add the script

fsmt-make-super-tiny-model.py

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+#!/usr/bin/env python
+# coding: utf-8
+# Copyright 2020 The HuggingFace Team. 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.
+
+# This script creates a super tiny model that is useful inside tests, when we just want to test that
+# the machinery works, without needing to the check the quality of the outcomes.
+#
+# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
+# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
+# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
+# The latter is done by `fsmt-make-super-tiny-model.py`.
+#
+# It will be used then as "stas/tiny-wmt19-en-ru"
+
+from pathlib import Path
+import json
+import tempfile
+
+from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
+from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
+
+mname_tiny = "tiny-wmt19-en-ru"
+
+# Build
+
+# borrowed from a test 
+vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "w</w>", "r</w>", "t</w>", "lo", "low", "er</w>", "low</w>", "lowest</w>", "newer</w>", "wider</w>", "<unk>", ]
+vocab_tokens = dict(zip(vocab, range(len(vocab))))
+merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
+
+with tempfile.TemporaryDirectory() as tmpdirname:
+    build_dir = Path(tmpdirname)
+    src_vocab_file = build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
+    tgt_vocab_file = build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
+    merges_file = build_dir / VOCAB_FILES_NAMES["merges_file"]
+    with open(src_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens))
+    with open(tgt_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens))
+    with open(merges_file, "w") as fp   : fp.write("\n".join(merges))
+
+    tokenizer = FSMTTokenizer(
+        langs=["en", "ru"],
+        src_vocab_size = len(vocab),
+        tgt_vocab_size = len(vocab),
+        src_vocab_file=src_vocab_file,
+        tgt_vocab_file=tgt_vocab_file,
+        merges_file=merges_file,
+    )
+    
+config = FSMTConfig(
+    langs=['ru', 'en'],
+    src_vocab_size=1000, tgt_vocab_size=1000,
+    d_model=4,
+    encoder_layers=1, decoder_layers=1,
+    encoder_ffn_dim=4, decoder_ffn_dim=4,
+    encoder_attention_heads=1, decoder_attention_heads=1,
+)
+
+tiny_model = FSMTForConditionalGeneration(config)
+print(f"num of params {tiny_model.num_parameters()}")
+
+# Test
+batch = tokenizer(["Making tiny model"], return_tensors="pt")
+outputs = tiny_model(**batch)
+
+print("test output:", len(outputs.logits[0]))
+
+# Save
+tiny_model.half() # makes it smaller
+tiny_model.save_pretrained(mname_tiny)
+tokenizer.save_pretrained(mname_tiny)
+
+print(f"Generated {mname_tiny}")
+
+# Upload
+# transformers-cli upload tiny-wmt19-en-ru