Delete src

src/run-exo.py

@@ -1,90 +0,0 @@
-# In this example, a user is running a home cluster with 3 shards.
-# They are prompting the cluster to generate a response to a question.
-# The cluster is given the question, and the user is given the response.
-
-from exo.inference.mlx.sharded_utils import get_model_path, load_tokenizer
-from exo.inference.shard import Shard
-from exo.networking.peer_handle import PeerHandle
-from exo.networking.grpc.grpc_peer_handle import GRPCPeerHandle
-from exo.topology.device_capabilities import DeviceCapabilities, DeviceFlops
-from typing import List
-import asyncio
-import argparse
-import uuid
-
-models = {
-    "mlx-community/Meta-Llama-3-8B-Instruct-4bit": Shard(model_id="mlx-community/Meta-Llama-3-8B-Instruct-4bit", start_layer=0, end_layer=0, n_layers=32),
-    "mlx-community/Meta-Llama-3-70B-Instruct-4bit": Shard(model_id="mlx-community/Meta-Llama-3-70B-Instruct-4bit", start_layer=0, end_layer=0, n_layers=80),
-    ""
-}
-
-path_or_hf_repo = "mlx-community/Meta-Llama-3-8B-Instruct-4bit"
-model_path = get_model_path(path_or_hf_repo)
-tokenizer_config = {}
-tokenizer = load_tokenizer(model_path, tokenizer_config)
-
-# we intentionally leave out peer1 to demonstrate equality of nodes in exo.
-# there is no "master" node in exo, all nodes are equal and can take on any role.
-# peer1 = GRPCPeerHandle(
-#     "node1",
-#     "localhost:8080",
-#     DeviceCapabilities(model="placeholder", chip="placeholder", memory=0)
-# )
-peer2 = GRPCPeerHandle(
-    "node2",
-    "localhost:8081",
-    DeviceCapabilities(model="placeholder", chip="placeholder", memory=0, flops=DeviceFlops(fp32=0, fp16=0, int8=0))
-)
-shard = models[path_or_hf_repo]
-request_id = str(uuid.uuid4())
-
-async def run_prompt(prompt: str):
-    if tokenizer.chat_template is None:
-        tokenizer.chat_template = tokenizer.default_chat_template
-    if (
-        hasattr(tokenizer, "apply_chat_template")
-        and tokenizer.chat_template is not None
-    ):
-        messages = [{"role": "user", "content": prompt}]
-        prompt = tokenizer.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True
-        )
-
-    await peer2.connect()
-    await peer2.global_reset(shard, set(), 2)
-
-    try:
-        await peer2.send_prompt(shard, prompt, request_id)
-    except Exception as e:
-        print(e)
-
-    import time
-    # poll 10 times per second for result (even though generation is faster, any more than this it's not nice for the user)
-    previous_length = 0
-    n_tokens = 0
-    start_time = time.perf_counter()
-    while True:
-        try:
-            result, is_finished = await peer2.get_inference_result(request_id)
-        except Exception as e:
-            continue
-        await asyncio.sleep(0.1)
-
-        # Print the updated string in place
-        updated_string = tokenizer.decode(result)
-        n_tokens = len(result)
-        print(updated_string[previous_length:], end='', flush=True)
-        previous_length = len(updated_string)
-
-        if is_finished:
-            print("\nDone")
-            break
-    end_time = time.perf_counter()
-    print(f"\nDone. Processed {n_tokens} tokens in {end_time - start_time:.2f} seconds ({n_tokens / (end_time - start_time):.2f} tokens/second)")
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run prompt")
-    parser.add_argument("--prompt", type=str, help="The prompt to run")
-    args = parser.parse_args()
-
-    asyncio.run(run_prompt(args.prompt))

src/run-hf.py

@@ -1,4 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-405B")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-405B")