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fix: Bugfix in shape analysis for multi-GPU systems #1765

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Merged
merged 1 commit into from
Mar 24, 2023
Merged

fix: Bugfix in shape analysis for multi-GPU systems #1765

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20 changes: 16 additions & 4 deletions core/partitioning/partitioning.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -542,14 +542,26 @@ bool isInputDynamic(PartitioningCtx* ctx) {
void populateInputIValues(PartitioningCtx* ctx) {
if (isInputDynamic(ctx)) {
ctx->min_input_ivalues_map = partitioning::generateRandomInputs(
ctx->settings.collection_input_spec_map, ctx->input_types_map, ir::ShapeMode::kMIN);
ctx->settings.collection_input_spec_map,
ctx->input_types_map,
ir::ShapeMode::kMIN,
ctx->settings.target_device.gpu_id);
ctx->opt_input_ivalues_map = partitioning::generateRandomInputs(
ctx->settings.collection_input_spec_map, ctx->input_types_map, ir::ShapeMode::kOPT);
ctx->settings.collection_input_spec_map,
ctx->input_types_map,
ir::ShapeMode::kOPT,
ctx->settings.target_device.gpu_id);
ctx->max_input_ivalues_map = partitioning::generateRandomInputs(
ctx->settings.collection_input_spec_map, ctx->input_types_map, ir::ShapeMode::kMAX);
ctx->settings.collection_input_spec_map,
ctx->input_types_map,
ir::ShapeMode::kMAX,
ctx->settings.target_device.gpu_id);
} else {
ctx->opt_input_ivalues_map = partitioning::generateRandomInputs(
ctx->settings.collection_input_spec_map, ctx->input_types_map, ir::ShapeMode::kOPT);
ctx->settings.collection_input_spec_map,
ctx->input_types_map,
ir::ShapeMode::kOPT,
ctx->settings.target_device.gpu_id);
}
}

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3 changes: 2 additions & 1 deletion core/partitioning/partitioning.h
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,8 @@ const std::unordered_set<c10::Symbol> CollectionNodeKinds = {
ExampleIValues generateRandomInputs(
ir::CollectionInputSpecMap& input_ranges,
ir::CollectionTypeMap& input_types,
const ir::ShapeMode& shape_mode = ir::ShapeMode::kOPT);
const ir::ShapeMode& shape_mode = ir::ShapeMode::kOPT,
int64_t gpu_id = 0);

void populateInputIValues(PartitioningCtx* ctx);

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15 changes: 9 additions & 6 deletions core/partitioning/shape_analysis.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,8 @@ namespace partitioning {
at::Tensor generateSingleInput(
ir::Input& input,
c10::optional<at::ScalarType>& type_opt,
const ir::ShapeMode& shape_mode) {
const ir::ShapeMode& shape_mode,
int64_t gpu_id) {
nvinfer1::Dims input_shape = input.input_shape;
if (input.input_is_dynamic) {
if (shape_mode == ir::ShapeMode::kMIN) {
Expand Down Expand Up @@ -42,15 +43,17 @@ at::Tensor generateSingleInput(

// Make the value range for input tensor a uniform (float) distribution
// over [LoValIncl, HiValExcl), then cast to the desired dtype
auto in = ((HiValExcl - LoValIncl) * at::rand(util::toVec(input_shape), {at::kCUDA}) + LoValIncl).to(type);
auto in = ((HiValExcl - LoValIncl) * at::rand(util::toVec(input_shape)) + LoValIncl)
.to(at::Device(at::kCUDA, gpu_id), type);

return in;
}

std::unordered_map<const torch::jit::Value*, torch::jit::IValue> generateRandomInputs(
std::unordered_map<const torch::jit::Value*, std::vector<ir::Input>>& inputs,
std::unordered_map<const torch::jit::Value*, std::vector<c10::optional<at::ScalarType>>>& types,
const ir::ShapeMode& shape_mode) {
const ir::ShapeMode& shape_mode,
int64_t gpu_id) {
// generate random inputs for running pytorch segments
std::unordered_map<const torch::jit::Value*, torch::jit::IValue> ivalue_map;

Expand All @@ -59,21 +62,21 @@ std::unordered_map<const torch::jit::Value*, torch::jit::IValue> generateRandomI
c10::TypePtr elementType = c10::TensorType::get();
auto generic_list = c10::impl::GenericList(elementType);
for (size_t i = 0; i < input.second.size(); i++) {
auto in = generateSingleInput(input.second[i], types[input.first][i], shape_mode);
auto in = generateSingleInput(input.second[i], types[input.first][i], shape_mode, gpu_id);
generic_list.push_back(in.clone());
}
ivalue_map[input.first] = c10::IValue(generic_list);
} else if (input.first->type()->kind() == torch::jit::TypeKind::TupleType) {
// create tuple
std::vector<torch::jit::IValue> list;
for (size_t i = 0; i < input.second.size(); i++) {
auto in = generateSingleInput(input.second[i], types[input.first][i], shape_mode);
auto in = generateSingleInput(input.second[i], types[input.first][i], shape_mode, gpu_id);
list.push_back(in.clone());
}
auto tuple = c10::ivalue::Tuple::create(list); // create tuple ptr
ivalue_map[input.first] = c10::IValue(tuple);
} else {
auto in = generateSingleInput(input.second[0], types[input.first][0], shape_mode);
auto in = generateSingleInput(input.second[0], types[input.first][0], shape_mode, gpu_id);
ivalue_map[input.first] = in.clone();
}
}
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