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tidying up comments and methods

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Shannon Sands 2025-05-12 07:41:59 +10:00
parent 137f8381ec
commit 04b32fd8f3
1 changed files with 16 additions and 112 deletions
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128
environments/infinimath/infinimath_env.py
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@ -45,25 +45,21 @@ Remember to format your final answer correctly as this is important for evaluati
class InfiniteMathEnvConfig(BaseEnvConfig):
"""Configuration for the InfiniteMath environment."""
# Curriculum parameters
starting_level: int = 1
progress_threshold: float = 0.8
min_evaluations: int = 5
# Environment parameters
max_attempts_per_problem: int = 3
correct_reward: float = 1.0
incorrect_reward: float = -1.0
think_block_bonus: float = 0.2 # Bonus for a well-formed think block
boxed_answer_bonus: float = 0.2 # Bonus for a well-formed boxed answer
think_block_bonus: float = 0.2
boxed_answer_bonus: float = 0.2
# Length penalty parameters
apply_length_penalty: bool = True
length_threshold_ratio: float = (
0.5 # Percentage of max_token_length before penalties apply
0.5
)
# Completion parameters
temperature: float = 0.7
top_p: float = 0.9
@ -79,40 +75,34 @@ class InfiniteMathEnv(BaseEnv):
testing=False,
):
super().__init__(config, server_configs, slurm, testing)
self.config = config # Override with our specific config class
self.config = config
# Initialize tracking metrics
self.percent_correct_buffer = []
self.level_correct_buffer = {
i: [] for i in range(1, 8)
} # Track correctness for each level
}
self.eval_metrics = []
# Curriculum will be initialized in setup()
self.curriculum = None
# Set the system prompt
self.system_prompt = system_prompt
async def setup(self):
"""Initialize the environment and curriculum."""
logger.info("Setting up InfiniteMathEnv")
# Initialize curriculum
self.curriculum = MathCurriculum(
starting_level=self.config.starting_level,
progress_threshold=self.config.progress_threshold,
min_evaluations=self.config.min_evaluations,
)
# Generate some test problems for each level for evaluation
self.eval_problems = {}
for level in range(1, 8):
self.eval_problems[level] = []
temp_curriculum = MathCurriculum(starting_level=level)
# Generate 10 test problems for each level
attempts = 0
max_attempts_per_level = 20 # Try at most 20 problems to get 10 valid ones
max_attempts_per_level = 20
while (
len(self.eval_problems[level]) < 10
@ -120,9 +110,8 @@ class InfiniteMathEnv(BaseEnv):
):
try:
problem, solution, generator_id = temp_curriculum.get_problem()
# Strip LaTeX delimiters
problem = self.strip_latex_delimiters(problem)
solution = self.strip_latex_delimiters(solution)
problem = self._strip_latex_delimiters(problem)
solution = self._strip_latex_delimiters(solution)
self.eval_problems[level].append((problem, solution, generator_id))
except Exception as e:
logger.warning(
@ -134,7 +123,6 @@ class InfiniteMathEnv(BaseEnv):
f"Generated {len(self.eval_problems[level])} evaluation problems for level {level}"
)
# If any levels have no problems, add a simple fallback
for level in range(1, 8):
if not self.eval_problems[level]:
logger.warning(
@ -169,9 +157,8 @@ class InfiniteMathEnv(BaseEnv):
else:
self.eval_problems[level].append(("What is |3 - 10|?", "7", 71))
def strip_latex_delimiters(self, text: str) -> str:
def _strip_latex_delimiters(self, text: str) -> str:
"""Strip LaTeX delimiters ($...$) from text."""
# Handle both inline expressions $...$ and expressions that make up the entire string
return re.sub(r"\$(.*?)\$", r"\1", text)
def save_checkpoint(self, step, data=None):
@ -179,7 +166,6 @@ class InfiniteMathEnv(BaseEnv):
if data is None:
data = {}
# Save curriculum state
data["curriculum_level"] = self.curriculum.get_current_level()
data["performance_history"] = {
str(k): v for k, v in self.curriculum.performance_history.items()
@ -191,19 +177,16 @@ class InfiniteMathEnv(BaseEnv):
"""Load curriculum state from checkpoint."""
super().load_checkpoint()
# Check if we have curriculum data in the checkpoint
checkpoint_path = f"{self.checkpoint_dir}/env_checkpoints/{self.wandb_prepend}/step-{self.curr_step}.json"
try:
with open(checkpoint_path, "r") as f:
data = json.load(f)
# Restore curriculum state if available
if "curriculum_level" in data:
level = data["curriculum_level"]
self.curriculum.current_level = level
if "performance_history" in data:
# Convert string keys back to integers
self.curriculum.performance_history = {
int(k): v for k, v in data["performance_history"].items()
}
@ -215,7 +198,6 @@ class InfiniteMathEnv(BaseEnv):
if wandb_metrics is None:
wandb_metrics = {}
# Log overall correct percentage
try:
wandb_metrics["train/percent_correct"] = sum(
self.percent_correct_buffer
@ -223,7 +205,6 @@ class InfiniteMathEnv(BaseEnv):
except ZeroDivisionError:
pass
# Log per-level metrics
for level, buffer in self.level_correct_buffer.items():
if buffer:
wandb_metrics[f"train/level_{level}_correct"] = sum(buffer) / len(
@ -231,7 +212,6 @@ class InfiniteMathEnv(BaseEnv):
)
wandb_metrics[f"train/level_{level}_count"] = len(buffer)
# Log current level and curriculum progress
if self.curriculum:
current_level = self.curriculum.get_current_level()
max_level = max(self.curriculum.DIFFICULTY_LEVELS.keys())
@ -242,12 +222,10 @@ class InfiniteMathEnv(BaseEnv):
current_level / max_level
) * 100
# Log level description
wandb_metrics["curriculum/level_description"] = (
self.curriculum.get_level_description()
)
# Log performance history for current level
if current_level in self.curriculum.performance_history:
history = self.curriculum.performance_history[current_level]
if history:
@ -266,49 +244,25 @@ class InfiniteMathEnv(BaseEnv):
0, self.curriculum.progress_threshold - success_rate
)
# Log reward function metrics
# REMOVED: Specific reward function config logging as it's not used anymore
# if hasattr(self, "reward_function") and self.wandb:
# if hasattr(self.reward_function, "set_wandb_logger"):
# self.reward_function.set_wandb_logger(self.wandb)
# # Log the reward configurations
# if isinstance(self.config.reward_functions, list) and self.config.reward_functions:
# # Log the reward configuration
# wandb_metrics["reward/format_reward_enabled"] = "format" in self.config.reward_functions
# wandb_metrics["reward/boxed_reward_enabled"] = "boxed" in self.config.reward_functions
# if hasattr(self.config, "format_reward_weight"):
# wandb_metrics["reward/format_reward_weight"] = self.config.format_reward_weight
# if hasattr(self.config, "boxed_reward_weight"):
# wandb_metrics["reward/boxed_reward_weight"] = self.config.boxed_reward_weight
# Add eval metrics
for item in self.eval_metrics:
wandb_metrics[item[0]] = item[1]
# Reset buffers
self.percent_correct_buffer = []
for level in self.level_correct_buffer:
self.level_correct_buffer[level] = []
self.eval_metrics = []
# Call the parent method to handle remaining metrics
await super().wandb_log(wandb_metrics)
async def get_next_item(self):
"""Get the next problem based on current curriculum level."""
problem, solution, generator_id = self.curriculum.get_problem()
# Strip LaTeX delimiters from problem and solution
problem = self.strip_latex_delimiters(problem)
solution = self.strip_latex_delimiters(solution)
problem = self._strip_latex_delimiters(problem)
solution = self._strip_latex_delimiters(solution)
# Create a message with the problem
prompt = tuple([frozenset({"role": "user", "content": problem}.items())])
# Return the problem with metadata
return (prompt, solution, generator_id)
async def evaluate(self, *args, **kwargs):
@ -316,7 +270,6 @@ class InfiniteMathEnv(BaseEnv):
current_level = self.curriculum.get_current_level()
logger.info(f"Starting evaluation for curriculum level {current_level}")
# Only evaluate problems at the current level
eval_tasks = []
eval_generator_ids = []
if current_level in self.eval_problems:
@ -332,11 +285,9 @@ class InfiniteMathEnv(BaseEnv):
)
return []
# Run evaluation tasks
logger.info(f"Evaluating {len(eval_tasks)} problems at level {current_level}")
results = await asyncio.gather(*eval_tasks)
# Calculate accuracy for the current level
correct_count = sum(1 for _, is_correct in results if is_correct)
total_count = len(results)
accuracy = correct_count / total_count if total_count > 0 else 0
@ -345,23 +296,18 @@ class InfiniteMathEnv(BaseEnv):
f"Level {current_level} accuracy: {accuracy:.2f} ({correct_count}/{total_count})"
)
# Record metrics for the current level
self.eval_metrics.append((f"eval/level_{current_level}_accuracy", accuracy))
self.eval_metrics.append(("eval/current_level", current_level))
# Record the actual evaluation results in the curriculum's performance history
for i, (_, is_correct) in enumerate(results):
if i < len(eval_generator_ids):
# Record the actual result
self.curriculum.record_performance(eval_generator_ids[i], is_correct)
else:
# Fallback if somehow the lists are different lengths
sample_generator_id = random.choice(
self.curriculum.DIFFICULTY_LEVELS[current_level]
)
self.curriculum.record_performance(sample_generator_id, is_correct)
# Try to advance to the next level
advanced = self.curriculum.advance_difficulty()
new_level = self.curriculum.get_current_level()
@ -381,74 +327,62 @@ class InfiniteMathEnv(BaseEnv):
try:
logger.debug(f"Evaluating level {level} problem: {problem[:30]}...")
# Convert messages to a single prompt using the tokenizer
messages = [
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": problem},
]
prompt = self.tokenizer.apply_chat_template(messages, tokenize=False)
# Add prefilled thinking starter
prefill = "\n<think>\n"
prefilled_prompt = prompt + prefill
# Generate completion using the prompt
logger.debug(f"Requesting completion for problem: {problem[:30]}...")
completion = await self.server.completion(
prompt=prefilled_prompt,
n=1,
max_tokens=self.config.max_token_length,
temperature=0.0, # Use 0 temperature for deterministic results
temperature=0.0,
top_p=1.0,
split="eval",
)
# Extract the completion text and prepend the thinking starter
model_answer = prefill + (
completion.choices[0].text
if hasattr(completion.choices[0], "text")
else completion.choices[0].message.content
)
# Check if the answer is correct
is_correct = self.check_answer(model_answer, solution)
logger.debug(f"Problem evaluated: level={level}, correct={is_correct}")
return level, is_correct
except Exception as e:
logger.error(f"Error evaluating problem: {e}")
# Return a failed result in case of error
return level, False
def check_answer(self, model_answer: str, solution: str) -> bool:
"""Check if the model's answer matches the solution."""
# Extract the part after the thinking block
after_think_part = (
model_answer.split("</think>")[-1].strip()
if "</think>" in model_answer
else model_answer
)
# Extract the boxed answer if present
boxed_answer = self._extract_boxed_answer(after_think_part)
if not boxed_answer:
# Try to find the answer in the last line
lines = after_think_part.strip().split("\n")
if lines:
boxed_answer = lines[-1].strip()
# Clean up answers for comparison (remove spaces, convert to lowercase)
model_clean = self._clean_for_comparison(
boxed_answer if boxed_answer else after_think_part
)
solution_clean = self._clean_for_comparison(solution)
# Check if they match
return model_clean == solution_clean
def _extract_boxed_answer(self, text: str) -> Optional[str]:
"""Extract answer from a LaTeX boxed expression."""
# Try to find boxed content
boxed_match = re.search(r"\\boxed{([^}]*)}", text)
if boxed_match:
return boxed_match.group(1)
@ -456,7 +390,6 @@ class InfiniteMathEnv(BaseEnv):
def _clean_for_comparison(self, text: str) -> str:
"""Clean text for comparison."""
# Remove LaTeX commands, spaces, commas, and convert to lowercase
cleaned = re.sub(r"\\[a-zA-Z]+", "", text)
cleaned = re.sub(r"[,\s]", "", cleaned)
cleaned = cleaned.lower()
@ -464,11 +397,8 @@ class InfiniteMathEnv(BaseEnv):
async def collect_trajectories(self, item) -> Tuple[List, List]:
"""Collect trajectories for the current item."""
# Extract information from the item
problem_prompt, solution, generator_id = item
# Create prompt using tokenizer's chat template
# Add prefilled thinking starter
prefill = "\n<think>\n"
messages = [
{"role": "system", "content": self.system_prompt},
@ -477,7 +407,6 @@ class InfiniteMathEnv(BaseEnv):
]
prompt = self.tokenizer.apply_chat_template(messages, tokenize=False)
# Generate completions using completion API
completions = await self.server.completion(
prompt=prompt,
n=self.config.group_size,
@ -486,19 +415,15 @@ class InfiniteMathEnv(BaseEnv):
top_p=self.config.top_p,
)
# Prepare data for scoring
to_score = []
# Track level for metrics
level = None
for lvl, generator_ids in self.curriculum.DIFFICULTY_LEVELS.items():
if generator_id in generator_ids:
level = lvl
break
# Process each completion
for i, completion in enumerate(completions.choices):
# Get the completion text and prepend the thinking starter
model_answer = prefill + (
completion.text
if hasattr(completion, "text")
@ -506,20 +431,14 @@ class InfiniteMathEnv(BaseEnv):
)
print("model_answer", model_answer)
# Build complete message sequence
full_messages = [
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": dict(problem_prompt[0])["content"]},
{"role": "assistant", "content": model_answer},
]
# Add to scoring list
to_score.append((full_messages, solution, generator_id, level))
# Record performance in curriculum for each item we're scoring
# This will be called again after scoring, but that's fine
# No additional items for backlog
backlog = []
return to_score, backlog
@ -532,57 +451,45 @@ class InfiniteMathEnv(BaseEnv):
scored_data["scores"] = []
scored_data["messages"] = []
# Process each item in the rollout data
for i, (messages, solution, generator_id, level) in enumerate(rollout_group_data):
model_answer = messages[-1]["content"]
current_score = 0.0
# 1. Accuracy Check
is_correct = self.check_answer(model_answer, solution)
if is_correct:
current_score += self.config.correct_reward
else:
current_score += self.config.incorrect_reward
# Record answer correctness for tracking and curriculum
self.percent_correct_buffer.append(1 if is_correct else 0)
if level is not None:
self.level_correct_buffer[level].append(1 if is_correct else 0)
self.curriculum.record_performance(generator_id, is_correct)
# 2. Thinking Block Check
think_match = re.search(r"<think>(.*?)</think>", model_answer, re.DOTALL)
if think_match:
think_content = think_match.group(1).strip()
if think_content: # Check if there's actual content
if think_content:
current_score += self.config.think_block_bonus
# else: penalty for empty think block, or neutral
# else: penalty for missing think block, or neutral
# 3. Boxed Answer Check
# Extract the part after the thinking block for boxed answer validation
after_think_part = model_answer.split("</think>")[-1].strip() if "</think>" in model_answer else model_answer
boxed_answer_content = self._extract_boxed_answer(after_think_part)
if boxed_answer_content is not None: # Check if \boxed{} is present and has content
if boxed_answer_content is not None:
current_score += self.config.boxed_answer_bonus
# else: penalty for missing/malformed boxed answer, or neutral
logger.info(f"Item {i}: Correct: {is_correct}, Think Bonus: {self.config.think_block_bonus if think_match and think_match.group(1).strip() else 0}, Boxed Bonus: {self.config.boxed_answer_bonus if boxed_answer_content is not None else 0}, Final Score: {current_score}")
# Tokenize for the trainer
tokens_dict = tokenize_for_trainer(
self.tokenizer,
messages, # These are the full messages including system, user, assistant
None, # Not used by this tokenizer function apparently
messages,
None,
)
# Add to scored data
scored_data["tokens"].append(tokens_dict["tokens"])
scored_data["masks"].append(tokens_dict["masks"])
scored_data["scores"].append(current_score)
scored_data["messages"].append(messages)
# Advance difficulty if criteria met
self.curriculum.advance_difficulty()
return scored_data
@ -605,7 +512,6 @@ if __name__ == "__main__":
inference_weight=1.0,
wandb_name="infinite_math",
data_path_to_save_groups="data/infinite_math_groups.jsonl",
# InfiniteMath specific config
starting_level=1,
progress_threshold=0.8,
min_evaluations=10,
@ -613,10 +519,8 @@ if __name__ == "__main__":
incorrect_reward=-0.5,
apply_length_penalty=True,
length_threshold_ratio=0.6,
# Completion parameters
temperature=0.7,
top_p=0.9,
# Reward function configuration - use name directly
reward_functions=["accuracy", "format", "boxed"],
accuracy_reward_weight=1.0,
format_reward_weight=0.2,