File size: 3,984 Bytes
fe6a83c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | """HF Inference Endpoint custom handler for state-spaces/mamba-2.8b-hf.
Deploys pure-state-space Mamba via the standard HF Endpoints custom-handler
interface. The handler loads the base model at init time and serves a single
endpoint that accepts Bench 1.6-A's completion-format prompts.
Input schema (Bench 1.6-A concatenated completion format):
{
"inputs": "<flat text prompt with system + user turns concatenated>",
"parameters": {
"max_new_tokens": 512,
"temperature": 0.7,
"top_p": 0.95,
"do_sample": true,
}
}
Output schema:
{
"generated_text": "<model completion>",
"input_tokens": <int>,
"output_tokens": <int>,
"model": "state-spaces/mamba-2.8b-hf"
}
Preregistered per docs/BENCH-1.6A-PREREG.md §5.5 (base-model asymmetry):
Base Mamba receives completion-format prompts, NOT chat-template formatted
messages. The caller (scripts/nsi_bench_hf.py) is responsible for
concatenating the Bench 1 [system, user_1, assistant_1, user_2, ...]
structure into the flat text the base model expects.
"""
from __future__ import annotations
from typing import Any
import torch
from transformers import AutoTokenizer, MambaForCausalLM
MODEL_ID = "state-spaces/mamba-2.8b-hf"
class EndpointHandler:
"""HF Endpoints custom handler entry point.
HF Endpoints constructs this class once at boot and calls __call__ per
request. The class name MUST be EndpointHandler.
"""
def __init__(self, path: str = "") -> None:
# HF Endpoints passes the model path in `path`. For our handler the
# model ID is canonical and the weights are either shipped in `path`
# or pulled from the hub. We prefer the hub ID so the handler is
# independent of the deployment repo layout.
self.model_id = MODEL_ID
self.device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if self.device == "cuda" else torch.float32
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
self.model = MambaForCausalLM.from_pretrained(
self.model_id,
torch_dtype=dtype,
).to(self.device)
self.model.eval()
def __call__(self, data: dict[str, Any]) -> dict[str, Any]:
prompt: str = data.get("inputs", "")
params: dict[str, Any] = data.get("parameters", {}) or {}
max_new_tokens: int = int(params.get("max_new_tokens", 512))
temperature: float = float(params.get("temperature", 0.7))
top_p: float = float(params.get("top_p", 0.95))
do_sample: bool = bool(params.get("do_sample", True))
if not prompt:
return {
"generated_text": "",
"input_tokens": 0,
"output_tokens": 0,
"model": self.model_id,
"error": "empty_input",
}
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
input_tokens = int(inputs["input_ids"].shape[-1])
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature if do_sample else 1.0,
top_p=top_p,
do_sample=do_sample,
pad_token_id=self.tokenizer.eos_token_id
if self.tokenizer.pad_token_id is None
else self.tokenizer.pad_token_id,
)
full_text = self.tokenizer.decode(
outputs[0],
skip_special_tokens=True,
)
generated_only = full_text[len(prompt):] if full_text.startswith(prompt) else full_text
output_tokens = int(outputs.shape[-1]) - input_tokens
return {
"generated_text": generated_only,
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"model": self.model_id,
}
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