Add copyright and license info (#719)

Summary:
## Types of changes

- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to change)
- [x] Docs change / refactoring / dependency upgrade

## Motivation and Context / Related issue

## How Has This Been Tested (if it applies)

## Checklist

- [x] The documentation is up-to-date with the changes I made.
- [x] I have read the **CONTRIBUTING** document and completed the CLA (see **CONTRIBUTING**).
- [ ] All tests passed, and additional code has been covered with new tests.

Pull Request resolved: #719

Reviewed By: iden-kalemaj

Differential Revision: D68476050

fbshipit-source-id: 2330aeccdc2fa6540dc70ae35b7e42d51871d6f8

Author: 564612540

research/disk_optimizer/KFprivacy_engine.py

@@ -1,3 +1,17 @@
+# Copyright (c) Xinwei Zhang
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from typing import List, Union
 
 from opacus.optimizers import DPOptimizer

research/disk_optimizer/ReadMe.md

@@ -3,7 +3,9 @@
 ## Introduction
 This part of the code introduces a new component to the optimizer named DiSK. The code uses a simplifed Kalman to improve the privatized gradient estimate. Speficially, the privatized minibatch gradient is replaced with:
 
-$$\mathbb{g_{t+\frac{1}{2}}} = \frac{1}{B}\sum_{\xi \in \mathcal{B}_t} \mathrm{clip}_C\left(\frac{1-\kappa}{\kappa\gamma}\nabla f(x_t + \gamma(x_t-x_{t-1});\xi) + \Big(1- \frac{1-\kappa}{\kappa\gamma}\Big)\nabla f(x_t;\xi)\right) + w_t$$
+
+$$\mathbb{g}_{t+\frac{1}{2}}(\xi) = \frac{1-\kappa}{\kappa\gamma}\nabla f(x_t + \gamma(x_t-x_{t-1});\xi) + \Big(1- \frac{1-\kappa}{\kappa\gamma}\Big)\nabla f(x_t;\xi)$$
+$$\mathbb{g_{t+\frac{1}{2}}} = \frac{1}{B}\sum_{\xi \in \mathcal{B}_t} \mathrm{clip}_C\left(\mathbb{g}_{t+\frac{1}{2}}(\xi)\right) + w_t$$
 $$g_{t}= (1-\kappa)g_{t-1} + \kappa g_{t+\frac{1}{2}}$$
 
 A detailed description of the algorithm can be found at [Here](https://arxiv.org/abs/2410.03883).

research/disk_optimizer/optimizers/KFadaclipoptimizer.py

@@ -1,3 +1,17 @@
+# Copyright (c) Xinwei Zhang
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import annotations
 
 import logging
@@ -33,6 +47,7 @@ def __init__(
         secure_mode: bool = False,
         kappa: float = 0.7,
         gamma: float = 0.5,
+        **kwargs,
     ):
         if gamma == 0 or abs(gamma - (1 - kappa) / kappa) < 1e-3:
             gamma = (1 - kappa) / kappa
@@ -58,6 +73,7 @@ def __init__(
             max_clipbound=max_clipbound,
             min_clipbound=min_clipbound,
             unclipped_num_std=unclipped_num_std,
+            **kwargs,
         )
         self.kappa = kappa
         self.gamma = gamma
@@ -79,7 +95,7 @@ def step(self, closure=required) -> Optional[float]:
                     first_step = True
                     state["kf_d_t"] = torch.zeros_like(p.data).to(p.data)
                     state["kf_m_t"] = grad.clone().to(p.data)
-                state["kf_m_t"].lerp_(grad, weight=self.kappa)
+                state["kf_m_t"].lerp_(grad, weight=1 - self.kappa)
                 p.grad = state["kf_m_t"].clone().to(p.data)
                 state["kf_d_t"] = -p.data.clone().to(p.data)
                 if first_step:

research/disk_optimizer/optimizers/KFddp_perlayeroptimizer.py

@@ -1,3 +1,17 @@
+# Copyright (c) Xinwei Zhang
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import annotations
 
 from functools import partial
@@ -29,6 +43,7 @@ def __init__(
         secure_mode: bool = False,
         kappa: float = 0.7,
         gamma: float = 0.5,
+        **kwargs,
     ):
         self.rank = torch.distributed.get_rank()
         self.world_size = torch.distributed.get_world_size()
@@ -43,6 +58,7 @@ def __init__(
             secure_mode=secure_mode,
             kappa=kappa,
             gamma=gamma,
+            **kwargs,
         )
 
 
@@ -64,6 +80,7 @@ def __init__(
         secure_mode: bool = False,
         kappa: float = 0.7,
         gamma: float = 0.5,
+        **kwargs,
     ):
         self.rank = torch.distributed.get_rank()
         self.world_size = torch.distributed.get_world_size()
@@ -79,6 +96,7 @@ def __init__(
             secure_mode=secure_mode,
             kappa=kappa,
             gamma=gamma,
+            **kwargs,
         )
         self._register_hooks()
 

research/disk_optimizer/optimizers/KFddpoptimizer.py

@@ -1,3 +1,17 @@
+# Copyright (c) Xinwei Zhang
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import annotations
 
 import logging
@@ -32,6 +46,7 @@ def __init__(
         secure_mode: bool = False,
         kappa=0.7,
         gamma=0.5,
+        **kwargs,
     ):
         super().__init__(
             optimizer,
@@ -43,6 +58,7 @@ def __init__(
             secure_mode=secure_mode,
             kappa=kappa,
             gamma=gamma,
+            **kwargs,
         )
         self.rank = torch.distributed.get_rank()
         self.world_size = torch.distributed.get_world_size()
@@ -80,7 +96,7 @@ def step(self, closure=required) -> Optional[float]:
                     first_step = True
                     state["kf_d_t"] = torch.zeros_like(p.data).to(p.data)
                     state["kf_m_t"] = grad.clone().to(p.data)
-                state["kf_m_t"].lerp_(grad, weight=self.kappa)
+                state["kf_m_t"].lerp_(grad, weight=1 - self.kappa)
                 p.grad = state["kf_m_t"].clone().to(p.data)
                 state["kf_d_t"] = -p.data.clone().to(p.data)
                 if first_step:

research/disk_optimizer/optimizers/KFddpoptimizer_fast_gradient_clipping.py

@@ -1,3 +1,17 @@
+# Copyright (c) Xinwei Zhang
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import annotations
 
 import logging
@@ -32,6 +46,7 @@ def __init__(
         secure_mode: bool = False,
         kappa=0.7,
         gamma=0.5,
+        **kwargs,
     ):
         super().__init__(
             optimizer,
@@ -43,6 +58,7 @@ def __init__(
             secure_mode=secure_mode,
             kappa=kappa,
             gamma=gamma,
+            **kwargs,
         )
         self.rank = torch.distributed.get_rank()
         self.world_size = torch.distributed.get_world_size()
@@ -80,7 +96,7 @@ def step(self, closure=required) -> Optional[float]:
                     first_step = True
                     state["kf_d_t"] = torch.zeros_like(p.data).to(p.data)
                     state["kf_m_t"] = grad.clone().to(p.data)
-                state["kf_m_t"].lerp_(grad, weight=self.kappa)
+                state["kf_m_t"].lerp_(grad, weight=1 - self.kappa)
                 p.grad = state["kf_m_t"].clone().to(p.data)
                 state["kf_d_t"] = -p.data.clone().to(p.data)
                 if first_step:

research/disk_optimizer/optimizers/KFoptimizer.py

@@ -1,3 +1,17 @@
+# Copyright (c) Xinwei Zhang
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import annotations
 
 import logging
@@ -27,6 +41,7 @@ def __init__(
         secure_mode: bool = False,
         kappa=0.7,
         gamma=0.5,
+        **kwargs,
     ):
         if gamma == 0 or abs(gamma - (1 - kappa) / kappa) < 1e-3:
             gamma = (1 - kappa) / kappa
@@ -47,6 +62,7 @@ def __init__(
             loss_reduction=loss_reduction,
             generator=generator,
             secure_mode=secure_mode,
+            **kwargs,
         )
         self.kappa = kappa
         self.gamma = gamma
@@ -131,7 +147,7 @@ def step(self, closure=required) -> Optional[float]:
                     first_step = True
                     state["kf_d_t"] = torch.zeros_like(p.data).to(p.data)
                     state["kf_m_t"] = grad.clone().to(p.data)
-                state["kf_m_t"].lerp_(grad, weight=self.kappa)
+                state["kf_m_t"].lerp_(grad, weight=1 - self.kappa)
                 p.grad = state["kf_m_t"].clone().to(p.data)
                 state["kf_d_t"] = -p.data.clone().to(p.data)
                 if first_step:

research/disk_optimizer/optimizers/KFoptimizer_fast_gradient_clipping.py

@@ -1,9 +1,26 @@
+# Copyright (c) Xinwei Zhang
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import annotations
 
 import logging
 from typing import Optional
 
 import torch
+from opacus.optimizers.optimizer_fast_gradient_clipping import (
+    DPOptimizerFastGradientClipping,
+)
 from torch.optim import Optimizer
 from torch.optim.optimizer import required
 
@@ -13,8 +30,7 @@
 logger = logging.getLogger(__name__)
 logger.disabled = True
 
-
-class KF_DPOptimizerFastGradientClipping(KF_DPOptimizer):
+class KF_DPOptimizerFastGradientClipping(DPOptimizerFastGradientClipping, KF_DPOptimizer):
     def __init__(
         self,
         optimizer: Optimizer,
@@ -27,8 +43,9 @@ def __init__(
         secure_mode: bool = False,
         kappa=0.7,
         gamma=0.5,
+        **kwargs,
     ):
-        super().__init__(
+        super(KF_DPOptimizer).__init__(
             optimizer,
             noise_multiplier=noise_multiplier,
             max_grad_norm=max_grad_norm,
@@ -38,6 +55,7 @@ def __init__(
             secure_mode=secure_mode,
             kappa=kappa,
             gamma=gamma,
+            **kwargs,
         )
 
     def _compute_one_closure(self, closure=required):

research/disk_optimizer/optimizers/KFperlayeroptimizer.py

@@ -1,3 +1,17 @@
+# Copyright (c) Xinwei Zhang
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import annotations
 
 import logging
@@ -28,6 +42,7 @@ def __init__(
         secure_mode: bool = False,
         kappa=0.7,
         gamma=0.5,
+        **kwargs,
     ):
         assert len(max_grad_norm) == len(params(optimizer))
         self.max_grad_norms = max_grad_norm
@@ -42,6 +57,7 @@ def __init__(
             secure_mode=secure_mode,
             kappa=kappa,
             gamma=gamma,
+            **kwargs,
         )
 
     def clip_and_accumulate(self):

research/disk_optimizer/optimizers/__init__.py

@@ -1,3 +1,17 @@
+# Copyright (c) Xinwei Zhang
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from opacus.optimizers import (
     AdaClipDPOptimizer,
     DistributedDPOptimizer,