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 SolitaryThinker
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# Agent Infrastructure — Status Dashboard
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Developer-maintained overview of all agent components and their maturity.
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Use this to understand what exists, how complete it is, and how much to trust it.
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_Last synced: 2026-03-02_
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> To resync this dashboard, use the workflow: `.agents/workflows/sync-dashboard.md`
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---
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## Summary
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| Category | Total | ✅ Ready | 🟡 Draft | 🔴 Stub | Trust |
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|----------|-------|---------|---------|---------|-------|
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| Skills | 8 | 0 | 8 | 0 | Low — newly created, untested |
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| Workflows (SOPs) | 4 | 0 | 4 | 0 | Low — newly created, untested |
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| Memory files | 4 | 1 | 3 | 0 | Medium — codebase_map is solid |
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| Lessons | 0 | — | — | — | N/A — empty |
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| Exploration logs | 0 | — | — | — | N/A — empty |
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---
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## Skills (`.agents/skills/`)
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| Skill | File | Status | Trust | Tested | Notes |
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|-------|------|--------|-------|--------|-------|
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| Launch Experiment | `launch-experiment.md` | 🟡 Draft | Low | ❌ | Needs dry-run validation |
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| Monitor Experiment | `monitor-experiment.md` | 🟡 Draft | Low | ❌ | Requires W&B API access to test |
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| Summarize Run | `summarize-run.md` | 🟡 Draft | Low | ❌ | Pattern from existing test infra |
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| Log Experiment | `log-experiment.md` | 🟡 Draft | Low | ❌ | Journal formatting only |
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| Evaluate Video Quality | `evaluate-video-quality.md` | 🟡 Draft | Low | ❌ | SSIM section most mature |
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| Index Related Work | `index-related-work.md` | 🟡 Draft | Low | ❌ | Schema defined, no entries yet |
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| Search Related Work | `search-related-work.md` | 🟡 Draft | Low | ❌ | Depends on indexed entries |
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| Skill Template | `SKILL_TEMPLATE.md` | ✅ Ready | High | ✅ | Meta-template, stable |
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### Trust Level Definitions
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- **High**: Tested in production, validated against real experiments
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- **Medium**: Logic is sound, partially tested or based on existing patterns
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- **Low**: Newly written, not yet validated
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- **None**: Placeholder only
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---
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## Workflows / SOPs (`.agents/workflows/`)
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| Workflow | File | Status | Trust | Tested | Notes |
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|----------|------|--------|-------|--------|-------|
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| Experiment Lifecycle | `experiment-lifecycle.md` | 🟡 Draft | Low | ❌ | End-to-end flow, untested |
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| Evaluation Development | `evaluation-development.md` | 🟡 Draft | Low | ❌ | Metric dev process |
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| Experiment Journaling | `experiment-journaling.md` | 🟡 Draft | Low | ❌ | Journaling cadence |
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| Lesson Capture | `lesson-capture.md` | 🟡 Draft | Low | ❌ | Post-experiment reflection |
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| Sync Dashboard | `sync-dashboard.md` | 🟡 Draft | Low | ❌ | This dashboard's updater |
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---
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## Memory (`.agents/memory/`)
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| File | Status | Trust | Notes |
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|------|--------|-------|-------|
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| `codebase_map.md` | ✅ Ready | High | Synthesized from full repo research |
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| `experiment_journal.md` | 🟡 Draft | Medium | Schema defined, no entries yet |
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| `evaluation_registry.md` | 🟡 Draft | Medium | SSIM/loss metrics documented |
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| `related_work/README.md` | 🟡 Draft | Medium | Schema defined, no entries yet |
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---
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## Lessons (`.agents/lessons/`)
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| File | Category | Severity | Notes |
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|------|----------|----------|-------|
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_No lessons captured yet._
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---
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## Exploration Logs (`.agents/exploration/`)
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| File | Status | Topic | Notes |
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|------|--------|-------|-------|
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_No exploration logs yet._
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|
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---
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## What to Do Next
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1. **Validate skills**: Run a minimal training experiment using the
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`experiment-lifecycle` SOP to test `launch-experiment` → `monitor-experiment`
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→ `summarize-run` end-to-end.
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2. **Index first related work**: Use `index-related-work` to add at least one
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paper (e.g., the Self-Forcing paper used in the codebase).
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3. **Capture first lesson**: After the validation run, capture any findings.
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4. **Promote to Ready**: As each skill/SOP is tested, update its status here.
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# Exploration Logs
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This directory holds draft procedures and investigation notes for tasks that
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don't yet have a standardized skill or SOP. Each exploration should follow this
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template.
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## When to Create an Exploration Log
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- You are working on a task with no existing skill or workflow.
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- You are experimenting with a new metric, training technique, or tool.
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- You want to document findings before they are promoted to a standard.
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## File Naming
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`<topic-slug>.md` — e.g., `fvd-metric-investigation.md`
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## Template
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```markdown
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# Exploration Log: <Topic>
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## Status: draft | under_review | promoted | abandoned
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## Context
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<Why this exploration is needed — link to experiment or task if applicable.>
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## Progress
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- [ ] Step 1: ...
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- [ ] Step 2: ...
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## Findings
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<What you have learned so far.>
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## Mistakes / Dead Ends
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<What didn't work and why — these become lessons.>
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## Proposed Standardization
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<If this works, describe the skill/SOP/workflow to create.>
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```
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## Lifecycle
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1. **Create** during exploration mode.
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2. **Update** as you make progress.
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3. **Promote**: If findings are solid, create a skill in `.agents/skills/` or an SOP in `.agents/workflows/`.
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4. **Archive mistakes**: Move failures into `.agents/lessons/`.
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# Lessons Learned Database
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This directory stores documented mistakes, unexpected behaviors, and their fixes.
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Each lesson is a permanent record that helps agents and humans avoid repeating
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past errors.
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## When to Create a Lesson
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- An experiment failed for a non-obvious reason.
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- A configuration or hyperparameter choice led to wasted compute.
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- A porting, data, or infrastructure issue was discovered and resolved.
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- A workaround was needed for a known framework/library bug.
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## File Naming
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`<YYYY-MM-DD>_<short-slug>.md` — e.g., `2026-03-02_lr-too-high-for-lora.md`
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## Template
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```markdown
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---
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date: <ISO-8601>
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experiment: <reference to experiment_journal.md entry, if applicable>
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category: hyperparameter | data | infrastructure | evaluation | porting | other
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severity: critical | important | minor
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---
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# <Short Descriptive Title>
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## What Happened
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<Description of the problem and its symptoms.>
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## Root Cause
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<Analysis of why it happened.>
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## Fix / Workaround
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<What resolved the issue.>
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## Prevention
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<How to avoid this in the future — updated skills, SOPs, or checks.>
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```
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## Usage
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- Before starting a task, **search this directory** for relevant lessons.
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- After completing or failing a task, **check if a new lesson should be created**.
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- Periodically review lessons for **patterns** — recurring themes may warrant
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a new skill, SOP, or codebase fix.
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@@ -1,129 +0,0 @@
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# FastVideo-WorldModel — Codebase Map
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High-level structural index for agent orientation. Updated 2026-03-08.
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## Repository Layout
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```
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FastVideo-WorldModel/
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├── fastvideo/ # Core Python package
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│ ├── models/ # Model implementations
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│ │ ├── dits/ # DiT transformers (wanvideo, ltx2, ...)
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│ │ ├── vaes/ # VAE models
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│ │ ├── encoders/ # Text/image encoders (T5, CLIP)
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│ │ ├── schedulers/ # Noise schedulers
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│ │ ├── upsamplers/ # Super-resolution models
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│ │ ├── audio/ # Audio models
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│ │ └── loader/ # Component loaders for HF repos
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│ ├── configs/ # Configuration system
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│ │ ├── models/ # Arch configs + param_names_mapping
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│ │ ├── pipelines/ # Pipeline wiring
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│ │ └── sample/ # Default sampling parameters
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│ ├── pipelines/ # End-to-end pipelines
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│ │ ├── basic/ # Per-model pipelines (wan/, ltx2/, ...)
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│ │ └── stages/ # Reusable pipeline stages
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│ ├── train/ # Refactored training framework (YAML-driven, preferred)
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│ │ ├── trainer.py # Main training loop coordinator
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│ │ ├── entrypoint/ # Training entrypoint (train.py) + checkpoint conversion
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│ │ ├── methods/ # Training algorithms (FineTune, DFSFT, DMD2, SelfForcing)
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│ │ │ ├── base.py # TrainingMethod ABC
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│ │ │ ├── fine_tuning/ # FineTuneMethod, DiffusionForcingSFTMethod
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│ │ │ └── distribution_matching/ # DMD2Method, SelfForcingMethod
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│ │ ├── models/ # Per-role model wrappers (ModelBase, CausalModelBase)
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│ │ │ └── wan/ # WanModel, WanCausalModel
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│ │ ├── callbacks/ # Composable hooks (grad_clip, ema, validation)
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│ │ └── utils/ # Config, builder, checkpoint, optimizer, tracking
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│ ├── training/ # Legacy training infrastructure (being phased out)
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│ │ ├── trackers.py # W&B tracker (BaseTracker → WandbTracker)
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│ │ ├── training_utils.py # Checkpointing, grad clipping, state dicts
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│ │ ├── training_pipeline.py # Base training pipeline
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│ │ ├── wan_training_pipeline.py # Wan T2V training
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│ │ ├── wan_i2v_training_pipeline.py # Wan I2V training
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│ │ ├── distillation_pipeline.py # Distillation base
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│ │ ├── wan_distillation_pipeline.py # Wan distillation
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│ │ ├── self_forcing_distillation_pipeline.py # Self-forcing distill
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│ │ ├── ltx2_training_pipeline.py # LTX-2 training
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│ │ └── matrixgame_training_pipeline.py # MatrixGame training
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│ ├── attention/ # Attention backends
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│ ├── distributed/ # Sequence/tensor parallel utilities
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│ ├── layers/ # Tensor-parallel layers
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│ ├── tests/ # Package-level tests
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│ │ ├── training/ # Training regression tests (W&B summary comparison)
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│ │ ├── ssim/ # SSIM visual regression tests
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│ │ ├── encoders/ # Encoder parity tests
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│ │ └── modal/ # Modal CI test runner
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│ └── registry.py # Unified config registry
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├── fastvideo-kernel/ # CUDA/custom kernels (separate build: ./build.sh)
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├── scripts/ # Utility scripts
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│ ├── distill/ # Distillation launch scripts
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│ ├── inference/ # Inference scripts
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│ ├── checkpoint_conversion/ # Weight conversion tools
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│ ├── finetune/ # Finetune scripts
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│ └── preprocess/ # Data preprocessing
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├── examples/ # Ready-to-run examples
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│ ├── training/ # Training examples (finetune/, consistency_finetune/)
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│ ├── distill/ # Distillation examples
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│ ├── inference/ # Inference examples
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│ └── dataset/ # Dataset examples
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├── docs/ # MkDocs documentation source
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│ ├── design/overview.md # Architecture overview
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│ ├── training/ # Training guides
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│ └── contributing/ # Contributor guides + coding_agents.md
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├── tests/ # Top-level tests (local_tests/)
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├── AGENTS.md # Agent coding guidelines
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└── .agents/ # Agent infrastructure (you are here)
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```
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## Key Training Entrypoints
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### New framework (`fastvideo/train/`) — preferred
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|
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| Method | Config Example | Launch Pattern |
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|--------|---------------|----------------|
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| FineTune (Wan) | `examples/train/finetune_wan2.1_t2v_1.3B_vsa_*.yaml` | `torchrun -m fastvideo.train.entrypoint.train --config <yaml>` |
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| DFSFT (Wan causal) | `examples/train/dfsft_wan_causal_t2v_1.3B.yaml` | `torchrun -m fastvideo.train.entrypoint.train --config <yaml>` |
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| DMD2 distillation | `examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml` | `torchrun -m fastvideo.train.entrypoint.train --config <yaml>` |
|
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| Self-Forcing | `examples/train/self_forcing_wan_causal_t2v_1.3B.yaml` | `torchrun -m fastvideo.train.entrypoint.train --config <yaml>` |
|
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|
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### Legacy pipelines (`fastvideo/training/`) — being phased out
|
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|
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| Pipeline | Entrypoint | Launch Pattern |
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|----------|-----------|----------------|
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| Wan T2V finetune | `fastvideo/training/wan_training_pipeline.py` | `torchrun --nproc_per_node N` |
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| Wan I2V finetune | `fastvideo/training/wan_i2v_training_pipeline.py` | `torchrun --nproc_per_node N` |
|
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| Wan distillation (DMD) | `fastvideo/training/wan_distillation_pipeline.py` | `torchrun --nproc_per_node N` |
|
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| Self-forcing distill | `fastvideo/training/wan_self_forcing_distillation_pipeline.py` | `torchrun --nproc_per_node N` |
|
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| LTX-2 finetune | `fastvideo/training/ltx2_training_pipeline.py` | `torchrun --nproc_per_node N` |
|
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| MatrixGame | `fastvideo/training/matrixgame_training_pipeline.py` | `torchrun --nproc_per_node N` |
|
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## W&B Integration
|
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|
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- **Tracker classes**: `fastvideo/training/trackers.py`
|
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- `WandbTracker` — logs metrics, videos, timing
|
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- `SequentialTracker` — fan-out to multiple trackers
|
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- `DummyTracker` — no-op for offline/test
|
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- **Run summary location**: `<output_dir>/tracker/wandb/latest-run/files/wandb-summary.json`
|
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- **Reference summaries**: `fastvideo/tests/training/*/` (e.g., `a40_reference_wandb_summary.json`)
|
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- **Environment**: `WANDB_API_KEY`, `WANDB_BASE_URL`, `WANDB_MODE`
|
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|
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## Critical Environment Variables
|
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|
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| Variable | Purpose |
|
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|----------|---------|
|
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| `WANDB_API_KEY` | W&B authentication |
|
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| `WANDB_MODE` | `online` / `offline` |
|
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| `FASTVIDEO_ATTENTION_BACKEND` | `FLASH_ATTN` / `TORCH_SDPA` |
|
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| `TOKENIZERS_PARALLELISM` | Set `false` to avoid fork warnings |
|
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| `HF_HOME` | HuggingFace cache directory |
|
||||
|
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## Build & Test Commands
|
||||
|
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```bash
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uv pip install -e .[dev] # Editable install
|
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pre-commit run --all-files # Lint/format/spell
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pytest tests/ # Top-level tests
|
||||
pytest fastvideo/tests/ -v # Package tests
|
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pytest fastvideo/tests/training/Vanilla -srP # Training loss regression
|
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pytest fastvideo/tests/ssim/ -vs # SSIM visual regression
|
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cd fastvideo-kernel && ./build.sh # Build kernels
|
||||
```
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@@ -1,327 +0,0 @@
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# Evaluation Metrics Registry
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|
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Living catalog of all evaluation metrics for FastVideo-WorldModel video quality
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assessment. Each metric includes a detailed explanation, implementation status,
|
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usage instructions, and interpretation guide.
|
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|
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_Last updated: 2026-03-02_
|
||||
|
||||
---
|
||||
|
||||
## Metric Summary
|
||||
|
||||
| Metric | Category | Status | Location | Trust |
|
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|--------|----------|--------|----------|-------|
|
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| **FVD** | Distribution | ✅ Implemented | `benchmarks/fvd/` | High |
|
||||
| **SSIM** | Reference | ✅ Implemented | `fastvideo/tests/ssim/` | High |
|
||||
| **LPIPS** | Perceptual | ✅ Implemented | `scripts/lora_extraction/` | Medium |
|
||||
| **Loss trajectory** | Training signal | ✅ Implemented | W&B `train_loss` | Medium |
|
||||
| **Grad norm stability** | Training signal | ✅ Implemented | W&B `grad_norm` | Medium |
|
||||
| **GameWorld Score** | Multi-dim benchmark | 🟡 External | Matrix-Game repo | Low |
|
||||
| **Human preference** | Gold standard | 🔴 Manual | N/A | Highest |
|
||||
|
||||
---
|
||||
|
||||
## Implemented Metrics
|
||||
|
||||
### FVD — Fréchet Video Distance
|
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|
||||
**Category**: Distribution-level quality metric
|
||||
**Status**: ✅ Fully implemented in `benchmarks/fvd/`
|
||||
**Trust**: High — standard protocol, I3D feature extractor
|
||||
|
||||
#### What It Measures
|
||||
FVD measures the distance between the **distribution** of generated videos and
|
||||
a distribution of real/reference videos. It works by:
|
||||
1. Extracting spatiotemporal features from both real and generated video sets
|
||||
using a pretrained **I3D** (Inflated 3D ConvNet) model.
|
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2. Modeling each set of features as a multivariate Gaussian (mean + covariance).
|
||||
3. Computing the **Fréchet distance** between the two Gaussians.
|
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|
||||
Lower FVD = generated videos are more statistically similar to real videos.
|
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|
||||
#### Why It Matters
|
||||
- FVD is the **de facto standard** for benchmarking video generation models.
|
||||
- It captures both **visual quality** (are individual frames realistic?) and
|
||||
**temporal coherence** (do frames flow naturally?).
|
||||
- Matrix-Game 2.0, Open-Sora, and most video generation papers report FVD.
|
||||
|
||||
#### Limitations
|
||||
- Requires a **large sample set** (standard protocol uses 2048 videos) to
|
||||
produce stable statistics. Small sample sizes yield noisy results.
|
||||
- Measures **distributional similarity**, not per-video quality. A model could
|
||||
have low FVD by generating a diverse set of "roughly okay" videos.
|
||||
- The I3D model was trained on Kinetics-400 (human actions). It may be less
|
||||
sensitive to domain-specific artifacts in non-human-action videos (e.g.,
|
||||
driving, game environments).
|
||||
- Does not directly measure text-video alignment or action controllability.
|
||||
|
||||
#### How to Use
|
||||
|
||||
```python
|
||||
# Programmatic
|
||||
from benchmarks.fvd import compute_fvd_with_config, FVDConfig
|
||||
|
||||
config = FVDConfig.fvd2048_16f() # Standard: 2048 videos, 16 frames
|
||||
results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
|
||||
print(f"FVD: {results['fvd']:.2f}")
|
||||
```
|
||||
|
||||
```bash
|
||||
# CLI
|
||||
python -m benchmarks.fvd.cli \
|
||||
--real-path data/real/ \
|
||||
--gen-path outputs/gen/ \
|
||||
--protocol fvd2048_16f
|
||||
```
|
||||
|
||||
**Preset protocols**:
|
||||
| Protocol | Videos | Frames | Use Case |
|
||||
|----------|--------|--------|----------|
|
||||
| `fvd2048_16f` | 2048 | 16 | Standard benchmark (papers) |
|
||||
| `fvd2048_128f` | 2048 | 128 | Long video evaluation |
|
||||
| `quick_test` | 100 | 16 | Fast dev iteration |
|
||||
|
||||
**Feature extractors**: `i3d` (default, standard), `clip`, `videomae`
|
||||
|
||||
#### Interpretation
|
||||
| FVD Range | Interpretation |
|
||||
|-----------|---------------|
|
||||
| < 100 | Excellent — near-real quality |
|
||||
| 100–300 | Good — competitive with SOTA |
|
||||
| 300–600 | Fair — noticeable gap from real |
|
||||
| > 600 | Poor — significant quality issues |
|
||||
|
||||
> FVD values are dataset-dependent. Always compare against baselines evaluated
|
||||
> on the same real video distribution.
|
||||
|
||||
---
|
||||
|
||||
### SSIM — Structural Similarity Index
|
||||
|
||||
**Category**: Per-frame reference comparison
|
||||
**Status**: ✅ Implemented in `fastvideo/tests/ssim/`
|
||||
**Trust**: High — used in CI regression tests
|
||||
|
||||
#### What It Measures
|
||||
SSIM compares two images (or video frames) based on three components:
|
||||
1. **Luminance**: brightness similarity
|
||||
2. **Contrast**: dynamic range similarity
|
||||
3. **Structure**: spatial pattern similarity
|
||||
|
||||
The final score is a value in [0, 1] where 1.0 = identical.
|
||||
|
||||
#### Why It Matters
|
||||
- Used as a **regression guard** in CI: ensures model updates don't degrade
|
||||
visual output below a threshold.
|
||||
- More perceptually meaningful than raw pixel MSE.
|
||||
- Fast to compute — suitable for automated testing.
|
||||
|
||||
#### Limitations
|
||||
- Requires a **pixel-aligned reference** video. Cannot compare videos with
|
||||
different seeds, prompts, or angles.
|
||||
- Operates **per-frame** — does not capture temporal coherence.
|
||||
- Insensitive to some perceptual artifacts (color shifts, high-frequency noise).
|
||||
|
||||
#### How to Use
|
||||
|
||||
```bash
|
||||
pytest fastvideo/tests/ssim/ -vs
|
||||
```
|
||||
|
||||
#### Interpretation
|
||||
| SSIM Range | Quality |
|
||||
|------------|---------|
|
||||
| > 0.90 | Excellent — very close to reference |
|
||||
| 0.80–0.90 | Good — acceptable for most uses |
|
||||
| 0.70–0.80 | Fair — noticeable differences |
|
||||
| < 0.70 | Poor — significant divergence |
|
||||
|
||||
---
|
||||
|
||||
### LPIPS — Learned Perceptual Image Patch Similarity
|
||||
|
||||
**Category**: Per-frame perceptual distance
|
||||
**Status**: ✅ Implemented in `scripts/lora_extraction/lora_inference_comparison.py`
|
||||
**Trust**: Medium — available but only used for LoRA comparison currently
|
||||
|
||||
#### What It Measures
|
||||
LPIPS uses a pretrained neural network (AlexNet by default) to extract
|
||||
deep features from two images and computes the distance between them in
|
||||
feature space. Unlike SSIM, LPIPS correlates much more strongly with
|
||||
**human perceptual judgments**.
|
||||
|
||||
Lower LPIPS = more perceptually similar.
|
||||
|
||||
#### Why It Matters
|
||||
- Best available automated proxy for **human visual judgments** at the frame
|
||||
level.
|
||||
- Captures semantic and structural differences that SSIM misses (e.g., texture
|
||||
changes, minor recoloring).
|
||||
- Used for validating LoRA merge quality.
|
||||
|
||||
#### Limitations
|
||||
- Per-frame metric — no temporal awareness.
|
||||
- Requires reference video (paired comparison only).
|
||||
- Slightly slower than SSIM due to neural network forward pass.
|
||||
|
||||
#### How to Use
|
||||
|
||||
```bash
|
||||
python scripts/lora_extraction/lora_inference_comparison.py \
|
||||
--base merged_model \
|
||||
--ft path/to/finetuned \
|
||||
--adapter NONE \
|
||||
--output-dir results \
|
||||
--prompt "A cat" \
|
||||
--compute-lpips
|
||||
```
|
||||
|
||||
#### Interpretation
|
||||
| LPIPS Range | Quality |
|
||||
|-------------|---------|
|
||||
| < 0.10 | Excellent — nearly indistinguishable |
|
||||
| 0.10–0.20 | Good — minor perceptual differences |
|
||||
| 0.20–0.40 | Fair — noticeable differences |
|
||||
| > 0.40 | Poor — clearly different |
|
||||
|
||||
---
|
||||
|
||||
### Loss Trajectory
|
||||
|
||||
**Category**: Training signal proxy
|
||||
**Status**: ✅ Active (from W&B `train_loss`)
|
||||
**Trust**: Medium — proxy, not direct quality measure
|
||||
|
||||
#### What It Measures
|
||||
Tracks the training loss over time. A healthy training run shows:
|
||||
- **Decreasing loss** over the first hundreds of steps.
|
||||
- **Stable gradient norms** (no wild spikes).
|
||||
- **Consistent step times** (no infrastructure issues).
|
||||
|
||||
#### Why It Matters
|
||||
- Cheapest evaluation signal — available in real-time from W&B.
|
||||
- Critical for the **30-minute quality check** workflow.
|
||||
- At later training stages (when loss becomes meaningful), trajectory shape
|
||||
can predict final model quality.
|
||||
|
||||
#### Context: How This Evolves
|
||||
The team's experience shows evaluation signals change during a project:
|
||||
- **Early stage**: Loss may be flat or meaningless → focus on SSIM & visual
|
||||
inspection instead.
|
||||
- **Mid stage**: Loss starts decreasing → trajectory shape becomes useful.
|
||||
- **Late stage**: Loss is meaningful → can compare trajectories across runs.
|
||||
|
||||
This dynamic is a key insight from the team's workflow: don't over-rely on
|
||||
loss early; don't ignore it late.
|
||||
|
||||
---
|
||||
|
||||
### Grad Norm Stability
|
||||
|
||||
**Category**: Training health diagnostic
|
||||
**Status**: ✅ Active (from W&B `grad_norm`)
|
||||
**Trust**: Medium — diagnostic, not quality metric
|
||||
|
||||
#### What It Measures
|
||||
The magnitude of gradients during training. Stable grad norms indicate
|
||||
healthy optimization. Spikes or NaN values indicate training instability.
|
||||
|
||||
#### Alert Thresholds
|
||||
| Condition | Meaning |
|
||||
|-----------|---------|
|
||||
| Stable ~0.3–0.5 | Normal training |
|
||||
| Single spike > 3× average | Possible bad batch, monitor |
|
||||
| NaN or Inf | 🔴 Training has diverged — stop run |
|
||||
| Increasing trend | Learning rate may be too high |
|
||||
|
||||
---
|
||||
|
||||
## External Benchmarks
|
||||
|
||||
### GameWorld Score Benchmark (Matrix-Game)
|
||||
|
||||
**Category**: Multi-dimensional evaluation framework for interactive world models
|
||||
**Status**: 🟡 External — not implemented in-repo
|
||||
**Source**: [Matrix-Game 1.0 benchmark](https://github.com/SkyworkAI/Matrix-Game), used in [Matrix-Game 2.0 paper](https://arxiv.org/abs/2508.13009)
|
||||
|
||||
#### What It Measures
|
||||
A comprehensive benchmark examining **four critical capabilities**:
|
||||
|
||||
| Dimension | What It Evaluates | Example Signals |
|
||||
|-----------|-------------------|-----------------|
|
||||
| **Visual quality** | Frame-level realism, absence of artifacts | Color fidelity, sharpness, coherence |
|
||||
| **Temporal quality** | Smoothness across frames, motion consistency | Jitter, flickering, temporal aliasing |
|
||||
| **Action controllability** | Response to input actions (keyboard/mouse) | Action delay, correctness, smoothness |
|
||||
| **Physical rule understanding** | Adherence to physics (gravity, collision) | Object persistence, plausible motion |
|
||||
|
||||
#### Context from Matrix-Game 2.0
|
||||
- Evaluation uses **597-frame composite action sequences** over 32 Minecraft
|
||||
scenes and 16 wild scenes.
|
||||
- Action controllability assessment is **Minecraft-specific** — cannot be
|
||||
directly applied to wild/general scenes.
|
||||
- The paper notes that models that "collapse" to static frames can
|
||||
paradoxically score higher on consistency metrics — beware of this confound.
|
||||
|
||||
#### Relevance to FastVideo
|
||||
- Matrix-Game 2.0 is built on SkyReels-V2/Wan2.1 architecture — **same model
|
||||
family as FastVideo**.
|
||||
- Their distillation uses DMD-based Self-Forcing — **same technique** as our
|
||||
`self_forcing_distillation_pipeline.py`.
|
||||
- GameWorld Score dimensions are a useful framework for thinking about world
|
||||
model quality even outside gaming contexts.
|
||||
|
||||
---
|
||||
|
||||
## Human Preference Evaluation
|
||||
|
||||
**Category**: Gold-standard quality assessment
|
||||
**Status**: 🔴 Manual process — no automated implementation
|
||||
**Priority**: **Highest** — this is the most important evaluation signal
|
||||
**Trust**: Highest — but expensive
|
||||
|
||||
### What It Measures
|
||||
Human evaluators compare generated videos and rate them on dimensions like:
|
||||
- Overall quality and realism
|
||||
- Temporal coherence and smoothness
|
||||
- Prompt adherence / action correctness
|
||||
- Absence of artifacts
|
||||
|
||||
#### Why It's the Most Important Metric
|
||||
All automated metrics are **proxies** for human judgment. They can be gamed
|
||||
or may miss artifacts that humans easily notice. Human preference is the
|
||||
ultimate ground truth for video generation quality.
|
||||
|
||||
#### Cost & Practicality
|
||||
| Approach | Cost | Scale | When to Use |
|
||||
|----------|------|-------|-------------|
|
||||
| Internal team review | Low | ~10–50 videos | Every major checkpoint |
|
||||
| Crowdsource (MTurk, Scale) | Medium | 100+ videos | Pre-release validation |
|
||||
| A/B preference test | Medium | Pairs | Comparing two model versions |
|
||||
|
||||
#### Recommended Protocol
|
||||
1. Sample 10–20 videos from the model at a checkpoint.
|
||||
2. Include diverse prompts (easy + hard, short + long).
|
||||
3. Have 2–3 evaluators score each video 1–5 on: quality, coherence, fidelity.
|
||||
4. Record scores in the experiment journal.
|
||||
|
||||
---
|
||||
|
||||
## Metrics NOT Used
|
||||
|
||||
| Metric | Reason |
|
||||
|--------|--------|
|
||||
| ~~CLIP-Score~~ | Not used by the team. Measures text-image alignment using CLIP embeddings, but not well-suited for video temporal quality. |
|
||||
| Inception Score (IS) | Less informative than FVD for video; primarily an image metric. |
|
||||
| PSNR | Pixel-level metric; less perceptually meaningful than SSIM/LPIPS. |
|
||||
|
||||
---
|
||||
|
||||
## Adding a New Metric
|
||||
|
||||
Follow the SOP: `.agents/workflows/evaluation-development.md`
|
||||
|
||||
1. Prototype in `.agents/exploration/`
|
||||
2. Validate on known-good and known-bad samples
|
||||
3. Add to this registry
|
||||
4. Update the `evaluate-video-quality` skill
|
||||
@@ -1,21 +0,0 @@
|
||||
# Experiment Journal
|
||||
|
||||
Living log of all experiments. Each entry captures what was tried, the result,
|
||||
and any insights. Newest entries go at the top.
|
||||
|
||||
_No experiments logged yet. Use the `log-experiment` skill to add entries._
|
||||
|
||||
<!-- TEMPLATE — copy and fill for each new experiment:
|
||||
|
||||
## [YYYY-MM-DD] Experiment: <name>
|
||||
- **Hypothesis**: <what you expected to learn>
|
||||
- **Config**: model=..., lr=..., sp_size=..., gpus=..., script=...
|
||||
- **W&B run**: <run_id or URL>
|
||||
- **Duration**: <total wall time>
|
||||
- **Key metrics**: loss=..., step_time=..., grad_norm=...
|
||||
- **Checkpoint**: <path>
|
||||
- **Insight**: <what was learned>
|
||||
- **Status**: running | completed | failed | abandoned
|
||||
- **Related lessons**: `.agents/lessons/<filename>.md`
|
||||
|
||||
-->
|
||||
@@ -1,4 +0,0 @@
|
||||
{"name": "codebase-map", "description": "High-level structural index of the FastVideo-WorldModel repository", "path": "codebase-map/README.md", "status": "ready", "trust": "high"}
|
||||
{"name": "evaluation-registry", "description": "Catalog of all evaluation metrics with detailed explanations, implementation status, and usage guides", "path": "evaluation-registry/README.md", "status": "draft", "trust": "medium"}
|
||||
{"name": "experiment-journal", "description": "Living log of all experiments with hypotheses, configs, metrics, and insights", "path": "experiment-journal/README.md", "status": "draft", "trust": "medium"}
|
||||
{"name": "related-work", "description": "Index of related papers, repos, and blog posts with structured comparisons to FastVideo", "path": "related-work/README.md", "status": "draft", "trust": "low"}
|
||||
@@ -1,34 +0,0 @@
|
||||
# Related Work Index
|
||||
|
||||
Each file in this directory is a structured summary of a related paper, repo,
|
||||
or blog post relevant to FastVideo-WorldModel training.
|
||||
|
||||
## File Format
|
||||
|
||||
Each file is named `<slug>.md` and follows this structure:
|
||||
|
||||
```markdown
|
||||
---
|
||||
title: <paper/repo title>
|
||||
source: <URL or citation>
|
||||
type: paper | repo | blog
|
||||
date_indexed: <ISO-8601>
|
||||
tags: [world-model, distillation, evaluation, reward-shaping, ...]
|
||||
---
|
||||
|
||||
## Summary
|
||||
<1-2 paragraph summary of the work.>
|
||||
|
||||
## Key Differences from FastVideo
|
||||
- <Bullet points comparing their approach to ours.>
|
||||
|
||||
## Actionable Insights
|
||||
- <What we could adopt or adapt.>
|
||||
```
|
||||
|
||||
## How to Add New Entries
|
||||
|
||||
Use the `index-related-work` skill, or manually create a file following the
|
||||
template above.
|
||||
|
||||
_No related work indexed yet._
|
||||
@@ -1,76 +0,0 @@
|
||||
# Agent Onboarding — FastVideo-WorldModel
|
||||
|
||||
Welcome, agent. This is the **master onboarding** guide. Follow the steps below,
|
||||
then check if a **domain-specific onboarding** exists for your task.
|
||||
|
||||
## Domain-Specific Onboarding
|
||||
|
||||
If your task falls into one of these areas, read the specialized guide **after**
|
||||
completing the general steps below:
|
||||
|
||||
| Domain | Guide | When to Use |
|
||||
|--------|-------|-------------|
|
||||
| **WorldModel Training** | `worldmodel-training/README.md` | Training, finetuning, distillation, experiment management |
|
||||
|
||||
---
|
||||
|
||||
## Step 1: Understand the Codebase
|
||||
|
||||
Read these files to build your context:
|
||||
|
||||
| Priority | File | What you learn |
|
||||
|----------|------|----------------|
|
||||
| 1 | `AGENTS.md` | Coding guidelines, build/test commands, PR conventions |
|
||||
| 2 | `docs/design/overview.md` | Architecture: models, pipelines, configs, registry |
|
||||
| 3 | `fastvideo/train/` | Refactored training framework (YAML-driven, modular methods/models/callbacks) |
|
||||
| 4 | `docs/training/overview.md` | Training data flow and preprocessing |
|
||||
| 5 | `docs/training/finetune.md` | Training arguments, parallelism, LoRA, validation |
|
||||
| 6 | `docs/contributing/coding_agents.md` | How to add model pipelines with agent assistance |
|
||||
|
||||
## Step 2: Discover Available Resources
|
||||
|
||||
Read these two index files to see what skills and memory modules exist:
|
||||
|
||||
- **`.agents/skills/index.jsonl`** — catalog of all agent skills (name + description)
|
||||
- **`.agents/memory/index.jsonl`** — catalog of all memory modules (name + description)
|
||||
|
||||
Each entry has a `path` field pointing to the full content. Only load the
|
||||
full README.md for modules relevant to your current task.
|
||||
|
||||
## Step 3: Check for Existing Skills & SOPs
|
||||
|
||||
Before writing new code or procedures:
|
||||
|
||||
1. **Skills**: Read `.agents/skills/index.jsonl` — find a matching skill by description.
|
||||
2. **Workflows/SOPs**: Browse `.agents/workflows/` — step-by-step procedures for common tasks.
|
||||
3. **Lessons**: Browse `.agents/lessons/` — known pitfalls and their fixes.
|
||||
|
||||
If a skill or SOP exists for your task, **use it**. If not, you are in **exploration mode** — see Step 4.
|
||||
|
||||
## Step 4: Exploration Mode
|
||||
|
||||
If no existing skill/SOP covers your task:
|
||||
|
||||
1. Document your progress in `.agents/exploration/<topic>.md` using the template in `.agents/exploration/README.md`.
|
||||
2. At the end of your session, reflect:
|
||||
- **What worked** → propose a new skill or SOP in the exploration log.
|
||||
- **What failed** → create a lesson in `.agents/lessons/`.
|
||||
3. Flag the exploration log for human review.
|
||||
|
||||
## Quick Reference
|
||||
|
||||
```
|
||||
.agents/
|
||||
├── ONBOARDING.md ← you are here
|
||||
├── STATUS.md ← dashboard: completeness & trust of all components
|
||||
├── skills/ ← reusable agent skills
|
||||
├── workflows/ ← SOPs and procedures
|
||||
├── memory/ ← persistent context (folder per topic + index.jsonl)
|
||||
│ ├── index.jsonl
|
||||
│ ├── codebase-map/
|
||||
│ ├── experiment-journal/
|
||||
│ ├── evaluation-registry/
|
||||
│ └── related-work/
|
||||
├── lessons/ ← mistakes and fixes
|
||||
└── exploration/ ← draft procedures
|
||||
```
|
||||
@@ -1,302 +0,0 @@
|
||||
# WorldModel Training — Agent Onboarding
|
||||
|
||||
Specialized onboarding for agents working on FastVideo-WorldModel training,
|
||||
distillation, and evaluation. Read the master onboarding (`.agents/onboarding/README.md`)
|
||||
first, then come here.
|
||||
|
||||
---
|
||||
|
||||
## Domain Context
|
||||
|
||||
FastVideo-WorldModel trains **interactive world models** — video generation systems
|
||||
that respond to user actions (keyboard/mouse) in real-time. The architecture is
|
||||
based on **Wan2.1** (SkyReels-V2) DiT models with causal attention for
|
||||
auto-regressive streaming generation.
|
||||
|
||||
**Key techniques you will work with:**
|
||||
- Full finetuning and LoRA on Wan / LTX-2 / MatrixGame models
|
||||
- DMD-based distillation (few-step generation)
|
||||
- Self-Forcing distillation (causal streaming)
|
||||
- Diffusion-Forcing SFT (DFSFT) for causal models
|
||||
- VSA (Variable Sparsity Acceleration) for efficient training
|
||||
|
||||
---
|
||||
|
||||
## Training Code: Two Generations
|
||||
|
||||
### New modular framework: `fastvideo/train/` (preferred)
|
||||
|
||||
The refactored training code uses a **YAML-only config-driven** architecture
|
||||
with composable methods, per-role models, and a callback system. All new
|
||||
training work should use this framework.
|
||||
|
||||
### Legacy pipelines: `fastvideo/training/` (deprecated)
|
||||
|
||||
The old monolithic pipeline classes (`WanTrainingPipeline`,
|
||||
`DistillationPipeline`, etc.) still exist but are being phased out. The new
|
||||
framework imports select utilities from `fastvideo/training/` for backward
|
||||
compatibility (EMA, gradient clipping, checkpoint wrappers).
|
||||
|
||||
---
|
||||
|
||||
## Essential Reading (Training-Specific)
|
||||
|
||||
Read these **in order** before touching any training code:
|
||||
|
||||
| # | File | What You Learn |
|
||||
|---|------|----------------|
|
||||
| 1 | `docs/training/overview.md` | Training data flow: raw video → text embeddings + video latents → training |
|
||||
| 2 | `docs/training/finetune.md` | Training arguments, parallelism (SP/TP), LoRA, validation settings |
|
||||
| 3 | `docs/training/data_preprocess.md` | How to preprocess datasets into the expected format |
|
||||
| 4 | `docs/design/overview.md` | Architecture: models, pipelines, configs, registry |
|
||||
|
||||
---
|
||||
|
||||
## New Training Framework (`fastvideo/train/`)
|
||||
|
||||
### Architecture Overview
|
||||
|
||||
```
|
||||
fastvideo/train/
|
||||
├── __init__.py → exports Trainer
|
||||
├── trainer.py → main training loop coordinator
|
||||
├── entrypoint/
|
||||
│ ├── train.py → YAML-only training entrypoint
|
||||
│ └── dcp_to_diffusers.py → checkpoint conversion utility
|
||||
├── methods/ → training algorithms (TrainingMethod ABC)
|
||||
│ ├── base.py → TrainingMethod base class
|
||||
│ ├── fine_tuning/
|
||||
│ │ ├── finetune.py → FineTuneMethod (supervised finetuning)
|
||||
│ │ └── dfsft.py → DiffusionForcingSFTMethod (causal)
|
||||
│ ├── distribution_matching/
|
||||
│ │ ├── dmd2.py → DMD2Method (distribution matching distill)
|
||||
│ │ └── self_forcing.py → SelfForcingMethod (causal streaming)
|
||||
│ ├── knowledge_distillation/ → (stub, not yet implemented)
|
||||
│ └── consistency_model/ → (stub, not yet implemented)
|
||||
├── models/ → per-role model instances
|
||||
│ ├── base.py → ModelBase & CausalModelBase (ABC)
|
||||
│ └── wan/
|
||||
│ ├── wan.py → WanModel (non-causal)
|
||||
│ └── wan_causal.py → WanCausalModel (causal streaming)
|
||||
├── callbacks/ → training hooks & monitoring
|
||||
│ ├── callback.py → Callback base class + CallbackDict
|
||||
│ ├── grad_clip.py → GradNormClipCallback
|
||||
│ ├── ema.py → EMACallback (shadow weights)
|
||||
│ └── validation.py → ValidationCallback (sampling + eval)
|
||||
└── utils/ → configuration, building, checkpointing
|
||||
├── builder.py → build_from_config() (config → runtime)
|
||||
├── checkpoint.py → CheckpointManager (DCP-based)
|
||||
├── config.py → load_run_config() (YAML → RunConfig)
|
||||
├── training_config.py → TypedConfig dataclasses
|
||||
├── optimizer.py → build_optimizer_and_scheduler()
|
||||
├── instantiate.py → resolve_target() + instantiate()
|
||||
├── tracking.py → build_tracker() (W&B, etc.)
|
||||
├── dataloader.py → dataloader utilities
|
||||
├── module_state.py → apply_trainable()
|
||||
└── moduleloader.py → load_module_from_path()
|
||||
```
|
||||
|
||||
### Key Concepts
|
||||
|
||||
**TrainingMethod** (`methods/base.py`): Abstract base class for all training
|
||||
algorithms. Owns role models (student, teacher, critic), manages checkpoint
|
||||
state, and defines the training step interface.
|
||||
|
||||
**ModelBase** (`models/base.py`): Per-role model wrapper. Each role (student,
|
||||
teacher, critic) gets its own `ModelBase` instance owning a `transformer` and
|
||||
`noise_scheduler`. `CausalModelBase` extends this for streaming models.
|
||||
|
||||
**Callback system** (`callbacks/`): Composable hooks for gradient clipping,
|
||||
EMA, validation, etc. Configured via YAML, dispatched by `CallbackDict`.
|
||||
|
||||
**Config system** (`utils/config.py`, `utils/training_config.py`): YAML files
|
||||
are parsed into typed `RunConfig` dataclass trees. Models and methods use
|
||||
`_target_` fields for instantiation (similar to Hydra).
|
||||
|
||||
### Training Flow
|
||||
|
||||
```
|
||||
run_training_from_config(config_path)
|
||||
→ load_run_config() # YAML → RunConfig
|
||||
→ init_distributed() # TP/SP setup
|
||||
→ build_from_config() # instantiate models, method, dataloader
|
||||
→ Trainer.run() # main loop:
|
||||
├─ callbacks.on_train_start()
|
||||
├─ checkpoint_manager.maybe_resume()
|
||||
├─ for step in range(max_steps):
|
||||
│ ├─ method.single_train_step(batch)
|
||||
│ ├─ method.backward()
|
||||
│ ├─ callbacks.on_before_optimizer_step()
|
||||
│ ├─ method.optimizers_schedulers_step()
|
||||
│ ├─ tracker.log(metrics, step)
|
||||
│ ├─ callbacks.on_training_step_end()
|
||||
│ └─ checkpoint_manager.maybe_save(step)
|
||||
├─ callbacks.on_train_end()
|
||||
└─ checkpoint_manager.save_final()
|
||||
```
|
||||
|
||||
### Training Methods
|
||||
|
||||
| Method | Class | Use Case |
|
||||
|--------|-------|----------|
|
||||
| **FineTune** | `FineTuneMethod` | Single-role supervised finetuning |
|
||||
| **DFSFT** | `DiffusionForcingSFTMethod` | Diffusion-forcing SFT with inhomogeneous timesteps |
|
||||
| **DMD2** | `DMD2Method` | Multi-role distribution matching distillation (student + teacher + critic) |
|
||||
| **Self-Forcing** | `SelfForcingMethod` | Extends DMD2 for causal student rollouts |
|
||||
|
||||
### Launching Training (New Framework)
|
||||
|
||||
Training is launched via `torchrun` with a single YAML config:
|
||||
|
||||
```bash
|
||||
torchrun --nproc_per_node <N_GPUS> \
|
||||
-m fastvideo.train.entrypoint.train \
|
||||
--config examples/train/<config>.yaml
|
||||
```
|
||||
|
||||
### Example YAML Configs
|
||||
|
||||
| Config | Method | Description |
|
||||
|--------|--------|-------------|
|
||||
| `examples/train/finetune_wan2.1_t2v_1.3B_vsa_phase3.4_0.9sparsity.yaml` | FineTune | Wan 1.3B finetuning with VSA sparsity |
|
||||
| `examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml` | DMD2 | Wan 1.3B distillation (student + teacher + critic) |
|
||||
| `examples/train/dfsft_wan_causal_t2v_1.3B.yaml` | DFSFT | Causal Wan 1.3B diffusion-forcing SFT |
|
||||
| `examples/train/self_forcing_wan_causal_t2v_1.3B.yaml` | Self-Forcing | Causal streaming distillation |
|
||||
|
||||
### Checkpointing (New Framework)
|
||||
|
||||
**CheckpointManager** (`utils/checkpoint.py`) saves via `torch.distributed.checkpoint`:
|
||||
|
||||
```
|
||||
output_dir/
|
||||
└─ checkpoint-{step}/
|
||||
├─ dcp/ # DCP state dict
|
||||
├─ config.json # resolved training config
|
||||
└─ .fastvideo_metadata.json
|
||||
```
|
||||
|
||||
Checkpoint state includes: role model weights, per-role optimizers/schedulers,
|
||||
CUDA RNG state, and callback state (e.g., EMA shadow weights).
|
||||
|
||||
### Config Structure
|
||||
|
||||
A YAML config defines the full training pipeline:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
student:
|
||||
_target_: fastvideo.train.models.wan.WanModel
|
||||
model_path: ...
|
||||
trainable: true
|
||||
teacher: # optional, for distillation
|
||||
_target_: fastvideo.train.models.wan.WanModel
|
||||
model_path: ...
|
||||
trainable: false
|
||||
|
||||
method:
|
||||
_target_: fastvideo.train.methods.fine_tuning.FineTuneMethod
|
||||
# method-specific params...
|
||||
|
||||
training:
|
||||
distributed: { num_gpus: 8, tp_size: 1, sp_size: 8 }
|
||||
data: { data_path: ..., batch_size: 1 }
|
||||
optimizer: { lr: 1e-5, lr_scheduler: constant_with_warmup }
|
||||
loop: { max_train_steps: 1000 }
|
||||
checkpoint: { output_dir: ./outputs }
|
||||
tracker: { trackers: [wandb], project_name: ... }
|
||||
|
||||
callbacks:
|
||||
grad_clip:
|
||||
_target_: fastvideo.train.callbacks.GradNormClipCallback
|
||||
max_grad_norm: 1.0
|
||||
validation:
|
||||
_target_: fastvideo.train.callbacks.ValidationCallback
|
||||
validation_steps: 100
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Legacy Training Pipelines (`fastvideo/training/`)
|
||||
|
||||
> **Note:** Use the new `fastvideo/train/` framework for new work. This section
|
||||
> is retained for reference on existing pipelines not yet migrated.
|
||||
|
||||
| Pipeline | Entrypoint | Use Case |
|
||||
|----------|-----------|----------|
|
||||
| Wan T2V finetune | `fastvideo/training/wan_training_pipeline.py` | Standard text-to-video finetune / LoRA |
|
||||
| Wan I2V finetune | `fastvideo/training/wan_i2v_training_pipeline.py` | Image-to-video (first frame conditioned) |
|
||||
| MatrixGame finetune | `fastvideo/training/matrixgame_training_pipeline.py` | Action-conditioned world model |
|
||||
| LTX-2 finetune | `fastvideo/training/ltx2_training_pipeline.py` | LTX-2 architecture finetuning |
|
||||
| Wan DMD distillation | `fastvideo/training/wan_distillation_pipeline.py` | Few-step distillation via DMD |
|
||||
| Self-Forcing distill | `fastvideo/training/wan_self_forcing_distillation_pipeline.py` | Causal streaming distillation |
|
||||
|
||||
---
|
||||
|
||||
## Key Infrastructure
|
||||
|
||||
### W&B Integration
|
||||
- **Tracker**: `fastvideo/training/trackers.py` — `WandbTracker` class
|
||||
- **New framework tracker**: `fastvideo/train/utils/tracking.py` — `build_tracker()`
|
||||
- **Env vars**: `WANDB_API_KEY`, `WANDB_BASE_URL`, `WANDB_MODE`
|
||||
|
||||
### Parallelism
|
||||
- **SP** (Sequence Parallel): splits video frames across GPUs — `sp_size: N`
|
||||
- **TP** (Tensor Parallel): splits model layers across GPUs — `tp_size: N`
|
||||
- Typical configs: SP=2–8, TP=1–2
|
||||
|
||||
---
|
||||
|
||||
## Evaluation (for training runs)
|
||||
|
||||
Read `.agents/memory/evaluation-registry/README.md` for the full metric catalog.
|
||||
|
||||
**Quick summary for training agents:**
|
||||
| Metric | When to Use | Trust |
|
||||
|--------|-------------|-------|
|
||||
| **Loss trajectory** | Every run, real-time from W&B | Medium |
|
||||
| **SSIM** | When comparing against reference outputs | High |
|
||||
| **FVD** | For benchmarking model quality (`benchmarks/fvd/`) | High |
|
||||
| **LPIPS** | LoRA merge validation | Medium |
|
||||
| **Human preference** | Major checkpoints | Highest |
|
||||
|
||||
---
|
||||
|
||||
## Common Workflows
|
||||
|
||||
| Task | Skill / SOP |
|
||||
|------|-------------|
|
||||
| Launch a training run | `.agents/skills/launch-experiment/SKILL.md` |
|
||||
| Monitor a running experiment | `.agents/skills/monitor-experiment/SKILL.md` |
|
||||
| Summarize final results | `.agents/skills/summarize-run/SKILL.md` |
|
||||
| Full experiment lifecycle | `.agents/workflows/experiment-lifecycle.md` |
|
||||
| Capture lessons from failures | `.agents/workflows/lesson-capture.md` |
|
||||
|
||||
---
|
||||
|
||||
## World Model–Specific Concepts
|
||||
|
||||
### Action Injection (MatrixGame)
|
||||
The MatrixGame pipeline adds **action modules** to each DiT block, enabling
|
||||
frame-level mouse/keyboard input conditioning. The action sequence is injected
|
||||
per-frame alongside the latent video tokens.
|
||||
|
||||
### Causal Architecture
|
||||
For streaming generation, the model uses **causal attention** (each frame only
|
||||
attends to previous frames). This enables auto-regressive chunk-by-chunk
|
||||
generation — critical for real-time interactive world models.
|
||||
|
||||
### Self-Forcing Distillation
|
||||
A **data-free** distillation method where the student model is trained to
|
||||
generate coherent video sequences by being forced to use its own previous
|
||||
outputs (rather than ground-truth) as context. This produces models robust to
|
||||
their own error accumulation during long auto-regressive generation.
|
||||
|
||||
### DMD Distillation (Distribution Matching Distillation)
|
||||
Reduces inference steps from ~50 to 3–4 by training a student model to match
|
||||
the output distribution of the teacher model. Uses a critic network to estimate
|
||||
distribution divergence.
|
||||
|
||||
### Diffusion-Forcing SFT (DFSFT)
|
||||
Supervised finetuning with **inhomogeneous timesteps** across chunks — each
|
||||
chunk in a causal sequence can have a different noise level, training the model
|
||||
to handle mixed-fidelity contexts.
|
||||
@@ -1,57 +0,0 @@
|
||||
---
|
||||
name: <skill-name>
|
||||
description: <one-line description — Codex uses this for implicit invocation matching>
|
||||
---
|
||||
|
||||
# <Skill Name>
|
||||
|
||||
## Purpose
|
||||
<Why this skill exists and when to use it.>
|
||||
|
||||
## Prerequisites
|
||||
- <What must be true before using this skill>
|
||||
|
||||
## Inputs
|
||||
| Parameter | Required | Description |
|
||||
|-----------|----------|-------------|
|
||||
| `param1` | Yes | ... |
|
||||
|
||||
## Steps
|
||||
|
||||
1. **Step 1 title**
|
||||
- Detail...
|
||||
|
||||
2. **Step 2 title**
|
||||
- Detail...
|
||||
|
||||
## Outputs
|
||||
- <What this skill produces>
|
||||
|
||||
## Example Usage
|
||||
|
||||
```
|
||||
<Example invocation or prompt snippet>
|
||||
```
|
||||
|
||||
## References
|
||||
- <Links to relevant files in the codebase>
|
||||
|
||||
---
|
||||
|
||||
## Folder Structure
|
||||
|
||||
Each skill lives in its own directory under `.agents/skills/`:
|
||||
|
||||
```
|
||||
.agents/skills/<skill-name>/
|
||||
├── SKILL.md # Required: instructions + metadata (this file)
|
||||
├── scripts/ # Optional: executable helper scripts
|
||||
├── references/ # Optional: documentation, papers
|
||||
└── assets/ # Optional: templates, resources
|
||||
```
|
||||
|
||||
After creating a new skill, add an entry to `.agents/skills/index.jsonl`:
|
||||
|
||||
```json
|
||||
{"name": "<skill-name>", "description": "<description>", "path": "<skill-name>/SKILL.md", "status": "draft", "trust": "low"}
|
||||
```
|
||||
@@ -1,128 +0,0 @@
|
||||
---
|
||||
name: evaluate-video-quality
|
||||
description: Evaluate generated video quality using available metrics (SSIM, loss trajectory, caption consistency)
|
||||
---
|
||||
|
||||
# Evaluate Video Quality
|
||||
|
||||
## Purpose
|
||||
Assess the quality of videos generated by a training run. Combines multiple
|
||||
signals to give a holistic quality assessment. This skill is **evolving** —
|
||||
new metrics will be added as they are developed.
|
||||
|
||||
## Prerequisites
|
||||
- Generated videos available locally or via W&B artifacts.
|
||||
- For SSIM: reference videos from official implementations.
|
||||
- For caption consistency: LLM access (optional, stub for now).
|
||||
|
||||
## Inputs
|
||||
|
||||
| Parameter | Required | Description |
|
||||
|-----------|----------|-------------|
|
||||
| `video_paths` | Yes | List of paths to generated videos |
|
||||
| `reference_paths` | No | Paths to reference videos (for SSIM) |
|
||||
| `prompts` | No | Prompts used to generate videos (for caption check) |
|
||||
| `loss_summary` | No | Path to W&B summary JSON (for loss trajectory) |
|
||||
| `metrics` | No | Which metrics to run (default: all available) |
|
||||
|
||||
## Available Metrics
|
||||
|
||||
Check `.agents/memory/evaluation-registry/README.md` for the current catalog.
|
||||
|
||||
### SSIM (Active)
|
||||
|
||||
Leverages the existing infrastructure in `fastvideo/tests/ssim/`.
|
||||
|
||||
```bash
|
||||
pytest fastvideo/tests/ssim/ -vs --video-path <generated> --reference-path <reference>
|
||||
```
|
||||
|
||||
Or use the SSIM utility directly:
|
||||
|
||||
```python
|
||||
from fastvideo.tests.ssim.ssim_utils import compute_ssim
|
||||
score = compute_ssim(generated_video, reference_video)
|
||||
# score > 0.85 is typically "acceptable"
|
||||
```
|
||||
|
||||
**Interpretation**:
|
||||
| SSIM Range | Quality |
|
||||
|------------|---------|
|
||||
| > 0.90 | Excellent — very close to reference |
|
||||
| 0.80–0.90 | Good — acceptable for most uses |
|
||||
| 0.70–0.80 | Fair — noticeable differences |
|
||||
| < 0.70 | Poor — significant quality issues |
|
||||
|
||||
### Loss Trajectory (Active)
|
||||
|
||||
Analyze the loss curve shape from W&B summary:
|
||||
|
||||
```python
|
||||
import json
|
||||
with open(loss_summary_path) as f:
|
||||
summary = json.load(f)
|
||||
|
||||
final_loss = summary["train_loss"]
|
||||
runtime = summary["_runtime"]
|
||||
steps = summary["_step"]
|
||||
```
|
||||
|
||||
**Early-stage heuristics** (first 500 steps):
|
||||
- Loss should be decreasing (even slightly).
|
||||
- Grad norm should be stable (no wild oscillations).
|
||||
- If loss is flat or increasing, flag for review.
|
||||
|
||||
### Caption Consistency (Draft — Not Yet Calibrated)
|
||||
|
||||
Use an LLM to evaluate whether the video content matches the input prompt.
|
||||
|
||||
```
|
||||
Prompt: "A golden retriever playing in the snow"
|
||||
Video: <path>
|
||||
|
||||
Score the video on:
|
||||
1. Object presence (is there a golden retriever?)
|
||||
2. Action accuracy (is it playing?)
|
||||
3. Environment match (is there snow?)
|
||||
4. Overall coherence (does it look natural?)
|
||||
|
||||
Each 1-5, total /20.
|
||||
```
|
||||
|
||||
> ⚠️ This metric is in **draft** status. Results should not be treated as
|
||||
> ground truth until calibrated against human judgments.
|
||||
|
||||
## Steps
|
||||
|
||||
1. **Identify available metrics** — Check `.agents/memory/evaluation-registry/README.md`.
|
||||
2. **Run each metric** — Collect scores.
|
||||
3. **Aggregate** — Produce a combined quality report.
|
||||
4. **Log** — Update the experiment journal with quality results.
|
||||
|
||||
## Outputs
|
||||
|
||||
```markdown
|
||||
## Video Quality Report: <experiment_name>
|
||||
|
||||
| Metric | Score | Threshold | Status |
|
||||
|--------|-------|-----------|--------|
|
||||
| SSIM (avg) | 0.87 | > 0.80 | ✅ Pass |
|
||||
| Loss trajectory | decreasing | decreasing | ✅ Pass |
|
||||
| Caption consistency | 16/20 | > 14/20 | ✅ Pass |
|
||||
|
||||
### Per-Video Scores
|
||||
| Video | SSIM | Caption |
|
||||
|-------|------|---------|
|
||||
| video_001.mp4 | 0.89 | 17/20 |
|
||||
| video_002.mp4 | 0.85 | 15/20 |
|
||||
```
|
||||
|
||||
## References
|
||||
- `fastvideo/tests/ssim/` — SSIM test infrastructure
|
||||
- `fastvideo/tests/training/Vanilla/test_training_loss.py` — loss comparison
|
||||
- `.agents/memory/evaluation-registry/README.md` — metric catalog
|
||||
|
||||
## Changelog
|
||||
| Date | Change |
|
||||
|------|--------|
|
||||
| 2026-03-02 | Initial version with SSIM, loss trajectory, caption consistency stub |
|
||||
@@ -1,94 +0,0 @@
|
||||
---
|
||||
name: index-related-work
|
||||
description: Ingest a paper or repository into the related work index
|
||||
---
|
||||
|
||||
# Index Related Work
|
||||
|
||||
## Purpose
|
||||
Create a structured summary of a related paper, repository, or blog post and
|
||||
add it to `.agents/memory/related-work/` for future reference. This builds the
|
||||
agent's knowledge base for making informed decisions about training, evaluation,
|
||||
and architecture choices.
|
||||
|
||||
## Prerequisites
|
||||
- Access to the paper/repo (URL, PDF, or local clone).
|
||||
|
||||
## Inputs
|
||||
|
||||
| Parameter | Required | Description |
|
||||
|-----------|----------|-------------|
|
||||
| `source` | Yes | URL, citation, or local path |
|
||||
| `type` | Yes | `paper`, `repo`, or `blog` |
|
||||
| `tags` | No | List of tags (default: inferred from content) |
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Extract key information
|
||||
|
||||
For **papers**: Read abstract, method section, experimental setup, and results.
|
||||
For **repos**: Read README, key source files, and training scripts.
|
||||
For **blogs**: Read the full post.
|
||||
|
||||
Focus on:
|
||||
- What problem does it solve?
|
||||
- What architecture/technique is used?
|
||||
- How does it relate to FastVideo's approach?
|
||||
|
||||
### 2. Create the index entry
|
||||
|
||||
Write to `.agents/memory/related-work/<slug>.md`:
|
||||
|
||||
```markdown
|
||||
---
|
||||
title: <title>
|
||||
source: <URL or citation>
|
||||
type: paper | repo | blog
|
||||
date_indexed: <ISO-8601>
|
||||
tags: [world-model, distillation, evaluation, ...]
|
||||
---
|
||||
|
||||
## Summary
|
||||
<1-2 paragraph summary.>
|
||||
|
||||
## Key Differences from FastVideo
|
||||
- <comparison points>
|
||||
|
||||
## Actionable Insights
|
||||
- <what we could adopt or adapt>
|
||||
```
|
||||
|
||||
### 3. Update the catalog
|
||||
|
||||
If `.agents/memory/related-work/_catalog.md` exists, append the new entry.
|
||||
If not, create it:
|
||||
|
||||
```markdown
|
||||
# Related Work Catalog
|
||||
|
||||
| Slug | Title | Type | Tags | Date |
|
||||
|------|-------|------|------|------|
|
||||
| <slug> | <title> | <type> | <tags> | <date> |
|
||||
```
|
||||
|
||||
## Outputs
|
||||
- New file in `.agents/memory/related-work/<slug>.md`.
|
||||
- Updated catalog.
|
||||
|
||||
## Example Usage
|
||||
|
||||
```
|
||||
Index the Self-Forcing paper:
|
||||
|
||||
source: https://arxiv.org/abs/2406.xxxxx
|
||||
type: paper
|
||||
tags: [world-model, self-forcing, distillation]
|
||||
```
|
||||
|
||||
## References
|
||||
- `.agents/memory/related-work/README.md` — schema documentation
|
||||
|
||||
## Changelog
|
||||
| Date | Change |
|
||||
|------|--------|
|
||||
| 2026-03-02 | Initial version |
|
||||
@@ -1,7 +0,0 @@
|
||||
{"name": "launch-experiment", "description": "Generate and execute a training launch command for FastVideo models", "path": "launch-experiment/SKILL.md", "status": "draft", "trust": "low"}
|
||||
{"name": "monitor-experiment", "description": "Poll a running W&B training run for progress and emit structured alerts", "path": "monitor-experiment/SKILL.md", "status": "draft", "trust": "low"}
|
||||
{"name": "summarize-run", "description": "Extract a W&B run summary into a structured experiment report", "path": "summarize-run/SKILL.md", "status": "draft", "trust": "low"}
|
||||
{"name": "log-experiment", "description": "Append or update an experiment entry in the experiment journal", "path": "log-experiment/SKILL.md", "status": "draft", "trust": "low"}
|
||||
{"name": "evaluate-video-quality", "description": "Evaluate generated video quality using available metrics (SSIM, loss trajectory, caption consistency)", "path": "evaluate-video-quality/SKILL.md", "status": "draft", "trust": "low"}
|
||||
{"name": "index-related-work", "description": "Ingest a paper or repository into the related work index", "path": "index-related-work/SKILL.md", "status": "draft", "trust": "low"}
|
||||
{"name": "search-related-work", "description": "Query the related work index for relevant papers, repos, or comparisons", "path": "search-related-work/SKILL.md", "status": "draft", "trust": "low"}
|
||||
@@ -1,127 +0,0 @@
|
||||
---
|
||||
name: launch-experiment
|
||||
description: Generate and execute a training launch command for FastVideo models
|
||||
---
|
||||
|
||||
# Launch Experiment
|
||||
|
||||
## Purpose
|
||||
Construct a fully-specified `torchrun` training command for a FastVideo model
|
||||
given a target pipeline, dataset, and hyperparameter overrides. This skill
|
||||
automates the boilerplate of setting environment variables, picking the right
|
||||
entrypoint, and applying defaults from the closest example script.
|
||||
|
||||
## Prerequisites
|
||||
- The repo is cloned and `fastvideo` is installed (`uv pip install -e .[dev]`).
|
||||
- Dataset is preprocessed (see `docs/training/data_preprocess.md`).
|
||||
- `WANDB_API_KEY` is set in the environment (or `WANDB_MODE=offline` for local).
|
||||
- GPU resources are available (multi-GPU requires NCCL).
|
||||
|
||||
## Inputs
|
||||
|
||||
| Parameter | Required | Description |
|
||||
|-----------|----------|-------------|
|
||||
| `pipeline` | Yes | Training pipeline type: `finetune`, `distill-dmd`, `self-forcing`, `lora`, `consistency` |
|
||||
| `model` | Yes | Model family: `wan-t2v-1.3B`, `wan-i2v-14B`, `ltx2`, `matrixgame` |
|
||||
| `data_path` | Yes | Path to preprocessed dataset (parquet) |
|
||||
| `num_gpus` | Yes | Number of GPUs |
|
||||
| `overrides` | No | Dict of hyperparameter overrides (any CLI arg) |
|
||||
| `output_dir` | No | Output directory (default: `outputs/<model>_<pipeline>`) |
|
||||
| `run_name` | No | W&B run name (default: auto-generated) |
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Identify the training entrypoint
|
||||
|
||||
| Pipeline | Entrypoint |
|
||||
|----------|-----------|
|
||||
| `finetune` (Wan T2V) | `fastvideo/training/wan_training_pipeline.py` |
|
||||
| `finetune` (Wan I2V) | `fastvideo/training/wan_i2v_training_pipeline.py` |
|
||||
| `finetune` (LTX-2) | `fastvideo/training/ltx2_training_pipeline.py` |
|
||||
| `finetune` (MatrixGame) | `fastvideo/training/matrixgame_training_pipeline.py` |
|
||||
| `distill-dmd` | `fastvideo/training/wan_distillation_pipeline.py` |
|
||||
| `self-forcing` | `fastvideo/training/wan_self_forcing_distillation_pipeline.py` |
|
||||
|
||||
### 2. Resolve default hyperparameters
|
||||
|
||||
Find the closest example script in `examples/training/` for the model:
|
||||
|
||||
| Model | Example Script Directory |
|
||||
|-------|-------------------------|
|
||||
| `wan-t2v-1.3B` | `examples/training/finetune/wan_t2v_1.3B/crush_smol/` |
|
||||
| `wan-i2v-14B` | `examples/training/finetune/wan_i2v_14B_480p/crush_smol/` |
|
||||
| `ltx2` | `examples/training/finetune/ltx2/` |
|
||||
| `matrixgame` | `examples/training/finetune/MatrixGame2.0/` |
|
||||
| `distill-dmd` | `scripts/distill/v1_distill_dmd_wan.sh` |
|
||||
|
||||
Read the script to extract default values for:
|
||||
- `--learning_rate`, `--train_batch_size`, `--sp_size`, `--tp_size`
|
||||
- `--num_latent_t`, `--num_height`, `--num_width`, `--num_frames`
|
||||
- `--gradient_accumulation_steps`, `--max_train_steps`
|
||||
- `--mixed_precision`, `--weight_decay`, `--max_grad_norm`
|
||||
- `--validation_steps`, `--validation_sampling_steps`
|
||||
|
||||
### 3. Set environment variables
|
||||
|
||||
```bash
|
||||
export WANDB_API_KEY="${WANDB_API_KEY}"
|
||||
export WANDB_BASE_URL="https://api.wandb.ai"
|
||||
export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
|
||||
export TOKENIZERS_PARALLELISM=false
|
||||
export TRITON_CACHE_DIR=/tmp/triton_cache
|
||||
```
|
||||
|
||||
### 4. Construct the torchrun command
|
||||
|
||||
```bash
|
||||
torchrun --nnodes 1 --nproc_per_node <num_gpus> \
|
||||
<entrypoint> \
|
||||
--pretrained_model_name_or_path <model_hf_id> \
|
||||
--data_path "<data_path>" \
|
||||
--output_dir "<output_dir>" \
|
||||
--wandb_run_name "<run_name>" \
|
||||
--tracker_project_name "<project_name>" \
|
||||
--log_validation \
|
||||
<...all hyperparameters...>
|
||||
```
|
||||
|
||||
### 5. Log to experiment journal
|
||||
|
||||
After launching, append an entry to `.agents/memory/experiment-journal/README.md`:
|
||||
|
||||
```markdown
|
||||
## [YYYY-MM-DD] Experiment: <run_name>
|
||||
- **Hypothesis**: <user-provided or auto-generated>
|
||||
- **Config**: model=<model>, lr=<lr>, sp_size=<sp>, gpus=<n>, script=<entrypoint>
|
||||
- **W&B run**: <pending — will be updated by monitor skill>
|
||||
- **Status**: running
|
||||
```
|
||||
|
||||
## Outputs
|
||||
- A ready-to-execute shell command.
|
||||
- An experiment journal entry.
|
||||
|
||||
## Example Usage
|
||||
|
||||
```
|
||||
Launch a Wan T2V 1.3B finetune on 4 GPUs with lr=5e-5 and max_train_steps=1000:
|
||||
|
||||
pipeline: finetune
|
||||
model: wan-t2v-1.3B
|
||||
data_path: data/crush_smol_preprocessed/
|
||||
num_gpus: 4
|
||||
overrides:
|
||||
learning_rate: 5e-5
|
||||
max_train_steps: 1000
|
||||
```
|
||||
|
||||
## References
|
||||
- `examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh`
|
||||
- `scripts/distill/v1_distill_dmd_wan.sh`
|
||||
- `docs/training/finetune.md` (training arguments table)
|
||||
- `fastvideo/training/trackers.py` (tracker initialization)
|
||||
|
||||
## Changelog
|
||||
| Date | Change |
|
||||
|------|--------|
|
||||
| 2026-03-02 | Initial version |
|
||||
@@ -1,87 +0,0 @@
|
||||
---
|
||||
name: log-experiment
|
||||
description: Append or update an experiment entry in the experiment journal
|
||||
---
|
||||
|
||||
# Log Experiment
|
||||
|
||||
## Purpose
|
||||
Create or update an entry in `.agents/memory/experiment-journal/README.md` to maintain
|
||||
a living record of all experiments and their outcomes.
|
||||
|
||||
## Prerequisites
|
||||
- `.agents/memory/experiment-journal/README.md` exists.
|
||||
|
||||
## Inputs
|
||||
|
||||
| Parameter | Required | Description |
|
||||
|-----------|----------|-------------|
|
||||
| `name` | Yes | Experiment name / identifier |
|
||||
| `hypothesis` | No | What you expected to learn |
|
||||
| `config` | Yes | Key config: model, lr, sp_size, gpus, script |
|
||||
| `wandb_run` | No | W&B run ID or URL |
|
||||
| `duration` | No | Total wall time |
|
||||
| `metrics` | No | Key metrics dict (loss, step_time, grad_norm) |
|
||||
| `checkpoint` | No | Path to checkpoint |
|
||||
| `insight` | No | What was learned |
|
||||
| `status` | Yes | `running`, `completed`, `failed`, `abandoned` |
|
||||
| `lessons` | No | Paths to related lesson files |
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Check for existing entry
|
||||
|
||||
Search `.agents/memory/experiment-journal/README.md` for an entry with the same name.
|
||||
If found, update it instead of creating a duplicate.
|
||||
|
||||
### 2. Format the entry
|
||||
|
||||
```markdown
|
||||
## [YYYY-MM-DD] Experiment: <name>
|
||||
- **Hypothesis**: <hypothesis or "N/A">
|
||||
- **Config**: model=<model>, lr=<lr>, sp_size=<sp>, gpus=<n>, script=<script>
|
||||
- **W&B run**: <wandb_run or "pending">
|
||||
- **Duration**: <duration or "in progress">
|
||||
- **Key metrics**: loss=<loss>, step_time=<step_time>, grad_norm=<grad_norm>
|
||||
- **Checkpoint**: <checkpoint or "N/A">
|
||||
- **Insight**: <insight or "pending">
|
||||
- **Status**: <status>
|
||||
- **Related lessons**: <lessons or "none">
|
||||
```
|
||||
|
||||
### 3. Insert at the top of the journal
|
||||
|
||||
New entries go at the top of the file (after the header), so the most recent
|
||||
experiments are always visible first.
|
||||
|
||||
### 4. Warn on duplicates
|
||||
|
||||
If a similar experiment name exists with `status: completed`, warn that this
|
||||
may be a repeat. If it's `status: running`, assume this is an update.
|
||||
|
||||
## Outputs
|
||||
- Updated `.agents/memory/experiment-journal/README.md`.
|
||||
|
||||
## Example Usage
|
||||
|
||||
```
|
||||
Log a completed experiment:
|
||||
|
||||
name: wan-t2v-finetune-lr5e5-sp4
|
||||
config: model=wan-t2v-1.3B, lr=5e-5, sp_size=4, gpus=4
|
||||
wandb_run: fastvideo/training/run_abc123
|
||||
duration: 2h 15m
|
||||
metrics: {loss: 0.065, step_time: 2.3, grad_norm: 0.35}
|
||||
checkpoint: outputs/wan_finetune/checkpoint-1000
|
||||
insight: LR 5e-5 converges 30% faster than 1e-5 with no quality loss
|
||||
status: completed
|
||||
```
|
||||
|
||||
## References
|
||||
- `.agents/memory/experiment-journal/README.md` — journal file
|
||||
- `.agents/workflows/experiment-lifecycle.md` — when to log
|
||||
|
||||
## Changelog
|
||||
| Date | Change |
|
||||
|------|--------|
|
||||
| 2026-03-02 | Initial version |
|
||||
@@ -1,134 +0,0 @@
|
||||
---
|
||||
name: monitor-experiment
|
||||
description: Poll a running W&B training run for progress and emit structured alerts
|
||||
---
|
||||
|
||||
# Monitor Experiment
|
||||
|
||||
## Purpose
|
||||
Continuously (or on-demand) check a running experiment's W&B metrics and emit
|
||||
alerts for anomalies. Supports the "30-minute quality check" paradigm: after
|
||||
the first 30 minutes of a long training run, produce a checkpoint quality
|
||||
report before committing more resources.
|
||||
|
||||
## Prerequisites
|
||||
- `WANDB_API_KEY` is set in the environment.
|
||||
- The experiment is actively logging to W&B (not in `WANDB_MODE=offline`).
|
||||
- For offline mode: read from local `wandb-summary.json` instead.
|
||||
|
||||
## Inputs
|
||||
|
||||
| Parameter | Required | Description |
|
||||
|-----------|----------|-------------|
|
||||
| `run_id` | Yes* | W&B run ID (e.g., `entity/project/run_id`) |
|
||||
| `output_dir` | Yes* | Local output directory (for offline mode fallback) |
|
||||
| `poll_interval` | No | Seconds between polls (default: 60) |
|
||||
| `alert_on` | No | List of alert conditions to enable (default: all) |
|
||||
|
||||
\* One of `run_id` or `output_dir` is required.
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Connect to the run
|
||||
|
||||
**Online mode** (preferred):
|
||||
|
||||
```python
|
||||
import wandb
|
||||
api = wandb.Api()
|
||||
run = api.run("<run_id>")
|
||||
```
|
||||
|
||||
**Offline fallback**:
|
||||
|
||||
```python
|
||||
import json
|
||||
summary_path = f"{output_dir}/tracker/wandb/latest-run/files/wandb-summary.json"
|
||||
with open(summary_path) as f:
|
||||
summary = json.load(f)
|
||||
```
|
||||
|
||||
### 2. Track key metrics
|
||||
|
||||
| Metric | W&B Key | Description |
|
||||
|--------|---------|-------------|
|
||||
| Training loss | `train_loss` | Primary training loss |
|
||||
| Gradient norm | `grad_norm` | Gradient magnitude |
|
||||
| Step time | `step_time` | Wall-clock seconds per step |
|
||||
| Learning rate | `learning_rate` | Current LR |
|
||||
| Avg step time | `avg_step_time` | Running average step time |
|
||||
| Validation videos | `validation_videos_*` | Generated validation samples |
|
||||
|
||||
### 3. Evaluate alert conditions
|
||||
|
||||
| Alert | Condition | Severity |
|
||||
|-------|-----------|----------|
|
||||
| **Loss spike** | `current_loss > 3 × rolling_avg_loss` | 🔴 Critical |
|
||||
| **NaN/Inf gradient** | `grad_norm` is NaN or Inf | 🔴 Critical |
|
||||
| **Step time regression** | `step_time > 2 × baseline_step_time` | 🟡 Warning |
|
||||
| **No progress** | No new W&B logs for > 10 minutes | 🟡 Warning |
|
||||
| **Loss plateau** | Loss change < 1% over last 100 steps | 🟢 Info |
|
||||
|
||||
### 4. Emit structured status
|
||||
|
||||
Output format (agent-consumable):
|
||||
|
||||
```json
|
||||
{
|
||||
"run_id": "...",
|
||||
"step": 500,
|
||||
"metrics": {
|
||||
"train_loss": 0.078,
|
||||
"grad_norm": 0.41,
|
||||
"step_time": 2.5,
|
||||
"learning_rate": 1e-6
|
||||
},
|
||||
"alerts": [
|
||||
{"type": "loss_spike", "severity": "critical", "message": "Loss jumped to 0.45 (avg: 0.08)"}
|
||||
],
|
||||
"status": "running"
|
||||
}
|
||||
```
|
||||
|
||||
### 5. 30-Minute Quality Check
|
||||
|
||||
After the first 30 minutes of wall-clock time:
|
||||
1. Summarize the loss curve shape (decreasing? at what rate?).
|
||||
2. Check if validation videos have been generated.
|
||||
3. Report step count, loss at start vs. current, and estimated time to completion.
|
||||
4. Produce a go/no-go recommendation.
|
||||
|
||||
```markdown
|
||||
## 30-Minute Check: <run_name>
|
||||
- **Steps completed**: 150
|
||||
- **Loss**: 0.12 → 0.08 (↓ 33%)
|
||||
- **Grad norm**: stable at ~0.4
|
||||
- **Step time**: 2.5s/step (consistent)
|
||||
- **Validation videos**: 5 generated at step 100
|
||||
- **Recommendation**: ✅ Continue — loss is decreasing normally
|
||||
```
|
||||
|
||||
## Outputs
|
||||
- Structured JSON status updates.
|
||||
- Alert messages for anomalous conditions.
|
||||
- 30-minute checkpoint quality report.
|
||||
|
||||
## Example Usage
|
||||
|
||||
```
|
||||
Monitor W&B run "fastvideo/Wan_distillation/abc123":
|
||||
|
||||
run_id: fastvideo/Wan_distillation/abc123
|
||||
poll_interval: 120
|
||||
alert_on: [loss_spike, nan_gradient, step_time_regression]
|
||||
```
|
||||
|
||||
## References
|
||||
- `fastvideo/training/trackers.py` — `WandbTracker` implementation
|
||||
- `fastvideo/tests/training/Vanilla/test_training_loss.py` — how summaries are compared
|
||||
- `fastvideo/tests/training/Vanilla/a40_reference_wandb_summary.json` — reference summary format
|
||||
|
||||
## Changelog
|
||||
| Date | Change |
|
||||
|------|--------|
|
||||
| 2026-03-02 | Initial version |
|
||||
@@ -1,82 +0,0 @@
|
||||
---
|
||||
name: search-related-work
|
||||
description: Query the related work index for relevant papers, repos, or comparisons
|
||||
---
|
||||
|
||||
# Search Related Work
|
||||
|
||||
## Purpose
|
||||
Search through `.agents/memory/related-work/` to find indexed papers, repos,
|
||||
or blog posts relevant to a query. Use this when you need to understand how
|
||||
other work compares to FastVideo's approach, or when looking for techniques
|
||||
to adopt.
|
||||
|
||||
## Prerequisites
|
||||
- The related work index has entries (`.agents/memory/related-work/*.md`).
|
||||
|
||||
## Inputs
|
||||
|
||||
| Parameter | Required | Description |
|
||||
|-----------|----------|-------------|
|
||||
| `query` | Yes | Natural language query |
|
||||
| `tags` | No | Filter by tags (e.g., `[distillation, evaluation]`) |
|
||||
| `type` | No | Filter by type (`paper`, `repo`, `blog`) |
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Search the index
|
||||
|
||||
Use grep-based search through `.agents/memory/related-work/`:
|
||||
|
||||
```bash
|
||||
# Search by content
|
||||
grep -rl "<query>" .agents/memory/related-work/
|
||||
|
||||
# Search by tags (in frontmatter)
|
||||
grep -l "tags:.*<tag>" .agents/memory/related-work/*.md
|
||||
```
|
||||
|
||||
### 2. Rank results
|
||||
|
||||
For each matching file:
|
||||
1. Read the file.
|
||||
2. Score relevance to the query based on:
|
||||
- Title match
|
||||
- Tag match
|
||||
- Content match (summary, differences, insights)
|
||||
3. Return top results.
|
||||
|
||||
### 3. Format output
|
||||
|
||||
```markdown
|
||||
## Related Work Search: "<query>"
|
||||
|
||||
### 1. <Title> (relevance: high)
|
||||
- **Source**: <URL>
|
||||
- **Tags**: <tags>
|
||||
- **Key insight**: <most relevant excerpt>
|
||||
- **File**: `.agents/memory/related-work/<slug>.md`
|
||||
|
||||
### 2. <Title> (relevance: medium)
|
||||
...
|
||||
```
|
||||
|
||||
## Outputs
|
||||
- Ranked list of relevant related work entries with excerpts.
|
||||
|
||||
## Example Usage
|
||||
|
||||
```
|
||||
Search for work related to video quality evaluation metrics:
|
||||
|
||||
query: "video generation quality evaluation metrics"
|
||||
tags: [evaluation]
|
||||
```
|
||||
|
||||
## References
|
||||
- `.agents/memory/related-work/README.md` — index schema
|
||||
|
||||
## Changelog
|
||||
| Date | Change |
|
||||
|------|--------|
|
||||
| 2026-03-02 | Initial version |
|
||||
@@ -1,137 +0,0 @@
|
||||
---
|
||||
name: summarize-run
|
||||
description: Extract a W&B run summary into a structured experiment report
|
||||
---
|
||||
|
||||
# Summarize Run
|
||||
|
||||
## Purpose
|
||||
After a training run completes (or at any checkpoint), extract key metrics from
|
||||
the W&B run summary and produce a structured markdown report. Supports both
|
||||
online (W&B API) and offline (local `wandb-summary.json`) modes.
|
||||
|
||||
## Prerequisites
|
||||
- Run has completed or reached a checkpoint with a saved summary.
|
||||
- For online: `WANDB_API_KEY` set in environment.
|
||||
- For offline: access to `<output_dir>/tracker/wandb/latest-run/files/wandb-summary.json`.
|
||||
|
||||
## Inputs
|
||||
|
||||
| Parameter | Required | Description |
|
||||
|-----------|----------|-------------|
|
||||
| `run_id` | Yes* | W&B run ID for online access |
|
||||
| `output_dir` | Yes* | Local output dir for offline access |
|
||||
| `reference_run` | No | Path to reference `wandb-summary.json` for comparison |
|
||||
| `experiment_name` | No | Name for the journal entry (default: from W&B) |
|
||||
|
||||
\* One of `run_id` or `output_dir` is required.
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Load run summary
|
||||
|
||||
**Online**:
|
||||
|
||||
```python
|
||||
import wandb
|
||||
api = wandb.Api()
|
||||
run = api.run("<run_id>")
|
||||
summary = dict(run.summary)
|
||||
config = dict(run.config)
|
||||
```
|
||||
|
||||
**Offline** (existing codebase pattern from `fastvideo/tests/training/`):
|
||||
|
||||
```python
|
||||
import json
|
||||
summary_path = f"{output_dir}/tracker/wandb/latest-run/files/wandb-summary.json"
|
||||
with open(summary_path) as f:
|
||||
summary = json.load(f)
|
||||
```
|
||||
|
||||
### 2. Extract key fields
|
||||
|
||||
| Field | Source | Description |
|
||||
|-------|--------|-------------|
|
||||
| `train_loss` | `summary["train_loss"]` | Final training loss |
|
||||
| `avg_step_time` | `summary["avg_step_time"]` | Average seconds per step |
|
||||
| `step_time` | `summary["step_time"]` | Last step time |
|
||||
| `grad_norm` | `summary["grad_norm"]` | Final gradient norm |
|
||||
| `learning_rate` | `summary["learning_rate"]` | Final LR |
|
||||
| `_step` | `summary["_step"]` | Total steps completed |
|
||||
| `_runtime` | `summary["_runtime"]` | Total wall-clock seconds |
|
||||
| `validation_videos_*` | `summary[key]` | Validation video artifacts |
|
||||
|
||||
### 3. Compare against reference (optional)
|
||||
|
||||
Follow the pattern in `fastvideo/tests/training/Vanilla/test_training_loss.py`:
|
||||
|
||||
```python
|
||||
# Fields to compare
|
||||
compare_fields = ["train_loss", "grad_norm", "avg_step_time"]
|
||||
tolerance = 0.05 # 5% relative tolerance
|
||||
|
||||
for field in compare_fields:
|
||||
ref_val = reference_summary[field]
|
||||
cur_val = summary[field]
|
||||
diff_pct = abs(cur_val - ref_val) / abs(ref_val) * 100
|
||||
status = "✅" if diff_pct < tolerance * 100 else "⚠️"
|
||||
print(f"{status} {field}: {cur_val:.4f} (ref: {ref_val:.4f}, diff: {diff_pct:.1f}%)")
|
||||
```
|
||||
|
||||
### 4. Generate report
|
||||
|
||||
```markdown
|
||||
# Run Summary: <experiment_name>
|
||||
|
||||
| Metric | Value | Reference | Diff |
|
||||
|--------|-------|-----------|------|
|
||||
| Train Loss | 0.0788 | 0.0800 | -1.5% ✅ |
|
||||
| Avg Step Time | 2.81s | 2.80s | +0.4% ✅ |
|
||||
| Grad Norm | 0.408 | 0.410 | -0.5% ✅ |
|
||||
| Total Steps | 500 | — | — |
|
||||
| Wall Time | 23m 30s | — | — |
|
||||
|
||||
## Configuration
|
||||
- Model: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
|
||||
- Learning Rate: 1e-6
|
||||
- Batch Size: 1
|
||||
- GPUs: 8 × (SP=1, TP=1)
|
||||
- Mixed Precision: bf16
|
||||
|
||||
## Validation Videos
|
||||
<list of validation video paths if available>
|
||||
|
||||
## Notes
|
||||
<any observations or anomalies>
|
||||
```
|
||||
|
||||
### 5. Update experiment journal
|
||||
|
||||
Append or update the experiment's entry in `.agents/memory/experiment-journal/README.md`
|
||||
with the final metrics and status.
|
||||
|
||||
## Outputs
|
||||
- Structured markdown report.
|
||||
- Updated experiment journal entry.
|
||||
|
||||
## Example Usage
|
||||
|
||||
```
|
||||
Summarize the run in output directory "outputs/wan_finetune":
|
||||
|
||||
output_dir: outputs/wan_finetune
|
||||
reference_run: fastvideo/tests/training/Vanilla/a40_reference_wandb_summary.json
|
||||
experiment_name: wan-t2v-finetune-lr1e6
|
||||
```
|
||||
|
||||
## References
|
||||
- `fastvideo/tests/training/Vanilla/test_training_loss.py` — reference comparison pattern
|
||||
- `fastvideo/tests/training/Vanilla/a40_reference_wandb_summary.json` — example summary
|
||||
- `fastvideo/tests/training/lora/test_lora_training.py` — LoRA summary comparison
|
||||
- `fastvideo/training/trackers.py` — tracker summary generation
|
||||
|
||||
## Changelog
|
||||
| Date | Change |
|
||||
|------|--------|
|
||||
| 2026-03-02 | Initial version |
|
||||
@@ -1,54 +0,0 @@
|
||||
---
|
||||
description: How to develop, validate, and register a new evaluation metric
|
||||
---
|
||||
|
||||
# Evaluation Development SOP
|
||||
|
||||
Standard procedure for adding new video quality evaluation metrics to the
|
||||
FastVideo agent toolkit.
|
||||
|
||||
## When to Use
|
||||
|
||||
- You need a metric that doesn't exist in `.agents/memory/evaluation-registry/README.md`.
|
||||
- An existing metric needs significant changes to its methodology.
|
||||
- You're exploring a new evaluation approach.
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Research
|
||||
|
||||
- Search `.agents/memory/related-work/` for existing evaluation approaches.
|
||||
- Check the `evaluation_registry.md` for current metrics and their limitations.
|
||||
- Review literature: FVD, CLIP-Score, human preference, etc.
|
||||
|
||||
### 2. Prototype
|
||||
|
||||
- Write a standalone script in `.agents/exploration/<metric-name>.md`.
|
||||
- Keep it simple: one script, minimal dependencies.
|
||||
- Test on a few known-good and known-bad video samples.
|
||||
|
||||
### 3. Validate
|
||||
|
||||
- **Known-good test**: Metric should score high on reference-quality videos.
|
||||
- **Known-bad test**: Metric should score low on degraded/unrelated videos.
|
||||
- **Sensitivity test**: Small quality differences should produce meaningful
|
||||
score differences.
|
||||
- Document thresholds and their justification.
|
||||
|
||||
### 4. Register
|
||||
|
||||
Update `.agents/memory/evaluation-registry/README.md`:
|
||||
- Add the metric with status `Active`.
|
||||
- Document location, thresholds, and trust level.
|
||||
|
||||
### 5. Integrate
|
||||
|
||||
Update `.agents/skills/evaluate-video-quality.md`:
|
||||
- Add the new metric as a section.
|
||||
- Include code examples and interpretation guide.
|
||||
|
||||
### 6. Document
|
||||
|
||||
- Move the exploration log content into the skill.
|
||||
- Clean up the exploration file or mark it as `promoted`.
|
||||
- If anything went wrong during development, create a lesson.
|
||||
@@ -1,47 +0,0 @@
|
||||
---
|
||||
description: When and how to log experiments in the experiment journal
|
||||
---
|
||||
|
||||
# Experiment Journaling SOP
|
||||
|
||||
Ensures every experiment is properly recorded with context and outcomes.
|
||||
|
||||
## When to Log
|
||||
|
||||
**Always.** Every experiment — even quick tests — should be journaled.
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Before Launch — Create Draft Entry
|
||||
|
||||
Use the `log-experiment` skill with `status: running`:
|
||||
- Include hypothesis and config.
|
||||
- Leave metrics, duration, and insight blank.
|
||||
|
||||
### 2. After 30-Minute Check — Update with Initial Metrics
|
||||
|
||||
Update the entry with:
|
||||
- Current loss and its trajectory direction.
|
||||
- Step time.
|
||||
- Number of validation videos generated.
|
||||
- Preliminary go/no-go assessment.
|
||||
|
||||
### 3. On Completion — Fill Final Entry
|
||||
|
||||
Update the entry with `status: completed`:
|
||||
- Final loss, grad norm, avg step time.
|
||||
- Total duration and steps.
|
||||
- Checkpoint path.
|
||||
- Key insight.
|
||||
|
||||
### 4. On Failure — Document Failure Mode
|
||||
|
||||
Update the entry with `status: failed`:
|
||||
- What went wrong (OOM, NaN, crash, etc.).
|
||||
- At what step the failure occurred.
|
||||
- Create a lesson in `.agents/lessons/` for non-trivial failures.
|
||||
|
||||
### 5. Cross-Reference
|
||||
|
||||
- Link related lessons: `**Related lessons**: .agents/lessons/<filename>.md`
|
||||
- Link related experiments: if this is a follow-up, reference the prior entry.
|
||||
@@ -1,87 +0,0 @@
|
||||
---
|
||||
description: End-to-end experiment lifecycle from hypothesis to lessons learned
|
||||
---
|
||||
|
||||
# Experiment Lifecycle SOP
|
||||
|
||||
Standard operating procedure for running ML training experiments on
|
||||
FastVideo-WorldModel. Every experiment should follow this flow.
|
||||
|
||||
## Overview
|
||||
|
||||
```
|
||||
Plan → Launch → Monitor → Summarize → Journal → Reflect
|
||||
```
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Plan the Experiment
|
||||
|
||||
Before launching:
|
||||
- [ ] Define a clear **hypothesis** (what you expect to learn).
|
||||
- [ ] Select the **model** and **pipeline** type (finetune, distill, lora, etc.).
|
||||
- [ ] Prepare the **dataset** (preprocessed into parquet format).
|
||||
- [ ] Review existing experiments in `.agents/memory/experiment-journal/README.md` for related work.
|
||||
- [ ] Check `.agents/lessons/` for known pitfalls with this configuration.
|
||||
- [ ] Document the plan in the experiment journal as a draft entry.
|
||||
|
||||
### 2. Launch the Experiment
|
||||
|
||||
Use the `launch-experiment` skill:
|
||||
- Provide: pipeline, model, data_path, num_gpus, and any hyperparameter overrides.
|
||||
- The skill generates the `torchrun` command and creates a journal entry.
|
||||
- Verify the command looks correct before executing.
|
||||
|
||||
Reference: `.agents/skills/launch-experiment.md`
|
||||
|
||||
### 3. Monitor the Experiment
|
||||
|
||||
Use the `monitor-experiment` skill:
|
||||
- Provide the W&B run ID (or output_dir for offline).
|
||||
- Monitor alerts: loss spikes, NaN gradients, step time regressions.
|
||||
- At the **30-minute mark**: perform the quality check.
|
||||
- Is loss decreasing?
|
||||
- Are validation videos reasonable?
|
||||
- Is step time consistent?
|
||||
- **Decision point**: Continue or abort based on the 30-min check.
|
||||
|
||||
Reference: `.agents/skills/monitor-experiment.md`
|
||||
|
||||
### 4. Summarize the Run
|
||||
|
||||
After completion (or at any checkpoint), use the `summarize-run` skill:
|
||||
- Extract final metrics from W&B summary.
|
||||
- Compare against reference runs if available.
|
||||
- Generate a structured report.
|
||||
|
||||
Reference: `.agents/skills/summarize-run.md`
|
||||
|
||||
### 5. Update the Experiment Journal
|
||||
|
||||
Use the `log-experiment` skill to update the journal entry:
|
||||
- Fill in final metrics, duration, checkpoint paths.
|
||||
- Record the key insight learned.
|
||||
- Set status to `completed`, `failed`, or `abandoned`.
|
||||
|
||||
Reference: `.agents/skills/log-experiment.md`
|
||||
|
||||
### 6. Reflect and Capture Lessons
|
||||
|
||||
After every experiment:
|
||||
- **What went right?** → Note in the journal insight field.
|
||||
- **What went wrong?** → Create a lesson in `.agents/lessons/`:
|
||||
- Use the template in `.agents/lessons/README.md`.
|
||||
- Cross-reference the experiment journal entry.
|
||||
- **What was surprising?** → Consider creating an exploration log if this
|
||||
warrants further investigation.
|
||||
|
||||
Reference: `.agents/workflows/lesson-capture.md`
|
||||
|
||||
## Validation Criteria
|
||||
|
||||
This SOP is validated when an agent can:
|
||||
1. Follow steps 1–6 end-to-end for a minimal training run
|
||||
(e.g., `examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh`
|
||||
with `--max_train_steps 5`).
|
||||
2. Produce a complete experiment journal entry.
|
||||
3. Generate a run summary report.
|
||||
@@ -1,71 +0,0 @@
|
||||
---
|
||||
description: Post-experiment reflection to capture lessons learned
|
||||
---
|
||||
|
||||
# Lesson Capture SOP
|
||||
|
||||
Systematic procedure for turning experiment outcomes into persistent knowledge.
|
||||
|
||||
## When to Use
|
||||
|
||||
After **every** completed or failed experiment. Even successful experiments
|
||||
can yield lessons (e.g., "LR 5e-5 works better than 1e-5 for LoRA").
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Review the Experiment
|
||||
|
||||
Read the experiment journal entry. Ask:
|
||||
- Did anything go wrong?
|
||||
- Was anything surprising?
|
||||
- Did anything take longer than expected?
|
||||
- Was a workaround needed?
|
||||
|
||||
### 2. Decide: Lesson or Not?
|
||||
|
||||
| Situation | Action |
|
||||
|-----------|--------|
|
||||
| Something broke | Create a lesson (category: `infrastructure` or `data`) |
|
||||
| Hyperparameter choice mattered | Create a lesson (category: `hyperparameter`) |
|
||||
| Porting issue found | Create a lesson (category: `porting`) |
|
||||
| Evaluation metric was misleading | Create a lesson (category: `evaluation`) |
|
||||
| Everything went smoothly | No lesson needed, but note in the journal insight |
|
||||
|
||||
### 3. Create the Lesson File
|
||||
|
||||
In `.agents/lessons/`, create `<YYYY-MM-DD>_<short-slug>.md`:
|
||||
|
||||
```markdown
|
||||
---
|
||||
date: <ISO-8601>
|
||||
experiment: <journal entry reference>
|
||||
category: hyperparameter | data | infrastructure | evaluation | porting
|
||||
severity: critical | important | minor
|
||||
---
|
||||
|
||||
# <Short Descriptive Title>
|
||||
|
||||
## What Happened
|
||||
<description>
|
||||
|
||||
## Root Cause
|
||||
<analysis>
|
||||
|
||||
## Fix / Workaround
|
||||
<resolution>
|
||||
|
||||
## Prevention
|
||||
<how to avoid in future>
|
||||
```
|
||||
|
||||
### 4. Cross-Reference
|
||||
|
||||
- Update the experiment journal entry with a link to the lesson file.
|
||||
- If a similar lesson already exists, add a reference or update it.
|
||||
|
||||
### 5. Periodic Pattern Review
|
||||
|
||||
Every ~10 lessons, scan for patterns:
|
||||
- Multiple lessons in the same category → consider a new skill or SOP.
|
||||
- Repeated mistakes → strengthen the relevant SOP with a checklist item.
|
||||
- Infrastructure issues → propose a codebase fix.
|
||||
@@ -1,67 +0,0 @@
|
||||
---
|
||||
description: Synchronize the STATUS.md dashboard by scanning .agents/ directories
|
||||
---
|
||||
|
||||
# Sync Dashboard
|
||||
|
||||
Updates `.agents/STATUS.md` by scanning the skills, workflows, memory, lessons,
|
||||
and exploration directories to reflect what actually exists on disk.
|
||||
|
||||
## When to Use
|
||||
|
||||
- After adding, removing, or renaming any file in `.agents/`.
|
||||
- Periodically (e.g., at end of each conversation session).
|
||||
- When the dashboard feels out of date.
|
||||
|
||||
## Steps
|
||||
|
||||
### 1. Scan directories
|
||||
|
||||
List all files in each directory:
|
||||
|
||||
```bash
|
||||
echo "=== Skills ==="
|
||||
ls -1 .agents/skills/*.md 2>/dev/null | grep -v SKILL_TEMPLATE
|
||||
|
||||
echo "=== Workflows ==="
|
||||
ls -1 .agents/workflows/*.md 2>/dev/null
|
||||
|
||||
echo "=== Memory ==="
|
||||
ls -1 .agents/memory/*.md 2>/dev/null
|
||||
ls -1 .agents/memory/related-work/*.md 2>/dev/null | grep -v README
|
||||
|
||||
echo "=== Lessons ==="
|
||||
ls -1 .agents/lessons/*.md 2>/dev/null | grep -v README
|
||||
|
||||
echo "=== Exploration ==="
|
||||
ls -1 .agents/exploration/*.md 2>/dev/null | grep -v README
|
||||
```
|
||||
|
||||
### 2. Compare with STATUS.md
|
||||
|
||||
For each file found:
|
||||
- If it's in STATUS.md → leave it (preserve status/trust/tested fields).
|
||||
- If it's NOT in STATUS.md → add it with status `🔴 Stub`, trust `None`, tested `❌`.
|
||||
|
||||
For each entry in STATUS.md:
|
||||
- If the file no longer exists → mark it as `❌ Removed` or delete the row.
|
||||
|
||||
### 3. Update counts
|
||||
|
||||
Recalculate the summary table at the top:
|
||||
- Count files per category.
|
||||
- Count by status (Ready, Draft, Stub).
|
||||
|
||||
### 4. Update timestamp
|
||||
|
||||
Set `_Last synced: <current date>_` at the top of STATUS.md.
|
||||
|
||||
### 5. Review
|
||||
|
||||
Read through the updated STATUS.md for accuracy. Flag anything that looks wrong.
|
||||
|
||||
## Notes
|
||||
|
||||
- Do NOT change trust levels during sync — those are set manually after testing.
|
||||
- Do NOT change status during sync — status changes require actual validation.
|
||||
- This workflow only handles structural sync (file existence), not content review.
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"benchmark_id": "wan-t2v-1.3b-2gpu",
|
||||
"description": "Wan2.1 T2V 1.3B inference performance",
|
||||
"model": {
|
||||
"model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
|
||||
"model_short_name": "Wan2.1-T2V-1.3B"
|
||||
},
|
||||
"init_kwargs": {
|
||||
"num_gpus": 2,
|
||||
"flow_shift": 7.0,
|
||||
"sp_size": 2,
|
||||
"tp_size": 1,
|
||||
"vae_sp": true,
|
||||
"vae_tiling": true,
|
||||
"text_encoder_precisions": ["fp32"]
|
||||
},
|
||||
"generation_kwargs": {
|
||||
"height": 480,
|
||||
"width": 832,
|
||||
"num_frames": 45,
|
||||
"num_inference_steps": 4,
|
||||
"guidance_scale": 3,
|
||||
"embedded_cfg_scale": 6,
|
||||
"seed": 1024,
|
||||
"fps": 24,
|
||||
"neg_prompt": "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
|
||||
},
|
||||
"test_prompts": [
|
||||
"Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting."
|
||||
],
|
||||
"run_config": {
|
||||
"num_warmup_runs": 1,
|
||||
"num_measurement_runs": 3,
|
||||
"required_gpus": 2
|
||||
},
|
||||
"thresholds": {
|
||||
"L40S": {
|
||||
"max_generation_time_s": 34.0,
|
||||
"max_peak_memory_mb": 11000.0
|
||||
},
|
||||
"default": {
|
||||
"max_generation_time_s": 120.0,
|
||||
"max_peak_memory_mb": 30000.0
|
||||
}
|
||||
}
|
||||
}
|
||||
+99
-118
@@ -2,54 +2,28 @@ env:
|
||||
IMAGE_VERSION: "py3.12-latest"
|
||||
BUILDKITE_CLEAN_CHECKOUT: true
|
||||
|
||||
notify:
|
||||
- github_commit_status:
|
||||
context: "fastcheck-passed"
|
||||
if: build.env("TEST_SCOPE") == "fastcheck" || build.env("TEST_SCOPE") == null
|
||||
- github_commit_status:
|
||||
context: "full-suite-passed"
|
||||
if: build.env("TEST_SCOPE") == "full"
|
||||
|
||||
steps:
|
||||
# ============================================================
|
||||
- label: ":dart: Direct Test (${TEST_TYPE})"
|
||||
if: build.env("TEST_SCOPE") == "direct"
|
||||
command: "timeout 90m .buildkite/scripts/pr_test.sh"
|
||||
retry:
|
||||
automatic:
|
||||
- exit_status: 128
|
||||
limit: 3
|
||||
- exit_status: -1
|
||||
limit: 2
|
||||
agents:
|
||||
queue: "default"
|
||||
- label: "pre-commit"
|
||||
command: ".buildkite/scripts/pre_commit.sh"
|
||||
agents:
|
||||
queue: "default"
|
||||
|
||||
# ============================================================
|
||||
# Fastcheck: Runs on every PR (~10-15 min parallel)
|
||||
# Core component validation: encoders, VAEs, transformers,
|
||||
# CUDA kernels, and unit tests.
|
||||
# ============================================================
|
||||
- label: "Trigger Fastcheck"
|
||||
if: build.env("TEST_SCOPE") == "fastcheck" || build.env("TEST_SCOPE") == null
|
||||
retry:
|
||||
automatic:
|
||||
- exit_status: 128
|
||||
limit: 3
|
||||
- exit_status: -1
|
||||
limit: 2
|
||||
plugins:
|
||||
- monorepo-diff#v1.4.0:
|
||||
diff: 'git fetch origin "${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}" && git diff --name-only "origin/${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}...HEAD"'
|
||||
watch:
|
||||
- path:
|
||||
- wait
|
||||
|
||||
- label: "Trigger Tests"
|
||||
plugins:
|
||||
- monorepo-diff#v1.4.0:
|
||||
diff: 'git fetch origin "$BUILDKITE_PULL_REQUEST_BASE_BRANCH" && git diff --name-only origin/"$BUILDKITE_PULL_REQUEST_BASE_BRANCH"...HEAD'
|
||||
watch:
|
||||
- path:
|
||||
- "fastvideo/models/encoders/**"
|
||||
- "fastvideo/models/loader/**"
|
||||
- "fastvideo/tests/encoders/**"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 20m .buildkite/scripts/pr_test.sh"
|
||||
label: ":microscope: Encoder Tests"
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: "Encoder Tests"
|
||||
env:
|
||||
- TEST_TYPE=encoder
|
||||
agents:
|
||||
@@ -61,8 +35,8 @@ steps:
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 20m .buildkite/scripts/pr_test.sh"
|
||||
label: ":microscope: VAE Tests"
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: "VAE Tests"
|
||||
env:
|
||||
- TEST_TYPE=vae
|
||||
agents:
|
||||
@@ -77,62 +51,18 @@ steps:
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: ":microscope: Transformer Tests"
|
||||
label: "Transformer Tests"
|
||||
env:
|
||||
- TEST_TYPE=transformer
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "fastvideo-kernel/**"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: ":microscope: Kernel Tests"
|
||||
env:
|
||||
- TEST_TYPE=kernel_tests
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "fastvideo/**"
|
||||
- ".buildkite/**"
|
||||
- ".github/**"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: ":microscope: Unit Tests"
|
||||
env:
|
||||
- TEST_TYPE=unit_test
|
||||
agents:
|
||||
queue: "default"
|
||||
|
||||
# ============================================================
|
||||
# Full Suite: Runs when TEST_SCOPE=full
|
||||
# Triggered by adding the 'ready' label (via ci-trigger-full-suite.yml)
|
||||
# or on-demand via /test full slash command.
|
||||
# Includes integration tests, SSIM regression, training pipelines,
|
||||
# and performance benchmarks.
|
||||
# ============================================================
|
||||
- label: "Trigger Full Suite"
|
||||
if: build.env("TEST_SCOPE") == "full"
|
||||
retry:
|
||||
automatic:
|
||||
- exit_status: 128
|
||||
limit: 3
|
||||
- exit_status: -1
|
||||
limit: 2
|
||||
plugins:
|
||||
- monorepo-diff#v1.4.0:
|
||||
diff: 'git fetch origin "${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}" && git diff --name-only "origin/${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}...HEAD"'
|
||||
watch:
|
||||
- path:
|
||||
- path:
|
||||
- "fastvideo/**/*.py"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 90m .buildkite/scripts/pr_test.sh"
|
||||
label: ":bar_chart: SSIM Tests"
|
||||
command: "timeout 45m .buildkite/scripts/pr_test.sh"
|
||||
label: "SSIM Tests"
|
||||
env:
|
||||
- TEST_TYPE=ssim
|
||||
agents:
|
||||
@@ -146,8 +76,8 @@ steps:
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 20m .buildkite/scripts/pr_test.sh"
|
||||
label: ":test_tube: LoRA Inference Tests"
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: "LoRA Inference Tests"
|
||||
env:
|
||||
- TEST_TYPE=inference_lora
|
||||
agents:
|
||||
@@ -158,7 +88,7 @@ steps:
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: ":test_tube: Training Tests"
|
||||
label: "Training Tests"
|
||||
env:
|
||||
- TEST_TYPE=training
|
||||
agents:
|
||||
@@ -169,7 +99,7 @@ steps:
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: ":test_tube: Distillation DMD Tests"
|
||||
label: "Distillation DMDTests"
|
||||
env:
|
||||
- TEST_TYPE=distillation_dmd
|
||||
agents:
|
||||
@@ -181,7 +111,7 @@ steps:
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: ":test_tube: Self-Forcing Tests"
|
||||
label: "Self-Forcing Tests"
|
||||
env:
|
||||
- TEST_TYPE=self_forcing
|
||||
agents:
|
||||
@@ -192,63 +122,114 @@ steps:
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: ":test_tube: LoRA Training Tests"
|
||||
label: "LoRA Training Tests"
|
||||
env:
|
||||
- TEST_TYPE=training_lora
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "fastvideo/**"
|
||||
- "fastvideo-kernel/**"
|
||||
- "csrc/attn/video_sparse_attn/**"
|
||||
- "csrc/attn/video_sparse_attn/tk/**"
|
||||
- "csrc/attn/video_sparse_attn/setup.py"
|
||||
- "csrc/attn/video_sparse_attn/config_vsa.py"
|
||||
- "csrc/attn/video_sparse_attn/vsa.cpp"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: ":test_tube: Training Tests VSA"
|
||||
label: "Training Tests VSA"
|
||||
env:
|
||||
- TEST_TYPE=training_vsa
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "fastvideo-kernel/**"
|
||||
- "fastvideo/**"
|
||||
- "csrc/attn/sliding_tile_attn/**"
|
||||
- "csrc/attn/sliding_tile_attn/setup.py"
|
||||
- "csrc/attn/sliding_tile_attn/config_sta.py"
|
||||
- "csrc/attn/sliding_tile_attn/st_attn.cpp"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: "Inference Tests STA"
|
||||
env:
|
||||
- TEST_TYPE=inference_sta
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "csrc/attn/sliding_tile_attn/**"
|
||||
- "csrc/attn/sliding_tile_attn/setup.py"
|
||||
- "csrc/attn/sliding_tile_attn/config_sta.py"
|
||||
- "csrc/attn/sliding_tile_attn/st_attn.cpp"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: "Precision Tests STA"
|
||||
env:
|
||||
- TEST_TYPE=precision_sta
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "csrc/attn/video_sparse_attn/**"
|
||||
- "csrc/attn/video_sparse_attn/tk/**"
|
||||
- "csrc/attn/tests/test_vsa.py"
|
||||
- "csrc/attn/video_sparse_attn/setup.py"
|
||||
- "csrc/attn/video_sparse_attn/config_vsa.py"
|
||||
- "csrc/attn/video_sparse_attn/vsa.cpp"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: "Precision Tests VSA"
|
||||
env:
|
||||
- TEST_TYPE=precision_vsa
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "csrc/attn/vmoba_attn/**"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: "Precision Tests VMoBA"
|
||||
env:
|
||||
- TEST_TYPE=precision_vmoba
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "csrc/attn/vmoba_attn/vmoba/**"
|
||||
- "fastvideo/attention/backends/vmoba.py"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: ":test_tube: Inference Tests VMoBA"
|
||||
env:
|
||||
label: "Inference Tests VMoBA"
|
||||
env:
|
||||
- TEST_TYPE=inference_vmoba
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "fastvideo/models/dits/**"
|
||||
- "fastvideo/pipelines/**"
|
||||
- "fastvideo/attention/**"
|
||||
- "fastvideo/layers/**"
|
||||
- "fastvideo/worker/**"
|
||||
- "fastvideo/entrypoints/**"
|
||||
- "fastvideo/tests/performance/**"
|
||||
- ".buildkite/performance-benchmarks/**"
|
||||
- "fastvideo/**"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 30m .buildkite/scripts/pr_test.sh"
|
||||
label: ":test_tube: Performance Tests"
|
||||
command: "timeout 15m .buildkite/scripts/pr_test.sh"
|
||||
label: "Unit Tests"
|
||||
env:
|
||||
- TEST_TYPE=performance
|
||||
- TEST_TYPE=unit_test
|
||||
agents:
|
||||
queue: "default"
|
||||
- path:
|
||||
- "fastvideo/entrypoints/openai/**"
|
||||
- "fastvideo/entrypoints/cli/serve.py"
|
||||
- "fastvideo/tests/entrypoints/test_openai_api_integration.py"
|
||||
- "scripts/lora_extraction/**"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
config:
|
||||
command: "timeout 30m .buildkite/scripts/pr_test.sh"
|
||||
label: ":test_tube: API Server Tests"
|
||||
command: "timeout 90m .buildkite/scripts/pr_test.sh"
|
||||
label: "LoRA Extraction Tests"
|
||||
env:
|
||||
- TEST_TYPE=api_server
|
||||
- TEST_TYPE=lora_extraction
|
||||
agents:
|
||||
queue: "default"
|
||||
|
||||
@@ -51,7 +51,6 @@ else
|
||||
fi
|
||||
|
||||
MODAL_TEST_FILE="fastvideo/tests/modal/pr_test.py"
|
||||
MODAL_SSIM_TEST_FILE="fastvideo/tests/modal/ssim_test.py"
|
||||
|
||||
if [ -z "${TEST_TYPE:-}" ]; then
|
||||
log "Error: TEST_TYPE environment variable is not set"
|
||||
@@ -59,11 +58,7 @@ if [ -z "${TEST_TYPE:-}" ]; then
|
||||
fi
|
||||
log "Test type: $TEST_TYPE"
|
||||
|
||||
EFFECTIVE_PR=${BUILDKITE_PULL_REQUEST:-false}
|
||||
if [ "$EFFECTIVE_PR" = "false" ] && [ -n "${PR_NUMBER:-}" ]; then
|
||||
EFFECTIVE_PR=$PR_NUMBER
|
||||
fi
|
||||
MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$EFFECTIVE_PR IMAGE_VERSION=$IMAGE_VERSION"
|
||||
MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$BUILDKITE_PULL_REQUEST IMAGE_VERSION=$IMAGE_VERSION"
|
||||
|
||||
case "$TEST_TYPE" in
|
||||
"encoder")
|
||||
@@ -80,7 +75,7 @@ case "$TEST_TYPE" in
|
||||
;;
|
||||
"ssim")
|
||||
log "Running SSIM tests..."
|
||||
MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_SSIM_TEST_FILE::run_ssim_tests"
|
||||
MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
|
||||
;;
|
||||
"training")
|
||||
log "Running training tests..."
|
||||
@@ -94,9 +89,17 @@ case "$TEST_TYPE" in
|
||||
log "Running training VSA tests..."
|
||||
MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_training_tests_VSA"
|
||||
;;
|
||||
"kernel_tests")
|
||||
log "Running kernel tests..."
|
||||
MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_kernel_tests"
|
||||
"inference_sta")
|
||||
log "Running inference STA tests..."
|
||||
MODAL_COMMAND="$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 $MODAL_TEST_FILE::run_precision_tests_VSA"
|
||||
;;
|
||||
"inference_lora")
|
||||
log "Running LoRA tests..."
|
||||
@@ -115,6 +118,10 @@ case "$TEST_TYPE" in
|
||||
log "Running V-MoBA inference tests..."
|
||||
MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_tests_vmoba"
|
||||
;;
|
||||
"precision_vmoba")
|
||||
log "Running V-MoBA precision tests..."
|
||||
MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_vmoba"
|
||||
;;
|
||||
"unit_test")
|
||||
log "Running unit tests..."
|
||||
MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_unit_test"
|
||||
@@ -123,14 +130,6 @@ case "$TEST_TYPE" in
|
||||
log "Running LoRA extraction tests..."
|
||||
MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_lora_extraction_tests"
|
||||
;;
|
||||
"performance")
|
||||
log "Running performance tests..."
|
||||
MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_performance_tests"
|
||||
;;
|
||||
"api_server")
|
||||
log "Running API server integration tests..."
|
||||
MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_api_server_tests"
|
||||
;;
|
||||
*)
|
||||
log "Error: Unknown test type: $TEST_TYPE"
|
||||
exit 1
|
||||
|
||||
@@ -1,62 +0,0 @@
|
||||
<!--
|
||||
PR TITLE: Must start with a type tag, e.g.:
|
||||
[feat] Add new model [bugfix] Fix VAE tiling [refactor] Restructure pipeline
|
||||
[perf] Optimize kernel [ci] Update tests [docs] Add guide
|
||||
[misc] Cleanup configs [new-model] Port Flux2
|
||||
|
||||
MERGE WORKFLOW:
|
||||
1. Ensure pre-commit passes and you have at least 1 approval
|
||||
2. Comment /merge (or add the "ready" label) to enter the Merge Queue
|
||||
3. Full Test Suite runs automatically on a staging branch → auto-merge on success
|
||||
|
||||
ON-DEMAND TESTING (write access required):
|
||||
/test full — Full Test Suite /test ssim — SSIM regression
|
||||
/test training — Training pipeline /test encoder — Encoder tests
|
||||
/test transformer — Transformer tests /test vae — VAE tests
|
||||
/test kernel — CUDA kernel tests /test unit — Unit tests
|
||||
See docs/contributing/pull_requests.md for all 17 test commands
|
||||
-->
|
||||
|
||||
## Purpose
|
||||
|
||||
<!-- What does this PR do? Link the related issue if applicable. -->
|
||||
|
||||
Fixes #
|
||||
|
||||
## Changes
|
||||
|
||||
<!-- Describe your changes concisely. What approach did you take? -->
|
||||
|
||||
-
|
||||
|
||||
## Test Plan
|
||||
|
||||
<!-- How did you verify your changes? Paste exact commands and output. -->
|
||||
|
||||
```bash
|
||||
# Commands you ran
|
||||
```
|
||||
|
||||
## Test Results
|
||||
|
||||
<!-- Paste test output, before/after comparisons, or SSIM scores for model changes. -->
|
||||
|
||||
<details>
|
||||
<summary>Test output</summary>
|
||||
|
||||
```
|
||||
# Paste output here
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## Checklist
|
||||
|
||||
- [ ] I ran `pre-commit run --all-files` and fixed all issues
|
||||
- [ ] I added or updated tests for my changes
|
||||
- [ ] I updated documentation if needed
|
||||
- [ ] I considered GPU memory impact of my changes
|
||||
|
||||
**For model/pipeline changes, also check:**
|
||||
- [ ] I verified SSIM regression tests pass
|
||||
- [ ] I updated the support matrix if adding a new model
|
||||
@@ -1,321 +0,0 @@
|
||||
merge_protections:
|
||||
- name: PR merge requirements
|
||||
if:
|
||||
- base = main
|
||||
success_conditions:
|
||||
- "title~=(?i)^\\[(feat|feature|bugfix|fix|refactor|perf|ci|doc|docs|misc|chore|kernel|new.?model)\\]"
|
||||
- check-success~=pre-commit
|
||||
- check-success=fastcheck-passed
|
||||
|
||||
pull_request_rules:
|
||||
|
||||
# ============================================================
|
||||
# Type labels (from PR title prefix)
|
||||
# ============================================================
|
||||
|
||||
- name: "label type: feat"
|
||||
conditions:
|
||||
- "title~=(?i)^\\[(feat|feature)\\]"
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["type: feat"]
|
||||
|
||||
- name: "label type: bugfix"
|
||||
conditions:
|
||||
- "title~=(?i)^\\[(bug)?fix\\]"
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["type: bugfix"]
|
||||
|
||||
- name: "label type: refactor"
|
||||
conditions:
|
||||
- "title~=(?i)^\\[refactor\\]"
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["type: refactor"]
|
||||
|
||||
- name: "label type: perf"
|
||||
conditions:
|
||||
- "title~=(?i)^\\[perf\\]"
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["type: perf"]
|
||||
|
||||
- name: "label type: ci"
|
||||
conditions:
|
||||
- "title~=(?i)^\\[ci\\]"
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["type: ci"]
|
||||
|
||||
- name: "label type: docs"
|
||||
conditions:
|
||||
- "title~=(?i)^\\[(doc|docs)\\]"
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["type: docs"]
|
||||
|
||||
- name: "label type: misc"
|
||||
conditions:
|
||||
- "title~=(?i)^\\[(misc|chore)\\]"
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["type: misc"]
|
||||
|
||||
- name: "label type: new-model"
|
||||
conditions:
|
||||
- "title~=(?i)^\\[new.?model\\]"
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["type: new-model"]
|
||||
|
||||
# ============================================================
|
||||
# Scope labels (from changed files)
|
||||
# ============================================================
|
||||
|
||||
- name: "label scope: training"
|
||||
conditions:
|
||||
- or:
|
||||
- files~=^fastvideo/train/
|
||||
- files~=^fastvideo/training/
|
||||
- files~=^fastvideo/distillation/
|
||||
- files~=^examples/train/
|
||||
- files~=^examples/training/
|
||||
- files~=^examples/distill/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: training"]
|
||||
|
||||
- name: "label scope: inference"
|
||||
conditions:
|
||||
- or:
|
||||
- files~=^fastvideo/pipelines/basic/
|
||||
- files~=^fastvideo/pipelines/stages/
|
||||
- files~=^fastvideo/pipelines/samplers/
|
||||
- files~=^fastvideo/entrypoints/
|
||||
- files~=^fastvideo/worker/
|
||||
- files~=^fastvideo/configs/sample/
|
||||
- files~=^fastvideo/configs/pipelines/
|
||||
- files~=^examples/inference/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: inference"]
|
||||
|
||||
- name: "label scope: attention"
|
||||
conditions:
|
||||
- files~=^fastvideo/attention/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: attention"]
|
||||
|
||||
- name: "label scope: kernel"
|
||||
conditions:
|
||||
- or:
|
||||
- files~=^fastvideo-kernel/
|
||||
- files~=^csrc/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: kernel"]
|
||||
|
||||
- name: "label scope: data"
|
||||
conditions:
|
||||
- or:
|
||||
- files~=^fastvideo/dataset/
|
||||
- files~=^fastvideo/pipelines/preprocess/
|
||||
- files~=^examples/preprocessing/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: data"]
|
||||
|
||||
- name: "label scope: infra"
|
||||
conditions:
|
||||
- or:
|
||||
- files~=^\.github/
|
||||
- files~=^\.buildkite/
|
||||
- files~=^fastvideo/tests/
|
||||
- files~=^docker/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: infra"]
|
||||
|
||||
- name: "label scope: distributed"
|
||||
conditions:
|
||||
- files~=^fastvideo/distributed/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: distributed"]
|
||||
|
||||
- name: "label scope: docs"
|
||||
conditions:
|
||||
- files~=^docs/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: docs"]
|
||||
|
||||
- name: "label scope: ui"
|
||||
conditions:
|
||||
- files~=^ui/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: ui"]
|
||||
|
||||
- name: "label scope: model"
|
||||
conditions:
|
||||
- or:
|
||||
- files~=^fastvideo/models/
|
||||
- files~=^fastvideo/layers/
|
||||
- files~=^fastvideo/configs/models/
|
||||
- -closed
|
||||
actions:
|
||||
label:
|
||||
add: ["scope: model"]
|
||||
|
||||
# ============================================================
|
||||
# Pre-commit failure help comment
|
||||
# ============================================================
|
||||
|
||||
- name: comment on pre-commit failure
|
||||
conditions:
|
||||
- check-failure~=pre-commit
|
||||
- -closed
|
||||
actions:
|
||||
comment:
|
||||
message: |
|
||||
## Pre-commit checks failed
|
||||
|
||||
Hi @{{author}}, the pre-commit checks have failed. To fix them locally:
|
||||
|
||||
```bash
|
||||
# Install pre-commit if you haven't already
|
||||
uv pip install pre-commit
|
||||
pre-commit install
|
||||
|
||||
# Run all checks and auto-fix what's possible
|
||||
pre-commit run --all-files
|
||||
```
|
||||
|
||||
Common fixes:
|
||||
- **yapf**: `yapf -i <file>` (formatting)
|
||||
- **ruff**: `ruff check --fix <file>` (linting)
|
||||
- **codespell**: `codespell --write-changes <file>` (spelling)
|
||||
|
||||
After fixing, commit and push the changes. The checks will re-run automatically.
|
||||
|
||||
For future commits, `pre-commit` will run automatically on changed files before each commit.
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Merge conflict detection
|
||||
# ============================================================
|
||||
|
||||
- name: label conflicting PRs
|
||||
conditions:
|
||||
- conflict
|
||||
- -closed
|
||||
- label!=stale
|
||||
actions:
|
||||
label:
|
||||
add: [needs-rebase]
|
||||
comment:
|
||||
message: |
|
||||
This PR has merge conflicts with the base branch. Please rebase:
|
||||
|
||||
```bash
|
||||
git fetch origin main
|
||||
git rebase origin/main
|
||||
# Resolve any conflicts, then:
|
||||
git push --force-with-lease
|
||||
```
|
||||
|
||||
- name: remove conflict label when resolved
|
||||
conditions:
|
||||
- -conflict
|
||||
- -closed
|
||||
- label=needs-rebase
|
||||
actions:
|
||||
label:
|
||||
remove: [needs-rebase]
|
||||
|
||||
# ============================================================
|
||||
# Auto-merge and auto-rebase
|
||||
# ============================================================
|
||||
|
||||
- name: auto-merge when ready and all checks pass
|
||||
conditions:
|
||||
- label=ready
|
||||
- "title~=(?i)^\\[(feat|feature|bugfix|fix|refactor|perf|ci|doc|docs|misc|chore|kernel|new.?model)\\]"
|
||||
- "#approved-reviews-by>=1"
|
||||
- check-success~=pre-commit
|
||||
- check-success=fastcheck-passed
|
||||
- check-success=full-suite-passed
|
||||
- -conflict
|
||||
- -closed
|
||||
- -draft
|
||||
actions:
|
||||
merge:
|
||||
method: squash
|
||||
|
||||
- name: auto-rebase when ready and Full Suite passed
|
||||
conditions:
|
||||
- label=ready
|
||||
- "#approved-reviews-by>=1"
|
||||
- check-success=full-suite-passed
|
||||
- -conflict
|
||||
- -closed
|
||||
- -draft
|
||||
actions:
|
||||
rebase: {}
|
||||
|
||||
- name: remove ready label on Full Suite failure
|
||||
conditions:
|
||||
- label=ready
|
||||
- check-failure=full-suite-passed
|
||||
actions:
|
||||
label:
|
||||
remove: [ready]
|
||||
|
||||
# ============================================================
|
||||
# PR title format help
|
||||
# ============================================================
|
||||
|
||||
- name: comment on invalid PR title format
|
||||
conditions:
|
||||
- -closed
|
||||
- -draft
|
||||
- "-title~=(?i)^\\[(feat|feature|bugfix|fix|refactor|perf|ci|doc|docs|misc|chore|kernel|new.?model)\\]"
|
||||
actions:
|
||||
comment:
|
||||
message: |
|
||||
## ⚠️ PR title format required
|
||||
|
||||
Your PR title must start with a type tag in brackets. Examples:
|
||||
- `[feat] Add new model support`
|
||||
- `[bugfix] Fix VAE tiling corruption`
|
||||
- `[refactor] Restructure training pipeline`
|
||||
- `[perf] Optimize attention kernel`
|
||||
- `[ci] Update test infrastructure`
|
||||
- `[docs] Add inference guide`
|
||||
- `[misc] Clean up configs`
|
||||
- `[new-model] Port Flux2 to FastVideo`
|
||||
|
||||
Valid tags: `feat`, `feature`, `bugfix`, `fix`, `refactor`, `perf`, `ci`, `doc`, `docs`, `misc`, `chore`, `kernel`, `new-model`
|
||||
|
||||
Please update your PR title and the merge protection check will pass automatically.
|
||||
|
||||
@@ -0,0 +1,249 @@
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
|
||||
import requests
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
"""Parse command line arguments"""
|
||||
parser = argparse.ArgumentParser(description='Run tests on RunPod GPU')
|
||||
parser.add_argument('--gpu-type', type=str, help='GPU type to use')
|
||||
parser.add_argument('--gpu-count',
|
||||
type=int,
|
||||
help='Number of GPUs to use',
|
||||
default=1)
|
||||
parser.add_argument('--test-command', type=str, help='Test command to run')
|
||||
parser.add_argument('--disk-size',
|
||||
type=int,
|
||||
default=20,
|
||||
help='Container disk size in GB (default: 20)')
|
||||
parser.add_argument('--volume-size',
|
||||
type=int,
|
||||
default=20,
|
||||
help='Persistent volume size in GB (default: 20)')
|
||||
parser.add_argument(
|
||||
'--image',
|
||||
type=str,
|
||||
required=True,
|
||||
help='Docker image to use')
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
API_KEY = os.environ['RUNPOD_API_KEY']
|
||||
RUN_ID = os.environ['GITHUB_RUN_ID']
|
||||
JOB_ID = os.environ['JOB_ID']
|
||||
PODS_API = "https://rest.runpod.io/v1/pods"
|
||||
HEADERS = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {API_KEY}"
|
||||
}
|
||||
|
||||
|
||||
def create_pod():
|
||||
"""Create a RunPod instance"""
|
||||
# Ensure image name is lowercase (Docker requirement)
|
||||
image_name = args.image.lower()
|
||||
print(f"Using specified image: {image_name}")
|
||||
|
||||
docker_start_cmd = [
|
||||
"bash",
|
||||
"-c",
|
||||
"apt update;DEBIAN_FRONTEND=noninteractive apt-get install openssh-server -y;mkdir -p ~/.ssh;cd $_;chmod 700 ~/.ssh;echo \"$PUBLIC_KEY\" >> authorized_keys;chmod 700 authorized_keys;service ssh start;sleep infinity"
|
||||
]
|
||||
|
||||
print(f"Creating RunPod instance with GPU: {args.gpu_type}...")
|
||||
payload = {
|
||||
"name": f"fastvideo-{JOB_ID}-{RUN_ID}",
|
||||
"containerDiskInGb": args.disk_size,
|
||||
"volumeInGb": args.volume_size,
|
||||
"gpuTypeIds": [args.gpu_type],
|
||||
"gpuCount": args.gpu_count,
|
||||
"imageName": image_name,
|
||||
"allowedCudaVersions": ["12.4"],
|
||||
"dockerStartCmd": docker_start_cmd
|
||||
}
|
||||
|
||||
response = requests.post(PODS_API, headers=HEADERS, json=payload)
|
||||
response_data = response.json()
|
||||
print(f"Response: {json.dumps(response_data, indent=2)}")
|
||||
|
||||
return response_data["id"]
|
||||
|
||||
|
||||
def wait_for_pod(pod_id):
|
||||
"""Wait for pod to be in RUNNING state and fully ready with SSH access"""
|
||||
print("Waiting for RunPod to be ready...")
|
||||
|
||||
# First wait for RUNNING status
|
||||
max_attempts = 10
|
||||
attempts = 0
|
||||
while attempts < max_attempts:
|
||||
response = requests.get(f"{PODS_API}/{pod_id}", headers=HEADERS)
|
||||
pod_data = response.json()
|
||||
status = pod_data["desiredStatus"]
|
||||
|
||||
if status == "RUNNING":
|
||||
print("RunPod is running! Now waiting for ports to be assigned...")
|
||||
break
|
||||
|
||||
print(
|
||||
f"Current status: {status}, waiting... (attempt {attempts+1}/{max_attempts})"
|
||||
)
|
||||
time.sleep(2)
|
||||
attempts += 1
|
||||
|
||||
if attempts >= max_attempts:
|
||||
raise TimeoutError(
|
||||
"Timed out waiting for RunPod to reach RUNNING state")
|
||||
|
||||
# Wait for ports to be assigned
|
||||
max_attempts = 50
|
||||
attempts = 0
|
||||
while attempts < max_attempts:
|
||||
response = requests.get(f"{PODS_API}/{pod_id}", headers=HEADERS)
|
||||
pod_data = response.json()
|
||||
port_mappings = pod_data.get("portMappings")
|
||||
|
||||
if (port_mappings is not None and "22" in port_mappings
|
||||
and pod_data.get("publicIp", "") != ""):
|
||||
print("RunPod is ready with SSH access!")
|
||||
print(f"SSH IP: {pod_data['publicIp']}")
|
||||
print(f"SSH Port: {port_mappings['22']}")
|
||||
break
|
||||
|
||||
print(
|
||||
f"Waiting for SSH port and public IP to be available... (attempt {attempts+1}/{max_attempts})"
|
||||
)
|
||||
time.sleep(20)
|
||||
attempts += 1
|
||||
|
||||
if attempts >= max_attempts:
|
||||
raise TimeoutError("Timed out waiting for RunPod SSH access")
|
||||
|
||||
|
||||
def execute_command(pod_id):
|
||||
"""Execute command on the pod via SSH using system SSH client"""
|
||||
print(f"Running command: {args.test_command}")
|
||||
|
||||
response = requests.get(f"{PODS_API}/{pod_id}", headers=HEADERS)
|
||||
pod_data = response.json()
|
||||
ssh_ip = pod_data["publicIp"]
|
||||
ssh_port = pod_data["portMappings"]["22"]
|
||||
|
||||
# Copy the repository to the pod using scp
|
||||
repo_dir = os.path.abspath(os.getcwd())
|
||||
repo_name = os.path.basename(repo_dir)
|
||||
|
||||
print(f"Copying repository from {repo_dir} to RunPod...")
|
||||
|
||||
tar_command = [
|
||||
"tar", "-czf", "/tmp/repo.tar.gz", "-C",
|
||||
os.path.dirname(repo_dir), repo_name
|
||||
]
|
||||
subprocess.run(tar_command, check=True)
|
||||
|
||||
# Copy the tarball to the pod
|
||||
scp_command = [
|
||||
"scp", "-o", "StrictHostKeyChecking=no", "-o",
|
||||
"UserKnownHostsFile=/dev/null", "-o", "ServerAliveInterval=60", "-o",
|
||||
"ServerAliveCountMax=10", "-P",
|
||||
str(ssh_port), "/tmp/repo.tar.gz", f"root@{ssh_ip}:/tmp/"
|
||||
]
|
||||
subprocess.run(scp_command, check=True)
|
||||
|
||||
# For custom image, we can use the pre-configured environment
|
||||
setup_steps = [
|
||||
"tar -xzf /tmp/repo.tar.gz --no-same-owner -C /workspace/",
|
||||
f"cd /workspace/{repo_name}",
|
||||
"source $HOME/.local/bin/env && source /opt/venv/bin/activate",
|
||||
args.test_command
|
||||
]
|
||||
|
||||
remote_command = " && ".join(setup_steps)
|
||||
|
||||
ssh_command = [
|
||||
"ssh", "-o", "StrictHostKeyChecking=no", "-o",
|
||||
"UserKnownHostsFile=/dev/null", "-o", "ServerAliveInterval=60", "-o",
|
||||
"ServerAliveCountMax=10", "-p",
|
||||
str(ssh_port), f"root@{ssh_ip}", remote_command
|
||||
]
|
||||
|
||||
print(f"Connecting to {ssh_ip}:{ssh_port}...")
|
||||
|
||||
try:
|
||||
process = subprocess.Popen(ssh_command,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
universal_newlines=True,
|
||||
bufsize=0)
|
||||
|
||||
stdout_lines = []
|
||||
|
||||
print("Command output:")
|
||||
|
||||
for line in iter(process.stdout.readline, ''):
|
||||
print(line.strip())
|
||||
stdout_lines.append(line)
|
||||
|
||||
process.wait()
|
||||
|
||||
return_code = process.returncode
|
||||
success = return_code == 0
|
||||
|
||||
stdout_str = "".join(stdout_lines)
|
||||
|
||||
if success:
|
||||
print("Command executed successfully")
|
||||
else:
|
||||
print(f"Command failed with exit code {return_code}")
|
||||
|
||||
result = {
|
||||
"success": success,
|
||||
"return_code": return_code,
|
||||
"stdout": stdout_str,
|
||||
"stderr": ""
|
||||
}
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error executing SSH command: {str(e)}")
|
||||
result = {"success": False, "error": str(e), "stdout": "", "stderr": ""}
|
||||
return result
|
||||
|
||||
|
||||
def terminate_pod(pod_id):
|
||||
"""Terminate the pod"""
|
||||
print("Terminating RunPod...")
|
||||
requests.delete(f"{PODS_API}/{pod_id}", headers=HEADERS)
|
||||
print(f"Terminated pod {pod_id}")
|
||||
|
||||
|
||||
def main():
|
||||
pod_id = None
|
||||
try:
|
||||
pod_id = create_pod()
|
||||
wait_for_pod(pod_id)
|
||||
result = execute_command(pod_id)
|
||||
|
||||
if result.get("error") is not None:
|
||||
print(f"Error executing command: {result['error']}")
|
||||
sys.exit(1)
|
||||
|
||||
if not result.get("success", False):
|
||||
print(
|
||||
"Tests failed - check the output above for details on which tests failed"
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
finally:
|
||||
if pod_id:
|
||||
terminate_pod(pod_id)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,90 @@
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import uuid
|
||||
|
||||
import requests
|
||||
|
||||
API_KEY = os.environ['RUNPOD_API_KEY']
|
||||
RUN_ID = os.environ.get('GITHUB_RUN_ID', str(uuid.uuid4()))
|
||||
PODS_API = "https://rest.runpod.io/v1/pods"
|
||||
HEADERS = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {API_KEY}"
|
||||
}
|
||||
|
||||
|
||||
def get_job_ids():
|
||||
"""Parse job IDs from environment variable"""
|
||||
job_ids_str = os.environ.get('JOB_IDS')
|
||||
try:
|
||||
job_ids = json.loads(job_ids_str)
|
||||
if not isinstance(job_ids, list):
|
||||
print("Error: JOB_IDS is not a list.")
|
||||
sys.exit(1)
|
||||
return job_ids
|
||||
except json.JSONDecodeError as e:
|
||||
print(f"Error parsing JOB_IDS: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def cleanup_pods():
|
||||
"""Find and terminate RunPod instances"""
|
||||
print(f"Run ID: {RUN_ID}")
|
||||
|
||||
single_job_id = os.environ.get('JOB_ID')
|
||||
|
||||
if single_job_id:
|
||||
job_ids = [single_job_id]
|
||||
print(f"Job ID: {single_job_id}")
|
||||
else:
|
||||
job_ids = get_job_ids()
|
||||
print(f"Job IDs: {job_ids}")
|
||||
|
||||
# Get all pods associated with RunPod API_KEY
|
||||
try:
|
||||
response = requests.get(PODS_API, headers=HEADERS)
|
||||
response.raise_for_status()
|
||||
pods = response.json()
|
||||
except requests.exceptions.RequestException as e:
|
||||
print(f"Error getting pods: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Find and terminate pods created by this workflow run
|
||||
terminated_pods = []
|
||||
for pod in pods:
|
||||
pod_name = pod.get("name", "")
|
||||
pod_id = pod.get("id")
|
||||
|
||||
# Check if this pod was created by one of our jobs
|
||||
if any(f"{job_id}-{RUN_ID}" in pod_name for job_id in job_ids):
|
||||
print(f"Found pod: {pod_id} ({pod_name})")
|
||||
try:
|
||||
print(f"Terminating pod {pod_id}...")
|
||||
term_response = requests.delete(f"{PODS_API}/{pod_id}",
|
||||
headers=HEADERS)
|
||||
term_response.raise_for_status()
|
||||
terminated_pods.append(pod_id)
|
||||
print(f"Successfully terminated pod {pod_id}")
|
||||
except requests.exceptions.RequestException as e:
|
||||
print(f"Error terminating pod {pod_id}: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
if terminated_pods:
|
||||
if single_job_id:
|
||||
print(f"Terminated pod: {terminated_pods[0]}")
|
||||
else:
|
||||
print(f"Terminated {len(terminated_pods)} pods: {terminated_pods}")
|
||||
else:
|
||||
if single_job_id:
|
||||
print(f"No pod found matching pattern: {single_job_id}-{RUN_ID}")
|
||||
else:
|
||||
print("No pods found to terminate.")
|
||||
|
||||
|
||||
def main():
|
||||
cleanup_pods()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -32,7 +32,7 @@ permissions:
|
||||
jobs:
|
||||
build-python-3-10:
|
||||
if: ${{ github.event.inputs.python_3_10 == 'true' }}
|
||||
uses: ./.github/workflows/_template-build-image.yml
|
||||
uses: ./.github/workflows/build-image-template.yml
|
||||
with:
|
||||
python_version: '3.10'
|
||||
dockerfile_path: docker/Dockerfile.python3.10
|
||||
@@ -41,7 +41,7 @@ jobs:
|
||||
|
||||
build-python-3-11:
|
||||
if: ${{ github.event.inputs.python_3_11 == 'true' }}
|
||||
uses: ./.github/workflows/_template-build-image.yml
|
||||
uses: ./.github/workflows/build-image-template.yml
|
||||
with:
|
||||
python_version: '3.11'
|
||||
dockerfile_path: docker/Dockerfile.python3.11
|
||||
@@ -50,7 +50,7 @@ jobs:
|
||||
|
||||
build-python-3-12:
|
||||
if: ${{ github.event.inputs.python_3_12 == 'true' }}
|
||||
uses: ./.github/workflows/_template-build-image.yml
|
||||
uses: ./.github/workflows/build-image-template.yml
|
||||
with:
|
||||
python_version: '3.12'
|
||||
dockerfile_path: docker/Dockerfile.python3.12
|
||||
@@ -59,9 +59,9 @@ jobs:
|
||||
|
||||
build-python-3-12-cuda-12-9:
|
||||
if: ${{ github.event.inputs.python_3_12_cuda_12_9 == 'true' }}
|
||||
uses: ./.github/workflows/_template-build-image.yml
|
||||
uses: ./.github/workflows/build-image-template.yml
|
||||
with:
|
||||
python_version: '3.12'
|
||||
dockerfile_path: docker/Dockerfile.python3.12.cuda12.9.1
|
||||
tag_suffix: py3.12-cuda12.9.1
|
||||
secrets: inherit
|
||||
secrets: inherit
|
||||
@@ -1,29 +0,0 @@
|
||||
name: pre-commit
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
branches: [main]
|
||||
workflow_call:
|
||||
|
||||
concurrency:
|
||||
group: pre-commit-${{ github.ref }}
|
||||
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
pre-commit:
|
||||
if: github.event_name == 'workflow_call' || github.event.pull_request.draft != true
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.12"
|
||||
- run: echo "::add-matcher::.github/workflows/matchers/actionlint.json"
|
||||
- run: echo "::add-matcher::.github/workflows/matchers/mypy.json"
|
||||
- run: echo "::add-matcher::.github/workflows/matchers/ruff.json"
|
||||
- uses: pre-commit/action@v3.0.1
|
||||
with:
|
||||
extra_args: --all-files --hook-stage manual
|
||||
@@ -1,230 +0,0 @@
|
||||
name: Slash Commands
|
||||
|
||||
on:
|
||||
issue_comment:
|
||||
types: [created]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: write
|
||||
|
||||
jobs:
|
||||
handle-merge:
|
||||
if: >-
|
||||
github.event.issue.pull_request != null
|
||||
&& startsWith(github.event.comment.body, '/merge')
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check write permission
|
||||
id: perm
|
||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||
with:
|
||||
script: |
|
||||
const { data: perm } = await github.rest.repos.getCollaboratorPermissionLevel({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
username: context.payload.comment.user.login,
|
||||
});
|
||||
const hasWrite = ['admin', 'write'].includes(perm.permission);
|
||||
if (!hasWrite) {
|
||||
core.setFailed(`User ${context.payload.comment.user.login} lacks write permission (has: ${perm.permission}).`);
|
||||
}
|
||||
core.setOutput('has_write', String(hasWrite));
|
||||
|
||||
- name: Add ready label and react
|
||||
if: steps.perm.outputs.has_write == 'true'
|
||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||
with:
|
||||
script: |
|
||||
const owner = context.repo.owner;
|
||||
const repo = context.repo.repo;
|
||||
const prNumber = context.payload.issue.number;
|
||||
// Remove ready first to allow re-trigger (labeled event fires on add, not if already present)
|
||||
try { await github.rest.issues.removeLabel({ owner, repo, issue_number: prNumber, name: 'ready' }); } catch {}
|
||||
await github.rest.issues.addLabels({ owner, repo, issue_number: prNumber, labels: ['ready'] });
|
||||
await github.rest.reactions.createForIssueComment({
|
||||
owner, repo,
|
||||
comment_id: context.payload.comment.id,
|
||||
content: 'rocket',
|
||||
});
|
||||
parse-command:
|
||||
if: >-
|
||||
github.event.issue.pull_request != null
|
||||
&& startsWith(github.event.comment.body, '/test')
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
test_type: ${{ steps.parse.outputs.test_type }}
|
||||
test_scope: ${{ steps.parse.outputs.test_scope }}
|
||||
full_suite: ${{ steps.parse.outputs.full_suite }}
|
||||
pr_sha: ${{ steps.pr.outputs.sha }}
|
||||
pr_branch: ${{ steps.pr.outputs.branch }}
|
||||
has_write: ${{ steps.perm.outputs.has_write }}
|
||||
steps:
|
||||
- name: Check write permission
|
||||
id: perm
|
||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||
with:
|
||||
script: |
|
||||
const { data: perm } = await github.rest.repos.getCollaboratorPermissionLevel({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
username: context.payload.comment.user.login,
|
||||
});
|
||||
const hasWrite = ['admin', 'write'].includes(perm.permission);
|
||||
core.setOutput('has_write', String(hasWrite));
|
||||
if (!hasWrite) {
|
||||
core.info(`User ${context.payload.comment.user.login} lacks write permission — ignoring.`);
|
||||
}
|
||||
|
||||
- name: Parse /test command
|
||||
id: parse
|
||||
if: steps.perm.outputs.has_write == 'true'
|
||||
shell: bash
|
||||
env:
|
||||
COMMENT: ${{ github.event.comment.body }}
|
||||
run: |
|
||||
set -euo pipefail
|
||||
TEST_NAME=$(echo "$COMMENT" | grep -oP '(?<=/test\s)\S+' | head -1 || true)
|
||||
|
||||
VALID="encoder vae transformer kernel unit ssim training lora-inference lora-training distillation self-forcing vsa vmoba performance api full fastcheck pre-commit"
|
||||
if [ -z "$TEST_NAME" ] || ! echo "$VALID" | grep -qw "$TEST_NAME"; then
|
||||
echo "Unknown test: '$TEST_NAME'. Valid: $VALID"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
declare -A MAP=(
|
||||
[encoder]=encoder [vae]=vae [transformer]=transformer
|
||||
[kernel]=kernel_tests [unit]=unit_test
|
||||
[ssim]=ssim [training]=training
|
||||
[lora-inference]=inference_lora [lora-training]=training_lora
|
||||
[distillation]=distillation_dmd [self-forcing]=self_forcing
|
||||
[vsa]=training_vsa [vmoba]=inference_vmoba
|
||||
[performance]=performance [api]=api_server
|
||||
)
|
||||
|
||||
if [ "$TEST_NAME" = "full" ]; then
|
||||
{
|
||||
echo "test_type=all"
|
||||
echo "test_scope=full"
|
||||
echo "full_suite=true"
|
||||
} >> "$GITHUB_OUTPUT"
|
||||
elif [ "$TEST_NAME" = "fastcheck" ]; then
|
||||
{
|
||||
echo "test_type=fastcheck"
|
||||
echo "test_scope=fastcheck"
|
||||
echo "full_suite=false"
|
||||
} >> "$GITHUB_OUTPUT"
|
||||
elif [ "$TEST_NAME" = "pre-commit" ]; then
|
||||
{
|
||||
echo "test_type="
|
||||
echo "test_scope=precommit"
|
||||
echo "full_suite=false"
|
||||
} >> "$GITHUB_OUTPUT"
|
||||
else
|
||||
{
|
||||
echo "test_type=${MAP[$TEST_NAME]}"
|
||||
echo "test_scope=direct"
|
||||
echo "full_suite=false"
|
||||
} >> "$GITHUB_OUTPUT"
|
||||
fi
|
||||
|
||||
- name: Get PR details
|
||||
id: pr
|
||||
if: steps.perm.outputs.has_write == 'true'
|
||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||
with:
|
||||
script: |
|
||||
const { data: pr } = await github.rest.pulls.get({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
pull_number: context.payload.issue.number,
|
||||
});
|
||||
core.setOutput('sha', pr.head.sha);
|
||||
core.setOutput('branch', pr.head.ref);
|
||||
|
||||
pre-commit:
|
||||
needs: parse-command
|
||||
if: >-
|
||||
needs.parse-command.outputs.has_write == 'true'
|
||||
&& needs.parse-command.outputs.test_scope == 'precommit'
|
||||
uses: ./.github/workflows/ci-precommit.yml
|
||||
|
||||
post-precommit-status:
|
||||
needs: [parse-command, pre-commit]
|
||||
if: always() && needs.parse-command.outputs.test_scope == 'precommit'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Post commit status
|
||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||
env:
|
||||
PR_SHA: ${{ needs.parse-command.outputs.pr_sha }}
|
||||
RESULT: ${{ needs.pre-commit.result }}
|
||||
with:
|
||||
script: |
|
||||
const state = process.env.RESULT === 'success' ? 'success' : 'failure';
|
||||
await github.rest.repos.createCommitStatus({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
sha: process.env.PR_SHA,
|
||||
state,
|
||||
context: 'pre-commit',
|
||||
description: `Triggered via /test pre-commit (${state})`,
|
||||
});
|
||||
|
||||
trigger-buildkite:
|
||||
needs: parse-command
|
||||
if: >-
|
||||
needs.parse-command.outputs.has_write == 'true'
|
||||
&& needs.parse-command.outputs.test_type != ''
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: React to comment
|
||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||
with:
|
||||
script: |
|
||||
await github.rest.reactions.createForIssueComment({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
comment_id: context.payload.comment.id,
|
||||
content: 'rocket',
|
||||
});
|
||||
|
||||
- name: Trigger Buildkite
|
||||
env:
|
||||
BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
|
||||
PR_SHA: ${{ needs.parse-command.outputs.pr_sha }}
|
||||
PR_BRANCH: ${{ needs.parse-command.outputs.pr_branch }}
|
||||
PR_NUMBER: ${{ github.event.issue.number }}
|
||||
TEST_SCOPE: ${{ needs.parse-command.outputs.test_scope }}
|
||||
FULL_SUITE: ${{ needs.parse-command.outputs.full_suite }}
|
||||
TEST_TYPE: ${{ needs.parse-command.outputs.test_type }}
|
||||
BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
|
||||
BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
|
||||
run: |
|
||||
curl -sS --fail-with-body -X POST \
|
||||
"https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds" \
|
||||
-H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
|
||||
-H "Content-Type: application/json" \
|
||||
--data-raw "$(jq -n \
|
||||
--arg commit "$PR_SHA" \
|
||||
--arg branch "$PR_BRANCH" \
|
||||
--arg message "/test ${TEST_TYPE} on PR #${PR_NUMBER}" \
|
||||
--argjson pr_id "$PR_NUMBER" \
|
||||
--arg test_scope "$TEST_SCOPE" \
|
||||
--arg full_suite "$FULL_SUITE" \
|
||||
--arg test_type "$TEST_TYPE" \
|
||||
--arg pr_number "$PR_NUMBER" \
|
||||
'{
|
||||
commit: $commit,
|
||||
branch: $branch,
|
||||
message: $message,
|
||||
ignore_pipeline_branch_filters: true,
|
||||
pull_request_id: $pr_id,
|
||||
pull_request_base_branch: "main",
|
||||
env: {
|
||||
TEST_SCOPE: $test_scope,
|
||||
FULL_SUITE: $full_suite,
|
||||
TEST_TYPE: $test_type,
|
||||
PR_NUMBER: $pr_number
|
||||
}
|
||||
}')"
|
||||
@@ -1,83 +0,0 @@
|
||||
name: Trigger Full Suite
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
types: [labeled, synchronize]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: read
|
||||
|
||||
concurrency:
|
||||
group: full-suite-${{ github.event.pull_request.number }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
trigger:
|
||||
if: >-
|
||||
(github.event.action == 'labeled' && github.event.label.name == 'ready')
|
||||
|| github.event.action == 'synchronize'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check ready label
|
||||
id: check
|
||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||
with:
|
||||
script: |
|
||||
const { data: pr } = await github.rest.pulls.get({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
pull_number: context.payload.pull_request.number,
|
||||
});
|
||||
const hasReady = pr.labels.some(l => l.name === 'ready');
|
||||
core.setOutput('has_ready', String(hasReady));
|
||||
if (!hasReady) core.info('No ready label — skipping Full Suite trigger.');
|
||||
|
||||
- name: Cancel previous Buildkite builds
|
||||
if: steps.check.outputs.has_ready == 'true'
|
||||
env:
|
||||
BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
|
||||
PR_BRANCH: ${{ github.event.pull_request.head.ref }}
|
||||
run: |
|
||||
# Find running builds for this branch with TEST_SCOPE=full and cancel them
|
||||
builds=$(curl -sS -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
|
||||
"https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds?branch=${PR_BRANCH}&state=running,scheduled" \
|
||||
| jq -r '.[] | select(.env.TEST_SCOPE == "full") | .number')
|
||||
for build_num in $builds; do
|
||||
echo "Cancelling Buildkite build #$build_num"
|
||||
curl -sS -X PUT -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
|
||||
"https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds/${build_num}/cancel"
|
||||
done
|
||||
|
||||
- name: Trigger Buildkite Full Suite
|
||||
if: steps.check.outputs.has_ready == 'true'
|
||||
env:
|
||||
BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
|
||||
PR_SHA: ${{ github.event.pull_request.head.sha }}
|
||||
PR_BRANCH: ${{ github.event.pull_request.head.ref }}
|
||||
PR_NUMBER: ${{ github.event.pull_request.number }}
|
||||
BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
|
||||
BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
|
||||
run: |
|
||||
curl -sS --fail-with-body -X POST \
|
||||
"https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds" \
|
||||
-H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
|
||||
-H "Content-Type: application/json" \
|
||||
--data-raw "$(jq -n \
|
||||
--arg commit "$PR_SHA" \
|
||||
--arg branch "$PR_BRANCH" \
|
||||
--arg message "Full Suite for PR #${PR_NUMBER}" \
|
||||
--argjson pr_id "$PR_NUMBER" \
|
||||
'{
|
||||
commit: $commit,
|
||||
branch: $branch,
|
||||
message: $message,
|
||||
ignore_pipeline_branch_filters: true,
|
||||
pull_request_id: $pr_id,
|
||||
pull_request_base_branch: "main",
|
||||
env: {
|
||||
TEST_SCOPE: "full",
|
||||
FULL_SUITE: "true",
|
||||
PR_NUMBER: ($pr_id | tostring)
|
||||
}
|
||||
}')"
|
||||
@@ -1,65 +0,0 @@
|
||||
name: Auto-Label Issues
|
||||
|
||||
on:
|
||||
issues:
|
||||
types: [opened, edited]
|
||||
|
||||
permissions:
|
||||
issues: write
|
||||
|
||||
jobs:
|
||||
label-issues:
|
||||
if: github.repository == 'hao-ai-lab/FastVideo'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Label by keywords
|
||||
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
|
||||
with:
|
||||
script: |
|
||||
const title = context.payload.issue.title.toLowerCase();
|
||||
const body = (context.payload.issue.body || '').toLowerCase();
|
||||
const text = title + ' ' + body;
|
||||
const labels = [];
|
||||
|
||||
const rules = [
|
||||
// scope labels (shared with PR labeling via Mergify)
|
||||
// Mapping: label → repo directories
|
||||
// scope: training → fastvideo/train/, fastvideo/training/, fastvideo/distillation/
|
||||
// scope: inference → fastvideo/pipelines/, fastvideo/entrypoints/, fastvideo/worker/
|
||||
// scope: attention → fastvideo/attention/
|
||||
// scope: kernel → fastvideo-kernel/, csrc/
|
||||
// scope: model → fastvideo/models/, fastvideo/layers/, fastvideo/configs/models/
|
||||
// scope: data → fastvideo/dataset/, fastvideo/pipelines/preprocess/
|
||||
// scope: distributed → fastvideo/distributed/
|
||||
// scope: docs → docs/
|
||||
{ keywords: ['training', 'finetune', 'fine-tune', 'lora', 'fsdp', 'distill'], label: 'scope: training' },
|
||||
{ keywords: ['inference', 'generate', 'pipeline', 'slow', 'latency'], label: 'scope: inference' },
|
||||
{ keywords: ['attention', 'vsa', 'flash', 'sta', 'vmoba', 'sparse attn'], label: 'scope: attention' },
|
||||
{ keywords: ['kernel', 'csrc', 'cuda kernel', 'thunderkittens'], label: 'scope: kernel' },
|
||||
{ keywords: ['wan', 'hunyuan', 'mochi', 'ltx', 'cogvideo', 'flux', 'sd3', 'cosmos'], label: 'scope: model' },
|
||||
{ keywords: ['dataset', 'dataloader', 'preprocessing', 'preprocess'], label: 'scope: data' },
|
||||
{ keywords: ['distributed', 'sequence parallel', 'fsdp', 'tensor parallel', 'multi-node', 'multi-gpu'], label: 'scope: distributed' },
|
||||
{ keywords: ['docs', 'documentation', 'tutorial', 'example'], label: 'scope: docs' },
|
||||
// issue-only labels (cross-module, no single repo directory)
|
||||
{ keywords: ['install', 'setup', 'pip', 'cuda', 'uv ', 'import error', 'modulenotfound'], label: 'installation' },
|
||||
{ keywords: ['memory', 'oom', 'out of memory', 'gpu memory', 'vram'], label: 'performance' },
|
||||
{ keywords: ['windows', 'macos', 'mac os', 'apple', 'mps', 'rocm', 'amd', 'npu'], label: 'platform' },
|
||||
];
|
||||
|
||||
for (const rule of rules) {
|
||||
if (rule.keywords.some(kw => text.includes(kw))) {
|
||||
labels.push(rule.label);
|
||||
}
|
||||
}
|
||||
|
||||
if (labels.length > 0) {
|
||||
await github.rest.issues.addLabels({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
issue_number: context.payload.issue.number,
|
||||
labels: labels,
|
||||
});
|
||||
console.log(`Added labels: ${labels.join(', ')}`);
|
||||
} else {
|
||||
console.log('No keyword matches found');
|
||||
}
|
||||
@@ -1,51 +0,0 @@
|
||||
name: Close Stale Issues and PRs
|
||||
|
||||
on:
|
||||
schedule:
|
||||
# Daily at 1:30 AM UTC
|
||||
- cron: '30 1 * * *'
|
||||
|
||||
jobs:
|
||||
stale:
|
||||
if: github.repository == 'hao-ai-lab/FastVideo'
|
||||
permissions:
|
||||
issues: write
|
||||
pull-requests: write
|
||||
actions: write
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v10.1.1
|
||||
with:
|
||||
operations-per-run: 500
|
||||
|
||||
exempt-draft-pr: true
|
||||
exempt-issue-labels: 'keep-open,pinned,security,Bug,RFC'
|
||||
exempt-pr-labels: 'keep-open,pinned'
|
||||
|
||||
labels-to-add-when-unstale: 'unstale'
|
||||
labels-to-remove-when-stale: 'unstale'
|
||||
|
||||
days-before-issue-stale: 90
|
||||
days-before-issue-close: 30
|
||||
stale-issue-label: 'stale'
|
||||
stale-issue-message: >
|
||||
This issue has been automatically marked as stale because it has not
|
||||
had any activity within 90 days. It will be automatically closed if
|
||||
no further activity occurs within 30 days. Leave a comment if you
|
||||
feel this issue should remain open. Thank you!
|
||||
close-issue-message: >
|
||||
This issue has been automatically closed due to inactivity. Please
|
||||
feel free to reopen if you feel it is still relevant. Thank you!
|
||||
|
||||
days-before-pr-stale: 60
|
||||
days-before-pr-close: 14
|
||||
stale-pr-label: 'stale'
|
||||
stale-pr-message: >
|
||||
This pull request has been automatically marked as stale because it
|
||||
has not had any activity within 60 days. It will be automatically
|
||||
closed if no further activity occurs within 14 days. Leave a comment
|
||||
if you feel this pull request should remain open. Thank you!
|
||||
close-pr-message: >
|
||||
This pull request has been automatically closed due to inactivity.
|
||||
Please feel free to reopen if you intend to continue working on it.
|
||||
Thank you!
|
||||
@@ -1,56 +0,0 @@
|
||||
name: Welcome First-Time Contributors
|
||||
|
||||
on:
|
||||
issues:
|
||||
types: [opened]
|
||||
pull_request_target:
|
||||
types: [opened]
|
||||
|
||||
permissions:
|
||||
issues: write
|
||||
pull-requests: write
|
||||
|
||||
jobs:
|
||||
welcome:
|
||||
if: github.repository == 'hao-ai-lab/FastVideo'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/first-interaction@34f15e814fe48ac9312ccf29db4e74fa767cbab7 # v1.3.0
|
||||
with:
|
||||
repo-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
issue-message: |
|
||||
Welcome to FastVideo! Thanks for opening your first issue.
|
||||
|
||||
To help us investigate, please include:
|
||||
- **FastVideo version**: `pip show fastvideo`
|
||||
- **GPU**: `nvidia-smi` output (GPU model, driver, CUDA version)
|
||||
- **Python version**: `python --version`
|
||||
- **OS**: e.g., Ubuntu 22.04
|
||||
|
||||
If this is a bug, a minimal reproduction script helps us fix it faster.
|
||||
|
||||
Useful links:
|
||||
- [Documentation](https://hao-ai-lab.github.io/FastVideo)
|
||||
- [Contributing Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)
|
||||
- [Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ)
|
||||
pr-message: |
|
||||
Welcome to FastVideo! Thanks for your first pull request.
|
||||
|
||||
**How our CI works:**
|
||||
|
||||
PRs run a two-tier CI system:
|
||||
1. **Pre-commit** — formatting (yapf), linting (ruff), type checking (mypy). Runs immediately on every PR.
|
||||
2. **Fastcheck** — core GPU tests (encoders, VAEs, transformers, kernels, unit tests). Runs automatically via Buildkite on relevant file changes (~10-15 min).
|
||||
3. **Full Suite** — integration tests, training pipelines, SSIM regression. Runs only when a reviewer adds the `ready` label.
|
||||
|
||||
**Before your PR is reviewed:**
|
||||
- [ ] `pre-commit run --all-files` passes locally
|
||||
- [ ] You've added or updated tests for your changes
|
||||
- [ ] The PR description explains what and why
|
||||
|
||||
If pre-commit fails, a bot comment will explain how to fix it. Fastcheck and Full Suite results appear in the Checks section below.
|
||||
|
||||
**Useful links:**
|
||||
- [Contributing Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)
|
||||
- [Development Roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899)
|
||||
- [Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ)
|
||||
@@ -7,14 +7,14 @@ on:
|
||||
- 'docs/**'
|
||||
- 'mkdocs.yml'
|
||||
- 'requirements-mkdocs.txt'
|
||||
- '.github/workflows/infra-docs.yml'
|
||||
- '.github/workflows/docs.yml'
|
||||
pull_request:
|
||||
branches: [ main ]
|
||||
paths:
|
||||
- 'docs/**'
|
||||
- 'mkdocs.yml'
|
||||
- 'requirements-mkdocs.txt'
|
||||
- '.github/workflows/infra-docs.yml'
|
||||
- '.github/workflows/docs.yml'
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
@@ -45,12 +45,6 @@ jobs:
|
||||
- name: Setup Pages
|
||||
uses: actions/configure-pages@v4
|
||||
|
||||
- name: Generate docs examples
|
||||
run: python docs/generate_examples.py
|
||||
|
||||
- name: Check docs links
|
||||
run: python scripts/check_docs_links.py
|
||||
|
||||
- name: Build documentation
|
||||
run: mkdocs build
|
||||
|
||||
@@ -69,4 +63,4 @@ jobs:
|
||||
steps:
|
||||
- name: Deploy to GitHub Pages
|
||||
id: deployment
|
||||
uses: actions/deploy-pages@v4
|
||||
uses: actions/deploy-pages@v4
|
||||
+78
-67
@@ -5,7 +5,7 @@ on:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- "fastvideo-kernel/pyproject.toml"
|
||||
- "csrc/fastvideo_kernel/pyproject.toml"
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
@@ -23,24 +23,22 @@ jobs:
|
||||
- name: Check if version changed
|
||||
id: check-version
|
||||
run: |
|
||||
cd fastvideo-kernel
|
||||
cd csrc/fastvideo_kernel
|
||||
# Get current commit's version from pyproject.toml
|
||||
# Use ^ to match start of line to avoid matching minimum-version
|
||||
NEW_VERSION=$(grep -oP '^version\s*=\s*"\K[^"]+' pyproject.toml)
|
||||
NEW_VERSION=$(grep -oP 'version\s*=\s*"\K[^"]+' pyproject.toml)
|
||||
echo "New version: $NEW_VERSION"
|
||||
|
||||
# Get previous version from git history
|
||||
# Note: git show expects path relative to repo root
|
||||
OLD_VERSION=$(git show HEAD~1:fastvideo-kernel/pyproject.toml | grep -oP '^version\s*=\s*"\K[^"]+' || echo "0.0.0")
|
||||
OLD_VERSION=$(git show HEAD~1:./pyproject.toml | grep -oP 'version\s*=\s*"\K[^"]+' || echo "0.0.0")
|
||||
echo "Old version: $OLD_VERSION"
|
||||
|
||||
if [ "$NEW_VERSION" != "$OLD_VERSION" ]; then
|
||||
echo "Version changed from $OLD_VERSION to $NEW_VERSION"
|
||||
echo "changed=true" >> "$GITHUB_OUTPUT"
|
||||
echo "new-version=$NEW_VERSION" >> "$GITHUB_OUTPUT"
|
||||
echo "changed=true" >> $GITHUB_OUTPUT
|
||||
echo "new-version=$NEW_VERSION" >> $GITHUB_OUTPUT
|
||||
else
|
||||
echo "Version did not change"
|
||||
echo "changed=false" >> "$GITHUB_OUTPUT"
|
||||
echo "changed=false" >> $GITHUB_OUTPUT
|
||||
fi
|
||||
|
||||
build_wheels:
|
||||
@@ -53,21 +51,15 @@ jobs:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
os: [ubuntu-22.04]
|
||||
python-version: ['3.10', '3.11', '3.12']
|
||||
python-version: ['3.10', '3.11', '3.12', '3.13']
|
||||
torch-cuda:
|
||||
# - torch-version: '2.5.1'
|
||||
# cuda-version: '12.4.1'
|
||||
# torch-cuda-short: 'cu124'
|
||||
# - torch-version: '2.6.0'
|
||||
# cuda-version: '12.6.3'
|
||||
# torch-cuda-short: 'cu126'
|
||||
# - torch-version: '2.7.1'
|
||||
# cuda-version: '12.8.0'
|
||||
# torch-cuda-short: 'cu128'
|
||||
# - torch-version: '2.9.1'
|
||||
# cuda-version: '12.8.0'
|
||||
# torch-cuda-short: 'cu128'
|
||||
- torch-version: '2.10.0'
|
||||
- torch-version: '2.5.1'
|
||||
cuda-version: '12.4.1'
|
||||
torch-cuda-short: 'cu124'
|
||||
- torch-version: '2.6.0'
|
||||
cuda-version: '12.6.3'
|
||||
torch-cuda-short: 'cu126'
|
||||
- torch-version: '2.7.1'
|
||||
cuda-version: '12.8.0'
|
||||
torch-cuda-short: 'cu128'
|
||||
|
||||
@@ -146,47 +138,30 @@ jobs:
|
||||
run: |
|
||||
export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
|
||||
|
||||
pip install setuptools ninja packaging wheel triton scikit-build-core cmake build
|
||||
pip install setuptools ninja packaging wheel triton
|
||||
|
||||
cd fastvideo-kernel
|
||||
cd csrc/fastvideo_kernel
|
||||
git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
|
||||
# Release builds are produced on GPU-less runners, so force-enable TK and target Hopper.
|
||||
export TORCH_CUDA_ARCH_LIST="9.0a"
|
||||
export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON -DCMAKE_CUDA_ARCHITECTURES=90a"
|
||||
|
||||
# Build standard wheel (no local version suffix) for PyPI
|
||||
python -m build --wheel --outdir dist
|
||||
|
||||
# Fix the wheel to be manylinux compliant
|
||||
pip install auditwheel
|
||||
# Point auditwheel at torch libs, but do not vendor them into the wheel.
|
||||
TORCH_LIB_DIR=$(python - <<'PY'
|
||||
import os
|
||||
import torch
|
||||
python setup.py bdist_wheel --dist-dir=dist
|
||||
|
||||
print(os.path.join(os.path.dirname(torch.__file__), "lib"))
|
||||
PY
|
||||
)
|
||||
export LD_LIBRARY_PATH="${TORCH_LIB_DIR}:${LD_LIBRARY_PATH}"
|
||||
# Target manylinux_2_35 (Ubuntu 22.04 native)
|
||||
auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist \
|
||||
--exclude libtorch_cuda.so \
|
||||
--exclude libtorch_cpu.so \
|
||||
--exclude libtorch.so \
|
||||
--exclude libc10.so \
|
||||
--exclude libc10_cuda.so \
|
||||
--exclude libtorch_python.so
|
||||
# Move fixed wheels back to dist for upload consistency
|
||||
rm dist/*.whl
|
||||
mv fixed_dist/*.whl dist/
|
||||
- name: Rename wheel file
|
||||
run: |
|
||||
cd csrc/fastvideo_kernel
|
||||
|
||||
CUDA_SHORT_VERSION=$(echo ${{ matrix.torch-cuda.cuda-version }} | cut -d. -f1,2 | sed 's/\.//g')
|
||||
TORCH_SHORT_VERSION=$(echo ${{ matrix.torch-cuda.torch-version }} | cut -d. -f1,2)
|
||||
# Get the correct version format
|
||||
tmpname=cu${CUDA_SHORT_VERSION}torch${TORCH_SHORT_VERSION}
|
||||
wheel_name=$(ls dist/*whl | xargs -n 1 basename | sed "s/-/+$tmpname-/2")
|
||||
# Rename with version information
|
||||
ls dist/*whl |xargs -I {} mv {} dist/${wheel_name}
|
||||
echo "wheel_name=${wheel_name}" >> $GITHUB_ENV
|
||||
|
||||
- name: Upload wheel artifact
|
||||
# Only upload if it's the "main" CUDA version we want on PyPI
|
||||
# We upload all to artifacts for inspection/GH releases, but give them distinct artifact names
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: fastvideo_kernel-py${{ matrix.python-version }}-${{ matrix.torch-cuda.torch-cuda-short }}-torch${{ matrix.torch-cuda.torch-version }}
|
||||
path: fastvideo-kernel/dist/*.whl
|
||||
name: ${{ env.wheel_name }}-py${{ matrix.python-version }}
|
||||
path: csrc/fastvideo_kernel/dist/*.whl
|
||||
retention-days: 90
|
||||
|
||||
publish_package:
|
||||
@@ -204,22 +179,58 @@ jobs:
|
||||
with:
|
||||
python-version: '3.10'
|
||||
|
||||
- name: Download PyPI wheels
|
||||
uses: actions/download-artifact@v4
|
||||
- name: Install CUDA 12.4.1
|
||||
uses: Jimver/cuda-toolkit@v0.2.21
|
||||
id: cuda-toolkit
|
||||
with:
|
||||
path: fastvideo-kernel/dist/
|
||||
pattern: 'fastvideo_kernel-py*'
|
||||
merge-multiple: true
|
||||
cuda: 12.4.1
|
||||
linux-local-args: '["--toolkit"]'
|
||||
method: 'network'
|
||||
sub-packages: '["nvcc"]'
|
||||
|
||||
- name: Install dependencies (GCC, Clang, CUDA Paths, Git)
|
||||
run: |
|
||||
sudo apt update
|
||||
sudo apt install -y git patchelf gcc-11 g++-11 clang-11
|
||||
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
|
||||
|
||||
# Allow Git to Access Safe Directory
|
||||
git config --global --add safe.directory /__w/FastVideo/FastVideo
|
||||
|
||||
# Set CUDA environment variables
|
||||
export CUDA_HOME=/usr/local/cuda-12.4.1
|
||||
export PATH=${CUDA_HOME}/bin:${PATH}
|
||||
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
|
||||
|
||||
# Verify installation
|
||||
gcc --version
|
||||
g++ --version
|
||||
clang-11 --version
|
||||
nvcc --version
|
||||
|
||||
- name: Install PyTorch 2.5.1+cu12.4.1
|
||||
run: |
|
||||
pip install --upgrade pip
|
||||
pip install typing-extensions==4.12.2
|
||||
export TORCH_CUDA_VERSION=124
|
||||
pip install --no-cache-dir torch==2.5.1 --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION}
|
||||
nvcc --version
|
||||
python --version
|
||||
python -c "import torch; print('PyTorch:', torch.__version__)"
|
||||
python -c "import torch; print('CUDA:', torch.version.cuda)"
|
||||
python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
|
||||
|
||||
- name: Build source distribution
|
||||
run: |
|
||||
pip install build scikit-build-core cmake ninja
|
||||
export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
|
||||
|
||||
cd fastvideo-kernel
|
||||
# We don't need full CUDA/Torch to just package the source (sdist)
|
||||
python -m build --sdist --outdir dist
|
||||
pip install setuptools ninja packaging wheel triton
|
||||
|
||||
cd csrc/fastvideo_kernel
|
||||
git submodule update --init --recursive
|
||||
python setup.py sdist --dist-dir=dist
|
||||
|
||||
- name: Publish release distributions to PyPI
|
||||
uses: pypa/gh-action-pypi-publish@release/v1
|
||||
with:
|
||||
packages-dir: fastvideo-kernel/dist/
|
||||
packages-dir: csrc/fastvideo_kernel/dist/
|
||||
@@ -1,17 +0,0 @@
|
||||
{
|
||||
"problemMatcher": [
|
||||
{
|
||||
"owner": "ruff",
|
||||
"pattern": [
|
||||
{
|
||||
"regexp": "^(.+):(\\d+):(\\d+): (\\w+) (.+)$",
|
||||
"file": 1,
|
||||
"line": 2,
|
||||
"column": 3,
|
||||
"code": 4,
|
||||
"message": 5
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,401 @@
|
||||
name: PR Test
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [main]
|
||||
paths:
|
||||
- "fastvideo/**/*.py"
|
||||
- ".github/workflows/pr-test.yml"
|
||||
pull_request:
|
||||
branches: [main]
|
||||
types: [opened, ready_for_review, synchronize, reopened]
|
||||
paths:
|
||||
- "fastvideo/**/*.py"
|
||||
- ".github/workflows/pr-test.yml"
|
||||
- "pyproject.toml"
|
||||
- "docker/Dockerfile.python3.12"
|
||||
- "csrc/**"
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
run_encoder_test:
|
||||
description: "Run encoder-test"
|
||||
required: false
|
||||
default: false
|
||||
type: boolean
|
||||
run_vae_test:
|
||||
description: "Run vae-test"
|
||||
required: false
|
||||
default: false
|
||||
type: boolean
|
||||
run_transformer_test:
|
||||
description: "Run transformer-test"
|
||||
required: false
|
||||
default: false
|
||||
type: boolean
|
||||
run_ssim_test:
|
||||
description: "Run ssim-test"
|
||||
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_unit_test:
|
||||
description: "Run unit-test"
|
||||
required: false
|
||||
default: false
|
||||
type: boolean
|
||||
|
||||
env:
|
||||
PYTHONUNBUFFERED: "1"
|
||||
|
||||
|
||||
concurrency:
|
||||
group: pr-test-${{ github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
pre-commit:
|
||||
uses: ./.github/workflows/pre-commit.yml
|
||||
|
||||
change-filter:
|
||||
runs-on: ubuntu-latest
|
||||
needs: pre-commit
|
||||
if: ${{ github.event.pull_request.draft == false || github.event_name == 'workflow_dispatch' }}
|
||||
outputs:
|
||||
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 }}
|
||||
unit-test: ${{ steps.filter.outputs.unit-test }}
|
||||
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.python3.10'
|
||||
- 'docker/Dockerfile.python3.11'
|
||||
- 'docker/Dockerfile.python3.12'
|
||||
sta-kernel-paths: &sta-kernel-paths
|
||||
- 'csrc/attn/sliding_tile_attn/**'
|
||||
- 'csrc/attn/sliding_tile_attn/tk/**'
|
||||
- 'csrc/attn/sliding_tile_attn/setup.py'
|
||||
- 'csrc/attn/sliding_tile_attn/config_sta.py'
|
||||
- 'csrc/attn/sliding_tile_attn/st_attn.cpp'
|
||||
vsa-kernel-paths: &vsa-kernel-paths
|
||||
- 'csrc/attn/video_sparse_attn/**'
|
||||
- 'csrc/attn/video_sparse_attn/tk/**'
|
||||
- 'csrc/attn/video_sparse_attn/setup.py'
|
||||
- 'csrc/attn/video_sparse_attn/config_vsa.py'
|
||||
- 'csrc/attn/video_sparse_attn/vsa.cpp'
|
||||
vsa-paths: &vsa-paths
|
||||
- 'fastvideo/**'
|
||||
- *common-paths
|
||||
- *vsa-kernel-paths
|
||||
|
||||
# Actual tests
|
||||
encoder-test:
|
||||
- 'fastvideo/models/encoders/**'
|
||||
- 'fastvideo/models/loader/**'
|
||||
- 'fastvideo/tests/encoders/**'
|
||||
- *common-paths
|
||||
vae-test:
|
||||
- 'fastvideo/models/vaes/**'
|
||||
- 'fastvideo/models/loader/**'
|
||||
- 'fastvideo/tests/vaes/**'
|
||||
- *common-paths
|
||||
transformer-test:
|
||||
- 'fastvideo/models/dits/**'
|
||||
- 'fastvideo/models/loader/**'
|
||||
- 'fastvideo/tests/transformers/**'
|
||||
- 'fastvideo/layers/**'
|
||||
- 'fastvideo/attention/**'
|
||||
- *common-paths
|
||||
training-test:
|
||||
- 'fastvideo/**'
|
||||
- *common-paths
|
||||
training-test-VSA:
|
||||
- 'fastvideo/**'
|
||||
- *common-paths
|
||||
- *vsa-kernel-paths
|
||||
inference-test-STA:
|
||||
- 'fastvideo/**'
|
||||
- *common-paths
|
||||
- *sta-kernel-paths
|
||||
precision-test-STA:
|
||||
- *common-paths
|
||||
- *sta-kernel-paths
|
||||
precision-test-VSA:
|
||||
- *common-paths
|
||||
- *vsa-kernel-paths
|
||||
unit-test:
|
||||
- 'fastvideo/**'
|
||||
- *common-paths
|
||||
|
||||
encoder-test:
|
||||
needs: change-filter
|
||||
if: >-
|
||||
(github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.encoder-test == 'true') ||
|
||||
(github.event_name == 'workflow_dispatch' && github.event.inputs.run_encoder_test == 'true')
|
||||
uses: ./.github/workflows/runpod-test.yml
|
||||
with:
|
||||
job_id: "encoder-test"
|
||||
gpu_type: "NVIDIA A40"
|
||||
gpu_count: 1
|
||||
volume_size: 100
|
||||
image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
|
||||
test_command: "uv pip install -e .[test] && pytest ./fastvideo/tests/encoders -s"
|
||||
timeout_minutes: 30
|
||||
secrets:
|
||||
RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
|
||||
RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
|
||||
|
||||
vae-test:
|
||||
needs: change-filter
|
||||
if: >-
|
||||
(github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.vae-test == 'true') ||
|
||||
(github.event_name == 'workflow_dispatch' && github.event.inputs.run_vae_test == 'true')
|
||||
uses: ./.github/workflows/runpod-test.yml
|
||||
with:
|
||||
job_id: "vae-test"
|
||||
gpu_type: "NVIDIA A40"
|
||||
gpu_count: 1
|
||||
volume_size: 100
|
||||
image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
|
||||
test_command: "uv pip install -e .[test] && pytest ./fastvideo/tests/vaes -s"
|
||||
timeout_minutes: 30
|
||||
secrets:
|
||||
RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
|
||||
RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
|
||||
|
||||
transformer-test:
|
||||
needs: change-filter
|
||||
if: >-
|
||||
(github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.transformer-test == 'true') ||
|
||||
(github.event_name == 'workflow_dispatch' && github.event.inputs.run_transformer_test == 'true')
|
||||
uses: ./.github/workflows/runpod-test.yml
|
||||
with:
|
||||
job_id: "transformer-test"
|
||||
gpu_type: "NVIDIA L40S"
|
||||
gpu_count: 1
|
||||
volume_size: 100
|
||||
image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
|
||||
test_command: "uv pip install -e .[test] && pytest ./fastvideo/tests/transformers -s"
|
||||
timeout_minutes: 30
|
||||
secrets:
|
||||
RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
|
||||
RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
|
||||
|
||||
ssim-test:
|
||||
needs: change-filter
|
||||
if: >-
|
||||
github.event_name != 'workflow_dispatch' || (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
python-version: [
|
||||
# {version: "3.10", tag: "latest"},
|
||||
# {version: "3.11", tag: "py3.11-latest"},
|
||||
{version: "3.12", tag: "py3.12-latest"}
|
||||
]
|
||||
uses: ./.github/workflows/runpod-test.yml
|
||||
with:
|
||||
job_id: "ssim-test-py${{ matrix.python-version.version }}"
|
||||
gpu_type: "NVIDIA A40"
|
||||
gpu_count: 2
|
||||
volume_size: 200
|
||||
disk_size: 200
|
||||
image: "ghcr.io/${{ github.repository }}/fastvideo-dev:${{ matrix.python-version.tag }}"
|
||||
test_command: "uv pip install -e .[test] && pytest ./fastvideo/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/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: ${{ secrets.WANDB_API_KEY }}
|
||||
|
||||
training-test-VSA:
|
||||
needs: change-filter
|
||||
if: >-
|
||||
(github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test-VSA == 'true') ||
|
||||
(github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test_VSA == 'true')
|
||||
uses: ./.github/workflows/runpod-test.yml
|
||||
with:
|
||||
job_id: "training-test-VSA"
|
||||
gpu_type: "NVIDIA H100 NVL"
|
||||
gpu_count: 2
|
||||
volume_size: 100
|
||||
disk_size: 100
|
||||
image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
|
||||
test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/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: ${{ secrets.WANDB_API_KEY }}
|
||||
|
||||
inference-test-STA:
|
||||
needs: change-filter
|
||||
if: >-
|
||||
(github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.inference-test-STA == 'true') ||
|
||||
(github.event_name == 'workflow_dispatch' && github.event.inputs.run_inference_test_STA == 'true')
|
||||
uses: ./.github/workflows/runpod-test.yml
|
||||
with:
|
||||
job_id: "inference-test-STA"
|
||||
gpu_type: "NVIDIA H100 NVL"
|
||||
gpu_count: 2
|
||||
volume_size: 100
|
||||
disk_size: 100
|
||||
image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
|
||||
test_command: "uv pip install -e .[test] && pytest ./fastvideo/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_vsa.py"
|
||||
timeout_minutes: 30
|
||||
secrets:
|
||||
RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
|
||||
RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
|
||||
|
||||
unit-test:
|
||||
needs: change-filter
|
||||
if: >-
|
||||
(github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.unit-test == 'true') ||
|
||||
(github.event_name == 'workflow_dispatch' && github.event.inputs.run_unit_test == 'true')
|
||||
uses: ./.github/workflows/runpod-test.yml
|
||||
with:
|
||||
job_id: "unit-test"
|
||||
gpu_type: "NVIDIA L40S"
|
||||
gpu_count: 1
|
||||
volume_size: 100
|
||||
disk_size: 100
|
||||
image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
|
||||
test_command: "uv pip install -e .[test] && pytest ./fastvideo/dataset/ -vs && pytest ./fastvideo/workflow/ -vs && pytest ./fastvideo/entrypoints/ -vs"
|
||||
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/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:
|
||||
# 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:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.10"
|
||||
|
||||
- name: Install dependencies
|
||||
run: pip install requests
|
||||
|
||||
- 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", "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
|
||||
@@ -0,0 +1,18 @@
|
||||
name: pre-commit
|
||||
|
||||
on:
|
||||
workflow_call:
|
||||
|
||||
jobs:
|
||||
pre-commit:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.12"
|
||||
- run: echo "::add-matcher::.github/workflows/matchers/actionlint.json"
|
||||
- run: echo "::add-matcher::.github/workflows/matchers/mypy.json"
|
||||
- uses: pre-commit/action@v3.0.1
|
||||
with:
|
||||
extra_args: --all-files --hook-stage manual
|
||||
@@ -0,0 +1,94 @@
|
||||
name: RunPod Test
|
||||
|
||||
on:
|
||||
workflow_call:
|
||||
inputs:
|
||||
job_id:
|
||||
required: true
|
||||
type: string
|
||||
description: "Unique identifier for this test job"
|
||||
gpu_type:
|
||||
required: true
|
||||
type: string
|
||||
description: "GPU type to use (e.g. NVIDIA A40, NVIDIA L40S)"
|
||||
gpu_count:
|
||||
required: true
|
||||
type: number
|
||||
description: "Number of GPUs to use"
|
||||
volume_size:
|
||||
required: false
|
||||
type: number
|
||||
default: 20
|
||||
description: "Volume size in GB"
|
||||
disk_size:
|
||||
required: false
|
||||
type: number
|
||||
default: 20
|
||||
description: "Disk size in GB"
|
||||
image:
|
||||
required: true
|
||||
type: string
|
||||
description: "Docker image to use"
|
||||
test_command:
|
||||
required: true
|
||||
type: string
|
||||
description: "Command to run tests"
|
||||
timeout_minutes:
|
||||
required: false
|
||||
type: number
|
||||
default: 30
|
||||
description: "Timeout in minutes"
|
||||
secrets:
|
||||
RUNPOD_API_KEY:
|
||||
required: true
|
||||
RUNPOD_PRIVATE_KEY:
|
||||
required: true
|
||||
WANDB_API_KEY:
|
||||
required: false
|
||||
|
||||
jobs:
|
||||
run-test:
|
||||
runs-on: ubuntu-latest
|
||||
environment: runpod-runners
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.12"
|
||||
|
||||
- name: Set up SSH key
|
||||
run: |
|
||||
mkdir -p ~/.ssh
|
||||
echo "${{ secrets.RUNPOD_PRIVATE_KEY }}" > ~/.ssh/id_rsa
|
||||
chmod 600 ~/.ssh/id_rsa
|
||||
ssh-keygen -y -f ~/.ssh/id_rsa > ~/.ssh/id_rsa.pub
|
||||
|
||||
- name: Install dependencies
|
||||
run: pip install requests
|
||||
|
||||
- name: Run tests on RunPod
|
||||
env:
|
||||
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: ${{ inputs.timeout_minutes }}
|
||||
run: >-
|
||||
python .github/scripts/runpod_api.py
|
||||
--gpu-type "${{ inputs.gpu_type }}"
|
||||
--gpu-count ${{ inputs.gpu_count }}
|
||||
--volume-size ${{ inputs.volume_size }}
|
||||
--disk-size ${{ inputs.disk_size }}
|
||||
--image "${{ inputs.image }}"
|
||||
--test-command "${{ inputs.test_command }}"
|
||||
|
||||
- name: Terminate RunPod Instances
|
||||
if: ${{ always() }}
|
||||
env:
|
||||
RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
|
||||
GITHUB_RUN_ID: ${{ github.run_id }}
|
||||
JOB_ID: ${{ inputs.job_id }}
|
||||
run: python .github/scripts/runpod_cleanup.py
|
||||
@@ -0,0 +1,249 @@
|
||||
name: Publish Sliding Tile Attention Kernel to PyPI on Version Change
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- "csrc/attn/sliding_tile_attn/setup.py"
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
check-version-change:
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
version-changed: ${{ steps.check-version.outputs.changed }}
|
||||
new-version: ${{ steps.check-version.outputs.new-version }}
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 2
|
||||
|
||||
- name: Check if version changed
|
||||
id: check-version
|
||||
run: |
|
||||
cd csrc/attn/sliding_tile_attn
|
||||
# Get current commit's version
|
||||
NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup.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")
|
||||
echo "Old version: $OLD_VERSION"
|
||||
|
||||
if [ "$NEW_VERSION" != "$OLD_VERSION" ]; then
|
||||
echo "Version changed from $OLD_VERSION to $NEW_VERSION"
|
||||
echo "changed=true" >> $GITHUB_OUTPUT
|
||||
echo "new-version=$NEW_VERSION" >> $GITHUB_OUTPUT
|
||||
else
|
||||
echo "Version did not change"
|
||||
echo "changed=false" >> $GITHUB_OUTPUT
|
||||
fi
|
||||
|
||||
build_wheels:
|
||||
name: Build Wheel
|
||||
needs: check-version-change
|
||||
if: ${{ needs.check-version-change.outputs.version-changed == 'true' || github.event_name == 'workflow_dispatch' }}
|
||||
runs-on: ${{ matrix.os }}
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
# Using ubuntu-20.04 instead of 22.04 for more compatibility (glibc). Ideally we'd use the
|
||||
# manylinux docker image, but I haven't figured out how to install CUDA on manylinux.
|
||||
os: [ubuntu-22.04]
|
||||
python-version: ['3.10', '3.11', '3.12', '3.13']
|
||||
torch-version: ['2.5.1', '2.6.0']
|
||||
cuda-version: ['12.4.1', '12.5.1', '12.6.3']
|
||||
|
||||
steps:
|
||||
- name: Free up disk space
|
||||
run: |
|
||||
echo "Initial disk space:"
|
||||
df -h
|
||||
|
||||
# Remove large directories
|
||||
sudo rm -rf /usr/share/dotnet
|
||||
sudo rm -rf /usr/local/lib/android
|
||||
sudo rm -rf /opt/ghc
|
||||
sudo rm -rf /usr/local/share/boost
|
||||
sudo rm -rf /usr/share/swift
|
||||
sudo rm -rf /usr/local/lib/node_modules
|
||||
sudo rm -rf /usr/local/share/powershell
|
||||
sudo rm -rf /usr/share/rust
|
||||
sudo rm -rf /usr/local/.ghcup
|
||||
|
||||
# Remove cached files
|
||||
sudo rm -rf /var/lib/apt/lists/*
|
||||
sudo rm -rf /var/cache/apt/archives/*
|
||||
|
||||
echo "Disk space after cleanup:"
|
||||
df -h
|
||||
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
|
||||
- name: Install CUDA ${{ matrix.cuda-version }}
|
||||
uses: Jimver/cuda-toolkit@v0.2.21
|
||||
id: cuda-toolkit
|
||||
with:
|
||||
cuda: ${{ matrix.cuda-version }}
|
||||
linux-local-args: '["--toolkit"]'
|
||||
method: 'network'
|
||||
|
||||
- name: Install dependencies (GCC, Clang, CUDA Paths, Git)
|
||||
run: |
|
||||
sudo apt update
|
||||
sudo apt install -y git patchelf gcc-11 g++-11 clang-11
|
||||
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
|
||||
|
||||
# Allow Git to Access Safe Directory
|
||||
git config --global --add safe.directory /__w/FastVideo/FastVideo
|
||||
|
||||
# Set CUDA environment variables
|
||||
export CUDA_HOME=/usr/local/cuda-${{ matrix.cuda-version }}
|
||||
export PATH=${CUDA_HOME}/bin:${PATH}
|
||||
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
|
||||
|
||||
# Verify installation
|
||||
gcc --version
|
||||
g++ --version
|
||||
clang-11 --version
|
||||
nvcc --version
|
||||
|
||||
- name: Install PyTorch ${{ matrix.torch-version }}+cu${{ matrix.cuda-version }}
|
||||
run: |
|
||||
pip install --upgrade pip
|
||||
# With python 3.13 and torch 2.5.1, unless we update typing-extensions, we get error
|
||||
# AttributeError: attribute '__default__' of 'typing.ParamSpec' objects is not writable
|
||||
pip install typing-extensions==4.12.2
|
||||
# We want to figure out the CUDA version to download pytorch
|
||||
# e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116
|
||||
# see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix
|
||||
export TORCH_CUDA_VERSION=124
|
||||
pip install --no-cache-dir torch==${{ matrix.torch-version }} --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION}
|
||||
nvcc --version
|
||||
python --version
|
||||
python -c "import torch; print('PyTorch:', torch.__version__)"
|
||||
python -c "import torch; print('CUDA:', torch.version.cuda)"
|
||||
python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
|
||||
|
||||
- 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/attn/sliding_tile_attn # Move into the correct folder
|
||||
git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
|
||||
python setup.py bdist_wheel --dist-dir=dist
|
||||
|
||||
- name: Rename wheel file
|
||||
run: |
|
||||
cd csrc/attn/sliding_tile_attn
|
||||
|
||||
CUDA_SHORT_VERSION=$(echo ${{ matrix.cuda-version }} | cut -d. -f1,2 | sed 's/\.//g')
|
||||
TORCH_SHORT_VERSION=$(echo ${{ matrix.torch-version }} | cut -d. -f1,2)
|
||||
# Get the correct version format
|
||||
tmpname=cu${CUDA_SHORT_VERSION}torch${TORCH_SHORT_VERSION}
|
||||
wheel_name=$(ls dist/*whl | xargs -n 1 basename | sed "s/-/+$tmpname-/2")
|
||||
# Rename with version information
|
||||
ls dist/*whl |xargs -I {} mv {} dist/${wheel_name}
|
||||
echo "wheel_name=${wheel_name}" >> $GITHUB_ENV
|
||||
|
||||
- name: Upload wheel artifact
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: ${{ env.wheel_name }}
|
||||
path: csrc/attn/sliding_tile_attn/dist/*.whl
|
||||
retention-days: 90
|
||||
|
||||
publish_package:
|
||||
name: Publish package
|
||||
needs: [build_wheels, check-version-change]
|
||||
if: ${{ needs.check-version-change.outputs.version-changed == 'true' || github.event_name == 'workflow_dispatch' }}
|
||||
runs-on: ubuntu-22.04
|
||||
permissions:
|
||||
id-token: write # Needed for OIDC Trusted Publishing
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.10'
|
||||
|
||||
- name: Install CUDA 12.4.1
|
||||
uses: Jimver/cuda-toolkit@v0.2.21
|
||||
id: cuda-toolkit
|
||||
with:
|
||||
cuda: 12.4.1
|
||||
linux-local-args: '["--toolkit"]'
|
||||
method: 'network'
|
||||
sub-packages: '["nvcc"]'
|
||||
|
||||
- name: Install dependencies (GCC, Clang, CUDA Paths, Git)
|
||||
run: |
|
||||
sudo apt update
|
||||
sudo apt install -y git patchelf gcc-11 g++-11 clang-11
|
||||
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
|
||||
|
||||
# Allow Git to Access Safe Directory
|
||||
git config --global --add safe.directory /__w/FastVideo/FastVideo
|
||||
|
||||
# Set CUDA environment variables
|
||||
export CUDA_HOME=/usr/local/cuda-12.4.1
|
||||
export PATH=${CUDA_HOME}/bin:${PATH}
|
||||
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
|
||||
|
||||
# Verify installation
|
||||
gcc --version
|
||||
g++ --version
|
||||
clang-11 --version
|
||||
nvcc --version
|
||||
|
||||
- name: Install PyTorch 2.5.1+cu12.4.1
|
||||
run: |
|
||||
pip install --upgrade pip
|
||||
# With python 3.13 and torch 2.5.1, unless we update typing-extensions, we get error
|
||||
# AttributeError: attribute '__default__' of 'typing.ParamSpec' objects is not writable
|
||||
pip install typing-extensions==4.12.2
|
||||
# We want to figure out the CUDA version to download pytorch
|
||||
# e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116
|
||||
# see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix
|
||||
export TORCH_CUDA_VERSION=124
|
||||
pip install --no-cache-dir torch==2.5.1 --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION}
|
||||
nvcc --version
|
||||
python --version
|
||||
python -c "import torch; print('PyTorch:', torch.__version__)"
|
||||
python -c "import torch; print('CUDA:', torch.version.cuda)"
|
||||
python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
|
||||
|
||||
- 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/attn/sliding_tile_attn # Move into the correct folder
|
||||
git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
|
||||
python setup.py sdist --dist-dir=dist
|
||||
|
||||
- name: Publish release distributions to PyPI
|
||||
uses: pypa/gh-action-pypi-publish@release/v1
|
||||
with:
|
||||
packages-dir: csrc/attn/sliding_tile_attn/dist/
|
||||
@@ -0,0 +1,31 @@
|
||||
name: Run Tests
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ main ]
|
||||
pull_request:
|
||||
branches: [ main ]
|
||||
|
||||
jobs:
|
||||
test:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Check out repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.12' # or any version you need
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip setuptools wheel
|
||||
pip install torch
|
||||
pip install packaging ninja
|
||||
pip install -e .
|
||||
pip install pytest
|
||||
|
||||
- name: Run Pytest
|
||||
run: |
|
||||
pytest --ignore csrc/attn/test
|
||||
@@ -0,0 +1,257 @@
|
||||
name: Publish Video Sparse Attention Kernel to PyPI on Version Change
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- "csrc/attn/video_sparse_attn/setup.py"
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
check-version-change:
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
version-changed: ${{ steps.check-version.outputs.changed }}
|
||||
new-version: ${{ steps.check-version.outputs.new-version }}
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 2
|
||||
|
||||
- name: Check if version changed
|
||||
id: check-version
|
||||
run: |
|
||||
cd csrc/attn/video_sparse_attn
|
||||
# Get current commit's version
|
||||
NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup.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")
|
||||
echo "Old version: $OLD_VERSION"
|
||||
|
||||
if [ "$NEW_VERSION" != "$OLD_VERSION" ]; then
|
||||
echo "Version changed from $OLD_VERSION to $NEW_VERSION"
|
||||
echo "changed=true" >> $GITHUB_OUTPUT
|
||||
echo "new-version=$NEW_VERSION" >> $GITHUB_OUTPUT
|
||||
else
|
||||
echo "Version did not change"
|
||||
echo "changed=false" >> $GITHUB_OUTPUT
|
||||
fi
|
||||
|
||||
build_wheels:
|
||||
name: Build Wheel
|
||||
needs: check-version-change
|
||||
if: ${{ needs.check-version-change.outputs.version-changed == 'true' || github.event_name == 'workflow_dispatch' }}
|
||||
runs-on: ${{ matrix.os }}
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
# Using ubuntu-20.04 instead of 22.04 for more compatibility (glibc). Ideally we'd use the
|
||||
# manylinux docker image, but I haven't figured out how to install CUDA on manylinux.
|
||||
os: [ubuntu-22.04]
|
||||
python-version: ['3.10', '3.11', '3.12', '3.13']
|
||||
# For version reference https://pytorch.org/get-started/previous-versions/
|
||||
torch-cuda:
|
||||
- torch-version: '2.5.1'
|
||||
cuda-version: '12.4.1'
|
||||
torch-cuda-short: 'cu124'
|
||||
- torch-version: '2.6.0'
|
||||
cuda-version: '12.6.3'
|
||||
torch-cuda-short: 'cu126'
|
||||
- torch-version: '2.7.1'
|
||||
cuda-version: '12.8.0'
|
||||
torch-cuda-short: 'cu128'
|
||||
|
||||
steps:
|
||||
- name: Free up disk space
|
||||
run: |
|
||||
echo "Initial disk space:"
|
||||
df -h
|
||||
|
||||
# Remove large directories
|
||||
sudo rm -rf /usr/share/dotnet
|
||||
sudo rm -rf /usr/local/lib/android
|
||||
sudo rm -rf /opt/ghc
|
||||
sudo rm -rf /usr/local/share/boost
|
||||
sudo rm -rf /usr/share/swift
|
||||
sudo rm -rf /usr/local/lib/node_modules
|
||||
sudo rm -rf /usr/local/share/powershell
|
||||
sudo rm -rf /usr/share/rust
|
||||
sudo rm -rf /usr/local/.ghcup
|
||||
|
||||
# Remove cached files
|
||||
sudo rm -rf /var/lib/apt/lists/*
|
||||
sudo rm -rf /var/cache/apt/archives/*
|
||||
|
||||
echo "Disk space after cleanup:"
|
||||
df -h
|
||||
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
|
||||
- name: Install CUDA ${{ matrix.torch-cuda.cuda-version }}
|
||||
uses: Jimver/cuda-toolkit@v0.2.21
|
||||
id: cuda-toolkit
|
||||
with:
|
||||
cuda: ${{ matrix.torch-cuda.cuda-version }}
|
||||
linux-local-args: '["--toolkit"]'
|
||||
method: 'network'
|
||||
|
||||
- name: Install dependencies (GCC, Clang, CUDA Paths, Git)
|
||||
run: |
|
||||
sudo apt update
|
||||
sudo apt install -y git patchelf gcc-11 g++-11 clang-11
|
||||
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
|
||||
|
||||
# Allow Git to Access Safe Directory
|
||||
git config --global --add safe.directory /__w/FastVideo/FastVideo
|
||||
|
||||
# Set CUDA environment variables
|
||||
export CUDA_HOME=/usr/local/cuda-${{ matrix.torch-cuda.cuda-version }}
|
||||
export PATH=${CUDA_HOME}/bin:${PATH}
|
||||
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
|
||||
|
||||
# Verify installation
|
||||
gcc --version
|
||||
g++ --version
|
||||
clang-11 --version
|
||||
nvcc --version
|
||||
|
||||
- name: Install PyTorch ${{ matrix.torch-cuda.torch-version }}+cu${{ matrix.torch-cuda.cuda-version }}
|
||||
run: |
|
||||
pip install --upgrade pip
|
||||
# With python 3.13 and torch 2.5.1, unless we update typing-extensions, we get error
|
||||
# AttributeError: attribute '__default__' of 'typing.ParamSpec' objects is not writable
|
||||
pip install typing-extensions==4.12.2
|
||||
# We want to figure out the CUDA version to download pytorch
|
||||
# e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116
|
||||
# see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix
|
||||
pip install --no-cache-dir torch==${{ matrix.torch-cuda.torch-version }} --index-url https://download.pytorch.org/whl/${{matrix.torch-cuda.torch-cuda-short}}
|
||||
nvcc --version
|
||||
python --version
|
||||
python -c "import torch; print('PyTorch:', torch.__version__)"
|
||||
python -c "import torch; print('CUDA:', torch.version.cuda)"
|
||||
python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
|
||||
|
||||
- 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/attn/video_sparse_attn # Move into the correct folder
|
||||
git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
|
||||
python setup.py bdist_wheel --dist-dir=dist
|
||||
|
||||
- name: Rename wheel file
|
||||
run: |
|
||||
cd csrc/attn/video_sparse_attn
|
||||
|
||||
CUDA_SHORT_VERSION=$(echo ${{ matrix.torch-cuda.cuda-version }} | cut -d. -f1,2 | sed 's/\.//g')
|
||||
TORCH_SHORT_VERSION=$(echo ${{ matrix.torch-cuda.torch-version }} | cut -d. -f1,2)
|
||||
# Get the correct version format
|
||||
tmpname=cu${CUDA_SHORT_VERSION}torch${TORCH_SHORT_VERSION}
|
||||
wheel_name=$(ls dist/*whl | xargs -n 1 basename | sed "s/-/+$tmpname-/2")
|
||||
# Rename with version information
|
||||
ls dist/*whl |xargs -I {} mv {} dist/${wheel_name}
|
||||
echo "wheel_name=${wheel_name}" >> $GITHUB_ENV
|
||||
|
||||
- name: Upload wheel artifact
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: ${{ env.wheel_name }}
|
||||
path: csrc/attn/video_sparse_attn/dist/*.whl
|
||||
retention-days: 90
|
||||
|
||||
publish_package:
|
||||
name: Publish package
|
||||
needs: [build_wheels, check-version-change]
|
||||
if: ${{ needs.check-version-change.outputs.version-changed == 'true' || github.event_name == 'workflow_dispatch' }}
|
||||
runs-on: ubuntu-22.04
|
||||
permissions:
|
||||
id-token: write # Needed for OIDC Trusted Publishing
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.10'
|
||||
|
||||
- name: Install CUDA 12.4.1
|
||||
uses: Jimver/cuda-toolkit@v0.2.21
|
||||
id: cuda-toolkit
|
||||
with:
|
||||
cuda: 12.4.1
|
||||
linux-local-args: '["--toolkit"]'
|
||||
method: 'network'
|
||||
sub-packages: '["nvcc"]'
|
||||
|
||||
- name: Install dependencies (GCC, Clang, CUDA Paths, Git)
|
||||
run: |
|
||||
sudo apt update
|
||||
sudo apt install -y git patchelf gcc-11 g++-11 clang-11
|
||||
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
|
||||
|
||||
# Allow Git to Access Safe Directory
|
||||
git config --global --add safe.directory /__w/FastVideo/FastVideo
|
||||
|
||||
# Set CUDA environment variables
|
||||
export CUDA_HOME=/usr/local/cuda-12.4.1
|
||||
export PATH=${CUDA_HOME}/bin:${PATH}
|
||||
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
|
||||
|
||||
# Verify installation
|
||||
gcc --version
|
||||
g++ --version
|
||||
clang-11 --version
|
||||
nvcc --version
|
||||
|
||||
- name: Install PyTorch 2.5.1+cu12.4.1
|
||||
run: |
|
||||
pip install --upgrade pip
|
||||
# With python 3.13 and torch 2.5.1, unless we update typing-extensions, we get error
|
||||
# AttributeError: attribute '__default__' of 'typing.ParamSpec' objects is not writable
|
||||
pip install typing-extensions==4.12.2
|
||||
# We want to figure out the CUDA version to download pytorch
|
||||
# e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116
|
||||
# see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix
|
||||
export TORCH_CUDA_VERSION=124
|
||||
pip install --no-cache-dir torch==2.5.1 --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION}
|
||||
nvcc --version
|
||||
python --version
|
||||
python -c "import torch; print('PyTorch:', torch.__version__)"
|
||||
python -c "import torch; print('CUDA:', torch.version.cuda)"
|
||||
python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
|
||||
|
||||
- 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/attn/video_sparse_attn # Move into the correct folder
|
||||
git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
|
||||
python setup.py sdist --dist-dir=dist
|
||||
|
||||
- name: Publish release distributions to PyPI
|
||||
uses: pypa/gh-action-pypi-publish@release/v1
|
||||
with:
|
||||
packages-dir: csrc/attn/video_sparse_attn/dist/
|
||||
-19
@@ -18,7 +18,6 @@ venv/
|
||||
.venv/
|
||||
runs/
|
||||
samples/
|
||||
Miniconda3-latest-Linux-x86_64.sh
|
||||
*validation/
|
||||
data/
|
||||
outputs/
|
||||
@@ -33,12 +32,6 @@ env
|
||||
**.txt
|
||||
*.log
|
||||
weights/
|
||||
logs/
|
||||
|
||||
# SSIM test outputs
|
||||
fastvideo/tests/ssim/generated_videos/
|
||||
**/.cache/**
|
||||
|
||||
|
||||
# Distribution / packaging
|
||||
build/
|
||||
@@ -76,18 +69,6 @@ docs/distillation/examples/
|
||||
!docs/assets/images/**/*.png
|
||||
!comfyui/assets/**/*.png
|
||||
!comfyui/assets/**/*.gif
|
||||
!assets/images/**/*.png
|
||||
!assets/images/**/*.jpg
|
||||
!assets/images/**/*.jpeg
|
||||
!assets/images/**/*.gif
|
||||
!assets/videos/**/*.mp4
|
||||
|
||||
dmd_t2v_output/
|
||||
preprocess_output_text/
|
||||
|
||||
|
||||
.claude/
|
||||
.codex/
|
||||
.sisyphus/
|
||||
openspec/
|
||||
fastvideo/tests/ssim/reference_videos/**
|
||||
|
||||
+6
-5
@@ -1,6 +1,7 @@
|
||||
[submodule "fastvideo-kernel/include/tk"]
|
||||
path = fastvideo-kernel/include/tk
|
||||
[submodule "csrc/attn/video_sparse_attn/tk"]
|
||||
path = csrc/attn/video_sparse_attn/tk
|
||||
url = https://github.com/HazyResearch/ThunderKittens.git
|
||||
|
||||
[submodule "csrc/attn/sliding_tile_attn/tk"]
|
||||
path = csrc/attn/sliding_tile_attn/tk
|
||||
url = https://github.com/HazyResearch/ThunderKittens.git
|
||||
[submodule "fastvideo-kernel/include/cutlass"]
|
||||
path = fastvideo-kernel/include/cutlass
|
||||
url = https://github.com/NVIDIA/cutlass.git
|
||||
|
||||
@@ -4,13 +4,13 @@ default_stages:
|
||||
exclude: |
|
||||
(?x)(
|
||||
fastvideo/third_party/.*|
|
||||
fastvideo-kernel/.*|
|
||||
csrc/.*|
|
||||
assets/.*|
|
||||
tests/.*|
|
||||
demo/.*|
|
||||
predict\.py|
|
||||
scripts/.*|
|
||||
assets/prompts/.*|
|
||||
prompts/.*|
|
||||
fastvideo/data_preprocess/.*|
|
||||
fastvideo/dataset/.*|
|
||||
fastvideo/models/.*|
|
||||
@@ -18,8 +18,10 @@ exclude: |
|
||||
fastvideo/train\.py|
|
||||
fastvideo/utils/.*|
|
||||
examples/.*|
|
||||
.github/workflows/publish-fastvideo.yml|
|
||||
.github/workflows/_template-build-image.yml|
|
||||
.github/workflows/fastvideo-publish.yml|
|
||||
.github/workflows/sta-publish.yml|
|
||||
.github/workflows/vsa-publish.yml|
|
||||
.github/workflows/build-image-template.yml|
|
||||
docs/source/inference/support_matrix.md
|
||||
)
|
||||
repos:
|
||||
@@ -58,7 +60,7 @@ repos:
|
||||
hooks:
|
||||
- id: mypy
|
||||
args: [--python-version, '3.10', --follow-imports, "skip", "--disable-error-code", "union-attr", "--disable-error-code", "override" ]
|
||||
additional_dependencies: [types-aiofiles, types-cachetools, types-setuptools, types-PyYAML, types-requests]
|
||||
additional_dependencies: [types-cachetools, types-setuptools, types-PyYAML, types-requests]
|
||||
- repo: local
|
||||
hooks:
|
||||
- id: check-filenames
|
||||
@@ -66,7 +68,7 @@ repos:
|
||||
entry: bash
|
||||
args:
|
||||
- -c
|
||||
- 'git ls-files | grep -v "^\"*fastvideo/tests/ssim/" | grep -v "^\"*fastvideo/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
|
||||
- 'git ls-files | grep -v "^fastvideo/tests/ssim/" | grep -v "^fastvideo/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
|
||||
language: system
|
||||
always_run: true
|
||||
pass_filenames: false
|
||||
|
||||
@@ -1,56 +0,0 @@
|
||||
# Repository Guidelines
|
||||
|
||||
## Project Structure & Module Organization
|
||||
- Core Python package: `fastvideo/` (models, pipelines, training, distributed runtime, CLI entrypoints).
|
||||
- CUDA/custom kernels: `fastvideo-kernel/` (separate build/test flow).
|
||||
- Tests:
|
||||
- `fastvideo/tests/` for package-level tests (dataset, encoders, inference, training, SSIM, workflow).
|
||||
- `tests/local_tests/` for additional local/component checks.
|
||||
- Docs and guides: `docs/` (MkDocs source), with contributor docs in `docs/contributing/`.
|
||||
- Runnable examples and scripts: `examples/` and `scripts/`.
|
||||
- Static assets: `assets/` (including `assets/images/`, `assets/videos/`, and `assets/prompts/`) and `comfyui/assets/`.
|
||||
|
||||
## Build, Test, and Development Commands
|
||||
- `uv pip install -e .[dev]`: editable install with lint/test extras.
|
||||
- `pre-commit install --hook-type pre-commit --hook-type commit-msg`: enable local hooks.
|
||||
- `pre-commit run --all-files`: run formatter/lint/type/spelling checks.
|
||||
- `pytest tests/`: run top-level test suite.
|
||||
- `pytest fastvideo/tests/ -v`: run package tests.
|
||||
- `pytest fastvideo/tests/ssim/ -vs`: run SSIM regression tests (GPU-heavy).
|
||||
- `cd fastvideo-kernel && ./build.sh`: build kernel extensions.
|
||||
|
||||
## Coding Style & Naming Conventions
|
||||
- Python 3.10+; 4-space indentation; keep code and imports readable and explicit.
|
||||
- Style tools are configured in `pyproject.toml` and `.pre-commit-config.yaml`:
|
||||
- `yapf` (format), `ruff` (lint, auto-fix), `mypy` (typing), `codespell`.
|
||||
- Target line length is 80.
|
||||
- Naming: `snake_case` for functions/files, `PascalCase` for classes, `UPPER_SNAKE_CASE` for constants.
|
||||
|
||||
## Testing Guidelines
|
||||
- Use `pytest` and place tests near relevant domains (e.g., `fastvideo/tests/encoders/`).
|
||||
- Prefer descriptive names like `test_<feature>_<expected_behavior>.py`.
|
||||
- For new pipelines/backends, include at least one regression-oriented test; add SSIM coverage when output quality must be preserved.
|
||||
- Document GPU assumptions in tests that require specific hardware.
|
||||
|
||||
## Commit & Pull Request Guidelines
|
||||
- Follow existing commit style: short subject with optional tag prefix, e.g. `[bugfix]: ...`, `[feat]: ...`, `[misc]: ...`, and include PR reference like `(#1234)` when applicable.
|
||||
- Keep commits focused by concern (feature, refactor, fix).
|
||||
- PRs should include:
|
||||
- clear problem/solution summary,
|
||||
- test evidence (`pytest`/SSIM outputs or rationale if skipped),
|
||||
- linked issue/PR context,
|
||||
- screenshots or sample outputs for UI/demo/docs changes.
|
||||
|
||||
## Agent Infrastructure
|
||||
|
||||
This repository is agent-friendly. Before doing any work, read:
|
||||
|
||||
1. `.agents/onboarding/README.md` — full onboarding guide with step-by-step instructions.
|
||||
2. `.agents/memory/codebase-map/README.md` — structural index of the entire repository.
|
||||
3. `.agents/skills/` — available agent skills (check if one exists before writing code).
|
||||
4. `.agents/workflows/` — SOPs for common procedures (experiment lifecycle, evaluation, etc.).
|
||||
5. `.agents/lessons/` — known pitfalls and their documented fixes.
|
||||
|
||||
If you are exploring a new procedure that has no existing SOP, document your
|
||||
progress in `.agents/exploration/` and flag it for review at the end of your
|
||||
session.
|
||||
@@ -1,79 +1,70 @@
|
||||
<div align="center">
|
||||
<img src=assets/logos/logo.svg width="30%"/>
|
||||
</div>
|
||||
**FastVideo is a unified post-training and inference framework for accelerated video generation.**
|
||||
|
||||
FastVideo features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
|
||||
|
||||
<p align="center">
|
||||
| <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982" target="_blank"><b>Weekly Dev Meeting</b></a> | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> | 🟣💬 <a href="https://github.com/hao-ai-lab/FastVideo/discussions/1097" target="_blank"> <b> WeChat </b> </a> |
|
||||
| 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408" target="_blank"><b>FastWan</b></a> | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> | 🟣💬 <a href="https://ibb.co/c7g1qdD" target="_blank"> <b> WeChat </b> </a> |
|
||||
</p>
|
||||
|
||||
**FastVideo is a unified post-training and real-time inference framework for accelerated video generation.**
|
||||
<div align="center">
|
||||
<img src=assets/fastwan.png width="90%"/>
|
||||
</div>
|
||||
|
||||
## NEWS
|
||||
- `2026/03/17`: Release Live demo: [Into the Dreamverse: Vibe Directing in FastVideo](https://dreamverse.fastvideo.org/), check out the [Blog](https://haoailab.com/blogs/dreamverse/).
|
||||
- `2026/03/13`: Release Live demo: [Create a 5s 1080p Video in 4.5s with FastVideo on a Single GPU](https://1080p.fastvideo.org/), check out the [Blog](https://haoailab.com/blogs/fastvideo_realtime_1080p/).
|
||||
- `2025/11/19`: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py).
|
||||
- `2025/08/04`: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
|
||||
|
||||
### More News
|
||||
|
||||
- `2025/06/14`: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389).
|
||||
- `2025/04/24`: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
|
||||
- `2025/02/18`: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
|
||||
- ```2025/11/19```: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
|
||||
- ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
|
||||
- ```2025/06/14```: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
|
||||
- ```2025/04/24```: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
|
||||
- ```2025/02/18```: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
|
||||
|
||||
## Key Features
|
||||
|
||||
FastVideo has the following features:
|
||||
|
||||
- End-to-end post-training support for bidirectional and autoregressive models:
|
||||
- End-to-end post-training support:
|
||||
- [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) for Wan2.1 and Wan2.2 to achineve >50x denoising speedup
|
||||
- Data preprocessing pipeline for video data
|
||||
- Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs
|
||||
- Data preprocessing pipeline for video, image, and text data
|
||||
- Distribution Matching Distillation (DMD2) stepwise distillation.
|
||||
- Sparse attention with [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
|
||||
- [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >50x denoising speedup
|
||||
- Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
|
||||
- Causal distillation through Self-Forcing
|
||||
- See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for full list of supported models and recipes.
|
||||
- Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs
|
||||
- State-of-the-art performance optimizations for inference
|
||||
- Sequence Parallelism for distributed inference
|
||||
- Multiple state-of-the-art attention backends
|
||||
- User-friendly CLI and Python API
|
||||
- See this [page](https://hao-ai-lab.github.io/FastVideo/inference/optimizations/) for full list of supported optimizations.
|
||||
- [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
|
||||
- [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
|
||||
- [TeaCache](https://arxiv.org/pdf/2411.19108)
|
||||
- [Sage Attention](https://arxiv.org/abs/2410.02367)
|
||||
- Diverse hardware and OS support
|
||||
- Support H100, A100, 4090
|
||||
- Support Linux, Windows, MacOS
|
||||
- See this [page](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for full list of supported models, hardware assumptions, and optimization compatibility.
|
||||
|
||||
## Getting Started
|
||||
|
||||
We recommend using [uv](https://docs.astral.sh/uv/) to create a clean environment. If you previously used Conda, switching to uv generally gives faster and more stable installs.
|
||||
We recommend using an environment manager such as `Conda` to create a clean environment:
|
||||
|
||||
```bash
|
||||
# Create and activate a new uv environment
|
||||
uv venv --python 3.12 --seed
|
||||
source .venv/bin/activate
|
||||
# Create and activate a new conda environment
|
||||
conda create -n fastvideo python=3.12
|
||||
conda activate fastvideo
|
||||
|
||||
# Install FastVideo
|
||||
uv pip install fastvideo
|
||||
pip install fastvideo
|
||||
```
|
||||
|
||||
Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.
|
||||
|
||||
## Sparse Distillation
|
||||
|
||||
For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
|
||||
|
||||
See below for recipes and datasets:
|
||||
|
||||
| Model | Sparse Distillation | Dataset |
|
||||
| ------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
|
||||
| [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P) | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k) |
|
||||
| [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free) | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k) |
|
||||
| Model | Sparse Distillation | Dataset |
|
||||
|:-------------------------------------------------------------------------------------------: |:---------------------------------------------------------------------------------------------------------------: |:--------------------------------------------------------------------------------------------------------: |
|
||||
| [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P) | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k) |
|
||||
| [FastWan2.1-T2V-14B-Preview](https://huggingface.co/FastVideo/FastWan2.1-T2V-14B-Diffusers) | Coming soon! | [FastVideo Synthetic Wan2.1 720P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x768x1280_250k) |
|
||||
| [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free) | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k) |
|
||||
|
||||
## Inference
|
||||
|
||||
### Generating Your First Video
|
||||
|
||||
Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://hao-ai-lab.github.io/FastVideo/attention/vsa/#installation). Create a file called `example.py` with the following code:
|
||||
Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation/). Create a file called `example.py` with the following code:
|
||||
|
||||
```python
|
||||
import os
|
||||
@@ -94,6 +85,7 @@ def main():
|
||||
# Generate the video
|
||||
video = generator.generate_video(
|
||||
prompt,
|
||||
return_frames=True, # Also return frames from this call (defaults to False)
|
||||
output_path="my_videos/", # Controls where videos are saved
|
||||
save_video=True
|
||||
)
|
||||
@@ -110,37 +102,55 @@ python example.py
|
||||
|
||||
For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/).
|
||||
|
||||
## More Guides
|
||||
### Other docs:
|
||||
|
||||
- [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/)
|
||||
- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/)
|
||||
|
||||
## Distillation and Finetuning
|
||||
- [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/)
|
||||
- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)
|
||||
<!-- - [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetune.html) -->
|
||||
|
||||
## Awesome work using FastVideo or our research projects
|
||||
|
||||
- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based on a fork of FastVideo on Sept. 24, 2025.
|
||||
- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo.
|
||||
- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo.
|
||||
- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo.
|
||||
- [HY-WorldPlay](https://github.com/Tencent-Hunyuan/HY-WorldPlay): An action-conditioned world model model trained using FastVideo framework.
|
||||
- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention.
|
||||
- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch.
|
||||
- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention.
|
||||
- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based on a fork of FastVideo on Sept. 24, 2025. [](https://github.com/sgl-project/sglang)
|
||||
|
||||
- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. [](https://github.com/XueZeyue/DanceGRPO)
|
||||
- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo. [](https://github.com/Tencent-Hunyuan/SRPO)
|
||||
- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. [](https://github.com/Vchitect/DCM)
|
||||
- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention. [](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5)
|
||||
- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch. [](https://github.com/kandinskylab/kandinsky-5)
|
||||
- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention. [](https://github.com/meituan-longcat/LongCat-Video)
|
||||
|
||||
## 🤝 Contributing
|
||||
|
||||
We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/).
|
||||
See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899).
|
||||
|
||||
## Acknowledgement
|
||||
We learned and reused code from the following projects:
|
||||
- [Wan-Video](https://github.com/Wan-Video)
|
||||
- [ThunderKittens](https://github.com/HazyResearch/ThunderKittens)
|
||||
- [Triton](https://github.com/triton-lang/triton)
|
||||
- [DMD2](https://github.com/tianweiy/DMD2)
|
||||
- [diffusers](https://github.com/huggingface/diffusers)
|
||||
- [xDiT](https://github.com/xdit-project/xDiT)
|
||||
- [vLLM](https://github.com/vllm-project/vllm)
|
||||
- [SGLang](https://github.com/sgl-project/sglang)
|
||||
|
||||
We learned the design and reused code from the following projects: [Wan-Video](https://github.com/Wan-Video), [ThunderKittens](https://github.com/HazyResearch/ThunderKittens), [DMD2](https://github.com/tianweiy/DMD2), [diffusers](https://github.com/huggingface/diffusers), [xDiT](https://github.com/xdit-project/xDiT), [vLLM](https://github.com/vllm-project/vllm), [SGLang](https://github.com/sgl-project/sglang). We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project.
|
||||
We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project.
|
||||
|
||||
## Citation
|
||||
|
||||
If you find FastVideo useful, please consider citing our research work:
|
||||
If you find FastVideo useful, please considering citing our work:
|
||||
|
||||
```bibtex
|
||||
@software{fastvideo2024,
|
||||
title = {FastVideo: A Unified Framework for Accelerated Video Generation},
|
||||
author = {The FastVideo Team},
|
||||
url = {https://github.com/hao-ai-lab/FastVideo},
|
||||
month = apr,
|
||||
year = {2024},
|
||||
}
|
||||
|
||||
@article{zhang2025vsa,
|
||||
title={Vsa: Faster video diffusion with trainable sparse attention},
|
||||
author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
|
||||
|
||||
+8
-3
@@ -1,10 +1,15 @@
|
||||
try:
|
||||
from .comfyui.video_generator.nodes import (NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS)
|
||||
from .comfyui.video_generator.nodes import (NODE_CLASS_MAPPINGS,
|
||||
NODE_DISPLAY_NAME_MAPPINGS)
|
||||
WEB_DIRECTORY = "./web"
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
|
||||
__all__ = [
|
||||
'NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY'
|
||||
]
|
||||
except ImportError:
|
||||
# ComfyUI environment not available, skip comfyui imports
|
||||
NODE_CLASS_MAPPINGS = {}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {}
|
||||
WEB_DIRECTORY = "./web"
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
|
||||
__all__ = [
|
||||
'NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY'
|
||||
]
|
||||
|
||||
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|
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|
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Load Diff
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@@ -1,7 +0,0 @@
|
||||
# FastVideo/assets/videos
|
||||
|
||||
This folder is used to store **video assets for examples**, primarily **input videos** consumed by scripts under `FastVideo/examples/`.
|
||||
|
||||
- **Typical contents**: short input clips for demos (e.g., video2world / image2video examples).
|
||||
- **Non-critical**: these assets are for convenience and are not required to use the FastVideo library.
|
||||
- **Large files**: avoid committing large videos to git; prefer shared storage or download-on-demand.
|
||||
Binary file not shown.
@@ -38,8 +38,7 @@ python -m benchmarks.fvd.cli \
|
||||
--num-frames 32 \
|
||||
--clip-strategy random \
|
||||
--batch-size 32 \
|
||||
--seed 42 \
|
||||
--extractor clip
|
||||
--seed 42
|
||||
```
|
||||
|
||||
**Standard protocols:**
|
||||
@@ -52,8 +51,6 @@ python -m benchmarks.fvd.cli \
|
||||
--protocol fvd2048_16f # or fvd2048_128f, quick_test, etc.
|
||||
```
|
||||
|
||||
This would use i3d model by default as the feature extractor
|
||||
|
||||
**Feature caching** (speed up repeated evaluations):
|
||||
|
||||
```bash
|
||||
@@ -61,7 +58,7 @@ python -m benchmarks.fvd.cli \
|
||||
--real-path data/real/ \
|
||||
--gen-path outputs/gen/ \
|
||||
--protocol fvd2048_16f \
|
||||
--cache-real-features fvd-cache/extractor_name # Directory path (will save/load fvd-cache/extractor_name/extractor-name_real_features.pkl)
|
||||
--cache-real-features cache/real # Directory path (will save/load cache/real/real_features.pkl)
|
||||
```
|
||||
|
||||
Run `python -m benchmarks.fvd.cli --help` for all options.
|
||||
@@ -86,7 +83,6 @@ batch_size=32, # GPU batch size
|
||||
device='cuda', # cuda|cpu
|
||||
cache_real_features=None, # Cache path for speed
|
||||
seed=42, # Reproducibility
|
||||
extractor='i3d', # i3d|clip|videomae
|
||||
```
|
||||
|
||||
## Programmatic Usage
|
||||
@@ -101,6 +97,7 @@ print(f"FVD: {results['fvd']:.2f}")
|
||||
|
||||
## Notes
|
||||
|
||||
- I3D model auto-downloads from Hugging Face on first run
|
||||
- Requires minimum 10 frames per clip
|
||||
- Supports both video files (.mp4, .avi, etc.) and frame directories
|
||||
- `--cache-real-features` expects a **directory path** (e.g., `cache/real`), it will automatically create/load `real_features.pkl` inside that directory
|
||||
|
||||
@@ -13,7 +13,7 @@ from .fvd import (
|
||||
compute_statistics,
|
||||
FVDConfig,
|
||||
)
|
||||
from .feature_extractors import (BaseFeatureExtractor, I3DFeatureExtractor, load_extractor)
|
||||
from .i3d_model import I3DFeatureExtractor
|
||||
from .video_utils import (
|
||||
load_video_auto,
|
||||
sample_clips_from_video,
|
||||
@@ -27,9 +27,7 @@ __all__ = [
|
||||
'compute_frechet_distance',
|
||||
'compute_statistics',
|
||||
'FVDConfig',
|
||||
'BaseFeatureExtractor',
|
||||
'I3DFeatureExtractor',
|
||||
'load_extractor',
|
||||
'load_video_auto',
|
||||
'sample_clips_from_video',
|
||||
'load_video_clips_streaming',
|
||||
|
||||
+145
-37
@@ -1,37 +1,125 @@
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
import traceback
|
||||
from pathlib import Path
|
||||
|
||||
from .fvd import compute_fvd_with_config, FVDConfig
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description='Compute Fréchet Video Distance (FVD)')
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Compute Fréchet Video Distance (FVD)',
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
# Standard FVD2048_16f protocol
|
||||
python -m fastvideo.benchmarks.fvd.cli \\
|
||||
--real-path data/real/ \\
|
||||
--gen-path outputs/gen/ \\
|
||||
--protocol fvd2048_16f
|
||||
|
||||
# Custom configuration
|
||||
python -m fastvideo.benchmarks.fvd.cli \\
|
||||
--real-path data/real/ \\
|
||||
--gen-path outputs/gen/ \\
|
||||
--num-videos 1024 \\
|
||||
--num-frames 32 \\
|
||||
--clip-strategy random \\
|
||||
--frame-stride 2
|
||||
""")
|
||||
|
||||
# Required arguments
|
||||
parser.add_argument('--real-path', type=str, required=True, help='Path to real videos')
|
||||
parser.add_argument('--gen-path', type=str, required=True, help='Path to generated videos')
|
||||
|
||||
# Extractor selection
|
||||
parser.add_argument('--extractor',
|
||||
parser.add_argument('--real-path',
|
||||
type=str,
|
||||
default='i3d',
|
||||
choices=['i3d', 'clip', 'videomae'],
|
||||
help='Feature extractor model to use (default: i3d)')
|
||||
required=True,
|
||||
help='Path to real videos directory')
|
||||
parser.add_argument('--gen-path',
|
||||
type=str,
|
||||
required=True,
|
||||
help='Path to generated videos directory')
|
||||
|
||||
# Standard args
|
||||
parser.add_argument('--seed', type=int, default=None, help='Random seed for reproducibility')
|
||||
# Reproducibility
|
||||
parser.add_argument(
|
||||
'--seed',
|
||||
type=int,
|
||||
default=None,
|
||||
help='Random seed for reproducibility (np.random, random, torch)')
|
||||
|
||||
# Protocol presets
|
||||
parser.add_argument('--protocol',
|
||||
type=str,
|
||||
default=None,
|
||||
choices=['fvd2048_16f', 'fvd2048_128f', 'quick_test'],
|
||||
choices=[
|
||||
'fvd2048_16f', 'fvd2048_128f',
|
||||
'fvd2048_128f_subsample8', 'quick_test'
|
||||
],
|
||||
help='Use standard protocol (overrides other settings)')
|
||||
parser.add_argument('--num-videos', type=int, default=2048, help='Number of videos to use')
|
||||
parser.add_argument('--num-frames', type=int, default=16, help='Number of frames per clip')
|
||||
parser.add_argument('--clip-strategy', type=str, default='beginning', help='Clip sampling strategy')
|
||||
parser.add_argument('--batch-size', type=int, default=32, help='Batch size for feature extraction')
|
||||
parser.add_argument('--device', type=str, default='cuda', help='Device to use (cuda or cpu)')
|
||||
parser.add_argument('--cache-real-features', type=str, default=None, help='Path to cache real video features')
|
||||
parser.add_argument('--quiet', action='store_true', help='Suppress progress output')
|
||||
|
||||
# Video selection
|
||||
parser.add_argument('--num-videos',
|
||||
type=int,
|
||||
default=2048,
|
||||
help='Number of videos to use (default: 2048)')
|
||||
|
||||
# Clip sampling
|
||||
parser.add_argument('--num-frames',
|
||||
type=int,
|
||||
default=16,
|
||||
help='Number of frames per clip (default: 16)')
|
||||
parser.add_argument('--num-clips',
|
||||
type=int,
|
||||
default=1,
|
||||
help='Number of clips per video (default: 1)')
|
||||
parser.add_argument(
|
||||
'--clip-strategy',
|
||||
type=str,
|
||||
default='beginning',
|
||||
choices=['beginning', 'random', 'uniform', 'middle', 'sliding', 'all'],
|
||||
help='Clip sampling strategy (default: beginning)')
|
||||
parser.add_argument(
|
||||
'--frame-stride',
|
||||
type=int,
|
||||
default=1,
|
||||
help='Frame stride for FPS subsampling (default: 1, no subsampling)')
|
||||
parser.add_argument('--temporal-stride',
|
||||
type=int,
|
||||
default=1,
|
||||
help='Temporal stride for sliding window (default: 1)')
|
||||
|
||||
# Data processing
|
||||
parser.add_argument('--no-frame-dirs',
|
||||
action='store_true',
|
||||
help='Disable frame directory support')
|
||||
|
||||
# Computation
|
||||
parser.add_argument('--batch-size',
|
||||
type=int,
|
||||
default=32,
|
||||
help='Batch size for feature extraction (default: 32)')
|
||||
parser.add_argument('--device',
|
||||
type=str,
|
||||
default='cuda',
|
||||
choices=['cuda', 'cpu'],
|
||||
help='Device to use (default: cuda)')
|
||||
|
||||
# Caching
|
||||
parser.add_argument('--cache-real-features',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Path to cache real video features')
|
||||
parser.add_argument('--i3d-model-path',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Custom cache path for I3D model')
|
||||
|
||||
# Output
|
||||
parser.add_argument('--output',
|
||||
type=str,
|
||||
default='fvd_results.json',
|
||||
help='Output JSON file (default: fvd_results.json)')
|
||||
parser.add_argument('--quiet',
|
||||
action='store_true',
|
||||
help='Suppress progress output')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
@@ -40,36 +128,56 @@ def main() -> int:
|
||||
protocol_map = {
|
||||
'fvd2048_16f': FVDConfig.fvd2048_16f,
|
||||
'fvd2048_128f': FVDConfig.fvd2048_128f,
|
||||
'fvd2048_128f_subsample8': FVDConfig.fvd2048_128f_subsample8,
|
||||
'quick_test': FVDConfig.quick_test,
|
||||
}
|
||||
config = protocol_map[args.protocol]()
|
||||
# Apply overrides
|
||||
|
||||
# Override device and caching from args
|
||||
config.device = args.device
|
||||
config.cache_real_features = args.cache_real_features
|
||||
config.extractor_model = args.extractor # Apply extractor arg
|
||||
config.i3d_model_path = args.i3d_model_path
|
||||
config.batch_size = args.batch_size
|
||||
config.seed = args.seed
|
||||
else:
|
||||
config = FVDConfig(
|
||||
num_videos=args.num_videos,
|
||||
num_frames_per_clip=args.num_frames,
|
||||
extractor_model=args.extractor, # Apply extractor arg
|
||||
clip_strategy=args.clip_strategy,
|
||||
batch_size=args.batch_size,
|
||||
device=args.device,
|
||||
cache_real_features=args.cache_real_features,
|
||||
seed=args.seed)
|
||||
# Custom config from args
|
||||
config = FVDConfig(num_videos=args.num_videos,
|
||||
num_frames_per_clip=args.num_frames,
|
||||
num_clips_per_video=args.num_clips,
|
||||
clip_strategy=args.clip_strategy,
|
||||
frame_stride=args.frame_stride,
|
||||
temporal_stride=args.temporal_stride,
|
||||
support_frame_dirs=not args.no_frame_dirs,
|
||||
batch_size=args.batch_size,
|
||||
device=args.device,
|
||||
cache_real_features=args.cache_real_features,
|
||||
i3d_model_path=args.i3d_model_path,
|
||||
seed=args.seed)
|
||||
|
||||
# Compute FVD
|
||||
try:
|
||||
_ = compute_fvd_with_config(
|
||||
args.real_path, # noqa: F841
|
||||
args.gen_path,
|
||||
config,
|
||||
verbose=not args.quiet)
|
||||
results = compute_fvd_with_config(real_videos=args.real_path,
|
||||
gen_videos=args.gen_path,
|
||||
config=config,
|
||||
verbose=not args.quiet)
|
||||
|
||||
# Save results
|
||||
output_path = Path(args.output)
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
with open(output_path, 'w') as f:
|
||||
json.dump(results, f, indent=2)
|
||||
|
||||
print(f"\nResults saved to {output_path}")
|
||||
print(f"FVD: {results['fvd']:.2f}")
|
||||
print(f"Protocol: {results['protocol']}")
|
||||
|
||||
return 0
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error: {e}", file=sys.stderr)
|
||||
traceback.print_exc(file=sys.stderr)
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
|
||||
|
||||
@@ -1,232 +0,0 @@
|
||||
"""
|
||||
Pluggable Feature Extractors for FVD Computation.
|
||||
Supports I3D (standard), CLIP, and VideoMAE via a common interface.
|
||||
"""
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from abc import ABC, abstractmethod
|
||||
from huggingface_hub import hf_hub_download
|
||||
from tqdm import tqdm
|
||||
|
||||
try:
|
||||
from transformers import CLIPModel, CLIPProcessor, VideoMAEModel
|
||||
TRANSFORMERS_AVAILABLE = True
|
||||
except ImportError:
|
||||
TRANSFORMERS_AVAILABLE = False
|
||||
|
||||
|
||||
class BaseFeatureExtractor(ABC, nn.Module):
|
||||
"""Abstract base class for all video feature extractors."""
|
||||
|
||||
def __init__(self, device: str = 'cuda'):
|
||||
super().__init__()
|
||||
self.device = torch.device(device if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def feature_dim(self) -> int:
|
||||
"""Dimension of the output feature vector."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def preprocess(self, videos: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Args:
|
||||
videos: [B, T, C, H, W] in [0, 255] range.
|
||||
Returns:
|
||||
Preprocessed tensor ready for the model.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def extract_features_batch(self, videos: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Extract features for a single batch.
|
||||
Args:
|
||||
videos: [B, T, C, H, W] (raw input)
|
||||
Returns:
|
||||
Features: [B, feature_dim]
|
||||
"""
|
||||
pass
|
||||
|
||||
@torch.no_grad()
|
||||
def extract_features(self, videos: torch.Tensor, batch_size: int = 32, verbose: bool = True) -> torch.Tensor:
|
||||
"""
|
||||
Extract features for a large tensor of videos by batching.
|
||||
"""
|
||||
N = len(videos)
|
||||
all_features = []
|
||||
|
||||
iterator = range(0, N, batch_size)
|
||||
if verbose:
|
||||
iterator = tqdm(iterator, desc=f"Extracting features ({self.__class__.__name__})")
|
||||
|
||||
for i in iterator:
|
||||
batch = videos[i:i + batch_size].to(self.device)
|
||||
features = self.extract_features_batch(batch)
|
||||
all_features.append(features.cpu())
|
||||
|
||||
return torch.cat(all_features, dim=0)
|
||||
|
||||
|
||||
# 1. I3D Extractor (The Standard FVD Metric)
|
||||
class I3DFeatureExtractor(BaseFeatureExtractor):
|
||||
REPO_ID = 'flateon/FVD-I3D-torchscript'
|
||||
MODEL_FILENAME = 'i3d_torchscript.pt'
|
||||
|
||||
def __init__(self, device: str = 'cuda', cache_dir: str | None = None):
|
||||
super().__init__(device)
|
||||
self.cache_dir = cache_dir
|
||||
self.model = self._load_model()
|
||||
self.model.eval()
|
||||
self.model.to(self.device)
|
||||
|
||||
@property
|
||||
def feature_dim(self) -> int:
|
||||
return 400
|
||||
|
||||
def _load_model(self) -> torch.nn.Module:
|
||||
try:
|
||||
model_path = hf_hub_download(repo_id=self.REPO_ID, filename=self.MODEL_FILENAME, cache_dir=self.cache_dir)
|
||||
return torch.jit.load(model_path, map_location=self.device)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load I3D model: {e}") from e
|
||||
|
||||
def preprocess(self, videos: torch.Tensor) -> torch.Tensor:
|
||||
"""Standard I3D preprocessing: Resize to 224, Norm to [-1, 1]."""
|
||||
B, T, C, H, W = videos.shape
|
||||
|
||||
if T < 10:
|
||||
raise ValueError(f"I3D requires at least 10 frames, got {T}")
|
||||
|
||||
# Normalize to [0, 1]
|
||||
if videos.max() > 1.0:
|
||||
videos = videos / 255.0
|
||||
|
||||
# Scale to [-1, 1]
|
||||
videos = videos * 2.0 - 1.0
|
||||
|
||||
# Resize to 224x224
|
||||
if H != 224 or W != 224:
|
||||
videos = videos.reshape(B * T, C, H, W)
|
||||
videos = F.interpolate(videos, size=(224, 224), mode='bilinear', align_corners=False)
|
||||
videos = videos.reshape(B, T, C, 224, 224)
|
||||
|
||||
# [B, T, C, H, W] -> [B, C, T, H, W]
|
||||
return videos.permute(0, 2, 1, 3, 4).contiguous()
|
||||
|
||||
def extract_features_batch(self, videos: torch.Tensor) -> torch.Tensor:
|
||||
batch = self.preprocess(videos)
|
||||
# TorchScript I3D returns raw logits when return_features=True
|
||||
return self.model(batch, rescale=False, resize=False, return_features=True)
|
||||
|
||||
|
||||
# 2. CLIP Extractor (Semantic/Content Quality)
|
||||
class CLIPFeatureExtractor(BaseFeatureExtractor):
|
||||
|
||||
def __init__(self, device: str = 'cuda', model_name: str = "openai/clip-vit-base-patch32"):
|
||||
if not TRANSFORMERS_AVAILABLE:
|
||||
raise ImportError("Please install transformers: pip install transformers")
|
||||
super().__init__(device)
|
||||
self.processor = CLIPProcessor.from_pretrained(model_name)
|
||||
self.model = CLIPModel.from_pretrained(model_name).to(self.device)
|
||||
self.model.eval()
|
||||
self._feature_dim = self.model.config.projection_dim
|
||||
|
||||
@property
|
||||
def feature_dim(self) -> int:
|
||||
return self._feature_dim
|
||||
|
||||
def preprocess(self, videos: torch.Tensor) -> torch.Tensor:
|
||||
# Ensure values are [0, 255]
|
||||
if videos.max() <= 1.0:
|
||||
videos = videos * 255.0
|
||||
|
||||
return videos.to(torch.uint8)
|
||||
|
||||
def extract_features_batch(self, videos: torch.Tensor) -> torch.Tensor:
|
||||
# Input: [B, T, C, H, W]
|
||||
B, T, C, H, W = videos.shape
|
||||
videos = self.preprocess(videos)
|
||||
|
||||
# Flatten B*T to treat frames as images
|
||||
images = videos.view(B * T, C, H, W)
|
||||
|
||||
# HF Processor
|
||||
inputs = self.processor(images=images, return_tensors="pt", padding=True)
|
||||
inputs = {k: v.to(self.device) for k, v in inputs.items()}
|
||||
|
||||
# Extract features [B*T, Dim]
|
||||
outputs = self.model.get_image_features(**inputs)
|
||||
|
||||
# Reshape [B, T, Dim] and Average Pooling over time
|
||||
outputs = outputs.view(B, T, -1)
|
||||
return outputs.mean(dim=1)
|
||||
|
||||
|
||||
# 3. VideoMAE Extractor (Structure/Motion Quality)
|
||||
class VideoMAEFeatureExtractor(BaseFeatureExtractor):
|
||||
|
||||
def __init__(self, device: str = 'cuda', model_name: str = "MCG-NJU/videomae-base"):
|
||||
if not TRANSFORMERS_AVAILABLE:
|
||||
raise ImportError("Please install transformers: pip install transformers")
|
||||
super().__init__(device)
|
||||
self.model = VideoMAEModel.from_pretrained(model_name).to(self.device)
|
||||
self.model.eval()
|
||||
|
||||
self.register_buffer('mean', torch.tensor([0.485, 0.456, 0.406], device=self.device).view(1, 1, 3, 1, 1))
|
||||
self.register_buffer('std', torch.tensor([0.229, 0.224, 0.225], device=self.device).view(1, 1, 3, 1, 1))
|
||||
|
||||
@property
|
||||
def feature_dim(self) -> int:
|
||||
return self.model.config.hidden_size
|
||||
|
||||
def preprocess(self, videos: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Efficient GPU-based preprocessing.
|
||||
Input: [B, T, C, H, W] in range [0, 255]
|
||||
"""
|
||||
B, T, C, H, W = videos.shape
|
||||
|
||||
# 1. Resize to 224x224
|
||||
if H != 224 or W != 224:
|
||||
videos = videos.view(B * T, C, H, W)
|
||||
videos = F.interpolate(videos, size=(224, 224), mode='bilinear', align_corners=False)
|
||||
videos = videos.view(B, T, C, 224, 224)
|
||||
|
||||
# 2. Normalize to [0, 1]
|
||||
if videos.dtype != torch.float32:
|
||||
videos = videos.float()
|
||||
|
||||
if videos.max() > 1.0:
|
||||
videos = videos / 255.0
|
||||
|
||||
# 3. Apply ImageNet Mean/Std
|
||||
return (videos - self.mean) / self.std
|
||||
|
||||
def extract_features_batch(self, videos: torch.Tensor) -> torch.Tensor:
|
||||
# Input: [B, T, C, H, W]
|
||||
|
||||
# Fast GPU Preprocessing
|
||||
pixel_values = self.preprocess(videos)
|
||||
|
||||
# Forward pass
|
||||
outputs = self.model(pixel_values)
|
||||
|
||||
# Global Average Pooling of last hidden state [B, T_patches, 768] -> [B, 768]
|
||||
return outputs.last_hidden_state.mean(dim=1)
|
||||
|
||||
|
||||
# Factory
|
||||
def load_extractor(name: str, device: str = 'cuda') -> BaseFeatureExtractor:
|
||||
name = name.lower()
|
||||
if name == 'i3d':
|
||||
return I3DFeatureExtractor(device)
|
||||
elif name == 'clip':
|
||||
return CLIPFeatureExtractor(device)
|
||||
elif name == 'videomae':
|
||||
return VideoMAEFeatureExtractor(device)
|
||||
else:
|
||||
raise ValueError(f"Unknown extractor: {name}. Options: i3d, clip, videomae")
|
||||
+191
-128
@@ -5,7 +5,8 @@ from pathlib import Path
|
||||
from collections.abc import Iterator
|
||||
import pickle
|
||||
from dataclasses import dataclass, field
|
||||
from .feature_extractors import BaseFeatureExtractor, load_extractor
|
||||
|
||||
from .i3d_model import I3DFeatureExtractor
|
||||
from .video_utils import ClipSamplingStrategy, load_video_clips_streaming
|
||||
|
||||
|
||||
@@ -36,7 +37,8 @@ def compute_frechet_distance(mu1: np.ndarray,
|
||||
|
||||
if np.iscomplexobj(covmean):
|
||||
if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
|
||||
print(f"Warning: Imaginary component: {np.max(np.abs(covmean.imag))}")
|
||||
print(
|
||||
f"Warning: Imaginary component: {np.max(np.abs(covmean.imag))}")
|
||||
covmean = covmean.real
|
||||
|
||||
trace_product = np.trace(covmean)
|
||||
@@ -53,9 +55,6 @@ class FVDConfig:
|
||||
# Video selection
|
||||
num_videos: int = 2048
|
||||
|
||||
# Feature Extractor Selection
|
||||
extractor_model: str = 'i3d' # Options: 'i3d', 'clip', 'videomae'
|
||||
|
||||
# Clip sampling
|
||||
num_frames_per_clip: int = 16
|
||||
num_clips_per_video: int = 1
|
||||
@@ -66,7 +65,8 @@ class FVDConfig:
|
||||
temporal_stride: int = 1 # For sliding window clips
|
||||
|
||||
# Data processing
|
||||
video_extensions: list[str] = field(default_factory=lambda: ['.mp4', '.avi', '.mov', '.mkv'])
|
||||
video_extensions: list[str] = field(
|
||||
default_factory=lambda: ['.mp4', '.avi', '.mov', '.mkv'])
|
||||
support_frame_dirs: bool = True
|
||||
|
||||
# Computation
|
||||
@@ -85,28 +85,61 @@ class FVDConfig:
|
||||
|
||||
@classmethod
|
||||
def fvd2048_16f(cls) -> 'FVDConfig':
|
||||
"""Standard FVD protocol: 2048 videos, 16 frames, beginning clip."""
|
||||
return cls(num_videos=2048, num_frames_per_clip=16, clip_strategy='beginning', use_streaming=True)
|
||||
"""
|
||||
Standard FVD protocol: 2048 videos, 16 frames, beginning clip.
|
||||
|
||||
most common FVD configuration used in papers
|
||||
"""
|
||||
return cls(num_videos=2048,
|
||||
num_frames_per_clip=16,
|
||||
clip_strategy='beginning',
|
||||
use_streaming=True)
|
||||
|
||||
@classmethod
|
||||
def fvd2048_128f(cls) -> 'FVDConfig':
|
||||
"""Long video protocol: 2048 videos, 128 frames."""
|
||||
return cls(num_videos=2048, num_frames_per_clip=128, clip_strategy='beginning', use_streaming=True)
|
||||
return cls(num_videos=2048,
|
||||
num_frames_per_clip=128,
|
||||
clip_strategy='beginning',
|
||||
use_streaming=True)
|
||||
|
||||
@classmethod
|
||||
def fvd2048_128f_subsample8(cls) -> 'FVDConfig':
|
||||
"""
|
||||
Long video with FPS subsampling: 2048 videos, 128 frames (every 8th).
|
||||
Used for very long videos - samples every 8th frame
|
||||
"""
|
||||
return cls(num_videos=2048,
|
||||
num_frames_per_clip=16,
|
||||
frame_stride=8,
|
||||
clip_strategy='beginning',
|
||||
use_streaming=True)
|
||||
|
||||
@classmethod
|
||||
def quick_test(cls) -> 'FVDConfig':
|
||||
"""Quick test config: 100 videos, 16 frames."""
|
||||
return cls(num_videos=100, num_frames_per_clip=16, clip_strategy='beginning')
|
||||
return cls(num_videos=100,
|
||||
num_frames_per_clip=16,
|
||||
clip_strategy='beginning')
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""Export config to dict for logging"""
|
||||
d = self.__dict__.copy()
|
||||
d['clip_strategy'] = str(self.clip_strategy)
|
||||
return d
|
||||
return {
|
||||
'num_videos': self.num_videos,
|
||||
'num_frames_per_clip': self.num_frames_per_clip,
|
||||
'num_clips_per_video': self.num_clips_per_video,
|
||||
'clip_strategy': str(self.clip_strategy),
|
||||
'frame_stride': self.frame_stride,
|
||||
'temporal_stride': self.temporal_stride,
|
||||
'batch_size': self.batch_size,
|
||||
'device': self.device,
|
||||
'seed': self.seed,
|
||||
'use_streaming': self.use_streaming,
|
||||
}
|
||||
|
||||
def __str__(self) -> str:
|
||||
"""Human-readable protocol name"""
|
||||
desc = f"FVD_{self.extractor_model.upper()}_{self.num_videos}_{self.num_frames_per_clip}f"
|
||||
desc = f"FVD{self.num_videos}_{self.num_frames_per_clip}f"
|
||||
if self.frame_stride > 1:
|
||||
desc += f"_subsample{self.frame_stride}"
|
||||
if self.num_clips_per_video > 1:
|
||||
@@ -117,42 +150,57 @@ class FVDConfig:
|
||||
|
||||
|
||||
def extract_features_streaming(video_generator: Iterator[torch.Tensor],
|
||||
extractor: BaseFeatureExtractor,
|
||||
extractor: I3DFeatureExtractor,
|
||||
batch_size: int = 32,
|
||||
max_clips: int | None = None,
|
||||
verbose: bool = True) -> np.ndarray:
|
||||
"""
|
||||
Extract features from a video clip generator using streaming.
|
||||
|
||||
Args:
|
||||
video_generator: Iterator yielding clips [T, C, H, W]
|
||||
extractor: I3D feature extractor
|
||||
batch_size: Batch size for processing
|
||||
max_clips: Maximum clips to process (for validation)
|
||||
verbose: Show progress
|
||||
|
||||
Returns:
|
||||
features: [N, 400] numpy array
|
||||
"""
|
||||
all_features = []
|
||||
batch = []
|
||||
clip_count = 0
|
||||
|
||||
if verbose:
|
||||
print(f"Extracting features with batch_size={batch_size}...")
|
||||
|
||||
with torch.no_grad():
|
||||
for clip_count, clip in enumerate(video_generator):
|
||||
batch.append(clip)
|
||||
for clip_count, clip in enumerate(video_generator):
|
||||
batch.append(clip)
|
||||
|
||||
# Process batch when full
|
||||
if len(batch) == batch_size:
|
||||
batch_tensor = torch.stack(batch).to(extractor.device)
|
||||
features = extractor.extract_features_batch(batch_tensor)
|
||||
|
||||
all_features.append(features.detach().cpu().numpy())
|
||||
batch = []
|
||||
|
||||
if verbose and clip_count % (batch_size * 10) == 0:
|
||||
print(f"Processed {clip_count} clips...")
|
||||
|
||||
if max_clips is not None and clip_count >= max_clips:
|
||||
break
|
||||
|
||||
# Process remaining clips
|
||||
if len(batch) > 0:
|
||||
# Process batch when full
|
||||
if len(batch) == batch_size:
|
||||
batch_tensor = torch.stack(batch).to(extractor.device)
|
||||
features = extractor.extract_features_batch(batch_tensor)
|
||||
all_features.append(features.detach().cpu().numpy())
|
||||
features = extractor.extract_features(batch_tensor,
|
||||
batch_size=batch_size,
|
||||
verbose=False)
|
||||
all_features.append(features.cpu().numpy())
|
||||
|
||||
batch = [] # Clear batch
|
||||
|
||||
if verbose and clip_count % (batch_size * 10) == 0:
|
||||
print(f"Processed {clip_count} clips...")
|
||||
|
||||
# Stop if we've reached max_clips
|
||||
if max_clips is not None and clip_count >= max_clips:
|
||||
break
|
||||
|
||||
# Process remaining clips
|
||||
if len(batch) > 0:
|
||||
batch_tensor = torch.stack(batch).to(extractor.device)
|
||||
features = extractor.extract_features(batch_tensor,
|
||||
batch_size=len(batch),
|
||||
verbose=False)
|
||||
all_features.append(features.cpu().numpy())
|
||||
|
||||
if len(all_features) == 0:
|
||||
raise RuntimeError("No features extracted - check video loading")
|
||||
@@ -166,17 +214,14 @@ def extract_features_streaming(video_generator: Iterator[torch.Tensor],
|
||||
|
||||
|
||||
def load_or_compute_features(videos: str | Path | torch.Tensor,
|
||||
extractor: BaseFeatureExtractor,
|
||||
extractor: I3DFeatureExtractor,
|
||||
config: FVDConfig,
|
||||
cache_path: str | None = None,
|
||||
cache_name: str = "real_features") -> np.ndarray:
|
||||
"""Load features from cache or compute (with streaming support)"""
|
||||
|
||||
if cache_path is not None:
|
||||
script_dir = Path(__file__).parent
|
||||
cache_dir = script_dir / cache_path
|
||||
cache_file = cache_dir / f"{config.extractor_model}_{cache_name}.pkl"
|
||||
|
||||
cache_file = Path(cache_path) / f"{cache_name}.pkl"
|
||||
if cache_file.exists():
|
||||
print(f"Loading cached features from {cache_file}")
|
||||
with open(cache_file, 'rb') as f:
|
||||
@@ -184,33 +229,32 @@ def load_or_compute_features(videos: str | Path | torch.Tensor,
|
||||
|
||||
# Validate and limit based on config
|
||||
max_features = config.num_videos * config.num_clips_per_video
|
||||
|
||||
if len(features) < max_features:
|
||||
print(f"WARNING: Cache has {len(features)} features but need {max_features}")
|
||||
print("Cached features insufficient - will recompute...")
|
||||
print(
|
||||
f"WARNING: Cache has {len(features)} features but need {max_features}"
|
||||
)
|
||||
print("Recomputing features...")
|
||||
elif len(features) > max_features:
|
||||
print(f"Using {max_features} features from cache (truncated from {len(features)})")
|
||||
features = features[:max_features]
|
||||
return features
|
||||
else:
|
||||
print(f"Using all {len(features)} cached features")
|
||||
return features
|
||||
|
||||
print("Computing features from scratch...")
|
||||
|
||||
if isinstance(videos, (str | Path)):
|
||||
# Compute features
|
||||
if isinstance(videos, str | Path):
|
||||
target_size = (224, 224) if config.resize_before_extraction else None
|
||||
|
||||
video_generator = load_video_clips_streaming(videos,
|
||||
num_frames=config.num_frames_per_clip,
|
||||
max_videos=config.num_videos,
|
||||
clip_strategy=config.clip_strategy,
|
||||
frame_stride=config.frame_stride,
|
||||
num_clips_per_video=config.num_clips_per_video,
|
||||
video_extensions=config.video_extensions,
|
||||
support_frame_dirs=config.support_frame_dirs,
|
||||
target_size=target_size,
|
||||
verbose=True)
|
||||
video_generator = load_video_clips_streaming(
|
||||
videos,
|
||||
num_frames=config.num_frames_per_clip,
|
||||
max_videos=config.num_videos,
|
||||
clip_strategy=config.clip_strategy,
|
||||
frame_stride=config.frame_stride,
|
||||
num_clips_per_video=config.num_clips_per_video,
|
||||
video_extensions=config.video_extensions,
|
||||
support_frame_dirs=config.support_frame_dirs,
|
||||
target_size=target_size,
|
||||
verbose=True)
|
||||
|
||||
max_clips = config.num_videos * config.num_clips_per_video
|
||||
features = extract_features_streaming(video_generator,
|
||||
@@ -218,26 +262,30 @@ def load_or_compute_features(videos: str | Path | torch.Tensor,
|
||||
batch_size=config.batch_size,
|
||||
max_clips=max_clips,
|
||||
verbose=True)
|
||||
|
||||
else:
|
||||
# Already a tensor
|
||||
print(f"Extracting features from {len(videos)} video tensors...")
|
||||
features = extractor.extract_features(videos, batch_size=config.batch_size, verbose=True)
|
||||
features = extractor.extract_features(videos,
|
||||
batch_size=config.batch_size,
|
||||
verbose=True)
|
||||
features = features.numpy()
|
||||
|
||||
# Validate feature count
|
||||
expected_count = config.num_videos * config.num_clips_per_video
|
||||
if len(features) < expected_count:
|
||||
raise ValueError(f"ERROR: Only extracted {len(features)} features, but need {expected_count}!\n"
|
||||
f"Found fewer videos than expected. Check your video directory.")
|
||||
raise ValueError(
|
||||
f"ERROR: Only extracted {len(features)} features, but need {expected_count}!\n"
|
||||
f"Found fewer videos than expected. Check your video directory.")
|
||||
elif len(features) > expected_count:
|
||||
print(f"Truncating {len(features)} features to {expected_count}")
|
||||
features = features[:expected_count]
|
||||
|
||||
# Cache features if requested
|
||||
if cache_path is not None:
|
||||
script_dir = Path(__file__).parent
|
||||
cache_dir = script_dir / cache_path
|
||||
cache_dir = Path(cache_path)
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
cache_file = cache_dir / f"{config.extractor_model}_{cache_name}.pkl"
|
||||
cache_file = cache_dir / f"{cache_name}.pkl"
|
||||
print(f"Caching features to {cache_file}")
|
||||
with open(cache_file, 'wb') as f:
|
||||
pickle.dump(features, f)
|
||||
@@ -245,34 +293,79 @@ def load_or_compute_features(videos: str | Path | torch.Tensor,
|
||||
return features
|
||||
|
||||
|
||||
def compute_fvd(real_videos: str | Path | torch.Tensor,
|
||||
gen_videos: str | Path | torch.Tensor,
|
||||
num_frames: int = 16,
|
||||
batch_size: int = 32,
|
||||
device: str = 'cuda',
|
||||
num_videos: int | None = 2048,
|
||||
cache_real_features: str | None = None,
|
||||
i3d_model_path: str | None = None,
|
||||
seed: int | None = None,
|
||||
verbose: bool = True) -> float:
|
||||
"""
|
||||
Compute Fréchet Video Distance (FVD)
|
||||
|
||||
For advanced control, use compute_fvd_with_config() instead.
|
||||
|
||||
Args:
|
||||
real_videos: Path to real videos or tensor [N, T, C, H, W]
|
||||
gen_videos: Path to generated videos or tensor [N, T, C, H, W]
|
||||
num_frames: Frames per video (default: 16)
|
||||
batch_size: Batch size (default: 32)
|
||||
device: 'cuda' or 'cpu' (default: 'cuda')
|
||||
num_videos: Max videos (default: 2048)
|
||||
cache_real_features: Cache path for real features
|
||||
i3d_model_path: Custom I3D model cache path
|
||||
seed: Random seed for reproducibility
|
||||
verbose: Print progress
|
||||
|
||||
Returns:
|
||||
FVD score (float). Lower is better.
|
||||
"""
|
||||
num_videos = num_videos if num_videos is not None else 2048
|
||||
|
||||
config = FVDConfig(
|
||||
num_videos=num_videos,
|
||||
num_frames_per_clip=num_frames,
|
||||
batch_size=batch_size,
|
||||
device=device,
|
||||
cache_real_features=cache_real_features,
|
||||
i3d_model_path=i3d_model_path,
|
||||
seed=seed,
|
||||
)
|
||||
|
||||
result = compute_fvd_with_config(real_videos, gen_videos, config, verbose)
|
||||
return result['fvd']
|
||||
|
||||
|
||||
def compute_fvd_with_config(real_videos: str | Path | torch.Tensor,
|
||||
gen_videos: str | Path | torch.Tensor,
|
||||
config: FVDConfig,
|
||||
verbose: bool = True) -> dict:
|
||||
"""
|
||||
Compute FVD using a standardized configuration.
|
||||
|
||||
This is the recommended way to compute FVD for reproducibility.
|
||||
|
||||
Args:
|
||||
real_videos: Path or tensors
|
||||
gen_videos: Path or tensors
|
||||
config: FVDConfig specifying protocol
|
||||
verbose: Print progress
|
||||
|
||||
Returns:
|
||||
results: Dictionary with:
|
||||
- 'fvd': FVD score (float)
|
||||
- 'protocol': Protocol name (str)
|
||||
- 'model': Feature extractor model name (str)
|
||||
- 'config': Configuration dict
|
||||
|
||||
Example:
|
||||
>>> config = FVDConfig.fvd2048_16f()
|
||||
>>> results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
|
||||
>>> print(f"FVD: {results['fvd']:.2f}")
|
||||
Compute FVD using a standardized configuration.
|
||||
|
||||
This is the recommended way to compute FVD for reproducibility.
|
||||
|
||||
Args:
|
||||
real_videos: Path or tensors
|
||||
gen_videos: Path or tensors
|
||||
config: FVDConfig specifying protocol
|
||||
verbose: Print progress
|
||||
|
||||
Returns:
|
||||
results: Dictionary with:
|
||||
- 'fvd': FVD score (float)
|
||||
- 'protocol': Protocol name (str)
|
||||
- 'config': Configuration dict
|
||||
|
||||
Example:
|
||||
>>> config = FVDConfig.fvd2048_16f()
|
||||
>>> results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
|
||||
>>> print(f"FVD: {results['fvd']:.2f}")
|
||||
>>> print(f"Protocol: {results['protocol']}") # "FVD2048_16f"
|
||||
"""
|
||||
|
||||
# Seed for reproducibility
|
||||
if config.seed is not None:
|
||||
import random as _rnd
|
||||
@@ -285,18 +378,18 @@ def compute_fvd_with_config(real_videos: str | Path | torch.Tensor,
|
||||
if verbose:
|
||||
print("=" * 70)
|
||||
print(f"Computing FVD with protocol: {config}")
|
||||
print(f"Model: {config.extractor_model.upper()}")
|
||||
print("=" * 70)
|
||||
print("\nConfiguration:")
|
||||
for key, value in config.to_dict().items():
|
||||
print(f" {key}: {value}")
|
||||
print()
|
||||
|
||||
# Initialize Extractor using Factory
|
||||
# Initialize I3D
|
||||
if verbose:
|
||||
print(f"\nInitializing {config.extractor_model.upper()} model on {config.device}...")
|
||||
print(f"\nInitializing I3D model on {config.device}...")
|
||||
|
||||
extractor = load_extractor(config.extractor_model, device=config.device)
|
||||
extractor = I3DFeatureExtractor(device=config.device,
|
||||
cache_dir=config.i3d_model_path)
|
||||
|
||||
# Extract features
|
||||
if verbose:
|
||||
@@ -304,11 +397,12 @@ def compute_fvd_with_config(real_videos: str | Path | torch.Tensor,
|
||||
print("Extracting REAL video features...")
|
||||
print(f"{'='*70}")
|
||||
|
||||
real_features = load_or_compute_features(videos=real_videos,
|
||||
extractor=extractor,
|
||||
config=config,
|
||||
cache_path=config.cache_real_features,
|
||||
cache_name="real_features")
|
||||
real_features = load_or_compute_features(
|
||||
videos=real_videos,
|
||||
extractor=extractor,
|
||||
config=config,
|
||||
cache_path=config.cache_real_features,
|
||||
cache_name="real_features")
|
||||
|
||||
if verbose:
|
||||
print(f"\n{'='*70}")
|
||||
@@ -340,45 +434,14 @@ def compute_fvd_with_config(real_videos: str | Path | torch.Tensor,
|
||||
|
||||
if verbose:
|
||||
print(f"\n{'='*70}")
|
||||
print(f"FVD Score ({config.extractor_model.upper()}): {fvd:.4f}")
|
||||
print(f"FVD Score: {fvd:.4f}")
|
||||
print(f"Protocol: {config}")
|
||||
print(f"{'='*70}\n")
|
||||
|
||||
results = {
|
||||
'fvd': fvd,
|
||||
'protocol': str(config),
|
||||
'model': config.extractor_model,
|
||||
'config': config.to_dict(),
|
||||
}
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def compute_fvd(real_videos: str | Path | torch.Tensor,
|
||||
gen_videos: str | Path | torch.Tensor,
|
||||
num_frames: int = 16,
|
||||
batch_size: int = 32,
|
||||
device: str = 'cuda',
|
||||
num_videos: int | None = 2048,
|
||||
cache_real_features: str | None = None,
|
||||
i3d_model_path: str | None = None,
|
||||
seed: int | None = None,
|
||||
verbose: bool = True) -> float:
|
||||
"""
|
||||
Backward compatibility wrapper for computing FVD (defaults to I3D).
|
||||
"""
|
||||
num_videos = num_videos if num_videos is not None else 2048
|
||||
|
||||
config = FVDConfig(
|
||||
num_videos=num_videos,
|
||||
num_frames_per_clip=num_frames,
|
||||
extractor_model='i3d', # Default to I3D
|
||||
batch_size=batch_size,
|
||||
device=device,
|
||||
cache_real_features=cache_real_features,
|
||||
i3d_model_path=i3d_model_path,
|
||||
seed=seed,
|
||||
)
|
||||
|
||||
result = compute_fvd_with_config(real_videos, gen_videos, config, verbose)
|
||||
return result['fvd']
|
||||
|
||||
+28
-10
@@ -19,12 +19,16 @@ class I3DFeatureExtractor(nn.Module):
|
||||
REPO_ID = 'flateon/FVD-I3D-torchscript'
|
||||
MODEL_FILENAME = 'i3d_torchscript.pt'
|
||||
|
||||
def __init__(self, device: str = 'cuda', cache_dir: str | Path | None = None):
|
||||
def __init__(self,
|
||||
device: str = 'cuda',
|
||||
cache_dir: str | Path | None = None):
|
||||
super().__init__()
|
||||
|
||||
self.device_str = device
|
||||
if device == 'cuda' and not torch.cuda.is_available():
|
||||
print("Warning: CUDA requested but not available – falling back to CPU")
|
||||
print(
|
||||
"Warning: CUDA requested but not available – falling back to CPU"
|
||||
)
|
||||
self.device = torch.device('cpu')
|
||||
else:
|
||||
self.device = torch.device(device)
|
||||
@@ -47,7 +51,9 @@ class I3DFeatureExtractor(nn.Module):
|
||||
|
||||
try:
|
||||
# Download model from Hugging Face Hub
|
||||
model_path = hf_hub_download(repo_id=self.REPO_ID, filename=self.MODEL_FILENAME, cache_dir=self.cache_dir)
|
||||
model_path = hf_hub_download(repo_id=self.REPO_ID,
|
||||
filename=self.MODEL_FILENAME,
|
||||
cache_dir=self.cache_dir)
|
||||
|
||||
# Load directly to chosen device
|
||||
model = torch.jit.load(model_path, map_location=self.device)
|
||||
@@ -55,9 +61,10 @@ class I3DFeatureExtractor(nn.Module):
|
||||
return model
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load I3D model from Hugging Face Hub. Error: {e}\n"
|
||||
f"Ensure you have internet connection and huggingface_hub installed:\n"
|
||||
f"pip install huggingface_hub") from e
|
||||
raise RuntimeError(
|
||||
f"Failed to load I3D model from Hugging Face Hub. Error: {e}\n"
|
||||
f"Ensure you have internet connection and huggingface_hub installed:\n"
|
||||
f"pip install huggingface_hub") from e
|
||||
|
||||
def preprocess(self, videos: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
@@ -81,7 +88,10 @@ class I3DFeatureExtractor(nn.Module):
|
||||
# Resize to 224x224 if needed
|
||||
if H != 224 or W != 224:
|
||||
videos = videos.reshape(B * T, C, H, W)
|
||||
videos = F.interpolate(videos, size=(224, 224), mode='bilinear', align_corners=False)
|
||||
videos = F.interpolate(videos,
|
||||
size=(224, 224),
|
||||
mode='bilinear',
|
||||
align_corners=False)
|
||||
videos = videos.reshape(B, T, C, 224, 224)
|
||||
|
||||
# Convert to [B, C, T, H, W] format
|
||||
@@ -90,7 +100,10 @@ class I3DFeatureExtractor(nn.Module):
|
||||
return videos
|
||||
|
||||
@torch.no_grad()
|
||||
def extract_features(self, videos: torch.Tensor, batch_size: int = 32, verbose: bool = True) -> torch.Tensor:
|
||||
def extract_features(self,
|
||||
videos: torch.Tensor,
|
||||
batch_size: int = 32,
|
||||
verbose: bool = True) -> torch.Tensor:
|
||||
"""
|
||||
Extract I3D features
|
||||
|
||||
@@ -114,11 +127,16 @@ class I3DFeatureExtractor(nn.Module):
|
||||
batch = self.preprocess(batch) # Now returns [B, C, T, H, W]
|
||||
|
||||
# Use the HF model without rescale/resize (we handle it in preprocess)
|
||||
features = self.model(batch, rescale=False, resize=False, return_features=True)
|
||||
features = self.model(batch,
|
||||
rescale=False,
|
||||
resize=False,
|
||||
return_features=True)
|
||||
|
||||
all_features.append(features.cpu())
|
||||
|
||||
return torch.cat(all_features, dim=0)
|
||||
|
||||
def __call__(self, videos: torch.Tensor, batch_size: int = 32) -> torch.Tensor:
|
||||
def __call__(self,
|
||||
videos: torch.Tensor,
|
||||
batch_size: int = 32) -> torch.Tensor:
|
||||
return self.extract_features(videos, batch_size=batch_size)
|
||||
|
||||
+17
-34
@@ -1,50 +1,33 @@
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from benchmarks.fvd.fvd import FVDConfig, compute_fvd_with_config
|
||||
|
||||
root_dir = Path(__file__).parent.parent.parent
|
||||
sys.path.insert(0, str(root_dir))
|
||||
|
||||
from benchmarks.fvd.fvd import FVDConfig, compute_fvd_with_config # noqa: E402
|
||||
|
||||
|
||||
def main() -> None:
|
||||
# Get script directory
|
||||
script_dir = Path(__file__).parent.resolve()
|
||||
|
||||
# Define directories
|
||||
clip_strategy = 'beginning' # Options: 'uniform', 'random', 'beginning', 'end', 'all'
|
||||
cfg = FVDConfig(
|
||||
num_videos=650,
|
||||
num_frames_per_clip=16,
|
||||
num_clips_per_video=1,
|
||||
clip_strategy=clip_strategy,
|
||||
frame_stride=1,
|
||||
batch_size=32,
|
||||
device='cuda',
|
||||
seed=42,
|
||||
cache_real_features=str(script_dir / f'fvd-cache/{clip_strategy}'),
|
||||
)
|
||||
|
||||
real_dir = "benchmarks/data/real_videos"
|
||||
gen_dir = "benchmarks/data/generated_videos"
|
||||
|
||||
# Compare all 3 models
|
||||
models_to_test = ['i3d', 'clip', 'videomae']
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print("STARTING COMPARISON BENCHMARK")
|
||||
print(f"{'='*60}")
|
||||
|
||||
for model_name in models_to_test:
|
||||
print(f"\n>>> Running evaluation with {model_name.upper()}...")
|
||||
|
||||
try:
|
||||
cfg = FVDConfig(
|
||||
num_videos=650,
|
||||
num_frames_per_clip=16,
|
||||
extractor_model=model_name,
|
||||
clip_strategy='beginning',
|
||||
device='cuda',
|
||||
seed=42,
|
||||
# Use separate cache folders for each model to avoid conflicts
|
||||
cache_real_features=str(script_dir / f'fvd-cache/{model_name}'),
|
||||
)
|
||||
|
||||
results = compute_fvd_with_config(real_dir, gen_dir, cfg, verbose=False)
|
||||
print(f"FVD: {results['fvd']}\nModel: {results['model']}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"{model_name.upper()} Failed: {e}")
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print("BENCHMARK COMPLETE")
|
||||
print(f"{'='*60}")
|
||||
results = compute_fvd_with_config(real_dir, gen_dir, cfg, verbose=True)
|
||||
print(f"FVD = {results['fvd']:.2f}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -59,7 +59,10 @@ def validate_fvd(subset_a: Path, subset_b: Path, num_videos: int):
|
||||
print("TEST 1: Identity Test")
|
||||
print("=" * 70)
|
||||
|
||||
result1 = compute_fvd_with_config(real_videos=str(subset_a), gen_videos=str(subset_a), config=config, verbose=False)
|
||||
result1 = compute_fvd_with_config(real_videos=str(subset_a),
|
||||
gen_videos=str(subset_a),
|
||||
config=config,
|
||||
verbose=False)
|
||||
fvd_identity = result1['fvd']
|
||||
print(f"\nIdentity FVD: {fvd_identity:.2f}")
|
||||
|
||||
@@ -67,7 +70,10 @@ def validate_fvd(subset_a: Path, subset_b: Path, num_videos: int):
|
||||
print("TEST 2: Real vs Real")
|
||||
print("=" * 70)
|
||||
|
||||
result2 = compute_fvd_with_config(real_videos=str(subset_a), gen_videos=str(subset_b), config=config, verbose=False)
|
||||
result2 = compute_fvd_with_config(real_videos=str(subset_a),
|
||||
gen_videos=str(subset_b),
|
||||
config=config,
|
||||
verbose=False)
|
||||
fvd_real = result2['fvd']
|
||||
print(f"\nReal vs Real FVD: {fvd_real:.2f}")
|
||||
|
||||
@@ -81,7 +87,9 @@ def validate_fvd(subset_a: Path, subset_b: Path, num_videos: int):
|
||||
def main() -> None:
|
||||
bair_dir = Path('benchmarks/data/bair_full_videos')
|
||||
|
||||
subset_a, subset_b, count = split_videos(bair_dir, n_per_subset=128, seed=42)
|
||||
subset_a, subset_b, count = split_videos(bair_dir,
|
||||
n_per_subset=128,
|
||||
seed=42)
|
||||
validate_fvd(subset_a, subset_b, count)
|
||||
|
||||
|
||||
|
||||
@@ -54,7 +54,8 @@ def _load_video_cv2(video_path: str | Path,
|
||||
raise RuntimeError(f"Video has 0 frames: {video_path}")
|
||||
|
||||
frames = np.stack(frames) # [T, H, W, C]
|
||||
frames = torch.from_numpy(frames).permute(0, 3, 1, 2).float() # [T, C, H, W]
|
||||
frames = torch.from_numpy(frames).permute(0, 3, 1,
|
||||
2).float() # [T, C, H, W]
|
||||
return frames
|
||||
|
||||
if total_frames == 0:
|
||||
@@ -62,11 +63,14 @@ def _load_video_cv2(video_path: str | Path,
|
||||
|
||||
# Determine frame indices for sampling
|
||||
if total_frames < num_frames:
|
||||
frame_indices = list(range(total_frames)) + [total_frames - 1] * (num_frames - total_frames)
|
||||
frame_indices = list(range(
|
||||
total_frames)) + [total_frames - 1] * (num_frames - total_frames)
|
||||
elif sample_strategy == 'uniform':
|
||||
frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int).tolist()
|
||||
frame_indices = np.linspace(0, total_frames - 1, num_frames,
|
||||
dtype=int).tolist()
|
||||
elif sample_strategy == 'random':
|
||||
frame_indices = sorted(np.random.choice(total_frames, num_frames, replace=False))
|
||||
frame_indices = sorted(
|
||||
np.random.choice(total_frames, num_frames, replace=False))
|
||||
else:
|
||||
raise ValueError(f"Unknown sample_strategy: {sample_strategy}")
|
||||
|
||||
@@ -90,15 +94,17 @@ def _load_video_cv2(video_path: str | Path,
|
||||
cap.release()
|
||||
|
||||
frames = np.stack(frames) # [T, H, W, C]
|
||||
frames = torch.from_numpy(frames).permute(0, 3, 1, 2).float() # [T, C, H, W]
|
||||
frames = torch.from_numpy(frames).permute(0, 3, 1,
|
||||
2).float() # [T, C, H, W]
|
||||
|
||||
return frames
|
||||
|
||||
|
||||
def _load_video_from_frames(frame_dir: str | Path,
|
||||
num_frames: int | None = 16,
|
||||
sample_strategy: str = 'uniform',
|
||||
frame_extensions: list[str] | None = None) -> torch.Tensor:
|
||||
def _load_video_from_frames(
|
||||
frame_dir: str | Path,
|
||||
num_frames: int | None = 16,
|
||||
sample_strategy: str = 'uniform',
|
||||
frame_extensions: list[str] | None = None) -> torch.Tensor:
|
||||
"""
|
||||
Load video from directory of frame images.
|
||||
|
||||
@@ -125,7 +131,9 @@ def _load_video_from_frames(frame_dir: str | Path,
|
||||
frame_files.extend(frame_dir.glob(f"*{ext}"))
|
||||
|
||||
if len(frame_files) == 0:
|
||||
raise ValueError(f"No frames found in {frame_dir} with extensions {frame_extensions}")
|
||||
raise ValueError(
|
||||
f"No frames found in {frame_dir} with extensions {frame_extensions}"
|
||||
)
|
||||
|
||||
frame_files = sorted(frame_files, key=lambda x: x.name)
|
||||
total_frames = len(frame_files)
|
||||
@@ -135,11 +143,16 @@ def _load_video_from_frames(frame_dir: str | Path,
|
||||
frame_indices = list(range(total_frames))
|
||||
else:
|
||||
if total_frames < num_frames:
|
||||
frame_indices = list(range(total_frames)) + [total_frames - 1] * (num_frames - total_frames)
|
||||
frame_indices = list(range(total_frames)) + [total_frames - 1] * (
|
||||
num_frames - total_frames)
|
||||
elif sample_strategy == 'uniform':
|
||||
frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int).tolist()
|
||||
frame_indices = np.linspace(0,
|
||||
total_frames - 1,
|
||||
num_frames,
|
||||
dtype=int).tolist()
|
||||
elif sample_strategy == 'random':
|
||||
frame_indices = sorted(np.random.choice(total_frames, num_frames, replace=False))
|
||||
frame_indices = sorted(
|
||||
np.random.choice(total_frames, num_frames, replace=False))
|
||||
else:
|
||||
raise ValueError(f"Unknown sample_strategy: {sample_strategy}")
|
||||
|
||||
@@ -157,7 +170,8 @@ def _load_video_from_frames(frame_dir: str | Path,
|
||||
|
||||
# Stack and convert to tensor
|
||||
frames = np.stack(frames) # [T, H, W, C]
|
||||
frames = torch.from_numpy(frames).permute(0, 3, 1, 2).float() # [T, C, H, W]
|
||||
frames = torch.from_numpy(frames).permute(0, 3, 1,
|
||||
2).float() # [T, C, H, W]
|
||||
|
||||
return frames
|
||||
|
||||
@@ -212,12 +226,13 @@ def load_video_auto(video_path: str | Path,
|
||||
raise ValueError(f"Unknown video format at {video_path}")
|
||||
|
||||
|
||||
def sample_clips_from_video(video: torch.Tensor,
|
||||
num_frames_per_clip: int = 16,
|
||||
num_clips: int = 1,
|
||||
strategy: str | ClipSamplingStrategy = ClipSamplingStrategy.BEGINNING,
|
||||
frame_stride: int = 1,
|
||||
temporal_stride: int = 1) -> list[torch.Tensor]:
|
||||
def sample_clips_from_video(
|
||||
video: torch.Tensor,
|
||||
num_frames_per_clip: int = 16,
|
||||
num_clips: int = 1,
|
||||
strategy: str | ClipSamplingStrategy = ClipSamplingStrategy.BEGINNING,
|
||||
frame_stride: int = 1,
|
||||
temporal_stride: int = 1) -> list[torch.Tensor]:
|
||||
"""
|
||||
Sample clips from a video with various strategies.
|
||||
|
||||
@@ -281,7 +296,10 @@ def sample_clips_from_video(video: torch.Tensor,
|
||||
elif strategy == ClipSamplingStrategy.RANDOM:
|
||||
# Sample N random clips
|
||||
for _ in range(num_clips):
|
||||
start = 0 if effective_clip_length == T else np.random.randint(0, T - effective_clip_length + 1)
|
||||
if effective_clip_length == T:
|
||||
start = 0
|
||||
else:
|
||||
start = np.random.randint(0, T - effective_clip_length + 1)
|
||||
clip = video[start:start + effective_clip_length]
|
||||
clips.append(clip)
|
||||
|
||||
@@ -294,7 +312,8 @@ def sample_clips_from_video(video: torch.Tensor,
|
||||
clips.append(clip)
|
||||
else:
|
||||
# Multiple uniformly spaced clips
|
||||
step = (T - effective_clip_length) / (num_clips - 1) if num_clips > 1 else 0
|
||||
step = (T - effective_clip_length) / (num_clips -
|
||||
1) if num_clips > 1 else 0
|
||||
for i in range(num_clips):
|
||||
start = int(i * step)
|
||||
start = min(start, T - effective_clip_length)
|
||||
@@ -403,12 +422,15 @@ def load_video_clips_streaming(directory: str | Path,
|
||||
failed_count = 0
|
||||
total_clips = 0
|
||||
|
||||
iterator = tqdm(video_paths, desc="Loading videos") if verbose else video_paths
|
||||
iterator = tqdm(video_paths,
|
||||
desc="Loading videos") if verbose else video_paths
|
||||
|
||||
for video_path in iterator:
|
||||
try:
|
||||
# Load full video
|
||||
video = load_video_auto(video_path, num_frames=None, sample_strategy='uniform')
|
||||
video = load_video_auto(video_path,
|
||||
num_frames=None,
|
||||
sample_strategy='uniform')
|
||||
|
||||
# Sample clips from video
|
||||
clips = sample_clips_from_video(video,
|
||||
@@ -423,14 +445,18 @@ def load_video_clips_streaming(directory: str | Path,
|
||||
T, C, H, W = clip.shape
|
||||
if target_size != (H, W):
|
||||
# Resize to target size
|
||||
clip = clip.contiguous() # Fix non-contiguous tensors first
|
||||
clip_flat = clip.view(T * C, H, W).unsqueeze(0) # [1, T*C, H, W]
|
||||
clip_resized = torch.nn.functional.interpolate(clip_flat,
|
||||
size=target_size,
|
||||
mode='bilinear',
|
||||
align_corners=False)
|
||||
clip = clip_resized.squeeze(0).view(T, C, target_size[0],
|
||||
target_size[1]) # Back to [T, C, H, W]
|
||||
clip = clip.contiguous(
|
||||
) # Fix non-contiguous tensors first
|
||||
clip_flat = clip.view(T * C, H,
|
||||
W).unsqueeze(0) # [1, T*C, H, W]
|
||||
clip_resized = torch.nn.functional.interpolate(
|
||||
clip_flat,
|
||||
size=target_size,
|
||||
mode='bilinear',
|
||||
align_corners=False)
|
||||
clip = clip_resized.squeeze(0).view(
|
||||
T, C, target_size[0],
|
||||
target_size[1]) # Back to [T, C, H, W]
|
||||
resized_clips.append(clip)
|
||||
clips = resized_clips
|
||||
|
||||
@@ -454,7 +480,11 @@ def load_video_clips_streaming(directory: str | Path,
|
||||
|
||||
failure_rate = failed_count / len(video_paths)
|
||||
if failure_rate > 0.1: # More than 10% failed
|
||||
print(f"\nWARNING: {failure_rate:.1%} of videos failed to load ({failed_count}/{len(video_paths)})")
|
||||
print(
|
||||
f"\nWARNING: {failure_rate:.1%} of videos failed to load ({failed_count}/{len(video_paths)})"
|
||||
)
|
||||
|
||||
if verbose:
|
||||
print(f"\nSuccessfully loaded {total_clips} clips from {len(video_paths) - failed_count} videos")
|
||||
print(
|
||||
f"\nSuccessfully loaded {total_clips} clips from {len(video_paths) - failed_count} videos"
|
||||
)
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
#!/bin/bash
|
||||
|
||||
# 1. Install missing dependency
|
||||
pip install -q opencv-python-headless transformers huggingface_hub
|
||||
pip install -q opencv-python-headless
|
||||
|
||||
# 2. Run FVD script
|
||||
python benchmarks/fvd/run_fvd.py
|
||||
|
||||
+68
-35
@@ -9,12 +9,11 @@ import datetime
|
||||
import locale
|
||||
import os
|
||||
import re
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
# This script outputs relevant system environment info.
|
||||
# Run it with: python collect_env.py
|
||||
# Requires Python 3.10+ (matches fastvideo); uses shutil.which (Python 3.3+).
|
||||
# Unlike the rest of the PyTorch this file must be python2 compliant.
|
||||
# This script outputs relevant system environment info
|
||||
# Run it with `python collect_env.py` or `python -m torch.utils.collect_env`
|
||||
from collections import namedtuple
|
||||
|
||||
from fastvideo.envs import environment_variables
|
||||
@@ -100,7 +99,10 @@ DEFAULT_PIP_PATTERNS = {
|
||||
def run(command):
|
||||
"""Return (return-code, stdout, stderr)."""
|
||||
shell = True if type(command) is str else False
|
||||
p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell)
|
||||
p = subprocess.Popen(command,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE,
|
||||
shell=shell)
|
||||
raw_output, raw_err = p.communicate()
|
||||
rc = p.returncode
|
||||
if get_platform() == 'win32':
|
||||
@@ -154,7 +156,8 @@ def get_conda_packages(run_lambda, patterns=None):
|
||||
return out
|
||||
|
||||
return "\n".join(line for line in out.splitlines()
|
||||
if not line.startswith("#") and any(name in line for name in patterns))
|
||||
if not line.startswith("#") and any(name in line
|
||||
for name in patterns))
|
||||
|
||||
|
||||
def get_gcc_version(run_lambda):
|
||||
@@ -162,24 +165,27 @@ def get_gcc_version(run_lambda):
|
||||
|
||||
|
||||
def get_clang_version(run_lambda):
|
||||
return run_and_parse_first_match(run_lambda, 'clang --version', r'clang version (.*)')
|
||||
return run_and_parse_first_match(run_lambda, 'clang --version',
|
||||
r'clang version (.*)')
|
||||
|
||||
|
||||
def get_cmake_version(run_lambda):
|
||||
return run_and_parse_first_match(run_lambda, 'cmake --version', r'cmake (.*)')
|
||||
return run_and_parse_first_match(run_lambda, 'cmake --version',
|
||||
r'cmake (.*)')
|
||||
|
||||
|
||||
def get_nvidia_driver_version(run_lambda):
|
||||
if get_platform() == 'darwin':
|
||||
cmd = 'kextstat | grep -i cuda'
|
||||
return run_and_parse_first_match(run_lambda, cmd, r'com[.]nvidia[.]CUDA [(](.*?)[)]')
|
||||
return run_and_parse_first_match(run_lambda, cmd,
|
||||
r'com[.]nvidia[.]CUDA [(](.*?)[)]')
|
||||
smi = get_nvidia_smi()
|
||||
return run_and_parse_first_match(run_lambda, smi, r'Driver Version: (.*?) ')
|
||||
|
||||
|
||||
def get_gpu_info(run_lambda):
|
||||
if get_platform() == 'darwin' or (TORCH_AVAILABLE and hasattr(torch.version, 'hip')
|
||||
and torch.version.hip is not None):
|
||||
if get_platform() == 'darwin' or (TORCH_AVAILABLE and hasattr(
|
||||
torch.version, 'hip') and torch.version.hip is not None):
|
||||
if TORCH_AVAILABLE and torch.cuda.is_available():
|
||||
if torch.version.hip is not None:
|
||||
prop = torch.cuda.get_device_properties(0)
|
||||
@@ -201,7 +207,8 @@ def get_gpu_info(run_lambda):
|
||||
|
||||
|
||||
def get_running_cuda_version(run_lambda):
|
||||
return run_and_parse_first_match(run_lambda, 'nvcc --version', r'release .+ V(.*)')
|
||||
return run_and_parse_first_match(run_lambda, 'nvcc --version',
|
||||
r'release .+ V(.*)')
|
||||
|
||||
|
||||
def get_cudnn_version(run_lambda):
|
||||
@@ -247,7 +254,8 @@ def get_nvidia_smi():
|
||||
if get_platform() == 'win32':
|
||||
system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
|
||||
program_files_root = os.environ.get('PROGRAMFILES', 'C:\\Program Files')
|
||||
legacy_path = os.path.join(program_files_root, 'NVIDIA Corporation', 'NVSMI', smi)
|
||||
legacy_path = os.path.join(program_files_root, 'NVIDIA Corporation',
|
||||
'NVSMI', smi)
|
||||
new_path = os.path.join(system_root, 'System32', smi)
|
||||
smis = [new_path, legacy_path]
|
||||
for candidate_smi in smis:
|
||||
@@ -379,8 +387,10 @@ def get_cpu_info(run_lambda):
|
||||
if get_platform() == 'linux':
|
||||
rc, out, err = run_lambda('lscpu')
|
||||
elif get_platform() == 'win32':
|
||||
rc, out, err = run_lambda('wmic cpu get Name,Manufacturer,Family,Architecture,ProcessorType,DeviceID, \
|
||||
CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision /VALUE')
|
||||
rc, out, err = run_lambda(
|
||||
'wmic cpu get Name,Manufacturer,Family,Architecture,ProcessorType,DeviceID, \
|
||||
CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision /VALUE'
|
||||
)
|
||||
elif get_platform() == 'darwin':
|
||||
rc, out, err = run_lambda("sysctl -n machdep.cpu.brand_string")
|
||||
cpu_info = 'None'
|
||||
@@ -405,22 +415,27 @@ def get_platform():
|
||||
|
||||
|
||||
def get_mac_version(run_lambda):
|
||||
return run_and_parse_first_match(run_lambda, 'sw_vers -productVersion', r'(.*)')
|
||||
return run_and_parse_first_match(run_lambda, 'sw_vers -productVersion',
|
||||
r'(.*)')
|
||||
|
||||
|
||||
def get_windows_version(run_lambda):
|
||||
system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
|
||||
wmic_cmd = os.path.join(system_root, 'System32', 'Wbem', 'wmic')
|
||||
findstr_cmd = os.path.join(system_root, 'System32', 'findstr')
|
||||
return run_and_read_all(run_lambda, '{} os get Caption | {} /v Caption'.format(wmic_cmd, findstr_cmd))
|
||||
return run_and_read_all(
|
||||
run_lambda,
|
||||
'{} os get Caption | {} /v Caption'.format(wmic_cmd, findstr_cmd))
|
||||
|
||||
|
||||
def get_lsb_version(run_lambda):
|
||||
return run_and_parse_first_match(run_lambda, 'lsb_release -a', r'Description:\t(.*)')
|
||||
return run_and_parse_first_match(run_lambda, 'lsb_release -a',
|
||||
r'Description:\t(.*)')
|
||||
|
||||
|
||||
def check_release_file(run_lambda):
|
||||
return run_and_parse_first_match(run_lambda, 'cat /etc/*-release', r'PRETTY_NAME="(.*)"')
|
||||
return run_and_parse_first_match(run_lambda, 'cat /etc/*-release',
|
||||
r'PRETTY_NAME="(.*)"')
|
||||
|
||||
|
||||
def get_os(run_lambda):
|
||||
@@ -480,13 +495,17 @@ def get_pip_packages(run_lambda, patterns=None):
|
||||
|
||||
if pip_available:
|
||||
cmd = [sys.executable, '-mpip', 'list', '--format=freeze']
|
||||
elif shutil.which("uv") is not None:
|
||||
elif os.environ.get("UV") is not None:
|
||||
print("uv is set")
|
||||
cmd = ["uv", "pip", "list", "--format=freeze"]
|
||||
else:
|
||||
raise RuntimeError("Could not collect pip list output (pip or uv module not available)")
|
||||
raise RuntimeError(
|
||||
"Could not collect pip list output (pip or uv module not available)"
|
||||
)
|
||||
|
||||
out = run_and_read_all(run_lambda, cmd)
|
||||
return "\n".join(line for line in out.splitlines() if any(name in line for name in patterns))
|
||||
return "\n".join(line for line in out.splitlines()
|
||||
if any(name in line for name in patterns))
|
||||
|
||||
pip_version = 'pip3' if sys.version[0] == '3' else 'pip'
|
||||
out = run_with_pip()
|
||||
@@ -518,7 +537,8 @@ def is_xnnpack_available():
|
||||
def get_env_vars():
|
||||
env_vars = ''
|
||||
secret_terms = ('secret', 'token', 'api', 'access', 'password')
|
||||
report_prefix = ("TORCH", "NCCL", "PYTORCH", "CUDA", "CUBLAS", "CUDNN", "OMP_", "MKL_", "NVIDIA")
|
||||
report_prefix = ("TORCH", "NCCL", "PYTORCH", "CUDA", "CUBLAS", "CUDNN",
|
||||
"OMP_", "MKL_", "NVIDIA")
|
||||
for k, v in os.environ.items():
|
||||
if any(term in k.lower() for term in secret_terms):
|
||||
continue
|
||||
@@ -539,7 +559,8 @@ def get_env_info():
|
||||
debug_mode_str = str(torch.version.debug)
|
||||
cuda_available_str = str(torch.cuda.is_available())
|
||||
cuda_version_str = torch.version.cuda
|
||||
if not hasattr(torch.version, 'hip') or torch.version.hip is None: # cuda version
|
||||
if not hasattr(torch.version,
|
||||
'hip') or torch.version.hip is None: # cuda version
|
||||
hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
|
||||
else: # HIP version
|
||||
|
||||
@@ -567,8 +588,9 @@ def get_env_info():
|
||||
return SystemEnv(
|
||||
torch_version=version_str,
|
||||
is_debug_build=debug_mode_str,
|
||||
python_version='{} ({}-bit runtime)'.format(sys_version,
|
||||
sys.maxsize.bit_length() + 1),
|
||||
python_version='{} ({}-bit runtime)'.format(
|
||||
sys_version,
|
||||
sys.maxsize.bit_length() + 1),
|
||||
python_platform=get_python_platform(),
|
||||
is_cuda_available=cuda_available_str,
|
||||
cuda_compiled_version=cuda_version_str,
|
||||
@@ -692,8 +714,10 @@ def pretty_str(envinfo):
|
||||
'nvidia_driver_version',
|
||||
]
|
||||
all_cuda_fields = dynamic_cuda_fields + ['cudnn_version']
|
||||
all_dynamic_cuda_fields_missing = all(mutable_dict[field] is None for field in dynamic_cuda_fields)
|
||||
if TORCH_AVAILABLE and not torch.cuda.is_available() and all_dynamic_cuda_fields_missing:
|
||||
all_dynamic_cuda_fields_missing = all(mutable_dict[field] is None
|
||||
for field in dynamic_cuda_fields)
|
||||
if TORCH_AVAILABLE and not torch.cuda.is_available(
|
||||
) and all_dynamic_cuda_fields_missing:
|
||||
for field in all_cuda_fields:
|
||||
mutable_dict[field] = 'No CUDA'
|
||||
if envinfo.cuda_compiled_version is None:
|
||||
@@ -706,15 +730,19 @@ def pretty_str(envinfo):
|
||||
mutable_dict = replace_nones(mutable_dict)
|
||||
|
||||
# If either of these are '', replace with 'No relevant packages'
|
||||
mutable_dict['pip_packages'] = replace_if_empty(mutable_dict['pip_packages'])
|
||||
mutable_dict['conda_packages'] = replace_if_empty(mutable_dict['conda_packages'])
|
||||
mutable_dict['pip_packages'] = replace_if_empty(
|
||||
mutable_dict['pip_packages'])
|
||||
mutable_dict['conda_packages'] = replace_if_empty(
|
||||
mutable_dict['conda_packages'])
|
||||
|
||||
# Tag conda and pip packages with a prefix
|
||||
# If they were previously None, they'll show up as ie '[conda] Could not collect'
|
||||
if mutable_dict['pip_packages']:
|
||||
mutable_dict['pip_packages'] = prepend(mutable_dict['pip_packages'], '[{}] '.format(envinfo.pip_version))
|
||||
mutable_dict['pip_packages'] = prepend(
|
||||
mutable_dict['pip_packages'], '[{}] '.format(envinfo.pip_version))
|
||||
if mutable_dict['conda_packages']:
|
||||
mutable_dict['conda_packages'] = prepend(mutable_dict['conda_packages'], '[conda] ')
|
||||
mutable_dict['conda_packages'] = prepend(mutable_dict['conda_packages'],
|
||||
'[conda] ')
|
||||
mutable_dict['cpu_info'] = envinfo.cpu_info
|
||||
return env_info_fmt.format(**mutable_dict)
|
||||
|
||||
@@ -728,13 +756,18 @@ def main():
|
||||
output = get_pretty_env_info()
|
||||
print(output)
|
||||
|
||||
if TORCH_AVAILABLE and hasattr(torch, 'utils') and hasattr(torch.utils, '_crash_handler'):
|
||||
if TORCH_AVAILABLE and hasattr(torch, 'utils') and hasattr(
|
||||
torch.utils, '_crash_handler'):
|
||||
minidump_dir = torch.utils._crash_handler.DEFAULT_MINIDUMP_DIR
|
||||
if sys.platform == "linux" and os.path.exists(minidump_dir):
|
||||
dumps = [os.path.join(minidump_dir, dump) for dump in os.listdir(minidump_dir)]
|
||||
dumps = [
|
||||
os.path.join(minidump_dir, dump)
|
||||
for dump in os.listdir(minidump_dir)
|
||||
]
|
||||
latest = max(dumps, key=os.path.getctime)
|
||||
ctime = os.path.getctime(latest)
|
||||
creation_time = datetime.datetime.fromtimestamp(ctime).strftime('%Y-%m-%d %H:%M:%S')
|
||||
creation_time = datetime.datetime.fromtimestamp(ctime).strftime(
|
||||
'%Y-%m-%d %H:%M:%S')
|
||||
msg = "\n*** Detected a minidump at {} created on {}, ".format(latest, creation_time) + \
|
||||
"if this is related to your bug please include it when you file a report ***"
|
||||
print(msg, file=sys.stderr)
|
||||
|
||||
+3
-2
@@ -1,4 +1,5 @@
|
||||
from .video_generator.nodes import (NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS)
|
||||
from .video_generator.nodes import (NODE_CLASS_MAPPINGS,
|
||||
NODE_DISPLAY_NAME_MAPPINGS)
|
||||
|
||||
WEB_DIRECTORY = "./web"
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
|
||||
@@ -443,6 +443,7 @@
|
||||
1025,
|
||||
"fixed",
|
||||
24,
|
||||
-99999,
|
||||
-99999
|
||||
],
|
||||
"auto_widget_states": {
|
||||
@@ -490,6 +491,11 @@
|
||||
"isAuto": true,
|
||||
"value": -99999,
|
||||
"cachedValue": "X://insert/path/here.mp4"
|
||||
},
|
||||
"enable_teacache": {
|
||||
"isAuto": true,
|
||||
"value": -99999,
|
||||
"cachedValue": true
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -636,4 +642,4 @@
|
||||
"VHS_KeepIntermediate": true
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
||||
@@ -353,7 +353,8 @@
|
||||
1024,
|
||||
"fixed",
|
||||
24,
|
||||
"X://insert/path/here.mp4"
|
||||
"X://insert/path/here.mp4",
|
||||
true
|
||||
],
|
||||
"auto_widget_states": {
|
||||
"height": {
|
||||
@@ -400,6 +401,11 @@
|
||||
"isAuto": true,
|
||||
"value": "X://insert/path/here.mp4",
|
||||
"cachedValue": "X://insert/path/here.mp4"
|
||||
},
|
||||
"enable_teacache": {
|
||||
"isAuto": true,
|
||||
"value": true,
|
||||
"cachedValue": true
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -688,4 +694,4 @@
|
||||
"VHS_KeepIntermediate": true
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
||||
@@ -31,6 +31,9 @@ class InferenceArgs:
|
||||
"image_path": ("STRING", {
|
||||
"default": "X://insert/path/here.mp4"
|
||||
}),
|
||||
"enable_teacache": ([True, False], {
|
||||
"default": False
|
||||
}),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -54,6 +57,7 @@ class InferenceArgs:
|
||||
seed,
|
||||
fps,
|
||||
image_path,
|
||||
enable_teacache,
|
||||
):
|
||||
raw_args = {
|
||||
"height": height,
|
||||
@@ -65,6 +69,7 @@ class InferenceArgs:
|
||||
"seed": seed,
|
||||
"fps": fps,
|
||||
"image_path": image_path,
|
||||
"enable_teacache": enable_teacache,
|
||||
}
|
||||
|
||||
# Filter out keys where value is -99999, handling different types properly
|
||||
|
||||
@@ -14,7 +14,10 @@ class LoadImagePath:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
input_dir = folder_paths.get_input_directory()
|
||||
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
|
||||
files = [
|
||||
f for f in os.listdir(input_dir)
|
||||
if os.path.isfile(os.path.join(input_dir, f))
|
||||
]
|
||||
files = folder_paths.filter_files_content_types(files, ["image"])
|
||||
return {
|
||||
"required": {
|
||||
@@ -62,10 +65,13 @@ class LoadImagePath:
|
||||
None,
|
||||
]
|
||||
if 'A' in processed_image.getbands():
|
||||
mask = np.array(processed_image.getchannel('A')).astype(np.float32) / 255.0
|
||||
mask = np.array(processed_image.getchannel('A')).astype(
|
||||
np.float32) / 255.0
|
||||
mask = 1. - torch.from_numpy(mask)
|
||||
elif processed_image.mode == 'P' and 'transparency' in processed_image.info:
|
||||
mask = np.array(processed_image.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
|
||||
mask = np.array(
|
||||
processed_image.convert('RGBA').getchannel('A')).astype(
|
||||
np.float32) / 255.0
|
||||
mask = 1. - torch.from_numpy(mask)
|
||||
else:
|
||||
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
|
||||
|
||||
@@ -9,7 +9,8 @@ from PIL import ImageFile, UnidentifiedImageError
|
||||
T = TypeVar('T')
|
||||
|
||||
|
||||
def conditioning_set_values(conditioning: list[Any], values: dict[str, Any] | None = None) -> list[Any]:
|
||||
def conditioning_set_values(conditioning: list[Any],
|
||||
values: dict[str, Any] | None = None) -> list[Any]:
|
||||
if values is None:
|
||||
values = {}
|
||||
c = []
|
||||
@@ -26,7 +27,8 @@ def pillow(fn: Callable[[Any], T], arg: Any) -> T:
|
||||
prev_value = None
|
||||
try:
|
||||
x = fn(arg)
|
||||
except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416
|
||||
except (OSError, UnidentifiedImageError, ValueError
|
||||
): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416
|
||||
prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
|
||||
ImageFile.LOAD_TRUNCATED_IMAGES = True
|
||||
x = fn(arg)
|
||||
@@ -37,7 +39,12 @@ def pillow(fn: Callable[[Any], T], arg: Any) -> T:
|
||||
|
||||
|
||||
def hasher() -> Callable[[], Any]:
|
||||
hashfuncs = {"md5": hashlib.md5, "sha1": hashlib.sha1, "sha256": hashlib.sha256, "sha512": hashlib.sha512}
|
||||
hashfuncs = {
|
||||
"md5": hashlib.md5,
|
||||
"sha1": hashlib.sha1,
|
||||
"sha256": hashlib.sha256,
|
||||
"sha512": hashlib.sha512
|
||||
}
|
||||
return hashfuncs[args.default_hashing_function]
|
||||
|
||||
|
||||
@@ -51,7 +58,8 @@ def string_to_torch_dtype(string: str) -> torch.dtype | None:
|
||||
return None
|
||||
|
||||
|
||||
def image_alpha_fix(destination: torch.Tensor, source: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
def image_alpha_fix(destination: torch.Tensor,
|
||||
source: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
if destination.shape[-1] < source.shape[-1]:
|
||||
source = source[..., :destination.shape[-1]]
|
||||
elif destination.shape[-1] > source.shape[-1]:
|
||||
|
||||
@@ -26,7 +26,11 @@ class TextEncoderConfig:
|
||||
CATEGORY = "fastvideo"
|
||||
|
||||
def set_args(self, prefix, quant_config, lora_config):
|
||||
raw_args = {"prefix": prefix, "quant_config": quant_config, "lora_config": lora_config}
|
||||
raw_args = {
|
||||
"prefix": prefix,
|
||||
"quant_config": quant_config,
|
||||
"lora_config": lora_config
|
||||
}
|
||||
|
||||
# Filter out keys where value is -99999
|
||||
args = {k: v for k, v in raw_args.items() if str(int(v)) != str(-99999)}
|
||||
|
||||
@@ -13,7 +13,11 @@ from comfy.model_management import processing_interrupted
|
||||
from fastvideo import PipelineConfig
|
||||
from fastvideo import VideoGenerator as FastVideoGenerator
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
|
||||
sys.path.insert(
|
||||
0,
|
||||
os.path.dirname(
|
||||
os.path.dirname(
|
||||
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
|
||||
|
||||
|
||||
# Custom exception for interruption
|
||||
@@ -24,7 +28,8 @@ class GenerationInterruptedException(Exception):
|
||||
# Custom exception for interruption that ComfyUI will recognize
|
||||
class GenerationCancelledException(Exception):
|
||||
|
||||
def __init__(self, message: str = "Generation was cancelled by user") -> None:
|
||||
def __init__(self,
|
||||
message: str = "Generation was cancelled by user") -> None:
|
||||
self.message = message
|
||||
super().__init__(self.message)
|
||||
|
||||
@@ -134,7 +139,8 @@ class VideoGenerator:
|
||||
self._generation_interrupted = True
|
||||
|
||||
# Try to send interrupt signal to worker processes
|
||||
if self.generator is not None and hasattr(self.generator, 'executor'):
|
||||
if self.generator is not None and hasattr(
|
||||
self.generator, 'executor'):
|
||||
try:
|
||||
# The MultiprocExecutor has a workers attribute
|
||||
if hasattr(self.generator.executor, 'workers'):
|
||||
@@ -150,12 +156,16 @@ class VideoGenerator:
|
||||
break
|
||||
time.sleep(0.5)
|
||||
|
||||
def _run_generation(self, prompt: str, output_path: str, inference_args: dict[str, Any]) -> None:
|
||||
def _run_generation(self, prompt: str, output_path: str,
|
||||
inference_args: dict[str, Any]) -> None:
|
||||
"""Thread function to run the generation"""
|
||||
try:
|
||||
if self.generator is not None:
|
||||
self.generator.generate_video(prompt=prompt, output_path=output_path, **inference_args)
|
||||
self._generation_result = os.path.join(output_path, f"{prompt[:100]}.mp4")
|
||||
self.generator.generate_video(prompt=prompt,
|
||||
output_path=output_path,
|
||||
**inference_args)
|
||||
self._generation_result = os.path.join(output_path,
|
||||
f"{prompt[:100]}.mp4")
|
||||
else:
|
||||
raise RuntimeError("Generator is not initialized")
|
||||
except Exception as e:
|
||||
@@ -216,7 +226,8 @@ class VideoGenerator:
|
||||
update_config_from_args(pipeline_config.vae_config, vae_config)
|
||||
|
||||
if text_encoder_config is not None:
|
||||
update_config_from_args(pipeline_config.text_encoder_configs, text_encoder_config)
|
||||
update_config_from_args(pipeline_config.text_encoder_configs,
|
||||
text_encoder_config)
|
||||
|
||||
# Update top-level pipeline config with remaining arguments
|
||||
raw_pipeline_args = {}
|
||||
@@ -234,7 +245,10 @@ class VideoGenerator:
|
||||
raw_pipeline_args['text_encoder_precision'] = text_encoder_precision
|
||||
|
||||
# Filter out any value explicitly set to -99999 (auto values)
|
||||
pipeline_args = {k: v for k, v in raw_pipeline_args.items() if str(int(v)) != str(-99999)}
|
||||
pipeline_args = {
|
||||
k: v
|
||||
for k, v in raw_pipeline_args.items() if str(int(v)) != str(-99999)
|
||||
}
|
||||
|
||||
update_config_from_args(pipeline_config, pipeline_args)
|
||||
|
||||
@@ -248,30 +262,38 @@ class VideoGenerator:
|
||||
if dit_cpu_offload is not None:
|
||||
raw_generation_args['dit_cpu_offload'] = dit_cpu_offload
|
||||
|
||||
generation_args = {k: v for k, v in raw_generation_args.items() if str(int(v)) != str(-99999)}
|
||||
generation_args = {
|
||||
k: v
|
||||
for k, v in raw_generation_args.items()
|
||||
if str(int(v)) != str(-99999)
|
||||
}
|
||||
|
||||
if self.generator is None:
|
||||
print('generation_args', generation_args)
|
||||
print('pipeline_config', pipeline_config)
|
||||
self.generator = FastVideoGenerator.from_pretrained(model_path=model_path,
|
||||
**generation_args,
|
||||
pipeline_config=pipeline_config)
|
||||
self.generator = FastVideoGenerator.from_pretrained(
|
||||
model_path=model_path,
|
||||
**generation_args,
|
||||
pipeline_config=pipeline_config)
|
||||
|
||||
print('inference_args', inference_args)
|
||||
|
||||
# Start a thread to run the generation
|
||||
self._generation_thread = threading.Thread(target=self._run_generation,
|
||||
args=(prompt, output_path, inference_args),
|
||||
args=(prompt, output_path,
|
||||
inference_args),
|
||||
daemon=True)
|
||||
self._generation_thread.start()
|
||||
|
||||
# Start a background thread to monitor for interruptions
|
||||
self._generation_active = True
|
||||
self._interrupt_thread = threading.Thread(target=self._monitor_for_interruption, daemon=True)
|
||||
self._interrupt_thread = threading.Thread(
|
||||
target=self._monitor_for_interruption, daemon=True)
|
||||
self._interrupt_thread.start()
|
||||
|
||||
# Wait for either completion or interruption
|
||||
while self._generation_thread.is_alive() and not self._interrupt_event.is_set():
|
||||
while self._generation_thread.is_alive(
|
||||
) and not self._interrupt_event.is_set():
|
||||
self._generation_thread.join(timeout=0.5)
|
||||
|
||||
self._generation_active = False
|
||||
|
||||
@@ -552,7 +552,7 @@ app.registerExtension({
|
||||
]
|
||||
const floatWidgetNames = ["embedded_cfg_scale", "guidance_scale"]
|
||||
const comboWidgetNames = ["vae_tiling", "vae_precision", "vae_sp", "text_encoder_precision", "precision",
|
||||
"load_encoder", "load_decoder", "use_tiling", "use_temporal_tiling", "use_parallel_tiling", "dit_cpu_offload"
|
||||
"load_encoder", "load_decoder", "use_tiling", "use_temporal_tiling", "use_parallel_tiling", "dit_cpu_offload", "enable_teacache"
|
||||
]
|
||||
const stringWidgetNames = ["prefix", "quant_config", "lora_config", "image_path"]
|
||||
|
||||
|
||||
@@ -0,0 +1,113 @@
|
||||
|
||||
|
||||
# Attention Kernel Used in FastVideo
|
||||
|
||||
|
||||
|
||||
## Video Sparse Attention (VSA)
|
||||
|
||||
### Installation
|
||||
We support H100 (via TK) and any other GPU (via triton) for VSA.
|
||||
```bash
|
||||
git submodule update --init --recursive
|
||||
python setup_vsa.py install
|
||||
```
|
||||
|
||||
|
||||
If you encounter error during installation, try below:
|
||||
Install C++20 for ThunderKittens:
|
||||
```bash
|
||||
sudo apt update
|
||||
sudo apt install gcc-11 g++-11
|
||||
|
||||
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
|
||||
|
||||
sudo apt update
|
||||
sudo apt install clang-11
|
||||
```
|
||||
(If you use CUDA12.8)
|
||||
```bash
|
||||
export CUDA_HOME=/usr/local/cuda-12.8
|
||||
export PATH=${CUDA_HOME}/bin:${PATH}
|
||||
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
|
||||
```
|
||||
|
||||
### Verify if you have successfully installed
|
||||
|
||||
```bash
|
||||
# test numerical
|
||||
python tests/test_vsa.py
|
||||
# (For H100) test speed
|
||||
python benchmarks/bench_vsa_hopper.py
|
||||
```
|
||||
bench_vsa_hopper.py should print something like this:
|
||||
```bash
|
||||
Using topk=76 kv blocks per q block (out of 768 total kv blocks)
|
||||
|
||||
=== BLOCK SPARSE ATTENTION BENCHMARK ===
|
||||
Block Sparse Forward - TFLOPS: 5622.26
|
||||
Block Sparse Backward - TFLOPS: 3865.68
|
||||
```
|
||||
|
||||
|
||||
## Sliding Tile Attention (STA)
|
||||
We only support H100 for STA.
|
||||
```bash
|
||||
git submodule update --init --recursive
|
||||
python setup_sta.py install
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
### Usage
|
||||
End-2-end inference with FastVideo:
|
||||
```bash
|
||||
bash scripts/inference/v1_inference_wan_STA.sh
|
||||
```
|
||||
|
||||
If you want to use sliding tile attention in your custom model:
|
||||
```python
|
||||
from st_attn import sliding_tile_attention
|
||||
# assuming video size (T, H, W) = (30, 48, 80), text tokens = 256 with padding.
|
||||
# q, k, v: [batch_size, num_heads, seq_length, head_dim], seq_length = T*H*W + 256
|
||||
# a tile is a cube of size (6, 8, 8)
|
||||
# window_size in tiles: [(window_t, window_h, window_w), (..)...]. For example, window size (3, 3, 3) means a query can attend to (3x6, 3x8, 3x8) = (18, 24, 24) tokens out of the total 30x48x80 video.
|
||||
# text_length: int ranging from 0 to 256
|
||||
# If your attention contains text token (Hunyuan)
|
||||
out = sliding_tile_attention(q, k, v, window_size, text_length)
|
||||
# If your attention does not contain text token (StepVideo)
|
||||
out = sliding_tile_attention(q, k, v, window_size, 0, False)
|
||||
```
|
||||
|
||||
|
||||
### Test
|
||||
```bash
|
||||
python tests/test_sta.py # test STA
|
||||
python tests/test_vsa.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.
|
||||
|
||||
|
||||
https://github.com/user-attachments/assets/f3b6dd79-7b43-4b60-a0fa-3d6495ec5747
|
||||
|
||||
## Why is STA Fast?
|
||||
2D/3D Sliding Window Attention (SWA) creates many mixed blocks in the attention map. Even though mixed blocks have less output value,a mixed block is significantly slower than a dense block due to the GPU-unfriendly masking operation.
|
||||
|
||||
STA removes mixed blocks.
|
||||
|
||||
|
||||
<div align="center">
|
||||
<img src=../../assets/sliding_tile_attn_map.png width="80%"/>
|
||||
</div>
|
||||
|
||||
## Acknowledgement
|
||||
|
||||
We learned or reuse code from FlexAtteniton, NATEN, and ThunderKittens.
|
||||
@@ -0,0 +1,145 @@
|
||||
import os
|
||||
from collections import defaultdict
|
||||
|
||||
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"):
|
||||
assert mode in ["fwd", "bwd", "fwd_bwd"]
|
||||
f = 4 * batch * seqlen**2 * nheads * headdim // (2 if causal else 1)
|
||||
return f if mode == "fwd" else (2.5 * f if mode == "bwd" else 3.5 * f)
|
||||
|
||||
|
||||
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, 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}")
|
||||
|
||||
q = torch.randn(B, H, N, D, dtype=torch.bfloat16, device='cuda', requires_grad=False).contiguous()
|
||||
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()
|
||||
# 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()
|
||||
|
||||
|
||||
# # Warmup for forward pass
|
||||
# for _ in range(10):
|
||||
# o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, dit_seq_shape)
|
||||
|
||||
# # 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))
|
||||
|
||||
# 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 = 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 (ms): {ms:.2f}")
|
||||
print(f"Average TFLOPS: {tflops_fwd}")
|
||||
print("-" * 60)
|
||||
|
||||
# torch.cuda.empty_cache()
|
||||
# torch.cuda.synchronize()
|
||||
|
||||
# # Prepare for timing backward pass
|
||||
# start_events_bwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)]
|
||||
# end_events_bwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)]
|
||||
|
||||
# # Warmup for backward pass
|
||||
# for _ in range(10):
|
||||
# qg, kg, vg = tk.mha_backward(q, k, v, o, l_vec, grad_output, causal)
|
||||
|
||||
# # Time the backward pass
|
||||
# for i in range(10):
|
||||
# start_events_bwd[i].record()
|
||||
# qg, kg, vg = tk.mha_backward(q, k, v, o, l_vec, grad_output, causal)
|
||||
# end_events_bwd[i].record()
|
||||
|
||||
# torch.cuda.synchronize()
|
||||
# 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 = 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(ms): {ms:.2f}")
|
||||
# print(f"Average TFLOPS: {tflops_bwd}")
|
||||
# print("=" * 60)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def plot_results(results):
|
||||
os.makedirs('benchmark_results', exist_ok=True)
|
||||
for mode in ['fwd', 'bwd']:
|
||||
for (D, causal), values in results[mode].items():
|
||||
seq_lens = [x[0] for x in values]
|
||||
tflops = [x[1] for x in values]
|
||||
|
||||
plt.figure(figsize=(10, 6))
|
||||
bars = plt.bar(range(len(seq_lens)), tflops, tick_label=seq_lens)
|
||||
plt.xlabel('Sequence Length')
|
||||
plt.ylabel('TFLOPS')
|
||||
plt.title(f'{mode.upper()} Pass - Head Dim: {D}, Causal: {causal}')
|
||||
plt.grid(True)
|
||||
|
||||
# Adding the numerical y value on top of each bar
|
||||
for bar in bars:
|
||||
yval = bar.get_height()
|
||||
plt.text(bar.get_x() + bar.get_width() / 2, yval, round(yval, 2), ha='center', va='bottom')
|
||||
|
||||
filename = f'benchmark_results/{mode}_D{D}_causal{causal}.png'
|
||||
plt.savefig(filename)
|
||||
plt.close()
|
||||
|
||||
|
||||
# Example list of configurations to test
|
||||
configurations = [
|
||||
(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),
|
||||
# (16, 16, 768*8, 128, False),
|
||||
# (16, 16, 768*16, 128, False),
|
||||
# (16, 16, 768, 128, True),
|
||||
# (16, 16, 768*2, 128, True),
|
||||
# (16, 16, 768*4, 128, True),
|
||||
# (16, 16, 768*8, 128, True),
|
||||
# (16, 16, 768*16, 128, True),
|
||||
# (16, 32, 768, 64, False),
|
||||
# (16, 32, 768*2, 64, False),
|
||||
# (16, 32, 768*4, 64, False),
|
||||
# (16, 32, 768*8, 64, False),
|
||||
# (16, 32, 768*16, 64, False),
|
||||
# (16, 32, 768, 64, True),
|
||||
# (16, 32, 768*2, 64, True),
|
||||
# (16, 32, 768*4, 64, True),
|
||||
# (16, 32, 768*8, 64, True),
|
||||
# (16, 32, 768*16, 64, True),
|
||||
]
|
||||
|
||||
results = benchmark_attention(configurations)
|
||||
# plot_results(results)
|
||||
@@ -0,0 +1,224 @@
|
||||
import torch
|
||||
import argparse
|
||||
from triton.testing import do_bench
|
||||
from vsa import block_sparse_fwd, block_sparse_bwd
|
||||
from vsa import BLOCK_M, BLOCK_N
|
||||
import triton
|
||||
import numpy as np
|
||||
import random
|
||||
|
||||
def set_seed(seed: int = 42):
|
||||
# Python random module
|
||||
random.seed(seed)
|
||||
|
||||
# NumPy
|
||||
np.random.seed(seed)
|
||||
|
||||
# PyTorch
|
||||
torch.manual_seed(seed)
|
||||
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=1, help='Batch size')
|
||||
parser.add_argument('--num_heads', type=int, default=12, help='Number of heads')
|
||||
parser.add_argument('--head_dim', type=int, default=128, help='Head dimension')
|
||||
parser.add_argument('--topk', type=int, default=None, help='Number of kv blocks each q block attends to')
|
||||
parser.add_argument('--seq_lengths', type=int, nargs='+', default=[49152], help='Sequence lengths to benchmark')
|
||||
return parser.parse_args()
|
||||
|
||||
def create_input_tensors(batch, head, seq_len, headdim):
|
||||
"""Create random input tensors for attention."""
|
||||
q = torch.randn(batch, head, seq_len, headdim, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(batch, head, seq_len, headdim, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(batch, head, seq_len, headdim, dtype=torch.bfloat16, device="cuda")
|
||||
return q, k, v
|
||||
|
||||
def generate_block_sparse_pattern(bs, h, num_q_blocks, num_kv_blocks, k, device="cuda"):
|
||||
"""
|
||||
Generate a block sparse pattern where each q block attends to exactly k kv blocks.
|
||||
|
||||
Args:
|
||||
bs: batch size
|
||||
h: number of heads
|
||||
num_q_blocks: number of query blocks
|
||||
num_kv_blocks: number of key-value blocks
|
||||
k: number of kv blocks each q block attends to
|
||||
device: device to create tensors on
|
||||
|
||||
Returns:
|
||||
q2k_block_sparse_index: [bs, h, num_q_blocks, k]
|
||||
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 k).
|
||||
k2q_block_sparse_index: [bs, h, num_kv_blocks, num_q_blocks]
|
||||
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.
|
||||
block_sparse_mask: [bs, h, num_q_blocks, num_kv_blocks]
|
||||
Binary mask where 1 indicates attention connection.
|
||||
"""
|
||||
# Ensure k is not larger than num_kv_blocks
|
||||
k = min(k, num_kv_blocks)
|
||||
|
||||
# Create random scores for sampling
|
||||
scores = torch.rand(bs, h, num_q_blocks, num_kv_blocks, device=device)
|
||||
|
||||
# Get top-k indices for each q block
|
||||
_, q2k_block_sparse_index = torch.topk(scores, k, dim=-1)
|
||||
q2k_block_sparse_index = q2k_block_sparse_index.to(torch.int32)
|
||||
|
||||
# sort q2k_block_sparse_index
|
||||
q2k_block_sparse_index, _ = torch.sort(q2k_block_sparse_index, dim=-1)
|
||||
|
||||
# All q blocks attend to exactly k kv blocks
|
||||
q2k_block_sparse_num = torch.full((bs, h, num_q_blocks), k, dtype=torch.int32, device=device)
|
||||
|
||||
# Create the corresponding mask
|
||||
block_sparse_mask = torch.zeros(bs, h, num_q_blocks, num_kv_blocks, dtype=torch.bool, device=device)
|
||||
|
||||
# Fill in the mask based on the indices
|
||||
for b in range(bs):
|
||||
for head in range(h):
|
||||
for q_idx in range(num_q_blocks):
|
||||
kv_indices = q2k_block_sparse_index[b, head, q_idx]
|
||||
block_sparse_mask[b, head, q_idx, kv_indices] = True
|
||||
|
||||
# Create the reverse mapping (k2q)
|
||||
# First, initialize lists to collect q indices for each kv block
|
||||
k2q_indices_list = [[[] for _ in range(num_kv_blocks)] for _ in range(bs * h)]
|
||||
|
||||
# Populate the lists based on q2k mapping
|
||||
for b in range(bs):
|
||||
for head in range(h):
|
||||
flat_idx = b * h + head
|
||||
for q_idx in range(num_q_blocks):
|
||||
kv_indices = q2k_block_sparse_index[b, head, q_idx].tolist()
|
||||
for kv_idx in kv_indices:
|
||||
k2q_indices_list[flat_idx][kv_idx].append(q_idx)
|
||||
|
||||
# Find the maximum number of q blocks that attend to any kv block
|
||||
max_q_per_kv = 0
|
||||
for flat_idx in range(bs * h):
|
||||
for kv_idx in range(num_kv_blocks):
|
||||
max_q_per_kv = max(max_q_per_kv, len(k2q_indices_list[flat_idx][kv_idx]))
|
||||
|
||||
# Create tensors for k2q mapping
|
||||
k2q_block_sparse_index = torch.full((bs, h, num_kv_blocks, max_q_per_kv), -1,
|
||||
dtype=torch.int32, device=device)
|
||||
k2q_block_sparse_num = torch.zeros((bs, h, num_kv_blocks),
|
||||
dtype=torch.int32, device=device)
|
||||
|
||||
# Fill the tensors
|
||||
for b in range(bs):
|
||||
for head in range(h):
|
||||
flat_idx = b * h + head
|
||||
for kv_idx in range(num_kv_blocks):
|
||||
q_indices = k2q_indices_list[flat_idx][kv_idx]
|
||||
num_q = len(q_indices)
|
||||
k2q_block_sparse_num[b, head, kv_idx] = num_q
|
||||
if num_q > 0:
|
||||
k2q_block_sparse_index[b, head, kv_idx, :num_q] = torch.tensor(
|
||||
q_indices, dtype=torch.int32, device=device)
|
||||
|
||||
return q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, block_sparse_mask
|
||||
|
||||
def benchmark_block_sparse_attention(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, flops):
|
||||
"""Benchmark block sparse attention forward and backward passes."""
|
||||
print("\n=== BLOCK SPARSE ATTENTION BENCHMARK ===")
|
||||
|
||||
# Forward pass
|
||||
# Warm-up run
|
||||
variable_block_sizes = torch.ones(q2k_block_sparse_index.shape[2], device=q.device).int() * BLOCK_M
|
||||
o, l_vec = block_sparse_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, variable_block_sizes)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
# Benchmark forward
|
||||
fwd_time = do_bench(
|
||||
lambda: block_sparse_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, variable_block_sizes),
|
||||
warmup=5,
|
||||
rep=20,
|
||||
quantiles=None
|
||||
)
|
||||
|
||||
sparse_tflops = flops / fwd_time * 1e-12 * 1e3
|
||||
print(f"Block Sparse Forward - TFLOPS: {sparse_tflops:.2f}")
|
||||
|
||||
# Backward pass
|
||||
grad_output = torch.randn_like(o)
|
||||
|
||||
# Warm-up runs
|
||||
for _ in range(5):
|
||||
block_sparse_bwd(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num, variable_block_sizes)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
# Benchmark backward
|
||||
bwd_time = do_bench(
|
||||
lambda: block_sparse_bwd(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num, variable_block_sizes),
|
||||
warmup=5,
|
||||
rep=20,
|
||||
quantiles=None
|
||||
)
|
||||
bwd_flops = 2.5 * flops # Approximation
|
||||
|
||||
sparse_bwd_tflops = bwd_flops / bwd_time * 1e-12 * 1e3
|
||||
print(f"Block Sparse Backward - TFLOPS: {sparse_bwd_tflops:.2f}")
|
||||
|
||||
return sparse_tflops, sparse_bwd_tflops
|
||||
|
||||
def main():
|
||||
args = parse_arguments()
|
||||
|
||||
set_seed(42)
|
||||
|
||||
# Extract parameters
|
||||
batch = args.batch_size
|
||||
head = args.num_heads
|
||||
headdim = args.head_dim
|
||||
|
||||
print(f"Block Sparse Attention Benchmark")
|
||||
print(f"batch: {batch}, head: {head}, headdim: {headdim}")
|
||||
|
||||
# Test with different sequence lengths
|
||||
for seq_len in args.seq_lengths:
|
||||
# Skip very long sequences if they might cause OOM
|
||||
if seq_len > 16384 and batch > 1:
|
||||
continue
|
||||
|
||||
print("="*100)
|
||||
print(f"\nSequence length: {seq_len}")
|
||||
|
||||
# Calculate theoretical FLOPs for attention
|
||||
flops = 4 * batch * head * headdim * seq_len * seq_len
|
||||
|
||||
# Create input tensors
|
||||
q, k, v = create_input_tensors(batch, head, seq_len, headdim)
|
||||
|
||||
# Setup block sparse parameters
|
||||
num_q_blocks = seq_len // BLOCK_M
|
||||
num_kv_blocks = seq_len // BLOCK_N
|
||||
|
||||
# Determine k value (number of kv blocks per q block)
|
||||
topk = args.topk
|
||||
if topk is None:
|
||||
topk = num_kv_blocks // 10 # Default to ~90% sparsity if k is not specified
|
||||
topk = max(1, topk)
|
||||
print(f"Using topk={topk} kv blocks per q block (out of {num_kv_blocks} total kv blocks)")
|
||||
|
||||
# Generate block sparse pattern
|
||||
q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, _ = generate_block_sparse_pattern(
|
||||
batch, head, num_q_blocks, num_kv_blocks, topk, device="cuda")
|
||||
|
||||
# Benchmark block sparse attention
|
||||
sparse_fwd, sparse_bwd = benchmark_block_sparse_attention(
|
||||
q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, flops
|
||||
)
|
||||
|
||||
# Print results
|
||||
print("\n=== PERFORMANCE RESULTS ===")
|
||||
print(f"Block Sparse Forward - TFLOPS: {sparse_fwd:.2f}")
|
||||
print(f"Block Sparse Backward - TFLOPS: {sparse_bwd:.2f}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,217 @@
|
||||
import torch
|
||||
import argparse
|
||||
import triton.testing
|
||||
from vsa import block_sparse_attn
|
||||
from vsa import BLOCK_M, BLOCK_N
|
||||
|
||||
import numpy as np
|
||||
import random
|
||||
|
||||
def set_seed(seed: int = 42):
|
||||
# Python random module
|
||||
random.seed(seed)
|
||||
|
||||
# NumPy
|
||||
np.random.seed(seed)
|
||||
|
||||
# PyTorch
|
||||
torch.manual_seed(seed)
|
||||
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=1, help='Batch size')
|
||||
parser.add_argument('--num_heads', type=int, default=12, help='Number of heads')
|
||||
parser.add_argument('--head_dim', type=int, default=64, help='Head dimension')
|
||||
parser.add_argument('--topk', type=int, default=None, help='Number of kv blocks each q block attends to')
|
||||
parser.add_argument('--seq_lengths', type=int, nargs='+', default=[49152], help='Sequence lengths to benchmark')
|
||||
return parser.parse_args()
|
||||
|
||||
def create_input_tensors(batch, head, seq_len, headdim):
|
||||
"""Create random input tensors for attention."""
|
||||
q = torch.randn(batch, head, seq_len, headdim, dtype=torch.bfloat16, device="cuda")
|
||||
k = torch.randn(batch, head, seq_len, headdim, dtype=torch.bfloat16, device="cuda")
|
||||
v = torch.randn(batch, head, seq_len, headdim, dtype=torch.bfloat16, device="cuda")
|
||||
return q, k, v
|
||||
|
||||
def generate_block_sparse_pattern(bs, h, num_q_blocks, num_kv_blocks, k, device="cuda"):
|
||||
"""
|
||||
Generate a block sparse pattern where each q block attends to exactly k kv blocks.
|
||||
|
||||
Args:
|
||||
bs: batch size
|
||||
h: number of heads
|
||||
num_q_blocks: number of query blocks
|
||||
num_kv_blocks: number of key-value blocks
|
||||
k: number of kv blocks each q block attends to
|
||||
device: device to create tensors on
|
||||
|
||||
Returns:
|
||||
q2k_block_sparse_index: [bs, h, num_q_blocks, k]
|
||||
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 k).
|
||||
k2q_block_sparse_index: [bs, h, num_kv_blocks, num_q_blocks]
|
||||
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.
|
||||
block_sparse_mask: [bs, h, num_q_blocks, num_kv_blocks]
|
||||
Binary mask where 1 indicates attention connection.
|
||||
"""
|
||||
# Ensure k is not larger than num_kv_blocks
|
||||
k = min(k, num_kv_blocks)
|
||||
|
||||
# Create random scores for sampling
|
||||
scores = torch.rand(bs, h, num_q_blocks, num_kv_blocks, device=device)
|
||||
|
||||
# Get top-k indices for each q block
|
||||
_, q2k_block_sparse_index = torch.topk(scores, k, dim=-1)
|
||||
q2k_block_sparse_index = q2k_block_sparse_index.to(torch.int32)
|
||||
|
||||
# sort q2k_block_sparse_index
|
||||
q2k_block_sparse_index, _ = torch.sort(q2k_block_sparse_index, dim=-1)
|
||||
|
||||
# All q blocks attend to exactly k kv blocks
|
||||
q2k_block_sparse_num = torch.full((bs, h, num_q_blocks), k, dtype=torch.int32, device=device)
|
||||
|
||||
# Create the corresponding mask
|
||||
block_sparse_mask = torch.zeros(bs, h, num_q_blocks, num_kv_blocks, dtype=torch.bool, device=device)
|
||||
|
||||
# Fill in the mask based on the indices
|
||||
for b in range(bs):
|
||||
for head in range(h):
|
||||
for q_idx in range(num_q_blocks):
|
||||
kv_indices = q2k_block_sparse_index[b, head, q_idx]
|
||||
block_sparse_mask[b, head, q_idx, kv_indices] = True
|
||||
|
||||
# Create the reverse mapping (k2q)
|
||||
# First, initialize lists to collect q indices for each kv block
|
||||
k2q_indices_list = [[[] for _ in range(num_kv_blocks)] for _ in range(bs * h)]
|
||||
|
||||
# Populate the lists based on q2k mapping
|
||||
for b in range(bs):
|
||||
for head in range(h):
|
||||
flat_idx = b * h + head
|
||||
for q_idx in range(num_q_blocks):
|
||||
kv_indices = q2k_block_sparse_index[b, head, q_idx].tolist()
|
||||
for kv_idx in kv_indices:
|
||||
k2q_indices_list[flat_idx][kv_idx].append(q_idx)
|
||||
|
||||
# Find the maximum number of q blocks that attend to any kv block
|
||||
max_q_per_kv = 0
|
||||
for flat_idx in range(bs * h):
|
||||
for kv_idx in range(num_kv_blocks):
|
||||
max_q_per_kv = max(max_q_per_kv, len(k2q_indices_list[flat_idx][kv_idx]))
|
||||
|
||||
# Create tensors for k2q mapping
|
||||
k2q_block_sparse_index = torch.full((bs, h, num_kv_blocks, max_q_per_kv), -1,
|
||||
dtype=torch.int32, device=device)
|
||||
k2q_block_sparse_num = torch.zeros((bs, h, num_kv_blocks),
|
||||
dtype=torch.int32, device=device)
|
||||
|
||||
# Fill the tensors
|
||||
for b in range(bs):
|
||||
for head in range(h):
|
||||
flat_idx = b * h + head
|
||||
for kv_idx in range(num_kv_blocks):
|
||||
q_indices = k2q_indices_list[flat_idx][kv_idx]
|
||||
num_q = len(q_indices)
|
||||
k2q_block_sparse_num[b, head, kv_idx] = num_q
|
||||
if num_q > 0:
|
||||
k2q_block_sparse_index[b, head, kv_idx, :num_q] = torch.tensor(
|
||||
q_indices, dtype=torch.int32, device=device)
|
||||
|
||||
return q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, block_sparse_mask
|
||||
|
||||
def benchmark_block_sparse_attention(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, flops):
|
||||
"""Benchmark block sparse attention forward+backward pass."""
|
||||
print("\n=== BLOCK SPARSE ATTENTION FORWARD+BACKWARD BENCHMARK ===")
|
||||
|
||||
# Combined forward+backward pass
|
||||
# Warm-up run
|
||||
q_fwd = q.clone().requires_grad_(True)
|
||||
k_fwd = k.clone().requires_grad_(True)
|
||||
v_fwd = v.clone().requires_grad_(True)
|
||||
o = block_sparse_attn(q_fwd, k_fwd, v_fwd, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num)
|
||||
grad_output = torch.randn_like(o)
|
||||
o.backward(grad_output)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
# Benchmark forward+backward
|
||||
def forward_backward_fn():
|
||||
q_fwd = q.clone().requires_grad_(True)
|
||||
k_fwd = k.clone().requires_grad_(True)
|
||||
v_fwd = v.clone().requires_grad_(True)
|
||||
o = block_sparse_attn(q_fwd, k_fwd, v_fwd, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num)
|
||||
grad_output = torch.randn_like(o)
|
||||
o.backward(grad_output)
|
||||
|
||||
total_time = triton.testing.do_bench(
|
||||
forward_backward_fn,
|
||||
warmup=25,
|
||||
rep=100,
|
||||
return_mode='mean'
|
||||
)
|
||||
|
||||
# Total flops for forward + backward (forward + 2.5x backward approximation)
|
||||
total_flops = flops + 2.5 * flops # 3.5x the forward flops
|
||||
sparse_tflops = total_flops / total_time * 1e-12 * 1e3
|
||||
print(f"Block Sparse Forward+Backward - TFLOPS: {sparse_tflops:.2f}")
|
||||
|
||||
return sparse_tflops
|
||||
|
||||
def main():
|
||||
args = parse_arguments()
|
||||
|
||||
set_seed(42)
|
||||
|
||||
# Extract parameters
|
||||
batch = args.batch_size
|
||||
head = args.num_heads
|
||||
headdim = args.head_dim
|
||||
|
||||
print(f"Block Sparse Attention Benchmark")
|
||||
print(f"batch: {batch}, head: {head}, headdim: {headdim}")
|
||||
|
||||
# Test with different sequence lengths
|
||||
for seq_len in args.seq_lengths:
|
||||
# Skip very long sequences if they might cause OOM
|
||||
if seq_len > 16384 and batch > 1:
|
||||
continue
|
||||
|
||||
print("="*100)
|
||||
print(f"\nSequence length: {seq_len}")
|
||||
|
||||
# Calculate theoretical FLOPs for attention
|
||||
flops = 4 * batch * head * headdim * seq_len * seq_len
|
||||
|
||||
# Create input tensors
|
||||
q, k, v = create_input_tensors(batch, head, seq_len, headdim)
|
||||
|
||||
# Setup block sparse parameters
|
||||
num_q_blocks = seq_len // BLOCK_M
|
||||
num_kv_blocks = seq_len // BLOCK_N
|
||||
|
||||
# Determine k value (number of kv blocks per q block)
|
||||
topk = args.topk
|
||||
if topk is None:
|
||||
topk = num_kv_blocks // 10 # Default to ~90% sparsity if k is not specified
|
||||
topk = max(1, topk)
|
||||
print(f"Using topk={topk} kv blocks per q block (out of {num_kv_blocks} total kv blocks)")
|
||||
|
||||
# Generate block sparse pattern
|
||||
q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, _ = generate_block_sparse_pattern(
|
||||
batch, head, num_q_blocks, num_kv_blocks, topk, device="cuda")
|
||||
|
||||
# Benchmark block sparse attention
|
||||
sparse_fwd = benchmark_block_sparse_attention(
|
||||
q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, flops
|
||||
)
|
||||
|
||||
# Print results
|
||||
print("\n=== PERFORMANCE RESULTS ===")
|
||||
print(f"Block Sparse Forward+Backward - TFLOPS: {sparse_fwd:.2f}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,2 @@
|
||||
recursive-include tk *
|
||||
include config_sta.py
|
||||
@@ -0,0 +1,103 @@
|
||||
|
||||
# Attention Kernel Used in FastVideo
|
||||
|
||||
## Sliding Tile Attention (STA)
|
||||
We support H100 (via TK) and any other GPU (via triton) for STA.
|
||||
|
||||
### Installation
|
||||
```bash
|
||||
pip install st_attn
|
||||
```
|
||||
|
||||
Install from source:
|
||||
|
||||
```bash
|
||||
git submodule update --init --recursive
|
||||
python setup.py install
|
||||
```
|
||||
|
||||
If you want to skip the compilation of the TK kernel and only use the Triton version, try below:
|
||||
|
||||
```bash
|
||||
SKIP_SM90_EXT=1 python setup.py install
|
||||
or
|
||||
SKIP_SM90_EXT=1 pip install --no-build-isolation .
|
||||
```
|
||||
|
||||
If you encounter error during installation, try below:
|
||||
Install C++20 for ThunderKittens:
|
||||
```bash
|
||||
sudo apt update
|
||||
sudo apt install gcc-11 g++-11
|
||||
|
||||
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
|
||||
|
||||
sudo apt update
|
||||
sudo apt install clang-11
|
||||
```
|
||||
(If you use CUDA12.8)
|
||||
```bash
|
||||
export CUDA_HOME=/usr/local/cuda-12.8
|
||||
export PATH=${CUDA_HOME}/bin:${PATH}
|
||||
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
|
||||
```
|
||||
|
||||
### Usage
|
||||
End-2-end inference with FastVideo:
|
||||
```bash
|
||||
bash scripts/inference/v1_inference_wan_STA.sh
|
||||
```
|
||||
|
||||
If you want to use sliding tile attention in your custom model:
|
||||
```python
|
||||
from st_attn import sliding_tile_attention
|
||||
# assuming video size (T, H, W) = (30, 48, 80), text tokens = 256 with padding.
|
||||
# q, k, v: [batch_size, num_heads, seq_length, head_dim], seq_length = T*H*W + 256
|
||||
# a tile is a cube of size (6, 8, 8)
|
||||
# window_size in tiles: [(window_t, window_h, window_w), (..)...]. For example, window size (3, 3, 3) means a query can attend to (3x6, 3x8, 3x8) = (18, 24, 24) tokens out of the total 30x48x80 video.
|
||||
# text_length: int ranging from 0 to 256
|
||||
# If your attention contains text token (Hunyuan)
|
||||
out = sliding_tile_attention(q, k, v, window_size, text_length)
|
||||
# If your attention does not contain text token (StepVideo)
|
||||
out = sliding_tile_attention(q, k, v, window_size, 0, False)
|
||||
```
|
||||
|
||||
|
||||
### Test
|
||||
```bash
|
||||
python ../tests/test_sta.py # test STA
|
||||
```
|
||||
### 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.
|
||||
|
||||
|
||||
https://github.com/user-attachments/assets/f3b6dd79-7b43-4b60-a0fa-3d6495ec5747
|
||||
|
||||
|
||||
## STA Configuration Logic
|
||||
Here is a diagram of how the window is configured and passed through the FastVideo pipeline:
|
||||
|
||||
<div align="center">
|
||||
<img src="../../../docs/assets/images/STA_configuration.png" width="80%"/>
|
||||
</div>
|
||||
|
||||
|
||||
## Why is STA Fast?
|
||||
2D/3D Sliding Window Attention (SWA) creates many mixed blocks in the attention map. Even though mixed blocks have less output value,a mixed block is significantly slower than a dense block due to the GPU-unfriendly masking operation.
|
||||
|
||||
STA removes mixed blocks.
|
||||
|
||||
|
||||
<div align="center">
|
||||
<img src=../../../assets/sliding_tile_attn_map.png width="80%"/>
|
||||
</div>
|
||||
|
||||
## Acknowledgement
|
||||
|
||||
We learned or reuse code from FlexAtteniton, NATEN, and ThunderKittens.
|
||||
@@ -0,0 +1,15 @@
|
||||
### ADD TO THIS TO REGISTER NEW KERNELS
|
||||
sources = {
|
||||
'st_attn': {
|
||||
'source_files': {
|
||||
'h100': 'st_attn/st_attn_h100.cu' # define these source files for each GPU target desired.
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
### WHICH KERNELS DO WE WANT TO BUILD?
|
||||
# (oftentimes during development work you don't need to redefine them all.)
|
||||
kernels = ['st_attn']
|
||||
|
||||
### WHICH GPU TARGET DO WE WANT TO BUILD FOR?
|
||||
target = 'h100'
|
||||
@@ -0,0 +1,83 @@
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
from config_sta import kernels, sources, target
|
||||
from setuptools import find_packages, setup
|
||||
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
|
||||
|
||||
target = target.lower()
|
||||
|
||||
# Package metadata
|
||||
PACKAGE_NAME = "st_attn"
|
||||
VERSION = "0.0.6"
|
||||
AUTHOR = "Hao AI Lab"
|
||||
DESCRIPTION = "Sliding Tile Atteniton Kernel Used in FastVideo"
|
||||
URL = "https://github.com/hao-ai-lab/FastVideo/tree/main/csrc/sliding_tile_attention"
|
||||
|
||||
# Set environment variables
|
||||
tk_root = os.getenv('THUNDERKITTENS_ROOT', os.path.abspath(os.path.join(os.getcwd(), 'tk/')))
|
||||
python_include = subprocess.check_output(['python', '-c',
|
||||
"import sysconfig; print(sysconfig.get_path('include'))"]).decode().strip()
|
||||
torch_include = subprocess.check_output([
|
||||
'python', '-c',
|
||||
"import torch; from torch.utils.cpp_extension import include_paths; print(' '.join(['-I' + p for p in include_paths()]))"
|
||||
]).decode().strip()
|
||||
print('st_attn root:', tk_root)
|
||||
print('Python include:', python_include)
|
||||
print('Torch include directories:', torch_include)
|
||||
|
||||
# CUDA flags
|
||||
cuda_flags = [
|
||||
'-DNDEBUG', '-Xcompiler=-Wno-psabi', '-Xcompiler=-fno-strict-aliasing', '--expt-extended-lambda',
|
||||
'--expt-relaxed-constexpr', '-forward-unknown-to-host-compiler', '--use_fast_math', '-std=c++20', '-O3',
|
||||
'-Xnvlink=--verbose', '-Xptxas=--verbose', '-Xptxas=--warn-on-spills', f'-I{tk_root}/include',
|
||||
f'-I{tk_root}/prototype', f'-I{python_include}', '-DTORCH_COMPILE'
|
||||
] + torch_include.split()
|
||||
cpp_flags = ['-std=c++20', '-O3']
|
||||
|
||||
if target == 'h100':
|
||||
cuda_flags.append('-DKITTENS_HOPPER')
|
||||
cuda_flags.append('-arch=sm_90a')
|
||||
else:
|
||||
raise ValueError(f'Target {target} not supported')
|
||||
|
||||
source_files = ['st_attn.cpp']
|
||||
for k in kernels:
|
||||
if target not in sources[k]['source_files']:
|
||||
raise KeyError(f'Target {target} not found in source files for kernel {k}')
|
||||
if isinstance(sources[k]['source_files'][target], list):
|
||||
source_files.extend(sources[k]['source_files'][target])
|
||||
else:
|
||||
source_files.append(sources[k]['source_files'][target])
|
||||
cpp_flags.append(f'-DTK_COMPILE_{k.replace(" ", "_").upper()}')
|
||||
|
||||
ext_modules = []
|
||||
|
||||
if os.environ.get("SKIP_SM90_EXT", "0") != "1":
|
||||
ext_modules.append(
|
||||
CUDAExtension('st_attn_cuda',
|
||||
sources=source_files,
|
||||
extra_compile_args={
|
||||
'cxx': cpp_flags,
|
||||
'nvcc': cuda_flags
|
||||
},
|
||||
libraries=['cuda'])
|
||||
)
|
||||
else:
|
||||
print("ENV SKIP_SM90_EXT=1, skip st_attn_cuda compile")
|
||||
|
||||
setup(name=PACKAGE_NAME,
|
||||
version=VERSION,
|
||||
author=AUTHOR,
|
||||
description=DESCRIPTION,
|
||||
url=URL,
|
||||
packages=find_packages(),
|
||||
ext_modules=ext_modules,
|
||||
cmdclass={'build_ext': BuildExtension},
|
||||
classifiers=[
|
||||
"Programming Language :: Python :: 3",
|
||||
"Environment :: GPU :: NVIDIA CUDA :: 12",
|
||||
"License :: OSI Approved :: Apache Software License",
|
||||
],
|
||||
python_requires='>=3.10',
|
||||
install_requires=["torch>=2.5.0"])
|
||||
@@ -0,0 +1,23 @@
|
||||
#include <torch/extension.h>
|
||||
#include <ATen/ATen.h>
|
||||
|
||||
#include <vector>
|
||||
#include <cuda_fp16.h>
|
||||
#include <cuda_bf16.h>
|
||||
|
||||
#include <cuda_runtime.h>
|
||||
|
||||
#ifdef TK_COMPILE_ST_ATTN
|
||||
extern torch::Tensor sta_forward(
|
||||
torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o, int kernel_t_size, int kernel_w_size, int kernel_h_size, int text_length, bool process_text, bool has_text, int kernel_aspect_ratio_flag
|
||||
);
|
||||
#endif
|
||||
|
||||
|
||||
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
|
||||
m.doc() = "Sliding Block Attention Kernels"; // optional module docstring
|
||||
|
||||
#ifdef TK_COMPILE_ST_ATTN
|
||||
m.def("sta_fwd", torch::wrap_pybind_function(sta_forward), "sliding tile attention, assuming tile size is (6,8,8)");
|
||||
#endif
|
||||
}
|
||||
@@ -0,0 +1,63 @@
|
||||
import math
|
||||
|
||||
import torch
|
||||
from torch.utils.checkpoint import detach_variable
|
||||
try:
|
||||
from st_attn_cuda import sta_fwd
|
||||
except ImportError:
|
||||
sta_fwd = None
|
||||
|
||||
try:
|
||||
from st_attn.st_attn_triton import sliding_tile_attention_triton
|
||||
except ImportError:
|
||||
sliding_tile_attention_triton = None
|
||||
|
||||
def sliding_tile_attention_SM90(q_all, k_all, v_all, window_size, text_length, has_text=True, dit_seq_shape='30x48x80'):
|
||||
seq_length = q_all.shape[2]
|
||||
dit_seq_shape_mapping = {
|
||||
'30x48x80':1,
|
||||
'36x48x48':2,
|
||||
'18x48x80':3,
|
||||
}
|
||||
if has_text:
|
||||
assert q_all.shape[
|
||||
2] >= 115200 and q_all.shape[2] <= 115456, f"Unsupported {dit_seq_shape}, current shape is {q_all.shape}, only support '30x48x80' for HunyuanVideo"
|
||||
assert q_all.shape[1] == len(window_size), "Number of heads must match the number of window sizes"
|
||||
target_size = math.ceil(seq_length / 384) * 384
|
||||
pad_size = target_size - seq_length
|
||||
if pad_size > 0:
|
||||
q_all = torch.cat([q_all, q_all[:, :, -pad_size:]], dim=2)
|
||||
k_all = torch.cat([k_all, k_all[:, :, -pad_size:]], dim=2)
|
||||
v_all = torch.cat([v_all, v_all[:, :, -pad_size:]], dim=2)
|
||||
else:
|
||||
if dit_seq_shape == '36x48x48': # Stepvideo 204x768x68
|
||||
assert q_all.shape[2] == 82944
|
||||
elif dit_seq_shape == '18x48x80': # Wan 69x768x1280
|
||||
assert q_all.shape[2] == 69120
|
||||
else:
|
||||
raise ValueError(f"Unsupported {dit_seq_shape}, current shape is {q_all.shape}, only support '36x48x48' for Stepvideo and '18x48x80' for Wan")
|
||||
|
||||
kernel_aspect_ratio_flag = dit_seq_shape_mapping[dit_seq_shape]
|
||||
hidden_states = torch.empty_like(q_all)
|
||||
# This for loop is ugly. but it is actually quite efficient. The sequence dimension alone can already oversubscribe SMs
|
||||
for head_index, (t_kernel, h_kernel, w_kernel) in enumerate(window_size):
|
||||
for batch in range(q_all.shape[0]):
|
||||
q_head, k_head, v_head, o_head = (q_all[batch:batch + 1, head_index:head_index + 1],
|
||||
k_all[batch:batch + 1,
|
||||
head_index:head_index + 1], v_all[batch:batch + 1,
|
||||
head_index:head_index + 1],
|
||||
hidden_states[batch:batch + 1, head_index:head_index + 1])
|
||||
|
||||
_ = sta_fwd(q_head, k_head, v_head, o_head, t_kernel, h_kernel, w_kernel, text_length, False, has_text, kernel_aspect_ratio_flag)
|
||||
if has_text:
|
||||
_ = sta_fwd(q_all, k_all, v_all, hidden_states, 3, 3, 3, text_length, True, True, kernel_aspect_ratio_flag)
|
||||
return hidden_states[:, :, :seq_length]
|
||||
|
||||
def sliding_tile_attention(q_all, k_all, v_all, window_size, text_length, has_text=True, dit_seq_shape='30x48x80'):
|
||||
major, minor = torch.cuda.get_device_capability(q_all.device)
|
||||
if major == 9 and minor == 0 and sta_fwd is not None:
|
||||
return sliding_tile_attention_SM90(q_all, k_all, v_all, window_size, text_length, has_text, dit_seq_shape)
|
||||
elif sliding_tile_attention_triton is not None:
|
||||
return sliding_tile_attention_triton(q_all, k_all, v_all, window_size, text_length, has_text, dit_seq_shape)
|
||||
else:
|
||||
raise ImportError("No suitable sliding tile attention implementation found.")
|
||||
@@ -0,0 +1,841 @@
|
||||
// # Define TORCH_COMPILE macro
|
||||
|
||||
#include "kittens.cuh"
|
||||
#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;
|
||||
}
|
||||
|
||||
|
||||
constexpr int CONSUMER_WARPGROUPS = (3);
|
||||
constexpr int PRODUCER_WARPGROUPS = (1);
|
||||
constexpr int NUM_WARPGROUPS = (CONSUMER_WARPGROUPS+PRODUCER_WARPGROUPS);
|
||||
constexpr int NUM_WORKERS = (NUM_WARPGROUPS*kittens::WARPGROUP_WARPS);
|
||||
|
||||
using namespace kittens;
|
||||
namespace cg = cooperative_groups;
|
||||
|
||||
template<int D> struct fwd_attend_ker_tile_dims {};
|
||||
template<> struct fwd_attend_ker_tile_dims<64> {
|
||||
constexpr static int tile_width = (64);
|
||||
constexpr static int qo_height = (4*16);
|
||||
constexpr static int kv_height = (8*16);
|
||||
constexpr static int stages = (4);
|
||||
};
|
||||
template<> struct fwd_attend_ker_tile_dims<128> {
|
||||
constexpr static int tile_width = (128);
|
||||
constexpr static int qo_height = (4*16);
|
||||
constexpr static int kv_height = (8*16);
|
||||
constexpr static int stages = (2);
|
||||
};
|
||||
|
||||
template<int D> struct fwd_globals {
|
||||
using q_tile = st_bf<fwd_attend_ker_tile_dims<D>::qo_height, fwd_attend_ker_tile_dims<D>::tile_width>;
|
||||
using k_tile = st_bf<fwd_attend_ker_tile_dims<D>::kv_height, fwd_attend_ker_tile_dims<D>::tile_width>;
|
||||
using v_tile = st_bf<fwd_attend_ker_tile_dims<D>::kv_height, fwd_attend_ker_tile_dims<D>::tile_width>;
|
||||
using l_col_vec = col_vec<st_fl<fwd_attend_ker_tile_dims<D>::qo_height, fwd_attend_ker_tile_dims<D>::tile_width>>;
|
||||
using o_tile = st_bf<fwd_attend_ker_tile_dims<D>::qo_height, fwd_attend_ker_tile_dims<D>::tile_width>;
|
||||
|
||||
using q_gl = gl<bf16, -1, -1, -1, -1, q_tile>;
|
||||
using k_gl = gl<bf16, -1, -1, -1, -1, k_tile>;
|
||||
using v_gl = gl<bf16, -1, -1, -1, -1, v_tile>;
|
||||
using l_gl = gl<float, -1, -1, -1, -1, l_col_vec>;
|
||||
using o_gl = gl<bf16, -1, -1, -1, -1, o_tile>;
|
||||
|
||||
q_gl q;
|
||||
k_gl k;
|
||||
v_gl v;
|
||||
l_gl l;
|
||||
o_gl o;
|
||||
|
||||
const int N;
|
||||
const int text_L;
|
||||
const int hr;
|
||||
};
|
||||
|
||||
|
||||
template<int D, bool is_causal, bool text_q, bool text_kv, int DT, int DH, int DW, int CT, int CH, int CW>
|
||||
__global__ __launch_bounds__((NUM_WORKERS)*kittens::WARP_THREADS, 1)
|
||||
void fwd_attend_ker(const __grid_constant__ fwd_globals<D> g) {
|
||||
extern __shared__ int __shm[];
|
||||
tma_swizzle_allocator al((int*)&__shm[0]);
|
||||
int warpid = kittens::warpid(), warpgroupid = warpid/kittens::WARPGROUP_WARPS;
|
||||
|
||||
using K = fwd_attend_ker_tile_dims<D>;
|
||||
|
||||
using q_tile = st_bf<K::qo_height, K::tile_width>;
|
||||
using k_tile = st_bf<K::kv_height, K::tile_width>;
|
||||
using v_tile = st_bf<K::kv_height, K::tile_width>;
|
||||
using l_col_vec = col_vec<st_fl<K::qo_height, K::tile_width>>;
|
||||
using o_tile = st_bf<K::qo_height, K::tile_width>;
|
||||
|
||||
q_tile (&q_smem)[CONSUMER_WARPGROUPS] = al.allocate<q_tile, CONSUMER_WARPGROUPS>();
|
||||
k_tile (&k_smem)[K::stages] = al.allocate<k_tile, K::stages >();
|
||||
v_tile (&v_smem)[K::stages] = al.allocate<v_tile, K::stages >();
|
||||
l_col_vec (&l_smem)[CONSUMER_WARPGROUPS] = al.allocate<l_col_vec, CONSUMER_WARPGROUPS>();
|
||||
auto (*o_smem) = reinterpret_cast<o_tile(*)>(q_smem);
|
||||
int img_kv_blocks;
|
||||
int kv_blocks = g.N / (K::kv_height);
|
||||
if constexpr (text_kv) {
|
||||
img_kv_blocks = kv_blocks - 3;
|
||||
} else {
|
||||
img_kv_blocks = kv_blocks;
|
||||
}
|
||||
int kv_head_idx = blockIdx.y / g.hr;
|
||||
int seq_idx;
|
||||
if constexpr (text_q) {
|
||||
seq_idx = CT * CH * CW * 6.0 + blockIdx.x * CONSUMER_WARPGROUPS;
|
||||
} else {
|
||||
seq_idx = blockIdx.x * CONSUMER_WARPGROUPS;
|
||||
}
|
||||
__shared__ kittens::semaphore qsmem_semaphore, k_smem_arrived[K::stages], v_smem_arrived[K::stages], compute_done[K::stages];
|
||||
if (threadIdx.x == 0) {
|
||||
init_semaphore(qsmem_semaphore, 0, 1);
|
||||
for(int j = 0; j < K::stages; j++) {
|
||||
init_semaphore(k_smem_arrived[j], 0, 1);
|
||||
init_semaphore(v_smem_arrived[j], 0, 1);
|
||||
init_semaphore(compute_done[j], CONSUMER_WARPGROUPS, 0);
|
||||
}
|
||||
|
||||
tma::expect_bytes(qsmem_semaphore, sizeof(q_smem));
|
||||
|
||||
for (int wg = 0; wg < CONSUMER_WARPGROUPS; wg++) {
|
||||
coord<q_tile> q_tile_idx = {blockIdx.z, blockIdx.y, (seq_idx) + wg, 0};
|
||||
tma::load_async(q_smem[wg], g.q, q_tile_idx, qsmem_semaphore);
|
||||
}
|
||||
|
||||
if constexpr (text_q){
|
||||
for (int j = 0; j < K::stages - 1; j++) {
|
||||
coord<k_tile> kv_tile_idx = {blockIdx.z, kv_head_idx, j, 0};
|
||||
tma::expect_bytes(k_smem_arrived[j], sizeof(k_tile));
|
||||
tma::load_async(k_smem[j], g.k, kv_tile_idx, k_smem_arrived[j]);
|
||||
tma::expect_bytes(v_smem_arrived[j], sizeof(v_tile));
|
||||
tma::load_async(v_smem[j], g.v, kv_tile_idx, v_smem_arrived[j]);
|
||||
}
|
||||
} else {
|
||||
int qt = seq_idx / 6 / (CH * CW);
|
||||
int qh = (seq_idx / 6) % (CH * CW) / CW;
|
||||
int qw = (seq_idx / 6) % CW;
|
||||
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_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));
|
||||
tma::load_async(k_smem[count], g.k, kv_tile_idx, k_smem_arrived[count]);
|
||||
tma::expect_bytes(v_smem_arrived[count], sizeof(v_tile));
|
||||
tma::load_async(v_smem[count], g.v, kv_tile_idx, v_smem_arrived[count]);
|
||||
count += 1;
|
||||
}
|
||||
j += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
int pipe_idx = K::stages - 1;
|
||||
|
||||
if(warpgroupid == NUM_WARPGROUPS-1) {
|
||||
warpgroup::decrease_registers<32>();
|
||||
|
||||
int kv_iters;
|
||||
if constexpr (is_causal) {
|
||||
kv_iters = (seq_idx * (K::qo_height/kittens::TILE_ROW_DIM<bf16>)) - 1 + (CONSUMER_WARPGROUPS * (K::qo_height/kittens::TILE_ROW_DIM<bf16>));
|
||||
kv_iters = ((kv_iters / (K::kv_height/kittens::TILE_ROW_DIM<bf16>)) == 0) ? (0) : ((kv_iters / (K::kv_height/kittens::TILE_ROW_DIM<bf16>)) - 1);
|
||||
}
|
||||
else { kv_iters = kv_blocks-2;}
|
||||
|
||||
if(warpid == NUM_WORKERS-4) {
|
||||
if constexpr (text_q){
|
||||
for (auto kv_idx = pipe_idx - 1; kv_idx <= kv_iters; kv_idx++) {
|
||||
coord<k_tile> kv_tile_idx = {blockIdx.z, kv_head_idx, kv_idx + 1, 0};
|
||||
tma::expect_bytes(k_smem_arrived[(kv_idx+1)%K::stages], sizeof(k_tile));
|
||||
tma::load_async(k_smem[(kv_idx+1)%K::stages], g.k, kv_tile_idx, k_smem_arrived[(kv_idx+1)%K::stages]);
|
||||
tma::expect_bytes(v_smem_arrived[(kv_idx+1)%K::stages], sizeof(v_tile));
|
||||
tma::load_async(v_smem[(kv_idx+1)%K::stages], g.v, kv_tile_idx, v_smem_arrived[(kv_idx+1)%K::stages]);
|
||||
kittens::wait(compute_done[(kv_idx)%K::stages], (kv_idx/K::stages)%2);
|
||||
}
|
||||
} else {
|
||||
int qt = seq_idx / 6 / (CH * CW);
|
||||
int qh = (seq_idx / 6) % (CH * CW) / CW;
|
||||
int qw = (seq_idx / 6) % CW;
|
||||
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++) {
|
||||
for (int kw = k_w_min; kw <= k_w_max; kw++) {
|
||||
for (int j = 0; j <= 2; j++){
|
||||
if (count >= K::stages - 1) {
|
||||
int index = ((kt * (CH * CW)) + (kh * CW) + kw) * 3 + j;
|
||||
coord<k_tile> kv_tile_idx = {blockIdx.z, kv_head_idx, index, 0};
|
||||
tma::expect_bytes(k_smem_arrived[count%K::stages], sizeof(k_tile));
|
||||
tma::load_async(k_smem[count%K::stages], g.k, kv_tile_idx, k_smem_arrived[count%K::stages]);
|
||||
tma::expect_bytes(v_smem_arrived[count%K::stages], sizeof(v_tile));
|
||||
tma::load_async(v_smem[count%K::stages], g.v, kv_tile_idx, v_smem_arrived[count%K::stages]);
|
||||
kittens::wait(compute_done[(count - 1)%K::stages], ((count - 1)/K::stages)%2);
|
||||
count += 1;
|
||||
} else {
|
||||
count += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
// for text
|
||||
for (int index = img_kv_blocks; index < kv_blocks; index++) {
|
||||
coord<k_tile> kv_tile_idx = {blockIdx.z, kv_head_idx, index, 0};
|
||||
tma::expect_bytes(k_smem_arrived[count%K::stages], sizeof(k_tile));
|
||||
tma::load_async(k_smem[count%K::stages], g.k, kv_tile_idx, k_smem_arrived[count%K::stages]);
|
||||
tma::expect_bytes(v_smem_arrived[count%K::stages], sizeof(v_tile));
|
||||
tma::load_async(v_smem[count%K::stages], g.v, kv_tile_idx, v_smem_arrived[count%K::stages]);
|
||||
kittens::wait(compute_done[(count - 1)%K::stages], ((count - 1)/K::stages)%2);
|
||||
count += 1;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
}
|
||||
else {
|
||||
warpgroup::increase_registers<160>();
|
||||
|
||||
rt_fl<16, K::kv_height> att_block;
|
||||
rt_bf<16, K::kv_height> att_block_mma;
|
||||
rt_fl<16, K::tile_width> o_reg;
|
||||
|
||||
col_vec<rt_fl<16, K::kv_height>> max_vec, norm_vec, max_vec_last_scaled, max_vec_scaled;
|
||||
|
||||
neg_infty(max_vec);
|
||||
zero(norm_vec);
|
||||
zero(o_reg);
|
||||
|
||||
int kv_iters;
|
||||
if constexpr (is_causal) {
|
||||
kv_iters = (seq_idx * 4) - 1 + (CONSUMER_WARPGROUPS * 4);
|
||||
kv_iters = (kv_iters/8);
|
||||
}
|
||||
else if constexpr (text_q){
|
||||
// the last three kv blocks are for text, we process them separately
|
||||
kv_iters = img_kv_blocks - 1;
|
||||
} else {
|
||||
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);
|
||||
for (auto kv_idx = 0; kv_idx <= kv_iters; kv_idx++) {
|
||||
|
||||
kittens::wait(k_smem_arrived[(kv_idx)%K::stages], (kv_idx/K::stages)%2);
|
||||
warpgroup::mm_ABt(att_block, q_smem[warpgroupid], k_smem[(kv_idx)%K::stages]);
|
||||
|
||||
copy(max_vec_last_scaled, max_vec);
|
||||
if constexpr (D == 64) { mul(max_vec_last_scaled, max_vec_last_scaled, 1.44269504089f*0.125f); }
|
||||
else { mul(max_vec_last_scaled, max_vec_last_scaled, 1.44269504089f*0.08838834764f); }
|
||||
|
||||
warpgroup::mma_async_wait();
|
||||
|
||||
row_max(max_vec, att_block, max_vec);
|
||||
|
||||
if constexpr (D == 64) {
|
||||
mul(att_block, att_block, 1.44269504089f*0.125f);
|
||||
mul(max_vec_scaled, max_vec, 1.44269504089f*0.125f);
|
||||
}
|
||||
else {
|
||||
mul(att_block, att_block, 1.44269504089f*0.08838834764f);
|
||||
mul(max_vec_scaled, max_vec, 1.44269504089f*0.08838834764f);
|
||||
}
|
||||
|
||||
sub_row(att_block, att_block, max_vec_scaled);
|
||||
exp2(att_block, att_block);
|
||||
sub(max_vec_last_scaled, max_vec_last_scaled, max_vec_scaled);
|
||||
exp2(max_vec_last_scaled, max_vec_last_scaled);
|
||||
mul(norm_vec, norm_vec, max_vec_last_scaled);
|
||||
row_sum(norm_vec, att_block, norm_vec);
|
||||
add(att_block, att_block, 0.f);
|
||||
copy(att_block_mma, att_block);
|
||||
mul_row(o_reg, o_reg, max_vec_last_scaled);
|
||||
|
||||
kittens::wait(v_smem_arrived[(kv_idx)%K::stages], (kv_idx/K::stages)%2);
|
||||
|
||||
warpgroup::mma_AB(o_reg, att_block_mma, v_smem[(kv_idx)%K::stages]);
|
||||
warpgroup::mma_async_wait();
|
||||
|
||||
if(warpgroup::laneid() == 0) arrive(compute_done[(kv_idx)%K::stages], 1);
|
||||
}
|
||||
// the last three kv blocks are for text, we process them separately
|
||||
if constexpr(text_kv) {
|
||||
for (auto kv_idx = kv_iters + 1; kv_idx <= kv_iters + 3; kv_idx++) {
|
||||
|
||||
kittens::wait(k_smem_arrived[(kv_idx)%K::stages], (kv_idx/K::stages)%2);
|
||||
warpgroup::mm_ABt(att_block, q_smem[warpgroupid], k_smem[(kv_idx)%K::stages]);
|
||||
|
||||
copy(max_vec_last_scaled, max_vec);
|
||||
if constexpr (D == 64) { mul(max_vec_last_scaled, max_vec_last_scaled, 1.44269504089f*0.125f); }
|
||||
else { mul(max_vec_last_scaled, max_vec_last_scaled, 1.44269504089f*0.08838834764f); }
|
||||
|
||||
warpgroup::mma_async_wait();
|
||||
// apply non-pad mask
|
||||
int offset = g.text_L - (kv_idx - (kv_iters + 1)) * K::kv_height;
|
||||
// printf("k_idx_start: %d, k_idx_end: %d, text_end: %d, offset: %d\n", k_idx_start, k_idx_end, text_end, offset);
|
||||
right_fill(att_block, att_block, offset, base_types::constants<float>::neg_infty());
|
||||
|
||||
|
||||
row_max(max_vec, att_block, max_vec);
|
||||
|
||||
if constexpr (D == 64) {
|
||||
mul(att_block, att_block, 1.44269504089f*0.125f);
|
||||
mul(max_vec_scaled, max_vec, 1.44269504089f*0.125f);
|
||||
}
|
||||
else {
|
||||
mul(att_block, att_block, 1.44269504089f*0.08838834764f);
|
||||
mul(max_vec_scaled, max_vec, 1.44269504089f*0.08838834764f);
|
||||
}
|
||||
|
||||
sub_row(att_block, att_block, max_vec_scaled);
|
||||
exp2(att_block, att_block);
|
||||
sub(max_vec_last_scaled, max_vec_last_scaled, max_vec_scaled);
|
||||
exp2(max_vec_last_scaled, max_vec_last_scaled);
|
||||
mul(norm_vec, norm_vec, max_vec_last_scaled);
|
||||
row_sum(norm_vec, att_block, norm_vec);
|
||||
add(att_block, att_block, 0.f);
|
||||
copy(att_block_mma, att_block);
|
||||
mul_row(o_reg, o_reg, max_vec_last_scaled);
|
||||
|
||||
kittens::wait(v_smem_arrived[(kv_idx)%K::stages], (kv_idx/K::stages)%2);
|
||||
|
||||
warpgroup::mma_AB(o_reg, att_block_mma, v_smem[(kv_idx)%K::stages]);
|
||||
warpgroup::mma_async_wait();
|
||||
|
||||
if(warpgroup::laneid() == 0) arrive(compute_done[(kv_idx)%K::stages], 1);
|
||||
}
|
||||
}
|
||||
|
||||
div_row(o_reg, o_reg, norm_vec);
|
||||
warpgroup::store(o_smem[warpgroupid], o_reg);
|
||||
warpgroup::sync(warpgroupid+4);
|
||||
|
||||
if (warpid % 4 == 0) {
|
||||
coord<o_tile> o_tile_idx = {blockIdx.z, blockIdx.y, (seq_idx) + warpgroupid, 0};
|
||||
tma::store_async(g.o, o_smem[warpgroupid], o_tile_idx);
|
||||
}
|
||||
|
||||
mul(max_vec_scaled, max_vec_scaled, 0.69314718056f);
|
||||
log(norm_vec, norm_vec);
|
||||
add(norm_vec, norm_vec, max_vec_scaled);
|
||||
|
||||
if constexpr (D == 64) { mul(norm_vec, norm_vec, -8.0f); }
|
||||
else { mul(norm_vec, norm_vec, -11.313708499f); }
|
||||
|
||||
warpgroup::store(l_smem[warpgroupid], norm_vec);
|
||||
warpgroup::sync(warpgroupid+4);
|
||||
|
||||
if (warpid % 4 == 0) {
|
||||
coord<l_col_vec> tile_idx = {blockIdx.z, blockIdx.y, 0, (seq_idx) + warpgroupid};
|
||||
tma::store_async(g.l, l_smem[warpgroupid], tile_idx);
|
||||
}
|
||||
tma::store_async_wait();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
#include "pyutils/torch_helpers.cuh"
|
||||
#include <ATen/cuda/CUDAContext.h>
|
||||
#include <iostream>
|
||||
|
||||
torch::Tensor
|
||||
sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o, int kernel_t_size, int kernel_h_size, int kernel_w_size, int text_length, bool process_text, bool has_text, int kernel_aspect_ratio_flag)
|
||||
{
|
||||
CHECK_INPUT(q);
|
||||
CHECK_INPUT(k);
|
||||
CHECK_INPUT(v);
|
||||
|
||||
auto batch = q.size(0);
|
||||
auto seq_len = q.size(2);
|
||||
auto head_dim = q.size(3);
|
||||
auto qo_heads = q.size(1);
|
||||
auto kv_heads = k.size(1);
|
||||
|
||||
// check to see that these dimensions match for all inputs
|
||||
TORCH_CHECK(q.size(0) == batch, "Q batch dimension - idx 0 - must match for all inputs");
|
||||
TORCH_CHECK(k.size(0) == batch, "K batch dimension - idx 0 - must match for all inputs");
|
||||
TORCH_CHECK(v.size(0) == batch, "V batch dimension - idx 0 - must match for all inputs");
|
||||
|
||||
TORCH_CHECK(q.size(2) == seq_len, "Q sequence length dimension - idx 2 - must match for all inputs");
|
||||
TORCH_CHECK(k.size(2) == seq_len, "K sequence length dimension - idx 2 - must match for all inputs");
|
||||
TORCH_CHECK(v.size(2) == seq_len, "V sequence length dimension - idx 2 - must match for all inputs");
|
||||
|
||||
TORCH_CHECK(q.size(3) == head_dim, "Q head dimension - idx 3 - must match for all non-vector inputs");
|
||||
TORCH_CHECK(k.size(3) == head_dim, "K head dimension - idx 3 - must match for all non-vector inputs");
|
||||
TORCH_CHECK(v.size(3) == head_dim, "V head dimension - idx 3 - must match for all non-vector inputs");
|
||||
|
||||
TORCH_CHECK(qo_heads >= kv_heads, "QO heads must be greater than or equal to KV heads");
|
||||
TORCH_CHECK(qo_heads % kv_heads == 0, "QO heads must be divisible by KV heads");
|
||||
TORCH_CHECK(q.size(1) == qo_heads, "QO head dimension - idx 1 - must match for all inputs");
|
||||
TORCH_CHECK(k.size(1) == kv_heads, "KV head dimension - idx 1 - must match for all inputs");
|
||||
TORCH_CHECK(v.size(1) == kv_heads, "KV head dimension - idx 1 - must match for all inputs");
|
||||
|
||||
auto hr = qo_heads / kv_heads;
|
||||
|
||||
c10::BFloat16* q_ptr = q.data_ptr<c10::BFloat16>();
|
||||
c10::BFloat16* k_ptr = k.data_ptr<c10::BFloat16>();
|
||||
c10::BFloat16* v_ptr = v.data_ptr<c10::BFloat16>();
|
||||
|
||||
bf16* d_q = reinterpret_cast<bf16*>(q_ptr);
|
||||
bf16* d_k = reinterpret_cast<bf16*>(k_ptr);
|
||||
bf16* d_v = reinterpret_cast<bf16*>(v_ptr);
|
||||
|
||||
|
||||
|
||||
torch::Tensor l_vec = torch::empty({static_cast<const uint>(batch),
|
||||
static_cast<const uint>(qo_heads),
|
||||
static_cast<const uint>(seq_len),
|
||||
static_cast<const uint>(1)},
|
||||
torch::TensorOptions().dtype(torch::kFloat).device(q.device()).memory_format(at::MemoryFormat::Contiguous));
|
||||
|
||||
|
||||
bf16* o_ptr = reinterpret_cast<bf16*>(o.data_ptr<c10::BFloat16>());
|
||||
bf16* d_o = reinterpret_cast<bf16*>(o_ptr);
|
||||
|
||||
float* l_ptr = reinterpret_cast<float*>(l_vec.data_ptr<float>());
|
||||
float* d_l = reinterpret_cast<float*>(l_ptr);
|
||||
|
||||
//cudadevicesynchronize();
|
||||
const c10::cuda::OptionalCUDAGuard device_guard(q.device());
|
||||
const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
|
||||
|
||||
|
||||
if (head_dim == 128) {
|
||||
using q_tile = st_bf<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>;
|
||||
using k_tile = st_bf<fwd_attend_ker_tile_dims<128>::kv_height, fwd_attend_ker_tile_dims<128>::tile_width>;
|
||||
using v_tile = st_bf<fwd_attend_ker_tile_dims<128>::kv_height, fwd_attend_ker_tile_dims<128>::tile_width>;
|
||||
using l_col_vec = col_vec<st_fl<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>>;
|
||||
using o_tile = st_bf<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>;
|
||||
|
||||
using q_global = gl<bf16, -1, -1, -1, -1, q_tile>;
|
||||
using k_global = gl<bf16, -1, -1, -1, -1, k_tile>;
|
||||
using v_global = gl<bf16, -1, -1, -1, -1, v_tile>;
|
||||
using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
|
||||
using o_global = gl<bf16, -1, -1, -1, -1, o_tile>;
|
||||
|
||||
using globals = fwd_globals<128>;
|
||||
|
||||
q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
|
||||
k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
|
||||
v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
|
||||
l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U, static_cast<unsigned int>(seq_len)};
|
||||
o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
|
||||
|
||||
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)};
|
||||
|
||||
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);
|
||||
dim3 grid_text(2, qo_heads, batch);
|
||||
if (!process_text) {
|
||||
if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 3) {
|
||||
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 1, 1, 1, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 1, 1, 1, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 5) {
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 1, 1, 2, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true,1, 1, 2, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 5 && kernel_h_size == 3 && kernel_w_size == 3) {
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 2, 1, 1, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 2, 1, 1, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
}else if (kernel_t_size ==3 && kernel_h_size == 5 && kernel_w_size == 5){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 1, 2, 2, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 1, 2, 2, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size ==5 && kernel_h_size == 6 && kernel_w_size == 1){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 2, 3, 0, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 2, 3, 0, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size ==5 && kernel_h_size == 3 && kernel_w_size == 5){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 2, 1, 2, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 2, 1, 2, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 5 && kernel_h_size == 5 && kernel_w_size == 5){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 2, 2, 2, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 2, 2, 2, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 5 && kernel_h_size == 5 && kernel_w_size == 7){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 2, 2, 3, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 2, 2, 3, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 5 && kernel_h_size == 6 && kernel_w_size == 10){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 2, 3, 5, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 2, 3, 5, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 5 && kernel_h_size == 1 && kernel_w_size == 1){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 2, 0, 0, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 2, 0, 0, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 10){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 0, 3, 5, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true, 0, 3, 5, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 5 && kernel_h_size == 1 && kernel_w_size == 10){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, true, 2, 0, 5, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, true,2, 0, 5, 5, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else {
|
||||
// print error
|
||||
std::cout << "Invalid kernel size" << std::endl;
|
||||
//print kernel size
|
||||
std::cout << "Kernel size: " << kernel_t_size << " " << kernel_h_size << " " << kernel_w_size << std::endl;
|
||||
}
|
||||
} else {
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, true, true, 1, 1, 1, 5, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, true, true, 1, 1, 1, 5, 6, 10><<<grid_text, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
}
|
||||
|
||||
} else {
|
||||
dim3 grid_image(seq_len/(CONSUMER_WARPGROUPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
|
||||
if (kernel_aspect_ratio_flag == 2){
|
||||
if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 3) {
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 1, 1, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 1, 1, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 6) {
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 1, 3, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false,1, 1, 3, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 6 && kernel_h_size == 3 && kernel_w_size == 3) {
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 3, 1, 1, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 3, 1, 1, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size ==3 && kernel_h_size == 6 && kernel_w_size == 6){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 3, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 3, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
}else if (kernel_t_size ==3 && kernel_h_size == 6 && kernel_w_size == 3){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 1, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 1, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size ==6 && kernel_h_size == 3 && kernel_w_size == 6){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 3, 1, 3, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 3, 1, 3, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 6 && kernel_h_size == 6 && kernel_w_size == 6){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 3, 3, 3, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 3, 3, 3, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 6 && kernel_h_size == 1 && kernel_w_size == 1){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 3, 0, 0, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 3, 0, 0, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 6 && kernel_h_size == 1 && kernel_w_size == 6){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 3, 0, 3, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 3, 0, 3, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 6 && kernel_h_size == 6 && kernel_w_size == 1){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 3, 3, 0, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 3, 3, 0, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 6){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 3, 3, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 0, 3, 3, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 1 && kernel_w_size == 6){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 0, 3, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 0, 0, 3, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 1){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 3, 0, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 0, 3, 0, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 6 && kernel_h_size == 6 && kernel_w_size == 1){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 3, 3, 0, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 3, 3, 0, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 6 && kernel_h_size == 1 && kernel_w_size == 6){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 3, 0, 3, 6, 6, 6>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 3, 0, 3, 6, 6, 6><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else {
|
||||
// print error
|
||||
std::cout << "Invalid kernel size" << std::endl;
|
||||
//print kernel size
|
||||
std::cout << "Kernel size: " << kernel_t_size << " " << kernel_h_size << " " << kernel_w_size << std::endl;
|
||||
}
|
||||
}
|
||||
else if (kernel_aspect_ratio_flag == 3) {
|
||||
if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 3) {
|
||||
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 1, 1, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 1, 1, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 5) {
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 1, 2, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false,1, 1, 2, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 5 && kernel_w_size == 5){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 2, 2, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 2, 2, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 6 && kernel_w_size == 1){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 0, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 0, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 5 && kernel_w_size == 7){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 2, 3, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 2, 3, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 5 && kernel_w_size == 9){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 2, 4, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 2, 4, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 6 && kernel_w_size == 10){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 5, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 5, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 6 && kernel_w_size == 3){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 1, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 3, 1, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 1 && kernel_w_size == 1){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 0, 0, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 1, 0, 0, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 10){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 3, 5, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 0, 3, 5, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 5 && kernel_w_size == 10){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 2, 5, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 0, 2, 5, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 7){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 3, 3, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 0, 3, 3, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 5 && kernel_w_size == 7){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 2, 3, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 0, 2, 3, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 5 && kernel_w_size == 9){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 2, 4, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false, 0, 2, 4, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 1 && kernel_w_size == 10){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 0, 5, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false,1, 0, 5, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 3 && kernel_h_size == 3 && kernel_w_size == 10){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 1, 1, 5, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false,1, 1, 5, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 3 && kernel_w_size == 10){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 1, 5, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false,0, 1, 5, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else if (kernel_t_size == 1 && kernel_h_size == 6 && kernel_w_size == 5){
|
||||
cudaFuncSetAttribute(
|
||||
fwd_attend_ker<128, false, false, false, 0, 3, 2, 3, 6, 10>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
mem_size
|
||||
);
|
||||
fwd_attend_ker<128, false, false, false,0, 3, 2, 3, 6, 10><<<grid_image, (32*NUM_WORKERS), mem_size, stream>>>(g);
|
||||
} else {
|
||||
// print error
|
||||
std::cout << "Invalid kernel size" << std::endl;
|
||||
//print kernel size
|
||||
std::cout << "Kernel size: " << kernel_t_size << " " << kernel_h_size << " " << kernel_w_size << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
else {
|
||||
std::cout << "Unsupported kernel_aspect_ratio_flag: " << kernel_aspect_ratio_flag << std::endl;
|
||||
}
|
||||
|
||||
}
|
||||
CHECK_CUDA_ERROR(cudaGetLastError());
|
||||
// cudaStreamSynchronize(stream);
|
||||
}
|
||||
|
||||
return o;
|
||||
//cudadevicesynchronize();
|
||||
}
|
||||
|
||||
+1
-9
@@ -11,20 +11,12 @@ def is_hip():
|
||||
target = triton.runtime.driver.active.get_current_target()
|
||||
return target.backend == 'hip'
|
||||
|
||||
|
||||
def is_cdna3_cdna4():
|
||||
target = triton.runtime.driver.active.get_current_target()
|
||||
return (target.arch == 'gfx1201' or target.arch == 'gfx1101' or target.arch == 'gfx1100' or target.arch == 'gfx1030')
|
||||
|
||||
|
||||
def get_common_autotune_config():
|
||||
# cdna arch does not support a 4-stage software pipeline, see https://github.com/ROCm/triton/issues/916
|
||||
supported_num_staged = [1, 2] if is_cdna3_cdna4() else [1, 2, 3, 4]
|
||||
configs = [
|
||||
triton.Config({'BLOCK_Q': BLOCK_Q, 'BLOCK_KV': BLOCK_KV}, num_stages=s, num_warps=w) \
|
||||
for BLOCK_Q in [32, 64, 128]\
|
||||
for BLOCK_KV in [32, 64, 128]\
|
||||
for s in supported_num_staged\
|
||||
for s in [1, 2, 3, 4]\
|
||||
for w in [4, 8]\
|
||||
]
|
||||
return configs
|
||||
@@ -1,6 +1,6 @@
|
||||
import torch
|
||||
from .support_flex_sta import get_sliding_tile_attention_mask
|
||||
from fastvideo_kernel import sliding_tile_attention
|
||||
from flex_sta_ref import get_sliding_tile_attention_mask
|
||||
from st_attn import sliding_tile_attention
|
||||
from torch.nn.attention.flex_attention import flex_attention
|
||||
# from flash_attn_interface import flash_attn_func
|
||||
from tqdm import tqdm
|
||||
@@ -73,23 +73,15 @@ def check_correctness(b, h, n, d, causal, mean, std, num_iterations=50, error_mo
|
||||
|
||||
|
||||
# Example usage
|
||||
def test_sliding_tile_attention():
|
||||
if not torch.cuda.is_available():
|
||||
return
|
||||
|
||||
b, h, d = 2, 24, 128
|
||||
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']}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_sliding_tile_attention()
|
||||
b, h, d = 2, 24, 128
|
||||
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']}")
|
||||
@@ -1,13 +1,13 @@
|
||||
import torch
|
||||
import pytest
|
||||
import sys
|
||||
import os
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
|
||||
from .utils import generate_block_sparse_mask_for_function, create_full_mask_from_block_mask
|
||||
# Use installed package
|
||||
# from fastvideo_kernel import video_sparse_attn as block_sparse_attn
|
||||
# Add the parent directory to the path to import block_sparse_attn
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
from tests.utils import generate_block_sparse_mask_for_function, create_full_mask_from_block_mask
|
||||
from vsa import block_sparse_attn
|
||||
|
||||
BLOCK_M = 64
|
||||
BLOCK_N = 64
|
||||
@@ -42,13 +42,8 @@ def block_sparse_kernel_test(Q, K, V, block_sparse_mask, variable_block_sizes, q
|
||||
q_padded = vsa_pad(Q, q_non_pad_index, q_num_blocks, BLOCK_M)
|
||||
k_padded = vsa_pad(K, kv_non_pad_index, kv_num_blocks, BLOCK_M)
|
||||
v_padded = vsa_pad(V, kv_non_pad_index, kv_num_blocks, BLOCK_M)
|
||||
# Use autograd-enabled wrapper (internally dispatches to SM90 kernel or Triton)
|
||||
from fastvideo_kernel.block_sparse_attn import block_sparse_attn
|
||||
output_padded, _aux = block_sparse_attn(
|
||||
q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes
|
||||
)
|
||||
|
||||
output = output_padded[:, :, q_non_pad_index, :]
|
||||
output, _= block_sparse_attn(q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes)
|
||||
output = output[:, :, q_non_pad_index, :]
|
||||
output.backward(dO)
|
||||
return output, Q.grad, K.grad, V.grad
|
||||
|
||||
@@ -240,18 +235,11 @@ def generate_error_graphs_qkdiff(h, d, error_mode='all'):
|
||||
|
||||
print("-" * 150)
|
||||
|
||||
def test_video_sparse_attention_backward():
|
||||
if not torch.cuda.is_available():
|
||||
return
|
||||
|
||||
if __name__ == "__main__":
|
||||
h, d = 16, 128
|
||||
print("Block Sparse Attention with Variable Block Sizes Analysis")
|
||||
print("=" * 60)
|
||||
for mode in ['backward']:
|
||||
generate_error_graphs(h, d, error_mode=mode)
|
||||
generate_error_graphs_qkdiff(h, d, error_mode=mode)
|
||||
print("\nAnalysis completed for all modes.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_video_sparse_attention_backward()
|
||||
|
||||
print("\nAnalysis completed for all modes.")
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user