Ring allreduce in multitensor (#3000)

* Ring allreduce v3

* Configurable size, number of gpus and jit in benchmark

* ScheduleBarrier v0

* GB/s that make sense

* ScheduleBarrier v0.1

* Fallback on 2 GPUs

* ScheduleBarrier v0.2

* ScheduleBarrier v0.3

* ScheduleBarrier v0.3.1

* ScheduleBarrier v0.3.2

* Replace ScheduleBarrier with automatic optimization

* unused import

* fix comment

* typing

* better fallback

* python 3.8

---------

Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
Co-authored-by: chenyu <chenyu@fastmail.com>
Co-authored-by: nimlgen <138685161+nimlgen@users.noreply.github.com>

test/external/external_benchmark_multitensor_allreduce.py

@@ -0,0 +1,63 @@
+import time
+import functools
+from tinygrad import Tensor, Device
+from tinygrad.lazy import LazyBuffer
+from tinygrad.ops import ReduceOps, BinaryOps, GlobalCounters
+from tinygrad.features.multi import MultiLazyBuffer, ring_allreduce
+from tinygrad.features.jit import TinyJit
+from tinygrad.realize import create_schedule, run_schedule
+from tinygrad.helpers import getenv
+from typing import List, Union
+
+def naive_allreduce(op: ReduceOps, lbs: List[LazyBuffer]):
+  bop = {ReduceOps.SUM:BinaryOps.ADD, ReduceOps.MAX:BinaryOps.MAX}[op]
+  return [functools.reduce(lambda x,y: x.e(bop, y), [x.copy_to_device(lb.device) for x in lbs]) for lb in lbs]
+
+def realize(x: Union[LazyBuffer, List[LazyBuffer]]):
+  x = x if isinstance(x, list) else [x]
+  run_schedule(create_schedule(x))
+  for lb in x: Device[lb.device].synchronize()
+
+def test(impl, devs: List[str], N: int, iters:int = 10):
+  def _wrapped(impl, op: ReduceOps, t: Tensor) -> Tensor:
+    return Tensor(MultiLazyBuffer(impl(op, t.lazydata.lbs), 0), device=devs)
+  _jitted = TinyJit(_wrapped) if getenv("USEJIT", 1) == 1 else _wrapped
+
+  secs, gflops, gbs = 0, 0, 0
+  for i in range(-2, iters):
+    GlobalCounters.reset()
+    lbs = [Tensor.full((N,), float(1+i), device=d).contiguous().lazydata for i,d in enumerate(devs)]
+    realize(lbs)
+    start = time.time()
+    realize(_jitted(impl, ReduceOps.SUM, Tensor(MultiLazyBuffer(lbs, 0), device=devs)).lazydata.lbs)
+    end = time.time()
+    if i < 0:
+      # First time is slow due to kernel compilation
+      continue
+    i_secs = end-start
+    i_gflops = GlobalCounters.global_ops/i_secs/10**9
+    i_gbs = (N*4)/i_secs/10**9
+    print(f"{impl.__name__} iter {i+1}/{iters}: {i_secs:.6f} sec {i_gflops:.2f} GFLOP/s {i_gbs:.2f} GB/s")
+    secs += i_secs
+    gflops += i_gflops
+    gbs += i_gbs
+
+  return (gflops/iters, gbs/iters, secs/iters)
+
+
+def main():
+  dev, n_gpus = Device.DEFAULT, getenv("GPUS", 6) # number of gpus
+  devs = tuple([f"{dev}:{x}" for x in range(n_gpus)])
+
+  sz = getenv("SZ", 1000) * 10**6 # size of data on each gpu
+  f32 = 4 # 4 bytes
+  N = sz//f32
+
+  print(f"Using {sz/10**9:.2f} GB of numbers on each of {n_gpus} GPUs, {n_gpus*sz/10**9:.2f} GB total.")
+  (ring_gflops, ring_gbs, ring_secs) = test(ring_allreduce, devs, N)
+  (naive_gflops, naive_gbs, naive_secs) = test(naive_allreduce, devs, N)
+  print(f"Ring:\n  {ring_secs:.6f} seconds/iter\n  {ring_gflops:.2f} GFLOP/s\n  {ring_gbs:.2f} GB/s")
+  print(f"Naive:\n  {naive_secs:.6f} seconds/iter\n  {naive_gflops:.2f} GFLOP/s\n  {naive_gbs:.2f} GB/s")
+
+if __name__ == "__main__":
+  main()

test/test_multitensor.py

@@ -1,11 +1,13 @@
-import unittest, functools
+import unittest, functools, random
 from typing import List
 from tinygrad import Tensor, Device, nn, GlobalCounters, TinyJit
 from tinygrad.device import BufferCopy
-from tinygrad.ops import LoadOps, ReduceOps
-from tinygrad.helpers import CI
+from tinygrad.ops import LoadOps, ReduceOps, BinaryOps
+from tinygrad.helpers import CI, prod
 from tinygrad.nn.state import get_parameters, get_state_dict
 from tinygrad.realize import create_schedule
+from tinygrad.features.multi import ring_allreduce, MultiLazyBuffer
+from random import randint
 import numpy as np
 from hypothesis import given, strategies as strat, settings
 
@@ -90,6 +92,24 @@ class TestMultiTensor(unittest.TestCase):
     fn = f(n)
     np.testing.assert_allclose(fX.numpy(), fn, rtol=1e-6, atol=1e-6)
 
+  @unittest.skipIf(CI and Device.DEFAULT == "CLANG", "clang is slow")
+  def test_fuzz_allreduce(self):
+    def naive_allreduce(lbs):
+      return [functools.reduce(lambda x,y: x.e(BinaryOps.ADD, y), [x.copy_to_device(lb.device) for x in lbs]) for lb in lbs]
+
+    random.seed(41)
+    for it in range(100):
+      for n in range(2, 4+1):
+        t = Tensor.rand(tuple([(n if i == 0 else 1) * randint(1, 10) for i in range(randint(1, 4))])).shard_(tuple([d0, d1, d2, d3][:n]), 0)
+        a = Tensor(MultiLazyBuffer(naive_allreduce(t.lazydata.lbs), 0))
+        b = Tensor(MultiLazyBuffer(ring_allreduce(ReduceOps.SUM, t.lazydata.lbs), 0))
+        diff = a - b
+        mean_err = diff.reshape((prod(diff.shape),)).abs().mean().numpy()
+        max_err = diff.reshape((prod(diff.shape),)).abs().max().numpy()
+        assert mean_err < 1e-6, f"big mean error, iteration {it}_{n}"
+        assert max_err < 1e-6, f"big max error, iteration {it}_{n}"
+
+
   def _test_matmul_shard_axis(self, shard_x, shard_w, device):
     X = Tensor.kaiming_uniform(N, N).realize()
     W = Tensor.kaiming_uniform(N, N).realize()