mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-09 15:08:02 -05:00
ea1be2e4cd
* Modify tests and start work towards removing symbolic reshape * Refactor symbolic reshape * fix small error * much cleaner + fix more tests * Can remove this now * Update test_symbolic_ops and test_tiny * Couple more tests * Unused import * More tests and add EXPAND to Tensor.empty * Fix test beam search * all int * Fix rangeify by adding shrink * Remove OOB check and so fix test_symbolic_jit * test_symbolic_jit doesn't need OOB Context anymore either * Should remove that test now * Cleanups part 1 * fix linters * Final cleanups * Don't reassign inside for loop --------- Co-authored-by: chenyu <chenyu@fastmail.com>
319 lines
11 KiB
Python
319 lines
11 KiB
Python
import unittest
|
|
|
|
from test.helpers import assert_jit_cache_len
|
|
from tinygrad import Variable, Tensor, TinyJit
|
|
import numpy as np
|
|
|
|
class TestSymbolicJit(unittest.TestCase):
|
|
def test_plus1(self):
|
|
def f(a): return (a+1).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(3, 10)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = jf(a[:, :vi]).reshape(3, i).numpy()
|
|
expected = f(a[:, :i]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_add(self):
|
|
def f(a, b): return (a+b).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(3, 10)
|
|
b = Tensor.rand(3, 10)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = jf(a[:, :vi], b[:, :vi]).reshape(3, i).numpy()
|
|
expected = f(a[:, :i], b[:, :i]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_matmul(self):
|
|
def f(a, b): return (a@b).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(3, 10)
|
|
b = Tensor.rand(10, 5)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = jf(a[:, :vi], b[:vi, :]).numpy()
|
|
expected = f(a[:, :i], b[:i, :]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_mixed_with_no_symbol_kernel(self):
|
|
def f(a, b):
|
|
s = (a@b).realize()
|
|
s = (s+s).realize() # this one does not have symbols in input
|
|
return s
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(3, 10)
|
|
b = Tensor.rand(10, 5)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = jf(a[:, :vi], b[:vi, :]).numpy()
|
|
expected = f(a[:, :i], b[:i, :]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 2)
|
|
|
|
def test_attention(self):
|
|
def f(q, k, v): return Tensor.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)).realize()
|
|
jf = TinyJit(f)
|
|
q = Tensor.rand(2, 1, 4, 8)
|
|
k = Tensor.rand(2, 10, 4, 8)
|
|
v = Tensor.rand(2, 10, 4, 8)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = jf(q, k[:, :vi], v[:, :vi]).reshape(2, 4, 1, 8).numpy()
|
|
expected = f(q, k[:, :i], v[:, :i]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 5)
|
|
|
|
def test_cat_dim0(self):
|
|
def f(a, b): return a.cat(b, dim=0).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(10, 3)
|
|
b = Tensor.rand(2, 3)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = jf(a[:vi], b).reshape(i+2, 3).numpy()
|
|
expected = f(a[:i], b).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_cat_dim1(self):
|
|
def f(a, b): return a.cat(b, dim=1).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(3, 10)
|
|
b = Tensor.rand(3, 2)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = jf(a[:, :vi], b).reshape(3, i+2).numpy()
|
|
expected = f(a[:, :i], b).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_cat_dim0_two_vars(self):
|
|
def f(a, b): return a.cat(b, dim=0).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(10, 3)
|
|
b = Tensor.rand(10, 3)
|
|
for i in range(1, 5):
|
|
for j in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
vj = Variable("j", 1, 10).bind(j)
|
|
symbolic = jf(a[:vi], b[:vj]).reshape(i+j, 3).numpy()
|
|
expected = f(a[:i], b[:j]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_cat_dim1_two_vars(self):
|
|
def f(a, b): return a.cat(b, dim=1).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(3, 10)
|
|
b = Tensor.rand(3, 10)
|
|
for i in range(1, 5):
|
|
for j in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
vj = Variable("j", 1, 10).bind(j)
|
|
symbolic = jf(a[:, :vi], b[:, :vj]).reshape(3, i+j).numpy()
|
|
expected = f(a[:, :i], b[:, :j]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_two_vars_plus1_ij(self):
|
|
def f(a, b): return (a@b+1).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(10, 3)
|
|
b = Tensor.rand(3, 10)
|
|
for i in range(1, 5):
|
|
for j in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
vj = Variable("j", 1, 10).bind(j)
|
|
symbolic = jf(a[:vi, :], b[:, :vj]).reshape(i, j).numpy()
|
|
expected = f(a[:i, :], b[:, :j]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_two_vars_plus1_ji(self):
|
|
def f(a, b): return (a@b+1).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(10, 3)
|
|
b = Tensor.rand(3, 10)
|
|
for i in range(1, 5):
|
|
for j in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
vj = Variable("j", 1, 10).bind(j)
|
|
symbolic = jf(a[:vj, :], b[:, :vi]).reshape(j, i).numpy()
|
|
expected = f(a[:j, :], b[:, :i]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_jit_symbolic_shape_mismatch(self):
|
|
@TinyJit
|
|
def add(a, b): return (a+b).realize()
|
|
a = Tensor.rand(3, 10)
|
|
b = Tensor.rand(3, 10)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
add(a[:, :vi], b[:, :vi])
|
|
vi2 = Variable("i", 1, 10).bind(7)
|
|
a = Tensor.rand(3, 7)[:, :vi2]
|
|
bad = Tensor.rand(4, 7)[:, :vi2]
|
|
with self.assertRaises(AssertionError):
|
|
add(a, bad)
|
|
|
|
def test_shrink(self):
|
|
# shrink is a movement, so we pair it with a simple function to test the JIT interaction
|
|
def f(a): return (a+1).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(7, 11)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = a.shrink(((3,5),(vi,vi+2)))
|
|
symbolic = jf(symbolic).numpy()
|
|
expected = f(a.shrink(((3,5),(i,i+2)))).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_slice(self):
|
|
# slice is a movement, so we pair it with a simple function to test the JIT interaction
|
|
def f(a): return (a+1).realize()
|
|
jf = TinyJit(f)
|
|
a = Tensor.rand(7, 11)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = a[3:5, vi:vi+2]
|
|
symbolic = jf(symbolic).numpy()
|
|
expected = f(a[3:5, i:i+2]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_slice_var_shape(self):
|
|
def f(a): return (a+1).realize()
|
|
jf = TinyJit(f)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
a = Tensor.ones(vi, 11).contiguous()
|
|
symbolic = a[:, 1:2]
|
|
symbolic = jf(symbolic).reshape(i, 1).numpy()
|
|
expected = f(a.reshape(i, 11)[:, 1:2]).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
assert_jit_cache_len(jf, 1)
|
|
|
|
def test_ones_sum(self):
|
|
def f(a): return a.sum().realize()
|
|
jf = TinyJit(f)
|
|
t = Tensor.ones(10)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
symbolic = jf(t[:vi]).item()
|
|
expected = f(t[:i]).item()
|
|
np.testing.assert_equal(symbolic, expected)
|
|
|
|
def test_mean(self):
|
|
def f(a): return a.mean().realize()
|
|
def f0(a): return a.mean(0).realize()
|
|
def f1(a): return a.mean(1).realize()
|
|
jf = TinyJit(f)
|
|
jf0 = TinyJit(f0)
|
|
jf1 = TinyJit(f1)
|
|
a = Tensor.rand(10, 3)
|
|
b = Tensor.rand(10, 3)
|
|
c = Tensor.rand(10, 3)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
# axis = None
|
|
symbolic = jf(a[:vi]).numpy()
|
|
expected = a[:i].mean().numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
# axis = 0
|
|
symbolic = jf0(b[:vi]).numpy()
|
|
expected = b[:i].mean(0).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
# axis = 1
|
|
symbolic = jf1(c[:vi]).reshape(i).numpy()
|
|
expected = c[:i].mean(1).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
|
|
def test_mean_2d(self):
|
|
def f(a): return a.mean().realize()
|
|
def f0(a): return a.mean(0).realize()
|
|
def f1(a): return a.mean(1).realize()
|
|
jf = TinyJit(f)
|
|
jf0 = TinyJit(f0)
|
|
jf1 = TinyJit(f1)
|
|
a = Tensor.rand(10, 10)
|
|
b = Tensor.rand(10, 10)
|
|
c = Tensor.rand(10, 10)
|
|
for i in range(1, 5):
|
|
for j in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
vj = Variable("j", 1, 10).bind(j)
|
|
# axis = None
|
|
symbolic = jf(a[:vi, :vj]).numpy()
|
|
expected = a[:i, :j].mean().numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
# axis = 0
|
|
symbolic = jf0(b[:vi, :vj]).reshape(j).numpy()
|
|
expected = b[:i, :j].mean(0).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
# axis = 1
|
|
symbolic = jf1(c[:vi, :vj]).reshape(i).numpy()
|
|
expected = c[:i, :j].mean(1).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
|
|
def test_var(self):
|
|
def f(a): return a.var().realize()
|
|
def f0(a): return a.var(0).realize()
|
|
def f1(a): return a.var(1).realize()
|
|
jf = TinyJit(f)
|
|
jf0 = TinyJit(f0)
|
|
jf1 = TinyJit(f1)
|
|
a = Tensor.rand(10, 3)
|
|
b = Tensor.rand(10, 3)
|
|
c = Tensor.rand(10, 3)
|
|
for i in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
# axis = None
|
|
symbolic = jf(a[:vi]).numpy()
|
|
expected = a[:i].var().numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
# axis = 0
|
|
symbolic = jf0(b[:vi]).numpy()
|
|
expected = b[:i].var(0).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
# axis = 1
|
|
symbolic = jf1(c[:vi]).reshape(i).numpy()
|
|
expected = c[:i].var(1).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
|
|
def test_var_2d(self):
|
|
def f(a): return a.var().realize()
|
|
def f0(a): return a.var(0).realize()
|
|
def f1(a): return a.var(1).realize()
|
|
jf = TinyJit(f)
|
|
jf0 = TinyJit(f0)
|
|
jf1 = TinyJit(f1)
|
|
a = Tensor.rand(10, 10)
|
|
b = Tensor.rand(10, 10)
|
|
c = Tensor.rand(10, 10)
|
|
for i in range(1, 5):
|
|
for j in range(1, 5):
|
|
vi = Variable("i", 1, 10).bind(i)
|
|
vj = Variable("j", 1, 10).bind(j)
|
|
# axis = None
|
|
symbolic = jf(a[:vi, :vj]).numpy()
|
|
expected = a[:i, :j].var().numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
# axis = 0
|
|
symbolic = jf0(b[:vi, :vj]).reshape(j).numpy()
|
|
expected = b[:i, :j].var(0).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
# axis = 1
|
|
symbolic = jf1(c[:vi, :vj]).reshape(i).numpy()
|
|
expected = c[:i, :j].var(1).numpy()
|
|
np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|