From c0d2c008b0d05302e08f2f9dd00f9aec21532254 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=A5=9A=E6=AF=93?= Date: Wed, 23 Sep 2026 10:11:41 +0800 Subject: [PATCH] fix: bound LLM inference memory for batch screening --- Dockerfile | 5 +- SERVER_DEPLOY.md | 59 + app/api.py | 8 +- app/optimize.py | 5 +- docker-compose-gpu.yml | 10 +- lnp_ml/modeling/layers/llm_prompt.py | 105 +- lnp_ml/modeling/predict.py | 2 +- pixi.toml | 1 + requirements.txt | 1 + server_gpu_test.sh | 108 + server_test_cases/README.md | 88 + server_test_cases/generate_mock_batch.py | 102 + server_test_cases/mixed_valid_invalid.json | 56 + server_test_cases/mock_batch_128_no_llm.json | 1286 +++++ server_test_cases/mock_batch_32_llm.json | 326 ++ server_test_cases/mock_batch_500_llm.json | 5006 +++++++++++++++++ server_test_cases/optimize_smoke.json | 31 + server_test_cases/run_cases.sh | 187 + server_test_cases/run_lnp_batch_stress.sh | 146 + server_test_cases/single_valid.json | 10 + server_test_cases/ui_batch_screening_32.csv | 33 + .../ui_batch_screening_matrix.csv | 3 + .../ui_batch_screening_mixed.csv | 7 + tests/test_llm_memory.py | 58 + 24 files changed, 7618 insertions(+), 25 deletions(-) create mode 100644 SERVER_DEPLOY.md create mode 100755 server_gpu_test.sh create mode 100644 server_test_cases/README.md create mode 100755 server_test_cases/generate_mock_batch.py create mode 100644 server_test_cases/mixed_valid_invalid.json create mode 100644 server_test_cases/mock_batch_128_no_llm.json create mode 100644 server_test_cases/mock_batch_32_llm.json create mode 100644 server_test_cases/mock_batch_500_llm.json create mode 100644 server_test_cases/optimize_smoke.json create mode 100755 server_test_cases/run_cases.sh create mode 100755 server_test_cases/run_lnp_batch_stress.sh create mode 100644 server_test_cases/single_valid.json create mode 100644 server_test_cases/ui_batch_screening_32.csv create mode 100644 server_test_cases/ui_batch_screening_matrix.csv create mode 100644 server_test_cases/ui_batch_screening_mixed.csv create mode 100644 tests/test_llm_memory.py diff --git a/Dockerfile b/Dockerfile index 260ed56..eeb4692 100644 --- a/Dockerfile +++ b/Dockerfile @@ -36,8 +36,8 @@ COPY app/ ./app/ # 安装项目包 RUN pip install -e . -# 复制模型文件 -COPY models/final/ ./models/final/ +# 模型、Qwen 权重和 RAG 数据在运行时以只读 volume 挂载。 +# 不把数 GB 权重烘进镜像，也避免误把 Git LFS pointer 当成模型。 # ============ API 服务 ============ FROM base AS api @@ -60,4 +60,3 @@ ENV STREAMLIT_SERVER_PORT=8501 \ STREAMLIT_BROWSER_GATHER_USAGE_STATS=false CMD ["streamlit", "run", "app/app.py"] - diff --git a/SERVER_DEPLOY.md b/SERVER_DEPLOY.md new file mode 100644 index 0000000..290c99a --- /dev/null +++ b/SERVER_DEPLOY.md @@ -0,0 +1,59 @@ +# GPU 服务器测试与部署 + +这个代码包不包含大模型权重。解压后，请先在项目根目录放好以下运行时文件： + +```text +models/final/model.pt +models/mpnn/all_amine_split_for_LiON/cv_0/fold_0/model_0/model.pt +models/mpnn/all_amine_split_for_LiON/cv_1/fold_0/model_0/model.pt +models/mpnn/all_amine_split_for_LiON/cv_2/fold_0/model_0/model.pt +models/mpnn/all_amine_split_for_LiON/cv_3/fold_0/model_0/model.pt +models/mpnn/all_amine_split_for_LiON/cv_4/fold_0/model_0/model.pt +models/qwen2.5-7b-instruct/config.json +models/qwen2.5-7b-instruct/ # 其余 tokenizer/权重文件 +data/interim/internal.csv +``` + +`.pt` 必须是真实二进制权重，不能是 100 多字节的 Git LFS pointer。 + +## 1. 服务器预检 + +```bash +nvidia-smi +docker compose -f docker-compose-gpu.yml config +bash server_gpu_test.sh preflight +``` + +## 2. 构建并启动测试服务 + +```bash +LLM_INFERENCE_BATCH_SIZE=4 bash server_gpu_test.sh start +``` + +对 24GB 显卡可将微批设为 `2`；48GB/80GB 显卡通常可使用 `4`。这个参数只控制 Qwen 内部微批，不会限制 API 一次提交的条数。 + +## 3. 单条与 32 条 LLM 冒烟测试 + +```bash +bash server_gpu_test.sh smoke +``` + +另开一个终端观察峰值： + +```bash +watch -n 0.5 nvidia-smi +``` + +预期：两个请求都返回 HTTP 200，日志中出现 `LLM inference micro-batching`，32 条请求不再把 32 条同时送入 Qwen。 + +## 4. 确认后上线 + +如果当前机器就是正式机，`start` 启动的已是 Compose 中的正式服务。检查： + +```bash +docker compose -f docker-compose-gpu.yml ps +docker compose -f docker-compose-gpu.yml logs --tail=200 api +curl -fsS http://127.0.0.1:18000/ +``` + +回滚时保留旧代码目录，然后在旧目录里重新执行 `docker compose up -d --build`。 diff --git a/app/api.py b/app/api.py index 9dee506..275109e 100644 --- a/app/api.py +++ b/app/api.py @@ -229,7 +229,12 @@ class BatchPredictRequest(BaseModel): items: List[PredictRequest] = Field( ..., min_length=1, max_length=500, description="配方列表，单次最多 500 条" ) - batch_size: int = Field(default=32, ge=1, le=256, description="模型前向批大小") + batch_size: int = Field( + default=32, + ge=1, + le=256, + description="外层模型前向批大小；7B LLM 会按显存安全值内部微批", + ) use_llm: bool = Field( default=True, description="是否启用 LLM 分支；关闭可大幅提速，但会损失 delivery 精度", @@ -668,4 +673,3 @@ if __name__ == "__main__": port=8000, reload=True, ) - diff --git a/app/optimize.py b/app/optimize.py index a37f6be..1c110e1 100644 --- a/app/optimize.py +++ b/app/optimize.py @@ -571,7 +571,9 @@ def predict_all( all_ee_preds = [] all_toxic_preds = [] - with torch.no_grad(): + # inference_mode 比 no_grad 还会关闭 version counter/view tracking， + # 对大批量 LLM 推理可再减少一部分临时内存。 + with torch.inference_mode(): for batch in dataloader: smiles = batch["smiles"] tabular = {k: v.to(device) for k, v in batch["tabular"].items()} @@ -1145,4 +1147,3 @@ def main( if __name__ == "__main__": app() - diff --git a/docker-compose-gpu.yml b/docker-compose-gpu.yml index ae78141..b39b699 100644 --- a/docker-compose-gpu.yml +++ b/docker-compose-gpu.yml @@ -6,12 +6,20 @@ services: dockerfile: Dockerfile target: api container_name: lnp-api + ports: + # 仅绑定本机，供服务器上的冒烟测试使用，不直接暴露到公网。 + - "127.0.0.1:${API_PORT:-18000}:8000" environment: - MODEL_PATH=/app/models/final/model.pt + - RAG_POOL_CSV=/app/data/interim/internal.csv + # 7B LLM 内部微批；外层批量筛选仍可一次提交更多条。 + - LLM_INFERENCE_BATCH_SIZE=${LLM_INFERENCE_BATCH_SIZE:-4} volumes: - # 挂载模型目录以便更新模型 + # 权重和 RAG 数据不进镜像，服务器上需预先放到这些路径。 - ./models/final:/app/models/final:ro - ./models/mpnn:/app/models/mpnn:ro + - ./models/qwen2.5-7b-instruct:/app/models/qwen2.5-7b-instruct:ro + - ./data/interim/internal.csv:/app/data/interim/internal.csv:ro restart: unless-stopped healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8000/"] diff --git a/lnp_ml/modeling/layers/llm_prompt.py b/lnp_ml/modeling/layers/llm_prompt.py index c530f00..183dc74 100644 --- a/lnp_ml/modeling/layers/llm_prompt.py +++ b/lnp_ml/modeling/layers/llm_prompt.py @@ -9,6 +9,16 @@ import torch.nn as nn DEFAULT_MOLT5_PATH = os.environ.get("MOLT5_PATH", "models/molt5-base") + +def _positive_int_env(name: str, default: int) -> int: + """Read a positive integer environment variable with a safe fallback.""" + try: + value = int(os.environ.get(name, str(default))) + except (TypeError, ValueError): + return default + return value if value > 0 else default + + # 检索池支持的额外多任务（除 delivery 外） _EXTRA_TASKS = ["size", "pdi", "ee", "toxic", "biodist"] @@ -58,6 +68,9 @@ class LLMPromptEncoder(nn.Module): self.use_rag = use_rag self.rag_top_k = rag_top_k self.use_soft_prompt = use_soft_prompt + # 7B 模型即使在 no_grad 下，大批量长 prompt 的临时激活也很大。 + # 外层 DataLoader 仍可用大 batch；这里只把 LLM 前向切成小块。 + self.inference_batch_size = _positive_int_env("LLM_INFERENCE_BATCH_SIZE", 4) _name_l = model_name_or_path.lower() _is_t5 = "t5" in _name_l @@ -145,16 +158,18 @@ class LLMPromptEncoder(nn.Module): def _apply_lora(self, r, alpha, dropout, prepare_kbit=False): from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training - if prepare_kbit: + # 4-bit/8-bit 基座只 prepare 一次。PEFT 会处理输入梯度并开启 + # gradient checkpointing；后面再显式开一次，兼容不同 PEFT 版本。 + is_kbit = ( + prepare_kbit + or getattr(self.encoder, "is_loaded_in_4bit", False) + or getattr(self.encoder, "is_loaded_in_8bit", False) + ) + if is_kbit: self.encoder = prepare_model_for_kbit_training(self.encoder) else: for p in self.encoder.parameters(): p.requires_grad = False - - # 4-bit 量化模型(QLoRA)需先 prepare，才能正确接收梯度 - if getattr(self.encoder, "is_loaded_in_4bit", False) or getattr(self.encoder, "is_loaded_in_8bit", False): - from peft import prepare_model_for_kbit_training - self.encoder = prepare_model_for_kbit_training(self.encoder) for p in self.encoder.parameters(): p.requires_grad = False # 不同架构注意力层命名不同： @@ -170,6 +185,24 @@ class LLMPromptEncoder(nn.Module): target_modules=_targets, bias="none") self.encoder = get_peft_model(self.encoder, cfg) + # Decoder-only LLM 做表征编码不需要 KV cache。训练时保留 cache + # 只会额外占显存；梯度检查点则用计算换显存。 + if hasattr(self.encoder, "config"): + self.encoder.config.use_cache = False + if hasattr(self.encoder, "gradient_checkpointing_enable"): + try: + self.encoder.gradient_checkpointing_enable( + gradient_checkpointing_kwargs={"use_reentrant": False} + ) + except TypeError: + self.encoder.gradient_checkpointing_enable() + + def _encoder_forward(self, **kwargs): + """Forward the backbone without allocating an unused decoder KV cache.""" + if self._is_qwen: + kwargs["use_cache"] = False + return self.encoder(**kwargs) + # ---------- 检索池 ---------- def set_retrieval_pool(self, smiles_list, labels, pool_id="train", extra_labels=None): """设置 RAG 检索池（防泄漏：只传训练集分子与标签）。 @@ -326,14 +359,15 @@ class LLMPromptEncoder(nn.Module): def _rag_encode_batch(self, prompts, device): """编码一批 RAG prompt，取最后有效 token。grad 由调用方上下文决定。""" outs = [] - for i in range(0, len(prompts), 4): - bt = prompts[i:i+4] + chunk_size = self.inference_batch_size + for i in range(0, len(prompts), chunk_size): + bt = prompts[i:i + chunk_size] enc = self.tokenizer( bt, padding=True, truncation=True, max_length=self.max_length, return_tensors="pt", ).to(device) self._warn_if_truncated(enc) - out = self.encoder(**enc).last_hidden_state # [B,L,H] + out = self._encoder_forward(**enc).last_hidden_state # [B,L,H] lengths = enc["attention_mask"].sum(1) - 1 b = torch.arange(out.size(0), device=device) outs.append(out[b, lengths.long(), :].float()) # [B,H] @@ -359,7 +393,8 @@ class LLMPromptEncoder(nn.Module): return self._rag_encode_batch(prompts, device) # ---------- soft-prompt 编码（带梯度，不缓存特征）---------- - def _encode_softrag(self, smiles, chem, tab, device) -> torch.Tensor: + def _encode_softrag_chunk(self, smiles, chem, tab, device) -> torch.Tensor: + """单个 LLM micro-batch 的 soft-RAG 前向。""" prompts = [self._get_prompt(s) for s in smiles] enc = self.tokenizer( prompts, padding=True, truncation=True, @@ -393,7 +428,7 @@ class LLMPromptEncoder(nn.Module): position_ids = attn_mask.long().cumsum(-1) - 1 position_ids = position_ids.masked_fill(attn_mask == 0, 1) - out = self.encoder( + out = self._encoder_forward( inputs_embeds=inputs_embeds, attention_mask=attn_mask, position_ids=position_ids, @@ -407,6 +442,43 @@ class LLMPromptEncoder(nn.Module): feat = out[b, lengths.long(), :] return self.proj_down(feat.float()) + def _encode_softrag(self, smiles, chem, tab, device) -> torch.Tensor: + """ + soft-RAG 编码。推理时自动微批，避免 batch API 把 7B LLM 的 + 临时激活按请求 batch_size 线性放大。训练仍保持原 batch， + 由 gradient checkpointing 降低激活显存。 + """ + batch_size = len(smiles) + micro_batch = self.inference_batch_size + should_chunk = ( + not self.training + and not torch.is_grad_enabled() + and batch_size > micro_batch + ) + if not should_chunk: + return self._encode_softrag_chunk(smiles, chem, tab, device) + + if not getattr(self, "_microbatch_logged", False): + from loguru import logger + + logger.info( + f"LLM inference micro-batching: batch={batch_size}, " + f"micro_batch={micro_batch} (override with LLM_INFERENCE_BATCH_SIZE)" + ) + self._microbatch_logged = True + + outputs = [] + for start in range(0, batch_size, micro_batch): + end = min(start + micro_batch, batch_size) + chem_chunk = chem[start:end] if chem is not None else None + tab_chunk = tab[start:end] if tab is not None else None + outputs.append( + self._encode_softrag_chunk( + smiles[start:end], chem_chunk, tab_chunk, device + ) + ) + return torch.cat(outputs, dim=0) + # ---------- 旧路径（保留，向后兼容）---------- def _mean_pool(self, last_hidden, mask): m = mask.unsqueeze(-1).float() @@ -417,12 +489,13 @@ class LLMPromptEncoder(nn.Module): missing = [s for s in smiles if s not in self._cache] if missing: uniq = list(dict.fromkeys(missing)) - for i in range(0, len(uniq), 256): - chunk = uniq[i:i + 256] + chunk_size = self.inference_batch_size if self._is_qwen else 256 + for i in range(0, len(uniq), chunk_size): + chunk = uniq[i:i + chunk_size] mols = [self._fmt_mol(s) for s in chunk] enc = self.tokenizer(mols, padding=True, truncation=True, max_length=self.max_length, return_tensors="pt").to(device) - out = self.encoder(**enc).last_hidden_state + out = self._encoder_forward(**enc).last_hidden_state pooled = self._mean_pool(out, enc["attention_mask"]) for s, v in zip(chunk, pooled): self._cache[s] = v.cpu() @@ -432,7 +505,7 @@ class LLMPromptEncoder(nn.Module): mols = [self._fmt_mol(s) for s in smiles] enc = self.tokenizer(mols, padding=True, truncation=True, max_length=self.max_length, return_tensors="pt").to(device) - out = self.encoder(**enc).last_hidden_state + out = self._encoder_forward(**enc).last_hidden_state return self._mean_pool(out, enc["attention_mask"]) def forward(self, smiles: List[str], chem: Optional[torch.Tensor] = None, @@ -448,4 +521,4 @@ class LLMPromptEncoder(nn.Module): def clear_cache(self) -> None: self._cache.clear() - self._prompt_cache.clear() \ No newline at end of file + self._prompt_cache.clear() diff --git a/lnp_ml/modeling/predict.py b/lnp_ml/modeling/predict.py index 1b8cc3c..448f552 100644 --- a/lnp_ml/modeling/predict.py +++ b/lnp_ml/modeling/predict.py @@ -118,7 +118,7 @@ def load_model( return model -@torch.no_grad() +@torch.inference_mode() def predict_batch( model: Union[LNPModel, LNPModelWithoutMPNN], loader: DataLoader, diff --git a/pixi.toml b/pixi.toml index a996b3f..b0811f6 100644 --- a/pixi.toml +++ b/pixi.toml @@ -31,5 +31,6 @@ uvicorn = ">=0.33.0, <0.34" optuna = ">=4.5.0, <5" captum = ">=0.7.0, <0.8" transformers = ">=4.30, <4.46" +peft = "==0.13.2" sentencepiece = "*" protobuf = "*" diff --git a/requirements.txt b/requirements.txt index 0eb08c4..d7a1d64 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,6 +21,7 @@ uvicorn>=0.33.0,<0.34 optuna>=4.5.0,<5 captum>=0.7.0 transformers>=4.30,<4.46 +peft==0.13.2 sentencepiece protobuf bitsandbytes diff --git a/server_gpu_test.sh b/server_gpu_test.sh new file mode 100755 index 0000000..8b5fbcc --- /dev/null +++ b/server_gpu_test.sh @@ -0,0 +1,108 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +cd "${ROOT_DIR}" + +COMPOSE_FILE=${COMPOSE_FILE:-docker-compose-gpu.yml} +API_PORT=${API_PORT:-18000} +API_URL=${API_URL:-http://127.0.0.1:${API_PORT}} +LLM_INFERENCE_BATCH_SIZE=${LLM_INFERENCE_BATCH_SIZE:-4} +export API_PORT +export LLM_INFERENCE_BATCH_SIZE + +fail() { + echo "ERROR: $*" >&2 + exit 1 +} + +assert_real_file() { + local path=$1 + [ -f "${path}" ] || fail "missing file: ${path}" + if head -n 1 "${path}" 2>/dev/null | grep -q "git-lfs.github.com/spec"; then + fail "${path} is a Git LFS pointer, not the real artifact" + fi +} + +preflight() { + command -v docker >/dev/null || fail "docker is not installed" + command -v nvidia-smi >/dev/null || fail "nvidia-smi is not installed" + nvidia-smi >/dev/null || fail "NVIDIA driver/GPU is unavailable" + docker compose version >/dev/null || fail "docker compose plugin is unavailable" + + assert_real_file models/final/model.pt + assert_real_file data/interim/internal.csv + assert_real_file models/qwen2.5-7b-instruct/config.json + + local fold + for fold in 0 1 2 3 4; do + assert_real_file "models/mpnn/all_amine_split_for_LiON/cv_${fold}/fold_0/model_0/model.pt" + done + + find models/qwen2.5-7b-instruct -maxdepth 1 -type f \ + \( -name '*.safetensors' -o -name '*.bin' \) -print -quit | grep -q . \ + || fail "Qwen weight shards (*.safetensors or *.bin) are missing" + + docker compose -f "${COMPOSE_FILE}" config >/dev/null + echo "preflight: OK" +} + +wait_for_api() { + local attempt + for attempt in $(seq 1 120); do + if curl -fsS "${API_URL}/" >/dev/null 2>&1; then + echo "API ready after ${attempt} checks" + return 0 + fi + sleep 2 + done + docker compose -f "${COMPOSE_FILE}" logs --tail=200 api || true + fail "API did not become healthy within 240 seconds" +} + +start() { + preflight + docker compose -f "${COMPOSE_FILE}" build + docker compose -f "${COMPOSE_FILE}" up -d + wait_for_api + docker compose -f "${COMPOSE_FILE}" ps + curl -fsS "${API_URL}/" + echo +} + +request_item() { + printf '%s' '{"smiles":"CC(C)NCCNC(C)C","cationic_lipid_to_mrna_ratio":10.0,"cationic_lipid_mol_ratio":35.0,"phospholipid_mol_ratio":16.0,"cholesterol_mol_ratio":46.5,"peg_lipid_mol_ratio":2.5,"helper_lipid":"DOPE","route":"intravenous"}' +} + +smoke() { + wait_for_api + + local item payload i + item=$(request_item) + curl -fsS -H 'Content-Type: application/json' \ + -d "${item}" "${API_URL}/predict" >/tmp/lnp-single-response.json + echo "single prediction: OK" + + payload='{"items":[' + for i in $(seq 1 32); do + [ "${i}" -eq 1 ] || payload+=',' + payload+="${item}" + done + payload+='],"batch_size":32,"use_llm":true}' + + curl -fsS -H 'Content-Type: application/json' \ + -d "${payload}" "${API_URL}/predict/batch" >/tmp/lnp-batch-response.json + grep -q '"n_succeeded":32' /tmp/lnp-batch-response.json \ + || fail "batch response did not report 32 successes" + echo "32-item LLM prediction: OK" + + docker compose -f "${COMPOSE_FILE}" logs --tail=200 api \ + | grep -E 'LLM inference micro-batching|ERROR|CUDA out of memory' || true +} + +case "${1:-}" in + preflight) preflight ;; + start) start ;; + smoke) smoke ;; + *) echo "Usage: $0 {preflight|start|smoke}" >&2; exit 2 ;; +esac diff --git a/server_test_cases/README.md b/server_test_cases/README.md new file mode 100644 index 0000000..284e366 --- /dev/null +++ b/server_test_cases/README.md @@ -0,0 +1,88 @@ +# LNP API 与 GPU 显存测试 + +这里有两套测试： + +- `run_lnp_batch_stress.sh`：专门复现 LNP 批量调用的显存问题。 +- `run_cases.sh`：检查健康状态、器官列表、单条预测、批量预测、异常输入和小规模配方优化。 + +## 1. 复现 LNP 批量显存场景 + +推荐先运行与问题最接近的场景： + +```bash +bash server_test_cases/run_lnp_batch_stress.sh repro +``` + +`repro` 会依次提交： + +1. 32 条不同 LNP 配方，`use_llm=true`、外层 `batch_size=32`。这是原先最容易从约 10GB 涨到 63GB 的场景。 +2. 500 条不同 LNP 配方，仍使用外层 `batch_size=32`，检查多轮处理后是否持续涨显存。 + +逐级观察批量大小与显存的关系： + +```bash +bash server_test_cases/run_lnp_batch_stress.sh ladder +``` + +完整测试（关闭 LLM 对照 + 阶梯 + 500 条持续压力）： + +```bash +bash server_test_cases/run_lnp_batch_stress.sh full +``` + +每个场景都会打印并写入 CSV：运行前显存、采样峰值、显存增量、耗时、HTTP 状态。结果文件名形如 `vram-results-20260922-160000.csv`。 + +可以设置显存上限，让超过阈值时测试直接失败。例如期望不超过 20GB： + +```bash +MAX_GPU_MIB=20480 bash server_test_cases/run_lnp_batch_stress.sh repro +``` + +这里采样的是整张卡的显存；测试时请尽量不要让其他任务共用该卡。 + +## 2. Mock 数据 + +已附带三组可直接提交的数据： + +- `mock_batch_32_llm.json`：核心显存回归场景。 +- `mock_batch_500_llm.json`：最大请求量与持续处理场景。 +- `mock_batch_128_no_llm.json`：关闭 LLM 的对照组。 + +这些数据从项目 `data/interim/internal.csv` 抽取不同 SMILES，再随机生成满足总和 100% 的组分比例。也可以自行生成： + +```bash +python3 server_test_cases/generate_mock_batch.py \ + --count 200 \ + --batch-size 32 \ + --use-llm \ + --source-csv data/interim/internal.csv \ + --output /tmp/lnp-batch-200.json +``` + +生成过程使用固定随机种子，便于前后版本用完全相同的数据对比。 + +## 3. 其他功能测试 + +服务启动后执行： + +```bash +bash server_test_cases/run_cases.sh +``` + +如果 API 端口或 GPU 编号不同： + +```bash +API_PORT=18000 GPU_INDEX=0 bash server_test_cases/run_cases.sh +``` + +用例包含： + +1. 健康检查与模型状态。 +2. 可用器官列表。 +3. 启用 LLM 的单条预测，检查输出完整性、有限数和 biodistribution 归一化。 +4. 64 条关闭 LLM 的吞吐对照。 +5. 32 条启用 LLM 的显存回归测试。 +6. 2 条合法 + 3 条非法输入，检查批量接口的局部容错。 +7. 小范围 `/optimize` 配方搜索，检查优化功能和返回结果。 + +GPU 显存采样来自整张卡；如果还有其他进程使用同一张卡，请以 `nvidia-smi` 中 API 进程的变化为准。 diff --git a/server_test_cases/generate_mock_batch.py b/server_test_cases/generate_mock_batch.py new file mode 100755 index 0000000..0b28107 --- /dev/null +++ b/server_test_cases/generate_mock_batch.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +"""Generate reproducible /predict/batch payloads for GPU memory tests.""" + +from __future__ import annotations + +import argparse +import csv +import json +import random +from pathlib import Path + + +# Used only when data/interim/internal.csv is not available. These are valid, +# deliberately varied SMILES; the project dataset is preferred for realistic RAG prompts. +FALLBACK_SMILES = [ + "CC(C)NCCNC(C)C", + "CCCCN(CC)CC", + "CCCCCCCCN(C)C", + "CCN(CC)CCOC(=O)CCCC", + "CCCCCCCCCCN(CC)CC", + "CC(C)(C)OC(=O)NCCN(C)C", + "CCCCCCCCCCCCN(C)C", + "CCOC(=O)CN(C)C", + "CCCCCCN(CC)CC", + "CN(C)CCOC(=O)CCCCCCCC", +] + + +def load_smiles(source: Path | None) -> list[str]: + if source and source.is_file(): + with source.open(encoding="utf-8-sig", newline="") as handle: + rows = csv.DictReader(handle) + values = [] + seen = set() + for row in rows: + value = (row.get("smiles") or "").strip() + if value and value.lower() != "nan" and value not in seen: + seen.add(value) + values.append(value) + if values: + return values + return FALLBACK_SMILES + + +def make_composition(rng: random.Random) -> tuple[float, float, float, float]: + # Match the ranges used by the optimizer and guarantee an exact 100% sum. + for _ in range(1000): + peg = round(rng.uniform(1.0, 5.0), 2) + cationic = round(rng.uniform(25.0, 53.0), 2) + phospholipid = round(rng.uniform(8.0, 30.0), 2) + cholesterol = round(100.0 - peg - cationic - phospholipid, 2) + if 15.0 <= cholesterol <= 46.0: + return cationic, phospholipid, cholesterol, peg + raise RuntimeError("unable to generate a valid composition") + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--count", type=int, required=True, choices=range(1, 501)) + parser.add_argument("--batch-size", type=int, default=32, choices=range(1, 257)) + parser.add_argument("--use-llm", action=argparse.BooleanOptionalAction, default=True) + parser.add_argument("--seed", type=int, default=20260922) + parser.add_argument("--source-csv", type=Path) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + + rng = random.Random(args.seed) + smiles_pool = load_smiles(args.source_csv) + rng.shuffle(smiles_pool) + items = [] + for index in range(args.count): + cationic, phospholipid, cholesterol, peg = make_composition(rng) + items.append( + { + "smiles": smiles_pool[index % len(smiles_pool)], + "cationic_lipid_to_mrna_ratio": round(rng.uniform(7.0, 20.0), 2), + "cationic_lipid_mol_ratio": cationic, + "phospholipid_mol_ratio": phospholipid, + "cholesterol_mol_ratio": cholesterol, + "peg_lipid_mol_ratio": peg, + "helper_lipid": ("DOPE", "DSPC")[index % 2], + "route": ("intravenous", "intramuscular")[index % 2], + } + ) + + payload = { + "items": items, + "batch_size": args.batch_size, + "use_llm": args.use_llm, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text( + json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8" + ) + print( + f"generated {args.output}: items={args.count}, unique_smiles={len(set(x['smiles'] for x in items))}, " + f"batch_size={args.batch_size}, use_llm={args.use_llm}" + ) + + +if __name__ == "__main__": + main() diff --git a/server_test_cases/mixed_valid_invalid.json b/server_test_cases/mixed_valid_invalid.json new file mode 100644 index 0000000..7a40eaa --- /dev/null +++ b/server_test_cases/mixed_valid_invalid.json @@ -0,0 +1,56 @@ +{ + "items": [ + { + "smiles": "CC(C)NCCNC(C)C", + "cationic_lipid_to_mrna_ratio": 10.0, + "cationic_lipid_mol_ratio": 35.0, + "phospholipid_mol_ratio": 16.0, + "cholesterol_mol_ratio": 46.5, + "peg_lipid_mol_ratio": 2.5, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "CCCCCCCCCCCCCCCCN", + "cationic_lipid_to_mrna_ratio": 12.0, + "cationic_lipid_mol_ratio": 40.0, + "phospholipid_mol_ratio": 15.0, + "cholesterol_mol_ratio": 42.5, + "peg_lipid_mol_ratio": 2.5, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "this-is-not-smiles", + "cationic_lipid_to_mrna_ratio": 10.0, + "cationic_lipid_mol_ratio": 35.0, + "phospholipid_mol_ratio": 16.0, + "cholesterol_mol_ratio": 46.5, + "peg_lipid_mol_ratio": 2.5, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "CCN(CC)CC", + "cationic_lipid_to_mrna_ratio": 10.0, + "cationic_lipid_mol_ratio": 35.0, + "phospholipid_mol_ratio": 16.0, + "cholesterol_mol_ratio": 30.0, + "peg_lipid_mol_ratio": 2.5, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "C1CCN(CC1)CCO", + "cationic_lipid_to_mrna_ratio": 10.0, + "cationic_lipid_mol_ratio": 35.0, + "phospholipid_mol_ratio": 16.0, + "cholesterol_mol_ratio": 46.5, + "peg_lipid_mol_ratio": 2.5, + "helper_lipid": "INVALID_HELPER", + "route": "intravenous" + } + ], + "batch_size": 5, + "use_llm": false +} diff --git a/server_test_cases/mock_batch_128_no_llm.json b/server_test_cases/mock_batch_128_no_llm.json new file mode 100644 index 0000000..ec508d3 --- /dev/null +++ b/server_test_cases/mock_batch_128_no_llm.json @@ -0,0 +1,1286 @@ +{ + "items": [ + { + "smiles": "O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O)CCNC(C[C@@H](CN)CC(C)C)=O", + "cationic_lipid_to_mrna_ratio": 7.46, + "cationic_lipid_mol_ratio": 37.58, + "phospholipid_mol_ratio": 23.5, + "cholesterol_mol_ratio": 36.53, + "peg_lipid_mol_ratio": 2.39, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCCCCCCCCC)CCN(CCC(OCCCCCCCCCCCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCCCC)=O)CCNC(CCC(NCC1=CC=C(B(O)O)C=C1)=O)=O", + "cationic_lipid_to_mrna_ratio": 16.82, + "cationic_lipid_mol_ratio": 50.09, + "phospholipid_mol_ratio": 13.01, + "cholesterol_mol_ratio": 33.14, + "peg_lipid_mol_ratio": 3.76, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(OCCCCCCN([C@H](C(NCCCN(CCCCCCOC(CCCCCCC)=O)CCCCCCOC(CCCCCCC)=O)=O)CO)CCCCCCOC(CCCCCCC)=O)CCCCCCC", + "cationic_lipid_to_mrna_ratio": 9.92, + "cationic_lipid_mol_ratio": 46.82, + "phospholipid_mol_ratio": 16.37, + "cholesterol_mol_ratio": 33.48, + "peg_lipid_mol_ratio": 3.33, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCN([C@H](C(NCCCCCCN(CCCCCCOC(CCCCC)=O)CCCCCCOC(CCCCC)=O)=O)CO)CCCCCCOC(CCCCC)=O)CCCCC", + "cationic_lipid_to_mrna_ratio": 10.73, + "cationic_lipid_mol_ratio": 36.31, + "phospholipid_mol_ratio": 23.42, + "cholesterol_mol_ratio": 35.49, + "peg_lipid_mol_ratio": 4.78, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCN(CC1=NC=CS1)CC2=NC=CS2)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O", + "cationic_lipid_to_mrna_ratio": 19.35, + "cationic_lipid_mol_ratio": 51.39, + "phospholipid_mol_ratio": 10.87, + "cholesterol_mol_ratio": 35.21, + "peg_lipid_mol_ratio": 2.53, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCCCCCCCCC)CCN(CCC(OCCCCCCCCCCCCCC)=O)CCCCCCN(CCCCCCN(CCC(OCCCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCCCC)=O)CCCN1CCOCC1", + "cationic_lipid_to_mrna_ratio": 17.21, + "cationic_lipid_mol_ratio": 46.17, + "phospholipid_mol_ratio": 19.64, + "cholesterol_mol_ratio": 31.96, + "peg_lipid_mol_ratio": 2.23, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "CCCCCCCCCCCC(OCCCCCCN(CCCCCCOC(CCCCCCCCCCC)=O)CCCOC([C@@H](N(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)CO)=O)=O", + "cationic_lipid_to_mrna_ratio": 10.34, + "cationic_lipid_mol_ratio": 44.28, + "phospholipid_mol_ratio": 17.74, + "cholesterol_mol_ratio": 34.79, + "peg_lipid_mol_ratio": 3.19, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCN([C@H](C(NCCN(C)CCN(CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCOC(CCCCCCCCC=C)=O)=O)CO)CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCCCC=C", + "cationic_lipid_to_mrna_ratio": 9.15, + "cationic_lipid_mol_ratio": 42.88, + "phospholipid_mol_ratio": 10.3, + "cholesterol_mol_ratio": 43.76, + "peg_lipid_mol_ratio": 3.06, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "CC(CNC([C@@H](N(CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCOC(CCCCCCCCC=C)=O)CO)=O)(CN(CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCOC(CCCCCCCCC=C)=O)C", + "cationic_lipid_to_mrna_ratio": 10.12, + "cationic_lipid_mol_ratio": 49.74, + "phospholipid_mol_ratio": 12.42, + "cholesterol_mol_ratio": 35.63, + "peg_lipid_mol_ratio": 2.21, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(NCCN(CCCCN(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)CCCCN(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)CCCN", + "cationic_lipid_to_mrna_ratio": 19.72, + "cationic_lipid_mol_ratio": 32.8, + "phospholipid_mol_ratio": 25.26, + "cholesterol_mol_ratio": 38.21, + "peg_lipid_mol_ratio": 3.73, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCN1CC(C(OC)=O)CC1=O)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O", + "cationic_lipid_to_mrna_ratio": 13.7, + "cationic_lipid_mol_ratio": 43.91, + "phospholipid_mol_ratio": 21.35, + "cholesterol_mol_ratio": 31.92, + "peg_lipid_mol_ratio": 2.82, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "CCCCCCCCCCCCOC(CCN([C@H](C(NCCCCCCN(CCC(OCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCC)=O)=O)CO)CCC(OCCCCCCCCCCCC)=O)=O", + "cationic_lipid_to_mrna_ratio": 10.47, + "cationic_lipid_mol_ratio": 32.49, + "phospholipid_mol_ratio": 24.48, + "cholesterol_mol_ratio": 40.96, + "peg_lipid_mol_ratio": 2.07, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C([C@H](CO)N(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)N(CC1)CCN1C2=NC=CC=N2", + "cationic_lipid_to_mrna_ratio": 12.86, + "cationic_lipid_mol_ratio": 51.8, + "phospholipid_mol_ratio": 19.97, + "cholesterol_mol_ratio": 25.07, + "peg_lipid_mol_ratio": 3.16, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(CN(CC(O)CCCCCCCC)CC(O)CCCCCCCC)NCCCN(CC(O)CCCCCCCC)CC(O)CCCCCCCC", + "cationic_lipid_to_mrna_ratio": 18.73, + "cationic_lipid_mol_ratio": 45.45, + "phospholipid_mol_ratio": 13.35, + "cholesterol_mol_ratio": 38.13, + "peg_lipid_mol_ratio": 3.07, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "CCCCCCCCCCCC(OCCCCCCN(CCN(C)CCNC([C@@H](N(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)CO)=O)CCCCCCOC(CCCCCCCCCCC)=O)=O", + "cationic_lipid_to_mrna_ratio": 10.09, + "cationic_lipid_mol_ratio": 37.43, + "phospholipid_mol_ratio": 11.94, + "cholesterol_mol_ratio": 45.78, + "peg_lipid_mol_ratio": 4.85, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "OC[C@@H](C(NCCCCCCN(CC(O)CCCCCCCCCC)CC(O)CCCCCCCCCC)=O)N(CC(CCCCCCCCCC)O)CC(CCCCCCCCCC)O", + "cationic_lipid_to_mrna_ratio": 13.05, + "cationic_lipid_mol_ratio": 52.84, + "phospholipid_mol_ratio": 8.6, + "cholesterol_mol_ratio": 37.06, + "peg_lipid_mol_ratio": 1.5, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(OCCCCCCCCCCCCCC)CCN(CCC(OCCCCCCCCCCCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCCCC)=O)CCNC(C[C@@H](CN)CC(C)C)=O", + "cationic_lipid_to_mrna_ratio": 11.09, + "cationic_lipid_mol_ratio": 48.25, + "phospholipid_mol_ratio": 14.52, + "cholesterol_mol_ratio": 32.66, + "peg_lipid_mol_ratio": 4.57, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCCC/C=C\\C/C=C\\CCCCC)CCN(CCC(OCCCCCCCC/C=C\\C/C=C\\CCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCC/C=C\\C/C=C\\CCCCC)=O)CCC(OCCCCCCCC/C=C\\C/C=C\\CCCCC)=O)CCNC(CCC(NCCC1=CC=C(O)C(O)=C1)=O)=O", + "cationic_lipid_to_mrna_ratio": 19.63, + "cationic_lipid_mol_ratio": 31.98, + "phospholipid_mol_ratio": 25.5, + "cholesterol_mol_ratio": 40.57, + "peg_lipid_mol_ratio": 1.95, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(NCCCCCCN(CCCCCCCCCCCC)CCCCCCCCCCCC)[C@H](C)N(CCCCCCCCCCCC)CCCCCCCCCCCC", + "cationic_lipid_to_mrna_ratio": 16.96, + "cationic_lipid_mol_ratio": 28.21, + "phospholipid_mol_ratio": 28.57, + "cholesterol_mol_ratio": 39.51, + "peg_lipid_mol_ratio": 3.71, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCN(CCC(F)(F)F)CCC(F)(F)F)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O", + "cationic_lipid_to_mrna_ratio": 19.73, + "cationic_lipid_mol_ratio": 37.62, + "phospholipid_mol_ratio": 22.08, + "cholesterol_mol_ratio": 36.42, + "peg_lipid_mol_ratio": 3.88, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "CCCCCCCCC(CN(CCCNC([C@@H](N(CC(CCCCCCCC)O)CC(CCCCCCCC)O)CO)=O)CC(CCCCCCCC)O)O", + "cationic_lipid_to_mrna_ratio": 15.62, + "cationic_lipid_mol_ratio": 51.29, + "phospholipid_mol_ratio": 27.02, + "cholesterol_mol_ratio": 18.83, + "peg_lipid_mol_ratio": 2.86, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C([C@H](CO)N(CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCOC(CCCCCCCCC=C)=O)N(CC1)CCN1C2=NC=CC=N2", + "cationic_lipid_to_mrna_ratio": 14.57, + "cationic_lipid_mol_ratio": 38.85, + "phospholipid_mol_ratio": 18.16, + "cholesterol_mol_ratio": 40.17, + "peg_lipid_mol_ratio": 2.82, + "helper_lipid": "DSPC", + "route": 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"cholesterol_mol_ratio": 22.55, + "peg_lipid_mol_ratio": 1.95, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "CCCCCCCCC(CN(CCCNC([C@@H](N(CC(CCCCCCCC)O)CC(CCCCCCCC)O)CO)=O)CC(CCCCCCCC)O)O", + "cationic_lipid_to_mrna_ratio": 15.82, + "cationic_lipid_mol_ratio": 48.84, + "phospholipid_mol_ratio": 20.37, + "cholesterol_mol_ratio": 27.92, + "peg_lipid_mol_ratio": 2.87, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C([C@H](CO)N(CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCOC(CCCCCCCCC=C)=O)N(CC1)CCN1C2=NC=CC=N2", + "cationic_lipid_to_mrna_ratio": 17.02, + "cationic_lipid_mol_ratio": 45.11, + "phospholipid_mol_ratio": 10.96, + "cholesterol_mol_ratio": 41.42, + "peg_lipid_mol_ratio": 2.51, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "CCCCCCCCCCCC(OCCCCCCN([C@H](C(NCCCCCCN(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)=O)CO)CCCCCCOC(CCCCCCCCCCC)=O)=O", + "cationic_lipid_to_mrna_ratio": 7.55, + "cationic_lipid_mol_ratio": 47.16, + "phospholipid_mol_ratio": 23.26, + "cholesterol_mol_ratio": 24.88, + "peg_lipid_mol_ratio": 4.7, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(OCCCCCCCCCCCC)CCN(CCC(OCCCCCCCCCCCC)=O)CCCCCCN(CCCCCCN(CCC(OCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCC)=O)CCN1CCOCC1", + "cationic_lipid_to_mrna_ratio": 8.03, + "cationic_lipid_mol_ratio": 44.06, + "phospholipid_mol_ratio": 8.8, + "cholesterol_mol_ratio": 45.01, + "peg_lipid_mol_ratio": 2.13, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(CN(CCCCCCCCCCCCCC)CCCCCCCCCCCCCC)NCCCN(CCCCCCCCCCCCCC)CCCCCCCCCCCCCC", + "cationic_lipid_to_mrna_ratio": 9.37, + "cationic_lipid_mol_ratio": 50.22, + "phospholipid_mol_ratio": 11.54, + "cholesterol_mol_ratio": 35.23, + "peg_lipid_mol_ratio": 3.01, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCCCN(CCCCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O)CCNC(CCC(NCCC1=CC=C(O)C(O)=C1)=O)=O", + "cationic_lipid_to_mrna_ratio": 10.15, + "cationic_lipid_mol_ratio": 43.34, + "phospholipid_mol_ratio": 11.46, + "cholesterol_mol_ratio": 42.51, + "peg_lipid_mol_ratio": 2.69, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O)CCNC([C@@H](N)CCCCN)=O", + "cationic_lipid_to_mrna_ratio": 12.48, + "cationic_lipid_mol_ratio": 51.19, + "phospholipid_mol_ratio": 23.28, + "cholesterol_mol_ratio": 22.37, + "peg_lipid_mol_ratio": 3.16, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(CN(CCCCCCCCCCCC)CCCCCCCCCCCC)NCCCN(CCCCCCCCCCCC)CCCCCCCCCCCC", + "cationic_lipid_to_mrna_ratio": 12.45, + "cationic_lipid_mol_ratio": 34.48, + "phospholipid_mol_ratio": 21.98, + "cholesterol_mol_ratio": 39.61, + "peg_lipid_mol_ratio": 3.93, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCN(CC1=COC=N1)CC2=COC=N2)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O", + "cationic_lipid_to_mrna_ratio": 19.35, + "cationic_lipid_mol_ratio": 34.7, + "phospholipid_mol_ratio": 23.31, + "cholesterol_mol_ratio": 40.94, + "peg_lipid_mol_ratio": 1.05, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCNC(CCCN)=O)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O", + "cationic_lipid_to_mrna_ratio": 8.84, + "cationic_lipid_mol_ratio": 40.58, + "phospholipid_mol_ratio": 25.04, + "cholesterol_mol_ratio": 32.22, + "peg_lipid_mol_ratio": 2.16, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(CN(CCCCCCCCCC)CCCCCCCCCC)NCCCN(CCCCCCCCCC)CCCCCCCCCC", + "cationic_lipid_to_mrna_ratio": 13.21, + "cationic_lipid_mol_ratio": 50.84, + "phospholipid_mol_ratio": 21.13, + "cholesterol_mol_ratio": 23.55, + "peg_lipid_mol_ratio": 4.48, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(OCCCCCCCC/C=C\\C/C=C\\CCCCC)CCN(CCC(OCCCCCCCC/C=C\\C/C=C\\CCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCC/C=C\\C/C=C\\CCCCC)=O)CCC(OCCCCCCCC/C=C\\C/C=C\\CCCCC)=O)CCNC(CCCN)=O", + "cationic_lipid_to_mrna_ratio": 10.28, + "cationic_lipid_mol_ratio": 42.8, + "phospholipid_mol_ratio": 29.63, + "cholesterol_mol_ratio": 25.79, + "peg_lipid_mol_ratio": 1.78, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCNC(C1=CC=CC2=C1N=CN2)=O)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O", + "cationic_lipid_to_mrna_ratio": 17.74, + "cationic_lipid_mol_ratio": 44.83, + "phospholipid_mol_ratio": 17.52, + "cholesterol_mol_ratio": 34.18, + "peg_lipid_mol_ratio": 3.47, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "CCCCCCCCCCCC(OCCCCCCN([C@H](C(NCCN(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)=O)CO)CCCCCCOC(CCCCCCCCCCC)=O)=O", + "cationic_lipid_to_mrna_ratio": 14.92, + "cationic_lipid_mol_ratio": 48.15, + "phospholipid_mol_ratio": 26.0, + "cholesterol_mol_ratio": 24.78, + "peg_lipid_mol_ratio": 1.07, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "CCCCCCCCCCCCCCCCOC(CCN([C@H](C(NCCCCCCN(CCC(OCCCCCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCCCCCC)=O)=O)CO)CCC(OCCCCCCCCCCCCCCCC)=O)=O", + "cationic_lipid_to_mrna_ratio": 11.12, + "cationic_lipid_mol_ratio": 47.54, + "phospholipid_mol_ratio": 17.02, + "cholesterol_mol_ratio": 30.79, + "peg_lipid_mol_ratio": 4.65, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "CCCCCCCCCCC(CN([C@H](C(NCCCN(CC(CCCCCCCCCC)O)CC(CCCCCCCCCC)O)=O)CO)CC(CCCCCCCCCC)O)O", + "cationic_lipid_to_mrna_ratio": 15.0, + "cationic_lipid_mol_ratio": 46.03, + "phospholipid_mol_ratio": 27.7, + "cholesterol_mol_ratio": 23.3, + "peg_lipid_mol_ratio": 2.97, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCCC/C=C\\C/C=C\\CCCCC)CCN(CCC(OCCCCCCCC/C=C\\C/C=C\\CCCCC)=O)CCCCCCN(CCCCCCN(CCC(OCCCCCCCC/C=C\\C/C=C\\CCCCC)=O)CCC(OCCCCCCCC/C=C\\C/C=C\\CCCCC)=O)CCCN1CCOCC1", + "cationic_lipid_to_mrna_ratio": 15.02, + "cationic_lipid_mol_ratio": 34.17, + "phospholipid_mol_ratio": 22.92, + "cholesterol_mol_ratio": 41.29, + "peg_lipid_mol_ratio": 1.62, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "O=C(CN(CC(O)CCCCCCCCCCCCCCCC)CC(O)CCCCCCCCCCCCCCCC)NCCCN(CC(O)CCCCCCCCCCCCCCCC)CC(O)CCCCCCCCCCCCCCCC", + "cationic_lipid_to_mrna_ratio": 9.66, + "cationic_lipid_mol_ratio": 46.21, + "phospholipid_mol_ratio": 27.2, + "cholesterol_mol_ratio": 24.04, + "peg_lipid_mol_ratio": 2.55, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(OCCCCCCN([C@H](C(NCCCN(CCCCCCOC(CCCCCCCCC)=O)CCCCCCOC(CCCCCCCCC)=O)=O)CO)CCCCCCOC(CCCCCCCCC)=O)CCCCCCCCC", + "cationic_lipid_to_mrna_ratio": 13.33, + "cationic_lipid_mol_ratio": 32.5, + "phospholipid_mol_ratio": 21.05, + "cholesterol_mol_ratio": 41.76, + "peg_lipid_mol_ratio": 4.69, + "helper_lipid": "DSPC", + "route": "intramuscular" + }, + { + "smiles": "CCCCCCCCCCCCC(CN([C@H](C(NCCCN(CC(CCCCCCCCCCCC)O)CC(CCCCCCCCCCCC)O)=O)CO)CC(CCCCCCCCCCCC)O)O", + "cationic_lipid_to_mrna_ratio": 14.0, + "cationic_lipid_mol_ratio": 36.24, + "phospholipid_mol_ratio": 18.31, + "cholesterol_mol_ratio": 42.47, + "peg_lipid_mol_ratio": 2.98, + "helper_lipid": "DOPE", + "route": "intravenous" + }, + { + "smiles": "O=C(NCCCN(CCCCCCCCCCCC)CCCCCCCCCCCC)[C@H]([C@H](O)C)N(CCCCCCCCCCCC)CCCCCCCCCCCC", + "cationic_lipid_to_mrna_ratio": 15.53, + "cationic_lipid_mol_ratio": 49.02, + "phospholipid_mol_ratio": 14.07, + "cholesterol_mol_ratio": 33.54, + "peg_lipid_mol_ratio": 3.37, + "helper_lipid": "DSPC", + "route": "intramuscular" + } + ], + "batch_size": 32, + "use_llm": true +} diff --git a/server_test_cases/optimize_smoke.json b/server_test_cases/optimize_smoke.json new file mode 100644 index 0000000..05d0ade --- /dev/null +++ b/server_test_cases/optimize_smoke.json @@ -0,0 +1,31 @@ +{ + "smiles": "CC(C)NCCNC(C)C", + "organ": "liver", + "top_k": 3, + "num_seeds": 5, + "top_per_seed": 1, + "rerank_top_n": 8, + "step_sizes": [5], + "wr_step_sizes": [5], + "routes": ["intravenous"], + "comp_ranges": { + "weight_ratio_min": 10, + "weight_ratio_max": 15, + "cationic_mol_min": 40, + "cationic_mol_max": 50, + "phospholipid_mol_min": 10, + "phospholipid_mol_max": 20, + "cholesterol_mol_min": 28, + "cholesterol_mol_max": 45, + "peg_mol_min": 1, + "peg_mol_max": 5 + }, + "scoring_weights": { + "biodist_weight": 1, + "delivery_weight": 0, + "size_weight": 0, + "ee_class_weights": [0, 0, 0], + "pdi_class_weights": [0, 0], + "toxic_class_weights": [0, 0] + } +} diff --git a/server_test_cases/run_cases.sh b/server_test_cases/run_cases.sh new file mode 100755 index 0000000..5259860 --- /dev/null +++ b/server_test_cases/run_cases.sh @@ -0,0 +1,187 @@ +#!/usr/bin/env bash +set -euo pipefail + +CASE_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +API_PORT=${API_PORT:-18000} +API_URL=${API_URL:-http://127.0.0.1:${API_PORT}} +GPU_INDEX=${GPU_INDEX:-0} +CURL_TIMEOUT=${CURL_TIMEOUT:-1800} + +PASS=0 +FAIL=0 + +run_case() { + local name=$1 + shift + echo + echo "===== ${name} =====" + if "$@"; then + PASS=$((PASS + 1)) + echo "PASS: ${name}" + else + FAIL=$((FAIL + 1)) + echo "FAIL: ${name}" >&2 + fi +} + +validate_health() { + local response=/tmp/lnp-test-health.json + curl -fsS --max-time 30 "${API_URL}/" >"${response}" + python3 - "${response}" <<'PY' +import json, sys +data = json.load(open(sys.argv[1], encoding="utf-8")) +assert data["status"] == "healthy", data +assert data["model_loaded"] is True, data +assert data["use_llm"] is True, data +print(json.dumps(data, ensure_ascii=False, indent=2)) +PY +} + +validate_organs() { + local response=/tmp/lnp-test-organs.json + curl -fsS --max-time 30 "${API_URL}/organs" >"${response}" + python3 - "${response}" <<'PY' +import json, sys +data = json.load(open(sys.argv[1], encoding="utf-8")) +expected = {"lymph_nodes", "heart", "liver", "spleen", "lung", "kidney", "muscle"} +assert set(data) == expected, data +print(json.dumps(data, ensure_ascii=False)) +PY +} + +validate_single() { + local response=/tmp/lnp-test-single.json + curl -fsS --max-time "${CURL_TIMEOUT}" \ + -H 'Content-Type: application/json' \ + --data-binary "@${CASE_DIR}/single_valid.json" \ + "${API_URL}/predict" >"${response}" + python3 - "${response}" <<'PY' +import json, math, sys +data = json.load(open(sys.argv[1], encoding="utf-8")) +required = ["biodist", "quantified_delivery", "pdi_class", "ee_class", "toxic_class"] +assert all(k in data for k in required), data +values = list(data["biodist"].values()) +assert len(values) == 7, values +assert all(math.isfinite(float(x)) for x in values), values +assert abs(sum(values) - 1.0) < 1e-4, sum(values) +assert math.isfinite(float(data["quantified_delivery"])) +print(json.dumps(data, ensure_ascii=False, indent=2)) +PY +} + +make_batch_payload() { + local count=$1 + local use_llm=$2 + local batch_size=$3 + python3 - "${CASE_DIR}/single_valid.json" "${count}" "${use_llm}" "${batch_size}" <<'PY' +import json, sys +item = json.load(open(sys.argv[1], encoding="utf-8")) +print(json.dumps({ + "items": [item for _ in range(int(sys.argv[2]))], + "batch_size": int(sys.argv[4]), + "use_llm": sys.argv[3].lower() == "true", +})) +PY +} + +validate_batch_response() { + local response=$1 + local expected=$2 + python3 - "${response}" "${expected}" <<'PY' +import json, math, sys +data = json.load(open(sys.argv[1], encoding="utf-8")) +expected = int(sys.argv[2]) +assert data["n_requested"] == expected, data +assert data["n_succeeded"] == expected, data +assert data["errors"] == [], data["errors"] +assert len(data["predictions"]) == expected +for pred in data["predictions"]: + values = list(pred["biodist"].values()) + assert len(values) == 7 + assert all(math.isfinite(float(x)) for x in values) + assert abs(sum(values) - 1.0) < 1e-4 +print(f"n_succeeded={data['n_succeeded']}, errors={len(data['errors'])}") +PY +} + +run_batch() { + local count=$1 + local use_llm=$2 + local batch_size=$3 + local response="/tmp/lnp-test-batch-${count}-${use_llm}.json" + local payload before peak current curl_pid start_ms end_ms + + payload=$(make_batch_payload "${count}" "${use_llm}" "${batch_size}") + before=$(nvidia-smi --id="${GPU_INDEX}" --query-gpu=memory.used \ + --format=csv,noheader,nounits | head -n 1 | tr -d ' ') + peak=${before} + start_ms=$(date +%s%3N) + + curl -fsS --max-time "${CURL_TIMEOUT}" \ + -H 'Content-Type: application/json' \ + -d "${payload}" "${API_URL}/predict/batch" >"${response}" & + curl_pid=$! + while kill -0 "${curl_pid}" 2>/dev/null; do + current=$(nvidia-smi --id="${GPU_INDEX}" --query-gpu=memory.used \ + --format=csv,noheader,nounits | head -n 1 | tr -d ' ') + if [ "${current}" -gt "${peak}" ]; then peak=${current}; fi + sleep 0.2 + done + wait "${curl_pid}" + end_ms=$(date +%s%3N) + + validate_batch_response "${response}" "${count}" + echo "use_llm=${use_llm}, count=${count}, outer_batch=${batch_size}" + echo "GPU used before=${before} MiB, observed peak=${peak} MiB, delta=$((peak - before)) MiB" + echo "elapsed=$((end_ms - start_ms)) ms" +} + +validate_mixed_errors() { + local response=/tmp/lnp-test-mixed.json + curl -fsS --max-time "${CURL_TIMEOUT}" \ + -H 'Content-Type: application/json' \ + --data-binary "@${CASE_DIR}/mixed_valid_invalid.json" \ + "${API_URL}/predict/batch" >"${response}" + python3 - "${response}" <<'PY' +import json, sys +data = json.load(open(sys.argv[1], encoding="utf-8")) +assert data["n_requested"] == 5, data +assert data["n_succeeded"] == 2, data +assert len(data["errors"]) == 3, data +assert {x["index"] for x in data["errors"]} == {2, 3, 4}, data["errors"] +print(json.dumps(data["errors"], ensure_ascii=False, indent=2)) +PY +} + +validate_optimize() { + local response=/tmp/lnp-test-optimize.json + curl -fsS --max-time "${CURL_TIMEOUT}" \ + -H 'Content-Type: application/json' \ + --data-binary "@${CASE_DIR}/optimize_smoke.json" \ + "${API_URL}/optimize" >"${response}" + python3 - "${response}" <<'PY' +import json, math, sys +data = json.load(open(sys.argv[1], encoding="utf-8")) +assert data["target_organ"] == "liver", data +assert 1 <= len(data["formulations"]) <= 3, data +for row in data["formulations"]: + assert math.isfinite(float(row["target_biodist"])), row + assert abs(sum(float(x) for x in row["all_biodist"].values()) - 1.0) < 1e-4, row +print(f"optimize returned {len(data['formulations'])} formulations") +PY +} + +run_case "health" validate_health +run_case "available organs" validate_organs +run_case "single prediction with LLM" validate_single +run_case "64 items without LLM (throughput baseline)" run_batch 64 false 64 +run_case "32 items with LLM (VRAM regression)" run_batch 32 true 32 +run_case "mixed valid/invalid items" validate_mixed_errors +run_case "small optimize search" validate_optimize + +echo +echo "===== SUMMARY =====" +echo "passed=${PASS}, failed=${FAIL}" +if [ "${FAIL}" -ne 0 ]; then + exit 1 +fi diff --git a/server_test_cases/run_lnp_batch_stress.sh b/server_test_cases/run_lnp_batch_stress.sh new file mode 100755 index 0000000..887ad08 --- /dev/null +++ b/server_test_cases/run_lnp_batch_stress.sh @@ -0,0 +1,146 @@ +#!/usr/bin/env bash +set -uo pipefail + +CASE_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +API_PORT=${API_PORT:-18000} +API_URL=${API_URL:-http://127.0.0.1:${API_PORT}} +GPU_INDEX=${GPU_INDEX:-0} +CURL_TIMEOUT=${CURL_TIMEOUT:-3600} +MAX_GPU_MIB=${MAX_GPU_MIB:-0} +PROFILE=${1:-repro} +RESULT_CSV=${RESULT_CSV:-"${CASE_DIR}/vram-results-$(date +%Y%m%d-%H%M%S).csv"} + +if ! command -v curl >/dev/null || ! command -v python3 >/dev/null; then + echo "curl and python3 are required" >&2 + exit 2 +fi +if ! command -v nvidia-smi >/dev/null; then + echo "nvidia-smi is required for the GPU memory test" >&2 + exit 2 +fi +if ! curl -fsS --max-time 30 "${API_URL}/" >/tmp/lnp-stress-health.json; then + echo "API is not reachable: ${API_URL}" >&2 + exit 2 +fi + +gpu_used_mib() { + nvidia-smi --id="${GPU_INDEX}" --query-gpu=memory.used --format=csv,noheader,nounits \ + | head -n 1 | tr -d ' ' +} + +now_ms() { + python3 -c 'import time; print(int(time.time() * 1000))' +} + +validate_response() { + local response=$1 expected=$2 + python3 - "${response}" "${expected}" <<'PY' +import json, math, sys +data = json.load(open(sys.argv[1], encoding="utf-8")) +expected = int(sys.argv[2]) +assert data["n_requested"] == expected, data +assert data["n_succeeded"] == expected, data +assert data["errors"] == [], data["errors"] +assert len(data["predictions"]) == expected, len(data["predictions"]) +for pred in data["predictions"]: + values = list(pred["biodist"].values()) + assert len(values) == 7 + assert all(math.isfinite(float(value)) for value in values) + assert abs(sum(values) - 1.0) < 1e-4, sum(values) +print(f"validated {expected} predictions") +PY +} + +run_payload() { + local label=$1 payload=$2 expected=$3 + local response="/tmp/lnp-${label}-response.json" + local http_file="/tmp/lnp-${label}-http.txt" + local before peak current curl_pid curl_status http_code start_ms end_ms elapsed delta status + + echo + echo "===== ${label} =====" + before=$(gpu_used_mib) + peak=${before} + start_ms=$(now_ms) + + curl -sS --max-time "${CURL_TIMEOUT}" -o "${response}" -w '%{http_code}' \ + -H 'Content-Type: application/json' --data-binary "@${payload}" \ + "${API_URL}/predict/batch" >"${http_file}" & + curl_pid=$! + while kill -0 "${curl_pid}" 2>/dev/null; do + current=$(gpu_used_mib 2>/dev/null || echo 0) + if [[ "${current}" =~ ^[0-9]+$ ]] && [ "${current}" -gt "${peak}" ]; then + peak=${current} + fi + sleep 0.1 + done + wait "${curl_pid}" + curl_status=$? + end_ms=$(now_ms) + http_code=$(cat "${http_file}" 2>/dev/null || echo 000) + elapsed=$((end_ms - start_ms)) + delta=$((peak - before)) + status=PASS + + if [ "${curl_status}" -ne 0 ] || [ "${http_code}" != "200" ]; then + status=FAIL + echo "request failed: curl_status=${curl_status}, HTTP=${http_code}" >&2 + sed -n '1,30p' "${response}" 2>/dev/null || true + elif ! validate_response "${response}" "${expected}"; then + status=FAIL + elif [ "${MAX_GPU_MIB}" -gt 0 ] && [ "${peak}" -gt "${MAX_GPU_MIB}" ]; then + status=FAIL + echo "peak ${peak} MiB exceeds MAX_GPU_MIB=${MAX_GPU_MIB}" >&2 + fi + + echo "${label}: status=${status}, before=${before} MiB, peak=${peak} MiB, delta=${delta} MiB, elapsed=${elapsed} ms" + printf '%s,%s,%s,%s,%s,%s,%s\n' \ + "${label}" "${status}" "${before}" "${peak}" "${delta}" "${elapsed}" "${http_code}" >>"${RESULT_CSV}" + [ "${status}" = PASS ] +} + +make_payload() { + local count=$1 outer_batch=$2 use_llm=$3 output=$4 + local llm_flag=--use-llm + [ "${use_llm}" = false ] && llm_flag=--no-use-llm + python3 "${CASE_DIR}/generate_mock_batch.py" \ + --count "${count}" --batch-size "${outer_batch}" "${llm_flag}" \ + --source-csv "${CASE_DIR}/../data/interim/internal.csv" --output "${output}" +} + +printf 'case,status,before_mib,peak_mib,delta_mib,elapsed_ms,http_code\n' >"${RESULT_CSV}" +FAILED=0 + +case "${PROFILE}" in + repro) + run_payload "llm-32-outer32" "${CASE_DIR}/mock_batch_32_llm.json" 32 || FAILED=1 + run_payload "llm-500-outer32" "${CASE_DIR}/mock_batch_500_llm.json" 500 || FAILED=1 + ;; + ladder) + for count in 1 8 16 32 64 128; do + payload="/tmp/lnp-ladder-${count}.json" + make_payload "${count}" "${count}" true "${payload}" + run_payload "llm-${count}-outer${count}" "${payload}" "${count}" || FAILED=1 + curl -fsS --max-time 30 "${API_URL}/" >/dev/null || break + done + ;; + full) + run_payload "baseline-no-llm-128" "${CASE_DIR}/mock_batch_128_no_llm.json" 128 || FAILED=1 + for count in 1 8 16 32 64 128; do + payload="/tmp/lnp-ladder-${count}.json" + make_payload "${count}" "${count}" true "${payload}" + run_payload "llm-${count}-outer${count}" "${payload}" "${count}" || FAILED=1 + curl -fsS --max-time 30 "${API_URL}/" >/dev/null || break + done + curl -fsS --max-time 30 "${API_URL}/" >/dev/null \ + && run_payload "llm-500-outer32" "${CASE_DIR}/mock_batch_500_llm.json" 500 || FAILED=1 + ;; + *) + echo "Usage: $0 [repro|ladder|full]" >&2 + exit 2 + ;; +esac + +echo +echo "Results: ${RESULT_CSV}" +exit "${FAILED}" diff --git a/server_test_cases/single_valid.json b/server_test_cases/single_valid.json new file mode 100644 index 0000000..f16d8b7 --- /dev/null +++ b/server_test_cases/single_valid.json @@ -0,0 +1,10 @@ +{ + "smiles": "CC(C)NCCNC(C)C", + "cationic_lipid_to_mrna_ratio": 10.0, + "cationic_lipid_mol_ratio": 35.0, + "phospholipid_mol_ratio": 16.0, + "cholesterol_mol_ratio": 46.5, + "peg_lipid_mol_ratio": 2.5, + "helper_lipid": "DOPE", + "route": "intravenous" +} diff --git a/server_test_cases/ui_batch_screening_32.csv b/server_test_cases/ui_batch_screening_32.csv new file mode 100644 index 0000000..a2ffd80 --- /dev/null +++ b/server_test_cases/ui_batch_screening_32.csv @@ -0,0 +1,33 @@ +name,SMILES +LNP-001,O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O)CCNC(C[C@@H](CN)CC(C)C)=O +LNP-002,O=C(OCCCCCCCCCCCCCC)CCN(CCC(OCCCCCCCCCCCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCCCC)=O)CCNC(CCC(NCC1=CC=C(B(O)O)C=C1)=O)=O +LNP-003,O=C(OCCCCCCN([C@H](C(NCCCN(CCCCCCOC(CCCCCCC)=O)CCCCCCOC(CCCCCCC)=O)=O)CO)CCCCCCOC(CCCCCCC)=O)CCCCCCC +LNP-004,O=C(OCCCCCCN([C@H](C(NCCCCCCN(CCCCCCOC(CCCCC)=O)CCCCCCOC(CCCCC)=O)=O)CO)CCCCCCOC(CCCCC)=O)CCCCC +LNP-005,O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCN(CC1=NC=CS1)CC2=NC=CS2)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O +LNP-006,O=C(OCCCCCCCCCCCCCC)CCN(CCC(OCCCCCCCCCCCCCC)=O)CCCCCCN(CCCCCCN(CCC(OCCCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCCCC)=O)CCCN1CCOCC1 +LNP-007,CCCCCCCCCCCC(OCCCCCCN(CCCCCCOC(CCCCCCCCCCC)=O)CCCOC([C@@H](N(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)CO)=O)=O +LNP-008,O=C(OCCCCCCN([C@H](C(NCCN(C)CCN(CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCOC(CCCCCCCCC=C)=O)=O)CO)CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCCCC=C +LNP-009,CC(CNC([C@@H](N(CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCOC(CCCCCCCCC=C)=O)CO)=O)(CN(CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCOC(CCCCCCCCC=C)=O)C +LNP-010,O=C(NCCN(CCCCN(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)CCCCN(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)CCCN +LNP-011,O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCN1CC(C(OC)=O)CC1=O)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O +LNP-012,CCCCCCCCCCCCOC(CCN([C@H](C(NCCCCCCN(CCC(OCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCC)=O)=O)CO)CCC(OCCCCCCCCCCCC)=O)=O +LNP-013,O=C([C@H](CO)N(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)N(CC1)CCN1C2=NC=CC=N2 +LNP-014,O=C(CN(CC(O)CCCCCCCC)CC(O)CCCCCCCC)NCCCN(CC(O)CCCCCCCC)CC(O)CCCCCCCC +LNP-015,CCCCCCCCCCCC(OCCCCCCN(CCN(C)CCNC([C@@H](N(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)CO)=O)CCCCCCOC(CCCCCCCCCCC)=O)=O +LNP-016,OC[C@@H](C(NCCCCCCN(CC(O)CCCCCCCCCC)CC(O)CCCCCCCCCC)=O)N(CC(CCCCCCCCCC)O)CC(CCCCCCCCCC)O +LNP-017,O=C(OCCCCCCCCCCCCCC)CCN(CCC(OCCCCCCCCCCCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCCCC)=O)CCNC(C[C@@H](CN)CC(C)C)=O +LNP-018,O=C(OCCCCCCCC/C=C\C/C=C\CCCCC)CCN(CCC(OCCCCCCCC/C=C\C/C=C\CCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCC/C=C\C/C=C\CCCCC)=O)CCC(OCCCCCCCC/C=C\C/C=C\CCCCC)=O)CCNC(CCC(NCCC1=CC=C(O)C(O)=C1)=O)=O +LNP-019,O=C(NCCCCCCN(CCCCCCCCCCCC)CCCCCCCCCCCC)[C@H](C)N(CCCCCCCCCCCC)CCCCCCCCCCCC +LNP-020,O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCN(CCC(F)(F)F)CCC(F)(F)F)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O +LNP-021,CCCCCCCCC(CN(CCCNC([C@@H](N(CC(CCCCCCCC)O)CC(CCCCCCCC)O)CO)=O)CC(CCCCCCCC)O)O +LNP-022,O=C([C@H](CO)N(CCCCCCOC(CCCCCCCCC=C)=O)CCCCCCOC(CCCCCCCCC=C)=O)N(CC1)CCN1C2=NC=CC=N2 +LNP-023,CCCCCCCCCCCC(OCCCCCCN([C@H](C(NCCCCCCN(CCCCCCOC(CCCCCCCCCCC)=O)CCCCCCOC(CCCCCCCCCCC)=O)=O)CO)CCCCCCOC(CCCCCCCCCCC)=O)=O +LNP-024,O=C(OCCCCCCCCCCCC)CCN(CCC(OCCCCCCCCCCCC)=O)CCCCCCN(CCCCCCN(CCC(OCCCCCCCCCCCC)=O)CCC(OCCCCCCCCCCCC)=O)CCN1CCOCC1 +LNP-025,O=C(CN(CCCCCCCCCCCCCC)CCCCCCCCCCCCCC)NCCCN(CCCCCCCCCCCCCC)CCCCCCCCCCCCCC +LNP-026,O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCCCN(CCCCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O)CCNC(CCC(NCCC1=CC=C(O)C(O)=C1)=O)=O +LNP-027,O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O)CCNC([C@@H](N)CCCCN)=O +LNP-028,O=C(CN(CCCCCCCCCCCC)CCCCCCCCCCCC)NCCCN(CCCCCCCCCCCC)CCCCCCCCCCCC +LNP-029,O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCN(CC1=COC=N1)CC2=COC=N2)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O +LNP-030,O=C(OCCCCCCCC)CCN(CCC(OCCCCCCCC)=O)CCCCN(CCNC(CCCN)=O)CCCCN(CCC(OCCCCCCCC)=O)CCC(OCCCCCCCC)=O +LNP-031,O=C(CN(CCCCCCCCCC)CCCCCCCCCC)NCCCN(CCCCCCCCCC)CCCCCCCCCC +LNP-032,O=C(OCCCCCCCC/C=C\C/C=C\CCCCC)CCN(CCC(OCCCCCCCC/C=C\C/C=C\CCCCC)=O)CCCCN(CCCCN(CCC(OCCCCCCCC/C=C\C/C=C\CCCCC)=O)CCC(OCCCCCCCC/C=C\C/C=C\CCCCC)=O)CCNC(CCCN)=O diff --git a/server_test_cases/ui_batch_screening_matrix.csv b/server_test_cases/ui_batch_screening_matrix.csv new file mode 100644 index 0000000..4dd6a44 --- /dev/null +++ b/server_test_cases/ui_batch_screening_matrix.csv @@ -0,0 +1,3 @@ +head,Tail-A,Tail-B +H-01,CC(C)NCCNC(C)C,CCCCCCCCCCN(C)C +H-02,CCN(CC)CCOC(=O)CCCC,CCCCCCCCCCCCN(C)C diff --git a/server_test_cases/ui_batch_screening_mixed.csv b/server_test_cases/ui_batch_screening_mixed.csv new file mode 100644 index 0000000..e6d0122 --- /dev/null +++ b/server_test_cases/ui_batch_screening_mixed.csv @@ -0,0 +1,7 @@ +name,SMILES +valid-1,CC(C)NCCNC(C)C +valid-2,CCCCCCCCCCN(C)C +invalid-backend-1,C1INVALIDCCCC +filtered-short,abc +duplicate-valid-1,CC(C)NCCNC(C)C +invalid-backend-2,NOT_A_VALID_CCCC diff --git a/tests/test_llm_memory.py b/tests/test_llm_memory.py new file mode 100644 index 0000000..6faac34 --- /dev/null +++ b/tests/test_llm_memory.py @@ -0,0 +1,58 @@ +"""LLM 显存保护逻辑的轻量测试（不需要下载模型）。""" + +from types import MethodType, SimpleNamespace +from unittest.mock import patch + +import torch + +from lnp_ml.modeling.layers.llm_prompt import LLMPromptEncoder, _positive_int_env + + +def _empty_encoder(micro_batch: int = 2) -> LLMPromptEncoder: + encoder = LLMPromptEncoder.__new__(LLMPromptEncoder) + torch.nn.Module.__init__(encoder) + encoder.inference_batch_size = micro_batch + return encoder + + +def test_invalid_inference_batch_env_falls_back() -> None: + with patch.dict("os.environ", {"LLM_INFERENCE_BATCH_SIZE": "invalid"}): + assert _positive_int_env("LLM_INFERENCE_BATCH_SIZE", 4) == 4 + with patch.dict("os.environ", {"LLM_INFERENCE_BATCH_SIZE": "0"}): + assert _positive_int_env("LLM_INFERENCE_BATCH_SIZE", 4) == 4 + + +def test_softrag_inference_is_microbatched() -> None: + encoder = _empty_encoder(micro_batch=2).eval() + calls = [] + + def fake_chunk(self, smiles, chem, tab, device): + calls.append((list(smiles), chem.clone(), tab.clone())) + return chem[:, 0, :] + tab[:, 0, :] + + encoder._encode_softrag_chunk = MethodType(fake_chunk, encoder) + chem = torch.arange(5 * 2 * 3, dtype=torch.float32).reshape(5, 2, 3) + tab = torch.ones(5, 1, 3) + + with torch.no_grad(): + output = encoder._encode_softrag( + [f"SMILES-{i}" for i in range(5)], chem, tab, torch.device("cpu") + ) + + assert [len(smiles) for smiles, _, _ in calls] == [2, 2, 1] + assert torch.equal(output, chem[:, 0, :] + tab[:, 0, :]) + + +def test_qwen_forward_disables_kv_cache() -> None: + encoder = _empty_encoder() + encoder._is_qwen = True + received = {} + + class FakeBackbone(torch.nn.Module): + def forward(self, **kwargs): + received.update(kwargs) + return SimpleNamespace(last_hidden_state=torch.zeros(1, 1, 1)) + + encoder.encoder = FakeBackbone() + encoder._encoder_forward(input_ids=torch.ones(1, 1, dtype=torch.long)) + assert received["use_cache"] is False