From fa0bbbf3e5ad94e7ddcf02afe2bc37b82be662bf Mon Sep 17 00:00:00 2001 From: yuekaizhang Date: Tue, 24 May 2022 23:48:44 -0400 Subject: [PATCH 1/2] Add triton offline ASR server --- triton/Dockerfile/Dockerfile.client | 7 + triton/Dockerfile/Dockerfile.server | 10 + triton/README.md | 257 ++++ triton/client/1089-134686-0001.json | 1 + triton/client/client.py | 127 ++ triton/client/generate_perf_input.py | 24 + triton/client/offline_client.py | 48 + triton/client/test_wavs/1089-134686-0001.wav | Bin 0 -> 212044 bytes triton/client/test_wavs/1221-135766-0001.wav | Bin 0 -> 534924 bytes triton/client/test_wavs/1221-135766-0002.wav | Bin 0 -> 154444 bytes triton/client/test_wavs/trans.txt | 3 + triton/client/test_wavs/wav.scp | 3 + triton/client/utils.py | 59 + .../conformer_transducer/config.pbtxt | 99 ++ triton/model_repo/decoder/config.pbtxt | 58 + triton/model_repo/encoder/config.pbtxt | 69 ++ .../model_repo/feature_extractor/1/model.py | 144 +++ .../model_repo/feature_extractor/config.pbtxt | 73 ++ triton/model_repo/greedy_search/1/model.py | 236 ++++ triton/model_repo/greedy_search/config.pbtxt | 65 + triton/model_repo/joiner/config.pbtxt | 63 + triton/scripts/conformer_triton.py | 1066 +++++++++++++++++ triton/scripts/export_jit.py | 422 +++++++ triton/scripts/start_server.sh | 27 + 24 files changed, 2861 insertions(+) create mode 100755 triton/Dockerfile/Dockerfile.client create mode 100755 triton/Dockerfile/Dockerfile.server create mode 100755 triton/README.md create mode 100755 triton/client/1089-134686-0001.json create mode 100755 triton/client/client.py create mode 100755 triton/client/generate_perf_input.py create mode 100755 triton/client/offline_client.py create mode 100755 triton/client/test_wavs/1089-134686-0001.wav create mode 100755 triton/client/test_wavs/1221-135766-0001.wav create mode 100755 triton/client/test_wavs/1221-135766-0002.wav create mode 100755 triton/client/test_wavs/trans.txt create mode 100755 triton/client/test_wavs/wav.scp create mode 100755 triton/client/utils.py create mode 100755 triton/model_repo/conformer_transducer/config.pbtxt create mode 100755 triton/model_repo/decoder/config.pbtxt create mode 100755 triton/model_repo/encoder/config.pbtxt create mode 100755 triton/model_repo/feature_extractor/1/model.py create mode 100755 triton/model_repo/feature_extractor/config.pbtxt create mode 100755 triton/model_repo/greedy_search/1/model.py create mode 100755 triton/model_repo/greedy_search/config.pbtxt create mode 100755 triton/model_repo/joiner/config.pbtxt create mode 100755 triton/scripts/conformer_triton.py create mode 100755 triton/scripts/export_jit.py create mode 100755 triton/scripts/start_server.sh diff --git a/triton/Dockerfile/Dockerfile.client b/triton/Dockerfile/Dockerfile.client new file mode 100755 index 000000000..c4f393343 --- /dev/null +++ b/triton/Dockerfile/Dockerfile.client @@ -0,0 +1,7 @@ +FROM nvcr.io/nvidia/tritonserver:21.10-py3-sdk +LABEL maintainer="NVIDIA" +LABEL repository="tritonserver" + +RUN apt-get update && apt-get install -y libsndfile1 +RUN pip3 install soundfile +WORKDIR /workspace diff --git a/triton/Dockerfile/Dockerfile.server b/triton/Dockerfile/Dockerfile.server new file mode 100755 index 000000000..1cb0e6440 --- /dev/null +++ b/triton/Dockerfile/Dockerfile.server @@ -0,0 +1,10 @@ +FROM nvcr.io/nvidia/tritonserver:21.10-py3 +LABEL maintainer="NVIDIA" +LABEL repository="tritonserver" + +RUN apt-get update && apt-get -y install swig && apt-get -y install python3-dev && apt-get install -y cmake +RUN pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html +RUN pip3 install -v kaldifeat +RUN pip3 install pyyaml +WORKDIR /workspace +COPY ./scripts scripts diff --git a/triton/README.md b/triton/README.md new file mode 100755 index 000000000..350843a04 --- /dev/null +++ b/triton/README.md @@ -0,0 +1,257 @@ +# Inference Serving Practice for Transducer Non-streaming ASR based on Icefall + +In this tutorial, we'll go through how to run a non-streaming (offline) ASR Transducer model trained by [Icefall](https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless3) **on GPUs**, and deploy it as service with NVIDIA [Triton Inference Server](https://github.com/triton-inference-server/server). +## Table of Contents + +- [Preparation](#preparation) + - [Prepare Environment](#prepare-environment) + - [Prepare Models](#prepare-models) +- [Deploy on Triton Inference Server](#deploy-on-triton-inference-server) + - [Quick Start](#quick-start) + - [Advanced](#advanced) + - [Specify which GPUs for deployment](#specify-which-gpus-for-deployment) + - [Set the number of model instances per GPU](#set-the-number-of-model-instances-per-gpu) + - [Set the ports exposed by server](#set-the-ports-exposed-by-server) + - [Set the request scheduler](#set-the-request-scheduler) +- [Inference Client](#inference-client) + - [Quick Start](#quick-start-1) + - [Performance Test](#performance-test) +- [Benchmarks](#benchmarks) + - [FP32 Performance on Single GPU](#fp32-performance-on-single-gpu) + - [FP32 Performance of Small Model for Librispeech](#fp32-performance-of-small-model-for-librispeech) + - [Reference Accuracy](#reference-accuracy) + +## Preparation + +First of all, we need to get environment, models, codes, and data ready. + +### Prepare Environment + +Clone the repository: + +```bash +# Clond Icefall repo +git clone https://github.com/k2-fsa/icefall.git +cd icefall +export ICEFALL_DIR=$PWD + +# Clone k2 sherpa repo +git clone https://github.com/k2-fsa/sherpa.git +cd sherpa +export SHERPA_SRC=$PWD + +``` + +Next, install dependencies (you need to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before going ahead): + +``` +TODO +``` + +### Prepare Models + +``` +# Download pretrained models +git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-04-29 + +# export them to three jit models: encoder_jit.pt, decoder_jit.pt, joiner_jit.pt +cp $SHERPA/triton/scripts/conformer_triton.py $ICEFALL_DIR/egs/librispeech/ASR/pruned_stateless_transducer3/ +cp $SHERPA/triton/scripts/export_jit.py $ICEFALL_DIR/egs/librispeech/ASR/pruned_stateless_transducer3/ + +cd $ICEFALL_DIR/egs/librispeech/ASR/pruned_stateless_transducer3 +python3 export_jit.py --pretrained-model --output-dir --bpe-model +``` + + +## Deploy on Triton Inference Server + +Now we have exported pretrained non-streaming ASR model, then we need to consider how to deploy the model on the server as non-streaming ASR service, to allow users to send audio requests and get recognition results. Actually, [Triton Inference Server](https://github.com/triton-inference-server/server) does the most of serving work for us, it handles requests/results sending and receiving, request scheduling, load balance, and inference execution. In this part, we'll go through how to deploy the model on Triton. + +Build the server docker image: +``` +docker build . -f Dockerfile/Dockerfile.server -t sherpa_triton_server:latest --network host +``` + +Build the client docker image: +``` +docker build . -f Dockerfile/Dockerfile.client -t sherpa_triton_client:latest --network host +``` + +The model repository is provided in `model_repo` directory, you can find directories standing for each of the components. And there is a `conformer_transducer` dir which ensemble all the components into a whole pipeline. Each of those component directory contains a config file `config.pbtxt` and a version directory containing the model file. However, the version directories of encoder and decoder are still empty since we have not put the exported models into them. + +### Quick Start + +Now start server: + +```bash +# Start the docker container +docker run --gpus all -v $PWD/model_repo:/ws/model_repo -v :/ws/jit_model/ --name sherpa_server --net host --shm-size=1g --ulimit memlock=-1 -p 8000:8000 -p 8001:8001 -p 8002:8002 --ulimit stack=67108864 -it sherpa_triton_server:latest + +# Inside the docker container +bash /workspace/scripts/start_server.sh +``` + +If you meet any issues during the process, please file an issue. + +### Advanced + +Here we introduce some advanced configuration/options for deploying the ASR server. + +#### Specify which GPUs for deployment + +If you have multiple GPUs on the server machine, you can specify which GPUs will be used for deploying ASR service. To do so, just change the `-e CUDA_VISIBLE_DEVICES=` option or just use to specify which GPU to make visible in the container when starting server container. + +For example, if you just want to use GPU 1, 2, 3 for deployment, then use the following options to start the server: + +```bash +docker run --gpus '"device=1,2,3"' -v $PWD/model_repo:/ws/model_repo -v :/ws/jit_model/ --name sherpa_server --net host --shm-size=1g --ulimit memlock=-1 -p 8000:8000 -p 8001:8001 -p 8002:8002 --ulimit stack=67108864 -it sherpa_triton_server:latest +``` + +#### Set the number of model instances per GPU + +Triton can provide multiple [instances of a model](https://github.com/triton-inference-server/server/blob/master/docs/architecture.md#concurrent-model-execution) so that multiple inference requests for that model can be handled simultaneously. You can set the number of model instances on each GPU by modifying the `config.pbtxt` file of the any of the component model in the `model_repo`, as [Triton document](https://github.com/triton-inference-server/server/blob/master/docs/model_configuration.md#instance-groups) says. + +For example, if you want to set 4 model instances for encoder component on each GPU (given that each GPU memory is large enough to handle to instances), just edit the following lines of config file model_repo/encoder/config.pbtxt`: + +``` +instance_group [ + { + count: 4 + kind: KIND_GPU + } +] +``` + +Elsewise, if you want to set only 1 model instance on each GPU, just change the `count:` field to `1`. + +You can also specify which GPUs to use for deployment, and how many model instances running on those GPUs. For example, you can set 1 execution instance on GPU 0, 2 execution instances on GPU 1 and 2: + +``` +instance_group [ + { + count: 1 + kind: KIND_GPU + gpus: [ 0 ] + }, + { + count: 2 + kind: KIND_GPU + gpus: [ 1, 2 ] + } + ] +``` + +#### Set the ports exposed by server + +The default ports exposed by server to clients is: port 8000 for HTTP inference service; port 8001 for gRPC inference service; port 8002 for Metrics service. If the default ports are occupied by other services, you can change the ports that are exposed to the clients, by specify `-p` option when starting the server. + +For example, if you want to set port 8003 for HTTP inference service, 8004 for gRPC inference service, and 8005 for Metrics service, then use the following command to start the server: + +```bash +docker run --gpus all -v $PWD/model_repo:/ws/model_repo -v :/ws/jit_model/ --name sherpa_server --net host --shm-size=1g --ulimit memlock=-1 -p 8003:8003 -p 8004:8004 -p 8005:8005 --ulimit stack=67108864 -it sherpa_triton_server:latest +``` + +And then add the following options when start the Triton server: + +```bash +tritonserver --model-repository=/ws/model_repo --http-port=8003 --grpc-port=8004 --metrics-port=8005 +``` + +Please note that: if you change the exposed ports of server, you should also specify the same ports when sending requests via client program. For how to specify server port for sending requests. + +#### Set the request scheduler + +With Triton, you can choose various scheduler modes and batching strategies for coming requests, as described in [Triton document](https://github.com/triton-inference-server/server/blob/master/docs/model_configuration.md#scheduling-and-batching). In our project, we use [dynamic batcher](https://github.com/triton-inference-server/server/blob/master/docs/model_configuration.md#dynamic-batcher) by default, which allows inference requests to be combined by the server, so that a batch is created dynamically. + +You can change the settings of the dynamic batcher by editting `config.pbtxt` file of each model: + +``` +dynamic_batching { + preferred_batch_size: [8, 16, 32, 64] + max_queue_delay_microseconds: 10000 +} +``` + +The [preferred_batch_size](https://github.com/triton-inference-server/server/blob/master/docs/model_configuration.md#preferred-batch-sizes) property indicates the batch sizes that the dynamic batcher should attempt to create. For example, the above configuration enables dynamic batching with preferred batch sizes of 8, 16, 32 and 64. + +The `max_queue_delay_microseconds` property setting changes the dynamic batcher behavior when a batch of a preferred size cannot be created. When a batch of a preferred size cannot be created from the available requests, the dynamic batcher will delay sending the batch as long as no request is delayed longer than the configured max_queue_delay_microseconds value. If a new request arrives during this delay and allows the dynamic batcher to form a batch of a preferred batch size, then that batch is sent immediately for inferencing. If the delay expires the dynamic batcher sends the batch as is, even though it is not a preferred size. + +## Inference Client + +In this section, we will show how to send requests to our deployed non-streaming ASR service, and receive the recognition results. Also, we can use client to test the accuracy of the ASR service on a test dataset. In addition, we can use [perf_analyzer](https://github.com/triton-inference-server/server/blob/main/docs/perf_analyzer.md) provided by Triton to test the performance of the service. + +### Quick Start + +We do it in the built client container, now let's start the container. + +```bash +docker run -ti --net host --name sherpa_client -v $PWD/client:/ws/client sherpa_triton_client:latest +cd /ws/client +``` + +In the docker container, run the client script to do ASR inference. + +```bash +# Test one audio +python3 client.py --audio_file=./test_wavs/1089-134686-0001.wav --url=localhost:8001 +``` + +The above command sends a single audio `1089-134686-0001.wav` to the server and get the result. `--url` option specifies the IP and port of the server, in our example, we set the server and client on the same machine, therefore IP is `localhost`, and we use port `8001` since it is the default port for gRPC in Triton. But if your client is not on the same machine as the server, you should change this option. + +You can also test a bunch of audios together with the client. Just specify the path of `wav.scp` with `--wavscp` option, set the path of test set directory with `--data_dir` option, and set the path of ground-truth transcript file with `--trans` option, the client will infer all the audios in test set and calculate the CER upon the test set. + +```bash +# Test a bunch of audios +python3 client.py --wavscp=./test_wavs/wav.scp --data_dir=./test_wavs/ --trans=./test_wavs/trans.txt +``` + +### Performance Test + +We use [perf_analyzer](https://github.com/triton-inference-server/server/blob/main/docs/perf_analyzer.md) to test the performance of non-streaming ASR service. Before this, we need to generate the test input data. + +Still in the container client, run: + +```bash +cd /ws/client +python3 generate_perf_input.py test_wavs/1089-134686-0001.wav +``` + +The first argument is the path of audio file used for testing, and the second argument is the output json file loaded by perf_analyzer. + +Then in client docker container, we can simply run the testing: + +```bash +perf_analyzer -m conformer_transducer -b 1 -a -p 20000 --concurrency-range 100:200:50 -i gRPC --input-data=./input.json -u localhost:8001 +``` + +Where: +- `-m` option indicates the name of the served model; +- `-p` option is the mearsurement window, which indicates in what time duration to collect the metrics; +- `-v` option turns on the verbose model; +- `-i` option is for choosing the networking protocol, you can choose `HTTP` or `gRPC` here; +- `-u` option sets the url of the service in the form of `:`, but notice that port `8000` corresponds to HTTP protocol while port `8001` corresponds to gRPC protocol; +- `-b` option indicates the batch size of the input requests used fo testing; since we simulate individual users sending requests, we set batch size here to `1`; +- `-a` option controls the analyzer to send requests in an asynchronous way, if this option is not applied, the requests will be sent in synchronous way; +- `--input-data` option points to the path of the json file containing the real input data +- `--concurrency-range` option is an important one, it indicates the concurrency of the requests which defines the pressure we will give to the server. +- You can also set `-f` option to set the path of testing result file; +- You can also set `--max-threads` option to set the number of threads used to send test request, it should be set to the number of CPU cores in your test machine. + +As described above, if you want to send request with batch size > 1: + +```bash +perf_analyzer -m attention_rescoring -b 16 -a -p20000 --concurrency-range 100:200:50 -i gRPC --input-data=./input.json -u localhost:8001 +``` + +## Benchmarks + +In this section, we show a reference performance benchamrk for the non-streaming ASR based on Icefall. + +Notice that we send the test requests all with same length, so that Triton server will pack several requests into a batch. But in the real production cases, the lengths of all the incoming requests will not be the same, therefore, in order to allow Triton batch the requests to improve throughput, you need to try to padding the input to a specific length at the client side. + +### FP32 Performance on Single GPU + +First we give the performance benchmark of FP32 precision tested on single T4. We use test audios with two different lengths, 5 seconds, 8 seconds and 10 seconds. 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import multiprocessing +from multiprocessing import Pool + +import argparse +import os +import tritonclient.grpc as grpcclient +from utils import cal_cer +from offline_client import SpeechClient +import numpy as np + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-v', + '--verbose', + action="store_true", + required=False, + default=False, + help='Enable verbose output') + parser.add_argument('-u', + '--url', + type=str, + required=False, + default='localhost:8001', + help='Inference server URL. Default is ' + 'localhost:8001.') + parser.add_argument('--model_name', + required=False, + default='conformer_transducer', + help='the model to send request to') + parser.add_argument('--wavscp', + type=str, + required=False, + default=None, + help='audio_id \t absolute_wav_path') + parser.add_argument('--trans', + type=str, + required=False, + default=None, + help='audio_id \t text') + parser.add_argument('--data_dir', + type=str, + required=False, + default=None, + help='data dir will be append to audio file if given') + parser.add_argument( + '--audio_file', + type=str, + required=False, + default=None, + help='single wav file' + ) + + FLAGS = parser.parse_args() + + # load data + filenames = [] + transcripts = [] + if FLAGS.audio_file is not None: + path = FLAGS.audio_file + if FLAGS.data_dir: + path = os.path.join(FLAGS.data_dir, path) + if os.path.exists(path): + filenames = [path] + elif FLAGS.wavscp is not None: + audio_data = {} + with open(FLAGS.wavscp, "r", encoding="utf-8") as f: + for line in f: + aid, path = line.strip().split('\t') + if FLAGS.data_dir: + path = os.path.join(FLAGS.data_dir, path) + audio_data[aid] = {'path': path} + with open(FLAGS.trans, "r", encoding="utf-8") as f: + for line in f: + aid, text = line.strip().split('\t') + audio_data[aid]['text'] = text + + for key, value in audio_data.items(): + filenames.append(value['path']) + transcripts.append(value['text']) + + num_workers = multiprocessing.cpu_count() // 2 + + def single_job(client_files): + with grpcclient.InferenceServerClient(url=FLAGS.url, + verbose=FLAGS.verbose) as triton_client: + protocol_client = grpcclient + speech_client = SpeechClient(triton_client, FLAGS.model_name, + protocol_client) + idx, audio_files = client_files + predictions = [] + for li in audio_files: + result = speech_client.recognize(li, idx) + print("Recognized {}:{}".format(li, result[0])) + predictions += result + return predictions + + # start to do inference + # Group requests in batches + predictions = [] + tasks = [] + splits = np.array_split(filenames, num_workers) + + for idx, per_split in enumerate(splits): + cur_files = per_split.tolist() + tasks.append((idx, cur_files)) + + with Pool(processes=num_workers) as pool: + predictions = pool.map(single_job, tasks) + + predictions = [item for sublist in predictions for item in sublist] + if transcripts: + cer = cal_cer(predictions, transcripts) + print("CER is: {}".format(cer)) diff --git a/triton/client/generate_perf_input.py b/triton/client/generate_perf_input.py new file mode 100755 index 000000000..9b4c39955 --- /dev/null +++ b/triton/client/generate_perf_input.py @@ -0,0 +1,24 @@ +import sys +import json +import soundfile as sf +import numpy as np + + +def generate(wav_file, out): + """ + inp: a single channel, 16kHz wav file + out: the generated json test data + """ + print("Reading {}".format(wav_file)) + waveform, sample_rate = sf.read(wav_file) + batch_size = 1 + mat = np.array([waveform] * batch_size, dtype=np.float32) + + out_dict = {"data": [{"WAV_LENS": [len(waveform)], + "WAV": {"shape": [len(waveform)], "content": mat.flatten().tolist()}}]} + json.dump(out_dict, open(out, "w")) + +if __name__ == "__main__": + inp = sys.argv[1] + out = sys.argv[2] + generate(inp, out) diff --git a/triton/client/offline_client.py b/triton/client/offline_client.py new file mode 100755 index 000000000..79b60a599 --- /dev/null +++ b/triton/client/offline_client.py @@ -0,0 +1,48 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tritonclient.utils import np_to_triton_dtype +import numpy as np +import soundfile as sf + +class SpeechClient(object): + def __init__(self, triton_client, model_name, protocol_client): + self.triton_client = triton_client + self.protocol_client = protocol_client + self.model_name = model_name + + def recognize(self, wav_file, idx=0): + waveform, sample_rate = sf.read(wav_file) + + samples = np.array([waveform], dtype=np.float32) + lengths = np.array([[len(waveform)]], dtype=np.int32) + sequence_id = 10086 + idx + result = '' + inputs = [ + self.protocol_client.InferInput("WAV", samples.shape, + np_to_triton_dtype(samples.dtype)), + self.protocol_client.InferInput("WAV_LENS", lengths.shape, + np_to_triton_dtype(lengths.dtype)) + ] + inputs[0].set_data_from_numpy(samples) + inputs[1].set_data_from_numpy(lengths) + outputs = [self.protocol_client.InferRequestedOutput("TRANSCRIPTS")] + response = self.triton_client.infer(self.model_name, + inputs, + request_id=str(sequence_id), + outputs=outputs) + tmp = response.as_numpy("TRANSCRIPTS") + result = response.as_numpy("TRANSCRIPTS")[0].tolist() + result = b' '.join(result).decode('utf-8') + return [result] diff --git a/triton/client/test_wavs/1089-134686-0001.wav b/triton/client/test_wavs/1089-134686-0001.wav new file mode 100755 index 0000000000000000000000000000000000000000..bfe1519ead65b33e26f8b81f25b1c072cbb90d13 GIT binary patch literal 212044 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--- /dev/null +++ b/triton/client/test_wavs/trans.txt @@ -0,0 +1,3 @@ +1089-134686-0001 AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS +1221-135766-0001 GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN +1221-135766-0002 YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION diff --git a/triton/client/test_wavs/wav.scp b/triton/client/test_wavs/wav.scp new file mode 100755 index 000000000..3ffe2e510 --- /dev/null +++ b/triton/client/test_wavs/wav.scp @@ -0,0 +1,3 @@ +1089-134686-0001 1089-134686-0001.wav +1221-135766-0001 1221-135766-0001.wav +1221-135766-0002 1221-135766-0002.wav diff --git a/triton/client/utils.py b/triton/client/utils.py new file mode 100755 index 000000000..66415ec64 --- /dev/null +++ b/triton/client/utils.py @@ -0,0 +1,59 @@ +import numpy as np + +def _levenshtein_distance(ref, hyp): + """Levenshtein distance is a string metric for measuring the difference + between two sequences. Informally, the levenshtein disctance is defined as + the minimum number of single-character edits (substitutions, insertions or + deletions) required to change one word into the other. We can naturally + extend the edits to word level when calculate levenshtein disctance for + two sentences. + """ + m = len(ref) + n = len(hyp) + + # special case + if ref == hyp: + return 0 + if m == 0: + return n + if n == 0: + return m + + if m < n: + ref, hyp = hyp, ref + m, n = n, m + + # use O(min(m, n)) space + distance = np.zeros((2, n + 1), dtype=np.int32) + + # initialize distance matrix + for j in range(n + 1): + distance[0][j] = j + + # calculate levenshtein distance + for i in range(1, m + 1): + prev_row_idx = (i - 1) % 2 + cur_row_idx = i % 2 + distance[cur_row_idx][0] = i + for j in range(1, n + 1): + if ref[i - 1] == hyp[j - 1]: + distance[cur_row_idx][j] = distance[prev_row_idx][j - 1] + else: + s_num = distance[prev_row_idx][j - 1] + 1 + i_num = distance[cur_row_idx][j - 1] + 1 + d_num = distance[prev_row_idx][j] + 1 + distance[cur_row_idx][j] = min(s_num, i_num, d_num) + + return distance[m % 2][n] + + +def cal_cer(references, predictions): + errors = 0 + lengths = 0 + for ref, pred in zip(references, predictions): + cur_ref = list(ref) + cur_hyp = list(pred) + cur_error = _levenshtein_distance(cur_ref, cur_hyp) + errors += cur_error + lengths += len(cur_ref) + return float(errors) / lengths diff --git a/triton/model_repo/conformer_transducer/config.pbtxt b/triton/model_repo/conformer_transducer/config.pbtxt new file mode 100755 index 000000000..cb1532cbb --- /dev/null +++ b/triton/model_repo/conformer_transducer/config.pbtxt @@ -0,0 +1,99 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "conformer_transducer" +platform: "ensemble" +max_batch_size: 512 #MAX_BATCH + +input [ + { + name: "WAV" + data_type: TYPE_FP32 + dims: [-1] + }, + { + name: "WAV_LENS" + data_type: TYPE_INT32 + dims: [1] + } +] + +output [ + { + name: "TRANSCRIPTS" + data_type: TYPE_STRING + dims: [1] + } +] + +ensemble_scheduling { + step [ + { + model_name: "feature_extractor" + model_version: -1 + input_map { + key: "wav" + value: "WAV" + } + input_map { + key: "wav_lens" + value: "WAV_LENS" + } + output_map { + key: "speech__0" + value: "SPEECH" + } + output_map { + key: "speech_lengths__1" + value: "SPEECH_LENGTHS" + } + }, + { + model_name: "encoder" + model_version: -1 + input_map { + key: "speech__0" + value: "SPEECH" + } + input_map { + key: "speech_lengths__1" + value: "SPEECH_LENGTHS" + } + output_map { + key: "encoder_out__0" + value: "encoder_out__0" + } + output_map { + key: "encoder_out_lens__1" + value: "encoder_out_lens__1" + } + }, + { + model_name: "greedy_search" + model_version: -1 + input_map { + key: "encoder_out__0" + value: "encoder_out__0" + } + input_map { + key: "encoder_out_lens__1" + value: "encoder_out_lens__1" + } + output_map { + key: "OUTPUT0" + value: "TRANSCRIPTS" + } + } + ] +} diff --git a/triton/model_repo/decoder/config.pbtxt b/triton/model_repo/decoder/config.pbtxt new file mode 100755 index 000000000..510a9fca3 --- /dev/null +++ b/triton/model_repo/decoder/config.pbtxt @@ -0,0 +1,58 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "decoder" +backend: "pytorch" +default_model_filename: "decoder_jit.pt" + +max_batch_size: 512 +input [ + { + name: "decoder_input__0" + data_type: TYPE_INT32 + dims: [2] # [context_size] + } +] + +output [ + { + name: "decoder_output__0" + data_type: TYPE_FP32 + dims: [1, 512] # [context_size, decoder_dim] + } +] + +dynamic_batching { + preferred_batch_size: [ 16, 32 ] + } + +instance_group [ + { + count: 1 + kind: KIND_GPU + } +] + +parameters: { +key: "INFERENCE_MODE" + value: { + string_value:"true" + } +} +parameters: { +key: "DISABLE_OPTIMIZED_EXECUTION" + value: { + string_value:"true" + } +} \ No newline at end of file diff --git a/triton/model_repo/encoder/config.pbtxt b/triton/model_repo/encoder/config.pbtxt new file mode 100755 index 000000000..021505452 --- /dev/null +++ b/triton/model_repo/encoder/config.pbtxt @@ -0,0 +1,69 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "encoder" +backend: "pytorch" +default_model_filename: "encoder_jit.pt" + +max_batch_size: 512 +input [ + { + name: "speech__0" + data_type: TYPE_FP32 + dims: [-1, 80] # 80 + }, + { + name: "speech_lengths__1" + data_type: TYPE_INT32 + dims: [1] + reshape: { shape: [ ] } + } +] +output [ + { + name: "encoder_out__0" + data_type: TYPE_FP32 + dims: [-1, 512] # [-1, encoder_dim] + }, + { + name: "encoder_out_lens__1" + data_type: TYPE_INT32 + dims: [1] + reshape: { shape: [ ] } + } +] + +dynamic_batching { + preferred_batch_size: [ 16, 32 ] + } + +instance_group [ + { + count: 1 + kind: KIND_GPU + } +] + +parameters: { +key: "INFERENCE_MODE" + value: { + string_value:"true" + } +} +parameters: { +key: "DISABLE_OPTIMIZED_EXECUTION" + value: { + string_value:"true" + } +} \ No newline at end of file diff --git a/triton/model_repo/feature_extractor/1/model.py b/triton/model_repo/feature_extractor/1/model.py new file mode 100755 index 000000000..581f9f236 --- /dev/null +++ b/triton/model_repo/feature_extractor/1/model.py @@ -0,0 +1,144 @@ +import triton_python_backend_utils as pb_utils +from torch.utils.dlpack import to_dlpack +import torch +import numpy as np +import kaldifeat +import _kaldifeat +from typing import List +import json + +class Fbank(torch.nn.Module): + def __init__(self, opts): + super(Fbank, self).__init__() + self.fbank = kaldifeat.Fbank(opts) + + def forward(self, waves: List[torch.Tensor]): + return self.fbank(waves) + + +class TritonPythonModel: + """Your Python model must use the same class name. Every Python model + that is created must have "TritonPythonModel" as the class name. + """ + + def initialize(self, args): + """`initialize` is called only once when the model is being loaded. + Implementing `initialize` function is optional. This function allows + the model to initialize any state associated with this model. + + Parameters + ---------- + args : dict + Both keys and values are strings. The dictionary keys and values are: + * model_config: A JSON string containing the model configuration + * model_instance_kind: A string containing model instance kind + * model_instance_device_id: A string containing model instance device ID + * model_repository: Model repository path + * model_version: Model version + * model_name: Model name + """ + self.model_config = model_config = json.loads(args['model_config']) + self.max_batch_size = max(model_config["max_batch_size"], 1) + + if "GPU" in model_config["instance_group"][0]["kind"]: + self.device = "cuda" + else: + self.device = "cpu" + + # Get OUTPUT0 configuration + output0_config = pb_utils.get_output_config_by_name( + model_config, "speech__0") + # Convert Triton types to numpy types + output0_dtype = pb_utils.triton_string_to_numpy( + output0_config['data_type']) + if output0_dtype == np.float32: + self.output0_dtype = torch.float32 + else: + self.output0_dtype = torch.float16 + + # Get OUTPUT1 configuration + output1_config = pb_utils.get_output_config_by_name( + model_config, "speech_lengths__1") + # Convert Triton types to numpy types + self.output1_dtype = pb_utils.triton_string_to_numpy( + output1_config['data_type']) + + params = self.model_config['parameters'] + opts = kaldifeat.FbankOptions() + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + for li in params.items(): + key, value = li + value = value["string_value"] + if key == "num_mel_bins": + opts.mel_opts.num_bins = int(value) + # elif key == "frame_shift_in_ms": + # opts.frame_opts.frame_shift_ms = float(value) + # elif key == "frame_length_in_ms": + # opts.frame_opts.frame_length_ms = float(value) + elif key == "sample_rate": + opts.frame_opts.samp_freq = int(value) + opts.device = torch.device(self.device) + self.opts = opts + self.feature_extractor = Fbank(self.opts) + self.feature_size = opts.mel_opts.num_bins + + def execute(self, requests): + """`execute` must be implemented in every Python model. `execute` + function receives a list of pb_utils.InferenceRequest as the only + argument. This function is called when an inference is requested + for this model. + + Parameters + ---------- + requests : list + A list of pb_utils.InferenceRequest + + Returns + ------- + list + A list of pb_utils.InferenceResponse. The length of this list must + be the same as `requests` + """ + batch_count = [] + total_waves = [] + batch_len = [] + responses = [] + for request in requests: + input0 = pb_utils.get_input_tensor_by_name(request, "wav") + input1 = pb_utils.get_input_tensor_by_name(request, "wav_lens") + + cur_b_wav = input0.as_numpy() + cur_b_wav_lens = input1.as_numpy() # b x 1 + cur_batch = cur_b_wav.shape[0] + cur_len = cur_b_wav.shape[1] + batch_count.append(cur_batch) + batch_len.append(cur_len) + for wav, wav_len in zip(cur_b_wav, cur_b_wav_lens): + wav_len = wav_len[0] + wav = torch.tensor(wav[0:wav_len], dtype=torch.float32, + device=self.device) + total_waves.append(wav) + + features = self.feature_extractor(total_waves) + for b, l in zip(batch_count, batch_len): + expect_feat_len = _kaldifeat.num_frames(l, self.opts.frame_opts) + speech = torch.zeros((b, expect_feat_len, self.feature_size), + dtype=self.output0_dtype, device=self.device) + speech_lengths = torch.zeros((b, 1), dtype=torch.int32, device=self.device) + for i in range(b): + f = features.pop(0) + f_l = f.shape[0] + speech[i, 0: f_l, :] = f.to(self.output0_dtype) + speech_lengths[i][0] = f_l + # put speech feature on device will cause empty output + # we will follow this issue and now temporarily put it on cpu + speech = speech.cpu() + speech_lengths = speech_lengths.cpu() + + out0 = pb_utils.Tensor.from_dlpack("speech__0", to_dlpack(speech)) + out1 = pb_utils.Tensor.from_dlpack("speech_lengths__1", + to_dlpack(speech_lengths)) + inference_response = pb_utils.InferenceResponse(output_tensors=[out0, out1]) + responses.append(inference_response) + return responses diff --git a/triton/model_repo/feature_extractor/config.pbtxt b/triton/model_repo/feature_extractor/config.pbtxt new file mode 100755 index 000000000..5f954ef9b --- /dev/null +++ b/triton/model_repo/feature_extractor/config.pbtxt @@ -0,0 +1,73 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "feature_extractor" +backend: "python" +max_batch_size: 512 + +parameters [ + { + key: "num_mel_bins", + value: { string_value: "80"} + }, + { + key: "frame_shift_in_ms" + value: { string_value: "10"} + }, + { + key: "frame_length_in_ms" + value: { string_value: "25"} + }, + { + key: "sample_rate" + value: { string_value: "16000"} + } + +] + +input [ + { + name: "wav" + data_type: TYPE_FP32 + dims: [-1] + }, + { + name: "wav_lens" + data_type: TYPE_INT32 + dims: [1] + } +] + +output [ + { + name: "speech__0" + data_type: TYPE_FP32 + dims: [-1, 80] # 80 + }, + { + name: "speech_lengths__1" + data_type: TYPE_INT32 + dims: [1] + } +] + +dynamic_batching { + preferred_batch_size: [ 16, 32 ] + } +instance_group [ + { + count: 1 + kind: KIND_GPU + } +] diff --git a/triton/model_repo/greedy_search/1/model.py b/triton/model_repo/greedy_search/1/model.py new file mode 100755 index 000000000..8d8dc226b --- /dev/null +++ b/triton/model_repo/greedy_search/1/model.py @@ -0,0 +1,236 @@ +import triton_python_backend_utils as pb_utils +import numpy as np + +import json + +import torch +import sentencepiece as spm + +class TritonPythonModel: + """Your Python model must use the same class name. Every Python model + that is created must have "TritonPythonModel" as the class name. + """ + + def initialize(self, args): + """`initialize` is called only once when the model is being loaded. + Implementing `initialize` function is optional. This function allows + the model to initialize any state associated with this model. + + Parameters + ---------- + args : dict + Both keys and values are strings. The dictionary keys and values are: + * model_config: A JSON string containing the model configuration + * model_instance_kind: A string containing model instance kind + * model_instance_device_id: A string containing model instance device ID + * model_repository: Model repository path + * model_version: Model version + * model_name: Model name + """ + self.model_config = model_config = json.loads(args['model_config']) + self.max_batch_size = max(model_config["max_batch_size"], 1) + + # Get OUTPUT0 configuration + output0_config = pb_utils.get_output_config_by_name( + model_config, "OUTPUT0") + # Convert Triton types to numpy types + self.out0_dtype = pb_utils.triton_string_to_numpy( + output0_config['data_type']) + + # Get INPUT configuration + + encoder_config = pb_utils.get_input_config_by_name( + model_config, "encoder_out__0") + self.data_type = pb_utils.triton_string_to_numpy( + encoder_config['data_type']) + + self.encoder_dim = encoder_config['dims'][-1] + + + self.init_sentence_piece(self.model_config['parameters']) + + def init_sentence_piece(self, parameters): + for key,value in parameters.items(): + parameters[key] = value["string_value"] + self.context_size = int(parameters['context_size']) + sp = spm.SentencePieceProcessor() + sp.load(parameters['bpe_model']) + self.blank_id = sp.piece_to_id("") + self.unk_id = sp.piece_to_id("") + self.vocab_size = sp.get_piece_size() + self.sp = sp + + def execute(self, requests): + """`execute` must be implemented in every Python model. `execute` + function receives a list of pb_utils.InferenceRequest as the only + argument. This function is called when an inference is requested + for this model. + + Parameters + ---------- + requests : list + A list of pb_utils.InferenceRequest + + Returns + ------- + list + A list of pb_utils.InferenceResponse. The length of this list must + be the same as `requests` + """ + # Every Python backend must iterate through list of requests and create + # an instance of pb_utils.InferenceResponse class for each of them. You + # should avoid storing any of the input Tensors in the class attributes + # as they will be overridden in subsequent inference requests. You can + # make a copy of the underlying NumPy array and store it if it is + # required. + + batch_encoder_out_list, batch_encoder_lens_list = [], [] + batchsize_lists = [] + total_seqs = 0 + encoder_max_len = 0 + + for request in requests: + # Perform inference on the request and append it to responses list... + in_0 = pb_utils.get_input_tensor_by_name(request, "encoder_out__0") + in_1 = pb_utils.get_input_tensor_by_name(request, "encoder_out_lens__1") + batch_encoder_out_list.append(in_0.as_numpy()) + encoder_max_len = max(encoder_max_len, batch_encoder_out_list[-1].shape[1]) + cur_b_lens = in_1.as_numpy() + batch_encoder_lens_list.append(cur_b_lens) + cur_batchsize = cur_b_lens.shape[0] + batchsize_lists.append(cur_batchsize) + total_seqs += cur_batchsize + + + encoder_out_array = np.zeros((total_seqs, encoder_max_len, self.encoder_dim), + dtype=self.data_type) + encoder_out_lens_array = np.zeros(total_seqs, dtype=np.int32) + st = 0 + for b in batchsize_lists: + t = batch_encoder_out_list.pop(0) + encoder_out_array[st:st + b, 0:t.shape[1]] = t + encoder_out_lens_array[st:st + b] = batch_encoder_lens_list.pop(0) + st += b + + encoder_out = torch.from_numpy(encoder_out_array) + + encoder_out_lens = torch.from_numpy(encoder_out_lens_array) + + packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence( + input=encoder_out, + lengths=encoder_out_lens.cpu(), + batch_first=True, + enforce_sorted=False + ) + pack_batch_size_list = packed_encoder_out.batch_sizes.tolist() + + hyps = [[self.blank_id] * self.context_size for _ in range(total_seqs)] + decoder_input = np.asarray(hyps,dtype=np.int32) + in_decoder_input_tensor = pb_utils.Tensor("decoder_input__0", decoder_input) + + inference_request = pb_utils.InferenceRequest( + model_name='decoder', + requested_output_names=['decoder_output__0'], + inputs=[in_decoder_input_tensor]) + + inference_response = inference_request.exec() + if inference_response.has_error(): + raise pb_utils.TritonModelException(inference_response.error().message()) + else: + # Extract the output tensors from the inference response. + decoder_out = pb_utils.get_output_tensor_by_name(inference_response, + 'decoder_output__0') + decoder_out = torch.utils.dlpack.from_dlpack(decoder_out.to_dlpack()).cpu().numpy() + #decoder_out = decoder_out.as_numpy() + + offset = 0 + + for batch_size in pack_batch_size_list: + + start = offset + end = offset + batch_size + current_encoder_out = packed_encoder_out.data[start:end] + current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1).cpu().numpy() + # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim) + + offset = end + + decoder_out = decoder_out[:batch_size] + + in_joiner_tensor_0 = pb_utils.Tensor("encoder_out__0", current_encoder_out) + in_joiner_tensor_1 = pb_utils.Tensor("decoder_out__1", np.expand_dims(decoder_out, axis=1)) + + inference_request = pb_utils.InferenceRequest( + model_name='joiner', + requested_output_names=['logit__0'], + inputs=[in_joiner_tensor_0, in_joiner_tensor_1]) + inference_response = inference_request.exec() + if inference_response.has_error(): + raise pb_utils.TritonModelException(inference_response.error().message()) + else: + # Extract the output tensors from the inference response. + logits = pb_utils.get_output_tensor_by_name(inference_response, + 'logit__0') + logits = torch.utils.dlpack.from_dlpack(logits.to_dlpack()).cpu().numpy() + + #logits = logits.as_numpy() + logits = torch.from_numpy(logits) + logits = logits.squeeze(1).squeeze(1) # (batch_size, vocab_size) + + assert logits.ndim == 2, logits.shape + y = logits.argmax(dim=1).tolist() + + emitted = False + for i, v in enumerate(y): + if v not in (self.blank_id, self.unk_id): + hyps[i].append(v) + emitted = True + if emitted: + # update decoder output + decoder_input = [h[-self.context_size:] for h in hyps[:batch_size]] + + decoder_input = np.asarray(decoder_input,dtype=np.int32) + + in_decoder_input_tensor = pb_utils.Tensor("decoder_input__0", decoder_input) + + inference_request = pb_utils.InferenceRequest( + model_name='decoder', + requested_output_names=['decoder_output__0'], + inputs=[in_decoder_input_tensor]) + + inference_response = inference_request.exec() + if inference_response.has_error(): + raise pb_utils.TritonModelException(inference_response.error().message()) + else: + # Extract the output tensors from the inference response. + decoder_out = pb_utils.get_output_tensor_by_name(inference_response, + 'decoder_output__0') + decoder_out = torch.utils.dlpack.from_dlpack(decoder_out.to_dlpack()).cpu().numpy() + #decoder_out = decoder_out.as_numpy() + + sorted_ans = [h[self.context_size:] for h in hyps] + ans = [] + unsorted_indices = packed_encoder_out.unsorted_indices.tolist() + for i in range(total_seqs): + ans.append(sorted_ans[unsorted_indices[i]]) + + results = [] + for hyp in self.sp.decode(ans): + results.append(hyp.split()) + + st = 0 + responses = [] + for b in batchsize_lists: + sents = np.array(results[st:st + b]) + out0 = pb_utils.Tensor("OUTPUT0", sents.astype(self.out0_dtype)) + inference_response = pb_utils.InferenceResponse(output_tensors=[out0]) + responses.append(inference_response) + st += b + return responses + + def finalize(self): + """`finalize` is called only once when the model is being unloaded. + Implementing `finalize` function is optional. This function allows + the model to perform any necessary clean ups before exit. + """ + print('Cleaning up...') \ No newline at end of file diff --git a/triton/model_repo/greedy_search/config.pbtxt b/triton/model_repo/greedy_search/config.pbtxt new file mode 100755 index 000000000..0d13c4277 --- /dev/null +++ b/triton/model_repo/greedy_search/config.pbtxt @@ -0,0 +1,65 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "greedy_search" +backend: "python" +max_batch_size: 512 + +parameters [ + { + key: "context_size", + value: { string_value: "2"} + }, + { + key: "bpe_model", + value: { string_value: "/ws/yuekaiz/icefall-asr-librispeech-pruned-transducer-stateless3-2022-04-29/data/lang_bpe_500/bpe.model"} + }, + { + key: "FORCE_CPU_ONLY_INPUT_TENSORS", + value: {string_value:"yes"} + } +] + + +input [ + { + name: "encoder_out__0" + data_type: TYPE_FP32 + dims: [-1, 512] # [-1, feature_size] + }, + { + name: "encoder_out_lens__1" + data_type: TYPE_INT32 + dims: [1] + reshape: { shape: [ ] } + } +] + +output [ + { + name: "OUTPUT0" + data_type: TYPE_STRING + dims: [1] + } +] + +dynamic_batching { + preferred_batch_size: [ 16, 32 ] + } +instance_group [ + { + count: 2 + kind: KIND_CPU + } + ] diff --git a/triton/model_repo/joiner/config.pbtxt b/triton/model_repo/joiner/config.pbtxt new file mode 100755 index 000000000..e55ec401c --- /dev/null +++ b/triton/model_repo/joiner/config.pbtxt @@ -0,0 +1,63 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "joiner" +backend: "pytorch" +default_model_filename: "joiner_jit.pt" + +max_batch_size: 512 +input [ + { + name: "encoder_out__0" + data_type: TYPE_FP32 + dims: [1,1,512] + }, + { + name: "decoder_out__1" + data_type: TYPE_FP32 + dims: [1,1,512] + } +] + +output [ + { + name: "logit__0" + data_type: TYPE_FP32 + dims: [1,1,500] + } +] + +dynamic_batching { + preferred_batch_size: [ 16, 32 ] + } + +instance_group [ + { + count: 1 + kind: KIND_GPU + } +] + +parameters: { +key: "INFERENCE_MODE" + value: { + string_value:"true" + } +} +parameters: { +key: "DISABLE_OPTIMIZED_EXECUTION" + value: { + string_value:"true" + } +} \ No newline at end of file diff --git a/triton/scripts/conformer_triton.py b/triton/scripts/conformer_triton.py new file mode 100755 index 000000000..553c050d5 --- /dev/null +++ b/triton/scripts/conformer_triton.py @@ -0,0 +1,1066 @@ +#!/usr/bin/env python3 +# Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import math +import warnings +from typing import Optional, Tuple + +import torch +from encoder_interface import EncoderInterface +from scaling import ( + ActivationBalancer, + BasicNorm, + DoubleSwish, + ScaledConv1d, + ScaledConv2d, + ScaledLinear, +) +from torch import Tensor, nn + +#from icefall.utils import make_pad_mask +def make_pad_mask(lengths: torch.Tensor) -> torch.Tensor: + """ + Args: + lengths: + A 1-D tensor containing sentence lengths. + Returns: + Return a 2-D bool tensor, where masked positions + are filled with `True` and non-masked positions are + filled with `False`. + + >>> lengths = torch.tensor([1, 3, 2, 5]) + >>> make_pad_mask(lengths) + tensor([[False, True, True, True, True], + [False, False, False, True, True], + [False, False, True, True, True], + [False, False, False, False, False]]) + """ + assert lengths.ndim == 1, lengths.ndim + + max_len = lengths.max() + n = lengths.size(0) + + expaned_lengths = torch.arange(max_len).expand(n, max_len).to(lengths) + + return expaned_lengths >= lengths.unsqueeze(1) + +class Conformer(EncoderInterface): + """ + Args: + num_features (int): Number of input features + subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers) + d_model (int): attention dimension, also the output dimension + nhead (int): number of head + dim_feedforward (int): feedforward dimention + num_encoder_layers (int): number of encoder layers + dropout (float): dropout rate + layer_dropout (float): layer-dropout rate. + cnn_module_kernel (int): Kernel size of convolution module + vgg_frontend (bool): whether to use vgg frontend. + """ + + def __init__( + self, + num_features: int, + subsampling_factor: int = 4, + d_model: int = 256, + nhead: int = 4, + dim_feedforward: int = 2048, + num_encoder_layers: int = 12, + dropout: float = 0.1, + layer_dropout: float = 0.075, + cnn_module_kernel: int = 31, + ) -> None: + super(Conformer, self).__init__() + + self.num_features = num_features + self.subsampling_factor = subsampling_factor + if subsampling_factor != 4: + raise NotImplementedError("Support only 'subsampling_factor=4'.") + + # self.encoder_embed converts the input of shape (N, T, num_features) + # to the shape (N, T//subsampling_factor, d_model). + # That is, it does two things simultaneously: + # (1) subsampling: T -> T//subsampling_factor + # (2) embedding: num_features -> d_model + self.encoder_embed = Conv2dSubsampling(num_features, d_model) + + self.encoder_pos = RelPositionalEncoding(d_model, dropout) + + encoder_layer = ConformerEncoderLayer( + d_model, + nhead, + dim_feedforward, + dropout, + layer_dropout, + cnn_module_kernel, + ) + self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers) + + def forward( + self, x: torch.Tensor, x_lens: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + x: + The input tensor. Its shape is (batch_size, seq_len, feature_dim). + x_lens: + A tensor of shape (batch_size,) containing the number of frames in + `x` before padding. + warmup: + A floating point value that gradually increases from 0 throughout + training; when it is >= 1.0 we are "fully warmed up". It is used + to turn modules on sequentially. + Returns: + Return a tuple containing 2 tensors: + - embeddings: its shape is (batch_size, output_seq_len, d_model) + - lengths, a tensor of shape (batch_size,) containing the number + of frames in `embeddings` before padding. + """ + warmup = 1.0 + x = self.encoder_embed(x) + x, pos_emb = self.encoder_pos(x) + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + + # Caution: We assume the subsampling factor is 4! + + # lengths = ((x_lens - 1) // 2 - 1) // 2 # issue an warning + # + # Note: rounding_mode in torch.div() is available only in torch >= 1.8.0 + lengths = (((x_lens - 1) >> 1) - 1) >> 1 + + assert x.size(0) == lengths.max().item() + mask = make_pad_mask(lengths) + + x = self.encoder( + x, pos_emb, src_key_padding_mask=mask, warmup=warmup + ) # (T, N, C) + + x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + + return x, lengths + + +class ConformerEncoderLayer(nn.Module): + """ + ConformerEncoderLayer is made up of self-attn, feedforward and convolution networks. + See: "Conformer: Convolution-augmented Transformer for Speech Recognition" + + Args: + d_model: the number of expected features in the input (required). + nhead: the number of heads in the multiheadattention models (required). + dim_feedforward: the dimension of the feedforward network model (default=2048). + dropout: the dropout value (default=0.1). + cnn_module_kernel (int): Kernel size of convolution module. + + Examples:: + >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) + >>> src = torch.rand(10, 32, 512) + >>> pos_emb = torch.rand(32, 19, 512) + >>> out = encoder_layer(src, pos_emb) + """ + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + layer_dropout: float = 0.075, + cnn_module_kernel: int = 31, + ) -> None: + super(ConformerEncoderLayer, self).__init__() + + self.layer_dropout = layer_dropout + + self.d_model = d_model + + self.self_attn = RelPositionMultiheadAttention( + d_model, nhead, dropout=0.0 + ) + + self.feed_forward = nn.Sequential( + ScaledLinear(d_model, dim_feedforward), + ActivationBalancer(channel_dim=-1), + DoubleSwish(), + nn.Dropout(dropout), + ScaledLinear(dim_feedforward, d_model, initial_scale=0.25), + ) + + self.feed_forward_macaron = nn.Sequential( + ScaledLinear(d_model, dim_feedforward), + ActivationBalancer(channel_dim=-1), + DoubleSwish(), + nn.Dropout(dropout), + ScaledLinear(dim_feedforward, d_model, initial_scale=0.25), + ) + + self.conv_module = ConvolutionModule(d_model, cnn_module_kernel) + + self.norm_final = BasicNorm(d_model) + + # try to ensure the output is close to zero-mean (or at least, zero-median). + self.balancer = ActivationBalancer( + channel_dim=-1, min_positive=0.45, max_positive=0.55, max_abs=6.0 + ) + + self.dropout = nn.Dropout(dropout) + + def forward( + self, + src: Tensor, + pos_emb: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + warmup: float = 1.0, + ) -> Tensor: + """ + Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + pos_emb: Positional embedding tensor (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + warmup: controls selective bypass of of layers; if < 1.0, we will + bypass layers more frequently. + + Shape: + src: (S, N, E). + pos_emb: (N, 2*S-1, E) + src_mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, N is the batch size, E is the feature number + """ + src_orig = src + + warmup_scale = min(0.1 + warmup, 1.0) + # alpha = 1.0 means fully use this encoder layer, 0.0 would mean + # completely bypass it. + if self.training: + alpha = ( + warmup_scale + if torch.rand(()).item() <= (1.0 - self.layer_dropout) + else 0.1 + ) + else: + alpha = 1.0 + + # macaron style feed forward module + src = src + self.dropout(self.feed_forward_macaron(src)) + + # multi-headed self-attention module + src_att = self.self_attn( + src, + src, + src, + pos_emb=pos_emb, + attn_mask=src_mask, + key_padding_mask=src_key_padding_mask, + )[0] + src = src + self.dropout(src_att) + + # convolution module + src = src + self.dropout(self.conv_module(src)) + + # feed forward module + src = src + self.dropout(self.feed_forward(src)) + + src = self.norm_final(self.balancer(src)) + + if alpha != 1.0: + src = alpha * src + (1 - alpha) * src_orig + + return src + + +class ConformerEncoder(nn.Module): + r"""ConformerEncoder is a stack of N encoder layers + + Args: + encoder_layer: an instance of the ConformerEncoderLayer() class (required). + num_layers: the number of sub-encoder-layers in the encoder (required). + + Examples:: + >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) + >>> conformer_encoder = ConformerEncoder(encoder_layer, num_layers=6) + >>> src = torch.rand(10, 32, 512) + >>> pos_emb = torch.rand(32, 19, 512) + >>> out = conformer_encoder(src, pos_emb) + """ + + def __init__(self, encoder_layer: nn.Module, num_layers: int) -> None: + super().__init__() + self.layers = nn.ModuleList( + [copy.deepcopy(encoder_layer) for i in range(num_layers)] + ) + self.num_layers = num_layers + + def forward( + self, + src: Tensor, + pos_emb: Tensor, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + warmup: float = 1.0, + ) -> Tensor: + r"""Pass the input through the encoder layers in turn. + + Args: + src: the sequence to the encoder (required). + pos_emb: Positional embedding tensor (required). + mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + src: (S, N, E). + pos_emb: (N, 2*S-1, E) + mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number + + """ + output = src + + for i, mod in enumerate(self.layers): + output = mod( + output, + pos_emb, + src_mask=mask, + src_key_padding_mask=src_key_padding_mask, + warmup=warmup, + ) + + return output + + +class RelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module. + + See : Appendix B in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" + Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py + + Args: + d_model: Embedding dimension. + dropout_rate: Dropout rate. + max_len: Maximum input length. + + """ + + def __init__( + self, d_model: int, dropout_rate: float, max_len: int = 5000 + ) -> None: + """Construct an PositionalEncoding object.""" + super(RelPositionalEncoding, self).__init__() + self.d_model = d_model + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: Tensor) -> None: + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + # Note: TorchScript doesn't implement operator== for torch.Device + if self.pe.dtype != x.dtype or str(self.pe.device) != str( + x.device + ): + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[Tensor, Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Encoded tensor (batch, 2*time-1, `*`). + + """ + self.extend_pe(x) + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 + - x.size(1) + + 1 : self.pe.size(1) // 2 # noqa E203 + + x.size(1), + ] + return self.dropout(x), self.dropout(pos_emb) + + +class RelPositionMultiheadAttention(nn.Module): + r"""Multi-Head Attention layer with relative position encoding + + See reference: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" + + Args: + embed_dim: total dimension of the model. + num_heads: parallel attention heads. + dropout: a Dropout layer on attn_output_weights. Default: 0.0. + + Examples:: + + >>> rel_pos_multihead_attn = RelPositionMultiheadAttention(embed_dim, num_heads) + >>> attn_output, attn_output_weights = multihead_attn(query, key, value, pos_emb) + """ + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + ) -> None: + super(RelPositionMultiheadAttention, self).__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + + self.in_proj = ScaledLinear(embed_dim, 3 * embed_dim, bias=True) + self.out_proj = ScaledLinear( + embed_dim, embed_dim, bias=True, initial_scale=0.25 + ) + + # linear transformation for positional encoding. + self.linear_pos = ScaledLinear(embed_dim, embed_dim, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(num_heads, self.head_dim)) + self.pos_bias_v = nn.Parameter(torch.Tensor(num_heads, self.head_dim)) + self.pos_bias_u_scale = nn.Parameter(torch.zeros(()).detach()) + self.pos_bias_v_scale = nn.Parameter(torch.zeros(()).detach()) + self._reset_parameters() + + def _pos_bias_u(self): + return self.pos_bias_u * self.pos_bias_u_scale.exp() + + def _pos_bias_v(self): + return self.pos_bias_v * self.pos_bias_v_scale.exp() + + def _reset_parameters(self) -> None: + nn.init.normal_(self.pos_bias_u, std=0.01) + nn.init.normal_(self.pos_bias_v, std=0.01) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: Tensor, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + pos_emb: Positional embedding tensor + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. When given a binary mask and a value is True, + the corresponding value on the attention layer will be ignored. When given + a byte mask and a value is non-zero, the corresponding value on the attention + layer will be ignored + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + + Shape: + - Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - pos_emb: :math:`(N, 2*L-1, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the position + with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + + - Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + return self.multi_head_attention_forward( + query, + key, + value, + pos_emb, + self.embed_dim, + self.num_heads, + self.in_proj.get_weight(), + self.in_proj.get_bias(), + self.dropout, + self.out_proj.get_weight(), + self.out_proj.get_bias(), + training=self.training, + key_padding_mask=key_padding_mask, + need_weights=need_weights, + attn_mask=attn_mask, + ) + + def rel_shift(self, x: Tensor) -> Tensor: + """Compute relative positional encoding. + + Args: + x: Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + Tensor: tensor of shape (batch, head, time1, time2) + (note: time2 has the same value as time1, but it is for + the key, while time1 is for the query). + """ + (batch_size, num_heads, time1, n) = x.shape + assert n == 2 * time1 - 1 + # Note: TorchScript requires explicit arg for stride() + batch_stride = x.stride(0) + head_stride = x.stride(1) + time1_stride = x.stride(2) + n_stride = x.stride(3) + return x.as_strided( + (batch_size, num_heads, time1, time1), + (batch_stride, head_stride, time1_stride - n_stride, n_stride), + storage_offset=n_stride * (time1 - 1), + ) + + def multi_head_attention_forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: Tensor, + embed_dim_to_check: int, + num_heads: int, + in_proj_weight: Tensor, + in_proj_bias: Tensor, + dropout_p: float, + out_proj_weight: Tensor, + out_proj_bias: Tensor, + training: bool = True, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + pos_emb: Positional embedding tensor + embed_dim_to_check: total dimension of the model. + num_heads: parallel attention heads. + in_proj_weight, in_proj_bias: input projection weight and bias. + dropout_p: probability of an element to be zeroed. + out_proj_weight, out_proj_bias: the output projection weight and bias. + training: apply dropout if is ``True``. + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. This is an binary mask. When the value is True, + the corresponding value on the attention layer will be filled with -inf. + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + + Shape: + Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - pos_emb: :math:`(N, 2*L-1, E)` or :math:`(1, 2*L-1, E)` where L is the target sequence + length, N is the batch size, E is the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions + will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + + Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == embed_dim_to_check + assert key.size(0) == value.size(0) and key.size(1) == value.size(1) + + head_dim = embed_dim // num_heads + assert ( + head_dim * num_heads == embed_dim + ), "embed_dim must be divisible by num_heads" + + scaling = float(head_dim) ** -0.5 + + if torch.equal(query, key) and torch.equal(key, value): + # self-attention + q, k, v = nn.functional.linear( + query, in_proj_weight, in_proj_bias + ).chunk(3, dim=-1) + + elif torch.equal(key, value): + # encoder-decoder attention + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = nn.functional.linear(query, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + k, v = nn.functional.linear(key, _w, _b).chunk(2, dim=-1) + + else: + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = nn.functional.linear(query, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = embed_dim * 2 + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + k = nn.functional.linear(key, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim * 2 + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + v = nn.functional.linear(value, _w, _b) + + if attn_mask is not None: + assert ( + attn_mask.dtype == torch.float32 + or attn_mask.dtype == torch.float64 + or attn_mask.dtype == torch.float16 + or attn_mask.dtype == torch.uint8 + or attn_mask.dtype == torch.bool + ), "Only float, byte, and bool types are supported for attn_mask, not {}".format( + attn_mask.dtype + ) + if attn_mask.dtype == torch.uint8: + warnings.warn( + "Byte tensor for attn_mask is deprecated. Use bool tensor instead." + ) + attn_mask = attn_mask.to(torch.bool) + + if attn_mask.dim() == 2: + attn_mask = attn_mask.unsqueeze(0) + if list(attn_mask.size()) != [1, query.size(0), key.size(0)]: + raise RuntimeError( + "The size of the 2D attn_mask is not correct." + ) + elif attn_mask.dim() == 3: + if list(attn_mask.size()) != [ + bsz * num_heads, + query.size(0), + key.size(0), + ]: + raise RuntimeError( + "The size of the 3D attn_mask is not correct." + ) + else: + raise RuntimeError( + "attn_mask's dimension {} is not supported".format( + attn_mask.dim() + ) + ) + # attn_mask's dim is 3 now. + + # convert ByteTensor key_padding_mask to bool + if ( + key_padding_mask is not None + and key_padding_mask.dtype == torch.uint8 + ): + warnings.warn( + "Byte tensor for key_padding_mask is deprecated. Use bool tensor instead." + ) + key_padding_mask = key_padding_mask.to(torch.bool) + + q = (q * scaling).contiguous().view(tgt_len, bsz, num_heads, head_dim) + k = k.contiguous().view(-1, bsz, num_heads, head_dim) + v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1) + + src_len = k.size(0) + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz, "{} == {}".format( + key_padding_mask.size(0), bsz + ) + assert key_padding_mask.size(1) == src_len, "{} == {}".format( + key_padding_mask.size(1), src_len + ) + + q = q.transpose(0, 1) # (batch, time1, head, d_k) + + pos_emb_bsz = pos_emb.size(0) + assert pos_emb_bsz in (1, bsz) # actually it is 1 + p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim) + p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) + + q_with_bias_u = (q + self._pos_bias_u()).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + + q_with_bias_v = (q + self._pos_bias_v()).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + + # compute attention score + # first compute matrix a and matrix c + # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 + k = k.permute(1, 2, 3, 0) # (batch, head, d_k, time2) + matrix_ac = torch.matmul( + q_with_bias_u, k + ) # (batch, head, time1, time2) + + # compute matrix b and matrix d + matrix_bd = torch.matmul( + q_with_bias_v, p.transpose(-2, -1) + ) # (batch, head, time1, 2*time1-1) + matrix_bd = self.rel_shift(matrix_bd) + + attn_output_weights = ( + matrix_ac + matrix_bd + ) # (batch, head, time1, time2) + + attn_output_weights = attn_output_weights.view( + bsz * num_heads, tgt_len, -1 + ) + + assert list(attn_output_weights.size()) == [ + bsz * num_heads, + tgt_len, + src_len, + ] + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + attn_output_weights.masked_fill_(attn_mask, float("-inf")) + else: + attn_output_weights += attn_mask + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + attn_output_weights = attn_output_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2), + float("-inf"), + ) + attn_output_weights = attn_output_weights.view( + bsz * num_heads, tgt_len, src_len + ) + + attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1) + attn_output_weights = nn.functional.dropout( + attn_output_weights, p=dropout_p, training=training + ) + + attn_output = torch.bmm(attn_output_weights, v) + assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim] + attn_output = ( + attn_output.transpose(0, 1) + .contiguous() + .view(tgt_len, bsz, embed_dim) + ) + attn_output = nn.functional.linear( + attn_output, out_proj_weight, out_proj_bias + ) + + if need_weights: + # average attention weights over heads + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + return attn_output, attn_output_weights.sum(dim=1) / num_heads + else: + return attn_output, None + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model. + Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py + + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernerl size of conv layers. + bias (bool): Whether to use bias in conv layers (default=True). + + """ + + def __init__( + self, channels: int, kernel_size: int, bias: bool = True + ) -> None: + """Construct an ConvolutionModule object.""" + super(ConvolutionModule, self).__init__() + # kernerl_size should be a odd number for 'SAME' padding + assert (kernel_size - 1) % 2 == 0 + + self.pointwise_conv1 = ScaledConv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + + # after pointwise_conv1 we put x through a gated linear unit (nn.functional.glu). + # For most layers the normal rms value of channels of x seems to be in the range 1 to 4, + # but sometimes, for some reason, for layer 0 the rms ends up being very large, + # between 50 and 100 for different channels. This will cause very peaky and + # sparse derivatives for the sigmoid gating function, which will tend to make + # the loss function not learn effectively. (for most layers the average absolute values + # are in the range 0.5..9.0, and the average p(x>0), i.e. positive proportion, + # at the output of pointwise_conv1.output is around 0.35 to 0.45 for different + # layers, which likely breaks down as 0.5 for the "linear" half and + # 0.2 to 0.3 for the part that goes into the sigmoid. The idea is that if we + # constrain the rms values to a reasonable range via a constraint of max_abs=10.0, + # it will be in a better position to start learning something, i.e. to latch onto + # the correct range. + self.deriv_balancer1 = ActivationBalancer( + channel_dim=1, max_abs=10.0, min_positive=0.05, max_positive=1.0 + ) + + self.depthwise_conv = ScaledConv1d( + channels, + channels, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + groups=channels, + bias=bias, + ) + + self.deriv_balancer2 = ActivationBalancer( + channel_dim=1, min_positive=0.05, max_positive=1.0 + ) + + self.activation = DoubleSwish() + + self.pointwise_conv2 = ScaledConv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + initial_scale=0.25, + ) + + def forward(self, x: Tensor) -> Tensor: + """Compute convolution module. + + Args: + x: Input tensor (#time, batch, channels). + + Returns: + Tensor: Output tensor (#time, batch, channels). + + """ + # exchange the temporal dimension and the feature dimension + x = x.permute(1, 2, 0) # (#batch, channels, time). + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channels, time) + + x = self.deriv_balancer1(x) + x = nn.functional.glu(x, dim=1) # (batch, channels, time) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + + x = self.deriv_balancer2(x) + x = self.activation(x) + + x = self.pointwise_conv2(x) # (batch, channel, time) + + return x.permute(2, 0, 1) + + +class Conv2dSubsampling(nn.Module): + """Convolutional 2D subsampling (to 1/4 length). + + Convert an input of shape (N, T, idim) to an output + with shape (N, T', odim), where + T' = ((T-1)//2 - 1)//2, which approximates T' == T//4 + + It is based on + https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py # noqa + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + layer1_channels: int = 8, + layer2_channels: int = 32, + layer3_channels: int = 128, + ) -> None: + """ + Args: + in_channels: + Number of channels in. The input shape is (N, T, in_channels). + Caution: It requires: T >=7, in_channels >=7 + out_channels + Output dim. The output shape is (N, ((T-1)//2 - 1)//2, out_channels) + layer1_channels: + Number of channels in layer1 + layer1_channels: + Number of channels in layer2 + """ + assert in_channels >= 7 + super().__init__() + + self.conv = nn.Sequential( + ScaledConv2d( + in_channels=1, + out_channels=layer1_channels, + kernel_size=3, + padding=1, + ), + ActivationBalancer(channel_dim=1), + DoubleSwish(), + ScaledConv2d( + in_channels=layer1_channels, + out_channels=layer2_channels, + kernel_size=3, + stride=2, + ), + ActivationBalancer(channel_dim=1), + DoubleSwish(), + ScaledConv2d( + in_channels=layer2_channels, + out_channels=layer3_channels, + kernel_size=3, + stride=2, + ), + ActivationBalancer(channel_dim=1), + DoubleSwish(), + ) + self.out = ScaledLinear( + layer3_channels * (((in_channels - 1) // 2 - 1) // 2), out_channels + ) + # set learn_eps=False because out_norm is preceded by `out`, and `out` + # itself has learned scale, so the extra degree of freedom is not + # needed. + self.out_norm = BasicNorm(out_channels, learn_eps=False) + # constrain median of output to be close to zero. + self.out_balancer = ActivationBalancer( + channel_dim=-1, min_positive=0.45, max_positive=0.55 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Subsample x. + + Args: + x: + Its shape is (N, T, idim). + + Returns: + Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim) + """ + # On entry, x is (N, T, idim) + x = x.unsqueeze(1) # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W) + x = self.conv(x) + # Now x is of shape (N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + # Now x is of shape (N, ((T-1)//2 - 1))//2, odim) + x = self.out_norm(x) + x = self.out_balancer(x) + return x + + +if __name__ == "__main__": + feature_dim = 50 + c = Conformer(num_features=feature_dim, d_model=128, nhead=4) + batch_size = 5 + seq_len = 20 + # Just make sure the forward pass runs. + f = c( + torch.randn(batch_size, seq_len, feature_dim), + torch.full((batch_size,), seq_len, dtype=torch.int64), + warmup=0.5, + ) diff --git a/triton/scripts/export_jit.py b/triton/scripts/export_jit.py new file mode 100755 index 000000000..ba33709cb --- /dev/null +++ b/triton/scripts/export_jit.py @@ -0,0 +1,422 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts a pretrained transducer model into three jit models: +# encoder_jit.pt decoder_jit.pt joiner_jit.pt +""" +Usage: +./pruned_transducer_stateless3/export_jit.py \ + --pretrained-model ./pruned_transducer_stateless3/exp \ + --output-dir ./pruned_transducer_stateless3/exp \ + --bpe-model ./bpe.model + +It will generate three jit files under ouput_dir, then you should put these +models under corresponding trtion mdoel_repo modules. + +""" + +import argparse +import logging +import os + +import sentencepiece as spm +import torch +from torch import nn + +import torch.nn.functional as F +from scaling import ScaledConv1d, ScaledEmbedding, ScaledLinear + +from conformer_triton import Conformer + +class Decoder(nn.Module): + """This class modifies the stateless decoder from the following paper: + + RNN-transducer with stateless prediction network + https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419 + + It removes the recurrent connection from the decoder, i.e., the prediction + network. Different from the above paper, it adds an extra Conv1d + right after the embedding layer. + + TODO: Implement https://arxiv.org/pdf/2109.07513.pdf + """ + + def __init__( + self, + vocab_size: int, + decoder_dim: int, + blank_id: int, + context_size: int, + ): + """ + Args: + vocab_size: + Number of tokens of the modeling unit including blank. + decoder_dim: + Dimension of the input embedding, and of the decoder output. + blank_id: + The ID of the blank symbol. + context_size: + Number of previous words to use to predict the next word. + 1 means bigram; 2 means trigram. n means (n+1)-gram. + """ + super().__init__() + + self.embedding = ScaledEmbedding( + num_embeddings=vocab_size, + embedding_dim=decoder_dim, + padding_idx=blank_id, + ) + self.blank_id = blank_id + + assert context_size >= 1, context_size + self.context_size = context_size + self.vocab_size = vocab_size + if context_size > 1: + self.conv = ScaledConv1d( + in_channels=decoder_dim, + out_channels=decoder_dim, + kernel_size=context_size, + padding=0, + groups=decoder_dim, + bias=False, + ) + + def forward(self, y: torch.Tensor) -> torch.Tensor: + """ + Args: + y: + A 2-D tensor of shape (N, U). + need_pad: + True to left pad the input. Should be True during training. + False to not pad the input. Should be False during inference. + Returns: + Return a tensor of shape (N, U, decoder_dim). + """ + y = y.to(torch.int64) + embedding_out = self.embedding(y) + if self.context_size > 1: + embedding_out = embedding_out.permute(0, 2, 1) + + # During inference time, there is no need to do extra padding + # as we only need one output + assert embedding_out.size(-1) == self.context_size + embedding_out = self.conv(embedding_out) + embedding_out = embedding_out.permute(0, 2, 1) + embedding_out = F.relu(embedding_out) + return embedding_out + +class Joiner(nn.Module): + def __init__( + self, + encoder_dim: int, + decoder_dim: int, + joiner_dim: int, + vocab_size: int, + ): + super().__init__() + + self.encoder_proj = ScaledLinear(encoder_dim, joiner_dim) + self.decoder_proj = ScaledLinear(decoder_dim, joiner_dim) + self.output_linear = ScaledLinear(joiner_dim, vocab_size) + + def forward( + self, + encoder_out: torch.Tensor, + decoder_out: torch.Tensor, + ) -> torch.Tensor: + """ + Args: + encoder_out: + Output from the encoder. Its shape is (N, T, s_range, C). + decoder_out: + Output from the decoder. Its shape is (N, T, s_range, C). + project_input: + If true, apply input projections encoder_proj and decoder_proj. + If this is false, it is the user's responsibility to do this + manually. + Returns: + Return a tensor of shape (N, T, s_range, C). + """ + + assert encoder_out.ndim == decoder_out.ndim == 4 + assert encoder_out.shape[:-1] == decoder_out.shape[:-1] + + + logit = self.encoder_proj(encoder_out) + self.decoder_proj( + decoder_out + ) + + + logit = self.output_linear(torch.tanh(logit)) + + return logit + +class AttributeDict(dict): + def __getattr__(self, key): + if key in self: + return self[key] + raise AttributeError(f"No such attribute '{key}'") + + def __setattr__(self, key, value): + self[key] = value + + def __delattr__(self, key): + if key in self: + del self[key] + return + raise AttributeError(f"No such attribute '{key}'") + +def str2bool(v): + """Used in argparse.ArgumentParser.add_argument to indicate + that a type is a bool type and user can enter + + - yes, true, t, y, 1, to represent True + - no, false, f, n, 0, to represent False + + See https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse # noqa + """ + if isinstance(v, bool): + return v + if v.lower() in ("yes", "true", "t", "y", "1"): + return True + elif v.lower() in ("no", "false", "f", "n", "0"): + return False + else: + raise argparse.ArgumentTypeError("Boolean value expected.") + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + "encoder_dim": 512, + "nhead": 8, + "dim_feedforward": 2048, + "num_encoder_layers": 12, + # parameters for decoder + "decoder_dim": 512, + # parameters for joiner + "joiner_dim": 512, + # parameters for Noam + "model_warm_step": 3000, # arg given to model, not for lrate + "env_info": {}, # remove k2 etc dependency + } + ) + + return params + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Conformer and Transformer + encoder = Conformer( + num_features=params.feature_dim, + subsampling_factor=params.subsampling_factor, + d_model=params.encoder_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + ) + return encoder + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--pretrained-model", + type=str, + required=True, + help="""It specifies the path of input pretrained_transducer.pt torch model. + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--output-dir", + type=str, + default="exp", + help="""It specifies the directory where output jit models located + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + return parser + + +def main(): + args = get_parser().parse_args() + + params = get_params() + params.update(vars(args)) + + assert torch.cuda.is_available() + # for cpu models, you need to modify the config.pbtxt files under model_repo + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create models") + + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + checkpoint = torch.load(args.pretrained_model, map_location="cpu") + + # remove the prefix, e.g. encoder.encoder.layre1.bias --> encoder.layer1.bias + for old_key in list(checkpoint["model"].keys()): + key_list = old_key.split(".")[1:] + if len(key_list) > 1: + new_key = ".".join(key_list) + else: + new_key = old_key + checkpoint["model"][new_key] = checkpoint["model"].pop(old_key) + + miss_keys,_ = encoder.load_state_dict(checkpoint["model"], strict=False) + assert len(miss_keys) == 0 + miss_keys,_ = decoder.load_state_dict(checkpoint["model"], strict=False) + assert len(miss_keys) == 0 + miss_keys,_ = joiner.load_state_dict(checkpoint["model"], strict=False) + assert len(miss_keys) == 0 + + encoder.cuda() + encoder.eval() + + decoder.cuda() + decoder.eval() + + joiner.cuda() + joiner.eval() + + + os.makedirs(args.output_dir, exist_ok=True) + encoder = torch.jit.script(encoder) + filename = args.output_dir + "/encoder_jit.pt" + encoder.save(filename) + logging.info("Export encoder jit finished.") + + decoder = torch.jit.script(decoder) + filename = args.output_dir + "/decoder_jit.pt" + decoder.save(filename) + logging.info("Export decoder jit finished.") + + joiner = torch.jit.script(joiner) + filename = args.output_dir + "/joiner_jit.pt" + joiner.save(filename) + logging.info("Export joiner jit finished.") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/triton/scripts/start_server.sh b/triton/scripts/start_server.sh new file mode 100755 index 000000000..55fcbaac4 --- /dev/null +++ b/triton/scripts/start_server.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +jit_model_dir=/ws/jit_model +model_repo=/ws/model_repo + +cp $jit_model_dir/encoder_jit.pt $model_rep/encoder/1 +cp $jit_model_dir/decoder_jit.pt $model_rep/decoder/1 +cp $jit_model_dir/joiner_jit.pt $model_rep/joiner/1 + + +# Start server +tritonserver --model-repository=$model_repo \ No newline at end of file From c58e1474eb012b4feab13717149852409476f088 Mon Sep 17 00:00:00 2001 From: yuekaizhang Date: Sun, 29 May 2022 09:13:31 -0400 Subject: [PATCH 2/2] Dockerfile update, replace wer, hardcode path change etc. --- triton/Dockerfile/Dockerfile.client | 2 + triton/Dockerfile/Dockerfile.server | 1 + triton/README.md | 5 +- triton/client/1089-134686-0001.json | 1 - triton/client/client.py | 20 +- triton/client/utils.py | 236 +++++++++++++----- .../model_repo/feature_extractor/1/model.py | 26 +- triton/model_repo/greedy_search/config.pbtxt | 4 +- triton/scripts/start_server.sh | 8 +- 9 files changed, 227 insertions(+), 76 deletions(-) delete mode 100755 triton/client/1089-134686-0001.json diff --git a/triton/Dockerfile/Dockerfile.client b/triton/Dockerfile/Dockerfile.client index c4f393343..83bf3052b 100755 --- a/triton/Dockerfile/Dockerfile.client +++ b/triton/Dockerfile/Dockerfile.client @@ -4,4 +4,6 @@ LABEL repository="tritonserver" RUN apt-get update && apt-get install -y libsndfile1 RUN pip3 install soundfile +RUN pip3 install kaldialign + WORKDIR /workspace diff --git a/triton/Dockerfile/Dockerfile.server b/triton/Dockerfile/Dockerfile.server index 1cb0e6440..2663b5280 100755 --- a/triton/Dockerfile/Dockerfile.server +++ b/triton/Dockerfile/Dockerfile.server @@ -5,6 +5,7 @@ LABEL repository="tritonserver" RUN apt-get update && apt-get -y install swig && apt-get -y install python3-dev && apt-get install -y cmake RUN pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html RUN pip3 install -v kaldifeat +RUN pip3 install sentencepiece RUN pip3 install pyyaml WORKDIR /workspace COPY ./scripts scripts diff --git a/triton/README.md b/triton/README.md index 350843a04..8175d4298 100755 --- a/triton/README.md +++ b/triton/README.md @@ -60,6 +60,9 @@ cp $SHERPA/triton/scripts/export_jit.py $ICEFALL_DIR/egs/librispeech/ASR/pruned_ cd $ICEFALL_DIR/egs/librispeech/ASR/pruned_stateless_transducer3 python3 export_jit.py --pretrained-model --output-dir --bpe-model + +# copy bpe file to , later we would mount to the triton docker container +cp ``` @@ -221,7 +224,7 @@ The first argument is the path of audio file used for testing, and the second ar Then in client docker container, we can simply run the testing: ```bash -perf_analyzer -m conformer_transducer -b 1 -a -p 20000 --concurrency-range 100:200:50 -i gRPC --input-data=./input.json -u localhost:8001 +perf_analyzer -m conformer_transducer -b 1 -a -p 20000 --concurrency-range 100:200:50 -i gRPC --input-data= -u localhost:8001 ``` Where: diff --git a/triton/client/1089-134686-0001.json b/triton/client/1089-134686-0001.json deleted file mode 100755 index 9327037d3..000000000 --- a/triton/client/1089-134686-0001.json +++ /dev/null @@ -1 +0,0 @@ -{"data": [{"WAV_LENS": [106000], "WAV": {"shape": [106000], "content": [0.0003662109375, 0.000640869140625, 0.000762939453125, 0.0008544921875, 0.00079345703125, 0.000640869140625, 0.000518798828125, 0.000152587890625, -6.103515625e-05, 0.0, 0.0, 6.103515625e-05, 0.00018310546875, 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required=False, + default="./wer_results.txt", + help='output of wer anaylasis' + ) FLAGS = parser.parse_args() @@ -122,6 +130,12 @@ def single_job(client_files): predictions = pool.map(single_job, tasks) predictions = [item for sublist in predictions for item in sublist] + results = [] if transcripts: - cer = cal_cer(predictions, transcripts) - print("CER is: {}".format(cer)) + assert len(transcripts) == len(predictions) + for i in range(len(transcripts)): + results.append((transcripts[i], predictions[i])) + with open(FLAGS.errs_file, "w") as f: + wer = write_error_stats( + f, f"Testset", results, enable_log=True + ) \ No newline at end of file diff --git a/triton/client/utils.py b/triton/client/utils.py index 66415ec64..d15fb6156 100755 --- a/triton/client/utils.py +++ b/triton/client/utils.py @@ -1,59 +1,181 @@ -import numpy as np - -def _levenshtein_distance(ref, hyp): - """Levenshtein distance is a string metric for measuring the difference - between two sequences. Informally, the levenshtein disctance is defined as - the minimum number of single-character edits (substitutions, insertions or - deletions) required to change one word into the other. We can naturally - extend the edits to word level when calculate levenshtein disctance for - two sentences. +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang +# Mingshuang Luo) +# +# See ../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from collections import defaultdict +from typing import Dict, List, TextIO, Tuple + +import kaldialign + + +# https://github.com/k2-fsa/icefall/blob/master/icefall/utils.py#L336 +# Directly copy from above link, would remove this copy after dockerfile update +def write_error_stats( + f: TextIO, + test_set_name: str, + results: List[Tuple[str, str]], + enable_log: bool = True, +) -> float: + """Write statistics based on predicted results and reference transcripts. + It will write the following to the given file: + - WER + - number of insertions, deletions, substitutions, corrects and total + reference words. For example:: + Errors: 23 insertions, 57 deletions, 212 substitutions, over 2606 + reference words (2337 correct) + - The difference between the reference transcript and predicted result. + An instance is given below:: + THE ASSOCIATION OF (EDISON->ADDISON) ILLUMINATING COMPANIES + The above example shows that the reference word is `EDISON`, + but it is predicted to `ADDISON` (a substitution error). + Another example is:: + FOR THE FIRST DAY (SIR->*) I THINK + The reference word `SIR` is missing in the predicted + results (a deletion error). + results: + An iterable of tuples. The first element is the reference transcript + while the second element is the predicted result. + enable_log: + If True, also print detailed WER to the console. + Otherwise, it is written only to the given file. + Returns: + Return None. """ - m = len(ref) - n = len(hyp) - - # special case - if ref == hyp: - return 0 - if m == 0: - return n - if n == 0: - return m - - if m < n: - ref, hyp = hyp, ref - m, n = n, m - - # use O(min(m, n)) space - distance = np.zeros((2, n + 1), dtype=np.int32) - - # initialize distance matrix - for j in range(n + 1): - distance[0][j] = j - - # calculate levenshtein distance - for i in range(1, m + 1): - prev_row_idx = (i - 1) % 2 - cur_row_idx = i % 2 - distance[cur_row_idx][0] = i - for j in range(1, n + 1): - if ref[i - 1] == hyp[j - 1]: - distance[cur_row_idx][j] = distance[prev_row_idx][j - 1] + subs: Dict[Tuple[str, str], int] = defaultdict(int) + ins: Dict[str, int] = defaultdict(int) + dels: Dict[str, int] = defaultdict(int) + + # `words` stores counts per word, as follows: + # corr, ref_sub, hyp_sub, ins, dels + words: Dict[str, List[int]] = defaultdict(lambda: [0, 0, 0, 0, 0]) + num_corr = 0 + ERR = "*" + for ref, hyp in results: + ali = kaldialign.align(ref, hyp, ERR) + for ref_word, hyp_word in ali: + if ref_word == ERR: + ins[hyp_word] += 1 + words[hyp_word][3] += 1 + elif hyp_word == ERR: + dels[ref_word] += 1 + words[ref_word][4] += 1 + elif hyp_word != ref_word: + subs[(ref_word, hyp_word)] += 1 + words[ref_word][1] += 1 + words[hyp_word][2] += 1 else: - s_num = distance[prev_row_idx][j - 1] + 1 - i_num = distance[cur_row_idx][j - 1] + 1 - d_num = distance[prev_row_idx][j] + 1 - distance[cur_row_idx][j] = min(s_num, i_num, d_num) - - return distance[m % 2][n] - - -def cal_cer(references, predictions): - errors = 0 - lengths = 0 - for ref, pred in zip(references, predictions): - cur_ref = list(ref) - cur_hyp = list(pred) - cur_error = _levenshtein_distance(cur_ref, cur_hyp) - errors += cur_error - lengths += len(cur_ref) - return float(errors) / lengths + words[ref_word][0] += 1 + num_corr += 1 + ref_len = sum([len(r) for r, _ in results]) + sub_errs = sum(subs.values()) + ins_errs = sum(ins.values()) + del_errs = sum(dels.values()) + tot_errs = sub_errs + ins_errs + del_errs + tot_err_rate = "%.2f" % (100.0 * tot_errs / ref_len) + + if enable_log: + logging.info( + f"[{test_set_name}] %WER {tot_errs / ref_len:.2%} " + f"[{tot_errs} / {ref_len}, {ins_errs} ins, " + f"{del_errs} del, {sub_errs} sub ]" + ) + + print(f"%WER = {tot_err_rate}", file=f) + print( + f"Errors: {ins_errs} insertions, {del_errs} deletions, " + f"{sub_errs} substitutions, over {ref_len} reference " + f"words ({num_corr} correct)", + file=f, + ) + print( + "Search below for sections starting with PER-UTT DETAILS:, " + "SUBSTITUTIONS:, DELETIONS:, INSERTIONS:, PER-WORD STATS:", + file=f, + ) + + print("", file=f) + print("PER-UTT DETAILS: corr or (ref->hyp) ", file=f) + for ref, hyp in results: + ali = kaldialign.align(ref, hyp, ERR) + combine_successive_errors = True + if combine_successive_errors: + ali = [[[x], [y]] for x, y in ali] + for i in range(len(ali) - 1): + if ali[i][0] != ali[i][1] and ali[i + 1][0] != ali[i + 1][1]: + ali[i + 1][0] = ali[i][0] + ali[i + 1][0] + ali[i + 1][1] = ali[i][1] + ali[i + 1][1] + ali[i] = [[], []] + ali = [ + [ + list(filter(lambda a: a != ERR, x)), + list(filter(lambda a: a != ERR, y)), + ] + for x, y in ali + ] + ali = list(filter(lambda x: x != [[], []], ali)) + ali = [ + [ + ERR if x == [] else " ".join(x), + ERR if y == [] else " ".join(y), + ] + for x, y in ali + ] + + print( + " ".join( + ( + ref_word + if ref_word == hyp_word + else f"({ref_word}->{hyp_word})" + for ref_word, hyp_word in ali + ) + ), + file=f, + ) + + print("", file=f) + print("SUBSTITUTIONS: count ref -> hyp", file=f) + + for count, (ref, hyp) in sorted( + [(v, k) for k, v in subs.items()], reverse=True + ): + print(f"{count} {ref} -> {hyp}", file=f) + + print("", file=f) + print("DELETIONS: count ref", file=f) + for count, ref in sorted([(v, k) for k, v in dels.items()], reverse=True): + print(f"{count} {ref}", file=f) + + print("", file=f) + print("INSERTIONS: count hyp", file=f) + for count, hyp in sorted([(v, k) for k, v in ins.items()], reverse=True): + print(f"{count} {hyp}", file=f) + + print("", file=f) + print( + "PER-WORD STATS: word corr tot_errs count_in_ref count_in_hyp", file=f + ) + for _, word, counts in sorted( + [(sum(v[1:]), k, v) for k, v in words.items()], reverse=True + ): + (corr, ref_sub, hyp_sub, ins, dels) = counts + tot_errs = ref_sub + hyp_sub + ins + dels + ref_count = corr + ref_sub + dels + hyp_count = corr + hyp_sub + ins + + print(f"{word} {corr} {tot_errs} {ref_count} {hyp_count}", file=f) + return float(tot_err_rate) \ No newline at end of file diff --git a/triton/model_repo/feature_extractor/1/model.py b/triton/model_repo/feature_extractor/1/model.py index 581f9f236..6dbc58424 100755 --- a/triton/model_repo/feature_extractor/1/model.py +++ b/triton/model_repo/feature_extractor/1/model.py @@ -1,3 +1,17 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import triton_python_backend_utils as pb_utils from torch.utils.dlpack import to_dlpack import torch @@ -72,10 +86,10 @@ def initialize(self, args): value = value["string_value"] if key == "num_mel_bins": opts.mel_opts.num_bins = int(value) - # elif key == "frame_shift_in_ms": - # opts.frame_opts.frame_shift_ms = float(value) - # elif key == "frame_length_in_ms": - # opts.frame_opts.frame_length_ms = float(value) + elif key == "frame_shift_in_ms": + opts.frame_opts.frame_shift_ms = float(value) + elif key == "frame_length_in_ms": + opts.frame_opts.frame_length_ms = float(value) elif key == "sample_rate": opts.frame_opts.samp_freq = int(value) opts.device = torch.device(self.device) @@ -131,10 +145,6 @@ def execute(self, requests): f_l = f.shape[0] speech[i, 0: f_l, :] = f.to(self.output0_dtype) speech_lengths[i][0] = f_l - # put speech feature on device will cause empty output - # we will follow this issue and now temporarily put it on cpu - speech = speech.cpu() - speech_lengths = speech_lengths.cpu() out0 = pb_utils.Tensor.from_dlpack("speech__0", to_dlpack(speech)) out1 = pb_utils.Tensor.from_dlpack("speech_lengths__1", diff --git a/triton/model_repo/greedy_search/config.pbtxt b/triton/model_repo/greedy_search/config.pbtxt index 0d13c4277..34e83da1f 100755 --- a/triton/model_repo/greedy_search/config.pbtxt +++ b/triton/model_repo/greedy_search/config.pbtxt @@ -23,7 +23,7 @@ parameters [ }, { key: "bpe_model", - value: { string_value: "/ws/yuekaiz/icefall-asr-librispeech-pruned-transducer-stateless3-2022-04-29/data/lang_bpe_500/bpe.model"} + value: { string_value: "/ws/bpe.model"} }, { key: "FORCE_CPU_ONLY_INPUT_TENSORS", @@ -36,7 +36,7 @@ input [ { name: "encoder_out__0" data_type: TYPE_FP32 - dims: [-1, 512] # [-1, feature_size] + dims: [-1, 512] # [-1, encoder_out_dim] }, { name: "encoder_out_lens__1" diff --git a/triton/scripts/start_server.sh b/triton/scripts/start_server.sh index 55fcbaac4..6fec73ad4 100755 --- a/triton/scripts/start_server.sh +++ b/triton/scripts/start_server.sh @@ -18,10 +18,10 @@ jit_model_dir=/ws/jit_model model_repo=/ws/model_repo -cp $jit_model_dir/encoder_jit.pt $model_rep/encoder/1 -cp $jit_model_dir/decoder_jit.pt $model_rep/decoder/1 -cp $jit_model_dir/joiner_jit.pt $model_rep/joiner/1 - +cp $jit_model_dir/encoder_jit.pt $model_repo/encoder/1 +cp $jit_model_dir/decoder_jit.pt $model_repo/decoder/1 +cp $jit_model_dir/joiner_jit.pt $model_repo/joiner/1 +cp $jit_model_dir/bpe.model /ws # Start server tritonserver --model-repository=$model_repo \ No newline at end of file

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