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未验证 提交 3a7761a0 编写于 作者: C Chen Weihang 提交者： GitHub
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remove comment yamls, test=document_fix (#41221)

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python/paddle/utils/code_gen/api.yaml
浏览文件 @ 3a7761a0
# - api : norm
# args : (Tensor x, int axis, float epsilon, bool is_test)
# output : Tensor(out), Tensor(norm)
# infer_meta :
# func : NormInferMeta
# kernel :
# func : norm
# intermediate : norm
# backward : norm_grad
# # maxout
# - api : maxout
# args : (Tensor x, int groups, int axis)
# output : Tensor
# infer_meta :
# func : MaxoutInferMeta
# kernel :
# func : maxout
# backward : maxout_grad
# # batch_norm
# - api : batch_norm
# args : (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)
# output : Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
# infer_meta :
# func : XXXXInferMeta
# kernel :
# func : batch_norm
# backward: batch_norm_grad
# # bilinear_tensor_product ?? optional
# - api : bilinear_tensor_product
# args : (Tensor x, Tensor y, Tensor weight, Tensor bias)
# output : Tensor
# infer_meta :
# func : BilinearTensorProductInferMeta
# kernel :
# func : bilinear_tensor_product
# backward : bilinear_tensor_product_grad
# optional : bias
# broadcast_tensors
# - api : broadcast_tensors
# args : (Tensor[] x)
# output : Tensor[]
# infer_meta :
# func : BroadcastTensorsInferMeta
# kernel :
# func : broadcast_tensors
# backward : broadcast_tensors_grad
# # dropout
# - api : dropout
# args : (Tensor x, Tensor seed_tensor, float p, bool is_test, str mode, int seed, bool fix_seed)
# output : Tensor(out), Tensor(mask)
# infer_meta :
# func : DropoutInferMeta
# kernel :
# func : dropout
# # expand
# - api : expand
# args : (Tensor x, IntArray shape)
# output : Tensor
# infer_meta :
# func : ExpandInferMeta
# kernel :
# func : expand
# backward : expand_grad
# eye
# - api : eye
# args : (int64_t num_rows, int64_t num_colums, DataType dtype = DataType::FLOAT32)
# output : Tensor
# infer_meta :
# func : EyeInferMeta
# kernel :
# func : eye
# gaussian_random
# - api : gaussian_random
# args : (IntArray shape, float mean, float std, int seed, DataType dtype=DataType::FLOAT32)
# output : Tensor
# infer_meta :
# func : CreateInferMeta
# param : [shape, dtype]
# kernel :
# func : gaussian_random
# data_type : dtype
# # graph_send_recv
# - api : graph_send_recv
# args : (Tensor x, Tensor src_index, Tensor dst_index, str pool_type)
# output : Tensor(out), Tensor(dst_count)
# infer_meta :
# func : GraphSendRecvInferMeta
# kernel :
# func : graph_send_recv
# backward : graph_send_recv_grad
# # label_smooth
# - api : label_smooth
# args : (Tensor label, Tensor prior_dist, float epsilon)
# output : Tensor
# infer_meta :
# func : UnchangedInferMeta
# param : [label]
# kernel :
# func : label_smooth
# data_type : label
# optional : prior_dist
# backward : label_smooth_grad
# linspace start stop number
# - api : linspace
# args : (Tensor start, Tensor stop, Tensor number, DataType dtype=DataType::FLOAT32)
# output : Tensor
# infer_meta :
# func : LinspaceInferMeta
# kernel :
# func : linspace
# # multi_dot
# - api : multi_dot
# args : (Tensor[] x)
# output : Tensor
# infer_meta :
# func : MultiDotInferMeta
# kernel :
# func : multi_dot
# backward : multi_dot_grad
# # nll_loss
# - api : nll_loss
# args : (Tensor x, Tensor label, Tensor weight, int64_t ignore_index, str reduction)
# output : Tensor(out), Tensor(total_weight)
# infer_meta :
# func : NllLossRawInferMeta
# kernel :
# func : nll_loss
# data_type : x
# optional : weight
# backward : nll_loss_grad
# # psroi_pool
# - api : psroi_pool
# args : (Tensor x, Tensor rois, Tensor rois_num, int pooled_weight, int pooled_width, int output_channels, float spatial_scale )
# output : Tensor
# infer_meta :
# func : PsroiPoolInferMeta
# kernel :
# func : psroi_pool
# backward : psroi_pool_grad
# optional : rois_num
# # randint
# - api : randint
# args : (int low, int high, IntArray shape, DataType dtype)
# output : Tensor
# infer_meta :
# func : RandintInferMeta
# kernel :
# func : randint
# # randperm
# - api : randperm
# args : (int n, DataType dtype)
# output : Tensor
# infer_meta :
# func : RandpermInferMeta
# kernel :
# func : randperm
# # max
# - api : max
# args : (Tensor x, int64_t[] dims, bool keep_dim)
# output : Tensor
# infer_meta :
# func : MaxInferMeta
# kernel :
# func : max
# # phi_transfer_layout | not have python api
# # truncated_gaussian_random
# - api : truncated_gaussian_random
# args : (int[] shape, float mean, float std, int seed, DataType dtype)
# output : Tensor
# infer_meta :
# func : TruncatedGaussianRandomInferMeta
# kernel :
# func : truncated_gaussian_random
# # unbind
# - api : unbind
# args : (Tensor x, int axis)
# output : Tensor[]
# infer_meta :
# func : UnbindInferMeta
# kernel :
# func : unbind
# # uniform_random_raw selected rows ??
# - api : pixel_shuffle
# args : (Tensor x, int upscale_factor, const std::string& data_format)
# output : Tensor
# infer_meta :
# func : PixelShuffleInferMeta
# kernel :
# func : pixel_shuffle
# BilinearTensorProductInferMeta
# BroadcastTensorsInferMeta
# bincount
# - api : bincount
# args : (Tensor x, Tensor weight, int minlength)
# output : Tensor
# infer_meta :
# func : BincountInferMeta
# kernel :
# func : bincount
# optional : weight
# expand_as
# - api : expand_as
# args : (Tensor x, Tensor y, int[] target_shape)
# output : Tensor
# infer_meta :
# func : ExpandAsInferMeta
# kernel :
# func : expand_as
# optional : y
# # backward : expand_as_grad
# # optional : y
# - api : equal_all
# args : (Tensor x, Tensor y)
# output : Tensor
# infer_meta :
# func : CompareAllInferMeta
# kernel :
# func : equal_all
# histogram
# - api : histogram
# args : (Tensor x, int64_t bins, int min, int max)
# output : Tensor
# infer_meta :
# func : HistogramInferMeta
# kernel :
# func : histogram
- api : abs
args : (Tensor x)
output : Tensor
......
python/paddle/utils/code_gen/backward.yaml
浏览文件 @ 3a7761a0
# - backward_api : 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_api : matmul_triple_grad
# forward : matmul_double_grad (Tensor x, Tensor y, Tensor out_grad, Tensor dx_grad, Tensor dy_grad, bool transpose_x, bool transpose_y) -> Tensor(d2x), Tensor(d2y), Tensor(dout_grad)
# args : (Tensor x, Tensor y, Tensor out_grad, Tensor dx_grad, Tensor dy_grad, Tensor d2x_grad, Tensor d2y_grad, Tensor dout_grad_grad, bool transpose_x, bool transpose_y)
# output : Tensor(d3x), Tensor(d3y), Tensor(d2out_grad), Tensor(ddx_grad), Tensor(ddy_grad)
# infer_meta :
# func : MatmulTripleGradInferMeta
# kernel :
# func : matmul_triple_grad
# - backward_api : maxout_grad
# forward : maxout (Tensor x, int groups, int axis) -> Tensor(out)
# args : (Tensor x, Tensor out, Tensor out_grad, int groups, int axis)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : maxout_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 indices, Tensor x, Tensor out_grad, int axis, bool descending)
# output : Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad)
# infer_meta :
# func : GeneralTernaryGradInferMeta
# param : [x, scale, bias]
# kernel :
# func : batch_norm_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 : FourXXXXInferMeta
# param : [x, y, weight, bias]
# kernel :
# func : bilinear_tensor_product_grad
# optional : bias
# - backward_api : broadcast_tensor_grad
# forward : broadcast_tensors (Tensor[] x) -> Tensor [] (out)
# args : (Tensor [] out_grad)
# output : Tensor [] (x_grad)
# infer_meta :
# func : XXXXInferMeta
# param : [out_grad]
# kernel :
# func : broadcast_tensor_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
# - backward_api : 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 : [x, y]
# kernel :
# func : where_grad
# - backward_api : triangular_solve_grad
# forward : triangular_solve (Tensor x, Tensor y, bool upper, bool tranpose, bool unitriangular) -> Tensor(out)
# args : (Tensor x, Tensor y, Tensor out, Tensor out_grad, bool upper, bool tranpose, bool unitriangular)
# output : Tensor(x_grad), Tensor(y_grad)
# infer_meta :
# func : GeneralBinaryGradInferMeta
# param : [x, y]
# kernel :
# func : triangular_solve_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 : 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
# - 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 : UnchangedGradInferMeta
# param : [x]
# kernel :
# func : expand_grad
# - backward_api : graph_send_recv_grad
# forward : graph_send_recv (Tensor x, Tensor src_index, Tensor dst_index, str pool_type) -> Tensor(out), Tensor(dst_count)
# args : (Tensor out_grad, Tensor x, Tensor out, Tensor src_index, Tensor dst_index, Tensor dst_count, str pool_type)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : graph_send_recv_grad
# - backward_api : multi_dot_grad
# forward : multi_dot (Tensor[] x) -> Tensor(out)
# args : (Tensor out_grad, Tensor[] x)
# output : Tensor[] (x_grad)
# infer_meta :
# func : XXXXInferMeta
# param : [x]
# kernel :
# func : multi_dot_grad
# - backward_api : pad_grad
# forward : pad (Tensor x, int[] paddings, float pad_value) -> Tensor(out)
# args : (Tensor out_grad, int[] paddings, float pad_value)
# output : Tensor(x_grad)
# infer_meta :
# func : XXXXXInferMeta
# param : [x]
# kernel :
# func : pad_grad
# - backward_api : pixel_shuffle_grad
# forward : pixel_shuffle (Tensor x, int upscale_factor, str data_format) -> Tensor(out)
# args : (Tensor out_grad, int upscale_factor, str data_format)
# output : Tensor(x_grad)
# infer_meta :
# func : XXXXXInferMeta
# param : [x]
# kernel :
# func : pixel_shuffle_grad
# - backward_api : poisson_grad
# forward : poisson (Tensor x) -> Tensor(out)
# args : ()
# output : Tensor(x_grad)
# infer_meta :
# func : XXXXXInferMeta
# param : [x]
# kernel :
# func : poisson_grad
# - backward_api : where_index_grad
# forward : where_index (Tensor condition) -> Tensor(out)
# args : (Tensor out_grad, Tensor x, int offset, int axis1, int axis2)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : where_index_grad
- backward_api : abs_grad
forward : abs (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
......
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