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<h1 id="logits-processors">Logits Processors</h1>
<h2 id="overview">Overview</h2>
<p>A <strong>Logits Processor (LP)</strong> sits between <em>model output logits</em> and the <em>sampler</em> (top-k/top-p/temperature…). It applies pluggable transformations to logits <strong>before</strong> sampling (e.g., weighting, masking, penalties, biases).</p>
<h2 id="key-features">Key Features</h2>
<ul>
<li><strong>Server-level registration</strong>: declare available processors at startup via <code>--logits-processors</code>. The declaration order is the execution order.</li>
<li><strong>Per-request control</strong>: enable and configure processors via the <code>logits_processors_args</code> field in the request body.</li>
<li><strong>Built-in processor</strong>: commonly used processors are provided, e.g., <code>LogitBiasLogitsProcessor</code>, which can be loaded directly by class name.</li>
<li><strong>Extensible interface</strong>: a standard <code>LogitsProcessor</code> interface is provided for user-defined processors, which can be loaded by FQCN <code>module.path:ClassName</code>.</li>
</ul>
<h2 id="usage">Usage</h2>
<h3 id="online-service">Online Service</h3>
<h4 id="1-start-the-service-register-logits-processors">1. Start the service (register logits processors)</h4>
<p>Register processors with <code>--logits-processors</code> when starting the service. For a built-in processor like <code>LogitBiasLogitsProcessor</code>, pass the class name directly:</p>
<pre><code class="language-bash">python -m fastdeploy.entrypoints.openai.api_server \
--model /path/to/model \
--port 8180 --metrics-port 8181 --engine-worker-queue-port 8182 --cache-queue-port 8183 \
--logits-processors LogitBiasLogitsProcessor
</code></pre>
<h4 id="2-send-a-request-enable-and-configure-as-needed">2. Send a request (enable and configure as needed)</h4>
<p>Use the <code>logits_processors_args</code> field in the REST request body to enable and configure processors. Example with <code>LogitBiasLogitsProcessor</code>, which adds a bias to specified tokens. It accepts a <code>logit_bias</code> dictionary mapping <em>token_id</em> → <em>bias value</em>:</p>
<pre><code class="language-bash">curl -X POST &quot;http://0.0.0.0:8180/v1/chat/completions&quot; -H &quot;Content-Type: application/json&quot; -d '{
&quot;messages&quot;: [{&quot;role&quot;:&quot;user&quot;, &quot;content&quot;:&quot;Who is Lu Xun?&quot;}],
&quot;logits_processors_args&quot;: {
&quot;logit_bias&quot;: {&quot;128&quot;: 5.0, &quot;50256&quot;: -10.0}
}
}'
</code></pre>
<p>When using the OpenAI Python SDK, pass <code>logits_processors_args</code> through <code>extra_body</code>:</p>
<pre><code class="language-python">import openai
client = openai.Client(base_url=&quot;http://0.0.0.0:8180/v1&quot;, api_key=&quot;EMPTY_API_KEY&quot;)
response = client.chat.completions.create(
model=&quot;default&quot;,
messages=[{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Who is Lu Xun?&quot;}],
extra_body={
&quot;logits_processors_args&quot;: {
&quot;logit_bias&quot;: {&quot;128&quot;: 5.0, &quot;50256&quot;: -10.0}
}
}
)
</code></pre>
<h3 id="offline-inference">Offline Inference</h3>
<p>For offline inference, pass the <code>logits_processors</code> argument (type <code>list[str]</code>) when initializing the <code>LLM</code> instance. When generating text via the offline <code>chat()</code> or <code>generate()</code> APIs, provide the logits-processor parameters through <code>sampling_params</code>.<code>logits_processors_args</code> to enable and pass arguments to the corresponding processors.</p>
<pre><code class="language-python">from fastdeploy import LLM, SamplingParams
llm = LLM(
model=&quot;path/to/model&quot;,
engine_worker_queue_port=8282,
cache_queue_port=8383,
logits_processors=['LogitBiasLogitsProcessor'],
)
messages = [{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Who is Lu Xun?&quot;}]
sampling_params = SamplingParams(
top_p=0.95,
max_tokens=128,
logits_processors_args={&quot;logit_bias&quot;: {128: 5.0, 50256: -10.0}},
)
outputs = llm.chat(messages, sampling_params)
print(outputs[0].outputs.text)
</code></pre>
<h2 id="custom-logits-processor">Custom Logits Processor</h2>
<h3 id="1-define-your-own-logitsprocessor-class">1. Define your own LogitsProcessor class</h3>
<p>Inherit the <code>fastdeploy.openai.logits_processor.LogitsProcessor</code> class and implement the <code>update_state()</code> and <code>apply()</code> methods.</p>
<ul>
<li><strong><code>update_state()</code> is used to update the logits processor state.</strong> The input is the inference backend’s runtime state <code>share_inputs</code>, and it returns nothing. You need to extract useful information from the runtime state to update the logits processor’s internal state.</li>
<li>For example, in the following example, we retrieve the current batch’s <code>logits_processors_args</code> from <code>share_inputs</code>, and then bulk-modify the enablement status of the logits processor for the current batch;</li>
<li>When writing your class, you should predefine the parameter names for your logits processor, such as adding a request parameter <code>enable_your_logits_processor</code> to control whether your logits processor is enabled for a request;</li>
<li><strong><code>apply()</code> is used to actually modify the logits tensor.</strong> Before <code>apply()</code> runs, the model will call <code>update_state()</code> to refresh the logits processor state. Therefore, ensure your <code>update_state()</code> correctly updates the state variables used by the logits processor.</li>
<li>In the following example, we use <code>self.enabled</code>to determine whether each request in the current batch enables your logits processor, and adjust the logits tensor dynamically.</li>
</ul>
<pre><code class="language-python">from paddle import Tensor
from fastdeploy.config import FDConfig
from fastdeploy.openai.logits_processor import LogitsProcessor
class YourLogitsProcessor(LogitsProcessor):
def __init__(self, fd_config: FDConfig) -&gt; None:
# Initialize your state variables here, for example obtain dtype, device, etc.
# from fd_config. You can freely set the state variables you need, and update them
# during each step of inference update_state()
self.enabled = None
return
def update_state(self, share_inputs: dict) -&gt; None:
&quot;&quot;&quot;Called when there are new output tokens, prior to each forward pass.
Each field in the `share_inputs` dict typically stores information for all request
slots. It has a `stop_flags` array that indicates whether a slot currently has a
running request (`False` means the slot is active). Therefore, it is recommended to
filter entries by `stop_flags` to keep only data for the current batch.
&quot;&quot;&quot;
stop_flags = share_inputs[&quot;stop_flags&quot;]
logits_processors_args = share_inputs[&quot;logits_processors_args&quot;]
logits_processors_args = [a for a, f in zip(logits_processors_args, stop_flags) if not f]
# Update your state variables here to facilitate dynamically
# adjusting your logits processor behavior at each step of inference.
# The latest state should be read and used in the apply() method
self.enabled = [a.enable_your_logits_processor for a in logits_processors_args]
return
def apply(self, logits: Tensor) -&gt; Tensor:
&quot;&quot;&quot;Apply LogitsProcessor to batch logits tensor.
The updated tensor must be returned but may be modified in-place.
&quot;&quot;&quot;
for i, e in enumerate(self.enabled):
# Implement your core logits transformation here, and return the modified logits tensor
logits[i] = ...
return logits
</code></pre>
<h3 id="2-use-your-logits-processor-via-online-service">2. Use your logits processor via online service</h3>
<h4 id="22-start-the-service-register-your-logits-processor">2.2. Start the service (register your logits processor)</h4>
<p>When registering a custom processor, pass its <strong>FQCN</strong> (<code>module.path:ClassName</code>) to <code>--logits-processors</code>:</p>
<pre><code class="language-bash">python -m fastdeploy.entrypoints.openai.api_server \
--model /path/to/model \
--port 8180 --metrics-port 8181 --engine-worker-queue-port 8182 --cache-queue-port 8183 \
--logits-processors your.dotted.path.to.module:YourLogitsProcessor
</code></pre>
<h4 id="22-send-a-request-enable-and-configure-as-needed">2.2. Send a request (enable and configure as needed)</h4>
<p>Enable your processor per request via <code>logits_processors_args</code>:</p>
<pre><code class="language-bash">curl -X POST &quot;http://0.0.0.0:8180/v1/chat/completions&quot; -H &quot;Content-Type: application/json&quot; -d '{
&quot;messages&quot;: [{&quot;role&quot;:&quot;user&quot;, &quot;content&quot;:&quot;Who is Lu Xun?&quot;}],
&quot;logits_processors_args&quot;: {
&quot;enable_your_logits_processor&quot;: true
}
}'
</code></pre>
<p>Using the OpenAI Python SDK:</p>
<pre><code class="language-python">import openai
client = openai.Client(base_url=&quot;http://0.0.0.0:8180/v1&quot;, api_key=&quot;EMPTY_API_KEY&quot;)
response = client.chat.completions.create(
model=&quot;default&quot;,
messages=[{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Who is Lu Xun?&quot;}],
extra_body={
&quot;logits_processors_args&quot;: {
&quot;enable_your_logits_processor&quot;: True
}
}
)
</code></pre>
<h3 id="3-use-your-logits-processor-via-offline-inference">3. Use your logits processor via offline inference</h3>
<p>For offline inference, pass the <code>logits_processors</code> argument (type <code>list[str]</code>) when initializing the <code>LLM</code> instance. To specify your custom logits processor, pass its FQCN (<code>module.path:ClassName</code>). When generating text via the offline <code>chat()</code> or <code>generate()</code> APIs, provide the logits-processor parameters through <code>sampling_params</code>.<code>logits_processors_args</code> to enable and pass arguments to the corresponding processors.</p>
<pre><code class="language-python">from fastdeploy import LLM, SamplingParams
llm = LLM(
model=&quot;path/to/model&quot;,
engine_worker_queue_port=8282,
cache_queue_port=8383,
logits_processors=['your.dotted.path.to.module:YourLogitsProcessor'],
)
messages = [{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Who is Lu Xun?&quot;}]
sampling_params = SamplingParams(
top_p=0.95,
max_tokens=128,
logits_processors_args={&quot;enable_your_logits_processor&quot;: True},
)
outputs = llm.chat(messages, sampling_params)
print(outputs[0].outputs.text)
</code></pre>
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