mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-26 23:38:58 -05:00
287 lines
11 KiB
Python
287 lines
11 KiB
Python
import functools
|
|
import unittest
|
|
from tinygrad import Tensor, Device, nn, GlobalCounters
|
|
from tinygrad.device import _BufferCopy
|
|
from tinygrad.helpers import CI
|
|
from tinygrad.jit import TinyJit
|
|
from tinygrad.nn.state import get_parameters
|
|
import numpy as np
|
|
|
|
d_zero = f"{Device.DEFAULT}:0"
|
|
d0, d1 = f"{Device.DEFAULT}:1", f"{Device.DEFAULT}:2"
|
|
d2, d3 = f"{Device.DEFAULT}:3", f"{Device.DEFAULT}:4"
|
|
devices_2 = (d0, d1)
|
|
devices_3 = (d0, d1, d2)
|
|
N = 128
|
|
|
|
# shard_x is "data parallel"
|
|
# shard_w is "model parallel"
|
|
|
|
@unittest.skipIf(CI and Device.DEFAULT in {"GPU", "CUDA", "METAL"}, "no GPU CI")
|
|
class TestMultiTensor(unittest.TestCase):
|
|
def test_to(self):
|
|
X = Tensor.ones(256).contiguous().realize()
|
|
X.to_((d0, d1))
|
|
for lb in X.lazydata.lbs:
|
|
assert lb.shape == (256,)
|
|
(X + X).realize()
|
|
|
|
def test_shard(self):
|
|
X = Tensor.ones(256).contiguous().realize()
|
|
X.shard_((d0, d1), 0)
|
|
for lb in X.lazydata.lbs:
|
|
assert lb.shape == (128,)
|
|
(X + X).realize()
|
|
|
|
def test_shard_same_device(self):
|
|
X = Tensor.ones(256).contiguous().realize()
|
|
X.shard_((d0, X.device), 0)
|
|
(X + X).realize()
|
|
|
|
def test_shard_plus_one_sum(self):
|
|
X = Tensor.ones(256).contiguous().realize()
|
|
X.shard_([d0, d1], 0)
|
|
(X + 1).sum().realize()
|
|
|
|
def test_shard_plus_one_sum_d_zero(self):
|
|
X = Tensor.ones(256).contiguous().realize()
|
|
X.shard_([d_zero, d1], 0)
|
|
(X + 1).sum().realize()
|
|
|
|
def test_numpy(self):
|
|
X = Tensor.ones(256)
|
|
X.shard_((d0, d1), 0)
|
|
np.testing.assert_allclose(X.numpy(), 1)
|
|
|
|
def _test_simple_add_axis(self, shard_x, shard_w):
|
|
X = Tensor.ones(256).contiguous().realize()
|
|
W = Tensor.ones(256).contiguous().realize()
|
|
X.shard_((d0, d1), shard_x)
|
|
W.shard_((d0, d1), shard_w)
|
|
O = X + W
|
|
np.testing.assert_allclose(O.numpy(), 2)
|
|
|
|
def test_simple_add(self): return self._test_simple_add_axis(None, None)
|
|
def test_simple_add_X(self): return self._test_simple_add_axis(0, None)
|
|
def test_simple_add_W(self): return self._test_simple_add_axis(None, 0)
|
|
def test_simple_add_XW(self): return self._test_simple_add_axis(0, 0)
|
|
|
|
def test_four_add(self):
|
|
X = Tensor.ones(256, 256).contiguous().realize()
|
|
W = Tensor.ones(256, 256).contiguous().realize()
|
|
X.shard_((d0, d1, d2, d3), 1)
|
|
W.shard_((d0, d1, d2, d3), None)
|
|
O = X + W
|
|
np.testing.assert_allclose(O.numpy(), 2)
|
|
|
|
def _test_simple_reduce_axis(self, shard_x):
|
|
X = Tensor.ones(256, 256).contiguous().realize()
|
|
X.shard_((d0, d1), shard_x)
|
|
O = X.sum(axis=1)
|
|
np.testing.assert_allclose(O.numpy(), 256)
|
|
|
|
def test_simple_reduce(self): return self._test_simple_reduce_axis(None)
|
|
def test_simple_reduce_0(self): return self._test_simple_reduce_axis(0)
|
|
def test_simple_reduce_1(self): return self._test_simple_reduce_axis(1)
|
|
|
|
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()
|
|
Xs = X.shard(device, shard_x)
|
|
Ws = W.shard(device, shard_w)
|
|
O = (Xs@Ws)
|
|
np.testing.assert_allclose(X.numpy() @ W.numpy(), O.to(Device.DEFAULT).numpy(), atol=1e-5)
|
|
|
|
def _test_double_matmul_shard_axis(self, shard_x, shard_w, device):
|
|
X = Tensor.kaiming_uniform(N, N).realize()
|
|
W1 = Tensor.kaiming_uniform(N, N).realize()
|
|
W2 = Tensor.kaiming_uniform(N, N).realize()
|
|
Xs = X.shard(device, shard_x)
|
|
W1s = W1.shard(device, shard_w)
|
|
W2s = W2.shard(device, shard_w)
|
|
O = (Xs@W1s)@W2s
|
|
np.testing.assert_allclose((X.numpy() @ W1.numpy()) @ W2.numpy(), O.to(Device.DEFAULT).numpy(), atol=1e-5)
|
|
|
|
def test_matmul_shard_none(self): return self._test_matmul_shard_axis(None, None, devices_2)
|
|
def test_matmul_shard_X_0(self): return self._test_matmul_shard_axis(0, None, devices_2)
|
|
def test_matmul_shard_X_1(self): return self._test_matmul_shard_axis(1, None, devices_2)
|
|
def test_matmul_shard_W_0(self): return self._test_matmul_shard_axis(None, 0, devices_2)
|
|
def test_matmul_shard_W_1(self): return self._test_matmul_shard_axis(None, 1, devices_2)
|
|
|
|
def test_matmul_shard_0_0(self): return self._test_matmul_shard_axis(0, 0, devices_2)
|
|
def test_matmul_shard_0_1(self): return self._test_matmul_shard_axis(0, 1, devices_2)
|
|
def test_matmul_shard_1_0(self): return self._test_matmul_shard_axis(1, 0, devices_2)
|
|
def test_matmul_shard_1_1(self): return self._test_matmul_shard_axis(1, 1, devices_2)
|
|
|
|
def test_double_matmul_shard_X_0(self): return self._test_double_matmul_shard_axis(0, None, devices_2)
|
|
def test_double_matmul_shard_X_1(self): return self._test_double_matmul_shard_axis(1, None, devices_2)
|
|
def test_double_matmul_shard_W_0(self): return self._test_double_matmul_shard_axis(None, 0, devices_2)
|
|
def test_double_matmul_shard_W_1(self): return self._test_double_matmul_shard_axis(None, 1, devices_2)
|
|
|
|
def test_conv_data_shard(self):
|
|
conv = nn.Conv2d(3, 16, 3, bias=False)
|
|
for p in get_parameters(conv): p.shard_((d0, d1))
|
|
fake_image = Tensor.rand((2, 3, 32, 32)).shard((d0, d1), axis=0)
|
|
out = conv(fake_image)
|
|
out.numpy()
|
|
|
|
def test_conv_bias_data_shard(self):
|
|
conv = nn.Conv2d(3, 16, 3)
|
|
for p in get_parameters(conv): p.shard_((d0, d1))
|
|
fake_image = Tensor.rand((2, 3, 32, 32)).shard((d0, d1), axis=0)
|
|
out = conv(fake_image)
|
|
out.numpy()
|
|
|
|
def test_backprop_conv(self):
|
|
conv = nn.Conv2d(3, 16, 3)
|
|
for p in get_parameters(conv): p.shard_((d0, d1))
|
|
optim = nn.optim.Adam(get_parameters(conv))
|
|
fake_image = Tensor.rand((2, 3, 32, 32)).shard((d0, d1), axis=0)
|
|
out = conv(fake_image)
|
|
optim.zero_grad()
|
|
out.mean().backward()
|
|
#for p in get_parameters(conv): p.grad.realize()
|
|
optim.step()
|
|
|
|
def test_lr_scheduler_OneCycleLR(self):
|
|
from extra.lr_scheduler import OneCycleLR
|
|
conv = nn.Conv2d(3, 16, 3)
|
|
for p in get_parameters(conv): p.shard_((d0, d1))
|
|
optim = nn.optim.SGD(get_parameters(conv))
|
|
lr_sched = OneCycleLR(optim, max_lr=0.1, pct_start=0.1, div_factor=100, final_div_factor=0.1, total_steps=10)
|
|
lr_sched.step()
|
|
|
|
def test_embedding(self):
|
|
B, T, embed_size, vocab_size = 4, 10, 20, 28
|
|
|
|
layer = nn.Embedding(vocab_size, embed_size)
|
|
x = Tensor(np.random.randint(0, vocab_size, (B, T)))
|
|
z = layer(x)
|
|
|
|
layer_sharded = nn.Embedding(vocab_size, embed_size)
|
|
layer_sharded.weight.assign(layer.weight.shard((d0, d1), axis=1)).realize()
|
|
x_sharded = x.shard((d0, d1), axis=None)
|
|
z_shard = layer_sharded(x_sharded)
|
|
|
|
np.testing.assert_allclose(z.numpy(), z_shard.numpy(), atol=1e-6, rtol=1e-6)
|
|
|
|
def test_rmsnorm(self):
|
|
from extra.models.llama import RMSNorm
|
|
B, T, embed_size = 4, 10, 20
|
|
|
|
layer_norm = RMSNorm(embed_size)
|
|
x = Tensor.rand((B, T, embed_size)).contiguous().realize()
|
|
y = layer_norm(x)
|
|
|
|
# for norm layers, the correct way to shard weights is duplication
|
|
layer_norm_sharded = RMSNorm(embed_size)
|
|
layer_norm_sharded.weight.shard_((d0, d1), axis=None).realize()
|
|
|
|
# if x is being sharded, then all-reduce is involved
|
|
x_sharded = x.shard((d0, d1), axis=2).realize()
|
|
y_shard = layer_norm_sharded(x_sharded).realize()
|
|
np.testing.assert_allclose(y.numpy(), y_shard.numpy(), atol=1e-6, rtol=1e-6)
|
|
|
|
# if x is being duplicated, then the operations remain inside each GPU
|
|
# which is the common case
|
|
x_sharded = x.shard((d0, d1), axis=None).realize()
|
|
y_shard = layer_norm_sharded(x_sharded).realize()
|
|
np.testing.assert_allclose(y.numpy(), y_shard.numpy(), atol=1e-6, rtol=1e-6)
|
|
|
|
def test_data_parallel_resnet(self):
|
|
import sys, pathlib
|
|
sys.path.append((pathlib.Path(__file__).parent.parent / "extra" / "models").as_posix())
|
|
from resnet import ResNet18
|
|
|
|
fake_image = Tensor.rand((2, 3, 224, 224))
|
|
fake_image_sharded = fake_image.shard((d0, d1), axis=0)
|
|
m = ResNet18()
|
|
m.load_from_pretrained()
|
|
real_output = m(fake_image).numpy()
|
|
for p in get_parameters(m): p.shard_((d0, d1)).realize()
|
|
GlobalCounters.reset()
|
|
shard_output = m(fake_image_sharded).realize()
|
|
assert shard_output.lazydata.lbs[0].shape == (1, 1000)
|
|
assert shard_output.lazydata.lbs[1].shape == (1, 1000)
|
|
shard_output_np = shard_output.numpy()
|
|
np.testing.assert_allclose(real_output, shard_output_np, atol=1e-6, rtol=1e-6)
|
|
|
|
def test_multi_tensor_jit_param(self):
|
|
@TinyJit
|
|
def jf(a, b) -> Tensor:
|
|
return (a + b).realize()
|
|
|
|
for _ in range(5):
|
|
a = Tensor.ones(256).contiguous().realize()
|
|
b = Tensor.ones(256).contiguous().realize()
|
|
a.shard_((d0, d1))
|
|
b.shard_((d0, d1))
|
|
c = jf(a, b)
|
|
np.testing.assert_allclose(c.numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
|
|
assert len(jf.jit_cache) > 0
|
|
|
|
def test_multi_tensor_jit_body(self):
|
|
@TinyJit
|
|
def jf() -> Tensor:
|
|
a = Tensor.ones(256).contiguous().realize()
|
|
b = Tensor.ones(256).contiguous().realize()
|
|
a.shard_((d0, d1))
|
|
b.shard_((d0, d1))
|
|
return (a + b).realize()
|
|
|
|
for _ in range(5):
|
|
r = jf()
|
|
np.testing.assert_allclose(r.numpy(), np.ones(256)+np.ones(256), atol=1e-4, rtol=1e-5)
|
|
assert len(jf.jit_cache) > 0
|
|
|
|
@unittest.skipIf(CI and Device.DEFAULT=="METAL", "no ICB in CI, creation of graph fails")
|
|
def test_multi_device_jit_graph(self):
|
|
if Device[d0].graph is None or Device[d1].graph is None: raise unittest.SkipTest("only test graphs")
|
|
|
|
@TinyJit
|
|
def jf(a: Tensor, b: Tensor, c: Tensor, d:Tensor):
|
|
# Create 80 entries on device 0: 2 batches.
|
|
for _ in range(40):
|
|
a = ((a + b).realize() + (a * b).realize()).realize()
|
|
# Create 80 entries on device 1: 2 batches.
|
|
for _ in range(40):
|
|
c = ((c + d).realize() + (c * d).realize()).realize()
|
|
# Create a copy from device 0 to 1: 1 entry.
|
|
a = a.to(d1).realize()
|
|
# Creates one last entry on device 1: 1 batch.
|
|
return (a + c).realize()
|
|
|
|
a = Tensor.randn(10, 10, device=d0).realize()
|
|
b = Tensor.randn(10, 10, device=d0).realize()
|
|
c = Tensor.randn(10, 10, device=d1).realize()
|
|
d = Tensor.randn(10, 10, device=d1).realize()
|
|
|
|
ref = jf(a, b, c, d).numpy()
|
|
for _ in range(5):
|
|
o = jf(a, b, c, d).numpy()
|
|
np.testing.assert_allclose(ref, o, atol=1e-4, rtol=1e-5)
|
|
|
|
graph_d0 = Device[d0].graph.func if isinstance(Device[d0].graph, functools.partial) else Device[d0].graph
|
|
graph_d1 = Device[d1].graph.func if isinstance(Device[d1].graph, functools.partial) else Device[d1].graph
|
|
# Checking that 2 graphs per device, 1 copy and 1 last graph on device 1 are created.
|
|
assert isinstance(jf.jit_cache[0].prg, graph_d0)
|
|
assert isinstance(jf.jit_cache[1].prg, graph_d0)
|
|
assert isinstance(jf.jit_cache[2].prg, graph_d1)
|
|
assert isinstance(jf.jit_cache[3].prg, graph_d1)
|
|
assert isinstance(jf.jit_cache[4].prg, _BufferCopy)
|
|
assert isinstance(jf.jit_cache[5].prg, graph_d1)
|
|
|
|
def test_uneven_shard(self):
|
|
for N in range(1, 6):
|
|
X = Tensor.rand(4, 1, 257).contiguous().realize()
|
|
n = X.numpy()
|
|
devices = tuple(f"{Device.DEFAULT}:{i}" for i in range(N))
|
|
X.shard_(devices, 2)
|
|
np.testing.assert_equal(X.numpy(), n)
|
|
np.testing.assert_equal(X.reshape(2, 2, 257).numpy(), n.reshape((2, 2, 257)))
|
|
np.testing.assert_equal(X.shrink(((0,2), (0, 1), (0,257))).numpy(), n[0:2, 0:1, 0:257])
|
|
np.testing.assert_equal(X.expand((4, 4, 257)).numpy(), np.tile(n, (1, 4, 1)))
|
|
np.testing.assert_equal(X.permute((0, 2, 1)).numpy(), np.transpose(n, (0, 2, 1)))
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main() |