From 4dddbf5b7508e9dd4347de4a5349667a459b739f Mon Sep 17 00:00:00 2001
From: Nikhilesh Bhatnagar <tingc9@gmail.com>
Date: Tue, 25 Jul 2023 09:15:09 +0000
Subject: [PATCH] Updated langcodes

---
 README.md                                   | 6 +++---
 triton_models/ssmt_model_demuxer/1/model.py | 2 +-
 triton_models/ssmt_tokenizer/1/model.py     | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 3ad2698..91f8988 100644
--- a/README.md
+++ b/README.md
@@ -18,12 +18,11 @@ nvidia-docker run --gpus=all --rm --shm-size 5g --network=host --name dhruva-ssm
 
 * This repo contains the templates and component triton models for the SSMT project.
 * Also contained is a Dockerfile to construct the triton server instance.
-* Given a URL and quantization method (those supported by CTranslate2 i.e. `int8`, `int8_float16`, `int8_bfloat16`, `int16`, `float16` and `bfloat16`) it will download, quantize and construct the SSMT Tritom Repository in `./ssmt_triton_repo`.
+* Given a URL and quantization method (those supported by CTranslate2 i.e. `int8`, `int8_float16`, `int8_bfloat16`, `int16`, `float16` and `bfloat16`) it will download, quantize and construct the SSMT Triton Repository in `./ssmt_triton_repo`.
 * Dynamic batching and caching is supported and enabled by default.
 * The repository folder can me mounted to the dhruva ssmt triton server on `/models` and can be queried via a client.
 * Sample client code is also given as an ipython notebook.
-* The `model.zip` package needs to contain a folder of `.pt` and `.src` files named `1` through `9` with each file corresponding to the following mapping:
-`{'eng-hin': 1, 'hin-eng': 2, 'eng-tel': 3, 'tel-eng': 4, 'hin-tel': 6, 'tel-hin': 7, 'eng-guj': 8, 'guj-eng': 9}`
+* The `model.zip` package needs to contain a folder of `.pt` and `.src` files named `1` through `9` with each file corresponding to the following mapping: `{'en-hi': 1, 'hi-en': 2, 'en-te': 3, 'te-en': 4, 'hi-te': 6, 'te-hi': 7, 'en-gu': 8, 'gu-en': 9}`
 
 ## Architecture of the pipeline
 
@@ -32,6 +31,7 @@ The pipeline consists of 4 components, executed in order:
 * Model Demuxer - This model is CPU only and depending on the language pair requested, queues up an `InferenceRequest` for the appropriate model and returns it as the response.
 * Model - This is a GPU based model and it processes the tokenized text and returns the final form of the translated text to the caller.
 * Pipeline - This is an ensemble model that wraps the above three components together and is the one meant to be exposed to the client.
+
 The exact specifications of the model inputs and outputs can be looked at in the corresponding `config.pbtxt` files.
 One can construct the triton repo like so:
 ```bash
diff --git a/triton_models/ssmt_model_demuxer/1/model.py b/triton_models/ssmt_model_demuxer/1/model.py
index 5c4e289..e8c6744 100644
--- a/triton_models/ssmt_model_demuxer/1/model.py
+++ b/triton_models/ssmt_model_demuxer/1/model.py
@@ -7,7 +7,7 @@ class TritonPythonModel:
         self.model_config = json.loads(args["model_config"])
         target_config = pb_utils.get_output_config_by_name(self.model_config, "OUTPUT_TEXT")
         self.target_dtype = pb_utils.triton_string_to_numpy(target_config["data_type"])
-        self.lang_pair_map = {'eng-hin': 1, 'hin-eng': 2, 'eng-tel': 3, 'tel-eng': 4, 'hin-tel': 6, 'tel-hin': 7, 'eng-guj': 8, 'guj-eng': 9}
+        self.lang_pair_map = {'en-hi': 1, 'hi-en': 2, 'en-te': 3, 'te-en': 4, 'hi-te': 6, 'te-hi': 7, 'en-gu': 8, 'gu-en': 9}
 
     async def execute(self, requests):
         responses = []
diff --git a/triton_models/ssmt_tokenizer/1/model.py b/triton_models/ssmt_tokenizer/1/model.py
index 7a85827..76433e8 100644
--- a/triton_models/ssmt_tokenizer/1/model.py
+++ b/triton_models/ssmt_tokenizer/1/model.py
@@ -11,7 +11,7 @@ class TritonPythonModel:
         self.model_config = json.loads(args["model_config"])
         target_config = pb_utils.get_output_config_by_name(self.model_config, "INPUT_TEXT_TOKENIZED")
         self.target_dtype = pb_utils.triton_string_to_numpy(target_config["data_type"])
-        self.lang_pair_map = {'eng-hin': 1, 'hin-eng': 2, 'eng-tel': 3, 'tel-eng': 4, 'hin-tel': 6, 'tel-hin': 7, 'eng-guj': 8, 'guj-eng': 9}
+        self.lang_pair_map = {'en-hi': 1, 'hi-en': 2, 'en-te': 3, 'te-en': 4, 'hi-te': 6, 'te-hi': 7, 'en-gu': 8, 'gu-en': 9}
         self.bpes = {lang_pair: BPE(open(os.path.join(current_path, f'bpe_src/{model_id}.src'), encoding='utf-8')) for lang_pair, model_id in self.lang_pair_map.items()}
 
     def execute(self, requests):
-- 
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