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import dsp
import dspy
from dspy.teleprompt.teleprompt import Teleprompter
from dspy.signatures import Signature
from dspy.evaluate.evaluate import Evaluate
from collections import defaultdict
"""
USAGE SUGGESTIONS:
The following code can be used to compile a optimized signature teleprompter, and evaluate it on an end task:
teleprompter = SignatureOptimizer(prompt_model=prompt_model, metric=metric, breadth=BREADTH, depth=DEPTH, init_temperature=INIT_TEMPERATURE)
kwargs = dict(num_threads=NUM_THREADS, display_progress=True, display_table=0)
compiled_prompt_opt = teleprompter.compile(program.deepcopy(), devset=devset[:DEV_NUM], eval_kwargs=kwargs)
eval_score = evaluate(compiled_prompt_opt, devset=evalset[:EVAL_NUM], **kwargs)
Note that this teleprompter takes in the following parameters:
* prompt_model: The model used for prompt generation. When unspecified, defaults to the model set in settings (ie. dspy.settings.configure(lm=task_model)).
* metric: The task metric used for optimization.
* breadth: The number of new prompts to generate at each iteration. Default=10.
* depth: The number of times we should ask our prompt model to generate new prompts, with the history of the past prompts as input. Default=3.
* init_temperature: The temperature used to generate new prompts. Higher roughly equals more creative. Default=1.4.
* verbose: Tells the method whether or not to print intermediate steps.
* track_stats: Tells the method whether or not to track statistics about the optimization process.
If True, the method will track the following statistics:
* results_best: The min,max,avg,stddev of top 10 scores for each predictor at each depth.
* results_latest: The min,max,avg,stddev of newest prompt scores for each predictor at each depth.
* total_calls: The total number of calls to the task metric.
These statistics will be returned as attributes of the best program.
"""
class BasicGenerateInstruction(Signature):
"""You are an instruction optimizer for large language models. I will give you a ``signature`` of fields (inputs and outputs) in English. Your task is to propose an instruction that will lead a good language model to perform the task well. Don't be afraid to be creative."""
basic_instruction = dspy.InputField(desc="The initial instructions before optimization")
proposed_instruction = dspy.OutputField(desc="The improved instructions for the language model")
proposed_prefix_for_output_field = dspy.OutputField(desc="The string at the end of the prompt, which will help the model start solving the task")
class GenerateInstructionGivenAttempts(dspy.Signature):
"""You are an instruction optimizer for large language models. I will give some task instructions I've tried, along with their corresponding validation scores. The instructions are arranged in increasing order based on their scores, where higher scores indicate better quality.
Your task is to propose a new instruction that will lead a good language model to perform the task even better. Don't be afraid to be creative."""
attempted_instructions = dspy.InputField(format=dsp.passages2text)
proposed_instruction = dspy.OutputField(desc="The improved instructions for the language model")
proposed_prefix_for_output_field = dspy.OutputField(desc="The string at the end of the prompt, which will help the model start solving the task")
class SignatureOptimizer(Teleprompter):
def __init__(self, prompt_model=None, metric=None, breadth=10, depth=3, init_temperature=1.4, verbose=False, track_stats=False):
self.metric = metric
self.breadth = breadth
self.depth = depth
self.init_temperature = init_temperature
self.prompt_model = prompt_model
self.verbose = verbose
self.track_stats = track_stats
def _check_candidates_equal(self, candidate1, candidate2):
for p1, p2 in zip(candidate1["program"].predictors(), candidate2["program"].predictors()):
if not p1.extended_signature.instructions == p2.extended_signature.instructions:
return False
if not p1.extended_signature.fields[-1] == p2.extended_signature.fields[-1]:
return False
return True
def _drop_duplicates(self, candidates):
final_candidates = []
last_batch = []
last_batch_score = -1
for c in candidates:
repeat = False
if c['score'] == last_batch_score:
for c2 in last_batch:
if (self._check_candidates_equal(c, c2)):
repeat = True
break
if not repeat:
last_batch.append(c)
else:
last_batch = [c]
last_batch_score = c['score']
if not repeat:
final_candidates.append(c)
return final_candidates
def compile(self, student, *, devset, eval_kwargs):
"""student is a program that needs to be optimized, note that it may be zero-shot or already pre-optimized for demos != []"""
module = student.deepcopy()
evaluate = Evaluate(devset=devset, metric=self.metric, **eval_kwargs)
total_calls = 0
results_best = {id(p):{"depth": [], "max": [], "average": [], "min":[], "std": []} for p in module.predictors()}
results_latest = {id(p):{"depth": [], "max": [], "average": [], "min":[], "std": []} for p in module.predictors()}
if self.track_stats:
import numpy as np
candidates = {}
evaluated_candidates = defaultdict(dict)
# Seed the prompt optimizer zero shot with just the instruction, generate BREADTH new prompts
for predictor in module.predictors():
basic_instruction = None
basic_prefix = None
if (hasattr(predictor, 'extended_signature')):
basic_instruction = predictor.extended_signature.instructions
basic_prefix = predictor.extended_signature.fields[-1].name
else:
basic_instruction = predictor.extended_signature1.instructions
basic_prefix = predictor.extended_signature1.fields[-1].name
if self.prompt_model:
with dspy.settings.context(lm=self.prompt_model):
instruct = dspy.Predict(BasicGenerateInstruction, n=self.breadth-1, temperature=self.init_temperature)(basic_instruction=basic_instruction)
else:
instruct = dspy.Predict(BasicGenerateInstruction, n=self.breadth-1, temperature=self.init_temperature)(basic_instruction=basic_instruction)
# Add in our initial prompt as a candidate as well
instruct.completions.proposed_instruction.append(basic_instruction)
instruct.completions.proposed_prefix_for_output_field.append(basic_prefix)
candidates[id(predictor)] = instruct.completions
evaluated_candidates[id(predictor)] = {}
if self.verbose and self.prompt_model: print(f"{self.prompt_model.inspect_history(n=1)}")
latest_candidates = candidates
all_candidates = candidates
module_clone = module.deepcopy()
# For each iteration in depth...
for d in range(self.depth): # TODO: fix this so that we eval the new batch of predictors with the new best followoing predictors
if self.verbose: print(f"Starting iteration {d}/{self.depth}.")
latest_scores = []
# Go through our module's predictors
for p_i, (p_old, p_new) in enumerate(zip(module.predictors(), module_clone.predictors())):
candidates_ = latest_candidates[id(p_old)] # Use the most recently generated candidates for evaluation
if len(module.predictors()) > 1:
candidates_ = all_candidates[id(p_old)] # Unless our program has multiple predictors, in which case we need to reevaluate all prompts with the new prompt(s) for the other predictor(s)
# For each candidate
for c_i, c in enumerate(candidates_):
# Get the candidate instruction and prefix
instruction, prefix = c.proposed_instruction.strip('"').strip(), c.proposed_prefix_for_output_field.strip('"').strip()
# Set this new module with our instruction / prefix
if (hasattr(p_new, 'extended_signature')):
p_new.extended_signature.instructions = instruction
p_new.extended_signature.fields[-1] = p_new.extended_signature.fields[-1]._replace(name=prefix)
else:
p_new.extended_signature1.instructions = instruction
p_new.extended_signature1.fields[-1] = p_new.extended_signature1.fields[-1]._replace(name=prefix)
p_new.extended_signature2.instructions = instruction
p_new.extended_signature2.fields[-1] = p_new.extended_signature2.fields[-1]._replace(name=prefix)
# Score the instruction / prefix
if self.verbose: print(f"----------------")
for i,predictor in enumerate(module_clone.predictors()):
if self.verbose: print(f"Predictor {i}")
if (hasattr(predictor, 'extended_signature')):
if self.verbose: print(f"i: {predictor.extended_signature.instructions}")
if self.verbose: print(f"p: {predictor.extended_signature.fields[-1].name}")
else:
if self.verbose: print(f"i: {predictor.extended_signature1.instructions}")
if self.verbose: print(f"p: {predictor.extended_signature1.fields[-1].name}")
if self.verbose: print()
if self.verbose: print(f"At Depth {d}/{self.depth}, Evaluating Prompt Candidate #{c_i}/{len(candidates_)} for Predictor {p_i} of {len(module.predictors())}.")
score = evaluate(module_clone, devset=devset, **eval_kwargs)
if self.verbose and self.prompt_model: print(f"prompt_model.inspect_history(n=1) {self.prompt_model.inspect_history(n=1)}")
total_calls += 1
if self.verbose: print(f"----------------")
replace_entry = True
if self.verbose: print(f"(instruction, prefix) {(instruction, prefix)}")
# if verbose: print(f"evaluated_candidates[id(p_old)] {evaluated_candidates[id(p_old)]}")
if ((instruction, prefix) in evaluated_candidates[id(p_old)]):
# if verbose: print(f"if evaluated_candidates[id(p_old)][(instruction, prefix)] {evaluated_candidates[id(p_old)][(instruction, prefix)]}")
if evaluated_candidates[id(p_old)][(instruction, prefix)]["score"] >= score:
replace_entry = False
if replace_entry:
# Add it to our evaluated candidates list
evaluated_candidates[id(p_old)][(instruction, prefix)] = {
"score": score,
"program": module_clone.deepcopy(),
"instruction": instruction,
"prefix": prefix,
"depth": d
}
if (len(candidates_)-self.breadth <= c_i):
latest_scores.append(score)
if self.track_stats:
results_latest[id(p_old)]["depth"].append(d)
results_latest[id(p_old)]["max"].append(max(latest_scores))
results_latest[id(p_old)]["average"].append(sum(latest_scores)/len(latest_scores))
results_latest[id(p_old)]["min"].append(min(latest_scores))
results_latest[id(p_old)]["std"].append(np.std(latest_scores))
# Now that we've evaluated the candidates, set this predictor to the best performing version
# to ensure the next round of scores reflect the best possible version
best_candidate = max(evaluated_candidates[id(p_old)].values(), key=lambda candidate: candidate['score'])
if (hasattr(p_new, 'extended_signature')):
p_new.extended_signature.instructions = best_candidate["instruction"]
p_new.extended_signature.fields[-1] = p_new.extended_signature.fields[-1]._replace(name=best_candidate["prefix"])
else:
p_new.extended_signature1.instructions = best_candidate["instruction"]
p_new.extended_signature1.fields[-1] = p_new.extended_signature1.fields[-1]._replace(name=best_candidate["prefix"])
p_new.extended_signature2.instructions = best_candidate["instruction"]
p_new.extended_signature2.fields[-1] = p_new.extended_signature2.fields[-1]._replace(name=best_candidate["prefix"])
if self.verbose: print(f"Updating Predictor {id(p_old)} to:\ni: {best_candidate['instruction']}\np: {best_candidate['prefix']}")
if self.verbose: print(f"Full predictor with update: ")
for i,predictor in enumerate(module_clone.predictors()):
if self.verbose: print(f"Predictor {i}")
if (hasattr(predictor, 'extended_signature')):
if self.verbose: print(f"i: {predictor.extended_signature.instructions}")
if self.verbose: print(f"p: {predictor.extended_signature.fields[-1].name}")
else:
if self.verbose: print(f"i: {predictor.extended_signature1.instructions}")
if self.verbose: print(f"p: {predictor.extended_signature1.fields[-1].name}")
if self.verbose: print()
if d == self.depth-1:
break
new_candidates = {}
for p_base in module.predictors():
# Build Few-Shot Example of Optimized Prompts
attempts = []
shortest_len = self.breadth
shortest_len = min(len(evaluated_candidates[id(p_base)]),shortest_len)
best_predictors = list(evaluated_candidates[id(p_base)].values())
# best_predictors = evaluated_candidates[id(p_base)].values()[:]
best_predictors.sort(key=lambda x: x['score'], reverse=True)
if self.track_stats:
scores = [x['score'] for x in best_predictors][:10]
results_best[id(p_base)]["depth"].append(d)
results_best[id(p_base)]["max"].append(max(scores))
results_best[id(p_base)]["average"].append(sum(scores)/len(scores))
results_best[id(p_base)]["min"].append(min(scores))
results_best[id(p_base)]["std"].append(np.std(scores))
for i in range(shortest_len-1,-1,-1):
# breakpoint()
attempts.append(f'Instruction #{shortest_len-i}: {best_predictors[i]["instruction"]}')
attempts.append(f'Prefix #{shortest_len-i}: {best_predictors[i]["prefix"]}')
attempts.append(f'Resulting Score #{shortest_len-i}: {best_predictors[i]["score"]}')
# Generate next batch of potential prompts to optimize, with previous attempts as input
if self.prompt_model:
with dspy.settings.context(lm=self.prompt_model):
instr = dspy.Predict(GenerateInstructionGivenAttempts, n=self.breadth, temperature=self.init_temperature)(attempted_instructions=attempts)
else:
instr = dspy.Predict(GenerateInstructionGivenAttempts, n=self.breadth, temperature=self.init_temperature)(attempted_instructions=attempts)
if self.verbose and self.prompt_model: print(f"{self.prompt_model.inspect_history(n=1)}")
# Get candidates for each predictor
new_candidates[id(p_base)] = instr.completions
all_candidates[id(p_base)].proposed_instruction.extend(instr.completions.proposed_instruction)
all_candidates[id(p_base)].proposed_prefix_for_output_field.extend(instr.completions.proposed_prefix_for_output_field)
if self.verbose and self.prompt_model: print(f"{self.prompt_model.inspect_history(n=1)}")
latest_candidates = new_candidates
candidates = []
for predictor in module.predictors():
candidates.extend(list(evaluated_candidates[id(predictor)].values()))
if self.track_stats:
best_predictors = list(evaluated_candidates[id(predictor)].values())
best_predictors.sort(key=lambda x: x['score'], reverse=True)
scores = [x['score'] for x in best_predictors][:10]
results_best[id(predictor)]["depth"].append(d)
results_best[id(predictor)]["max"].append(max(scores))
results_best[id(predictor)]["average"].append(sum(scores)/len(scores))
results_best[id(predictor)]["min"].append(min(scores))
results_best[id(predictor)]["std"].append(np.std(scores))
# if verbose: print(f"candidates: {candidates}")
candidates.sort(key=lambda x: x['score'], reverse=True)
candidates = self._drop_duplicates(candidates)
best_program = candidates[0]["program"]
best_program.candidate_programs = candidates
best_program.total_calls = total_calls
if self.track_stats:
best_program.results_best = results_best
best_program.results_latest = results_latest
return best_program |