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authorTim Dettmers <tim.dettmers@gmail.com>2021-11-29 09:32:13 -0800
committerTim Dettmers <tim.dettmers@gmail.com>2021-11-29 09:32:13 -0800
commit20e1677dfdc4495038fd780807c8cbc253adf921 (patch)
tree42011169e55eab3f4226ff171d84edac84ec6f8f
parent3cff6795fb70dd99b4802593f3c70d291e0cd1dc (diff)
Added module override, bnb.nn.Embedding #13 #15 #19
-rw-r--r--CHANGELOG.md6
-rw-r--r--README.md1
-rw-r--r--bitsandbytes/nn/__init__.py2
-rw-r--r--bitsandbytes/nn/modules.py33
-rw-r--r--bitsandbytes/optim/optimizer.py31
-rw-r--r--errors_and_solutions.md13
-rw-r--r--howto_config_override.md14
-rw-r--r--tests/test_modules.py46
8 files changed, 140 insertions, 6 deletions
diff --git a/CHANGELOG.md b/CHANGELOG.md
index d12af22..fa20b15 100644
--- a/CHANGELOG.md
+++ b/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.
diff --git a/README.md b/README.md
index 4a731b0..4b7db17 100644
--- a/README.md
+++ b/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
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:
diff --git a/errors_and_solutions.md b/errors_and_solutions.md
index dd99f7c..5e8b2d2 100644
--- a/errors_and_solutions.md
+++ b/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.
diff --git a/howto_config_override.md b/howto_config_override.md
index 11e9d49..4680776 100644
--- a/howto_config_override.md
+++ b/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)
+
+```
diff --git a/tests/test_modules.py b/tests/test_modules.py
new file mode 100644
index 0000000..6cbee7b
--- /dev/null
+++ b/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
+
+