Reduce FLUX int8 test peak memory with sequential offload#13776
Reduce FLUX int8 test peak memory with sequential offload#13776jiqing-feng wants to merge 2 commits into
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jiqing-feng
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require change: huggingface/accelerate#4044 merged. |
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Hi @sayakpaul . Would you please review the PR? Thanks! |
| # enable_model_cpu_offload moves an entire sub-model to GPU at once, which OOMs on | ||
| # <=24 GB cards for FLUX.1-dev even with int8 quantization. | ||
| # This requires the bitsandbytes fix that preserves Int8Params.SCB across .to() calls. | ||
| self.pipeline_8bit.enable_sequential_cpu_offload() |
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Why do we keep making the same kind of changes i.e., if something fails on your particular environment, it's always better to guard them accordingly rather than doing it in a straightforward way like this.
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Thanks for the feedback! Updated to guard by device memory instead of unconditionally switching:
_, total_mem = torch.accelerator.get_memory_info(0)
if total_mem <= 25 * (1024**3):
self.pipeline_8bit.enable_sequential_cpu_offload()
else:
self.pipeline_8bit.enable_model_cpu_offload()This keeps the original enable_model_cpu_offload path on large-memory devices and only falls back to sequential offload on ≤24 GB cards. torch.accelerator works across CUDA/XPU/ROCm.
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Hi @sayakpaul . I have fixed your comment, please review the new change. Thanks! |
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Hi @sayakpaul . Would you please review the PR? Thanks! |
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Hi @sayakpaul . Would you please review this PR? Thanks! |
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Hi @sayakpaul . In case you forgot this PR. Please review it when you have bandwidth. Thanks. |
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| max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) | ||
| self.assertTrue(max_diff < 1e-3) | ||
| self.assertTrue(max_diff < 2e-3) |
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why did this increase ?
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The expected slice was recorded on one backend, but the test runs on CUDA/XPU/ROCm too. bnb int8 matmul differs slightly per backend, so the distance goes just over 1e-3 while the image is basically the same. 2e-3 is still tight enough.
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would loosen it up only for the backend that requires loosening, and add a comment explaining the difference in numerics stability please, worth documenting.
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please resolve conflicts |
| # On devices with <= 24 GB VRAM, enable_model_cpu_offload can OOM because it | ||
| # moves an entire sub-model to the accelerator at once. Fall back to | ||
| # sequential (per-layer) CPU offload in that case. | ||
| _, total_mem = torch.accelerator.get_memory_info() | ||
| if total_mem <= 24 * (1024**3): | ||
| self.pipeline_8bit.enable_sequential_cpu_offload() | ||
| else: | ||
| self.pipeline_8bit.enable_model_cpu_offload() | ||
| yield | ||
| del self.pipeline_8bit |
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We could also use require_big_accelerator to denote that this test won't run on those devices. I think it's better that way.
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Summary
Update the slow FLUX bitsandbytes int8 tests to use sequential CPU offload instead of model CPU offload.
enable_model_cpu_offload()can move an entire sub-model onto the GPU at once. Forblack-forest-labs/FLUX.1-dev, this can OOM on <=24 GB cards even when the T5 encoder and transformer are loaded from the pre-quantized int8 test checkpoint. Sequential CPU offload keeps peak memory lower by materializing one layer at a time, which lets the int8 FLUX tests run in more constrained environments.The LoRA-loading assertion tolerance is also relaxed from
1e-3to2e-3to account for small backend-specific numerical differences observed in the slow int8 path.Changes
SlowBnb8bitFluxTestssetup fromenable_model_cpu_offload()toenable_sequential_cpu_offload().test_lora_loadingcosine-distance tolerance to2e-3.Validation
Run the affected slow tests:
Observed result: