mirror of
https://github.com/ml-explore/mlx.git
synced 2025-12-14 09:07:12 +08:00
27778156dc
* Added reduce scatter and all gather for nccl * fix unused import, delete unused file * small fix * deleted useless condition * fixed comments * fix bug in eval_gpu, renamed to sum_scatter, fix docs * final fix docs * remove and * Update mlx/distributed/mpi/mpi.cpp Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * fix broken set input output * fixes set output * typo * fix typo * no cpu, no gpu for reduce scatter --------- Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
315 lines
9.7 KiB
Python
315 lines
9.7 KiB
Python
# Copyright © 2025 Apple Inc.
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import mlx.core as mx
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import mlx.nn as nn
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import mlx_tests
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from mlx.nn.layers.distributed import shard_inplace, shard_linear
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from mlx.nn.utils import average_gradients
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class MLXDistributedCommonTestCase(mlx_tests.MLXTestCase):
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def test_average_gradients(self):
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original_all_sum = mx.distributed.all_sum
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n_calls = 0
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xtype = None
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def new_all_sum(x, **kwargs):
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nonlocal n_calls
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nonlocal xtype
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n_calls += 1
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if xtype is not None:
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self.assertEqual(xtype, x.dtype)
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return original_all_sum(x, **kwargs)
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mx.distributed.all_sum = new_all_sum
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try:
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grads = [mx.ones(10) for i in range(10)]
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new_grads = average_gradients(grads)
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mx.eval(new_grads)
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self.assertEqual(len(new_grads), 10)
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self.assertTrue(all(mx.all(g == 1) for g in new_grads))
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self.assertEqual(n_calls, 1)
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n_calls = 0
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new_grads = average_gradients(grads, all_reduce_size=4 * 50)
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mx.eval(new_grads)
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self.assertEqual(len(new_grads), 10)
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self.assertTrue(all(mx.all(g == 1) for g in new_grads))
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self.assertEqual(n_calls, 2)
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n_calls = 0
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new_grads = average_gradients(grads, all_reduce_size=0)
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mx.eval(new_grads)
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self.assertEqual(len(new_grads), 10)
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self.assertTrue(all(mx.all(g == 1) for g in new_grads))
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self.assertEqual(n_calls, 10)
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n_calls = 0
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xtype = mx.float16
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new_grads = average_gradients(
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grads, all_reduce_size=2 * 50, communication_type=mx.float16
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)
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mx.eval(new_grads)
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self.assertEqual(len(new_grads), 10)
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self.assertTrue(all(g.dtype == mx.float32 for g in new_grads))
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self.assertTrue(all(mx.all(g == 1) for g in new_grads))
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self.assertEqual(n_calls, 2)
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finally:
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mx.distributed.all_sum = original_all_sum
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def test_all_reduce(self):
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g = mx.distributed.init()
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dtypes = [
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(mx.int8, 0),
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(mx.uint8, 0),
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(mx.int32, 0),
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(mx.uint32, 0),
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(mx.float32, 1e-6),
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(mx.float16, 5e-3),
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(mx.bfloat16, 1e-1),
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]
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sizes = [
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(7,),
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(10,),
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(1024,),
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(1024, 1024),
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]
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key = mx.random.key(0)
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for dt, rtol in dtypes:
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for sh in sizes:
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x = (mx.random.uniform(shape=(g.size(),) + sh, key=key) * 10).astype(dt)
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# All sum
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y = mx.distributed.all_sum(x[g.rank()], group=g)
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z = x.sum(0)
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maxrelerror = (y - z).abs()
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if rtol > 0:
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maxrelerror /= z.abs()
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maxrelerror = maxrelerror.max()
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self.assertLessEqual(maxrelerror, rtol)
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# All max
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y = mx.distributed.all_max(x[g.rank()], group=g)
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z = x.max(0)
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self.assertTrue(mx.all(y == z))
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# All min
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y = mx.distributed.all_min(x[g.rank()], group=g)
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z = x.min(0)
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self.assertTrue(mx.all(y == z))
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def test_donation(self):
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x = mx.random.normal((1024,))
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mx.eval(x)
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mx.synchronize()
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mx.reset_peak_memory()
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scale = mx.array(2.0)
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y = mx.distributed.all_sum(x)
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mx.eval(y)
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mx.synchronize()
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all_sum_only = mx.get_peak_memory()
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y = mx.distributed.all_sum(x) * scale
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mx.eval(y)
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mx.synchronize()
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all_sum_with_binary = mx.get_peak_memory()
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self.assertEqual(all_sum_only, all_sum_with_binary)
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def test_shard_linear(self):
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# Seed the prng to have the same inputs and weights generated everywhere
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mx.random.seed(0xF0F0F0F0)
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# Prepare inputs
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world = mx.distributed.init()
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part = (
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slice(None),
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slice(
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world.rank() * 1024 // world.size(),
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(world.rank() + 1) * 1024 // world.size(),
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),
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)
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x = mx.random.normal((4, 1024))
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# Create and shard some linear layers
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lin = nn.Linear(1024, 1024, bias=True)
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slin1 = shard_linear(lin, "all-to-sharded")
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slin2 = shard_linear(lin, "sharded-to-all")
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y = lin(x)
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y1 = slin1(x)
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y2 = slin2(x[part])
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self.assertTrue(mx.allclose(y, y2, atol=self.atol, rtol=self.rtol))
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self.assertTrue(mx.allclose(y[part], y1, atol=self.atol, rtol=self.rtol))
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# And their quant versions (QuintizedMatmul is not supported on CUDA)
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if not mx.cuda.is_available():
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qlin = lin.to_quantized()
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slin1 = shard_linear(qlin, "all-to-sharded")
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slin2 = shard_linear(qlin, "sharded-to-all")
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y = qlin(x)
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y1 = slin1(x)
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y2 = slin2(x[part])
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self.assertTrue(mx.allclose(y, y2, atol=self.atol, rtol=self.rtol))
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self.assertTrue(mx.allclose(y[part], y1))
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# Check the backward works as expected
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def dummy_loss(model, x, y):
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return (model(x) * y).sum()
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mod = nn.Sequential(
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nn.Linear(128, 128),
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nn.Linear(128, 128),
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nn.Linear(128, 128),
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nn.Linear(128, 128),
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)
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smod = nn.Sequential(
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shard_linear(mod.layers[0], "all-to-sharded"),
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shard_linear(mod.layers[1], "sharded-to-all"),
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shard_linear(mod.layers[2], "all-to-sharded"),
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shard_linear(mod.layers[3], "sharded-to-all"),
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)
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grad1 = nn.value_and_grad(mod, dummy_loss)
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grad2 = nn.value_and_grad(smod, dummy_loss)
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x = mx.random.normal((4, 128))
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y = mx.random.normal((4, 128))
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l1, g1 = grad1(mod, x, y)
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l2, g2 = grad2(smod, x, y)
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mx.eval(l1, g1, l2, g2)
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part = slice(
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world.rank() * 128 // world.size(), (world.rank() + 1) * 128 // world.size()
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)
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self.assertTrue(mx.allclose(l1, l2))
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self.assertTrue(
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mx.allclose(
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g1["layers"][0]["weight"][part],
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g2["layers"][0]["weight"],
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atol=1e-6,
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rtol=1e-4,
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)
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)
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self.assertTrue(
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mx.allclose(
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g1["layers"][2]["weight"][part],
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g2["layers"][2]["weight"],
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atol=1e-6,
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rtol=1e-4,
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)
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)
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self.assertTrue(
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mx.allclose(
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g1["layers"][1]["weight"][:, part],
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g2["layers"][1]["weight"],
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atol=1e-6,
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rtol=1e-4,
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)
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)
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self.assertTrue(
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mx.allclose(
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g1["layers"][3]["weight"][:, part],
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g2["layers"][3]["weight"],
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atol=1e-6,
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rtol=1e-4,
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)
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)
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self.assertTrue(
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mx.allclose(
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g1["layers"][0]["bias"][part],
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g2["layers"][0]["bias"],
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atol=1e-6,
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rtol=1e-4,
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)
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)
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self.assertTrue(
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mx.allclose(
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g1["layers"][2]["bias"][part],
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g2["layers"][2]["bias"],
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atol=1e-6,
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rtol=1e-4,
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)
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)
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self.assertTrue(
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mx.allclose(
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g1["layers"][1]["bias"],
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g2["layers"][1]["bias"],
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atol=self.atol,
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rtol=self.rtol,
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)
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)
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self.assertTrue(
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mx.allclose(
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g1["layers"][3]["bias"],
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g2["layers"][3]["bias"],
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atol=self.atol,
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rtol=self.rtol,
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)
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)
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def test_shard_predicate(self):
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mx.random.seed(0xF0F0F0F0)
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class MyConv(nn.Module):
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def __init__(self, *args, **kwargs):
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super().__init__()
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self.aggregate = kwargs.pop("aggregate", False)
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self.conv = nn.Conv2d(*args, **kwargs)
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def __call__(self, x):
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x = self.conv(x)
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if self.aggregate:
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x = mx.distributed.all_sum(x)
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return x
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def sharding(path, weight):
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parts = path.split(".")
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even = int(parts[1]) % 2 == 0
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if even:
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return 0
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else:
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return -1 if parts[-1] != "bias" else None
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mod = nn.Sequential(
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MyConv(3, 128, kernel_size=3),
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MyConv(128, 128, kernel_size=3),
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MyConv(128, 128, kernel_size=3),
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MyConv(128, 3, kernel_size=3),
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)
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smod = nn.Sequential(
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MyConv(3, 128, kernel_size=3),
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MyConv(128, 128, kernel_size=3, aggregate=True),
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MyConv(128, 128, kernel_size=3),
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MyConv(128, 3, kernel_size=3, aggregate=True),
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)
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smod.update(mod.parameters())
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shard_inplace(smod, sharding)
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x = mx.random.normal((4, 16, 16, 3))
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y1 = mod(x)
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y2 = smod(x)
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self.assertTrue(mx.allclose(y1, y2, atol=1e-6, rtol=1e-4))
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def test_all_gather(self):
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world = mx.distributed.init()
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dtypes = [
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mx.int8,
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mx.uint8,
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mx.int32,
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mx.uint32,
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mx.float32,
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mx.float16,
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mx.bfloat16,
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]
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for dt in dtypes:
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x = mx.ones((2, 2, 4), dtype=dt)
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y = mx.distributed.all_gather(x)
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self.assertEqual(y.shape, (world.size() * 2, 2, 4))
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self.assertTrue(mx.all(y == 1))
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