!13001 Auto_tune add sync fusion env.

From: @linqingke
Reviewed-by: @jjfeing,@xu-yfei
Signed-off-by: @xu-yfei

mindspore/_extends/parallel_compile/tbe_compiler/tuner.py

@@ -22,7 +22,7 @@ from te.platform.cce_conf import te_set_version
 from te.platform.fusion_manager import set_current_op_name
 from te.platform.fusion_util import fusion_op, dump_fusion_json
 from te.platform.parallel_compilation import init_multi_process_env, get_finished_compilation_task, \
-    deinit_multi_process_env, dispatch_autotune_task, start_ga_multi_process
+    deinit_multi_process_env, dispatch_autotune_task, start_ga_multi_process, import_py_module
 import auto_tune
 from schedule_search.rl_online_tune import rl_tune_init, dispatch_fusion_tune_task, dispatch_single_tune_task, \
     rl_tune_deinit
@@ -48,6 +48,8 @@ class TbeTuner:
         if os.environ.get("TUNE_DUMP_PATH") is not None:
             self.offline_dump_path = os.getenv("TUNE_DUMP_PATH", "")
         self._creating_custom_path(tune_mode)
+        self.fusion_need_sync = 0
+        self.module_list = {}
 
     def init_tune_interface(self, json_str, process_num):
         """
@@ -222,6 +224,24 @@ class TbeTuner:
             log.info("GA Tune init success.")
         return True
 
+    def sync_fusion_env(self):
+        """
+        Sync fusion env
+        :return: None
+        """
+        if self.fusion_need_sync == 0:
+            return
+
+        module_using = []
+        for key, value in self.module_list.items():
+            if value > 0:
+                module_using.append(str(key))
+            self.module_list[key] = 0
+
+        module_str = ",".join(module_using)
+        import_py_module(module_str)
+        self.fusion_need_sync = 0
+
     def rl_tune(self, task_id, op_json):
         """
         RL tune for single op and fusion op
@@ -231,6 +251,7 @@ class TbeTuner:
         """
         json_info = json.loads(op_json)
         if "fusion_op" in json_info:
+            self.sync_fusion_env()
             ret = self.fusion_rl_tune(task_id, json_info)
         else:
             ret = self.single_rl_tune(task_id, json_info)
@@ -244,6 +265,7 @@ class TbeTuner:
         """
         json_info = json.loads(op_json)
         if "fusion_op" in json_info:
+            self.sync_fusion_env()
             self.fusion_ga_tune(task_id, json_info)
         else:
             self.single_ga_tune(task_id, json_info)
@@ -289,6 +311,9 @@ class TbeTuner:
         l1size = 0  # todo need to verify
         ret = dispatch_single_tune_task(graph_id, task_id, l1size, base_kernel, kernel_name, op_module_name,
                                         op_module_name + "@" + op_module_name, op_type, op_type, op_args)
+
+        self.module_list[op_module_name] = 1
+        self.fusion_need_sync += 1
         return ret, job_type
 
     def get_op_module_names(self, json_info):

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_input_gpu_kernel.h

@@ -20,6 +20,7 @@
 #include <algorithm>
 #include <string>
 #include <vector>
+#include <map>
 
 #include "backend/kernel_compiler/gpu/cuda_impl/pad_impl.cuh"
 #include "backend/kernel_compiler/gpu/gpu_kernel.h"
@@ -28,6 +29,8 @@
 
 namespace mindspore {
 namespace kernel {
+const std::map<std::string, size_t> kFormatIndexMap = {{"NCHW", 2}, {"HWCN", 0}, {"NHWC", 1}};
+
 template <typename T>
 class ConvGradInputGpuBkwKernel : public GpuKernel {
  public:
@@ -339,7 +342,16 @@ class ConvGradInputGpuBkwKernel : public GpuKernel {
   void SetStrideAndDilation(const CNodePtr &kernel_node) {
     std::vector<int64_t> stride_me = AnfAlgo::GetNodeAttr<std::vector<int64_t>>(kernel_node, "stride");
     std::vector<int64_t> dilation_me = AnfAlgo::GetNodeAttr<std::vector<int64_t>>(kernel_node, "dilation");
-    (void)std::transform(stride_me.begin(), stride_me.end(), std::back_inserter(stride_),
+    std::string format_me = AnfAlgo::GetNodeAttr<std::string>(kernel_node, "format");
+    auto iter = kFormatIndexMap.find(format_me);
+    if (iter == kFormatIndexMap.end()) {
+      MS_LOG(EXCEPTION) << "OriFormat is " << format_me << ", Please confirm that in {NCHW, HWCN, NHWC}.";
+    }
+    size_t h_index = iter->second;
+    if (stride_me.size() < h_index + 2) {
+      MS_LOG(EXCEPTION) << "Strides should greater than " << h_index + 1 << ", but got " << stride_me.size();
+    }
+    (void)std::transform(stride_me.begin() + h_index, stride_me.begin() + h_index + 2, std::back_inserter(stride_),
                          [](const int64_t &value) { return static_cast<int>(value); });
     (void)std::transform(dilation_me.begin(), dilation_me.end(), std::back_inserter(dilation_),
                          [](const int64_t &value) { return static_cast<int>(value); });

mindspore/ops/operations/nn_ops.py

@@ -1981,7 +1981,7 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output'])
         self.out_channel = validator.check_positive_int(out_channel, 'out_channel', self.name)
         self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name)
-        self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=False)
+        self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=True)
         self.add_prim_attr('stride', self.stride)
         self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True)
         self.add_prim_attr('dilation', self.dilation)