auto parallel context modify

mindspore/ccsrc/frontend/parallel/context.cc

@@ -42,15 +42,12 @@ std::shared_ptr<ParallelContext> ParallelContext::GetInstance() {
   return inst_context_;
 }
 
-ParallelContext::ParallelContext() {
+ParallelContext::ParallelContext() { Reset(); }
-  communication_backend_ = HCCL_BACKEND;
-  Reset();
-}
 
 void ParallelContext::Reset() {
   mirror_mean_ = false;
   full_batch_ = false;
-  cast_before_mirror_ = true;
+  gradient_fp32_sync_ = true;
   loss_repeated_mean_ = true;
   device_num_ = 1;
   global_rank_ = 0;
@@ -81,14 +78,10 @@ void ParallelContext::set_mirror_mean(bool mirror_mean) { mirror_mean_ = mirror_
 
 void ParallelContext::set_full_batch(bool full_batch) { full_batch_ = full_batch; }
 
-void ParallelContext::set_cast_before_mirror(bool cast_before_mirror) { cast_before_mirror_ = cast_before_mirror; }
+void ParallelContext::set_gradient_fp32_sync(bool gradient_fp32_sync) { gradient_fp32_sync_ = gradient_fp32_sync; }
 
 void ParallelContext::set_loss_repeated_mean(bool loss_repeated_mean) { loss_repeated_mean_ = loss_repeated_mean; }
 
-void ParallelContext::set_communication_backend(const std::string &communication_backend) {
-  communication_backend_ = communication_backend;
-}
-
 bool ParallelContext::set_parallel_mode(const std::string &parallel_mode) {
   auto iter = std::find(PARALLEL_MODE_LIST.begin(), PARALLEL_MODE_LIST.end(), parallel_mode);
   if (iter == PARALLEL_MODE_LIST.end()) {

mindspore/ccsrc/frontend/parallel/context.h

@@ -58,8 +58,8 @@ class ParallelContext {
   void set_full_batch(bool full_batch);
   bool full_batch() const { return full_batch_; }
 
-  void set_cast_before_mirror(bool cast_before_mirror);
+  void set_gradient_fp32_sync(bool gradient_fp32_sync);
-  bool cast_before_mirror() const { return cast_before_mirror_; }
+  bool gradient_fp32_sync() const { return gradient_fp32_sync_; }
 
   void set_loss_repeated_mean(bool loss_repeated_mean);
   bool loss_repeated_mean() const { return loss_repeated_mean_; }
@@ -70,9 +70,6 @@ class ParallelContext {
   void set_global_rank(int32_t global_rank);
   int32_t global_rank() const { return global_rank_; }
 
-  void set_communication_backend(const std::string &communication_backend);
-  std::string communication_backend() const { return communication_backend_; }
-
   bool set_parallel_mode(const std::string &parallel_mode);
   std::string parallel_mode() const { return parallel_mode_; }
 
@@ -112,11 +109,10 @@ class ParallelContext {
   static std::shared_ptr<ParallelContext> inst_context_;
   bool mirror_mean_;
   bool full_batch_;
-  bool cast_before_mirror_;
+  bool gradient_fp32_sync_;
   bool loss_repeated_mean_;
   int32_t device_num_;
   int32_t global_rank_;
-  std::string communication_backend_;
   std::string parallel_mode_;
   std::string strategy_search_mode_;
   bool parameter_broadcast_;

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -43,6 +43,7 @@
 #include "frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h"
 #include "utils/comm_manager.h"
 #include "utils/symbolic.h"
+#include "utils/ms_context.h"
 
 using mindspore::tensor::Tensor;
 
@@ -869,8 +870,8 @@ std::pair<bool, CNodePtr> FindCNode(const AnfNodePtr &anode, const std::string &
 }
 
 bool IsCastBeforMirror(const CNodePtr &node, size_t index) {
-  // only if cast_before_mirror is true, pre node is cast and type is not float32 return true
+  // only if gradient_fp32_sync is true, pre node is cast and type is not float32 return true
-  if (!ParallelContext::GetInstance()->cast_before_mirror()) {
+  if (!ParallelContext::GetInstance()->gradient_fp32_sync()) {
     return false;
   }
   auto pre_node = node->input(index);
@@ -2421,13 +2422,17 @@ Status ParallelInit() {
   MS_EXCEPTION_IF_NULL(ParallelContext::GetInstance());
   int32_t device_num = ParallelContext::GetInstance()->device_num();
   int32_t global_rank = ParallelContext::GetInstance()->global_rank();
-  std::string backend = ParallelContext::GetInstance()->communication_backend();
+  auto ms_context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(ms_context);
+  std::string backend = ms_context->get_param<std::string>(MS_CTX_DEVICE_TARGET);
   std::string world_group;
-
+  std::string communication_backend;
-  if (backend == HCCL_BACKEND) {
+  if (backend == kAscendDevice || backend == kDavinciDevice) {
     world_group = HCCL_WORLD_GROUP;
-  } else if (backend == NCCL_BACKEND) {
+    communication_backend = HCCL_BACKEND;
+  } else if (backend == kGPUDevice) {
     world_group = NCCL_WORLD_GROUP;
+    communication_backend = NCCL_BACKEND;
   } else {
     MS_LOG(EXCEPTION) << "Invalid communication backend: " << backend;
   }
@@ -2450,14 +2455,14 @@ Status ParallelInit() {
     MS_LOG(INFO) << "Get global rank from communication model, the global rank is  " << global_rank;
   }
 
-  if (!InitDevice(device_num, global_rank, backend)) {
+  if (!InitDevice(device_num, global_rank, communication_backend)) {
     MS_LOG(ERROR) << "Init device failed";
     return FAILED;
   }
 
   MS_LOG(INFO) << "The parallel context: dev num: " << device_num << ", global rank: " << global_rank
                << ", backend: " << backend << ", mirror_mean: " << ParallelContext::GetInstance()->mirror_mean()
-               << ", cast_before_mirror: " << ParallelContext::GetInstance()->cast_before_mirror();
+               << ", gradient_fp32_sync: " << ParallelContext::GetInstance()->gradient_fp32_sync();
   return SUCCESS;
 }
 

mindspore/ccsrc/pipeline/jit/init.cc

@@ -209,12 +209,10 @@ PYBIND11_MODULE(_c_expression, m) {
     .def("get_global_rank_is_set", &ParallelContext::global_rank_is_set, "Get global rank is set.")
     .def("get_mirror_mean", &ParallelContext::mirror_mean, "Get mirror mean.")
     .def("set_mirror_mean", &ParallelContext::set_mirror_mean, "Set mirror mean.")
-    .def("get_cast_before_mirror", &ParallelContext::cast_before_mirror, "Get cast before mirror.")
+    .def("get_gradient_fp32_sync", &ParallelContext::gradient_fp32_sync, "Get cast before mirror.")
-    .def("set_cast_before_mirror", &ParallelContext::set_cast_before_mirror, "Set cast before mirror.")
+    .def("set_gradient_fp32_sync", &ParallelContext::set_gradient_fp32_sync, "Set cast before mirror.")
     .def("get_loss_repeated_mean", &ParallelContext::loss_repeated_mean, "Get loss repeated mean.")
     .def("set_loss_repeated_mean", &ParallelContext::set_loss_repeated_mean, "Set loss repeated mean.")
-    .def("get_communication_backend", &ParallelContext::communication_backend, "Get communication backend.")
-    .def("set_communication_backend", &ParallelContext::set_communication_backend, "Set communication backend.")
     .def("get_parallel_mode", &ParallelContext::parallel_mode, "Get parallel mode.")
     .def("set_parallel_mode", &ParallelContext::set_parallel_mode, "Set parallel mode.")
     .def("get_strategy_search_mode", &ParallelContext::strategy_search_mode, "Get strategy search mode.")

mindspore/communication/management.py

@@ -15,7 +15,6 @@
 """Communication management API"""
 import os
 from mindspore import context
-from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from ._comm_helper import Backend, _get_rank_helper, _get_size_helper, \
     _get_world_rank_from_group_rank_helper, _get_group_rank_from_world_rank_helper, \
     _create_group_helper, _destroy_group_helper, HCCL_WORLD_COMM_GROUP, NCCL_WORLD_COMM_GROUP, \
@@ -86,9 +85,6 @@ def init(backend_name=None):
     else:
         raise RuntimeError("Backend name {} is not supported.".format(backend_name))
 
-    auto_parallel_context().set_communication_backend(backend_name)
-
-
 def release():
     """
     Release distributed resource. e.g., hccl/nccl.

mindspore/context.py

@@ -434,7 +434,7 @@ def _context():
     return _k_context
 
 
-@args_type_check(device_num=int, global_rank=int, mirror_mean=bool, cast_before_mirror=bool, parallel_mode=str,
+@args_type_check(device_num=int, global_rank=int, mirror_mean=bool, gradient_fp32_sync=bool, parallel_mode=str,
                  auto_parallel_search_mode=str, parameter_broadcast=bool, strategy_ckpt_load_file=str,
                  strategy_ckpt_save_file=str, full_batch=bool, enable_parallel_optimizer=bool)
 def set_auto_parallel_context(**kwargs):
@@ -454,9 +454,9 @@ def set_auto_parallel_context(**kwargs):
         global_rank (int): Global rank id, the value must be in [0, 4095]. Default: 0.
         mirror_mean (bool): Whether to perform mean operator after all-reduce of mirror.
                      "stand_alone" do not support mirror_mean. Default: False.
-        cast_before_mirror (bool): Insert Mirror Op after the cast if this flag is True.
+        gradient_fp32_sync (bool): Gradients allreduce by fp32 even though gradients is fp16 if this flag is True..
                      "stand_alone", "data_parallel" and "hybrid_parallel" do not support
-                     cast_before_mirror. Default: True.
+                     gradient_fp32_sync. Default: True.
         parallel_mode (str): There are five kinds of parallel modes, "stand_alone", "data_parallel",
                      "hybrid_parallel", "semi_auto_parallel" and "auto_parallel". Default: "stand_alone".
 
@@ -492,7 +492,7 @@ def set_auto_parallel_context(**kwargs):
         >>> context.set_auto_parallel_context(device_num=8)
         >>> context.set_auto_parallel_context(global_rank=0)
         >>> context.set_auto_parallel_context(mirror_mean=True)
-        >>> context.set_auto_parallel_context(cast_before_mirror=False)
+        >>> context.set_auto_parallel_context(gradient_fp32_sync=False)
         >>> context.set_auto_parallel_context(parallel_mode="auto_parallel")
         >>> context.set_auto_parallel_context(parameter_broadcast=False)
         >>> context.set_auto_parallel_context(strategy_ckpt_load_file="./strategy_stage1.ckpt")
@@ -524,7 +524,7 @@ def reset_auto_parallel_context():
     - device_num: 1.
     - global_rank: 0.
     - mirror_mean: False.
-    - cast_before_mirror: True.
+    - gradient_fp32_sync: True.
     - parallel_mode: "stand_alone".
     - parameter_broadcast: False.
     - strategy_ckpt_load_file: "".

mindspore/parallel/_auto_parallel_context.py

@@ -113,24 +113,24 @@ class _AutoParallelContext:
         self.check_context_handle()
         return self._context_handle.get_mirror_mean()
 
-    def set_cast_before_mirror(self, cast_before_mirror):
+    def set_gradient_fp32_sync(self, gradient_fp32_sync):
         """
-        Set cast_before_mirror.
+        Set gradient_fp32_sync.
 
         Note:
-            If cast_before_mirror is true,
+            If gradient_fp32_sync is true,
             it will convert tensor type from fp16 to fp32 before parameter gradients allreduce.
 
         Args:
-            cast_before_mirror (bool): The cast_before_mirror flag.
+            gradient_fp32_sync (bool): The gradient_fp32_sync flag.
         """
         self.check_context_handle()
-        self._context_handle.set_cast_before_mirror(cast_before_mirror)
+        self._context_handle.set_gradient_fp32_sync(gradient_fp32_sync)
 
-    def get_cast_before_mirror(self):
+    def get_gradient_fp32_sync(self):
-        """Get cast_before_mirror flag."""
+        """Get gradient_fp32_sync flag."""
         self.check_context_handle()
-        return self._context_handle.get_cast_before_mirror()
+        return self._context_handle.get_gradient_fp32_sync()
 
     def set_loss_repeated_mean(self, loss_repeated_mean):
         """
@@ -152,21 +152,6 @@ class _AutoParallelContext:
         self.check_context_handle()
         return self._context_handle.get_loss_repeated_mean()
 
-    def set_communication_backend(self, communication_backend):
-        """
-        Set communication backend.
-
-        Args:
-            communication_backend (str): The communication backend.
-        """
-        self.check_context_handle()
-        self._context_handle.set_communication_backend(communication_backend)
-
-    def get_communication_backend(self):
-        """Get communication backend."""
-        self.check_context_handle()
-        return self._context_handle.get_communication_backend()
-
     def set_parallel_mode(self, parallel_mode):
         """
         Set parallel mode for auto parallel.
@@ -469,7 +454,7 @@ _set_auto_parallel_context_func_map = {
     "device_num": auto_parallel_context().set_device_num,
     "global_rank": auto_parallel_context().set_global_rank,
     "mirror_mean": auto_parallel_context().set_mirror_mean,
-    "cast_before_mirror": auto_parallel_context().set_cast_before_mirror,
+    "gradient_fp32_sync": auto_parallel_context().set_gradient_fp32_sync,
     "loss_repeated_mean": auto_parallel_context().set_loss_repeated_mean,
     "parallel_mode": auto_parallel_context().set_parallel_mode,
     "auto_parallel_search_mode": auto_parallel_context().set_strategy_search_mode,
@@ -484,7 +469,7 @@ _get_auto_parallel_context_func_map = {
     "device_num": auto_parallel_context().get_device_num,
     "global_rank": auto_parallel_context().get_global_rank,
     "mirror_mean": auto_parallel_context().get_mirror_mean,
-    "cast_before_mirror": auto_parallel_context().get_cast_before_mirror,
+    "gradient_fp32_sync": auto_parallel_context().get_gradient_fp32_sync,
     "loss_repeated_mean": auto_parallel_context().get_loss_repeated_mean,
     "parallel_mode": auto_parallel_context().get_parallel_mode,
     "auto_parallel_search_mode": auto_parallel_context().get_strategy_search_mode,
@@ -495,7 +480,7 @@ _get_auto_parallel_context_func_map = {
     "enable_parallel_optimizer": auto_parallel_context().get_enable_parallel_optimizer}
 
 
-@args_type_check(device_num=int, global_rank=int, mirror_mean=bool, cast_before_mirror=bool,
+@args_type_check(device_num=int, global_rank=int, mirror_mean=bool, gradient_fp32_sync=bool,
                  loss_repeated_mean=bool, parallel_mode=str, auto_parallel_search_mode=str,
                  parameter_broadcast=bool, strategy_ckpt_load_file=str,
                  strategy_ckpt_save_file=str, full_batch=bool, enable_parallel_optimizer=bool)
@@ -512,8 +497,9 @@ def _set_auto_parallel_context(**kwargs):
         global_rank (int): Global rank id, the value must be in [0, 4095]. Default: 0.
         mirror_mean (bool): Whether to perform mean operator after all-reduce of mirror. Default: False.
         loss_repeated_mean (bool): Whether to perform mean operator in backward in the case of repeated
-                          calculations. Default: True.
+                        calculations. Default: True.
-        cast_before_mirror (bool): Insert Mirror Op after the cast if this flag is True. Default: True.
+        gradient_fp32_sync (bool): Gradients allreduce by fp32 even though gradients is fp16 if this flag is True.
+                        Default: True.
         parallel_mode (str): There are five kinds of parallel modes, "stand_alone", "data_parallel",
                      "hybrid_parallel", "semi_auto_parallel" and "auto_parallel". Default: "stand_alone".
 
@@ -577,7 +563,7 @@ def _reset_auto_parallel_context():
     - device_num: 1.
     - global_rank: 0.
     - mirror_mean: False.
-    - cast_before_mirror: True.
+    - gradient_fp32_sync: True.
     - parallel_mode: "stand_alone".
     - parameter_broadcast: False.
     - strategy_ckpt_load_file: ""

tests/ut/python/hccl_test/manage/api.py

@@ -61,7 +61,7 @@ def get_rank_id(group=None):
 
 def get_rank_size(group=None):
     hccl = Hccl()
-    if group is None:
+    if group is None or "nccl_world_group" in group:
         return hccl.rank_size
     if isinstance(group, str):
         return int(group.split("-")[0])

tests/ut/python/parallel/test_element_wise_function.py

@@ -830,7 +830,7 @@ def test_matmul_cast():
     compile_net(net, x, y, b)
 
 
-def test_cast_before_mirror():
+def test_gradient_fp32_sync():
     class Net(nn.Cell):
         def __init__(self, strategy1):
             super().__init__()
@@ -843,7 +843,7 @@ def test_cast_before_mirror():
             out = self.matmul(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0, cast_before_mirror=True)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, gradient_fp32_sync=True)
     strategy1 = ((2, 2), (2, 2))
     net = GradWrap(NetWithLoss(Net(strategy1)))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
@@ -854,7 +854,7 @@ def test_cast_before_mirror():
     compile_net(net, x, y, b)
 
 
-def test_cast_before_mirror1():
+def test_gradient_fp32_sync1():
     class Net(nn.Cell):
         def __init__(self, strategy1):
             super().__init__()
@@ -867,7 +867,7 @@ def test_cast_before_mirror1():
             out = self.matmul(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0, cast_before_mirror=True)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, gradient_fp32_sync=True)
     strategy1 = ((2, 2), (2, 2))
     net = GradWrap(NetWithLoss(Net(strategy1)))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
@@ -878,7 +878,7 @@ def test_cast_before_mirror1():
     compile_net(net, x, y, b)
 
 
-def test_cast_before_mirror2():
+def test_gradient_fp32_sync2():
     class Net(nn.Cell):
         def __init__(self, strategy1):
             super().__init__()
@@ -891,7 +891,7 @@ def test_cast_before_mirror2():
             out = self.matmul(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0, cast_before_mirror=False)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, gradient_fp32_sync=False)
     strategy1 = ((2, 2), (2, 2))
     net = GradWrap(NetWithLoss(Net(strategy1)))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
@@ -902,7 +902,7 @@ def test_cast_before_mirror2():
     compile_net(net, x, y, b)
 
 
-def test_cast_before_mirror3():
+def test_gradient_fp32_sync3():
     class Net(nn.Cell):
         def __init__(self, strategy1):
             super().__init__()

tests/ut/python/parallel/test_set_auto_parallel_context.py

@@ -20,25 +20,21 @@ from mindspore.parallel._auto_parallel_context import auto_parallel_context
 
 
 def test_set_auto_parallel_context():
-    context.set_auto_parallel_context(device_num=4, global_rank=3, mirror_mean=True, cast_before_mirror=False,
+    context.set_auto_parallel_context(device_num=4, global_rank=3, mirror_mean=True, gradient_fp32_sync=False,
                                       parallel_mode="auto_parallel", parameter_broadcast=False)
     device_num = context.get_auto_parallel_context("device_num")
     global_rank = context.get_auto_parallel_context("global_rank")
     mirror_mean = context.get_auto_parallel_context("mirror_mean")
-    cast_before_mirror = context.get_auto_parallel_context("cast_before_mirror")
+    gradient_fp32_sync = context.get_auto_parallel_context("gradient_fp32_sync")
     parallel_mode = context.get_auto_parallel_context("parallel_mode")
     parameter_broadcast = context.get_auto_parallel_context("parameter_broadcast")
     assert device_num == 4
     assert global_rank == 3
     assert mirror_mean
-    assert not cast_before_mirror
+    assert not gradient_fp32_sync
     assert parallel_mode == "auto_parallel"
     assert not parameter_broadcast
 
-    auto_parallel_context().set_communication_backend("hccl")
-    backend = auto_parallel_context().get_communication_backend()
-    assert backend == "hccl"
-
     auto_parallel_context().set_device_num(4)
     device_num = auto_parallel_context().get_device_num()
     device_num_is_set = auto_parallel_context().get_device_num_is_set()
@@ -53,9 +49,9 @@ def test_set_auto_parallel_context():
     mirror_mean = auto_parallel_context().get_mirror_mean()
     assert mirror_mean
 
-    auto_parallel_context().set_cast_before_mirror(False)
+    auto_parallel_context().set_gradient_fp32_sync(False)
-    cast_before_mirror = auto_parallel_context().get_cast_before_mirror()
+    gradient_fp32_sync = auto_parallel_context().get_gradient_fp32_sync()
-    assert not cast_before_mirror
+    assert not gradient_fp32_sync
 
     parameter_broadcast_is_set = auto_parallel_context().get_parameter_broadcast_is_set()
     assert parameter_broadcast_is_set
@@ -91,7 +87,7 @@ def test_reset_auto_parallel_context():
     device_num = context.get_auto_parallel_context("device_num")
     global_rank = context.get_auto_parallel_context("global_rank")
     mirror_mean = context.get_auto_parallel_context("mirror_mean")
-    cast_before_mirror = context.get_auto_parallel_context("cast_before_mirror")
+    gradient_fp32_sync = context.get_auto_parallel_context("gradient_fp32_sync")
     parallel_mode = context.get_auto_parallel_context("parallel_mode")
     parameter_broadcast = context.get_auto_parallel_context("parameter_broadcast")
     device_num_is_set = auto_parallel_context().get_device_num_is_set()
@@ -99,7 +95,7 @@ def test_reset_auto_parallel_context():
     assert device_num == 1
     assert global_rank == 0
     assert not mirror_mean
-    assert cast_before_mirror
+    assert gradient_fp32_sync
     assert parallel_mode == "stand_alone"
     assert not parameter_broadcast
     assert not device_num_is_set