[bounty] Don't use numpy inside hlb_cifar10 training loop (#10777)

* Don't use numpy inside hlb_cifar10 training loop

* Lint it

* jit it

* Drop the last half-batch

* Use gather for random_crop and reuse perms

* Wrap train_cifar in FUSE_ARANGE context

* No need to pass FUSE_ARANGE=1 to hlb_cifar10.py

* Add cutmix to jittable augmentations

* Remove .contiguous() from fetch_batches

* Fix indexing boundary

---------

Co-authored-by: Irwin1138 <irwin1139@gmail.com>

.github/workflows/benchmark_search.yml

@@ -27,7 +27,7 @@ jobs:
         BENCHMARK_LOG=search_sdxl_cached PYTHONPATH=. AMD=1 JITBEAM=2 python examples/sdxl.py --noshow --timing --seed 0
     - name: Run winograd cifar with new search
       run: |
-        BENCHMARK_LOG=search_wino_cifar WINO=1 DEFAULT_FLOAT=HALF FUSE_ARANGE=1 JITBEAM=4 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 BS=1024 STEPS=500 python examples/hlb_cifar10.py
+        BENCHMARK_LOG=search_wino_cifar WINO=1 DEFAULT_FLOAT=HALF JITBEAM=4 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 BS=1024 STEPS=500 python examples/hlb_cifar10.py
     - name: Run winograd cifar with cached search
       run: |
-        BENCHMARK_LOG=search_wino_cifar_cached WINO=1 DEFAULT_FLOAT=HALF FUSE_ARANGE=1 JITBEAM=4 BS=1024 STEPS=500 python examples/hlb_cifar10.py
+        BENCHMARK_LOG=search_wino_cifar_cached WINO=1 DEFAULT_FLOAT=HALF JITBEAM=4 BS=1024 STEPS=500 python examples/hlb_cifar10.py

examples/hlb_cifar10.py

@@ -7,7 +7,7 @@ import random, time
 import numpy as np
 from typing import Optional
 from extra.lr_scheduler import OneCycleLR
-from tinygrad import nn, dtypes, Tensor, Device, GlobalCounters, TinyJit
+from tinygrad import nn, dtypes, Tensor, Device, GlobalCounters, TinyJit, Variable
 from tinygrad.nn.state import get_state_dict, get_parameters
 from tinygrad.nn import optim
 from tinygrad.helpers import Context, BEAM, WINO, getenv, colored, prod
@@ -145,6 +145,7 @@ hyp = {
   },
 }
 
+@Context(FUSE_ARANGE=getenv("FUSE_ARANGE", 1))
 def train_cifar():
 
   def set_seed(seed):
@@ -201,24 +202,37 @@ def train_cifar():
     idx_y = Tensor.arange(H, dtype=dtypes.int32).reshape((1,1,H,1))
     return (idx_x >= low_x) * (idx_x < (low_x + mask_size)) * (idx_y >= low_y) * (idx_y < (low_y + mask_size))
 
-  def random_crop(X:Tensor, crop_size=32):
-    mask = make_square_mask(X.shape, crop_size)
-    mask = mask.expand((-1,3,-1,-1))
-    X_cropped = Tensor(X.numpy()[mask.numpy()])
-    return X_cropped.reshape((-1, 3, crop_size, crop_size))
+  # Similar, but different enough.
+  def make_random_crop_indices(shape, mask_size) -> Tensor:
+    BS, _, H, W = shape
+    low_x = Tensor.randint(BS, low=0, high=W-mask_size).reshape(BS,1,1,1)
+    low_y = Tensor.randint(BS, low=0, high=H-mask_size).reshape(BS,1,1,1)
+    idx_x = Tensor.arange(mask_size, dtype=dtypes.int32).reshape((1,1,1,mask_size))
+    idx_y = Tensor.arange(mask_size, dtype=dtypes.int32).reshape((1,1,mask_size,1))
+    return low_x, low_y, idx_x, idx_y
 
-  def cutmix(X:Tensor, Y:Tensor, mask_size=3):
-    # fill the square with randomly selected images from the same batch
+  def random_crop(X:Tensor, crop_size=32):
+    Xs, Ys, Xi, Yi = make_random_crop_indices(X.shape, crop_size)
+    return X.gather(-1, (Xs + Xi).expand(-1, 3, X.shape[2], -1)).gather(-2, ((Ys+Yi).expand(-1, 3, crop_size, crop_size)))
+
+  def cutmix(X, Y, order, mask_size=3):
     mask = make_square_mask(X.shape, mask_size)
-    order = list(range(0, X.shape[0]))
-    random.shuffle(order)
-    X_patch = Tensor(X.numpy()[order], device=X.device, dtype=X.dtype)
-    Y_patch = Tensor(Y.numpy()[order], device=Y.device, dtype=Y.dtype)
+    X_patch, Y_patch = X[order], Y[order]
     X_cutmix = mask.where(X_patch, X)
     mix_portion = float(mask_size**2)/(X.shape[-2]*X.shape[-1])
     Y_cutmix = mix_portion * Y_patch + (1. - mix_portion) * Y
     return X_cutmix, Y_cutmix
 
+  @TinyJit
+  def augmentations(X:Tensor, Y:Tensor):
+    perms = Tensor.randperm(X.shape[0], device=X.device) # We reuse perms for cutmix, because they are expensivne to generate
+    if getenv("RANDOM_CROP", 1):
+      X = random_crop(X, crop_size=32)
+    if getenv("RANDOM_FLIP", 1):
+      X = (Tensor.rand(X.shape[0],1,1,1) < 0.5).where(X.flip(-1), X) # flip LR
+    X, Y = X[perms], Y[perms]
+    return X, Y, *cutmix(X, Y, perms, mask_size=hyp['net']['cutmix_size'])
+
   # the operations that remain inside batch fetcher is the ones that involves random operations
   def fetch_batches(X_in:Tensor, Y_in:Tensor, BS:int, is_train:bool):
     step, epoch = 0, 0
@@ -226,28 +240,16 @@ def train_cifar():
       st = time.monotonic()
       X, Y = X_in, Y_in
       if is_train:
-        # TODO: these are not jitted
-        if getenv("RANDOM_CROP", 1):
-          X = random_crop(X, crop_size=32)
-        if getenv("RANDOM_FLIP", 1):
-          X = (Tensor.rand(X.shape[0],1,1,1) < 0.5).where(X.flip(-1), X) # flip LR
-        if getenv("CUTMIX", 1):
-          if step >= hyp['net']['cutmix_steps']:
-            X, Y = cutmix(X, Y, mask_size=hyp['net']['cutmix_size'])
-        order = list(range(0, X.shape[0]))
-        random.shuffle(order)
-        X, Y = X.numpy()[order], Y.numpy()[order]
-      else:
-        X, Y = X.numpy(), Y.numpy()
+        X, Y, X_cm, Y_cm = augmentations(X, Y)
+        if getenv("CUTMIX", 1) and step >= hyp['net']['cutmix_steps']: X, Y = X_cm, Y_cm
       et = time.monotonic()
       print(f"shuffling {'training' if is_train else 'test'} dataset in {(et-st)*1e3:.2f} ms ({epoch=})")
-      for i in range(0, X.shape[0], BS):
-        # pad the last batch  # TODO: not correct for test
-        batch_end = min(i+BS, Y.shape[0])
-        x = Tensor(X[batch_end-BS:batch_end], device=X_in.device, dtype=X_in.dtype)
-        y = Tensor(Y[batch_end-BS:batch_end], device=Y_in.device, dtype=Y_in.dtype)
+
+      vi = Variable("i", 0, (full_batches := (X.shape[0] // BS) * BS) - BS)
+      for i in range(0, full_batches, BS):
         step += 1
-        yield x, y
+        vib = vi.bind(i)
+        yield X[vib:vib+BS], Y[vib:vib+BS]
       epoch += 1
       if not is_train: break