[Runtime] Enable set_input_zero_copy in GraphRuntime

src/runtime/graph/graph_runtime.cc

@@ -6,9 +6,9 @@
  * to you under the Apache License, Version 2.0 (the
  * "License"); you may not use this file except in compliance
  * with the License.  You may obtain a copy of the License at
- * 
+ *
  *   http://www.apache.org/licenses/LICENSE-2.0
- * 
+ *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
@@ -23,6 +23,7 @@
  */
 #include "graph_runtime.h"
 
+#include <tvm/runtime/device_api.h>
 #include <tvm/runtime/ndarray.h>
 #include <tvm/runtime/packed_func.h>
 #include <tvm/runtime/registry.h>
@@ -38,6 +39,13 @@
 
 namespace tvm {
 namespace runtime {
+namespace details {
+inline size_t GetDataAlignment(const DLTensor& arr) {
+  size_t align = (arr.dtype.bits / 8) * arr.dtype.lanes;
+  if (align < kAllocAlignment) return kAllocAlignment;
+  return align;
+}
+}  // namespace details
 
 /*!
  * \brief Run all the operations one by one.
@@ -96,6 +104,39 @@ void GraphRuntime::SetInput(int index, DLTensor* data_in) {
   uint32_t eid = this->entry_id(input_nodes_[index], 0);
   data_entry_[eid].CopyFrom(data_in);
 }
+/*!
+ * \brief set index-th input to the graph without copying the data.
+ * \param index The input index.
+ * \param data_ref The input data that is referred.
+ */
+void GraphRuntime::SetInputZeroCopy(int index, DLTensor* data_ref) {
+  CHECK_LT(static_cast<size_t>(index), input_nodes_.size());
+  uint32_t eid = this->entry_id(input_nodes_[index], 0);
+  const DLTensor* old_t = data_entry_[eid].operator->();
+
+  // check the consistency of input
+  CHECK_EQ(data_alignment_[eid], details::GetDataAlignment(*data_ref));
+  CHECK_EQ(reinterpret_cast<size_t>(data_ref->data) % kAllocAlignment, 0);
+  CHECK_EQ(old_t->ndim, static_cast<size_t>(data_ref->ndim));
+  CHECK_EQ(old_t->ctx.device_type, data_ref->ctx.device_type);
+  CHECK_EQ(old_t->ctx.device_id, data_ref->ctx.device_id);
+  for (auto i = 0; i < data_ref->ndim; ++i) {
+    CHECK_EQ(old_t->shape[i], data_ref->shape[i]);
+  }
+
+  // Update the data pointer for each argument of each op
+  for (auto& op_arg : op_args_) {
+    if (op_arg) {
+      const auto it = op_arg->input_entry_ids.find(eid);
+      if (it != op_arg->input_entry_ids.end()) {
+        for (const auto i : it->second) {
+          DLTensor* t = static_cast<DLTensor*>(op_arg->arg_values[i].v_handle);
+          t->data = data_ref->data;
+        }
+      }
+    }
+  }
+}
 /*!
  * \brief Get the number of outputs
  *
@@ -184,7 +225,7 @@ void GraphRuntime::LoadParams(dmlc::Stream* strm) {
   }
 }
 
-  void GraphRuntime::ShareParams(const GraphRuntime& other, dmlc::Stream* strm) {
+void GraphRuntime::ShareParams(const GraphRuntime& other, dmlc::Stream* strm) {
     uint64_t header, reserved;
     CHECK(strm->Read(&header))
       << "Invalid parameters file format";
@@ -206,6 +247,8 @@ void GraphRuntime::LoadParams(dmlc::Stream* strm) {
     CHECK_EQ(data_entry_[eid].use_count(), 1);
     data_entry_[eid] = other.GetInput(GetInputIndex(names[i]));
     CHECK_GT(data_entry_[eid].use_count(), 1);
+    const DLTensor* tmp = data_entry_[eid].operator->();
+    data_alignment_[eid] = details::GetDataAlignment(*tmp);
   }
   this->SetupOpExecs();
 }
@@ -268,53 +311,65 @@ void GraphRuntime::SetupStorage() {
   // memory assignment for each node entry. The allocated memory on each device
   // is mapped to this pool.
   data_entry_.resize(num_node_entries());
+  data_alignment_.resize(num_node_entries());
   for (size_t i = 0; i < data_entry_.size(); ++i) {
     int storage_id = attrs_.storage_id[i];
     CHECK_LT(static_cast<size_t>(storage_id), storage_pool_.size());
     data_entry_[i] =
         storage_pool_[storage_id].CreateView(attrs_.shape[i], vtype[i]);
+    const DLTensor* tmp = data_entry_[i].operator->();
+    data_alignment_[i] = details::GetDataAlignment(*tmp);
   }
 }
 
 void GraphRuntime::SetupOpExecs() {
   op_execs_.resize(this->GetNumOfNodes());
+  op_args_.resize(this->GetNumOfNodes());
   // setup the array and requirements.
   for (uint32_t nid = 0; nid < this->GetNumOfNodes(); ++nid) {
     const auto& inode = nodes_[nid];
     if (inode.op_type == "null") continue;
     std::vector<DLTensor> args;
+    std::vector<uint32_t> input_entry_ids;
     for (const auto& e : inode.inputs) {
-      args.push_back(*(data_entry_[this->entry_id(e)].operator->()));
+      uint32_t eid = this->entry_id(e);
+      args.push_back(*(data_entry_[eid].operator->()));
+      input_entry_ids.push_back(eid);
     }
     for (uint32_t index = 0; index < inode.param.num_outputs; ++index) {
       uint32_t eid = this->entry_id(nid, index);
       args.push_back(*(data_entry_[eid].operator->()));
     }
     CHECK(inode.op_type == "tvm_op") << "Can only take tvm_op as op";
 
-    op_execs_[nid] = CreateTVMOp(inode.param, args, inode.inputs.size());
+    std::tie(op_execs_[nid], op_args_[nid]) =
+        CreateTVMOp(inode.param, args, inode.inputs.size());
+    auto& entry_to_input_pos = op_args_[nid]->input_entry_ids;
+    for (uint32_t i = 0; i < input_entry_ids.size(); ++i) {
+      const auto eid = input_entry_ids[i];
+      auto it = entry_to_input_pos.find(eid);
+      if (it == entry_to_input_pos.end()) {
+        entry_to_input_pos.emplace(eid, std::vector<uint32_t>{i});
+      } else {
+        it->second.push_back(i);
+      }
+    }
   }
 }
 
-std::function<void()> GraphRuntime::CreateTVMOp(
+std::pair<std::function<void()>, std::shared_ptr<GraphRuntime::OpArgs> > GraphRuntime::CreateTVMOp(
     const TVMOpParam& param,
     const std::vector<DLTensor>& args,
     size_t num_inputs) {
-  struct OpArgs {
-    std::vector<DLTensor> args;
-    std::vector<TVMValue> arg_values;
-    std::vector<int> arg_tcodes;
-    std::vector<int64_t> shape_data;
-  };
-  std::shared_ptr<OpArgs> arg_ptr = std::make_shared<OpArgs>();
+  std::shared_ptr<GraphRuntime::OpArgs> arg_ptr = std::make_shared<GraphRuntime::OpArgs>();
   // setup address.
-  arg_ptr->args = std::move(args);
+  arg_ptr->args = args;
   if (param.flatten_data) {
     arg_ptr->shape_data.resize(arg_ptr->args.size());
   }
   for (size_t i = 0; i < arg_ptr->args.size(); ++i) {
     TVMValue v;
-    DLTensor* t = &(arg_ptr->args[i]);
+    DLTensor* t = &arg_ptr->args[i];
     v.v_handle = t;
     arg_ptr->arg_values.push_back(v);
     arg_ptr->arg_tcodes.push_back(kArrayHandle);
@@ -327,7 +382,7 @@ std::function<void()> GraphRuntime::CreateTVMOp(
   }
 
   if (param.func_name == "__nop") {
-    return [](){};
+    return {[](){}, arg_ptr};
   } else if (param.func_name == "__copy") {
     // Perform cross device data copy.
     // Directly copy data from the input to the output.
@@ -336,7 +391,7 @@ std::function<void()> GraphRuntime::CreateTVMOp(
       DLTensor* to = static_cast<DLTensor*>(arg_ptr->arg_values[1].v_handle);
       TVM_CCALL(TVMArrayCopyFromTo(from, to, nullptr));
     };
-    return fexec;
+    return {fexec, arg_ptr};
   }
 
   // Get compiled function from the module that contains both host and device
@@ -351,7 +406,7 @@ std::function<void()> GraphRuntime::CreateTVMOp(
                   static_cast<int>(arg_ptr->arg_values.size()));
     pf.CallPacked(targs, &rv);
   };
-  return fexec;
+  return {fexec, arg_ptr};
 }
 
 PackedFunc GraphRuntime::GetFunction(
@@ -367,14 +422,23 @@ PackedFunc GraphRuntime::GetFunction(
           this->SetInput(args[0], args[1]);
         }
       });
+  } else if (name == "set_input_zero_copy") {
+    return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
+      if (args[0].type_code() == kStr) {
+        int in_idx = this->GetInputIndex(args[0]);
+        if (in_idx >= 0) this->SetInputZeroCopy(in_idx, args[1]);
+      } else {
+        this->SetInputZeroCopy(args[0], args[1]);
+      }
+    });
   } else if (name == "get_output") {
     return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
-        if (args.num_args == 2) {
-          this->CopyOutputTo(args[0], args[1]);
-        } else {
-          *rv = this->GetOutput(args[0]);
-        }
-      });
+      if (args.num_args == 2) {
+        this->CopyOutputTo(args[0], args[1]);
+      } else {
+        *rv = this->GetOutput(args[0]);
+      }
+    });
   } else if (name == "get_input") {
     return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
         int in_idx = 0;

src/runtime/graph/graph_runtime.h

@@ -6,9 +6,9 @@
  * to you under the Apache License, Version 2.0 (the
  * "License"); you may not use this file except in compliance
  * with the License.  You may obtain a copy of the License at
- * 
+ *
  *   http://www.apache.org/licenses/LICENSE-2.0
- * 
+ *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
@@ -34,6 +34,7 @@
 #include <tvm/runtime/packed_func.h>
 
 #include <memory>
+#include <unordered_map>
 #include <utility>
 #include <vector>
 #include <string>
@@ -67,6 +68,14 @@ struct TVMOpParam {
  *  TVM runtime PackedFunc API.
  */
 class GraphRuntime : public ModuleNode {
+  struct OpArgs {
+    std::vector<DLTensor> args;
+    std::unordered_map<uint32_t, std::vector<uint32_t> > input_entry_ids;
+    std::vector<TVMValue> arg_values;
+    std::vector<int> arg_tcodes;
+    std::vector<int64_t> shape_data;
+  };
+
  public:
   /*!
    * \brief Get member function to front-end
@@ -111,6 +120,12 @@ class GraphRuntime : public ModuleNode {
    * \param data_in The input data.
    */
   void SetInput(int index, DLTensor* data_in);
+  /*!
+   * \brief set index-th input to the graph without copying the data
+   * \param index The input index.
+   * \param data_ref The input data that is referred.
+   */
+  void SetInputZeroCopy(int index, DLTensor* data_ref);
   /*!
    * \brief Get the number of outputs
    *
@@ -365,9 +380,9 @@ class GraphRuntime : public ModuleNode {
    * \param num_inputs Number of inputs.
    * \return The created executor.
    */
-  std::function<void()> CreateTVMOp(const TVMOpParam& attrs,
-                                    const std::vector<DLTensor>& args,
-                                    size_t num_inputs);
+  std::pair<std::function<void()>, std::shared_ptr<OpArgs> > CreateTVMOp(
+      const TVMOpParam& attrs, const std::vector<DLTensor>& args,
+      size_t num_inputs);
   // Get node entry index.
   uint32_t entry_id(uint32_t nid, uint32_t index) const {
     return node_row_ptr_[nid] + index;
@@ -398,8 +413,12 @@ class GraphRuntime : public ModuleNode {
   std::vector<NDArray> storage_pool_;
   /*! \brief Data entry of each node. */
   std::vector<NDArray> data_entry_;
+  /*! \brief Data alignment of each node. */
+  std::vector<size_t> data_alignment_;
   /*! \brief Operator on each node. */
   std::vector<std::function<void()> > op_execs_;
+  /*! \brief Arg info of TVM ops */
+  std::vector<std::shared_ptr<OpArgs> > op_args_;
 };
 
 std::vector<TVMContext> GetAllContext(const TVMArgs& args);

src/runtime/ndarray.cc

@@ -6,9 +6,9 @@
  * to you under the Apache License, Version 2.0 (the
  * "License"); you may not use this file except in compliance
  * with the License.  You may obtain a copy of the License at
- * 
+ *
  *   http://www.apache.org/licenses/LICENSE-2.0
- * 
+ *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY

tests/cpp/relay_build_module_test.cc

@@ -84,18 +84,41 @@ TEST(Relay, BuildModule) {
   auto ctx = A->ctx;
   auto pfr = tvm::runtime::Registry::Get("tvm.graph_runtime.create");
   tvm::runtime::Module run_mod = (*pfr)(json, mod, (int)ctx.device_type, (int)ctx.device_id);
-  auto set_input_f = run_mod.GetFunction("set_input", false);
+  auto set_input_f = run_mod.GetFunction("set_input_zero_copy", false);
   auto run_f = run_mod.GetFunction("run", false);
   auto get_output_f = run_mod.GetFunction("get_output", false);
-  set_input_f("a", A);
-  set_input_f("b", B);
-  set_input_f("c", C);
+  set_input_f("a", &A.ToDLPack()->dl_tensor);
+  set_input_f("b", &B.ToDLPack()->dl_tensor);
+  set_input_f("c", &C.ToDLPack()->dl_tensor);
   run_f();
   tvm::runtime::NDArray Y = get_output_f(0);
   auto pY = (float*)Y.ToDLPack()->dl_tensor.data;
   for (int i = 0; i < 6; ++i) {
     CHECK_LT(fabs(pY[i] - (i + (i + 1) + (i + 2))), 1e-4);
   }
+  // mutate the input a bit and run it again
+  for (int i = 0; i < 6; ++i) {
+    pB[i] = i + 3;
+  }
+  run_f();
+  tvm::runtime::NDArray Y2 = get_output_f(0);
+  auto pY2 = (float*)Y2.ToDLPack()->dl_tensor.data;
+  for (int i = 0; i < 6; ++i) {
+    CHECK_LT(fabs(pY2[i] - (i + (i + 3) + (i + 2))), 1e-4);
+  }
+  // attach a different input and run it again
+  auto C2 = tvm::runtime::NDArray::Empty({2, 3}, {kDLFloat, 32, 1}, {kDLCPU, 0});
+  auto pC2 = (float*)C2.ToDLPack()->dl_tensor.data;
+  for (int i = 0; i < 6; ++i) {
+    pC2[i] = i + 4;
+  }
+  set_input_f("c", &C2.ToDLPack()->dl_tensor);
+  run_f();
+  tvm::runtime::NDArray Y3 = get_output_f(0);
+  auto pY3 = (float*)Y3.ToDLPack()->dl_tensor.data;
+  for (int i = 0; i < 6; ++i) {
+    CHECK_LT(fabs(pY3[i] - (i + (i + 3) + (i + 4))), 1e-4);
+  }
 }
 
 int main(int argc, char ** argv) {