Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

paddle/framework/CMakeLists.txt

@@ -23,7 +23,7 @@ cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc
 cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
 cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
 cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto proto_desc)
-cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope)
+cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope proto_desc)
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
 
 cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator proto_desc)

paddle/framework/block_desc.cc

@@ -34,6 +34,10 @@ VarDescBind *BlockDescBind::Var(const std::string &name) const {
   return it->second.get();
 }
 
+bool BlockDescBind::HasVar(const std::string &name) const {
+  return vars_.find(name) != vars_.end();
+}
+
 std::vector<VarDescBind *> BlockDescBind::AllVars() const {
   std::vector<VarDescBind *> res;
   for (const auto &p : vars_) {

paddle/framework/block_desc.h

@@ -43,6 +43,8 @@ class BlockDescBind {
 
   VarDescBind *Var(const std::string &name_bytes) const;
 
+  bool HasVar(const std::string &var_name) const;
+
   std::vector<VarDescBind *> AllVars() const;
 
   BlockDescBind *ParentBlock() const;

paddle/framework/operator.h

@@ -22,6 +22,7 @@ limitations under the License. */
 
 #include "op_info.h"
 #include "paddle/framework/attribute.h"
+#include "paddle/framework/block_desc.h"
 #include "paddle/framework/data_type.h"
 #include "paddle/framework/framework.pb.h"
 #include "paddle/framework/lod_tensor.h"
@@ -317,26 +318,122 @@ class ExecutionContext : public InferShapeContext {
   const platform::DeviceContext& device_context_;
 };
 
+class CompileTimeInferShapeContext : public InferShapeContextBase {
+ public:
+  CompileTimeInferShapeContext(const OpDescBind& op, const BlockDescBind& block)
+      : op_(op), block_(block) {}
+
+  bool HasInput(const std::string& name) const override {
+    const std::vector<std::string>& input_names = op_.Input(name);
+    auto length = input_names.size();
+    PADDLE_ENFORCE_EQ(length, 1UL,
+                      "Input(%s) should have only one value, "
+                      "but it have %d now",
+                      name, length);
+    return block_.HasVar(input_names[0]);
+  }
+
+  bool HasOutput(const std::string& name) const override {
+    const std::vector<std::string>& output_names = op_.Output(name);
+    auto length = output_names.size();
+    PADDLE_ENFORCE_EQ(length, 1UL,
+                      "Output(%s) should have only one value, "
+                      "but it have %d now",
+                      name, length);
+    return block_.HasVar(output_names[0]);
+  }
+
+  bool HasInputs(const std::string& name) const override {
+    const std::vector<std::string>& input_names = op_.Input(name);
+    PADDLE_ENFORCE(!input_names.empty(), "Inputs(%s) length is 0", name);
+    for (auto& input : input_names) {
+      if (!block_.HasVar(input)) return false;
+    }
+    return true;
+  }
+
+  bool HasOutputs(const std::string& name) const override {
+    const std::vector<std::string>& output_names = op_.Output(name);
+    PADDLE_ENFORCE(!output_names.empty(), "Inputs(%s) length is 0", name);
+    for (auto& output : output_names) {
+      if (!block_.HasVar(output)) return false;
+    }
+    return true;
+  }
+
+  DDim GetInputDim(const std::string& name) const override {
+    std::vector<DDim> ddims = GetInputsDim(name);
+    auto length = ddims.size();
+    PADDLE_ENFORCE_EQ(length, 1UL,
+                      "Input(%s) should have 1 value, "
+                      "but it has %d now",
+                      name, length);
+    return ddims[0];
+  }
+
+  void SetInputDim(const std::string& name, const DDim& dim) override {
+    SetInputsDim(name, {dim});
+  }
+
+  DDim GetOutputDim(const std::string& name) const override {
+    std::vector<DDim> ddims = GetOutputsDim(name);
+    auto length = ddims.size();
+    PADDLE_ENFORCE_EQ(length, 1UL,
+                      "Output(%s) should have 1 value, "
+                      "but it has %d now",
+                      name, length);
+    return ddims[0];
+  }
+
+  void SetOutputDim(const std::string& name, const DDim& dim) override {
+    SetOutputsDim(name, {dim});
+  }
+
+  AttrReader Attrs() const override { return AttrReader(op_.GetAttrMap()); }
+
+  const std::vector<std::string>& Inputs(
+      const std::string& name) const override {
+    return op_.Input(name);
+  }
+
+  const std::vector<std::string>& Outputs(
+      const std::string& name) const override {
+    return op_.Output(name);
+  }
+
+ private:
+  DDim GetDim(const std::string& name) const override {
+    return framework::make_ddim(block_.Var(name)->Shape());
+  }
+
+  void SetDim(const std::string& name, const DDim& dim) override {
+    block_.Var(name)->SetShape(framework::vectorize(dim));
+  }
+
+  const OpDescBind& op_;
+  const BlockDescBind& block_;
+};
+
 class RuntimeInferShapeContext : public InferShapeContextBase {
  public:
   RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope)
       : op_(op), scope_(scope) {}
 
-  bool HasInput(const std::string& name) const {
+  bool HasInput(const std::string& name) const override {
     auto ipt = op_.Input(name);
     auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
     return var != nullptr;
   }
 
-  bool HasOutput(const std::string& name) const {
+  bool HasOutput(const std::string& name) const override {
     auto ipt = op_.Output(name);
     auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
     return var != nullptr;
   }
 
-  bool HasInputs(const std::string& name) const {
+  bool HasInputs(const std::string& name) const override {
     auto inputs = op_.Inputs(name);
-    if (inputs.size() == 0UL) {
+    if (inputs.empty()) {
       return false;
     }
     for (auto& input : inputs) {
@@ -347,9 +444,9 @@ class RuntimeInferShapeContext : public InferShapeContextBase {
     return true;
   }
 
-  bool HasOutputs(const std::string& name) const {
+  bool HasOutputs(const std::string& name) const override {
     auto outputs = op_.Outputs(name);
-    if (outputs.size() == 0UL) {
+    if (outputs.empty()) {
       return false;
     }
     for (auto& output : outputs) {
@@ -360,29 +457,31 @@ class RuntimeInferShapeContext : public InferShapeContextBase {
     return true;
   }
 
-  DDim GetInputDim(const std::string& name) const {
+  DDim GetInputDim(const std::string& name) const override {
     return GetDim(op_.Input(name));
   }
 
-  void SetInputDim(const std::string& name, const DDim& dim) {
+  void SetInputDim(const std::string& name, const DDim& dim) override {
     SetDim(op_.Input(name), dim);
   }
 
-  DDim GetOutputDim(const std::string& name) const {
+  DDim GetOutputDim(const std::string& name) const override {
     return GetDim(op_.Output(name));
   }
 
-  void SetOutputDim(const std::string& name, const DDim& dim) {
+  void SetOutputDim(const std::string& name, const DDim& dim) override {
     SetDim(op_.Output(name), dim);
   }
 
-  AttrReader Attrs() const { return AttrReader(op_.Attrs()); }
+  AttrReader Attrs() const override { return AttrReader(op_.Attrs()); }
 
-  const std::vector<std::string>& Inputs(const std::string& name) const {
+  const std::vector<std::string>& Inputs(
+      const std::string& name) const override {
     return op_.Inputs(name);
   }
 
-  const std::vector<std::string>& Outputs(const std::string& name) const {
+  const std::vector<std::string>& Outputs(
+      const std::string& name) const override {
     return op_.Outputs(name);
   }
 
@@ -403,11 +502,11 @@ class RuntimeInferShapeContext : public InferShapeContextBase {
     return t;
   }
 
-  DDim GetDim(const std::string& name) const {
+  DDim GetDim(const std::string& name) const override {
     return GetTensor<false>(name)->dims();
   }
 
-  void SetDim(const std::string& name, const DDim& dim) {
+  void SetDim(const std::string& name, const DDim& dim) override {
     GetTensor<true>(name)->Resize(dim);
   }
 
@@ -513,9 +612,9 @@ class OperatorWithKernel : public OperatorBase {
                        });
   }
 
- protected:
   virtual void InferShape(InferShapeContextBase* ctx) const = 0;
 
+ protected:
   // indicate kernel DataType by input data. Defaultly all input data must be
   // same.
   virtual DataType IndicateDataType(const ExecutionContext& ctx) const {

paddle/framework/shape_inference.h

@@ -19,6 +19,9 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+// TODO(longfei): Once after both CompileTimeInferShapeContext and
+// RuntimeInferShapeContext get merged, we can rename InferShapeContextBase into
+// InferShapeContext so to replace the current InferShapeContext.
 class InferShapeContextBase {
  public:
   virtual ~InferShapeContextBase() {}

paddle/framework/tensor_array.cc

@@ -217,12 +217,11 @@ LoDTensor DynamicBatchUnpacker::GetBatch(size_t index) {
 
   // collect indice need to copy to the batch
   std::vector<size_t> indice;
-  for (size_t seq_id = 0; seq_id < meta.size(); seq_id++) {
-    const auto& seq_meta = meta[seq_id];
-    if (index >= seq_meta.end) break;
-    indice.push_back(seq_meta.begin + index);
+  for (const auto& seq : meta) {
+    size_t id = seq.begin + index;
+    if (id >= seq.end) break;
+    indice.push_back(id);
   }
-
   PADDLE_ENFORCE(!indice.empty(), "invalid batch at %d", index);
 
   // copy the indice of records in LoDTensor
@@ -232,16 +231,18 @@ LoDTensor DynamicBatchUnpacker::GetBatch(size_t index) {
   result.Resize(make_ddim(record_dims_vec));
   result.mutable_data<value_type>(platform::CPUPlace());
 
-  for (size_t i = 0; i < indice.size() - 1; i++) {
+  for (size_t i = 0; i < indice.size(); i++) {
     auto index = indice[i];
     auto target = result.Slice<value_type>(i, i + 1);
     auto source_ = source->Slice<value_type>(index, index + 1);
+
     target.CopyFrom<value_type>(source_, platform::CPUPlace());
   }
 
   return result;
 }
 
+// TODO(supejom) to cache lod if reasonable
 LoDTensor PackDynamicBatch(const std::vector<LoDTensor>& source,
                            const std::vector<DySeqMeta>& meta, const LoD& lod,
                            size_t level) {
@@ -273,7 +274,6 @@ LoDTensor PackDynamicBatch(const std::vector<LoDTensor>& source,
   }
 
   result.set_lod(lod);
-
   return result;
 }
 

paddle/pybind/pybind.cc

@@ -230,6 +230,21 @@ All parameter, weight, gradient are variables in Paddle.
                                    desc.InitializationErrorString());
                     return OpRegistry::CreateOp(desc);
                   })
+      .def_static("infer_shape",
+                  [](OpDescBind &op_desc, BlockDescBind &block) {
+                    auto op = OpRegistry::CreateOp(*op_desc.Proto());
+                    auto *op_with_kernel =
+                        dynamic_cast<OperatorWithKernel *>(op.get());
+                    if (op_with_kernel != nullptr) {
+                      auto ctx = CompileTimeInferShapeContext(op_desc, block);
+                      op_with_kernel->InferShape(&ctx);
+                    } else {
+                      PADDLE_THROW(
+                          "OP(%s) is not type of OperatorWithKernel, "
+                          "should not call this function",
+                          op_desc.Type());
+                    }
+                  })
       .def("backward",
            [](const OperatorBase &forwardOp,
               const std::unordered_set<std::string> &no_grad_vars) {

python/paddle/v2/framework/tests/test_infer_shape.py

+import unittest
+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
+
+
+class TestInferShape(unittest.TestCase):
+    def test_sum_op(self):
+        prog = core.ProgramDesc.__create_program_desc__()
+        self.assertIsNotNone(prog)
+        block = prog.block(0)
+        self.assertIsNotNone(block)
+
+        shape = [10, 20]
+
+        # prepare input/output
+        x1 = block.new_var("x1")
+        x1.set_shape(shape)
+        x2 = block.new_var("x2")
+        x2.set_shape(shape)
+
+        out = block.new_var("out")
+
+        # prepare the operator
+        sum_op_desc = block.append_op()
+        sum_op_desc.set_type("sum")
+        sum_op_desc.set_input("X", ["x1", "x2"])
+        sum_op_desc.set_output("Out", ["out"])
+
+        core.Operator.infer_shape(sum_op_desc, block)
+        self.assertEqual(out.shape(), shape)
+
+    def test_mul_op(self):
+        prog = core.ProgramDesc.__create_program_desc__()
+        self.assertIsNotNone(prog)
+        block = prog.block(0)
+        self.assertIsNotNone(block)
+
+        x_shape = [10, 20]
+        y_shape = [20, 30]
+
+        # prepare input/output
+        x1 = block.new_var("x")
+        x1.set_shape(x_shape)
+        x2 = block.new_var("y")
+        x2.set_shape(y_shape)
+
+        out = block.new_var("out")
+
+        # prepare the operator
+        mul_op_desc = block.append_op()
+        mul_op_desc.set_type("mul")
+        mul_op_desc.set_input("X", ["x"])
+        mul_op_desc.set_input("Y", ["y"])
+        mul_op_desc.set_output("Out", ["out"])
+        mul_op_desc.set_attr("x_num_col_dims", 1)
+        mul_op_desc.set_attr("y_num_col_dims", 1)
+
+        core.Operator.infer_shape(mul_op_desc, block)
+        self.assertEqual(out.shape(), [x_shape[0], y_shape[1]])
+
+
+if __name__ == '__main__':
+    unittest.main()