wehub-resource-sync
320 lines
9.2 KiB
Python
320 lines
9.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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WARNING: This test runs in both single-node (4 GPUs) and multi-node
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(2 node with 2 GPUs each) modes. If the test only uses 2 GPUs, it is
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important to set the distributed backend to "mp" to avoid Ray scheduling
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all workers in a node other than the head node, which can cause the test
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to fail.
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"""
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import json
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import os
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from dataclasses import dataclass
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from typing import Literal, NamedTuple
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import lm_eval
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import pytest
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import torch
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from tests.utils import RemoteOpenAIServer, create_new_process_for_each_test
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from vllm.config.model import RunnerOption
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from vllm.logger import init_logger
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from vllm.platforms import current_platform
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from ..models.registry import HF_EXAMPLE_MODELS
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logger = init_logger("test_context_parallel")
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VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
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CP_TEST_MODELS = [
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# TODO support other models
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# [LANGUAGE GENERATION]
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"deepseek-ai/DeepSeek-V2-Lite-Chat",
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"Qwen/Qwen2.5-1.5B-Instruct",
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"Qwen/Qwen3.5-0.8B", # hybrid attention model
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]
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# GSM8K eval configuration
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NUM_SHOTS = 5 # Few-shot examples
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TASK = "gsm8k"
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FILTER = "exact_match,strict-match"
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NUM_CONCURRENT = 128
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# tp accuracy with 2% buffer
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MIN_ACCURACY = {
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# .buildkite/lm-eval-harness/configs/DeepSeek-V2-Lite-Chat.yaml
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"deepseek-ai/DeepSeek-V2-Lite-Chat": 0.64,
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# .buildkite/lm-eval-harness/configs/Qwen2.5-1.5B-Instruct.yaml
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"Qwen/Qwen2.5-1.5B-Instruct": 0.52,
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"Qwen/Qwen3.5-0.8B": 0.33,
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}
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class ParallelSetup(NamedTuple):
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tp_size: int
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pp_size: int
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dcp_size: int
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cp_kv_cache_interleave_size: int
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eager_mode: bool
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chunked_prefill: bool
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class CPTestOptions(NamedTuple):
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multi_node_only: bool
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attn_backend: str | None = None
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@dataclass
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class CPTestSettings:
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parallel_setups: list[ParallelSetup]
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distributed_backends: list[str]
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runner: RunnerOption
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test_options: CPTestOptions
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@staticmethod
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def detailed(
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*,
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tp_base: int = 4,
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pp_base: int = 1,
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dcp_multipliers: list[float] | None = None,
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cp_kv_cache_interleave_size: int = 1,
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multi_node_only: bool = False,
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runner: RunnerOption = "auto",
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attn_backend: str | None = None,
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):
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parallel_setups = []
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if dcp_multipliers is None:
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dcp_multipliers = [
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0.5,
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]
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for eager_mode_val in [False]:
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for pp_multiplier in [1]:
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for dcp_multiplier in dcp_multipliers:
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for chunked_prefill_val in [True]:
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parallel_setups.append(
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ParallelSetup(
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tp_size=tp_base,
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pp_size=pp_multiplier * pp_base,
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dcp_size=int(dcp_multiplier * tp_base),
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cp_kv_cache_interleave_size=cp_kv_cache_interleave_size,
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eager_mode=eager_mode_val,
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chunked_prefill=chunked_prefill_val,
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)
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)
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return CPTestSettings(
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parallel_setups=parallel_setups,
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distributed_backends=["mp"],
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runner=runner,
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test_options=CPTestOptions(
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multi_node_only=multi_node_only,
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attn_backend=attn_backend,
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),
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)
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def iter_params(self, model_id: str):
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opts = self.test_options
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for parallel_setup in self.parallel_setups:
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for backend in self.distributed_backends:
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yield (
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model_id,
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parallel_setup,
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backend,
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self.runner,
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opts,
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)
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if current_platform.is_rocm():
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CP_TEXT_GENERATION_MODELS = {
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"deepseek-ai/DeepSeek-V2-Lite-Chat": [
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CPTestSettings.detailed(dcp_multipliers=[1]),
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],
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"Qwen/Qwen2.5-1.5B-Instruct": [
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CPTestSettings.detailed(dcp_multipliers=[1]),
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],
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}
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else:
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CP_TEXT_GENERATION_MODELS = {
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"deepseek-ai/DeepSeek-V2-Lite-Chat": [
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CPTestSettings.detailed(dcp_multipliers=[1]),
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CPTestSettings.detailed(
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dcp_multipliers=[0.5],
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cp_kv_cache_interleave_size=64,
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attn_backend="FLASHMLA",
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),
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],
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"Qwen/Qwen2.5-1.5B-Instruct": [
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CPTestSettings.detailed(
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cp_kv_cache_interleave_size=16, attn_backend="FLASH_ATTN"
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),
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CPTestSettings.detailed(
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cp_kv_cache_interleave_size=16, attn_backend="FLASHINFER"
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),
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],
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"Qwen/Qwen3.5-0.8B": [
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CPTestSettings.detailed(
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cp_kv_cache_interleave_size=16,
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attn_backend="FLASH_ATTN",
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),
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],
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}
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def _test_cp_gsm8k(
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model_id: str,
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parallel_setup: ParallelSetup,
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distributed_backend: str,
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runner: RunnerOption,
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test_options: CPTestOptions,
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num_gpus_available: int,
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*,
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method: Literal["generate"],
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is_multimodal: bool,
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):
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(
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tp_size,
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pp_size,
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dcp_size,
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cp_kv_cache_interleave_size,
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eager_mode,
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chunked_prefill,
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) = parallel_setup
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multi_node_only, attn_backend = test_options
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model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
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model_info.check_transformers_version(on_fail="skip")
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trust_remote_code = model_info.trust_remote_code
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tokenizer_mode = model_info.tokenizer_mode
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hf_overrides = model_info.hf_overrides
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model_info.check_available_online(on_fail="skip")
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if num_gpus_available < tp_size * pp_size:
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pytest.skip(f"Need at least {tp_size} x {pp_size} GPUs")
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if VLLM_MULTI_NODE and distributed_backend == "mp":
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pytest.skip(
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"Skipping multi-node pipeline parallel test for "
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"multiprocessing distributed backend"
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)
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if multi_node_only and not VLLM_MULTI_NODE:
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pytest.skip("Not in multi-node setting")
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server_args = [
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# use half precision for speed and memory savings in CI environment
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"--dtype",
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"bfloat16",
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"--max-model-len",
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"4096",
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"--max-num-seqs",
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"64",
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]
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if chunked_prefill:
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server_args.append("--enable-chunked-prefill")
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if eager_mode:
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server_args.append("--enforce-eager")
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if runner != "auto":
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server_args.extend(["--runner", runner])
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if trust_remote_code:
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server_args.append("--trust-remote-code")
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if tokenizer_mode:
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server_args.extend(["--tokenizer-mode", tokenizer_mode])
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if hf_overrides:
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server_args.extend(["--hf-overrides", json.dumps(hf_overrides)])
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server_args.extend(
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[
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"--tensor-parallel-size",
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str(tp_size),
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"--pipeline-parallel-size",
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str(pp_size),
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"--decode-context-parallel-size",
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str(dcp_size),
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"--dcp-kv-cache-interleave-size",
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str(cp_kv_cache_interleave_size),
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"--distributed-executor-backend",
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distributed_backend,
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]
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)
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if attn_backend:
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server_args.append(f"--attention-backend={attn_backend}")
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with RemoteOpenAIServer(
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model_id,
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server_args,
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max_wait_seconds=720,
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) as remote_server:
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url = f"{remote_server.url_for('v1')}/completions"
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model_args = (
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f"model={model_id},"
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f"base_url={url},"
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f"num_concurrent={NUM_CONCURRENT},tokenized_requests=False"
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)
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results = lm_eval.simple_evaluate(
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model="local-completions",
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model_args=model_args,
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tasks=TASK,
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num_fewshot=NUM_SHOTS,
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)
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# Validate accuracy is reasonable
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accuracy = results["results"][TASK][FILTER]
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min_accuracy = MIN_ACCURACY[model_id]
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assert accuracy >= min_accuracy, (
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f"TP+DCP accuracy too low: {accuracy:.3f} < {min_accuracy:.3f}"
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)
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@pytest.mark.parametrize(
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(
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"model_id",
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"parallel_setup",
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"distributed_backend",
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"runner",
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"test_options",
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),
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[
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params
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for model_id, settings in CP_TEXT_GENERATION_MODELS.items()
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for setting in settings
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for params in setting.iter_params(model_id)
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if model_id in CP_TEST_MODELS
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],
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)
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@create_new_process_for_each_test()
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def test_cp_generation(
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model_id: str,
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parallel_setup: ParallelSetup,
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distributed_backend: str,
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runner: RunnerOption,
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test_options: CPTestOptions,
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num_gpus_available,
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):
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if (
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model_id == "deepseek-ai/DeepSeek-V2-Lite-Chat"
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and torch.cuda.get_device_capability() < (9, 0)
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):
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pytest.skip(reason="MLA+DCP requires compute capability of 9.0 or higher")
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if (
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model_id == "Qwen/Qwen2.5-1.5B-Instruct"
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and torch.cuda.get_device_capability() != (9, 0)
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):
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pytest.skip(reason="GQA+DCP currently requires compute capability of 9.0")
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_test_cp_gsm8k(
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model_id,
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parallel_setup,
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distributed_backend,
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runner,
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test_options,
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num_gpus_available,
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method="generate",
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is_multimodal=False,
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)
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