mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-10 07:28:15 -05:00
255 lines
8.9 KiB
Python
255 lines
8.9 KiB
Python
import os, random, pickle, functools, itertools
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from typing import List, Tuple
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from pathlib import Path
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import numpy as np
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from PIL import Image
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from tqdm import tqdm
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from tinygrad import dtypes, Tensor
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from tinygrad.helpers import getenv, prod, Context, round_up
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from collections import deque
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from multiprocessing import Queue, Process, shared_memory, connection, Lock, cpu_count, Pool
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class MyQueue:
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def __init__(self, multiple_readers=True, multiple_writers=True):
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self._reader, self._writer = connection.Pipe(duplex=False)
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self._rlock = Lock() if multiple_readers else None
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self._wlock = Lock() if multiple_writers else None
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def get(self):
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if self._rlock: self._rlock.acquire()
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ret = pickle.loads(self._reader.recv_bytes())
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if self._rlock: self._rlock.release()
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return ret
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def put(self, obj):
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if self._wlock: self._wlock.acquire()
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self._writer.send_bytes(pickle.dumps(obj))
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if self._wlock: self._wlock.release()
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def shuffled_indices(n, seed=None):
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rng = random.Random(seed)
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indices = {}
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for i in range(n-1, -1, -1):
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j = rng.randint(0, i)
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if i not in indices: indices[i] = i
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if j not in indices: indices[j] = j
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indices[i], indices[j] = indices[j], indices[i]
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yield indices[i]
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del indices[i]
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def loader_process(q_in, q_out, X:Tensor, seed):
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import signal
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signal.signal(signal.SIGINT, lambda _, __: exit(0))
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from extra.datasets.imagenet import center_crop, preprocess_train
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with Context(DEBUG=0):
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while (_recv := q_in.get()) is not None:
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idx, fn, val = _recv
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if fn is not None:
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img = Image.open(fn)
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img = img.convert('RGB') if img.mode != "RGB" else img
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if val:
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# eval: 76.08%, load in 0m7.366s (0m5.301s with simd)
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# sudo apt-get install libjpeg-dev
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# CC="cc -mavx2" pip install -U --force-reinstall pillow-simd
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img = center_crop(img)
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img = np.array(img)
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else:
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# reseed rng for determinism
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if seed is not None:
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np.random.seed(seed * 2 ** 10 + idx)
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random.seed(seed * 2 ** 10 + idx)
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img = preprocess_train(img)
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else:
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# pad zeros
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img = np.zeros((224, 224, 3), dtype=np.uint8)
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# broken out
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#img_tensor = Tensor(img.tobytes(), device='CPU')
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#storage_tensor = X[idx].contiguous().realize().lazydata.realized
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#storage_tensor._copyin(img_tensor.numpy())
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# faster
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X[idx].contiguous().realize().lazydata.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
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# ideal
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#X[idx].assign(img.tobytes()) # NOTE: this is slow!
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q_out.put(idx)
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q_out.put(None)
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def batch_load_resnet(batch_size=64, val=False, shuffle=True, seed=None, pad_first_batch=False):
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from extra.datasets.imagenet import get_train_files, get_val_files
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files = get_val_files() if val else get_train_files()
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from extra.datasets.imagenet import get_imagenet_categories
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cir = get_imagenet_categories()
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if pad_first_batch:
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FIRST_BATCH_PAD = round_up(len(files), batch_size) - len(files)
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else:
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FIRST_BATCH_PAD = 0
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file_count = FIRST_BATCH_PAD + len(files)
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BATCH_COUNT = min(32, file_count // batch_size)
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def _gen():
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for _ in range(FIRST_BATCH_PAD): yield -1
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yield from shuffled_indices(len(files), seed=seed) if shuffle else iter(range(len(files)))
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gen = iter(_gen())
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def enqueue_batch(num):
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for idx in range(num*batch_size, (num+1)*batch_size):
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fidx = next(gen)
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if fidx != -1:
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fn = files[fidx]
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q_in.put((idx, fn, val))
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Y[idx] = cir[fn.split("/")[-2]]
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else:
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# padding
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q_in.put((idx, None, val))
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Y[idx] = -1
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shutdown = False
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class Cookie:
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def __init__(self, num): self.num = num
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def __del__(self):
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if not shutdown:
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try: enqueue_batch(self.num)
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except StopIteration: pass
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gotten = [0]*BATCH_COUNT
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def receive_batch():
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while 1:
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num = q_out.get()//batch_size
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gotten[num] += 1
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if gotten[num] == batch_size: break
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gotten[num] = 0
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return X[num*batch_size:(num+1)*batch_size], Y[num*batch_size:(num+1)*batch_size], Cookie(num)
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#q_in, q_out = MyQueue(multiple_writers=False), MyQueue(multiple_readers=False)
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q_in, q_out = Queue(), Queue()
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sz = (batch_size*BATCH_COUNT, 224, 224, 3)
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if os.path.exists("/dev/shm/resnet_X"): os.unlink("/dev/shm/resnet_X")
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shm = shared_memory.SharedMemory(name="resnet_X", create=True, size=prod(sz))
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procs = []
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try:
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# disk:shm is slower
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#X = Tensor.empty(*sz, dtype=dtypes.uint8, device=f"disk:shm:{shm.name}")
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X = Tensor.empty(*sz, dtype=dtypes.uint8, device=f"disk:/dev/shm/resnet_X")
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Y = [None] * (batch_size*BATCH_COUNT)
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for _ in range(cpu_count()):
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p = Process(target=loader_process, args=(q_in, q_out, X, seed))
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p.daemon = True
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p.start()
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procs.append(p)
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for bn in range(BATCH_COUNT): enqueue_batch(bn)
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# NOTE: this is batch aligned, last ones are ignored unless pad_first_batch is True
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for _ in range(0, file_count//batch_size): yield receive_batch()
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finally:
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shutdown = True
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# empty queues
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for _ in procs: q_in.put(None)
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q_in.close()
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for _ in procs:
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while q_out.get() is not None: pass
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q_out.close()
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# shutdown processes
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for p in procs: p.join()
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shm.close()
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try:
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shm.unlink()
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except FileNotFoundError:
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# happens with BENCHMARK set
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pass
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@functools.lru_cache(maxsize=128)
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def load_bert_file(fn:str) -> List[dict]:
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with open(fn, "rb") as f: data = pickle.load(f)
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return data
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def process_batch_bert(data: List[dict]) -> dict[str, Tensor]:
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return {
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"input_ids": Tensor(np.concatenate([s["input_ids"] for s in data], axis=0), dtype=dtypes.float32),
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"input_mask": Tensor(np.concatenate([s["input_mask"] for s in data], axis=0), dtype=dtypes.default_float),
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"segment_ids": Tensor(np.concatenate([s["segment_ids"] for s in data], axis=0), dtype=dtypes.float32),
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"masked_lm_positions": Tensor(np.concatenate([s["masked_lm_positions"] for s in data], axis=0), dtype=dtypes.float32),
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"masked_lm_ids": Tensor(np.concatenate([s["masked_lm_ids"] for s in data], axis=0), dtype=dtypes.float32),
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"masked_lm_weights": Tensor(np.concatenate([s["masked_lm_weights"] for s in data], axis=0), dtype=dtypes.float32),
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"next_sentence_labels": Tensor(np.concatenate([s["next_sentence_labels"] for s in data], axis=0), dtype=dtypes.float32),
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}
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def shuffle_parts(file_paths: List[str]) -> List[str]:
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parts = {}
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for f in file_paths:
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part = Path(f).stem.split('_')[0]
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if part not in parts: parts[part] = []
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parts[part].append(f)
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part_ids = list(parts.keys())
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random.shuffle(part_ids)
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shuffled_files = []
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for p in part_ids:
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parts[p].sort(key=lambda x: int(Path(x).stem.split('_')[1]))
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shuffled_files.extend(parts[p])
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return shuffled_files
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def random_sample(data: List[str]):
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index = random.randint(0, len(data) - 1)
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selected_sample = data[index]
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return selected_sample, index
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def load_datasample(file_and_offset:Tuple[str, int]) -> List[dict]:
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data = load_bert_file(file_and_offset[0])
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return data[file_and_offset[1]]
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# Reference: https://github.com/mlcommons/training/blob/1c8a098ae3e70962a4f7422c0b0bd35ae639e357/language_model/tensorflow/bert/run_pretraining.py, Line 394
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def batch_load_train_bert(BS:int):
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from extra.datasets.wikipedia import get_wiki_train_files
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files = shuffle_parts(get_wiki_train_files())
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dataset = []
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for f in files:
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lists = [(f, o) for o in range(int(Path(f).stem.split("_")[3].split(".")[0]))]
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dataset.extend(lists)
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active_set = deque(dataset[:1000])
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remaining_set = deque(dataset[1000:])
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while dataset:
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blob = []
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for _ in range(BS):
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if active_set:
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index = random.randint(0, len(active_set) - 1)
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sample = active_set[index]
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active_set.remove(sample)
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blob.append(sample)
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if remaining_set:
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active_set.append(remaining_set.popleft())
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yield process_batch_bert([load_datasample(sample) for sample in blob])
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# Reference: https://github.com/mlcommons/training/blob/1c8a098ae3e70962a4f7422c0b0bd35ae639e357/language_model/tensorflow/bert/run_pretraining.py, Line 416
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def batch_load_val_bert(BS:int):
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from extra.datasets.wikipedia import get_wiki_val_files
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files = get_wiki_val_files()
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dataset = list(itertools.chain.from_iterable([load_bert_file(f) for f in files]))
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idx = 0
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while True:
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start_idx = (idx * BS) % len(dataset)
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end_idx = ((idx + 1) * BS) % len(dataset)
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if start_idx < end_idx:
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yield process_batch_bert(dataset[start_idx:end_idx])
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else: # wrap around the end to the beginning of the dataset
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yield process_batch_bert(dataset[start_idx:] + dataset[:end_idx])
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idx += 1
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if __name__ == "__main__":
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from extra.datasets.imagenet import get_train_files, get_val_files
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VAL = getenv("VAL", 1)
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files = get_val_files() if VAL else get_train_files()
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with tqdm(total=len(files)) as pbar:
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for x,y,c in batch_load_resnet(val=VAL):
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pbar.update(x.shape[0])
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