[TPU][V1] Add support for top-logprobs (#17072)

Signed-off-by: NickLucche <nlucches@redhat.com>

tests/v1/tpu/test_sampler.py

@@ -61,3 +61,51 @@ def test_sampler_different(model_name: str):
         # to have deterministic results over many tokens, tests the first ~20
         # tokens match.
         assert output[0].outputs[0].text[:20] == output[1].outputs[0].text[:20]
+
+
+@pytest.mark.parametrize("model_name", ["Qwen/Qwen2.5-1.5B-Instruct"])
+# TODO TPU will appear busy if we fan-out test params here
+@pytest.mark.parametrize("n_prompts", [1])
+@pytest.mark.skipif(not current_platform.is_tpu(),
+                    reason="This test needs a TPU")
+def test_logprobs(model_name: str, n_prompts: int):
+    """
+    Request top logprobs with different sampling settings and check
+    that results contains the requested number, ordered ascendingly.  
+    """
+
+    def check_num_logprobs(logprobs, expected_num: int):
+        for step in logprobs:
+            prev_logp = 1.0
+            # order by rank
+            sorted_step = dict(
+                sorted(step.items(), key=lambda item: item[1].rank))
+
+            # Can contain the sampled token
+            assert len(step) == expected_num or len(step) == expected_num + 1
+            # Check results are ordered by prob value
+            for rankno, (tid, logp) in enumerate(sorted_step.items()):
+                assert logp.logprob <= prev_logp
+                prev_logp = logp.logprob
+                assert logp.rank == rankno + 1
+
+    llm = LLM(model_name,
+              enforce_eager=False,
+              max_num_seqs=1,
+              max_model_len=128,
+              max_num_batched_tokens=128)
+    prompts = [
+        "Write a short story about a robot that dreams for the first time."
+    ] * n_prompts
+    greedy_sampling_params = SamplingParams(temperature=0.0, max_tokens=64,\
+         logprobs=4)
+    regular_sampling_params = SamplingParams(temperature=0.4, max_tokens=64,\
+         logprobs=4)
+    topkp_sampling_params = SamplingParams(temperature=0.4, max_tokens=64,\
+         logprobs=4, top_k=12, top_p=0.5)
+
+    for sp in [greedy_sampling_params, regular_sampling_params, \
+               topkp_sampling_params]:
+        output = llm.generate(prompts, sp)
+        for o in output:
+            check_num_logprobs(o.outputs[0].logprobs, 4)