feat(neuron): dynamic-resolution images via Qwen smart_resize (#14)

Replace the fixed 448×448-square preprocess with native-aspect
`smart_resize`, and thread the resulting per-image grid through the LM
so spatial structure survives non-square images (documents, screenshots,
charts, panoramas, OCR) instead of being squished into a square.

- preprocess.rs: port Qwen `smart_resize` (factor = patch×merge = 32;
  pixel budget [min,max], default 256²–1024² → 64–1024 LM tokens).
  `PreprocessProfile` drops the fixed target dims for `factor`/`min_pixels`/
  `max_pixels`; `preprocess`/`preprocess_data_uri` now return the resized
  `(h, w)`; add `resized_dims_for_uri` (decode + resize, no normalize) for
  the TP leader's token count.
- rope.rs: `compute_mrope_index`/`get_rope_index` take per-image
  `grids: &[(lm_gh, lm_gw)]` instead of assuming a square `isqrt(run)`.
  Walk image runs in order, validate `run == gh*gw`, emit row-major
  positions, resume the shared counter at `base + max(gh,gw)`. Correct
  for multiple images of differing grids interleaved with text.
- candle.rs: `VisionMeta`/`LoadedModel`/`TpLoadedModel` carry the
  `image_grid_factor` (patch×merge) instead of the constant 196; all four
  prompt-build sites compute per-image counts from each image's resized
  grid (single-GPU from the extracted `ImageInput.h/w`, TP from
  `resized_dims_for_uri`). `ModelArch` gains `vision_grid_factor`.
- single-GPU (`mod.rs`, `dispatch.rs`) and TP
  (`tp_qwen3_5.rs::prefill_with_images_chunked`, `dispatch.rs`,
  `tp/worker.rs`) thread the grids into `get_rope_index`. Each TP rank
  recomputes grids from its own deterministic preprocess — no rpc.rs
  change, single source of truth.

The vision tower itself was already grid-general (recent pos-embed
interpolation + 2D rotary fix). No patch-count cap: pos-embed is
interpolated to any grid; `max_pixels` bounds cost (O(patches²) ViT
attention + prefill) instead.

Tests: smart_resize (aspect/cap/floor/reject), `compute_mrope_index`
non-square + two-image + mismatch cases, square-grid regression guard.
Non-cuda build + clippy + full workspace tests green; TP load/dispatch
paths are cuda-gated → Gitea CUDA type-check. Operator pixel-budget
config + remaining doc cleanup follow in C5.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

crates/neuron/src/harness/candle.rs

@@ -210,13 +210,11 @@ pub struct LoadedModel {
     /// targets and the worker forward uses it to locate splice
     /// positions in the LM input embeddings.
     pub image_token_id: Option<u32>,
-    /// LM-side tokens this model's vision tower emits per image at
-    /// the Stage B fixed resolution (448×448 → 196 for Qwen3.6).
-    /// `None` for text-only models. Set at load time so the
-    /// hot path doesn't recompute it per request. Stage B fixed
-    /// resolution → constant; dynamic resolution per #14 makes it
-    /// per-image.
-    pub lm_tokens_per_image: Option<usize>,
+    /// `patch_size × spatial_merge_size` — divides a resized pixel
+    /// dimension into LM-grid units. Per-image LM token count is
+    /// `(h/factor) × (w/factor)` (#14 dynamic resolution). `None` for
+    /// text-only models. Set at load time.
+    pub image_grid_factor: Option<usize>,
 }
 
 impl LoadedModel {
@@ -288,9 +286,9 @@ pub struct TpLoadedModel {
     pub has_vision: bool,
     /// `<|image_pad|>` token id — same as [`LoadedModel::image_token_id`].
     pub image_token_id: Option<u32>,
-    /// LM-side tokens per image at the fixed 448×448 resolution — same
-    /// as [`LoadedModel::lm_tokens_per_image`].
-    pub lm_tokens_per_image: Option<usize>,
+    /// Pixel→LM-grid divisor — same as
+    /// [`LoadedModel::image_grid_factor`].
+    pub image_grid_factor: Option<usize>,
 }
 
 #[cfg(feature = "cuda")]
@@ -394,10 +392,11 @@ impl ModelArch {
         offset: usize,
         image_embeds: &Tensor,
         image_token_id: u32,
+        grids: &[(usize, usize)],
     ) -> Result<Tensor> {
         let raw = match self {
             ModelArch::Qwen3_5Dense(m) => {
-                m.forward_with_vision(input, offset, image_embeds, image_token_id)?
+                m.forward_with_vision(input, offset, image_embeds, image_token_id, grids)?
             }
             other => anyhow::bail!(
                 "forward_with_vision: architecture {} has no vision tower",
@@ -407,6 +406,20 @@ impl ModelArch {
         squeeze_to_vocab(&raw)
     }
 
+    /// `patch_size × spatial_merge_size` for the loaded vision tower —
+    /// divides a resized pixel dim into LM-grid units (an image of
+    /// resized `(h, w)` yields the LM grid `(h/factor, w/factor)`).
+    /// `None` for architectures/checkpoints without a vision tower.
+    pub fn vision_grid_factor(&self) -> Option<usize> {
+        match self {
+            ModelArch::Qwen3_5Dense(m) => m.vision().map(|v| {
+                let c = v.config();
+                c.patch_size * c.spatial_merge_size
+            }),
+            _ => None,
+        }
+    }
+
     /// Encode a preprocessed image into LM-side token embeddings via
     /// the loaded vision tower. Stage A5.
     ///
@@ -1683,11 +1696,11 @@ impl CandleHarness {
                     .ok_or_else(|| InferenceError::VisionUnsupported {
                         model_id: request.model.clone(),
                     })?;
-                let patches_per_image = loaded
-                    .lm_tokens_per_image
-                    .ok_or_else(|| InferenceError::VisionUnsupported {
+                let factor = loaded.image_grid_factor.ok_or_else(|| {
+                    InferenceError::VisionUnsupported {
                         model_id: request.model.clone(),
-                    })?;
+                    }
+                })?;
                 let profile = super::preprocess::PreprocessProfile::qwen3_6();
                 let images = extract_images_from_request(&request, &profile).map_err(|e| {
                     InferenceError::Other(anyhow::anyhow!("extract_images: {e}"))
@@ -1699,7 +1712,12 @@ impl CandleHarness {
                         "request has image content but extractor produced zero images"
                     )));
                 }
-                let per_image_counts: Vec<usize> = vec![patches_per_image; images.len()];
+                // Per-image LM token count from each image's resized grid
+                // (#14 dynamic resolution; was a constant 196).
+                let per_image_counts: Vec<usize> = images
+                    .iter()
+                    .map(|im| (im.h / factor) * (im.w / factor))
+                    .collect();
                 prompt_tokens =
                     expand_image_pad_tokens(&prompt_tokens, image_token_id, &per_image_counts)
                         .map_err(InferenceError::Other)?;
@@ -2059,11 +2077,12 @@ impl CandleHarness {
                         .ok_or_else(|| InferenceError::VisionUnsupported {
                             model_id: request.model.clone(),
                         })?;
-                let patches_per_image = loaded.lm_tokens_per_image.ok_or_else(|| {
-                    InferenceError::VisionUnsupported {
-                        model_id: request.model.clone(),
-                    }
-                })?;
+                let factor =
+                    loaded
+                        .image_grid_factor
+                        .ok_or_else(|| InferenceError::VisionUnsupported {
+                            model_id: request.model.clone(),
+                        })?;
                 let profile = super::preprocess::PreprocessProfile::qwen3_6();
                 let images = extract_images_from_request(&request, &profile)
                     .map_err(|e| InferenceError::Other(anyhow::anyhow!("extract_images: {e}")))?;
@@ -2072,7 +2091,11 @@ impl CandleHarness {
                         "request has image content but extractor produced zero images"
                     )));
                 }
-                let per_image_counts: Vec<usize> = vec![patches_per_image; images.len()];
+                // Per-image LM token count from each image's resized grid (#14).
+                let per_image_counts: Vec<usize> = images
+                    .iter()
+                    .map(|im| (im.h / factor) * (im.w / factor))
+                    .collect();
                 prompt_tokens =
                     expand_image_pad_tokens(&prompt_tokens, image_token_id, &per_image_counts)
                         .map_err(InferenceError::Other)?;
@@ -2526,7 +2549,7 @@ impl Harness for CandleHarness {
             chat_template,
             has_vision: vision_meta.has_vision,
             image_token_id: vision_meta.image_token_id,
-            lm_tokens_per_image: vision_meta.lm_tokens_per_image,
+            image_grid_factor: vision_meta.image_grid_factor,
         });
 
         let mut models = self.models.write().await;
@@ -2742,7 +2765,7 @@ impl CandleHarness {
             tracing::info!(
                 model = %spec.model_id,
                 image_token_id = ?vision_meta.image_token_id,
-                lm_tokens_per_image = ?vision_meta.lm_tokens_per_image,
+                image_grid_factor = ?vision_meta.image_grid_factor,
                 "TP load: vision tower present, advertising vision capability"
             );
         }
@@ -2764,7 +2787,7 @@ impl CandleHarness {
             chat_template,
             has_vision: vision_meta.has_vision,
             image_token_id: vision_meta.image_token_id,
-            lm_tokens_per_image: vision_meta.lm_tokens_per_image,
+            image_grid_factor: vision_meta.image_grid_factor,
         });
 
         let mut models = self.models.write().await;
@@ -2938,18 +2961,32 @@ impl CandleHarness {
                     .ok_or_else(|| InferenceError::VisionUnsupported {
                         model_id: request.model.clone(),
                     })?;
-            let patches_per_image =
-                tp.lm_tokens_per_image
-                    .ok_or_else(|| InferenceError::VisionUnsupported {
-                        model_id: request.model.clone(),
-                    })?;
+            let factor = tp
+                .image_grid_factor
+                .ok_or_else(|| InferenceError::VisionUnsupported {
+                    model_id: request.model.clone(),
+                })?;
             let data_uris = extract_image_data_uris(&request);
             if data_uris.is_empty() {
                 return Err(InferenceError::Other(anyhow::anyhow!(
                     "request has image content but extractor produced zero data URIs"
                 )));
             }
-            let per_image_counts: Vec<usize> = vec![patches_per_image; data_uris.len()];
+            // Per-image LM token count from each image's resized grid (#14).
+            // Decode header + smart_resize only; the workers re-derive the
+            // same dims when they preprocess for the replicated tower.
+            let profile = super::preprocess::PreprocessProfile::qwen3_6();
+            let per_image_counts: Vec<usize> = data_uris
+                .iter()
+                .enumerate()
+                .map(|(i, uri)| {
+                    let (h, w) =
+                        super::preprocess::resized_dims_for_uri(uri, &profile).map_err(|e| {
+                            InferenceError::Other(anyhow::anyhow!("resized_dims image #{i}: {e}"))
+                        })?;
+                    Ok::<usize, InferenceError>((h as usize / factor) * (w as usize / factor))
+                })
+                .collect::<Result<Vec<_>, _>>()?;
             prompt_tokens =
                 expand_image_pad_tokens(&prompt_tokens, image_token_id, &per_image_counts)
                     .map_err(InferenceError::Other)?;
@@ -3457,18 +3494,30 @@ async fn chat_completion_tp_inner(
                 .ok_or_else(|| InferenceError::VisionUnsupported {
                     model_id: request.model.clone(),
                 })?;
-        let patches_per_image =
-            tp.lm_tokens_per_image
-                .ok_or_else(|| InferenceError::VisionUnsupported {
-                    model_id: request.model.clone(),
-                })?;
+        let factor = tp
+            .image_grid_factor
+            .ok_or_else(|| InferenceError::VisionUnsupported {
+                model_id: request.model.clone(),
+            })?;
         let data_uris = extract_image_data_uris(&request);
         if data_uris.is_empty() {
             return Err(InferenceError::Other(anyhow::anyhow!(
                 "request has image content but extractor produced zero data URIs"
             )));
         }
-        let per_image_counts: Vec<usize> = vec![patches_per_image; data_uris.len()];
+        // Per-image LM token count from each image's resized grid (#14).
+        let profile = super::preprocess::PreprocessProfile::qwen3_6();
+        let per_image_counts: Vec<usize> = data_uris
+            .iter()
+            .enumerate()
+            .map(|(i, uri)| {
+                let (h, w) =
+                    super::preprocess::resized_dims_for_uri(uri, &profile).map_err(|e| {
+                        InferenceError::Other(anyhow::anyhow!("resized_dims image #{i}: {e}"))
+                    })?;
+                Ok::<usize, InferenceError>((h as usize / factor) * (w as usize / factor))
+            })
+            .collect::<Result<Vec<_>, _>>()?;
         prompt_tokens = expand_image_pad_tokens(&prompt_tokens, image_token_id, &per_image_counts)
             .map_err(InferenceError::Other)?;
         Some((data_uris, image_token_id))
@@ -3917,10 +3966,12 @@ fn build_prompt_for_request(
 struct VisionMeta {
     has_vision: bool,
     image_token_id: Option<u32>,
-    /// LM-side tokens this model's vision tower emits per image at
-    /// the Stage B fixed `PreprocessProfile::qwen3_6()` resolution
-    /// (448×448). Equal to `(H/patch_size/spatial_merge_size)²`.
-    lm_tokens_per_image: Option<usize>,
+    /// `patch_size × spatial_merge_size` — the divisor that turns a
+    /// resized pixel dimension into an LM-grid dimension. An image of
+    /// resized `(h, w)` emits `(h/factor) × (w/factor)` LM tokens (#14
+    /// dynamic resolution; was a constant 196 at the old fixed 448²).
+    /// `None` for text-only models.
+    image_grid_factor: Option<usize>,
 }
 
 impl VisionMeta {
@@ -3949,22 +4000,18 @@ impl VisionMeta {
             .get("image_token_id")
             .and_then(|x| x.as_u64())
             .map(|n| n as u32);
-        // Compute LM tokens per image at the Stage B fixed resolution
-        // (PreprocessProfile::qwen3_6() → 448×448). One LM token per
-        // spatial-merge group of patches.
-        let target_h = super::preprocess::PreprocessProfile::qwen3_6().target_height as usize;
-        let target_w = super::preprocess::PreprocessProfile::qwen3_6().target_width as usize;
-        let lm_tokens_per_image = if patch_size > 0 && spatial_merge_size > 0 {
-            let gh = target_h / patch_size / spatial_merge_size;
-            let gw = target_w / patch_size / spatial_merge_size;
-            Some(gh * gw)
+        // The pixel→LM-grid divisor. An image resized to (h, w) emits
+        // (h/factor) × (w/factor) LM tokens — computed per image at
+        // request time now that resolution is dynamic (#14).
+        let image_grid_factor = if patch_size > 0 && spatial_merge_size > 0 {
+            Some(patch_size * spatial_merge_size)
         } else {
             None
         };
         Self {
             has_vision: true,
             image_token_id,
-            lm_tokens_per_image,
+            image_grid_factor,
         }
     }
 }
@@ -4011,13 +4058,13 @@ fn extract_images_from_request(
                     .and_then(|v| v.get("url"))
                     .and_then(|v| v.as_str())
                     .ok_or_else(|| anyhow::anyhow!("image_url part missing url field"))?;
-                let pixels = super::preprocess::preprocess_data_uri(url, profile)
+                let (pixels, h, w) = super::preprocess::preprocess_data_uri(url, profile)
                     .with_context(|| format!("preprocess image #{}", out.len()))?;
                 out.push(super::device_worker::jobs::ImageInput {
                     pixels,
                     c: 3,
-                    h: profile.target_height as usize,
-                    w: profile.target_width as usize,
+                    h: h as usize,
+                    w: w as usize,
                 });
             }
         }