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.github/workflows/README.md

@@ -43,10 +43,18 @@ I will provide the details of each workflow below.
 
 | Workflow Name          | File name                  | Description                                                                                                                                       |
 | ---------------------- | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `Build on PR`          | `build_on_pr.yml`          | This workflow is triggered when a PR changes essential files. It will run all the unit tests in the repository with 4 GPUs. |
+| `Build on PR`          | `build_on_pr.yml`          | This workflow is triggered when a PR changes essential files and a branch is created/deleted. It will run all the unit tests in the repository with 4 GPUs. |
 | `Build on Schedule`    | `build_on_schedule.yml`    | This workflow will run the unit tests everyday with 8 GPUs. The result is sent to Lark.                                                           |
 | `Report test coverage` | `report_test_coverage.yml` | This PR will put up a comment to report the test coverage results when `Build` is done.                                                           |
 
+To reduce the average time of the unit test on PR, `Build on PR` workflow manages testmon cache.
+
+1. When creating a new branch, it copies `cache/main/.testmondata*` to `cache/<branch>/`.
+2. When creating a new PR or change the base branch of a PR, it copies `cache/<base_ref>/.testmondata*` to `cache/_pull/<pr_number>/`.
+3. When running unit tests for each PR, it restores testmon cache from `cache/_pull/<pr_number>/`. After the test, it stores the cache back to `cache/_pull/<pr_number>/`.
+4. When a PR is closed, if it's merged, it copies `cache/_pull/<pr_number>/.testmondata*` to `cache/<base_ref>/`. Otherwise, it just removes `cache/_pull/<pr_number>`.
+5. When a branch is deleted, it removes `cache/<ref>`.
+
 ### Example Test
 
 | Workflow Name              | File name                       | Description                                                                    |

.github/workflows/build_on_pr.yml

@@ -2,7 +2,7 @@ name: Build on PR
 
 on:
   pull_request:
-    types: [synchronize, opened, reopened]
+    types: [synchronize, opened, reopened, ready_for_review, closed, edited]
     branches:
       - "main"
       - "develop"
@@ -18,11 +18,63 @@ on:
       - "!tests/**.md" # ignore doc change
       - "pytest.ini" # test config change
       - "setup.py" # install command change
+  create:
+  delete:
 
 jobs:
+  prepare_cache:
+    name: Prepare testmon cache
+    if: |
+      github.event_name == 'create' &&
+      github.event.ref_type == 'branch' &&
+      github.event.repository.full_name == 'hpcaitech/ColossalAI'
+    runs-on: [self-hosted, gpu]
+    container:
+      image: hpcaitech/pytorch-cuda:1.12.0-11.3.0
+      options: --rm
+    timeout-minutes: 5
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Copy testmon cache
+        run: | # branch name may contain slash, we need to replace it with space
+          export REF_BRANCH=$(echo ${{ github.event.ref }} | sed "s/\// /")
+          if [ -d /github/home/testmon_cache/${MAIN_BRANCH} ]; then
+            [ ! -z "$(ls -A /github/home/testmon_cache/${MAIN_BRANCH})" ] && cp -p -r /github/home/testmon_cache/${MAIN_BRANCH} "/github/home/testmon_cache/${REF_BRANCH}"
+          fi
+        env:
+          MAIN_BRANCH: ${{ github.event.master_branch }}
+
+  prepare_cache_for_pr:
+    name: Prepare testmon cache for PR
+    if: |
+      github.event_name == 'pull_request' &&
+      (github.event.action == 'opened' || github.event.action == 'reopened' || (github.event.action == 'edited' && github.event.changes.base != null)) &&
+      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
+    runs-on: [self-hosted, gpu]
+    container:
+      image: hpcaitech/pytorch-cuda:1.12.0-11.3.0
+      options: --rm
+    timeout-minutes: 5
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Copy testmon cache
+        run: | # branch name may contain slash, we need to replace it with space
+          export BASE=$(echo ${{ github.event.pull_request.base.ref }} | sed "s/\// /")
+          if [ -d "/github/home/testmon_cache/${BASE}" ]; then
+            [ ! -z "$(ls -A "/github/home/testmon_cache/${BASE}")" ] && mkdir -p /github/home/testmon_cache/_pull && cp -p -r "/github/home/testmon_cache/${BASE}" /github/home/testmon_cache/_pull/${PR_NUMBER}
+          fi
+        env:
+          PR_NUMBER: ${{ github.event.number }}
+
   detect:
     name: Detect file change
     if: |
+      github.event_name == 'pull_request' &&
+      (github.event.action == 'synchronize' || github.event.action == 'opened' || github.event.action == 'reopened' || github.event.action == 'ready_for_review') &&
       github.event.pull_request.draft == false &&
       github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
     outputs:
@@ -135,9 +187,11 @@ jobs:
 
       - name: Restore Testmon Cache
         run: |
-          if [ -d /github/home/testmon_cache ]; then
-            [ ! -z "$(ls -A /github/home/testmon_cache)" ] && cp -p -r /github/home/testmon_cache/.testmondata* /__w/ColossalAI/ColossalAI/
+          if [ -d /github/home/testmon_cache/_pull/${PR_NUMBER} ]; then
+            [ ! -z "$(ls -A /github/home/testmon_cache/_pull/${PR_NUMBER})" ] && cp -p -r /github/home/testmon_cache/_pull/${PR_NUMBER}/.testmondata* /__w/ColossalAI/ColossalAI/
           fi
+        env:
+          PR_NUMBER: ${{ github.event.number }}
 
       - name: Execute Unit Testing
         run: |
@@ -149,8 +203,10 @@ jobs:
 
       - name: Store Testmon Cache
         run: |
-          [ -d /github/home/testmon_cache ] || mkdir /github/home/testmon_cache
-          cp -p -r /__w/ColossalAI/ColossalAI/.testmondata* /github/home/testmon_cache/
+          mkdir -p /github/home/testmon_cache/_pull/${PR_NUMBER}
+          cp -p -r /__w/ColossalAI/ColossalAI/.testmondata* /github/home/testmon_cache/_pull/${PR_NUMBER}/
+        env:
+          PR_NUMBER: ${{ github.event.number }}
 
       - name: Collate artifact
         env:
@@ -188,3 +244,54 @@ jobs:
         with:
           name: report
           path: report/
+
+  store_cache:
+    name: Store testmon cache for PR
+    if: |
+      github.event_name == 'pull_request' &&
+      github.event.action == 'closed' &&
+      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
+    runs-on: [self-hosted, gpu]
+    container:
+      image: hpcaitech/pytorch-cuda:1.12.0-11.3.0
+      options: --rm
+    timeout-minutes: 5
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Store testmon cache if possible
+        if: github.event.pull_request.merged == true
+        run: | # branch name may contain slash, we need to replace it with space
+          export BASE=$(echo ${{ github.event.pull_request.base.ref }} | sed "s/\// /")
+          if [ -d /github/home/testmon_cache/_pull/${PR_NUMBER} ]; then
+            [ ! -z "$(ls -A /github/home/testmon_cache/_pull/${PR_NUMBER})" ] && cp -p -r /github/home/testmon_cache/_pull/${PR_NUMBER}/.testmondata* "/github/home/testmon_cache/${BASE}/"
+          fi
+        env:
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+
+      - name: Remove testmon cache
+        run: |
+          rm -rf /github/home/testmon_cache/_pull/${PR_NUMBER}
+        env:
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+
+  remove_cache:
+    name: Remove testmon cache
+    if: |
+      github.event_name == 'delete' &&
+      github.event.ref_type == 'branch' &&
+      github.event.repository.full_name == 'hpcaitech/ColossalAI'
+    runs-on: [self-hosted, gpu]
+    container:
+      image: hpcaitech/pytorch-cuda:1.12.0-11.3.0
+      options: --rm
+    timeout-minutes: 5
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Remove testmon cache
+        run: | # branch name may contain slash, we need to replace it with space
+          export BASE=$(echo ${{ github.event.ref }} | sed "s/\// /")
+          rm -rf "/github/home/testmon_cache/${BASE}"

.github/workflows/release_docker_after_publish.yml

@@ -23,8 +23,11 @@ jobs:
         run: |
           version=$(cat version.txt)
           tag=hpcaitech/colossalai:$version
+          latest=hpcaitech/colossalai:latest
           docker build --build-arg http_proxy=http://172.17.0.1:7890 --build-arg https_proxy=http://172.17.0.1:7890 --build-arg VERSION=v${version} -t $tag ./docker
+          docker tag $tag $latest
           echo "tag=${tag}" >> $GITHUB_OUTPUT
+          echo "latest=${latest}" >> $GITHUB_OUTPUT
 
       - name: Log in to Docker Hub
         uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9
@@ -36,6 +39,7 @@ jobs:
         id: docker-push
         run: |
           docker push ${{ steps.build.outputs.tag }}
+          docker push ${{ steps.build.outputs.latest }}
 
   notify:
     name: Notify Lark via webhook

.github/workflows/run_chatgpt_examples.yml

@@ -20,7 +20,7 @@ jobs:
     runs-on: [self-hosted, gpu]
     container:
       image: hpcaitech/pytorch-cuda:1.12.0-11.3.0
-      options: --gpus all --rm -v /data/scratch/github_actions/chat:/data/scratch/github_actions/chat
+      options: --gpus all --rm -v /data/scratch/github_actions/chat:/data/scratch/github_actions/chat --shm-size=10.24gb
     timeout-minutes: 30
     defaults:
       run:

applications/Chat/benchmarks/ray/1mmt_dummy.py

@@ -0,0 +1,178 @@
+import argparse
+import os
+import socket
+from functools import partial
+
+import ray
+import torch
+from coati.quant import llama_load_quant, low_resource_init
+from coati.ray.detached_trainer_ppo import DetachedPPOTrainer
+from coati.ray.experience_maker_holder import ExperienceMakerHolder
+from coati.ray.utils import (
+    get_actor_from_args,
+    get_critic_from_args,
+    get_receivers_per_sender,
+    get_reward_model_from_args,
+    get_strategy_from_args,
+)
+from torch.utils.data import DataLoader
+from transformers import AutoConfig, AutoTokenizer
+from transformers.modeling_utils import no_init_weights
+
+
+def get_free_port():
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(('', 0))
+        return s.getsockname()[1]
+
+
+def get_local_ip():
+    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
+        s.connect(('8.8.8.8', 80))
+        return s.getsockname()[0]
+
+
+def main(args):
+    master_addr = str(get_local_ip())
+    # trainer_env_info
+    trainer_port = str(get_free_port())
+    env_info_trainers = [{
+        'local_rank': '0',
+        'rank': str(rank),
+        'world_size': str(args.num_trainers),
+        'master_port': trainer_port,
+        'master_addr': master_addr
+    } for rank in range(args.num_trainers)]
+
+    # maker_env_info
+    maker_port = str(get_free_port())
+    env_info_maker = {
+        'local_rank': '0',
+        'rank': '0',
+        'world_size': '1',
+        'master_port': maker_port,
+        'master_addr': master_addr
+    }
+
+    # configure tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(args.pretrain)
+    tokenizer.pad_token = tokenizer.eos_token
+
+    def model_fn():
+        actor_cfg = AutoConfig.from_pretrained(args.pretrain)
+        critic_cfg = AutoConfig.from_pretrained(args.critic_pretrain)
+        actor = get_actor_from_args(args.model, config=actor_cfg).requires_grad_(False).half().cuda()
+        critic = get_critic_from_args(args.critic_model, config=critic_cfg).requires_grad_(False).half().cuda()
+        reward_model = get_reward_model_from_args(args.critic_model,
+                                                  config=critic_cfg).requires_grad_(False).half().cuda()
+        if args.initial_model_quant_ckpt is not None and args.model == 'llama':
+            # quantize initial model
+            with low_resource_init(), no_init_weights():
+                initial_model = get_actor_from_args(args.model, config=actor_cfg)
+            initial_model.model = llama_load_quant(initial_model.model, args.initial_model_quant_ckpt, args.quant_bits,
+                                                   args.quant_group_size).cuda().requires_grad_(False)
+        else:
+            initial_model = get_actor_from_args(args.model, config=actor_cfg).requires_grad_(False).half().cuda()
+        return actor, critic, reward_model, initial_model
+
+    # configure Experience Maker
+    experience_holder_ref = ExperienceMakerHolder.options(name="maker0", num_gpus=1, max_concurrency=2).remote(
+        detached_trainer_name_list=[f'trainer{i}' for i in range(args.num_trainers)],
+        strategy_fn=partial(get_strategy_from_args, args.maker_strategy),
+        model_fn=model_fn,
+        env_info=env_info_maker,
+        kl_coef=0.1,
+        debug=args.debug,
+    # sync_models_from_trainers=True,
+    # generation kwargs:
+        max_length=512,
+        do_sample=True,
+        temperature=1.0,
+        top_k=50,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        eval_performance=True,
+        use_cache=True,
+    )
+
+    def trainer_model_fn():
+        actor = get_actor_from_args(args.model, config=AutoConfig.from_pretrained(args.pretrain)).half().cuda()
+        critic = get_critic_from_args(args.critic_model,
+                                      config=AutoConfig.from_pretrained(args.critic_pretrain)).half().cuda()
+        return actor, critic
+
+    # configure Trainer
+    trainer_refs = [
+        DetachedPPOTrainer.options(name=f"trainer{i}", num_gpus=1, max_concurrency=2).remote(
+            experience_maker_holder_name_list=[
+                f'maker{x}' for x in get_receivers_per_sender(i, args.num_trainers, 1, allow_idle_sender=True)
+            ],
+            strategy_fn=partial(get_strategy_from_args, args.trainer_strategy),
+            model_fn=trainer_model_fn,
+            env_info=env_info_trainer,
+            train_batch_size=args.train_batch_size,
+            buffer_limit=16,
+            eval_performance=True,
+            debug=args.debug,
+        ) for i, env_info_trainer in enumerate(env_info_trainers)
+    ]
+
+    dataset_size = args.experience_batch_size * 4
+
+    def data_gen_fn():
+        input_ids = torch.randint(tokenizer.vocab_size, (256,), device=torch.cuda.current_device())
+        attn_mask = torch.ones_like(input_ids)
+        return {'input_ids': input_ids, 'attention_mask': attn_mask}
+
+    def build_dataloader(size):
+        dataset = [data_gen_fn() for _ in range(size)]
+        dataloader = DataLoader(dataset, batch_size=args.experience_batch_size)
+        return dataloader
+
+    # uncomment this function if sync_models_from_trainers is True
+    # ray.get([
+    #     trainer_ref.sync_models_to_remote_makers.remote()
+    #     for trainer_ref in trainer_refs
+    # ])
+
+    wait_tasks = []
+
+    wait_tasks.append(
+        experience_holder_ref.workingloop.remote(partial(build_dataloader, dataset_size),
+                                                 num_steps=args.experience_steps))
+
+    total_steps = args.experience_batch_size * args.experience_steps // (args.num_trainers * args.train_batch_size)
+    for trainer_ref in trainer_refs:
+        wait_tasks.append(trainer_ref.fit.remote(total_steps, args.update_steps, args.train_epochs))
+
+    ray.get(wait_tasks)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--num_trainers', type=int, default=1)
+    parser.add_argument('--trainer_strategy',
+                        choices=[
+                            'naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu',
+                            'colossalai_zero2_cpu'
+                        ],
+                        default='naive')
+    parser.add_argument('--maker_strategy', choices=['naive'], default='naive')
+    parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama'])
+    parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama'])
+    parser.add_argument('--pretrain', type=str, default=None)
+    parser.add_argument('--critic_pretrain', type=str, default=None)
+    parser.add_argument('--experience_steps', type=int, default=4)
+    parser.add_argument('--experience_batch_size', type=int, default=8)
+    parser.add_argument('--train_epochs', type=int, default=1)
+    parser.add_argument('--update_steps', type=int, default=2)
+    parser.add_argument('--train_batch_size', type=int, default=8)
+    parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank")
+
+    parser.add_argument('--initial_model_quant_ckpt', type=str, default=None)
+    parser.add_argument('--quant_bits', type=int, default=4)
+    parser.add_argument('--quant_group_size', type=int, default=128)
+    parser.add_argument('--debug', action='store_true')
+    args = parser.parse_args()
+    ray.init(namespace=os.environ["RAY_NAMESPACE"], runtime_env={"env_vars": dict(os.environ)})
+    main(args)