Added module override, bnb.nn.Embedding #13 #15 #19

3 files changed, 60 insertions, 6 deletions

diff --git a/bitsandbytes/nn/__init__.py b/bitsandbytes/nn/__init__.py
index 177540f..27ad6ca 100644
--- a/bitsandbytes/nn/__init__.py
+++ b/bitsandbytes/nn/__init__.py
@@ -2,4 +2,4 @@
 #   
 # This source code is licensed under the MIT license found in the 
 # LICENSE file in the root directory of this source tree.
-from .modules import StableEmbedding
+from .modules import StableEmbedding, Embedding
diff --git a/bitsandbytes/nn/modules.py b/bitsandbytes/nn/modules.py
index ce2f3a4..dc0a171 100644
--- a/bitsandbytes/nn/modules.py
+++ b/bitsandbytes/nn/modules.py
@@ -18,8 +18,7 @@ class StableEmbedding(torch.nn.Embedding):
                  sparse: bool = False, _weight: Optional[Tensor] = None) -> None:
         super(StableEmbedding, self).__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse, _weight)
         self.norm = torch.nn.LayerNorm(embedding_dim)
-        GlobalOptimManager.get_instance().register_parameters(self.weight)
-        GlobalOptimManager.get_instance().override_config(self.weight, 'optim_bits', 32)
+        GlobalOptimManager.get_instance().register_module_override(self, 'weight', {'optim_bits': 32})
 
     def reset_parameters(self) -> None:
         torch.nn.init.xavier_uniform_(self.weight)
@@ -42,3 +41,33 @@ class StableEmbedding(torch.nn.Embedding):
             self.norm_type, self.scale_grad_by_freq, self.sparse)
 
         return self.norm(emb)
+
+
+class Embedding(torch.nn.Embedding):
+    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None,
+                 max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
+                 sparse: bool = False, _weight: Optional[Tensor] = None) -> None:
+        super(Embedding, self).__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse, _weight)
+        GlobalOptimManager.get_instance().register_module_override(self, 'weight', {'optim_bits': 32})
+
+    def reset_parameters(self) -> None:
+        torch.nn.init.xavier_uniform_(self.weight)
+        self._fill_padding_idx_with_zero()
+
+    ''' !!! This is a redefinition of _fill_padding_idx_with_zero in torch.nn.Embedding
+        to make the Layer compatible with Pytorch < 1.9.
+        This means that if this changes in future PyTorch releases this need to change too
+        which is cumbersome. However, with this we can ensure compatibility with previous
+        PyTorch releases.
+    '''
+    def _fill_padding_idx_with_zero(self) -> None:
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                self.weight[self.padding_idx].fill_(0)
+
+    def forward(self, input: Tensor) -> Tensor:
+        emb = F.embedding(
+            input, self.weight, self.padding_idx, self.max_norm,
+            self.norm_type, self.scale_grad_by_freq, self.sparse)
+
+        return emb
diff --git a/bitsandbytes/optim/optimizer.py b/bitsandbytes/optim/optimizer.py
index cfbd72e..5a5bb1e 100644
--- a/bitsandbytes/optim/optimizer.py
+++ b/bitsandbytes/optim/optimizer.py
@@ -26,6 +26,7 @@ class GlobalOptimManager(object):
         self.index2config = {}
         self.optimizer = None
         self.uses_config_override = False
+        self.module_weight_config_triple = []
 
     @classmethod
     def get_instance(cls):
@@ -77,12 +78,16 @@ class GlobalOptimManager(object):
                 if id(p) in self.pid2config:self.pid2config[id(p)].update(key_value_dict)
                 else: self.pid2config[id(p)] = key_value_dict
 
+    def register_module_override(self, module, param_name, config):
+        self.module_weight_config_triple.append((module, param_name, config))
+
+
 
 class Optimizer8bit(torch.optim.Optimizer):
 
     def __init__(self, params, defaults, optim_bits=32):
         super(Optimizer8bit, self).__init__(params, defaults)
-        self.checked_if_on_gpu = False
+        self.initialized = False
         self.name2qmap = {}
 
         self.mng = GlobalOptimManager.get_instance()
@@ -172,7 +177,6 @@ class Optimizer8bit(torch.optim.Optimizer):
         self.__setstate__({'state': state, 'param_groups': param_groups})
 
     def to_gpu(self):
-        self.checked_if_on_gpu = True
         for gindex, group in enumerate(self.param_groups):
             for pindex, p in enumerate(group['params']):
                 if p in self.state:
@@ -181,6 +185,23 @@ class Optimizer8bit(torch.optim.Optimizer):
                         if isinstance(v, torch.Tensor):
                             self.state[p][k] = v.to(p.device)
 
+    def check_overrides(self):
+        for module, attr, config in self.mng.module_weight_config_triple:
+            pmodule = getattr(module, attr)
+            assert pmodule is not None
+            assert isinstance(pmodule, torch.Tensor) or isinstance(pmodule, torch.Parameter)
+            found = False
+            for gindex, group in enumerate(self.param_groups):
+                if found: break
+                for pindex, p in enumerate(group['params']):
+                    if found: break
+                    if id(p) == id(pmodule):
+                        # found the matching parameter
+                        # init override
+                        self.mng.pid2config[id(p)] = config
+                        self.mng.index2config[(gindex, pindex)] = self.mng.pid2config[id(p)]
+                        found = True
+
     @torch.no_grad()
     def step(self, closure=None):
         """Performs a single optimization step.
@@ -196,7 +217,11 @@ class Optimizer8bit(torch.optim.Optimizer):
 
         overflows = []
 
-        if not self.checked_if_on_gpu: self.to_gpu() # needed for fairseq pure fp16 training
+        if not self.initialized:
+            self.check_overrides()
+            self.to_gpu() # needed for fairseq pure fp16 training
+            self.initialized = True
+
         for gindex, group in enumerate(self.param_groups):
             for pindex, p in enumerate(group['params']):
                 if p.grad is None: