Add Voxtral runner #13663

jackzhxng · 2025-08-25T22:01:36Z

Summary

Utilize multimodal_runner.h to run Voxtral exported from Optimum Executorch.

The runner takes in a .pt file of a preprocessed audio recording and feeds it a C++ multimodal runner.

Example output:

This audio is a casual and somewhat silly conversation between two speakers who seem to be discussing their tattoos. The speakers are engaging in a game where they ask each other what their tattoos say, but both repeatedly say "sweet" instead of the actual words. The speakers are aware of their mistake and try to correct it by asking the other what their tattoo says, but they still end up saying "sweet" again. The conversation ends with a speaker telling the other that their tattoo says "
PyTorchObserver {"prompt_tokens":1138,"generated_tokens":99,"model_load_start_ms":0,"model_load_end_ms":0,"inference_start_ms":1756159197436,"inference_end_ms":1756159222710,"prompt_eval_end_ms":1756159209605,"first_token_ms":1756159209605,"aggregate_sampling_time_ms":96,"SCALING_FACTOR_UNITS_PER_SECOND":1000}
I 00:00:33.116291 executorch:stats.h:104]       Prompt Tokens: 1138    Generated Tokens: 99
I 00:00:33.116304 executorch:stats.h:110]       Model Load Time:                0.000000 (seconds)
I 00:00:33.116312 executorch:stats.h:117]       Total inference time:           25.274000 (seconds)              Rate:  3.917069 (tokens/second)
I 00:00:33.116320 executorch:stats.h:127]               Prompt evaluation:      12.169000 (seconds)              Rate:  93.516312 (tokens/second)
I 00:00:33.116327 executorch:stats.h:136]               Generated 99 tokens:    13.105000 (seconds)              Rate:  7.554369 (tokens/second)
I 00:00:33.116338 executorch:stats.h:147]       Time to first generated token:  12.169000 (seconds)
I 00:00:33.116344 executorch:stats.h:153]       Sampling time over 1237 tokens: 0.096000 (seconds)

Test plan

Build and run:

# Build and install ExecuTorch
cmake --preset llm -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=cmake-out -DEXECUTORCH_ENABLE_LOGGING=ON && cmake --build cmake-out -j16 --target install --config Release

# Build and install Voxtral runner
cmake -DCMAKE_INSTALL_PREFIX=cmake-out -DBUILD_TESTING=OFF -DCMAKE_BUILD_TYPE=Release -Bcmake-out/examples/models/voxtral examples/models/voxtral && cmake --build cmake-out/examples/models/voxtral -j16 --config Release

# Run Voxtral runner
./cmake-out/examples/models/voxtral/voxtral_runner --model_path ~/models/voxtral/voxtral_q8da4w_edm_qe4w_d_split_metadata_unsqueeze.pte --tokenizer_path ~/hf/models--mistralai--Voxtral-Mini-3B-2507/snapshots/3060fe34b35ba5d44202ce9ff3c097642914f8f3/tekken.json --prompt "What can you tell me about this audio?" --audio_path ~/models/voxtral/input_features.bin

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pytorch-bot · 2025-08-25T22:01:40Z

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/13663

📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

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This comment was automatically generated by Dr. CI and updates every 15 minutes.

ghstack-source-id: 0fbf7c4 Pull Request resolved: #13663

### Summary Utilize `multimodal_runner.h` to run [Voxtral exported from Optimum Executorch](huggingface/optimum-executorch#126). The runner takes in a `.pt` file of a preprocessed audio recording and feeds it a C++ multimodal runner. Example output: ``` This audio is a casual and somewhat silly conversation between two speakers who seem to be discussing their tattoos. The speakers are engaging in a game where they ask each other what their tattoos say, but both repeatedly say "sweet" instead of the actual words. The speakers are aware of their mistake and try to correct it by asking the other what their tattoo says, but they still end up saying "sweet" again. The conversation ends with a speaker telling the other that their tattoo says " PyTorchObserver {"prompt_tokens":1138,"generated_tokens":99,"model_load_start_ms":0,"model_load_end_ms":0,"inference_start_ms":1756159197436,"inference_end_ms":1756159222710,"prompt_eval_end_ms":1756159209605,"first_token_ms":1756159209605,"aggregate_sampling_time_ms":96,"SCALING_FACTOR_UNITS_PER_SECOND":1000} I 00:00:33.116291 executorch:stats.h:104] Prompt Tokens: 1138 Generated Tokens: 99 I 00:00:33.116304 executorch:stats.h:110] Model Load Time: 0.000000 (seconds) I 00:00:33.116312 executorch:stats.h:117] Total inference time: 25.274000 (seconds) Rate: 3.917069 (tokens/second) I 00:00:33.116320 executorch:stats.h:127] Prompt evaluation: 12.169000 (seconds) Rate: 93.516312 (tokens/second) I 00:00:33.116327 executorch:stats.h:136] Generated 99 tokens: 13.105000 (seconds) Rate: 7.554369 (tokens/second) I 00:00:33.116338 executorch:stats.h:147] Time to first generated token: 12.169000 (seconds) I 00:00:33.116344 executorch:stats.h:153] Sampling time over 1237 tokens: 0.096000 (seconds) ``` ### Test plan Build and run: ``` # Build and install ExecuTorch cmake --preset llm -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=cmake-out -DEXECUTORCH_ENABLE_LOGGING=ON && cmake --build cmake-out -j16 --target install --config Release # Build and install Voxtral runner cmake -DCMAKE_INSTALL_PREFIX=cmake-out -DBUILD_TESTING=OFF -DCMAKE_BUILD_TYPE=Release -Bcmake-out/examples/models/voxtral examples/models/voxtral && cmake --build cmake-out/examples/models/voxtral -j16 --config Release # Run Voxtral runner ./cmake-out/examples/models/voxtral/voxtral_runner --model_path ~/models/voxtral/voxtral_q8da4w_edm_qe4w_d_split_metadata_unsqueeze.pte --tokenizer_path ~/hf/models--mistralai--Voxtral-Mini-3B-2507/snapshots/3060fe34b35ba5d44202ce9ff3c097642914f8f3/tekken.json --prompt "What can you tell me about this audio?" --audio_path ~/models/voxtral/input_features.bin ``` [ghstack-poisoned]

examples/models/voxtral/multimodal.cpp

ghstack-source-id: c51dcf5 Pull Request resolved: #13663

### Summary Utilize `multimodal_runner.h` to run [Voxtral exported from Optimum Executorch](huggingface/optimum-executorch#126). The runner takes in a `.pt` file of a preprocessed audio recording and feeds it a C++ multimodal runner. Example output: ``` This audio is a casual and somewhat silly conversation between two speakers who seem to be discussing their tattoos. The speakers are engaging in a game where they ask each other what their tattoos say, but both repeatedly say "sweet" instead of the actual words. The speakers are aware of their mistake and try to correct it by asking the other what their tattoo says, but they still end up saying "sweet" again. The conversation ends with a speaker telling the other that their tattoo says " PyTorchObserver {"prompt_tokens":1138,"generated_tokens":99,"model_load_start_ms":0,"model_load_end_ms":0,"inference_start_ms":1756159197436,"inference_end_ms":1756159222710,"prompt_eval_end_ms":1756159209605,"first_token_ms":1756159209605,"aggregate_sampling_time_ms":96,"SCALING_FACTOR_UNITS_PER_SECOND":1000} I 00:00:33.116291 executorch:stats.h:104] Prompt Tokens: 1138 Generated Tokens: 99 I 00:00:33.116304 executorch:stats.h:110] Model Load Time: 0.000000 (seconds) I 00:00:33.116312 executorch:stats.h:117] Total inference time: 25.274000 (seconds) Rate: 3.917069 (tokens/second) I 00:00:33.116320 executorch:stats.h:127] Prompt evaluation: 12.169000 (seconds) Rate: 93.516312 (tokens/second) I 00:00:33.116327 executorch:stats.h:136] Generated 99 tokens: 13.105000 (seconds) Rate: 7.554369 (tokens/second) I 00:00:33.116338 executorch:stats.h:147] Time to first generated token: 12.169000 (seconds) I 00:00:33.116344 executorch:stats.h:153] Sampling time over 1237 tokens: 0.096000 (seconds) ``` ### Test plan Build and run: ``` # Build and install ExecuTorch cmake --preset llm -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=cmake-out -DEXECUTORCH_ENABLE_LOGGING=ON && cmake --build cmake-out -j16 --target install --config Release # Build and install Voxtral runner cmake -DCMAKE_INSTALL_PREFIX=cmake-out -DBUILD_TESTING=OFF -DCMAKE_BUILD_TYPE=Release -Bcmake-out/examples/models/voxtral examples/models/voxtral && cmake --build cmake-out/examples/models/voxtral -j16 --config Release # Run Voxtral runner ./cmake-out/examples/models/voxtral/voxtral_runner --model_path ~/models/voxtral/voxtral_q8da4w_edm_qe4w_d_split_metadata_unsqueeze.pte --tokenizer_path ~/hf/models--mistralai--Voxtral-Mini-3B-2507/snapshots/3060fe34b35ba5d44202ce9ff3c097642914f8f3/tekken.json --prompt "What can you tell me about this audio?" --audio_path ~/models/voxtral/input_features.bin ``` [ghstack-poisoned]

ghstack-source-id: 22066a6 Pull Request resolved: #13663

### Summary Utilize `multimodal_runner.h` to run [Voxtral exported from Optimum Executorch](huggingface/optimum-executorch#126). The runner takes in a `.pt` file of a preprocessed audio recording and feeds it a C++ multimodal runner. Example output: ``` This audio is a casual and somewhat silly conversation between two speakers who seem to be discussing their tattoos. The speakers are engaging in a game where they ask each other what their tattoos say, but both repeatedly say "sweet" instead of the actual words. The speakers are aware of their mistake and try to correct it by asking the other what their tattoo says, but they still end up saying "sweet" again. The conversation ends with a speaker telling the other that their tattoo says " PyTorchObserver {"prompt_tokens":1138,"generated_tokens":99,"model_load_start_ms":0,"model_load_end_ms":0,"inference_start_ms":1756159197436,"inference_end_ms":1756159222710,"prompt_eval_end_ms":1756159209605,"first_token_ms":1756159209605,"aggregate_sampling_time_ms":96,"SCALING_FACTOR_UNITS_PER_SECOND":1000} I 00:00:33.116291 executorch:stats.h:104] Prompt Tokens: 1138 Generated Tokens: 99 I 00:00:33.116304 executorch:stats.h:110] Model Load Time: 0.000000 (seconds) I 00:00:33.116312 executorch:stats.h:117] Total inference time: 25.274000 (seconds) Rate: 3.917069 (tokens/second) I 00:00:33.116320 executorch:stats.h:127] Prompt evaluation: 12.169000 (seconds) Rate: 93.516312 (tokens/second) I 00:00:33.116327 executorch:stats.h:136] Generated 99 tokens: 13.105000 (seconds) Rate: 7.554369 (tokens/second) I 00:00:33.116338 executorch:stats.h:147] Time to first generated token: 12.169000 (seconds) I 00:00:33.116344 executorch:stats.h:153] Sampling time over 1237 tokens: 0.096000 (seconds) ``` ### Test plan Build and run: ``` # Build and install ExecuTorch cmake --preset llm -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=cmake-out -DEXECUTORCH_ENABLE_LOGGING=ON && cmake --build cmake-out -j16 --target install --config Release # Build and install Voxtral runner cmake -DCMAKE_INSTALL_PREFIX=cmake-out -DBUILD_TESTING=OFF -DCMAKE_BUILD_TYPE=Release -Bcmake-out/examples/models/voxtral examples/models/voxtral && cmake --build cmake-out/examples/models/voxtral -j16 --config Release # Run Voxtral runner ./cmake-out/examples/models/voxtral/voxtral_runner --model_path ~/models/voxtral/voxtral_q8da4w_edm_qe4w_d_split_metadata_unsqueeze.pte --tokenizer_path ~/hf/models--mistralai--Voxtral-Mini-3B-2507/snapshots/3060fe34b35ba5d44202ce9ff3c097642914f8f3/tekken.json --prompt "What can you tell me about this audio?" --audio_path ~/models/voxtral/input_features.bin ``` [ghstack-poisoned]

kimishpatel · 2025-08-26T21:21:16Z

extension/llm/runner/llm_runner_helper.cpp

    size_t bos_token_index,
    size_t eos_token_index) {
  runtime::runtime_init();
+  auto tekken_tokenizer = std::make_unique<tokenizers::Tekken>();


not sure I follow what this is doing

hf tokenizer can "load" the tekken tokenizer since it's also a json, which we don't want since the pattern I see here is that we keep loading different tokenizers until one works

### Summary Utilize `multimodal_runner.h` to run [Voxtral exported from Optimum Executorch](huggingface/optimum-executorch#126). The runner takes in a `.pt` file of a preprocessed audio recording and feeds it a C++ multimodal runner. Example output: ``` This audio is a casual and somewhat silly conversation between two speakers who seem to be discussing their tattoos. The speakers are engaging in a game where they ask each other what their tattoos say, but both repeatedly say "sweet" instead of the actual words. The speakers are aware of their mistake and try to correct it by asking the other what their tattoo says, but they still end up saying "sweet" again. The conversation ends with a speaker telling the other that their tattoo says " PyTorchObserver {"prompt_tokens":1138,"generated_tokens":99,"model_load_start_ms":0,"model_load_end_ms":0,"inference_start_ms":1756159197436,"inference_end_ms":1756159222710,"prompt_eval_end_ms":1756159209605,"first_token_ms":1756159209605,"aggregate_sampling_time_ms":96,"SCALING_FACTOR_UNITS_PER_SECOND":1000} I 00:00:33.116291 executorch:stats.h:104] Prompt Tokens: 1138 Generated Tokens: 99 I 00:00:33.116304 executorch:stats.h:110] Model Load Time: 0.000000 (seconds) I 00:00:33.116312 executorch:stats.h:117] Total inference time: 25.274000 (seconds) Rate: 3.917069 (tokens/second) I 00:00:33.116320 executorch:stats.h:127] Prompt evaluation: 12.169000 (seconds) Rate: 93.516312 (tokens/second) I 00:00:33.116327 executorch:stats.h:136] Generated 99 tokens: 13.105000 (seconds) Rate: 7.554369 (tokens/second) I 00:00:33.116338 executorch:stats.h:147] Time to first generated token: 12.169000 (seconds) I 00:00:33.116344 executorch:stats.h:153] Sampling time over 1237 tokens: 0.096000 (seconds) ``` ### Test plan Build and run: ``` # Build and install ExecuTorch cmake --preset llm -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=cmake-out -DEXECUTORCH_ENABLE_LOGGING=ON && cmake --build cmake-out -j16 --target install --config Release # Build and install Voxtral runner cmake -DCMAKE_INSTALL_PREFIX=cmake-out -DBUILD_TESTING=OFF -DCMAKE_BUILD_TYPE=Release -Bcmake-out/examples/models/voxtral examples/models/voxtral && cmake --build cmake-out/examples/models/voxtral -j16 --config Release # Run Voxtral runner ./cmake-out/examples/models/voxtral/voxtral_runner --model_path ~/models/voxtral/voxtral_q8da4w_edm_qe4w_d_split_metadata_unsqueeze.pte --tokenizer_path ~/hf/models--mistralai--Voxtral-Mini-3B-2507/snapshots/3060fe34b35ba5d44202ce9ff3c097642914f8f3/tekken.json --prompt "What can you tell me about this audio?" --audio_path ~/models/voxtral/input_features.bin ``` [ghstack-poisoned]

kirklandsign · 2025-08-28T20:22:21Z

examples/models/voxtral/multimodal.cpp

+  // Prepare inputs
+  std::vector<MultimodalInput> inputs;
+
+  // 1. Add start bos-related text inputs and modality start token.


This is show we run audio inputs with multimodal runner?

(Messed up the merge for the original stack, this is reland. Original PR with comments here - #13663) Differential Revision: [D81498749](https://our.internmc.facebook.com/intern/diff/D81498749) [ghstack-poisoned]

### Summary Utilize `multimodal_runner.h` to run [Voxtral exported from Optimum Executorch](huggingface/optimum-executorch#126). The runner takes in a `.pt` file of a preprocessed audio recording and feeds it a C++ multimodal runner. Example output: ``` This audio is a casual and somewhat silly conversation between two speakers who seem to be discussing their tattoos. The speakers are engaging in a game where they ask each other what their tattoos say, but both repeatedly say "sweet" instead of the actual words. The speakers are aware of their mistake and try to correct it by asking the other what their tattoo says, but they still end up saying "sweet" again. The conversation ends with a speaker telling the other that their tattoo says " PyTorchObserver {"prompt_tokens":1138,"generated_tokens":99,"model_load_start_ms":0,"model_load_end_ms":0,"inference_start_ms":1756159197436,"inference_end_ms":1756159222710,"prompt_eval_end_ms":1756159209605,"first_token_ms":1756159209605,"aggregate_sampling_time_ms":96,"SCALING_FACTOR_UNITS_PER_SECOND":1000} I 00:00:33.116291 executorch:stats.h:104] Prompt Tokens: 1138 Generated Tokens: 99 I 00:00:33.116304 executorch:stats.h:110] Model Load Time: 0.000000 (seconds) I 00:00:33.116312 executorch:stats.h:117] Total inference time: 25.274000 (seconds) Rate: 3.917069 (tokens/second) I 00:00:33.116320 executorch:stats.h:127] Prompt evaluation: 12.169000 (seconds) Rate: 93.516312 (tokens/second) I 00:00:33.116327 executorch:stats.h:136] Generated 99 tokens: 13.105000 (seconds) Rate: 7.554369 (tokens/second) I 00:00:33.116338 executorch:stats.h:147] Time to first generated token: 12.169000 (seconds) I 00:00:33.116344 executorch:stats.h:153] Sampling time over 1237 tokens: 0.096000 (seconds) ``` ### Test plan Build and run: ``` # Build and install ExecuTorch cmake --preset llm -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=cmake-out -DEXECUTORCH_ENABLE_LOGGING=ON && cmake --build cmake-out -j16 --target install --config Release # Build and install Voxtral runner cmake -DCMAKE_INSTALL_PREFIX=cmake-out -DBUILD_TESTING=OFF -DCMAKE_BUILD_TYPE=Release -Bcmake-out/examples/models/voxtral examples/models/voxtral && cmake --build cmake-out/examples/models/voxtral -j16 --config Release # Run Voxtral runner ./cmake-out/examples/models/voxtral/voxtral_runner --model_path ~/models/voxtral/voxtral_q8da4w_edm_qe4w_d_split_metadata_unsqueeze.pte --tokenizer_path ~/hf/models--mistralai--Voxtral-Mini-3B-2507/snapshots/3060fe34b35ba5d44202ce9ff3c097642914f8f3/tekken.json --prompt "What can you tell me about this audio?" --audio_path ~/models/voxtral/input_features.bin ``` Pull Request resolved: #13663

(Messed up the merge for the original stack, this is reland. Original PR with comments here - #13663) Differential Revision: [D81498749](https://our.internmc.facebook.com/intern/diff/D81498749) [ghstack-poisoned]

Add Voxtral runner

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jackzhxng requested review from kirklandsign, larryliu0820, lucylq, mergennachin and swolchok as code owners August 25, 2025 22:01

This was referenced Aug 25, 2025

Add audio to multimodal runner #13662

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Make token and stat callback optional in multimodal runner #13664

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Add Voxtral runner

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ghstack-source-id: 0fbf7c4 Pull Request resolved: #13663

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Add Voxtral runner

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ghstack-source-id: 0fbf7c4 Pull Request resolved: #13663

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Add Voxtral runner

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ghstack-source-id: 0fbf7c4 Pull Request resolved: #13663

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Add Voxtral runner

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ghstack-source-id: c51dcf5 Pull Request resolved: #13663

jackzhxng added the release notes: examples Changes to any of our example LLMs integrations, such as Llama3 and Llava label Aug 26, 2025

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Add Voxtral runner

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ghstack-source-id: c51dcf5 Pull Request resolved: #13663

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Add Voxtral runner

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ghstack-source-id: c51dcf5 Pull Request resolved: #13663

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Add Voxtral runner

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ghstack-source-id: 22066a6 Pull Request resolved: #13663

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Include audio preprocessing for raw audio tensor #13752

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jackzhxng merged commit f9c1771 into gh/jackzhxng/31/base Sep 2, 2025
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