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

CHANGELOG.md

@@ -43,7 +43,13 @@ Docs:
 Features:
  - Added Adagrad (without grad clipping) as 32-bit and 8-bit block-wise optimizer.
  - Added AdamW (copy of Adam with weight decay init 1e-2). #10
+ - Introduced ModuleConfig overrides which can be seamlessly be used at initialization time of a module.
+ - Added `bnb.nn.Embedding` layer which runs at 32-bit but without the layernorm. This works well if you need to fine-tune pretrained models that do not have a embedding layer norm. #19
 
 Bug fixes:
  - Fixed a bug where weight decay was incorrectly applied to 32-bit Adam. #13
  - Fixed an unsafe use of eval. #8
+ - Fixed a bug where the StableEmbedding layer 32-bit optimizer override would not work without registering the whole model first (`bnb.optim.GlobalOptimManager.get_instance().register_parameters(model.parameters())`).  #13 #15 
+
+Docs:
+ - Added instructions how to solve "\_\_fatbinwrap_" errors.

README.md

@@ -83,6 +83,7 @@ For upcoming features and changes and full history see [Patch Notes](CHANGELOG.m
 ## Errors
 
 1. RuntimeError: CUDA error: no kernel image is available for execution on the device. [Solution](errors_and_solutions.md#No-kernel-image-available)
+2. __fatbinwrap_.. [Solution](errors_and_solutions.md#fatbinwrap_)
 
 ## Compile from source
 

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

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

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:

errors_and_solutions.md

@@ -6,3 +6,16 @@ If you are feeling lucky, you can also try to compile the library from source. T
 
 
 __If you encounter any other error not listed here please create an issue. This will help resolve your problem and will help out others in the future.
+
+
+# fatbinwrap
+
+This error occurs if there is a mismatch between CUDA versions in the C++ library and the CUDA part. Make sure you have right CUDA in your $PATH and $LD_LIBRARY_PATH variable. In the conda base environment you can find the library under:
+```bash
+ls $CONDA_PREFIX/lib/*cudart*
+```
+Make sure this path is appended to the `LD_LIBRARY_PATH` so bnb can find the CUDA runtime environment library (cudart).
+
+If this does not fix the issue, please try [compilation from source](compile_from_source.md) next.
+
+If this does not work, please open an issue and paste the printed environment if you call `make` and the associated error when running bnb.

howto_config_override.md

@@ -2,6 +2,7 @@
 
 If you want to optimize some unstable parameters with 32-bit Adam and others with 8-bit Adam, you can use the `GlobalOptimManager`. With this, we can also configure specific hyperparameters for particular layers, such as embedding layers. To do that, we need two things: (1) register the parameter while they are still on the CPU, (2) override the config with the new desired hyperparameters (anytime, anywhere). See our [guide](howto_config_override.md) for more details
 
+For global overrides in many different places in your code you can do:
 ```python
 import torch
 import bitsandbytes as bnb
@@ -24,3 +25,16 @@ mng.override_config([model.special.weight, model.also_special.weight],
                     key_value_dict ={'is_sparse': True, 'lr': 1e-5, 'betas'=(0.9, 0.98)}) 
 ``` 
 Possible options for the config override are: `betas, eps, weight_decay, lr, optim_bits, min_8bit_size, percentile_clipping, block_wise, max_unorm`
+
+For overrides for particular layers we recommend overriding locally in each module. You can do this by passing the module, the parameter, and its attribute name to the GlobalOptimManager:
+```python
+class MyModule(torch.nn.Module):
+  def __init__(din, dout):
+    super(MyModule, self).__init__()
+    self.linear = torch.nn.Linear(din, dout)
+    # optimization will happen in 32-bit and
+    # learning rate will be set to 0.0001 independent of the main learning rate
+    config = {'optim_bits': 32, 'lr' : 0.0001}
+    GlobalOptimManager.get_instance().register_module_override(self, 'weight', config)
+
+```

tests/test_modules.py

@@ -0,0 +1,46 @@
+# Copyright (c) Facebook, Inc. and its affiliates. 
+#   
+# This source code is licensed under the MIT license found in the 
+# LICENSE file in the root directory of this source tree.
+import pytest
+import torch
+import bitsandbytes as bnb
+
+from itertools import product
+
+from bitsandbytes import functional as F
+
+
+@pytest.mark.parametrize("embcls", [bnb.nn.Embedding, bnb.nn.StableEmbedding], ids=['Embedding', 'StableEmbedding'])
+def test_embeddings(embcls):
+    bnb.optim.GlobalOptimManager.get_instance().initialize()
+    emb1 = torch.nn.Embedding(100, 512).cuda()
+    emb2 = embcls(100, 512).cuda()
+
+    adam1 = bnb.optim.Adam8bit(emb1.parameters())
+    adam2 = bnb.optim.Adam8bit(emb2.parameters())
+
+    batches = torch.randint(1, 100, size=(100, 4, 32)).cuda()
+
+    for i in range(100):
+        batch = batches[i]
+
+        embedded1 = emb1(batch)
+        embedded2 = emb2(batch)
+
+        l1 = embedded1.mean()
+        l2 = embedded2.mean()
+
+        l1.backward()
+        l2.backward()
+
+        adam1.step()
+        adam2.step()
+
+        adam1.zero_grad()
+        adam2.zero_grad()
+
+        assert adam1.state[emb1.weight]['state1'].dtype == torch.uint8
+        assert adam2.state[emb2.weight]['state1'].dtype == torch.float32
+
+