From e7d0b559485e723104bff40776f01eedeffecffe Mon Sep 17 00:00:00 2001 From: liuxinyang <292384718@qq.com> Date: Tue, 8 Apr 2025 06:13:56 +0000 Subject: [PATCH 01/25] =?UTF-8?q?!176=20=E5=A2=9E=E5=8A=A0=E5=85=A8?= =?UTF-8?q?=E5=85=B1=E5=8D=A1=E5=8A=9F=E8=83=BD=20Merge=20pull=20request?= =?UTF-8?q?=20!176=20from=20liuxinyang/master?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cli/train_grpo.py | 103 ++++++++---- configs/r1_zero_qwen25_7b_integrated.yaml | 107 +++++++++++++ docs/features/hybrid_actor.md | 38 ++--- mindspeed_rl/config_cls/rl_config.py | 2 + mindspeed_rl/config_cls/validate_config.py | 39 ++++- mindspeed_rl/models/rule_verifier.py | 6 +- mindspeed_rl/trainer/grpo_trainer_hybrid.py | 19 ++- mindspeed_rl/utils/utils.py | 50 +++++- mindspeed_rl/workers/actor_hybrid_worker.py | 81 ++++++++-- mindspeed_rl/workers/integrated_worker.py | 112 +++++++++++++ mindspeed_rl/workers/reference_woker.py | 40 ++++- .../resharding/megatron_sharding_manager.py | 150 +++++++++++++----- mindspeed_rl/workers/reward_woker.py | 47 ++++-- mindspeed_rl/workers/scheduler/launcher.py | 10 ++ tests/st/resharding/test_resharding.py | 6 +- 15 files changed, 657 insertions(+), 153 deletions(-) create mode 100644 configs/r1_zero_qwen25_7b_integrated.yaml create mode 100644 mindspeed_rl/workers/integrated_worker.py diff --git a/cli/train_grpo.py b/cli/train_grpo.py index daa53451..bd64a91b 100644 --- a/cli/train_grpo.py +++ b/cli/train_grpo.py @@ -31,6 +31,7 @@ from mindspeed_rl.workers.scheduler.launcher import RayActorGroup from mindspeed_rl.workers.actor_hybrid_worker import ActorHybridWorker from mindspeed_rl.workers.reference_woker import ReferenceWorker from mindspeed_rl.workers.reward_woker import RewardWorker +from mindspeed_rl.workers.integrated_worker import IntegratedWorker cur_file_dir = Path(__file__).absolute().parent.parent logger = Loggers("grpo_train") @@ -45,47 +46,65 @@ def train(config): logger.info('start async initializing ray actor groups') - actor_worker = RayActorGroup( - worker=ActorHybridWorker, - placement_group=None, - megatron_config=actor_config, - rl_config=rl_config, - generate_config=generate_config, - model_provider=gpt_model_provider, - tokenizer=tokenizer, - initialize_func=initialize_megatron, - get_megatron_module=get_megatron_module, - global_batch_size=actor_config.global_batch_size * rl_config.n_samples_per_prompt - ).initialize() - - reference_worker = RayActorGroup( - worker=ReferenceWorker, - placement_group=None, - megatron_config=ref_config, - rl_config=rl_config, - model_provider=gpt_model_provider, - tokenizer=tokenizer, - initialize_func=initialize_megatron, - get_megatron_module=get_megatron_module, - global_batch_size=actor_config.global_batch_size * rl_config.n_samples_per_prompt - ).initialize() - reward_list = [] - if rl_config.reward_resource: - reward_worker = RayActorGroup( - worker=RewardWorker, + if rl_config.use_integrated_worker: + integrated_worker = RayActorGroup( + worker=IntegratedWorker, placement_group=None, - megatron_config=reward_config, + megatron_config=actor_config, rl_config=rl_config, - model_provider=rm_model_provider, + generate_config=generate_config, + model_provider=gpt_model_provider, + tokenizer=tokenizer, + initialize_func=initialize_megatron, + get_megatron_module=get_megatron_module, + global_batch_size=actor_config.global_batch_size * rl_config.n_samples_per_prompt, + ).initialize() + + actor_worker = integrated_worker + reference_worker = integrated_worker + + else: + actor_worker = RayActorGroup( + worker=ActorHybridWorker, + placement_group=None, + megatron_config=actor_config, + rl_config=rl_config, + generate_config=generate_config, + model_provider=gpt_model_provider, tokenizer=tokenizer, initialize_func=initialize_megatron, get_megatron_module=get_megatron_module, global_batch_size=actor_config.global_batch_size * rl_config.n_samples_per_prompt ).initialize() - reward_list.append(reward_worker) + reference_worker = RayActorGroup( + worker=ReferenceWorker, + placement_group=None, + megatron_config=ref_config, + rl_config=rl_config, + model_provider=gpt_model_provider, + tokenizer=tokenizer, + initialize_func=initialize_megatron, + get_megatron_module=get_megatron_module, + global_batch_size=actor_config.global_batch_size * rl_config.n_samples_per_prompt + ).initialize() + + if rl_config.reward_resource: + reward_worker = RayActorGroup( + worker=RewardWorker, + placement_group=None, + megatron_config=reward_config, + rl_config=rl_config, + model_provider=rm_model_provider, + tokenizer=tokenizer, + initialize_func=initialize_megatron, + get_megatron_module=get_megatron_module, + global_batch_size=actor_config.global_batch_size * rl_config.n_samples_per_prompt + ).initialize() + + reward_list.append(reward_worker) if rl_config.rule_reward: rule_reward = RuleReward.options(num_cpus=rl_config.num_cpus_for_local_task).remote() @@ -145,11 +164,25 @@ def parse_training_config(config: Dict): """ actor_config = MegatronConfig({**config.get("megatron_training"), **config.get("actor_config")}, config.get('model')) - ref_config = MegatronConfig({**config.get("megatron_training"), **config.get("ref_config")}, - config.get('model')) - reward_config = MegatronConfig({**config.get("megatron_training"), **config.get("reward_config")}, - config.get('model')) rl_config = RLConfig(config.get("rl_config")) + + if rl_config.use_integrated_worker: + if "ref_config" in config: + raise ValueError( + f"ref_config should not be set when use_integrated_worker mode is on.") + ref_config = actor_config + + if "reward_config" in config: + raise ValueError( + f"reward_config should not be set when use_integrated_worker mode is on.") + reward_config = actor_config + + else: + ref_config = MegatronConfig({**config.get("megatron_training"), **config.get("ref_config")}, + config.get('model')) + + reward_config = MegatronConfig({**config.get("megatron_training"), **config.get("reward_config")}, + config.get('model')) generate_config = GenerateConfig(config.get("generate_config")) validate_rl_args(actor_config, ref_config, reward_config, rl_config, generate_config) diff --git a/configs/r1_zero_qwen25_7b_integrated.yaml b/configs/r1_zero_qwen25_7b_integrated.yaml new file mode 100644 index 00000000..41fc2a7e --- /dev/null +++ b/configs/r1_zero_qwen25_7b_integrated.yaml @@ -0,0 +1,107 @@ +defaults: + - model: + - qwen25_7b + +megatron_training: + model: qwen25_7b + use_fused_rmsnorm: true + use_mcore_models: true + sequence_parallel: true + use_flash_attn: true + no_masked_softmax_fusion: true + attention_softmax_in_fp32: true + no_gradient_accumulation_fusion: true + use_fused_swiglu: true + use_fused_rotary_pos_emb: true + bf16: true + use_distributed_optimizer: true + tokenizer_type: PretrainedFromHF + tokenizer_name_or_path: ./data/models/Qwen2.5-7B + global_batch_size: 32 + seq_length: 1024 + save_interval: 50 + train_iters: 300 + stage: ray_grpo + attention_dropout: 0.0 + init_method_std: 0.01 + hidden_dropout: 0.0 + distributed_backend: nccl + no_shared_storage: true + variable_seq_lengths: true + dataset_additional_keys: ['labels',] + data_path: ./dataset/data + split: 100,0,0 + +actor_config: + model: qwen25_7b + micro_batch_size: 16 + tensor_model_parallel_size: 4 + pipeline_model_parallel_size: 1 + lr: 5e-7 + lr_decay_style: cosine + min_lr: 5e-8 + weight_decay: 0.0 + lr_warmup_fraction: 0.0 + clip_grad: 10000.0 + adam_beta1: 0.9 + adam_beta2: 0.95 + initial_loss_scale: 4096 + finetune: true + load: ./ckpt + save: ./ckpt + no_load_optim: true + no_load_rng: true + +rl_config: + use_integrated_worker: true + blocking: true + update_micro_batch_size: 2 + experience_count: 16 + gamma: 1.0 + lam: 0.95 + adv_estimator: group_norm + kl_penalty: kl + kl_ctrl_type: fixed + init_kl_coef: 0.0001 + mini_batch_size: 256 + max_prompt_length: 2048 + epochs: 1 + clip_ratio: 0.2 + entropy_coeff: 0.001 + n_samples_per_prompt: 8 + rule_reward: true + verifier_function: ["base_acc"] + verifier_weight: [1.0] + verifier_parallel: 4 + verifier_timeout: 120 + use_tensorboard: true + actor_resource: + num_npus: 8 + +generate_config: + trust_remote_code: true + offload_train_optimizer: true + offload_train_grad: true + offload_train_param: true + + # 推理时的并行配置 + infer_tensor_parallel_size: 2 + infer_pipeline_parallel_size: 1 + infer_expert_parallel_size: 1 + + # vllm 模型相关设置 + max_num_seqs: 512 + max_model_len: 4096 + dtype: "bfloat16" + gpu_memory_utilization: 0.9 + num_scheduler_steps: 1 + + # 采样配置 + sampling_config: + logprobs: 1 + max_tokens: 2048 + top_p: 0.9 + top_k: 50 + min_p: 0.01 + temperature: 0.8 + detokenize: false diff --git a/docs/features/hybrid_actor.md b/docs/features/hybrid_actor.md index e4d9126c..077ac36f 100644 --- a/docs/features/hybrid_actor.md +++ b/docs/features/hybrid_actor.md @@ -70,22 +70,22 @@ generate_config: def initialize(self): # 初始化分布式环境 self.setup_distributed_rank() - + # 初始化训练态模型及卸载器 self.model, self.optimizer, self.opt_param_scheduler = self._build_model_optimizer() - self.megatron_offloader = MegatronOffLoader(self.optimizer, self.model) - + self.actor_offloader = MegatronOffLoader(self.model, self.optimizer) + # 在初始化推理态模型之前，首先卸载训练态的模型，这样才能让推理模型在初始化时正确计算KV Block的数量 if self.generate_config.offload_train_optimizer: - self.megatron_offloader.offload_optimizer() - if self.generate_config.offload_train_grad: - self.megatron_offloader.offload_grad() - if self.generate_config.offload_train_param: - self.megatron_offloader.offload_train_param() + self.actor_offloader.offload_optimizer() + if self.generate_config.offload_grad: + self.actor_offloader.offload_grad() + if self.generate_config.offload_param: + self.actor_offloader.offload_param() # 初始化推理态模型 self.inference_model = self._build_rollout() - + # 初始化 sharding_manager self.sharding_manager = self._build_sharding_manager() ... @@ -221,44 +221,44 @@ def generate_sequences(self): # mindspeed_rl/workers/resharding/megatron_sharding_manager.py class MegatronShardingManager: - + def reshard_to_train_mode(self): # 卸载推理态相关权重 self.inference_engine.offload_model_weights() self.offload_infer_params() torch.cuda.empty_cache() - + # 重新加载回训练态所需的内容 if self.optimizer_offload: self.megatron_offloader.onload_optimizer() if self.train_param_offload: - self.megatron_offloader.onload_train_param() + self.megatron_offloader.onload_param() if self.grad_offload: self.megatron_offloader.onload_grad() torch.cuda.empty_cache() def reshard_to_infer_mode(self): - + # 卸载训练态所需的相关参数 if self.optimizer_offload: self.megatron_offloader.offload_optimizer() if self.grad_offload: self.megatron_offloader.offload_grad() torch.cuda.empty_cache() - + # 训练态权重要在完成推理权重构建之后才能进行卸载 # 这里是为了对应初始化后第一次推理时，训练态权重不在显存上的情况 if self.train_param_offload: - self.megatron_offloader.onload_train_param() - + self.megatron_offloader.onload_param() + # 根据训练态和推理态的切分策略，完成推理态权重的构建 self.onload_infer_params() infer_params = self.vllm_weight_container.get_infer_params() - + # 开始推理前，将训练态权重进行卸载 if self.train_param_offload: - self.megatron_offloader.offload_train_param() - + self.megatron_offloader.offload_param() + # 将推理态权重从 weight_buffer 绑定到推理引擎上 self.inference_engine.sync_model_weights(infer_params, load_format='megatron') torch.cuda.empty_cache() diff --git a/mindspeed_rl/config_cls/rl_config.py b/mindspeed_rl/config_cls/rl_config.py index 650a5db1..994bfc4f 100644 --- a/mindspeed_rl/config_cls/rl_config.py +++ b/mindspeed_rl/config_cls/rl_config.py @@ -86,6 +86,7 @@ class RLConfig(BaseConfig): self.n_samples_per_prompt = 1 self.enable_sharding_validate = False self.tp_split_expert = False + self.update_micro_batch_size = None self.use_tensorboard = False self.use_wandb = False @@ -94,5 +95,6 @@ class RLConfig(BaseConfig): self.wandb_save_dir = "" self.blocking = False self.num_cpus_for_local_task = 1 + self.use_integrated_worker = False self.update(config_dict) diff --git a/mindspeed_rl/config_cls/validate_config.py b/mindspeed_rl/config_cls/validate_config.py index bba4ae96..00e400fa 100644 --- a/mindspeed_rl/config_cls/validate_config.py +++ b/mindspeed_rl/config_cls/validate_config.py @@ -1,8 +1,29 @@ # Copyright (c) 2025, HUAWEI CORPORATION. All rights reserved. import os +from mindspeed_rl.config_cls.rl_config import RLConfig +from mindspeed_rl.config_cls.megatron_config import MegatronConfig +from mindspeed_rl.config_cls.generate_config import GenerateConfig + + +def validate_rl_args( + actor_config: MegatronConfig, + ref_config: MegatronConfig, + reward_config: MegatronConfig, + rl_config: RLConfig, + generate_config: GenerateConfig + ): + + # 检查全共卡情况下参数设置 + if rl_config.use_integrated_worker: + if rl_config.reference_resource is not None: + raise ValueError( + f"reference_resource should not be set when use_integrated_worker mode is on.") + rl_config.reference_resource = rl_config.actor_resource + if rl_config.reward_resource is not None: + raise ValueError( + f" Reward model is not supported when use_integrated_worker mode is on.") -def validate_rl_args(actor_config, ref_config, reward_config, rl_config, generate_config): # 校验序列长度与模型最大长度 if generate_config.max_model_len < actor_config.seq_length: raise ValueError( @@ -102,10 +123,11 @@ def validate_rl_args(actor_config, ref_config, reward_config, rl_config, generat rl_config.n_samples_per_prompt, "Reference") - _validate_batch_ratio(reward_config.global_batch_size, - reward_config.micro_batch_size, - rl_config.n_samples_per_prompt, - "Reward") + if rl_config.reward_resource: + _validate_batch_ratio(reward_config.global_batch_size, + reward_config.micro_batch_size, + rl_config.n_samples_per_prompt, + "Reward") # 校验经验计数与全局批次关系 def _validate_experience_ratio(global_batch, experience_count, component): @@ -122,9 +144,10 @@ def validate_rl_args(actor_config, ref_config, reward_config, rl_config, generat rl_config.experience_count_ref, "Reference") - _validate_experience_ratio(reward_config.global_batch_size, - rl_config.experience_count_reward, - "Reward") + if rl_config.reward_resource: + _validate_experience_ratio(reward_config.global_batch_size, + rl_config.experience_count_reward, + "Reward") _validate_experience_ratio(reward_config.global_batch_size, rl_config.experience_count_rule_reward, diff --git a/mindspeed_rl/models/rule_verifier.py b/mindspeed_rl/models/rule_verifier.py index 482d16d5..3bf64d7f 100644 --- a/mindspeed_rl/models/rule_verifier.py +++ b/mindspeed_rl/models/rule_verifier.py @@ -112,7 +112,11 @@ def compute_verifier_score(batch, megatron_config, rl_config, ignore_token=-100) scores = (scores - scores.mean(dim=1, keepdim=True)) / (scores.std(dim=1, keepdim=True) + 1e-8) scores = scores.reshape(reward_index.shape) - metrics["timing/rule_reward"] = [round(time.time(), 4), round(start_time, 4)] + end_time = time.time() + metrics["timing/rule_reward"] = [round(end_time, 4), round(start_time, 4)] + metrics["start_time/rule_reward"] = [round(start_time, 4)] + metrics["end_time/rule_reward"] = [round(end_time, 4)] + return scores, metrics diff --git a/mindspeed_rl/trainer/grpo_trainer_hybrid.py b/mindspeed_rl/trainer/grpo_trainer_hybrid.py index daf77a56..079a3f30 100644 --- a/mindspeed_rl/trainer/grpo_trainer_hybrid.py +++ b/mindspeed_rl/trainer/grpo_trainer_hybrid.py @@ -14,7 +14,7 @@ from mindspeed_rl.trainer.utils.compute_utils import compute_advantage, compute_ from mindspeed_rl.workers.scheduler.launcher import RayActorGroup from mindspeed_rl.utils.loggers import Loggers from mindspeed_rl.utils.metrics import Metric -from mindspeed_rl.utils.utils import metrics_post_processing, compute_tps +from mindspeed_rl.utils.utils import metrics_post_processing, compute_tps, metrics_sort class RayGRPOTrainer(RayBaseTrainer): @@ -148,23 +148,23 @@ class RayGRPOTrainer(RayBaseTrainer): # generate sequences self.actor_worker.generate_sequences(blocking=self.blocking) - # compute reference log_prob - self.ref_worker.compute_log_prob(blocking=self.blocking) - # compute rm scores. for reward_worker in self.reward_list: if isinstance(reward_worker, RayActorGroup): reward_worker.compute_rm_score(blocking=self.blocking) else: - self.rule_reward_compute_rm_score(reward_worker, blocking=self.blocking) + self.rule_reward_compute_rm_score(reward_worker, blocking=False) # compute advantages, executed on the driver process - self.compute_advantage(blocking=self.blocking) + self.compute_advantage(blocking=False) + + # compute reference log_prob + self.ref_worker.compute_ref_log_prob(blocking=self.blocking) - self.actor_worker.wait_all_ref_objs_run_over() # compute old log_prob self.actor_worker.compute_log_prob(blocking=self.blocking) + self.actor_worker.wait_all_ref_objs_run_over() self.ref_worker.wait_all_ref_objs_run_over() for reward in self.reward_list: if hasattr(reward, 'wait_all_ref_objs_run_over'): @@ -180,11 +180,10 @@ class RayGRPOTrainer(RayBaseTrainer): metrics_result = ray.get(self.transfer_dock.get_metrics.remote()) metrics_result = metrics_post_processing(metrics_result) + metrics_result = metrics_sort(metrics_result, all_timer.last) tps = compute_tps(self.kwargs, grpo_data_metrics, self.global_batch_size, self.n_samples_per_prompt, all_timer.last) - - metrics.update(value=grpo_data_metrics) metrics.update(value=metrics_result) - metrics.update("timing/all", all_timer.last) + metrics.update(value=grpo_data_metrics) metrics.update("tokens/p/s", tps) iteration += 1 logger.info(metrics.metric, iteration, self.train_iters) diff --git a/mindspeed_rl/utils/utils.py b/mindspeed_rl/utils/utils.py index e703ca25..4a547283 100644 --- a/mindspeed_rl/utils/utils.py +++ b/mindspeed_rl/utils/utils.py @@ -181,7 +181,16 @@ def metrics_post_processing(metrics) -> Dict[str, Tensor]: new_metrics[key] = metrics.compute_max(key, value) - metrics.compute_min(key, value) else: new_metrics[key] = value - + elif "start_time" in key: + if isinstance(value, list): + new_metrics[key] = metrics.compute_min(key, value) + else: + new_metrics[key] = value + elif "end_time" in key: + if isinstance(value, list): + new_metrics[key] = metrics.compute_max(key, value) + else: + new_metrics[key] = value elif isinstance(value, list): new_metrics[key] = metrics.compute_mean(key, value) else: @@ -189,17 +198,54 @@ def metrics_post_processing(metrics) -> Dict[str, Tensor]: return new_metrics +def metrics_sort(metrics, time_all) -> Dict[str, Tensor]: + + old_log_p_end_time = metrics.pop('end_time/old_log_p', None) + + reference_start_time = metrics.pop('start_time/reference_model', None) + reference_end_time = metrics.pop('end_time/reference', None) + non_overlap_reference_model_time = max(reference_end_time - max(old_log_p_end_time, reference_start_time), 0) + + if "timing/rule_reward" in metrics.keys(): + reward_start_time = metrics.pop('start_time/rule_reward', None) + reward_end_time = metrics.pop('end_time/rule_reward', None) + non_overlap_rule_reward_time = max(reward_end_time - max(old_log_p_end_time, reward_start_time), 0) + metrics["timing/non_overlap_rule_reward"] = non_overlap_rule_reward_time + if "timing/reward_model" in metrics.keys(): + reward_start_time = metrics.pop('start_time/reward_model', None) + reward_end_time = metrics.pop('end_time/reward_model', None) + non_overlap_reward_model_time = max(reward_end_time - max(old_log_p_end_time, reward_start_time), 0) + metrics["timing/non_overlap_reward_model"] = non_overlap_reward_model_time + + + metrics["timing/non_overlap_reference_model"] = non_overlap_reference_model_time + metrics["timing/all"] = time_all + + sort_metrics = dict(sorted(metrics.items())) + custom_order = ['timing/all', 'timing/update', 'timing/rollout', 'timing/old_log_p', 'timing/reference_model', 'timing/non_overlap_reference_model'] + special_keys = ['timing/non_overlap_rule_reward', 'timing/non_overlap_reward_model', 'timing/rule_reward', 'timing/reward_model'] + keys_to_move = [key for key in sort_metrics.keys() if key in special_keys] + remaining_keys = [] + for key in sort_metrics: + if key not in custom_order and key not in special_keys: + remaining_keys.append(key) + new_order = custom_order + keys_to_move + remaining_keys + sorted_metric = {key: sort_metrics[key] for key in new_order} + return sorted_metric + + def compute_tps(compute_kwargs, metrics_result, gbs, n_samples, time_all): actor_resource = compute_kwargs.get('actor_resource', {}) reference_resource = compute_kwargs.get('reference_resource', {}) reward_resource = compute_kwargs.get('reward_resource', None) + actor_resource_only = compute_kwargs.get('use_integrated_worker', False) actor_npus = actor_resource.get('num_npus', 0) reference_npus = reference_resource.get('num_npus', 0) reward_npus = reward_resource.get('num_npus', 0) if reward_resource is not None else 0 - world_size = actor_npus + reference_npus + reward_npus + world_size = actor_npus + reference_npus + reward_npus if not actor_resource_only else actor_npus tps = (metrics_result['response_length/mean'] + metrics_result['prompt_length/mean']) * gbs * n_samples / world_size / time_all return tps diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index df4990c6..a7f2204c 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -21,10 +21,11 @@ from mindspeed_rl.workers.base_worker import BaseWorker from mindspeed_rl.workers.resharding.megatron_sharding_manager import MegatronShardingManager, MegatronOffLoader from mindspeed_rl.utils.utils import num_floating_point_operations from mindspeed_rl.utils.pad_process import remove_padding_and_split_to_list, truncate_rows +from mindspeed_rl.utils.compute import get_parallel_state +from mindspeed_rl.trainer.utils.parallel_state import is_pipeline_last_stage, get_tensor_model_parallel_rank -@ray.remote(resources={"NPU": 0.7}) -class ActorHybridWorker(BaseWorker): +class ActorHybridWorkerBase(BaseWorker): """ ActorHybridWorker class. This class implements the hybrid worker logic for training and inference. @@ -63,18 +64,18 @@ class ActorHybridWorker(BaseWorker): self.num_floating_point_operations_so_far = 0 self.actor_hybrid = None - self.megatron_offloader = None + self.actor_offloader = None def initialize(self): self.setup_distributed_rank() self.model, self.optimizer, self.opt_param_scheduler = self._build_model_optimizer() - self.megatron_offloader = MegatronOffLoader(self.optimizer, self.model) + self.actor_offloader = MegatronOffLoader(self.model, self.optimizer) if self.generate_config.offload_train_optimizer: - self.megatron_offloader.offload_optimizer() + self.actor_offloader.offload_optimizer() if self.generate_config.offload_train_grad: - self.megatron_offloader.offload_grad() + self.actor_offloader.offload_grad() if self.generate_config.offload_train_param: - self.megatron_offloader.offload_train_param() + self.actor_offloader.offload_param() self.inference_model = self._build_rollout() self.sharding_manager = self._build_sharding_manager() @@ -109,7 +110,8 @@ class ActorHybridWorker(BaseWorker): return self.args.consumed_train_samples def update(self, kl_ctrl=None): - start_time = time.time() + self.sharding_manager.enter_train_mode() + experience_consumer_stage = 'actor_train' experience_colums = ['responses', 'advantages', 'old_log_prob', 'ref_log_prob', 'input_ids', 'response_length', 'prompt_length'] @@ -121,10 +123,14 @@ class ActorHybridWorker(BaseWorker): learning_rate = param_group['lr'] ray.get(self.td.update_metrics.remote(key='grpo/lr', value=learning_rate)) + start_time_defined = False while self.all_consumed(experience_consumer_stage) > 0: batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_colums, experience_count, self.rl_config.n_samples_per_prompt, self.megatron_config.tensor_model_parallel_size) + if not start_time_defined: + start_time = time.time() + start_time_defined = True if batch_data and index: metrics = self.actor_hybrid.update_actor(batch_data, kl_ctrl) self.empty_cache() @@ -141,19 +147,22 @@ class ActorHybridWorker(BaseWorker): ) ) + self.sharding_manager.exit_train_mode() + def save_ckpt(self, iteration: int): self.save_checkpoint(iteration, self.model, self.optimizer, self.opt_param_scheduler, self.num_floating_point_operations_so_far) def generate_sequences(self): - start_time = time.time() + self.sharding_manager.enter_infer_mode() + experience_consumer_stage = 'actor_rollout' experience_colums = ['prompts', 'prompt_length'] experience_count = self.rl_config.experience_count_actor // self.generate_config.data_parallel_size - self.sharding_manager.reshard_to_infer_mode() pad_token_id = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod + start_time_defined = False while self.all_consumed(experience_consumer_stage) > 0: batch_data, index = self.dispatch_transfer_dock_data( experience_consumer_stage, @@ -163,6 +172,9 @@ class ActorHybridWorker(BaseWorker): tp_size=self.megatron_config.tensor_model_parallel_size, use_vllm=True ) + if not start_time_defined: + start_time = time.time() + start_time_defined = True if batch_data and index: indexes = list(range(0, experience_count * self.rl_config.n_samples_per_prompt, self.rl_config.n_samples_per_prompt)) @@ -193,26 +205,44 @@ class ActorHybridWorker(BaseWorker): 'input_ids': input_ids_list, 'response_length': responses_length } + self.collect_transfer_dock_data(outputs, index, self.rl_config.n_samples_per_prompt, use_vllm=True) + end_time = time.time() ray.get( self.td.update_metrics.remote( "timing/rollout", - value=[round(time.time(), 4), round(start_time, 4)], + value=[round(end_time, 4), round(start_time, 4)], cumulate=True ) ) - self.collect_transfer_dock_data(outputs, index, self.rl_config.n_samples_per_prompt, use_vllm=True) - self.sharding_manager.reshard_to_train_mode() - self.empty_cache() + generate_end_time = time.time() + parallel_state = get_parallel_state() + use_vllm = True + if is_pipeline_last_stage(parallel_state, use_vllm) and get_tensor_model_parallel_rank(parallel_state, use_vllm) == 0: + ray.get( + self.td.update_metrics.remote( + "end_time/generate", + value=[round(generate_end_time, 4)], + cumulate=True + ) + ) + + self.sharding_manager.exit_infer_mode() def compute_log_prob(self): + self.sharding_manager.enter_forward_mode() + experience_consumer_stage = 'actor_log_prob' experience_colums = ['input_ids', 'responses', 'response_length', 'prompt_length'] experience_count = self.rl_config.experience_count_actor // self.parallel_state.get_data_parallel_world_size() + start_time_defined = False while self.all_consumed(experience_consumer_stage) > 0: batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_colums, experience_count, self.rl_config.n_samples_per_prompt, tp_size=self.megatron_config.tensor_model_parallel_size) + if not start_time_defined: + start_time = time.time() + start_time_defined = True if batch_data and index: output, batch = self.actor_hybrid.compute_log_prob(batch_data) if self.parallel_state.is_pipeline_last_stage(ignore_virtual=True): @@ -222,6 +252,22 @@ class ActorHybridWorker(BaseWorker): log_probs = truncate_rows(log_probs, batch['response_length']) output = {'old_log_prob': log_probs} self.collect_transfer_dock_data(output, index, self.rl_config.n_samples_per_prompt) + end_time = time.time() + ray.get( + self.td.update_metrics.remote( + "timing/old_log_p", + value=[round(end_time, 4), round(start_time, 4)], + cumulate=True + ) + ) + ray.get( + self.td.update_metrics.remote( + "end_time/old_log_p", + value=[round(end_time, 4)], + cumulate=True + ) + ) + self.empty_cache() @@ -306,6 +352,11 @@ class ActorHybridWorker(BaseWorker): grad_offload=self.generate_config.offload_train_grad, train_param_offload=self.generate_config.offload_train_param, enable_validate=self.rl_config.enable_sharding_validate, - megatron_offloader=self.megatron_offloader + megatron_offloader=self.actor_offloader ) return sharding_manager + + +@ray.remote(resources={"NPU": 0.7}) +class ActorHybridWorker(ActorHybridWorkerBase): + pass \ No newline at end of file diff --git a/mindspeed_rl/workers/integrated_worker.py b/mindspeed_rl/workers/integrated_worker.py new file mode 100644 index 00000000..cc091943 --- /dev/null +++ b/mindspeed_rl/workers/integrated_worker.py @@ -0,0 +1,112 @@ +# Copyright (c) 2025, HUAWEI CORPORATION. All rights reserved. + +import dataclasses +from typing import Callable + +import ray +import torch + +from mindspeed_rl.config_cls.megatron_config import MegatronConfig +from mindspeed_rl.config_cls.rl_config import RLConfig +from mindspeed_rl.config_cls.generate_config import GenerateConfig +from mindspeed_rl.utils.tokenizer import BaseTokenizer +from mindspeed_rl.workers.resharding.megatron_sharding_manager import MegatronOffLoader + +from mindspeed_rl.workers.actor_hybrid_worker import ActorHybridWorkerBase +from mindspeed_rl.workers.reference_woker import ReferenceWorkerBase +from mindspeed_rl.workers.reward_woker import RewardWorkerBase +from mindspeed_rl.models.reference import Reference +from mindspeed_rl.models.reward import Reward + + +@ray.remote(resources={"NPU": 0.7}) +class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerBase): + """ + IntegratedWorker class. This class implements the integrated worker for the Actor, Reference and Reward Worker. + + Args: + megatron_config: MegatronConfig Configuration for Megatron-LM (e.g., model parallelism settings). + rl_config: RLConfig Configuration for reinforcement learning (e.g., PPO settings). + generate_config: GenerateConfig Configuration for generation/inference (e.g., vLLM settings). + model_provider: Callable Function to provide the model instance. + initialize_func: Callable Function to initialize the model and environment. + tokenizer: BaseTokenizer = None Object to retrieve the tokenizer. + get_megatron_module: Callable = megatron_module from get_megatron_module. + **kwargs: Additional parameters for base class argument passing. + """ + + def __init__( + self, + megatron_config: MegatronConfig, + rl_config: RLConfig, + generate_config: GenerateConfig, + model_provider: Callable, + initialize_func: Callable, + tokenizer: BaseTokenizer = None, + get_megatron_module: Callable = None, + **kwargs + ): + + # We use Actor as main worker, so only do init for Actor here. + ActorHybridWorkerBase.__init__( + self, + megatron_config, + rl_config, + generate_config, + model_provider=model_provider, + initialize_func=initialize_func, + tokenizer=tokenizer, + get_megatron_module=get_megatron_module, + **kwargs + ) + + self.update_micro_batch_size = rl_config.update_micro_batch_size + + self.reference = None + self.ref_model = None + self.ref_manager = None + + + def initialize(self): + + # Based on Actor + ActorHybridWorkerBase.initialize(self) + + # Add Reference + self.ref_model = self.get_model(self.model_provider, self.model_type, wrap_with_ddp=False) + self.load_checkpoint(self.ref_model, None, None) + self.ref_manager = MegatronOffLoader(self.ref_model, wrap_with_ddp=False) + self.ref_manager.offload_param() + self.reference = Reference( + self.ref_model, + beta=self.rl_config.beta, + mini_batch_size=self.rl_config.mini_batch_size, + epochs=self.rl_config.epochs, + shuffle_mini_batch=self.rl_config.shuffle_mini_batch, + generate_config=self.generate_config, + stage=self.megatron_config.stage, + forward_backward_func=self.forward_backward_func, + micro_batch_size=self.megatron_config.micro_batch_size + ) + + def compute_ref_log_prob(self): + self.ref_manager.onload_param() + ReferenceWorkerBase.compute_ref_log_prob(self) + self.ref_manager.offload_param() + + def update(self, kl_ctrl=None): + # set update mbs + update_mbs = self.update_micro_batch_size + mbs = self.actor_hybrid.train_actor.micro_batch_size + + from megatron.training import get_args + args = get_args() + + if update_mbs is not None: + self.actor_hybrid.train_actor.micro_batch_size = update_mbs + args.micro_batch_size = update_mbs + + ActorHybridWorkerBase.update(self, kl_ctrl) + + args.micro_batch_size = mbs + self.actor_hybrid.train_actor.micro_batch_size = mbs diff --git a/mindspeed_rl/workers/reference_woker.py b/mindspeed_rl/workers/reference_woker.py index 4fa50e4a..fd465a77 100644 --- a/mindspeed_rl/workers/reference_woker.py +++ b/mindspeed_rl/workers/reference_woker.py @@ -13,10 +13,11 @@ from mindspeed_rl.models.reference import Reference from mindspeed_rl.utils.pad_process import truncate_rows from mindspeed_rl.utils.tokenizer import BaseTokenizer from mindspeed_rl.workers.base_worker import BaseWorker +from mindspeed_rl.utils.compute import get_parallel_state +from mindspeed_rl.trainer.utils.parallel_state import is_pipeline_last_stage, get_tensor_model_parallel_rank -@ray.remote(resources={"NPU": 0.3}) -class ReferenceWorker(BaseWorker): +class ReferenceWorkerBase(BaseWorker): """ ReferenceWorker class. This class implements the worker logic for reference model inference. @@ -80,16 +81,26 @@ class ReferenceWorker(BaseWorker): def init_transfer_dock(self, td): self.td = td - def compute_log_prob(self): - start_time = time.time() + def compute_ref_log_prob(self): experience_consumer_stage = 'ref_log_prob' experience_columns = ['input_ids', 'responses', 'response_length', 'prompt_length'] experience_count = self.rl_config.experience_count_ref // self.parallel_state.get_data_parallel_world_size() + start_time_defined = False while self.all_consumed(experience_consumer_stage) > 0: batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, experience_count, self.rl_config.n_samples_per_prompt, tp_size=self.megatron_config.tensor_model_parallel_size) + if not start_time_defined: + start_time = time.time() + start_time_defined = True + ray.get( + self.td.update_metrics.remote( + "start_time/reference_model", + value=[round(start_time, 4)], + cumulate=True + ) + ) if batch_data and index: output, batch = self.reference.compute_log_prob(batch_data) @@ -99,13 +110,28 @@ class ReferenceWorker(BaseWorker): log_probs = log_probs.to(torch.float32) log_probs = truncate_rows(log_probs, batch['response_length']) output = {'ref_log_prob': log_probs} + self.collect_transfer_dock_data(output, index, self.rl_config.n_samples_per_prompt) + end_time = time.time() ray.get( self.td.update_metrics.remote( "timing/reference_model", - value=[round(time.time(), 4), round(start_time, 4)], + value=[round(end_time, 4), round(start_time, 4)], cumulate=True ) ) - self.collect_transfer_dock_data(output, index, self.rl_config.n_samples_per_prompt) - + parallel_state = get_parallel_state() + use_vllm = False + if is_pipeline_last_stage(parallel_state, use_vllm) and get_tensor_model_parallel_rank(parallel_state, use_vllm) == 0: + ref_end_time = time.time() + ray.get( + self.td.update_metrics.remote( + "end_time/reference", + value=[round(ref_end_time, 4)] + ) + ) self.empty_cache() + + +@ray.remote(resources={"NPU": 0.3}) +class ReferenceWorker(ReferenceWorkerBase): + pass \ No newline at end of file diff --git a/mindspeed_rl/workers/resharding/megatron_sharding_manager.py b/mindspeed_rl/workers/resharding/megatron_sharding_manager.py index d74fd0c1..c7016e40 100644 --- a/mindspeed_rl/workers/resharding/megatron_sharding_manager.py +++ b/mindspeed_rl/workers/resharding/megatron_sharding_manager.py @@ -29,23 +29,20 @@ from mindspeed_rl.workers.resharding.weight_adaptor import get_weight_adaptor class MegatronOffLoader: - def __init__( - self, - optimizer=None, - megatron_model=None - ): + def __init__(self, megatron_model=None, optimizer=None, wrap_with_ddp=True): self.optimizer = optimizer - self.train_model = megatron_model + self.model = megatron_model + self.wrap_with_ddp = wrap_with_ddp self.tensor_to_cpu_states_map = dict() def offload_grad(self): - for model_idx, model in enumerate(self.train_model): + for model in self.model: for buffer in chain(model.buffers, model.expert_parallel_buffers): self.swap_tensors_to_host(buffer.grad_data) def onload_grad(self): - for model_idx, model in enumerate(self.train_model): + for model in self.model: for buffer in chain(model.buffers, model.expert_parallel_buffers): self.swap_tensors_to_device(buffer.grad_data) @@ -73,15 +70,23 @@ class MegatronOffLoader: else: return data - def offload_train_param(self): - for model_idx, model in enumerate(self.train_model): - for buffer in chain(model.buffers, model.expert_parallel_buffers): - self.swap_tensors_to_host(buffer.param_data) + def offload_param(self): + if self.wrap_with_ddp: + for model in self.model: + for buffer in chain(model.buffers, model.expert_parallel_buffers): + self.swap_tensors_to_host(buffer.param_data) + else: + for item in self.model: + item.to('cpu') - def onload_train_param(self): - for model_idx, model in enumerate(self.train_model): - for buffer in chain(model.buffers, model.expert_parallel_buffers): - self.swap_tensors_to_device(buffer.param_data) + def onload_param(self): + if self.wrap_with_ddp: + for model in self.model: + for buffer in chain(model.buffers, model.expert_parallel_buffers): + self.swap_tensors_to_device(buffer.param_data) + else: + for item in self.model: + item.to(torch.cuda.current_device()) def swap_tensors_to_host(self, tensor): if tensor not in self.tensor_to_cpu_states_map: @@ -162,47 +167,106 @@ class MegatronShardingManager: self.inference_engine.offload_model_weights() self.megatron_offloader = megatron_offloader - def __enter__(self): - self.reshard_to_infer_mode() + def offload_infer_params(self): + infer_weight_buffers = self.vllm_weight_container.weight_buffers + for buffer in infer_weight_buffers: + buffer.offload() + + def onload_infer_params(self): + infer_weight_buffers = self.vllm_weight_container.weight_buffers + for buffer in infer_weight_buffers: + buffer.onload() + + def enter_infer_mode(self): + """ + Before: + Empty or with training param on NPU. + + After: + Empty. + + Process: + 1. onload training param if needed + 2. onload inference param + 3. do resharding + 4. offload training param + """ + if self.train_param_offload: + self.megatron_offloader.onload_param() + + self.onload_infer_params() + + infer_params = self.vllm_weight_container.get_infer_params() + + if self.train_param_offload: + self.megatron_offloader.offload_param() + + self.inference_engine.sync_model_weights(infer_params, load_format='megatron') + torch.cuda.empty_cache() + + def exit_infer_mode(self): + """ + Before: + With inference param on NPU. - def __exit__(self, exc_type, exc_value, traceback): - self.reshard_to_train_mode() + After: + Empty. - def reshard_to_train_mode(self): + Process: + 1. offload inference param + """ self.inference_engine.offload_model_weights() self.offload_infer_params() torch.cuda.empty_cache() + + def enter_forward_mode(self): + """ + Before: + Empty. + + After: + With training param on NPU. + + Process: + 1. onload training param + """ + if self.train_param_offload: + self.megatron_offloader.onload_param() + torch.cuda.empty_cache() + + def enter_train_mode(self): + """ + Before: + With training param on NPU. + + After: + With training param, optimizer and grad on NPU. + + Process: + 1. onload training optimizer + 2. onload training grad + """ if self.optimizer_offload: self.megatron_offloader.onload_optimizer() - if self.train_param_offload: - self.megatron_offloader.onload_train_param() if self.grad_offload: self.megatron_offloader.onload_grad() - # add empty cache after each compute torch.cuda.empty_cache() - def reshard_to_infer_mode(self): + def exit_train_mode(self): + """ + Before: + With training param, optimizer and grad on NPU. + + After: + With training param on NPU. + + Process: + 1. offload training optimizer + 2. offload training grad + """ if self.optimizer_offload: self.megatron_offloader.offload_optimizer() if self.grad_offload: self.megatron_offloader.offload_grad() torch.cuda.empty_cache() - if self.train_param_offload: - self.megatron_offloader.onload_train_param() - self.onload_infer_params() - infer_params = self.vllm_weight_container.get_infer_params() - if self.train_param_offload: - self.megatron_offloader.offload_train_param() - self.inference_engine.sync_model_weights(infer_params, load_format='megatron') - torch.cuda.empty_cache() - - def offload_infer_params(self): - infer_weight_buffers = self.vllm_weight_container.weight_buffers - for buffer in infer_weight_buffers: - buffer.offload() - - def onload_infer_params(self): - infer_weight_buffers = self.vllm_weight_container.weight_buffers - for buffer in infer_weight_buffers: - buffer.onload() diff --git a/mindspeed_rl/workers/reward_woker.py b/mindspeed_rl/workers/reward_woker.py index ab7fc23f..2948d8ea 100644 --- a/mindspeed_rl/workers/reward_woker.py +++ b/mindspeed_rl/workers/reward_woker.py @@ -13,10 +13,11 @@ from mindspeed_rl.models.reward import Reward from mindspeed_rl.trainer.utils.compute_utils import get_last_reward from mindspeed_rl.utils.tokenizer import BaseTokenizer from mindspeed_rl.workers.base_worker import BaseWorker +from mindspeed_rl.utils.compute import get_parallel_state +from mindspeed_rl.trainer.utils.parallel_state import is_pipeline_last_stage, get_tensor_model_parallel_rank -@ray.remote(resources={"NPU": 0.1}) -class RewardWorker(BaseWorker): +class RewardWorkerBase(BaseWorker): """ RewardWorker class. This class implements the worker logic for reward model training and inference. @@ -77,16 +78,26 @@ class RewardWorker(BaseWorker): self.td = td def compute_rm_score(self): - start_time = time.time() experience_consumer_stage = 'reward_scores' experience_columns = ['input_ids', 'prompt_length', "responses", "response_length", *self.megatron_config.dataset_additional_keys] experience_count = self.rl_config.experience_count_reward // self.parallel_state.get_data_parallel_world_size() + start_time_defined = False while not ray.get(self.td.all_consumed.remote(experience_consumer_stage)): batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, experience_count, self.rl_config.n_samples_per_prompt, tp_size=self.megatron_config.tensor_model_parallel_size) + if not start_time_defined: + start_time = time.time() + start_time_defined = True + ray.get( + self.td.update_metrics.remote( + "start_time/reward_model", + value=[round(start_time, 4)], + cumulate=True + ) + ) if batch_data and index: output, batch = self.reward.compute_rm_score(batch_data) if self.parallel_state.is_pipeline_last_stage(): @@ -98,12 +109,28 @@ class RewardWorker(BaseWorker): n_sample_batch=self.rl_config.n_samples_per_prompt ) output = {'rm_scores': rm_score, 'token_level_rewards': last_rewards} - ray.get( - self.td.update_metrics.remote( - "timing/reward_model", - value=[round(time.time(), 4), round(start_time, 4)], - cumulate=True - ) - ) self.collect_transfer_dock_data(output, index, self.rl_config.n_samples_per_prompt) + end_time = time.time() + ray.get( + self.td.update_metrics.remote( + "timing/reward_model", + value=[round(end_time, 4), round(start_time, 4)], + cumulate=True + ) + ) + parallel_state = get_parallel_state() + use_vllm = False + if is_pipeline_last_stage(parallel_state, use_vllm) and get_tensor_model_parallel_rank(parallel_state, use_vllm) == 0: + rwd_end_time = time.time() + ray.get( + self.td.update_metrics.remote( + "end_time/reward_model", + value=[round(rwd_end_time, 4)] + ) + ) self.empty_cache() + + +@ray.remote(resources={"NPU": 0.1}) +class RewardWorker(RewardWorkerBase): + pass \ No newline at end of file diff --git a/mindspeed_rl/workers/scheduler/launcher.py b/mindspeed_rl/workers/scheduler/launcher.py index ea9c4e80..2b6d3099 100644 --- a/mindspeed_rl/workers/scheduler/launcher.py +++ b/mindspeed_rl/workers/scheduler/launcher.py @@ -35,12 +35,16 @@ from mindspeed_rl.workers.base_worker import BaseWorker from mindspeed_rl.workers.actor_hybrid_worker import ActorHybridWorker from mindspeed_rl.workers.reference_woker import ReferenceWorker from mindspeed_rl.workers.reward_woker import RewardWorker +from mindspeed_rl.workers.integrated_worker import IntegratedWorker def get_rl_resource_by_worker_type(rl_config: RLConfig, worker: Type[BaseWorker]): if (worker.__ray_actor_class__.__name__ == ActorHybridWorker.__ray_actor_class__.__name__): return rl_config.actor_resource + elif (worker.__ray_actor_class__.__name__ == + IntegratedWorker.__ray_actor_class__.__name__): + return rl_config.actor_resource elif (worker.__ray_actor_class__.__name__ == RewardWorker.__ray_actor_class__.__name__): return rl_config.reward_resource @@ -264,6 +268,12 @@ class RayActorGroup: if blocking: ray.get(self.temp_actor_ref_objs) + def compute_ref_log_prob(self, blocking=False): + for actor in self.actor_handlers: + self.temp_actor_ref_objs.append(actor.compute_ref_log_prob.remote()) + if blocking: + ray.get(self.temp_actor_ref_objs) + def compute_rm_score(self, blocking=False): for actor in self.actor_handlers: self.temp_actor_ref_objs.append(actor.compute_rm_score.remote()) diff --git a/tests/st/resharding/test_resharding.py b/tests/st/resharding/test_resharding.py index 670a9d26..676d4131 100644 --- a/tests/st/resharding/test_resharding.py +++ b/tests/st/resharding/test_resharding.py @@ -317,7 +317,7 @@ class TestActor(): trust_remote_code=True, megatron_config=megatron_config ) - self.megatron_offloader = MegatronOffLoader(self.optimizer, self.model) + self.megatron_offloader = MegatronOffLoader(self.model, self.optimizer) self.sharding_manager = MegatronShardingManager( megatron_model=self.model, model_config=model_config_mock, @@ -342,9 +342,9 @@ class TestActor(): tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_path) input_ids = tokenizer(prompts, padding=True, truncation=True, return_tensors="pt")["input_ids"].tolist() - self.sharding_manager.reshard_to_infer_mode() + self.sharding_manager.enter_infer_mode() outputs = self.inference_engine.generate_sequences(idx_list=input_ids)[0] - self.sharding_manager.reshard_to_train_mode() + self.sharding_manager.exit_infer_mode() rank = torch.distributed.get_rank() for output in outputs: -- Gitee From e99fea1d6321443cf09b5407320eb4cc776610c3 Mon Sep 17 00:00:00 2001 From: yuqi Date: Wed, 9 Apr 2025 07:54:40 +0000 Subject: [PATCH 02/25] =?UTF-8?q?!179=20=E3=80=90Bugfix=E3=80=91=E5=A2=9E?= =?UTF-8?q?=E5=8A=A0Prompt=20Dataset=E7=9A=84=5F=5Flen=5F=5F=E5=87=BD?= =?UTF-8?q?=E6=95=B0=20Merge=20pull=20request=20!179=20from=20yuqi/len=5Ff?= =?UTF-8?q?ix2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mindspeed_rl/datasets/prompt_dataset.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/mindspeed_rl/datasets/prompt_dataset.py b/mindspeed_rl/datasets/prompt_dataset.py index 283605fd..346996f1 100644 --- a/mindspeed_rl/datasets/prompt_dataset.py +++ b/mindspeed_rl/datasets/prompt_dataset.py @@ -60,6 +60,9 @@ class PromptDataset(BaseDataset): super().__init__(self.res_dataset, dataset_type) + def __len__(self): + return len(self.shuffle_index) + def __getitem__(self, index): doc_idx = self.shuffle_index[index] -- Gitee From dc540475eb9fc4c973afe8f74e333b843543a246 Mon Sep 17 00:00:00 2001 From: liuxinyang <292384718@qq.com> Date: Fri, 11 Apr 2025 08:02:55 +0000 Subject: [PATCH 03/25] =?UTF-8?q?!184=20resharding=20=E8=80=97=E6=97=B6?= =?UTF-8?q?=E4=BC=98=E5=8C=96=20Merge=20pull=20request=20!184=20from=20liu?= =?UTF-8?q?xinyang/dev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../models/base/base_training_engine.py | 2 +- mindspeed_rl/workers/actor_hybrid_worker.py | 4 +- .../resharding/megatron_sharding_manager.py | 30 ++++++------ .../workers/resharding/memory_buffer.py | 17 +++++-- .../resharding/vllm_weight_container.py | 47 ------------------- mindspeed_rl/workers/reward_woker.py | 2 +- 6 files changed, 34 insertions(+), 68 deletions(-) diff --git a/mindspeed_rl/models/base/base_training_engine.py b/mindspeed_rl/models/base/base_training_engine.py index e72a1a6d..b374ca84 100644 --- a/mindspeed_rl/models/base/base_training_engine.py +++ b/mindspeed_rl/models/base/base_training_engine.py @@ -182,7 +182,7 @@ class BaseTrainingEngine(ABC): append_to_dict(metrics, metric) # append the metric from this micro-batch to global metrics. # add empty cache after each compute - torch.cuda.empty_cache() + # torch.cuda.empty_cache() grad_norm = sum(grad_norm_list) / len(grad_norm_list) metrics["grad_norm"] = grad_norm_list return metrics diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index a7f2204c..96987d07 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -133,7 +133,7 @@ class ActorHybridWorkerBase(BaseWorker): start_time_defined = True if batch_data and index: metrics = self.actor_hybrid.update_actor(batch_data, kl_ctrl) - self.empty_cache() + # self.empty_cache() self.args.consumed_train_samples += self.megatron_config.global_batch_size // self.rl_config.n_samples_per_prompt self.num_floating_point_operations_so_far += num_floating_point_operations(self.args, self.megatron_config.global_batch_size) @@ -269,7 +269,7 @@ class ActorHybridWorkerBase(BaseWorker): ) - self.empty_cache() + # self.empty_cache() def _get_megatron_optimizer( self, diff --git a/mindspeed_rl/workers/resharding/megatron_sharding_manager.py b/mindspeed_rl/workers/resharding/megatron_sharding_manager.py index c7016e40..b7e200b6 100644 --- a/mindspeed_rl/workers/resharding/megatron_sharding_manager.py +++ b/mindspeed_rl/workers/resharding/megatron_sharding_manager.py @@ -39,12 +39,12 @@ class MegatronOffLoader: def offload_grad(self): for model in self.model: for buffer in chain(model.buffers, model.expert_parallel_buffers): - self.swap_tensors_to_host(buffer.grad_data) + self.swap_tensors_to_host(buffer.grad_data, copy_data=False) def onload_grad(self): for model in self.model: for buffer in chain(model.buffers, model.expert_parallel_buffers): - self.swap_tensors_to_device(buffer.grad_data) + self.swap_tensors_to_device(buffer.grad_data, copy_data=False) def offload_optimizer(self): for param_group in self.optimizer.optimizer.param_groups: @@ -88,19 +88,21 @@ class MegatronOffLoader: for item in self.model: item.to(torch.cuda.current_device()) - def swap_tensors_to_host(self, tensor): + def swap_tensors_to_host(self, tensor, copy_data=True): if tensor not in self.tensor_to_cpu_states_map: self.tensor_to_cpu_states_map[tensor] = torch.empty_like(tensor, device='cpu') if tensor.storage().size() != 0: - cpu_state = self.tensor_to_cpu_states_map[tensor] - cpu_state.copy_(tensor, non_blocking=False) + if copy_data: + cpu_state = self.tensor_to_cpu_states_map[tensor] + cpu_state.copy_(tensor, non_blocking=False) tensor.storage().resize_(0) - def swap_tensors_to_device(self, tensor): + def swap_tensors_to_device(self, tensor, copy_data=True): if tensor.storage().size() == 0: cpu_state = self.tensor_to_cpu_states_map[tensor] tensor.storage().resize_(cpu_state.storage().size()) - tensor.copy_(cpu_state, non_blocking=False) + if copy_data: + tensor.copy_(cpu_state, non_blocking=False) class MegatronShardingManager: @@ -170,12 +172,12 @@ class MegatronShardingManager: def offload_infer_params(self): infer_weight_buffers = self.vllm_weight_container.weight_buffers for buffer in infer_weight_buffers: - buffer.offload() + buffer.destroy() def onload_infer_params(self): infer_weight_buffers = self.vllm_weight_container.weight_buffers for buffer in infer_weight_buffers: - buffer.onload() + buffer.rebuild() def enter_infer_mode(self): """ @@ -202,7 +204,7 @@ class MegatronShardingManager: self.megatron_offloader.offload_param() self.inference_engine.sync_model_weights(infer_params, load_format='megatron') - torch.cuda.empty_cache() + # torch.cuda.empty_cache() def exit_infer_mode(self): """ @@ -217,7 +219,7 @@ class MegatronShardingManager: """ self.inference_engine.offload_model_weights() self.offload_infer_params() - torch.cuda.empty_cache() + # torch.cuda.empty_cache() def enter_forward_mode(self): """ @@ -232,7 +234,7 @@ class MegatronShardingManager: """ if self.train_param_offload: self.megatron_offloader.onload_param() - torch.cuda.empty_cache() + # torch.cuda.empty_cache() def enter_train_mode(self): """ @@ -250,7 +252,7 @@ class MegatronShardingManager: self.megatron_offloader.onload_optimizer() if self.grad_offload: self.megatron_offloader.onload_grad() - torch.cuda.empty_cache() + # torch.cuda.empty_cache() def exit_train_mode(self): """ @@ -268,5 +270,5 @@ class MegatronShardingManager: self.megatron_offloader.offload_optimizer() if self.grad_offload: self.megatron_offloader.offload_grad() - torch.cuda.empty_cache() + # torch.cuda.empty_cache() diff --git a/mindspeed_rl/workers/resharding/memory_buffer.py b/mindspeed_rl/workers/resharding/memory_buffer.py index 830c1b5c..022a9469 100644 --- a/mindspeed_rl/workers/resharding/memory_buffer.py +++ b/mindspeed_rl/workers/resharding/memory_buffer.py @@ -33,7 +33,8 @@ class MemoryBuffer: self.numel_padded = numel_padded self.dtype = dtype # Create a memory buffer with padding for alignment (e.g., 128-bit alignment) - self.data = torch.zeros(self.numel_padded, dtype=self.dtype, device='cpu', requires_grad=False) + self.data = torch.zeros(self.numel_padded, dtype=self.dtype, + device=torch.cuda.current_device(), requires_grad=False) self.tensor_indices = {} # Stores the mapping of parameter names to their position in the buffer def zero(self): @@ -137,7 +138,8 @@ class ModelWeightBuffer: self.get_weight_buffer_meta = get_weight_buffer_meta self.weight_buffer_meta = self.get_weight_buffer_meta(self.model, weight_names) self.weight_names = list(self.weight_buffer_meta.keys()) - self.memory_buffers = build_memory_buffer(self.weight_buffer_meta) + self.memory_buffers = None + # self.memory_buffers = build_memory_buffer(self.weight_buffer_meta) def __getitem__(self, weight_name: str) -> torch.Tensor: return self.get_weight_by_name(weight_name) @@ -156,4 +158,13 @@ class ModelWeightBuffer: def onload(self): for memory_buffer in self.memory_buffers.values(): - memory_buffer.data = memory_buffer.data.to(torch.cuda.current_device(), non_blocking=False) \ No newline at end of file + memory_buffer.data = memory_buffer.data.to(torch.cuda.current_device(), non_blocking=False) + + def destroy(self): + for memory_buffer in self.memory_buffers.values(): + memory_buffer = None + self.memory_buffers = None + + def rebuild(self): + if self.memory_buffers is None: + self.memory_buffers = build_memory_buffer(self.weight_buffer_meta) \ No newline at end of file diff --git a/mindspeed_rl/workers/resharding/vllm_weight_container.py b/mindspeed_rl/workers/resharding/vllm_weight_container.py index 4bac7fa7..a783e906 100644 --- a/mindspeed_rl/workers/resharding/vllm_weight_container.py +++ b/mindspeed_rl/workers/resharding/vllm_weight_container.py @@ -161,53 +161,6 @@ class MegatronStyleVllmWeightContainer: raise ValueError("num_layers % pp_size == 0, please specify num_layer_list") return [self._num_hidden_layers // self._pp_size for _ in range(self._pp_size)] - def _get_weight_names_per_pp(self): - end_layer = self.model_config.num_hidden_layers - 1 - - def get_weight_names_in_range(layer_range, names: list, layer_name='layers') -> list: - """ - Extract weights in a given range and also include the weights before and after the range as needed. - """ - start, end = layer_range - last_layer_index = end_layer - names_in_range = [] - - # add names before decoder layers - if start == 0: - for name in names: - if layer_name not in name: - names_in_range.append(name) - else: - break - - for name in names: - # Extract layer number from weight - match = re.match(r'.*\.layers\.(\d+)', name) - if match: - layer_num = int(match.group(1)) - if start <= layer_num <= end: - names_in_range.append(name) - - # add names after decode layers - if end == last_layer_index: - for name in reversed(names): - if layer_name not in name: - names_in_range.append(name) - else: - break - return names_in_range - - pp_size = self._pp_size - vllm_names = list(dict(self.vllm_model.named_parameters()).keys()) - num_layers = self.model_config.num_hidden_layers - pp_layers_range = [] - start_layer = 0 - for layers_in_pp_rank in self._num_layer_list: - pp_layers_range.append((start_layer, start_layer + layers_in_pp_rank - 1)) - start_layer += layers_in_pp_rank - weight_names_per_pp = [get_weight_names_in_range(layer_range, vllm_names) for layer_range in pp_layers_range] - return weight_names_per_pp - def _unwrap_megatron_model(self, model): """ Remove consecutive 'module.' prefixes from the model based on the state_dict's first key. diff --git a/mindspeed_rl/workers/reward_woker.py b/mindspeed_rl/workers/reward_woker.py index 2948d8ea..5d544bcf 100644 --- a/mindspeed_rl/workers/reward_woker.py +++ b/mindspeed_rl/workers/reward_woker.py @@ -128,7 +128,7 @@ class RewardWorkerBase(BaseWorker): value=[round(rwd_end_time, 4)] ) ) - self.empty_cache() + # self.empty_cache() @ray.remote(resources={"NPU": 0.1}) -- Gitee From baa2a3e5d034cd278f582d5ccf4ba5924b3e3d6e Mon Sep 17 00:00:00 2001 From: TJJ Date: Sat, 12 Apr 2025 09:13:15 +0000 Subject: [PATCH 04/25] !195 requirement Merge pull request !195 from TJJ/dev --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 76f10561..49d9a910 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,7 +9,7 @@ ray==2.42.1 regex sympy tensorboard -tensordict +tensordict==0.1.2 transformers==4.48.2 word2number wandb -- Gitee From f9622fb9931b4ad59e582203e851b356c404f391 Mon Sep 17 00:00:00 2001 From: yuqi Date: Tue, 15 Apr 2025 02:11:13 +0000 Subject: [PATCH 05/25] =?UTF-8?q?!192=20=E4=BF=AE=E5=A4=8DSFT=20Trainer=20?= =?UTF-8?q?Bug=20Merge=20pull=20request=20!192=20from=20yuqi/sft=5Ffix2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mindspeed_rl/trainer/sft_trainer.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/mindspeed_rl/trainer/sft_trainer.py b/mindspeed_rl/trainer/sft_trainer.py index a2a1616a..5d8741fb 100644 --- a/mindspeed_rl/trainer/sft_trainer.py +++ b/mindspeed_rl/trainer/sft_trainer.py @@ -70,20 +70,20 @@ class SFTTrainer(ABC): keys += ['position_ids'] data_type = torch.int64 - if (not self.parallel_state.is_pipeline_first_stage()) and ( - not self.parallel_state.is_pipeline_last_stage()): - tokens, attention_mask = get_finetune_data_on_this_tp_rank(data_iterator, - self.parallel_state, - self.args.reset_attention_mask, - self.args.tokenizer_padding_side) + if (not self.parallel_state.is_pipeline_first_stage()) and (not self.parallel_state.is_pipeline_last_stage()): if self.args.variable_seq_lengths and self.args.pipeline_model_parallel_size > 2: + tokens, attention_mask = get_finetune_data_on_this_tp_rank(data_iterator, + self.parallel_state, + self.args.reset_position_ids, + self.args.tokenizer_padding_side) return tokens, None, None, attention_mask, None else: + # Broadcast data. + data_b = broadcast_data(keys, next(data_iterator), data_type) if self.args.reset_position_ids: - # Broadcast data. - data_b = broadcast_data(keys, next(data_iterator), data_type, - self.parallel_state) self.generate_seq_len_fun(data_b) + attention_mask_1d = data_b.get('attention_mask').long() + attention_mask = get_tune_attention_mask(attention_mask_1d) batch = {'attention_mask': attention_mask} batch = self.get_batch_on_this_cp_rank(batch) return None, None, None, batch['attention_mask'], None -- Gitee From e646805cf17dd9588410a727b5b9fb6d9bb88fa3 Mon Sep 17 00:00:00 2001 From: sunjianping Date: Wed, 16 Apr 2025 01:28:48 +0000 Subject: [PATCH 06/25] =?UTF-8?q?!197=20fix:=20=E5=B0=86ray=20worker?= =?UTF-8?q?=E7=9A=84placement=20group=E8=B5=84=E6=BA=90=E5=88=86=E9=85=8D?= =?UTF-8?q?=E7=9A=84cpu=E6=A0=B8=E6=95=B0=E4=BF=AE=E6=94=B9=E4=B8=BA?= =?UTF-8?q?=E9=85=8D=E7=BD=AE=E9=A1=B9=E6=8E=A7=E5=88=B6=20Merge=20pull=20?= =?UTF-8?q?request=20!197=20from=20sunjianping/dev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- configs/grpo_trainer_qwen25_32b.yaml | 1 + configs/grpo_trainer_qwen25_7b.yaml | 1 + docs/solutions/r1_zero_qwen25_32b.md | 1 + docs/solutions/r1_zero_qwen25_7b.md | 1 + mindspeed_rl/config_cls/rl_config.py | 2 ++ mindspeed_rl/workers/scheduler/launcher.py | 12 +++++------- 6 files changed, 11 insertions(+), 7 deletions(-) diff --git a/configs/grpo_trainer_qwen25_32b.yaml b/configs/grpo_trainer_qwen25_32b.yaml index 5362787a..aaf7194e 100644 --- a/configs/grpo_trainer_qwen25_32b.yaml +++ b/configs/grpo_trainer_qwen25_32b.yaml @@ -91,6 +91,7 @@ rl_config: verifier_weight: [1.0] verifier_parallel: 4 num_cpus_for_local_task: 1.0 + num_cpus_for_placement_group: 8 actor_resource: num_npus: 16 reference_resource: diff --git a/configs/grpo_trainer_qwen25_7b.yaml b/configs/grpo_trainer_qwen25_7b.yaml index 74bafee3..e6437d7e 100644 --- a/configs/grpo_trainer_qwen25_7b.yaml +++ b/configs/grpo_trainer_qwen25_7b.yaml @@ -89,6 +89,7 @@ rl_config: n_samples_per_prompt: 2 rule_reward: false num_cpus_for_local_task: 1.0 + num_cpus_for_placement_group: 8 actor_resource: num_npus: 4 reference_resource: diff --git a/docs/solutions/r1_zero_qwen25_32b.md b/docs/solutions/r1_zero_qwen25_32b.md index d85ee88b..c2ad5121 100644 --- a/docs/solutions/r1_zero_qwen25_32b.md +++ b/docs/solutions/r1_zero_qwen25_32b.md @@ -159,6 +159,7 @@ rl_config: verifier_function: ["acc", "strict_format"] <-- 规则奖励模型方法 verifier_weight: [1.0, 1.0] <-- 规则奖励模型权重 use_tensorboard: true <-- 开启tensorboard日志功能 + num_cpus_for_placement_group: 8 <-- 给每个worker的ray placement分配cpu核数 actor_resource: <-- actor worker资源分配 num_npus: 16 reference_resource: <-- ref worker资源分配 diff --git a/docs/solutions/r1_zero_qwen25_7b.md b/docs/solutions/r1_zero_qwen25_7b.md index 9d1f2dec..88555381 100644 --- a/docs/solutions/r1_zero_qwen25_7b.md +++ b/docs/solutions/r1_zero_qwen25_7b.md @@ -157,6 +157,7 @@ rl_config: verifier_function: ["base_acc"] <-- 规则奖励模型方法 verifier_weight: [1.0] <-- 规则奖励模型权重 use_tensorboard: true <-- 开启tensorboard日志功能 + num_cpus_for_placement_group: 8 <-- 给每个worker的ray placement分配cpu核数 actor_resource: <-- actor worker资源分配 num_npus: 8 reference_resource: <-- ref worker资源分配 diff --git a/mindspeed_rl/config_cls/rl_config.py b/mindspeed_rl/config_cls/rl_config.py index 994bfc4f..b743b40f 100644 --- a/mindspeed_rl/config_cls/rl_config.py +++ b/mindspeed_rl/config_cls/rl_config.py @@ -46,6 +46,7 @@ class RLConfig(BaseConfig): wandb_save_dir: Path to save the wandb results locally. (default: "") blocking: Whether to enable blocking mode (default: False) num_cpus_for_local_task: Number of CPUs for local ray task (default: 1) + num_cpus_for_placement_group: Number of CPUs for ray worker placement group # Default values can still be defined if no config is provided ''' @@ -95,6 +96,7 @@ class RLConfig(BaseConfig): self.wandb_save_dir = "" self.blocking = False self.num_cpus_for_local_task = 1 + self.num_cpus_for_placement_group = 8 self.use_integrated_worker = False self.update(config_dict) diff --git a/mindspeed_rl/workers/scheduler/launcher.py b/mindspeed_rl/workers/scheduler/launcher.py index 2b6d3099..ceaed6fb 100644 --- a/mindspeed_rl/workers/scheduler/launcher.py +++ b/mindspeed_rl/workers/scheduler/launcher.py @@ -78,7 +78,7 @@ def get_device_information(num_npus: int) \ (num_npus + num_devices_per_node - 1) // num_devices_per_node) -def construct_placement_groups(num_npus, num_devices_per_node, num_nodes) \ +def construct_placement_groups(num_npus, num_cpus, num_devices_per_node, num_nodes) \ -> List[PlacementGroup]: """ 构造ray placement group @@ -87,18 +87,15 @@ def construct_placement_groups(num_npus, num_devices_per_node, num_nodes) \ 1 基于节点连续分配资源 2 共置情形，相同rank索引使用相同placement group索引 3 STRICT_PACK部署策略，强制一个placement group部署在同一节点上 - 4 默认运行环境CPU有192核，NPU卡数量为8/16，允许NPU:CPU为1:8 """ placement_groups = [] - # default cpu 192 cores, npu 8/16 - default_cpu_per_npu = 8 for index in range(num_nodes): if (num_npus % num_devices_per_node != 0 and index == num_nodes - 1): - bundles = [{"NPU": 1, "CPU": default_cpu_per_npu} + bundles = [{"NPU": 1, "CPU": num_cpus} for _ in range(num_npus % num_devices_per_node)] else: - bundles = [{"NPU": 1, "CPU": default_cpu_per_npu} + bundles = [{"NPU": 1, "CPU": num_cpus} for _ in range(num_devices_per_node)] placement_group = ray.util.placement_group(bundles, strategy="STRICT_PACK") @@ -180,7 +177,8 @@ class RayActorGroup: def get_placement_group(self, placement_group: PlacementGroup = None) -> PlacementGroup: if placement_group is not None: return placement_group - return construct_placement_groups(self.num_npus, self.num_devices_per_node, self.num_nodes) + return construct_placement_groups(self.num_npus, self.rl_config.num_cpus_for_placement_group, + self.num_devices_per_node, self.num_nodes) def create_actor_handlers(self, param: ActorHandlerParams) \ -> ray.actor.ActorHandle: -- Gitee From 31b0682dea089406dff1ba692944ebc5663a36e9 Mon Sep 17 00:00:00 2001 From: liuxinyang <292384718@qq.com> Date: Fri, 18 Apr 2025 02:53:57 +0000 Subject: [PATCH 07/25] !207 add kwai reward Merge pull request !207 from liuxinyang/dev --- mindspeed_rl/models/rule_verifier.py | 10 +- .../evaluation_utils/code_util/__init__.py | 150 ++++ .../code_util/testing_util.py | 752 ++++++++++++++++++ .../utils/evaluation_utils/code_util/utils.py | 45 ++ .../evaluation_utils/math_util/__init__.py | 450 +++++++++++ .../evaluation_utils/math_util/grader.py | 388 +++++++++ .../math_util/math_normalize.py | 163 ++++ mindspeed_rl/utils/kwai.py | 237 ++++++ 8 files changed, 2193 insertions(+), 2 deletions(-) create mode 100644 mindspeed_rl/utils/evaluation_utils/code_util/__init__.py create mode 100644 mindspeed_rl/utils/evaluation_utils/code_util/testing_util.py create mode 100644 mindspeed_rl/utils/evaluation_utils/code_util/utils.py create mode 100644 mindspeed_rl/utils/evaluation_utils/math_util/__init__.py create mode 100644 mindspeed_rl/utils/evaluation_utils/math_util/grader.py create mode 100644 mindspeed_rl/utils/evaluation_utils/math_util/math_normalize.py create mode 100644 mindspeed_rl/utils/kwai.py diff --git a/mindspeed_rl/models/rule_verifier.py b/mindspeed_rl/models/rule_verifier.py index 3bf64d7f..3af03287 100644 --- a/mindspeed_rl/models/rule_verifier.py +++ b/mindspeed_rl/models/rule_verifier.py @@ -9,6 +9,7 @@ from transformers import AutoTokenizer from mindspeed_rl.utils.loggers import Loggers from mindspeed_rl.utils.math_eval_toolkit.grader import math_equal from mindspeed_rl.utils.math_eval_toolkit.parser import extract_answer +from mindspeed_rl.utils.kwai import compute_score as kwai_score logger = Loggers("Rule verify") @@ -142,7 +143,8 @@ def verifier(responses, data, config, **kwargs): "format": format_reward, "step": reasoning_steps_reward, "strict_format": strict_format_reward, - "base_acc": base_model_accuracy_reward + "base_acc": base_model_accuracy_reward, + "kwai": kwai_score } labels = data["labels"] @@ -152,6 +154,10 @@ def verifier(responses, data, config, **kwargs): verifier_function = config.verifier_function verifier_weight = config.verifier_weight + tasks = None + if "kwai" in verifier_function: + tasks = data["tasks"] + for idx, fun_verifier in enumerate(verifier_function): if fun_verifier not in rule_verifier_function: continue @@ -165,7 +171,7 @@ def verifier(responses, data, config, **kwargs): max_num_workers=config.verifier_parallel ) else: - scores = rule_verifier_function[fun_verifier](queue=None, sequences=responses, answers=labels) + scores = rule_verifier_function[fun_verifier](queue=None, sequences=responses, answers=labels, tasks=tasks) metrics[f'grpo/{fun_verifier}_rewards/mean'] = scores rewards = [all_score + tmp_score * verifier_weight[idx] diff --git a/mindspeed_rl/utils/evaluation_utils/code_util/__init__.py b/mindspeed_rl/utils/evaluation_utils/code_util/__init__.py new file mode 100644 index 00000000..e2c268dc --- /dev/null +++ b/mindspeed_rl/utils/evaluation_utils/code_util/__init__.py @@ -0,0 +1,150 @@ +from .utils import check_correctness as apps_check_correctness +import json +import re +import traceback + +# def evaluate_code(completion, test_cases): +# # try to get code solution from completion. if the completion is pure code, this will not take effect. +# solution = completion.split('```python')[-1].split('```')[0] +# try: +# try: +# if not isinstance(test_cases, dict): +# test_cases = json.loads(test_cases) +# except Exception as e: +# print(f"Error:{e}") + +# # 先检查正确性，如果正确，则再one by one 检查test case +# try: +# res, metadata = apps_check_correctness( +# in_outs=test_cases, +# generation=solution, +# timeout=5, +# debug=False +# ) +# metadata = dict(enumerate(metadata))[0] +# success = all(map(lambda x: x == True, res)) +# if success: +# return success, metadata +# except Exception as e: +# pass + +# test_cases_list = [] +# inputs = test_cases["inputs"] +# outputs = test_cases["outputs"] +# for i in range(len(inputs)): +# test_cases_list.append({ +# "inputs": [inputs[i]], +# "outputs": [outputs[i]] +# }) + +# metadata_list = [] +# res_list = [] +# for test_case_id, test_case in enumerate(test_cases_list): +# res, metadata = apps_check_correctness( +# in_outs=test_case, +# generation=solution, +# timeout=5, +# debug=False +# ) +# try: +# metadata = dict(enumerate(metadata))[0] # 运算失败时metadata有可能为空 +# except Exception as e: +# metadata={} +# metadata["test_case"] = {} +# metadata["test_case"]["input"] = str(test_case["inputs"][0]) +# metadata["test_case"]["output"] = str(test_case["outputs"][0]) +# metadata["test_case"]["res"] = str(res) +# metadata_list.append(metadata) +# res_list.extend(res) + +# if test_case_id>=9: +# break + +# success = all(map(lambda x: x == True, res_list)) +# except Exception as e: +# traceback.print_exc(10) +# success = False +# metadata_list = None +# return success, metadata_list + +import concurrent.futures +import json +import traceback +from functools import partial + +def evaluate_code(completion, test_cases): + # 提取代码解决方案 + solution = completion.split('```python')[-1].split('```')[0].strip() + + try: + # 解析测试用例 + if not isinstance(test_cases, dict): + test_cases = json.loads(test_cases) + + # 预检查完整测试用例集 + try: + res, metadata = apps_check_correctness( + in_outs=test_cases, + generation=solution, + timeout=5, + debug=False + ) + if all(map(lambda x: x == True, res)): + return True, dict(enumerate(metadata))[0] + except: + pass + + # 准备并行测试用例 + test_cases_list = [{ + "inputs": [inputs], + "outputs": [outputs] + } for inputs, outputs in zip(test_cases["inputs"], test_cases["outputs"])][:10] # 保持前10个用例的限制 + + # 并行处理函数 + def process_test_case(test_case, solution): + try: + res, metadata = apps_check_correctness( + in_outs=test_case, + generation=solution, + timeout=5, + debug=False + ) + metadata = dict(enumerate(metadata))[0] if metadata else {} + except Exception as e: + return {"error": str(e), "test_case": test_case} + + return { + "metadata": { + **metadata, + "test_case": { + "input": str(test_case["inputs"][0]), + "output": str(test_case["outputs"][0]), + "res": str(res) + } + }, + "result": res + } + + # 使用线程池并行执行 + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = [executor.submit(process_test_case, tc, solution) for tc in test_cases_list] + results = [future.result() for future in concurrent.futures.as_completed(futures)] + + # 处理结果 + res_list = [] + metadata_list = [] + for result in results: + if "error" in result: + print(f"Error in test case: {result['error']}") + continue + res_list.extend(result["result"]) + metadata_list.append(result["metadata"]) + success = all(map(lambda x: x == True, res_list)) + return success, metadata_list + + except Exception as e: + traceback.print_exc() + return False, [] + +if __name__ == "__main__": + pass \ No newline at end of file diff --git a/mindspeed_rl/utils/evaluation_utils/code_util/testing_util.py b/mindspeed_rl/utils/evaluation_utils/code_util/testing_util.py new file mode 100644 index 00000000..7db0edfa --- /dev/null +++ b/mindspeed_rl/utils/evaluation_utils/code_util/testing_util.py @@ -0,0 +1,752 @@ +import ast +import json +import sys +import faulthandler +import platform + +# used for debugging to time steps +from datetime import datetime + +# to run the solution files we're using a timing based approach +import signal + +import numpy as np + +# for capturing the stdout +from io import StringIO + +# used for testing the code that reads from input +from unittest.mock import patch, mock_open + +from pyext import RuntimeModule + +from enum import Enum + +import traceback + +def truncatefn(s, length=300): + assert isinstance(s, str) + if len(s) <= length: + return s + + return s[: length // 2] + "...(truncated) ..." + s[-length // 2 :] + + +class CODE_TYPE(Enum): + call_based = 0 + standard_input = 1 + + +# stuff for setting up signal timer +class TimeoutException(Exception): + pass + + +def timeout_handler(signum, frame): + print("alarm went off") + return + # raise TimeoutException # this is an unhandled exception. just return None is OK + + +signal.signal(signal.SIGALRM, timeout_handler) +# timeout = 6 # seconds + + +# used to capture stdout as a list +# from https://stackoverflow.com/a/16571630/6416660 +# alternative use redirect_stdout() from contextlib +class Capturing(list): + def __enter__(self): + self._stdout = sys.stdout + sys.stdout = self._stringio = StringIO() + # Make closing the StringIO a no-op + self._stringio.close = lambda x: 1 + return self + + def __exit__(self, *args): + self.append(self._stringio.getvalue()) + del self._stringio # free up some memory + sys.stdout = self._stdout + + +def only_int_check(val): + return isinstance(val, int) + + +def string_int_check(val): + return isinstance(val, str) and val.isdigit() + + +def combined_int_check(val): + return only_int_check(val) or string_int_check(val) + +def clean_traceback(error_traceback): + file_start = error_traceback.find('File \"\"') + # print(file_start) + error_traceback = "Traceback (most recent call last):\n " + error_traceback[file_start:] + return error_traceback + +def run_test(in_outs, test=None, debug=False, timeout=15): + """ + if test(generated_code) is not None it'll try to run the code. + otherwise it'll just return an input and output pair. + """ + # Disable functionalities that can make destructive changes to the test. + reliability_guard() + + if debug: + print(f"start = {datetime.now().time()}") + + # try: + # in_outs = json.loads(sample["input_output"]) + # except ValueError: + # in_outs = None + if in_outs: + if in_outs.get("fn_name") is None: + which_type = CODE_TYPE.standard_input # Standard input + method_name = None + else: + which_type = CODE_TYPE.call_based # Call-based + method_name = in_outs["fn_name"] + + if debug: + print(f"loaded input_output = {datetime.now().time()}") + + if test is None: + assert False, "should not happen: test code is none" + return in_outs, {"error": "no test code provided"} + elif test is not None: + results = [] + sol = "from string import *\nfrom re import *\nfrom datetime import *\nfrom collections import *\nfrom heapq import *\nfrom bisect import *\nfrom copy import *\nfrom math import *\nfrom random import *\nfrom statistics import *\nfrom itertools import *\nfrom functools import *\nfrom operator import *\nfrom io import *\nfrom sys import *\nfrom json import *\nfrom builtins import *\nfrom typing import *\nimport string\nimport re\nimport datetime\nimport collections\nimport heapq\nimport bisect\nimport copy\nimport math\nimport random\nimport statistics\nimport itertools\nimport functools\nimport operator\nimport io\nimport sys\nimport json\nsys.setrecursionlimit(6*10**5)\n" + if debug: + print(f"loading test code = {datetime.now().time()}") + + if which_type == CODE_TYPE.call_based: + + sol += test + if debug: + print(f"sol = {sol}") + signal.alarm(timeout) + try: + tmp_sol = RuntimeModule.from_string("tmp_sol", "", sol) + if "class Solution" not in test: + tmp = tmp_sol + else: + tmp = tmp_sol.Solution() + signal.alarm(0) + except Exception as e: + signal.alarm(0) + error_traceback = traceback.format_exc() + if debug: + print(f"type 0 compilation error = {e}") + results.append(-2) + return results, { + "error": repr(e), + # "error_code": -1, + # "error_message": "Compilation Error", + "traceback": clean_traceback(error_traceback), + } + signal.alarm(0) + + elif which_type == CODE_TYPE.standard_input: + # sol + # if code has if __name__ == "__main__": then remove it + try: + astree = ast.parse(test) + last_block = astree.body[-1] + if isinstance(last_block, ast.If): + condition = last_block.test + if ast.unparse(condition).strip() == "__name__ == '__main__'": + test = ( + ast.unparse(astree.body[:-1]) + + "\n" + + ast.unparse(last_block.body) + ) + except: + pass + + tmp_test = test.split("\n") + + new_test = [] + for x in tmp_test: + if (not x.startswith("from ")) and (not x.startswith("import ")): + new_test.append("\t" + x + "\n") + else: + new_test.append(x + "\n") + tmp_test = new_test + + new_test = "" + started = False + for i in tmp_test: + if i.startswith("\t") and not started: + new_test += "stdin = sys.stdin\nstdout = sys.stdout\n" + new_test += "def code():\n" + new_test += i + started = True + elif started and ((i.startswith("from ")) or (i.startswith("import "))): + new_test += "\t" + i + else: + new_test += i + tmp_test = new_test + + sol += tmp_test + if debug: + print(f"sol = {sol}") + method_name = "code" + signal.alarm(timeout) + try: + tmp_sol = RuntimeModule.from_string("tmp_sol", "", sol) + tmp = tmp_sol + signal.alarm(0) + except Exception as e: + signal.alarm(0) + error_traceback = traceback.format_exc() + if debug: + print(f"type 1 compilation error = {e}") + results.append(-2) + return results, { + "error": repr(e), + # "error_code": -1, + # "error_message": "Compilation Error", + "traceback": clean_traceback(error_traceback), + } + signal.alarm(0) + if debug: + print(f"get method = {datetime.now().time()}") + + try: + method = getattr(tmp, method_name) # get_attr second arg must be str + except: + signal.alarm(0) + error_traceback = traceback.format_exc() + e = sys.exc_info() + print(f"unable to get function error = {e}") + results.append(-2) + return results, { + "error": repr(e), + # "error_code": -1, + # "error_message": "Unable to extract code", + "traceback": clean_traceback(error_traceback), + } + + for index, inputs in enumerate(in_outs["inputs"]): + raw_inputs = inputs + raw_outputs = in_outs["outputs"][index] + if which_type == CODE_TYPE.call_based: + inputs = [json.loads(line) for line in inputs.split("\n")] + in_outs["outputs"][index] = json.loads(in_outs["outputs"][index]) + + truncate_line_size = 300 // (raw_inputs.count("\n") + 1) + raw_inputs = "\n".join( + [ + truncatefn(line, truncate_line_size) + for line in raw_inputs.strip().split("\n") + ] + ) + raw_outputs = truncatefn(raw_outputs, 200) + else: + raw_inputs = truncatefn(raw_inputs) + raw_outputs = truncatefn(raw_outputs, 200) + # JSON forces dictionaries to have string keys; this undoes this (assuming a singleton list) + try: + if isinstance(inputs[0], dict): + inputs = [{int(k): v for k, v in inputs[0].items()}] + except: + True + try: + if isinstance(in_outs["outputs"][index], dict): + in_outs["outputs"][index] = [ + {int(k): v for k, v in in_outs["outputs"][index].items()} + ] + except: + True + try: + if isinstance(in_outs["outputs"][index][0], dict): + in_outs["outputs"][index] = [ + {int(k): v for k, v in in_outs["outputs"][index][0].items()} + ] + except: + True + + if debug: + print( + f"time: {datetime.now().time()} testing index = {index} inputs = {inputs}, {type(inputs)}. type = {which_type}" + ) + if which_type == CODE_TYPE.call_based: # Call-based + signal.alarm(timeout) + faulthandler.enable() + try: + output = method(*inputs) + raw_true_output = output + + raw_true_output_copy = json.dumps(output) + raw_true_output_copy = truncatefn(raw_true_output_copy, 200) + + # ground truth sequences are not tuples + if isinstance(output, tuple): + output = list(output) + + tmp_result = output == in_outs["outputs"][index] + if ( + isinstance(in_outs["outputs"][index], list) + and in_outs["outputs"][index] + ): + tmp_result = tmp_result or ( + output == in_outs["outputs"][index][0] + ) + + # ground truth sequences are not tuples + try: + if isinstance(output[0], tuple): + tmp_result = tmp_result or ( + [list(x) for x in output] + == in_outs["outputs"][index][0] + ) + except: + True + results.append(tmp_result) + if tmp_result != True: + return results, { + "output": raw_true_output_copy, + "expected": raw_outputs, + "inputs": raw_inputs, + # "error_code": -2, + "error_message": "Wrong Answer", + } + # reset the alarm + signal.alarm(0) + except Exception as e: + signal.alarm(0) + error_traceback = traceback.format_exc() + faulthandler.disable() + if debug: + print( + f"Standard input runtime error or time limit exceeded error = {e}" + ) + results.append(-1) + if "timeoutexception" in repr(e).lower(): + return results, { + "error": repr(e), + # "error_code": -3, + # "error_message": "Time Limit Exceeded", + # "inputs": raw_inputs, + # "expected": raw_outputs, + "traceback": clean_traceback(error_traceback), + } + else: + return results, { + "error": repr(e), + # "error_code": -4, + # "error_message": "Runtime Error", + # "inputs": raw_inputs, + # "expected": raw_outputs, + "traceback": clean_traceback(error_traceback), + } + faulthandler.disable() + signal.alarm(0) + if debug: + print( + f"outputs = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" + ) + elif which_type == CODE_TYPE.standard_input: # Standard input + faulthandler.enable() + passed = False + + if isinstance(inputs, list): + inputs = "\n".join(inputs) + if isinstance(in_outs["outputs"][index], list): + in_outs["outputs"][index] = "\n".join(in_outs["outputs"][index]) + + signal.alarm(timeout) + with Capturing() as output: + try: + call_method(method, inputs) + # reset the alarm + signal.alarm(0) + passed = True + except Exception as e: + # runtime error or took too long + signal.alarm(0) + error_traceback = traceback.format_exc() + print( + f"Call-based runtime error or time limit exceeded error = {repr(e)}{e}" + ) + results.append(-1) + if "timeoutexception" in repr(e).lower(): + return results, { + "error": repr(e), + # "error_code": -3, + # "error_message": "Time Limit Exceeded", + # "inputs": raw_inputs, + # "expected": raw_outputs, + "traceback": clean_traceback(error_traceback), + } + else: + return results, { + "error": repr(e), + # "error_code": -4, + # "error_message": "Runtime Error", + # "inputs": raw_inputs, + # "expected": raw_outputs, + "traceback": clean_traceback(error_traceback), + } + signal.alarm(0) + raw_true_output = output[0] + raw_true_output_copy = truncatefn(raw_true_output, 200) + output = raw_true_output.splitlines() + if not passed: + if debug: + nl = "\n" + if not isinstance(inputs, list): + print( + f"not passed output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" + ) + else: + print( + f"not passed output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" + ) + continue + + if passed and debug: + print( + f"==> output = {output}, test outputs = {in_outs['outputs'][index]}" + ) + + if custom_compare_(output, in_outs["outputs"][index]): + tmp_result = True + results.append(tmp_result) + continue + + # ground truth sequences are expressed as lists not tuples + if isinstance(output, tuple): + output = list(output) + + tmp_result = False + try: + tmp_result = output == [in_outs["outputs"][index]] + if isinstance(in_outs["outputs"][index], list): + tmp_result = tmp_result or (output == in_outs["outputs"][index]) + if isinstance(output[0], str): + tmp_result = tmp_result or ( + [e.strip() for e in output] == in_outs["outputs"][index] + ) + except Exception as e: + if debug: + print(f"Failed check1 exception = {e}") + pass + + if tmp_result == True: + results.append(tmp_result) + continue + + # try one more time without \n + if isinstance(in_outs["outputs"][index], list): + for tmp_index, i in enumerate(in_outs["outputs"][index]): + in_outs["outputs"][index][tmp_index] = i.split("\n") + in_outs["outputs"][index][tmp_index] = [ + x.strip() for x in in_outs["outputs"][index][tmp_index] if x + ] + else: + in_outs["outputs"][index] = in_outs["outputs"][index].split("\n") + in_outs["outputs"][index] = list( + filter(len, in_outs["outputs"][index]) + ) + in_outs["outputs"][index] = list( + map(lambda x: x.strip(), in_outs["outputs"][index]) + ) + + try: + tmp_result = output == [in_outs["outputs"][index]] + if isinstance(in_outs["outputs"][index], list): + tmp_result = tmp_result or (output == in_outs["outputs"][index]) + except Exception as e: + if debug: + print(f"Failed check2 exception = {e}") + pass + + if tmp_result == True: + results.append(tmp_result) + continue + + # try by converting the output into a split up list too + if isinstance(output, list): + output = list(filter(len, output)) + + if debug: + nl = "\n" + if not isinstance(inputs, list): + print( + f"@1 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]} {tmp_result=}" + ) + else: + print( + f"@1 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]} {tmp_result=}" + ) + + if tmp_result == True: + results.append(tmp_result) + continue + + if debug: + print(f"{tmp_result=} @a") + + try: + tmp_result = output == [in_outs["outputs"][index]] + if isinstance(in_outs["outputs"][index], list): + tmp_result = tmp_result or (output == in_outs["outputs"][index]) + except Exception as e: + if debug: + print(f"Failed check3 exception = {e}") + pass + + if debug: + print(f"{tmp_result=} @b") + + try: + all_ints = all( + combined_int_check(e1) and combined_int_check(e2) + for e1, e2 in zip(output, in_outs["outputs"][index]) + ) + if not all_ints: + if debug: + print( + [ + combined_int_check(e1) and combined_int_check(e2) + for e1, e2 in zip(output, in_outs["outputs"][index]) + ] + ) + output_float = [float(e) for e in output] + gt_float = [float(e) for e in in_outs["outputs"][index]] + tmp_result = tmp_result or ( + (len(output_float) == len(gt_float)) + and np.allclose(output_float, gt_float) + ) + except Exception as e: + pass + + if debug: + print(f"{tmp_result=} @c") + + try: + if isinstance(output[0], list): + all_ints = all( + combined_int_check(e1) and combined_int_check(e2) + for e1, e2 in zip(output[0], in_outs["outputs"][index]) + ) + if not all_ints: + output_float = [float(e) for e in output[0]] + gt_float = [float(e) for e in in_outs["outputs"][index][0]] + tmp_result = tmp_result or ( + (len(output_float) == len(gt_float)) + and np.allclose(output_float, gt_float) + ) + except Exception as e: + pass + + if tmp_result == True: + results.append(tmp_result) + continue + + if debug: + print(f"{tmp_result=} @d") + # try by converting the stuff into split up list + if isinstance(in_outs["outputs"][index], list): + for tmp_index, i in enumerate(in_outs["outputs"][index]): + in_outs["outputs"][index][tmp_index] = set(i.split()) + else: + in_outs["outputs"][index] = set(in_outs["outputs"][index].split()) + + if debug: + print(f"{tmp_result=} @e") + + try: + tmp_result = output == in_outs["outputs"][index] + except Exception as e: + if debug: + print(f"Failed check4 exception = {e}") + continue + + if tmp_result == True: + results.append(tmp_result) + continue + + if debug: + print(f"{tmp_result=} @f") + + # try by converting the output into a split up list too + if isinstance(output, list): + for tmp_index, i in enumerate(output): + output[tmp_index] = i.split() + output = list(filter(len, output)) + for tmp_index, i in enumerate(output): + output[tmp_index] = set(i) + else: + output = output.split() + output = list(filter(len, output)) + output = set(output) + + if debug: + print(f"{tmp_result=} @g") + + if tmp_result == True and debug: + print("PASSED") + + results.append(tmp_result) + if tmp_result != True: + return results, { + "output": raw_true_output_copy, + "expected": raw_outputs, + "inputs": raw_inputs, + # "error_code": -2, + "error_message": "Wrong Answer", + } + + if debug: + nl = "\n" + if not isinstance(inputs, list): + print( + f"@2 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" + ) + else: + print( + f"@2 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" + ) + + print(f"results = {results}") + + return results, {} + + +def custom_compare_(output, ground_truth): + + if isinstance(output, list): + output_1 = "\n".join(output) + if stripped_string_compare(output_1, ground_truth): + return True + + if isinstance(output, list): + output_2 = [o.lstrip().rstrip() for o in output] + output_2 = "\n".join(output_2) + if stripped_string_compare(output_2, ground_truth): + return True + + return False + + +def stripped_string_compare(s1, s2): + s1 = s1.lstrip().rstrip() + s2 = s2.lstrip().rstrip() + return s1 == s2 + + +def call_method(method, inputs): + + if isinstance(inputs, list): + inputs = "\n".join(inputs) + + inputs_line_iterator = iter(inputs.split("\n")) + + # sys.setrecursionlimit(10000) + + # @patch('builtins.input', side_effect=inputs.split("\n")) + @patch("builtins.open", mock_open(read_data=inputs)) + @patch("sys.stdin", StringIO(inputs)) + @patch("sys.stdin.readline", lambda *args: next(inputs_line_iterator)) + @patch("sys.stdin.readlines", lambda *args: inputs.split("\n")) + @patch("sys.stdin.read", lambda *args: inputs) + # @patch('sys.stdout.write', print) + def _inner_call_method(_method): + try: + return _method() + except SystemExit as e: + pass + finally: + pass + + return _inner_call_method(method) + + +def reliability_guard(maximum_memory_bytes=None): + """ + This disables various destructive functions and prevents the generated code + from interfering with the test (e.g. fork bomb, killing other processes, + removing filesystem files, etc.) + WARNING + This function is NOT a security sandbox. Untrusted code, including, model- + generated code, should not be blindly executed outside of one. See the + Codex paper for more information about OpenAI's code sandbox, and proceed + with caution. + """ + + if maximum_memory_bytes is not None: + import resource + + resource.setrlimit( + resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes) + ) + resource.setrlimit( + resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes) + ) + if not platform.uname().system == "Darwin": + resource.setrlimit( + resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes) + ) + + faulthandler.disable() + + import builtins + + builtins.exit = None + builtins.quit = None + + import os + + os.environ["OMP_NUM_THREADS"] = "1" + + os.kill = None + os.system = None # 防止干扰repl评测 + os.putenv = None + os.remove = None + os.removedirs = None + os.rmdir = None + os.fchdir = None + os.setuid = None + os.fork = None + os.forkpty = None + os.killpg = None + os.rename = None + os.renames = None + os.truncate = None + os.replace = None + os.unlink = None + os.fchmod = None + os.fchown = None + os.chmod = None + os.chown = None + os.chroot = None + os.fchdir = None + os.lchflags = None + os.lchmod = None + os.lchown = None + os.getcwd = None + os.chdir = None + + import shutil + + shutil.rmtree = None + shutil.move = None + shutil.chown = None + + import subprocess + + subprocess.Popen = None # type: ignore + + __builtins__["help"] = None + + import sys + + sys.modules["ipdb"] = None + sys.modules["joblib"] = None + sys.modules["resource"] = None + sys.modules["psutil"] = None + sys.modules["tkinter"] = None \ No newline at end of file diff --git a/mindspeed_rl/utils/evaluation_utils/code_util/utils.py b/mindspeed_rl/utils/evaluation_utils/code_util/utils.py new file mode 100644 index 00000000..3ef741bf --- /dev/null +++ b/mindspeed_rl/utils/evaluation_utils/code_util/utils.py @@ -0,0 +1,45 @@ +# Borrowed from: https://huggingface.co/spaces/codeparrot/apps_metric/blob/main/utils.py + + +import multiprocessing +from typing import Dict, Optional +from datasets import load_dataset +from .testing_util import run_test +import traceback +import os,sys + +def _temp_run(sample, generation, debug, result,metadata_list,timeout): + # test is run silently. If it is killed, nothing will be printed + with open(os.devnull, 'w') as devnull: + sys.stdout = devnull + sys.stderr = devnull + try: + res, metadata= run_test(in_outs=sample, test=generation, debug=debug,timeout=timeout) + result.append(res) + metadata_list.append(metadata) + except Exception as e: + # print(e) # some tracebacks are extremely long. + traceback.print_exc(10) + result.append([-1 for i in range(len(sample['inputs']))]) + metadata_list.append({}) + +def check_correctness(in_outs: Optional[dict], generation, timeout=10, debug=True): + """Check correctness of code generation with a global timeout. + The global timeout is to catch some extreme/rare cases not handled by the timeouts + inside `run_test`""" + + manager = multiprocessing.Manager() + result = manager.list() + metadata_list = manager.list() + p = multiprocessing.Process(target=_temp_run, args=(in_outs, generation, debug, result,metadata_list,timeout)) + p.start() + p.join(timeout=timeout + 1) + if p.is_alive(): + p.kill() + # p.terminate() + if not result: + # consider that all tests failed + result = [[-1 for i in range(len(in_outs["inputs"]))]] + if debug: + print(f"global timeout") + return result[0], metadata_list diff --git a/mindspeed_rl/utils/evaluation_utils/math_util/__init__.py b/mindspeed_rl/utils/evaluation_utils/math_util/__init__.py new file mode 100644 index 00000000..26249936 --- /dev/null +++ b/mindspeed_rl/utils/evaluation_utils/math_util/__init__.py @@ -0,0 +1,450 @@ +""" +Answer checker API that uses sympy to simplify expressions and check for equality. + +Call grade_answer(given_answer: str, ground_truth: str). + +FROM: https://github.com/openai/prm800k/blob/main/prm800k/grading/grader.py +""" +import re +import sympy +from pylatexenc import latex2text +from sympy.parsing import sympy_parser + +from . import math_normalize +from .grader import math_equal +# import math_normalize +# from grader import math_equal + +# sympy might hang -- we don't care about trying to be lenient in these cases +BAD_SUBSTRINGS = ["^{", "^("] +BAD_REGEXES = ["\^[0-9]+\^", "\^[0-9][0-9]+"] +TUPLE_CHARS = "()[]" + + +def _sympy_parse(expr: str): + """Parses an expression with sympy.""" + py_expr = expr.replace("^", "**") + return sympy_parser.parse_expr( + py_expr, + transformations=( + sympy_parser.standard_transformations + + (sympy_parser.implicit_multiplication_application,) + ), + ) + + +def _parse_latex(expr: str) -> str: + """Attempts to parse latex to an expression sympy can read.""" + expr = expr.replace("\\tfrac", "\\frac") + expr = expr.replace("\\dfrac", "\\frac") + expr = expr.replace("\\frac", " \\frac") # Play nice with mixed numbers. + expr = latex2text.LatexNodes2Text().latex_to_text(expr) + + # Replace the specific characters that this parser uses. + expr = expr.replace("√", "sqrt") + expr = expr.replace("π", "pi") + expr = expr.replace("∞", "inf") + expr = expr.replace("∪", "U") + expr = expr.replace("·", "*") + expr = expr.replace("×", "*") + + return expr.strip() + + +def _is_float(num: str) -> bool: + try: + float(num) + return True + except ValueError: + return False + + +def _is_int(x: float) -> bool: + try: + return abs(x - int(round(x))) <= 1e-7 + except: + return False + + +def _is_frac(expr: str) -> bool: + return bool(re.search(r"^-?[0-9]+.?/0*[1-9][0-9]*.?$", expr)) + + +def _str_is_int(x: str) -> bool: + try: + x = _strip_properly_formatted_commas(x) + x = float(x) + return abs(x - int(round(x))) <= 1e-7 + except: + return False + + +def _str_to_int(x: str) -> bool: + x = x.replace(",", "") + x = float(x) + return int(x) + + +def _inject_implicit_mixed_number(step: str): + """ + Automatically make a mixed number evalable + e.g. 7 3/4 => 7+3/4 + """ + p1 = re.compile("([0-9]) +([0-9])") + step = p1.sub("\\1+\\2", step) ## implicit mults + return step + + +def _strip_properly_formatted_commas(expr: str): + # We want to be careful because we don't want to strip tuple commas + p1 = re.compile("(\d)(,)(\d\d\d)($|\D)") + while True: + next_expr = p1.sub("\\1\\3\\4", expr) + if next_expr == expr: + break + expr = next_expr + return next_expr + + +def _normalize(expr: str) -> str: + """Normalize answer expressions.""" + if expr is None: + return None + + # Remove enclosing `\text{}`. + m = re.search("^\\\\text\{(?P.+?)\}$", expr) + if m is not None: + expr = m.group("text") + + expr = expr.replace("\\%", "%") + expr = expr.replace("\\$", "$") + expr = expr.replace("$", "") + expr = expr.replace("%", "") + expr = expr.replace(" or ", " , ") + expr = expr.replace(" and ", " , ") + + expr = expr.replace("million", "*10^6") + expr = expr.replace("billion", "*10^9") + expr = expr.replace("trillion", "*10^12") + + for unit in [ + "degree", + "cm", + "centimeter", + "meter", + "mile", + "second", + "minute", + "hour", + "day", + "week", + "month", + "year", + "foot", + "feet", + "inch", + "yard", + "liter", + ]: + expr = re.sub(f"{unit}(es)?(s)? *(\^[0-9]+)?", "", expr) + expr = re.sub(f"\^ *\\\\circ", "", expr) + # expr = re.sub(f"\^*\\\\circ", "", expr) + + if len(expr) > 0 and expr[0] == "{" and expr[-1] == "}": + expr = expr[1:-1] + + expr = re.sub(",\\\\! *", "", expr) + if _is_float(expr) and _is_int(float(expr)): + expr = str(int(round(float(expr)))) + if "\\" in expr: + try: + expr = _parse_latex(expr) + except: + pass + + # edge case with mixed numbers and negative signs + expr = re.sub("- *", "-", expr) + + expr = _inject_implicit_mixed_number(expr) + # expr = expr.replace(" ", "") + + # # if we somehow still have latex braces here, just drop them + # expr = expr.replace("{", "") + # expr = expr.replace("}", "") + + # don't be case sensitive for text answers + expr = expr.lower() + + if _str_is_int(expr): + expr = str(_str_to_int(expr)) + + return expr + + +def count_unknown_letters_in_expr(expr: str): + expr = expr.replace("sqrt", "") + expr = expr.replace("frac", "") + letters_in_expr = set([x for x in expr if x.isalpha()]) + return len(letters_in_expr) + + +def should_allow_eval(expr: str): + # we don't want to try parsing unknown text or functions of more than two variables + if count_unknown_letters_in_expr(expr) > 2: + return False + + for bad_string in BAD_SUBSTRINGS: + if bad_string in expr: + return False + + for bad_regex in BAD_REGEXES: + if re.search(bad_regex, expr) is not None: + return False + + return True + + +def are_equal_under_sympy(ground_truth_normalized: str, given_normalized: str): + are_equal = False + try: + expr = f"({ground_truth_normalized})-({given_normalized})" + if should_allow_eval(expr): + sympy_diff = _sympy_parse(expr) + simplified = sympy.simplify(sympy_diff) + if simplified == 0: + are_equal = True + except: + pass + return are_equal + + +def split_tuple(expr: str): + """ + Split the elements in a tuple/interval, while handling well-formatted commas in large numbers + """ + expr = _strip_properly_formatted_commas(expr) + if len(expr) == 0: + return [] + if ( + len(expr) > 2 + and expr[0] in TUPLE_CHARS + and expr[-1] in TUPLE_CHARS + and all([ch not in expr[1:-1] for ch in TUPLE_CHARS]) + ): + elems = [elem.strip() for elem in expr[1:-1].split(",")] + else: + elems = [expr] + return elems + + +def grade_answer(given_answer: str, ground_truth: str) -> bool: + """ + The answer will be considered correct if: + (a) it normalizes to the same string as the ground truth answer + OR + (b) sympy can simplify the difference between the expressions to 0 + """ + if given_answer is None: + return False + + ground_truth_normalized_mathd = math_normalize.normalize_answer(ground_truth) + given_answer_normalized_mathd = math_normalize.normalize_answer(given_answer) + + # be at least as lenient as mathd + if ground_truth_normalized_mathd == given_answer_normalized_mathd: + return True + + ground_truth_normalized = _normalize(ground_truth) + given_normalized = _normalize(given_answer) + + if ground_truth_normalized is None: + return False + + if ground_truth_normalized == given_normalized: + return True + + if len(given_normalized) == 0: + return False + + ground_truth_elems = split_tuple(ground_truth_normalized) + given_elems = split_tuple(given_normalized) + + if len(ground_truth_elems) > 1 and ( + ground_truth_normalized[0] != given_normalized[0] + or ground_truth_normalized[-1] != given_normalized[-1] + ): + is_correct = False + elif len(ground_truth_elems) != len(given_elems): + is_correct = False + else: + for ground_truth_elem, given_elem in zip(ground_truth_elems, given_elems): + if _is_frac(ground_truth_elem) and _is_frac(given_elem): + # if fractions aren't reduced, then shouldn't be marked as correct + # so, we don't want to allow sympy.simplify in this case + is_correct = ground_truth_elem == given_elem + elif _str_is_int(ground_truth_elem) != _str_is_int(given_elem): + # if the ground truth answer is an integer, we require the given answer to be a strict match (no sympy.simplify) + is_correct = False + else: + is_correct = are_equal_under_sympy(ground_truth_elem, given_elem) + if not is_correct: + break + + return is_correct + +def remove_boxed(s): + left = "\\boxed{" + try: + assert s[:len(left)] == left + assert s[-1] == "}" + return s[len(left):-1] + except: + return None + +def _last_boxed_only_string(string): + idx = string.rfind("\\boxed") + if idx < 0: + idx = string.rfind("\\fbox") + if idx < 0: + return None + + i = idx + left_brace_idx = None + right_brace_idx = None + num_left_braces_open = 0 + while i < len(string): + if string[i] == "{": + num_left_braces_open += 1 + if left_brace_idx is None: + left_brace_idx = i + elif string[i] == "}": + num_left_braces_open -= 1 + if num_left_braces_open == 0: + right_brace_idx = i + break + + i += 1 + + if left_brace_idx is None or right_brace_idx is None: + return None + + return string[left_brace_idx + 1: right_brace_idx].strip() + +def match_answer(response): + """优先从标签中提取答案，如果没有则从后提取最后一个boxed内容""" + try: + # 首先尝试从标签中提取 + answer_pattern = r'(.*?)' + matches = list(re.finditer(answer_pattern, response, re.DOTALL)) + + if matches and matches[-1].group(1).strip(): + # 取最后一个标签内容 + answer_content = matches[-1].group(1).strip() + + # 尝试从answer内容中提取boxed内容 + boxed_content = _last_boxed_only_string(answer_content) + if boxed_content: + return True, boxed_content + + # 如果没有boxed内容，返回整个answer内容 + return True, answer_content + + # 如果没有标签或内容为空，尝试从后提取 + think_end_pos = response.find('') + if think_end_pos > 0: + # 只处理后的内容 + after_think = response[think_end_pos + len(''):].strip() + + # 从after_think中提取最后一个boxed内容 + boxed_content = _last_boxed_only_string(after_think) + if boxed_content: + return True, boxed_content + + # 如果都没有找到，返回失败 + return False, "" + + except Exception as e: + print(f"Error in match_answer: {str(e)}") + return False, "" + +def normalize_math_answer(answer): + """简化版数学答案标准化，只处理单位和基本格式""" + if not answer: + return answer + + # 格式化答案，移除多余空格 + answer = answer.strip() + + # 移除单位文本 + for unit in ["square feet", "feet", "foot", "meters", "meter", "cm", "mm", "km", "inches", "inch"]: + if answer.endswith(unit): + answer = answer[:-len(unit)].strip() + + return answer + +import math +import threading +def limited_time_execution(timeout=10.0): + """全局超时保护机制，不依赖于信号机制""" + + class TimeoutError(Exception): + pass + + def check_timeout(): + thread_id = threading.current_thread().ident + if thread_id in timeout_flags and timeout_flags[thread_id]: + raise TimeoutError("Operation timed out") + + timeout_flags = {} + thread_id = threading.current_thread().ident + timeout_flags[thread_id] = False + + def set_timeout_flag(): + timeout_flags[thread_id] = True + + timer = threading.Timer(timeout, set_timeout_flag) + timer.daemon = True # 确保线程不会阻止程序退出 + timer.start() + + try: + yield check_timeout + finally: + timer.cancel() + if thread_id in timeout_flags: + del timeout_flags[thread_id] + +def evaluate_math(model_output: str, ground_truth: str, timeout=5.0) -> bool: + model_output = str(model_output) + ground_truth = str(ground_truth) + + # 提取答案 + try: + is_matched, extracted_model_output = match_answer(model_output) + except Exception as e: + print(f"Error extracting answer: {str(e)}") + return False, "Error extracting answer" + + if not is_matched or not extracted_model_output: + return False, "No answer found" + + # 标准化答案进行简单比较和调试 + try: + normalized_model_answer = normalize_math_answer(extracted_model_output) + normalized_ground_truth = normalize_math_answer(ground_truth) + + # 打印标准化后的答案用于调试 + print(f"Normalized model answer: '{normalized_model_answer}'") + print(f"Normalized ground truth: '{normalized_ground_truth}'") + except Exception as e: + print(f"Error in answer normalization: {str(e)}") + + # 使用grade_answer进行等价性判断 + try: + if grade_answer(extracted_model_output, ground_truth): + return True, extracted_model_output + except Exception as e: + print(f"Error in grade_answer: {str(e)}") + + # 如果grade_answer失败，返回False + return False, extracted_model_output + diff --git a/mindspeed_rl/utils/evaluation_utils/math_util/grader.py b/mindspeed_rl/utils/evaluation_utils/math_util/grader.py new file mode 100644 index 00000000..f7ce9772 --- /dev/null +++ b/mindspeed_rl/utils/evaluation_utils/math_util/grader.py @@ -0,0 +1,388 @@ +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Copyright (c) Microsoft Corporation. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE + +# Copyright (c) 2023 OpenAI +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# Copyright (c) 2021 Dan Hendrycks +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + + +""" +This logic is largely copied from the Hendrycks' MATH release (math_equivalence), and borrowed from: +- https://github.com/microsoft/ToRA/blob/main/src/eval/grader.py +- https://github.com/microsoft/ProphetNet/tree/master/CRITIC +- https://github.com/openai/prm800k +""" + + +import contextlib +import re +import signal +import math +from math import isclose +from typing import Union + +from sympy import N, simplify +from sympy.parsing.latex import parse_latex +from sympy.parsing.sympy_parser import parse_expr + + +def is_digit(s): + try: + if "{,}" in str(s): + num = float(str(s).replace("{,}", "")) + return True, num + + num = float(str(s).replace(",", "")) + return True, num + except ValueError: + return False, None + + +def normalize(answer, pi) -> str: + # checking if answer is $ and removing $ in that case to compare + if isinstance(answer, str) and bool(re.match(r'\$\d+(\.\d+)?', answer)): + return answer[1:] + + # checking if answer is % or \\% and removing % + if isinstance(answer, str) and ( + bool(re.match(r'^\d+(\.\d+)?%$', answer)) or bool(re.match(r'^\d+(\.\d+)?\\%$', answer)) + ): + return answer.replace("\\%", "").replace("%", "") + + # handle base + answer = handle_base(answer) + + # handle pi + answer = handle_pi(answer, pi) + + return answer + +def handle_base(x) -> str: + if isinstance(x, str) and "_" in x: + # Due to base + x = x.split("_")[0] + x = float(x) + return int(x) + return x + + +def handle_pi(string, pi): + + if isinstance(string, str) and "\pi" in string: + # Find the first occurrence of "\pi" + idx = string.find("\pi") + + # Iterate over the string and find all occurrences of "\pi" with a valid previous character + while idx != -1: + + if idx > 0 and string[idx-1].isdigit(): + # Replace "\pi" with "*math.pi" if the previous character is a digit + string = string[:idx] + f"*{pi}" + string[idx+3:] + else: + # Replace "\pi" with "1*math.pi" if the previous character is not a digit + string = string[:idx] + f"1*{pi}" + string[idx+3:] + + # Find the next occurrence of "\pi" + idx = string.find("\pi", idx + 1) + + # Evaluate the expression using eval() function + try: + string = eval(string) + except: + pass + + return string + +def math_equal( + prediction: Union[bool, float, str], + reference: Union[float, str], + include_percentage: bool = True, + tolerance: float = 1e-4, + timeout: float = 10.0, + pi: float = math.pi +) -> bool: + """ + Exact match of math if and only if: + 1. numerical equal: both can convert to float and are equal + 2. symbolic equal: both can convert to sympy expression and are equal + """ + + prediction = normalize(prediction, pi) + reference = normalize(reference, pi) + + if isinstance(prediction, str) and len(prediction) > 1000: # handling weird corner-cases + prediction = prediction[:1000] + + # 0. string comparison + if isinstance(prediction, str) and isinstance(reference, str): + if prediction.strip().lower() == reference.strip().lower(): + return True + if prediction.replace(" ", "") == reference.replace(" ", ""): + return True + + try: # 1. numerical equal + if is_digit(prediction)[0] and is_digit(reference)[0]: + prediction = is_digit(prediction)[1] + reference = is_digit(reference)[1] + # number questions + if include_percentage: + gt_result = [reference / 100, reference, reference * 100] + else: + gt_result = [reference] + for item in gt_result: + try: + if isclose(item, prediction, rel_tol=tolerance): + return True + except Exception: + continue + return False + except Exception: + pass + + if not prediction and prediction not in [0, False]: + return False + + # 2. symbolic equal + reference = str(reference).strip() + prediction = str(prediction).strip() + + ## deal with [], (), {} + prediction = format_intervals(prediction) + + pred_str, ref_str = prediction, reference + if (prediction.startswith("[") and prediction.endswith("]") and not reference.startswith("(")) or ( + prediction.startswith("(") and prediction.endswith(")") and not reference.startswith("[") + ): + pred_str = pred_str.strip("[]()") + ref_str = ref_str.strip("[]()") + for s in ["{", "}", "(", ")"]: + ref_str = ref_str.replace(s, "") + pred_str = pred_str.replace(s, "") + if pred_str == ref_str: + return True + + ## [a, b] vs. [c, d], return a==c and b==d + if ( + prediction + and reference + and prediction[0] in "([" + and prediction[-1] in ")]" + and prediction[0] == reference[0] + and prediction[-1] == reference[-1] + ): + pred_parts = prediction[1:-1].split(",") + ref_parts = reference[1:-1].split(",") + if len(pred_parts) == len(ref_parts): + if all( + [ + math_equal(pred_pt, ref_pt, include_percentage, tolerance) + for pred_pt, ref_pt in zip(pred_parts, ref_parts) + ] + ): + return True + + if "," in prediction and "," in reference: + pred_parts = [item.strip() for item in prediction.split(",")] + ref_parts = [item.strip() for item in reference.split(",")] + + if len(pred_parts) == len(ref_parts): + if all( + [ + math_equal(pred_parts[i], ref_parts[i], include_percentage, tolerance) + for i in range(len(pred_parts)) + ] + ): + return True + else: + return False + + # if we have point == tuple of values + if prediction.startswith("Point") and reference[0] == "(" and reference[-1] == ")": + pred_parts = prediction[prediction.find("(") + 1 : -1].split(",") + ref_parts = reference[1:-1].split(",") + if len(pred_parts) == len(ref_parts): + if all( + [ + math_equal(pred_pt, ref_pt, include_percentage, tolerance) + for pred_pt, ref_pt in zip(pred_parts, ref_parts) + ] + ): + return True + + # if reference is a matrix + if "\begin{pmatrix}" in reference and prediction.startswith("Matrix"): + try: + pred_matrix = parse_expr(prediction) + ref_matrix_items = reference.split()[1:-1:2] + if len(pred_matrix) == len(ref_matrix_items): + if all( + [ + math_equal(pred, ref, include_percentage, tolerance) + for ref, pred in zip(ref_matrix_items, pred_matrix) + ] + ): + return True + except Exception: + pass + elif "\begin{pmatrix}" in reference and prediction.startswith("[") and prediction.endswith("]"): + if isinstance(eval(prediction), list): + try: + pred_matrix = eval(prediction) + # ref_matrix_items = reference.split()[1:-1:2] + ref_matrix_items = reference.lstrip("\\begin{pmatrix}").lstrip("\begin{pmatrix}").rstrip("\\end{pmatrix}").rstrip("\end{pmatrix}") + ref_matrix_items = ref_matrix_items.split("\\") + ref_matrix_items = [row.split("&") if "&" in row else row for row in ref_matrix_items] + if len(pred_matrix) == len(ref_matrix_items): + if all( + [ + math_equal(pred, ref, include_percentage, tolerance) + for ref, pred in zip(ref_matrix_items, pred_matrix) + ] + ): + return True + except Exception: + pass + + return symbolic_equal(prediction, reference, tolerance, timeout) + + +def symbolic_equal(a, b, tolerance, timeout=10.0): + def _parse(s): + for f in [parse_expr, parse_latex]: + try: + with time_limit(timeout): + return f(s) + except Exception: + pass + return s + + a = _parse(a) + b = _parse(b) + + try: + with time_limit(timeout): + if simplify(a - b) == 0: + return True + except Exception: + pass + + try: + with time_limit(timeout): + if isclose(N(a), N(b), rel_tol=tolerance): + return True + except Exception: + pass + return False + + +class TimeoutException(Exception): + pass + + +@contextlib.contextmanager +def time_limit(seconds: float): + def signal_handler(signum, frame): + raise TimeoutException("Timed out!") + + # 设置备份计时器以避免无限挂起 + original_handler = signal.getsignal(signal.SIGALRM) + try: + signal.setitimer(signal.ITIMER_REAL, seconds) + signal.signal(signal.SIGALRM, signal_handler) + yield + finally: + # 确保总是重置计时器和信号处理 + signal.setitimer(signal.ITIMER_REAL, 0) + signal.signal(signal.SIGALRM, original_handler) + + +def format_intervals(prediction): + patterns = { + "Interval(": r"^Interval\((.*)\)$", + "Interval.Ropen(": r"^Interval\.Ropen\((.*)\)$", + "Interval.Lopen(": r"^Interval\.Lopen\((.*)\)$", + "Interval.open(": r"^Interval\.open\((.*)\)$", + } + + for key, pattern in patterns.items(): + match = re.match(pattern, prediction) + if match: + inner_content = match.group(1) + + if key == "Interval(": # Intarval(a, b) == [a, b] + return f"[{inner_content}]" + elif key == "Interval.Ropen(": # Intarval.Ropen(a, b) == [a, b) + return f"[{inner_content})" + elif key == "Interval.Lopen(": # Intarval.Lopen(a, b) == (a, b] + return f"({inner_content}]" + elif key == "Interval.open(": # Intarval.open(a, b) == (a, b) + return f"({inner_content})" + + return prediction \ No newline at end of file diff --git a/mindspeed_rl/utils/evaluation_utils/math_util/math_normalize.py b/mindspeed_rl/utils/evaluation_utils/math_util/math_normalize.py new file mode 100644 index 00000000..6d30af34 --- /dev/null +++ b/mindspeed_rl/utils/evaluation_utils/math_util/math_normalize.py @@ -0,0 +1,163 @@ +""" +This logic is largely copied from the Hendrycks' MATH release (math_equivalence). + +From: https://github.com/openai/prm800k/blob/main/prm800k/grading/math_normalize.py +""" +import re +from typing import Optional + + +def normalize_answer(answer: Optional[str]) -> Optional[str]: + if answer is None: + return None + answer = answer.strip() + try: + # Remove enclosing `\text{}`. + m = re.search("^\\\\text\{(?P.+?)\}$", answer) + if m is not None: + answer = m.group("text").strip() + return _strip_string(answer) + except: + return answer + + +def _fix_fracs(string): + substrs = string.split("\\frac") + new_str = substrs[0] + if len(substrs) > 1: + substrs = substrs[1:] + for substr in substrs: + new_str += "\\frac" + if substr[0] == "{": + new_str += substr + else: + try: + assert len(substr) >= 2 + except: + return string + a = substr[0] + b = substr[1] + if b != "{": + if len(substr) > 2: + post_substr = substr[2:] + new_str += "{" + a + "}{" + b + "}" + post_substr + else: + new_str += "{" + a + "}{" + b + "}" + else: + if len(substr) > 2: + post_substr = substr[2:] + new_str += "{" + a + "}" + b + post_substr + else: + new_str += "{" + a + "}" + b + string = new_str + return string + + +def _fix_a_slash_b(string): + if len(string.split("/")) != 2: + return string + a = string.split("/")[0] + b = string.split("/")[1] + try: + a = int(a) + b = int(b) + assert string == "{}/{}".format(a, b) + new_string = "\\frac{" + str(a) + "}{" + str(b) + "}" + return new_string + except: + return string + + +def _remove_right_units(string): + # "\\text{ " only ever occurs (at least in the val set) when describing units + if "\\text{ " in string: + splits = string.split("\\text{ ") + assert len(splits) == 2 + return splits[0] + else: + return string + + +def _fix_sqrt(string): + if "\\sqrt" not in string: + return string + splits = string.split("\\sqrt") + new_string = splits[0] + for split in splits[1:]: + if split[0] != "{": + a = split[0] + new_substr = "\\sqrt{" + a + "}" + split[1:] + else: + new_substr = "\\sqrt" + split + new_string += new_substr + return new_string + + +def _strip_string(string): + # linebreaks + string = string.replace("\n", "") + # print(string) + + # remove inverse spaces + string = string.replace("\\!", "") + # print(string) + + # replace \\ with \ + string = string.replace("\\\\", "\\") + # print(string) + + # replace tfrac and dfrac with frac + string = string.replace("tfrac", "frac") + string = string.replace("dfrac", "frac") + # print(string) + + # remove \left and \right + string = string.replace("\\left", "") + string = string.replace("\\right", "") + # print(string) + + # Remove circ (degrees) + string = string.replace("^{\\circ}", "") + string = string.replace("^\\circ", "") + + # remove dollar signs + string = string.replace("\\$", "") + + # remove units (on the right) + string = _remove_right_units(string) + + # remove percentage + string = string.replace("\\%", "") + string = string.replace("\%", "") + + # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string + string = string.replace(" .", " 0.") + string = string.replace("{.", "{0.") + # if empty, return empty string + if len(string) == 0: + return string + if string[0] == ".": + string = "0" + string + + # to consider: get rid of e.g. "k = " or "q = " at beginning + if len(string.split("=")) == 2: + if len(string.split("=")[0]) <= 2: + string = string.split("=")[1] + + # fix sqrt3 --> sqrt{3} + string = _fix_sqrt(string) + + # remove spaces + string = string.replace(" ", "") + + # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b} + string = _fix_fracs(string) + + # manually change 0.5 --> \frac{1}{2} + if string == "0.5": + string = "\\frac{1}{2}" + + # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y + string = _fix_a_slash_b(string) + + return string \ No newline at end of file diff --git a/mindspeed_rl/utils/kwai.py b/mindspeed_rl/utils/kwai.py new file mode 100644 index 00000000..89d6466b --- /dev/null +++ b/mindspeed_rl/utils/kwai.py @@ -0,0 +1,237 @@ +import asyncio +import json +import traceback +from concurrent.futures import ProcessPoolExecutor +from functools import partial +from typing import Optional + +import math +import random +from .evaluation_utils.code_util import evaluate_code +from .evaluation_utils.math_util import evaluate_math +import time +import ray +from tqdm.asyncio import tqdm + + +def validate_response_structure(processed_str: str, task: str) -> bool: + """只检查是否有标签且标签前有内容""" + + # print("\n[Structure Validation]") + + # 检查标签是否存在 + think_end_pos = processed_str.find('') + + # 验证标签存在且前面有内容 + validation_passed = think_end_pos > 0 + + # print(f" : position={think_end_pos}") + # print(f" Validation: {'PASS' if validation_passed else 'FAIL'}") + + return validation_passed + + +def process_completion(completion, task, reference): + if task == "code": + return evaluate_code(completion, reference) + elif task == "math": + return evaluate_math(completion, str(reference)) + else: + print('task') + raise NotImplementedError + + +def get_format_score(validation_passed): + format_reward = 1 + format_reward if validation_passed else -abs(format_reward) + print(f"\n Format validation: {'PASS' if validation_passed else 'FAIL'}") + print(f" Format score: {format_reward}") + return format_reward + + + +async def process_row_with_timeout(completion, reference, task, executor, timeout=300.0): + """ + Process a single row with a timeout. + """ + loop = asyncio.get_running_loop() + try: + # Ensure process_completion is called properly + tasks = [asyncio.wait_for( + loop.run_in_executor( + executor, + partial(process_completion, completion, task, reference) # Ensure synchronous + ), + timeout=timeout + ) + ] + return await asyncio.gather(*tasks) + except asyncio.TimeoutError: + print(f"Timeout occurred for completion: {completion}") + return None # Default value for timed-out rows + except Exception as e: + print(f"Error processing completion: {completion[:10]}, Error: {e}") + return None # Default value for failed rows + +async def parallel_evaluate_continual_async(completions, references, tasks, num_processes, task_timeout=300.0): + """ + Evaluate rows in parallel with a process pool and timeout handling. + """ + scores = [] + with ProcessPoolExecutor(max_workers=num_processes) as executor: + # Create tasks for all rows + tasks_async = [ + process_row_with_timeout(completion, reference, task, executor, timeout=task_timeout) + for completion, reference, task in zip(completions, references, tasks) + ] + # Use tqdm for progress tracking + results = await tqdm.gather(*tasks_async, disable=True) + + # Process results + for result, completion, reference, task in zip(results, completions, references, tasks): + validation_passed = validate_response_structure(completion, task) + + if isinstance(result, Exception) or result is None: + # Handle failed or timed-out tasks + # scores.append(0.0) + score = 0 + continue + print(f"Test for result {result}") + try: + # Process result based on task type + if task == 'code' and not result[0][0]: # if task is code, the reference should be json string + correct = 0 + error = 0 + total = min( + len(json.loads(reference)['inputs'] if not isinstance(reference, dict) else reference['inputs']), + 10) + for run in result[0][1]: + if 'test_case' in run and 'res' in run['test_case'] and run['test_case']['res'] == '[True]': + correct += 1 + if 'test_case' in run and 'res' in run['test_case'] and run['test_case']['res'] == '[-2]': + error += 1 + score = correct / total + # scores.append(correct / total) # 添加小数奖励 + else: + score = float(int(result[0][0])) + # scores.append(float(int(result[0][0]))) + except Exception as e: + print(f"Error processing result for row: {completion[:10]}, Error: {e}") + # scores.append(0.0) + score = 0 + + # answer_score = 0 + # # 添加整数奖励 + # if score>0: + # if score==1: + # answer_score = 2 + # else: + # answer_score = -1.5 + # else: + # answer_score = -2 + # format score + validation_passed = validate_response_structure(completion, task) + # format_score = get_format_score(validation_passed) + format_score = 1 + # 格式优先 + if validation_passed == False: + format_score = -1 + total_score = format_score + else: + total_score = format_score + score + # print(" completion ".center(80, '=')+"\n\n" + str(completion) + "\n\n" + # + " validate_response_structure ".center(80, '=')+"\n\n" + str(validation_passed) + "\n\n" + # + " format_score ".center(80, '=')+"\n\n" + str(format_score) + "\n\n" + # + " total_score ".center(80, '=')+"\n\n" + str(total_score) + "\n\n" + # + " score ".center(80, '=')+"\n\n" + str(score) + "\n\n" + # ) + + scores.append(total_score) + return scores + +@ray.remote +def process_row(completion, reference, task, timeout=300.0): + """ + Process a single row synchronously. + """ + try: + result = process_completion(completion, task, reference) + return [result] + except Exception as e: + print(f"Error processing completion in Process_Row function: {completion[:10]}, Error: {e}") + return None + +def sequential_evaluate_continual(completions, references, tasks): + """ + Evaluate rows sequentially without concurrency. + """ + scores = [] + futures = [] + for completion, reference, task in zip(completions, references, tasks): + futures.append(process_row.remote(completion, reference, task )) + results = ray.get(futures) + for completion, reference, task, result in zip(completions, references, tasks, results): + validation_passed = validate_response_structure(completion, task) + + if isinstance(result, Exception) or result is None: + # Handle failed or timed-out tasks + # scores.append(0.0) + score = 0 + continue + else: + try: + if task == 'code' and not result[0][0]: + correct = 0 + error = 0 + total = min( + len(json.loads(reference)['inputs'] if not isinstance(reference, dict) else reference['inputs']), + 10) + for run in result[0][1]: + if 'test_case' in run and 'res' in run['test_case'] and run['test_case']['res'] == '[True]': + correct += 1 + if 'test_case' in run and 'res' in run['test_case'] and run['test_case']['res'] == '[-2]': + error += 1 + score = correct / total + else: + score = float(int(result[0][0])) + except Exception as e: + print(f"Error processing result for row in sequential function: {completion[:10]}, Error: {e}") + print(result) + score = 0 + + format_score = 0.2 if validation_passed else 0 + total_score = format_score + score + scores.append(total_score) + + return scores + + + + +def compute_score(queue, sequences, answers, tasks, *kwargs): + do_print = True + # completions = [completions] + # references = [references] + # tasks = [tasks] + if do_print: + print("completions:", sequences[0]) + print("references:", answers[0]) + print("tasks:", tasks[0]) + # three lists should have identical length + # TODO: make this one completely asynchronous, which means the main process can do other things(e.g., forwarding reward model) while computing score + assert len(sequences) == len(answers) == len(tasks) + try: + res = sequential_evaluate_continual(sequences, answers, tasks) + print("res:", res[0]) + # return res + except asyncio.TimeoutError as e: + print('Global timeout in reward computing! Setting all as 0.5.') + res = [0.02 for _ in range(len(sequences))] + except Exception as e: + print(f"Unexpected error: {e}") + res = [0.01 for _ in range(len(sequences))] + + if queue is not None: + queue.put(res) + + return res \ No newline at end of file -- Gitee From e98298533d7539f49c4b4f51ce9458bdcbd7716d Mon Sep 17 00:00:00 2001 From: yuqi Date: Fri, 18 Apr 2025 03:06:47 +0000 Subject: [PATCH 08/25] =?UTF-8?q?!213=20=E3=80=90bugfix=E3=80=91actor?= =?UTF-8?q?=E5=86=85=E4=BD=BF=E7=94=A8setup=5Fmodel=5Fand=5Foptimizer?= =?UTF-8?q?=E6=9B=BF=E6=8D=A2get=5Fmodel=E9=80=BB=E8=BE=91=20Merge=20pull?= =?UTF-8?q?=20request=20!213=20from=20yuqi/build=5Fmodule2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cli/train_grpo.py | 4 ++++ mindspeed_rl/workers/actor_hybrid_worker.py | 11 +++++++---- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/cli/train_grpo.py b/cli/train_grpo.py index bd64a91b..76deceb7 100644 --- a/cli/train_grpo.py +++ b/cli/train_grpo.py @@ -202,6 +202,8 @@ def get_megatron_module(): from megatron.training.training import get_model, unwrap_model from megatron.core.distributed.distributed_data_parallel_config import DistributedDataParallelConfig from megatron.core.tensor_parallel.cross_entropy import vocab_parallel_cross_entropy + from megatron.training.training import setup_model_and_optimizer + from megatron.core.enums import ModelType return { 'parallel_state': parallel_state, @@ -217,6 +219,8 @@ def get_megatron_module(): 'local_ddp': LocalDDP, 'distributed_data_parallel_config': DistributedDataParallelConfig, 'vocab_parallel_cross_entropy': vocab_parallel_cross_entropy, + 'setup_model_and_optimizer': setup_model_and_optimizer, + 'model_type': ModelType, } diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index 96987d07..403725b2 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -112,6 +112,8 @@ class ActorHybridWorkerBase(BaseWorker): def update(self, kl_ctrl=None): self.sharding_manager.enter_train_mode() + self.args.curr_iteration = self.iteration + experience_consumer_stage = 'actor_train' experience_colums = ['responses', 'advantages', 'old_log_prob', 'ref_log_prob', 'input_ids', 'response_length', 'prompt_length'] @@ -147,6 +149,7 @@ class ActorHybridWorkerBase(BaseWorker): ) ) + self.iteration += 1 self.sharding_manager.exit_train_mode() def save_ckpt(self, iteration: int): @@ -291,10 +294,8 @@ class ActorHybridWorkerBase(BaseWorker): return optimizer, opt_param_scheduler def _build_model_optimizer(self): - actor_module = self.get_model(self.model_provider, None) - if isinstance(actor_module, nn.ModuleList): - actor_module = [actor_module[0]] - optimizer, opt_param_scheduler = self._get_megatron_optimizer(model=actor_module) + actor_module, optimizer, opt_param_scheduler = self.setup_model_and_optimizer( + self.model_provider, self.model_type.encoder_or_decoder) # load checkpoint if self.megatron_config.load is not None or self.megatron_config.pretrained_checkpoint is not None: @@ -304,6 +305,8 @@ class ActorHybridWorkerBase(BaseWorker): self.megatron_config.iteration = 0 self.megatron_config.num_floating_point_operations_so_far = 0 + self.iteration = self.get_iteration() + return actor_module, optimizer, opt_param_scheduler def _build_rollout(self): -- Gitee From c65c595faba64386649b2e95b6ae41b4aeff2a8a Mon Sep 17 00:00:00 2001 From: LookAround Date: Fri, 18 Apr 2025 03:07:59 +0000 Subject: [PATCH 09/25] =?UTF-8?q?!210=20=E3=80=90=E4=BF=AE=E6=94=B9?= =?UTF-8?q?=E8=AF=B4=E6=98=8E=E3=80=91fix=20vllm=20bug=20Merge=20pull=20re?= =?UTF-8?q?quest=20!210=20from=20LookAround/pr=5Fdev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mindspeed_rl/trainer/utils/parallel_state.py | 11 ++++++++--- mindspeed_rl/workers/actor_hybrid_worker.py | 2 +- mindspeed_rl/workers/base_worker.py | 8 ++++++-- 3 files changed, 15 insertions(+), 6 deletions(-) diff --git a/mindspeed_rl/trainer/utils/parallel_state.py b/mindspeed_rl/trainer/utils/parallel_state.py index da714f04..8243d33b 100644 --- a/mindspeed_rl/trainer/utils/parallel_state.py +++ b/mindspeed_rl/trainer/utils/parallel_state.py @@ -59,9 +59,14 @@ def get_tensor_model_parallel_group(mpu, use_vllm=False): def get_model_parallel_group(mpu, use_vllm=False): if use_vllm: + import vllm from vllm.distributed import parallel_state as vpu - if not hasattr(vpu, "get_tensor_model_parallel_group"): - vpu = mpu - return vpu.get_model_parallel_group() + + if vllm.__version__ == "0.7.3": + return vpu.get_tensor_model_parallel_group().device_group + else: + if not hasattr(vpu, "get_tensor_model_parallel_group"): + vpu = mpu + return vpu.get_model_parallel_group() else: return mpu.get_model_parallel_group() \ No newline at end of file diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index 403725b2..9c367283 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -166,7 +166,7 @@ class ActorHybridWorkerBase(BaseWorker): pad_token_id = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod start_time_defined = False - while self.all_consumed(experience_consumer_stage) > 0: + while self.all_consumed(experience_consumer_stage, use_vllm=True) > 0: batch_data, index = self.dispatch_transfer_dock_data( experience_consumer_stage, experience_colums, diff --git a/mindspeed_rl/workers/base_worker.py b/mindspeed_rl/workers/base_worker.py index 4b0a7c2b..6fed82cb 100644 --- a/mindspeed_rl/workers/base_worker.py +++ b/mindspeed_rl/workers/base_worker.py @@ -145,11 +145,15 @@ class BaseWorker(BaseRayWorker, ABC): self.args = None def all_consumed(self, experience_consumer_stage, use_vllm=False): - status = torch.tensor(0, device=next(self.model[0].parameters()).device) + if use_vllm: + current_device = next(self.inference_model.model.parameters()).device + else: + current_device = next(self.model[0].parameters()).device + status = torch.tensor(0, device=current_device) if get_tensor_model_parallel_rank(self.parallel_state, use_vllm) == 0 and \ get_pipeline_model_parallel_rank(self.parallel_state, use_vllm) == 0: status = torch.tensor(int(not ray.get(self.td.all_consumed.remote(experience_consumer_stage))), - device=next(self.model[0].parameters()).device) + device=current_device) torch.distributed.all_reduce(status, group=get_model_parallel_group(self.parallel_state, use_vllm), op=torch.distributed.ReduceOp.MAX) return status -- Gitee From 10ba6218d7321f3193031b75f976f28aca28b5e9 Mon Sep 17 00:00:00 2001 From: luokui Date: Sat, 19 Apr 2025 02:42:48 +0000 Subject: [PATCH 10/25] =?UTF-8?q?!221=20=E5=8F=82=E6=95=B0=E4=B8=8D?= =?UTF-8?q?=E5=8F=98=E5=9B=9E=E8=B0=83=20Merge=20pull=20request=20!221=20f?= =?UTF-8?q?rom=20luokui/dev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cli/train_grpo.py | 1 + configs/grpo_trainer_qwen25_32b.yaml | 1 - configs/grpo_trainer_qwen25_7b.yaml | 1 - configs/r1_zero_deepseekv3_671b.yaml | 1 - configs/r1_zero_qwen25_32b.yaml | 2 +- configs/r1_zero_qwen25_7b.yaml | 2 +- configs/r1_zero_qwen25_7b_integrated.yaml | 1 - mindspeed_rl/config_cls/generate_config.py | 3 + mindspeed_rl/config_cls/rl_config.py | 12 - mindspeed_rl/config_cls/validate_config.py | 157 ++--- mindspeed_rl/trainer/base.py | 4 +- mindspeed_rl/trainer/grpo_trainer_hybrid.py | 45 +- mindspeed_rl/trainer/utils/compute_utils.py | 13 +- mindspeed_rl/trainer/utils/transfer_dock.py | 555 ++++++++++++------ mindspeed_rl/utils/__init__.py | 6 +- mindspeed_rl/utils/pad_process.py | 4 +- mindspeed_rl/utils/utils.py | 5 + mindspeed_rl/workers/actor_hybrid_worker.py | 44 +- mindspeed_rl/workers/base_worker.py | 24 +- mindspeed_rl/workers/reference_woker.py | 15 +- mindspeed_rl/workers/reward_woker.py | 14 +- mindspeed_rl/workers/rule_reward.py | 6 +- .../configs/test_grpo_trainer_qwen25_7b.yaml | 4 +- tests/st/infer_engine/test_vllm_engine.py | 1 - .../test_vllm_engine_multistep_decode.py | 1 - tests/ut/utils/test_transfer_dock.py | 86 +-- tests/ut/workers/test_base_worker.py | 4 +- 27 files changed, 547 insertions(+), 465 deletions(-) diff --git a/cli/train_grpo.py b/cli/train_grpo.py index 76deceb7..dbae265d 100644 --- a/cli/train_grpo.py +++ b/cli/train_grpo.py @@ -145,6 +145,7 @@ def train(config): reward_list, tokenizer=tokenizer, global_batch_size=actor_config.global_batch_size, + micro_batch_size=actor_config.micro_batch_size, train_iters=actor_config.train_iters, save_interval=actor_config.save_interval, dataset_additional_keys=actor_config.dataset_additional_keys, diff --git a/configs/grpo_trainer_qwen25_32b.yaml b/configs/grpo_trainer_qwen25_32b.yaml index aaf7194e..e92a5ad6 100644 --- a/configs/grpo_trainer_qwen25_32b.yaml +++ b/configs/grpo_trainer_qwen25_32b.yaml @@ -72,7 +72,6 @@ reward_config: rl_config: blocking: false - experience_count: 2 gamma: 1.0 lam: 0.95 adv_estimator: group_norm diff --git a/configs/grpo_trainer_qwen25_7b.yaml b/configs/grpo_trainer_qwen25_7b.yaml index e6437d7e..41a262c6 100644 --- a/configs/grpo_trainer_qwen25_7b.yaml +++ b/configs/grpo_trainer_qwen25_7b.yaml @@ -73,7 +73,6 @@ reward_config: rl_config: blocking: false - experience_count: 2 gamma: 1.0 lam: 0.95 adv_estimator: group_norm diff --git a/configs/r1_zero_deepseekv3_671b.yaml b/configs/r1_zero_deepseekv3_671b.yaml index b8cf10b1..e6a22b95 100644 --- a/configs/r1_zero_deepseekv3_671b.yaml +++ b/configs/r1_zero_deepseekv3_671b.yaml @@ -72,7 +72,6 @@ reward_config: rl_config: blocking: false - experience_count: 12 gamma: 1.0 lam: 0.95 adv_estimator: group_norm diff --git a/configs/r1_zero_qwen25_32b.yaml b/configs/r1_zero_qwen25_32b.yaml index 240b0bfa..37379ca5 100644 --- a/configs/r1_zero_qwen25_32b.yaml +++ b/configs/r1_zero_qwen25_32b.yaml @@ -72,7 +72,6 @@ reward_config: rl_config: blocking: false - experience_count: 32 gamma: 1.0 lam: 0.95 adv_estimator: group_norm @@ -97,6 +96,7 @@ rl_config: num_npus: 16 generate_config: + micro_batch_size: 1 trust_remote_code: true offload_train_optimizer: true offload_train_grad: true diff --git a/configs/r1_zero_qwen25_7b.yaml b/configs/r1_zero_qwen25_7b.yaml index d67454f1..b6c7e254 100644 --- a/configs/r1_zero_qwen25_7b.yaml +++ b/configs/r1_zero_qwen25_7b.yaml @@ -72,7 +72,6 @@ reward_config: rl_config: blocking: true - experience_count: 64 gamma: 1.0 lam: 0.95 adv_estimator: group_norm @@ -97,6 +96,7 @@ rl_config: num_npus: 8 generate_config: + micro_batch_size: 4 trust_remote_code: true offload_train_optimizer: true offload_train_grad: true diff --git a/configs/r1_zero_qwen25_7b_integrated.yaml b/configs/r1_zero_qwen25_7b_integrated.yaml index 41fc2a7e..075c6ef8 100644 --- a/configs/r1_zero_qwen25_7b_integrated.yaml +++ b/configs/r1_zero_qwen25_7b_integrated.yaml @@ -56,7 +56,6 @@ rl_config: use_integrated_worker: true blocking: true update_micro_batch_size: 2 - experience_count: 16 gamma: 1.0 lam: 0.95 adv_estimator: group_norm diff --git a/mindspeed_rl/config_cls/generate_config.py b/mindspeed_rl/config_cls/generate_config.py index 6cf7642a..7ccc61cf 100644 --- a/mindspeed_rl/config_cls/generate_config.py +++ b/mindspeed_rl/config_cls/generate_config.py @@ -44,6 +44,9 @@ class GenerateConfig(BaseConfig): # 是否信任远程代码，例如用于自定义 tokenizer，默认为 True self.trust_remote_code = True + # 推理时的微批次处理数据 + self.micro_batch_size = None + # 推理时的张量并行大小，默认为 8 self.infer_tensor_parallel_size = 8 diff --git a/mindspeed_rl/config_cls/rl_config.py b/mindspeed_rl/config_cls/rl_config.py index b743b40f..de2ae915 100644 --- a/mindspeed_rl/config_cls/rl_config.py +++ b/mindspeed_rl/config_cls/rl_config.py @@ -29,12 +29,6 @@ class RLConfig(BaseConfig): adv_estimator: Method for estimating advantages (e.g., 'group_norm', 'gae') (default: 'group_norm') kl_penalty: Type of KL penalty to apply (e.g., 'kl', 'reverse_kl') (default: 'kl') - experience_count: The batch size of prompts per pipeline stage in each data fetch operation from the TransferDock (TD). (default: 1) - experience_count_actor: experience count every forward step for actor (default: same as experience_count_all) - experience_count_reward: experience count every forward step for reward (default: same as experience_count_all) - experience_count_ref: experience count every forward step for reference (default: same as experience_count_all) - experience_count_rule_reward: experience count every forward step for rule reward (default: same as experience_count_all) - shuffle_mini_batch: Whether to shuffle minibatch (default: False) n_samples_per_prompt: Number of samples per prompt (default: 1) enable_sharding_validate: Whether to enable sharding validation (default: False) @@ -77,12 +71,6 @@ class RLConfig(BaseConfig): self.verifier_parallel = 1 self.verifier_timeout = 30 - self.experience_count = 1 - self.experience_count_actor = None - self.experience_count_reward = None - self.experience_count_ref = None - self.experience_count_rule_reward = None - self.shuffle_mini_batch = False self.n_samples_per_prompt = 1 self.enable_sharding_validate = False diff --git a/mindspeed_rl/config_cls/validate_config.py b/mindspeed_rl/config_cls/validate_config.py index 00e400fa..09c8748e 100644 --- a/mindspeed_rl/config_cls/validate_config.py +++ b/mindspeed_rl/config_cls/validate_config.py @@ -31,12 +31,6 @@ def validate_rl_args( f"Actor.seq_length={actor_config.seq_length} vs " f"GenerateConfig.max_model_len={generate_config.max_model_len}") - # 初始化经验计数配置 - rl_config.experience_count_actor = rl_config.experience_count_actor or rl_config.experience_count - rl_config.experience_count_ref = rl_config.experience_count_ref or rl_config.experience_count - rl_config.experience_count_reward = rl_config.experience_count_reward or rl_config.experience_count - rl_config.experience_count_rule_reward = rl_config.experience_count_rule_reward or rl_config.experience_count - # 校验资源分配合理性 def _validate_resource(resource, t_size, p_size, c_size, component): product = t_size * p_size * c_size @@ -62,49 +56,7 @@ def validate_rl_args( reward_config.pipeline_model_parallel_size, reward_config.context_parallel_size, "Reward") - - # 计算数据并行度 - actor_data_parallel_size = rl_config.actor_resource.num_npus // ( - actor_config.tensor_model_parallel_size * - actor_config.pipeline_model_parallel_size * - actor_config.context_parallel_size) - - ref_data_parallel_size = rl_config.reference_resource.num_npus // ( - ref_config.tensor_model_parallel_size * - ref_config.pipeline_model_parallel_size * - ref_config.context_parallel_size) - - # 校验微批次经验分配 - def _validate_micro_batch(experience_count, n_samples_per_prompt, data_parallel, micro_batch, component): - per_device = experience_count * n_samples_per_prompt // data_parallel - if per_device % micro_batch != 0: - raise ValueError( - f"{component} per-device experience count {per_device} " - f"must be divisible by micro_batch_size {micro_batch}") - - # 校验奖励模型配置 - if rl_config.reward_resource: - reward_data_parallel_size = rl_config.reward_resource.num_npus // ( - reward_config.tensor_model_parallel_size * - reward_config.pipeline_model_parallel_size * - reward_config.context_parallel_size) - - if rl_config.experience_count_reward % reward_data_parallel_size != 0: - raise ValueError( - f"Reward experience count {rl_config.experience_count_reward} " - f"must be divisible by reward_data_parallel_size {reward_data_parallel_size}") - - _validate_micro_batch(rl_config.experience_count_actor, - rl_config.n_samples_per_prompt, - reward_data_parallel_size, - reward_config.micro_batch_size, - "Reward") - - # 校验生成配置数据并行度 - generate_config.data_parallel_size = rl_config.actor_resource.num_npus // ( - generate_config.infer_tensor_parallel_size * - generate_config.infer_pipeline_parallel_size) - + # 校验批次大小与微批次关系 def _validate_batch_ratio(global_batch, micro_batch, n_samples, component): if (global_batch * n_samples) % micro_batch != 0: @@ -129,68 +81,65 @@ def validate_rl_args( rl_config.n_samples_per_prompt, "Reward") - # 校验经验计数与全局批次关系 - def _validate_experience_ratio(global_batch, experience_count, component): - if global_batch % experience_count != 0: + # 校验数据并行与批次关系 + def _validate_data_parallel(global_batch_size, data_parallel, micro_batch_size, n_samples, component): + if global_batch_size % data_parallel != 0: raise ValueError( - f"{component} global_batch_size {global_batch} " - f"must be divisible by experience_count {experience_count}") - - _validate_experience_ratio(actor_config.global_batch_size, - rl_config.experience_count_actor, - "Actor") - - _validate_experience_ratio(ref_config.global_batch_size, - rl_config.experience_count_ref, - "Reference") - - if rl_config.reward_resource: - _validate_experience_ratio(reward_config.global_batch_size, - rl_config.experience_count_reward, - "Reward") - - _validate_experience_ratio(reward_config.global_batch_size, - rl_config.experience_count_rule_reward, - "Rule Reward") - - # 校验数据并行与微批次关系 - if actor_config.global_batch_size % actor_data_parallel_size != 0: - raise ValueError( - f"Actor global_batch_size {actor_config.global_batch_size} " - f"must be divisible by data_parallel_size {actor_data_parallel_size}") + f"{component} global_batch_size {global_batch_size} " + f"must be divisible by data_parallel_size {data_parallel}") + + if (global_batch_size // data_parallel * n_samples) % micro_batch_size != 0: + raise ValueError( + f"{component} global_batch_size {actor_config.global_batch_size} " + f" // data_parallel {data_parallel} * n_samples {n_samples} " + f"must be divisible by micro_batch_size {micro_batch_size} ") - if (actor_config.global_batch_size // actor_data_parallel_size) % actor_config.micro_batch_size != 0: - raise ValueError( - f"Actor per-device batch size {actor_config.global_batch_size // actor_data_parallel_size} " - f"must be divisible by micro_batch_size {actor_config.micro_batch_size}") + # 计算数据并行度 + actor_data_parallel_size = rl_config.actor_resource.num_npus // ( + actor_config.tensor_model_parallel_size * + actor_config.pipeline_model_parallel_size * + actor_config.context_parallel_size) - # 校验经验计数与并行度关系 - if rl_config.experience_count_actor % generate_config.data_parallel_size != 0: - raise ValueError( - f"Actor experience_count {rl_config.experience_count_actor} " - f"must be divisible by generate_data_parallel_size {generate_config.data_parallel_size}") + generate_config.data_parallel_size = rl_config.actor_resource.num_npus // ( + generate_config.infer_tensor_parallel_size * + generate_config.infer_pipeline_parallel_size) - if rl_config.experience_count_actor % actor_data_parallel_size != 0: - raise ValueError( - f"Actor experience_count {rl_config.experience_count_actor} " - f"must be divisible by actor_data_parallel_size {actor_data_parallel_size}") + ref_data_parallel_size = rl_config.reference_resource.num_npus // ( + ref_config.tensor_model_parallel_size * + ref_config.pipeline_model_parallel_size * + ref_config.context_parallel_size) - if rl_config.experience_count_ref % ref_data_parallel_size != 0: - raise ValueError( - f"Reference experience_count {rl_config.experience_count_ref} " - f"must be divisible by ref_data_parallel_size {ref_data_parallel_size}") + _validate_data_parallel(actor_config.global_batch_size, + actor_data_parallel_size, + actor_config.micro_batch_size, + rl_config.n_samples_per_prompt, + "Actor") + + _validate_data_parallel(actor_config.global_batch_size, + generate_config.data_parallel_size, + generate_config.micro_batch_size, + rl_config.n_samples_per_prompt, + "Generation") + + if not rl_config.use_integrated_worker: + _validate_data_parallel(ref_config.global_batch_size, + ref_data_parallel_size, + ref_config.micro_batch_size, + rl_config.n_samples_per_prompt, + "Reference") - _validate_micro_batch(rl_config.experience_count_actor, - rl_config.n_samples_per_prompt, - actor_data_parallel_size, - actor_config.micro_batch_size, - "Actor") + if rl_config.reward_resource: + reward_data_parallel_size = rl_config.reward_resource.num_npus // ( + reward_config.tensor_model_parallel_size * + reward_config.pipeline_model_parallel_size * + reward_config.context_parallel_size) - _validate_micro_batch(rl_config.experience_count_ref, - rl_config.n_samples_per_prompt, - ref_data_parallel_size, - ref_config.micro_batch_size, - "Reference") + if not rl_config.use_integrated_worker: + _validate_data_parallel(reward_config.global_batch_size, + reward_data_parallel_size, + reward_config.micro_batch_size, + rl_config.n_samples_per_prompt, + "Reference") # 检查验证器参数匹配 if len(rl_config.verifier_function) != len(rl_config.verifier_weight): diff --git a/mindspeed_rl/trainer/base.py b/mindspeed_rl/trainer/base.py index fcf56113..7b59228f 100644 --- a/mindspeed_rl/trainer/base.py +++ b/mindspeed_rl/trainer/base.py @@ -32,7 +32,7 @@ class RayBaseTrainer(object): kl_target: float = 100.0, init_kl_coef: float = 0.001, global_batch_size: int = 32, - experience_count: int = 1, + micro_batch_size: int = 1, n_samples_per_prompt: int = 1, tokenizer: BaseTokenizer = None, dataset_additional_keys: List[str] = None, @@ -55,7 +55,7 @@ class RayBaseTrainer(object): self.kl_target = kl_target self.init_kl_coef = init_kl_coef self.global_batch_size = global_batch_size - self.experience_count = experience_count + self.micro_batch_size = micro_batch_size self.n_samples_per_prompt = n_samples_per_prompt self.tokenizer = tokenizer self.dataset_additional_keys = dataset_additional_keys diff --git a/mindspeed_rl/trainer/grpo_trainer_hybrid.py b/mindspeed_rl/trainer/grpo_trainer_hybrid.py index 079a3f30..d2aef2b4 100644 --- a/mindspeed_rl/trainer/grpo_trainer_hybrid.py +++ b/mindspeed_rl/trainer/grpo_trainer_hybrid.py @@ -14,7 +14,7 @@ from mindspeed_rl.trainer.utils.compute_utils import compute_advantage, compute_ from mindspeed_rl.workers.scheduler.launcher import RayActorGroup from mindspeed_rl.utils.loggers import Loggers from mindspeed_rl.utils.metrics import Metric -from mindspeed_rl.utils.utils import metrics_post_processing, compute_tps, metrics_sort +from mindspeed_rl.utils.utils import metrics_post_processing, compute_tps, metrics_sort, get_least_common_multiple class RayGRPOTrainer(RayBaseTrainer): @@ -33,7 +33,6 @@ class RayGRPOTrainer(RayBaseTrainer): kl_target: float = 100.0 The target value for KL divergence (used in adaptive methods). init_kl_coef: float = 0.01 The initial coefficient for KL divergence penalty. global_batch_size: int = 1 The global batch size for training (number of prompts per iteration). - experience_count: int = 1 The batch size of prompts per pipeline stage in each data fetch operation from the TransferDock (TD). n_samples_per_prompt: int = 1 The number of samples generated per prompt. tokenizer: BaseTokenizer = None tokenizer to use. dataset_additional_keys: List[str] = None Additional keys to include in the dataset. @@ -55,7 +54,7 @@ class RayGRPOTrainer(RayBaseTrainer): kl_target: float = 100.0, init_kl_coef: float = 0.01, global_batch_size: int = 1, - experience_count: int = 1, + micro_batch_size: int = 1, n_samples_per_prompt: int = 1, tokenizer: BaseTokenizer = None, dataset_additional_keys: List[str] = None, @@ -75,7 +74,7 @@ class RayGRPOTrainer(RayBaseTrainer): adv_estimator=adv_estimator, init_kl_coef=init_kl_coef, global_batch_size=global_batch_size, - experience_count=experience_count, + micro_batch_size=micro_batch_size, n_samples_per_prompt=n_samples_per_prompt, tokenizer=tokenizer, dataset_additional_keys=dataset_additional_keys, @@ -90,7 +89,8 @@ class RayGRPOTrainer(RayBaseTrainer): self.kwargs = kwargs def transfer_dock_init(self): - self.transfer_dock = GRPOTransferDock.remote(self.global_batch_size, self.metrics, addition_columns=self.dataset_additional_keys) + self.transfer_dock = GRPOTransferDock.remote(self.global_batch_size, self.n_samples_per_prompt, + self.metrics, addition_columns=self.dataset_additional_keys) self.actor_worker.sync_init_transfer_dock(self.transfer_dock) self.ref_worker.sync_init_transfer_dock(self.transfer_dock) for reward in self.reward_list: @@ -116,33 +116,10 @@ class RayGRPOTrainer(RayBaseTrainer): logger.info('async start grpo training at iteration: {}/{} ...'.format(iteration, self.train_iters)) while iteration < self.train_iters: + ray.get(self.transfer_dock.clear.remote()) batch = next(data_iters) - prompts = batch['prompts'] - prompt_length = [] - for prompt in prompts: - for _ in range(self.n_samples_per_prompt): - prompt_length.append(torch.tensor([len(prompt)])) - - prompts_data = prompts - prompts = [] - for prompt in prompts_data: - for _ in range(self.n_samples_per_prompt): - prompts.append(copy.deepcopy(prompt)) - - add_vals = {} - for add_keys in self.dataset_additional_keys: - if add_keys in batch.keys(): - values = [] - for value in batch[add_keys]: - for _ in range(self.n_samples_per_prompt): - values.append(value) - add_vals[add_keys] = values - - ray.get(self.transfer_dock.clear.remote()) - ray.get(self.transfer_dock.put_experience.remote( - data_dict=dict({'prompt_length': prompt_length, 'prompts': prompts}, **add_vals), - num_responses=self.n_samples_per_prompt)) + ray.get(self.transfer_dock.put_prompts_experience.remote(batch, self.dataset_additional_keys)) with Timer(name='iteration', logger=None) as all_timer: # generate sequences @@ -175,7 +152,7 @@ class RayGRPOTrainer(RayBaseTrainer): # collect metrics grpo_data_metrics = compute_grpo_data_metrics(self.transfer_dock, - self.global_batch_size, + self.global_batch_size * self.n_samples_per_prompt, self.tokenizer) metrics_result = ray.get(self.transfer_dock.get_metrics.remote()) @@ -198,14 +175,16 @@ class RayGRPOTrainer(RayBaseTrainer): logger.info('after grpo training is done') def compute_advantage(self, blocking=False): + experience_count = get_least_common_multiple(self.micro_batch_size, + self.n_samples_per_prompt) + compute_advantage_ref = compute_advantage.options(num_cpus=self.num_cpus_for_local_task).remote( self.transfer_dock, self.gamma, self.lam, adv_estimator=self.adv_estimator, - experience_count=self.experience_count, + experience_count=experience_count, tokenizer=self.tokenizer, - n_samples_per_prompt=self.n_samples_per_prompt, ) if blocking: ray.get(compute_advantage_ref) diff --git a/mindspeed_rl/trainer/utils/compute_utils.py b/mindspeed_rl/trainer/utils/compute_utils.py index e766b43a..e096573e 100644 --- a/mindspeed_rl/trainer/utils/compute_utils.py +++ b/mindspeed_rl/trainer/utils/compute_utils.py @@ -124,7 +124,7 @@ def compute_group_norm_advantage_return(token_level_rewards: torch.Tensor, eos_m @ray.remote -def compute_advantage(rb, gamma, lam, adv_estimator, experience_count, tokenizer, n_samples_per_prompt): +def compute_advantage(rb, gamma, lam, adv_estimator, experience_count, tokenizer): """ Compute the advantage function based on different adv_estimator @@ -135,18 +135,17 @@ def compute_advantage(rb, gamma, lam, adv_estimator, experience_count, tokenizer adv_estimator: The type of advantage estimator, which can be "gae" or "group_norm" experience_count: The number of experiences to retrieve from the experience rb tokenizer: The pre-trained tokenizer - n_samples_per_prompt: The number of samples per prompt Returns: None """ experience_consumer_stage = "compute_advantage" - experience_colums = ["responses", "token_level_rewards", "response_length"] + experience_columns = ["responses", "token_level_rewards", "response_length"] pad_token_id = tokenizer.pad if tokenizer.pad is not None else tokenizer.eod while not ray.get(rb.all_consumed.remote(experience_consumer_stage)): batch_data, index = ray.get( rb.get_experience.remote( - experience_consumer_stage, experience_colums, experience_count, pad_id=pad_token_id + experience_consumer_stage, experience_columns, experience_count, pad_id=pad_token_id ) ) if batch_data and index: @@ -170,7 +169,7 @@ def compute_advantage(rb, gamma, lam, adv_estimator, experience_count, tokenizer "advantages": advantages, "returns": returns, } - rb.put_experience.remote(data_dict=output, indexes=index, num_responses=n_samples_per_prompt) + rb.put_experience.remote(data_dict=output, indexes=index) def get_last_reward(rm_scores, n_sample_batch: int): @@ -205,7 +204,7 @@ def compute_grpo_data_metrics( Dictionary containing various metric values """ experience_consumer_stage = "grpo_metrics" - experience_colums = [ + experience_columns = [ "rm_scores", "token_level_rewards", "responses", @@ -217,7 +216,7 @@ def compute_grpo_data_metrics( pad_id = tokenizer.pad if tokenizer.pad is not None else tokenizer.eod while not ray.get(rb.all_consumed.remote(experience_consumer_stage)): batch, index = ray.get( - rb.get_experience.remote(experience_consumer_stage, experience_colums, experience_count, pad_id=pad_id) + rb.get_experience.remote(experience_consumer_stage, experience_columns, experience_count, pad_id=pad_id) ) if batch and index: sequence_score = batch["rm_scores"].sum(-1) diff --git a/mindspeed_rl/trainer/utils/transfer_dock.py b/mindspeed_rl/trainer/utils/transfer_dock.py index f866cc47..e3685664 100644 --- a/mindspeed_rl/trainer/utils/transfer_dock.py +++ b/mindspeed_rl/trainer/utils/transfer_dock.py @@ -1,7 +1,8 @@ # coding=utf-8 # Copyright (c) 2025, HUAWEI CORPORATION. All rights reserved. - +import copy import time +import threading from abc import ABC from typing import List, Dict, Union, Optional from operator import itemgetter @@ -13,12 +14,12 @@ from torch.nn import functional as F from torch.nn.utils.rnn import pad_sequence from mindspeed_rl.utils.loggers import Loggers - logger = Loggers("transfer_dock") class TimeoutException(Exception): """Custom Timeout Exception""" + def __init__(self, message="TIMEOUT: Time Sleep Too Long"): super().__init__(message) @@ -29,30 +30,49 @@ class TransferDock(ABC): providing data storage and retrieval functions. """ - def __init__(self, max_len: int, experience_columns: Union[List[str], None], - timeout: Union[int, None], timeout_interval: Union[int, None]) -> None: + def __init__( + self, + prompts_num: int, + n_samples_per_prompt: int, + experience_columns: Union[List[str], None], + timeout: Union[int, None], + timeout_interval: Union[int, None], + ) -> None: """TransferDock initialize. Args: - max_len: The maximum length of data that can be stored in TransferDock. + prompts_num: The number of prompts loaded from the dataset. + n_samples_per_prompt: The number of responses for a prompt. experience_columns: Data columns in TransferDock. + timeout: The waiting time for over time printing + timeout_interval: Time interval for timeout printing """ super().__init__() - self.max_len = max_len - self.experience_columns = experience_columns if experience_columns is not None else [] - self.experience_data = {key: [None for _ in range(self.max_len)] for key in self.experience_columns} + + self.prompts_num = prompts_num + self.n_samples_per_prompt = n_samples_per_prompt + self.max_len = prompts_num * n_samples_per_prompt + + self.experience_columns = ( + experience_columns if experience_columns is not None else [] + ) + self.experience_data = { + key: [None for _ in range(self.max_len)] + for key in self.experience_columns + } self.experience_data_status = { - key: torch.zeros(self.max_len, dtype=torch.int32) for key in self.experience_columns + key: torch.zeros(self.max_len, dtype=torch.int32) + for key in self.experience_columns } - self.index_dispatch_stop_signal = False - self.timeout = timeout if timeout is not None else 300 # waite over 300s to log - self.timeout_interval = timeout_interval if timeout_interval is not None else 5 # logger ever 5s + + self.timeout = timeout if timeout is not None else 300 + self.timeout_interval = timeout_interval if timeout_interval is not None else 5 def _put( - self, - experience_columns: List[str], - experience: List[List[List[torch.Tensor]]], - indexes: List[int] = None, + self, + experience_columns: List[str], + experience: List[List[List[torch.Tensor]]], + indexes: List[int] = None, ): """Put data into specified columns and rows. @@ -62,96 +82,45 @@ class TransferDock(ABC): experience: Data for the corresponding columns. [ [ - [tensor([1, 1, 1, 1]), tensor([2, 2, 2, 2])], - [tensor([3, 3, 3, 3]), tensor(4, 4, 4, 4)] + tensor([1, 1, 1, 1]), + tensor([2, 2, 2, 2]), + tensor([3, 3, 3, 3]), + tensor([4, 4, 4, 4]) ], [ - [tensor([1]), tensor([2, 2])], - [tensor([3, 3, 3]), tensor([4, 4, 4, 4])] + tensor([1]), + tensor([2, 2]), + tensor([3, 3, 3]), + tensor([4, 4, 4, 4]) ] ] indexes: Rows to put data in. - [0, 2] + [0, 1, 2, 4] Returns: None """ - # If experience_columns not in TD, raise ValueError for experience_column in experience_columns: if experience_column not in self.experience_columns: - raise ValueError(f"put experience ERROR: {experience_column} not in TD experience_column {self.experience_columns}") - - if indexes is not None: - self._put_with_index(experience_columns, experience, indexes) - else: - self._put_without_index(experience_columns, experience) - - def _put_with_index( - self, - experience_columns: List[str], - experience: List[List[List[torch.Tensor]]], - indexes: List[int], - ): - """Put data into specified columns and rows. - - Args: - experience_columns: Columns to put data in. - experience: Data for the corresponding columns. - indexes: Rows to put data in. + raise ValueError( + f"put experience ERROR: {experience_column} not in TD experience_column {self.experience_columns}" + ) - Returns: None + if not indexes: + raise ValueError( + "put experience into TD without indexes, indexes must be provided" + ) - """ if max(indexes) >= self.max_len: - raise ValueError(f"Put experience index {max(indexes)} exceeds the Transfer Dock range {self.max_len}.") + raise ValueError( + f"Put experience index {max(indexes)} exceeds the Transfer Dock range {self.max_len}." + ) for column_idx, single_column in enumerate(experience_columns): for i, index in enumerate(indexes): - # input can rewrite experience data in TD self.experience_data[single_column][index] = experience[column_idx][i] self.experience_data_status[single_column][index] = 1 - def _put_without_index(self, experience_columns: List[str], experience: List[List[List[torch.Tensor]]]): - """Put data into specified columns and random rows. - - Args: - experience_columns: Columns to put data in. - experience: Data for the corresponding columns. - - Returns: None - - """ - start_time = time.time() - while self.index_dispatch_stop_signal: - elapsed_time = time.time() - start_time - if elapsed_time > self.timeout and elapsed_time % self.timeout_interval < 0.1: # 每隔2s打印一次 - logger.warning(f"TIMEOUT: index_dispatch_stop_signal has slept {elapsed_time} second") - # put_without_index to the TD. Only one process can sample the index at a time. The others are waiting. - time.sleep(0.1) - - self.index_dispatch_stop_signal = True - writable_indexes = ( - torch.all( - torch.stack( - [self.experience_data_status[single_column] == 0 for single_column in experience_columns], - dim=0, - ), - dim=0, - keepdim=True, - ) - .reshape(-1) - .nonzero(as_tuple=True)[0] - ) - sampled_indexes_idx = torch.multinomial( - torch.ones(len(writable_indexes)), len(experience[0]), replacement=False - ).tolist() - sampled_indexes = [int(writable_indexes[i]) for i in sampled_indexes_idx] - - for single_column in experience_columns: - self.experience_data_status[single_column][sampled_indexes] = -1 - self.index_dispatch_stop_signal = False - self._put_with_index(experience_columns, experience, sampled_indexes) - def _get(self, experience_columns: List[str], indexes: List[int]): """Get data based on row and column numbers. @@ -164,18 +133,27 @@ class TransferDock(ABC): Returns: Data list. [ [ - [tensor([1, 1, 1, 1]), tensor([2, 2, 2, 2])], - [tensor([3, 3, 3, 3]), tensor(4, 4, 4, 4)] + tensor([1, 1, 1, 1]), + tensor([2, 2, 2, 2]), + tensor([3, 3, 3, 3]), + tensor([4, 4, 4, 4]) ], [ - [tensor([1]), tensor([2, 2])], - [tensor([3, 3, 3]), tensor([4, 4, 4, 4])] + tensor([1]), + tensor([2, 2]), + tensor([3, 3, 3]), + tensor([4, 4, 4, 4]) ] ] """ + if len(indexes) == 0: + return [[] for _ in range(len(experience_columns))] + if max(indexes) >= self.max_len: - raise ValueError(f"Get experience index {max(indexes)} exceeds the Transfer Dock range {self.max_len}.") + raise ValueError( + f"Get experience index {max(indexes)} exceeds the Transfer Dock range {self.max_len}." + ) experience = [] for single_column in experience_columns: @@ -188,7 +166,7 @@ class TransferDock(ABC): return experience def _wait_for_data(self, single_column: str, indexes: List[int]): - """ Wait for data in which column and row to be ready. + """Wait for data in which column and row to be ready. Args: single_column: Column that need to wait for data to be ready. @@ -201,29 +179,44 @@ class TransferDock(ABC): data_ready = self.experience_data_status[single_column][indexes] == 1 else: data_ready = sum(itemgetter(*indexes)(self.experience_data_status[single_column])) == len(indexes) - + start_time = time.time() while not data_ready: elapsed_time = time.time() - start_time - if elapsed_time > self.timeout and elapsed_time % self.timeout_interval < 0.1: + if ( + elapsed_time > self.timeout + and elapsed_time % self.timeout_interval < 0.1 + ): logger.warning(f"TIMEOUT: data_ready has slept {elapsed_time} second") - # Wait until the data in a single column is ready. time.sleep(0.1) if len(indexes) == 1: data_ready = self.experience_data_status[single_column][indexes] == 1 else: - data_ready = sum(itemgetter(*indexes)(self.experience_data_status[single_column])) == len(indexes) + data_ready = sum( + itemgetter(*indexes)(self.experience_data_status[single_column]) + ) == len(indexes) - def _clear_experience_data_and_status(self): + def _clear_experience_data_and_status(self, indexes=None): """Clear data and data status in TransferDock. Returns: None """ - self.experience_data = {key: [None for _ in range(self.max_len)] for key in self.experience_columns} - self.experience_data_status = { - key: torch.zeros(self.max_len, dtype=torch.int32) for key in self.experience_columns - } + if indexes is None: + self.experience_data = { + key: [None for _ in range(self.max_len)] + for key in self.experience_columns + } + self.experience_data_status = { + key: torch.zeros(self.max_len, dtype=torch.int32) + for key in self.experience_columns + } + else: + for key in self.experience_columns: + self.experience_data_status[key][indexes] = 0 + for key in self.experience_columns: + for idx in indexes: + self.experience_data[key][idx] = None def get_experience_data(self): """Get all data in TransferDock. @@ -257,19 +250,32 @@ class GRPOTransferDock(TransferDock): GRPO asynchronous tasks in the Ray cluster. """ - def __init__(self, max_len: int, metrics=None, addition_columns: Union[List[str], None] = None, - addition_consumers: Union[List[str], None] = None, timeout: Union[int, None] = None, - timeout_interval: Union[int, None] = None) -> None: + def __init__( + self, + prompts_num: int, + n_samples_per_prompt: int, + metrics=None, + addition_columns: Union[List[str], None] = None, + addition_consumers: Union[List[str], None] = None, + timeout: Union[int, None] = None, + timeout_interval: Union[int, None] = None, + ) -> None: """GRPOTransferDock initialize. Args: - max_len: The maximum length of data that can be stored in GRPOTransferDock. + prompts_num: The number of prompts loaded from the dataset. + n_samples_per_prompt: The number of responses for a prompt. + metrics: The metrics stored in TransferDock. + addition_columns: Additional experience columns in TransferDock. + addition_consumers: Additional consumers in TransferDock. + timeout: The waiting time for over time printing. + timeout_interval: Time interval for timeout printing. """ self.experience_columns = [ "prompts", - 'prompt_length', - 'responses', - 'response_length', + "prompt_length", + "responses", + "response_length", "attention_mask", "labels", "input_ids", @@ -278,10 +284,9 @@ class GRPOTransferDock(TransferDock): "token_level_rewards", "old_log_prob", "ref_log_prob", - 'advantages', - 'returns' + "advantages", + "returns", ] - self.max_len = max_len self.experience_consumers = [ "trainer", "actor_rollout", @@ -293,24 +298,32 @@ class GRPOTransferDock(TransferDock): "reward_scores", "grpo_metrics", ] - # Initialize to add additional experience columns if addition_columns: for column in addition_columns: if column not in self.experience_columns: self.experience_columns.append(column) - # Initialize to add additional experience consumers if addition_consumers: for consumer in addition_consumers: if consumer not in self.experience_consumers: self.experience_consumers.append(consumer) + super().__init__( + prompts_num, + n_samples_per_prompt, + self.experience_columns, + timeout, + timeout_interval, + ) self.experience_consumer_status = { - key: torch.zeros(self.max_len, dtype=torch.int32) for key in self.experience_consumers + key: torch.zeros(self.max_len, dtype=torch.int32) + for key in self.experience_consumers + } + self.consumer_sampling_lock = { + key: threading.Lock() + for key in self.experience_consumers } - self.consumer_sampling_signal = {key: False for key in self.experience_consumers} self.metrics = metrics - super().__init__(self.max_len, self.experience_columns, timeout, timeout_interval) def get_metrics(self): return self.metrics @@ -319,13 +332,14 @@ class GRPOTransferDock(TransferDock): self.metrics.update(key, value, cumulate=cumulate) def get_experience( - self, - consumer: str, - experience_columns: List[str], - experience_count: int = None, - indexes: List[int] = None, - pad_id: int = None, - multiple: int = 1, + self, + consumer: str, + experience_columns: List[str], + experience_count: int = None, + indexes: List[int] = None, + pad_id: int = None, + multiple: int = 1, + get_n_samples: bool = True, ): """Get padded experience data from GRPOTransferDock. @@ -336,29 +350,48 @@ class GRPOTransferDock(TransferDock): indexes: Rows from which to get data. pad_id: Pad token. multiple: The multiple of TP to pad. + get_n_samples: Whether to get n samples at the same time. + target_seq_len: Target sequence length. Returns: Data dict and row numbers. """ - # If consumer not in TD, raise ValueError if consumer not in self.experience_consumers: - raise ValueError(f"get experience ERROR: {consumer} not in TD experience_consumers {self.experience_consumers}") + raise ValueError( + f"get experience ERROR: {consumer} not in TD experience_consumers {self.experience_consumers}" + ) - # If experience_columns not in TD, raise ValueError for experience_column in experience_columns: if experience_column not in self.experience_columns: - raise ValueError(f"get experience ERROR: {experience_column} not in TD experience_column {self.experience_columns}") - + raise ValueError( + f"get experience ERROR: {experience_column} not in TD experience_column {self.experience_columns}" + ) + if indexes is None: - # If experience_count > the number of TD (self.max_len), raise ValueError if experience_count > self.max_len: - raise ValueError(f"max_len:{self.max_len} need >= experience_count: {experience_count}") + raise ValueError( + f"TD max_len: {self.max_len} need >= experience_count: {experience_count}" + ) - # If max_len is not divisible by experience_count, raise ValueError if self.max_len % experience_count != 0: - raise ValueError(f"max_len:{self.max_len} need be divisible by experience_count: {experience_count}") - - indexes = self._sample_ready_index(consumer, experience_count, experience_columns) + raise ValueError( + f"TD max_len:{self.max_len} need be divisible by experience_count: {experience_count}" + ) + + if get_n_samples: + if experience_count % self.n_samples_per_prompt != 0: + raise ValueError( + f"get_n_samples need experience_count:{experience_count} must be divisible by " + f"n_samples_per_prompt: {self.n_samples_per_prompt}" + ) + indexes = self._sample_ready_index_n_samples( + consumer, experience_count, experience_columns + ) + else: + indexes = self._sample_ready_index( + consumer, experience_count, experience_columns + ) + if not indexes: return None, None experience = self._get(experience_columns, indexes) @@ -370,60 +403,179 @@ class GRPOTransferDock(TransferDock): return experience_batch, indexes def put_experience( - self, - data_dict: Dict[str, Union[Tensor, List[Tensor]]], - indexes: List[int] = None, - num_responses: int = 1, + self, + data_dict: Dict[str, Union[Tensor, List[Tensor]]], + indexes: List[int] = None ): """Put data into specified columns and rows. Args: data_dict: Data dict to put in GRPOTransferDock. indexes: Rows to put data in. - num_responses: The number of data to put in each row. Returns: None """ - experience_columns, experience = trans_input_to_experience(data_dict, num_responses) + + if not indexes: + raise ValueError( + "put experience into TD without indexes, indexes must be provided" + ) + experience_columns, experience = trans_input_to_experience(data_dict) + self._put(experience_columns, experience, indexes) + + def put_prompts_experience( + self, batch: Dict[str, Tensor], dataset_additional_keys: List[str] = None + ): + """Put data into specified columns and rows. + + Args: + batch: Batch datas from original dataloader. + dataset_additional_keys: The additional experience types from the dataset. + + Returns: None + + """ + + prompts = batch["prompts"] + prompt_length = [] + for prompt in prompts: + for _ in range(self.n_samples_per_prompt): + prompt_length.append(torch.tensor([len(prompt)])) + + prompts_data = prompts + prompts = [] + for prompt in prompts_data: + for _ in range(self.n_samples_per_prompt): + prompts.append(copy.deepcopy(prompt)) + + add_vals = {} + for add_keys in dataset_additional_keys: + if add_keys in batch.keys(): + values = [] + for value in batch[add_keys]: + for _ in range(self.n_samples_per_prompt): + values.append(value) + add_vals[add_keys] = values + + indexes = [i for i in range(len(prompt_length))] + data_dict = dict( + {"prompt_length": prompt_length, "prompts": prompts}, **add_vals + ) + experience_columns, experience = trans_input_to_experience(data_dict) + self._put(experience_columns, experience, indexes) - def _sample_ready_index(self, consumer: str, mbs: int, experience_columns: List[str]) -> Optional[List[int]]: - """Wait for random sampling rows to be ready. + def _sample_ready_index( + self, + consumer: str, + experience_count: int, + experience_columns: List[str], + target_seq_len: int = None, + ) -> Optional[List[int]]: + """Randomly select a specified number of prepared experiences from TransferDock. Args: consumer: GRPO task stage to sample in. - mbs: Number for rows to sample. + experience_count: Number for rows to sample. experience_columns: Columns from which to sample. Returns: Sampled row numbers. """ - start_time = time.time() - while self.consumer_sampling_signal[consumer]: - elapsed_time = time.time() - start_time - if elapsed_time > self.timeout and elapsed_time % self.timeout_interval < 0.1: - logger.warning(f"TIMEOUT: consumer_sampling_signal has slept {elapsed_time} second") - # only one process can sampele index. The others are waiting. - time.sleep(0.1) + with self.consumer_sampling_lock[consumer]: + not_consumed_indexes = self.experience_consumer_status[consumer] == 0 + data_ready_indexes = torch.all( + torch.stack( + [self.experience_data_status[single_column] == 1 for single_column in experience_columns] + ), dim=0, + ) + usable_indexes = (not_consumed_indexes & data_ready_indexes).nonzero(as_tuple=True)[0] - self.consumer_sampling_signal[consumer] = True - not_consumed_indexes = self.experience_consumer_status[consumer] == 0 - data_ready_indexes = torch.all( - torch.stack([self.experience_data_status[single_column] == 1 for single_column in experience_columns]), - dim=0, - ) - usable_indexes = (not_consumed_indexes & data_ready_indexes).nonzero(as_tuple=True)[0] + if len(usable_indexes) < experience_count: + return None - if len(usable_indexes) < mbs: - self.consumer_sampling_signal[consumer] = False - return None + if experience_count > 0: + sampled_indexes = self.batch_balencing_sampler( + experience_columns, usable_indexes, experience_count, target_seq_len + ) + self.experience_consumer_status[consumer][sampled_indexes] = 1 + else: + sampled_indexes = None - sampled_indexes_idx = torch.multinomial(torch.ones(len(usable_indexes)), mbs, replacement=False).tolist() - sampled_indexes = [int(usable_indexes[i]) for i in sampled_indexes_idx] - self.experience_consumer_status[consumer][sampled_indexes] = 1 - self.consumer_sampling_signal[consumer] = False + return sampled_indexes + + def _sample_ready_index_n_samples( + self, + consumer: str, + experience_count: int, + experience_columns: List[str], + target_seq_len: int = None, + ) -> Optional[List[int]]: + """Randomly select a specified number of prepared experiences from TransferDock at multiples of n_sample. + + Args: + consumer: GRPO task stage to sample in. + experience_count: Number for rows to sample. + experience_columns: Columns from which to sample. + target_seq_len: Sample according with seq_len and target_seq_len. + + Returns: Sampled row numbers. + + """ + experience_count_n_samples = experience_count // self.n_samples_per_prompt + with self.consumer_sampling_lock[consumer]: + experience_consumer_status_n_samples = ( + 1 - torch.all( + torch.tensor( + torch.reshape( + self.experience_consumer_status[consumer], + (self.prompts_num, self.n_samples_per_prompt), + ) == 0 + ), dim=1, + ).int() + ) + not_consumed_indexes = experience_consumer_status_n_samples == 0 + + experience_data_status_n_samples = {} + for key, value in self.experience_data_status.items(): + experience_data_status_n_samples[key] = torch.all( + torch.tensor( + torch.reshape(value, (self.prompts_num, self.n_samples_per_prompt)) == 1 + ), dim=1, + ).int() + + data_ready_indexes = torch.all( + torch.stack( + [experience_data_status_n_samples.get(single_column) == 1 for single_column in experience_columns]), + dim=0, + ) + + usable_indexes = (not_consumed_indexes & data_ready_indexes).nonzero(as_tuple=True)[0] + + if len(usable_indexes) < experience_count_n_samples: + return None + + sampled_indexes_n_sample = self.batch_balencing_sampler( + experience_columns, + usable_indexes, + experience_count_n_samples, + target_seq_len, + ) + + sampled_indexes = [] + for n_sample_index in sampled_indexes_n_sample: + index_list = [] + for index in range( + n_sample_index * self.n_samples_per_prompt, + (n_sample_index + 1) * self.n_samples_per_prompt + ): + index_list.append(index) + + sampled_indexes += index_list + + self.experience_consumer_status[consumer][sampled_indexes] = 1 return sampled_indexes @@ -445,7 +597,8 @@ class GRPOTransferDock(TransferDock): """ self.experience_consumer_status = { - key: torch.zeros(self.max_len, dtype=torch.int32) for key in self.experience_consumers + key: torch.zeros(self.max_len, dtype=torch.int32) + for key in self.experience_consumers } self.metrics.reset() self._clear_experience_data_and_status() @@ -458,12 +611,31 @@ class GRPOTransferDock(TransferDock): """ return self.experience_consumer_status + def batch_balencing_sampler( + self, experience_columns, usable_indexes, experience_count, target_seq_len=None + ): + if target_seq_len is None: + weights = torch.ones(len(usable_indexes)) + else: + seq_len = torch.tensor( + [ + sum([self.experience_data[key][idx].numel() for key in experience_columns]) + for idx in usable_indexes + ] + ) + weights = torch.sigmoid(1 / (torch.abs(seq_len - target_seq_len) + 0.001), dim=0) + + sampled_indexes_idx = torch.multinomial(weights, experience_count, replacement=False).tolist() + sampled_indexes = [int(usable_indexes[i]) for i in sampled_indexes_idx] + + return sampled_indexes + def trans_experience_to_output( - experience: List[List[List[Tensor]]], - experience_columns: List[str], - pad_id: int, - multiple: int, + experience: List[List[Tensor]], + experience_columns: List[str], + pad_id: int, + multiple: int, ): """Merge and pad data into dict. @@ -471,12 +643,16 @@ def trans_experience_to_output( experience: Data list. [ [ - [tensor([1, 1, 1, 1]), tensor([2, 2, 2, 2])], - [tensor([3, 3, 3, 3]), tensor(4, 4, 4, 4)] + tensor([1, 1, 1, 1]), + tensor([2, 2, 2, 2]), + tensor([3, 3, 3, 3]), + tensor([4, 4, 4, 4]) ], [ - [tensor([1]), tensor([2, 2])], - [tensor([3, 3, 3]), tensor([4, 4, 4, 4])] + tensor([1]), + tensor([2, 2]), + tensor([3, 3, 3]), + tensor([4, 4, 4, 4]) ] ] experience_columns: Columns for the corresponding data. @@ -503,7 +679,7 @@ def trans_experience_to_output( """ batch = {} for i, experience_column in enumerate(experience_columns): - experience_i_all = [item for sublist in experience[i] for item in sublist] + experience_i_all = experience[i] if experience_column in ["prompt_length", "response_length"]: padded = torch.cat(experience_i_all).reshape(-1, 1) elif experience_i_all[0].is_floating_point(): @@ -516,7 +692,7 @@ def trans_experience_to_output( return batch -def trans_input_to_experience(experience_dict: Dict[str, Union[Tensor, List[Tensor]]], num_responses: int): +def trans_input_to_experience(experience_dict: Dict[str, Union[Tensor, List[Tensor]]]): """Split data dict into columns and data list. Args: @@ -540,12 +716,16 @@ def trans_input_to_experience(experience_dict: Dict[str, Union[Tensor, List[Tens ['prompts', 'attention_mask'] [ [ - [tensor([1, 1, 1, 1]), tensor([2, 2, 2, 2])], - [tensor([3, 3, 3, 3]), tensor(4, 4, 4, 4)] + tensor([1, 1, 1, 1]), + tensor([2, 2, 2, 2]), + tensor([3, 3, 3, 3]), + tensor([4, 4, 4, 4]) ], [ - [tensor([1)], tensor([2, 2])], - [tensor([3, 3, 3]), tensor([4, 4, 4, 4])] + tensor([1)], + tensor([2, 2]), + tensor([3, 3, 3]), + tensor([4, 4, 4, 4]) ] ] @@ -555,25 +735,16 @@ def trans_input_to_experience(experience_dict: Dict[str, Union[Tensor, List[Tens for key, value in experience_dict.items(): if value is not None: experience_columns.append(key) - if len(value) % num_responses != 0: - raise ValueError( - f"value must divide num_responses, but got value: {len(value)}, num_responses: {num_responses}" - ) if isinstance(value, Tensor): - value = value.reshape(len(value) // num_responses, num_responses, value.shape[1]) - slices = torch.unbind(value, dim=0) - value = [[t.detach().clone() for t in torch.unbind(t_slice, dim=0)] for t_slice in slices] - elif isinstance(value, List): - value = [ - [v.detach().clone() for v in value[i: i + num_responses]] - for i in range(0, len(value), num_responses) - ] + value = list(torch.unbind(value, dim=0)) experience_list.append(value) return experience_columns, experience_list -def pad_multiples(data_list: List[Tensor], pad_id: Union[float, int], multiple: int = 1) -> Tensor: +def pad_multiples( + data_list: List[Tensor], pad_id: Union[float, int], multiple: int = 1 +) -> Tensor: """Pad method for data list. Args: diff --git a/mindspeed_rl/utils/__init__.py b/mindspeed_rl/utils/__init__.py index 2afebe25..9e904ae1 100644 --- a/mindspeed_rl/utils/__init__.py +++ b/mindspeed_rl/utils/__init__.py @@ -7,10 +7,12 @@ from .metrics import Metric from .math_eval_toolkit import extract_answer, choice_answer_clean, math_equal from .utils import ( get_batch_metrices_mean, num_floating_point_operations, - seed_all, synchronize_time, parse_args_from_config, get_tune_attention_mask + seed_all, synchronize_time, parse_args_from_config, get_tune_attention_mask, + get_least_common_multiple ) __all__ = ['get_tokenizer', 'Loggers', 'WandbLogger', 'Metric', 'get_batch_metrices_mean', 'num_floating_point_operations', 'seed_all', 'synchronize_time', 'parse_args_from_config', - 'extract_answer', 'choice_answer_clean', 'math_equal', 'get_tune_attention_mask'] + 'extract_answer', 'choice_answer_clean', 'math_equal', + 'get_tune_attention_mask', 'get_least_common_multiple'] diff --git a/mindspeed_rl/utils/pad_process.py b/mindspeed_rl/utils/pad_process.py index a112a0ec..26c644c8 100644 --- a/mindspeed_rl/utils/pad_process.py +++ b/mindspeed_rl/utils/pad_process.py @@ -7,7 +7,7 @@ from torch.nn.utils.rnn import pad_sequence from torch.nn import functional as F -def remove_padding_and_split_to_list(responses: torch.Tensor, eos_token_id: int, pad_token_id: int) -> List[ +def remove_padding_and_split_to_list(responses: torch.Tensor, eos_token_id: int, pad_token_id: int, to_list: bool = False) -> List[ torch.Tensor]: output = [] for i in range(responses.shape[0]): @@ -21,6 +21,8 @@ def remove_padding_and_split_to_list(responses: torch.Tensor, eos_token_id: int, response = response[:first_pad_index + 1] else: response = response[:first_pad_index] + if to_list: + response = response[:-1].cpu().numpy().tolist() output.append(response) return output diff --git a/mindspeed_rl/utils/utils.py b/mindspeed_rl/utils/utils.py index 4a547283..385bb0e8 100644 --- a/mindspeed_rl/utils/utils.py +++ b/mindspeed_rl/utils/utils.py @@ -5,6 +5,7 @@ import os import sys import time +import math import random from typing import Dict, List @@ -276,3 +277,7 @@ def parse_args_from_config(config): continue else: sys.argv.append(f"--{key.replace('_', '-')}={value}") + + +def get_least_common_multiple(num_1: int, num_2: int): + return abs(num_1 * num_2) // math.gcd(num_1, num_2) \ No newline at end of file diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index 9c367283..902cb273 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -19,7 +19,7 @@ from mindspeed_rl.models.rollout.vllm_engine import VLLMInferEngine from mindspeed_rl.utils.tokenizer import BaseTokenizer from mindspeed_rl.workers.base_worker import BaseWorker from mindspeed_rl.workers.resharding.megatron_sharding_manager import MegatronShardingManager, MegatronOffLoader -from mindspeed_rl.utils.utils import num_floating_point_operations +from mindspeed_rl.utils.utils import num_floating_point_operations, get_least_common_multiple from mindspeed_rl.utils.pad_process import remove_padding_and_split_to_list, truncate_rows from mindspeed_rl.utils.compute import get_parallel_state from mindspeed_rl.trainer.utils.parallel_state import is_pipeline_last_stage, get_tensor_model_parallel_rank @@ -115,9 +115,9 @@ class ActorHybridWorkerBase(BaseWorker): self.args.curr_iteration = self.iteration experience_consumer_stage = 'actor_train' - experience_colums = ['responses', 'advantages', 'old_log_prob', + experience_columns = ['responses', 'advantages', 'old_log_prob', 'ref_log_prob', 'input_ids', 'response_length', 'prompt_length'] - experience_count = self.megatron_config.global_batch_size // self.rl_config.n_samples_per_prompt // self.parallel_state.get_data_parallel_world_size() + experience_count = self.megatron_config.global_batch_size // self.parallel_state.get_data_parallel_world_size() #get lr learning_rate = None @@ -127,9 +127,11 @@ class ActorHybridWorkerBase(BaseWorker): start_time_defined = False while self.all_consumed(experience_consumer_stage) > 0: - batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_colums, - experience_count, self.rl_config.n_samples_per_prompt, - self.megatron_config.tensor_model_parallel_size) + batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, + experience_columns, + experience_count, + self.megatron_config.tensor_model_parallel_size, + get_n_samples=False) if not start_time_defined: start_time = time.time() start_time_defined = True @@ -160,8 +162,9 @@ class ActorHybridWorkerBase(BaseWorker): self.sharding_manager.enter_infer_mode() experience_consumer_stage = 'actor_rollout' - experience_colums = ['prompts', 'prompt_length'] - experience_count = self.rl_config.experience_count_actor // self.generate_config.data_parallel_size + experience_columns = ['prompts', 'prompt_length'] + experience_count = get_least_common_multiple(self.generate_config.micro_batch_size, + self.rl_config.n_samples_per_prompt) pad_token_id = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod @@ -169,9 +172,8 @@ class ActorHybridWorkerBase(BaseWorker): while self.all_consumed(experience_consumer_stage, use_vllm=True) > 0: batch_data, index = self.dispatch_transfer_dock_data( experience_consumer_stage, - experience_colums, + experience_columns, experience_count, - n_samples_per_prompt=self.rl_config.n_samples_per_prompt, tp_size=self.megatron_config.tensor_model_parallel_size, use_vllm=True ) @@ -179,15 +181,13 @@ class ActorHybridWorkerBase(BaseWorker): start_time = time.time() start_time_defined = True if batch_data and index: - indexes = list(range(0, experience_count * self.rl_config.n_samples_per_prompt, - self.rl_config.n_samples_per_prompt)) + indexes = list(range(0, experience_count, self.rl_config.n_samples_per_prompt)) prompts_data = batch_data['prompts'][indexes] prompt_length_data = batch_data['prompt_length'][indexes] # preprocess, remove padding prompts = truncate_rows(prompts_data, prompt_length_data) prompts_list = [prompt.numpy().tolist() for prompt in prompts] - # inference responses_pad_right = self.actor_hybrid.generate_sequences(copy.deepcopy(prompts_list)) responses = remove_padding_and_split_to_list(responses_pad_right, self.tokenizer.eod, pad_token_id) @@ -208,7 +208,7 @@ class ActorHybridWorkerBase(BaseWorker): 'input_ids': input_ids_list, 'response_length': responses_length } - self.collect_transfer_dock_data(outputs, index, self.rl_config.n_samples_per_prompt, use_vllm=True) + self.collect_transfer_dock_data(outputs, index, use_vllm=True) end_time = time.time() ray.get( self.td.update_metrics.remote( @@ -217,6 +217,7 @@ class ActorHybridWorkerBase(BaseWorker): cumulate=True ) ) + generate_end_time = time.time() parallel_state = get_parallel_state() use_vllm = True @@ -235,14 +236,17 @@ class ActorHybridWorkerBase(BaseWorker): self.sharding_manager.enter_forward_mode() experience_consumer_stage = 'actor_log_prob' - experience_colums = ['input_ids', 'responses', 'response_length', 'prompt_length'] - experience_count = self.rl_config.experience_count_actor // self.parallel_state.get_data_parallel_world_size() + experience_columns = ['input_ids', 'responses', 'response_length', 'prompt_length'] + experience_count = get_least_common_multiple(self.megatron_config.micro_batch_size, + self.rl_config.n_samples_per_prompt) start_time_defined = False while self.all_consumed(experience_consumer_stage) > 0: - batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_colums, - experience_count, self.rl_config.n_samples_per_prompt, - tp_size=self.megatron_config.tensor_model_parallel_size) + batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, + experience_columns, + experience_count, + tp_size=self.megatron_config.tensor_model_parallel_size, + get_n_samples=False) if not start_time_defined: start_time = time.time() start_time_defined = True @@ -254,7 +258,7 @@ class ActorHybridWorkerBase(BaseWorker): log_probs = log_probs.to(torch.float32) log_probs = truncate_rows(log_probs, batch['response_length']) output = {'old_log_prob': log_probs} - self.collect_transfer_dock_data(output, index, self.rl_config.n_samples_per_prompt) + self.collect_transfer_dock_data(output, index) end_time = time.time() ray.get( self.td.update_metrics.remote( diff --git a/mindspeed_rl/workers/base_worker.py b/mindspeed_rl/workers/base_worker.py index 6fed82cb..72169fb5 100644 --- a/mindspeed_rl/workers/base_worker.py +++ b/mindspeed_rl/workers/base_worker.py @@ -203,8 +203,9 @@ class BaseWorker(BaseRayWorker, ABC): torch.cuda.empty_cache() def dispatch_transfer_dock_data(self, experience_consumer_stage, - experience_colums, experience_count, n_samples_per_prompt=1, tp_size=1, - use_vllm=False): + experience_columns, experience_count, tp_size=1, + use_vllm=False, + get_n_samples=True): pad_id = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod batch_data = {} @@ -212,13 +213,14 @@ class BaseWorker(BaseRayWorker, ABC): # in case of rank0 get_experience before other ranks judge td.all_consumed if get_tensor_model_parallel_rank(self.parallel_state, use_vllm) == 0 and \ get_pipeline_model_parallel_rank(self.parallel_state, use_vllm) == 0: - batch_data, index = ray.get(self.td.get_experience.remote(experience_consumer_stage, experience_colums, + batch_data, index = ray.get(self.td.get_experience.remote(experience_consumer_stage, experience_columns, experience_count, pad_id=pad_id, - multiple=tp_size)) # cpu数据 + multiple=tp_size, + get_n_samples=get_n_samples)) # cpu数据 if not index: # 判断是否取出数据，未取出数据为-1 index = [-1] * experience_count - index = torch.tensor(index).cuda() + index = torch.tensor(index + ([-1] * (experience_count - len(index)))).cuda() else: index = torch.empty(experience_count, device=torch.cuda.current_device(), dtype=torch.int64) @@ -235,7 +237,8 @@ class BaseWorker(BaseRayWorker, ABC): if index[0].item() == -1: return None, None - for key in experience_colums: + index_without_pad = [] + for key in experience_columns: if get_tensor_model_parallel_rank(self.parallel_state, use_vllm) == 0 and \ get_pipeline_model_parallel_rank(self.parallel_state, use_vllm) == 0: batch_data_shape = torch.tensor(batch_data[key].shape, @@ -289,11 +292,12 @@ class BaseWorker(BaseRayWorker, ABC): batch_data[key].cuda(), get_pipeline_model_parallel_src_rank(self.parallel_state, use_vllm), group=get_pipeline_model_parallel_group(self.parallel_state, use_vllm) ) - index = index.cpu().numpy().tolist() - return batch_data, index + if not index_without_pad: + index_without_pad = index.cpu().numpy().tolist()[:batch_data_shape[0]] + return batch_data, index_without_pad - def collect_transfer_dock_data(self, output, index, n_samples_per_prompt=1, use_vllm=False): + def collect_transfer_dock_data(self, output, index, use_vllm=False): if is_pipeline_last_stage(self.parallel_state, use_vllm) and get_tensor_model_parallel_rank(self.parallel_state, use_vllm) == 0: output = {key: value.cpu() if not isinstance(value, List) else value for key, value in output.items()} - self.td.put_experience.remote(data_dict=output, indexes=index, num_responses=n_samples_per_prompt) + self.td.put_experience.remote(data_dict=output, indexes=index) diff --git a/mindspeed_rl/workers/reference_woker.py b/mindspeed_rl/workers/reference_woker.py index fd465a77..1937fad2 100644 --- a/mindspeed_rl/workers/reference_woker.py +++ b/mindspeed_rl/workers/reference_woker.py @@ -10,6 +10,7 @@ from mindspeed_rl.config_cls.megatron_config import MegatronConfig from mindspeed_rl.config_cls.rl_config import RLConfig from mindspeed_rl.config_cls.generate_config import GenerateConfig from mindspeed_rl.models.reference import Reference +from mindspeed_rl.utils.utils import get_least_common_multiple from mindspeed_rl.utils.pad_process import truncate_rows from mindspeed_rl.utils.tokenizer import BaseTokenizer from mindspeed_rl.workers.base_worker import BaseWorker @@ -84,13 +85,16 @@ class ReferenceWorkerBase(BaseWorker): def compute_ref_log_prob(self): experience_consumer_stage = 'ref_log_prob' experience_columns = ['input_ids', 'responses', 'response_length', 'prompt_length'] - experience_count = self.rl_config.experience_count_ref // self.parallel_state.get_data_parallel_world_size() + experience_count = get_least_common_multiple(self.megatron_config.micro_batch_size, + self.rl_config.n_samples_per_prompt) start_time_defined = False while self.all_consumed(experience_consumer_stage) > 0: - batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, - experience_count, self.rl_config.n_samples_per_prompt, - tp_size=self.megatron_config.tensor_model_parallel_size) + batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, + experience_columns, + experience_count, + tp_size=self.megatron_config.tensor_model_parallel_size, + get_n_samples=False) if not start_time_defined: start_time = time.time() start_time_defined = True @@ -110,7 +114,7 @@ class ReferenceWorkerBase(BaseWorker): log_probs = log_probs.to(torch.float32) log_probs = truncate_rows(log_probs, batch['response_length']) output = {'ref_log_prob': log_probs} - self.collect_transfer_dock_data(output, index, self.rl_config.n_samples_per_prompt) + self.collect_transfer_dock_data(output, index) end_time = time.time() ray.get( self.td.update_metrics.remote( @@ -129,6 +133,7 @@ class ReferenceWorkerBase(BaseWorker): value=[round(ref_end_time, 4)] ) ) + self.empty_cache() diff --git a/mindspeed_rl/workers/reward_woker.py b/mindspeed_rl/workers/reward_woker.py index 5d544bcf..675ea439 100644 --- a/mindspeed_rl/workers/reward_woker.py +++ b/mindspeed_rl/workers/reward_woker.py @@ -11,6 +11,7 @@ from mindspeed_rl.config_cls.rl_config import RLConfig from mindspeed_rl.config_cls.generate_config import GenerateConfig from mindspeed_rl.models.reward import Reward from mindspeed_rl.trainer.utils.compute_utils import get_last_reward +from mindspeed_rl.utils.utils import get_least_common_multiple from mindspeed_rl.utils.tokenizer import BaseTokenizer from mindspeed_rl.workers.base_worker import BaseWorker from mindspeed_rl.utils.compute import get_parallel_state @@ -81,13 +82,16 @@ class RewardWorkerBase(BaseWorker): experience_consumer_stage = 'reward_scores' experience_columns = ['input_ids', 'prompt_length', "responses", "response_length", *self.megatron_config.dataset_additional_keys] - experience_count = self.rl_config.experience_count_reward // self.parallel_state.get_data_parallel_world_size() + experience_count = get_least_common_multiple(self.megatron_config.micro_batch_size, + self.rl_config.n_samples_per_prompt) start_time_defined = False while not ray.get(self.td.all_consumed.remote(experience_consumer_stage)): - batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, - experience_count, self.rl_config.n_samples_per_prompt, - tp_size=self.megatron_config.tensor_model_parallel_size) + batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, + experience_columns, + experience_count, + tp_size=self.megatron_config.tensor_model_parallel_size, + get_n_samples=False) if not start_time_defined: start_time = time.time() start_time_defined = True @@ -109,7 +113,7 @@ class RewardWorkerBase(BaseWorker): n_sample_batch=self.rl_config.n_samples_per_prompt ) output = {'rm_scores': rm_score, 'token_level_rewards': last_rewards} - self.collect_transfer_dock_data(output, index, self.rl_config.n_samples_per_prompt) + self.collect_transfer_dock_data(output, index) end_time = time.time() ray.get( self.td.update_metrics.remote( diff --git a/mindspeed_rl/workers/rule_reward.py b/mindspeed_rl/workers/rule_reward.py index 393a4cef..b1a1e711 100644 --- a/mindspeed_rl/workers/rule_reward.py +++ b/mindspeed_rl/workers/rule_reward.py @@ -3,6 +3,7 @@ import ray from mindspeed_rl.models.rule_verifier import compute_verifier_score from mindspeed_rl.utils.loggers import Loggers +from mindspeed_rl.utils.utils import get_least_common_multiple logger = Loggers("rule_reward") @@ -22,7 +23,8 @@ class RuleReward(object): def compute_rm_score(self): experience_consumer_stage = 'rule_reward' experience_columns = ['prompts', 'responses', 'response_length', *self.megatron_config.dataset_additional_keys] - experience_count = self.rl_config.experience_count_rule_reward + experience_count = get_least_common_multiple(self.megatron_config.micro_batch_size, + self.rl_config.n_samples_per_prompt) pad_token_id = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod while not ray.get(self.td.all_consumed.remote(experience_consumer_stage)): batch_data, index = ray.get( @@ -50,4 +52,4 @@ class RuleReward(object): ray.get(self.td.update_metrics.remote(key, value=value, cumulate=True)) output = {"rm_scores": token_level_rewards, "token_level_rewards": token_level_rewards} - self.td.put_experience.remote(data_dict=output, indexes=index, num_responses=self.n_samples_per_prompt) + self.td.put_experience.remote(data_dict=output, indexes=index) diff --git a/tests/st/configs/test_grpo_trainer_qwen25_7b.yaml b/tests/st/configs/test_grpo_trainer_qwen25_7b.yaml index d40a0f7e..72f15781 100644 --- a/tests/st/configs/test_grpo_trainer_qwen25_7b.yaml +++ b/tests/st/configs/test_grpo_trainer_qwen25_7b.yaml @@ -35,7 +35,7 @@ megatron_training: actor_config: model: qwen25_7b - micro_batch_size: 1 + micro_batch_size: 4 tensor_model_parallel_size: 4 pipeline_model_parallel_size: 1 lr: 1e-7 @@ -73,7 +73,6 @@ reward_config: rl_config: blocking: false - experience_count: 1 gamma: 1.0 lam: 0.95 adv_estimator: group_norm @@ -100,6 +99,7 @@ rl_config: generate_config: # tokenizer相关配置 trust_remote_code: true + micro_batch_size: 1 # 推理时的并行配置 infer_tensor_parallel_size: 4 diff --git a/tests/st/infer_engine/test_vllm_engine.py b/tests/st/infer_engine/test_vllm_engine.py index ece02392..a4536dbf 100644 --- a/tests/st/infer_engine/test_vllm_engine.py +++ b/tests/st/infer_engine/test_vllm_engine.py @@ -131,7 +131,6 @@ def main(): ) logger.info("generate_sequences output is:") logger.info(outputs[0]) - logger.info(outputs[0].shape) logger.info("input") logger.info(idx_list[0]) diff --git a/tests/st/infer_engine/test_vllm_engine_multistep_decode.py b/tests/st/infer_engine/test_vllm_engine_multistep_decode.py index bfc9a7ec..d99d0eb4 100644 --- a/tests/st/infer_engine/test_vllm_engine_multistep_decode.py +++ b/tests/st/infer_engine/test_vllm_engine_multistep_decode.py @@ -132,7 +132,6 @@ def main(): ) logger.info("generate_sequences output is:") logger.info(outputs[0]) - logger.info(outputs[0].shape) logger.info("input") logger.info(idx_list[0]) diff --git a/tests/ut/utils/test_transfer_dock.py b/tests/ut/utils/test_transfer_dock.py index 28ab7678..6f1721a8 100644 --- a/tests/ut/utils/test_transfer_dock.py +++ b/tests/ut/utils/test_transfer_dock.py @@ -14,11 +14,14 @@ from mindspeed_rl.utils.metrics import Metric @pytest.fixture(scope="function") def setup_teardown_transfer_dock(request): self = request.instance - self.max_len = 16 + self.prompts_num = 16 + self.n_samples_per_prompt = 1 + self.max_len = self.prompts_num * self.n_samples_per_prompt self.timeout = 10 self.timeout_interval = 2 self.default_experience_columns = ["default"] - self.td = TransferDock(max_len=self.max_len, + self.td = TransferDock(prompts_num=self.prompts_num, + n_samples_per_prompt=self.n_samples_per_prompt, experience_columns=self.default_experience_columns, timeout=self.timeout, timeout_interval=self.timeout_interval) @@ -29,9 +32,13 @@ def setup_teardown_transfer_dock(request): @pytest.fixture(scope="function") def setup_teardown_grpo_transfer_dock_function(request): self = request.instance - self.max_len = 16 + self.prompts_num = 16 + self.n_samples_per_prompt = 1 + self.max_len = self.prompts_num * self.n_samples_per_prompt metrics = Metric() - self.td = GRPOTransferDock.remote(max_len=self.max_len, metrics=metrics) + self.td = GRPOTransferDock.remote(prompts_num=self.prompts_num, + n_samples_per_prompt=self.n_samples_per_prompt, + metrics=metrics) yield ray.get(self.td.clear.remote()) @@ -48,20 +55,21 @@ def setup_teardown_grpo_transfer_dock_class(request): @pytest.mark.usefixtures("setup_teardown_transfer_dock") class TestTransferDock(DistributedTest): def test_init(self): - assert self.td.max_len == self.max_len + assert self.td.prompts_num == self.prompts_num + assert self.td.n_samples_per_prompt == self.n_samples_per_prompt assert self.td.timeout == self.timeout assert self.td.timeout_interval == self.timeout_interval + assert self.td.max_len == self.max_len for experience_column in self.default_experience_columns: assert experience_column in self.td.experience_data assert experience_column in self.td.experience_data_status - assert not self.td.index_dispatch_stop_signal def test_put(self): mbs = 4 experience_columns = ["default"] indexes = random.sample(range(16), mbs) experience = [ - [[torch.randn(1, 8)] for _ in range(mbs)], + [torch.randn(1, 8) for _ in range(mbs)], ] self.td._put( @@ -73,48 +81,9 @@ class TestTransferDock(DistributedTest): for index_idx, index in enumerate(indexes): for column_idx, experience_column in enumerate(experience_columns): assert torch.equal( - self.td.experience_data[experience_column][index][0], - experience[column_idx][index_idx][0], - ) - - def test_put_with_index(self): - mbs = 4 - experience_columns = ["default"] - indexes = random.sample(range(16), mbs) - experience = [[[torch.randn(1, 8)] for _ in range(mbs)]] - - self.td._put_with_index( - experience_columns=experience_columns, - experience=experience, - indexes=indexes, - ) - - for index_idx, index in enumerate(indexes): - for column_idx, experience_column in enumerate(experience_columns): - assert torch.equal( - self.td.experience_data[experience_column][index][0], - experience[column_idx][index_idx][0], - ) - - def test_put_without_index(self): - mbs = 4 - experience_columns = ["default"] - experience = [[[torch.randn(1, 8)] for _ in range(mbs)]] - - self.td._put_without_index(experience_columns=experience_columns, experience=experience) - - indexes = torch.nonzero(self.td.experience_data_status[experience_columns[0]], as_tuple=True)[0].tolist() - for index in indexes: - exp_index = [] - for column_idx, experience_column in enumerate(experience_columns): - exp_index.extend( - [ - idx - for idx, t in enumerate(experience[column_idx]) - if torch.equal(t[0], self.td.experience_data[experience_column][index][0]) - ] + self.td.experience_data[experience_column][index], + experience[column_idx][index_idx], ) - assert len(set(exp_index)) == 1 def test_get(self): put_mbs = 4 @@ -122,7 +91,7 @@ class TestTransferDock(DistributedTest): experience_columns = ["default"] put_indexes = random.sample(range(16), put_mbs) put_experience = [ - [[torch.randn(1, 8)] for _ in range(put_mbs)], + [torch.randn(1, 8) for _ in range(put_mbs)], ] self.td._put( experience_columns=experience_columns, @@ -139,8 +108,8 @@ class TestTransferDock(DistributedTest): for experience_column in experience_columns: column_index = experience_columns.index(experience_column) assert torch.equal( - get_experience[column_index][get_index][0], - put_experience[column_index][put_index][0], + get_experience[column_index][get_index], + put_experience[column_index][put_index], ) @@ -159,7 +128,7 @@ class TestGRPOTransferDock(DistributedTest): put_indexes = random.sample(range(16), put_mbs) get_indexes = random.sample(put_indexes, get_mbs) - ray.get(self.td.put_experience.remote(data_dict={"prompts": prompts}, indexes=put_indexes, num_responses=1)) + ray.get(self.td.put_experience.remote(data_dict={"prompts": prompts}, indexes=put_indexes)) experience_batch, output_indexes = ray.get( self.td.get_experience.remote( @@ -175,7 +144,7 @@ class TestGRPOTransferDock(DistributedTest): prompts = [torch.randn(1, 8) for _ in range(put_mbs)] put_indexes = random.sample(range(16), put_mbs) - ray.get(self.td.put_experience.remote(data_dict={"prompts": prompts}, indexes=put_indexes, num_responses=1)) + ray.get(self.td.put_experience.remote(data_dict={"prompts": prompts}, indexes=put_indexes)) for index in put_indexes: experience_batch, output_indexes = ray.get( @@ -190,12 +159,13 @@ class TestGRPOTransferDock(DistributedTest): n_actor = 4 @ray.remote(num_cpus=1) - def actor_put(td, n_actor, gbs): - for i in range(gbs // n_actor): - data = torch.randn(1, 1024) - ray.get(td.put_experience.remote({f"prompts": data})) + def actor_put(td, n_actor, gbs, actor_id): + put_num = gbs // n_actor + data = torch.randn(put_num, 1024) + ray.get(td.put_experience.remote({f"prompts": data}, + indexes=[index for index in range(actor_id * put_num, (actor_id + 1) * put_num)])) - ray.get([actor_put.remote(self.td, n_actor, self.max_len) for _ in range(n_actor)]) + ray.get([actor_put.remote(self.td, n_actor, self.max_len, actor_id) for actor_id in range(n_actor)]) _, output_indexes = ray.get( self.td.get_experience.remote( diff --git a/tests/ut/workers/test_base_worker.py b/tests/ut/workers/test_base_worker.py index 1c9eb810..f6bc36e9 100644 --- a/tests/ut/workers/test_base_worker.py +++ b/tests/ut/workers/test_base_worker.py @@ -129,7 +129,7 @@ class TestBaseWorker(DistributedTest): mock_cuda.return_value = 'cpu' experience_consumer_stage = 'actor_train' - experience_colums = [] + experience_columns = [] experience_count = 1 worker = BaseWorker( @@ -146,6 +146,6 @@ class TestBaseWorker(DistributedTest): worker.parallel_state = MagicMock() _, _ = worker.dispatch_transfer_dock_data(experience_consumer_stage, - experience_colums, experience_count) + experience_columns, experience_count) assert mock_broadcast.call_count == 2 -- Gitee From edf113d49ea289e6f4d25a7231ee1400674c8b25 Mon Sep 17 00:00:00 2001 From: yuqi Date: Mon, 21 Apr 2025 09:19:22 +0000 Subject: [PATCH 11/25] =?UTF-8?q?!225=20=E3=80=90bugfix=E3=80=91=E6=AD=A3?= =?UTF-8?q?=E7=A1=AE=E8=AE=BE=E7=BD=AEno=5Fsync=5Ffunc=EF=BC=8C=E4=BF=AE?= =?UTF-8?q?=E5=A4=8Doverlap=5Fgrad=5Freduce=E6=8A=A5=E9=94=99=20Merge=20pu?= =?UTF-8?q?ll=20request=20!225=20from=20yuqi/nosyncfunc2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cli/train_grpo.py | 2 ++ mindspeed_rl/utils/utils.py | 27 ++++++++++++++++++++- mindspeed_rl/workers/actor_hybrid_worker.py | 12 ++++++++- 3 files changed, 39 insertions(+), 2 deletions(-) diff --git a/cli/train_grpo.py b/cli/train_grpo.py index dbae265d..08370d46 100644 --- a/cli/train_grpo.py +++ b/cli/train_grpo.py @@ -193,6 +193,7 @@ def parse_training_config(config: Dict): def get_megatron_module(): from megatron.core import parallel_state + from megatron.core import DistributedDataParallel from megatron.core.optimizer import get_megatron_optimizer from megatron.training.checkpointing import load_checkpoint, save_checkpoint from megatron.training.training import get_optimizer_param_scheduler @@ -222,6 +223,7 @@ def get_megatron_module(): 'vocab_parallel_cross_entropy': vocab_parallel_cross_entropy, 'setup_model_and_optimizer': setup_model_and_optimizer, 'model_type': ModelType, + 'distributed_data_parallel': DistributedDataParallel } diff --git a/mindspeed_rl/utils/utils.py b/mindspeed_rl/utils/utils.py index 385bb0e8..64eb0daf 100644 --- a/mindspeed_rl/utils/utils.py +++ b/mindspeed_rl/utils/utils.py @@ -280,4 +280,29 @@ def parse_args_from_config(config): def get_least_common_multiple(num_1: int, num_2: int): - return abs(num_1 * num_2) // math.gcd(num_1, num_2) \ No newline at end of file + return abs(num_1 * num_2) // math.gcd(num_1, num_2) + + +def get_attr_wrapped_model(model, attr, allow_none=True, return_model_obj=False): + if isinstance(model, list): + raise RuntimeError("_get_attr_wrapped_model given a list of models") + + if allow_none: + + def condition(model, attr): + return not hasattr(model, attr) + + else: + + def condition(model, attr): + return getattr(model, attr, None) is None + + while condition(model, attr): + if not hasattr(model, "module"): + raise RuntimeError(f"_get_attr_wrapped_model couldn't find attribute {attr}") + + model = model.module + + if return_model_obj: + return model + return getattr(model, attr) \ No newline at end of file diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index 902cb273..1a1bd0b4 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -19,7 +19,7 @@ from mindspeed_rl.models.rollout.vllm_engine import VLLMInferEngine from mindspeed_rl.utils.tokenizer import BaseTokenizer from mindspeed_rl.workers.base_worker import BaseWorker from mindspeed_rl.workers.resharding.megatron_sharding_manager import MegatronShardingManager, MegatronOffLoader -from mindspeed_rl.utils.utils import num_floating_point_operations, get_least_common_multiple +from mindspeed_rl.utils.utils import num_floating_point_operations, get_least_common_multiple, get_attr_wrapped_model from mindspeed_rl.utils.pad_process import remove_padding_and_split_to_list, truncate_rows from mindspeed_rl.utils.compute import get_parallel_state from mindspeed_rl.trainer.utils.parallel_state import is_pipeline_last_stage, get_tensor_model_parallel_rank @@ -69,6 +69,7 @@ class ActorHybridWorkerBase(BaseWorker): def initialize(self): self.setup_distributed_rank() self.model, self.optimizer, self.opt_param_scheduler = self._build_model_optimizer() + self._set_no_sync_func() self.actor_offloader = MegatronOffLoader(self.model, self.optimizer) if self.generate_config.offload_train_optimizer: self.actor_offloader.offload_optimizer() @@ -363,6 +364,15 @@ class ActorHybridWorkerBase(BaseWorker): ) return sharding_manager + def _set_no_sync_func(self): + config = get_attr_wrapped_model(self.model[0], 'config', allow_none=False) + + if isinstance(self.model[0], self.distributed_data_parallel) and config.no_sync_func is None: + # Megatron requires no_sync_func properly to correctly trigger DP reduce + config.no_sync_func = [model_chunk.no_sync for model_chunk in self.model] + if len(self.model) == 1: + config.no_sync_func = config.no_sync_func[0] + @ray.remote(resources={"NPU": 0.7}) class ActorHybridWorker(ActorHybridWorkerBase): -- Gitee From a44ee1a9c4a150ee8d1bd4d9edf0ca13a97c99b6 Mon Sep 17 00:00:00 2001 From: TJJ Date: Mon, 21 Apr 2025 13:47:28 +0000 Subject: [PATCH 12/25] =?UTF-8?q?!236=20=E3=80=90bugfix=E3=80=91pg=5Floss?= =?UTF-8?q?=20for=20dp=20Merge=20pull=20request=20!236=20from=20TJJ/dev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mindspeed_rl/utils/metrics.py | 23 ++++++++++++++++----- mindspeed_rl/workers/actor_hybrid_worker.py | 15 +------------- 2 files changed, 19 insertions(+), 19 deletions(-) diff --git a/mindspeed_rl/utils/metrics.py b/mindspeed_rl/utils/metrics.py index 69225902..b4c1a3e2 100644 --- a/mindspeed_rl/utils/metrics.py +++ b/mindspeed_rl/utils/metrics.py @@ -18,13 +18,26 @@ class Metric(ABC): value: dict|list|tensor. when key is None, maybe value is a dict """ if cumulate: - if key in self.metric: - if isinstance(self.metric[key], list): - self.metric[key].extend(value) + if isinstance(value, Dict): + if cumulate: + for key in value: + if key in self.metric: + if isinstance(self.metric[key], list): + self.metric[key].extend(value[key]) + else: + self.metric[key] = value[key] + else: + self.metric[key] = [*value[key]] else: - self.metric[key] = value + self.metric.update(value) else: - self.metric[key] = [*value] + if key in self.metric: + if isinstance(self.metric[key], list): + self.metric[key].extend(value) + else: + self.metric[key] = value + else: + self.metric[key] = [*value] else: if isinstance(value, Dict): self.metric.update(value) diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index 1a1bd0b4..49c1521e 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -143,7 +143,7 @@ class ActorHybridWorkerBase(BaseWorker): self.num_floating_point_operations_so_far += num_floating_point_operations(self.args, self.megatron_config.global_batch_size) if self.parallel_state.is_pipeline_last_stage() and self.parallel_state.get_tensor_model_parallel_rank() == 0: - ray.get(self.td.update_metrics.remote(value=metrics)) + ray.get(self.td.update_metrics.remote(value=metrics, cumulate=True)) ray.get( self.td.update_metrics.remote( "timing/update", @@ -218,19 +218,6 @@ class ActorHybridWorkerBase(BaseWorker): cumulate=True ) ) - - generate_end_time = time.time() - parallel_state = get_parallel_state() - use_vllm = True - if is_pipeline_last_stage(parallel_state, use_vllm) and get_tensor_model_parallel_rank(parallel_state, use_vllm) == 0: - ray.get( - self.td.update_metrics.remote( - "end_time/generate", - value=[round(generate_end_time, 4)], - cumulate=True - ) - ) - self.sharding_manager.exit_infer_mode() def compute_log_prob(self): -- Gitee From b84a82b684c82ce639716d9d84191491cf2ed24c Mon Sep 17 00:00:00 2001 From: LookAround Date: Tue, 22 Apr 2025 12:59:24 +0000 Subject: [PATCH 13/25] !227 remove pad Merge pull request !227 from LookAround/remove_pad --- mindspeed_rl/trainer/grpo_trainer_hybrid.py | 6 +- mindspeed_rl/trainer/utils/compute_utils.py | 28 +-- mindspeed_rl/trainer/utils/transfer_dock.py | 218 +++++++++++++++----- mindspeed_rl/workers/actor_hybrid_worker.py | 8 +- mindspeed_rl/workers/base_worker.py | 48 +++-- mindspeed_rl/workers/reference_woker.py | 3 + mindspeed_rl/workers/reward_woker.py | 3 +- mindspeed_rl/workers/rule_reward.py | 6 +- tests/ut/utils/test_transfer_dock.py | 10 +- 9 files changed, 243 insertions(+), 87 deletions(-) diff --git a/mindspeed_rl/trainer/grpo_trainer_hybrid.py b/mindspeed_rl/trainer/grpo_trainer_hybrid.py index d2aef2b4..80864db9 100644 --- a/mindspeed_rl/trainer/grpo_trainer_hybrid.py +++ b/mindspeed_rl/trainer/grpo_trainer_hybrid.py @@ -14,7 +14,8 @@ from mindspeed_rl.trainer.utils.compute_utils import compute_advantage, compute_ from mindspeed_rl.workers.scheduler.launcher import RayActorGroup from mindspeed_rl.utils.loggers import Loggers from mindspeed_rl.utils.metrics import Metric -from mindspeed_rl.utils.utils import metrics_post_processing, compute_tps, metrics_sort, get_least_common_multiple +from mindspeed_rl.utils.utils import metrics_post_processing, compute_tps, metrics_sort +from mindspeed_rl.utils.utils import num_floating_point_operations, get_least_common_multiple class RayGRPOTrainer(RayBaseTrainer): @@ -142,6 +143,7 @@ class RayGRPOTrainer(RayBaseTrainer): self.actor_worker.compute_log_prob(blocking=self.blocking) self.actor_worker.wait_all_ref_objs_run_over() + self.ref_worker.wait_all_ref_objs_run_over() for reward in self.reward_list: if hasattr(reward, 'wait_all_ref_objs_run_over'): @@ -177,7 +179,7 @@ class RayGRPOTrainer(RayBaseTrainer): def compute_advantage(self, blocking=False): experience_count = get_least_common_multiple(self.micro_batch_size, self.n_samples_per_prompt) - + compute_advantage_ref = compute_advantage.options(num_cpus=self.num_cpus_for_local_task).remote( self.transfer_dock, self.gamma, diff --git a/mindspeed_rl/trainer/utils/compute_utils.py b/mindspeed_rl/trainer/utils/compute_utils.py index e096573e..ea103346 100644 --- a/mindspeed_rl/trainer/utils/compute_utils.py +++ b/mindspeed_rl/trainer/utils/compute_utils.py @@ -24,6 +24,7 @@ import numpy as np import mindspeed_rl.utils.torch_functional as F from mindspeed_rl.utils.pad_process import truncate_rows from mindspeed_rl.utils.utils import generate_mask +from mindspeed_rl.trainer.utils.transfer_dock import pad_experience class AdaptiveKLController: @@ -124,16 +125,16 @@ def compute_group_norm_advantage_return(token_level_rewards: torch.Tensor, eos_m @ray.remote -def compute_advantage(rb, gamma, lam, adv_estimator, experience_count, tokenizer): +def compute_advantage(td, gamma, lam, adv_estimator, experience_count, tokenizer): """ Compute the advantage function based on different adv_estimator Args: - rb: A data queue object + td: A data queue object gamma: The reward discount factor lam: The lambda parameter in advantage estimation adv_estimator: The type of advantage estimator, which can be "gae" or "group_norm" - experience_count: The number of experiences to retrieve from the experience rb + experience_count: The number of experiences to retrieve from the experience td tokenizer: The pre-trained tokenizer Returns: @@ -142,13 +143,15 @@ def compute_advantage(rb, gamma, lam, adv_estimator, experience_count, tokenizer experience_consumer_stage = "compute_advantage" experience_columns = ["responses", "token_level_rewards", "response_length"] pad_token_id = tokenizer.pad if tokenizer.pad is not None else tokenizer.eod - while not ray.get(rb.all_consumed.remote(experience_consumer_stage)): + + while not ray.get(td.all_consumed.remote(experience_consumer_stage)): batch_data, index = ray.get( - rb.get_experience.remote( - experience_consumer_stage, experience_columns, experience_count, pad_id=pad_token_id + td.get_experience.remote( + experience_consumer_stage, experience_columns, experience_count, # pad_id=pad_token_id ) ) if batch_data and index: + batch_data = pad_experience(batch_data, pad_token_id) # multiple, tp_size response_mask = generate_mask(batch_data["responses"], batch_data["response_length"]) token_level_rewards = batch_data["token_level_rewards"] @@ -169,7 +172,7 @@ def compute_advantage(rb, gamma, lam, adv_estimator, experience_count, tokenizer "advantages": advantages, "returns": returns, } - rb.put_experience.remote(data_dict=output, indexes=index) + td.put_experience.remote(data_dict=output, indexes=index) def get_last_reward(rm_scores, n_sample_batch: int): @@ -190,13 +193,13 @@ def get_last_reward(rm_scores, n_sample_batch: int): def compute_grpo_data_metrics( - rb, experience_count, tokenizer + td, experience_count, tokenizer ): """ Calculate various metrics for GRPO data Args: - rb: A data queue object + td: A data queue object experience_count: Number of experiences to retrieve tokenizer: The pre-trained tokenizer @@ -213,12 +216,13 @@ def compute_grpo_data_metrics( "prompt_length", "response_length", ] - pad_id = tokenizer.pad if tokenizer.pad is not None else tokenizer.eod - while not ray.get(rb.all_consumed.remote(experience_consumer_stage)): + pad_token_id = tokenizer.pad if tokenizer.pad is not None else tokenizer.eod + while not ray.get(td.all_consumed.remote(experience_consumer_stage)): batch, index = ray.get( - rb.get_experience.remote(experience_consumer_stage, experience_columns, experience_count, pad_id=pad_id) + td.get_experience.remote(experience_consumer_stage, experience_columns, experience_count) ) if batch and index: + batch = pad_experience(batch, pad_token_id) # multiple, tp_size sequence_score = batch["rm_scores"].sum(-1) sequence_reward = batch["token_level_rewards"].sum(-1) prompt_length = batch["prompt_length"] diff --git a/mindspeed_rl/trainer/utils/transfer_dock.py b/mindspeed_rl/trainer/utils/transfer_dock.py index e3685664..c9a324d6 100644 --- a/mindspeed_rl/trainer/utils/transfer_dock.py +++ b/mindspeed_rl/trainer/utils/transfer_dock.py @@ -337,8 +337,6 @@ class GRPOTransferDock(TransferDock): experience_columns: List[str], experience_count: int = None, indexes: List[int] = None, - pad_id: int = None, - multiple: int = 1, get_n_samples: bool = True, ): """Get padded experience data from GRPOTransferDock. @@ -399,13 +397,15 @@ class GRPOTransferDock(TransferDock): self.experience_consumer_status[consumer][indexes] = 1 experience = self._get(experience_columns, indexes) - experience_batch = trans_experience_to_output(experience, experience_columns, pad_id, multiple) + experience_batch = {} + for i, experience_column in enumerate(experience_columns): + experience_batch[experience_column] = experience[i] return experience_batch, indexes def put_experience( self, data_dict: Dict[str, Union[Tensor, List[Tensor]]], - indexes: List[int] = None + indexes: List[int] = None, ): """Put data into specified columns and rows. @@ -631,32 +631,25 @@ class GRPOTransferDock(TransferDock): return sampled_indexes -def trans_experience_to_output( - experience: List[List[Tensor]], - experience_columns: List[str], - pad_id: int, - multiple: int, +def pad_experience( + experience_batch: Dict[str, List[Tensor]], + pad_id: int, + multiple: int = 1, ): - """Merge and pad data into dict. + """ Pad dict data. Args: - experience: Data list. - [ - [ - tensor([1, 1, 1, 1]), - tensor([2, 2, 2, 2]), - tensor([3, 3, 3, 3]), - tensor([4, 4, 4, 4]) - ], - [ - tensor([1]), - tensor([2, 2]), - tensor([3, 3, 3]), - tensor([4, 4, 4, 4]) - ] - ] - experience_columns: Columns for the corresponding data. - ['prompts', 'attention_mask'] + experience_batch: Dict + { + 'prompts': [ tensor([1, 1, 1, 1]), + tensor([2, 2, 2, 2]), + tensor([3, 3, 3, 3]), + tensor([4, 4, 4, 4])], + 'attention_mask': [ tensor([1]), + tensor([2, 2]), + tensor([3, 3, 3]), + tensor([4, 4, 4, 4])], + } pad_id: Pad token. 0.0 multiple: The multiple of TP to pad. @@ -677,17 +670,36 @@ def trans_experience_to_output( } """ + def pad_multiples(data_list: List[Tensor], pad_id: Union[float, int], multiple: int = 1) -> Tensor: + """Pad method for data list. + + Args: + data_list: Data list. + pad_id: Pad token. + multiple: The multiple of TP to pad. + + Returns: Padded tensor. + + """ + padded = pad_sequence(data_list, batch_first=True, padding_value=pad_id) + max_len = padded.size(1) + target_len = ((max_len + multiple - 1) // multiple) * multiple + padded = F.pad(padded, (0, target_len - max_len), value=pad_id) + return padded + batch = {} - for i, experience_column in enumerate(experience_columns): - experience_i_all = experience[i] - if experience_column in ["prompt_length", "response_length"]: - padded = torch.cat(experience_i_all).reshape(-1, 1) - elif experience_i_all[0].is_floating_point(): - padded = pad_multiples(experience_i_all, pad_id=0.0, multiple=multiple) - else: - padded = pad_multiples(experience_i_all, pad_id=pad_id, multiple=multiple) + if not experience_batch: + raise ValueError("ERROR: when pad, get an empty experience_batch") + else: + for experience_column, experience in experience_batch.items(): + if experience_column in ["prompt_length", "response_length"]: + padded = torch.cat(experience).reshape(-1, 1) + elif experience[0].is_floating_point(): + padded = pad_multiples(experience, pad_id=0.0, multiple=multiple) + else: + padded = pad_multiples(experience, pad_id=pad_id, multiple=multiple) - batch[experience_column] = padded + batch[experience_column] = padded return batch @@ -736,27 +748,133 @@ def trans_input_to_experience(experience_dict: Dict[str, Union[Tensor, List[Tens if value is not None: experience_columns.append(key) if isinstance(value, Tensor): + if value.dtype == torch.int64: + value = value.to(torch.int32) value = list(torch.unbind(value, dim=0)) + elif isinstance(value, List): + value = [val.to(torch.int32) if val.dtype == torch.int64 else val for val in value] + else: + raise ValueError(f"value type {type(value)} not supported") experience_list.append(value) return experience_columns, experience_list -def pad_multiples( - data_list: List[Tensor], pad_id: Union[float, int], multiple: int = 1 -) -> Tensor: - """Pad method for data list. +def pack_experience_columns(experience_dict, experience_count): + """ + Compress experiences by packing tensors into ONE. + from experience_dict + { + 'prompts': [ tensor([1, 1, 1]), + tensor([2, 2, 2, 2]), + tensor([3, 3, 3]), + tensor([4, 4, 4, 4])], + 'attention_mask': [ tensor([1]), + tensor([2, 2]), + tensor([3, 3, 3]), + tensor([4, 4, 4, 4])], + } + To batch_data + { + 'prompts': tensor([1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4]), + 'attention_mask': tensor([1, 2, 2, 3, 3, 3, 4, 4, 4, 4]) + } + batch_data_length + { + 'prompts': tensor([3, 4, 3, 4]), + 'attention_mask': tensor([1, 2, 3, 4]) + } + """ + + if not experience_dict: + raise ValueError(f"ERROR: when pack, get an empty experience_dict") + + batch_data = {} + batch_data_length = {} - Args: - data_list: Data list. - pad_id: Pad token. - multiple: The multiple of TP to pad. + for key, value in experience_dict.items(): + if len(value) != experience_count: + raise ValueError(f"ERROR: when pack, experience '{key}' number does not match experience_count") + packed_experience = [] + data_length = [] + for i in range(experience_count): + packed_experience.extend(value[i].tolist()) + data_length.append(len(value[i])) + + batch_data[key] = torch.tensor(packed_experience, dtype=value[0].dtype) + batch_data_length[key] = torch.tensor(data_length, dtype=torch.int32) - Returns: Padded tensor. + return batch_data, batch_data_length + +def unpack_pad_experience(batch_data, batch_data_length, pad_id, multiple): """ - padded = pad_sequence(data_list, batch_first=True, padding_value=pad_id) - max_len = padded.size(1) - target_len = ((max_len + multiple - 1) // multiple) * multiple - padded = F.pad(padded, (0, target_len - max_len), value=pad_id) - return padded + 1. restore the received experience dict + 2. pad the tensor (consider the requirement of multiple) + from batch_data + { + 'prompts': tensor([1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4]), + 'attention_mask': tensor([1, 2, 2, 3, 3, 3, 4, 4, 4, 4]) + } + batch_data_length + { + 'prompts': tensor([3, 4, 3, 4]), + 'attention_mask': tensor([1, 2, 3, 4]) + } + To padded_batch_data (multiple=2) + { + "prompts": tensor( + [[1, 1, 1, -1, -1, -1, -1, -1], + [2, 2, 2, 2, -1, -1, -1, -1], + [3, 3, 3, -1, -1, -1, -1, -1], + [4, 4, 4, 4, -1, -1, -1, -1]]), + "attention_mask": tensor( + [[1, -1, -1, -1, -1, -1, -1, -1], + [2, 2, -1, -1, -1, -1, -1, -1], + [3, 3, 3, -1, -1, -1, -1, -1], + [4, 4, 4, 4, -1, -1, -1, -1]]), + } + """ + if not batch_data: + raise ValueError(f"ERROR: empty batch_data") + + if set(batch_data.keys()) != set(batch_data_length.keys()): + raise ValueError(f"ERROR: when unpack, keys from batch_data and batch_data_length dictionaries do not match") + + data_device = batch_data[list(batch_data.keys())[0]].device + + padded_batch_data = {} + for key, length_list in batch_data_length.items(): + if key in ['prompt_length', 'response_length']: + padded_batch_data[key] = batch_data[key].view(-1, 1) + continue + data = batch_data[key] + data_dtype = batch_data[key].dtype + + lengths = length_list.to(data_device) + + # 计算最大长度 + max_row_len = torch.max(lengths).item() + if multiple > 1: + max_row_len = ((max_row_len + multiple - 1) // multiple) * multiple + + # 预分配张量 + if data[0].is_floating_point(): + padded_tensor = torch.full((len(lengths), max_row_len), 0.0, + dtype=data_dtype, device=data_device) + else: + padded_tensor = torch.full((len(lengths), max_row_len), pad_id, + dtype=data_dtype, device=data_device) + + # 向量化填充 + cum_length = torch.cat([torch.tensor([0], device=data_device + ), torch.cumsum(lengths, 0)]) + + for i, _ in enumerate(lengths): + seq_len = lengths[i] + padded_tensor[i, :seq_len] = data[cum_length[i]:cum_length[i + 1]] + padded_batch_data[key] = padded_tensor + + return padded_batch_data + + diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index 49c1521e..f8b8cc60 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -116,17 +116,20 @@ class ActorHybridWorkerBase(BaseWorker): self.args.curr_iteration = self.iteration experience_consumer_stage = 'actor_train' + experience_columns = ['responses', 'advantages', 'old_log_prob', 'ref_log_prob', 'input_ids', 'response_length', 'prompt_length'] + experience_count = self.megatron_config.global_batch_size // self.parallel_state.get_data_parallel_world_size() #get lr learning_rate = None for param_group in self.optimizer.param_groups: learning_rate = param_group['lr'] - ray.get(self.td.update_metrics.remote(key='grpo/lr', value=learning_rate)) + ray.get(self.td.update_metrics.remote(key='grpo/lr', value=learning_rate)) start_time_defined = False + count = 0 while self.all_consumed(experience_consumer_stage) > 0: batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, @@ -138,6 +141,7 @@ class ActorHybridWorkerBase(BaseWorker): start_time_defined = True if batch_data and index: metrics = self.actor_hybrid.update_actor(batch_data, kl_ctrl) + # self.empty_cache() self.args.consumed_train_samples += self.megatron_config.global_batch_size // self.rl_config.n_samples_per_prompt self.num_floating_point_operations_so_far += num_floating_point_operations(self.args, @@ -193,7 +197,7 @@ class ActorHybridWorkerBase(BaseWorker): responses = remove_padding_and_split_to_list(responses_pad_right, self.tokenizer.eod, pad_token_id) responses_length = [torch.tensor([len(response)]) for response in responses] - # copy prompts (from 1 to n_samples_per_prompt) + prompts_data = prompts prompts = [] for prompt in prompts_data: diff --git a/mindspeed_rl/workers/base_worker.py b/mindspeed_rl/workers/base_worker.py index 72169fb5..eaa9bab6 100644 --- a/mindspeed_rl/workers/base_worker.py +++ b/mindspeed_rl/workers/base_worker.py @@ -28,6 +28,7 @@ from mindspeed_rl.trainer.utils.parallel_state import ( get_model_parallel_group ) from mindspeed_rl.utils.compute import set_parallel_state, set_vocab_parallel +from mindspeed_rl.trainer.utils.transfer_dock import pack_experience_columns, unpack_pad_experience logger = Loggers("base_worker") @@ -209,14 +210,14 @@ class BaseWorker(BaseRayWorker, ABC): pad_id = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod batch_data = {} + batch_data_length = {} # make sure that all ranks in tp/pp group enter dispatch_transfer_dock_data, # in case of rank0 get_experience before other ranks judge td.all_consumed if get_tensor_model_parallel_rank(self.parallel_state, use_vllm) == 0 and \ get_pipeline_model_parallel_rank(self.parallel_state, use_vllm) == 0: batch_data, index = ray.get(self.td.get_experience.remote(experience_consumer_stage, experience_columns, - experience_count, pad_id=pad_id, - multiple=tp_size, - get_n_samples=get_n_samples)) # cpu数据 + experience_count, get_n_samples=get_n_samples)) # cpu数据 + if not index: # 判断是否取出数据，未取出数据为-1 index = [-1] * experience_count @@ -237,22 +238,28 @@ class BaseWorker(BaseRayWorker, ABC): if index[0].item() == -1: return None, None - index_without_pad = [] + if get_tensor_model_parallel_rank(self.parallel_state, use_vllm) == 0 and \ + get_pipeline_model_parallel_rank(self.parallel_state, use_vllm) == 0: + batch_data, batch_data_length = pack_experience_columns(batch_data, experience_count) + for key in experience_columns: if get_tensor_model_parallel_rank(self.parallel_state, use_vllm) == 0 and \ get_pipeline_model_parallel_rank(self.parallel_state, use_vllm) == 0: batch_data_shape = torch.tensor(batch_data[key].shape, dtype=torch.int64, device=torch.cuda.current_device()) - if batch_data[key].dtype == torch.int64: + batch_data_length_shape = torch.tensor(batch_data_length[key].shape, dtype=torch.int64, device=torch.cuda.current_device()) + + if batch_data[key].dtype == torch.int32: batch_data_dtype = torch.tensor(1, dtype=torch.int64, device=torch.cuda.current_device()) else: batch_data_dtype = torch.tensor(2, dtype=torch.int64, device=torch.cuda.current_device()) else: - batch_data_shape = torch.empty(2, device=torch.cuda.current_device(), dtype=torch.int64) + batch_data_shape = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.int64) batch_data_dtype = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.int64) + batch_data_length_shape = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.int64) # 传输tensor数据形状和类型 torch.distributed.broadcast( @@ -272,16 +279,22 @@ class BaseWorker(BaseRayWorker, ABC): group=get_pipeline_model_parallel_group(self.parallel_state, use_vllm) ) + torch.distributed.broadcast(batch_data_length_shape, get_tensor_model_parallel_src_rank(self.parallel_state, use_vllm), + group=get_tensor_model_parallel_group(self.parallel_state, use_vllm)) + torch.distributed.broadcast(batch_data_length_shape, get_pipeline_model_parallel_src_rank(self.parallel_state, use_vllm), + group=get_pipeline_model_parallel_group(self.parallel_state, use_vllm)) + if get_tensor_model_parallel_rank(self.parallel_state, use_vllm) != 0 or \ get_pipeline_model_parallel_rank(self.parallel_state, use_vllm) != 0: if batch_data_dtype == 1: - batch_data[key] = torch.empty(batch_data_shape[0], batch_data_shape[1], + batch_data[key] = torch.empty(batch_data_shape[0], # batch_data_shape[1], device=torch.cuda.current_device(), - dtype=torch.int64) + dtype=torch.int32) else: - batch_data[key] = torch.empty(batch_data_shape[0], batch_data_shape[1], + batch_data[key] = torch.empty(batch_data_shape[0], # batch_data_shape[1], device=torch.cuda.current_device(), dtype=torch.float32) + batch_data_length[key] = torch.empty(batch_data_length_shape[0], device=torch.cuda.current_device(), dtype=torch.int32) # 传输tensor数据 torch.distributed.broadcast( @@ -292,9 +305,20 @@ class BaseWorker(BaseRayWorker, ABC): batch_data[key].cuda(), get_pipeline_model_parallel_src_rank(self.parallel_state, use_vllm), group=get_pipeline_model_parallel_group(self.parallel_state, use_vllm) ) - if not index_without_pad: - index_without_pad = index.cpu().numpy().tolist()[:batch_data_shape[0]] - return batch_data, index_without_pad + + torch.distributed.broadcast(batch_data_length[key].cuda(), get_tensor_model_parallel_src_rank(self.parallel_state, use_vllm), + group=get_tensor_model_parallel_group(self.parallel_state, use_vllm)) + + torch.distributed.broadcast(batch_data_length[key].cuda(), get_pipeline_model_parallel_src_rank(self.parallel_state, use_vllm), + group=get_pipeline_model_parallel_group(self.parallel_state, use_vllm)) + + index_without_pad = index.cpu().numpy().tolist()[:batch_data_shape[0]] + + if batch_data: + padded_batch_data = unpack_pad_experience(batch_data, batch_data_length, pad_id, tp_size) + return padded_batch_data, index_without_pad + else: + return {}, [] def collect_transfer_dock_data(self, output, index, use_vllm=False): if is_pipeline_last_stage(self.parallel_state, use_vllm) and get_tensor_model_parallel_rank(self.parallel_state, diff --git a/mindspeed_rl/workers/reference_woker.py b/mindspeed_rl/workers/reference_woker.py index 1937fad2..61f9bebe 100644 --- a/mindspeed_rl/workers/reference_woker.py +++ b/mindspeed_rl/workers/reference_woker.py @@ -95,6 +95,7 @@ class ReferenceWorkerBase(BaseWorker): experience_count, tp_size=self.megatron_config.tensor_model_parallel_size, get_n_samples=False) + if not start_time_defined: start_time = time.time() start_time_defined = True @@ -134,6 +135,8 @@ class ReferenceWorkerBase(BaseWorker): ) ) + print('finish calc log prob') + self.empty_cache() diff --git a/mindspeed_rl/workers/reward_woker.py b/mindspeed_rl/workers/reward_woker.py index 675ea439..fccdf609 100644 --- a/mindspeed_rl/workers/reward_woker.py +++ b/mindspeed_rl/workers/reward_woker.py @@ -90,8 +90,7 @@ class RewardWorkerBase(BaseWorker): batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, experience_count, - tp_size=self.megatron_config.tensor_model_parallel_size, - get_n_samples=False) + tp_size=self.megatron_config.tensor_model_parallel_size) if not start_time_defined: start_time = time.time() start_time_defined = True diff --git a/mindspeed_rl/workers/rule_reward.py b/mindspeed_rl/workers/rule_reward.py index b1a1e711..47c32f0d 100644 --- a/mindspeed_rl/workers/rule_reward.py +++ b/mindspeed_rl/workers/rule_reward.py @@ -4,6 +4,7 @@ import ray from mindspeed_rl.models.rule_verifier import compute_verifier_score from mindspeed_rl.utils.loggers import Loggers from mindspeed_rl.utils.utils import get_least_common_multiple +from mindspeed_rl.trainer.utils.transfer_dock import pad_experience logger = Loggers("rule_reward") @@ -32,11 +33,11 @@ class RuleReward(object): experience_consumer_stage, experience_columns, experience_count, - pad_id=pad_token_id ) ) # cpu数据 if batch_data and index: + batch_data = pad_experience(batch_data, pad_token_id) # multiple, tp_size if "categories" in batch_data.keys(): use_verifier_mask = batch_data["categories"][:, 0].squeeze().bool() selected_index = [index[i] for i in range(len(index)) if use_verifier_mask[i]] @@ -46,7 +47,8 @@ class RuleReward(object): if "categories" in batch_data.keys(): batch_data = {key: value[use_verifier_mask] if key != 'prompts' else value[ use_verifier_mask[::self.n_samples_per_prompt]] for key, value in batch_data.items()} - token_level_rewards, metrics = compute_verifier_score(batch_data, self.megatron_config, self.rl_config) + ignore_token = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod + token_level_rewards, metrics = compute_verifier_score(batch_data, self.megatron_config, self.rl_config, ignore_token) for key, value in metrics.items(): ray.get(self.td.update_metrics.remote(key, value=value, cumulate=True)) diff --git a/tests/ut/utils/test_transfer_dock.py b/tests/ut/utils/test_transfer_dock.py index 6f1721a8..18f42847 100644 --- a/tests/ut/utils/test_transfer_dock.py +++ b/tests/ut/utils/test_transfer_dock.py @@ -132,12 +132,12 @@ class TestGRPOTransferDock(DistributedTest): experience_batch, output_indexes = ray.get( self.td.get_experience.remote( - consumer="actor_rollout", experience_columns=["prompts"], indexes=get_indexes, pad_id=0.0 + consumer="actor_rollout", experience_columns=["prompts"], indexes=get_indexes ) ) assert output_indexes == get_indexes - assert experience_batch["prompts"].size(0) == get_mbs + assert len(experience_batch["prompts"]) == get_mbs def test_put_experience(self): put_mbs = 4 @@ -149,7 +149,7 @@ class TestGRPOTransferDock(DistributedTest): for index in put_indexes: experience_batch, output_indexes = ray.get( self.td.get_experience.remote( - consumer="actor_rollout", experience_columns=["prompts"], indexes=[index], pad_id=0.0 + consumer="actor_rollout", experience_columns=["prompts"], indexes=[index] ) ) assert output_indexes[0] == index @@ -169,7 +169,7 @@ class TestGRPOTransferDock(DistributedTest): _, output_indexes = ray.get( self.td.get_experience.remote( - consumer="actor_rollout", experience_columns=["prompts"], experience_count=self.max_len, pad_id=0.0 + consumer="actor_rollout", experience_columns=["prompts"], experience_count=self.max_len ) ) assert len(output_indexes) == self.max_len @@ -182,7 +182,7 @@ class TestGRPOTransferDock(DistributedTest): while not ray.get(td.all_consumed.remote("actor_rollout")): ray.get( td.get_experience.remote( - consumer=f"actor_rollout", experience_columns=["prompts"], experience_count=1, pad_id=0.0 + consumer=f"actor_rollout", experience_columns=["prompts"], experience_count=1, ) ) -- Gitee From c557aa5ef9391314a8ca86aa5c7a340d352c1a90 Mon Sep 17 00:00:00 2001 From: zengshu Date: Tue, 22 Apr 2025 13:48:42 +0000 Subject: [PATCH 14/25] =?UTF-8?q?!244=20ReadMe=E4=BF=AE=E6=94=B9=20Merge?= =?UTF-8?q?=20pull=20request=20!244=20from=20zengshu/dev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 38072396..f17010a0 100644 --- a/README.md +++ b/README.md @@ -183,7 +183,7 @@ MindSpeed-RL已发布版本维护策略： | **MindSpeed-RL版本** | **对应标签** | **维护策略** | **当前状态** | **发布时间** | **后续状态** | **EOL日期** | |--------------------| ------------ |----------|----------|-----------|----------|-----------| -| 0.0.1 | \ | 预览版本 | 待发布 | 2025/3/30 | \ | 2024/9/30 | +| 2.0.0 | \ | 预览版本 | 预览 | \ | \ | 2025/9/30 | ## 致谢 -- Gitee From 13819d2ab54c8545e82dd7b34f5a368b7d10d4ab Mon Sep 17 00:00:00 2001 From: xiecheng Date: Wed, 23 Apr 2025 03:28:29 +0000 Subject: [PATCH 15/25] =?UTF-8?q?!241=20[dev=E5=88=86=E6=94=AF]=E5=85=B1?= =?UTF-8?q?=E5=8D=A1=E6=96=B9=E6=A1=88=E6=97=B6=E9=97=B4=E6=89=93=E7=82=B9?= =?UTF-8?q?=E6=B7=BB=E5=8A=A0=20Merge=20pull=20request=20!241=20from=20xie?= =?UTF-8?q?cheng/dev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- OWNERS | 2 ++ mindspeed_rl/trainer/grpo_trainer_hybrid.py | 23 ++++++++++++++++++-- mindspeed_rl/utils/utils.py | 24 ++++++++++++++++++--- mindspeed_rl/workers/actor_hybrid_worker.py | 23 ++++++++++++++++++++ mindspeed_rl/workers/integrated_worker.py | 21 ++++++++++++++++++ 5 files changed, 88 insertions(+), 5 deletions(-) diff --git a/OWNERS b/OWNERS index b6502ba6..52a3031f 100644 --- a/OWNERS +++ b/OWNERS @@ -34,3 +34,5 @@ reviewers: - yangguangthu - indigoluo - chantcalf +- TJJ5 +- zengshu19991010 diff --git a/mindspeed_rl/trainer/grpo_trainer_hybrid.py b/mindspeed_rl/trainer/grpo_trainer_hybrid.py index 80864db9..e361fcd6 100644 --- a/mindspeed_rl/trainer/grpo_trainer_hybrid.py +++ b/mindspeed_rl/trainer/grpo_trainer_hybrid.py @@ -1,6 +1,7 @@ # Copyright (c) 2025, HUAWEI CORPORATION. All rights reserved. import copy from typing import List, Union +import time import ray import torch from codetiming import Timer @@ -14,7 +15,7 @@ from mindspeed_rl.trainer.utils.compute_utils import compute_advantage, compute_ from mindspeed_rl.workers.scheduler.launcher import RayActorGroup from mindspeed_rl.utils.loggers import Loggers from mindspeed_rl.utils.metrics import Metric -from mindspeed_rl.utils.utils import metrics_post_processing, compute_tps, metrics_sort +from mindspeed_rl.utils.utils import metrics_post_processing, compute_tps, compute_vllm_throughput, metrics_sort from mindspeed_rl.utils.utils import num_floating_point_operations, get_least_common_multiple @@ -161,9 +162,11 @@ class RayGRPOTrainer(RayBaseTrainer): metrics_result = metrics_post_processing(metrics_result) metrics_result = metrics_sort(metrics_result, all_timer.last) tps = compute_tps(self.kwargs, grpo_data_metrics, self.global_batch_size, self.n_samples_per_prompt, all_timer.last) + vllm_throughput = compute_vllm_throughput(self.kwargs, grpo_data_metrics, self.global_batch_size, self.n_samples_per_prompt, metrics_result["timing/rollout"]) metrics.update(value=metrics_result) metrics.update(value=grpo_data_metrics) metrics.update("tokens/p/s", tps) + metrics.update("vllm_throughput", vllm_throughput) iteration += 1 logger.info(metrics.metric, iteration, self.train_iters) if self.tensorboard is not None: @@ -179,7 +182,8 @@ class RayGRPOTrainer(RayBaseTrainer): def compute_advantage(self, blocking=False): experience_count = get_least_common_multiple(self.micro_batch_size, self.n_samples_per_prompt) - + + start_adv_time = time.time() compute_advantage_ref = compute_advantage.options(num_cpus=self.num_cpus_for_local_task).remote( self.transfer_dock, self.gamma, @@ -190,6 +194,21 @@ class RayGRPOTrainer(RayBaseTrainer): ) if blocking: ray.get(compute_advantage_ref) + end_adv_time = time.time() + ray.get( + self.transfer_dock.update_metrics.remote( + "timing/adv", + value=[round(end_adv_time, 4), round(start_adv_time, 4)], + cumulate=True + ) + ) + ray.get( + self.transfer_dock.update_metrics.remote( + "end_time/end_adv_time", + value=[round(end_adv_time, 4)], + cumulate=True + ) + ) @staticmethod def rule_reward_compute_rm_score(reward_worker, blocking=False): diff --git a/mindspeed_rl/utils/utils.py b/mindspeed_rl/utils/utils.py index 64eb0daf..5293d9e1 100644 --- a/mindspeed_rl/utils/utils.py +++ b/mindspeed_rl/utils/utils.py @@ -202,10 +202,12 @@ def metrics_post_processing(metrics) -> Dict[str, Tensor]: def metrics_sort(metrics, time_all) -> Dict[str, Tensor]: old_log_p_end_time = metrics.pop('end_time/old_log_p', None) + end_adv_time = metrics.pop('end_time/end_adv_time', None) reference_start_time = metrics.pop('start_time/reference_model', None) reference_end_time = metrics.pop('end_time/reference', None) - non_overlap_reference_model_time = max(reference_end_time - max(old_log_p_end_time, reference_start_time), 0) + non_overlap_reference_model_time = max(reference_end_time - max(old_log_p_end_time, reference_start_time), 0) + non_overlap_adv_time = max(max(old_log_p_end_time, end_adv_time) - old_log_p_end_time, 0) if "timing/rule_reward" in metrics.keys(): reward_start_time = metrics.pop('start_time/rule_reward', None) @@ -220,11 +222,12 @@ def metrics_sort(metrics, time_all) -> Dict[str, Tensor]: metrics["timing/non_overlap_reference_model"] = non_overlap_reference_model_time + metrics["timing/non_overlap_adv"] = non_overlap_adv_time metrics["timing/all"] = time_all sort_metrics = dict(sorted(metrics.items())) - custom_order = ['timing/all', 'timing/update', 'timing/rollout', 'timing/old_log_p', 'timing/reference_model', 'timing/non_overlap_reference_model'] - special_keys = ['timing/non_overlap_rule_reward', 'timing/non_overlap_reward_model', 'timing/rule_reward', 'timing/reward_model'] + custom_order = ['timing/all', 'timing/update', 'timing/resharding_to_train', 'timing/rollout', 'timing/resharding_to_infer', 'timing/old_log_p', 'timing/reference_model', 'timing/non_overlap_reference_model'] + special_keys = ['timing/non_overlap_rule_reward', 'timing/non_overlap_reward_model', 'timing/rule_reward', 'timing/reward_model', 'timing/adv', 'timing/non_overlap_adv', 'timing/onload', 'timing/offload'] keys_to_move = [key for key in sort_metrics.keys() if key in special_keys] remaining_keys = [] for key in sort_metrics: @@ -251,6 +254,21 @@ def compute_tps(compute_kwargs, metrics_result, gbs, n_samples, time_all): return tps +def compute_vllm_throughput(compute_kwargs, metrics_result, gbs, n_samples, time_rollout): + actor_resource = compute_kwargs.get('actor_resource', {}) + reference_resource = compute_kwargs.get('reference_resource', {}) + reward_resource = compute_kwargs.get('reward_resource', None) + actor_resource_only = compute_kwargs.get('use_integrated_worker', False) + + actor_npus = actor_resource.get('num_npus', 0) + reference_npus = reference_resource.get('num_npus', 0) + reward_npus = reward_resource.get('num_npus', 0) if reward_resource is not None else 0 + + world_size = actor_npus + reference_npus + reward_npus if not actor_resource_only else actor_npus + vllm_throughput = metrics_result['response_length/mean'] * gbs * n_samples / world_size / time_rollout + return vllm_throughput + + def seed_all(seed=1234): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index f8b8cc60..51d69beb 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -111,7 +111,9 @@ class ActorHybridWorkerBase(BaseWorker): return self.args.consumed_train_samples def update(self, kl_ctrl=None): + start_sharding_enter_train = time.time() self.sharding_manager.enter_train_mode() + sharding_train_interval = time.time() - start_sharding_enter_train self.args.curr_iteration = self.iteration @@ -157,14 +159,25 @@ class ActorHybridWorkerBase(BaseWorker): ) self.iteration += 1 + start_sharding_exit_train = time.time() self.sharding_manager.exit_train_mode() + sharding_train_interval += (time.time() - start_sharding_exit_train) + ray.get( + self.td.update_metrics.remote( + "timing/resharding_to_train", + value=[sharding_train_interval], + cumulate=True + ) + ) def save_ckpt(self, iteration: int): self.save_checkpoint(iteration, self.model, self.optimizer, self.opt_param_scheduler, self.num_floating_point_operations_so_far) def generate_sequences(self): + start_sharding_enter_infer = time.time() self.sharding_manager.enter_infer_mode() + sharding_infer_interval = time.time() - start_sharding_enter_infer experience_consumer_stage = 'actor_rollout' experience_columns = ['prompts', 'prompt_length'] @@ -222,7 +235,17 @@ class ActorHybridWorkerBase(BaseWorker): cumulate=True ) ) + + start_sharding_exit_infer = time.time() self.sharding_manager.exit_infer_mode() + sharding_infer_interval += (time.time() - start_sharding_exit_infer) + ray.get( + self.td.update_metrics.remote( + "timing/resharding_to_infer", + value=[sharding_infer_interval], + cumulate=True + ) + ) def compute_log_prob(self): self.sharding_manager.enter_forward_mode() diff --git a/mindspeed_rl/workers/integrated_worker.py b/mindspeed_rl/workers/integrated_worker.py index cc091943..9d2da179 100644 --- a/mindspeed_rl/workers/integrated_worker.py +++ b/mindspeed_rl/workers/integrated_worker.py @@ -3,6 +3,7 @@ import dataclasses from typing import Callable +import time import ray import torch @@ -90,9 +91,29 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB ) def compute_ref_log_prob(self): + start_onload_time = time.time() self.ref_manager.onload_param() + end_onload_time = time.time() + ray.get( + self.td.update_metrics.remote( + "timing/onload", + value=[round(end_onload_time, 4), round(start_onload_time, 4)], + cumulate=True + ) + ) + ReferenceWorkerBase.compute_ref_log_prob(self) + + start_offload_time = time.time() self.ref_manager.offload_param() + end_offload_time = time.time() + ray.get( + self.td.update_metrics.remote( + "timing/offload", + value=[round(end_offload_time, 4), round(start_offload_time, 4)], + cumulate=True + ) + ) def update(self, kl_ctrl=None): # set update mbs -- Gitee From 7657332680661674a1d6bbb1010389f159b703f2 Mon Sep 17 00:00:00 2001 From: xiecheng Date: Thu, 24 Apr 2025 12:55:25 +0000 Subject: [PATCH 16/25] =?UTF-8?q?!247=20[dev]readme=E6=9B=B4=E6=96=B0=20Me?= =?UTF-8?q?rge=20pull=20request=20!247=20from=20xiecheng/dev=5Freadme?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/install_guide.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/install_guide.md b/docs/install_guide.md index a96c6a6c..03f78239 100644 --- a/docs/install_guide.md +++ b/docs/install_guide.md @@ -35,8 +35,9 @@ pip install apex-0.1.dev*.whl ### vllm及相关依赖安装： （注：环境中需要安装git，因为vllm的安装过程依赖git） ```shell -git clone -b v0.7.3 https://github.com/vllm-project/vllm.git +git clone https://github.com/vllm-project/vllm.git cd vllm +git checkout v0.7.3 pip install -r requirements-build.txt VLLM_TARGET_DEVICE=empty pip install . ``` -- Gitee From 04aa084503a63af3e989f48abcd8293e7cdadbe8 Mon Sep 17 00:00:00 2001 From: tanwenqin Date: Fri, 25 Apr 2025 03:36:58 +0000 Subject: [PATCH 17/25] =?UTF-8?q?!239=20=E7=A1=AE=E5=AE=9A=E6=80=A7?= =?UTF-8?q?=E8=AE=A1=E7=AE=97=E4=BB=A5=E5=8F=8ATD=E4=BF=9D=E5=BA=8F?= =?UTF-8?q?=E7=89=B9=E6=80=A7=20Merge=20pull=20request=20!239=20from=20tan?= =?UTF-8?q?wenqin/dev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- configs/grpo_trainer_qwen25_32b.yaml | 1 + configs/grpo_trainer_qwen25_7b.yaml | 1 + docs/algorithms/grpo.md | 1 + mindspeed_rl/config_cls/rl_config.py | 1 + .../vllm_adapter/vllm_parallel_state.py | 14 ++++++- mindspeed_rl/trainer/base.py | 2 + mindspeed_rl/trainer/grpo_trainer_hybrid.py | 12 ++++-- mindspeed_rl/trainer/utils/compute_utils.py | 19 ++++++--- mindspeed_rl/utils/utils.py | 10 +++++ mindspeed_rl/workers/actor_hybrid_worker.py | 20 ++++++--- mindspeed_rl/workers/base_worker.py | 42 +++++++++++++++++-- mindspeed_rl/workers/reference_woker.py | 6 ++- mindspeed_rl/workers/reward_woker.py | 9 +++- mindspeed_rl/workers/rule_reward.py | 8 +++- 14 files changed, 122 insertions(+), 24 deletions(-) diff --git a/configs/grpo_trainer_qwen25_32b.yaml b/configs/grpo_trainer_qwen25_32b.yaml index e92a5ad6..d9d78907 100644 --- a/configs/grpo_trainer_qwen25_32b.yaml +++ b/configs/grpo_trainer_qwen25_32b.yaml @@ -71,6 +71,7 @@ reward_config: no_load_rng: true rl_config: + guarantee_order: false blocking: false gamma: 1.0 lam: 0.95 diff --git a/configs/grpo_trainer_qwen25_7b.yaml b/configs/grpo_trainer_qwen25_7b.yaml index 41a262c6..85f0cb03 100644 --- a/configs/grpo_trainer_qwen25_7b.yaml +++ b/configs/grpo_trainer_qwen25_7b.yaml @@ -72,6 +72,7 @@ reward_config: no_load_rng: true rl_config: + guarantee_order: false blocking: false gamma: 1.0 lam: 0.95 diff --git a/docs/algorithms/grpo.md b/docs/algorithms/grpo.md index 24f2fab4..4a06f68f 100644 --- a/docs/algorithms/grpo.md +++ b/docs/algorithms/grpo.md @@ -184,6 +184,7 @@ bash examples/r1/qwen25/r1_zero_qwen25_32b_worker.sh ### `rl_config:` * `blocking`：是否开启异步，默认为 False； +* `guarantee_order`: 是否开启TransferDock保序，默认 False * `n_samples_per_prompt`：每条prompt的重用次数，一条 prompt 输入能输出 n 条 responese； * `max_prompt_length`：GRPO 训练中最大 prompt 长度，默认为512； * `clip_ratio`：Actor 模型训练计算损失函数时的 clip 比例，默认为0.2 一般取值范围 [0.1，0.3] 最大取值范围[0，1] 该数值越大允许策略更新的幅度越大，反之不然； diff --git a/mindspeed_rl/config_cls/rl_config.py b/mindspeed_rl/config_cls/rl_config.py index de2ae915..339b06d3 100644 --- a/mindspeed_rl/config_cls/rl_config.py +++ b/mindspeed_rl/config_cls/rl_config.py @@ -83,6 +83,7 @@ class RLConfig(BaseConfig): self.wandb_exp_name = "" self.wandb_save_dir = "" self.blocking = False + self.guarantee_order = False self.num_cpus_for_local_task = 1 self.num_cpus_for_placement_group = 8 self.use_integrated_worker = False diff --git a/mindspeed_rl/models/rollout/vllm_adapter/vllm_parallel_state.py b/mindspeed_rl/models/rollout/vllm_adapter/vllm_parallel_state.py index f4c6bc80..abcd38be 100644 --- a/mindspeed_rl/models/rollout/vllm_adapter/vllm_parallel_state.py +++ b/mindspeed_rl/models/rollout/vllm_adapter/vllm_parallel_state.py @@ -32,6 +32,13 @@ _TP = None # Pipeline model parallel group that the current rank belongs to. _PP = None +# Tensor model parallel group +_TP_GROUP_RANKS = None + + +def get_vllm_tp_group_ranks(): + return _TP_GROUP_RANKS + # This method is for initializing the ParallelGroup when using HybridEngine def initialize_parallel_state( @@ -187,9 +194,12 @@ def initialize_model_parallel_for_vllm( def get_tp_group_ranks(): if infer_tensor_model_parallel_size > train_tensor_model_parallel_size: - return get_split_tp_group_ranks() + tp_group_ranks = get_split_tp_group_ranks() else: - return get_allgather_tp_group_ranks() + tp_group_ranks = get_allgather_tp_group_ranks() + global _TP_GROUP_RANKS + _TP_GROUP_RANKS = tp_group_ranks + return tp_group_ranks _TP = init_model_parallel_group( diff --git a/mindspeed_rl/trainer/base.py b/mindspeed_rl/trainer/base.py index 7b59228f..4ad0b2c2 100644 --- a/mindspeed_rl/trainer/base.py +++ b/mindspeed_rl/trainer/base.py @@ -37,6 +37,7 @@ class RayBaseTrainer(object): tokenizer: BaseTokenizer = None, dataset_additional_keys: List[str] = None, blocking: bool = False, + guarantee_order: bool = False, num_cpus_for_local_task: float = 0.1, **kwargs): @@ -60,6 +61,7 @@ class RayBaseTrainer(object): self.tokenizer = tokenizer self.dataset_additional_keys = dataset_additional_keys self.blocking = blocking + self.guarantee_order = guarantee_order self.num_cpus_for_local_task = num_cpus_for_local_task self.kwargs = kwargs diff --git a/mindspeed_rl/trainer/grpo_trainer_hybrid.py b/mindspeed_rl/trainer/grpo_trainer_hybrid.py index e361fcd6..e9f1d3a9 100644 --- a/mindspeed_rl/trainer/grpo_trainer_hybrid.py +++ b/mindspeed_rl/trainer/grpo_trainer_hybrid.py @@ -61,6 +61,7 @@ class RayGRPOTrainer(RayBaseTrainer): tokenizer: BaseTokenizer = None, dataset_additional_keys: List[str] = None, blocking: bool = False, + guarantee_order: bool = False, num_cpus_for_local_task: int = 1, **kwargs ): @@ -81,6 +82,7 @@ class RayGRPOTrainer(RayBaseTrainer): tokenizer=tokenizer, dataset_additional_keys=dataset_additional_keys, blocking=blocking, + guarantee_order=guarantee_order, num_cpus_for_local_task=num_cpus_for_local_task, **kwargs ) @@ -135,7 +137,7 @@ class RayGRPOTrainer(RayBaseTrainer): self.rule_reward_compute_rm_score(reward_worker, blocking=False) # compute advantages, executed on the driver process - self.compute_advantage(blocking=False) + self.compute_advantage(blocking=False, guarantee_order=self.guarantee_order) # compute reference log_prob self.ref_worker.compute_ref_log_prob(blocking=self.blocking) @@ -156,7 +158,9 @@ class RayGRPOTrainer(RayBaseTrainer): # collect metrics grpo_data_metrics = compute_grpo_data_metrics(self.transfer_dock, self.global_batch_size * self.n_samples_per_prompt, - self.tokenizer) + self.tokenizer, + self.global_batch_size * self.n_samples_per_prompt, + self.guarantee_order) metrics_result = ray.get(self.transfer_dock.get_metrics.remote()) metrics_result = metrics_post_processing(metrics_result) @@ -179,7 +183,7 @@ class RayGRPOTrainer(RayBaseTrainer): logger.info('after grpo training is done') - def compute_advantage(self, blocking=False): + def compute_advantage(self, blocking=False, guarantee_order=False): experience_count = get_least_common_multiple(self.micro_batch_size, self.n_samples_per_prompt) @@ -191,6 +195,8 @@ class RayGRPOTrainer(RayBaseTrainer): adv_estimator=self.adv_estimator, experience_count=experience_count, tokenizer=self.tokenizer, + global_batch_size=self.global_batch_size * self.n_samples_per_prompt, + guarantee_order=guarantee_order ) if blocking: ray.get(compute_advantage_ref) diff --git a/mindspeed_rl/trainer/utils/compute_utils.py b/mindspeed_rl/trainer/utils/compute_utils.py index ea103346..bf51679d 100644 --- a/mindspeed_rl/trainer/utils/compute_utils.py +++ b/mindspeed_rl/trainer/utils/compute_utils.py @@ -23,7 +23,7 @@ import numpy as np import mindspeed_rl.utils.torch_functional as F from mindspeed_rl.utils.pad_process import truncate_rows -from mindspeed_rl.utils.utils import generate_mask +from mindspeed_rl.utils.utils import generate_mask, get_current_dp_range_indexes from mindspeed_rl.trainer.utils.transfer_dock import pad_experience @@ -125,7 +125,7 @@ def compute_group_norm_advantage_return(token_level_rewards: torch.Tensor, eos_m @ray.remote -def compute_advantage(td, gamma, lam, adv_estimator, experience_count, tokenizer): +def compute_advantage(td, gamma, lam, adv_estimator, experience_count, tokenizer, global_batch_size, guarantee_order): """ Compute the advantage function based on different adv_estimator @@ -136,6 +136,8 @@ def compute_advantage(td, gamma, lam, adv_estimator, experience_count, tokenizer adv_estimator: The type of advantage estimator, which can be "gae" or "group_norm" experience_count: The number of experiences to retrieve from the experience td tokenizer: The pre-trained tokenizer + global_batch_size: The number of global batch size + guarantee_order: The switch of guarantee order Returns: None @@ -143,11 +145,13 @@ def compute_advantage(td, gamma, lam, adv_estimator, experience_count, tokenizer experience_consumer_stage = "compute_advantage" experience_columns = ["responses", "token_level_rewards", "response_length"] pad_token_id = tokenizer.pad if tokenizer.pad is not None else tokenizer.eod - + sorted_indexes = get_current_dp_range_indexes(experience_count=experience_count, + assign_batch_size=global_batch_size) if guarantee_order else None while not ray.get(td.all_consumed.remote(experience_consumer_stage)): batch_data, index = ray.get( td.get_experience.remote( experience_consumer_stage, experience_columns, experience_count, # pad_id=pad_token_id + indexes=sorted_indexes.pop(0) if guarantee_order else None ) ) if batch_data and index: @@ -193,7 +197,7 @@ def get_last_reward(rm_scores, n_sample_batch: int): def compute_grpo_data_metrics( - td, experience_count, tokenizer + td, experience_count, tokenizer, global_batch_size, guarantee_order ): """ Calculate various metrics for GRPO data @@ -202,6 +206,8 @@ def compute_grpo_data_metrics( td: A data queue object experience_count: Number of experiences to retrieve tokenizer: The pre-trained tokenizer + global_batch_size: The number of global batch size + guarantee_order: The switch of guarantee order Returns: Dictionary containing various metric values @@ -217,9 +223,12 @@ def compute_grpo_data_metrics( "response_length", ] pad_token_id = tokenizer.pad if tokenizer.pad is not None else tokenizer.eod + sorted_indexes = get_current_dp_range_indexes(experience_count=experience_count, + assign_batch_size=global_batch_size) if guarantee_order else None while not ray.get(td.all_consumed.remote(experience_consumer_stage)): batch, index = ray.get( - td.get_experience.remote(experience_consumer_stage, experience_columns, experience_count) + td.get_experience.remote(experience_consumer_stage, experience_columns, experience_count, + indexes=sorted_indexes.pop(0) if guarantee_order else None) ) if batch and index: batch = pad_experience(batch, pad_token_id) # multiple, tp_size diff --git a/mindspeed_rl/utils/utils.py b/mindspeed_rl/utils/utils.py index 5293d9e1..8526cc83 100644 --- a/mindspeed_rl/utils/utils.py +++ b/mindspeed_rl/utils/utils.py @@ -16,6 +16,11 @@ import torch_npu from torch import Tensor +def get_current_dp_range_indexes(experience_count, assign_batch_size, current_dp_rank=0): + all_indexes = list(range(assign_batch_size * current_dp_rank, assign_batch_size * (current_dp_rank + 1))) + return [all_indexes[i:i + experience_count] for i in range(0, len(all_indexes), experience_count)] + + def synchronize_time(): """Synchronize training start time across all distributed processes.""" cur_time = time.time() @@ -272,6 +277,11 @@ def compute_vllm_throughput(compute_kwargs, metrics_result, gbs, n_samples, time def seed_all(seed=1234): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) + os.environ['HCCL_DETERMINISTIC'] = str(True) + os.environ['LCCL_DETERMINISTIC'] = str(1) + os.environ['CLOSE_MATMUL_K_SHIFT'] = str(1) + os.environ['ATB_MATMUL_SHUFFLE_K_ENABLE'] = "0" + os.environ['ATB_LLM_LCOC_ENABLE'] = "0" np.random.seed(seed) torch.manual_seed(seed) torch.use_deterministic_algorithms(True) diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index 51d69beb..5055f194 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -121,7 +121,7 @@ class ActorHybridWorkerBase(BaseWorker): experience_columns = ['responses', 'advantages', 'old_log_prob', 'ref_log_prob', 'input_ids', 'response_length', 'prompt_length'] - + experience_count = self.megatron_config.global_batch_size // self.parallel_state.get_data_parallel_world_size() #get lr @@ -129,14 +129,17 @@ class ActorHybridWorkerBase(BaseWorker): for param_group in self.optimizer.param_groups: learning_rate = param_group['lr'] ray.get(self.td.update_metrics.remote(key='grpo/lr', value=learning_rate)) - + sorted_indexes = self.get_dp_range_indexes(experience_count, + use_vllm=False) if self.rl_config.guarantee_order else None start_time_defined = False count = 0 - while self.all_consumed(experience_consumer_stage) > 0: + while self.all_consumed(experience_consumer_stage, sorted_indexes) > 0: batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, experience_count, self.megatron_config.tensor_model_parallel_size, + indexes=sorted_indexes.pop( + 0) if self.rl_config.guarantee_order else None, get_n_samples=False) if not start_time_defined: start_time = time.time() @@ -185,14 +188,17 @@ class ActorHybridWorkerBase(BaseWorker): self.rl_config.n_samples_per_prompt) pad_token_id = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod + sorted_indexes = self.get_dp_range_indexes(experience_count, + use_vllm=True) if self.rl_config.guarantee_order else None start_time_defined = False - while self.all_consumed(experience_consumer_stage, use_vllm=True) > 0: + while self.all_consumed(experience_consumer_stage, sorted_indexes, use_vllm=True) > 0: batch_data, index = self.dispatch_transfer_dock_data( experience_consumer_stage, experience_columns, experience_count, tp_size=self.megatron_config.tensor_model_parallel_size, + indexes=sorted_indexes.pop(0) if self.rl_config.guarantee_order else None, use_vllm=True ) if not start_time_defined: @@ -254,13 +260,17 @@ class ActorHybridWorkerBase(BaseWorker): experience_columns = ['input_ids', 'responses', 'response_length', 'prompt_length'] experience_count = get_least_common_multiple(self.megatron_config.micro_batch_size, self.rl_config.n_samples_per_prompt) + sorted_indexes = self.get_dp_range_indexes(experience_count, + use_vllm=False) if self.rl_config.guarantee_order else None start_time_defined = False - while self.all_consumed(experience_consumer_stage) > 0: + while self.all_consumed(experience_consumer_stage, sorted_indexes) > 0: batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, experience_count, tp_size=self.megatron_config.tensor_model_parallel_size, + indexes=sorted_indexes.pop( + 0) if self.rl_config.guarantee_order else None, get_n_samples=False) if not start_time_defined: start_time = time.time() diff --git a/mindspeed_rl/workers/base_worker.py b/mindspeed_rl/workers/base_worker.py index eaa9bab6..3470a191 100644 --- a/mindspeed_rl/workers/base_worker.py +++ b/mindspeed_rl/workers/base_worker.py @@ -11,6 +11,7 @@ import torch import torch_npu import ray +from mindspeed_rl.models.rollout.vllm_adapter.vllm_parallel_state import get_vllm_tp_group_ranks from mindspeed_rl.utils.loggers import Loggers from mindspeed_rl.utils.tokenizer import BaseTokenizer @@ -28,10 +29,15 @@ from mindspeed_rl.trainer.utils.parallel_state import ( get_model_parallel_group ) from mindspeed_rl.utils.compute import set_parallel_state, set_vocab_parallel +from mindspeed_rl.utils.utils import get_current_dp_range_indexes from mindspeed_rl.trainer.utils.transfer_dock import pack_experience_columns, unpack_pad_experience logger = Loggers("base_worker") +_DP_RANGE_DATA_CONSUMED_FLAG = 0 + +_DP_RANGE_DATA_NOT_CONSUMED_FLAG = 1 + class BaseRayWorker: def __init__(self): @@ -145,7 +151,11 @@ class BaseWorker(BaseRayWorker, ABC): self.td = None self.args = None - def all_consumed(self, experience_consumer_stage, use_vllm=False): + def all_consumed(self, experience_consumer_stage, sorted_indexes, use_vllm=False): + if self.rl_config.guarantee_order and not sorted_indexes: + return _DP_RANGE_DATA_CONSUMED_FLAG + elif self.rl_config.guarantee_order: + return _DP_RANGE_DATA_NOT_CONSUMED_FLAG if use_vllm: current_device = next(self.inference_model.model.parameters()).device else: @@ -205,7 +215,7 @@ class BaseWorker(BaseRayWorker, ABC): def dispatch_transfer_dock_data(self, experience_consumer_stage, experience_columns, experience_count, tp_size=1, - use_vllm=False, + use_vllm=False, indexes=None, get_n_samples=True): pad_id = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod @@ -216,8 +226,8 @@ class BaseWorker(BaseRayWorker, ABC): if get_tensor_model_parallel_rank(self.parallel_state, use_vllm) == 0 and \ get_pipeline_model_parallel_rank(self.parallel_state, use_vllm) == 0: batch_data, index = ray.get(self.td.get_experience.remote(experience_consumer_stage, experience_columns, - experience_count, get_n_samples=get_n_samples)) # cpu数据 - + experience_count, indexes=indexes, + get_n_samples=get_n_samples)) # cpu数据 if not index: # 判断是否取出数据，未取出数据为-1 index = [-1] * experience_count @@ -325,3 +335,27 @@ class BaseWorker(BaseRayWorker, ABC): use_vllm) == 0: output = {key: value.cpu() if not isinstance(value, List) else value for key, value in output.items()} self.td.put_experience.remote(data_dict=output, indexes=index) + + def get_dp_range_indexes(self, experience_count, use_vllm=False): + if use_vllm: + current_dp_rank, dp_world_size = self.get_vllm_dp_rank() + else: + current_dp_rank = self.parallel_state.get_data_parallel_rank() + dp_world_size = self.parallel_state.get_data_parallel_world_size() + assign_batch_size = self.megatron_config.global_batch_size // dp_world_size + return get_current_dp_range_indexes(experience_count=experience_count, + assign_batch_size=assign_batch_size, + current_dp_rank=current_dp_rank) + + @staticmethod + def get_vllm_dp_rank(): + get_rollout_data_parallel_rank = torch.distributed.get_rank() + vllm_dp_groups = get_vllm_tp_group_ranks() + if vllm_dp_groups is None: + raise ValueError("vllm dp groups is None") + for index, dp_group in enumerate(vllm_dp_groups): + if get_rollout_data_parallel_rank in dp_group: + current_dp_rank = index + return current_dp_rank, len(vllm_dp_groups) + + diff --git a/mindspeed_rl/workers/reference_woker.py b/mindspeed_rl/workers/reference_woker.py index 61f9bebe..7fd06586 100644 --- a/mindspeed_rl/workers/reference_woker.py +++ b/mindspeed_rl/workers/reference_woker.py @@ -87,13 +87,17 @@ class ReferenceWorkerBase(BaseWorker): experience_columns = ['input_ids', 'responses', 'response_length', 'prompt_length'] experience_count = get_least_common_multiple(self.megatron_config.micro_batch_size, self.rl_config.n_samples_per_prompt) + sorted_indexes = self.get_dp_range_indexes(experience_count, + use_vllm=False) if self.rl_config.guarantee_order else None start_time_defined = False - while self.all_consumed(experience_consumer_stage) > 0: + while self.all_consumed(experience_consumer_stage, sorted_indexes) > 0: batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, experience_count, tp_size=self.megatron_config.tensor_model_parallel_size, + indexes=sorted_indexes.pop( + 0) if self.rl_config.guarantee_order else None, get_n_samples=False) if not start_time_defined: diff --git a/mindspeed_rl/workers/reward_woker.py b/mindspeed_rl/workers/reward_woker.py index fccdf609..a556904d 100644 --- a/mindspeed_rl/workers/reward_woker.py +++ b/mindspeed_rl/workers/reward_woker.py @@ -84,13 +84,18 @@ class RewardWorkerBase(BaseWorker): *self.megatron_config.dataset_additional_keys] experience_count = get_least_common_multiple(self.megatron_config.micro_batch_size, self.rl_config.n_samples_per_prompt) + sorted_indexes = self.get_dp_range_indexes(experience_count, + use_vllm=False) if self.rl_config.guarantee_order else None start_time_defined = False - while not ray.get(self.td.all_consumed.remote(experience_consumer_stage)): + while self.all_consumed(experience_consumer_stage, sorted_indexes) > 0: batch_data, index = self.dispatch_transfer_dock_data(experience_consumer_stage, experience_columns, experience_count, - tp_size=self.megatron_config.tensor_model_parallel_size) + tp_size=self.megatron_config.tensor_model_parallel_size, + indexes=sorted_indexes.pop( + 0) if self.rl_config.guarantee_order else None, + ) if not start_time_defined: start_time = time.time() start_time_defined = True diff --git a/mindspeed_rl/workers/rule_reward.py b/mindspeed_rl/workers/rule_reward.py index 47c32f0d..80778c37 100644 --- a/mindspeed_rl/workers/rule_reward.py +++ b/mindspeed_rl/workers/rule_reward.py @@ -3,8 +3,8 @@ import ray from mindspeed_rl.models.rule_verifier import compute_verifier_score from mindspeed_rl.utils.loggers import Loggers -from mindspeed_rl.utils.utils import get_least_common_multiple from mindspeed_rl.trainer.utils.transfer_dock import pad_experience +from mindspeed_rl.utils.utils import get_least_common_multiple, get_current_dp_range_indexes logger = Loggers("rule_reward") @@ -26,13 +26,17 @@ class RuleReward(object): experience_columns = ['prompts', 'responses', 'response_length', *self.megatron_config.dataset_additional_keys] experience_count = get_least_common_multiple(self.megatron_config.micro_batch_size, self.rl_config.n_samples_per_prompt) + assign_batch_size = self.megatron_config.global_batch_size * self.rl_config.n_samples_per_prompt + sorted_indexes = get_current_dp_range_indexes(experience_count=experience_count, + assign_batch_size=assign_batch_size) if self.rl_config.guarantee_order else None + pad_token_id = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod while not ray.get(self.td.all_consumed.remote(experience_consumer_stage)): batch_data, index = ray.get( self.td.get_experience.remote( experience_consumer_stage, experience_columns, - experience_count, + experience_count ) ) # cpu数据 -- Gitee From bae12608676f88f18647ddd71a3f51e25cc8165c Mon Sep 17 00:00:00 2001 From: tanwenqin Date: Sun, 27 Apr 2025 06:07:22 +0000 Subject: [PATCH 18/25] =?UTF-8?q?!250=20[bugfix]TD=E4=BF=9D=E5=BA=8F=20Mer?= =?UTF-8?q?ge=20pull=20request=20!250=20from=20tanwenqin/dev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mindspeed_rl/workers/rule_reward.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mindspeed_rl/workers/rule_reward.py b/mindspeed_rl/workers/rule_reward.py index 80778c37..51c5c38e 100644 --- a/mindspeed_rl/workers/rule_reward.py +++ b/mindspeed_rl/workers/rule_reward.py @@ -36,7 +36,8 @@ class RuleReward(object): self.td.get_experience.remote( experience_consumer_stage, experience_columns, - experience_count + experience_count, + indexes=sorted_indexes.pop(0) if self.rl_config.guarantee_order else None ) ) # cpu数据 -- Gitee From 3ad75b39f958b818044af10924c61fd0770dff76 Mon Sep 17 00:00:00 2001 From: liuxinyang <292384718@qq.com> Date: Mon, 28 Apr 2025 01:09:39 +0000 Subject: [PATCH 19/25] =?UTF-8?q?!248=20actor=5Flog=5Fp=20=E5=92=8C=20rule?= =?UTF-8?q?=20reward=E8=AE=A1=E7=AE=97=E5=8A=A0=E9=80=9F=20Merge=20pull=20?= =?UTF-8?q?request=20!248=20from=20liuxinyang/reward?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cli/train_grpo.py | 20 ++++++++++-- mindspeed_rl/config_cls/rl_config.py | 1 + .../models/loss/grpo_actor_loss_func.py | 2 ++ mindspeed_rl/models/rule_verifier.py | 4 +-- mindspeed_rl/trainer/grpo_trainer_hybrid.py | 31 +++++++++++-------- mindspeed_rl/utils/utils.py | 11 +++++-- mindspeed_rl/workers/actor_hybrid_worker.py | 6 +++- mindspeed_rl/workers/integrated_worker.py | 4 +-- mindspeed_rl/workers/rule_reward.py | 9 ++++-- mindspeed_rl/workers/scheduler/launcher.py | 4 +-- 10 files changed, 64 insertions(+), 28 deletions(-) diff --git a/cli/train_grpo.py b/cli/train_grpo.py index 08370d46..27b3b323 100644 --- a/cli/train_grpo.py +++ b/cli/train_grpo.py @@ -13,6 +13,7 @@ import hydra import ray import torch import yaml +from ray.util import placement_group from mindspeed_rl.config_cls.validate_config import validate_rl_args from mindspeed_rl.utils import get_tokenizer @@ -106,10 +107,23 @@ def train(config): reward_list.append(reward_worker) + def get_node_nums(): + nodes = ray.nodes() + return len([node for node in nodes if node.get("Alive", False)]) + + rule_reward_num_process = get_node_nums() if rl_config.rule_reward: - rule_reward = RuleReward.options(num_cpus=rl_config.num_cpus_for_local_task).remote() - rule_reward.initialize.remote(reward_config, rl_config, tokenizer) - reward_list.append(rule_reward) + pg = placement_group( + [{"CPU": rl_config.num_cpus_for_local_task} for _ in range(rule_reward_num_process)], + strategy='SPREAD' + ) + + ray.get(pg.ready()) + + for i in range(rule_reward_num_process): + rule_reward = RuleReward.options(placement_group=pg, placement_group_bundle_index=i).remote() + rule_reward.initialize.remote(reward_config, rl_config, tokenizer) + reward_list.append(rule_reward) train_ds, _, _ = build_train_valid_test_datasets( data_prefix=[actor_config.data_path, ], diff --git a/mindspeed_rl/config_cls/rl_config.py b/mindspeed_rl/config_cls/rl_config.py index 339b06d3..df8e6deb 100644 --- a/mindspeed_rl/config_cls/rl_config.py +++ b/mindspeed_rl/config_cls/rl_config.py @@ -70,6 +70,7 @@ class RLConfig(BaseConfig): self.verifier_weight = [1.0, ] self.verifier_parallel = 1 self.verifier_timeout = 30 + self.verifier_micro_batch_size = 16 self.shuffle_mini_batch = False self.n_samples_per_prompt = 1 diff --git a/mindspeed_rl/models/loss/grpo_actor_loss_func.py b/mindspeed_rl/models/loss/grpo_actor_loss_func.py index 676df778..67915862 100644 --- a/mindspeed_rl/models/loss/grpo_actor_loss_func.py +++ b/mindspeed_rl/models/loss/grpo_actor_loss_func.py @@ -93,6 +93,8 @@ class GRPOActorLossFunc(BaseLossFunc): a float number indicating the fraction of policy gradient loss being clipped """ + if old_log_prob is None: + old_log_prob = log_prob.detach().clone() negative_approx_kl = log_prob - old_log_prob ratio = torch.exp(negative_approx_kl) ppo_kl = F.masked_mean(-negative_approx_kl, eos_mask) diff --git a/mindspeed_rl/models/rule_verifier.py b/mindspeed_rl/models/rule_verifier.py index 3af03287..7bed3e79 100644 --- a/mindspeed_rl/models/rule_verifier.py +++ b/mindspeed_rl/models/rule_verifier.py @@ -4,7 +4,6 @@ import multiprocessing as mp from multiprocessing import Process, Queue import torch -from transformers import AutoTokenizer from mindspeed_rl.utils.loggers import Loggers from mindspeed_rl.utils.math_eval_toolkit.grader import math_equal @@ -72,9 +71,8 @@ def _validate_response_structure(processed_str: str) -> bool: return validation_passed -def compute_verifier_score(batch, megatron_config, rl_config, ignore_token=-100): +def compute_verifier_score(batch, megatron_config, rl_config, tokenizer, ignore_token=-100): start_time = time.time() - tokenizer = AutoTokenizer.from_pretrained(megatron_config.tokenizer_name_or_path, trust_remote_code=True) question = batch["prompts"] indexes = [i for i in range(0, question.size(0), rl_config.n_samples_per_prompt)] question = question[indexes] diff --git a/mindspeed_rl/trainer/grpo_trainer_hybrid.py b/mindspeed_rl/trainer/grpo_trainer_hybrid.py index e9f1d3a9..7392576d 100644 --- a/mindspeed_rl/trainer/grpo_trainer_hybrid.py +++ b/mindspeed_rl/trainer/grpo_trainer_hybrid.py @@ -91,6 +91,7 @@ class RayGRPOTrainer(RayBaseTrainer): self.metrics = Metric() self.transfer_dock_init() self.kwargs = kwargs + self.set_actor_log_prob_skip_flag() def transfer_dock_init(self): self.transfer_dock = GRPOTransferDock.remote(self.global_batch_size, self.n_samples_per_prompt, @@ -103,6 +104,14 @@ class RayGRPOTrainer(RayBaseTrainer): else: reward.init_transfer_dock.remote(self.transfer_dock) + def set_actor_log_prob_skip_flag(self): + global_batch_size = self.actor_worker.megatron_config.global_batch_size + mini_batch_size = self.actor_worker.rl_config.mini_batch_size + n_samples_per_prompt = self.actor_worker.rl_config.n_samples_per_prompt + epochs = self.actor_worker.rl_config.epochs + self.skip_actor_log_prob = (global_batch_size * n_samples_per_prompt == mini_batch_size and epochs == 1) + self.actor_worker.skip_actor_log_prob = self.skip_actor_log_prob + def fit(self, data_loader: DataLoader): """ The utils loop of GRPO @@ -130,11 +139,12 @@ class RayGRPOTrainer(RayBaseTrainer): self.actor_worker.generate_sequences(blocking=self.blocking) # compute rm scores. + rule_reward = [] for reward_worker in self.reward_list: if isinstance(reward_worker, RayActorGroup): reward_worker.compute_rm_score(blocking=self.blocking) else: - self.rule_reward_compute_rm_score(reward_worker, blocking=False) + rule_reward.append(reward_worker.compute_rm_score.remote()) # compute advantages, executed on the driver process self.compute_advantage(blocking=False, guarantee_order=self.guarantee_order) @@ -143,17 +153,18 @@ class RayGRPOTrainer(RayBaseTrainer): self.ref_worker.compute_ref_log_prob(blocking=self.blocking) # compute old log_prob - self.actor_worker.compute_log_prob(blocking=self.blocking) + if not self.skip_actor_log_prob: + self.actor_worker.compute_log_prob(blocking=self.blocking) self.actor_worker.wait_all_ref_objs_run_over() - + self.ref_worker.wait_all_ref_objs_run_over() for reward in self.reward_list: if hasattr(reward, 'wait_all_ref_objs_run_over'): reward.wait_all_ref_objs_run_over() # update actor - self.actor_worker.update(self.kl_ctrl) + self.actor_worker.update(self.kl_ctrl, self.skip_actor_log_prob) # collect metrics grpo_data_metrics = compute_grpo_data_metrics(self.transfer_dock, @@ -187,7 +198,7 @@ class RayGRPOTrainer(RayBaseTrainer): experience_count = get_least_common_multiple(self.micro_batch_size, self.n_samples_per_prompt) - start_adv_time = time.time() + start_adv_time = time.time() compute_advantage_ref = compute_advantage.options(num_cpus=self.num_cpus_for_local_task).remote( self.transfer_dock, self.gamma, @@ -203,11 +214,11 @@ class RayGRPOTrainer(RayBaseTrainer): end_adv_time = time.time() ray.get( self.transfer_dock.update_metrics.remote( - "timing/adv", + "timing/adv", value=[round(end_adv_time, 4), round(start_adv_time, 4)], cumulate=True ) - ) + ) ray.get( self.transfer_dock.update_metrics.remote( "end_time/end_adv_time", @@ -216,11 +227,5 @@ class RayGRPOTrainer(RayBaseTrainer): ) ) - @staticmethod - def rule_reward_compute_rm_score(reward_worker, blocking=False): - rule_reward_compute_rm_score_ref = reward_worker.compute_rm_score.remote() - if blocking: - ray.get(rule_reward_compute_rm_score_ref) - def save_checkpoint(self, iteration: int): self.actor_worker.save_checkpoint(iteration) diff --git a/mindspeed_rl/utils/utils.py b/mindspeed_rl/utils/utils.py index 8526cc83..d2ec6230 100644 --- a/mindspeed_rl/utils/utils.py +++ b/mindspeed_rl/utils/utils.py @@ -205,12 +205,20 @@ def metrics_post_processing(metrics) -> Dict[str, Tensor]: def metrics_sort(metrics, time_all) -> Dict[str, Tensor]: + custom_order = ['timing/all', 'timing/update', 'timing/resharding_to_train', 'timing/rollout', 'timing/resharding_to_infer', + 'timing/old_log_p', 'timing/reference_model', 'timing/non_overlap_reference_model'] + special_keys = ['timing/non_overlap_rule_reward', 'timing/non_overlap_reward_model', 'timing/rule_reward', + 'timing/reward_model', 'timing/adv', 'timing/non_overlap_adv', 'timing/onload', 'timing/offload'] old_log_p_end_time = metrics.pop('end_time/old_log_p', None) end_adv_time = metrics.pop('end_time/end_adv_time', None) reference_start_time = metrics.pop('start_time/reference_model', None) reference_end_time = metrics.pop('end_time/reference', None) + + if old_log_p_end_time is None: + old_log_p_end_time = reference_end_time + custom_order.remove('timing/old_log_p') non_overlap_reference_model_time = max(reference_end_time - max(old_log_p_end_time, reference_start_time), 0) non_overlap_adv_time = max(max(old_log_p_end_time, end_adv_time) - old_log_p_end_time, 0) @@ -231,8 +239,7 @@ def metrics_sort(metrics, time_all) -> Dict[str, Tensor]: metrics["timing/all"] = time_all sort_metrics = dict(sorted(metrics.items())) - custom_order = ['timing/all', 'timing/update', 'timing/resharding_to_train', 'timing/rollout', 'timing/resharding_to_infer', 'timing/old_log_p', 'timing/reference_model', 'timing/non_overlap_reference_model'] - special_keys = ['timing/non_overlap_rule_reward', 'timing/non_overlap_reward_model', 'timing/rule_reward', 'timing/reward_model', 'timing/adv', 'timing/non_overlap_adv', 'timing/onload', 'timing/offload'] + keys_to_move = [key for key in sort_metrics.keys() if key in special_keys] remaining_keys = [] for key in sort_metrics: diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index 5055f194..106760b0 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -110,7 +110,9 @@ class ActorHybridWorkerBase(BaseWorker): def get_consumed_train_samples(self): return self.args.consumed_train_samples - def update(self, kl_ctrl=None): + def update(self, kl_ctrl=None, skip_actor_log_prob=False): + if skip_actor_log_prob: + self.sharding_manager.enter_forward_mode() start_sharding_enter_train = time.time() self.sharding_manager.enter_train_mode() sharding_train_interval = time.time() - start_sharding_enter_train @@ -123,6 +125,8 @@ class ActorHybridWorkerBase(BaseWorker): 'ref_log_prob', 'input_ids', 'response_length', 'prompt_length'] experience_count = self.megatron_config.global_batch_size // self.parallel_state.get_data_parallel_world_size() + if skip_actor_log_prob: + experience_columns.remove('old_log_prob') #get lr learning_rate = None diff --git a/mindspeed_rl/workers/integrated_worker.py b/mindspeed_rl/workers/integrated_worker.py index 9d2da179..fee0bc6e 100644 --- a/mindspeed_rl/workers/integrated_worker.py +++ b/mindspeed_rl/workers/integrated_worker.py @@ -115,7 +115,7 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB ) ) - def update(self, kl_ctrl=None): + def update(self, kl_ctrl=None, skip_actor_log_prob=False): # set update mbs update_mbs = self.update_micro_batch_size mbs = self.actor_hybrid.train_actor.micro_batch_size @@ -127,7 +127,7 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB self.actor_hybrid.train_actor.micro_batch_size = update_mbs args.micro_batch_size = update_mbs - ActorHybridWorkerBase.update(self, kl_ctrl) + ActorHybridWorkerBase.update(self, kl_ctrl, skip_actor_log_prob) args.micro_batch_size = mbs self.actor_hybrid.train_actor.micro_batch_size = mbs diff --git a/mindspeed_rl/workers/rule_reward.py b/mindspeed_rl/workers/rule_reward.py index 51c5c38e..410da3d0 100644 --- a/mindspeed_rl/workers/rule_reward.py +++ b/mindspeed_rl/workers/rule_reward.py @@ -1,5 +1,6 @@ # Copyright (c) 2025, HUAWEI CORPORATION. All rights reserved. import ray +from transformers import AutoTokenizer from mindspeed_rl.models.rule_verifier import compute_verifier_score from mindspeed_rl.utils.loggers import Loggers @@ -17,6 +18,8 @@ class RuleReward(object): self.megatron_config = megatron_config self.n_samples_per_prompt = rl_config.n_samples_per_prompt self.tokenizer = tokenizer + self.hf_tokenizer = AutoTokenizer.from_pretrained(megatron_config.tokenizer_name_or_path, + trust_remote_code=True) def init_transfer_dock(self, td): self.td = td @@ -24,7 +27,7 @@ class RuleReward(object): def compute_rm_score(self): experience_consumer_stage = 'rule_reward' experience_columns = ['prompts', 'responses', 'response_length', *self.megatron_config.dataset_additional_keys] - experience_count = get_least_common_multiple(self.megatron_config.micro_batch_size, + experience_count = get_least_common_multiple(self.rl_config.verifier_micro_batch_size, self.rl_config.n_samples_per_prompt) assign_batch_size = self.megatron_config.global_batch_size * self.rl_config.n_samples_per_prompt sorted_indexes = get_current_dp_range_indexes(experience_count=experience_count, @@ -53,7 +56,9 @@ class RuleReward(object): batch_data = {key: value[use_verifier_mask] if key != 'prompts' else value[ use_verifier_mask[::self.n_samples_per_prompt]] for key, value in batch_data.items()} ignore_token = self.tokenizer.pad if self.tokenizer.pad else self.tokenizer.eod - token_level_rewards, metrics = compute_verifier_score(batch_data, self.megatron_config, self.rl_config, ignore_token) + + token_level_rewards, metrics = compute_verifier_score(batch_data, self.megatron_config, self.rl_config, + self.hf_tokenizer, ignore_token) for key, value in metrics.items(): ray.get(self.td.update_metrics.remote(key, value=value, cumulate=True)) diff --git a/mindspeed_rl/workers/scheduler/launcher.py b/mindspeed_rl/workers/scheduler/launcher.py index ceaed6fb..0722c8e9 100644 --- a/mindspeed_rl/workers/scheduler/launcher.py +++ b/mindspeed_rl/workers/scheduler/launcher.py @@ -278,10 +278,10 @@ class RayActorGroup: if blocking: ray.get(self.temp_actor_ref_objs) - def update(self, kl_ctrl): + def update(self, kl_ctrl, skip_actor_log_prob): actor_train_objs = [] for actor in self.actor_handlers: - actor_train_objs.append(actor.update.remote(kl_ctrl)) + actor_train_objs.append(actor.update.remote(kl_ctrl, skip_actor_log_prob)) return ray.get(actor_train_objs) def save_checkpoint(self, iteration): -- Gitee From 3a4c3e8043de493babb910fd2fe56441a4fd00a9 Mon Sep 17 00:00:00 2001 From: Nurxat Date: Mon, 28 Apr 2025 01:38:18 +0000 Subject: [PATCH 20/25] =?UTF-8?q?!254=20=E6=96=AD=E7=82=B9=E7=BB=AD?= =?UTF-8?q?=E8=AE=ADbug=E4=BF=AE=E5=A4=8D=20Merge=20pull=20request=20!254?= =?UTF-8?q?=20from=20Nurxat/dev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mindspeed_rl/workers/actor_hybrid_worker.py | 30 +-------------------- 1 file changed, 1 insertion(+), 29 deletions(-) diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index 106760b0..fa8e7c56 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -105,7 +105,7 @@ class ActorHybridWorkerBase(BaseWorker): self.empty_cache() def get_iteration(self): - return self.megatron_config.iteration + return self.args.iteration def get_consumed_train_samples(self): return self.args.consumed_train_samples @@ -304,40 +304,12 @@ class ActorHybridWorkerBase(BaseWorker): ) ) - # self.empty_cache() - def _get_megatron_optimizer( - self, - model, - no_wd_decay_cond=None, - scale_lr_cond=None, - lr_mult=1.0 - ): - args = self.args - kwargs = {} - for f in dataclasses.fields(OptimizerConfig): - if hasattr(args, f.name): - kwargs[f.name] = getattr(args, f.name) - config = OptimizerConfig(**kwargs) - - optimizer = self.get_megatron_optimizer(config, model, no_wd_decay_cond, - scale_lr_cond, lr_mult) - opt_param_scheduler = self.get_optimizer_param_scheduler(optimizer) - return optimizer, opt_param_scheduler - def _build_model_optimizer(self): actor_module, optimizer, opt_param_scheduler = self.setup_model_and_optimizer( self.model_provider, self.model_type.encoder_or_decoder) - # load checkpoint - if self.megatron_config.load is not None or self.megatron_config.pretrained_checkpoint is not None: - self.megatron_config.iteration, self.megatron_config.num_floating_point_operations_so_far = self.load_checkpoint( - actor_module, optimizer, opt_param_scheduler) - else: - self.megatron_config.iteration = 0 - self.megatron_config.num_floating_point_operations_so_far = 0 - self.iteration = self.get_iteration() return actor_module, optimizer, opt_param_scheduler -- Gitee From 2c1e4326fef31bc8efcd83cf8e058ba416c6b6e7 Mon Sep 17 00:00:00 2001 From: LookAround Date: Mon, 28 Apr 2025 15:12:29 +0000 Subject: [PATCH 21/25] =?UTF-8?q?!251=20=20dataset=20shuffle=E9=80=BB?= =?UTF-8?q?=E8=BE=91=E4=BF=AE=E5=A4=8D=E4=B8=8E=E5=8F=98=E6=9B=B4=20Merge?= =?UTF-8?q?=20pull=20request=20!251=20from=20LookAround/fix=5Fdataset?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cli/train_grpo.py | 12 ++++++--- mindspeed_rl/datasets/build_dataset.py | 2 +- mindspeed_rl/datasets/dataloader.py | 27 +++++++++++---------- mindspeed_rl/datasets/prompt_dataset.py | 3 ++- mindspeed_rl/datasets/utils.py | 16 ++++++++---- mindspeed_rl/trainer/grpo_trainer_hybrid.py | 4 +-- mindspeed_rl/workers/reference_woker.py | 5 ++-- tests/ut/datasets/test_prompt_dataset.py | 8 +++--- 8 files changed, 44 insertions(+), 33 deletions(-) diff --git a/cli/train_grpo.py b/cli/train_grpo.py index 27b3b323..bc018caf 100644 --- a/cli/train_grpo.py +++ b/cli/train_grpo.py @@ -132,7 +132,6 @@ def train(config): train_valid_test_num_samples=[ actor_config.train_iters * actor_config.global_batch_size, 0, 0 ], - no_shuffle=actor_config.no_shuffle, seed=actor_config.seed, dataset_cls=PromptDataset, extra_param=actor_config @@ -142,10 +141,15 @@ def train(config): actor_worker.wait_all_ref_objs_run_over() consumed_train_samples = actor_worker.get_consumed_train_samples() + data_loader = PromptDataLoader( - train_ds, consumed_train_samples, actor_config.global_batch_size, - actor_config.num_workers, actor_config.seed, actor_config.dataset_additional_keys + train_ds, actor_config.global_batch_size, + actor_config.num_workers, actor_config.seed, actor_config.dataset_additional_keys, + actor_config.no_shuffle ) + data_iters = iter(data_loader) + [next(data_iters) for _ in range(consumed_train_samples // actor_config.global_batch_size)] + logger.info('after dataloader is built') reference_worker.wait_all_ref_objs_run_over() @@ -166,7 +170,7 @@ def train(config): **rl_config.dict() ) - trainer.fit(data_loader) + trainer.fit(data_iters) logger.info("training process successfully!") diff --git a/mindspeed_rl/datasets/build_dataset.py b/mindspeed_rl/datasets/build_dataset.py index 50ad3899..239e8793 100644 --- a/mindspeed_rl/datasets/build_dataset.py +++ b/mindspeed_rl/datasets/build_dataset.py @@ -76,8 +76,8 @@ def build_train_valid_test_datasets( dataset_cls=dataset_cls, parallel_state=parallel_state, full_shuffle_instruction_dataset=full_shuffle_instruction_dataset, - no_shuffle=no_shuffle, reset_position_ids=reset_position_ids, + no_shuffle=no_shuffle, prompt_type=prompt_type, prompt_type_path=prompt_type_path, seed=seed, diff --git a/mindspeed_rl/datasets/dataloader.py b/mindspeed_rl/datasets/dataloader.py index 2394ce3f..5c3353b9 100644 --- a/mindspeed_rl/datasets/dataloader.py +++ b/mindspeed_rl/datasets/dataloader.py @@ -5,10 +5,10 @@ from dataclasses import dataclass from typing import Sequence, Dict, Any import torch +from torch.utils.data import RandomSampler, SequentialSampler from transformers import DataCollatorForSeq2Seq from .data_samplers import PretrainingSampler -from .data_samplers import PromptSampler class InstructionDataLoader(torch.utils.data.DataLoader): @@ -184,18 +184,11 @@ class PromptDataLoader(torch.utils.data.DataLoader): """ def __init__(self, dataset, - consumed_samples, global_batch_size, num_workers, seed, - dataset_additional_keys): - - - batch_sampler = PromptSampler( - total_samples=len(dataset), - consumed_samples=consumed_samples, - batch_size=global_batch_size) - + dataset_additional_keys, + no_shuffle): def collator(features, return_tensors=None): features_dict = {} @@ -206,10 +199,18 @@ class PromptDataLoader(torch.utils.data.DataLoader): return features_dict + if not no_shuffle: + train_dataloader_generator = torch.Generator() + train_dataloader_generator.manual_seed(seed) + sampler = RandomSampler(data_source=dataset, generator=train_dataloader_generator) + else: + sampler = SequentialSampler(data_source=dataset) + super().__init__(dataset, - batch_sampler=batch_sampler, num_workers=num_workers, generator=torch.Generator().manual_seed(seed), collate_fn=collator, - pin_memory=True - ) + pin_memory=True, + sampler=sampler, + batch_size=global_batch_size, + drop_last=True) diff --git a/mindspeed_rl/datasets/prompt_dataset.py b/mindspeed_rl/datasets/prompt_dataset.py index 346996f1..1ab63bc1 100644 --- a/mindspeed_rl/datasets/prompt_dataset.py +++ b/mindspeed_rl/datasets/prompt_dataset.py @@ -53,7 +53,8 @@ class PromptDataset(BaseDataset): num_samples=self.num_samples, seed=seed, full_shuffle_instruction_dataset=full_shuffle_instruction_dataset, - parallel_state=None) + parallel_state=None, + no_shuffle=True) dataset_type = "Prompt_DS_Packed" else: raise NotImplementedError('non packed data are not supported yet.') diff --git a/mindspeed_rl/datasets/utils.py b/mindspeed_rl/datasets/utils.py index 2ee2324c..1e3cdf77 100644 --- a/mindspeed_rl/datasets/utils.py +++ b/mindspeed_rl/datasets/utils.py @@ -103,7 +103,8 @@ def _build_index_mappings( num_samples: int, seed, full_shuffle_instruction_dataset, - parallel_state + parallel_state, + no_shuffle=False ): """ - `shuffle_index` is [num_epoch * len(self.mtf)] @@ -129,7 +130,12 @@ def _build_index_mappings( epoch = 0 shuffle_idx = [] while len(shuffle_idx) < num_samples: - new_document_ids = _build_shuffle_idx(nb_documents=nb_documents, start_index=start_index, np_rng=np_rng) + new_document_ids = _build_shuffle_idx( + nb_documents=nb_documents, + start_index=start_index, + np_rng=np_rng, + no_shuffle=no_shuffle + ) shuffle_idx.extend(new_document_ids.tolist()) epoch += 1 @@ -163,10 +169,10 @@ def _build_sequential_idx(nb_documents: int, start_index): return result -def _build_shuffle_idx(nb_documents: int, start_index, np_rng): +def _build_shuffle_idx(nb_documents: int, start_index, np_rng, no_shuffle): """Build the range [0, dataset_size) and shuffle.""" - result = _build_sequential_idx(nb_documents, start_index) # in-place shuffling - np_rng.shuffle(result) + if not no_shuffle: + np_rng.shuffle(result) return result diff --git a/mindspeed_rl/trainer/grpo_trainer_hybrid.py b/mindspeed_rl/trainer/grpo_trainer_hybrid.py index 7392576d..e29c4635 100644 --- a/mindspeed_rl/trainer/grpo_trainer_hybrid.py +++ b/mindspeed_rl/trainer/grpo_trainer_hybrid.py @@ -112,15 +112,13 @@ class RayGRPOTrainer(RayBaseTrainer): self.skip_actor_log_prob = (global_batch_size * n_samples_per_prompt == mini_batch_size and epochs == 1) self.actor_worker.skip_actor_log_prob = self.skip_actor_log_prob - def fit(self, data_loader: DataLoader): + def fit(self, data_iters): """ The utils loop of GRPO """ logger = Loggers('grpo_trainer_hybrid') metrics = Metric() - data_iters = iter(data_loader) - iteration = self.actor_worker.get_iteration() if self.blocking: diff --git a/mindspeed_rl/workers/reference_woker.py b/mindspeed_rl/workers/reference_woker.py index 7fd06586..4c98b133 100644 --- a/mindspeed_rl/workers/reference_woker.py +++ b/mindspeed_rl/workers/reference_woker.py @@ -2,6 +2,7 @@ import time from typing import Callable +import logging as logger import ray import torch @@ -138,9 +139,7 @@ class ReferenceWorkerBase(BaseWorker): value=[round(ref_end_time, 4)] ) ) - - print('finish calc log prob') - + logger.info("finish compute ref log prob") self.empty_cache() diff --git a/tests/ut/datasets/test_prompt_dataset.py b/tests/ut/datasets/test_prompt_dataset.py index cd469da5..6a9f8c51 100644 --- a/tests/ut/datasets/test_prompt_dataset.py +++ b/tests/ut/datasets/test_prompt_dataset.py @@ -2,6 +2,7 @@ # Copyright (c) 2025, HUAWEI CORPORATION. All rights reserved. import numpy as np +from torch.utils.data import SequentialSampler from mindspeed_rl.datasets.prompt_dataset import PromptDataset from mindspeed_rl.datasets.dataloader import PromptDataLoader @@ -21,8 +22,8 @@ class TestPromptDataset(DistributedTest): dummy_config = MegatronConfig({}, {}) dummy_config.stage = "ray_ppo" dummy_config.full_shuffle_instruction_dataset = False - dummy_config.no_shuffle = True dummy_config.global_batch_size = 1 + dummy_config.no_shuffle = True documents = np.arange(start=0, stop=3000, step=1, dtype=np.int32) dataset = PromptDataset( data_prefix=packed_data_prefix, @@ -33,9 +34,10 @@ class TestPromptDataset(DistributedTest): documents=documents, extra_param=dummy_config, ) - dataloader = PromptDataLoader(dataset, 0, dummy_config.global_batch_size, + dataloader = PromptDataLoader(dataset, dummy_config.global_batch_size, dummy_config.num_workers, dummy_config.seed, - dummy_config.dataset_additional_keys) + dummy_config.dataset_additional_keys, + dummy_config.no_shuffle) for item in dataloader: assert item['prompts'][0][0] == 151644 -- Gitee From a412032cb36b7f1cece53acb0de2463852fd2427 Mon Sep 17 00:00:00 2001 From: liuxinyang <292384718@qq.com> Date: Mon, 28 Apr 2025 15:25:51 +0000 Subject: [PATCH 22/25] =?UTF-8?q?!259=20=E5=85=A8=E5=85=B1=E5=8D=A1?= =?UTF-8?q?=E6=A8=A1=E5=BC=8F=E6=94=AF=E6=8C=81=E6=96=AD=E7=82=B9=E7=BB=AD?= =?UTF-8?q?=E8=AE=AD=E5=8D=95=E7=8B=AC=E5=8A=A0=E8=BD=BDref=E6=9D=83?= =?UTF-8?q?=E9=87=8D=20Merge=20pull=20request=20!259=20from=20liuxinyang/d?= =?UTF-8?q?ev?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mindspeed_rl/config_cls/rl_config.py | 1 + mindspeed_rl/config_cls/validate_config.py | 5 +++++ mindspeed_rl/workers/integrated_worker.py | 18 +++++++++++++++--- 3 files changed, 21 insertions(+), 3 deletions(-) diff --git a/mindspeed_rl/config_cls/rl_config.py b/mindspeed_rl/config_cls/rl_config.py index df8e6deb..8eaa1206 100644 --- a/mindspeed_rl/config_cls/rl_config.py +++ b/mindspeed_rl/config_cls/rl_config.py @@ -71,6 +71,7 @@ class RLConfig(BaseConfig): self.verifier_parallel = 1 self.verifier_timeout = 30 self.verifier_micro_batch_size = 16 + self.integrated_mode_config = None self.shuffle_mini_batch = False self.n_samples_per_prompt = 1 diff --git a/mindspeed_rl/config_cls/validate_config.py b/mindspeed_rl/config_cls/validate_config.py index 09c8748e..64e1fffd 100644 --- a/mindspeed_rl/config_cls/validate_config.py +++ b/mindspeed_rl/config_cls/validate_config.py @@ -24,6 +24,11 @@ def validate_rl_args( raise ValueError( f" Reward model is not supported when use_integrated_worker mode is on.") + else: + if rl_config.integrated_mode_config is not None: + raise ValueError( + f"integrated_mode_config should not be set when use_integrated_worker mode is off.") + # 校验序列长度与模型最大长度 if generate_config.max_model_len < actor_config.seq_length: raise ValueError( diff --git a/mindspeed_rl/workers/integrated_worker.py b/mindspeed_rl/workers/integrated_worker.py index fee0bc6e..08456373 100644 --- a/mindspeed_rl/workers/integrated_worker.py +++ b/mindspeed_rl/workers/integrated_worker.py @@ -75,7 +75,10 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB # Add Reference self.ref_model = self.get_model(self.model_provider, self.model_type, wrap_with_ddp=False) - self.load_checkpoint(self.ref_model, None, None) + ref_model_load_path = getattr( + self.rl_config.integrated_mode_config, "ref_model_load_path", None + ) if self.rl_config.integrated_mode_config is not None else None + self.load_checkpoint_with_path(self.ref_model, ref_model_load_path) self.ref_manager = MegatronOffLoader(self.ref_model, wrap_with_ddp=False) self.ref_manager.offload_param() self.reference = Reference( @@ -120,8 +123,7 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB update_mbs = self.update_micro_batch_size mbs = self.actor_hybrid.train_actor.micro_batch_size - from megatron.training import get_args - args = get_args() + args = self.get_args() if update_mbs is not None: self.actor_hybrid.train_actor.micro_batch_size = update_mbs @@ -131,3 +133,13 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB args.micro_batch_size = mbs self.actor_hybrid.train_actor.micro_batch_size = mbs + + def load_checkpoint_with_path(self, model, path, info=None): + if path is None: + self.load_checkpoint(model, None, None) + else: + origin_load_dir = getattr(self.get_args(), "load") + setattr(self.get_args(), "load", path) + self.load_checkpoint(model, None, None) + setattr(self.get_args(), "load", origin_load_dir) + -- Gitee From 594ae6c3495b526ddac0f29bd6a7bdcb9dbd2ffd Mon Sep 17 00:00:00 2001 From: liuxinyang Date: Thu, 8 May 2025 14:12:04 +0800 Subject: [PATCH 23/25] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E8=AE=AD=E7=BB=83DP?= =?UTF-8?q?=E5=92=8C=E6=96=AD=E7=82=B9=E7=BB=AD=E8=AE=ADbug?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cli/train_grpo.py | 4 +- mindspeed_rl/workers/actor_hybrid_worker.py | 16 ++++-- mindspeed_rl/workers/integrated_worker.py | 61 ++++++++++++++++----- 3 files changed, 61 insertions(+), 20 deletions(-) diff --git a/cli/train_grpo.py b/cli/train_grpo.py index bc018caf..61047b83 100644 --- a/cli/train_grpo.py +++ b/cli/train_grpo.py @@ -224,6 +224,7 @@ def get_megatron_module(): from megatron.core.tensor_parallel.cross_entropy import vocab_parallel_cross_entropy from megatron.training.training import setup_model_and_optimizer from megatron.core.enums import ModelType + from megatron.core.distributed import finalize_model_grads return { 'parallel_state': parallel_state, @@ -241,7 +242,8 @@ def get_megatron_module(): 'vocab_parallel_cross_entropy': vocab_parallel_cross_entropy, 'setup_model_and_optimizer': setup_model_and_optimizer, 'model_type': ModelType, - 'distributed_data_parallel': DistributedDataParallel + 'distributed_data_parallel': DistributedDataParallel, + 'finalize_model_grads': finalize_model_grads } diff --git a/mindspeed_rl/workers/actor_hybrid_worker.py b/mindspeed_rl/workers/actor_hybrid_worker.py index fa8e7c56..38743b15 100644 --- a/mindspeed_rl/workers/actor_hybrid_worker.py +++ b/mindspeed_rl/workers/actor_hybrid_worker.py @@ -159,8 +159,8 @@ class ActorHybridWorkerBase(BaseWorker): ray.get(self.td.update_metrics.remote(value=metrics, cumulate=True)) ray.get( self.td.update_metrics.remote( - "timing/update", - value=[round(time.time(), 4), round(start_time, 4)], + "timing/update", + value=[round(time.time(), 4), round(start_time, 4)], cumulate=True ) ) @@ -240,7 +240,7 @@ class ActorHybridWorkerBase(BaseWorker): end_time = time.time() ray.get( self.td.update_metrics.remote( - "timing/rollout", + "timing/rollout", value=[round(end_time, 4), round(start_time, 4)], cumulate=True ) @@ -367,12 +367,18 @@ class ActorHybridWorkerBase(BaseWorker): def _set_no_sync_func(self): config = get_attr_wrapped_model(self.model[0], 'config', allow_none=False) - if isinstance(self.model[0], self.distributed_data_parallel) and config.no_sync_func is None: - # Megatron requires no_sync_func properly to correctly trigger DP reduce + config.grad_scale_func = self.optimizer.scale_loss + + if isinstance(self.model[0], self.distributed_data_parallel) and self.megatron_config.overlap_grad_reduce: + if config.no_sync_func is not None: + raise ValueError('When overlap_grad_reduce is True, config.no_sync_func must be None; ' + 'a custom no_sync_func is not supported when overlapping grad-reduce') config.no_sync_func = [model_chunk.no_sync for model_chunk in self.model] if len(self.model) == 1: config.no_sync_func = config.no_sync_func[0] + config.finalize_model_grads_func = self.finalize_model_grads + @ray.remote(resources={"NPU": 0.7}) class ActorHybridWorker(ActorHybridWorkerBase): diff --git a/mindspeed_rl/workers/integrated_worker.py b/mindspeed_rl/workers/integrated_worker.py index 08456373..4a1a2f1b 100644 --- a/mindspeed_rl/workers/integrated_worker.py +++ b/mindspeed_rl/workers/integrated_worker.py @@ -67,7 +67,6 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB self.ref_model = None self.ref_manager = None - def initialize(self): # Based on Actor @@ -78,7 +77,7 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB ref_model_load_path = getattr( self.rl_config.integrated_mode_config, "ref_model_load_path", None ) if self.rl_config.integrated_mode_config is not None else None - self.load_checkpoint_with_path(self.ref_model, ref_model_load_path) + self.load_checkpoint_with_path(self.ref_model, ref_model_load_path, ckpt_only=True) self.ref_manager = MegatronOffLoader(self.ref_model, wrap_with_ddp=False) self.ref_manager.offload_param() self.reference = Reference( @@ -99,14 +98,14 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB end_onload_time = time.time() ray.get( self.td.update_metrics.remote( - "timing/onload", + "timing/onload", value=[round(end_onload_time, 4), round(start_onload_time, 4)], cumulate=True ) - ) - + ) + ReferenceWorkerBase.compute_ref_log_prob(self) - + start_offload_time = time.time() self.ref_manager.offload_param() end_offload_time = time.time() @@ -134,12 +133,46 @@ class IntegratedWorker(ActorHybridWorkerBase, ReferenceWorkerBase, RewardWorkerB args.micro_batch_size = mbs self.actor_hybrid.train_actor.micro_batch_size = mbs - def load_checkpoint_with_path(self, model, path, info=None): - if path is None: - self.load_checkpoint(model, None, None) - else: - origin_load_dir = getattr(self.get_args(), "load") - setattr(self.get_args(), "load", path) - self.load_checkpoint(model, None, None) - setattr(self.get_args(), "load", origin_load_dir) + def load_checkpoint_with_path(self, model, path, ckpt_only=False): + """Load model checkpoint from a specified path with flexible control. + + Args: + model: The model to load checkpoint into. + path: Path to the checkpoint file/directory. If None, use the path in megatron args. + ckpt_only: If True, only loads model weights (skips optimizer/RNG states). + """ + + # Backup original arguments if needed + original_args = { + 'no_load_optim': getattr(self.get_args(), "no_load_optim", None), + 'no_load_rng': getattr(self.get_args(), "no_load_rng", None), + 'load': getattr(self.get_args(), "load", None), + 'iteration': getattr(self.get_args(), "iteration", None), + 'finetune': getattr(self.get_args(), "finetune", None), + 'consumed_train_samples': getattr(self.get_args(), "consumed_train_samples", None), + 'consumed_valid_samples': getattr(self.get_args(), "consumed_valid_samples", None), + } if ckpt_only or path else {} + + if ckpt_only: + self._set_args({ + "no_load_optim": True, + "no_load_rng": True, + "finetune": True, + 'consumed_train_samples': 0, + 'consumed_valid_samples': 0 + }) + + if path is not None: + self._set_args({"load": path}) + + self.load_checkpoint(model, None, None) + + if original_args: + 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