remove test_skip_speculation from dist tests

tests/spec_decode/e2e/test_integration_dist_tp2.py

@@ -109,69 +109,3 @@ def test_draft_model_tp_lt_target_model_tp2(test_llm_generator,
                                          batch_size,
                                          max_output_len=32,
                                          force_output_len=True)
-
-
-@pytest.mark.skipif(torch.cuda.device_count() < 2,
-                    reason="Need at least 2 GPUs to run the test.")
-@pytest.mark.parametrize(
-    "common_llm_kwargs",
-    [{
-        "model": "JackFram/llama-160m",
-
-        # Skip cuda graph recording for fast test.
-        "enforce_eager": True,
-
-        # Required for spec decode.
-        "use_v2_block_manager": True,
-        "tensor_parallel_size": 2,
-
-        # Use AsyncLLM engine, so that the engine runs in its own process.
-        # Otherwise, since vLLM does not follow true SPMD, the test runner
-        # process will have both the engine and the rank0 worker. NCCL is not
-        # cleaned up properly, and its server host thread leaks, causing the
-        # second run of the test to fail with internal NCCL error.
-        "use_async": True,
-    }])
-@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
-@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
-@pytest.mark.parametrize("test_llm_kwargs", [
-    {
-        "speculative_model": "JackFram/llama-68m",
-        "num_speculative_tokens": 5,
-
-        # Artificially limit the draft model max model len; this forces vLLM
-        # to skip speculation once the sequences grow beyond 32-k tokens.
-        "speculative_max_model_len": 32,
-    },
-    {
-        "speculative_model": "JackFram/llama-68m",
-        "num_speculative_tokens": 5,
-
-        # Artificially limit the draft model max model len; this forces vLLM
-        # to skip speculation once the sequences grow beyond 32-k tokens.
-        "speculative_max_model_len": 32,
-        "speculative_draft_tensor_parallel_size": 1,
-    },
-])
-@pytest.mark.parametrize("batch_size", [8])
-@pytest.mark.parametrize(
-    "output_len",
-    [
-        # This must be a good bit larger than speculative_max_model_len so that
-        # we can test the case where all seqs are skipped, but still small to
-        # ensure fast test.
-        64,
-    ])
-@pytest.mark.parametrize("seed", [1])
-def test_skip_speculation(baseline_llm_generator, test_llm_generator,
-                          batch_size: int, output_len: int):
-    """Verify greedy equality when some (or all) sequences skip speculation.
-    We do this by setting the max model len of the draft model to an
-    artificially low value, such that when the sequences grow beyond it, they
-    are skipped in speculative decoding.
-    """
-    run_greedy_equality_correctness_test(baseline_llm_generator,
-                                         test_llm_generator,
-                                         batch_size,
-                                         max_output_len=output_len,
-                                         force_output_len=True)

tests/spec_decode/e2e/test_integration_dist_tp4.py

@@ -39,6 +39,7 @@
 ])
 @pytest.mark.parametrize("baseline_llm_kwargs", [{}])
 @pytest.mark.parametrize("test_llm_kwargs", [
+    #TODO(wooyeon): add spec_draft_dp=2 case
     {
         "speculative_draft_tensor_parallel_size": 1,
     },
@@ -55,69 +56,3 @@ def test_draft_model_tp_lt_target_model_tp4(test_llm_generator,
                                          batch_size,
                                          max_output_len=32,
                                          force_output_len=True)
-
-
-@pytest.mark.skipif(torch.cuda.device_count() < 4,
-                    reason="Need at least 4 GPUs to run the test.")
-@pytest.mark.parametrize(
-    "common_llm_kwargs",
-    [{
-        "model": "JackFram/llama-160m",
-
-        # Skip cuda graph recording for fast test.
-        "enforce_eager": True,
-
-        # Required for spec decode.
-        "use_v2_block_manager": True,
-        "tensor_parallel_size": 4,
-
-        # Use AsyncLLM engine, so that the engine runs in its own process.
-        # Otherwise, since vLLM does not follow true SPMD, the test runner
-        # process will have both the engine and the rank0 worker. NCCL is not
-        # cleaned up properly, and its server host thread leaks, causing the
-        # second run of the test to fail with internal NCCL error.
-        "use_async": True,
-    }])
-@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
-@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
-@pytest.mark.parametrize("test_llm_kwargs", [
-    {
-        "speculative_model": "JackFram/llama-68m",
-        "num_speculative_tokens": 5,
-
-        # Artificially limit the draft model max model len; this forces vLLM
-        # to skip speculation once the sequences grow beyond 32-k tokens.
-        "speculative_max_model_len": 32,
-    },
-    {
-        "speculative_model": "JackFram/llama-68m",
-        "num_speculative_tokens": 5,
-
-        # Artificially limit the draft model max model len; this forces vLLM
-        # to skip speculation once the sequences grow beyond 32-k tokens.
-        "speculative_max_model_len": 32,
-        "speculative_draft_tensor_parallel_size": 1,
-    },
-])
-@pytest.mark.parametrize("batch_size", [8])
-@pytest.mark.parametrize(
-    "output_len",
-    [
-        # This must be a good bit larger than speculative_max_model_len so that
-        # we can test the case where all seqs are skipped, but still small to
-        # ensure fast test.
-        64,
-    ])
-@pytest.mark.parametrize("seed", [1])
-def test_skip_speculation(baseline_llm_generator, test_llm_generator,
-                          batch_size: int, output_len: int):
-    """Verify greedy equality when some (or all) sequences skip speculation.
-    We do this by setting the max model len of the draft model to an
-    artificially low value, such that when the sequences grow beyond it, they
-    are skipped in speculative decoding.
-    """
-    run_greedy_equality_correctness_test(baseline_llm_generator,
-                                         test_llm_generator,
-                                         batch_size,
-                                         max_output_len=output_len,
-                                         force_output_len=True)