Merge branch 'master' into carmocca/bwd-sync-enabled

Author: carmocca

.github/workflows/ci-pkg-install.yml

@@ -115,7 +115,7 @@ jobs:
           done
       - name: Install pytest doctest extension
         run: |
-          pip install -q "pytest-doctestplus>=0.9.0"
+          pip install -q -r requirements/doctests.txt
           pip list
 
       - name: DocTest package

docs/source-fabric/advanced/compile.rst

@@ -3,7 +3,7 @@ Speed up models by compiling them
 #################################
 
 Compiling your PyTorch model can result in significant speedups, especially on the latest generations of GPUs.
-This guide shows you how to apply ``torch.compile`` correctly in your code.
+This guide shows you how to apply `torch.compile <https://pytorch.org/docs/2.2/generated/torch.compile.html>`_ correctly in your code.
 
 .. note::
 
@@ -132,7 +132,7 @@ If there are regions in the code that it doesn't understand, it will introduce a
 Graph breaks aren't a deal breaker, since the optimized parts should still run faster.
 But if you want to get the most out of ``torch.compile``, you might want to invest rewriting the problematic section of the code that produce the breaks.
 
-You can check whether your model produces graph breaks by calling ``torch.compile`` with ``fullraph=True``:
+You can check whether your model produces graph breaks by calling ``torch.compile`` with ``fullgraph=True``:
 
 .. code-block:: python
 
@@ -223,6 +223,9 @@ On PyTorch 2.2 and later, ``torch.compile`` will detect dynamism automatically a
     Numbers produced with NVIDIA A100 SXM4 40GB, PyTorch 2.2.0, CUDA 12.1.
 
 
+If you still see recompilation issues after dealing with the aforementioned cases, there is a `Compile Profiler in PyTorch <https://pytorch.org/docs/stable/torch.compiler_troubleshooting.html#excessive-recompilation>`_ for further investigation.
+
+
 ----
 
 
@@ -266,11 +269,11 @@ You can find a full list of compile options in the `PyTorch documentation <https
 A note about torch.compile in practice
 **************************************
 
-In practice, you will find that ``torch.compile`` often doesn't work well and can even be counter-productive.
-Compilation may fail with cryptic error messages that are impossible to debug without help from the PyTorch team.
-It is also not uncommon that ``torch.compile`` will produce a significantly *slower* model or one with much higher memory usage.
-On top of that, the compilation phase itself can be incredibly slow, taking several minutes to finish.
-For these reasons, we recommend that you don't waste too much time trying to apply ``torch.compile`` during development, and rather evaluate its effectiveness toward the end when you are about to launch long-running, expensive experiments.
+In practice, you will find that ``torch.compile`` may not work well at first or may be counter-productive to performance.
+Compilation may fail with cryptic error messages that are hard to debug, luckily the PyTorch team is responsive and it's likely that messaging will improve in time.
+It is not uncommon that ``torch.compile`` will produce a significantly *slower* model or one with higher memory usage. You'll need to invest time in this phase if the model is not among the ones that have a happy path.
+As a note, the compilation phase itself will take some time, taking up to several minutes.
+For these reasons, we recommend that you don't invest too much time trying to apply ``torch.compile`` during development, and rather evaluate its effectiveness toward the end when you are about to launch long-running, expensive experiments.
 Always compare the speed and memory usage of the compiled model against the original model!
 
 
@@ -301,4 +304,18 @@ However, should you have issues compiling DDP and FSDP models, you can opt out o
     model = fabric.setup(model, _reapply_compile=False)
 
 
+----
+
+
+********************
+Additional Resources
+********************
+
+Here are a few resources for further reading after you complete this tutorial:
+
+- `PyTorch 2.0 Paper <https://pytorch.org/blog/pytorch-2-paper-tutorial/>`_
+- `GenAI with PyTorch 2.0 blog post series <https://pytorch.org/blog/accelerating-generative-ai-4/>`_
+- `Training Production AI Models with PyTorch 2.0 <https://pytorch.org/blog/training-production-ai-models/>`_
+- `Empowering Models with Performance: The Art of Generalized Model Transformation Approach <https://pytorch.org/blog/empowering-models-performance/>`_
+
 |

docs/source-fabric/api/fabric_args.rst

@@ -36,13 +36,16 @@ See also: :doc:`../fundamentals/accelerators`
 strategy
 ========
 
-Choose a training strategy: ``"dp"``, ``"ddp"``, ``"ddp_spawn"``, ``"xla"``, ``"deepspeed"``, ``"fsdp"````.
+Choose a training strategy: ``"dp"``, ``"ddp"``, ``"ddp_spawn"``, ``"ddp_find_unused_parameters_true"``, ``"xla"``, ``"deepspeed"``, ``"fsdp"``.
 
 .. code-block:: python
 
     # Running with the DistributedDataParallel strategy on 4 GPUs
     fabric = Fabric(strategy="ddp", accelerator="gpu", devices=4)
 
+    # Running with the DDP strategy with find unused parameters enabled on 4 GPUs
+    fabric = Fabric(strategy="ddp_find_unused_parameters_true", accelerator="gpu", devices=4)
+
     # Running with the DDP Spawn strategy using 4 CPU processes
     fabric = Fabric(strategy="ddp_spawn", accelerator="cpu", devices=4)
 

docs/source-fabric/guide/checkpoint/distributed_checkpoint.rst

@@ -187,7 +187,7 @@ It is possible to convert a distributed checkpoint to a regular, single-file che
 
 .. code-block:: bash
 
-    python -m lightning.fabric.utilities.consolidate_checkpoint path/to/my/checkpoint
+    fabric consolidate path/to/my/checkpoint
 
 You will need to do this for example if you want to load the checkpoint into a script that doesn't use FSDP, or need to export the checkpoint to a different format for deployment, evaluation, etc.
 
@@ -202,7 +202,7 @@ You will need to do this for example if you want to load the checkpoint into a s
 
     .. code-block:: bash
 
-        python -m lightning.fabric.utilities.consolidate_checkpoint my-checkpoint.ckpt
+        fabric consolidate my-checkpoint.ckpt
 
     This saves a new file ``my-checkpoint.ckpt.consolidated`` next to the sharded checkpoint which you can load normally in PyTorch:
 

docs/source-pytorch/advanced/compile.rst

@@ -0,0 +1,290 @@
+#################################
+Speed up models by compiling them
+#################################
+
+Compiling your LightningModule can result in significant speedups, especially on the latest generations of GPUs.
+This guide shows you how to apply `torch.compile <https://pytorch.org/docs/2.2/generated/torch.compile.html>`_ correctly in your code.
+
+.. note::
+
+    This requires PyTorch >= 2.0.
+
+
+----
+
+
+*******************************************
+Apply torch.compile to your LightningModule
+*******************************************
+
+Compiling a LightningModule is as simple as adding one line of code, calling :func:`torch.compile`:
+
+.. code-block:: python
+
+    import torch
+    import lightning as L
+
+    # Define the model
+    model = MyLightningModule()
+
+    # Compile the model
+    model = torch.compile(model)
+
+    # Run with the Trainer
+    trainer = L.Trainer()
+    trainer.fit(model)
+
+
+.. important::
+
+    You should compile the model **before** calling ``trainer.fit()`` as shown above for an optimal integration with features in Trainer.
+
+The newly added call to ``torch.compile()`` by itself doesn't do much. It just wraps the model in a "compiled model".
+The actual optimization will start when calling the ``forward()`` method for the first time:
+
+.. code-block:: python
+
+    # 1st execution compiles the model (slow)
+    output = model(input)
+
+    # All future executions will be fast (for inputs of the same size)
+    output = model(input)
+    output = model(input)
+    ...
+
+**When you pass the LightningModule to the Trainer, it will automatically also compile the ``*_step()`` methods.**
+
+When measuring the speed of a compiled model and comparing it to a regular model, it is important to
+always exclude the first call to ``forward()``/``*_step()`` from your measurements, since it includes the compilation time.
+
+
+.. collapse:: Full example with benchmark
+
+    Below is an example that measures the speedup you get when compiling the InceptionV3 from TorchVision.
+
+    .. code-block:: python
+
+        import statistics
+        import torch
+        import torchvision.models as models
+        import lightning as L
+        from torch.utils.data import DataLoader
+
+
+        class MyLightningModule(L.LightningModule):
+            def __init__(self):
+                super().__init__()
+                self.model = models.inception_v3()
+
+            def training_step(self, batch):
+                return self.model(batch).logits.sum()
+
+            def train_dataloader(self):
+                return DataLoader([torch.randn(3, 512, 512) for _ in range(256)], batch_size=16)
+
+            def configure_optimizers(self):
+                return torch.optim.SGD(self.parameters(), lr=0.01)
+
+
+        class Benchmark(L.Callback):
+            """A callback that measures the median execution time between the start and end of a batch."""
+            def __init__(self):
+                self.start = torch.cuda.Event(enable_timing=True)
+                self.end = torch.cuda.Event(enable_timing=True)
+                self.times = []
+
+            def median_time(self):
+                return statistics.median(self.times)
+
+            def on_train_batch_start(self, trainer, *args, **kwargs):
+                self.start.record()
+
+            def on_train_batch_end(self, trainer, *args, **kwargs):
+                # Exclude the first iteration to let the model warm up
+                if trainer.global_step > 1:
+                    self.end.record()
+                    torch.cuda.synchronize()
+                    self.times.append(self.start.elapsed_time(self.end) / 1000)
+
+
+        model = MyLightningModule()
+
+        # Compile!
+        compiled_model = torch.compile(model)
+
+        # Measure the median iteration time with uncompiled model
+        benchmark = Benchmark()
+        trainer = L.Trainer(accelerator="cuda", devices=1, max_steps=10, callbacks=[benchmark])
+        trainer.fit(model)
+        eager_time = benchmark.median_time()
+
+        # Measure the median iteration time with compiled model
+        benchmark = Benchmark()
+        trainer = L.Trainer(accelerator="cuda", devices=1, max_steps=10, callbacks=[benchmark])
+        trainer.fit(compiled_model)
+        compile_time = benchmark.median_time()
+
+        # Compare the speedup for the compiled execution
+        speedup = eager_time / compile_time
+        print(f"Eager median time: {eager_time:.4f} seconds")
+        print(f"Compile median time: {compile_time:.4f} seconds")
+        print(f"Speedup: {speedup:.1f}x")
+
+
+    On an NVIDIA A100 SXM4 40GB with PyTorch 2.2.0, CUDA 12.1, we get the following speedup:
+
+    .. code-block:: text
+
+        Eager median time: 0.0863 seconds
+        Compile median time: 0.0709 seconds
+        Speedup: 1.2x
+
+
+----
+
+
+******************
+Avoid graph breaks
+******************
+
+When ``torch.compile`` looks at the code in your model's ``forward()`` or ``*_step()`` method, it will try to compile as much of the code as possible.
+If there are regions in the code that it doesn't understand, it will introduce a so-called "graph break" that essentially splits the code in optimized and unoptimized parts.
+Graph breaks aren't a deal breaker, since the optimized parts should still run faster.
+But if you want to get the most out of ``torch.compile``, you might want to invest rewriting the problematic section of the code that produce the breaks.
+
+You can check whether your model produces graph breaks by calling ``torch.compile`` with ``fullgraph=True``:
+
+.. code-block:: python
+
+    # Force an error if there is a graph break in the model
+    model = torch.compile(model, fullgraph=True)
+
+Be aware that the error messages produced here are often quite cryptic, so you will likely have to do some `troubleshooting <https://pytorch.org/docs/stable/torch.compiler_troubleshooting.html>`_ to fully optimize your model.
+
+
+----
+
+
+*******************
+Avoid recompilation
+*******************
+
+As mentioned before, the compilation of the model happens the first time you call ``forward()`` or the first time the Trainer calls the ``*_step()`` methods.
+At this point, PyTorch will inspect the input tensor(s) and optimize the compiled code for the particular shape, data type and other properties the input has.
+If the shape of the input remains the same across all calls, PyTorch will reuse the compiled code it generated and you will get the best speedup.
+However, if these properties change across subsequent calls to ``forward()``/``*_step()``, PyTorch will be forced to recompile the model for the new shapes, and this will significantly slow down your training if it happens on every iteration.
+
+**When your training suddenly becomes slow, it's probably because PyTorch is recompiling the model!**
+Here are some common scenarios when this can happen:
+
+- You are using dataset with different inputs or shapes for validation than for training, causing a recompilation whenever the Trainer switches between training and validation.
+- Your dataset size is not divisible by the batch size, and the dataloader has ``drop_last=False`` (the default).
+  The last batch in your training loop will be smaller and trigger a recompilation.
+
+Ideally, you should try to make the input shape(s) to ``forward()`` static.
+However, when this is not possible, you can request PyTorch to compile the code by taking into account possible changes to the input shapes.
+
+.. code-block:: python
+
+    # On PyTorch < 2.2
+    model = torch.compile(model, dynamic=True)
+
+A model compiled with ``dynamic=True`` will typically be slower than a model compiled with static shapes, but it will avoid the extreme cost of recompilation every iteration.
+On PyTorch 2.2 and later, ``torch.compile`` will detect dynamism automatically and you should no longer need to set this.
+
+If you still see recompilation issues after dealing with the aforementioned cases, there is a `Compile Profiler in PyTorch <https://pytorch.org/docs/stable/torch.compiler_troubleshooting.html#excessive-recompilation>`_ for further investigation.
+
+
+----
+
+
+***********************************
+Experiment with compilation options
+***********************************
+
+There are optional settings that, depending on your model, can give additional speedups.
+
+**CUDA Graphs:** By enabling CUDA Graphs, CUDA will record all computations in a graph and replay it every time forward and backward is called.
+The requirement is that your model must be static, i.e., the input shape must not change and your model must execute the same operations every time.
+Enabling CUDA Graphs often results in a significant speedup, but sometimes also increases the memory usage of your model.
+
+.. code-block:: python
+
+    # Enable CUDA Graphs
+    compiled_model = torch.compile(model, mode="reduce-overhead")
+
+    # This does the same
+    compiled_model = torch.compile(model, options={"triton.cudagraphs": True})
+
+|
+
+**Shape padding:** The specific shape/size of the tensors involved in the computation of your model (input, activations, weights, gradients, etc.) can have an impact on the performance.
+With shape padding enabled, ``torch.compile`` can extend the tensors by padding to a size that gives a better memory alignment.
+Naturally, the tradoff here is that it will consume a bit more memory.
+
+.. code-block:: python
+
+    # Default is False
+    compiled_model = torch.compile(model, options={"shape_padding": True})
+
+
+You can find a full list of compile options in the `PyTorch documentation <https://pytorch.org/docs/stable/generated/torch.compile.html>`_.
+
+
+----
+
+
+**************************************
+A note about torch.compile in practice
+**************************************
+
+In practice, you will find that ``torch.compile`` may not work well at first or may be counter-productive to performance.
+Compilation may fail with cryptic error messages that are hard to debug, luckily the PyTorch team is responsive and it's likely that messaging will improve in time.
+It is not uncommon that ``torch.compile`` will produce a significantly *slower* model or one with higher memory usage. You'll need to invest time in this phase if the model is not among the ones that have a happy path.
+As a note, the compilation phase itself will take some time, taking up to several minutes.
+For these reasons, we recommend that you don't invest too much time trying to apply ``torch.compile`` during development, and rather evaluate its effectiveness toward the end when you are about to launch long-running, expensive experiments.
+Always compare the speed and memory usage of the compiled model against the original model!
+
+
+----
+
+
+***********
+Limitations
+***********
+
+There are a few limitations you should be aware of when using ``torch.compile`` **in conjunction with the Trainer**:
+
+* The Trainer currently does not reapply ``torch.compile`` over DDP/FSDP, meaning distributed operations can't benefit from speed ups at the moment.
+  This limitation will be lifted in the future.
+
+* In some cases, using ``self.log()`` in your LightningModule will cause compilation errors.
+  Until addressed, you can work around these issues by applying ``torch.compile`` to the submodule(s) of your LightningModule rather than to the entire LightningModule at once.
+
+  .. code-block:: python
+
+      import lightning as L
+
+      class MyLightningModule(L.LightningModule):
+          def __init__(self):
+              super().__init__()
+              self.model = MySubModule()
+              self.model = torch.compile(self.model)
+              ...
+
+
+----
+
+
+********************
+Additional Resources
+********************
+
+Here are a few resources for further reading after you complete this tutorial:
+
+- `PyTorch 2.0 Paper <https://pytorch.org/blog/pytorch-2-paper-tutorial/>`_
+- `GenAI with PyTorch 2.0 blog post series <https://pytorch.org/blog/accelerating-generative-ai-4/>`_
+- `Training Production AI Models with PyTorch 2.0 <https://pytorch.org/blog/training-production-ai-models/>`_
+- `Empowering Models with Performance: The Art of Generalized Model Transformation Approach <https://pytorch.org/blog/empowering-models-performance/>`_
+
+|