fix(teleprompt): make COPRO.compile eval_kwargs optional (#10087)

* fix(teleprompt): make COPRO.compile eval_kwargs optional

COPRO.compile marked eval_kwargs as a required keyword-only argument, but the docstring documents it as optional, so compile(student, trainset=...) raised TypeError. Default it to None and normalize to {} at the top of compile(); both call sites already splat the dict, so an empty one is a no-op. Adds a regression test that compiles without eval_kwargs.

Fixes #9080

* docs(teleprompt): state COPRO eval_kwargs default in docstring

---------

Co-authored-by: Katherine Ahn <ka552@cornell.edu>

Author: katherineahn

dspy/teleprompt/copro_optimizer.py

@@ -121,18 +121,19 @@ def _set_signature(self, predictor, updated_signature):
         assert hasattr(predictor, "signature")
         predictor.signature = updated_signature
 
-    def compile(self, student, *, trainset, eval_kwargs):
+    def compile(self, student, *, trainset, eval_kwargs=None):
         """
         optimizes `signature` of `student` program - note that it may be zero-shot or already pre-optimized (demos already chosen - `demos != []`)
 
         parameters:
         student: program to optimize and left modified.
         trainset: iterable of `Example`s
-        eval_kwargs: optional, dict
+        eval_kwargs: optional, dict, defaults to None (no extra kwargs)
            Additional keywords to go into `Evaluate` for the metric.
 
         Returns optimized version of `student`.
         """
+        eval_kwargs = eval_kwargs or {}
         module = student.deepcopy()
         evaluate = Evaluate(devset=trainset, metric=self.metric, **eval_kwargs)
         total_calls = 0

tests/teleprompt/test_copro_optimizer.py

@@ -65,6 +65,28 @@ def test_signature_optimizer_optimization_process():
     # such as checking the instructions of the optimized student's predictors.
 
 
+def test_compile_without_eval_kwargs():
+    # `eval_kwargs` is documented as optional, so compile() must run when it is omitted.
+    # Regression test for #9080 (previously raised TypeError: missing keyword-only argument).
+    optimizer = COPRO(metric=simple_metric, breadth=2, depth=1, init_temperature=1.4)
+    dspy.configure(
+        lm=DummyLM(
+            [
+                {
+                    "proposed_instruction": "Optimized instruction 1",
+                    "proposed_prefix_for_output_field": "Optimized instruction 2",
+                },
+            ]
+        )
+    )
+
+    student = SimpleModule("input -> output")
+
+    optimized_student = optimizer.compile(student, trainset=trainset)
+
+    assert optimized_student is not student, "Optimization did not modify the student"
+
+
 def test_signature_optimizer_statistics_tracking():
     optimizer = COPRO(metric=simple_metric, breadth=2, depth=1, init_temperature=1.4)
     optimizer.track_stats = True  # Enable statistics tracking