SpecDec Bench: February Update #875
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| # Porting Spec-Bench Inference Runners to specdec_bench | ||
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| This guide explains how to convert any `inference_*.py` runner from [Spec-Bench](https://github.com/hemingkx/Spec-Bench) to a model class compatible with `specdec_bench`. | ||
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| ## Overview | ||
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| Spec-Bench inference runners follow a pattern where: | ||
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| 1. A `*_forward()` function handles the speculative decoding logic | ||
| 2. The `run_eval()` function orchestrates evaluation with tokenized inputs | ||
| 3. Models are loaded in `__main__` and passed to `run_eval()` | ||
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| In contrast, `specdec_bench` uses a class-based approach where: | ||
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| 1. Models inherit from the `Model` base class | ||
| 2. `__init__()` handles model loading | ||
| 3. `run()` is an async method that processes single requests | ||
| 4. `stop()` handles cleanup | ||
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| ## The specdec_bench Model Interface | ||
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| ```python | ||
| class Model: | ||
| def __init__(self, model_dir, tokenizer, max_draft_length): | ||
| raise NotImplementedError | ||
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| async def run(self, prompt_ids, sampling_params, request_id, turn_id): | ||
| """ | ||
| prompt_ids: list of token IDs (not a tensor!) | ||
| Returns dict with: | ||
| - output_ids: list of list of token chunks per step [[chunk1, chunk2, ...]] | ||
| - output_logits: optional logits (usually None) | ||
| - token_times: list of timestamps per decoding step | ||
| """ | ||
| raise NotImplementedError | ||
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| def stop(self): | ||
| pass | ||
| ``` | ||
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Contributor
There was a problem hiding this comment. Interface definition contradicts implementation examples within the same document. The Model interface at Line 25 declares Align all examples with whichever signature is chosen as the canonical API. 🤖 Prompt for AI Agents |
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| ## Step-by-Step Porting Guide | ||
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| ### Step 1: Identify the Key Components in Spec-Bench | ||
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| Look at the `inference_*.py` file and identify: | ||
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| 1. **The forward function** (e.g., `medusa_forward`, `ea_forward`) | ||
| - This contains the core speculative decoding loop | ||
| - Signature: `forward_func(inputs, model, tokenizer, max_new_tokens, **kwargs)` | ||
| - Returns: `(output_ids, new_token_count, num_steps, accept_length_list)` | ||
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| 2. **The model class** (e.g., `MedusaModel`, `EaModel`) | ||
| - Found in `model/<method>/` directory | ||
| - Has a `from_pretrained()` class method | ||
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| 3. **Required utilities** from the method's module: | ||
| - Buffer generation (e.g., `generate_medusa_buffers`) | ||
| - Initialization functions (e.g., `initialize_medusa`, `initialize_past_key_values`) | ||
| - Decoding functions (e.g., `tree_decoding`, `generate_candidates`) | ||
| - State update functions (e.g., `update_inference_inputs`) | ||
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| 4. **Method-specific choices/configs** (e.g., `mc_sim_7b_63` for Medusa) | ||
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| ### Step 2: Create the specdec_bench Model Class | ||
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| ```python | ||
| # specdec_bench/specdec_bench/models/specbench_<method>.py | ||
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| from .base import Model | ||
| import asyncio | ||
| import time | ||
| import torch | ||
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| # Import dependencies from Spec-Bench | ||
| try: | ||
| import sys | ||
| import os | ||
| spec_bench_path = os.path.join(os.getcwd(), "Spec-Bench") | ||
| sys.path.insert(0, spec_bench_path) | ||
| from model.<method>.<model_file> import <ModelClass> | ||
| from model.<method>.kv_cache import initialize_past_key_values | ||
| from model.<method>.utils import ( | ||
| # Import all required utilities | ||
| ) | ||
| from model.<method>.<choices_file> import <default_choices> | ||
| except ImportError as e: | ||
| print(f"<Method> dependencies not found: {e}") | ||
| <ModelClass> = None | ||
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| class SpecBench<Method>Model(Model): | ||
| def __init__(self, model_dir, max_concurrent_requests, sampling_kwargs, **kwargs): | ||
| # 1. Validate dependencies | ||
| if <ModelClass> is None: | ||
| raise ImportError("<Method> dependencies not found.") | ||
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| # 2. Extract configuration from kwargs | ||
| self.dtype = kwargs.get("dtype", "float16") | ||
| self.max_steps = kwargs.get("max_steps", 512) | ||
| self.temperature = sampling_kwargs.get("temperature", 0.0) | ||
| # ... other method-specific parameters | ||
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| # 3. Set up device (avoid device_map="auto" for multi-GPU issues) | ||
| self.device = torch.device(kwargs.get("device", "cuda:0")) | ||
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| # 4. Convert dtype string to torch dtype | ||
| dtype_map = { | ||
| "float32": torch.float32, | ||
| "float16": torch.float16, | ||
| "bfloat16": torch.bfloat16, | ||
| } | ||
| torch_dtype = dtype_map.get(self.dtype, torch.float16) | ||
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| # 5. Load the model | ||
| self.model = <ModelClass>.from_pretrained( | ||
| model_dir, | ||
| # ... other args from Spec-Bench's __main__ | ||
| torch_dtype=torch_dtype, | ||
| low_cpu_mem_usage=True, | ||
| ) | ||
| self.model = self.model.to(self.device) | ||
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| self.sampling_kwargs = sampling_kwargs | ||
| ``` | ||
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| ### Step 3: Port the Forward Function | ||
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| Convert the standalone `*_forward()` function to an internal method: | ||
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| ```python | ||
| def _forward(self, input_ids, max_new_tokens, end_id): | ||
| """ | ||
| Port of the original *_forward function. | ||
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| Key changes from Spec-Bench: | ||
| 1. input_ids is already a tensor (converted in run()) | ||
| 2. Add timing list to track per-step timestamps | ||
| 3. Use self.device instead of model.base_model.device | ||
| 4. Return timing along with other outputs | ||
| """ | ||
| accept_length_list = [] | ||
| timing = [time.perf_counter()] # ADD: Track timing | ||
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| # === COPY THE FORWARD LOGIC FROM SPEC-BENCH === | ||
| # Replace: device=model.base_model.device | ||
| # With: device=self.device | ||
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| # Initialize buffers... | ||
| # Initialize KV cache... | ||
| # Main decoding loop... | ||
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| for idx in range(self.max_steps): | ||
| # Generate candidates... | ||
| # Tree decoding... | ||
| # Evaluate posterior... | ||
| # Update inputs... | ||
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| timing.append(time.perf_counter()) # ADD: Record time per step | ||
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| # Check for EOS | ||
| if end_id in input_ids[0, input_len:].tolist(): | ||
| break | ||
| if new_token > max_new_tokens: | ||
| break | ||
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| return input_ids, new_token, idx + 1, accept_length_list, timing # ADD timing | ||
| ``` | ||
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| ### Step 4: Implement the run() Method | ||
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| ```python | ||
| async def run(self, prompt_ids, max_length, end_id, request_id, turn_id): | ||
| """ | ||
| Async interface for specdec_bench. | ||
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| Args: | ||
| prompt_ids: List of input token IDs (NOT a tensor) | ||
| max_length: Maximum new tokens to generate | ||
| end_id: EOS token ID | ||
| request_id: Request identifier | ||
| turn_id: Turn identifier | ||
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| Returns: | ||
| dict with output_ids, output_logits, token_times | ||
| """ | ||
| output_dict = {} | ||
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| # Convert prompt_ids list to tensor | ||
| input_ids = torch.tensor( | ||
| [prompt_ids], dtype=torch.long, device=self.device | ||
| ) | ||
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| # Run forward pass (use asyncio.to_thread for sync code) | ||
| result = await asyncio.to_thread( | ||
| self._forward, input_ids, max_length, end_id | ||
| ) | ||
| input_ids_out, new_token, num_steps, accept_length_list, timing = result | ||
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| # Extract generated tokens (excluding prompt) | ||
| original_len = len(prompt_ids) | ||
| generated_tokens = input_ids_out[0, original_len:].tolist() | ||
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| # Remove EOS token if present | ||
| if end_id in generated_tokens: | ||
| eos_idx = generated_tokens.index(end_id) | ||
| generated_tokens = generated_tokens[:eos_idx] | ||
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| # Format output_ids as list of token chunks per step | ||
| # This matches specdec_bench's expected format | ||
| reformatted_output_ids = [[]] | ||
| start = 0 | ||
| for accept_len in accept_length_list: | ||
| if accept_len > 0 and start < len(generated_tokens): | ||
| chunk = generated_tokens[start:start + accept_len] | ||
| if chunk: | ||
| reformatted_output_ids[0].append(chunk) | ||
| start += accept_len | ||
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| # Handle remaining tokens | ||
| if start < len(generated_tokens): | ||
| reformatted_output_ids[0].append(generated_tokens[start:]) | ||
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| output_dict['output_ids'] = reformatted_output_ids | ||
| output_dict['output_logits'] = None | ||
| output_dict['token_times'] = timing | ||
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| return output_dict | ||
| ``` | ||
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| ### Step 5: Implement stop() for Cleanup | ||
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| ```python | ||
| def stop(self): | ||
| """Clean up resources.""" | ||
| # Clear any cached states | ||
| if hasattr(self.model, "past_key_values"): | ||
| del self.model.past_key_values | ||
| del self.model.past_key_values_data | ||
| del self.model.current_length_data | ||
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| # Clear method-specific buffers | ||
| if hasattr(self.model, "<method>_buffers"): | ||
| del self.model.<method>_buffers | ||
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| # Free GPU memory | ||
| if hasattr(self, 'model') and self.model is not None: | ||
| del self.model | ||
| torch.cuda.empty_cache() | ||
| ``` | ||
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| ### Step 6: Register the Model (Optional) | ||
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| Add to `specdec_bench/specdec_bench/models/__init__.py`: | ||
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| ```python | ||
| from .specbench_<method> import SpecBench<Method>Model | ||
| ``` | ||
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| ## Key Differences Summary | ||
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| | Aspect | Spec-Bench | specdec_bench | | ||
| |--------|-----------|---------------| | ||
| | Input format | `inputs.input_ids` (tensor from tokenizer) | `prompt_ids` (list of ints) | | ||
| | Output format | `(output_ids, new_token, steps, accept_lengths)` | `dict` with `output_ids`, `output_logits`, `token_times` | | ||
| | Output IDs | Full sequence tensor | List of token chunks per step | | ||
| | Timing | External (in `run_eval`) | Internal (in `run()`) | | ||
| | Device | `device_map="auto"` | Explicit single device | | ||
| | Interface | Function-based | Class-based with async `run()` | | ||
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| ## Common Pitfalls | ||
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| 1. **Device Mismatch**: Avoid `device_map="auto"` which spreads model across GPUs. Use explicit `.to(device)`. | ||
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| 2. **Tensor vs List**: `prompt_ids` in specdec_bench is a Python list, not a tensor. Convert it in `run()`. | ||
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| 3. **Output Format**: specdec_bench expects `output_ids` as `[[chunk1, chunk2, ...]]` (list of lists of lists for beam_width=1). | ||
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| 4. **Timing**: Add `time.perf_counter()` calls to track per-step latency. | ||
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| 5. **EOS Handling**: Strip EOS tokens from output before formatting. | ||
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| 6. **Async Wrapper**: Use `asyncio.to_thread()` to wrap synchronous forward passes. | ||
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| ## Example: Mapping Spec-Bench Methods | ||
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| | Spec-Bench File | Model Class | Forward Function | Key Utils | | ||
| |-----------------|-------------|------------------|-----------| | ||
| | `inference_medusa.py` | `MedusaModel` | `medusa_forward` | `generate_medusa_buffers`, `initialize_medusa` | | ||
| | `inference_eagle.py` | `EaModel` | `ea_forward` | `generate_tree_buffers`, `initialize_tree` | | ||
| | `inference_eagle2.py` | `EaModel` | `ea_forward` | Same as EAGLE | | ||
| | `inference_hydra.py` | `HydraModel` | `hydra_forward` | `generate_hydra_buffers`, `initialize_hydra` | | ||
| | `inference_lookahead.py` | `LookaheadModel` | `lookahead_forward` | Lookahead-specific utils | | ||
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| ## Testing Your Port | ||
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| ```python | ||
| import asyncio | ||
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| async def test(): | ||
| model = SpecBench<Method>Model( | ||
| model_dir="/path/to/model", | ||
| max_concurrent_requests=1, | ||
| sampling_kwargs={"temperature": 0.0}, | ||
| # method-specific kwargs... | ||
| ) | ||
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| result = await model.run( | ||
| prompt_ids=[1, 2, 3, 4, 5], # Example token IDs | ||
| max_length=100, | ||
| end_id=2, # EOS token | ||
| request_id="test", | ||
| turn_id=0 | ||
| ) | ||
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| print("Output chunks:", result['output_ids']) | ||
| print("Timing:", result['token_times']) | ||
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| model.stop() | ||
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| asyncio.run(test()) | ||
| ``` | ||
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| Adjust the vicuna chat template to be in the tokenizer_config to be | ||
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| Insert to tokenizer_config (for vicuna) | ||
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| ```json | ||
| "chat_template": "{% set ns = namespace(system='') %}{% for m in messages %}{% if m['role'] == 'system' %}{% set ns.system = m['content'] %}{% endif %}{% endfor %}{{ ns.system | trim }}{% if ns.system | trim != '' %} {% endif %}{% for m in messages %}{% if m['role'] == 'user' %}USER: {{ m['content'] | trim }} ASSISTANT:{% elif m['role'] == 'assistant' %}{{ m['content'] | trim }}{% endif %}{% endfor %}" | ||
| ``` | ||
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Repository: NVIDIA/Model-Optimizer
Length of output: 724
Remove or provide the missing
runtime_args_long_context.yamlfile.The README references
--runtime_params runtime_args_long_context.yaml, but this file does not exist in the repository. Either create the file with appropriate configuration values or remove the parameter from the example command.🤖 Prompt for AI Agents