MakotoUwu commented on PR #19771:
URL: https://github.com/apache/tvm/pull/19771#issuecomment-4971544558

   Thanks for the detailed review. I've pushed a narrower revision limited to 
the
   four chunking items you identified: planning from encoded and decoded sizes,
   4-byte-aligned WebGPU copy ranges, safe chunk-view cleanup, and honoring
   `cpu_arr->byte_offset`.
   
   I removed the native-float32 `f32-to-bf16` fallback, Shape callback handling,
   generic int64 marshalling, and the storage-size helper.
   
   I also added the requested same-artifact validation using this
   [pinned 1120 MiB tensor-cache 
record](https://huggingface.co/welcoma/gemma-4-E2B-it-q4f16_1-MLC/blob/4e7d43f11998bac8aa25e46bc43d6a16e6d78131/tensor-cache.json).
   The record has dtype `uint32`, so it uses the existing raw-copy path and does
   not require a format-semantics change.
   
   Results:
   
   - clean `apache/main` and a validation-only no-chunk control both fail at
     `CachedCallStack.allocThenSetArgBytes`, before bytes are copied or the C++
     packed function is entered
   - the two failures are controls, not failures of the proposed revision
   - the actual proposed revision succeeds by loading the 1120 MiB CPU record in
     9 chunks
   - the adapter advertises 4,294,967,292-byte buffer limits and the requested
     device exposes 1 GiB limits
   - the current 384 MiB shard fits those requested limits, is successfully
     synchronized and copied back, and matches the source contents
   - a 256 MiB record produces identical contents through full-record and 
chunked
     loading
   
   The chunk-size sweep passes at 32, 64, 128, and 256 MiB. It fails at 512 and
   1024 MiB as retained call-stack growth drives the next Wasm allocation close 
to
   1 GiB. I kept 128 MiB as a conservative cap to leave headroom for the final
   tensor, retained staging memory, and call metadata.
   
   Fresh CI is green on this head. The updated PR description contains the exact
   traces, limits, hashes, sweep results, and validation commands.
   
   Could you please take another look when you have bandwidth?
   


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