189 lines
6.8 KiB
Python
189 lines
6.8 KiB
Python
import argparse
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import glob
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from dataclasses import dataclass
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from sglang.test.test_utils import run_unittest_files
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@dataclass
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class TestFile:
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name: str
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estimated_time: float = 60
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suites = {
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"per-commit": [
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TestFile("models/lora/test_lora.py", 76),
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TestFile("models/lora/test_lora_backend.py", 420),
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TestFile("models/lora/test_multi_lora_backend.py", 144),
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TestFile("models/test_embedding_models.py", 119),
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TestFile("models/test_generation_models.py", 103),
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TestFile("models/test_grok_models.py", 60),
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TestFile("models/test_qwen_models.py", 82),
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TestFile("models/test_reward_models.py", 83),
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TestFile("models/test_gme_qwen_models.py", 45),
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TestFile("models/test_clip_models.py", 100),
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TestFile("test_abort.py", 51),
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TestFile("test_block_int8.py", 22),
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TestFile("test_chunked_prefill.py", 336),
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TestFile("test_eagle_infer.py", 447),
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TestFile("test_ebnf_constrained.py"),
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TestFile("test_fp8_kernel.py", 2),
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TestFile("test_embedding_openai_server.py", 36),
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TestFile("test_hidden_states.py", 55),
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TestFile("test_int8_kernel.py", 1),
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TestFile("test_input_embeddings.py", 38),
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TestFile("test_json_constrained.py", 98),
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TestFile("test_large_max_new_tokens.py", 41),
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TestFile("test_metrics.py", 32),
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TestFile("test_mla.py", 92),
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TestFile("test_mla_deepseek_v3.py", 221),
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TestFile("test_mla_int8_deepseek_v3.py", 421),
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TestFile("test_mla_flashinfer.py", 395),
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TestFile("test_mla_fp8.py", 93),
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TestFile("test_no_chunked_prefill.py", 126),
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TestFile("test_no_overlap_scheduler.py", 262),
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TestFile("test_openai_server.py", 124),
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TestFile("test_penalty.py", 41),
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TestFile("test_page_size.py", 60),
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TestFile("test_pytorch_sampling_backend.py", 66),
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TestFile("test_radix_attention.py", 167),
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TestFile("test_reasoning_content.py", 89),
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TestFile("test_regex_constrained.py", 64),
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TestFile("test_release_memory_occupation.py", 44),
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TestFile("test_request_length_validation.py", 31),
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TestFile("test_retract_decode.py", 54),
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TestFile("test_server_args.py", 1),
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TestFile("test_skip_tokenizer_init.py", 72),
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TestFile("test_srt_engine.py", 237),
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TestFile("test_srt_endpoint.py", 94),
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TestFile("test_torch_compile.py", 76),
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TestFile("test_torch_compile_moe.py", 85),
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TestFile("test_torch_native_attention_backend.py", 149),
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TestFile("test_torchao.py", 70),
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TestFile("test_triton_attention_kernels.py", 4),
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TestFile("test_triton_attention_backend.py", 134),
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TestFile("test_update_weights_from_disk.py", 114),
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TestFile("test_update_weights_from_tensor.py", 48),
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TestFile("test_vertex_endpoint.py", 31),
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TestFile("test_vision_chunked_prefill.py", 223),
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TestFile("test_vlm_accuracy.py", 60),
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TestFile("test_vision_openai_server.py", 344),
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TestFile("test_fim_completion.py", 120),
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TestFile("test_w8a8_quantization.py", 46),
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TestFile("test_eval_fp8_accuracy.py", 172),
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TestFile("test_create_kvindices.py", 2),
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TestFile("test_hicache.py", 60),
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TestFile("test_hicache_mla.py", 90),
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],
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"nightly": [
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TestFile("test_nightly_gsm8k_eval.py"),
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],
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"vllm_dependency_test": [
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TestFile("test_vllm_dependency.py"),
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TestFile("test_awq.py"),
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TestFile("test_gguf.py", 78),
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TestFile("test_gptqmodel_dynamic.py", 72),
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],
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}
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def auto_partition(files, rank, size):
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"""
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Partition files into size sublists with approximately equal sums of estimated times
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using stable sorting, and return the partition for the specified rank.
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Args:
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files (list): List of file objects with estimated_time attribute
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rank (int): Index of the partition to return (0 to size-1)
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size (int): Number of partitions
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Returns:
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list: List of file objects in the specified rank's partition
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"""
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weights = [f.estimated_time for f in files]
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if not weights or size <= 0 or size > len(weights):
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return []
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# Create list of (weight, original_index) tuples
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# Using negative index as secondary key to maintain original order for equal weights
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indexed_weights = [(w, -i) for i, w in enumerate(weights)]
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# Stable sort in descending order by weight
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# If weights are equal, larger (negative) index comes first (i.e., earlier original position)
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indexed_weights = sorted(indexed_weights, reverse=True)
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# Extract original indices (negate back to positive)
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indexed_weights = [(w, -i) for w, i in indexed_weights]
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# Initialize partitions and their sums
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partitions = [[] for _ in range(size)]
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sums = [0.0] * size
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# Greedy approach: assign each weight to partition with smallest current sum
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for weight, idx in indexed_weights:
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# Find partition with minimum sum
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min_sum_idx = sums.index(min(sums))
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partitions[min_sum_idx].append(idx)
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sums[min_sum_idx] += weight
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# Return the files corresponding to the indices in the specified rank's partition
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indices = partitions[rank]
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return [files[i] for i in indices]
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if __name__ == "__main__":
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument(
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"--timeout-per-file",
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type=int,
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default=1800,
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help="The time limit for running one file in seconds.",
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)
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arg_parser.add_argument(
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"--suite",
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type=str,
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default=list(suites.keys())[0],
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choices=list(suites.keys()) + ["all"],
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help="The suite to run",
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)
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arg_parser.add_argument(
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"--range-begin",
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type=int,
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default=0,
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help="The begin index of the range of the files to run.",
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)
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arg_parser.add_argument(
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"--range-end",
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type=int,
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default=None,
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help="The end index of the range of the files to run.",
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)
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arg_parser.add_argument(
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"--auto-partition-id",
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type=int,
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help="Use auto load balancing. The part id.",
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)
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arg_parser.add_argument(
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"--auto-partition-size",
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type=int,
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help="Use auto load balancing. The number of parts.",
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)
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args = arg_parser.parse_args()
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print(f"{args=}")
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if args.suite == "all":
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files = glob.glob("**/test_*.py", recursive=True)
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else:
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files = suites[args.suite]
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if args.auto_partition_size:
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files = auto_partition(files, args.auto_partition_id, args.auto_partition_size)
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else:
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files = files[args.range_begin : args.range_end]
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print("The running tests are ", [f.name for f in files])
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exit_code = run_unittest_files(files, args.timeout_per_file)
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exit(exit_code)
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