Merge pull request #15959 from luotao1/infershape_refine

refine infershape of sequence_enumerate, hash and fuse_emb_seq_pool

paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc

@@ -23,6 +23,9 @@ class FusedEmbeddingSeqPoolOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
+    if (ctx->IsRuntime()) {
+      return;
+    }
     PADDLE_ENFORCE(ctx->HasInput("W"),
                    "Input W of FusedEmbeddingSeqPoolOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Ids"),
@@ -42,36 +45,15 @@ class FusedEmbeddingSeqPoolOp : public framework::OperatorWithKernel {
     // we only support sum now
     PADDLE_ENFORCE_EQ(combiner, "sum");
 
-    int64_t last_dim = table_dims[1];
-    for (int i = 1; i != ids_dims.size(); ++i) {
-      last_dim *= ids_dims[i];
-    }
-
-    if (ctx->IsRuntime()) {
-      framework::Variable* ids_var =
-          boost::get<framework::Variable*>(ctx->GetInputVarPtrs("Ids")[0]);
-      const auto& ids_lod = ids_var->Get<LoDTensor>().lod();
+    int64_t last_dim = FusedEmbeddingSeqPoolLastDim(table_dims, ids_dims);
+    // in compile time, the lod level of ids must be 1
+    framework::VarDesc* ids_desc =
+        boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("Ids")[0]);
+    PADDLE_ENFORCE_EQ(ids_desc->GetLoDLevel(), 1);
 
-      // in run time, the LoD of ids must be 1
-      PADDLE_ENFORCE(ids_lod.size(), 1u,
-                     "The LoD level of Input(Ids) must be 1");
-      PADDLE_ENFORCE_GE(ids_lod[0].size(), 1u, "The LoD could NOT be empty");
-
-      int64_t batch_size = ids_lod[0].size() - 1;
-
-      // in run time, the shape from Ids -> output
-      // should be [seq_length, 1] -> [batch_size, embedding_size]
-      ctx->SetOutputDim("Out", framework::make_ddim({batch_size, last_dim}));
-    } else {
-      // in compile time, the lod level of ids must be 1
-      framework::VarDesc* ids_desc =
-          boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("Ids")[0]);
-      PADDLE_ENFORCE_EQ(ids_desc->GetLoDLevel(), 1);
-
-      // in compile time, the shape from Ids -> output
-      // should be [-1, 1] -> [-1, embedding_size]
-      ctx->SetOutputDim("Out", framework::make_ddim({-1, last_dim}));
-    }
+    // in compile time, the shape from Ids -> output
+    // should be [-1, 1] -> [-1, embedding_size]
+    ctx->SetOutputDim("Out", framework::make_ddim({-1, last_dim}));
   }
 
  protected:

paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h

@@ -61,6 +61,15 @@ struct EmbeddingVSumFunctor {
   }
 };
 
+inline int FusedEmbeddingSeqPoolLastDim(const framework::DDim &table_dims,
+                                        const framework::DDim &ids_dims) {
+  int64_t last_dim = table_dims[1];
+  for (int i = 1; i != ids_dims.size(); ++i) {
+    last_dim *= ids_dims[i];
+  }
+  return last_dim;
+}
+
 template <typename T>
 class FusedEmbeddingSeqPoolKernel : public framework::OpKernel<T> {
  public:
@@ -70,6 +79,17 @@ class FusedEmbeddingSeqPoolKernel : public framework::OpKernel<T> {
     const LoDTensor *table_var = context.Input<LoDTensor>("W");
     const std::string &combiner_type = context.Attr<std::string>("combiner");
 
+    int64_t last_dim =
+        FusedEmbeddingSeqPoolLastDim(table_var->dims(), ids_t->dims());
+    const auto &ids_lod = ids_t->lod();
+    // in run time, the LoD of ids must be 1
+    PADDLE_ENFORCE(ids_lod.size(), 1u, "The LoD level of Input(Ids) must be 1");
+    PADDLE_ENFORCE_GE(ids_lod[0].size(), 1u, "The LoD could NOT be empty");
+    int64_t batch_size = ids_lod[0].size() - 1;
+    // in run time, the shape from Ids -> output
+    // should be [seq_length, 1] -> [batch_size, embedding_size]
+    output_t->Resize({batch_size, last_dim});
+
     if (combiner_type == "sum") {
       EmbeddingVSumFunctor<T> functor;
       functor(context, table_var, ids_t, output_t);

paddle/fluid/operators/hash_op.cc

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/hash_op.h"
 #include <string>
-#include <vector>
 
 namespace paddle {
 namespace operators {
@@ -27,6 +26,9 @@ class HashOp : public framework::OperatorWithKernel {
       : OperatorWithKernel(type, inputs, outputs, attrs) {}
 
   void InferShape(framework::InferShapeContext *ctx) const override {
+    if (ctx->IsRuntime()) {
+      return;
+    }
     PADDLE_ENFORCE(ctx->HasInput("X"),
                    "Input(X) of HashOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
@@ -36,15 +38,8 @@ class HashOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(dims.size(), 2UL,
                       "The input of hash_op's dimensions must be 2");
     std::vector<int64_t> out_dims;
-    out_dims.reserve(dims.size() + 1);
-    // copy all dims except the last one
-    for (int i = 0u; i != dims.size() - 1; ++i) {
-      out_dims.emplace_back(dims[i]);
-    }
     int num_hash = ctx->Attrs().Get<int>("num_hash");
-    out_dims.emplace_back(num_hash);
-    // keep the last dim to 1
-    out_dims.emplace_back(1);
+    HashOutputSize(dims, out_dims, num_hash);
 
     ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
     ctx->ShareLoD("X", /*->*/ "Out");
@@ -71,4 +66,4 @@ $$Out = scale * X$$
 namespace ops = paddle::operators;
 
 REGISTER_OP_WITHOUT_GRADIENT(hash, ops::HashOp, ops::HashOpMaker);
-REGISTER_OP_CPU_KERNEL(hash, ops::HashKerel<int>, ops::HashKerel<int64_t>);
+REGISTER_OP_CPU_KERNEL(hash, ops::HashKernel<int>, ops::HashKernel<int64_t>);

paddle/fluid/operators/hash_op.h

@@ -17,21 +17,34 @@ limitations under the License. */
 extern "C" {
 #include <xxhash.h>
 }
+#include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
-// template <typename DeviceContext, typename T>
+
+inline void HashOutputSize(const framework::DDim& in_dims,
+                           std::vector<int64_t>& out_dims,  // NOLINT
+                           int num_hash) {
+  out_dims.reserve(in_dims.size() + 1);
+  // copy all dims except the last one
+  for (int i = 0u; i != in_dims.size() - 1; ++i) {
+    out_dims.emplace_back(in_dims[i]);
+  }
+  out_dims.emplace_back(num_hash);
+  // keep the last dim to 1
+  out_dims.emplace_back(1);
+}
+
 template <typename T>
-class HashKerel : public framework::OpKernel<T> {
+class HashKernel : public framework::OpKernel<T> {
  public:
   virtual void Compute(const framework::ExecutionContext& context) const {
     auto* out_t = context.Output<framework::LoDTensor>("Out");
     auto* in_t = context.Input<framework::LoDTensor>("X");
     int mod_by = context.Attr<int>("mod_by");
     int num_hash = context.Attr<int>("num_hash");
-    auto* output = out_t->mutable_data<T>(context.GetPlace());
 
     auto in_dims = in_t->dims();
     auto in_lod = in_t->lod();
@@ -39,6 +52,11 @@ class HashKerel : public framework::OpKernel<T> {
         static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
         "The actual input data's size mismatched with LoD information.");
 
+    std::vector<int64_t> out_dims;
+    HashOutputSize(in_dims, out_dims, num_hash);
+    out_t->Resize(framework::make_ddim(out_dims));
+    auto* output = out_t->mutable_data<T>(context.GetPlace());
+
     auto seq_length = in_dims[0];
     auto last_dim = in_dims[in_dims.size() - 1];
     auto* input = in_t->data<T>();
@@ -49,6 +67,7 @@ class HashKerel : public framework::OpKernel<T> {
       }
       input += last_dim;
     }
+    out_t->set_lod(in_t->lod());
   }
 };
 

paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc

@@ -22,6 +22,9 @@ class SequenceEnumerateOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
+    if (ctx->IsRuntime()) {
+      return;
+    }
     PADDLE_ENFORCE(
         ctx->HasInput("X"),
         "Input(X) of SequecceEnumerate operator should not be null.");
@@ -33,9 +36,9 @@ class SequenceEnumerateOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(
         x_dims.size(), 2,
         "Input(X) of SequenceEnumerate operator's rank should be 2.");
-    PADDLE_ENFORCE_EQ(
-        x_dims[1], 1,
-        "Input(X) of SequenceEnumerate operator's 2nd dimension should be 1.");
+    PADDLE_ENFORCE_EQ(x_dims[1], 1,
+                      "Input(X) of SequenceEnumerate operator's 2nd "
+                      "dimension should be 1.");
 
     const auto win_size = ctx->Attrs().Get<int>("win_size");
     ctx->SetOutputDim("Out", {x_dims[0], win_size});

paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cu

@@ -65,6 +65,7 @@ class SequenceEnumerateOpCUDAKernel : public framework::OpKernel<T> {
     auto lod0 = in_lod[0];
     auto in_len = in->numel();
     auto in_data = in->data<T>();
+    out->Resize({in_dims[0], win_size});
     auto out_data = out->mutable_data<T>(context.GetPlace());
     // Copy LoD to GPU
     const size_t* dev_in_lod_ptr = lod0.CUDAData(context.GetPlace());
@@ -72,6 +73,7 @@ class SequenceEnumerateOpCUDAKernel : public framework::OpKernel<T> {
     CalcOutPut<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
                  PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
         in_data, dev_in_lod_ptr, lod0.size(), win_size, pad_value, out_data);
+    out->set_lod(in->lod());
   }
 };
 

paddle/fluid/operators/sequence_ops/sequence_enumerate_op.h

@@ -39,6 +39,7 @@ class SequenceEnumerateKernel : public framework::OpKernel<T> {
     // Generate enumerate sequence set
     auto lod0 = in_lod[0];
     auto in_data = in->data<T>();
+    out->Resize({in_dims[0], win_size});
     auto out_data = out->mutable_data<T>(context.GetPlace());
     for (size_t i = 0; i < lod0.size() - 1; ++i) {
       for (size_t idx = lod0[i]; idx < lod0[i + 1]; ++idx) {
@@ -49,6 +50,7 @@ class SequenceEnumerateKernel : public framework::OpKernel<T> {
         }
       }
     }
+    out->set_lod(in->lod());
   }
 };