Pantogloss

Pantogloss is a TensorFlow/Keras many-to-English machine-translation library. Its first model, pantogloss-500-en, was converted and numerically validated from the model described in Many-to-English Machine Translation Tools, Data, and Pretrained Models (ACL-IJCNLP 2021).

The Python package is distributed through PyPI, while the initial model is kept in a separate private Hugging Face repository. Installing Pantogloss does not grant model access; users must be authorized for chrismattmann/pantogloss-500-en and authenticate with hf auth login.

The codebase and converted model are licensed under Apache-2.0. This repository is private during initial development.

Intended API

from pantogloss import Translator

translator = Translator.from_pretrained("pantogloss-500-en")
print(translator.translate("Comment allez-vous ?"))

RTG-compatible beam search is available without changing the return type:

print(
    translator.translate(
        "Comment allez-vous ?",
        beam_size=4,
        length_penalty=0.6,
    )
)

Pantogloss selects the first TensorFlow GPU automatically and enables memory growth. Device choice can also be made explicit:

translator = Translator.from_pretrained("pantogloss-500-en", device="gpu")
print(translator.device_info)

Using device="gpu" fails clearly if TensorFlow cannot see a GPU; use device="cpu" to force CPU inference.

Install the accelerator backend for the machine:

# Linux with an NVIDIA GPU
pip install 'pantogloss[cuda]'

# Apple Silicon
pip install 'pantogloss[metal]'

Both use the same device="auto" or device="gpu" Python API. The CUDA extra does not install or replace the host NVIDIA driver. The Metal extra uses Apple's TensorFlow PluggableDevice and the TensorFlow 2.18 runtime combination validated by the Bytewise project.

The model is stored separately in the private Hugging Face repository chrismattmann/pantogloss-500-en; it is never included in the Python wheel.

Development status

The complete 307-variable Keras model has been converted locally from all 308 learned PyTorch tensors (the target embedding and output projection are tied). Greedy parity against the archived RTG implementation passes across a ten-language batch: token IDs and translations match exactly, while final logits have a maximum absolute error of 1.24e-5. With the original beam size 4 and length penalty 0.6, all decoded four-best candidate sets match. One near-tied example changes top rank because of framework floating-point ordering. Model version 0.1.0 is released in the private Hugging Face repository at an immutable commit.

The source model and generated artifacts stay under the ignored artifacts/ directory. To reproduce conversion after acquiring the source archive:

python tools/convert_rtg_checkpoint.py \
  artifacts/source/rtg500eng-tfm9L6L768d-bsz720k-stp200k-ens05 \
  artifacts/converted/pantogloss-500-en-candidate

Run the reference parity harness with:

CUDA_VISIBLE_DEVICES=-1 python tools/check_parity.py \
  artifacts/source/rtg500eng-tfm9L6L768d-bsz720k-stp200k-ens05 \
  artifacts/converted/pantogloss-500-en-candidate

To require and verify real GPU placement:

python tools/check_gpu.py artifacts/converted/pantogloss-500-en-candidate

Apple Silicon validation

Pantogloss uses the same hardware-neutral GPU API for CUDA and Metal. On an M-series Mac with Python 3.12 and Xcode command-line tools installed:

python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e '.[metal,test]'
hf auth login
python tools/check_platform.py --device cpu
python tools/check_platform.py --device gpu
python tools/benchmark_inference.py --device gpu --runs 5

The portable platform report identifies the selected backend as cpu, cuda, or metal, verifies the first model variable's actual TensorFlow placement, and runs a real translation. Metal placement and inference are validated on an Apple M3 Max with TensorFlow 2.18.1. For a short batch-one sentence, its warmed median was 0.545 seconds on CPU and 0.633 seconds on Metal; accelerator benefits are expected primarily from batching and future compiled decoding.