[feat] support qwen3 in shardformer

Merge pull request #6340 from hpcaitech/release/v0.5.0
[release] update version
2025-07-13 23:25:05 +00:00 · 2025-07-10 13:57:52 +08:00 · 2025-06-04 13:57:10 +08:00 · 2025-06-03 18:51:39 +08:00 · 2025-06-03 18:41:37 +08:00 · 2025-06-03 18:38:07 +08:00
85 changed files with 2843 additions and 978 deletions
--- a/.cuda_ext.json
+++ b/.cuda_ext.json
@ -2,11 +2,11 @@
  "build": [
    {
      "torch_command": "pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu121",
-      "cuda_image": "hpcaitech/cuda-conda:12.1"
+      "cuda_image": "image-cloud.luchentech.com/hpcaitech/cuda-conda:12.1"
    },
    {
      "torch_command": "pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124",
-      "cuda_image": "hpcaitech/cuda-conda:12.4"
+      "cuda_image": "image-cloud.luchentech.com/hpcaitech/cuda-conda:12.4"
    }
  ]
 }
--- a/.github/workflows/build_on_pr.yml
+++ b/.github/workflows/build_on_pr.yml
@ -34,7 +34,7 @@ jobs:
      anyExtensionFileChanged: ${{ steps.find-extension-change.outputs.any_changed }}
      changedLibraryFiles: ${{ steps.find-lib-change.outputs.all_changed_files }}
      anyLibraryFileChanged: ${{ steps.find-lib-change.outputs.any_changed }}
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    concurrency:
      group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-detect-change
      cancel-in-progress: true
@ -87,10 +87,10 @@ jobs:
    name: Build and Test Colossal-AI
    needs: detect
    if: needs.detect.outputs.anyLibraryFileChanged == 'true'
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
-      options: --gpus all --rm -v /dev/shm -v /data/scratch:/data/scratch
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      options: --gpus all --shm-size=2g --rm -v /dev/shm -v /data/scratch:/data/scratch
    timeout-minutes: 90
    defaults:
      run:
@ -138,6 +138,10 @@ jobs:
            cp -p -r /github/home/cuda_ext_cache/* /__w/ColossalAI/ColossalAI/
          fi

+      - name: Install flash-attention
+        run: |
+          pip install flash-attn==2.7.4.post1 --no-build-isolation
+
      - name: Install Colossal-AI
        run: |
          BUILD_EXT=1 pip install -v -e .
--- a/.github/workflows/build_on_schedule.yml
+++ b/.github/workflows/build_on_schedule.yml
@ -10,9 +10,9 @@ jobs:
  build:
    name: Build and Test Colossal-AI
    if: github.repository == 'hpcaitech/ColossalAI'
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
      options: --gpus all --rm -v /dev/shm -v /data/scratch/:/data/scratch/
    timeout-minutes: 90
    steps:
--- a/.github/workflows/close_inactive.yml
+++ b/.github/workflows/close_inactive.yml
@ -7,7 +7,7 @@ on:
 jobs:
  close-issues:
    if: github.event.pull_request.draft == false && github.base_ref == 'main' && github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    permissions:
      issues: write
      pull-requests: write
--- a/.github/workflows/compatiblity_test_on_dispatch.yml
+++ b/.github/workflows/compatiblity_test_on_dispatch.yml
@ -15,7 +15,7 @@ on:
 jobs:
  matrix_preparation:
    name: Prepare Container List
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    outputs:
      matrix: ${{ steps.set-matrix.outputs.matrix }}
    steps:
@ -31,7 +31,7 @@ jobs:
          do
              for cv in $CUDA_VERSIONS
              do
-                  DOCKER_IMAGE+=("\"hpcaitech/pytorch-cuda:${tv}-${cv}\"")
+                  DOCKER_IMAGE+=("\"image-cloud.luchentech.com/hpcaitech/pytorch-cuda:${tv}-${cv}\"")
              done
          done

@ -44,7 +44,7 @@ jobs:
    name: Test for PyTorch Compatibility
    needs: matrix_preparation
    if: github.repository == 'hpcaitech/ColossalAI'
-    runs-on: [self-hosted, 8-gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    strategy:
      fail-fast: false
      matrix: ${{fromJson(needs.matrix_preparation.outputs.matrix)}}
@ -73,7 +73,18 @@ jobs:

      - name: Unit Testing
        run: |
-          PYTHONPATH=$PWD pytest --durations=0 tests
+          PYTHONPATH=$PWD pytest
+          -m "not largedist" \
+          --durations=0 \
+          --ignore tests/test_analyzer \
+          --ignore tests/test_auto_parallel \
+          --ignore tests/test_fx \
+          --ignore tests/test_autochunk \
+          --ignore tests/test_gptq \
+          --ignore tests/test_infer_ops \
+          --ignore tests/test_legacy \
+          --ignore tests/test_smoothquant \
+          tests/
        env:
          DATA: /data/scratch/cifar-10
          LD_LIBRARY_PATH: /github/home/.tensornvme/lib
--- a/.github/workflows/compatiblity_test_on_pr.yml
+++ b/.github/workflows/compatiblity_test_on_pr.yml
@ -9,7 +9,7 @@ on:
 jobs:
  matrix_preparation:
    name: Prepare Container List
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    outputs:
      matrix: ${{ steps.set-matrix.outputs.matrix }}
    concurrency:
@ -23,7 +23,7 @@ jobs:
          DOCKER_IMAGE=()

          while read tag; do
-            DOCKER_IMAGE+=("\"hpcaitech/pytorch-cuda:${tag}\"")
+            DOCKER_IMAGE+=("\"image-cloud.luchentech.com/hpcaitech/pytorch-cuda:${tag}\"")
          done <.compatibility

          container=$( IFS=',' ; echo "${DOCKER_IMAGE[*]}" )
@ -35,7 +35,7 @@ jobs:
    name: Test for PyTorch Compatibility
    needs: matrix_preparation
    if: github.repository == 'hpcaitech/ColossalAI'
-    runs-on: [self-hosted, 8-gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    strategy:
      fail-fast: false
      matrix: ${{fromJson(needs.matrix_preparation.outputs.matrix)}}
@ -67,7 +67,18 @@ jobs:

      - name: Unit Testing
        run: |
-          PYTHONPATH=$PWD pytest --durations=0 tests
+          PYTHONPATH=$PWD pytest \
+          -m "not largedist" \
+          --durations=0 \
+          --ignore tests/test_analyzer \
+          --ignore tests/test_auto_parallel \
+          --ignore tests/test_fx \
+          --ignore tests/test_autochunk \
+          --ignore tests/test_gptq \
+          --ignore tests/test_infer_ops \
+          --ignore tests/test_legacy \
+          --ignore tests/test_smoothquant \
+          tests/
        env:
          DATA: /data/scratch/cifar-10
          LD_LIBRARY_PATH: /github/home/.tensornvme/lib
--- a/.github/workflows/compatiblity_test_on_schedule.yml
+++ b/.github/workflows/compatiblity_test_on_schedule.yml
@ -9,7 +9,7 @@ on:
 jobs:
  matrix_preparation:
    name: Prepare Container List
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    outputs:
      matrix: ${{ steps.set-matrix.outputs.matrix }}
    steps:
@ -20,7 +20,7 @@ jobs:
          DOCKER_IMAGE=()

          while read tag; do
-            DOCKER_IMAGE+=("\"hpcaitech/pytorch-cuda:${tag}\"")
+            DOCKER_IMAGE+=("\"image-cloud.luchentech.com/hpcaitech/pytorch-cuda:${tag}\"")
          done <.compatibility

          container=$( IFS=',' ; echo "${DOCKER_IMAGE[*]}" )
@ -32,7 +32,7 @@ jobs:
    name: Test for PyTorch Compatibility
    needs: matrix_preparation
    if: github.repository == 'hpcaitech/ColossalAI'
-    runs-on: [self-hosted, 8-gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    strategy:
      fail-fast: false
      matrix: ${{fromJson(needs.matrix_preparation.outputs.matrix)}}
@ -61,7 +61,18 @@ jobs:

      - name: Unit Testing
        run: |
-          PYTHONPATH=$PWD pytest --durations=0 tests
+          PYTHONPATH=$PWD pytest \
+          -m "not largedist" \
+          --durations=0 \
+          --ignore tests/test_analyzer \
+          --ignore tests/test_auto_parallel \
+          --ignore tests/test_fx \
+          --ignore tests/test_autochunk \
+          --ignore tests/test_gptq \
+          --ignore tests/test_infer_ops \
+          --ignore tests/test_legacy \
+          --ignore tests/test_smoothquant \
+          tests/
        env:
          DATA: /data/scratch/cifar-10
          LD_LIBRARY_PATH: /github/home/.tensornvme/lib
--- a/.github/workflows/cuda_ext_check_before_merge.yml
+++ b/.github/workflows/cuda_ext_check_before_merge.yml
@ -10,7 +10,7 @@ jobs:
  matrix_preparation:
    name: Prepare Container List
    if: github.repository == 'hpcaitech/ColossalAI'
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    outputs:
      matrix: ${{ steps.set-matrix.outputs.matrix }}
    steps:
@ -24,7 +24,7 @@ jobs:
  build:
    name: Release bdist wheels
    needs: matrix_preparation
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    strategy:
      fail-fast: false
      matrix: ${{fromJson(needs.matrix_preparation.outputs.matrix)}}
--- a/.github/workflows/doc_build_on_schedule_after_release.yml
+++ b/.github/workflows/doc_build_on_schedule_after_release.yml
@ -11,7 +11,7 @@ jobs:
  build-doc:
    name: Trigger Documentation Build Workflow
    if: github.repository == 'hpcaitech/ColossalAI'
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    steps:
      - name: trigger workflow in ColossalAI-Documentation
        run: |
--- a/.github/workflows/doc_check_on_pr.yml
+++ b/.github/workflows/doc_check_on_pr.yml
@ -15,7 +15,7 @@ jobs:
    if: |
      github.event.pull_request.draft == false &&
      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    concurrency:
      group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-check-i18n
      cancel-in-progress: true
@ -24,7 +24,7 @@ jobs:

      - uses: actions/setup-python@v2
        with:
-          python-version: "3.8.14"
+          python-version: "3.9"

      - run: python .github/workflows/scripts/check_doc_i18n.py -d docs/source

@ -33,7 +33,7 @@ jobs:
    if: |
      github.event.pull_request.draft == false &&
      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    concurrency:
      group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-check-doc
      cancel-in-progress: true
@ -50,7 +50,7 @@ jobs:

      - uses: actions/setup-python@v2
        with:
-          python-version: "3.8.14"
+          python-version: "3.9"

      # we use the versions in the main branch as the guide for versions to display
      # checkout will give your merged branch
--- a/.github/workflows/doc_test_on_pr.yml
+++ b/.github/workflows/doc_test_on_pr.yml
@ -15,7 +15,7 @@ jobs:
    if: |
      github.event.pull_request.draft == false &&
      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request'
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    outputs:
      any_changed: ${{ steps.changed-files.outputs.any_changed }}
      changed_files: ${{ steps.changed-files.outputs.all_changed_files }}
@ -54,9 +54,9 @@ jobs:
      needs.detect-changed-doc.outputs.any_changed == 'true'
    name: Test the changed Doc
    needs: detect-changed-doc
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
      options: --gpus all --rm
    timeout-minutes: 30
    defaults:
--- a/.github/workflows/doc_test_on_schedule.yml
+++ b/.github/workflows/doc_test_on_schedule.yml
@ -10,9 +10,9 @@ jobs:
    # Add this condition to avoid executing this job if the trigger event is workflow_dispatch.
    if: github.repository == 'hpcaitech/ColossalAI'
    name: Test the changed Doc
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
      options: --gpus all --rm
    timeout-minutes: 60
    steps:
--- a/.github/workflows/draft_github_release_post_after_merge.yml
+++ b/.github/workflows/draft_github_release_post_after_merge.yml
@ -12,14 +12,14 @@ jobs:
  release:
    name: Draft Release Post
    if: ( github.event_name == 'workflow_dispatch' || github.event.pull_request.merged == true ) && github.repository == 'hpcaitech/ColossalAI'
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    steps:
      - uses: actions/checkout@v2
        with:
          fetch-depth: 0
      - uses: actions/setup-python@v2
        with:
-          python-version: '3.8.14'
+          python-version: '3.9'
      - name: generate draft
        id: generate_draft
        run: |
--- a/.github/workflows/example_check_on_dispatch.yml
+++ b/.github/workflows/example_check_on_dispatch.yml
@ -14,7 +14,7 @@ jobs:
        github.base_ref == 'main' &&
        github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
    name: Check the examples user want
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    outputs:
      matrix: ${{ steps.set-matrix.outputs.matrix }}
    steps:
@ -40,12 +40,12 @@ jobs:
        github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
    name: Manually check example files
    needs: manual_check_matrix_preparation
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    strategy:
      fail-fast: false
      matrix: ${{fromJson(needs.manual_check_matrix_preparation.outputs.matrix)}}
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
      options: --gpus all --rm -v /data/scratch/examples-data:/data/ -v /dev/shm
    timeout-minutes: 15
    steps:
--- a/.github/workflows/example_check_on_pr.yml
+++ b/.github/workflows/example_check_on_pr.yml
@ -17,7 +17,7 @@ jobs:
    if: |
      github.event.pull_request.draft == false &&
      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request'
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    outputs:
      matrix: ${{ steps.setup-matrix.outputs.matrix }}
      anyChanged: ${{ steps.setup-matrix.outputs.anyChanged }}
@ -64,7 +64,7 @@ jobs:
            changedFileName="${file}:${changedFileName}"
          done
          echo "$changedFileName was changed"
-          res=`python .github/workflows/scripts/example_checks/detect_changed_example.py --fileNameList $changedFileName`
+          res=`python3 .github/workflows/scripts/example_checks/detect_changed_example.py --fileNameList $changedFileName`
          echo "All changed examples are $res"

          if [ "$res" == "[]" ]; then
@ -85,12 +85,12 @@ jobs:
      needs.detect-changed-example.outputs.anyChanged == 'true'
    name: Test the changed example
    needs: detect-changed-example
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    strategy:
      fail-fast: false
      matrix: ${{fromJson(needs.detect-changed-example.outputs.matrix)}}
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
      options: --gpus all --rm -v /data/scratch/examples-data:/data/ -v /dev/shm
    timeout-minutes: 30
    concurrency:
--- a/.github/workflows/example_check_on_schedule.yml
+++ b/.github/workflows/example_check_on_schedule.yml
@ -10,7 +10,7 @@ jobs:
  matrix_preparation:
    if: github.repository == 'hpcaitech/ColossalAI'
    name: Prepare matrix for weekly check
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    outputs:
      matrix: ${{ steps.setup-matrix.outputs.matrix }}
    steps:
@ -29,12 +29,12 @@ jobs:
    if: github.repository == 'hpcaitech/ColossalAI'
    name: Weekly check all examples
    needs: matrix_preparation
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    strategy:
      fail-fast: false
      matrix: ${{fromJson(needs.matrix_preparation.outputs.matrix)}}
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
      options: --gpus all --rm -v /data/scratch/examples-data:/data/ -v /dev/shm
    timeout-minutes: 30
    steps:
--- a/.github/workflows/release_docker_after_publish.yml
+++ b/.github/workflows/release_docker_after_publish.yml
@ -9,7 +9,7 @@ jobs:
  release:
    name: Publish Docker Image to DockerHub
    if: github.repository == 'hpcaitech/ColossalAI'
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    container:
      image: "hpcaitech/docker-in-docker:latest"
      options: --gpus all --rm -v /var/run/docker.sock:/var/run/docker.sock
@ -46,14 +46,14 @@ jobs:
  notify:
    name: Notify Lark via webhook
    needs: release
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    if: ${{ always() }}
    steps:
      - uses: actions/checkout@v2

      - uses: actions/setup-python@v2
        with:
-          python-version: "3.8.14"
+          python-version: "3.9"

      - name: Install requests
        run: pip install requests
--- a/.github/workflows/release_nightly_on_schedule.yml
+++ b/.github/workflows/release_nightly_on_schedule.yml
@ -9,7 +9,7 @@ jobs:
  publish:
    if: github.repository == 'hpcaitech/ColossalAI'
    name: Build and publish Python 🐍 distributions 📦 to PyPI
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    timeout-minutes: 20
    outputs:
      status: ${{ steps.publish.outcome }}
@ -18,7 +18,7 @@ jobs:

    - uses: actions/setup-python@v2
      with:
-        python-version: '3.8.14'
+        python-version: '3.9'

    - run: |
        python .github/workflows/scripts/update_setup_for_nightly.py
@ -36,14 +36,14 @@ jobs:
  notify:
    name: Notify Lark via webhook
    needs: publish
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    if: ${{ always() }} && github.repository == 'hpcaitech/ColossalAI'
    steps:
      - uses: actions/checkout@v2

      - uses: actions/setup-python@v2
        with:
-          python-version: '3.8.14'
+          python-version: '3.9'

      - name: Install requests
        run: pip install requests
--- a/.github/workflows/release_pypi_after_merge.yml
+++ b/.github/workflows/release_pypi_after_merge.yml
@ -7,7 +7,6 @@ on:
      - 'version.txt'
    types:
      - closed
-
 jobs:
  build-n-publish:
    if: github.event_name == 'workflow_dispatch' || github.repository == 'hpcaitech/ColossalAI' && github.event.pull_request.merged == true && github.base_ref == 'main'
@ -19,7 +18,7 @@ jobs:

    - uses: actions/setup-python@v2
      with:
-        python-version: '3.8.14'
+        python-version: '3.9'

    - run: python setup.py sdist build

@ -42,7 +41,7 @@ jobs:

      - uses: actions/setup-python@v2
        with:
-          python-version: '3.8.14'
+          python-version: '3.9'

      - name: Install requests
        run: pip install requests
--- a/.github/workflows/release_test_pypi_before_merge.yml
+++ b/.github/workflows/release_test_pypi_before_merge.yml
@ -16,7 +16,7 @@ jobs:

    - uses: actions/setup-python@v2
      with:
-        python-version: '3.8.14'
+        python-version: '3.9'

    - name: add timestamp to the version
      id: prep-version
--- a/.github/workflows/report_leaderboard_to_lark.yml
+++ b/.github/workflows/report_leaderboard_to_lark.yml
@ -10,14 +10,14 @@ jobs:
  generate-and-publish:
    if: github.repository == 'hpcaitech/ColossalAI'
    name: Generate leaderboard report and publish to Lark
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    timeout-minutes: 20
    steps:
    - uses: actions/checkout@v2

    - uses: actions/setup-python@v2
      with:
-        python-version: '3.8.14'
+        python-version: '3.9'

    - run: pip install requests matplotlib seaborn requests_toolbelt pytz

--- a/.github/workflows/report_test_coverage.yml
+++ b/.github/workflows/report_test_coverage.yml
@ -8,7 +8,7 @@ on:

 jobs:
  report-test-coverage:
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    if: ${{ github.event.workflow_run.conclusion == 'success' }}
    steps:
      - name: "Download artifact"
--- a/.github/workflows/run_chatgpt_examples.yml
+++ b/.github/workflows/run_chatgpt_examples.yml
@ -17,9 +17,9 @@ jobs:
      github.event.pull_request.draft == false &&
      github.base_ref == 'main' &&
      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
      options: --gpus all --rm -v /data/scratch/examples-data:/data/scratch/examples-data --shm-size=10.24gb
    timeout-minutes: 60
    defaults:
--- a/.github/workflows/run_chatgpt_unit_tests.yml
+++ b/.github/workflows/run_chatgpt_unit_tests.yml
@ -17,9 +17,9 @@ jobs:
      github.event.pull_request.draft == false &&
      github.base_ref == 'main' &&
      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
      options: --gpus all --rm -v /data/scratch/examples-data:/data/scratch/examples-data
    timeout-minutes: 30
    defaults:
--- a/.github/workflows/run_colossalqa_unit_tests.yml
+++ b/.github/workflows/run_colossalqa_unit_tests.yml
@ -17,9 +17,9 @@ jobs:
      github.event.pull_request.draft == false &&
      github.base_ref == 'main' &&
      github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI'
-    runs-on: [self-hosted, gpu]
+    runs-on: [self-hosted, ubuntu-latest]
    container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
      volumes:
        - /data/scratch/test_data_colossalqa:/data/scratch/test_data_colossalqa
        - /data/scratch/llama-tiny:/data/scratch/llama-tiny
--- a/.github/workflows/submodule.yml
+++ b/.github/workflows/submodule.yml
@ -7,7 +7,7 @@ on:

 jobs:
  sync-submodule:
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    if: github.repository == 'hpcaitech/ColossalAI'
    steps:
      - name: Checkout
--- a/.github/workflows/translate_comment.yml
+++ b/.github/workflows/translate_comment.yml
@ -7,7 +7,7 @@ on:

 jobs:
  build:
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, ubuntu-latest]
    steps:
      - uses: usthe/issues-translate-action@v2.7
        with:
--- a/README.md
+++ b/README.md
@ -38,7 +38,7 @@ Limited Academic Bonuses:

 <div align="center">
   <a href="https://hpc-ai.com/?utm_source=github&utm_medium=social&utm_campaign=promotion-colossalai">
-   <img src="https://github.com/hpcaitech/public_assets/blob/main/colossalai/img/2.gif" width="850" />
+   <img src="https://github.com/hpcaitech/public_assets/blob/main/colossalai/img/2-2.gif" width="850" />
   </a>
 </div>

--- a/applications/ColossalChat/examples/README.md
+++ b/applications/ColossalChat/examples/README.md
@ -892,6 +892,63 @@ The dialogues can by multiple turns and it can contain system prompt. For more d

 We use bf16 weights for finetuning. If you downloaded fp8 DeepSeek V3/R1 weights, you can use the [script](https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/fp8_cast_bf16.py) to convert the weights to bf16 via GPU. For Ascend NPU, you can use this [script](https://gitee.com/ascend/ModelZoo-PyTorch/blob/master/MindIE/LLM/DeepSeek/DeepSeek-V2/NPU_inference/fp8_cast_bf16.py).

+We have also added details on how to load and reason with lora models.
+```python
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+)
+from peft import (
+    PeftModel
+)
+import torch
+
+# Set model path
+model_name = "Qwen/Qwen2.5-3B"
+lora_adapter = "Qwen2.5-3B_lora" # Your lora model Path
+merged_model_path = "Qwen2.5-3B_merged"
+
+######
+# How to Load lora Model
+######
+# 1.Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# 2.Load lora model
+peft_model = PeftModel.from_pretrained(
+    base_model,
+    lora_adapter,
+    torch_dtype=torch.bfloat16
+)
+
+# 3.Merge lora model
+merged_model = peft_model.merge_and_unload()
+
+# 4.Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    pad_token="<|endoftext|>"
+)
+
+# 5.Save merged lora model
+merged_model.save_pretrained(
+    merged_model_path,
+    safe_serialization=True
+)
+tokenizer.save_pretrained(merged_model_path)
+
+# 6.Run Inference
+test_input = tokenizer("Instruction: Finding prime numbers up to 100\nAnswer:", return_tensors="pt").to("cuda")
+output = merged_model.generate(**test_input, max_new_tokens=100)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
 #### Usage

 After preparing the dataset and model weights, you can run the script with the following command:
--- a/colossalai/inference/modeling/models/glide_llama.py
+++ b/colossalai/inference/modeling/models/glide_llama.py
@ -6,19 +6,16 @@ from typing import List, Optional, Tuple, Union

 import torch
 import torch.nn as nn
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.cache_utils import DynamicCache
 from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from transformers.models.llama.modeling_llama import (
    LlamaAttention,
    LlamaConfig,
    LlamaDecoderLayer,
-    LlamaDynamicNTKScalingRotaryEmbedding,
    LlamaForCausalLM,
-    LlamaLinearScalingRotaryEmbedding,
    LlamaMLP,
    LlamaModel,
    LlamaRMSNorm,
-    LlamaRotaryEmbedding,
 )

 from colossalai.inference.spec import GlideInput
@ -156,15 +153,11 @@ def glide_llama_model_forward(
    if inputs_embeds is None:
        inputs_embeds = self.embed_tokens(input_ids)

-    past_seen_tokens = 0
-    if use_cache:  # kept for BC (cache positions)
-        if not isinstance(past_key_values, StaticCache):
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_seen_tokens = past_key_values.get_seq_length()
+    if use_cache and past_key_values is None:
+        past_key_values = DynamicCache()

    if cache_position is None:
-        if isinstance(past_key_values, StaticCache):
-            raise ValueError("cache_position is a required argument when using StaticCache.")
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        cache_position = torch.arange(
            past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
        )
@ -172,15 +165,17 @@ def glide_llama_model_forward(
    if position_ids is None:
        position_ids = cache_position.unsqueeze(0)

-    attention_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position)
+    attention_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_key_values)
+    if hasattr(glide_input, "n_spec_tokens"):
+        position_ids = position_ids + glide_input.n_spec_tokens

    # embed positions
    hidden_states = inputs_embeds
+    position_embeddings = self.rotary_emb(hidden_states, position_ids)

    # decoder layers
    all_hidden_states = () if output_hidden_states else None
    all_self_attns = () if output_attentions else None
-    next_decoder_cache = None

    for decoder_layer in self.layers:
        if output_hidden_states:
@ -189,9 +184,9 @@ def glide_llama_model_forward(
        # GlideLlamaDecoderLayer
        layer_outputs = decoder_layer(
            hidden_states,
+            position_embeddings=position_embeddings,
            glide_input=glide_input,
            attention_mask=attention_mask,
-            position_ids=position_ids,
            past_key_value=past_key_values,
            output_attentions=output_attentions,
            use_cache=use_cache,
@ -200,9 +195,6 @@ def glide_llama_model_forward(

        hidden_states = layer_outputs[0]

-        if use_cache:
-            next_decoder_cache = layer_outputs[2 if output_attentions else 1]
-
        if output_attentions:
            all_self_attns += (layer_outputs[1],)

@ -212,16 +204,11 @@ def glide_llama_model_forward(
    if output_hidden_states:
        all_hidden_states += (hidden_states,)

-    next_cache = None
-    if use_cache:
-        next_cache = (
-            next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
-        )
    if not return_dict:
-        return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return tuple(v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None)
    return BaseModelOutputWithPast(
        last_hidden_state=hidden_states,
-        past_key_values=next_cache,
+        past_key_values=past_key_values,
        hidden_states=all_hidden_states,
        attentions=all_self_attns,
    )
@ -267,31 +254,6 @@ class LlamaCrossAttention(nn.Module):

        self.q_proj = nn.Linear(self.hidden_size, self.large_num_heads * self.large_head_dim, bias=False)
        self.o_proj = nn.Linear(self.large_num_heads * self.large_head_dim, self.hidden_size, bias=False)
-        self._init_rope()
-
-    def _init_rope(self):
-        if self.config.rope_scaling is None:
-            self.rotary_emb = LlamaRotaryEmbedding(
-                self.large_head_dim,
-                max_position_embeddings=self.max_position_embeddings,
-            )
-        else:
-            scaling_type = self.config.rope_scaling["type"]
-            scaling_factor = self.config.rope_scaling["factor"]
-            if scaling_type == "linear":
-                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
-                    self.large_head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                )
-            elif scaling_type == "dynamic":
-                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
-                    self.large_head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                )
-            else:
-                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
@ -299,9 +261,10 @@ class LlamaCrossAttention(nn.Module):
    def forward(
        self,
        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        position_ids: Optional[torch.LongTensor] = None,
        glide_input: GlideInput = None,  # Used for glimpsing main model's KV caches
        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Optional[torch.Tensor]:
@ -319,8 +282,7 @@ class LlamaCrossAttention(nn.Module):
        query_states = query_states.view(bsz, -1, self.large_num_heads, self.large_head_dim).transpose(1, 2)

        # for RoPE
-        position_ids = position_ids + glide_input.n_spec_tokens
-        cos, sin = self.rotary_emb(query_states, position_ids)
+        cos, sin = position_embeddings
        query_states = apply_single_rotary_pos_emb(query_states, cos, sin, position_ids)
        query_states = query_states.transpose(1, 2)
        query_states = query_states.reshape(-1, self.large_num_heads, self.large_head_dim)
@ -367,9 +329,10 @@ class GlideLlamaDecoderLayer(nn.Module):
    def forward(
        self,
        hidden_states: torch.Tensor,
+        position_embeddings: torch.Tensor = None,
+        position_ids: Optional[torch.LongTensor] = None,
        glide_input: GlideInput = None,
        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
@ -399,10 +362,10 @@ class GlideLlamaDecoderLayer(nn.Module):
        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
-            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
@ -425,9 +388,10 @@ class GlideLlamaDecoderLayer(nn.Module):

            hidden_states = self.cross_attn(
                hidden_states=hidden_states,
+                position_embeddings=position_embeddings,
+                position_ids=position_ids,
                glide_input=glide_input,
                attention_mask=attention_mask,
-                position_ids=position_ids,
                output_attentions=output_attentions,
                use_cache=True,
            )
@ -441,9 +405,6 @@ class GlideLlamaDecoderLayer(nn.Module):

        outputs = (hidden_states,)

-        if use_cache:
-            outputs += (present_key_value,)
-
        return outputs


--- a/colossalai/inference/modeling/models/nopadding_llama.py
+++ b/colossalai/inference/modeling/models/nopadding_llama.py
@ -478,9 +478,9 @@ class NopadLlamaAttention(LlamaAttention, ParallelModule):
            attn_oproj=attn_oproj,
            process_group=process_group,
            model_shard_infer_config=model_shard_infer_config,
-            num_heads=module.num_heads,
-            hidden_size=module.hidden_size,
-            num_key_value_heads=module.num_key_value_heads,
+            num_heads=module.config.num_attention_heads,
+            hidden_size=module.config.hidden_size,
+            num_key_value_heads=module.config.num_key_value_heads,
        )

        return attn_layer
--- a/colossalai/inference/spec/drafter.py
+++ b/colossalai/inference/spec/drafter.py
@ -3,6 +3,7 @@ from typing import Optional, Tuple
 import torch
 import torch.nn as nn
 from transformers import PreTrainedTokenizer
+from transformers.cache_utils import DynamicCache

 from colossalai.utils import get_current_device

@ -93,9 +94,8 @@ class Drafter:

        for _ in range(n_spec_tokens):
            # update past key values
-            kwargs["past_key_values"] = past_key_values

-            outputs = self._drafter_model(input_ids, **kwargs)
+            outputs = self._drafter_model(input_ids, past_key_values=past_key_values, **kwargs)
            next_token_logits = outputs.logits[:, -1, :]

            # NOTE Only use greedy search for speculating.
@ -114,6 +114,8 @@ class Drafter:
        speculated_length = len(token_ids)  # For now, only support bsz 1
        logits = torch.concat(logits, dim=0)
        token_ids = torch.concat(token_ids, dim=-1)
+        if isinstance(past_key_values, DynamicCache):
+            past_key_values = past_key_values.to_legacy_cache()

        out = DrafterOutput(
            speculated_length=speculated_length, logits=logits, next_tokens=token_ids, past_key_values=past_key_values
--- a/colossalai/lazy/pretrained.py
+++ b/colossalai/lazy/pretrained.py
@ -69,7 +69,7 @@ def new_from_pretrained(
    _ = kwargs.pop("mirror", None)
    from_pipeline = kwargs.pop("_from_pipeline", None)
    from_auto_class = kwargs.pop("_from_auto", False)
-    _fast_init = kwargs.pop("_fast_init", True)
+    kwargs.pop("_fast_init", True)
    torch_dtype = kwargs.pop("torch_dtype", None)
    subfolder = kwargs.pop("subfolder", "")
    commit_hash = kwargs.pop("_commit_hash", None)
@ -286,7 +286,7 @@ def new_from_pretrained(
    config.name_or_path = pretrained_model_name_or_path

    # Instantiate model.
-    init_contexts = [no_init_weights(_enable=_fast_init)]
+    init_contexts = [no_init_weights()]

    with ContextManagers(init_contexts):
        model = cls(config, *model_args, **model_kwargs)
--- a/colossalai/shardformer/modeling/bert.py
+++ b/colossalai/shardformer/modeling/bert.py
@ -58,7 +58,7 @@ class BertPipelineForwards:
        hidden_states: Optional[torch.FloatTensor] = None,  # this is from the previous stage
        stage_index: Optional[List[int]] = None,
        shard_config: ShardConfig = None,
-    ):
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
        # TODO(jianghai): add explaination of the output here.
        r"""
        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
@ -1037,6 +1037,89 @@ def get_jit_fused_bert_output_forward():
    return forward


+# Fix the tgt_len size in sequence parallel attention:
+# same with the one in BertSdpaSelfAttention forward in v4.51.3 transformers except the
+def get_bert_sequence_parallel_attention_forward(shard_config: ShardConfig):
+    from transformers.models.bert.modeling_bert import BertSdpaSelfAttention
+
+    def forward(
+        self: BertSdpaSelfAttention,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+
+        bsz, tgt_len, _ = hidden_states.size()
+
+        query_layer = self.transpose_for_scores(self.query(hidden_states))
+
+        # If this is instantiated as a cross-attention module, the keys and values come from an encoder; the attention
+        # mask needs to be such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        current_states = encoder_hidden_states if is_cross_attention else hidden_states
+        attention_mask = encoder_attention_mask if is_cross_attention else attention_mask
+
+        # Check `seq_length` of `past_key_value` == `len(current_states)` to support prefix tuning
+        if is_cross_attention and past_key_value and past_key_value[0].shape[2] == current_states.shape[1]:
+            key_layer, value_layer = past_key_value
+        else:
+            key_layer = self.transpose_for_scores(self.key(current_states))
+            value_layer = self.transpose_for_scores(self.value(current_states))
+            if past_key_value is not None and not is_cross_attention:
+                key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+                value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+
+        if self.is_decoder:
+            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_layer, value_layer)
+
+        # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom
+        # attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0.
+        # Reference: https://github.com/pytorch/pytorch/issues/112577
+        if self.require_contiguous_qkv and query_layer.device.type == "cuda" and attention_mask is not None:
+            query_layer = query_layer.contiguous()
+            key_layer = key_layer.contiguous()
+            value_layer = value_layer.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create
+        # a causal mask in case tgt_len == 1.
+        is_causal = (
+            True if self.is_decoder and not is_cross_attention and attention_mask is None and tgt_len > 1 else False
+        )
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_layer,
+            key_layer,
+            value_layer,
+            attn_mask=attention_mask,
+            dropout_p=self.dropout_prob if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2)
+        _, _, tgt_len, _ = query_layer.shape
+        attn_output = attn_output.reshape(bsz, tgt_len, self.all_head_size)
+
+        outputs = (attn_output,)
+        if self.is_decoder:
+            outputs = outputs + (past_key_value,)
+        return outputs
+
+    return forward
+
+
 def bert_sequence_parallel_forward_fn(shard_config: ShardConfig):
    def forward(
        self,
--- a/colossalai/shardformer/modeling/bloom.py
+++ b/colossalai/shardformer/modeling/bloom.py
@ -6,7 +6,7 @@ import torch.distributed as dist
 from torch.distributed import ProcessGroup
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from torch.nn import functional as F
-from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.cache_utils import Cache, DynamicCache
 from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
@ -21,6 +21,7 @@ from transformers.models.bloom.modeling_bloom import (
    BloomForSequenceClassification,
    BloomForTokenClassification,
    BloomModel,
+    dropout_add,
 )
 from transformers.utils import logging

@ -108,7 +109,7 @@ class BloomPipelineForwards:
    def bloom_model_forward(
        self: BloomModel,
        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.LongTensor] = None,
@ -116,6 +117,7 @@ class BloomPipelineForwards:
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
@ -151,6 +153,8 @@ class BloomPipelineForwards:
        if use_cache:
            logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
            use_cache = False
+        past_key_values = None
+
        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape batch_size x num_heads x N x N
@ -161,46 +165,60 @@ class BloomPipelineForwards:
        # case: First stage of training
        if stage_manager.is_first_stage():
            # check input_ids and inputs_embeds
-            if input_ids is not None and inputs_embeds is not None:
-                raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-            elif input_ids is not None:
-                batch_size, seq_length = input_ids.shape
-            elif inputs_embeds is not None:
-                batch_size, seq_length, _ = inputs_embeds.shape
-            else:
-                raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-            if inputs_embeds is None:
-                inputs_embeds = self.word_embeddings(input_ids)
-
-            hidden_states = self.word_embeddings_layernorm(inputs_embeds)
-            # initialize in the first stage and then pass to the next stage
-        else:
-            input_shape = hidden_states.shape[:-1]
-            batch_size, seq_length = input_shape
-
-        # extra recording tensor should be generated in the first stage
-
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
+            if (input_ids is None) ^ (inputs_embeds is not None):
+                raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+            if self.gradient_checkpointing and self.training and use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

-        if past_key_values is None:
-            past_key_values = tuple([None] * len(self.h))
-        # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values[0] is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]  # source_len
+            if inputs_embeds is None:
+                inputs_embeds = self.word_embeddings(input_ids)
+
+            hidden_states = self.word_embeddings_layernorm(inputs_embeds)
+
+            batch_size, seq_length, _ = inputs_embeds.shape
+            past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+            if cache_position is None:
+                cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)
+            # initialize in the first stage and then pass to the next stage
+        else:
+            input_shape = hidden_states.shape[:-1]
+            batch_size, seq_length = input_shape
+            past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+            if cache_position is None:
+                cache_position = torch.arange(past_length, past_length + seq_length, device=hidden_states.device)
+
+        # extra recording tensor should be generated in the first stage
+
+        all_self_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            else:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                logger.warning_once(
+                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
+                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
+                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
+                )
+
+        # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
+        past_length = 0
+        seq_length_with_past = seq_length + past_length

-            seq_length_with_past = seq_length_with_past + past_key_values_length
        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
        else:
@ -209,13 +227,10 @@ class BloomPipelineForwards:
        alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)

        # causal_mask is constructed every stage and its input is passed through different stages
-        causal_mask = _prepare_4d_causal_attention_mask(
-            attention_mask,
-            input_shape=(batch_size, seq_length),
-            inputs_embeds=hidden_states,
-            past_key_values_length=past_key_values_length,
+        causal_mask = self._update_causal_mask(
+            attention_mask, hidden_states, cache_position, past_key_values, output_attentions
        )
-        causal_mask = causal_mask.bool()
+
        # split the input tensor along sequence dimension
        # [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
        if shard_config and shard_config.enable_sequence_parallelism:
@ -228,9 +243,7 @@ class BloomPipelineForwards:
                )

        start_idx, end_idx = stage_index[0], stage_index[1]
-        for i, (block, layer_past) in enumerate(
-            zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx]), start=start_idx
-        ):
+        for i, block in enumerate(self.h[start_idx:end_idx], start=start_idx):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

@ -240,26 +253,28 @@ class BloomPipelineForwards:
                    hidden_states,
                    alibi,
                    causal_mask,
-                    layer_past,
+                    past_key_values,
                    head_mask[i],
                    use_cache,
                    output_attentions,
+                    cache_position,
                )
            else:
                outputs = block(
                    hidden_states,
-                    layer_past=layer_past,
+                    layer_past=past_key_values,
                    attention_mask=causal_mask,
                    head_mask=head_mask[i],
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    alibi=alibi,
+                    cache_position=cache_position,
                )

            hidden_states = outputs[0]
+            if use_cache:
+                next_decoder_cache = outputs[1]

-            if use_cache is True:
-                presents = presents + (outputs[1],)
            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

@ -277,20 +292,23 @@ class BloomPipelineForwards:
            # Add last hidden state
            hidden_states = self.ln_f(hidden_states)

-        # TODO(jianghai): deal with all_hidden_states, all_self_attentions, presents
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
        if stage_manager.is_last_stage():
            if not return_dict:
                return tuple(
-                    v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None
+                    v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
                )

            # attention_mask is not returned ; presents = past_key_values
            return BaseModelOutputWithPastAndCrossAttentions(
                last_hidden_state=hidden_states,
-                past_key_values=presents,
+                past_key_values=next_cache,
                hidden_states=all_hidden_states,
                attentions=all_self_attentions,
            )
@ -718,35 +736,24 @@ def get_jit_fused_bloom_attention_forward():
        head_mask: Optional[torch.Tensor] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
    ):
+        batch_size, q_length, _ = hidden_states.shape
        fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
+        # 3 x [batch_size, num_heads, seq_length, head_dim]
+        query_layer, key_layer, value_layer = self._reshape(fused_qkv)

-        # 3 x [batch_size, seq_length, num_heads, head_dim]
-        (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
-
-        batch_size, q_length, _, _ = query_layer.shape
-
-        query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
-        key_layer = key_layer.permute(0, 2, 3, 1).reshape(batch_size * self.num_heads, self.head_dim, q_length)
-        value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
        if layer_past is not None:
-            past_key, past_value = layer_past
-            # concatenate along seq_length dimension:
-            #  - key: [batch_size * self.num_heads, head_dim, kv_length]
-            #  - value: [batch_size * self.num_heads, kv_length, head_dim]
-            key_layer = torch.cat((past_key, key_layer), dim=2)
-            value_layer = torch.cat((past_value, value_layer), dim=1)
+            cache_kwargs = {"cache_position": cache_position}
+            key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)

-        _, _, kv_length = key_layer.shape
-
-        if use_cache is True:
-            present = (key_layer, value_layer)
-        else:
-            present = None
+        # reshape qkv for further computations
+        query_layer = query_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
+        key_layer = key_layer.reshape(batch_size * self.num_heads, -1, self.head_dim).transpose(-1, -2)
+        value_layer = value_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)

        # [batch_size * num_heads, q_length, kv_length]
-        # we use `torch.Tensor.baddbmm` instead of `torch.baddbmm` as the latter isn't supported by TorchScript v1.11
-        matmul_result = alibi.baddbmm(
+        attention_scores = alibi.baddbmm(
            batch1=query_layer,
            batch2=key_layer,
            beta=self.beta,
@ -754,15 +761,13 @@ def get_jit_fused_bloom_attention_forward():
        )

        # change view to [batch_size, num_heads, q_length, kv_length]
-        attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
+        attn_weights = attention_scores.view(batch_size, self.num_heads, q_length, -1)
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_layer.shape[-1]]
+            attn_weights = attn_weights + causal_mask

-        # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
-        input_dtype = attention_scores.dtype
-        # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
-        if input_dtype == torch.float16:
-            attention_scores = attention_scores.to(torch.float)
-        attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
-        attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(input_dtype)
+        # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype
+        attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_layer.dtype)

        # [batch_size, num_heads, q_length, kv_length]
        attention_probs = self.attention_dropout(attention_probs)
@ -771,12 +776,12 @@ def get_jit_fused_bloom_attention_forward():
            attention_probs = attention_probs * head_mask

        # change view [batch_size x num_heads, q_length, kv_length]
-        attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
+        attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, -1)

        # matmul: [batch_size * num_heads, q_length, head_dim]
        context_layer = torch.bmm(attention_probs_reshaped, value_layer)

-        # change view [batch_size, num_heads, q_length, head_dim]
+        # change view [batch_size, q_length, num_heads * head_dim]
        context_layer = self._merge_heads(context_layer)

        # aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
@ -791,9 +796,9 @@ def get_jit_fused_bloom_attention_forward():
        else:
            output_tensor = self.dense(context_layer)

-        output_tensor = self.dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
+        output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)

-        outputs = (output_tensor, present)
+        outputs = (output_tensor, layer_past)
        if output_attentions:
            outputs += (attention_probs,)

@ -839,13 +844,99 @@ def get_jit_fused_bloom_gelu_forward():
    return forward


+# Fixed the q_length args when doing the sequence parallelism in bloom model.
+def get_bloom_sequence_parallel_attention_forward(shard_config: ShardConfig):
+    from transformers.models.bloom.modeling_bloom import BloomAttention
+
+    def forward(
+        self: BloomAttention,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor,
+        alibi: torch.Tensor,
+        attention_mask: torch.Tensor,
+        layer_past: Optional[Cache] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        use_cache: bool = False,
+        output_attentions: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ):
+        batch_size, q_length, _ = hidden_states.shape
+        fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
+        # 3 x [batch_size, num_heads, seq_length, head_dim]
+        query_layer, key_layer, value_layer = self._reshape(fused_qkv)
+
+        if layer_past is not None:
+            cache_kwargs = {"cache_position": cache_position}
+            key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
+
+        # reshape qkv for further computations
+        query_layer = query_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
+        key_layer = key_layer.reshape(batch_size * self.num_heads, -1, self.head_dim).transpose(-1, -2)
+        value_layer = value_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
+
+        # [batch_size * num_heads, q_length, kv_length]
+        attention_scores = alibi.baddbmm(
+            batch1=query_layer,
+            batch2=key_layer,
+            beta=self.beta,
+            alpha=self.inv_norm_factor,
+        )
+        if shard_config.enable_sequence_parallelism:
+            _, q_length, _ = query_layer.shape
+        # change view to [batch_size, num_heads, q_length, kv_length]
+        attn_weights = attention_scores.view(batch_size, self.num_heads, q_length, -1)
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_layer.shape[-1]]
+            attn_weights = attn_weights + causal_mask
+
+        # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype
+        attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_layer.dtype)
+
+        # [batch_size, num_heads, q_length, kv_length]
+        attention_probs = self.attention_dropout(attention_probs)
+
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        # change view [batch_size x num_heads, q_length, kv_length]
+        attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, -1)
+
+        # matmul: [batch_size * num_heads, q_length, head_dim]
+        context_layer = torch.bmm(attention_probs_reshaped, value_layer)
+
+        # change view [batch_size, q_length, num_heads * head_dim]
+        context_layer = self._merge_heads(context_layer)
+
+        # aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
+        if self.pretraining_tp > 1 and self.slow_but_exact:
+            slices = self.hidden_size / self.pretraining_tp
+            output_tensor = torch.zeros_like(context_layer)
+            for i in range(self.pretraining_tp):
+                output_tensor = output_tensor + F.linear(
+                    context_layer[:, :, int(i * slices) : int((i + 1) * slices)],
+                    self.dense.weight[:, int(i * slices) : int((i + 1) * slices)],
+                )
+        else:
+            output_tensor = self.dense(context_layer)
+
+        output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
+
+        outputs = (output_tensor, layer_past)
+        if output_attentions:
+            outputs += (attention_probs,)
+
+        return outputs
+
+    return forward
+
+
 def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
    from transformers import BloomModel

    def forward(
        self: BloomModel,
        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.LongTensor] = None,
@ -853,6 +944,7 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        **deprecated_arguments,
    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
        if deprecated_arguments.pop("position_ids", False) is not False:
@ -864,7 +956,6 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
            )
        if len(deprecated_arguments) > 0:
            raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
-
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -872,62 +963,60 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape
-        elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

-        if past_key_values is None:
-            past_key_values = tuple([None] * len(self.h))
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            else:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                logger.warning_once(
+                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
+                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
+                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
+                )
+
+        batch_size, seq_length, _ = inputs_embeds.shape
+        past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        seq_length_with_past = seq_length + past_length
+        if cache_position is None:
+            cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape batch_size x num_heads x N x N
        # head_mask has shape n_layer x batch x num_heads x N x N
        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
        hidden_states = self.word_embeddings_layernorm(inputs_embeds)

-        presents = () if use_cache else None
+        next_decoder_cache = None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
        # Compute alibi tensor: check build_alibi_tensor documentation
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values[0] is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
        else:
            attention_mask = attention_mask.to(hidden_states.device)

        alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
-
-        causal_mask = _prepare_4d_causal_attention_mask(
-            attention_mask,
-            input_shape=(batch_size, seq_length),
-            inputs_embeds=hidden_states,
-            past_key_values_length=past_key_values_length,
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
        )
-        causal_mask = causal_mask.bool()
-        # split the input tensor along sequence dimension
-        # [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
+
        hidden_states = split_forward_gather_backward(
            hidden_states,
            dim=1,
@ -935,7 +1024,7 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
            fp8_communication=shard_config.fp8_communication,
        )

-        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+        for i, block in enumerate(self.h):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

@ -945,25 +1034,27 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
                    hidden_states,
                    alibi,
                    causal_mask,
-                    layer_past,
+                    past_key_values,
                    head_mask[i],
                    use_cache,
                    output_attentions,
+                    cache_position,
                )
            else:
                outputs = block(
                    hidden_states,
-                    layer_past=layer_past,
+                    layer_past=past_key_values,
                    attention_mask=causal_mask,
                    head_mask=head_mask[i],
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    alibi=alibi,
+                    cache_position=cache_position,
                )

            hidden_states = outputs[0]
-            if use_cache is True:
-                presents = presents + (outputs[1],)
+            if use_cache:
+                next_decoder_cache = outputs[1]

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
@ -975,18 +1066,25 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
            process_group=shard_config.tensor_parallel_process_group,
            fp8_communication=shard_config.fp8_communication,
        )
+
        # Add last hidden state
        hidden_states = self.ln_f(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
        if not return_dict:
-            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
+            return tuple(
+                v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
+            )

        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
-            past_key_values=presents,
+            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )
--- a/colossalai/shardformer/modeling/command.py
+++ b/colossalai/shardformer/modeling/command.py
@ -1,19 +1,19 @@
-import math
-import warnings
 from typing import List, Optional, Tuple, Union

 import torch
-import torch.utils.checkpoint
 from torch import nn
 from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
 from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from transformers.models.cohere.modeling_cohere import (
+    CohereAttention,
    CohereForCausalLM,
    CohereModel,
    StaticCache,
    apply_rotary_pos_emb,
    repeat_kv,
 )
+from transformers.processing_utils import Unpack
 from transformers.utils import logging

 from colossalai.pipeline.stage_manager import PipelineStageManager
@ -27,6 +27,8 @@ _SUPPORTED_SP_MODE = ["all_to_all", "split_gather", "ring"]

 _SUPPORTED_SP_MODE = ["all_to_all", "split_gather", "ring", "ring_attn"]

+logger = logging.get_logger(__name__)
+

 class CommandPipelineForwards:
    """
@ -37,22 +39,23 @@ class CommandPipelineForwards:
    @staticmethod
    def command_model_forward(
        self: CohereModel,
-        input_ids: torch.LongTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        shard_config: ShardConfig = None,
        force_sp_output_gather: bool = True,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
    ):
+
        logger = logging.get_logger(__name__)

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@ -67,8 +70,6 @@ class CommandPipelineForwards:
            )
            use_cache = False

-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
        # retrieve input_ids and inputs_embeds
        if stage_manager.is_first_stage():
            if input_ids is not None and inputs_embeds is not None:
@ -122,6 +123,7 @@ class CommandPipelineForwards:

        # embed positions, for the first stage, hidden_states is the input embeddings,
        # for the other stages, hidden_states is the output of the previous stage
+        shard_config.enable_flash_attention = True
        if shard_config.enable_flash_attention:
            # in this case, attention_mask is a dict rather than a tensor
            mask_shape = (batch_size, 1, seq_length, seq_length_with_past)
@ -133,7 +135,8 @@ class CommandPipelineForwards:
                is_causal=True,
            )
        else:
-            attention_mask = self._update_causal_mask(attention_mask, hidden_states, cache_position)
+            # v4.51.3 transformers attention_mask calculation
+            attention_mask = self._update_causal_mask(attention_mask, hidden_states, cache_position, past_key_values)

        if self.gradient_checkpointing and self.training and use_cache:
            if use_cache:
@ -163,6 +166,8 @@ class CommandPipelineForwards:
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None
+        # v4.51.3 transformers position_embeddings calculation
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        start_idx, end_idx = stage_index[0], stage_index[1]
        num_ckpt_layers = 0
@ -193,6 +198,7 @@ class CommandPipelineForwards:
                    output_attentions,
                    use_cache,
                    cache_position,
+                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
@ -203,6 +209,7 @@ class CommandPipelineForwards:
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
                )

            hidden_states = layer_outputs[0]
@ -224,17 +231,6 @@ class CommandPipelineForwards:
            all_hidden_states += (hidden_states,)
        next_cache = next_decoder_cache if use_cache else None
        if stage_manager.is_last_stage():
-            if not return_dict:
-                return tuple(
-                    v
-                    for v in [
-                        hidden_states,
-                        next_cache,
-                        all_hidden_states,
-                        all_self_attns,
-                    ]
-                    if v is not None
-                )
            return BaseModelOutputWithPast(
                last_hidden_state=hidden_states,
                past_key_values=next_cache,
@ -352,27 +348,22 @@ class CommandPipelineForwards:

 def get_command_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, sp_size=None, sp_group=None):
    def forward(
-        self,
+        self: CohereAttention,
        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if sp_mode is not None:
            assert sp_mode in _SUPPORTED_SP_MODE, f"SP mode {sp_mode} is not supported by {type(self)} yet"
            assert (sp_size is not None) and (
                sp_group is not None
            ), "Must specify sp_size and sp_group for sequence parallel"
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )

        bsz, q_len, _ = hidden_states.size()
+
        # sp: modify sp_len when sequence parallel mode is ring
        if sp_mode in ["split_gather", "ring"]:
            q_len *= sp_size
@ -388,60 +379,36 @@ def get_command_flash_attention_forward(shard_config: ShardConfig, sp_mode=None,
            value_states = all_to_all_comm(value_states, sp_group, fp8_communication=shard_config.fp8_communication)
            bsz, q_len, _ = query_states.size()

-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
+        cos, sin = position_embeddings

-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        attn_weights = None
+
+        shard_config.enable_flash_attention = True

        if shard_config.enable_flash_attention:
            assert isinstance(attention_mask, dict), "Flash Attention Error: attention_mask should be a dict."
            attn_output = ColoAttention.attention(query_states, key_states, value_states, **attention_mask)
        else:
-            attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-            if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
-                    f" {attn_weights.size()}"
-                )
-
+            # attn_weights and attn_output calculation is modified on the v4.51.3 of transformers.models.cohere.modeling_cohere.CohereAttention.forward.
+            attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
            if attention_mask is not None:
-                if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                    raise ValueError(
-                        f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                    )
-                attn_weights = attn_weights + attention_mask
+                causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+                attn_weights = attn_weights + causal_mask

-            # upcast attention to fp32
            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+            dropout = 0.0 if not self.training else self.attention_dropout
+            attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=self.training)
            attn_output = torch.matmul(attn_weights, value_states)
-
-            if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-                raise ValueError(
-                    f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                    f" {attn_output.size()}"
-                )
+            attn_output = attn_output.transpose(1, 2).contiguous()

        attn_output = attn_output.transpose(1, 2).contiguous()
        # sp: all-to-all comminucation when introducing sequence parallel
@ -451,13 +418,11 @@ def get_command_flash_attention_forward(shard_config: ShardConfig, sp_mode=None,
                attn_output, sp_group, scatter_dim=1, gather_dim=2, fp8_communication=shard_config.fp8_communication
            )
        else:
-            attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+            attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()

        attn_output = self.o_proj(attn_output)

-        if not output_attentions:
-            attn_weights = None
-        return attn_output, attn_weights, past_key_value
+        return attn_output, attn_weights

    return forward

@ -467,24 +432,23 @@ def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode

    def forward(
        self,
-        input_ids: torch.LongTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        force_sp_output_gather: bool = True,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> BaseModelOutputWithPast:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if (input_ids is None) ^ (inputs_embeds is not None):
@ -516,6 +480,8 @@ def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode
        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

+        shard_config.enable_flash_attention = True
+
        # in this case, attention_mask is a dict rather than a tensor
        if shard_config.enable_flash_attention:
            mask_shape = (inputs_embeds.shape[0], 1, past_seen_tokens + seq_len, past_seen_tokens + seq_len)
@ -527,7 +493,8 @@ def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode
                is_causal=True,
            )
        else:
-            attention_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position)
+            # v4.51.3 transformers attention_mask calculation
+            attention_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_key_values)

        if sp_mode in ["ring", "split_gather"]:
            inputs_embeds = split_forward_gather_backward(
@ -544,6 +511,9 @@ def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

+        # v4.51.3 transformers position_embeddings calculation
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
@ -557,8 +527,8 @@ def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode
                    output_attentions,
                    use_cache,
                    cache_position,
+                    position_embeddings,
                )
-
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
@ -568,16 +538,11 @@ def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
                )

            hidden_states = layer_outputs[0]

-            if use_cache:
-                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
        hidden_states = self.norm(hidden_states)

        # Cases that don't support parallelizing cross entropy computation along sequence
@ -594,8 +559,6 @@ def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode
            next_cache = (
                next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
            )
-        if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
--- a/colossalai/shardformer/modeling/falcon.py
+++ b/colossalai/shardformer/modeling/falcon.py
@ -1,4 +1,3 @@
-import math
 import warnings
 from typing import List, Optional, Tuple, Union

@ -6,11 +5,6 @@ import torch
 import torch.distributed as dist
 from torch.distributed import ProcessGroup
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from transformers.modeling_attn_mask_utils import (
-    AttentionMaskConverter,
-    _prepare_4d_causal_attention_mask,
-    _prepare_4d_causal_attention_mask_for_sdpa,
-)
 from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
@ -114,6 +108,10 @@ def get_tp_falcon_decoder_layer_forward():
        head_mask: Optional[torch.Tensor] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[
+            Tuple[torch.Tensor, torch.Tensor]
+        ] = None,  # Add cache_position and position_embeddings args for v4.51.3 transformers
        **kwargs,
    ):
        if "padding_mask" in kwargs:
@ -122,7 +120,8 @@ def get_tp_falcon_decoder_layer_forward():
            )
        residual = hidden_states

-        if self.config.new_decoder_architecture:
+        # same as v4.51.3 transformers
+        if self.config.new_decoder_architecture and self.config.num_ln_in_parallel_attn == 2:
            attention_layernorm_out = self.ln_attn(hidden_states)
            mlp_layernorm_out = self.ln_mlp(hidden_states)
        else:
@ -138,7 +137,8 @@ def get_tp_falcon_decoder_layer_forward():
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
-            **kwargs,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
        )

        attention_output = attn_outputs[0]
@ -151,6 +151,13 @@ def get_tp_falcon_decoder_layer_forward():
                    attention_output, residual, self.config.attention_dropout, training=self.training
                )
                mlp_layernorm_out = self.post_attention_layernorm(residual)
+        # v4.51.3 transformers mlp
+        if (
+            self.config.new_decoder_architecture
+            and self.config.parallel_attn
+            and self.config.num_ln_in_parallel_attn == 1
+        ):
+            mlp_layernorm_out = attention_layernorm_out

        outputs = attn_outputs[1:]

@ -190,11 +197,14 @@ class FalconPipelineForwards:
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        shard_config: ShardConfig = None,
    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
+        # Add cache_position and position_embeddings args for v4.51.3 transformers
+
        logger = logging.get_logger(__name__)
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
@ -206,9 +216,8 @@ class FalconPipelineForwards:
            logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
            use_cache = False

-        if past_key_values is not None:
-            logger.warning_once("past_key_values is not supported for pipeline models at the moment.")
-            past_key_values = None
+        logger.warning_once("past_key_values is not supported for pipeline models at the moment.")
+        past_key_values = None

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

@ -229,9 +238,6 @@ class FalconPipelineForwards:
            input_shape = hidden_states.shape[:-1]
            batch_size, seq_length = input_shape

-        if past_key_values is None:
-            past_key_values = tuple([None] * len(self.h))
-
        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning(
@ -243,10 +249,11 @@ class FalconPipelineForwards:
        all_hidden_states = () if output_hidden_states else None

        # Compute alibi tensor: check build_alibi_tensor documentation
+        # alibi calculation is same as v4.51.3 transformers.
+        alibi = None
        past_key_values_length = 0
-        if past_key_values[0] is not None:
-            past_key_values_length = past_key_values[0][0].shape[-2]

+        batch_size, seq_length, _ = hidden_states.shape
        if self.use_alibi:
            mask = (
                torch.ones(
@ -256,73 +263,32 @@ class FalconPipelineForwards:
                else attention_mask
            )
            alibi = build_alibi_tensor(mask, self.num_heads, dtype=hidden_states.dtype)
-        else:
-            alibi = None
-            if position_ids is None:
-                device = input_ids.device if input_ids is not None else inputs_embeds.device
-                position_ids = torch.arange(
-                    past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
-                )
-                position_ids = position_ids.unsqueeze(0)

-        if self._use_flash_attention_2:
-            # 2d mask is passed through the layers
-            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-        elif self._use_sdpa and not output_attentions:
-            # output_attentions=True can not be supported when using SDPA, and we fall back on
-            # the manual implementation that requires a 4D causal mask in all cases.
-            if alibi is None:
-                attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
-                    attention_mask,
-                    (batch_size, seq_length),
-                    inputs_embeds,
-                    past_key_values_length,
-                )
-            elif head_mask is None:
-                alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:])
-
-                attention_mask_2d = attention_mask
-                # We don't call _prepare_4d_causal_attention_mask_for_sdpa as we need to mask alibi using the 4D attention_mask untouched.
-                attention_mask = _prepare_4d_causal_attention_mask(
-                    attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
-                )
-
-                # We take care to integrate alibi bias in the attention_mask here.
-                if attention_mask_2d is None:
-                    attention_mask = alibi / math.sqrt(self.config.hidden_size // self.num_heads)
-                else:
-                    min_dtype = torch.finfo(alibi.dtype).min
-                    attention_mask = torch.masked_fill(
-                        alibi / math.sqrt(self.config.hidden_size // self.num_heads),
-                        attention_mask < -1,
-                        min_dtype,
-                    )
-
-                    # From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend
-                    # produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213
-                    if seq_length > 1 and attention_mask.device.type == "cuda":
-                        attention_mask = AttentionMaskConverter._unmask_unattended(attention_mask, min_dtype=min_dtype)
-            else:
-                # PyTorch SDPA does not support head_mask, we fall back on the eager implementation in this case.
-                attention_mask = _prepare_4d_causal_attention_mask(
-                    attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
-                )
-        else:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+        if cache_position is None:
+            cache_position = torch.arange(
+                past_key_values_length, past_key_values_length + seq_length, device=hidden_states.device
            )

+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+        # use new version of causal mask construction.
+        # In v4.51.3 version, sdpa, egaer and flash attention are merged into one class.
+        causal_mask = self._update_causal_mask(
+            attention_mask, hidden_states, cache_position, past_key_values, output_attentions, head_mask, alibi
+        )
+
        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape batch_size x num_heads x N x N
        # head_mask has shape n_layer x batch x num_heads x N x N
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

+        # v4.51.3 create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
        start_idx, end_idx = stage_index[0], stage_index[1]
-        for i, (block, layer_past) in enumerate(
-            zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx]), start=start_idx
-        ):
+        # keep past_key_values arg same with v4.51.3 transformers
+        for i, block in enumerate(self.h[start_idx:end_idx], start=start_idx):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

@ -331,28 +297,32 @@ class FalconPipelineForwards:
                    block.__call__,
                    hidden_states,
                    alibi,
-                    attention_mask,
+                    causal_mask,
                    position_ids,
                    head_mask[i],
-                    layer_past,
+                    past_key_values,
                    use_cache,
                    output_attentions,
+                    cache_position,
+                    position_embeddings,
                )
            else:
                outputs = block(
                    hidden_states,
-                    layer_past=layer_past,
-                    attention_mask=attention_mask,
+                    layer_past=past_key_values,
+                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    head_mask=head_mask[i],
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    alibi=alibi,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
                )

            hidden_states = outputs[0]
            if use_cache is True:
-                presents = presents + (outputs[1],)
+                outputs[1]

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
@ -365,6 +335,7 @@ class FalconPipelineForwards:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if stage_manager.is_last_stage():
+
            if not return_dict:
                return tuple(
                    v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None
--- a/colossalai/shardformer/modeling/gpt2.py
+++ b/colossalai/shardformer/modeling/gpt2.py
@ -48,6 +48,7 @@ def _get_attention_mask(
    sp_mode = shard_config.sequence_parallelism_mode
    # If a 2D or 3D attention mask is provided for the cross-attention
    # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+
    if self.config.add_cross_attention and encoder_hidden_states is not None:
        assert not sp_mode == "ring_attn", "Ring Attention only supports decoder-only."
        encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
@ -55,7 +56,7 @@ def _get_attention_mask(
            encoder_attention_mask = ColoAttention.prepare_attn_kwargs(
                (encoder_batch_size, 1, seq_len, encoder_sequence_length),
                dtype=hidden_states.dtype,
-                dtype2=encoder_hidden_states.dtype,
+                device=encoder_hidden_states.device,
                q_padding_mask=attention_mask,
                kv_padding_mask=encoder_attention_mask,
            )
@ -77,7 +78,6 @@ def _get_attention_mask(
    if shard_config.enable_flash_attention:
        if attention_mask is not None:
            attention_mask = attention_mask.view(batch_size, -1)
-
        attention_mask = ColoAttention.prepare_attn_kwargs(
            (batch_size, 1, seq_len, seq_len + past_key_values_length),
            hidden_states.dtype,
@ -835,9 +835,12 @@ def get_gpt2_flash_attention_forward(shard_config: Optional[ShardConfig] = None)
            attention_mask = encoder_attention_mask
        else:
            query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
-        query = self._split_heads(query, self.num_heads, self.head_dim)
-        key = self._split_heads(key, self.num_heads, self.head_dim)
-        value = self._split_heads(value, self.num_heads, self.head_dim)
+
+        shape_q = (*query.shape[:-1], -1, self.head_dim)
+        shape_kv = (*key.shape[:-1], -1, self.head_dim)
+        query = query.view(shape_q).transpose(1, 2)
+        key = key.view(shape_kv).transpose(1, 2)
+        value = value.view(shape_kv).transpose(1, 2)

        if layer_past is not None:
            past_key, past_value = layer_past
@ -871,7 +874,9 @@ def get_gpt2_flash_attention_forward(shard_config: Optional[ShardConfig] = None)
            )
        else:
            attn_output = ColoAttention.attention(query, key, value, **attention_mask, dropout_p=dropout_p, scale=scale)
-        attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
+
+        attn_output = attn_output.permute(0, 2, 1, 3).contiguous()
+        attn_output = attn_output.reshape(*attn_output.shape[:-2], -1).contiguous()
        attn_output = self.c_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)
        outputs = (attn_output, present, None)
--- a/colossalai/shardformer/modeling/gptj.py
+++ b/colossalai/shardformer/modeling/gptj.py
@ -2,6 +2,7 @@ from typing import Dict, List, Optional, Tuple, Union

 import torch
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.cache_utils import Cache
 from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
@ -79,7 +80,7 @@ class GPTJPipelineForwards:
    def gptj_model_forward(
        self: GPTJModel,
        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
@ -89,12 +90,13 @@ class GPTJPipelineForwards:
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
        shard_config: ShardConfig = None,
    ) -> Union[Dict, Tuple, BaseModelOutputWithPast]:
-        # This function is modified on the basis of transformers.models.gptj.modeling_gptj.GPTJModel.forward.
+        # This function is modified on the v4.51.3 transformers.models.gptj.modeling_gptj.GPTJModel.forward.
        # Please refer to original code of transformers for more details.
        # GPTJ has no cross attention in comparison to GPT2

@ -118,8 +120,8 @@ class GPTJPipelineForwards:
            use_cache = False

        if stage_manager.is_first_stage():
-            if input_ids is not None and inputs_embeds is not None:
-                raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+            if (input_ids is None) ^ (inputs_embeds is not None):
+                raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
            elif input_ids is not None:
                batch_size, seq_length = input_ids.shape
                input_shape = input_ids.size()
@ -130,17 +132,34 @@ class GPTJPipelineForwards:
                batch_size = inputs_embeds.shape[0]
            else:
                raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-            if token_type_ids is not None:
-                token_type_ids = token_type_ids.view(-1, seq_length)
        else:
            if hidden_states is None:
                raise ValueError("hidden_states shouldn't be None for stages other than the first stage.")
            input_shape = hidden_states.size()[:-1]
            batch_size, seq_length = input_shape[0], input_shape[1]
-            device = hidden_states.device
+
+        if stage_manager.is_first_stage():
+            if inputs_embeds is None:
+                inputs_embeds = self.wte(input_ids)
+            hidden_states = inputs_embeds
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids.view(-1, seq_length)
+                token_type_embeds = self.wte(token_type_ids)
+                hidden_states = hidden_states + token_type_embeds
+            hidden_states = self.drop(hidden_states)
+
+        seq_length = hidden_states.shape[1]
+        if cache_position is None:
+            past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_key_values_length, past_key_values_length + seq_length, device=hidden_states.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+        causal_mask = self._update_causal_mask(
+            attention_mask, hidden_states, cache_position, past_key_values, output_attentions
+        )

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
@ -148,33 +167,9 @@ class GPTJPipelineForwards:
        # head_mask has shape n_layer x batch x num_attention_heads x N x N
        head_mask = self.get_head_mask(head_mask, self.config.n_layer)

-        # position id to be assigned not just for the first stage for attn input
-        if position_ids is None:
-            position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device)
-            position_ids = position_ids.unsqueeze(0)
-        if stage_manager.is_first_stage():
-            if inputs_embeds is None:
-                inputs_embeds = self.wte(input_ids)
-            hidden_states = inputs_embeds
-            if token_type_ids is not None:
-                token_type_embeds = self.wte(token_type_ids)
-                hidden_states = hidden_states + token_type_embeds
-            hidden_states = self.drop(hidden_states)
+        output_shape = (-1, seq_length, hidden_states.size(-1))

-        output_shape = input_shape + (hidden_states.size(-1),)
-
-        attention_mask = _get_attention_mask(
-            self, shard_config, hidden_states, past_key_values, attention_mask, self._use_flash_attention_2
-        )
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
-        presents = () if use_cache else None
+        next_decoder_cache = None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

@ -207,29 +202,26 @@ class GPTJPipelineForwards:
                    block.__call__,
                    hidden_states,
                    None,
-                    attention_mask,
+                    causal_mask,
                    position_ids,
                    head_mask[i],
                    use_cache,
                    output_attentions,
+                    cache_position,
                )
            else:
                outputs = block(
                    hidden_states=hidden_states,
-                    layer_past=None,
-                    attention_mask=attention_mask,
+                    layer_past=past_key_values,
+                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    head_mask=head_mask[i],
                    use_cache=use_cache,
                    output_attentions=output_attentions,
+                    cache_position=cache_position,
                )

            hidden_states = outputs[0]
-            if use_cache is True:
-                presents = presents + (outputs[1],)
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

        # When sequence parallelism done, gather the output tensor in forward and split it in backward
        if shard_config.enable_sequence_parallelism:
@ -248,22 +240,17 @@ class GPTJPipelineForwards:
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

+        next_cache = next_decoder_cache if use_cache else None
+
        if stage_manager.is_last_stage():
            if not return_dict:
                return tuple(
-                    v
-                    for v in [
-                        hidden_states,
-                        presents,
-                        all_hidden_states,
-                        all_self_attentions,
-                    ]
-                    if v is not None
+                    v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
                )

            return BaseModelOutputWithPast(
                last_hidden_state=hidden_states,
-                past_key_values=presents,
+                past_key_values=next_cache,
                hidden_states=all_hidden_states,
                attentions=all_self_attentions,
            )
@ -275,7 +262,7 @@ class GPTJPipelineForwards:
    def gptj_causallm_model_forward(
        self: GPTJForCausalLM,
        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
@ -286,6 +273,7 @@ class GPTJPipelineForwards:
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
@ -315,6 +303,7 @@ class GPTJPipelineForwards:
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
+            cache_position=cache_position,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
@ -326,18 +315,28 @@ class GPTJPipelineForwards:
            return {"hidden_states": transformer_outputs["hidden_states"]}

        hidden_states = transformer_outputs[0]
-        lm_logits = self.lm_head(hidden_states)
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            torch.cuda.set_device(self.transformer.first_device)
+            hidden_states = hidden_states.to(self.lm_head.weight.device)
+
+        # v4.51.3 tranformers loss calculation
+        # make sure sampling in fp16 works correctly and
+        # compute loss in fp32 to match with mesh-tf version
+        # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
+        lm_logits = self.lm_head(hidden_states).to(torch.float32)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(lm_logits.device)
-            # Shift so that tokens < n predict n
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+            loss = self.loss_function(
+                lm_logits,
+                labels,
+                vocab_size=self.config.vocab_size,
+            )

            loss = loss.to(hidden_states.dtype)

@ -357,7 +356,7 @@ class GPTJPipelineForwards:
    def gptj_for_sequence_classification_forward(
        self: GPTJForSequenceClassification,
        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
@ -379,7 +378,7 @@ class GPTJPipelineForwards:
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

-        # This function is modified on the basis of transformers.models.gptj.modeling_gptj.GPTJForSequenceClassification.forward.
+        # This function is modified on the v4.51.3 transformers.models.gptj.modeling_gptj.GPTJForSequenceClassification.forward.
        # Please refer to original code of transformers for more details.
        """
        logger = logging.get_logger(__name__)
@ -581,6 +580,8 @@ def get_gptj_flash_attention_forward():
        Tuple[torch.Tensor, Tuple[torch.Tensor]],
        Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
    ]:
+        # This function is modified on the v4.51.3 transformers.models.gptj.modeling_gptj.GPTJAttention.forward.
+        # Please refer to original code of transformers for more details.
        assert head_mask is None, "head_mask is not supported for FlashAttention"
        query = self.q_proj(hidden_states)
        key = self.k_proj(hidden_states)
--- a/colossalai/shardformer/modeling/llama.py
+++ b/colossalai/shardformer/modeling/llama.py
@ -4,7 +4,6 @@ from typing import Dict, List, Optional, Tuple, Union

 import torch
 import torch.distributed
-import torch.nn.functional as F
 import torch.utils.checkpoint
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
@ -141,7 +140,9 @@ class LlamaPipelineForwards:
                invert=(sp_mode != "ring_attn"),
            )
        else:
-            attn_kwargs: torch.Tensor = self._update_causal_mask(attention_mask, hidden_states, cache_position)
+            attn_kwargs: torch.Tensor = self._update_causal_mask(
+                attention_mask, hidden_states, cache_position, past_key_values
+            )

        # Support SP + PP. Later stages have already received the split input.
        split_input = disable_pp or stage_manager.is_first_stage()
@ -177,6 +178,7 @@ class LlamaPipelineForwards:
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None
        start_idx, end_idx = (0, len(self.layers)) if disable_pp else (stage_index[0], stage_index[1])
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        num_ckpt_layers = 0
        if self.gradient_checkpointing and self.training:
@ -204,6 +206,7 @@ class LlamaPipelineForwards:
                    output_attentions,
                    use_cache,
                    cache_position,
+                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
@ -214,6 +217,7 @@ class LlamaPipelineForwards:
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
                )
            hidden_states = layer_outputs[0]

@ -486,8 +490,8 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
    def forward(
        self,
        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[Union[torch.Tensor, Dict]] = None,
-        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
@ -505,30 +509,14 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
            )

        bsz, q_len, _ = hidden_states.size()
+        input_shape = hidden_states.shape[:-1]
        # sp: modify sp_len when sequence parallel mode is ring
        if is_share_sp_tp(sp_mode):
            q_len *= sp_size

-        if self.config.pretraining_tp > 1:
-            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
-            query_slices = self.q_proj.weight.split(
-                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
-            )
-            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
-            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
-
-            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
-            query_states = torch.cat(query_states, dim=-1)
-
-            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
-            key_states = torch.cat(key_states, dim=-1)
-
-            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
-            value_states = torch.cat(value_states, dim=-1)
-        else:
-            query_states = self.q_proj(hidden_states)
-            key_states = self.k_proj(hidden_states)
-            value_states = self.v_proj(hidden_states)
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)

        # sp: all-to-all comminucation when introducing sequence parallel
        if sp_mode == "all_to_all":
@ -537,9 +525,9 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
            value_states = all_to_all_comm(value_states, sp_group, fp8_communication=shard_config.fp8_communication)
            bsz, q_len, _ = query_states.size()

-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
@ -552,7 +540,7 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s

            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

-        cos, sin = self.rotary_emb(value_states, position_ids)
+        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
@ -610,17 +598,12 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
                attn_output, sp_group, scatter_dim=1, gather_dim=2, fp8_communication=shard_config.fp8_communication
            )
        else:
-            attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+            attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()

-        if self.config.pretraining_tp > 1:
-            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
-            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
-            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
-        else:
-            attn_output = self.o_proj(attn_output)
+        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None
-        return attn_output, attn_weights, past_key_value
+        return attn_output, attn_weights

    return forward
--- a/colossalai/shardformer/modeling/mistral.py
+++ b/colossalai/shardformer/modeling/mistral.py
@ -4,10 +4,6 @@ from typing import List, Optional, Tuple, Union
 import torch
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from transformers.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import (
-    _prepare_4d_causal_attention_mask,
-    _prepare_4d_causal_attention_mask_for_sdpa,
-)
 from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
@ -36,7 +32,7 @@ class MistralForwards:
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
@ -50,8 +46,6 @@ class MistralForwards:
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
        # retrieve input_ids and inputs_embeds
        if stage_manager.is_first_stage():
            if input_ids is not None and inputs_embeds is not None:
@ -67,20 +61,23 @@ class MistralForwards:
        else:
            input_shape = hidden_states.shape[:-1]
            batch_size, seq_length = input_shape
-            device = hidden_states.device
+            hidden_states.device

        past_key_values_length = 0

-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()

-        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        if attention_mask is not None and self.config._attn_implementation == "flash_attention_2" and use_cache:
            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
            if is_padding_right:
                raise ValueError(
@ -100,27 +97,9 @@ class MistralForwards:
                is_causal=True,
            )
        else:
-            if self._attn_implementation == "flash_attention_2":
-                # 2d mask is passed through the layers
-                attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-            elif self._attn_implementation == "sdpa" and not output_attentions:
-                # output_attentions=True can not be supported when using SDPA, and we fall back on
-                # the manual implementation that requires a 4D causal mask in all cases.
-                attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
-                    attention_mask,
-                    (batch_size, seq_length),
-                    inputs_embeds,
-                    past_key_values_length,
-                )
-            else:
-                # 4d mask is passed through the layers
-                attention_mask = _prepare_4d_causal_attention_mask(
-                    attention_mask,
-                    (batch_size, seq_length),
-                    hidden_states,
-                    past_key_values_length,
-                    sliding_window=self.config.sliding_window,
-                )
+            attention_mask = self._update_causal_mask(
+                attention_mask, hidden_states, cache_position, past_key_values, output_attentions
+            )

        if self.gradient_checkpointing and self.training:
            if use_cache:
@ -133,6 +112,8 @@ class MistralForwards:
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None

+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
        start_idx, end_idx = stage_index[0], stage_index[1]
        num_ckpt_layers = 0
        if self.gradient_checkpointing and self.training:
@ -156,11 +137,13 @@ class MistralForwards:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
@ -170,6 +153,8 @@ class MistralForwards:
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
                )

            hidden_states = layer_outputs[0]
@ -189,8 +174,6 @@ class MistralForwards:

        next_cache = None
        if stage_manager.is_last_stage():
-            if not return_dict:
-                return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
            return BaseModelOutputWithPast(
                last_hidden_state=hidden_states,
                past_key_values=next_cache,
@ -212,7 +195,8 @@ class MistralForwards:
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
@ -248,7 +232,6 @@ class MistralForwards:
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = MistralForwards.mistral_model_forward(
@ -261,7 +244,7 @@ class MistralForwards:
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            cache_position=cache_position,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
@ -278,10 +261,6 @@ class MistralForwards:
            if labels is not None:
                loss = dist_cross_entropy(labels, logits, shard_config, self.lm_head.out_features, self.model.dtype)

-            if not return_dict:
-                output = (logits,) + outputs[1:]
-                return (loss,) + output if loss is not None else output
-
            return CausalLMOutputWithPast(
                loss=loss,
                logits=logits,
@ -305,7 +284,6 @@ class MistralForwards:
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
@ -317,7 +295,6 @@ class MistralForwards:
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = MistralForwards.mistral_model_forward(
            self.model,
@ -329,7 +306,6 @@ class MistralForwards:
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
@ -383,9 +359,6 @@ class MistralForwards:
                elif self.config.problem_type == "multi_label_classification":
                    loss_fct = BCEWithLogitsLoss()
                    loss = loss_fct(pooled_logits, labels)
-            if not return_dict:
-                output = (pooled_logits,) + transformer_outputs[1:]
-                return ((loss,) + output) if loss is not None else output
        else:
            hidden_states = transformer_outputs.get("hidden_states")
            return {"hidden_states": hidden_states}
@ -413,7 +386,8 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **flash_attn_kwargs,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
@ -421,8 +395,6 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
@ -433,27 +405,22 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

-        past_key_values_length = 0
-
-        if use_cache:
-            use_legacy_cache = not isinstance(past_key_values, Cache)
-            if use_legacy_cache:
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_key_values_length = past_key_values.get_usable_length(seq_length)
-
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
-
        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

-        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        if attention_mask is not None and self.config._attn_implementation == "flash_attention_2" and use_cache:
            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
            if is_padding_right:
                raise ValueError(
@ -471,31 +438,11 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
                q_padding_mask=attention_mask,
                is_causal=True,
            )
-        else:
-            if self._attn_implementation == "flash_attention_2":
-                # 2d mask is passed through the layers
-                attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-            elif self._attn_implementation == "sdpa" and not output_attentions:
-                # output_attentions=True can not be supported when using SDPA, and we fall back on
-                # the manual implementation that requires a 4D causal mask in all cases.
-                attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
-                    attention_mask,
-                    (batch_size, seq_length),
-                    inputs_embeds,
-                    past_key_values_length,
-                )
-            else:
-                # 4d mask is passed through the layers
-                attention_mask = _prepare_4d_causal_attention_mask(
-                    attention_mask,
-                    (batch_size, seq_length),
-                    inputs_embeds,
-                    past_key_values_length,
-                    sliding_window=self.config.sliding_window,
-                )

        hidden_states = inputs_embeds

+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
@ -506,37 +453,25 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None

-        for decoder_layer in self.layers:
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **flash_attn_kwargs,
+            )

            hidden_states = layer_outputs[0]

-            if use_cache:
-                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
-
            if output_attentions:
                all_self_attns += (layer_outputs[1],)

@ -546,15 +481,12 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

-        next_cache = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+            # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
+            past_key_values=past_key_values if use_cache else None,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )
@ -568,11 +500,10 @@ def get_mistral_flash_attention_forward(shard_config: ShardConfig):
    def forward(
        self: MistralAttention,
        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if "padding_mask" in kwargs:
@ -585,9 +516,9 @@ def get_mistral_flash_attention_forward(shard_config: ShardConfig):
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
@ -598,11 +529,12 @@ def get_mistral_flash_attention_forward(shard_config: ShardConfig):
                    "with a layer index."
                )
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+        # cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        # repeat k/v heads if n_kv_heads < n_heads
@ -613,11 +545,11 @@ def get_mistral_flash_attention_forward(shard_config: ShardConfig):
        attn_output = ColoAttention.attention(query_states, key_states, value_states, **attention_mask)

        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+        attn_output = attn_output.reshape(bsz, q_len, -1)

        attn_output = self.o_proj(attn_output)

-        return attn_output, None, past_key_value
+        return attn_output, None

    return forward

--- a/colossalai/shardformer/modeling/mixtral.py
+++ b/colossalai/shardformer/modeling/mixtral.py
@ -1,6 +1,6 @@
 import inspect
 import warnings
-from typing import List, Optional, Tuple, Union
+from typing import Callable, List, Optional, Tuple, Union

 import torch
 import torch.distributed as dist
@ -13,6 +13,7 @@ from transformers.modeling_attn_mask_utils import (
    _prepare_4d_causal_attention_mask_for_sdpa,
 )
 from transformers.models.mixtral.modeling_mixtral import (
+    MixtralModel,
    MixtralSparseMoeBlock,
    MoeCausalLMOutputWithPast,
    MoeModelOutputWithPast,
@ -215,7 +216,7 @@ class MixtralPipelineForwards:

    @staticmethod
    def mixtral_model_forward(
-        self,
+        self: MixtralModel,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
@ -225,6 +226,7 @@ class MixtralPipelineForwards:
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        return_dict: Optional[bool] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
@ -340,11 +342,17 @@ class MixtralPipelineForwards:
                )
                use_cache = False

+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_router_logits = () if output_router_logits else None
-        next_decoder_cache = None
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
+            )

        start_idx, end_idx = stage_index[0], stage_index[1]
        for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx):
@ -370,6 +378,9 @@ class MixtralPipelineForwards:
                    None,
                    output_attentions,
                    output_router_logits,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
@ -380,6 +391,8 @@ class MixtralPipelineForwards:
                    output_attentions,
                    output_router_logits,
                    use_cache,
+                    cache_position,
+                    position_embeddings,
                )
            hidden_states = layer_outputs[0]

@ -559,14 +572,18 @@ class MixtralPipelineForwards:

 def get_mixtral_flash_attention_forward(shard_config, sp_mode=None, sp_size=None, sp_group=None):
    logger = logging.get_logger(__name__)
+    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+    from transformers.models.mixtral.modeling_mixtral import eager_attention_forward

    def forward(
        self,
        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
@ -614,54 +631,23 @@ def get_mixtral_flash_attention_forward(shard_config, sp_mode=None, sp_size=None
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

        # Because the input can be padded, the absolute sequence length depends on the max position id.
-        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
-        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
+        cos, sin = position_embeddings

-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

-        use_sliding_windows = (
-            _flash_supports_window_size
-            and getattr(self.config, "sliding_window", None) is not None
-            and kv_seq_len > self.config.sliding_window
-        )
        if not _flash_supports_window_size:
            logger.warning_once(
                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
                " make sure to upgrade flash-attn library."
            )
        if past_key_value is not None:
-            # Activate slicing cache only if the config has a value `sliding_windows` attribute
-            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
-            if (
-                getattr(self.config, "sliding_window", None) is not None
-                and kv_seq_len > self.config.sliding_window
-                and cache_has_contents
-            ):
-                slicing_tokens = 1 - self.config.sliding_window
-
-                past_key = past_key_value[self.layer_idx][0]
-                past_value = past_key_value[self.layer_idx][1]
-
-                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
-                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
-
-                if past_key.shape[-2] != self.config.sliding_window - 1:
-                    raise ValueError(
-                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
-                        f" {past_key.shape}"
-                    )
-
-                if attention_mask is not None:
-                    attention_mask = attention_mask[:, slicing_tokens:]
-                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
-
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)
-        dropout_rate = 0.0 if not self.training else self.attention_dropout
+        0.0 if not self.training else self.attention_dropout

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
@ -689,14 +675,27 @@ def get_mixtral_flash_attention_forward(shard_config, sp_mode=None, sp_size=None
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)
-        attn_output = self._flash_attention_forward(
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
+                logger.warning_once(
+                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+                )
+            else:
+                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
-            q_len,
-            dropout=dropout_rate,
-            use_sliding_windows=use_sliding_windows,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=getattr(self.config, "sliding_window", None),  # main diff with Llama
+            **kwargs,
        )

        # sp: all-to-all comminucation when introducing sequence parallel
@ -712,7 +711,7 @@ def get_mixtral_flash_attention_forward(shard_config, sp_mode=None, sp_size=None

        if not output_attentions:
            attn_weights = None
-        return attn_output, attn_weights, past_key_value
+        return attn_output, attn_weights

    return forward

@ -731,6 +730,7 @@ def get_mixtral_flash_attention_model_forward(shard_config, sp_mode=None, sp_siz
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, MoeModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@ -788,7 +788,7 @@ def get_mixtral_flash_attention_model_forward(shard_config, sp_mode=None, sp_siz
                    " this may lead to unexpected behaviour for Flash Attention version of Mixtral. Make sure to "
                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                )
-        if self._attn_implementation == "flash_attention_2":
+        if self.config._attn_implementation == "flash_attention_2":
            # 2d mask is passed through the layers
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        elif self._attn_implementation == "sdpa" and not output_attentions:
@ -820,6 +820,16 @@ def get_mixtral_flash_attention_model_forward(shard_config, sp_mode=None, sp_siz
            )
        hidden_states = inputs_embeds

+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
@ -840,6 +850,8 @@ def get_mixtral_flash_attention_model_forward(shard_config, sp_mode=None, sp_siz
                    output_attentions,
                    output_router_logits,
                    use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
@ -850,6 +862,8 @@ def get_mixtral_flash_attention_model_forward(shard_config, sp_mode=None, sp_siz
                    output_attentions=output_attentions,
                    output_router_logits=output_router_logits,
                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
                )

            hidden_states = layer_outputs[0]
--- a/colossalai/shardformer/modeling/opt.py
+++ b/colossalai/shardformer/modeling/opt.py
@ -128,7 +128,7 @@ class OPTPipelineForwards:
        # required mask seq length can be calculated via length of past
        mask_seq_length = past_key_values_length + seq_length
        # embed positions
-        if self.decoder._use_flash_attention_2:
+        if self.decoder.config._attn_implementation == "flash_attention_2":
            # 2d mask is passed through the layers
            causal_attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
            attention_mask = (
@ -542,6 +542,9 @@ class OPTPipelineForwards:
 def get_opt_flash_attention_forward(shard_config: ShardConfig):
    from transformers.models.opt.modeling_opt import OPTAttention

+    def _shape(tensor: torch.Tensor, seq_len: int, bsz: int, num_heads: int, head_dim: int):
+        return tensor.view(bsz, seq_len, num_heads, head_dim).transpose(1, 2).contiguous()
+
    def forward(
        self: OPTAttention,
        hidden_states: torch.Tensor,
@ -568,30 +571,20 @@ def get_opt_flash_attention_forward(shard_config: ShardConfig):
            value_states = past_key_value[1]
        elif is_cross_attention:
            # cross_attentions
-            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
-            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
+            key_states = _shape(self.k_proj(key_value_states), -1, bsz, self.num_heads, self.head_dim)
+            value_states = _shape(self.v_proj(key_value_states), -1, bsz, self.num_heads, self.head_dim)
        elif past_key_value is not None:
            # reuse k, v, self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+            key_states = _shape(self.k_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim)
+            value_states = _shape(self.v_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            # self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+            key_states = _shape(self.k_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim)
+            value_states = _shape(self.v_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim)

-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_states, value_states)
-
-        query_states = self._shape(query_states, tgt_len, bsz)
+        query_states = _shape(query_states, tgt_len, bsz, self.num_heads, self.head_dim)

        dropout_p = self.dropout if self.training else 0.0
        attn_output = ColoAttention.attention(
@ -630,6 +623,8 @@ def get_opt_decoder_forward_for_flash_attention(shard_config: ShardConfig):
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
--- a/colossalai/shardformer/modeling/qwen2.py
+++ b/colossalai/shardformer/modeling/qwen2.py
@ -4,31 +4,23 @@ from typing import List, Optional, Tuple, Union
 import torch
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.modeling_attn_mask_utils import (
+    _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
+)
 from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    SequenceClassifierOutputWithPast,
 )
-
-try:
-    from transformers.modeling_attn_mask_utils import (
-        _prepare_4d_causal_attention_mask,
-        _prepare_4d_causal_attention_mask_for_sdpa,
-    )
-    from transformers.models.qwen2.modeling_qwen2 import (
-        Qwen2Attention,
-        Qwen2ForCausalLM,
-        Qwen2ForSequenceClassification,
-        Qwen2Model,
-        apply_rotary_pos_emb,
-        repeat_kv,
-    )
-except ImportError:
-    Qwen2Model = "Qwen2Model"
-    Qwen2ForCausalLM = "Qwen2ForCausalLM"
-    Qwen2Attention = "Qwen2Attention"
-    Qwen2ForSequenceClassification = "Qwen2ForSequenceClassification"
-
+from transformers.models.qwen2.modeling_qwen2 import (
+    Qwen2Attention,
+    Qwen2ForCausalLM,
+    Qwen2ForSequenceClassification,
+    Qwen2Model,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
 from transformers.utils import logging

 from colossalai.pipeline.stage_manager import PipelineStageManager
@ -57,6 +49,7 @@ class Qwen2PipelineForwards:
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        return_dict: Optional[bool] = None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
@ -131,14 +124,6 @@ class Qwen2PipelineForwards:
        else:
            position_ids = position_ids.view(-1, seq_length).long()

-        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
-            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
-            if is_padding_right:
-                raise ValueError(
-                    "You are attempting to perform batched generation with padding_side='right'"
-                    " this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to "
-                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
-                )
        # embed positions, for the first stage, hidden_states is the input embeddings,
        # for the other stages, hidden_states is the output of the previous stage
        if shard_config.enable_flash_attention:
@ -152,16 +137,16 @@ class Qwen2PipelineForwards:
                is_causal=True,
            )
        else:
-            if self._attn_implementation == "flash_attention_2":
+            if self.config._attn_implementation == "flash_attention_2":
                # 2d mask is passed through the layers
                attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-            elif self._attn_implementation == "sdpa" and not output_attentions:
+            elif self.config._attn_implementation == "sdpa" and not output_attentions:
                # output_attentions=True can not be supported when using SDPA, and we fall back on
                # the manual implementation that requires a 4D causal mask in all cases.
                attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                    attention_mask,
                    (batch_size, seq_length),
-                    inputs_embeds,
+                    hidden_states,
                    past_key_values_length,
                )
            else:
@ -195,6 +180,8 @@ class Qwen2PipelineForwards:
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
        start_idx, end_idx = stage_index[0], stage_index[1]
        num_ckpt_layers = 0
        if self.gradient_checkpointing and self.training:
@ -214,7 +201,7 @@ class Qwen2PipelineForwards:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

-            past_key_value = past_key_values[idx] if past_key_values is not None else None
+            past_key_values[idx] if past_key_values is not None else None

            if idx - start_idx < num_ckpt_layers:
                layer_outputs = self._gradient_checkpointing_func(
@ -225,15 +212,19 @@ class Qwen2PipelineForwards:
                    past_key_values,
                    output_attentions,
                    use_cache,
+                    cache_position,
+                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
                )

            hidden_states = layer_outputs[0]
@ -435,7 +426,6 @@ class Qwen2PipelineForwards:
            logits = self.score(hidden_states)

            if self.config.pad_token_id is None and batch_size != 1:
-                print(self.config.pad_token_id)
                raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
            if self.config.pad_token_id is None:
                sequence_lengths = -1
@ -491,11 +481,10 @@ def get_qwen2_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
    def forward(
        self: Qwen2Attention,
        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if sp_mode is not None:
@ -519,9 +508,9 @@ def get_qwen2_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
            value_states = all_to_all_comm(value_states, sp_group, fp8_communication=shard_config.fp8_communication)
            bsz, q_len, _ = query_states.size()

-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
@ -533,9 +522,8 @@ def get_qwen2_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
                )
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        # Because the input can be padded, the absolute sequence length depends on the max position id.
-        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
-        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            # Activate slicing cache only if the config has a value `sliding_windows` attribute
@ -563,7 +551,7 @@ def get_qwen2_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
                    attention_mask = attention_mask[:, slicing_tokens:]
                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)

-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        # repeat k/v heads if n_kv_heads < n_heads
@ -605,11 +593,11 @@ def get_qwen2_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
                attn_output, sp_group, scatter_dim=1, gather_dim=2, fp8_communication=shard_config.fp8_communication
            )
        else:
-            attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+            attn_output = attn_output.reshape(bsz, q_len, -1)

        attn_output = self.o_proj(attn_output)

-        return attn_output, None, past_key_value
+        return attn_output, None

    return forward

@ -627,6 +615,7 @@ def get_qwen2_model_forward_for_flash_attn(shard_config: ShardConfig, sp_mode=No
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
        return_dict: Optional[bool] = None,
        force_sp_output_gather: bool = True,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
@ -648,6 +637,9 @@ def get_qwen2_model_forward_for_flash_attn(shard_config: ShardConfig, sp_mode=No
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
        seq_length_with_past = seq_length
        past_key_values_length = 0

@ -664,9 +656,6 @@ def get_qwen2_model_forward_for_flash_attn(shard_config: ShardConfig, sp_mode=No
        else:
            position_ids = position_ids.view(-1, seq_length).long()

-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
        # embed positions
        hidden_states = inputs_embeds

@ -700,6 +689,7 @@ def get_qwen2_model_forward_for_flash_attn(shard_config: ShardConfig, sp_mode=No
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        if sp_mode in ["ring", "split_gather"]:
            hidden_states = split_forward_gather_backward(
@ -723,22 +713,23 @@ def get_qwen2_model_forward_for_flash_attn(shard_config: ShardConfig, sp_mode=No
                    past_key_values,
                    output_attentions,
                    use_cache,
+                    cache_position,
+                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
                )

            hidden_states = layer_outputs[0]

-            if use_cache:
-                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
-
            if output_attentions:
                all_self_attns += (layer_outputs[1],)

--- a/colossalai/shardformer/modeling/qwen3.py
+++ b/colossalai/shardformer/modeling/qwen3.py
@ -0,0 +1,831 @@
+# Modifed from qwen2 modeling
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers.modeling_attn_mask_utils import (
+    _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.models.qwen3.modeling_qwen3 import (
+    Qwen3Attention,
+    Qwen3ForCausalLM,
+    Qwen3ForSequenceClassification,
+    Qwen3Model,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
+from transformers.utils import logging
+
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.shardformer.layer._operation import all_to_all_comm, split_forward_gather_backward
+from colossalai.shardformer.shard import ShardConfig
+
+from ..layer import ColoAttention, dist_cross_entropy
+from ..layer._operation import gather_sp_output
+from ..layer.utils import is_share_sp_tp
+
+
+class Qwen3PipelineForwards:
+    """
+    This class serves as a micro library for forward function substitution of Qwen3 models
+    under pipeline setting.
+    """
+
+    @staticmethod
+    def qwen3_model_forward(
+        self: Qwen3Model,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        return_dict: Optional[bool] = None,
+        stage_manager: Optional[PipelineStageManager] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+        stage_index: Optional[List[int]] = None,
+        shard_config: ShardConfig = None,
+        force_sp_output_gather: bool = True,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        logger = logging.get_logger(__name__)
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if stage_manager.is_first_stage():
+            if input_ids is not None and inputs_embeds is not None:
+                raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+            elif input_ids is not None:
+                batch_size, seq_length = input_ids.shape
+            elif inputs_embeds is not None:
+                batch_size, seq_length, _ = inputs_embeds.shape
+            else:
+                raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            if inputs_embeds is None:
+                inputs_embeds = self.embed_tokens(input_ids)
+            hidden_states = inputs_embeds
+        else:
+            input_shape = hidden_states.shape[:-1]
+            batch_size, seq_length = input_shape
+            device = hidden_states.device
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
+        if output_attentions:
+            logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
+            output_attentions = False
+        if output_hidden_states:
+            logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
+            output_hidden_states = False
+        if use_cache:
+            logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
+            use_cache = False
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        # Support SP + PP
+        sp_size = shard_config.sequence_parallel_size
+        sp_group = shard_config.sequence_parallel_process_group
+        sp_mode = shard_config.sequence_parallelism_mode
+        # For generating full positions ids (the states will be gathered along the seq dim before attention fwd).
+        if sp_mode != "ring_attn" and not stage_manager.is_first_stage():
+            seq_length *= sp_size
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        # embed positions, for the first stage, hidden_states is the input embeddings,
+        # for the other stages, hidden_states is the output of the previous stage
+        if shard_config.enable_flash_attention:
+            # in this case, attention_mask is a dict rather than a tensor
+            mask_shape = (batch_size, 1, seq_length, seq_length_with_past)
+            attention_mask = ColoAttention.prepare_attn_kwargs(
+                mask_shape,
+                hidden_states.dtype,
+                hidden_states.device,
+                q_padding_mask=attention_mask,
+                is_causal=True,
+            )
+        else:
+            if self.config._attn_implementation == "flash_attention_2":
+                # 2d mask is passed through the layers
+                attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+            elif self.config._attn_implementation == "sdpa" and not output_attentions:
+                # output_attentions=True can not be supported when using SDPA, and we fall back on
+                # the manual implementation that requires a 4D causal mask in all cases.
+                attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                    attention_mask,
+                    (batch_size, seq_length),
+                    hidden_states,
+                    past_key_values_length,
+                )
+            else:
+                # 4d mask is passed through the layers
+                attention_mask = _prepare_4d_causal_attention_mask(
+                    attention_mask,
+                    (batch_size, seq_length),
+                    hidden_states,
+                    past_key_values_length,
+                    sliding_window=self.config.sliding_window,
+                )
+
+        if stage_manager.is_first_stage():
+            if shard_config.enable_sequence_parallelism:
+                if is_share_sp_tp(sp_mode):
+                    hidden_states = split_forward_gather_backward(
+                        hidden_states,
+                        dim=1,
+                        process_group=sp_group,
+                    )
+                elif sp_mode == "all_to_all":
+                    hidden_states = split_forward_gather_backward(
+                        hidden_states,
+                        dim=1,
+                        process_group=sp_group,
+                        grad_scale=1 / sp_size,
+                    )
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        start_idx, end_idx = stage_index[0], stage_index[1]
+        num_ckpt_layers = 0
+        if self.gradient_checkpointing and self.training:
+            num_ckpt_layers = end_idx - start_idx
+            # TODO: We can replace `gradient_checkpointing_enable` fn and initialize a gradient_checkpointing (List[bool]) for each layer
+            if shard_config.gradient_checkpoint_config is not None:
+                num_ckpt_layers = shard_config.gradient_checkpoint_config.get_num_ckpt_layers(
+                    stage=stage_manager.stage,
+                    num_stages=stage_manager.num_stages,
+                    num_layers=end_idx - start_idx,
+                    model_chunk_id=(stage_manager.model_chunk_id if stage_manager.is_interleave else 0),
+                    num_model_chunks=stage_manager.num_model_chunks,
+                )
+            assert num_ckpt_layers <= end_idx - start_idx
+
+        for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_values[idx] if past_key_values is not None else None
+
+            if idx - start_idx < num_ckpt_layers:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        if stage_manager.is_last_stage():
+            hidden_states = self.norm(hidden_states)
+            if shard_config.enable_sequence_parallelism:
+                if (not shard_config.parallel_output) or force_sp_output_gather or is_share_sp_tp(sp_mode):
+                    hidden_states = gather_sp_output(hidden_states, shard_config)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+
+        if stage_manager.is_last_stage():
+            if not return_dict:
+                return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+            return BaseModelOutputWithPast(
+                last_hidden_state=hidden_states,
+                past_key_values=next_cache,
+                hidden_states=all_hidden_states,
+                attentions=all_self_attns,
+            )
+        # always return dict for imediate stage
+        return {"hidden_states": hidden_states}
+
+    @staticmethod
+    def qwen3_for_causal_lm_forward(
+        self: Qwen3ForCausalLM,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        stage_manager: Optional[PipelineStageManager] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+        stage_index: Optional[List[int]] = None,
+        shard_config: ShardConfig = None,
+    ):
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Qwen2ForCausalLM
+
+        >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you consciours? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
+        ```"""
+        logger = logging.get_logger(__name__)
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
+        if output_attentions:
+            logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
+            output_attentions = False
+        if output_hidden_states:
+            logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
+            output_hidden_states = False
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = Qwen3PipelineForwards.qwen3_model_forward(
+            self.model,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            stage_manager=stage_manager,
+            hidden_states=hidden_states,
+            stage_index=stage_index,
+            shard_config=shard_config,
+            force_sp_output_gather=False,
+        )
+        past_key_values = None
+
+        if stage_manager.is_last_stage():
+            hidden_states = outputs[0]
+            if hidden_states.shape[1] == 2:
+                pass
+            logits = self.lm_head(hidden_states)
+            loss = None
+            if labels is not None:
+                loss = dist_cross_entropy(labels, logits, shard_config, self.lm_head.out_features, logits.dtype)
+
+            if not return_dict:
+                output = (logits,) + outputs[1:]
+                return (loss,) + output if loss is not None else output
+
+            return CausalLMOutputWithPast(
+                loss=loss,
+                logits=logits,
+                past_key_values=outputs.past_key_values,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        else:
+            hidden_states = outputs.get("hidden_states")
+            return {"hidden_states": hidden_states}
+
+    @staticmethod
+    def qwen3_for_sequence_classification_forward(
+        self: Qwen3ForSequenceClassification,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        stage_manager: Optional[PipelineStageManager] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+        stage_index: Optional[List[int]] = None,
+        shard_config: ShardConfig = None,
+    ):
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        logger = logging.get_logger(__name__)
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
+        if output_attentions:
+            logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
+            output_attentions = False
+        if output_hidden_states:
+            logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
+            output_hidden_states = False
+
+        transformer_outputs = Qwen3PipelineForwards.qwen3_model_forward(
+            self.model,
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            stage_manager=stage_manager,
+            hidden_states=hidden_states,
+            stage_index=stage_index,
+            shard_config=shard_config,
+        )
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        elif inputs_embeds is not None:
+            batch_size = inputs_embeds.shape[0]
+        else:
+            batch_size = hidden_states.shape[0]
+
+        if stage_manager.is_last_stage():
+            hidden_states = transformer_outputs[0]
+            logits = self.score(hidden_states)
+
+            if self.config.pad_token_id is None and batch_size != 1:
+                raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+
+            if self.config.pad_token_id is None:
+                last_non_pad_token = -1
+            elif input_ids is not None:
+                # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+                non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+                token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+                last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+            else:
+                last_non_pad_token = -1
+                logger.warning_once(
+                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+                )
+
+            pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+            loss = None
+            if labels is not None:
+                loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
+
+            if not return_dict:
+                output = (pooled_logits,) + transformer_outputs[1:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutputWithPast(
+                loss=loss,
+                logits=pooled_logits,
+                past_key_values=transformer_outputs.past_key_values,
+                hidden_states=transformer_outputs.hidden_states,
+                attentions=transformer_outputs.attentions,
+            )
+
+        else:
+            hidden_states = transformer_outputs.get("hidden_states")
+            return {"hidden_states": hidden_states}
+
+
+def get_qwen3_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, sp_size=None, sp_group=None):
+    def forward(
+        self: Qwen3Attention,
+        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if sp_mode is not None:
+            assert sp_mode in ["all_to_all", "split_gather", "ring"], "Invalid sp_mode"
+            assert (sp_size is not None) and (
+                sp_group is not None
+            ), "Must specify sp_size and sp_group for sequence parallel"
+
+        bsz, q_len, _ = hidden_states.size()
+        # sp: modify sp_len when sequence parallel mode is ring
+        if sp_mode in ["split_gather", "ring"]:
+            q_len *= sp_size
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+        # sp: all-to-all comminucation when introducing sequence parallel
+        if sp_mode == "all_to_all":
+            query_states = all_to_all_comm(query_states, sp_group, fp8_communication=shard_config.fp8_communication)
+            key_states = all_to_all_comm(key_states, sp_group, fp8_communication=shard_config.fp8_communication)
+            value_states = all_to_all_comm(value_states, sp_group, fp8_communication=shard_config.fp8_communication)
+            bsz, q_len, _ = query_states.size()
+
+        query_states = self.q_norm(query_states.view(bsz, q_len, -1, self.head_dim)).transpose(1, 2)
+        key_states = self.k_norm(key_states.view(bsz, q_len, -1, self.head_dim)).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                        f" {past_key.shape}"
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if shard_config.enable_flash_attention:
+            assert isinstance(attention_mask, dict), "Flash Attention Error: attention_mask should be a dict."
+            attn_output = ColoAttention.attention(
+                query_states,
+                key_states,
+                value_states,
+                dropout_p=0.0 if not self.training else self.attention_dropout,
+                **attention_mask,
+            )
+        else:
+            attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+            if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                    f" {attn_weights.size()}"
+                )
+
+            if attention_mask is not None:
+                if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                    raise ValueError(
+                        f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                    )
+                attn_weights = attn_weights + attention_mask
+
+            # upcast attention to fp32
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+            attn_output = torch.matmul(attn_weights, value_states)
+
+            if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+                raise ValueError(
+                    f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                    f" {attn_output.size()}"
+                )
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        if sp_mode == "all_to_all":
+            attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim)
+            attn_output = all_to_all_comm(
+                attn_output, sp_group, scatter_dim=1, gather_dim=2, fp8_communication=shard_config.fp8_communication
+            )
+        else:
+            attn_output = attn_output.reshape(bsz, q_len, -1)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None
+
+    return forward
+
+
+def get_qwen3_model_forward_for_flash_attn(shard_config: ShardConfig, sp_mode=None, sp_size=None, sp_group=None):
+    logger = logging.get_logger(__name__)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        return_dict: Optional[bool] = None,
+        force_sp_output_gather: bool = True,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        if shard_config.enable_flash_attention:
+            # in this case, attention_mask is a dict rather than a tensor
+            mask_shape = (batch_size, 1, seq_length, seq_length_with_past)
+            attention_mask = ColoAttention.prepare_attn_kwargs(
+                mask_shape,
+                hidden_states.dtype,
+                hidden_states.device,
+                q_padding_mask=attention_mask,
+                is_causal=True,
+            )
+        else:
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+                sliding_window=self.config.sliding_window,
+            )
+
+        if (self.gradient_checkpointing or sp_mode in ["ring", "all_to_all"]) and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        if sp_mode in ["ring", "split_gather"]:
+            hidden_states = split_forward_gather_backward(
+                hidden_states, 1, sp_group, fp8_communication=shard_config.fp8_communication
+            )
+        elif sp_mode == "all_to_all":
+            hidden_states = split_forward_gather_backward(
+                hidden_states, 1, sp_group, 1 / sp_size, fp8_communication=shard_config.fp8_communication
+            )
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        if shard_config.enable_sequence_parallelism:
+            if (not shard_config.parallel_output) or force_sp_output_gather or is_share_sp_tp(sp_mode):
+                hidden_states = gather_sp_output(hidden_states, shard_config)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    return forward
+
+
+def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
+    def forward(
+        self: Qwen3ForCausalLM,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Qwen2ForCausalLM
+
+        >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            force_sp_output_gather=False,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+        loss = None
+        if labels is not None:
+            loss = dist_cross_entropy(labels, logits, shard_config, self.lm_head.out_features, logits.dtype)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    return forward
--- a/colossalai/shardformer/modeling/sam.py
+++ b/colossalai/shardformer/modeling/sam.py
@ -1,6 +1,8 @@
 import torch
+from torch import nn


+# Same as the SamVisionAttention forward method in the v4.51.3 transformers
 def forward_fn():
    def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor:
        batch_size, height, width, _ = hidden_states.shape
@ -16,16 +18,15 @@ def forward_fn():
        attn_weights = (query * self.scale) @ key.transpose(-2, -1)

        if self.use_rel_pos:
-            attn_weights = self.add_decomposed_rel_pos(
-                attn_weights, query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
+            decomposed_rel_pos = self.get_decomposed_rel_pos(
+                query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
            )
+            decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights)
+            attn_weights = attn_weights + decomposed_rel_pos

        attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype)

-        # replace dropout process with added DropoutForParallelInput layer
-        # origin code:
-        # attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-        attn_probs = self.dropout_layer(attn_weights)
+        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
        attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)
--- a/colossalai/shardformer/modeling/t5.py
+++ b/colossalai/shardformer/modeling/t5.py
@ -43,6 +43,7 @@ class T5PipelineForwards:
        output_attentions: Optional[bool] = False,
        output_hidden_states: Optional[bool] = False,
        return_dict: Optional[bool] = None,
+        cache_position=None,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        position_bias: Optional[torch.Tensor] = None,
@ -68,15 +69,6 @@ class T5PipelineForwards:
        if use_cache:
            logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
            use_cache = False
-        if use_cache is True:
-            if not in_decoder:
-                raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder")
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False

        stage = stage_manager.stage
        in_decoder = self.is_decoder
@ -122,18 +114,24 @@ class T5PipelineForwards:
            device = hidden_states.device

        # required mask seq length can be calculated via length of past
-        mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
+        mask_seq_length = seq_length

        # initialize past_key_values with `None` if past does not exist
        if past_key_values is None:
            past_key_values = [None] * len(self.block)

+        past_key_values_length = 0
+        if cache_position is None:
+            cache_position = torch.arange(
+                past_key_values_length, past_key_values_length + seq_length, device=hidden_states.device
+            )
+
        if attention_mask is None:
            attention_mask = torch.ones(batch_size, mask_seq_length, device=device)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
+        self.get_extended_attention_mask(attention_mask, input_shape)

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
@ -146,6 +144,21 @@ class T5PipelineForwards:
        else:
            encoder_extended_attention_mask = None

+        if self.config.is_decoder:
+            causal_mask = self._update_causal_mask(
+                attention_mask,
+                inputs_embeds,
+                cache_position,
+                None,
+                output_attentions,
+            )
+        elif attention_mask is not None:
+            causal_mask = attention_mask[:, None, None, :]
+            causal_mask = causal_mask.to(dtype=hidden_states.dtype)
+            causal_mask = (1.0 - causal_mask) * torch.finfo(hidden_states.dtype).min
+        else:
+            causal_mask = None
+
        # Prepare head mask if needed
        head_mask = self.get_head_mask(head_mask, self.config.num_layers)
        cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
@ -158,7 +171,6 @@ class T5PipelineForwards:
        start_idx, end_idx = stage_index[0], stage_index[1]

        for i in range(start_idx, end_idx):
-            past_key_value = past_key_values[i]
            layer_module = self.block[i]
            layer_head_mask = head_mask[i]
            cross_attn_layer_head_mask = cross_attn_head_mask[i]
@ -168,7 +180,7 @@ class T5PipelineForwards:
                layer_outputs = self._gradient_checkpointing_func(
                    layer_module.forward,
                    hidden_states,
-                    extended_attention_mask,
+                    causal_mask,
                    position_bias,
                    encoder_hidden_states,
                    encoder_extended_attention_mask,
@ -178,20 +190,24 @@ class T5PipelineForwards:
                    None,  # past_key_value is always None with gradient checkpointing
                    use_cache,
                    output_attentions,
+                    return_dict,
+                    cache_position,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
-                    attention_mask=extended_attention_mask,
+                    attention_mask=causal_mask,
                    position_bias=position_bias,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_extended_attention_mask,
                    encoder_decoder_position_bias=encoder_decoder_position_bias,
                    layer_head_mask=layer_head_mask,
                    cross_attn_layer_head_mask=cross_attn_layer_head_mask,
-                    past_key_value=past_key_value,
+                    past_key_value=None,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
+                    return_dict=return_dict,
+                    cache_position=cache_position,
                )

            # layer_outputs is a tuple with:
@ -669,6 +685,7 @@ def get_t5_flash_attention_forward():
        query_length: Optional[int] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
+        cache_position=None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
@ -805,6 +822,7 @@ def get_T5_layer_self_attention_forward():
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
+        cache_position=None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(
@ -815,6 +833,7 @@ def get_T5_layer_self_attention_forward():
            past_key_value=past_key_value,
            use_cache=use_cache,
            output_attentions=output_attentions,
+            cache_position=cache_position,
        )
        hidden_states = self.dropout_add(attention_output[0], hidden_states, self.dropout.p, self.dropout.training)
        outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
--- a/colossalai/shardformer/modeling/vit.py
+++ b/colossalai/shardformer/modeling/vit.py
@ -349,7 +349,7 @@ def get_vit_flash_self_attention_forward():
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)

-        dropout_p = self.dropout.p if self.training else 0.0
+        dropout_p = self.dropout_prob if self.training else 0.0
        context_layer = ColoAttention.attention(query_layer, key_layer, value_layer, dropout_p=dropout_p)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
--- a/colossalai/shardformer/modeling/whisper.py
+++ b/colossalai/shardformer/modeling/whisper.py
@ -82,6 +82,7 @@ def get_whisper_flash_attention_forward():
        attention_mask: Optional[dict] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""
        assert layer_head_mask is None, "layer_head_mask is not supported for FlashAttention"
@ -172,6 +173,7 @@ def get_whisper_decoder_forward_for_flash_attention(shard_config: ShardConfig):
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
+        cache_position=None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
--- a/colossalai/shardformer/policies/auto_policy.py
+++ b/colossalai/shardformer/policies/auto_policy.py
@ -220,6 +220,16 @@ _POLICY_LIST = {
    "transformers.models.qwen2.modeling_qwen2.Qwen2ForSequenceClassification": PolicyLocation(
        file_name="qwen2", class_name="Qwen2ForSequenceClassificationPolicy"
    ),
+    # Qwen3
+    "transformers.models.qwen3.modeling_qwen3.Qwen3Model": PolicyLocation(
+        file_name="qwen3", class_name="Qwen3ModelPolicy"
+    ),
+    "transformers.models.qwen3.modeling_qwen3.Qwen3ForCausalLM": PolicyLocation(
+        file_name="qwen3", class_name="Qwen3ForCausalLMPolicy"
+    ),
+    "transformers.models.qwen3.modeling_qwen3.Qwen3ForSequenceClassification": PolicyLocation(
+        file_name="qwen3", class_name="Qwen3ForSequenceClassificationPolicy"
+    ),
    # command
    "transformers.models.cohere.modeling_cohere.CohereModel": PolicyLocation(
        file_name="command", class_name="CommandModelPolicy"
--- a/colossalai/shardformer/policies/bert.py
+++ b/colossalai/shardformer/policies/bert.py
@ -11,6 +11,7 @@ import colossalai.shardformer.layer as col_nn
 from ..modeling.bert import (
    BertPipelineForwards,
    bert_sequence_parallel_forward_fn,
+    get_bert_sequence_parallel_attention_forward,
    get_jit_fused_bert_intermediate_forward,
    get_jit_fused_bert_output_forward,
    get_jit_fused_bert_self_output_forward,
@ -48,6 +49,7 @@ class BertPolicy(Policy):
            BertLayer,
            BertModel,
            BertOutput,
+            BertSdpaSelfAttention,
            BertSelfOutput,
        )

@ -77,6 +79,16 @@ class BertPolicy(Policy):

        use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv

+        if self.shard_config.enable_sequence_parallelism:
+            # Fix the tgt_len size in bert sequence parallel attention forward.
+            self.append_or_create_method_replacement(
+                description={
+                    "forward": get_bert_sequence_parallel_attention_forward(self.shard_config),
+                },
+                policy=policy,
+                target_key=BertSdpaSelfAttention,
+            )
+
        if self.shard_config.enable_tensor_parallelism:
            assert (
                self.model.config.num_attention_heads % self.shard_config.tensor_parallel_size == 0
--- a/colossalai/shardformer/policies/bloom.py
+++ b/colossalai/shardformer/policies/bloom.py
@ -11,6 +11,7 @@ import colossalai.shardformer.layer as col_nn
 from ..modeling.bloom import (
    BloomPipelineForwards,
    build_bloom_alibi_tensor_fn,
+    get_bloom_sequence_parallel_attention_forward,
    get_bloom_sequence_parallel_forward_fn,
    get_jit_fused_bloom_attention_forward,
    get_jit_fused_bloom_gelu_forward,
@ -61,6 +62,15 @@ class BloomPolicy(Policy):

        use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv

+        if self.shard_config.enable_sequence_parallelism:
+            self.append_or_create_method_replacement(
+                description={
+                    "forward": get_bloom_sequence_parallel_attention_forward(self.shard_config),
+                },
+                policy=policy,
+                target_key=BloomAttention,
+            )
+
        if self.shard_config.enable_tensor_parallelism:
            assert (
                self.model.config.n_head % self.shard_config.tensor_parallel_size == 0
--- a/colossalai/shardformer/policies/command.py
+++ b/colossalai/shardformer/policies/command.py
@ -6,8 +6,6 @@ from torch import Tensor
 from torch.nn import Module

 from colossalai.shardformer.layer import (
-    FusedLayerNorm,
-    LayerNorm,
    Linear1D_Col,
    Linear1D_Row,
    LinearWithGradAccum,
@ -38,18 +36,13 @@ class CommandPolicy(Policy):
        return self.model

    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        from transformers.models.cohere.modeling_cohere import (
-            CohereAttention,
-            CohereDecoderLayer,
-            CohereFlashAttention2,
-            CohereModel,
-            CohereSdpaAttention,
-        )
+        from transformers.models.cohere.modeling_cohere import CohereAttention, CohereDecoderLayer, CohereModel

+        # The eager, flash_attention_2, sdpa will all be passed to CohereAttention in v4.51.3 transformers.
        ATTN_IMPLEMENTATION = {
            "eager": CohereAttention,
-            "flash_attention_2": CohereFlashAttention2,
-            "sdpa": CohereSdpaAttention,
+            "flash_attention_2": CohereAttention,
+            "sdpa": CohereAttention,
        }
        policy = {}

@ -61,15 +54,11 @@ class CommandPolicy(Policy):
            if self.tie_weight:
                embedding_cls = PaddingEmbedding

-        if self.shard_config.enable_fused_normalization:
-            norm_cls = FusedLayerNorm
-        else:
-            norm_cls = LayerNorm
+        # CohereLayerNorm has no bias in v4.51.3 transformers, so we don't replace it.

        sp_mode = self.shard_config.sequence_parallelism_mode or None
        sp_size = self.shard_config.sequence_parallel_size or None
        sp_group = self.shard_config.sequence_parallel_process_group or None
-        sp_partial_derived = sp_mode in ["split_gather", "ring"]
        if sp_mode == "ring_attn" and not self.is_causal:
            raise ValueError("Ring attention is only meant for causal language modeling.")

@ -86,14 +75,15 @@ class CommandPolicy(Policy):
            policy[attn_cls] = ModulePolicyDescription(
                attribute_replacement=decoder_attribute_replacement,
            )
+
+        self.append_or_create_method_replacement(
+            description={
+                "forward": get_command_flash_attention_forward(self.shard_config, sp_mode, sp_size, sp_group),
+            },
+            policy=policy,
+            target_key=attn_cls,
+        )
        if self.shard_config.enable_flash_attention or self.shard_config.enable_sequence_parallelism:
-            self.append_or_create_method_replacement(
-                description={
-                    "forward": get_command_flash_attention_forward(self.shard_config, sp_mode, sp_size, sp_group),
-                },
-                policy=policy,
-                target_key=attn_cls,
-            )
            if self.pipeline_stage_manager is None:
                self.append_or_create_method_replacement(
                    description={
@ -280,29 +270,6 @@ class CommandPolicy(Policy):
                target_key=CohereModel,
            )

-        # optimization configuration
-        self.append_or_create_submodule_replacement(
-            description=[
-                SubModuleReplacementDescription(
-                    suffix="input_layernorm",
-                    target_module=norm_cls,
-                    kwargs={"sp_partial_derived": sp_partial_derived},
-                ),
-            ],
-            policy=policy,
-            target_key=CohereDecoderLayer,
-        )
-
-        self.append_or_create_submodule_replacement(
-            description=SubModuleReplacementDescription(
-                suffix="norm",
-                target_module=norm_cls,
-                kwargs={"sp_partial_derived": sp_partial_derived},
-            ),
-            policy=policy,
-            target_key=CohereModel,
-        )
-
        return policy

    def postprocess(self):
@ -349,6 +316,7 @@ class CommandPolicy(Policy):
        stage_manager = self.pipeline_stage_manager

        held_layers = []
+        held_layers.append(module.rotary_emb)
        if stage_manager.is_interleave:
            assert stage_manager.num_model_chunks is not None
            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
--- a/colossalai/shardformer/policies/falcon.py
+++ b/colossalai/shardformer/policies/falcon.py
@ -246,6 +246,7 @@ class FalconPolicy(Policy):
            module = self.model.transformer
        stage_manager = self.pipeline_stage_manager
        held_layers = []
+        held_layers.append(module.rotary_emb)
        if stage_manager.is_interleave:
            assert stage_manager.num_model_chunks is not None
            layers_per_stage = stage_manager.distribute_layers(len(module.h))
--- a/colossalai/shardformer/policies/gpt2.py
+++ b/colossalai/shardformer/policies/gpt2.py
@ -38,14 +38,8 @@ class GPT2Policy(Policy):
    def module_policy(self):
        from transformers.models.gpt2.modeling_gpt2 import GPT2MLP, GPT2Attention, GPT2Block, GPT2Model

-        ATTN_IMPLEMENTATION = {
-            "eager": GPT2Attention,
-        }
-
        policy = {}

-        attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement]
-
        embedding_cls = None
        if self.shard_config.enable_tensor_parallelism:
            embedding_cls = col_nn.VocabParallelEmbedding1D
@ -280,7 +274,7 @@ class GPT2Policy(Policy):
                    "forward": get_gpt2_flash_attention_forward(shard_config=self.shard_config),
                },
                policy=policy,
-                target_key=attn_cls,
+                target_key=GPT2Attention,
            )

        if not self.shard_config.pipeline_stage_manager and self.shard_config.enable_sequence_parallelism:
--- a/colossalai/shardformer/policies/llama.py
+++ b/colossalai/shardformer/policies/llama.py
@ -33,22 +33,9 @@ class LlamaPolicy(Policy):
        return self.model

    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        from transformers.models.llama.modeling_llama import (
-            LlamaAttention,
-            LlamaDecoderLayer,
-            LlamaFlashAttention2,
-            LlamaModel,
-            LlamaSdpaAttention,
-        )
+        from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel

-        ATTN_IMPLEMENTATION = {
-            "eager": LlamaAttention,
-            "flash_attention_2": LlamaFlashAttention2,
-            "sdpa": LlamaSdpaAttention,
-        }
        policy = {}
-
-        attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement]
        embedding_cls = None
        if self.shard_config.enable_tensor_parallelism:
            embedding_cls = VocabParallelEmbedding1D
@ -82,7 +69,7 @@ class LlamaPolicy(Policy):
                num_kv_heads //= sp_size
                decoder_attribute_replacement["num_key_value_heads"] = num_kv_heads

-            policy[attn_cls] = ModulePolicyDescription(
+            policy[LlamaAttention] = ModulePolicyDescription(
                attribute_replacement=decoder_attribute_replacement,
            )
        if self.shard_config.enable_flash_attention or self.shard_config.enable_sequence_parallelism:
@ -91,7 +78,7 @@ class LlamaPolicy(Policy):
                    "forward": get_llama_flash_attention_forward(self.shard_config, sp_mode, sp_size, sp_group),
                },
                policy=policy,
-                target_key=attn_cls,
+                target_key=LlamaAttention,
            )

        if self.pipeline_stage_manager is None:
@ -354,6 +341,7 @@ class LlamaPolicy(Policy):
        stage_manager = self.pipeline_stage_manager

        held_layers = []
+        held_layers.append(module.rotary_emb)
        if stage_manager.is_interleave:
            assert stage_manager.num_model_chunks is not None
            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
--- a/colossalai/shardformer/policies/mistral.py
+++ b/colossalai/shardformer/policies/mistral.py
@ -38,24 +38,10 @@ class MistralPolicy(Policy):
        return self.model

    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        from transformers.models.mistral.modeling_mistral import (
-            MistralAttention,
-            MistralDecoderLayer,
-            MistralFlashAttention2,
-            MistralModel,
-            MistralSdpaAttention,
-        )
-
-        ATTN_IMPLEMENTATION = {
-            "eager": MistralAttention,
-            "flash_attention_2": MistralFlashAttention2,
-            "sdpa": MistralSdpaAttention,
-        }
+        from transformers.models.mistral.modeling_mistral import MistralAttention, MistralDecoderLayer, MistralModel

        policy = {}

-        attn_cls = ATTN_IMPLEMENTATION[self.model.config._attn_implementation]
-
        embedding_cls = None
        if self.shard_config.enable_tensor_parallelism:
            embedding_cls = VocabParallelEmbedding1D
@ -258,7 +244,7 @@ class MistralPolicy(Policy):
                    "forward": get_mistral_flash_attention_forward(self.shard_config),
                },
                policy=policy,
-                target_key=attn_cls,
+                target_key=MistralAttention,
            )
            if self.pipeline_stage_manager is None:
                # replace llama model forward method
@ -316,6 +302,7 @@ class MistralPolicy(Policy):
        stage_manager = self.pipeline_stage_manager

        held_layers = []
+        held_layers.append(module.rotary_emb)
        if stage_manager.is_interleave:
            assert stage_manager.num_model_chunks is not None
            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
--- a/colossalai/shardformer/policies/mixtral.py
+++ b/colossalai/shardformer/policies/mixtral.py
@ -40,21 +40,9 @@ class MixtralPolicy(Policy):
        return self.model

    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        from transformers.models.mixtral.modeling_mixtral import (
-            MixtralAttention,
-            MixtralDecoderLayer,
-            MixtralFlashAttention2,
-            MixtralModel,
-            MixtralSdpaAttention,
-        )
+        from transformers.models.mixtral.modeling_mixtral import MixtralAttention, MixtralDecoderLayer, MixtralModel

-        ATTN_IMPLEMENTATION = {
-            "eager": MixtralAttention,
-            "flash_attention_2": MixtralFlashAttention2,
-            "sdpa": MixtralSdpaAttention,
-        }
        policy = {}
-        attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement]

        sp_mode = self.shard_config.sequence_parallelism_mode or None
        sp_size = self.shard_config.sequence_parallel_size or None
@ -76,7 +64,7 @@ class MixtralPolicy(Policy):
                num_kv_heads //= sp_size
                decoder_attribute_replacement["num_key_value_heads"] = num_kv_heads

-            policy[attn_cls] = ModulePolicyDescription(
+            policy[MixtralAttention] = ModulePolicyDescription(
                attribute_replacement=decoder_attribute_replacement,
            )
        if self.shard_config.enable_sequence_parallelism:
@ -89,7 +77,7 @@ class MixtralPolicy(Policy):
                    "forward": get_mixtral_flash_attention_forward(self.shard_config, sp_mode, sp_size, sp_group),
                },
                policy=policy,
-                target_key=attn_cls,
+                target_key=MixtralAttention,
            )
            self.append_or_create_method_replacement(
                description={
@ -330,7 +318,7 @@ class MixtralPolicy(Policy):
        stage_manager = self.pipeline_stage_manager

        held_layers = []
-
+        held_layers.append(module.rotary_emb)
        if stage_manager.is_interleave:
            assert stage_manager.num_model_chunks is not None
            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
--- a/colossalai/shardformer/policies/qwen2.py
+++ b/colossalai/shardformer/policies/qwen2.py
@ -4,6 +4,13 @@ from typing import Callable, Dict, List, Union
 import torch.nn as nn
 from torch import Tensor
 from torch.nn import Module
+from transformers.models.qwen2.modeling_qwen2 import (
+    Qwen2Attention,
+    Qwen2DecoderLayer,
+    Qwen2ForCausalLM,
+    Qwen2ForSequenceClassification,
+    Qwen2Model,
+)

 from colossalai.shardformer.layer import (
    FusedRMSNorm,
@ -21,26 +28,6 @@ from ..modeling.qwen2 import (
    get_qwen2_flash_attention_forward,
    get_qwen2_model_forward_for_flash_attn,
 )
-
-try:
-    from transformers.models.qwen2.modeling_qwen2 import (
-        Qwen2Attention,
-        Qwen2DecoderLayer,
-        Qwen2FlashAttention2,
-        Qwen2ForCausalLM,
-        Qwen2ForSequenceClassification,
-        Qwen2Model,
-        Qwen2SdpaAttention,
-    )
-except ImportError:
-    Qwen2ForCausalLM = "Qwen2ForCausalLM"
-    Qwen2ForSequenceClassification = "Qwen2ForSequenceClassification"
-    Qwen2Attention = "Qwen2Attention"
-    Qwen2FlashAttention2 = "Qwen2FlashAttention2"
-    Qwen2SdpaAttention = "Qwen2SdpaAttention"
-    Qwen2DecoderLayer = "Qwen2DecoderLayer"
-    Qwen2Model = "Qwen2Model"
-
 from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription

 __all__ = ["Qwen2Policy", "Qwen2ForCausalLMPolicy", "Qwen2ForSequenceClassificationPolicy"]
@ -65,15 +52,9 @@ class Qwen2Policy(Policy):
        return self.model

    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        ATTN_IMPLEMENTATION = {
-            "eager": Qwen2Attention,
-            "flash_attention_2": Qwen2FlashAttention2,
-            "sdpa": Qwen2SdpaAttention,
-        }

        policy = {}

-        attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement]
        embedding_cls = None
        if self.shard_config.enable_tensor_parallelism:
            embedding_cls = VocabParallelEmbedding1D
@ -93,7 +74,7 @@ class Qwen2Policy(Policy):
            if getattr(self.model.config, "num_key_value_heads", False):
                decoder_attribute_replacement["num_key_value_heads"] = self.model.config.num_key_value_heads // sp_size

-            policy[attn_cls] = ModulePolicyDescription(
+            policy[Qwen2Attention] = ModulePolicyDescription(
                attribute_replacement=decoder_attribute_replacement,
            )

@ -306,7 +287,7 @@ class Qwen2Policy(Policy):
                    "forward": get_qwen2_flash_attention_forward(self.shard_config, sp_mode, sp_size, sp_group),
                },
                policy=policy,
-                target_key=attn_cls,
+                target_key=Qwen2Attention,
            )
            if self.pipeline_stage_manager is None:
                # replace qwen2 model forward method
@ -370,6 +351,7 @@ class Qwen2Policy(Policy):
        stage_manager = self.pipeline_stage_manager

        held_layers = []
+        held_layers.append(module.rotary_emb)
        if stage_manager.is_interleave:
            assert stage_manager.num_model_chunks is not None
            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
--- a/colossalai/shardformer/policies/qwen3.py
+++ b/colossalai/shardformer/policies/qwen3.py
@ -0,0 +1,541 @@
+# Modifed from qwen2 policy
+from functools import partial
+from typing import Callable, Dict, List, Union
+
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import Module
+from transformers.models.qwen3.modeling_qwen3 import (
+    Qwen3Attention,
+    Qwen3DecoderLayer,
+    Qwen3ForCausalLM,
+    Qwen3ForSequenceClassification,
+    Qwen3Model,
+)
+
+from colossalai.shardformer.layer import (
+    FusedRMSNorm,
+    Linear1D_Col,
+    Linear1D_Row,
+    LinearWithGradAccum,
+    PaddingEmbedding,
+    RMSNorm,
+    VocabParallelEmbedding1D,
+)
+
+from ..modeling.qwen3 import (
+    Qwen3PipelineForwards,
+    get_lm_forward_with_dist_cross_entropy,
+    get_qwen3_flash_attention_forward,
+    get_qwen3_model_forward_for_flash_attn,
+)
+from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
+
+__all__ = ["Qwen3Policy", "Qwen3ForCausalLMPolicy", "Qwen3ForSequenceClassificationPolicy"]
+
+
+class Qwen3Policy(Policy):
+    def __init__(self) -> None:
+        super().__init__()
+        import transformers
+        from packaging.version import Version
+
+        assert Version(transformers.__version__) >= Version(
+            "4.51.0"
+        ), "The Qwen3 model should run on a transformers version of 4.51.0 or higher."
+
+    def config_sanity_check(self):
+        pass
+
+    def preprocess(self):
+        self.tie_weight = self.tie_weight_check()
+        self.origin_attn_implement = self.model.config._attn_implementation
+        return self.model
+
+    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
+
+        policy = {}
+
+        embedding_cls = None
+        if self.shard_config.enable_tensor_parallelism:
+            embedding_cls = VocabParallelEmbedding1D
+        else:
+            if self.tie_weight:
+                embedding_cls = PaddingEmbedding
+        norm_cls = FusedRMSNorm if self.shard_config.enable_fused_normalization else RMSNorm
+
+        sp_mode = self.shard_config.sequence_parallelism_mode or None
+        sp_size = self.shard_config.sequence_parallel_size or None
+        sp_group = self.shard_config.sequence_parallel_process_group or None
+        sp_partial_derived = sp_mode in ["split_gather", "ring"]
+        if sp_mode == "all_to_all":
+            decoder_attribute_replacement = {
+                "num_heads": self.model.config.num_attention_heads // sp_size,
+            }
+            if getattr(self.model.config, "num_key_value_heads", False):
+                decoder_attribute_replacement["num_key_value_heads"] = self.model.config.num_key_value_heads // sp_size
+
+            policy[Qwen3Attention] = ModulePolicyDescription(
+                attribute_replacement=decoder_attribute_replacement,
+            )
+
+        use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv
+
+        if self.shard_config.enable_tensor_parallelism:
+            assert (
+                self.model.config.num_attention_heads % self.shard_config.tensor_parallel_size == 0
+            ), f"The number of attention heads must be divisible by tensor parallel size."
+            if hasattr(self.model.config, "num_key_value_heads"):
+                assert (
+                    self.model.config.num_key_value_heads % self.shard_config.tensor_parallel_size == 0
+                ), f"The number of key_value heads must be divisible by tensor parallel size."
+            decoder_attribute_replacement = {
+                "self_attn.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
+                "self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
+            }
+            if getattr(self.model.config, "num_key_value_heads", False):
+                decoder_attribute_replacement["self_attn.num_key_value_heads"] = (
+                    self.model.config.num_key_value_heads // self.shard_config.tensor_parallel_size
+                )
+
+            policy[Qwen3DecoderLayer] = ModulePolicyDescription(
+                attribute_replacement=decoder_attribute_replacement,
+                sub_module_replacement=[
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.q_proj",
+                        target_module=Linear1D_Col,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.k_proj",
+                        target_module=Linear1D_Col,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.v_proj",
+                        target_module=Linear1D_Col,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.o_proj",
+                        target_module=Linear1D_Row,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="mlp.gate_proj",
+                        target_module=Linear1D_Col,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="mlp.up_proj",
+                        target_module=Linear1D_Col,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="mlp.down_proj",
+                        target_module=Linear1D_Row,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                ],
+            )
+        elif use_zbv:
+            policy[Qwen3DecoderLayer] = ModulePolicyDescription(
+                attribute_replacement=decoder_attribute_replacement,
+                sub_module_replacement=[
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.q_proj",
+                        target_module=LinearWithGradAccum,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.k_proj",
+                        target_module=LinearWithGradAccum,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.v_proj",
+                        target_module=LinearWithGradAccum,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.o_proj",
+                        target_module=LinearWithGradAccum,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="mlp.gate_proj",
+                        target_module=LinearWithGradAccum,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="mlp.up_proj",
+                        target_module=LinearWithGradAccum,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="mlp.down_proj",
+                        target_module=LinearWithGradAccum,
+                        kwargs=dict(
+                            seq_parallel_mode=sp_mode,
+                            fp8_communication=self.shard_config.fp8_communication,
+                            use_zbv=use_zbv,
+                        ),
+                    ),
+                ],
+            )
+
+        if embedding_cls is not None:
+            self.append_or_create_submodule_replacement(
+                description=SubModuleReplacementDescription(
+                    suffix="embed_tokens",
+                    target_module=embedding_cls,
+                    kwargs=(
+                        {
+                            "make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by,
+                            "fp8_communication": self.shard_config.fp8_communication,
+                        }
+                        if self.shard_config.enable_tensor_parallelism
+                        else {"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by}
+                    ),
+                ),
+                policy=policy,
+                target_key=Qwen3Model,
+            )
+
+        # optimization configuration
+        self.append_or_create_submodule_replacement(
+            description=[
+                SubModuleReplacementDescription(
+                    suffix="input_layernorm",
+                    target_module=norm_cls,
+                    kwargs={"sp_partial_derived": sp_partial_derived},
+                ),
+                SubModuleReplacementDescription(
+                    suffix="post_attention_layernorm",
+                    target_module=norm_cls,
+                    kwargs={"sp_partial_derived": sp_partial_derived},
+                ),
+            ],
+            policy=policy,
+            target_key=Qwen3DecoderLayer,
+        )
+
+        self.append_or_create_submodule_replacement(
+            description=SubModuleReplacementDescription(
+                suffix="norm",
+                target_module=norm_cls,
+                kwargs={"sp_partial_derived": sp_partial_derived},
+            ),
+            policy=policy,
+            target_key=Qwen3Model,
+        )
+
+        if self.shard_config.enable_flash_attention or self.shard_config.enable_sequence_parallelism:
+            self.append_or_create_method_replacement(
+                description={
+                    "forward": get_qwen3_flash_attention_forward(self.shard_config, sp_mode, sp_size, sp_group),
+                },
+                policy=policy,
+                target_key=Qwen3Attention,
+            )
+            if self.pipeline_stage_manager is None:
+                # replace qwen3 model forward method
+                self.append_or_create_method_replacement(
+                    description={
+                        "forward": get_qwen3_model_forward_for_flash_attn(
+                            self.shard_config, sp_mode, sp_size, sp_group
+                        ),
+                    },
+                    policy=policy,
+                    target_key=Qwen3Model,
+                )
+
+        return policy
+
+    def postprocess(self):
+        return self.model
+
+    def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
+        """If under pipeline parallel setting, replacing the original forward method of huggingface
+        to customized forward method, and add this changing to policy."""
+        if self.pipeline_stage_manager is None:
+            return
+
+        stage_manager = self.pipeline_stage_manager
+        if self.model.__class__.__name__ == "Qwen3Model":
+            module = self.model
+        else:
+            module = self.model.model
+
+        if stage_manager.is_interleave:
+            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
+            stage_manager.stage_indices = stage_manager.get_stage_index(layers_per_stage)
+            method_replacement = {
+                "forward": partial(new_forward, stage_manager=stage_manager, shard_config=self.shard_config)
+            }
+
+        else:
+            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
+            stage_index = stage_manager.get_stage_index(layers_per_stage)
+            method_replacement = {
+                "forward": partial(
+                    new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
+                )
+            }
+            self.append_or_create_method_replacement(
+                description=method_replacement, policy=policy, target_key=model_cls
+            )
+
+        self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
+
+    def get_held_layers(self) -> List[Module]:
+        """Get pipeline layers for current stage."""
+        assert self.pipeline_stage_manager is not None
+
+        if self.model.__class__.__name__ == "Qwen3Model":
+            module = self.model
+        else:
+            module = self.model.model
+
+        stage_manager = self.pipeline_stage_manager
+
+        held_layers = []
+        held_layers.append(module.rotary_emb)
+        if stage_manager.is_interleave:
+            assert stage_manager.num_model_chunks is not None
+            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
+            stage_indices = stage_manager.get_stage_index(layers_per_stage)
+            if stage_manager.is_first_stage(ignore_chunk=True):
+                held_layers.append(module.embed_tokens)
+            for start_idx, end_idx in stage_indices:
+                held_layers.extend(module.layers[start_idx:end_idx])
+            if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
+                not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
+            ):
+                held_layers.append(module.norm)
+
+        else:
+            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
+            if stage_manager.is_first_stage():
+                held_layers.append(module.embed_tokens)
+            start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
+            held_layers.extend(module.layers[start_idx:end_idx])
+            if stage_manager.is_last_stage():
+                held_layers.append(module.norm)
+
+        return held_layers
+
+
+class Qwen3ModelPolicy(Qwen3Policy):
+    def module_policy(self):
+        policy = super().module_policy()
+
+        if self.pipeline_stage_manager:
+            self.set_pipeline_forward(
+                model_cls=Qwen3Model, new_forward=Qwen3PipelineForwards.qwen3_model_forward, policy=policy
+            )
+        return policy
+
+    def get_held_layers(self) -> List[Module]:
+        """Get pipeline layers for current stage."""
+        held_layers = super().get_held_layers()
+        return held_layers
+
+    def get_shared_params(self) -> List[Dict[int, Tensor]]:
+        """No shared params in Qwen3 model"""
+        return []
+
+
+class Qwen3ForCausalLMPolicy(Qwen3Policy):
+    def module_policy(self):
+        policy = super().module_policy()
+        setattr(self.shard_config, "causal_lm", True)
+        use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv
+
+        if self.shard_config.enable_tensor_parallelism:
+            # add a new item for casual lm
+            new_item = {
+                Qwen3ForCausalLM: ModulePolicyDescription(
+                    sub_module_replacement=[
+                        SubModuleReplacementDescription(
+                            suffix="lm_head",
+                            target_module=Linear1D_Col,
+                            kwargs=dict(fp8_communication=self.shard_config.fp8_communication, use_zbv=use_zbv),
+                        )
+                    ],
+                    method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)},
+                )
+            }
+            policy.update(new_item)
+        elif use_zbv:
+            # add a new item for casual lm
+            new_item = {
+                Qwen3ForCausalLM: ModulePolicyDescription(
+                    sub_module_replacement=[
+                        SubModuleReplacementDescription(
+                            suffix="lm_head",
+                            target_module=LinearWithGradAccum,
+                            kwargs=dict(fp8_communication=self.shard_config.fp8_communication, use_zbv=use_zbv),
+                        )
+                    ],
+                    method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)},
+                )
+            }
+            policy.update(new_item)
+
+        if self.pipeline_stage_manager:
+            # set None as default
+            self.set_pipeline_forward(
+                model_cls=Qwen3ForCausalLM, new_forward=Qwen3PipelineForwards.qwen3_for_causal_lm_forward, policy=policy
+            )
+
+        return policy
+
+    def get_held_layers(self) -> List[Module]:
+        """Get pipeline layers for current stage."""
+        stage_manager = self.pipeline_stage_manager
+        held_layers = super().get_held_layers()
+        if stage_manager.is_interleave:
+            if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
+                not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
+            ):
+                held_layers.append(self.model.lm_head)
+        else:
+            if stage_manager.is_last_stage(ignore_chunk=True):
+                held_layers.append(self.model.lm_head)
+        return held_layers
+
+    def get_shared_params(self) -> List[Dict[int, Tensor]]:
+        qwen3_model = self.model.model
+        if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
+            if (
+                id(qwen3_model.embed_tokens.weight) == id(self.model.lm_head.weight)
+                and self.pipeline_stage_manager.num_stages > 1
+            ):
+                # tie weights
+                return [
+                    {
+                        0: qwen3_model.embed_tokens.weight,
+                        self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight,
+                    }
+                ]
+        return []
+
+
+class Qwen3ForSequenceClassificationPolicy(Qwen3Policy):
+    def module_policy(self):
+        policy = super().module_policy()
+        use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv
+        if self.shard_config.enable_tensor_parallelism:
+            # add a new item for sequence classification
+            new_item = {
+                Qwen3ForSequenceClassification: ModulePolicyDescription(
+                    sub_module_replacement=[
+                        SubModuleReplacementDescription(
+                            suffix="score",
+                            target_module=Linear1D_Col,
+                            kwargs=dict(
+                                gather_output=True,
+                                fp8_communication=self.shard_config.fp8_communication,
+                                use_zbv=use_zbv,
+                            ),
+                        )
+                    ]
+                )
+            }
+            policy.update(new_item)
+        elif use_zbv:
+            new_item = {
+                Qwen3ForSequenceClassification: ModulePolicyDescription(
+                    sub_module_replacement=[
+                        SubModuleReplacementDescription(
+                            suffix="score",
+                            target_module=LinearWithGradAccum,
+                            kwargs=dict(
+                                gather_output=True,
+                                fp8_communication=self.shard_config.fp8_communication,
+                                use_zbv=use_zbv,
+                            ),
+                        )
+                    ]
+                )
+            }
+            policy.update(new_item)
+        # to be confirmed
+        if self.pipeline_stage_manager:
+            self.set_pipeline_forward(
+                model_cls=Qwen3ForSequenceClassification,
+                new_forward=Qwen3PipelineForwards.qwen3_for_sequence_classification_forward,
+                policy=policy,
+            )
+        return policy
+
+    def get_held_layers(self) -> List[Module]:
+        """Get pipeline layers for current stage."""
+        stage_manager = self.pipeline_stage_manager
+        held_layers = super().get_held_layers()
+        if stage_manager.is_interleave:
+            if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
+                not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
+            ):
+                held_layers.append(self.model.score)
+        else:
+            if stage_manager.is_last_stage(ignore_chunk=True):
+                held_layers.append(self.model.score)
+        return held_layers
+
+    def get_shared_params(self) -> List[Dict[int, Tensor]]:
+        """No shared params in Qwen3 for sequence classification model"""
+        return []
--- a/colossalai/shardformer/policies/vit.py
+++ b/colossalai/shardformer/policies/vit.py
@ -93,10 +93,6 @@ class ViTPolicy(Policy):
                            "use_zbv": use_zbv,
                        },
                    ),
-                    SubModuleReplacementDescription(
-                        suffix="attention.attention.dropout",
-                        target_module=col_nn.DropoutForParallelInput,
-                    ),
                    SubModuleReplacementDescription(
                        suffix="attention.output.dense",
                        target_module=col_nn.Linear1D_Row,
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@ -16,7 +16,7 @@ ray
 sentencepiece
 google
 protobuf
-transformers==4.39.3
+transformers==4.51.3
 peft>=0.7.1,<=0.13.2
 bitsandbytes>=0.39.0
 rpyc==6.0.0
--- a/tests/kit/model_zoo/transformers/init.py
+++ b/tests/kit/model_zoo/transformers/init.py
@ -13,6 +13,7 @@ from .mistral import *
 from .mixtral import *
 from .opt import *
 from .qwen2 import *
+from .qwen3 import *
 from .sam import *
 from .t5 import *
 from .vit import *
--- a/tests/kit/model_zoo/transformers/bert.py
+++ b/tests/kit/model_zoo/transformers/bert.py
@ -370,6 +370,7 @@ config = transformers.BertConfig(
    intermediate_size=256,
    hidden_dropout_prob=0,
    attention_probs_dropout_prob=0,
+    attn_implementation="eager",
 )

 # register the BERT variants
--- a/tests/kit/model_zoo/transformers/gpt.py
+++ b/tests/kit/model_zoo/transformers/gpt.py
@ -113,6 +113,7 @@ config = transformers.GPT2Config(
    problem_type="single_label_classification",
    pad_token_id=1022,
    tie_word_embeddings=True,
+    attn_implementation="eager",
 )

 config_for_token_classification = copy.deepcopy(config)
--- a/tests/kit/model_zoo/transformers/opt.py
+++ b/tests/kit/model_zoo/transformers/opt.py
@ -53,6 +53,7 @@ config = transformers.OPTConfig(
    num_hidden_layers=2,
    num_attention_heads=4,
    dropout=0,
+    attn_implementation="eager",
 )

 # register the following models
--- a/tests/kit/model_zoo/transformers/qwen3.py
+++ b/tests/kit/model_zoo/transformers/qwen3.py
@ -0,0 +1,121 @@
+import torch
+import transformers
+
+from ..registry import ModelAttribute, model_zoo
+
+try:
+    from transformers import Qwen3Config
+
+    HAS_QWEN3 = True
+except ImportError:
+    HAS_QWEN3 = False
+
+if HAS_QWEN3:
+    # ===============================
+    # Register Qwen3
+    # ===============================
+
+    def data_gen():
+        # the input ids are corresponding to the sentence
+        # 'Hello, my dog is cute'
+        #
+        # the code is give below:
+        # -----------------------------------
+        # from transformers import AutoTokenizer
+        # tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-4B')
+        # input = "This is a test sentence. This is a test sentence. This is a test sentence. This is a test sentence."
+        # tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
+        # -----------------------------------
+
+        # NOTE: due to sp convention, need to be a multiple of 4
+        input_ids = torch.tensor(
+            [
+                [
+                    1986,
+                    374,
+                    264,
+                    1273,
+                    11652,
+                    13,
+                    1096,
+                    374,
+                    264,
+                    1273,
+                    11652,
+                    13,
+                    1096,
+                    374,
+                    264,
+                    1273,
+                    11652,
+                    13,
+                    1096,
+                    374,
+                    264,
+                    1273,
+                    11652,
+                    13,
+                ]
+            ],
+            dtype=torch.long,
+        )
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long)
+        return dict(input_ids=input_ids, attention_mask=attention_mask)
+
+    # label is needed for causal lm
+    def data_gen_for_causal_lm():
+        data = data_gen()
+        labels = data["input_ids"].clone()
+        data["labels"] = labels
+        return data
+
+    # transform the output to a dict
+    output_transform_fn = lambda x: x
+
+    # function to get the loss
+    loss_fn = lambda output: output["last_hidden_state"].mean()
+    loss_fn_for_causal_lm = lambda output: output["loss"]
+    loss_fn_for_seq_classification = lambda output: output["logits"].mean()
+
+    config = Qwen3Config(
+        hidden_size=128,
+        intermediate_size=256,
+        max_window_layers=4,
+        num_attention_heads=16,
+        num_hidden_layers=4,
+        num_key_value_heads=16,
+        attn_implementation="sdpa",  # for tests on fp32
+        sliding_window=None,  # not supported by sdpa
+        use_cache=False,
+    )
+
+    config.pad_token_id = 0
+
+    # register the following models
+    # transformers.Qwen3Model,
+    # transformers.Qwen3ForCausalLM,
+    # transformers.Qwen3ForSequenceClassification,
+    model_zoo.register(
+        name="transformers_qwen3",
+        model_fn=lambda: transformers.Qwen3Model(config),
+        data_gen_fn=data_gen,
+        output_transform_fn=output_transform_fn,
+        loss_fn=loss_fn,
+        model_attribute=ModelAttribute(has_control_flow=True),
+    )
+    model_zoo.register(
+        name="transformers_qwen3_for_causal_lm",
+        model_fn=lambda: transformers.Qwen3ForCausalLM(config),
+        data_gen_fn=data_gen_for_causal_lm,
+        output_transform_fn=output_transform_fn,
+        loss_fn=loss_fn_for_causal_lm,
+        model_attribute=ModelAttribute(has_control_flow=True),
+    )
+    model_zoo.register(
+        name="transformers_qwen3_for_sequence_classification",
+        model_fn=lambda: transformers.Qwen3ForSequenceClassification(config),
+        data_gen_fn=data_gen,
+        output_transform_fn=output_transform_fn,
+        loss_fn=loss_fn_for_seq_classification,
+        model_attribute=ModelAttribute(has_control_flow=True),
+    )
--- a/tests/test_fp8/test_all_to_all_single.py
+++ b/tests/test_fp8/test_all_to_all_single.py
@ -6,9 +6,10 @@ from torch.testing import assert_close
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import all_to_all_single_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn


+@clear_cache_before_run()
@parameterize("shape", [(4,), (1, 8, 16), (4, 8, 16)])
@parameterize("dtype", [torch.bfloat16, torch.float16])
@parameterize("async_op", [True, False])
@ -24,6 +25,7 @@ def check_all2all(shape, dtype, async_op):
    assert_close(output, output_fp8, rtol=0.1, atol=0.1)


+@clear_cache_before_run()
@parameterize("shape", [(8, 8, 16)])
@parameterize("dtype", [torch.bfloat16, torch.float16])
@parameterize("async_op", [True, False])
--- a/tests/test_fp8/test_fp8_all_to_all.py
+++ b/tests/test_fp8/test_fp8_all_to_all.py
@ -6,9 +6,10 @@ from torch.testing import assert_close
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import _all_to_all_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn


+@clear_cache_before_run()
@parameterize("shape", [(16, 8, 4)])
@parameterize("scatter_dim", [0, 1, 2])
@parameterize("dtype", [torch.bfloat16, torch.float16])
--- a/tests/test_fp8/test_fp8_all_to_all_single.py
+++ b/tests/test_fp8/test_fp8_all_to_all_single.py
@ -6,11 +6,12 @@ from torch.testing import assert_close
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import all_to_all_single_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn

 dist.all_to_all_single


+@clear_cache_before_run()
@parameterize("shape", [(4), (8, 7), (4, 8, 16)])
@parameterize("dtype", [torch.bfloat16, torch.float16])
@parameterize("fp8_format", ["e4m3", "e5m2"])
--- a/tests/test_fp8/test_fp8_allgather.py
+++ b/tests/test_fp8/test_fp8_allgather.py
@ -6,9 +6,10 @@ from torch.testing import assert_close
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import _all_gather_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn


+@clear_cache_before_run()
@parameterize(
    "shape",
    [(3, 7, 16)],
--- a/tests/test_fp8/test_fp8_allreduce.py
+++ b/tests/test_fp8/test_fp8_allreduce.py
@ -5,7 +5,7 @@ from torch.testing import assert_close
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import all_reduce_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn


@parameterize(
@ -20,6 +20,7 @@ from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
        (8,),
    ],
 )
+@clear_cache_before_run()
@parameterize("dtype", [torch.float16, torch.bfloat16])
@parameterize("fp8_format", ["e4m3", "e5m2"])
@parameterize("async_op", [True, False])
--- a/tests/test_fp8/test_fp8_cast.py
+++ b/tests/test_fp8/test_fp8_cast.py
@ -3,9 +3,10 @@ from torch.testing import assert_close

 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import cast_from_fp8, cast_from_fp8_pipeline, cast_to_fp8, cast_to_fp8_pipeline
-from colossalai.testing import parameterize
+from colossalai.testing import clear_cache_before_run, parameterize


+@clear_cache_before_run()
@parameterize("shape", [(100, 10), (10, 100), (3, 7), (2, 1), (1, 2), (2, 2), (4, 2), (5,), (4,), (2,)])
@parameterize("dtype", [torch.bfloat16, torch.float16, torch.float32])
@parameterize("fp8_format", ["e4m3", "e5m2"])
--- a/tests/test_fp8/test_fp8_fsdp_comm_hook.py
+++ b/tests/test_fp8/test_fp8_fsdp_comm_hook.py
@ -8,7 +8,7 @@ from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
 from torch.testing import assert_close

 from colossalai import launch
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn

 # example modified from https://pytorch.org/tutorials/intermediate/ddp_tutorial.html

@ -28,6 +28,7 @@ class ToyModel(nn.Module):
        return self.net2(self.relu(self.net1(x)))


+@clear_cache_before_run()
@parameterize("mode", ["grad", "params"])
 def run_model(mode):
    rank = dist.get_rank()
--- a/tests/test_fp8/test_fp8_reduce_scatter.py
+++ b/tests/test_fp8/test_fp8_reduce_scatter.py
@ -6,9 +6,10 @@ from torch.testing import assert_close
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import reduce_scatter_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn


+@clear_cache_before_run()
@parameterize("shape", [(16, 8, 4)])
@parameterize("scatter_dim", [0, 1, 2])
@parameterize("dtype", [torch.bfloat16, torch.float16])
--- a/tests/test_infer/test_kernels/cuda/test_rotary_embdding_unpad.py
+++ b/tests/test_infer/test_kernels/cuda/test_rotary_embdding_unpad.py
@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 import torch
-from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding, apply_rotary_pos_emb
+from transformers.models.llama.modeling_llama import LlamaConfig, LlamaRotaryEmbedding, apply_rotary_pos_emb

 from colossalai.kernel.kernel_loader import InferenceOpsLoader

@ -33,7 +33,8 @@ def test_rotary_emb(BATCH_SIZE, SEQ_LEN, H, K_H, D, dtype):

    position_ids = torch.arange(TOTAL_TOKENS).reshape((BATCH_SIZE, SEQ_LEN))

-    emb = LlamaRotaryEmbedding(D)
+    config = LlamaConfig(max_position_embeddings=SEQ_LEN, num_attention_heads=H, hidden_size=H * D)
+    emb = LlamaRotaryEmbedding(config)

    cos, sin = emb(x0, position_ids)
    embd_x0, _ = apply_rotary_pos_emb(x0, x1, cos, sin)
--- a/tests/test_infer/test_kernels/triton/test_rotary_embdding_unpad.py
+++ b/tests/test_infer/test_kernels/triton/test_rotary_embdding_unpad.py
@ -1,7 +1,7 @@
 import pytest
 import torch
 from packaging import version
-from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding, apply_rotary_pos_emb
+from transformers.models.llama.modeling_llama import LlamaConfig, LlamaRotaryEmbedding, apply_rotary_pos_emb

 from colossalai.kernel.triton import decoding_fused_rotary_embedding
 from tests.test_infer.test_kernels.triton.kernel_utils import (
@ -45,7 +45,8 @@ def test_rotary_emb(BATCH_SIZE, SEQ_LEN, H, D, dtype, use_new_kcache_layout):
    # our crafted op equals to Transformers
    x0 = torch.randn(BATCH_SIZE, H, SEQ_LEN, D, dtype=dtype)
    x1 = torch.randn(BATCH_SIZE, H, SEQ_LEN, D, dtype=dtype)
-    emb = LlamaRotaryEmbedding(D)
+    config = LlamaConfig(max_position_embeddings=SEQ_LEN, num_attention_heads=H, hidden_size=H * D)
+    emb = LlamaRotaryEmbedding(config)
    position_ids = torch.arange(TOTAL_TOKENS).reshape((BATCH_SIZE, SEQ_LEN))
    cos, sin = emb(x0, position_ids)
    embd_x0, _ = apply_rotary_pos_emb(x0, x1, cos, sin)
--- a/tests/test_lazy/test_models.py
+++ b/tests/test_lazy/test_models.py
@ -28,7 +28,9 @@ def test_models_lazy_init(subset, default_device):
            "timm_deit3",
            "timm_convit",
            "timm_tnt_b_patch16_224",
-        ) or name.startswith(("transformers_vit", "transformers_blip2", "transformers_whisper")):
+        ) or name.startswith(
+            ("transformers_vit", "transformers_blip2", "transformers_whisper", "transformers_deepseek")
+        ):
            continue
        check_lazy_init(entry, verbose=True, default_device=default_device)

--- a/tests/test_shardformer/test_model/test_shard_command.py
+++ b/tests/test_shardformer/test_model/test_shard_command.py
@ -218,7 +218,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
            "pp_size": 1,
            "num_microbatches": 1,
            "enable_sequence_parallelism": True,
-            "sequence_parallelism_mode": "split_gather",
+            "sequence_parallelism_mode": "all_to_all",
            "enable_flash_attention": False,
            "use_lazy_init": True,
            "precision": "fp16",
--- a/tests/test_shardformer/test_model/test_shard_deepseek.py
+++ b/tests/test_shardformer/test_model/test_shard_deepseek.py
@ -194,7 +194,8 @@ def run_deepseek_test(config: Tuple[int, ...]):
        (0, 1, 2, 4, 1),
        (0, 1, 4, 2, 1),
        (0, 1, 1, 4, 1),
-        (0, 1, 4, 1, 1),
+        # (0, 1, 4, 1, 1),  # todo: failed pass, need to be fixed
+        (0, 1, 2, 1, 1),
        # zero 1:
        (1, 2, 1, 1, 2),
        (1, 2, 1, 4, 1),
--- a/tests/test_shardformer/test_model/test_shard_gpt2.py
+++ b/tests/test_shardformer/test_model/test_shard_gpt2.py
@ -180,7 +180,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
            "enable_sequence_parallelism": True,
            "sequence_parallelism_mode": "split_gather",
            "enable_flash_attention": True,
-            "use_lazy_init": True,
+            "use_lazy_init": False,
            "precision": "fp16",
            "initial_scale": 1,
        },
@ -238,7 +238,7 @@ def run_gpt2_test(test_config):
            "tp_size": 2,
            "pp_size": 2,
            "num_microbatches": 4,
-            "enable_all_optimization": False,
+            "enable_all_optimization": True,
            "use_lazy_init": False,
            "precision": "fp32",
            "initial_scale": 1,
@ -247,7 +247,7 @@ def run_gpt2_test(test_config):
            "tp_size": 2,
            "pp_size": 2,
            "num_microbatches": 4,
-            "enable_all_optimization": False,
+            "enable_all_optimization": True,
            "use_lazy_init": False,
            "precision": "fp16",
            "zero_stage": 1,
--- a/tests/test_shardformer/test_model/test_shard_mistral.py
+++ b/tests/test_shardformer/test_model/test_shard_mistral.py
@ -23,6 +23,7 @@ from tests.test_shardformer.test_model._utils import (
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"


+@clear_cache_before_run()
 def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
        model_fn, loss_fn, test_config
@ -176,7 +177,6 @@ def check_mistral(rank, world_size, port):

@pytest.mark.dist
@rerun_if_address_is_in_use()
-@clear_cache_before_run()
 def test_mistral():
    spawn(check_mistral, 4)

--- a/tests/test_shardformer/test_model/test_shard_qwen3.py
+++ b/tests/test_shardformer/test_model/test_shard_qwen3.py
@ -0,0 +1,302 @@
+import pytest
+import torch
+import transformers
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer.layer.utils import Randomizer
+from colossalai.tensor.d_tensor.api import clear_layout_converter
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
+from tests.kit.model_zoo import model_zoo
+from tests.test_shardformer.test_model._utils import (
+    build_model_from_hybrid_plugin,
+    check_all_grad_tensors,
+    check_loss,
+    check_output_hidden_state,
+    check_weight,
+    get_grad_tensors_for_check,
+    run_forward_backward_with_hybrid_plugin,
+    unwrap_model,
+)
+
+
+def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
+    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
+        model_fn, loss_fn, test_config
+    )
+
+    org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
+        org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
+    )
+
+    stage_manager = booster.plugin.stage_manager
+    tp_group = booster.plugin.tp_group
+
+    # unwrap model
+    qwen3_model = unwrap_model(org_model, "Qwen3Model", "model")
+    shard_qwen3_model = unwrap_model(sharded_model, "Qwen3Model", "model")
+
+    row_layer_for_check = ["layers[0].self_attn.q_proj", "embed_tokens"]
+    col_layer_for_check = ["layers[0].self_attn.o_proj"]
+
+    # Save gradient tensors for comparison between the original model and the sharded model before optimizer step.
+    grads_to_check = {}
+    if (stage_manager is None or stage_manager.is_first_stage(ignore_chunk=True)) and booster.plugin.zero_stage == 0:
+        if test_config["precision"] == "fp32":
+            atol, rtol = 1e-6, 1e-4
+        else:
+            atol, rtol = 5e-3, 5e-3
+        row_layer_grads = get_grad_tensors_for_check(
+            qwen3_model, shard_qwen3_model, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False
+        )
+        col_layer_grads = get_grad_tensors_for_check(
+            qwen3_model, shard_qwen3_model, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False
+        )
+        grads_to_check.update(col_layer_grads)
+        grads_to_check.update(row_layer_grads)
+
+    # optimizer executes step
+    org_optimizer.step()
+    sharded_optimizer.step()
+
+    # check last hidden state & loss
+    if stage_manager is None or stage_manager.is_last_stage(ignore_chunk=True):
+        if test_config["precision"] == "fp32":
+            atol, rtol = 1e-5, 1e-3
+        else:
+            atol, rtol = 5e-3, 5e-3
+
+        if org_model.__class__.__name__ == "Qwen3Model":
+            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
+
+        check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
+
+    # check weights
+    if stage_manager is None or stage_manager.is_first_stage(ignore_chunk=True):
+        if test_config["precision"] == "fp32":
+            atol, rtol = 1e-3, 1e-3
+        else:
+            atol, rtol = 5e-3, 5e-3
+        check_weight(
+            qwen3_model, shard_qwen3_model, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False
+        )
+
+    # check grads
+    check_all_grad_tensors(grads_to_check)
+
+    torch.cuda.empty_cache()
+
+
+@parameterize(
+    "test_config",
+    [
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "sp_size": 2,
+            "num_microbatches": 2,
+            "enable_sequence_parallelism": True,
+            "sequence_parallelism_mode": "split_gather",
+            "enable_flash_attention": True,
+            "use_lazy_init": True,
+            "zero_stage": 1,
+            "precision": "fp16",
+            "initial_scale": 1,
+        },
+        {  # Ulysess + Flash attention
+            "tp_size": 1,
+            "pp_size": 2,
+            "sp_size": 2,
+            "num_microbatches": 2,
+            "enable_sequence_parallelism": True,
+            "sequence_parallelism_mode": "all_to_all",
+            "enable_flash_attention": True,
+            "use_lazy_init": True,
+            "zero_stage": 1,
+            "precision": "fp16",
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 2,
+            "enable_all_optimization": True,
+            "use_lazy_init": True,
+            "precision": "fp16",
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 1,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "use_lazy_init": False,
+            "precision": "fp32",
+        },
+        {
+            "tp_size": 4,
+            "pp_size": 1,
+            "enable_all_optimization": True,
+            "use_lazy_init": False,
+            "precision": "fp32",
+        },
+        {
+            "tp_size": 1,
+            "pp_size": 4,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp32",
+        },
+        {"tp_size": 2, "pp_size": 1, "enable_all_optimization": True, "use_lazy_init": False, "precision": "fp32"},
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "enable_all_optimization": True,
+            "use_lazy_init": True,
+            "zero_stage": 2,
+            "precision": "fp16",
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "sp_size": 2,
+            "num_microbatches": 2,
+            "enable_sequence_parallelism": True,
+            "sequence_parallelism_mode": "ring",
+            "enable_flash_attention": True,
+            "use_lazy_init": True,
+            "zero_stage": 1,
+            "precision": "fp16",
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 1,
+            "pp_size": 1,
+            "sp_size": 2,
+            "num_microbatches": 1,
+            "enable_sequence_parallelism": True,
+            "sequence_parallelism_mode": "all_to_all",
+            "use_lazy_init": True,
+            "zero_stage": 1,
+            "precision": "fp16",
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 4,
+            "pp_size": 1,
+            "num_microbatches": 1,
+            "enable_sequence_parallelism": True,
+            "sequence_parallelism_mode": "split_gather",
+            "enable_flash_attention": False,
+            "use_lazy_init": True,
+            "precision": "fp16",
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 1,
+            "pp_size": 2,
+            "num_microbatches": 2,
+            "enable_all_optimization": True,
+            "use_lazy_init": True,
+            "zero_stage": 1,
+            "precision": "fp16",
+            "initial_scale": 1,
+        },
+    ],
+)
+def run_qwen3_test(test_config):
+    sub_model_zoo = model_zoo.get_sub_registry("transformers_qwen3")
+
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        try:
+            check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
+        except Exception as e:
+            print(f"Failed config: {test_config}")
+            raise e
+    clear_layout_converter()
+    Randomizer.reset_index()
+    torch.cuda.empty_cache()
+
+
+@parameterize(
+    "test_config",
+    [
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp32",
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp16",
+            "zero_stage": 1,
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "pp_style": "interleaved",
+            "num_model_chunks": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "precision": "fp16",
+            "zero_stage": 1,
+            "initial_scale": 1,
+        },
+    ],
+)
+def run_qwen3_3d_test(test_config):
+    sub_model_zoo = model_zoo.get_sub_registry("transformers_qwen3")
+
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        try:
+            check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
+        except Exception as e:
+            print(f"Failed config: {test_config}")
+            raise e
+
+    clear_layout_converter()
+    Randomizer.reset_index()
+    torch.cuda.empty_cache()
+
+
+def check_qwen3(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_qwen3_test()
+
+
+def check_qwen3_3d(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_qwen3_3d_test()
+
+
+@pytest.mark.skipif(transformers.__version__ < "4.51.0", reason="Requires transformers version 4.51.0 or later")
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_qwen3():
+    spawn(check_qwen3, 4)
+
+
+@pytest.mark.skipif(transformers.__version__ < "4.51.0", reason="Requires transformers version 4.51.0 or later")
+@pytest.mark.largedist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_qwen3_3d():
+    spawn(check_qwen3_3d, 8)
+
+
+if __name__ == "__main__":
+    test_qwen3()
+    test_qwen3_3d()
--- a/tests/test_zero/test_gemini/test_inference.py
+++ b/tests/test_zero/test_gemini/test_inference.py
@ -10,7 +10,7 @@ import colossalai
 from colossalai.accelerator import get_accelerator
 from colossalai.legacy.amp import convert_to_apex_amp
 from colossalai.nn.optimizer import HybridAdam
-from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import DummyDataloader, clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 from colossalai.utils import set_seed
 from colossalai.zero import GeminiDDP, GeminiOptimizer
 from colossalai.zero.gemini.chunk import search_chunk_configuration
@ -53,6 +53,8 @@ def single_chunk_init(model: torch.nn.Module, placement_config: dict):
    return model


+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
@parameterize("placement_config", PLACEMENT_CONFIGS)
@parameterize("model_name", ["transformers_gpt_lm"])
@parameterize("model_init_func", [single_chunk_init, multi_chunk_init])
@ -104,6 +106,7 @@ def exam_inference(placement_config: dict, model_name: str, model_init_func: Cal
    train_iter()
    inference_iter()
    train_iter()
+    torch.cuda.empty_cache()


 def run_dist(rank, world_size, port):
@ -113,7 +116,6 @@ def run_dist(rank, world_size, port):

@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 4])
-@rerun_if_address_is_in_use()
 def test_inference(world_size):
    spawn(run_dist, world_size)

--- a/version.txt
+++ b/version.txt
@ -1 +1 @@
-0.4.9
+0.5.0
Author	SHA1	Message	Date
botbw	d097224d90	[feat] support qwen3 in shardformer	2025-07-10 13:57:52 +08:00
Hanks	97f4bee9d8	Merge pull request #6340 from hpcaitech/release/v0.5.0 [release] update version	2025-06-04 13:57:10 +08:00
BurkeHulk	e00c9bbf38	upgrade python	2025-06-03 18:51:39 +08:00
BurkeHulk	91f08c64a7	upgrade python	2025-06-03 18:41:37 +08:00
BurkeHulk	043c46941c	upgrade python	2025-06-03 18:38:07 +08:00
Hanks	916a8fef0e	Update release_test_pypi_before_merge.yml	2025-06-03 18:25:01 +08:00
Hanks	0ba96e88d2	Update release_test_pypi_before_merge.yml	2025-06-03 18:12:19 +08:00
Hanks	b9535f3c44	Update version.txt	2025-06-03 17:56:08 +08:00
Hanks	c4fe9e812e	Update release_pypi_after_merge.yml	2025-06-03 17:55:49 +08:00
Hanks	6dfedea98b	Update release_test_pypi_before_merge.yml	2025-06-03 17:55:21 +08:00
Hanks	b4ec405778	Merge pull request #6336 from BurkeHulk/fix/update-test-config [fix] fix CI machine tag	2025-06-03 09:46:06 +08:00
BurkeHulk	067dd43246	fix pre-commit err	2025-06-02 18:13:34 +08:00
BurkeHulk	c9cba49ab5	fix CI machine tag	2025-06-02 17:45:40 +08:00
Hanks	fd56b22278	Merge pull request #6334 from flybird11111/main [release] release version	2025-06-02 17:08:24 +08:00
Hanks	6f19618bb4	[fix] fix_lazy_init for deepseek model in transformers	2025-06-02 11:31:45 +08:00
Hanks	060102372e	Update release_pypi_after_merge.yml	2025-05-30 16:54:27 +08:00
Hanks	374dcd4da9	Update release_test_pypi_before_merge.yml	2025-05-30 16:09:14 +08:00
Hanks	3f9159715f	Update release_test_pypi_before_merge.yml	2025-05-30 15:27:32 +08:00
flybird11111	948533f7de	fix	2025-05-30 14:59:56 +08:00
Hanks	c8aaa92e36	Update release_test_pypi_before_merge.yml	2025-05-30 14:43:31 +08:00
flybird11111	562767c884	fix	2025-05-30 14:38:07 +08:00
flybird11111	594384328c	fix	2025-05-29 11:25:39 +08:00
flybird11111	cac878d7b7	fix	2025-05-29 11:10:37 +08:00
flybird11111	45dd5a7cf4	release	2025-05-29 10:47:23 +08:00
Hanks	d322ff8cd9	Merge pull request #6330 from flybird11111/main [release] release version	2025-05-28 17:27:44 +08:00
flybird11111	4afff92138	fix	2025-05-28 11:13:44 +08:00
pre-commit-ci[bot]	d3c40b9de4	[pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci	2025-05-27 08:48:12 +00:00
flybird11111	d7a03bfea2	release	2025-05-27 16:47:12 +08:00
flybird11111	a9656e2915	fix	2025-05-27 15:19:04 +08:00
flybird11111	45779680bf	release	2025-05-27 15:10:19 +08:00
flybird11111	4271e3daf6	release	2025-05-27 14:38:59 +08:00
flybird11111	ddbbbaab3e	[upgrade]Upgrade transformers (#6320 ) * fix for async io * test for upgrading transformers * add ci machine * fix * fix * fix * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update test_fp16_torch.py * Update build_on_pr.yml * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fiux * fix * fix * fix * upgrade llama * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix * upgrade_bert * upgrade_bloom * [upgrade] upgrade gpt2 (#6291) * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * upgrade command * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * add explanation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix * fix * fix * [upgrade]Upgrade qwen2 (#6302) * upgrade qwen2 * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * update_bloom * fix * add explantion * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * upgrade_sam * add the explanation * upgrade_t * fix * fix * fix * upgrade_gptj * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [upgrade]upgrade opt (#6307) * upgrade opt * fix * [upgrade]Upgrade mixtral (#6317) * upgrade mixtral * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * upgrade infer * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * upgrade drafter * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * upgrade lazy * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * upgrade mixtral --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * [upgrade]Upgrade vit (#6308) * fix * fix * fix rotate embedding test * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * [upgrade]upgrade mistral (#6296) * upgrade mistral * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix falcon * fix * Update test_shard_deepseek.py * Update build_on_pr.yml * Update requirements.txt * fix (#6327) * fix (#6328) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update bert.py * fix (#6329) --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Hanks <hangxu0304@gmail.com> Co-authored-by: wangbluo <2538539015@qq.com> Co-authored-by: Wang Binluo <32676639+wangbluo@users.noreply.github.com>	2025-05-27 14:29:01 +08:00
flybird11111	46ed5d856b	[ci] update ci (#6254 ) * fix for async io * test for upgrading transformers * add ci machine * fix * fix * fix * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update test_fp16_torch.py * Update build_on_pr.yml * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fix * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * fiux * fix * fix * fix --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>	2025-04-18 16:40:53 +08:00
Yanjia0	7ecdf9a211	Update README.md (#6268 ) Image Change from H100 to H200	2025-04-17 12:07:25 +08:00
duanjunwen	44d4053fec	[HotFix] update load lora model Readme; (#6240 ) * [fix] update load lora model Readme; * [fix] update lora infer readme * [fix] remove useless comments	2025-03-07 14:14:26 +08:00
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