Enabled Eager AutoCodeGen for 40+ more operators (#37910)

* Rearranged Eager AutoCodeGen directory structure

* Removed USE_OP in Eager AutoCodeGen

* Enabled generation for Operators without Grad/Inputs/Outputs

* Resolved operators without input

* Fixed merge conflicts

* Enabled Eager AutoCodeGen for 10+ more operators

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -33,14 +33,18 @@ static std::unordered_map<std::string, paddle::framework::AttributeMap>
     operators_with_attrs = {};
 
 static std::unordered_set<std::string> operators_to_skip = {
-    "chunk_eval",  // Stupid tensor name
-    "minus",          "pull_sparse",     "pull_box_extended_sparse",
-    "pull_sparse_v2", "pull_box_sparse", "fused_attention",
-    "diag_v2",        "c_split"};
+    "minus",
+};
 
 static std::unordered_set<std::string> operators_to_codegen = {};
 static std::unordered_set<std::string> skipped_operators = {};
 
+static std::string LegalizeVariableName(const std::string& var_name) {
+  std::string ret = var_name;
+  std::replace(ret.begin(), ret.end(), '-', '_');  // replace all '-' to '_'
+  return ret;
+}
+
 static std::string AttrTypeToString(const proto::AttrType& type) {
   std::string ret;
   switch (type) {
@@ -608,6 +612,9 @@ static bool CollectGradInformationFromOpInfo(
   }
 
   VLOG(6) << "Prepared Default Attributes Map, size = " << default_attrs.size();
+  for (const auto& iter : default_attrs) {
+    VLOG(6) << iter.first;
+  }
 
   /* ---------------------------- */
   /* --------- Backward --------- */
@@ -1052,24 +1059,25 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
     const std::string& output_name = output.name();
     std::string out_tensor_str;
     size_t return_position = fwd_outputs_name_pos_map.at(output_name);
+    std::string output_varname = LegalizeVariableName(output_name);
 
     if (output.duplicable()) {
       const char* FWD_OUT_TENSORS_TEMPLATE =
           "  std::vector<egr::EagerTensor> %s = "
           "egr::EagerUtils::GetOutputs(outs[\"%s\"]);\n";
       out_tensor_str = paddle::string::Sprintf(FWD_OUT_TENSORS_TEMPLATE,
-                                               output_name, output_name);
+                                               output_varname, output_name);
       return_types[return_position] = "std::vector<egr::EagerTensor>";
     } else {
       const char* FWD_OUT_TENSOR_TEMPLATE =
           "  egr::EagerTensor %s = "
           "egr::EagerUtils::GetOutput(outs[\"%s\"][0]);\n";
       out_tensor_str = paddle::string::Sprintf(FWD_OUT_TENSOR_TEMPLATE,
-                                               output_name, output_name);
+                                               output_varname, output_name);
       return_types[return_position] = "egr::EagerTensor";
     }
 
-    return_contents[return_position] = output_name;
+    return_contents[return_position] = output_varname;
     generated_function_body += out_tensor_str;
   }
   generated_function_body += "\n";
@@ -1280,23 +1288,76 @@ static std::string GenerateGradNodeCCContents(
 
     if (grad_outs_slotname_map.count(grad_output_name)) {
       // Fwd Tensor
-      const std::string& fwd_input_name =
-          grad_outs_slotname_map.at(grad_output_name);
-      size_t fwd_input_position = fwd_inputs_name_pos_map.at(fwd_input_name);
-
-      if (duplicable_input_name_set.count(fwd_input_name)) {
-        const char* GRAD_OUTS_CONTENT_TEMPLATE =
-            "{ \"%s\", egr::EagerUtils::ConstructDuplicableOutput( "
-            "this->OutputMeta()[%d].Size() ) },";
+      const std::string& fwd_name = grad_outs_slotname_map.at(grad_output_name);
+
+      /* Handle Special Case: "PullSparseOp", etc
+
+          Forward:
+
+             Ids  W
+              |   |
+           PullSparseOp
+                |
+               Out
+
+          Backward:
+
+             Ids  GradOut  W
+              |      |     |
+             PullSparseGradOp
+                     |
+                  GradOut
+
+          Its grad output "GradOut" corresponds to forward output "Out",
+          where there is a hiden inplace involved. So we find "GradOut"'s index
+         in
+          grads, and perform the inplace operation by constructing outs =
+         {{"Out", grads[i]}}
+
+          GradOut -> Out -> fwd_output_pos -> grads position -> grads[i]
+          outs = {{"Out", grads[i]}}
+
+          For returns, append "GradOut" to the very end of return list.
+      */
+      if (!fwd_inputs_name_pos_map.count(fwd_name)) {
+        PADDLE_ENFORCE(fwd_outputs_name_pos_map.count(fwd_name),
+                       paddle::platform::errors::Fatal(
+                           "fwd_name not found in fwd_inputs_name_pos_map nor "
+                           "fwd_outputs_name_pos_map"));
+
+        size_t grads_position = fwd_outputs_name_pos_map.at(fwd_name);
+        std::string grad_ptr_name = fwd_name + "_ptrs";
+        const char* GET_GRADS_PTR_TEMPLATE =
+            "  std::vector<std::shared_ptr<egr::EagerTensor>> %s;\n"
+            "  for(const auto& t : grads[%d]) {\n    "
+            "%s.emplace_back(std::move(std::make_shared<egr::EagerTensor>(t)));"
+            "\n  }\n";
+        std::string grads_ptr_str =
+            paddle::string::Sprintf(GET_GRADS_PTR_TEMPLATE, grad_ptr_name,
+                                    grads_position, grad_ptr_name);
+        generated_grad_function_body += grads_ptr_str;
+        generated_grad_function_body += "\n";
+
+        const char* GRAD_OUTS_CONTENT_TEMPLATE = "{ \"%s\", %s },";
         outs_contents_str += paddle::string::Sprintf(
-            GRAD_OUTS_CONTENT_TEMPLATE, grad_output_name, fwd_input_position);
+            GRAD_OUTS_CONTENT_TEMPLATE, grad_output_name, grad_ptr_name);
+
       } else {
-        const char* GRAD_OUTS_CONTENT_TEMPLATE =
-            "{ \"%s\", "
-            "{std::make_shared<egr::EagerTensor>(egr::Controller::Instance()."
-            "GenerateUniqueName())}},";
-        outs_contents_str += paddle::string::Sprintf(GRAD_OUTS_CONTENT_TEMPLATE,
-                                                     grad_output_name);
+        size_t fwd_input_position = fwd_inputs_name_pos_map.at(fwd_name);
+        if (duplicable_input_name_set.count(fwd_name)) {
+          const char* GRAD_OUTS_CONTENT_TEMPLATE =
+              "{ \"%s\", egr::EagerUtils::ConstructDuplicableOutput( "
+              "this->OutputMeta()[%d].Size() ) },";
+          outs_contents_str += paddle::string::Sprintf(
+              GRAD_OUTS_CONTENT_TEMPLATE, grad_output_name, fwd_input_position);
+        } else {
+          const char* GRAD_OUTS_CONTENT_TEMPLATE =
+              "{ \"%s\", "
+              "{std::make_shared<egr::EagerTensor>(egr::Controller::Instance()."
+              "GenerateUniqueName())}},";
+          outs_contents_str += paddle::string::Sprintf(
+              GRAD_OUTS_CONTENT_TEMPLATE, grad_output_name);
+        }
       }
     } else {
       PADDLE_THROW(platform::errors::Fatal(
@@ -1340,15 +1401,39 @@ static std::string GenerateGradNodeCCContents(
 
   // [Generation] Get Return
   std::string outputs_str = "";
+  size_t num_appended_outputs = 0;
   for (auto iter : grad_outs) {
     const std::string& grad_out_name = iter.first;
-    size_t fwd_input_position =
-        fwd_inputs_name_pos_map.at(grad_outs_slotname_map.at(grad_out_name));
+    const std::string& fwd_name = grad_outs_slotname_map.at(grad_out_name);
+
+    if (fwd_inputs_name_pos_map.count(fwd_name)) {
+      size_t fwd_input_position = fwd_inputs_name_pos_map.at(fwd_name);
+      const char* BWD_OUTPUT_TEMPLATE =
+          "  outputs[%d] = egr::EagerUtils::GetOutputs(outs[\"%s\"]);\n";
+      outputs_str += paddle::string::Sprintf(BWD_OUTPUT_TEMPLATE,
+                                             fwd_input_position, grad_out_name);
+      num_appended_outputs++;
+    } else {
+      PADDLE_ENFORCE(fwd_outputs_name_pos_map.count(fwd_name),
+                     paddle::platform::errors::Fatal(
+                         "fwd_name not found in fwd_inputs_name_pos_map nor "
+                         "fwd_outputs_name_pos_map"));
+    }
+  }
 
-    const char* BWD_OUTPUT_TEMPLATE =
-        "  outputs[%d] = egr::EagerUtils::GetOutputs(outs[\"%s\"]);\n";
-    outputs_str += paddle::string::Sprintf(BWD_OUTPUT_TEMPLATE,
-                                           fwd_input_position, grad_out_name);
+  /* Handle Special Case: "PullSparseOp", etc
+     For returns, append "GradOut" to the very end of return list. */
+  for (auto iter : grad_outs) {
+    const std::string& grad_out_name = iter.first;
+    const std::string& fwd_name = grad_outs_slotname_map.at(grad_out_name);
+
+    if (fwd_outputs_name_pos_map.count(fwd_name)) {
+      const char* BWD_OUTPUT_TEMPLATE =
+          "  outputs[%d] = egr::EagerUtils::GetOutputs(outs[\"%s\"]);\n";
+      outputs_str += paddle::string::Sprintf(
+          BWD_OUTPUT_TEMPLATE, num_appended_outputs, grad_out_name);
+      num_appended_outputs++;
+    }
   }
 
   const char* BWD_RETURN_TEMPLATE =
@@ -1722,6 +1807,10 @@ static void PrepareAttrMapForOps() {
   operators_with_attrs["transfer_dtype"] = {};
   operators_with_attrs["transfer_dtype"]["out_dtype"] = 5;
   operators_with_attrs["transfer_dtype"]["in_dtype"] = 5;
+
+  // Handle "c_split"
+  operators_with_attrs["c_split"] = {};
+  operators_with_attrs["c_split"]["nranks"] = 1;
 }
 
 static void CollectOperatorsToCodeGen(const std::string& op_list_path) {

paddle/fluid/eager/auto_code_generator/op_list.txt

-sigmoid
-matmul_v2
-reduce_sum
-elementwise_add
 rsqrt
 multihead_matmul
 addmm
@@ -19,7 +15,9 @@ pow2_decay_with_linear_warmup
 split
 fc
 clear_float_status
+matmul_v2
 load
+c_embedding
 elementwise_max
 adadelta
 chunk_eval
@@ -43,8 +41,10 @@ expand_v2
 lgamma
 solve
 deformable_psroi_pooling
+transfer_layout
 instance_norm
 decode_jpeg
+distributed_push_sparse
 gather_nd
 reduce_prod
 matrix_rank
@@ -57,10 +57,12 @@ sequence_slice
 lookup_table
 softplus
 depthwise_conv2d
+c_allreduce_sum
 fused_fc_elementwise_layernorm
 sigmoid_cross_entropy_with_logits
 exp
 scatter
+c_allreduce_min
 equal_all
 searchsorted
 fusion_squared_mat_sub
@@ -73,6 +75,7 @@ momentum
 temporal_shift
 nce
 mv
+global_scatter
 proximal_gd
 memcpy_h2d
 add_position_encoding
@@ -90,13 +93,18 @@ randperm
 sequence_scatter
 partial_sum
 relu6
+partial_allgather
+c_scatter
+alltoall
 conv3d
 lstm_unit
 not_equal
 transpose2
+c_sync_comm_stream
 uniform_random_batch_size_like
 unfold
 lrn
+isclose
 softmax_with_cross_entropy
 isfinite_v2
 bernoulli
@@ -105,6 +113,7 @@ gaussian_random
 flatten2
 matmul
 cvm
+recv_v2
 adamax
 masked_select
 range
@@ -112,6 +121,7 @@ bitwise_not
 trace
 multinomial
 modified_huber_loss
+c_reduce_prod
 roll
 squared_l2_distance
 conv3d_transpose
@@ -128,8 +138,10 @@ multiclass_nms2
 bpr_loss
 fft_c2c
 bicubic_interp_v2
+angle
 reshape
 coalesce_tensor
+dgc
 roi_align
 reshape2
 reduce_any
@@ -139,6 +151,7 @@ sequence_reshape
 bilateral_slice
 fill_any_like
 empty
+partial_recv
 pad_constant_like
 pool2d
 size
@@ -148,11 +161,14 @@ stack
 dgc_momentum
 lamb
 generate_proposals_v2
+c_sync_calc_stream
 bitwise_or
 gru_unit
 fake_channel_wise_quantize_dequantize_abs_max
 sampling_id
 unsqueeze2
+transfer_dtype
+allreduce
 average_accumulates
 sequence_enumerate
 fusion_seqconv_eltadd_relu
@@ -160,6 +176,7 @@ bce_loss
 generate_proposal_labels
 im2sequence
 isinf
+c_reducescatter
 adagrad
 linear_chain_crf
 retinanet_target_assign
@@ -170,6 +187,7 @@ lookup_table_v2
 detection_map
 l1_norm
 sqrt
+partial_send
 fused_elemwise_activation
 slogdeterminant
 share_buffer
@@ -191,7 +209,10 @@ linear_interp
 auc
 logical_or
 batch_norm
+c_reduce_sum
+elementwise_add
 acos
+send_and_recv
 unpool
 cumprod
 sample_logits
@@ -206,6 +227,7 @@ matrix_power
 greater_equal
 generate_proposals
 bilinear_interp
+sigmoid
 inplace_abn
 softshrink
 mul
@@ -243,6 +265,8 @@ overlap_add
 fill_constant_batch_size_like
 fill_any
 dequantize_log
+c_split
+barrier
 max_pool2d_with_index
 pad3d
 norm
@@ -258,6 +282,7 @@ pow
 stanh
 label_smooth
 merged_momentum
+c_reduce_min
 ascend_trigger
 fused_feedforward
 rpn_target_assign
@@ -271,6 +296,7 @@ frame
 bincount
 shape
 group_norm
+c_softmax_with_cross_entropy
 resnet_unit
 sequence_expand_as
 cos_sim
@@ -319,6 +345,7 @@ adamw
 elementwise_pow
 prior_box
 p_norm
+c_concat
 unique_consecutive
 lod_reset
 pad
@@ -339,6 +366,7 @@ pad2d
 inverse
 spectral_norm
 shuffle_channel
+send_v2
 psroi_pool
 seed
 ceil
@@ -347,6 +375,7 @@ reduce_min
 cos
 ncclAllReduce
 cudnn_lstm
+reduce_sum
 digamma
 assign_value
 increment
@@ -366,6 +395,8 @@ atan
 less_than
 unsqueeze
 crf_decoding
+global_gather
+c_allreduce_prod
 log_softmax
 ftrl
 matrix_nms
@@ -374,6 +405,7 @@ cast
 tanh_shrink
 hard_shrink
 multiclass_nms
+c_broadcast
 fusion_transpose_flatten_concat
 sequence_unpad
 fused_elemwise_add_activation
@@ -393,6 +425,7 @@ alloc_float_status
 sequence_concat
 fusion_seqpool_cvm_concat
 similarity_focus
+c_allreduce_max
 argsort
 sequence_expand
 sgd
@@ -413,6 +446,7 @@ pyramid_hash
 fake_quantize_dequantize_moving_average_abs_max
 multi_dot
 sequence_pool
+broadcast
 transpose
 top_k
 dist
@@ -466,10 +500,13 @@ squared_l2_norm
 elementwise_sub
 margin_rank_loss
 faster_tokenizer
+c_identity
+c_reduce_max
 relu
 is_empty
 reduce_all
 edit_distance
+distributed_lookup_table
 bmm
 yolo_box
 soft_relu
@@ -484,6 +521,7 @@ nearest_interp
 gather
 trilinear_interp_v2
 box_clip
+c_allgather
 isnan_v2
 softmax
 conv2d_fusion