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Merged
merged 4 commits into from
Jan 25, 2024
Merged

Optimize index put preprocess #61060

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e5396b3
reduce vector operations when no bool index
zoooo0820 Jan 11, 2024
ed77742
reduce vector in index_put
zoooo0820 Jan 11, 2024
46b2488
reduce vector operations
zoooo0820 Jan 17, 2024
3407011
no change for value
zoooo0820 Jan 23, 2024
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156 changes: 75 additions & 81 deletions paddle/phi/kernels/funcs/index_put_utils.h
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Original file line number Diff line number Diff line change
Expand Up @@ -73,67 +73,73 @@ std::vector<const phi::DenseTensor*> DealWithBoolIndices(
const Context& dev_ctx,
const std::vector<const phi::DenseTensor*>& indices_v,
std::vector<phi::DenseTensor>* tmp_indices_v) {
std::vector<const phi::DenseTensor*> res(indices_v.begin(), indices_v.end());
bool contains_bool_tensor = false;
std::vector<const phi::DenseTensor*> res;

bool contains_bool_tensor = false;
for (size_t i = 0; i < indices_v.size(); ++i) {
if (indices_v[i]->dtype() == phi::DataType::BOOL) {
contains_bool_tensor = true;
int rank = indices_v[i]->dims().size();
PADDLE_ENFORCE_GE(
rank,
1UL,
phi::errors::InvalidArgument("the only bool tensor in indices should "
"have number of dimension at least 1"));
phi::DenseTensor nonzero_indices(phi::DataType::INT64);
nonzero_indices.Resize(common::make_ddim({-1, rank}));
NonZeroKernel<bool, Context>(dev_ctx, *indices_v[i], &nonzero_indices);

if (nonzero_indices.numel() == 0) {
std::vector<const phi::DenseTensor*> empty_indices;
return empty_indices;
}
break;
}
}

std::vector<phi::DenseTensor*> integer_indices(rank, nullptr);
const int tmp_ix = tmp_indices_v->size();
for (int i = 0; i < rank; ++i) {
tmp_indices_v->emplace_back(
DenseTensor(phi::DataType::INT64)
.Resize(common::make_ddim({nonzero_indices.dims()[0]})));
}
for (int i = 0; i < rank; ++i) {
integer_indices[i] = &((*tmp_indices_v)[i + tmp_ix]);
}
SplitWithNumKernel<int64_t, Context>(
dev_ctx, nonzero_indices, rank, 1, integer_indices);
if (contains_bool_tensor) {
for (size_t i = 0; i < indices_v.size(); ++i) {
if (indices_v[i]->dtype() == phi::DataType::BOOL) {
int rank = indices_v[i]->dims().size();
PADDLE_ENFORCE_GE(rank,
1UL,
phi::errors::InvalidArgument(
"the only bool tensor in indices should "
"have number of dimension at least 1"));
phi::DenseTensor nonzero_indices(phi::DataType::INT64);
nonzero_indices.Resize(common::make_ddim({-1, rank}));
NonZeroKernel<bool, Context>(dev_ctx, *indices_v[i], &nonzero_indices);

if (nonzero_indices.numel() == 0) {
std::vector<const phi::DenseTensor*> empty_indices;
return empty_indices;
}

std::vector<phi::DenseTensor*> integer_indices(rank, nullptr);
const int tmp_ix = tmp_indices_v->size();
for (int i = 0; i < rank; ++i) {
tmp_indices_v->emplace_back(
DenseTensor(phi::DataType::INT64)
.Resize(common::make_ddim({nonzero_indices.dims()[0]})));
}
for (int i = 0; i < rank; ++i) {
integer_indices[i] = &((*tmp_indices_v)[i + tmp_ix]);
}
SplitWithNumKernel<int64_t, Context>(
dev_ctx, nonzero_indices, rank, 1, integer_indices);
#ifdef PADDLE_WITH_XPU
auto place = dev_ctx.GetPlace();
if (place.GetType() == phi::AllocationType::XPU) {
auto& pool = phi::DeviceContextPool::Instance();
auto* xpu_ctx = static_cast<phi::XPUContext*>(pool.Get(place));
if (xpu_ctx->x_context()->xpu_stream) {
dev_ctx.Wait();
auto place = dev_ctx.GetPlace();
if (place.GetType() == phi::AllocationType::XPU) {
auto& pool = phi::DeviceContextPool::Instance();
auto* xpu_ctx = static_cast<phi::XPUContext*>(pool.Get(place));
if (xpu_ctx->x_context()->xpu_stream) {
dev_ctx.Wait();
}
}
}
#endif

} else if ((indices_v[i]->dtype() == phi::DataType::INT64) ||
(indices_v[i]->dtype() == phi::DataType::INT32)) {
tmp_indices_v->emplace_back(*indices_v[i]);
} else {
PADDLE_THROW(phi::errors::InvalidArgument(
"data type of tensor in indices must be int32, int64 or bool"));
} else if ((indices_v[i]->dtype() == phi::DataType::INT64) ||
(indices_v[i]->dtype() == phi::DataType::INT32)) {
tmp_indices_v->emplace_back(*indices_v[i]);
} else {
PADDLE_THROW(phi::errors::InvalidArgument(
"data type of tensor in indices must be int32, int64 or bool"));
}
}
}
if (contains_bool_tensor) {
std::vector<const phi::DenseTensor*> res_tmp(tmp_indices_v->size(),
nullptr);
for (size_t i = 0; i < res_tmp.size(); ++i) {
res_tmp[i] = &((*tmp_indices_v)[i]);

res.reserve(tmp_indices_v->size());
for (size_t i = 0; i < tmp_indices_v->size(); ++i) {
res.emplace_back(&((*tmp_indices_v)[i]));
}
res.swap(res_tmp);
} else {
res = indices_v;
}

return res;
}

Expand Down Expand Up @@ -212,62 +218,50 @@ void DealWithIndices(const Context& dev_ctx,
res_dim_v->insert(res_dim_v->end(),
tmp_x_dims.begin() + int_indices_v.size(),
tmp_x_dims.end());

std::vector<DenseTensor> reshaped_indices_v;
phi::DDim res_dim = common::make_ddim(*res_dim_v);
for (size_t i = 0; i < int_indices_v.size(); ++i) {
phi::DenseTensor index_tensor;
if (int_indices_v[i]->dtype() == phi::DataType::INT32) {
reshaped_indices_v.emplace_back(phi::Cast<int, Context>(
dev_ctx, *int_indices_v[i], phi::DataType::INT64));
index_tensor = phi::Cast<int, Context>(
dev_ctx, *int_indices_v[i], phi::DataType::INT64);
} else {
reshaped_indices_v.emplace_back(*int_indices_v[i]);
index_tensor = *int_indices_v[i];
}
tmp_res_indices_v->emplace_back(
GetReshapeAndExpandTensor<int64_t, Context>(
dev_ctx, index_tensor, res_dim, bd_dim, 0));
}
reshaped_indices_v.insert(
reshaped_indices_v.end(), range_tensor_v.begin(), range_tensor_v.end());

phi::DDim res_dim = common::make_ddim(*res_dim_v);

for (size_t i = 0; i < reshaped_indices_v.size(); ++i) {
for (size_t i = 0; i < range_tensor_v.size(); ++i) {
tmp_res_indices_v->emplace_back(
GetReshapeAndExpandTensor<int64_t, Context>(
dev_ctx,
reshaped_indices_v[i],
res_dim,
bd_dim,
((i < int_indices_v.size())
? 0
: i - int_indices_v.size() + len_bd_dim)));
dev_ctx, range_tensor_v[i], res_dim, bd_dim, i + len_bd_dim));
}
for (size_t i = 0; i < res_indices_v->size(); ++i) {
(*res_indices_v)[i] = &(*tmp_res_indices_v)[i];
}

} else {
std::vector<DenseTensor> int_indices_v_tmp;

for (size_t i = 0; i < int_indices_v.size(); ++i) {
phi::DenseTensor index_tensor;
phi::DenseTensor expand_index;
if (int_indices_v[i]->dtype() == phi::DataType::INT32) {
int_indices_v_tmp.emplace_back(phi::Cast<int, Context>(
dev_ctx, *int_indices_v[i], phi::DataType::INT64));
index_tensor = phi::Cast<int, Context>(
dev_ctx, *int_indices_v[i], phi::DataType::INT64);
} else {
int_indices_v_tmp.emplace_back(*int_indices_v[i]);
index_tensor = *int_indices_v[i];
}
}

for (size_t i = 0; i < int_indices_v.size(); ++i) {
if (bd_dim != int_indices_v[i]->dims()) {
tmp_res_indices_v->emplace_back(
DenseTensor(phi::DataType::INT64).Resize(bd_dim));
expand_index = DenseTensor(phi::DataType::INT64).Resize(bd_dim);
ExpandKernel<int64_t, Context>(
dev_ctx,
int_indices_v_tmp[i],
index_tensor,
IntArray(common::vectorize<int64_t>(bd_dim)),
&(*tmp_res_indices_v)[i]);
&expand_index);
} else {
tmp_res_indices_v->emplace_back(int_indices_v_tmp[i]);
expand_index = index_tensor;
}
tmp_res_indices_v->emplace_back(expand_index);
}

for (size_t i = 0; i < res_indices_v->size(); ++i) {
(*res_indices_v)[i] = &(*tmp_res_indices_v)[i];
}
Expand Down