openpilot1/tinygrad_repo/test/test_symbolic_shapetracker.py

173 lines
6.3 KiB
Python

import unittest
from tinygrad.shape.shapetracker import ShapeTracker, View
from tinygrad.shape.symbolic import Variable
from tinygrad.tensor import Tensor
class TestSymbolic(unittest.TestCase):
def test_symbolic_st(self):
x = Variable("x", 1, 100)
st = ShapeTracker.from_shape((x, 3))
assert st.shape == (x, 3)
assert st.real_strides() == (3, 1)
def test_expr_idxs(self):
x = Variable("x", 1, 100)
st = ShapeTracker.from_shape((x, 3))
idxs = [Variable("x", 0, 100), Variable("y", 0, 100)]
e1, e2 = st.expr_idxs(idxs)
assert e1.render() == "((x*3)+y)"
assert e2.render() == "1"
st = st.permute((1, 0))
e1, e2 = st.expr_idxs(idxs)
assert e1.render() == "((y*3)+x)"
assert e2.render() == "1"
def test_cat_dim0_strides(self):
i = Variable("i", 1, 5).bind(3)
j = Variable("j", 1, 5).bind(3)
k = Variable("k", 1, 5).bind(3)
t = Tensor.rand(3, 4).reshape(i, 4).cat(Tensor.rand(3, 4).reshape(j, 4), dim=0).cat(Tensor.rand(3, 4).reshape(k, 4), dim=0)
st = t.lazydata.st
assert st.shape == (i+j+k, 4)
assert st.real_strides() == (4, 1)
t = Tensor.rand(3, 3).reshape(i, 3).cat(Tensor.rand(3, 3).reshape(i, 3), dim=0).cat(Tensor.rand(3, 3), dim=0)
st = t.lazydata.st
assert st.shape == (2*i+3, 3)
assert st.real_strides() == (3, 1)
def test_cat_dim1_strides(self):
i = Variable("i", 1, 5).bind(4)
j = Variable("j", 1, 5).bind(4)
k = Variable("k", 1, 5).bind(4)
t = Tensor.rand(3, 4).reshape(3, i).cat(Tensor.rand(3, 4).reshape(3, j), dim=1).cat(Tensor.rand(3, 4).reshape(3, k), dim=1)
st = t.lazydata.st
assert st.shape == (3, i+j+k)
assert st.real_strides() == (i+j+k, 1)
class TestSymbolicVarVals(unittest.TestCase):
def test_var_vals_empty(self):
assert ShapeTracker.from_shape((3, 4, 5)).var_vals == {}
def test_var_vals_shape(self):
x = Variable("x", 1, 100).bind(3)
assert ShapeTracker.from_shape((x, 3)).var_vals == {Variable("x", 1, 100): 3}
def test_var_vals_offset(self):
x = Variable("x", 1, 100).bind(3)
st = ShapeTracker.from_shape((4, 3)).shrink(((x, x+1), (0, 3)))
assert st.real_offset() == x * 3
assert st.var_vals == {Variable("x", 1, 100): 3}
def test_var_vals_mask(self):
x = Variable("x", 1, 100).bind(3)
view = View.create(shape=(3,4), strides=(4,1), offset=0, mask=((0, x), (0, 4)))
st = ShapeTracker(views=(view,))
assert st.var_vals == {Variable("x", 1, 100): 3}
def test_var_vals_complex(self):
x = Variable("x", 1, 100).bind(3)
y = Variable("y", 1, 100).bind(4)
z = Variable("z", 1, 100).bind(5)
st = ShapeTracker.from_shape((x, 5, y)).shrink(((0, x), (z, z+1), (0, 3)))
assert st.real_offset() == y * z
assert st.var_vals == {Variable("x", 1, 100): 3, Variable("y", 1, 100):4, Variable("z", 1, 100): 5}
def test_shrink_reshape(self):
x = Variable("x", 1, 100).bind(3)
st = ShapeTracker.from_shape((10, 10, 10)).shrink(((x, x+3), (3, 7), (2, 5)))
st = st.reshape((3*4*3,))
assert st.var_vals == {Variable("x", 1, 100): 3}
class TestShapeTrackerUnbind(unittest.TestCase):
def test_view_unbind(self):
v = Variable("v", 1, 100)
bv = Variable("v", 1, 100).bind(3)
assert View.create(shape=(bv, 4)).unbind() == View.create(shape=(v, 4))
def test_reshape_unbind(self):
v = Variable("v", 1, 100)
bv = Variable("v", 1, 100).bind(3)
t = Tensor.rand(3, 4).reshape(bv, 4)
assert t.lazydata.st.unbind() == ShapeTracker((View.create(shape=(v, 4)),))
def test_shrink_unbind(self):
v = Variable("v", 1, 100)
bv = Variable("v", 1, 100).bind(2)
t = Tensor.rand(3, 4).shrink(((bv, bv+1), (0, 4)))
assert t.lazydata.st.unbind() == ShapeTracker((View.create(shape=(1, 4), offset=4*v),))
class TestSymbolicReshape(unittest.TestCase):
def test_reshape_into_symbols_simple(self):
for i in range(1, 6):
vi = Variable("i", 1, 5).bind(i)
t = Tensor.rand(i, 4).reshape(vi, 4)
assert t.shape == (vi, 4)
t = Tensor.rand(i, 6).reshape(vi, 2, 3)
assert t.shape == (vi, 2, 3)
def test_reshape_symbols_reshape_ints(self):
for i in range(1, 6):
vi = Variable("i", 1, 5).bind(i)
t = Tensor.rand(i, 4).reshape(vi, 4)
assert t.shape == (vi, 4)
t = t.reshape(i, 4)
assert t.shape == (i, 4)
def test_reshape_into_symbols_bad_shape(self):
vi = Variable("i", 1, 10).bind(4)
with self.assertRaises(AssertionError):
t = Tensor.rand(4, 6).reshape(vi, 6).reshape(1, 77) # reshape to a different size new shape through symbolic shape
with self.assertRaises(AssertionError):
t = Tensor.rand(3, 4).reshape(3, (vi+1)) # reshape into non-Variable Node
def test_two_symbol_reshape(self):
for i in range(1, 6):
for j in range(1, 6):
vi = Variable("i", 1, 5).bind(i)
vj = Variable("j", 1, 5).bind(j)
t = Tensor.rand(i, j).reshape(vi, vj)
assert t.shape == (vi, vj)
# NOTE: this is currently not allowed
# t = t.reshape(1, vi*vj)
# assert t.shape == (1, vi*vj)
t = t.reshape(vj, vi)
assert t.shape == (vj, vi)
class TestSymbolicExpand(unittest.TestCase):
def test_expand_into_symbols(self):
# TODO: enfore expand only into bound variables
vi = Variable("i", 1, 5)
vj = Variable("j", 1, 5)
a = Tensor([[1], [2], [3]]).expand((3, vi))
assert a.shape == (3, vi)
a = a.reshape(3, vi, 1).expand((3, vi, vj))
assert a.shape == (3, vi, vj)
def test_plus_expands_constant(self):
for i in range(1, 6):
vi = Variable("i", 1, 5).bind(i)
a = Tensor.rand(3, i).reshape(3, vi)
a = a + 1
assert a.shape == (3, vi)
class TestSymbolicShrink(unittest.TestCase):
def test_shrink_symbols(self):
vi = Variable("i", 1, 5)
t = Tensor.rand(3, 5).shrink(((0, 2), (vi, vi+1)))
assert t.shape == (2, 1)
class TestSymbolicShapeExpr(unittest.TestCase):
def test_symbolic_expr_idxs(self):
# taken from symbolic shape llama
i = Variable("i", 1, 120)
gidx0 = Variable("gidx0", 0, i)
lidx1 = Variable("lidx1", 0, 7)
idx = (gidx0, lidx1, Variable.num(1))
shape = (i+1, 8, 4)
strides = (1, (i*4)+4, i+1)
st = ShapeTracker((View.create(shape, strides), ))
idx, valid = st.expr_idxs(idx)
assert idx.render() == "((lidx1*((i*4)+4))+1+gidx0+i)"
if __name__ == '__main__':
unittest.main()