formatted files and made imports relative to a2perf

Author: uchendui

CODE_OF_CONDUCT.md

@@ -14,22 +14,22 @@ race, religion, or sexual identity and orientation.
 Examples of behavior that contributes to creating a positive environment
 include:
 
-*   Using welcoming and inclusive language
-*   Being respectful of differing viewpoints and experiences
-*   Gracefully accepting constructive criticism
-*   Focusing on what is best for the community
-*   Showing empathy towards other community members
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
 
 Examples of unacceptable behavior by participants include:
 
-*   The use of sexualized language or imagery and unwelcome sexual attention or
-    advances
-*   Trolling, insulting/derogatory comments, and personal or political attacks
-*   Public or private harassment
-*   Publishing others' private information, such as a physical or electronic
-    address, without explicit permission
-*   Other conduct which could reasonably be considered inappropriate in a
-    professional setting
+* The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
 
 ## Our Responsibilities
 

PRINCIPLES.md

@@ -125,19 +125,18 @@ In addition to the above objectives, we will not design or deploy AI in the
 following application areas:
 
 * Technologies that cause or are likely to cause overall harm. Where there is a
-material risk of harm, we will proceed only where we believe that the benefits
-substantially outweigh the risks, and will incorporate appropriate safety
-constraints.
+  material risk of harm, we will proceed only where we believe that the benefits
+  substantially outweigh the risks, and will incorporate appropriate safety
+  constraints.
 
 * Weapons or other technologies whose principal purpose or implementation is to
-cause or directly facilitate injury to people.
+  cause or directly facilitate injury to people.
 
 * Technologies that gather or use information for surveillance violating
-internationally accepted norms.
+  internationally accepted norms.
 
 * Technologies whose purpose contravenes widely accepted principles of
-international law and human rights.
-
+  international law and human rights.
 
 ## AI for the long term
 

README.md

@@ -53,24 +53,25 @@ scaling to 100s of actors.
 
 ## Features
 
-*   Places netlists with hundreds of macros and millions of stdcells (in
-    clustered format).
-*   Computes both macro location and orientation (flipping).
-*   Optimizes multiple objectives including wirelength, congestion, and density.
-*   Supports alignment of blocks to the grid, to model clock strap or macro
-    blockage.
-*   Supports macro-to-macro, macro-to-boundary spacing constraints.
-*   Supports fixed macros.
-*   Supports [DREAMPlace](https://github.com/limbo018/DREAMPlace) as the stdcell placer.
-*   Allows users to specify their own technology parameters, e.g. and routing
-    resources (in routes per micron) and macro routing allocation.
-*   Generates
-    [clustered netlists](https://github.com/google-research/circuit_training/tree/main/circuit_training/grouping).
-*   [TILOS-AI-Institute](https://www.tilos.ai/) has created a
-    [script](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/FormatTranslators)
-    to convert LEF/DEF and Bookshelf to the
-    [Netlist Protocol Buffer](https://github.com/google-research/circuit_training/blob/main/docs/NETLIST_FORMAT.md)
-    used as the input for circuit training.
+* Places netlists with hundreds of macros and millions of stdcells (in
+  clustered format).
+* Computes both macro location and orientation (flipping).
+* Optimizes multiple objectives including wirelength, congestion, and density.
+* Supports alignment of blocks to the grid, to model clock strap or macro
+  blockage.
+* Supports macro-to-macro, macro-to-boundary spacing constraints.
+* Supports fixed macros.
+* Supports [DREAMPlace](https://github.com/limbo018/DREAMPlace) as the stdcell
+  placer.
+* Allows users to specify their own technology parameters, e.g. and routing
+  resources (in routes per micron) and macro routing allocation.
+* Generates
+  [clustered netlists](https://github.com/google-research/circuit_training/tree/main/circuit_training/grouping).
+* [TILOS-AI-Institute](https://www.tilos.ai/) has created a
+  [script](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/FormatTranslators)
+  to convert LEF/DEF and Bookshelf to the
+  [Netlist Protocol Buffer](https://github.com/google-research/circuit_training/blob/main/docs/NETLIST_FORMAT.md)
+  used as the input for circuit training.
 
 <a id='Installation'></a>
 
@@ -91,9 +92,8 @@ The steps below install the most recent branch and the archive is in the
 [releases section](#releases). There are two methods for installing; but before
 doing either one you need to run the preliminary setup](#preliminary-setup).
 
-*  [Use the docker](#using-the-docker) (**Highly Recommended**)
-*  [Install locally](#install-locally)
-
+* [Use the docker](#using-the-docker) (**Highly Recommended**)
+* [Install locally](#install-locally)
 
 ### Preliminary Setup
 
@@ -143,17 +143,15 @@ Do not forget to do the [prelimary setup](#preliminary-setup).
 
 Circuit Training installation steps:
 
-*   Install our DREAMPlace binary.
-*   Install TF-Agents and The Placement Cost Binary
-*   Run a test
-
+* Install our DREAMPlace binary.
+* Install TF-Agents and The Placement Cost Binary
+* Run a test
 
 #### Install DREAMPlace
 
 Follow the [instructions](#install-dreamplace) for DREAMPlace but do not change
 the ENV VARS that you already exported previously.
 
-
 #### Install TF-Agents and the Placement Cost binary
 
 These commands install TF-Agents and the placement cost binary.
@@ -177,7 +175,6 @@ to `tf-agents[reverb]~=<version you want>`
 tox -e py39-stable -- circuit_training/grouping/grouping_test.py
 ```
 
-
 ## HEAD
 
 We recommand using [stable](#stable) branches; but our team does work from the
@@ -188,9 +185,8 @@ The steps below install the most recent branch and the archive is in the
 [releases section](#releases). There are two methods for installing; but before
 doing either one you need to run the [preliminary setup](#preliminary-setup-2).
 
-*  [Use the docker](#using-the-docker-2) (**Highly Recommended**)
-*  [Install locally](#install-locally-2)
-
+* [Use the docker](#using-the-docker-2) (**Highly Recommended**)
+* [Install locally](#install-locally-2)
 
 ### Preliminary Setup
 
@@ -231,16 +227,15 @@ $ docker run --rm -v ${REPO_ROOT}:/workspace --workdir /workspace circuit_traini
 
 Circuit Training installation steps:
 
-*   Install our DREAMPlace binary.
-*   Install TF-Agents Nightly and the placement cost binary
-*   Run a test
+* Install our DREAMPlace binary.
+* Install TF-Agents Nightly and the placement cost binary
+* Run a test
 
 #### Install DREAMPlace
 
 Follow the [instructions](#install-dreamplace) for DREAMPlace but do not change
 the ENV VARS that you already exported previously.
 
-
 #### Install TF-Agents and the Placement Cost binary
 
 These commands install TF-Agents and the placement cost binary.
@@ -264,7 +259,7 @@ tox -e py39-nightly -- circuit_training/grouping/grouping_test.py
 
 ## Install DREAMPlace
 
-DREAMPlace is **not** provided as a PyPi package and needs to be compilede. We 
+DREAMPlace is **not** provided as a PyPi package and needs to be compilede. We
 provide compiled versions of DREAMPlace taken from our
 [branch](https://github.com/esonghori/DREAMPlace/tree/circuit_training) for a
 range of Python versions built for our docker image (Ubuntu 20.4). We also use
@@ -355,12 +350,11 @@ the filename pattern can be used to install DREAMPle for the versions of Python
 supported. For the Placement Cost binary, the ULR is to the version of the PLC
 used at the time the branch was cut.
 
-Release | Branch / Tag                                                              | TF-Agents                 | DREAMPlace                       | PL
-------- | ------------------------------------------------------------------------- | ------------------------- | -------------------------------- | -------------- |
-HEAD    | [main](https://github.com/google-research/circuit-training)               | tf-agents-nightly[reverb] |
-0.0.3   | [v0.0.3](https://github.com/google-research/circuit_training/tree/v0.0.3) | tf-agents[reverb]~=0.16.0 | dreamplace_20230414_b31e8af_python3.9.tar.gz | [placement cost binary](https://storage.googleapis.com/rl-infra-public/circuit-training/placement_cost/plc_wrapper_main_0.0.3)
-0.0.2   | [v0.0.2](https://github.com/google-research/circuit_training/tree/v0.0.2) | tf-agents[reverb]~=0.16.0 |
-
+ Release | Branch / Tag                                                              | TF-Agents                 | DREAMPlace                                   | PL                                                                                                                             
+---------|---------------------------------------------------------------------------|---------------------------|----------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|
+ HEAD    | [main](https://github.com/google-research/circuit-training)               | tf-agents-nightly[reverb] |
+ 0.0.3   | [v0.0.3](https://github.com/google-research/circuit_training/tree/v0.0.3) | tf-agents[reverb]~=0.16.0 | dreamplace_20230414_b31e8af_python3.9.tar.gz | [placement cost binary](https://storage.googleapis.com/rl-infra-public/circuit-training/placement_cost/plc_wrapper_main_0.0.3) 
+ 0.0.2   | [v0.0.2](https://github.com/google-research/circuit_training/tree/v0.0.2) | tf-agents[reverb]~=0.16.0 |
 
 <a id='Results'></a>
 
@@ -382,20 +376,20 @@ for 3 different seeds run 3 times each. This is slightly different than what was
 used in the paper (8 runs each with a different seed), but better captures the
 different sources of variability.
 
- Metric  | Proxy Wirelength | Proxy Congestion | Proxy Density
--------- | ---------------- | ---------------- | -------------
-**mean** | 0.1013           | 0.9174           | 0.5502
-**std**  | 0.0036           | 0.0647           | 0.0568
+ Metric   | Proxy Wirelength | Proxy Congestion | Proxy Density 
+----------|------------------|------------------|---------------
+ **mean** | 0.1013           | 0.9174           | 0.5502        
+ **std**  | 0.0036           | 0.0647           | 0.0568        
 
 The table below summarizes the
 [paper](https://www.nature.com/articles/s41586-021-03544-w.epdf?sharing_token=tYaxh2mR5EozfsSL0WHZLdRgN0jAjWel9jnR3ZoTv0PW0K0NmVrRsFPaMa9Y5We9O4Hqf_liatg-lvhiVcYpHL_YQpqkurA31sxqtmA-E1yNUWVMMVSBxWSp7ZFFIWawYQYnEXoBE4esRDSWqubhDFWUPyI5wK_5B_YIO-D_kS8%3D)
 result for fine-tuning from a pre-trained model over 8 runs with each one using
 a different seed.
 
- Metric  | Proxy Wirelength | Proxy Congestion | Proxy Density
--------- | ---------------- | ---------------- | -------------
-**mean** | 0.1198           | 0.9718           | 0.5729
-**std**  | 0.0019           | 0.0346           | 0.0086
+ Metric   | Proxy Wirelength | Proxy Congestion | Proxy Density 
+----------|------------------|------------------|---------------
+ **mean** | 0.1198           | 0.9718           | 0.5729        
+ **std**  | 0.0019           | 0.0346           | 0.0086        
 
 <a id='FAQ'></a>
 
@@ -547,20 +541,20 @@ We would like to recognize the following individuals for their code
 contributions, discussions, and other work to make the release of the Circuit
 Training library possible.
 
-*   Sergio Guadarrama
-*   Summer Yue
-*   Ebrahim Songhori
-*   Joe Jiang
-*   Toby Boyd
-*   Azalia Mirhoseini
-*   Anna Goldie
-*   Mustafa Yazgan
-*   Shen Wang
-*   Terence Tam
-*   Young-Joon Lee
-*   Roger Carpenter
-*   Quoc Le
-*   Ed Chi
+* Sergio Guadarrama
+* Summer Yue
+* Ebrahim Songhori
+* Joe Jiang
+* Toby Boyd
+* Azalia Mirhoseini
+* Anna Goldie
+* Mustafa Yazgan
+* Shen Wang
+* Terence Tam
+* Young-Joon Lee
+* Roger Carpenter
+* Quoc Le
+* Ed Chi
 
 <a id='Citation'></a>
 

__init__.py

@@ -0,0 +1,18 @@
+import gym as legacy_gym
+import gymnasium as gym
+
+# Register the environments with the relative path
+gym.envs.register(
+    id='QuadrupedLocomotion-v0',
+    apply_api_compatibility=True,
+    entry_point='a2perf.domains.quadruped_locomotion.motion_imitation.envs.env_builder:build_imitation_env',
+    kwargs={
+    }
+)
+
+legacy_gym.envs.register(
+    id='QuadrupedLocomotion-v0',
+    entry_point='a2perf.domains.quadruped_locomotion.motion_imitation.envs.env_builder:build_imitation_env',
+    kwargs={
+    }
+)

circuit_training/dreamplace/dreamplace_core.py

@@ -15,12 +15,15 @@
 """A soft macro placer using Dreamplace."""
 import time
 
-from absl import logging
-from circuit_training.dreamplace import dreamplace_util
-from circuit_training.dreamplace import placedb_plc
-from dreamplace import NonLinearPlace
 import gin
 import timeout_decorator
+from absl import logging
+from dreamplace import NonLinearPlace
+
+from a2perf.domains.circuit_training.circuit_training.dreamplace import \
+  dreamplace_util
+from a2perf.domains.circuit_training.circuit_training.dreamplace import \
+  placedb_plc
 
 
 @gin.configurable(allowlist=['enable_timeout'])

circuit_training/dreamplace/dreamplace_main.py

@@ -16,12 +16,15 @@
 
 import random
 
-from absl import app
-from absl import flags
-from circuit_training.dreamplace import dreamplace_core
-from circuit_training.dreamplace import dreamplace_util
 import numpy as np
 import torch
+from absl import app
+from absl import flags
+
+from a2perf.domains.circuit_training.circuit_training.dreamplace import \
+  dreamplace_core
+from a2perf.domains.circuit_training.circuit_training.dreamplace import \
+  dreamplace_util
 
 _SEED = flags.DEFINE_integer(
     'seed', 0, 'RNG seed for all algorithms.If None, RNG is not seeded.'

circuit_training/dreamplace/dreamplace_util.py

@@ -19,10 +19,12 @@
 import time
 from typing import Optional
 
+import gin
 from absl import logging
-import circuit_training.environment.placement_util as util
 from dreamplace import Params
-import gin
+
+import \
+  a2perf.domains.circuit_training.circuit_training.environment.placement_util as util
 
 
 def print_and_save_result(

circuit_training/dreamplace/dreamplace_util_test.py

@@ -14,8 +14,9 @@
 # limitations under the License.
 """Test for dreamplace_util."""
 
-from circuit_training.dreamplace import dreamplace_util
-from circuit_training.utils import test_utils
+from a2perf.domains.circuit_training.circuit_training.dreamplace import \
+  dreamplace_util
+from a2perf.domains.circuit_training.circuit_training.utils import test_utils
 
 
 class DreamplaceUtilTest(test_utils.TestCase):

circuit_training/dreamplace/placedb_plc.py

@@ -18,7 +18,9 @@
 Realistic placement is done in dreamplace_core.
 """
 from absl import logging
-from circuit_training.dreamplace import plc_converter
+
+from a2perf.domains.circuit_training.circuit_training.dreamplace import \
+  plc_converter
 
 
 class PlacedbPlc(object):

circuit_training/dreamplace/plc_converter.py

@@ -20,11 +20,10 @@
 """
 import pickle
 
-from absl import logging
-from dreamplace import PlaceDB
 import numpy as np
-
 import tensorflow.io.gfile as gfile
+from absl import logging
+from dreamplace import PlaceDB
 
 
 def blockage_area(plc):