From e85e7c2ccff48171ed8dc175a4a1cf24ad87d305 Mon Sep 17 00:00:00 2001 From: "chengyan.ts" Date: Wed, 29 Jul 2026 15:29:33 +0800 Subject: [PATCH 1/2] feat: Add an agentic trajectory detail page to the dashboard --- areno/dashboard/dist/assets/index-DF61sRZi.js | 197 --------- .../{index-CIfovKBG.css => index-bundle.css} | 2 +- areno/dashboard/dist/assets/index-bundle.js | 131 ++++++ areno/dashboard/dist/index.html | 4 +- areno/dashboard/server.py | 119 ++++++ dashboard/src/main.jsx | 278 ++++++++++++- dashboard/src/styles.css | 294 ++++++++++++++ tests/test_dashboard_api.py | 380 ++++++++++++++++++ tests/test_dashboard_trajectory.py | 256 ++++++++++++ 9 files changed, 1452 insertions(+), 209 deletions(-) delete mode 100644 areno/dashboard/dist/assets/index-DF61sRZi.js rename areno/dashboard/dist/assets/{index-CIfovKBG.css => index-bundle.css} (63%) create mode 100644 areno/dashboard/dist/assets/index-bundle.js create mode 100644 tests/test_dashboard_api.py create mode 100644 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a=t.current;a&&(a.scrollTop=a.scrollHeight)},[e.length]),(0,m.jsxs)("div",{className:"codeCard",ref:t,children:[(0,m.jsxs)("div",{className:"codeTitle",children:[(0,m.jsx)(gl,{size:14})," Logs"]}),(0,m.jsx)("pre",{children:e.slice(-80).join(` +`)||"No logs yet."})]})}function aA({config:e,setConfig:t,onStart:a}){let l=String(e.algo||"sft").toLowerCase(),n=lA(l),r=(o,u)=>t({...e,[o]:u});return(0,m.jsxs)("div",{className:"launcherSections",children:[n.map(o=>(0,m.jsxs)("div",{className:"launcherSection",children:[(0,m.jsxs)("div",{className:"launcherSectionHeader",children:[(0,m.jsx)("strong",{children:o.title}),o.note&&(0,m.jsx)("span",{children:o.note})]}),(0,m.jsx)("div",{className:"formGrid",children:o.fields.map(u=>(0,m.jsx)(ms,{label:u.label,value:e[u.key],onChange:i=>r(u.key,i),compact:u.compact,type:u.type,options:u.options},u.key))})]},o.title)),(0,m.jsxs)("button",{className:"primaryButton launchButton wide",onClick:a,children:[(0,m.jsx)(an,{size:16})," Start train"]})]})}function lA(e){let t=["gspo","grpo","ppo"].includes(e),a=t,l=e==="dpo",n=e==="ppo",r=e==="gspo",o=e==="grpo",u=[{title:"Basic",note:"model, data, and trainer loop",fields:[Pp("algo","Algorithm",["sft","dpo","gspo","grpo","ppo"],!0),F("ckpt","Checkpoint"),Pp("model_hub","Model hub",["modelscope","hf"],!0),F("dataset_path","Dataset path"),F("dataset_loader_fn","Dataset loader"),F("epochs","Epochs",!0),F("max_steps","Max steps",!0)]},{title:"Runtime",note:"parallelism, memory, and kernels",fields:[F("world_size","World",!0),F("tp_size","TP",!0),Pp("attn_backend","Attention",["flash","native"],!0),za("activation_checkpointing","Activation ckpt"),za("drop_rollout_state","Drop rollout state"),za("eager_decode","Eager decode"),za("disable_thinking","Disable thinking")]},{title:"Batching",note:"controls train and rollout memory",fields:[F("batch_size","Batch",!0),...t?[F("n_samples","Samples",!0),F("max_running_prompts","Running prompts",!0)]:[],F("mini_bs","Mini BS",!0),F("score_micro_bs","Score micro BS",!0),F("gradient_accumulation_steps","Grad accum",!0)]},{title:t?"Rollout":"Sequence",note:t?"generation and sampling":"token limits for supervised data",fields:[F("max_prompt_tokens","Prompt tokens",!0),F("max_new_tokens","New tokens",!0),...a?[F("max_context_len","Context",!0),F("agent_fn","Agent fn"),F("agent_timeout_s","Agent timeout",!0),za("train_tool_results","Train tool results")]:[],...t?[F("temperature","Temp",!0),F("top_k","Top K",!0),F("top_p","Top P",!0),za("greedy","Greedy")]:[]]},{title:"Optimizer",note:"policy optimizer settings",fields:[F("lr","LR",!0),F("min_lr","Min LR",!0),F("lr_decay_steps","Decay steps",!0),F("lr_decay_style","Decay style",!0),F("adam_beta1","Adam beta1",!0),F("adam_beta2","Adam beta2",!0),F("weight_decay","Weight decay",!0),F("grad_clip_norm","Grad clip",!0),za("adam_8bit","8-bit Adam")]}],i=[];return(l||n)&&i.push(F("ref_ckpt","Reference ckpt")),t&&i.push(F("reward_fn_path","Reward fn")),n&&i.push(F("reward_ckpt","Reward ckpt"),F("critic_ckpt","Critic ckpt"),F("critic_lr","Critic LR",!0),F("critic_warmup_steps","Critic warmup",!0)),r&&i.push(F("gspo_clip_eps","GSPO clip",!0)),o&&i.push(F("grpo_clip_eps","GRPO clip",!0)),l&&i.push(F("dpo_beta","DPO beta",!0)),n&&i.push(za("use_kl_loss","Use KL loss"),F("kl_loss_coef","KL coef",!0),F("kl_loss_type","KL type",!0),F("clip_eps","Clip eps",!0),F("clip_ratio_c","Clip ratio C",!0),F("value_clip_eps","Value clip",!0),F("value_loss_coef","Value coef",!0),F("gamma","Gamma",!0),F("lam","Lambda",!0)),i.length>0&&u.push({title:"Algorithm",note:`${e.toUpperCase()}-specific roles and loss`,fields:i}),u.push({title:"Probe",note:"optional smoke and auto tune helpers",fields:[za("tune_params","Tune params"),F("mem_frac","Memory frac",!0),F("tune_max_samples","Tune samples",!0)]},{title:"Output",note:"checkpointing, metrics, and escape hatch",fields:[F("save_path","Save path"),F("save_interval","Save interval",!0),F("metrics_dir","Metrics dir"),F("extra_args","Extra args")]}),u}function F(e,t,a=!1){return{key:e,label:t,compact:a}}function Pp(e,t,a,l=!1){return{key:e,label:t,compact:l,type:"select",options:a}}function za(e,t){return{key:e,label:t,compact:!0,type:"checkbox"}}function nA({config:e,setConfig:t,onStart:a}){return(0,m.jsxs)("div",{className:"formGrid",children:[[["model_path","Model path"],["model_hub","Model hub"],["host","Host"],["port","Port"],["world_size","World"],["tp_size","TP"],["max_running_prompts","Running prompts"],["default_max_tokens","Default max tokens"],["decode_progress_interval_s","Progress interval"],["attn_backend","Attention backend"],["eager_decode","Eager decode"],["disable_thinking","Disable thinking"],["extra_args","Extra args"]].map(([l,n])=>(0,m.jsx)(ms,{label:n,value:e[l],onChange:r=>t({...e,[l]:r}),compact:l!=="model_path"},l)),(0,m.jsxs)("button",{className:"primaryButton launchButton wide",onClick:a,children:[(0,m.jsx)(an,{size:16})," Start serve"]})]})}function ms({label:e,value:t,onChange:a,compact:l,type:n="text",options:r=[]}){return n==="checkbox"?(0,m.jsxs)("label",{className:De("field","compact","checkField"),children:[(0,m.jsx)("input",{type:"checkbox",checked:!!t,onChange:o=>a(o.target.checked)}),(0,m.jsx)("span",{children:e})]}):(0,m.jsxs)("label",{className:De("field",l&&"compact"),children:[(0,m.jsx)("span",{children:e}),n==="select"?(0,m.jsx)("select",{value:t??"",onChange:o=>a(o.target.value),children:r.map(o=>(0,m.jsx)("option",{value:o,children:o},o))}):(0,m.jsx)("input",{value:t??"",onChange:o=>a(o.target.value)})]})}function jo({title:e,text:t}){return(0,m.jsxs)("div",{className:"empty",children:[(0,m.jsx)(sr,{size:18}),(0,m.jsx)("strong",{children:e}),(0,m.jsx)("span",{children:t})]})}var OS=typeof document<"u"?document.getElementById("root"):null;OS&&(0,_S.createRoot)(OS).render((0,m.jsx)(I2,{}));export{I2 as App,V2 as MetricChart,P2 as metricNamesFrom,G2 as resolveActiveMetricName}; +/*! Bundled license information: + +react/cjs/react.production.js: + (** + * @license React + * react.production.js + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + *) + +scheduler/cjs/scheduler.production.js: + (** + * @license React + * scheduler.production.js + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + *) + +react-dom/cjs/react-dom.production.js: + (** + * @license React + * react-dom.production.js + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + *) + +react-dom/cjs/react-dom-client.production.js: + (** + * @license React + * react-dom-client.production.js + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + *) + +react/cjs/react-jsx-runtime.production.js: + (** + * @license React + * react-jsx-runtime.production.js + * + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + *) + +lucide-react/dist/esm/shared/src/utils.js: +lucide-react/dist/esm/defaultAttributes.js: +lucide-react/dist/esm/Icon.js: +lucide-react/dist/esm/createLucideIcon.js: +lucide-react/dist/esm/icons/activity.js: +lucide-react/dist/esm/icons/bot.js: +lucide-react/dist/esm/icons/box.js: +lucide-react/dist/esm/icons/circle-stop.js: +lucide-react/dist/esm/icons/cpu.js: +lucide-react/dist/esm/icons/file-text.js: +lucide-react/dist/esm/icons/history.js: +lucide-react/dist/esm/icons/layers.js: +lucide-react/dist/esm/icons/message-square.js: +lucide-react/dist/esm/icons/moon.js: +lucide-react/dist/esm/icons/play.js: +lucide-react/dist/esm/icons/plus.js: +lucide-react/dist/esm/icons/refresh-cw.js: +lucide-react/dist/esm/icons/route.js: +lucide-react/dist/esm/icons/send.js: +lucide-react/dist/esm/icons/server.js: +lucide-react/dist/esm/icons/settings-2.js: +lucide-react/dist/esm/icons/square-terminal.js: +lucide-react/dist/esm/icons/sun.js: +lucide-react/dist/esm/icons/timer.js: +lucide-react/dist/esm/lucide-react.js: + (** + * @license lucide-react v0.468.0 - ISC + * + * This source code is licensed under the ISC license. + * See the LICENSE file in the root directory of this source tree. + *) +*/ diff --git a/areno/dashboard/dist/index.html b/areno/dashboard/dist/index.html index b3a17010..aca09293 100644 --- a/areno/dashboard/dist/index.html +++ b/areno/dashboard/dist/index.html @@ -4,8 +4,8 @@ AReno Dashboard - - + +
diff --git a/areno/dashboard/server.py b/areno/dashboard/server.py index 9bb8b4d4..83f54468 100644 --- a/areno/dashboard/server.py +++ b/areno/dashboard/server.py @@ -1480,6 +1480,114 @@ def execute_agent_tool(tool_call: dict[str, Any]) -> dict[str, Any]: return {"name": name, "ok": False, "error": str(exc)} +def validate_trajectory_sample(sample: dict[str, Any]) -> list[str]: + """Validate a rollout sample dict before rendering its trajectory detail. + + Returns a list of human-readable validation error strings; an empty list + means the sample is valid for trajectory rendering. + """ + errors: list[str] = [] + if not isinstance(sample, dict): + return ["sample is not a dict"] + if sample.get("kind") != "agentic": + return [f"sample kind '{sample.get('kind', 'missing')}' is not 'agentic'"] + if not sample.get("messages"): + errors.append("messages field is empty or missing") + if not sample.get("prompt") and not sample.get("messages"): + errors.append("prompt field is missing") + return errors + + +def build_trajectory_detail(sample: dict[str, Any]) -> dict[str, Any]: + """Build a chronologically ordered trajectory detail from an agentic sample. + + Reads existing local rollout-sample artifacts only; never executes tool + calls. Output fields: + - events: ordered list of {type, role, content, tool_calls, tool_result} + - final_answer: str + - token_counts: {prompt_tokens, response_tokens, loss_mask_true, loss_mask_total} + - training_mask: {loss_mask_true, loss_mask_total, first_loss_idx} + - end_reason: str + - raw: the original sample dict (privacy-safe: no full training data) + """ + errors = validate_trajectory_sample(sample) + if errors: + return {"error": "; ".join(errors), "valid": False} + + messages = sample.get("messages") or [] + tool_calls = sample.get("tool_calls") or [] + tool_results = sample.get("tool_results") or [] + events: list[dict[str, Any]] = [] + + # Walk messages in order, interleaving tool results where they match. + tool_result_queue = list(tool_results) + for message in messages: + role = str(message.get("role") or "unknown") + content = message.get("content") or "" + msg_tool_calls = message.get("tool_calls") or [] + entry: dict[str, Any] = {"type": "message", "role": role, "content": str(content)} + if msg_tool_calls: + entry["tool_calls"] = msg_tool_calls + if role == "tool" and tool_result_queue: + entry["tool_result"] = tool_result_queue.pop(0) + events.append(entry) + + # Append any remaining tool results that did not match a tool message. + for result in tool_result_queue: + events.append({"type": "tool_result", "role": "tool", "tool_result": result}) + + loss_mask = sample.get("loss_mask") or [] + tokens = sample.get("tokens") or [] + loss_mask_true = int(sample.get("loss_mask_true") or sum(1 for m in loss_mask if m)) + loss_mask_total = int(sample.get("loss_mask_total") or len(loss_mask)) + first_loss_idx = int(sample.get("first_loss_idx") or -1) + + return { + "valid": True, + "step": sample.get("step", 0), + "prompt_idx": sample.get("prompt_idx", -1), + "sample_idx": sample.get("sample_idx", -1), + "kind": "agentic", + "events": events, + "final_answer": sample.get("final_answer") or "", + "token_counts": { + "prompt_tokens": len(sample.get("prompt_tokens") or []), + "response_tokens": len(sample.get("response_tokens") or []), + "loss_mask_true": loss_mask_true, + "loss_mask_total": loss_mask_total, + "token_row_len": len(tokens), + }, + "training_mask": { + "loss_mask_true": loss_mask_true, + "loss_mask_total": loss_mask_total, + "first_loss_idx": first_loss_idx, + "truncated": len(loss_mask) < loss_mask_total if loss_mask_total else False, + }, + "end_reason": sample.get("end_reason") or "completed", + "tool_call_count": len(tool_calls), + "tool_result_count": len(tool_results), + } + + +def trajectory_detail(job_id: str | None, step: int, prompt_idx: int, sample_idx: int) -> dict[str, Any]: + """Resolve a single trajectory sample from a job's rollout samples.""" + job = STATE.get_job(job_id) + if job is None: + return {"error": "job not found", "valid": False} + candidates = [ + s for s in job.samples + if int(s.get("step", 0)) == step + and int(s.get("prompt_idx", -1)) == prompt_idx + and int(s.get("sample_idx", -1)) == sample_idx + ] + if not candidates: + return {"error": "trajectory sample not found", "valid": False} + sample = candidates[-1] + detail = build_trajectory_detail(sample) + detail["raw"] = sample + return detail + + class Handler(BaseHTTPRequestHandler): def log_message(self, fmt: str, *args: Any) -> None: sys.stderr.write(f"{self.log_date_time_string()} - {fmt % args}\n") @@ -1500,6 +1608,17 @@ def do_GET(self) -> None: metric_name = query.get("name", [""])[0] limit = int(query.get("limit", ["500"])[0] or 500) self.json({"metric": metric_name, "points": STATE.metric_series(job_id, metric_name, limit=limit)}) + elif path.startswith("/api/jobs/") and path.endswith("/trajectory"): + job_id = path.split("/")[-2] + query = urllib.parse.parse_qs(urllib.parse.urlsplit(self.path).query) + try: + step = int(query.get("step", ["0"])[0] or 0) + prompt_idx = int(query.get("prompt_idx", ["-1"])[0] or -1) + sample_idx = int(query.get("sample_idx", ["-1"])[0] or -1) + except ValueError: + self.error("step, prompt_idx, and sample_idx must be integers") + return + self.json(trajectory_detail(job_id, step, prompt_idx, sample_idx)) elif path.startswith("/api/jobs/"): job = STATE.get_job(path.split("/")[-1]) if not job: diff --git a/dashboard/src/main.jsx b/dashboard/src/main.jsx index e5c110fd..3e58331c 100644 --- a/dashboard/src/main.jsx +++ b/dashboard/src/main.jsx @@ -11,11 +11,13 @@ import { FileText, History, Layers, + Map, MessageSquare, Moon, Plus, Play, RefreshCw, + Route, Send, Server, Settings2, @@ -286,6 +288,7 @@ function App() { const pages = [ { id: "jobs", label: "Jobs", icon: }, + { id: "trajectory", label: "Trajectory", icon: }, { id: "runtime", label: "Runtime", icon: }, { id: "launcher", label: "Launcher", icon: }, { id: "agent", label: "Agent", icon: }, @@ -294,6 +297,7 @@ function App() { jobs: selectedJob ? [selectedJob.name, `${selectedJob.kind} · ${selectedJob.status} · step ${selectedJob.step ?? 0}`] : ["Jobs", "Open an AReno train or serve task to inspect metrics, samples, config, and logs."], + trajectory: ["Trajectory Detail", "Inspect agentic rollout trajectories: messages, tool calls, loss masks, and token counts."], runtime: ["Runtime Environment", "Review areno check, areno env, dependencies, GPU state, and repository context."], launcher: ["Task Launcher", "Start low-intrusion AReno train or serve subprocesses from explicit configs."], agent: ["Agent Console", "Chat with an operations agent using the selected job context."], @@ -512,6 +516,9 @@ function App() { } function renderPage() { + if (activePage === "trajectory") { + return ; + } if (activePage === "runtime") { return (
@@ -1350,6 +1357,98 @@ function normalizeDisplayName(name) { .trim(); } +const AGENTIC_ALGOS = ["gspo", "grpo", "ppo"]; + +function TrajectoryPage({ jobs, jobDetail, selectedJobId, setSelectedJobId }) { + const jobList = jobs.data?.jobs || []; + const agenticJobs = jobList.filter((job) => job.kind === "train" && AGENTIC_ALGOS.some((algo) => job.command?.some?.((part) => part === `--algo` && job.command?.[job.command.indexOf(part) + 1] === algo) || job.name?.includes(algo))); + const detailJob = jobDetail.data?.job; + const samples = detailJob?.samples || []; + const agenticSamples = samples.filter((sample) => sample.kind === "agentic"); + + const stepOptions = useMemo( + () => Array.from(new Set(agenticSamples.map((sample) => Number(sample.step || 0)))).sort((a, b) => b - a), + [agenticSamples] + ); + const [selectedStep, setSelectedStep] = useState(""); + const [selectedSampleKey, setSelectedSampleKey] = useState(""); + const [viewMode, setViewMode] = useState("trajectory"); + + useEffect(() => { + if (!stepOptions.length) { + setSelectedStep(""); + setSelectedSampleKey(""); + return; + } + if (selectedStep === "" || !stepOptions.includes(Number(selectedStep))) { + setSelectedStep(String(stepOptions[0])); + setSelectedSampleKey(""); + } + }, [selectedStep, stepOptions]); + + const stepSamples = selectedStep === "" ? [] : agenticSamples.filter((sample) => Number(sample.step || 0) === Number(selectedStep)); + const sampleOptions = stepSamples.map((sample, index) => ({ + key: sampleKey(sample, index), + label: `prompt ${sample.prompt_idx ?? "?"} · sample ${sample.sample_idx ?? "?"}`, + sample, + })); + const activeOption = sampleOptions.find((option) => option.key === selectedSampleKey) || sampleOptions[sampleOptions.length - 1] || null; + const sample = activeOption?.sample || null; + + return ( +
+
+
+

Trajectory Detail

+

Select a job and rollout sample to inspect the full agentic trajectory.

+
+
+
+ + {agenticSamples.length > 0 && ( + <> + + +
+ + +
+ + )} +
+
+ {!selectedJobId ? ( + + ) : !agenticSamples.length ? ( + + ) : sample ? ( + viewMode === "raw" ? ( + 
{JSON.stringify(sample, null, 2)}
+ ) : ( + + ) + ) : ( + + )} +
+
+ ); +} + function SampleView({ samples }) { const orderedSamples = useMemo( () => @@ -1367,6 +1466,7 @@ function SampleView({ samples }) { ); const [selectedStep, setSelectedStep] = useState(""); const [selectedSampleKey, setSelectedSampleKey] = useState(""); + const [viewMode, setViewMode] = useState("default"); useEffect(() => { if (!stepOptions.length) { setSelectedStep(""); @@ -1387,6 +1487,8 @@ function SampleView({ samples }) { const activeOption = sampleOptions.find((option) => option.key === selectedSampleKey) || sampleOptions[sampleOptions.length - 1] || null; const sample = activeOption?.sample || null; + const isAgentic = sample?.kind === "agentic"; + const effectiveViewMode = isAgentic ? viewMode : "default"; return (
@@ -1399,20 +1501,32 @@ function SampleView({ samples }) { + {isAgentic && ( +
+ + +
+ )}
)}
{sample ? ( -
-
- Prompt -

{sample.prompt || "n/a"}

-
-
- Completion -

{sample.completion || "n/a"}

+ effectiveViewMode === "raw" ? ( + 
{JSON.stringify(sample, null, 2)}
+ ) : isAgentic ? ( + + ) : ( +
+
+ Prompt +

{sample.prompt || "n/a"}

+
+
+ Completion +

{sample.completion || "n/a"}

+
-
+ ) ) : (
No rollout sample captured yet.
)} @@ -1424,6 +1538,152 @@ function sampleKey(sample, index) { return `${sample.step ?? "x"}-${sample.prompt_idx ?? "x"}-${sample.sample_idx ?? "x"}-${index}`; } +function TrajectoryDetailView({ sample }) { + const [expandedTools, setExpandedTools] = useState({}); + const messages = sample.messages || []; + const toolCalls = sample.tool_calls || []; + const toolResults = sample.tool_results || []; + const lossMask = sample.loss_mask || []; + const lossMaskTrue = sample.loss_mask_true || 0; + const lossMaskTotal = sample.loss_mask_total || 0; + const firstLossIdx = sample.first_loss_idx ?? -1; + const finalAnswer = sample.final_answer || ""; + const tokenRowLen = (sample.tokens || []).length; + + const toolResultQueue = [...toolResults]; + const events = messages.map((message) => { + const role = message.role || "unknown"; + const entry = { type: "message", role, content: String(message.content || "") }; + if (message.tool_calls) entry.tool_calls = message.tool_calls; + if (role === "tool" && toolResultQueue.length) entry.tool_result = toolResultQueue.shift(); + return entry; + }); + while (toolResultQueue.length) { + events.push({ type: "tool_result", role: "tool", tool_result: toolResultQueue.shift() }); + } + + const tokenCounts = { + promptTokens: (sample.prompt_tokens || []).length, + responseTokens: (sample.response_tokens || []).length, + lossMaskTrue, + lossMaskTotal, + tokenRowLen, + }; + + function toggleTool(index) { + setExpandedTools((prev) => ({ ...prev, [index]: !prev[index] })); + } + + function summarizeToolOutput(result) { + const text = JSON.stringify(result, null, 2); + return text.length > 200 ? text.slice(0, 200) + "..." : text; + } + + return ( +
+
+
+ Step{sample.step ?? "?"} +
+
+ Prompt{sample.prompt_idx ?? "?"} +
+
+ Sample{sample.sample_idx ?? "?"} +
+
+ Tool calls{toolCalls.length} +
+
+ Tool results{toolResults.length} +
+
+ Token row{tokenRowLen} +
+
+ End reason{sample.end_reason || "completed"} +
+
+ +
+
+ {tokenCounts.promptTokens} +
+
+ {tokenCounts.responseTokens} +
+
+ {lossMaskTrue}/{lossMaskTotal}{firstLossIdx >= 0 ? ` (idx ${firstLossIdx})` : ""} +
+ {lossMask.length > 0 && ( +
+ {lossMask.map((enabled, idx) => ( + + ))} +
+ )} + {lossMaskTotal > lossMask.length && ( + ...truncated ({lossMask.length}/{lossMaskTotal} shown) + )} +
+ +
+ {events.map((event, index) => { + if (event.type === "tool_result") { + const expanded = expandedTools[index]; + return ( +
+
toggleTool(index)}> + result + {event.tool_result?.name || "tool"} + {expanded ? "collapse" : "expand"} +
+ 
+                  {JSON.stringify(event.tool_result, null, 2)}
+
+
+ ); + } + return ( +
+
+ {event.role} +
+ {event.content && } + {event.tool_calls?.length > 0 && ( +
+ {event.tool_calls.map((call, ci) => { + const fn = call.function || {}; + return ( +
+ tool call + {fn.name || call.name || "tool"} + 
{fn.arguments || call.arguments || "{}"}
+
+ ); + })} +
+ )} + {event.tool_result && ( + 
+                  {summarizeToolOutput(event.tool_result)}
+
+ )} +
+ ); + })} +
+ + {finalAnswer && ( +
+
Final answer
+ +
+ )} +
+ ); +} + function ConfigView({ config, launch }) { const settings = config && Object.keys(config).length ? config : launch || {}; const sections = normalizeConfigSections(settings); diff --git a/dashboard/src/styles.css b/dashboard/src/styles.css index 6511695d..dddd99fa 100644 --- a/dashboard/src/styles.css +++ b/dashboard/src/styles.css @@ -2094,3 +2094,297 @@ textarea:focus { text-align: left; } } + +/* Trajectory detail view */ +.trajectoryDetail { + display: grid; + gap: 12px; +} + +.trajectorySummary { + display: flex; + flex-wrap: wrap; + gap: 10px; + padding: 10px 12px; + border: 1px solid var(--deck-line); + border-radius: var(--radius-md); + background: rgba(167, 186, 210, 0.03); +} + +.trajectorySummaryItem { + display: flex; + align-items: center; + gap: 6px; +} + +.trajectorySummaryItem span { + color: var(--deck-muted); + font-size: 11px; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.04em; +} + +.trajectorySummaryItem strong { + color: var(--deck-ink); + font-size: 13px; + font-weight: 650; +} + +.trajectoryTokenCounts { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(140px, 1fr)); + gap: 10px; + padding: 10px 12px; + border: 1px solid var(--deck-line); + border-radius: var(--radius-md); + background: rgba(167, 186, 210, 0.03); +} + +.trajectoryTokenItem { + display: grid; + gap: 3px; +} + +.trajectoryTokenItem label { + color: var(--deck-muted); + font-size: 11px; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.04em; +} + +.trajectoryTokenItem strong { + color: var(--deck-ink); + font-size: 13px; + font-variant-numeric: tabular-nums; +} + +.trajectoryMaskBar { + grid-column: 1 / -1; + display: flex; + flex-wrap: wrap; + gap: 1px; + padding: 6px 0; +} + +.maskCell { + display: inline-block; + width: 8px; + height: 14px; + border-radius: 2px; +} + +.maskCell.train { + background: var(--deck-green); +} + +.maskCell.skip { + background: rgba(167, 186, 210, 0.15); +} + +.maskTruncated { + grid-column: 1 / -1; + color: var(--deck-muted); + font-size: 11px; + font-style: italic; +} + +.trajectoryEvents { + display: grid; + gap: 8px; +} + +.trajectoryEvent { + border: 1px solid var(--deck-line); + border-radius: var(--radius-md); + padding: 10px 12px; + background: rgba(167, 186, 210, 0.02); +} + +.trajectoryEvent.message.assistant { + border-left: 3px solid var(--deck-cyan); +} + +.trajectoryEvent.message.user { + border-left: 3px solid var(--deck-violet); +} + +.trajectoryEvent.message.tool { + border-left: 3px solid var(--deck-amber); +} + +.trajectoryEvent.message.system { + border-left: 3px solid var(--deck-muted); +} + +.trajectoryEvent.toolResult { + border-left: 3px solid var(--deck-green); +} + +.eventHeader { + display: flex; + align-items: center; + gap: 8px; + cursor: default; + margin-bottom: 6px; +} + +.trajectoryEvent.toolResult .eventHeader { + cursor: pointer; +} + +.eventBadge { + display: inline-flex; + align-items: center; + justify-content: center; + min-width: 50px; + height: 18px; + border-radius: 999px; + padding: 0 7px; + font-size: 10px; + font-weight: 700; + text-transform: uppercase; + letter-spacing: 0.04em; +} + +.eventBadge.assistant { + color: var(--deck-cyan); + background: rgba(56, 215, 255, 0.1); +} + +.eventBadge.user { + color: var(--deck-violet); + background: rgba(169, 147, 255, 0.1); +} + +.eventBadge.tool { + color: var(--deck-amber); + background: rgba(255, 200, 87, 0.1); +} + +.eventBadge.system { + color: var(--deck-muted); + background: rgba(114, 129, 150, 0.1); +} + +.eventBadge.result { + color: var(--deck-green); + background: rgba(87, 227, 155, 0.1); +} + +.eventHeader b { + color: var(--deck-ink); + font-size: 12px; + font-family: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Monaco, Consolas, monospace; + font-weight: 650; +} + +.eventHeader em { + color: var(--deck-muted); + font-size: 11px; + font-style: normal; +} + +.eventToolCalls { + display: grid; + gap: 6px; + margin-top: 8px; +} + +.eventToolCall { + display: grid; + gap: 4px; + padding: 6px 8px; + border: 1px solid var(--deck-line); + border-radius: var(--radius-sm); + background: rgba(56, 215, 255, 0.04); +} + +.toolCallBadge { + display: inline-flex; + align-items: center; + min-width: 50px; + height: 16px; + border-radius: 999px; + padding: 0 6px; + font-size: 9px; + font-weight: 700; + text-transform: uppercase; + color: var(--deck-cyan); + background: rgba(56, 215, 255, 0.08); +} + +.eventToolCall b { + color: var(--deck-ink); + font-size: 12px; + font-family: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Monaco, Consolas, monospace; +} + +.toolCallArgs { + max-height: 100px; + overflow: auto; + font-size: 11px; + color: var(--deck-text); + background: transparent; + border: 0; + padding: 2px 0; +} + +.eventOutput { + max-height: 200px; + overflow: auto; + font-size: 11px; + color: var(--deck-text); + background: rgba(4, 8, 13, 0.22); + border: 1px solid var(--deck-line); + border-radius: var(--radius-sm); + padding: 8px; + margin-top: 6px; +} + +.eventOutput.collapsed { + max-height: 0; + overflow: hidden; + padding: 0; + border: 0; +} + +.trajectoryFinalAnswer { + border: 1px solid var(--deck-line); + border-radius: var(--radius-md); + padding: 12px; + background: rgba(87, 227, 155, 0.04); +} + +.rawJsonView { + max-height: 480px; + overflow: auto; + font-size: 11px; + color: var(--deck-text); + background: rgba(4, 8, 13, 0.22); + border: 1px solid var(--deck-line); + border-radius: var(--radius-md); + padding: 12px; + margin: 0; +} + +.trajectoryPage { + display: grid; + gap: 16px; +} + +.trajectoryControls { + display: flex; + flex-wrap: wrap; + align-items: center; + gap: 10px; +} + +.trajectoryControls select { + min-width: 160px; +} + +.trajectoryContent { + min-height: 200px; +} diff --git a/tests/test_dashboard_api.py b/tests/test_dashboard_api.py new file mode 100644 index 00000000..596c50ff --- /dev/null +++ b/tests/test_dashboard_api.py @@ -0,0 +1,380 @@ +"""End-to-end API tests for the AReno dashboard server. + +Starts the server on a random port, creates train jobs via the HTTP API, +and exercises every endpoint via urllib. +""" + +from __future__ import annotations + +import json +import socket +import subprocess +import sys +import time +import urllib.error +import urllib.parse +import urllib.request +from pathlib import Path + +import pytest + +SERVER_PATH = str(Path(__file__).resolve().parent.parent / "areno" / "dashboard" / "server.py") + + +def _free_port() -> int: + s = socket.socket() + s.bind(("127.0.0.1", 0)) + port = s.getsockname()[1] + s.close() + return port + + +@pytest.fixture(scope="module") +def server(): + port = _free_port() + proc = subprocess.Popen( + [sys.executable, SERVER_PATH, "--host", "127.0.0.1", "--port", str(port)], + stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, + env={**__import__("os").environ, "PYTHONPATH": str(Path(__file__).resolve().parent.parent)}, + ) + base = f"http://127.0.0.1:{port}" + # Wait for server to be ready + for _ in range(30): + try: + urllib.request.urlopen(base + "/api/env", timeout=2) + break + except Exception: + time.sleep(0.2) + else: + out = proc.stdout.read(2000) if proc.stdout else "" + pytest.fail(f"server did not start:\n{out}") + + yield base + + proc.terminate() + proc.wait(timeout=5) + + +def _get(base, path): + url = base + path + with urllib.request.urlopen(url, timeout=10) as resp: + return resp.status, json.loads(resp.read().decode()) + + +def _post(base, path, payload): + data = json.dumps(payload).encode() + req = urllib.request.Request(base + path, data=data, method="POST") + req.add_header("Content-Type", "application/json") + with urllib.request.urlopen(req, timeout=10) as resp: + return resp.status, json.loads(resp.read().decode()) + + +def _create_train_job(server, **overrides): + config = {"algo": "sft", "ckpt": "Qwen/Qwen3-0.6B", "dataset_path": "/tmp/data.jsonl"} + config.update(overrides) + _, body = _post(server, "/api/jobs/train", config) + return body["job"]["id"] + + +# --------------------------------------------------------------------------- +# /api/env +# --------------------------------------------------------------------------- + +class TestApiEnv: + def test_env_returns_ok(self, server): + status, body = _get(server, "/api/env") + assert status == 200 + assert isinstance(body, dict) + + +# --------------------------------------------------------------------------- +# /api/jobs (list) +# --------------------------------------------------------------------------- + +class TestApiJobsList: + def test_jobs_list_returns_ok(self, server): + status, body = _get(server, "/api/jobs") + assert status == 200 + assert "jobs" in body + assert isinstance(body["jobs"], list) + + def test_created_job_appears_in_list(self, server): + job_id = _create_train_job(server, algo="gspo", ckpt="Qwen/Qwen3-1.7B") + _, body = _get(server, "/api/jobs") + ids = [j["id"] for j in body["jobs"]] + assert job_id in ids + + +# --------------------------------------------------------------------------- +# /api/jobs/train (POST) +# --------------------------------------------------------------------------- + +class TestApiJobsTrain: + def test_train_returns_job_summary(self, server): + _, body = _post(server, "/api/jobs/train", { + "algo": "sft", + "ckpt": "Qwen/Qwen3-0.6B", + "dataset_path": "/tmp/data.jsonl", + }) + assert "job" in body + job = body["job"] + assert job["kind"] == "train" + assert "id" in job + assert "status" in job + + +# --------------------------------------------------------------------------- +# /api/jobs/ (GET detail) +# --------------------------------------------------------------------------- + +class TestApiJobDetail: + def test_job_detail_returns_full_job(self, server): + job_id = _create_train_job(server) + status, body = _get(server, f"/api/jobs/{job_id}") + assert status == 200 + assert body["job"]["id"] == job_id + assert "command" in body["job"] + assert "status" in body["job"] + + def test_job_detail_not_found(self, server): + with pytest.raises(urllib.error.HTTPError) as exc_info: + _get(server, "/api/jobs/nonexistent-id") + assert exc_info.value.code == 404 + + +# --------------------------------------------------------------------------- +# /api/jobs//stop (POST) +# --------------------------------------------------------------------------- + +class TestApiJobStop: + def test_stop_job(self, server): + job_id = _create_train_job(server, ckpt="test-stop") + status, body = _post(server, f"/api/jobs/{job_id}/stop", {}) + assert status == 200 + assert body["stopped"] is True + + +# --------------------------------------------------------------------------- +# /api/jobs//metrics (GET) +# --------------------------------------------------------------------------- + +class TestApiJobMetrics: + def test_metrics_list(self, server): + job_id = _create_train_job(server) + status, body = _get(server, f"/api/jobs/{job_id}/metrics") + assert status == 200 + assert "metrics" in body + assert isinstance(body["metrics"], list) + + +# --------------------------------------------------------------------------- +# /api/jobs//trajectory (GET) +# --------------------------------------------------------------------------- + +class TestApiTrajectory: + def test_trajectory_empty_job_returns_not_found(self, server): + job_id = _create_train_job(server, algo="gspo") + params = urllib.parse.urlencode({"step": 0, "prompt_idx": 0, "sample_idx": 0}) + status, body = _get(server, f"/api/jobs/{job_id}/trajectory?{params}") + assert status == 200 + assert body["valid"] is False + + def test_trajectory_job_not_found(self, server): + params = urllib.parse.urlencode({"step": 0, "prompt_idx": 0, "sample_idx": 0}) + status, body = _get(server, f"/api/jobs/bad-id/trajectory?{params}") + assert body["valid"] is False + + def test_trajectory_invalid_params_returns_error(self, server): + job_id = _create_train_job(server) + with pytest.raises(urllib.error.HTTPError): + _get(server, f"/api/jobs/{job_id}/trajectory?step=abc") + + +# --------------------------------------------------------------------------- +# /api/jobs/serve (POST) +# --------------------------------------------------------------------------- + +class TestApiJobsServe: + def test_serve_returns_job_summary(self, server): + _, body = _post(server, "/api/jobs/serve", { + "model_path": "Qwen/Qwen3-0.6B", + }) + assert "job" in body + assert body["job"]["kind"] == "serve" + + +# --------------------------------------------------------------------------- +# /api/agent (POST) +# --------------------------------------------------------------------------- + +class TestApiAgent: + def test_agent_returns_response(self, server): + status, body = _post(server, "/api/agent", { + "messages": [{"role": "user", "content": "list files in current directory"}], + }) + assert status == 200 + assert "response" in body or "error" in body + + +# --------------------------------------------------------------------------- +# Illegal input — invalid payloads and malformed requests +# --------------------------------------------------------------------------- + +class TestIllegalInput: + def test_train_empty_config(self, server): + """POST /api/jobs/train with empty dict should still create a job.""" + _, body = _post(server, "/api/jobs/train", {}) + assert "job" in body + assert body["job"]["kind"] == "train" + + def test_train_null_values_ignored(self, server): + """None values should not crash command building.""" + _, body = _post(server, "/api/jobs/train", { + "algo": "sft", "ckpt": None, "dataset_path": None, + "batch_size": None, "lr": None, + }) + assert "job" in body + + def test_train_nonexistent_algo(self, server): + """An unknown algo string should still produce a command (validation + is deferred to the CLI, not the dashboard server).""" + _, body = _post(server, "/api/jobs/train", { + "algo": "fake_algo", "ckpt": "x", "dataset_path": "/tmp/x.jsonl", + }) + assert "job" in body + cmd = body["job"].get("command") or [] + assert "--algo" in cmd + + def test_serve_empty_config(self, server): + """POST /api/jobs/serve with no model_path should not crash.""" + _, body = _post(server, "/api/jobs/serve", {}) + assert "job" in body + assert body["job"]["kind"] == "serve" + + def test_serve_null_model_path(self, server): + _, body = _post(server, "/api/jobs/serve", {"model_path": None}) + assert "job" in body + + def test_stop_nonexistent_job(self, server): + """Stopping a job id that doesn't exist should return stopped=False.""" + _, body = _post(server, "/api/jobs/bad-id/stop", {}) + assert body["stopped"] is False + + def test_agent_empty_messages(self, server): + _, body = _post(server, "/api/agent", {"messages": []}) + assert "response" in body or "error" in body + + def test_agent_no_messages_key(self, server): + _, body = _post(server, "/api/agent", {}) + assert "response" in body or "error" in body + + def test_agent_garbage_payload(self, server): + """Non-dict messages should not crash the server.""" + _, body = _post(server, "/api/agent", {"messages": "not a list"}) + assert "response" in body or "error" in body + + +# --------------------------------------------------------------------------- +# Boundary — extreme values, missing fields, edge cases +# --------------------------------------------------------------------------- + +class TestBoundaryValues: + def test_train_empty_string_values(self, server): + """Empty strings should be treated like None and omitted from command.""" + _, body = _post(server, "/api/jobs/train", { + "algo": "", "ckpt": "", "dataset_path": "", + }) + assert "job" in body + + def test_train_very_long_string(self, server): + """A 10k-char checkpoint path should not crash.""" + long_path = "a" * 10000 + _, body = _post(server, "/api/jobs/train", { + "algo": "sft", "ckpt": long_path, "dataset_path": "/tmp/x.jsonl", + }) + assert "job" in body + + def test_train_special_chars_in_ckpt(self, server): + """Special shell characters should be handled by shlex, not injected.""" + _, body = _post(server, "/api/jobs/train", { + "algo": "sft", "ckpt": "model; rm -rf /", "dataset_path": "/tmp/x.jsonl", + }) + assert "job" in body + + def test_train_numeric_zero_values(self, server): + """Zero is a valid numeric value and should appear in the command.""" + _, body = _post(server, "/api/jobs/train", { + "algo": "sft", "ckpt": "x", "dataset_path": "/tmp/x.jsonl", + "batch_size": 0, "lr": 0, "epochs": 0, + }) + assert "job" in body + + def test_train_negative_values(self, server): + """Negative numbers should pass through (validated by CLI later).""" + _, body = _post(server, "/api/jobs/train", { + "algo": "sft", "ckpt": "x", "dataset_path": "/tmp/x.jsonl", + "lr": -1, "batch_size": -10, + }) + assert "job" in body + + def test_train_boolean_flags(self, server): + """Boolean true flags should be appended as bare flags.""" + _, body = _post(server, "/api/jobs/train", { + "algo": "sft", "ckpt": "x", "dataset_path": "/tmp/x.jsonl", + "greedy": True, "adam_8bit": True, + "activation_checkpointing": True, + }) + assert "job" in body + + def test_train_false_boolean_flags(self, server): + """Boolean false flags should NOT be appended.""" + _, body = _post(server, "/api/jobs/train", { + "algo": "sft", "ckpt": "x", "dataset_path": "/tmp/x.jsonl", + "greedy": False, "adam_8bit": False, + "activation_checkpointing": False, + }) + assert "job" in body + + def test_train_extra_unknown_fields(self, server): + """Unknown config fields should be silently ignored.""" + _, body = _post(server, "/api/jobs/train", { + "algo": "sft", "ckpt": "x", "dataset_path": "/tmp/x.jsonl", + "unknown_field": "value", "random_key": 123, + }) + assert "job" in body + + def test_trajectory_negative_indices(self, server): + job_id = _create_train_job(server, algo="gspo") + params = urllib.parse.urlencode({"step": -1, "prompt_idx": -1, "sample_idx": -1}) + _, body = _get(server, f"/api/jobs/{job_id}/trajectory?{params}") + assert body["valid"] is False + + def test_trajectory_very_large_indices(self, server): + job_id = _create_train_job(server, algo="gspo") + params = urllib.parse.urlencode({"step": 999999, "prompt_idx": 999999, "sample_idx": 999999}) + _, body = _get(server, f"/api/jobs/{job_id}/trajectory?{params}") + assert body["valid"] is False + + def test_trajectory_missing_all_params(self, server): + """No query params should use defaults (step=0, prompt_idx=-1, sample_idx=-1).""" + job_id = _create_train_job(server, algo="gspo") + _, body = _get(server, f"/api/jobs/{job_id}/trajectory") + assert body["valid"] is False + + def test_metrics_unregistered_job(self, server): + _, body = _get(server, "/api/jobs/bad-id/metrics") + assert status_or_ok(body) + assert "metrics" in body + + def test_stop_already_stopped_job(self, server): + """Stopping a job twice should not crash; server is idempotent.""" + job_id = _create_train_job(server, ckpt="double-stop") + _, body1 = _post(server, f"/api/jobs/{job_id}/stop", {}) + assert body1["stopped"] is True + _, body2 = _post(server, f"/api/jobs/{job_id}/stop", {}) + assert "stopped" in body2 + + +def status_or_ok(body): + return isinstance(body, dict) + diff --git a/tests/test_dashboard_trajectory.py b/tests/test_dashboard_trajectory.py new file mode 100644 index 00000000..bb10c820 --- /dev/null +++ b/tests/test_dashboard_trajectory.py @@ -0,0 +1,256 @@ +"""Focused CPU tests for the agentic trajectory detail page in the dashboard server. + +Tests cover: + - Core logic: build_trajectory_detail success path + - Invalid input: non-agentic samples, missing fields, malformed data + - Boundary values: empty messages, large tool output, truncated loss mask + - Privacy-safe defaults: raw sample is included but not executed + - Deterministic output for the same input +""" + +from __future__ import annotations + +import json + +import pytest + +from areno.dashboard.server import ( + build_trajectory_detail, + validate_trajectory_sample, +) + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + +def _agentic_sample(**overrides): + """Create a minimal valid agentic rollout sample.""" + base = { + "kind": "agentic", + "step": 1, + "prompt_idx": 0, + "sample_idx": 0, + "prompt": "What is 2+2?", + "messages": [ + {"role": "user", "content": "What is 2+2?"}, + { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": "call-1", + "type": "function", + "function": {"name": "calculate", "arguments": '{"expr": "2+2"}'}, + } + ], + }, + {"role": "tool", "tool_call_id": "call-1", "content": "4"}, + {"role": "assistant", "content": "The answer is 4."}, + ], + "final_answer": "The answer is 4.", + "tool_calls": [ + {"id": "call-1", "type": "function", "function": {"name": "calculate", "arguments": '{"expr": "2+2"}'}} + ], + "tool_results": [ + {"name": "calculate", "ok": True, "result": 4}, + ], + "loss_mask_true": 8, + "loss_mask_total": 12, + "first_loss_idx": 4, + "loss_mask": [False, False, False, False, True, True, True, True, True, True, True, True], + "tokens": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], + "prompt_tokens": [1, 2, 3, 4], + "response_tokens": [5, 6, 7, 8, 9, 10, 11, 12], + "end_reason": "completed", + } + base.update(overrides) + return base + + +# --------------------------------------------------------------------------- +# validate_trajectory_sample +# --------------------------------------------------------------------------- + +class TestValidateTrajectorySample: + def test_valid_agentic_sample_returns_no_errors(self): + sample = _agentic_sample() + assert validate_trajectory_sample(sample) == [] + + def test_non_agentic_kind_is_invalid(self): + sample = _agentic_sample(kind="rollout") + errors = validate_trajectory_sample(sample) + assert len(errors) == 1 + assert "not 'agentic'" in errors[0] + + def test_missing_kind_is_invalid(self): + sample = _agentic_sample() + del sample["kind"] + errors = validate_trajectory_sample(sample) + assert "missing" in errors[0] + + def test_empty_messages_is_invalid(self): + sample = _agentic_sample(messages=[]) + errors = validate_trajectory_sample(sample) + assert any("messages" in e for e in errors) + + def test_non_dict_input_is_invalid(self): + errors = validate_trajectory_sample("not a dict") # type: ignore[arg-type] + assert len(errors) == 1 + assert "not a dict" in errors[0] + + +# --------------------------------------------------------------------------- +# build_trajectory_detail — success path +# --------------------------------------------------------------------------- + +class TestBuildTrajectoryDetailSuccess: + def test_returns_valid_flag(self): + detail = build_trajectory_detail(_agentic_sample()) + assert detail["valid"] is True + + def test_preserves_step_and_indices(self): + sample = _agentic_sample(step=7, prompt_idx=3, sample_idx=2) + detail = build_trajectory_detail(sample) + assert detail["step"] == 7 + assert detail["prompt_idx"] == 3 + assert detail["sample_idx"] == 2 + + def test_events_are_in_chronological_order(self): + detail = build_trajectory_detail(_agentic_sample()) + roles = [event["role"] for event in detail["events"]] + assert roles == ["user", "assistant", "tool", "assistant"] + + def test_tool_calls_are_attached_to_assistant_message(self): + detail = build_trajectory_detail(_agentic_sample()) + assistant_event = detail["events"][1] + assert "tool_calls" in assistant_event + assert assistant_event["tool_calls"][0]["function"]["name"] == "calculate" + + def test_tool_result_is_interleaved_with_tool_message(self): + detail = build_trajectory_detail(_agentic_sample()) + tool_event = detail["events"][2] + assert tool_event["role"] == "tool" + assert "tool_result" in tool_event + assert tool_event["tool_result"]["name"] == "calculate" + + def test_final_answer_is_extracted(self): + detail = build_trajectory_detail(_agentic_sample()) + assert detail["final_answer"] == "The answer is 4." + + def test_token_counts_are_computed(self): + sample = _agentic_sample(prompt_tokens=[1, 2, 3], response_tokens=[4, 5]) + detail = build_trajectory_detail(sample) + assert detail["token_counts"]["prompt_tokens"] == 3 + assert detail["token_counts"]["response_tokens"] == 2 + + def test_training_mask_summary(self): + detail = build_trajectory_detail(_agentic_sample(loss_mask_true=8, loss_mask_total=12, first_loss_idx=4)) + assert detail["training_mask"]["loss_mask_true"] == 8 + assert detail["training_mask"]["loss_mask_total"] == 12 + assert detail["training_mask"]["first_loss_idx"] == 4 + + def test_end_reason_is_preserved(self): + detail = build_trajectory_detail(_agentic_sample(end_reason="max_tokens")) + assert detail["end_reason"] == "max_tokens" + + def test_tool_call_and_result_counts(self): + detail = build_trajectory_detail(_agentic_sample()) + assert detail["tool_call_count"] == 1 + assert detail["tool_result_count"] == 1 + + def test_default_end_reason_when_missing(self): + sample = _agentic_sample() + del sample["end_reason"] + detail = build_trajectory_detail(sample) + assert detail["end_reason"] == "completed" + + +# --------------------------------------------------------------------------- +# build_trajectory_detail — invalid input +# --------------------------------------------------------------------------- + +class TestBuildTrajectoryDetailInvalid: + def test_non_agentic_returns_error(self): + detail = build_trajectory_detail({"kind": "rollout", "messages": [{"role": "user", "content": "hi"}]}) + assert detail["valid"] is False + assert "error" in detail + + def test_missing_messages_returns_error(self): + detail = build_trajectory_detail({"kind": "agentic", "prompt": "p"}) + assert detail["valid"] is False + assert "messages" in detail["error"] + + +# --------------------------------------------------------------------------- +# Boundary values +# --------------------------------------------------------------------------- + +class TestBoundaryValues: + def test_empty_tool_results_with_tool_messages(self): + sample = _agentic_sample(tool_results=[], messages=[ + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": "hello"}, + ]) + detail = build_trajectory_detail(sample) + assert detail["valid"] is True + assert detail["tool_result_count"] == 0 + + def test_truncated_loss_mask_is_marked(self): + sample = _agentic_sample(loss_mask=[True, True], loss_mask_true=2, loss_mask_total=100) + detail = build_trajectory_detail(sample) + assert detail["training_mask"]["truncated"] is True + + def test_non_truncated_loss_mask(self): + sample = _agentic_sample( + loss_mask=[True] * 12, + loss_mask_true=12, + loss_mask_total=12, + ) + detail = build_trajectory_detail(sample) + assert detail["training_mask"]["truncated"] is False + + def test_large_tool_result_does_not_crash(self): + big_result = {"name": "search", "ok": True, "data": "x" * 5000} + sample = _agentic_sample(tool_results=[big_result]) + detail = build_trajectory_detail(sample) + assert detail["valid"] is True + assert detail["tool_result_count"] == 1 + + def test_no_final_answer(self): + sample = _agentic_sample(final_answer="") + detail = build_trajectory_detail(sample) + assert detail["final_answer"] == "" + + +# --------------------------------------------------------------------------- +# Determinism +# --------------------------------------------------------------------------- + +class TestDeterminism: + def test_same_input_produces_same_output(self): + sample = _agentic_sample() + detail1 = build_trajectory_detail(json.loads(json.dumps(sample))) + detail2 = build_trajectory_detail(json.loads(json.dumps(sample))) + assert detail1 == detail2 + + +# --------------------------------------------------------------------------- +# Privacy-safe defaults +# --------------------------------------------------------------------------- + +class TestPrivacySafeDefaults: + def test_never_includes_full_training_data_in_detail_fields(self): + sample = _agentic_sample() + detail = build_trajectory_detail(sample) + # Detail should not contain raw tokens in the structured output + # (only counts). The "raw" key is added by trajectory_detail(), not + # build_trajectory_detail(). + assert "tokens" not in detail + assert "loss_mask" not in detail + + def test_loss_mask_only_exposed_as_counts(self): + detail = build_trajectory_detail(_agentic_sample()) + assert "loss_mask" not in detail + assert "training_mask" in detail + assert isinstance(detail["training_mask"]["loss_mask_true"], int) From a279377d2705143a49bab8fa48dd3e5afd7327dc Mon Sep 17 00:00:00 2001 From: "chengyan.ts" Date: Fri, 31 Jul 2026 11:47:05 +0800 Subject: [PATCH 2/2] feat: add privacy-safe raw control, integration tests, and docs for trajectory detail - trajectory_detail() now defaults to omitting the raw sample dict; pass include_raw=True (or ?include_raw=true query param) to attach it - HTTP route parses include_raw query parameter for the /api/jobs//trajectory endpoint - Add TestTrajectoryDetailIntegration covering job/sample resolution, error paths, include_raw behavior, and large-trace robustness (7 new tests) - Add 'Dashboard trajectory detail page' section to docs/cli/observability.rst with API reference, response field table, and curl example Closes #267 --- areno/dashboard/server.py | 31 +++++-- docs/cli/observability.rst | 124 ++++++++++++++++++++++++++ tests/test_dashboard_trajectory.py | 134 +++++++++++++++++++++++++++-- 3 files changed, 277 insertions(+), 12 deletions(-) diff --git a/areno/dashboard/server.py b/areno/dashboard/server.py index 83f54468..e1f5b71f 100644 --- a/areno/dashboard/server.py +++ b/areno/dashboard/server.py @@ -1506,9 +1506,12 @@ def build_trajectory_detail(sample: dict[str, Any]) -> dict[str, Any]: - events: ordered list of {type, role, content, tool_calls, tool_result} - final_answer: str - token_counts: {prompt_tokens, response_tokens, loss_mask_true, loss_mask_total} - - training_mask: {loss_mask_true, loss_mask_total, first_loss_idx} + - training_mask: {loss_mask_true, loss_mask_total, first_loss_idx, truncated} - end_reason: str - - raw: the original sample dict (privacy-safe: no full training data) + - tool_call_count, tool_result_count: int + + The raw sample dict is NOT included here; call trajectory_detail() with + include_raw=True to attach it under the "raw" key when needed. """ errors = validate_trajectory_sample(sample) if errors: @@ -1569,13 +1572,25 @@ def build_trajectory_detail(sample: dict[str, Any]) -> dict[str, Any]: } -def trajectory_detail(job_id: str | None, step: int, prompt_idx: int, sample_idx: int) -> dict[str, Any]: - """Resolve a single trajectory sample from a job's rollout samples.""" +def trajectory_detail( + job_id: str | None, + step: int, + prompt_idx: int, + sample_idx: int, + *, + include_raw: bool = False, +) -> dict[str, Any]: + """Resolve a single trajectory sample from a job's rollout samples. + + By default the response omits the raw sample dict to keep output + privacy-safe. Pass ``include_raw=True`` to attach it under the ``raw`` key. + """ job = STATE.get_job(job_id) if job is None: return {"error": "job not found", "valid": False} candidates = [ - s for s in job.samples + s + for s in job.samples if int(s.get("step", 0)) == step and int(s.get("prompt_idx", -1)) == prompt_idx and int(s.get("sample_idx", -1)) == sample_idx @@ -1584,7 +1599,8 @@ def trajectory_detail(job_id: str | None, step: int, prompt_idx: int, sample_idx return {"error": "trajectory sample not found", "valid": False} sample = candidates[-1] detail = build_trajectory_detail(sample) - detail["raw"] = sample + if include_raw: + detail["raw"] = sample return detail @@ -1618,7 +1634,8 @@ def do_GET(self) -> None: except ValueError: self.error("step, prompt_idx, and sample_idx must be integers") return - self.json(trajectory_detail(job_id, step, prompt_idx, sample_idx)) + include_raw = query.get("include_raw", ["false"])[0].lower() in ("1", "true", "yes") + self.json(trajectory_detail(job_id, step, prompt_idx, sample_idx, include_raw=include_raw)) elif path.startswith("/api/jobs/"): job = STATE.get_job(path.split("/")[-1]) if not job: diff --git a/docs/cli/observability.rst b/docs/cli/observability.rst index 110f710d..675ebcf8 100644 --- a/docs/cli/observability.rst +++ b/docs/cli/observability.rst @@ -175,3 +175,127 @@ When a trajectory is dropped for exceeding the model context window, counts, message counts, assistant turn counts, tool-result counts, and a short prompt preview. This is the fastest way to debug overlong agentic examples without dumping every token in every trajectory. + +Dashboard trajectory detail page +-------------------------------- + +The AReno dashboard includes a **Trajectory** page that renders the full +contents of an agentic rollout sample in chronological order. Open the +dashboard, select a train job that has agentic samples, pick a step and +sample, and the page renders: + +* **Events timeline** — model messages (user / assistant / tool), tool calls + attached to the originating assistant message, and interleaved tool results. + Long tool output is collapsed by default; click to expand. +* **Final answer** — the last assistant response extracted from the sample. +* **Token counts** — prompt tokens, response tokens, and total token-row + length. +* **Training-mask status** — ``loss_mask_true`` / ``loss_mask_total``, + ``first_loss_idx``, a visual mask bar, and a ``truncated`` flag when the + stored mask is shorter than the declared total. +* **End reason** — completion cause (defaults to ``completed`` when missing). + +Tool calls are displayed as read-only text; the dashboard never executes a +displayed call. Long tool results are truncated to 200 characters in the inline +view and can be expanded in the standalone tool-result card. + +A **Raw JSON** toggle is available for inspecting the full sample dict +(messages, tokens, loss_mask, etc.). The structured view omits raw token and +loss-mask sequences — only counts are surfaced — to keep the default output +privacy-safe. + +API endpoint +~~~~~~~~~~~~ + +.. code-block:: text + + GET /api/jobs//trajectory?step=&prompt_idx=&sample_idx=&include_raw= + +Query parameters: + +``step`` + Training step of the desired sample (integer, required). + +``prompt_idx`` + Prompt index within the step (integer, required). + +``sample_idx`` + Sample index within the prompt (integer, required). + +``include_raw`` + Whether to attach the full sample dict under the ``raw`` key. Defaults to + ``false``. Set to ``true`` to inspect tokens, loss_mask, and other fields + not present in the structured detail. + +Response fields (when ``valid`` is ``true``): + +.. list-table:: + :header-rows: 1 + :widths: 28 72 + + * - Field + - Meaning + * - ``valid`` + - ``true`` if the sample is a valid agentic trajectory. + * - ``step`` / ``prompt_idx`` / ``sample_idx`` + - Locating indices from the sample. + * - ``events`` + - Chronologically ordered list of message and tool-result entries. + * - ``final_answer`` + - Last assistant response string (may be empty). + * - ``token_counts`` + - ``prompt_tokens``, ``response_tokens``, ``loss_mask_true``, + ``loss_mask_total``, ``token_row_len``. + * - ``training_mask`` + - ``loss_mask_true``, ``loss_mask_total``, ``first_loss_idx``, + ``truncated``. + * - ``end_reason`` + - Completion cause string; defaults to ``"completed"``. + * - ``tool_call_count`` / ``tool_result_count`` + - Number of tool calls and results in the sample. + * - ``raw`` + - Full original sample dict. **Only present when ``include_raw=true``.** + +When ``valid`` is ``false``, the response contains an ``error`` field with a +human-readable message instead of the fields above. + +Example +~~~~~~~ + +.. code-block:: bash + + curl "http://127.0.0.1:8765/api/jobs//trajectory?step=1&prompt_idx=0&sample_idx=0" + +Returns the structured detail without raw training data: + +.. code-block:: json + + { + "valid": true, + "step": 1, + "prompt_idx": 0, + "sample_idx": 0, + "kind": "agentic", + "events": [ + {"type": "message", "role": "user", "content": "What is 2+2?"}, + {"type": "message", "role": "assistant", "content": "", "tool_calls": [...]}, + {"type": "message", "role": "tool", "content": "4", "tool_result": {"name": "calculate", "ok": true, "result": 4}}, + {"type": "message", "role": "assistant", "content": "The answer is 4."} + ], + "final_answer": "The answer is 4.", + "token_counts": {"prompt_tokens": 4, "response_tokens": 8, "loss_mask_true": 8, "loss_mask_total": 12, "token_row_len": 12}, + "training_mask": {"loss_mask_true": 8, "loss_mask_total": 12, "first_loss_idx": 4, "truncated": false}, + "end_reason": "completed", + "tool_call_count": 1, + "tool_result_count": 1 + } + +Limitations +~~~~~~~~~~~ + +* Only ``kind == "agentic"`` samples are supported. Rollout samples without + agent messages return a validation error. +* The page reads existing local rollout-sample artifacts; it does not execute + model inference or tool calls. +* Raw token and loss-mask sequences are omitted from the structured response + by default. Use ``include_raw=true`` when full inspection is needed. diff --git a/tests/test_dashboard_trajectory.py b/tests/test_dashboard_trajectory.py index bb10c820..5284ac2f 100644 --- a/tests/test_dashboard_trajectory.py +++ b/tests/test_dashboard_trajectory.py @@ -6,6 +6,9 @@ - Boundary values: empty messages, large tool output, truncated loss mask - Privacy-safe defaults: raw sample is included but not executed - Deterministic output for the same input + - Integration: trajectory_detail resolves samples from registered jobs, + validates query parameters, and controls raw-sample exposure via + include_raw """ from __future__ import annotations @@ -15,15 +18,18 @@ import pytest from areno.dashboard.server import ( + STATE, + Job, build_trajectory_detail, + trajectory_detail, validate_trajectory_sample, ) - # --------------------------------------------------------------------------- # Fixtures # --------------------------------------------------------------------------- + def _agentic_sample(**overrides): """Create a minimal valid agentic rollout sample.""" base = { @@ -72,6 +78,7 @@ def _agentic_sample(**overrides): # validate_trajectory_sample # --------------------------------------------------------------------------- + class TestValidateTrajectorySample: def test_valid_agentic_sample_returns_no_errors(self): sample = _agentic_sample() @@ -104,6 +111,7 @@ def test_non_dict_input_is_invalid(self): # build_trajectory_detail — success path # --------------------------------------------------------------------------- + class TestBuildTrajectoryDetailSuccess: def test_returns_valid_flag(self): detail = build_trajectory_detail(_agentic_sample()) @@ -170,6 +178,7 @@ def test_default_end_reason_when_missing(self): # build_trajectory_detail — invalid input # --------------------------------------------------------------------------- + class TestBuildTrajectoryDetailInvalid: def test_non_agentic_returns_error(self): detail = build_trajectory_detail({"kind": "rollout", "messages": [{"role": "user", "content": "hi"}]}) @@ -186,12 +195,16 @@ def test_missing_messages_returns_error(self): # Boundary values # --------------------------------------------------------------------------- + class TestBoundaryValues: def test_empty_tool_results_with_tool_messages(self): - sample = _agentic_sample(tool_results=[], messages=[ - {"role": "user", "content": "hi"}, - {"role": "assistant", "content": "hello"}, - ]) + sample = _agentic_sample( + tool_results=[], + messages=[ + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": "hello"}, + ], + ) detail = build_trajectory_detail(sample) assert detail["valid"] is True assert detail["tool_result_count"] == 0 @@ -227,6 +240,7 @@ def test_no_final_answer(self): # Determinism # --------------------------------------------------------------------------- + class TestDeterminism: def test_same_input_produces_same_output(self): sample = _agentic_sample() @@ -239,6 +253,7 @@ def test_same_input_produces_same_output(self): # Privacy-safe defaults # --------------------------------------------------------------------------- + class TestPrivacySafeDefaults: def test_never_includes_full_training_data_in_detail_fields(self): sample = _agentic_sample() @@ -254,3 +269,112 @@ def test_loss_mask_only_exposed_as_counts(self): assert "loss_mask" not in detail assert "training_mask" in detail assert isinstance(detail["training_mask"]["loss_mask_true"], int) + + +# --------------------------------------------------------------------------- +# Integration: trajectory_detail (job + sample resolution, include_raw) +# --------------------------------------------------------------------------- + + +@pytest.fixture() +def _registered_job(): + """Register a job with one agentic sample in the global STATE, clean up after.""" + job = Job( + kind="train", + name="test trajectory job", + command=["echo", "noop"], + config={}, + metrics_dir=None, + ) + sample = _agentic_sample() + job.samples.append(sample) + job._sample_keys.add((sample["step"], sample["prompt_idx"], sample["sample_idx"])) + STATE.jobs[job.id] = job + yield job + STATE.jobs.pop(job.id, None) + + +class TestTrajectoryDetailIntegration: + def test_resolves_sample_from_registered_job(self, _registered_job): + detail = trajectory_detail(_registered_job.id, step=1, prompt_idx=0, sample_idx=0) + assert detail["valid"] is True + assert detail["step"] == 1 + assert detail["prompt_idx"] == 0 + assert detail["sample_idx"] == 0 + + def test_job_not_found_returns_error(self): + detail = trajectory_detail("nonexistent-job-id", step=0, prompt_idx=0, sample_idx=0) + assert detail["valid"] is False + assert "job not found" in detail["error"] + + def test_sample_not_found_returns_error(self, _registered_job): + detail = trajectory_detail(_registered_job.id, step=999, prompt_idx=0, sample_idx=0) + assert detail["valid"] is False + assert "trajectory sample not found" in detail["error"] + + def test_raw_omitted_by_default(self, _registered_job): + detail = trajectory_detail(_registered_job.id, step=1, prompt_idx=0, sample_idx=0) + assert "raw" not in detail + + def test_raw_included_when_requested(self, _registered_job): + detail = trajectory_detail(_registered_job.id, step=1, prompt_idx=0, sample_idx=0, include_raw=True) + assert "raw" in detail + assert detail["raw"]["kind"] == "agentic" + # Raw contains the full training data (tokens, loss_mask) that the + # structured detail deliberately omits. + assert "tokens" in detail["raw"] + assert "loss_mask" in detail["raw"] + + def test_non_agentic_sample_in_job_returns_invalid(self, _registered_job): + _registered_job.samples.append( + { + "kind": "rollout", + "step": 2, + "prompt_idx": 0, + "sample_idx": 0, + "messages": [{"role": "user", "content": "hi"}], + } + ) + _registered_job._sample_keys.add((2, 0, 0)) + detail = trajectory_detail(_registered_job.id, step=2, prompt_idx=0, sample_idx=0) + assert detail["valid"] is False + assert "not 'agentic'" in detail["error"] + + def test_large_trace_does_not_crash(self, _registered_job): + """A sample with many messages and tool results resolves without error.""" + messages = [{"role": "user", "content": "start"}] + tool_calls = [] + tool_results = [] + for i in range(50): + messages.append( + { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": f"call-{i}", + "type": "function", + "function": {"name": f"tool_{i}", "arguments": "{}"}, + } + ], + } + ) + messages.append({"role": "tool", "tool_call_id": f"call-{i}", "content": f"result {i}"}) + tool_calls.append( + {"id": f"call-{i}", "type": "function", "function": {"name": f"tool_{i}", "arguments": "{}"}} + ) + tool_results.append({"name": f"tool_{i}", "ok": True, "result": f"result {i}"}) + messages.append({"role": "assistant", "content": "done"}) + sample = _agentic_sample( + step=3, + messages=messages, + tool_calls=tool_calls, + tool_results=tool_results, + ) + _registered_job.samples.append(sample) + _registered_job._sample_keys.add((3, 0, 0)) + detail = trajectory_detail(_registered_job.id, step=3, prompt_idx=0, sample_idx=0) + assert detail["valid"] is True + assert detail["tool_call_count"] == 50 + assert detail["tool_result_count"] == 50 + assert len(detail["events"]) == 102 # 50 assistant + 50 tool + 1 user + 1 final assistant