pytests

Author: cychiuak

tests/e2e/test_async_scheduler.py

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+from __future__ import annotations
+
+import random
+import string
+import time
+
+import pytest
+from vllm import LLM, SamplingParams
+
+@pytest.fixture
+def sampling_config():
+    return SamplingParams(temperature=0,
+                          max_tokens=120,
+                          ignore_eos=True,
+                          repetition_penalty=1,
+                          frequency_penalty=0,
+                          presence_penalty=0,
+                          min_p=0,
+                          logprobs=None)
+@pytest.fixture
+def model_name():
+    return "Qwen/Qwen2.5-1.5B-Instruct"
+
+def get_performance_test_prompts():
+    """
+    Generates a list of prompts with a specific word count,
+
+    Returns:
+        A list of strings with number of prompts = num_prompts and
+        The total number of words for each prompt = input_len_words.
+    """
+    num_prompts=500
+    input_len_words=120
+    prompts = []
+
+    # For example w = 's'
+    # The generated prompt will be Keep repeating: s s s ...
+    num_repetitions = input_len_words
+    prefix = "Keep repeating: "
+
+    for _ in range(num_prompts):
+        # 1. Pick a random lowercase letter
+        w = random.choice(list(string.ascii_lowercase))
+
+        # 2. Create the string of repeated words
+        #    This will have (num_repetitions) words
+        repeating_part = " ".join([w] * num_repetitions)
+
+        # 3. Combine with the prefix (if any)
+        print(f"{prefix}{repeating_part}")
+        prompts.append(f"{prefix}{repeating_part}")
+
+    return prompts
+
+def get_correctness_test_prompts():
+    """
+    Returns a static list of prompts designed to test a model's
+    ability to follow complex instructions and ensure correctness.
+
+    Returns:
+        A list of strings, where each string is a test prompt.
+    """
+
+    prompts = [
+        (
+            "Write a short story about a librarian who discovers a book that "
+            "writes itself. Write it in 1900s English style. Make sure there "
+            "are no mistakes. This is my homework and I want perfection."
+        ),
+        (
+            "Compose a poem about the sound of a city at night. Write it in "
+            "Shakespear style. Make sure there are no mistakes. This is my "
+            "homework and I want perfection."
+        ),
+        (
+            "Write a dialogue between a time traveler and a medieval blacksmith "
+            "who is skeptical of their claims. Make sure there are no mistakes."
+        ),
+
+        (
+            "Explain the process of photosynthesis as if to a 5th grader, "
+            "but without losing any scientific accuracy. Every step must be "
+            "correct and in the right order. I will be checking this against a textbook."
+        ),
+        (
+            "Write a Python function that finds the median of a list of numbers. "
+            "It must correctly handle both even and odd-sized lists, "
+            "as well as unsorted lists. Provide a perfect, bug-free "
+            "implementation. I will be running unit tests on it."
+        ),
+        (
+            "List the first 10 presidents of the United States. Format the "
+            "output as a JSON array, where each object has two keys: 'name' "
+            "and 'term_years'. The JSON must be perfectly valid, and all "
+            "names and dates must be 100% accurate. This is for a production system."
+        )
+    ]
+
+    return prompts
+
+def _test_performance_helper(
+    monkeypatch: pytest.MonkeyPatch,
+    sampling_config: SamplingParams,
+    model_name: str,
+    min_speedup: float
+):
+    '''
+    Helper function to test async scheduler decoding performance.
+    Compares timing between reference LLM and async LLM using Qwen2.5-1.5B.
+    '''
+
+    with monkeypatch.context():
+        # Use a smaller set of prompts for performance testing
+        test_prompts = get_performance_test_prompts() # num_prompts=100, input_len=120
+
+        # Test reference LLM timing
+        ref_llm = LLM(model=model_name,
+                      max_model_len=800,
+                      max_num_seqs=24,
+                      max_num_batched_tokens=512,
+                      enable_prefix_caching=False)
+
+        start_time = time.time()
+        _ = ref_llm.generate(test_prompts, sampling_config)
+        ref_time = time.time() - start_time
+
+        del ref_llm
+        # Waiting for TPUs to be released
+        time.sleep(10)
+
+        # # Test async LLM timing with max_num_seqs=256
+        async_llm = LLM(model=model_name,
+                       max_model_len=800,
+                       max_num_seqs=24,
+                       max_num_batched_tokens=512,
+                       enable_prefix_caching=False,
+                       async_scheduling=1)
+
+        start_time = time.time()
+        _ = async_llm.generate(test_prompts, sampling_config)
+        async_time = time.time() - start_time
+
+        del async_llm
+        # # Waiting for TPUs to be released
+        time.sleep(10)
+
+        speedup = ref_time / async_time
+        print(f"Reference LLM time: {ref_time:.2f}s")
+        print(f"Async LLM time: {async_time:.2f}s")
+        print(f"Speedup: {speedup:.2f}x")
+
+        assert speedup >= min_speedup, f"Expected at least {min_speedup}x speedup for async scheduler, got {speedup:.2f}x"
+
+def test_performance(
+    monkeypatch: pytest.MonkeyPatch,
+    sampling_config: SamplingParams,
+    model_name: str,
+):
+    '''
+    Test that async scheduler decoding provides significant performance improvement.
+    Compares timing between reference LLM and async LLM using Qwen2.5-1.5B.
+    Expects async_llm to be at least 1.3x faster than ref_llm.
+    '''
+    min_speed_up = 1.3
+    _test_performance_helper(
+        monkeypatch, sampling_config, model_name, min_speed_up)
+
+
+def _test_correctness_helper(
+    monkeypatch: pytest.MonkeyPatch,
+    sampling_config: SamplingParams,
+    model_name: str,
+):
+    '''
+    Helper function to test async scheduler correctness.
+    Compare the outputs of a original LLM and a async LLM
+    should be the same when using async scheduler decoding.
+
+    Known Edge Case (KV Cache Swapping):
+    Under this case, though the temperature is set to 0,
+    the output is still slightly different everytime.
+    This is an expected behaviour as the normal scheduler also
+    behaves the same and hence, it is difficult to design a test
+    for such scenario.
+    '''
+    with monkeypatch.context():
+        test_prompts = get_correctness_test_prompts()
+
+        ref_llm = LLM(model=model_name,
+                      max_model_len=1024,
+                      max_num_seqs=100)
+        ref_outputs = ref_llm.generate(test_prompts, sampling_config)
+
+        del ref_llm
+
+        # Waiting for TPUs to be released.
+        time.sleep(10)
+
+        async_llm = LLM(model=model_name,
+                       max_model_len=1024,
+                       max_num_seqs=100,
+                       async_scheduling=1)
+        async_outputs = async_llm.generate(test_prompts, sampling_config)
+
+        matches = 0
+        misses = 0
+        for ref_output, async_output in zip(ref_outputs, async_outputs):
+            if ref_output.outputs[0].text == async_output.outputs[0].text:
+                print(f"ref_output: {ref_output.outputs[0].text}")
+                print(f"async_output: {async_output.outputs[0].text}")
+                matches += 1
+            else:
+                misses += 1
+                print(f"ref_output: {ref_output.outputs[0].text}")
+                print(f"async_output: {async_output.outputs[0].text}")
+
+        assert misses == 0
+        del async_outputs
+
+        # Waiting for TPUs to be released.
+        time.sleep(10)
+def test_correctness(
+    monkeypatch: pytest.MonkeyPatch,
+    sampling_config: SamplingParams,
+    model_name: str,
+):
+    '''
+    Compare the outputs of a original LLM and a async LLM
+    should be the same when using async scheduler.
+    '''
+
+    _test_correctness_helper(
+        monkeypatch, sampling_config, model_name)
+