Enable auto parameters tying for TPUs

CHANGELOG.md

@@ -166,6 +166,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for `torch.use_deterministic_algorithms` ([#9121](https://github.com/PyTorchLightning/pytorch-lightning/pull/9121))
 
 
+- Enabled automatic parameters tying for TPUs ([#9525](https://github.com/PyTorchLightning/pytorch-lightning/pull/9525))
+
+
 ### Changed
 
 - `pytorch_lightning.loggers.neptune.NeptuneLogger` is now consistent with new [neptune-client](https://github.com/neptune-ai/neptune-client) API ([#6867](https://github.com/PyTorchLightning/pytorch-lightning/pull/6867)).
@@ -296,6 +299,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated Accelerator collective API `barrier`, `broadcast`, and `all_gather`, call `TrainingTypePlugin` collective API directly ([#9677](https://github.com/PyTorchLightning/pytorch-lightning/pull/9677))
 
 
+- Deprecated the `LightningModule.on_post_move_to_device` method ([#9525](https://github.com/PyTorchLightning/pytorch-lightning/pull/9525))
+
+
+- Deprecated `pytorch_lightning.core.decorators.parameter_validation` in favor of `pytorch_lightning.utilities.parameter_tying.set_shared_parameters` ([#9525](https://github.com/PyTorchLightning/pytorch-lightning/pull/9525))
+
+
 ### Removed
 
 - Removed deprecated `metrics` ([#8586](https://github.com/PyTorchLightning/pytorch-lightning/pull/8586/))

pyproject.toml

@@ -80,6 +80,7 @@ module = [
     "pytorch_lightning.utilities.distributed",
     "pytorch_lightning.utilities.memory",
     "pytorch_lightning.utilities.model_summary",
+    "pytorch_lightning.utilities.parameter_tying",
     "pytorch_lightning.utilities.parsing",
     "pytorch_lightning.utilities.seed",
     "pytorch_lightning.utilities.xla_device",

pytorch_lightning/core/decorators.py

@@ -11,12 +11,18 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Decorator for LightningModule methods."""
+from pytorch_lightning.utilities import rank_zero_deprecation
 
-from functools import wraps
-from typing import Callable
+rank_zero_deprecation(
+    "Using `pytorch_lightning.core.decorators.parameter_validation` is deprecated in v1.5, "
+    "and will be removed in v1.7. It has been replaced by automatic parameters tying with "
+    "`pytorch_lightning.utilities.params_tying.set_shared_parameters`"
+)
 
-from pytorch_lightning.utilities import rank_zero_warn
+from functools import wraps  # noqa: E402
+from typing import Callable  # noqa: E402
+
+from pytorch_lightning.utilities import rank_zero_warn  # noqa: E402
 
 
 def parameter_validation(fn: Callable) -> Callable:

pytorch_lightning/plugins/training_type/single_tpu.py

@@ -14,12 +14,17 @@
 import os
 from typing import Any, Dict
 
-from pytorch_lightning.core.decorators import parameter_validation
 from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
 from pytorch_lightning.plugins.training_type.single_device import SingleDevicePlugin
-from pytorch_lightning.utilities import _OMEGACONF_AVAILABLE, _TPU_AVAILABLE
+from pytorch_lightning.utilities import (
+    _OMEGACONF_AVAILABLE,
+    _TPU_AVAILABLE,
+    find_shared_parameters,
+    set_shared_parameters,
+)
 from pytorch_lightning.utilities.apply_func import apply_to_collection
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.types import _PATH
 
 if _TPU_AVAILABLE:
@@ -49,7 +54,14 @@ def __init__(
     def is_distributed(self) -> bool:
         return False
 
-    @parameter_validation
+    def setup(self) -> None:
+        shared_params = find_shared_parameters(self.model)
+        self.model_to_device()
+        if is_overridden("on_post_move_to_device", self.lightning_module):
+            self.model.on_post_move_to_device()
+        else:
+            set_shared_parameters(self.model, shared_params)
+
     def model_to_device(self) -> None:
         self.model.to(self.root_device)
 

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -23,17 +23,23 @@
 from torch.utils.data import DataLoader
 
 import pytorch_lightning as pl
-from pytorch_lightning.core.decorators import parameter_validation
 from pytorch_lightning.overrides import LightningDistributedModule
 from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
 from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin
 from pytorch_lightning.trainer.connectors.data_connector import _PatchDataLoader
 from pytorch_lightning.trainer.states import TrainerFn
-from pytorch_lightning.utilities import _OMEGACONF_AVAILABLE, _TPU_AVAILABLE, rank_zero_warn
+from pytorch_lightning.utilities import (
+    _OMEGACONF_AVAILABLE,
+    _TPU_AVAILABLE,
+    find_shared_parameters,
+    rank_zero_warn,
+    set_shared_parameters,
+)
 from pytorch_lightning.utilities.apply_func import apply_to_collection
 from pytorch_lightning.utilities.data import has_len
 from pytorch_lightning.utilities.distributed import rank_zero_only, ReduceOp
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.seed import reset_seed
 from pytorch_lightning.utilities.types import _PATH, STEP_OUTPUT
 
@@ -156,7 +162,13 @@ def new_process(self, process_idx: int, trainer, mp_queue) -> None:
         if self.tpu_global_core_rank != 0 and trainer.progress_bar_callback is not None:
             trainer.progress_bar_callback.disable()
 
+        shared_params = find_shared_parameters(self.model)
         self.model_to_device()
+        if is_overridden("on_post_move_to_device", self.lightning_module):
+            self.model.module.on_post_move_to_device()
+        else:
+            set_shared_parameters(self.model.module, shared_params)
+
         trainer.accelerator.setup_optimizers(trainer)
         trainer.precision_plugin.connect(self._model, None, None)
 
@@ -176,7 +188,6 @@ def new_process(self, process_idx: int, trainer, mp_queue) -> None:
         # ensure that spawned processes go through teardown before joining
         trainer._call_teardown_hook()
 
-    @parameter_validation
     def model_to_device(self) -> None:
         self.model = self.wrapped_model.to(self.root_device)
 

pytorch_lightning/trainer/configuration_validator.py

@@ -46,6 +46,8 @@ def verify_loop_configurations(self, model: "pl.LightningModule") -> None:
         self._check_add_get_queue(model)
         # TODO(@daniellepintz): Delete _check_progress_bar in v1.7
         self._check_progress_bar(model)
+        # TODO: Delete _check_on_post_move_to_device in v1.7
+        self._check_on_post_move_to_device(model)
         # TODO: Delete _check_on_keyboard_interrupt in v1.7
         self._check_on_keyboard_interrupt()
 
@@ -127,6 +129,19 @@ def _check_progress_bar(self, model: "pl.LightningModule") -> None:
                 " Please use the `ProgressBarBase.get_metrics` instead."
             )
 
+    def _check_on_post_move_to_device(self, model: "pl.LightningModule") -> None:
+        r"""
+        Checks if `on_post_move_to_device` method is overriden and sends a deprecation warning.
+
+        Args:
+            model: The model to check the `on_post_move_to_device` method.
+        """
+        if is_overridden("on_post_move_to_device", model):
+            rank_zero_deprecation(
+                "Method `on_post_move_to_device` has been deprecated in v1.5 and will be removed in v1.7. "
+                "We perform automatic parameters tying without the need of implementing `on_post_move_to_device`."
+            )
+
     def __verify_eval_loop_configuration(self, model: "pl.LightningModule", stage: str) -> None:
         loader_name = f"{stage}_dataloader"
         step_name = "validation_step" if stage == "val" else "test_step"

pytorch_lightning/utilities/__init__.py

@@ -57,6 +57,7 @@
     _TPU_AVAILABLE,
     _XLA_AVAILABLE,
 )
+from pytorch_lightning.utilities.parameter_tying import find_shared_parameters, set_shared_parameters  # noqa: F401
 from pytorch_lightning.utilities.parsing import AttributeDict, flatten_dict, is_picklable  # noqa: F401
 from pytorch_lightning.utilities.warnings import rank_zero_deprecation, rank_zero_warn  # noqa: F401
 

pytorch_lightning/utilities/parameter_tying.py

@@ -0,0 +1,71 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Utilities for automatic parameters tying.
+
+Reference:
+    https://github.com/pytorch/fairseq/blob/1f7ef9ed1e1061f8c7f88f8b94c7186834398690/fairseq/trainer.py#L110-L118
+"""
+from typing import Dict, List, Optional
+
+from torch import nn
+from torch.nn import Parameter
+
+
+def find_shared_parameters(module: nn.Module) -> List[str]:
+    """Returns a list of names of shared parameters set in the module."""
+    return _find_shared_parameters(module)
+
+
+def _find_shared_parameters(module: nn.Module, tied_parameters: Optional[Dict] = None, prefix: str = "") -> List[str]:
+    if tied_parameters is None:
+        first_call = True
+        tied_parameters = {}
+    else:
+        first_call = False
+    for name, param in module._parameters.items():
+        param_prefix = prefix + ("." if prefix else "") + name
+        if param is None:
+            continue
+        if param not in tied_parameters:
+            tied_parameters[param] = []
+        tied_parameters[param].append(param_prefix)
+    for name, m in module._modules.items():
+        if m is None:
+            continue
+        submodule_prefix = prefix + ("." if prefix else "") + name
+        _find_shared_parameters(m, tied_parameters, submodule_prefix)
+    if first_call:
+        return [x for x in tied_parameters.values() if len(x) > 1]
+
+
+def set_shared_parameters(module: nn.Module, shared_params: list) -> nn.Module:
+    for shared_param in shared_params:
+        ref = _get_module_by_path(module, shared_param[0])
+        for path in shared_param[1:]:
+            _set_module_by_path(module, path, ref)
+    return module
+
+
+def _get_module_by_path(module: nn.Module, path: str) -> nn.Module:
+    path = path.split(".")
+    for name in path:
+        module = getattr(module, name)
+    return module
+
+
+def _set_module_by_path(module: nn.Module, path: str, value: Parameter) -> None:
+    path = path.split(".")
+    for name in path[:-1]:
+        module = getattr(module, name)
+    setattr(module, path[-1], value)

tests/accelerators/test_tpu_backend.py

@@ -23,6 +23,7 @@
 from pytorch_lightning.accelerators.tpu import TPUAccelerator
 from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.plugins import TPUSpawnPlugin
+from pytorch_lightning.utilities import find_shared_parameters
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers.boring_model import BoringModel
 from tests.helpers.runif import RunIf
@@ -80,37 +81,6 @@ def test_if_test_works_after_train(tmpdir):
     assert len(trainer.test(model)) == 1
 
 
-@RunIf(tpu=True)
-@pl_multi_process_test
-def test_weight_tying_warning(tmpdir, capsys=None):
-    """Ensure a warning is thrown if model parameter lengths do not match post moving to device."""
-
-    model = WeightSharingModule()
-    trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
-
-    with pytest.warns(UserWarning, match=r"The model layers do not match after moving to the target device."):
-        trainer.fit(model)
-
-
-@RunIf(tpu=True)
-@pl_multi_process_test
-def test_if_weights_tied(tmpdir, capsys=None):
-    """Test if weights are properly tied on `on_post_move_to_device`.
-
-    Ensure no warning for parameter mismatch is thrown.
-    """
-
-    class Model(WeightSharingModule):
-        def on_post_move_to_device(self):
-            self.layer_3.weight = self.layer_1.weight
-
-    model = Model()
-    trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
-
-    with pytest.warns(UserWarning, match="The model layers do not match"):
-        trainer.fit(model)
-
-
 @RunIf(tpu=True)
 def test_accelerator_tpu():
 
@@ -257,3 +227,49 @@ def test_ddp_cpu_not_supported_on_tpus():
 
     with pytest.raises(MisconfigurationException, match="`accelerator='ddp_cpu'` is not supported on TPU machines"):
         Trainer(accelerator="ddp_cpu")
+
+
+@RunIf(tpu=True)
+def test_auto_parameters_tying_tpus(tmpdir):
+
+    model = WeightSharingModule()
+    shared_params = find_shared_parameters(model)
+
+    assert shared_params[0] == ["layer_1.weight", "layer_3.weight"]
+
+    trainer = Trainer(default_root_dir=tmpdir, limit_train_batches=5, tpu_cores=8, max_epochs=1)
+    trainer.fit(model)
+
+    assert torch.all(torch.eq(model.layer_1.weight, model.layer_3.weight))
+
+
+@RunIf(tpu=True)
+def test_auto_parameters_tying_tpus_nested_module(tmpdir):
+    class SubModule(nn.Module):
+        def __init__(self, layer):
+            super().__init__()
+            self.layer = layer
+
+        def forward(self, x):
+            return self.layer(x)
+
+    class NestedModule(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.layer = nn.Linear(32, 10, bias=False)
+            self.net_a = SubModule(self.layer)
+            self.layer_2 = nn.Linear(10, 32, bias=False)
+            self.net_b = SubModule(self.layer)
+
+        def forward(self, x):
+            x = self.net_a(x)
+            x = self.layer_2(x)
+            x = self.net_b(x)
+            return x
+
+    model = NestedModule()
+
+    trainer = Trainer(default_root_dir=tmpdir, limit_train_batches=5, tpu_cores=8, max_epochs=1)
+    trainer.fit(model)
+
+    assert torch.all(torch.eq(model.net_a.layer.weight, model.net_b.layer.weight))

tests/deprecated_api/test_remove_1-7.py

@@ -255,3 +255,27 @@ def test_v1_7_0_deprecate_lightning_distributed(tmpdir):
         from pytorch_lightning.distributed.dist import LightningDistributed
 
         _ = LightningDistributed()
+
+
+def test_v1_7_0_deprecate_on_post_move_to_device(tmpdir):
+    class TestModel(BoringModel):
+        def on_post_move_to_device(self):
+            print("on_post_move_to_device")
+
+    model = TestModel()
+
+    trainer = Trainer(default_root_dir=tmpdir, limit_train_batches=5, max_epochs=1)
+
+    with pytest.deprecated_call(
+        match=r"Method `on_post_move_to_device` has been deprecated in v1.5 and will be removed in v1.7"
+    ):
+        trainer.fit(model)
+
+
+def test_v1_7_0_deprecate_parameter_validation():
+
+    _soft_unimport_module("pytorch_lightning.core.decorators")
+    with pytest.deprecated_call(
+        match="Using `pytorch_lightning.core.decorators.parameter_validation` is deprecated in v1.5"
+    ):
+        from pytorch_lightning.core.decorators import parameter_validation  # noqa: F401