ckpt

[CI] Add tests to Modal (#562 )
[CI] Fix SSIM and transformers CI (#564 )
2025-06-28 14:56:21 -07:00 · 2025-06-28 14:02:16 -05:00 · 2025-06-28 00:26:20 -05:00 · 2025-06-27 22:52:40 -05:00 · 2025-06-27 14:50:42 -05:00 · 2025-06-27 02:19:23 -05:00
211 changed files with 8166 additions and 3174 deletions
@@ -0,0 +1,148 @@
+env:
+  IMAGE_VERSION: "py3.12-latest"
+
+steps:
+  - label: "pre-commit"
+    command: ".buildkite/scripts/pre_commit.sh"
+    agents:
+      queue: "default"
+    env:
+      - BUILDKITE_CLEAN_CHECKOUT=true
+
+  - wait
+
+  - label: "Trigger Tests"
+    plugins:
+      - monorepo-diff#v1.4.0:
+          diff: "git diff --name-only $BUILDKITE_PULL_REQUEST_BASE_BRANCH...HEAD"
+          watch:
+            - path: 
+                - "fastvideo/v1/models/encoders/**"
+                - "fastvideo/v1/models/loader/**"
+                - "fastvideo/v1/tests/encoders/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: "Encoder Tests"
+                env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
+                  - TEST_TYPE=encoder
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/v1/models/vaes/**"
+                - "fastvideo/v1/models/loader/**"
+                - "fastvideo/v1/tests/vaes/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: "VAE Tests"
+                env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
+                  - TEST_TYPE=vae
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/v1/models/dits/**"
+                - "fastvideo/v1/models/loader/**"
+                - "fastvideo/v1/tests/transformers/**"
+                - "fastvideo/v1/layers/**"
+                - "fastvideo/v1/attention/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: "Transformer Tests"
+                env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
+                  - TEST_TYPE=transformer
+                agents:
+                  queue: "default"
+            - path: 
+                - "fastvideo/v1/**/*.py"
+              config:
+                command: "timeout 60m .buildkite/scripts/pr_test.sh"
+                label: "SSIM Tests"
+                env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
+                  - TEST_TYPE=ssim
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/v1/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: "Training Tests"
+                env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
+                  - TEST_TYPE=training
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/v1/**"
+                - "csrc/attn/vsa/**"
+                - "csrc/attn/tk/**"
+                - "csrc/attn/setup_vsa.py"
+                - "csrc/attn/config_vsa.py"
+                - "csrc/attn/vsa.cpp"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: "Training Tests VSA"
+                env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
+                  - TEST_TYPE=training_vsa
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/v1/**"
+                - "csrc/attn/st_attn/**"
+                - "csrc/attn/setup_sta.py"
+                - "csrc/attn/config_sta.py"
+                - "csrc/attn/st_attn.cpp"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: "Inference Tests STA"
+                env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
+                  - TEST_TYPE=inference_sta
+                agents:
+                  queue: "default"
+            - path:
+                - "csrc/attn/st_attn/**"
+                - "csrc/attn/setup_sta.py"
+                - "csrc/attn/config_sta.py"
+                - "csrc/attn/st_attn.cpp"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: "Precision Tests STA"
+                env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
+                  - TEST_TYPE=precision_sta
+                agents:
+                  queue: "default"
+            - path:
+                - "csrc/attn/vsa/**"
+                - "csrc/attn/tk/**"
+                - "csrc/attn/setup_vsa.py"
+                - "csrc/attn/config_vsa.py"
+                - "csrc/attn/vsa.cpp"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: "Precision Tests VSA"
+                env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
+                  - TEST_TYPE=precision_vsa
+                agents:
+                  queue: "default"
@@ -0,0 +1,117 @@
+#!/bin/bash
+set -uo pipefail
+
+log() {
+  echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
+}
+
+log "=== Starting Modal test execution ==="
+
+# Change to the project directory
+cd "$(dirname "$0")/../.."
+PROJECT_ROOT=$(pwd)
+log "Project root: $PROJECT_ROOT"
+
+# Install Modal if not available
+if ! python3 -m modal --version &> /dev/null; then
+    log "Modal not found, installing..."
+    python3 -m pip install modal
+    
+    # Verify installation
+    if ! python3 -m modal --version &> /dev/null; then
+        log "Error: Failed to install modal. Please install it manually."
+        exit 1
+    fi
+fi
+
+log "modal version: $(python3 -m modal --version)"
+
+# Set up Modal authentication using Buildkite secrets
+log "Setting up Modal authentication from Buildkite secrets..."
+MODAL_TOKEN_ID=$(buildkite-agent secret get modal_token_id)
+MODAL_TOKEN_SECRET=$(buildkite-agent secret get modal_token_secret)
+
+WANDB_API_KEY=$(buildkite-agent secret get wandb_api_key)
+
+WANDB_API_KEY=$(buildkite-agent secret get wandb_api_key)
+
+if [ -n "$MODAL_TOKEN_ID" ] && [ -n "$MODAL_TOKEN_SECRET" ]; then
+    log "Retrieved Modal credentials from Buildkite secrets"
+    python3 -m modal token set --token-id "$MODAL_TOKEN_ID" --token-secret "$MODAL_TOKEN_SECRET" --profile buildkite-ci --activate --verify
+    if [ $? -eq 0 ]; then
+        log "Modal authentication successful"
+    else
+        log "Error: Failed to set Modal credentials"
+        exit 1
+    fi
+else
+    log "Error: Could not retrieve Modal credentials from Buildkite secrets."
+    log "Please ensure 'modal_token_id' and 'modal_token_secret' secrets are set in Buildkite."
+    exit 1
+fi
+
+MODAL_TEST_FILE="fastvideo/v1/tests/modal/pr_test.py"
+
+if [ -z "${TEST_TYPE:-}" ]; then
+    log "Error: TEST_TYPE environment variable is not set"
+    exit 1
+fi
+log "Test type: $TEST_TYPE"
+
+MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT IMAGE_VERSION=$IMAGE_VERSION"
+
+case "$TEST_TYPE" in
+    "encoder")
+        log "Running encoder tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_encoder_tests"
+        ;;
+    "vae")
+        log "Running VAE tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_vae_tests"
+        ;;
+    "transformer")
+        log "Running transformer tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
+        ;;
+    "ssim")
+        log "Running SSIM tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
+        ;;
+    "training")
+        log "Running training tests..."
+        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_training_tests"
+        ;;
+    "training_vsa")
+        log "Running training VSA tests..."
+        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_training_tests_VSA"
+        ;;
+    "inference_sta")
+        log "Running inference STA tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_tests_STA"
+        ;;
+    "precision_sta")
+        log "Running precision STA tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_STA"
+        ;;
+    "precision_vsa")
+        log "Running precision VSA tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_VSA"
+        ;;
+    *)
+        log "Error: Unknown test type: $TEST_TYPE"
+        exit 1
+        ;;
+esac
+
+log "Executing: $MODAL_COMMAND"
+eval "$MODAL_COMMAND"
+TEST_EXIT_CODE=$?
+
+if [ $TEST_EXIT_CODE -eq 0 ]; then
+    log "Modal test completed successfully"
+else
+    log "Error: Modal test failed with exit code: $TEST_EXIT_CODE"
+fi
+
+log "=== Test execution completed with exit code: $TEST_EXIT_CODE ==="
+exit $TEST_EXIT_CODE
@@ -0,0 +1,40 @@
+#!/bin/bash
+set -uo pipefail
+
+log() {
+  echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
+}
+
+log "=== Starting pre-commit checks ==="
+
+cd "$(dirname "$0")/../.."
+PROJECT_ROOT=$(pwd)
+log "Project root: $PROJECT_ROOT"
+
+if ! python3 -m pre_commit --version &> /dev/null; then
+    log "pre-commit not found, installing..."
+    python3 -m pip install --user pre-commit==4.0.1
+    
+    if ! python3 -m pre_commit --version &> /dev/null; then
+        log "Error: Failed to install pre-commit."
+        exit 1
+    fi
+fi
+
+log "Pre-commit version: $(python3 -m pre_commit --version)"
+
+log "Installing/updating pre-commit hooks..."
+python3 -m pre_commit install --install-hooks
+
+log "Running pre-commit checks on all files..."
+python3 -m pre_commit run --all-files
+PRE_COMMIT_EXIT_CODE=$?
+
+if [ $PRE_COMMIT_EXIT_CODE -eq 0 ]; then
+    log "Pre-commit checks completed successfully"
+else
+    log "Error: Pre-commit checks failed with exit code: $PRE_COMMIT_EXIT_CODE"
+fi
+
+log "=== Pre-commit checks completed with exit code: $PRE_COMMIT_EXIT_CODE ==="
+exit $PRE_COMMIT_EXIT_CODE 
@@ -160,8 +160,7 @@ def execute_command(pod_id):
    setup_steps = [
        "tar -xzf /tmp/repo.tar.gz --no-same-owner -C /workspace/",
        f"cd /workspace/{repo_name}",
-        "source /opt/conda/etc/profile.d/conda.sh",
-        "conda activate fastvideo-dev",
+        "source $HOME/.local/bin/env && source /opt/venv/bin/activate",
        args.test_command
    ]
    
@@ -12,13 +12,11 @@ on:
    paths:
      - "fastvideo/**/*.py"
      - ".github/workflows/pr-test.yml"
+      - "pyproject.toml"
+      - "docker/Dockerfile.python3.12"
+      - "csrc/**"
  workflow_dispatch:
    inputs:
-      custom_image:
-        description: "Custom image from this repository (default: fastvideo-dev:latest)"
-        required: false
-        default: "fastvideo-dev:latest"
-        type: string
      run_encoder_test:
        description: "Run encoder-test"
        required: false
@@ -39,10 +37,41 @@ on:
        required: false
        default: false
        type: boolean
+      run_training_test:
+        description: "Run training-test"
+        required: false
+        default: false
+        type: boolean
+      run_training_test_VSA:
+        description: "Run training-test-VSA"
+        required: false
+        default: false
+        type: boolean
+      run_inference_test_STA:
+        description: "Run inference-test-STA"
+        required: false
+        default: false
+        type: boolean
+      run_precision_test_STA:
+        description: "Run precision-test-STA"
+        required: false
+        default: false
+        type: boolean
+      run_precision_test_VSA:
+        description: "Run precision-test-VSA"
+        required: false
+        default: false
+        type: boolean
+      run_nightly_test:
+        description: "Run nightly-test"
+        required: false
+        default: false
+        type: boolean

 env:
  PYTHONUNBUFFERED: "1"

+
 concurrency:
  group: pr-test-${{ github.ref }}
  cancel-in-progress: true
@@ -59,26 +88,72 @@ jobs:
      encoder-test: ${{ steps.filter.outputs.encoder-test }}
      vae-test: ${{ steps.filter.outputs.vae-test }}
      transformer-test: ${{ steps.filter.outputs.transformer-test }}
+      training-test: ${{ steps.filter.outputs.training-test }}
+      training-test-VSA: ${{ steps.filter.outputs.training-test-VSA }}
+      inference-test-STA: ${{ steps.filter.outputs.inference-test-STA }}
+      precision-test-STA: ${{ steps.filter.outputs.precision-test-STA }}
+      precision-test-VSA: ${{ steps.filter.outputs.precision-test-VSA }}
    steps:
      - uses: actions/checkout@v4
      - uses: dorny/paths-filter@v3
        id: filter
        with:
          filters: |
+            # Define reusable path patterns
+            common-paths: &common-paths
+              - 'pyproject.toml'
+              - 'docker/Dockerfile.python3.12'
+            sta-kernel-paths: &sta-kernel-paths
+              - 'csrc/attn/st_attn/**'
+              - 'csrc/attn/setup_sta.py'
+              - 'csrc/attn/config_sta.py'
+              - 'csrc/attn/st_attn.cpp'
+            vsa-kernel-paths: &vsa-kernel-paths
+              - 'csrc/attn/vsa/**'
+              - 'csrc/attn/tk/**'
+              - 'csrc/attn/setup_vsa.py'
+              - 'csrc/attn/config_vsa.py'
+              - 'csrc/attn/vsa.cpp'
+            vsa-paths: &vsa-paths
+              - 'fastvideo/v1/**'
+              - *common-paths
+              - *vsa-kernel-paths
+            
+            # Actual tests
            encoder-test:
              - 'fastvideo/v1/models/encoders/**'
-              - 'fastvideo/v1/models/loaders/**'
+              - 'fastvideo/v1/models/loader/**'
              - 'fastvideo/v1/tests/encoders/**'
+              - *common-paths
            vae-test:
              - 'fastvideo/v1/models/vaes/**'
-              - 'fastvideo/v1/models/loaders/**'
+              - 'fastvideo/v1/models/loader/**'
              - 'fastvideo/v1/tests/vaes/**'
+              - *common-paths
            transformer-test:
              - 'fastvideo/v1/models/dits/**'
-              - 'fastvideo/v1/models/loaders/**'
+              - 'fastvideo/v1/models/loader/**'
              - 'fastvideo/v1/tests/transformers/**'
              - 'fastvideo/v1/layers/**'
              - 'fastvideo/v1/attention/**'
+              - *common-paths
+            training-test:
+              - 'fastvideo/v1/**'
+              - *common-paths
+            training-test-VSA:
+              - 'fastvideo/v1/**'
+              - *common-paths
+              - *vsa-kernel-paths
+            inference-test-STA:
+              - 'fastvideo/v1/**'
+              - *common-paths
+              - *sta-kernel-paths
+            precision-test-STA:
+              - *common-paths
+              - *sta-kernel-paths
+            precision-test-VSA:
+              - *common-paths
+              - *vsa-kernel-paths

  encoder-test:
    needs: change-filter
@@ -91,8 +166,8 @@ jobs:
      gpu_type: "NVIDIA A40"
      gpu_count: 1
      volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
-      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/encoders -s"
+      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/encoders -s"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -109,8 +184,8 @@ jobs:
      gpu_type: "NVIDIA A40"
      gpu_count: 1
      volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
-      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/vaes -s"
+      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/vaes -s"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -127,8 +202,8 @@ jobs:
      gpu_type: "NVIDIA L40S"
      gpu_count: 1
      volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
-      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/transformers -s"
+      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/transformers -s"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -137,8 +212,7 @@ jobs:
  ssim-test:
    needs: change-filter
    if: >-
-      (github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
+      github.event_name != 'workflow_dispatch' || (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
    strategy:
      fail-fast: false
      matrix:
@@ -155,14 +229,130 @@ jobs:
      volume_size: 200
      disk_size: 200
      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:${{ matrix.python-version.tag }}"
-      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/ssim -vs"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/ssim -vs"
      timeout_minutes: 60
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
+  
+  training-test:
+    needs: change-filter
+    if: >-
+      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test == 'true') || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test == 'true')
+    uses: ./.github/workflows/runpod-test.yml
+    with:
+      job_id: "training-test"
+      gpu_type: "NVIDIA A40"
+      gpu_count: 4
+      volume_size: 100
+      disk_size: 100
+      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/Vanilla -srP"
+      timeout_minutes: 30
+    secrets:
+      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
+      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
+      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
+
+  training-test-VSA:
+    needs: change-filter
+    if: >-
+      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test-VSA == 'true') || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test_VSA == 'true')
+    uses: ./.github/workflows/runpod-test.yml
+    with:
+      job_id: "training-test-VSA"
+      gpu_type: "NVIDIA H100 NVL"
+      gpu_count: 1
+      volume_size: 100
+      disk_size: 100
+      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/VSA -srP"
+      timeout_minutes: 30
+    secrets:
+      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
+      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
+      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
+
+  inference-test-STA:
+    needs: change-filter
+    if: >-
+      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.inference-test-STA == 'true') || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_inference_test_STA == 'true')
+    uses: ./.github/workflows/runpod-test.yml
+    with:
+      job_id: "inference-test-STA"
+      gpu_type: "NVIDIA H100 NVL"
+      gpu_count: 1
+      volume_size: 100
+      disk_size: 100
+      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/inference/STA -srP"
+      timeout_minutes: 30
+    secrets:
+      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
+      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
+
+  precision-test-STA:
+    needs: change-filter
+    if: >-
+      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.precision-test-STA == 'true') || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_precision_test_STA == 'true')
+    uses: ./.github/workflows/runpod-test.yml
+    with:
+      job_id: "precision-test-STA"
+      gpu_type: "NVIDIA H100 NVL"
+      gpu_count: 1
+      volume_size: 100
+      disk_size: 100
+      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      test_command: "uv pip install -e .[test] && python csrc/attn/tests/test_sta.py"
+      timeout_minutes: 30
+    secrets:
+      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
+      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
+
+  precision-test-VSA:
+    needs: change-filter
+    if: >-
+      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.precision-test-VSA == 'true') || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_precision_test_VSA == 'true')
+    uses: ./.github/workflows/runpod-test.yml
+    with:
+      job_id: "precision-test-VSA"
+      gpu_type: "NVIDIA H100 NVL"
+      gpu_count: 1
+      volume_size: 100
+      disk_size: 100
+      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      test_command: "uv pip install -e .[test] && python csrc/attn/tests/test_block_sparse.py"
+      timeout_minutes: 30
+    secrets:
+      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
+      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
+
+  nightly-test:
+    if: >-
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
+    uses: ./.github/workflows/runpod-test.yml
+    with:
+      job_id: "nightly-test"
+      gpu_type: "NVIDIA A40"
+      gpu_count: 4
+      volume_size: 100
+      disk_size: 100
+      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/nightly/test_e2e_overfit_single_sample.py -vs"
+      timeout_minutes: 30
+    secrets:
+      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
+      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
+      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}

  runpod-cleanup:
-    needs: [encoder-test, vae-test, transformer-test, ssim-test] # Add other jobs to this list as you create them
+    # Add other jobs to this list as you create them
+    needs: [encoder-test, vae-test, transformer-test, ssim-test, training-test, training-test-VSA, inference-test-STA, precision-test-STA, precision-test-VSA] 
    if: ${{ always() && ((github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || github.event_name == 'workflow_dispatch') }}
    runs-on: ubuntu-latest
    steps:
@@ -179,7 +369,7 @@ jobs:

      - name: Cleanup all RunPod instances
        env:
-          JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12"]'
+          JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12", "training-test", "training-test-VSA", "inference-test-STA", "precision-test-STA", "precision-test-VSA"]'
          RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
          GITHUB_RUN_ID: ${{ github.run_id }}
        run: python .github/scripts/runpod_cleanup.py
@@ -43,6 +43,8 @@ on:
        required: true
      RUNPOD_PRIVATE_KEY:
        required: true
+      WANDB_API_KEY:
+        required: false

 jobs:
  run-test:
@@ -55,7 +57,7 @@ jobs:
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
-          python-version: "3.10"
+          python-version: "3.12"

      - name: Set up SSH key
        run: |
@@ -72,6 +74,7 @@ jobs:
          JOB_ID: ${{ inputs.job_id }}
          RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
          GITHUB_RUN_ID: ${{ github.run_id }}
+          WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
        timeout-minutes: ${{ inputs.timeout_minutes }}
        run: >-
          python .github/scripts/runpod_api.py 
@@ -5,7 +5,7 @@ on:
    branches:
      - main
    paths:
-      - "csrc/sliding_tile_attention/setup.py"
+      - "csrc/attn/setup_sta.py"
  workflow_dispatch:

 jobs:
@@ -23,13 +23,13 @@ jobs:
      - name: Check if version changed
        id: check-version
        run: |
-          cd csrc/sliding_tile_attention
+          cd csrc/attn
          # Get current commit's version
-          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup.py)
+          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup_sta.py)
          echo "New version: $NEW_VERSION"
          
          # Get previous version from git history
-          OLD_VERSION=$(git show HEAD~1:./setup.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
+          OLD_VERSION=$(git show HEAD~1:./setup_sta.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
          echo "Old version: $OLD_VERSION"
          
          if [ "$NEW_VERSION" != "$OLD_VERSION" ]; then
@@ -136,19 +136,21 @@ jobs:

      - name: Build wheel
        run: |
+          export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
+          
          # We want setuptools >= 49.6.0 otherwise we can't compile the extension if system CUDA version is 11.7 and pytorch cuda version is 11.6
          # https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/utils/cpp_extension.py#L810
          # However this still fails so I'm using a newer version of setuptools
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/sliding_tile_attention  # Move into the correct folder
-          git submodule update --init --recursive tk # Ensure ThunderKittens submodule is initialized
-          python setup.py bdist_wheel --dist-dir=dist
+          cd csrc/attn  # Move into the correct folder
+          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
+          python setup_sta.py bdist_wheel --dist-dir=dist

      - name: Rename wheel file
        run: |
-          cd csrc/sliding_tile_attention
+          cd csrc/attn

          CUDA_SHORT_VERSION=$(echo ${{ matrix.cuda-version }} | cut -d. -f1,2 | sed 's/\.//g')
          TORCH_SHORT_VERSION=$(echo ${{ matrix.torch-version }} | cut -d. -f1,2)
@@ -163,7 +165,7 @@ jobs:
        uses: actions/upload-artifact@v4
        with:
          name: ${{ env.wheel_name }}
-          path: csrc/sliding_tile_attention/dist/*.whl
+          path: csrc/attn/dist/*.whl
          retention-days: 90

  publish_package:
@@ -229,17 +231,19 @@ jobs:

      - name: Build source distribution
        run: |
+          export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
+          
          # We want setuptools >= 49.6.0 otherwise we can't compile the extension if system CUDA version is 11.7 and pytorch cuda version is 11.6
          # https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/utils/cpp_extension.py#L810
          # However this still fails so I'm using a newer version of setuptools
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/sliding_tile_attention  # Move into the correct folder
-          git submodule update --init --recursive tk # Ensure ThunderKittens submodule is initialized
-          python setup.py sdist --dist-dir=dist
+          cd csrc/attn  # Move into the correct folder
+          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
+          python setup_sta.py sdist --dist-dir=dist

      - name: Publish release distributions to PyPI
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
-          packages-dir: csrc/sliding_tile_attention/dist/
+          packages-dir: csrc/attn/dist/
@@ -28,4 +28,4 @@ jobs:

      - name: Run Pytest
        run: |
-          pytest --ignore csrc/sliding_tile_attention/test
+          pytest --ignore csrc/attn/test
@@ -27,7 +27,6 @@ env
 **/build/
 **.pyc
 **.txt
-csrc/attn/tk/

 # Distribution / packaging
 build/
@@ -1,3 +1,3 @@
-[submodule "csrc/sliding_tile_attention/tk"]
-	path = csrc/sliding_tile_attention/tk
+[submodule "csrc/attn/tk"]
+	path = csrc/attn/tk
 	url = https://github.com/HazyResearch/ThunderKittens.git
@@ -91,7 +91,7 @@ For a more detailed guide, please see our [inference quick start](https://hao-ai

 ## Distillation and Finetuning
 - [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/training/distillation.html)
- [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetuning.html)
+- [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetune.html)

 ## 📑 Development Plan

@@ -111,7 +111,7 @@ For a more detailed guide, please see our [inference quick start](https://hao-ai

 ## 🤝 Contributing

-We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/developer_guide/overview.html)
+We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview.html)

 ## Acknowledgement
 We learned and reused code from the following projects:
@@ -4,9 +4,8 @@


 ## Installation
-We test our code on Pytorch 2.5.0 and CUDA>=12.4. Currently we only have implementation on H100.
+We test our code on Pytorch 2.5.0 and CUDA>=12.4. Currently we only support H100/H200, because ThunderKittens uses TMA but doesn't support Blackwell yet.
 First, install C++20 for ThunderKittens:
-
 ```bash
 sudo apt update
 sudo apt install gcc-11 g++-11
@@ -16,17 +15,27 @@ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave
 sudo apt update
 sudo apt install clang-11
 ```
-Install STA:
+
+## Environment Setup
+First, set up your CUDA environment:
 ```bash
 export CUDA_HOME=/usr/local/cuda-12.4
 export PATH=${CUDA_HOME}/bin:${PATH} 
 export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
 git submodule update --init --recursive
-python setup.py install
+```
+
+## Install Sliding Tile Attention (STA)
+```bash
+python setup_sta.py install
+```
+
+## Install Video Sparse Attention (VSA)
+```bash
+python setup_vsa.py install
 ```

 ## Usage
-
 ```python
 from st_attn import sliding_tile_attention
 # assuming video size (T, H, W) = (30, 48, 80), text tokens = 256 with padding. 
@@ -44,8 +53,14 @@ out = sliding_tile_attention(q, k, v, window_size, 0, False)

 ## Test
 ```bash
-python test/test_sta.py
+python tests/test_sta.py # test STA
+python tests/test_block_sparse.py # test VSA
 ```
+## Benchmark
+```bash
+python benchmarks/bench_sta.py
+```
+

 ## How Does STA Work?
 We give a demo for 2D STA with window size (6,6) operating on a (10, 10) image. 
@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from st_attn import sliding_tile_attention
+from triton.testing import do_bench


 def flops(batch, seqlen, nheads, headdim, causal, mode="fwd"):
@@ -13,16 +14,16 @@ def flops(batch, seqlen, nheads, headdim, causal, mode="fwd"):
    return f if mode == "fwd" else (2.5 * f if mode == "bwd" else 3.5 * f)


-def efficiency(flop, time):
-    flop = flop / 1e12
-    time = time / 1e6
-    return flop / time
+def compute_TFLOPS(flops, ms):
+    flops = flops / 1e12
+    ms = ms / 1e3
+    return flops / ms


 def benchmark_attention(configurations):
    results = {'fwd': defaultdict(list), 'bwd': defaultdict(list)}

-    for B, H, N, D, causal in configurations:
+    for B, H, N, D, causal, dit_seq_shape, window_size in configurations:
        print("=" * 60)
        print(f"Timing forward and backward pass for B={B}, H={H}, N={N}, D={D}, causal={causal}")

@@ -30,38 +31,31 @@ def benchmark_attention(configurations):
        k = torch.randn(B, H, N, D, dtype=torch.bfloat16, device='cuda', requires_grad=False).contiguous()
        v = torch.randn(B, H, N, D, dtype=torch.bfloat16, device='cuda', requires_grad=False).contiguous()

-        grad_output = torch.randn_like(q, requires_grad=False).contiguous()
+        # grad_output = torch.randn_like(q, requires_grad=False).contiguous()
+        # qg = torch.zeros_like(q, requires_grad=False, dtype=torch.float).contiguous()
+        # kg = torch.zeros_like(k, requires_grad=False, dtype=torch.float).contiguous()
+        # vg = torch.zeros_like(v, requires_grad=False, dtype=torch.float).contiguous()

-        qg = torch.zeros_like(q, requires_grad=False, dtype=torch.float).contiguous()
-        kg = torch.zeros_like(k, requires_grad=False, dtype=torch.float).contiguous()
-        vg = torch.zeros_like(v, requires_grad=False, dtype=torch.float).contiguous()

-        # Prepare for timing forward pass
-        start_events_fwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)]
-        end_events_fwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)]
+        # # Warmup for forward pass
+        # for _ in range(10):
+        #     o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, dit_seq_shape)

-        torch.cuda.empty_cache()
-        torch.cuda.synchronize()
+        # # Time the forward pass
+        # for i in range(10):
+        #     start_events_fwd[i].record()
+        #     o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, dit_seq_shape)
+        #     end_events_fwd[i].record()
+        ms = do_bench(lambda: sliding_tile_attention(q, k, v, [window_size] * 24, 0, False, dit_seq_shape))

-        # Warmup for forward pass
-        for _ in range(10):
-            o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, '18x48x80')
+        # times_fwd = [s.elapsed_time(e) for s, e in zip(start_events_fwd, end_events_fwd)]
+        # time_us_fwd = np.mean(times_fwd) * 1000

-        # Time the forward pass
-        for i in range(10):
-            start_events_fwd[i].record()
-            o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, '18x48x80')
-            end_events_fwd[i].record()
-
-        torch.cuda.synchronize()
-        times_fwd = [s.elapsed_time(e) for s, e in zip(start_events_fwd, end_events_fwd)]
-        time_us_fwd = np.mean(times_fwd) * 1000
-
-        tflops_fwd = efficiency(flops(B, N, H, D, causal, 'fwd'), time_us_fwd)
+        tflops_fwd = compute_TFLOPS(flops(B, N, H, D, causal, 'fwd'), ms)
        results['fwd'][(D, causal)].append((N, tflops_fwd))

-        print(f"Average time for forward pass in us: {time_us_fwd:.2f}")
-        print(f"Average efficiency for forward pass in TFLOPS: {tflops_fwd}")
+        print(f"Average time for forward pass (ms): {ms:.2f}")
+        print(f"Average TFLOPS: {tflops_fwd}")
        print("-" * 60)

        # torch.cuda.empty_cache()
@@ -85,15 +79,14 @@ def benchmark_attention(configurations):
        # times_bwd = [s.elapsed_time(e) for s, e in zip(start_events_bwd, end_events_bwd)]
        # time_us_bwd = np.mean(times_bwd) * 1000

-        # tflops_bwd = efficiency(flops(B, N, H, D, causal, 'bwd'), time_us_bwd)
+        # tflops_bwd = compute_TFLOPS(flops(B, N, H, D, causal, 'bwd'), ms)
        # results['bwd'][(D, causal)].append((N, tflops_bwd))

-        # print(f"Average time for backward pass in us: {time_us_bwd:.2f}")
-        # print(f"Average efficiency for backward pass in TFLOPS: {tflops_bwd}")
-        print("=" * 60)
+        # print(f"Average time for backward pass(ms): {ms:.2f}")
+        # print(f"Average TFLOPS: {tflops_bwd}")
+        # print("=" * 60)

        torch.cuda.empty_cache()
-        torch.cuda.synchronize()

    return results

@@ -124,7 +117,10 @@ def plot_results(results):

 # Example list of configurations to test
 configurations = [
-    (2, 24, 69120, 128, False),
+    (2, 24, 69120, 128, False, '18x48x80', [3, 6, 10]),
+    (2, 24, 69120, 128, True, '18x48x80', [3, 6, 10]),
+    (2, 24, 82944, 128, False, '36x48x48', [3, 3, 6]), # Stepvideo
+    (2, 24, 82944, 128, True, '36x48x48', [3, 3, 6]),
    # (16, 16, 768*16,  128, False),
    # (16, 16, 768*2,  128, False),
    # (16, 16, 768*4,  128, False),
@@ -4,9 +4,17 @@
 #include <cooperative_groups.h>
 #include <iostream>
 #include <stdio.h>
+#include <c10/cuda/CUDAGuard.h>
+
+// #define CLAMP(value, min, max) ((value) < (min) ? (min) : ((value) > (max) ? (max) : (value)))
+__device__ __forceinline__ int clamp_int(int value, int min, int max) {
+    return (value < min) ? min : ((value > max) ? max : value);
+}
+// #define ABS(x) ((x) < 0 ? -(x) : (x))
+__device__ __forceinline__ int abs_int(int value) {
+    return (value < 0) ? -value : value;
+}

-#define CLAMP(value, min, max) ((value) < (min) ? (min) : ((value) > (max) ? (max) : (value)))
-#define ABS(x) ((x) < 0 ? -(x) : (x))

 constexpr int CONSUMER_WARPGROUPS = (3); 
 constexpr int PRODUCER_WARPGROUPS = (1); 
@@ -117,16 +125,16 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals<D> g) {
            int qt = seq_idx / 6 / (CH * CW);
            int qh = (seq_idx / 6) % (CH * CW) / CW;
            int qw = (seq_idx / 6) % CW;
-            qt = CLAMP(qt, DT, CT-DT-1);
-            qh = CLAMP(qh, DH, CH-DH-1);
-            qw = CLAMP(qw, DW, CW-DW-1);
+            qt = clamp_int(qt, DT, CT-DT-1);
+            qh = clamp_int(qh, DH, CH-DH-1);
+            qw = clamp_int(qw, DW, CW-DW-1);
            int count = 0;
            int j = 0;
            while (count < K::stages - 1) {
                int kt = j / 3 / (CH * CW);
                int kh = (j / 3) % (CH * CW) / CW;
                int kw = (j / 3) % CW;
-                bool mask = (ABS(qt - kt) <= DT) && (ABS(qh - kh) <= DH) && (ABS(qw - kw) <= DW);
+                bool mask = (abs_int(qt - kt) <= DT) && (abs_int(qh - kh) <= DH) && (abs_int(qw - kw) <= DW);
                if (mask){
                    coord<k_tile> kv_tile_idx = {blockIdx.z, kv_head_idx, j, 0};
                    tma::expect_bytes(k_smem_arrived[count], sizeof(k_tile));
@@ -167,15 +175,15 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals<D> g) {
                int qt = seq_idx / 6 / (CH * CW);
                int qh = (seq_idx / 6) % (CH * CW) / CW;
                int qw = (seq_idx / 6) % CW;
-                qt = CLAMP(qt, DT, CT-DT-1);
-                qh = CLAMP(qh, DH, CH-DH-1);
-                qw = CLAMP(qw, DW, CW-DW-1);
-                int k_t_min = CLAMP(qt-DT, 0, CT-1);
-                int k_t_max = CLAMP(qt+DT, 0, CT-1);
-                int k_h_min = CLAMP(qh-DH, 0, CH-1);
-                int k_h_max = CLAMP(qh+DH, 0, CH-1);
-                int k_w_min = CLAMP(qw-DW, 0, CW-1);
-                int k_w_max = CLAMP(qw+DW, 0, CW-1);
+                qt = clamp_int(qt, DT, CT-DT-1);
+                qh = clamp_int(qh, DH, CH-DH-1);
+                qw = clamp_int(qw, DW, CW-DW-1);
+                int k_t_min = clamp_int(qt-DT, 0, CT-1);
+                int k_t_max = clamp_int(qt+DT, 0, CT-1);
+                int k_h_min = clamp_int(qh-DH, 0, CH-1);
+                int k_h_max = clamp_int(qh+DH, 0, CH-1);
+                int k_w_min = clamp_int(qw-DW, 0, CW-1);
+                int k_w_max = clamp_int(qw+DW, 0, CW-1);
                int count = 0;
                for (int kt = k_t_min; kt <= k_t_max; kt++) {
                    for (int kh = k_h_min; kh <= k_h_max; kh++) {
@@ -234,7 +242,7 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals<D> g) {
            // the last three kv blocks are for text, we process them separately
            kv_iters = img_kv_blocks - 1;
        } else {
-            kv_iters = CLAMP(DT*2+1, 1, CT) * CLAMP(DH*2+1, 1, CH) * CLAMP(DW*2+1, 1, CW) * 3 - 1 ; 
+            kv_iters = clamp_int(DT*2+1, 1, CT) * clamp_int(DH*2+1, 1, CH) * clamp_int(DW*2+1, 1, CW) * 3 - 1 ; 
        }

        kittens::wait(qsmem_semaphore, 0);
@@ -415,8 +423,9 @@ sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o,
    float* l_ptr = reinterpret_cast<float*>(l_vec.data_ptr<float>());
    float* d_l   = reinterpret_cast<float*>(l_ptr);

-    cudaDeviceSynchronize();
-    auto stream = at::cuda::getCurrentCUDAStream().stream(); 
+    //cudadevicesynchronize();
+    const c10::cuda::OptionalCUDAGuard device_guard(q.device());
+    const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream(); 


    if (head_dim == 128) {
@@ -442,8 +451,8 @@ sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o,

        globals g{qg_arg, kg_arg, vg_arg, lg_arg, og_arg, static_cast<int>(seq_len),  static_cast<int>(text_length), static_cast<int>(hr)};

-        auto mem_size = kittens::MAX_SHARED_MEMORY;
-        auto threads  = NUM_WORKERS * kittens::WARP_THREADS;
+        constexpr int mem_size = kittens::MAX_SHARED_MEMORY;
+        int threads = NUM_WORKERS * kittens::WARP_THREADS;
        if (has_text) {
            // TORCH_CHECK(seq_len % (CONSUMER_WARPGROUPS*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 192");
            dim3 grid_image(seq_len/(CONSUMER_WARPGROUPS*kittens::TILE_ROW_DIM<bf16>*4)-2, qo_heads, batch);
@@ -823,10 +832,10 @@ sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o,

        }
        CHECK_CUDA_ERROR(cudaGetLastError());
-        cudaStreamSynchronize(stream);
+        // cudaStreamSynchronize(stream);
    }

    return o;
-    cudaDeviceSynchronize();
+    //cudadevicesynchronize();
 }

@@ -2,11 +2,12 @@ import torch
 import argparse
 from flash_attn.utils.benchmark import benchmark_forward
 from flash_attn import flash_attn_func
-from st_attn import block_sparse_attention_fwd, block_sparse_attention_backward, BlockSparseAttentionFunction
-from st_attn import BLOCK_M, BLOCK_N
+from vsa import block_sparse_attention_fwd, block_sparse_attention_backward, BlockSparseAttentionFunction
+from vsa import BLOCK_M, BLOCK_N

 import numpy as np
 import random
+import gc

 def set_seed(seed: int = 42):
    # Python random module
@@ -20,15 +21,6 @@ def set_seed(seed: int = 42):
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)  # if using multi-GPU

-def parse_arguments():
-    parser = argparse.ArgumentParser(description='Benchmark Block Sparse Attention')
-    parser.add_argument('--batch_size', type=int, default=4, help='Batch size')
-    parser.add_argument('--num_heads', type=int, default=6, help='Number of heads')
-    parser.add_argument('--head_dim', type=int, default=128, help='Head dimension')
-    parser.add_argument('--topk', type=int, default=64, help='Number of kv blocks each q block attends to')
-    parser.add_argument('--seq_lengths', type=int, nargs='+', default=[29120], help='Sequence lengths to benchmark')
-    parser.add_argument('--num_iterations', type=int, default=100, help='Number of test iterations to run')
-    return parser.parse_args()

@torch.no_grad
 def precision_metric(quant_o, fa2_o): 
@@ -135,9 +127,7 @@ def generate_block_sparse_pattern(bs, h, num_q_blocks, num_kv_blocks, k, device=
                
    return q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, block_sparse_mask

-def main():
-    args = parse_arguments()
-
+def main(args):
    set_seed(42)
    
    # Extract parameters
@@ -191,23 +181,36 @@ def main():
            block_mask_expanded = block_sparse_mask.unsqueeze(-1).unsqueeze(-2)  # [b, h, num_q_blocks, num_kv_blocks, 1, 1]
            block_mask_expanded = block_mask_expanded.expand(-1, -1, -1, -1, BLOCK_M, BLOCK_N)  # [b, h, num_q_blocks, num_kv_blocks, BLOCK_M, BLOCK_N]
            full_mask = block_mask_expanded.permute(0, 1, 2, 4, 3, 5).reshape(batch, head, seq_len, seq_len)
-            
-            q_sdpa = q.clone()
-            k_sdpa = k.clone()
-            v_sdpa = v.clone()

            q.requires_grad = True
            k.requires_grad = True
            v.requires_grad = True
-            q_sdpa.requires_grad = True
-            k_sdpa.requires_grad = True
-            v_sdpa.requires_grad = True
+

            # testing forward
            o = BlockSparseAttentionFunction.apply(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num)
+            del q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, block_sparse_mask, block_mask_expanded
+            grad_o = torch.randn_like(o)
+            o.backward(grad_o)
+            # clear memory
+            q_sdpa = q.detach().clone()
+            k_sdpa = k.detach().clone()
+            v_sdpa = v.detach().clone()
+            q_sdpa.requires_grad = True
+            k_sdpa.requires_grad = True
+            v_sdpa.requires_grad = True
+            q.data = torch.empty(0, device=q.device)
+            k.data = torch.empty(0, device=k.device)
+            v.data = torch.empty(0, device=v.device)
+            torch.cuda.empty_cache()
+
            o_sdpa = torch.nn.functional.scaled_dot_product_attention(q_sdpa, k_sdpa, v_sdpa, attn_mask=full_mask)

+
            sim, l1, rmse = precision_metric(o, o_sdpa)
+            assert sim > 0.9999, f"SSIM too low: {sim}"
+            assert l1 < 8e-5, f"l1 too large: {l1}"
+            assert rmse < 2e-5, f"RMSE too large: {rmse}"
            forward_metrics['sim'].append(sim)
            forward_metrics['l1'].append(l1)
            forward_metrics['rmse'].append(rmse)
@@ -215,52 +218,72 @@ def main():
            print(f"block_sparse_attention_fwd vs torch.nn.functional.scaled_dot_product_attention:\nsim: {sim}, l1: {l1}, rmse: {rmse}")

            # test backward
-            grad_o = torch.randn_like(o)
-            o.backward(grad_o)
            o_sdpa.backward(grad_o)

            sim, l1, rmse = precision_metric(q.grad, q_sdpa.grad)
+            # Error bounds collected on H100
+            assert sim > 0.9999, f"SSIM too low: {sim}"
+            assert l1 < 4e-3, f"l1 too large: {l1}"
+            assert rmse < 3e-4, f"RMSE too large: {rmse}"
            grad_q_metrics['sim'].append(sim)
            grad_q_metrics['l1'].append(l1)
            grad_q_metrics['rmse'].append(rmse)
            print(f"block_sparse_attention_bwd vs torch.nn.functional.scaled_dot_product_attention grad_q:\nsim: {sim}, l1: {l1}, rmse: {rmse}")        
            
            sim, l1, rmse = precision_metric(k.grad, k_sdpa.grad)
+            assert sim > 0.9999, f"SSIM too low: {sim}"
+            assert l1 < 4e-3, f"l1 too large: {l1}"
+            assert rmse < 2e-4, f"RMSE too large: {rmse}"
            grad_k_metrics['sim'].append(sim)
            grad_k_metrics['l1'].append(l1)
            grad_k_metrics['rmse'].append(rmse)
            print(f"block_sparse_attention_bwd vs torch.nn.functional.scaled_dot_product_attention grad_k:\nsim: {sim}, l1: {l1}, rmse: {rmse}")
            
            sim, l1, rmse = precision_metric(v.grad, v_sdpa.grad)
+            assert sim > 0.9999, f"SSIM too low: {sim}"
+            assert l1 < 1e-4, f"l1 too large: {l1}"
+            assert rmse < 2e-5, f"RMSE too large: {rmse}"
            grad_v_metrics['sim'].append(sim)
            grad_v_metrics['l1'].append(l1)
            grad_v_metrics['rmse'].append(rmse)
            print(f"block_sparse_attention_bwd vs torch.nn.functional.scaled_dot_product_attention grad_v:\nsim: {sim}, l1: {l1}, rmse: {rmse}")
-        
+            
+            del o, o_sdpa, grad_o, q_sdpa, k_sdpa, v_sdpa
+            gc.collect()
+            torch.cuda.empty_cache()
+
        # Print summary statistics if multiple iterations were run
        if num_iterations > 1:
            print("\n" + "="*50)
            print(f"Summary Statistics (over {num_iterations} iterations):")
            
            print("\nForward metrics:")
-            print(f"Similarity: mean={np.mean(forward_metrics['sim']):.6f}, std={np.std(forward_metrics['sim']):.6f}")
-            print(f"L1 error: mean={np.mean(forward_metrics['l1']):.6f}, std={np.std(forward_metrics['l1']):.6f}")
-            print(f"RMSE: mean={np.mean(forward_metrics['rmse']):.6f}, std={np.std(forward_metrics['rmse']):.6f}")
+            print(f"Similarity: mean={np.mean(forward_metrics['sim']):.6f}, std={np.std(forward_metrics['sim']):.6f}, min={np.min(forward_metrics['sim']):.6f}")
+            print(f"L1 error: mean={np.mean(forward_metrics['l1']):.6f}, std={np.std(forward_metrics['l1']):.6f}, max={np.max(forward_metrics['l1']):.6f}")
+            print(f"RMSE: mean={np.mean(forward_metrics['rmse']):.6f}, std={np.std(forward_metrics['rmse']):.6f}, max={np.max(forward_metrics['rmse']):.6f}")
            
            print("\nGradient Q metrics:")
-            print(f"Similarity: mean={np.mean(grad_q_metrics['sim']):.6f}, std={np.std(grad_q_metrics['sim']):.6f}")
-            print(f"L1 error: mean={np.mean(grad_q_metrics['l1']):.6f}, std={np.std(grad_q_metrics['l1']):.6f}")
-            print(f"RMSE: mean={np.mean(grad_q_metrics['rmse']):.6f}, std={np.std(grad_q_metrics['rmse']):.6f}")
+            print(f"Similarity: mean={np.mean(grad_q_metrics['sim']):.6f}, std={np.std(grad_q_metrics['sim']):.6f}, min={np.min(grad_q_metrics['sim']):.6f}")
+            print(f"L1 error: mean={np.mean(grad_q_metrics['l1']):.6f}, std={np.std(grad_q_metrics['l1']):.6f}, max={np.max(grad_q_metrics['l1']):.6f}")
+            print(f"RMSE: mean={np.mean(grad_q_metrics['rmse']):.6f}, std={np.std(grad_q_metrics['rmse']):.6f}, max={np.max(grad_q_metrics['rmse']):.6f}")
            
            print("\nGradient K metrics:")
-            print(f"Similarity: mean={np.mean(grad_k_metrics['sim']):.6f}, std={np.std(grad_k_metrics['sim']):.6f}")
-            print(f"L1 error: mean={np.mean(grad_k_metrics['l1']):.6f}, std={np.std(grad_k_metrics['l1']):.6f}")
-            print(f"RMSE: mean={np.mean(grad_k_metrics['rmse']):.6f}, std={np.std(grad_k_metrics['rmse']):.6f}")
+            print(f"Similarity: mean={np.mean(grad_k_metrics['sim']):.6f}, std={np.std(grad_k_metrics['sim']):.6f}, min={np.min(grad_k_metrics['sim']):.6f}")
+            print(f"L1 error: mean={np.mean(grad_k_metrics['l1']):.6f}, std={np.std(grad_k_metrics['l1']):.6f}, max={np.max(grad_k_metrics['l1']):.6f}")
+            print(f"RMSE: mean={np.mean(grad_k_metrics['rmse']):.6f}, std={np.std(grad_k_metrics['rmse']):.6f}, max={np.max(grad_k_metrics['rmse']):.6f}")
            
            print("\nGradient V metrics:")
-            print(f"Similarity: mean={np.mean(grad_v_metrics['sim']):.6f}, std={np.std(grad_v_metrics['sim']):.6f}")
-            print(f"L1 error: mean={np.mean(grad_v_metrics['l1']):.6f}, std={np.std(grad_v_metrics['l1']):.6f}")
-            print(f"RMSE: mean={np.mean(grad_v_metrics['rmse']):.6f}, std={np.std(grad_v_metrics['rmse']):.6f}")
+            print(f"Similarity: mean={np.mean(grad_v_metrics['sim']):.6f}, std={np.std(grad_v_metrics['sim']):.6f}, min={np.min(grad_v_metrics['sim']):.6f}")
+            print(f"L1 error: mean={np.mean(grad_v_metrics['l1']):.6f}, std={np.std(grad_v_metrics['l1']):.6f}, max={np.max(grad_v_metrics['l1']):.6f}")
+            print(f"RMSE: mean={np.mean(grad_v_metrics['rmse']):.6f}, std={np.std(grad_v_metrics['rmse']):.6f}, max={np.max(grad_v_metrics['rmse']):.6f}")

 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser(description='Benchmark Block Sparse Attention')
+    parser.add_argument('--batch_size', type=int, default=4, help='Batch size')
+    parser.add_argument('--num_heads', type=int, default=6, help='Number of heads')
+    parser.add_argument('--head_dim', type=int, default=128, help='Head dimension')
+    parser.add_argument('--topk', type=int, default=64, help='Number of kv blocks each q block attends to')
+    parser.add_argument('--seq_lengths', type=int, nargs='+', default=[29120], help='Sequence lengths to benchmark')
+    parser.add_argument('--num_iterations', type=int, default=50, help='Number of test iterations to run')
+    args = parser.parse_args()
+    main(args)
@@ -9,14 +9,14 @@ flex_attention = torch.compile(flex_attention, dynamic=False)


 def flex_test(Q, K, V, kernel_size):
-    mask = get_sliding_tile_attention_mask(kernel_size, (6, 8, 8), (36, 48, 48), 39, 'cuda', 0)
+    mask = get_sliding_tile_attention_mask(kernel_size, (6, 8, 8), (18, 48, 80), 0, 'cuda', 0)
    output = flex_attention(Q, K, V, block_mask=mask)

    return output


 def h100_fwd_kernel_test(Q, K, V, kernel_size):
-    o = sliding_tile_attention(Q, K, V, [kernel_size] * 24, 39, False)
+    o = sliding_tile_attention(Q, K, V, [kernel_size] * 24, 0, False, '18x48x80')
    return o


@@ -37,7 +37,7 @@ def check_correctness(b, h, n, d, causal, mean, std, num_iterations=50, error_mo
            'max_diff': 0
        },
    }
-    kernel_size_ls = [(6, 1, 6), (6, 6, 1)]
+    kernel_size_ls = [(3, 3, 5), (3, 1, 10)]
    from tqdm import tqdm
    for kernel_size in tqdm(kernel_size_ls):
        for _ in range(num_iterations):
@@ -74,12 +74,14 @@ def check_correctness(b, h, n, d, causal, mean, std, num_iterations=50, error_mo

 # Example usage
 b, h, d = 2, 24, 128
-n = 82944  # Sequence length
+n = 69120  # Sequence length
 causal = False
 mean = 1e-1
 std = 10

 # Run correctness check directly
 results = check_correctness(b, h, n, d, causal, mean, std, error_mode='output')
+assert results['TK vs FLEX']['avg_diff'] < 3e-6, f"Average difference: {results['TK vs FLEX']['avg_diff']} is too large"
+assert results['TK vs FLEX']['max_diff'] < 4e-2, f"Maximum difference: {results['TK vs FLEX']['max_diff']} is too large"
 print(f"Average difference: {results['TK vs FLEX']['avg_diff']}")
 print(f"Maximum difference: {results['TK vs FLEX']['max_diff']}")
@@ -2,7 +2,7 @@ import math

 import torch
 from torch.utils.checkpoint import detach_variable
-
+from typing import Tuple
 try:
    from vsa_cuda import block_sparse_fwd, block_sparse_bwd
 except ImportError:
@@ -12,33 +12,116 @@ except ImportError:

 BLOCK_M = 64
 BLOCK_N = 64
-def block_sparse_attention_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num):
+
+def video_sparse_attn(q, k, v, topk, block_size, compress_attn_weight=None):
    """
-    block_sparse_mask: [bs, h, num_q_blocks, num_kv_blocks]. 
-        [*, *, i, j] = 1 means the i-th q block should attend to the j-th kv block.
+    q: [batch_size, num_heads, seq_len, head_dim]
+    k: [batch_size, num_heads, seq_len, head_dim]
+    v: [batch_size, num_heads, seq_len, head_dim]
+    topk: int
+    block_size: int or tuple of 3 ints
+    video_shape: tuple of (T, H, W)
+    compress_attn_weight: [batch_size, num_heads, seq_len, head_dim]
+    select_attn_weight: [batch_size, num_heads, seq_len, head_dim]
+    
+    V1 of sparse attention. Include compress attn and sparse attn branch, use average pooling to compress. 
+    Assume q, k, v is flattened in this way: [batch_size, num_heads, T//block_size[0], H//block_size[1], W//block_size[2], block_size[0], block_size[1], block_size[2]]
    """
-    # assert all elements in q2k_block_sparse_num can be devisible by 2
-    o, lse = block_sparse_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num)
-    return o, lse

-def block_sparse_attention_backward(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num):
-    grad_q, grad_k, grad_v = block_sparse_bwd(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num)
-    return grad_q, grad_k, grad_v
+    if isinstance(block_size, int):
+        block_size = (block_size, block_size, block_size)

-class BlockSparseAttentionFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num):
-        o, lse = block_sparse_attention_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num)
-        ctx.save_for_backward(q, k, v, o, lse, k2q_block_sparse_index, k2q_block_sparse_num)
-        return o
+    block_elements = block_size[0] * block_size[1] * block_size[2]
+    assert block_elements % 64 == 0 and block_elements >= 64
+    assert q.shape[2] % block_elements == 0
+    batch_size, num_heads, seq_len, head_dim = q.shape
+    # compress attn
+    q_compress = q.view(batch_size, num_heads, seq_len // block_elements,
+                        block_elements, head_dim).mean(dim=3)
+    k_compress = k.view(batch_size, num_heads, seq_len // block_elements,
+                        block_elements, head_dim).mean(dim=3)
+    v_compress = v.view(batch_size, num_heads, seq_len // block_elements,
+                        block_elements, head_dim).mean(dim=3)

-    @staticmethod
-    def backward(ctx, grad_output):
-        q, k, v, o, lse, k2q_block_sparse_index, k2q_block_sparse_num = ctx.saved_tensors
-        grad_q, grad_k, grad_v = block_sparse_attention_backward(
-            q, k, v, o, lse, grad_output, k2q_block_sparse_index, k2q_block_sparse_num
-        )
-        return grad_q, grad_k, grad_v, None, None, None, None
+    output_compress, block_attn_score = torch_attention(q_compress, k_compress,
+                                                        v_compress)
+
+    output_compress = output_compress.view(batch_size, num_heads,
+                                           seq_len // block_elements, 1,
+                                           head_dim)
+    output_compress = output_compress.repeat(1, 1, 1, block_elements,
+                                             1).view(batch_size, num_heads,
+                                                     seq_len, head_dim)
+
+    q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num = generate_topk_block_sparse_pattern(
+        block_attn_score, topk)
+
+    output_select = block_sparse_attn(q, k, v, q2k_block_sparse_index,
+                                      q2k_block_sparse_num,
+                                      k2q_block_sparse_index,
+                                      k2q_block_sparse_num)
+
+    if compress_attn_weight is not None:
+        final_output = output_compress * compress_attn_weight + output_select
+    else:
+        final_output = output_compress + output_select
+    return final_output
+
+def torch_attention(q, k, v) -> Tuple[torch.Tensor, torch.Tensor]:
+    QK = torch.matmul(q, k.transpose(-2, -1))
+    QK /= (q.size(-1)**0.5)
+
+    # Causal mask removed since causal is always false
+
+    QK = torch.nn.functional.softmax(QK, dim=-1)
+    output = torch.matmul(QK, v)
+    return output, QK
+
+def generate_topk_block_sparse_pattern(block_attn_score: torch.Tensor,
+                                       topk: int):
+    """
+    Generate a block sparse pattern where each q block attends to exactly topk kv blocks,
+    based on the provided attention scores.
+    
+    Args:
+        block_attn_score: [bs, h, num_q_blocks, num_kv_blocks]
+            Attention scores between query and key blocks
+        topk: int
+            Number of kv blocks each q block attends to
+        
+    Returns:
+        q2k_block_sparse_index: [bs, h, num_q_blocks, topk]
+            Contains the indices of kv blocks that each q block attends to.
+        q2k_block_sparse_num: [bs, h, num_q_blocks]
+            Contains the number of kv blocks that each q block attends to (all equal to topk).
+        k2q_block_sparse_index: [bs, h, num_kv_blocks, max_q_per_kv]
+            Contains the indices of q blocks that attend to each kv block.
+        k2q_block_sparse_num: [bs, h, num_kv_blocks]
+            Contains the number of q blocks that attend to each kv block.
+    """
+    device = block_attn_score.device
+    # Extract dimensions from block_attn_score
+    bs, h, num_q_blocks, num_kv_blocks = block_attn_score.shape
+
+    sorted_result = torch.sort(block_attn_score, dim=-1, descending=True)
+
+    sorted_indice = sorted_result.indices
+
+    q2k_block_sparse_index, _ = torch.sort(sorted_indice[:, :, :, :topk],
+                                           dim=-1)
+    q2k_block_sparse_index = q2k_block_sparse_index.to(dtype=torch.int32)
+    q2k_block_sparse_num = torch.full((bs, h, num_q_blocks),
+                                      topk,
+                                      device=device,
+                                      dtype=torch.int32)
+
+    block_map = topk_index_to_map(q2k_block_sparse_index,
+                                  num_kv_blocks,
+                                  transpose_map=True)
+    k2q_block_sparse_index, k2q_block_sparse_num = map_to_index(
+        block_map.transpose(2, 3))
+
+    return q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num

@torch._dynamo.disable
 def block_sparse_attn(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num):
@@ -61,6 +144,20 @@ def block_sparse_attn(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q
        q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num
    )

+def block_sparse_attention_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num):
+    """
+    block_sparse_mask: [bs, h, num_q_blocks, num_kv_blocks]. 
+        [*, *, i, j] = 1 means the i-th q block should attend to the j-th kv block.
+    """
+    # assert all elements in q2k_block_sparse_num can be devisible by 2
+    o, lse = block_sparse_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num)
+    return o, lse
+
+def block_sparse_attention_backward(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num):
+    grad_output = grad_output.contiguous()
+    grad_q, grad_k, grad_v = block_sparse_bwd(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num)
+    return grad_q, grad_k, grad_v
+
 ## pytorch sdpa version of block sparse ##
 import triton
 import triton.language as tl
@@ -128,6 +225,169 @@ def index_to_mask(q2k_block_sparse_index, q2k_block_sparse_num, BLOCK_Q, BLOCK_K

    return mask

+@triton.jit
+def topk_index_to_map_kernel(
+    map_ptr,
+    index_ptr,
+    map_bs_stride,
+    map_h_stride,
+    map_q_stride,
+    map_kv_stride,
+    index_bs_stride,
+    index_h_stride,
+    index_q_stride,
+    index_kv_stride,
+    topk: tl.constexpr,
+):
+    b, h, q = tl.program_id(0), tl.program_id(1), tl.program_id(2)
+    index_ptr_base = index_ptr + b * index_bs_stride + h * index_h_stride + q * index_q_stride
+    map_ptr_base = map_ptr + b * map_bs_stride + h * map_h_stride + q * map_q_stride
+
+    for i in tl.static_range(topk):
+        index = tl.load(index_ptr_base + i * index_kv_stride)
+        tl.store(map_ptr_base + index * map_kv_stride, 1.0)
+
+@triton.jit
+def map_to_index_kernel(
+    map_ptr,
+    index_ptr,
+    index_num_ptr,
+    map_bs_stride,
+    map_h_stride,
+    map_q_stride,
+    map_kv_stride,
+    index_bs_stride,
+    index_h_stride,
+    index_q_stride,
+    index_kv_stride,
+    index_num_bs_stride,
+    index_num_h_stride,
+    index_num_q_stride,
+    num_kv_blocks: tl.constexpr,
+):
+    b, h, q = tl.program_id(0), tl.program_id(1), tl.program_id(2)
+    index_ptr_base = index_ptr + b * index_bs_stride + h * index_h_stride + q * index_q_stride
+    map_ptr_base = map_ptr + b * map_bs_stride + h * map_h_stride + q * map_q_stride
+
+    num = 0
+    for i in tl.static_range(num_kv_blocks):
+        map_entry = tl.load(map_ptr_base + i * map_kv_stride)
+        if map_entry:
+            tl.store(index_ptr_base + num * index_kv_stride, i)
+            num += 1
+
+    tl.store(
+        index_num_ptr + b * index_num_bs_stride + h * index_num_h_stride +
+        q * index_num_q_stride, num)
+
+def topk_index_to_map(index: torch.Tensor,
+                      num_kv_blocks: int,
+                      transpose_map: bool = False):
+    """
+    Convert topk indices to a map.
+    
+    Args:
+        index: [bs, h, num_q_blocks, topk]
+            The topk indices tensor.
+        num_kv_blocks: int
+            The number of key-value blocks in the block_map returned
+        transpose_map: bool
+            If True, the block_map will be transposed on the final two dimensions.
+    
+    Returns:
+        block_map: [bs, h, num_q_blocks, num_kv_blocks]
+            A binary map where 1 indicates that the q block attends to the kv block.
+    """
+    bs, h, num_q_blocks, topk = index.shape
+
+    if transpose_map is False:
+        block_map = torch.zeros((bs, h, num_q_blocks, num_kv_blocks),
+                                dtype=torch.bool,
+                                device=index.device)
+    else:
+        block_map = torch.zeros((bs, h, num_kv_blocks, num_q_blocks),
+                                dtype=torch.bool,
+                                device=index.device)
+        block_map = block_map.transpose(2, 3)
+
+    grid = (bs, h, num_q_blocks)
+    topk_index_to_map_kernel[grid](
+        block_map,
+        index,
+        block_map.stride(0),
+        block_map.stride(1),
+        block_map.stride(2),
+        block_map.stride(3),
+        index.stride(0),
+        index.stride(1),
+        index.stride(2),
+        index.stride(3),
+        topk=topk,
+    )
+
+    return block_map
+
+def map_to_index(block_map: torch.Tensor):
+    """
+    Convert a block map to indices and counts.
+    
+    Args:
+        block_map: [bs, h, num_q_blocks, num_kv_blocks]
+            The block map tensor.
+    
+    Returns:
+        index: [bs, h, num_q_blocks, num_kv_blocks]
+            The indices of the blocks.
+        index_num: [bs, h, num_q_blocks]
+            The number of blocks for each q block.
+    """
+    bs, h, num_q_blocks, num_kv_blocks = block_map.shape
+
+    index = torch.full((block_map.shape),
+                       -1,
+                       dtype=torch.int32,
+                       device=block_map.device)
+    index_num = torch.empty((bs, h, num_q_blocks),
+                            dtype=torch.int32,
+                            device=block_map.device)
+
+    grid = (bs, h, num_q_blocks)
+    map_to_index_kernel[grid](
+        block_map,
+        index,
+        index_num,
+        block_map.stride(0),
+        block_map.stride(1),
+        block_map.stride(2),
+        block_map.stride(3),
+        index.stride(0),
+        index.stride(1),
+        index.stride(2),
+        index.stride(3),
+        index_num.stride(0),
+        index_num.stride(1),
+        index_num.stride(2),
+        num_kv_blocks=num_kv_blocks,
+    )
+
+    return index, index_num
+
+class BlockSparseAttentionFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num):
+        o, lse = block_sparse_attention_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num)
+        ctx.save_for_backward(q, k, v, o, lse, k2q_block_sparse_index, k2q_block_sparse_num)
+        return o
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        q, k, v, o, lse, k2q_block_sparse_index, k2q_block_sparse_num = ctx.saved_tensors
+        grad_q, grad_k, grad_v = block_sparse_attention_backward(
+            q, k, v, o, lse, grad_output, k2q_block_sparse_index, k2q_block_sparse_num
+        )
+        return grad_q, grad_k, grad_v, None, None, None, None
+
+
 class DummyOperator(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
@@ -207,4 +467,4 @@ class BlockSparseAttnTorch:

        o = DummyOperator.apply(output)
        o.register_hook(self.recompute)
-        return o
+        return o
@@ -3,6 +3,8 @@
 #include "kittens.cuh"
 #include <cooperative_groups.h>
 #include <iostream>
+#include <c10/cuda/CUDAGuard.h>
+

 using namespace kittens;
 namespace cg = cooperative_groups;
@@ -940,8 +942,9 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v
    float* l_ptr = reinterpret_cast<float*>(l_vec.data_ptr<float>());
    float* d_l   = reinterpret_cast<float*>(l_ptr);

-    cudaDeviceSynchronize();
-    auto stream = at::cuda::getCurrentCUDAStream().stream(); 
+    //cudadevicesynchronize();
+    const c10::cuda::OptionalCUDAGuard device_guard(q.device());    
+    const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream(); 

    if (head_dim == 64) {
        using q_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>;
@@ -966,7 +969,7 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v

        globals g{qg_arg, kg_arg, vg_arg, lg_arg, og_arg, static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_kv_blocks_per_q), reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr())};

-        auto mem_size = 54000;
+        constexpr int mem_size = 54000;

        dim3 grid(seq_len/(64), qo_heads, batch);

@@ -979,7 +982,7 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v
        fwd_attend_ker<64><<<grid, (128), mem_size, stream>>>(g);

        CHECK_CUDA_ERROR(cudaGetLastError());
-        cudaStreamSynchronize(stream);
+        // cudaStreamSynchronize(stream);
    }

    if (head_dim == 128) {
@@ -1005,7 +1008,7 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v

        globals g{qg_arg, kg_arg, vg_arg, lg_arg, og_arg, static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_kv_blocks_per_q), reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr())};

-        auto mem_size = 54000;
+        constexpr int mem_size = 54000;

        dim3 grid(seq_len/(64), qo_heads, batch);

@@ -1018,11 +1021,11 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v
        fwd_attend_ker<128><<<grid, (128), mem_size, stream>>>(g);

        CHECK_CUDA_ERROR(cudaGetLastError());
-        cudaStreamSynchronize(stream);
+        // cudaStreamSynchronize(stream);
    }

    return {o, l_vec};
-    cudaDeviceSynchronize();
+    //cudadevicesynchronize();
 }

 std::vector<torch::Tensor> 
@@ -1132,13 +1135,14 @@ block_sparse_attention_backward(torch::Tensor q,
    float* d_kg = reinterpret_cast<float*>(kg_ptr);
    float* d_vg = reinterpret_cast<float*>(vg_ptr);

-    auto mem_size = kittens::MAX_SHARED_MEMORY; 
-    auto threads  = 4 * kittens::WARP_THREADS;
+    constexpr int mem_size = kittens::MAX_SHARED_MEMORY; 
+    int threads  = 4 * kittens::WARP_THREADS;

-    cudaDeviceSynchronize();
-    auto stream = at::cuda::getCurrentCUDAStream().stream();
+    //cudadevicesynchronize();
+    const c10::cuda::OptionalCUDAGuard device_guard(q.device());    
+    const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream(); 

-    cudaStreamSynchronize(stream);
+    //  cudaStreamSynchronize(stream);

    // TORCH_CHECK(seq_len % (4*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 256");
    dim3 grid_bwd(seq_len/(4*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
@@ -1222,7 +1226,7 @@ block_sparse_attention_backward(torch::Tensor q,
        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
        threads = 128;

-        cudaDeviceSynchronize();
+        //cudadevicesynchronize();

        {
            cudaFuncSetAttribute(
@@ -1240,8 +1244,8 @@ block_sparse_attention_backward(torch::Tensor q,
        }

        // CHECK_CUDA_ERROR(cudaGetLastError());
-        cudaStreamSynchronize(stream);
-        cudaDeviceSynchronize();
+        // cudaStreamSynchronize(stream);
+        //cudadevicesynchronize();
        // const auto kernel_end = std::chrono::high_resolution_clock::now();
        // std::cout << "Kernel Time: " << std::chrono::duration_cast<std::chrono::microseconds>(kernel_end - start).count() << "us" << std::endl;
        // std::cout << "---" << std::endl;
@@ -1326,7 +1330,7 @@ block_sparse_attention_backward(torch::Tensor q,
        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
        threads = 128;

-        cudaDeviceSynchronize();
+        //cudadevicesynchronize();

        {
            cudaFuncSetAttribute(
@@ -1338,10 +1342,10 @@ block_sparse_attention_backward(torch::Tensor q,
            bwd_attend_ker<128><<<grid_bwd_2, threads, 113000, stream>>>(bwd_global); 
        }

-        cudaStreamSynchronize(stream);
-        cudaDeviceSynchronize();
+        // cudaStreamSynchronize(stream);
+        //cudadevicesynchronize();
    }

    return {qg, kg, vg};
-    cudaDeviceSynchronize();
+    //cudadevicesynchronize();
 }
@@ -1,7 +1,9 @@
-FROM nvidia/cuda:12.4.1-devel-ubuntu20.04
+FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04

 ENV DEBIAN_FRONTEND=noninteractive

+SHELL ["/bin/bash", "-c"]
+
 WORKDIR /FastVideo

 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -9,17 +11,25 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    ca-certificates \
    openssh-server \
+    zsh \
+    vim \
+    curl \
+    gcc-11 \
+    g++-11 \
+    clang-11 \
    && rm -rf /var/lib/apt/lists/*

-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
-    bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda && \
-    rm Miniconda3-latest-Linux-x86_64.sh
+# Set up C++20 compilers for ThunderKittens
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11

-ENV PATH=/opt/conda/bin:$PATH
+# Set CUDA environment variables
+ENV CUDA_HOME=/usr/local/cuda-12.8
+ENV PATH=${CUDA_HOME}/bin:${PATH}
+ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH

-RUN conda create --name fastvideo-dev python=3.12.9 -y
-
-SHELL ["/bin/bash", "-c"]
+# Install uv and source its environment
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    echo 'source $HOME/.local/bin/env' >> /root/.bashrc

 # Copy just the pyproject.toml first to leverage Docker cache
 COPY pyproject.toml ./
@@ -27,22 +37,36 @@ COPY pyproject.toml ./
 # Create a dummy README to satisfy the installation
 RUN echo "# Placeholder" > README.md

-RUN conda run -n fastvideo-dev pip install --no-cache-dir --upgrade pip && \
-    conda run -n fastvideo-dev pip install --no-cache-dir .[dev] && \
-    conda run -n fastvideo-dev pip install --no-cache-dir flash-attn==2.7.4.post1 --no-build-isolation && \
-    conda clean -afy
+# Create and activate virtual environment with specific Python version and seed
+RUN source $HOME/.local/bin/env && \
+    uv venv --python 3.12 --seed /opt/venv && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir --upgrade pip && \
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir flash-attn==2.8.0.post2 --no-build-isolation

 COPY . .

-RUN conda run -n fastvideo-dev pip install --no-cache-dir -e .[dev]
+# Install dependencies using uv and set up shell configuration
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir -e .[dev] && \
+    git config --unset-all http.https://github.com/.extraheader || true && \
+    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
+    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile

-# Remove authentication headers
-RUN git config --unset-all http.https://github.com/.extraheader || true
+# Install STA (Sliding Tile Attention)
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn && \
+    git submodule update --init --recursive && \
+    python setup_sta.py install

-# Set up automatic conda environment activation for all shells
-RUN echo 'source /opt/conda/etc/profile.d/conda.sh' >> /root/.bashrc && \
-    echo 'conda activate fastvideo-dev' >> /root/.bashrc && \
-    # Ensure .bashrc is sourced for SSH login shells
-    echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
+# Install VSA 
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn && \
+    git submodule update --init --recursive && \
+    python setup_vsa.py install

 EXPOSE 22
@@ -1,7 +1,7 @@
 (sta-demo)=

 # 🔍 Demo
-There is a demo for 2D STA with window size (6,6) operating on a (10, 10) image.
+This is is a demo for 2D STA with window size (6,6) operating on a (10, 10) image.

 <div style="text-align: center;">
  <video controls width="800">
@@ -9,3 +9,9 @@ There is a demo for 2D STA with window size (6,6) operating on a (10, 10) image.
    Your browser does not support the video tag.
  </video>
 </div>
+
+You can run STA using the following command:
+
+```bash
+bash scripts/inference/v1_inference_wan_STA.sh
+```
@@ -7,70 +7,40 @@ To save GPU memory, we precompute text embeddings and VAE latents to eliminate t
 We provide a sample dataset to help you get started. Download the source media using the following command:

 ```bash
-python scripts/huggingface/download_hf.py --repo_id=FastVideo/Image-Vid-Finetune-Src --local_dir=data/Image-Vid-Finetune-Src --repo_type=dataset
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/mini_i2v_dataset --local_dir=FastVideo/mini_i2v_dataset --repo_type=dataset
 ```

+The folder `crush-smol_raw/` contains raw videos and captions for testing preprocessing, while `crush-smol_preprocessed/` contains latents prepared for testing training.
+
 To preprocess the dataset for fine-tuning or distillation, run:

 ```
-bash scripts/preprocess/preprocess_mochi_data.sh # for mochi
-bash scripts/preprocess/preprocess_hunyuan_data.sh # for hunyuan
+bash scripts/preprocess/v1_preprocess_wan_data_t2v # for wan
 ```

-The preprocessed dataset will be stored in `Image-Vid-Finetune-Mochi` or `Image-Vid-Finetune-HunYuan` correspondingly.
-
 ## Process your own dataset

-If you wish to create your own dataset for finetuning or distillation, please structure you video dataset in the following format:
+If you wish to create your own dataset for finetuning or distillation, please refer `mini_i2v_dataset/crush-smol_raw/` to structure you video dataset in the following format:

 ```
-path_to_dataset_folder/
-├── media/
-│   ├── 0.jpg
+path_to_your_dataset_folder/
+├── videos/
+│   ├── 0.mp4
 │   ├── 1.mp4
-│   ├── 2.jpg
-├── video2caption.json
-└── merge.txt
+├── videos.txt
+└── prompt.txt
 ```

-Format the JSON file as a list, where each item represents a media source:
+To geranate the `videos2caption.json` and `merge.txt`, run

-For image media,
-
-```
-{
-    "path": "0.jpg",
-    "cap": ["captions"]
-}
+``` python
+python scripts/dataset_preparation/prepare_json_file.py --data_folder mini_i2v_dataset/crush-smol_raw/ --output your_output_folder
 ```

-For video media,
+Adjust the `DATA_MERGE_PATH` and `OUTPUT_DIR` in `scripts/preprocess/v1_preprocess_****.sh` accordingly and run:

 ```
-{
-    "path": "1.mp4",
-    "resolution": {
-      "width": 848,
-      "height": 480
-    },
-    "fps": 30.0,
-    "duration": 6.033333333333333,
-    "cap": [
-      "caption"
-    ]
-  }
-```
-
-Use a txt file (merge.txt) to contain the source folder for media and the JSON file for meta information:
-
-```
-path_to_media_source_foder,path_to_json_file
-```
-
-Adjust the `DATA_MERGE_PATH` and `OUTPUT_DIR` in `scripts/preprocess/preprocess_****_data.sh` accordingly and run:
-
-```
-bash scripts/preprocess/preprocess_****_data.sh
+bash scripts/preprocess/v1_preprocess_****.sh
 ```

 The preprocessed data will be put into the `OUTPUT_DIR` and the `videos2caption.json` can be used in finetune and distill scripts.
@@ -16,6 +16,13 @@ bash scripts/finetune/finetune_mochi.sh # for mochi
 ```

 **Note that for finetuning, we did not tune the hyperparameters in the provided script.**
+## ⚡ Finetune with VSA
+Follow [data_preprocess.md](#v0-data-preprocess) to get parquet files for preproccessed latent, and then run:
+
+```bash
+bash scripts/finetune/finetune_v1_VSA.sh
+```
+
 ## ⚡ Lora Finetune

 Hunyuan supports Lora fine-tuning of videos up to 720p. Demos and prompts of Black-Myth-Wukong can be found in [here](https://huggingface.co/FastVideo/Hunyuan-Black-Myth-Wukong-lora-weight). You can download the Lora weight through:
@@ -10,7 +10,7 @@ def main():
    # attempt to identify the optimal arguments.
    generator = VideoGenerator.from_pretrained(
        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        # if num_gpus > 1, FastVideo will automatically handle distributed setup
+        # FastVideo will automatically handle distributed setup
        num_gpus=2,
        use_fsdp_inference=True,
        use_cpu_offload=False
@@ -5,7 +5,7 @@ export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
 export MODEL_BASE=Wan-AI/Wan2.1-T2V-14B-Diffusers

 base_port=29503
-num_gpu=$(nvidia-smi --query-gpu=gpu_name --format=csv,noheader | wc -l)
+num_gpu=1
 gpu_ids=$(seq 0 $((num_gpu-1)))
 skip_time_steps=12

@@ -14,7 +14,7 @@ STA_mode="STA_searching"
 for i in $gpu_ids; do
    port=$((base_port+i))
    CUDA_VISIBLE_DEVICES=$i MASTER_PORT=$port python examples/inference/sta_mask_search/wan_example.py \
-        --prompt_path ./assets/prompt_extend_${i}.txt \
+        --prompt_path ./assets/prompt_${i}.txt \
        --output_path $output_path \
        --STA_mode $STA_mode &
    sleep 1
@@ -27,7 +27,7 @@ STA_mode="STA_tuning"
 for i in $gpu_ids; do
    port=$((base_port+i))
    CUDA_VISIBLE_DEVICES=$i MASTER_PORT=$port python examples/inference/sta_mask_search/wan_example.py \
-        --prompt_path ./assets/prompt_extend_${i}.txt \
+        --prompt_path ./assets/prompt_${i}.txt \
        --output_path $output_path \
        --STA_mode $STA_mode \
        --skip_time_steps $skip_time_steps &
@@ -0,0 +1,10 @@
+This directory contain e2e examples scripts for finetuning Wan2.1 I2V. 
+
+Execute the following commands from `FastVideo/` to run training:
+
+- Download crush-smol dataset:
+`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/download_dataset.sh`
+- Preprocess the videos and captions into latents:
+`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh`
+- Edit the following file and run finetuning:
+`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh`
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -0,0 +1,91 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
+VALIDATION_DIR="data/crush-smol_processed_i2v/validation_parquet_dataset/"
+NUM_GPUS=8
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_i2v_finetune"
+  --output_dir "$DATA_DIR/outputs/wan_i2v_finetune"
+  --max_train_steps 2000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 8
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size 8
+  --tp_size 8
+  --hsdp_replicate_dim 1
+  --hsdp_shard_dim 8
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR"
+  --dataloader_num_workers 1
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_preprocessed_path "$VALIDATION_DIR"
+  --validation_steps 100
+  --validation_sampling_steps "40"
+  --validation_guidance_scale "1.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --checkpointing_steps 1000
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --allow_tf32
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,130 @@
+#!/bin/bash
+#SBATCH --job-name=i2v
+#SBATCH --partition=main
+#SBATCH --qos=hao
+#SBATCH --nodes=4
+#SBATCH --ntasks=4
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --nodelist=fs-mbz-gpu-[100-850]
+#SBATCH --mem=1440G
+#SBATCH --output=i2v_output/i2v_%j.out
+#SBATCH --error=i2v_output/i2v_%j.err
+#SBATCH --exclusive
+set -e -x
+
+# Environment Setup
+source ~/conda/miniconda/bin/activate
+conda activate will-fv
+
+# Basic Info
+export WANDB_MODE="online"
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
+export TOKENIZERS_PARALLELISM=false
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+echo "MASTER_ADDR: $MASTER_ADDR"
+echo "NODE_RANK: $NODE_RANK"
+
+# Configs
+NUM_GPUS=8
+
+MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
+VALIDATION_DIR="data/crush-smol_processed_i2v/validation_parquet_dataset/"
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+
+# Training arguments
+training_args=(
+  --tracker_project_name wan_i2v_finetune
+  --output_dir="$DATA_DIR/outputs/wan_i2v_finetune_2n"
+  --max_train_steps=2000
+  --train_batch_size=2
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps=1
+  --num_latent_t 8
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size $NUM_GPUS
+  --tp_size $NUM_GPUS
+  --hsdp_replicate_dim $SLURM_JOB_NUM_NODES
+  --hsdp_shard_dim $NUM_GPUS
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR"
+  --dataloader_num_workers 10
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_preprocessed_path "$VALIDATION_DIR"
+  --validation_steps 100
+  --validation_sampling_steps "40"
+  --validation_guidance_scale "1.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate=1e-5
+  --mixed_precision="bf16"
+  --checkpointing_steps=1000
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --allow_tf32
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+)
+
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,25 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+MODEL_TYPE="wan"
+DATA_MERGE_PATH="data/crush-smol/merge.txt"
+OUTPUT_DIR="data/crush-smol_processed_i2v/"
+VALIDATION_PATH="examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation.json"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
+    --model_path $MODEL_PATH \
+    --data_merge_path $DATA_MERGE_PATH \
+    --preprocess_video_batch_size 8 \
+    --max_height 480 \
+    --max_width 832 \
+    --num_frames 77 \
+    --dataloader_num_workers 0 \
+    --output_dir=$OUTPUT_DIR \
+    --model_type $MODEL_TYPE \
+    --train_fps 16 \
+    --validation_dataset_file $VALIDATION_PATH \
+    --samples_per_file 8 \
+    --flush_frequency 8 \
+    --preprocess_task "i2v" 
@@ -0,0 +1,31 @@
+{
+  "data": [
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}
@@ -0,0 +1,10 @@
+This directory contain e2e examples scripts for finetuning Wan2.1 T2v. 
+
+Execute the following commands from `FastVideo/` to run training:
+
+- Download crush-smol dataset:
+`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/download_dataset.sh`
+- Preprocess the videos and captions into latents:
+`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/preprocess_wan_data_t2v.sh`
+- Edit the following file and run finetuning:
+`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.sh`
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -0,0 +1,90 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
+VALIDATION_DIR="data/crush-smol_processed_t2v/validation_parquet_dataset/"
+NUM_GPUS=4
+# export CUDA_VISIBLE_DEVICES=4,5
+
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_t2v_finetune"
+  --output_dir "outputs/wan_t2v_finetune"
+  --max_train_steps 5000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 8
+  --num_latent_t 8 
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS 
+  --sp_size $NUM_GPUS 
+  --tp_size $NUM_GPUS
+  --hsdp_replicate_dim 1
+  --hsdp_shard_dim $NUM_GPUS
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path $DATA_DIR
+  --dataloader_num_workers 1
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation 
+  --validation_preprocessed_path $VALIDATION_DIR
+  --validation_steps 50
+  --validation_sampling_steps "50" 
+  --validation_guidance_scale "1.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 5e-5
+  --mixed_precision "bf16"
+  --checkpointing_steps 6000
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --allow_tf32
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+)
+
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+    fastvideo/v1/training/wan_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,127 @@
+#!/bin/bash
+#SBATCH --job-name=t2v
+#SBATCH --partition=main
+#SBATCH --qos=hao
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --nodelist=fs-mbz-gpu-[100-850]
+#SBATCH --mem=1440G
+#SBATCH --output=t2v_output/t2v_%j.out
+#SBATCH --error=t2v_output/t2v_%j.err
+#SBATCH --exclusive
+set -e -x
+
+# Environment Setup
+source ~/conda/miniconda/bin/activate
+conda activate will-fv
+
+# Basic Info
+export WANDB_MODE="online"
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
+export TOKENIZERS_PARALLELISM=false
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+echo "MASTER_ADDR: $MASTER_ADDR"
+echo "NODE_RANK: $NODE_RANK"
+
+# Configs
+NUM_GPUS=8
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
+VALIDATION_DIR="data/crush-smol_processed_t2v/validation_parquet_dataset/"
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name wan_t2v_finetune
+  --output_dir="outputs/wan_t2v_finetune"
+  --max_train_steps=1000
+  --train_batch_size=4
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps=1
+  --num_latent_t 8
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size 4
+  --tp_size 4
+  --hsdp_replicate_dim 2
+  --hsdp_shard_dim 4
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR"
+  --dataloader_num_workers 10
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_preprocessed_path "$VALIDATION_DIR"
+  --validation_steps 100
+  --validation_sampling_steps "50"
+  --validation_guidance_scale "1.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate=5e-5
+  --mixed_precision="bf16"
+  --checkpointing_steps=500
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --allow_tf32
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+)
+
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/v1/training/wan_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,25 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+MODEL_TYPE="wan"
+DATA_MERGE_PATH="data/crush-smol/merge.txt"
+OUTPUT_DIR="data/crush-smol_processed_t2v/"
+VALIDATION_PATH="examples/training/finetune/wan_t2v_1_3b/crush_smol/validation.json"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
+    --model_path $MODEL_PATH \
+    --data_merge_path $DATA_MERGE_PATH \
+    --preprocess_video_batch_size 8 \
+    --max_height 480 \
+    --max_width 832 \
+    --num_frames 77 \
+    --dataloader_num_workers 0 \
+    --output_dir=$OUTPUT_DIR \
+    --model_type $MODEL_TYPE \
+    --train_fps 16 \
+    --validation_dataset_file $VALIDATION_PATH \
+    --samples_per_file 8 \
+    --flush_frequency 8 \
+    --preprocess_task "t2v" 
@@ -0,0 +1,31 @@
+{
+  "data": [
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}
@@ -104,13 +104,7 @@ if __name__ == "__main__":
        default=None,
        help="The output directory where the model predictions and checkpoints will be written.",
    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
-    )
+

    args = parser.parse_args()
    main(args)
@@ -671,13 +671,6 @@ if __name__ == "__main__":
        help=("Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
              ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'),
    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
-    )

    # optimizer & scheduler & Training
    parser.add_argument("--num_train_epochs", type=int, default=100)
@@ -693,13 +693,6 @@ if __name__ == "__main__":
        help=("Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
              ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'),
    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
-    )

    # optimizer & scheduler & Training
    parser.add_argument("--num_train_epochs", type=int, default=100)
@@ -520,13 +520,7 @@ if __name__ == "__main__":
        help=("Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
              ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'),
    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
-    )
+

    # optimizer & scheduler & Training
    parser.add_argument("--num_train_epochs", type=int, default=100)
@@ -6,6 +6,8 @@ from typing import Any, Dict, List, Optional, Tuple

 import numpy as np

+from fastvideo.v1.utils import dict_to_3d_list
+

 def configure_sta(mode: str = 'STA_searching',
                  layer_num: int = 40,
@@ -349,21 +351,6 @@ def select_best_mask_strategy(
    return best_mask_strategy, overall_sparsity, strategy_counts


-def dict_to_3d_list(mask_strategy: Optional[Dict[str, List[int]]],
-                    t_max: int = 50,
-                    l_max: int = 60,
-                    h_max: int = 24) -> List[List[List[Optional[List[int]]]]]:
-    result: List[List[List[Optional[List[int]]]]] = [[[
-        None for _ in range(h_max)
-    ] for _ in range(l_max)] for _ in range(t_max)]
-    if mask_strategy is None:
-        return result
-    for key, value in mask_strategy.items():
-        t, layer_idx, h = map(int, key.split('_'))
-        result[t][layer_idx][h] = value
-    return result
-
-
 def save_mask_search_results(
        mask_search_final_result: List[Dict[str, List[float]]],
        prompt: str,
@@ -10,8 +10,8 @@ from fastvideo.v1.attention.selector import get_attn_backend

 __all__ = [
    "DistributedAttention",
-    "DistributedAttention_VSA",
    "LocalAttention",
+    "DistributedAttention_VSA",
    "AttentionBackend",
    "AttentionMetadata",
    "AttentionMetadataBuilder",
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import json
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Type
+from typing import Any, List, Optional, Type

 import torch
 from einops import rearrange
@@ -17,33 +17,11 @@ from fastvideo.v1.fastvideo_args import FastVideoArgs
 from fastvideo.v1.forward_context import ForwardContext, get_forward_context
 from fastvideo.v1.logger import init_logger
 from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.v1.utils import dict_to_3d_list

 logger = init_logger(__name__)


-# TODO(will-refactor): move this to a utils file
-def dict_to_3d_list(
-        mask_strategy: Dict[str,
-                            Any]) -> List[List[List[Optional[torch.Tensor]]]]:
-    indices = [tuple(map(int, key.split('_'))) for key in mask_strategy]
-
-    max_timesteps_idx = max(
-        timesteps_idx for timesteps_idx, layer_idx, head_idx in indices) + 1
-    max_layer_idx = max(layer_idx
-                        for timesteps_idx, layer_idx, head_idx in indices) + 1
-    max_head_idx = max(head_idx
-                       for timesteps_idx, layer_idx, head_idx in indices) + 1
-
-    result = [[[None for _ in range(max_head_idx)]
-               for _ in range(max_layer_idx)] for _ in range(max_timesteps_idx)]
-
-    for key, value in mask_strategy.items():
-        timesteps_idx, layer_idx, head_idx = map(int, key.split('_'))
-        result[timesteps_idx][layer_idx][head_idx] = value
-
-    return result
-
-
 class RangeDict(dict):

    def __getitem__(self, item: int) -> str:
@@ -287,16 +265,10 @@ class SlidingTileAttentionImpl(AttentionImpl):
                    forward_batch.mask_search_final_result_pos[timestep].append(
                        layer_loss_save)
        else:
-            # windows = [
-            #     self.mask_strategy[timestep][layer_idx][head_idx + start_head]
-            #     for head_idx in range(head_num)
-            # ]
            windows = [
                STA_param[head_idx + start_head] for head_idx in range(head_num)
            ]
-            # if has_text is False:
-            #     from IPython import embed
-            #     embed()
+
            hidden_states = sliding_tile_attention(
                query, key, value, windows, text_length, has_text,
                self.dit_seq_shape_str).transpose(1, 2)
@@ -1,18 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 import math
 from dataclasses import dataclass
-from typing import Any, List, Optional, Type, cast
+from typing import List, Optional, Type

 import torch
-import triton
-import triton.language as tl
 from einops import rearrange

 try:
-    from vsa import block_sparse_attn
-except ImportError:  # noqa: E722
-    block_sparse_attn = None
-from typing import Tuple
+    from vsa import video_sparse_attn
+except ImportError:
+    video_sparse_attn = None

 from fastvideo.v1.attention.backends.abstract import (AttentionBackend,
                                                      AttentionImpl,
@@ -75,14 +72,18 @@ class VideoSparseAttentionMetadataBuilder(AttentionMetadataBuilder):
        if forward_batch.latents is None:
            raise ValueError("latents cannot be None")

-        raw_latent_shape = forward_batch.latents.shape
-        patch_size = fastvideo_args.dit_config.patch_size
+        raw_latent_shape = forward_batch.raw_latent_shape
+        if raw_latent_shape is None:
+            raise ValueError("raw_latent_shape cannot be None")
+
+        patch_size = fastvideo_args.pipeline_config.dit_config.patch_size
        dit_seq_shape = [
            raw_latent_shape[2] // patch_size[0],
            raw_latent_shape[3] // patch_size[1],
            raw_latent_shape[4] // patch_size[2]
        ]
        VSA_sparsity = forward_batch.VSA_sparsity
+
        return VideoSparseAttentionMetadata(current_timestep=current_timestep,
                                            dit_seq_shape=dit_seq_shape,
                                            VSA_sparsity=VSA_sparsity)
@@ -177,12 +178,16 @@ class VideoSparseAttentionImpl(AttentionImpl):
        value = value.transpose(1, 2).contiguous()
        gate_compress = gate_compress.transpose(1, 2).contiguous()

+        VSA_sparsity = attn_metadata.VSA_sparsity
+
        cur_topk = math.ceil(
-            (1 - attn_metadata.VSA_sparsity) *
+            (1 - VSA_sparsity) *
            (self.img_seq_length / math.prod(self.VSA_base_tile_size)))

-        # Cast to Any to bypass type checking for untyped function
-        hidden_states = cast(Any, sparse_attn_c_s_p)(
+        if video_sparse_attn is None:
+            raise NotImplementedError("video_sparse_attn is not installed")
+
+        hidden_states = video_sparse_attn(
            query,
            key,
            value,
@@ -191,267 +196,3 @@ class VideoSparseAttentionImpl(AttentionImpl):
            compress_attn_weight=gate_compress).transpose(1, 2)

        return hidden_states
-
-
-def torch_attention(q, k, v) -> Tuple[torch.Tensor, torch.Tensor]:
-    QK = torch.matmul(q, k.transpose(-2, -1))
-    QK /= (q.size(-1)**0.5)
-
-    # Causal mask removed since causal is always false
-
-    QK = torch.nn.functional.softmax(QK, dim=-1)
-    output = torch.matmul(QK, v)
-    return output, QK
-
-
-def sparse_attn_c_s_p(q, k, v, topk, block_size, compress_attn_weight=None):
-    """
-    q: [batch_size, num_heads, seq_len, head_dim]
-    k: [batch_size, num_heads, seq_len, head_dim]
-    v: [batch_size, num_heads, seq_len, head_dim]
-    topk: int
-    block_size: int or tuple of 3 ints
-    video_shape: tuple of (T, H, W)
-    compress_attn_weight: [batch_size, num_heads, seq_len, head_dim]
-    select_attn_weight: [batch_size, num_heads, seq_len, head_dim]
-    
-    V1 of sparse attention. Include compress attn and sparse attn branch, use average pooling to compress. 
-    Assume q, k, v is flattened in this way: [batch_size, num_heads, T//block_size[0], H//block_size[1], W//block_size[2], block_size[0], block_size[1], block_size[2]]
-    """
-
-    if isinstance(block_size, int):
-        block_size = (block_size, block_size, block_size)
-
-    block_elements = block_size[0] * block_size[1] * block_size[2]
-    assert block_elements % 64 == 0 and block_elements >= 64
-    assert q.shape[2] % block_elements == 0
-    batch_size, num_heads, seq_len, head_dim = q.shape
-    # compress attn
-    q_compress = q.view(batch_size, num_heads, seq_len // block_elements,
-                        block_elements, head_dim).mean(dim=3)
-    k_compress = k.view(batch_size, num_heads, seq_len // block_elements,
-                        block_elements, head_dim).mean(dim=3)
-    v_compress = v.view(batch_size, num_heads, seq_len // block_elements,
-                        block_elements, head_dim).mean(dim=3)
-
-    output_compress, block_attn_score = torch_attention(q_compress, k_compress,
-                                                        v_compress)
-
-    output_compress = output_compress.view(batch_size, num_heads,
-                                           seq_len // block_elements, 1,
-                                           head_dim)
-    output_compress = output_compress.repeat(1, 1, 1, block_elements,
-                                             1).view(batch_size, num_heads,
-                                                     seq_len, head_dim)
-
-    q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num = generate_topk_block_sparse_pattern(
-        block_attn_score, topk)
-
-    output_select = block_sparse_attn(q, k, v, q2k_block_sparse_index,
-                                      q2k_block_sparse_num,
-                                      k2q_block_sparse_index,
-                                      k2q_block_sparse_num)
-
-    if compress_attn_weight is not None:
-        final_output = output_compress * compress_attn_weight + output_select
-    else:
-        final_output = output_compress + output_select
-    return final_output
-
-
-@triton.jit
-def topk_index_to_map_kernel(
-    map_ptr,
-    index_ptr,
-    map_bs_stride,
-    map_h_stride,
-    map_q_stride,
-    map_kv_stride,
-    index_bs_stride,
-    index_h_stride,
-    index_q_stride,
-    index_kv_stride,
-    topk: tl.constexpr,
-):
-    b, h, q = tl.program_id(0), tl.program_id(1), tl.program_id(2)
-    index_ptr_base = index_ptr + b * index_bs_stride + h * index_h_stride + q * index_q_stride
-    map_ptr_base = map_ptr + b * map_bs_stride + h * map_h_stride + q * map_q_stride
-
-    for i in tl.static_range(topk):
-        index = tl.load(index_ptr_base + i * index_kv_stride)
-        tl.store(map_ptr_base + index * map_kv_stride, 1.0)
-
-
-@triton.jit
-def map_to_index_kernel(
-    map_ptr,
-    index_ptr,
-    index_num_ptr,
-    map_bs_stride,
-    map_h_stride,
-    map_q_stride,
-    map_kv_stride,
-    index_bs_stride,
-    index_h_stride,
-    index_q_stride,
-    index_kv_stride,
-    index_num_bs_stride,
-    index_num_h_stride,
-    index_num_q_stride,
-    num_kv_blocks: tl.constexpr,
-):
-    b, h, q = tl.program_id(0), tl.program_id(1), tl.program_id(2)
-    index_ptr_base = index_ptr + b * index_bs_stride + h * index_h_stride + q * index_q_stride
-    map_ptr_base = map_ptr + b * map_bs_stride + h * map_h_stride + q * map_q_stride
-
-    num = 0
-    for i in tl.static_range(num_kv_blocks):
-        map_entry = tl.load(map_ptr_base + i * map_kv_stride)
-        if map_entry:
-            tl.store(index_ptr_base + num * index_kv_stride, i)
-            num += 1
-
-    tl.store(
-        index_num_ptr + b * index_num_bs_stride + h * index_num_h_stride +
-        q * index_num_q_stride, num)
-
-
-def topk_index_to_map(index: torch.Tensor,
-                      num_kv_blocks: int,
-                      transpose_map: bool = False):
-    """
-    Convert topk indices to a map.
-    
-    Args:
-        index: [bs, h, num_q_blocks, topk]
-            The topk indices tensor.
-        num_kv_blocks: int
-            The number of key-value blocks in the block_map returned
-        transpose_map: bool
-            If True, the block_map will be transposed on the final two dimensions.
-    
-    Returns:
-        block_map: [bs, h, num_q_blocks, num_kv_blocks]
-            A binary map where 1 indicates that the q block attends to the kv block.
-    """
-    bs, h, num_q_blocks, topk = index.shape
-
-    if transpose_map is False:
-        block_map = torch.zeros((bs, h, num_q_blocks, num_kv_blocks),
-                                dtype=torch.bool,
-                                device=index.device)
-    else:
-        block_map = torch.zeros((bs, h, num_kv_blocks, num_q_blocks),
-                                dtype=torch.bool,
-                                device=index.device)
-        block_map = block_map.transpose(2, 3)
-
-    grid = (bs, h, num_q_blocks)
-    topk_index_to_map_kernel[grid](
-        block_map,
-        index,
-        block_map.stride(0),
-        block_map.stride(1),
-        block_map.stride(2),
-        block_map.stride(3),
-        index.stride(0),
-        index.stride(1),
-        index.stride(2),
-        index.stride(3),
-        topk=topk,
-    )
-
-    return block_map
-
-
-def map_to_index(block_map: torch.Tensor):
-    """
-    Convert a block map to indices and counts.
-    
-    Args:
-        block_map: [bs, h, num_q_blocks, num_kv_blocks]
-            The block map tensor.
-    
-    Returns:
-        index: [bs, h, num_q_blocks, num_kv_blocks]
-            The indices of the blocks.
-        index_num: [bs, h, num_q_blocks]
-            The number of blocks for each q block.
-    """
-    bs, h, num_q_blocks, num_kv_blocks = block_map.shape
-
-    index = torch.full((block_map.shape),
-                       -1,
-                       dtype=torch.int32,
-                       device=block_map.device)
-    index_num = torch.empty((bs, h, num_q_blocks),
-                            dtype=torch.int32,
-                            device=block_map.device)
-
-    grid = (bs, h, num_q_blocks)
-    map_to_index_kernel[grid](
-        block_map,
-        index,
-        index_num,
-        block_map.stride(0),
-        block_map.stride(1),
-        block_map.stride(2),
-        block_map.stride(3),
-        index.stride(0),
-        index.stride(1),
-        index.stride(2),
-        index.stride(3),
-        index_num.stride(0),
-        index_num.stride(1),
-        index_num.stride(2),
-        num_kv_blocks=num_kv_blocks,
-    )
-
-    return index, index_num
-
-
-def generate_topk_block_sparse_pattern(block_attn_score: torch.Tensor,
-                                       topk: int):
-    """
-    Generate a block sparse pattern where each q block attends to exactly topk kv blocks,
-    based on the provided attention scores.
-    
-    Args:
-        block_attn_score: [bs, h, num_q_blocks, num_kv_blocks]
-            Attention scores between query and key blocks
-        topk: int
-            Number of kv blocks each q block attends to
-        
-    Returns:
-        q2k_block_sparse_index: [bs, h, num_q_blocks, topk]
-            Contains the indices of kv blocks that each q block attends to.
-        q2k_block_sparse_num: [bs, h, num_q_blocks]
-            Contains the number of kv blocks that each q block attends to (all equal to topk).
-        k2q_block_sparse_index: [bs, h, num_kv_blocks, max_q_per_kv]
-            Contains the indices of q blocks that attend to each kv block.
-        k2q_block_sparse_num: [bs, h, num_kv_blocks]
-            Contains the number of q blocks that attend to each kv block.
-    """
-    device = block_attn_score.device
-    # Extract dimensions from block_attn_score
-    bs, h, num_q_blocks, num_kv_blocks = block_attn_score.shape
-
-    sorted_result = torch.sort(block_attn_score, dim=-1, descending=True)
-
-    sorted_indice = sorted_result.indices
-
-    q2k_block_sparse_index, _ = torch.sort(sorted_indice[:, :, :, :topk],
-                                           dim=-1)
-    q2k_block_sparse_index = q2k_block_sparse_index.to(dtype=torch.int32)
-    q2k_block_sparse_num = torch.full((bs, h, num_q_blocks),
-                                      topk,
-                                      device=device,
-                                      dtype=torch.int32)
-
-    block_map = topk_index_to_map(q2k_block_sparse_index,
-                                  num_kv_blocks,
-                                  transpose_map=True)
-    k2q_block_sparse_index, k2q_block_sparse_num = map_to_index(
-        block_map.transpose(2, 3))
-
-    return q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num
@@ -12,7 +12,7 @@ from fastvideo.v1.distributed.communication_op import (
 from fastvideo.v1.distributed.parallel_state import (get_sp_parallel_rank,
                                                     get_sp_world_size)
 from fastvideo.v1.forward_context import ForwardContext, get_forward_context
-from fastvideo.v1.platforms import _Backend
+from fastvideo.v1.platforms import AttentionBackendEnum
 from fastvideo.v1.utils import get_compute_dtype


@@ -26,8 +26,8 @@ class DistributedAttention(nn.Module):
                 num_kv_heads: Optional[int] = None,
                 softmax_scale: Optional[float] = None,
                 causal: bool = False,
-                 supported_attention_backends: Optional[Tuple[_Backend,
-                                                              ...]] = None,
+                 supported_attention_backends: Optional[Tuple[
+                     AttentionBackendEnum, ...]] = None,
                 prefix: str = "",
                 **extra_impl_args) -> None:
        super().__init__()
@@ -45,13 +45,13 @@ class DistributedAttention(nn.Module):
            dtype,
            supported_attention_backends=supported_attention_backends)
        impl_cls = attn_backend.get_impl_cls()
-        self.impl = impl_cls(num_heads=num_heads,
-                             head_size=head_size,
-                             causal=causal,
-                             softmax_scale=self.softmax_scale,
-                             num_kv_heads=num_kv_heads,
-                             prefix=f"{prefix}.impl",
-                             **extra_impl_args)
+        self.attn_impl = impl_cls(num_heads=num_heads,
+                                  head_size=head_size,
+                                  causal=causal,
+                                  softmax_scale=self.softmax_scale,
+                                  num_kv_heads=num_kv_heads,
+                                  prefix=f"{prefix}.impl",
+                                  **extra_impl_args)
        self.num_heads = num_heads
        self.head_size = head_size
        self.num_kv_heads = num_kv_heads
@@ -100,7 +100,7 @@ class DistributedAttention(nn.Module):
                                                    scatter_dim=2,
                                                    gather_dim=1)
        # Apply backend-specific preprocess_qkv
-        qkv = self.impl.preprocess_qkv(qkv, ctx_attn_metadata)
+        qkv = self.attn_impl.preprocess_qkv(qkv, ctx_attn_metadata)

        # Concatenate with replicated QKV if provided
        if replicated_q is not None:
@@ -116,7 +116,7 @@ class DistributedAttention(nn.Module):

        q, k, v = qkv.chunk(3, dim=0)

-        output = self.impl.forward(q, k, v, ctx_attn_metadata)
+        output = self.attn_impl.forward(q, k, v, ctx_attn_metadata)

        # Redistribute back if using sequence parallelism
        replicated_output = None
@@ -127,7 +127,7 @@ class DistributedAttention(nn.Module):
            replicated_output = sequence_model_parallel_all_gather(
                replicated_output.contiguous(), dim=2)
        # Apply backend-specific postprocess_output
-        output = self.impl.postprocess_output(output, ctx_attn_metadata)
+        output = self.attn_impl.postprocess_output(output, ctx_attn_metadata)

        output = sequence_model_parallel_all_to_all_4D(output,
                                                       scatter_dim=1,
@@ -183,18 +183,17 @@ class DistributedAttention_VSA(DistributedAttention):
                                                     scatter_dim=2,
                                                     gather_dim=1)

-        qkvg = self.impl.preprocess_qkv(
-            qkvg, ctx_attn_metadata)  # (yongqi) pass latent shape here?
+        qkvg = self.attn_impl.preprocess_qkv(qkvg, ctx_attn_metadata)

        q, k, v, gate_compress = qkvg.chunk(4, dim=0)
-        output = self.impl.forward(q, k, v, gate_compress,
-                                   ctx_attn_metadata)  # type: ignore[call-arg]
+        output = self.attn_impl.forward(
+            q, k, v, gate_compress, ctx_attn_metadata)  # type: ignore[call-arg]

        # Redistribute back if using sequence parallelism
        replicated_output = None

        # Apply backend-specific postprocess_output
-        output = self.impl.postprocess_output(output, ctx_attn_metadata)
+        output = self.attn_impl.postprocess_output(output, ctx_attn_metadata)

        output = sequence_model_parallel_all_to_all_4D(output,
                                                       scatter_dim=1,
@@ -212,8 +211,8 @@ class LocalAttention(nn.Module):
                 num_kv_heads: Optional[int] = None,
                 softmax_scale: Optional[float] = None,
                 causal: bool = False,
-                 supported_attention_backends: Optional[Tuple[_Backend,
-                                                              ...]] = None,
+                 supported_attention_backends: Optional[Tuple[
+                     AttentionBackendEnum, ...]] = None,
                 **extra_impl_args) -> None:
        super().__init__()
        if softmax_scale is None:
@@ -229,12 +228,12 @@ class LocalAttention(nn.Module):
            dtype,
            supported_attention_backends=supported_attention_backends)
        impl_cls = attn_backend.get_impl_cls()
-        self.impl = impl_cls(num_heads=num_heads,
-                             head_size=head_size,
-                             softmax_scale=self.softmax_scale,
-                             num_kv_heads=num_kv_heads,
-                             causal=causal,
-                             **extra_impl_args)
+        self.attn_impl = impl_cls(num_heads=num_heads,
+                                  head_size=head_size,
+                                  softmax_scale=self.softmax_scale,
+                                  num_kv_heads=num_kv_heads,
+                                  causal=causal,
+                                  **extra_impl_args)
        self.num_heads = num_heads
        self.head_size = head_size
        self.num_kv_heads = num_kv_heads
@@ -265,5 +264,5 @@ class LocalAttention(nn.Module):
        forward_context: ForwardContext = get_forward_context()
        ctx_attn_metadata = forward_context.attn_metadata

-        output = self.impl.forward(q, k, v, ctx_attn_metadata)
+        output = self.attn_impl.forward(q, k, v, ctx_attn_metadata)
        return output
@@ -11,13 +11,13 @@ import torch
 import fastvideo.v1.envs as envs
 from fastvideo.v1.attention.backends.abstract import AttentionBackend
 from fastvideo.v1.logger import init_logger
-from fastvideo.v1.platforms import _Backend, current_platform
+from fastvideo.v1.platforms import AttentionBackendEnum, current_platform
 from fastvideo.v1.utils import STR_BACKEND_ENV_VAR, resolve_obj_by_qualname

 logger = init_logger(__name__)


-def backend_name_to_enum(backend_name: str) -> Optional[_Backend]:
+def backend_name_to_enum(backend_name: str) -> Optional[AttentionBackendEnum]:
    """
    Convert a string backend name to a _Backend enum value.

@@ -27,11 +27,11 @@ def backend_name_to_enum(backend_name: str) -> Optional[_Backend]:
            loaded.
    """
    assert backend_name is not None
-    return _Backend[backend_name] if backend_name in _Backend.__members__ else \
+    return AttentionBackendEnum[backend_name] if backend_name in AttentionBackendEnum.__members__ else \
          None


-def get_env_variable_attn_backend() -> Optional[_Backend]:
+def get_env_variable_attn_backend() -> Optional[AttentionBackendEnum]:
    '''
    Get the backend override specified by the FastVideo attention
    backend environment variable, if one is specified.
@@ -53,10 +53,11 @@ def get_env_variable_attn_backend() -> Optional[_Backend]:
 #
 # THIS SELECTION TAKES PRECEDENCE OVER THE
 # FASTVIDEO ATTENTION BACKEND ENVIRONMENT VARIABLE
-forced_attn_backend: Optional[_Backend] = None
+forced_attn_backend: Optional[AttentionBackendEnum] = None


-def global_force_attn_backend(attn_backend: Optional[_Backend]) -> None:
+def global_force_attn_backend(
+        attn_backend: Optional[AttentionBackendEnum]) -> None:
    '''
    Force all attention operations to use a specified backend.

@@ -71,7 +72,7 @@ def global_force_attn_backend(attn_backend: Optional[_Backend]) -> None:
    forced_attn_backend = attn_backend


-def get_global_forced_attn_backend() -> Optional[_Backend]:
+def get_global_forced_attn_backend() -> Optional[AttentionBackendEnum]:
    '''
    Get the currently-forced choice of attention backend,
    or None if auto-selection is currently enabled.
@@ -82,7 +83,8 @@ def get_global_forced_attn_backend() -> Optional[_Backend]:
 def get_attn_backend(
    head_size: int,
    dtype: torch.dtype,
-    supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
+    supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
+                                                 ...]] = None,
 ) -> Type[AttentionBackend]:
    return _cached_get_attn_backend(head_size, dtype,
                                    supported_attention_backends)
@@ -92,7 +94,8 @@ def get_attn_backend(
 def _cached_get_attn_backend(
    head_size: int,
    dtype: torch.dtype,
-    supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
+    supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
+                                                 ...]] = None,
 ) -> Type[AttentionBackend]:
    # Check whether a particular choice of backend was
    # previously forced.
@@ -102,7 +105,7 @@ def _cached_get_attn_backend(
    if not supported_attention_backends:
        raise ValueError("supported_attention_backends is empty")
    selected_backend = None
-    backend_by_global_setting: Optional[_Backend] = (
+    backend_by_global_setting: Optional[AttentionBackendEnum] = (
        get_global_forced_attn_backend())
    if backend_by_global_setting is not None:
        selected_backend = backend_by_global_setting
@@ -125,7 +128,7 @@ def _cached_get_attn_backend(

@contextmanager
 def global_force_attn_backend_context_manager(
-        attn_backend: _Backend) -> Generator[None, None, None]:
+        attn_backend: AttentionBackendEnum) -> Generator[None, None, None]:
    '''
    Globally force a FastVideo attention backend override within a
    context manager, reverting the global attention backend
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field, fields
-from typing import Any, Dict
+from typing import Any, Dict, List, Tuple

 from fastvideo.v1.logger import init_logger

@@ -12,7 +12,9 @@ logger = init_logger(__name__)
 # 3. Any field in ArchConfig is fixed upon initialization, and should be hidden away from users
@dataclass
 class ArchConfig:
-    pass
+    stacked_params_mapping: List[Tuple[str, str, str]] = field(
+        default_factory=list
+    )  # mapping from huggingface weight names to custom names


@dataclass
@@ -4,7 +4,7 @@ from typing import Any, List, Optional, Tuple

 from fastvideo.v1.configs.models.base import ArchConfig, ModelConfig
 from fastvideo.v1.layers.quantization import QuantizationConfig
-from fastvideo.v1.platforms import _Backend
+from fastvideo.v1.platforms import AttentionBackendEnum


@dataclass
@@ -12,13 +12,12 @@ class DiTArchConfig(ArchConfig):
    _fsdp_shard_conditions: list = field(default_factory=list)
    _compile_conditions: list = field(default_factory=list)
    _param_names_mapping: dict = field(default_factory=dict)
+    _reverse_param_names_mapping: dict = field(default_factory=dict)
    _lora_param_names_mapping: dict = field(default_factory=dict)
-    _supported_attention_backends: Tuple[_Backend,
-                                         ...] = (_Backend.SLIDING_TILE_ATTN,
-                                                 _Backend.SAGE_ATTN,
-                                                 _Backend.FLASH_ATTN,
-                                                 _Backend.TORCH_SDPA,
-                                                 _Backend.VIDEO_SPARSE_ATTN)
+    _supported_attention_backends: Tuple[AttentionBackendEnum, ...] = (
+        AttentionBackendEnum.SLIDING_TILE_ATTN, AttentionBackendEnum.SAGE_ATTN,
+        AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA,
+        AttentionBackendEnum.VIDEO_SPARSE_ATTN)

    hidden_size: int = 0
    num_attention_heads: int = 0
@@ -147,6 +147,9 @@ class HunyuanVideoArchConfig(DiTArchConfig):
            r"final_layer.linear.\1",
        })

+    # Reverse mapping for saving checkpoints: training -> diffusers
+    _reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+
    patch_size: int = 2
    patch_size_t: int = 1
    in_channels: int = 16
@@ -5,13 +5,11 @@ from typing import List, Optional, Tuple, Union
 from fastvideo.v1.configs.models.dits.base import DiTArchConfig, DiTConfig


-def is_blocks(n: str, m) -> bool:
-    return "blocks" in n and str.isdigit(n.split(".")[-1])
-
-
@dataclass
 class StepVideoArchConfig(DiTArchConfig):
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda:
+        [lambda n, m: "transformer_blocks" in n and n.split(".")[-1].isdigit()])

    _param_names_mapping: dict = field(
        default_factory=lambda: {
@@ -49,9 +49,13 @@ class WanVideoArchConfig(DiTArchConfig):
            r"blocks.\1.ffn.fc_in.\2",
            r"^blocks\.(\d+)\.ffn\.net\.2\.(.*)$":
            r"blocks.\1.ffn.fc_out.\2",
-            r"blocks\.(\d+)\.norm2\.(.*)$":
+            r"^blocks\.(\d+)\.norm2\.(.*)$":
            r"blocks.\1.self_attn_residual_norm.norm.\2",
        })
+
+    # Reverse mapping for saving checkpoints: training -> diffusers
+    _reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+
    # Some LoRA adapters use the original official layer names instead of hf layer names,
    # so apply this before the param_names_mapping
    _lora_param_names_mapping: dict = field(
@@ -6,14 +6,14 @@ import torch

 from fastvideo.v1.configs.models.base import ArchConfig, ModelConfig
 from fastvideo.v1.layers.quantization import QuantizationConfig
-from fastvideo.v1.platforms import _Backend
+from fastvideo.v1.platforms import AttentionBackendEnum


@dataclass
 class EncoderArchConfig(ArchConfig):
    architectures: List[str] = field(default_factory=lambda: [])
-    _supported_attention_backends: Tuple[_Backend, ...] = (_Backend.FLASH_ATTN,
-                                                           _Backend.TORCH_SDPA)
+    _supported_attention_backends: Tuple[AttentionBackendEnum, ...] = (
+        AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA)
    output_hidden_states: bool = False
    use_return_dict: bool = True

@@ -32,8 +32,11 @@ class TextEncoderArchConfig(EncoderArchConfig):
    output_past: bool = True
    scalable_attention: bool = True
    tie_word_embeddings: bool = False
-
+    stacked_params_mapping: List[Tuple[str, str, str]] = field(
+        default_factory=list
+    )  # mapping from huggingface weight names to custom names
    tokenizer_kwargs: Dict[str, Any] = field(default_factory=dict)
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [])

    def __post_init__(self) -> None:
        self.tokenizer_kwargs = {
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
-from typing import Optional
+from typing import List, Optional, Tuple

 from fastvideo.v1.configs.models.encoders.base import (ImageEncoderArchConfig,
                                                       ImageEncoderConfig,
@@ -8,6 +8,14 @@ from fastvideo.v1.configs.models.encoders.base import (ImageEncoderArchConfig,
                                                       TextEncoderConfig)


+def _is_transformer_layer(n: str, m) -> bool:
+    return "layers" in n and str.isdigit(n.split(".")[-1])
+
+
+def _is_embeddings(n: str, m) -> bool:
+    return n.endswith("embeddings")
+
+
@dataclass
 class CLIPTextArchConfig(TextEncoderArchConfig):
    vocab_size: int = 49408
@@ -27,6 +35,15 @@ class CLIPTextArchConfig(TextEncoderArchConfig):
    bos_token_id: int = 49406
    eos_token_id: int = 49407
    text_len: int = 77
+    stacked_params_mapping: List[Tuple[str, str,
+                                       str]] = field(default_factory=lambda: [
+                                           # (param_name, shard_name, shard_id)
+                                           ("qkv_proj", "q_proj", "q"),
+                                           ("qkv_proj", "k_proj", "k"),
+                                           ("qkv_proj", "v_proj", "v"),
+                                       ])
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda: [_is_transformer_layer, _is_embeddings])


@dataclass
@@ -1,11 +1,23 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
-from typing import Optional
+from typing import List, Optional, Tuple

 from fastvideo.v1.configs.models.encoders.base import (TextEncoderArchConfig,
                                                       TextEncoderConfig)


+def _is_transformer_layer(n: str, m) -> bool:
+    return "layers" in n and str.isdigit(n.split(".")[-1])
+
+
+def _is_embeddings(n: str, m) -> bool:
+    return n.endswith("embed_tokens")
+
+
+def _is_final_norm(n: str, m) -> bool:
+    return n.endswith("norm")
+
+
@dataclass
 class LlamaArchConfig(TextEncoderArchConfig):
    vocab_size: int = 32000
@@ -32,6 +44,18 @@ class LlamaArchConfig(TextEncoderArchConfig):
    head_dim: Optional[int] = None
    hidden_state_skip_layer: int = 2
    text_len: int = 256
+    stacked_params_mapping: List[Tuple[str, str, str]] = field(
+        default_factory=lambda: [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),  # type: ignore
+            (".gate_up_proj", ".up_proj", 1),  # type: ignore
+        ])
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda:
+        [_is_transformer_layer, _is_embeddings, _is_final_norm])


@dataclass
@@ -1,11 +1,23 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
-from typing import Optional
+from typing import List, Optional, Tuple

 from fastvideo.v1.configs.models.encoders.base import (TextEncoderArchConfig,
                                                       TextEncoderConfig)


+def _is_transformer_layer(n: str, m) -> bool:
+    return "block" in n and str.isdigit(n.split(".")[-1])
+
+
+def _is_embeddings(n: str, m) -> bool:
+    return n.endswith("shared")
+
+
+def _is_final_layernorm(n: str, m) -> bool:
+    return n.endswith("final_layer_norm")
+
+
@dataclass
 class T5ArchConfig(TextEncoderArchConfig):
    vocab_size: int = 32128
@@ -29,6 +41,16 @@ class T5ArchConfig(TextEncoderArchConfig):
    eos_token_id: int = 1
    classifier_dropout: float = 0.0
    text_len: int = 512
+    stacked_params_mapping: List[Tuple[str, str,
+                                       str]] = field(default_factory=lambda: [
+                                           # (param_name, shard_name, shard_id)
+                                           (".qkv_proj", ".q", "q"),
+                                           (".qkv_proj", ".k", "k"),
+                                           (".qkv_proj", ".v", "v"),
+                                       ])
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda:
+        [_is_transformer_layer, _is_embeddings, _is_final_layernorm])

    # Referenced from https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/configuration_t5.py
    def __post_init__(self):
@@ -1,4 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
+import argparse
+import dataclasses
 from dataclasses import dataclass, field
 from typing import Any, Union

@@ -129,3 +131,12 @@ class VAEConfig(ModelConfig):
        )

        return parser
+
+    @classmethod
+    def from_cli_args(cls, args: argparse.Namespace) -> "VAEConfig":
+        kwargs = {}
+        for attr in dataclasses.fields(cls):
+            value = getattr(args, attr.name, None)
+            if value is not None:
+                kwargs[attr.name] = value
+        return cls(**kwargs)
@@ -3,7 +3,7 @@ from fastvideo.v1.configs.pipelines.base import (PipelineConfig,
 from fastvideo.v1.configs.pipelines.hunyuan import (FastHunyuanConfig,
                                                    HunyuanConfig)
 from fastvideo.v1.configs.pipelines.registry import (
-    get_pipeline_config_cls_for_name)
+    get_pipeline_config_cls_from_name)
 from fastvideo.v1.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.v1.configs.pipelines.wan import (WanI2V480PConfig,
                                                WanI2V720PConfig,
@@ -14,5 +14,5 @@ __all__ = [
    "HunyuanConfig", "FastHunyuanConfig", "PipelineConfig",
    "SlidingTileAttnConfig", "WanT2V480PConfig", "WanI2V480PConfig",
    "WanT2V720PConfig", "WanI2V720PConfig", "StepVideoT2VConfig",
-    "get_pipeline_config_cls_for_name"
+    "get_pipeline_config_cls_from_name"
 ]
@@ -1,19 +1,31 @@
 # SPDX-License-Identifier: Apache-2.0
 import json
 from dataclasses import asdict, dataclass, field, fields
-from typing import Any, Callable, Dict, Optional, Tuple, cast
+from enum import Enum
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast

 import torch

 from fastvideo.v1.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
                                         VAEConfig)
 from fastvideo.v1.configs.models.encoders import BaseEncoderOutput
+from fastvideo.v1.configs.utils import update_config_from_args
 from fastvideo.v1.logger import init_logger
-from fastvideo.v1.utils import shallow_asdict
+from fastvideo.v1.utils import (FlexibleArgumentParser, StoreBoolean,
+                                shallow_asdict)

 logger = init_logger(__name__)


+class STA_Mode(str, Enum):
+    """STA (Sliding Tile Attention) modes."""
+    STA_INFERENCE = "STA_inference"
+    STA_SEARCHING = "STA_searching"
+    STA_TUNING = "STA_tuning"
+    STA_TUNING_CFG = "STA_tuning_cfg"
+    NONE = None
+
+
 def preprocess_text(prompt: str) -> str:
    return prompt

@@ -22,59 +34,282 @@ def postprocess_text(output: BaseEncoderOutput) -> torch.tensor:
    raise NotImplementedError


+# config for a single pipeline
@dataclass
 class PipelineConfig:
    """Base configuration for all pipeline architectures."""
+    model_path: str = ""
+    pipeline_config_path: Optional[str] = None
+
    # Video generation parameters
    embedded_cfg_scale: float = 6.0
    flow_shift: Optional[float] = None
    disable_autocast: bool = False

    # Model configuration
-    precision: str = "bf16"
+    dit_config: DiTConfig = field(default_factory=DiTConfig)
+    dit_precision: str = "bf16"

    # VAE configuration
+    vae_config: VAEConfig = field(default_factory=VAEConfig)
    vae_precision: str = "fp16"
    vae_tiling: bool = True
    vae_sp: bool = True
-    vae_config: VAEConfig = field(default_factory=VAEConfig)

-    # DiT configuration
-    dit_config: DiTConfig = field(default_factory=DiTConfig)
+    # Image encoder configuration
+    image_encoder_config: EncoderConfig = field(default_factory=EncoderConfig)
+    image_encoder_precision: str = "fp32"

    # Text encoder configuration
-    text_encoder_precisions: Tuple[str, ...] = field(
-        default_factory=lambda: ("fp16", ))
+    DEFAULT_TEXT_ENCODER_PRECISIONS = ("fp16", )
    text_encoder_configs: Tuple[EncoderConfig, ...] = field(
        default_factory=lambda: (EncoderConfig(), ))
+    text_encoder_precisions: Tuple[str, ...] = field(
+        default_factory=lambda: ("fp16", ))
    preprocess_text_funcs: Tuple[Callable[[str], str], ...] = field(
        default_factory=lambda: (preprocess_text, ))
    postprocess_text_funcs: Tuple[Callable[[BaseEncoderOutput], torch.tensor],
                                  ...] = field(default_factory=lambda:
                                               (postprocess_text, ))

-    # STA (Spatial-Temporal Attention) parameters
+    # LoRA parameters
+    lora_path: Optional[str] = None
+    lora_nickname: Optional[
+        str] = "default"  # for swapping adapters in the pipeline
+    lora_target_names: Optional[List[
+        str]] = None  # can restrict list of layers to adapt, e.g. ["q_proj"]
+
+    # StepVideo specific parameters
+    pos_magic: Optional[str] = None
+    neg_magic: Optional[str] = None
+    timesteps_scale: Optional[bool] = None
+
+    # STA (Sliding Tile Attention) parameters
    mask_strategy_file_path: Optional[str] = None
-    STA_mode: str = "STA_inference"
+    STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
    skip_time_steps: int = 15

    # Compilation
-    enable_torch_compile: bool = False
+    # enable_torch_compile: bool = False
+
+    @staticmethod
+    def add_cli_args(parser: FlexibleArgumentParser,
+                     prefix: str = "") -> FlexibleArgumentParser:
+        prefix_with_dot = f"{prefix}." if (prefix.strip() != "") else ""
+
+        # model_path will be conflicting with the model_path in FastVideoArgs,
+        # so we add it separately if prefix is not empty
+        if prefix_with_dot != "":
+            parser.add_argument(
+                f"--{prefix_with_dot}model-path",
+                type=str,
+                dest=f"{prefix_with_dot.replace('-', '_')}model_path",
+                default=PipelineConfig.model_path,
+                help="Path to the pretrained model",
+            )
+
+        parser.add_argument(
+            f"--{prefix_with_dot}pipeline-config-path",
+            type=str,
+            dest=f"{prefix_with_dot.replace('-', '_')}pipeline_config_path",
+            default=PipelineConfig.pipeline_config_path,
+            help="Path to the pipeline config",
+        )
+        parser.add_argument(
+            f"--{prefix_with_dot}embedded-cfg-scale",
+            type=float,
+            dest=f"{prefix_with_dot.replace('-', '_')}embedded_cfg_scale",
+            default=PipelineConfig.embedded_cfg_scale,
+            help="Embedded CFG scale",
+        )
+        parser.add_argument(
+            f"--{prefix_with_dot}flow-shift",
+            type=float,
+            dest=f"{prefix_with_dot.replace('-', '_')}flow_shift",
+            default=PipelineConfig.flow_shift,
+            help="Flow shift parameter",
+        )
+
+        # DiT configuration
+        parser.add_argument(
+            f"--{prefix_with_dot}dit-precision",
+            type=str,
+            dest=f"{prefix_with_dot.replace('-', '_')}dit_precision",
+            default=PipelineConfig.dit_precision,
+            choices=["fp32", "fp16", "bf16"],
+            help="Precision for the DiT model",
+        )
+
+        # VAE configuration
+        parser.add_argument(
+            f"--{prefix_with_dot}vae-precision",
+            type=str,
+            dest=f"{prefix_with_dot.replace('-', '_')}vae_precision",
+            default=PipelineConfig.vae_precision,
+            choices=["fp32", "fp16", "bf16"],
+            help="Precision for VAE",
+        )
+        parser.add_argument(
+            f"--{prefix_with_dot}vae-tiling",
+            action=StoreBoolean,
+            dest=f"{prefix_with_dot.replace('-', '_')}vae_tiling",
+            default=PipelineConfig.vae_tiling,
+            help="Enable VAE tiling",
+        )
+        parser.add_argument(
+            f"--{prefix_with_dot}vae-sp",
+            action=StoreBoolean,
+            dest=f"{prefix_with_dot.replace('-', '_')}vae_sp",
+            help="Enable VAE spatial parallelism",
+        )
+
+        # Text encoder configuration
+        parser.add_argument(
+            f"--{prefix_with_dot}text-encoder-precisions",
+            nargs="+",
+            type=str,
+            dest=f"{prefix_with_dot.replace('-', '_')}text_encoder_precisions",
+            default=PipelineConfig.DEFAULT_TEXT_ENCODER_PRECISIONS,
+            choices=["fp32", "fp16", "bf16"],
+            help="Precision for each text encoder",
+        )
+
+        # Image encoder configuration
+        parser.add_argument(
+            f"--{prefix_with_dot}image-encoder-precision",
+            type=str,
+            dest=f"{prefix_with_dot.replace('-', '_')}image_encoder_precision",
+            default=PipelineConfig.image_encoder_precision,
+            choices=["fp32", "fp16", "bf16"],
+            help="Precision for image encoder",
+        )
+        parser.add_argument(
+            f"--{prefix_with_dot}pos_magic",
+            type=str,
+            dest=f"{prefix_with_dot.replace('-', '_')}pos_magic",
+            default=PipelineConfig.pos_magic,
+            help="Positive magic prompt for sampling, used in stepvideo",
+        )
+        parser.add_argument(
+            f"--{prefix_with_dot}neg_magic",
+            type=str,
+            dest=f"{prefix_with_dot.replace('-', '_')}neg_magic",
+            default=PipelineConfig.neg_magic,
+            help="Negative magic prompt for sampling, used in stepvideo",
+        )
+        parser.add_argument(
+            f"--{prefix_with_dot}timesteps_scale",
+            type=bool,
+            dest=f"{prefix_with_dot.replace('-', '_')}timesteps_scale",
+            default=PipelineConfig.timesteps_scale,
+            help=
+            "Bool for applying scheduler scale in set_timesteps, used in stepvideo",
+        )
+
+        # Add VAE configuration arguments
+        from fastvideo.v1.configs.models.vaes.base import VAEConfig
+        VAEConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}vae-config")
+
+        # Add DiT configuration arguments
+        from fastvideo.v1.configs.models.dits.base import DiTConfig
+        DiTConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}dit-config")
+
+        return parser
+
+    def update_config_from_dict(self,
+                                args: Dict[str, Any],
+                                prefix: str = "") -> None:
+        prefix_with_dot = f"{prefix}." if (prefix.strip() != "") else ""
+        update_config_from_args(self, args, prefix, pop_args=True)
+        update_config_from_args(self.vae_config,
+                                args,
+                                f"{prefix_with_dot}vae_config",
+                                pop_args=True)
+        update_config_from_args(self.dit_config,
+                                args,
+                                f"{prefix_with_dot}dit_config",
+                                pop_args=True)

    @classmethod
    def from_pretrained(cls, model_path: str) -> "PipelineConfig":
+        """
+        use the pipeline class setting from model_path to match the pipeline config
+        """
        from fastvideo.v1.configs.pipelines.registry import (
-            get_pipeline_config_cls_for_name)
-        pipeline_config_cls = get_pipeline_config_cls_for_name(model_path)
-        if pipeline_config_cls is not None:
-            pipeline_config = pipeline_config_cls()
-        else:
+            get_pipeline_config_cls_from_name)
+        pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)
+
+        return cast(PipelineConfig, pipeline_config_cls(model_path=model_path))
+
+    @classmethod
+    def from_kwargs(cls,
+                    kwargs: Dict[str, Any],
+                    config_cli_prefix: str = "") -> "PipelineConfig":
+        """
+        Load PipelineConfig from kwargs Dictionary.
+        kwargs: dictionary of kwargs
+        config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance
+        """
+        from fastvideo.v1.configs.pipelines.registry import (
+            get_pipeline_config_cls_from_name)
+
+        prefix_with_dot = f"{config_cli_prefix}." if (config_cli_prefix.strip()
+                                                      != "") else ""
+        model_path: Optional[str] = kwargs.get(prefix_with_dot + 'model_path',
+                                               None) or kwargs.get('model_path')
+        pipeline_config_or_path: Optional[Union[str, PipelineConfig, Dict[
+            str, Any]]] = kwargs.get(prefix_with_dot + 'pipeline_config',
+                                     None) or kwargs.get('pipeline_config')
+        if model_path is None:
+            raise ValueError("model_path is required in kwargs")
+
+        # 1. Get the pipeline config class from the registry
+        pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)
+
+        # 2. Instantiate PipelineConfig
+        if pipeline_config_cls is None:
            logger.warning(
-                "Couldn't find an optimal sampling param for %s. Using the default sampling param.",
+                "Couldn't find pipeline config for %s. Using the default pipeline config.",
                model_path)
            pipeline_config = cls()
+        else:
+            pipeline_config = pipeline_config_cls()

-        return cast(PipelineConfig, pipeline_config)
+        # 3. Load PipelineConfig from a json file or a PipelineConfig object if provided
+        if isinstance(pipeline_config_or_path, str):
+            pipeline_config.load_from_json(pipeline_config_or_path)
+            kwargs[prefix_with_dot +
+                   'pipeline_config_path'] = pipeline_config_or_path
+        elif isinstance(pipeline_config_or_path, PipelineConfig):
+            pipeline_config = pipeline_config_or_path
+        elif isinstance(pipeline_config_or_path, dict):
+            pipeline_config.update_pipeline_config(pipeline_config_or_path)
+
+        # 4. Update PipelineConfig from CLI arguments if provided
+        kwargs[prefix_with_dot + 'model_path'] = model_path
+        pipeline_config.update_config_from_dict(kwargs, config_cli_prefix)
+        return pipeline_config
+
+    def check_pipeline_config(self) -> None:
+        if self.vae_sp and not self.vae_tiling:
+            raise ValueError(
+                "Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True."
+            )
+
+        if len(self.text_encoder_configs) != len(self.text_encoder_precisions):
+            raise ValueError(
+                f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text encoder precisions ({len(self.text_encoder_precisions)})"
+            )
+
+        if len(self.text_encoder_configs) != len(self.preprocess_text_funcs):
+            raise ValueError(
+                f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"
+            )
+
+        if len(self.preprocess_text_funcs) != len(self.postprocess_text_funcs):
+            raise ValueError(
+                f"Length of text postprocess functions ({len(self.postprocess_text_funcs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"
+            )

    def dump_to_json(self, file_path: str):
        output_dict = shallow_asdict(self)
@@ -80,7 +80,7 @@ class HunyuanConfig(PipelineConfig):
                     (llama_postprocess_text, clip_postprocess_text))

    # Precision for each component
-    precision: str = "bf16"
+    dit_precision: str = "bf16"
    vae_precision: str = "fp16"
    text_encoder_precisions: Tuple[str, ...] = field(
        default_factory=lambda: ("fp16", "fp16"))
@@ -19,7 +19,7 @@ from fastvideo.v1.utils import (maybe_download_model_index,
 logger = init_logger(__name__)

 # Registry maps specific model weights to their config classes
-WEIGHT_CONFIG_REGISTRY: Dict[str, Type[PipelineConfig]] = {
+PIPE_NAME_TO_CONFIG: Dict[str, Type[PipelineConfig]] = {
    "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
    "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
@@ -51,37 +51,74 @@ PIPELINE_FALLBACK_CONFIG: Dict[str, Type[PipelineConfig]] = {
 }


-def get_pipeline_config_cls_for_name(
-        pipeline_name_or_path: str) -> Optional[type[PipelineConfig]]:
-    """Get the appropriate config class for specific pretrained weights."""
+def get_pipeline_config_cls_from_name(
+        pipeline_name_or_path: str) -> Type[PipelineConfig]:
+    """Get the appropriate configuration class for a given pipeline name or path.

-    if os.path.exists(pipeline_name_or_path):
-        config = verify_model_config_and_directory(pipeline_name_or_path)
-        logger.warning(
-            "FastVideo may not correctly identify the optimal config for this model, as the local directory may have been renamed."
-        )
-    else:
-        config = maybe_download_model_index(pipeline_name_or_path)
+    This function implements a multi-step lookup process to find the most suitable
+    configuration class for a given pipeline. It follows this order:
+    1. Exact match in the PIPE_NAME_TO_CONFIG
+    2. Partial match in the PIPE_NAME_TO_CONFIG
+    3. Fallback to class name in the model_index.json
+    4. else raise an error

-    pipeline_name = config["_class_name"]
+    Args:
+        pipeline_name_or_path (str): The name or path of the pipeline. This can be:
+            - A registered model ID (e.g., "FastVideo/FastHunyuan-diffusers")
+            - A local path to a model directory
+            - A model ID that will be downloaded
+
+    Returns:
+        Type[PipelineConfig]: The configuration class that best matches the pipeline.
+            This will be one of:
+            - A specific weight configuration class if an exact match is found
+            - A fallback configuration class based on the pipeline architecture
+            - The base PipelineConfig class if no matches are found
+
+    Note:
+        - For local paths, the function will verify the model configuration
+        - For remote models, it will attempt to download the model index
+        - Warning messages are logged when falling back to less specific configurations
+    """
+
+    pipeline_config_cls: Optional[Type[PipelineConfig]] = None

    # First try exact match for specific weights
-    if pipeline_name_or_path in WEIGHT_CONFIG_REGISTRY:
-        return WEIGHT_CONFIG_REGISTRY[pipeline_name_or_path]
+    if pipeline_name_or_path in PIPE_NAME_TO_CONFIG:
+        pipeline_config_cls = PIPE_NAME_TO_CONFIG[pipeline_name_or_path]

    # Try partial matches (for local paths that might include the weight ID)
-    for registered_id, config_class in WEIGHT_CONFIG_REGISTRY.items():
+    for registered_id, config_class in PIPE_NAME_TO_CONFIG.items():
        if registered_id in pipeline_name_or_path:
-            return config_class
-
-    # If no match, try to use the fallback config
-    fallback_config = None
-    # Try to determine pipeline architecture for fallback
-    for pipeline_type, detector in PIPELINE_DETECTOR.items():
-        if detector(pipeline_name.lower()):
-            fallback_config = PIPELINE_FALLBACK_CONFIG.get(pipeline_type)
+            pipeline_config_cls = config_class
            break

-    logger.warning("No match found for pipeline %s, using fallback config %s.",
-                   pipeline_name_or_path, fallback_config)
-    return fallback_config
+    # If no match, try to use the fallback config
+    if pipeline_config_cls is None:
+        if os.path.exists(pipeline_name_or_path):
+            config = verify_model_config_and_directory(pipeline_name_or_path)
+        else:
+            config = maybe_download_model_index(pipeline_name_or_path)
+        logger.warning(
+            "Trying to use the config from the model_index.json. FastVideo may not correctly identify the optimal config for this model in this situation."
+        )
+
+        pipeline_name = config["_class_name"]
+        # Try to determine pipeline architecture for fallback
+        for pipeline_type, detector in PIPELINE_DETECTOR.items():
+            if detector(pipeline_name.lower()):
+                pipeline_config_cls = PIPELINE_FALLBACK_CONFIG.get(
+                    pipeline_type)
+                break
+
+        if pipeline_config_cls is not None:
+            logger.warning(
+                "No match found for pipeline %s, using fallback config %s.",
+                pipeline_name_or_path, pipeline_config_cls)
+
+    if pipeline_config_cls is None:
+        raise ValueError(
+            f"No match found for pipeline {pipeline_name_or_path}, please check the pipeline name or path."
+        )
+
+    return pipeline_config_cls
@@ -39,7 +39,6 @@ class SamplingParam:
    num_inference_steps: int = 50
    guidance_scale: float = 1.0
    guidance_rescale: float = 0.0
-    VSA_sparsity: float = 0.0

    # TeaCache parameters
    enable_teacache: bool = False
@@ -185,12 +184,6 @@ class SamplingParam:
            default=SamplingParam.image_path,
            help="Path to input image for image-to-video generation",
        )
-        parser.add_argument(
-            "--VSA-sparsity",
-            type=float,
-            default=SamplingParam.VSA_sparsity,
-            help="VSA attention sparsity",
-        )
        return parser


@@ -0,0 +1,45 @@
+from typing import Any, Dict
+
+
+def update_config_from_args(config: Any,
+                            args_dict: Dict[str, Any],
+                            prefix: str = "",
+                            pop_args: bool = False) -> None:
+    """
+    Update configuration object from arguments dictionary.
+    
+    Args:
+        config: The configuration object to update
+        args_dict: Dictionary containing arguments
+        prefix: Prefix for the configuration parameters in the args_dict.
+               If None, assumes direct attribute mapping without prefix.
+    """
+    # Handle top-level attributes (no prefix)
+    args_not_to_remove = [
+        'model_path',
+    ]
+    args_to_remove = []
+    if prefix.strip() == "":
+        for key, value in args_dict.items():
+            if hasattr(config, key) and value is not None:
+                if key == "text_encoder_precisions" and isinstance(value, list):
+                    setattr(config, key, tuple(value))
+                else:
+                    setattr(config, key, value)
+                if pop_args:
+                    args_to_remove.append(key)
+    else:
+        # Handle nested attributes with prefix
+        prefix_with_dot = f"{prefix}."
+        for key, value in args_dict.items():
+            if key.startswith(prefix_with_dot) and value is not None:
+                attr_name = key[len(prefix_with_dot):]
+                if hasattr(config, attr_name):
+                    setattr(config, attr_name, value)
+                if pop_args:
+                    args_to_remove.append(key)
+
+    if pop_args:
+        for key in args_to_remove:
+            if key not in args_not_to_remove:
+                args_dict.pop(key)
@@ -1,19 +1,17 @@
-import os
-
+# SPDX-License-Identifier: Apache-2.0
 from torchvision import transforms
 from torchvision.transforms import Lambda
-from transformers import AutoTokenizer

-from fastvideo.v1.dataset.t2v_datasets import T2V_dataset
+from fastvideo.v1.dataset.parquet_dataset_map_style import (
+    build_parquet_map_style_dataloader)
+from fastvideo.v1.dataset.preprocessing_datasets import (
+    VideoCaptionMergedDataset)
 from fastvideo.v1.dataset.transform import (CenterCropResizeVideo, Normalize255,
                                            TemporalRandomCrop)
-
-from .parquet_dataset_map_style import build_parquet_map_style_dataloader
-
-__all__ = ["build_parquet_map_style_dataloader"]
+from fastvideo.v1.dataset.validation_dataset import ValidationDataset


-def getdataset(args, start_idx=0) -> T2V_dataset:
+def getdataset(args) -> VideoCaptionMergedDataset:
    temporal_sample = TemporalRandomCrop(args.num_frames)  # 16 x
    norm_fun = Lambda(lambda x: 2.0 * x - 1.0)
    resize_topcrop = [
@@ -31,15 +29,14 @@ def getdataset(args, start_idx=0) -> T2V_dataset:
        *resize_topcrop,
        norm_fun,
    ])
-    tokenizer_path = os.path.join(args.model_path, "tokenizer")
-    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
-                                              cache_dir=args.cache_dir)
-    if args.dataset == "t2v":
-        return T2V_dataset(args,
-                           transform=transform,
-                           temporal_sample=temporal_sample,
-                           tokenizer=tokenizer,
-                           transform_topcrop=transform_topcrop,
-                           start_idx=start_idx)
+    return VideoCaptionMergedDataset(data_merge_path=args.data_merge_path,
+                                     args=args,
+                                     transform=transform,
+                                     temporal_sample=temporal_sample,
+                                     transform_topcrop=transform_topcrop)

-    raise NotImplementedError(args.dataset)
+
+__all__ = [
+    "build_parquet_map_style_dataloader", "ValidationDataset",
+    "VideoCaptionMergedDataset"
+]
@@ -0,0 +1,185 @@
+# SPDX-License-Identifier: Apache-2.0
+import argparse
+import os
+import pathlib
+import time
+
+import torch.distributed as dist
+import torch.distributed.checkpoint as dist_cp
+
+from fastvideo.v1.dataset.parquet_dataset_iterable_style import (
+    build_parquet_iterable_style_dataloader)
+from fastvideo.v1.distributed import get_world_rank
+from fastvideo.v1.distributed.parallel_state import (
+    cleanup_dist_env_and_memory, get_local_torch_device,
+    maybe_init_distributed_environment_and_model_parallel)
+from fastvideo.v1.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Benchmark parquet iterable style dataset loading speed")
+    parser.add_argument(
+        "--path",
+        type=str,
+        help="Path to parquet dataset",
+    )
+    parser.add_argument("--batch_size",
+                        type=int,
+                        default=4,
+                        help="Batch size for DataLoader")
+    parser.add_argument("--num_data_workers",
+                        type=int,
+                        help="Number of DataLoader workers")
+    parser.add_argument("--num_epoch",
+                        type=int,
+                        default=2,
+                        help="Number of epoches to benchmark")
+    parser.add_argument("--verify_resume",
+                        action="store_true",
+                        help="Verify resume")
+    parser.add_argument(
+        "--num_batches_per_epoch",
+        type=int,
+        default=1000,
+        help="Number of batches to benchmark",
+    )
+    parser.add_argument('--checkpoint_path',
+                        type=str,
+                        default='dataloader_checkpoint',
+                        help='Path to save/load checkpoint')
+    '''
+    example launch command:
+    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 2 --num_epoch 2 --num_batches_per_epoch 2 --verify_resume
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5 --verify_resume
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path /mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn/latents/ --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 100 
+    '''
+    args = parser.parse_args()
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    maybe_init_distributed_environment_and_model_parallel(
+        tp_size=(world_size + 1) // 2, sp_size=(world_size + 1) // 2)
+    logger.info("Initialized distributed environment with world_size=%d",
+                world_size)
+
+    # Create DataLoader with proper settings
+    dataset, dataloader = build_parquet_iterable_style_dataloader(
+        args.path, args.batch_size, args.num_data_workers)
+    logger.info("Initialized dataloader")
+
+    if args.verify_resume:
+        # First pass - record latent sums
+        first_pass_sums = []
+        for i, (latents, embeddings, masks,
+                caption_text) in enumerate(dataloader):
+            latent_sum = latents.sum().item()
+            first_pass_sums.append(latent_sum)
+            logger.info("Batch %d latent sum: %f", i, latent_sum)
+            if i >= args.num_batches_per_epoch - 1:
+                break
+
+        # Save dataloader state using distributed checkpoint
+        checkpoint_dir = pathlib.Path(args.checkpoint_path)
+        logger.info("Rank %d: Saving dataloader state to %s", get_world_rank(),
+                    checkpoint_dir)
+        states = {"dataloader": dataloader}
+
+        begin_time = time.monotonic()
+        dist_cp.save(states, checkpoint_id=checkpoint_dir.as_posix())
+        end_time = time.monotonic()
+
+        logger.info("Rank %d: Saved checkpoint in %.2f seconds",
+                    get_world_rank(), end_time - begin_time)
+
+        # Make sure all processes wait for checkpoint to be saved
+        if world_size > 1:
+            dist.barrier()
+
+        # Recreate dataloader and load state
+        dataset, dataloader = build_parquet_iterable_style_dataloader(
+            args.path, args.batch_size, args.num_data_workers)
+        load_states = {"dataloader": dataloader}
+        dist_cp.load(load_states, checkpoint_id=checkpoint_dir.as_posix())
+        logger.info("Rank %d: Loaded dataloader state from %s",
+                    get_world_rank(), checkpoint_dir)
+
+        # Second pass - verify latent sums match
+        for i, (latents, embeddings, masks) in enumerate(dataloader):
+            latent_sum = latents.sum().item()
+            first_pass_sums.append(latent_sum)
+            logger.info("Batch %d latent sum: %f",
+                        i + args.num_batches_per_epoch, latent_sum)
+            if i >= args.num_batches_per_epoch - 1:
+                break
+
+        dataset, dataloader = build_parquet_iterable_style_dataloader(
+            args.path, args.batch_size, args.num_data_workers)
+        # Second pass - verify latent sums match
+        second_pass_sums = []
+        for i, (latents, embeddings, masks,
+                caption_text) in enumerate(dataloader):
+            latent_sum = latents.sum().item()
+            second_pass_sums.append(latent_sum)
+            logger.info("Batch %d latent sum: %f (should match first pass: %f)",
+                        i, latent_sum, first_pass_sums[i])
+            if i >= args.num_batches_per_epoch * 2 - 1:
+                break
+
+        # Verify all sums match
+        if all(
+                abs(a - b) < 1e-6
+                for a, b in zip(first_pass_sums, second_pass_sums)):
+            logger.info(
+                "All latent sums match between passes - resume verification successful!"
+            )
+        else:
+            raise ValueError(
+                "Latent sums do not match between passes - resume verification failed!"
+            )
+
+    start_time = time.time()
+    total_samples = 0
+    total_batches = 0
+    for _ in range(args.num_epoch):
+        for i, (latents, embeddings, masks,
+                caption_text) in enumerate(dataloader):
+            if i >= args.num_batches_per_epoch:
+                break
+
+            # Move data to device
+            latents = latents.to(get_local_torch_device())
+            embeddings = embeddings.to(get_local_torch_device())
+
+            # Calculate actual batch size
+            batch_size = latents.size(0)
+            total_samples += batch_size
+            total_batches += 1
+
+            # Print progress only from rank 0
+            if get_world_rank() == 0 and (i + 1) % 10 == 0:
+                elapsed = time.time() - start_time
+                samples_per_sec = total_samples / elapsed
+                logger.info("Batch %d/%d, Speed: %.2f samples/sec", i + 1,
+                            args.num_batches_per_epoch, samples_per_sec)
+
+    # Final statistics
+    if world_size > 1:
+        dist.barrier()
+
+    if get_world_rank() == 0:
+        elapsed = time.time() - start_time
+        samples_per_sec = total_samples / elapsed
+
+        logger.info("\nBenchmark Results:")
+        logger.info("Total time: %.2f seconds", elapsed)
+        logger.info("Total samples: %d", total_samples)
+        logger.info("Average speed: %.2f samples/sec", samples_per_sec)
+        logger.info("Time per batch: %.2f ms", elapsed / total_batches * 1000)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    finally:
+        cleanup_dist_env_and_memory()
@@ -8,11 +8,12 @@ import torch
 import torch.distributed as dist
 import torch.distributed.checkpoint as dist_cp

+from fastvideo.v1.dataset.dataloader.schema import pyarrow_schema_t2v
 from fastvideo.v1.dataset.parquet_dataset_map_style import (
    build_parquet_map_style_dataloader)
 from fastvideo.v1.distributed import get_world_rank
 from fastvideo.v1.distributed.parallel_state import (
-    cleanup_dist_env_and_memory, get_torch_device,
+    cleanup_dist_env_and_memory, get_local_torch_device,
    maybe_init_distributed_environment_and_model_parallel)
 from fastvideo.v1.logger import init_logger

@@ -54,9 +55,9 @@ def main() -> None:
                        help='Path to save/load checkpoint')
    '''
    example launch command:
-    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/v1/dataset/parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 4 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 4 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 2 --verify_resume
+    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 4 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 3 --verify_resume
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5 --verify_resume
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path /mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn/latents/ --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 100 
    '''
    args = parser.parse_args()
    world_size = int(os.environ.get("WORLD_SIZE", 1))
@@ -66,14 +67,22 @@ def main() -> None:
                world_size)

    # Create DataLoader with proper settings
-    dataloader = build_parquet_map_style_dataloader(args.path, args.batch_size,
-                                                    args.num_data_workers)
+    dataset, dataloader = build_parquet_map_style_dataloader(
+        args.path,
+        args.batch_size,
+        parquet_schema=pyarrow_schema_t2v,
+        num_data_workers=args.num_data_workers)
    logger.info("Initialized dataloader with %d batches", len(dataloader))

    if args.verify_resume:
-        for i, (latents, embeddings, masks,
-                data_indices) in enumerate(dataloader):
-            logger.info("Batch %d data_indices: %s", i, data_indices)
+        # First pass - record latent sums
+        first_pass_sums = []
+        for i, batch in enumerate(dataloader):
+            latents = batch['vae_latent']
+            embeddings = batch['text_embedding']
+            latent_sum = latents.sum().item()
+            first_pass_sums.append(latent_sum)
+            logger.info("Batch %d latent sum: %f", i, latent_sum)
            if i >= args.num_batches_per_epoch - 1:
                break

@@ -94,47 +103,70 @@ def main() -> None:
        if world_size > 1:
            dist.barrier()

-        dataloader = build_parquet_map_style_dataloader(args.path,
-                                                        args.batch_size,
-                                                        args.num_data_workers)
-        # Load dataloader state using distributed checkpoint
-        logger.info("Rank %d: Loading dataloader state from %s",
-                    get_world_rank(), checkpoint_dir)
+        # Recreate dataloader and load state
+        dataset, dataloader = build_parquet_map_style_dataloader(
+            args.path,
+            args.batch_size,
+            parquet_schema=pyarrow_schema_t2v,
+            num_data_workers=args.num_data_workers)
        load_states = {"dataloader": dataloader}
        dist_cp.load(load_states, checkpoint_id=checkpoint_dir.as_posix())
        logger.info("Rank %d: Loaded dataloader state from %s",
                    get_world_rank(), checkpoint_dir)

-        for i, (latents, embeddings, masks,
-                data_indices) in enumerate(dataloader):
-            logger.info("Batch %d data_indices: %s", i, data_indices)
+        for i, batch in enumerate(dataloader):
+            latents = batch['vae_latent']
+            embeddings = batch['text_embedding']
+            latent_sum = latents.sum().item()
+            first_pass_sums.append(latent_sum)
+            logger.info("Batch %d latent sum: %f",
+                        i + args.num_batches_per_epoch, latent_sum)
            if i >= args.num_batches_per_epoch - 1:
                break

-        logger.info("Restart from the beginning")
+        dataset, dataloader = build_parquet_map_style_dataloader(
+            args.path,
+            args.batch_size,
+            parquet_schema=pyarrow_schema_t2v,
+            num_data_workers=args.num_data_workers)

-        dataloader = build_parquet_map_style_dataloader(args.path,
-                                                        args.batch_size,
-                                                        args.num_data_workers)
-
-        for i, (latents, embeddings, masks,
-                data_indices) in enumerate(dataloader):
-            logger.info("Batch %d data_indices: %s", i, data_indices)
+        # Second pass - verify latent sums match
+        second_pass_sums = []
+        for i, batch in enumerate(dataloader):
+            latents = batch['vae_latent']
+            embeddings = batch['text_embedding']
+            latent_sum = latents.sum().item()
+            second_pass_sums.append(latent_sum)
+            logger.info("Batch %d latent sum: %f (should match first pass: %f)",
+                        i, latent_sum, first_pass_sums[i])
            if i >= args.num_batches_per_epoch * 2 - 1:
                break

+        # Verify all sums match
+        if all(
+                abs(a - b) < 1e-6
+                for a, b in zip(first_pass_sums, second_pass_sums)):
+            logger.info(
+                "All latent sums match between passes - resume verification successful!"
+            )
+        else:
+            raise ValueError(
+                "Latent sums do not match between passes - resume verification failed!"
+            )
+
    start_time = time.time()
    total_samples = 0
    total_batches = 0
    for _ in range(args.num_epoch):
-        for i, (latents, embeddings, masks,
-                data_indices) in enumerate(dataloader):
+        for i, batch in enumerate(dataloader):
+            latents = batch['vae_latent']
+            embeddings = batch['text_embedding']
            if i >= args.num_batches_per_epoch:
                break

            # Move data to device
-            latents = latents.to(get_torch_device())
-            embeddings = embeddings.to(get_torch_device())
+            latents = latents.to(get_local_torch_device())
+            embeddings = embeddings.to(get_local_torch_device())

            # Calculate actual batch size
            batch_size = latents.size(0)
@@ -26,15 +26,47 @@ pyarrow_schema_i2v = pa.schema([
    pa.field("text_embedding_shape", pa.list_(pa.int64())),
    # e.g., 'bfloat16' or 'float32'
    pa.field("text_embedding_dtype", pa.string()),
-    pa.field("text_attention_mask_bytes", pa.binary()),
-    # e.g., [SeqLen]
-    pa.field("text_attention_mask_shape", pa.list_(pa.int64())),
-    # e.g., 'bool' or 'int8'
-    pa.field("text_attention_mask_dtype", pa.string()),
    #I2V
    pa.field("clip_feature_bytes", pa.binary()),
    pa.field("clip_feature_shape", pa.list_(pa.int64())),
    pa.field("clip_feature_dtype", pa.string()),
+    pa.field("first_frame_latent_bytes", pa.binary()),
+    pa.field("first_frame_latent_shape", pa.list_(pa.int64())),
+    pa.field("first_frame_latent_dtype", pa.string()),
+    # I2V Validation
+    pa.field("pil_image_bytes", pa.binary()),
+    pa.field("pil_image_shape", pa.list_(pa.int64())),
+    pa.field("pil_image_dtype", pa.string()),
+    # --- Metadata ---
+    pa.field("file_name", pa.string()),
+    pa.field("caption", pa.string()),
+    pa.field("media_type", pa.string()),  # 'image' or 'video'
+    pa.field("width", pa.int64()),
+    pa.field("height", pa.int64()),
+    # -- Video-specific (can be null/default for images) ---
+    # Number of frames processed (e.g., 1 for image, N for video)
+    pa.field("num_frames", pa.int64()),
+    pa.field("duration_sec", pa.float64()),
+    pa.field("fps", pa.float64()),
+])
+
+pyarrow_schema_i2v_validation = pa.schema([
+    pa.field("id", pa.string()),
+    # --- Text encoder output tensor ---
+    # Tensors are stored as raw bytes with shape and dtype info for loading
+    pa.field("text_embedding_bytes", pa.binary()),
+    # e.g., [SeqLen, Dim]
+    pa.field("text_embedding_shape", pa.list_(pa.int64())),
+    # e.g., 'bfloat16' or 'float32'
+    pa.field("text_embedding_dtype", pa.string()),
+    #I2V
+    pa.field("clip_feature_bytes", pa.binary()),
+    pa.field("clip_feature_shape", pa.list_(pa.int64())),
+    pa.field("clip_feature_dtype", pa.string()),
+    # I2V Validation
+    pa.field("pil_image_bytes", pa.binary()),
+    pa.field("pil_image_shape", pa.list_(pa.int64())),
+    pa.field("pil_image_dtype", pa.string()),
    # --- Metadata ---
    pa.field("file_name", pa.string()),
    pa.field("caption", pa.string()),
@@ -64,11 +96,6 @@ pyarrow_schema_t2v = pa.schema([
    pa.field("text_embedding_shape", pa.list_(pa.int64())),
    # e.g., 'bfloat16' or 'float32'
    pa.field("text_embedding_dtype", pa.string()),
-    pa.field("text_attention_mask_bytes", pa.binary()),
-    # e.g., [SeqLen]
-    pa.field("text_attention_mask_shape", pa.list_(pa.int64())),
-    # e.g., 'bool' or 'int8'
-    pa.field("text_attention_mask_dtype", pa.string()),
    # --- Metadata ---
    pa.field("file_name", pa.string()),
    pa.field("caption", pa.string()),
@@ -80,4 +107,26 @@ pyarrow_schema_t2v = pa.schema([
    pa.field("num_frames", pa.int64()),
    pa.field("duration_sec", pa.float64()),
    pa.field("fps", pa.float64()),
-])
+])
+
+pyarrow_schema_t2v_validation = pa.schema([
+    pa.field("id", pa.string()),
+    # --- Text encoder output tensor ---
+    # Tensors are stored as raw bytes with shape and dtype info for loading
+    pa.field("text_embedding_bytes", pa.binary()),
+    # e.g., [SeqLen, Dim]
+    pa.field("text_embedding_shape", pa.list_(pa.int64())),
+    # e.g., 'bfloat16' or 'float32'
+    pa.field("text_embedding_dtype", pa.string()),
+    # --- Metadata ---
+    pa.field("file_name", pa.string()),
+    pa.field("caption", pa.string()),
+    pa.field("media_type", pa.string()),  # 'image' or 'video'
+    pa.field("width", pa.int64()),
+    pa.field("height", pa.int64()),
+    # -- Video-specific (can be null/default for images) ---
+    # Number of frames processed (e.g., 1 for image, N for video)
+    pa.field("num_frames", pa.int64()),
+    pa.field("duration_sec", pa.float64()),
+    pa.field("fps", pa.float64()),
+])
@@ -1,137 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import argparse
-import json
-import os
-import time
-from multiprocessing import Pool, cpu_count
-from pathlib import Path
-
-import torchvision
-from tqdm import tqdm
-
-
-def get_video_info(video_path):
-    """Get video information using torchvision."""
-    # Read video tensor (T, C, H, W)
-    video_tensor, _, info = torchvision.io.read_video(str(video_path),
-                                                      output_format="TCHW",
-                                                      pts_unit="sec")
-
-    num_frames = video_tensor.shape[0]
-    height = video_tensor.shape[2]
-    width = video_tensor.shape[3]
-    fps = info.get("video_fps", 0)
-    duration = num_frames / fps if fps > 0 else 0
-
-    # Extract name
-    _, _, videos_dir, video_name = str(video_path).split("/")
-
-    return {
-        "path": str(video_name),
-        "resolution": {
-            "width": width,
-            "height": height
-        },
-        "size": os.path.getsize(video_path),
-        "fps": fps,
-        "duration": duration,
-        "num_frames": num_frames
-    }
-
-
-def prepare_dataset_json(folder_path,
-                         output_name="videos2caption.json",
-                         num_workers=None) -> None:
-    """Prepare dataset information from a folder containing videos and prompt.txt."""
-    folder_path = Path(folder_path)
-
-    # Read prompt file
-    prompt_file = folder_path / "prompt.txt"
-    if not prompt_file.exists():
-        raise FileNotFoundError(f"prompt.txt not found in {folder_path}")
-
-    with open(prompt_file) as f:
-        prompts = [line.strip() for line in f.readlines() if line.strip()]
-
-    # Read videos file
-    videos_file = folder_path / "videos.txt"
-    if not videos_file.exists():
-        raise FileNotFoundError(f"videos.txt not found in {folder_path}")
-
-    with open(videos_file) as f:
-        video_paths = [line.strip() for line in f.readlines() if line.strip()]
-
-    if len(prompts) != len(video_paths):
-        raise ValueError(
-            f"Number of prompts ({len(prompts)}) does not match number of videos ({len(video_paths)})"
-        )
-
-    # Prepare arguments for multiprocessing
-    process_args = [folder_path / video_path for video_path in video_paths]
-
-    # Determine number of workers
-    if num_workers is None:
-        num_workers = max(1, cpu_count() - 1)  # Leave one CPU free
-
-    # Process videos in parallel
-    start_time = time.time()
-    with Pool(num_workers) as pool:
-        results = list(
-            tqdm(pool.imap(get_video_info, process_args),
-                 total=len(process_args),
-                 desc="Processing videos",
-                 unit="video"))
-
-    # Combine results with prompts
-    dataset_info = []
-    for result, prompt in zip(results, prompts):
-        result["cap"] = [prompt]
-        dataset_info.append(result)
-
-    # Calculate total processing time
-    total_time = time.time() - start_time
-    total_videos = len(dataset_info)
-    avg_time_per_video = total_time / total_videos if total_videos > 0 else 0
-
-    print("\nProcessing completed:")
-    print(f"Total videos processed: {total_videos}")
-    print(f"Total time: {total_time:.2f} seconds")
-    print(f"Average time per video: {avg_time_per_video:.2f} seconds")
-
-    # Save to JSON file
-    output_file = folder_path / output_name
-    with open(output_file, 'w') as f:
-        json.dump(dataset_info, f, indent=2)
-
-    # Create merge.txt
-    merge_file = folder_path / "merge.txt"
-    with open(merge_file, 'w') as f:
-        f.write(f"{folder_path}/videos,{output_file}\n")
-
-    print(f"Dataset information saved to {output_file}")
-    print(f"Merge file created at {merge_file}")
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description='Prepare video dataset information in JSON format')
-    parser.add_argument(
-        '--folder',
-        type=str,
-        required=True,
-        help='Path to the folder containing videos and prompt.txt')
-    parser.add_argument(
-        '--output',
-        type=str,
-        default='videos2caption.json',
-        help='Name of the output JSON file (default: videos2caption.json)')
-    parser.add_argument('--workers',
-                        type=int,
-                        default=32,
-                        help='Number of worker processes (default: 16)')
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    prepare_dataset_json(args.folder, args.output, args.workers)
@@ -0,0 +1,278 @@
+import os
+import pickle
+import random
+from typing import Dict, List, Tuple
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import torch
+import tqdm
+from torch.utils.data import IterableDataset, get_worker_info
+from torchdata.stateful_dataloader import StatefulDataLoader
+
+from fastvideo.v1.dataset.utils import collate_latents_embs_masks
+from fastvideo.v1.distributed import (get_sp_world_size, get_world_rank,
+                                      get_world_size)
+from fastvideo.v1.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class BatchIterator:
+    # TODO: Implement state_dict and load_state_dict to support resume.
+    def __init__(self, files, batch_size, text_padding_length, keys,
+                 worker_num_samples, read_batch_size):
+        self.files = files
+        self.batch_size = batch_size
+        self.text_padding_length = text_padding_length
+        self.keys = keys
+        self.worker_num_samples = worker_num_samples
+        self.processed_samples = 0
+        self.buffer = []
+        self.read_batch_size = read_batch_size
+
+    def __iter__(self):
+        for file in self.files:
+            if self.processed_samples >= self.worker_num_samples:
+                return
+
+            reader = pq.ParquetFile(file)
+            for batch in reader.iter_batches(batch_size=self.read_batch_size):
+                if self.processed_samples >= self.worker_num_samples:
+                    return
+
+                self.buffer.extend(batch.to_pylist())
+
+                while len(self.buffer) >= self.batch_size:
+                    if self.processed_samples >= self.worker_num_samples:
+                        return
+
+                    batch_to_process = self.buffer[:self.batch_size]
+                    self.buffer = self.buffer[self.batch_size:]
+
+                    all_latents, all_embs, all_masks, caption_text = collate_latents_embs_masks(
+                        batch_to_process, self.text_padding_length, self.keys)
+                    self.processed_samples += self.batch_size
+                    yield all_latents, all_embs, all_masks, caption_text
+
+
+class LatentsParquetIterStyleDataset(IterableDataset):
+    """Efficient loader for video-text data from a directory of Parquet files."""
+
+    # Modify this in the future if we want to add more keys, for example, in image to video.
+    keys = [("vae_latent", "latent"), ("text_embedding")]
+
+    def __init__(self,
+                 path: str,
+                 batch_size: int = 1024,
+                 cfg_rate: float = 0.1,
+                 num_workers: int = 1,
+                 drop_last: bool = True,
+                 text_padding_length: int = 512,
+                 seed: int = 42,
+                 read_batch_size: int = 32,
+                 parquet_schema: pa.Schema = None):
+        super().__init__()
+        self.path = str(path)
+        self.batch_size = batch_size
+        self.parquet_schema = parquet_schema
+        self.cfg_rate = cfg_rate
+        self.text_padding_length = text_padding_length
+        self.seed = seed
+        self.read_batch_size = read_batch_size
+        # Get distributed training info
+        self.global_rank = get_world_rank()
+        self.world_size = get_world_size()
+        self.sp_world_size = get_sp_world_size()
+        self.num_sp_groups = self.world_size // self.sp_world_size
+        num_workers = 1 if num_workers == 0 else num_workers
+        # Get sharding info
+        shard_parquet_files, shard_total_samples, shard_parquet_lengths = shard_parquet_files_across_sp_groups_and_workers(
+            self.path, self.num_sp_groups, num_workers, seed)
+
+        if drop_last:
+            self.worker_num_samples = min(
+                shard_total_samples) // batch_size * batch_size
+            # Assign files to current rank's SP group
+            ith_sp_group = self.global_rank // self.sp_world_size
+            self.sp_group_parquet_files = shard_parquet_files[ith_sp_group::self
+                                                              .num_sp_groups]
+            self.sp_group_parquet_lengths = shard_parquet_lengths[
+                ith_sp_group::self.num_sp_groups]
+            self.sp_group_num_samples = shard_total_samples[ith_sp_group::self.
+                                                            num_sp_groups]
+            logger.info(
+                "In total %d parquet files, %d samples, after sharding we retain %d samples due to drop_last",
+                sum([len(shard) for shard in shard_parquet_files]),
+                sum(shard_total_samples),
+                self.worker_num_samples * self.num_sp_groups * num_workers)
+        else:
+            raise ValueError("drop_last must be True")
+        logger.info("Each dataloader worker will load %d samples",
+                    self.worker_num_samples)
+
+    def __iter__(self):
+        worker_info = get_worker_info()
+        worker_id = worker_info.id if worker_info is not None else 1
+
+        worker_files = self.sp_group_parquet_files[worker_id]
+
+        batch_iterator = BatchIterator(
+            files=worker_files,
+            batch_size=self.batch_size,
+            text_padding_length=self.text_padding_length,
+            keys=self.keys,
+            worker_num_samples=self.worker_num_samples,
+            read_batch_size=self.read_batch_size)  # type: ignore
+
+        yield from batch_iterator
+
+        if batch_iterator.processed_samples != self.worker_num_samples:
+            raise ValueError(
+                "Rank %d, Worker %d: Not enough samples to process, this should not happen",
+                self.global_rank, worker_id)
+
+
+def shard_parquet_files_across_sp_groups_and_workers(
+    path: str,
+    num_sp_groups: int,
+    num_workers: int,
+    seed: int = 42,
+) -> Tuple[List[List[str]], List[int], List[Dict[str, int]]]:
+    """
+    Shard parquet files across SP groups and workers in a balanced way.
+    
+    Args:
+        path: Directory containing parquet files
+        num_sp_groups: Number of SP groups to shard across
+        num_workers: Number of workers per SP group
+        seed: Random seed for shuffling
+        
+    Returns:
+        Tuple containing:
+        - List of lists of parquet files for each shard
+        - List of total samples per shard
+        - List of dictionaries mapping file paths to their lengths
+    """
+    # Check if sharding plan already exists
+    sharding_info_dir = os.path.join(
+        path, f"sharding_info_{num_sp_groups}_sp_groups_{num_workers}_workers")
+    if os.path.exists(sharding_info_dir):
+        logger.info("Sharding plan already exists")
+        logger.info("Loading sharding plan from %s", sharding_info_dir)
+        try:
+            with open(
+                    os.path.join(sharding_info_dir, "shard_parquet_files.pkl"),
+                    "rb") as f:
+                shard_parquet_files = pickle.load(f)
+            with open(
+                    os.path.join(sharding_info_dir, "shard_total_samples.pkl"),
+                    "rb") as f:
+                shard_total_samples = pickle.load(f)
+            with open(
+                    os.path.join(sharding_info_dir,
+                                 "shard_parquet_lengths.pkl"), "rb") as f:
+                shard_parquet_lengths = pickle.load(f)
+            return shard_parquet_files, shard_total_samples, shard_parquet_lengths
+        except Exception as e:
+            logger.error("Error loading sharding plan: %s", str(e))
+            logger.info("Falling back to creating new sharding plan")
+
+    if get_world_rank() == 0:
+        logger.info("Scanning for parquet files in %s", path)
+
+        # Find all parquet files
+        parquet_files = []
+
+        for root, _, files in os.walk(path):
+            for file in files:
+                if file.endswith('.parquet'):
+                    parquet_files.append(os.path.join(root, file))
+
+        if not parquet_files:
+            raise ValueError("No parquet files found in %s", path)
+
+        # Calculate file lengths efficiently using a single pass
+        logger.info("Calculating file lengths...")
+        lengths = []
+        for file in tqdm.tqdm(parquet_files, desc="Reading parquet files"):
+            lengths.append(pq.ParquetFile(file).metadata.num_rows)
+
+        total_samples = sum(lengths)
+        logger.info("Found %d files with %d total samples", len(parquet_files),
+                    total_samples)
+
+        # Sort files by length for better balancing
+        sorted_indices = np.argsort(lengths)
+        sorted_files = [parquet_files[i] for i in sorted_indices]
+        sorted_lengths = [lengths[i] for i in sorted_indices]
+
+        # Create shards
+        num_shards = num_sp_groups * num_workers
+        shard_parquet_files = [[] for _ in range(num_shards)]
+        shard_total_samples = [0] * num_shards
+        shard_parquet_lengths = [{} for _ in range(num_shards)]
+
+        # Distribute files to shards using a greedy approach
+        logger.info("Distributing files to shards...")
+        for file, length in zip(reversed(sorted_files),
+                                reversed(sorted_lengths)):
+            # Find shard with minimum current length
+            target_shard = np.argmin(shard_total_samples)
+            shard_parquet_files[target_shard].append(file)
+            shard_total_samples[target_shard] += length
+            shard_parquet_lengths[target_shard][file] = length
+        #randomize each shard
+        for shard in shard_parquet_files:
+            random.seed(seed)
+            random.shuffle(shard)
+
+        save_dir = os.path.join(
+            path,
+            f"sharding_info_{num_sp_groups}_sp_groups_{num_workers}_workers")
+        os.makedirs(save_dir, exist_ok=True)
+        with open(os.path.join(save_dir, "shard_parquet_files.pkl"), "wb") as f:
+            pickle.dump(shard_parquet_files, f)
+        with open(os.path.join(save_dir, "shard_total_samples.pkl"), "wb") as f:
+            pickle.dump(shard_total_samples, f)
+        with open(os.path.join(save_dir, "shard_parquet_lengths.pkl"),
+                  "wb") as f:
+            pickle.dump(shard_parquet_lengths, f)
+        logger.info("Saved sharding info to %s", save_dir)
+
+    # wait for all ranks to finish
+    torch.distributed.barrier()
+    # recursive call
+    return shard_parquet_files_across_sp_groups_and_workers(
+        path, num_sp_groups, num_workers, seed)
+
+
+def build_parquet_iterable_style_dataloader(
+    path: str,
+    batch_size: int,
+    num_data_workers: int,
+    cfg_rate: float = 0.0,
+    drop_last: bool = True,
+    text_padding_length: int = 512,
+    seed: int = 42,
+    read_batch_size: int = 32
+) -> Tuple[LatentsParquetIterStyleDataset, StatefulDataLoader]:
+    """Build a dataloader for the LatentsParquetIterStyleDataset."""
+    dataset = LatentsParquetIterStyleDataset(
+        path=path,
+        batch_size=batch_size,
+        cfg_rate=cfg_rate,
+        num_workers=num_data_workers,
+        drop_last=drop_last,
+        text_padding_length=text_padding_length,
+        seed=seed,
+        read_batch_size=read_batch_size)
+
+    loader = StatefulDataLoader(
+        dataset,
+        batch_size=1,
+        num_workers=num_data_workers,
+        pin_memory=True,
+    )
+    return dataset, loader
@@ -1,15 +1,18 @@
 # SPDX-License-Identifier: Apache-2.0
 import os
+import pickle
 from typing import Any, Dict, List, Tuple

-import numpy as np
+import pyarrow as pa
 import pyarrow.parquet as pq
 # Torch in general
 import torch
+import tqdm
 # Dataset
 from torch.utils.data import Dataset, Sampler
 from torchdata.stateful_dataloader import StatefulDataLoader

+from fastvideo.v1.dataset.utils import collate_rows_from_parquet_schema
 from fastvideo.v1.distributed import (get_sp_world_size, get_world_rank,
                                      get_world_size)
 from fastvideo.v1.logger import init_logger
@@ -30,6 +33,7 @@ class DP_SP_BatchSampler(Sampler[List[int]]):
        sp_world_size: int,
        global_rank: int,
        drop_last: bool = True,
+        drop_first_row: bool = False,
        seed: int = 0,
    ):
        self.batch_size = batch_size
@@ -45,6 +49,11 @@ class DP_SP_BatchSampler(Sampler[List[int]]):
        # Create a random permutation of all indices
        global_indices = torch.randperm(self.dataset_size, generator=rng)

+        if drop_first_row:
+            # drop 0 in global_indices
+            global_indices = global_indices[global_indices != 0]
+            self.dataset_size = self.dataset_size - 1
+
        if self.drop_last:
            # For drop_last=True, we:
            # 1. Ensure total samples is divisible by (batch_size * num_sp_groups)
@@ -56,19 +65,22 @@ class DP_SP_BatchSampler(Sampler[List[int]]):
                                            self.num_sp_groups *
                                            self.batch_size]
        else:
-            # add more indices to make it divisible by (batch_size * num_sp_groups)
-            padding_size = self.num_sp_groups * self.batch_size - (
-                self.dataset_size % (self.num_sp_groups * self.batch_size))
-            global_indices = torch.cat(
-                [global_indices, global_indices[:padding_size]])
+            if self.dataset_size % (self.num_sp_groups * self.batch_size) != 0:
+                # add more indices to make it divisible by (batch_size * num_sp_groups)
+                padding_size = self.num_sp_groups * self.batch_size - (
+                    self.dataset_size % (self.num_sp_groups * self.batch_size))
+                logger.info("Padding the dataset from %d to %d",
+                            self.dataset_size, self.dataset_size + padding_size)
+                global_indices = torch.cat(
+                    [global_indices, global_indices[:padding_size]])

        # shard the indices to each sp group
        ith_sp_group = self.global_rank // self.sp_world_size
        sp_group_local_indices = global_indices[ith_sp_group::self.
                                                num_sp_groups]
-
        self.sp_group_local_indices = sp_group_local_indices
-        logger.info("sp_group_local_indices: %d", len(sp_group_local_indices))
+        logger.info("Dataset size for each sp group: %d",
+                    len(sp_group_local_indices))

    def __iter__(self):
        indices = self.sp_group_local_indices
@@ -81,20 +93,49 @@ class DP_SP_BatchSampler(Sampler[List[int]]):


 def get_parquet_files_and_length(path: str):
-    lengths = []
-    file_names = []
-    for root, _, files in os.walk(path):
-        for file in sorted(files):
-            if file.endswith('.parquet'):
-                file_path = os.path.join(root, file)
-                num_rows = pq.ParquetFile(file_path).metadata.num_rows
-                lengths.append(num_rows)
-                file_names.append(file_path)
-    # sort according to file name to ensure all rank has the same order (in case os.walk is not sorted)
-    file_names_sorted, lengths_sorted = zip(
-        *sorted(zip(file_names, lengths), key=lambda x: x[0]))
-    assert len(file_names_sorted) != 0, "No parquet files found in the dataset"
-    return file_names_sorted, lengths_sorted
+    # Check if cached info exists
+    cache_dir = os.path.join(path, "map_style_cache")
+    cache_file = os.path.join(cache_dir, "file_info.pkl")
+
+    if os.path.exists(cache_file):
+        logger.info("Loading cached file info from %s", cache_file)
+        try:
+            with open(cache_file, "rb") as f:
+                file_names_sorted, lengths_sorted = pickle.load(f)
+            return file_names_sorted, lengths_sorted
+        except Exception as e:
+            logger.error("Error loading cached file info: %s", str(e))
+            logger.info("Falling back to scanning files")
+
+    # If no cache exists or loading failed, scan files
+    if get_world_rank() == 0:
+        lengths = []
+        file_names = []
+        for root, _, files in os.walk(path):
+            for file in sorted(files):
+                if file.endswith('.parquet'):
+                    file_path = os.path.join(root, file)
+                    file_names.append(file_path)
+        for file_path in tqdm.tqdm(file_names,
+                                   desc="Reading parquet files to get lengths"):
+            num_rows = pq.ParquetFile(file_path).metadata.num_rows
+            lengths.append(num_rows)
+        # sort according to file name to ensure all rank has the same order (in case os.walk is not sorted)
+        file_names_sorted, lengths_sorted = zip(
+            *sorted(zip(file_names, lengths), key=lambda x: x[0]))
+        assert len(
+            file_names_sorted) != 0, "No parquet files found in the dataset"
+
+        os.makedirs(cache_dir, exist_ok=True)
+        with open(cache_file, "wb") as f:
+            pickle.dump((file_names_sorted, lengths_sorted), f)
+        logger.info("Saved file info to %s", cache_file)
+
+    # Wait for rank 0 to finish saving
+    if get_world_size() > 1:
+        torch.distributed.barrier()
+
+    return get_parquet_files_and_length(path)


 def read_row_from_parquet_file(parquet_files: List[str], global_row_idx: int,
@@ -144,39 +185,27 @@ class LatentsParquetMapStyleDataset(Dataset):
    Note: 
    Using parquet for map style dataset is not efficient, we mainly keep it for backward compatibility and debugging.
    """
-    # Modify this in the future if we want to add more keys, for example, in image to video.
-    keys = ["vae_latent", "text_embedding"]

    def __init__(
        self,
        path: str,
        batch_size: int,
+        parquet_schema: pa.Schema,
        cfg_rate: float = 0.0,
        seed: int = 42,
        drop_last: bool = True,
+        drop_first_row: bool = False,
        text_padding_length: int = 512,
    ):
        super().__init__()
        self.path = path
        self.cfg_rate = cfg_rate
-        if cfg_rate > 0.0:
-            raise ValueError(
-                "cfg_rate > 0.0 is not supported for now because it will trigger bug when num_data_workers > 0"
-            )
+        self.parquet_schema = parquet_schema
        logger.info("Initializing LatentsParquetMapStyleDataset with path: %s",
                    path)
        self.parquet_files, self.lengths = get_parquet_files_and_length(path)
        self.batch = batch_size
        self.text_padding_length = text_padding_length
-        self._cols = [
-            "vae_latent_bytes",
-            "vae_latent_shape",
-            "text_embedding_bytes",
-            "text_embedding_shape",
-            "text_embedding_dtype",
-            "height",
-            "width",
-        ]
        self.sampler = DP_SP_BatchSampler(
            batch_size=batch_size,
            dataset_size=sum(self.lengths),
@@ -184,27 +213,14 @@ class LatentsParquetMapStyleDataset(Dataset):
            sp_world_size=get_sp_world_size(),
            global_rank=get_world_rank(),
            drop_last=drop_last,
+            drop_first_row=drop_first_row,
            seed=seed,
        )
        logger.info("Dataset initialized with %d parquet files and %d rows",
                    len(self.parquet_files), sum(self.lengths))

-    def _get_torch_tensors_from_row_dict(
-            self, row_dict: Dict[str, Any]) -> Dict[str, torch.Tensor]:
-        """
-        Get the latents and prompts from a row dictionary.
-        """
-        return_dict = {}
-        for key in self.keys:
-            shape = row_dict[f"{key}_shape"]
-            bytes = row_dict[f"{key}_bytes"]
-            # TODO (peiyuan): read precision
-            data = np.frombuffer(bytes, dtype=np.float32).reshape(shape).copy()
-            data = torch.from_numpy(data)
-            return_dict[key] = data
-        return return_dict
-
-    def get_validation_negative_prompt(self) -> tuple[Any, Any, Any, Any]:
+    def get_validation_negative_prompt(
+            self) -> tuple[torch.Tensor, torch.Tensor, str]:
        """
        Get the negative prompt for validation. 
        This method ensures the negative prompt is loaded and cached properly.
@@ -218,39 +234,23 @@ class LatentsParquetMapStyleDataset(Dataset):
        row_dict = read_row_from_parquet_file([file_path], row_idx,
                                              [self.lengths[0]])

-        # Get tensors using the existing helper method
-        data = self._get_torch_tensors_from_row_dict(row_dict)
-        emb = data["text_embedding"]
+        batch = collate_rows_from_parquet_schema([row_dict],
+                                                 self.parquet_schema,
+                                                 self.text_padding_length,
+                                                 cfg_rate=0.0)
+        negative_prompt = batch['info_list'][0]['prompt']
+        negative_prompt_embedding = batch['text_embedding']
+        negative_prompt_attention_mask = batch['text_attention_mask']
+        if len(negative_prompt_embedding.shape) == 2:
+            negative_prompt_embedding = negative_prompt_embedding.unsqueeze(0)
+        if len(negative_prompt_attention_mask.shape) == 1:
+            negative_prompt_attention_mask = negative_prompt_attention_mask.unsqueeze(
+                0).unsqueeze(0)

-        # Pad the embedding and get mask
-        padded_emb, mask = self._pad(emb, self.text_padding_length)
-
-        # Pin memory for faster transfer to GPU
-        padded_emb = padded_emb
-        mask = mask
-
-        return None, padded_emb, mask, None
-
-    def _pad(self, t: torch.Tensor, padding_length: int) -> torch.Tensor:
-        """
-        Pad or crop an embedding [L, D] to exactly padding_length tokens.
-        Return:
-        - [L, D] tensor in pinned CPU memory
-        - [L] attention mask in pinned CPU memory
-        """
-        L, D = t.shape
-        if padding_length > L:  # pad
-            pad = torch.zeros(padding_length - L,
-                              D,
-                              dtype=t.dtype,
-                              device=t.device)
-            return torch.cat([t, pad], 0), torch.cat(
-                [torch.ones(L), torch.zeros(padding_length - L)], 0)
-        else:  # crop
-            return t[:padding_length], torch.ones(padding_length)
+        return negative_prompt_embedding, negative_prompt_attention_mask, negative_prompt

    # PyTorch calls this ONLY because the batch_sampler yields a list
-    def __getitems__(self, indices: List[int]):
+    def __getitems__(self, indices: List[int]) -> Dict[str, Any]:
        """
        Batch fetch using read_row_from_parquet_file for each index.
        """
@@ -259,29 +259,11 @@ class LatentsParquetMapStyleDataset(Dataset):
            for idx in indices
        ]

-        # Initialize tensors to hold padded embeddings and masks
-        all_latents = []
-        all_embs = []
-        all_masks = []
-
-        # Process each row individually
-        for i, row in enumerate(rows):
-            # Get tensors from row
-            data = self._get_torch_tensors_from_row_dict(row)
-            latents, emb = data["vae_latent"], data["text_embedding"]
-
-            padded_emb, mask = self._pad(emb, self.text_padding_length)
-            # Store in batch tensors
-            all_latents.append(latents)
-            all_embs.append(padded_emb)
-            all_masks.append(mask)
-
-        # Pin memory for faster transfer to GPU
-        all_latents = torch.stack(all_latents)
-        all_embs = torch.stack(all_embs)
-        all_masks = torch.stack(all_masks)
-
-        return all_latents, all_embs, all_masks, indices
+        batch = collate_rows_from_parquet_schema(rows,
+                                                 self.parquet_schema,
+                                                 self.text_padding_length,
+                                                 cfg_rate=self.cfg_rate)
+        return batch

    def __len__(self):
        return sum(self.lengths)
@@ -298,8 +280,10 @@ def build_parquet_map_style_dataloader(
        path,
        batch_size,
        num_data_workers,
+        parquet_schema,
        cfg_rate=0.0,
        drop_last=True,
+        drop_first_row=False,
        text_padding_length=512,
        seed=42) -> Tuple[LatentsParquetMapStyleDataset, StatefulDataLoader]:
    dataset = LatentsParquetMapStyleDataset(
@@ -307,7 +291,9 @@ def build_parquet_map_style_dataloader(
        batch_size,
        cfg_rate=cfg_rate,
        drop_last=drop_last,
+        drop_first_row=drop_first_row,
        text_padding_length=text_padding_length,
+        parquet_schema=parquet_schema,
        seed=seed)

    loader = StatefulDataLoader(
@@ -0,0 +1,615 @@
+# SPDX-License-Identifier: Apache-2.0
+import json
+import math
+import os
+import random
+from abc import ABC, abstractmethod
+from collections import Counter
+from dataclasses import dataclass
+from os.path import join as opj
+from typing import Any, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+import torchvision
+from einops import rearrange
+from PIL import Image
+from transformers import AutoTokenizer
+
+from fastvideo.v1.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+@dataclass
+class PreprocessBatch:
+    """
+    Batch information for dataset processing stages.
+    
+    This class holds all the information about a video-caption or image-caption pair
+    as it moves through the processing pipeline. Fields are populated by different stages.
+    """
+    # Raw metadata
+    path: str
+    cap: Union[str, List[str]]
+    resolution: Optional[Dict] = None
+    fps: Optional[float] = None
+    duration: Optional[float] = None
+
+    # Processed metadata
+    num_frames: Optional[int] = None
+    sample_frame_index: Optional[List[int]] = None
+    sample_num_frames: Optional[int] = None
+
+    # Processed data
+    pixel_values: Optional[torch.Tensor] = None
+    text: Optional[str] = None
+    input_ids: Optional[torch.Tensor] = None
+    cond_mask: Optional[torch.Tensor] = None
+
+    @property
+    def is_video(self) -> bool:
+        """Check if this is a video item."""
+        return self.path.endswith(".mp4")
+
+    @property
+    def is_image(self) -> bool:
+        """Check if this is an image item."""
+        return self.path.endswith(".jpg")
+
+
+class DatasetStage(ABC):
+    """
+    Abstract base class for dataset processing stages.
+    
+    Similar to PipelineStage but designed for dataset preprocessing operations.
+    """
+
+    @abstractmethod
+    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
+        """
+        Process the dataset batch.
+        
+        Args:
+            batch: Dataset batch to process
+            **kwargs: Additional processing parameters
+            
+        Returns:
+            Processed batch
+        """
+        raise NotImplementedError
+
+
+class DatasetFilterStage(ABC):
+    """
+    Abstract base class for dataset filtering stages.
+    
+    These stages can filter out items during metadata processing.
+    """
+
+    @abstractmethod
+    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
+        """
+        Check if batch should be kept.
+        
+        Args:
+            batch: Dataset batch to check
+            **kwargs: Additional parameters
+            
+        Returns:
+            True if batch should be kept, False otherwise
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
+        """
+        Process the dataset batch (for non-filtering operations).
+        
+        Args:
+            batch: Dataset batch to process
+            **kwargs: Additional processing parameters
+            
+        Returns:
+            Processed batch
+        """
+        raise NotImplementedError
+
+
+class DataValidationStage(DatasetFilterStage):
+    """Stage for validating data items."""
+
+    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
+        """
+        Validate data item.
+        
+        Args:
+            batch: Dataset batch to validate
+            
+        Returns:
+            True if valid, False if invalid
+        """
+        # Check for caption
+        if batch.cap is None:
+            return False
+
+        if batch.is_video:
+            # Validate video-specific fields
+            if batch.duration is None or batch.fps is None:
+                return False
+        elif not batch.is_image:
+            return False
+
+        return True
+
+    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
+        """Process does nothing for validation - filtering is handled by should_keep."""
+        return batch
+
+
+class ResolutionFilterStage(DatasetFilterStage):
+    """Stage for filtering data items based on resolution constraints."""
+
+    def __init__(self,
+                 max_h_div_w_ratio: float = 17 / 16,
+                 min_h_div_w_ratio: float = 8 / 16,
+                 max_height: int = 1024,
+                 max_width: int = 1024):
+        self.max_h_div_w_ratio = max_h_div_w_ratio
+        self.min_h_div_w_ratio = min_h_div_w_ratio
+        self.max_height = max_height
+        self.max_width = max_width
+
+    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
+        """
+        Check if data item passes resolution filtering.
+        
+        Args:
+            batch: Dataset batch with resolution information
+            
+        Returns:
+            True if passes filter, False otherwise
+        """
+        # Only apply to videos
+        if not batch.is_video:
+            return True
+
+        if batch.resolution is None:
+            return False
+
+        height = batch.resolution.get("height", None)
+        width = batch.resolution.get("width", None)
+        if height is None or width is None:
+            return False
+
+        # Check aspect ratio
+        aspect = self.max_height / self.max_width
+        hw_aspect_thr = 1.5
+
+        return self.filter_resolution(
+            height,
+            width,
+            max_h_div_w_ratio=hw_aspect_thr * aspect,
+            min_h_div_w_ratio=1 / hw_aspect_thr * aspect,
+        )
+
+    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
+        """Process does nothing for resolution filtering - filtering is handled by should_keep."""
+        return batch
+
+    def filter_resolution(self, h: int, w: int, max_h_div_w_ratio: float,
+                          min_h_div_w_ratio: float) -> bool:
+        """Filter based on height/width ratio."""
+        return h / w <= max_h_div_w_ratio and h / w >= min_h_div_w_ratio
+
+
+class FrameSamplingStage(DatasetFilterStage):
+    """Stage for temporal frame sampling and indexing."""
+
+    def __init__(self,
+                 num_frames: int,
+                 train_fps: int,
+                 speed_factor: int = 1,
+                 video_length_tolerance_range: float = 5.0,
+                 drop_short_ratio: float = 0.0):
+        self.num_frames = num_frames
+        self.train_fps = train_fps
+        self.speed_factor = speed_factor
+        self.video_length_tolerance_range = video_length_tolerance_range
+        self.drop_short_ratio = drop_short_ratio
+
+    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
+        """
+        Check if video should be kept based on length constraints.
+        
+        Args:
+            batch: Dataset batch
+            
+        Returns:
+            True if should be kept, False otherwise
+        """
+        if batch.is_image:
+            return True
+
+        if batch.duration is None or batch.fps is None:
+            return False
+
+        num_frames = math.ceil(batch.fps * batch.duration)
+
+        # Check if video is too long
+        if (num_frames / batch.fps > self.video_length_tolerance_range *
+            (self.num_frames / self.train_fps * self.speed_factor)):
+            return False
+
+        # Resample frame indices to check length
+        frame_interval = batch.fps / self.train_fps
+        start_frame_idx = 0
+        frame_indices = np.arange(start_frame_idx, num_frames,
+                                  frame_interval).astype(int)
+
+        # Filter short videos
+        return not (len(frame_indices) < self.num_frames
+                    and random.random() < self.drop_short_ratio)
+
+    def process(self,
+                batch: PreprocessBatch,
+                temporal_sample_fn=None,
+                **kwargs) -> PreprocessBatch:
+        """
+        Process frame sampling for video data items.
+        
+        Args:
+            batch: Dataset batch
+            temporal_sample_fn: Function for temporal sampling
+            
+        Returns:
+            Updated batch with frame sampling info
+        """
+        if batch.is_image:
+            # For images, just add sample info
+            batch.sample_frame_index = [0]
+            batch.sample_num_frames = 1
+            return batch
+
+        assert batch.duration is not None and batch.fps is not None
+        batch.num_frames = math.ceil(batch.fps * batch.duration)
+
+        # Resample frame indices
+        frame_interval = batch.fps / self.train_fps
+        start_frame_idx = 0
+        frame_indices = np.arange(start_frame_idx, batch.num_frames,
+                                  frame_interval).astype(int)
+
+        # Temporal crop if too long
+        if len(frame_indices
+               ) > self.num_frames and temporal_sample_fn is not None:
+            begin_index, end_index = temporal_sample_fn(len(frame_indices))
+            frame_indices = frame_indices[begin_index:end_index]
+
+        batch.sample_frame_index = frame_indices.tolist()
+        batch.sample_num_frames = len(frame_indices)
+
+        return batch
+
+
+class VideoTransformStage(DatasetStage):
+    """Stage for video data transformation."""
+
+    def __init__(self, transform) -> None:
+        self.transform = transform
+
+    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
+        """
+        Transform video data.
+        
+        Args:
+            batch: Dataset batch with video information
+            
+        Returns:
+            Batch with transformed video tensor
+        """
+        if not batch.is_video:
+            return batch
+
+        assert os.path.exists(batch.path), f"file {batch.path} do not exist!"
+        assert batch.sample_frame_index is not None, "Frame indices must be set before transformation"
+
+        torchvision_video, _, metadata = torchvision.io.read_video(
+            batch.path, output_format="TCHW")
+        video = torchvision_video[batch.sample_frame_index]
+        if self.transform is not None:
+            video = self.transform(video)
+        video = rearrange(video, "t c h w -> c t h w")
+        video = video.to(torch.uint8)
+
+        h, w = video.shape[-2:]
+        assert (
+            h / w <= 17 / 16 and h / w >= 8 / 16
+        ), f"Only videos with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But video ({batch.path}) found ratio is {round(h / w, 2)} with the shape of {video.shape}"
+
+        video = video.float() / 127.5 - 1.0
+        batch.pixel_values = video
+        return batch
+
+
+class ImageTransformStage(DatasetStage):
+    """Stage for image data transformation."""
+
+    def __init__(self, transform, transform_topcrop) -> None:
+        self.transform = transform
+        self.transform_topcrop = transform_topcrop
+
+    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
+        """
+        Transform image data.
+        
+        Args:
+            batch: Dataset batch with image information
+            
+        Returns:
+            Batch with transformed image tensor
+        """
+        if not batch.is_image:
+            return batch
+
+        image = Image.open(batch.path).convert("RGB")
+        image = torch.from_numpy(np.array(image))
+        image = rearrange(image, "h w c -> c h w").unsqueeze(0)
+
+        if self.transform_topcrop is not None:
+            image = self.transform_topcrop(image)
+        elif self.transform is not None:
+            image = self.transform(image)
+
+        image = image.transpose(0, 1)  # [1 C H W] -> [C 1 H W]
+        image = image.float() / 127.5 - 1.0
+        batch.pixel_values = image
+        return batch
+
+
+class TextEncodingStage(DatasetStage):
+    """Stage for text tokenization and encoding."""
+
+    def __init__(self, tokenizer, text_max_length: int, cfg_rate: float = 0.0):
+        self.tokenizer = tokenizer
+        self.text_max_length = text_max_length
+        self.cfg_rate = cfg_rate
+
+    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
+        """
+        Process text data.
+        
+        Args:
+            batch: Dataset batch with caption information
+            
+        Returns:
+            Batch with encoded text information
+        """
+        text = batch.cap
+        if not isinstance(text, list):
+            text = [text]
+        text = [random.choice(text)]
+
+        text = text[0] if random.random() > self.cfg_rate else ""
+        text_tokens_and_mask = self.tokenizer(
+            text,
+            max_length=self.text_max_length,
+            padding="max_length",
+            truncation=True,
+            return_attention_mask=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+
+        batch.text = text
+        batch.input_ids = text_tokens_and_mask["input_ids"]
+        batch.cond_mask = text_tokens_and_mask["attention_mask"]
+        return batch
+
+
+class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
+                                torch.distributed.checkpoint.stateful.Stateful):
+    """
+    Merged dataset for video and caption data with stage-based processing.
+    Assumes that data_merge_path is a txt file with the following format:
+    <folder_path>,<json_file_path>
+
+        The folder should contain videos.
+
+        The json file should be a list of dictionaries with the following format:
+        [
+        {
+            "path": "1gGQy4nxyUo-Scene-016.mp4",
+            "resolution": {
+            "width": 1920,
+            "height": 1080
+            },
+            "size": 2439112,
+            "fps": 25.0,
+            "duration": 6.88,
+            "num_frames": 172,
+            "cap": [
+            "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open."
+            ]
+        },
+        ...
+        ]
+    
+    This dataset processes video and image data through a series of stages:
+    - Data validation
+    - Resolution filtering  
+    - Frame sampling
+    - Transformation
+    - Text encoding
+    """
+
+    def __init__(self,
+                 data_merge_path: str,
+                 args,
+                 transform,
+                 temporal_sample,
+                 transform_topcrop,
+                 start_idx: int = 0):
+        self.data_merge_path = data_merge_path
+        self.start_idx = start_idx
+        self.args = args
+        self.temporal_sample = temporal_sample
+
+        # Initialize tokenizer
+        tokenizer_path = os.path.join(args.model_path, "tokenizer")
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
+                                                  cache_dir=args.cache_dir)
+
+        # Initialize processing stages
+        self._init_stages(args, transform, transform_topcrop, tokenizer)
+
+        # Process metadata
+        self.processed_batches = self._process_metadata()
+
+    def _init_stages(self, args, transform, transform_topcrop,
+                     tokenizer) -> None:
+        """Initialize all processing stages."""
+        self.validation_stage = DataValidationStage()
+        self.resolution_filter_stage = ResolutionFilterStage(
+            max_height=args.max_height, max_width=args.max_width)
+        self.frame_sampling_stage = FrameSamplingStage(
+            num_frames=args.num_frames,
+            train_fps=args.train_fps,
+            speed_factor=args.speed_factor,
+            video_length_tolerance_range=args.video_length_tolerance_range,
+            drop_short_ratio=args.drop_short_ratio)
+        self.video_transform_stage = VideoTransformStage(transform)
+        self.image_transform_stage = ImageTransformStage(
+            transform, transform_topcrop)
+        self.text_encoding_stage = TextEncodingStage(
+            tokenizer=tokenizer,
+            text_max_length=args.text_max_length,
+            cfg_rate=args.training_cfg_rate)
+
+    def _load_raw_data(self) -> List[Dict]:
+        """Load raw data from JSON files."""
+        # Read folder-annotation pairs
+        with open(self.data_merge_path) as f:
+            folder_anno_pairs = [
+                line.strip().split(",") for line in f if line.strip()
+            ]
+        assert len(
+            folder_anno_pairs) == 1, "Only support one folder-annotation pair"
+        assert len(folder_anno_pairs[0]
+                   ) == 2, "Folder-annotation pair should have two elements"
+        folder, annotation_file = folder_anno_pairs[0]
+
+        data_items: List[Dict] = []
+        with open(annotation_file) as f:
+            data_items = json.load(f)
+
+        # Update paths with folder prefix
+        for item in data_items:
+            item["path"] = opj(folder, item["path"])
+
+        return data_items
+
+    def _process_metadata(self) -> List[PreprocessBatch]:
+        """Process the raw metadata through all filtering stages."""
+        raw_data = self._load_raw_data()
+        processed_batches = []
+
+        # Initialize counters
+        filter_counts = {
+            "validation_failed": 0,
+            "resolution_failed": 0,
+            "frame_sampling_failed": 0
+        }
+        sample_num_frames: List[int] = []
+
+        for item in raw_data:
+            batch = PreprocessBatch(path=item["path"],
+                                    cap=item["cap"],
+                                    resolution=item.get("resolution"),
+                                    fps=item.get("fps"),
+                                    duration=item.get("duration"))
+
+            # Apply filtering stages
+            if not self._apply_filter_stages(batch, filter_counts):
+                continue
+
+            # Apply frame sampling processing
+            batch = self.frame_sampling_stage.process(
+                batch, temporal_sample_fn=self.temporal_sample)
+
+            processed_batches.append(batch)
+            assert batch.sample_num_frames is not None
+            sample_num_frames.append(batch.sample_num_frames)
+
+        self._log_filtering_stats(filter_counts, sample_num_frames,
+                                  len(raw_data), len(processed_batches))
+        return processed_batches
+
+    def _apply_filter_stages(self, batch: PreprocessBatch,
+                             filter_counts: Dict[str, int]) -> bool:
+        """Apply all filter stages and update counters. Returns True if batch should be kept."""
+        if not self.validation_stage.should_keep(batch):
+            filter_counts["validation_failed"] += 1
+            return False
+
+        if not self.resolution_filter_stage.should_keep(batch):
+            filter_counts["resolution_failed"] += 1
+            return False
+
+        if not self.frame_sampling_stage.should_keep(batch):
+            filter_counts["frame_sampling_failed"] += 1
+            return False
+
+        return True
+
+    def _log_filtering_stats(self, filter_counts: Dict[str, int],
+                             sample_num_frames: List[int], before_count: int,
+                             after_count: int):
+        """Log filtering statistics."""
+        logger.info(
+            "validation_failed: %d, resolution_failed: %d, frame_sampling_failed: %d, "
+            "Counter(sample_num_frames): %s, before filter: %d, after filter: %d",
+            filter_counts['validation_failed'],
+            filter_counts['resolution_failed'],
+            filter_counts['frame_sampling_failed'], Counter(sample_num_frames),
+            before_count, after_count)
+
+    def __iter__(self):
+        """Iterate through processed data items."""
+        for idx in range(len(self.processed_batches)):
+            yield self._get_item(idx)
+
+    def __len__(self):
+        return len(self.processed_batches)
+
+    def _get_item(self, idx: int) -> Dict:
+        """Get a single processed data item."""
+        batch = self.processed_batches[idx]
+
+        # Apply transformation stages
+        batch = self.video_transform_stage.process(batch)
+        batch = self.image_transform_stage.process(batch)
+        batch = self.text_encoding_stage.process(batch)
+
+        # Build result dictionary
+        result = {
+            "pixel_values": batch.pixel_values,
+            "text": batch.text,
+            "input_ids": batch.input_ids,
+            "cond_mask": batch.cond_mask,
+            "path": batch.path,
+        }
+
+        # Add video-specific fields
+        if batch.is_video:
+            result.update({"fps": batch.fps, "duration": batch.duration})
+
+        return result
+
+    def state_dict(self) -> Dict[str, Any]:
+        """Return state dict for checkpointing."""
+        return {"processed_batches": self.processed_batches}
+
+    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
+        """Load state dict from checkpoint."""
+        self.processed_batches = state_dict["processed_batches"]
@@ -1,352 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import json
-import math
-import os
-import random
-from collections import Counter
-from os.path import join as opj
-
-import numpy as np
-import torch
-import torchvision
-from einops import rearrange
-from PIL import Image
-from torch.utils.data import Dataset
-
-from fastvideo.utils.dataset_utils import DecordInit
-from fastvideo.utils.logging_ import main_print
-
-
-class SingletonMeta(type):
-    _instances: dict[type, 'SingletonMeta'] = {}
-
-    def __call__(cls, *args, **kwargs):
-        if cls not in cls._instances:
-            instance = super().__call__(*args, **kwargs)
-            cls._instances[cls] = instance
-        return cls._instances[cls]
-
-
-class DataSetProg(metaclass=SingletonMeta):
-
-    def __init__(self) -> None:
-        self.cap_list: list[dict] = []
-        self.elements: list[int] = []
-        self.num_workers = 1
-        self.n_elements = 0
-        self.worker_elements: dict[int, list[int]] = {}
-        self.n_used_elements: dict[int, int] = {}
-
-    def set_cap_list(self, num_workers, cap_list, n_elements) -> None:
-        self.num_workers = num_workers
-        self.cap_list = cap_list
-        self.n_elements = n_elements
-        self.elements = list(range(n_elements))
-        random.shuffle(self.elements)
-        print(f"n_elements: {len(self.elements)}", flush=True)
-
-        for i in range(self.num_workers):
-            self.n_used_elements[i] = 0
-            per_worker = int(
-                math.ceil(len(self.elements) / float(self.num_workers)))
-            start = i * per_worker
-            end = min(start + per_worker, len(self.elements))
-            self.worker_elements[i] = self.elements[start:end]
-
-    def get_item(self, work_info) -> int:
-        worker_id = 0 if work_info is None else work_info.id
-
-        idx = self.worker_elements[worker_id][
-            self.n_used_elements[worker_id] %
-            len(self.worker_elements[worker_id])]
-        self.n_used_elements[worker_id] += 1
-        return idx
-
-
-dataset_prog = DataSetProg()
-
-
-def filter_resolution(h: int,
-                      w: int,
-                      max_h_div_w_ratio: float = 17 / 16,
-                      min_h_div_w_ratio: float = 8 / 16) -> bool:
-    return h / w <= max_h_div_w_ratio and h / w >= min_h_div_w_ratio
-
-
-class T2V_dataset(Dataset):
-
-    def __init__(self,
-                 args,
-                 transform,
-                 temporal_sample,
-                 tokenizer,
-                 transform_topcrop,
-                 start_idx=0) -> None:
-        self.start_idx = start_idx
-        self.data = args.data_merge_path
-        self.num_frames = args.num_frames
-        self.train_fps = args.train_fps
-        self.use_image_num = args.use_image_num
-        self.transform = transform
-        self.transform_topcrop = transform_topcrop
-        self.temporal_sample = temporal_sample
-        self.tokenizer = tokenizer
-        self.text_max_length = args.text_max_length
-        self.cfg = args.cfg
-        self.speed_factor = args.speed_factor
-        self.max_height = args.max_height
-        self.max_width = args.max_width
-        self.drop_short_ratio = args.drop_short_ratio
-        assert self.speed_factor >= 1
-        self.v_decoder = DecordInit()
-        self.video_length_tolerance_range = args.video_length_tolerance_range
-        self.support_Chinese = True
-        if "mt5" not in args.text_encoder_name:
-            self.support_Chinese = False
-
-        cap_list = self.get_cap_list()
-
-        assert len(cap_list) > 0
-        cap_list, self.sample_num_frames = self.define_frame_index(cap_list)
-        self.lengths = self.sample_num_frames
-
-        n_elements = len(cap_list)
-        dataset_prog.set_cap_list(args.dataloader_num_workers, cap_list,
-                                  n_elements)
-
-        print(f"video length: {len(dataset_prog.cap_list)}", flush=True)
-
-    def set_checkpoint(self, n_used_elements):
-        for i in range(len(dataset_prog.n_used_elements)):
-            dataset_prog.n_used_elements[i] = n_used_elements
-
-    def __len__(self):
-        return dataset_prog.n_elements
-
-    def __getitem__(self, idx):
-
-        data = self.get_data(idx)
-        return data
-
-    def get_data(self, idx) -> dict:
-        path = dataset_prog.cap_list[idx]["path"]
-        if path.endswith(".mp4"):
-            return self.get_video(idx)
-        else:
-            return self.get_image(idx)
-
-    def get_video(self, idx) -> dict:
-        video_path = dataset_prog.cap_list[idx]["path"]
-        assert os.path.exists(video_path), f"file {video_path} do not exist!"
-        frame_indices = dataset_prog.cap_list[idx]["sample_frame_index"]
-
-        torchvision_video, _, metadata = torchvision.io.read_video(
-            video_path, output_format="TCHW")
-        video = torchvision_video[frame_indices]
-        video = self.transform(video)
-        video = rearrange(video, "t c h w -> c t h w")
-        video = video.to(torch.uint8)
-        assert video.dtype == torch.uint8
-
-        h, w = video.shape[-2:]
-        assert (
-            h / w <= 17 / 16 and h / w >= 8 / 16
-        ), f"Only videos with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But video ({video_path}) found ratio is {round(h / w, 2)} with the shape of {video.shape}"
-
-        video = video.float() / 127.5 - 1.0
-
-        text = dataset_prog.cap_list[idx]["cap"]
-        if not isinstance(text, list):
-            text = [text]
-        text = [random.choice(text)]
-
-        text = text[0] if random.random() > self.cfg else ""
-        text_tokens_and_mask = self.tokenizer(
-            text,
-            max_length=self.text_max_length,
-            padding="max_length",
-            truncation=True,
-            return_attention_mask=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        )
-        input_ids = text_tokens_and_mask["input_ids"]
-        cond_mask = text_tokens_and_mask["attention_mask"]
-        return dict(pixel_values=video,
-                    text=text,
-                    input_ids=input_ids,
-                    cond_mask=cond_mask,
-                    path=video_path,
-                    fps=dataset_prog.cap_list[idx]["fps"],
-                    duration=dataset_prog.cap_list[idx]["duration"])
-
-    def get_image(self, idx) -> dict:
-        image_data = dataset_prog.cap_list[
-            idx]  # [{'path': path, 'cap': cap}, ...]
-
-        image = Image.open(image_data["path"]).convert("RGB")  # [h, w, c]
-        image = torch.from_numpy(np.array(image))  # [h, w, c]
-        image = rearrange(image, "h w c -> c h w").unsqueeze(0)  #  [1 c h w]
-        # for i in image:
-        #     h, w = i.shape[-2:]
-        #     assert h / w <= 17 / 16 and h / w >= 8 / 16, f'Only image with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But found ratio is {round(h / w, 2)} with the shape of {i.shape}'
-
-        image = (self.transform_topcrop(image) if "human_images"
-                 in image_data["path"] else self.transform(image)
-                 )  #  [1 C H W] -> num_img [1 C H W]
-        image = image.transpose(0, 1)  # [1 C H W] -> [C 1 H W]
-
-        image = image.float() / 127.5 - 1.0
-
-        caps: list[str] = (image_data["cap"] if isinstance(
-            image_data["cap"], list) else [image_data["cap"]])
-        caps = [random.choice(caps)]
-        text = caps
-        input_ids, cond_mask = [], []
-        single_text = text[0] if random.random() > self.cfg else ""
-        text_tokens_and_mask = self.tokenizer(
-            single_text,
-            max_length=self.text_max_length,
-            padding="max_length",
-            truncation=True,
-            return_attention_mask=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        )
-        input_ids = text_tokens_and_mask["input_ids"]  # 1, l
-        cond_mask = text_tokens_and_mask["attention_mask"]  # 1, l
-        return dict(
-            pixel_values=image,
-            text=text,
-            input_ids=input_ids,
-            cond_mask=cond_mask,
-            path=image_data["path"],
-        )
-
-    def define_frame_index(self, cap_list) -> tuple[list[dict], list[int]]:
-        new_cap_list = []
-        sample_num_frames = []
-        cnt_too_long = 0
-        cnt_too_short = 0
-        cnt_no_cap = 0
-        cnt_no_resolution = 0
-        cnt_resolution_mismatch = 0
-        cnt_movie = 0
-        cnt_img = 0
-        for i in cap_list:
-            path = i["path"]
-            cap = i.get("cap", None)
-            # ======no caption=====
-            if cap is None:
-                cnt_no_cap += 1
-                continue
-            if path.endswith(".mp4"):
-                # ======no fps and duration=====
-                duration = i.get("duration", None)
-                fps = i.get("fps", None)
-                if fps is None or duration is None:
-                    continue
-
-                # ======resolution mismatch=====
-                resolution = i.get("resolution", None)
-                if resolution is None:
-                    cnt_no_resolution += 1
-                    continue
-                else:
-                    if (resolution.get("height", None) is None
-                            or resolution.get("width", None) is None):
-                        cnt_no_resolution += 1
-                        continue
-                    height, width = i["resolution"]["height"], i["resolution"][
-                        "width"]
-                    aspect = self.max_height / self.max_width
-                    hw_aspect_thr = 1.5
-                    is_pick = filter_resolution(
-                        height,
-                        width,
-                        max_h_div_w_ratio=hw_aspect_thr * aspect,
-                        min_h_div_w_ratio=1 / hw_aspect_thr * aspect,
-                    )
-                    if not is_pick:
-                        print("resolution mismatch")
-                        cnt_resolution_mismatch += 1
-                        continue
-
-                # if path == 'finetrainers/3dgs-dissolve/videos/1.mp4':
-                #     from IPython import embed; embed()
-                i["num_frames"] = math.ceil(fps * duration)
-                # max 5.0 and min 1.0 are just thresholds to filter some videos which have suitable duration.
-                if i["num_frames"] / fps > self.video_length_tolerance_range * (
-                        self.num_frames / self.train_fps * self.speed_factor
-                ):  # too long video is not suitable for this training stage (self.num_frames)
-                    cnt_too_long += 1
-                    continue
-
-                # resample in case high fps, such as 50/60/90/144 -> train_fps(e.g, 24)
-                frame_interval = fps / self.train_fps
-                start_frame_idx = 0
-                frame_indices = np.arange(start_frame_idx, i["num_frames"],
-                                          frame_interval).astype(int)
-
-                # comment out it to enable dynamic frames training
-                if (len(frame_indices) < self.num_frames
-                        and random.random() < self.drop_short_ratio):
-                    cnt_too_short += 1
-                    continue
-
-                #  too long video will be temporal-crop randomly
-                if len(frame_indices) > self.num_frames:
-                    begin_index, end_index = self.temporal_sample(
-                        len(frame_indices))
-                    frame_indices = frame_indices[begin_index:end_index]
-                    # frame_indices = frame_indices[:self.num_frames]  # head crop
-                i["sample_frame_index"] = frame_indices.tolist()
-                new_cap_list.append(i)
-                i["sample_num_frames"] = len(
-                    i["sample_frame_index"]
-                )  # will use in dataloader(group sampler)
-                sample_num_frames.append(i["sample_num_frames"])
-            elif path.endswith(".jpg"):  # image
-                cnt_img += 1
-                new_cap_list.append(i)
-                i["sample_num_frames"] = 1
-                sample_num_frames.append(i["sample_num_frames"])
-            else:
-                raise NameError(
-                    f"Unknown file extension {path.split('.')[-1]}, only support .mp4 for video and .jpg for image"
-                )
-        # import ipdb;ipdb.set_trace()
-        main_print(
-            f"no_cap: {cnt_no_cap}, too_long: {cnt_too_long}, too_short: {cnt_too_short}, "
-            f"no_resolution: {cnt_no_resolution}, resolution_mismatch: {cnt_resolution_mismatch}, "
-            f"Counter(sample_num_frames): {Counter(sample_num_frames)}, cnt_movie: {cnt_movie}, cnt_img: {cnt_img}, "
-            f"before filter: {len(cap_list)}, after filter: {len(new_cap_list)}"
-        )
-        return new_cap_list, sample_num_frames
-
-    def decord_read(self, path, frame_indices) -> torch.Tensor:
-        decord_vr = self.v_decoder(path)
-        video_data = decord_vr.get_batch(frame_indices).asnumpy()
-        video_data = torch.from_numpy(video_data)
-        video_data = video_data.permute(0, 3, 1, 2)  # (T, H, W, C) -> (T C H W)
-        return video_data
-
-    def read_jsons(self, data) -> list[dict]:
-        cap_lists = []
-        with open(data) as f:
-            folder_anno = [
-                i.strip().split(",") for i in f.readlines()
-                if len(i.strip()) > 0
-            ]
-        print(folder_anno)
-        for folder, anno in folder_anno:
-            with open(anno) as f:
-                sub_list = json.load(f)
-            for i in range(len(sub_list)):
-                sub_list[i]["path"] = opj(folder, sub_list[i]["path"])
-            cap_lists += sub_list
-        return cap_lists
-
-    def get_cap_list(self) -> list:
-        cap_lists = self.read_jsons(self.data)[self.start_idx:]
-        return cap_lists
@@ -0,0 +1,220 @@
+import random
+from typing import Any, Dict, List, cast
+
+import numpy as np
+import torch
+
+
+def pad(t: torch.Tensor, padding_length: int) -> torch.Tensor:
+    """
+    Pad or crop an embedding [L, D] to exactly padding_length tokens.
+    Return:
+    - [L, D] tensor in pinned CPU memory
+    - [L] attention mask in pinned CPU memory
+    """
+    L, D = t.shape
+    if padding_length > L:  # pad
+        pad = torch.zeros(padding_length - L, D, dtype=t.dtype, device=t.device)
+        return torch.cat([t, pad], 0), torch.cat(
+            [torch.ones(L), torch.zeros(padding_length - L)], 0)
+    else:  # crop
+        return t[:padding_length], torch.ones(padding_length)
+
+
+def get_torch_tensors_from_row_dict(row_dict, keys, cfg_rate) -> Dict[str, Any]:
+    """
+    Get the latents and prompts from a row dictionary.
+    """
+    return_dict = {}
+    for key in keys:
+        shape, bytes = None, None
+        if isinstance(key, tuple):
+            for k in key:
+                try:
+                    shape = row_dict[f"{k}_shape"]
+                    bytes = row_dict[f"{k}_bytes"]
+                except KeyError:
+                    continue
+            key = key[0]
+            if shape is None or bytes is None:
+                raise ValueError(f"Key {key} not found in row_dict")
+        else:
+            shape = row_dict[f"{key}_shape"]
+            bytes = row_dict[f"{key}_bytes"]
+
+        # TODO (peiyuan): read precision
+        if key == 'text_embedding' and random.random() < cfg_rate:
+            data = np.zeros((512, 4096), dtype=np.float32)
+        else:
+            data = np.frombuffer(bytes, dtype=np.float32).reshape(shape).copy()
+        data = torch.from_numpy(data)
+        if len(data.shape) == 3:
+            B, L, D = data.shape
+            assert B == 1, "Batch size must be 1"
+            data = data.squeeze(0)
+        return_dict[key] = data
+    return return_dict
+
+
+def collate_latents_embs_masks(
+        batch_to_process,
+        text_padding_length,
+        keys,
+        cfg_rate=0.0
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[str]]:
+    # Initialize tensors to hold padded embeddings and masks
+    all_latents = []
+    all_embs = []
+    all_masks = []
+    caption_text = []
+    # Process each row individually
+    for i, row in enumerate(batch_to_process):
+        # Get tensors from row
+        data = get_torch_tensors_from_row_dict(row, keys, cfg_rate)
+        latents, emb = data["vae_latent"], data["text_embedding"]
+
+        padded_emb, mask = pad(emb, text_padding_length)
+        # Store in batch tensors
+        all_latents.append(latents)
+        all_embs.append(padded_emb)
+        all_masks.append(mask)
+        # TODO(py): remove this once we fix preprocess
+        try:
+            caption_text.append(row["prompt"])
+        except KeyError:
+            caption_text.append(row["caption"])
+
+    # Pin memory for faster transfer to GPU
+    all_latents = torch.stack(all_latents)
+    all_embs = torch.stack(all_embs)
+    all_masks = torch.stack(all_masks)
+
+    return all_latents, all_embs, all_masks, caption_text
+
+
+def collate_rows_from_parquet_schema(rows,
+                                     parquet_schema,
+                                     text_padding_length,
+                                     cfg_rate=0.0) -> Dict[str, Any]:
+    """
+    Collate rows from parquet files based on the provided schema.
+    Dynamically processes tensor fields based on schema and returns batched data.
+    
+    Args:
+        rows: List of row dictionaries from parquet files
+        parquet_schema: PyArrow schema defining the structure of the data
+    
+    Returns:
+        Dict containing batched tensors and metadata
+    """
+    if not rows:
+        return cast(Dict[str, Any], {})
+
+    # Initialize containers for different data types
+    batch_data: Dict[str, Any] = {}
+
+    # Get tensor and metadata field names from schema (fields ending with '_bytes')
+    tensor_fields = []
+    metadata_fields = []
+    for field in parquet_schema.names:
+        if field.endswith('_bytes'):
+            shape_field = field.replace('_bytes', '_shape')
+            dtype_field = field.replace('_bytes', '_dtype')
+            tensor_name = field.replace('_bytes', '')
+            tensor_fields.append(tensor_name)
+            assert shape_field in parquet_schema.names, f"Shape field {shape_field} not found in schema for field {field}. Currently we only support *_bytes fields for tensors."
+            assert dtype_field in parquet_schema.names, f"Dtype field {dtype_field} not found in schema for field {field}. Currently we only support *_bytes fields for tensors."
+        elif not field.endswith('_shape') and not field.endswith('_dtype'):
+            # Only add actual metadata fields, not the shape/dtype helper fields
+            metadata_fields.append(field)
+
+    # Process each tensor field
+    for tensor_name in tensor_fields:
+        tensor_list = []
+
+        for row in rows:
+            # Get tensor data from row using the existing helper function pattern
+            shape_key = f"{tensor_name}_shape"
+            bytes_key = f"{tensor_name}_bytes"
+
+            if shape_key in row and bytes_key in row:
+                shape = row[shape_key]
+                bytes_data = row[bytes_key]
+
+                if len(bytes_data) == 0:
+                    tensor = torch.zeros(0, dtype=torch.bfloat16)
+                else:
+                    # Convert bytes to tensor using float32 as default
+                    if tensor_name == 'text_embedding' and random.random(
+                    ) < cfg_rate:
+                        data = np.zeros((512, 4096), dtype=np.float32)
+                    else:
+                        data = np.frombuffer(
+                            bytes_data, dtype=np.float32).reshape(shape).copy()
+                    tensor = torch.from_numpy(data)
+                    # if len(data.shape) == 3:
+                    #     B, L, D = tensor.shape
+                    #     assert B == 1, "Batch size must be 1"
+                    #     tensor = tensor.squeeze(0)
+
+                tensor_list.append(tensor)
+            else:
+                # Handle missing tensor data
+                tensor_list.append(torch.zeros(0, dtype=torch.bfloat16))
+
+        # Stack tensors with special handling for text embeddings
+        if tensor_name == 'text_embedding':
+            # Handle text embeddings with padding
+            padded_tensors = []
+            attention_masks = []
+
+            for tensor in tensor_list:
+                if tensor.numel() > 0:
+                    padded_tensor, mask = pad(tensor, text_padding_length)
+                    padded_tensors.append(padded_tensor)
+                    attention_masks.append(mask)
+                else:
+                    # Handle empty embeddings - assume default embedding dimension
+                    padded_tensors.append(
+                        torch.zeros(text_padding_length,
+                                    768,
+                                    dtype=torch.bfloat16))
+                    attention_masks.append(torch.zeros(text_padding_length))
+
+            batch_data[tensor_name] = torch.stack(padded_tensors)
+            batch_data['text_attention_mask'] = torch.stack(attention_masks)
+        else:
+            # Stack all tensors to preserve batch consistency
+            # Don't filter out None or empty tensors as this breaks batch sizing
+            try:
+                batch_data[tensor_name] = torch.stack(tensor_list)
+            except ValueError as e:
+                shapes = [
+                    t.shape
+                    if t is not None and hasattr(t, 'shape') else 'None/Invalid'
+                    for t in tensor_list
+                ]
+                raise ValueError(
+                    f"Failed to stack tensors for field '{tensor_name}'. "
+                    f"Tensor shapes: {shapes}. "
+                    f"All tensors in a batch must have compatible shapes. "
+                    f"Original error: {e}") from e
+
+    # Process metadata fields into info_list
+    info_list = []
+    for row in rows:
+        info = {}
+        for field in metadata_fields:
+            info[field] = row.get(field, "")
+
+        # Add prompt field for backward compatibility
+        info["prompt"] = info.get("caption", "")
+        info_list.append(info)
+
+    batch_data['info_list'] = info_list
+
+    # Add caption_text for backward compatibility
+    if info_list and 'caption' in info_list[0]:
+        batch_data['caption_text'] = [info['caption'] for info in info_list]
+
+    return batch_data
@@ -0,0 +1,103 @@
+# SPDX-License-Identifier: Apache-2.0
+# adapted from: https://github.com/a-r-r-o-w/finetrainers/blob/main/finetrainers/data/dataset.py
+import os
+import pathlib
+
+import datasets
+import torch
+
+from fastvideo.v1.logger import init_logger
+from fastvideo.v1.models.vision_utils import load_image, load_video
+
+logger = init_logger(__name__)
+
+
+class ValidationDataset(torch.utils.data.IterableDataset):
+
+    def __init__(self, filename: str):
+        super().__init__()
+
+        self.filename = pathlib.Path(filename)
+        # get directory of filename
+        self.dir = os.path.abspath(self.filename.parent)
+
+        if not self.filename.exists():
+            raise FileNotFoundError(
+                f"File {self.filename.as_posix()} does not exist")
+
+        if self.filename.suffix == ".csv":
+            data = datasets.load_dataset("csv",
+                                         data_files=self.filename.as_posix(),
+                                         split="train")
+        elif self.filename.suffix == ".json":
+            data = datasets.load_dataset("json",
+                                         data_files=self.filename.as_posix(),
+                                         split="train",
+                                         field="data")
+        elif self.filename.suffix == ".parquet":
+            data = datasets.load_dataset("parquet",
+                                         data_files=self.filename.as_posix(),
+                                         split="train")
+        elif self.filename.suffix == ".arrow":
+            data = datasets.load_dataset("arrow",
+                                         data_files=self.filename.as_posix(),
+                                         split="train")
+        else:
+            _SUPPORTED_FILE_FORMATS = [".csv", ".json", ".parquet", ".arrow"]
+            raise ValueError(
+                f"Unsupported file format {self.filename.suffix} for validation dataset. Supported formats are: {_SUPPORTED_FILE_FORMATS}"
+            )
+
+        self._data = data.to_iterable_dataset()
+
+    def __iter__(self):
+        for sample in self._data:
+            # For consistency reasons, we mandate that "caption" is always present in the validation dataset.
+            # However, since the model specifications use "prompt", we create an alias here.
+            sample["prompt"] = sample["caption"]
+
+            # Load image or video if the path is provided
+            # TODO(aryan): need to handle custom columns here for control conditions
+            sample["image"] = None
+            sample["video"] = None
+
+            if sample.get("image_path", None) is not None:
+                image_path = sample["image_path"]
+                image_path = os.path.join(self.dir, image_path)
+                if not pathlib.Path(image_path).is_file(
+                ) and not image_path.startswith("http"):
+                    logger.warning("Image file %s does not exist.", image_path)
+                else:
+                    sample["image"] = load_image(image_path)
+
+            if sample.get("video_path", None) is not None:
+                video_path = sample["video_path"]
+                video_path = os.path.join(self.dir, video_path)
+                if not pathlib.Path(video_path).is_file(
+                ) and not video_path.startswith("http"):
+                    logger.warning("Video file %s does not exist.", video_path)
+                else:
+                    sample["video"] = load_video(video_path)
+
+            if sample.get("control_image_path", None) is not None:
+                control_image_path = sample["control_image_path"]
+                control_image_path = os.path.join(self.dir, control_image_path)
+                if not pathlib.Path(control_image_path).is_file(
+                ) and not control_image_path.startswith("http"):
+                    logger.warning("Control Image file %s does not exist.",
+                                   control_image_path)
+                else:
+                    sample["control_image"] = load_image(control_image_path)
+
+            if sample.get("control_video_path", None) is not None:
+                control_video_path = sample["control_video_path"]
+                control_video_path = os.path.join(self.dir, control_video_path)
+                if not pathlib.Path(control_video_path).is_file(
+                ) and not control_video_path.startswith("http"):
+                    logger.warning("Control Video file %s does not exist.",
+                                   control_video_path)
+                else:
+                    sample["control_video"] = load_video(control_video_path)
+
+            sample = {k: v for k, v in sample.items() if v is not None}
+            yield sample
@@ -3,10 +3,10 @@
 from fastvideo.v1.distributed.communication_op import *
 from fastvideo.v1.distributed.parallel_state import (
    cleanup_dist_env_and_memory, get_dp_group, get_dp_rank, get_dp_world_size,
-    get_sp_group, get_sp_parallel_rank, get_sp_world_size, get_torch_device,
-    get_tp_group, get_tp_rank, get_tp_world_size, get_world_group,
-    get_world_rank, get_world_size, init_distributed_environment,
-    initialize_model_parallel,
+    get_local_torch_device, get_sp_group, get_sp_parallel_rank,
+    get_sp_world_size, get_tp_group, get_tp_rank, get_tp_world_size,
+    get_world_group, get_world_rank, get_world_size,
+    init_distributed_environment, initialize_model_parallel,
    maybe_init_distributed_environment_and_model_parallel,
    model_parallel_is_initialized)
 from fastvideo.v1.distributed.utils import *
@@ -40,5 +40,5 @@ __all__ = [
    "get_tp_world_size",

    # Get torch device
-    "get_torch_device",
+    "get_local_torch_device",
 ]
@@ -36,6 +36,7 @@ from unittest.mock import patch

 import torch
 import torch.distributed
+import torch.distributed as dist
 from torch.distributed import Backend, ProcessGroup, ReduceOp

 import fastvideo.v1.envs as envs
@@ -692,6 +693,7 @@ class GroupCoordinator:


 _WORLD: Optional[GroupCoordinator] = None
+_NODE: Optional[GroupCoordinator] = None


 def get_world_group() -> GroupCoordinator:
@@ -699,6 +701,11 @@ def get_world_group() -> GroupCoordinator:
    return _WORLD


+def get_node_group() -> GroupCoordinator:
+    assert _NODE is not None, ("node group is not initialized")
+    return _NODE
+
+
 def init_world_group(ranks: List[int], local_rank: int,
                     backend: str) -> GroupCoordinator:
    return GroupCoordinator(
@@ -710,6 +717,18 @@ def init_world_group(ranks: List[int], local_rank: int,
    )


+def init_node_group(local_rank: int, backend: str):
+    cpu_group = get_world_group().cpu_group
+    node_ranks = same_node_ranks(cpu_group)
+    node_size = len(node_ranks)
+    all_node_ranks = [
+        list(range(i * node_size, (i + 1) * node_size))
+        for i in range(dist.get_world_size() // node_size)
+    ]
+    global _NODE
+    _NODE = init_model_parallel_group(all_node_ranks, local_rank, backend)
+
+
 def init_model_parallel_group(
    group_ranks: List[List[int]],
    local_rank: int,
@@ -782,6 +801,8 @@ def init_distributed_environment(
    else:
        assert _WORLD.world_size == torch.distributed.get_world_size(), (
            "world group already initialized with a different world size")
+    # Init a group for each node
+    init_node_group(local_rank, backend)


 _SP: Optional[GroupCoordinator] = None
@@ -904,7 +925,7 @@ def get_dp_rank() -> int:
    return get_dp_group().rank_in_group


-def get_torch_device() -> torch.device:
+def get_local_torch_device() -> torch.device:
    """Return the torch device for the current rank."""
    return torch.device(f"cuda:{envs.LOCAL_RANK}")

@@ -1021,17 +1042,22 @@ def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
            "torch._C._host_emptyCache() only available in Pytorch >=2.5")


-def in_the_same_node_as(pg: Union[ProcessGroup, StatelessProcessGroup],
-                        source_rank: int = 0) -> List[bool]:
+def same_node_ranks(pg: Union[ProcessGroup, StatelessProcessGroup],
+                    source_rank: int = 0) -> List[int]:
    """
-    This is a collective operation that returns if each rank is in the same node
+    This is a collective operation that returns ranks that are in the same node
    as the source rank. It tests if processes are attached to the same
    memory system (shared access to shared memory).
+    Args:
+        pg: the global process group to test
+        source_rank: the rank to test against
+    Returns:
+        A list of ranks that are in the same node as the source rank.
    """
    if isinstance(pg, ProcessGroup):
        assert torch.distributed.get_backend(
            pg) != torch.distributed.Backend.NCCL, (
-                "in_the_same_node_as should be tested with a non-NCCL group.")
+                "same_node_ranks should be tested with a non-NCCL group.")
        # local rank inside the group
        rank = torch.distributed.get_rank(group=pg)
        world_size = torch.distributed.get_world_size(group=pg)
@@ -1103,7 +1129,7 @@ def in_the_same_node_as(pg: Union[ProcessGroup, StatelessProcessGroup],
            rank_data = pg.broadcast_obj(is_in_the_same_node, src=i)
            aggregated_data += rank_data

-    return [x == 1 for x in aggregated_data.tolist()]
+    return [i for i, x in enumerate(aggregated_data.tolist()) if x == 1]


 def initialize_tensor_parallel_group(
@@ -4,9 +4,9 @@
 import argparse
 import dataclasses
 import os
-from typing import Any, Dict, List, Optional, cast
+from typing import List, cast

-from fastvideo import PipelineConfig, VideoGenerator
+from fastvideo import VideoGenerator
 from fastvideo.v1.configs.sample.base import SamplingParam
 from fastvideo.v1.entrypoints.cli.cli_types import CLISubcommand
 from fastvideo.v1.entrypoints.cli.utils import RaiseNotImplementedAction
@@ -37,8 +37,6 @@ class GenerateSubcommand(CLISubcommand):
    def cmd(self, args: argparse.Namespace) -> None:
        excluded_args = ['subparser', 'config', 'dispatch_function']

-        FastVideoArgs.from_cli_args(args)
-
        provided_args = {}
        for k, v in vars(args).items():
            if (k not in excluded_args and v is not None
@@ -66,27 +64,19 @@ class GenerateSubcommand(CLISubcommand):

        init_args = {
            k: v
-            for k, v in merged_args.items() if k in self.init_arg_names
+            for k, v in merged_args.items()
+            if k not in self.generation_arg_names
        }
        generation_args = {
            k: v
            for k, v in merged_args.items() if k in self.generation_arg_names
        }

-        pipeline_config = PipelineConfig.from_pretrained(
-            merged_args['model_path'])
-
-        update_config_from_args(pipeline_config.dit_config, merged_args,
-                                "dit_config")
-        update_config_from_args(pipeline_config.vae_config, merged_args,
-                                "vae_config")
-        update_config_from_args(pipeline_config, merged_args)
-
        model_path = init_args.pop('model_path')
        prompt = generation_args.pop('prompt')

-        generator = VideoGenerator.from_pretrained(
-            model_path=model_path, **init_args, pipeline_config=pipeline_config)
+        generator = VideoGenerator.from_pretrained(model_path=model_path,
+                                                   **init_args)

        generator.generate_video(prompt=prompt, **generation_args)

@@ -132,34 +122,3 @@ class GenerateSubcommand(CLISubcommand):

 def cmd_init() -> List[CLISubcommand]:
    return [GenerateSubcommand()]
-
-
-def update_config_from_args(config: Any,
-                            args_dict: Dict[str, Any],
-                            prefix: Optional[str] = None) -> None:
-    """
-    Update configuration object from arguments dictionary.
-    
-    Args:
-        config: The configuration object to update
-        args_dict: Dictionary containing arguments
-        prefix: Prefix for the configuration parameters in the args_dict.
-               If None, assumes direct attribute mapping without prefix.
-    """
-    # Handle top-level attributes (no prefix)
-    if prefix is None:
-        for key, value in args_dict.items():
-            if hasattr(config, key) and value is not None:
-                if key == "text_encoder_precisions" and isinstance(value, list):
-                    setattr(config, key, tuple(value))
-                else:
-                    setattr(config, key, value)
-        return
-
-    # Handle nested attributes with prefix
-    prefix_with_dot = f"{prefix}."
-    for key, value in args_dict.items():
-        if key.startswith(prefix_with_dot) and value is not None:
-            attr_name = key[len(prefix_with_dot):]
-            if hasattr(config, attr_name):
-                setattr(config, attr_name, value)
@@ -18,8 +18,6 @@ import torch
 import torchvision
 from einops import rearrange

-from fastvideo.v1.configs.pipelines import (PipelineConfig,
-                                            get_pipeline_config_cls_for_name)
 from fastvideo.v1.configs.sample import SamplingParam
 from fastvideo.v1.fastvideo_args import FastVideoArgs
 from fastvideo.v1.logger import init_logger
@@ -55,9 +53,6 @@ class VideoGenerator:
                        model_path: str,
                        device: Optional[str] = None,
                        torch_dtype: Optional[torch.dtype] = None,
-                        pipeline_config: Optional[
-                            Union[str
-                                  | PipelineConfig]] = None,
                        **kwargs) -> "VideoGenerator":
        """
        Create a video generator from a pretrained model.
@@ -66,35 +61,17 @@ class VideoGenerator:
            model_path: Path or identifier for the pretrained model
            device: Device to load the model on (e.g., "cuda", "cuda:0", "cpu")
            torch_dtype: Data type for model weights (e.g., torch.float16)
-            **kwargs: Additional arguments to customize model loading
+            pipeline_config: Pipeline config to use for inference
+            **kwargs: Additional arguments to customize model loading, set any FastVideoArgs or PipelineConfig attributes here.
                
        Returns:
            The created video generator

        Priority level: Default pipeline config < User's pipeline config < User's kwargs
        """
-        config = None
-        # 1. If users provide a pipeline config, it will override the default pipeline config
-        if isinstance(pipeline_config, PipelineConfig):
-            config = pipeline_config
-        else:
-            config_cls = get_pipeline_config_cls_for_name(model_path)
-            if config_cls is not None:
-                config = config_cls()
-                if isinstance(pipeline_config, str):
-                    config.load_from_json(pipeline_config)
-
-        # 2. If users also provide some kwargs, it will override the pipeline config.
-        # The user kwargs shouldn't contain model config parameters!
-        if config is None:
-            logger.warning("No config found for model %s, using default config",
-                           model_path)
-            config_args = kwargs
-        else:
-            config_args = shallow_asdict(config)
-            config_args.update(kwargs)
-
-        fastvideo_args = FastVideoArgs(model_path=model_path, **config_args)
+        # If users also provide some kwargs, it will override the FastVideoArgs and PipelineConfig.
+        kwargs['model_path'] = model_path
+        fastvideo_args = FastVideoArgs.from_kwargs(kwargs)

        return cls.from_fastvideo_args(fastvideo_args)

@@ -150,16 +127,17 @@ class VideoGenerator:
        """
        # Create a copy of inference args to avoid modifying the original
        fastvideo_args = self.fastvideo_args
+        pipeline_config = fastvideo_args.pipeline_config

        # Validate inputs
        if not isinstance(prompt, str):
            raise TypeError(
                f"`prompt` must be a string, but got {type(prompt)}")
        prompt = prompt.strip()
-
        if sampling_param is None:
            sampling_param = SamplingParam.from_pretrained(
                fastvideo_args.model_path)
+
        kwargs["prompt"] = prompt
        sampling_param.update(kwargs)

@@ -176,10 +154,10 @@ class VideoGenerator:
                f"height={sampling_param.height}, width={sampling_param.width}, "
                f"num_frames={sampling_param.num_frames}")

-        temporal_scale_factor = fastvideo_args.vae_config.arch_config.temporal_compression_ratio
+        temporal_scale_factor = pipeline_config.vae_config.arch_config.temporal_compression_ratio
        num_frames = sampling_param.num_frames
        num_gpus = fastvideo_args.num_gpus
-        use_temporal_scaling_frames = fastvideo_args.vae_config.use_temporal_scaling_frames
+        use_temporal_scaling_frames = pipeline_config.vae_config.use_temporal_scaling_frames

        # Adjust number of frames based on number of GPUs
        if use_temporal_scaling_frames:
@@ -238,18 +216,18 @@ class VideoGenerator:
       num_videos_per_prompt: {sampling_param.num_videos_per_prompt}
              guidance_scale: {sampling_param.guidance_scale}
                    n_tokens: {n_tokens}
-                  flow_shift: {fastvideo_args.flow_shift}
-     embedded_guidance_scale: {fastvideo_args.embedded_cfg_scale}
+                  flow_shift: {fastvideo_args.pipeline_config.flow_shift}
+     embedded_guidance_scale: {fastvideo_args.pipeline_config.embedded_cfg_scale}
                  save_video: {sampling_param.save_video}
                  output_path: {sampling_param.output_path}
        """ # type: ignore[attr-defined]
        logger.info(debug_str)
-
        # Prepare batch
        batch = ForwardBatch(
            **shallow_asdict(sampling_param),
            eta=0.0,
            n_tokens=n_tokens,
+            VSA_sparsity=fastvideo_args.VSA_sparsity,
            extra={},
        )

@@ -6,26 +6,32 @@ import argparse
 import dataclasses
 from contextlib import contextmanager
 from dataclasses import field
-from typing import Any, Callable, List, Optional, Tuple
+from typing import Any, Dict, List, Optional

-from fastvideo.v1.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from fastvideo.v1.configs.pipelines.base import PipelineConfig, STA_Mode
 from fastvideo.v1.logger import init_logger
 from fastvideo.v1.utils import FlexibleArgumentParser, StoreBoolean

 logger = init_logger(__name__)


-def preprocess_text(prompt: str) -> str:
-    return prompt
+def clean_cli_args(args: argparse.Namespace) -> Dict[str, Any]:
+    """
+    Clean the arguments by removing the ones that not explicitly provided by the user.
+    """
+    provided_args = {}
+    for k, v in vars(args).items():
+        if (v is not None and hasattr(args, '_provided')
+                and k in args._provided):
+            provided_args[k] = v

-
-def postprocess_text(output: Any) -> Any:
-    raise NotImplementedError
+    return provided_args


+# args for fastvideo framework
@dataclasses.dataclass
 class FastVideoArgs:
-    # Model and path configuration
+    # Model and path configuration (for convenience)
    model_path: str

    # Cache strategy
@@ -44,70 +50,34 @@ class FastVideoArgs:
    num_gpus: int = 1
    tp_size: int = -1
    sp_size: int = -1
-    dp_size: int = 1
-    dp_shards: int = -1
+    hsdp_replicate_dim: int = 1
+    hsdp_shard_dim: int = -1
    dist_timeout: Optional[int] = None  # timeout for torch.distributed

-    # Video generation parameters
-    embedded_cfg_scale: float = 6.0
-    flow_shift: Optional[float] = None
+    pipeline_config: PipelineConfig = field(default_factory=PipelineConfig)

    output_type: str = "pil"

-    # DiT configuration
-    dit_config: DiTConfig = field(default_factory=DiTConfig)
-    precision: str = "bf16"
-    use_cpu_offload: bool = True
+    use_cpu_offload: bool = True  # For DiT
    use_fsdp_inference: bool = True
+    text_encoder_offload: bool = True
+    pin_cpu_memory: bool = True

-    # VAE configuration
-    vae_precision: str = "fp16"
-    vae_tiling: bool = True  # Might change in between forward passes
-    vae_sp: bool = False  # Might change in between forward passes
-    # vae_scale_factor: Optional[int] = None # Deprecated
-    vae_config: VAEConfig = field(default_factory=VAEConfig)
-
-    # Image encoder configuration
-    image_encoder_precision: str = "fp32"
-    image_encoder_config: EncoderConfig = field(default_factory=EncoderConfig)
-
-    # Text encoder configuration
-    DEFAULT_TEXT_ENCODER_PRECISIONS = (
-        "fp16",
-        # "fp16",
-    )
-    text_encoder_precisions: Tuple[str, ...] = field(
-        default_factory=lambda: FastVideoArgs.DEFAULT_TEXT_ENCODER_PRECISIONS)
-    text_encoder_configs: Tuple[EncoderConfig, ...] = field(
-        default_factory=lambda: (EncoderConfig(), ))
-    preprocess_text_funcs: Tuple[Callable[[str], str], ...] = field(
-        default_factory=lambda: (preprocess_text, ))
-    postprocess_text_funcs: Tuple[Callable[[Any], Any], ...] = field(
-        default_factory=lambda: (postprocess_text, ))
-
-    # STA parameters
-    STA_mode: Optional[str] = None
-    skip_time_steps: int = 15
-    # LoRA parameters
-    lora_path: Optional[str] = None
-    lora_nickname: Optional[
-        str] = "default"  # for swapping adapters in the pipeline
-    lora_target_names: Optional[List[
-        str]] = None  # can restrict list of layers to adapt, e.g. ["q_proj"]
-
-    # STA parameters
+    # STA (Sliding Tile Attention) parameters
    mask_strategy_file_path: Optional[str] = None
+    STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
+    skip_time_steps: int = 15
+
+    # Compilation
    enable_torch_compile: bool = False

    disable_autocast: bool = False

-    # StepVideo specific parameters
-    pos_magic: Optional[str] = None
-    neg_magic: Optional[str] = None
-    timesteps_scale: Optional[bool] = None
+    # VSA parameters
+    VSA_sparsity: float = 0.0  # inference/validation sparsity

-    # Logging
-    log_level: str = "info"
+    # Stage verification
+    enable_stage_verification: bool = True

    @property
    def training_mode(self) -> bool:
@@ -125,11 +95,6 @@ class FastVideoArgs:
            help=
            "The path of the model weights. This can be a local folder or a Hugging Face repo ID.",
        )
-        parser.add_argument(
-            "--dit-weight",
-            type=str,
-            help="Path to the DiT model weights",
-        )
        parser.add_argument(
            "--model-dir",
            type=str,
@@ -175,31 +140,27 @@ class FastVideoArgs:
            help="The number of GPUs to use.",
        )
        parser.add_argument(
-            "--tensor-parallel-size",
            "--tp-size",
            type=int,
            default=FastVideoArgs.tp_size,
            help="The tensor parallelism size.",
        )
        parser.add_argument(
-            "--sequence-parallel-size",
            "--sp-size",
            type=int,
            default=FastVideoArgs.sp_size,
            help="The sequence parallelism size.",
        )
        parser.add_argument(
-            "--data-parallel-size",
-            "--dp-size",
+            "--hsdp-replicate-dim",
            type=int,
-            default=FastVideoArgs.dp_size,
+            default=FastVideoArgs.hsdp_replicate_dim,
            help="The data parallelism size.",
        )
        parser.add_argument(
-            "--data-parallel-shards",
-            "--dp-shards",
+            "--hsdp-shard-dim",
            type=int,
-            default=FastVideoArgs.dp_shards,
+            default=FastVideoArgs.hsdp_shard_dim,
            help="The data parallelism shards.",
        )
        parser.add_argument(
@@ -209,19 +170,7 @@ class FastVideoArgs:
            help="Set timeout for torch.distributed initialization.",
        )

-        parser.add_argument(
-            "--embedded-cfg-scale",
-            type=float,
-            default=FastVideoArgs.embedded_cfg_scale,
-            help="Embedded CFG scale",
-        )
-        parser.add_argument(
-            "--flow-shift",
-            "--shift",
-            type=float,
-            default=FastVideoArgs.flow_shift,
-            help="Flow shift parameter",
-        )
+        # Output type
        parser.add_argument(
            "--output-type",
            type=str,
@@ -230,62 +179,14 @@ class FastVideoArgs:
            help="Output type for the generated video",
        )

-        parser.add_argument(
-            "--precision",
-            type=str,
-            default=FastVideoArgs.precision,
-            choices=["fp32", "fp16", "bf16"],
-            help="Precision for the model",
-        )
-
-        # VAE configuration
-        parser.add_argument(
-            "--vae-precision",
-            type=str,
-            default=FastVideoArgs.vae_precision,
-            choices=["fp32", "fp16", "bf16"],
-            help="Precision for VAE",
-        )
-        parser.add_argument(
-            "--vae-tiling",
-            action=StoreBoolean,
-            default=FastVideoArgs.vae_tiling,
-            help="Enable VAE tiling",
-        )
-        parser.add_argument(
-            "--vae-sp",
-            action=StoreBoolean,
-            help="Enable VAE spatial parallelism",
-        )
-
-        parser.add_argument(
-            "--text-encoder-precisions",
-            nargs="+",
-            type=str,
-            default=FastVideoArgs.DEFAULT_TEXT_ENCODER_PRECISIONS,
-            choices=["fp32", "fp16", "bf16"],
-            help="Precision for each text encoder",
-        )
-
-        # Image encoder config
-        parser.add_argument(
-            "--image-encoder-precision",
-            type=str,
-            default=FastVideoArgs.image_encoder_precision,
-            choices=["fp32", "fp16", "bf16"],
-            help="Precision for image encoder",
-        )
-
-        # STA parameters
+        # STA (Sliding Tile Attention) parameters
        parser.add_argument(
            "--STA-mode",
            type=str,
-            default=FastVideoArgs.STA_mode,
-            choices=[
-                "STA_inference", "STA_searching", "STA_tuning",
-                "STA_tuning_cfg", None
-            ],
-            help="STA mode",
+            default=FastVideoArgs.STA_mode.value,
+            choices=[mode.value for mode in STA_Mode],
+            help=
+            "STA mode contains STA_inference, STA_searching, STA_tuning, STA_tuning_cfg, None",
        )
        parser.add_argument(
            "--skip-time-steps",
@@ -309,7 +210,7 @@ class FastVideoArgs:
            "--use-cpu-offload",
            action=StoreBoolean,
            help=
-            "Use CPU offload for model inference. Enable if run out of memory with FSDP.",
+            "Use CPU offload for DiT inference. Enable if run out of memory with FSDP.",
        )
        parser.add_argument(
            "--use-fsdp-inference",
@@ -317,7 +218,19 @@ class FastVideoArgs:
            help=
            "Use FSDP for inference by sharding the model weights. Latency is very low due to prefetch--enable if run out of memory.",
        )
-
+        parser.add_argument(
+            "--text-encoder-cpu-offload",
+            action=StoreBoolean,
+            help=
+            "Use CPU offload for text encoder. Enable if run out of memory.",
+        )
+        parser.add_argument(
+            "--pin-cpu-memory",
+            action=StoreBoolean,
+            help=
+            "Pin memory for CPU offload. Only added as a temp workaround if it throws \"CUDA error: invalid argument\". "
+            "Should be enabled in almost all cases",
+        )
        parser.add_argument(
            "--disable-autocast",
            action=StoreBoolean,
@@ -325,91 +238,69 @@ class FastVideoArgs:
            "Disable autocast for denoising loop and vae decoding in pipeline sampling",
        )

+        # VSA parameters
        parser.add_argument(
-            "--pos_magic",
-            type=str,
-            default=FastVideoArgs.pos_magic,
-            help="Positive magic prompt for sampling",
-        )
-        parser.add_argument(
-            "--neg_magic",
-            type=str,
-            default=FastVideoArgs.neg_magic,
-            help="Negative magic prompt for sampling",
-        )
-        parser.add_argument(
-            "--timesteps_scale",
-            type=bool,
-            default=FastVideoArgs.timesteps_scale,
-            help="Bool for applying scheduler scale in set_timesteps",
+            "--VSA-sparsity",
+            type=float,
+            default=FastVideoArgs.VSA_sparsity,
+            help="Validation sparsity for VSA",
        )

-        # Logging
+        # Stage verification
        parser.add_argument(
-            "--log-level",
-            type=str,
-            default=FastVideoArgs.log_level,
-            help="The logging level of all loggers.",
+            "--enable-stage-verification",
+            action=StoreBoolean,
+            default=FastVideoArgs.enable_stage_verification,
+            help="Enable input/output verification for pipeline stages",
        )

-        # Add VAE configuration arguments
-        from fastvideo.v1.configs.models.vaes.base import VAEConfig
-        VAEConfig.add_cli_args(parser)
-
-        # Add DiT configuration arguments
-        from fastvideo.v1.configs.models.dits.base import DiTConfig
-        DiTConfig.add_cli_args(parser)
+        # Add pipeline configuration arguments
+        PipelineConfig.add_cli_args(parser)

        return parser

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace) -> "FastVideoArgs":
-        args.tp_size = args.tensor_parallel_size
-        args.sp_size = args.sequence_parallel_size
-        args.flow_shift = getattr(args, "shift", args.flow_shift)
-
+        provided_args = clean_cli_args(args)
        # Get all fields from the dataclass
        attrs = [attr.name for attr in dataclasses.fields(cls)]

        # Create a dictionary of attribute values, with defaults for missing attributes
        kwargs = {}
        for attr in attrs:
-            # Handle renamed attributes or those with multiple CLI names
-            if attr == 'tp_size' and hasattr(args, 'tensor_parallel_size'):
-                kwargs[attr] = args.tensor_parallel_size
-            elif attr == 'sp_size' and hasattr(args, 'sequence_parallel_size'):
-                kwargs[attr] = args.sequence_parallel_size
-            elif attr == 'dp_size' and hasattr(args, 'data_parallel_size'):
-                kwargs[attr] = args.data_parallel_size
-            elif attr == 'dp_shards' and hasattr(args, 'data_parallel_shards'):
-                kwargs[attr] = args.data_parallel_shards
-            elif attr == 'flow_shift' and hasattr(args, 'shift'):
-                kwargs[attr] = args.shift
+            if attr == 'pipeline_config':
+                pipeline_config = PipelineConfig.from_kwargs(provided_args)
+                kwargs[attr] = pipeline_config
            # Use getattr with default value from the dataclass for potentially missing attributes
            else:
                default_value = getattr(cls, attr, None)
                value = getattr(args, attr, default_value)
-                if value is not None:
-                    kwargs[attr] = value
+                kwargs[attr] = value  # type: ignore

+        return cls(**kwargs)  # type: ignore
+
+    @classmethod
+    def from_kwargs(cls, kwargs: Dict[str, Any]) -> "FastVideoArgs":
+        kwargs['pipeline_config'] = PipelineConfig.from_kwargs(kwargs)
        return cls(**kwargs)

    def check_fastvideo_args(self) -> None:
        """Validate inference arguments for consistency"""
        if not self.inference_mode:
-            assert self.dp_size is not -1, "dp_size must be set for training"
-            assert self.dp_shards is not -1, "dp_shards must be set for training"
-            assert self.sp_size is not -1, "sp_size must be set for training"
+            assert self.hsdp_replicate_dim != -1, "hsdp_replicate_dim must be set for training"
+            assert self.hsdp_shard_dim != -1, "hsdp_shard_dim must be set for training"
+            assert self.sp_size != -1, "sp_size must be set for training"

-        if self.tp_size is -1:
+        if self.tp_size == -1:
            self.tp_size = self.num_gpus
-        if self.sp_size is -1:
+        if self.sp_size == -1:
            self.sp_size = self.num_gpus
-        if self.dp_shards is -1:
-            self.dp_shards = self.num_gpus
+        if self.hsdp_shard_dim == -1:
+            self.hsdp_shard_dim = self.num_gpus
+
        assert self.sp_size <= self.num_gpus and self.num_gpus % self.sp_size == 0, "num_gpus must >= and be divisible by sp_size"
-        assert self.dp_size <= self.num_gpus and self.num_gpus % self.dp_size == 0, "num_gpus must >= and be divisible by dp_size"
-        assert self.dp_shards <= self.num_gpus and self.num_gpus % self.dp_shards == 0, "num_gpus must >= and be divisible by dp_shards"
+        assert self.hsdp_replicate_dim <= self.num_gpus and self.num_gpus % self.hsdp_replicate_dim == 0, "num_gpus must >= and be divisible by hsdp_replicate_dim"
+        assert self.hsdp_shard_dim <= self.num_gpus and self.num_gpus % self.hsdp_shard_dim == 0, "num_gpus must >= and be divisible by hsdp_shard_dim"

        if self.num_gpus < max(self.tp_size, self.sp_size):
            self.num_gpus = max(self.tp_size, self.sp_size)
@@ -419,33 +310,17 @@ class FastVideoArgs:
                f"tp_size ({self.tp_size}) must be equal to sp_size ({self.sp_size})"
            )

-        # Validate VAE spatial parallelism with VAE tiling
-        if self.vae_sp and not self.vae_tiling:
-            raise ValueError(
-                "Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True."
-            )
-
-        if len(self.text_encoder_configs) != len(self.text_encoder_precisions):
-            raise ValueError(
-                f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text encoder precisions ({len(self.text_encoder_precisions)})"
-            )
-
-        if len(self.text_encoder_configs) != len(self.preprocess_text_funcs):
-            raise ValueError(
-                f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"
-            )
-
-        if len(self.preprocess_text_funcs) != len(self.postprocess_text_funcs):
-            raise ValueError(
-                f"Length of text postprocess functions ({len(self.postprocess_text_funcs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"
-            )
-
        if self.enable_torch_compile and self.num_gpus > 1:
            logger.warning(
                "Currently torch compile does not work with multi-gpu. Setting enable_torch_compile to False"
            )
            self.enable_torch_compile = False

+        if self.pipeline_config is None:
+            raise ValueError("pipeline_config is not set in FastVideoArgs")
+
+        self.pipeline_config.check_pipeline_config()
+

 _current_fastvideo_args = None

@@ -519,21 +394,22 @@ class TrainingArgs(FastVideoArgs):
    # text encoder & vae & diffusion model
    pretrained_model_name_or_path: str = ""
    dit_model_name_or_path: str = ""
-    cache_dir: str = ""

    # diffusion setting
    ema_decay: float = 0.0
    ema_start_step: int = 0
-    cfg: float = 0.0
+    training_cfg_rate: float = 0.0
    precondition_outputs: bool = False

    # validation & logs
-    validation_prompt_dir: str = ""
+    validation_dataset_file: str = ""
+    validation_preprocessed_path: str = ""
    validation_sampling_steps: str = ""
    validation_guidance_scale: str = ""
    validation_steps: float = 0.0
    log_validation: bool = False
    tracker_project_name: str = ""
+    wandb_run_name: str = ""
    seed: Optional[int] = None

    # output
@@ -541,7 +417,6 @@ class TrainingArgs(FastVideoArgs):
    checkpoints_total_limit: int = 0
    checkpointing_steps: int = 0
    resume_from_checkpoint: bool = False
-    logging_dir: str = ""

    # optimizer & scheduler
    num_train_epochs: int = 0
@@ -582,36 +457,29 @@ class TrainingArgs(FastVideoArgs):
    # master_weight_type
    master_weight_type: str = ""

-    # For fast checking in LoRA pipeline
-    training_mode: bool = True
+    # VSA training decay parameters
+    VSA_decay_rate: float = 0.01  # decay rate -> 0.02
+    VSA_decay_interval_steps: int = 1  # decay interval steps -> 50

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace) -> "TrainingArgs":
+        provided_args = clean_cli_args(args)
        # Get all fields from the dataclass
        attrs = [attr.name for attr in dataclasses.fields(cls)]
-
+        logger.info(provided_args)
        # Create a dictionary of attribute values, with defaults for missing attributes
        kwargs = {}
        for attr in attrs:
-            # Handle renamed attributes or those with multiple CLI names
-            if attr == 'tp_size' and hasattr(args, 'tensor_parallel_size'):
-                kwargs[attr] = args.tensor_parallel_size
-            elif attr == 'sp_size' and hasattr(args, 'sequence_parallel_size'):
-                kwargs[attr] = args.sequence_parallel_size
-            elif attr == 'flow_shift' and hasattr(args, 'shift'):
-                kwargs[attr] = args.shift
-            elif attr == 'dp_size' and hasattr(args, 'data_parallel_size'):
-                kwargs[attr] = args.data_parallel_size
-            elif attr == 'dp_shards' and hasattr(args, 'data_parallel_shards'):
-                kwargs[attr] = args.data_parallel_shards
+            if attr == 'pipeline_config':
+                pipeline_config = PipelineConfig.from_kwargs(provided_args)
+                kwargs[attr] = pipeline_config
            # Use getattr with default value from the dataclass for potentially missing attributes
            else:
                default_value = getattr(cls, attr, None)
                value = getattr(args, attr, default_value)
-                if value is not None:
-                    kwargs[attr] = value
+                kwargs[attr] = value  # type: ignore

-        return cls(**kwargs)
+        return cls(**kwargs)  # type: ignore

    @staticmethod
    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
@@ -674,7 +542,7 @@ class TrainingArgs(FastVideoArgs):
                            type=int,
                            default=0,
                            help="Step to start EMA")
-        parser.add_argument("--cfg",
+        parser.add_argument("--training-cfg-rate",
                            type=float,
                            help="Classifier-free guidance scale")
        parser.add_argument(
@@ -683,9 +551,12 @@ class TrainingArgs(FastVideoArgs):
            help="Whether to precondition the outputs of the model")

        # Validation and logging
-        parser.add_argument("--validation-prompt-dir",
+        parser.add_argument("--validation-dataset-file",
                            type=str,
-                            help="Directory containing validation prompts")
+                            help="Path to unprocessed validation dataset")
+        parser.add_argument("--validation-preprocessed-path",
+                            type=str,
+                            help="Path to processed validation dataset")
        parser.add_argument("--validation-sampling-steps",
                            type=str,
                            help="Validation sampling steps")
@@ -701,6 +572,9 @@ class TrainingArgs(FastVideoArgs):
        parser.add_argument("--tracker-project-name",
                            type=str,
                            help="Project name for tracking")
+        parser.add_argument("--wandb-run-name",
+                            type=str,
+                            help="Run name for wandb")
        parser.add_argument("--seed",
                            type=int,
                            default=42,
@@ -839,4 +713,16 @@ class TrainingArgs(FastVideoArgs):
                            type=str,
                            help="Master weight type")

+        # VSA parameters for training with dense to sparse adaption
+        parser.add_argument(
+            "--VSA-decay-rate",  # decay rate, how much sparsity you want to decay each step
+            type=float,
+            default=TrainingArgs.VSA_decay_rate,
+            help="VSA decay rate")
+        parser.add_argument(
+            "--VSA-decay-interval-steps",  # how many steps for training with current sparsity
+            type=int,
+            default=TrainingArgs.VSA_decay_interval_steps,
+            help="VSA decay interval steps")
+
        return parser
@@ -5,15 +5,16 @@ import time
 from collections import defaultdict
 from contextlib import contextmanager
 from dataclasses import dataclass
-from typing import Optional
+from typing import TYPE_CHECKING, Optional

 import torch

-# if TYPE_CHECKING:
-from fastvideo.v1.attention import AttentionMetadata
 from fastvideo.v1.fastvideo_args import FastVideoArgs
 from fastvideo.v1.logger import init_logger
-from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
+
+if TYPE_CHECKING:
+    from fastvideo.v1.attention import AttentionMetadata
+    from fastvideo.v1.pipelines import ForwardBatch

 logger = init_logger(__name__)

@@ -36,13 +37,13 @@ class ForwardContext:
    # attn_layers: Dict[str, Any]
    # TODO: extend to support per-layer dynamic forward context
    attn_metadata: "AttentionMetadata"  # set dynamically for each forward pass
-    forward_batch: Optional[ForwardBatch] = None
+    forward_batch: Optional["ForwardBatch"] = None


-_forward_context: Optional[ForwardContext] = None
+_forward_context: Optional["ForwardContext"] = None


-def get_forward_context() -> ForwardContext:
+def get_forward_context() -> "ForwardContext":
    """Get the current forward context."""
    assert _forward_context is not None, (
        "Forward context is not set. "
@@ -54,7 +55,7 @@ def get_forward_context() -> ForwardContext:
@contextmanager
 def set_forward_context(current_timestep,
                        attn_metadata,
-                        forward_batch: Optional[ForwardBatch] = None,
+                        forward_batch: Optional["ForwardBatch"] = None,
                        fastvideo_args: Optional[FastVideoArgs] = None):
    """A context manager that stores the current forward context,
    can be attention metadata, etc.
@@ -5,6 +5,8 @@ from typing import Optional, Tuple, Union

 import torch
 import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributed.tensor import DTensor

 from fastvideo.v1.layers.custom_op import CustomOp

@@ -69,7 +71,12 @@ class RMSNorm(CustomOp):
        x = x * torch.rsqrt(variance + self.variance_epsilon)
        x = x.to(orig_dtype)
        if self.has_weight:
-            x = x * self.weight
+            # TODO(wenxuan): When using CPU offload, FSDP has a bug that doesn't unwrap DTensor in final_layer_norm.
+            # Report this
+            if isinstance(self.weight, DTensor):
+                x = x * self.weight.to(x.device).full_tensor()
+            else:
+                x = x * self.weight
        if residual is None:
            return x
        else:
@@ -95,6 +102,22 @@ class ScaleResidual(nn.Module):
        return residual + x * gate


+# adapted from Diffusers: https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py
+# NOTE(will): Needed to match behavior of diffusers and wan2.1 even while using
+# FSDP's MixedPrecisionPolicy
+class FP32LayerNorm(nn.LayerNorm):
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        origin_dtype = inputs.dtype
+        return F.layer_norm(
+            inputs.float(),
+            self.normalized_shape,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        ).to(origin_dtype)
+
+
 class ScaleResidualLayerNormScaleShift(nn.Module):
    """
    Fused operation that combines:
@@ -112,6 +135,7 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
        eps: float = 1e-6,
        elementwise_affine: bool = False,
        dtype: torch.dtype = torch.float32,
+        compute_dtype: torch.dtype | None = None,
        prefix: str = "",
    ):
        super().__init__()
@@ -121,10 +145,15 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
                                eps=eps,
                                dtype=dtype)
        elif norm_type == "layer":
-            self.norm = nn.LayerNorm(hidden_size,
-                                     elementwise_affine=elementwise_affine,
-                                     eps=eps,
-                                     dtype=dtype)
+            if compute_dtype == torch.float32:
+                self.norm = FP32LayerNorm(hidden_size,
+                                          elementwise_affine=elementwise_affine,
+                                          eps=eps)
+            else:
+                self.norm = nn.LayerNorm(hidden_size,
+                                         elementwise_affine=elementwise_affine,
+                                         eps=eps,
+                                         dtype=dtype)
        else:
            raise NotImplementedError(f"Norm type {norm_type} not implemented")

@@ -163,18 +192,25 @@ class LayerNormScaleShift(nn.Module):
        eps: float = 1e-6,
        elementwise_affine: bool = False,
        dtype: torch.dtype = torch.float32,
+        compute_dtype: torch.dtype | None = None,
        prefix: str = "",
    ):
        super().__init__()
+        self.compute_dtype = compute_dtype
        if norm_type == "rms":
            self.norm = RMSNorm(hidden_size,
                                has_weight=elementwise_affine,
                                eps=eps)
        elif norm_type == "layer":
-            self.norm = nn.LayerNorm(hidden_size,
-                                     elementwise_affine=elementwise_affine,
-                                     eps=eps,
-                                     dtype=dtype)
+            if self.compute_dtype == torch.float32:
+                self.norm = FP32LayerNorm(hidden_size,
+                                          elementwise_affine=elementwise_affine,
+                                          eps=eps)
+            else:
+                self.norm = nn.LayerNorm(hidden_size,
+                                         elementwise_affine=elementwise_affine,
+                                         eps=eps,
+                                         dtype=dtype)
        else:
            raise NotImplementedError(f"Norm type {norm_type} not implemented")

@@ -182,4 +218,7 @@ class LayerNormScaleShift(nn.Module):
                scale: torch.Tensor) -> torch.Tensor:
        """Apply ln followed by scale and shift in a single fused operation."""
        normalized = self.norm(x)
-        return normalized * (1.0 + scale) + shift
+        if self.compute_dtype == torch.float32:
+            return (normalized.float() * (1.0 + scale) + shift).to(x.dtype)
+        else:
+            return normalized * (1.0 + scale) + shift
@@ -114,7 +114,7 @@ def _info(logger: Logger,

    if (main_process_only and is_main_process) or (local_main_process_only
                                                   and is_local_main_process):
-        logger.log(logging.INFO, msg, *args, **kwargs)
+        logger.log(logging.INFO, msg, *args, stacklevel=2, **kwargs)

    global _warned_local_main_process, _warned_main_process

@@ -134,7 +134,7 @@ def _info(logger: Logger,
        _warned_main_process = True

    if not main_process_only and not local_main_process_only:
-        logger.log(logging.INFO, msg, *args, **kwargs)
+        logger.log(logging.INFO, msg, *args, stacklevel=2, **kwargs)


 class _FastvideoLogger(Logger):
@@ -6,7 +6,7 @@ import torch
 from torch import nn

 from fastvideo.v1.configs.models import DiTConfig
-from fastvideo.v1.platforms import _Backend
+from fastvideo.v1.platforms import AttentionBackendEnum


 # TODO
@@ -14,12 +14,13 @@ class BaseDiT(nn.Module, ABC):
    _fsdp_shard_conditions: list = []
    _compile_conditions: list = []
    _param_names_mapping: dict
+    _reverse_param_names_mapping: dict
    hidden_size: int
    num_attention_heads: int
    num_channels_latents: int
    # always supports torch_sdpa
    _supported_attention_backends: Tuple[
-        _Backend, ...] = DiTConfig()._supported_attention_backends
+        AttentionBackendEnum, ...] = DiTConfig()._supported_attention_backends

    def __init_subclass__(cls) -> None:
        required_class_attrs = [
@@ -65,7 +66,7 @@ class BaseDiT(nn.Module, ABC):
                )

    @property
-    def supported_attention_backends(self) -> Tuple[_Backend, ...]:
+    def supported_attention_backends(self) -> Tuple[AttentionBackendEnum, ...]:
        return self._supported_attention_backends


@@ -78,6 +79,7 @@ class CachableDiT(BaseDiT):
    # These are required class attributes that should be overridden by concrete implementations
    _fsdp_shard_conditions = []
    _param_names_mapping = {}
+    _reverse_param_names_mapping = {}
    _lora_param_names_mapping: dict = {}
    # Ensure these instance attributes are properly defined in subclasses
    hidden_size: int
@@ -85,7 +87,7 @@ class CachableDiT(BaseDiT):
    num_channels_latents: int
    # always supports torch_sdpa
    _supported_attention_backends: Tuple[
-        _Backend, ...] = DiTConfig()._supported_attention_backends
+        AttentionBackendEnum, ...] = DiTConfig()._supported_attention_backends

    def __init__(self, config: DiTConfig, **kwargs) -> None:
        super().__init__(config, **kwargs)
@@ -23,7 +23,7 @@ from fastvideo.v1.layers.visual_embedding import (ModulateProjection,
                                                  unpatchify)
 from fastvideo.v1.models.dits.base import CachableDiT
 from fastvideo.v1.models.utils import modulate
-from fastvideo.v1.platforms import _Backend
+from fastvideo.v1.platforms import AttentionBackendEnum


 class HunyuanRMSNorm(nn.Module):
@@ -96,7 +96,8 @@ class MMDoubleStreamBlock(nn.Module):
        num_attention_heads: int,
        mlp_ratio: float,
        dtype: Optional[torch.dtype] = None,
-        supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
+        supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
+                                                     ...]] = None,
        prefix: str = "",
    ):
        super().__init__()
@@ -303,7 +304,8 @@ class MMSingleStreamBlock(nn.Module):
        num_attention_heads: int,
        mlp_ratio: float = 4.0,
        dtype: Optional[torch.dtype] = None,
-        supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
+        supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
+                                                     ...]] = None,
        prefix: str = "",
    ):
        super().__init__()
@@ -440,6 +442,8 @@ class HunyuanVideoTransformer3DModel(CachableDiT):
    _supported_attention_backends = HunyuanVideoConfig(
    )._supported_attention_backends
    _param_names_mapping = HunyuanVideoConfig()._param_names_mapping
+    _reverse_param_names_mapping = HunyuanVideoConfig(
+    )._reverse_param_names_mapping
    _lora_param_names_mapping = HunyuanVideoConfig()._lora_param_names_mapping

    def __init__(self, config: HunyuanVideoConfig, hf_config: dict[str, Any]):
@@ -876,8 +880,8 @@ class IndividualTokenRefinerBlock(nn.Module):
            num_heads=num_attention_heads,
            head_size=hidden_size // num_attention_heads,
            # TODO: remove hardcode; remove STA
-            supported_attention_backends=(_Backend.FLASH_ATTN,
-                                          _Backend.TORCH_SDPA),
+            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                          AttentionBackendEnum.TORCH_SDPA),
        )

    def forward(self, x, c):
@@ -26,7 +26,7 @@ from fastvideo.v1.layers.rotary_embedding import (_apply_rotary_emb,
                                                  get_rotary_pos_embed)
 from fastvideo.v1.layers.visual_embedding import TimestepEmbedder
 from fastvideo.v1.models.dits.base import BaseDiT
-from fastvideo.v1.platforms import _Backend
+from fastvideo.v1.platforms import AttentionBackendEnum


 class PatchEmbed2D(nn.Module):
@@ -139,16 +139,17 @@ class StepVideoRMSNorm(nn.Module):

 class SelfAttention(nn.Module):

-    def __init__(self,
-                 hidden_dim,
-                 head_dim,
-                 rope_split: Tuple[int, int, int] = (64, 32, 32),
-                 bias: bool = False,
-                 with_rope: bool = True,
-                 with_qk_norm: bool = True,
-                 attn_type: str = "torch",
-                 supported_attention_backends=(_Backend.FLASH_ATTN,
-                                               _Backend.TORCH_SDPA)):
+    def __init__(
+        self,
+        hidden_dim,
+        head_dim,
+        rope_split: Tuple[int, int, int] = (64, 32, 32),
+        bias: bool = False,
+        with_rope: bool = True,
+        with_qk_norm: bool = True,
+        attn_type: str = "torch",
+        supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                      AttentionBackendEnum.TORCH_SDPA)):
        super().__init__()
        self.head_dim = head_dim
        self.hidden_dim = hidden_dim
@@ -257,7 +258,8 @@ class CrossAttention(nn.Module):
        head_dim,
        bias=False,
        with_qk_norm=True,
-        supported_attention_backends=(_Backend.FLASH_ATTN, _Backend.TORCH_SDPA)
+        supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                      AttentionBackendEnum.TORCH_SDPA)
    ) -> None:
        super().__init__()
        self.head_dim = head_dim
@@ -453,11 +455,10 @@ class StepVideoTransformerBlock(nn.Module):

 class StepVideoModel(BaseDiT):
    # (Optional) Keep the same attribute for compatibility with splitting, etc.
-    _fsdp_shard_conditions = [
-        lambda n, m: "transformer_blocks" in n and n.split(".")[-1].isdigit(),
-        # lambda n, m: "pos_embed" in n  # If needed for the patch embedding.
-    ]
+    _fsdp_shard_conditions = StepVideoConfig()._fsdp_shard_conditions
    _param_names_mapping = StepVideoConfig()._param_names_mapping
+    _reverse_param_names_mapping = StepVideoConfig(
+    )._reverse_param_names_mapping
    _lora_param_names_mapping = StepVideoConfig()._lora_param_names_mapping
    _supported_attention_backends = StepVideoConfig(
    )._supported_attention_backends
@@ -14,8 +14,8 @@ from fastvideo.v1.configs.models.dits import WanVideoConfig
 from fastvideo.v1.configs.sample.wan import WanTeaCacheParams
 from fastvideo.v1.distributed.parallel_state import get_sp_world_size
 from fastvideo.v1.forward_context import get_forward_context
-from fastvideo.v1.layers.layernorm import (LayerNormScaleShift, RMSNorm,
-                                           ScaleResidual,
+from fastvideo.v1.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift,
+                                           RMSNorm, ScaleResidual,
                                           ScaleResidualLayerNormScaleShift)
 from fastvideo.v1.layers.linear import ReplicatedLinear
 # from torch.nn import RMSNorm
@@ -26,7 +26,7 @@ from fastvideo.v1.layers.rotary_embedding import (_apply_rotary_emb,
 from fastvideo.v1.layers.visual_embedding import (ModulateProjection,
                                                  PatchEmbed, TimestepEmbedder)
 from fastvideo.v1.models.dits.base import CachableDiT
-from fastvideo.v1.platforms import _Backend
+from fastvideo.v1.platforms import AttentionBackendEnum


 class WanImageEmbedding(torch.nn.Module):
@@ -34,9 +34,9 @@ class WanImageEmbedding(torch.nn.Module):
    def __init__(self, in_features: int, out_features: int):
        super().__init__()

-        self.norm1 = nn.LayerNorm(in_features)
+        self.norm1 = FP32LayerNorm(in_features)
        self.ff = MLP(in_features, in_features, out_features, act_type="gelu")
-        self.norm2 = nn.LayerNorm(out_features)
+        self.norm2 = FP32LayerNorm(out_features)

    def forward(self,
                encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
@@ -125,8 +125,8 @@ class WanSelfAttention(nn.Module):
            dropout_rate=0,
            softmax_scale=None,
            causal=False,
-            supported_attention_backends=(_Backend.FLASH_ATTN,
-                                          _Backend.TORCH_SDPA))
+            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                          AttentionBackendEnum.TORCH_SDPA))

    def forward(self, x: torch.Tensor, context: torch.Tensor,
                context_lens: int):
@@ -174,7 +174,8 @@ class WanI2VCrossAttention(WanSelfAttention):
        window_size=(-1, -1),
        qk_norm=True,
        eps=1e-6,
-        supported_attention_backends: Optional[Tuple[_Backend, ...]] = None
+        supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
+                                                     ...]] = None
    ) -> None:
        super().__init__(dim, num_heads, window_size, qk_norm, eps,
                         supported_attention_backends)
@@ -216,21 +217,22 @@ class WanI2VCrossAttention(WanSelfAttention):

 class WanTransformerBlock(nn.Module):

-    def __init__(self,
-                 dim: int,
-                 ffn_dim: int,
-                 num_heads: int,
-                 qk_norm: str = "rms_norm_across_heads",
-                 cross_attn_norm: bool = False,
-                 eps: float = 1e-6,
-                 added_kv_proj_dim: Optional[int] = None,
-                 supported_attention_backends: Optional[Tuple[_Backend,
-                                                              ...]] = None,
-                 prefix: str = ""):
+    def __init__(
+            self,
+            dim: int,
+            ffn_dim: int,
+            num_heads: int,
+            qk_norm: str = "rms_norm_across_heads",
+            cross_attn_norm: bool = False,
+            eps: float = 1e-6,
+            added_kv_proj_dim: Optional[int] = None,
+            supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
+                                                         ...]] = None,
+            prefix: str = ""):
        super().__init__()

        # 1. Self-attention
-        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
        self.to_q = ReplicatedLinear(dim, dim, bias=True)
        self.to_k = ReplicatedLinear(dim, dim, bias=True)
        self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -261,7 +263,8 @@ class WanTransformerBlock(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=True,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 2. Cross-attention
        if added_kv_proj_dim is not None:
@@ -281,7 +284,8 @@ class WanTransformerBlock(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=False,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 3. Feed-forward
        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -358,21 +362,22 @@ class WanTransformerBlock(nn.Module):

 class WanTransformerBlock_VSA(nn.Module):

-    def __init__(self,
-                 dim: int,
-                 ffn_dim: int,
-                 num_heads: int,
-                 qk_norm: str = "rms_norm_across_heads",
-                 cross_attn_norm: bool = False,
-                 eps: float = 1e-6,
-                 added_kv_proj_dim: Optional[int] = None,
-                 supported_attention_backends: Optional[Tuple[_Backend,
-                                                              ...]] = None,
-                 prefix: str = ""):
+    def __init__(
+            self,
+            dim: int,
+            ffn_dim: int,
+            num_heads: int,
+            qk_norm: str = "rms_norm_across_heads",
+            cross_attn_norm: bool = False,
+            eps: float = 1e-6,
+            added_kv_proj_dim: Optional[int] = None,
+            supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
+                                                         ...]] = None,
+            prefix: str = ""):
        super().__init__()

        # 1. Self-attention
-        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
        self.to_q = ReplicatedLinear(dim, dim, bias=True)
        self.to_k = ReplicatedLinear(dim, dim, bias=True)
        self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -404,7 +409,8 @@ class WanTransformerBlock_VSA(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=True,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 2. Cross-attention
        if added_kv_proj_dim is not None:
@@ -424,7 +430,8 @@ class WanTransformerBlock_VSA(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=False,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 3. Feed-forward
        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -511,6 +518,7 @@ class WanTransformer3DModel(CachableDiT):
    _supported_attention_backends = WanVideoConfig(
    )._supported_attention_backends
    _param_names_mapping = WanVideoConfig()._param_names_mapping
+    _reverse_param_names_mapping = WanVideoConfig()._reverse_param_names_mapping
    _lora_param_names_mapping = WanVideoConfig()._lora_param_names_mapping

    def __init__(self, config: WanVideoConfig, hf_config: dict[str,
@@ -561,7 +569,8 @@ class WanTransformer3DModel(CachableDiT):
                                            norm_type="layer",
                                            eps=config.eps,
                                            elementwise_affine=False,
-                                            dtype=torch.float32)
+                                            dtype=torch.float32,
+                                            compute_dtype=torch.float32)
        self.proj_out = nn.Linear(
            inner_dim, config.out_channels * math.prod(config.patch_size))
        self.scale_shift_table = nn.Parameter(
@@ -569,6 +578,17 @@ class WanTransformer3DModel(CachableDiT):

        self.gradient_checkpointing = False

+        # For type checking
+        self.previous_e0_even = None
+        self.previous_e0_odd = None
+        self.previous_residual_even = None
+        self.previous_residual_odd = None
+        self.is_even = True
+        self.should_calc_even = True
+        self.should_calc_odd = True
+        self.accumulated_rel_l1_distance_even = 0
+        self.accumulated_rel_l1_distance_odd = 0
+        self.cnt = 0
        self.__post_init__()

    def forward(self,
@@ -655,7 +675,7 @@ class WanTransformer3DModel(CachableDiT):
        # 5. Output norm, projection & unpatchify
        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
                                                                          dim=1)
-        hidden_states = self.norm_out(hidden_states.float(), shift, scale)
+        hidden_states = self.norm_out(hidden_states, shift, scale)
        hidden_states = self.proj_out(hidden_states)

        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from abc import ABC, abstractmethod
-from typing import Optional, Tuple
+from dataclasses import field
+from typing import List, Optional, Tuple

 import torch
 from torch import nn
@@ -8,16 +9,22 @@ from torch import nn
 from fastvideo.v1.configs.models.encoders import (BaseEncoderOutput,
                                                  ImageEncoderConfig,
                                                  TextEncoderConfig)
-from fastvideo.v1.platforms import _Backend
+from fastvideo.v1.platforms import AttentionBackendEnum


 class TextEncoder(nn.Module, ABC):
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [])
+    _stacked_params_mapping: List[Tuple[str, str,
+                                        str]] = field(default_factory=list)
    _supported_attention_backends: Tuple[
-        _Backend, ...] = TextEncoderConfig()._supported_attention_backends
+        AttentionBackendEnum,
+        ...] = TextEncoderConfig()._supported_attention_backends

    def __init__(self, config: TextEncoderConfig) -> None:
        super().__init__()
        self.config = config
+        self._fsdp_shard_conditions = config._fsdp_shard_conditions
+        self._stacked_params_mapping = config.arch_config.stacked_params_mapping
        if not self.supported_attention_backends:
            raise ValueError(
                f"Subclass {self.__class__.__name__} must define _supported_attention_backends"
@@ -34,13 +41,14 @@ class TextEncoder(nn.Module, ABC):
        pass

    @property
-    def supported_attention_backends(self) -> Tuple[_Backend, ...]:
+    def supported_attention_backends(self) -> Tuple[AttentionBackendEnum, ...]:
        return self._supported_attention_backends


 class ImageEncoder(nn.Module, ABC):
    _supported_attention_backends: Tuple[
-        _Backend, ...] = ImageEncoderConfig()._supported_attention_backends
+        AttentionBackendEnum,
+        ...] = ImageEncoderConfig()._supported_attention_backends

    def __init__(self, config: ImageEncoderConfig) -> None:
        super().__init__()
@@ -56,5 +64,5 @@ class ImageEncoder(nn.Module, ABC):
        pass

    @property
-    def supported_attention_backends(self) -> Tuple[_Backend, ...]:
+    def supported_attention_backends(self) -> Tuple[AttentionBackendEnum, ...]:
        return self._supported_attention_backends
@@ -596,12 +596,7 @@ class CLIPVisionModel(ImageEncoder):
    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-        ]
+
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()
        layer_count = len(self.vision_model.encoder.layers)
@@ -620,7 +615,8 @@ class CLIPVisionModel(ImageEncoder):
                if layer_idx >= layer_count:
                    continue

-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+            for (param_name, weight_name,
+                 shard_id) in self.config.arch_config.stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
@@ -369,14 +369,7 @@ class LlamaModel(TextEncoder):

    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            (".qkv_proj", ".q_proj", "q"),
-            (".qkv_proj", ".k_proj", "k"),
-            (".qkv_proj", ".v_proj", "v"),
-            (".gate_up_proj", ".gate_proj", 0),
-            (".gate_up_proj", ".up_proj", 1),
-        ]
+
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()
        for name, loaded_weight in weights:
@@ -406,7 +399,7 @@ class LlamaModel(TextEncoder):
                    continue
                else:
                    name = kv_scale_name
-            for param_name, weight_name, shard_id in stacked_params_mapping:
+            for param_name, weight_name, shard_id in self.config.arch_config.stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
SolitaryThinker	ea8c154176	ckpt	2025-06-28 14:56:21 -07:00
Kevin Lin	580d6dfe1f	[CI] Add tests to Modal (#562 )	2025-06-28 14:02:16 -05:00
Wenxuan Tan	344e43006a	[CI] Fix SSIM and transformers CI (#564 )	2025-06-28 00:26:20 -05:00
Wenxuan Tan	c5155b256e	[Feature] Load weights from distributed (#470 )	2025-06-27 22:52:40 -05:00
William Lin	e005c7f3ac	[Docs] [Training] add readme for example training (#563 )	2025-06-27 14:50:42 -05:00
Yongqi Chen	ff5a79ef60	[Feature][Inference] Add VSA inference script (#561 )	2025-06-27 02:19:23 -05:00
William Lin	ab01dc4ba5	[Feature] [Training] Add i2v training (#559 )	2025-06-27 01:56:50 -05:00
William Lin	285a950c1b	[CI] fix vae and ssim tests (#557 )	2025-06-26 23:53:01 -05:00
William Lin	46a0a85d85	[Training] Fixes SP for training; Improve Datasets and schema (#555 )	2025-06-26 21:13:28 -05:00
Yongqi Chen	4aeabbc629	[Feature][Training] Add cfg rate for dataset loader (#556 )	2025-06-26 18:22:37 -04:00
Wenxuan Tan	949bb5c835	[CI] Fix CI checks (#553 )	2025-06-25 14:07:51 -05:00
Wenxuan Tan	aab74c1271	[Kernel] Remove all syncs from STA & VSA kernels (#517 )	2025-06-23 13:13:09 -07:00
Yongqi Chen	f89d86944f	[Feature][Training]Add diffusers format checkpoint saving for inference (#542 )	2025-06-22 01:23:41 -04:00
William Lin	8741d204a5	[Training] Refactor and improve validation datasets (#539 )	2025-06-21 17:58:35 -07:00
Wenxuan Tan	cdc85f58a8	[chore] Bump torch to 2.7.1 to support Blackwell (#483 )	2025-06-20 22:10:56 -07:00
William Lin	0262d2f089	[misc] [training] Reorganize training pipeline (#533 )	2025-06-20 20:42:25 -07:00
William Lin	62c0343465	[bugfix] [VSA] Fix layernorm type for VSA Wan2.1 TransformerBlock (#534 )	2025-06-20 00:24:51 -07:00
William Lin	1e1a023fb0	[bugfix] Fix stage validator for multi text encoder models (#535 )	2025-06-19 22:49:16 -07:00
William Lin	1d2517ad8e	[misc] Remove gradient checking code (#532 )	2025-06-18 23:29:25 -07:00
William Lin	d41186cb4a	[Feat] Add Stage input and output verification (#523 )	2025-06-18 23:29:11 -07:00
Kevin Lin Edenzzzz Wenxuan Tan	78e0c7eec9	Specify cu128 Pytorch installation (#530 ) Co-authored-by: Edenzzzz <wtan45@wisc.edu> Co-authored-by: Wenxuan Tan <wenxuan.tan@wisc.edu>	2025-06-18 20:02:50 -05:00
Wenxuan Tan	1c41a94b62	[Refactor] Move dict_to_3d_list under utils (#507 )	2025-06-18 13:34:37 -07:00
Yongqi Chen	2e66aafe20	[Bugfix][Readme]Fix readme website bugs and add VSA finetune docs (#531 )	2025-06-17 22:48:29 -07:00
Yongqi ChenandWill Lin	55074bda76	[CI] Add STA-inference/VSA-training test (#527 ) Co-authored-by: Will Lin <wlsaidhi@gmail.com>	2025-06-17 21:13:06 -07:00
William Lin	de65bec2b7	[Ci] add sta and vsa install to docker image (#528 )	2025-06-17 18:09:48 -07:00
Yongqi Chen	7664dd0de3	[Bugfix][Inference]Fix envs.attn_backend (#525 )	2025-06-17 18:38:06 -05:00
William Linandkevin314	019a88ced4	[CI][bugfix] Use new 3.12 docker image (#526 ) Co-authored-by: kevin314 <kevin.lin.cs1@gmail.com>	2025-06-17 15:37:08 -07:00
Kevin Lin	72de11abcc	[CI] Add current PR test workflow to Buildkite/Modal (#512 )	2025-06-17 13:29:22 -07:00
Kevin Lin	d71a4ebffc	[CI] Update Docker image to flash-attn 2.8.0 / CUDA 12.8 (#524 )	2025-06-16 17:48:23 -07:00
William Lin	1089ab43bf	[bugfix] [Training] use diffusers fp32layernorm for wan2.1 (#490 )	2025-06-15 22:45:48 -07:00
William Lin	97d4b984c9	[misc] [ci] fix e2e preprocess+training data path (#521 )	2025-06-14 22:37:51 -07:00
Wenxuan Tan	2a8953d74d	[Refactor] Fix attn backend selection not correctly setting env variable (#516 )	2025-06-15 00:04:54 -05:00
Yongqi Chen	8801b10da7	[Bugfix][Preprocess]fix mini dataset name (#520 )	2025-06-14 22:03:22 -07:00
William Lin	6b413f2ec4	[CI] [Training] drop negative prompt in validation dataset and CI test for preprocess + training overfit (#519 )	2025-06-14 18:50:17 -07:00
Yongqi Chen	28b72694aa	[Feature][Preprocess]Add Readme doc for preprocess (#518 )	2025-06-14 20:41:13 -04:00
Yongqi Chen	4afb0cfe4f	[Feature][Training]vsa for t2v training ready (#513 )	2025-06-14 01:08:00 -04:00
Zhang Peiyuan	3eec1281cf	[misc] Fix preprocessing and dataloader extra padding (#514 )	2025-06-13 15:15:33 -07:00
Wenxuan Tan	0660489e38	[CI] Restrict training CI to v1 (#508 )	2025-06-12 15:26:05 -07:00
Zhang Peiyuan	dd871a17bf	fix logging (#509 )	2025-06-12 15:24:12 -07:00
Jinzhe Pan Peiyuan Zhang Will Lin	dc11529862	[Refactor][Configurations] clean config orgnization (#505 ) Co-authored-by: Peiyuan Zhang <a1286225768@gmail.com> Co-authored-by: Will Lin <wlsaidhi@gmail.com>	2025-06-12 13:27:08 -07:00
Zhang Peiyuan	ffabf85e31	[feat] Add parquet iterable dataset. (#506 )	2025-06-12 04:30:56 -04:00
William Lin	c0026ca5ba	[CI] [Training] Initial e2e small training test (#504 )	2025-06-11 13:53:36 -07:00
Zhang Peiyuan	0f2bbe71ac	[misc] rename dp_size to hdsp_replicate_dim (#491 )	2025-06-10 16:36:56 -07:00
Yongqi ChenandJerryZhou54	2a46902ecb	[Feature][VSA]Update STA publish workflow (#498 ) Co-authored-by: JerryZhou54 <zhouw.jerry2017@outlook.com>	2025-06-10 19:33:34 -04:00