Migrate llama_classification to use the /classify interface

python/sglang/srt/models/llama_classification.py

@@ -18,7 +18,7 @@
 from torch import nn
 from transformers import LlamaConfig
 
-from sglang.srt.layers.logits_processor import LogitsProcessorOutput
+from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
@@ -40,7 +40,7 @@ def __init__(
         self.classification_head = nn.Linear(
             config.hidden_size, config.classification_out_size, bias=False
         )
-        self.eos_token_id = config.eos_token_id
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=False)
 
     @torch.no_grad()
     def forward(
@@ -49,28 +49,17 @@ def forward(
         positions: torch.Tensor,
         forward_batch: ForwardBatch,
         input_embeds: torch.Tensor = None,
-    ) -> torch.Tensor:
-        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
-        is_eos_token = input_ids == self.eos_token_id
-        hidden_states = hidden_states[is_eos_token]
-        scores = self.classification_head(hidden_states)
-
-        if scores.shape[0] != forward_batch.batch_size:
-            print("Warning: the EOS tokens are missing in some sentences.")
-            scores = torch.ones(
-                (forward_batch.batch_size, self.config.classification_out_size)
-            ).to(input_ids.device)
+        get_embedding: bool = True,
+    ) -> EmbeddingPoolerOutput:
+        assert (
+            get_embedding
+        ), "LlamaForClassification is only used for embedding. Please add --is-embedding when you launch the server."
 
-        logits_output = LogitsProcessorOutput(
-            next_token_logits=scores,
-            next_token_logprobs=scores,
-            normalized_prompt_logprobs=scores,
-            input_token_logprobs=torch.ones_like(input_ids),
-            input_top_logprobs=None,
-            output_top_logprobs=None,
-        )
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        last_token_hidden = self.pooler(hidden_states, forward_batch).embeddings
+        scores = self.classification_head(last_token_hidden)
 
-        return logits_output
+        return EmbeddingPoolerOutput(scores)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         params_dict = dict(self.named_parameters())

scripts/deprecated/test_httpserver_classify.py

@@ -1,6 +1,6 @@
 """
 Usage:
-python3 -m sglang.launch_server --disable-cuda-graph --model-path /model/llama-classification
+python3 -m sglang.launch_server --model-path /model/llama-classification --is-embedding --disable-radix-cache
 
 python3 test_httpserver_classify.py
 """
@@ -11,7 +11,7 @@
 import requests
 
 
-def get_logits(url, prompt):
+def get_logits_deprecated(url: str, prompt: str):
     response = requests.post(
         url + "/generate",
         json={
@@ -25,7 +25,7 @@ def get_logits(url, prompt):
     return response.json()["meta_info"]["normalized_prompt_logprob"]
 
 
-def get_logits_batch(url, prompts):
+def get_logits_batch_deprecated(url: str, prompts: list[str]):
     response = requests.post(
         url + "/generate",
         json={
@@ -46,6 +46,22 @@ def get_logits_batch(url, prompts):
     return logits
 
 
+def get_logits(url: str, prompt: str):
+    response = requests.post(
+        url + "/classify",
+        json={"text": prompt},
+    )
+    return response.json()["embedding"]
+
+
+def get_logits_batch(url: str, prompts: list[str]):
+    response = requests.post(
+        url + "/classify",
+        json={"text": prompts},
+    )
+    return np.array([x["embedding"] for x in response.json()])
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="http://127.0.0.1")