From 7baf1064251146afa2353fbfe723c9b01cdec21f Mon Sep 17 00:00:00 2001 From: Jacob-Junqi Tian Date: Mon, 8 Sep 2025 16:17:05 -0400 Subject: [PATCH 1/4] Minimalistic GRPO optimizer step implementation. (to be numerically verified.) --- download_model.py | 26 + pyproject.toml | 4 +- starters/llm_fine_tuning/README.md | 6 + .../rlvr/data_collation/batch.py | 125 +++ .../rlvr/data_collation/data_types.py | 50 + .../llm_fine_tuning/rlvr/grpo/data_types.py | 326 +++++++ starters/llm_fine_tuning/rlvr/grpo/grpo.py | 193 ++++ starters/llm_fine_tuning/rlvr/grpo/trainer.py | 171 ++++ uv.lock | 892 ++++++++++-------- 9 files changed, 1402 insertions(+), 391 deletions(-) create mode 100644 download_model.py create mode 100644 starters/llm_fine_tuning/README.md create mode 100644 starters/llm_fine_tuning/rlvr/data_collation/batch.py create mode 100644 starters/llm_fine_tuning/rlvr/data_collation/data_types.py create mode 100644 starters/llm_fine_tuning/rlvr/grpo/data_types.py create mode 100644 starters/llm_fine_tuning/rlvr/grpo/grpo.py create mode 100644 starters/llm_fine_tuning/rlvr/grpo/trainer.py diff --git a/download_model.py b/download_model.py new file mode 100644 index 0000000..95dada3 --- /dev/null +++ b/download_model.py @@ -0,0 +1,26 @@ +""" +Download model from Hub to a local path. + +Usage: +uv run download_model.py \ +--repo_name Qwen/Qwen3-0.6B \ +--local_path /model-weights/Qwen3-0.6B + +uv run download_model.py \ +--repo_name Qwen/Qwen3-4B-Thinking-2507 \ +--local_path /model-weights/Qwen3-4B-Thinking-2507 +""" + +import argparse + +from transformers import AutoModel, AutoTokenizer + + +parser = argparse.ArgumentParser() +parser.add_argument("--repo_name", required=True) +parser.add_argument("--local_path", required=True) + +if __name__ == "__main__": + args = parser.parse_args() + AutoTokenizer.from_pretrained(args.repo_name).save_pretrained(args.local_path) + AutoModel.from_pretrained(args.repo_name).save_pretrained(args.local_path) diff --git a/pyproject.toml b/pyproject.toml index 36d8be9..bc6f07d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,6 +24,8 @@ dependencies = [ "pillow>=10.0.0,<11.0.0", "ruff>=0.1.0,<1.0.0", "pre-commit>=3.0.0,<4.0.0", + "pydantic>=2.11.7", + "rich>=14.1.0", ] [tool.ruff] @@ -38,7 +40,7 @@ docstring-code-format = true [tool.ruff.lint] select = ["A","B","COM","C4","RET","SIM","ICN","Q","RSE","D","E","F","I","W","N","ERA","PL"] fixable = ["A","B","COM","C4","RET","SIM","ICN","Q","RSE","D","E","F","I","W","N","ERA","PL"] -ignore = ["B905","E501","D203","D213","PLR2004","PLR0913","COM812"] +ignore = ["B905","E501","D203","D213","PLC0415","PLR2004","PLR0913","COM812", "ERA001"] [tool.ruff.lint.per-file-ignores] "__init__.py" = ["E402","F401","F403","F811"] diff --git a/starters/llm_fine_tuning/README.md b/starters/llm_fine_tuning/README.md new file mode 100644 index 0000000..4879325 --- /dev/null +++ b/starters/llm_fine_tuning/README.md @@ -0,0 +1,6 @@ +### LLM fine-tuning starters + +This directory includes minimal examples for LLM fine-tuning with vec-tool: + +- [text_classification](text_classification/): Fine-tunes a small Transformer on AG News using Hugging Face Trainer, with Submitit-compatible checkpointing and resume. +- [rlvr](rlvr/): RL fine-tuning, where reward is extrinsic and verifiable. \ No newline at end of file diff --git a/starters/llm_fine_tuning/rlvr/data_collation/batch.py b/starters/llm_fine_tuning/rlvr/data_collation/batch.py new file mode 100644 index 0000000..1ebbc6b --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/data_collation/batch.py @@ -0,0 +1,125 @@ +"""Utils for generating strongly-typed batches.""" + +from typing import Annotated, Generic, Iterator, Type, TypeVar + +import numpy as np +import pydantic +from pydantic.json_schema import SkipJsonSchema + +from starters.llm_fine_tuning.rlvr.data_collation.data_types import TypedBatch + + +T = TypeVar("T", bound=TypedBatch) + + +class FieldConfig(pydantic.BaseModel): + """Field configs. + + Extra dimension(s) will be added after the sequence dimension. + To add extra dimensions, specify the shape along these dimensions. + For (batch, size, vocab), set extra_dimensions to (vocab,) + """ + + model_config = pydantic.ConfigDict(arbitrary_types_allowed=True) + padding_value: float | int | bool + dtype: Annotated[type, SkipJsonSchema[None]] + extra_dimensions: tuple[int, ...] = () + + +class TypedBatcher(Generic[T]): + """Util for generating strongly typed batches.""" + + def __init__( + self, + field_data: dict[str, list[list]], + batch_model: Type[T], + field_configs: dict[str, FieldConfig], + batch_size: int, + pad_to_length: int | None = None, + ): + self.field_data = field_data + self.batch_model = batch_model + self.batch_size = batch_size + self.fields = field_configs + + # Use length of longest sequence as padding length if not provided. + # TODO: implement dynamic padding (each batch is padded to longest seq.) + if not pad_to_length: + pad_to_length = max( + len(seq) for sequences in field_data.values() for seq in sequences + ) + self.pad_to_length = pad_to_length + + # Validate all fields have same number of sequences + seq_lens = {k: len(seqs) for k, seqs in field_data.items()} + assert len(set(seq_lens.values())) == 1, f"Inconsistent seq count: {seq_lens}" + + # Check for additional dimensions. + token_data_shapes = { + k: np.asarray(v[0][0]).shape for k, v in field_data.items() + } + extra_dimensions = { + k: _cfg.extra_dimensions for k, _cfg in field_configs.items() + } + for k, _token_data_shape in token_data_shapes.items(): + if _token_data_shape != extra_dimensions[k]: + msg = ( + "Extra dimension mismatch. Did you mean to specify extra dimensions? " + f"token_data_shapes: {token_data_shapes} vs " + f"extra_dimensions from field_configs: {extra_dimensions}, " + f"mismatch at field '{k}'" + ) + raise ValueError(msg) + + def __len__(self) -> int: + """Estimate number of batches to return.""" + num_sequences = len(next(iter(self.field_data.values()))) + return (num_sequences + self.batch_size - 1) // self.batch_size + + def __iter__(self) -> Iterator[T]: + """Yield strongly-typed batches.""" + # Initialize batch arrays + batch_arrays = { + name: np.full( + # place extra dimensions after the token dimension. + (self.batch_size, self.pad_to_length, *cfg.extra_dimensions), + cfg.padding_value, + dtype=cfg.dtype, + ) + for name, cfg in self.fields.items() + } + attention_mask = np.zeros((self.batch_size, self.pad_to_length), dtype=bool) + + pos_in_batch = 0 + num_sequences = len(next(iter(self.field_data.values()))) + + for i in range(num_sequences): + # Copy data for all fields + for name, arrays in batch_arrays.items(): + # actual sequence length, capped at pad_to_length + seq_len = min(len(self.field_data[name][i]), self.pad_to_length) + arrays[pos_in_batch, :seq_len] = self.field_data[name][i][:seq_len] + attention_mask[pos_in_batch, :seq_len] = True + + pos_in_batch += 1 + + if pos_in_batch == self.batch_size: + yield self.batch_model( + **{k: v.copy() for k, v in batch_arrays.items()}, + attention_mask=attention_mask.copy(), + num_valid=pos_in_batch, + ) + + # Reset batch arrays + attention_mask.fill(False) + for k, array in batch_arrays.items(): + array.fill(self.fields[k].padding_value) + + pos_in_batch = 0 + + if pos_in_batch > 0: + yield self.batch_model( + **{k: v.copy() for k, v in batch_arrays.items()}, + attention_mask=attention_mask.copy(), + num_valid=pos_in_batch, + ) diff --git a/starters/llm_fine_tuning/rlvr/data_collation/data_types.py b/starters/llm_fine_tuning/rlvr/data_collation/data_types.py new file mode 100644 index 0000000..455bae8 --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/data_collation/data_types.py @@ -0,0 +1,50 @@ +"""Types for RLVR.""" + +from typing import TYPE_CHECKING, Annotated, Type, TypeVar + +import numpy as np +import pydantic +from pydantic.json_schema import SkipJsonSchema + + +if TYPE_CHECKING: + import torch + + +T = TypeVar("T", bound=pydantic.BaseModel) + + +class TypedBatch(pydantic.BaseModel): + """A batch of data with dynamic fields.""" + + model_config = pydantic.ConfigDict(arbitrary_types_allowed=True) + attention_mask: Annotated[np.ndarray, SkipJsonSchema[None]] + num_valid: int + + def __init__(self, **kwargs): + super().__init__(**kwargs) + # Validate all arrays have same batch dimension + batch_dims = {k: len(v) for k, v in kwargs.items() if k != "num_valid"} + assert ( + len(set(batch_dims.values())) == 1 + ), f"Inconsistent batch dim: {batch_dims}" + + def to_torch(self, device: "torch.device", target_class: Type[T]) -> T: + """Transfer tensors to torch. + + Params: + device: target torch device. + target_class: should be a Pydantic class + where the fields are named the same but + of type torch.Tensor in place of np.ndarray. + """ + import torch + + output_dict = {} + for k, v in self.model_dump().items(): + if isinstance(v, np.ndarray): + output_dict[k] = torch.Tensor(v).to(device) + else: + output_dict[k] = v + + return target_class(**output_dict) diff --git a/starters/llm_fine_tuning/rlvr/grpo/data_types.py b/starters/llm_fine_tuning/rlvr/grpo/data_types.py new file mode 100644 index 0000000..cd1e754 --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/grpo/data_types.py @@ -0,0 +1,326 @@ +"""Data types for GRPO.""" + +from typing import TYPE_CHECKING, Annotated, Any, Sequence + +import numpy as np +import pydantic +from pydantic.json_schema import SkipJsonSchema +from transformers import PreTrainedTokenizerFast + +from starters.llm_fine_tuning.rlvr.data_collation.batch import ( + FieldConfig, + TypedBatcher, +) +from starters.llm_fine_tuning.rlvr.data_collation.data_types import TypedBatch + + +if TYPE_CHECKING: + import torch + + +def _assistant_char_spans( + messages: Sequence[dict[str, str]], formatted: str +) -> list[tuple[int, int]]: + """Find [start, end) character spans of assistant contents inside formatted string. + + We search sequentially to disambiguate duplicate substrings across turns. + If a piece isn't found (e.g., template alters content), it is skipped. + """ + spans: list[tuple[int, int]] = [] + cursor = 0 + for message in messages: + if message.get("role") != "assistant": + continue + content = message.get("content", "") + if not content: + continue + idx = formatted.find(content, cursor) + if idx != -1: + spans.append((idx, idx + len(content))) + cursor = idx + len(content) + return spans + + +class RewardDetail(pydantic.BaseModel): + """Reward details for one rollout, outcome-supervised only. + + Attention mask is not included. + Rather, input_ids should be truncated on both sides. + """ + + input_ids: list[int] + loss_mask: list[bool] + reward: float + + def __init__(self, input_ids: list[int], loss_mask: list[bool], **kwargs): + """Verify input_ids and loss_mask shape.""" + input_ids_np = np.asarray(input_ids) + loss_mask_np = np.asarray(loss_mask) + + _shape = input_ids_np.shape + assert len(_shape) == 1, f"Flat list of indices required; got {_shape}" + assert loss_mask_np.shape == _shape, "Loss mask shape must match input_ids." + + super().__init__(input_ids=input_ids, loss_mask=loss_mask, **kwargs) + + @staticmethod + def from_messages( + messages: list[dict[str, str]], + reward: float, + tokenizer: PreTrainedTokenizerFast, + ) -> "RewardDetail": + """Tokenize a chat and create a per-token boolean loss mask.""" + chat_template = getattr(tokenizer, "chat_template", None) + if chat_template is None: + raise ValueError(f"Template is required but not available in: {tokenizer}") + formatted = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=False, + ) + assert isinstance(formatted, str) + add_special_tokens = False # per HF docs when tokenizing the formatted string + + assistant_spans = _assistant_char_spans(messages, formatted) + + # TODO: Verify HF typing + enc: Any = tokenizer( + formatted, + add_special_tokens=add_special_tokens, + return_offsets_mapping=True, # requires FastTokenizer + truncation=False, + ) + input_ids: list[int] = enc["input_ids"] + offsets: list[tuple[int, int]] = enc["offset_mapping"] + + # One bool per token. + # Mark as True if the token span is completely in any assistant span + loss_mask: list[bool] = [ + any(span_a <= _a < _b <= span_b for span_a, span_b in assistant_spans) + for _a, _b in offsets + ] + + return RewardDetail(input_ids=input_ids, loss_mask=loss_mask, reward=reward) + + +class _AdvantageDetail(RewardDetail): + """Reward details plus group-relative advantage info. + + Important: + advantages is a per-token list for extensibility. + However, in outcome supervision formulations (e.g., DeepSeek Math) + advantages is the same number repeated for all tokens. + + """ + + advantages: list[float] # per-token, same length as input_ids. + + +class PerTokenProbs(pydantic.BaseModel): + """Per-token probability output. + + If input_id is int array of shape (batch, length, vocab_size), then + - "full" would be float array of shape (batch, length - 1, vocab_size) + - "selected" would be float array of shape (batch, length - 1) + """ + + full: list[list[list[float]]] + selected: list[list[float]] + + def __add__(self, other: "PerTokenProbs") -> "PerTokenProbs": + """Concatenate along the batch axis (first dimension).""" + return PerTokenProbs( + full=self.full + other.full, + selected=self.selected + other.selected, + ) + + def __radd__(self, other: object) -> "PerTokenProbs": + """Support sum(..., other=0).""" + if not isinstance(other, PerTokenProbs): + return self + + return self + other + + @staticmethod + def from_batch( + attention_mask: np.ndarray, + num_valid: int, + full: np.ndarray, + selected: np.ndarray, + ): + """Slice batch and extract non-padding probs only. + + The outputs (full and selected) both start at the second token. + The first token in attention mask is not included in the output. + + Params: + ------- + attention_mask: bool array of shape (batch, input_tokens) + num_valid: keep up to the first num_valid items. + (assume that the following rows are paddings.) + full: float array of shape (batch, input_tokens - 1, vocab) + selected: float array of shape (batch, input_tokens - 1) + """ + batch_size, num_input_tokens = attention_mask.shape + if not ( + (full.shape[:2] == (batch_size, num_input_tokens - 1)) + and (selected.shape == (batch_size, num_input_tokens - 1)) + ): + msg = ( + f"attention_mask.shape {attention_mask.shape} " + "(batch, num_gen_tokens) did not match " + f"full.shape {full.shape} (batch, num_gen_tokens - 1, vocab) " + f"selected.shape {selected.shape} (batch, num_gen_tokens - 1)." + ) + raise ValueError(msg) + + # Exclude first input token, which is not part of any output. + attention_mask_shifted = attention_mask[:, 1:] + + return PerTokenProbs( + full=[ + np.compress(_mask, _full, axis=0).tolist() + for _mask, _full in zip( + attention_mask_shifted[:num_valid], full[:num_valid] + ) + ], + selected=[ + np.compress(_mask, _selected, axis=0).tolist() + for _mask, _selected in zip( + attention_mask_shifted[:num_valid], selected[:num_valid] + ) + ], + ) + + +class BatchForInference(TypedBatch): + """Typed batch for getting probs.""" + + # (batch, input_tokens) + input_ids: Annotated[np.ndarray, SkipJsonSchema[None]] + + +class BatchForGRPO(TypedBatch): + """Typed batch for GRPO training.""" + + # (batch, input_tokens) + input_ids: Annotated[np.ndarray, SkipJsonSchema[None]] + loss_masks: Annotated[np.ndarray, SkipJsonSchema[None]] + per_token_advantage: Annotated[np.ndarray, SkipJsonSchema[None]] + + # (batch, input_tokens - 1, vocab) + pi_selected_ref: Annotated[np.ndarray, SkipJsonSchema[None]] + pi_selected_base: Annotated[np.ndarray, SkipJsonSchema[None]] + + +class BatchForGRPOTorch(pydantic.BaseModel): + """Typed batch for GRPO training, but in Torch format.""" + + model_config = pydantic.ConfigDict(arbitrary_types_allowed=True) + + num_valid: int + attention_mask: Annotated["torch.Tensor", SkipJsonSchema[None]] + + # (batch, input_tokens) for all of the following + # int/long + input_ids: Annotated["torch.Tensor", SkipJsonSchema[None]] + + # bool + loss_masks: Annotated["torch.Tensor", SkipJsonSchema[None]] + + # float + per_token_advantage: Annotated["torch.Tensor", SkipJsonSchema[None]] + pi_selected_ref: Annotated["torch.Tensor", SkipJsonSchema[None]] + pi_selected_base: Annotated["torch.Tensor", SkipJsonSchema[None]] + + +class AdvantageData(pydantic.BaseModel): + """Batch of outputs for group-relative advantage estimate.""" + + advantage_details: list[_AdvantageDetail] + + def _group_rewards(self) -> list[float]: + """All rewards in this group.""" + return [_detail.reward for _detail in self.advantage_details] + + @staticmethod + def from_list_of_rewards(reward_details: list[RewardDetail]) -> "AdvantageData": + """Compute advantage for a given batch of rewards.""" + if len(reward_details) == 0: + msg = "reward_details must be a non-empty list, but an empty list is given." + raise ValueError(msg) + + # per-trace only, not token-level + rewards = np.asarray([_detail.reward for _detail in reward_details]) + advantages_np: np.ndarray = np.asarray( + (rewards - rewards.mean()) / rewards.std() + ) + advantages: list[float] = advantages_np.flatten().tolist() + + advantage_details: list[_AdvantageDetail] = [] + for _reward_detail, _advantage in zip(reward_details, advantages): + # Same (sequence-level) advantage for each token. + _per_token_advantages = [_advantage] * len(_reward_detail.input_ids) + _advantage_detail = _AdvantageDetail( + **_reward_detail.model_dump(), advantages=_per_token_advantages + ) + advantage_details.append(_advantage_detail) + + return AdvantageData(advantage_details=advantage_details) + + def get_iterator_for_inference( + self, batch_size: int, pad_to_length: int | None = None + ) -> TypedBatcher[BatchForInference]: + """Obtain batched iterator for inference.""" + return TypedBatcher( + field_data={ + "input_ids": [ + _advantage.input_ids for _advantage in self.advantage_details + ] + }, + batch_model=BatchForInference, + field_configs={"input_ids": FieldConfig(padding_value=0, dtype=int)}, + batch_size=batch_size, + pad_to_length=pad_to_length, + ) + + +class GRPOData(AdvantageData): + """Advantage data, plus probabilities from ref policy and optionally base policy.""" + + ref_probs: PerTokenProbs + base_probs: PerTokenProbs + + def get_iterator_for_training( + self, batch_size: int, pad_to_length: int | None = None + ) -> TypedBatcher[BatchForGRPO]: + """Obtain batched iterator for training.""" + probability_selected_field = FieldConfig(padding_value=0, dtype=float) + return TypedBatcher( + field_data={ + "input_ids": [ + _advantage.input_ids for _advantage in self.advantage_details + ], + "loss_masks": [ + _advantage.loss_mask for _advantage in self.advantage_details + ], + "per_token_advantage": [ + _advantage.advantages for _advantage in self.advantage_details + ], + "pi_selected_ref": self.ref_probs.selected, + "pi_selected_base": self.base_probs.selected, + }, + batch_model=BatchForGRPO, + field_configs={ + "input_ids": FieldConfig(padding_value=0, dtype=int), + "loss_masks": FieldConfig(padding_value=False, dtype=bool), + "per_token_advantage": FieldConfig(padding_value=0, dtype=float), + "pi_selected_ref": probability_selected_field, + "pi_selected_base": probability_selected_field, + }, + batch_size=batch_size, + pad_to_length=pad_to_length, + ) + + +GRPOBatcher = TypedBatcher[BatchForGRPO] diff --git a/starters/llm_fine_tuning/rlvr/grpo/grpo.py b/starters/llm_fine_tuning/rlvr/grpo/grpo.py new file mode 100644 index 0000000..4a8b84d --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/grpo/grpo.py @@ -0,0 +1,193 @@ +"""GRPO logic.""" + +import math + +import torch +from rich.progress import Progress +from torch.nn.functional import log_softmax +from torch.optim import AdamW +from transformers import PreTrainedModel + +from starters.llm_fine_tuning.rlvr.grpo.data_types import GRPOBatcher, BatchForGRPOTorch + + +def compute_grpo_loss( + *, + logits_next: torch.Tensor, + target_token_ids: torch.Tensor, + valid_token_mask: torch.Tensor, + taken_prob_old: torch.Tensor, + taken_prob_ref: torch.Tensor, + advantage_per_token: torch.Tensor, + kl_coefficient: float, + numerical_stability_eps: float = 1e-12, +) -> torch.Tensor: + """ + Compute the GRPO loss for a mini-batch. + + The per-token objective (to MAXIMIZE) is: + J_token = r * A - β * (x - log x - 1), + + where: + • r = π_current(a|s) / π_old(a|s) + • x = π_ref(a|s) / π_current(a|s) + • A is the rollout-level normalized advantage (broadcast across tokens in that rollout) + • β is the KL coefficient. + + This function returns the NEGATIVE batch objective (i.e., a loss to MINIMIZE). + Reduction matches Eq. (19) of the GRPO formulation: + 1) mean over valid tokens for each rollout, + 2) mean over rollouts in the mini-batch. + + Args: + logits_next: Logits for next-token prediction at positions [:, :-1], (B, T, V). + target_token_ids: Gold next-token ids, (B, T). + valid_token_mask: Boolean mask selecting generated tokens to train on, (B, T). + taken_prob_old: Probability under the pi_frozen for the taken token, (B, T). + taken_prob_ref: Probability under the pi_kl_ref for the taken token, (B, T). + advantage_per_token: Token-level normalized advantages, (B, T). + kl_coefficient: Strength of the KL penalty to the reference policy. + numerical_stability_eps: Small epsilon to avoid log(0). + + Returns + ------- + A scalar tensor: the loss to minimize (negative batch objective). + """ + # Log-probabilities of all tokens under current policy + log_prob_all = log_softmax(logits_next, dim=-1) # (B, T, V) + + # Log-probability of the *taken* tokens under current policy + log_prob_taken_current = torch.gather( + log_prob_all, dim=-1, index=target_token_ids.unsqueeze(-1) + ).squeeze( + -1 + ) # (B, T) + + # Clamp old/ref probabilities for stability and take logs + taken_prob_old = taken_prob_old.clamp(min=numerical_stability_eps) + taken_prob_ref = taken_prob_ref.clamp(min=numerical_stability_eps) + log_prob_taken_old = torch.log(taken_prob_old) # (B, T) + log_prob_taken_ref = torch.log(taken_prob_ref) # (B, T) + + # Importance ratio: r = π_current / π_old (for the taken token) + log_ratio_current_over_old = log_prob_taken_current - log_prob_taken_old # (B, T) + ratio_current_over_old = torch.exp(log_ratio_current_over_old) # (B, T) + + # KL penalty term (per taken token): x - log x - 1, where x = pi_ref / pi_current + log_ratio_ref_over_current = log_prob_taken_ref - log_prob_taken_current # (B, T) + ratio_ref_over_current = torch.exp(log_ratio_ref_over_current) # (B, T) + kl_scalar_taken = ( + ratio_ref_over_current - log_ratio_ref_over_current - 1.0 + ) # (B, T) + + # Per-token objective contribution + token_objective = ( + ratio_current_over_old * advantage_per_token - kl_coefficient * kl_scalar_taken + ) # (B, T) + + # Mask to only include valid positions + token_objective = token_objective * valid_token_mask.float() # (B, T) + + # Mean over valid tokens per rollout (avoid division by zero) + tokens_per_rollout = valid_token_mask.sum(dim=1) # (B,) + per_rollout_mean = token_objective.sum(dim=1) / torch.clamp( + tokens_per_rollout, min=1 + ) # (B,) + + # Batch mean; return negative to obtain a loss to minimize + batch_objective_mean = per_rollout_mean.mean() + return -batch_objective_mean + + +def optimize_grpo_one_epoch( + *, + batcher: GRPOBatcher, + model: PreTrainedModel, + gradient_accumulation_steps: int, + learning_rate: float = 1e-5, + kl_coefficient: float = 0.05, + adam_betas: tuple[float, float] = (0.9, 0.999), + adam_weight_decay: float = 0.0, + max_grad_norm: float = 1.0, + use_mixed_precision: bool = True, + numerical_stability_eps: float = 1e-12, +) -> PreTrainedModel: + """ + Perform one optimization epoch of GRPO on a HuggingFace causal language model. + + This revision reduces line count by inlining the mini-batch slicing directly at + the call sites (model forward and loss computation). See `compute_grpo_loss` + for the objective definition and reduction details. + """ + model.train() + device = next(model.parameters()).device + + optimizer = AdamW( + model.parameters(), + lr=learning_rate, + betas=adam_betas, + weight_decay=adam_weight_decay, + ) + scaler = torch.cuda.amp.GradScaler( + enabled=(use_mixed_precision and torch.cuda.is_available()) + ) + + num_steps = len(batcher) + expected_updates = math.ceil(num_steps / max(1, gradient_accumulation_steps)) + + accumulated_microbatches = 0 + optimizer.zero_grad(set_to_none=True) + + with Progress() as progress: + step_task_id = progress.add_task( + "[cyan]GRPO optimization[/cyan] (optimizer steps)", total=expected_updates + ) + + for batch_np in batcher: + batch = batch_np.to_torch(device=device, target_class=BatchForGRPOTorch) + with torch.amp.autocast( + "cuda", enabled=(use_mixed_precision and torch.cuda.is_available()) + ): + outputs = model( + input_ids=batch.input_ids.to(torch.long), + attention_mask=batch.attention_mask.to(torch.long), + ) + logits_next = outputs.logits[:, :-1, :] # (B, T, V) + + # Note that the first input token is skipped. + loss_to_minimize = compute_grpo_loss( + logits_next=logits_next, + target_token_ids=batch.input_ids[:, 1:].to(torch.long), + valid_token_mask=batch.loss_masks[:, 1:], + taken_prob_old=batch.pi_selected_base[:, 1:].to(torch.float32), + taken_prob_ref=batch.pi_selected_ref[:, 1:].to(torch.float32), + advantage_per_token=batch.per_token_advantage[:, 1:].to( + torch.float32 + ), + kl_coefficient=kl_coefficient, + numerical_stability_eps=numerical_stability_eps, + ) / max(1, gradient_accumulation_steps) + + scaler.scale(loss_to_minimize).backward() + accumulated_microbatches += 1 + + if accumulated_microbatches % max(1, gradient_accumulation_steps) == 0: + if max_grad_norm and max_grad_norm > 0: + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad(set_to_none=True) + progress.update(step_task_id, advance=1) + + # Flush leftover microbatches + if accumulated_microbatches % max(1, gradient_accumulation_steps) != 0: + if max_grad_norm and max_grad_norm > 0: + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad(set_to_none=True) + progress.update(step_task_id, advance=1) + + return model diff --git a/starters/llm_fine_tuning/rlvr/grpo/trainer.py b/starters/llm_fine_tuning/rlvr/grpo/trainer.py new file mode 100644 index 0000000..08a2cfa --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/grpo/trainer.py @@ -0,0 +1,171 @@ +""" +GRPO Trainer for RL with Verifiable Reward. + +See: +arxiv.org/pdf/2402.03300 +(Algorithm 1 on page 14) for exact formulation + +Usage: + +PYTHONPATH="." \ +uv run starters/llm_fine_tuning/rlvr/grpo/trainer.py \ +--tokenizer /model-weights/Qwen3-0.6B \ +--base_model /model-weights/Qwen3-0.6B \ +--kl_ref_model /model-weights/Qwen3-0.6B +""" + +import argparse +import logging + +import torch +from rich.progress import track +from torch.nn.functional import softmax +from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + PreTrainedModel, +) + +from starters.llm_fine_tuning.rlvr.grpo.data_types import ( + AdvantageData, + BatchForInference, + GRPOData, + PerTokenProbs, + RewardDetail, +) +from starters.llm_fine_tuning.rlvr.grpo.grpo import optimize_grpo_one_epoch + + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + + +def get_per_token_probs( + batch: BatchForInference, model: "PreTrainedModel" +) -> PerTokenProbs: + """Obtain per-token probs for a given batch and a given model. + + Params: + batch: Tokenized _Batch (batch, length). + model: Pretrained Causal LM. + + Return: + ------- + np.ndarray (batch, length - 1, vocab_size) + """ + device = next(model.parameters()).device + input_ids = torch.as_tensor(batch.input_ids, dtype=torch.long, device=device) + attention_mask = torch.as_tensor( + batch.attention_mask, dtype=torch.long, device=device + ) + + with torch.inference_mode(): + output = model(input_ids=input_ids, attention_mask=attention_mask) + # logits: (batch, length, vocab) where + # (j, k, :) contains distribution for tokens k+1 of batch j. + logits = output.logits + # (batch, length - 1, vocab) skipping last token which has no label. + # for all possible vocabs. + probabilities_all = softmax(logits, dim=-1)[:, :-1, :] + # (batch, length - 1, 1) skipping first token which is always given. + target_token_ids = input_ids[:, 1:].unsqueeze(-1) + # (batch, length - 1) for input_ids only. + probabilities_selected = torch.gather( + probabilities_all, dim=-1, index=target_token_ids + ).squeeze(-1) + + return PerTokenProbs.from_batch( + attention_mask=batch.attention_mask, + num_valid=batch.num_valid, + full=probabilities_all.float().cpu().numpy(), + selected=probabilities_selected.float().cpu().numpy(), + ) + + +parser = argparse.ArgumentParser() +parser.add_argument("--base_model", required=True) +parser.add_argument("--kl_ref_model", required=True) +parser.add_argument("--tokenizer", required=True) +parser.add_argument( + "--bsz_inference", type=int, default=16, help="batch size for getting pi_ref" +) +parser.add_argument("--bsz_train", type=int, default=8, help="batch size for backprop") + + +# def parameter_update(model: AutoModelForCausalLM, advantage_batch: AdvantageBatch) + + +if __name__ == "__main__": + args = parser.parse_args() + + tokenizer = AutoTokenizer.from_pretrained(args.tokenizer) + logger.info(f"Tokenizer: {tokenizer}") + example_reward_details = [ + RewardDetail.from_messages( + messages=[ + {"role": "system", "content": "Example system instructions."}, + {"role": "user", "content": "Example user message."}, + {"role": "assistant", "content": _message}, + ], + reward=_reward, + tokenizer=tokenizer, + ) + for _reward, _message in [ + (1.0, "Example correct response."), + (1.0, "Another correct response."), + (0.0, "Example incorrect response."), + (1.0, "Example correct response 1."), + (1.0, "Another correct response 1."), + (0.0, "Example incorrect response 1."), + ] + ] + + advantage_data = AdvantageData.from_list_of_rewards(example_reward_details) + + device = torch.device("cuda:0") + + logger.info("Loading kl_ref weights") + kl_ref_model = AutoModelForCausalLM.from_pretrained(args.kl_ref_model) + logger.info("Loading kl_ref weights to CUDA.") + kl_ref_model = kl_ref_model.to(device) + + per_token_probs_ref = sum( + get_per_token_probs(_batch, kl_ref_model) + for _batch in track( + advantage_data.get_iterator_for_inference(batch_size=5), + description="Inference: pi_ref", + ) + ) + del kl_ref_model + + base_model = AutoModelForCausalLM.from_pretrained(args.base_model) + logger.info("Loading weights to CUDA.") + base_model = base_model.to(device) + + per_token_probs_base = sum( + get_per_token_probs(_batch, base_model) + for _batch in track( + advantage_data.get_iterator_for_inference(batch_size=5), + description="Inference: pi_old", + ) + ) + + assert isinstance(per_token_probs_ref, PerTokenProbs) + assert isinstance(per_token_probs_base, PerTokenProbs) + grpo_data = GRPOData( + **advantage_data.model_dump(), + ref_probs=per_token_probs_ref, + base_probs=per_token_probs_base, + ) + + for training_batch in grpo_data.get_iterator_for_training( + batch_size=args.bsz_train + ): + print({k: type(v) for k, v in training_batch.model_dump().items()}) + break + + base_model = optimize_grpo_one_epoch( + batcher=grpo_data.get_iterator_for_training(batch_size=args.bsz_train), + model=base_model, + gradient_accumulation_steps=1, + ) diff --git a/uv.lock b/uv.lock index 0ff9ef8..f4822af 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 2 requires-python = "==3.12.*" [[package]] @@ -15,18 +15,18 @@ dependencies = [ { name = 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-0400 Subject: [PATCH 2/4] GRPO for Openai-Agents implementation. (Missing vec-inf component.) --- .vscode/settings.json | 3 + download_model.py | 10 +- pyproject.toml | 8 + scripts.md | 21 + .../llm_fine_tuning/rlvr/agents/examples.py | 176 ++++++++ starters/llm_fine_tuning/rlvr/agents/notes.md | 104 +++++ .../rlvr/agents/rollout_translation.py | 314 ++++++++++++++ starters/llm_fine_tuning/rlvr/async_utils.py | 59 +++ .../llm_fine_tuning/rlvr/grpo/data_types.py | 23 +- starters/llm_fine_tuning/rlvr/grpo/grpo.py | 2 +- .../rlvr/grpo/rollout_generation.py | 176 ++++++++ starters/llm_fine_tuning/rlvr/grpo/trainer.py | 188 +++++---- starters/llm_fine_tuning/rlvr/types.py | 20 + uv.lock | 395 ++++++++++++++++++ 14 files changed, 1417 insertions(+), 82 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 scripts.md create mode 100644 starters/llm_fine_tuning/rlvr/agents/examples.py create mode 100644 starters/llm_fine_tuning/rlvr/agents/notes.md create mode 100644 starters/llm_fine_tuning/rlvr/agents/rollout_translation.py create mode 100644 starters/llm_fine_tuning/rlvr/async_utils.py create mode 100644 starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py create mode 100644 starters/llm_fine_tuning/rlvr/types.py diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..1dc2d9f --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "chatgpt.commentCodeLensEnabled": false +} \ No newline at end of file diff --git a/download_model.py b/download_model.py index 95dada3..eb01f62 100644 --- a/download_model.py +++ b/download_model.py @@ -9,11 +9,15 @@ uv run download_model.py \ --repo_name Qwen/Qwen3-4B-Thinking-2507 \ --local_path /model-weights/Qwen3-4B-Thinking-2507 + +uv run download_model.py \ +--repo_name Qwen/Qwen3-1.7B \ +--local_path /model-weights/Qwen3-1.7B """ import argparse -from transformers import AutoModel, AutoTokenizer +from transformers import AutoModelForCausalLM, AutoTokenizer parser = argparse.ArgumentParser() @@ -23,4 +27,6 @@ if __name__ == "__main__": args = parser.parse_args() AutoTokenizer.from_pretrained(args.repo_name).save_pretrained(args.local_path) - AutoModel.from_pretrained(args.repo_name).save_pretrained(args.local_path) + AutoModelForCausalLM.from_pretrained(args.repo_name).save_pretrained( + args.local_path + ) diff --git a/pyproject.toml b/pyproject.toml index bc6f07d..c7342ed 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,14 @@ dependencies = [ "pre-commit>=3.0.0,<4.0.0", "pydantic>=2.11.7", "rich>=14.1.0", + "openai-agents>=0.2.11", + "vec-inf==0.6.1", + "basedpyright>=1.31.4", +] + +[dependency-groups] +dev = [ + "basedpyright>=1.31.4", ] [tool.ruff] diff --git a/scripts.md b/scripts.md new file mode 100644 index 0000000..8e05d37 --- /dev/null +++ b/scripts.md @@ -0,0 +1,21 @@ +# Scripts + +## RLVR + +```bash +uv run starters/llm_fine_tuning/rlvr/grpo_trainer.py +``` + +## vec-inf Placeholder + +```bash +singularity exec --nv --bind /model-weights/Qwen3-0.6B --containall /model-weights/vec-inf-shared/vector-inference_0.10.0.sif \ +vllm serve /model-weights/Qwen3-0.6B \ +--served-model-name Qwen3-0.6B \ +--host "0.0.0.0" \ +--port 8000 \ +--max-model-len 40960 \ +--max-num-seqs 256 \ +--enable-auto-tool-choice \ +--tool-call-parser hermes +``` diff --git a/starters/llm_fine_tuning/rlvr/agents/examples.py b/starters/llm_fine_tuning/rlvr/agents/examples.py new file mode 100644 index 0000000..fbadece --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/agents/examples.py @@ -0,0 +1,176 @@ +""" +OpenAI Agents SDK demo: function tool `get_weather` + minimal agent. + +Setup (with astral-uv): + uv venv && uv pip install -U pip + uv add openai-agents pydantic + # Or, if you prefer pip: `pip install openai-agents pydantic` + +Environment: + export OPENAI_API_KEY=... # Required for the default OpenAI client + +Run: + uv run python agents_sdk_get_weather_demo.py +""" + +from __future__ import annotations + +from datetime import date, datetime, timedelta, timezone +from typing import Literal + +from agents import Agent, Runner, function_tool +from pydantic import BaseModel, Field + + +class WeatherReport(BaseModel): + """Structured output for a weather report. + + Attributes + ---------- + city: + Echo of the requested city (canonicalized). + unit: + "c" for Celsius or "f" for Fahrenheit. + temperature: + Air temperature in the requested unit. + feels_like: + Apparent temperature in the requested unit. + condition: + One of: "clear", "partly cloudy", "cloudy", "rain", "snow", "windy". + humidity: + Relative humidity percentage (0–100). + wind_kph: + Wind speed in kilometers per hour. + observation_time: + UTC timestamp when the reading was generated. + """ + + city: str + unit: Literal["c", "f"] + temperature: float + feels_like: float + condition: Literal["clear", "partly cloudy", "cloudy", "rain", "snow", "windy"] + humidity: int = Field(ge=0, le=100) + wind_kph: float = Field(ge=0) + observation_time: datetime + + +@function_tool +def get_weather( + city: str, + unit: Literal["c", "f"] = "c", + when: Literal["now", "today", "tomorrow"] = "now", +) -> str: + """Return a deterministic, mock weather report for demos. + + The function is *offline* and *stable across runs* for a given `(city, date)` + so it's ideal for showcasing **function-tool** calls without network flakiness. + + Args: + city: + Human-readable city name (e.g., "Vancouver"). + unit: + Temperature unit: "c" for Celsius, "f" for Fahrenheit. Defaults to "c". + when: + Time window for the report: "now", "today", or "tomorrow". Defaults to "now". + + Returns: + JSON string representing a `WeatherReport`. + """ + canonical = city.strip() + + # City baselines (°C). Extend this mapping to taste. + baselines: dict[str, float] = { + "vancouver": 14.0, + "new york": 12.0, + "london": 11.0, + "singapore": 28.0, + "shanghai": 28.0, + "auckland": 20.0, + "tokyo": 17.0, + "paris": 13.0, + "san francisco": 16.0, + "berlin": 12.0, + "mexico city": 19.0, + } + + key = canonical.lower() + base_c = baselines.get(key, 15.0) + + # Reference date for deterministic seeding + today = date.today() + ref_date = today if when in ("now", "today") else today + timedelta(days=1) + + # Seeded pseudo-randoms derived from (city, date) + seed = abs(hash(f"{key}|{ref_date.isoformat()}")) + + def prand(a: float, b: float, salt: int) -> float: + # Deterministic pseudo-random in [a, b] + return a + (seed ^ salt) % 10 / 9.0 * (b - a) + + temp_c = base_c + prand(-4.0, 4.0, 0xA5A5) - 0.5 + humidity = int(round(prand(40, 90, 0xB6B6))) + wind_kph = round(prand(0.0, 30.0, 0xC7C7), 1) + + band = seed % 100 + if band < 20: + condition = "clear" + elif band < 45: + condition = "partly cloudy" + elif band < 65: + condition = "cloudy" + elif band < 85: + condition = "rain" + elif band < 95: + condition = "windy" + else: + condition = "snow" + + feels_c = temp_c - 0.1 * wind_kph + 0.02 * (humidity - 50) + + def to_unit(tc: float, u: Literal["c", "f"]) -> float: + return round(tc if u == "c" else (tc * 9 / 5 + 32), 1) + + report = WeatherReport( + city=canonical, + unit=unit, + temperature=to_unit(temp_c, unit), + feels_like=to_unit(feels_c, unit), + condition=condition, # type: ignore[arg-type] + humidity=humidity, + wind_kph=wind_kph, + observation_time=datetime.now(timezone.utc), + ) + + # Agents SDK tools should return a string (or something that stringifies cleanly). + return report.model_dump_json() + + +# --- Minimal agent wiring ---------------------------------------------------- +weather_agent = Agent( + name="Weather Helper", + instructions=( + "You answer weather questions. When the user asks about weather, " + "call the `get_weather` tool. If it returns JSON, parse it and reply " + "concisely with temperature, feels-like, condition, and units." + ), + tools=[get_weather], # register the function tool +) + + +def main() -> None: + """Run a single demo turn with the agent and print the final output.""" + # Example inputs that strongly encourage tool use + user_inputs: list[str] = [ + "What's the weather in Vancouver today in celsius?", + "NYC now, in Fahrenheit — include feels-like and wind, please.", + ] + + for i, prompt in enumerate(user_inputs, start=1): + print(f"\n=== Demo turn {i} ===") + result = Runner.run_sync(weather_agent, prompt) + print(result.final_output) + + +if __name__ == "__main__": + main() diff --git a/starters/llm_fine_tuning/rlvr/agents/notes.md b/starters/llm_fine_tuning/rlvr/agents/notes.md new file mode 100644 index 0000000..e48b1be --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/agents/notes.md @@ -0,0 +1,104 @@ +# Agent SDK output to HuggingFace + +HuggingFace transformer requires the following format: `messages` and `tools`. + +Messages in the usual OpenAI Chat Completion format, including a special "tool" role for tool outputs. + +```json +[ + { "role": "user", "content": "Turn on the living room lights." }, + { + "role": "assistant", + "tool_calls": [ + { + "type": "function", + "function": { + "name": "control_light", + "arguments": { "room": "living room", "state": "on" } + } + } + ] + }, + { + "role": "tool", + "name": "control_light", + "content": "The lights in the living room are now on." + }, + { "role": "assistant", "content": "Done!" } +] +``` + +Tools as a list of dict of the following format: + +```json +{ + "type": "function", + "function": { + "name": "control_light", + "description": "Controls the lights in a room.", + "parameters": { + "type": "object", + "properties": { + "room": { "type": "string", "description": "The name of the room." }, + "state": { + "type": "string", + "description": "The desired state of the light (\"on\" or \"off\")." + } + }, + "required": ["room", "state"] + }, + "return": { + "type": "string", + "description": "str: A message indicating the new state of the lights." + } + } +} +``` + +Test the format using `tokenizer.apply_chat_template`: + +```python +formatted = tokenizer.apply_chat_template( + messages, + tools=(tools or None), # type: ignore[arg-type] + tokenize=False, + add_generation_prompt=False, +) +``` + +Run the following to see the output: + +```bash +PYTHONPATH="." uv run starters/llm_fine_tuning/rlvr/agents/main.py' +``` + +Example output from Qwen3 format: + +``` +<|im_start|>system +# Tools + +You may call one or more functions to assist with the user query. + +You are provided with function signatures within XML tags: + +{"type": "function", "function": {"name": "get_weather", "parameters": {"properties": {"city": {"description": "\nHuman-readable city name (e.g., \"Vancouver\").", "title": "City", "type": "string"}, "unit": {"default": "c", "description": "\nTemperature unit: \"c\" for Celsius, \"f\" for Fahrenheit. Defaults to \"c\".", "enum": ["c", "f"], "title": "Unit", "type": "string"}, "when": {"default": "now", "description": "\nTime window for the report: \"now\", \"today\", or \"tomorrow\". Defaults to \"now\".", "enum": ["now", "today", "tomorrow"], "title": "When", "type": "string"}}, "required": ["city", "unit", "when"], "title": "get_weather_args", "type": "object", "additionalProperties": false}, "description": "Return a deterministic, mock weather report for demos.\n\nThe function is *offline* and *stable across runs* for a given `(city, date)`\nso it's ideal for showcasing **function-tool** calls without network flakiness."}} + + +For each function call, return a json object with function name and arguments within XML tags: + +{"name": , "arguments": } +<|im_end|> +<|im_start|>user +Weather in Auckland<|im_end|> +<|im_start|>user + +{"city":"Auckland","unit":"c","temperature":16.4,"feels_like":16.5,"condition":"cloudy","humidity":73,"wind_kph":3.3,"observation_time":"2025-09-12T18:01:56.767763Z"} +<|im_end|> +<|im_start|>assistant + +Okay, let me process the tool response. The user asked for the weather in Auckland, so I need to present the data concisely. The temperature is 16.4°C, feels like 16.5°C. The condition is cloudy. Humidity is 73%, wind speed 3.3 kph. I should mention the time as well. Let me check the units again to make sure they're correct. Celsius for temperature and feels-like, and the condition. No need for extra info unless specified. Alright, putting it all together in a clear sentence. + + +The weather in Auckland is currently 16.4°C with a feels-like temperature of 16.5°C, with a cloudy condition and 73% humidity. Wind is blowing at 3.3 kph.<|im_end|> +``` diff --git a/starters/llm_fine_tuning/rlvr/agents/rollout_translation.py b/starters/llm_fine_tuning/rlvr/agents/rollout_translation.py new file mode 100644 index 0000000..7881d87 --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/agents/rollout_translation.py @@ -0,0 +1,314 @@ +"""Entrypoint for testing agents SDK integration. + +Usage: +PYTHONPATH="." uv run starters/llm_fine_tuning/rlvr/agents/main.py +""" + +import asyncio +from typing import Any, cast + +import agents +import openai +import pydantic +import transformers +from openai.types.chat import ( + ChatCompletionAssistantMessageParam, + ChatCompletionMessageFunctionToolCallParam, + ChatCompletionToolMessageParam, + ChatCompletionUserMessageParam, +) + +from starters.llm_fine_tuning.rlvr.agents.examples import weather_agent +from starters.llm_fine_tuning.rlvr.types import ChatMessage + + +class Rollout(pydantic.BaseModel): + """Rollout in ChatCompletion / HF-compatible format.""" + + messages: list[ChatMessage] + tools: list[dict[str, Any]] + + +def _introspect_tools(agent_obj: agents.AgentBase) -> list[dict[str, Any]]: + """Convert registered agent tools to OpenAI-compatible tools schema. + + Best-effort introspection; silently falls back to no tools on failure. + """ + tools_out: list[dict[str, Any]] = [] + try: + agent_tools = getattr(agent_obj, "tools", []) + for t in agent_tools or []: + name = getattr(t, "name", None) + params = getattr(t, "params_json_schema", None) or getattr( + t, "parameters", None + ) + desc = getattr(t, "description", None) + if name and params: + fn = {"name": name, "parameters": params} + if isinstance(desc, str) and desc: + fn["description"] = desc + tools_out.append({"type": "function", "function": fn}) + except Exception: + tools_out = [] + return tools_out + + +def _derive_final_assistant_text(resp_obj: agents.RunResult) -> str: + """Extract a plain assistant text as a fallback.""" + for attr in ("final_output", "output", "text"): + if hasattr(resp_obj, attr): + val = getattr(resp_obj, attr) + if isinstance(val, str) and val.strip(): + return val.strip() + return str(resp_obj) + + +def _get_items_list(resp_obj: agents.RunResult) -> list[object] | None: + """Return a list of new items from a run, if available.""" + for attr in ("new_items", "items"): + if hasattr(resp_obj, attr): + value = getattr(resp_obj, attr) + if isinstance(value, list): + return value + return None + + +def _collect_call_id_to_name(items: list[object]) -> dict[str, str]: + """Build a mapping from tool call id to function name.""" + call_id_to_name: dict[str, str] = {} + for item in items: + item_type = getattr(item, "type", None) + if item_type != "tool_call_item": + continue + + raw = getattr(item, "raw_item", None) + call_id = getattr(raw, "call_id", None) if raw is not None else None + # name is not needed for tool message shape + + if call_id is None and isinstance(raw, dict): + call_id = raw.get("call_id") + name = raw.get("name") + + if isinstance(call_id, str) and isinstance(name, str): + call_id_to_name[call_id] = name + + return call_id_to_name + + +def _append_tool_output_messages( + messages_out: list[ChatMessage], + items: list[object], + call_id_to_name: dict[str, str], +) -> None: + """Append tool output messages to the chat messages list. + + Produces OpenAI-compatible tool messages including optional `name` and + `tool_call_id` fields when available. + """ + for item in items: + if getattr(item, "type", None) != "tool_call_output_item": + continue + + raw = getattr(item, "raw_item", None) + call_id = getattr(raw, "call_id", None) if raw is not None else None + output_text = getattr(raw, "output", None) if raw is not None else None + # No need to extract name for tool messages + + if call_id is None and isinstance(raw, dict): + call_id = raw.get("call_id") + output_text = raw.get("output") + + if output_text is None: + v = getattr(item, "output", None) + if isinstance(v, str) and v.strip(): + output_text = v.strip() + + if isinstance(output_text, str) and output_text: + # ChatCompletionToolMessageParam requires role, content, and tool_call_id + tool_msg_typed: ChatCompletionToolMessageParam = { + "role": "tool", + "content": output_text, + "tool_call_id": call_id or "", + } + messages_out.append(cast(ChatMessage, tool_msg_typed)) + + +def _find_first_tool_output_index(items: list[object]) -> int | None: + """Return the index of the first tool output item, if any.""" + for i, it in enumerate(items): + if getattr(it, "type", None) == "tool_call_output_item": + return i + return None + + +def _collect_reason_text_before(items: list[object], end: int) -> list[str]: + """Collect assistant "reason" texts before the first tool output.""" + parts: list[str] = [] + for it in items[:end]: + if getattr(it, "type", None) != "message_output_item": + continue + try: + from agents.items import ItemHelpers + + it_any: Any = it + text = ItemHelpers.text_message_output(it_any) + except Exception: + text = None + if isinstance(text, str) and text.strip(): + parts.append(text.strip()) + return parts + + +def _collect_function_tool_calls_before(items: list[object], end: int) -> list[dict[str, Any]]: + """Collect function tool calls before the first tool output as `tool_calls`.""" + out: list[dict[str, Any]] = [] + for it in items[:end]: + if getattr(it, "type", None) != "tool_call_item": + continue + raw = getattr(it, "raw_item", None) + name = getattr(raw, "name", None) if raw is not None else None + arguments = getattr(raw, "arguments", None) if raw is not None else None + _type = getattr(raw, "type", None) if raw is not None else None + call_id = getattr(raw, "call_id", None) if raw is not None else None + + if isinstance(raw, dict): + name = raw.get("name") + arguments = raw.get("arguments") + _type = raw.get("type") + call_id = raw.get("call_id") + + if _type == "function_call" and isinstance(name, str) and isinstance(arguments, str): + entry: dict[str, Any] = { + "type": "function", + "function": {"name": name, "arguments": arguments}, + } + if isinstance(call_id, str) and call_id: + entry["id"] = call_id + out.append(entry) + return out + + +def _build_assistant_tool_invocation_message(items: list[object]) -> ChatMessage | None: + """Build an assistant message that invokes tools via `tool_calls`. + + - Aggregates function tool calls from run items. + - Includes preceding assistant text ("reason") before the first tool output, if any. + - Note: HF chat templates (e.g., Qwen3) ignore `function_call` and expect + `tool_calls`. + """ + first_tool_output_idx = _find_first_tool_output_index(items) + scan_upto = first_tool_output_idx if first_tool_output_idx is not None else len(items) + + reason_text_parts = _collect_reason_text_before(items, scan_upto) + tool_calls = _collect_function_tool_calls_before(items, scan_upto) + + if not tool_calls: + return None + + content_str = "\n".join(reason_text_parts).strip() + asst_typed: ChatCompletionAssistantMessageParam = { + "role": "assistant", + "content": content_str or None, + "tool_calls": cast("list[ChatCompletionMessageFunctionToolCallParam]", tool_calls), + } + return cast(ChatMessage, asst_typed) + + +def translate_rollout( + resp_obj: agents.RunResult, user_text: str, agent_obj: agents.AgentBase +) -> Rollout: + """Build messages and tools suitable for HF chat templates. + + - Includes tool outputs and subsequent assistant messages when present. + - Falls back to a simple user/assistant exchange otherwise. + """ + messages_out: list[ChatMessage] = [ + cast( + ChatMessage, ChatCompletionUserMessageParam(role="user", content=user_text) + ) + ] + + tools_out = _introspect_tools(agent_obj) + + items = _get_items_list(resp_obj) + if items: + # Add assistant message that invokes tools (if any) + assistant_invocation = _build_assistant_tool_invocation_message(items) + if assistant_invocation is not None: + messages_out.append(assistant_invocation) + + # Add tool output messages + call_id_to_name = _collect_call_id_to_name(items) + _append_tool_output_messages(messages_out, items, call_id_to_name) + + # Append final assistant response derived from run + last_asst: ChatCompletionAssistantMessageParam = { + "role": "assistant", + "content": _derive_final_assistant_text(resp_obj), + } + messages_out.append(cast(ChatMessage, last_asst)) + + return Rollout( + messages=messages_out, + tools=tools_out, + ) + + +# Integration test +async def main(): + """Run async logic.""" + client = openai.AsyncOpenAI(api_key="EMPTY") + run_config = agents.RunConfig( + model=agents.OpenAIChatCompletionsModel(model=MODEL_NAME, openai_client=client) + ) + return await agents.Runner.run( + weather_agent, input="Weather in Auckland", run_config=run_config + ) + + +if __name__ == "__main__": + from transformers.tokenization_utils_fast import PreTrainedTokenizerFast + + MODEL_NAME = "Qwen3-0.6B" + MODEL_PATH = f"/model-weights/{MODEL_NAME}" + + agent_response = asyncio.run(main()) + # Build messages and tools for the tokenizer, including tool outputs + user_input = "Weather in Auckland" + rollout = translate_rollout(agent_response, user_input, weather_agent) + + # Ensure output is serializable + assert isinstance(rollout.model_dump_json(indent=2), str) + + # Verify roles in rollout + rollout_roles = [_message["role"] for _message in rollout.messages] + print(rollout_roles) + print(rollout.model_dump_json(indent=2)) + + assert rollout_roles == [ + "user", # "input" + "assistant", # assistant invokes tool + "tool", # response from tool + "assistant", # message to user + ], rollout_roles + + # Verify compatibility with HF tokenizer + tokenizer: "PreTrainedTokenizerFast" = transformers.AutoTokenizer.from_pretrained( + MODEL_PATH + ) + formatted = tokenizer.apply_chat_template( + cast("list[dict[str, Any]]", rollout.messages), + tools=cast(Any, (rollout.tools or None)), + tokenize=False, + add_generation_prompt=False, + ) + assert isinstance(formatted, str) + print(formatted) + + # Verify function call details appear in the tokenizer output + # Previously, using `function_call` was ignored by Qwen3 chat template, + # so the tool invocation JSON was missing from `formatted`. + assert "" in formatted, "Missing block in template output" + assert '"name": "get_weather"' in formatted, "Missing function name in tool_call" + assert '"arguments":' in formatted, "Missing arguments field in tool_call" + assert "Auckland" in formatted, "Missing argument value in tool_call" diff --git a/starters/llm_fine_tuning/rlvr/async_utils.py b/starters/llm_fine_tuning/rlvr/async_utils.py new file mode 100644 index 0000000..923f605 --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/async_utils.py @@ -0,0 +1,59 @@ +"""Utils for async workflows.""" + +import asyncio +import types +from typing import Any, Awaitable, Callable, Coroutine, Sequence, TypeVar + +from rich.progress import Progress + + +T = TypeVar("T") + + +async def indexed(index: int, coro: Coroutine[None, None, T]) -> tuple[int, T]: + """Return (index, await coro).""" + return index, (await coro) + + +async def rate_limited( + _fn: Callable[[], Awaitable[T]], semaphore: asyncio.Semaphore +) -> T: + """Run _fn with semaphore rate limit.""" + async with semaphore: + return await _fn() + + +async def gather_with_progress( + coros: "list[types.CoroutineType[Any, Any, T]]", + description: str = "Running tasks", +) -> Sequence[T]: + """ + Run a list of coroutines concurrently, display a rich.Progress bar as each finishes. + + Returns the results in the same order as the input list. + + :param coros: List of coroutines to run. + :return: List of results, ordered to match the input coroutines. + """ + # Wrap each coroutine in a Task and remember its original index + tasks = [ + asyncio.create_task(indexed(index=index, coro=coro)) + for index, coro in enumerate(coros) + ] + + # Pre‐allocate a results list; we'll fill in each slot as its Task completes + results: list[T | None] = [None] * len(tasks) + + # Create and start a Progress bar with a total equal to the number of tasks + with Progress() as progress: + progress_task = progress.add_task(description, total=len(tasks)) + + # as_completed yields each Task as soon as it finishes + for finished in asyncio.as_completed(tasks): + index, result = await finished + results[index] = result + progress.update(progress_task, advance=1) + + # At this point, every slot in `results` is guaranteed to be non‐None + # so we can safely cast it back to List[T] + return results # type: ignore \ No newline at end of file diff --git a/starters/llm_fine_tuning/rlvr/grpo/data_types.py b/starters/llm_fine_tuning/rlvr/grpo/data_types.py index cd1e754..704f0b4 100644 --- a/starters/llm_fine_tuning/rlvr/grpo/data_types.py +++ b/starters/llm_fine_tuning/rlvr/grpo/data_types.py @@ -12,6 +12,7 @@ TypedBatcher, ) from starters.llm_fine_tuning.rlvr.data_collation.data_types import TypedBatch +from starters.llm_fine_tuning.rlvr.types import ChatMessage if TYPE_CHECKING: @@ -19,7 +20,7 @@ def _assistant_char_spans( - messages: Sequence[dict[str, str]], formatted: str + messages: Sequence[ChatMessage], formatted: str ) -> list[tuple[int, int]]: """Find [start, end) character spans of assistant contents inside formatted string. @@ -31,7 +32,7 @@ def _assistant_char_spans( for message in messages: if message.get("role") != "assistant": continue - content = message.get("content", "") + content: str = message.get("content", "") # type: ignore if not content: continue idx = formatted.find(content, cursor) @@ -41,7 +42,7 @@ def _assistant_char_spans( return spans -class RewardDetail(pydantic.BaseModel): +class RewardDetailTokenized(pydantic.BaseModel): """Reward details for one rollout, outcome-supervised only. Attention mask is not included. @@ -65,16 +66,16 @@ def __init__(self, input_ids: list[int], loss_mask: list[bool], **kwargs): @staticmethod def from_messages( - messages: list[dict[str, str]], + messages: list[ChatMessage], reward: float, tokenizer: PreTrainedTokenizerFast, - ) -> "RewardDetail": + ) -> "RewardDetailTokenized": """Tokenize a chat and create a per-token boolean loss mask.""" chat_template = getattr(tokenizer, "chat_template", None) if chat_template is None: raise ValueError(f"Template is required but not available in: {tokenizer}") formatted = tokenizer.apply_chat_template( - messages, + messages, # type: ignore[arg-type] tokenize=False, add_generation_prompt=False, ) @@ -100,10 +101,12 @@ def from_messages( for _a, _b in offsets ] - return RewardDetail(input_ids=input_ids, loss_mask=loss_mask, reward=reward) + return RewardDetailTokenized( + input_ids=input_ids, loss_mask=loss_mask, reward=reward + ) -class _AdvantageDetail(RewardDetail): +class _AdvantageDetail(RewardDetailTokenized): """Reward details plus group-relative advantage info. Important: @@ -244,7 +247,9 @@ def _group_rewards(self) -> list[float]: return [_detail.reward for _detail in self.advantage_details] @staticmethod - def from_list_of_rewards(reward_details: list[RewardDetail]) -> "AdvantageData": + def from_list_of_rewards( + reward_details: list[RewardDetailTokenized], + ) -> "AdvantageData": """Compute advantage for a given batch of rewards.""" if len(reward_details) == 0: msg = "reward_details must be a non-empty list, but an empty list is given." diff --git a/starters/llm_fine_tuning/rlvr/grpo/grpo.py b/starters/llm_fine_tuning/rlvr/grpo/grpo.py index 4a8b84d..d4d6cdc 100644 --- a/starters/llm_fine_tuning/rlvr/grpo/grpo.py +++ b/starters/llm_fine_tuning/rlvr/grpo/grpo.py @@ -8,7 +8,7 @@ from torch.optim import AdamW from transformers import PreTrainedModel -from starters.llm_fine_tuning.rlvr.grpo.data_types import GRPOBatcher, BatchForGRPOTorch +from starters.llm_fine_tuning.rlvr.grpo.data_types import BatchForGRPOTorch, GRPOBatcher def compute_grpo_loss( diff --git a/starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py b/starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py new file mode 100644 index 0000000..491516d --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py @@ -0,0 +1,176 @@ +"""Rollout Generator for RL use cases. + +Usage: +PYTHONPATH="." uv run starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py +""" + +import asyncio +from typing import Any, Sequence + +import agents +import pydantic + +from starters.llm_fine_tuning.rlvr.agents.rollout_translation import ( + Rollout, + translate_rollout, +) +from starters.llm_fine_tuning.rlvr.async_utils import gather_with_progress, rate_limited + + +class RLVRDataItem(pydantic.BaseModel): + """One row in the RLVR training dataset.""" + + query: str + target: str | float + metadata: dict[str, Any] = pydantic.Field(default_factory=dict) + + +class _EvalResult(pydantic.BaseModel): + """Evaluation score, including explanation.""" + + explanation: str + score: float + + +class RewardDetails(_EvalResult): + """_EvalResult plus full ChatCompletion/HF-compatible rollout info.""" + + source_item: RLVRDataItem + rollout: Rollout + + +EVAL_AGENT_INSTRUCTIONS = """\ +Evaluate if the `proposed` response matches the ground-truth `target`. + +Give a score of 0.0 if incorrect and 1.0 if correct. +""" + +# Eval agent must support structured output and use output_type _EvalResult +eval_agent = agents.Agent( + "Evaluator", instructions=EVAL_AGENT_INSTRUCTIONS, output_type=_EvalResult +) + + +class RLVREvaluator: + """Base class for stateful evaluator of verifiable rewards.""" + + def __init__( + self, + evaluator_agent: "agents.Agent", + agent_run_config: "agents.RunConfig", + semaphore: asyncio.Semaphore, + ): + self.agent = evaluator_agent + self.semaphore = semaphore + self.agent_run_config = agent_run_config + + async def evaluate(self, item: RLVRDataItem, proposed: str) -> RewardDetails: + """Evaluate one proposed response to one data item.""" + import agents + + query = f"Ground Truth: {item.model_dump_json(indent=2)} \nProposed: {proposed}" + async with self.semaphore: + response = await agents.Runner.run( + self.agent, input=query, run_config=self.agent_run_config + ) + + return response.final_output_as(RewardDetails) + + +class GRPORollout: + """GRPO Rollout Generation and reward calculation. + + TODO: create a base class interface based on this class. + """ + + def __init__( + self, + agent: "agents.Agent", + evaluator: RLVREvaluator, + ): + self.agent = agent + self.evaluator = evaluator + + async def generate( + self, + data: Sequence[RLVRDataItem], + agent_run_config: "agents.RunConfig", + semaphore: asyncio.Semaphore, + ) -> list[RewardDetails]: + """Generate RLVR reward details on given data and policy. + + Specify LLM client and model name in agent_run_config. + """ + import agents + + # Generate agent rollouts + rollout_coros = [ + rate_limited( + lambda _item=_item: agents.Runner.run( + self.agent, _item.query, run_config=agent_run_config + ), + semaphore=semaphore, + ) + for _item in data + ] + + outputs = await gather_with_progress(rollout_coros, description="Rollout ...") + text_responses = [str(_result.final_output) for _result in outputs] + + # Translate Agent SDK rollouts to Chat Completion format + full_rollouts = [ + translate_rollout( + resp_obj=_output, user_text=_item.query, agent_obj=self.agent + ) + for _item, _output in zip(data, outputs) + ] + + # Obtain verifiable reward scores + eval_coros = [ + self.evaluator.evaluate(item=_item, proposed=_response_text) + for _item, _response_text in zip(data, text_responses) + ] + eval_results = await gather_with_progress( + eval_coros, description="Evaluate ..." + ) + + return [ + RewardDetails(**_eval.model_dump(), source_item=_item, rollout=_rollout) + for _item, _eval, _rollout in zip(data, eval_results, full_rollouts) + ] + + +async def main(): + """Integration tests.""" + import openai + + from starters.llm_fine_tuning.rlvr.agents.examples import weather_agent + + client = openai.AsyncOpenAI() + example_agent_config = agents.RunConfig( + model=agents.OpenAIChatCompletionsModel( + model="Qwen3-0.6B", openai_client=client + ) + ) + evaluator = RLVREvaluator( + eval_agent, + agent_run_config=example_agent_config, + semaphore=asyncio.Semaphore(1), + ) + + rollout_generator = GRPORollout(weather_agent, evaluator=evaluator) + example_data = [ + RLVRDataItem(query="Weather in Shanghai", target="28 degrees Celsius, clear"), + RLVRDataItem(query="Weather in Vancouver", target="10 degrees Celsius, cloudy"), + ] + rollouts = await rollout_generator.generate( + data=example_data, + agent_run_config=example_agent_config, + semaphore=asyncio.Semaphore(1), + ) + + print([_rollout.model_dump() for _rollout in rollouts][0]) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/starters/llm_fine_tuning/rlvr/grpo/trainer.py b/starters/llm_fine_tuning/rlvr/grpo/trainer.py index 08a2cfa..4d77d22 100644 --- a/starters/llm_fine_tuning/rlvr/grpo/trainer.py +++ b/starters/llm_fine_tuning/rlvr/grpo/trainer.py @@ -15,8 +15,13 @@ """ import argparse +import asyncio import logging +from typing import Literal, Sequence +import agents +import numpy as np +import openai import torch from rich.progress import track from torch.nn.functional import softmax @@ -26,14 +31,22 @@ PreTrainedModel, ) +from starters.llm_fine_tuning.rlvr.agents.examples import weather_agent from starters.llm_fine_tuning.rlvr.grpo.data_types import ( AdvantageData, BatchForInference, GRPOData, PerTokenProbs, - RewardDetail, + RewardDetailTokenized, ) from starters.llm_fine_tuning.rlvr.grpo.grpo import optimize_grpo_one_epoch +from starters.llm_fine_tuning.rlvr.grpo.rollout_generation import ( + GRPORollout, + RewardDetails, + RLVRDataItem, + RLVREvaluator, + eval_agent, +) logger = logging.getLogger(__name__) @@ -82,6 +95,31 @@ def get_per_token_probs( ) +async def roll_out( + data: Sequence[RLVRDataItem], + client: "openai.AsyncOpenAI", + model_name: str, + semaphore_rollout: "asyncio.Semaphore", + semaphore_llm_judge: "asyncio.Semaphore", +) -> list[RewardDetails]: + """Rollout online to get reward details, not yet tokenized.""" + example_agent_config = agents.RunConfig( + model=agents.OpenAIChatCompletionsModel(model=model_name, openai_client=client) + ) + evaluator = RLVREvaluator( + eval_agent, + agent_run_config=example_agent_config, + semaphore=semaphore_llm_judge, + ) + + rollout_generator = GRPORollout(weather_agent, evaluator=evaluator) + return await rollout_generator.generate( + data=data, + agent_run_config=example_agent_config, + semaphore=semaphore_rollout, + ) + + parser = argparse.ArgumentParser() parser.add_argument("--base_model", required=True) parser.add_argument("--kl_ref_model", required=True) @@ -90,82 +128,92 @@ def get_per_token_probs( "--bsz_inference", type=int, default=16, help="batch size for getting pi_ref" ) parser.add_argument("--bsz_train", type=int, default=8, help="batch size for backprop") - - -# def parameter_update(model: AutoModelForCausalLM, advantage_batch: AdvantageBatch) - +parser.add_argument("--concurrency_rollout", type=int, default=36) +parser.add_argument("--concurrency_llm_judge", type=int, default=36) + +example_data = [ + RLVRDataItem(query="Weather in Shanghai", target="28 degrees Celsius, clear"), + RLVRDataItem( + query="Weather in Vancouver", target="-10 degrees Celsius, heavy snow" + ), +] +example_dataset: dict[Literal["train", "test"], Sequence[RLVRDataItem]] = { + "train": example_data, + "test": example_data, +} if __name__ == "__main__": args = parser.parse_args() - tokenizer = AutoTokenizer.from_pretrained(args.tokenizer) logger.info(f"Tokenizer: {tokenizer}") - example_reward_details = [ - RewardDetail.from_messages( - messages=[ - {"role": "system", "content": "Example system instructions."}, - {"role": "user", "content": "Example user message."}, - {"role": "assistant", "content": _message}, - ], - reward=_reward, - tokenizer=tokenizer, + semaphore_rollout = asyncio.Semaphore(args.concurrency_rollout) + semaphore_llm_judge = asyncio.Semaphore(args.concurrency_llm_judge) + + for _step in range(72): + # Generate on-policy rollouts and rewards- not tokenized yet. + # TODO: replace client and model_name with OpenAI-compatible vec-inf wrapper. + advantage_data = { + _split: AdvantageData.from_list_of_rewards( + [ + RewardDetailTokenized.from_messages( + _detail.rollout.messages, + reward=_detail.score, + tokenizer=tokenizer, + ) + for _detail in asyncio.run( + roll_out( + data=_data, + client=openai.AsyncOpenAI(api_key="EMPTY"), + model_name="Qwen3-0.6B", + semaphore_rollout=semaphore_rollout, + semaphore_llm_judge=semaphore_llm_judge, + ) + ) + ] + ) + for _split, _data in example_dataset.items() + } + eval_advantage = np.mean(advantage_data["test"]._group_rewards()).item() + print(f"Step {_step}, eval_advantage: {eval_advantage:.3f}") + + device = torch.device("cuda:0") + + logger.info("Loading kl_ref weights") + kl_ref_model = AutoModelForCausalLM.from_pretrained(args.kl_ref_model) + logger.info("Loading kl_ref weights to CUDA.") + kl_ref_model = kl_ref_model.to(device) + + per_token_probs_ref = sum( + get_per_token_probs(_batch, kl_ref_model) + for _batch in track( + advantage_data["train"].get_iterator_for_inference(batch_size=5), + description="Inference: pi_ref", + ) ) - for _reward, _message in [ - (1.0, "Example correct response."), - (1.0, "Another correct response."), - (0.0, "Example incorrect response."), - (1.0, "Example correct response 1."), - (1.0, "Another correct response 1."), - (0.0, "Example incorrect response 1."), - ] - ] - - advantage_data = AdvantageData.from_list_of_rewards(example_reward_details) - - device = torch.device("cuda:0") - - logger.info("Loading kl_ref weights") - kl_ref_model = AutoModelForCausalLM.from_pretrained(args.kl_ref_model) - logger.info("Loading kl_ref weights to CUDA.") - kl_ref_model = kl_ref_model.to(device) - - per_token_probs_ref = sum( - get_per_token_probs(_batch, kl_ref_model) - for _batch in track( - advantage_data.get_iterator_for_inference(batch_size=5), - description="Inference: pi_ref", + del kl_ref_model + + base_model = AutoModelForCausalLM.from_pretrained(args.base_model) + logger.info("Loading weights to CUDA.") + base_model = base_model.to(device) + + per_token_probs_base = sum( + get_per_token_probs(_batch, base_model) + for _batch in track( + advantage_data["train"].get_iterator_for_inference(batch_size=5), + description="Inference: pi_old", + ) ) - ) - del kl_ref_model - - base_model = AutoModelForCausalLM.from_pretrained(args.base_model) - logger.info("Loading weights to CUDA.") - base_model = base_model.to(device) - per_token_probs_base = sum( - get_per_token_probs(_batch, base_model) - for _batch in track( - advantage_data.get_iterator_for_inference(batch_size=5), - description="Inference: pi_old", + assert isinstance(per_token_probs_ref, PerTokenProbs) + assert isinstance(per_token_probs_base, PerTokenProbs) + grpo_data = GRPOData( + **advantage_data["train"].model_dump(), + ref_probs=per_token_probs_ref, + base_probs=per_token_probs_base, ) - ) - assert isinstance(per_token_probs_ref, PerTokenProbs) - assert isinstance(per_token_probs_base, PerTokenProbs) - grpo_data = GRPOData( - **advantage_data.model_dump(), - ref_probs=per_token_probs_ref, - base_probs=per_token_probs_base, - ) - - for training_batch in grpo_data.get_iterator_for_training( - batch_size=args.bsz_train - ): - print({k: type(v) for k, v in training_batch.model_dump().items()}) - break - - base_model = optimize_grpo_one_epoch( - batcher=grpo_data.get_iterator_for_training(batch_size=args.bsz_train), - model=base_model, - gradient_accumulation_steps=1, - ) + base_model = optimize_grpo_one_epoch( + batcher=grpo_data.get_iterator_for_training(batch_size=args.bsz_train), + model=base_model, + gradient_accumulation_steps=1, + ) diff --git a/starters/llm_fine_tuning/rlvr/types.py b/starters/llm_fine_tuning/rlvr/types.py new file mode 100644 index 0000000..b239ab7 --- /dev/null +++ b/starters/llm_fine_tuning/rlvr/types.py @@ -0,0 +1,20 @@ +""" +Shared types. + +With reference to github.com/willccbb/verifiers +""" + +from typing import TYPE_CHECKING, Annotated + +from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam +from pydantic import SkipValidation + + +# typing aliases +if TYPE_CHECKING: + ChatMessage = ChatCompletionMessageParam +else: + # Use SkipValidation to preserve arbitrary keys like `tool_calls` as raw dicts + # without Pydantic coercion that can break downstream consumption by + # transformers' chat templates. + ChatMessage = Annotated[ChatCompletionMessageParam, SkipValidation] diff --git a/uv.lock b/uv.lock index f4822af..9935517 100644 --- a/uv.lock +++ b/uv.lock @@ -91,6 +91,20 @@ version = "4.9.3" source = { registry = 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index c7342ed..22b63b6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,9 @@ dependencies = [ "openai-agents>=0.2.11", "vec-inf==0.6.1", "basedpyright>=1.31.4", + "langfuse>=3.3.4", + "nest-asyncio>=1.6.0", + "pydantic-ai[logfire]>=1.0.6", ] [dependency-groups] diff --git a/starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py b/starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py index 491516d..4881421 100644 --- a/starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py +++ b/starters/llm_fine_tuning/rlvr/grpo/rollout_generation.py @@ -5,6 +5,8 @@ """ import asyncio +import contextlib +import os from typing import Any, Sequence import agents @@ -17,6 +19,47 @@ from starters.llm_fine_tuning.rlvr.async_utils import gather_with_progress, rate_limited +# Optional: Instrument OpenAI Agents SDK with Langfuse via Logfire (OTel) +def _setup_langfuse_instrumentation(service_name: str = "rlvr-grpo") -> None: + """Configure Logfire to instrument OpenAI Agents and initialize Langfuse. + + Follows guidance from Langfuse docs on OpenAI Agents SDK instrumentation. + This is a no-op if required packages or credentials are missing. + """ + # Only attempt setup if Langfuse credentials seem present + if not (os.getenv("LANGFUSE_PUBLIC_KEY") and os.getenv("LANGFUSE_SECRET_KEY")): + return + + try: + import logfire # type: ignore + except Exception: + return + + try: + # Configure logfire to export spans via OTLP (Langfuse client handles export) + logfire.configure( + service_name=os.getenv("LANGFUSE_SERVICE_NAME", service_name), + send_to_logfire=False, + ) + # Patch OpenAI Agents SDK to emit spans + logfire.instrument_openai_agents() + + # Initialize Langfuse client (reads env vars for auth and host) + from langfuse import get_client # type: ignore + + _lf = get_client() + # Optionally validate credentials; do not raise to avoid breaking flows + with contextlib.suppress(Exception): + _ = _lf.auth_check() + except Exception: + # Swallow all errors to keep core functionality intact + return + + +# Perform best-effort setup at import time. This is safe and no-ops if disabled. +_setup_langfuse_instrumentation() + + class RLVRDataItem(pydantic.BaseModel): """One row in the RLVR training dataset.""" diff --git a/uv.lock b/uv.lock index 9935517..86c0dc0 100644 --- a/uv.lock +++ b/uv.lock @@ -20,6 +20,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b0/5e/80cee674cdbe529ef008721d7eebb50ae5def4314211d82123aa23e828f8/accelerate-0.34.2-py3-none-any.whl", hash = "sha256:d69159e2c4e4a473d14443b27d2d732929254e826b3ab4813b3785b5ac616c7c", size = 324366, upload-time = "2024-09-05T16:45:17.121Z" }, ] +[[package]] +name = "ag-ui-protocol" +version = "0.1.8" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pydantic" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/27/de/0bddf7f26d5f38274c99401735c82ad59df9cead6de42f4bb2ad837286fe/ag_ui_protocol-0.1.8.tar.gz", hash = "sha256:eb745855e9fc30964c77e953890092f8bd7d4bbe6550d6413845428dd0faac0b", size = 5323, upload-time = 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c1a088f07fda479318b982ade0763775738ce2e8 Mon Sep 17 00:00:00 2001 From: Jacob-Junqi Tian Date: Mon, 15 Sep 2025 18:24:46 -0400 Subject: [PATCH 4/4] Added AGENTS.md for programming agents. --- AGENTS.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 AGENTS.md diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..84d53be --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,19 @@ +# AGENTS.md + +## Testing + +- Ruff Lint: `uv run ruff check [files]` +- Lint: `uv run basedpyright [files]` + - Pyright Warnings are okay but not nice- you don't have to fix existing ones, but try not to introduce additional ones. + +## Code style + +- Use type hints where ever appropriate. +- The codebase uses a modern version of Python. Use `list[...]` instead of `typing.List[...]`. No need to import `annotations`. +- Try to minimize indentation. + +## Notes for agents + +- Always load environment variables from `.envrc` before executing commands. +- All python commands should be invoked through `uv run`. +- If some code looks out of place or could be simplified, go ahead and fix the code, even if that is not directly part of the request.