)]}'
{
  "commit": "774d7bfdfc07a85e6582201b4d72fd0bd10e8b21",
  "tree": "3d04d12094cb0ad90f35bfc625decb3b6c9f1fe9",
  "parents": [
    "6caa91743beb38c369d45f5ccf10f6bd5fc2fd57"
  ],
  "author": {
    "name": "Roberto Laudani",
    "email": "laudani@roofline.ai",
    "time": "Thu Jul 09 15:02:43 2026 +0200"
  },
  "committer": {
    "name": "GitHub",
    "email": "noreply@github.com",
    "time": "Thu Jul 09 15:02:43 2026 +0200"
  },
  "message": "[GlobalOpt] Add pass to convert broadcast batch_matmul to matmul (#24670)\n\nWhen a `linalg.batch_matmul` multiplies a batched input by a weight that\nis the same for every batch, every batch slice runs the same matmul\nagainst the same weight. This pass rewrites that case into a single\n`linalg.matmul`.\n\nFor an input of shape `[B, M, K]` and a weight `[K, N]` broadcast to\n`[B, K, N]`, the pass:\n\n  1. collapses the input to `[B*M, K]` using tensor.collapse_shape\n  2. multiplies it by the `[K, N]` weight using linalg.matmul\n  3. expands the result back to `[B, M, N]` using tensor.expand_shape\n\nThis removes the broadcast and turns the batched contraction into a\nplain\nmatmul, which downstream passes can handle better.\n\n---------\n\nSigned-off-by: Roberto Laudani \u003claudani@roofline.ai\u003e\nCo-authored-by: ziereis \u003cziereis@roofline.ai\u003e",
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