs :
    out : Out

- op : clip
  backward : clip_grad
  extra :
    attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32"]

- op : concat
  backward : concat_grad
  extra :
    attrs : [bool use_mkldnn = false, bool use_quantizer = false, str mkldnn_data_type = "float32"]

- op : conditional_block
  backward : conditional_block_grad
  extra :
    attrs : ['str[] skip_eager_deletion_vars = {}']

- op : conj
  inputs :
    x : X
  outputs :
    out : Out

- op : conv2d
  backward : conv2d_grad
  extra :
    attrs : [bool is_test = false, bool use_cudnn = true, bool fuse_relu_before_depthwise_conv = false, bool use_mkldnn = false,
             bool use_quantizer = false, str mkldnn_data_type = "float32", bool fuse_relu = false,
             str fuse_activation = "", float fuse_alpha = 0.0f, float fuse_beta = 0.0f, bool use_addto = false,
             bool fuse_residual_connection = false, float Scale_in = 1.0f, float Scale_out = 1.0f,
             float Scale_in_eltwise = 1.0f, 'float[] Scale_weights = {1.0f}', bool force_fp32_output = false,
             int workspace_size_MB = platform::GetDefaultConvWorkspaceSizeLimitMB(), bool exhaustive_search = false]

- op : conv2d_fusion
  extra :
    attrs : [bool is_test = false, bool use_cudnn = false, bool fuse_relu_before_depthwise_conv = false, bool use_mkldnn = false,
             bool use_quantizer = false, str mkldnn_data_type = "float32", bool fuse_relu = false,
             str fuse_activation = "", float fuse_alpha = 0.0f, float fuse_beta = 0.0f, bool use_addto = false,
             bool fuse_residual_connection = false, float Scale_in = 1.0f, float Scale_out = 1.0f,
             float Scale_in_eltwise = 1.0f, 'float[] Scale_weights = {1.0f}', bool force_fp32_output = false,
             int workspace_size_MB = platform::GetDefaultConvWorkspaceSizeLimitMB(), bool exhaustive_search = false]

- op : conv2d_transpose
  backward : conv2d_transpose_grad
  extra :
    attrs : [bool is_test = false, bool use_cudnn = true, bool use_mkldnn = false, bool force_fp32_output = false,
             str mkldnn_data_type = "float32", bool fuse_relu = false,
             str fuse_activation = "", float fuse_alpha = 0.0f, float fuse_beta = 0.0f,
             int workspace_size_MB = platform::GetDefaultConvWorkspaceSizeLimitMB()]

- op : conv3d
  backward : conv3d_grad
  extra :
    attrs : [bool is_test = false, bool use_cudnn = true, bool use_mkldnn = false, str mkldnn_data_type = "float32", bool fuse_relu = false,
             str fuse_activation = "", float fuse_alpha = 0.0f, float fuse_beta = 0.0f,
             bool use_addto = false, bool fuse_residual_connection = false, bool force_fp32_output = false,
             int workspace_size_MB = platform::GetDefaultConvWorkspaceSizeLimitMB(), bool exhaustive_search = false]

- op : conv3d_transpose
  backward : conv3d_transpose_grad
  extra :
    attrs : [bool use_cudnn = true, bool use_mkldnn = false, int workspace_size_MB = platform::GetDefaultConvWorkspaceSizeLimitMB()]

- op : cos
  backward : cos_grad, cos_double_grad, cos_triple_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : cosh
  backward : cosh_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : cross
  inputs :
    {x : X, y : Y}
  attrs :
    axis : dim
  outputs :
    out : Out

- op : data_norm
  backward : data_norm_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : depthwise_conv2d
  backward : depthwise_conv2d_grad
  extra :
    attrs : [bool is_test = false, bool fuse_relu_before_depthwise_conv = false, bool use_mkldnn = false,
             bool use_quantizer = false, str mkldnn_data_type = "float32", bool fuse_relu = false,
             str fuse_activation = "", float fuse_alpha = 0.0f, float fuse_beta = 0.0f, bool use_addto = false,
             bool fuse_residual_connection = false, float Scale_in = 1.0f, float Scale_out = 1.0f,
             float Scale_in_eltwise = 1.0f, 'float[] Scale_weights = {1.0f}', bool force_fp32_output = false,
             int workspace_size_MB = platform::GetDefaultConvWorkspaceSizeLimitMB(), bool exhaustive_search = false]

- op : depthwise_conv2d_transpose
  backward : depthwise_conv2d_transpose_grad
  extra :
    attrs : [bool is_test = false, bool use_cudnn = false, bool use_mkldnn = false, bool force_fp32_output = false,
             str mkldnn_data_type = "float32", bool fuse_relu = false,
             str fuse_activation = "", float fuse_alpha = 0.0f, float fuse_beta = 0.0f,
             int workspace_size_MB = platform::GetDefaultConvWorkspaceSizeLimitMB()]

- op : dequantize_linear
  extra :
    attrs : [float moving_rate = 0.9]

- op : det (determinant)
  backward : det_grad (determinant_grad)
  inputs :
    x : Input
  outputs :
    out : Out

- op : diag (diag_v2)
  backward : diag_grad (diag_v2_grad)
  inputs :
    x : X
  outputs :
    out : Out

- op : diag_embed
  inputs :
    input : Input
  outputs :
    out : Out

- op : diagonal
  inputs :
    x : Input
  outputs :
    out : Out

- op : digamma
  inputs :
    x : X
  outputs :
    out : Out

- op : dist
  inputs :
    {x : X, y : Y}
  outputs :
    out : Out

- op : distributed_push_sparse
  extra :
    attrs : ['int[] slots = {}']

- op : divide (elementwise_div)
  backward : divide_grad (elementwise_div)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : dot
  inputs :
    {x : X, y : Y}
  outputs :
    out : Out

- op : dropout
  backward : dropout_grad
  extra :
    attrs : [bool fix_seed = false, int seed = 0]

- op : dropout_nd
  backward : dropout_nd_grad
  extra :
    attrs : [bool fix_seed = false, int seed = 0]

- op : eig
  inputs :
    x : X
  outputs :
    out_w : Eigenvalues
    out_v : Eigenvectors

- op : eigh
  inputs :
    x : X
  outputs :
    out_w : Eigenvalues
    out_v : Eigenvectors

- op : eigvals
  inputs :
    x : X
  outputs :
    out : Out

- op : elementwise_pow
  backward : elementwise_pow_grad
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : elu
  backward : elu_grad, elu_double_grad (elu_grad_grad)
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false]

- op : embedding (lookup_table_v2)
  backward : embedding_grad (lookup_table_v2_grad)
  extra :
    attrs : [bool is_sparse = false, bool is_distributed = false, bool remote_prefetch = false,
             int trainer_id = 0, int slot = 0, 'int64_t[] height_sections = {}', 'str[] epmap = {}',
             'str[] table_names = {}']

- op : equal_all
  inputs :
    {x : X, y : Y}
  outputs :
    out : Out

- op : erf
  inputs :
    x : X
  outputs :
    out : Out

- op : erfinv
  inputs :
    x : X
  outputs :
    out : Out

- op : exp
  backward : exp_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : expand (expand_v2)
  backward : expand_grad (expand_v2_grad)
  extra :
    attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32"]

- op : expm1
  backward : expm1_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : fake_channel_wise_quantize_abs_max
  extra :
    attrs : [int round_type = 1]

- op : fake_channel_wise_quantize_dequantize_abs_max
  extra :
    attrs : [int round_type = 1]

- op : fake_quantize_abs_max
  extra :
    attrs : [int round_type = 1]

- op : fake_quantize_dequantize_abs_max
  extra :
    attrs : [int round_type = 1]

- op : fake_quantize_dequantize_moving_average_abs_max
  extra :
    attrs : [int round_type = 1]

- op : fake_quantize_moving_average_abs_max
  extra :
    attrs : [int round_type = 1]

- op : fake_quantize_range_abs_max
  extra :
    attrs : [int round_type = 1]

- op : fft_c2c
  inputs: {x: X}
  outputs: {out: Out}

- op : fft_c2r
  inputs: {x: X}
  outputs: {out: Out}

- op : fft_r2c
  inputs: {x: X}
  outputs: {out: Out}

- op : fill_diagonal_tensor
  inputs :
    {x : X, y : Y}
  outputs :
    out : Out

- op : flip
  inputs :
    x : X
  outputs :
    out : Out

- op : floor
  backward : floor_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : floor_divide (elementwise_floordiv)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : fmax (elementwise_fmax)
  backward : fmax_grad (elementwise_fmax_grad)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : fmin (elementwise_fmin)
  backward : fmin_grad (elementwise_fmin_grad)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : fold
  inputs :
    x : X
  outputs :
    out : Y

- op : frobenius_norm
  backward : frobenius_norm_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : full (fill_constant)
  extra :
    attrs : [bool use_mkldnn = false]

- op : gather
  backward : gather_grad
  extra :
    attrs : [bool overwrite = true]

- op : gather_tree
  inputs :
    {ids : Ids, parents : Parents}
  outputs :
    out : Out

- op : gelu
  backward : gelu_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32"]

- op : grad_add
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : grid_sample(grid_sampler)
  backward : grid_sample_grad (grid_sampler_grad)
  inputs :
    {x : X, grid : Grid}
  outputs :
    out : Output
  extra :
    attrs : [bool use_cudnn = true]

- op : gru
  backward : gru_grad
  extra :
    attrs : [bool is_test = false]

- op : gumbel_softmax
  inputs :
    x : X
  outputs :
    out : Out

- op : hard_swish
  backward : hard_swish_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : hardshrink (hard_shrink)
  backward : hardshrink_grad (hard_shrink_grad)
  inputs :
    x : X
  outputs :
    out : Out

- op : hardsigmoid (hard_sigmoid)
  backward : hardsigmoid_grad (hard_sigmoid_grad)
  inputs :
    x : X
  outputs :
    out : Out

- op : heaviside (elementwise_heaviside)
  backward : heaviside_grad (elementwise_heaviside_grad)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : histogram
  inputs :
    input : X
  outputs :
    out : Out

- op : index_sample
  inputs :
    {x : X, index : Index}
  outputs :
    out : Out

- op : index_select
  inputs :
    {x : X, index : Index}
  outputs :
    out : Out
  attrs :
    axis : dim

- op : inplace_abn
  backward : inplace_abn_grad
  extra :
    attrs : [bool use_mkldnn = false, bool fuse_with_relu = false]

- op : inverse
  inputs :
    x : Input
  outputs :
    out : Output

- op : is_empty
  inputs :
    x : X
  outputs :
    out : Out

- op : isfinite (isfinite_v2)
  inputs :
    x : X
  outputs :
    out : Out

- op : isinf (isinf_v2)
  inputs :
    x : X
  outputs :
    out : Out

- op : isnan (isnan_v2)
  inputs :
    x : X
  outputs :
    out : Out

- op : kthvalue
  inputs :
    x : X
  outputs :
    {out : Out, indices : Indices}

- op : label_smooth
  inputs :
    {label : X, prior_dist : PriorDist}
  outputs :
    out : Out

- op : layer_norm
  backward : layer_norm_grad
  extra :
    attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32", bool is_test = false]

- op : leaky_relu
  backward : leaky_relu_grad, leaky_relu_double_grad (leaky_relu_grad_grad)
  inputs :
    x : X
  outputs :
    out : Out
  attrs:
    negative_slope : alpha
  extra :
    attrs : [bool use_mkldnn = false]

- op : lerp
  backward : lerp_grad
  inputs :
    {x : X, y : Y, weight : Weight}
  outputs :
    out : Out

- op : lgamma
  inputs :
    x : X
  outputs :
    out : Out

- op : linear_interp (linear_interp_v2)
  backward : linear_interp_grad (linear_interp_v2_grad)
  extra :
    attrs : [bool use_mkldnn = false]

- op : log
  backward : log_grad, log_double_grad (log_grad_grad)
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : log10
  backward : log10_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : log1p
  backward : log1p_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : log2
  backward : log2_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : log_softmax
  backward : log_softmax_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : logit
  inputs :
    x : X
  outputs :
    out : Out

- op : logsigmoid
  inputs :
    x : X
  outputs :
    out : Out

- op : logsigmoid
  backward : logsigmoid_grad
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : lrn
  backward : lrn_grad
  extra :
    attrs : [bool use_mkldnn = false, bool is_test = false]

- op : masked_select
  inputs :
    {x : X, mask : Mask}
  outputs :
    out : Y

- op : matmul (matmul_v2)
  backward : matmul_grad (matmul_v2_grad)
  extra :
    attrs : [bool use_mkldnn = false, 'int[] fused_reshape_Out = {}', 'int[] fused_transpose_Out = {}',
             str mkldnn_data_type = "float32", 'int[] fused_reshape_X = {}', 'int[] fused_reshape_Y = {}',
             'int[] fused_transpose_X = {}', 'int[] fused_transpose_Y = {}']

- op : matmul_with_flatten (mul)
  backward : matmul_with_flatten_grad (mul_grad)
  extra :
    attrs : [bool use_mkldnn = false, float scale_x = 1.0f, 'float[] scale_y = {1.0f}',
             float scale_out = 1.0f, bool force_fp32_output = false]

- op : matrix_power
  inputs :
    x : X
  outputs :
    out : Out

- op : maximum (elementwise_max)
  backward : maximum_grad (elementwise_max_grad)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : maximum (elementwise_min)
  backward : maximum_grad (elementwise_min_grad)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : maxout
  inputs :
    x : X
  outputs :
    out : Out

- op : mish
  backward : mish_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : multiply (elementwise_mul)
  backward : multiply_grad (elementwise_mul_grad)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : mv
  inputs :
    {x : X, vec : Vec}
  outputs :
    out : Out

- op : nce
  backward : nce_grad
  extra :
    attrs : [int trainer_id = 0, 'int64_t[] height_sections = {}', 'str[] epmap = {}',
             'str[] table_names = {}', 'int[] custom_neg_classes = {}']

- op : nearest_interp (nearest_interp_v2)
  backward : nearest_interp_grad (nearest_interp_v2_grad)
  extra :
    attrs : [bool use_mkldnn = false]

- op : pad2d
  backward : pad2d_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : pad3d
  backward : pad3d_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : partial_sum
  backward : partial_sum_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : poisson
  inputs :
    x : X
  outputs :
    out : Out

- op : pool2d
  backward : pool2d_grad
  extra :
    attrs : [bool use_mkldnn = false, bool use_quantizer = false,
              str mkldnn_data_type = "float32", bool is_test = false]

- op : pool3d
  backward : pool3d_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : prelu
  backward : prelu_grad
  extra :
    attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32", bool is_test = false]

- op : quantize_linear
  extra :
    attrs : [float moving_rate = 0.9]

- op : reciprocal
  backward : reciprocal_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : reduce_all
  extra :
    attrs : [bool use_mkldnn = false]

- op : reduce_amax
  backward : reduce_amax_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : reduce_amin
  backward : reduce_amin_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : reduce_any
  extra :
    attrs : [bool use_mkldnn = false]

- op : reduce_max
  backward : reduce_max_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : reduce_mean
  backward : reduce_mean_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : reduce_min
  backward : reduce_min_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : reduce_prod
  backward : reduce_prod_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : reduce_sum
  backward : reduce_sum_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : relu
  backward : relu_grad, relu_double_grad (relu_grad_grad)
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : relu6
  backward : relu6_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : remainder (elementwise_mod)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : renorm
  backward : renorm_grad
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : round
  backward : round_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : rsqrt
  backward : rsqrt_grad, rsqrt_double_grad (rsqrt_grad_grad)
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : scale
  extra :
    attrs : [bool use_mkldnn = false]

- op : seed
  extra :
    attrs : [bool deterministic = false, str rng_name = "", bool force_cpu = false]

- op : send_uv (graph_send_uv)
  backward : send_uv_grad (graph_send_uv_grad)

- op : sequence_softmax
  backward : sequence_softmax_grad
  extra :
    attrs : [str data_format = "AnyLayout"]

- op : shape
  extra :
    attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32"]

- op : shuffle_channel
  backward : shuffle_channel_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : sigmoid
  backward : sigmoid_grad, sigmoid_double_grad (sigmoid_grad_grad), sigmoid_triple_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : silu
  backward : silu_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : sin
  backward : sin_grad, sin_double_grad, sin_triple_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : sinh
  backward : sinh_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : slice
  backward : slice_grad
  extra :
    attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32"]

- op : softmax
  backward : softmax_grad
  extra :
    attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32", bool is_test = false]

- op : softplus
  backward : softplus_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false, str fuse_activation_type = "", float fuse_activation_alpha = 0.0f,
             float fuse_activation_beta = 0.0f, float fuse_activation_scale = 1.0f]

- op : softshrink
  backward : softshrink_grad
  inputs :
    x : X
  outputs :
    out : Out
  attrs :
    threshold : lambda

- op : softsign
  backward : softsign_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : solve
  inputs :
    {x : X, y : Y}
  outputs :
    out : Out

- op : sqrt
  backward : sqrt_grad, sqrt_double_grad (sqrt_grad_grad)
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : square
  backward : square_grad, square_double_grad (square_grad_grad)
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : squeeze (squeeze2)
  backward : squeeze_grad (squeeze2_grad)
  extra :
    attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32"]

- op : stack
  backward : stack_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : stack
  backward : stack_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : subtract (elementwise_sub)
  backward : subtract_grad (elementwise_sub_grad)
  extra :
    attrs : [bool use_mkldnn = false, str x_data_format = "", str y_data_format = "", str mkldnn_data_type = "float32",
             bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f]

- op : swish
  backward : swish_grad
  extra :
    attrs : [bool use_mkldnn = false]

- op : sync_batch_norm
  backward : sync_batch_norm_grad
  extra :
    attrs : [bool use_mkldnn = false, bool fuse_with_relu = false]

- op : tan
  backward : tan_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : tanh
  backward : tanh_grad, tanh_double_grad (tanh_grad_grad), tanh_triple_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : tanh_shrink
  backward : tanh_shrink_grad
  inputs :
    x : X
  outputs :
    out : Out
  extra :
    attrs : [bool use_mkldnn = false, bool use_cudnn = false]

- op : thresholded_relu
  inputs :
    x : X
  outputs :
    out : Out

- op : trace
  inputs :
    x : Input
  outputs :
    out : Out

- op : transpose (transpose2)
  backward : transpose_grad (transpose2_grad)
  extra :
    attrs : [bool use_mkldnn = false, str data_format = "AnyLayout", bool use_quantizer = false,
              str mkldnn_data_type = "float32"]

- op : trilinear_interp (trilinear_interp_v2)
  backward : trilinear_interp_grad (trilinear_interp_v2_grad)
  extra :
    attrs : [bool use_mkldnn = false]

- op : trunc
  inputs :
    input : X
  outputs :
    out : Out

- op : unfold
  inputs :
    x : X
  outputs :
    out : Y

- op : while
  backward : while_grad
  extra :
    attrs : ['str[] skip_eager_deletion_vars = {}']