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| 1 | +# SPDX-License-Identifier: Apache-2.0 |
| 2 | +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project |
| 3 | +""" |
| 4 | +HTTP-based batch invariance test: send requests to a running |
| 5 | +vLLM server and compare BS=1 vs BS=N results (tokens and per-step logprobs). |
| 6 | +
|
| 7 | +Environment variables: |
| 8 | + - VLLM_TEST_MODEL: served model name (e.g., Qwen/Qwen3-1.7B / DeepSeek-R1) |
| 9 | + - VLLM_TP_SIZE: tensor parallelism size (e.g., 4) |
| 10 | +
|
| 11 | +""" |
| 12 | + |
| 13 | +import os |
| 14 | +import random |
| 15 | +import sys |
| 16 | +from typing import Any |
| 17 | + |
| 18 | +import openai |
| 19 | +from utils import _random_prompt, skip_unsupported |
| 20 | + |
| 21 | +from tests.utils import RemoteOpenAIServer |
| 22 | + |
| 23 | + |
| 24 | +def _request_completion( |
| 25 | + client: openai.OpenAI, |
| 26 | + model: str, |
| 27 | + prompt: Any, |
| 28 | + sp: dict[str, Any], |
| 29 | + max_retries: int = 3, |
| 30 | + retry_backoff: float = 0.5, |
| 31 | +) -> dict[str, Any] | None: |
| 32 | + payload: dict[str, Any] = {"model": model, "prompt": prompt} |
| 33 | + payload.update(sp) |
| 34 | + |
| 35 | + for attempt in range(max_retries + 1): |
| 36 | + try: |
| 37 | + completion = client.completions.create(**payload) |
| 38 | + # Convert to plain dict so downstream logic can keep using |
| 39 | + # dict-style access just like with raw HTTP JSON. |
| 40 | + return completion.model_dump() |
| 41 | + except Exception as e: # pragma: no cover |
| 42 | + if attempt < max_retries: |
| 43 | + import time as _t |
| 44 | + |
| 45 | + _t.sleep(retry_backoff * (2**attempt)) |
| 46 | + continue |
| 47 | + sys.stderr.write(f"Error: {e}\n") |
| 48 | + return None |
| 49 | + return None |
| 50 | + |
| 51 | + |
| 52 | +def _extract_tokens_and_logprobs( |
| 53 | + choice: dict[str, Any], |
| 54 | +) -> tuple[list[Any], list[float] | None]: |
| 55 | + tokens: list[Any] = [] |
| 56 | + token_logprobs: list[float] | None = None |
| 57 | + lp = choice.get("logprobs") |
| 58 | + if lp and isinstance(lp, dict): |
| 59 | + tokens = lp.get("token_ids") or lp.get("tokens") or [] |
| 60 | + token_logprobs = lp.get("token_logprobs", None) |
| 61 | + return tokens, token_logprobs |
| 62 | + |
| 63 | + |
| 64 | +def _compare_bs1_vs_bsn_single_process( |
| 65 | + prompts: list[str], |
| 66 | + sp_kwargs: dict[str, Any], |
| 67 | + client: openai.OpenAI, |
| 68 | + model_name: str, |
| 69 | +) -> None: |
| 70 | + # BS=1 |
| 71 | + bs1_tokens_per_prompt: list[list[Any]] = [] |
| 72 | + bs1_logprobs_per_prompt: list[list[float] | None] = [] |
| 73 | + for p in prompts: |
| 74 | + resp = _request_completion(client, model_name, p, sp_kwargs) |
| 75 | + if resp is None or not resp.get("choices"): |
| 76 | + raise AssertionError("BS=1 empty/failed response") |
| 77 | + choice = resp["choices"][0] |
| 78 | + toks, lps = _extract_tokens_and_logprobs(choice) |
| 79 | + if lps is None: |
| 80 | + raise AssertionError( |
| 81 | + "logprobs not returned; ensure server supports 'logprobs'" |
| 82 | + ) |
| 83 | + bs1_tokens_per_prompt.append(list(toks)) |
| 84 | + bs1_logprobs_per_prompt.append(list(lps)) |
| 85 | + |
| 86 | + # BS=N |
| 87 | + bsN_tokens_per_prompt: list[list[Any]] = [None] * len(prompts) # type: ignore[list-item] |
| 88 | + bsN_logprobs_per_prompt: list[list[float] | None] = [None] * len(prompts) |
| 89 | + resp = _request_completion(client, model_name, prompts, sp_kwargs) |
| 90 | + if resp is None or not resp.get("choices"): |
| 91 | + raise AssertionError("BS=N empty/failed batched response") |
| 92 | + choices = resp.get("choices", []) |
| 93 | + if len(choices) != len(prompts): |
| 94 | + raise AssertionError( |
| 95 | + f"BS=N choices length {len(choices)} != num prompts {len(prompts)}" |
| 96 | + ) |
| 97 | + for idx, choice in enumerate(choices): |
| 98 | + toks, lps = _extract_tokens_and_logprobs(choice) |
| 99 | + if lps is None: |
| 100 | + raise AssertionError(f"BS=N missing logprobs for prompt {idx}") |
| 101 | + bsN_tokens_per_prompt[idx] = list(toks) |
| 102 | + bsN_logprobs_per_prompt[idx] = list(lps) |
| 103 | + |
| 104 | + # compare |
| 105 | + for i, (tokens_bs1, tokens_bsN, logprobs_bs1, logprobs_bsN) in enumerate( |
| 106 | + zip( |
| 107 | + bs1_tokens_per_prompt, |
| 108 | + bsN_tokens_per_prompt, |
| 109 | + bs1_logprobs_per_prompt, |
| 110 | + bsN_logprobs_per_prompt, |
| 111 | + ) |
| 112 | + ): |
| 113 | + if tokens_bs1 != tokens_bsN: |
| 114 | + raise AssertionError( |
| 115 | + f"Prompt {i} (sampling): Different tokens sampled. " |
| 116 | + f"BS=1 tokens: {tokens_bs1} BS=N tokens: {tokens_bsN}" |
| 117 | + ) |
| 118 | + if logprobs_bs1 is None or logprobs_bsN is None: |
| 119 | + raise AssertionError(f"Prompt {i}: Missing logprobs in one of the runs") |
| 120 | + if len(logprobs_bs1) != len(logprobs_bsN): |
| 121 | + raise AssertionError( |
| 122 | + f"Prompt {i}: Different number of steps: " |
| 123 | + f"{len(logprobs_bs1)} (BS=1) vs {len(logprobs_bsN)} (BS=N)." |
| 124 | + ) |
| 125 | + for t, (a, b) in enumerate(zip(logprobs_bs1, logprobs_bsN)): |
| 126 | + if a != b: |
| 127 | + diff = abs(a - b) |
| 128 | + raise AssertionError( |
| 129 | + f"Prompt {i} Step {t}: Bitwise mismatch " |
| 130 | + f"(abs diff={diff:.6e}). " |
| 131 | + f"BS=1 tokens: {tokens_bs1} BS=N tokens: {tokens_bsN}" |
| 132 | + ) |
| 133 | + |
| 134 | + |
| 135 | +@skip_unsupported |
| 136 | +def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(): |
| 137 | + random.seed(int(os.getenv("VLLM_TEST_SEED", "12345"))) |
| 138 | + model_name = os.getenv("VLLM_TEST_MODEL", "Qwen/Qwen3-1.7B") |
| 139 | + prompts_all = [_random_prompt(10, 50) for _ in range(32)] |
| 140 | + |
| 141 | + sp_kwargs: dict[str, Any] = { |
| 142 | + "temperature": 0.6, |
| 143 | + "top_p": 1.0, |
| 144 | + "max_tokens": 8, |
| 145 | + "seed": 42, |
| 146 | + "logprobs": 5, |
| 147 | + } |
| 148 | + |
| 149 | + tp_size = os.getenv("VLLM_TP_SIZE", "1") |
| 150 | + server_args: list[str] = [] |
| 151 | + if tp_size: |
| 152 | + server_args += ["-tp", tp_size] |
| 153 | + |
| 154 | + with RemoteOpenAIServer(model_name, server_args) as server: |
| 155 | + client = server.get_client() |
| 156 | + _compare_bs1_vs_bsn_single_process( |
| 157 | + prompts=prompts_all, |
| 158 | + sp_kwargs=sp_kwargs, |
| 159 | + client=client, |
| 160 | + model_name=model_name, |
| 161 | + ) |
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