all2all (#13902)

* all2all

* um

* fix

* x

* um

* simler

* mypy

* fix

* t

* cmnts

test/external/external_benchmark_multitensor_allreduce.py

@@ -1,7 +1,7 @@
 from tinygrad import Tensor, Device, GlobalCounters, TinyJit, dtypes
-from tinygrad.helpers import getenv, Context, RING, DEBUG
+from tinygrad.helpers import getenv, Context, DEBUG
 
-def test(devs: list[str], N: int, iters:int = 10):
+def test(devs: list[str], N: int, iters:int = 10, name:str = "allreduce"):
   @TinyJit
   def f(t: Tensor) -> Tensor: t.sum(0).realize()
 
@@ -17,39 +17,33 @@ def test(devs: list[str], N: int, iters:int = 10):
     i_secs = GlobalCounters.time_sum_s
     i_gflops = GlobalCounters.global_ops/i_secs/10**9
     i_gbs = (N*4)/i_secs/10**9
-    print(f"{'ring_allreduce' if RING >= 2 else 'naive_allreduce'} iter {i+1}/{iters}: {i_secs:.6f} sec {i_gflops:.2f} GFLOP/s {i_gbs:.2f} GB/s")
+    print(f"{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 run(sz, n_gpus=6, iters=10, use_ring=False):
+def run(sz, n_gpus=6, iters=10, ring=0, all2all=0):
   devs = tuple([f"{Device.DEFAULT}:{x}" for x in range(n_gpus)])
   N = sz // dtypes.float32.itemsize
-  with Context(RING=(2 if use_ring else 0), DEBUG=max(DEBUG.value, 2)): return test(devs, N, iters=iters)
+  name = "all2all" if all2all else ("ring" if ring else "naive")
+  with Context(RING=(2 if ring else 0), ALL2ALL=(2 if all2all else 0), JIT_BATCH_SIZE=0, DEBUG=max(DEBUG.value, 2)):
+    return test(devs, N, iters=iters, name=name)
 
 def main():
-  ONLY_RING = getenv("ONLY_RING", 0)
   n_gpus = getenv("GPUS", 6)
   iters = getenv("ITERS", 10)
+  sz = getenv("SZ", 1000) * 10**6 # size of data on each gpu
+  print(f"Using {sz/10**9:.2f} GB of numbers on each of {n_gpus} GPUs, {n_gpus*sz/10**9:.2f} GB total.")
 
-  if getenv("BENCHMARK_SPLIT"):
-    l, r = 0, 512
-    while r - l > 1:
-      m = (l + r) // 2
-      (ring_gflops, ring_gbs, ring_secs) = run(m * 1024 * 4, n_gpus=n_gpus, iters=100, use_ring=True)
-      (naive_gflops, naive_gbs, naive_secs) = run(m * 1024 * 4, n_gpus=n_gpus, iters=100, use_ring=False)
-      if ring_secs > naive_secs: l = m
-      else: r = m
-    print("Better split", r * 1024, "elements")
-  else:
-    sz = getenv("SZ", 1000) * 10**6 # size of data on each gpu
-    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) = run(sz, use_ring=True, n_gpus=n_gpus, iters=iters)
-    if not ONLY_RING: (naive_gflops, naive_gbs, naive_secs) = run(sz, use_ring=False, n_gpus=n_gpus, iters=iters)
-    print(f"Ring:\n  {ring_secs:.6f} seconds/iter\n  {ring_gflops:.2f} GFLOP/s\n  {ring_gbs:.2f} GB/s")
-    if not ONLY_RING: print(f"Naive:\n  {naive_secs:.6f} seconds/iter\n  {naive_gflops:.2f} GFLOP/s\n  {naive_gbs:.2f} GB/s")
+  results = {}
+  for name, kwargs in [("naive", {}), ("ring", {"ring": 2}), ("all2all", {"all2all": 2})]:
+    results[name] = run(sz, n_gpus=n_gpus, iters=iters, **kwargs)
+
+  print("\n=== RESULTS ===")
+  for name, (gflops, gbs, secs) in results.items():
+    print(f"{name.upper()}:\n  {secs:.6f} seconds/iter\n  {gflops:.2f} GFLOP/s\n  {gbs:.2f} GB/s")
 
 if __name__ == "__main__":
   main()

test/test_multitensor.py

@@ -256,6 +256,11 @@ class TestMultiTensor(unittest.TestCase):
       a,b = _test_allreduce(Tensor.rand(256, 256))
       np.testing.assert_almost_equal(a.numpy(), b.numpy(), decimal=5)
 
+  def test_allreduce_all2all(self):
+    with Context(ALL2ALL=2):
+      a,b = _test_allreduce(Tensor.rand(256, 256))
+      np.testing.assert_almost_equal(a.numpy(), b.numpy(), decimal=5)
+
   def test_copy_jit(self):
     @TinyJit
     def copy_tensor(x:Tensor): return (x.to(f"{x.device.split(':')[0]}:1") + 1)

tinygrad/helpers.py

@@ -181,8 +181,8 @@ JIT, JIT_BATCH_SIZE = ContextVar("JIT", 2 if OSX and ARCH_X86 else 1), ContextVa
 WINO, CAPTURING, TRACEMETA = ContextVar("WINO", 0), ContextVar("CAPTURING", 1), ContextVar("TRACEMETA", 1)
 USE_TC, TC_SELECT, TC_OPT, AMX = ContextVar("TC", 1), ContextVar("TC_SELECT", -1), ContextVar("TC_OPT", 0), ContextVar("AMX", 0)
 TRANSCENDENTAL, NOLOCALS = ContextVar("TRANSCENDENTAL", 1), ContextVar("NOLOCALS", 0)
-SPLIT_REDUCEOP, NO_MEMORY_PLANNER, RING = ContextVar("SPLIT_REDUCEOP", 1), ContextVar("NO_MEMORY_PLANNER", 0), ContextVar("RING", 1)
-LRU = ContextVar("LRU", 1)
+SPLIT_REDUCEOP, NO_MEMORY_PLANNER, LRU = ContextVar("SPLIT_REDUCEOP", 1), ContextVar("NO_MEMORY_PLANNER", 0), ContextVar("LRU", 1)
+RING, ALL2ALL = ContextVar("RING", 1), ContextVar("ALL2ALL", 0)
 CACHELEVEL, IGNORE_BEAM_CACHE, DEVECTORIZE = ContextVar("CACHELEVEL", 2), ContextVar("IGNORE_BEAM_CACHE", 0), ContextVar("DEVECTORIZE", 1)
 VALIDATE_WITH_CPU, DISABLE_FAST_IDIV = ContextVar("VALIDATE_WITH_CPU", 0), ContextVar("DISABLE_FAST_IDIV", 0)
 CORRECT_DIVMOD_FOLDING, FUSE_OPTIM = ContextVar("CORRECT_DIVMOD_FOLDING", 0), ContextVar("FUSE_OPTIM", 0)

tinygrad/runtime/graph/hcq.py

@@ -1,6 +1,6 @@
-import collections, time
+import collections, itertools, time
 from typing import Any, cast
-from tinygrad.helpers import round_up, PROFILE, merge_dicts, getenv, dedup, suppress_finalizing
+from tinygrad.helpers import round_up, PROFILE, ALL2ALL, merge_dicts, getenv, dedup, suppress_finalizing
 from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQSignal, HCQBuffer, HWQueue, HCQArgsState, BumpAllocator, MMIOInterface
 from tinygrad.device import Buffer, BufferSpec, Compiled, Device, ProfileGraphEntry, ProfileGraphEvent
 from tinygrad.dtype import dtypes
@@ -49,7 +49,9 @@ class HCQGraph(MultiGraphRunner):
     self.ji_schedule: dict[int, tuple[HCQCompiled, HWQueue, list, list, HCQSignal, int|None]] = {}
 
     self.comp_queues: dict[HCQCompiled, HWQueue] = {dev: dev.hw_compute_queue_t() for dev in self.devices}
-    self.copy_queues: dict[HCQCompiled, HWQueue] = {} # lazy allocation
+    self.copy_queues: dict[tuple[HCQCompiled, int], HWQueue] = {} # lazy allocation, keyed by (device, queue_idx)
+    self.num_copy_queues: int = getenv("HCQ_NUM_SDMA", 2 if ALL2ALL >= 1 else 1)
+    self.copy_queue_cnt: collections.defaultdict[HCQCompiled, itertools.count] = collections.defaultdict(itertools.count)
 
     self.signals: dict[Any, HCQSignal] = {**{dev: dev.new_signal(value=0) for dev in self.devices if not dev._is_cpu()},
       **{"KICK": self.devices[0].new_signal(value=0)}, **{dev: self.devices[0].new_signal(value=0) for dev in self.devices if dev._is_cpu()}}
@@ -85,7 +87,8 @@ class HCQGraph(MultiGraphRunner):
         enqueue_queue = self.comp_queues[enqueue_dev]
       else:
         assert (enqueue_dev.hw_copy_queue_t is not None), "device must implement a copy queue"
-        enqueue_queue = self.copy_queues.setdefault(enqueue_dev, enqueue_dev.hw_copy_queue_t())
+        queue_idx = next(self.copy_queue_cnt[enqueue_dev]) % self.num_copy_queues
+        enqueue_queue = self.copy_queues.setdefault((enqueue_dev, queue_idx), enqueue_dev.hw_copy_queue_t(queue_idx=queue_idx))
 
       out_signal = self.signals.setdefault(enqueue_queue, self.devices[0].new_signal(value=0))
 
@@ -175,14 +178,17 @@ class HCQGraph(MultiGraphRunner):
 
     for dev in self.devices:
       for dep_dev in list(self.copy_to_devs[dev]) + [dev]:
-        if dep_dev in self.copy_queues: self.comp_queues[dev].wait(self.signals[(copy_q:=self.copy_queues[dep_dev])], cast(int, last_j[copy_q]) + 1)
+        for copy_q in self._dev_copy_queues(dep_dev):
+          if copy_q in self.signals: self.comp_queues[dev].wait(self.signals[copy_q], cast(int, last_j[copy_q]) + 1)
 
       self.comp_queues[dev].signal(self.virt_timeline_signals[dev], self.virt_timeline_vals[dev] + 1).bind(dev)
-      if dev in self.copy_queues: self.copy_queues[dev].bind(dev)
+      for copy_q in self._dev_copy_queues(dev): copy_q.bind(dev)
 
     self.last_timeline: dict[HCQCompiled, tuple[HCQSignal, int]] = {dev: (dev.timeline_signal, 0) for dev in self.devices}
     self.queue_signals_to_reset = [self.signals[q] for q in list(self.comp_queues.values()) + list(self.copy_queues.values()) if q in self.signals]
 
+  def _dev_copy_queues(self, dev): return [q for (d, _), q in self.copy_queues.items() if d == dev]
+
   def __call__(self, input_rawbuffers: list[Buffer], var_vals: dict[str, int], wait=False) -> float|None:
     # Wait and restore signals
     self.kickoff_value += 1
@@ -205,8 +211,7 @@ class HCQGraph(MultiGraphRunner):
 
     for dev in self.devices:
       self.comp_queues[dev].submit(dev, hcq_var_vals_local:=hcq_var_vals|self.fixedvars.get(dev, {}))
-      if (copy_queue:=self.copy_queues.get(dev, None)) is not None: copy_queue.submit(dev, hcq_var_vals_local)
-
+      for copy_queue in self._dev_copy_queues(dev): copy_queue.submit(dev, hcq_var_vals_local)
       self.last_timeline[dev] = (dev.timeline_signal, dev.next_timeline())
 
     if wait:

tinygrad/runtime/support/hcq.py

@@ -354,7 +354,7 @@ class HCQCompiled(Compiled, Generic[SignalType]):
   cpu_devices: list[HCQCompiled] = []
 
   def __init__(self, device:str, allocator:HCQAllocatorBase, compilers:CompilerSet, runtime, signal_t:Type[SignalType],
-               comp_queue_t:Callable[[], HWQueue], copy_queue_t:Callable[[], HWQueue]|None=None, kernargs_size=(16 << 20), sigalloc_size=0x1000):
+               comp_queue_t:Callable[..., HWQueue], copy_queue_t:Callable[..., HWQueue]|None=None, kernargs_size=(16 << 20), sigalloc_size=0x1000):
     self.device_id:int = int(device.split(":")[1]) if ":" in device else 0
 
     from tinygrad.runtime.graph.hcq import HCQGraph

tinygrad/schedule/multi.py

@@ -1,6 +1,6 @@
 from typing import cast
 import functools, itertools, operator
-from tinygrad.helpers import all_same, all_int, prod, DEBUG, RING, getenv
+from tinygrad.helpers import all_same, all_int, prod, DEBUG, RING, ALL2ALL, getenv
 from tinygrad.uop.ops import Ops, UOp, sint, PatternMatcher, UPat, GroupOp, graph_rewrite_map, graph_rewrite
 from tinygrad.device import Device
 
@@ -35,45 +35,49 @@ def handle_allreduce(buf:UOp, red:UOp) -> UOp|None:
   if not isinstance(buf.device, tuple): return None
   assert all_int(buf.shape), f"does not support symbolic shape {buf.shape}"
   n_lbs, shape, numel = len(buf.device), buf.shape, prod(buf.shape)
+
   # ring allreduce doesn't provide a benefit with only 2 nodes or where number of elements is less than 256k (empirically)
   # fallback to naive allreduce to save on kernel dispatch, chunking and reassembling chunks.
-  use_ring = (RING >= 2 or (n_lbs > 2 and numel > getenv("RING_ALLREDUCE_THRESHOLD", 256_000) and RING >= 1))
-  if DEBUG >= 2: print(f"{'RING ALLREDUCE' if use_ring else 'NAIVE ALLREDUCE'} {n_lbs}x{numel} | {buf.dtype}")
+  use_all2all = (ALL2ALL >= 2 or (n_lbs > 2 and numel > getenv("RING_ALLREDUCE_THRESHOLD", 256_000) and ALL2ALL >= 1))
+  use_ring = not use_all2all and (RING >= 2 or (n_lbs > 2 and numel > getenv("RING_ALLREDUCE_THRESHOLD", 256_000) and RING >= 1))
+  if DEBUG >= 2: print(f"{'ALL2ALL' if use_all2all else 'RING' if use_ring else 'NAIVE'} ALLREDUCE {n_lbs}x{numel} | {buf.dtype}")
 
   # contiguous before we copy it
   buf = buf.contiguous()
 
-  # copy to all devices. if you shrink later, that'll be handled
-  if not use_ring: return functools.reduce(lambda x,y: x.alu(red.arg, y),
-                                           [UOp(Ops.COPY, buf.dtype, (buf.mselect(i), red.src[1])) for i in range(len(buf.device))])
+  # naive: copy to all devices. if you shrink later, that'll be handled
+  if not use_ring and not use_all2all:
+    return functools.reduce(lambda x,y: x.alu(red.arg, y), [UOp(Ops.COPY, buf.dtype, (buf.mselect(i), red.src[1])) for i in range(n_lbs)])
 
-  # new ring reduce
+  # chunk data into n_lbs pieces
   factor = next((f for f in [32, 16, 8, 4, 2] if numel % f == 0), 1)
   base, left = (numel // factor) // n_lbs, (numel // factor) % n_lbs
-  chunk_sizes = [(base + 1) * factor] * left + [base * factor] * (n_lbs - left)
-  chunks = list(itertools.pairwise(itertools.accumulate(chunk_sizes, initial=0)))
+  chunks = list(itertools.pairwise(itertools.accumulate([(base + 1) * factor] * left + [base * factor] * (n_lbs - left), initial=0)))
 
-  # extract chunks and scatter-reduce
+  # reduce-scatter
   reduced_chunks = []
   for i,(s,e) in enumerate(chunks):
-    chunk = buf.reshape((numel,)).shrink(((s,e),))
-    reduced_chunk = chunk
-    for step in range(n_lbs-1):
-      src, dest = (i+step)%n_lbs, (i+step+1)%n_lbs
-      # copy the chunk from the src device to the dest (operating device), and select the chunk on the dest device
-      reduced_chunk = reduced_chunk.copy_to_device(buf.device[dest], src if isinstance(reduced_chunk.device, tuple) else None) \
-        .alu(red.arg, chunk.copy_to_device(buf.device[dest], dest))
-    reduced_chunks.append(reduced_chunk)
+    if use_all2all:
+      chunks_on_i = [buf.mselect(j).reshape((numel,)).shrink(((s,e),)).copy_to_device(buf.device[i]) for j in range(n_lbs)]
+      reduced_chunks.append(functools.reduce(lambda x,y: x.alu(red.arg, y), chunks_on_i))
+    else:
+      chunk, reduced = buf.reshape((numel,)).shrink(((s,e),)), buf.reshape((numel,)).shrink(((s,e),))
+      for step in range(n_lbs-1):
+        src, dest = (i+step)%n_lbs, (i+step+1)%n_lbs
+        cp = reduced.copy_to_device(buf.device[dest], src if isinstance(reduced.device, tuple) else None)
+        reduced = cp.alu(red.arg, chunk.copy_to_device(buf.device[dest], dest))
+      reduced_chunks.append(reduced)
 
   # allgather
   copied_chunks = []
-  for i,c in enumerate(reduced_chunks):
-    this_chunk: list[UOp|None] = [None] * len(buf.device)
-    this_chunk[(i+len(buf.device)-1)%n_lbs] = c
-    for step in range(n_lbs-1):
-      dest = (i+step)%n_lbs
-      this_chunk[dest] = c = c.copy_to_device(buf.device[dest])
-    copied_chunks.append(UOp(Ops.MSTACK, buf.dtype, tuple(cast(list[UOp], this_chunk))))
+  for i,rc in enumerate(reduced_chunks):
+    if use_all2all: copied_chunks.append(UOp(Ops.MSTACK, buf.dtype, tuple(rc.copy_to_device(buf.device[j]) for j in range(n_lbs))))
+    else:
+      this_chunk: list[UOp|None] = [None] * n_lbs
+      this_chunk[(i+n_lbs-1)%n_lbs] = rc
+      for step in range(n_lbs-1):
+        this_chunk[(i+step)%n_lbs] = rc = rc.copy_to_device(buf.device[(i+step)%n_lbs])
+      copied_chunks.append(UOp(Ops.MSTACK, buf.dtype, tuple(cast(list[UOp], this_chunk))))
 
   # reassemble
   pads = [((s,numel-e),) for s,e in chunks]