KyleandClaude Opus 5
43644de268
A pipeline materializes every component before the first stage runs, so peak memory is the sum of all components even though no two are needed at the same moment. The CPU offload flags cannot help with this: they act after loading, and on a unified-memory device moving weights to the host frees nothing because it is the same pool. Add lazy_module_load, off by default. Heavy components become a LazyModule proxy that loads on first use, and the pipeline installs a release hook on the last stage that holds each one. Peak becomes the largest overlapping set instead of the sum. Measured on MiniMax-H3 r16, 121 GiB GB10, 1 GPU, 192x320, 4 steps. Peak CUDA allocated is 57.7 GiB, reached during conditioning where the text encoder and video VAE overlap. The four deferred components account for 96.3 GiB together, which is what stays resident without the flag: text encoder 48.0, DiT 37.8, video VAE 9.7, audio VAE 0.8. Generation completed and wrote a video. Details worth flagging: The release hook lives on PipelineStage.__call__, not in the pipeline stage loop, so pipelines that override forward still free. The proxy forwards __class__, so isinstance stays honest. A proxy answering False to isinstance(module, FSDPModule) would take the wrong branch silently. Self-returning methods hand back the proxy rather than the component. Stages write self.vae = self.vae.to(device) in a dozen places; returning the component there would replace the proxy with a reference the pipeline cannot release, and the run would look normal while freeing nothing. Releasing is a latency cost, never a correctness one: a released component reloads on next access. Training keeps everything resident and warns if the flag is set. If no stage holds a deferred component the pipeline warns rather than silently doing nothing. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
185 lines
6.6 KiB
Markdown
185 lines
6.6 KiB
Markdown
# Offloading
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This page describes how to use offloading techniques for inference to reduce GPU memory usage while maintaining acceptable performance.
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## Default Behavior
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```python
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dit_cpu_offload: bool = True
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use_fsdp_inference: bool = False
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dit_layerwise_offload: bool = True
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text_encoder_cpu_offload: bool = True
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image_encoder_cpu_offload: bool = True
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vae_cpu_offload: bool = True
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pin_cpu_memory: bool = True
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lazy_module_load: bool = False
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```
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On unified-memory accelerators such as NVIDIA GB10 and Apple silicon, FastVideo
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detects the selected device inside each worker and disables all five host-offload
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modes before loading modules. Host and accelerator allocations share one physical
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pool there, so offload adds transfers and duplicate residency instead of freeing
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memory. CUDA FSDP sharding remains enabled when requested; MPS continues to
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disable FSDP. `pin_cpu_memory` is not an offload mode and is left unchanged.
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## Behavior Explanation
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!!! note
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For CLI usage, replace underscores (`_`) with hyphens (`-`).
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### `use_fsdp_inference`
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Enables [FSDP](https://docs.pytorch.org/tutorials/intermediate/FSDP_tutorial.html) for inference. The model weights are sharded across multiple GPUs to reduce memory usage per GPU, and weights are broadcast to all GPUs layer by layer during inference.
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#### Performance Impact
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FSDP inference introduces negligible performance overhead due to weight prefetching. Performance overhead may be visible when GPU interconnect is slow (e.g., multiple consumer-level GPUs connected by slow PCIe without GPU P2P support).
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#### Usage Recommendation
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We recommend enabling this option when multiple GPUs are available.
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### `dit_cpu_offload`
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Enables CPU offloading for FSDP inference. When enabled, the model weights are offloaded to CPU memory, and the weight of each layer is moved to GPU memory only when that layer is being computed.
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#### Performance Impact
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The PyTorch FSDP implementation does not overlap computation and data transfer perfectly for inference, so enabling this option will harm performance.
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#### Usage Recommendation
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This option only takes effect when FSDP is enabled. For single GPU usage, we recommend using `dit_layerwise_offload` instead.
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### `dit_layerwise_offload`
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This option is similar to `dit_cpu_offload`, but with two key differences:
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1. It overlaps computation and PCIe data transfer
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2. It only works for single GPU inference
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#### Performance Impact
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This option introduces negligible performance overhead.
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#### Usage Recommendation
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We recommend enabling this option for single GPU usage. This option is not compatible with FSDP.
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### `text_encoder_cpu_offload`
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When enabled, the text encoder model weights are offloaded to CPU memory, and text encoding is computed on CPU.
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#### Performance Impact
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This option significantly slows down text encoding computation, but text encoding is usually not the bottleneck.
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#### Usage Recommendation
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We recommend enabling this option only when OOM happens.
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### `image_encoder_cpu_offload` and `vae_cpu_offload`
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When enabled, the weights are stored in CPU memory and moved to GPU memory when the corresponding module is being computed. After computation, the weights are moved back to CPU memory.
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#### Performance Impact
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These options introduce performance overhead due to PCIe data transfer.
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#### Usage Recommendation
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We recommend enabling these options when OOM happens.
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### `lazy_module_load`
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Every option above moves weights between host and device. This one changes
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whether they are in memory at all.
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By default a pipeline loads every component before the first stage runs, so
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peak memory is the sum of all of them even though no two are needed at the same
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moment. With `lazy_module_load` enabled, each heavy component loads on first use
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and is freed once the last stage that needs it has returned, so peak memory
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becomes the largest overlapping set instead of the sum. For a text-to-video
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pipeline that is roughly `max(text encoder, DiT + VAE)` rather than
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`text encoder + DiT + VAE`.
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#### Performance Impact
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A freed component is read from disk again on the next generation, so a
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multi-prompt run pays one reload per component per request. For a large text
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encoder that is tens of seconds.
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#### Usage Recommendation
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Enable this when a model does not fit at load time, which the CPU offload
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options above cannot help with because they act after loading. It is
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particularly relevant on unified-memory devices, where host and device draw on
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the same pool and moving weights to the host frees nothing. Leave it off when
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the model already fits.
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This option applies to inference only. Training keeps every component resident
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and logs a warning if the flag is set.
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## General Recommendations
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### Single GPU Inference
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We recommend enabling `dit_layerwise_offload`. If OOM happens, also enable `image_encoder_cpu_offload` and `vae_cpu_offload`. If OOM still happens, consider enabling `text_encoder_cpu_offload`.
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### Multi-GPU Inference
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We recommend enabling `use_fsdp_inference` and disabling both `dit_layerwise_offload` and `dit_cpu_offload`. If OOM happens, consider enabling `text_encoder_cpu_offload`, `image_encoder_cpu_offload`, and `vae_cpu_offload`. If OOM still happens, consider enabling `dit_cpu_offload`.
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### When the Model Does Not Fit at Load Time
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The offload options only help once loading has finished. If the run dies while
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components are still being placed, or if the machine has unified memory so
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there is no separate host pool to offload into, enable `lazy_module_load`.
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## Examples
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### Single GPU with Layerwise Offloading
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```python
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from fastvideo import VideoGenerator
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generator = VideoGenerator.from_pretrained(
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"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
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num_gpus=1,
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# Recommended for single GPU
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dit_layerwise_offload=True,
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# Enable if OOM happens
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vae_cpu_offload=True,
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image_encoder_cpu_offload=True,
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text_encoder_cpu_offload=True,
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# Speeds up CPU-GPU transfer
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pin_cpu_memory=True,
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)
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prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers."
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video = generator.generate_video(prompt, output_path="output/", save_video=True)
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```
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### Multi-GPU with FSDP
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```python
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from fastvideo import VideoGenerator
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generator = VideoGenerator.from_pretrained(
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"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
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num_gpus=2,
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# Recommended for multi-GPU
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use_fsdp_inference=True,
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dit_layerwise_offload=False,
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dit_cpu_offload=False,
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# Enable if OOM happens
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vae_cpu_offload=True,
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image_encoder_cpu_offload=True,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=True,
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)
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prompt = "A majestic lion strides across the golden savanna."
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video = generator.generate_video(prompt, output_path="output/", save_video=True)
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```
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