recompute_fuse

mindspore/_extends/graph_kernel/model/graph_split.py

@@ -16,7 +16,7 @@
 import os
 from functools import reduce
 from mindspore import log as logger
-from .model import PrimLib, Graph, Tensor
+from .model import PrimLib, Graph, Tensor, Operator
 from .model import DataFormat as DF
 
 
@@ -65,13 +65,16 @@ class GraphSplitByPattern:
                 self.stitch_ops = set()
                 self.stitch_atomic_ops = set()
 
-        def __init__(self, init_op, is_output, unique_id, reach_tab):
-            self.pattern = PrimLib.iter_type(init_op)
-            self.ops = [init_op]
+        def __init__(self, init_op, is_output, unique_id, reach_tab, recompute_ops=None):
+            self.pattern = PrimLib.iter_type(init_op) if init_op is not None else PrimLib.UNKNOWN
+            self.ops = [] if init_op is None else [init_op]
             self.in_relations = dict()  # {area1: relation1, area2: relation2, ...}
             self.out_relations = dict()  # {area1: relation1, area2: relation2, ...}
             self.mode = None
             self.stitch_info = self.StitchInfo()
+            self.recompute_ops = [] if recompute_ops is None else recompute_ops
+            self.ori_op_map = {}
+            self.is_recompute = False
             self.is_output = is_output
             self.output_excluded = set()
             if self.pattern == PrimLib.REDUCE:
@@ -143,6 +146,8 @@ class GraphSplitByPattern:
                     r = rels.pop(area)
                     _update_relation(rels, self, r)
 
+            if area.is_recompute:
+                self.cp_ops(area)
             if self.pattern >= area.pattern:
                 self.ops.extend(area.ops)
             else:
@@ -161,7 +166,9 @@ class GraphSplitByPattern:
             if area.output_excluded:
                 self.output_excluded.update(area.output_excluded)
             self.update_stitch_info(area.stitch_info)
-            self.reach_tab.fuse(self.unique_id, area.unique_id)
+            if not area.is_recompute:
+                self.reach_tab.fuse(self.unique_id, area.unique_id)
+            self.recompute_ops.extend(area.recompute_ops)
 
         def check_acyclic(self, to):
             """Check circle. It returns false if circle exists"""
@@ -180,25 +187,73 @@ class GraphSplitByPattern:
                         return True
             return False
 
+        def cp_ops(self, area):
+            """copy recompute_ops in area to ops, self is area's user"""
+            tail_tensor = area.recompute_ops[-1].output
+            #copy tensors, all copied are Tensor.PARA_NONE
+            tensor_map = {}
+            tensor_map[area.recompute_ops[0].inputs[0]] = area.recompute_ops[0].inputs[0]
+            for op in area.recompute_ops:
+                orig_tensor = op.output
+                cp_tensor = Tensor(orig_tensor.name, orig_tensor.shape, orig_tensor.dtype, orig_tensor.data_format)
+                tensor_map[orig_tensor] = cp_tensor
+            #copy ops
+            cp_ops = []
+            for op in area.recompute_ops:
+                cp_op = Operator(op.prim, [tensor_map[op.inputs[0]]], tensor_map[op.output], op.attrs)
+                cp_op.all_inputs = cp_op.inputs
+                cp_ops.append(cp_op)
+                area.ori_op_map[cp_op] = op
+            #connect copied ops
+            for op in self.ops:
+                if tail_tensor in op.inputs:
+                    op.inputs.remove(tail_tensor)
+                    op.inputs.append(tensor_map[tail_tensor])
+                    tail_tensor.to_ops.remove(op)
+                    tensor_map[tail_tensor].to_ops.append(op)
+            #fill cp_ops in self.recompute_area
+            cp_dom_op = None
+            for cp, ori in area.ori_op_map.items():
+                if ori == area.dom_op():
+                    cp_dom_op = cp
+            area.ops.clear()
+            area.ops.append(cp_dom_op)
+            area.ops.extend([op for op in cp_ops if op != cp_dom_op])
+
     def __init__(self, graph, flags):
         self.graph = graph
         self.areas = []
         self.flags = flags
-        self.reach_tab = self.ReachTable(len(graph.ops))
-        area_map = {}
+        self.enable_recompute = self.flags.get("enable_recompute_fusion", False)
+        self.reach_tab = self.ReachTable(len(graph.ops) + 1 if self.enable_recompute else len(graph.ops))
+        self.area_map = {}
         _, outputs = graph.deduce_parameters()
-        idx = 0
+        self.idx = 0
         for op in graph.ops:
             is_output = op.output in outputs
-            a = self.Area(op, is_output, idx, self.reach_tab)
-            idx += 1
+            a = self.Area(op, is_output, self.idx, self.reach_tab)
+            self.idx += 1
             self.set_default_mode(a)
             self.areas.append(a)
-            area_map[op] = a
+            self.set_area_map([op], a)
         for a in self.areas:
-            a.link_input(area_map)
+            a.link_input(self.area_map)
         for i in range(len(self.areas)-1, -1, -1):
             self.areas[i].link_output()
+        if self.enable_recompute:
+            self.recom_area = self.Area(None, False, self.idx, self.reach_tab)
+            self.recom_area.is_recompute = True
+            self.recom_pre = None
+            self.recom_user = None
+            self.recom_dom = None
+            self.dom_user_r = PrimLib.UNKNOWN
+            self.recom_res = False
+            self.orig_op_map = {}
+
+    def set_area_map(self, ops, area):
+        """update area_map after op fused to area"""
+        for op in ops:
+            self.area_map[op] = area
 
     def set_default_mode(self, area):
         area.mode = self.get_default_mode(area.ops[0])
@@ -234,11 +289,13 @@ class GraphSplitByPattern:
                     if is_forward:
                         for area in fuse_areas:
                             dominant.fuse(area)
+                            self.set_area_map(area.ops, dominant)
                             self.areas.remove(area)
                     else:
                         forward_area = dominant
                         for area in fuse_areas:
                             area.fuse(forward_area)
+                            self.set_area_map(forward_area.ops, area)
                             self.areas.remove(forward_area)
                             forward_area = area
                     changed = True
@@ -246,16 +303,39 @@ class GraphSplitByPattern:
             else:
                 return changed
 
-    def to_subgraphs(self):
-        """Transform op groups to subgraphs"""
+    def fuse_recom(self, selector):
+        """Fuse recompute area to its user"""
+        for dominant in [self.recom_area, self.recom_user]:
+            result = selector(dominant)
+            if result is not None and result[0]:
+                fuse_areas, _ = result
+                fuse_areas = self.limit_area_size(dominant, fuse_areas)
+                if not fuse_areas:
+                    continue
+                if fuse_areas[0] in [self.recom_area, self.recom_user]:
+                    self.recom_user.fuse(self.recom_area)
+                    self.recom_res = True
+                    return True
+        return False
+
+    def index_op(self):
+        """index op by order, the copied op share id with original op, for topo-sort"""
         ids = {}
         for i, op in enumerate(self.graph.ops):
             ids[op] = i
+        if hasattr(self, 'orig_op_map'):
+            for k, v in self.orig_op_map.items():
+                ids[k] = ids[v]
+        return ids
+
+    def to_subgraphs(self):
+        """Transform op groups to subgraphs"""
+        ids = self.index_op()
         subgraphs = []
         graphmodes = []
         for i, area in enumerate(self.areas):
             area.ops.sort(key=lambda op: ids[op])
-            subgraphs.append(Graph('{}_{}'.format(self.graph.name, i), area.ops, area.stitch_info))
+            subgraphs.append(Graph('{}_{}'.format(self.graph.name, i), area.ops, area.stitch_info, area.recompute_ops))
             graphmodes.append("basic" if area.mode == self.Area.MODE_BASIC else "composite")
         return subgraphs, graphmodes
 
@@ -274,13 +354,14 @@ class GraphSplitByPattern:
             with os.fdopen(os.open(filename, os.O_RDWR | os.O_CREAT), 'w+') as f:
                 f.write(subgraphs_str)
 
-    def do_split(self):
-        """Split graph by pattern"""
-        raise Exception("do_split() is not implemented in {}".format(self.__class__.__name__))
+    def pattern_fuse(self, select=None):
+        """fuse Areas by pattern repeatedly"""
+        raise Exception("pattern_fuse() is not implemented in {}".format(self.__class__.__name__))
 
     def split(self):
         """Split graph by pattern"""
-        self.do_split()
+        self.pattern_fuse()
+        self.recompute_fuse()
         # The reshape should not be output node
         # Note: after this function, the input output relation is not maintained.
         self.split_output_reshapes()
@@ -316,6 +397,159 @@ class GraphSplitByPattern:
         if new_areas:
             self.areas += new_areas
 
+    def set_recompute(self, dom_area, ops, user_area):
+        """set the recompute area and connect with other areas"""
+        self.recom_area.recompute_ops.extend(ops)
+        #recom_area: set dom_op and correct ops length
+        patterns = [PrimLib.iter_type(op) for op in ops]
+        self.recom_area.pattern = max(patterns)
+        for i, pat in enumerate(patterns):
+            if pat == self.recom_area.pattern:
+                self.recom_area.ops = [ops[i]] * len(ops)
+                break
+        #disconnect dom_area and user_area
+        self.dom_user_r = dom_area.out_relations[user_area]
+        dom_area.out_relations.pop(user_area)
+        user_area.in_relations.pop(dom_area)
+        #connect recom_area and user_area
+        user_area.in_relations[self.recom_area] = self.dom_user_r
+        self.recom_area.out_relations[user_area] = self.dom_user_r
+        #connect recom_pre and recom_area
+        self.recom_pre = self.area_map[ops[0].inputs[0].op] if ops[0].inputs[0].op else None
+        if self.recom_pre is not None:
+            self.recom_area.in_relations[self.recom_pre] = dom_area.in_relations[self.recom_pre]
+            self.recom_pre.out_relations[self.recom_area] = dom_area.in_relations[self.recom_pre]
+        #set related areas
+        self.recom_user = user_area
+        self.recom_dom = dom_area
+        self.recom_res = False
+
+    def clear_recompute(self):
+        """disconnect recom_area from other areas, and clear recom_area"""
+        self.recom_area.out_relations.clear()
+        self.recom_area.in_relations.clear()
+        if not self.recom_res:
+            self.recom_user.in_relations.pop(self.recom_area)
+            self.recom_user.in_relations[self.recom_dom] = self.dom_user_r
+            self.recom_dom.out_relations[self.recom_user] = self.dom_user_r
+            if self.recom_pre:
+                self.recom_pre.out_relations.pop(self.recom_area)
+        self.recom_area.ops.clear()
+        self.recom_area.recompute_ops.clear()
+        self.orig_op_map.update(self.recom_area.ori_op_map)
+        self.recom_area.ori_op_map.clear()
+
+
+    def to_subgraph(self, dom):
+        """Transform area to subgraphs"""
+        ids = self.index_op()
+        dom_ops = list()
+        dom_ops.extend(dom.ops)
+        dom_ops.sort(key=lambda op: ids[op])
+        subgraph = []
+        subgraph = Graph('{}_area'.format(self.graph.name), dom_ops)
+        return subgraph
+
+    def find_cheap_regions(self, dom):
+        """extract all the cheap regions in dom area, toposort each region before return"""
+        def _grow_region(region_ops, op, weight, inputs):
+            """include op to region_ops if region grow"""
+            # region successfully ends at input
+            if op.inputs[0] in inputs and len(op.inputs) == 1 and \
+                PrimLib.iter_type(op) <= PrimLib.BROADCAST:
+                region_ops.append(op)
+                return False, None, weight
+            #region fails to grow
+            MAX_WEIGHT = 20
+            if weight > MAX_WEIGHT or len(op.inputs) > 1 or PrimLib.iter_type(op) > PrimLib.BROADCAST:
+                return False, None, weight
+            #region grows successfully
+            weight = weight + 1
+            region_ops.append(op)
+            return True, op.inputs[0].op, weight
+
+        def _find_cheap_regions(dom):
+            sub = self.to_subgraph(dom)
+            inputs, outputs = sub.deduce_parameters()
+            cheap_regions = []
+            for output in outputs:
+                #  tensor should have user other than user_area to be fused
+                if output.para_type != Tensor.PARA_OUTPUT and len(output.to_ops) < 2:
+                    continue
+                region_ops = []
+                grow = True
+                candidate_op = output.op
+                weight = 1
+                while grow:
+                    grow, candidate_op, weight = _grow_region(region_ops, candidate_op, weight, inputs)
+                # region ends at input and not empty
+                if region_ops and region_ops[-1].inputs[0] in inputs:
+                    region_ops.reverse()
+                    # tensor size should equal or becomes larger(cast up, broadcast)
+                    if region_ops[0].inputs[0].get_size() > region_ops[-1].output.get_size():
+                        continue
+                    cheap_regions.append(region_ops)
+            return cheap_regions
+
+        return _find_cheap_regions(dom)
+
+    def select_user_area(self, tail_tensor):
+        """select the user area has only one edge to dom area"""
+        def _get_edge_num(dom_area, user_area):
+            """get edge num between two areas"""
+            dom_graph = self.to_subgraph(dom_area)
+            _, dom_outputs = dom_graph.deduce_parameters()
+            user_graph = self.to_subgraph(user_area)
+            user_inputs, _ = user_graph.deduce_parameters()
+            edge = [t for t in dom_outputs if t in user_inputs]
+            return len(edge)
+
+        def _select_user_area(tail_tensor):
+            user_areas = []
+            for user_op in tail_tensor.to_ops:
+                user_area = self.area_map[user_op]
+                if len(user_area.ops) == 1 and user_area.pattern == PrimLib.RESHAPE:
+                    continue
+                edge_num = _get_edge_num(self.area_map[tail_tensor.op], user_area)
+                if edge_num == 1 and not user_area in user_areas:
+                    user_areas.append(user_area)
+            return user_areas
+
+        return _select_user_area(tail_tensor)
+
+    def recompute_fuse(self):
+        """find recompute regions and copy them out to new Areas"""
+        def do_recompute_fuse():
+            """split the unfusing pattern by add recompute area"""
+            recompute_suc = False
+            orig_areas = []
+            orig_areas.extend(self.areas)
+            for dom in orig_areas:
+                if dom not in self.areas or not dom.out_relations:
+                    continue
+                cheap_regions = self.find_cheap_regions(dom)
+                dom_changed = False
+                for cheap_region in cheap_regions:
+                    user_areas = self.select_user_area(cheap_region[-1].output)
+                    if not user_areas:
+                        continue
+                    for user_area in user_areas:
+                        self.set_recompute(dom, cheap_region, user_area)
+                        self.pattern_fuse(self.fuse_recom)
+                        self.clear_recompute()
+                        if self.recom_res:
+                            recompute_suc = True
+                            #Copy region at most once for this dom
+                            dom_changed = True
+                            break
+                    if dom_changed:
+                        break
+            return recompute_suc
+
+        if self.enable_recompute:
+            while do_recompute_fuse():
+                self.pattern_fuse()
+
 use_poly_reduce = True
 
 
@@ -331,8 +565,8 @@ class GraphSplitGpu(GraphSplitByPattern):
         pattern = PrimLib.iter_type(op)
         return self.Area.MODE_BASIC if pattern == PrimLib.RESHAPE else self.Area.MODE_COMPOSITE
 
-    def do_split(self):
-        """Split graph by pattern"""
+    def pattern_fuse(self, fuse_func=None):
+        """fuse Areas by pattern"""
         def _reshape(dom):
             if dom.pattern != PrimLib.RESHAPE:
                 return None
@@ -551,21 +785,38 @@ class GraphSplitGpu(GraphSplitByPattern):
                     fused.append(a)
             return fused, True
 
-        enable_stitch_fusion = self.flags.get("enable_stitch_fusion", False)
-        changed = True
-        while changed:
-            changed = self.fuse(_reshape)
-            changed = self.fuse(_elemwise_depth) or changed
-            changed = self.fuse(_elemwise_width) or changed
-            changed = self.fuse(_reduce_depth) or changed
-            changed = self.fuse(_reduce_width) or changed
-            changed = self.fuse(_broadcast_depth) or changed
-            changed = self.fuse(_broadcast_width) or changed
+        def _fuse_loop():
+            changed = True
+            while changed:
+                changed = self.fuse(_reshape)
+                changed = self.fuse(_elemwise_depth) or changed
+                changed = self.fuse(_elemwise_width) or changed
+                changed = self.fuse(_reduce_depth) or changed
+                changed = self.fuse(_reduce_width) or changed
+                changed = self.fuse(_broadcast_depth) or changed
+                changed = self.fuse(_broadcast_width) or changed
+                if use_poly_reduce:
+                    changed = self.fuse(_reduce_output) or changed
+                    if enable_stitch_fusion:
+                        changed = self.fuse(_reduce_stitch) or changed
+            self.fuse(_transpose)
+
+        def _fuse_once(fuse_func):
+            if fuse_func(_reshape) or fuse_func(_elemwise_depth) or fuse_func(_elemwise_width) or \
+                fuse_func(_reduce_depth) or fuse_func(_reduce_width) or fuse_func(_broadcast_depth) or \
+                fuse_func(_broadcast_width):
+                return
             if use_poly_reduce:
-                changed = self.fuse(_reduce_output) or changed
-                if enable_stitch_fusion:
-                    changed = self.fuse(_reduce_stitch) or changed
-        self.fuse(_transpose)
+                if fuse_func(_reduce_output) or (enable_stitch_fusion and fuse_func(_reduce_stitch)):
+                    return
+            fuse_func(_transpose)
+            return
+
+        enable_stitch_fusion = self.flags.get("enable_stitch_fusion", False)
+        if fuse_func is None:
+            _fuse_loop()
+        else:
+            _fuse_once(fuse_func)
 
 
 class GraphSplitAscend(GraphSplitByPattern):
@@ -580,8 +831,8 @@ class GraphSplitAscend(GraphSplitByPattern):
             return self.Area.MODE_COMPOSITE
         return self.Area.MODE_BASIC
 
-    def do_split(self):
-        """Split graph by pattern"""
+    def pattern_fuse(self, fuse_func=None):
+        """fuse Areas by pattern"""
         def _tensor_size(tensor):
             size = 1
             for i in tensor.shape:
@@ -685,6 +936,19 @@ class GraphSplitAscend(GraphSplitByPattern):
                     fused.append(a)
             return fused, False
 
+        def _reduce_output(dom):
+            if dom.pattern != PrimLib.REDUCE:
+                return None
+            op_attrs = dom.dom_op().attrs
+            if not op_attrs.get('reduce_output_fuse'):
+                return None
+            fused = []
+            for a, r in dom.out_relations.items():
+                if a.pattern <= PrimLib.BROADCAST and r <= PrimLib.BROADCAST and \
+                        dom.check_acyclic(a):
+                    fused.append(a)
+            return fused, False
+
         def _transdata_pattern_support(dom, a):
             transdata_op = dom.dom_op()
 
@@ -733,32 +997,31 @@ class GraphSplitAscend(GraphSplitByPattern):
                     fused.append(a)
             return fused, True
 
-        def _reduce_output(dom):
-            if dom.pattern != PrimLib.REDUCE:
-                return None
-            op_attrs = dom.dom_op().attrs
-            if not op_attrs.get('reduce_output_fuse'):
-                return None
-            fused = []
-            for a, r in dom.out_relations.items():
-                if a.pattern <= PrimLib.BROADCAST and r <= PrimLib.BROADCAST and \
-                        dom.check_acyclic(a):
-                    fused.append(a)
-            return fused, False
-
-        changed = True
-        while changed:
-            changed = self.fuse(_reshape)
-            changed = self.fuse(_elemwise_depth) or changed
-            changed = self.fuse(_elemwise_width) or changed
-            changed = self.fuse(_reduce_depth) or changed
-            changed = self.fuse(_reduce_width) or changed
-            changed = self.fuse(_broadcast_depth) or changed
-            changed = self.fuse(_broadcast_width) or changed
-            changed = self.fuse(_matmul_depth) or changed
-            changed = self.fuse(_reduce_output) or changed
-        self.fuse(_transdata)
-
+        def _fuse_loop():
+            changed = True
+            while changed:
+                changed = self.fuse(_reshape)
+                changed = self.fuse(_elemwise_depth) or changed
+                changed = self.fuse(_elemwise_width) or changed
+                changed = self.fuse(_reduce_depth) or changed
+                changed = self.fuse(_reduce_width) or changed
+                changed = self.fuse(_broadcast_depth) or changed
+                changed = self.fuse(_broadcast_width) or changed
+                changed = self.fuse(_matmul_depth) or changed
+                changed = self.fuse(_reduce_output) or changed
+            self.fuse(_transdata)
+
+        def _fuse_once(fuse_func):
+            if fuse_func(_reshape) or fuse_func(_elemwise_depth) or fuse_func(_elemwise_width) or \
+                fuse_func(_reduce_depth) or fuse_func(_reduce_width) or fuse_func(_broadcast_depth) or \
+                fuse_func(_broadcast_width) or fuse_func(_matmul_depth) or fuse_func(_reduce_output) or \
+                fuse_func(_transdata):
+                pass
+
+        if fuse_func is None:
+            _fuse_loop()
+        else:
+            _fuse_once(fuse_func)
 
 
 def split(graph, target, flags):

mindspore/_extends/graph_kernel/model/model.py

@@ -320,8 +320,8 @@ class Operator:
 
     def __str__(self):
         args = ', '.join([str(t) for t in self.all_inputs])
-        expr = "%s = %s.%s(%s)" % (
-            str(self.output), self.prim, self.output.dtype, args)
+        expr = "%s = %s.%s(%s) id:%s" % (
+            str(self.output), self.prim, self.output.dtype, args, id(self))
         return expr if not self.attrs else '%s // %s' % (expr, str(self.attrs))
 
     def __repr__(self):
@@ -331,12 +331,13 @@ class Operator:
 class Graph:
     """Graph"""
 
-    def __init__(self, name, ops, stitch_info=None):
+    def __init__(self, name, ops, stitch_info=None, recompute_ops=None):
         self.name = name
         self.ops = ops  # in topo order, can not use set
         self.inputs = []
         self.outputs = []
         self.stitch_info = stitch_info
+        self.recompute_ops = recompute_ops
 
     def set_processor(self, processor):
         """Set processor"""

mindspore/_extends/graph_kernel/model/model_builder.py

@@ -203,11 +203,13 @@ class CompositeGraph:
             desc['buffer_stitch'] = buffer_stitch
         return desc
 
-    def dump(self, subgraph):
-        """Dump Graph to json"""
-        desc = {}
-        inputs, outputs = subgraph.deduce_parameters()
-        graph_ops = set(subgraph.ops)
+    def add_recompute_ops(self, subgraph, desc):
+        if subgraph.recompute_ops:
+            desc['recompute_ops'] = [op.output.name for op in subgraph.recompute_ops]
+        return desc
+
+    def _pre_dump(self, outputs):
+        """restore name to before load"""
         inplace_assign = {}  # y_name, output_name
         inplace_assign_z = None
         for op in self.desc['op_desc']:
@@ -217,6 +219,14 @@ class CompositeGraph:
             for t in outputs:
                 if t.name not in inplace_assign:
                     inplace_assign_z = t
+        return inplace_assign, inplace_assign_z
+
+    def dump(self, subgraph):
+        """Dump Graph to json"""
+        desc = {}
+        inputs, outputs = subgraph.deduce_parameters()
+        graph_ops = set(subgraph.ops)
+        inplace_assign, inplace_assign_z = self._pre_dump(outputs)
         for key in self.desc:
             if key == 'input_desc':
                 desc[key] = [
@@ -251,7 +261,7 @@ class CompositeGraph:
                             op_desc.append(inplace_desc)
                     else:
                         op = self.tensors[d['output_desc'][0]['tensor_name']].op
-                        if op in graph_ops:
+                        if op in graph_ops or op in subgraph.recompute_ops:
                             op_desc.append(d)
                 desc[key] = op_desc
             elif key == 'op':
@@ -260,6 +270,7 @@ class CompositeGraph:
                 desc[key] = self.desc[key]
 
         desc = self.add_stitch_info(subgraph, desc)
+        desc = self.add_recompute_ops(subgraph, desc)
         return desc
 
 

mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_json_decoder.cc

@@ -433,68 +433,5 @@ FuncGraphPtr AkgKernelJsonDecoder::DecodeFusedNodes(const std::string &kernel_js
   auto kernel_json = nlohmann::json::parse(kernel_json_str);
   return DecodeFusedNodes(kernel_json);
 }
-
-StitchInfo AkgKernelJsonDecoder::GetStitchInfo(const nlohmann::json &kernel_json) const {
-  StitchInfo info;
-  if (kernel_json.find(kJsonKeyBufferStitch) != kernel_json.end()) {
-    nlohmann::json buffer_stitch = kernel_json[kJsonKeyBufferStitch];
-    if (buffer_stitch.find(kJsonKeyStitchOp) != buffer_stitch.end()) {
-      std::vector<std::string> stitch_ops = buffer_stitch[kJsonKeyStitchOp];
-      info.stitch_ops = stitch_ops;
-    }
-    if (buffer_stitch.find(kJsonKeyStitchAtomicOp) != buffer_stitch.end()) {
-      std::vector<std::string> stitch_atomic_ops = buffer_stitch[kJsonKeyStitchAtomicOp];
-      info.stitch_atomic_ops = stitch_atomic_ops;
-    }
-  }
-  return info;
-}
-
-void AkgKernelJsonDecoder::SetStitchAttr(const nlohmann::json &op_desc, const StitchInfo &info,
-                                         const CNodePtr &node) const {
-  std::vector<nlohmann::json> output_descs = op_desc[kJsonKeyOutputDesc];
-  if (output_descs.empty() || output_descs[0].find(kJsonKeyTensorName) == output_descs[0].end()) return;
-  std::string tensor_name = output_descs[0][kJsonKeyTensorName];
-  if (std::find(info.stitch_ops.begin(), info.stitch_ops.end(), tensor_name) != info.stitch_ops.end()) {
-    AnfAlgo::SetNodeAttr(kAttrStitch, MakeValue("common"), node);
-    MS_LOG(INFO) << "Enable common stitch fusion by " << node->fullname_with_scope();
-  }
-  if (std::find(info.stitch_atomic_ops.begin(), info.stitch_atomic_ops.end(), tensor_name) !=
-      info.stitch_atomic_ops.end()) {
-    AnfAlgo::SetNodeAttr(kAttrStitch, MakeValue("atomic"), node);
-    MS_LOG(INFO) << "Enable atomic add stitch fusion by " << node->fullname_with_scope();
-  }
-}
-
-bool AkgKernelJsonDecoder::DecodeSplitNodes(const nlohmann::json &kernel_json,
-                                            const std::map<std::string, AnfNodePtr> &address_node_map,
-                                            AnfNodePtrList *res_graphs) {
-  MS_EXCEPTION_IF_NULL(res_graphs);
-  MS_LOG(DEBUG) << "start decode, " << kernel_json;
-  // decode cnodes in graph.
-  std::vector<nlohmann::json> op_node_descs = kernel_json[kJsonKeyOpDesc];
-  if (op_node_descs.empty()) {
-    MS_LOG(ERROR) << "Error decode, no cnodes for graph." << kernel_json;
-    return false;
-  }
-  StitchInfo info = GetStitchInfo(kernel_json);
-  for (const auto &op_desc : op_node_descs) {
-    if (op_desc.find(kJsonKeyPtrAddress) == op_desc.end() || op_desc[kJsonKeyPtrAddress].is_null()) {
-      MS_LOG(ERROR) << "Decode failed, key: " << kJsonKeyPtrAddress << " not found in: " << op_desc;
-      return false;
-    }
-
-    std::string ptr_address = op_desc[kJsonKeyPtrAddress];
-    if (address_node_map.count(ptr_address) == 0) {
-      MS_LOG(ERROR) << "Decode failed, ptr_address not found in map.";
-      return false;
-    }
-    auto node = address_node_map.at(ptr_address)->cast<CNodePtr>();
-    SetStitchAttr(op_desc, info, node);
-    res_graphs->push_back(node);
-  }
-  MS_LOG(DEBUG) << "decode cnodes success, size: " << res_graphs->size();
-  return true;
-}
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_json_decoder.h

@@ -26,10 +26,6 @@
 
 namespace mindspore {
 namespace kernel {
-struct StitchInfo {
-  std::vector<std::string> stitch_ops;
-  std::vector<std::string> stitch_atomic_ops;
-};
 class AkgKernelJsonDecoder {
  public:
   AkgKernelJsonDecoder() { nodes_map_.clear(); }
@@ -37,15 +33,11 @@ class AkgKernelJsonDecoder {
 
   FuncGraphPtr DecodeFusedNodes(const nlohmann::json &kernel_json);
   FuncGraphPtr DecodeFusedNodes(const std::string &kernel_json_str);
-  bool DecodeSplitNodes(const nlohmann::json &kernel_json, const std::map<std::string, AnfNodePtr> &address_node_map,
-                        AnfNodePtrList *res_graphs);
 
  private:
   ParameterPtr DecodeParameter(const nlohmann::json &parameter_json, const FuncGraphPtr &func_graph);
   CNodePtr DecodeCNode(const nlohmann::json &cnode_json, const FuncGraphPtr &func_graph, const std::string &processor);
   AnfNodePtr DecodeOutput(const std::vector<nlohmann::json> &output_descs, const FuncGraphPtr &func_graph);
-  StitchInfo GetStitchInfo(const nlohmann::json &kernel_json) const;
-  void SetStitchAttr(const nlohmann::json &op_desc, const StitchInfo &info, const CNodePtr &node) const;
   std::map<std::string, AnfNodePtr> nodes_map_;
 };
 }  // namespace kernel

mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_json_generator.h

@@ -54,6 +54,7 @@ constexpr auto kJsonKeyFusionType = "fusion_type";
 constexpr auto kJsonKeySubGraph = "sub_graph";
 constexpr auto kJsonKeyCoreNum = "core_num";
 constexpr auto kJsonKeyTypeInfo = "type_info";
+constexpr auto kJsonKeyRecomputeOps = "recompute_ops";
 constexpr auto kJsonKeyBufferStitch = "buffer_stitch";
 constexpr auto kJsonKeyStitchOp = "stitch_op";
 constexpr auto kJsonKeyStitchAtomicOp = "stitch_atomic_op";

mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_optimization.cc

@@ -117,7 +117,7 @@ PassManagerPtr GraphKernelOptimizer::Split() const {
   // which can avoid unnecessary input-output and get better performance.
   // preprocess for ShapeOpsSplitter
   pm->AddPass(std::make_shared<ExtendOutputForUpdateState>(), OptLevel_1);
-  std::vector<PrimitivePtr> duplicated_ops = {prim::kPrimReshape, prim::kPrimExpandDims, prim::kPrimCast};
+  std::vector<PrimitivePtr> duplicated_ops = {prim::kPrimReshape};
   pm->AddPass(std::make_shared<ShapeOpsSplitter>(duplicated_ops), OptLevel_1);
 
   // Split kernel according to costmodel

mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_splitter.cc

@@ -32,6 +32,150 @@
 #include "utils/context/graph_kernel_flags.h"
 
 namespace mindspore {
+namespace kernel {
+namespace {
+StitchInfo GetStitchInfo(const nlohmann::json &kernel_json) {
+  StitchInfo info;
+  if (kernel_json.find(kJsonKeyBufferStitch) != kernel_json.end()) {
+    nlohmann::json buffer_stitch = kernel_json[kJsonKeyBufferStitch];
+    if (buffer_stitch.find(kJsonKeyStitchOp) != buffer_stitch.end()) {
+      std::vector<std::string> stitch_ops = buffer_stitch[kJsonKeyStitchOp];
+      info.stitch_ops = stitch_ops;
+    }
+    if (buffer_stitch.find(kJsonKeyStitchAtomicOp) != buffer_stitch.end()) {
+      std::vector<std::string> stitch_atomic_ops = buffer_stitch[kJsonKeyStitchAtomicOp];
+      info.stitch_atomic_ops = stitch_atomic_ops;
+    }
+  }
+  return info;
+}
+
+std::set<std::string> GetRecomputeOps(const nlohmann::json &kernel_json) {
+  if (kernel_json.find(kJsonKeyRecomputeOps) != kernel_json.end()) {
+    std::vector<std::string> recompute_ops = kernel_json[kJsonKeyRecomputeOps];
+    return std::set<std::string>(recompute_ops.begin(), recompute_ops.end());
+  }
+  return std::set<std::string>();
+}
+
+bool IsRecomputeOp(const nlohmann::json &op_desc, const std::set<std::string> &recompute_ops) {
+  std::vector<nlohmann::json> output_descs = op_desc[kJsonKeyOutputDesc];
+  if (output_descs.empty() || output_descs[0].find(kJsonKeyTensorName) == output_descs[0].end()) {
+    return false;
+  }
+  std::string tensor_name = output_descs[0][kJsonKeyTensorName];
+  if (recompute_ops.count(tensor_name)) {
+    return true;
+  }
+  return false;
+}
+
+CNodePtr NewRecomputeNode(const AnfNodePtr &orig_node, std::map<AnfNodePtr, AnfNodePtr> *node_map) {
+  auto func_graph = orig_node->func_graph();
+  MS_EXCEPTION_IF_NULL(func_graph);
+  auto cnode = orig_node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  TraceGuard guard(std::make_shared<TraceOpt>(cnode->debug_info()));
+  auto orig_inputs = cnode->inputs();
+  std::vector<AnfNodePtr> inputs;
+  for (auto inp : orig_inputs) {
+    if (node_map->find(inp) == node_map->end()) {
+      inputs.push_back(inp);
+      continue;
+    }
+    inputs.push_back((*node_map)[inp]);
+  }
+  CNodePtr cp_node = func_graph->NewCNode(inputs);
+  func_graph->AddNode(cp_node);
+  cp_node->set_abstract(cnode->abstract());
+  cp_node->set_forward(cnode->forward().first, cnode->forward().second);
+  cp_node->set_inputs_value(cnode->inputs_value());
+  ScopePtr scope = (orig_node->scope() != kDefaultScope) ? orig_node->scope() : kDefaultScope;
+  cp_node->set_scope(scope);
+  cp_node->set_kernel_info(cnode->kernel_info_ptr());
+  (*node_map)[orig_node] = cp_node;
+  return cp_node->cast<CNodePtr>();
+}
+
+void SetStitchAttr(const nlohmann::json &op_desc, const StitchInfo &info, const CNodePtr &node) {
+  std::vector<nlohmann::json> output_descs = op_desc[kJsonKeyOutputDesc];
+  if (output_descs.empty() || output_descs[0].find(kJsonKeyTensorName) == output_descs[0].end()) return;
+  std::string tensor_name = output_descs[0][kJsonKeyTensorName];
+  if (std::find(info.stitch_ops.begin(), info.stitch_ops.end(), tensor_name) != info.stitch_ops.end()) {
+    AnfAlgo::SetNodeAttr(kAttrStitch, MakeValue("common"), node);
+    MS_LOG(INFO) << "Enable common stitch fusion by " << node->fullname_with_scope();
+  }
+  if (std::find(info.stitch_atomic_ops.begin(), info.stitch_atomic_ops.end(), tensor_name) !=
+      info.stitch_atomic_ops.end()) {
+    AnfAlgo::SetNodeAttr(kAttrStitch, MakeValue("atomic"), node);
+    MS_LOG(INFO) << "Enable atomic add stitch fusion by " << node->fullname_with_scope();
+  }
+}
+
+// replace original region root op by its copy in this res_graphs
+void ConnectRecomputeOps(AnfNodePtrList *res_graphs, const AnfNodePtr &orig_region_root,
+                         const AnfNodePtr &cp_region_root) {
+  for (auto &node : *res_graphs) {
+    auto cnode = node->cast<CNodePtr>();
+    auto inputs = cnode->inputs();
+    for (size_t i = 1; i < inputs.size(); ++i) {
+      if (inputs[i] != orig_region_root) continue;
+      cnode->set_input(i, cp_region_root);
+    }
+  }
+}
+}  // namespace
+
+bool SplitNodesDecoder::DecodeSplitNodes(const nlohmann::json &kernel_json,
+                                         const std::map<std::string, AnfNodePtr> &address_node_map,
+                                         AnfNodePtrList *res_graphs) {
+  MS_EXCEPTION_IF_NULL(res_graphs);
+  MS_LOG(DEBUG) << "start decode, " << kernel_json;
+  // decode cnodes in graph.
+  std::vector<nlohmann::json> op_node_descs = kernel_json[kJsonKeyOpDesc];
+  if (op_node_descs.empty()) {
+    MS_LOG(ERROR) << "Error decode, no cnodes for graph." << kernel_json;
+    return false;
+  }
+  StitchInfo info = GetStitchInfo(kernel_json);
+  auto recompute_ops = GetRecomputeOps(kernel_json);
+  // key_value: original_copied
+  std::map<AnfNodePtr, AnfNodePtr> node_map;
+  // nodes would be copied
+  AnfNodePtrList orig_region_nodes;
+  // nodes would not be copied
+  AnfNodePtrList no_cp_nodes;
+  for (const auto &op_desc : op_node_descs) {
+    if (op_desc.find(kJsonKeyPtrAddress) == op_desc.end() || op_desc[kJsonKeyPtrAddress].is_null()) {
+      MS_LOG(ERROR) << "Decode failed, key: " << kJsonKeyPtrAddress << " not found in: " << op_desc;
+      return false;
+    }
+
+    std::string ptr_address = op_desc[kJsonKeyPtrAddress];
+    if (address_node_map.count(ptr_address) == 0) {
+      MS_LOG(ERROR) << "Decode failed, ptr_address not found in map.";
+      return false;
+    }
+    auto node = address_node_map.at(ptr_address)->cast<CNodePtr>();
+    if (IsRecomputeOp(op_desc, recompute_ops)) {
+      auto cp_node = NewRecomputeNode(node, &node_map);
+      orig_region_nodes.push_back(node);
+      SetStitchAttr(op_desc, info, cp_node);
+      res_graphs->push_back(cp_node);
+      continue;
+    }
+    SetStitchAttr(op_desc, info, node);
+    res_graphs->push_back(node);
+    no_cp_nodes.push_back(node);
+  }
+  for (auto orig_node : orig_region_nodes) {
+    ConnectRecomputeOps(&no_cp_nodes, orig_node, node_map[orig_node]);
+  }
+  MS_LOG(DEBUG) << "decode cnodes success, size: " << res_graphs->size();
+  return true;
+}
+}  // namespace kernel
+
 namespace opt {
 namespace {
 void TraverseFuncGraphFromCNode(const CNodePtr &cnode, const std::function<void(AnfNodePtr &)> &callback) {
@@ -620,7 +764,7 @@ class CostModelSplitSchemer : public SplitSchemer {
     split_plan_.clear();
     for (const auto &graph_desc : graph_descs) {
       AnfNodePtrList res_graph;
-      if (!akg_kernel_json_decoder.DecodeSplitNodes(graph_desc, address_node_map, &res_graph)) {
+      if (!kernel::SplitNodesDecoder::DecodeSplitNodes(graph_desc, address_node_map, &res_graph)) {
         MS_LOG(ERROR) << "Failed decode sub graph, " << graph_desc;
         return false;
       }
@@ -731,6 +875,7 @@ class CostModelSplitSchemer : public SplitSchemer {
     nlohmann::json flag_json;
     flag_json["dump_as_text"] = flags.dump_as_text;
     flag_json["enable_stitch_fusion"] = flags.enable_stitch_fusion;
+    flag_json["enable_recompute_fusion"] = flags.enable_recompute_fusion;
     return flag_json.dump();
   }
 

mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_splitter.h

@@ -15,11 +15,31 @@
  */
 #ifndef MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GRAPH_KERNEL_GRAPH_KERNEL_SPLITTER_H_
 #define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GRAPH_KERNEL_GRAPH_KERNEL_SPLITTER_H_
+#include <map>
 #include <memory>
+#include <set>
+#include <string>
+#include <vector>
+#include <nlohmann/json.hpp>
 #include "ir/func_graph.h"
 #include "backend/optimizer/common/pass.h"
 
 namespace mindspore {
+namespace kernel {
+struct StitchInfo {
+  std::vector<std::string> stitch_ops;
+  std::vector<std::string> stitch_atomic_ops;
+};
+
+class SplitNodesDecoder {
+ public:
+  SplitNodesDecoder() {}
+  ~SplitNodesDecoder() = default;
+  static bool DecodeSplitNodes(const nlohmann::json &kernel_json,
+                               const std::map<std::string, AnfNodePtr> &address_node_map, AnfNodePtrList *res_graphs);
+};
+}  // namespace kernel
+
 namespace opt {
 class GraphKernelSplitter : public Pass {
  public:

mindspore/ccsrc/utils/context/graph_kernel_flags.cc

@@ -181,6 +181,7 @@ void GraphKernelFlags::RegisterFlags(std::map<std::string, std::string> *flag_ma
   // Boolean flags
   reg.AddFlag("dump_as_text", &dump_as_text);
   reg.AddFlag("enable_stitch_fusion", &enable_stitch_fusion, opt_level == OptLevel_3);
+  reg.AddFlag("enable_recompute_fusion", &enable_recompute_fusion, opt_level == OptLevel_2);
   reg.AddFlag("enable_parallel_fusion", &enable_parallel_fusion, opt_level == OptLevel_3);
 
   // Integer flags
@@ -203,6 +204,7 @@ std::string GraphKernelFlags::DumpAllFlags() const {
 
   json["dump_as_text"] = dump_as_text;
   json["enable_stitch_fusion"] = enable_stitch_fusion;
+  json["enable_recompute_fusion"] = enable_recompute_fusion;
   json["enable_parallel_fusion"] = enable_parallel_fusion;
 
   json["opt_level"] = opt_level;

mindspore/ccsrc/utils/context/graph_kernel_flags.h

@@ -67,6 +67,11 @@ class GraphKernelFlags {
    */
   bool enable_stitch_fusion;
 
+  /**
+   * Enable recompute fusion in graph kernel fusion strategy.
+   */
+  bool enable_recompute_fusion{true};
+
   /**
    * Enable parallel fusion in graph kernel fusion strategy.
    *

tests/st/ops/graph_kernel/test_recompute.py

@@ -0,0 +1,223 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed 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 KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.common import dtype as mstype
+from mindspore.nn import Cell
+import mindspore.ops.operations as P
+
+#{cast} would be recompute and fused
+class Net1(Cell):
+    def __init__(self):
+        super(Net1, self).__init__()
+        self.cast = P.Cast()
+        self.sum = P.ReduceSum(keep_dims=False)
+
+    def construct(self, x):
+        cast_res = self.cast(x, mstype.float32)
+        sum1_res = self.sum(cast_res, (0,))
+        sum2_res = self.sum(cast_res, (1,))
+        return sum1_res, sum2_res
+
+#{sqrt} would be recompute on Ascend
+class Net2(Cell):
+    def __init__(self):
+        super(Net2, self).__init__()
+        self.sqrt = P.Sqrt()
+        self.sum = P.ReduceSum(keep_dims=True)
+        self.add = P.Add()
+        self.neg = P.Neg()
+
+    def construct(self, x0, x1):
+        sqrt_res = self.sqrt(x0)
+        neg_res = self.neg(sqrt_res)
+        add_res = self.add(x1, sqrt_res)
+        sum_res = self.sum(add_res, (0,))
+        return neg_res, sum_res
+
+#{sqrt} would be recompute
+class Net3(Cell):
+    def __init__(self):
+        super(Net3, self).__init__()
+        self.sqrt = P.Sqrt()
+        self.add = P.Add()
+        self.neg = P.Neg()
+
+    def construct(self, x0, x1):
+        sqrt_res = self.sqrt(x0)
+        neg_res = self.neg(sqrt_res)
+        add_res = self.add(x1, sqrt_res)
+        return neg_res, add_res
+
+#{sqrt neg} would be recompute
+class Net4(Cell):
+    def __init__(self):
+        super(Net4, self).__init__()
+        self.sqrt = P.Sqrt()
+        self.neg = P.Neg()
+        self.sum = P.ReduceSum(keep_dims=False)
+
+    def construct(self, x):
+        sqrt_res = self.sqrt(x)
+        neg_res = self.neg(sqrt_res)
+        sum1_res = self.sum(neg_res, (0,))
+        sum2_res = self.sum(neg_res, (1,))
+        return sum1_res, sum2_res
+
+#{sqrt} would be recompute
+class Net5(Cell):
+    def __init__(self):
+        super(Net5, self).__init__()
+        self.sqrt = P.Sqrt()
+        self.add = P.Add()
+
+    def construct(self, x0, x1, x2):
+        sqrt_res = self.sqrt(x0)
+        add1_res = self.add(sqrt_res, x1)
+        add2_res = self.add(sqrt_res, x2)
+        return add1_res, add2_res
+
+def test_basic1(net):
+    def get_output(i0, net, enable_graph_kernel=False):
+        context.set_context(enable_graph_kernel=enable_graph_kernel)
+        net_obj = net()
+        output = net_obj(i0)
+        return output
+
+    i0 = Tensor(np.random.uniform(1, 2, [1024, 1024]).astype(np.float16))
+    expect = get_output(i0, net, False)
+    output = get_output(i0, net, True)
+    expect0_np = expect[0].asnumpy().copy()
+    output0_np = output[0].asnumpy().copy()
+    expect1_np = expect[1].asnumpy().copy()
+    output1_np = output[1].asnumpy().copy()
+    assert np.allclose(expect0_np, output0_np, 1.e-3, 1.e-3)
+    assert np.allclose(expect1_np, output1_np, 1.e-3, 1.e-3)
+
+
+def test_basic2(net):
+    def get_output(i0, i1, net, enable_graph_kernel=False):
+        context.set_context(enable_graph_kernel=enable_graph_kernel)
+        net_obj = net()
+        output = net_obj(i0, i1)
+        return output
+
+    i0 = Tensor(np.random.uniform(1, 2, [1, 1024]).astype(np.float32))
+    i1 = Tensor(np.random.uniform(1, 2, [1024, 1024]).astype(np.float32))
+    expect = get_output(i0, i1, net, False)
+    output = get_output(i0, i1, net, True)
+    expect0_np = expect[0].asnumpy().copy()
+    output0_np = output[0].asnumpy().copy()
+    expect1_np = expect[1].asnumpy().copy()
+    output1_np = output[1].asnumpy().copy()
+    assert np.allclose(expect0_np, output0_np, 1.e-3, 1.e-3)
+    assert np.allclose(expect1_np, output1_np, 1.e-3, 1.e-3)
+
+def test_basic3(net):
+    def get_output(i0, i1, i2, net, enable_graph_kernel=False):
+        context.set_context(enable_graph_kernel=enable_graph_kernel)
+        net_obj = net()
+        output = net_obj(i0, i1, i2)
+        return output
+
+    i0 = Tensor(np.random.uniform(1, 2, [1, 1024]).astype(np.float16))
+    i1 = Tensor(np.random.uniform(1, 2, [1024, 1024]).astype(np.float16))
+    i2 = Tensor(np.random.uniform(1, 2, [2048, 1024]).astype(np.float16))
+    expect = get_output(i0, i1, i2, net, False)
+    output = get_output(i0, i1, i2, net, True)
+    expect0_np = expect[0].asnumpy().copy()
+    output0_np = output[0].asnumpy().copy()
+    expect1_np = expect[1].asnumpy().copy()
+    output1_np = output[1].asnumpy().copy()
+    assert np.allclose(expect0_np, output0_np, 1.e-3, 1.e-3)
+    assert np.allclose(expect1_np, output1_np, 1.e-3, 1.e-3)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_gpu_1():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    test_basic1(Net1)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_gpu_2():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    test_basic2(Net2)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_gpu_3():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    test_basic2(Net3)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_gpu_4():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    test_basic1(Net4)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_gpu_5():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    test_basic3(Net5)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_ascend_1():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    test_basic1(Net1)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_ascend_2():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    test_basic2(Net2)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_ascend_3():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    test_basic2(Net3)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_ascend_4():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    test_basic1(Net4)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_ascend_5():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    test_basic3(Net5)