test_flatten_params_wrapper.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.

""" Test FlattenParamsWrapper on CPU and GPU (FP32 & FP16 on GPU). """

from collections import OrderedDict
import unittest

import torch

from fairscale.nn import FlattenParamsWrapper
from fairscale.utils.testing import objects_are_equal


class TestFlattenParams(unittest.TestCase):
    """ Base test class and used for CPU case. """

    def _get_module_init_fns(self):
        return [
            self._get_basic_linear_module,
            self._get_shared_params_transformer,
        ]

    def _get_empty_module(self, seed=0):
        torch.manual_seed(seed)  # keep everything deterministic

        class Test(torch.nn.Module):
            def forward(self, x):
                return x + 1

        module = Test()

        def get_input(device, dtype):
            torch.manual_seed(1)  # keep everything deterministic
            return torch.rand(1).to(device=device, dtype=dtype)

        module.get_input = get_input
        return module

    def _get_transformer(self, seed=0):
        torch.manual_seed(seed)  # keep everything deterministic
        module = torch.nn.Transformer(
            d_model=32, num_encoder_layers=2, num_decoder_layers=2, dim_feedforward=128, dropout=0.1,
        )
        module.register_buffer("dummy_buffer", torch.tensor(1.0))

        def get_input(device, dtype):
            torch.manual_seed(1)  # keep everything deterministic
            src = torch.rand(20, 8, 32).to(device=device, dtype=dtype)  # T x B x C
            tgt = torch.rand(10, 8, 32).to(device=device, dtype=dtype)  # T x B x C
            return (src, tgt)

        module.get_input = get_input
        return module

    def _get_shared_params_transformer(self, seed=0):
        module = self._get_transformer(seed=seed)
        # share the FFNs
        for enc_layer, dec_layer in zip(module.encoder.layers, module.decoder.layers):
            dec_layer.linear1.weight = enc_layer.linear1.weight
            dec_layer.linear2.weight = enc_layer.linear2.weight
        return module

    def _get_basic_linear_module(self, seed=0):
        module = torch.nn.Sequential(
            torch.nn.Sequential(torch.nn.Linear(4, 8), torch.nn.Linear(8, 8)),
            torch.nn.Sequential(torch.nn.Linear(8, 16)),
            torch.nn.Linear(16, 4),
        )

        def get_input(device, dtype):
            torch.manual_seed(1)  # keep everything deterministic
            return (torch.rand(8, 4).to(device=device, dtype=dtype),)

        module.get_input = get_input
        return module

    def _get_output(self, module):
        device = next(module.parameters()).device
        dtype = next(module.parameters()).dtype
        input = module.get_input(device, dtype)
        return module(*input)

    def _get_pnorm_after_step(self, module):
        optim = torch.optim.SGD(module.parameters(), lr=0.01)
        loss = self._get_output(module).sum()
        loss.backward()
        optim.step()
        return torch.norm(torch.stack([p.detach().norm() for p in module.parameters()]))

    def _test_num_params(self, module):
        ref_num_params = sum(p.numel() for p in module.parameters())

        flat_module = FlattenParamsWrapper(module)
        flat_num_params = sum(p.numel() for p in flat_module.parameters())

        assert ref_num_params == flat_num_params
        assert flat_num_params == flat_module.flat_param.numel()

    def _test_output(self, module):
        ref_output = self._get_output(module)

        flat_module = FlattenParamsWrapper(module)
        flat_output = self._get_output(flat_module)
        assert objects_are_equal(ref_output, flat_output)

    def test_partial_flattening(self):
        module = self._get_transformer()
        num_params = sum(p.numel() for p in module.parameters())

        params_to_flatten = list(module.encoder.layers[1].parameters()) + list(module.decoder.layers[0].parameters())
        num_params_to_flatten = sum(p.numel() for p in params_to_flatten)

        module = FlattenParamsWrapper(module, param_list=params_to_flatten)
        assert module.flat_param.numel() == num_params_to_flatten
        assert sum(p.numel() for p in module.parameters()) == num_params

        # flattened parameters are removed
        assert len(list(module.encoder.layers[1].parameters())) == 0
        assert len(list(module.decoder.layers[0].parameters())) == 0

        # non-flattened parameters remain
        assert len(list(module.encoder.layers[0].parameters())) > 0
        assert len(list(module.decoder.layers[1].parameters())) > 0

        # test that changing the module dtype works properly
        orig_dtype = params_to_flatten[0].dtype
        new_dtype = torch.float32 if orig_dtype == torch.float16 else torch.float16
        assert module.flat_param.dtype == orig_dtype
        assert all(p.dtype == orig_dtype for p in module.encoder.layers[0].parameters())
        module = module.to(dtype=new_dtype)
        assert module.flat_param.dtype == new_dtype
        assert all(p.dtype == new_dtype for p in module.encoder.layers[0].parameters())

    def test_two_flattening_group(self):
        module = self._get_transformer()
        num_params = sum(p.numel() for p in module.parameters())

        params_to_flatten1 = list(module.encoder.layers[1].parameters()) + list(module.decoder.layers[0].parameters())
        params_to_flatten2 = list(module.encoder.layers[0].parameters()) + list(module.decoder.layers[1].parameters())
        num_params_to_flatten1 = sum(p.numel() for p in params_to_flatten1)
        num_params_to_flatten2 = sum(p.numel() for p in params_to_flatten2)

        module = FlattenParamsWrapper(module, param_list=[params_to_flatten1, params_to_flatten2])
        assert module.flat_params[0].numel() == num_params_to_flatten1
        assert module.flat_params[1].numel() == num_params_to_flatten2
        assert sum(p.numel() for p in module.parameters()) == num_params

    def test_flatten_nothing(self):
        module = self._get_transformer()
        ref_out = self._get_output(module)
        ref_state_dict = module.state_dict()
        for k, v in ref_state_dict.items():
            ref_state_dict[k] = v.clone()
        module = FlattenParamsWrapper(module, param_list=[[]])
        fpw_state_dict = module.state_dict()
        assert ref_state_dict.keys() == fpw_state_dict.keys()
        for k, v in ref_state_dict.items():
            torch.testing.assert_allclose(v, fpw_state_dict[k])
        fpw_out = self._get_output(module)
        torch.testing.assert_allclose(ref_out, fpw_out)

    def test_empty_module(self):
        module = self._get_empty_module()
        in_data = torch.rand(1)
        ref_out = module(in_data)
        module = FlattenParamsWrapper(module)
        assert len(list(module.parameters())) == 0
        assert len(module.state_dict()) == 0
        fpw_out = module(in_data)
        torch.testing.assert_allclose(ref_out, fpw_out)

    def test_num_params(self):
        module = self._get_transformer()
        self._test_num_params(module)

    def test_shared_params_num_params(self):
        module = self._get_shared_params_transformer()
        self._test_num_params(module)

    def test_output(self):
        module = self._get_transformer()
        self._test_output(module)

    def test_shared_params_output(self):
        module = self._get_shared_params_transformer()
        self._test_output(module)

    def test_shared_params_pnorm_after_step(self):
        # incorrect parameter sharing is likely to cause problems after an
        # optimization step
        module = self._get_shared_params_transformer()
        ref_pnorm_after_step = self._get_pnorm_after_step(module)

        module = self._get_shared_params_transformer()  # recreate
        flat_module = FlattenParamsWrapper(module)
        flat_pnorm_after_step = self._get_pnorm_after_step(flat_module)

        torch.testing.assert_allclose(ref_pnorm_after_step, flat_pnorm_after_step)

    def test_state_dict_equality(self):
        """Test that unflattened state dict matches original (unwrapped) one."""
        modules_to_test = [init_fn() for init_fn in self._get_module_init_fns()]
        for module in modules_to_test:
            ref_state_dict = module.state_dict()

            flat_module = FlattenParamsWrapper(module)
            flat_state_dict = flat_module.state_dict()

            assert (
                ref_state_dict.keys() == flat_state_dict.keys()
            ), f"{ref_state_dict.keys()} != {flat_state_dict.keys()}"
            assert objects_are_equal(ref_state_dict, flat_state_dict), f"{ref_state_dict} != {flat_state_dict}"

    def test_load_state_dict(self):
        """Test that original (unwrapped) state_dict can be loaded in wrapped module."""
        for module_init_fn in self._get_module_init_fns():
            module = module_init_fn()
            ref_state_dict = module.state_dict()
            ref_output = self._get_output(module)

            module = module_init_fn(seed=1234)
            flat_module = FlattenParamsWrapper(module)

            # This should work without the unflatten_params context manager
            flat_module.load_state_dict(ref_state_dict)
            flat_output = self._get_output(flat_module)
            assert objects_are_equal(ref_output, flat_output)

            # And it should work with the context manager too
            with flat_module.unflatten_params():
                flat_module.load_state_dict(ref_state_dict)
            flat_output = self._get_output(flat_module)
            assert objects_are_equal(ref_output, flat_output)

    def test_flat_state_dict(self):
        """Test that flat state dict can be reloaded and produces the same results."""
        for module_init_fn in self._get_module_init_fns():
            flat_module = FlattenParamsWrapper(module_init_fn())
            ref_output = self._get_output(flat_module)

            flat_state_dict = flat_module.flat_state_dict()

            new_module = FlattenParamsWrapper(module_init_fn(seed=1234))
            new_module.load_state_dict(flat_state_dict)
            new_output = self._get_output(new_module)

            assert objects_are_equal(ref_output, new_output)

    def test_unflatten_params(self):
        for module_init_fn in self._get_module_init_fns():
            module = FlattenParamsWrapper(module_init_fn())
            buffers = {k.replace("_fpw_module.", "") for k, _ in module.named_buffers()}

            def clone_state_dict():
                return OrderedDict((k, v.clone()) for k, v in module.state_dict().items())

            ref_flat_param = module.flat_param.clone()
            with module.unflatten_params():
                ref_state_dict = clone_state_dict()
            assert not torch.all(ref_flat_param == 0)

            # confirm that unflatten_params reflects values from new_flat_param
            new_flat_param = torch.full_like(module.flat_param, fill_value=42.0)
            with module.unflatten_params(flat_params=[new_flat_param]):
                new_state_dict = clone_state_dict()
                assert new_state_dict.keys() == ref_state_dict.keys()
                for k, v in new_state_dict.items():
                    if k in buffers:  # buffers are not changed
                        torch.testing.assert_allclose(v, ref_state_dict[k])
                    else:  # params reflect new_flat_param value
                        torch.testing.assert_allclose(v, torch.ones_like(v) * 42.0)

            # after context manager exits, we go back to previous (reference) state
            torch.testing.assert_allclose(module.flat_param, ref_flat_param)
            with module.unflatten_params():
                ref_state_dict2 = clone_state_dict()
                assert objects_are_equal(ref_state_dict, ref_state_dict2)

            # if we load the new_state_dict, then the flat param should match new_flat_param
            module.load_state_dict(new_state_dict)
            torch.testing.assert_allclose(module.flat_param, new_flat_param)


@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestFlattenParamsCUDA(TestFlattenParams):
    def _get_transformer(self, seed=0):
        module = super()._get_transformer(seed=seed)
        return module.cuda()


@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
class TestFlattenParamsCUDAHalf(TestFlattenParams):
    def _get_transformer(self, seed=0):
        module = super()._get_transformer(seed=seed)
        return module.cuda().half()


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