[ExecuTorch] Complete Muse Glimmer rename

examples/models/muse-glimmer/runtime/runners/solo.cpp

@@ -1,3 +1,3 @@
 /*
  * Copyright (c) Meta Platforms, Inc. and affiliates.
  * All rights reserved.
@@ -252,7 +252,7 @@
   return Error::Ok;
 }
 
-// Muse Glimmer image special-token ids (from the tokenizer / OnyxConfig).
+// Muse Glimmer image special-token ids (from the tokenizer / MuseGlimmerConfig).
 
 // Preprocess one image file, run the exported vision_encoder, and copy the
 // resulting soft-token embeddings ([num_soft_tokens, hidden] bf16) to host.

examples/models/muse-glimmer/tests/gen_prompt_golden.py

@@ -11,29 +11,6 @@
 
 Only lengths, the special-token skeleton, and a digest of the full id sequence
 are written. The text ids themselves stay out of the repo.
-
-Re-validating after a regeneration
-----------------------------------
-``tokenizer.json`` and the golden are pinned to each other by sha256, so drift is
-caught automatically and the checks below are not worth running on a schedule.
-Run them once whenever you bump the pinned revision, to confirm the new
-tokenizer still agrees with the independent sources.
-
-1. tiktoken over ``l4_200k_base`` from the quantized repo. A different BPE
-   implementation reading a different vocab file. Build the Encoding as
-   ``meta_reference_implementation/standalone_inference.py`` does, registering
-   ``tokenizer_config.json``'s ``extra_special_tokens`` at ``200000 + index``,
-   then compare ``encode(prompt, allowed_special="all")`` against this
-   tokenizer for every case prompt. Last run: identical ids, all 7 cases.
-
-2. The vocab embedded in ``onyx-rl_v2-q4km-gs128.gguf``. Compare all 202048
-   id-to-string pairs against ``tokenizer.json``. Last run: zero mismatches.
-
-3. ``transformers`` with the Onyx wheel from the transformers_onyx repo, to
-   check assembly rather than the BPE. Pass ``current_date`` explicitly: the
-   template calls ``strftime_now``, so a rendering left to default is not
-   reproducible tomorrow. Last run: ``apply_chat_template`` output matched a
-   raw encode of the same text, 64 ids.
 """
 
 import json

examples/models/muse-glimmer/tests/prompt_cases.py

@@ -22,8 +22,8 @@
 IMAGE_MARKER = "<img>"
 
 # Tokens the image span costs beyond the patches themselves. Zero: the canonical
-# format is a bare <|patch|> run, which is what OnyxProcessor.replace_image_token
-# emits and what meta_reference_implementation splices on.
+# format is a bare <|patch|> run, which is what the Muse Glimmer processor's
+# replace_image_token method emits and what meta_reference_implementation splices on.
 IMAGE_WRAPPER_TOKENS = 0
 
 HF_DIR_ENV = "MUSE_GLIMMER_HF_DIR"

examples/models/muse-glimmer/tests/test_vision_precompute.py

@@ -8,7 +8,7 @@
 
 Verifies the host-side precomputed tensors (patchify, positional-embedding
 interpolation, 2D-RoPE, sparse permutation, block-diagonal masks, pixel-shuffle
-permutation) match the eager ``OnyxVisionEncoder`` math. Runs on CPU.
+permutation) match the eager ``MuseGlimmerVisionEncoder`` math. Runs on CPU.
 """
 
 import unittest
@@ -88,7 +88,7 @@ def test_matches_eager_complex_form(self):
         grid_h = grid_w = 5
         cos, sin = make_2d_rope(grid_h, grid_w, cfg)
 
-        # Eager reference (OnyxVisionEncoder._make_2d_rope), inline.
+        # Eager reference (MuseGlimmerVisionEncoder._make_2d_rope), inline.
         head_dim = cfg.head_dim
         half_dim = head_dim // 2
         quarter = half_dim // 2

examples/models/muse-glimmer/tests/test_vision_tower.py

@@ -7,8 +7,8 @@
 """Unit tests for the export-friendly Muse Glimmer vision encoder (vision_tower.py).
 
 Checks (CPU):
-  * ``MuseGlimmerVisionEncoder`` reproduces an inline eager reference (the onyx vision
-    math) to bf16 tolerance, given shared random weights.
+  * ``MuseGlimmerVisionEncoder`` reproduces an inline eager reference (the Muse
+    Glimmer vision math) to bf16 tolerance, given shared random weights.
   * The forward runs on a single-tile image (identity sparse perm) and a
     multi-tile image (non-trivial sparse perm), producing the right shapes.
   * ``torch.export(strict=True)`` traces the encoder with a dynamic num_patches.
@@ -60,11 +60,11 @@ def _init_random(model: torch.nn.Module, seed: int = 0) -> None:
 
 
 # ---------------------------------------------------------------------------
-# Inline eager reference (mirrors OnyxVisionEncoder math on float32).
+# Inline eager reference (mirrors MuseGlimmerVisionEncoder math on float32).
 
 
 def _rotate_interleaved_complex(x, cos, sin):
-    """Adjacent-pair rotation via complex mul (eager onyx formulation)."""
+    """Adjacent-pair rotation via complex mul (eager Muse Glimmer formulation)."""
     freqs = torch.complex(cos, sin)  # [P, d/2]
     xc = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
     out = torch.view_as_real(xc * freqs.unsqueeze(0).unsqueeze(2)).flatten(-2)