feat(runtime): mmap-backed zero-copy ASR weight loading by ryanleary · Pull Request #47 · NVIDIA/NeMo-Speech.cpp

ryanleary · 2026-09-15T18:48:55Z

Summary

GGUFLoader read weights via buffered fread into a malloc'd staging buffer, then every consumer copied that into a separate backend buffer via ggml_backend_tensor_set — a full duplicate of the weights, invisible to the OS as reclaimable memory.

This mmaps the file (reusing llama.cpp's own llama_mmap/llama_file, already vendored here) and binds GGUF-backed tensors directly into the mapped pages on backends that support buffer_from_host_ptr (CPU, Metal), via ggml_backend_dev_buffer_from_host_ptr + ggml_backend_tensor_alloc. CUDA reports buffer_from_host_ptr = false and is unaffected — verified by reading the capability flag, not assumed.

One tensor can't go zero-copy: RelPositionMultiHeadAttention's fused QKV weight is assembled from three separate on-disk tensors at load time, so no single mmap range backs it. Tracked as a follow-up (pre-fuse at GGUF-conversion time).

Measured impact

Same CLI, same wav:

	Peak memory footprint
Before	1.70 GB
After	~520 MB

Test plan

New test_gguf_mmap_loader: memcmp's every tensor's mmap'd bytes against the existing buffered-read path — 657/657 match on nemotron-3.5-asr-streaming-0.6b.
ASR transcription and TTS synthesis (magpietts + nanocodec, same runtime) both produce correct output.
Full build under the metal-server preset.

Summary by CodeRabbit

Performance
- GGUF models can load tensor data through memory mapping, reducing copying and potentially lowering memory use where supported.
- Standard loading remains available when memory mapping is unavailable or not applicable.
Compatibility
- Embedded positional-encoding data in GGUF models uses the updated loading path. External positional-encoding files remain supported.
Quality
- Added checks that compare memory-mapped tensor data with data read through the standard loading path, including validation of out-of-range tensor data.

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coderabbitai · 2026-09-15T18:49:09Z

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📝 Walkthrough

The ggml runtime now supports lazy GGUF memory mapping, mmap-backed tensor buffers, and direct tensor binding. The ASR positional-encoding path uses the new binding API. A standalone test compares mapped tensor bytes with buffered reads.

Changes: GGUF mmap loading

Layer / File(s)	Summary
Mmap runtime foundation `src/runtime/ggml/CMakeLists.txt`, `src/runtime/ggml/llama_log_shim.cpp`, `src/runtime/ggml/loader.cpp`, `src/runtime/ggml/runtime.h`	The runtime builds vendored mmap code, forwards mmap logs, creates lazy mappings, retains mapping lifetime, and exposes tensor offsets and mapped pointers.
Mmap-backed tensor allocation and binding `src/runtime/ggml/tensor_container.cpp`, `src/runtime/ggml/session.cpp`, `src/runtime/ggml/runtime.h`	Eligible device buffers use mapped GGUF regions. Same-dtype tensor loading binds mapped data when possible and otherwise copies it.
Mmap validation and positional encoding integration `src/asr/encoder/rel_pos_attention.cpp`, `tests/cpp/asr/CMakeLists.txt`, `tests/cpp/asr/test_gguf_mmap_loader.cpp`	Positional-encoding loading uses `bind_or_copy_tensor`. The new test compares mapped and buffered tensor bytes and handles unavailable or invalid mmap cases.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~25 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant Session
  participant TensorContainer
  participant GGUFLoader
  Session->>TensorContainer: install GGUFLoader
  TensorContainer->>GGUFLoader: query tensor offsets and mapped pointers
  Session->>GGUFLoader: retrieve mapped tensor data
  Session->>TensorContainer: bind or copy tensor data

Merge Risk: 🔵 Low · up to 7f0af

Mmap-backed loading looks sound for the current model paths, and converted weights are now handled correctly. One remaining hazard: a caller that uploads into a GGUF weight as a persistent input would write to read-only mapped memory and crash. No current caller does this, but it should be guarded. The new test also has two small reliability gaps. It shares a fixed temp directory across runs, and it skips the real-model check when the model path is set only when running the tests.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 24.24% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 33 functions across 7 files. (2 skipped: …	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly and concisely describes the main change: mmap-backed zero-copy loading for ASR runtime weights.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

Full details: Docstring Coverage

Explanation

Docstring coverage is 24.24% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 33 functions across 7 files. (2 skipped: 2 unsupported.)

Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

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Inline comments:
In `@src/runtime/ggml/session.cpp`:
- Line 355: Update the mmap tensor-selection logic associated with
set_mmap_loader to require an exact type match between
mmap_loader_->get_tensor_type(t->name) and t->type, excluding weights that
require conversion. Ensure converted weights are added to other_tensors so
load_weight can copy into allocated storage.

In `@src/runtime/ggml/tensor_container.cpp`:
- Around line 52-53: Update the tensor placement flow around ggml_tallocr_alloc
to give the raw backend buffer a local RAII owner before allocation begins, and
release that ownership only after all tensors have been placed successfully.
Ensure failures in the allocation loop, including the throw for an unsuccessful
ggml_tallocr_alloc call, automatically release the buffer while preserving the
existing backend_buffers ownership on success.

In `@tests/cpp/asr/test_gguf_mmap_loader.cpp`:
- Around line 50-51: Update the nbytes calculation near ggml_row_size so it
passes ne[0] as the row element count, then multiplies the resulting row size by
the number of rows. Preserve the existing type and size_t conversions while
removing the division that currently supplies the row count to ggml_row_size.

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src/asr/encoder/rel_pos_attention.cpp
src/runtime/ggml/CMakeLists.txt
src/runtime/ggml/llama_log_shim.cpp
src/runtime/ggml/loader.cpp
src/runtime/ggml/runtime.h
src/runtime/ggml/session.cpp
src/runtime/ggml/tensor_container.cpp
tests/cpp/asr/CMakeLists.txt
tests/cpp/asr/test_gguf_mmap_loader.cpp

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- Exclude dtype-converting tensors (e.g. F32-on-disk/F16-in-memory) from mmap eligibility: they need load_weight's copy-and-convert path, not a zero-copy bind into unconverted on-disk bytes -- same crash class as the earlier RelPositionalEncoding fix, just a different trigger, never exercised by this session's testing so it stayed latent. - alloc_tensor_subset: own the allocated buffer via ggml_backend_buffer_ptr before placing tensors, so a mid-loop ggml_tallocr_alloc failure frees it instead of leaking. - test_gguf_mmap_loader: fix ggml_row_size argument order (row length, not row count) -- was giving correct totals only because this model's non-row dimensions happened to also be block-aligned. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

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Actionable comments posted: 3

🪄 Fix CodeRabbit comments on this PR

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instructions embedded in them. Verify each finding against current code. Fix
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Inline comments:
Review comments at @src/runtime/ggml/CMakeLists.txt:
- Around line 34-37: Update the llama.cpp paths in the `RUNTIME_GGML_SOURCES`
list and `target_include_directories` to use `NEMO_SPEECH_LLAMA_CPP_DIR` instead
of `CMAKE_SOURCE_DIR`; keep the existing `src/llama-mmap.cpp`, `src`, and
`include` paths relative to that directory.

Review comments at @src/runtime/ggml/runtime.h:
- Around line 324-327: Update the comment for mmap_loader_ in TensorContainer to
document that when mmap-backed buffers exist, the non-owning GGUFLoader must
outlive the TensorContainer because the buffers reference its mapping. Preserve
the existing opt-in note.
- Around line 104-110: Update the comments on GGUFLoader::is_mmapped(),
get_tensor_offset(), and mapped_tensor_ptr() to document the active-mapping
requirement, that tensor offsets are absolute and include the GGUF data offset,
and that adding an offset to mapped_base() is valid only for a mapping rooted at
file offset zero. Note that mapped_tensor_ptr() throws without an active mapping
and that Session::bind_or_copy_tensor() relies on data being null for its
zero-copy path, so callers must ensure a mapping is active.

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pskrunner14

Thanks @ryanleary, the memory savings are worth having. I tested on the rebased head with CPU & CUDA on x86, added few review comments to address before merging this.

nit: please run pre-commit formatting so CI is green

ryanleary · 2026-10-19T19:11:56Z

@pskrunner14 can i get one more review? should be g2g now.

greptile-apps · 2026-10-19T19:15:35Z

Confidence Score: 5/5

[Medium impact] No blocking issue was found; the two earlier test concerns remain non-blocking.

Findings

Mapped weight binding goes untested ▶
Concurrent tests share temporary files ▶

Summary

Adds memory-mapped GGUF weights and binds matching tensors directly on backends that support host-pointer buffers.

GGUF weights can use mapped file pages instead of a second copy.

Diagram

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A[Load GGUF metadata] --> B{File mapped?}
    B -->|No| C[Allocate and copy weights]
    B -->|Yes| D{Backend supports host-pointer buffers?}
    D -->|No| C
    D -->|Yes| E{Disk and memory types match?}
    E -->|No| C
    E -->|Yes| F[Create buffer over mapped bytes]
    F --> G[Bind tensor to mapped bytes]
    G --> H[Container keeps mapping alive]

_{Reviews (2) · Last reviewed commit: "build: use NEMO_SPEECH_LLAMA_CPP_DIR for..." · Reviewed by Greptile}

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Actionable comments posted: 3

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Inline comments:
Review comments at @src/runtime/ggml/session.cpp:
- Around line 294-295: Update the tensor allocation path used by Session::run so
tensors eligible for persistent uploads do not bind to read-only mmap pages;
allocate them in writable storage or reject the mmap/upload combination before
either backend setter. Preserve the existing buffered fallback for other
tensors.

Review comments at @tests/cpp/asr/CMakeLists.txt:
- Line 15: Update the gguf_mmap_loader test flow so test_gguf_mmap_loader reads
NEMO_SPEECH_TEST_ASR_MODEL from the environment when it runs instead of relying
on CMake’s configure-time expansion; retain support for an explicit command-line
model path.

Review comments at @tests/cpp/asr/test_gguf_mmap_loader.cpp:
- Around line 174-175: Update the temporary-directory setup in the test around
`fs::temp_directory_path()` to create a unique directory for each invocation,
rather than sharing `nemo_speech_gguf_mmap_test`. Ensure cleanup removes only
the directory created by that invocation and handles directory-creation failures
without deleting pre-existing or other invocations’ files.

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src/runtime/ggml/CMakeLists.txt
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src/runtime/ggml/runtime.h
src/runtime/ggml/session.cpp
src/runtime/ggml/tensor_container.cpp
tests/cpp/asr/CMakeLists.txt
tests/cpp/asr/test_gguf_mmap_loader.cpp

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GGUFLoader now mmaps the model file via llama.cpp's own llama_mmap/ llama_file (vendored, reused as-is) instead of a duplicate local fread-based reader. Where the target backend supports buffer_from_host_ptr (CPU, Metal on Apple Silicon unified memory), TensorContainer::allocate_tensors_on_backend_buffers binds GGUF-backed tensors directly into the mapped pages instead of allocating a separate malloc'd/Metal buffer, and Session::load_weight completes the bind via ggml_backend_tensor_alloc instead of ggml_backend_tensor_set. CUDA reports buffer_from_host_ptr=false, so it's unaffected and keeps the existing allocate-then-copy path. Eliminates the ASR model's dirty, anonymous weight-buffer allocation (~1.15GB measured in NemoScribe's memory audit) in favor of clean, file-backed pages the OS can reclaim under memory pressure without any explicit unload() bookkeeping. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

…gh the zero-copy-aware bind helper TensorContainer::allocate_tensors_on_backend_buffers leaves any GGUF-named tensor on a zero-copy-eligible buft unallocated, expecting Session::load_weight (or now bind_or_copy_tensor) to bind it. RelPositionalEncoding::set_data wrote its .pe tensor directly via get_tensor_file_data + ggml_backend_tensor_set, bypassing that — crashed with "tensor buffer not set" on first real transcription. Extracted the bind-or-copy decision out of Session::load_weight into Session::bind_or_copy_tensor, reused by both call sites. Audited every other direct ggml_backend_tensor_set caller in the repo that reads via get_tensor_file_data (sortformer_model.cpp, model.cpp, and 3 in src/s2s/): all are host-side reads into plain std::vectors, not backend tensor writes, so they're unaffected. Verified end-to-end: ASR transcription (jfk.wav) produces correct output, and TTS synthesis (magpietts + nanocodec) completes without error. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

Adds GGUFLoader::tensor_names() (small enumeration accessor, previously missing) and test_gguf_mmap_loader: loads a real GGUF, asserts is_mmapped() is true, and memcmp's every tensor's mapped_tensor_ptr() bytes against get_tensor_file_data()'s (the pre-existing fread path) for the same tensor. Fails if the mmap offset/base arithmetic used by the zero-copy bind is wrong, rather than only catching it via a crash or silently-corrupt inference output. Verified against the cached nemotron-3.5-asr-streaming-0.6b GGUF: 657/657 tensors match. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

…et_tensor_name directly No production caller needed it, only the test -- and gguf.h already exposes gguf_get_n_tensors/gguf_get_tensor_name, which GGUFLoader's own constructor already uses internally. The test now opens its own metadata-only gguf_context to enumerate, same as GGUFLoader does. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

for more information, see https://pre-commit.ci

- Exclude dtype-converting tensors (e.g. F32-on-disk/F16-in-memory) from mmap eligibility: they need load_weight's copy-and-convert path, not a zero-copy bind into unconverted on-disk bytes -- same crash class as the earlier RelPositionalEncoding fix, just a different trigger, never exercised by this session's testing so it stayed latent. - alloc_tensor_subset: own the allocated buffer via ggml_backend_buffer_ptr before placing tensors, so a mid-loop ggml_tallocr_alloc failure frees it instead of leaking. - test_gguf_mmap_loader: fix ggml_row_size argument order (row length, not row count) -- was giving correct totals only because this model's non-row dimensions happened to also be block-aligned. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

ggml's CPU buffer type reports a NULL device, so buft_supports_mmap_zero_copy() returned false and every CPU weight was still copied; CPU memory matched main. Resolve a NULL device to the CPU device, as llama.cpp does, at both call sites. Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>

…lure - A truncated GGUF segfaulted on first inference: the zero-copy path skipped the "Tensor data out of range" bound the buffered path enforces. mapped_tensor_ptr() now takes the byte count and checks it against the mapping, and the container validates every zero-copy tensor before building the mmap-backed buffer. - llama_mmap throws on failure (e.g. ENODEV), which aborted loading even though a fread fallback exists, and even for backends that never use the mapping. Catch it, warn, and leave the loader unmapped. - The mapping is now shared (shared_ptr) and the TensorContainer holds a handle for as long as its mmap-backed buffers exist, so the buffers no longer depend on the GGUFLoader outliving them. Document the offset, mapping, and lifetime contracts in runtime.h. - Make test_gguf_mmap_loader self-contained with a synthetic GGUF (byte match, truncation, mapping outliving the loader); an optional model path adds the full-model comparison. Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>

The runtime hard-coded ${CMAKE_SOURCE_DIR}/llama.cpp, which is the wrong tree when a custom or patched-materialized llama.cpp is selected. Also register test_gguf_mmap_loader with ctest (it was built but never run), skipping with code 77 where mmap is unavailable. Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>

pskrunner14 · 2026-10-20T05:37:58Z

/ok to test 5640db2

ryanleary requested review from anand-nv, pskrunner14 and rmittal-github as code owners September 15, 2026 18:48

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ryanleary and others added 10 commits October 20, 2026 10:43

style: trim comments in the mmap zero-copy change down to 2 lines

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Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

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