[New Model]: Support Qwen3 Embedding & Reranker (#19260)

tests/models/language/pooling/test_qwen3_reranker_seq_cls.py

@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+import pytest
+
+model_name = "tomaarsen/Qwen3-Reranker-0.6B-seq-cls"
+
+text_1 = "What is the capital of France?"
+texts_2 = [
+    "The capital of Brazil is Brasilia.",
+    "The capital of France is Paris.",
+]
+
+
+def vllm_reranker(model_name):
+    from vllm import LLM
+
+    model = LLM(model=model_name, task="score")
+    outputs = model.score(text_1, texts_2)
+
+    return [output.outputs.score for output in outputs]
+
+
+def hf_reranker(model_name):
+    import torch
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
+    model = AutoModelForCausalLM.from_pretrained(model_name).eval()
+
+    token_false_id = tokenizer.convert_tokens_to_ids("no")
+    token_true_id = tokenizer.convert_tokens_to_ids("yes")
+
+    max_length = 8192
+
+    def process_inputs(pairs):
+        inputs = tokenizer(pairs,
+                           padding=False,
+                           truncation='longest_first',
+                           return_attention_mask=False,
+                           max_length=max_length)
+        for i, ele in enumerate(inputs['input_ids']):
+            inputs['input_ids'][i] = ele
+        inputs = tokenizer.pad(inputs,
+                               padding=True,
+                               return_tensors="pt",
+                               max_length=max_length)
+        for key in inputs:
+            inputs[key] = inputs[key].to(model.device)
+        return inputs
+
+    @torch.no_grad()
+    def compute_logits(inputs, **kwargs):
+        batch_scores = model(**inputs).logits[:, -1, :]
+        true_vector = batch_scores[:, token_true_id]
+        false_vector = batch_scores[:, token_false_id]
+        batch_scores = torch.stack([false_vector, true_vector], dim=1)
+        batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
+        scores = batch_scores[:, 1].exp().tolist()
+        return scores
+
+    pairs = [(text_1, texts_2[0]), (text_1, texts_2[1])]
+    inputs = process_inputs(pairs)
+    scores = compute_logits(inputs)
+
+    return scores
+
+
+@pytest.mark.parametrize("model_name", [model_name])
+def test_model(model_name):
+    hf_outputs = hf_reranker(model_name)
+    vllm_outputs = vllm_reranker(model_name)
+
+    assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
+    assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)