FM / operators

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators/

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators

Name	Size	Mode	Actions
abs_op.h	705	0644	edit dl rm
accumulate_op.h	1073	0644	edit dl rm
accuracy_op.h	652	0644	edit dl rm
acos_op.h	711	0644	edit dl rm
activation_ops_cudnn.h	4122	0644	edit dl rm
affine_channel_op.h	3450	0644	edit dl rm
alias_with_name.h	1234	0644	edit dl rm
apmeter_op.h	1027	0644	edit dl rm
arg_ops.h	2319	0644	edit dl rm
asin_op.h	711	0644	edit dl rm
assert_op.h	1335	0644	edit dl rm
async_net_barrier_op.h	904	0644	edit dl rm
atan_op.h	711	0644	edit dl rm
batch_box_cox_op.h	2287	0644	edit dl rm
batch_bucketize_op.h	720	0644	edit dl rm
batch_gather_ops.h	5264	0644	edit dl rm
batch_matmul_op.h	9602	0644	edit dl rm
batch_moments_op.h	3364	0644	edit dl rm
batch_permutation_op.h	954	0644	edit dl rm
batch_sparse_to_dense_op.h	6147	0644	edit dl rm
bbox_transform_op.h	2668	0644	edit dl rm
bisect_percentile_op.h	4921	0644	edit dl rm
boolean_mask_ops.h	2665	0644	edit dl rm
boolean_unmask_ops.h	378	0644	edit dl rm
box_with_nms_limit_op.h	4960	0644	edit dl rm
bucketize_op.h	1361	0644	edit dl rm
byte_weight_dequant_op.h	1722	0644	edit dl rm
cast_op.h	1393	0644	edit dl rm
cbrt_op.h	723	0644	edit dl rm
cc_bmm_bg_op.h	3894	0644	edit dl rm
ceil_op.h	782	0644	edit dl rm
channel_backprop_stats_op.h	737	0644	edit dl rm
channel_shuffle_op.h	1902	0644	edit dl rm
channel_stats_op.h	1807	0644	edit dl rm
clip_op.h	1639	0644	edit dl rm
collect_and_distribute_fpn_rpn_proposals_op.h	6875	0644	edit dl rm
concat_split_op.h	11850	0644	edit dl rm
conditional_op.h	487	0644	edit dl rm
conv_op.h	3125	0644	edit dl rm
conv_op_cache_cudnn.h	1935	0644	edit dl rm
conv_op_impl.h	28729	0644	edit dl rm
conv_op_shared.h	672	0644	edit dl rm
conv_pool_op_base.h	32109	0644	edit dl rm
conv_transpose_op.h	1727	0644	edit dl rm
conv_transpose_op_impl.h	18264	0644	edit dl rm
conv_transpose_op_mobile.h	1470	0644	edit dl rm
conv_transpose_op_mobile_impl.h	19587	0644	edit dl rm
conv_transpose_unpool_op_base.h	10303	0644	edit dl rm
copy_op.h	1296	0644	edit dl rm
copy_rows_to_tensor_op.h	2599	0644	edit dl rm
cosh_op.h	711	0644	edit dl rm
cosine_embedding_criterion_op.h	1127	0644	edit dl rm
cos_op.h	705	0644	edit dl rm
counter_ops.h	4596	0644	edit dl rm
create_scope_op.h	5232	0644	edit dl rm
cross_entropy_op.h	4420	0644	edit dl rm
ctc_beam_search_decoder_op.h	1102	0644	edit dl rm
ctc_greedy_decoder_op.h	817	0644	edit dl rm
cube_op.h	723	0644	edit dl rm
dataset_ops.h	5501	0644	edit dl rm
data_couple.h	464	0644	edit dl rm
deform_conv_op.h	3543	0644	edit dl rm
deform_conv_op_impl.h	13171	0644	edit dl rm
dense_vector_to_id_list_op.h	1797	0644	edit dl rm
distance_op.h	8419	0644	edit dl rm
do_op.h	6981	0644	edit dl rm
dropout_op.h	1516	0644	edit dl rm
elementwise_add_op.h	2024	0644	edit dl rm
elementwise_div_op.h	1224	0644	edit dl rm
elementwise_linear_op.h	1170	0644	edit dl rm
elementwise_logical_ops.h	5083	0644	edit dl rm
elementwise_mul_op.h	1224	0644	edit dl rm
elementwise_ops.h	19115	0644	edit dl rm
elementwise_ops_utils.h	1008	0644	edit dl rm
elementwise_op_test.h	9237	0644	edit dl rm
elementwise_sub_op.h	2025	0644	edit dl rm
elu_op.h	875	0644	edit dl rm
enforce_finite_op.h	2303	0644	edit dl rm
ensure_clipped_op.h	1608	0644	edit dl rm
ensure_cpu_output_op.h	1465	0644	edit dl rm
erf_op.h	751	0644	edit dl rm
expand_op.h	3877	0644	edit dl rm
expand_squeeze_dims_op.h	3451	0644	edit dl rm
exp_op.h	425	0644	edit dl rm
fc_inference.h	775	0644	edit dl rm
feature_maps_ops.h	32437	0644	edit dl rm
feed_blob_op.h	802	0644	edit dl rm
filler_op.h	18431	0644	edit dl rm
find_duplicate_elements_op.h	1563	0644	edit dl rm
find_op.h	2055	0644	edit dl rm
flatten_op.h	1525	0644	edit dl rm
flexible_top_k.h	936	0644	edit dl rm
floor_op.h	788	0644	edit dl rm
free_op.h	777	0644	edit dl rm
fully_connected_op.h	9351	0644	edit dl rm
fused_rowwise_8bit_conversion_ops.h	6601	0644	edit dl rm
fused_rowwise_nbitfake_conversion_ops.h	4375	0644	edit dl rm
fused_rowwise_nbit_conversion_ops.h	8723	0644	edit dl rm
fused_rowwise_random_quantization_ops.h	2607	0644	edit dl rm
gather_fused_8bit_rowwise_op.h	2179	0644	edit dl rm
gather_op.h	7505	0644	edit dl rm
gather_ranges_to_dense_op.h	8188	0644	edit dl rm
gelu_op.h	1452	0644	edit dl rm
generate_proposals_op.h	6256	0644	edit dl rm
generate_proposals_op_util_boxes.h	14309	0644	edit dl rm
generate_proposals_op_util_nms.h	26214	0644	edit dl rm
generate_proposals_op_util_nms_gpu.h	2128	0644	edit dl rm
given_tensor_byte_string_to_uint8_fill_op.h	2150	0644	edit dl rm
given_tensor_fill_op.h	3002	0644	edit dl rm
glu_op.h	1458	0644	edit dl rm
group_norm_op.h	8967	0644	edit dl rm
gru_unit_op.h	6626	0644	edit dl rm
half_float_ops.h	2732	0644	edit dl rm
hard_sigmoid_op.h	994	0644	edit dl rm
heatmap_max_keypoint_op.h	939	0644	edit dl rm
histogram_op.h	2421	0644	edit dl rm
h_softmax_op.h	4954	0644	edit dl rm
if_op.h	1764	0644	edit dl rm
im2col_op.h	8943	0644	edit dl rm
index_hash_ops.h	2232	0644	edit dl rm
index_ops.h	3155	0644	edit dl rm
inference_lstm_op.h	9881	0644	edit dl rm
instance_norm_op.h	7441	0644	edit dl rm
integral_image_op.h	923	0644	edit dl rm
is_empty_op.h	558	0644	edit dl rm
jsd_op.h	721	0644	edit dl rm
key_split_ops.h	1400	0644	edit dl rm
layer_norm_op.h	8098	0644	edit dl rm
leaky_relu_op.h	1111	0644	edit dl rm
lengths_pad_op.h	2574	0644	edit dl rm
lengths_reducer_fused_8bit_rowwise_ops.h	5532	0644	edit dl rm
lengths_reducer_fused_nbit_rowwise_ops.h	23465	0644	edit dl rm
lengths_reducer_ops.h	23315	0644	edit dl rm
lengths_reducer_rowwise_8bit_ops.h	6180	0644	edit dl rm
lengths_tile_op.h	582	0644	edit dl rm
lengths_top_k_op.h	1358	0644	edit dl rm
length_split_op.h	2259	0644	edit dl rm
listwise_l2r_op.h	1677	0644	edit dl rm
load_save_op.h	14091	0644	edit dl rm
load_save_op_util.h	1642	0644	edit dl rm
locally_connected_op.h	3872	0644	edit dl rm
locally_connected_op_impl.h	26495	0644	edit dl rm
locally_connected_op_util.h	1332	0644	edit dl rm
local_response_normalization_op.h	2804	0644	edit dl rm
log1p_op.h	717	0644	edit dl rm
logit_op.h	1129	0644	edit dl rm
log_op.h	431	0644	edit dl rm
loss_op.h	1058	0644	edit dl rm
lpnorm_op.h	1279	0644	edit dl rm
lstm_unit_op.h	6733	0644	edit dl rm
lstm_utils.h	9424	0644	edit dl rm
map_ops.h	8011	0644	edit dl rm
margin_ranking_criterion_op.h	1113	0644	edit dl rm
matmul_op.h	2843	0644	edit dl rm
max_pool_with_index_gpu.h	1155	0644	edit dl rm
mean_op.h	3252	0644	edit dl rm
merge_id_lists_op.h	2570	0644	edit dl rm
minmax_ops.h	3829	0644	edit dl rm
mish_op.h	794	0644	edit dl rm
mod_op.h	984	0644	edit dl rm
moments_op.h	4051	0644	edit dl rm
multi_class_accuracy_op.h	539	0644	edit dl rm
negate_gradient_op.h	566	0644	edit dl rm
negative_op.h	451	0644	edit dl rm
ngram_ops.h	2644	0644	edit dl rm
normalize_l1_op.h	1075	0644	edit dl rm
normalize_op.h	3013	0644	edit dl rm
no_default_engine_op.h	1063	0644	edit dl rm
numpy_tile_op.h	3643	0644	edit dl rm
one_hot_ops.h	2562	0644	edit dl rm
onnx_while_op.h	10655	0644	edit dl rm
operator_fallback_gpu.h	4155	0644	edit dl rm
op_utils_cudnn.h	2112	0644	edit dl rm
order_switch_ops.h	2149	0644	edit dl rm
pack_rnn_sequence_op.h	3074	0644	edit dl rm
pack_segments.h	2729	0644	edit dl rm
pad_op.h	2902	0644	edit dl rm
partition_ops.h	9958	0644	edit dl rm
percentile_op.h	1009	0644	edit dl rm
perplexity_op.h	447	0644	edit dl rm
piecewise_linear_transform_op.h	8281	0644	edit dl rm
pool_op.h	8525	0644	edit dl rm
pool_op_util.h	1105	0644	edit dl rm
pow_op.h	4677	0644	edit dl rm
prefetch_op.h	4661	0644	edit dl rm
prelu_op.h	1067	0644	edit dl rm
prepend_dim_op.h	2760	0644	edit dl rm
quantile_op.h	4120	0644	edit dl rm
quant_decode_op.h	5370	0644	edit dl rm
rank_loss_op.h	820	0644	edit dl rm
reciprocal_op.h	721	0644	edit dl rm
reducer_functors.h	24556	0644	edit dl rm
reduce_front_back_max_ops.h	4399	0644	edit dl rm
reduce_front_back_sum_mean_ops.h	5337	0644	edit dl rm
reduce_ops.h	9962	0644	edit dl rm
reduction_ops.h	5944	0644	edit dl rm
relu_n_op.h	990	0644	edit dl rm
relu_op.h	624	0644	edit dl rm
remove_data_blocks_op.h	2651	0644	edit dl rm
replace_nan_op.h	1170	0644	edit dl rm
reshape_op.h	5723	0644	edit dl rm
resize_3d_op.h	2677	0644	edit dl rm
resize_op.h	2307	0644	edit dl rm
reverse_packed_segs_op.h	2772	0644	edit dl rm
rmac_regions_op.h	708	0644	edit dl rm
rms_norm_op.h	2968	0644	edit dl rm
roi_align_gradient_op.h	1486	0644	edit dl rm
roi_align_op.h	2857	0644	edit dl rm
roi_align_rotated_gradient_op.h	1369	0644	edit dl rm
roi_align_rotated_op.h	1636	0644	edit dl rm
roi_pool_op.h	2503	0644	edit dl rm
rowmul_op.h	1947	0644	edit dl rm
rsqrt_op.h	729	0644	edit dl rm
scale_blobs_op.h	1458	0644	edit dl rm
scale_op.h	1019	0644	edit dl rm
segment_reduction_op.h	71022	0644	edit dl rm
self_binning_histogram_op.h	6258	0644	edit dl rm
selu_op.h	1545	0644	edit dl rm
sequence_ops.h	8264	0644	edit dl rm
shape_op.h	1638	0644	edit dl rm
sigmoid_op.h	639	0644	edit dl rm
sinh_op.h	711	0644	edit dl rm
sinusoid_position_encoding_op.h	2834	0644	edit dl rm
sin_op.h	705	0644	edit dl rm
slice_op.h	10071	0644	edit dl rm
softmax_op.h	1174	0644	edit dl rm
softmax_utils.h	447	0644	edit dl rm
softmax_with_loss_op.h	2883	0644	edit dl rm
softplus_op.h	781	0644	edit dl rm
softsign_op.h	675	0644	edit dl rm
space_batch_op.h	6848	0644	edit dl rm
sparse_dropout_with_replacement_op.h	1122	0644	edit dl rm
sparse_itemwise_dropout_with_replacement_op.h	1163	0644	edit dl rm
sparse_lp_regularizer_op.h	1130	0644	edit dl rm
sparse_normalize_op.h	834	0644	edit dl rm
sparse_to_dense_mask_op.h	10051	0644	edit dl rm
sparse_to_dense_op.h	3977	0644	edit dl rm
spatial_batch_norm_op.h	15175	0644	edit dl rm
spatial_softmax_with_loss_op.h	2182	0644	edit dl rm
sqrt_op.h	448	0644	edit dl rm
sqr_op.h	431	0644	edit dl rm
square_root_divide_op.h	1857	0644	edit dl rm
stats_put_ops.h	2813	0644	edit dl rm
stop_gradient.h	548	0644	edit dl rm
string_ops.h	2067	0644	edit dl rm
stump_func_op.h	2112	0644	edit dl rm
summarize_op.h	1875	0644	edit dl rm
swish_op.h	772	0644	edit dl rm
tanh_op.h	723	0644	edit dl rm
tan_op.h	705	0644	edit dl rm
tensor_protos_db_input.h	3633	0644	edit dl rm
text_file_reader_utils.h	2900	0644	edit dl rm
thresholded_relu_op.h	1137	0644	edit dl rm
tile_op.h	8741	0644	edit dl rm
top_k.h	1061	0644	edit dl rm
transpose_op.h	2082	0644	edit dl rm
tt_linear_op.h	6501	0644	edit dl rm
unique_ops.h	1666	0644	edit dl rm
unsafe_coalesce.h	2481	0644	edit dl rm
upsample_op.h	2246	0644	edit dl rm
utility_ops.h	49994	0644	edit dl rm
variable_length_sequence_padding.h	1378	0644	edit dl rm
weighted_multi_sampling_op.h	602	0644	edit dl rm
weighted_sample_op.h	739	0644	edit dl rm
while_op.h	1961	0644	edit dl rm
zero_gradient_op.h	347	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/operators/elementwise_ops.h (19115B)

#ifndef CAFFE2_OPERATORS_ELEMENTWISE_OPS_H_
#define CAFFE2_OPERATORS_ELEMENTWISE_OPS_H_

#include <iterator>
#include <string>
#include <tuple>
#include <vector>

#include "caffe2/core/common_omp.h"
#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
#include "caffe2/operators/elementwise_ops_utils.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"

namespace caffe2 {

using NumericTypes = TensorTypes<int32_t, int64_t, float, double>;
using IntTypes = TensorTypes<int32_t, int64_t>;
using BoolTypes = TensorTypes<bool>;
using IntBoolTypes = TensorTypes<int32_t, int64_t, bool>; // discrete types

struct SameTypeAsInput {
  template <typename T>
  using type = T;
};

template <typename R>
struct FixedType {
  template <typename T>
  using type = R;
};

template <
    typename InputTypes,
    class Context,
    class Functor,
    class OutputTypeMap = SameTypeAsInput>
class UnaryElementwiseWithArgsOp final : public Operator<Context> {
 public:
  USE_OPERATOR_CONTEXT_FUNCTIONS;

template <class... Args>
  explicit UnaryElementwiseWithArgsOp(Args&&... args)
      : Operator<Context>(std::forward<Args>(args)...), functor_(*this) {}

bool RunOnDevice() override {
    return DispatchHelper<InputTypes>::call(this, Input(0));
  }

template <typename T>
  bool DoRunWithType() {
    const auto& X = Input(0);

auto* Y = Output(
        0, X.sizes(), at::dtype<typename OutputTypeMap::template type<T>>());
    return functor_(
        X.numel(),
        X.template data<T>(),
        Y->template mutable_data<typename OutputTypeMap::template type<T>>(),
        &context_);
  }

private:
  Functor functor_;
};

// UnaryFunctorWithDefaultCtor is a functor that can be used as the functor of
// an UnaryElementwiseWithArgsOp. It simply forwards the operator() call into
// another functor that doesn't accept arguments in its constructor.
template <class Functor>
struct UnaryFunctorWithDefaultCtor {
  explicit UnaryFunctorWithDefaultCtor(OperatorBase& /* op */) {}

template <typename TIn, typename TOut, class Context>
  bool operator()(const int size, const TIn* X, TOut* Y, Context* context)
      const {
    return functor(size, X, Y, context);
  }

Functor functor{};
};

// UnaryElementwiseOp is a wrapper around UnaryElementwiseWithArgsOp, with the
// difference that it takes a functor with default constructor, e.g. that does
// not need to take into consideration any arguments during operator creation.
template <
    typename InputTypes,
    class Context,
    class Functor,
    class OutputTypeMap = SameTypeAsInput>
using UnaryElementwiseOp = UnaryElementwiseWithArgsOp<
    InputTypes,
    Context,
    UnaryFunctorWithDefaultCtor<Functor>,
    OutputTypeMap>;

template <
    typename InputTypes,
    class Context,
    class Functor,
    class OutputTypeMap = SameTypeAsInput>
class BinaryElementwiseWithArgsOp final : public Operator<Context> {
 public:
  USE_OPERATOR_CONTEXT_FUNCTIONS;

template <class... Args>
  explicit BinaryElementwiseWithArgsOp(Args&&... args)
      : Operator<Context>(std::forward<Args>(args)...),
        OP_SINGLE_ARG(bool, "broadcast", legacy_broadcast_, false),
        OP_SINGLE_ARG(int, "axis", axis_, -1),
        OP_SINGLE_ARG(string, "axis_str", axis_str_, string("")),
        OP_SINGLE_ARG(string, "order", order_, "NCHW"),
        functor_(*this) {
    if (legacy_broadcast_) {
      if (axis_ != -1) {
        // Get axis from an explicit axis argument.
        CAFFE_ENFORCE_EQ(
            axis_str_.size(),
            0U,
            "Args axis and axis_str cannot be used simultaneously.");
      } else if (axis_str_.size()) {
        // Get the axis index semantically.
        CAFFE_ENFORCE_EQ(
            axis_str_.size(), 1U, "Unsupported axis string", axis_str_);
        const size_t semantic_axis_ = order_.find(axis_str_);
        CAFFE_ENFORCE_NE(
            semantic_axis_,
            string::npos,
            "Unrecognizable axis string ",
            axis_str_,
            " from order string ",
            order_);
        axis_ = semantic_axis_;
      } else {
        CAFFE_ENFORCE(
            axis_ == -1 && axis_str_.empty(),
            "Do not specify axis or axis_str if broadcast is not enabled.");
      }
    }
  }

bool RunOnDevice() override {
    return DispatchHelper<InputTypes>::call(this, Input(0));
  }

template <typename T>
  bool DoRunWithType() {
    const auto& A = Input(0);
    const auto& B = Input(1);

const T* A_data = A.template data<T>();
    const T* B_data = B.template data<T>();
    std::vector<int> A_dims;
    std::vector<int> B_dims;
    std::vector<int64_t> C_dims;

if (legacy_broadcast_) {
      CAFFE_ENFORCE(
          !IsInputOutputAlias(1, 0),
          "In-place is allowed only with the first tensor when "
          "legacy-broadcasting");
      C_dims = A.sizes().vec();
      if (B.numel() == 1) {
        A_dims = {static_cast<int>(A.numel())};
        B_dims = {1};
      } else {
        size_t pre, n, post;
        std::tie(pre, n, post) =
            elementwise_ops_utils::ComputeLegacyBroadcastSizes(A, B, axis_);
        A_dims = {
            static_cast<int>(pre), static_cast<int>(n), static_cast<int>(post)};
        B_dims = {static_cast<int>(n), 1};
      }
    } else {
      std::copy(
          A.sizes().cbegin(), A.sizes().cend(), std::back_inserter(A_dims));
      std::copy(
          B.sizes().cbegin(), B.sizes().cend(), std::back_inserter(B_dims));
      // TODO: change the types to vector<int64_t>
      auto C_dims_int =
          elementwise_ops_utils::ComputeBinaryBroadcastForwardDims(
              A_dims, B_dims);
      std::copy(
          C_dims_int.cbegin(), C_dims_int.cend(), std::back_inserter(C_dims));
      if (IsInputOutputAlias(0, 0)) {
        CAFFE_ENFORCE_EQ(C_dims_int, A_dims);
      } else if (IsInputOutputAlias(1, 0)) {
        CAFFE_ENFORCE_EQ(C_dims_int, B_dims);
      }
    }

auto* C = Output(
        0, C_dims, at::dtype<typename OutputTypeMap::template type<T>>());
    auto* C_data =
        C->template mutable_data<typename OutputTypeMap::template type<T>>();
    return functor_.Forward(A_dims, B_dims, A_data, B_data, C_data, &context_);
  }

private:
  const bool legacy_broadcast_;
  int axis_;
  const std::string axis_str_;
  const std::string order_;

Functor functor_;
};

template <
    typename InputTypes,
    class Context,
    class Functor,
    class OutputTypeMap = SameTypeAsInput,
    class GradientTypeMap = SameTypeAsInput>
class BinaryElementwiseWithArgsGradientOp final : public Operator<Context> {
 public:
  USE_OPERATOR_CONTEXT_FUNCTIONS;

template <class... Args>
  explicit BinaryElementwiseWithArgsGradientOp(Args&&... args)
      : Operator<Context>(std::forward<Args>(args)...),
        OP_SINGLE_ARG(bool, "broadcast", legacy_broadcast_, false),
        OP_SINGLE_ARG(int, "axis", axis_, -1),
        OP_SINGLE_ARG(string, "axis_str", axis_str_, ""),
        OP_SINGLE_ARG(string, "order", order_, "NCHW"),
        functor_(*this) {
    if (legacy_broadcast_) {
      if (axis_ != -1) {
        // Get axis from an explicit axis argument.
        CAFFE_ENFORCE_EQ(
            axis_str_.size(),
            0U,
            "Args axis and axis_str cannot be used simultaneously.");
      } else if (axis_str_.size()) {
        // Get the axis index semantically.
        CAFFE_ENFORCE_EQ(
            axis_str_.size(), 1U, "Unsupported axis string", axis_str_);
        const size_t semantic_axis_ = order_.find(axis_str_);
        CAFFE_ENFORCE_NE(
            semantic_axis_,
            string::npos,
            "Unrecognizable axis string ",
            axis_str_,
            " from order string ",
            order_);
        axis_ = semantic_axis_;
      } else {
        CAFFE_ENFORCE(
            axis_ == -1 && axis_str_.empty(),
            "Do not specify axis or axis_str if broadcast is not enabled.");
      }
    }
  }

bool RunOnDevice() override {
    return DispatchHelper<InputTypes>::call(this, Input(1));
  }

template <typename T>
  bool DoRunWithType() {
    const auto& dC = Input(0);
    const auto& A = Input(1);
    const auto& B = Input(2);

vector<int> A_dims;
    vector<int> B_dims;
    if (legacy_broadcast_) {
      if (B.numel() == 1) {
        A_dims = {static_cast<int>(A.numel())};
        B_dims = {1};
      } else {
        size_t pre, n, post;
        std::tie(pre, n, post) =
            elementwise_ops_utils::ComputeLegacyBroadcastSizes(A, B, axis_);
        A_dims = {
            static_cast<int>(pre), static_cast<int>(n), static_cast<int>(post)};
        B_dims = {static_cast<int>(n), 1};
      }
    } else {
      std::copy(
          A.sizes().cbegin(), A.sizes().cend(), std::back_inserter(A_dims));
      std::copy(
          B.sizes().cbegin(), B.sizes().cend(), std::back_inserter(B_dims));
    }
    const typename OutputTypeMap::template type<T>* C_data = nullptr;
    if (InputSize() == 4) {
      const auto& C = Input(3);
      C_data = C.template data<typename OutputTypeMap::template type<T>>();
    }
    const auto* dC_data =
        dC.template data<typename GradientTypeMap::template type<T>>();
    const T* A_data = A.template data<T>();
    const T* B_data = B.template data<T>();
    auto* dA = Output(
        0, A.sizes(), at::dtype<typename GradientTypeMap::template type<T>>());
    auto* dB = Output(
        1, B.sizes(), at::dtype<typename GradientTypeMap::template type<T>>());
    auto* dA_data =
        dA->template mutable_data<typename GradientTypeMap::template type<T>>();
    auto* dB_data =
        dB->template mutable_data<typename GradientTypeMap::template type<T>>();
    return functor_.Backward(
        A_dims,
        B_dims,
        dC_data,
        A_data,
        B_data,
        C_data,
        dA_data,
        dB_data,
        &context_);
  }

private:
  const bool legacy_broadcast_;
  int axis_;
  const std::string axis_str_;
  const std::string order_;

Functor functor_;
};

template <class Functor>
struct BinaryFunctorWithDefaultCtor {
  explicit BinaryFunctorWithDefaultCtor(OperatorBase& /* op */) {}

template <typename TIn, typename TOut, class Context>
  bool Forward(
      const std::vector<int>& A_dims,
      const std::vector<int>& B_dims,
      const TIn* A_data,
      const TIn* B_data,
      TOut* C_data,
      Context* context) const {
    return functor.Forward(A_dims, B_dims, A_data, B_data, C_data, context);
  }

template <typename TGrad, typename TIn, typename TOut, class Context>
  bool Backward(
      const std::vector<int>& A_dims,
      const std::vector<int>& B_dims,
      const TGrad* dC_data,
      const TIn* A_data,
      const TIn* B_data,
      const TOut* C_data,
      TGrad* dA_data,
      TGrad* dB_data,
      Context* context) const {
    return functor.Backward(
        A_dims,
        B_dims,
        dC_data,
        A_data,
        B_data,
        C_data,
        dA_data,
        dB_data,
        context);
  }

Functor functor{};
};

template <class Functor>
struct BinaryFunctorWithBroadcastOptionsCtor {
  explicit BinaryFunctorWithBroadcastOptionsCtor(OperatorBase& op)
      : functor{op.GetSingleArgument<bool>("allow_broadcast_fastpath", false)} {}

Functor functor;
};

// BinaryElementwiseOp is a wrapper around BinaryElementwiseWithArgsOp, with the
// difference that it takes a functor with default constructor, e.g. that does
// not need to take into consideration any arguments during operator creation.
template <
    typename InputTypes,
    class Context,
    class Functor,
    class TypeMap = SameTypeAsInput>
using BinaryElementwiseOp = BinaryElementwiseWithArgsOp<
    InputTypes,
    Context,
    BinaryFunctorWithDefaultCtor<Functor>,
    TypeMap>;

// BinaryElementwiseGradientOp is a wrapper around
// BinaryElementwiseGradientWithArgsOp, with the difference that it takes a
// functor with default constructor, e.g. that does not need to take into
// consideration any arguments during operator creation.
template <
    typename InputTypes,
    class Context,
    class Functor,
    class OutputTypeMap = SameTypeAsInput,
    class GradientTypeMap = SameTypeAsInput>
using BinaryElementwiseGradientOp = BinaryElementwiseWithArgsGradientOp<
    InputTypes,
    Context,
    BinaryFunctorWithDefaultCtor<Functor>,
    OutputTypeMap,
    GradientTypeMap>;

// BinaryElementwiseBroadcastOp is a wrapper around BinaryElementwiseWithArgsOp,
// with the difference that it takes a functor with a constructor that accepts
// broadcast-related arguments (just a single boolean for whether broadcast
// fastpaths are allowed at the time this comment was written).
template <
    typename InputTypes,
    class Context,
    class Functor,
    class TypeMap = SameTypeAsInput>
using BinaryElementwiseBroadcastOp = BinaryElementwiseWithArgsOp<
    InputTypes,
    Context,
    BinaryFunctorWithBroadcastOptionsCtor<Functor>,
    TypeMap>;

// BinaryElementwiseGradientBroadcastOp is a wrapper around
// BinaryElementwiseWithArgsGradientOp, with the difference that it takes a
// functor with a constructor that accepts broadcast-related arguments (just a
// single boolean for whether broadcast fastpaths are allowed at the time this
// comment was written).
template <
    typename InputTypes,
    class Context,
    class Functor,
    class OutputTypeMap = SameTypeAsInput,
    class GradientTypeMap = SameTypeAsInput>
using BinaryElementwiseGradientBroadcastOp = BinaryElementwiseWithArgsGradientOp<
    InputTypes,
    Context,
    BinaryFunctorWithBroadcastOptionsCtor<Functor>,
    OutputTypeMap,
    GradientTypeMap>;

// Forward-only Unary Functors.
template <class Context>
struct NotFunctor {
  bool operator()(const int N, const bool* X, bool* Y, Context* context) const {
    math::Not(N, X, Y, context);
    return true;
  }
};

template <class Context>
struct SignFunctor {
  template <typename T>
  bool operator()(const int N, const T* X, T* Y, Context* context) const {
    math::Sign(N, X, Y, context);
    return true;
  }
};

// Forward-only Binary Functors.
#define C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(FunctorName) \
  template <class Context>                                   \
  struct FunctorName##Functor {                              \
    template <typename TIn, typename TOut>                   \
    bool Forward(                                            \
        const std::vector<int>& A_dims,                      \
        const std::vector<int>& B_dims,                      \
        const TIn* A,                                        \
        const TIn* B,                                        \
        TOut* C,                                             \
        Context* context) const {                            \
      math::FunctorName(                                     \
          A_dims.size(),                                     \
          A_dims.data(),                                     \
          B_dims.size(),                                     \
          B_dims.data(),                                     \
          A,                                                 \
          B,                                                 \
          C,                                                 \
          context);                                          \
      return true;                                           \
    }                                                        \
  };

// Compare functors.
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(EQ);
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(NE);
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(LT);
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(LE);
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(GT);
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(GE);

// Logical functors.
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(And);
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(Or);
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(Xor);

// Bitwise functors.
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(BitwiseAnd);
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(BitwiseOr);
C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR(BitwiseXor);

#undef C10_DECLARE_FORWARD_ONLY_BINARY_FUNCTOR

namespace SRLHelper {

template <typename T>
void sum2one(const T* a, T* y, size_t n);

template <typename T>
void RunWithBroadcastFront(const T* a, T* y, size_t pre, size_t n, CPUContext*);

template <typename T>
void RunWithBroadcastBack(const T* a, T* y, size_t post, size_t n, CPUContext*);

template <typename T>
void RunWithBroadcast2(
    const T* a,
    T* y,
    size_t pre,
    size_t n,
    size_t post,
    CPUContext*);

} // namespace SRLHelper

// Sum reduction operator that is used for computing the gradient in cases
// where the forward op is in broadcast mode.
template <class Context>
class SumReduceLikeOp final : public Operator<Context> {
 public:
  USE_OPERATOR_CONTEXT_FUNCTIONS;
  template <class... Args>
  explicit SumReduceLikeOp(Args&&... args)
      : Operator<Context>(std::forward<Args>(args)...),
        OP_SINGLE_ARG(int, "axis", axis_, -1),
        OP_SINGLE_ARG(string, "axis_str", axis_str_, ""),
        OP_SINGLE_ARG(string, "order", order_, "NCHW") {
    if (axis_ != -1) {
      // Get axis from an explicit axis argument.
      CAFFE_ENFORCE_EQ(
          axis_str_.size(),
          0U,
          "Args axis and axis_str cannot be used simultaneously.");
    } else if (axis_str_.size()) {
      // Get the axis index semantically.
      CAFFE_ENFORCE_EQ(
          axis_str_.size(), 1U, "Unsupported axis string", axis_str_);
      size_t semantic_axis = order_.find(axis_str_);
      CAFFE_ENFORCE_NE(
          semantic_axis,
          string::npos,
          "Unrecognizable axis string ",
          axis_str_,
          " from order string ",
          order_);
      axis_ = semantic_axis;
    }
  }

bool RunOnDevice() override {
    return DispatchHelper<TensorTypes<float, double>>::call(this, Input(0));
  }

template <typename T>
  bool DoRunWithType();

private:
  int axis_;
  string axis_str_;
  string order_;
  Tensor ones_{Context::GetDeviceType()};
  Tensor sum_buffer_{Context::GetDeviceType()};
};

} // namespace caffe2

#endif // CAFFE2_OPERATORS_ELEMENTWISE_OPS_H_

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