From 23d120b86824da9b2cbc008aa86def27615d554a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 10:10:00 +0000 Subject: [PATCH 01/10] Bump the all-dependencies group across 1 directory with 3 updates Bumps the all-dependencies group with 3 updates in the / directory: [psycopg2-binary](https://github.com/psycopg/psycopg2), [boto3](https://github.com/boto/boto3) and [google-cloud-storage](https://github.com/googleapis/python-storage). Updates `psycopg2-binary` from 2.9.11 to 2.9.12 - [Changelog](https://github.com/psycopg/psycopg2/blob/master/NEWS) - [Commits](https://github.com/psycopg/psycopg2/compare/2.9.11...2.9.12) Updates `boto3` from 1.42.51 to 1.43.21 - [Release notes](https://github.com/boto/boto3/releases) - [Commits](https://github.com/boto/boto3/compare/1.42.51...1.43.21) Updates `google-cloud-storage` from 3.9.0 to 3.10.1 - [Release notes](https://github.com/googleapis/python-storage/releases) - [Changelog](https://github.com/googleapis/python-storage/blob/main/CHANGELOG.md) - [Commits](https://github.com/googleapis/python-storage/compare/v3.9.0...v3.10.1) --- updated-dependencies: - dependency-name: boto3 dependency-version: 1.43.2 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: all-dependencies - dependency-name: google-cloud-storage dependency-version: 3.10.1 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: all-dependencies - dependency-name: psycopg2-binary dependency-version: 2.9.12 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: all-dependencies ... Signed-off-by: dependabot[bot] --- pyproject.toml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c6c5d36..58eaeaf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,9 +25,9 @@ dependencies = [ "graphviz>=0.20,<1", # the below 4 are tied to the Dockerfile.mlflow versions; need to be upgraded together "mlflow[auth]==3.7.0", - "psycopg2-binary==2.9.11", - "boto3==1.42.51", - "google-cloud-storage==3.9.0", + "psycopg2-binary==2.9.12", + "boto3==1.43.21", + "google-cloud-storage==3.10.1", ] [project.scripts] From aee9ab64db2a41e9764eb65a1da75f4570f22db7 Mon Sep 17 00:00:00 2001 From: Vishal Doshi Date: Wed, 10 Jun 2026 09:17:17 -0400 Subject: [PATCH 02/10] Add method for metadata updates in EvalContext. --- src/modelplane/evaluator/context.py | 17 +++++++++++++++++ tests/unit/evaluator/test_context.py | 18 +++++++++++++++++- 2 files changed, 34 insertions(+), 1 deletion(-) diff --git a/src/modelplane/evaluator/context.py b/src/modelplane/evaluator/context.py index 04d575d..1c2546b 100644 --- a/src/modelplane/evaluator/context.py +++ b/src/modelplane/evaluator/context.py @@ -87,6 +87,23 @@ def with_response(self, new_response: str) -> EvalContext: ) def with_metadata(self, new_metadata: dict[str, Any]) -> EvalContext: + """ + Return a new EvalContext with the provided metadata replacing the + original metadata. + """ + return EvalContext( + prompt=self.prompt, + response=self.response, + metadata=new_metadata, + ) + + def with_metadata_updates(self, updates: dict[str, Any]) -> EvalContext: + """ + Return a new EvalContext with the original metadata updated with the + provided updates. + """ + new_metadata = self.metadata.copy() + new_metadata.update(updates) return EvalContext( prompt=self.prompt, response=self.response, diff --git a/tests/unit/evaluator/test_context.py b/tests/unit/evaluator/test_context.py index 31ec8f9..7c6fdb0 100644 --- a/tests/unit/evaluator/test_context.py +++ b/tests/unit/evaluator/test_context.py @@ -27,6 +27,18 @@ def test_metadata_replacement(sample_ctx): assert new_ctx.metadata == new_metadata +def test_metadata_updates(): + original_metadata = {"key1": "value1"} + sample_ctx = EvalContext( + prompt="Prompt", response="Response", metadata=original_metadata + ) + additional_metadata = {"key2": "value2"} + new_ctx = sample_ctx.with_metadata_updates(additional_metadata) + assert new_ctx.prompt == sample_ctx.prompt + assert new_ctx.response == sample_ctx.response + assert new_ctx.metadata == {**sample_ctx.metadata, **additional_metadata} + + def test_with_updates(sample_ctx): new_prompt = "Updated prompt" new_response = "Updated response" @@ -69,5 +81,9 @@ def test_hash(sample_ctx): other_ctx = EvalContext(prompt=sample_ctx.prompt, response=sample_ctx.response) assert sample_ctx.hash() == other_ctx.hash() - other_ctx = EvalContext(prompt=sample_ctx.prompt, response=sample_ctx.response, metadata={"key": "value"}) + other_ctx = EvalContext( + prompt=sample_ctx.prompt, + response=sample_ctx.response, + metadata={"key": "value"}, + ) assert sample_ctx.hash() != other_ctx.hash() From cd3cdb3ab7a624f81c46890f6625d3bf0af067df Mon Sep 17 00:00:00 2001 From: Barbara Korycki Date: Thu, 11 Jun 2026 16:23:37 -0700 Subject: [PATCH 03/10] bug fix --- src/modelplane/evaluator/context.py | 3 ++- tests/unit/evaluator/test_context.py | 19 +++++++++++++++++++ 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/src/modelplane/evaluator/context.py b/src/modelplane/evaluator/context.py index 1c2546b..bd9fe8f 100644 --- a/src/modelplane/evaluator/context.py +++ b/src/modelplane/evaluator/context.py @@ -1,5 +1,6 @@ from __future__ import annotations +import json from dataclasses import dataclass, field from typing import Any, Optional @@ -123,7 +124,7 @@ def with_updates( ) def hash(self): - return hash((self.prompt, self.response, frozenset(self.metadata.items()))) + return hash((self.prompt, self.response, json.dumps(self.metadata, sort_keys=True, separators=(',', ':'), ensure_ascii=False))) def __eq__(self, value: EvalContext) -> bool: if not isinstance(value, EvalContext): diff --git a/tests/unit/evaluator/test_context.py b/tests/unit/evaluator/test_context.py index 7c6fdb0..86cb9e7 100644 --- a/tests/unit/evaluator/test_context.py +++ b/tests/unit/evaluator/test_context.py @@ -87,3 +87,22 @@ def test_hash(sample_ctx): metadata={"key": "value"}, ) assert sample_ctx.hash() != other_ctx.hash() + + # Make sure can handle nested dictionaries in metadata + nested_ctx_1 = EvalContext( + prompt=sample_ctx.prompt, + response=sample_ctx.response, + metadata={"key": "value", "nested": {"key": "value_1"}}, + ) + nested_ctx_2 = EvalContext( + prompt=sample_ctx.prompt, + response=sample_ctx.response, + metadata={"key": "value", "nested": {"key": "value_2"}}, + ) + nested_ctx_2_copy = EvalContext( + prompt=sample_ctx.prompt, + response=sample_ctx.response, + metadata={"key": "value", "nested": {"key": "value_2"}}, + ) + assert nested_ctx_1.hash() != nested_ctx_2.hash() + assert nested_ctx_2_copy.hash() == nested_ctx_2.hash() \ No newline at end of file From ae5b1b738a83f6b34d7bf188d378cf50c715d281 Mon Sep 17 00:00:00 2001 From: Vishal Doshi Date: Wed, 10 Jun 2026 15:43:54 -0400 Subject: [PATCH 04/10] Remove code. --- src/modelplane/evaluator/annotator.py | 36 - src/modelplane/evaluator/context.py | 136 ---- src/modelplane/evaluator/cost.py | 52 -- src/modelplane/evaluator/dag.py | 698 ------------------- src/modelplane/evaluator/nodes.py | 192 ----- src/modelplane/evaluator/prompt_enricher.py | 74 -- src/modelplane/evaluator/safety.py | 56 -- src/modelplane/evaluator/verdict.py | 13 - tests/unit/evaluator/__init__.py | 0 tests/unit/evaluator/conftest.py | 253 ------- tests/unit/evaluator/mocks.py | 300 -------- tests/unit/evaluator/test_composer.py | 405 ----------- tests/unit/evaluator/test_context.py | 108 --- tests/unit/evaluator/test_nodes.py | 128 ---- tests/unit/evaluator/test_prompt_enricher.py | 33 - tests/unit/evaluator/test_safety.py | 64 -- 16 files changed, 2548 deletions(-) delete mode 100644 src/modelplane/evaluator/annotator.py delete mode 100644 src/modelplane/evaluator/context.py delete mode 100644 src/modelplane/evaluator/cost.py delete mode 100644 src/modelplane/evaluator/dag.py delete mode 100644 src/modelplane/evaluator/nodes.py delete mode 100644 src/modelplane/evaluator/prompt_enricher.py delete mode 100644 src/modelplane/evaluator/safety.py delete mode 100644 src/modelplane/evaluator/verdict.py delete mode 100644 tests/unit/evaluator/__init__.py delete mode 100644 tests/unit/evaluator/conftest.py delete mode 100644 tests/unit/evaluator/mocks.py delete mode 100644 tests/unit/evaluator/test_composer.py delete mode 100644 tests/unit/evaluator/test_context.py delete mode 100644 tests/unit/evaluator/test_nodes.py delete mode 100644 tests/unit/evaluator/test_prompt_enricher.py delete mode 100644 tests/unit/evaluator/test_safety.py diff --git a/src/modelplane/evaluator/annotator.py b/src/modelplane/evaluator/annotator.py deleted file mode 100644 index ae18783..0000000 --- a/src/modelplane/evaluator/annotator.py +++ /dev/null @@ -1,36 +0,0 @@ -from modelgauge.annotator import Annotator -from modelgauge.prompt import ChatPrompt, TextPrompt -from modelgauge.prompt_formatting import format_chat -from modelgauge.sut import SUTResponse - -from modelplane.evaluator.context import EvalContext -from modelplane.evaluator.dag import Composer, SuccessfulDAGOutput -from modelplane.evaluator.verdict import Verdict - - -class DAGAnnotator(Annotator): - """Annotator that executes a DAG.""" - - def __init__(self, uid: str, dag: Composer) -> None: - super().__init__(uid) - self.dag = dag - - def translate_prompt( - self, - prompt: TextPrompt | ChatPrompt, - response: SUTResponse, - ) -> EvalContext: - prompt_str = ( - prompt.text if isinstance(prompt, TextPrompt) else format_chat(prompt) - ) - return EvalContext( - prompt=prompt_str, - response=response.text, - ) - - def annotate(self, annotation_request: EvalContext) -> Verdict: - dag_output = self.dag.run(annotation_request) - if isinstance(dag_output, SuccessfulDAGOutput): - return dag_output.verdict - else: - raise dag_output.error diff --git a/src/modelplane/evaluator/context.py b/src/modelplane/evaluator/context.py deleted file mode 100644 index bd9fe8f..0000000 --- a/src/modelplane/evaluator/context.py +++ /dev/null @@ -1,136 +0,0 @@ -from __future__ import annotations - -import json -from dataclasses import dataclass, field -from typing import Any, Optional - -from modelplane.evaluator.cost import RealizedCost - - -@dataclass -class NodeOutput: - value: Any - original_ctx: EvalContext - realized_cost: RealizedCost = field(default_factory=RealizedCost) - updated_ctx: Optional[EvalContext] = None - - def to_dict(self) -> dict: - return { - "value": str(self.value), - "realized_cost": self.realized_cost.to_dict(), - "updated_ctx": self.updated_ctx.to_dict() if self.updated_ctx else None, - "original_ctx": self.original_ctx.to_dict(), - } - - -class EvalContext: - """Context state passed around during DAG execution.""" - - def __init__( - self, - prompt: str, - response: str, - metadata: Optional[dict[str, Any]] = None, - ) -> None: - self.prompt = prompt - self.response = response - self.metadata = metadata or {} - self._parent_outputs = {} - - def with_parent_outputs(self, outputs: dict[str, NodeOutput]) -> EvalContext: - updated_ctx = None - for node_output in outputs.values(): - if node_output.updated_ctx: - if updated_ctx and node_output.updated_ctx != updated_ctx: - raise ValueError( - "If context is updated, all parent outputs must have the same updated context." - ) - elif not updated_ctx: - updated_ctx = node_output.updated_ctx - if updated_ctx: - ctx = EvalContext( - prompt=updated_ctx.prompt, - response=updated_ctx.response, - metadata=updated_ctx.metadata, - ) - else: - ctx = EvalContext( - prompt=self.prompt, - response=self.response, - metadata=self.metadata, - ) - ctx._parent_outputs = outputs - return ctx - - def parent_outputs(self) -> list[NodeOutput]: - """Return the NodeOutput for a specific node, or None if it was skipped.""" - return list(self._parent_outputs.values()) - - def to_dict(self) -> dict: - return { - "prompt": self.prompt, - "response": self.response, - "metadata": self.metadata, - } - - def with_prompt(self, new_prompt: str) -> EvalContext: - return EvalContext( - prompt=new_prompt, - response=self.response, - metadata=self.metadata, - ) - - def with_response(self, new_response: str) -> EvalContext: - return EvalContext( - prompt=self.prompt, - response=new_response, - metadata=self.metadata, - ) - - def with_metadata(self, new_metadata: dict[str, Any]) -> EvalContext: - """ - Return a new EvalContext with the provided metadata replacing the - original metadata. - """ - return EvalContext( - prompt=self.prompt, - response=self.response, - metadata=new_metadata, - ) - - def with_metadata_updates(self, updates: dict[str, Any]) -> EvalContext: - """ - Return a new EvalContext with the original metadata updated with the - provided updates. - """ - new_metadata = self.metadata.copy() - new_metadata.update(updates) - return EvalContext( - prompt=self.prompt, - response=self.response, - metadata=new_metadata, - ) - - def with_updates( - self, - prompt: Optional[str] = None, - response: Optional[str] = None, - metadata: Optional[dict[str, Any]] = None, - ) -> EvalContext: - return EvalContext( - prompt=prompt or self.prompt, - response=response or self.response, - metadata=metadata or self.metadata, - ) - - def hash(self): - return hash((self.prompt, self.response, json.dumps(self.metadata, sort_keys=True, separators=(',', ':'), ensure_ascii=False))) - - def __eq__(self, value: EvalContext) -> bool: - if not isinstance(value, EvalContext): - return False - return ( - self.prompt == value.prompt - and self.response == value.response - and self.metadata == value.metadata - ) diff --git a/src/modelplane/evaluator/cost.py b/src/modelplane/evaluator/cost.py deleted file mode 100644 index f68c477..0000000 --- a/src/modelplane/evaluator/cost.py +++ /dev/null @@ -1,52 +0,0 @@ -from dataclasses import dataclass - - -@dataclass -class CostInfo: - input_cost_per_token: float = 0.0 - output_cost_per_token: float = 0.0 - fixed_cost: float = 0.0 - latency_seconds: float = 0.0 - - def __add__(self, other: "CostInfo") -> "CostInfo": - return CostInfo( - input_cost_per_token=self.input_cost_per_token + other.input_cost_per_token, - output_cost_per_token=self.output_cost_per_token - + other.output_cost_per_token, - fixed_cost=self.fixed_cost + other.fixed_cost, - latency_seconds=self.latency_seconds + other.latency_seconds, - ) - - -@dataclass -class RealizedCost: - input_token_cost: float = 0.0 - output_token_cost: float = 0.0 - fixed_cost: float = 0.0 - latency_seconds: float = 0.0 - - @property - def total_token_cost(self) -> float: - return self.input_token_cost + self.output_token_cost - - @property - def total_cost(self) -> float: - return self.total_token_cost + self.fixed_cost - - def __add__(self, other: "RealizedCost") -> "RealizedCost": - return RealizedCost( - input_token_cost=self.input_token_cost + other.input_token_cost, - output_token_cost=self.output_token_cost + other.output_token_cost, - fixed_cost=self.fixed_cost + other.fixed_cost, - latency_seconds=self.latency_seconds + other.latency_seconds, - ) - - def to_dict(self) -> dict: - return { - "input_token_cost": self.input_token_cost, - "output_token_cost": self.output_token_cost, - "fixed_cost": self.fixed_cost, - "latency_seconds": self.latency_seconds, - "total_token_cost": self.total_token_cost, - "total_cost": self.total_cost, - } diff --git a/src/modelplane/evaluator/dag.py b/src/modelplane/evaluator/dag.py deleted file mode 100644 index 6768b5a..0000000 --- a/src/modelplane/evaluator/dag.py +++ /dev/null @@ -1,698 +0,0 @@ -"""Composer implementation.""" - -import collections -import functools -import json -import os -import traceback -from concurrent.futures import ThreadPoolExecutor -from dataclasses import dataclass -from itertools import product -from pathlib import Path -from typing import Any, Optional - -import pandas as pd -from airrlogger.log_config import get_logger -from modelbench.cache import DiskCache, NullCache -from tqdm import tqdm - -from modelplane.evaluator.context import EvalContext, NodeOutput -from modelplane.evaluator.cost import CostInfo, RealizedCost -from modelplane.evaluator.nodes import Arbiter, CacheableNodeMixin, ComposerNode, Gate -from modelplane.evaluator.verdict import Verdict - -logger = get_logger(__name__) - - -def requires_validate_and_build(method): - @functools.wraps(method) - def wrapper(self, *args, **kwargs): - self._validate_and_build() - return method(self, *args, **kwargs) - - return wrapper - - -@dataclass -class _DAGOutput: - node_outputs: dict[str, NodeOutput] - total_cost: RealizedCost - - -@dataclass -class SuccessfulDAGOutput(_DAGOutput): - verdict: Verdict - - -@dataclass -class FailedDAGOutput(_DAGOutput): - error: Exception - - -class NodeExecutionError(Exception): - def __init__(self, node_name: str, original_error: Exception): - formatted = traceback.format_exception(original_error) - self.node_name = node_name - self.original_error = original_error - super().__init__(f"Error executing node '{node_name}': {''.join(formatted)}") - - -class ComposerColumnNames: - def __init__( - self, - composer_name: Optional[str] = None, - output_col_name: Optional[str] = None, - error_col_name: Optional[str] = None, - dag_run_col_name: Optional[str] = None, - cost_col_name: Optional[str] = None, - ): - if ( - any( - not name - for name in [ - output_col_name, - error_col_name, - dag_run_col_name, - cost_col_name, - ] - ) - and composer_name is None - ): - raise ValueError( - "If any of the column names are not provided, composer_name must be provided to generate default column names." - ) - - self.output_col = output_col_name or f"{composer_name}_output" - self.error_col = error_col_name or f"{composer_name}_error" - self.dag_run_col = dag_run_col_name or f"{composer_name}_dag_run" - self.cost_col = cost_col_name or f"{composer_name}_dag_cost" - - -class Composer: - """DAG of ComposerNodes. - - Usage: - - refusal_gate = MyRefusalGate("RefusalGate", routes_true=[Score(value=1)], routes_false=["NonRefusal"]) - eval_non_refusal = MyNonRefusalEvaluator("NonRefusal", routes=["Arbiter"]) - arbiter = MyArbiter("Arbiter") - - dag = ( - Composer("refusal_gated_safety_evaluator", verdict_type=Safety) - .add_node(refusal_gate) - .add_node(eval_non_refusal) - .add_node(arbiter) - ) - - # run single - result = dag.run(prompt_uid="123", prompt="...", response="...") - # run batch - results_df = dag.run_dataframe(df) - """ - - def __init__( - self, - name: str, - verdict_type: type, - cache_path: Optional[Path] = None, - col_names: Optional[ComposerColumnNames] = None, - ) -> None: - self.name = name - self._nodes: dict[str, ComposerNode] = {} - self._root_nodes: list[str] = [] - self._ordered: list[str] = [] - self._validated: bool = False - self._predecessors: dict[str, list[str]] = collections.defaultdict(list) - if not issubclass(verdict_type, Verdict): - raise ValueError("verdict_type must be a subclass of Verdict.") - self._verdict_type = verdict_type - self._cache_path = cache_path - self._node_caches = {} - self._col_names = col_names or ComposerColumnNames(composer_name=name) - - @property - def verdict_type(self) -> type: - return self._verdict_type - - @property - def df_output_col(self) -> str: - return self._col_names.output_col - - @property - def df_error_col(self) -> str: - return self._col_names.error_col - - @property - def df_dag_run_col(self) -> str: - return self._col_names.dag_run_col - - @property - def df_cost_col(self) -> str: - return self._col_names.cost_col - - def add_node( - self, - node: ComposerNode, - ) -> "Composer": - """Register a node with its routes.""" - - if node.name in self._nodes: - raise ValueError( - f"A different node named {node.name} is already registered." - ) - self._nodes[node.name] = node - self._validated = False - if isinstance(node, CacheableNodeMixin): - self._node_caches[node.name] = ( - DiskCache(self._cache_path / node.name) - if self._cache_path - else NullCache() - ) - return self - - def _validate_and_build(self) -> None: - """ - Validate the DAG: - - All routes reference registered nodes or instances of the output type. - - No cycles. - - All paths lead to an instance of the output type. - - Build: - - _predecessors: dict mapping node name to list of parent node names (for context during execution) - - _root_nodes: list of node names with no incoming routes (starting points) - - _ordered: list of node names in topological order (valid execution order) - """ - # skip validation if we've already done it and the DAG hasn't changed - if self._validated: - return - - # check that all route targets reference registered nodes or instances - # of the output type, and that all Arbiters have compatible output types - for node_name, node in self._nodes.items(): - if isinstance(node, Arbiter): - if not issubclass(node.verdict_type, self.verdict_type): - raise ValueError( - f"Node {node_name} is an Arbiter with verdict_type {node.verdict_type.__name__}, which is not compatible with the DAG's verdict_type {self.verdict_type.__name__}." - ) - for target in node.all_routes(): - if target not in self._nodes and not isinstance( - target, self.verdict_type - ): - raise ValueError( - f"Node {node_name} routes to unregistered node {target} or incompatible output." - ) - - # check for cycles (kahn's algorithm) - all_routes = {name: node.all_routes() for name, node in self._nodes.items()} - in_degree: dict[str, int] = {n: 0 for n in self._nodes} - for routes in all_routes.values(): - for route in routes: - if isinstance(route, Verdict): - continue - in_degree[route] += 1 - - root_nodes = [n for n in self._nodes if in_degree[n] == 0] - queue = collections.deque(root_nodes) - ordered: list[str] = [] - while queue: - current = queue.popleft() - ordered.append(current) - for child in all_routes.get(current, []): - if isinstance(child, Verdict): - continue - in_degree[child] -= 1 - if in_degree[child] == 0: - queue.append(child) - - if len(ordered) != len(self._nodes): - nodes_in_cycle = set(self._nodes) - set(ordered) - raise ValueError(f"DAG contains a cycle. Nodes in cycle: {nodes_in_cycle}") - - # build predecessors - for name, node in self._nodes.items(): - for target in node.all_routes(): - if isinstance(target, Verdict): - continue - self._predecessors[target].append(name) - - self._validated = True - self._root_nodes = root_nodes - self._ordered = ordered - - def _run_node(self, node: ComposerNode, ctx: EvalContext) -> NodeOutput: - if isinstance(node, CacheableNodeMixin): - key = node.cache_key(ctx) - if key in self._node_caches[node.name]: - return self._node_caches[node.name][key] - else: - output = node.run(ctx) - self._node_caches[node.name][key] = output - return output - else: - return node.run(ctx) - - def _run_traced( - self, ctx: EvalContext - ) -> tuple[SuccessfulDAGOutput | FailedDAGOutput, set[tuple[str, str]]]: - """Execute the DAG and return (final verdict, node outputs, realized costs, traversed edges).""" - node_outputs: dict[str, NodeOutput] = {} - traversed_edges: set[tuple[str, str]] = set() - total_cost = RealizedCost() - reachable: set[str] = set(self._root_nodes) - for node_name in self._ordered: - if node_name not in reachable: - continue - ctx = ctx.with_parent_outputs( - { - pred: node_outputs[pred] - for pred in self._predecessors[node_name] - if pred in node_outputs - } - ) - node = self._nodes[node_name] - try: - output = self._run_node(node, ctx) - except Exception as e: - wrapped_error = NodeExecutionError(node.name, e) - return ( - FailedDAGOutput( - node_outputs=node_outputs, - total_cost=total_cost, - error=wrapped_error, - ), - traversed_edges, - ) - node_outputs[node_name] = output - total_cost += output.realized_cost - if isinstance(output.value, Verdict): - traversed_edges.add((node_name, output.value.name)) - dag_output = SuccessfulDAGOutput( - verdict=output.value, - node_outputs=node_outputs, - total_cost=total_cost, - ) - return dag_output, traversed_edges - for target in node.next_nodes(output.value): - t = target if isinstance(target, str) else target.name - traversed_edges.add((node_name, t)) - if isinstance(target, Verdict): - return ( - SuccessfulDAGOutput( - verdict=target, - node_outputs=node_outputs, - total_cost=total_cost, - ), - traversed_edges, - ) - reachable.add(t) - raise ValueError("DAG execution completed without reaching a Verdict node.") - - @requires_validate_and_build - def run(self, ctx: EvalContext) -> SuccessfulDAGOutput | FailedDAGOutput: - """Execute the DAG on a single prompt/response and get the output, - node outputs, and overall realized cost.""" - dag_output, _ = self._run_traced(ctx) - return dag_output - - @requires_validate_and_build - def run_dataframe( - self, - df: pd.DataFrame, - prompt_col: str = "prompt", - response_col: str = "response", - metadata_col: Optional[str] = None, - n_jobs: int = 1, - ) -> pd.DataFrame: - """Run the DAG over every row of a DataFrame.""" - - def _run_row(row: Any) -> SuccessfulDAGOutput | FailedDAGOutput: - metadata = None - if metadata_col: - row_val = row[metadata_col] - if row_val: - try: - metadata = json.loads(row_val) - except Exception as e: - logger.warning( - "Failed to parse json metadata in row. Proceeding with no metadata." - ) - logger.debug(f"Metadata parsing error: {e}") - ctx = EvalContext( - prompt=str(row[prompt_col]), - response=str(row[response_col]), - metadata=metadata, - ) - return self.run(ctx) - - rows = [row for _, row in df.iterrows()] - - if n_jobs == 1: - records = [_run_row(row) for row in tqdm(rows, desc=self.name)] - else: - max_workers = os.cpu_count() if n_jobs == -1 else n_jobs - with ThreadPoolExecutor(max_workers=max_workers) as executor: - records = list( - tqdm(executor.map(_run_row, rows), total=len(rows), desc=self.name) - ) - - result_df = pd.DataFrame( - { - self.df_output_col: [ - r.verdict.name if isinstance(r, SuccessfulDAGOutput) else None - for r in records - ], - self.df_error_col: [ - str(r.error) if isinstance(r, FailedDAGOutput) else None - for r in records - ], - self.df_dag_run_col: [ - json.dumps({k: v.to_dict() for k, v in r.node_outputs.items()}) - for r in records - ], - self.df_cost_col: [json.dumps(r.total_cost.to_dict()) for r in records], - }, - index=df.index, - ) - return pd.concat([df, result_df], axis=1) - - @requires_validate_and_build - def potential_costs(self) -> dict[str, CostInfo]: - """Run the DAG on all terminal paths and report total costs per path.""" - gates = [name for name, node in self._nodes.items() if isinstance(node, Gate)] - path_costs: dict[str, CostInfo] = {} - - for combo in product([True, False], repeat=len(gates)): - gate_outcomes = dict(zip(gates, combo)) - reachable: set[str] = set(self._root_nodes) - path: list[str] = [] - total = CostInfo() - - for node_name in self._ordered: - if node_name not in reachable: - continue - node = self._nodes[node_name] - total += node.cost - path.append(node_name) - if isinstance(node, Gate): - targets = ( - node.routes_true - if gate_outcomes[node_name] - else node.routes_false - ) - elif isinstance(node, Arbiter): - targets = [] - else: - targets = node.routes - for target in targets: - if not isinstance(target, Verdict): - reachable.add( - target if isinstance(target, str) else target.name - ) - - base_path = " -> ".join(path) - path_costs[f"{base_path} -> Out ({self.verdict_type.__name__})"] = total - - return path_costs - - def _visualize( - self, - node_outputs: Optional[dict[str, Any]] = None, - traversed_edges: Optional[set[tuple[str, str]]] = None, - final_output: Optional[Verdict] = None, - ctx: Optional[EvalContext] = None, - ): # pragma: no cover - """Render the DAG as a PNG image. In a Jupyter notebook the image is displayed inline. - - When node_outputs/traversed_edges/final_output are provided (via visualize_run), - the hot path is highlighted and each node shows its output value. - - NOTE: this helper method is vibe-coded and provided as-is. - """ - import graphviz - from IPython.display import Image - - traced = node_outputs is not None - - _NODE_STYLES: dict[type, dict] = { - Gate: {"shape": "diamond", "style": "filled", "fillcolor": "#ffe082"}, - Arbiter: {"shape": "hexagon", "style": "filled", "fillcolor": "#e1bee7"}, - Verdict: { - "shape": "rectangle", - "style": "filled,rounded", - "fillcolor": "#dcedc8", - }, - } - _OUTPUT_TYPE_STYLE = { - "shape": "rectangle", - "style": "filled,rounded,dashed", - "fillcolor": "#dcedc8", - } - _DEFAULT_STYLE = { - "shape": "rectangle", - "style": "filled", - "fillcolor": "#eeeeee", - } - _DIM = { - "style": "filled", - "fillcolor": "#f0f0f0", - "color": "#bbbbbb", - "fontcolor": "#aaaaaa", - } - - _NODE_W, _NODE_H = 1.5, 0.5 # inches, fixed for all nodes - - def _fontsize( - label: str, max_fs: float = 11.0, min_fs: float = 7.0, fill: float = 0.8 - ) -> str: - """Scale font size so the longest line fits within _NODE_W. - - fill: fraction of the node width usable for text. Shapes like diamonds, - hexagons, and parallelograms have less usable area than rectangles, so - pass a smaller fill value for those. - """ - longest = max((len(line) for line in label.split("\n")), default=1) - # approx: each char ≈ 0.55 × fontsize points - fs = (_NODE_W * 72 * fill) / (longest * 0.55) - return f"{max(min_fs, min(max_fs, fs)):.1f}" - - dot = graphviz.Digraph(name=self.name) - dot.attr( - label=self.name, - labelloc="t", - fontsize="13", - fontname="Helvetica", - rankdir="LR", - ranksep="0.5", - nodesep="0.4", - ) - dot.attr( - "node", - fontname="Helvetica", - fontsize="11", - width=str(_NODE_W), - height=str(_NODE_H), - fixedsize="true", - ) - dot.attr("edge", fontname="Helvetica", fontsize="9") - - # implicit input node pinned to the left - top = graphviz.Digraph() - top.attr(rank="min") - - def _truncate(s: str, n: int = 24) -> str: - return s if len(s) <= n else s[: n - 1] + "…" - - if ctx is not None: - input_label = f"p: {_truncate(ctx.prompt)}\nr: {_truncate(ctx.response)}" - else: - input_label = "prompt\nresponse" - top.node( - "__input__", - input_label, - shape="parallelogram", - style="filled", - fillcolor="#b2dfdb", - color="#4db6ac", - fontcolor="#00695c", - fontsize=_fontsize(input_label, fill=0.45), - ) - dot.subgraph(top) - - # collect Verdict instances directly referenced in routes (from non-Arbiter nodes) - direct_verdicts: dict[str, Verdict] = {} - has_arbiter = any(isinstance(n, Arbiter) for n in self._nodes.values()) - for node in self._nodes.values(): - if not isinstance(node, Arbiter): - for target in node.all_routes(): - if isinstance(target, Verdict): - direct_verdicts[target.name] = target - - # whether the final output came from a direct route or an arbiter - final_from_direct = traced and final_output in direct_verdicts.values() - - bottom = graphviz.Digraph() - bottom.attr(rank="max") - - # individual nodes for directly-routed Verdict instances, shown with their repr - for out_name, out_inst in direct_verdicts.items(): - attrs = dict(_NODE_STYLES[Verdict]) - if traced: - if out_inst is final_output: - attrs["penwidth"] = "2.5" - else: - attrs = dict(_DIM, shape="rectangle", style="filled,rounded") - bottom.node( - out_name, repr(out_inst), fontsize=_fontsize(repr(out_inst)), **attrs - ) - - # synthetic output type node for Arbiters - if has_arbiter: - output_node_id = f"__output_{self.verdict_type.__name__}__" - output_label = f"{self.verdict_type.__name__} (?)" - attrs = dict(_OUTPUT_TYPE_STYLE) - if traced: - if not final_from_direct and final_output is not None: - attrs = dict(_NODE_STYLES[Verdict]) - attrs["penwidth"] = "2.5" - output_label = repr(final_output) - elif final_from_direct: - attrs = dict(_DIM, shape="rectangle", style="filled,rounded") - bottom.node( - output_node_id, output_label, fontsize=_fontsize(output_label), **attrs - ) - - dot.subgraph(bottom) - - # processing nodes - for node_name, node in self._nodes.items(): - base_style = next( - (s for t, s in _NODE_STYLES.items() if isinstance(node, t)), - _DEFAULT_STYLE, - ) - node_was_active = ( - node_outputs is not None and node_name in node_outputs - ) or ( - traversed_edges is not None - and any(src == node_name for src, _ in traversed_edges) - ) - if traced and not node_was_active: - attrs = dict(_DIM, shape=base_style.get("shape", "box")) - label = node_name - else: - attrs = dict(base_style) - if traced: - raw = node_outputs[node_name] # type: ignore[index] - label = f"{node_name}\n{node.format_output(raw.value)}" - attrs["penwidth"] = "2.5" - else: - label = node_name - _fill = ( - 0.45 - if isinstance(node, Gate) - else 0.65 if isinstance(node, Arbiter) else 0.8 - ) - dot.node(node_name, label, fontsize=_fontsize(label, fill=_fill), **attrs) - - # edges from implicit input to root nodes - for root in self._root_nodes: - dot.edge("__input__", root, color="#888888") - - # edges between processing nodes - for node_name, node in self._nodes.items(): - if isinstance(node, Gate): - for target in node.routes_true: - t = target if isinstance(target, str) else target.name - hot = not traced or (node_name, t) in traversed_edges # type: ignore[operator] - dot.edge( - node_name, - t, - label=" True", - color="#2e7d32" if hot else "#cccccc", - fontcolor="#2e7d32" if hot else "#cccccc", - penwidth="2" if hot and traced else "1", - ) - for target in node.routes_false: - t = target if isinstance(target, str) else target.name - hot = not traced or (node_name, t) in traversed_edges # type: ignore[operator] - dot.edge( - node_name, - t, - label=" False", - color="#c62828" if hot else "#cccccc", - fontcolor="#c62828" if hot else "#cccccc", - penwidth="2" if hot and traced else "1", - ) - elif isinstance(node, Arbiter): - output_node_id = f"__output_{self.verdict_type.__name__}__" - hot = not traced or node_name in (node_outputs or {}) - dot.edge( - node_name, - output_node_id, - color="#555555" if hot else "#cccccc", - penwidth="2" if hot and traced else "1", - ) - else: - for target in node.routes: - t = target if isinstance(target, str) else target.name - hot = not traced or (node_name, t) in traversed_edges # type: ignore[operator] - edge_label = "" - if traced and hot and node_name in (node_outputs or {}): - edge_label = f" {node.format_output(node_outputs[node_name].value)}" # type: ignore[index] - dot.edge( - node_name, - t, - label=edge_label, - color="#555555" if hot else "#cccccc", - fontcolor="#555555" if hot else "#cccccc", - penwidth="2" if hot and traced else "1", - ) - - try: - return Image(dot.pipe(format="png")) - except graphviz.ExecutableNotFound as e: - raise RuntimeError( - "Graphviz system binaries not found. Install them with:\n" - " macOS: brew install graphviz\n" - " Ubuntu: apt-get install graphviz\n" - " conda: conda install graphviz" - ) from e - - @requires_validate_and_build - def visualize(self): # pragma: no cover - """Render the DAG structure as a PNG image (inline in Jupyter notebooks). - - The graph flows left to right. Node shapes and colors: - - Input — teal parallelogram (implicit; represents the prompt/response pair) - - Gate — amber diamond; edges labelled "True" (green) / "False" (red) - - Enricher — light grey rectangle; edges are unlabelled - - Arbiter — light purple hexagon; edge labelled with the output type name - - Output (direct instance) — soft green rounded rectangle, solid border; - label is repr(output) - - Output (type placeholder) — soft green rounded rectangle, dashed border; - label is the class name; shown when the DAG contains - an Arbiter whose concrete value is only known at runtime - - Raises: - RuntimeError: if the Graphviz system binaries are not installed. - """ - return self._visualize() - - @requires_validate_and_build - def visualize_run(self, ctx: EvalContext): # pragma: no cover - """Run the DAG on ctx and return a visualization with the executed path highlighted. - - Identical layout to visualize(), with the following additions: - - Active nodes are bolded and show their output value beneath the node name. - - Inactive nodes are greyed out. - """ - dag_output, traversed_edges = self._run_traced(ctx) - return self._visualize( - node_outputs=dag_output.node_outputs, - traversed_edges=traversed_edges, - final_output=( - dag_output.verdict - if isinstance(dag_output, SuccessfulDAGOutput) - else None - ), - ctx=ctx, - ) diff --git a/src/modelplane/evaluator/nodes.py b/src/modelplane/evaluator/nodes.py deleted file mode 100644 index 746b0d3..0000000 --- a/src/modelplane/evaluator/nodes.py +++ /dev/null @@ -1,192 +0,0 @@ -""" -Node types for the Composer pipeline. - -Class hierarchy: - - ComposerNode (ABC) - ├── Gate (binary test; routes on True/False) - ├── Enricher (produces arbitary output; routes forward unconditionally) - └── Arbiter (produces Output) - Output (terminal; carries a verdict value) -""" - -from abc import ABC, abstractmethod -from typing import Any, Optional, Sequence - -from modelplane.evaluator.context import EvalContext, NodeOutput -from modelplane.evaluator.cost import CostInfo, RealizedCost -from modelplane.evaluator.verdict import Verdict - - -class ComposerNode(ABC): - def __init__( - self, - name: str, - routes_true: Optional[Sequence[str | Verdict]] = None, - routes_false: Optional[Sequence[str | Verdict]] = None, - routes: Optional[Sequence[str | Verdict]] = None, - ) -> None: - self.name = name - self._routes_true: tuple[str | Verdict, ...] = tuple(routes_true or []) - self._routes_false: tuple[str | Verdict, ...] = tuple(routes_false or []) - self._routes: tuple[str | Verdict, ...] = tuple(routes or []) - self.validate() - - @property - def routes_true(self) -> tuple[str | Verdict, ...]: - return self._routes_true - - @property - def routes_false(self) -> tuple[str | Verdict, ...]: - return self._routes_false - - @property - def routes(self) -> tuple[str | Verdict, ...]: - return self._routes - - @abstractmethod - def run(self, ctx: EvalContext) -> NodeOutput: - """Execute the node and return its output and realized cost.""" - raise NotImplementedError # pragma: no cover - - def build_output( - self, - value: Any, - ctx: EvalContext, - updated_ctx: Optional[EvalContext] = None, - ) -> NodeOutput: - """Helper method for building a NodeOutput with the node's realized cost - when the cost doesn't have to be computed concurrently with the output value. - - This helper assumes the context is not updated. - """ - return NodeOutput( - value=value, - realized_cost=self.realized_cost(ctx), - original_ctx=ctx, - updated_ctx=updated_ctx, - ) - - @property - def cost(self) -> CostInfo: - """Override this to represent the cost of running this node.""" - return CostInfo() - - def realized_cost(self, ctx: EvalContext) -> RealizedCost: - """Base realized cost when ctx doesn't affect (see LLMNodeMixin for context-aware cost).""" - return RealizedCost( - fixed_cost=self.cost.fixed_cost, - latency_seconds=self.cost.latency_seconds, - ) - - def __repr__(self) -> str: - return f"{self.name!r}: ({self.__class__.__name__})" - - @staticmethod - def format_output(output: Any) -> str: - """Convenience method to format the node's output for debugging/visualization.""" - if isinstance(output, float): - return f"{output:.3g}" - s = str(output) - return s if len(s) <= 30 else s[:27] + "..." - - def all_routes(self) -> list[str | Verdict]: - """Return a list of all route targets from this node.""" - return [*self.routes_true, *self.routes_false, *self.routes] - - def next_nodes(self, output_value: Any) -> tuple[str | Verdict, ...]: - """Given the node's output value, return the tuple of next node names to activate.""" - if isinstance(self, Gate): - return self.routes_true if output_value else self.routes_false - else: - return self.routes - - def validate(self) -> None: - """Validate that the node's routing configuration is consistent with its type.""" - # validate that routes with Verdicts only have one Verdict - for route_list in [self.routes_true, self.routes_false, self.routes]: - output_routes = [r for r in route_list if isinstance(r, Verdict)] - if len(output_routes) > 1: - raise ValueError( - f"{self!r} has multiple Verdict routes {output_routes}, which is not allowed." - ) - - -class CacheableNodeMixin(ComposerNode, ABC): - """Mixin for nodes whose outputs should be cached.""" - - def cache_key(self, ctx: EvalContext) -> tuple: - return ctx.hash() - - -class LLMCostMixin(ComposerNode): - """Mixin for nodes that involve LLM calls, to simplify cost calculation.""" - - @abstractmethod - def input_tokens(self, ctx: EvalContext) -> int: - raise NotImplementedError # pragma: no cover - - @abstractmethod - def output_tokens(self, ctx: EvalContext) -> int: - raise NotImplementedError # pragma: no cover - - def realized_cost(self, ctx: EvalContext) -> RealizedCost: - return RealizedCost( - input_token_cost=self.input_tokens(ctx) * self.cost.input_cost_per_token, - output_token_cost=self.output_tokens(ctx) * self.cost.output_cost_per_token, - fixed_cost=self.cost.fixed_cost, - latency_seconds=self.cost.latency_seconds, - ) - - -def _validate_binary_routes(node: ComposerNode) -> None: - if not node.routes_true or not node.routes_false: - raise ValueError(f"{node!r} requires both routes_true and routes_false") - if node.routes: - raise ValueError( - f"{node!r} should not have routes= (use routes_true= / routes_false=)" - ) - - -def _validate_unary_routes(node: ComposerNode) -> None: - if not node.routes: - raise ValueError(f"{node!r} requires routes=") - if node.routes_true or node.routes_false: - raise ValueError( - f"{node!r} should not have routes_true= / routes_false= (use routes=)" - ) - - -def _validate_terminal(node: ComposerNode) -> None: - if node.routes_true or node.routes_false or node.routes: - raise ValueError(f"{node!r} is terminal and cannot have routing kwargs") - - -class Gate(ComposerNode): - """Binary test node.""" - - def validate(self) -> None: - super().validate() - _validate_binary_routes(self) - - -class Enricher(ComposerNode): - """Context transformation node.""" - - def validate(self) -> None: - super().validate() - _validate_unary_routes(self) - - -class Arbiter(ComposerNode): - """Takes context and returns a Verdict indicating the final verdict (based on routes).""" - - def validate(self) -> None: - super().validate() - _validate_terminal(self) - - @property - @abstractmethod - def verdict_type(self) -> type: - """Return the expected type of the Verdict's value for validation.""" - raise NotImplementedError # pragma: no cover diff --git a/src/modelplane/evaluator/prompt_enricher.py b/src/modelplane/evaluator/prompt_enricher.py deleted file mode 100644 index a9d8de7..0000000 --- a/src/modelplane/evaluator/prompt_enricher.py +++ /dev/null @@ -1,74 +0,0 @@ -import string -from typing import Optional, Sequence - -from modelgauge.config import load_secrets_from_config -from modelgauge.model_options import ModelOptions -from modelgauge.secret_values import RawSecrets -from modelgauge.sut import PromptResponseSUT, TextPrompt -from modelgauge.sut_factory import SUT_FACTORY - -from modelplane.evaluator.context import EvalContext, NodeOutput -from modelplane.evaluator.cost import RealizedCost -from modelplane.evaluator.nodes import Enricher - - -class PromptEngineeredNode(Enricher): - """Node that enriches the context by making an LLM call with a prompt template.""" - - def __init__( - self, - name: str, - routes: Sequence[str], - prompt_template: string.Template, - sut_id: str, - model_options=None, - sut_secrets: Optional[RawSecrets] = None, - **sut_kwargs, - ) -> None: - super().__init__(name=name, routes=routes) - - subs = prompt_template.get_identifiers() - if not set(subs).issubset({"prompt", "response"}): - raise ValueError( - "Prompt template may only have 'prompt' and 'response' placeholders." - ) - self.prompt_template = prompt_template - - if model_options is None: - model_options = ModelOptions() - self.model_options = model_options - - if sut_secrets is None: - sut_secrets = load_secrets_from_config() - sut = SUT_FACTORY.make_instance(uid=sut_id, secrets=sut_secrets, **sut_kwargs) - if not isinstance(sut, PromptResponseSUT): - raise ValueError( - f"PromptEngineeredAnnotator only works with PromptResponseSUTs. SUT {sut_id} is of type {type(sut)}" - ) - self.sut: PromptResponseSUT = sut - - def _build_prompt(self, ctx: EvalContext) -> TextPrompt: - return TextPrompt( - text=self.prompt_template.safe_substitute( - prompt=ctx.prompt, response=ctx.response - ) - ) - - def _count_tokens(self, text: str) -> int: - # Simple tokenizer. - return len(text.split()) - - def run(self, ctx: EvalContext) -> NodeOutput: - prompt = self._build_prompt(ctx) - sut_request = self.sut.translate_text_prompt(prompt, options=self.model_options) - resp = self.sut.evaluate(sut_request) - sut_response = self.sut.translate_response(sut_request, resp) - return NodeOutput( - value=sut_response.text, - realized_cost=RealizedCost( - input_token_cost=self._count_tokens(prompt.text), - output_token_cost=self._count_tokens(sut_response.text), - ), - original_ctx=ctx, - updated_ctx=ctx.with_response(sut_response.text), - ) diff --git a/src/modelplane/evaluator/safety.py b/src/modelplane/evaluator/safety.py deleted file mode 100644 index 32ccae5..0000000 --- a/src/modelplane/evaluator/safety.py +++ /dev/null @@ -1,56 +0,0 @@ -from modelgauge.annotation import SafetyAnnotation -from modelgauge.annotator import Annotator, SUTResponse, TextPrompt - -from modelplane.evaluator.annotator import DAGAnnotator -from modelplane.evaluator.context import EvalContext -from modelplane.evaluator.dag import Composer -from modelplane.evaluator.nodes import Arbiter, CacheableNodeMixin, NodeOutput -from modelplane.evaluator.verdict import Verdict - - -class Safety(Verdict): - - def __init__(self, is_safe: bool) -> None: - self.is_safe = is_safe - - @property - def name(self) -> str: - return "SAFE" if self.is_safe else "UNSAFE" - - -class SafetyArbiter(Arbiter): - @property - def verdict_type(self) -> type: - return Safety - - -class SafetyDAGAnnotator(DAGAnnotator): - """Implementation of DAGAnnotator that produces a SafetyAnnotation.""" - - def __init__(self, uid: str, dag: Composer) -> None: - super().__init__(uid, dag) - if not issubclass(dag.verdict_type, Safety): - raise ValueError("All outputs of the DAG must be of type Safety.") - - def translate_response( - self, - request: EvalContext, - response: Safety, - ) -> SafetyAnnotation: - """Map DAGResult verdict to a SafetyAnnotation (is_safe bool).""" - return SafetyAnnotation(is_safe=response.is_safe) - - -class AnnotatorArbiter(SafetyArbiter, CacheableNodeMixin): - """Arbiter that outputs SAFE or UNSAFE based on the output of a (safety) Annotator.""" - - def __init__(self, name: str, annotator: Annotator) -> None: - super().__init__(name=name) - self.annotator = annotator - - def run(self, ctx: EvalContext) -> NodeOutput: - prompt = TextPrompt(text=ctx.prompt) - response = SUTResponse(text=ctx.response) - annotation = self.annotator.process(prompt, response) - val = Safety(is_safe=annotation.is_safe) - return NodeOutput(value=val, original_ctx=ctx) diff --git a/src/modelplane/evaluator/verdict.py b/src/modelplane/evaluator/verdict.py deleted file mode 100644 index 5066d6f..0000000 --- a/src/modelplane/evaluator/verdict.py +++ /dev/null @@ -1,13 +0,0 @@ -from abc import abstractmethod - - -class Verdict: - """DAG outputs.""" - - @property - @abstractmethod - def name(self) -> str: - """Return a string name for this output, used for routing and debugging.""" - - def __repr__(self) -> str: - return f"{self.__class__.__name__} ({self.name})" diff --git a/tests/unit/evaluator/__init__.py b/tests/unit/evaluator/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/unit/evaluator/conftest.py b/tests/unit/evaluator/conftest.py deleted file mode 100644 index de83a5c..0000000 --- a/tests/unit/evaluator/conftest.py +++ /dev/null @@ -1,253 +0,0 @@ -"""Shared mock node implementations and helpers for evaluator tests.""" - -import os -import string - -import pytest - -from modelplane.evaluator.context import EvalContext -from modelplane.evaluator.dag import Composer -from modelplane.evaluator.prompt_enricher import PromptEngineeredNode -from modelplane.evaluator.safety import Safety -from modelplane.evaluator.verdict import Verdict - -from .mocks import ( - AlwaysFalse, - AlwaysSafe, - AlwaysTrue, - AlwaysTrueCacheable, - AlwaysUnsafe, - BadArbiter, - FixedScorer, - LLMEnricher, - LowerCaser, - LowerCaseScorer, - PromptLengthGate, - ThresholdArbiter, - UnexpectedArbiter, - UnexpectedOutput, - UpperCaser, - UpperCaseScorer, -) - -TRUE_BRANCH: tuple[str | Verdict] = ("true_branch",) -FALSE_BRANCH: tuple[str | Verdict] = ("false_branch",) -DEFAULT_BRANCH: tuple[str | Verdict] = ("next_node",) -BAD_BRANCH: tuple[str | Verdict] = ("undefined_node",) -SCORE1 = 1.0 -SCORE2 = 2.0 - -skip_in_ci = pytest.mark.skipif(os.getenv("CI") == "true", reason="skipped in CI") - - -@pytest.fixture -def always_true_gate() -> AlwaysTrue: - return AlwaysTrue( - name="always_true", routes_true=TRUE_BRANCH, routes_false=FALSE_BRANCH - ) - - -@pytest.fixture -def bad_gate() -> AlwaysTrue: - return AlwaysTrue( - name="bad_gate", routes_true=BAD_BRANCH, routes_false=FALSE_BRANCH - ) - - -@pytest.fixture -def always_false_gate() -> AlwaysFalse: - return AlwaysFalse( - name="always_false", routes_true=TRUE_BRANCH, routes_false=FALSE_BRANCH - ) - - -@pytest.fixture -def lower_caser() -> LowerCaser: - return LowerCaser(name="lower_caser", routes=DEFAULT_BRANCH) - - -@pytest.fixture -def score_1() -> FixedScorer: - return FixedScorer(name="score_1", value=SCORE1, routes=DEFAULT_BRANCH) - - -@pytest.fixture -def score_2() -> FixedScorer: - return FixedScorer(name="score_2", value=SCORE2, routes=DEFAULT_BRANCH) - - -@pytest.fixture -def costly_enricher() -> LLMEnricher: - return LLMEnricher(name="costly_enricher", routes=DEFAULT_BRANCH) - - -@pytest.fixture -def sample_ctx() -> EvalContext: - return EvalContext(prompt="Hello, world", response="This is a response.") - - -@pytest.fixture -def always_unsafe() -> AlwaysUnsafe: - return AlwaysUnsafe(name="always_unsafe") - - -@pytest.fixture -def always_safe() -> AlwaysSafe: - return AlwaysSafe(name="always_safe") - - -@pytest.fixture -def threshold_arbiter() -> ThresholdArbiter: - return ThresholdArbiter(name="threshold_arbiter", threshold=1.5) - - -@pytest.fixture -def one_step_dag(): - return ( - Composer("one_step", verdict_type=Safety) - .add_node( - AlwaysFalse( - name="gate", - routes_true=[Safety(is_safe=True)], - routes_false=["always_unsafe"], - ) - ) - .add_node(AlwaysUnsafe(name="always_unsafe")) - ) - - -@pytest.fixture -def cached_minimal_dag(tmp_path): - return ( - Composer("cached_minimal", verdict_type=Safety, cache_path=tmp_path) - .add_node( - AlwaysTrueCacheable( - name="always_true", - routes_true=[Safety(is_safe=True)], - routes_false=[Safety(is_safe=False)], - ) - ) - ) - - -@pytest.fixture -def cached_simple_dag(tmp_path): - return ( - Composer("simple_cached", verdict_type=Safety, cache_path=tmp_path) - .add_node( - AlwaysTrueCacheable( - name="always_true", - routes_true=["lower_caser", "prompt_parity"], - routes_false=["always_safe"], - ) - ) - .add_node(AlwaysSafe(name="always_safe")) - .add_node( - PromptLengthGate( - name="prompt_parity", - routes_true=[Safety(is_safe=False)], - routes_false=["upper_scorer"], - ) - ) - .add_node( - LowerCaser(name="lower_caser", routes=["lower_scorer", "upper_scorer"]) - ) - .add_node(LowerCaseScorer(name="lower_scorer", routes=["threshold_arbiter"])) - .add_node(UpperCaseScorer(name="upper_scorer", routes=["threshold_arbiter"])) - .add_node(ThresholdArbiter(name="threshold_arbiter", threshold=0.5)) - ) - - -@pytest.fixture -def simple_dag(): - return ( - Composer("simple", verdict_type=Safety) - .add_node( - AlwaysTrue( - name="always_true", - routes_true=["lower_caser", "prompt_parity"], - routes_false=["always_safe"], - ) - ) - .add_node(AlwaysSafe(name="always_safe")) - .add_node( - PromptLengthGate( - name="prompt_parity", - routes_true=[Safety(is_safe=False)], - routes_false=["upper_scorer"], - ) - ) - .add_node( - LowerCaser(name="lower_caser", routes=["lower_scorer", "upper_scorer"]) - ) - .add_node(LowerCaseScorer(name="lower_scorer", routes=["threshold_arbiter"])) - .add_node(UpperCaseScorer(name="upper_scorer", routes=["threshold_arbiter"])) - .add_node(ThresholdArbiter(name="threshold_arbiter", threshold=0.5)) - ) - - -@pytest.fixture() -def bad_dag_with_cycle(): - return ( - Composer("cyclic", verdict_type=Safety) - .add_node( - AlwaysTrue( - name="node1", - routes_true=["node2"], - routes_false=["node3"], - ) - ) - .add_node( - AlwaysTrue( - name="node2", - routes_true=["node3"], - routes_false=["node1"], - ) - ) - .add_node( - AlwaysTrue( - name="node3", - routes_true=[Safety(is_safe=True)], - routes_false=[Safety(is_safe=False)], - ) - ) - ) - - -@pytest.fixture -def bad_dag_with_undefined_output(simple_dag): - bad_arbiter = UnexpectedArbiter(name="arbiter") - simple_dag.add_node(bad_arbiter) - return simple_dag - - -@pytest.fixture -def bad_dag_with_bad_arbiter(): - dag = Composer("test", verdict_type=Safety) - dag.add_node(BadArbiter(name="bad_arbiter")) - return dag - - -@pytest.fixture -def bad_one_step_dag(): - return ( - Composer("one_step", verdict_type=Safety) - .add_node( - AlwaysFalse( - name="gate", - routes_true=[UnexpectedOutput()], - routes_false=["always_unsafe"], - ) - ) - .add_node(AlwaysUnsafe(name="always_unsafe")) - ) - - -@pytest.fixture -def prompt_enricher() -> PromptEngineeredNode: - return PromptEngineeredNode( - name="prompt_enricher", - routes=["next_node"], - prompt_template=string.Template("$prompt\n$response"), - sut_id="demo_yes_no", - ) diff --git a/tests/unit/evaluator/mocks.py b/tests/unit/evaluator/mocks.py deleted file mode 100644 index 9d27876..0000000 --- a/tests/unit/evaluator/mocks.py +++ /dev/null @@ -1,300 +0,0 @@ -from modelplane.evaluator.context import EvalContext, NodeOutput -from modelplane.evaluator.cost import CostInfo -from modelplane.evaluator.nodes import ( - Arbiter, - CacheableNodeMixin, - Enricher, - Gate, - LLMCostMixin, -) -from modelplane.evaluator.safety import Safety -from modelplane.evaluator.verdict import Verdict - - -def context_token_count(ctx: EvalContext) -> int: - return len(ctx.prompt.split() + ctx.response.split()) - - -class FailingNode(Enricher): - - def run(self, ctx: EvalContext) -> NodeOutput: - raise RuntimeError("I'm afraid I can't do that, Dave.") - - -class PassthroughGate(Gate, LLMCostMixin): - ROUTE_TO_TAKE: bool - - def run(self, ctx: EvalContext) -> NodeOutput: - return self.build_output(self.ROUTE_TO_TAKE, ctx) - - def input_tokens(self, ctx: EvalContext) -> int: - return context_token_count(ctx) - - def output_tokens(self, ctx: EvalContext) -> int: - return 1 - - -class AlwaysTrue(PassthroughGate): - ROUTE_TO_TAKE = True - - -class AlwaysTrueCacheable(AlwaysTrue, CacheableNodeMixin): - """Always-true gate that participates in Composer node-level disk caching.""" - - run_count = 0 - - def run(self, ctx: EvalContext) -> NodeOutput: - type(self).run_count += 1 - return super().run(ctx) - - -class AlwaysFalse(PassthroughGate): - ROUTE_TO_TAKE = False - - @property - def cost(self) -> CostInfo: - return CostInfo( - input_cost_per_token=0.1, - output_cost_per_token=0.2, - fixed_cost=0.1, - latency_seconds=0.1, - ) - - -class PromptLengthGate(Gate): - def run(self, ctx: EvalContext) -> NodeOutput: - return self.build_output(len(ctx.prompt) % 2 == 0, ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - fixed_cost=0.2, - latency_seconds=0.2, - ) - - -class Caser(Enricher, LLMCostMixin): - def input_tokens(self, ctx: EvalContext) -> int: - return len(ctx.response.split()) - - def output_tokens(self, ctx: EvalContext) -> int: - return len(ctx.response.split()) - - -class LowerCaser(Caser): - """Enriches by returning the response lowercased.""" - - def run(self, ctx: EvalContext) -> NodeOutput: - output = ctx.response.lower() - return self.build_output( - output, - ctx, - updated_ctx=ctx.with_response(output), - ) - - @property - def cost(self) -> CostInfo: - return CostInfo( - input_cost_per_token=0.3, - output_cost_per_token=0.4, - fixed_cost=0.3, - latency_seconds=0.3, - ) - - -class UpperCaser(Caser): - """Enriches by returning the response uppercased.""" - - def run(self, ctx: EvalContext) -> NodeOutput: - output = ctx.response.upper() - return self.build_output( - output, - ctx, - updated_ctx=ctx.with_response(output), - ) - - @property - def cost(self) -> CostInfo: - return CostInfo( - input_cost_per_token=0.4, - output_cost_per_token=0.5, - fixed_cost=0.4, - latency_seconds=0.4, - ) - - -class LLMEnricher(Enricher, LLMCostMixin): - - def run(self, ctx: EvalContext) -> NodeOutput: - return self.build_output(ctx.response, ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - input_cost_per_token=0.5, - output_cost_per_token=0.6, - fixed_cost=0.5, - latency_seconds=0.5, - ) - - def input_tokens(self, ctx: EvalContext) -> int: - return context_token_count(ctx) - - def output_tokens(self, ctx: EvalContext) -> int: - return context_token_count(ctx) - - -class NoOpEnricher(Enricher): - """Passes context through without changing it.""" - - def run(self, ctx: EvalContext) -> NodeOutput: - return self.build_output(None, ctx) - - -class FixedScorer(Enricher): - """Returns a fixed float score regardless of context.""" - - def __init__(self, name: str, value: float, **kwargs): - super().__init__(name, **kwargs) - self.value = value - - def run(self, ctx: EvalContext) -> NodeOutput: - return self.build_output(self.value, ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - fixed_cost=0.6, - latency_seconds=0.6, - ) - - -class LowerCaseScorer(Enricher): - """Scores based on the percentage of lowercase characters in the response.""" - - def run(self, ctx: EvalContext) -> NodeOutput: - if not ctx.response: - return self.build_output(0.0, ctx) - num_lower = sum(1 for c in ctx.response if c.islower()) - return self.build_output(num_lower / len(ctx.response), ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - fixed_cost=0.7, - latency_seconds=0.7, - ) - - -class UpperCaseScorer(Enricher): - """Scores based on the percentage of uppercase characters in the response.""" - - def run(self, ctx: EvalContext) -> NodeOutput: - if not ctx.response: - return self.build_output(0.0, ctx) - num_upper = sum(1 for c in ctx.response if c.isupper()) - return self.build_output(num_upper / len(ctx.response), ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - fixed_cost=0.8, - latency_seconds=0.8, - ) - - -class AlwaysUnsafe(Arbiter): - def run(self, ctx: EvalContext) -> NodeOutput: - return self.build_output(Safety(is_safe=False), ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - fixed_cost=0.9, - latency_seconds=0.9, - ) - - @property - def verdict_type(self) -> type: - return Safety - - -class AlwaysSafe(Arbiter): - def run(self, ctx: EvalContext) -> NodeOutput: - return self.build_output(Safety(is_safe=True), ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - fixed_cost=1.0, - latency_seconds=1.0, - ) - - @property - def verdict_type(self) -> type: - return Safety - - -class ThresholdArbiter(Arbiter): - def __init__(self, name: str, threshold: float, **kwargs): - super().__init__(name, **kwargs) - self.threshold = threshold - - def run(self, ctx: EvalContext) -> NodeOutput: - score_outputs = ctx.parent_outputs() - scores = [output.value for output in score_outputs] - score = sum(scores) / len(scores) - return self.build_output(Safety(is_safe=score < self.threshold), ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - fixed_cost=1.1, - latency_seconds=1.1, - ) - - @property - def verdict_type(self) -> type: - return Safety - - -class UnexpectedOutput(Verdict): - @property - def name(self) -> str: - return "UNEXPECTED_OUTPUT" - - -class UnexpectedArbiter(Arbiter): - """An arbiter that returns an output not declared in outputs().""" - - def run(self, ctx: EvalContext) -> NodeOutput: - return self.build_output(UnexpectedOutput(), ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - fixed_cost=1.2, - latency_seconds=1.2, - ) - - @property - def verdict_type(self) -> type: - return UnexpectedOutput - - -class BadArbiter(Arbiter): - """An arbiter that violates the contract by returning a non-Output value.""" - - def run(self, ctx: EvalContext) -> NodeOutput: - return self.build_output("safe", ctx) - - @property - def cost(self) -> CostInfo: - return CostInfo( - fixed_cost=1.3, - latency_seconds=1.3, - ) - - @property - def verdict_type(self) -> type: - return Safety diff --git a/tests/unit/evaluator/test_composer.py b/tests/unit/evaluator/test_composer.py deleted file mode 100644 index 5c24388..0000000 --- a/tests/unit/evaluator/test_composer.py +++ /dev/null @@ -1,405 +0,0 @@ -"""Unit tests for Composer construction, validation, execution, and visualization.""" - -import json -from unittest.mock import patch - -import pandas as pd -import pytest - -from modelplane.evaluator.context import EvalContext -from modelplane.evaluator.dag import Composer, ComposerColumnNames -from modelplane.evaluator.safety import Safety - -from .conftest import skip_in_ci -from .mocks import ( - AlwaysSafe, - AlwaysTrue, - AlwaysTrueCacheable, - LowerCaser, - LowerCaseScorer, - NoOpEnricher, - ThresholdArbiter, - UpperCaser, -) - - -def test_dag_outputs(simple_dag): - assert simple_dag.verdict_type == Safety - - -def test_dag_with_bad_verdict_type(): - with pytest.raises( - ValueError, - match="verdict_type must be a subclass of Verdict", - ): - Composer(name="bad_dag", verdict_type=str) - - -def test_add_node_with_same_name_as_existing_node(simple_dag, always_true_gate): - always_true_gate.name = next(iter(simple_dag._nodes)) - with pytest.raises(ValueError, match="is already registered"): - simple_dag.add_node(always_true_gate) # same name as existing node - - -def test_add_node_with_undefined_target_node(simple_dag, bad_gate): - simple_dag.add_node(bad_gate) - with pytest.raises(ValueError, match="routes to unregistered node"): - simple_dag._validate_and_build() - - -def test_dag_with_cycle(bad_dag_with_cycle): - with pytest.raises(ValueError, match="DAG contains a cycle"): - bad_dag_with_cycle._validate_and_build() - - -def test_dag_with_undefined_output(bad_dag_with_undefined_output): - with pytest.raises( - ValueError, match=r"which is not compatible with the DAG\'s verdict_type" - ): - bad_dag_with_undefined_output._validate_and_build() - - -def test_dag_with_bad_arbiter(bad_dag_with_bad_arbiter, sample_ctx): - with pytest.raises( - ValueError, - match=r"DAG execution completed without reaching a Verdict node", - ): - bad_dag_with_bad_arbiter.run(sample_ctx) - - -def test_dag_with_bad_output_route(bad_one_step_dag, sample_ctx): - with pytest.raises( - ValueError, - match=r"incompatible output", - ): - bad_one_step_dag.run(sample_ctx) - - -def test_dag_run_with_cache(cached_minimal_dag, sample_ctx): - output = cached_minimal_dag.run(sample_ctx) - assert output.verdict.name == "SAFE" - - always_true = cached_minimal_dag._nodes["always_true"] - always_true.run = lambda ctx: (_ for _ in ()).throw( - ValueError("Should not call run") - ) - output_cached = cached_minimal_dag.run(sample_ctx) - assert output_cached.verdict.name == "SAFE" - - -def test_dag_uses_per_node_disk_cache(tmp_path, sample_ctx): - """Each cacheable node must use cache_path / node.name, not a shared store.""" - gate_a = AlwaysTrueCacheable( - name="gate_a", - routes_true=["gate_b"], - routes_false=[Safety(is_safe=False)], - ) - gate_b = AlwaysTrueCacheable( - name="gate_b", - routes_true=[Safety(is_safe=True)], - routes_false=[Safety(is_safe=False)], - ) - dag = ( - Composer("per_node_cache", verdict_type=Safety, cache_path=tmp_path) - .add_node(gate_a) - .add_node(gate_b) - ) - - assert dag._node_caches["gate_a"].cache_path == tmp_path / "gate_a" - assert dag._node_caches["gate_b"].cache_path == tmp_path / "gate_b" - - AlwaysTrueCacheable.run_count = 0 - dag.run(sample_ctx) - assert AlwaysTrueCacheable.run_count == 2 - - dag._node_caches["gate_b"].raw_cache.clear() - gate_a.run = lambda ctx: (_ for _ in ()).throw(ValueError("gate_a cache miss")) - - AlwaysTrueCacheable.run_count = 0 - dag.run(sample_ctx) - # gate_a hits its own cache; gate_b misses after clear and runs once - assert AlwaysTrueCacheable.run_count == 1 - - -def test_dag_cache_miss_on_different_context(cached_minimal_dag): - AlwaysTrueCacheable.run_count = 0 - ctx_a = EvalContext(prompt="Hello", response="world") - ctx_b = EvalContext(prompt="Goodbye", response="world") - - cached_minimal_dag.run(ctx_a) - cached_minimal_dag.run(ctx_b) - assert AlwaysTrueCacheable.run_count == 2 - - -def test_dag_cacheable_node_without_cache_path_runs_each_time(sample_ctx): - AlwaysTrueCacheable.run_count = 0 - dag = Composer("no_cache", verdict_type=Safety).add_node( - AlwaysTrueCacheable( - name="always_true", - routes_true=[Safety(is_safe=True)], - routes_false=[Safety(is_safe=False)], - ) - ) - dag.run(sample_ctx) - dag.run(sample_ctx) - assert AlwaysTrueCacheable.run_count == 2 - - -def test_dag_run_with_cached_simple_dag(cached_simple_dag, sample_ctx): - AlwaysTrueCacheable.run_count = 0 - - def assert_output(dag_output): - assert dag_output.total_cost.input_token_cost == pytest.approx(1.2) - assert dag_output.total_cost.output_token_cost == pytest.approx(1.6) - assert dag_output.total_cost.fixed_cost == pytest.approx(0.5) - assert dag_output.total_cost.latency_seconds == pytest.approx(0.5) - assert dag_output.total_cost.total_token_cost == pytest.approx(2.8) - assert dag_output.total_cost.total_cost == pytest.approx(3.3) - assert dag_output.verdict.name == "UNSAFE" - - dag_output = cached_simple_dag.run(sample_ctx) - assert_output(dag_output) - assert AlwaysTrueCacheable.run_count == 1 - - dag_output = cached_simple_dag.run(sample_ctx) - assert_output(dag_output) - assert AlwaysTrueCacheable.run_count == 1 - - -def test_dag_run(simple_dag, sample_ctx): - dag_output = simple_dag.run(sample_ctx) - - # lower_caser and prompt_parity are at the same level from always_true - assert dag_output.total_cost.input_token_cost == pytest.approx( - 1.2 - ) # (lower_caser - 4 * 0.3) + (prompt_parity - 4 * 0) - assert dag_output.total_cost.output_token_cost == pytest.approx( - 1.6 - ) # (lower_caser - 4 * 0.4) + (prompt_parity - 4 * 0) - assert dag_output.total_cost.fixed_cost == pytest.approx( - 0.5 - ) # (lower_caser - 0.3) + (prompt_parity - 0.2) - assert dag_output.total_cost.latency_seconds == pytest.approx( - 0.5 - ) # (lower_caser - 0.3) + (prompt_parity - 0.2) - assert dag_output.total_cost.total_token_cost == pytest.approx(2.8) - assert dag_output.total_cost.total_cost == pytest.approx(3.3) - - assert dag_output.verdict.name == "UNSAFE" - - -def test_dag_passes_updated_context_to_downstream_nodes(): - ctx = EvalContext(prompt="x", response="HELLO") - dag = ( - Composer("ctx_update", verdict_type=Safety) - .add_node( - AlwaysTrue( - name="always_true", - routes_true=["lower_caser"], - routes_false=["always_safe"], - ) - ) - .add_node(AlwaysSafe(name="always_safe")) - .add_node(LowerCaser(name="lower_caser", routes=["noop"])) - .add_node(NoOpEnricher(name="noop", routes=["lower_scorer"])) - .add_node(LowerCaseScorer(name="lower_scorer", routes=["threshold_arbiter"])) - .add_node(ThresholdArbiter(name="threshold_arbiter", threshold=0.5)) - ) - dag_output = dag.run(ctx) - assert dag_output.node_outputs["lower_caser"].updated_ctx is not None - assert dag_output.node_outputs["lower_caser"].updated_ctx.response == "hello" - # Scorer reads ctx.response; 1.0 only if it saw the lowercased update from lower_caser. - assert dag_output.node_outputs["lower_scorer"].value == pytest.approx(1.0) - - -def test_dag_updated_context_not_passed_to_parallel_nodes(): - # noop and lower caser are parallel nodes. noop should not see the updated context from lower_caser. - ctx = EvalContext(prompt="x", response="HELLO") - dag = ( - Composer("ctx_update", verdict_type=Safety) - .add_node( - AlwaysTrue( - name="always_true", - routes_true=["lower_caser", "noop"], - routes_false=["always_safe"], - ) - ) - .add_node(AlwaysSafe(name="always_safe")) - .add_node(LowerCaser(name="lower_caser", routes=["lower_scorer"])) - .add_node(NoOpEnricher(name="noop", routes=["lower_scorer"])) - .add_node(LowerCaseScorer(name="lower_scorer", routes=["threshold_arbiter"])) - .add_node(ThresholdArbiter(name="threshold_arbiter", threshold=0.5)) - ) - dag_output = dag.run(ctx) - - assert dag_output.node_outputs["lower_caser"].original_ctx.response == "HELLO" - assert dag_output.node_outputs["lower_caser"].updated_ctx is not None - assert dag_output.node_outputs["lower_caser"].updated_ctx.response == "hello" - - assert dag_output.node_outputs["noop"].original_ctx.response == "HELLO" - assert dag_output.node_outputs["noop"].updated_ctx is None - - assert dag_output.node_outputs["lower_scorer"].original_ctx.response == "hello" - # Scorer reads ctx.response; 1.0 only if it saw the lowercased update from lower_caser. - assert dag_output.node_outputs["lower_scorer"].value == pytest.approx(1.0) - - -def test_dag_parallel_nodes_different_updated_contexts_raises_error(): - # upper caser and lower caser are parallel nodes, they update the dontext differently which should raise an error. - ctx = EvalContext(prompt="x", response="HELLO") - dag = ( - Composer("ctx_update", verdict_type=Safety) - .add_node( - AlwaysTrue( - name="always_true", - routes_true=["lower_caser", "upper_caser"], - routes_false=["always_safe"], - ) - ) - .add_node(AlwaysSafe(name="always_safe")) - .add_node(LowerCaser(name="lower_caser", routes=["lower_scorer"])) - .add_node(UpperCaser(name="upper_caser", routes=["lower_scorer"])) - .add_node(LowerCaseScorer(name="lower_scorer", routes=["threshold_arbiter"])) - .add_node(ThresholdArbiter(name="threshold_arbiter", threshold=0.5)) - ) - with pytest.raises( - ValueError, - match="all parent outputs must have the same updated context", - ): - dag.run(ctx) - - -def test_dag_run_with_dataframe(simple_dag, tmp_path): - # "hello world" (space lowers avg below threshold) → safe - # "helloworld" (no space, avg = 0.5 = threshold) → unsafe - # Alternate even/odd prompt lengths to exercise both enricher paths. - df = pd.DataFrame( - { - "prompt": ["a", "ab", "abc", "abcd"], # odd, even, odd, even - "response": ["Hello world", "Helloworld", "Hello world", "Helloworld"], - "metadata": [ - json.dumps({"key": "value1"}), - json.dumps({"key": "value2"}), - "notvalidjson", - None, - ], - } - ) - result_df = simple_dag.run_dataframe(df, metadata_col="metadata") - - assert len(result_df) == len(df) - assert "prompt" in result_df.columns - assert "response" in result_df.columns - verdicts = result_df[simple_dag.df_output_col].tolist() - expected_verdicts = ["SAFE", "UNSAFE", "SAFE", "UNSAFE"] - assert verdicts == expected_verdicts - - for cost_json in result_df[simple_dag.df_cost_col]: - cost = json.loads(cost_json) - assert "input_token_cost" in cost - assert "output_token_cost" in cost - assert "fixed_cost" in cost - assert "latency_seconds" in cost - assert "total_token_cost" in cost - assert "total_cost" in cost - - for dag_run_json in result_df[simple_dag.df_dag_run_col]: - dag_run = json.loads(dag_run_json) - assert "always_true" in dag_run - if "lower_caser" in dag_run: - # confirm original ctx is present and different from updated ctx - assert ( - dag_run["lower_caser"]["original_ctx"]["response"].lower() - == dag_run["lower_caser"]["updated_ctx"]["response"] - ) - - -def test_dag_run_with_dataframe_parallel(simple_dag): - df = pd.DataFrame( - { - "prompt": ["a", "ab", "abc", "abcd"], # odd, even, odd, even - "response": ["hello world", "helloworld", "hello world", "helloworld"], - } - ) - result_df = simple_dag.run_dataframe(df, n_jobs=-1) - - assert len(result_df) == len(df) - assert "prompt" in result_df.columns - assert "response" in result_df.columns - verdicts = result_df[simple_dag.df_output_col].tolist() - expected_verdicts = ["SAFE", "UNSAFE", "SAFE", "UNSAFE"] - assert verdicts == expected_verdicts - - -def test_dag_cost_all_paths(simple_dag): - costs = simple_dag.potential_costs() - assert costs.keys() == { - "always_true -> always_safe -> Out (Safety)", - "always_true -> lower_caser -> prompt_parity -> lower_scorer -> upper_scorer -> threshold_arbiter -> Out (Safety)", - } - - key = "always_true -> always_safe -> Out (Safety)" - assert costs[key].input_cost_per_token == pytest.approx(0.0) - assert costs[key].output_cost_per_token == pytest.approx(0.0) - assert costs[key].fixed_cost == pytest.approx(1.0) - assert costs[key].latency_seconds == pytest.approx(1.0) - - key = "always_true -> lower_caser -> prompt_parity -> lower_scorer -> upper_scorer -> threshold_arbiter -> Out (Safety)" - # 0.3 (lower_caser) + 0.0 (prompt_parity) + 0.0 (lower_scorer) + 0.0 (upper_scorer) + 0.0 (threshold_arbiter) - assert costs[key].input_cost_per_token == pytest.approx(0.3) - # 0.4 (lower_caser) + 0.0 (prompt_parity) + 0.0 (lower_scorer) + 0.0 (upper_scorer) + 0.0 (threshold_arbiter) - assert costs[key].output_cost_per_token == pytest.approx(0.4) - # 0.3 (lower_caser) + 0.2 (prompt_parity) + 0.7 (lower_scorer) + 0.8 (upper_scorer) + 1.1 (threshold_arbiter) - assert costs[key].fixed_cost == pytest.approx(3.1) - # 0.3 (lower_caser) + 0.2 (prompt_parity) + 0.7 (lower_scorer) + 0.8 (upper_scorer) + 1.1 (threshold_arbiter) - assert costs[key].latency_seconds == pytest.approx(3.1) - - -@skip_in_ci -def test_dag_visualize_runs(simple_dag, one_step_dag, sample_ctx): - simple_dag.visualize() - simple_dag.visualize_run(sample_ctx) - one_step_dag.visualize() - one_step_dag.visualize_run(sample_ctx) - - -def test_visualize_raises_when_graphviz_binary_missing(simple_dag): - import graphviz - - with patch.object( - graphviz.Digraph, - "pipe", - side_effect=graphviz.ExecutableNotFound(["dot"]), - ): - with pytest.raises( - RuntimeError, - match="Graphviz system binaries not found", - ): - simple_dag.visualize() - - -def test_composer_names_orig(simple_dag): - assert simple_dag.name == "simple" - assert simple_dag.df_output_col == "simple_output" - assert simple_dag.df_error_col == "simple_error" - assert simple_dag.df_dag_run_col == "simple_dag_run" - assert simple_dag.df_cost_col == "simple_dag_cost" - - -def test_composer_names_override(): - dag = Composer( - name="dag_name", - verdict_type=Safety, - col_names=ComposerColumnNames( - composer_name="dag_name", output_col_name="my_output" - ), - ) - assert dag.df_output_col == "my_output" - assert dag.df_error_col == "dag_name_error" - assert dag.df_dag_run_col == "dag_name_dag_run" - assert dag.df_cost_col == "dag_name_dag_cost" - - -def test_composer_names_partial_override_no_name_raises(): - with pytest.raises(ValueError, match="composer_name must be provided"): - ComposerColumnNames(output_col_name="my_output") diff --git a/tests/unit/evaluator/test_context.py b/tests/unit/evaluator/test_context.py deleted file mode 100644 index 86cb9e7..0000000 --- a/tests/unit/evaluator/test_context.py +++ /dev/null @@ -1,108 +0,0 @@ -import pytest - -from modelplane.evaluator.context import EvalContext, NodeOutput - - -def test_prompt_replacement(sample_ctx): - new_prompt = "New prompt" - new_ctx = sample_ctx.with_prompt(new_prompt) - assert new_ctx.prompt == new_prompt - assert new_ctx.response == sample_ctx.response - assert new_ctx.metadata == sample_ctx.metadata - - -def test_response_replacement(sample_ctx): - new_response = "New response" - new_ctx = sample_ctx.with_response(new_response) - assert new_ctx.prompt == sample_ctx.prompt - assert new_ctx.response == new_response - assert new_ctx.metadata == sample_ctx.metadata - - -def test_metadata_replacement(sample_ctx): - new_metadata = {"key": "value"} - new_ctx = sample_ctx.with_metadata(new_metadata) - assert new_ctx.prompt == sample_ctx.prompt - assert new_ctx.response == sample_ctx.response - assert new_ctx.metadata == new_metadata - - -def test_metadata_updates(): - original_metadata = {"key1": "value1"} - sample_ctx = EvalContext( - prompt="Prompt", response="Response", metadata=original_metadata - ) - additional_metadata = {"key2": "value2"} - new_ctx = sample_ctx.with_metadata_updates(additional_metadata) - assert new_ctx.prompt == sample_ctx.prompt - assert new_ctx.response == sample_ctx.response - assert new_ctx.metadata == {**sample_ctx.metadata, **additional_metadata} - - -def test_with_updates(sample_ctx): - new_prompt = "Updated prompt" - new_response = "Updated response" - new_metadata = {"updated": True} - new_ctx = sample_ctx.with_updates( - prompt=new_prompt, - response=new_response, - metadata=new_metadata, - ) - assert new_ctx.prompt == new_prompt - assert new_ctx.response == new_response - assert new_ctx.metadata == new_metadata - - -def test_with_different_parent_outputs(sample_ctx): - parent_outputs = { - "parent1": NodeOutput( - value="output1", - original_ctx=sample_ctx, - updated_ctx=sample_ctx.with_response("Updated response for parent1"), - ), - "parent2": NodeOutput( - value="output2", - original_ctx=sample_ctx, - updated_ctx=sample_ctx.with_prompt("Updated prompt for parent2"), - ), - } - with pytest.raises( - ValueError, - match="all parent outputs must have the same updated context", - ): - sample_ctx.with_parent_outputs(parent_outputs) - - -def test_eq_with_non_eval_context(sample_ctx): - assert sample_ctx != "not an EvalContext" - - -def test_hash(sample_ctx): - other_ctx = EvalContext(prompt=sample_ctx.prompt, response=sample_ctx.response) - assert sample_ctx.hash() == other_ctx.hash() - - other_ctx = EvalContext( - prompt=sample_ctx.prompt, - response=sample_ctx.response, - metadata={"key": "value"}, - ) - assert sample_ctx.hash() != other_ctx.hash() - - # Make sure can handle nested dictionaries in metadata - nested_ctx_1 = EvalContext( - prompt=sample_ctx.prompt, - response=sample_ctx.response, - metadata={"key": "value", "nested": {"key": "value_1"}}, - ) - nested_ctx_2 = EvalContext( - prompt=sample_ctx.prompt, - response=sample_ctx.response, - metadata={"key": "value", "nested": {"key": "value_2"}}, - ) - nested_ctx_2_copy = EvalContext( - prompt=sample_ctx.prompt, - response=sample_ctx.response, - metadata={"key": "value", "nested": {"key": "value_2"}}, - ) - assert nested_ctx_1.hash() != nested_ctx_2.hash() - assert nested_ctx_2_copy.hash() == nested_ctx_2.hash() \ No newline at end of file diff --git a/tests/unit/evaluator/test_nodes.py b/tests/unit/evaluator/test_nodes.py deleted file mode 100644 index 56e09cc..0000000 --- a/tests/unit/evaluator/test_nodes.py +++ /dev/null @@ -1,128 +0,0 @@ -"""Unit tests for individual ComposerNode subclasses.""" - -import pytest - -from modelplane.evaluator.context import NodeOutput -from modelplane.evaluator.nodes import ComposerNode -from modelplane.evaluator.safety import Safety - -from .conftest import DEFAULT_BRANCH, FALSE_BRANCH, SCORE1, SCORE2, TRUE_BRANCH -from .mocks import AlwaysTrue, AlwaysUnsafe, LowerCaser - - -def test_true_routes_to_true_branch(sample_ctx, always_true_gate): - output = always_true_gate.run(sample_ctx) - assert output.value - assert always_true_gate.next_nodes(output.value) == TRUE_BRANCH - - -def test_false_routes_to_false_branch(sample_ctx, always_false_gate): - output = always_false_gate.run(sample_ctx) - assert not output.value - assert always_false_gate.next_nodes(output.value) == FALSE_BRANCH - - -def test_lower_caser(sample_ctx, lower_caser): - output = lower_caser.run(sample_ctx) - assert output.value == sample_ctx.response.lower() - assert lower_caser.next_nodes(output.value) == DEFAULT_BRANCH - - -def test_fixed_scorer(sample_ctx, score_1): - output = score_1.run(sample_ctx) - assert output.value == SCORE1 - assert score_1.next_nodes(output.value) == DEFAULT_BRANCH - - -def test_consistent_arbiters(sample_ctx, score_1, score_2, always_unsafe, always_safe): - parent_outputs = { - score_1.name: NodeOutput(value=SCORE1, original_ctx=sample_ctx), - score_2.name: NodeOutput(value=SCORE2, original_ctx=sample_ctx), - } - run_ctx = sample_ctx.with_parent_outputs(parent_outputs) - output = always_unsafe.run(run_ctx) - assert output.value.name == "UNSAFE" - output = always_safe.run(run_ctx) - assert output.value.name == "SAFE" - - -def test_threshold_arbiter_true(sample_ctx, threshold_arbiter): - run_ctx = sample_ctx.with_parent_outputs( - { - "parent0": NodeOutput(value=SCORE2, original_ctx=sample_ctx), - "parent1": NodeOutput(value=SCORE2, original_ctx=sample_ctx), - } - ) - output = threshold_arbiter.run(run_ctx) - assert output.value.name == "UNSAFE" - - -def test_threshold_arbiter_false(sample_ctx, threshold_arbiter): - run_ctx = sample_ctx.with_parent_outputs( - { - "parent0": NodeOutput(value=SCORE1, original_ctx=sample_ctx), - "parent1": NodeOutput(value=SCORE1, original_ctx=sample_ctx), - } - ) - output = threshold_arbiter.run(run_ctx) - assert output.value.name == "SAFE" - - -def test_gate_with_two_outputs(): - with pytest.raises(ValueError, match="has multiple Verdict routes"): - AlwaysTrue( - name="bad_gate", - routes_true=[Safety(is_safe=True), Safety(is_safe=False)], - routes_false=FALSE_BRANCH, - ) - - -def test_gate_with_no_true_route(): - with pytest.raises(ValueError, match="requires both routes_true and routes_false"): - AlwaysTrue( - name="bad_gate", - routes_false=FALSE_BRANCH, - ) - - -def test_gate_with_routes(): - with pytest.raises(ValueError, match="should not have routes"): - AlwaysTrue( - name="bad_gate", - routes_true=TRUE_BRANCH, - routes_false=FALSE_BRANCH, - routes=DEFAULT_BRANCH, - ) - - -def test_enricher_with_binary_routes(): - with pytest.raises( - ValueError, match="should not have routes_true= / routes_false=" - ): - LowerCaser( - name="bad_enricher", - routes_true=TRUE_BRANCH, - routes=DEFAULT_BRANCH, - ) - - -def test_enricher_with_no_routes(): - with pytest.raises(ValueError, match="requires routes="): - LowerCaser( - name="bad_enricher", - ) - - -def test_arbiter_with_routes(): - with pytest.raises(ValueError, match="is terminal and cannot have routing kwargs"): - AlwaysUnsafe( - name="bad_arbiter", - routes=DEFAULT_BRANCH, - ) - - -def test_note_format_output(): - assert ComposerNode.format_output(3.1415926535) == "3.14" - assert ComposerNode.format_output("short string") == "short string" - long_string = "x" * 50 - assert ComposerNode.format_output(long_string) == "x" * 27 + "..." diff --git a/tests/unit/evaluator/test_prompt_enricher.py b/tests/unit/evaluator/test_prompt_enricher.py deleted file mode 100644 index 5603188..0000000 --- a/tests/unit/evaluator/test_prompt_enricher.py +++ /dev/null @@ -1,33 +0,0 @@ -import string - -import pytest - -from modelplane.evaluator.context import EvalContext -from modelplane.evaluator.prompt_enricher import PromptEngineeredNode - - -def test_prompt_enricher_run(prompt_enricher, sample_ctx): - even_ctx = EvalContext(prompt="this is even", response="with the response") - output = prompt_enricher.run(even_ctx) - assert isinstance(output.value, str) - assert output.value == "Yes" - - odd_ctx = EvalContext(prompt="this is not even", response="with the response") - output = prompt_enricher.run(odd_ctx) - assert isinstance(output.value, str) - assert output.value == "No" - - -def test_prompt_enricher_bad_template(): - with pytest.raises( - ValueError, - match="Prompt template may only have 'prompt' and 'response' placeholders.", - ): - PromptEngineeredNode( - name="bad_enricher", - routes=["next_node"], - prompt_template=string.Template( - "This template has an invalid placeholder: $invalid" - ), - sut_id="demo_yes_no", - ) diff --git a/tests/unit/evaluator/test_safety.py b/tests/unit/evaluator/test_safety.py deleted file mode 100644 index f46543c..0000000 --- a/tests/unit/evaluator/test_safety.py +++ /dev/null @@ -1,64 +0,0 @@ -import pytest -from modelgauge.annotation import SafetyAnnotation -from modelgauge.annotators.demo_annotator import DemoYBadAnnotator -from modelgauge.prompt import TextPrompt -from modelgauge.sut import SUTResponse - -from modelplane.evaluator.dag import Composer, FailedDAGOutput, NodeExecutionError -from modelplane.evaluator.safety import AnnotatorArbiter, Safety, SafetyDAGAnnotator -from modelplane.evaluator.verdict import Verdict - -from .mocks import FailingNode - - -def test_safety_annotator_arbiter(sample_ctx): - annotator = DemoYBadAnnotator("demo_annotator") - arbiter = AnnotatorArbiter(name="demo_arbiter", annotator=annotator) - output = arbiter.run(sample_ctx) - assert output.value.is_safe - assert isinstance(output.value, Safety) - assert arbiter.verdict_type == Safety - - -def test_safety_dag_run(simple_dag, sample_ctx): - safety_annotator = SafetyDAGAnnotator("safety", simple_dag) - output = safety_annotator.process( - prompt=TextPrompt(text=sample_ctx.prompt), - response=SUTResponse(text=sample_ctx.response), - ) - assert not output.is_safe - assert isinstance(output, SafetyAnnotation) - - -def test_safety_dag_with_bad_verdict_type(): - with pytest.raises( - ValueError, - match="All outputs of the DAG must be of type Safety.", - ): - SafetyDAGAnnotator("bad_dag", Composer("bad_dag", verdict_type=Verdict)) - - -def test_safety_dag_with_bad_node(sample_ctx, threshold_arbiter): - failing_node = FailingNode(name="failing_node", routes=["threshold_arbiter"]) - dag = ( - Composer( - "bad_node_dag", - verdict_type=Safety, - ) - .add_node(failing_node) - .add_node(threshold_arbiter) - ) - dag_output = dag.run(sample_ctx) - assert isinstance(dag_output, FailedDAGOutput) - assert str(dag_output.error.original_error) == "I'm afraid I can't do that, Dave." - - dag_annotator = SafetyDAGAnnotator("safety_annotator", dag) - with pytest.raises( - NodeExecutionError, match=r"Error executing node 'failing_node'" - ) as e: - dag_annotator.process( - prompt=TextPrompt(text=sample_ctx.prompt), - response=SUTResponse(text=sample_ctx.response), - ) - assert type(e.value.original_error) == type(dag_output.error) - assert "Traceback" in str(e.value) From 8d21cea25e621044669ecfddcd219c7cbd6ff699 Mon Sep 17 00:00:00 2001 From: Vishal Doshi Date: Wed, 10 Jun 2026 16:05:43 -0400 Subject: [PATCH 05/10] Update modelbench to latest with composer. --- pyproject.toml | 6 +- uv.lock | 1951 ++++++++++++++++++++++++------------------------ 2 files changed, 1000 insertions(+), 957 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 58eaeaf..57babda 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,7 @@ dependencies = [ "jupyterlab-git", "scikit-learn>=1.5.0,<2.0.0", "pandas>=2.2.2,<4", - "modelbench @ git+https://github.com/mlcommons/modelbench.git", + "modelbench[composer]", "graphviz>=0.20,<1", # the below 4 are tied to the Dockerfile.mlflow versions; need to be upgraded together "mlflow[auth]==3.7.0", @@ -30,6 +30,10 @@ dependencies = [ "google-cloud-storage==3.10.1", ] +[tool.uv.sources] +# TODO: remove this +modelbench = { git = "https://github.com/mlcommons/modelbench.git", rev = "dag-evaluator-migration" } + [project.scripts] modelplane = "modelplane.cli:cli" diff --git a/uv.lock b/uv.lock index 728aabc..f5de4d2 100644 --- a/uv.lock +++ b/uv.lock @@ -9,16 +9,16 @@ resolution-markers = [ [[package]] name = "aiohappyeyeballs" -version = "2.6.1" +version = "2.6.2" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" } +sdist = { url = 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