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#include "caffe2/opt/custom/in_batch_broadcast.h"
#include "caffe2/utils/proto_utils.h"
namespace caffe2 {
namespace opt {
const std::string kFP16_SUFFIX = "_fp16";
const std::string kFP32_SUFFIX = "_fp32";
const std::string kTILE_SUFFIX = "_tile";
void inBatchBroadcast(
NetDef* net,
const std::unordered_set<std::string>& to_broadcast_blobs,
int32_t batch_size,
ShapeInfoMap& shape_hints) {
int current_pos = net->op_size();
caffe2::NetDef broadcast_net;
broadcast_net.CopyFrom(*net);
broadcast_net.clear_op();
std::vector<OperatorDef> pre_ops;
std::vector<OperatorDef> post_ops;
// Heuristic: if any of to_broadcast_blobs is connected to
// Fused8BitRowwiseQuantizedToFloat only, we move Tile after
// Fused8BitRowwiseQuantizedToFloat to save some compute.
std::unordered_map<std::string, int> consumers;
for (const auto& op : net->op()) {
for (const auto& i : op.input()) {
if (to_broadcast_blobs.count(i)) {
consumers[i] += 1;
}
}
}
std::unordered_map<std::string, std::string> to_broadcast_replace;
for (const auto& op : net->op()) {
bool match = false;
if (op.type() == "Fused8BitRowwiseQuantizedToFloat") {
CAFFE_ENFORCE_EQ(
op.input_size(),
1,
"Fused8BitRowwiseQuantizedToFloat can only have 1 input");
CAFFE_ENFORCE_EQ(
op.output_size(),
1,
"Fused8BitRowwiseQuantizedToFloat can only have 1 output");
const auto it = consumers.find(op.input(0));
if (it != consumers.end() && it->second == 1) {
match = true;
}
}
if (match) {
to_broadcast_replace.emplace(op.input(0), op.output(0));
pre_ops.emplace_back(op);
} else {
post_ops.emplace_back(op);
}
}
// Build a reverse mapping. Not that such mapping is bijective, because if it
// is not, some key will have multiple consumers, which violates the single
// consumer condition above.
std::unordered_map<std::string, std::string> reversed;
for (const auto& kv : to_broadcast_replace) {
reversed.emplace(kv.second, kv.first);
}
std::unordered_set<std::string> to_broadcast_copy;
for (const auto& b : to_broadcast_blobs) {
const auto it = to_broadcast_replace.find(b);
if (it != to_broadcast_replace.end()) {
to_broadcast_copy.emplace(it->second);
} else {
to_broadcast_copy.emplace(b);
}
}
for (const auto& op : pre_ops) {
broadcast_net.add_op()->CopyFrom(op);
}
auto setShape = [&shape_hints, batch_size](
const std::string& blob,
const std::string& new_blob) mutable {
auto it = shape_hints.find(blob);
CAFFE_ENFORCE(it != shape_hints.end(), "Cannot find shape info for ", blob);
auto& shape = it->second;
CAFFE_ENFORCE(shape.shape.dims_size(), "Dim size for ", blob, " is 0");
if (!new_blob.empty()) {
shape_hints.emplace(new_blob, shape);
}
CAFFE_ENFORCE_EQ(
shape.shape.dims(0) % batch_size,
0,
"Dims(0) for ",
blob,
": ",
shape.shape.dims(0),
" cannot be divided by batch_size ",
batch_size);
shape.shape.set_dims(0, shape.shape.dims(0) / batch_size);
shape.setDimType(0, TensorBoundShape_DimType_CONSTANT);
};
for (const auto& blob : to_broadcast_copy) {
auto it = shape_hints.find(blob);
CAFFE_ENFORCE(it != shape_hints.end(), "Cannot find shape info for ", blob);
const auto& shape = it->second;
CAFFE_ENFORCE_GT(shape.shape.dims_size(), 0, "Dim size for ", blob, " is 0");
// If an op like Fused8BitRowwiseQuantizedToFloat ends up on CPU and
// Tile ends up on an accelerator and only FP16 is supported, then we want
// to make sure conversion from FP32 to FP16 is done on CPU to save cycles
// on accelerator.
const std::string blob_fp16 = blob + kFP16_SUFFIX;
const std::string blob_fp32 = blob + kFP32_SUFFIX;
const bool isFp32Optimization =
(shape.shape.data_type() == TensorProto_DataType_FLOAT);
if (isFp32Optimization) {
auto* op_fp16 = broadcast_net.add_op();
op_fp16->CopyFrom(CreateOperatorDef(
"FloatToHalf",
"",
{blob},
{blob_fp16},
{MakeArgument<int>("net_pos", current_pos++)}));
auto* op_fp32 = broadcast_net.add_op();
op_fp32->CopyFrom(CreateOperatorDef(
"HalfToFloat",
"",
{blob_fp16},
{blob_fp32},
{MakeArgument<int>("net_pos", current_pos++)}));
}
std::string blob_tile = blob + kTILE_SUFFIX;
auto* op_tile = broadcast_net.add_op();
op_tile->CopyFrom(CreateOperatorDef(
"Tile",
"",
{isFp32Optimization ? blob_fp32 : blob},
{blob_tile},
{MakeArgument<int>("tiles", batch_size),
MakeArgument<int>("axis", 0),
// Indicating that we are tiling to max_batch_size
MakeArgument<int>("dynamic", 1),
MakeArgument<int>("net_pos", current_pos++)}));
setShape(blob, blob_tile);
if (isFp32Optimization) {
const auto adjusted_shape = shape_hints[blob];
auto shape_fp16 = adjusted_shape;
shape_fp16.shape.set_data_type(TensorProto_DataType_FLOAT16);
shape_hints.emplace(blob_fp16, shape_fp16);
shape_hints.emplace(blob_fp32, adjusted_shape);
}
const auto rit = reversed.find(blob);
if (rit != reversed.end()) {
const auto& original_input = rit->second;
setShape(original_input, "");
}
}
for (auto& op : post_ops) {
for (int j = 0; j < op.input_size(); j++) {
if (to_broadcast_copy.count(op.input(j))) {
*op.mutable_input(j) = op.input(j) + kTILE_SUFFIX;
}
}
broadcast_net.add_op()->CopyFrom(op);
}
net->Swap(&broadcast_net);
}
} // namespace opt
} // namespace caffe2
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