[XNNPACK] Serialize weights as fp16 rather than fp32#9753
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mcr229 merged 1 commit intopytorch:mainfrom Mar 31, 2025
Merged
[XNNPACK] Serialize weights as fp16 rather than fp32#9753mcr229 merged 1 commit intopytorch:mainfrom
mcr229 merged 1 commit intopytorch:mainfrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9753
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digantdesai
reviewed
Mar 31, 2025
| swap will happen before converting to nhwc. | ||
| quant_params: Quantization meta data for this tensor, None if it is not quantized | ||
| fp32_static_weights: XNN_FLAG_FP32_STATIC_WEIGHTS for fp16 conv | ||
| force_fp32: forces tensor to be serialize as fp32, used for bias of dynamically quantized ops |
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s/fp32_static_weight/force_fp32 - seems a little too vague if you ask me.
digantdesai
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Mar 31, 2025
kirklandsign
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Apr 11, 2025
### Summary Previously we've used FP32_STATIC_WEIGHTS flag in xnnpack to coerce fp32 weights into fp16 for linear and conv. This allowed us to mimc fp16 computation because the weights would be converted and packed as fp16 at runtime. However, this means we lose the benefit of the smaller .pte file because the weights are serialized as fp32 rather than fp16. Additionally, we still have to load the weights as fp32, since they are converted at runtime. This has some poor effects on performance ### Test plan ``` python -m unittest backends.xnnpack.test.ops.test_linear.TestLinear.test_fp16_linear python -m unittest backends.xnnpack.test.ops.test_linear.TestLinear python -m unittest backends.xnnpack.test.ops.test_conv2d.TestConv2d ``` Llama 3.2 with bf16 weights: Before: ``` -rw-r--r-- 1 maxren staff 5468937344 Mar 28 17:00 llama3_2_fp16_direct_convert_runtime.pte ``` After: ``` -rw-r--r-- 1 maxren staff 2997443712 Mar 28 16:57 llama3_2_fp16_direct_convert_runtime.pte ```
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Summary
Previously we've used FP32_STATIC_WEIGHTS flag in xnnpack to coerce fp32 weights into fp16 for linear and conv. This allowed us to mimc fp16 computation because the weights would be converted and packed as fp16 at runtime. However, this means we lose the benefit of the smaller .pte file because the weights are serialized as fp32 rather than fp16. Additionally, we still have to load the weights as fp32, since they are converted at runtime. This has some poor effects on performance
Test plan
Llama 3.2 with bf16 weights:
Before:
After: