tinygrad/test/test_pickle.py

import unittest, pickle, types
import numpy as np
from test.helpers import assert_equiv_uops
from tinygrad import Tensor, TinyJit, Variable, dtypes
from tinygrad.engine.schedule import create_schedule
from tinygrad.ops import PatternMatcher, UPat, UOp

class TestPickle(unittest.TestCase):
  def test_pickle_code_object(self):
    y = lambda x: x*2  # noqa: E731
    code_str = pickle.dumps(y.__code__)
    fxn = types.FunctionType(pickle.loads(code_str), globals())
    self.assertEqual(fxn(2), 4)

  def test_pickle_pattern_matcher(self):
    pm = PatternMatcher([(UPat.cvar('x'), lambda x: x*2)])
    sink = UOp.const(dtypes.int, 2)
    tt = pm.rewrite(sink)
    pm_str = pickle.dumps(pm)
    pm2 = pickle.loads(pm_str)
    self.assertEqual(pm2.rewrite(sink).key, tt.key)

  def test_pickle_main_pattern_matcher(self):
    from tinygrad.codegen.uopgraph import sym
    pickle.dumps(sym)

  def test_pickle_realized_tensor(self):
    t = Tensor.rand(10, 10).realize()
    st = pickle.dumps(t)
    t2:Tensor = pickle.loads(st)
    np.testing.assert_equal(t.numpy(), t2.numpy())

  def test_pickle_unrealized_tensor(self):
    t = Tensor.ones(10, 10)
    st = pickle.dumps(t)
    t2:Tensor = pickle.loads(st)
    np.testing.assert_equal(t.numpy(), t2.numpy())

  def test_pickle_variable(self):
    v = Variable("i", 1, 20).bind(10)
    t1 = Tensor.ones(10, v).contiguous()
    t2 = Tensor.ones(10, v).contiguous()
    ret = (t1+t2).sum(1)
    st = pickle.dumps(ret)
    del ret
    vt2 = pickle.loads(st)
    np.testing.assert_equal(vt2.numpy(), 20)

  def test_pickle_buffer_view(self):
    t = Tensor.arange(10, device="CLANG").contiguous().realize()
    vt = t[3:5].contiguous().realize()
    assert hasattr(vt.lazydata.buffer, 'base')
    ref_value = vt.tolist()
    st = pickle.dumps(vt)
    del t, vt
    vt2 = pickle.loads(st)
    assert hasattr(vt2.lazydata.buffer, 'base')
    assert ref_value == vt2.tolist()

  def test_pickle_numpy(self):
    t = Tensor(np.array([1,2,3,4.]))
    st = pickle.dumps(t)
    t2:Tensor = pickle.loads(st)
    np.testing.assert_equal(t.numpy(), t2.numpy())

  def test_pickle_jit(self):
    @TinyJit
    def add(a, b): return a.sum()+b+1
    for _ in range(3): add(Tensor.rand(10, 10), Tensor.rand(10, 10))
    st = pickle.dumps(add)
    del add

    add_fxn = pickle.loads(st)
    x = Tensor.ones(10, 10).contiguous().realize()
    y = Tensor.ones(10, 10).contiguous().realize()
    print("post jit")
    out = add_fxn(x, y)
    np.testing.assert_equal(out.numpy(), 102)

  def test_pickle_schedule(self):
    a = Tensor([1,2])
    out = a + 2
    sched = create_schedule([out.lazydata])
    pk = pickle.dumps(sched)
    sched_pk = pickle.loads(pk)
    assert_equiv_uops(sched_pk[-1].ast, sched[-1].ast)

  def test_pickle_renderer(self):
    from tinygrad.device import Device
    pk = pickle.dumps(Device.default.renderer)
    pickle.loads(pk)

class TestPickleJIT(unittest.TestCase):
  @classmethod
  def setUpClass(cls):
    @TinyJit
    def add(a, b): return a.sum()+b+1
    for _ in range(3): add(Tensor.rand(1000, 1000), Tensor.rand(1000, 1000))
    cls.st = pickle.dumps(add)
    del add

  def test_inspect(self):
    import io
    class FakeClass:
      def __init__(self, *args, **kwargs):
        print(self.module, self.name)
    class InspectUnpickler(pickle.Unpickler):
      def find_class(self, module, name): return type("SpecializedFakeClass", (FakeClass,), {"name": name, "module": module})
    InspectUnpickler(io.BytesIO(self.st)).load()

  @unittest.skip("we are still saving intermediate buffers")
  def test_size(self):
    # confirm no intermediate buffers are saved
    self.assertLess(len(self.st), 1_000_000)

if __name__ == '__main__':
  unittest.main()