127 lines
4.1 KiB
C++
127 lines
4.1 KiB
C++
// Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#define HAVE_CUDA 1 // Loading Kaldi headers with GPU
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#include <cfloat>
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#include <sstream>
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#include "cudadecoder/batched-threaded-nnet3-cuda-online-pipeline.h"
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#include "fstext/fstext-lib.h"
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#include "lat/kaldi-lattice.h"
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#include "lat/lattice-functions.h"
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#include "nnet3/am-nnet-simple.h"
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#include "nnet3/nnet-utils.h"
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#include "util/kaldi-thread.h"
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#include "src/core/model_config.h"
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#include "src/core/model_config.pb.h"
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#include "src/custom/sdk/custom_instance.h"
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using kaldi::BaseFloat;
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namespace nvidia {
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namespace inferenceserver {
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namespace custom {
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namespace kaldi_cbe {
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// Context object. All state must be kept in this object.
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class Context {
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public:
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Context(const std::string& instance_name, const ModelConfig& config,
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const int gpu_device);
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virtual ~Context();
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// Initialize the context. Validate that the model configuration,
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// etc. is something that we can handle.
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int Init();
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// Perform custom execution on the payloads.
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int Execute(const uint32_t payload_cnt, CustomPayload* payloads,
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CustomGetNextInputFn_t input_fn, CustomGetOutputFn_t output_fn);
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private:
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// init kaldi pipeline
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int InitializeKaldiPipeline();
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int InputOutputSanityCheck();
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int ReadModelParameters();
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int GetSequenceInput(CustomGetNextInputFn_t& input_fn, void* input_context,
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CorrelationID* corr_id, int32_t* start, int32_t* ready,
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int32_t* dim, int32_t* end,
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const kaldi::BaseFloat** wave_buffer,
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std::vector<uint8_t>* input_buffer);
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int SetOutputs(kaldi::CompactLattice& clat,
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CustomGetOutputFn_t output_fn, CustomPayload payload);
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int SetOutputByName(const char* output_name,
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const std::string& out_bytes,
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CustomGetOutputFn_t output_fn,
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CustomPayload payload);
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bool CheckPayloadError(const CustomPayload& payload);
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int FlushBatch();
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// The name of this instance of the backend.
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const std::string instance_name_;
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// The model configuration.
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const ModelConfig model_config_;
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// The GPU device ID to execute on or CUSTOM_NO_GPU_DEVICE if should
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// execute on CPU.
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const int gpu_device_;
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// Models paths
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std::string nnet3_rxfilename_, fst_rxfilename_;
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std::string word_syms_rxfilename_;
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// batch_size
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int max_batch_size_;
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int num_channels_;
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int num_worker_threads_;
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std::vector<CorrelationID> batch_corr_ids_;
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std::vector<kaldi::SubVector<kaldi::BaseFloat>> batch_wave_samples_;
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std::vector<bool> batch_is_first_chunk_;
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std::vector<bool> batch_is_last_chunk_;
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BaseFloat sample_freq_, seconds_per_chunk_;
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int chunk_num_bytes_, chunk_num_samps_;
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// feature_config includes configuration for the iVector adaptation,
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// as well as the basic features.
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kaldi::cuda_decoder::BatchedThreadedNnet3CudaOnlinePipelineConfig
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batched_decoder_config_;
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std::unique_ptr<kaldi::cuda_decoder::BatchedThreadedNnet3CudaOnlinePipeline>
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cuda_pipeline_;
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// Maintain the state of some shared objects
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kaldi::TransitionModel trans_model_;
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kaldi::nnet3::AmNnetSimple am_nnet_;
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fst::SymbolTable* word_syms_;
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const uint64_t int32_byte_size_;
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const uint64_t int64_byte_size_;
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std::vector<int64_t> output_shape_;
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std::vector<uint8_t> byte_buffer_;
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std::vector<std::vector<uint8_t>> wave_byte_buffers_;
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};
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} // namespace kaldi_cbe
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} // namespace custom
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} // namespace inferenceserver
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} // namespace nvidia
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