[core] remove beam search from the core (#9105)

tests/samplers/test_sampler.py

@@ -159,26 +159,6 @@ def test_sampler_all_random_seed_deterministic(seed: int, device: str):
     assert first_sampler_output == second_sampler_output
 
 
-@pytest.mark.parametrize("seed", RANDOM_SEEDS)
-@pytest.mark.parametrize("device", CUDA_DEVICES)
-def test_sampler_all_beam(seed: int, device: str):
-    set_random_seed(seed)
-    torch.set_default_device(device)
-    batch_size = random.randint(1, 256)
-    _, fake_logits, sampler = _prepare_test(batch_size)
-
-    sampling_params = SamplingParams(
-        temperature=0,
-        best_of=2,
-        use_beam_search=True,
-    )
-    _do_sample(batch_size, fake_logits, sampler, sampling_params, device)
-    # no assertion here as I am not sure how to determine whether
-    # the outputs are expected - in other words, this just tests
-    # whether there are no exceptions in the sampler
-    # when handling an all-beam search case.
-
-
 @pytest.mark.parametrize("seed", RANDOM_SEEDS)
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 def test_sampler_min_tokens_penalty(seed: int, device: str):
@@ -479,7 +459,7 @@ def test_sampler_mixed(seed: int, device: str):
     seq_lens: List[int] = []
     for i in range(batch_size):
         expected: Optional[List[int]] = None
-        sampling_type = random.randint(0, 3)
+        sampling_type = random.randint(0, 2)
         if sampling_type == 0:
             sampling_params = SamplingParams(temperature=0)
             expected = [int(torch.argmax(fake_logits[i], dim=-1).item())]
@@ -498,10 +478,7 @@ def test_sampler_mixed(seed: int, device: str):
                 for idx in range(n):
                     fake_logits[i, i + idx] = 1e2
                 expected = list(range(i, i + n))
-        else:
-            sampling_params = SamplingParams(temperature=0,
-                                             use_beam_search=True,
-                                             best_of=2)
+
         expected_tokens.append(expected)
         seq_group_metadata_list.append(
             SequenceGroupMetadata(
@@ -530,9 +507,6 @@ def test_sampler_mixed(seed: int, device: str):
                 zip(sampler_output, seq_group_metadata_list)):
             assert metadata.sampling_params is not None
 
-            if metadata.sampling_params.use_beam_search:
-                continue
-
             if (metadata.sampling_params.seed is not None
                     and expected_tokens[i] is None):
                 # Record seeded random result to compare with results of