exo/exo/inference/tinygrad/inference.py

from pathlib import Path
import json
import os
from exo.inference.tinygrad.models.llama import Transformer, convert_from_huggingface, fix_bf16
from exo.inference.shard import Shard
from tinygrad.nn.state import safe_load, torch_load, load_state_dict
from tinygrad import Tensor, dtypes, nn, Context
from transformers import AutoTokenizer
from exo.inference.inference_engine import InferenceEngine
from typing import Optional, Tuple
import numpy as np
from exo.inference.tinygrad.tinygrad_helpers import concat_weights, load
from exo.download.shard_download import ShardDownloader

Tensor.no_grad = True
# default settings
TEMPERATURE = int(os.getenv("TEMPERATURE", 0.85))
TOP_K = 25
TOP_P = 0.9
ALPHA_F = 0.1
ALPHA_P = 0.0
MODEL_PARAMS = {
  "8B": {
    "args": {"dim": 4096, "n_heads": 32, "n_kv_heads": 8, "n_layers": 32, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 14336},
    "files": 1
  },
  "70B": {
    "args": {"dim": 8192, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256,  "hidden_dim": 28672},
    "files": 8
  }
}

def build_transformer(model_path: Path, shard: Shard, model_size="8B", device=None):
  # build model
  linear = nn.Linear
  with Context(THREEFRY=0):
    model = Transformer(**MODEL_PARAMS[model_size]["args"], linear=linear, max_context=8192, jit=True, shard=shard)

  # load weights
  if model_path.is_dir():
    if (model_path / "model.safetensors.index.json").exists(): weights = load(str(model_path / "model.safetensors.index.json"), shard)
    elif (model_path / "model.safetensors").exists(): weights = load(str(model_path / "model.safetensors"), shard)
    else: weights = concat_weights([load(str(model_path / f"consolidated.{i:02d}.pth"), shard) for i in range(MODEL_PARAMS[model_size]["files"])], device[0] if isinstance(device, tuple) else device)
  else:
    weights = load(str(model_path), shard)
  weights = convert_from_huggingface(weights, model, MODEL_PARAMS[model_size]["args"]["n_heads"], MODEL_PARAMS[model_size]["args"]["n_kv_heads"])
  weights = fix_bf16(weights)

  with Context(BEAM=0):
    # replace weights in model
    load_state_dict(model, weights, strict=False, consume=False) # consume=True
  return model

class TinygradDynamicShardInferenceEngine(InferenceEngine):
  def __init__(self, shard_downloader: ShardDownloader):
    self.shard = None
    self.shard_downloader = shard_downloader

  async def infer_prompt(self, request_id: str, shard: Shard, prompt: str, image_str: Optional[str] = None, inference_state: Optional[str] = None) -> (np.ndarray, str, bool):
    await self.ensure_shard(shard)
    start_pos = json.loads(inference_state or "{}").get("start_pos", 0)
    n_captured_toks = json.loads(inference_state or "{}").get("n_captured_toks", 0)

    toks = self.tokenizer.encode(prompt)
    h = self.model(Tensor([toks]), start_pos, TEMPERATURE).realize()

    if h.shape == (1,):
      start_pos += len(toks)
      start_pos += 1
      n_captured_toks = 0
      return np.array([[h.item()]]), json.dumps({"start_pos": start_pos, "n_captured_toks": n_captured_toks}), h.item() == self.tokenizer.eos_token_id
    else:
      n_captured_toks = len(toks)
      return h.numpy(), json.dumps({"start_pos": start_pos, "n_captured_toks": n_captured_toks}), False

  async def infer_tensor(self, request_id: str, shard: Shard, input_data: np.ndarray, inference_state: Optional[str] = None) -> Tuple[np.ndarray, str, bool]:
    await self.ensure_shard(shard)
    start_pos = json.loads(inference_state or "{}").get("start_pos", 0)
    n_captured_toks = json.loads(inference_state or "{}").get("n_captured_toks", 0)

    h = self.model(Tensor(input_data), start_pos, TEMPERATURE).realize()

    if h.shape == (1,):
      start_pos += n_captured_toks
      start_pos += 1
      n_captured_toks = 0
      return np.array([[h.item()]]), json.dumps({"start_pos": start_pos, "n_captured_toks": n_captured_toks}), h.item() == self.tokenizer.eos_token_id
    else:
      return h.numpy(), json.dumps({"start_pos": start_pos, "n_captured_toks": n_captured_toks}), False

  async def ensure_shard(self, shard: Shard):
    if self.shard == shard:
      return

    model_path = await self.shard_downloader.ensure_shard(shard)
    self.model = build_transformer(model_path, shard, model_size="8B" if "8b" in shard.model_id.lower() else "70B")
    self.tokenizer = AutoTokenizer.from_pretrained(str((model_path if model_path.is_dir() else model_path.parent)))
    self.shard = shard