From 391a060f214e6edc0b29477673b1e10f78847117 Mon Sep 17 00:00:00 2001 From: jinyaohui Date: Tue, 12 May 2020 22:11:50 +0800 Subject: [PATCH] remove two context param --- example/bert_clue/README.md | 1 - example/bert_clue/run_distribute_pretrain.sh | 1 - example/bert_clue/run_pretrain.py | 4 +-- example/bert_clue/run_standalone_pretrain.sh | 1 - example/googlenet_cifar10/train.py | 1 - example/mobilenetv2_imagenet2012/eval.py | 1 - example/mobilenetv2_imagenet2012/train.py | 1 - example/resnet101_imagenet2012/eval.py | 1 - example/resnet101_imagenet2012/train.py | 1 - example/resnet50_cifar10/eval.py | 2 +- example/resnet50_cifar10/train.py | 2 +- example/ssd_coco2017/eval.py | 2 +- example/ssd_coco2017/train.py | 2 +- example/vgg16_cifar10/train.py | 1 - example/yolov3_coco2017/eval.py | 2 +- example/yolov3_coco2017/train.py | 2 +- mindspore/ccsrc/pipeline/init.cc | 4 --- mindspore/ccsrc/utils/context/ms_context.h | 2 -- mindspore/context.py | 28 ++----------------- .../st/auto_parallel/onehot_model_parallel.py | 12 ++++---- .../soft_entropy_loss_expand_parallel.py | 18 ++++++------ .../test_resnet50_expand_loss_2p.py | 17 ++++++----- tests/st/control/test_multigraph_sink.py | 6 +--- tests/st/control/test_while.py | 1 - tests/st/fusion/test_conv_bn1_fusion.py | 2 +- tests/st/mem_reuse/resnet_cifar_memreuse.py | 2 +- tests/st/mem_reuse/resnet_cifar_normal.py | 3 +- .../models/bert/bert_tdt_lossscale.py | 1 - tests/st/ops/ascend/test_add.py | 4 --- .../ascend/test_tbe_ops/test_apply_adam.py | 1 - tests/st/tbe_networks/export_geir.py | 18 +++--------- tests/st/tbe_networks/resnet_cifar.py | 3 +- tests/st/tbe_networks/test_resnet_cifar_1p.py | 4 +-- tests/st/tbe_networks/test_resnet_cifar_8p.py | 11 +++----- .../parallel/test_auto_parallel_resnet.py | 21 ++++++++------ .../python/predict/test_predict_save_model.py | 1 - .../pynative_mode/test_multigraph_sink.py | 12 ++++---- 37 files changed, 66 insertions(+), 130 deletions(-) diff --git a/example/bert_clue/README.md b/example/bert_clue/README.md index 93b5b2774..f61cb5ddc 100644 --- a/example/bert_clue/README.md +++ b/example/bert_clue/README.md @@ -58,7 +58,6 @@ options: --epoch_size epoch size: N, default is 1 --device_num number of used devices: N, default is 1 --device_id device id: N, default is 0 - --enable_task_sink enable task sink: "true" | "false", default is "true" --enable_loop_sink enable loop sink: "true" | "false", default is "true" --enable_mem_reuse enable memory reuse: "true" | "false", default is "true" --enable_save_ckpt enable save checkpoint: "true" | "false", default is "true" diff --git a/example/bert_clue/run_distribute_pretrain.sh b/example/bert_clue/run_distribute_pretrain.sh index b5e342069..aeef7b04d 100644 --- a/example/bert_clue/run_distribute_pretrain.sh +++ b/example/bert_clue/run_distribute_pretrain.sh @@ -50,7 +50,6 @@ do --epoch_size=$EPOCH_SIZE \ --device_id=$DEVICE_ID \ --device_num=$RANK_SIZE \ - --enable_task_sink="true" \ --enable_loop_sink="true" \ --enable_mem_reuse="true" \ --enable_save_ckpt="true" \ diff --git a/example/bert_clue/run_pretrain.py b/example/bert_clue/run_pretrain.py index 8448248c4..4fa09347f 100644 --- a/example/bert_clue/run_pretrain.py +++ b/example/bert_clue/run_pretrain.py @@ -59,7 +59,6 @@ def run_pretrain(): parser.add_argument("--epoch_size", type=int, default="1", help="Epoch size, default is 1.") parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.") - parser.add_argument("--enable_task_sink", type=str, default="true", help="Enable task sink, default is true.") parser.add_argument("--enable_loop_sink", type=str, default="true", help="Enable loop sink, default is true.") parser.add_argument("--enable_mem_reuse", type=str, default="true", help="Enable mem reuse, default is true.") parser.add_argument("--enable_save_ckpt", type=str, default="true", help="Enable save checkpoint, default is true.") @@ -76,8 +75,7 @@ def run_pretrain(): args_opt = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) - context.set_context(enable_task_sink=(args_opt.enable_task_sink == "true"), - enable_loop_sink=(args_opt.enable_loop_sink == "true"), + context.set_context(enable_loop_sink=(args_opt.enable_loop_sink == "true"), enable_mem_reuse=(args_opt.enable_mem_reuse == "true")) context.set_context(reserve_class_name_in_scope=False) diff --git a/example/bert_clue/run_standalone_pretrain.sh b/example/bert_clue/run_standalone_pretrain.sh index 058509505..94d769fc1 100644 --- a/example/bert_clue/run_standalone_pretrain.sh +++ b/example/bert_clue/run_standalone_pretrain.sh @@ -29,7 +29,6 @@ python run_pretrain.py \ --distribute="false" \ --epoch_size=$EPOCH_SIZE \ --device_id=$DEVICE_ID \ - --enable_task_sink="true" \ --enable_loop_sink="true" \ --enable_mem_reuse="true" \ --enable_save_ckpt="true" \ diff --git a/example/googlenet_cifar10/train.py b/example/googlenet_cifar10/train.py index 442a2320e..6f9801325 100644 --- a/example/googlenet_cifar10/train.py +++ b/example/googlenet_cifar10/train.py @@ -70,7 +70,6 @@ if __name__ == '__main__': context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target) context.set_context(device_id=args_opt.device_id) - context.set_context(enable_task_sink=True) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/example/mobilenetv2_imagenet2012/eval.py b/example/mobilenetv2_imagenet2012/eval.py index f7085859b..71986c1b2 100644 --- a/example/mobilenetv2_imagenet2012/eval.py +++ b/example/mobilenetv2_imagenet2012/eval.py @@ -34,7 +34,6 @@ args_opt = parser.parse_args() device_id = int(os.getenv('DEVICE_ID')) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False) -context.set_context(enable_task_sink=True) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/example/mobilenetv2_imagenet2012/train.py b/example/mobilenetv2_imagenet2012/train.py index d22e97a29..d7549c184 100644 --- a/example/mobilenetv2_imagenet2012/train.py +++ b/example/mobilenetv2_imagenet2012/train.py @@ -54,7 +54,6 @@ rank_size = int(os.getenv('RANK_SIZE')) run_distribute = rank_size > 1 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False) -context.set_context(enable_task_sink=True) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/example/resnet101_imagenet2012/eval.py b/example/resnet101_imagenet2012/eval.py index bdf6e89ca..5bc651a96 100755 --- a/example/resnet101_imagenet2012/eval.py +++ b/example/resnet101_imagenet2012/eval.py @@ -46,7 +46,6 @@ args_opt = parser.parse_args() device_id = int(os.getenv('DEVICE_ID')) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id) -context.set_context(enable_task_sink=True) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/example/resnet101_imagenet2012/train.py b/example/resnet101_imagenet2012/train.py index 365a85939..2a049db42 100755 --- a/example/resnet101_imagenet2012/train.py +++ b/example/resnet101_imagenet2012/train.py @@ -49,7 +49,6 @@ args_opt = parser.parse_args() device_id = int(os.getenv('DEVICE_ID')) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id) -context.set_context(enable_task_sink=True) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/example/resnet50_cifar10/eval.py b/example/resnet50_cifar10/eval.py index cedcf2eed..872f27d72 100755 --- a/example/resnet50_cifar10/eval.py +++ b/example/resnet50_cifar10/eval.py @@ -39,7 +39,7 @@ args_opt = parser.parse_args() device_id = int(os.getenv('DEVICE_ID')) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False) -context.set_context(enable_task_sink=True, device_id=device_id) +context.set_context(device_id=device_id) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/example/resnet50_cifar10/train.py b/example/resnet50_cifar10/train.py index db7f08b2b..448ea9b05 100755 --- a/example/resnet50_cifar10/train.py +++ b/example/resnet50_cifar10/train.py @@ -42,7 +42,7 @@ args_opt = parser.parse_args() device_id = int(os.getenv('DEVICE_ID')) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False) -context.set_context(enable_task_sink=True, device_id=device_id) +context.set_context(device_id=device_id) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/example/ssd_coco2017/eval.py b/example/ssd_coco2017/eval.py index 6b222093a..8612a3779 100644 --- a/example/ssd_coco2017/eval.py +++ b/example/ssd_coco2017/eval.py @@ -71,7 +71,7 @@ if __name__ == '__main__': args_opt = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) - context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True) + context.set_context(enable_loop_sink=True, enable_mem_reuse=True) config = ConfigSSD() prefix = "ssd_eval.mindrecord" diff --git a/example/ssd_coco2017/train.py b/example/ssd_coco2017/train.py index 65dfe5db8..9a6a4ece7 100644 --- a/example/ssd_coco2017/train.py +++ b/example/ssd_coco2017/train.py @@ -93,7 +93,7 @@ def main(): args_opt = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) - context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True) + context.set_context(enable_loop_sink=True, enable_mem_reuse=True) if args_opt.distribute: device_num = args_opt.device_num diff --git a/example/vgg16_cifar10/train.py b/example/vgg16_cifar10/train.py index 8cfcc5fd9..fcf5ea701 100644 --- a/example/vgg16_cifar10/train.py +++ b/example/vgg16_cifar10/train.py @@ -64,7 +64,6 @@ if __name__ == '__main__': context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target) context.set_context(device_id=args_opt.device_id) - context.set_context(enable_task_sink=True) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/example/yolov3_coco2017/eval.py b/example/yolov3_coco2017/eval.py index f85fa95d7..3bc302726 100644 --- a/example/yolov3_coco2017/eval.py +++ b/example/yolov3_coco2017/eval.py @@ -82,7 +82,7 @@ if __name__ == '__main__': args_opt = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) - context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True) + context.set_context(enable_loop_sink=True, enable_mem_reuse=True) # It will generate mindrecord file in args_opt.mindrecord_dir, # and the file name is yolo.mindrecord0, 1, ... file_num. diff --git a/example/yolov3_coco2017/train.py b/example/yolov3_coco2017/train.py index 999cbdc62..987aac196 100644 --- a/example/yolov3_coco2017/train.py +++ b/example/yolov3_coco2017/train.py @@ -85,7 +85,7 @@ def main(): args_opt = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) - context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True) + context.set_context(enable_loop_sink=True, enable_mem_reuse=True) if args_opt.distribute: device_num = args_opt.device_num context.reset_auto_parallel_context() diff --git a/mindspore/ccsrc/pipeline/init.cc b/mindspore/ccsrc/pipeline/init.cc index 3bd9cda8e..90bae0a38 100644 --- a/mindspore/ccsrc/pipeline/init.cc +++ b/mindspore/ccsrc/pipeline/init.cc @@ -115,12 +115,8 @@ PYBIND11_MODULE(_c_expression, m) { .def("set_device_id", &mindspore::MsContext::set_device_id, "Set device id.") .def("open_tsd", &mindspore::MsContext::OpenTsd, "Open tdt dataset client.") .def("close_tsd", &mindspore::MsContext::CloseTsd, "Close tdt dataset client.") - .def("set_task_sink_flag", &mindspore::MsContext::set_enable_task_sink, "Set enable task sink.") - .def("get_task_sink_flag", &mindspore::MsContext::enable_task_sink, "Get whether to enable task sink.") .def("get_save_graphs_flag", &mindspore::MsContext::save_graphs_flag, "Get whether to save graphs.") .def("set_save_graphs_flag", &mindspore::MsContext::set_save_graphs_flag, "Set whether to save graphs.") - .def("get_ir_fusion_flag", &mindspore::MsContext::ir_fusion_flag, "Get whether to enable ir fusion.") - .def("set_ir_fusion_flag", &mindspore::MsContext::set_ir_fusion_flag, "Set whether to enable ir fusion.") .def("get_auto_mixed_precision_flag", &mindspore::MsContext::auto_mixed_precision_flag, "Get whether to enable auto mixed precision.") .def("set_auto_mixed_precision_flag", &mindspore::MsContext::set_auto_mixed_precision_flag, diff --git a/mindspore/ccsrc/utils/context/ms_context.h b/mindspore/ccsrc/utils/context/ms_context.h index 9895e7046..ef13d2a1b 100644 --- a/mindspore/ccsrc/utils/context/ms_context.h +++ b/mindspore/ccsrc/utils/context/ms_context.h @@ -62,7 +62,6 @@ class MsContext { bool enable_pynative_infer() const { return enable_pynative_infer_; } void set_enable_pynative_infer(bool enable_pynative_infer) { enable_pynative_infer_ = enable_pynative_infer; } - void set_enable_task_sink(bool enable_task_sink) { enable_task_sink_ = enable_task_sink; } bool enable_task_sink() const { return enable_task_sink_; } void set_precompile_only(bool precompile_only) { precompile_only_ = precompile_only; } @@ -90,7 +89,6 @@ class MsContext { bool enable_hccl() const { return enable_hccl_; } bool PynativeInitGe(); - void set_ir_fusion_flag(bool ir_fusion_flag) { ir_fusion_flag_ = ir_fusion_flag; } bool ir_fusion_flag() const { return ir_fusion_flag_; } void set_loop_sink_flag(bool loop_sink_flag) { enable_loop_sink_ = loop_sink_flag; } diff --git a/mindspore/context.py b/mindspore/context.py index 147ae96a5..99307a7ac 100644 --- a/mindspore/context.py +++ b/mindspore/context.py @@ -142,15 +142,6 @@ class _Context: raise ValueError("Context handle is none in context!!!") return value - # For Ascend task sink mode execution - @property - def enable_task_sink(self): - return self._context_handle.get_task_sink_flag() - - @enable_task_sink.setter - def enable_task_sink(self, task_sink): - self._context_handle.set_task_sink_flag(task_sink) - @property def mode(self): return self._context_handle.get_execution_mode() @@ -224,14 +215,6 @@ class _Context: if not success: raise RuntimeError("Device id set failed!!!") - @property - def enable_ir_fusion(self): - return self._context_handle.get_ir_fusion_flag() - - @enable_ir_fusion.setter - def enable_ir_fusion(self, enable_ir_fusion): - self._context_handle.set_ir_fusion_flag(enable_ir_fusion) - @property def enable_loop_sink(self): return self._context_handle.get_loop_sink_flag() @@ -485,11 +468,9 @@ def reset_auto_parallel_context(): _reset_auto_parallel_context() -@args_type_check(mode=int, precompile_only=bool, device_target=str, - device_id=int, enable_ir_fusion=bool, save_graphs=bool, - enable_task_sink=bool, save_graphs_path=str, enable_loop_sink=bool, - enable_mem_reuse=bool, save_ms_model=bool, save_ms_model_path=str, - enable_auto_mixed_precision=bool, enable_dump=bool, save_dump_path=str, +@args_type_check(mode=int, precompile_only=bool, device_target=str, device_id=int, save_graphs=bool, + save_graphs_path=str, enable_loop_sink=bool, enable_mem_reuse=bool, save_ms_model=bool, + save_ms_model_path=str, enable_auto_mixed_precision=bool, enable_dump=bool, save_dump_path=str, enable_reduce_precision=bool, graph_memory_max_size=str, variable_memory_max_size=str, enable_profiling=bool, profiling_options=str) def set_context(**kwargs): @@ -517,10 +498,8 @@ def set_context(**kwargs): device_target (str): The target device to run, support "Ascend", "GPU", "CPU". Default: "Ascend". device_id (int): Id of target device, the value must be in [0, device_num_per_host-1], while device_num_per_host should no more than 4096. Default: 0. - enable_ir_fusion (bool): Whether to enable ir fusion. Default: True. save_graphs (bool): Whether to save graphs. Default: False. enable_loop_sink (bool): Whether to enable loop sink. Default: True. - enable_task_sink (bool): Whether to enable task sink. Default: True. enable_mem_reuse (bool): Whether to enable memory reuse. Default: True. save_ms_model (bool): Whether to save lite model converted by graph. Default: False. save_ms_model_path (str): Path to save converted lite model. Default: "." @@ -559,7 +538,6 @@ def set_context(**kwargs): >>> context.set_context(device_target="Ascend") >>> context.set_context(device_id=0) >>> context.set_context(save_graphs=True, save_graphs_path="./model.ms") - >>> context.set_context(enable_task_sink=True) >>> context.set_context(enable_mem_reuse=True) >>> context.set_context(enable_reduce_precision=True) >>> context.set_context(save_ms_model=True, save_ms_model_path=".") diff --git a/tests/st/auto_parallel/onehot_model_parallel.py b/tests/st/auto_parallel/onehot_model_parallel.py index 3c41e2975..e0ec25bd2 100644 --- a/tests/st/auto_parallel/onehot_model_parallel.py +++ b/tests/st/auto_parallel/onehot_model_parallel.py @@ -33,9 +33,7 @@ def setup_module(): global rank_id np.random.seed(0) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - context.set_context(enable_task_sink=True, - device_id=device_id) - context.set_context(enable_ir_fusion=True) + context.set_context(device_id=device_id) context.set_context(enable_loop_sink=False) distributedTool.init() device_num = distributedTool.get_group_size() @@ -86,15 +84,15 @@ class DataGenerator(): return data def input_data(self, shape): - data = (self.generate_data(shape)*2).astype(np.float32) - stra = [1]*len(shape) + data = (self.generate_data(shape) * 2).astype(np.float32) + stra = [1] * len(shape) stra[0] = device_num datas = self.get_parallel_blocks(data, stra) return Tensor(data), Tensor(datas[rank_id]) def label_data(self, shape, classes): - data = (self.generate_data(shape)*(classes-1)).astype(np.int32) - stra = [1]*len(shape) + data = (self.generate_data(shape) * (classes - 1)).astype(np.int32) + stra = [1] * len(shape) stra[0] = device_num datas = self.get_parallel_blocks(data, stra) return Tensor(data), Tensor(datas[rank_id]) diff --git a/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py b/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py index 767094c04..73a681d91 100644 --- a/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py +++ b/tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py @@ -37,7 +37,7 @@ device_id = int(os.getenv('DEVICE_ID')) rank_id = 0 embed = 128 classes = 32 -batch_size = 32*2 +batch_size = 32 * 2 MatmulParamShape = (classes, embed) @@ -46,9 +46,7 @@ def setup_module(): global rank_id np.random.seed(0) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - context.set_context(enable_task_sink=True, - device_id=device_id) - context.set_context(enable_ir_fusion=True) + context.set_context(device_id=device_id) context.set_context(enable_loop_sink=False) distributedTool.init() rank_id = distributedTool.get_rank() @@ -77,20 +75,20 @@ class DataGenerator(): def generate_data(self, shape): size = np.cumprod(shape)[-1] num_range = min(size, 1000) - data = (np.arange(0, size) % num_range)/num_range + data = (np.arange(0, size) % num_range) / num_range data = np.reshape(data, shape) return data def input_data(self, shape): - data = (self.generate_data(shape)*0.1).astype(np.float32) - stra = [1]*len(shape) + data = (self.generate_data(shape) * 0.1).astype(np.float32) + stra = [1] * len(shape) stra[0] = device_num datas = self.get_parallel_blocks(data, stra) return Tensor(data), Tensor(datas[rank_id]) def label_data(self, shape, embed): - data = (self.generate_data(shape)*(embed-1)).astype(np.int32) - stra = [1]*len(shape) + data = (self.generate_data(shape) * (embed - 1)).astype(np.int32) + stra = [1] * len(shape) stra[0] = device_num datas = self.get_parallel_blocks(data, stra) return Tensor(data), Tensor(datas[rank_id]) @@ -141,7 +139,7 @@ class SoftmaxCrossEntropyExpand(Cell): def __init__(self, sparse=False, stra_list=[]): super(SoftmaxCrossEntropyExpand, self).__init__() if len(stra_list) < 11: - stra_list = [None]*11 + stra_list = [None] * 11 self.exp = P.Exp() self.reduce_sum = P.ReduceSum(keep_dims=True).set_strategy(strategy=stra_list[1]) self.onehot = P.OneHot().set_strategy(strategy=stra_list[2]) diff --git a/tests/st/auto_parallel/test_resnet50_expand_loss_2p.py b/tests/st/auto_parallel/test_resnet50_expand_loss_2p.py index 41f08f54e..c168ea5b6 100644 --- a/tests/st/auto_parallel/test_resnet50_expand_loss_2p.py +++ b/tests/st/auto_parallel/test_resnet50_expand_loss_2p.py @@ -31,8 +31,7 @@ from mindspore.train.callback import Callback from mindspore.parallel import set_algo_parameters context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") -context.set_context(enable_task_sink=True, device_id=int(os.getenv('DEVICE_ID'))) -context.set_context(enable_ir_fusion=True) +context.set_context(device_id=int(os.getenv('DEVICE_ID'))) context.set_context(enable_loop_sink=False) init() context.set_auto_parallel_context(mirror_mean=True, parallel_mode=ParallelMode.AUTO_PARALLEL) @@ -316,14 +315,14 @@ class DataGenerator(): def input_data(self, shape): data = (self.generate_data(shape)).astype(np.float32) - stra = [1]*len(shape) + stra = [1] * len(shape) stra[0] = device_num datas = self.get_parallel_blocks(data, stra) return Tensor(data), Tensor(datas[rank_id]) def label_data(self, shape): - data = (self.generate_data(shape)*1000/np.prod(shape)).astype(np.int32) - stra = [1]*len(shape) + data = (self.generate_data(shape) * 1000 / np.prod(shape)).astype(np.int32) + stra = [1] * len(shape) stra[0] = device_num datas = self.get_parallel_blocks(data, stra) return Tensor(data), Tensor(datas[rank_id]) @@ -378,8 +377,8 @@ def test_train_feed(num_classes=8192): set_algo_parameters(elementwise_op_strategy_follow=True) parallel_callback = ModelCallback() dataGen = DataGenerator() - input_full, input_part = dataGen.input_data((32*2, 3, 224, 224)) - label_full, label_part = dataGen.label_data((32*2,)) + input_full, input_part = dataGen.input_data((32 * 2, 3, 224, 224)) + label_full, label_part = dataGen.label_data((32 * 2,)) dataset = Dataset(input_part, label_part) net = resnet50(num_classes) loss = SoftmaxCrossEntropyExpand(sparse=True) @@ -398,8 +397,8 @@ def test_train_feed2(num_classes=1001): set_algo_parameters(elementwise_op_strategy_follow=True) parallel_callback = ModelCallback() dataGen = DataGenerator() - input_full, input_part = dataGen.input_data((32*2, 3, 224, 224)) - label_full, label_part = dataGen.label_data((32*2,)) + input_full, input_part = dataGen.input_data((32 * 2, 3, 224, 224)) + label_full, label_part = dataGen.label_data((32 * 2,)) dataset = Dataset(input_part, label_part) net = resnet50(num_classes) loss = SoftmaxCrossEntropyExpand(sparse=True) diff --git a/tests/st/control/test_multigraph_sink.py b/tests/st/control/test_multigraph_sink.py index b145fb18f..c34ba8ae7 100644 --- a/tests/st/control/test_multigraph_sink.py +++ b/tests/st/control/test_multigraph_sink.py @@ -14,17 +14,14 @@ # ============================================================================ """ test_multigraph_sink """ import pytest -import numpy as np -import mindspore.nn as nn import mindspore.context as context from mindspore.common.tensor import Tensor from mindspore.common import dtype as mstype from mindspore.common import ms_function -from mindspore.ops import operations as P def setup_module(module): - context.set_context(mode = context.PYNATIVE_MODE, device_target = "Ascend") + context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend") c1 = Tensor([2], mstype.int32) @@ -208,4 +205,3 @@ def test_while_in_while_in_while(): output = while_in_while_in_while(c1, c2, c3) expect = Tensor([2534], mstype.int32) assert output == expect - diff --git a/tests/st/control/test_while.py b/tests/st/control/test_while.py index 6c659b658..0e578764d 100644 --- a/tests/st/control/test_while.py +++ b/tests/st/control/test_while.py @@ -31,7 +31,6 @@ def t1_while(x, y, z): def test_net(): context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - context.set_context(enable_task_sink=True) c1 = Tensor([2], mstype.int32) c2 = Tensor([14], mstype.int32) c3 = Tensor([1], mstype.int32) diff --git a/tests/st/fusion/test_conv_bn1_fusion.py b/tests/st/fusion/test_conv_bn1_fusion.py index c3547ae1c..36f7775de 100644 --- a/tests/st/fusion/test_conv_bn1_fusion.py +++ b/tests/st/fusion/test_conv_bn1_fusion.py @@ -21,7 +21,7 @@ from mindspore.common.initializer import initializer from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits from mindspore.nn.optim import Momentum -context.set_context(device_target="Ascend", enable_task_sink=True) +context.set_context(device_target="Ascend") input_channel = 2048 output_channel = 512 diff --git a/tests/st/mem_reuse/resnet_cifar_memreuse.py b/tests/st/mem_reuse/resnet_cifar_memreuse.py index 2604fe585..bfa03524b 100644 --- a/tests/st/mem_reuse/resnet_cifar_memreuse.py +++ b/tests/st/mem_reuse/resnet_cifar_memreuse.py @@ -53,7 +53,7 @@ device_id = int(os.getenv('DEVICE_ID')) data_home = args_opt.dataset_path context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") -context.set_context(enable_task_sink=True, device_id=device_id) +context.set_context(device_id=device_id) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/tests/st/mem_reuse/resnet_cifar_normal.py b/tests/st/mem_reuse/resnet_cifar_normal.py index 8e037212d..1bdef4c59 100644 --- a/tests/st/mem_reuse/resnet_cifar_normal.py +++ b/tests/st/mem_reuse/resnet_cifar_normal.py @@ -36,7 +36,6 @@ random.seed(1) np.random.seed(1) de.config.set_seed(1) - parser = argparse.ArgumentParser(description='Image classification') parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute') parser.add_argument('--device_num', type=int, default=1, help='Device num.') @@ -54,7 +53,7 @@ device_id = int(os.getenv('DEVICE_ID')) data_home = args_opt.dataset_path context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") -context.set_context(enable_task_sink=True, device_id=device_id) +context.set_context(device_id=device_id) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=False) diff --git a/tests/st/networks/models/bert/bert_tdt_lossscale.py b/tests/st/networks/models/bert/bert_tdt_lossscale.py index fc79718f1..2cb894b32 100644 --- a/tests/st/networks/models/bert/bert_tdt_lossscale.py +++ b/tests/st/networks/models/bert/bert_tdt_lossscale.py @@ -127,7 +127,6 @@ class ModelCallback(Callback): def test_bert_tdt(): """test bert tdt""" context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False) - context.set_context(enable_task_sink=True) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) ds = me_de_train_dataset() diff --git a/tests/st/ops/ascend/test_add.py b/tests/st/ops/ascend/test_add.py index 7d908158c..093f9e72d 100644 --- a/tests/st/ops/ascend/test_add.py +++ b/tests/st/ops/ascend/test_add.py @@ -15,14 +15,10 @@ from mindspore import Tensor from mindspore.ops import operations as P import mindspore.nn as nn -from mindspore.common.api import ms_function import numpy as np import mindspore.context as context -from mindspore.common.initializer import initializer -from mindspore.common.parameter import Parameter context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") -context.set_context(enable_task_sink=True) class Net(nn.Cell): diff --git a/tests/st/ops/ascend/test_tbe_ops/test_apply_adam.py b/tests/st/ops/ascend/test_tbe_ops/test_apply_adam.py index 3713de2c3..c2ae91f86 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_apply_adam.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_apply_adam.py @@ -21,7 +21,6 @@ from mindspore.nn import TrainOneStepCell, WithLossCell import mindspore.context as context context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", impl_type="tbe") -context.set_context(enable_task_sink=True) class Adam: diff --git a/tests/st/tbe_networks/export_geir.py b/tests/st/tbe_networks/export_geir.py index a4368e632..531c6a953 100644 --- a/tests/st/tbe_networks/export_geir.py +++ b/tests/st/tbe_networks/export_geir.py @@ -15,27 +15,17 @@ import os import numpy as np from resnet_torch import resnet50 -from mindspore.train.callback import Callback -from mindspore.nn.optim.momentum import Momentum -from mindspore.train.callback import ModelCheckpoint, CheckpointConfig -from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore import Tensor -import mindspore.nn as nn -from mindspore import context -from mindspore.train.serialization import save, load, save_checkpoint, load_checkpoint,\ - load_param_into_net, _exec_save_checkpoint,\ - _check_filedir_or_create, _chg_model_file_name_if_same_exist, \ +from mindspore.train.serialization import save, load, _check_filedir_or_create, _chg_model_file_name_if_same_exist, \ _read_file_last_line, context, export -context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", - enable_task_sink=True, enable_loop_sink=True, enable_ir_fusion=True) +context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", enable_loop_sink=True) def test_resnet50_export(batch_size=1, num_classes=5): - context.set_context(enable_ir_fusion=False) input_np = np.random.uniform(0.0, 1.0, size=[batch_size, 3, 224, 224]).astype(np.float32) net = resnet50(batch_size, num_classes) - #param_dict = load_checkpoint("./resnet50-1_103.ckpt") - #load_param_into_net(net, param_dict) + # param_dict = load_checkpoint("./resnet50-1_103.ckpt") + # load_param_into_net(net, param_dict) export(net, Tensor(input_np), file_name="./me_resnet50.pb", file_format="GEIR") diff --git a/tests/st/tbe_networks/resnet_cifar.py b/tests/st/tbe_networks/resnet_cifar.py index 4709b3ac7..38fcf42e9 100644 --- a/tests/st/tbe_networks/resnet_cifar.py +++ b/tests/st/tbe_networks/resnet_cifar.py @@ -32,6 +32,7 @@ from mindspore.communication.management import init from mindspore.parallel._auto_parallel_context import auto_parallel_context from resnet import resnet50 import random + random.seed(1) np.random.seed(1) ds.config.set_seed(1) @@ -53,7 +54,7 @@ device_id = int(os.getenv('DEVICE_ID')) data_home = args_opt.dataset_path context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") -context.set_context(enable_task_sink=True, device_id=device_id) +context.set_context(device_id=device_id) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/tests/st/tbe_networks/test_resnet_cifar_1p.py b/tests/st/tbe_networks/test_resnet_cifar_1p.py index 058ec3aee..b8c86932f 100644 --- a/tests/st/tbe_networks/test_resnet_cifar_1p.py +++ b/tests/st/tbe_networks/test_resnet_cifar_1p.py @@ -137,7 +137,7 @@ def train_process(device_id, epoch_size, num_classes, device_num, batch_size): os.system("mkdir " + str(device_id)) os.chdir(str(device_id)) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - context.set_context(enable_task_sink=True, device_id=device_id) + context.set_context(device_id=device_id) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) context.set_context(mode=context.GRAPH_MODE) @@ -159,7 +159,7 @@ def train_process(device_id, epoch_size, num_classes, device_num, batch_size): def eval(batch_size, num_classes): context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - context.set_context(enable_task_sink=True, device_id=0) + context.set_context(device_id=0) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) diff --git a/tests/st/tbe_networks/test_resnet_cifar_8p.py b/tests/st/tbe_networks/test_resnet_cifar_8p.py index 1e4372925..2bfe78631 100644 --- a/tests/st/tbe_networks/test_resnet_cifar_8p.py +++ b/tests/st/tbe_networks/test_resnet_cifar_8p.py @@ -24,8 +24,7 @@ import mindspore.common.dtype as mstype import os import numpy as np import mindspore.ops.functional as F -from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback -from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.train.callback import Callback import mindspore.dataset as ds import mindspore.dataset.transforms.c_transforms as C import mindspore.dataset.transforms.vision.c_transforms as vision @@ -34,8 +33,6 @@ from mindspore.parallel._auto_parallel_context import auto_parallel_context from resnet import resnet50 import random from multiprocessing import Process, Queue -from multiprocessing import Pool -import time random.seed(1) np.random.seed(1) @@ -150,7 +147,7 @@ def train_process(q, device_id, epoch_size, num_classes, device_num, batch_size, os.chdir(str(device_id)) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False) - context.set_context(enable_task_sink=True, device_id=device_id) + context.set_context(device_id=device_id) context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=True) os.environ['MINDSPORE_HCCL_CONFIG_PATH'] = MINDSPORE_HCCL_CONFIG_PATH @@ -206,9 +203,9 @@ def test_resnet_cifar_8p(): loss = 0.0 for i in range(device_num): loss += q.get() - loss = loss/device_num + loss = loss / device_num for i in range(device_num): os.system("rm -rf " + str(i)) print("End training...") - assert(loss < 2.0) + assert (loss < 2.0) diff --git a/tests/ut/python/parallel/test_auto_parallel_resnet.py b/tests/ut/python/parallel/test_auto_parallel_resnet.py index 1088ad736..0a05dc6de 100644 --- a/tests/ut/python/parallel/test_auto_parallel_resnet.py +++ b/tests/ut/python/parallel/test_auto_parallel_resnet.py @@ -22,7 +22,6 @@ from mindspore.common.initializer import TruncatedNormal from mindspore.communication.management import init from mindspore.train.model import Model, ParallelMode from mindspore import context -import os import re import mindspore.ops.functional as F from mindspore.nn.loss.loss import _Loss @@ -32,38 +31,43 @@ from mindspore.parallel import set_algo_parameters from mindspore.parallel import _cost_model_context as cost_model_context context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") -context.set_context(enable_task_sink=True, device_id= 0) -context.set_context(enable_ir_fusion=True) +context.set_context(device_id=0) context.set_context(enable_loop_sink=False) init() + def weight_variable(shape, factor=0.1): return TruncatedNormal(0.02) + def _conv3x3(in_channels, out_channels, stride=1, padding=0, pad_mode='same'): """Get a conv2d layer with 3x3 kernel size.""" init_value = weight_variable((out_channels, in_channels, 3, 3)) return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=padding, pad_mode=pad_mode, weight_init=init_value) + def _conv1x1(in_channels, out_channels, stride=1, padding=0, pad_mode='same'): """Get a conv2d layer with 1x1 kernel size.""" init_value = weight_variable((out_channels, in_channels, 1, 1)) return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=padding, pad_mode=pad_mode, weight_init=init_value) + def _conv7x7(in_channels, out_channels, stride=1, padding=0, pad_mode='same'): """Get a conv2d layer with 7x7 kernel size.""" init_value = weight_variable((out_channels, in_channels, 7, 7)) return nn.Conv2d(in_channels, out_channels, kernel_size=7, stride=stride, padding=padding, pad_mode=pad_mode, weight_init=init_value) + def _fused_bn(channels, momentum=0.9): """Get a fused batchnorm""" init_weight = weight_variable((channels,)) init_bias = weight_variable((channels,)) return nn.BatchNorm2d(channels, momentum=momentum) + class ResidualBlock(nn.Cell): expansion = 4 @@ -128,7 +132,7 @@ class ResNet(nn.Cell): layer_nums, in_channels, out_channels, - strides=[1,2,2,2], + strides=[1, 2, 2, 2], num_classes=100): super(ResNet, self).__init__() @@ -211,6 +215,7 @@ def resnet50(class_num=10): [2, 2, 2, 1], class_num) + class SoftmaxCrossEntropyExpand(_Loss): def __init__(self, sparse=False): super(SoftmaxCrossEntropyExpand, self).__init__() @@ -304,7 +309,7 @@ def test_train_32k_8p(epoch_size=3, batch_size=32, num_classes=32768): return allreduce_fusion_dict -def train_32k_8p_fusion1(epoch_size=3, batch_size=32, num_classes=32768): #1048576 #131072 #32768 #8192 +def train_32k_8p_fusion1(epoch_size=3, batch_size=32, num_classes=32768): # 1048576 #131072 #32768 #8192 cost_model_context.set_cost_model_context(costmodel_gamma=0.001, costmodel_beta=400.0) cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=1) cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_times=2) @@ -476,7 +481,7 @@ def train_32k_8p_fusion1(epoch_size=3, batch_size=32, num_classes=32768): #10485 cost_model_context.reset_cost_model_context() -def train_32k_8p_fusion2(epoch_size=3, batch_size=32, num_classes=32768): #1048576 #131072 #32768 #8192 +def train_32k_8p_fusion2(epoch_size=3, batch_size=32, num_classes=32768): # 1048576 #131072 #32768 #8192 cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_algorithm=2) cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_tail_time=0.1) cost_model_context.set_cost_model_context(costmodel_allreduce_fusion_allreduce_inherent_time=0.05) @@ -649,7 +654,7 @@ def train_32k_8p_fusion2(epoch_size=3, batch_size=32, num_classes=32768): #10485 cost_model_context.reset_cost_model_context() -def test_train_64k_8p(epoch_size=3, batch_size=32, num_classes=65536): #1048576 #131072 #32768 #8192 +def test_train_64k_8p(epoch_size=3, batch_size=32, num_classes=65536): # 1048576 #131072 #32768 #8192 dev_num = 8 context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, device_num=dev_num) cost_model_context.set_cost_model_context(costmodel_gamma=0.001, costmodel_beta=400.0) @@ -668,7 +673,7 @@ def test_train_64k_8p(epoch_size=3, batch_size=32, num_classes=65536): #1048576 model.train(5, dataset, dataset_sink_mode=False) strategies = _executor._get_strategy(model._train_network) for (k, v) in strategies.items(): - if re.search('Conv2D-op', k ) is not None: + if re.search('Conv2D-op', k) is not None: assert v[0][0] == dev_num elif re.search('MatMul-op', k) is not None: assert v == [[1, 1], [dev_num, 1]] diff --git a/tests/ut/python/predict/test_predict_save_model.py b/tests/ut/python/predict/test_predict_save_model.py index 074aa8282..63b71e9a1 100644 --- a/tests/ut/python/predict/test_predict_save_model.py +++ b/tests/ut/python/predict/test_predict_save_model.py @@ -64,7 +64,6 @@ parser.add_argument('--path', default='./lenet_model.ms', type=str, help='model if __name__ == '__main__': context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - context.set_context(enable_task_sink=True) print("test lenet predict start") seed = 0 diff --git a/tests/ut/python/pynative_mode/test_multigraph_sink.py b/tests/ut/python/pynative_mode/test_multigraph_sink.py index bf3d5b500..ad2a778fb 100644 --- a/tests/ut/python/pynative_mode/test_multigraph_sink.py +++ b/tests/ut/python/pynative_mode/test_multigraph_sink.py @@ -13,19 +13,15 @@ # limitations under the License. # ============================================================================ """ test_multigraph_sink """ -import pytest -import numpy as np -import mindspore.nn as nn import mindspore.context as context from mindspore.common.tensor import Tensor from mindspore.common import dtype as mstype from mindspore.common import ms_function -from mindspore.ops import operations as P def setup_module(module): context.set_context(mode = context.PYNATIVE_MODE, save_graphs = False, device_target = "Ascend") - context.set_context(enable_task_sink = True, device_id = 0) + context.set_context(device_id=0) c1 = Tensor([2], mstype.int32) @@ -86,6 +82,8 @@ def while_by_while(x, y, z): x = x + 1 x = x + 1 return x + + @ms_function def while_in_while(x, y, z): out = c4 @@ -98,6 +96,7 @@ def while_in_while(x, y, z): out = out + x return out + def test_simple_if(): output = simple_if(c1, c2, c3) expect = Tensor([6], mstype.int32) @@ -127,7 +126,8 @@ def test_while_by_while(): expect = Tensor([28], mstype.int32) assert output == expect + def test_while_in_while(): output = while_in_while(c1, c2, c3) expect = Tensor([1274], mstype.int32) - assert output == expect \ No newline at end of file + assert output == expect -- GitLab