Benjamin ChislettGitHubgemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>Signed-off-by: Benjamin Chislett <bchislett@nvidia.com> Signed-off-by: Benjamin Chislett <chislett.ben@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
87 lines
3.5 KiB
Markdown
87 lines
3.5 KiB
Markdown
# Hidden State Extraction
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The Hidden State Extraction feature allows vLLM to save intermediate layer activations from a target model during inference. This is useful for training [EAGLE](eagle.md)-style draft models, knowledge distillation, or offline analysis of model internals.
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!!! note
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It is possible to save the last-layer's output hidden states by passing `num_hidden_layers` as a layer id. Note that these are _not_ normalized using the output norm.
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## Offline Example
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```python
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import tempfile
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from vllm import LLM, SamplingParams
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from vllm.config.kv_transfer import KVTransferConfig
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from vllm.distributed.kv_transfer.kv_connector.v1 import (
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example_hidden_states_connector,
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)
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with tempfile.TemporaryDirectory() as tmpdir:
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llm = LLM(
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model="Qwen/Qwen3-8B",
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enable_chunked_prefill=False,
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speculative_config={
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"method": "extract_hidden_states",
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"num_speculative_tokens": 1,
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"draft_model_config": {
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"hf_config": {
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"eagle_aux_hidden_state_layer_ids": [1, 2, 3, 4],
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},
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},
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},
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kv_transfer_config=KVTransferConfig(
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kv_connector="ExampleHiddenStatesConnector",
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kv_role="kv_producer",
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kv_connector_extra_config={
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"shared_storage_path": tmpdir,
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},
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),
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)
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outputs = llm.generate(
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["The future of AI is"],
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SamplingParams(max_tokens=1),
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)
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for output in outputs:
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path = output.kv_transfer_params["hidden_states_path"]
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obj = example_hidden_states_connector.load_hidden_states(path)
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print(f"token_ids: {obj['token_ids'].shape}")
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print(f"hidden_states: {obj['hidden_states'].shape}")
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```
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A complete example is available at [`examples/features/speculative_decoding/extract_hidden_states_offline.py`](../../../examples/features/speculative_decoding/extract_hidden_states_offline.py).
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## Online Example
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For improved performance, it is recommended to use a RAM-mounted file system such as `/dev/shm/` for online usage in which the client cleans up the files soon after they are generated.
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```bash
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vllm serve Qwen/Qwen3-8B \
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--speculative_config '{"method": "extract_hidden_states", "num_speculative_tokens": 1, "draft_model_config": {"hf_config": {"eagle_aux_hidden_state_layer_ids": [1, 2, 3, 4]}}}' \
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--kv_transfer_config '{"kv_connector": "ExampleHiddenStatesConnector", "kv_role": "kv_producer", "kv_connector_extra_config": {"shared_storage_path": "/dev/shm/hidden_states"}}' \
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--no-enable-chunked-prefill
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```
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## Configuration
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The `kv_connector_extra_config` dict accepts these options:
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| Parameter | Default | Description |
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| --- | --- | --- |
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| `shared_storage_path` | `/tmp` | Directory where hidden state files are saved |
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| `num_writer_threads` | `8` | Thread pool size for async disk writes |
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| `use_synchronization_lock` | `True` | Use file locks so concurrent readers block until writes complete. Can be disabled for batch generation where synchronization is not needed. |
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## Output Format
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Each request produces a `.safetensors` file containing:
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- **`hidden_states`** — shape `[num_tokens, num_extracted_layers, hidden_size]`
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- **`token_ids`** — shape `[num_tokens]`
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The file path is returned in `output.kv_transfer_params["hidden_states_path"]`. Use `load_hidden_states()` from the connector module to read the file with proper synchronization.
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!!! note
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Chunked prefill is not compatible with this feature and must be disabled.
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