[VirtualMachine] new method allowing to set one input tensor by its i…

…ndex or name (#10293) * set_input_with_index was implemented for VM * clean code * add getInputIndexFromName. add function descriptions. lint fix * fix lint * transfer comparison of parameter names number and assigned devices number to VMFunction constructor * add GetVMFunctionWithName to Executable API * clean code * add SetInputWithName (set_input_with_name) to VM API * join SetInputWithIndex and SetInputWithName to SetOneInputTensor (set_one_input) to VM API, the joined methods were removed * fix lint * some fixes after review * add set_one_input method to python API of VirtualMachine * pytests for set_input and set_one_input methods of VirtualMachine were implemented and checked * CI restart * construct simple model for pytests by relay instead of onnx tools (need for correct CI) Co-authored-by: Valery Chernov <[email protected]>
apache · Feb 25, 2022 · d62a364 · d62a364
1 parent 308d320
commit d62a364
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Showing 6 changed files with 322 additions and 54 deletions.
diff --git a/include/tvm/runtime/vm/executable.h b/include/tvm/runtime/vm/executable.h
@@ -218,6 +218,13 @@ class Executable : public ModuleNode {
    */
   void SetLib(const runtime::Module& lib);
 
+  /*!
+   * \brief Get VMFunction.
+   * \param func_name The function's name.
+   * \return VMFunction.
+   */
+  const VMFunction& GetVMFunctionWithName(const std::string& func_name) const;
+
   /*!
    * \brief Get the arity of the VMFunction.
    * \param func Function name.

diff --git a/include/tvm/runtime/vm/vm.h b/include/tvm/runtime/vm/vm.h
@@ -93,7 +93,9 @@ struct VMFunction {
         params(std::move(params)),
         instructions(std::move(instructions)),
         register_file_size(register_file_size),
-        param_device_indexes(std::move(param_device_indexes)) {}
+        param_device_indexes(std::move(param_device_indexes)) {
+    ICHECK_EQ(params.size(), param_device_indexes.size());
+  }
 
   VMFunction() = default;
 
@@ -270,6 +272,15 @@ class VirtualMachine : public runtime::ModuleNode {
    */
   void SetInput(std::string name, TVMArgs args, int offset);
 
+  /*!
+   * \brief Set one input tensor with index or name to a function.
+   * \param name The function name.
+   * \param tag index or name of the input tensor .
+   * \param tensor the input tensor. If the tensor is not of the correct device for the function,
+   * they will be copied to the device.
+   */
+  void SetOneInput(std::string name, const TVMArgValue& tag, const TVMArgValue& tensor);
+
   /*!
    * \brief Internal hook for profiling the start of an op.
    *
@@ -286,6 +297,48 @@ class VirtualMachine : public runtime::ModuleNode {
    */
   virtual void OpStopHook();
 
+ private:
+  /*!
+   * \brief Get index of input tensor from its name.
+   * \param func_name The function's name.
+   * \param input_name The input tensor name.
+   * \return The input tensor index.
+   */
+  int64_t GetInputIndexFromVMFunction(const std::string& func_name,
+                                      const std::string& input_name) const;
+
+  /*!
+   * \brief Get index of input tensor from its name.
+   * \param params parameter names.
+   * \param input_name The input tensor name.
+   * \return The input tensor index.
+   */
+  int64_t GetInputIndexFromName(const std::vector<std::string>& params,
+                                const std::string& input_name) const;
+
+  /*!
+   * \brief Check executable exists and get VM function from it.
+   * \param func_name The function's name.
+   * \return VM function.
+   */
+  const VMFunction& CheckAndGetVMFunction(const std::string& func_name) const;
+
+  /*!
+   * \brief Creats inputs_ field, if it exists check its size.
+   * \param func_name The function's name.
+   * \param size inputs_ field size.
+   * \return VM function.
+   */
+  void CreateInputsOrCheckSize(const std::string& func_name, size_t size);
+
+  /*!
+   * \brief Set one input tensor with given index to set of input tensors if need copy to given
+   * device. \param tensors the input tensors set (destination) \param tensor some tensor (not
+   * neccessary DLTensor). \param index The input tensor index. \param dev device to copy if need.
+   */
+  void SetInputTensorWithIndex(std::vector<ObjectRef>& tensors,  // NOLINT(*)
+                               const TVMArgValue& tensor, int index, Device dev);
+
  protected:
   /*! \brief The virtual machine's packed function table. */
   std::vector<PackedFunc> packed_funcs_;

diff --git a/python/tvm/runtime/vm.py b/python/tvm/runtime/vm.py
@@ -380,6 +380,7 @@ def __init__(self, exe, device, memory_cfg=None):
         self._get_num_outputs = self.module["get_num_outputs"]
         self._get_input_index = self.module["get_input_index"]
         self._set_input = self.module["set_input"]
+        self._set_one_input = self.module["set_one_input"]
         self._setup_device(device, memory_cfg)
 
     def _setup_device(self, dev, memory_cfg):
@@ -450,6 +451,30 @@ def set_input(self, func_name, *args, **kwargs):
         cargs = convert(args)
         self._set_input(func_name, *cargs)
 
+    def set_one_input(self, func_name, *args, **kwargs):
+        """Set the one input tensor with tag to a function.
+
+        Parameters
+        ----------
+        func_name : str
+            The name of the function.
+        args : [str or int, tvm.runtime.NDArray]
+            name or index of tensor and input tensor, optional
+        kwargs: dict of str or int to tvm.runtime.NDArray, optional
+            taged arguments to the function.
+        Only args or kwargs should exist
+        """
+        if kwargs:
+            assert len(kwargs) == 1
+            tag = next(iter(kwargs))
+            if isinstance(tag, str):
+                func_params = self._exec.get_function_params(func_name)
+                assert tag in func_params
+            self._set_one_input(func_name, tag, kwargs[tag])
+        else:
+            assert len(args) == 2
+            self._set_one_input(func_name, args[0], args[1])
+
     def invoke(self, func_name, *args, **kwargs):
         """Invoke a function.
 

diff --git a/src/runtime/vm/executable.cc b/src/runtime/vm/executable.cc
@@ -109,27 +109,20 @@ PackedFunc Executable::GetFunction(const std::string& name, const ObjectPtr<Obje
   }
 }
 
-int Executable::GetFunctionArity(std::string func_name) const {
+const VMFunction& Executable::GetVMFunctionWithName(const std::string& func_name) const {
   auto it = global_map.find(func_name);
-  if (it == global_map.end()) {
-    LOG(ERROR) << "Cannot find function " << func_name << " in executable";
-    return -1;
-  }
-  const auto& func = functions[it->second];
+  ICHECK(it != global_map.end()) << "Cannot find function " << func_name << " in executable";
+  return functions[it->second];
+}
+
+int Executable::GetFunctionArity(std::string func_name) const {
+  const auto& func = GetVMFunctionWithName(func_name);
   return func.params.size();
 }
 
 std::string Executable::GetFunctionParameterName(std::string func_name, uint32_t index) const {
-  auto it = global_map.find(func_name);
-  if (it == global_map.end()) {
-    LOG(ERROR) << "Cannot find function " << func_name << " in executable";
-    return "";
-  }
-  const auto& func = functions[it->second];
-  if (index > func.params.size()) {
-    LOG(ERROR) << "Invalid parameter index";
-    return "";
-  }
+  const auto& func = GetVMFunctionWithName(func_name);
+  ICHECK_LT(index, func.params.size()) << "Invalid parameter index";
   return func.params[index];
 }
 

diff --git a/src/runtime/vm/vm.cc b/src/runtime/vm/vm.cc
@@ -190,17 +190,7 @@ PackedFunc VirtualMachine::GetFunction(const std::string& name,
   } else if (name == "get_input_index") {
     return TypedPackedFunc<int64_t(std::string, std::string)>(
         [this](std::string input_name, std::string func_name) {
-          auto gvit = exec_->global_map.find(func_name);
-          ICHECK(gvit != exec_->global_map.end()) << "Cannot find function " << func_name;
-          auto func_index = gvit->second;
-          const auto& vm_func = exec_->functions[func_index];
-          const auto& param_names = vm_func.params;
-          for (uint64_t i = 0; i < param_names.size(); i++) {
-            if (input_name == param_names[i]) {
-              return static_cast<int64_t>(i);
-            }
-          }
-          return static_cast<int64_t>(-1);
+          return GetInputIndexFromVMFunction(func_name, input_name);
         });
   } else if (name == "init") {
     return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
@@ -221,6 +211,12 @@ PackedFunc VirtualMachine::GetFunction(const std::string& name,
   } else if (name == "set_input") {
     return PackedFunc(
         [sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { SetInput(args[0], args, 1); });
+  } else if (name == "set_one_input") {
+    return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
+      ICHECK_EQ(args.size(), 3) << "The expected number of arguments is 3 "
+                                << "(func_name, index or name, tensor)";
+      SetOneInput(args[0], args[1], args[2]);
+    });
   } else if (name == "load_late_bound_consts") {
     return PackedFunc([this](TVMArgs args, TVMRetValue* rv) {
       CHECK_EQ(args.size(), 1);
@@ -234,39 +230,91 @@ PackedFunc VirtualMachine::GetFunction(const std::string& name,
 }
 
 void VirtualMachine::SetInput(std::string func_name, TVMArgs args, int offset) {
-  ICHECK(exec_) << "The executable is not created yet.";
-  auto gvit = exec_->global_map.find(func_name);
-  ICHECK(gvit != exec_->global_map.end()) << "Cannot find function " << func_name;
-  auto func_index = gvit->second;
-  const auto& vm_func = exec_->functions[func_index];
-  const auto& param_names = vm_func.params;
-  ICHECK_EQ(args.size() - offset, param_names.size())
+  const auto& vm_func = CheckAndGetVMFunction(func_name);
+  size_t params_num = vm_func.params.size();
+  ICHECK_EQ(args.size() - offset, params_num)
       << "The number of provided parameters doesn't match the number of arguments";
-  ICHECK_EQ(param_names.size(), vm_func.param_device_indexes.size())
-      << "The number of provided parameters doesn't match the number of assigned devices";
-  std::vector<ObjectRef> func_args(param_names.size());
+  std::vector<ObjectRef> func_args(params_num);
   for (int i = offset; i < args.size(); ++i) {
-    Device dev = GetDevice(vm_func.param_device_indexes[i - offset]);
-
-    if (args[i].type_code() == kTVMDLTensorHandle) {
-      // Automatically convert input DLTensors to NDArray
-      DLTensor* tensor = args[i];
-      std::vector<int64_t> shape;
-      for (int64_t i = 0; i < tensor->ndim; i++) {
-        shape.push_back(tensor->shape[i]);
-      }
-      NDArray ary = NDArray::Empty(shape, tensor->dtype, dev);
-      ary.CopyFrom(tensor);
-      func_args[i - offset] = ary;
-    } else {
-      ObjectRef obj = CopyTo(args[i], dev);
-      func_args[i - offset] = obj;
-    }
+    int index = i - offset;
+    Device dev = GetDevice(vm_func.param_device_indexes[index]);
+    SetInputTensorWithIndex(func_args, args[i], index, dev);
   }
   inputs_.erase(func_name);
   inputs_.emplace(func_name, func_args);
 }
 
+void VirtualMachine::SetOneInput(std::string func_name, const TVMArgValue& tag,
+                                 const TVMArgValue& tensor) {
+  const auto& vm_func = CheckAndGetVMFunction(func_name);
+  size_t params_num = vm_func.params.size();
+
+  int inp_index;
+  if (tag.type_code() == kTVMArgInt) {
+    inp_index = tag;
+  } else if (tag.type_code() == kTVMStr) {
+    inp_index = static_cast<int>(GetInputIndexFromName(vm_func.params, tag));
+  } else {
+    LOG(FATAL) << "The type of input tensor tag (" << tag.type_code()
+               << ") doesn't match integer or string";
+  }
+  ICHECK_LT(inp_index, params_num);
+
+  CreateInputsOrCheckSize(func_name, params_num);
+  Device dev = GetDevice(vm_func.param_device_indexes[inp_index]);
+  SetInputTensorWithIndex(inputs_[func_name], tensor, inp_index, dev);
+}
+
+int64_t VirtualMachine::GetInputIndexFromVMFunction(const std::string& func_name,
+                                                    const std::string& input_name) const {
+  const auto& vm_func = CheckAndGetVMFunction(func_name);
+  return GetInputIndexFromName(vm_func.params, input_name);
+}
+
+int64_t VirtualMachine::GetInputIndexFromName(const std::vector<std::string>& params,
+                                              const std::string& input_name) const {
+  // TODO(vvchernov): excess integer type?
+  for (uint64_t i = 0; i < params.size(); i++) {
+    if (input_name == params[i]) {
+      return static_cast<int64_t>(i);
+    }
+  }
+  return static_cast<int64_t>(-1);
+}
+
+const VMFunction& VirtualMachine::CheckAndGetVMFunction(const std::string& func_name) const {
+  ICHECK(exec_) << "The executable is not created yet.";
+  return exec_->GetVMFunctionWithName(func_name);
+}
+
+void VirtualMachine::CreateInputsOrCheckSize(const std::string& func_name, size_t size) {
+  if (inputs_.count(func_name)) {
+    ICHECK_EQ(inputs_[func_name].size(), size)
+        << "The size of function" << func_name
+        << " doesn't match the number of provided parameters";
+  } else {
+    std::vector<ObjectRef> func_args(size);
+    inputs_.emplace(func_name, func_args);
+  }
+}
+
+void VirtualMachine::SetInputTensorWithIndex(std::vector<ObjectRef>& tensors,
+                                             const TVMArgValue& inp_tensor, int index, Device dev) {
+  if (inp_tensor.type_code() == kTVMDLTensorHandle) {
+    // Automatically convert input DLTensors to NDArray
+    DLTensor* tensor = inp_tensor;
+    std::vector<int64_t> shape;
+    for (int64_t i = 0; i < tensor->ndim; i++) {
+      shape.push_back(tensor->shape[i]);
+    }
+    NDArray ary = NDArray::Empty(shape, tensor->dtype, dev);
+    ary.CopyFrom(tensor);
+    tensors[index] = ary;
+  } else {
+    tensors[index] = CopyTo(inp_tensor, dev);
+  }
+}
+
 inline Device VirtualMachine::GetDevice(Index device_index) const {
   ICHECK_GE(devices_.size(), device_index) << "invalid device index: " << device_index;
   return devices_[device_index];