Bamba VLLM Draft

tests/models/decoder_only/language/test_bamba.py

@@ -0,0 +1,329 @@
+"""Compare the outputs of HF and vLLM when using greedy sampling for Mamba.
+
+This actually is really indentical to test_mamba, so maybe we can reuse
+
+Run `pytest tests/models/decoder_only/language/test_bamba.py`.
+"""
+import pytest
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from vllm.sampling_params import SamplingParams
+from vllm.worker.model_runner import _get_graph_batch_size
+
+from ...utils import check_outputs_equal
+
+# will be ch
+MODELS = ["/workspace/bamba-ckpt",]
+
+
+# Use lower-level interfaces to create this greedy generator, as mamba will
+# choke on the model_kwarg 'attention_mask' if hf_model.generate_greedy is used.
+def generate_greedy(model_name, example_prompts, max_tokens):
+    # Create a text generation pipeline
+    # - in the original test_mamba.py they do not put the model to cuda 
+    #   maybe this affects the test.
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda')
+
+    # Generate texts from the prompts
+    outputs = []
+    for prompt in example_prompts:
+        # Tokenize the input prompt with truncation
+        inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
+        input_ids = inputs["input_ids"].to(model.device)
+
+        # Generate text using the model's generate method directly
+        generated_ids = model.generate(input_ids, max_new_tokens=max_tokens)
+        generated_text = tokenizer.decode(generated_ids[0],
+                                          skip_special_tokens=True)
+
+        outputs.append((generated_ids[0].tolist(), generated_text))
+
+    return outputs
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [96])
+def test_models(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    hf_outputs = generate_greedy(model, example_prompts, max_tokens)
+
+    with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+        # This test is for verifying whether the model's extra_repr
+        # can be printed correctly.
+        print(vllm_model.model.llm_engine.model_executor.driver_worker.
+              model_runner.model)
+
+    for i in range(len(example_prompts)):
+        hf_output_ids, hf_output_str = hf_outputs[i]
+        vllm_output_ids, vllm_output_str = vllm_outputs[i]
+        assert hf_output_str == vllm_output_str, (
+            f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
+        assert hf_output_ids == vllm_output_ids, (
+            f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [96])
+def test_batching(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    # To pass the small model tests, we need full precision.
+    for_loop_outputs = []
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        for prompt in example_prompts:
+            for_loop_outputs.append(
+                vllm_model.generate_greedy([prompt], max_tokens)[0])
+
+        batched_outputs = vllm_model.generate_greedy(example_prompts,
+                                                     max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=for_loop_outputs,
+        outputs_1_lst=batched_outputs,
+        name_0="for_loop_vllm",
+        name_1="batched_vllm",
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [10])
+def test_chunked_prefill_with_parallel_sampling(vllm_runner, example_prompts,
+                                                model: str, dtype: str,
+                                                max_tokens: int) -> None:
+    # Tests chunked prefill in conjunction with n>1. In this case, prefill is
+    # populated with decoding tokens and we test that it doesn't fail.
+    # This test might fail if cache is not allocated correctly for n > 1
+    # decoding steps inside a chunked prefill forward pass (where we have both
+    # prefill and decode together )
+    sampling_params = SamplingParams(n=3,
+                                     temperature=1,
+                                     seed=0,
+                                     max_tokens=max_tokens)
+    with vllm_runner(
+            model,
+            dtype=dtype,
+            enable_chunked_prefill=True,
+            max_num_batched_tokens=30,
+            max_num_seqs=10  # forces prefill chunks with decoding
+    ) as vllm_model:
+        vllm_outputs = vllm_model.generate(example_prompts, sampling_params)
+
+    for i, seqs in enumerate(vllm_outputs):
+        for j, (_, x) in enumerate(seqs):
+            print (f"Test {i}, Seq {j}: {x}")
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16])
+def test_chunked_prefill(vllm_runner, example_prompts, model: str, dtype: str,
+                         max_tokens: int,
+                         chunked_prefill_token_size: int) -> None:
+    """
+    Checks exact match decode between huggingface model and vllm runner with
+    chunked prefill.
+    """
+    max_num_seqs = chunked_prefill_token_size
+    max_num_batched_tokens = chunked_prefill_token_size
+
+    non_chunked = generate_greedy(model, example_prompts, max_tokens)
+
+    with vllm_runner(model,
+                     dtype=dtype,
+                     enable_chunked_prefill=True,
+                     max_num_batched_tokens=max_num_batched_tokens,
+                     max_num_seqs=max_num_seqs) as vllm_model:
+        chunked = vllm_model.generate_greedy(example_prompts,
+                                             max_tokens=max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=chunked,
+        outputs_1_lst=non_chunked,
+        name_0="chunked",
+        name_1="non_chunked",
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [15])
+def test_parallel_sampling(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        for_loop_outputs = []
+        for _ in range(10):
+            for_loop_outputs.append(
+                # using example_prompts index 1 instead of 0 since with 0 the
+                # logprobs get really close and the test doesn't pass
+                vllm_model.generate_greedy([example_prompts[1]], max_tokens)
+                [0])
+        sampling_params = SamplingParams(n=10,
+                                         temperature=0.001,
+                                         seed=0,
+                                         max_tokens=max_tokens)
+        n_lt_1_outputs = vllm_model.generate([example_prompts[1]],
+                                             sampling_params)
+    token_ids, texts = n_lt_1_outputs[0]
+    n_lt_1_outputs = [(token_id, text)
+                      for token_id, text in zip(token_ids, texts)]
+
+    check_outputs_equal(
+        outputs_0_lst=n_lt_1_outputs,
+        outputs_1_lst=for_loop_outputs,
+        name_0="vllm_n_lt_1_outputs",
+        name_1="vllm",
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [20])
+def test_mamba_cache_cg_padding(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    # This test is for verifying that mamba cache is padded to CG captured
+    # batch size. If it's not, a torch RuntimeError will be raised because
+    # tensor dimensions aren't compatible
+    while len(example_prompts) == _get_graph_batch_size(len(example_prompts)):
+        example_prompts.append(example_prompts[0])
+
+    try:
+        with vllm_runner(model, dtype=dtype) as vllm_model:
+            vllm_model.generate_greedy(example_prompts, max_tokens)
+    except RuntimeError:
+        pytest.fail(
+            "Couldn't run batch size which is not equal to a Cuda Graph "
+            "captured batch size. "
+            "Could be related to mamba cache not padded correctly")
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [20])
+def test_models_preemption_recompute(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    # Tests that outputs are identical with and w/o preemtions (recompute)
+    assert dtype == "float"
+
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        vllm_model.model.llm_engine.scheduler[
+            0].ENABLE_ARTIFICIAL_PREEMPT = True
+        preempt_vllm_outputs = vllm_model.generate_greedy(
+            example_prompts, max_tokens)
+
+        vllm_model.model.llm_engine.scheduler[
+            0].ENABLE_ARTIFICIAL_PREEMPT = False
+        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=preempt_vllm_outputs,
+        outputs_1_lst=vllm_outputs,
+        name_0="vllm_preepmtions",
+        name_1="vllm",
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+def test_fail_upon_inc_requests_and_finished_requests_lt_available_blocks(
+    vllm_runner,
+    model: str,
+    dtype: str,
+    example_prompts,
+) -> None:
+    # This test is for verifying that the Mamba inner state management doesn't
+    # collapse in case where the number of incoming requests and
+    # finished_requests_ids is larger than the maximum Mamba block capacity.
+    # This could generally happen due to the fact that Mamba does support
+    # statelessness mechanism where it can cleanup new incoming requests in
+    # a single step.
+    try:
+        with vllm_runner(model, dtype=dtype, max_num_seqs=10) as vllm_model:
+            vllm_model.generate_greedy([example_prompts[0]] * 100, 10)
+    except ValueError:
+        pytest.fail("Mamba inner state wasn't cleaned up properly between"
+                    "steps finished requests registered unnecessarily ")
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+def test_state_cleanup(
+    vllm_runner,
+    model: str,
+    dtype: str,
+    example_prompts,
+) -> None:
+    # This test is for verifying that the Mamba state is cleaned up between
+    # steps, If its not cleaned, an error would be expected.
+    try:
+        with vllm_runner(model, dtype=dtype) as vllm_model:
+            for _ in range(10):
+                vllm_model.generate_greedy([example_prompts[0]] * 100, 1)
+    except ValueError:
+        pytest.fail("Mamba inner state wasn't cleaned up between states, "
+                    "could be related to finished_requests_ids")
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+def test_multistep(
+    vllm_runner,
+    model: str,
+    dtype: str,
+    example_prompts,
+) -> None:
+    with vllm_runner(model, num_scheduler_steps=8,
+                     max_num_seqs=2) as vllm_model:
+        vllm_model.generate_greedy([example_prompts[0]] * 10, 1)
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [64])
+def test_multistep_correctness(vllm_runner, model: str, dtype: str,
+                               max_tokens: int, example_prompts) -> None:
+    with vllm_runner(model, num_scheduler_steps=8,
+                     max_num_seqs=2) as vllm_model:
+        vllm_outputs_multistep = vllm_model.generate_greedy(
+            example_prompts, max_tokens)
+
+    with vllm_runner(model, num_scheduler_steps=1,
+                     max_num_seqs=2) as vllm_model:
+        vllm_outputs_single_step = vllm_model.generate_greedy(
+            example_prompts, max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_outputs_multistep,
+        outputs_1_lst=vllm_outputs_single_step,
+        name_0="vllm_outputs_multistep",
+        name_1="vllm_outputs_single_step",
+    )