From 6891a4fe5f732033f0f03672753beb956ec7c2d1 Mon Sep 17 00:00:00 2001 From: Chen Weihang Date: Wed, 14 Sep 2022 17:48:20 +0800 Subject: [PATCH] normize yaml backward op label (#46028) --- .../generator/codegen_utils.py | 8 +- .../generator/eager_gen.py | 5 +- paddle/phi/api/yaml/backward.yaml | 42 +- .../api/yaml/generator/backward_api_gen.py | 2 +- paddle/phi/api/yaml/generator/parse_api.py | 2 +- paddle/phi/api/yaml/generator/parse_utils.py | 2 +- paddle/phi/api/yaml/legacy_backward.yaml | 508 +++++++++--------- paddle/phi/api/yaml/sparse_backward.yaml | 76 +-- 8 files changed, 322 insertions(+), 323 deletions(-) diff --git a/paddle/fluid/eager/auto_code_generator/generator/codegen_utils.py b/paddle/fluid/eager/auto_code_generator/generator/codegen_utils.py index 580dc14f43b..9022e800905 100644 --- a/paddle/fluid/eager/auto_code_generator/generator/codegen_utils.py +++ b/paddle/fluid/eager/auto_code_generator/generator/codegen_utils.py @@ -83,10 +83,10 @@ def ReadBwdFile(filepath): ret = {} if contents is not None: for content in contents: - assert 'backward_api' in content.keys(), AssertMessage( - 'backward_api', content.keys()) - if 'backward_api' in content.keys(): - api_name = content['backward_api'] + assert 'backward_op' in content.keys(), AssertMessage( + 'backward_op', content.keys()) + if 'backward_op' in content.keys(): + api_name = content['backward_op'] ret[api_name] = content f.close() diff --git a/paddle/fluid/eager/auto_code_generator/generator/eager_gen.py b/paddle/fluid/eager/auto_code_generator/generator/eager_gen.py index fd1acbf9329..100dfd57405 100644 --- a/paddle/fluid/eager/auto_code_generator/generator/eager_gen.py +++ b/paddle/fluid/eager/auto_code_generator/generator/eager_gen.py @@ -1485,7 +1485,7 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase): if next_grad_api_contents: # Fake forward_api_contents and backward_api_contents forward_api_contents = grad_api_contents - forward_api_contents['op'] = forward_api_contents['backward_api'] + forward_api_contents['op'] = forward_api_contents['backward_op'] backward_api_contents = next_grad_api_contents next_node_generator = DygraphFunctionGeneratorBase( @@ -1959,8 +1959,7 @@ class DygraphForwardAndNodesGenerator(GeneratorBase): forward_api_contents = backward_api_contents # Fake forward_api_content - forward_api_contents['op'] = forward_api_contents[ - 'backward_api'] + forward_api_contents['op'] = forward_api_contents['backward_op'] backward_api_contents = next_grad_api_contents if len(namespace) > 0: diff --git a/paddle/phi/api/yaml/backward.yaml b/paddle/phi/api/yaml/backward.yaml index d2ed2533ae0..9d81435169c 100644 --- a/paddle/phi/api/yaml/backward.yaml +++ b/paddle/phi/api/yaml/backward.yaml @@ -1,4 +1,4 @@ -- backward_api : atan2_grad +- backward_op : atan2_grad forward : atan2 (Tensor x, Tensor y) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out_grad) output : Tensor(x_grad), Tensor(y_grad) @@ -8,7 +8,7 @@ kernel : func : atan2_grad -- backward_api : cholesky_grad +- backward_op : cholesky_grad forward : cholesky (Tensor x, bool upper) -> Tensor(out) args : (Tensor out, Tensor out_grad, bool upper) output : Tensor(x_grad) @@ -18,7 +18,7 @@ kernel : func : cholesky_grad -- backward_api : cholesky_solve_grad +- backward_op : cholesky_solve_grad forward : cholesky_solve (Tensor x, Tensor y, bool upper) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out, Tensor out_grad, bool upper) output : Tensor(x_grad), Tensor(y_grad) @@ -28,7 +28,7 @@ kernel : func : cholesky_solve_grad -- backward_api : cross_grad +- backward_op : cross_grad forward : cross (Tensor x, Tensor y, int axis = 9) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out_grad, int axis) output : Tensor(x_grad), Tensor(y_grad) @@ -39,7 +39,7 @@ func : cross_grad data_type : out_grad -- backward_api : diag_grad +- backward_op : diag_grad forward : diag (Tensor x, int offset, float padding_value) -> Tensor(out) args : (Tensor x, Tensor out_grad, int offset) output : Tensor(x_grad) @@ -51,7 +51,7 @@ data_type : out_grad no_need_buffer : x -- backward_api : diagonal_grad +- backward_op : diagonal_grad forward : diagonal (Tensor x, int offset, int axis1, int axis2) -> Tensor(out) args : (Tensor x, Tensor out_grad, int offset = 0, int axis1 = 0, int axis2 = 1) output : Tensor(x_grad) @@ -63,7 +63,7 @@ data_type : out_grad no_need_buffer : x -- backward_api : digamma_grad +- backward_op : digamma_grad forward : digamma (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -73,7 +73,7 @@ kernel : func : digamma_grad -- backward_api : dist_grad +- backward_op : dist_grad forward : dist (Tensor x, Tensor y, float p) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out, Tensor out_grad, float p) output : Tensor(x_grad), Tensor(y_grad) @@ -83,7 +83,7 @@ kernel : func : dist_grad -- backward_api : dot_grad +- backward_op : dot_grad forward : dot (Tensor x, Tensor y) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out_grad) output : Tensor(x_grad), Tensor(y_grad) @@ -94,7 +94,7 @@ func : dot_grad data_type : out_grad -- backward_api : erf_grad +- backward_op : erf_grad forward : erf (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -105,7 +105,7 @@ func : erf_grad data_type : out_grad -- backward_api : erfinv_grad +- backward_op : erfinv_grad forward : erfinv (Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -115,7 +115,7 @@ kernel : func : erfinv_grad -- backward_api : fft_c2c_grad +- backward_op : fft_c2c_grad forward: fft_c2c(Tensor x, int64_t[] axes, str normalization, bool forward) -> Tensor(out) args : (Tensor out_grad, int64_t[] axes, str normalization, bool forward) output: Tensor(x_grad) @@ -125,7 +125,7 @@ kernel : func : fft_c2c_grad -- backward_api : fft_c2r_grad +- backward_op : fft_c2r_grad forward: fft_c2r(Tensor x, int64_t[] axes, str normalization, bool forward, int64_t last_dim_size) -> Tensor(out) args : (Tensor out_grad, int64_t[] axes, str normalization, bool forward, int64_t last_dim_size) output: Tensor(x_grad) @@ -135,7 +135,7 @@ func : fft_c2r_grad data_type: out_grad -- backward_api : fft_r2c_grad +- backward_op : fft_r2c_grad forward: fft_r2c(Tensor x, int64_t[] axes, str normalization, bool forward, bool onesided) -> Tensor(out) args : (Tensor x, Tensor out_grad, int64_t[] axes, str normalization, bool forward, bool onesided) output: Tensor(x_grad) @@ -147,7 +147,7 @@ data_type: out_grad no_need_buffer: x -- backward_api : graph_send_uv_grad +- backward_op : graph_send_uv_grad forward : graph_send_uv (Tensor x, Tensor y, Tensor src_index, Tensor dst_index, str message_op = "ADD") -> Tensor(out) args: (Tensor x, Tensor y, Tensor src_index, Tensor dst_index, Tensor out_grad, str message_op = "ADD") output : Tensor(x_grad), Tensor(y_grad) @@ -158,7 +158,7 @@ func : graph_send_uv_grad data_type : x -- backward_api : lgamma_grad +- backward_op : lgamma_grad forward : lgamma(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -168,7 +168,7 @@ kernel : func : lgamma_grad -- backward_api : mv_grad +- 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) @@ -178,7 +178,7 @@ kernel : func : mv_grad -- backward_api : poisson_grad +- backward_op : poisson_grad forward : poisson (Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) @@ -188,7 +188,7 @@ kernel : func : poisson_grad -- backward_api : solve_grad +- backward_op : solve_grad forward : solve (Tensor x, Tensor y) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out, Tensor out_grad) output : Tensor(x_grad), Tensor(y_grad) @@ -198,7 +198,7 @@ kernel : func : solve_grad -- backward_api : trace_grad +- backward_op : trace_grad forward : trace (Tensor x, int offset, int axis1, int axis2) -> Tensor(out) args : (Tensor x, Tensor out_grad, int offset, int axis1, int axis2) output : Tensor(x_grad) @@ -210,7 +210,7 @@ data_type : out_grad no_need_buffer : x -- backward_api : trunc_grad +- backward_op : trunc_grad forward : trunc (Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) diff --git a/paddle/phi/api/yaml/generator/backward_api_gen.py b/paddle/phi/api/yaml/generator/backward_api_gen.py index f168430ee7c..1a8b9cc4d39 100644 --- a/paddle/phi/api/yaml/generator/backward_api_gen.py +++ b/paddle/phi/api/yaml/generator/backward_api_gen.py @@ -28,7 +28,7 @@ class BackwardAPI(BaseAPI): self.no_need_buffer = self.parse_no_need_buffer(backward_item_yaml) def get_api_name(self, api_item_yaml): - return api_item_yaml['backward_api'] + return api_item_yaml['backward_op'] def parse_forward_config(self, forward_config): # api_name (const Tensor& input, ... , int attr, ...) -> Tensor(out) diff --git a/paddle/phi/api/yaml/generator/parse_api.py b/paddle/phi/api/yaml/generator/parse_api.py index bbecbcf26ad..91fd44b7093 100644 --- a/paddle/phi/api/yaml/generator/parse_api.py +++ b/paddle/phi/api/yaml/generator/parse_api.py @@ -27,7 +27,7 @@ def main(api_yaml_path, output_path, backward): apis = [] else: apis = [ - parse_api_entry(api, "backward_api" if backward else "op") + parse_api_entry(api, "backward_op" if backward else "op") for api in apis ] diff --git a/paddle/phi/api/yaml/generator/parse_utils.py b/paddle/phi/api/yaml/generator/parse_utils.py index 45aefdfd6dc..f617f166dd1 100644 --- a/paddle/phi/api/yaml/generator/parse_utils.py +++ b/paddle/phi/api/yaml/generator/parse_utils.py @@ -334,7 +334,7 @@ def parse_api_entry(api_entry: Dict[str, Any], name_field="op"): api["backward"] = backward # forward for backward_apis - is_backward_api = name_field == "backward_api" + is_backward_api = name_field == "backward_op" if is_backward_api: if "forward" in api_entry: forward = parse_forward(api_name, api_entry["forward"]) diff --git a/paddle/phi/api/yaml/legacy_backward.yaml b/paddle/phi/api/yaml/legacy_backward.yaml index 7471d310a64..76d60522d86 100755 --- a/paddle/phi/api/yaml/legacy_backward.yaml +++ b/paddle/phi/api/yaml/legacy_backward.yaml @@ -1,4 +1,4 @@ -- backward_api : abs_double_grad +- 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) @@ -10,7 +10,7 @@ data_transform: skip_transform : grad_x_grad -- backward_api : abs_grad +- backward_op : abs_grad forward : abs (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -23,7 +23,7 @@ skip_transform : out_grad backward : abs_double_grad -- backward_api : acos_grad +- backward_op : acos_grad forward : acos (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -34,7 +34,7 @@ func : acos_grad inplace : (out_grad -> x_grad) -- backward_api : acosh_grad +- backward_op : acosh_grad forward : acosh (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -45,7 +45,7 @@ func : acosh_grad inplace : (out_grad -> x_grad) -- backward_api : add_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) @@ -58,7 +58,7 @@ backward : add_triple_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : add_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) @@ -71,7 +71,7 @@ backward : add_double_grad inplace : (out_grad -> x_grad) -- backward_api : add_triple_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) @@ -82,7 +82,7 @@ func : add_triple_grad inplace : (grad_grad_out_grad -> grad_grad_x_grad) -- backward_api : addmm_grad +- backward_op : addmm_grad forward : addmm (Tensor input, Tensor x, Tensor y, float alpha, float beta) -> Tensor(out) args : (Tensor input, Tensor x, Tensor y, Tensor out_grad, float alpha, float beta) output : Tensor(input_grad), Tensor(x_grad), Tensor(y_grad) @@ -92,7 +92,7 @@ kernel : func : addmm_grad -- backward_api : affine_grid_grad +- backward_op : affine_grid_grad forward : affine_grid (Tensor input, IntArray outputShape, bool use_cudnn=true, bool align_corners=true) -> Tensor(output) args : (Tensor output_grad, IntArray outputShape, bool use_cudnn=true, bool align_corners=true) output : Tensor(input_grad) @@ -104,7 +104,7 @@ param : [output_grad, outputShape, align_corners] use_gpudnn: use_cudnn -- backward_api : amax_grad +- backward_op : amax_grad forward: amax (Tensor x, int64_t[] dims={}, bool keep_dim=false) -> Tensor(out) args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims={}, bool keep_dim=false, bool reduce_all=false) output : Tensor(x_grad) @@ -114,7 +114,7 @@ kernel : func : amax_grad -- backward_api : amin_grad +- backward_op : amin_grad forward: amin (Tensor x, int64_t[] dims={}, bool keep_dim=false) -> Tensor(out) args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims={}, bool keep_dim=false, bool reduce_all=false) output : Tensor(x_grad) @@ -124,7 +124,7 @@ kernel : func : amin_grad -- backward_api : angle_grad +- backward_op : angle_grad forward : angle (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -136,7 +136,7 @@ data_transform: skip_transform : out_grad -- backward_api : argsort_grad +- backward_op : argsort_grad forward : argsort (Tensor x, int axis, bool descending) -> Tensor(out), Tensor(indices) args : (Tensor indices, Tensor x, Tensor out_grad, int axis, bool descending) output : Tensor(x_grad) @@ -148,19 +148,19 @@ data_type : out_grad no_need_buffer : x -- backward_api : as_complex_grad +- backward_op : as_complex_grad forward : as_complex (Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) invoke : as_real(out_grad) -- backward_api : as_real_grad +- backward_op : as_real_grad forward : as_real (Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) invoke : as_complex(out_grad) -- backward_api : asin_grad +- backward_op : asin_grad forward : asin (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -171,7 +171,7 @@ func : asin_grad inplace : (out_grad -> x_grad) -- backward_api : asinh_grad +- backward_op : asinh_grad forward : asinh (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -182,13 +182,13 @@ func : asinh_grad inplace : (out_grad -> x_grad) -- backward_api : assign_grad +- backward_op : assign_grad forward : assign (Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) invoke : assign(out_grad) -- backward_api : 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) @@ -198,7 +198,7 @@ func : assign inplace : (out_grad -> x_grad) -- backward_api : atan_grad +- backward_op : atan_grad forward : atan (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -209,7 +209,7 @@ func : atan_grad inplace : (out_grad -> x_grad) -- backward_api : atanh_grad +- backward_op : atanh_grad forward : atanh (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -220,7 +220,7 @@ func : atanh_grad inplace : (out_grad -> x_grad) -- backward_api : batch_norm_double_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, bool fuse_with_relu) -> Tensor(grad_x), Tensor(grad_scale), Tensor(grad_bias) args : (Tensor x, Tensor scale, Tensor out_mean, Tensor out_variance, Tensor saved_mean, Tensor saved_variance, Tensor grad_out, Tensor grad_x_grad, Tensor grad_scale_grad, Tensor grad_bias_grad, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu) output : Tensor(x_grad), Tensor(scale_grad), Tensor(grad_out_grad) @@ -233,7 +233,7 @@ optional : out_mean, out_variance inplace : (grad_out -> grad_out_grad) -- backward_api : batch_norm_grad +- 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) @@ -246,7 +246,7 @@ optional : mean_out, variance_out, reserve_space backward : batch_norm_double_grad -- backward_api : bce_loss_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) @@ -257,7 +257,7 @@ func : bce_loss_grad inplace : (out_grad -> input_grad) -- backward_api : bicubic_interp_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) @@ -269,7 +269,7 @@ func : bicubic_interp_grad data_type : output_grad -- backward_api : bilinear_interp_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) @@ -281,7 +281,7 @@ func : bilinear_interp_grad data_type : output_grad -- backward_api : bilinear_tensor_product_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) @@ -290,7 +290,7 @@ kernel : func : bilinear_tensor_product_grad -- backward_api : bmm_grad +- backward_op : bmm_grad forward : bmm (Tensor x, Tensor y) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out_grad) output : Tensor(x_grad), Tensor(y_grad) @@ -299,7 +299,7 @@ kernel : func : bmm_grad -- backward_api : brelu_grad +- backward_op : brelu_grad forward : brelu (Tensor x, float t_min, float t_max) -> Tensor(out) args : (Tensor x, Tensor out_grad, float t_min, float t_max) output : Tensor(x_grad) @@ -310,7 +310,7 @@ func : brelu_grad inplace : (out_grad -> x_grad) -- backward_api : broadcast_tensors_grad +- backward_op : broadcast_tensors_grad forward : broadcast_tensors (Tensor[] x) -> Tensor[](out) args : (Tensor[] x, Tensor[] out_grad) output : Tensor[](x_grad) @@ -322,7 +322,7 @@ param : [out_grad] no_need_buffer : x -- backward_api : cast_grad +- backward_op : cast_grad forward : cast (Tensor x, DataType out_dtype) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -334,7 +334,7 @@ data_type : out_grad no_need_buffer : x -- backward_api : ceil_grad +- backward_op : ceil_grad forward : ceil(Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) @@ -345,7 +345,7 @@ func : ceil_grad inplace : (out_grad -> x_grad) -- backward_api : celu_double_grad +- backward_op : celu_double_grad forward : celu_grad(Tensor x, Tensor grad_out, float alpha) -> Tensor(grad_x) args : (Tensor x, Tensor grad_out, Tensor grad_x_grad, float alpha) output : Tensor(x_grad), Tensor(grad_out_grad) @@ -356,7 +356,7 @@ func : celu_double_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : celu_grad +- backward_op : celu_grad forward : celu(Tensor x, float alpha) -> Tensor(out) args : (Tensor x, Tensor out_grad, float alpha) output : Tensor(x_grad) @@ -368,7 +368,7 @@ backward : celu_double_grad inplace : (out_grad -> x_grad) -- backward_api : clip_double_grad +- backward_op : clip_double_grad forward : clip_grad (Tensor x, Tensor grad_out, Scalar min = 0., Scalar max = 0.) -> Tensor(grad_x) args : (Tensor x, Tensor grad_x_grad, Scalar min = 0., Scalar max = 0.) output : Tensor(grad_out_grad) @@ -378,7 +378,7 @@ kernel : func : clip_grad -- backward_api : clip_grad +- backward_op : clip_grad forward : clip (Tensor x, Scalar min, Scalar max) -> Tensor(out) args : (Tensor x, Tensor out_grad, Scalar min = 0., Scalar max = 0.) output : Tensor(x_grad) @@ -390,7 +390,7 @@ backward : clip_double_grad inplace : (out_grad -> x_grad) -- backward_api : complex_grad +- backward_op : complex_grad forward : complex (Tensor x, Tensor y) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out_grad) output : Tensor(x_grad), Tensor(y_grad) @@ -400,13 +400,13 @@ func : complex_grad data_type : x -- backward_api : concat_double_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_api : concat_grad +- 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()} @@ -418,7 +418,7 @@ no_need_buffer : x backward : concat_double_grad -- backward_api : conj_grad +- backward_op : conj_grad forward : conj (Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) @@ -428,7 +428,7 @@ kernel : func : conj -- backward_api : conv2d_grad +- backward_op : conv2d_grad forward : conv2d (Tensor input, Tensor filter, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) -> Tensor(out) args : (Tensor input, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) output : Tensor(input_grad), Tensor(filter_grad) @@ -440,7 +440,7 @@ use_gpudnn : true backward : conv2d_grad_grad -- backward_api : conv2d_grad_grad +- backward_op : conv2d_grad_grad forward : conv2d_grad (Tensor input, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) -> Tensor(grad_input), Tensor(grad_filter) args : (Tensor input, Tensor filter, Tensor grad_out, Tensor grad_input_grad, Tensor grad_filter_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) output : Tensor(input_grad), Tensor(filter_grad), Tensor(grad_out_grad) @@ -452,7 +452,7 @@ use_gpudnn : true optional : grad_input_grad, grad_filter_grad -- backward_api : conv2d_transpose_double_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) @@ -462,7 +462,7 @@ func : conv2d_transpose_grad_grad use_gpudnn : true -- backward_api : conv2d_transpose_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) @@ -473,7 +473,7 @@ use_gpudnn : true backward : conv2d_transpose_double_grad -- backward_api : conv3d_grad +- backward_op : conv3d_grad forward : conv3d (Tensor input, Tensor filter, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) -> Tensor(out) args : (Tensor input, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) output : Tensor(input_grad), Tensor(filter_grad) @@ -485,7 +485,7 @@ use_gpudnn : true backward : conv3d_grad_grad -- backward_api : conv3d_grad_grad +- backward_op : conv3d_grad_grad forward : conv3d_grad (Tensor input, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) -> Tensor(grad_input), Tensor(grad_filter) args : (Tensor input, Tensor filter, Tensor grad_out, Tensor grad_input_grad, Tensor grad_filter_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search) output : Tensor(input_grad), Tensor(filter_grad), Tensor(grad_out_grad) @@ -497,7 +497,7 @@ use_gpudnn : true optional : grad_input_grad, grad_filter_grad -- backward_api : conv3d_transpose_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) @@ -507,7 +507,7 @@ func : conv3d_transpose_grad use_gpudnn : true -- backward_api : cos_grad +- backward_op : cos_grad forward : cos (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -518,7 +518,7 @@ func : cos_grad inplace : (out_grad -> x_grad) -- backward_api : cosh_grad +- backward_op : cosh_grad forward : cosh (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -529,7 +529,7 @@ func : cosh_grad inplace : (out_grad -> x_grad) -- backward_api : crop_tensor_grad +- backward_op : crop_tensor_grad forward : crop_tensor (Tensor x, IntArray shape, IntArray offsets) -> Tensor(out) args : (Tensor x, Tensor out_grad, IntArray offsets) output : Tensor(x_grad) @@ -539,7 +539,7 @@ func : crop_tensor_grad data_type : x -- backward_api : cross_entropy_with_softmax_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) @@ -550,7 +550,7 @@ data_type : softmax inplace : (softmax -> input_grad) -- backward_api : cumprod_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) @@ -560,13 +560,13 @@ kernel : func : cumprod_grad -- backward_api : cumsum_grad +- backward_op : cumsum_grad forward : cumsum(Tensor x, Scalar axis, bool flatten, bool exclusive, bool reverse) -> Tensor(out) args : (Tensor out_grad, Scalar axis, bool flatten, bool exclusive, bool reverse) output : Tensor(x_grad) invoke : cumsum(out_grad, axis, flatten, exclusive, !reverse) -- backward_api : deformable_conv_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) @@ -577,7 +577,7 @@ data_type : x optional : mask -- backward_api : depthwise_conv2d_grad +- backward_op : depthwise_conv2d_grad forward : depthwise_conv2d (Tensor input, Tensor filter, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search, bool fuse_relu, bool use_gpudnn) -> Tensor(out) args : (Tensor input, Tensor filter, Tensor out_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search, bool fuse_relu, bool use_gpudnn) output : Tensor(input_grad), Tensor(filter_grad) @@ -590,7 +590,7 @@ use_gpudnn : use_gpudnn backward : depthwise_conv2d_grad_grad -- backward_api : depthwise_conv2d_grad_grad +- backward_op : depthwise_conv2d_grad_grad forward : depthwise_conv2d_grad (Tensor input, Tensor filter, Tensor grad_out, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search, bool fuse_relu, bool use_gpudnn) -> Tensor(grad_input), Tensor(grad_filter) args : (Tensor input, Tensor filter, Tensor grad_out, Tensor grad_input_grad, Tensor grad_filter_grad, int[] strides, int[] paddings, str paddding_algorithm, int groups, int[] dilations, str data_format, bool use_addto, int workspace_size_MB, bool exhaustive_search, bool fuse_relu) output : Tensor(input_grad), Tensor(filter_grad), Tensor(grad_out_grad) @@ -601,7 +601,7 @@ func : depthwise_conv2d_grad_grad optional : grad_input_grad, grad_filter_grad -- backward_api : depthwise_conv2d_transpose_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) @@ -610,7 +610,7 @@ kernel : func : depthwise_conv2d_transpose_grad -- backward_api : det_grad +- backward_op : det_grad forward : det (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -620,7 +620,7 @@ kernel : func : determinant_grad -- backward_api : divide_double_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) @@ -633,7 +633,7 @@ optional : grad_x_grad, grad_y_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : divide_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) @@ -644,7 +644,7 @@ func : divide_grad backward : divide_double_grad -- backward_api : dropout_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) @@ -654,7 +654,7 @@ kernel : func : dropout_grad -- backward_api : eig_grad +- backward_op : eig_grad forward : eig (Tensor x) -> Tensor(out_w), Tensor(out_v) args : (Tensor out_w, Tensor out_v, Tensor out_w_grad, Tensor out_v_grad) output : Tensor(x_grad) @@ -667,7 +667,7 @@ data_transform: skip_transform : out_w, out_w_grad -- backward_api : eigh_grad +- backward_op : eigh_grad forward : eigh (Tensor x, str uplo) -> Tensor(out_w), Tensor(out_v) args : (Tensor out_w, Tensor out_v, Tensor out_w_grad, Tensor out_v_grad) output : Tensor(x_grad) @@ -680,7 +680,7 @@ data_transform: skip_transform : out_w, out_w_grad -- backward_api : eigvalsh_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) @@ -692,7 +692,7 @@ data_transform : skip_transform : eigenvalues_grad -- backward_api : einsum_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()} @@ -702,7 +702,7 @@ kernel : func : einsum_grad -- backward_api : elementwise_pow_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) @@ -712,7 +712,7 @@ kernel : func : elementwise_pow_grad -- backward_api : elu_double_grad +- backward_op : elu_double_grad forward : elu_grad (Tensor x, Tensor out, Tensor grad_out, float alpha)-> Tensor(grad_x) args : (Tensor x, Tensor grad_out, Tensor grad_x_grad, float alpha) output : Tensor(x_grad), Tensor(grad_out_grad) @@ -723,7 +723,7 @@ func : elu_double_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : elu_grad +- backward_op : elu_grad forward : elu (Tensor x, float alpha) -> Tensor(out) args : (Tensor x, Tensor out, Tensor out_grad, float alpha) output : Tensor(x_grad) @@ -735,13 +735,13 @@ backward : elu_double_grad inplace : (out_grad -> x_grad) -- backward_api : embedding_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) -- backward_api : exp_grad +- backward_op : exp_grad forward : exp (Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -752,7 +752,7 @@ func : exp_grad inplace : (out_grad -> x_grad) -- backward_api : expand_as_grad +- 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) @@ -763,13 +763,13 @@ func : expand_as_grad no_need_buffer : x -- backward_api : expand_double_grad +- 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_api : expand_grad +- backward_op : expand_grad forward : expand (Tensor x, IntArray shape) -> Tensor(out) args : (Tensor x, Tensor out_grad, IntArray shape) output : Tensor(x_grad) @@ -781,7 +781,7 @@ no_need_buffer : x backward : expand_double_grad -- backward_api : expm1_grad +- backward_op : expm1_grad forward : expm1 (Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -792,7 +792,7 @@ func : expm1_grad inplace : (out_grad -> x_grad) -- backward_api : exponential__grad +- backward_op : exponential__grad forward : exponential_ (Tensor x, float lambda) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) @@ -800,7 +800,7 @@ func : UnchangedInferMeta invoke : zeros_like(out_grad) -- backward_api : fill_diagonal_grad +- backward_op : fill_diagonal_grad forward : fill_diagonal (Tensor x, float value, int offset, bool wrap) -> Tensor(out) args : (Tensor out_grad, float value, int offset, bool wrap) output : Tensor(x_grad) @@ -809,7 +809,7 @@ kernel : func : fill_diagonal_grad -- backward_api : fill_diagonal_tensor_grad +- backward_op : fill_diagonal_tensor_grad forward : fill_diagonal_tensor (Tensor x, Tensor y, int64_t offset, int dim1, int dim2) -> Tensor(out) args : (Tensor out_grad, int64_t offset, int dim1, int dim2) output : Tensor(x_grad) @@ -819,7 +819,7 @@ func : fill_diagonal_tensor_grad inplace : (out_grad -> x_grad) -- backward_api : fill_grad +- backward_op : fill_grad forward : fill (Tensor x, Scalar value) -> Tensor(out) args : (Tensor out_grad, Scalar value) output : Tensor(x_grad) @@ -830,7 +830,7 @@ func : fill_grad inplace : (out_grad -> x_grad) -- backward_api : flatten_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) @@ -844,13 +844,13 @@ layout: out_grad inplace : (out_grad -> x_grad) -- backward_api : flip_grad +- backward_op : flip_grad forward : flip (Tensor x, int[] axis) -> Tensor(out) args : (Tensor out_grad, int[] axis) output : Tensor(x_grad) invoke : flip(out_grad, axis) -- backward_api : floor_grad +- backward_op : floor_grad forward : floor(Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) @@ -861,7 +861,7 @@ func : floor_grad inplace : (out_grad -> x_grad) -- backward_api : fmax_grad +- backward_op : fmax_grad forward : fmax(Tensor x, Tensor y, int axis) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out_grad, int axis) output : Tensor(x_grad), Tensor(y_grad) @@ -871,7 +871,7 @@ kernel : func : fmax_grad -- backward_api : fmin_grad +- backward_op : fmin_grad forward : fmin(Tensor x, Tensor y, int axis) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out_grad, int axis) output : Tensor(x_grad), Tensor(y_grad) @@ -881,7 +881,7 @@ kernel : func : fmin_grad -- backward_api : frame_grad +- backward_op : frame_grad forward : frame(Tensor x, int frame_length, int hop_length, int axis) -> Tensor(out) args : (Tensor x, Tensor out_grad, int frame_length, int hop_length, int axis) output : Tensor(x_grad) @@ -891,7 +891,7 @@ kernel : func : frame_grad -- backward_api : frobenius_norm_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) @@ -901,7 +901,7 @@ kernel : func : frobenius_norm_grad -- backward_api : gather_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) @@ -913,7 +913,7 @@ func : gather_grad no_need_buffer : x -- backward_api : gather_nd_grad +- backward_op : gather_nd_grad forward : gather_nd (Tensor x, Tensor index) -> Tensor(out) args : (Tensor x, Tensor index, Tensor out_grad) output : Tensor(x_grad) @@ -924,7 +924,7 @@ func : gather_nd_grad no_need_buffer : x -- backward_api : gelu_grad +- backward_op : gelu_grad forward : gelu(Tensor x, bool approximate) -> Tensor(out) args : (Tensor x, Tensor out_grad, bool approximate) output : Tensor(x_grad) @@ -934,7 +934,7 @@ kernel : func : gelu_grad -- backward_api : graph_send_recv_grad +- backward_op : graph_send_recv_grad forward : graph_send_recv (Tensor x, Tensor src_index, Tensor dst_index, str reduce_op = "SUM", IntArray out_size = {0}) -> Tensor(out), Tensor(dst_count) args : (Tensor x, Tensor src_index, Tensor dst_index, Tensor out, Tensor dst_count, Tensor out_grad, str reduce_op = "SUM") output : Tensor(x_grad) @@ -946,7 +946,7 @@ data_type : out_grad optional: out, dst_count -- backward_api : graph_send_ue_recv_grad +- backward_op : graph_send_ue_recv_grad forward : graph_send_ue_recv (Tensor x, Tensor y, Tensor src_index, Tensor dst_index, str message_op, str reduce_op, IntArray out_size) -> Tensor(out), Tensor(dst_count) args : (Tensor x, Tensor y, Tensor src_index, Tensor dst_index, Tensor out, Tensor dst_count, Tensor out_grad, str message_op, str reduce_op) output : Tensor(x_grad), Tensor(y_grad) @@ -958,7 +958,7 @@ data_type : out_grad optional: out, dst_count -- backward_api : grid_sample_grad +- backward_op : grid_sample_grad forward : grid_sample (Tensor x, Tensor grid, str mode, str padding_mode, bool align_corners) -> Tensor(out) args : (Tensor x, Tensor grid, Tensor out_grad, str mode, str padding_mode, bool align_corners) output : Tensor(x_grad), Tensor(grid_grad) @@ -969,7 +969,7 @@ func : grid_sample_grad data_type : x -- backward_api : group_norm_grad +- 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) @@ -982,7 +982,7 @@ optional: scale, bias inplace : (y_grad -> x_grad) -- backward_api : gumbel_softmax_grad +- backward_op : 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) @@ -992,7 +992,7 @@ kernel : func : gumbel_softmax_grad -- backward_api : hard_shrink_grad +- backward_op : hard_shrink_grad forward : hard_shrink (Tensor x, float threshold) -> Tensor(out) args : (Tensor x, Tensor out_grad, float threshold) output : Tensor(x_grad) @@ -1003,7 +1003,7 @@ func : hard_shrink_grad inplace : (out_grad -> x_grad) -- backward_api : hard_sigmoid_grad +- backward_op : hard_sigmoid_grad forward : hard_sigmoid (Tensor x, float slope, float offset) -> Tensor(out) args : (Tensor out, Tensor out_grad, float slope, float offset) output : Tensor(x_grad) @@ -1014,7 +1014,7 @@ func : hard_sigmoid_grad inplace : (out_grad -> x_grad) -- backward_api : hard_swish_grad +- backward_op : hard_swish_grad forward : hard_swish (Tensor x, float threshold = 6.0, float scale = 6.0, float offset = 3.0) -> Tensor(out) args : (Tensor x, Tensor out_grad, float threshold, float scale, float offset) output : Tensor(x_grad) @@ -1025,7 +1025,7 @@ func : hard_swish_grad inplace : (out_grad -> x_grad) -- backward_api : hierarchical_sigmoid_grad +- backward_op : hierarchical_sigmoid_grad forward : hierarchical_sigmoid (Tensor x, Tensor w, Tensor label, Tensor path, Tensor code, Tensor bias, int num_classes, bool remote_prefetch, int trainer_id, int64_t[] height_sections, str[] epmap, str[] table_names, 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, int trainer_id, int64_t[] height_sections, str[] epmap, str[] table_names, bool is_sparse) output : Tensor(x_grad), Tensor(w_grad), Tensor(bias_grad) @@ -1036,7 +1036,7 @@ kernel : func : hierarchical_sigmoid_grad -- backward_api : huber_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) @@ -1046,13 +1046,13 @@ kernel : func : huber_loss_grad -- backward_api : imag_grad +- backward_op : imag_grad forward : imag (Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) invoke : imag_grad_impl(out_grad, x_grad) -- backward_api : index_add_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) @@ -1063,7 +1063,7 @@ data_type : out_grad inplace : (out_grad -> x_grad) -- backward_api : index_sample_grad +- backward_op : index_sample_grad forward : index_sample (Tensor x, Tensor index) -> Tensor(out) args : (Tensor x, Tensor index, Tensor out_grad) output : Tensor(x_grad) @@ -1075,7 +1075,7 @@ data_type : out_grad no_need_buffer : x -- backward_api : index_select_grad +- backward_op : index_select_grad forward : index_select(Tensor x, Tensor index, int dim) -> Tensor(out) args : (Tensor x, Tensor index, Tensor out_grad, int dim) output : Tensor(x_grad) @@ -1087,7 +1087,7 @@ data_type : x no_need_buffer : x -- backward_api : instance_norm_double_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) @@ -1098,7 +1098,7 @@ data_type : x optional : fwd_scale, grad_x_grad, grad_scale_grad, grad_bias_grad -- backward_api : instance_norm_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) @@ -1110,7 +1110,7 @@ optional : scale backward : instance_norm_double_grad -- backward_api : inverse_grad +- backward_op : inverse_grad forward : inverse(Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -1119,7 +1119,7 @@ kernel : func : inverse_grad -- backward_api : kldiv_loss_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) @@ -1130,7 +1130,7 @@ func : kldiv_loss_grad no_need_buffer : x -- backward_api : kron_grad +- backward_op : kron_grad forward : kron (Tensor x, Tensor y) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out_grad) output : Tensor(x_grad), Tensor(y_grad) @@ -1141,7 +1141,7 @@ func : kron_grad data_type : out_grad -- backward_api : kthvalue_grad +- backward_op : kthvalue_grad forward : kthvalue(Tensor x, int k, int axis, bool keepdim) -> Tensor(out), Tensor(indices) args : (Tensor x, Tensor indices, Tensor out_grad, int k, int axis, bool keepdim) output : Tensor(x_grad) @@ -1151,7 +1151,7 @@ kernel : func : kthvalue_grad -- backward_api : label_smooth_grad +- backward_op : label_smooth_grad forward : label_smooth (Tensor label, Tensor prior_dist, float epsilon) -> Tensor(out) args : (Tensor out_grad, float epsilon) output : Tensor(label_grad) @@ -1161,7 +1161,7 @@ kernel : func : label_smooth_grad -- backward_api : layer_norm_grad +- backward_op : layer_norm_grad forward : layer_norm (Tensor x, Tensor scale, Tensor bias, float epsilon, int begin_norm_axis, bool is_test) -> 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, bool is_test) output : Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad) @@ -1174,7 +1174,7 @@ no_need_buffer : bias optional : scale, bias -- backward_api : leaky_relu_double_grad +- backward_op : leaky_relu_double_grad forward : leaky_relu_grad (Tensor x, Tensor grad_out, float alpha) -> Tensor(grad_x) args : (Tensor x, Tensor grad_x_grad, float alpha) output : Tensor(grad_out_grad) @@ -1185,7 +1185,7 @@ func : leaky_relu_double_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : leaky_relu_grad +- 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) @@ -1197,7 +1197,7 @@ backward : leaky_relu_double_grad inplace : (out_grad -> x_grad) -- backward_api : lerp_grad +- backward_op : lerp_grad forward : lerp (Tensor x, Tensor y, Tensor weight) -> Tensor(out) args : (Tensor x, Tensor y, Tensor weight, Tensor out, Tensor out_grad) output : Tensor(x_grad), Tensor(y_grad) @@ -1207,7 +1207,7 @@ kernel : func : lerp_grad -- backward_api : linear_interp_grad +- 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) @@ -1219,7 +1219,7 @@ func : linear_interp_grad data_type : output_grad -- backward_api : log10_grad +- backward_op : log10_grad forward : log10 (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -1230,7 +1230,7 @@ func : log10_grad inplace : (out_grad -> x_grad) -- backward_api : log1p_grad +- backward_op : log1p_grad forward : log1p (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -1241,7 +1241,7 @@ func : log1p_grad inplace : (out_grad -> x_grad) -- backward_api : log2_grad +- backward_op : log2_grad forward : log2 (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -1252,7 +1252,7 @@ func : log2_grad inplace : (out_grad -> x_grad) -- backward_api : log_double_grad +- backward_op : log_double_grad forward : log_grad (Tensor x, Tensor grad_out) -> Tensor(grad_x) args : (Tensor x, Tensor grad_out, Tensor grad_x_grad) output : Tensor(x_grad), Tensor(grad_out_grad) @@ -1263,7 +1263,7 @@ func : log_double_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : log_grad +- backward_op : log_grad forward : log (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -1275,7 +1275,7 @@ backward : log_double_grad inplace : (out_grad -> x_grad) -- backward_api : log_loss_grad +- backward_op : log_loss_grad forward : log_loss (Tensor input, Tensor label, float epsilon) -> Tensor(out) args : (Tensor input, Tensor label, Tensor out_grad, float epsilon) output : Tensor(input_grad) @@ -1285,7 +1285,7 @@ kernel : func : log_loss_grad -- backward_api : log_softmax_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) @@ -1295,7 +1295,7 @@ kernel : func : log_softmax_grad -- backward_api : logcumsumexp_grad +- backward_op : logcumsumexp_grad forward : logcumsumexp(Tensor x, int axis, bool flatten, bool exclusive, bool reverse) -> Tensor(out) infer_meta : func : UnchangedInferMeta @@ -1305,7 +1305,7 @@ kernel : func : logcumsumexp_grad -- backward_api : logit_grad +- backward_op : logit_grad forward : logit (Tensor x, float eps = 1e-6f) -> Tensor(out) args : (Tensor x, Tensor out_grad, float eps) output : Tensor(x_grad) @@ -1315,7 +1315,7 @@ kernel : func : logit_grad -- backward_api : logsigmoid_grad +- backward_op : logsigmoid_grad forward : logsigmoid (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -1326,7 +1326,7 @@ func : logsigmoid_grad inplace : (out_grad -> x_grad) -- backward_api : logsumexp_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) @@ -1336,7 +1336,7 @@ kernel : func : logsumexp_grad -- backward_api : lu_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) @@ -1345,7 +1345,7 @@ kernel : func : lu_grad -- backward_api : lu_unpack_grad +- backward_op : lu_unpack_grad forward : lu_unpack (Tensor x, Tensor pivots, bool unpack_ludata, bool unpack_pivots) -> Tensor(pmat), Tensor(l), Tensor(u) args : (Tensor x, Tensor pivots, Tensor l, Tensor u, Tensor pmat, Tensor l_grad, Tensor u_grad, bool unpack_ludata, bool unpack_pivots) output : Tensor(x_grad) @@ -1354,7 +1354,7 @@ kernel : func : lu_unpack_grad -- backward_api : margin_cross_entropy_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) @@ -1365,7 +1365,7 @@ data_type : softmax inplace : (softmax -> logits_grad) -- backward_api : masked_select_grad +- backward_op : masked_select_grad forward : masked_select (Tensor x, Tensor mask) -> Tensor(out) args : (Tensor x, Tensor mask, Tensor out_grad) output : Tensor(x_grad) @@ -1377,7 +1377,7 @@ data_type : x no_need_buffer : x -- backward_api : matmul_double_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) @@ -1389,7 +1389,7 @@ backward : matmul_triple_grad optional : grad_x_grad, grad_y_grad -- backward_api : matmul_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) @@ -1400,7 +1400,7 @@ func : matmul_grad backward : matmul_double_grad -- backward_api : matmul_triple_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) @@ -1411,7 +1411,7 @@ func : matmul_triple_grad optional : grad_x_grad, grad_y_grad, grad_grad_out_grad -- backward_api : matrix_power_grad +- backward_op : matrix_power_grad forward : matrix_power (Tensor x, int n) -> Tensor(out) args : (Tensor x, Tensor out, Tensor out_grad, int n) output : Tensor(x_grad) @@ -1421,7 +1421,7 @@ kernel : func : matrix_power_grad -- backward_api : max_grad +- backward_op : max_grad forward: max (Tensor x, IntArray dims={}, bool keep_dim=false) -> Tensor(out) args : (Tensor x, Tensor out, Tensor out_grad, IntArray dims={}, bool keep_dim=false, bool reduce_all=false) output : Tensor(x_grad) @@ -1431,7 +1431,7 @@ kernel : func : max_grad -- backward_api : max_pool2d_with_index_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) @@ -1440,7 +1440,7 @@ kernel : func : max_pool2d_with_index_grad -- backward_api : max_pool3d_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) @@ -1449,7 +1449,7 @@ kernel : func : max_pool3d_with_index_grad -- backward_api : maximum_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) @@ -1459,7 +1459,7 @@ kernel : func : maximum_grad -- backward_api : maxout_grad +- backward_op : 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) @@ -1469,7 +1469,7 @@ kernel : func : maxout_grad -- backward_api : mean_all_grad +- backward_op : mean_all_grad forward : mean_all(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -1479,13 +1479,13 @@ kernel : func : mean_all_grad -- backward_api : mean_double_grad +- backward_op : mean_double_grad forward: mean_grad (Tensor x, Tensor grad_out, IntArray dims={}, bool keep_dim=false, bool reduce_all = false) -> Tensor(grad_x) args : (Tensor grad_x_grad, IntArray dims={}, bool keep_dim=false) output : Tensor(grad_out_grad) invoke : mean(grad_x_grad, dims, keep_dim) -- backward_api : mean_grad +- backward_op : mean_grad forward: mean (Tensor x, IntArray dims={}, bool keep_dim=false) -> Tensor(out) args : (Tensor x, Tensor out_grad, IntArray dims={}, bool keep_dim=false, bool reduce_all=false) output : Tensor(x_grad) @@ -1497,7 +1497,7 @@ backward : mean_double_grad no_need_buffer : x -- backward_api : meshgrid_grad +- backward_op : meshgrid_grad forward : meshgrid (Tensor[] inputs) -> Tensor[](outputs) args : (Tensor[] inputs, Tensor[] outputs_grad) output : Tensor[](inputs_grad){inputs.size()} @@ -1506,7 +1506,7 @@ kernel : func : meshgrid_grad -- backward_api : min_grad +- backward_op : min_grad forward: min (Tensor x, IntArray dims={}, bool keep_dim=false) -> Tensor(out) args : (Tensor x, Tensor out, Tensor out_grad, IntArray dims={}, bool keep_dim=false, bool reduce_all=false) output : Tensor(x_grad) @@ -1516,7 +1516,7 @@ kernel : func : min_grad -- backward_api : minimum_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) @@ -1526,7 +1526,7 @@ kernel : func : minimum_grad -- backward_api : mish_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) @@ -1537,7 +1537,7 @@ func : mish_grad inplace : (out_grad -> x_grad) -- backward_api : mode_grad +- backward_op : mode_grad forward : mode(Tensor x, int axis, bool keepdim) -> Tensor(out), Tensor(indices) args : (Tensor x, Tensor indices, Tensor out_grad, int axis, bool keepdim) output : Tensor(x_grad) @@ -1547,7 +1547,7 @@ kernel : func : mode_grad -- backward_api : multi_dot_grad +- backward_op : multi_dot_grad forward : multi_dot (Tensor[] x) -> Tensor(out) args : (Tensor[] x, Tensor out_grad) output : Tensor[](x_grad) {x.size()} @@ -1556,7 +1556,7 @@ kernel : func : multi_dot_grad -- backward_api : multiplex_grad +- backward_op : multiplex_grad forward : multiplex (Tensor[] ins, Tensor ids) -> Tensor(out) args : (Tensor[] ins, Tensor ids, Tensor out_grad) output : Tensor[](ins_grad){ins.size()} @@ -1567,7 +1567,7 @@ func : multiplex_grad param : [ids, out_grad] -- backward_api : multiply_double_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) @@ -1580,7 +1580,7 @@ backward : multiply_triple_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : multiply_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) @@ -1591,7 +1591,7 @@ func : multiply_grad backward : multiply_double_grad -- backward_api : multiply_triple_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) @@ -1602,7 +1602,7 @@ func : multiply_triple_grad optional : fwd_grad_grad_x, fwd_grad_grad_y, grad_grad_out_grad -- backward_api : nearest_interp_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) @@ -1614,7 +1614,7 @@ func : nearest_interp_grad data_type : output_grad -- backward_api : nll_loss_grad +- backward_op : nll_loss_grad forward : nll_loss (Tensor input, Tensor label, Tensor weight, int64_t ignore_index, str reduction) -> Tensor(out), Tensor(total_weight) args : (Tensor input, Tensor label, Tensor weight, Tensor total_weight, Tensor out_grad, int64_t ignore_index, str reduction) output : Tensor(input_grad) @@ -1625,7 +1625,7 @@ data_type : input optional : weight -- backward_api : norm_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) @@ -1635,7 +1635,7 @@ kernel : func : norm_grad -- backward_api : overlap_add_grad +- backward_op : overlap_add_grad forward : overlap_add(Tensor x, int hop_length, int axis) -> Tensor(out) args : (Tensor x, Tensor out_grad, int hop_length, int axis) output : Tensor(x_grad) @@ -1645,7 +1645,7 @@ func : overlap_add_grad data_type : x -- backward_api : p_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) @@ -1655,7 +1655,7 @@ kernel : func : p_norm_grad -- backward_api : pad3d_double_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) @@ -1664,7 +1664,7 @@ kernel : func : pad3d -- backward_api : pad3d_grad +- 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) @@ -1676,7 +1676,7 @@ no_need_buffer : x backward : pad3d_double_grad -- backward_api : pad_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) @@ -1685,7 +1685,7 @@ kernel : func : pad -- backward_api : pad_grad +- 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) @@ -1698,7 +1698,7 @@ no_need_buffer : x backward : pad_double_grad -- backward_api : pixel_shuffle_grad +- backward_op : 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) @@ -1707,7 +1707,7 @@ kernel : func : pixel_shuffle_grad -- backward_api : pool2d_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, bool use_gpudnn) -> Tensor(grad_x) args : (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, bool use_gpudnn) output : Tensor(grad_out_grad) @@ -1719,7 +1719,7 @@ param : [grad_x_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm] use_gpudnn : use_gpudnn -- backward_api : pool2d_grad +- 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, bool use_gpudnn) -> 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, bool use_gpudnn) output : Tensor(x_grad) @@ -1732,7 +1732,7 @@ use_gpudnn : use_gpudnn backward : pool2d_double_grad -- backward_api : pool3d_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, bool use_gpudnn) -> 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, bool use_gpudnn) output : Tensor(x_grad) @@ -1744,7 +1744,7 @@ param : [x, out, out_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm] use_gpudnn : use_gpudnn -- backward_api : pow_grad +- backward_op : pow_grad forward : pow(Tensor x, Scalar s) -> Tensor(out) args : (Tensor x, Tensor out_grad, Scalar s=-1) output : Tensor(x_grad) @@ -1755,7 +1755,7 @@ func : pow_grad inplace : (out_grad -> x_grad) -- backward_api : prelu_grad +- 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) @@ -1765,7 +1765,7 @@ kernel : func : prelu_grad -- backward_api : psroi_pool_grad +- backward_op : psroi_pool_grad forward : psroi_pool (Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height, int pooled_width, int output_channels, float spatial_scale) -> Tensor(out) args : (Tensor x, Tensor boxes, Tensor boxes_num, Tensor out_grad, int pooled_height, int pooled_width, int output_channels, float spatial_scale) output : Tensor(x_grad) @@ -1778,7 +1778,7 @@ optional : boxes_num # output is optional -- backward_api : put_along_axis_grad +- backward_op : put_along_axis_grad forward : put_along_axis (Tensor x, Tensor index, Tensor value, int axis, str reduce) -> Tensor(out) args : (Tensor x, Tensor index, Tensor out_grad, int axis, str reduce) output : Tensor(x_grad), Tensor(value_grad) @@ -1788,7 +1788,7 @@ kernel : func : put_along_axis_grad -- backward_api : qr_grad +- backward_op : qr_grad forward : qr (Tensor x, str mode) -> Tensor(q), Tensor(r) args : (Tensor x, Tensor q, Tensor r, Tensor q_grad, Tensor r_grad, str mode) output : Tensor(x_grad) @@ -1798,13 +1798,13 @@ kernel : func : qr_grad -- backward_api : real_grad +- backward_op : real_grad forward : real (Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) invoke : real_grad_impl(out_grad, x_grad) -- backward_api : reciprocal_grad +- backward_op : reciprocal_grad forward : reciprocal (Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -1815,7 +1815,7 @@ func : reciprocal_grad inplace : (out_grad -> x_grad) -- backward_api : reduce_prod_grad +- backward_op : reduce_prod_grad forward : reduce_prod (Tensor x, IntArray dims, bool keep_dim, bool reduce_all) -> Tensor(out) args : (Tensor x, Tensor out, Tensor out_grad, IntArray dims, bool keep_dim, bool reduce_all) output : Tensor(x_grad) @@ -1825,7 +1825,7 @@ kernel : func : prod_grad -- backward_api : relu6_grad +- backward_op : relu6_grad forward : relu6 (Tensor x, float threshold) -> Tensor(out) args : (Tensor out, Tensor out_grad, float threshold) output : Tensor(x_grad) @@ -1836,7 +1836,7 @@ func : relu6_grad inplace : (out_grad -> x_grad) -- backward_api : relu_double_grad +- backward_op : relu_double_grad forward : relu_grad (Tensor out, Tensor grad_out) -> Tensor(grad_x) args : (Tensor out, Tensor grad_x_grad) output : Tensor(grad_out_grad) @@ -1847,7 +1847,7 @@ func : relu_double_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : relu_grad +- backward_op : relu_grad forward : relu (Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -1859,7 +1859,7 @@ backward: relu_double_grad inplace : (out_grad -> x_grad) -- backward_api : renorm_grad +- backward_op : renorm_grad forward : renorm (Tensor x, float p, int axis, float max_norm) -> Tensor(out) args : (Tensor x, Tensor out_grad, float p, int axis, float max_norm) output : Tensor(x_grad) @@ -1869,7 +1869,7 @@ kernel : func : renorm_grad -- backward_api : repeat_interleave_grad +- backward_op : repeat_interleave_grad forward : repeat_interleave(Tensor x, int repeats, int dim) -> Tensor(out) args : (Tensor x, Tensor out_grad, int repeats, int dim) output : Tensor(x_grad) @@ -1879,7 +1879,7 @@ kernel : func : repeat_interleave_grad -- backward_api : repeat_interleave_with_tensor_index_grad +- backward_op : repeat_interleave_with_tensor_index_grad forward : repeat_interleave_with_tensor_index(Tensor x, Tensor repeats, int dim) -> Tensor(out) args : (Tensor x, Tensor repeats, Tensor out_grad, int dim) output : Tensor(x_grad) @@ -1890,7 +1890,7 @@ func : repeat_interleave_with_tensor_index_grad data_type : x -- backward_api : reshape_double_grad +- backward_op : reshape_double_grad forward : reshape_grad (Tensor xshape, Tensor grad_out) -> Tensor(grad_x) args : (Tensor grad_out, Tensor grad_x_grad) output : Tensor(grad_out_grad) @@ -1902,7 +1902,7 @@ no_need_buffer : grad_out inplace : (grad_x_grad -> grad_out_grad) -- backward_api : reshape_grad +- backward_op : reshape_grad forward : reshape (Tensor x, IntArray shape) -> Tensor(out), Tensor(xshape) args : (Tensor xshape, Tensor out_grad) output : Tensor(x_grad) @@ -1918,7 +1918,7 @@ backward : reshape_double_grad inplace : (out_grad -> x_grad) -- backward_api : reverse_array_grad +- backward_op : reverse_array_grad forward : reverse_array (Tensor[] x, IntArray axis) -> Tensor[](out) args : (Tensor[] out_grad, IntArray axis) output : Tensor[](x_grad){out_grad.size()} @@ -1927,13 +1927,13 @@ kernel : func : reverse -- backward_api : reverse_grad +- backward_op : reverse_grad forward : reverse (Tensor x, IntArray axis) -> Tensor(out) args : (Tensor out_grad, IntArray axis) output : Tensor(x_grad) invoke : reverse(out_grad, axis) -- backward_api : roi_align_grad +- backward_op : roi_align_grad forward : roi_align (Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height, int pooled_width, float spatial_scale, int sampling_ratio, bool aligned) -> Tensor(out) args : (Tensor x, Tensor boxes, Tensor boxes_num, Tensor out_grad, int pooled_height, int pooled_width, float spatial_scale, int sampling_ratio, bool aligned) output : Tensor(x_grad) @@ -1946,7 +1946,7 @@ no_need_buffer : x optional : boxes_num -- backward_api : roi_pool_grad +- backward_op : roi_pool_grad forward : roi_pool (Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height, int pooled_width, float spatial_scale) -> Tensor(out), Tensor(arg_max) args : (Tensor x, Tensor boxes, Tensor boxes_num, Tensor arg_max, Tensor out_grad, int pooled_height, int pooled_width, float spatial_scale) output : Tensor(x_grad) @@ -1958,7 +1958,7 @@ data_type : x optional : boxes_num -- backward_api : roll_grad +- backward_op : roll_grad forward : roll(Tensor x, IntArray shifts, int64_t[] axis) -> Tensor(out) args : (Tensor x, Tensor out_grad, IntArray shifts, int64_t[] axis) output : Tensor(x_grad) @@ -1970,7 +1970,7 @@ data_type : x no_need_buffer : x -- backward_api : round_grad +- backward_op : round_grad forward : round(Tensor x) -> Tensor(out) args : (Tensor out_grad) output : Tensor(x_grad) @@ -1981,7 +1981,7 @@ func : round_grad inplace : (out_grad -> x_grad) -- backward_api : rsqrt_double_grad +- backward_op : rsqrt_double_grad forward : rsqrt_grad (Tensor out, Tensor grad_out) -> Tensor(grad_x) args : (Tensor out, Tensor grad_x, Tensor grad_x_grad) output : Tensor(out_grad), Tensor(grad_out_grad) @@ -1992,7 +1992,7 @@ func : rsqrt_double_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : rsqrt_grad +- backward_op : rsqrt_grad forward : rsqrt (Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -2004,13 +2004,13 @@ backward : rsqrt_double_grad inplace : (out_grad -> x_grad) -- backward_api : scale_grad +- backward_op : scale_grad forward : scale (Tensor x, Scalar scale, float bias, bool bias_after_scale) -> Tensor(out) args : (Tensor out_grad, Scalar scale=1.0, bool bias_after_scale=true) output : Tensor(x_grad) invoke : scale(out_grad, scale, 0.0, bias_after_scale) -- backward_api : scatter_grad +- backward_op : scatter_grad forward : scatter (Tensor x, Tensor index, Tensor updates, bool overwrite) -> Tensor(out) args : (Tensor index, Tensor updates, Tensor out_grad, bool overwrite) output : Tensor(x_grad), Tensor(updates_grad) @@ -2021,7 +2021,7 @@ func : scatter_grad no_need_buffer : updates -- backward_api : scatter_nd_add_grad +- backward_op : scatter_nd_add_grad forward : scatter_nd_add (Tensor x, Tensor index, Tensor updates) -> Tensor(out) args : (Tensor index, Tensor updates, Tensor out_grad) output : Tensor(x_grad), Tensor(updates_grad) @@ -2032,7 +2032,7 @@ func : scatter_nd_add_grad no_need_buffer : updates -- backward_api : segment_pool_grad +- backward_op : segment_pool_grad forward : segment_pool (Tensor x, Tensor segment_ids, str pooltype) -> Tensor(out), Tensor(summed_ids) args : (Tensor x, Tensor segment_ids, Tensor out, Tensor summed_ids, Tensor out_grad, str pooltype) output : Tensor(x_grad) @@ -2044,7 +2044,7 @@ data_type : x optional : summed_ids -- backward_api : selu_grad +- backward_op : selu_grad forward : selu (Tensor x, float scale, float alpha) -> Tensor(out) args : (Tensor out, Tensor out_grad, float scale, float alpha) output : Tensor(x_grad) @@ -2054,7 +2054,7 @@ kernel : func : selu_grad -- backward_api : sigmoid_cross_entropy_with_logits_grad +- backward_op : sigmoid_cross_entropy_with_logits_grad forward : sigmoid_cross_entropy_with_logits (Tensor x, Tensor label, bool normalize, int ignore_index) -> Tensor(out) args : (Tensor x, Tensor label, Tensor out_grad, bool normalize, int ignore_index) output : Tensor(x_grad) @@ -2065,7 +2065,7 @@ func : sigmoid_cross_entropy_with_logits_grad inplace : (out_grad -> x_grad) -- backward_api : sigmoid_double_grad +- backward_op : sigmoid_double_grad forward : sigmoid_grad (Tensor out, Tensor fwd_grad_out) -> Tensor(grad_x) args : (Tensor out, Tensor fwd_grad_out, Tensor grad_x_grad) output : Tensor(out_grad), Tensor(fwd_grad_out_grad) @@ -2077,7 +2077,7 @@ backward : sigmoid_triple_grad inplace : (grad_x_grad -> fwd_grad_out_grad) -- backward_api : sigmoid_grad +- backward_op : sigmoid_grad forward : sigmoid (Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -2089,7 +2089,7 @@ backward : sigmoid_double_grad inplace : (out_grad -> x_grad) -- backward_api : sigmoid_triple_grad +- backward_op : sigmoid_triple_grad forward : sigmoid_double_grad (Tensor out, Tensor fwd_grad_out, Tensor grad_grad_x) -> Tensor(grad_out), Tensor(grad_grad_out) args : (Tensor out, Tensor fwd_grad_out, Tensor grad_grad_x, Tensor grad_out_grad, Tensor grad_grad_out_grad) output : Tensor(out_grad), Tensor(fwd_grad_out_grad), Tensor(grad_grad_x_grad) @@ -2101,7 +2101,7 @@ optional : grad_grad_out_grad inplace : (grad_grad_x -> fwd_grad_out_grad) -- backward_api : silu_grad +- backward_op : silu_grad forward : silu (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -2112,7 +2112,7 @@ func : silu_grad inplace : (out_grad -> x_grad) -- backward_api : sin_grad +- backward_op : sin_grad forward : sin (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -2123,7 +2123,7 @@ func : sin_grad inplace : (out_grad -> x_grad) -- backward_api : sinh_grad +- backward_op : sinh_grad forward : sinh (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -2134,13 +2134,13 @@ func : sinh_grad inplace : (out_grad -> x_grad) -- backward_api : slice_double_grad +- backward_op : slice_double_grad forward : slice_grad (Tensor input, Tensor grad_out, int64_t[] axes, IntArray starts, IntArray ends, int64_t[] infer_flags, int64_t[] decrease_axis) -> Tensor(grad_input) args : (Tensor grad_input_grad, int64_t[] axes, IntArray starts, IntArray ends, int64_t[] infer_flags, int64_t[] decrease_axis) output : Tensor(grad_out_grad) invoke : slice(grad_input_grad, axes, starts, ends, infer_flags, decrease_axis) -- backward_api : slice_grad +- backward_op : slice_grad forward : slice (Tensor input, int64_t[] axes, IntArray starts, IntArray ends, int64_t[] infer_flags, int64_t[] decrease_axis) -> Tensor(out) args : (Tensor input, Tensor out_grad, int64_t[] axes, IntArray starts, IntArray ends, int64_t[] infer_flags, int64_t[] decrease_axis) output : Tensor(input_grad) @@ -2152,7 +2152,7 @@ backward : slice_double_grad no_need_buffer : input -- backward_api : slogdet_grad +- backward_op : slogdet_grad forward : slogdet (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -2162,7 +2162,7 @@ kernel : func : slogdeterminant_grad -- backward_api : soft_shrink_grad +- backward_op : soft_shrink_grad forward : soft_shrink (Tensor x, float lambda) -> Tensor(out) args : (Tensor x, Tensor out_grad, float lambda) output : Tensor(x_grad) @@ -2173,7 +2173,7 @@ func : soft_shrink_grad inplace : (out_grad -> x_grad) -- backward_api : softmax_grad +- backward_op : softmax_grad forward : softmax (Tensor x, int axis) -> Tensor(out) args : (Tensor out, Tensor out_grad, int axis) output : Tensor(x_grad) @@ -2184,7 +2184,7 @@ func : softmax_grad use_gpudnn : true -- backward_api : softplus_grad +- backward_op : softplus_grad forward : softplus (Tensor x, float beta, float threshold) -> Tensor(out) args : (Tensor x, Tensor out_grad, float beta, float threshold) output : Tensor(x_grad) @@ -2195,7 +2195,7 @@ func : softplus_grad inplace : (out_grad -> x_grad) -- backward_api : softsign_grad +- backward_op : softsign_grad forward : softsign (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -2206,7 +2206,7 @@ func : softsign_grad inplace : (out_grad -> x_grad) -- backward_api : spectral_norm_grad +- backward_op : spectral_norm_grad forward : spectral_norm (Tensor weight, Tensor u, Tensor v, int dim, int power_iters, float eps) -> Tensor(out) args : (Tensor weight, Tensor u, Tensor v, Tensor out_grad, int dim, int power_iters, float eps) output : Tensor(weight_grad) @@ -2216,20 +2216,20 @@ func : spectral_norm_grad data_type : out_grad -- backward_api : split_grad +- backward_op : split_grad forward : split (Tensor x, IntArray num_or_sections, Scalar axis) -> Tensor[](out) args : (Tensor[] out_grad, Scalar axis = -1) output : Tensor(x_grad) invoke : concat( out_grad, axis) -- backward_api : split_with_num_grad +- backward_op : split_with_num_grad forward : split_with_num (Tensor x, int num, Scalar axis) -> Tensor[](out) args : (Tensor[] out_grad, Scalar axis = -1) output : Tensor(x_grad) invoke : concat( out_grad, axis) # TODO(zhangyunfei) The config of double grad and triple grad will be supported in the future. -- backward_api : sqrt_double_grad +- backward_op : sqrt_double_grad forward : sqrt_grad (Tensor out, Tensor grad_out) -> Tensor(grad_x) args : (Tensor out, Tensor grad_x, Tensor grad_x_grad) output : Tensor(out_grad), Tensor(grad_out_grad) @@ -2240,7 +2240,7 @@ func : sqrt_double_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : sqrt_grad +- backward_op : sqrt_grad forward : sqrt (Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -2252,7 +2252,7 @@ backward : sqrt_double_grad inplace : (out_grad -> x_grad) -- backward_api : square_double_grad +- backward_op : square_double_grad forward : square_grad (Tensor x, Tensor grad_out) -> Tensor(grad_x) args : (Tensor x, Tensor grad_out, Tensor grad_x_grad) output : Tensor(x_grad), Tensor(grad_out_grad) @@ -2263,7 +2263,7 @@ func : square_double_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : square_grad +- backward_op : square_grad forward : square (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -2275,7 +2275,7 @@ backward : square_double_grad inplace : (out_grad -> x_grad) -- backward_api : squared_l2_norm_grad +- backward_op : squared_l2_norm_grad forward : squared_l2_norm(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -2285,13 +2285,13 @@ kernel : func : squared_l2_norm_grad -- backward_api : squeeze_double_grad +- backward_op : squeeze_double_grad forward : squeeze_grad(Tensor xshape, Tensor grad_out, IntArray axes) -> Tensor(grad_x) args : (Tensor grad_x_grad, IntArray axes) output : Tensor(grad_out_grad) invoke: squeeze(grad_x_grad, axes) -- backward_api : squeeze_grad +- backward_op : squeeze_grad forward : squeeze(Tensor x, IntArray axes) -> Tensor(out), Tensor(xshape) args : (Tensor xshape, Tensor out_grad, IntArray axes) output : Tensor(x_grad) @@ -2303,7 +2303,7 @@ inplace : (out_grad -> x_grad) backward: squeeze_double_grad -- backward_api : stack_grad +- backward_op : stack_grad forward : stack (Tensor[] x, int axis) -> Tensor(out) args : (Tensor[] x, Tensor out_grad, int axis) output : Tensor[](x_grad){x.size()} @@ -2315,7 +2315,7 @@ param : [out_grad, axis] no_need_buffer : x -- backward_api : strided_slice_grad +- backward_op : strided_slice_grad forward : strided_slice (Tensor x, int[] axes, IntArray starts, IntArray ends, IntArray strides) -> Tensor(out) args : (Tensor x, Tensor out_grad, int[] axes, IntArray starts, IntArray ends, IntArray strides) output : Tensor(x_grad) @@ -2326,7 +2326,7 @@ func : strided_slice_grad no_need_buffer : x -- backward_api : subtract_double_grad +- backward_op : subtract_double_grad forward : subtract_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) @@ -2339,7 +2339,7 @@ no_need_buffer : y, grad_out inplace : (grad_x_grad -> grad_out_grad) -- backward_api : subtract_grad +- backward_op : subtract_grad forward : subtract (Tensor x, Tensor y) -> Tensor(out) args : (Tensor x, Tensor y, Tensor out_grad, int axis = -1) output : Tensor(x_grad), Tensor(y_grad) @@ -2352,13 +2352,13 @@ backward : subtract_double_grad inplace : (out_grad -> x_grad) -- backward_api : sum_double_grad +- backward_op : sum_double_grad forward : sum_grad (Tensor x, Tensor grad_out, IntArray dims, bool keep_dim, bool reduce_all=false) -> Tensor(grad_x) args : (Tensor grad_x_grad, IntArray dims={}, bool keep_dim=false) output : Tensor(grad_out_grad) invoke : sum(grad_x_grad, dims, grad_x_grad.dtype(), keep_dim) -- backward_api : sum_grad +- backward_op : sum_grad forward : sum (Tensor x, IntArray dims={}, DataType out_dtype=DataType::UNDEFINED, bool keep_dim=false) -> Tensor(out) args : (Tensor x, Tensor out_grad, IntArray dims, bool keep_dim, bool reduce_all=false) output : Tensor(x_grad) @@ -2370,7 +2370,7 @@ no_need_buffer : x backward : sum_double_grad -- backward_api : svd_grad +- backward_op : svd_grad forward : svd (Tensor x, bool full) -> Tensor(u), Tensor(s), Tensor(vh) args : (Tensor x, Tensor u, Tensor vh, Tensor s, Tensor u_grad, Tensor vh_grad, Tensor s_grad, bool full) output : Tensor(x_grad) @@ -2381,7 +2381,7 @@ func : svd_grad optional: u_grad, vh_grad, s_grad -- backward_api : swish_grad +- backward_op : swish_grad forward : swish (Tensor x, float beta=1.0) -> Tensor(out) args : (Tensor x, Tensor out_grad, float bete=1.0) output : Tensor(x_grad) @@ -2392,7 +2392,7 @@ func : swish_grad inplace : (out_grad -> x_grad) -- backward_api : sync_batch_norm_grad +- 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) @@ -2404,7 +2404,7 @@ data_type : out_grad optional : reserve_space -- backward_api : take_along_axis_grad +- backward_op : take_along_axis_grad forward : take_along_axis (Tensor x, Tensor index, int axis) -> Tensor(out) args : (Tensor x, Tensor index, Tensor out_grad, int axis) output : Tensor(x_grad) @@ -2414,7 +2414,7 @@ kernel : func : take_along_axis_grad -- backward_api : tan_grad +- backward_op : tan_grad forward : tan (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -2425,7 +2425,7 @@ func : tan_grad inplace : (out_grad -> x_grad) -- backward_api : tanh_double_grad +- backward_op : tanh_double_grad forward : tanh_grad (Tensor out, Tensor grad_out) -> Tensor(grad_x) args : (Tensor out, Tensor grad_out, Tensor grad_x_grad) output : Tensor(out_grad), Tensor(grad_out_grad) @@ -2437,7 +2437,7 @@ backward : tanh_triple_grad inplace : (grad_x_grad -> grad_out_grad) -- backward_api : tanh_grad +- backward_op : tanh_grad forward : tanh (Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -2449,7 +2449,7 @@ backward : tanh_double_grad inplace : (out_grad -> x_grad) -- backward_api : tanh_shrink_grad +- backward_op : tanh_shrink_grad forward : tanh_shrink (Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -2460,7 +2460,7 @@ func : tanh_shrink_grad inplace : (out_grad -> x_grad) -- backward_api : tanh_triple_grad +- backward_op : tanh_triple_grad forward : tanh_double_grad (Tensor out, Tensor grad_out_forward, Tensor grad_x_grad_forward) -> Tensor(grad_out_new), Tensor(grad_out_grad) args : (Tensor out, Tensor grad_out_forward, Tensor grad_x_grad_forward, Tensor grad_out_new_grad, Tensor grad_out_grad_grad) output : Tensor(out_grad), Tensor(grad_out_forward_grad), Tensor(grad_x_grad_forward_grad) @@ -2471,7 +2471,7 @@ func : tanh_triple_grad inplace : (grad_x_grad_forward -> grad_out_forward_grad) -- backward_api : temporal_shift_grad +- backward_op : temporal_shift_grad forward : temporal_shift(Tensor x, int seg_num, float shift_ratio, str data_format_str) -> Tensor(out) args : (Tensor out_grad, int seg_num, float shift_ratio, str data_format_str) output : Tensor(x_grad) @@ -2481,7 +2481,7 @@ kernel : func : temporal_shift_grad -- backward_api : thresholded_relu_grad +- backward_op : thresholded_relu_grad forward : thresholded_relu (Tensor x, float threshold) -> Tensor(out) args : (Tensor x, Tensor out_grad, float threshold) output : Tensor(x_grad) @@ -2492,13 +2492,13 @@ func : thresholded_relu_grad inplace : (out_grad -> x_grad) -- backward_api : tile_double_grad +- backward_op : tile_double_grad forward : tile_grad (Tensor x, Tensor grad_out, IntArray repeat_times) -> Tensor(grad_x) args : (Tensor grad_x_grad, IntArray repeat_times) output : Tensor(grad_out_grad) invoke : tile(grad_x_grad, repeat_times) -- backward_api : tile_grad +- backward_op : tile_grad forward : tile (Tensor x, IntArray repeat_times) -> Tensor(out) args : (Tensor x, Tensor out_grad, IntArray repeat_times) output : Tensor(x_grad) @@ -2510,7 +2510,7 @@ no_need_buffer : x backward : tile_double_grad -- backward_api : top_k_grad +- backward_op : top_k_grad forward : top_k (Tensor x, Scalar k, int axis = -1, bool largest = true, bool sorted = true) -> Tensor(out), Tensor(indices) args : (Tensor x, Tensor indices, Tensor out_grad, Scalar k = -1, int axis = -1, bool largest = true, bool sorted = true) output : Tensor(x_grad) @@ -2520,13 +2520,13 @@ kernel : func : top_k_grad -- backward_api : transpose_double_grad +- backward_op : transpose_double_grad forward : transpose_grad (Tensor grad_out, int[] axis) -> Tensor(grad_x) args : (Tensor grad_x_grad, int[] axis) output : Tensor(grad_out_grad) invoke : transpose(grad_x_grad, axis) -- backward_api : transpose_grad +- backward_op : transpose_grad forward : transpose (Tensor x, int[] axis) -> Tensor(out) args : (Tensor out_grad, int[] axis) output : Tensor(x_grad) @@ -2537,7 +2537,7 @@ func : transpose_grad backward : transpose_double_grad -- backward_api : triangular_solve_grad +- backward_op : 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) @@ -2547,7 +2547,7 @@ kernel : func : triangular_solve_grad -- backward_api : tril_triu_grad +- backward_op : tril_triu_grad forward : tril_triu(Tensor x, int diagonal, bool lower) -> Tensor(out) args : (Tensor out_grad, int diagonal, bool lower) output : Tensor(x_grad) @@ -2557,7 +2557,7 @@ kernel : func : tril_triu_grad -- backward_api : trilinear_interp_grad +- backward_op : trilinear_interp_grad forward : trilinear_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) @@ -2569,13 +2569,13 @@ func : trilinear_interp_grad data_type : output_grad -- backward_api : unbind_grad +- backward_op : unbind_grad forward : unbind (Tensor input, int axis) -> Tensor[](out) args : (Tensor[] out_grad, int axis) output : Tensor(input_grad) invoke : stack(out_grad, axis) -- backward_api : unfold_grad +- backward_op : unfold_grad forward : unfold (Tensor x, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations) -> Tensor(out) args : (Tensor x, Tensor out_grad, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations) output : Tensor(x_grad) @@ -2586,7 +2586,7 @@ func : unfold_grad no_need_buffer : x -- backward_api : uniform_random_inplace_grad +- backward_op : uniform_random_inplace_grad forward : uniform_random_inplace(Tensor x, float min, float max, int seed, int diag_num, int diag_step, float diag_val) -> Tensor(out) args : (Tensor out_grad, float min, float max, int seed, int diag_num, int diag_step, float diag_val) output : Tensor(x_grad) @@ -2596,13 +2596,13 @@ func : uniform_random_inplace_grad inplace : (out_grad -> x_grad) -- backward_api : unsqueeze_double_grad +- backward_op : unsqueeze_double_grad forward : unsqueeze_grad(Tensor xshape, Tensor grad_out, IntArray axes) -> Tensor(grad_x) args : (Tensor grad_x_grad, IntArray axes) output : Tensor(grad_out_grad) invoke : unsqueeze(grad_x_grad, axes) -- backward_api : unsqueeze_grad +- backward_op : unsqueeze_grad forward : unsqueeze(Tensor x, IntArray axes) -> Tensor(out), Tensor(xshape) args : (Tensor xshape, Tensor out_grad, IntArray axes) output : Tensor(x_grad) @@ -2615,7 +2615,7 @@ inplace : (out_grad -> x_grad) backward : unsqueeze_double_grad -- backward_api : unstack_grad +- backward_op : unstack_grad forward : unstack (Tensor x, int axis, int num) -> Tensor[](out) args : (Tensor[] out_grad, int axis) output : Tensor(x_grad) @@ -2625,7 +2625,7 @@ kernel : func : unstack_grad -- backward_api : warpctc_grad +- backward_op : warpctc_grad forward : warpctc (Tensor logits, Tensor label, Tensor logits_length, Tensor labels_length, int blank, bool norm_by_times) -> Tensor(loss), Tensor(warpctcgrad) args : (Tensor logits, Tensor logits_length, Tensor warpctcgrad, Tensor loss_grad, int blank, bool norm_by_times) output : Tensor(logits_grad) @@ -2637,7 +2637,7 @@ optional : logits_length no_need_buffer : logits -- backward_api : where_grad +- backward_op : where_grad forward : where (Tensor condition, Tensor x, Tensor y) -> Tensor(out) args : (Tensor condition, Tensor x, Tensor y, Tensor out_grad) output : Tensor(x_grad), Tensor(y_grad) @@ -2648,7 +2648,7 @@ func : where_grad no_need_buffer : x, y -- backward_api : yolov3_loss_grad +- backward_op : yolov3_loss_grad forward : yolov3_loss(Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, int[] anchors, int[] anchor_mask, int class_num, float ignore_thresh, int downsample_ratio, bool use_label_smooth=true, float scale_x_y=1.0) -> Tensor(loss), Tensor(objectness_mask), Tensor(gt_match_mask) args : (Tensor x, Tensor gt_box, Tensor gt_label, Tensor gt_score, Tensor objectness_mask, Tensor gt_match_mask, Tensor loss_grad, int[] anchors, int[] anchor_mask, int class_num, float ignore_thresh, int downsample_ratio, bool use_label_smooth=true, float scale_x_y=1.0) output : Tensor(x_grad), Tensor(gt_box_grad), Tensor(gt_label_grad), Tensor(gt_score_grad) @@ -2658,7 +2658,7 @@ func : yolov3_loss_grad optional : gt_score -- backward_api: fold_grad +- backward_op: fold_grad forward: fold (Tensor x, int[] output_sizes, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations) -> Tensor(out) args: (Tensor x, Tensor out_grad, int[] output_sizes, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations) output: Tensor(x_grad) @@ -2669,7 +2669,7 @@ func: fold_grad no_need_buffer : x -- backward_api: unpool3d_grad +- backward_op: unpool3d_grad forward: unpool3d (Tensor x, Tensor indices, int[] ksize, int[] strides, int[] padding, int[] output_size, str data_format) -> Tensor(out) args: (Tensor x, Tensor indices, Tensor out, Tensor out_grad, int[] ksize, int[] strides, int[] padding, int[] output_size, str data_format) output: Tensor(x_grad) @@ -2680,7 +2680,7 @@ func: unpool3d_grad data_type: x -- backward_api: unpool_grad +- backward_op: unpool_grad forward: unpool (Tensor x, Tensor indices, int[] ksize, int[] strides, int[] padding, IntArray output_size, str data_format) -> Tensor(out) args: (Tensor x, Tensor indices, Tensor out, Tensor out_grad, int[] ksize, int[] strides, int[] padding, IntArray output_size, str data_format) output: Tensor(x_grad) diff --git a/paddle/phi/api/yaml/sparse_backward.yaml b/paddle/phi/api/yaml/sparse_backward.yaml index e6242f178e5..4bc306388d1 100644 --- a/paddle/phi/api/yaml/sparse_backward.yaml +++ b/paddle/phi/api/yaml/sparse_backward.yaml @@ -1,4 +1,4 @@ -- backward_api : abs_grad +- backward_op : abs_grad forward : tanh(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -6,7 +6,7 @@ func : abs_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, abs_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : acos_grad +- backward_op : acos_grad forward : acos(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -14,7 +14,7 @@ func : acos_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, acos_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : acosh_grad +- backward_op : acosh_grad forward : acosh(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -22,7 +22,7 @@ func : acosh_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, acosh_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : add_grad +- 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) @@ -30,7 +30,7 @@ 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} -- backward_api : addmm_grad +- 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) @@ -40,7 +40,7 @@ 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_api : asin_grad +- backward_op : asin_grad forward : asin(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -48,7 +48,7 @@ func : asin_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, asin_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : asinh_grad +- backward_op : asinh_grad forward : asinh(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -56,7 +56,7 @@ func : asinh_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, asinh_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : atan_grad +- backward_op : atan_grad forward : atan(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -64,7 +64,7 @@ func : atan_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, atan_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : atanh_grad +- backward_op : atanh_grad forward : atanh(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -72,7 +72,7 @@ func : atanh_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, atanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : cast_grad +- 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) @@ -81,14 +81,14 @@ cast_csr_grad {sparse_csr, sparse_csr -> sparse_csr} data_type : out_grad -- backward_api : conv3d_coo_grad +- backward_op : conv3d_coo_grad forward : conv3d_coo (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) kernel : func : conv3d_coo_grad{sparse_coo, dense, sparse_coo, dense, dense, sparse_coo -> sparse_coo, dense} -- backward_api : divide_grad +- 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) @@ -96,13 +96,13 @@ 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_api : divide_scalar_grad +- 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_api : expm1_grad +- backward_op : expm1_grad forward : expm1(Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -110,7 +110,7 @@ func : expm1_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, expm1_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : leaky_relu_grad +- 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) @@ -118,7 +118,7 @@ func : leaky_relu_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, leaky_relu_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : log1p_grad +- backward_op : log1p_grad forward : log1p(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -126,14 +126,14 @@ func : log1p_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, log1p_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : masked_matmul_grad +- 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) kernel : func : masked_matmul_csr_grad{dense, dense, sparse_csr -> dense, dense} -- backward_api : matmul_grad +- 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) @@ -143,14 +143,14 @@ 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_api : maxpool_grad +- 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) kernel : func : maxpool_coo_grad {sparse_coo, dense, dense, sparse_coo, sparse_coo -> sparse_coo} -- backward_api : multiply_grad +- 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) @@ -158,7 +158,7 @@ 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_api : mv_grad +- 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) @@ -166,7 +166,7 @@ func : mv_coo_grad{sparse_coo, dense, dense -> sparse_coo, dense}, mv_csr_grad{sparse_csr, dense, dense -> sparse_csr, dense} -- backward_api : pow_grad +- backward_op : pow_grad forward : pow(Tensor x, float factor) -> Tensor(out) args : (Tensor x, Tensor out_grad, float factor) output : Tensor(x_grad) @@ -174,7 +174,7 @@ func : pow_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, pow_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : relu6_grad +- backward_op : relu6_grad forward : relu6(Tensor x, float threshold) -> Tensor(out) args : (Tensor out, Tensor out_grad, float threshold) output : Tensor(x_grad) @@ -182,7 +182,7 @@ func : relu6_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, relu6_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : relu_grad +- backward_op : relu_grad forward : relu(Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -190,13 +190,13 @@ func : relu_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, relu_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : scale_grad +- 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) invoke : scale(out_grad, scale, 0.0, true) -- backward_api : sin_grad +- backward_op : sin_grad forward : sin(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -204,7 +204,7 @@ func : sin_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, sin_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : sinh_grad +- backward_op : sinh_grad forward : sinh(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -212,21 +212,21 @@ func : sinh_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, sinh_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : softmax_grad +- 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) kernel : func : softmax_csr_grad{sparse_csr, sparse_csr -> sparse_csr} -- backward_api : sparse_coo_tensor_grad +- 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) kernel : func : sparse_coo_tensor_grad{dense, sparse_coo -> dense} -- backward_api : sqrt_grad +- backward_op : sqrt_grad forward : sqrt(Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -234,7 +234,7 @@ func : sqrt_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, sqrt_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : square_grad +- backward_op : square_grad forward : square(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -242,7 +242,7 @@ func : square_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, square_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : subtract_grad +- 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) @@ -250,7 +250,7 @@ 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_api : tan_grad +- backward_op : tan_grad forward : tan(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) @@ -258,7 +258,7 @@ func : tan_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, tan_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : tanh_grad +- backward_op : tanh_grad forward : tanh(Tensor x) -> Tensor(out) args : (Tensor out, Tensor out_grad) output : Tensor(x_grad) @@ -266,28 +266,28 @@ func : tanh_coo_grad {sparse_coo, sparse_coo -> sparse_coo}, tanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr} -- backward_api : to_dense_grad +- backward_op : to_dense_grad forward : to_dense(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) kernel : func : coo_to_dense_grad{sparse_coo, dense -> sparse_coo} -- backward_api : to_sparse_coo_grad +- 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) kernel : func : coo_to_dense { sparse_coo -> dense } -- backward_api : values_grad +- backward_op : values_grad forward : values_coo(Tensor x) -> Tensor(out) args : (Tensor x, Tensor out_grad) output : Tensor(x_grad) kernel : func : values_coo_grad{sparse_coo, dense-> sparse_coo} -- backward_api: fused_attention_grad +- 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) -- GitLab