[fix][FSDP] fix weight init when using apply() (fixes #490 and #444)

fairscale/nn/data_parallel/fully_sharded_data_parallel.py

@@ -9,7 +9,7 @@
 import functools
 from math import inf
 import traceback
-from typing import TYPE_CHECKING, Any, Callable, Dict, Generator, List, NamedTuple, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, Generator, List, NamedTuple, Optional, Set, Tuple, Union
 
 import torch
 from torch.autograd import Variable
@@ -150,6 +150,11 @@ class FullyShardedDataParallel(nn.Module):
             based on world_size, so the max shard size is roughly
             ``bucket_cap_mb / world_size``. Values <= 0 disable bucketing.
             Default: 25.
+        compute_device (torch.device, Optional):
+            device for computation. If not given and module params are on a CUDA
+            device, the param's device will be used. If not given and module
+            params are on CPU, then the current CUDA device (as indicated by
+            ``torch.cuda.current_device()`` will be used.
     """
 
     def __init__(
@@ -165,6 +170,7 @@ def __init__(
         buffer_dtype: Optional[torch.dtype] = None,
         move_grads_to_cpu: Optional[bool] = None,
         bucket_cap_mb: int = 25,
+        compute_device: Optional[torch.device] = None,
     ):
         super().__init__()
         self.process_group = process_group or dist.new_group()
@@ -179,14 +185,21 @@ def __init__(
         self.buffer_dtype = buffer_dtype or self.compute_dtype
         self.move_grads_to_cpu = cpu_offload if move_grads_to_cpu is None else move_grads_to_cpu
         self.bucket_cap_mb = bucket_cap_mb
+        self.compute_device = compute_device
 
         if self.fp32_reduce_scatter and not self.mixed_precision:
             raise ValueError("fp32_reduce_scatter requires mixed_precision=True")
         if self.cpu_offload and not self.mixed_precision:
             raise ValueError("cpu_offload requires mixed_precision=True")
 
-        compute_device = torch.device("cuda") if self.cpu_offload else next(module.parameters()).device
-        validate_process_group(compute_device, self.process_group)
+        if self.compute_device is None:
+            # Try to infer CUDA device from module parameters.
+            self.compute_device = next(module.parameters()).device
+            if self.compute_device.type != "cuda":
+                # Fall back to current CUDA device.
+                self.compute_device = torch.device("cuda")
+
+        validate_process_group(self.compute_device, self.process_group)
         enable_pytorch_sync_bn(module)
 
         # Only handle params which are not already sharded. This enables
@@ -239,11 +252,68 @@ def __init__(
     def module(self) -> nn.Module:
         return self._fsdp_wrapped_module  # note: may be a FlattenParamsWrapper instance
 
-    @torch.no_grad()
-    def _all_buffers_to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        """Move all buffers to the specified device and dtype, recursively."""
-        cast_fn = functools.partial(cast_buffers_, device=device, dtype=dtype)
-        self.apply(cast_fn)
+    def apply(self, fn: Callable[[nn.Module], None]) -> "FullyShardedDataParallel":
+        """
+        Applies ``fn`` recursively to every submodule (as returned by
+        ``.children()``) as well as self. Typical use includes initializing the
+        parameters of a model.
+
+        Compared to ``torch.nn.Module.apply``, this version additionally gathers
+        the full parameters before applying ``fn``. It should not be called from
+        within another ``summon_full_params`` context.
+
+        Args:
+            fn (nn.Module): function to be applied to each submodule
+
+        Returns:
+            Module: self
+        """
+        is_uninitialized = self._is_root is None
+        self.assert_state(TrainingState.IDLE)
+        with self.summon_full_params(recurse=False):
+            return_value = super().apply(fn)
+        # summon_full_params will call _lazy_init, which sets _is_root. However,
+        # apply() may be called directly on children instances to do weight
+        # init, so we should reset the _is_root flag in this case.
+        if is_uninitialized and self._is_root:
+            for module in self.modules():
+                if isinstance(module, FullyShardedDataParallel):
+                    module._reset_lazy_init()
+        return return_value
+
+    def _cast_buffers(
+        self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, memo: Optional[Set] = None
+    ) -> None:
+        """Move all buffers to the given *device* and *dtype*.
+
+        If *device* or *dtype* are not given, then they will default to
+        ``self.compute_device`` and ``self.buffer_dtype``, respectively. In the
+        case of nested FSDP instances, we will respect the child instance's
+        ``compute_device`` and ``buffer_dtype`` configuration.
+
+        Args:
+            device (torch.device, Optional):
+                device to cast buffers to (defaults to compute_device)
+            dtype (torch.dtype, Optional):
+                dtype to cast buffers to (defaults to buffer_dtype)
+            memo (Set, Optional):
+                set of modules that have already been processed
+        """
+        if memo is None:
+            memo = set()
+        for module in self.modules():
+            if module is not self and isinstance(module, FullyShardedDataParallel):
+                # Allow any child FSDP instances to handle their own buffers.
+                module._cast_buffers(device=device, dtype=dtype, memo=memo)
+            elif module not in memo:
+                memo.add(module)
+                for name, buf in module.named_buffers(recurse=False):
+                    if buf is None:
+                        continue
+                    buf = buf.to(device=device or self.compute_device)
+                    if torch.is_floating_point(buf):
+                        buf = buf.to(dtype=dtype or self.buffer_dtype)
+                    setattr(module, name, buf)
 
     @property
     def params_with_grad(self) -> List[Parameter]:
@@ -386,7 +456,10 @@ def extra_repr(self) -> str:
             f"flatten_parameters={self.flatten_parameters}, "
             f"cpu_offload={self.cpu_offload}, "
             f"compute_dtype={self.compute_dtype}, "
-            f"move_grads_to_cpu={self.move_grads_to_cpu}"
+            f"buffer_dtype={self.buffer_dtype}, "
+            f"move_grads_to_cpu={self.move_grads_to_cpu}, "
+            f"bucket_cap_mb={self.bucket_cap_mb}, "
+            f"compute_device={self.compute_device}"
         )
 
     def __getattr__(self, name: str) -> Any:
@@ -443,7 +516,7 @@ def state_dict(self, *args: Any, **kwargs: Any) -> "OrderedDict[str, torch.Tenso
         self._lazy_init()
         if self.mixed_precision:
             # Buffers dtype stays consistent with parameters.
-            self._all_buffers_to(dtype=torch.float32)
+            self._cast_buffers(dtype=torch.float32)
 
         if self._return_full_state_dict:
             if self.training_state != TrainingState.SUMMON_FULL_PARAMS:
@@ -463,8 +536,8 @@ def state_dict(self, *args: Any, **kwargs: Any) -> "OrderedDict[str, torch.Tenso
                 state_dict[k] = state_dict[k].cpu()
 
         if self.mixed_precision:
-            # In case we are in mixed precision, restore buffers back to fp16.
-            self._all_buffers_to(dtype=self.buffer_dtype)
+            # In case we are in mixed precision, restore buffers back to buffer_dtype.
+            self._cast_buffers()
         return state_dict
 
     # TODO (Min): figuring out how to do typing for this overloaded function.
@@ -572,7 +645,7 @@ def summon_full_params(self, recurse: bool = True, volatile: bool = False) -> Ge
             recurse (bool, Optional): recursively summon all params for nested
                 FSDP instances (default: True)
             volatile (bool, Optional): if ``True``, modifications to params are
-                not guaranteed persist after the context manager exists;
+                not guaranteed to persist after the context manager exists;
                 enabling this can be slightly more efficient (default: False)
         """
         if recurse:
@@ -625,6 +698,9 @@ def _reset_lazy_init(self) -> None:
         self._queue_wait_for_post_backward_closure: Optional[Callable] = None
         self._streams: Dict[str, torch.cuda.Stream] = {}
         self._reducer: Optional[ReduceScatterBucketer] = None
+        for p in self.params:
+            if hasattr(p, "_fp32_shard"):
+                del p._fp32_shard  # reset _init_param_attributes
 
     def _lazy_init(self) -> None:
         """Initialization steps that should happen lazily, typically right
@@ -642,12 +718,11 @@ def _lazy_init(self) -> None:
             self._set_is_root()
             self._setup_streams()
 
-        if self.cpu_offload:  # Buffers stay on GPU, and don't get sharded
-            self._all_buffers_to(device=torch.device("cuda"), dtype=self.buffer_dtype)
-        else:
-            self._all_buffers_to(dtype=self.buffer_dtype)
-
         if self._is_root:
+            # Buffers stay on GPU, and don't get sharded. Since _cast_buffers
+            # applies recursively, we only call this from the root instance.
+            self._cast_buffers()
+
             # Don't free the full params for the outer-most (root) instance,
             # since those params will be needed immediately after for the
             # backward pass.
@@ -684,10 +759,6 @@ def _init_param_attributes(self, p: Parameter) -> None:
         if hasattr(p, "_fp32_shard"):
             return
 
-        # Compute device defaults to CUDA when *cpu_offload* is enabled, or the
-        # param's current device otherwise (could be CPU).
-        compute_device = torch.device("cuda") if self.cpu_offload else p.device
-
         # A single shard of the parameters in full precision.
         p._fp32_shard = p.data
 
@@ -707,7 +778,7 @@ def _init_param_attributes(self, p: Parameter) -> None:
             # the computation in the forward/backward pass. We resize the
             # storage to size 0 at init (here) and re-materialize (by copying
             # from _fp32_shard) as needed.
-            p._fp16_shard = torch.zeros_like(p._fp32_shard, device=compute_device, dtype=self.compute_dtype)
+            p._fp16_shard = torch.zeros_like(p._fp32_shard, device=self.compute_device, dtype=self.compute_dtype)
             free_storage_(p._fp16_shard)
         else:
             p._fp16_shard = None  # use _fp32_shard
@@ -720,7 +791,7 @@ def _init_param_attributes(self, p: Parameter) -> None:
         # relevant computation.
         if p._is_sharded:
             p._full_param_padded = torch.zeros(
-                p.data.numel() * self.world_size, device=compute_device, dtype=self.compute_dtype
+                p.data.numel() * self.world_size, device=self.compute_device, dtype=self.compute_dtype
             )
             free_storage_(p._full_param_padded)
 
@@ -1290,20 +1361,6 @@ def fn(x: torch.Tensor) -> torch.Tensor:
     return apply_to_tensors(fn, args), apply_to_tensors(fn, kwargs)
 
 
-def cast_buffers_(
-    module: nn.Module, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None
-) -> None:
-    """Cast all of module.named_buffers to device and floating point buffers to dtype."""
-    # if buffers are already on the right device and/or dtype this is just python loop cost
-    assert dtype in {torch.float32, torch.float16}  # assumes compute_dtype == float16
-    for key, buf in module.named_buffers(recurse=False):
-        if buf is not None:
-            buf = buf.to(device=device)
-            if torch.is_floating_point(buf):
-                buf = buf.to(dtype=dtype)
-            setattr(module, key, buf)
-
-
 def free_storage_(data: torch.Tensor) -> None:
     """Free underlying storage of a Tensor."""
     if data.storage().size() > 0:

tests/ci_test_list_2.txt

@@ -33,3 +33,4 @@ tests/nn/pipe/test_deferred_batch_norm.py
 tests/nn/pipe/test_dependency.py
 tests/nn/pipe/test_stream.py
 tests/experimental/nn/test_multiprocess_pipe.py
+tests/nn/data_parallel/test_fsdp_apply.py

tests/nn/data_parallel/test_fsdp.py

@@ -77,6 +77,49 @@ def get_wrapped_model(group, cuda_first=False, config={}, **model_kwargs) -> Ful
             model = FullyShardedDataParallel(TransformerWithSharedParams(group, **model_kwargs), group, **config).cuda()
         return model
 
+    @classmethod
+    def _test_identical_outputs(
+        cls, model_init_fn, config, rank, group, num_steps=2, use_cuda=True, lr=0.01, ref_ddp_fn=None, norm_type=2,
+    ):
+        if config.get("mixed_precision", False):
+            autocast = True
+            # Force the compute dtype to be torch.float32 so that we get
+            # identical results as PyTorch DDP when using autocast. Note that
+            # this will cause the all-gather to happen in FP32, which is slower
+            # than necessary in most cases.
+            config["compute_dtype"] = torch.float32
+        else:
+            autocast = False
+
+        # Establish reference behavior with PyTorch DDP (+ optionally autocast).
+        model = model_init_fn(group=group, wrapper_config=None).cuda()
+        if ref_ddp_fn is None:
+            model = nn.parallel.DistributedDataParallel(
+                model, device_ids=[rank], output_device=rank, process_group=group
+            )
+        else:
+            model = ref_ddp_fn(model, group)
+        ref_loss = cls._train_for_several_steps(model, num_steps, autocast, lr=lr, norm_type=norm_type)
+        ref_state_dict = model.module.state_dict()
+        if config.get("cpu_offload", False):
+            for k in ref_state_dict.keys():
+                ref_state_dict[k] = ref_state_dict[k].cpu()
+
+        # Confirm we get the same behavior using FullyShardedDataParallel.
+        model = FullyShardedDataParallel(model_init_fn(group=group, wrapper_config=config), group, **config)
+        if use_cuda:
+            model = model.cuda()
+        else:
+            assert next(model.parameters()).device == torch.device("cpu")
+        shard_loss = cls._train_for_several_steps(model, num_steps, autocast, lr=lr, norm_type=norm_type)
+        shard_state_dict = model.state_dict()
+
+        try:
+            torch.testing.assert_allclose(ref_loss, shard_loss)
+            assert objects_are_equal(ref_state_dict, shard_state_dict, raise_exception=True)
+        except (AssertionError, RuntimeError) as e:
+            raise Exception(f"FullyShardedDataParallel didn't match PyTorch DDP using config: {config}\n\n {e}")
+
 
 class TestMixedPrecision(DistributedTest):
     def test_all_fp32(self):
@@ -313,49 +356,6 @@ def test_mixture_of_experts_grad_clip_breaks(self):
     def _dummy_ddp_fn(self, model, group):
         return DummyDDP(model)
 
-    @classmethod
-    def _test_identical_outputs(
-        cls, model_init_fn, config, rank, group, num_steps=2, use_cuda=True, lr=0.01, ref_ddp_fn=None, norm_type=2,
-    ):
-        if config.get("mixed_precision", False):
-            autocast = True
-            # Force the compute dtype to be torch.float32 so that we get
-            # identical results as PyTorch DDP when using autocast. Note that
-            # this will cause the all-gather to happen in FP32, which is slower
-            # than necessary in most cases.
-            config["compute_dtype"] = torch.float32
-        else:
-            autocast = False
-
-        # Establish reference behavior with PyTorch DDP (+ optionally autocast).
-        model = model_init_fn(group=group, wrapper_config=None).cuda()
-        if ref_ddp_fn is None:
-            model = nn.parallel.DistributedDataParallel(
-                model, device_ids=[rank], output_device=rank, process_group=group
-            )
-        else:
-            model = ref_ddp_fn(model, group)
-        ref_loss = cls._train_for_several_steps(model, num_steps, autocast, lr=lr, norm_type=norm_type)
-        ref_state_dict = model.module.state_dict()
-        if config.get("cpu_offload", False):
-            for k in ref_state_dict.keys():
-                ref_state_dict[k] = ref_state_dict[k].cpu()
-
-        # Confirm we get the same behavior using FullyShardedDataParallel.
-        model = FullyShardedDataParallel(model_init_fn(group=group, wrapper_config=config), group, **config)
-        if use_cuda:
-            model = model.cuda()
-        else:
-            assert next(model.parameters()).device == torch.device("cpu")
-        shard_loss = cls._train_for_several_steps(model, num_steps, autocast, lr=lr, norm_type=norm_type)
-        shard_state_dict = model.state_dict()
-
-        try:
-            torch.testing.assert_allclose(ref_loss, shard_loss)
-            assert objects_are_equal(ref_state_dict, shard_state_dict, raise_exception=True)
-        except (AssertionError, RuntimeError) as e:
-            raise Exception(f"FullyShardedDataParallel didn't match PyTorch DDP using config: {config}\n\n {e}")
-
     @parameterized.expand([[1], [inf]], name_func=rename_test)
     def test_clip_norm_transformer(self, norm_type):
         config = {"mixed_precision": True}