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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/11/30 01:03:41 UTC
[GitHub] piiswrong closed pull request #7512: Add two bit compression operator
piiswrong closed pull request #7512: Add two bit compression operator
URL: https://github.com/apache/incubator-mxnet/pull/7512
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/example/image-classification/common/fit.py b/example/image-classification/common/fit.py
index aeead0f82a..9657120093 100755
--- a/example/image-classification/common/fit.py
+++ b/example/image-classification/common/fit.py
@@ -113,8 +113,7 @@ def fit(args, network, data_loader, **kwargs):
data_loader : function that returns the train and val data iterators
"""
# kvstore
- kv = mx.kvstore.create(args.kv_store)
-
+ kv = mx.kvstore.create(args.kv_store, '2bit')
# logging
head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
logging.basicConfig(level=logging.DEBUG, format=head)
diff --git a/example/image-classification/hosts b/example/image-classification/hosts
new file mode 100644
index 0000000000..ea9f2cc6c0
--- /dev/null
+++ b/example/image-classification/hosts
@@ -0,0 +1,5 @@
+172.31.17.116
+172.31.34.134
+172.31.69.158
+172.31.67.2
+
diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h
index ef9d31ebc8..6420359e15 100644
--- a/include/mxnet/c_api.h
+++ b/include/mxnet/c_api.h
@@ -1511,7 +1511,22 @@ MXNET_DLL int MXInitPSEnv(mx_uint num_vars,
* \return 0 when success, -1 when failure happens
*/
MXNET_DLL int MXKVStoreCreate(const char *type,
+ const char *compress,
KVStoreHandle *out);
+
+/*!
+ * \brief Set to use low-bit compression
+ * \param handle handle to the kvstore
+ * \param compress set to use low-bit compression
+ * \param pos_threshold set the positive threshold in 2bit compress
+ * \param neg_threshold set the negative threshold in 2bit compress
+ * \return 0 when success, -1 when failure happens
+ */
+MXNET_DLL int MXKVStoreSetCompress(KVStoreHandle handle,
+ const char *compress,
+ const float pos_threshold,
+ const float neg_threshold);
+
/*!
* \brief Delete a KVStore handle.
* \param handle handle to the kvstore
diff --git a/include/mxnet/kvstore.h b/include/mxnet/kvstore.h
index ddaa207dab..f6d4f4589a 100644
--- a/include/mxnet/kvstore.h
+++ b/include/mxnet/kvstore.h
@@ -57,13 +57,25 @@ class KVStore {
* - 'dist_*' : multi-machines
* \return a new created KVStore.
*/
- static KVStore *Create(const char *type = "local");
+ static KVStore *Create(const char *type = "local",
+ const char *compress = "none");
/**
* \brief return the type
*/
inline const std::string& type() { return type_; }
+ /**
+ * \brief set to use low-bit compression
+ */
+ inline void SetCompress(const std::string& compress,
+ const float pos_threshold,
+ const float neg_threshold) {
+ compress_ = compress;
+ pos_threshold_ = pos_threshold;
+ neg_threshold_ = neg_threshold;
+ }
+
/*!
* \brief Initialize a list of key-value pair to the store.
*
@@ -387,6 +399,21 @@ class KVStore {
*/
std::string type_;
+ /**
+ * \brief whether using low-bit compression
+ */
+ std::string compress_ = "none";
+
+ /**
+ * \brief positive threshold
+ */
+ float pos_threshold_ = 0.1;
+
+ /**
+ * \brief negative threshold
+ */
+ float neg_threshold_ = -0.1;
+
/**
* \brief whether to do barrier when finalize
*/
diff --git a/include/mxnet/ndarray.h b/include/mxnet/ndarray.h
index 754bc28e7b..692f56d2ca 100644
--- a/include/mxnet/ndarray.h
+++ b/include/mxnet/ndarray.h
@@ -905,6 +905,12 @@ size_t num_aux_data(NDArrayStorageType stype);
*/
void CopyFromTo(const NDArray &from, NDArray *to, int priority = 0);
+void Quantize(const NDArray &from, NDArray *to, NDArray *residual,
+ const NDArray &pos_threshold, const NDArray &neg_threshold,
+ std::string& compress, int priority);
+
+void Dequantize(const NDArray &from, NDArray *to, std::string& compress, int priority);
+
/*!
* \brief Perform elementwise sum over each data from source, store result into out.
* \param source the ndarray we want to sum
diff --git a/python/mxnet/gluon/trainer.py b/python/mxnet/gluon/trainer.py
index 5f554073c1..31803abb10 100644
--- a/python/mxnet/gluon/trainer.py
+++ b/python/mxnet/gluon/trainer.py
@@ -43,6 +43,12 @@ class Trainer(object):
kvstore : str or KVStore
kvstore type for multi-gpu and distributed training. See help on
:any:`mxnet.kvstore.create` for more information.
+ compress : str
+ whether using low-bit compression. The argument can be 'none', '2bit', and '1bit'.
+ pos_threshold:
+ positive threshold used in 2bit compression.
+ neg_threshold:
+ negative threshold used in 2bit compression.
Properties
----------
@@ -50,7 +56,8 @@ class Trainer(object):
The current learning rate of the optimizer. Given an Optimizer object
optimizer, its learning rate can be accessed as optimizer.learning_rate.
"""
- def __init__(self, params, optimizer, optimizer_params=None, kvstore='device'):
+ def __init__(self, params, optimizer, optimizer_params=None, kvstore='device',
+ compress='none', pos_threshold=0.1, neg_threshold=-0.1):
if isinstance(params, (dict, ParameterDict)):
params = list(params.values())
if not isinstance(params, (list, tuple)):
@@ -64,13 +71,23 @@ def __init__(self, params, optimizer, optimizer_params=None, kvstore='device'):
"First argument must be a list or dict of Parameters, " \
"got list of %s."%(type(param)))
self._params.append(param)
-
+ if (compress != 'none' and
+ compress != '2bit' and compress != '1bit'):
+ raise ValueError("The compress argument can only be 'none', " \
+ "'2bit', or '1bit'.")
+ if (compress == '2bit' and
+ (pos_threshold <= 0 or neg_threshold >= 0)):
+ raise ValueError("The pos_threshold must be greater than 0, and " \
+ "the neg_threshold must be less than 0.")
optimizer_params = optimizer_params if optimizer_params else {}
self._scale = optimizer_params.get('rescale_grad', 1.0)
self._contexts = self._check_contexts()
self._init_optimizer(optimizer, optimizer_params)
self._kv_initialized = False
self._kvstore = kvstore
+ self._compress = compress
+ self._pos_threshold = pos_threshold
+ self._neg_threshold = neg_threshold
def _check_contexts(self):
contexts = None
@@ -100,9 +117,14 @@ def _init_optimizer(self, optimizer, optimizer_params):
def _init_kvstore(self):
arg_arrays = {param.name: param.data(self._contexts[0]) for param in self._params}
- kvstore, update_on_kvstore = _create_kvstore(self._kvstore, len(self._contexts),
- arg_arrays)
+ kvstore, update_on_kvstore = _create_kvstore(self._kvstore,
+ len(self._contexts),
+ arg_arrays,
+ self._compress)
if kvstore:
+ kvstore.set_compress(self._compress,
+ self._pos_threshold,
+ self._neg_threshold)
if 'dist' in kvstore.type:
update_on_kvstore = False
for i, param in enumerate(self._params):
diff --git a/python/mxnet/kvstore.py b/python/mxnet/kvstore.py
index cf5c15931b..cda8fe8613 100644
--- a/python/mxnet/kvstore.py
+++ b/python/mxnet/kvstore.py
@@ -24,7 +24,7 @@
from .ndarray import NDArray
from .ndarray import _ndarray_cls
from .base import _LIB
-from .base import check_call, c_array, c_str, string_types, mx_uint, py_str
+from .base import check_call, c_array, c_str, string_types, mx_uint, mx_float, py_str
from .base import NDArrayHandle, KVStoreHandle
from . import optimizer as opt
@@ -346,6 +346,20 @@ def row_sparse_pull(self, key, out=None, priority=0, row_ids=None):
self.handle, mx_uint(len(ckeys)), ckeys, cvals, crow_ids, ctypes.c_int(priority)))
+ def set_compress(self, compress='none',
+ pos_threshold=0.1,
+ neg_threshold=-0.1):
+ """ Set to use low-bit compression
+
+ compress can be 'none', '2bit', or '1bit'.
+ """
+ if not isinstance(compress, string_types):
+ raise TypeError('compress must be a string')
+ check_call(_LIB.MXKVStoreSetCompress(self.handle,
+ c_str(compress),
+ mx_float(pos_threshold),
+ mx_float(neg_threshold)))
+
def set_optimizer(self, optimizer):
""" Registers an optimizer with the kvstore.
@@ -527,7 +541,7 @@ def _send_command_to_servers(self, head, body):
check_call(_LIB.MXKVStoreSendCommmandToServers(
self.handle, mx_uint(head), c_str(body)))
-def create(name='local'):
+def create(name='local', compress='none'):
"""Creates a new KVStore.
For single machine training, there are two commonly used types:
@@ -566,5 +580,6 @@ def create(name='local'):
raise TypeError('name must be a string')
handle = KVStoreHandle()
check_call(_LIB.MXKVStoreCreate(c_str(name),
+ c_str(compress),
ctypes.byref(handle)))
return KVStore(handle)
diff --git a/python/mxnet/kvstore_server.py b/python/mxnet/kvstore_server.py
index 2504b4674a..423cce83ac 100644
--- a/python/mxnet/kvstore_server.py
+++ b/python/mxnet/kvstore_server.py
@@ -57,7 +57,7 @@ def server_controller(cmd_id, cmd_body, _):
raise
self.kvstore.set_optimizer(optimizer)
else:
- print("server %d, unknown command (%d, %s)" % (
+ print ("server %d, unknown command (%d, %s)" % (
self.kvstore.rank, cmd_id, cmd_body))
return server_controller
@@ -77,7 +77,7 @@ def _init_kvstore_server_module():
is_worker = ctypes.c_int()
check_call(_LIB.MXKVStoreIsWorkerNode(ctypes.byref(is_worker)))
if is_worker.value == 0:
- kvstore = create('dist')
+ kvstore = create('dist', '2bit')
server = KVStoreServer(kvstore)
server.run()
sys.exit()
diff --git a/python/mxnet/model.py b/python/mxnet/model.py
index 2444ca0dc5..e52f7aa8b2 100644
--- a/python/mxnet/model.py
+++ b/python/mxnet/model.py
@@ -54,7 +54,7 @@
'eval_metric',
'locals'])
-def _create_kvstore(kvstore, num_device, arg_params):
+def _create_kvstore(kvstore, num_device, arg_params, compress):
"""Create kvstore
This function select and create a proper kvstore if given the kvstore type.
@@ -78,7 +78,7 @@ def _create_kvstore(kvstore, num_device, arg_params):
# no need to use kv for single device and single machine
kv = None
else:
- kv = kvs.create(kvstore)
+ kv = kvs.create(kvstore, compress)
if kvstore == 'local':
# automatically select a proper local
max_size = max(np.prod(param.shape) for param in
diff --git a/python/mxnet/module/module.py b/python/mxnet/module/module.py
index d55b2117eb..22d6ab4bc6 100644
--- a/python/mxnet/module/module.py
+++ b/python/mxnet/module/module.py
@@ -484,7 +484,7 @@ def init_optimizer(self, kvstore='local', optimizer='sgd',
self._sync_params_from_devices()
(kvstore, update_on_kvstore) = \
- _create_kvstore(kvstore, len(self._context), self._arg_params)
+ _create_kvstore(kvstore, len(self._context), self._arg_params, '2bit')
batch_size = self._exec_group.batch_size
if kvstore and 'dist' in kvstore.type and '_sync' in kvstore.type:
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index 5171e2713c..2d162a39d2 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -721,9 +721,21 @@ int MXDataIterGetPadNum(DataIterHandle handle, int *pad) {
}
int MXKVStoreCreate(const char *type,
+ const char *compress,
KVStoreHandle *out) {
API_BEGIN();
- *out = KVStore::Create(type);
+ *out = KVStore::Create(type, compress);
+ API_END();
+}
+
+int MXKVStoreSetCompress(KVStoreHandle handle,
+ const char *compress,
+ const float pos_threshold,
+ const float neg_threshold) {
+ API_BEGIN();
+ static_cast<KVStore*>(handle)->SetCompress(compress,
+ pos_threshold,
+ neg_threshold);
API_END();
}
diff --git a/src/kvstore/comm.h b/src/kvstore/comm.h
index f5b6334315..5cb8962918 100644
--- a/src/kvstore/comm.h
+++ b/src/kvstore/comm.h
@@ -48,6 +48,7 @@ class Comm {
*/
virtual void Init(int key, const NDArrayStorageType stype,
const TShape& shape, int dtype = mshadow::kFloat32) = 0;
+
/**
* \brief returns src[0] + .. + src[src.size()-1]
*/
@@ -79,8 +80,22 @@ class Comm {
return pinned_ctx_;
}
+ /**
+ * \brief set to use low-bit compression
+ */
+ void SetCompress(const std::string& compress,
+ float const pos_threshold,
+ float const neg_threshold) {
+ compress_ = compress;
+ pos_threshold_ = pos_threshold;
+ neg_threshold_ = neg_threshold;
+ }
+
protected:
Context pinned_ctx_;
+ std::string compress_ = "none";
+ float pos_threshold_;
+ float neg_threshold_;
};
/**
@@ -497,23 +512,86 @@ class CommDevice : public Comm {
auto& buf = merge_buf_[key];
std::vector<NDArray> reduce(src.size());
- CopyFromTo(src[0], &(buf.merged), priority);
- reduce[0] = buf.merged;
if (buf.copy_buf.empty()) {
// TODO(mli) this results in large device memory usage for huge ndarray,
// such as the largest fullc in VGG. consider to do segment reduce with
// NDArray.Slice or gpu direct memory access. for the latter, we need to
// remove some ctx check, and also it reduces 20% perf
- buf.copy_buf.resize(src.size()-1);
- for (size_t i = 0; i < src.size()-1; ++i) {
+ buf.copy_buf.resize(src.size());
+ buf.small_recv_buf.resize(src.size());
+ buf.small_send_buf.resize(src.size());
+ buf.residual.resize(src.size());
+ pos_thre.resize(src.size());
+ neg_thre.resize(src.size());
+
+ for (size_t i = 0; i < src.size(); ++i) {
buf.copy_buf[i] = NDArray(
buf.merged.shape(), buf.merged.ctx(), false, buf.merged.dtype());
+ // allocation small buffer for compressed data
+ if (compress_.compare("none") != 0) {
+ // Residual
+ buf.residual[i] = NDArray(
+ buf.merged.shape(), src[i].ctx(), false, buf.merged.dtype());
+ buf.residual[i] = 0;
+ // recv buffer and send buffer
+ int bits = compress_ == "2bit" ? 16 : 32;
+ long int small_size = buf.merged.shape().Size() % bits == 0 ?
+ buf.merged.shape().Size() / bits + 3 :
+ buf.merged.shape().Size() / bits + 4;
+ buf.small_recv_buf[i] = NDArray(
+ TShape{small_size}, buf.merged.ctx(), false, buf.merged.dtype());
+ buf.small_send_buf[i] = NDArray(
+ TShape{small_size}, src[i].ctx(), false, buf.merged.dtype());
+ // The positive and negative threshold
+ if (compress_.compare("2bit") == 0) {
+ pos_thre[i] = NDArray(
+ TShape{1}, src[i].ctx(), false, buf.merged.dtype());
+ pos_thre[i] = pos_threshold_;
+ neg_thre[i] = NDArray(
+ TShape{1}, src[i].ctx(), false, buf.merged.dtype());
+ neg_thre[i] = neg_threshold_;
+ }
+ }
}
}
- for (size_t i = 0; i < src.size()-1; ++i) {
- CopyFromTo(src[i+1], &(buf.copy_buf[i]), priority);
- reduce[i+1] = buf.copy_buf[i];
+
+ for (size_t i = 0; i < src.size(); ++i) {
+ // compress before copy
+ if (compress_.compare("2bit") == 0) {
+ // TODO: New code: wrapper for NDArray quantize_2bit op
+ /*
+ Compress2Bit(src[i], buf.residual[i],
+ pos_thre[i], neg_thre[i],
+ &(buf.small_send_buf[i]), priority);
+ CopyFromTo(buf.small_send_buf[i],
+ &(buf.small_recv_buf[i]),
+ priority);
+ DeCompress2Bit(buf.small_recv_buf[i],
+ &(buf.copy_buf[i]),
+ priority);
+ */
+ Quantize(src[i], &(buf.small_send_buf[i]), &(buf.residual[i]),
+ pos_thre[i], neg_thre[i], compress_, priority);
+ CopyFromTo(buf.small_send_buf[i], &(buf.small_recv_buf[i]), priority);
+ Dequantize(buf.small_recv_buf[i], &(buf.copy_buf[i]), compress_, priority);
+ } else if (compress_.compare("1bit") == 0) {
+ // TODO: New code: wrapper for NDArray quantize_1bit op
+ /*
+ Compress1Bit(src[i], buf.residual[i],
+ &(buf.small_send_buf[i]),
+ priority);
+ CopyFromTo(buf.small_send_buf[i],
+ &(buf.small_recv_buf[i]),
+ priority);
+ DeCompress1Bit(buf.small_recv_buf[i],
+ &(buf.copy_buf[i]),
+ priority);
+ */
+ } else { // Do not compress
+ CopyFromTo(src[i], &(buf.copy_buf[i]), priority);
+ }
+ reduce[i] = buf.copy_buf[i];
}
ElementwiseSum(reduce, &buf.merged);
@@ -630,8 +708,18 @@ class CommDevice : public Comm {
NDArray merged;
/// \brief the gpu buffer
std::vector<NDArray> copy_buf;
+ /// \brief the residual buffer
+ std::vector<NDArray> residual;
+ /// \brief the small buffer for compressed data in sender
+ std::vector<NDArray> small_send_buf;
+ /// \brief the small buffer for compressed data in receiver
+ std::vector<NDArray> small_recv_buf;
};
std::unordered_map<int, BufferEntry> merge_buf_;
+
+ // \brief the positive and negative threshold
+ std::vector<NDArray> pos_thre;
+ std::vector<NDArray> neg_thre;
bool inited_;
};
diff --git a/src/kvstore/kvstore.cc b/src/kvstore/kvstore.cc
index a288676102..f6e8745a56 100644
--- a/src/kvstore/kvstore.cc
+++ b/src/kvstore/kvstore.cc
@@ -31,7 +31,7 @@
namespace mxnet {
-KVStore* KVStore::Create(const char *type_name) {
+KVStore* KVStore::Create(const char *type_name, const char *comp) {
std::string tname = type_name;
std::transform(tname.begin(), tname.end(), tname.begin(), ::tolower);
KVStore* kv = nullptr;
@@ -42,10 +42,11 @@ KVStore* KVStore::Create(const char *type_name) {
if (has("device")) {
use_device_comm = true;
}
+ std::string compress(comp);
if (has("dist")) {
#if MXNET_USE_DIST_KVSTORE
- kv = new kvstore::KVStoreDist(use_device_comm);
+ kv = new kvstore::KVStoreDist(use_device_comm, compress);
if (!has("_async") && kv->IsWorkerNode() && kv->get_rank() == 0) {
// configure the server to be the sync mode
kv->SendCommandToServers(kvstore::kSyncMode, "");
@@ -55,7 +56,7 @@ KVStore* KVStore::Create(const char *type_name) {
return nullptr;
#endif // MXNET_USE_DIST_KVSTORE
} else {
- kv = new kvstore::KVStoreLocal(use_device_comm);
+ kv = new kvstore::KVStoreLocal(use_device_comm, compress);
}
kv->type_ = tname;
return kv;
diff --git a/src/kvstore/kvstore_dist.h b/src/kvstore/kvstore_dist.h
index 6ce6b5adaf..5060565ae9 100644
--- a/src/kvstore/kvstore_dist.h
+++ b/src/kvstore/kvstore_dist.h
@@ -31,6 +31,7 @@
#include "mxnet/engine.h"
#include "ps/ps.h"
#include "./kvstore_dist_server.h"
+#include "../ndarray/ndarray_function.h"
#if MKL_EXPERIMENTAL == 1
#include <mkl_memory.h>
#include "../operator/mkl/mkl_memory-inl.h"
@@ -51,8 +52,8 @@ namespace kvstore {
*/
class KVStoreDist : public KVStoreLocal {
public:
- explicit KVStoreDist(bool use_device_comm)
- : KVStoreLocal(use_device_comm), ps_worker_(nullptr), server_(nullptr) {
+ explicit KVStoreDist(bool use_device_comm, std::string& comp)
+ : KVStoreLocal(use_device_comm, comp), ps_worker_(nullptr), server_(nullptr) {
if (IsWorkerNode()) {
ps_worker_ = new ps::KVWorker<real_t>(0);
ps::StartAsync("mxnet\0");
@@ -61,6 +62,7 @@ class KVStoreDist : public KVStoreLocal {
ps::kWorkerGroup + ps::kServerGroup + ps::kScheduler);
}
}
+ compress_ = comp;
bigarray_bound_ = dmlc::GetEnv("MXNET_KVSTORE_BIGARRAY_BOUND", 1000 * 1000);
log_verbose_ = dmlc::GetEnv("MXNET_KVSTORE_DIST_ROW_SPARSE_VERBOSE", false);
}
@@ -117,7 +119,7 @@ class KVStoreDist : public KVStoreLocal {
void RunServer(const Controller& controller) override {
CHECK(!IsWorkerNode());
if (IsServerNode()) {
- server_ = new KVStoreDistServer();
+ server_ = new KVStoreDistServer(compress_);
server_->set_controller(controller);
}
@@ -138,6 +140,9 @@ class KVStoreDist : public KVStoreLocal {
void InitImpl(const std::vector<int>& keys,
const std::vector<NDArray>& values) override {
CheckUnique(keys);
+ if (IsServerNode()) {
+ server_->set_compress(compress_);
+ }
for (size_t i = 0; i < keys.size(); ++i) {
comm_->Init(keys[i], values[i].storage_type(), values[i].shape(), values[i].dtype());
}
@@ -185,7 +190,7 @@ class KVStoreDist : public KVStoreLocal {
RunContext rctx, Engine::CallbackOnComplete cb) {
// convert to ps keys
size_t size = recv_buf.shape().Size();
- PSKV& pskv = EncodeKey(key, size);
+ PSKV& pskv = EncodeKey(key, size, false);
#if MKL_EXPERIMENTAL == 1
mkl_set_tblob_eager_mode(recv_buf.data());
#endif
@@ -285,18 +290,64 @@ class KVStoreDist : public KVStoreLocal {
CopyFromTo(merged, &send_buf);
}
+ auto& small_buf = comm_small_buf_[key];
+ auto& res_buf = residual_[key];
+
+ // Init the small buffer and residual_ buffer for quantize
+ if (small_buf.is_none() && compress_ != "none") {
+ int bits = compress_ == "2bit" ? 16 : 32;
+ long int small_size = merged.shape().Size() % bits == 0 ?
+ merged.shape().Size() / bits + 3 :
+ merged.shape().Size() / bits + 4;
+ // small buffer for quantize
+ small_buf = NDArray(TShape{small_size},
+ send_buf.ctx(), false, send_buf.dtype());
+ // residual buffer for quantize
+ res_buf = NDArray(merged.shape(), send_buf.ctx(),
+ false, send_buf.dtype());
+ res_buf = 0;
+ if (pos_thre_.is_none()) {
+ // positive threshold
+ pos_thre_ = NDArray(TShape{1}, send_buf.ctx(),
+ false, send_buf.dtype());
+ pos_thre_ = pos_threshold_;
+ // negative threshold
+ neg_thre_ = NDArray(TShape{1}, send_buf.ctx(),
+ false, send_buf.dtype());
+ neg_thre_ = neg_threshold_;
+ }
+ }
+
+ // Compress
+ if (compress_ == "2bit") {
+ Quantize(send_buf, &small_buf, &res_buf,
+ pos_thre_, neg_thre_,
+ compress_,
+ priority);
+ }
+
// push to servers
if (storage_type == kDefaultStorage) {
auto push_to_servers =
- [this, key, send_buf](RunContext rctx, Engine::CallbackOnComplete cb) {
+ [this, key, send_buf, small_buf](RunContext rctx, Engine::CallbackOnComplete cb) {
// convert to ps keys
- size_t size = send_buf.shape().Size();
- PSKV& pskv = EncodeKey(key, size);
+ size_t size = 0;
+ if (compress_ == "none") {
+ size = send_buf.shape().Size();
+ } else {
+ size = small_buf.shape().Size();
+ }
+ PSKV& pskv = EncodeKey(key, size, true);
#if MKL_EXPERIMENTAL == 1
mkl_set_tblob_eager_mode(send_buf.data());
#endif
- real_t* data = send_buf.data().dptr<real_t>();
+ real_t* data = nullptr;
+ if (compress_ == "none") {
+ data = send_buf.data().dptr<real_t>();
+ } else {
+ data = small_buf.data().dptr<real_t>();
+ }
// do push. false means no delete
ps::SArray<real_t> vals(data, size, false);
CHECK_NOTNULL(ps_worker_)->ZPush(
@@ -418,7 +469,8 @@ class KVStoreDist : public KVStoreLocal {
* \brief cache all key partitions
*/
std::unordered_map<int, PSKV> ps_kv_;
-
+ std::unordered_map<int, PSKV> push_ps_kv_;
+ std::unordered_map<int, PSKV> pull_ps_kv_;
/**
* \brief serizelize EncodeRowSparseKey and EncodeKey
*/
@@ -427,12 +479,12 @@ class KVStoreDist : public KVStoreLocal {
/**
* \brief convert to keys in ps
*/
- inline PSKV& EncodeKey(int key, size_t size) {
+ inline PSKV& EncodeKey(int key, size_t size, bool is_push) {
mu_.lock();
- PSKV& pskv = ps_kv_[key];
+ PSKV& pskv = (is_push) ? push_ps_kv_[key] : pull_ps_kv_[key];
mu_.unlock();
-
if (!pskv.keys.empty()) {
+ // For compress, we cannt check here
CHECK_EQ(static_cast<size_t>(pskv.size), size) << "The value size cannot be changed";
} else {
auto krs = ps::Postoffice::Get()->GetServerKeyRanges();
@@ -464,6 +516,7 @@ class KVStoreDist : public KVStoreLocal {
CHECK_EQ(static_cast<size_t>(pskv.size), size);
}
}
+
return pskv;
}
@@ -537,8 +590,16 @@ class KVStoreDist : public KVStoreLocal {
* \brief threshold for partition
*/
size_t bigarray_bound_;
- /// \brief send & recver buffer
+
std::unordered_map<int, NDArray> comm_buf_;
+ /// \brief small buffer for quantize
+ std::unordered_map<int, NDArray> comm_small_buf_;
+ /// \brief residual buffer for quantize
+ std::unordered_map<int, NDArray> residual_;
+ /// \brief threshold for quantize
+ NDArray pos_thre_;
+ NDArray neg_thre_;
+
bool log_verbose_;
};
diff --git a/src/kvstore/kvstore_dist_server.h b/src/kvstore/kvstore_dist_server.h
index bedb5398a0..8fedd7224a 100644
--- a/src/kvstore/kvstore_dist_server.h
+++ b/src/kvstore/kvstore_dist_server.h
@@ -33,8 +33,9 @@
#include <vector>
#include "ps/ps.h"
#include "mxnet/kvstore.h"
-#include "../operator/tensor/elemwise_binary_op-inl.h"
+#include "../operator/tensor/elemwise_binary_op.h"
#include "../operator/tensor/init_op.h"
+#include "../ndarray/ndarray_function.h"
namespace mxnet {
namespace kvstore {
@@ -108,7 +109,7 @@ class Executor {
class KVStoreDistServer {
public:
- KVStoreDistServer() {
+ KVStoreDistServer(std::string comp) {
using namespace std::placeholders;
ps_server_ = new ps::KVServer<float>(0);
static_cast<ps::SimpleApp*>(ps_server_)->set_request_handle(
@@ -117,6 +118,7 @@ class KVStoreDistServer {
std::bind(&KVStoreDistServer::DataHandleEx, this, _1, _2, _3));
sync_mode_ = false;
log_verbose_ = dmlc::GetEnv("MXNET_KVSTORE_DIST_ROW_SPARSE_VERBOSE", false);
+ compress_ = comp;
}
~KVStoreDistServer() {
@@ -128,6 +130,10 @@ class KVStoreDistServer {
controller_ = controller;
}
+ void set_compress(const std::string& compress) {
+ compress_ = compress;
+ }
+
void set_updater(const KVStore::Updater& updater) {
CHECK(updater);
updater_ = updater;
@@ -286,14 +292,14 @@ class KVStoreDistServer {
// instead of calling BinaryComputeRspRsp directly
using namespace mshadow;
Engine::Get()->PushSync([recved, merged, out](RunContext ctx) {
- std::vector<NDArray> inputs, outputs;
- inputs.push_back(recved);
- inputs.push_back(merged.array);
- outputs.push_back(out);
- op::ElemwiseBinaryOp::ComputeEx<cpu, mshadow::op::plus>(
- {}, {}, inputs, {kWriteTo}, outputs);
- }, recved.ctx(), const_vars, {out.var()},
- FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
+ std::vector<NDArray> inputs, outputs;
+ inputs.push_back(recved);
+ inputs.push_back(merged.array);
+ outputs.push_back(out);
+ op::ElemwiseBinaryOp::ComputeEx<cpu, mshadow::op::plus>(
+ {}, {}, inputs, {kWriteTo}, outputs);
+ }, recved.ctx(), const_vars, {out.var()},
+ FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
CopyFromTo(out, &merged.array, 0);
}
merged.request.push_back(req_meta);
@@ -361,6 +367,7 @@ class KVStoreDistServer {
void DataHandleDefault(const ps::KVMeta& req_meta,
const ps::KVPairs<real_t> &req_data,
ps::KVServer<real_t>* server) {
+
CHECK_EQ(req_meta.cmd, kDefaultPushPull);
// do some check
CHECK_EQ(req_data.keys.size(), (size_t)1);
@@ -381,10 +388,24 @@ class KVStoreDistServer {
TBlob recv_blob((real_t*)req_data.vals.data(), // NOLINT(*)
dshape, cpu::kDevMask);
NDArray recved = NDArray(recv_blob, 0);
+ // std::cout << "server: compress shape: " << dshape.Size() << std::endl;
+ NDArray comp_buf = compress_buf_[key];
+ if (compress_ != "none") {
+ long int original_size = (long int)(*(recv_blob.dptr<float>()+2));
+ dshape = TShape{original_size};
+ //std::cout << "server: Uncompress shape: " << dshape.Size() << std::endl;
+ if (comp_buf.is_none()) {
+ comp_buf = NDArray(dshape, Context());
+ }
+ }
if (stored.is_none()) {
// initialization
stored = NDArray(dshape, Context());
- CopyFromTo(recved, &stored, 0);
+ if (compress_ == "none") {
+ CopyFromTo(recved, &stored, 0);
+ } else {
+ Dequantize(recved, &stored, compress_, 0);
+ }
server->Response(req_meta);
stored.WaitToRead();
} else if (sync_mode_) {
@@ -394,9 +415,19 @@ class KVStoreDistServer {
merged.array = NDArray(dshape, Context());
}
if (merged.request.size() == 0) {
- CopyFromTo(recved, &merged.array, 0);
+ if (compress_ == "none") {
+ CopyFromTo(recved, &merged.array, 0);
+ } else {
+ Dequantize(recved, &comp_buf, compress_, 0);
+ CopyFromTo(comp_buf, &merged.array, 0);
+ }
} else {
- merged.array += recved;
+ if (compress_ == "none") {
+ merged.array += recved;
+ } else {
+ Dequantize(recved, &comp_buf, compress_, 0);
+ merged.array += comp_buf;
+ }
}
merged.request.push_back(req_meta);
ApplyUpdates(key, &merged, &stored, server);
@@ -436,12 +467,16 @@ class KVStoreDistServer {
std::unordered_map<int, NDArray> store_;
std::unordered_map<int, MergeBuf> merge_buf_;
+ std::unordered_map<int, NDArray> compress_buf_;
Executor exec_;
ps::KVServer<float>* ps_server_;
// whether to LOG verbose information
bool log_verbose_;
+
+ // set to use gradient compression
+ std::string compress_;
};
} // namespace kvstore
diff --git a/src/kvstore/kvstore_local.h b/src/kvstore/kvstore_local.h
index ac2968ba23..05eb441d29 100644
--- a/src/kvstore/kvstore_local.h
+++ b/src/kvstore/kvstore_local.h
@@ -51,13 +51,14 @@ class KVStoreLocal : public KVStore {
/*
* \param use_device_comm
*/
- explicit KVStoreLocal(bool use_device_comm) : KVStore() {
+ explicit KVStoreLocal(bool use_device_comm, std::string& comp) : KVStore() {
if (use_device_comm) {
comm_ = new CommDevice();
} else {
comm_ = new CommCPU();
}
pinned_ctx_ = comm_->pinned_ctx();
+ compress_ = comp;
}
virtual ~KVStoreLocal() {
@@ -144,6 +145,7 @@ class KVStoreLocal : public KVStore {
local_[keys[i]] = values[i].Copy(pinned_ctx_);
comm_->Init(keys[i], values[i].storage_type(), values[i].shape(), values[i].dtype());
}
+ comm_->SetCompress(compress_, pos_threshold_, neg_threshold_);
}
virtual void PushImpl(const std::vector<int>& keys,
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index 114e45f216..54ad466340 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -34,6 +34,7 @@
#include "../operator/tensor/matrix_op-inl.h"
#include "../operator/tensor/init_op.h"
#include "./autograd.h"
+#include "./ndarray_function.h"
#if MXNET_USE_OPENCV
#include <opencv2/opencv.hpp>
@@ -545,6 +546,97 @@ void CopyFromTo(const NDArray &from, NDArray *to, int priority) {
}
}
+void Quantize(const NDArray &from, NDArray *to, NDArray *residual,
+ const NDArray &pos_threshold, const NDArray &neg_threshold,
+ std::string& compress, int priority) {
+ CHECK(from.shape().ndim() != 0)
+ << "source operands have zero dimension shape";
+ // important: callback must always capture by value
+ NDArray ret = *to;
+ int a = from.ctx().dev_mask();
+ int b = to->ctx().dev_mask();
+ std::vector<Engine::VarHandle> const_vars;
+ const_vars.push_back(from.var());
+
+ std::vector<TBlob> inputs(5);
+ inputs[0] = from.data();
+ inputs[1] = residual->data();
+ inputs[2] = neg_threshold.data();
+ inputs[3] = pos_threshold.data();
+ inputs[4] = to->data();
+
+ if (a == cpu::kDevMask && b == cpu::kDevMask) {
+ if (compress == "2bit") {
+ Engine::Get()->PushSync([inputs](RunContext ctx) {
+ mxnet::ndarray::Quantize2BitDispatch<cpu>(ctx.get_stream<cpu>(), inputs);
+ }, from.ctx(), const_vars, {ret.var()},
+ FnProperty::kNormal, priority, PROFILER_MESSAGE("DequantizeCPU"));
+ } else {
+ LOG(FATAL) << "Unsupported dequantization";
+ }
+ } else {
+#if MXNET_USE_CUDA
+ if (a == gpu::kDevMask && b == gpu::kDevMask) {
+ if (compress == "2bit") {
+ Engine::Get()->PushSync([inputs](RunContext ctx) {
+ mxnet::ndarray::Quantize2BitDispatch<gpu>(ctx.get_stream<gpu>(), inputs);
+ }, from.ctx(), const_vars, {ret.var()},
+ FnProperty::kNormal, priority, PROFILER_MESSAGE("DequantizeGPU"));
+ } else {
+ LOG(FATAL) << "Unsupported dequantization";
+ }
+ } else {
+ LOG(FATAL) << "unknown device mask";
+ }
+#else
+ LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
+#endif
+ }
+}
+
+void Dequantize(const NDArray &from, NDArray *to, std::string& compress, int priority) {
+ CHECK(from.shape().ndim() != 0)
+ << "source operands have zero dimension shape";
+ // important: callback must always capture by value
+ NDArray ret = *to;
+ int a = from.ctx().dev_mask();
+ int b = to->ctx().dev_mask();
+ std::vector<Engine::VarHandle> const_vars;
+ const_vars.push_back(from.var());
+
+ std::vector<TBlob> inputs(2);
+ inputs[0] = from.data();
+ inputs[1] = to->data();
+
+ if (a == cpu::kDevMask && b == cpu::kDevMask) {
+ if (compress == "2bit") {
+ Engine::Get()->PushSync([inputs](RunContext ctx) {
+ mxnet::ndarray::Dequantize2BitDispatch<cpu>(ctx.get_stream<cpu>(), inputs);
+ }, from.ctx(), const_vars, {ret.var()},
+ FnProperty::kNormal, priority, PROFILER_MESSAGE("DequantizeCPU"));
+ } else {
+ LOG(FATAL) << "Unsupported dequantization";
+ }
+ } else {
+#if MXNET_USE_CUDA
+ if (a == gpu::kDevMask && b == gpu::kDevMask) {
+ if (compress == "2bit") {
+ Engine::Get()->PushSync([inputs](RunContext ctx) {
+ mxnet::ndarray::Dequantize2BitDispatch<gpu>(ctx.get_stream<gpu>(), inputs);
+ }, from.ctx(), const_vars, {ret.var()},
+ FnProperty::kNormal, priority, PROFILER_MESSAGE("DequantizeGPU"));
+ } else {
+ LOG(FATAL) << "Unsupported dequantization";
+ }
+ } else {
+ LOG(FATAL) << "unknown device mask";
+ }
+#else
+ LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
+#endif
+ }
+}
+
void ElementwiseSum(const std::vector<NDArray> &source, NDArray *out, int priority) {
std::vector<Engine::VarHandle> const_vars;
const_vars.reserve(source.size());
diff --git a/src/ndarray/ndarray_function.cc b/src/ndarray/ndarray_function.cc
index ef0adbe5f2..5028f82d17 100644
--- a/src/ndarray/ndarray_function.cc
+++ b/src/ndarray/ndarray_function.cc
@@ -26,6 +26,7 @@
#include "./ndarray_function.h"
#include "./ndarray_function-inl.h"
#include "../common/utils.h"
+#include "../operator/contrib/two_bit_quantize-inl.h"
namespace mxnet {
namespace ndarray {
@@ -183,5 +184,15 @@ void ElementwiseSum<cpu>(mshadow::Stream<cpu>* s,
}
}
+template<>
+void Dequantize2BitDispatch(mshadow::Stream<cpu>* s, const std::vector<TBlob>& inputs) {
+ mxnet::op::Dequantize2BitImpl<cpu>(s,inputs);
+}
+
+template<>
+void Quantize2BitDispatch(mshadow::Stream<cpu>* s, const std::vector<TBlob>& inputs) {
+ mxnet::op::Quantize2BitImpl<cpu>(s,inputs);
+}
+
} // namespace ndarray
} // namespace mxnet
diff --git a/src/ndarray/ndarray_function.cu b/src/ndarray/ndarray_function.cu
index 46f47ed237..8e6ed47b1a 100644
--- a/src/ndarray/ndarray_function.cu
+++ b/src/ndarray/ndarray_function.cu
@@ -32,6 +32,7 @@
#include "./ndarray_function.h"
#include "./ndarray_function-inl.h"
#include "./ndarray_function-inl.cuh"
+#include "../operator/contrib/two_bit_quantize-inl.h"
namespace mxnet {
namespace ndarray {
@@ -201,6 +202,16 @@ void ElementwiseSum<gpu>(mshadow::Stream<gpu>* s,
<< nds[0].storage_type();
}
}
+
+template<>
+void Dequantize2BitDispatch(mshadow::Stream<gpu>* s, const std::vector<TBlob>& inputs) {
+ mxnet::op::Dequantize2BitImpl<gpu>(s,inputs);
+}
+
+template<>
+void Quantize2BitDispatch(mshadow::Stream<gpu>* s, const std::vector<TBlob>& inputs) {
+ mxnet::op::Quantize2BitImpl<gpu>(s,inputs);
+}
} // namespace ndarray
} // namespace mxnet
diff --git a/src/ndarray/ndarray_function.h b/src/ndarray/ndarray_function.h
index f7da246014..10c2ffb144 100644
--- a/src/ndarray/ndarray_function.h
+++ b/src/ndarray/ndarray_function.h
@@ -164,6 +164,12 @@ void Copy(const TBlob &from, TBlob *to,
Context from_ctx, Context to_ctx,
RunContext ctx);
+template<typename xpu>
+void Dequantize2BitDispatch(mshadow::Stream<xpu>* s, const std::vector<TBlob>& inputs);
+
+template<typename xpu>
+void Quantize2BitDispatch(mshadow::Stream<xpu>* s, const std::vector<TBlob>& inputs);
+
template<typename Device>
void ElementwiseSum(const std::vector<TBlob> source,
TBlob *out,
diff --git a/src/operator/contrib/two_bit_quantize-inl.h b/src/operator/contrib/two_bit_quantize-inl.h
new file mode 100644
index 0000000000..d98d0c0405
--- /dev/null
+++ b/src/operator/contrib/two_bit_quantize-inl.h
@@ -0,0 +1,369 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+ /*!
+ * \file two_bit_quantize-inl.h
+ * \brief implementation of quantize_2bit operation
+ */
+#ifndef MXNET_OPERATOR_CONTRIB_TWO_BIT_QUANTIZE_INL_H_
+#define MXNET_OPERATOR_CONTRIB_TWO_BIT_QUANTIZE_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include <limits>
+#include "../elemwise_op_common.h"
+#include "../mshadow_op.h"
+#include "../mxnet_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct init_mem_2bit {
+ // Initialize output array
+ MSHADOW_XINLINE static void Map(int i, float* out) {
+ *reinterpret_cast<int*>(out+i) = 0x00000000;
+ }
+};
+
+template<typename xpu>
+void Create2BitArrayCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ // For now, this method can only compress the float data
+ using namespace mshadow;
+ using namespace mxnet_op;
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+ // Init the memory of output to 0x00000000
+ Kernel<init_mem_2bit, xpu>::Launch(s, outputs[0].Size(),
+ outputs[0].dptr<float>()); // compressed array
+}
+
+inline bool Create2BitArrayShape(const nnvm::NodeAttrs& attrs,
+ std::vector<TShape> *in_attrs,
+ std::vector<TShape> *out_attrs) {
+ // 0. input array
+ CHECK_EQ(in_attrs->size(), 1U);
+ // 0. output array
+ CHECK_EQ(out_attrs->size(), 1U);
+ // check input
+ CHECK(!shape_is_none(in_attrs->at(0)));
+ // output
+ int shape = in_attrs->at(0).Size() % 16 == 0 ?
+ in_attrs->at(0).Size() / 16 + 3:
+ in_attrs->at(0).Size() / 16 + 4;
+ SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape{shape});
+ return true;
+}
+
+inline bool Create2BitArray2BitType(const nnvm::NodeAttrs& attrs,
+ std::vector<int> *in_attrs,
+ std::vector<int> *out_attrs) {
+ // 0. input array
+ CHECK_EQ(in_attrs->size(), 1U);
+ // 0. output array
+ CHECK_EQ(out_attrs->size(), 1U);
+ // check input
+ CHECK_EQ((*in_attrs)[0], mshadow::kFloat32)
+ << "`create_2bit_` only supports float32 input for now";
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat32);
+ return true;
+}
+
+struct init_threshold_2bit {
+ MSHADOW_XINLINE static void Map(int i,
+ float *out,
+ const float *neg_threshold,
+ const float *pos_threshold,
+ int size) {
+ // The first two elments in output is threshold
+ // The third element is the original size of the array
+ out[0] = *neg_threshold;
+ out[1] = *pos_threshold;
+ out[2] = (float)size;
+ }
+};
+
+struct quantize_2bit {
+ MSHADOW_XINLINE static void Map(int i,
+ float *out,
+ float *grad,
+ float *residual,
+ const float *neg_threshold,
+ const float *pos_threshold) {
+ // Add residual to gradient
+ grad[i] += residual[i];
+ // get block id
+ int block_id = i / 16;
+ char* ch_ptr = reinterpret_cast<char*>(out+block_id);
+ // get row ptr
+ int row_id = (i%16)/4;
+ ch_ptr += row_id;
+ // get column id
+ int col_id = (i%16)%4;
+ // Compress
+ if (grad[i] <= *neg_threshold) { // set data to 01
+ // new residual
+ residual[i] = grad[i] - *neg_threshold;
+ switch (col_id) {
+ case 0:
+ (*ch_ptr) |= 0x40; // binary: (01)00 0000
+ break;
+ case 1:
+ (*ch_ptr) |= 0x10; // binary: 00(01) 0000
+ break;
+ case 2:
+ (*ch_ptr) |= 0x04; // binary: 0000 (01)00
+ break;
+ case 3:
+ (*ch_ptr) |= 0x01; // binary: 0000 00(01)
+ break;
+ default:
+ break;
+ }
+ } else if (grad[i] >= *pos_threshold) { // set data to 10
+ residual[i] = grad[i] - *pos_threshold;
+ switch (col_id) {
+ case 0:
+ (*ch_ptr) |= 0x80; // binary: (10)00 0000
+ break;
+ case 1:
+ (*ch_ptr) |= 0x20; // binary: 00(10) 0000
+ break;
+ case 2:
+ (*ch_ptr) |= 0x08; // binary: 0000 (10)00
+ break;
+ case 3:
+ (*ch_ptr) |= 0x02; // binary: 0000 00(10)
+ break;
+ default:
+ break;
+ }
+ } else { // else 00
+ residual[i] = grad[i];
+ }
+ }
+};
+
+template<typename xpu>
+void Quantize2BitImpl(mshadow::Stream<xpu>* s, const std::vector<TBlob>& inputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+ // First, init the memory of output to 0x00000000
+ Kernel<init_mem_2bit, xpu>::Launch(s, inputs[4].Size(),
+ inputs[4].dptr<float>()); // compressed array
+ // Then, init threshold and original size
+ Kernel<init_threshold_2bit, xpu>::Launch(s, 1,
+ inputs[4].dptr<float>(), // compressed array
+ inputs[2].dptr<float>(), // negative threshold
+ inputs[3].dptr<float>(), // positive threshold
+ inputs[0].Size()); // original size
+ // Finally, compress the data and calculate new residual
+ Kernel<quantize_2bit, xpu>::Launch(s, inputs[0].Size(),
+ inputs[4].dptr<float>()+3, // compressed array
+ inputs[0].dptr<float>(), // input array
+ inputs[1].dptr<float>(), // residual array
+ inputs[2].dptr<float>(), // negative threshold
+ inputs[3].dptr<float>()); // positive threshold
+}
+
+template<typename xpu>
+void Quantize2BitCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ // For now, this method can only compress the float data
+ mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+ Quantize2BitImpl<xpu>(s, inputs);
+}
+
+inline bool Quantize2BitShape(const nnvm::NodeAttrs& attrs,
+ std::vector<TShape> *in_attrs,
+ std::vector<TShape> *out_attrs) {
+ // 0. input array
+ // 1. residual array
+ // 2. negative threshold
+ // 3. positive threshold
+ // 4. compressed array
+ CHECK_EQ(in_attrs->size(), 5U);
+ CHECK(!shape_is_none(in_attrs->at(0)));
+ CHECK(!shape_is_none(in_attrs->at(1)));
+ CHECK_EQ(in_attrs->at(0).Size(),
+ in_attrs->at(1).Size());
+ CHECK(shape_is_scalar(in_attrs->at(2)));
+ CHECK(shape_is_scalar(in_attrs->at(3)));
+ int shape = in_attrs->at(0).Size() % 16 == 0 ?
+ in_attrs->at(0).Size() / 16 + 3:
+ in_attrs->at(0).Size() / 16 + 4;
+ CHECK_EQ(in_attrs->at(4).Size(), shape)
+ << "The size of output array is not equal to "
+ << "the size of compressed array";
+ return true;
+}
+
+inline bool Quantize2BitType(const nnvm::NodeAttrs& attrs,
+ std::vector<int> *in_attrs,
+ std::vector<int> *out_attrs) {
+ // 0. input array
+ // 1. residual array
+ // 2. negative threshold
+ // 3. positive threshold
+ // 4. compressed array
+ CHECK_EQ(in_attrs->size(), 5U);
+ // check input
+ CHECK_EQ((*in_attrs)[0], mshadow::kFloat32)
+ << "`quantize_2bit_` only supports float32 input for now";
+ CHECK_EQ((*in_attrs)[1], mshadow::kFloat32)
+ << "`quantize_2bit_` only supports float32 input for now";
+ CHECK_EQ((*in_attrs)[2], mshadow::kFloat32)
+ << "the third input of `quantize_2bit` should be "
+ << "a tensor with type of float";
+ CHECK_EQ((*in_attrs)[3], mshadow::kFloat32)
+ << "the fourth input of `quantize_2bit` should be "
+ << "a tensor with type of float";
+ CHECK_EQ((*in_attrs)[4], mshadow::kFloat32)
+ << "`quantize_2bit_` only supports float32 input for now";
+ return true;
+}
+
+struct dequantize_2bit {
+ // Decompress
+ MSHADOW_XINLINE static void Map(int i,
+ float *out,
+ float *in,
+ const float *neg_threshold,
+ const float *pos_threshold) {
+ // get block ptr
+ int block_id = i / 16;
+ char* ch_ptr = reinterpret_cast<char*>(in+block_id);
+ // get row ptr
+ int row_id = (i%16)/4;
+ ch_ptr += row_id;
+ // get column id
+ int col_id = (i%16)%4;
+ // Decompress
+ switch (col_id) {
+ case 0:
+ // positve
+ if (((*ch_ptr) & (0xc0)) == 0x80) { // binary: (10)00 0000
+ out[i] = *pos_threshold;
+ // negative
+ } else if (((*ch_ptr) & (0xc0)) == 0x40) { // binary: (01)00 0000
+ out[i] = *neg_threshold;
+ } else { // 0
+ out[i] = 0;
+ }
+ break;
+ case 1:
+ // positve
+ if (((*ch_ptr) & (0x30)) == 0x20) { // binary: 00(10) 0000
+ out[i] = *pos_threshold;
+ // negative
+ } else if (((*ch_ptr) & (0x30)) == 0x10) { // binary: 00(01) 0000
+ out[i] = *neg_threshold;
+ } else { // 0
+ out[i] = 0;
+ }
+ break;
+ case 2:
+ // positve
+ if (((*ch_ptr) & (0x0c)) == 0x08) { // binary: 00(10) 0000
+ out[i] = *pos_threshold;
+ // negative
+ } else if (((*ch_ptr) & (0x0c)) == 0x04) { // binary: 00(01) 0000
+ out[i] = *neg_threshold;
+ } else { // 0
+ out[i] = 0;
+ }
+ break;
+ case 3:
+ // positve
+ if (((*ch_ptr) & (0x03)) == 0x02) { // binary: 00(10) 0000
+ out[i] = *pos_threshold;
+ // negative
+ } else if (((*ch_ptr) & (0x03)) == 0x01) { // binary: 00(01) 0000
+ out[i] = *neg_threshold;
+ } else { // 0
+ out[i] = 0;
+ }
+ break;
+ default:
+ break;
+ }
+ }
+};
+
+template<typename xpu>
+void Dequantize2BitImpl(mshadow::Stream<xpu>* s, const std::vector<TBlob>& inputs) {
+ //using namespace mshadow;
+ // For now, this method can only decompress the float data
+ mxnet_op::Kernel<dequantize_2bit, xpu>::Launch(s, inputs[1].Size(), // original size
+ inputs[1].dptr<float>(), // out array
+ inputs[0].dptr<float>()+3, // compressed array
+ inputs[0].dptr<float>(), // negative threshold
+ inputs[0].dptr<float>()+1); // positve threshold
+}
+
+template<typename xpu>
+void Dequantize2BitCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+ Dequantize2BitImpl<xpu>(s, inputs);
+}
+
+inline bool Dequantize2BitShape(const nnvm::NodeAttrs& attrs,
+ std::vector<TShape> *in_attrs,
+ std::vector<TShape> *out_attrs) {
+ // 0. compressed array
+ // 1. original array
+ CHECK_EQ(in_attrs->size(), 2U);
+ // No output
+ CHECK_EQ(out_attrs->size(), 0U);
+ // check input
+ CHECK(!shape_is_none(in_attrs->at(0)));
+ CHECK(!shape_is_none(in_attrs->at(1)));
+ CHECK_LE(in_attrs->at(1).Size(),
+ in_attrs->at(0).Size()*16)
+ << "The shape of the second input array are "
+ << "not equal to the original array.";
+ return true;
+}
+
+inline bool Dequantize2BitType(const nnvm::NodeAttrs& attrs,
+ std::vector<int> *in_attrs,
+ std::vector<int> *out_attrs) {
+ // 0. compressed array
+ // 1. original array
+ CHECK_EQ(in_attrs->size(), 2U);
+ // check input
+ CHECK_EQ((*in_attrs)[0], mshadow::kFloat32)
+ << "`dequantize_2bit_` only supports float32 input for now";
+ CHECK_EQ((*in_attrs)[1], mshadow::kFloat32)
+ << "`dequantize_2bit_` only supports float32 input for now";
+ return true;
+}
+
+} // namespace op
+} // namespace mxnet
+#endif // MXNET_OPERATOR_CONTRIB_TWO_BIT_QUANTIZE_INL_H_
diff --git a/src/operator/contrib/two_bit_quantize.cc b/src/operator/contrib/two_bit_quantize.cc
new file mode 100644
index 0000000000..59136e2ef9
--- /dev/null
+++ b/src/operator/contrib/two_bit_quantize.cc
@@ -0,0 +1,97 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * \file two_bit_quantize.cc
+ * \brief
+ */
+#include "./two_bit_quantize-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_contrib_quantize_2bit)
+.describe(R"code(Quantize a input tensor using 2-bit compression with residual
+array and user-specified threshold.
+
+For example, assume the input array (gradient) is [-1.0, -5.0, -4.0], and the
+residual is [-2.0, 0, 1.0], and the threshold is -4.0 and +4.0, respectively.
+In this method, the elements (gradient + residual) >= pos_threshold will be
+compressed into a 2-bit data '01', and the elements <= neg_threshold will be
+compressed into a 2-bit data '10'. The other elements will be compressed
+into '00', which is represented as zero.
+
+In this example, invoke
+quantize_2bit(array, residual, neg_threshold, pos_threshold, out), the 'out'
+will be the compressed array. Note that, the out array can be generated by
+invoking create_2bit(array).
+
+In this example, the 'out' has 4 elements. The first element stores the
+neg_threshold (-0.4) and the second element stores the pos_threshold (+0.4), the
+third element stores the original size of the uncompressed array, and the
+original array will be compressed into a single element in the last element.
+In two bit compress, every 16 float data in original array
+will be packed into one float data in output array.
+)code" ADD_FILELINE)
+.set_num_inputs(5)
+.set_num_outputs(0)
+.set_attr<nnvm::FInferShape>("FInferShape", Quantize2BitShape)
+.set_attr<nnvm::FInferType>("FInferType", Quantize2BitType)
+.set_attr<FCompute>("FCompute<cpu>", Quantize2BitCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_quantize_2bit"})
+.add_argument("gradient_array", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`")
+.add_argument("residual_array", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`")
+.add_argument("neg_shreshold", "NDArray-or-Symbol", "The negative shreshold")
+.add_argument("compressed_array", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`")
+.add_argument("pos_shreshold", "NDArray-or-Symbol", "The positive shreshold");
+
+NNVM_REGISTER_OP(_contrib_create_2bit)
+.describe(R"code(Tp generate a compressed array with right shape.
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FInferShape>("FInferShape", Create2BitArrayShape)
+.set_attr<nnvm::FInferType>("FInferType", Create2BitArray2BitType)
+.set_attr<FCompute>("FCompute<cpu>", Create2BitArrayCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_create_2bit"})
+.add_argument("input", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`");
+
+NNVM_REGISTER_OP(_contrib_dequantize_2bit)
+.describe(R"code(Dequantize a input tensor compressed by quantize_2bit.
+
+The dequantize_2bit takes two input arguments. The first input is a NDArray,
+which has been generated by quantize_2bit(). The second input is also a
+NDArray that has the same shape with the original array before compressing.
+
+Using the example as was described above.
+
+Invoke dequantize_2bit(out, array), the 'array' argument will become
+[0, -4.0, 0], where -4.0 is the negative threshold.
+)code" ADD_FILELINE)
+.set_num_inputs(2)
+.set_num_outputs(0)
+.set_attr<nnvm::FInferShape>("FInferShape", Dequantize2BitShape)
+.set_attr<nnvm::FInferType>("FInferType", Dequantize2BitType)
+.set_attr<FCompute>("FCompute<cpu>", Dequantize2BitCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_dequantize_2bit"})
+.add_argument("input_1", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`")
+.add_argument("input_2", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`");
+
+} // namespace op
+} // namespace mxnet
diff --git a/src/operator/contrib/two_bit_quantize.cu b/src/operator/contrib/two_bit_quantize.cu
new file mode 100644
index 0000000000..20f21a295e
--- /dev/null
+++ b/src/operator/contrib/two_bit_quantize.cu
@@ -0,0 +1,39 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * \file two_bit_quantize_sim.cu
+ * \brief
+ */
+#include "./two_bit_quantize-inl.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_contrib_quantize_2bit)
+.set_attr<FCompute>("FCompute<gpu>", Quantize2BitCompute<gpu>);
+
+NNVM_REGISTER_OP(_contrib_dequantize_2bit)
+.set_attr<FCompute>("FCompute<gpu>", Dequantize2BitCompute<gpu>);
+
+NNVM_REGISTER_OP(_contrib_create_2bit)
+.set_attr<FCompute>("FCompute<gpu>", Create2BitArrayCompute<gpu>);
+
+} // namespace op
+} // namespace mxnet
diff --git a/tests/nightly/dist_sync_kvstore.py b/tests/nightly/dist_sync_kvstore.py
index af1ecfc503..0cd644f703 100644
--- a/tests/nightly/dist_sync_kvstore.py
+++ b/tests/nightly/dist_sync_kvstore.py
@@ -34,23 +34,26 @@ def check_diff_to_scalar(A, x, rank=None):
rsp_keys = ['9', '11', '13']
rate = 2
-shape = (2, 3)
+shape_1 = (2, 3)
+shape_2 = (12, 15)
big_shape = (1200, 1200) # bigger than BIGARRAY_BOUND
def init_kv():
- kv = mx.kv.create('dist_sync')
+ kv = mx.kv.create('dist_sync', '2bit')
# init kv dns keys
- kv.init(keys, [mx.nd.ones(shape)] * len(keys))
- kv.init('99', mx.nd.ones(big_shape))
+ #kv.init(keys, [mx.nd.ones(shape)] * len(keys))
+ #kv.init('99', mx.nd.ones(big_shape))
+ kv.init('99', mx.nd.zeros(shape_1))
+ kv.init('3', mx.nd.zeros(shape_2))
# init kv row_sparse keys
- kv.init(rsp_keys, [mx.nd.ones(shape).tostype('row_sparse')] * len(rsp_keys))
- kv.init('100', mx.nd.ones(big_shape).tostype('row_sparse'))
+ #kv.init(rsp_keys, [mx.nd.ones(shape).tostype('row_sparse')] * len(rsp_keys))
+ #kv.init('100', mx.nd.ones(big_shape).tostype('row_sparse'))
# worker info
my_rank = kv.rank
nworker = kv.num_workers
# init updater on servers
- kv.set_optimizer(mx.optimizer.create('test', rescale_grad=rate))
+ #kv.set_optimizer(mx.optimizer.create('test', rescale_grad=rate))
return kv, my_rank, nworker
def test_sync_push_pull():
@@ -172,5 +175,18 @@ def check_big_row_sparse_keys(kv, my_rank, nworker):
check_big_row_sparse_keys(kv, my_rank, nworker)
print('worker ' + str(my_rank) + ' is done')
+def test_sync_push():
+ kv, my_rank, nworker = init_kv()
+ val_1 = mx.nd.zeros(shape_1)
+ val_2 = mx.nd.zeros(shape_2)
+ for i in range(100):
+ kv.push('99', mx.nd.ones(shape_1))
+ kv.pull('99', out=val_1)
+ kv.push('3', mx.nd.ones(shape_2))
+ kv.pull('3', out=val_2)
+ print val_1
+ print val_2
+
if __name__ == "__main__":
- test_sync_push_pull()
+ #test_sync_push_pull()
+ test_sync_push()
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