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add dynamic shape support

This commit is contained in:
liyong 2020-11-17 15:07:37 +08:00
parent 68cb63d7f6
commit 48e688c166
12 changed files with 170 additions and 35 deletions
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6
mindspore/ccsrc/minddata/dataset/api/python/bindings.cc
View File

@ -76,6 +76,12 @@ PYBIND_REGISTER(
THROW_IF_ERROR(de.GetOutputTypes(&out));
return out;
})
.def("GetDataInfo",
[](DEPipeline &de) {
py::list types, shapes;
THROW_IF_ERROR(de.GetDataInfo(&types, &shapes));
return py::make_tuple(types, shapes);
})
.def("GetDatasetSize", &DEPipeline::GetDatasetSize)
.def("GetBatchSize", &DEPipeline::GetBatchSize)
.def("GetNumClasses", &DEPipeline::GetNumClasses)

26
mindspore/ccsrc/minddata/dataset/api/python/de_pipeline.cc
View File

@ -241,6 +241,30 @@ Status DEPipeline::GetNextAsList(py::list *output) {
return Status::OK();
}
Status DEPipeline::GetDataInfo(py::list *types, py::list *shapes) {
Status s;
DATA_INFO data_info;
// tree_.root() must be DeviceQueueOp
DeviceQueueOp *op = dynamic_cast<DeviceQueueOp *>(tree_->root().get());
if (op == nullptr) {
return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "GetDataInfo only supported by DeviceQueueOp");
}
{
py::gil_scoped_release gil_release;
s = op->GetDataInfo(&data_info);
}
RETURN_IF_NOT_OK(s);
for (auto el : data_info) {
types->append(el.first.AsNumpyType());
py::list shape;
for (auto dim : el.second.AsVector()) {
shape.append(dim);
}
shapes->append(shape);
}
return Status::OK();
}
Status DEPipeline::GetOutputShapes(py::list *output) {
std::vector<TensorShape> shapes;
Status s;
@ -1070,6 +1094,8 @@ Status DEPipeline::ParseDeviceQueueOp(const py::dict &args, std::shared_ptr<Data
(void)builder->SetSendEpochEnd(ToBool(value));
} else if (key == "total_batch") {
(void)builder->SetTotalBatch(ToInt(value));
} else if (key == "create_data_info_queue") {
(void)builder->SetCreateDataInfoQueue(ToBool(value));
}
}
}

2
mindspore/ccsrc/minddata/dataset/api/python/de_pipeline.h
View File

@ -111,6 +111,8 @@ class DEPipeline {
Status GetOutputTypes(py::list *output);
Status GetDataInfo(py::list *types, py::list *shapes);
Status SaveDataset(const std::vector<std::string> &file_names, const std::string &file_type);
int GetDatasetSize() const;

32
mindspore/ccsrc/minddata/dataset/engine/datasetops/device_queue_op.cc
View File

@ -33,7 +33,7 @@
namespace mindspore {
namespace dataset {
DeviceQueueOp::DeviceQueueOp(std::string channel_name, DeviceType device_type, int32_t device_id, int32_t prefetch_size,
bool send_epoch_end, int32_t total_batch)
bool send_epoch_end, int32_t total_batch, bool create_data_info_queue)
: PipelineOp(1),
channel_name_(channel_name),
device_type_(device_type),
@ -41,7 +41,8 @@ DeviceQueueOp::DeviceQueueOp(std::string channel_name, DeviceType device_type, i
prefetch_size_(prefetch_size),
send_epoch_end_(send_epoch_end),
stop_send_(false),
total_batch_(total_batch) {
total_batch_(total_batch),
create_data_info_queue_(create_data_info_queue) {
#ifdef ENABLE_TDTQUE
ascend_keep_waiting_ = true;
#endif
@ -87,6 +88,10 @@ Status DeviceQueueOp::operator()() {
if (device_type_ == DeviceType::Ascend) {
#ifdef ENABLE_TDTQUE
if (create_data_info_queue_) {
data_info_queue_ptr_ = std::make_unique<DATA_INFO_QUEUE>(kDataInfoQueueCapacity);
RETURN_IF_NOT_OK(data_info_queue_ptr_->Register(tree_->AllTasks()));
}
RETURN_IF_NOT_OK(SendDataToAscend());
#endif
} else if (device_type_ == DeviceType::GPU) {
@ -142,6 +147,13 @@ Status DeviceQueueOp::SendDataToAscend() {
return Status(StatusCode::kTDTPushFailure, "TDT Push Failed");
}
}
if (create_data_info_queue_) {
DATA_INFO data_info;
(void)std::transform(
currRow.begin(), currRow.end(), std::back_inserter(data_info),
[](const std::shared_ptr<Tensor> &ts) { return std::make_pair(ts->type(), ts->shape()); });
RETURN_IF_NOT_OK(data_info_queue_ptr_->Add(data_info));
}
if (isProfilingEnable) {
end_time = ProfilingTime::GetCurMilliSecond();
@ -157,6 +169,7 @@ Status DeviceQueueOp::SendDataToAscend() {
profiling_node->Record(CONNECTOR_DEPTH, connector_capacity, send_batch + 1, connector_size);
}
send_batch++;
if (total_batch_ > 0 && send_batch >= total_batch_) {
is_break_loop = true;
break;
@ -196,6 +209,21 @@ Status DeviceQueueOp::SendDataToAscend() {
return Status::OK();
}
#endif
#ifdef ENABLE_TDTQUE
Status DeviceQueueOp::GetDataInfo(DATA_INFO *data_info) {
if (!create_data_info_queue_) {
return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "DataInfo queue is not created.");
}
RETURN_IF_NOT_OK(data_info_queue_ptr_->PopFront(data_info));
return Status::OK();
}
#else
Status DeviceQueueOp::GetDataInfo(DATA_INFO *data_info) {
return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "GetDataInfo is not supported yet.");
}
#endif
#ifdef ENABLE_GPUQUE

21
mindspore/ccsrc/minddata/dataset/engine/datasetops/device_queue_op.h
View File

@ -18,6 +18,7 @@
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "minddata/dataset/engine/datasetops/pipeline_op.h"
@ -25,6 +26,7 @@
#include "minddata/dataset/util/status.h"
#ifdef ENABLE_TDTQUE
#include "minddata/dataset/util/queue.h"
#include "minddata/dataset/engine/tdt/tdt_plugin.h"
#endif
@ -37,6 +39,10 @@ using mindspore::device::GpuBufferMgr;
namespace mindspore {
namespace dataset {
using DATA_INFO = std::vector<std::pair<DataType, TensorShape>>;
using DATA_INFO_QUEUE = Queue<DATA_INFO>;
const int kDataInfoQueueCapacity = 128;
class DeviceQueueOp : public PipelineOp {
public:
static const uint32_t INVALID_HANDLE = 0xffffffffUL;
@ -91,13 +97,18 @@ class DeviceQueueOp : public PipelineOp {
return *this;
}
Builder &SetCreateDataInfoQueue(bool create_data_info_queue) {
builder_create_data_info_queue_ = create_data_info_queue;
return *this;
}
// Name: Build()
// Description: The final step for building a DeviceQueueOp via the Builder is
// to call this Build() method. It will instantiate the DeviceQueueOp
// and return it to caller as a shared pointer.
Status Build(std::shared_ptr<DeviceQueueOp> *ptr) {
*ptr = std::make_shared<DeviceQueueOp>(builder_channel_name_, builder_device_type_, builder_device_id_,
builder_prefetch_size_, builder_send_epoch_end_, builder_total_batch_);
builder_prefetch_size_, builder_send_epoch_end_, builder_total_batch_,
builder_create_data_info_queue_);
return Status::OK();
}
@ -108,12 +119,13 @@ class DeviceQueueOp : public PipelineOp {
std::string builder_channel_name_;
bool builder_send_epoch_end_;
int32_t builder_total_batch_;
bool builder_create_data_info_queue_;
};
// Name: constructor
// Description
DeviceQueueOp(std::string channel_name, DeviceType device_type, int32_t device_id, int32_t prefetch_size,
bool send_epoch_end, int32_t total_batch);
bool send_epoch_end, int32_t total_batch, bool create_data_info_queue);
// Name: destructor
// Description
@ -138,6 +150,8 @@ class DeviceQueueOp : public PipelineOp {
void StopWaiting() { ascend_keep_waiting_ = false; }
#endif
Status GetDataInfo(DATA_INFO *data_info);
// Name: Print()
// Description: A function that prints info about the node
void Print(std::ostream &out, // In: The output stream to print to
@ -170,6 +184,7 @@ class DeviceQueueOp : public PipelineOp {
#ifdef ENABLE_TDTQUE
Status SendDataToAscend();
bool ascend_keep_waiting_;
#endif
#ifdef ENABLE_GPUQUE
@ -190,6 +205,8 @@ class DeviceQueueOp : public PipelineOp {
const bool send_epoch_end_;
bool stop_send_;
int32_t total_batch_;
bool create_data_info_queue_;
std::unique_ptr<DATA_INFO_QUEUE> data_info_queue_ptr_;
#ifdef ENABLE_TDTQUE
std::shared_ptr<TdtPlugin> tdtInstancePtr;

5
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/transfer_node.cc
View File

@ -62,9 +62,8 @@ std::vector<std::shared_ptr<DatasetOp>> TransferNode::Build() {
} else if (device_type_ == "Ascend") {
type = DeviceQueueOp::DeviceType::Ascend;
}
node_ops.push_back(
std::make_shared<DeviceQueueOp>(queue_name_, type, device_id_, prefetch_size_, send_epoch_end_, total_batch_));
node_ops.push_back(std::make_shared<DeviceQueueOp>(queue_name_, type, device_id_, prefetch_size_, send_epoch_end_,
total_batch_, false));
return node_ops;
}

24
mindspore/dataset/engine/datasets.py
View File

@ -1005,7 +1005,7 @@ class Dataset:
return dataset
@check_device_send
def device_que(self, prefetch_size=None, send_epoch_end=True):
def device_que(self, prefetch_size=None, send_epoch_end=True, create_data_info_queue=False):
"""
Return a transferred Dataset that transfers data through a device.
@ -1013,6 +1013,8 @@ class Dataset:
prefetch_size (int, optional): Prefetch number of records ahead of the
user's request (default=None).
send_epoch_end (bool, optional): Whether to send end of sequence to device or not (default=True).
create_data_info_queue (bool, optional): Whether to create queue which stores
types and shapes of data or not(default=False).
Note:
If device is Ascend, features of data will be transferred one by one. The limitation
@ -1021,15 +1023,17 @@ class Dataset:
Return:
TransferDataset, dataset for transferring.
"""
return self.to_device(send_epoch_end=send_epoch_end)
return self.to_device(send_epoch_end=send_epoch_end, create_data_info_queue=create_data_info_queue)
@check_device_send
def to_device(self, send_epoch_end=True):
def to_device(self, send_epoch_end=True, create_data_info_queue=False):
"""
Transfer data through CPU, GPU or Ascend devices.
Args:
send_epoch_end (bool, optional): Whether to send end of sequence to device or not (default=True).
create_data_info_queue (bool, optional): Whether to create queue which stores
types and shapes of data or not(default=False).
Note:
If device is Ascend, features of data will be transferred one by one. The limitation
@ -1078,7 +1082,7 @@ class Dataset:
distribution_path, device_id = get_distribution(self)
if distribution_path == "":
return TransferDataset(self, queue_name, device_id, device_type, send_epoch_end)
return TransferDataset(self, queue_name, device_id, device_type, send_epoch_end, create_data_info_queue)
try:
with open(distribution_path, 'r') as distribution_f:
dist = json.load(distribution_f)
@ -1088,7 +1092,7 @@ class Dataset:
except Exception:
raise RuntimeError("Failed to read Distribution file.")
return TransferDataset(self, queue_name, device_id, device_type, send_epoch_end)
return TransferDataset(self, queue_name, device_id, device_type, send_epoch_end, create_data_info_queue)
@check_save
def save(self, file_name, num_files=1, file_type='mindrecord'):
@ -2640,9 +2644,12 @@ class TransferDataset(DatasetOp):
device_id (int): ID of device.
device_type (str): Type of device, including "CPU", "GPU", and "Ascend".
send_epoch_end (bool, optional): Whether to send end of sequence to device or not (default=True).
create_data_info_queue (bool, optional): Whether to create queue which stores
types and shapes of data or not(default=False).
"""
def __init__(self, input_dataset, queue_name, device_id, device_type, send_epoch_end=True):
def __init__(self, input_dataset, queue_name, device_id, device_type, send_epoch_end=True,
create_data_info_queue=False):
super().__init__()
self.children.append(input_dataset)
input_dataset.parent.append(self)
@ -2652,6 +2659,7 @@ class TransferDataset(DatasetOp):
self._device_id = device_id
self._send_epoch_end = send_epoch_end
self.iterator = None
self._create_data_info_queue = create_data_info_queue
def get_args(self):
args = super().get_args()
@ -2661,6 +2669,7 @@ class TransferDataset(DatasetOp):
args["send_epoch_end"] = self._send_epoch_end
if hasattr(self.children[0], "__total_batch__"):
args["total_batch"] = self.children[0].__total_batch__
args["create_data_info_queue"] = self._create_data_info_queue
return args
def create_dict_iterator(self, num_epochs=-1, output_numpy=False):
@ -2692,6 +2701,9 @@ class TransferDataset(DatasetOp):
def continue_send(self):
self.iterator.depipeline.ContinueSend()
def get_data_info(self):
return self.iterator.depipeline.GetDataInfo()
class RangeDataset(MappableDataset):
"""

4
mindspore/train/_utils.py
View File

@ -50,7 +50,7 @@ def _get_types_and_shapes(dataset):
return dataset_types, dataset_shapes
def _exec_datagraph(exec_dataset, dataset_size, phase='dataset'):
def _exec_datagraph(exec_dataset, dataset_size, phase='dataset', create_data_info_queue=False):
"""Initialize and execute the dataset graph."""
batch_size = exec_dataset.get_batch_size()
input_indexs = exec_dataset.input_indexs
@ -58,7 +58,7 @@ def _exec_datagraph(exec_dataset, dataset_size, phase='dataset'):
# transform data format
dataset_types, dataset_shapes = _get_types_and_shapes(exec_dataset)
send_epoch_end = bool(dataset_size == -1)
exec_dataset = exec_dataset.device_que(send_epoch_end=send_epoch_end)
exec_dataset = exec_dataset.device_que(send_epoch_end=send_epoch_end, create_data_info_queue=create_data_info_queue)
_executor.init_dataset(exec_dataset.queue_name,
dataset_size,

50
mindspore/train/dataset_helper.py
View File

@ -17,6 +17,7 @@ import math
import os
from mindspore._checkparam import Validator
from mindspore.common.dtype import pytype_to_dtype
from .. import context, nn
from ._utils import _exec_datagraph, _get_types_and_shapes, _construct_tensor_list
from ..nn.wrap import GetNextSingleOp
@ -31,6 +32,7 @@ def _send_data(dataset, epoch_num):
exec_dataset.send(epoch_num)
dataset.__has_sent__ = True
def _send_data_no_flag(dataset, epoch_num):
"""Engine dataset to write data to tdt queue directly."""
exec_dataset = dataset.__transfer_dataset__
@ -70,6 +72,7 @@ def connect_network_with_dataset(network, dataset_helper):
Wraps the input network with a dataset which automatically fetches data with 'GetNext' function from the
dataset channel 'queue_name' and performs the forward computation.
"""
def __init__(self, network, dataset_types, dataset_shapes, queue_name):
super(_DataWrapper, self).__init__(auto_prefix=False, flags=network.get_flags())
# Also copy the flag in `network` construct
@ -88,9 +91,30 @@ def connect_network_with_dataset(network, dataset_helper):
if isinstance(dataset_iter, _DatasetIterNormal):
raise RuntimeError("Dataset should be connected with network only in sink mode.")
if not hasattr(dataset, '__me_inited__') and (context.get_context("device_target") == "Ascend"
or context.get_context("device_target") == "GPU") and not \
context.get_context("enable_ge"):
if (hasattr(dataset_iter, "sink_size") and dataset_iter.sink_size == 1) \
and (hasattr(dataset_iter, "sink_count") and dataset_iter.sink_count == 1) \
and context.get_context("device_target") == "Ascend":
if not hasattr(dataset, '__network__'):
dataset.__network__ = network
network = dataset.__network__
dataset_types, dataset_shapes = dataset_helper.get_data_info()
dataset_types = [pytype_to_dtype(x) for x in dataset_types]
key = str(dataset_types) + str(dataset_shapes)
if hasattr(dataset, '__network_manage__') and key in dataset.__network_manage__:
network = dataset.__network_manage__[key]
else:
network = _DataWrapper(network, dataset_types, dataset_shapes, dataset.__transfer_dataset__.queue_name)
dataset.__network_manage__ = dataset.__network_manage__ if hasattr(
dataset, '__network_manage__') else dict()
dataset.__network_manage__[key] = network
return network
if not hasattr(dataset, '__me_inited__') and (context.get_context("device_target") == "Ascend" or \
context.get_context("device_target") == "GPU") and not context.get_context("enable_ge"):
dataset.__me_inited__ = True
dataset_types, dataset_shapes = dataset_helper.types_shapes()
@ -99,7 +123,6 @@ def connect_network_with_dataset(network, dataset_helper):
network = _DataWrapper(network, dataset_types, dataset_shapes, queue_name)
return network
class DatasetHelper:
"""
DatasetHelper is a class to process the MindData dataset and it provides the information of dataset.
@ -171,18 +194,25 @@ class DatasetHelper:
"""continue send data to device at the beginning of epoch."""
self.iter.continue_send()
def get_data_info(self):
return self.iter.get_data_info()
class _DatasetIter:
"""Base iter for dataset helper"""
def __init__(self, dataset, sink_size, epoch_num):
self.dataset = dataset
self.sink_size = sink_size
self.sink_count = 1
self.sink_count = self.get_sink_count(dataset)
if not hasattr(dataset, '__transfer_dataset__'):
if hasattr(dataset, '__loop_size__'):
self.sink_size = dataset.__loop_size__
dataset.__transfer_dataset__ = _exec_datagraph(dataset, self.sink_size)
create_data_info_queue = (sink_size == 1 and self.sink_count == 1 and context.get_context(
"device_target") == "Ascend")
dataset.__transfer_dataset__ = _exec_datagraph(dataset, self.sink_size,
create_data_info_queue=create_data_info_queue)
if not hasattr(dataset, '__no_send__'):
_send_data(dataset, epoch_num)
@ -191,6 +221,7 @@ class _DatasetIter:
self.stop_send = dataset.__transfer_dataset__.stop_send
self.continue_send = dataset.__transfer_dataset__.continue_send
self.get_data_info = dataset.__transfer_dataset__.get_data_info
self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset)
def __iter__(self):
@ -223,7 +254,7 @@ class _DatasetIter:
sink_size = self.dataset.__loop_size__
else:
if context.get_context("enable_ge") or context.get_context("device_target") == "Ascend" \
or context.get_context("device_target") == "GPU":
or context.get_context("device_target") == "GPU":
if self.sink_size > 0:
sink_size = self.sink_size
else:
@ -233,6 +264,7 @@ class _DatasetIter:
class _DatasetIterGE(_DatasetIter):
"""Iter for GE."""
def __init__(self, dataset, sink_size, epoch_num):
super().__init__(dataset, sink_size, epoch_num)
self.sink_count = self.get_sink_count(dataset)
@ -249,6 +281,7 @@ class _DatasetIterGE(_DatasetIter):
class _DatasetIterMSLoopSink(_DatasetIter):
"""Iter for context (device_target=Ascend)"""
def __init__(self, dataset, sink_size, epoch_num):
super().__init__(dataset, sink_size, epoch_num)
self.sink_count = self.get_sink_count(dataset)
@ -270,6 +303,7 @@ class _DatasetIterMSLoopSink(_DatasetIter):
class _DatasetIterMS(_DatasetIter):
"""Iter for MS(enable_loop_sink=False)."""
def __init__(self, dataset, sink_size, epoch_num):
super().__init__(dataset, sink_size, epoch_num)
if sink_size > 0:
@ -283,11 +317,13 @@ class _DatasetIterMS(_DatasetIter):
class _DatasetIterPSLite(_DatasetIter):
"""Iter for context (device_target=GPU) on MS_PSERVER or MS_SCHED"""
def __init__(self, dataset, sink_size, epoch_num):
super().__init__(dataset, sink_size, epoch_num)
self.sink_count = 1
self.sink_size = 1
self.op = None
def op():
return _construct_tensor_list(self.dataset_types, self.dataset_shapes, batch_expand_num=1)
self.op = op

28
mindspore/train/model.py
View File

@ -250,11 +250,14 @@ class Model:
scaling_sens /= self._device_number
return scaling_sens
def _exec_preprocess(self, network, is_train, phase, dataset, dataset_sink_mode, sink_size=-1, epoch_num=1):
def _exec_preprocess(self, network, is_train, phase, dataset,
dataset_sink_mode, sink_size=-1, epoch_num=1, dataset_helper=None):
"""Initializes dataset."""
if dataset_sink_mode and not is_train:
dataset.__loop_size__ = 1
dataset_helper = DatasetHelper(dataset, dataset_sink_mode, sink_size, epoch_num)
if dataset_helper is None:
dataset_helper = DatasetHelper(dataset, dataset_sink_mode, sink_size, epoch_num)
if dataset_sink_mode:
network = connect_network_with_dataset(network, dataset_helper)
@ -405,15 +408,6 @@ class Model:
epoch_num = math.ceil(epoch * sink_size / train_dataset.get_dataset_size())
train_dataset.__total_batch__ = epoch * sink_size
dataset_helper, train_network = self._exec_preprocess(self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=True,
sink_size=sink_size,
epoch_num=epoch_num)
self._train_network = train_network
cb_params.train_network = self._train_network
cb_params.cur_step_num = 0
run_context = RunContext(cb_params)
@ -421,9 +415,21 @@ class Model:
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
dataset_helper = None
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
dataset_helper, train_network = self._exec_preprocess(self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=True,
sink_size=sink_size,
epoch_num=epoch_num,
dataset_helper=dataset_helper)
self._train_network = train_network
cb_params.train_network = self._train_network
# for data sink dataset_helper only iter once, other wise iter epoch_size times.
for inputs in dataset_helper:

2
model_zoo/official/nlp/gpt/src/pre_process.py
View File

@ -133,7 +133,7 @@ def tokenize_lambada(file_path):
with open(file_path, 'r', encoding='utf-8') as f:
for line in f.readlines():
para = json.loads(line)['text'].replace(
"“", '""').replace("”", '"').strip().strip(".")
"“", '"').replace("”", '"').strip().strip(".")
tokenized_text = tokenizer.tokenize(para)
content += tokenizer.convert_tokens_to_ids(tokenized_text) + [EOT]
for chunk in chunks(content, SEQ_LEN):

5
tests/dataset_mock.py
View File

@ -50,7 +50,7 @@ class MindData:
def input_indexs(self):
return self._input_indexs
def device_que(self, send_epoch_end=True):
def device_que(self, send_epoch_end=True, create_data_info_queue=False):
self.queue_name = '6ba41974-209e-11ea-88b0-a24efeb2c736'
self.send_epoch_end = send_epoch_end
return self
@ -61,6 +61,9 @@ class MindData:
def send(self, num_epochs=-1):
pass
def get_data_info(self):
pass
def stop_send(self):
pass