Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Expose kWriteInplace for imperative execution (fcompute_ex and fstatefulcompute_ex) #133

Merged
merged 3 commits into from
Jul 28, 2017
Merged

Expose kWriteInplace for imperative execution (fcompute_ex and fstatefulcompute_ex) #133

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
156c5b9
expose kWriteInplace to FComputeEx and FStatefulComputeEx
eric-haibin-lin Jul 25, 2017
170f2fc
refactor ccode
eric-haibin-lin Jul 26, 2017
6255170
remove duplicated test
eric-haibin-lin Jul 26, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 17 additions & 0 deletions src/c_api/c_api_ndarray.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -270,6 +270,21 @@ void SetDependency(std::vector<engine::VarHandle> *p_read_vars,
Engine::Get()->DeduplicateVarHandle(&read_vars, &write_vars);
}

inline void SetWriteInplaceReq(const std::vector<NDArray> &ndinputs,
const std::vector<NDArray> &ndoutputs,
std::vector<OpReqType> *req) {
std::unordered_set<engine::VarHandle> in_vars;
for (auto &nd : ndinputs) {
in_vars.insert(nd.var());
}
for (size_t i = 0; i < ndoutputs.size(); i++) {
// output NDArray shares the memory with the input NDArray
if (in_vars.find(ndoutputs[i].var()) != in_vars.end()) {
req->at(i) = kWriteInplace;
}
}
}

void PushFCompute(const FCompute& fn,
const nnvm::Op* op,
const nnvm::NodeAttrs& attrs,
Expand Down Expand Up @@ -332,6 +347,7 @@ void PushFComputeEx(const FComputeEx& fn,
engine::CallbackOnComplete(),
requested};
std::vector<OpReqType> req(ndoutputs.size(), kWriteTo);
SetWriteInplaceReq(ndinputs, ndoutputs, &req);
fn(attrs, opctx, ndinputs, req, ndoutputs);
if (ctx.dev_mask() == gpu::kDevMask) {
rctx.get_stream<gpu>()->Wait();
Expand Down Expand Up @@ -406,6 +422,7 @@ void PushOperator(const OpStatePtr& state,
engine::CallbackOnComplete on_complete) {
OpContext opctx{is_train, rctx, on_complete, requested};
std::vector<OpReqType> req(ndoutputs.size(), kWriteTo);
SetWriteInplaceReq(ndinputs, ndoutputs, &req);
fcompute_ex(state, opctx, ndinputs, req, ndoutputs);
if (exec_type == ExecType::kSync) {
if (rctx.get_ctx().dev_mask() == gpu::kDevMask) {
Expand Down
45 changes: 17 additions & 28 deletions src/operator/optimizer_op-inl.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -93,13 +93,13 @@ struct SGDDnsRspKernel {
// IType is row sparse idx type
// i is the ith row in row sparse gradient
template<typename DType, typename IType>
MSHADOW_XINLINE static void Map(int i, size_t row_length, DType* out, const DType* weight,
MSHADOW_XINLINE static void Map(int i, const index_t row_length, DType* out, const DType* weight,
const IType* grad_idx, const DType *grad_val,
const DType clip_gradient, const DType lr,
const DType wd, const DType rescale_grad) {
for (size_t j = 0; j < row_length; j++) {
uint64_t data_i = grad_idx[i] * row_length + j;
uint64_t grad_i = i * row_length + j;
for (index_t j = 0; j < row_length; j++) {
index_t data_i = grad_idx[i] * row_length + j;
index_t grad_i = i * row_length + j;
if (clip_gradient >= 0.0f) {
KERNEL_ASSIGN(out[data_i], req, (1.f - lr * wd) * weight[data_i] -
(lr) * mshadow_op::clip::Map(rescale_grad * grad_val[grad_i], clip_gradient));
Expand All @@ -126,6 +126,7 @@ inline void SGDUpdateDnsRspImpl(const SGDParam& param,
CHECK_EQ(grad.storage_type(), kRowSparseStorage);
// if gradients are zeros, no weights are updated
if (!grad.storage_initialized() || req == kNullOp) return;
CHECK_EQ(req, kWriteInplace) << "kWriteInplace is expected for sparse sgd_mom_update";
CHECK_GT(weight.shape_.Size(), 0);

MSHADOW_REAL_TYPE_SWITCH(weight.type_flag_, DType, {
Expand All @@ -151,7 +152,7 @@ inline void SGDUpdateDnsRspImpl(const SGDParam& param,
template<int req>
struct SGDRspDnsKernel {
template<typename DType>
MSHADOW_XINLINE static void Map(int i, size_t num_cols, DType* out, const DType* weight,
MSHADOW_XINLINE static void Map(int i, const index_t num_cols, DType* out, const DType* weight,
const DType *grad, const DType clip_gradient, const DType lr,
const DType wd, const DType rescale_grad) {
bool contains_non_zeros = false;
Expand Down Expand Up @@ -191,6 +192,7 @@ inline void SGDUpdateRspDnsImpl(const SGDParam& param,
CHECK_RSP_ALL_ROWS_NON_ZERO(weight, "SGDUpdate", "weights");
CHECK_EQ(weight.storage_type(), kRowSparseStorage);
if (req == kNullOp) return;
CHECK_EQ(req, kWriteInplace) << "kWriteInplace is expected for sparse sgd_update";
CHECK(weight.storage_initialized());
Stream<xpu>* s = ctx.get_stream<xpu>();
MSHADOW_REAL_TYPE_SWITCH(weight.dtype(), DType, {
Expand All @@ -216,14 +218,9 @@ inline void SGDUpdateRspRspImpl(const SGDParam& param,
const OpReqType& req,
NDArray *out) {
CHECK_RSP_ALL_ROWS_NON_ZERO(weight, "SGDUpdate", "weights");
// TODO(haibin) this is a temporary solution, due to the fact that imperative_invoke only
// feed in kWriteTo as req for all operators.
// For sgd we don't want to assign zeros to the output values when req == kWriteTo
auto out_req = req;
if (out_req == kWriteTo) out_req = kWriteInplace;
// reuse dns rsp implementation when storage_shape == shape
TBlob out_blob = out->data();
SGDUpdateDnsRspImpl<xpu>(param, ctx, weight.data(), grad, out_req, &out_blob);
SGDUpdateDnsRspImpl<xpu>(param, ctx, weight.data(), grad, req, &out_blob);
}

template<typename xpu>
Expand Down Expand Up @@ -428,14 +425,14 @@ inline void MP_SGDMomUpdate(const nnvm::NodeAttrs& attrs,
template<int req>
struct SGDMomDnsRspDnsKernel {
template<typename DType, typename IType>
MSHADOW_XINLINE static void Map(int i, size_t row_length, DType* out_data,
MSHADOW_XINLINE static void Map(int i, index_t row_length, DType* out_data,
DType* mom_data, const DType* weight_data, const IType* grad_idx,
const DType* grad_data, const DType clip_gradient, const DType momentum,
const DType lr, const DType wd, const DType rescale_grad) {
const DType rate = lr * wd;
for (size_t j = 0; j < row_length; j++) {
uint64_t data_i = grad_idx[i] * row_length + j;
uint64_t grad_i = i * row_length + j;
for (index_t j = 0; j < row_length; j++) {
index_t data_i = grad_idx[i] * row_length + j;
index_t grad_i = i * row_length + j;
if (clip_gradient >= 0.0f) {
mom_data[data_i] = momentum * mom_data[data_i]
- rate * weight_data[data_i]
Expand Down Expand Up @@ -464,6 +461,7 @@ inline void SGDMomUpdateDnsRspDnsImpl(const SGDMomParam& param,
using namespace rowsparse;
Stream<xpu>* s = ctx.get_stream<xpu>();
if (!grad.storage_initialized() || req == kNullOp) return;
CHECK_EQ(req, kWriteInplace) << "kWriteInplace is expected for sparse sgd_mom_update";
CHECK_GT(weight.shape_.Size(), 0);
CHECK_GT(mom.shape_.Size(), 0);

Expand All @@ -490,7 +488,7 @@ inline void SGDMomUpdateDnsRspDnsImpl(const SGDMomParam& param,
template<int req>
struct SGDMomRspDnsKernel {
template<typename DType>
MSHADOW_XINLINE static void Map(int i, size_t num_cols, DType* out, DType* mom,
MSHADOW_XINLINE static void Map(int i, index_t num_cols, DType* out, DType* mom,
const DType* weight, const DType *grad,
const DType clip_gradient, const DType momentum,
const DType lr, const DType wd, const DType rescale_grad) {
Expand Down Expand Up @@ -534,19 +532,15 @@ inline void SGDMomUpdateRspDnsImpl(const SGDMomParam& param,
Stream<xpu>* s = ctx.get_stream<xpu>();
CHECK_EQ(weight.storage_type(), kRowSparseStorage);
if (req == kNullOp) return;
CHECK_EQ(req, kWriteInplace) << "kWriteInplace is expected for sparse sgd_mom_update";
CHECK(weight.storage_initialized());
// fill mom with zero values if not initialized yet
if (!mom.storage_initialized()) {
NDArray mom_zeros = mom;
FillDnsZerosRspImpl(s, &mom_zeros);
}
// TODO(haibin) this is a temporary solution, due to the fact that imperative_invoke only
// feed in kWriteTo as req for all operators.
// For sgd we don't want to assign zeros to the output values when req == kWriteTo
auto out_req = req;
if (out_req == kWriteTo) out_req = kWriteInplace;
MSHADOW_REAL_TYPE_SWITCH(weight.dtype(), DType, {
MXNET_ASSIGN_REQ_SWITCH(out_req, req_type, {
MXNET_ASSIGN_REQ_SWITCH(req, req_type, {
DType* weight_data = weight.data().dptr<DType>();
DType* grad_data = grad.dptr<DType>();
DType* mom_data = mom.data().dptr<DType>();
Expand Down Expand Up @@ -581,15 +575,10 @@ inline void SGDMomUpdateRspRspRspImpl(const SGDMomParam& param,
NDArray mom_zeros = mom;
FillDnsZerosRspImpl(s, &mom_zeros);
}
// TODO(haibin) this is a temporary solution, due to the fact that imperative_invoke only
// feed in kWriteTo as req for all operators.
// For sgd we don't want to assign zeros to the output values when req == kWriteTo
auto out_req = req;
if (out_req == kWriteTo) out_req = kWriteInplace;
TBlob out_blob = out->data();
// reuse dns rsp implementation when storage_shape == shape
SGDMomUpdateDnsRspDnsImpl<xpu>(param, ctx, weight.data(), grad,
mom.data(), out_req, &out_blob);
mom.data(), req, &out_blob);
}

template<typename xpu>
Expand Down
35 changes: 2 additions & 33 deletions tests/python/unittest/test_sparse_ndarray.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -81,45 +81,14 @@ def check_sparse_nd_copy(from_stype, to_stype, shape):
check_sparse_nd_copy('default', 'row_sparse', shape_3d)

def test_sparse_nd_basic():
def check_rsp_creation(values, indices, shape):
rsp = mx.nd.row_sparse(values, indices, shape)
dns = mx.nd.zeros(shape)
dns[1] = mx.nd.array(values[0])
dns[3] = mx.nd.array(values[1])
indices_np = mx.nd.array(indices, dtype='int64').asnumpy()
assert_almost_equal(rsp.indices.asnumpy(), indices_np)

def check_csr_creation(shape):
csr, (indptr, indices, values) = rand_sparse_ndarray(shape, 'csr')
assert_almost_equal(csr.indptr.asnumpy(), indptr)
assert_almost_equal(csr.indices.asnumpy(), indices)
assert_almost_equal(csr.data.asnumpy(), values)

def check_sparse_nd_rsp_aux():
def check_sparse_nd_basic_rsp():
storage_type = 'row_sparse'
shape = rand_shape_2d()
nd, (v, idx) = rand_sparse_ndarray(shape, storage_type)
assert(nd._num_aux == 1)
assert(nd.indices.dtype == np.int64)
assert(nd.stype == 'row_sparse')
assert_almost_equal(nd.indices.asnumpy(), idx)
assert_almost_equal(nd.data.asnumpy(), v)

shape = (4,2)
values = np.random.rand(2,2)
indices = np.array([1,3], dtype='int64')
check_rsp_creation(values, indices, shape)

values = mx.nd.array(np.random.rand(2,2))
indices = mx.nd.array([1,3], dtype='int64')
check_rsp_creation(values, indices, shape)

values = [[0.1, 0.2], [0.3, 0.4]]
indices = [1,3]
check_rsp_creation(values, indices, shape)

check_csr_creation(shape)
check_sparse_nd_rsp_aux()
check_sparse_nd_basic_rsp()


def test_sparse_nd_setitem():
Expand Down