[Doc] Add coverage banner (#533)

* add orb decov to circleci config.yml

* Add codecov badge to Readme

* Revert "[BugFix] Changing the dm_control import to fail if not installed (#515)"

This reverts commit d194735.

* codecov coverage w/o orb in circleci

* Revert "Revert "[BugFix] Changing the dm_control import to fail if not installed (#515)""

This reverts commit d0dc7de.

* [CI] generation of coverage reports (#534)

* update test scripts to add coverage

* update test scripts to add coverage

Co-authored-by: Silvestre Bahi <silvestrebahi@fb.com>

* [CI] Add xml coverage reports for codecov (#537)

* update test scripts to add coverage

* update test scripts to add coverage

* generate xml file for coverage

* Update run_test.sh

lint end of file

* Update run_test.sh

lint end of file

* Update run_test.sh

lint end of file

Co-authored-by: Silvestre Bahi <silvestrebahi@fb.com>

* permissions

* permissions

Co-authored-by: Silvestre Bahi <silvestrebahi@fb.com>
Co-authored-by: silvestrebahi <silvestre.bahi@gmail.com>

Author: 3 people

.circleci/config.yml

@@ -276,6 +276,12 @@ jobs:
       - run:
           name: Run tests
           command: .circleci/unittest/linux/scripts/run_test.sh
+
+      - run:
+          name: Codecov upload
+          command: |
+            bash <(curl -s https://codecov.io/bash) -Z -F linux-cpu
+
       - run:
           name: Post process
           command: .circleci/unittest/linux/scripts/post_process.sh
@@ -331,6 +337,10 @@ jobs:
           name: Run tests
           command: bash .circleci/unittest/linux/scripts/run_test.sh
 #          command: docker run --env-file ./env.list -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh
+      - run:
+          name: Codecov upload
+          command: |
+            bash <(curl -s https://codecov.io/bash) -Z -F linux-gpu
       - run:
           name: Post Process
           command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/post_process.sh
@@ -386,6 +396,10 @@ jobs:
           name: Run tests
           command: bash .circleci/unittest/linux_optdeps/scripts/run_test.sh
 #          command: docker run --env-file ./env.list -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh
+      - run:
+          name: Codecov upload
+          command: |
+            bash <(curl -s https://codecov.io/bash) -Z -F linux-outdeps-gpu
       - run:
           name: Post Process
           command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux_optdeps/scripts/post_process.sh
@@ -433,6 +447,10 @@ jobs:
       - run:
           name: Run tests
           command: .circleci/unittest/linux_stable/scripts/run_test.sh
+      - run:
+          name: Codecov upload
+          command: |
+            bash <(curl -s https://codecov.io/bash) -Z -F linux-stable-cpu
       - run:
           name: Post process
           command: .circleci/unittest/linux_stable/scripts/post_process.sh
@@ -488,6 +506,10 @@ jobs:
           name: Run tests
           command: bash .circleci/unittest/linux_stable/scripts/run_test.sh
 #          command: docker run --env-file ./env.list -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh
+      - run:
+          name: Codecov upload
+          command: |
+            bash <(curl -s https://codecov.io/bash) -Z -F linux-stable-gpu
       - run:
           name: Post Process
           command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux_stable/scripts/post_process.sh
@@ -532,6 +554,10 @@ jobs:
       - run:
           name: Run tests
           command: .circleci/unittest/linux/scripts/run_test.sh
+      - run:
+          name: Codecov upload
+          command: |
+            bash <(curl -s https://codecov.io/bash) -Z -F macos-cpu
       - run:
           name: Post process
           command: .circleci/unittest/linux/scripts/post_process.sh

.circleci/unittest/linux/scripts/environment.yml

@@ -27,3 +27,4 @@ dependencies:
     - dm_control
     - mlflow
     - av
+    - coverage

.circleci/unittest/linux/scripts/run_test.sh

@@ -18,6 +18,7 @@ lib_dir="${env_dir}/lib"
 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$lib_dir
 export MKL_THREADING_LAYER=GNU
 
-pytest test/smoke_test.py -v --durations 20
-pytest test/smoke_test_deps.py -v --durations 20
-pytest --instafail -v --durations 20
+coverage run -m pytest test/smoke_test.py -v --durations 20
+coverage run -m pytest test/smoke_test_deps.py -v --durations 20
+coverage run -m pytest --instafail -v --durations 20
+coverage xml

.circleci/unittest/linux_optdeps/scripts/environment.yml

@@ -15,3 +15,4 @@ dependencies:
     - pyyaml
     - scipy
     - hydra-core
+    - coverage

.circleci/unittest/linux_optdeps/scripts/run_test.sh

@@ -17,4 +17,5 @@ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/project/.mujoco/mujoco210/bin
 export MKL_THREADING_LAYER=GNU
 
 #MUJOCO_GL=glfw pytest --cov=torchrl --junitxml=test-results/junit.xml -v --durations 20
-MUJOCO_GL=egl pytest --instafail -v --durations 20
+MUJOCO_GL=egl coverage run -m pytest --instafail -v --durations 20
+coverage xml

.circleci/unittest/linux_stable/scripts/environment.yml

@@ -28,3 +28,4 @@ dependencies:
     - dm_control
     - mlflow
     - av
+    - coverage

.circleci/unittest/linux_stable/scripts/run_test.sh

@@ -18,6 +18,7 @@ lib_dir="${env_dir}/lib"
 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$lib_dir
 export MKL_THREADING_LAYER=GNU
 
-pytest test/smoke_test.py -v --durations 20
-pytest test/smoke_test_deps.py -v --durations 20
-pytest --instafail -v --durations 20
+coverage run -m pytest test/smoke_test.py -v --durations 20
+coverage run -m pytest test/smoke_test_deps.py -v --durations 20
+coverage run -m pytest --instafail -v --durations 20
+coverage xml

README.md

@@ -1,4 +1,5 @@
 [![pytorch](https://circleci.com/gh/pytorch/rl.svg?style=shield)](https://circleci.com/gh/pytorch/rl)
+[![codecov](https://codecov.io/gh/pytorch/rl/branch/main/graph/badge.svg?token=HcpK1ILV6r)](https://codecov.io/gh/pytorch/rl)
 
 # TorchRL
 
@@ -32,7 +33,7 @@ one object to another without friction.
 algorithms. For instance, here's how to code a rollout in TorchRL:
     <details>
       <summary>Code</summary>
-   
+
     ```diff
     - obs, done = env.reset()
     + tensordict = env.reset()
@@ -57,7 +58,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
     TensorDict abstracts away the input / output signatures of the modules, env, collectors, replay buffers and losses of the library, allowing its primitives
     to be easily recycled across settings.
     Here's another example of an off-policy training loop in TorchRL (assuming that a data collector, a replay buffer, a loss and an optimizer have been instantiated):
-   
+
     ```diff
     - for i, (obs, next_obs, action, hidden_state, reward, done) in enumerate(collector):
     + for i, tensordict in enumerate(collector):
@@ -73,7 +74,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
             optim.zero_grad()
     ```
     Again, this training loop can be re-used across algorithms as it makes a minimal number of assumptions about the structure of the data.
-   
+
     TensorDict supports multiple tensor operations on its device and shape
     (the shape of TensorDict, or its batch size, is the common arbitrary N first dimensions of all its contained tensors):
     ```python
@@ -96,11 +97,11 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
     </details>
 
     Check our [TensorDict tutorial](tutorials/tensordict.ipynb) for more information.
-   
+
 - An associated [`TensorDictModule` class](torchrl/modules/tensordict_module/common.py) which is [functorch](https://github.com/pytorch/functorch)-compatible!
     <details>
       <summary>Code</summary>
-   
+
     ```diff
     transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
     + td_module = TensorDictModule(transformer_model, in_keys=["src", "tgt"], out_keys=["out"])
@@ -111,7 +112,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
     + td_module(tensordict)
     + out = tensordict["out"]
     ```
- 
+
     The `TensorDictSequential` class allows to branch sequences of `nn.Module` instances in a highly modular way.
     For instance, here is an implementation of a transformer using the encoder and decoder blocks:
     ```python
@@ -123,7 +124,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
     assert transformer.in_keys == ["src", "src_mask", "tgt"]
     assert transformer.out_keys == ["memory", "output"]
     ```
-   
+
     `TensorDictSequential` allows to isolate subgraphs by querying a set of desired input / output keys:
     ```python
     transformer.select_subsequence(out_keys=["memory"])  # returns the encoder
@@ -144,7 +145,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
     A common pytorch-first class of [tensor-specification class](torchrl/data/tensor_specs.py) is also provided.
     <details>
       <summary>Code</summary>
-   
+
     ```python
     env_make = lambda: GymEnv("Pendulum-v1", from_pixels=True)
     env_parallel = ParallelEnv(4, env_make)  # creates 4 envs in parallel
@@ -159,7 +160,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
     learning (although the "dataloader" -- read data collector -- is modified on-the-fly):
     <details>
       <summary>Code</summary>
-   
+
     ```python
     env_make = lambda: GymEnv("Pendulum-v1", from_pixels=True)
     collector = MultiaSyncDataCollector(
@@ -182,7 +183,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
 - efficient<sup>(2)</sup> and generic<sup>(1)</sup> [replay buffers](torchrl/data/replay_buffers/replay_buffers.py) with modularized storage:
     <details>
       <summary>Code</summary>
-   
+
     ```python
     storage = LazyMemmapStorage(  # memory-mapped (physical) storage
         cfg.buffer_size,
@@ -205,7 +206,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
     which process and prepare the data coming out of the environments to be used by the agent:
     <details>
       <summary>Code</summary>
-   
+
     ```python
     env_make = lambda: GymEnv("Pendulum-v1", from_pixels=True)
     env_base = ParallelEnv(4, env_make, device="cuda:0")  # creates 4 envs in parallel
@@ -237,7 +238,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
 - various [architectures](torchrl/modules/models/) and models (e.g. [actor-critic](torchrl/modules/tensordict_module/actors.py))<sup>(1)</sup>:
     <details>
       <summary>Code</summary>
-   
+
     ```python
     # create an nn.Module
     common_module = ConvNet(
@@ -291,7 +292,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
     [modules](torchrl/modules/models/exploration.py) to easily swap between exploration and exploitation<sup>(1)</sup>:
     <details>
       <summary>Code</summary>
-   
+
     ```python
     policy_explore = EGreedyWrapper(policy)
     with set_exploration_mode("random"):
@@ -308,15 +309,15 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
 
     <details>
       <summary>Code</summary>
-   
+
     ### Loss modules
     ```python
     from torchrl.objectives.costs import DQNLoss
     loss_module = DQNLoss(value_network=value_network, gamma=0.99)
     tensordict = replay_buffer.sample(batch_size)
     loss = loss_module(tensordict)
     ```
- 
+
     ### Advantage computation
     ```python
     from torchrl.objectives.returns.functional import vec_td_lambda_return_estimate