mirror of https://github.com/commaai/tinygrad.git
381 lines
12 KiB
Python
381 lines
12 KiB
Python
#!/usr/bin/env python
|
|
import unittest
|
|
import numpy as np
|
|
from tinygrad import dtypes, Tensor, TinyJit, GlobalCounters, Variable
|
|
|
|
N = 200 # has to be bigger than the cache to fail
|
|
|
|
class TestAssign(unittest.TestCase):
|
|
def test_simple_assignment(self):
|
|
a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
|
|
b = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
|
|
a.realize()
|
|
b.realize()
|
|
ba1 = a.lazydata.base.realized
|
|
bb1 = b.lazydata.base.realized
|
|
a += b
|
|
a.realize()
|
|
ba2 = a.lazydata.base.realized
|
|
assert ba1 == ba2 and ba1 != bb1
|
|
np.testing.assert_allclose(a.numpy(), (np.arange(N*N)*2).reshape((N,N)))
|
|
|
|
def test_assign_zeros_good(self):
|
|
a = Tensor.zeros(10,10).contiguous()
|
|
a.assign(Tensor.ones(10,10))
|
|
b = Tensor.zeros(10,10).contiguous()
|
|
a.realize()
|
|
np.testing.assert_allclose(b.numpy(), 0)
|
|
|
|
def test_assign_zeros(self):
|
|
a = Tensor.zeros(10,10).contiguous()
|
|
b = Tensor.zeros(10,10).contiguous()
|
|
a.assign(Tensor.ones(10,10))
|
|
a.realize()
|
|
np.testing.assert_allclose(b.numpy(), 0)
|
|
|
|
def test_assign_add(self):
|
|
def f(x):
|
|
x += 1
|
|
x.realize()
|
|
x = Tensor([0])
|
|
f(x)
|
|
assert x.item() == 1
|
|
|
|
def test_assign_add_twice(self):
|
|
# NOTE: this has two kernels
|
|
def f(x):
|
|
x += 1
|
|
x += 1
|
|
x.realize()
|
|
x = Tensor([0])
|
|
f(x)
|
|
assert x.item() == 2
|
|
|
|
def test_assign_add_double(self):
|
|
def f(x):
|
|
x += 1
|
|
x.realize()
|
|
x = Tensor([0])
|
|
f(x)
|
|
out = x.item()
|
|
assert out == 1, f"expected 1, got {out}"
|
|
x = Tensor([0])
|
|
f(x)
|
|
out = x.item()
|
|
assert out == 1, f"expected 1, got {out}"
|
|
|
|
def test_assign_add_jit(self):
|
|
@TinyJit
|
|
def f(x):
|
|
x += 1
|
|
x.realize()
|
|
x = Tensor([0])
|
|
for _ in range(5): f(x)
|
|
assert x.item() == 5
|
|
|
|
def test_assign_add_jit_other(self):
|
|
@TinyJit
|
|
def f(x):
|
|
x += 1
|
|
x.realize()
|
|
x = Tensor([0])
|
|
for _ in range(5): f(x)
|
|
assert x.item() == 5
|
|
|
|
y = Tensor([0])
|
|
for _ in range(4): f(y)
|
|
assert y.item() == 4
|
|
|
|
def test_assign_other_jit(self):
|
|
@TinyJit
|
|
def f(x, a):
|
|
x.assign(a)
|
|
x.realize()
|
|
x = Tensor([0])
|
|
for i in range(1, 6):
|
|
f(x, x.full_like(i).contiguous()) # const would be implicitly folded without contiguous
|
|
assert x.item() == i
|
|
|
|
def test_assign_add_other_jit(self):
|
|
@TinyJit
|
|
def f(x, a):
|
|
x += a
|
|
x.realize()
|
|
x = Tensor([0])
|
|
a = 0
|
|
for i in range(1, 6):
|
|
a += i
|
|
f(x, x.full_like(i).contiguous())
|
|
assert x.item() == a
|
|
|
|
def test_assign_changes(self):
|
|
a = Tensor.ones(4).contiguous().realize()
|
|
old_a = a
|
|
a.assign(Tensor.full((4,), 2.).contiguous())
|
|
# NOTE: old_a is now 2, and this would match the behavior of pytorch
|
|
new = a + old_a
|
|
np.testing.assert_allclose(new.numpy(), 4)
|
|
|
|
def test_assign_diamond_cycle(self):
|
|
# NOTE: should *not* raise AssertionError from numpy
|
|
with self.assertRaisesRegex(RuntimeError, "cycle"):
|
|
a = Tensor.ones(4).contiguous().realize()
|
|
times_a = a*3
|
|
a.assign(Tensor.full((4,), 2.).contiguous())
|
|
new = a + (times_a-1)
|
|
np.testing.assert_allclose(new.numpy(), 4)
|
|
|
|
def test_assign_diamond_contiguous_cycle(self):
|
|
with self.assertRaisesRegex(RuntimeError, "cycle"):
|
|
a = Tensor.ones(4).contiguous().realize()
|
|
times_a = a*3
|
|
a.assign(Tensor.full((4,), 2.))
|
|
new = a.contiguous() + times_a-1
|
|
np.testing.assert_allclose(new.numpy(), 4)
|
|
|
|
def test_assign_diamond_possible(self):
|
|
a = Tensor.ones(4).contiguous().realize()
|
|
times_a = a*3
|
|
a.assign(Tensor.full((4,), 2.))
|
|
new = a + (times_a-1).contiguous()
|
|
np.testing.assert_allclose(new.numpy(), 4)
|
|
|
|
def test_assign_diamond_possible_contiguous(self):
|
|
a = Tensor.ones(4).contiguous().realize()
|
|
times_a = a*3
|
|
a.assign(Tensor.full((4,), 2.).contiguous())
|
|
new = a + (times_a-1).contiguous()
|
|
np.testing.assert_allclose(new.numpy(), 4)
|
|
|
|
def test_assign_diamond_both_contiguous(self):
|
|
a = Tensor.ones(4).contiguous().realize()
|
|
times_a = a*3
|
|
a.assign(Tensor.full((4,), 2.))
|
|
new = a.contiguous() + (times_a-1).contiguous()
|
|
np.testing.assert_allclose(new.numpy(), 4)
|
|
|
|
def test_assign_diamond_alt(self):
|
|
a = Tensor.ones(4).contiguous().realize()
|
|
a.assign(Tensor.full((4,), 2.).contiguous())
|
|
times_a = a*3
|
|
new = a + times_a
|
|
np.testing.assert_allclose(new.numpy(), 8)
|
|
|
|
def test_double_assign(self):
|
|
a = Tensor.ones(4).contiguous().realize()
|
|
a += 1
|
|
a += 1
|
|
np.testing.assert_allclose(a.numpy(), 3)
|
|
|
|
def test_crossover_assign(self):
|
|
a = Tensor.full((4,), 2).contiguous().realize()
|
|
b = Tensor.full((4,), 3).contiguous().realize()
|
|
a += b
|
|
b += a
|
|
Tensor.realize(a,b)
|
|
np.testing.assert_allclose(a.numpy(), 5)
|
|
np.testing.assert_allclose(b.numpy(), 8)
|
|
|
|
def test_assign_double_diamond(self):
|
|
a = Tensor.full((4,), 2).contiguous().realize()
|
|
b = Tensor.full((4,), 3).contiguous().realize()
|
|
a_prev = a*4
|
|
b_prev = b+3
|
|
b += a_prev.contiguous()
|
|
a += b_prev.contiguous()
|
|
Tensor.realize(a, b)
|
|
np.testing.assert_equal(b.numpy(), 11)
|
|
np.testing.assert_equal(a.numpy(), 8)
|
|
|
|
def test_assign_double_diamond_reduce(self):
|
|
a0 = Tensor.full((16, 16), 10).contiguous().realize()
|
|
a1 = Tensor.full((16, 16), 20).contiguous().realize()
|
|
b0 = Tensor.full((16, ), 1).contiguous().realize()
|
|
b1 = Tensor.full((16, ), 2).contiguous().realize()
|
|
|
|
r0 = (a0 - b1.contiguous()).sum(1)
|
|
r1 = (a1 - b0.contiguous()).sum(1)
|
|
b0.assign(r0 * b0)
|
|
b1.assign(r1 * b1)
|
|
Tensor.realize(b0, b1)
|
|
np.testing.assert_equal(b0.numpy(), 128)
|
|
np.testing.assert_equal(b1.numpy(), 608)
|
|
|
|
def test_crossunder_assign(self):
|
|
# NOTE: should *not* raise AssertionError from numpy
|
|
with self.assertRaisesRegex(RuntimeError, "cycle"):
|
|
a = Tensor.full((4,), 2).contiguous().realize()
|
|
b = Tensor.full((4,), 3).contiguous().realize()
|
|
c = a+9
|
|
a += b
|
|
b += c
|
|
Tensor.realize(a,b)
|
|
np.testing.assert_allclose(a.numpy(), 2+3)
|
|
np.testing.assert_allclose(b.numpy(), 3+2+9)
|
|
|
|
def test_assign_kv_cache(self):
|
|
bsz, max_context = 2, 8
|
|
|
|
class Attn:
|
|
@TinyJit
|
|
def __call__(self, xk:Tensor, start_pos:Variable):
|
|
seqlen = xk.shape[1]
|
|
if not hasattr(self, "cache_k"):
|
|
self.cache_k = Tensor.zeros(bsz, max_context, 1, 1).contiguous()
|
|
keys = self.cache_k.shrink((None, (0, start_pos), None, None)).cat(xk, dim=1).contiguous() if start_pos > 0 else xk
|
|
self.cache_k.assign(keys.pad((None,(0,max_context-start_pos-seqlen),None,None)).contiguous()).realize()
|
|
|
|
attn = Attn()
|
|
xk = Tensor.ones(bsz, 3, 1, 1).contiguous()
|
|
attn(xk, 0)
|
|
for i in range(3,6):
|
|
# copied from LLaMA
|
|
start_pos = Variable("start_pos", 1, max_context).bind(i)
|
|
xk = Tensor.ones(bsz, 1, 1, 1).contiguous()
|
|
attn(xk, start_pos)
|
|
|
|
out = attn.cache_k.flatten().numpy()
|
|
np.testing.assert_allclose(out, [1.,1.,1.,1.,1.,1.,0.,0.,1.,1.,1.,1.,1.,1.,0.,0.])
|
|
|
|
def test_assign_contiguous(self):
|
|
b = Tensor.rand(4,4).realize()
|
|
a = (Tensor.rand(4,4).realize() + 1)
|
|
kc = GlobalCounters.kernel_count
|
|
b.assign(a.contiguous()).realize()
|
|
assert GlobalCounters.kernel_count - kc == 2
|
|
|
|
def test_assign_contiguous_permute(self):
|
|
b = Tensor.rand(4,4).realize()
|
|
a = (Tensor.rand(4,4).realize() + 1).permute((1,0))
|
|
kc = GlobalCounters.kernel_count
|
|
b.assign(a.contiguous()).realize()
|
|
assert GlobalCounters.kernel_count - kc == 2
|
|
|
|
def test_permuted_assignment(self):
|
|
a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
|
|
b = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
|
|
a.realize()
|
|
b.realize()
|
|
ba1 = a.lazydata.base.realized
|
|
bb1 = b.lazydata.base.realized
|
|
with self.assertRaises((RuntimeError, AssertionError)):
|
|
a = a.permute(1,0)
|
|
a += b
|
|
a.realize()
|
|
ba2 = a.lazydata.base.realized
|
|
assert ba1 != ba2 and ba1 != bb1
|
|
np.testing.assert_allclose(a.numpy(), np.arange(N*N).reshape((N,N)) + np.arange(N*N).reshape((N,N)).transpose(1,0))
|
|
|
|
def test_post_permuted_assignment(self):
|
|
a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
|
|
b = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
|
|
a.realize()
|
|
b.realize()
|
|
#GlobalCounters.cache = []
|
|
ba1 = a.lazydata.base.realized # noqa: F841
|
|
bb1 = b.lazydata.base.realized # noqa: F841
|
|
with self.assertRaisesRegex(RuntimeError, "contiguous"):
|
|
a.assign(a.permute(1,0) + b) # this should not work!
|
|
a.realize()
|
|
ba2 = a.lazydata.base.realized # noqa: F841
|
|
# NOTE: don't test that it's assigned
|
|
#assert ba1 == ba2 and ba1 != bb1
|
|
np.testing.assert_allclose(a.numpy(), np.arange(N*N).reshape((N,N)) + np.arange(N*N).reshape((N,N)).transpose(1,0))
|
|
|
|
def test_simple_assignment_multioutput(self):
|
|
a = Tensor.randn(32, 32).realize()
|
|
b = Tensor.full((32, ), 1.).contiguous().realize()
|
|
c = Tensor.full((32, ), 2.).contiguous().realize()
|
|
d = Tensor.full((32, ), 3.).contiguous().realize()
|
|
|
|
r = a.sum(axis=1)
|
|
b.assign(r + b)
|
|
c.assign(r + c)
|
|
d.assign(r + d)
|
|
|
|
kc = GlobalCounters.kernel_count
|
|
Tensor.realize(b, c, d)
|
|
assert GlobalCounters.kernel_count - kc == 1
|
|
np.testing.assert_allclose(b.numpy(), a.sum(1).numpy()+1)
|
|
np.testing.assert_allclose(c.numpy(), a.sum(1).numpy()+2)
|
|
np.testing.assert_allclose(d.numpy(), a.sum(1).numpy()+3)
|
|
|
|
# NOTE: if the assign target is read/write in a single kernel, it should be contiguous
|
|
|
|
def test_permuted_assignment_correct(self):
|
|
a = Tensor.arange(4 * 4).reshape(4, 4).contiguous().realize()
|
|
b = Tensor.arange(4 * 4).reshape(4, 4).contiguous().realize()
|
|
# TODO: scheduler limitation, should NOT raise AssertionError from numpy.
|
|
with self.assertRaisesRegex(RuntimeError, "contiguous"):
|
|
a = a.permute(1, 0)
|
|
new_val = a + b
|
|
a.assign(new_val)
|
|
np.testing.assert_equal(a.numpy(), np.arange(4 * 4).reshape(4, 4).transpose(1, 0) + np.arange(4 * 4).reshape(4, 4))
|
|
|
|
def test_permuted_reduceop_child_dual_use(self):
|
|
a = Tensor.randn(32, 32, 32).realize()
|
|
b = Tensor.full((32, 32), 1.).contiguous().realize()
|
|
with self.assertRaisesRegex(RuntimeError, "contiguous"):
|
|
r = a.sum(axis=1)
|
|
b.assign(r + b.permute(1, 0))
|
|
b.realize()
|
|
|
|
def test_permuted_reduceop_multioutput_dual_use(self):
|
|
a = Tensor.randn(32, 32, 32).realize()
|
|
b = Tensor.full((32, 32), 1.).contiguous().realize()
|
|
c = Tensor.full((32, 32), 2.).contiguous().realize()
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "contiguous"):
|
|
r = a.sum(axis=1)
|
|
b_perm = b.permute(1, 0)
|
|
b.assign(r + b)
|
|
c.assign(r + b_perm)
|
|
Tensor.realize(b, c)
|
|
|
|
def test_permuted_reduceop_multioutput_dual_use_possible(self):
|
|
a = Tensor.randn(32, 32, 32, dtype=dtypes.int).realize()
|
|
b = Tensor.arange(32 * 32).reshape(32, 32).realize()
|
|
c = Tensor.arange(32 * 32).reshape(32, 32).realize()
|
|
|
|
kc = GlobalCounters.kernel_count
|
|
r = a.sum(axis=1)
|
|
b_perm = b.permute(1, 0)
|
|
b.assign(r + b)
|
|
c.assign(r + b_perm.contiguous())
|
|
Tensor.realize(b, c)
|
|
assert GlobalCounters.kernel_count - kc == 2
|
|
np.testing.assert_equal(b.numpy(), a.numpy().sum(1) + np.arange(32 * 32).reshape(32, 32))
|
|
np.testing.assert_equal(c.numpy(), a.numpy().sum(1) + np.arange(32 * 32).reshape(32, 32).transpose(1, 0))
|
|
|
|
def test_permuted_assignment_masked_view_possible(self):
|
|
a = Tensor.ones(4, 4).contiguous().realize()
|
|
b = a.shrink((None, (0, 2))).pad((None, (0, 2)), 2)
|
|
a.assign(a + b)
|
|
kc = GlobalCounters.kernel_count
|
|
a.realize()
|
|
assert GlobalCounters.kernel_count - kc == 1
|
|
np.testing.assert_equal(a.numpy(), np.ones((4, 4))+np.pad(np.ones((4, 4))[:, 0:2], ((0, 0), (0, 2)), constant_values=2))
|
|
|
|
def test_permuted_assignment_masked_view_not_contiguous(self):
|
|
a = Tensor.ones(4, 4).contiguous().realize()
|
|
with self.assertRaisesRegex(RuntimeError, "contiguous"):
|
|
b = a.shrink((None, (0, 2))).pad((None, (0, 2)), 2).permute(1, 0)
|
|
a.assign(a + b)
|
|
a.realize()
|
|
|
|
# TODO: is there a way to sneak in a permute such that it returns the wrong answer?
|
|
|
|
@unittest.skip("don't use output buffer, and mismatch dtype no longer supported")
|
|
def test_cast_assignment(self):
|
|
a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
|
|
a.realize()
|
|
oba1 = a.lazydata.base.output_buffer
|
|
a.assign(a.cast(dtypes.int32).realize())
|
|
a.realize()
|
|
oba2 = a.lazydata.base.output_buffer
|
|
assert oba1 is None and oba2 is None
|
|
np.testing.assert_allclose(a.numpy(), np.arange(N*N,dtype=np.int32).reshape((N,N)))
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|