mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-08-29 17:36:07 +00:00
geohot
53a11f59ab
value-producing calls: the body is a plain parametric program that stores outputs into output PARAMs (slots after the input PARAMs). the RETURNED placeholders are inputs to the call, bound to the output PARAMs positionally wherever the call is resolved, and callers AFTER on them like normal buffers. gradient flows through the generic AFTER rule; everything is just Ops.CALL.
228 lines
8.5 KiB
Python
228 lines
8.5 KiB
Python
import gc, unittest
|
|
from tinygrad import Tensor, UOp, GlobalCounters, dtypes
|
|
from tinygrad.engine.jit import TinyJit
|
|
from tinygrad.helpers import Context
|
|
|
|
class TestMultiRamUsage(unittest.TestCase):
|
|
def setUp(self):
|
|
gc.collect()
|
|
self.baseline = GlobalCounters.mem_used
|
|
self.baseline_per_device = dict(GlobalCounters.mem_used_per_device)
|
|
self.N = 100
|
|
def assertUsed(self, amt, strict=True):
|
|
gc.collect()
|
|
used = GlobalCounters.mem_used - self.baseline
|
|
print(f"used {used} bytes")
|
|
if strict: self.assertEqual(used, amt)
|
|
else: self.assertLessEqual(used, amt)
|
|
def assertDeviceUsed(self, expected:dict[str, int]):
|
|
gc.collect()
|
|
for dev, amt in expected.items():
|
|
used = GlobalCounters.mem_used_per_device[dev] - self.baseline_per_device.get(dev, 0)
|
|
self.assertEqual(used, amt, f"device {dev}: expected {amt} bytes used, got {used}")
|
|
|
|
def test_zeros(self):
|
|
_ = Tensor.zeros(self.N, self.N).contiguous().realize()
|
|
self.assertUsed(self.N*self.N*4)
|
|
|
|
def test_zeros_del(self):
|
|
_ = Tensor.zeros(self.N, self.N).contiguous().realize()
|
|
del _
|
|
self.assertUsed(0)
|
|
|
|
def test_zeros_copy(self):
|
|
devices_2 = ("NULL:1", "NULL:2")
|
|
_ = Tensor.zeros(self.N, self.N).contiguous().to(devices_2).realize()
|
|
# NOTE: the first one on the DEFAULT device should be freed
|
|
self.assertUsed(self.N*self.N*4*2)
|
|
|
|
def test_zeros_shard(self, devices=("NULL:1", "NULL:2")):
|
|
_ = Tensor.zeros(self.N, self.N).contiguous().shard(devices, axis=0).realize()
|
|
self.assertUsed(self.N*self.N*4) # sharding should not increase total ram usage
|
|
def test_zeros_shard_self(self): self.test_zeros_shard(("NULL:0", "NULL:1"))
|
|
|
|
def test_zeros_contiguous_shard(self):
|
|
devices_2 = ("NULL:1", "NULL:2")
|
|
_ = Tensor.zeros(self.N, self.N).contiguous().shard(devices_2, axis=0).contiguous().realize()
|
|
self.assertUsed(self.N*self.N*4) # sharding should not increase total ram usage
|
|
|
|
def test_sharded_memory_replicated(self):
|
|
devices_4 = tuple(f"NULL:{i+1}" for i in range(4))
|
|
X = Tensor.ones(256).contiguous().realize()
|
|
self.assertUsed(256 * 4)
|
|
X.shard_(devices_4).realize()
|
|
self.assertUsed(256 * 4 * 4)
|
|
|
|
def test_sharded_memory_replicated_const(self):
|
|
devices_4 = tuple(f"NULL:{i+1}" for i in range(4))
|
|
X = Tensor.ones(256, buffer=False).realize()
|
|
self.assertUsed(0)
|
|
X.shard_(devices_4).realize()
|
|
self.assertUsed(0)
|
|
|
|
def test_sharded_memory_axis_const(self):
|
|
devices_4 = tuple(f"NULL:{i+1}" for i in range(4))
|
|
X = Tensor.ones(256, buffer=False).realize()
|
|
self.assertUsed(0)
|
|
X.shard_(devices_4, axis=0).realize()
|
|
self.assertUsed(0)
|
|
|
|
def test_zeros_per_device(self):
|
|
_ = Tensor.zeros(self.N, self.N, device="NULL").contiguous().realize()
|
|
self.assertDeviceUsed({"NULL": self.N*self.N*4})
|
|
|
|
def test_zeros_del_per_device(self):
|
|
_ = Tensor.zeros(self.N, self.N, device="NULL").contiguous().realize()
|
|
del _
|
|
self.assertDeviceUsed({"NULL": 0})
|
|
|
|
def test_zeros_copy_per_device(self):
|
|
devices_2 = ("NULL:1", "NULL:2")
|
|
_ = Tensor.zeros(self.N, self.N).contiguous().to(devices_2).realize()
|
|
self.assertDeviceUsed({"NULL:1": self.N*self.N*4, "NULL:2": self.N*self.N*4})
|
|
|
|
def test_zeros_shard_per_device(self):
|
|
devices_2 = ("NULL:1", "NULL:2")
|
|
_ = Tensor.zeros(self.N, self.N).contiguous().shard(devices_2, axis=0).realize()
|
|
self.assertDeviceUsed({"NULL:1": self.N*(self.N//2)*4, "NULL:2": self.N*(self.N//2)*4})
|
|
|
|
def test_sharded_memory_replicated_per_device(self):
|
|
devices_4 = tuple(f"NULL:{i+1}" for i in range(4))
|
|
X = Tensor.ones(256, device="NULL").contiguous().realize()
|
|
self.assertDeviceUsed({"NULL": 256*4})
|
|
X.shard_(devices_4).realize()
|
|
for d in devices_4:
|
|
self.assertDeviceUsed({d: 256*4})
|
|
|
|
def _test_matmul_half(self, dev_count:int):
|
|
N = 32
|
|
total_mem = {}
|
|
devs = tuple(f"NULL:{i}" for i in range(dev_count))
|
|
for dtype in {dtypes.float, dtypes.half}:
|
|
GlobalCounters.reset()
|
|
a = Tensor.empty((N, N), dtype=dtype, device=devs[0]).shard(devs, axis=0)
|
|
b = Tensor.empty((N, N), dtype=dtype, device=devs[0]).shard(devs, axis=None)
|
|
(a @ b).realize()
|
|
total_mem[dtype] = GlobalCounters.global_mem
|
|
self.assertEqual(total_mem[dtypes.half], total_mem[dtypes.float] // 2)
|
|
|
|
def test_matmul_half(self): self._test_matmul_half(dev_count=2)
|
|
def test_matmul_half_alt(self): self._test_matmul_half(dev_count=4)
|
|
|
|
def test_multi_layer_allreduce(self):
|
|
N = 32
|
|
devices_2 = ("NULL:1", "NULL:2")
|
|
|
|
def make_inp():
|
|
x = Tensor.zeros(N, N).contiguous().shard(devices_2, axis=None).realize()
|
|
w1 = Tensor.zeros(N, N).contiguous().shard(devices_2, axis=1).realize()
|
|
w2 = Tensor.zeros(N, N).contiguous().shard(devices_2, axis=0).realize()
|
|
return x, w1, w2
|
|
|
|
def run_layers(n_layers):
|
|
GlobalCounters.reset()
|
|
|
|
@TinyJit
|
|
def f(x, w1, w2):
|
|
for _ in range(n_layers):
|
|
x = (x @ w1 @ w2)
|
|
return x.contiguous()
|
|
|
|
for _ in range(3):
|
|
a = make_inp()
|
|
r = f(*a)
|
|
del a, r
|
|
|
|
gc.collect()
|
|
return GlobalCounters.mem_used
|
|
|
|
mem_2 = run_layers(2)
|
|
mem_4 = run_layers(4)
|
|
self.assertEqual(mem_2, mem_4, f"graph memory should not grow with layers: 2 layers={mem_2}, 4 layers={mem_4}")
|
|
|
|
def test_allreduce_cast_dtype_memory(self):
|
|
N = 32
|
|
devices_2 = ("NULL:1", "NULL:2")
|
|
mem = {}
|
|
for allreduce_cast in (0, 1):
|
|
GlobalCounters.reset()
|
|
with Context(ALLREDUCE_CAST=allreduce_cast, SCACHE=0):
|
|
x = Tensor.empty((N, N), dtype=dtypes.bfloat16, device="NULL:1").shard(devices_2, axis=0)
|
|
x.sum(0).realize()
|
|
mem[allreduce_cast] = GlobalCounters.global_mem
|
|
# with ALLREDUCE_CAST, allreduce copies happen in bf16 (2 bytes) instead of fp32 (4 bytes)
|
|
self.assertLess(mem[1], mem[0])
|
|
|
|
class TestMultiScalarALU(unittest.TestCase):
|
|
"""Test that tuple-device scalars work correctly in ALU with MULTI tensors (_shard scalar fix)."""
|
|
def test_multi_times_replicated_scalar(self):
|
|
devices = ("NULL:0", "NULL:1")
|
|
x = Tensor.ones(4).contiguous().shard(devices, axis=0)
|
|
s = Tensor(2.0).to(devices)
|
|
result = x * s
|
|
self.assertEqual(result.shape, (4,))
|
|
self.assertEqual(result.uop.axis, 0)
|
|
|
|
def test_multi_add_replicated_scalar(self):
|
|
devices = ("NULL:0", "NULL:1")
|
|
x = Tensor.ones(4).contiguous().shard(devices, axis=0)
|
|
s = Tensor(1.0).to(devices)
|
|
result = x + s
|
|
self.assertEqual(result.shape, (4,))
|
|
self.assertEqual(result.uop.axis, 0)
|
|
|
|
def test_multi_times_call_scalar(self):
|
|
"""Per-device scalar from a CALL (like FP8 local amax) used in ALU with MULTI."""
|
|
import functools
|
|
from tinygrad.uop.ops import Ops
|
|
devices = ("NULL:0", "NULL:1")
|
|
x = Tensor.ones(4, 4).contiguous().shard(devices, axis=0)
|
|
# simulate per-device scalar via CALL (strips MULTI from param body → no allreduce)
|
|
@functools.cache
|
|
def _fxn(x_p, device):
|
|
t = Tensor(x_p, device=device)
|
|
inner = Tensor(t.uop.src[0]) if t.uop.op is Ops.UNSHARD else t
|
|
return (inner.sum(),)
|
|
param = x.as_param(0)
|
|
fxn = _fxn(param.uop, x.device)
|
|
per_dev_scalar = Tensor(fxn[0].uop.call(x.uop).returned_outputs[0])
|
|
result = x * per_dev_scalar
|
|
self.assertEqual(result.shape, (4, 4))
|
|
self.assertEqual(result.uop.axis, 0)
|
|
result.realize()
|
|
|
|
class TestMultiAxis(unittest.TestCase):
|
|
def test_reshape_shard_invalid(self):
|
|
devices = ("NULL:0", "NULL:1")
|
|
t = Tensor.ones(4, 3).shard(devices, axis=0)
|
|
with self.assertRaises(RuntimeError, msg="reshape cannot move items between shards"):
|
|
t.reshape(3, 4).uop.axis
|
|
|
|
def test_reshape_shard_valid(self):
|
|
devices = ("NULL:0", "NULL:1")
|
|
t = Tensor.ones(4, 8).shard(devices, axis=0)
|
|
self.assertEqual(t.reshape(2, 16).uop.axis, 0)
|
|
self.assertEqual(t.reshape(2, 2, 8).uop.axis, 0)
|
|
|
|
def test_uop_shard_axis_none(self):
|
|
devices = ("NULL:0", "NULL:1")
|
|
u = Tensor.ones(8).contiguous().realize().uop
|
|
self.assertIsNone(u.shard(devices).axis)
|
|
self.assertEqual(u.shard(devices, 0).axis, 0)
|
|
|
|
def test_empty_like_sharded(self):
|
|
t = Tensor.ones(4, 8).shard(("NULL:0", "NULL:1"), axis=0)
|
|
e = t.empty_like()
|
|
self.assertEqual(e.shape, t.shape)
|
|
self.assertEqual(e.device, t.device)
|
|
self.assertEqual(e.uop.axis, 0)
|
|
self.assertTrue(e.uop.has_buffer_identity())
|
|
|
|
def test_symbolic_reshape_shard_axis(self):
|
|
rows = UOp.variable("rows", 1, 4).bind(3)
|
|
x = Tensor.empty(4, 2).shard(("NULL:1", "NULL:2"), axis=1)[:rows]
|
|
self.assertEqual(x.reshape(rows, 1, 2).uop.axis, 2)
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|