Add LLaMA support

modules/LLaMA.py

@@ -0,0 +1,96 @@
+# 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.
+
+import json
+import os
+import sys
+import time
+from pathlib import Path
+from typing import Tuple
+
+import fire
+import torch
+from fairscale.nn.model_parallel.initialize import initialize_model_parallel
+from llama import LLaMA, ModelArgs, Tokenizer, Transformer
+
+os.environ['RANK'] = '0'
+os.environ['WORLD_SIZE'] = '1'
+os.environ['MP'] = '1'
+os.environ['MASTER_ADDR'] = '127.0.0.1'
+os.environ['MASTER_PORT'] = '2223'
+
+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("gloo")
+    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,
+    max_seq_len: int,
+    max_batch_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=max_seq_len, max_batch_size=max_batch_size, **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
+
+
+class LLaMAModel:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def from_pretrained(self, path, max_seq_len=512, max_batch_size=32):
+        tokenizer_path = path / "tokenizer.model"
+        path = os.path.abspath(path)
+        tokenizer_path = os.path.abspath(tokenizer_path)
+        
+        local_rank, world_size = setup_model_parallel()
+        if local_rank > 0:
+            sys.stdout = open(os.devnull, "w")
+
+        generator = load(
+            path, tokenizer_path, local_rank, world_size, max_seq_len, max_batch_size
+        )
+
+        result = self()
+        result.pipeline = generator
+        return result
+
+    def generate(self, prompt, token_count=512, temperature=0.8, top_p=0.95):
+
+        results = self.pipeline.generate(
+            [prompt], max_gen_len=token_count, temperature=temperature, top_p=top_p
+        )
+
+        return results[0]