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📝 WalkthroughWalkthroughRefactors Phi3/Phi4 vision preprocessing to resize raw images with CatmullRom and use a fused to_tensor_and_normalize path; removes tensor-level bicubic helpers from transforms; and adds a Criterion benchmark for Phi3 preprocessing. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant Caller as Caller
participant Processor as Phi3/Phi4Processor
participant ImageLib as image::DynamicImage / fast_image_resize
participant Transforms as transforms::to_tensor_and_normalize
Caller->>Processor: preprocess(images)
Processor->>ImageLib: resize(image) (CatmullRom)
ImageLib-->>Processor: resized_image
Processor->>Transforms: to_tensor_and_normalize(resized_image, mean,std)
Transforms-->>Processor: tensor_normalized
Processor->>Processor: tile/concat/mask/tokenize
Processor-->>Caller: PreprocessedOutput
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Code Review
This pull request replaces the custom bicubic interpolation logic with SIMD-accelerated FIR CatmullRom resizing for global image creation in both the Phi-3 and Phi-4 vision processors. Additionally, it introduces a fused to_tensor_and_normalize operation to optimize the image processing pipeline into a single pass. The manual bicubic implementation in transforms.rs has been removed as it is no longer needed. I have no feedback to provide.
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…Rom and fuse phi3/phi4 normalize Replace the hand-written per-pixel scalar bicubic_resize (which made ~339K calls to bicubic_interpolate with bounds-checked ndarray indexing) with SIMD-accelerated FIR CatmullRom from fast_image_resize. This operates on the raw DynamicImage before tensor conversion, avoiding the f32 intermediate. Additionally, fuse the separate to_tensor + normalize two-pass pipeline into a single to_tensor_and_normalize call for both the global image and HD tiles in Phi3Vision and Phi4Vision processors. Changes: - phi3_vision: create_global_image now takes DynamicImage, uses transforms::resize(CatmullRom) + to_tensor_and_normalize - phi4_vision: same pattern for create_global_image - phi3_vision/phi4_vision: HD tensor uses to_tensor_and_normalize (fused) - transforms.rs: remove cubic_weight, bicubic_interpolate, bicubic_resize (no remaining callers) Golden tests for phi3/phi4 may need regeneration due to numerical differences between FIR u8-space CatmullRom and the previous f32-space scalar bicubic. The golden fixture files are not present in CI worktrees; when fixtures are present the tests pass within existing tolerances (0.08 phi3, 0.05 phi4). Signed-off-by: Simo Lin <linsimo.mark@gmail.com>
Signed-off-by: Simo Lin <linsimo.mark@gmail.com>
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Benchmark ResultsBaseline (main) vs this PR: Qwen/LLaMA4 (unaffected paths — no regression):
Phi3-Vision (new benchmark, only available on this PR):
The flat scaling across input sizes confirms the SIMD FIR CatmullRom resize is now negligible cost — the bottleneck shifted to per-tile normalize+patchify. The old scalar Note: This changes pixel values for Phi3/Phi4 global images (FIR u8-space bicubic vs old f32-space scalar bicubic). Golden tests may need regeneration for phi3/phi4. |
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@crates/multimodal/src/vision/processors/phi4_vision.rs`:
- Around line 392-396: The code always passes self.mean/self.std to
create_global_image and transforms::to_tensor_and_normalize which ignores a
PreProcessorConfig that overrides only image_std; fix by computing effective
mean/std that honor optional overrides from PreProcessorConfig (e.g. use
config.image_mean.unwrap_or(self.mean) and config.image_std.unwrap_or(self.std)
or a helper like self.effective_mean_std()), then call
create_global_image(&hd_image, &effective_mean, &effective_std) and
transforms::to_tensor_and_normalize(&hd_image, &effective_mean, &effective_std);
also ensure preprocess() or the processor state exposes the PreProcessorConfig
used so image_std-only overrides are considered.
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Fix all unresolved CodeRabbit comments on this PR:
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📒 Files selected for processing (4)
crates/multimodal/benches/image_preprocess.rscrates/multimodal/src/vision/processors/phi3_vision.rscrates/multimodal/src/vision/processors/phi4_vision.rscrates/multimodal/src/vision/transforms.rs
💤 Files with no reviewable changes (1)
- crates/multimodal/src/vision/transforms.rs
| // Step 2: Create global image: FIR CatmullRom resize on raw image, then fused to_tensor+normalize | ||
| let global_tensor = self.create_global_image(&hd_image, &self.mean, &self.std); | ||
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| // Step 3: Create global image | ||
| let global_tensor = self.create_global_image(&hd_tensor); | ||
| // Step 3: Fused to_tensor + normalize on HD image (single pass) | ||
| let hd_tensor = transforms::to_tensor_and_normalize(&hd_image, &self.mean, &self.std); |
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Honor image_std-only overrides in this path.
Line 393 and Line 396 always use self.mean/self.std, but preprocess() only rebuilds a config-derived processor when dynamic_hd or image_mean is set. A PreProcessorConfig that overrides only image_std will still normalize with the default std here.
🔧 Minimal fix outside this hunk
- let processor = if config.dynamic_hd.is_some() || config.image_mean.is_some() {
+ let processor = if config.dynamic_hd.is_some()
+ || config.image_mean.is_some()
+ || config.image_std.is_some()
+ {
Self::from_preprocessor_config(config)
} else {
self.clone()
};🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@crates/multimodal/src/vision/processors/phi4_vision.rs` around lines 392 -
396, The code always passes self.mean/self.std to create_global_image and
transforms::to_tensor_and_normalize which ignores a PreProcessorConfig that
overrides only image_std; fix by computing effective mean/std that honor
optional overrides from PreProcessorConfig (e.g. use
config.image_mean.unwrap_or(self.mean) and config.image_std.unwrap_or(self.std)
or a helper like self.effective_mean_std()), then call
create_global_image(&hd_image, &effective_mean, &effective_std) and
transforms::to_tensor_and_normalize(&hd_image, &effective_mean, &effective_std);
also ensure preprocess() or the processor state exposes the PreProcessorConfig
used so image_std-only overrides are considered.
2 participants
Summary
bicubic_resize(per-pixel loop with ~339Kbicubic_interpolatecalls) with SIMD-accelerated FIR CatmullRom fromfast_image_resizeoperating on rawDynamicImagebefore tensor conversionto_tensor+normalizepipeline into a singleto_tensor_and_normalizecall for both the global image and HD tiles in Phi3Vision and Phi4Vision processorscubic_weight,bicubic_interpolate, andbicubic_resizefromtransforms.rs(no remaining callers)Notes
This changes numerical output for Phi3/Phi4 global images. FIR CatmullRom operates on u8 pixels vs the previous f32-space scalar bicubic. Golden tests for phi3/phi4 may need regeneration when fixture files are present. In the worktree where fixtures are absent, all tests pass (skip gracefully). The existing tolerances (0.08 for phi3, 0.05 for phi4) should accommodate the differences based on the algorithm similarity.
Test plan
cargo test -p llm-multimodal— all 135 unit tests passcargo test -p llm-multimodal -- vision_golden— all 81 golden tests pass (skip gracefully when fixtures absent)python crates/multimodal/scripts/generate_vision_golden.py --model phi3_vision phi4_visionand verify toleranceSummary by CodeRabbit
New Features
Performance Improvements
Breaking/Behavior Changes