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Add ragged batching support #95

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Mar 20, 2023
Merged

Add ragged batching support #95

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77 changes: 53 additions & 24 deletions src/libtorch.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1741,11 +1741,32 @@ ModelInstanceState::SetInputTensors(

input_names->emplace_back(input_name);

// The shape for the entire input patch, [total_batch_size, ...]
std::vector<int64_t> batchn_shape(
input_shape, input_shape + input_dims_count);
if (supports_batching_) {
batchn_shape[0] = total_batch_size;
// The shape for the entire input patch,
// [total_batch_size, ...] for non-ragged input and
// [total_element_count] for ragged input (non-nested tensor)
std::vector<int64_t> batchn_shape;
if (StateForModel()->IsInputRagged(input_name)) {
batchn_shape = std::vector<int64_t>{0};
for (size_t idx = 0; idx < request_count; idx++) {
TRITONBACKEND_Input* input;
RESPOND_AND_SET_NULL_IF_ERROR(
&((*responses)[idx]),
TRITONBACKEND_RequestInput(requests[idx], input_name, &input));
const int64_t* input_shape;
uint32_t input_dims_count;
RESPOND_AND_SET_NULL_IF_ERROR(
&((*responses)[idx]), TRITONBACKEND_InputProperties(
input, nullptr, nullptr, &input_shape,
&input_dims_count, nullptr, nullptr));

batchn_shape[0] += GetElementCount(input_shape, input_dims_count);
}
}
else {
batchn_shape = std::vector<int64_t>(input_shape, input_shape + input_dims_count);
if (supports_batching_) {
batchn_shape[0] = total_batch_size;
}
}

// The input must be in contiguous CPU/GPU memory.
Expand Down Expand Up @@ -1866,28 +1887,36 @@ ModelInstanceState::ReadOutputTensors(

// Output tensors may not reside on the same device as model
torch::Device tensor_device = output_flat.device();
const auto memory_type = (tensor_device.type() == torch::kCPU) ? TRITONSERVER_MEMORY_CPU
: TRITONSERVER_MEMORY_GPU;
const auto memory_id = (tensor_device.type() == torch::kCPU) ? 0 : tensor_device.index();

// Batch output doesn't support string data type yet, as it is not trivial
// to parse string output
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I think we need to mention this in the ragged batching docs too.

const BatchOutput* batch_output = StateForModel()->FindBatchOutput(name);
if (batch_output == nullptr) {
// Get output shape
std::vector<int64_t> batchn_shape;
auto shape = output_tensors[op_index].toTensor().sizes();
for (auto itr = shape.begin(); itr != shape.end(); itr++) {
batchn_shape.push_back(*itr);
}

// Get output shape
std::vector<int64_t> batchn_shape;
auto shape = output_tensors[op_index].toTensor().sizes();
for (auto itr = shape.begin(); itr != shape.end(); itr++) {
batchn_shape.push_back(*itr);
}
if (batchn_shape.size() == 0) {
return TRITONSERVER_ErrorNew(
TRITONSERVER_ERROR_INVALID_ARG,
(std::string("output '") + name +
"' is a scalar which is not supported.")
.c_str());
}

if (batchn_shape.size() == 0) {
return TRITONSERVER_ErrorNew(
TRITONSERVER_ERROR_INVALID_ARG,
(std::string("output '") + name +
"' is a scalar which is not supported.")
.c_str());
responder.ProcessTensor(
name, output_dtype, batchn_shape, output_buffer,
memory_type, memory_id);
} else {
responder.ProcessBatchOutput(
name, *batch_output, output_buffer, memory_type, memory_id);
}

responder.ProcessTensor(
name, output_dtype, batchn_shape, output_buffer,
(tensor_device.type() == torch::kCPU) ? TRITONSERVER_MEMORY_CPU
: TRITONSERVER_MEMORY_GPU,
(tensor_device.type() == torch::kCPU) ? 0 : tensor_device.index());

} else if (output_tensors[op_index].isList()) {
// Custom handling for string/bytes tensor...
torch::List<torch::jit::IValue> output_list =
Expand Down