test_vmap.py

from torch.testing._internal.common_utils import TestCase, run_tests
import torch
from torch import vmap

class TestVmapAPI(TestCase):
    def test_non_tensor_output_raises(self):
        with self.assertRaisesRegex(ValueError, "got type <class 'float'> as the return"):
            output = vmap(lambda x: 3.14)(torch.ones(3))

        def multiple_outputs(x):
            return x, 3

        with self.assertRaisesRegex(ValueError, "got type <class 'int'> for return 1"):
            vmap(multiple_outputs)(torch.ones(3))

    def test_different_map_dim_size_raises(self):
        x = torch.randn(2)
        y = torch.randn(3)
        expected_msg = 'Expected all tensors to have the same size in the mapped dimension'
        with self.assertRaisesRegex(ValueError, expected_msg):
            vmap(torch.mul)(x, y)

    def test_func_with_no_inputs(self):
        expected_msg = 'got no inputs'

        def foo():
            return torch.randn(3)

        def bar(x):
            return torch.randn(3)

        with self.assertRaisesRegex(ValueError, expected_msg):
            vmap(foo)()

        with self.assertRaisesRegex(ValueError, expected_msg):
            vmap(bar)()

    def test_constant_function(self):
        output = vmap(lambda x: torch.tensor(3.14))(torch.ones(3))
        self.assertEqual(output, torch.tensor([3.14, 3.14, 3.14]))

    def test_single_input(self):
        x = torch.randn(2, 3)

        def square(x):
            return x * x

        output = vmap(square)(x)
        self.assertEqual(output, x * x)

    def test_multiple_inputs(self):
        x = torch.randn(2, 3)
        y = torch.randn(2, 3)
        output = vmap(torch.mul)(x, y)
        self.assertEqual(output, x * y)

    def test_multiple_outputs(self):
        def foo(x):
            return x * x, x * x * x

        x = torch.randn(3)
        outputs = vmap(foo)(x)
        self.assertEqual(outputs[0], x * x)
        self.assertEqual(outputs[1], x * x * x)

    def test_multiple_outputs_error_cases(self):
        # This is the same thing as
        # def returns_tuple_of_tensors(x):
        #     return x, x
        def returns_tuple_of_tensors(x):
            return (x, x)

        def returns_list_of_two_tensors(x):
            return [x, x]

        def returns_list_of_one_tensor(x):
            return [x]

        x = torch.randn(3)

        # should not throw
        vmap(returns_tuple_of_tensors)(x)

        # jax supports these, but we don't yet
        msg = "must only return Tensors, got type <class 'list'>"
        with self.assertRaisesRegex(ValueError, msg):
            vmap(returns_list_of_two_tensors)(x)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(returns_list_of_one_tensor)(x)

    def test_nested_with_same_map_dim(self):
        x = torch.randn(2, 3, 5)
        y = torch.randn(2, 3, 5)
        output = vmap(vmap(torch.mul))(x, y)
        self.assertEqual(output, x * y)

        output = vmap(vmap(vmap(torch.mul)))(x, y)
        self.assertEqual(output, x * y)

    def test_nested_with_different_map_dim(self):
        x = torch.randn(2, 3)
        y = torch.randn(5, 3)
        output = vmap(lambda x: vmap(lambda y: x * y)(y))(x)
        self.assertEqual(output.shape, (2, 5, 3))
        self.assertEqual(output, x.view(2, 1, 3) * y)

        z = torch.randn(7, 3)
        output = vmap(lambda x: vmap(lambda y: vmap(lambda z: x * y * z)(z))(y))(x)
        self.assertEqual(output.shape, (2, 5, 7, 3))
        self.assertEqual(output, x.view(2, 1, 1, 3) * y.view(5, 1, 3) * z)

    def test_noop_in_inner_vmap(self):
        x = torch.randn(3)
        y = torch.randn(5)
        output = vmap(lambda x: vmap(lambda y: x)(y))(x)
        self.assertEqual(output, x.view(3, 1).expand(3, 5))

    def test_unsupported_op_err_msg(self):
        def foo(x):
            return torch.cos(x)

        x = torch.randn(3)
        with self.assertRaisesRegex(RuntimeError, 'NYI: Calling aten::cos inside of vmap'):
            vmap(foo)(x)

    def test_unsupported_inplace_op_err_msg(self):
        def foo(x):
            return x.cos_()

        x = torch.randn(3)
        # TODO(rzou): Yeah, this error message is pretty bad because the
        # dispatcher's fallback mechanism doesn't work for ops that don't support
        # boxing. Fix the error message at some point.
        with self.assertRaisesRegex(
                RuntimeError, 'Tried to call KernelFunction::call'):
            vmap(foo)(x)

    def test_nonzero_out_dims(self):
        # Basic test
        tensor = torch.randn(2, 3)
        result = vmap(lambda x: x, out_dims=1)(tensor)
        self.assertEqual(result, tensor.permute(1, 0))
        self.assertEqual(result.data_ptr(), tensor.data_ptr())

        # Test that the batch dimension gets permuted to dim 2
        tensor = torch.randn(2, 3, 5, 7)
        result = vmap(lambda x: x, out_dims=2)(tensor)
        self.assertEqual(result, tensor.permute(1, 2, 0, 3))
        self.assertEqual(result.data_ptr(), tensor.data_ptr())

        # negative out_dim
        tensor = torch.randn(2, 3, 5, 7)
        result = vmap(lambda x: x, out_dims=-1)(tensor)
        self.assertEqual(result, tensor.permute(1, 2, 3, 0))
        self.assertEqual(result.data_ptr(), tensor.data_ptr())

        # check that out_dims works on ALL outputs
        tensor = torch.randn(2, 3, 5, 7)
        other = torch.randn(2, 3, 5, 7)
        result = vmap(lambda x, y: (x, y), out_dims=2)(tensor, other)
        self.assertEqual(result, (tensor.permute(1, 2, 0, 3), other.permute(1, 2, 0, 3)))

        # use out_dims with the maximum vmap-able tensor dims (64 dims)
        ndims = 64
        shape = [2] + [1] * (ndims - 1)
        expected_shape = [1, 1, 2] + [1] * (ndims - 3)
        tensor = torch.randn(shape)
        result = vmap(lambda x: x, out_dims=2)(tensor)
        self.assertEqual(result.shape, expected_shape)

        # test something that is not the identity function
        def foo(x, y):
            return x, x * y, x * y * y
        x = torch.randn(2, 3, 5)
        y = torch.randn(2, 3, 5)
        result = vmap(foo, out_dims=1)(x, y)
        self.assertEqual(
            result,
            (x.permute(1, 0, 2), (x * y).permute(1, 0, 2), (x * y * y).permute(1, 0, 2)))

    def test_multiple_out_dims(self):
        def foo(x):
            return x, x

        def bar(x, y):
            return x, x, x, x * y

        x = torch.randn(2, 3, 5)
        y = torch.randn(2, 3, 5)
        result = vmap(foo, out_dims=(0, 1))(x)
        self.assertEqual(result, (x, x.permute(1, 0, 2)))

        result = vmap(bar, out_dims=(-1, 0, 1, 2))(x, y)
        expected = (
            x.permute(1, 2, 0),
            x,
            x.permute(1, 0, 2),
            (x * y).permute(1, 2, 0),
        )
        self.assertEqual(result, expected)

    def test_nested_out_dims(self):
        y = torch.randn(2, 3, 5, 7)

        # Inner vmap has non-zero out_dim
        result = vmap(lambda y: vmap(lambda x: x, out_dims=1)(y))(y)
        self.assertEqual(result.shape, (2, 5, 3, 7))
        self.assertEqual(result, y.permute(0, 2, 1, 3))

        # all vmaps have non-zero out_dim
        result = vmap(lambda y: vmap(lambda x: x, out_dims=1)(y), out_dims=1)(y)
        self.assertEqual(result.shape, (5, 2, 3, 7))
        self.assertEqual(result, y.permute(2, 0, 1, 3))

        # throwing in some negative out_dims
        result = vmap(lambda y: vmap(lambda x: x, out_dims=-1)(y), out_dims=-1)(y)
        self.assertEqual(result.shape, (5, 7, 3, 2))
        self.assertEqual(result, y.permute(2, 3, 1, 0))

        # testing fn that isn't the identity
        x = torch.randn(2, 3)
        y = torch.randn(5, 3)
        result = vmap(lambda y: vmap(lambda x: x * y, out_dims=1)(x), out_dims=-1)(y)
        self.assertEqual(result.shape, (3, 2, 5))
        self.assertEqual(result, (y.view(5, 1, 3) * x).permute(2, 1, 0))

    def test_out_dims_edge_case(self):
        def foo(x):
            return x

        # Test that we accept out_dims=(1,) for a function with one output.
        tensor = torch.randn(2, 3)
        expected = vmap(foo, out_dims=1)(tensor)
        result = vmap(foo, out_dims=(1,))(tensor)
        self.assertEqual(result, expected)

    def test_out_dims_must_be_int_or_tuple_of_int_err_msg(self):
        msg = '`out_dims` must be an int or a tuple of int'
        tensor = torch.randn(2, 3)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(lambda x: x, out_dims='lol')(tensor)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(lambda x: x, out_dims=('lol',))(tensor)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(lambda x: x, out_dims=None)(tensor)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(lambda x: x, out_dims=(None,))(tensor)

    def test_out_dims_and_num_outputs_mismatch_err_msg(self):
        msg = '`out_dims` must have one dim per output'
        x = torch.randn(2, 3, 5)

        # Too many out_dims
        with self.assertRaisesRegex(ValueError, msg):
            vmap(lambda x: x, out_dims=(0, 0))(x)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(lambda x: (x, x, x), out_dims=(0, 0, 0, 0))(x)

        # Too few out_dims
        with self.assertRaisesRegex(ValueError, msg):
            vmap(lambda x: (x, x), out_dims=(0,))(x)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(lambda x: (x, x, x), out_dims=(0, 0))(x)

    def test_out_dim_out_of_bounds_err_msg(self):
        # TODO(rzou): This error message isn't that great. It comes straight
        # from maybe_wrap_dim. Consider doing a try-catch-(add some context) to
        # the error message in the future in C++
        msg = 'Dimension out of range'
        x = torch.randn(2, 3, 5)
        with self.assertRaisesRegex(IndexError, msg):
            vmap(lambda x: x, out_dims=3)(x)
        with self.assertRaisesRegex(IndexError, msg):
            vmap(lambda x: x, out_dims=-4)(x)

    def test_non_zero_in_dims(self):
        tensor = torch.randn(2, 3, 5)

        # Implicit out_dims = 0; vmap will move the batch dim to the front.
        output = vmap(lambda x: x, (1,))(tensor)
        self.assertEqual(output, tensor.permute(1, 0, 2))
        self.assertEqual(output.data_ptr(), tensor.data_ptr())

        x = torch.randn(2, 3)
        y = torch.randn(3, 2)
        output = vmap(torch.mul, (0, 1))(x, y)
        self.assertEqual(output, x * y.t())
        output = vmap(torch.mul, (1, 0))(x, y)
        self.assertEqual(output, x.t() * y)

    def test_none_in_dims(self):
        x = torch.randn(2, 3)
        y = torch.randn(2, 3)

        # None in_dim for a Tensor means we don't map over it
        output = vmap(torch.mul, (0, None))(x, y)
        self.assertEqual(output.shape, (2, 2, 3))
        self.assertEqual(output, x.view(2, 1, 3) * y)

        # None in_dim for non-tensor arguments
        output = vmap(torch.mul, (0, None))(x, 2)
        self.assertEqual(output, x * 2)

    def test_nested_non_default_in_dims(self):
        x = torch.rand(5, 2, 3)
        y = torch.rand(3, 5, 2)
        result = vmap(vmap(vmap(torch.mul), (1, 0)), (1, 2))(x, y)
        self.assertEqual(result, x.permute(1, 2, 0) * y.permute(2, 0, 1))

    def test_non_default_in_dims_out_dims(self):
        x = torch.randn(2, 3, 5)

        # Same in_dim as out_dim, vmap over identity
        result = vmap(lambda x: x, in_dims=1, out_dims=1)(x)
        self.assertEqual(result, x)
        self.assertEqual(result.data_ptr(), x.data_ptr())

        # Different in_dim from out_dim, vmap over identity
        result = vmap(lambda x: x, in_dims=2, out_dims=1)(x)
        self.assertEqual(result.shape, (2, 5, 3))
        self.assertEqual(result, x.transpose(1, 2))
        self.assertEqual(result.data_ptr(), x.data_ptr())

        def foo(x):
            return x * 2

        # Same in_dim as out_dim, vmap over operation
        result = vmap(foo, in_dims=1, out_dims=1)(x)
        self.assertEqual(result, x * 2)

        # Different in_dim as out_dim, vmap over operation
        result = vmap(foo, in_dims=2, out_dims=1)(x)
        self.assertEqual(result.shape, (2, 5, 3))
        self.assertEqual(result, (x * 2).transpose(1, 2))

        # Basic nested test.
        result = vmap(vmap(foo, 1, 1), 1, 1)(x)
        self.assertEqual(result, x * 2)

    def test_in_dims_wrong_type_err_msg(self):
        x = torch.randn(3)
        y = torch.randn(3)
        msg = 'expected `in_dims` to be int or tuple'
        with self.assertRaisesRegex(ValueError, msg):
            vmap(torch.mul, [0, 0])(x, y)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(torch.mul, set({0, 0}))(x, y)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(torch.mul, 'lol')(x, y)
        # The following should not throw
        vmap(torch.mul, (0, 0))(x, y)

    def test_not_enough_in_dims_err_msg(self):
        x = torch.randn(3)
        y = torch.randn(3)
        msg = r'expected one `in_dim` per input \(got \w+ inputs\)'

        with self.assertRaisesRegex(ValueError, msg):
            vmap(torch.mul, (0,))(x, y)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(torch.mul, (0, 0, 0))(x, y)
        # The following should not throw
        vmap(torch.mul, (0, 0))(x, y)

    def test_in_dims_must_be_flat_tuple_err_msg(self):
        msg = 'in_dims must be a flat tuple containing ints and/or Nones'

        x = torch.randn(3)
        y = torch.randn(3)
        z = torch.randn(3)

        def foo(xy):
            return xy[0] * xy[1]

        def bar(x, yz):
            return x * yz[0] * yz[1]

        # NB: jax supports all of the following, we don't yet.
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo, ((0, 0),))((x, y))
        with self.assertRaisesRegex(ValueError, msg):
            vmap(bar, (0, (0, 0)))(x, (y, z))
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo, ({0: 0, 1: 0},))({0: x, 1: y})

    def test_integer_in_dim_but_not_tensor_input_err_msg(self):
        def foo(xy):
            return xy[0] * xy[1]

        def bar(x, yz):
            return x * yz[0] * yz[1]

        x = torch.randn(2, 3)
        y = torch.randn(2, 3)

        # jax supports these, we too can in the future.
        msg = 'Got in_dim=0 for input 0, but input 0 is not a Tensor'
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo)((x, y))
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo, (0,))((x, y))

        # jax supports these as well, we too can in the future.
        msg = 'Got in_dim=0 for input 1, but input 1 is not a Tensor'
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo)(x, (x, y))
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo, (0, 0))(x, (x, y))

        # the following are errors in jax (and will always be errors)
        msg = 'Got in_dim=0 for input 1, but input 1 is not a Tensor'
        with self.assertRaisesRegex(ValueError, msg):
            vmap(torch.sum)(x, 0)
        with self.assertRaisesRegex(ValueError, msg):
            vmap(torch.sum, (0, 0))(x, 0)
        # The following should not throw
        vmap(torch.sum, (0, None))(x, 0)

    def test_in_dim_not_in_tensor_err_msg(self):
        def foo(x):
            return x * x

        msg = r'Got in_dim=-?\w for input 0, but input 0 is a Tensor of dimensionality \w'
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo)(torch.randn([]))
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo, in_dims=(0,))(torch.randn([]))
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo, in_dims=(-1,))(torch.randn(2, 3))
        with self.assertRaisesRegex(ValueError, msg):
            vmap(foo, in_dims=(2,))(torch.randn(2, 3))
        # the following should not throw
        vmap(foo, in_dims=(0,))(torch.randn(2, 3))
        vmap(foo, in_dims=(1,))(torch.randn(2, 3))

if __name__ == '__main__':
    run_tests()