done

Author: Hangzhi

dspy/predict/chain_of_thought_with_hint.py

@@ -83,6 +83,7 @@ def compile(
             data_pred_ind = None if self.multitask else pred_ind
             lm = pred.lm or settings.lm
             training_key = (lm, data_pred_ind)
+            
             if training_key not in key_to_data:
                 train_data, data_format = self._prepare_finetune_data(
                     trace_data=trace_data, lm=lm, pred_ind=data_pred_ind
@@ -115,7 +116,7 @@ def compile(
         logger.info("Updating the student program with the fine-tuned LMs...")
         for pred_ind, pred in enumerate(student.predictors()):
             data_pred_ind = None if self.multitask else pred_ind
-            training_key = (pred.lm, data_pred_ind)
+            training_key = (pred.lm or settings.lm, data_pred_ind)
             finetuned_lm = key_to_lm[training_key]
             if isinstance(finetuned_lm, Exception):
                 raise RuntimeError(f"Finetuned LM for predictor {pred_ind} failed.") from finetuned_lm

dspy/teleprompt/bootstrap_finetune.py

@@ -0,0 +1,77 @@
+import dspy
+from dspy import Example
+from dspy.predict import Predict
+from dspy.teleprompt import BootstrapFinetune
+from dspy.utils.dummies import DummyLM
+
+
+# Define a simple metric function for testing
+def simple_metric(example, prediction, trace=None):
+    # Simplified metric for testing: true if prediction matches expected output
+    return example.output == prediction.output
+
+
+examples = [
+    Example(input="What is the color of the sky?", output="blue").with_inputs("input"),
+    Example(input="What does the fox say?", output="Ring-ding-ding-ding-dingeringeding!"),
+]
+trainset = [examples[0]]
+
+
+def test_bootstrap_finetune_initialization():
+    # Initialize BootstrapFinetune with a dummy metric and minimal setup
+    bootstrap = BootstrapFinetune(metric=simple_metric)
+    assert bootstrap.metric == simple_metric, "Metric not correctly initialized"
+    assert bootstrap.multitask == True, "Multitask should default to True"
+
+
+class SimpleModule(dspy.Module):
+    def __init__(self, signature, lm=None):
+        super().__init__()
+        self.predictor = Predict(signature)
+        if lm:
+            self.predictor.lm = lm
+
+    def forward(self, **kwargs):
+        return self.predictor(**kwargs)
+
+
+def test_compile_with_predict_instances_no_explicit_lm():
+    """Test BootstrapFinetune compile with predictors that don't have explicit LMs."""
+    from unittest.mock import patch
+    
+    # Create student and teacher modules without explicit LMs in predictors
+    # This tests the fix: lm = pred.lm or settings.lm
+    student = SimpleModule("input -> output")
+    teacher = SimpleModule("input -> output")
+
+    # Set up LM in settings - this will be the fallback
+    lm = DummyLM(["Initial thoughts", "Finish[blue]"])
+    original_lm = dspy.settings.lm
+    dspy.settings.configure(lm=lm)
+
+
+    # Verify that the predictor doesn't have an explicit LM
+    assert student.predictor.lm is None
+
+    # Initialize BootstrapFinetune - this should work without AttributeError
+    bootstrap = BootstrapFinetune(metric=simple_metric)
+    
+    # Mock all the components that would fail without proper setup
+    with patch('dspy.teleprompt.bootstrap_finetune.all_predictors_have_lms'), \
+            patch('dspy.teleprompt.bootstrap_finetune.prepare_teacher', return_value=teacher), \
+            patch('dspy.teleprompt.bootstrap_finetune.bootstrap_trace_data', return_value=[]), \
+            patch.object(bootstrap, '_prepare_finetune_data', return_value=([], 'openai')), \
+            patch.object(bootstrap, 'finetune_lms') as mock_finetune_lms:
+        
+        # Mock the finetune_lms to return a mapping from training key to LM
+        mock_finetune_lms.return_value = {(lm, None): lm}
+        
+        # This should not raise AttributeError due to the fix
+        compiled_student = bootstrap.compile(student, teacher=teacher, trainset=trainset)
+        
+        assert compiled_student is not None, "Failed to compile student"
+        # Verify that finetune_lms was called
+        mock_finetune_lms.assert_called_once()
+        
+