Neural Machine Translation Datasets

bleu_scores.txt

@@ -0,0 +1,100 @@
+1074	24.60
+1104	23.51
+1256	20.52
+0976	20.71
+2352	20.69
+1456	22.34
+2300	22.58
+0241	21.54
+2885	22.78
+0591	23.85
+2821	17.48
+1285	22.63
+1822	16.89
+2820	23.92
+2384	21.89
+1564	19.85
+0578	23.98
+0929	19.46
+2594	11.70
+0333	16.95
+0927	21.74
+1722	23.67
+0661	21.97
+1434	20.65
+2116	20.36
+2466	22.00
+2869	18.70
+0398	21.32
+1057	23.51
+1909	16.51
+2398	23.15
+0048	22.89
+2767	20.96
+2587	24.75
+1746	22.17
+1960	24.92
+2888	21.07
+0673	25.83
+0285	18.71
+2409	20.42
+1584	23.82
+0618	22.27
+2499	25.51
+0804	17.57
+1091	25.48
+1076	2.67
+1687	26.50
+2787	25.36
+1937	24.80
+0988	24.41
+2524	22.39
+2694	21.90
+2251	19.92
+2363	21.86
+0899	19.04
+0115	23.71
+0481	19.76
+2159	21.99
+2712	19.82
+0726	23.68
+1095	23.94
+1339	20.46
+0780	18.27
+2984	21.10
+2286	24.68
+1017	21.94
+2093	23.06
+0800	20.50
+0488	20.80
+0387	26.64
+2222	21.79
+2684	22.92
+0082	23.53
+0996	18.98
+0750	22.44
+1786	21.07
+2874	16.21
+2590	25.26
+1857	26.15
+1507	23.44
+2436	19.25
+0248	18.56
+1277	17.32
+0311	15.09
+0500	17.38
+1814	17.59
+1296	22.22
+2188	17.49
+0699	21.94
+1938	18.48
+2024	20.55
+1138	23.76
+0558	23.29
+2678	17.22
+0469	20.20
+1916	23.57
+2522	22.12
+2782	23.23
+1743	22.52
+1393	24.79

test.py

@@ -0,0 +1,115 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the GNU General Public License version 3.
+
+from typing import Tuple
+import os
+import sys
+import torch
+import fire
+import time
+import json
+
+from tqdm import tqdm
+
+from pathlib import Path
+
+from fairscale.nn.model_parallel.initialize import initialize_model_parallel
+
+from llama import ModelArgs, Transformer, Tokenizer, LLaMA
+
+
+def setup_model_parallel() -> Tuple[int, int]:
+    local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    world_size = int(os.environ.get("WORLD_SIZE", -1))
+
+    torch.distributed.init_process_group("nccl")
+    initialize_model_parallel(world_size)
+    torch.cuda.set_device(local_rank)
+
+    # seed must be the same in all processes
+    torch.manual_seed(1)
+    return local_rank, world_size
+
+
+def load(ckpt_dir: str, tokenizer_path: str, local_rank: int, world_size: int) -> LLaMA:
+    start_time = time.time()
+    checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
+    assert (
+        world_size == len(checkpoints)
+    ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
+    ckpt_path = checkpoints[local_rank]
+    print("Loading")
+    checkpoint = torch.load(ckpt_path, map_location="cpu")
+    with open(Path(ckpt_dir) / "params.json", "r") as f:
+        params = json.loads(f.read())
+
+    model_args: ModelArgs = ModelArgs(max_seq_len=1536, max_batch_size=32, **params)
+    tokenizer = Tokenizer(model_path=tokenizer_path)
+    model_args.vocab_size = tokenizer.n_words
+    torch.set_default_tensor_type(torch.cuda.HalfTensor)
+    model = Transformer(model_args)
+    torch.set_default_tensor_type(torch.FloatTensor)
+    model.load_state_dict(checkpoint, strict=False)
+
+    generator = LLaMA(model, tokenizer)
+    print(f"Loaded in {time.time() - start_time:.2f} seconds")
+    return generator
+
+
+def read(lang):
+
+    score_map = dict()
+
+    f = open("/data/wyt/in-context/xglm/bleu_scores.txt", 'r')
+    for line in f:
+        idx = int(line.split("\t")[0])
+        score = float(line.split("\t")[1])
+        score_map[idx] = score
+
+    new_map = sorted(score_map.items(), key=lambda x:x[1], reverse=True)
+    top_k = [i[0] for i in new_map]
+
+    example_src_list, example_tgt_list = [], []
+    with open("/data/wyt/in-context/xglm/corpus/dev.%s" % (lang[:2]), 'r') as f:
+        src_list = [line.strip() for line in f]
+    with open("/data/wyt/in-context/xglm/corpus/dev.%s" % (lang[-2:]), 'r') as f:
+        tgt_list = [line.strip() for line in f]
+
+    f = open("/data/wyt/in-context/xglm/corpus/wmt19/test.%s.%s" % (lang, lang[:2]), 'r')
+    test_src_list = [line.strip() for line in f if line != "\n"]
+
+    demonstration = "Translate these sentences from German to English:\n" if lang == "de-en" else "Translate these sentences from English to German:\n"
+    for idx in top_k[:16]:
+        demonstration = demonstration + "%s\n%s\n" % (src_list[idx], tgt_list[idx])
+    return demonstration, test_src_list
+
+
+def main(ckpt_dir: str, tokenizer_path: str, lang: str, temperature: float = 0.8, top_p: float = 0.95):
+    local_rank, world_size = setup_model_parallel()
+    if local_rank > 0:
+        sys.stdout = open(os.devnull, 'w')
+
+    print("temperature: ", temperature)
+    generator = load(ckpt_dir, tokenizer_path, local_rank, world_size)
+    
+    demonstration, test_src_list = read(lang)
+    step_len = 8
+    for i in tqdm(range(0, len(test_src_list), step_len)):
+        prompt_list = [demonstration + test_src + "\n" for test_src in test_src_list[i:i+step_len]]
+        results = generator.generate(prompt_list, max_gen_len=1536, temperature=temperature, top_p=top_p)
+
+        if local_rank == 0:
+            f = open("%s.%s.temp%02d.raw" % (lang, lang[-2:], temperature * 10), 'a')
+            for res_idx, result in enumerate(results):
+                start = result.find(test_src_list[i + res_idx])
+                if start == -1:
+                    tgt = ""
+                else:
+                    lines = result[start:].split("\n")
+                    tgt = lines[1] if len(lines) > 1 else ""
+
+                f.write(tgt + "\n")
+    
+
+if __name__ == "__main__":
+    fire.Fire(main)