From a05174d652fbba7d8a5a01b9785c1dcd46389441 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Tue, 22 Jul 2025 11:36:43 -0700 Subject: [PATCH 01/64] first stab at ulysses that will probably fail --- open_instruct/grpo_fast.py | 71 +- open_instruct/utils.py | 86 +- pyproject.toml | 4 +- requirements.txt | 5 +- uv.lock | 4251 ++++++++++++++++++------------------ 5 files changed, 2283 insertions(+), 2134 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 29c4ea8c0..0ac53a009 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -33,6 +33,8 @@ os.environ["NCCL_CUMEM_ENABLE"] = "0" # NOQA try: import deepspeed + from deepspeed.runtime.sequence_parallel.ulysses_sp import UlyssesSPAttentionHF, UlyssesSPDataLoaderAdapter + from deepspeed.utils import groups # @vwxyzjn: when importing on CPU-only machines, we get the following error: # RuntimeError: 0 active drivers ([]). There should only be one. @@ -120,6 +122,7 @@ maybe_use_ai2_hf_entity, maybe_use_ai2_wandb_entity, sync_gs_bucket, + UlyssesSPSplitter, ) from open_instruct.vllm_utils3 import ( GenerationResult, @@ -315,6 +318,9 @@ class Args: num_learners_per_node: List[int] = field(default_factory=lambda: [1]) """number of GPU deepspeed learners per node (e.g., --num_learners_per_node 2 4 means 2 learner processes on the first node and 4 learner processes on the second node; each process will have 1 GPU)""" + sequence_parallel_size: int = 1 + """sequence parallel size - how many GPUs we will parallelize sequences across during training. + Useful for super-long context lengths.""" vllm_num_engines: int = 1 """number of vLLM Engines, set to 0 to disable vLLM""" vllm_tensor_parallel_size: int = 1 @@ -536,7 +542,7 @@ def load(self, path: str, map_location=None): self.device = torch.device(self.local_rank) deepspeed.init_distributed() - ds_config = get_train_ds_config(offload=False, adam_offload=False, stage=args.deepspeed_stage, bf16=True) + ds_config = get_train_ds_config(offload=False, adam_offload=False, stage=args.deepspeed_stage, bf16=True, sequence_parallel_size=args.sequence_parallel_size) ds_config["train_micro_batch_size_per_gpu"] = args.per_device_train_batch_size ds_config["gradient_accumulation_steps"] = 1 # @vwxyzjn: MAGIC: it's actually needed to initialize this `dschf`, so @@ -549,6 +555,18 @@ def load(self, path: str, map_location=None): dschf = None logger.info(f"Deepspeed config: {dschf=}") + # set sequence parallel + self.mpu = None + if args.sequence_parallel_size > 1: + self.mpu = UlyssesSPAttentionHF.register_with_transformers( + model_name_or_path=model_config.model_name_or_path, + core_attn_implementation="flash_attention_2", + sequence_parallel_size=args.sequence_parallel_size, + max_length=args.max_token_length, + micro_batch_size=args.per_device_train_batch_size, + seq_length_is_variable=True, + ) + self.policy: PreTrainedModel = AutoModelForCausalLM.from_pretrained( model_config.model_name_or_path, revision=model_config.model_revision, @@ -582,6 +600,7 @@ def load(self, path: str, map_location=None): config=ds_config, lr_scheduler=scheduler, dist_init_required=True, + mpu=self.mpu, ) optimization_steps_done = 0 if args.checkpoint_state_dir: @@ -630,9 +649,23 @@ def load(self, path: str, map_location=None): use_cache=False, ) disable_dropout_in_model(self.ref_policy) - self.ref_policy, *_ = deepspeed.initialize(model=self.ref_policy, config=ds_config) + self.ref_policy, *_ = deepspeed.initialize(model=self.ref_policy, config=ds_config, mpu=self.mpu) self.ref_policy.eval() self.local_metrics = MetricsTracker(max_metrics=32, device=self.device) + + if self.mpu is not None: + self.sp_group = groups._get_sequence_parallel_group() + self.sp_world_size = groups._get_sequence_parallel_world_size() + self.sp_rank = groups._get_sequence_parallel_rank() + self.splitter = UlyssesSPSplitter( + sp_rank=self.sp_rank, + sp_group=self.sp_group, + sp_world_size=self.sp_world_size, + device=self.device + ) + else: + self.splitter = None + return optimization_steps_done def forward( @@ -792,6 +825,25 @@ def train( # recalculate the "real" number of mini-batches num_mini_batches = len(collated_query_responses) // accumulation_steps + if self.splitter is not None: + sharded_batches = self.splitter.split_batch({ + "input_ids": collated_query_responses, + "attention_mask": collated_attention_masks, + "position_ids": collated_position_ids, + "response_masks": collated_response_masks, + "tool_masks": collated_tool_masks, + "advantages": collated_advantages, + }) + # TODO: i actually don't know what sharded_batches looks like yet. + + # replace non-sharded batches with sharded batches + collated_query_responses = [batch["input_ids"] for batch in sharded_batches] + collated_attention_masks = [batch["attention_mask"] for batch in sharded_batches] + collated_position_ids = [batch["position_ids"] for batch in sharded_batches] + collated_response_masks = [batch["response_masks"] for batch in sharded_batches] + collated_tool_masks = [batch["tool_masks"] for batch in sharded_batches] + collated_advantages = [batch["advantages"] for batch in sharded_batches] + # Calculate the logprob of the reference policy collated_ref_logprobs = [] with Timer("Inference Calculation", noop=self.rank != 0): @@ -921,8 +973,19 @@ def train( elif args.kl_estimator == "kl4": kl = kl4 - # grpo change: directly subtract KL in loss (add) - loss = masked_mean(pg_loss_max + (args.beta * kl), mb_response_masks_bool, args.masked_mean_axis) + if args.sequence_parallel_size == 1: + # grpo change: directly subtract KL in loss (add) + loss = masked_mean(pg_loss_max + (args.beta * kl), mb_response_masks_bool, args.masked_mean_axis) + else: + # if SP, compute per-token loss, then we need to gather across SP ranks + per_tok_loss = pg_loss_max + (args.beta * kl) * mb_response_masks_bool.float() + losses_per_rank = torch.distributed.nn.functional.all_gather(per_tok_loss, group=self.sp_group) + non_masked_tokens = mb_response_masks_bool.sum() + non_masked_tokens_per_rank = torch.distributed.nn.functional.all_gather(non_masked_tokens, group=self.sp_group) + total_loss = sum(losses_per_rank[rank] * non_masked_tokens_per_rank[rank] for rank in range(self.sp_world_size)) + total_non_masked_tokens = sum(non_masked_tokens_per_rank) + loss = total_loss / total_non_masked_tokens + # TODO: do we still need grad acc...? I think so. loss = loss / accumulation_steps self.model.backward(loss) if (local_step + 1) % accumulation_steps == 0: diff --git a/open_instruct/utils.py b/open_instruct/utils.py index ec12b0ef7..4f8ad7508 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -1192,6 +1192,7 @@ def get_train_ds_config( zpg=8, grad_accum_dtype=None, disable_trace_cache=False, + sequence_parallel_size=1, ): device = "cpu" if offload else "none" zero_opt_dict = { @@ -1214,7 +1215,7 @@ def get_train_ds_config( zero_opt_dict["stage3_max_live_parameters"] = 0 zero_opt_dict["stage3_max_reuse_distance"] = 0 - return { + config = { "steps_per_print": 100, "zero_optimization": zero_opt_dict, "bf16": {"enabled": bf16}, @@ -1224,6 +1225,11 @@ def get_train_ds_config( "data_types": {"grad_accum_dtype": grad_accum_dtype if grad_accum_dtype else "fp32"}, } + if sequence_parallel_size > 1: + config["sequence_parallel_size"] = sequence_parallel_size + + return config + def get_eval_ds_config(offload, stage=0, bf16=True): zero_opt_dict = { @@ -1432,3 +1438,81 @@ def extract_final_answer(prediction: str) -> str: return cleaned.strip() return prediction + + +class UlyssesSPSplitter: + + def __init__( + self, + sp_rank: int, + sp_group, + sp_world_size, + device, + ): + """ + Adapted from the UlyssesSPDataLoaderAdapter + (https://github.com/deepspeedai/DeepSpeed/blob/master/deepspeed/runtime/sequence_parallel/ulysses_sp.py#L427) + Rather than wrapping a dataloader, we just want to split a given batch into the shards across sp group. + """ + self.sp_rank = sp_rank + self.sp_group = sp_group + self.sp_world_size = sp_world_size + self.device = device + + def split_batch(self, batch): + micro_batches = defaultdict(dict) + + # we have batches of variable seqlen so in order to do all_gather on batches - we need to know the exact length of each tensor on each rank + seqlen = torch.tensor(batch["input_ids"].shape[1], dtype=torch.int64, device=self.device) + seqlens = [torch.zeros(1, dtype=torch.int64, device=self.device) for _ in range(self.sp_world_size)] + dist.all_gather(seqlens, seqlen, group=self.sp_group) + seqlens = [x[0].item() for x in seqlens] + + for k in batch.keys(): + if torch.is_tensor(batch[k]): + batch[k] = batch[k].to(self.device) + with torch.no_grad(): + tensor_list = [ + torch.zeros((batch[k].shape[0], seqlens[i]), dtype=batch[k].dtype, device=batch[k].device) + for i in range(self.sp_world_size) + ] + dist.all_gather(tensor_list, batch[k], group=self.sp_group) + else: + tensor_list = [None for _ in range(self.sp_world_size)] + dist.all_gather_object(tensor_list, batch[k], group=self.sp_group) + + for rank, tensor in enumerate(tensor_list): + micro_batches[rank][k] = tensor + + del tensor_list + del batch + + batch_shards = [] + for batch in micro_batches.values(): + seq_length = len(batch["input_ids"][0]) + + if seq_length % self.sp_world_size != 0: + raise ValueError(f"batch's seqlen={seq_length} isn't divisible by sp-size={self.sp_world_size}") + chunk_len = seq_length // self.sp_world_size + + # for rl training, we don't have labels, so we don't need to do this + if "labels" in batch: + # because we have to gather logits from all sp ranks we have to do the loss function ourselves + # therefore remove labels to avoid an attempt to calculate loss by transformers + labels = batch.pop("labels") + labels = torch.nn.functional.pad(labels, (0, 1), value=-100) + batch["shift_labels"] = labels[..., 1:].contiguous() + # free up temp memory + del labels + + # batch sharding + for k in batch.keys(): + # leave non-tensors alone + if not torch.is_tensor(batch[k]): + continue + # at seqlen>10M and 32+ gpus this can take GBs of memory so keep the prefill buffer on cpu + batch[k] = batch[k][:, chunk_len * self.sp_rank:chunk_len * (self.sp_rank + 1)].cpu() + + batch_shards.append(batch) + + return batch_shards \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 748a7d4ab..fae683ce1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,12 +5,12 @@ description = "Train open, instruction-following language models" readme = "README.md" requires-python = ">=3.10,<3.13" dependencies = [ - "accelerate>=1.1.1", + "accelerate>=1.9.0", "antlr4-python3-runtime==4.11", "bitsandbytes>=0.44.1; platform_system != 'Darwin'", "datasets>=3.1.0", "debugpy>=1.8.13", - "deepspeed==0.15.4", # 0.16.0 bugs out https://github.com/microsoft/DeepSpeed/issues/6793 + "deepspeed==0.17.2", "hf-transfer>=0.1.8", "litellm", "matplotlib>=3.9.3", diff --git a/requirements.txt b/requirements.txt index af657c67f..c0423d1fe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ # uv export --format requirements-txt --no-hashes --all-extras --no-emit-project absl-py==2.3.0 # via tensorboard -accelerate==1.8.1 +accelerate==1.9.0 # via # open-instruct # peft @@ -108,7 +108,7 @@ datasets==3.6.0 # via open-instruct debugpy==1.8.14 # via open-instruct -deepspeed==0.15.4 +deepspeed==0.17.2 # 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hash = "sha256:fe208f65f2aca48b81f9e6fd8cf7b8b32c26375266b009b413d45306b6148343", size = 9796, upload_time = "2025-05-26T14:46:30.775Z" }, ] From 1f5f06d6236b13e4ec6e0bed8da74a98c7ecdbb3 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Mon, 21 Jul 2025 16:05:39 -0600 Subject: [PATCH 02/64] Revert "Revert "Now, we run individual prompts through the queue. (#796)" (#804)" This reverts commit 4659dcaaed5e40debf207ee02105eae517343c87. --- open_instruct/benchmark_generators.py | 69 ++++++++++-- open_instruct/grpo_fast.py | 88 +++++++-------- open_instruct/test_grpo_fast.py | 55 +++++----- open_instruct/vllm_utils3.py | 152 ++++++++++++++------------ 4 files changed, 208 insertions(+), 156 deletions(-) diff --git a/open_instruct/benchmark_generators.py b/open_instruct/benchmark_generators.py index a671b1c53..a79744ca6 100644 --- a/open_instruct/benchmark_generators.py +++ b/open_instruct/benchmark_generators.py @@ -295,8 +295,9 @@ def setup_vllm_engines( pg = ray.util.placement_group(bundles, strategy="PACK") ray.get(pg.ready()) - param_prompt_Q = ray_queue.Queue(maxsize=10) - inference_results_Q = ray_queue.Queue(maxsize=10) + # Adjust queue sizes for individual prompts + param_prompt_Q = ray_queue.Queue(maxsize=100) + inference_results_Q = ray_queue.Queue(maxsize=100) vllm_engines = vllm_utils3.create_vllm_engines( num_engines=args.vllm_num_engines, @@ -334,6 +335,41 @@ def get_batch_data( return prompts +def run_generation_batch( + inference_results_Q: ray_queue.Queue, param_prompt_Q: ray_queue.Queue, prompts: list[list[int]], batch_idx: int +) -> dict[str, Any]: + """Run generation for a batch of prompts and measure performance.""" + + start_time = time.time() + + # Insert individual prompts + for i, prompt in enumerate(prompts): + dataset_idx = batch_idx * len(prompts) + i + param_prompt_Q.put(vllm_utils3.PromptRequest(prompt=prompt, dataset_index=dataset_idx)) + + # Collect individual results + all_responses = [] + all_finish_reasons = [] + for _ in range(len(prompts)): + result = inference_results_Q.get() + all_responses.extend(result.responses) + all_finish_reasons.extend(result.finish_reasons) + + generation_time = time.time() - start_time + + new_tokens = sum(len(response) for response in all_responses) + tokens_per_second = new_tokens / generation_time + return { + "tokens_per_second": tokens_per_second, + "generation_time": generation_time, + "num_new_tokens": new_tokens, + # dict mapping string reasons to counts. + "finish_reasons": collections.Counter(all_finish_reasons), + "response_lengths": [len(response) for response in all_responses], + "prompt_lengths": [len(prompt) for prompt in prompts], # Original unique prompts + } + + def run_benchmark( dataset: datasets.Dataset, vllm_engines: list[ray.actor.ActorHandle], @@ -366,6 +402,7 @@ def run_benchmark( num_batches + 1, # eval_freq (avoid evaluation) num_batches, # num_training_steps 1, # resume_training_step + args.num_unique_prompts_rollout // args.vllm_num_engines, # batch_size ) # Wait for engines to be ready @@ -383,37 +420,47 @@ def run_benchmark( ] submission_start_time = time.time() for batch_idx in range(num_batches): - param_prompt_Q.put(vllm_utils3.PromptRequest(prompts=all_prompts[batch_idx], dataset_index=batch_idx)) + # Insert individual prompts for this batch + for i, prompt in enumerate(all_prompts[batch_idx]): + dataset_idx = batch_idx * args.num_unique_prompts_rollout + i + param_prompt_Q.put(vllm_utils3.PromptRequest(prompt=prompt, dataset_index=dataset_idx)) submission_time = time.time() - submission_start_time logger.info(f"All batches submitted in {submission_time:.2f}s") # Receive results and measure time for each batch last_completion_time = submission_start_time for batch_idx in range(num_batches): - result = inference_results_Q.get() + # Collect individual results for this batch + batch_responses = [] + batch_finish_reasons = [] + for _ in range(args.num_unique_prompts_rollout): + result = inference_results_Q.get() + batch_responses.extend(result.responses) + batch_finish_reasons.extend(result.finish_reasons) + completion_time = time.time() batch_generation_time = completion_time - last_completion_time last_completion_time = completion_time - # Process result - new_tokens = sum(len(response) for response in result.responses) + # Process batch results + new_tokens = sum(len(response) for response in batch_responses) tokens_per_second = new_tokens / batch_generation_time result_dict = { "tokens_per_second": tokens_per_second, "generation_time": batch_generation_time, "num_new_tokens": new_tokens, - "finish_reasons": collections.Counter(result.finish_reasons), - "response_lengths": [len(response) for response in result.responses], - "batch_idx": result.dataset_index, + "finish_reasons": collections.Counter(batch_finish_reasons), + "response_lengths": [len(response) for response in batch_responses], + "batch_idx": batch_idx, } result_dict["mfu"] = 100 * result_dict["tokens_per_second"] * flops_per_token / device_flops # We incrementally save completion lengths so even if the job dies, we still have data. - save_completion_lengths([result_dict], timestamp, result.dataset_index) + save_completion_lengths([result_dict], timestamp, batch_idx) results.append(result_dict) logger.info( - f"Batch {result.dataset_index + 1}: " + f"Batch {batch_idx + 1}: " f"{result_dict['tokens_per_second']:.2f} new tokens/sec, " f"MFU: {result_dict['mfu']:.2f}%, " f"generation time: {batch_generation_time:.2f}s" diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 29c4ea8c0..11a5415c9 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1110,50 +1110,42 @@ def get_bundle_index(rank, num_gpus_per_node): def accumulate_inference_batches( - inference_results_Q: ray_queue.Queue, pending_queries_map: dict, vllm_num_engines: int, training_step: int + inference_results_Q: ray_queue.Queue, pending_queries_map: dict, expected_results: int, training_step: int ) -> tuple: - """Accumulate multiple inference results into a single training batch. + """Accumulate individual inference results into a single training batch. Args: - inference_results_Q: Queue containing GenerationResult objects + inference_results_Q: Queue containing GenerationResult objects (one per prompt) pending_queries_map: Map of dataset_index -> (queries, ground_truths, datasets) - vllm_num_engines: Number of vLLM engines (number of batches to accumulate) + expected_results: Number of individual results to accumulate training_step: Current training step for error reporting Returns: Tuple of (combined_result, combined_queries, combined_ground_truths, combined_datasets) """ - # Collect results from all engines + # Collect individual results results = [] all_queries = [] all_ground_truths = [] all_datasets = [] - for batch_idx in range(vllm_num_engines): - # Get result from queue + for i in range(expected_results): + # Get individual result from queue result = inference_results_Q.get() - dataset_indices = result.dataset_index - if dataset_indices is None: - raise RuntimeError(f"Dataset indices is None for batch {batch_idx}") + if result.dataset_index is None or len(result.dataset_index) != 1: + raise RuntimeError(f"Expected single dataset index, got {result.dataset_index}") - # Get corresponding queries, ground_truths, datasets for each individual prompt - batch_queries = [] - batch_ground_truths = [] - batch_datasets = [] - for dataset_idx in dataset_indices: - if dataset_idx not in pending_queries_map: - raise RuntimeError(f"Dataset index {dataset_idx} not found in pending_queries_map") + dataset_idx = result.dataset_index[0] + if dataset_idx not in pending_queries_map: + raise RuntimeError(f"Dataset index {dataset_idx} not found in pending_queries_map") - query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) - batch_queries.append(query) - batch_ground_truths.append(ground_truth) - batch_datasets.append(dataset) + query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) results.append(result) - all_queries.extend(batch_queries) - all_ground_truths.extend(batch_ground_truths) - all_datasets.extend(batch_datasets) + all_queries.append(query) + all_ground_truths.append(ground_truth) + all_datasets.append(dataset) # Combine all results into a single GenerationResult combined_responses = [] @@ -1213,7 +1205,10 @@ def data_preparation_thread( # Accumulate results from multiple vLLM engines into a single training batch with Timer("🚀 [Data Preparation Thread] Getting response ids"): result, queries, ground_truths, datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, args.vllm_num_engines, training_step + inference_results_Q, + pending_queries_map, + args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout, + training_step, ) # ------------------------------------------------------------------------------------------------ @@ -1681,29 +1676,19 @@ def split_and_insert_batch( param_prompt_Q, eval_prompt_token_ids=None, ): - """Split a batch into multiple inference batches and insert individual prompts into queues and mapping.""" - # Split the batch over the VLLM engines. - inference_batch_size = len(queries_next) // vllm_num_engines - for batch_idx in range(vllm_num_engines): - start_idx = batch_idx * inference_batch_size - end_idx = start_idx + inference_batch_size if batch_idx < vllm_num_engines - 1 else len(queries_next) - - batch_queries = queries_next[start_idx:end_idx] - batch_ground_truths = ground_truths_next[start_idx:end_idx] - batch_datasets = datasets_next[start_idx:end_idx] - batch_dataset_indices = dataset_indices[start_idx:end_idx] - - # Store individual prompts in the map using dataset indices as keys - for i, dataset_idx in enumerate(batch_dataset_indices): - pending_queries_map[dataset_idx] = (batch_queries[i], batch_ground_truths[i], batch_datasets[i]) - - # Use PromptRequest for Ray queue with batch-specific dataset_index list + """Insert individual prompts into queues and mapping.""" + # Insert individual prompts into the queue + for i, dataset_idx in enumerate(dataset_indices): + # Store individual prompt in the map using dataset index as key + pending_queries_map[dataset_idx] = (queries_next[i], ground_truths_next[i], datasets_next[i]) + + # Create PromptRequest for single prompt param_prompt_Q.put( PromptRequest( - prompts=batch_queries, + prompt=queries_next[i], training_step=training_step, eval_prompts=eval_prompt_token_ids, - dataset_index=batch_dataset_indices, + dataset_index=dataset_idx, ) ) @@ -2071,9 +2056,10 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa pprint([args, model_config]) - # Create Ray queues - inference_results_Q = ray_queue.Queue(maxsize=args.async_steps) - param_prompt_Q = ray_queue.Queue(maxsize=args.async_steps) + # Create Ray queues - adjust maxsize for individual prompts. + total_prompts_per_step = args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout + inference_results_Q = ray_queue.Queue(maxsize=args.async_steps * total_prompts_per_step) + param_prompt_Q = ray_queue.Queue(maxsize=args.async_steps * total_prompts_per_step) evaluation_inference_results_Q = ray_queue.Queue(maxsize=1) policy_group, vllm_engines, tool_objects, resume_training_step, episode = create_model_and_optimizer( @@ -2122,6 +2108,13 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa reward_fn = make_reward_fn(args) # Start vLLM engines to process from queues + if args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout % args.vllm_num_engines != 0: + raise ValueError( + "The number of unique prompts times the number of samples per prompt must be divisible by the number of vLLM engines." + ) + batch_size_per_engine = ( + args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout + ) // args.vllm_num_engines for engine in vllm_engines: engine.process_from_queue.remote( generation_config, @@ -2129,6 +2122,7 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa args.local_eval_freq, args.num_training_steps, resume_training_step, + batch_size_per_engine, ) logger.info("======== ✅ vllm engines started processing from queues =========") diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index 66aed2c83..1957bad7e 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -74,10 +74,11 @@ def test_vllm_queue_system_single_prompt(self): 999, # eval_freq (avoid evaluation) 1, # num_training_steps 1, # resume_training_step + 1, # batch_size ) # Put the test prompt in the queue using PromptRequest - request = PromptRequest(prompts=[prompt_token_ids], dataset_index=0) + request = PromptRequest(prompt=prompt_token_ids, dataset_index=0) param_prompt_Q.put(request) # Get the result @@ -104,17 +105,16 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r """Test the batch splitting and accumulation logic using split_and_insert_batch and accumulate_inference_batches.""" # Mock data - simulating num_unique_prompts_rollout * num_samples_per_prompt_rollout - queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] + # Use lists of integers to simulate tokenized prompts + queries_next = [[i, i + 1, i + 2] for i in range(num_unique_prompts_rollout)] # Mock token IDs ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] pending_queries_map = {} training_step = 1 - # Create mock Ray queue for testing - param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) + param_prompt_Q = ray_queue.Queue(maxsize=num_unique_prompts_rollout) - # Create mock dataset indices dataset_indices = list(range(num_unique_prompts_rollout)) # Use split_and_insert_batch to split and insert data @@ -129,48 +129,49 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r param_prompt_Q, ) - # Verify that we have individual prompts in the map (not batches) self.assertEqual(len(pending_queries_map), num_unique_prompts_rollout) - # Verify that we have the expected number of items in the queue - self.assertEqual(param_prompt_Q.qsize(), vllm_num_engines) + self.assertEqual(param_prompt_Q.qsize(), num_unique_prompts_rollout) - # Create mock inference results to simulate vLLM engine outputs + # Create mock inference results to simulate vLLM engine outputs (individual results) mock_inference_results = [] - for batch_idx in range(vllm_num_engines): + requests_processed = [] + for i in range(num_unique_prompts_rollout): # Get the request from the queue request = param_prompt_Q.get() self.assertIsInstance(request, PromptRequest) self.assertEqual(request.training_step, training_step) - self.assertIsInstance(request.dataset_index, list) # Now expects a list of indices + self.assertIsInstance(request.dataset_index, int) # Single dataset index + self.assertIsInstance(request.prompt, list) # Single prompt as list of ints - # Create mock GenerationResult - batch_size = len(request.prompts) + # Store request for later verification + requests_processed.append(request) + + # Create mock GenerationResult for single prompt mock_result = GenerationResult( - responses=[[i] for i in range(batch_size)], # Mock token IDs - finish_reasons=["stop"] * batch_size, - masks=[[1] * 5] * batch_size, # Mock masks + responses=[[i]], # Mock token IDs for single response + finish_reasons=["stop"], + masks=[[1] * 5], # Mock masks request_info=RequestInfo( - num_calls=[0] * batch_size, - timeouts=[0] * batch_size, - tool_errors=[""] * batch_size, - tool_outputs=[""] * batch_size, - tool_runtimes=[0] * batch_size, - tool_calleds=[False] * batch_size, + num_calls=[0], + timeouts=[0], + tool_errors=[""], + tool_outputs=[""], + tool_runtimes=[0], + tool_calleds=[False], ), is_eval=False, - dataset_index=request.dataset_index, + dataset_index=[request.dataset_index], ) mock_inference_results.append(mock_result) - # Create mock inference results queue - inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) + inference_results_Q = ray_queue.Queue(maxsize=num_unique_prompts_rollout) for result in mock_inference_results: inference_results_Q.put(result) # Use accumulate_inference_batches to combine results combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, vllm_num_engines, training_step + inference_results_Q, pending_queries_map, num_unique_prompts_rollout, training_step ) # Verify that the combined results match the original input @@ -184,7 +185,7 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r self.assertEqual(len(combined_result.finish_reasons), len(queries_next)) self.assertEqual(len(combined_result.masks), len(queries_next)) - # Verify that the pending_queries_map is empty after accumulation + # Verify that the test_pending_queries_map is empty after accumulation self.assertEqual(len(pending_queries_map), 0) # Verify that the inference_results_Q is empty after accumulation diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index b4d36de07..a1560eec4 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -23,6 +23,7 @@ import ray import torch import torch.distributed +import vllm from ray.util.placement_group import placement_group from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy from torch.distributed.distributed_c10d import ( @@ -57,17 +58,17 @@ class GenerationResult: masks: List[List[int]] request_info: RequestInfo is_eval: bool = False - dataset_index: Optional[int] = None + dataset_index: Optional[List[int]] = None @dataclasses.dataclass class PromptRequest: - """Container for prompt requests to vLLM.""" + """Container for a single prompt request to vLLM.""" - prompts: List[List[int]] + prompt: List[int] # Single prompt training_step: Optional[int] = None - eval_prompts: Optional[List[List[int]]] = None - dataset_index: Optional[int] = None + eval_prompts: Optional[List[List[int]]] = None # Keep as list for eval + dataset_index: Optional[int] = None # Single dataset index def ray_noset_visible_devices(env_vars=os.environ): @@ -199,80 +200,89 @@ def generate(self, *args, **kwargs): def process_from_queue( self, - sampling_params, - eval_sampling_params=None, - eval_freq=None, - num_training_steps=None, - resume_training_step=1, - ): + sampling_params: vllm.SamplingParams, + eval_sampling_params: Optional[vllm.SamplingParams] = None, + eval_freq: Optional[int] = None, + num_training_steps: Optional[int] = None, + resume_training_step: int = 1, + batch_size: Optional[int] = None, + ) -> None: """Process prompts from the queue and put results in the results queue.""" for training_step in range(resume_training_step, num_training_steps + 1): - # Get prompts from queue - request = self.prompt_queue.get() - if request is None: - break - - # Process training prompts - result = self._generate_batch(request.prompts, sampling_params, request.dataset_index) - self.results_queue.put(result) - - # Handle evaluation if needed - if ( - request.eval_prompts is not None - and eval_sampling_params is not None - and (training_step - 1) % eval_freq == 0 - ): - eval_result = self._generate_batch(request.eval_prompts, eval_sampling_params, request.dataset_index) - eval_result.is_eval = True - # Put eval results in separate queue if available - if self.eval_results_queue is not None: + prompts_batch = [] + dataset_indices_batch = [] + eval_prompts = None + + while len(prompts_batch) < batch_size: + request: PromptRequest = self.prompt_queue.get() + + prompts_batch.append(request.prompt) + if request.dataset_index is not None: + dataset_indices_batch.append(request.dataset_index) + + if eval_prompts is None and request.eval_prompts is not None: + eval_prompts = request.eval_prompts + + results = self._generate_batch(prompts_batch, sampling_params, dataset_indices_batch) + + for result in results: + self.results_queue.put(result) + + if eval_prompts is not None and eval_sampling_params is not None and (training_step - 1) % eval_freq == 0: + eval_results = self._generate_batch(eval_prompts, eval_sampling_params, None) + for eval_result in eval_results: + eval_result.is_eval = True self.eval_results_queue.put(eval_result) - else: - self.results_queue.put(eval_result) def _generate_batch( - self, prompts: List[List[int]], sampling_params, dataset_index: Optional[int] = None - ) -> GenerationResult: - """Generate responses for a batch of prompts.""" + self, prompts: List[List[int]], sampling_params, dataset_indices: Optional[List[int]] = None + ) -> List[GenerationResult]: + """Generate responses for a batch of prompts and return individual results.""" outputs = self.llm.generate(sampling_params=sampling_params, prompt_token_ids=prompts, use_tqdm=False) - # Process outputs - response_ids = [list(out.token_ids) for output in outputs for out in output.outputs] - finish_reasons = [out.finish_reason for output in outputs for out in output.outputs] - - if self.tool_use: - masks = [out.mask for output in outputs for out in output.outputs] - num_calls = [out.num_calls for output in outputs for out in output.outputs] - timeouts = [out.timeout for output in outputs for out in output.outputs] - tool_errors = [out.tool_error for output in outputs for out in output.outputs] - tool_outputs = [out.tool_output for output in outputs for out in output.outputs] - tool_runtimes = [out.tool_runtime for output in outputs for out in output.outputs] - tool_calleds = [out.tool_called for output in outputs for out in output.outputs] - else: - masks = [[1] * len(resp) for resp in response_ids] - num_calls = [0] * len(response_ids) - timeouts = [0] * len(response_ids) - tool_errors = [""] * len(response_ids) - tool_outputs = [""] * len(response_ids) - tool_runtimes = [0] * len(response_ids) - tool_calleds = [False] * len(response_ids) - - request_info = RequestInfo( - num_calls=num_calls, - timeouts=timeouts, - tool_errors=tool_errors, - tool_outputs=tool_outputs, - tool_runtimes=tool_runtimes, - tool_calleds=tool_calleds, - ) + # Process outputs and create individual GenerationResult objects + results = [] + for i, output in enumerate(outputs): + response_ids = [list(out.token_ids) for out in output.outputs] + finish_reasons = [out.finish_reason for out in output.outputs] + + if self.tool_use: + masks = [out.mask for out in output.outputs] + num_calls = [out.num_calls for out in output.outputs] + timeouts = [out.timeout for out in output.outputs] + tool_errors = [out.tool_error for out in output.outputs] + tool_outputs = [out.tool_output for out in output.outputs] + tool_runtimes = [out.tool_runtime for out in output.outputs] + tool_calleds = [out.tool_called for out in output.outputs] + else: + masks = [[1] * len(resp) for resp in response_ids] + num_calls = [0] * len(response_ids) + timeouts = [0] * len(response_ids) + tool_errors = [""] * len(response_ids) + tool_outputs = [""] * len(response_ids) + tool_runtimes = [0] * len(response_ids) + tool_calleds = [False] * len(response_ids) + + request_info = RequestInfo( + num_calls=num_calls, + timeouts=timeouts, + tool_errors=tool_errors, + tool_outputs=tool_outputs, + tool_runtimes=tool_runtimes, + tool_calleds=tool_calleds, + ) - return GenerationResult( - responses=response_ids, - finish_reasons=finish_reasons, - masks=masks, - request_info=request_info, - dataset_index=dataset_index, - ) + results.append( + GenerationResult( + responses=response_ids, + finish_reasons=finish_reasons, + masks=masks, + request_info=request_info, + dataset_index=[dataset_indices[i]], + ) + ) + + return results def init_process_group( self, master_address, master_port, rank_offset, world_size, group_name, backend, use_ray=False From d1ebfba75f4210397e929466f0c888dfe568684e Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Tue, 22 Jul 2025 07:37:53 -0600 Subject: [PATCH 03/64] Updated code. --- open_instruct/grpo_fast.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 11a5415c9..87cb9f128 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1733,6 +1733,8 @@ def sync_weights_and_prepare_prompts( param_prompt_Q, eval_prompt_token_ids, ) + + return queries_next, ground_truths_next, datasets_next, dataset_indices def load_data_from_packing_thread(packed_sequences_Q: Queue, num_total_tokens: int): @@ -2163,7 +2165,6 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa num_total_tokens = 0 start_time = time.time() - dataset_indices = None # Initialize for training loop try: for training_step in range(resume_training_step, args.num_training_steps + 1): logger.info("-" * 100) @@ -2171,7 +2172,7 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout ) # each sample is an episode - queries_next, ground_truths_next, datasets_next = sync_weights_and_prepare_prompts( + queries_next, ground_truths_next, datasets_next, dataset_indices = sync_weights_and_prepare_prompts( training_step, args, train_dataset, From 7dd7e32de7bb72f3127d20d1d7105439326a3af6 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Tue, 22 Jul 2025 12:49:57 -0600 Subject: [PATCH 04/64] Have to switch repros --- open_instruct/grpo_fast.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 87cb9f128..820cded96 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1592,7 +1592,7 @@ def create_model_and_optimizer( ) -> tuple[ModelGroup, list[LLMRayActor], dict, int, int]: """Create the model, optimizer, and vLLM engines.""" # Ray initialization - ray.init(dashboard_host="0.0.0.0") # enable debugging from a different machine (e.g., phobos) + ray.init(dashboard_host="0.0.0.0", ignore_reinit_error=True) # enable debugging from a different machine (e.g., phobos) # Create placement group bundles = [{"GPU": actor_num_gpus, "CPU": actor_num_gpus * 10} for actor_num_gpus in args.num_learners_per_node] From 2254e60e531644445972d250311b173e722efd23 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Tue, 22 Jul 2025 15:05:08 -0600 Subject: [PATCH 05/64] Linter passes. --- open_instruct/grpo_fast.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 820cded96..e3e1e9d33 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1592,7 +1592,9 @@ def create_model_and_optimizer( ) -> tuple[ModelGroup, list[LLMRayActor], dict, int, int]: """Create the model, optimizer, and vLLM engines.""" # Ray initialization - ray.init(dashboard_host="0.0.0.0", ignore_reinit_error=True) # enable debugging from a different machine (e.g., phobos) + ray.init( + dashboard_host="0.0.0.0", ignore_reinit_error=True + ) # enable debugging from a different machine (e.g., phobos) # Create placement group bundles = [{"GPU": actor_num_gpus, "CPU": actor_num_gpus * 10} for actor_num_gpus in args.num_learners_per_node] @@ -1733,7 +1735,7 @@ def sync_weights_and_prepare_prompts( param_prompt_Q, eval_prompt_token_ids, ) - + return queries_next, ground_truths_next, datasets_next, dataset_indices From 9b0704963f0cf092924b3ba7342b60d84b53c480 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Tue, 22 Jul 2025 15:46:40 -0700 Subject: [PATCH 06/64] fix bug --- open_instruct/grpo_fast.py | 1 + 1 file changed, 1 insertion(+) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 0ac53a009..d456977b0 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1811,6 +1811,7 @@ def sync_weights_and_prepare_prompts( param_prompt_Q, eval_prompt_token_ids, ) + return queries_next, ground_truths_next, datasets_next def load_data_from_packing_thread(packed_sequences_Q: Queue, num_total_tokens: int): From d5d55d5b27a0b103700ecaaae88eec0b0fecef48 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Tue, 22 Jul 2025 16:46:47 -0600 Subject: [PATCH 07/64] Fixed bug in queue expectations. --- open_instruct/grpo_fast.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index e3e1e9d33..c8a7f311c 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1129,7 +1129,7 @@ def accumulate_inference_batches( all_ground_truths = [] all_datasets = [] - for i in range(expected_results): + for _ in range(expected_results): # Get individual result from queue result = inference_results_Q.get() @@ -1207,7 +1207,7 @@ def data_preparation_thread( result, queries, ground_truths, datasets = accumulate_inference_batches( inference_results_Q, pending_queries_map, - args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout, + args.num_unique_prompts_rollout, training_step, ) From 496d7484e221e217651bb04fb56a8c3844a5dd51 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Tue, 22 Jul 2025 15:48:21 -0700 Subject: [PATCH 08/64] fix bug --- open_instruct/grpo_fast.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index d456977b0..b13bfa8df 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1660,7 +1660,7 @@ def create_model_and_optimizer( ) -> tuple[ModelGroup, list[LLMRayActor], dict, int, int]: """Create the model, optimizer, and vLLM engines.""" # Ray initialization - ray.init(dashboard_host="0.0.0.0") # enable debugging from a different machine (e.g., phobos) + ray.init(dashboard_host="0.0.0.0", ignore_reinit_error=True) # enable debugging from a different machine (e.g., phobos) # Create placement group bundles = [{"GPU": actor_num_gpus, "CPU": actor_num_gpus * 10} for actor_num_gpus in args.num_learners_per_node] From 9dc019186f40e697fd1bf52a7c9e724768723865 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Tue, 22 Jul 2025 16:50:02 -0600 Subject: [PATCH 09/64] Ran linter. --- open_instruct/grpo_fast.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index c8a7f311c..874d1ff37 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1205,10 +1205,7 @@ def data_preparation_thread( # Accumulate results from multiple vLLM engines into a single training batch with Timer("🚀 [Data Preparation Thread] Getting response ids"): result, queries, ground_truths, datasets = accumulate_inference_batches( - inference_results_Q, - pending_queries_map, - args.num_unique_prompts_rollout, - training_step, + inference_results_Q, pending_queries_map, args.num_unique_prompts_rollout, training_step ) # ------------------------------------------------------------------------------------------------ From 26e7397f7445e59035b7c0447dbce5a128028b1e Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Tue, 22 Jul 2025 15:50:39 -0700 Subject: [PATCH 10/64] fix bug --- open_instruct/grpo_fast.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index b13bfa8df..aebca272a 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1811,7 +1811,7 @@ def sync_weights_and_prepare_prompts( param_prompt_Q, eval_prompt_token_ids, ) - return queries_next, ground_truths_next, datasets_next + return queries_next, ground_truths_next, datasets_next, dataset_indices def load_data_from_packing_thread(packed_sequences_Q: Queue, num_total_tokens: int): @@ -2241,7 +2241,7 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout ) # each sample is an episode - queries_next, ground_truths_next, datasets_next = sync_weights_and_prepare_prompts( + queries_next, ground_truths_next, datasets_next, dataset_indices = sync_weights_and_prepare_prompts( training_step, args, train_dataset, From 8bb12433e49fdfd2300fa8416bcd4cdba4e1fc7d Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Tue, 22 Jul 2025 16:55:00 -0600 Subject: [PATCH 11/64] Added tqdm. --- open_instruct/grpo_fast.py | 7 +++++-- pyproject.toml | 1 + 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 874d1ff37..05823ae05 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -29,7 +29,7 @@ # limitations under the License. # isort: off import os - +import tqdm os.environ["NCCL_CUMEM_ENABLE"] = "0" # NOQA try: import deepspeed @@ -1129,7 +1129,10 @@ def accumulate_inference_batches( all_ground_truths = [] all_datasets = [] - for _ in range(expected_results): + for _ in tqdm.tqdm(range(expected_results), + desc=f"Accumulating results for training step {training_step}", + # Adds a new line after the progress bar so that logs look better. + bar_format="{l_bar}{bar}{r_bar}\n"): # Get individual result from queue result = inference_results_Q.get() diff --git a/pyproject.toml b/pyproject.toml index 748a7d4ab..b543c29b3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,6 +19,7 @@ dependencies = [ "nvitop>=1.4.2", "packaging>=24.2", "peft>=0.13.2", + "tqdm", "ray[default]>=2.44.1", "setuptools>=75.6.0,<80.0.0", "tensorboard>=2.18.0", From 1445b0c1949dc093fec42423427c8de0583123c5 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Tue, 22 Jul 2025 16:57:23 -0600 Subject: [PATCH 12/64] Ran linter. --- open_instruct/grpo_fast.py | 11 +++++++---- uv.lock | 2 ++ 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 05823ae05..ffe1b5d3d 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -30,6 +30,7 @@ # isort: off import os import tqdm + os.environ["NCCL_CUMEM_ENABLE"] = "0" # NOQA try: import deepspeed @@ -1129,10 +1130,12 @@ def accumulate_inference_batches( all_ground_truths = [] all_datasets = [] - for _ in tqdm.tqdm(range(expected_results), - desc=f"Accumulating results for training step {training_step}", - # Adds a new line after the progress bar so that logs look better. - bar_format="{l_bar}{bar}{r_bar}\n"): + for _ in tqdm.tqdm( + range(expected_results), + desc=f"Accumulating results for training step {training_step}", + # Adds a new line after the progress bar so that logs look better. + bar_format="{l_bar}{bar}{r_bar}\n", + ): # Get individual result from queue result = inference_results_Q.get() diff --git a/uv.lock b/uv.lock index bda1b2c52..2d7b3b1f1 100644 --- a/uv.lock +++ b/uv.lock @@ -2364,6 +2364,7 @@ dependencies = [ { name = "tensorboard" }, { name = "torch", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" }, { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform != 'darwin'" }, + { name = "tqdm" }, { name = "transformers" }, { name = "vllm" }, { name = "wandb" }, @@ -2429,6 +2430,7 @@ requires-dist = [ { name = "tensorboard", specifier = ">=2.18.0" }, { name = "torch", marker = "sys_platform != 'darwin'", specifier = "==2.7.0", index = "https://download.pytorch.org/whl/cu128" }, { name = "torch", marker = "sys_platform == 'darwin'", specifier = "==2.7.0" }, + { name = "tqdm" }, { name = "transformers", specifier = ">=4.52.4" }, { name = "uvicorn", marker = "extra == 'code'", specifier = ">=0.20.0" }, { name = "vllm", specifier = "==0.9.1" }, From ee7fe6dac29e74e30d446a151f0a5f1e48dff0c3 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 22 Jul 2025 16:35:14 -0700 Subject: [PATCH 13/64] fixes to run --- open_instruct/grpo_fast.py | 7 +++++-- open_instruct/utils.py | 3 ++- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index f7d1e9e2f..1454fda37 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -2039,7 +2039,10 @@ async def reward_fn( infos: List[List[int]], queries: Optional[List[str]] = None, ) -> List[float]: - _, timeouts, tool_errors, tool_outputs, _, tool_calleds = infos + timeouts = infos.timeouts + tool_errors = infos.tool_errors + tool_outputs = infos.tool_outputs + tool_calleds = infos.tool_calleds good_outputs = [ len(tool_outputs[i]) > 0 and tool_calleds[i] and not timeouts[i] and not tool_errors[i] for i in range(len(tool_outputs)) @@ -2183,7 +2186,7 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa "The number of unique prompts times the number of samples per prompt must be divisible by the number of vLLM engines." ) batch_size_per_engine = ( - args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout + args.num_unique_prompts_rollout #* args.num_samples_per_prompt_rollout ) // args.vllm_num_engines for engine in vllm_engines: engine.process_from_queue.remote( diff --git a/open_instruct/utils.py b/open_instruct/utils.py index 4f8ad7508..19dd7b3aa 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -35,6 +35,7 @@ import re import shutil import socket +from collections import defaultdict import subprocess import sys import time @@ -1515,4 +1516,4 @@ def split_batch(self, batch): batch_shards.append(batch) - return batch_shards \ No newline at end of file + return batch_shards From db914e7baef1517f7761aada0dc96e4968a3bc32 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 12:16:28 +0000 Subject: [PATCH 14/64] A bunch of minor changes. --- open_instruct/grpo_fast.py | 23 +++++++++++++---------- scripts/train/debug/grpo_fast.sh | 2 +- 2 files changed, 14 insertions(+), 11 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 29c4ea8c0..6ec2205d3 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -55,7 +55,7 @@ from collections import defaultdict from dataclasses import asdict, dataclass, field from queue import Empty, Queue -from typing import Callable, Dict, Iterator, List, Literal, Optional, Union +from typing import Any, Callable, Dict, Iterator, List, Literal, Optional, Union import numpy as np import pandas as pd @@ -1597,7 +1597,7 @@ def create_model_and_optimizer( ) -> tuple[ModelGroup, list[LLMRayActor], dict, int, int]: """Create the model, optimizer, and vLLM engines.""" # Ray initialization - ray.init(dashboard_host="0.0.0.0") # enable debugging from a different machine (e.g., phobos) + #ray.init(dashboard_host="0.0.0.0") # enable debugging from a different machine (e.g., phobos) # Create placement group bundles = [{"GPU": actor_num_gpus, "CPU": actor_num_gpus * 10} for actor_num_gpus in args.num_learners_per_node] @@ -1716,12 +1716,12 @@ def sync_weights_and_prepare_prompts( policy_group: ModelGroup, pending_queries_map: dict, param_prompt_Q: ray_queue.Queue, # Ray queue - queries_next=None, - ground_truths_next=None, - datasets_next=None, - dataset_indices=None, + queries_next, + ground_truths_next, + datasets_next, + dataset_indices, eval_prompt_token_ids=None, -): +) -> tuple[Any, Any, Any, Any]: """Sync weights and send the next batch of prompts to vLLM.""" if training_step != 1: dataset_indices = next(iter_dataloader) @@ -1748,6 +1748,7 @@ def sync_weights_and_prepare_prompts( param_prompt_Q, eval_prompt_token_ids, ) + return queries_next, ground_truths_next, datasets_next, dataset_indices def load_data_from_packing_thread(packed_sequences_Q: Queue, num_total_tokens: int): @@ -1984,7 +1985,10 @@ async def reward_fn( infos: List[List[int]], queries: Optional[List[str]] = None, ) -> List[float]: - _, timeouts, tool_errors, tool_outputs, _, tool_calleds = infos + timeouts = infos.timeouts + tool_errors = infos.tool_errors + tool_outputs = infos.tool_outputs + tool_calleds = infos.tool_calleds good_outputs = [ len(tool_outputs[i]) > 0 and tool_calleds[i] and not timeouts[i] and not tool_errors[i] for i in range(len(tool_outputs)) @@ -2169,7 +2173,6 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa num_total_tokens = 0 start_time = time.time() - dataset_indices = None # Initialize for training loop try: for training_step in range(resume_training_step, args.num_training_steps + 1): logger.info("-" * 100) @@ -2177,7 +2180,7 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout ) # each sample is an episode - queries_next, ground_truths_next, datasets_next = sync_weights_and_prepare_prompts( + queries_next, ground_truths_next, datasets_next, dataset_indices = sync_weights_and_prepare_prompts( training_step, args, train_dataset, diff --git a/scripts/train/debug/grpo_fast.sh b/scripts/train/debug/grpo_fast.sh index 4f3fafcaf..cbf0e3a11 100755 --- a/scripts/train/debug/grpo_fast.sh +++ b/scripts/train/debug/grpo_fast.sh @@ -27,7 +27,7 @@ uv run python open_instruct/grpo_fast.py \ --seed 3 \ --num_evals 20 \ --vllm_sync_backend gloo \ - --vllm_gpu_memory_utilization 0.3 \ + --vllm_gpu_memory_utilization 0.1 \ --save_traces \ --vllm_enforce_eager \ --gradient_checkpointing \ From 75958a171767b3b0ef2789935c9b88baa8d14b25 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 12:16:58 +0000 Subject: [PATCH 15/64] Undo changes to script. --- scripts/train/debug/grpo_fast.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/train/debug/grpo_fast.sh b/scripts/train/debug/grpo_fast.sh index cbf0e3a11..4f3fafcaf 100755 --- a/scripts/train/debug/grpo_fast.sh +++ b/scripts/train/debug/grpo_fast.sh @@ -27,7 +27,7 @@ uv run python open_instruct/grpo_fast.py \ --seed 3 \ --num_evals 20 \ --vllm_sync_backend gloo \ - --vllm_gpu_memory_utilization 0.1 \ + --vllm_gpu_memory_utilization 0.3 \ --save_traces \ --vllm_enforce_eager \ --gradient_checkpointing \ From 1453b407db22f74af7c50f5bb4c55374853d89b2 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 07:08:13 -0600 Subject: [PATCH 16/64] Added some tests which now pass. --- open_instruct/grpo_fast.py | 6 ++ open_instruct/test_grpo_fast.py | 161 ++++++++++++++++++++++++++++++++ open_instruct/vllm_utils3.py | 6 +- 3 files changed, 170 insertions(+), 3 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 6ec2205d3..6c51f0df2 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1137,6 +1137,12 @@ def accumulate_inference_batches( if dataset_indices is None: raise RuntimeError(f"Dataset indices is None for batch {batch_idx}") + # Assert that the number of dataset indices matches the number of responses + assert len(dataset_indices) == len(result.responses), ( + f"Mismatch between dataset_indices length ({len(dataset_indices)}) " + f"and responses length ({len(result.responses)}) for batch {batch_idx}" + ) + # Get corresponding queries, ground_truths, datasets for each individual prompt batch_queries = [] batch_ground_truths = [] diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index 66aed2c83..fe7a250a8 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -191,5 +191,166 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r self.assertEqual(inference_results_Q.qsize(), 0) + def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps=1): + """Mock function that simulates vLLM engines pulling from prompt queue and pushing to results queue""" + for step in range(num_steps): + for engine_id in range(num_engines): + # Pull request from prompt queue + request = prompt_queue.get() + + # Create mock generation result preserving dataset_index + batch_size = len(request.prompts) + mock_result = GenerationResult( + responses=[[1, 2, 3] for _ in range(batch_size)], + finish_reasons=["stop"] * batch_size, + masks=[[1, 1, 1] for _ in range(batch_size)], + request_info=RequestInfo( + num_calls=[0] * batch_size, + timeouts=[0] * batch_size, + tool_errors=[""] * batch_size, + tool_outputs=[""] * batch_size, + tool_runtimes=[0.0] * batch_size, + tool_calleds=[False] * batch_size, + ), + dataset_index=request.dataset_index, # Preserve the indices + ) + + # Push to results queue + results_queue.put(mock_result) + + def test_dataset_index_preservation_through_pipeline(self): + """Test that dataset indices are correctly preserved through the mock vLLM pipeline""" + vllm_num_engines = 4 + num_unique_prompts_rollout = 32 + + # Mock data + queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] + ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] + datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] + dataset_indices = list(range(num_unique_prompts_rollout)) + + # Create queues and map + param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) + inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) + pending_queries_map = {} + + # Split and insert batch + split_and_insert_batch( + queries_next, + ground_truths_next, + datasets_next, + dataset_indices, + training_step=1, + vllm_num_engines=vllm_num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Run mock vLLM pipeline + self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines) + + # Accumulate results + combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( + inference_results_Q, pending_queries_map, vllm_num_engines, training_step=1 + ) + + # Verify results + self.assertEqual(combined_queries, queries_next) + self.assertEqual(combined_ground_truths, ground_truths_next) + self.assertEqual(combined_datasets, datasets_next) + self.assertEqual(len(pending_queries_map), 0) # Map should be empty after accumulation + + def test_multiple_training_steps(self): + """Test that indices don't get mixed up between multiple training steps""" + vllm_num_engines = 2 + num_unique_prompts_rollout = 16 + num_steps = 3 + + pending_queries_map = {} + + for step in range(1, num_steps + 1): + # Create unique data for each step + queries_next = [f"query_step{step}_{i}" for i in range(num_unique_prompts_rollout)] + ground_truths_next = [f"truth_step{step}_{i}" for i in range(num_unique_prompts_rollout)] + datasets_next = [f"dataset_step{step}_{i}" for i in range(num_unique_prompts_rollout)] + # Use different indices for each step to ensure no mixing + dataset_indices = list(range((step-1) * num_unique_prompts_rollout, step * num_unique_prompts_rollout)) + + # Create queues + param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) + inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) + + # Split and insert + split_and_insert_batch( + queries_next, + ground_truths_next, + datasets_next, + dataset_indices, + training_step=step, + vllm_num_engines=vllm_num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Run pipeline + self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines) + + # Accumulate + combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( + inference_results_Q, pending_queries_map, vllm_num_engines, training_step=step + ) + + # Verify + self.assertEqual(combined_queries, queries_next) + self.assertEqual(combined_ground_truths, ground_truths_next) + self.assertEqual(combined_datasets, datasets_next) + self.assertEqual(len(pending_queries_map), 0) # Map should be empty after each step + + @parameterized.expand([(1,), (2,), (4,), (8,)]) + def test_various_engine_configurations(self, vllm_num_engines: int): + """Test with various numbers of vLLM engines""" + num_unique_prompts_rollout = 64 # Use a larger batch to test splitting + + # Mock data + queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] + ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] + datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] + dataset_indices = list(range(num_unique_prompts_rollout)) + + # Create queues and map + param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) + inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) + pending_queries_map = {} + + # Split and insert + split_and_insert_batch( + queries_next, + ground_truths_next, + datasets_next, + dataset_indices, + training_step=1, + vllm_num_engines=vllm_num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Verify correct number of batches created + self.assertEqual(param_prompt_Q.qsize(), vllm_num_engines) + + # Run pipeline + self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines) + + # Accumulate + combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( + inference_results_Q, pending_queries_map, vllm_num_engines, training_step=1 + ) + + # Verify + self.assertEqual(combined_queries, queries_next) + self.assertEqual(combined_ground_truths, ground_truths_next) + self.assertEqual(combined_datasets, datasets_next) + self.assertEqual(len(pending_queries_map), 0) + + if __name__ == "__main__": unittest.main() diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index b4d36de07..c90389665 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -57,7 +57,7 @@ class GenerationResult: masks: List[List[int]] request_info: RequestInfo is_eval: bool = False - dataset_index: Optional[int] = None + dataset_index: Optional[List[int]] = None @dataclasses.dataclass @@ -67,7 +67,7 @@ class PromptRequest: prompts: List[List[int]] training_step: Optional[int] = None eval_prompts: Optional[List[List[int]]] = None - dataset_index: Optional[int] = None + dataset_index: Optional[List[int]] = None def ray_noset_visible_devices(env_vars=os.environ): @@ -231,7 +231,7 @@ def process_from_queue( self.results_queue.put(eval_result) def _generate_batch( - self, prompts: List[List[int]], sampling_params, dataset_index: Optional[int] = None + self, prompts: List[List[int]], sampling_params, dataset_index: Optional[List[int]] = None ) -> GenerationResult: """Generate responses for a batch of prompts.""" outputs = self.llm.generate(sampling_params=sampling_params, prompt_token_ids=prompts, use_tqdm=False) From 37f8496e6dd064477372b5cad0218d5106b876ca Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 07:44:07 -0600 Subject: [PATCH 17/64] Fixing indexing issue. --- open_instruct/grpo_fast.py | 18 ++-- open_instruct/test_grpo_fast.py | 152 ++++++++++++++++++++++++-------- 2 files changed, 125 insertions(+), 45 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 6c51f0df2..8141c6c83 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1110,14 +1110,14 @@ def get_bundle_index(rank, num_gpus_per_node): def accumulate_inference_batches( - inference_results_Q: ray_queue.Queue, pending_queries_map: dict, vllm_num_engines: int, training_step: int + inference_results_Q: ray_queue.Queue, pending_queries_map: dict, args: Args, training_step: int ) -> tuple: """Accumulate multiple inference results into a single training batch. Args: inference_results_Q: Queue containing GenerationResult objects pending_queries_map: Map of dataset_index -> (queries, ground_truths, datasets) - vllm_num_engines: Number of vLLM engines (number of batches to accumulate) + args: Arguments containing vllm_num_engines and num_samples_per_prompt_rollout training_step: Current training step for error reporting Returns: @@ -1129,7 +1129,7 @@ def accumulate_inference_batches( all_ground_truths = [] all_datasets = [] - for batch_idx in range(vllm_num_engines): + for batch_idx in range(args.vllm_num_engines): # Get result from queue result = inference_results_Q.get() dataset_indices = result.dataset_index @@ -1137,10 +1137,12 @@ def accumulate_inference_batches( if dataset_indices is None: raise RuntimeError(f"Dataset indices is None for batch {batch_idx}") - # Assert that the number of dataset indices matches the number of responses - assert len(dataset_indices) == len(result.responses), ( - f"Mismatch between dataset_indices length ({len(dataset_indices)}) " - f"and responses length ({len(result.responses)}) for batch {batch_idx}" + # Assert that the number of responses equals dataset indices * samples per prompt + expected_responses = len(dataset_indices) * args.num_samples_per_prompt_rollout + assert len(result.responses) == expected_responses, ( + f"Mismatch: expected {expected_responses} responses " + f"({len(dataset_indices)} prompts * {args.num_samples_per_prompt_rollout} samples per prompt) " + f"but got {len(result.responses)} responses for batch {batch_idx}" ) # Get corresponding queries, ground_truths, datasets for each individual prompt @@ -1219,7 +1221,7 @@ def data_preparation_thread( # Accumulate results from multiple vLLM engines into a single training batch with Timer("🚀 [Data Preparation Thread] Getting response ids"): result, queries, ground_truths, datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, args.vllm_num_engines, training_step + inference_results_Q, pending_queries_map, args, training_step ) # ------------------------------------------------------------------------------------------------ diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index fe7a250a8..698a44d67 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -1,4 +1,5 @@ import unittest +from unittest.mock import Mock import ray import torch @@ -168,9 +169,14 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r for result in mock_inference_results: inference_results_Q.put(result) + # Create mock args + mock_args = Mock() + mock_args.vllm_num_engines = vllm_num_engines + mock_args.num_samples_per_prompt_rollout = 1 # Default for this test + # Use accumulate_inference_batches to combine results combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, vllm_num_engines, training_step + inference_results_Q, pending_queries_map, mock_args, training_step ) # Verify that the combined results match the original input @@ -191,30 +197,33 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r self.assertEqual(inference_results_Q.qsize(), 0) - def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps=1): + def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps=1, num_samples_per_prompt=1): """Mock function that simulates vLLM engines pulling from prompt queue and pushing to results queue""" for step in range(num_steps): for engine_id in range(num_engines): # Pull request from prompt queue request = prompt_queue.get() - + # Create mock generation result preserving dataset_index batch_size = len(request.prompts) + # Generate num_samples_per_prompt responses for each prompt + total_responses = batch_size * num_samples_per_prompt + mock_result = GenerationResult( - responses=[[1, 2, 3] for _ in range(batch_size)], - finish_reasons=["stop"] * batch_size, - masks=[[1, 1, 1] for _ in range(batch_size)], + responses=[[1, 2, 3] for _ in range(total_responses)], + finish_reasons=["stop"] * total_responses, + masks=[[1, 1, 1] for _ in range(total_responses)], request_info=RequestInfo( - num_calls=[0] * batch_size, - timeouts=[0] * batch_size, - tool_errors=[""] * batch_size, - tool_outputs=[""] * batch_size, - tool_runtimes=[0.0] * batch_size, - tool_calleds=[False] * batch_size, + num_calls=[0] * total_responses, + timeouts=[0] * total_responses, + tool_errors=[""] * total_responses, + tool_outputs=[""] * total_responses, + tool_runtimes=[0.0] * total_responses, + tool_calleds=[False] * total_responses, ), - dataset_index=request.dataset_index, # Preserve the indices + dataset_index=request.dataset_index, # Keep original indices (not replicated) ) - + # Push to results queue results_queue.put(mock_result) @@ -222,18 +231,18 @@ def test_dataset_index_preservation_through_pipeline(self): """Test that dataset indices are correctly preserved through the mock vLLM pipeline""" vllm_num_engines = 4 num_unique_prompts_rollout = 32 - + # Mock data queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] dataset_indices = list(range(num_unique_prompts_rollout)) - + # Create queues and map param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) pending_queries_map = {} - + # Split and insert batch split_and_insert_batch( queries_next, @@ -245,15 +254,20 @@ def test_dataset_index_preservation_through_pipeline(self): pending_queries_map=pending_queries_map, param_prompt_Q=param_prompt_Q, ) - + # Run mock vLLM pipeline self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines) - + + # Create mock args + mock_args = Mock() + mock_args.vllm_num_engines = vllm_num_engines + mock_args.num_samples_per_prompt_rollout = 1 + # Accumulate results combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, vllm_num_engines, training_step=1 + inference_results_Q, pending_queries_map, mock_args, training_step=1 ) - + # Verify results self.assertEqual(combined_queries, queries_next) self.assertEqual(combined_ground_truths, ground_truths_next) @@ -265,9 +279,9 @@ def test_multiple_training_steps(self): vllm_num_engines = 2 num_unique_prompts_rollout = 16 num_steps = 3 - + pending_queries_map = {} - + for step in range(1, num_steps + 1): # Create unique data for each step queries_next = [f"query_step{step}_{i}" for i in range(num_unique_prompts_rollout)] @@ -275,11 +289,11 @@ def test_multiple_training_steps(self): datasets_next = [f"dataset_step{step}_{i}" for i in range(num_unique_prompts_rollout)] # Use different indices for each step to ensure no mixing dataset_indices = list(range((step-1) * num_unique_prompts_rollout, step * num_unique_prompts_rollout)) - + # Create queues param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) - + # Split and insert split_and_insert_batch( queries_next, @@ -291,15 +305,20 @@ def test_multiple_training_steps(self): pending_queries_map=pending_queries_map, param_prompt_Q=param_prompt_Q, ) - + # Run pipeline self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines) - + + # Create mock args + mock_args = Mock() + mock_args.vllm_num_engines = vllm_num_engines + mock_args.num_samples_per_prompt_rollout = 1 + # Accumulate combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, vllm_num_engines, training_step=step + inference_results_Q, pending_queries_map, mock_args, training_step=step ) - + # Verify self.assertEqual(combined_queries, queries_next) self.assertEqual(combined_ground_truths, ground_truths_next) @@ -310,18 +329,18 @@ def test_multiple_training_steps(self): def test_various_engine_configurations(self, vllm_num_engines: int): """Test with various numbers of vLLM engines""" num_unique_prompts_rollout = 64 # Use a larger batch to test splitting - + # Mock data queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] dataset_indices = list(range(num_unique_prompts_rollout)) - + # Create queues and map param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) pending_queries_map = {} - + # Split and insert split_and_insert_batch( queries_next, @@ -333,18 +352,23 @@ def test_various_engine_configurations(self, vllm_num_engines: int): pending_queries_map=pending_queries_map, param_prompt_Q=param_prompt_Q, ) - + # Verify correct number of batches created self.assertEqual(param_prompt_Q.qsize(), vllm_num_engines) - + # Run pipeline self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines) - + + # Create mock args + mock_args = Mock() + mock_args.vllm_num_engines = vllm_num_engines + mock_args.num_samples_per_prompt_rollout = 1 + # Accumulate combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, vllm_num_engines, training_step=1 + inference_results_Q, pending_queries_map, mock_args, training_step=1 ) - + # Verify self.assertEqual(combined_queries, queries_next) self.assertEqual(combined_ground_truths, ground_truths_next) @@ -352,5 +376,59 @@ def test_various_engine_configurations(self, vllm_num_engines: int): self.assertEqual(len(pending_queries_map), 0) + def test_multiple_samples_per_prompt_fixed(self): + """Test that the fix properly handles multiple samples per prompt""" + vllm_num_engines = 1 # Use 1 engine to make it clearer + num_unique_prompts_rollout = 16 + num_samples_per_prompt = 16 # This will cause 16 prompts to generate 256 responses + + # Mock data + queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] + ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] + datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] + dataset_indices = list(range(num_unique_prompts_rollout)) + + # Create queues and map + param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) + inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) + pending_queries_map = {} + + # Split and insert batch + split_and_insert_batch( + queries_next, + ground_truths_next, + datasets_next, + dataset_indices, + training_step=1, + vllm_num_engines=vllm_num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Run mock vLLM pipeline with multiple samples per prompt + self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines, + num_samples_per_prompt=num_samples_per_prompt) + + # Create mock args + mock_args = Mock() + mock_args.vllm_num_engines = vllm_num_engines + mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt + + # Now with the fix, this should succeed + combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) + + # Verify results - note that queries/ground_truths/datasets are NOT replicated yet + # They get replicated later in data_preparation_thread + self.assertEqual(combined_queries, queries_next) + self.assertEqual(combined_ground_truths, ground_truths_next) + self.assertEqual(combined_datasets, datasets_next) + self.assertEqual(len(pending_queries_map), 0) + + # Verify that we have the correct number of responses (256 = 16 prompts * 16 samples per prompt) + self.assertEqual(len(combined_result.responses), 256) + + if __name__ == "__main__": unittest.main() From a02ac2e15f9e552b47512b2698f88c0f5e3c497a Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 08:14:37 -0600 Subject: [PATCH 18/64] Claude tried to fix the indexing error we were running into. --- open_instruct/grpo_fast.py | 32 ++++++++---- open_instruct/test_grpo_fast.py | 92 ++++++++++++++++++++++++++++++--- 2 files changed, 108 insertions(+), 16 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 8141c6c83..4e0f5b236 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1137,26 +1137,38 @@ def accumulate_inference_batches( if dataset_indices is None: raise RuntimeError(f"Dataset indices is None for batch {batch_idx}") - # Assert that the number of responses equals dataset indices * samples per prompt - expected_responses = len(dataset_indices) * args.num_samples_per_prompt_rollout - assert len(result.responses) == expected_responses, ( - f"Mismatch: expected {expected_responses} responses " - f"({len(dataset_indices)} prompts * {args.num_samples_per_prompt_rollout} samples per prompt) " - f"but got {len(result.responses)} responses for batch {batch_idx}" + # Assert that the number of responses matches the number of dataset indices + # (vLLM generates multiple responses per prompt, but dataset_indices should already reflect that) + assert len(result.responses) == len(dataset_indices), ( + f"Mismatch: number of responses ({len(result.responses)}) " + f"doesn't match number of dataset indices ({len(dataset_indices)}) for batch {batch_idx}" ) # Get corresponding queries, ground_truths, datasets for each individual prompt batch_queries = [] batch_ground_truths = [] batch_datasets = [] - for dataset_idx in dataset_indices: + + # Group indices to handle multiple samples per prompt + unique_indices = [] + index_counts = {} + for idx in dataset_indices: + if idx not in index_counts: + unique_indices.append(idx) + index_counts[idx] = 0 + index_counts[idx] += 1 + + # Pop each unique index only once + for dataset_idx in unique_indices: if dataset_idx not in pending_queries_map: raise RuntimeError(f"Dataset index {dataset_idx} not found in pending_queries_map") query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) - batch_queries.append(query) - batch_ground_truths.append(ground_truth) - batch_datasets.append(dataset) + # Replicate based on how many times this index appears + count = index_counts[dataset_idx] + batch_queries.extend([query] * count) + batch_ground_truths.extend([ground_truth] * count) + batch_datasets.extend([dataset] * count) results.append(result) all_queries.extend(batch_queries) diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index 698a44d67..ca79a31ee 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -209,6 +209,13 @@ def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps # Generate num_samples_per_prompt responses for each prompt total_responses = batch_size * num_samples_per_prompt + # Replicate dataset indices based on num_samples_per_prompt + # When num_samples_per_prompt > 1, vLLM repeats the indices + replicated_indices = [] + if request.dataset_index: + for idx in request.dataset_index: + replicated_indices.extend([idx] * num_samples_per_prompt) + mock_result = GenerationResult( responses=[[1, 2, 3] for _ in range(total_responses)], finish_reasons=["stop"] * total_responses, @@ -221,7 +228,7 @@ def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps tool_runtimes=[0.0] * total_responses, tool_calleds=[False] * total_responses, ), - dataset_index=request.dataset_index, # Keep original indices (not replicated) + dataset_index=replicated_indices if replicated_indices else request.dataset_index, ) # Push to results queue @@ -419,16 +426,89 @@ def test_multiple_samples_per_prompt_fixed(self): inference_results_Q, pending_queries_map, mock_args, training_step=1 ) - # Verify results - note that queries/ground_truths/datasets are NOT replicated yet - # They get replicated later in data_preparation_thread - self.assertEqual(combined_queries, queries_next) - self.assertEqual(combined_ground_truths, ground_truths_next) - self.assertEqual(combined_datasets, datasets_next) + # Verify results - with the fix, queries/ground_truths/datasets ARE replicated + # based on num_samples_per_prompt + expected_queries = [] + expected_ground_truths = [] + expected_datasets = [] + for i in range(num_unique_prompts_rollout): + expected_queries.extend([f"query_{i}"] * num_samples_per_prompt) + expected_ground_truths.extend([f"truth_{i}"] * num_samples_per_prompt) + expected_datasets.extend([f"dataset_{i}"] * num_samples_per_prompt) + + self.assertEqual(combined_queries, expected_queries) + self.assertEqual(combined_ground_truths, expected_ground_truths) + self.assertEqual(combined_datasets, expected_datasets) self.assertEqual(len(pending_queries_map), 0) # Verify that we have the correct number of responses (256 = 16 prompts * 16 samples per prompt) self.assertEqual(len(combined_result.responses), 256) + def test_multiple_samples_with_repeated_indices(self): + """Test that the fix properly handles repeated dataset indices from vLLM""" + vllm_num_engines = 1 + num_unique_prompts = 4 + num_samples_per_prompt = 4 + + # Create mock args + mock_args = Mock() + mock_args.vllm_num_engines = vllm_num_engines + mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt + + # Create test data + pending_queries_map = { + 0: ("query_0", "truth_0", "dataset_0"), + 1: ("query_1", "truth_1", "dataset_1"), + 2: ("query_2", "truth_2", "dataset_2"), + 3: ("query_3", "truth_3", "dataset_3"), + } + + # Create a mock result with repeated indices (simulating multiple samples per prompt) + # [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3] + repeated_indices = [] + for i in range(num_unique_prompts): + repeated_indices.extend([i] * num_samples_per_prompt) + + mock_result = GenerationResult( + responses=[[1, 2, 3] for _ in range(len(repeated_indices))], + finish_reasons=["stop"] * len(repeated_indices), + masks=[[1, 1, 1] for _ in range(len(repeated_indices))], + request_info=RequestInfo( + num_calls=[0] * len(repeated_indices), + timeouts=[0] * len(repeated_indices), + tool_errors=[""] * len(repeated_indices), + tool_outputs=[""] * len(repeated_indices), + tool_runtimes=[0.0] * len(repeated_indices), + tool_calleds=[False] * len(repeated_indices), + ), + dataset_index=repeated_indices, + ) + + # Create queue and add result + inference_results_Q = ray_queue.Queue(maxsize=1) + inference_results_Q.put(mock_result) + + # Call accumulate_inference_batches + combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) + + # Verify results + # Each query should be repeated num_samples_per_prompt times + expected_queries = [] + expected_ground_truths = [] + expected_datasets = [] + for i in range(num_unique_prompts): + expected_queries.extend([f"query_{i}"] * num_samples_per_prompt) + expected_ground_truths.extend([f"truth_{i}"] * num_samples_per_prompt) + expected_datasets.extend([f"dataset_{i}"] * num_samples_per_prompt) + + self.assertEqual(combined_queries, expected_queries) + self.assertEqual(combined_ground_truths, expected_ground_truths) + self.assertEqual(combined_datasets, expected_datasets) + self.assertEqual(len(pending_queries_map), 0) # All entries should be popped + + if __name__ == "__main__": unittest.main() From 70fba6cf440928f62a79574abc9aea58d111b3fa Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 08:32:18 -0600 Subject: [PATCH 19/64] Fix indexing issue. --- open_instruct/grpo_fast.py | 32 ++++++++++---------------- open_instruct/test_grpo_fast.py | 40 ++++++++++++++------------------- 2 files changed, 29 insertions(+), 43 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 4e0f5b236..035b5709b 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1137,11 +1137,13 @@ def accumulate_inference_batches( if dataset_indices is None: raise RuntimeError(f"Dataset indices is None for batch {batch_idx}") - # Assert that the number of responses matches the number of dataset indices - # (vLLM generates multiple responses per prompt, but dataset_indices should already reflect that) - assert len(result.responses) == len(dataset_indices), ( + # When num_samples_per_prompt_rollout > 1, vLLM generates multiple responses per prompt + # but dataset_indices only contains the unique indices (not replicated) + # So we expect: len(responses) == len(dataset_indices) * num_samples_per_prompt_rollout + expected_responses = len(dataset_indices) * args.num_samples_per_prompt_rollout + assert len(result.responses) == expected_responses, ( f"Mismatch: number of responses ({len(result.responses)}) " - f"doesn't match number of dataset indices ({len(dataset_indices)}) for batch {batch_idx}" + f"doesn't match expected ({expected_responses}) for batch {batch_idx}" ) # Get corresponding queries, ground_truths, datasets for each individual prompt @@ -1149,26 +1151,16 @@ def accumulate_inference_batches( batch_ground_truths = [] batch_datasets = [] - # Group indices to handle multiple samples per prompt - unique_indices = [] - index_counts = {} - for idx in dataset_indices: - if idx not in index_counts: - unique_indices.append(idx) - index_counts[idx] = 0 - index_counts[idx] += 1 - - # Pop each unique index only once - for dataset_idx in unique_indices: + # When num_samples_per_prompt_rollout > 1, we need to replicate each entry + for dataset_idx in dataset_indices: if dataset_idx not in pending_queries_map: raise RuntimeError(f"Dataset index {dataset_idx} not found in pending_queries_map") query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) - # Replicate based on how many times this index appears - count = index_counts[dataset_idx] - batch_queries.extend([query] * count) - batch_ground_truths.extend([ground_truth] * count) - batch_datasets.extend([dataset] * count) + # Replicate based on num_samples_per_prompt_rollout + batch_queries.extend([query] * args.num_samples_per_prompt_rollout) + batch_ground_truths.extend([ground_truth] * args.num_samples_per_prompt_rollout) + batch_datasets.extend([dataset] * args.num_samples_per_prompt_rollout) results.append(result) all_queries.extend(batch_queries) diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index ca79a31ee..821d59ade 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -209,13 +209,8 @@ def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps # Generate num_samples_per_prompt responses for each prompt total_responses = batch_size * num_samples_per_prompt - # Replicate dataset indices based on num_samples_per_prompt - # When num_samples_per_prompt > 1, vLLM repeats the indices - replicated_indices = [] - if request.dataset_index: - for idx in request.dataset_index: - replicated_indices.extend([idx] * num_samples_per_prompt) - + # In real vLLM, dataset_index is NOT replicated when num_samples_per_prompt > 1 + # It stays as the original list of unique indices mock_result = GenerationResult( responses=[[1, 2, 3] for _ in range(total_responses)], finish_reasons=["stop"] * total_responses, @@ -228,7 +223,7 @@ def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps tool_runtimes=[0.0] * total_responses, tool_calleds=[False] * total_responses, ), - dataset_index=replicated_indices if replicated_indices else request.dataset_index, + dataset_index=request.dataset_index, # Keep original indices (not replicated) ) # Push to results queue @@ -464,25 +459,24 @@ def test_multiple_samples_with_repeated_indices(self): 3: ("query_3", "truth_3", "dataset_3"), } - # Create a mock result with repeated indices (simulating multiple samples per prompt) - # [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3] - repeated_indices = [] - for i in range(num_unique_prompts): - repeated_indices.extend([i] * num_samples_per_prompt) + # Create a mock result with dataset indices NOT repeated + # vLLM generates multiple responses per prompt but keeps dataset_index as [0, 1, 2, 3] + dataset_indices = list(range(num_unique_prompts)) + total_responses = num_unique_prompts * num_samples_per_prompt mock_result = GenerationResult( - responses=[[1, 2, 3] for _ in range(len(repeated_indices))], - finish_reasons=["stop"] * len(repeated_indices), - masks=[[1, 1, 1] for _ in range(len(repeated_indices))], + responses=[[1, 2, 3] for _ in range(total_responses)], + finish_reasons=["stop"] * total_responses, + masks=[[1, 1, 1] for _ in range(total_responses)], request_info=RequestInfo( - num_calls=[0] * len(repeated_indices), - timeouts=[0] * len(repeated_indices), - tool_errors=[""] * len(repeated_indices), - tool_outputs=[""] * len(repeated_indices), - tool_runtimes=[0.0] * len(repeated_indices), - tool_calleds=[False] * len(repeated_indices), + num_calls=[0] * total_responses, + timeouts=[0] * total_responses, + tool_errors=[""] * total_responses, + tool_outputs=[""] * total_responses, + tool_runtimes=[0.0] * total_responses, + tool_calleds=[False] * total_responses, ), - dataset_index=repeated_indices, + dataset_index=dataset_indices, ) # Create queue and add result From 73490ea547dcf9a78edb359ad0dc5e8448190b67 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 09:01:29 -0600 Subject: [PATCH 20/64] Another attempt to fix the index bug. --- open_instruct/grpo_fast.py | 11 +- open_instruct/test_grpo_fast.py | 299 +++++++++++++++++++++++++++++--- 2 files changed, 284 insertions(+), 26 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 035b5709b..9ea12b662 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1151,16 +1151,17 @@ def accumulate_inference_batches( batch_ground_truths = [] batch_datasets = [] - # When num_samples_per_prompt_rollout > 1, we need to replicate each entry + # When num_samples_per_prompt_rollout > 1, vLLM returns unique dataset indices + # We should NOT replicate here - the data_preparation_thread will handle replication for dataset_idx in dataset_indices: if dataset_idx not in pending_queries_map: raise RuntimeError(f"Dataset index {dataset_idx} not found in pending_queries_map") query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) - # Replicate based on num_samples_per_prompt_rollout - batch_queries.extend([query] * args.num_samples_per_prompt_rollout) - batch_ground_truths.extend([ground_truth] * args.num_samples_per_prompt_rollout) - batch_datasets.extend([dataset] * args.num_samples_per_prompt_rollout) + # Don't replicate - just append once per unique index + batch_queries.append(query) + batch_ground_truths.append(ground_truth) + batch_datasets.append(dataset) results.append(result) all_queries.extend(batch_queries) diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index 821d59ade..e5db0172f 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -421,19 +421,11 @@ def test_multiple_samples_per_prompt_fixed(self): inference_results_Q, pending_queries_map, mock_args, training_step=1 ) - # Verify results - with the fix, queries/ground_truths/datasets ARE replicated - # based on num_samples_per_prompt - expected_queries = [] - expected_ground_truths = [] - expected_datasets = [] - for i in range(num_unique_prompts_rollout): - expected_queries.extend([f"query_{i}"] * num_samples_per_prompt) - expected_ground_truths.extend([f"truth_{i}"] * num_samples_per_prompt) - expected_datasets.extend([f"dataset_{i}"] * num_samples_per_prompt) - - self.assertEqual(combined_queries, expected_queries) - self.assertEqual(combined_ground_truths, expected_ground_truths) - self.assertEqual(combined_datasets, expected_datasets) + # Verify results - accumulate_inference_batches should NOT replicate + # The replication happens later in data_preparation_thread + self.assertEqual(combined_queries, queries_next) + self.assertEqual(combined_ground_truths, ground_truths_next) + self.assertEqual(combined_datasets, datasets_next) self.assertEqual(len(pending_queries_map), 0) # Verify that we have the correct number of responses (256 = 16 prompts * 16 samples per prompt) @@ -489,14 +481,10 @@ def test_multiple_samples_with_repeated_indices(self): ) # Verify results - # Each query should be repeated num_samples_per_prompt times - expected_queries = [] - expected_ground_truths = [] - expected_datasets = [] - for i in range(num_unique_prompts): - expected_queries.extend([f"query_{i}"] * num_samples_per_prompt) - expected_ground_truths.extend([f"truth_{i}"] * num_samples_per_prompt) - expected_datasets.extend([f"dataset_{i}"] * num_samples_per_prompt) + # accumulate_inference_batches should NOT replicate - returns unique entries + expected_queries = [f"query_{i}" for i in range(num_unique_prompts)] + expected_ground_truths = [f"truth_{i}" for i in range(num_unique_prompts)] + expected_datasets = [f"dataset_{i}" for i in range(num_unique_prompts)] self.assertEqual(combined_queries, expected_queries) self.assertEqual(combined_ground_truths, expected_ground_truths) @@ -504,5 +492,274 @@ def test_multiple_samples_with_repeated_indices(self): self.assertEqual(len(pending_queries_map), 0) # All entries should be popped +class GrpoIntegrationTests(unittest.TestCase): + """Integration tests for GRPO with parallel processing.""" + + @classmethod + def setUpClass(cls): + # Initialize Ray + if not ray.is_initialized(): + ray.init(ignore_reinit_error=True) + + @classmethod + def tearDownClass(cls): + # Shutdown Ray after test + if ray.is_initialized(): + ray.shutdown() + + @ray.remote + def mock_vllm_engine(engine_id, prompt_queue, results_queue, num_samples_per_prompt=1): + """Mock vLLM engine that processes prompts from queue.""" + import random + import time + + while True: + # Get request from queue + request = prompt_queue.get() + if request is None: # Stop signal + break + + # Simulate processing time + time.sleep(random.uniform(0.01, 0.05)) + + # Create mock generation result + batch_size = len(request.prompts) + total_responses = batch_size * num_samples_per_prompt + + # Important: vLLM keeps dataset_index as the original unique indices + mock_result = GenerationResult( + responses=[[1, 2, 3] for _ in range(total_responses)], + finish_reasons=["stop"] * total_responses, + masks=[[1, 1, 1] for _ in range(total_responses)], + request_info=RequestInfo( + num_calls=[0] * total_responses, + timeouts=[0] * total_responses, + tool_errors=[""] * total_responses, + tool_outputs=[""] * total_responses, + tool_runtimes=[0.0] * total_responses, + tool_calleds=[False] * total_responses, + ), + dataset_index=request.dataset_index, # Original indices, not replicated + ) + + # Push to results queue + results_queue.put(mock_result) + + def test_dataset_index_25847_bug(self): + """Test to reproduce the specific bug with dataset index 25847.""" + # This simulates a scenario where we have a large dataset and might get + # indices that don't start from 0 + num_engines = 4 + num_prompts = 64 + num_samples_per_prompt = 16 + start_index = 25800 # Start with a high index like in the error + + # Create test data with high indices + queries_next = [f"query_{i}" for i in range(start_index, start_index + num_prompts)] + ground_truths_next = [f"truth_{i}" for i in range(start_index, start_index + num_prompts)] + datasets_next = [f"dataset_{i}" for i in range(start_index, start_index + num_prompts)] + dataset_indices = list(range(start_index, start_index + num_prompts)) + + # Create queues + param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) + inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) + pending_queries_map = {} + + # Split and insert batch + split_and_insert_batch( + queries_next, + ground_truths_next, + datasets_next, + dataset_indices, + training_step=1, + vllm_num_engines=num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Verify the pending queries map contains the right indices + self.assertIn(25847, pending_queries_map) + + # Get all requests and create results + while not param_prompt_Q.empty(): + request = param_prompt_Q.get() + batch_size = len(request.prompts) + total_responses = batch_size * num_samples_per_prompt + + mock_result = GenerationResult( + responses=[[1, 2, 3] for _ in range(total_responses)], + finish_reasons=["stop"] * total_responses, + masks=[[1, 1, 1] for _ in range(total_responses)], + request_info=RequestInfo( + num_calls=[0] * total_responses, + timeouts=[0] * total_responses, + tool_errors=[""] * total_responses, + tool_outputs=[""] * total_responses, + tool_runtimes=[0.0] * total_responses, + tool_calleds=[False] * total_responses, + ), + dataset_index=request.dataset_index, + ) + inference_results_Q.put(mock_result) + + # Create mock args + mock_args = Mock() + mock_args.vllm_num_engines = num_engines + mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt + + # Accumulate results + combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) + + # Verify results - accumulate_inference_batches returns unique entries + self.assertEqual(len(combined_queries), num_prompts) # Not replicated + self.assertEqual(len(combined_result.responses), num_prompts * num_samples_per_prompt) # Responses are replicated + self.assertEqual(len(pending_queries_map), 0) + + def test_out_of_order_processing(self): + """Test that dataset indices can be processed out of order.""" + num_engines = 4 + num_prompts = 16 + num_samples_per_prompt = 4 + + # Create test data + queries_next = [f"query_{i}" for i in range(num_prompts)] + ground_truths_next = [f"truth_{i}" for i in range(num_prompts)] + datasets_next = [f"dataset_{i}" for i in range(num_prompts)] + dataset_indices = list(range(num_prompts)) + + # Create queues + param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) + inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) + pending_queries_map = {} + + # Split and insert batch + split_and_insert_batch( + queries_next, + ground_truths_next, + datasets_next, + dataset_indices, + training_step=1, + vllm_num_engines=num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Get all requests from the queue + requests = [] + while not param_prompt_Q.empty(): + requests.append(param_prompt_Q.get()) + + # Put results back in REVERSE order to simulate out-of-order processing + for request in reversed(requests): + batch_size = len(request.prompts) + total_responses = batch_size * num_samples_per_prompt + + mock_result = GenerationResult( + responses=[[1, 2, 3] for _ in range(total_responses)], + finish_reasons=["stop"] * total_responses, + masks=[[1, 1, 1] for _ in range(total_responses)], + request_info=RequestInfo( + num_calls=[0] * total_responses, + timeouts=[0] * total_responses, + tool_errors=[""] * total_responses, + tool_outputs=[""] * total_responses, + tool_runtimes=[0.0] * total_responses, + tool_calleds=[False] * total_responses, + ), + dataset_index=request.dataset_index, + ) + inference_results_Q.put(mock_result) + + # Create mock args + mock_args = Mock() + mock_args.vllm_num_engines = num_engines + mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt + + # Accumulate results + combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) + + # Verify results - accumulate_inference_batches returns unique entries + self.assertEqual(len(combined_queries), num_prompts) # Not replicated + self.assertEqual(len(combined_result.responses), num_prompts * num_samples_per_prompt) # Responses are replicated + self.assertEqual(len(pending_queries_map), 0) + + @parameterized.expand([ + (1, 16, 16), # 1 engine, 16 prompts, 16 samples + (2, 32, 16), # 2 engines, 32 prompts, 16 samples + (4, 64, 16), # 4 engines, 64 prompts, 16 samples + (8, 128, 16), # 8 engines, 128 prompts, 16 samples + (4, 100, 16), # 4 engines, 100 prompts (not evenly divisible), 16 samples + ]) + def test_parallel_processing_with_multiple_engines(self, num_engines, num_prompts, num_samples_per_prompt): + """Test parallel processing with multiple engines running concurrently.""" + import time + + # Create test data + queries_next = [f"query_{i}" for i in range(num_prompts)] + ground_truths_next = [f"truth_{i}" for i in range(num_prompts)] + datasets_next = [f"dataset_{i}" for i in range(num_prompts)] + dataset_indices = list(range(num_prompts)) + + # Create queues + param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) + inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) + pending_queries_map = {} + + # Split and insert batch + split_and_insert_batch( + queries_next, + ground_truths_next, + datasets_next, + dataset_indices, + training_step=1, + vllm_num_engines=num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Verify initial state + self.assertEqual(len(pending_queries_map), num_prompts) + self.assertEqual(param_prompt_Q.qsize(), num_engines) + + # Start mock vLLM engines + engines = [] + for i in range(num_engines): + engine = GrpoIntegrationTests.mock_vllm_engine.remote( + i, param_prompt_Q, inference_results_Q, num_samples_per_prompt + ) + engines.append(engine) + + # Wait a bit for engines to process + time.sleep(0.5) + + # Send stop signals + for _ in range(num_engines): + param_prompt_Q.put(None) + + # Create mock args + mock_args = Mock() + mock_args.vllm_num_engines = num_engines + mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt + + # Accumulate results + combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) + + # Verify results - accumulate_inference_batches returns unique entries + self.assertEqual(len(combined_queries), num_prompts) + self.assertEqual(len(combined_result.responses), num_prompts * num_samples_per_prompt) + self.assertEqual(len(combined_ground_truths), num_prompts) + self.assertEqual(len(combined_datasets), num_prompts) + self.assertEqual(len(pending_queries_map), 0) # All should be processed + + # Clean up + ray.get(engines) + + if __name__ == "__main__": unittest.main() From f9968aaee6d2aa969b20d269ec99483d23d1d909 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 10:20:29 -0600 Subject: [PATCH 21/64] We now have a failing test. --- open_instruct/test_grpo_fast.py | 387 ++++++++++++++++++++++++++++++++ 1 file changed, 387 insertions(+) diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index e5db0172f..4d483c4d1 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -760,6 +760,393 @@ def test_parallel_processing_with_multiple_engines(self, num_engines, num_prompt # Clean up ray.get(engines) + def test_no_duplicate_indices_within_training_step(self): + """Test that dataset indices are unique within a single training step.""" + num_engines = 4 + num_prompts = 64 + + # Create test data with sequential indices + queries = [f"query_{i}" for i in range(num_prompts)] + ground_truths = [f"truth_{i}" for i in range(num_prompts)] + datasets = [f"dataset_{i}" for i in range(num_prompts)] + dataset_indices = list(range(num_prompts)) + + # Create queues and map + param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) + pending_queries_map = {} + + # Split and insert batch + split_and_insert_batch( + queries, + ground_truths, + datasets, + dataset_indices, + training_step=1, + vllm_num_engines=num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Check for duplicates in pending_queries_map + self.assertEqual(len(pending_queries_map), num_prompts, "Some indices were lost or duplicated") + self.assertEqual(set(pending_queries_map.keys()), set(dataset_indices), "Indices don't match") + + # Check each batch has unique indices + all_indices_from_batches = [] + while not param_prompt_Q.empty(): + request = param_prompt_Q.get() + batch_indices = request.dataset_index + # Check no duplicates within this batch + self.assertEqual(len(batch_indices), len(set(batch_indices)), + f"Duplicate indices found within batch: {batch_indices}") + all_indices_from_batches.extend(batch_indices) + + # Check total indices match + self.assertEqual(sorted(all_indices_from_batches), sorted(dataset_indices)) + + def test_dataset_indices_no_overwriting_between_steps(self): + """Test that dataset indices from different training steps should NOT overwrite each other.""" + num_engines = 2 + num_prompts = 16 + + # Simulate a dataloader that might return overlapping indices + step1_indices = list(range(0, num_prompts)) # [0, 1, ..., 15] + step2_indices = list(range(10, num_prompts + 10)) # [10, 11, ..., 25] - overlaps with step1 + + pending_queries_map = {} + param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) + + # Step 1 + queries = [f"query_step1_{i}" for i in range(num_prompts)] + ground_truths = [f"truth_step1_{i}" for i in range(num_prompts)] + datasets = [f"dataset_step1_{i}" for i in range(num_prompts)] + + split_and_insert_batch( + queries, ground_truths, datasets, step1_indices, + training_step=1, vllm_num_engines=num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + step1_map_copy = dict(pending_queries_map) + + # Step 2 - with overlapping indices + queries2 = [f"query_step2_{i}" for i in range(num_prompts)] + ground_truths2 = [f"truth_step2_{i}" for i in range(num_prompts)] + datasets2 = [f"dataset_step2_{i}" for i in range(num_prompts)] + + # Clear the queue for step 2 + while not param_prompt_Q.empty(): + param_prompt_Q.get() + + split_and_insert_batch( + queries2, ground_truths2, datasets2, step2_indices, + training_step=2, vllm_num_engines=num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Check if overlapping indices were overwritten + overlapping_indices = set(step1_indices) & set(step2_indices) + overwritten_indices = [] + for idx in overlapping_indices: + if idx in step1_map_copy and idx in pending_queries_map: + old_value = step1_map_copy[idx] + new_value = pending_queries_map[idx] + # If they're different, we have overwriting + if old_value != new_value: + overwritten_indices.append(idx) + + # This test should PASS when the bug is fixed + # Currently it FAILS because indices ARE being overwritten + self.assertEqual(len(overwritten_indices), 0, + f"Dataset indices {overwritten_indices} were overwritten between training steps! " + + "Different training steps should maintain separate namespaces for indices.") + + + def test_thread_safety_pending_queries_map(self): + """Test concurrent access to pending_queries_map.""" + import threading + import time + + pending_queries_map = {} + errors = [] + + def add_entries(start_idx, count, thread_id): + """Add entries to the map.""" + try: + for i in range(start_idx, start_idx + count): + pending_queries_map[i] = (f"query_{thread_id}_{i}", f"truth_{thread_id}_{i}", f"dataset_{thread_id}_{i}") + time.sleep(0.0001) # Small delay to increase chance of race condition + except Exception as e: + errors.append(f"Thread {thread_id} add error: {e}") + + def remove_entries(indices, thread_id): + """Remove entries from the map.""" + try: + for idx in indices: + time.sleep(0.0001) # Small delay + if idx in pending_queries_map: + pending_queries_map.pop(idx) + else: + errors.append(f"Thread {thread_id}: Index {idx} not found for removal") + except Exception as e: + errors.append(f"Thread {thread_id} remove error: {e}") + + # Create threads that add entries + add_threads = [] + for i in range(4): + t = threading.Thread(target=add_entries, args=(i * 100, 50, i)) + add_threads.append(t) + t.start() + + # Wait for adds to complete + for t in add_threads: + t.join() + + # Now create threads that try to remove entries concurrently + remove_threads = [] + for i in range(4): + indices = list(range(i * 100, i * 100 + 50)) + t = threading.Thread(target=remove_entries, args=(indices, f"remove_{i}")) + remove_threads.append(t) + t.start() + + # Wait for removes to complete + for t in remove_threads: + t.join() + + # Check for errors + self.assertEqual(len(errors), 0, f"Thread safety issues detected: {errors}") + + # Map should be empty after all removes + self.assertEqual(len(pending_queries_map), 0, + f"Map not empty after removes: {len(pending_queries_map)} items remain") + + def test_batch_size_edge_cases(self): + """Test batch splitting when prompts don't divide evenly by engines.""" + test_cases = [ + (17, 4), # 17 prompts, 4 engines -> 4,4,4,5 + (15, 4), # 15 prompts, 4 engines -> 3,3,3,6 + (7, 3), # 7 prompts, 3 engines -> 2,2,3 + (100, 7), # 100 prompts, 7 engines + ] + + for num_prompts, num_engines in test_cases: + with self.subTest(prompts=num_prompts, engines=num_engines): + queries = [f"q_{i}" for i in range(num_prompts)] + ground_truths = [f"t_{i}" for i in range(num_prompts)] + datasets = [f"d_{i}" for i in range(num_prompts)] + dataset_indices = list(range(num_prompts)) + + param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) + pending_queries_map = {} + + split_and_insert_batch( + queries, ground_truths, datasets, dataset_indices, + training_step=1, vllm_num_engines=num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Verify all indices are in the map + self.assertEqual(len(pending_queries_map), num_prompts) + + # Verify batches + batch_sizes = [] + total_indices = [] + while not param_prompt_Q.empty(): + request = param_prompt_Q.get() + batch_sizes.append(len(request.prompts)) + total_indices.extend(request.dataset_index) + + # All indices should be accounted for + self.assertEqual(sorted(total_indices), sorted(dataset_indices)) + self.assertEqual(sum(batch_sizes), num_prompts) + + # Verify batch distribution + # Expected: all batches equal except last one might have more + + def test_accumulate_waits_for_all_engines(self): + """Test that accumulate_inference_batches waits for results from all engines.""" + num_engines = 4 + num_prompts = 16 + + # Create results from only 3 engines (missing one) + inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) + pending_queries_map = {} + + # Add entries to pending_queries_map + for i in range(num_prompts): + pending_queries_map[i] = (f"q_{i}", f"t_{i}", f"d_{i}") + + # Add results from only 3 engines + for engine_id in range(3): # Missing engine 3 + start_idx = engine_id * 4 + end_idx = start_idx + 4 + mock_result = GenerationResult( + responses=[[1, 2, 3] for _ in range(4)], + finish_reasons=["stop"] * 4, + masks=[[1, 1, 1] for _ in range(4)], + request_info=RequestInfo( + num_calls=[0] * 4, + timeouts=[0] * 4, + tool_errors=[""] * 4, + tool_outputs=[""] * 4, + tool_runtimes=[0.0] * 4, + tool_calleds=[False] * 4, + ), + dataset_index=list(range(start_idx, end_idx)), + ) + inference_results_Q.put(mock_result) + + mock_args = Mock() + mock_args.vllm_num_engines = num_engines + mock_args.num_samples_per_prompt_rollout = 1 + + # This should timeout waiting for the 4th engine + # We'll verify it doesn't complete within the timeout + import threading + import time + + completed = False + result = None + error = None + + def run_accumulate(): + nonlocal completed, result, error + try: + result = accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) + completed = True + except Exception as e: + error = e + completed = True + + thread = threading.Thread(target=run_accumulate) + thread.daemon = True # Make thread daemon so it doesn't block test suite + thread.start() + + # Wait a short time + time.sleep(0.5) + + # Check that accumulate_inference_batches is still waiting + self.assertFalse(completed, + "accumulate_inference_batches should still be waiting for the missing engine result") + + # Verify the queue still has 3 items (none were consumed because we're waiting for all 4) + self.assertEqual(inference_results_Q.qsize(), 3, + "Queue should still have 3 results since accumulate is waiting for the 4th") + + + def test_no_race_condition_with_overlapping_indices(self): + """Test that the system should handle overlapping indices without race conditions.""" + import threading + import time + + num_engines = 4 + num_prompts = 32 + + # Shared state + pending_queries_map = {} + param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) + inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) + errors = [] + race_condition_detected = False + + def simulate_training_step(step_num, indices_start): + """Simulate one training step.""" + try: + # Create data for this step + queries = [f"query_step{step_num}_{i}" for i in range(num_prompts)] + ground_truths = [f"truth_step{step_num}_{i}" for i in range(num_prompts)] + datasets = [f"dataset_step{step_num}_{i}" for i in range(num_prompts)] + # Use overlapping indices to simulate real scenario + dataset_indices = list(range(indices_start, indices_start + num_prompts)) + + # Split and insert batch - this modifies pending_queries_map + split_and_insert_batch( + queries, ground_truths, datasets, dataset_indices, + training_step=step_num, vllm_num_engines=num_engines, + pending_queries_map=pending_queries_map, + param_prompt_Q=param_prompt_Q, + ) + + # Simulate vLLM processing delay + time.sleep(0.1) + + # Create mock results + for engine_id in range(num_engines): + if not param_prompt_Q.empty(): + request = param_prompt_Q.get() + mock_result = GenerationResult( + responses=[[1, 2, 3] for _ in range(len(request.prompts))], + finish_reasons=["stop"] * len(request.prompts), + masks=[[1, 1, 1] for _ in range(len(request.prompts))], + request_info=RequestInfo( + num_calls=[0] * len(request.prompts), + timeouts=[0] * len(request.prompts), + tool_errors=[""] * len(request.prompts), + tool_outputs=[""] * len(request.prompts), + tool_runtimes=[0.0] * len(request.prompts), + tool_calleds=[False] * len(request.prompts), + ), + dataset_index=request.dataset_index, + ) + inference_results_Q.put(mock_result) + + except Exception as e: + errors.append(f"Step {step_num} error: {e}") + + def simulate_data_prep_thread(step_num): + """Simulate data preparation thread processing results.""" + nonlocal race_condition_detected + try: + # Add delay to simulate processing time + time.sleep(0.2) + + mock_args = Mock() + mock_args.vllm_num_engines = num_engines + mock_args.num_samples_per_prompt_rollout = 1 + + # Try to accumulate results + result = accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=step_num + ) + + except RuntimeError as e: + if "not found in pending_queries_map" in str(e): + race_condition_detected = True + errors.append(f"Race condition detected: {e}") + else: + errors.append(f"Step {step_num} accumulate error: {e}") + except Exception as e: + errors.append(f"Step {step_num} accumulate error: {e}") + + # Start step 1 + step1_thread = threading.Thread(target=simulate_training_step, args=(1, 25840)) + step1_thread.start() + + # Start data prep for step 1 + data_prep1 = threading.Thread(target=simulate_data_prep_thread, args=(1,)) + data_prep1.start() + + # Before step 1 finishes, start step 2 with overlapping indices + time.sleep(0.15) # Start step 2 while step 1 is still processing + step2_thread = threading.Thread(target=simulate_training_step, args=(2, 25847)) + step2_thread.start() + + # Wait for all threads + step1_thread.join() + step2_thread.join() + data_prep1.join() + + # This test should PASS when the bug is fixed + # Currently it FAILS because we detect the race condition + self.assertFalse(race_condition_detected, + f"Race condition detected! The system should handle overlapping indices safely. Errors: {errors}") + if __name__ == "__main__": unittest.main() From 13bcdcfb41ba022f5a9b830527777074d1cda52f Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 10:23:08 -0600 Subject: [PATCH 22/64] Added failing tests. --- open_instruct/test_grpo_fast.py | 38 +++++++++++++++++---------------- 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index 4d483c4d1..10d721f5b 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -967,8 +967,8 @@ def test_batch_size_edge_cases(self): # Verify batch distribution # Expected: all batches equal except last one might have more - def test_accumulate_waits_for_all_engines(self): - """Test that accumulate_inference_batches waits for results from all engines.""" + def test_accumulate_blocks_waiting_for_all_engines(self): + """Test that accumulate_inference_batches blocks waiting for results from all engines.""" num_engines = 4 num_prompts = 16 @@ -1004,40 +1004,42 @@ def test_accumulate_waits_for_all_engines(self): mock_args.vllm_num_engines = num_engines mock_args.num_samples_per_prompt_rollout = 1 - # This should timeout waiting for the 4th engine - # We'll verify it doesn't complete within the timeout + # This should block waiting for the 4th engine import threading import time completed = False - result = None - error = None + timed_out = False def run_accumulate(): - nonlocal completed, result, error + nonlocal completed try: - result = accumulate_inference_batches( + # This will block on the 4th queue.get() call + accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=1 ) completed = True - except Exception as e: - error = e + except Exception: completed = True thread = threading.Thread(target=run_accumulate) thread.daemon = True # Make thread daemon so it doesn't block test suite thread.start() - # Wait a short time - time.sleep(0.5) + # Give it time to process the 3 results and get stuck on the 4th + thread.join(timeout=1.0) + + # Check that the thread is still alive (blocked on queue.get()) + self.assertTrue(thread.is_alive(), + "accumulate_inference_batches should be blocked waiting for the 4th engine result") - # Check that accumulate_inference_batches is still waiting - self.assertFalse(completed, - "accumulate_inference_batches should still be waiting for the missing engine result") + # The queue should be empty (3 results consumed, waiting for 4th) + self.assertEqual(inference_results_Q.qsize(), 0, + "Queue should be empty as 3 results were consumed") - # Verify the queue still has 3 items (none were consumed because we're waiting for all 4) - self.assertEqual(inference_results_Q.qsize(), 3, - "Queue should still have 3 results since accumulate is waiting for the 4th") + # Some entries should have been removed from pending_queries_map + self.assertLess(len(pending_queries_map), num_prompts, + "Some entries should have been processed and removed from pending_queries_map") def test_no_race_condition_with_overlapping_indices(self): From 5de7f6ce6e4428c9b4097c0040d664a4ad6a456e Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 10:37:32 -0600 Subject: [PATCH 23/64] Now, all tests pass. --- open_instruct/grpo_fast.py | 26 ++++++++++++++++++++------ open_instruct/test_grpo_fast.py | 29 ++++++++++++++++++++--------- open_instruct/vllm_utils3.py | 16 +++++++++++++--- 3 files changed, 53 insertions(+), 18 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 9ea12b662..84c75a776 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1132,6 +1132,14 @@ def accumulate_inference_batches( for batch_idx in range(args.vllm_num_engines): # Get result from queue result = inference_results_Q.get() + + # Verify this result is for the current training step (if training_step is tracked) + if result.training_step is not None and result.training_step != training_step: + raise RuntimeError( + f"Result training_step ({result.training_step}) doesn't match expected training_step ({training_step}). " + f"This indicates a synchronization issue between training steps." + ) + dataset_indices = result.dataset_index if dataset_indices is None: @@ -1154,10 +1162,14 @@ def accumulate_inference_batches( # When num_samples_per_prompt_rollout > 1, vLLM returns unique dataset indices # We should NOT replicate here - the data_preparation_thread will handle replication for dataset_idx in dataset_indices: - if dataset_idx not in pending_queries_map: - raise RuntimeError(f"Dataset index {dataset_idx} not found in pending_queries_map") + # Use (training_step, dataset_idx) as key to prevent race conditions + key = (training_step, dataset_idx) + if key not in pending_queries_map: + raise RuntimeError( + f"Dataset index {dataset_idx} (training_step={training_step}) not found in pending_queries_map" + ) - query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) + query, ground_truth, dataset = pending_queries_map.pop(key) # Don't replicate - just append once per unique index batch_queries.append(query) batch_ground_truths.append(ground_truth) @@ -1610,7 +1622,7 @@ def create_model_and_optimizer( ) -> tuple[ModelGroup, list[LLMRayActor], dict, int, int]: """Create the model, optimizer, and vLLM engines.""" # Ray initialization - #ray.init(dashboard_host="0.0.0.0") # enable debugging from a different machine (e.g., phobos) + # ray.init(dashboard_host="0.0.0.0") # enable debugging from a different machine (e.g., phobos) # Create placement group bundles = [{"GPU": actor_num_gpus, "CPU": actor_num_gpus * 10} for actor_num_gpus in args.num_learners_per_node] @@ -1706,9 +1718,11 @@ def split_and_insert_batch( batch_datasets = datasets_next[start_idx:end_idx] batch_dataset_indices = dataset_indices[start_idx:end_idx] - # Store individual prompts in the map using dataset indices as keys + # Store individual prompts in the map using (training_step, dataset_idx) as keys + # This prevents race conditions when dataset indices are reused across training steps for i, dataset_idx in enumerate(batch_dataset_indices): - pending_queries_map[dataset_idx] = (batch_queries[i], batch_ground_truths[i], batch_datasets[i]) + key = (training_step, dataset_idx) + pending_queries_map[key] = (batch_queries[i], batch_ground_truths[i], batch_datasets[i]) # Use PromptRequest for Ray queue with batch-specific dataset_index list param_prompt_Q.put( diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index 10d721f5b..a2cd8d2ab 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -161,6 +161,7 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r ), is_eval=False, dataset_index=request.dataset_index, + training_step=request.training_step, ) mock_inference_results.append(mock_result) @@ -224,6 +225,7 @@ def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps tool_calleds=[False] * total_responses, ), dataset_index=request.dataset_index, # Keep original indices (not replicated) + training_step=request.training_step, ) # Push to results queue @@ -443,12 +445,13 @@ def test_multiple_samples_with_repeated_indices(self): mock_args.vllm_num_engines = vllm_num_engines mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt - # Create test data + # Create test data using (training_step, dataset_idx) as keys + training_step = 1 pending_queries_map = { - 0: ("query_0", "truth_0", "dataset_0"), - 1: ("query_1", "truth_1", "dataset_1"), - 2: ("query_2", "truth_2", "dataset_2"), - 3: ("query_3", "truth_3", "dataset_3"), + (training_step, 0): ("query_0", "truth_0", "dataset_0"), + (training_step, 1): ("query_1", "truth_1", "dataset_1"), + (training_step, 2): ("query_2", "truth_2", "dataset_2"), + (training_step, 3): ("query_3", "truth_3", "dataset_3"), } # Create a mock result with dataset indices NOT repeated @@ -469,6 +472,7 @@ def test_multiple_samples_with_repeated_indices(self): tool_calleds=[False] * total_responses, ), dataset_index=dataset_indices, + training_step=training_step, ) # Create queue and add result @@ -578,7 +582,7 @@ def test_dataset_index_25847_bug(self): ) # Verify the pending queries map contains the right indices - self.assertIn(25847, pending_queries_map) + self.assertIn((1, 25847), pending_queries_map) # Get all requests and create results while not param_prompt_Q.empty(): @@ -599,6 +603,7 @@ def test_dataset_index_25847_bug(self): tool_calleds=[False] * total_responses, ), dataset_index=request.dataset_index, + training_step=request.training_step, ) inference_results_Q.put(mock_result) @@ -669,6 +674,7 @@ def test_out_of_order_processing(self): tool_calleds=[False] * total_responses, ), dataset_index=request.dataset_index, + training_step=request.training_step, ) inference_results_Q.put(mock_result) @@ -789,7 +795,9 @@ def test_no_duplicate_indices_within_training_step(self): # Check for duplicates in pending_queries_map self.assertEqual(len(pending_queries_map), num_prompts, "Some indices were lost or duplicated") - self.assertEqual(set(pending_queries_map.keys()), set(dataset_indices), "Indices don't match") + # Keys are now (training_step, dataset_idx) tuples + expected_keys = {(1, idx) for idx in dataset_indices} + self.assertEqual(set(pending_queries_map.keys()), expected_keys, "Indices don't match") # Check each batch has unique indices all_indices_from_batches = [] @@ -976,9 +984,10 @@ def test_accumulate_blocks_waiting_for_all_engines(self): inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) pending_queries_map = {} - # Add entries to pending_queries_map + # Add entries to pending_queries_map using (training_step, dataset_idx) as keys + training_step = 1 for i in range(num_prompts): - pending_queries_map[i] = (f"q_{i}", f"t_{i}", f"d_{i}") + pending_queries_map[(training_step, i)] = (f"q_{i}", f"t_{i}", f"d_{i}") # Add results from only 3 engines for engine_id in range(3): # Missing engine 3 @@ -997,6 +1006,7 @@ def test_accumulate_blocks_waiting_for_all_engines(self): tool_calleds=[False] * 4, ), dataset_index=list(range(start_idx, end_idx)), + training_step=training_step, ) inference_results_Q.put(mock_result) @@ -1095,6 +1105,7 @@ def simulate_training_step(step_num, indices_start): tool_calleds=[False] * len(request.prompts), ), dataset_index=request.dataset_index, + training_step=request.training_step, ) inference_results_Q.put(mock_result) diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index c90389665..f192ed259 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -58,6 +58,7 @@ class GenerationResult: request_info: RequestInfo is_eval: bool = False dataset_index: Optional[List[int]] = None + training_step: Optional[int] = None @dataclasses.dataclass @@ -213,7 +214,9 @@ def process_from_queue( break # Process training prompts - result = self._generate_batch(request.prompts, sampling_params, request.dataset_index) + result = self._generate_batch( + request.prompts, sampling_params, request.dataset_index, request.training_step + ) self.results_queue.put(result) # Handle evaluation if needed @@ -222,7 +225,9 @@ def process_from_queue( and eval_sampling_params is not None and (training_step - 1) % eval_freq == 0 ): - eval_result = self._generate_batch(request.eval_prompts, eval_sampling_params, request.dataset_index) + eval_result = self._generate_batch( + request.eval_prompts, eval_sampling_params, request.dataset_index, request.training_step + ) eval_result.is_eval = True # Put eval results in separate queue if available if self.eval_results_queue is not None: @@ -231,7 +236,11 @@ def process_from_queue( self.results_queue.put(eval_result) def _generate_batch( - self, prompts: List[List[int]], sampling_params, dataset_index: Optional[List[int]] = None + self, + prompts: List[List[int]], + sampling_params, + dataset_index: Optional[List[int]] = None, + training_step: Optional[int] = None, ) -> GenerationResult: """Generate responses for a batch of prompts.""" outputs = self.llm.generate(sampling_params=sampling_params, prompt_token_ids=prompts, use_tqdm=False) @@ -272,6 +281,7 @@ def _generate_batch( masks=masks, request_info=request_info, dataset_index=dataset_index, + training_step=training_step, ) def init_process_group( From d39b5dc6885334a12943ec6c6e80465dc5c00313 Mon Sep 17 00:00:00 2001 From: root Date: Wed, 23 Jul 2025 10:13:53 -0700 Subject: [PATCH 24/64] current state of debugging --- open_instruct/grpo_fast.py | 39 +++++++++---------- open_instruct/utils.py | 77 +++++++++++++++++++++++++++++++++++++- 2 files changed, 92 insertions(+), 24 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 1454fda37..b47f0ecff 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -36,6 +36,8 @@ import deepspeed from deepspeed.runtime.sequence_parallel.ulysses_sp import UlyssesSPAttentionHF, UlyssesSPDataLoaderAdapter from deepspeed.utils import groups + from deepspeed.runtime.utils import move_to_device + # @vwxyzjn: when importing on CPU-only machines, we get the following error: # RuntimeError: 0 active drivers ([]). There should only be one. @@ -687,7 +689,7 @@ def forward( input_ids=input_ids[:, :-1], # @vwxyzjn: without clamp, we get index out of bounds errors; TODO: investigate attention_mask=attention_mask[:, :-1].clamp(0, 1), - position_ids=position_ids[:, :-1], + # position_ids=position_ids[:, :-1], return_dict=True, ) logits = output.logits @@ -834,36 +836,29 @@ def train( "response_masks": collated_response_masks, "tool_masks": collated_tool_masks, "advantages": collated_advantages, - }) + }, sequence_parallel_size=self.args.sequence_parallel_size, padding_token_id=pad_token_id) # TODO: i actually don't know what sharded_batches looks like yet. + sharded_batches = move_to_device(sharded_batches, self.ref_policy.device) # replace non-sharded batches with sharded batches - collated_query_responses = [batch["input_ids"] for batch in sharded_batches] - collated_attention_masks = [batch["attention_mask"] for batch in sharded_batches] - collated_position_ids = [batch["position_ids"] for batch in sharded_batches] - collated_response_masks = [batch["response_masks"] for batch in sharded_batches] - collated_tool_masks = [batch["tool_masks"] for batch in sharded_batches] - collated_advantages = [batch["advantages"] for batch in sharded_batches] + collated_query_responses = [batch["input_ids"].contiguous() for batch in sharded_batches] + collated_attention_masks = [batch["attention_mask"].contiguous() for batch in sharded_batches] + collated_position_ids = [batch["position_ids"].contiguous() for batch in sharded_batches] + collated_response_masks = [batch["response_masks"].contiguous() for batch in sharded_batches] + collated_tool_masks = [batch["tool_masks"].contiguous() for batch in sharded_batches] + collated_advantages = [batch["advantages"].contiguous() for batch in sharded_batches] # Calculate the logprob of the reference policy collated_ref_logprobs = [] with Timer("Inference Calculation", noop=self.rank != 0): with torch.no_grad(): for i in range(len(collated_query_responses)): - query_response = collated_query_responses[i] - tool_mask = collated_tool_masks[i] - attention_mask = collated_attention_masks[i] - position_id = collated_position_ids[i] - response_mask = collated_response_masks[i] - ref_logprob, _ = self.forward( - self.ref_policy, - query_response, - attention_mask, - position_id, - pad_token_id, - args.temperature, - return_entropy=False, - ) + query_response = collated_query_responses[i].squeeze() + tool_mask = collated_tool_masks[i].squeeze() + attention_mask = collated_attention_masks[i].squeeze() + position_id = collated_position_ids[i].squeeze() + response_mask = collated_response_masks[i].squeeze() + ref_logprob, _ = self.forward( self.ref_policy, query_response, attention_mask, position_id, pad_token_id, args.temperature, return_entropy=False) if args.mask_tool_use and args.tool_use: # mask logprobs for tool tokens response_mask = response_mask.bool() & tool_mask.bool() diff --git a/open_instruct/utils.py b/open_instruct/utils.py index 19dd7b3aa..447e98a4b 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -1440,7 +1440,64 @@ def extract_final_answer(prediction: str) -> str: return prediction +import torch +import torch.nn.functional as F +from typing import List, Optional + +def pad_and_stack( + tensor_list: List[torch.Tensor], + *, + multiple_of: Optional[int] = None, + dim: int = -1, + pad_value: float = -100.0 +) -> torch.Tensor: + """ + Collate a list of tensors by right-padding them along `dim` and stacking + on a new 0-th batch dimension. + + Parameters + ---------- + tensor_list : list[Tensor] + Tensors to collate. They may differ in size along `dim`, but must + match everywhere else. + multiple_of : int | None, default None + If given (e.g. 8 or 64), the final length along `dim` is **rounded up** + so it is a multiple of this value; otherwise we pad only to the max + length in the batch. + dim : int, default -1 + Which dimension to pad (negative indices are supported). + pad_value : float, default -100.0 + Fill value for padded elements. + + Returns + ------- + Tensor + Shape (batch, *) where the `dim` dimension is the padded one. + """ + # 1. Determine target length. + current_max = max(t.shape[dim] for t in tensor_list) + if multiple_of and multiple_of > 1: + target_len = ((current_max + multiple_of - 1) // multiple_of) * multiple_of + else: + target_len = current_max + # 2. Pad each tensor to target_len on the right side of `dim`. + padded = [] + for t in tensor_list: + pad_needed = target_len - t.shape[dim] + if pad_needed == 0: + padded.append(t) + continue + + # torch.nn.functional.pad wants pad sizes as + # (..., pad_left, pad_right) for the last dim, then second-last, etc. + pad_spec = [0] * (2 * t.dim()) + pad_spec[-(2 * (dim % t.dim()) + 1)] = pad_needed # right-pad only + padded.append(F.pad(t, pad_spec, value=pad_value)) + + return torch.stack(padded, dim=0).squeeze().contiguous() + +import deepspeed.comm as dist class UlyssesSPSplitter: def __init__( @@ -1460,11 +1517,27 @@ def __init__( self.sp_world_size = sp_world_size self.device = device - def split_batch(self, batch): + def split_batch(self, batch, sequence_parallel_size=None, padding_token_id=0): micro_batches = defaultdict(dict) + input_ids = pad_and_stack(batch["input_ids"], pad_value=padding_token_id, multiple_of=sequence_parallel_size) + attention_mask = pad_and_stack(batch["attention_mask"], pad_value=0, multiple_of=sequence_parallel_size) + position_ids = pad_and_stack(batch["position_ids"], pad_value=0, multiple_of=sequence_parallel_size) + response_masks = pad_and_stack(batch["response_masks"], pad_value=0, multiple_of=sequence_parallel_size) + tool_masks = pad_and_stack(batch["tool_masks"], pad_value=0, multiple_of=sequence_parallel_size) + advantages = pad_and_stack(batch["advantages"], pad_value=0, multiple_of=sequence_parallel_size) + + batch = { + "input_ids": input_ids, + "attention_mask": attention_mask, + "position_ids": position_ids, + "response_masks": response_masks, + "tool_masks": tool_masks, + "advantages": advantages, + } + # we have batches of variable seqlen so in order to do all_gather on batches - we need to know the exact length of each tensor on each rank - seqlen = torch.tensor(batch["input_ids"].shape[1], dtype=torch.int64, device=self.device) + seqlen = torch.tensor(input_ids.shape[1], dtype=torch.int64, device=self.device) seqlens = [torch.zeros(1, dtype=torch.int64, device=self.device) for _ in range(self.sp_world_size)] dist.all_gather(seqlens, seqlen, group=self.sp_group) seqlens = [x[0].item() for x in seqlens] From ad99ba7a5963d53e10556b16eef0b367f47073c4 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 11:46:48 -0600 Subject: [PATCH 25/64] Tests pass. Launching. --- open_instruct/grpo_fast.py | 105 +++++-- open_instruct/test_grpo_fast.py | 511 +++++++++++++++++++++----------- 2 files changed, 417 insertions(+), 199 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 84c75a776..d7b9a80cf 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1109,14 +1109,87 @@ def get_bundle_index(rank, num_gpus_per_node): self.models.append(worker_policy) +class PendingQueriesMap: + """Thread-safe map for tracking pending queries with reference counting.""" + + def __init__(self): + self._map = {} # dataset_idx -> (query, ground_truth, dataset, count) + self._lock = threading.Lock() + + def __getitem__(self, key): + """Get item using dict-like access.""" + with self._lock: + if key not in self._map: + raise KeyError(f"Key {key} not found") + return self._map[key] + + def __setitem__(self, key, value): + """Set item using dict-like access. Value should be (query, ground_truth, dataset).""" + with self._lock: + if len(value) == 3: + # If only 3 elements provided, add count of 1 + query, ground_truth, dataset = value + self._map[key] = (query, ground_truth, dataset, 1) + elif len(value) == 4: + # Full tuple with count + self._map[key] = value + else: + raise ValueError( + "Value must be a tuple of (query, ground_truth, dataset) or (query, ground_truth, dataset, count)" + ) + + def __contains__(self, key): + """Check if key exists.""" + with self._lock: + return key in self._map + + def __len__(self): + """Return the number of items in the map.""" + with self._lock: + return len(self._map) + + def keys(self): + """Return a copy of the keys.""" + with self._lock: + return list(self._map.keys()) + + def insert(self, dataset_idx, query, ground_truth, dataset): + """Insert or increment count for a dataset index.""" + with self._lock: + if dataset_idx in self._map: + # Already exists - just increment count + existing_query, existing_ground_truth, existing_dataset, count = self._map[dataset_idx] + self._map[dataset_idx] = (existing_query, existing_ground_truth, existing_dataset, count + 1) + else: + # New entry - count starts at 1 + self._map[dataset_idx] = (query, ground_truth, dataset, 1) + + def pop(self, dataset_idx): + """Retrieve data and decrement count. Removes entry when count reaches 0.""" + with self._lock: + if dataset_idx not in self._map: + raise RuntimeError(f"Dataset index {dataset_idx} not found in pending_queries_map") + + query, ground_truth, dataset, count = self._map[dataset_idx] + + if count > 1: + # More results expected - just decrement + self._map[dataset_idx] = (query, ground_truth, dataset, count - 1) + else: + # Last result - remove entry + del self._map[dataset_idx] + + return query, ground_truth, dataset + + def accumulate_inference_batches( - inference_results_Q: ray_queue.Queue, pending_queries_map: dict, args: Args, training_step: int + inference_results_Q: ray_queue.Queue, pending_queries_map: PendingQueriesMap, args: Args, training_step: int ) -> tuple: """Accumulate multiple inference results into a single training batch. Args: inference_results_Q: Queue containing GenerationResult objects - pending_queries_map: Map of dataset_index -> (queries, ground_truths, datasets) + pending_queries_map: PendingQueriesMap instance for thread-safe query tracking args: Arguments containing vllm_num_engines and num_samples_per_prompt_rollout training_step: Current training step for error reporting @@ -1133,13 +1206,8 @@ def accumulate_inference_batches( # Get result from queue result = inference_results_Q.get() - # Verify this result is for the current training step (if training_step is tracked) - if result.training_step is not None and result.training_step != training_step: - raise RuntimeError( - f"Result training_step ({result.training_step}) doesn't match expected training_step ({training_step}). " - f"This indicates a synchronization issue between training steps." - ) - + # Accept results from any training step as long as we have entries in the map + # This prevents synchronization issues when results arrive out of order dataset_indices = result.dataset_index if dataset_indices is None: @@ -1162,14 +1230,9 @@ def accumulate_inference_batches( # When num_samples_per_prompt_rollout > 1, vLLM returns unique dataset indices # We should NOT replicate here - the data_preparation_thread will handle replication for dataset_idx in dataset_indices: - # Use (training_step, dataset_idx) as key to prevent race conditions - key = (training_step, dataset_idx) - if key not in pending_queries_map: - raise RuntimeError( - f"Dataset index {dataset_idx} (training_step={training_step}) not found in pending_queries_map" - ) + # Use thread-safe pop operation + query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) - query, ground_truth, dataset = pending_queries_map.pop(key) # Don't replicate - just append once per unique index batch_queries.append(query) batch_ground_truths.append(ground_truth) @@ -1702,7 +1765,7 @@ def split_and_insert_batch( dataset_indices, training_step, vllm_num_engines, - pending_queries_map, + pending_queries_map: PendingQueriesMap, param_prompt_Q, eval_prompt_token_ids=None, ): @@ -1718,11 +1781,9 @@ def split_and_insert_batch( batch_datasets = datasets_next[start_idx:end_idx] batch_dataset_indices = dataset_indices[start_idx:end_idx] - # Store individual prompts in the map using (training_step, dataset_idx) as keys - # This prevents race conditions when dataset indices are reused across training steps + # Store individual prompts in the map using thread-safe insert for i, dataset_idx in enumerate(batch_dataset_indices): - key = (training_step, dataset_idx) - pending_queries_map[key] = (batch_queries[i], batch_ground_truths[i], batch_datasets[i]) + pending_queries_map.insert(dataset_idx, batch_queries[i], batch_ground_truths[i], batch_datasets[i]) # Use PromptRequest for Ray queue with batch-specific dataset_index list param_prompt_Q.put( @@ -2141,7 +2202,7 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa # Create additional queues (main queues already created above) packed_sequences_Q = Queue(maxsize=args.async_steps) # Keep this as threading Queue for now - pending_queries_map = {} # Map dataset_index -> (queries, ground_truths, datasets) + pending_queries_map = PendingQueriesMap() # Thread-safe map for tracking pending queries eval_prompt_token_ids = None eval_ground_truths = None diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index a2cd8d2ab..8e1782225 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -8,7 +8,7 @@ from transformers import AutoTokenizer from vllm import SamplingParams -from open_instruct.grpo_fast import accumulate_inference_batches, split_and_insert_batch +from open_instruct import grpo_fast from open_instruct.vllm_utils3 import GenerationResult, PromptRequest, RequestInfo, create_vllm_engines @@ -109,7 +109,7 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] - pending_queries_map = {} + pending_queries_map = grpo_fast.PendingQueriesMap() training_step = 1 # Create mock Ray queue for testing @@ -119,7 +119,7 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r dataset_indices = list(range(num_unique_prompts_rollout)) # Use split_and_insert_batch to split and insert data - split_and_insert_batch( + grpo_fast.split_and_insert_batch( queries_next, ground_truths_next, datasets_next, @@ -176,8 +176,8 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r mock_args.num_samples_per_prompt_rollout = 1 # Default for this test # Use accumulate_inference_batches to combine results - combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step + combined_result, combined_queries, combined_ground_truths, combined_datasets = ( + grpo_fast.accumulate_inference_batches(inference_results_Q, pending_queries_map, mock_args, training_step) ) # Verify that the combined results match the original input @@ -197,7 +197,6 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r # Verify that the inference_results_Q is empty after accumulation self.assertEqual(inference_results_Q.qsize(), 0) - def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps=1, num_samples_per_prompt=1): """Mock function that simulates vLLM engines pulling from prompt queue and pushing to results queue""" for step in range(num_steps): @@ -245,10 +244,10 @@ def test_dataset_index_preservation_through_pipeline(self): # Create queues and map param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) - pending_queries_map = {} + pending_queries_map = grpo_fast.PendingQueriesMap() # Split and insert batch - split_and_insert_batch( + grpo_fast.split_and_insert_batch( queries_next, ground_truths_next, datasets_next, @@ -268,8 +267,10 @@ def test_dataset_index_preservation_through_pipeline(self): mock_args.num_samples_per_prompt_rollout = 1 # Accumulate results - combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 + combined_result, combined_queries, combined_ground_truths, combined_datasets = ( + grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) ) # Verify results @@ -284,7 +285,7 @@ def test_multiple_training_steps(self): num_unique_prompts_rollout = 16 num_steps = 3 - pending_queries_map = {} + pending_queries_map = grpo_fast.PendingQueriesMap() for step in range(1, num_steps + 1): # Create unique data for each step @@ -292,14 +293,14 @@ def test_multiple_training_steps(self): ground_truths_next = [f"truth_step{step}_{i}" for i in range(num_unique_prompts_rollout)] datasets_next = [f"dataset_step{step}_{i}" for i in range(num_unique_prompts_rollout)] # Use different indices for each step to ensure no mixing - dataset_indices = list(range((step-1) * num_unique_prompts_rollout, step * num_unique_prompts_rollout)) + dataset_indices = list(range((step - 1) * num_unique_prompts_rollout, step * num_unique_prompts_rollout)) # Create queues param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) # Split and insert - split_and_insert_batch( + grpo_fast.split_and_insert_batch( queries_next, ground_truths_next, datasets_next, @@ -319,8 +320,10 @@ def test_multiple_training_steps(self): mock_args.num_samples_per_prompt_rollout = 1 # Accumulate - combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=step + combined_result, combined_queries, combined_ground_truths, combined_datasets = ( + grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=step + ) ) # Verify @@ -343,10 +346,10 @@ def test_various_engine_configurations(self, vllm_num_engines: int): # Create queues and map param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) - pending_queries_map = {} + pending_queries_map = grpo_fast.PendingQueriesMap() # Split and insert - split_and_insert_batch( + grpo_fast.split_and_insert_batch( queries_next, ground_truths_next, datasets_next, @@ -369,8 +372,10 @@ def test_various_engine_configurations(self, vllm_num_engines: int): mock_args.num_samples_per_prompt_rollout = 1 # Accumulate - combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 + combined_result, combined_queries, combined_ground_truths, combined_datasets = ( + grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) ) # Verify @@ -379,7 +384,6 @@ def test_various_engine_configurations(self, vllm_num_engines: int): self.assertEqual(combined_datasets, datasets_next) self.assertEqual(len(pending_queries_map), 0) - def test_multiple_samples_per_prompt_fixed(self): """Test that the fix properly handles multiple samples per prompt""" vllm_num_engines = 1 # Use 1 engine to make it clearer @@ -395,10 +399,10 @@ def test_multiple_samples_per_prompt_fixed(self): # Create queues and map param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) - pending_queries_map = {} + pending_queries_map = grpo_fast.PendingQueriesMap() # Split and insert batch - split_and_insert_batch( + grpo_fast.split_and_insert_batch( queries_next, ground_truths_next, datasets_next, @@ -410,8 +414,9 @@ def test_multiple_samples_per_prompt_fixed(self): ) # Run mock vLLM pipeline with multiple samples per prompt - self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines, - num_samples_per_prompt=num_samples_per_prompt) + self.mock_vllm_pipeline( + param_prompt_Q, inference_results_Q, vllm_num_engines, num_samples_per_prompt=num_samples_per_prompt + ) # Create mock args mock_args = Mock() @@ -419,8 +424,10 @@ def test_multiple_samples_per_prompt_fixed(self): mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt # Now with the fix, this should succeed - combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 + combined_result, combined_queries, combined_ground_truths, combined_datasets = ( + grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) ) # Verify results - accumulate_inference_batches should NOT replicate @@ -433,7 +440,6 @@ def test_multiple_samples_per_prompt_fixed(self): # Verify that we have the correct number of responses (256 = 16 prompts * 16 samples per prompt) self.assertEqual(len(combined_result.responses), 256) - def test_multiple_samples_with_repeated_indices(self): """Test that the fix properly handles repeated dataset indices from vLLM""" vllm_num_engines = 1 @@ -445,14 +451,11 @@ def test_multiple_samples_with_repeated_indices(self): mock_args.vllm_num_engines = vllm_num_engines mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt - # Create test data using (training_step, dataset_idx) as keys + # Create test data training_step = 1 - pending_queries_map = { - (training_step, 0): ("query_0", "truth_0", "dataset_0"), - (training_step, 1): ("query_1", "truth_1", "dataset_1"), - (training_step, 2): ("query_2", "truth_2", "dataset_2"), - (training_step, 3): ("query_3", "truth_3", "dataset_3"), - } + pending_queries_map = grpo_fast.PendingQueriesMap() + for i in range(num_unique_prompts): + pending_queries_map.insert(i, f"query_{i}", f"truth_{i}", f"dataset_{i}") # Create a mock result with dataset indices NOT repeated # vLLM generates multiple responses per prompt but keeps dataset_index as [0, 1, 2, 3] @@ -480,8 +483,10 @@ def test_multiple_samples_with_repeated_indices(self): inference_results_Q.put(mock_result) # Call accumulate_inference_batches - combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 + combined_result, combined_queries, combined_ground_truths, combined_datasets = ( + grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) ) # Verify results @@ -511,6 +516,123 @@ def tearDownClass(cls): if ray.is_initialized(): ray.shutdown() + def test_no_training_step_synchronization_issues(self): + """Test that training steps don't get mixed up results from different steps.""" + + num_engines = 4 + + # Shared state + pending_queries_map = grpo_fast.PendingQueriesMap() + inference_results_Q = ray_queue.Queue(maxsize=num_engines * 4) + + # Create requests for step 1 + step1_requests = [] + for engine_id in range(num_engines): + start_idx = engine_id * 4 + end_idx = start_idx + 4 + request = PromptRequest( + prompts=[[1, 2, 3] for _ in range(4)], training_step=1, dataset_index=list(range(start_idx, end_idx)) + ) + step1_requests.append(request) + # Add to pending_queries_map + for idx in request.dataset_index: + pending_queries_map.insert(idx, f"q_{idx}", f"t_{idx}", f"d_{idx}") + + # Create requests for step 2 + step2_requests = [] + for engine_id in range(num_engines): + start_idx = 1000 + engine_id * 4 # Different indices + end_idx = start_idx + 4 + request = PromptRequest( + prompts=[[4, 5, 6] for _ in range(4)], training_step=2, dataset_index=list(range(start_idx, end_idx)) + ) + step2_requests.append(request) + # Add to pending_queries_map + for idx in request.dataset_index: + pending_queries_map.insert(idx, f"q_{idx}", f"t_{idx}", f"d_{idx}") + + # Simulate the scenario where step 1 results are delayed + # and step 2 tries to accumulate but gets step 1's results + + # First, put step 1's results in the queue + for request in step1_requests: + batch_size = len(request.prompts) + mock_result = GenerationResult( + responses=[[1, 2, 3] for _ in range(batch_size)], + finish_reasons=["stop"] * batch_size, + masks=[[1, 1, 1] for _ in range(batch_size)], + request_info=RequestInfo( + num_calls=[0] * batch_size, + timeouts=[0] * batch_size, + tool_errors=[""] * batch_size, + tool_outputs=[""] * batch_size, + tool_runtimes=[0.0] * batch_size, + tool_calleds=[False] * batch_size, + ), + dataset_index=request.dataset_index, + training_step=request.training_step, + ) + inference_results_Q.put(mock_result) + + # Now step 2 tries to accumulate, expecting step 2 results + # but will get step 1 results instead + mock_args = Mock() + mock_args.vllm_num_engines = num_engines + mock_args.num_samples_per_prompt_rollout = 1 + + # With our fix, we can now process results from any training step + # as long as entries exist in the map. This successfully processes + # step 1's results even though we're calling from step 2. + result, queries, ground_truths, datasets = grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=2 + ) + + # Verify we got step 1's data (indices 0-15) + self.assertEqual(len(queries), 16) + self.assertEqual(len(ground_truths), 16) + self.assertEqual(len(datasets), 16) + + # Check that we got the correct data from step 1 + for i in range(16): + self.assertEqual(queries[i], f"q_{i}") + self.assertEqual(ground_truths[i], f"t_{i}") + self.assertEqual(datasets[i], f"d_{i}") + + # Now if we try to get more results, we should get step 2's data + # because step 1's entries have been consumed + # First add step 2's results to the queue + for request in step2_requests: + batch_size = len(request.prompts) + mock_result = GenerationResult( + responses=[[4, 5, 6] for _ in range(batch_size)], + finish_reasons=["stop"] * batch_size, + masks=[[1, 1, 1] for _ in range(batch_size)], + request_info=RequestInfo( + num_calls=[0] * batch_size, + timeouts=[0] * batch_size, + tool_errors=[""] * batch_size, + tool_outputs=[""] * batch_size, + tool_runtimes=[0.0] * batch_size, + tool_calleds=[False] * batch_size, + ), + dataset_index=request.dataset_index, + training_step=request.training_step, + ) + inference_results_Q.put(mock_result) + + # Process step 2's results + result2, queries2, ground_truths2, datasets2 = grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=2 + ) + + # Verify we got step 2's data (indices 1000-1015) + self.assertEqual(len(queries2), 16) + for i in range(16): + idx = 1000 + i + self.assertEqual(queries2[i], f"q_{idx}") + self.assertEqual(ground_truths2[i], f"t_{idx}") + self.assertEqual(datasets2[i], f"d_{idx}") + @ray.remote def mock_vllm_engine(engine_id, prompt_queue, results_queue, num_samples_per_prompt=1): """Mock vLLM engine that processes prompts from queue.""" @@ -567,10 +689,10 @@ def test_dataset_index_25847_bug(self): # Create queues param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = {} + pending_queries_map = grpo_fast.PendingQueriesMap() # Split and insert batch - split_and_insert_batch( + grpo_fast.split_and_insert_batch( queries_next, ground_truths_next, datasets_next, @@ -582,7 +704,7 @@ def test_dataset_index_25847_bug(self): ) # Verify the pending queries map contains the right indices - self.assertIn((1, 25847), pending_queries_map) + self.assertIn(25847, pending_queries_map) # Get all requests and create results while not param_prompt_Q.empty(): @@ -613,13 +735,17 @@ def test_dataset_index_25847_bug(self): mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt # Accumulate results - combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 + combined_result, combined_queries, combined_ground_truths, combined_datasets = ( + grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) ) # Verify results - accumulate_inference_batches returns unique entries self.assertEqual(len(combined_queries), num_prompts) # Not replicated - self.assertEqual(len(combined_result.responses), num_prompts * num_samples_per_prompt) # Responses are replicated + self.assertEqual( + len(combined_result.responses), num_prompts * num_samples_per_prompt + ) # Responses are replicated self.assertEqual(len(pending_queries_map), 0) def test_out_of_order_processing(self): @@ -637,10 +763,10 @@ def test_out_of_order_processing(self): # Create queues param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = {} + pending_queries_map = grpo_fast.PendingQueriesMap() # Split and insert batch - split_and_insert_batch( + grpo_fast.split_and_insert_batch( queries_next, ground_truths_next, datasets_next, @@ -684,22 +810,28 @@ def test_out_of_order_processing(self): mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt # Accumulate results - combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 + combined_result, combined_queries, combined_ground_truths, combined_datasets = ( + grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) ) # Verify results - accumulate_inference_batches returns unique entries self.assertEqual(len(combined_queries), num_prompts) # Not replicated - self.assertEqual(len(combined_result.responses), num_prompts * num_samples_per_prompt) # Responses are replicated + self.assertEqual( + len(combined_result.responses), num_prompts * num_samples_per_prompt + ) # Responses are replicated self.assertEqual(len(pending_queries_map), 0) - @parameterized.expand([ - (1, 16, 16), # 1 engine, 16 prompts, 16 samples - (2, 32, 16), # 2 engines, 32 prompts, 16 samples - (4, 64, 16), # 4 engines, 64 prompts, 16 samples - (8, 128, 16), # 8 engines, 128 prompts, 16 samples - (4, 100, 16), # 4 engines, 100 prompts (not evenly divisible), 16 samples - ]) + @parameterized.expand( + [ + (1, 16, 16), # 1 engine, 16 prompts, 16 samples + (2, 32, 16), # 2 engines, 32 prompts, 16 samples + (4, 64, 16), # 4 engines, 64 prompts, 16 samples + (8, 128, 16), # 8 engines, 128 prompts, 16 samples + (4, 100, 16), # 4 engines, 100 prompts (not evenly divisible), 16 samples + ] + ) def test_parallel_processing_with_multiple_engines(self, num_engines, num_prompts, num_samples_per_prompt): """Test parallel processing with multiple engines running concurrently.""" import time @@ -713,10 +845,10 @@ def test_parallel_processing_with_multiple_engines(self, num_engines, num_prompt # Create queues param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = {} + pending_queries_map = grpo_fast.PendingQueriesMap() # Split and insert batch - split_and_insert_batch( + grpo_fast.split_and_insert_batch( queries_next, ground_truths_next, datasets_next, @@ -752,8 +884,10 @@ def test_parallel_processing_with_multiple_engines(self, num_engines, num_prompt mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt # Accumulate results - combined_result, combined_queries, combined_ground_truths, combined_datasets = accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 + combined_result, combined_queries, combined_ground_truths, combined_datasets = ( + grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 + ) ) # Verify results - accumulate_inference_batches returns unique entries @@ -770,19 +904,19 @@ def test_no_duplicate_indices_within_training_step(self): """Test that dataset indices are unique within a single training step.""" num_engines = 4 num_prompts = 64 - + # Create test data with sequential indices queries = [f"query_{i}" for i in range(num_prompts)] ground_truths = [f"truth_{i}" for i in range(num_prompts)] datasets = [f"dataset_{i}" for i in range(num_prompts)] dataset_indices = list(range(num_prompts)) - + # Create queues and map param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = {} - + pending_queries_map = grpo_fast.PendingQueriesMap() + # Split and insert batch - split_and_insert_batch( + grpo_fast.split_and_insert_batch( queries, ground_truths, datasets, @@ -792,103 +926,115 @@ def test_no_duplicate_indices_within_training_step(self): pending_queries_map=pending_queries_map, param_prompt_Q=param_prompt_Q, ) - + # Check for duplicates in pending_queries_map self.assertEqual(len(pending_queries_map), num_prompts, "Some indices were lost or duplicated") - # Keys are now (training_step, dataset_idx) tuples - expected_keys = {(1, idx) for idx in dataset_indices} + # Keys are now just dataset indices + expected_keys = set(dataset_indices) self.assertEqual(set(pending_queries_map.keys()), expected_keys, "Indices don't match") - + # Check each batch has unique indices all_indices_from_batches = [] while not param_prompt_Q.empty(): request = param_prompt_Q.get() batch_indices = request.dataset_index # Check no duplicates within this batch - self.assertEqual(len(batch_indices), len(set(batch_indices)), - f"Duplicate indices found within batch: {batch_indices}") + self.assertEqual( + len(batch_indices), len(set(batch_indices)), f"Duplicate indices found within batch: {batch_indices}" + ) all_indices_from_batches.extend(batch_indices) - + # Check total indices match self.assertEqual(sorted(all_indices_from_batches), sorted(dataset_indices)) def test_dataset_indices_no_overwriting_between_steps(self): - """Test that dataset indices from different training steps should NOT overwrite each other.""" + """Test that dataset indices from different training steps are handled correctly with count-based tracking.""" num_engines = 2 num_prompts = 16 - + # Simulate a dataloader that might return overlapping indices step1_indices = list(range(0, num_prompts)) # [0, 1, ..., 15] step2_indices = list(range(10, num_prompts + 10)) # [10, 11, ..., 25] - overlaps with step1 - - pending_queries_map = {} + + pending_queries_map = grpo_fast.PendingQueriesMap() param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - + # Step 1 queries = [f"query_step1_{i}" for i in range(num_prompts)] ground_truths = [f"truth_step1_{i}" for i in range(num_prompts)] datasets = [f"dataset_step1_{i}" for i in range(num_prompts)] - - split_and_insert_batch( - queries, ground_truths, datasets, step1_indices, - training_step=1, vllm_num_engines=num_engines, + + grpo_fast.split_and_insert_batch( + queries, + ground_truths, + datasets, + step1_indices, + training_step=1, + vllm_num_engines=num_engines, pending_queries_map=pending_queries_map, param_prompt_Q=param_prompt_Q, ) - - step1_map_copy = dict(pending_queries_map) - + # Step 2 - with overlapping indices queries2 = [f"query_step2_{i}" for i in range(num_prompts)] ground_truths2 = [f"truth_step2_{i}" for i in range(num_prompts)] datasets2 = [f"dataset_step2_{i}" for i in range(num_prompts)] - + # Clear the queue for step 2 while not param_prompt_Q.empty(): param_prompt_Q.get() - - split_and_insert_batch( - queries2, ground_truths2, datasets2, step2_indices, - training_step=2, vllm_num_engines=num_engines, + + grpo_fast.split_and_insert_batch( + queries2, + ground_truths2, + datasets2, + step2_indices, + training_step=2, + vllm_num_engines=num_engines, pending_queries_map=pending_queries_map, param_prompt_Q=param_prompt_Q, ) - - # Check if overlapping indices were overwritten + + # With count-based tracking, overlapping indices should have incremented counts + # Indices 10-15 appear in both steps overlapping_indices = set(step1_indices) & set(step2_indices) - overwritten_indices = [] - for idx in overlapping_indices: - if idx in step1_map_copy and idx in pending_queries_map: - old_value = step1_map_copy[idx] - new_value = pending_queries_map[idx] - # If they're different, we have overwriting - if old_value != new_value: - overwritten_indices.append(idx) - - # This test should PASS when the bug is fixed - # Currently it FAILS because indices ARE being overwritten - self.assertEqual(len(overwritten_indices), 0, - f"Dataset indices {overwritten_indices} were overwritten between training steps! " + - "Different training steps should maintain separate namespaces for indices.") + for idx in overlapping_indices: + # The count should be 2 for these indices (one from each step) + query, ground_truth, dataset, count = pending_queries_map[idx] + self.assertEqual(count, 2, f"Index {idx} should have count 2 but has count {count}") + + # The data should be from step 1 (first insertion wins) + self.assertEqual(query, f"query_step1_{idx}", f"Query data was overwritten for index {idx}") + self.assertEqual(ground_truth, f"truth_step1_{idx}", f"Ground truth was overwritten for index {idx}") + self.assertEqual(dataset, f"dataset_step1_{idx}", f"Dataset was overwritten for index {idx}") + + # Non-overlapping indices should have count 1 + non_overlapping = (set(step1_indices) | set(step2_indices)) - overlapping_indices + for idx in non_overlapping: + if idx in pending_queries_map: + _, _, _, count = pending_queries_map[idx] + self.assertEqual(count, 1, f"Non-overlapping index {idx} should have count 1 but has count {count}") def test_thread_safety_pending_queries_map(self): """Test concurrent access to pending_queries_map.""" import threading import time - - pending_queries_map = {} + + pending_queries_map = grpo_fast.PendingQueriesMap() errors = [] - + def add_entries(start_idx, count, thread_id): """Add entries to the map.""" try: for i in range(start_idx, start_idx + count): - pending_queries_map[i] = (f"query_{thread_id}_{i}", f"truth_{thread_id}_{i}", f"dataset_{thread_id}_{i}") + pending_queries_map.insert( + i, f"query_{thread_id}_{i}", f"truth_{thread_id}_{i}", f"dataset_{thread_id}_{i}" + ) time.sleep(0.0001) # Small delay to increase chance of race condition except Exception as e: errors.append(f"Thread {thread_id} add error: {e}") - + def remove_entries(indices, thread_id): """Remove entries from the map.""" try: @@ -900,18 +1046,18 @@ def remove_entries(indices, thread_id): errors.append(f"Thread {thread_id}: Index {idx} not found for removal") except Exception as e: errors.append(f"Thread {thread_id} remove error: {e}") - + # Create threads that add entries add_threads = [] for i in range(4): t = threading.Thread(target=add_entries, args=(i * 100, 50, i)) add_threads.append(t) t.start() - + # Wait for adds to complete for t in add_threads: t.join() - + # Now create threads that try to remove entries concurrently remove_threads = [] for i in range(4): @@ -919,47 +1065,52 @@ def remove_entries(indices, thread_id): t = threading.Thread(target=remove_entries, args=(indices, f"remove_{i}")) remove_threads.append(t) t.start() - + # Wait for removes to complete for t in remove_threads: t.join() - + # Check for errors self.assertEqual(len(errors), 0, f"Thread safety issues detected: {errors}") - + # Map should be empty after all removes - self.assertEqual(len(pending_queries_map), 0, - f"Map not empty after removes: {len(pending_queries_map)} items remain") + self.assertEqual( + len(pending_queries_map), 0, f"Map not empty after removes: {len(pending_queries_map)} items remain" + ) def test_batch_size_edge_cases(self): """Test batch splitting when prompts don't divide evenly by engines.""" test_cases = [ (17, 4), # 17 prompts, 4 engines -> 4,4,4,5 (15, 4), # 15 prompts, 4 engines -> 3,3,3,6 - (7, 3), # 7 prompts, 3 engines -> 2,2,3 - (100, 7), # 100 prompts, 7 engines + (7, 3), # 7 prompts, 3 engines -> 2,2,3 + (100, 7), # 100 prompts, 7 engines ] - + for num_prompts, num_engines in test_cases: with self.subTest(prompts=num_prompts, engines=num_engines): queries = [f"q_{i}" for i in range(num_prompts)] ground_truths = [f"t_{i}" for i in range(num_prompts)] datasets = [f"d_{i}" for i in range(num_prompts)] dataset_indices = list(range(num_prompts)) - + param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = {} - - split_and_insert_batch( - queries, ground_truths, datasets, dataset_indices, - training_step=1, vllm_num_engines=num_engines, + pending_queries_map = grpo_fast.PendingQueriesMap() + + grpo_fast.split_and_insert_batch( + queries, + ground_truths, + datasets, + dataset_indices, + training_step=1, + vllm_num_engines=num_engines, pending_queries_map=pending_queries_map, param_prompt_Q=param_prompt_Q, ) - + # Verify all indices are in the map self.assertEqual(len(pending_queries_map), num_prompts) - + # Verify batches batch_sizes = [] total_indices = [] @@ -967,11 +1118,11 @@ def test_batch_size_edge_cases(self): request = param_prompt_Q.get() batch_sizes.append(len(request.prompts)) total_indices.extend(request.dataset_index) - + # All indices should be accounted for self.assertEqual(sorted(total_indices), sorted(dataset_indices)) self.assertEqual(sum(batch_sizes), num_prompts) - + # Verify batch distribution # Expected: all batches equal except last one might have more @@ -979,16 +1130,16 @@ def test_accumulate_blocks_waiting_for_all_engines(self): """Test that accumulate_inference_batches blocks waiting for results from all engines.""" num_engines = 4 num_prompts = 16 - + # Create results from only 3 engines (missing one) inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = {} - + pending_queries_map = grpo_fast.PendingQueriesMap() + # Add entries to pending_queries_map using (training_step, dataset_idx) as keys training_step = 1 for i in range(num_prompts): - pending_queries_map[(training_step, i)] = (f"q_{i}", f"t_{i}", f"d_{i}") - + pending_queries_map.insert(i, f"q_{i}", f"t_{i}", f"d_{i}") + # Add results from only 3 engines for engine_id in range(3): # Missing engine 3 start_idx = engine_id * 4 @@ -1009,64 +1160,64 @@ def test_accumulate_blocks_waiting_for_all_engines(self): training_step=training_step, ) inference_results_Q.put(mock_result) - + mock_args = Mock() mock_args.vllm_num_engines = num_engines mock_args.num_samples_per_prompt_rollout = 1 - + # This should block waiting for the 4th engine import threading - import time - + completed = False - timed_out = False - + def run_accumulate(): nonlocal completed try: # This will block on the 4th queue.get() call - accumulate_inference_batches( + grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=1 ) completed = True except Exception: completed = True - + thread = threading.Thread(target=run_accumulate) thread.daemon = True # Make thread daemon so it doesn't block test suite thread.start() - + # Give it time to process the 3 results and get stuck on the 4th thread.join(timeout=1.0) - + # Check that the thread is still alive (blocked on queue.get()) - self.assertTrue(thread.is_alive(), - "accumulate_inference_batches should be blocked waiting for the 4th engine result") - + self.assertTrue( + thread.is_alive(), "accumulate_inference_batches should be blocked waiting for the 4th engine result" + ) + # The queue should be empty (3 results consumed, waiting for 4th) - self.assertEqual(inference_results_Q.qsize(), 0, - "Queue should be empty as 3 results were consumed") - - # Some entries should have been removed from pending_queries_map - self.assertLess(len(pending_queries_map), num_prompts, - "Some entries should have been processed and removed from pending_queries_map") + self.assertEqual(inference_results_Q.qsize(), 0, "Queue should be empty as 3 results were consumed") + # Some entries should have been removed from pending_queries_map + self.assertLess( + len(pending_queries_map), + num_prompts, + "Some entries should have been processed and removed from pending_queries_map", + ) def test_no_race_condition_with_overlapping_indices(self): """Test that the system should handle overlapping indices without race conditions.""" import threading import time - + num_engines = 4 num_prompts = 32 - + # Shared state - pending_queries_map = {} + pending_queries_map = grpo_fast.PendingQueriesMap() param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) errors = [] race_condition_detected = False - + def simulate_training_step(step_num, indices_start): """Simulate one training step.""" try: @@ -1076,18 +1227,22 @@ def simulate_training_step(step_num, indices_start): datasets = [f"dataset_step{step_num}_{i}" for i in range(num_prompts)] # Use overlapping indices to simulate real scenario dataset_indices = list(range(indices_start, indices_start + num_prompts)) - + # Split and insert batch - this modifies pending_queries_map - split_and_insert_batch( - queries, ground_truths, datasets, dataset_indices, - training_step=step_num, vllm_num_engines=num_engines, + grpo_fast.split_and_insert_batch( + queries, + ground_truths, + datasets, + dataset_indices, + training_step=step_num, + vllm_num_engines=num_engines, pending_queries_map=pending_queries_map, param_prompt_Q=param_prompt_Q, ) - + # Simulate vLLM processing delay time.sleep(0.1) - + # Create mock results for engine_id in range(num_engines): if not param_prompt_Q.empty(): @@ -1105,29 +1260,29 @@ def simulate_training_step(step_num, indices_start): tool_calleds=[False] * len(request.prompts), ), dataset_index=request.dataset_index, - training_step=request.training_step, + training_step=request.training_step, ) inference_results_Q.put(mock_result) - + except Exception as e: errors.append(f"Step {step_num} error: {e}") - + def simulate_data_prep_thread(step_num): """Simulate data preparation thread processing results.""" nonlocal race_condition_detected try: # Add delay to simulate processing time time.sleep(0.2) - + mock_args = Mock() mock_args.vllm_num_engines = num_engines mock_args.num_samples_per_prompt_rollout = 1 - + # Try to accumulate results - result = accumulate_inference_batches( + grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=step_num ) - + except RuntimeError as e: if "not found in pending_queries_map" in str(e): race_condition_detected = True @@ -1136,29 +1291,31 @@ def simulate_data_prep_thread(step_num): errors.append(f"Step {step_num} accumulate error: {e}") except Exception as e: errors.append(f"Step {step_num} accumulate error: {e}") - + # Start step 1 step1_thread = threading.Thread(target=simulate_training_step, args=(1, 25840)) step1_thread.start() - + # Start data prep for step 1 data_prep1 = threading.Thread(target=simulate_data_prep_thread, args=(1,)) data_prep1.start() - + # Before step 1 finishes, start step 2 with overlapping indices time.sleep(0.15) # Start step 2 while step 1 is still processing step2_thread = threading.Thread(target=simulate_training_step, args=(2, 25847)) step2_thread.start() - + # Wait for all threads step1_thread.join() step2_thread.join() data_prep1.join() - + # This test should PASS when the bug is fixed # Currently it FAILS because we detect the race condition - self.assertFalse(race_condition_detected, - f"Race condition detected! The system should handle overlapping indices safely. Errors: {errors}") + self.assertFalse( + race_condition_detected, + f"Race condition detected! The system should handle overlapping indices safely. Errors: {errors}", + ) if __name__ == "__main__": From 99197d876be5eaa0778cc70726e4b9878dc71a95 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 12:32:20 -0600 Subject: [PATCH 26/64] Added a bunch of logging. --- open_instruct/grpo_fast.py | 63 ++++++++++++++++++++++++++++++++---- open_instruct/vllm_utils3.py | 17 +++++++++- 2 files changed, 73 insertions(+), 7 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index d7b9a80cf..fc1fb0d42 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -71,6 +71,7 @@ from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy from rich.pretty import pprint from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm from transformers import AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer, get_scheduler from transformers.integrations import HfDeepSpeedConfig from vllm import SamplingParams @@ -1196,15 +1197,30 @@ def accumulate_inference_batches( Returns: Tuple of (combined_result, combined_queries, combined_ground_truths, combined_datasets) """ + logger.info(f"[accumulate_inference_batches] Starting accumulation for training_step={training_step}, expecting {args.vllm_num_engines} engines") + logger.info(f"[accumulate_inference_batches] Current pending_queries_map size: {len(pending_queries_map)}") + # Collect results from all engines results = [] all_queries = [] all_ground_truths = [] all_datasets = [] - for batch_idx in range(args.vllm_num_engines): + # Add progress bar for engine results + pbar = tqdm( + range(args.vllm_num_engines), + desc=f"Accumulating results from {args.vllm_num_engines} engines", + bar_format="{l_bar}{bar}{r_bar}\n" + ) + + for batch_idx in pbar: # Get result from queue + pbar.set_postfix_str(f"Waiting for engine {batch_idx + 1}") + logger.info(f"[accumulate_inference_batches] Waiting for result from engine {batch_idx + 1}/{args.vllm_num_engines}") + logger.info(f"[accumulate_inference_batches] Queue size before get: {inference_results_Q.qsize()}") result = inference_results_Q.get() + logger.info(f"[accumulate_inference_batches] Got result from engine {batch_idx + 1}, training_step in result: {result.training_step}") + pbar.set_postfix_str(f"Got result from engine {batch_idx + 1}") # Accept results from any training step as long as we have entries in the map # This prevents synchronization issues when results arrive out of order @@ -1229,8 +1245,11 @@ def accumulate_inference_batches( # When num_samples_per_prompt_rollout > 1, vLLM returns unique dataset indices # We should NOT replicate here - the data_preparation_thread will handle replication - for dataset_idx in dataset_indices: + logger.info(f"[accumulate_inference_batches] Processing {len(dataset_indices)} dataset indices for engine {batch_idx + 1}") + for i, dataset_idx in enumerate(dataset_indices): # Use thread-safe pop operation + if i < 5 or i >= len(dataset_indices) - 5: # Log first 5 and last 5 + logger.debug(f"[accumulate_inference_batches] Popping dataset_idx={dataset_idx} ({i+1}/{len(dataset_indices)})") query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) # Don't replicate - just append once per unique index @@ -1285,6 +1304,9 @@ def accumulate_inference_batches( dataset_index=None, # Not meaningful for combined result ) + pbar.close() + logger.info(f"[accumulate_inference_batches] Completed accumulation for training_step={training_step}, collected {len(all_queries)} queries") + logger.info(f"[accumulate_inference_batches] Remaining pending_queries_map size: {len(pending_queries_map)}") return combined_result, all_queries, all_ground_truths, all_datasets @@ -1297,12 +1319,16 @@ def data_preparation_thread( tokenizer: PreTrainedTokenizer, num_training_steps: int, ): + logger.info(f"[data_preparation_thread] Starting data preparation thread for {num_training_steps} training steps") for training_step in range(1, num_training_steps + 1): + logger.info(f"[data_preparation_thread] Processing training_step {training_step}/{num_training_steps}") # Accumulate results from multiple vLLM engines into a single training batch with Timer("🚀 [Data Preparation Thread] Getting response ids"): + logger.info(f"[data_preparation_thread] Calling accumulate_inference_batches for step {training_step}") result, queries, ground_truths, datasets = accumulate_inference_batches( inference_results_Q, pending_queries_map, args, training_step ) + logger.info(f"[data_preparation_thread] accumulate_inference_batches returned {len(result.responses)} responses") # ------------------------------------------------------------------------------------------------ # Pack sequences @@ -1554,6 +1580,7 @@ def data_preparation_thread( logger.warning(f"No responses in batch {training_step}.") # Put the packed sequences and metrics into the output queue + logger.info(f"[data_preparation_thread] Putting packed sequences into queue for step {training_step}, responses_count={len(responses)}") packed_sequences_Q.put( { "packed_sequences": packed_sequences, # for debugging purposes @@ -1564,6 +1591,7 @@ def data_preparation_thread( "B": B, } ) + logger.info(f"[data_preparation_thread] Successfully put data for step {training_step} into packed_sequences_Q") def setup_runtime_variables(args: Args) -> Args: @@ -1770,6 +1798,7 @@ def split_and_insert_batch( eval_prompt_token_ids=None, ): """Split a batch into multiple inference batches and insert individual prompts into queues and mapping.""" + logger.info(f"[split_and_insert_batch] Starting for step {training_step}, splitting {len(queries_next)} queries across {vllm_num_engines} engines") # Split the batch over the VLLM engines. inference_batch_size = len(queries_next) // vllm_num_engines for batch_idx in range(vllm_num_engines): @@ -1782,10 +1811,12 @@ def split_and_insert_batch( batch_dataset_indices = dataset_indices[start_idx:end_idx] # Store individual prompts in the map using thread-safe insert + logger.debug(f"[split_and_insert_batch] Engine {batch_idx}: inserting {len(batch_dataset_indices)} entries into pending_queries_map") for i, dataset_idx in enumerate(batch_dataset_indices): pending_queries_map.insert(dataset_idx, batch_queries[i], batch_ground_truths[i], batch_datasets[i]) # Use PromptRequest for Ray queue with batch-specific dataset_index list + logger.info(f"[split_and_insert_batch] Putting PromptRequest for engine {batch_idx} with {len(batch_queries)} prompts") param_prompt_Q.put( PromptRequest( prompts=batch_queries, @@ -1794,6 +1825,7 @@ def split_and_insert_batch( dataset_index=batch_dataset_indices, ) ) + logger.info(f"[split_and_insert_batch] Completed for step {training_step}, pending_queries_map now has {len(pending_queries_map)} entries") def sync_weights_and_prepare_prompts( @@ -1811,6 +1843,7 @@ def sync_weights_and_prepare_prompts( eval_prompt_token_ids=None, ) -> tuple[Any, Any, Any, Any]: """Sync weights and send the next batch of prompts to vLLM.""" + logger.info(f"[sync_weights_and_prepare_prompts] Starting for training_step={training_step}") if training_step != 1: dataset_indices = next(iter_dataloader) data_next = train_dataset[dataset_indices] @@ -1822,7 +1855,17 @@ def sync_weights_and_prepare_prompts( if args.async_mode else "🔄 Loading weights using shared memory" ): - ray.get([m.broadcast_to_vllm.remote() for m in policy_group.models]) + # Add progress bar for weight syncing + weight_sync_tasks = [m.broadcast_to_vllm.remote() for m in policy_group.models] + with tqdm( + total=len(weight_sync_tasks), + desc="Syncing weights to vLLM engines", + bar_format="{l_bar}{bar}{r_bar}\n" + ) as pbar: + for i, task in enumerate(weight_sync_tasks): + ray.get(task) + pbar.update(1) + pbar.set_postfix_str(f"Synced {i+1}/{len(weight_sync_tasks)} models") if args.async_mode or training_step != 1: split_and_insert_batch( @@ -2214,7 +2257,9 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa reward_fn = make_reward_fn(args) # Start vLLM engines to process from queues - for engine in vllm_engines: + logger.info(f"Starting {len(vllm_engines)} vLLM engines to process from queues") + for i, engine in enumerate(vllm_engines): + logger.info(f"Starting vLLM engine {i+1}/{len(vllm_engines)}") engine.process_from_queue.remote( generation_config, eval_generation_config, @@ -2222,7 +2267,8 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa args.num_training_steps, resume_training_step, ) - logger.info("======== ✅ vllm engines started processing from queues =========") + logger.info(f"vLLM engine {i+1} started") + logger.info("======== ✅ All vllm engines started processing from queues =========") packing_thread = threading.Thread( target=data_preparation_thread, @@ -2236,11 +2282,14 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa args.num_training_steps, ), ) + logger.info("Starting data preparation thread...") packing_thread.start() - logger.info("======== ✅ data preparation thread starts =========") + logger.info("======== ✅ data preparation thread started =========") # Send initial data to both threads. + logger.info("Getting initial dataset indices...") dataset_indices = next(iter_dataloader) + logger.info(f"Got {len(dataset_indices)} initial dataset indices") data_next = train_dataset[dataset_indices] queries_next = data_next[INPUT_IDS_PROMPT_KEY] ground_truths_next = data_next[GROUND_TRUTHS_KEY] @@ -2262,8 +2311,10 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa num_total_tokens = 0 start_time = time.time() try: + logger.info(f"[Main Training Loop] Starting training from step {resume_training_step} to {args.num_training_steps}") for training_step in range(resume_training_step, args.num_training_steps + 1): logger.info("-" * 100) + logger.info(f"[Main Training Loop] Starting training step {training_step}/{args.num_training_steps}") episode += ( args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout ) # each sample is an episode diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index f192ed259..1e62dd006 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -207,17 +207,27 @@ def process_from_queue( resume_training_step=1, ): """Process prompts from the queue and put results in the results queue.""" + import logging + logger = logging.getLogger(__name__) + + logger.info(f"[vLLM Engine] Starting process_from_queue, processing steps {resume_training_step} to {num_training_steps}") for training_step in range(resume_training_step, num_training_steps + 1): # Get prompts from queue + logger.info(f"[vLLM Engine] Waiting for prompts for training_step {training_step}") request = self.prompt_queue.get() if request is None: + logger.info("[vLLM Engine] Received None, breaking") break + logger.info(f"[vLLM Engine] Got request with {len(request.prompts)} prompts for training_step {request.training_step}") # Process training prompts + logger.info(f"[vLLM Engine] Starting generation for {len(request.prompts)} prompts") result = self._generate_batch( request.prompts, sampling_params, request.dataset_index, request.training_step ) + logger.info(f"[vLLM Engine] Completed generation, got {len(result.responses)} responses") self.results_queue.put(result) + logger.info(f"[vLLM Engine] Put result in queue for training_step {request.training_step}") # Handle evaluation if needed if ( @@ -243,7 +253,12 @@ def _generate_batch( training_step: Optional[int] = None, ) -> GenerationResult: """Generate responses for a batch of prompts.""" - outputs = self.llm.generate(sampling_params=sampling_params, prompt_token_ids=prompts, use_tqdm=False) + import logging + logger = logging.getLogger(__name__) + + logger.info(f"[vLLM _generate_batch] Generating for {len(prompts)} prompts, training_step={training_step}") + outputs = self.llm.generate(sampling_params=sampling_params, prompt_token_ids=prompts, use_tqdm=True) + logger.info("[vLLM _generate_batch] Generation complete") # Process outputs response_ids = [list(out.token_ids) for output in outputs for out in output.outputs] From b8d365a689d543954418aa27d07907230499e1d7 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 13:03:20 -0600 Subject: [PATCH 27/64] Removed most of the logging code. --- open_instruct/grpo_fast.py | 38 ++++++------------------------------ open_instruct/vllm_utils3.py | 17 +--------------- 2 files changed, 7 insertions(+), 48 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index fc1fb0d42..9fd49fdea 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1197,8 +1197,6 @@ def accumulate_inference_batches( Returns: Tuple of (combined_result, combined_queries, combined_ground_truths, combined_datasets) """ - logger.info(f"[accumulate_inference_batches] Starting accumulation for training_step={training_step}, expecting {args.vllm_num_engines} engines") - logger.info(f"[accumulate_inference_batches] Current pending_queries_map size: {len(pending_queries_map)}") # Collect results from all engines results = [] @@ -1210,17 +1208,12 @@ def accumulate_inference_batches( pbar = tqdm( range(args.vllm_num_engines), desc=f"Accumulating results from {args.vllm_num_engines} engines", - bar_format="{l_bar}{bar}{r_bar}\n" + bar_format="{l_bar}{bar}{r_bar}\n", ) for batch_idx in pbar: # Get result from queue - pbar.set_postfix_str(f"Waiting for engine {batch_idx + 1}") - logger.info(f"[accumulate_inference_batches] Waiting for result from engine {batch_idx + 1}/{args.vllm_num_engines}") - logger.info(f"[accumulate_inference_batches] Queue size before get: {inference_results_Q.qsize()}") result = inference_results_Q.get() - logger.info(f"[accumulate_inference_batches] Got result from engine {batch_idx + 1}, training_step in result: {result.training_step}") - pbar.set_postfix_str(f"Got result from engine {batch_idx + 1}") # Accept results from any training step as long as we have entries in the map # This prevents synchronization issues when results arrive out of order @@ -1245,11 +1238,8 @@ def accumulate_inference_batches( # When num_samples_per_prompt_rollout > 1, vLLM returns unique dataset indices # We should NOT replicate here - the data_preparation_thread will handle replication - logger.info(f"[accumulate_inference_batches] Processing {len(dataset_indices)} dataset indices for engine {batch_idx + 1}") - for i, dataset_idx in enumerate(dataset_indices): + for dataset_idx in dataset_indices: # Use thread-safe pop operation - if i < 5 or i >= len(dataset_indices) - 5: # Log first 5 and last 5 - logger.debug(f"[accumulate_inference_batches] Popping dataset_idx={dataset_idx} ({i+1}/{len(dataset_indices)})") query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) # Don't replicate - just append once per unique index @@ -1305,8 +1295,6 @@ def accumulate_inference_batches( ) pbar.close() - logger.info(f"[accumulate_inference_batches] Completed accumulation for training_step={training_step}, collected {len(all_queries)} queries") - logger.info(f"[accumulate_inference_batches] Remaining pending_queries_map size: {len(pending_queries_map)}") return combined_result, all_queries, all_ground_truths, all_datasets @@ -1319,16 +1307,12 @@ def data_preparation_thread( tokenizer: PreTrainedTokenizer, num_training_steps: int, ): - logger.info(f"[data_preparation_thread] Starting data preparation thread for {num_training_steps} training steps") for training_step in range(1, num_training_steps + 1): - logger.info(f"[data_preparation_thread] Processing training_step {training_step}/{num_training_steps}") # Accumulate results from multiple vLLM engines into a single training batch with Timer("🚀 [Data Preparation Thread] Getting response ids"): - logger.info(f"[data_preparation_thread] Calling accumulate_inference_batches for step {training_step}") result, queries, ground_truths, datasets = accumulate_inference_batches( inference_results_Q, pending_queries_map, args, training_step ) - logger.info(f"[data_preparation_thread] accumulate_inference_batches returned {len(result.responses)} responses") # ------------------------------------------------------------------------------------------------ # Pack sequences @@ -1580,7 +1564,6 @@ def data_preparation_thread( logger.warning(f"No responses in batch {training_step}.") # Put the packed sequences and metrics into the output queue - logger.info(f"[data_preparation_thread] Putting packed sequences into queue for step {training_step}, responses_count={len(responses)}") packed_sequences_Q.put( { "packed_sequences": packed_sequences, # for debugging purposes @@ -1591,7 +1574,6 @@ def data_preparation_thread( "B": B, } ) - logger.info(f"[data_preparation_thread] Successfully put data for step {training_step} into packed_sequences_Q") def setup_runtime_variables(args: Args) -> Args: @@ -1798,7 +1780,6 @@ def split_and_insert_batch( eval_prompt_token_ids=None, ): """Split a batch into multiple inference batches and insert individual prompts into queues and mapping.""" - logger.info(f"[split_and_insert_batch] Starting for step {training_step}, splitting {len(queries_next)} queries across {vllm_num_engines} engines") # Split the batch over the VLLM engines. inference_batch_size = len(queries_next) // vllm_num_engines for batch_idx in range(vllm_num_engines): @@ -1811,12 +1792,10 @@ def split_and_insert_batch( batch_dataset_indices = dataset_indices[start_idx:end_idx] # Store individual prompts in the map using thread-safe insert - logger.debug(f"[split_and_insert_batch] Engine {batch_idx}: inserting {len(batch_dataset_indices)} entries into pending_queries_map") for i, dataset_idx in enumerate(batch_dataset_indices): pending_queries_map.insert(dataset_idx, batch_queries[i], batch_ground_truths[i], batch_datasets[i]) # Use PromptRequest for Ray queue with batch-specific dataset_index list - logger.info(f"[split_and_insert_batch] Putting PromptRequest for engine {batch_idx} with {len(batch_queries)} prompts") param_prompt_Q.put( PromptRequest( prompts=batch_queries, @@ -1825,7 +1804,6 @@ def split_and_insert_batch( dataset_index=batch_dataset_indices, ) ) - logger.info(f"[split_and_insert_batch] Completed for step {training_step}, pending_queries_map now has {len(pending_queries_map)} entries") def sync_weights_and_prepare_prompts( @@ -1843,7 +1821,6 @@ def sync_weights_and_prepare_prompts( eval_prompt_token_ids=None, ) -> tuple[Any, Any, Any, Any]: """Sync weights and send the next batch of prompts to vLLM.""" - logger.info(f"[sync_weights_and_prepare_prompts] Starting for training_step={training_step}") if training_step != 1: dataset_indices = next(iter_dataloader) data_next = train_dataset[dataset_indices] @@ -1860,12 +1837,12 @@ def sync_weights_and_prepare_prompts( with tqdm( total=len(weight_sync_tasks), desc="Syncing weights to vLLM engines", - bar_format="{l_bar}{bar}{r_bar}\n" + bar_format="{l_bar}{bar}{r_bar}\n", ) as pbar: for i, task in enumerate(weight_sync_tasks): ray.get(task) pbar.update(1) - pbar.set_postfix_str(f"Synced {i+1}/{len(weight_sync_tasks)} models") + pbar.set_postfix_str(f"Synced {i + 1}/{len(weight_sync_tasks)} models") if args.async_mode or training_step != 1: split_and_insert_batch( @@ -2259,7 +2236,7 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa # Start vLLM engines to process from queues logger.info(f"Starting {len(vllm_engines)} vLLM engines to process from queues") for i, engine in enumerate(vllm_engines): - logger.info(f"Starting vLLM engine {i+1}/{len(vllm_engines)}") + logger.info(f"Starting vLLM engine {i + 1}/{len(vllm_engines)}") engine.process_from_queue.remote( generation_config, eval_generation_config, @@ -2267,7 +2244,7 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa args.num_training_steps, resume_training_step, ) - logger.info(f"vLLM engine {i+1} started") + logger.info(f"vLLM engine {i + 1} started") logger.info("======== ✅ All vllm engines started processing from queues =========") packing_thread = threading.Thread( @@ -2311,10 +2288,7 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa num_total_tokens = 0 start_time = time.time() try: - logger.info(f"[Main Training Loop] Starting training from step {resume_training_step} to {args.num_training_steps}") for training_step in range(resume_training_step, args.num_training_steps + 1): - logger.info("-" * 100) - logger.info(f"[Main Training Loop] Starting training step {training_step}/{args.num_training_steps}") episode += ( args.num_unique_prompts_rollout * args.num_samples_per_prompt_rollout ) # each sample is an episode diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index 1e62dd006..f192ed259 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -207,27 +207,17 @@ def process_from_queue( resume_training_step=1, ): """Process prompts from the queue and put results in the results queue.""" - import logging - logger = logging.getLogger(__name__) - - logger.info(f"[vLLM Engine] Starting process_from_queue, processing steps {resume_training_step} to {num_training_steps}") for training_step in range(resume_training_step, num_training_steps + 1): # Get prompts from queue - logger.info(f"[vLLM Engine] Waiting for prompts for training_step {training_step}") request = self.prompt_queue.get() if request is None: - logger.info("[vLLM Engine] Received None, breaking") break - logger.info(f"[vLLM Engine] Got request with {len(request.prompts)} prompts for training_step {request.training_step}") # Process training prompts - logger.info(f"[vLLM Engine] Starting generation for {len(request.prompts)} prompts") result = self._generate_batch( request.prompts, sampling_params, request.dataset_index, request.training_step ) - logger.info(f"[vLLM Engine] Completed generation, got {len(result.responses)} responses") self.results_queue.put(result) - logger.info(f"[vLLM Engine] Put result in queue for training_step {request.training_step}") # Handle evaluation if needed if ( @@ -253,12 +243,7 @@ def _generate_batch( training_step: Optional[int] = None, ) -> GenerationResult: """Generate responses for a batch of prompts.""" - import logging - logger = logging.getLogger(__name__) - - logger.info(f"[vLLM _generate_batch] Generating for {len(prompts)} prompts, training_step={training_step}") - outputs = self.llm.generate(sampling_params=sampling_params, prompt_token_ids=prompts, use_tqdm=True) - logger.info("[vLLM _generate_batch] Generation complete") + outputs = self.llm.generate(sampling_params=sampling_params, prompt_token_ids=prompts, use_tqdm=False) # Process outputs response_ids = [list(out.token_ids) for output in outputs for out in output.outputs] From e06fb2cec0a4e8567cf68d6a009785caf3f43427 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 13:07:07 -0600 Subject: [PATCH 28/64] Ran linter. --- open_instruct/grpo_fast.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 9fd49fdea..cce280e1d 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1834,15 +1834,10 @@ def sync_weights_and_prepare_prompts( ): # Add progress bar for weight syncing weight_sync_tasks = [m.broadcast_to_vllm.remote() for m in policy_group.models] - with tqdm( - total=len(weight_sync_tasks), - desc="Syncing weights to vLLM engines", - bar_format="{l_bar}{bar}{r_bar}\n", - ) as pbar: - for i, task in enumerate(weight_sync_tasks): - ray.get(task) - pbar.update(1) - pbar.set_postfix_str(f"Synced {i + 1}/{len(weight_sync_tasks)} models") + for task in tqdm( + weight_sync_tasks, desc="Syncing weights to vLLM engines", bar_format="{l_bar}{bar}{r_bar}\n" + ): + ray.get(task) if args.async_mode or training_step != 1: split_and_insert_batch( From b581d7935a3dce68795e336422606cf289e9bcd3 Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 15:38:34 -0600 Subject: [PATCH 29/64] Created stripped down version of the tests. --- open_instruct/test_grpo_fast.py | 1259 +++++++------------------------ 1 file changed, 285 insertions(+), 974 deletions(-) diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index 8e1782225..5af96690f 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -12,7 +12,9 @@ from open_instruct.vllm_utils3 import GenerationResult, PromptRequest, RequestInfo, create_vllm_engines -class TestGrpoFastVLLM(unittest.TestCase): +class TestGrpoFastBase(unittest.TestCase): + """Base class with common test utilities.""" + @classmethod def setUpClass(cls): # Initialize Ray @@ -24,6 +26,59 @@ def tearDownClass(cls): # Shutdown Ray after test if ray.is_initialized(): ray.shutdown() + + def create_test_data(self, num_prompts, prefix="", start_idx=0): + """Create test data with consistent naming.""" + indices = list(range(start_idx, start_idx + num_prompts)) + queries = [f"{prefix}query_{i}" for i in indices] + ground_truths = [f"{prefix}truth_{i}" for i in indices] + datasets = [f"{prefix}dataset_{i}" for i in indices] + return queries, ground_truths, datasets, indices + + def create_mock_args(self, num_engines=4, num_samples=1): + """Create mock args object.""" + mock_args = Mock() + mock_args.vllm_num_engines = num_engines + mock_args.num_samples_per_prompt_rollout = num_samples + return mock_args + + def create_mock_result(self, dataset_indices, training_step, num_samples_per_prompt=1): + """Create a mock GenerationResult.""" + batch_size = len(dataset_indices) + total_responses = batch_size * num_samples_per_prompt + + return GenerationResult( + responses=[[1, 2, 3] for _ in range(total_responses)], + finish_reasons=["stop"] * total_responses, + masks=[[1, 1, 1] for _ in range(total_responses)], + request_info=RequestInfo( + num_calls=[0] * total_responses, + timeouts=[0] * total_responses, + tool_errors=[""] * total_responses, + tool_outputs=[""] * total_responses, + tool_runtimes=[0.0] * total_responses, + tool_calleds=[False] * total_responses, + ), + dataset_index=dataset_indices, + training_step=training_step, + ) + + def setup_and_split_batch(self, queries, ground_truths, datasets, indices, + num_engines, training_step=1): + """Setup queues and split batch - common pattern.""" + param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) + inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) + pending_queries_map = grpo_fast.PendingQueriesMap() + + grpo_fast.split_and_insert_batch( + queries, ground_truths, datasets, indices, + training_step, num_engines, pending_queries_map, param_prompt_Q + ) + + return param_prompt_Q, inference_results_Q, pending_queries_map + + +class TestGrpoFastVLLM(TestGrpoFastBase): def test_vllm_queue_system_single_prompt(self): """Test the new queue-based vLLM system with a single prompt 'What is the capital of France?'""" @@ -100,34 +155,15 @@ def test_vllm_queue_system_single_prompt(self): # Send stop signal param_prompt_Q.put(None) - @parameterized.expand([(1,), (2,), (4,), (8,)]) - def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_rollout: int = 16): - """Test the batch splitting and accumulation logic using split_and_insert_batch and accumulate_inference_batches.""" - - # Mock data - simulating num_unique_prompts_rollout * num_samples_per_prompt_rollout - queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] - ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] - datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] - - pending_queries_map = grpo_fast.PendingQueriesMap() - training_step = 1 - - # Create mock Ray queue for testing - param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) - - # Create mock dataset indices - dataset_indices = list(range(num_unique_prompts_rollout)) - - # Use split_and_insert_batch to split and insert data - grpo_fast.split_and_insert_batch( - queries_next, - ground_truths_next, - datasets_next, - dataset_indices, - training_step, - vllm_num_engines, - pending_queries_map, - param_prompt_Q, + @parameterized.expand([(1, 16), (2, 32), (4, 64), (8, 128)]) + def test_batch_splitting_and_engine_configurations(self, vllm_num_engines: int, num_unique_prompts_rollout: int): + """Test batch splitting and accumulation with various engine configurations.""" + # Create test data + queries_next, ground_truths_next, datasets_next, dataset_indices = self.create_test_data(num_unique_prompts_rollout) + + # Setup and split batch + param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( + queries_next, ground_truths_next, datasets_next, dataset_indices, vllm_num_engines ) # Verify that we have individual prompts in the map (not batches) @@ -136,48 +172,20 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r # Verify that we have the expected number of items in the queue self.assertEqual(param_prompt_Q.qsize(), vllm_num_engines) - # Create mock inference results to simulate vLLM engine outputs - mock_inference_results = [] - for batch_idx in range(vllm_num_engines): - # Get the request from the queue + # Simulate vLLM processing + while not param_prompt_Q.empty(): request = param_prompt_Q.get() self.assertIsInstance(request, PromptRequest) - self.assertEqual(request.training_step, training_step) - self.assertIsInstance(request.dataset_index, list) # Now expects a list of indices - - # Create mock GenerationResult - batch_size = len(request.prompts) - mock_result = GenerationResult( - responses=[[i] for i in range(batch_size)], # Mock token IDs - finish_reasons=["stop"] * batch_size, - masks=[[1] * 5] * batch_size, # Mock masks - request_info=RequestInfo( - num_calls=[0] * batch_size, - timeouts=[0] * batch_size, - tool_errors=[""] * batch_size, - tool_outputs=[""] * batch_size, - tool_runtimes=[0] * batch_size, - tool_calleds=[False] * batch_size, - ), - is_eval=False, - dataset_index=request.dataset_index, - training_step=request.training_step, - ) - mock_inference_results.append(mock_result) - - # Create mock inference results queue - inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) - for result in mock_inference_results: - inference_results_Q.put(result) - - # Create mock args - mock_args = Mock() - mock_args.vllm_num_engines = vllm_num_engines - mock_args.num_samples_per_prompt_rollout = 1 # Default for this test + self.assertEqual(request.training_step, 1) + self.assertIsInstance(request.dataset_index, list) + + mock_result = self.create_mock_result(request.dataset_index, request.training_step) + inference_results_Q.put(mock_result) - # Use accumulate_inference_batches to combine results + # Accumulate results + mock_args = self.create_mock_args(vllm_num_engines) combined_result, combined_queries, combined_ground_truths, combined_datasets = ( - grpo_fast.accumulate_inference_batches(inference_results_Q, pending_queries_map, mock_args, training_step) + grpo_fast.accumulate_inference_batches(inference_results_Q, pending_queries_map, mock_args, 1) ) # Verify that the combined results match the original input @@ -197,76 +205,28 @@ def test_batch_splitting_logic(self, vllm_num_engines: int, num_unique_prompts_r # Verify that the inference_results_Q is empty after accumulation self.assertEqual(inference_results_Q.qsize(), 0) - def mock_vllm_pipeline(self, prompt_queue, results_queue, num_engines, num_steps=1, num_samples_per_prompt=1): - """Mock function that simulates vLLM engines pulling from prompt queue and pushing to results queue""" - for step in range(num_steps): - for engine_id in range(num_engines): - # Pull request from prompt queue - request = prompt_queue.get() - - # Create mock generation result preserving dataset_index - batch_size = len(request.prompts) - # Generate num_samples_per_prompt responses for each prompt - total_responses = batch_size * num_samples_per_prompt - - # In real vLLM, dataset_index is NOT replicated when num_samples_per_prompt > 1 - # It stays as the original list of unique indices - mock_result = GenerationResult( - responses=[[1, 2, 3] for _ in range(total_responses)], - finish_reasons=["stop"] * total_responses, - masks=[[1, 1, 1] for _ in range(total_responses)], - request_info=RequestInfo( - num_calls=[0] * total_responses, - timeouts=[0] * total_responses, - tool_errors=[""] * total_responses, - tool_outputs=[""] * total_responses, - tool_runtimes=[0.0] * total_responses, - tool_calleds=[False] * total_responses, - ), - dataset_index=request.dataset_index, # Keep original indices (not replicated) - training_step=request.training_step, - ) - - # Push to results queue - results_queue.put(mock_result) def test_dataset_index_preservation_through_pipeline(self): - """Test that dataset indices are correctly preserved through the mock vLLM pipeline""" + """Test that dataset indices are correctly preserved through the pipeline.""" vllm_num_engines = 4 num_unique_prompts_rollout = 32 - # Mock data - queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] - ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] - datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] - dataset_indices = list(range(num_unique_prompts_rollout)) - - # Create queues and map - param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) - inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) - pending_queries_map = grpo_fast.PendingQueriesMap() - - # Split and insert batch - grpo_fast.split_and_insert_batch( - queries_next, - ground_truths_next, - datasets_next, - dataset_indices, - training_step=1, - vllm_num_engines=vllm_num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, + # Create test data + queries_next, ground_truths_next, datasets_next, dataset_indices = self.create_test_data(num_unique_prompts_rollout) + + # Setup and split batch + param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( + queries_next, ground_truths_next, datasets_next, dataset_indices, vllm_num_engines ) - # Run mock vLLM pipeline - self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines) - - # Create mock args - mock_args = Mock() - mock_args.vllm_num_engines = vllm_num_engines - mock_args.num_samples_per_prompt_rollout = 1 + # Simulate vLLM processing + while not param_prompt_Q.empty(): + request = param_prompt_Q.get() + mock_result = self.create_mock_result(request.dataset_index, request.training_step) + inference_results_Q.put(mock_result) # Accumulate results + mock_args = self.create_mock_args(vllm_num_engines) combined_result, combined_queries, combined_ground_truths, combined_datasets = ( grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=1 @@ -277,153 +237,33 @@ def test_dataset_index_preservation_through_pipeline(self): self.assertEqual(combined_queries, queries_next) self.assertEqual(combined_ground_truths, ground_truths_next) self.assertEqual(combined_datasets, datasets_next) - self.assertEqual(len(pending_queries_map), 0) # Map should be empty after accumulation - - def test_multiple_training_steps(self): - """Test that indices don't get mixed up between multiple training steps""" - vllm_num_engines = 2 - num_unique_prompts_rollout = 16 - num_steps = 3 - - pending_queries_map = grpo_fast.PendingQueriesMap() - - for step in range(1, num_steps + 1): - # Create unique data for each step - queries_next = [f"query_step{step}_{i}" for i in range(num_unique_prompts_rollout)] - ground_truths_next = [f"truth_step{step}_{i}" for i in range(num_unique_prompts_rollout)] - datasets_next = [f"dataset_step{step}_{i}" for i in range(num_unique_prompts_rollout)] - # Use different indices for each step to ensure no mixing - dataset_indices = list(range((step - 1) * num_unique_prompts_rollout, step * num_unique_prompts_rollout)) - - # Create queues - param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) - inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) - - # Split and insert - grpo_fast.split_and_insert_batch( - queries_next, - ground_truths_next, - datasets_next, - dataset_indices, - training_step=step, - vllm_num_engines=vllm_num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, - ) - - # Run pipeline - self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines) - - # Create mock args - mock_args = Mock() - mock_args.vllm_num_engines = vllm_num_engines - mock_args.num_samples_per_prompt_rollout = 1 - - # Accumulate - combined_result, combined_queries, combined_ground_truths, combined_datasets = ( - grpo_fast.accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=step - ) - ) - - # Verify - self.assertEqual(combined_queries, queries_next) - self.assertEqual(combined_ground_truths, ground_truths_next) - self.assertEqual(combined_datasets, datasets_next) - self.assertEqual(len(pending_queries_map), 0) # Map should be empty after each step - - @parameterized.expand([(1,), (2,), (4,), (8,)]) - def test_various_engine_configurations(self, vllm_num_engines: int): - """Test with various numbers of vLLM engines""" - num_unique_prompts_rollout = 64 # Use a larger batch to test splitting - - # Mock data - queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] - ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] - datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] - dataset_indices = list(range(num_unique_prompts_rollout)) - - # Create queues and map - param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) - inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) - pending_queries_map = grpo_fast.PendingQueriesMap() - - # Split and insert - grpo_fast.split_and_insert_batch( - queries_next, - ground_truths_next, - datasets_next, - dataset_indices, - training_step=1, - vllm_num_engines=vllm_num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, - ) - - # Verify correct number of batches created - self.assertEqual(param_prompt_Q.qsize(), vllm_num_engines) - - # Run pipeline - self.mock_vllm_pipeline(param_prompt_Q, inference_results_Q, vllm_num_engines) - - # Create mock args - mock_args = Mock() - mock_args.vllm_num_engines = vllm_num_engines - mock_args.num_samples_per_prompt_rollout = 1 - - # Accumulate - combined_result, combined_queries, combined_ground_truths, combined_datasets = ( - grpo_fast.accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 - ) - ) - - # Verify - self.assertEqual(combined_queries, queries_next) - self.assertEqual(combined_ground_truths, ground_truths_next) - self.assertEqual(combined_datasets, datasets_next) self.assertEqual(len(pending_queries_map), 0) - def test_multiple_samples_per_prompt_fixed(self): - """Test that the fix properly handles multiple samples per prompt""" - vllm_num_engines = 1 # Use 1 engine to make it clearer - num_unique_prompts_rollout = 16 - num_samples_per_prompt = 16 # This will cause 16 prompts to generate 256 responses - - # Mock data - queries_next = [f"query_{i}" for i in range(num_unique_prompts_rollout)] - ground_truths_next = [f"truth_{i}" for i in range(num_unique_prompts_rollout)] - datasets_next = [f"dataset_{i}" for i in range(num_unique_prompts_rollout)] - dataset_indices = list(range(num_unique_prompts_rollout)) - # Create queues and map - param_prompt_Q = ray_queue.Queue(maxsize=vllm_num_engines) - inference_results_Q = ray_queue.Queue(maxsize=vllm_num_engines) - pending_queries_map = grpo_fast.PendingQueriesMap() - # Split and insert batch - grpo_fast.split_and_insert_batch( - queries_next, - ground_truths_next, - datasets_next, - dataset_indices, - training_step=1, - vllm_num_engines=vllm_num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, - ) + @parameterized.expand([(1, 16), (2, 8), (4, 4)]) + def test_multiple_samples_per_prompt(self, vllm_num_engines: int, num_samples_per_prompt: int): + """Test handling of multiple samples per prompt.""" + num_unique_prompts_rollout = 16 - # Run mock vLLM pipeline with multiple samples per prompt - self.mock_vllm_pipeline( - param_prompt_Q, inference_results_Q, vllm_num_engines, num_samples_per_prompt=num_samples_per_prompt + # Create test data + queries_next, ground_truths_next, datasets_next, dataset_indices = self.create_test_data(num_unique_prompts_rollout) + + # Setup and split batch + param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( + queries_next, ground_truths_next, datasets_next, dataset_indices, vllm_num_engines ) - # Create mock args - mock_args = Mock() - mock_args.vllm_num_engines = vllm_num_engines - mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt + # Simulate vLLM processing with multiple samples + while not param_prompt_Q.empty(): + request = param_prompt_Q.get() + mock_result = self.create_mock_result( + request.dataset_index, request.training_step, num_samples_per_prompt + ) + inference_results_Q.put(mock_result) - # Now with the fix, this should succeed + # Accumulate results + mock_args = self.create_mock_args(vllm_num_engines, num_samples_per_prompt) combined_result, combined_queries, combined_ground_truths, combined_datasets = ( grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=1 @@ -431,207 +271,63 @@ def test_multiple_samples_per_prompt_fixed(self): ) # Verify results - accumulate_inference_batches should NOT replicate - # The replication happens later in data_preparation_thread self.assertEqual(combined_queries, queries_next) self.assertEqual(combined_ground_truths, ground_truths_next) self.assertEqual(combined_datasets, datasets_next) self.assertEqual(len(pending_queries_map), 0) - # Verify that we have the correct number of responses (256 = 16 prompts * 16 samples per prompt) - self.assertEqual(len(combined_result.responses), 256) - - def test_multiple_samples_with_repeated_indices(self): - """Test that the fix properly handles repeated dataset indices from vLLM""" - vllm_num_engines = 1 - num_unique_prompts = 4 - num_samples_per_prompt = 4 - - # Create mock args - mock_args = Mock() - mock_args.vllm_num_engines = vllm_num_engines - mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt - - # Create test data - training_step = 1 - pending_queries_map = grpo_fast.PendingQueriesMap() - for i in range(num_unique_prompts): - pending_queries_map.insert(i, f"query_{i}", f"truth_{i}", f"dataset_{i}") - - # Create a mock result with dataset indices NOT repeated - # vLLM generates multiple responses per prompt but keeps dataset_index as [0, 1, 2, 3] - dataset_indices = list(range(num_unique_prompts)) - total_responses = num_unique_prompts * num_samples_per_prompt - - mock_result = GenerationResult( - responses=[[1, 2, 3] for _ in range(total_responses)], - finish_reasons=["stop"] * total_responses, - masks=[[1, 1, 1] for _ in range(total_responses)], - request_info=RequestInfo( - num_calls=[0] * total_responses, - timeouts=[0] * total_responses, - tool_errors=[""] * total_responses, - tool_outputs=[""] * total_responses, - tool_runtimes=[0.0] * total_responses, - tool_calleds=[False] * total_responses, - ), - dataset_index=dataset_indices, - training_step=training_step, - ) - - # Create queue and add result - inference_results_Q = ray_queue.Queue(maxsize=1) - inference_results_Q.put(mock_result) - - # Call accumulate_inference_batches - combined_result, combined_queries, combined_ground_truths, combined_datasets = ( - grpo_fast.accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 - ) - ) - - # Verify results - # accumulate_inference_batches should NOT replicate - returns unique entries - expected_queries = [f"query_{i}" for i in range(num_unique_prompts)] - expected_ground_truths = [f"truth_{i}" for i in range(num_unique_prompts)] - expected_datasets = [f"dataset_{i}" for i in range(num_unique_prompts)] + # Verify correct number of responses + expected_responses = num_unique_prompts_rollout * num_samples_per_prompt + self.assertEqual(len(combined_result.responses), expected_responses) - self.assertEqual(combined_queries, expected_queries) - self.assertEqual(combined_ground_truths, expected_ground_truths) - self.assertEqual(combined_datasets, expected_datasets) - self.assertEqual(len(pending_queries_map), 0) # All entries should be popped -class GrpoIntegrationTests(unittest.TestCase): +class GrpoIntegrationTests(TestGrpoFastBase): """Integration tests for GRPO with parallel processing.""" - @classmethod - def setUpClass(cls): - # Initialize Ray - if not ray.is_initialized(): - ray.init(ignore_reinit_error=True) - - @classmethod - def tearDownClass(cls): - # Shutdown Ray after test - if ray.is_initialized(): - ray.shutdown() - - def test_no_training_step_synchronization_issues(self): - """Test that training steps don't get mixed up results from different steps.""" - - num_engines = 4 - - # Shared state + def test_training_step_isolation(self): + """Test that different training steps are handled correctly.""" + num_engines = 2 + num_prompts = 16 + pending_queries_map = grpo_fast.PendingQueriesMap() inference_results_Q = ray_queue.Queue(maxsize=num_engines * 4) - - # Create requests for step 1 - step1_requests = [] - for engine_id in range(num_engines): - start_idx = engine_id * 4 - end_idx = start_idx + 4 - request = PromptRequest( - prompts=[[1, 2, 3] for _ in range(4)], training_step=1, dataset_index=list(range(start_idx, end_idx)) - ) - step1_requests.append(request) - # Add to pending_queries_map - for idx in request.dataset_index: - pending_queries_map.insert(idx, f"q_{idx}", f"t_{idx}", f"d_{idx}") - - # Create requests for step 2 - step2_requests = [] - for engine_id in range(num_engines): - start_idx = 1000 + engine_id * 4 # Different indices - end_idx = start_idx + 4 - request = PromptRequest( - prompts=[[4, 5, 6] for _ in range(4)], training_step=2, dataset_index=list(range(start_idx, end_idx)) - ) - step2_requests.append(request) - # Add to pending_queries_map - for idx in request.dataset_index: - pending_queries_map.insert(idx, f"q_{idx}", f"t_{idx}", f"d_{idx}") - - # Simulate the scenario where step 1 results are delayed - # and step 2 tries to accumulate but gets step 1's results - - # First, put step 1's results in the queue - for request in step1_requests: - batch_size = len(request.prompts) - mock_result = GenerationResult( - responses=[[1, 2, 3] for _ in range(batch_size)], - finish_reasons=["stop"] * batch_size, - masks=[[1, 1, 1] for _ in range(batch_size)], - request_info=RequestInfo( - num_calls=[0] * batch_size, - timeouts=[0] * batch_size, - tool_errors=[""] * batch_size, - tool_outputs=[""] * batch_size, - tool_runtimes=[0.0] * batch_size, - tool_calleds=[False] * batch_size, - ), - dataset_index=request.dataset_index, - training_step=request.training_step, + + # Create data for two different training steps + step1_data = self.create_test_data(num_prompts, "step1_", 0) + step2_data = self.create_test_data(num_prompts, "step2_", 1000) + + # Process both steps + for step, (queries, ground_truths, datasets, indices) in enumerate( + [step1_data, step2_data], 1 + ): + param_prompt_Q = ray_queue.Queue(maxsize=num_engines) + + grpo_fast.split_and_insert_batch( + queries, ground_truths, datasets, indices, + step, num_engines, pending_queries_map, param_prompt_Q ) - inference_results_Q.put(mock_result) - - # Now step 2 tries to accumulate, expecting step 2 results - # but will get step 1 results instead - mock_args = Mock() - mock_args.vllm_num_engines = num_engines - mock_args.num_samples_per_prompt_rollout = 1 - - # With our fix, we can now process results from any training step - # as long as entries exist in the map. This successfully processes - # step 1's results even though we're calling from step 2. - result, queries, ground_truths, datasets = grpo_fast.accumulate_inference_batches( + + # Create results for this step + while not param_prompt_Q.empty(): + request = param_prompt_Q.get() + mock_result = self.create_mock_result(request.dataset_index, step) + inference_results_Q.put(mock_result) + + # Process results from both steps + mock_args = self.create_mock_args(num_engines) + + # First accumulation gets step 1 results + result1, queries1, _, _ = grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=2 ) - - # Verify we got step 1's data (indices 0-15) - self.assertEqual(len(queries), 16) - self.assertEqual(len(ground_truths), 16) - self.assertEqual(len(datasets), 16) - - # Check that we got the correct data from step 1 - for i in range(16): - self.assertEqual(queries[i], f"q_{i}") - self.assertEqual(ground_truths[i], f"t_{i}") - self.assertEqual(datasets[i], f"d_{i}") - - # Now if we try to get more results, we should get step 2's data - # because step 1's entries have been consumed - # First add step 2's results to the queue - for request in step2_requests: - batch_size = len(request.prompts) - mock_result = GenerationResult( - responses=[[4, 5, 6] for _ in range(batch_size)], - finish_reasons=["stop"] * batch_size, - masks=[[1, 1, 1] for _ in range(batch_size)], - request_info=RequestInfo( - num_calls=[0] * batch_size, - timeouts=[0] * batch_size, - tool_errors=[""] * batch_size, - tool_outputs=[""] * batch_size, - tool_runtimes=[0.0] * batch_size, - tool_calleds=[False] * batch_size, - ), - dataset_index=request.dataset_index, - training_step=request.training_step, - ) - inference_results_Q.put(mock_result) - - # Process step 2's results - result2, queries2, ground_truths2, datasets2 = grpo_fast.accumulate_inference_batches( + self.assertTrue(all("step1_" in q for q in queries1)) + + # Second accumulation gets step 2 results + result2, queries2, _, _ = grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=2 ) - - # Verify we got step 2's data (indices 1000-1015) - self.assertEqual(len(queries2), 16) - for i in range(16): - idx = 1000 + i - self.assertEqual(queries2[i], f"q_{idx}") - self.assertEqual(ground_truths2[i], f"t_{idx}") - self.assertEqual(datasets2[i], f"d_{idx}") + self.assertTrue(all("step2_" in q for q in queries2)) @ray.remote def mock_vllm_engine(engine_id, prompt_queue, results_queue, num_samples_per_prompt=1): @@ -671,81 +367,40 @@ def mock_vllm_engine(engine_id, prompt_queue, results_queue, num_samples_per_pro # Push to results queue results_queue.put(mock_result) - def test_dataset_index_25847_bug(self): - """Test to reproduce the specific bug with dataset index 25847.""" - # This simulates a scenario where we have a large dataset and might get - # indices that don't start from 0 + def test_high_dataset_indices(self): + """Test handling of high dataset indices (bug #25847).""" num_engines = 4 num_prompts = 64 num_samples_per_prompt = 16 - start_index = 25800 # Start with a high index like in the error - + start_index = 25800 + # Create test data with high indices - queries_next = [f"query_{i}" for i in range(start_index, start_index + num_prompts)] - ground_truths_next = [f"truth_{i}" for i in range(start_index, start_index + num_prompts)] - datasets_next = [f"dataset_{i}" for i in range(start_index, start_index + num_prompts)] - dataset_indices = list(range(start_index, start_index + num_prompts)) - - # Create queues - param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = grpo_fast.PendingQueriesMap() - - # Split and insert batch - grpo_fast.split_and_insert_batch( - queries_next, - ground_truths_next, - datasets_next, - dataset_indices, - training_step=1, - vllm_num_engines=num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, + queries, ground_truths, datasets, indices = self.create_test_data( + num_prompts, start_idx=start_index ) - - # Verify the pending queries map contains the right indices + + # Setup and split batch + param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( + queries, ground_truths, datasets, indices, num_engines + ) + + # Verify the specific index from the bug is present self.assertIn(25847, pending_queries_map) - - # Get all requests and create results + + # Process all requests while not param_prompt_Q.empty(): request = param_prompt_Q.get() - batch_size = len(request.prompts) - total_responses = batch_size * num_samples_per_prompt - - mock_result = GenerationResult( - responses=[[1, 2, 3] for _ in range(total_responses)], - finish_reasons=["stop"] * total_responses, - masks=[[1, 1, 1] for _ in range(total_responses)], - request_info=RequestInfo( - num_calls=[0] * total_responses, - timeouts=[0] * total_responses, - tool_errors=[""] * total_responses, - tool_outputs=[""] * total_responses, - tool_runtimes=[0.0] * total_responses, - tool_calleds=[False] * total_responses, - ), - dataset_index=request.dataset_index, - training_step=request.training_step, - ) + mock_result = self.create_mock_result(request.dataset_index, 1, num_samples_per_prompt) inference_results_Q.put(mock_result) - - # Create mock args - mock_args = Mock() - mock_args.vllm_num_engines = num_engines - mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt - + # Accumulate results - combined_result, combined_queries, combined_ground_truths, combined_datasets = ( - grpo_fast.accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 - ) + mock_args = self.create_mock_args(num_engines, num_samples_per_prompt) + combined_result, _, _, _ = grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 ) - - # Verify results - accumulate_inference_batches returns unique entries - self.assertEqual(len(combined_queries), num_prompts) # Not replicated - self.assertEqual( - len(combined_result.responses), num_prompts * num_samples_per_prompt - ) # Responses are replicated + + # Verify results + self.assertEqual(len(combined_result.responses), num_prompts * num_samples_per_prompt) self.assertEqual(len(pending_queries_map), 0) def test_out_of_order_processing(self): @@ -755,183 +410,53 @@ def test_out_of_order_processing(self): num_samples_per_prompt = 4 # Create test data - queries_next = [f"query_{i}" for i in range(num_prompts)] - ground_truths_next = [f"truth_{i}" for i in range(num_prompts)] - datasets_next = [f"dataset_{i}" for i in range(num_prompts)] - dataset_indices = list(range(num_prompts)) - - # Create queues - param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = grpo_fast.PendingQueriesMap() - - # Split and insert batch - grpo_fast.split_and_insert_batch( - queries_next, - ground_truths_next, - datasets_next, - dataset_indices, - training_step=1, - vllm_num_engines=num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, + queries, ground_truths, datasets, indices = self.create_test_data(num_prompts) + + # Setup and split batch + param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( + queries, ground_truths, datasets, indices, num_engines ) - # Get all requests from the queue + # Get all requests and process in reverse order requests = [] while not param_prompt_Q.empty(): requests.append(param_prompt_Q.get()) # Put results back in REVERSE order to simulate out-of-order processing for request in reversed(requests): - batch_size = len(request.prompts) - total_responses = batch_size * num_samples_per_prompt - - mock_result = GenerationResult( - responses=[[1, 2, 3] for _ in range(total_responses)], - finish_reasons=["stop"] * total_responses, - masks=[[1, 1, 1] for _ in range(total_responses)], - request_info=RequestInfo( - num_calls=[0] * total_responses, - timeouts=[0] * total_responses, - tool_errors=[""] * total_responses, - tool_outputs=[""] * total_responses, - tool_runtimes=[0.0] * total_responses, - tool_calleds=[False] * total_responses, - ), - dataset_index=request.dataset_index, - training_step=request.training_step, + mock_result = self.create_mock_result( + request.dataset_index, request.training_step, num_samples_per_prompt ) inference_results_Q.put(mock_result) - # Create mock args - mock_args = Mock() - mock_args.vllm_num_engines = num_engines - mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt - - # Accumulate results - combined_result, combined_queries, combined_ground_truths, combined_datasets = ( - grpo_fast.accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 - ) - ) - - # Verify results - accumulate_inference_batches returns unique entries - self.assertEqual(len(combined_queries), num_prompts) # Not replicated - self.assertEqual( - len(combined_result.responses), num_prompts * num_samples_per_prompt - ) # Responses are replicated - self.assertEqual(len(pending_queries_map), 0) - - @parameterized.expand( - [ - (1, 16, 16), # 1 engine, 16 prompts, 16 samples - (2, 32, 16), # 2 engines, 32 prompts, 16 samples - (4, 64, 16), # 4 engines, 64 prompts, 16 samples - (8, 128, 16), # 8 engines, 128 prompts, 16 samples - (4, 100, 16), # 4 engines, 100 prompts (not evenly divisible), 16 samples - ] - ) - def test_parallel_processing_with_multiple_engines(self, num_engines, num_prompts, num_samples_per_prompt): - """Test parallel processing with multiple engines running concurrently.""" - import time - - # Create test data - queries_next = [f"query_{i}" for i in range(num_prompts)] - ground_truths_next = [f"truth_{i}" for i in range(num_prompts)] - datasets_next = [f"dataset_{i}" for i in range(num_prompts)] - dataset_indices = list(range(num_prompts)) - - # Create queues - param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = grpo_fast.PendingQueriesMap() - - # Split and insert batch - grpo_fast.split_and_insert_batch( - queries_next, - ground_truths_next, - datasets_next, - dataset_indices, - training_step=1, - vllm_num_engines=num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, - ) - - # Verify initial state - self.assertEqual(len(pending_queries_map), num_prompts) - self.assertEqual(param_prompt_Q.qsize(), num_engines) - - # Start mock vLLM engines - engines = [] - for i in range(num_engines): - engine = GrpoIntegrationTests.mock_vllm_engine.remote( - i, param_prompt_Q, inference_results_Q, num_samples_per_prompt - ) - engines.append(engine) - - # Wait a bit for engines to process - time.sleep(0.5) - - # Send stop signals - for _ in range(num_engines): - param_prompt_Q.put(None) - - # Create mock args - mock_args = Mock() - mock_args.vllm_num_engines = num_engines - mock_args.num_samples_per_prompt_rollout = num_samples_per_prompt - # Accumulate results - combined_result, combined_queries, combined_ground_truths, combined_datasets = ( - grpo_fast.accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=1 - ) + mock_args = self.create_mock_args(num_engines, num_samples_per_prompt) + combined_result, combined_queries, _, _ = grpo_fast.accumulate_inference_batches( + inference_results_Q, pending_queries_map, mock_args, training_step=1 ) - # Verify results - accumulate_inference_batches returns unique entries + # Verify results work correctly even with out-of-order processing self.assertEqual(len(combined_queries), num_prompts) self.assertEqual(len(combined_result.responses), num_prompts * num_samples_per_prompt) - self.assertEqual(len(combined_ground_truths), num_prompts) - self.assertEqual(len(combined_datasets), num_prompts) - self.assertEqual(len(pending_queries_map), 0) # All should be processed + self.assertEqual(len(pending_queries_map), 0) - # Clean up - ray.get(engines) def test_no_duplicate_indices_within_training_step(self): """Test that dataset indices are unique within a single training step.""" num_engines = 4 num_prompts = 64 - # Create test data with sequential indices - queries = [f"query_{i}" for i in range(num_prompts)] - ground_truths = [f"truth_{i}" for i in range(num_prompts)] - datasets = [f"dataset_{i}" for i in range(num_prompts)] - dataset_indices = list(range(num_prompts)) - - # Create queues and map - param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = grpo_fast.PendingQueriesMap() - - # Split and insert batch - grpo_fast.split_and_insert_batch( - queries, - ground_truths, - datasets, - dataset_indices, - training_step=1, - vllm_num_engines=num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, + # Create test data + queries, ground_truths, datasets, indices = self.create_test_data(num_prompts) + + # Setup and split batch + param_prompt_Q, _, pending_queries_map = self.setup_and_split_batch( + queries, ground_truths, datasets, indices, num_engines ) - # Check for duplicates in pending_queries_map - self.assertEqual(len(pending_queries_map), num_prompts, "Some indices were lost or duplicated") - # Keys are now just dataset indices - expected_keys = set(dataset_indices) - self.assertEqual(set(pending_queries_map.keys()), expected_keys, "Indices don't match") + # Check for duplicates + self.assertEqual(len(pending_queries_map), num_prompts) + self.assertEqual(set(pending_queries_map.keys()), set(indices)) # Check each batch has unique indices all_indices_from_batches = [] @@ -939,82 +464,55 @@ def test_no_duplicate_indices_within_training_step(self): request = param_prompt_Q.get() batch_indices = request.dataset_index # Check no duplicates within this batch - self.assertEqual( - len(batch_indices), len(set(batch_indices)), f"Duplicate indices found within batch: {batch_indices}" - ) + self.assertEqual(len(batch_indices), len(set(batch_indices))) all_indices_from_batches.extend(batch_indices) # Check total indices match - self.assertEqual(sorted(all_indices_from_batches), sorted(dataset_indices)) + self.assertEqual(sorted(all_indices_from_batches), sorted(indices)) - def test_dataset_indices_no_overwriting_between_steps(self): - """Test that dataset indices from different training steps are handled correctly with count-based tracking.""" + def test_overlapping_indices_between_steps(self): + """Test handling of overlapping dataset indices between training steps.""" num_engines = 2 num_prompts = 16 - # Simulate a dataloader that might return overlapping indices + # Simulate overlapping indices step1_indices = list(range(0, num_prompts)) # [0, 1, ..., 15] - step2_indices = list(range(10, num_prompts + 10)) # [10, 11, ..., 25] - overlaps with step1 + step2_indices = list(range(10, num_prompts + 10)) # [10, 11, ..., 25] pending_queries_map = grpo_fast.PendingQueriesMap() param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - # Step 1 - queries = [f"query_step1_{i}" for i in range(num_prompts)] - ground_truths = [f"truth_step1_{i}" for i in range(num_prompts)] - datasets = [f"dataset_step1_{i}" for i in range(num_prompts)] - + # Process step 1 + step1_data = self.create_test_data(num_prompts, "step1_") + queries1, ground_truths1, datasets1, _ = step1_data + grpo_fast.split_and_insert_batch( - queries, - ground_truths, - datasets, - step1_indices, - training_step=1, - vllm_num_engines=num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, + queries1, ground_truths1, datasets1, step1_indices, + 1, num_engines, pending_queries_map, param_prompt_Q ) - # Step 2 - with overlapping indices - queries2 = [f"query_step2_{i}" for i in range(num_prompts)] - ground_truths2 = [f"truth_step2_{i}" for i in range(num_prompts)] - datasets2 = [f"dataset_step2_{i}" for i in range(num_prompts)] - - # Clear the queue for step 2 + # Clear queue and process step 2 while not param_prompt_Q.empty(): param_prompt_Q.get() + step2_data = self.create_test_data(num_prompts, "step2_") + queries2, ground_truths2, datasets2, _ = step2_data + grpo_fast.split_and_insert_batch( - queries2, - ground_truths2, - datasets2, - step2_indices, - training_step=2, - vllm_num_engines=num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, + queries2, ground_truths2, datasets2, step2_indices, + 2, num_engines, pending_queries_map, param_prompt_Q ) - # With count-based tracking, overlapping indices should have incremented counts - # Indices 10-15 appear in both steps + # Verify overlapping indices handling overlapping_indices = set(step1_indices) & set(step2_indices) - + for idx in overlapping_indices: - # The count should be 2 for these indices (one from each step) query, ground_truth, dataset, count = pending_queries_map[idx] - self.assertEqual(count, 2, f"Index {idx} should have count 2 but has count {count}") - - # The data should be from step 1 (first insertion wins) - self.assertEqual(query, f"query_step1_{idx}", f"Query data was overwritten for index {idx}") - self.assertEqual(ground_truth, f"truth_step1_{idx}", f"Ground truth was overwritten for index {idx}") - self.assertEqual(dataset, f"dataset_step1_{idx}", f"Dataset was overwritten for index {idx}") - - # Non-overlapping indices should have count 1 - non_overlapping = (set(step1_indices) | set(step2_indices)) - overlapping_indices - for idx in non_overlapping: - if idx in pending_queries_map: - _, _, _, count = pending_queries_map[idx] - self.assertEqual(count, 1, f"Non-overlapping index {idx} should have count 1 but has count {count}") + self.assertEqual(count, 2) # Should have count 2 + # First insertion wins + self.assertTrue(query.startswith("step1_")) + self.assertTrue(ground_truth.startswith("step1_")) + self.assertTrue(dataset.startswith("step1_")) def test_thread_safety_pending_queries_map(self): """Test concurrent access to pending_queries_map.""" @@ -1023,299 +521,112 @@ def test_thread_safety_pending_queries_map(self): pending_queries_map = grpo_fast.PendingQueriesMap() errors = [] + num_threads = 4 + entries_per_thread = 50 - def add_entries(start_idx, count, thread_id): - """Add entries to the map.""" + def add_and_remove_entries(thread_id): + """Add and then remove entries from the map.""" try: - for i in range(start_idx, start_idx + count): + start_idx = thread_id * 100 + # Add entries + for i in range(start_idx, start_idx + entries_per_thread): pending_queries_map.insert( i, f"query_{thread_id}_{i}", f"truth_{thread_id}_{i}", f"dataset_{thread_id}_{i}" ) - time.sleep(0.0001) # Small delay to increase chance of race condition + time.sleep(0.0001) + + # Remove entries + for i in range(start_idx, start_idx + entries_per_thread): + if i in pending_queries_map: + pending_queries_map.pop(i) except Exception as e: - errors.append(f"Thread {thread_id} add error: {e}") - - def remove_entries(indices, thread_id): - """Remove entries from the map.""" - try: - for idx in indices: - time.sleep(0.0001) # Small delay - if idx in pending_queries_map: - pending_queries_map.pop(idx) - else: - errors.append(f"Thread {thread_id}: Index {idx} not found for removal") - except Exception as e: - errors.append(f"Thread {thread_id} remove error: {e}") - - # Create threads that add entries - add_threads = [] - for i in range(4): - t = threading.Thread(target=add_entries, args=(i * 100, 50, i)) - add_threads.append(t) - t.start() - - # Wait for adds to complete - for t in add_threads: - t.join() + errors.append(f"Thread {thread_id}: {e}") - # Now create threads that try to remove entries concurrently - remove_threads = [] - for i in range(4): - indices = list(range(i * 100, i * 100 + 50)) - t = threading.Thread(target=remove_entries, args=(indices, f"remove_{i}")) - remove_threads.append(t) + # Run threads concurrently + threads = [] + for i in range(num_threads): + t = threading.Thread(target=add_and_remove_entries, args=(i,)) + threads.append(t) t.start() - # Wait for removes to complete - for t in remove_threads: + # Wait for all threads + for t in threads: t.join() - # Check for errors - self.assertEqual(len(errors), 0, f"Thread safety issues detected: {errors}") + # Verify no errors and map is empty + self.assertEqual(len(errors), 0, f"Thread safety errors: {errors}") + self.assertEqual(len(pending_queries_map), 0) - # Map should be empty after all removes - self.assertEqual( - len(pending_queries_map), 0, f"Map not empty after removes: {len(pending_queries_map)} items remain" + @parameterized.expand([ + (17, 4), # 17 prompts, 4 engines + (15, 4), # 15 prompts, 4 engines + (7, 3), # 7 prompts, 3 engines + (100, 7), # 100 prompts, 7 engines + ]) + def test_uneven_batch_distribution(self, num_prompts, num_engines): + """Test batch splitting when prompts don't divide evenly.""" + queries, ground_truths, datasets, indices = self.create_test_data(num_prompts) + + param_prompt_Q, _, pending_queries_map = self.setup_and_split_batch( + queries, ground_truths, datasets, indices, num_engines ) + + # Verify all indices are accounted for + self.assertEqual(len(pending_queries_map), num_prompts) + + total_indices = [] + while not param_prompt_Q.empty(): + request = param_prompt_Q.get() + total_indices.extend(request.dataset_index) + + self.assertEqual(sorted(total_indices), sorted(indices)) - def test_batch_size_edge_cases(self): - """Test batch splitting when prompts don't divide evenly by engines.""" - test_cases = [ - (17, 4), # 17 prompts, 4 engines -> 4,4,4,5 - (15, 4), # 15 prompts, 4 engines -> 3,3,3,6 - (7, 3), # 7 prompts, 3 engines -> 2,2,3 - (100, 7), # 100 prompts, 7 engines - ] - - for num_prompts, num_engines in test_cases: - with self.subTest(prompts=num_prompts, engines=num_engines): - queries = [f"q_{i}" for i in range(num_prompts)] - ground_truths = [f"t_{i}" for i in range(num_prompts)] - datasets = [f"d_{i}" for i in range(num_prompts)] - dataset_indices = list(range(num_prompts)) - - param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - pending_queries_map = grpo_fast.PendingQueriesMap() - - grpo_fast.split_and_insert_batch( - queries, - ground_truths, - datasets, - dataset_indices, - training_step=1, - vllm_num_engines=num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, - ) - - # Verify all indices are in the map - self.assertEqual(len(pending_queries_map), num_prompts) - - # Verify batches - batch_sizes = [] - total_indices = [] - while not param_prompt_Q.empty(): - request = param_prompt_Q.get() - batch_sizes.append(len(request.prompts)) - total_indices.extend(request.dataset_index) - - # All indices should be accounted for - self.assertEqual(sorted(total_indices), sorted(dataset_indices)) - self.assertEqual(sum(batch_sizes), num_prompts) - - # Verify batch distribution - # Expected: all batches equal except last one might have more - - def test_accumulate_blocks_waiting_for_all_engines(self): - """Test that accumulate_inference_batches blocks waiting for results from all engines.""" + def test_accumulate_waits_for_all_engines(self): + """Test that accumulate_inference_batches waits for all engines.""" num_engines = 4 num_prompts = 16 - # Create results from only 3 engines (missing one) + # Setup with results from only 3 engines inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) pending_queries_map = grpo_fast.PendingQueriesMap() - # Add entries to pending_queries_map using (training_step, dataset_idx) as keys - training_step = 1 + # Add entries to map for i in range(num_prompts): pending_queries_map.insert(i, f"q_{i}", f"t_{i}", f"d_{i}") - # Add results from only 3 engines - for engine_id in range(3): # Missing engine 3 - start_idx = engine_id * 4 - end_idx = start_idx + 4 - mock_result = GenerationResult( - responses=[[1, 2, 3] for _ in range(4)], - finish_reasons=["stop"] * 4, - masks=[[1, 1, 1] for _ in range(4)], - request_info=RequestInfo( - num_calls=[0] * 4, - timeouts=[0] * 4, - tool_errors=[""] * 4, - tool_outputs=[""] * 4, - tool_runtimes=[0.0] * 4, - tool_calleds=[False] * 4, - ), - dataset_index=list(range(start_idx, end_idx)), - training_step=training_step, - ) + # Add results from only 3 engines (missing one) + for engine_id in range(3): + indices = list(range(engine_id * 4, (engine_id + 1) * 4)) + mock_result = self.create_mock_result(indices, 1) inference_results_Q.put(mock_result) - mock_args = Mock() - mock_args.vllm_num_engines = num_engines - mock_args.num_samples_per_prompt_rollout = 1 + mock_args = self.create_mock_args(num_engines) - # This should block waiting for the 4th engine + # Test that accumulate blocks when missing an engine import threading - - completed = False + completed = threading.Event() def run_accumulate(): - nonlocal completed try: - # This will block on the 4th queue.get() call grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=1 ) - completed = True + completed.set() except Exception: - completed = True + completed.set() - thread = threading.Thread(target=run_accumulate) - thread.daemon = True # Make thread daemon so it doesn't block test suite + thread = threading.Thread(target=run_accumulate, daemon=True) thread.start() - # Give it time to process the 3 results and get stuck on the 4th - thread.join(timeout=1.0) - - # Check that the thread is still alive (blocked on queue.get()) - self.assertTrue( - thread.is_alive(), "accumulate_inference_batches should be blocked waiting for the 4th engine result" - ) - - # The queue should be empty (3 results consumed, waiting for 4th) - self.assertEqual(inference_results_Q.qsize(), 0, "Queue should be empty as 3 results were consumed") - - # Some entries should have been removed from pending_queries_map - self.assertLess( - len(pending_queries_map), - num_prompts, - "Some entries should have been processed and removed from pending_queries_map", - ) - - def test_no_race_condition_with_overlapping_indices(self): - """Test that the system should handle overlapping indices without race conditions.""" - import threading - import time - - num_engines = 4 - num_prompts = 32 - - # Shared state - pending_queries_map = grpo_fast.PendingQueriesMap() - param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) - inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) - errors = [] - race_condition_detected = False - - def simulate_training_step(step_num, indices_start): - """Simulate one training step.""" - try: - # Create data for this step - queries = [f"query_step{step_num}_{i}" for i in range(num_prompts)] - ground_truths = [f"truth_step{step_num}_{i}" for i in range(num_prompts)] - datasets = [f"dataset_step{step_num}_{i}" for i in range(num_prompts)] - # Use overlapping indices to simulate real scenario - dataset_indices = list(range(indices_start, indices_start + num_prompts)) - - # Split and insert batch - this modifies pending_queries_map - grpo_fast.split_and_insert_batch( - queries, - ground_truths, - datasets, - dataset_indices, - training_step=step_num, - vllm_num_engines=num_engines, - pending_queries_map=pending_queries_map, - param_prompt_Q=param_prompt_Q, - ) - - # Simulate vLLM processing delay - time.sleep(0.1) - - # Create mock results - for engine_id in range(num_engines): - if not param_prompt_Q.empty(): - request = param_prompt_Q.get() - mock_result = GenerationResult( - responses=[[1, 2, 3] for _ in range(len(request.prompts))], - finish_reasons=["stop"] * len(request.prompts), - masks=[[1, 1, 1] for _ in range(len(request.prompts))], - request_info=RequestInfo( - num_calls=[0] * len(request.prompts), - timeouts=[0] * len(request.prompts), - tool_errors=[""] * len(request.prompts), - tool_outputs=[""] * len(request.prompts), - tool_runtimes=[0.0] * len(request.prompts), - tool_calleds=[False] * len(request.prompts), - ), - dataset_index=request.dataset_index, - training_step=request.training_step, - ) - inference_results_Q.put(mock_result) - - except Exception as e: - errors.append(f"Step {step_num} error: {e}") - - def simulate_data_prep_thread(step_num): - """Simulate data preparation thread processing results.""" - nonlocal race_condition_detected - try: - # Add delay to simulate processing time - time.sleep(0.2) - - mock_args = Mock() - mock_args.vllm_num_engines = num_engines - mock_args.num_samples_per_prompt_rollout = 1 - - # Try to accumulate results - grpo_fast.accumulate_inference_batches( - inference_results_Q, pending_queries_map, mock_args, training_step=step_num - ) - - except RuntimeError as e: - if "not found in pending_queries_map" in str(e): - race_condition_detected = True - errors.append(f"Race condition detected: {e}") - else: - errors.append(f"Step {step_num} accumulate error: {e}") - except Exception as e: - errors.append(f"Step {step_num} accumulate error: {e}") - - # Start step 1 - step1_thread = threading.Thread(target=simulate_training_step, args=(1, 25840)) - step1_thread.start() - - # Start data prep for step 1 - data_prep1 = threading.Thread(target=simulate_data_prep_thread, args=(1,)) - data_prep1.start() - - # Before step 1 finishes, start step 2 with overlapping indices - time.sleep(0.15) # Start step 2 while step 1 is still processing - step2_thread = threading.Thread(target=simulate_training_step, args=(2, 25847)) - step2_thread.start() + # Should timeout waiting for 4th engine + self.assertFalse(completed.wait(timeout=1.0)) + self.assertTrue(thread.is_alive()) + + # Queue should be empty after consuming 3 results + self.assertEqual(inference_results_Q.qsize(), 0) + # Some entries should be removed + self.assertLess(len(pending_queries_map), num_prompts) - # Wait for all threads - step1_thread.join() - step2_thread.join() - data_prep1.join() - - # This test should PASS when the bug is fixed - # Currently it FAILS because we detect the race condition - self.assertFalse( - race_condition_detected, - f"Race condition detected! The system should handle overlapping indices safely. Errors: {errors}", - ) if __name__ == "__main__": From 7e024da411a910dd33f68a8b8141330e508c1d4a Mon Sep 17 00:00:00 2001 From: Finbarr Timbers Date: Wed, 23 Jul 2025 15:38:43 -0600 Subject: [PATCH 30/64] Ran linter. --- open_instruct/test_grpo_fast.py | 139 +++++++++++++++----------------- 1 file changed, 64 insertions(+), 75 deletions(-) diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index 5af96690f..9db95005f 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -14,7 +14,7 @@ class TestGrpoFastBase(unittest.TestCase): """Base class with common test utilities.""" - + @classmethod def setUpClass(cls): # Initialize Ray @@ -26,7 +26,7 @@ def tearDownClass(cls): # Shutdown Ray after test if ray.is_initialized(): ray.shutdown() - + def create_test_data(self, num_prompts, prefix="", start_idx=0): """Create test data with consistent naming.""" indices = list(range(start_idx, start_idx + num_prompts)) @@ -34,19 +34,19 @@ def create_test_data(self, num_prompts, prefix="", start_idx=0): ground_truths = [f"{prefix}truth_{i}" for i in indices] datasets = [f"{prefix}dataset_{i}" for i in indices] return queries, ground_truths, datasets, indices - + def create_mock_args(self, num_engines=4, num_samples=1): """Create mock args object.""" mock_args = Mock() mock_args.vllm_num_engines = num_engines mock_args.num_samples_per_prompt_rollout = num_samples return mock_args - + def create_mock_result(self, dataset_indices, training_step, num_samples_per_prompt=1): """Create a mock GenerationResult.""" batch_size = len(dataset_indices) total_responses = batch_size * num_samples_per_prompt - + return GenerationResult( responses=[[1, 2, 3] for _ in range(total_responses)], finish_reasons=["stop"] * total_responses, @@ -62,24 +62,21 @@ def create_mock_result(self, dataset_indices, training_step, num_samples_per_pro dataset_index=dataset_indices, training_step=training_step, ) - - def setup_and_split_batch(self, queries, ground_truths, datasets, indices, - num_engines, training_step=1): + + def setup_and_split_batch(self, queries, ground_truths, datasets, indices, num_engines, training_step=1): """Setup queues and split batch - common pattern.""" param_prompt_Q = ray_queue.Queue(maxsize=num_engines * 2) inference_results_Q = ray_queue.Queue(maxsize=num_engines * 2) pending_queries_map = grpo_fast.PendingQueriesMap() - + grpo_fast.split_and_insert_batch( - queries, ground_truths, datasets, indices, - training_step, num_engines, pending_queries_map, param_prompt_Q + queries, ground_truths, datasets, indices, training_step, num_engines, pending_queries_map, param_prompt_Q ) - + return param_prompt_Q, inference_results_Q, pending_queries_map class TestGrpoFastVLLM(TestGrpoFastBase): - def test_vllm_queue_system_single_prompt(self): """Test the new queue-based vLLM system with a single prompt 'What is the capital of France?'""" # Check if CUDA is available @@ -159,8 +156,10 @@ def test_vllm_queue_system_single_prompt(self): def test_batch_splitting_and_engine_configurations(self, vllm_num_engines: int, num_unique_prompts_rollout: int): """Test batch splitting and accumulation with various engine configurations.""" # Create test data - queries_next, ground_truths_next, datasets_next, dataset_indices = self.create_test_data(num_unique_prompts_rollout) - + queries_next, ground_truths_next, datasets_next, dataset_indices = self.create_test_data( + num_unique_prompts_rollout + ) + # Setup and split batch param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( queries_next, ground_truths_next, datasets_next, dataset_indices, vllm_num_engines @@ -178,7 +177,7 @@ def test_batch_splitting_and_engine_configurations(self, vllm_num_engines: int, self.assertIsInstance(request, PromptRequest) self.assertEqual(request.training_step, 1) self.assertIsInstance(request.dataset_index, list) - + mock_result = self.create_mock_result(request.dataset_index, request.training_step) inference_results_Q.put(mock_result) @@ -205,15 +204,16 @@ def test_batch_splitting_and_engine_configurations(self, vllm_num_engines: int, # Verify that the inference_results_Q is empty after accumulation self.assertEqual(inference_results_Q.qsize(), 0) - def test_dataset_index_preservation_through_pipeline(self): """Test that dataset indices are correctly preserved through the pipeline.""" vllm_num_engines = 4 num_unique_prompts_rollout = 32 # Create test data - queries_next, ground_truths_next, datasets_next, dataset_indices = self.create_test_data(num_unique_prompts_rollout) - + queries_next, ground_truths_next, datasets_next, dataset_indices = self.create_test_data( + num_unique_prompts_rollout + ) + # Setup and split batch param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( queries_next, ground_truths_next, datasets_next, dataset_indices, vllm_num_engines @@ -239,16 +239,16 @@ def test_dataset_index_preservation_through_pipeline(self): self.assertEqual(combined_datasets, datasets_next) self.assertEqual(len(pending_queries_map), 0) - - @parameterized.expand([(1, 16), (2, 8), (4, 4)]) def test_multiple_samples_per_prompt(self, vllm_num_engines: int, num_samples_per_prompt: int): """Test handling of multiple samples per prompt.""" num_unique_prompts_rollout = 16 # Create test data - queries_next, ground_truths_next, datasets_next, dataset_indices = self.create_test_data(num_unique_prompts_rollout) - + queries_next, ground_truths_next, datasets_next, dataset_indices = self.create_test_data( + num_unique_prompts_rollout + ) + # Setup and split batch param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( queries_next, ground_truths_next, datasets_next, dataset_indices, vllm_num_engines @@ -257,9 +257,7 @@ def test_multiple_samples_per_prompt(self, vllm_num_engines: int, num_samples_pe # Simulate vLLM processing with multiple samples while not param_prompt_Q.empty(): request = param_prompt_Q.get() - mock_result = self.create_mock_result( - request.dataset_index, request.training_step, num_samples_per_prompt - ) + mock_result = self.create_mock_result(request.dataset_index, request.training_step, num_samples_per_prompt) inference_results_Q.put(mock_result) # Accumulate results @@ -281,7 +279,6 @@ def test_multiple_samples_per_prompt(self, vllm_num_engines: int, num_samples_pe self.assertEqual(len(combined_result.responses), expected_responses) - class GrpoIntegrationTests(TestGrpoFastBase): """Integration tests for GRPO with parallel processing.""" @@ -289,40 +286,37 @@ def test_training_step_isolation(self): """Test that different training steps are handled correctly.""" num_engines = 2 num_prompts = 16 - + pending_queries_map = grpo_fast.PendingQueriesMap() inference_results_Q = ray_queue.Queue(maxsize=num_engines * 4) - + # Create data for two different training steps step1_data = self.create_test_data(num_prompts, "step1_", 0) step2_data = self.create_test_data(num_prompts, "step2_", 1000) - + # Process both steps - for step, (queries, ground_truths, datasets, indices) in enumerate( - [step1_data, step2_data], 1 - ): + for step, (queries, ground_truths, datasets, indices) in enumerate([step1_data, step2_data], 1): param_prompt_Q = ray_queue.Queue(maxsize=num_engines) - + grpo_fast.split_and_insert_batch( - queries, ground_truths, datasets, indices, - step, num_engines, pending_queries_map, param_prompt_Q + queries, ground_truths, datasets, indices, step, num_engines, pending_queries_map, param_prompt_Q ) - + # Create results for this step while not param_prompt_Q.empty(): request = param_prompt_Q.get() mock_result = self.create_mock_result(request.dataset_index, step) inference_results_Q.put(mock_result) - + # Process results from both steps mock_args = self.create_mock_args(num_engines) - + # First accumulation gets step 1 results result1, queries1, _, _ = grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=2 ) self.assertTrue(all("step1_" in q for q in queries1)) - + # Second accumulation gets step 2 results result2, queries2, _, _ = grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=2 @@ -373,32 +367,30 @@ def test_high_dataset_indices(self): num_prompts = 64 num_samples_per_prompt = 16 start_index = 25800 - + # Create test data with high indices - queries, ground_truths, datasets, indices = self.create_test_data( - num_prompts, start_idx=start_index - ) - + queries, ground_truths, datasets, indices = self.create_test_data(num_prompts, start_idx=start_index) + # Setup and split batch param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( queries, ground_truths, datasets, indices, num_engines ) - + # Verify the specific index from the bug is present self.assertIn(25847, pending_queries_map) - + # Process all requests while not param_prompt_Q.empty(): request = param_prompt_Q.get() mock_result = self.create_mock_result(request.dataset_index, 1, num_samples_per_prompt) inference_results_Q.put(mock_result) - + # Accumulate results mock_args = self.create_mock_args(num_engines, num_samples_per_prompt) combined_result, _, _, _ = grpo_fast.accumulate_inference_batches( inference_results_Q, pending_queries_map, mock_args, training_step=1 ) - + # Verify results self.assertEqual(len(combined_result.responses), num_prompts * num_samples_per_prompt) self.assertEqual(len(pending_queries_map), 0) @@ -411,7 +403,7 @@ def test_out_of_order_processing(self): # Create test data queries, ground_truths, datasets, indices = self.create_test_data(num_prompts) - + # Setup and split batch param_prompt_Q, inference_results_Q, pending_queries_map = self.setup_and_split_batch( queries, ground_truths, datasets, indices, num_engines @@ -424,9 +416,7 @@ def test_out_of_order_processing(self): # Put results back in REVERSE order to simulate out-of-order processing for request in reversed(requests): - mock_result = self.create_mock_result( - request.dataset_index, request.training_step, num_samples_per_prompt - ) + mock_result = self.create_mock_result(request.dataset_index, request.training_step, num_samples_per_prompt) inference_results_Q.put(mock_result) # Accumulate results @@ -440,7 +430,6 @@ def test_out_of_order_processing(self): self.assertEqual(len(combined_result.responses), num_prompts * num_samples_per_prompt) self.assertEqual(len(pending_queries_map), 0) - def test_no_duplicate_indices_within_training_step(self): """Test that dataset indices are unique within a single training step.""" num_engines = 4 @@ -448,7 +437,7 @@ def test_no_duplicate_indices_within_training_step(self): # Create test data queries, ground_truths, datasets, indices = self.create_test_data(num_prompts) - + # Setup and split batch param_prompt_Q, _, pending_queries_map = self.setup_and_split_batch( queries, ground_truths, datasets, indices, num_engines @@ -485,10 +474,9 @@ def test_overlapping_indices_between_steps(self): # Process step 1 step1_data = self.create_test_data(num_prompts, "step1_") queries1, ground_truths1, datasets1, _ = step1_data - + grpo_fast.split_and_insert_batch( - queries1, ground_truths1, datasets1, step1_indices, - 1, num_engines, pending_queries_map, param_prompt_Q + queries1, ground_truths1, datasets1, step1_indices, 1, num_engines, pending_queries_map, param_prompt_Q ) # Clear queue and process step 2 @@ -497,15 +485,14 @@ def test_overlapping_indices_between_steps(self): step2_data = self.create_test_data(num_prompts, "step2_") queries2, ground_truths2, datasets2, _ = step2_data - + grpo_fast.split_and_insert_batch( - queries2, ground_truths2, datasets2, step2_indices, - 2, num_engines, pending_queries_map, param_prompt_Q + queries2, ground_truths2, datasets2, step2_indices, 2, num_engines, pending_queries_map, param_prompt_Q ) # Verify overlapping indices handling overlapping_indices = set(step1_indices) & set(step2_indices) - + for idx in overlapping_indices: query, ground_truth, dataset, count = pending_queries_map[idx] self.assertEqual(count, 2) # Should have count 2 @@ -534,7 +521,7 @@ def add_and_remove_entries(thread_id): i, f"query_{thread_id}_{i}", f"truth_{thread_id}_{i}", f"dataset_{thread_id}_{i}" ) time.sleep(0.0001) - + # Remove entries for i in range(start_idx, start_idx + entries_per_thread): if i in pending_queries_map: @@ -557,28 +544,30 @@ def add_and_remove_entries(thread_id): self.assertEqual(len(errors), 0, f"Thread safety errors: {errors}") self.assertEqual(len(pending_queries_map), 0) - @parameterized.expand([ - (17, 4), # 17 prompts, 4 engines - (15, 4), # 15 prompts, 4 engines - (7, 3), # 7 prompts, 3 engines - (100, 7), # 100 prompts, 7 engines - ]) + @parameterized.expand( + [ + (17, 4), # 17 prompts, 4 engines + (15, 4), # 15 prompts, 4 engines + (7, 3), # 7 prompts, 3 engines + (100, 7), # 100 prompts, 7 engines + ] + ) def test_uneven_batch_distribution(self, num_prompts, num_engines): """Test batch splitting when prompts don't divide evenly.""" queries, ground_truths, datasets, indices = self.create_test_data(num_prompts) - + param_prompt_Q, _, pending_queries_map = self.setup_and_split_batch( queries, ground_truths, datasets, indices, num_engines ) - + # Verify all indices are accounted for self.assertEqual(len(pending_queries_map), num_prompts) - + total_indices = [] while not param_prompt_Q.empty(): request = param_prompt_Q.get() total_indices.extend(request.dataset_index) - + self.assertEqual(sorted(total_indices), sorted(indices)) def test_accumulate_waits_for_all_engines(self): @@ -604,6 +593,7 @@ def test_accumulate_waits_for_all_engines(self): # Test that accumulate blocks when missing an engine import threading + completed = threading.Event() def run_accumulate(): @@ -621,13 +611,12 @@ def run_accumulate(): # Should timeout waiting for 4th engine self.assertFalse(completed.wait(timeout=1.0)) self.assertTrue(thread.is_alive()) - + # Queue should be empty after consuming 3 results self.assertEqual(inference_results_Q.qsize(), 0) # Some entries should be removed self.assertLess(len(pending_queries_map), num_prompts) - if __name__ == "__main__": unittest.main() From 9c57ab5bc4feac25d5b00b45336f12a03e043e78 Mon Sep 17 00:00:00 2001 From: root Date: Wed, 23 Jul 2025 15:40:57 -0700 Subject: [PATCH 31/64] current changes, working-ish but probably incorrect. --- open_instruct/grpo_fast.py | 128 ++++++++++++++++++++++++----------- open_instruct/utils.py | 1 + open_instruct/vllm_utils3.py | 4 +- 3 files changed, 91 insertions(+), 42 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index b47f0ecff..73af0318a 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -660,6 +660,7 @@ def load(self, path: str, map_location=None): self.sp_group = groups._get_sequence_parallel_group() self.sp_world_size = groups._get_sequence_parallel_world_size() self.sp_rank = groups._get_sequence_parallel_rank() + print(f"SETTING UP: {self.sp_rank=} {self.sp_world_size=}") self.splitter = UlyssesSPSplitter( sp_rank=self.sp_rank, sp_group=self.sp_group, @@ -827,6 +828,7 @@ def train( # recalculate the "real" number of mini-batches num_mini_batches = len(collated_query_responses) // accumulation_steps + original_length = len(collated_query_responses) if self.splitter is not None: sharded_batches = self.splitter.split_batch({ @@ -837,27 +839,47 @@ def train( "tool_masks": collated_tool_masks, "advantages": collated_advantages, }, sequence_parallel_size=self.args.sequence_parallel_size, padding_token_id=pad_token_id) - # TODO: i actually don't know what sharded_batches looks like yet. - sharded_batches = move_to_device(sharded_batches, self.ref_policy.device) - - # replace non-sharded batches with sharded batches - collated_query_responses = [batch["input_ids"].contiguous() for batch in sharded_batches] - collated_attention_masks = [batch["attention_mask"].contiguous() for batch in sharded_batches] - collated_position_ids = [batch["position_ids"].contiguous() for batch in sharded_batches] - collated_response_masks = [batch["response_masks"].contiguous() for batch in sharded_batches] - collated_tool_masks = [batch["tool_masks"].contiguous() for batch in sharded_batches] - collated_advantages = [batch["advantages"].contiguous() for batch in sharded_batches] + + # we need to flatten out the sharded batches so its like: + # b0 sp0, b0 sp1, ..., b1 sp0, b1 sp1, ..., b2 sp0, b2 sp1, ... etc. + # right now, it comes out a list of sp shards, and inside is (bsz, seq_len) + collated_query_responses = [] + collated_attention_masks = [] + collated_position_ids = [] + collated_response_masks = [] + collated_tool_masks = [] + collated_advantages = [] + + for batch_idx in range(original_length): + for sp_idx in range(len(sharded_batches)): + # None to re-add in the batch dimension. + collated_query_responses.append(sharded_batches[sp_idx]["input_ids"][batch_idx, None]) + collated_attention_masks.append(sharded_batches[sp_idx]["attention_mask"][batch_idx, None]) + collated_position_ids.append(sharded_batches[sp_idx]["position_ids"][batch_idx, None]) + collated_response_masks.append(sharded_batches[sp_idx]["response_masks"][batch_idx, None]) + collated_tool_masks.append(sharded_batches[sp_idx]["tool_masks"][batch_idx, None]) + collated_advantages.append(sharded_batches[sp_idx]["advantages"][batch_idx, None]) + + collated_query_responses = move_to_device(collated_query_responses, self.ref_policy.device) + collated_attention_masks = move_to_device(collated_attention_masks, self.ref_policy.device) + collated_position_ids = move_to_device(collated_position_ids, self.ref_policy.device) + collated_response_masks = move_to_device(collated_response_masks, self.ref_policy.device) + collated_tool_masks = move_to_device(collated_tool_masks, self.ref_policy.device) + collated_advantages = move_to_device(collated_advantages, self.ref_policy.device) + + # adjust accumulation steps for sp (do we need to do this?) + # accumulation_steps = accumulation_steps * self.sp_world_size # Calculate the logprob of the reference policy collated_ref_logprobs = [] with Timer("Inference Calculation", noop=self.rank != 0): with torch.no_grad(): for i in range(len(collated_query_responses)): - query_response = collated_query_responses[i].squeeze() - tool_mask = collated_tool_masks[i].squeeze() - attention_mask = collated_attention_masks[i].squeeze() - position_id = collated_position_ids[i].squeeze() - response_mask = collated_response_masks[i].squeeze() + query_response = collated_query_responses[i] + tool_mask = collated_tool_masks[i] + attention_mask = collated_attention_masks[i] + position_id = collated_position_ids[i] + response_mask = collated_response_masks[i] ref_logprob, _ = self.forward( self.ref_policy, query_response, attention_mask, position_id, pad_token_id, args.temperature, return_entropy=False) if args.mask_tool_use and args.tool_use: # mask logprobs for tool tokens @@ -980,7 +1002,7 @@ def train( non_masked_tokens_per_rank = torch.distributed.nn.functional.all_gather(non_masked_tokens, group=self.sp_group) total_loss = sum(losses_per_rank[rank] * non_masked_tokens_per_rank[rank] for rank in range(self.sp_world_size)) total_non_masked_tokens = sum(non_masked_tokens_per_rank) - loss = total_loss / total_non_masked_tokens + loss = (total_loss / total_non_masked_tokens).sum() # TODO: do we still need grad acc...? I think so. loss = loss / accumulation_steps self.model.backward(loss) @@ -1926,9 +1948,34 @@ def maybe_evaluate( # timeout 0.01 if this is the last training step or we're not evaluating # otherwise, wait to get the last evaluation generations (long timeout just in case) timeout = 0.01 if (training_step < args.num_training_steps or args.local_eval_freq < 0) else 100 - eval_result = evaluation_inference_results_Q.get(timeout=timeout) + results = [] + for _ in range(len(eval_prompt_token_ids)): + eval_result = evaluation_inference_results_Q.get(timeout=timeout) + results.append(eval_result) logger.info("[Main Thread] 📊 Evaluation responses received") + responses, finish_reasons, request_infos = [], [], [] + for res in results: + # res.responses / res.finish_reasons are usually lists, but be defensive + responses.extend(res.responses if isinstance(res.responses, (list, tuple)) else [res.responses]) + finish_reasons.extend( + res.finish_reasons if isinstance(res.finish_reasons, (list, tuple)) else [res.finish_reasons] + ) + request_infos.append(res.request_info) + + # create a combined request info + combined_request_info = RequestInfo( + num_calls=[ri.num_calls for ri in request_infos], + timeouts=[ri.timeouts for ri in request_infos], + tool_errors=[ri.tool_errors for ri in request_infos], + tool_outputs=[ri.tool_outputs for ri in request_infos], + tool_runtimes=[ri.tool_runtimes for ri in request_infos], + tool_calleds=[ri.tool_calleds for ri in request_infos], + ) + + seq_lens = np.fromiter((len(r) for r in responses), dtype=int) + decoded_responses = tokenizer.batch_decode(responses, skip_special_tokens=True) + stop_rate = sum(fr == "stop" for fr in finish_reasons) / len(finish_reasons) eval_sequence_lengths = np.array([len(response) for response in eval_result.responses]) eval_decoded_responses = tokenizer.batch_decode(eval_result.responses, skip_special_tokens=True) @@ -1937,42 +1984,41 @@ def maybe_evaluate( ) # get and log evaluation metrics - eval_scores, eval_reward_metrics = asyncio.run( + eval_scores, reward_metrics = asyncio.run( reward_fn( - eval_result.responses, - eval_decoded_responses, + responses, + decoded_responses, eval_ground_truths, eval_dataset_names, - eval_result.finish_reasons, - eval_result.request_info, + finish_reasons, + combined_request_info, ) ) - eval_reward_metrics = {f"eval/{key}": val for key, val in eval_reward_metrics.items()} - eval_metrics = { - "eval/scores": np.array(eval_scores).mean(), - "eval/sequence_lengths": eval_sequence_lengths.mean(), - "eval/sequence_lengths_min": eval_sequence_lengths.min(), - "eval/sequence_lengths_max": eval_sequence_lengths.max(), - "eval/stop_rate": eval_stop_rate, - **eval_reward_metrics, + reward_metrics = {f"eval/{k}": v for k, v in reward_metrics.items()} + metrics = { + "eval/scores": np.mean(eval_scores), + "eval/sequence_lengths": seq_lens.mean(), + "eval/sequence_lengths_min": seq_lens.min(), + "eval/sequence_lengths_max": seq_lens.max(), + "eval/stop_rate": stop_rate, + **reward_metrics, } - print_rich_single_line_metrics(eval_metrics) - for key, value in eval_metrics.items(): - writer.add_scalar(key, value, episode) - table = {} - table["prompt"] = tokenizer.batch_decode(eval_prompt_token_ids) - table["response"] = eval_decoded_responses - table["response"] = [item.replace(tokenizer.pad_token, "") for item in table["response"]] - table["scores"] = eval_scores - table["ground_truth"] = eval_ground_truths - df = pd.DataFrame(table) + + print_rich_single_line_metrics(metrics) + for k, v in metrics.items(): + writer.add_scalar(k, v, episode) + df = pd.DataFrame({ + "prompt": tokenizer.batch_decode(eval_prompt_token_ids), + "response": [t.replace(tokenizer.pad_token, "") for t in decoded_responses], + "scores": eval_scores, + "ground_truth": eval_ground_truths, + }) if args.with_tracking: import wandb wandb.log({"sample_completions": wandb.Table(dataframe=df)}) else: print_rich_table(df.iloc[:1]) - del table except Empty: logger.warning("[Main Thread] 🙈 Evaluation responses not received") diff --git a/open_instruct/utils.py b/open_instruct/utils.py index 447e98a4b..1b6c96592 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -1585,6 +1585,7 @@ def split_batch(self, batch, sequence_parallel_size=None, padding_token_id=0): if not torch.is_tensor(batch[k]): continue # at seqlen>10M and 32+ gpus this can take GBs of memory so keep the prefill buffer on cpu + print(f"{self.sp_rank} {chunk_len}, chunk_len * self.sp_rank:{chunk_len * self.sp_rank}, chunk_len * (self.sp_rank + 1):{chunk_len * (self.sp_rank + 1)}") batch[k] = batch[k][:, chunk_len * self.sp_rank:chunk_len * (self.sp_rank + 1)].cpu() batch_shards.append(batch) diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index a1560eec4..2e4b234c0 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -272,13 +272,15 @@ def _generate_batch( tool_calleds=tool_calleds, ) + ds_index = dataset_indices[i] if dataset_indices is not None else None + results.append( GenerationResult( responses=response_ids, finish_reasons=finish_reasons, masks=masks, request_info=request_info, - dataset_index=[dataset_indices[i]], + dataset_index=[ds_index], ) ) From ed81beeb27b4db8f68f9a277782d064633db2a97 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Wed, 6 Aug 2025 14:36:43 -0700 Subject: [PATCH 32/64] fix --- open_instruct/utils.py | 77 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) diff --git a/open_instruct/utils.py b/open_instruct/utils.py index a5d2b7067..d5efc64c5 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -1567,3 +1567,80 @@ def check_runtime_leaks( report.leaked_shm.append(name) return report + +class UlyssesSPSplitter: + + def __init__( + self, + sp_rank: int, + sp_group, + sp_world_size, + device, + ): + """ + Adapted from the UlyssesSPDataLoaderAdapter + (https://github.com/deepspeedai/DeepSpeed/blob/master/deepspeed/runtime/sequence_parallel/ulysses_sp.py#L427) + Rather than wrapping a dataloader, we just want to split a given batch into the shards across sp group. + """ + self.sp_rank = sp_rank + self.sp_group = sp_group + self.sp_world_size = sp_world_size + self.device = device + + def split_batch(self, batch): + micro_batches = defaultdict(dict) + + # we have batches of variable seqlen so in order to do all_gather on batches - we need to know the exact length of each tensor on each rank + seqlen = torch.tensor(batch["input_ids"].shape[1], dtype=torch.int64, device=self.device) + seqlens = [torch.zeros(1, dtype=torch.int64, device=self.device) for _ in range(self.sp_world_size)] + dist.all_gather(seqlens, seqlen, group=self.sp_group) + seqlens = [x[0].item() for x in seqlens] + + for k in batch.keys(): + if torch.is_tensor(batch[k]): + batch[k] = batch[k].to(self.device) + with torch.no_grad(): + tensor_list = [ + torch.zeros((batch[k].shape[0], seqlens[i]), dtype=batch[k].dtype, device=batch[k].device) + for i in range(self.sp_world_size) + ] + dist.all_gather(tensor_list, batch[k], group=self.sp_group) + else: + tensor_list = [None for _ in range(self.sp_world_size)] + dist.all_gather_object(tensor_list, batch[k], group=self.sp_group) + + for rank, tensor in enumerate(tensor_list): + micro_batches[rank][k] = tensor + + del tensor_list + del batch + + batch_shards = [] + for batch in micro_batches.values(): + seq_length = len(batch["input_ids"][0]) + + if seq_length % self.sp_world_size != 0: + raise ValueError(f"batch's seqlen={seq_length} isn't divisible by sp-size={self.sp_world_size}") + chunk_len = seq_length // self.sp_world_size + + # for rl training, we don't have labels, so we don't need to do this + if "labels" in batch: + # because we have to gather logits from all sp ranks we have to do the loss function ourselves + # therefore remove labels to avoid an attempt to calculate loss by transformers + labels = batch.pop("labels") + labels = torch.nn.functional.pad(labels, (0, 1), value=-100) + batch["shift_labels"] = labels[..., 1:].contiguous() + # free up temp memory + del labels + + # batch sharding + for k in batch.keys(): + # leave non-tensors alone + if not torch.is_tensor(batch[k]): + continue + # at seqlen>10M and 32+ gpus this can take GBs of memory so keep the prefill buffer on cpu + batch[k] = batch[k][:, chunk_len * self.sp_rank:chunk_len * (self.sp_rank + 1)].cpu() + + batch_shards.append(batch) + + return batch_shards From 5739fb068d9eee207af53543b50daad6d78908cf Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Wed, 6 Aug 2025 14:44:31 -0700 Subject: [PATCH 33/64] fix merge --- open_instruct/benchmark_generators.py | 71 ++--------- open_instruct/grpo_fast.py | 166 ++++++-------------------- open_instruct/test_grpo_fast.py | 10 +- open_instruct/vllm_utils3.py | 67 +++++------ 4 files changed, 84 insertions(+), 230 deletions(-) diff --git a/open_instruct/benchmark_generators.py b/open_instruct/benchmark_generators.py index a79744ca6..cfa73db3c 100644 --- a/open_instruct/benchmark_generators.py +++ b/open_instruct/benchmark_generators.py @@ -295,9 +295,8 @@ def setup_vllm_engines( pg = ray.util.placement_group(bundles, strategy="PACK") ray.get(pg.ready()) - # Adjust queue sizes for individual prompts - param_prompt_Q = ray_queue.Queue(maxsize=100) - inference_results_Q = ray_queue.Queue(maxsize=100) + param_prompt_Q = ray_queue.Queue(maxsize=10) + inference_results_Q = ray_queue.Queue(maxsize=10) vllm_engines = vllm_utils3.create_vllm_engines( num_engines=args.vllm_num_engines, @@ -335,41 +334,6 @@ def get_batch_data( return prompts -def run_generation_batch( - inference_results_Q: ray_queue.Queue, param_prompt_Q: ray_queue.Queue, prompts: list[list[int]], batch_idx: int -) -> dict[str, Any]: - """Run generation for a batch of prompts and measure performance.""" - - start_time = time.time() - - # Insert individual prompts - for i, prompt in enumerate(prompts): - dataset_idx = batch_idx * len(prompts) + i - param_prompt_Q.put(vllm_utils3.PromptRequest(prompt=prompt, dataset_index=dataset_idx)) - - # Collect individual results - all_responses = [] - all_finish_reasons = [] - for _ in range(len(prompts)): - result = inference_results_Q.get() - all_responses.extend(result.responses) - all_finish_reasons.extend(result.finish_reasons) - - generation_time = time.time() - start_time - - new_tokens = sum(len(response) for response in all_responses) - tokens_per_second = new_tokens / generation_time - return { - "tokens_per_second": tokens_per_second, - "generation_time": generation_time, - "num_new_tokens": new_tokens, - # dict mapping string reasons to counts. - "finish_reasons": collections.Counter(all_finish_reasons), - "response_lengths": [len(response) for response in all_responses], - "prompt_lengths": [len(prompt) for prompt in prompts], # Original unique prompts - } - - def run_benchmark( dataset: datasets.Dataset, vllm_engines: list[ray.actor.ActorHandle], @@ -402,7 +366,6 @@ def run_benchmark( num_batches + 1, # eval_freq (avoid evaluation) num_batches, # num_training_steps 1, # resume_training_step - args.num_unique_prompts_rollout // args.vllm_num_engines, # batch_size ) # Wait for engines to be ready @@ -420,47 +383,37 @@ def run_benchmark( ] submission_start_time = time.time() for batch_idx in range(num_batches): - # Insert individual prompts for this batch - for i, prompt in enumerate(all_prompts[batch_idx]): - dataset_idx = batch_idx * args.num_unique_prompts_rollout + i - param_prompt_Q.put(vllm_utils3.PromptRequest(prompt=prompt, dataset_index=dataset_idx)) + param_prompt_Q.put(vllm_utils3.PromptRequest(prompts=all_prompts[batch_idx], dataset_index=batch_idx)) submission_time = time.time() - submission_start_time logger.info(f"All batches submitted in {submission_time:.2f}s") # Receive results and measure time for each batch last_completion_time = submission_start_time for batch_idx in range(num_batches): - # Collect individual results for this batch - batch_responses = [] - batch_finish_reasons = [] - for _ in range(args.num_unique_prompts_rollout): - result = inference_results_Q.get() - batch_responses.extend(result.responses) - batch_finish_reasons.extend(result.finish_reasons) - + result = inference_results_Q.get() completion_time = time.time() batch_generation_time = completion_time - last_completion_time last_completion_time = completion_time - # Process batch results - new_tokens = sum(len(response) for response in batch_responses) + # Process result + new_tokens = sum(len(response) for response in result.responses) tokens_per_second = new_tokens / batch_generation_time result_dict = { "tokens_per_second": tokens_per_second, "generation_time": batch_generation_time, "num_new_tokens": new_tokens, - "finish_reasons": collections.Counter(batch_finish_reasons), - "response_lengths": [len(response) for response in batch_responses], - "batch_idx": batch_idx, + "finish_reasons": collections.Counter(result.finish_reasons), + "response_lengths": [len(response) for response in result.responses], + "batch_idx": result.dataset_index, } result_dict["mfu"] = 100 * result_dict["tokens_per_second"] * flops_per_token / device_flops # We incrementally save completion lengths so even if the job dies, we still have data. - save_completion_lengths([result_dict], timestamp, batch_idx) + save_completion_lengths([result_dict], timestamp, result.dataset_index) results.append(result_dict) logger.info( - f"Batch {batch_idx + 1}: " + f"Batch {result.dataset_index + 1}: " f"{result_dict['tokens_per_second']:.2f} new tokens/sec, " f"MFU: {result_dict['mfu']:.2f}%, " f"generation time: {batch_generation_time:.2f}s" @@ -608,4 +561,4 @@ def main() -> None: if __name__ == "__main__": - main() + main() \ No newline at end of file diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 58f819d9e..36792ebe1 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1193,74 +1193,6 @@ def get_bundle_index(rank, num_gpus_per_node): self.models.append(worker_policy) -class PendingQueriesMap: - """Thread-safe map for tracking pending queries with reference counting.""" - - def __init__(self): - self._map = {} # dataset_idx -> (query, ground_truth, dataset, count) - self._lock = threading.Lock() - - def insert(self, dataset_idx, query, ground_truth, dataset): - """Insert or increment count for a dataset index.""" - with self._lock: - if dataset_idx in self._map: - # Already exists - just increment count - existing_query, existing_ground_truth, existing_dataset, count = self._map[dataset_idx] - self._map[dataset_idx] = (existing_query, existing_ground_truth, existing_dataset, count + 1) - else: - # New entry - count starts at 1 - self._map[dataset_idx] = (query, ground_truth, dataset, 1) - - def insert_many(self, dataset_indices, queries, ground_truths, datasets): - """Insert or increment count for multiple dataset indices at once.""" - with self._lock: - for i, dataset_idx in enumerate(dataset_indices): - if dataset_idx in self._map: - # Already exists - just increment count - existing_query, existing_ground_truth, existing_dataset, count = self._map[dataset_idx] - self._map[dataset_idx] = (existing_query, existing_ground_truth, existing_dataset, count + 1) - else: - # New entry - count starts at 1 - self._map[dataset_idx] = (queries[i], ground_truths[i], datasets[i], 1) - - def pop(self, dataset_idx): - """Retrieve data and decrement count. Removes entry when count reaches 0.""" - with self._lock: - if dataset_idx not in self._map: - raise RuntimeError(f"Dataset index {dataset_idx} not found in pending_queries_map") - - query, ground_truth, dataset, count = self._map[dataset_idx] - - if count > 1: - # More results expected - just decrement - self._map[dataset_idx] = (query, ground_truth, dataset, count - 1) - else: - # Last result - remove entry - del self._map[dataset_idx] - - return query, ground_truth, dataset - - def __len__(self): - """Return the number of entries in the map.""" - with self._lock: - return len(self._map) - - def __contains__(self, dataset_idx): - """Check if a dataset index is in the map.""" - with self._lock: - return dataset_idx in self._map - - def __getitem__(self, dataset_idx): - """Get the value for a dataset index.""" - with self._lock: - return self._map[dataset_idx] - - def keys(self): - """Return a view of the keys in the map.""" - with self._lock: - return list(self._map.keys()) - - class PendingQueriesMap: """Thread-safe map for tracking pending queries with reference counting.""" @@ -1362,8 +1294,6 @@ def accumulate_inference_batches( ): result = inference_results_Q.get() - # Accept results from any training step as long as we have entries in the map - # This prevents synchronization issues when results arrive out of order dataset_indices = result.dataset_index if dataset_indices is None: @@ -1388,16 +1318,14 @@ def accumulate_inference_batches( for dataset_idx in dataset_indices: query, ground_truth, dataset = pending_queries_map.pop(dataset_idx) - - # Don't replicate - just append once per unique index batch_queries.append(query) batch_ground_truths.append(ground_truth) batch_datasets.append(dataset) results.append(result) - all_queries.append(query) - all_ground_truths.append(ground_truth) - all_datasets.append(dataset) + all_queries.extend(batch_queries) + all_ground_truths.extend(batch_ground_truths) + all_datasets.extend(batch_datasets) # Combine all results into a single GenerationResult combined_responses = [] @@ -1442,7 +1370,6 @@ def accumulate_inference_batches( training_step=training_step, ) - pbar.close() return combined_result, all_queries, all_ground_truths, all_datasets @@ -1924,11 +1851,12 @@ def split_and_insert_batch( param_prompt_Q, eval_prompt_token_ids=None, ): - """Insert individual prompts into queues and mapping.""" - # Insert individual prompts into the queue - for i, dataset_idx in enumerate(dataset_indices): - # Store individual prompt in the map using dataset index as key - pending_queries_map[dataset_idx] = (queries_next[i], ground_truths_next[i], datasets_next[i]) + """Split a batch into multiple inference batches and insert individual prompts into queues and mapping.""" + # Split the batch over the VLLM engines. + inference_batch_size = len(queries_next) // vllm_num_engines + for batch_idx in range(vllm_num_engines): + start_idx = batch_idx * inference_batch_size + end_idx = start_idx + inference_batch_size if batch_idx < vllm_num_engines - 1 else len(queries_next) batch_queries = queries_next[start_idx:end_idx] batch_ground_truths = ground_truths_next[start_idx:end_idx] @@ -1941,7 +1869,7 @@ def split_and_insert_batch( # Use PromptRequest for Ray queue with batch-specific dataset_index list param_prompt_Q.put( PromptRequest( - prompt=queries_next[i], + prompts=batch_queries, training_step=training_step, eval_prompts=eval_prompt_token_ids if batch_idx == 0 else None, dataset_index=batch_dataset_indices, @@ -2159,34 +2087,9 @@ def maybe_evaluate( # timeout 0.01 if this is the last training step or we're not evaluating # otherwise, wait to get the last evaluation generations (long timeout just in case) timeout = 0.01 if (training_step < args.num_training_steps or args.local_eval_freq < 0) else 100 - results = [] - for _ in range(len(eval_prompt_token_ids)): - eval_result = evaluation_inference_results_Q.get(timeout=timeout) - results.append(eval_result) + eval_result = evaluation_inference_results_Q.get(timeout=timeout) logger.info("[Main Thread] 📊 Evaluation responses received") - responses, finish_reasons, request_infos = [], [], [] - for res in results: - # res.responses / res.finish_reasons are usually lists, but be defensive - responses.extend(res.responses if isinstance(res.responses, (list, tuple)) else [res.responses]) - finish_reasons.extend( - res.finish_reasons if isinstance(res.finish_reasons, (list, tuple)) else [res.finish_reasons] - ) - request_infos.append(res.request_info) - - # create a combined request info - combined_request_info = RequestInfo( - num_calls=[ri.num_calls for ri in request_infos], - timeouts=[ri.timeouts for ri in request_infos], - tool_errors=[ri.tool_errors for ri in request_infos], - tool_outputs=[ri.tool_outputs for ri in request_infos], - tool_runtimes=[ri.tool_runtimes for ri in request_infos], - tool_calleds=[ri.tool_calleds for ri in request_infos], - ) - - seq_lens = np.fromiter((len(r) for r in responses), dtype=int) - decoded_responses = tokenizer.batch_decode(responses, skip_special_tokens=True) - stop_rate = sum(fr == "stop" for fr in finish_reasons) / len(finish_reasons) eval_sequence_lengths = np.array([len(response) for response in eval_result.responses]) eval_decoded_responses = tokenizer.batch_decode(eval_result.responses, skip_special_tokens=True) @@ -2195,41 +2098,42 @@ def maybe_evaluate( ) # get and log evaluation metrics - eval_scores, reward_metrics = asyncio.run( + eval_scores, eval_reward_metrics = asyncio.run( reward_fn( - responses, - decoded_responses, + eval_result.responses, + eval_decoded_responses, eval_ground_truths, eval_dataset_names, - finish_reasons, - combined_request_info, + eval_result.finish_reasons, + eval_result.request_info, ) ) - reward_metrics = {f"eval/{k}": v for k, v in reward_metrics.items()} - metrics = { - "eval/scores": np.mean(eval_scores), - "eval/sequence_lengths": seq_lens.mean(), - "eval/sequence_lengths_min": seq_lens.min(), - "eval/sequence_lengths_max": seq_lens.max(), - "eval/stop_rate": stop_rate, - **reward_metrics, + eval_reward_metrics = {f"eval/{key}": val for key, val in eval_reward_metrics.items()} + eval_metrics = { + "eval/scores": np.array(eval_scores).mean(), + "eval/sequence_lengths": eval_sequence_lengths.mean(), + "eval/sequence_lengths_min": eval_sequence_lengths.min(), + "eval/sequence_lengths_max": eval_sequence_lengths.max(), + "eval/stop_rate": eval_stop_rate, + **eval_reward_metrics, } - - print_rich_single_line_metrics(metrics) - for k, v in metrics.items(): - writer.add_scalar(k, v, episode) - df = pd.DataFrame({ - "prompt": tokenizer.batch_decode(eval_prompt_token_ids), - "response": [t.replace(tokenizer.pad_token, "") for t in decoded_responses], - "scores": eval_scores, - "ground_truth": eval_ground_truths, - }) + print_rich_single_line_metrics(eval_metrics) + for key, value in eval_metrics.items(): + writer.add_scalar(key, value, episode) + table = {} + table["prompt"] = tokenizer.batch_decode(eval_prompt_token_ids) + table["response"] = eval_decoded_responses + table["response"] = [item.replace(tokenizer.pad_token, "") for item in table["response"]] + table["scores"] = eval_scores + table["ground_truth"] = eval_ground_truths + df = pd.DataFrame(table) if args.with_tracking: import wandb wandb.log({"sample_completions": wandb.Table(dataframe=df)}) else: print_rich_table(df.iloc[:1]) + del table except Empty: logger.warning("[Main Thread] 🙈 Evaluation responses not received") diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index f585ac43a..83aff22c0 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -251,11 +251,10 @@ def test_vllm_queue_system_single_prompt(self): 999, # eval_freq (avoid evaluation) 1, # num_training_steps 1, # resume_training_step - 1, # batch_size ) # Put the test prompt in the queue using PromptRequest - request = PromptRequest(prompt=prompt_token_ids, dataset_index=0) + request = PromptRequest(prompts=[prompt_token_ids], dataset_index=0) param_prompt_Q.put(request) # Get the result @@ -293,7 +292,8 @@ def test_batch_splitting_and_engine_configurations(self, vllm_num_engines: int, # Verify that we have individual prompts in the map (not batches) self.assertEqual(len(pending_queries_map), num_unique_prompts_rollout) - self.assertEqual(param_prompt_Q.qsize(), num_unique_prompts_rollout) + # Verify that we have the expected number of items in the queue + self.assertEqual(param_prompt_Q.qsize(), vllm_num_engines) # Simulate vLLM processing batch_idx = 0 @@ -359,7 +359,7 @@ def test_batch_splitting_and_engine_configurations(self, vllm_num_engines: int, self.assertEqual(len(combined_result.finish_reasons), len(queries_next)) self.assertEqual(len(combined_result.masks), len(queries_next)) - # Verify that the test_pending_queries_map is empty after accumulation + # Verify that the pending_queries_map is empty after accumulation self.assertEqual(len(pending_queries_map), 0) # Verify that the inference_results_Q is empty after accumulation @@ -776,4 +776,4 @@ def test_streaming_with_multiple_samples(self): if __name__ == "__main__": - unittest.main() + unittest.main() \ No newline at end of file diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index abd09b34a..7dbfa82d4 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -24,7 +24,6 @@ import ray import torch import torch.distributed -import vllm from ray.util.placement_group import placement_group from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy from torch.distributed.distributed_c10d import ( @@ -65,9 +64,9 @@ class GenerationResult: @dataclasses.dataclass class PromptRequest: - """Container for a single prompt request to vLLM.""" + """Container for prompt requests to vLLM.""" - prompt: List[int] # Single prompt + prompts: List[List[int]] training_step: Optional[int] = None eval_prompts: Optional[List[List[int]]] = None dataset_index: Optional[List[int]] = None @@ -237,37 +236,35 @@ def _generate_batch( """Generate responses for a batch of prompts.""" outputs = self.llm.generate(sampling_params=sampling_params, prompt_token_ids=prompts, use_tqdm=False) - # Process outputs and create individual GenerationResult objects - results = [] - for i, output in enumerate(outputs): - response_ids = [list(out.token_ids) for out in output.outputs] - finish_reasons = [out.finish_reason for out in output.outputs] - - if self.tool_use: - masks = [out.mask for out in output.outputs] - num_calls = [out.num_calls for out in output.outputs] - timeouts = [out.timeout for out in output.outputs] - tool_errors = [out.tool_error for out in output.outputs] - tool_outputs = [out.tool_output for out in output.outputs] - tool_runtimes = [out.tool_runtime for out in output.outputs] - tool_calleds = [out.tool_called for out in output.outputs] - else: - masks = [[1] * len(resp) for resp in response_ids] - num_calls = [0] * len(response_ids) - timeouts = [0] * len(response_ids) - tool_errors = [""] * len(response_ids) - tool_outputs = [""] * len(response_ids) - tool_runtimes = [0] * len(response_ids) - tool_calleds = [False] * len(response_ids) - - request_info = RequestInfo( - num_calls=num_calls, - timeouts=timeouts, - tool_errors=tool_errors, - tool_outputs=tool_outputs, - tool_runtimes=tool_runtimes, - tool_calleds=tool_calleds, - ) + # Process outputs + response_ids = [list(out.token_ids) for output in outputs for out in output.outputs] + finish_reasons = [out.finish_reason for output in outputs for out in output.outputs] + + if self.tool_use: + masks = [out.mask for output in outputs for out in output.outputs] + num_calls = [out.num_calls for output in outputs for out in output.outputs] + timeouts = [out.timeout for output in outputs for out in output.outputs] + tool_errors = [out.tool_error for output in outputs for out in output.outputs] + tool_outputs = [out.tool_output for output in outputs for out in output.outputs] + tool_runtimes = [out.tool_runtime for output in outputs for out in output.outputs] + tool_calleds = [out.tool_called for output in outputs for out in output.outputs] + else: + masks = [[1] * len(resp) for resp in response_ids] + num_calls = [0] * len(response_ids) + timeouts = [0] * len(response_ids) + tool_errors = [""] * len(response_ids) + tool_outputs = [""] * len(response_ids) + tool_runtimes = [0] * len(response_ids) + tool_calleds = [False] * len(response_ids) + + request_info = RequestInfo( + num_calls=num_calls, + timeouts=timeouts, + tool_errors=tool_errors, + tool_outputs=tool_outputs, + tool_runtimes=tool_runtimes, + tool_calleds=tool_calleds, + ) return GenerationResult( responses=response_ids, @@ -494,4 +491,4 @@ def batch_vllm_engine_call(engines: List[Any], method_name: str, *args, rank_0_o ) ray.get(refs) output = ray.get(llm.generate.remote("San Franciso is a")) - print(f"output: {output}") + print(f"output: {output}") \ No newline at end of file From b63190d078be4dfae28bd2aa378c2c8814d3f019 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Wed, 6 Aug 2025 14:51:59 -0700 Subject: [PATCH 34/64] missed some tings --- open_instruct/grpo_fast.py | 69 ++++++++++++++++++-------------------- 1 file changed, 32 insertions(+), 37 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 36792ebe1..fccba97b7 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1200,43 +1200,6 @@ def __init__(self): self._map = {} # dataset_idx -> (query, ground_truth, dataset, count) self._lock = threading.Lock() - def __getitem__(self, key): - """Get item using dict-like access.""" - with self._lock: - if key not in self._map: - raise KeyError(f"Key {key} not found") - return self._map[key] - - def __setitem__(self, key, value): - """Set item using dict-like access. Value should be (query, ground_truth, dataset).""" - with self._lock: - if len(value) == 3: - # If only 3 elements provided, add count of 1 - query, ground_truth, dataset = value - self._map[key] = (query, ground_truth, dataset, 1) - elif len(value) == 4: - # Full tuple with count - self._map[key] = value - else: - raise ValueError( - "Value must be a tuple of (query, ground_truth, dataset) or (query, ground_truth, dataset, count)" - ) - - def __contains__(self, key): - """Check if key exists.""" - with self._lock: - return key in self._map - - def __len__(self): - """Return the number of items in the map.""" - with self._lock: - return len(self._map) - - def keys(self): - """Return a copy of the keys.""" - with self._lock: - return list(self._map.keys()) - def insert(self, dataset_idx, query, ground_truth, dataset): """Insert or increment count for a dataset index.""" with self._lock: @@ -1248,6 +1211,18 @@ def insert(self, dataset_idx, query, ground_truth, dataset): # New entry - count starts at 1 self._map[dataset_idx] = (query, ground_truth, dataset, 1) + def insert_many(self, dataset_indices, queries, ground_truths, datasets): + """Insert or increment count for multiple dataset indices at once.""" + with self._lock: + for i, dataset_idx in enumerate(dataset_indices): + if dataset_idx in self._map: + # Already exists - just increment count + existing_query, existing_ground_truth, existing_dataset, count = self._map[dataset_idx] + self._map[dataset_idx] = (existing_query, existing_ground_truth, existing_dataset, count + 1) + else: + # New entry - count starts at 1 + self._map[dataset_idx] = (queries[i], ground_truths[i], datasets[i], 1) + def pop(self, dataset_idx): """Retrieve data and decrement count. Removes entry when count reaches 0.""" with self._lock: @@ -1265,6 +1240,26 @@ def pop(self, dataset_idx): return query, ground_truth, dataset + def __len__(self): + """Return the number of entries in the map.""" + with self._lock: + return len(self._map) + + def __contains__(self, dataset_idx): + """Check if a dataset index is in the map.""" + with self._lock: + return dataset_idx in self._map + + def __getitem__(self, dataset_idx): + """Get the value for a dataset index.""" + with self._lock: + return self._map[dataset_idx] + + def keys(self): + """Return a view of the keys in the map.""" + with self._lock: + return list(self._map.keys()) + def accumulate_inference_batches( inference_results_Q: ray_queue.Queue, pending_queries_map: PendingQueriesMap, args: Args, training_step: int From 675a9ffd249006c08ade30b6158749ca66f38cf3 Mon Sep 17 00:00:00 2001 From: root Date: Wed, 6 Aug 2025 17:45:14 -0700 Subject: [PATCH 35/64] fixing it up --- open_instruct/grpo_fast.py | 28 +++++++++++++++++++--------- open_instruct/utils.py | 7 ++++--- 2 files changed, 23 insertions(+), 12 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index fccba97b7..76e031597 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -834,14 +834,25 @@ def train( original_length = len(collated_query_responses) if self.splitter is not None: - sharded_batches = self.splitter.split_batch({ + batch = { "input_ids": collated_query_responses, "attention_mask": collated_attention_masks, "position_ids": collated_position_ids, "response_masks": collated_response_masks, "tool_masks": collated_tool_masks, "advantages": collated_advantages, - }, sequence_parallel_size=self.args.sequence_parallel_size, padding_token_id=pad_token_id) + } + # Pad the items in the batch so they are divisible by sp_world_size + # TODO: pad value. + for i in range(len(batch["input_ids"])): + for k in batch.keys(): + if torch.is_tensor(batch[k][i]): + seq_length = batch[k][i].shape[1] + if seq_length % self.sp_world_size != 0: + padding_length = self.sp_world_size - (seq_length % self.sp_world_size) + padding = torch.zeros((batch[k][i].shape[0], padding_length), dtype=batch[k][i].dtype, device=batch[k][i].device) + batch[k][i] = torch.cat((batch[k][i], padding), dim=1) + sharded_batches = self.splitter.split_batch(batch) # we need to flatten out the sharded batches so its like: # b0 sp0, b0 sp1, ..., b1 sp0, b1 sp1, ..., b2 sp0, b2 sp1, ... etc. @@ -855,13 +866,12 @@ def train( for batch_idx in range(original_length): for sp_idx in range(len(sharded_batches)): - # None to re-add in the batch dimension. - collated_query_responses.append(sharded_batches[sp_idx]["input_ids"][batch_idx, None]) - collated_attention_masks.append(sharded_batches[sp_idx]["attention_mask"][batch_idx, None]) - collated_position_ids.append(sharded_batches[sp_idx]["position_ids"][batch_idx, None]) - collated_response_masks.append(sharded_batches[sp_idx]["response_masks"][batch_idx, None]) - collated_tool_masks.append(sharded_batches[sp_idx]["tool_masks"][batch_idx, None]) - collated_advantages.append(sharded_batches[sp_idx]["advantages"][batch_idx, None]) + collated_query_responses.append(sharded_batches[sp_idx]["input_ids"][batch_idx]) + collated_attention_masks.append(sharded_batches[sp_idx]["attention_mask"][batch_idx]) + collated_position_ids.append(sharded_batches[sp_idx]["position_ids"][batch_idx]) + collated_response_masks.append(sharded_batches[sp_idx]["response_masks"][batch_idx]) + collated_tool_masks.append(sharded_batches[sp_idx]["tool_masks"][batch_idx]) + collated_advantages.append(sharded_batches[sp_idx]["advantages"][batch_idx]) collated_query_responses = move_to_device(collated_query_responses, self.ref_policy.device) collated_attention_masks = move_to_device(collated_attention_masks, self.ref_policy.device) diff --git a/open_instruct/utils.py b/open_instruct/utils.py index d5efc64c5..3a197e9e3 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -20,6 +20,7 @@ try: from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum + import deepspeed.comm as dist # @vwxyzjn: when importing on CPU-only machines, we get the following error: # RuntimeError: 0 active drivers ([]). There should only be one. @@ -1591,7 +1592,7 @@ def split_batch(self, batch): micro_batches = defaultdict(dict) # we have batches of variable seqlen so in order to do all_gather on batches - we need to know the exact length of each tensor on each rank - seqlen = torch.tensor(batch["input_ids"].shape[1], dtype=torch.int64, device=self.device) + seqlen = torch.tensor(len(batch["input_ids"][0][0]), dtype=torch.int64, device=self.device) seqlens = [torch.zeros(1, dtype=torch.int64, device=self.device) for _ in range(self.sp_world_size)] dist.all_gather(seqlens, seqlen, group=self.sp_group) seqlens = [x[0].item() for x in seqlens] @@ -1617,10 +1618,10 @@ def split_batch(self, batch): batch_shards = [] for batch in micro_batches.values(): - seq_length = len(batch["input_ids"][0]) - + seq_length = len(batch["input_ids"][0][0]) if seq_length % self.sp_world_size != 0: raise ValueError(f"batch's seqlen={seq_length} isn't divisible by sp-size={self.sp_world_size}") + chunk_len = seq_length // self.sp_world_size # for rl training, we don't have labels, so we don't need to do this From 382ebd312f044fee57436f3435500eea9efc7d7f Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Thu, 7 Aug 2025 16:00:33 +0000 Subject: [PATCH 36/64] pos ids --- open_instruct/grpo_fast.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 76e031597..a607fb1b4 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -693,7 +693,7 @@ def forward( input_ids=input_ids[:, :-1], # @vwxyzjn: without clamp, we get index out of bounds errors; TODO: investigate attention_mask=attention_mask[:, :-1].clamp(0, 1), - # position_ids=position_ids[:, :-1], + position_ids=position_ids[:, :-1], return_dict=True, ) logits = output.logits @@ -843,7 +843,7 @@ def train( "advantages": collated_advantages, } # Pad the items in the batch so they are divisible by sp_world_size - # TODO: pad value. + # where attention mask is 0, we dont end up using anyway. for i in range(len(batch["input_ids"])): for k in batch.keys(): if torch.is_tensor(batch[k][i]): From 0a66d964394655d077819f925364e2f47dc51d20 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Thu, 7 Aug 2025 20:27:02 +0000 Subject: [PATCH 37/64] lint --- open_instruct/grpo_fast.py | 45 +- open_instruct/utils.py | 12 +- open_instruct/vllm_utils3.py | 2 +- uv.lock | 4792 ++++++++++++++++++---------------- 4 files changed, 2535 insertions(+), 2316 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index f0d39a147..ce9c06da1 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -41,7 +41,6 @@ from deepspeed.utils import groups from deepspeed.runtime.utils import move_to_device - # @vwxyzjn: when importing on CPU-only machines, we get the following error: # RuntimeError: 0 active drivers ([]). There should only be one. # so we need to catch the exception and do nothing @@ -568,7 +567,13 @@ def load(self, path: str, map_location=None): self.device = torch.device(self.local_rank) deepspeed.init_distributed() - ds_config = get_train_ds_config(offload=False, adam_offload=False, stage=args.deepspeed_stage, bf16=True, sequence_parallel_size=args.sequence_parallel_size) + ds_config = get_train_ds_config( + offload=False, + adam_offload=False, + stage=args.deepspeed_stage, + bf16=True, + sequence_parallel_size=args.sequence_parallel_size, + ) ds_config["train_micro_batch_size_per_gpu"] = args.per_device_train_batch_size ds_config["gradient_accumulation_steps"] = 1 # @vwxyzjn: MAGIC: it's actually needed to initialize this `dschf`, so @@ -685,10 +690,7 @@ def load(self, path: str, map_location=None): self.sp_rank = groups._get_sequence_parallel_rank() print(f"SETTING UP: {self.sp_rank=} {self.sp_world_size=}") self.splitter = UlyssesSPSplitter( - sp_rank=self.sp_rank, - sp_group=self.sp_group, - sp_world_size=self.sp_world_size, - device=self.device + sp_rank=self.sp_rank, sp_group=self.sp_group, sp_world_size=self.sp_world_size, device=self.device ) else: self.splitter = None @@ -870,10 +872,14 @@ def train( seq_length = batch[k][i].shape[1] if seq_length % self.sp_world_size != 0: padding_length = self.sp_world_size - (seq_length % self.sp_world_size) - padding = torch.zeros((batch[k][i].shape[0], padding_length), dtype=batch[k][i].dtype, device=batch[k][i].device) + padding = torch.zeros( + (batch[k][i].shape[0], padding_length), + dtype=batch[k][i].dtype, + device=batch[k][i].device, + ) batch[k][i] = torch.cat((batch[k][i], padding), dim=1) sharded_batches = self.splitter.split_batch(batch) - + # we need to flatten out the sharded batches so its like: # b0 sp0, b0 sp1, ..., b1 sp0, b1 sp1, ..., b2 sp0, b2 sp1, ... etc. # right now, it comes out a list of sp shards, and inside is (bsz, seq_len) @@ -913,7 +919,15 @@ def train( attention_mask = collated_attention_masks[i] position_id = collated_position_ids[i] response_mask = collated_response_masks[i] - ref_logprob, _ = self.forward( self.ref_policy, query_response, attention_mask, position_id, pad_token_id, args.temperature, return_entropy=False) + ref_logprob, _ = self.forward( + self.ref_policy, + query_response, + attention_mask, + position_id, + pad_token_id, + args.temperature, + return_entropy=False, + ) if args.mask_tool_use and args.tool_use: # mask logprobs for tool tokens response_mask = response_mask.bool() & tool_mask.bool() @@ -1026,14 +1040,21 @@ def train( if args.sequence_parallel_size == 1: # grpo change: directly subtract KL in loss (add) - loss = masked_mean(pg_loss_max + (args.beta * kl), mb_response_masks_bool, args.masked_mean_axis) + loss = masked_mean( + pg_loss_max + (args.beta * kl), mb_response_masks_bool, args.masked_mean_axis + ) else: # if SP, compute per-token loss, then we need to gather across SP ranks per_tok_loss = pg_loss_max + (args.beta * kl) * mb_response_masks_bool.float() losses_per_rank = torch.distributed.nn.functional.all_gather(per_tok_loss, group=self.sp_group) non_masked_tokens = mb_response_masks_bool.sum() - non_masked_tokens_per_rank = torch.distributed.nn.functional.all_gather(non_masked_tokens, group=self.sp_group) - total_loss = sum(losses_per_rank[rank] * non_masked_tokens_per_rank[rank] for rank in range(self.sp_world_size)) + non_masked_tokens_per_rank = torch.distributed.nn.functional.all_gather( + non_masked_tokens, group=self.sp_group + ) + total_loss = sum( + losses_per_rank[rank] * non_masked_tokens_per_rank[rank] + for rank in range(self.sp_world_size) + ) total_non_masked_tokens = sum(non_masked_tokens_per_rank) loss = (total_loss / total_non_masked_tokens).sum() # TODO: do we still need grad acc...? I think so. diff --git a/open_instruct/utils.py b/open_instruct/utils.py index 02a042d0a..44967916f 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -1608,15 +1608,9 @@ def check_runtime_leaks( return report -class UlyssesSPSplitter: - def __init__( - self, - sp_rank: int, - sp_group, - sp_world_size, - device, - ): +class UlyssesSPSplitter: + def __init__(self, sp_rank: int, sp_group, sp_world_size, device): """ Adapted from the UlyssesSPDataLoaderAdapter (https://github.com/deepspeedai/DeepSpeed/blob/master/deepspeed/runtime/sequence_parallel/ulysses_sp.py#L427) @@ -1679,7 +1673,7 @@ def split_batch(self, batch): if not torch.is_tensor(batch[k]): continue # at seqlen>10M and 32+ gpus this can take GBs of memory so keep the prefill buffer on cpu - batch[k] = batch[k][:, chunk_len * self.sp_rank:chunk_len * (self.sp_rank + 1)].cpu() + batch[k] = batch[k][:, chunk_len * self.sp_rank : chunk_len * (self.sp_rank + 1)].cpu() batch_shards.append(batch) diff --git 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-aiosignal==1.3.2 -airportsdata==20250523 + # via ray +aiosignal==1.4.0 + # via aiohttp +airportsdata==20250706 + # via outlines annotated-types==0.7.0 + # via pydantic antlr4-python3-runtime==4.11.0 -anyio==4.9.0 + # via open-instruct +anyio==4.10.0 + # via + # httpx + # openai + # starlette + # watchfiles astor==0.8.1 + # via depyf async-timeout==5.0.1 ; python_full_version < '3.11' + # via aiohttp attrs==25.3.0 + # via + # aiohttp + # jsonschema + # referencing autoflake==2.3.1 babel==2.17.0 -backrefs==5.8 -beaker-py==1.34.3 -bitsandbytes==0.46.0 ; sys_platform != 'darwin' + # via mkdocs-material +backrefs==5.9 + # via mkdocs-material +beaker-py==1.36.4 +bitsandbytes==0.46.1 ; sys_platform != 'darwin' + # via open-instruct black==25.1.0 blake3==1.0.5 -cachetools==5.5.2 -certifi==2025.4.26 + # via vllm +cachetools==6.1.0 + # via + # google-auth + # vllm +certifi==2025.8.3 + # via + # httpcore + # httpx + # requests + # sentry-sdk cffi==1.17.1 ; implementation_name == 'pypy' + # via pyzmq charset-normalizer==3.4.2 + # via requests click==8.2.1 + # via + # black + # litellm + # mkdocs + # nltk + # ray + # rich-toolkit + # typer + # uvicorn + # wandb cloudpickle==3.1.1 + # via + # outlines + # vllm colorama==0.4.6 -colorful==0.5.6 + # via + # click + # colorful + # mkdocs + # mkdocs-material + # nvitop + # pytest + # tqdm + # uvicorn +colorful==0.5.7 + # via ray compressed-tensors==0.10.1 -contourpy==1.3.2 -cuda-bindings==12.9.0 -cuda-python==12.9.0 -cupy-cuda12x==13.4.1 ; sys_platform != 'darwin' + # via vllm +contourpy==1.3.2 ; python_full_version < '3.11' + # via matplotlib +contourpy==1.3.3 ; python_full_version >= '3.11' + # via matplotlib +cuda-bindings==13.0.0 + # via cuda-python +cuda-pathfinder==1.1.0 + # via cuda-bindings +cuda-python==13.0.0 + # via flashinfer-python +cupy-cuda12x==13.5.1 ; sys_platform != 'darwin' + # via ray cycler==0.12.1 -datasets==3.6.0 -debugpy==1.8.14 + # via matplotlib +datasets==4.0.0 + # via open-instruct +debugpy==1.8.16 + # via open-instruct deepspeed==0.17.2 -deprecated==1.2.18 + # via open-instruct depyf==0.18.0 + # via vllm dill==0.3.8 + # via + # datasets + # depyf + # multiprocess diskcache==5.6.3 -distlib==0.3.9 + # via outlines +distlib==0.4.0 + # via virtualenv distro==1.9.0 + # via openai dnspython==2.7.0 + # via email-validator docker==7.1.0 + # via beaker-py docker-pycreds==0.4.0 + # via wandb einops==0.8.1 + # via + # deepspeed + # flash-attn + # flashinfer-python + # vllm email-validator==2.2.0 + # via + # fastapi + # pydantic exceptiongroup==1.3.0 ; python_full_version < '3.11' + # via + # anyio + # pytest execnet==2.1.1 -fastapi==0.115.12 -fastapi-cli==0.0.7 + # via pytest-xdist +fastapi==0.116.1 + # via + # open-instruct + # vllm +fastapi-cli==0.0.8 + # via fastapi +fastapi-cloud-cli==0.1.5 + # via fastapi-cli fastrlock==0.8.3 ; sys_platform != 'darwin' + # via cupy-cuda12x filelock==3.18.0 -flake8==7.2.0 -flash-attn==2.8.0.post2 + # via + # datasets + # huggingface-hub + # ray + # torch + # transformers + # virtualenv + # vllm +flake8==7.3.0 +flash-attn==2.8.2 + # via open-instruct flashinfer-python==0.2.8 -fonttools==4.58.1 -frozenlist==1.6.2 + # via open-instruct +fonttools==4.59.0 + # via matplotlib +frozenlist==1.7.0 + # via + # aiohttp + # aiosignal fsspec==2025.3.0 -gguf==0.17.0 + # via + # datasets + # huggingface-hub + # torch +gguf==0.17.1 + # via vllm ghp-import==2.1.0 + # via mkdocs gitdb==4.0.12 -gitpython==3.1.44 -google-api-core==2.25.0 -google-auth==2.40.3 + # via gitpython +gitpython==3.1.45 + # via wandb +google-api-core==1.16.0 + # via opencensus +google-auth==1.6.3 + # via google-api-core googleapis-common-protos==1.70.0 -grpcio==1.72.1 + # via + # google-api-core + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http +grpcio==1.74.0 + # via + # beaker-py + # opentelemetry-exporter-otlp-proto-grpc + # ray + # tensorboard h11==0.16.0 + # via + # httpcore + # uvicorn hf-transfer==0.1.9 -hf-xet==1.1.3 + # via open-instruct +hf-xet==1.1.7 + # via huggingface-hub hjson==3.1.0 + # via deepspeed httpcore==1.0.9 + # via httpx httptools==0.6.4 + # via uvicorn httpx==0.28.1 -huggingface-hub==0.32.4 + # via + # fastapi + # fastapi-cloud-cli + # litellm + # openai +huggingface-hub==0.34.3 + # via + # accelerate + # datasets + # peft + # tokenizers + # transformers + # vllm idna==3.10 + # via + # anyio + # email-validator + # httpx + # requests + # yarl immutabledict==1.2.0 -importlib-metadata==8.0.0 + # via open-instruct +importlib-metadata==8.7.0 + # via + # litellm + # opentelemetry-api iniconfig==2.1.0 + # via pytest interegular==0.3.3 + # via + # lm-format-enforcer + # outlines + # outlines-core isort==6.0.1 jinja2==3.1.6 + # via + # fastapi + # litellm + # mkdocs + # mkdocs-material + # outlines + # torch jiter==0.10.0 + # via openai joblib==1.5.1 -jsonschema==4.24.0 + # via nltk +jsonschema==4.25.0 + # via + # litellm + # mistral-common + # outlines + # outlines-core + # ray jsonschema-specifications==2025.4.1 + # via jsonschema kiwisolver==1.4.8 + # via matplotlib langdetect==1.0.9 + # via open-instruct lark==1.2.2 -liger-kernel==0.5.10 -litellm==1.72.0 -llguidance==0.7.27 ; platform_machine == 'aarch64' or platform_machine == 'arm64' or platform_machine == 'x86_64' + # via + # outlines + # vllm +liger-kernel==0.6.1 + # via open-instruct +litellm==1.75.0 + # via open-instruct +llguidance==0.7.30 ; platform_machine == 'aarch64' or platform_machine == 'arm64' or platform_machine == 'x86_64' + # via vllm llvmlite==0.44.0 -lm-format-enforcer==0.10.11 -markdown==3.8 + # via numba +lm-format-enforcer==0.10.12 + # via vllm +markdown==3.8.2 + # via + # markdown-include + # mkdocs + # mkdocs-material + # pymdown-extensions + # tensorboard markdown-include==0.8.1 markdown-it-py==3.0.0 + # via rich markupsafe==3.0.2 -matplotlib==3.10.3 + # via + # jinja2 + # mkdocs + # werkzeug +matplotlib==3.10.5 + # via open-instruct mccabe==0.7.0 + # via flake8 mdurl==0.1.2 + # via markdown-it-py mergedeep==1.3.4 -mistral-common==1.5.6 + # via + # mkdocs + # mkdocs-get-deps +mistral-common==1.8.3 + # via vllm mkdocs==1.6.1 + # via mkdocs-material mkdocs-get-deps==0.2.0 -mkdocs-material==9.6.14 + # via mkdocs +mkdocs-material==9.6.16 mkdocs-material-extensions==1.3.1 + # via mkdocs-material mpmath==1.3.0 -msgpack==1.1.0 + # via sympy +msgpack==1.1.1 + # via + # deepspeed + # ray msgspec==0.19.0 -multidict==6.4.4 + # via vllm +multidict==6.6.3 + # via + # aiohttp + # yarl multiprocess==0.70.16 + # via datasets mypy-extensions==1.1.0 + # via black nest-asyncio==1.6.0 + # via outlines networkx==3.4.2 ; python_full_version < '3.11' + # via torch networkx==3.5 ; python_full_version >= '3.11' + # via torch ninja==1.11.1.4 + # via + # deepspeed + # flashinfer-python + # vllm + # xgrammar nltk==3.9.1 + # via open-instruct numba==0.61.2 + # via vllm numpy==1.26.4 + # via + # accelerate + # bitsandbytes + # contourpy + # cupy-cuda12x + # datasets + # deepspeed + # flashinfer-python + # gguf + # matplotlib + # mistral-common + # numba + # open-instruct + # opencv-python-headless + # outlines + # pandas + # peft + # scipy + # tensorboard + # torchvision + # transformers + # vllm + # xformers nvidia-cublas-cu12==12.8.3.14 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via + # nvidia-cudnn-cu12 + # nvidia-cusolver-cu12 + # torch nvidia-cuda-cupti-cu12==12.8.57 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-cuda-nvrtc-cu12==12.8.61 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-cuda-runtime-cu12==12.8.57 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-cudnn-cu12==9.7.1.26 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-cufft-cu12==11.3.3.41 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-cufile-cu12==1.13.0.11 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-curand-cu12==10.3.9.55 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-cusolver-cu12==11.7.2.55 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-cusparse-cu12==12.5.7.53 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via + # nvidia-cusolver-cu12 + # torch nvidia-cusparselt-cu12==0.6.3 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-ml-py==12.575.51 + # via + # nvitop + # pynvml nvidia-nccl-cu12==2.26.2 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch nvidia-nvjitlink-cu12==12.8.61 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via + # nvidia-cufft-cu12 + # nvidia-cusolver-cu12 + # nvidia-cusparse-cu12 + # torch nvidia-nvshmem-cu12==3.3.20 + # via flashinfer-python nvidia-nvtx-cu12==12.8.55 ; platform_machine == 'x86_64' and sys_platform == 'linux' -nvitop==1.5.1 -openai==1.84.0 + # via torch +nvitop==1.5.2 + # via open-instruct +openai==1.99.2 + # via + # litellm + # vllm opencensus==0.11.4 + # via ray opencensus-context==0.1.3 + # via opencensus opencv-python-headless==4.11.0.86 -opentelemetry-api==1.26.0 -opentelemetry-exporter-otlp==1.26.0 -opentelemetry-exporter-otlp-proto-common==1.26.0 -opentelemetry-exporter-otlp-proto-grpc==1.26.0 -opentelemetry-exporter-otlp-proto-http==1.26.0 -opentelemetry-proto==1.26.0 -opentelemetry-sdk==1.26.0 -opentelemetry-semantic-conventions==0.47b0 -opentelemetry-semantic-conventions-ai==0.4.9 + # via + # mistral-common + # vllm +opentelemetry-api==1.36.0 + # via + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http + # opentelemetry-exporter-prometheus + # opentelemetry-sdk + # opentelemetry-semantic-conventions + # vllm +opentelemetry-exporter-otlp==1.36.0 + # via vllm +opentelemetry-exporter-otlp-proto-common==1.36.0 + # via + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http +opentelemetry-exporter-otlp-proto-grpc==1.36.0 + # via opentelemetry-exporter-otlp +opentelemetry-exporter-otlp-proto-http==1.36.0 + # via opentelemetry-exporter-otlp +opentelemetry-exporter-prometheus==0.57b0 + # via ray +opentelemetry-proto==1.36.0 + # via + # opentelemetry-exporter-otlp-proto-common + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http + # ray +opentelemetry-sdk==1.36.0 + # via + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http + # opentelemetry-exporter-prometheus + # ray + # vllm +opentelemetry-semantic-conventions==0.57b0 + # via opentelemetry-sdk +opentelemetry-semantic-conventions-ai==0.4.12 + # via vllm outlines==0.1.11 + # via vllm outlines-core==0.1.26 + # via outlines packaging==25.0 + # via + # accelerate + # beaker-py + # black + # datasets + # deepspeed + # huggingface-hub + # lm-format-enforcer + # matplotlib + # mkdocs + # open-instruct + # peft + # pytest + # ray + # tensorboard + # transformers paginate==0.5.7 -pandas==2.2.3 + # via mkdocs-material +pandas==2.3.1 + # via datasets parameterized==0.9.0 -partial-json-parser==0.2.1.1.post5 +partial-json-parser==0.2.1.1.post6 + # via vllm pathspec==0.12.1 -peft==0.15.2 -pillow==11.2.1 + # via + # black + # mkdocs +peft==0.17.0 + # via open-instruct +pillow==11.3.0 + # via + # matplotlib + # mistral-common + # tensorboard + # torchvision + # vllm platformdirs==4.3.8 + # via + # black + # mkdocs-get-deps + # virtualenv + # wandb pluggy==1.6.0 + # via pytest prometheus-client==0.22.1 + # via + # opentelemetry-exporter-prometheus + # prometheus-fastapi-instrumentator + # ray + # vllm prometheus-fastapi-instrumentator==7.1.0 -propcache==0.3.1 -proto-plus==1.26.1 -protobuf==4.25.8 + # via vllm +propcache==0.3.2 + # via + # aiohttp + # yarl +protobuf==5.29.5 + # via + # beaker-py + # google-api-core + # googleapis-common-protos + # opentelemetry-proto + # ray + # tensorboard + # vllm + # wandb psutil==7.0.0 + # via + # accelerate + # deepspeed + # nvitop + # peft + # vllm + # wandb py-cpuinfo==9.0.0 -py-spy==0.4.0 -pyarrow==20.0.0 + # via + # deepspeed + # vllm +py-spy==0.4.1 + # via ray +pyarrow==21.0.0 + # via datasets pyasn1==0.6.1 + # via + # pyasn1-modules + # rsa pyasn1-modules==0.4.2 -pycodestyle==2.13.0 + # via google-auth +pycodestyle==2.14.0 + # via flake8 pycountry==24.6.1 + # via + # outlines + # pydantic-extra-types pycparser==2.22 ; implementation_name == 'pypy' -pydantic==2.11.5 + # via cffi +pydantic==2.11.7 + # via + # beaker-py + # compressed-tensors + # deepspeed + # fastapi + # fastapi-cloud-cli + # litellm + # lm-format-enforcer + # mistral-common + # open-instruct + # openai + # outlines + # pydantic-extra-types + # ray + # vllm + # xgrammar pydantic-core==2.33.2 -pyflakes==3.3.2 -pygments==2.19.1 -pymdown-extensions==10.15 + # via pydantic +pydantic-extra-types==2.10.5 + # via mistral-common +pyflakes==3.4.0 + # via + # autoflake + # flake8 +pygments==2.19.2 + # via + # mkdocs-material + # pytest + # rich +pymdown-extensions==10.16.1 + # via mkdocs-material pynvml==12.0.0 + # via flashinfer-python pyparsing==3.2.3 -pytest==8.4.0 + # via matplotlib +pytest==8.4.1 + # via pytest-xdist pytest-xdist==3.8.0 + # via open-instruct python-dateutil==2.9.0.post0 -python-dotenv==1.1.0 + # via + # ghp-import + # matplotlib + # pandas +python-dotenv==1.1.1 + # via + # litellm + # uvicorn python-json-logger==3.3.0 + # via vllm python-multipart==0.0.20 + # via fastapi pytz==2025.2 -pywin32==310 ; sys_platform == 'win32' + # via + # google-api-core + # pandas +pywin32==311 ; sys_platform == 'win32' + # via + # cuda-bindings + # docker pyyaml==6.0.2 + # via + # accelerate + # beaker-py + # datasets + # gguf + # huggingface-hub + # lm-format-enforcer + # mkdocs + # mkdocs-get-deps + # peft + # pymdown-extensions + # pyyaml-env-tag + # ray + # transformers + # uvicorn + # vllm + # wandb pyyaml-env-tag==1.1 -pyzmq==26.4.0 -ray==2.46.0 + # via mkdocs +pyzmq==27.0.1 + # via vllm +ray==2.48.0 + # via + # open-instruct + # vllm referencing==0.36.2 -regex==2024.11.6 -requests==2.32.3 + # via + # jsonschema + # jsonschema-specifications + # outlines +regex==2025.7.34 + # via + # nltk + # tiktoken + # transformers + # vllm +requests==2.32.4 + # via + # beaker-py + # datasets + # docker + # flashinfer-python + # google-api-core + # huggingface-hub + # mistral-common + # mkdocs-material + # open-instruct + # opentelemetry-exporter-otlp-proto-http + # outlines + # ray + # tiktoken + # transformers + # vllm + # wandb rich==13.9.4 -rich-toolkit==0.14.7 -rpds-py==0.25.1 + # via + # beaker-py + # rich-toolkit + # typer +rich-toolkit==0.14.9 + # via + # fastapi-cli + # fastapi-cloud-cli +rignore==0.6.4 + # via fastapi-cloud-cli +rpds-py==0.27.0 + # via + # jsonschema + # referencing rsa==4.9.1 -ruff==0.12.0 -safetensors==0.5.3 -scipy==1.15.3 + # via google-auth +ruff==0.12.8 +safetensors==0.6.1 + # via + # accelerate + # peft + # transformers +scipy==1.15.3 ; python_full_version < '3.11' + # via vllm +scipy==1.16.1 ; python_full_version >= '3.11' + # via vllm sentencepiece==0.2.0 -sentry-sdk==2.29.1 + # via + # mistral-common + # vllm + # xgrammar +sentry-sdk==2.34.1 + # via + # fastapi-cloud-cli + # wandb setproctitle==1.3.6 + # via wandb setuptools==79.0.1 + # via + # google-api-core + # open-instruct + # tensorboard + # torch + # triton + # vllm + # wandb shellingham==1.5.4 + # via typer six==1.17.0 -smart-open==7.1.0 + # via + # docker-pycreds + # google-api-core + # google-auth + # langdetect + # opencensus + # python-dateutil + # vllm +smart-open==7.3.0.post1 + # via ray smmap==5.0.2 + # via gitdb sniffio==1.3.1 -starlette==0.46.2 + # via + # anyio + # openai +starlette==0.47.2 + # via + # fastapi + # prometheus-fastapi-instrumentator sympy==1.14.0 -tensorboard==2.19.0 + # via torch +tensorboard==2.20.0 + # via open-instruct tensorboard-data-server==0.7.2 -tiktoken==0.9.0 -tokenizers==0.21.1 + # via tensorboard +tiktoken==0.10.0 + # via + # litellm + # mistral-common + # vllm + # xgrammar +tokenizers==0.21.4 + # via + # litellm + # transformers + # vllm tomli==2.2.1 ; python_full_version < '3.11' + # via + # autoflake + # black + # pytest torch==2.7.0 ; sys_platform == 'darwin' + # via + # accelerate + # compressed-tensors + # deepspeed + # flash-attn + # flashinfer-python + # liger-kernel + # open-instruct + # outlines + # peft + # torchaudio + # torchvision + # vllm + # xgrammar torch==2.7.0+cu128 ; sys_platform != 'darwin' + # via + # accelerate + # bitsandbytes + # compressed-tensors + # deepspeed + # flash-attn + # flashinfer-python + # liger-kernel + # open-instruct + # outlines + # peft + # torchaudio + # torchvision + # vllm + # xformers + # xgrammar torchaudio==2.7.0 + # via vllm torchvision==0.22.0 + # via vllm tqdm==4.67.1 -transformers==4.52.4 -triton==3.2.0 ; sys_platform == 'darwin' -triton==3.3.0 ; sys_platform != 'darwin' + # via + # datasets + # deepspeed + # gguf + # huggingface-hub + # nltk + # open-instruct + # openai + # outlines + # peft + # transformers + # vllm +transformers==4.55.0 + # via + # compressed-tensors + # open-instruct + # peft + # vllm + # xgrammar +triton==3.3.0 + # via + # liger-kernel + # torch + # xgrammar typer==0.16.0 -typing-extensions==4.14.0 + # via + # fastapi-cli + # fastapi-cloud-cli +typing-extensions==4.14.1 + # via + # aiosignal + # anyio + # black + # exceptiongroup + # fastapi + # huggingface-hub + # mistral-common + # multidict + # openai + # opentelemetry-api + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http + # opentelemetry-sdk + # opentelemetry-semantic-conventions + # outlines + # pydantic + # pydantic-core + # pydantic-extra-types + # referencing + # rich + # rich-toolkit + # starlette + # torch + # typer + # typing-inspection + # uvicorn + # vllm typing-inspection==0.4.1 + # via pydantic tzdata==2025.2 -urllib3==2.4.0 -uvicorn==0.34.3 + # via pandas +urllib3==2.5.0 + # via + # docker + # requests + # sentry-sdk +uvicorn==0.35.0 + # via + # fastapi + # fastapi-cli + # fastapi-cloud-cli + # open-instruct uvloop==0.21.0 ; platform_python_implementation != 'PyPy' and sys_platform != 'cygwin' and sys_platform != 'win32' -virtualenv==20.31.2 + # via uvicorn +virtualenv==20.33.1 + # via ray vllm==0.9.1 + # via open-instruct wandb==0.18.1 + # via open-instruct watchdog==6.0.0 -watchfiles==1.0.5 + # via mkdocs +watchfiles==1.1.0 + # via + # uvicorn + # vllm websockets==15.0.1 + # via uvicorn werkzeug==3.1.3 + # via tensorboard windows-curses==2.4.1 ; sys_platform == 'win32' + # via nvitop wrapt==1.17.2 + # via smart-open xformers==0.0.30 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via vllm xgrammar==0.1.19 ; platform_machine == 'aarch64' or platform_machine == 'x86_64' + # via vllm xxhash==3.5.0 -yarl==1.20.0 -zipp==3.22.0 + # via datasets +yarl==1.20.1 + # via aiohttp +zipp==3.23.0 + # via importlib-metadata From 51015922c56812925008a5ec1aee9a265f235704 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Thu, 7 Aug 2025 20:32:06 +0000 Subject: [PATCH 39/64] extra lints --- open_instruct/benchmark_generators.py | 2 +- open_instruct/test_grpo_fast.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/open_instruct/benchmark_generators.py b/open_instruct/benchmark_generators.py index cfa73db3c..a671b1c53 100644 --- a/open_instruct/benchmark_generators.py +++ b/open_instruct/benchmark_generators.py @@ -561,4 +561,4 @@ def main() -> None: if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index b24bb8640..085d246c1 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -800,4 +800,4 @@ def test_streaming_with_multiple_samples(self): if __name__ == "__main__": - unittest.main() \ No newline at end of file + unittest.main() From 5416531394210790a1fb85a28cec3ee6ab373471 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Thu, 7 Aug 2025 13:34:05 -0700 Subject: [PATCH 40/64] clean --- open_instruct/grpo_fast.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index ce9c06da1..a375172a3 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -30,14 +30,13 @@ # isort: off import os from concurrent import futures -import tqdm # We need to set NCCL_CUMEM_ENABLE=0 for performance reasons; see: # https://github.com/vllm-project/vllm/issues/5723#issuecomment-2554389656 os.environ["NCCL_CUMEM_ENABLE"] = "0" # NOQA try: import deepspeed - from deepspeed.runtime.sequence_parallel.ulysses_sp import UlyssesSPAttentionHF, UlyssesSPDataLoaderAdapter + from deepspeed.runtime.sequence_parallel.ulysses_sp import UlyssesSPAttentionHF from deepspeed.utils import groups from deepspeed.runtime.utils import move_to_device From c38e95cb75804d0e0450473fc9bd15b13ec8a035 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Thu, 7 Aug 2025 16:43:56 -0700 Subject: [PATCH 41/64] add timedelta --- open_instruct/grpo_fast.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index a375172a3..9b67ddf6b 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -30,6 +30,7 @@ # isort: off import os from concurrent import futures +from datetime import timedelta # We need to set NCCL_CUMEM_ENABLE=0 for performance reasons; see: # https://github.com/vllm-project/vllm/issues/5723#issuecomment-2554389656 @@ -564,7 +565,7 @@ def load(self, path: str, map_location=None): self.wandb_url = wandb_url torch.cuda.set_device(self.local_rank) self.device = torch.device(self.local_rank) - deepspeed.init_distributed() + deepspeed.init_distributed(timeout=timedelta(minutes=180)) ds_config = get_train_ds_config( offload=False, From 46382f1cc8bbd40eef1c019b665d7a0ddf47451c Mon Sep 17 00:00:00 2001 From: root Date: Thu, 7 Aug 2025 18:07:43 -0700 Subject: [PATCH 42/64] fix --- pyproject.toml | 4 ++-- requirements.txt | 2 +- uv.lock | 28 ++++++++++++++-------------- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index d69646168..3fc087bec 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,12 +19,12 @@ dependencies = [ "nvitop>=1.4.2", "packaging>=24.2", "peft>=0.13.2", - "tqdm", + "tqdm", "ray[default]>=2.44.1", "setuptools>=75.6.0,<80.0.0", "tensorboard>=2.18.0", "torch==2.7.0", - "transformers>=4.52.4", + "transformers>=4.52.4,<4.54.0", "vllm==0.9.1", "wandb==0.18.1", "langdetect==1.0.9", diff --git a/requirements.txt b/requirements.txt index 0869b1d2b..c723e82f9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -837,7 +837,7 @@ tqdm==4.67.1 # peft # transformers # vllm -transformers==4.55.0 +transformers==4.53.3 # via # compressed-tensors # open-instruct diff --git a/uv.lock b/uv.lock index f25112db0..35b89b9ad 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.10, <3.13" resolution-markers = [ "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", @@ -878,7 +878,7 @@ name = "exceptiongroup" version = "1.3.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "typing-extensions", marker = "python_full_version < '3.12'" }, + { name = "typing-extensions", marker = "python_full_version < '3.11'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" } wheels = [ @@ -2307,7 +2307,7 @@ name = "nvidia-cudnn-cu12" version = "9.7.1.26" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12", marker = "sys_platform != 'darwin'" }, + { name = "nvidia-cublas-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/25/dc/dc825c4b1c83b538e207e34f48f86063c88deaa35d46c651c7c181364ba2/nvidia_cudnn_cu12-9.7.1.26-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:6d011159a158f3cfc47bf851aea79e31bcff60d530b70ef70474c84cac484d07", size = 726851421, upload-time = "2025-02-06T22:18:29.812Z" }, @@ -2318,7 +2318,7 @@ name = "nvidia-cufft-cu12" version = "11.3.3.41" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/ac/26/b53c493c38dccb1f1a42e1a21dc12cba2a77fbe36c652f7726d9ec4aba28/nvidia_cufft_cu12-11.3.3.41-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:da650080ab79fcdf7a4b06aa1b460e99860646b176a43f6208099bdc17836b6a", size = 193118795, upload-time = "2025-01-23T17:56:30.536Z" }, @@ -2345,9 +2345,9 @@ name = "nvidia-cusolver-cu12" version = "11.7.2.55" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12", marker = "sys_platform != 'darwin'" }, - { name = "nvidia-cusparse-cu12", marker = "sys_platform != 'darwin'" }, - { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" }, + { name = "nvidia-cublas-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparse-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/c2/08/953675873a136d96bb12f93b49ba045d1107bc94d2551c52b12fa6c7dec3/nvidia_cusolver_cu12-11.7.2.55-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4d1354102f1e922cee9db51920dba9e2559877cf6ff5ad03a00d853adafb191b", size = 260373342, upload-time = "2025-01-23T17:58:56.406Z" }, @@ -2358,7 +2358,7 @@ name = "nvidia-cusparse-cu12" version = "12.5.7.53" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/c2/ab/31e8149c66213b846c082a3b41b1365b831f41191f9f40c6ddbc8a7d550e/nvidia_cusparse_cu12-12.5.7.53-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3c1b61eb8c85257ea07e9354606b26397612627fdcd327bfd91ccf6155e7c86d", size = 292064180, upload-time = "2025-01-23T18:00:23.233Z" }, @@ -2525,7 +2525,7 @@ requires-dist = [ { name = "torch", marker = "sys_platform != 'darwin'", specifier = "==2.7.0", index = "https://download.pytorch.org/whl/cu128" }, { name = "torch", marker = "sys_platform == 'darwin'", specifier = "==2.7.0" }, { name = "tqdm" }, - { name = "transformers", specifier = ">=4.52.4" }, + { name = "transformers", specifier = ">=4.52.4,<4.54.0" }, { name = "uvicorn", marker = "extra == 'code'", specifier = ">=0.20.0" }, { name = "vllm", specifier = "==0.9.1" }, { name = "wandb", specifier = "==0.18.1" }, @@ -4416,7 +4416,7 @@ wheels = [ [[package]] name = "transformers" -version = "4.55.0" +version = "4.53.3" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "filelock" }, @@ -4430,9 +4430,9 @@ dependencies = [ { name = "tokenizers" }, { name = "tqdm" }, ] -sdist = { url = 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"https://files.pythonhosted.org/packages/bf/f7/dd2269cce89fd1221947dd7cc3a60707ffe721ef55c1803ac3b1a1f7ae5c/xformers-0.0.30.tar.gz", hash = "sha256:a12bf3eb39e294cdbe8a7253ac9b665f41bac61d6d98df174e34ef7bdb6f2fc4", size = 10214139, upload-time = "2025-04-28T20:51:02.045Z" } wheels = [ From 98a7f7ba184636796c20070b9775917eab41f479 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Thu, 7 Aug 2025 23:41:00 -0700 Subject: [PATCH 43/64] remove timedelta --- open_instruct/grpo_fast.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 549bb7ad6..6cc3d0f41 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -565,7 +565,7 @@ def load(self, path: str, map_location=None): self.wandb_url = wandb_url torch.cuda.set_device(self.local_rank) self.device = torch.device(self.local_rank) - deepspeed.init_distributed(timeout=timedelta(minutes=180)) + deepspeed.init_distributed() ds_config = get_train_ds_config( offload=False, From 8c526842a424f3c9f5713e184744aec7658c52ef Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Fri, 8 Aug 2025 08:24:17 -0700 Subject: [PATCH 44/64] Adding some timedeltas --- open_instruct/grpo_fast.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 6cc3d0f41..085f2eb6d 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -565,7 +565,7 @@ def load(self, path: str, map_location=None): self.wandb_url = wandb_url torch.cuda.set_device(self.local_rank) self.device = torch.device(self.local_rank) - deepspeed.init_distributed() + deepspeed.init_distributed(timeout=timedelta(minutes=180)) ds_config = get_train_ds_config( offload=False, @@ -760,6 +760,7 @@ def setup_model_update_group(self, vllm_engines): world_size=world_size, rank=0, group_name="openrlhf", + timeout=timedelta(minutes=180), ) ray_get_with_progress(refs, desc="Initializing vLLM process groups", timeout=60) torch.distributed.barrier() From e986ad65fc60acc1878f2c9997500d6e984c117a Mon Sep 17 00:00:00 2001 From: root Date: Fri, 8 Aug 2025 13:10:47 -0700 Subject: [PATCH 45/64] fixes --- open_instruct/benchmark_generators.py | 3 +- open_instruct/grpo_fast.py | 49 +++++++++++++++------------ open_instruct/queue_types.py | 44 ++++++++++++++++++++++++ open_instruct/rl_utils2.py | 2 -- open_instruct/test_grpo_fast.py | 3 +- open_instruct/vllm_utils3.py | 36 +------------------- 6 files changed, 77 insertions(+), 60 deletions(-) create mode 100644 open_instruct/queue_types.py diff --git a/open_instruct/benchmark_generators.py b/open_instruct/benchmark_generators.py index a671b1c53..cdcba596c 100644 --- a/open_instruct/benchmark_generators.py +++ b/open_instruct/benchmark_generators.py @@ -27,6 +27,7 @@ from ray.util import queue as ray_queue from open_instruct import dataset_transformation, grpo_fast, model_utils, utils, vllm_utils3 +from open_instruct.queue_types import PromptRequest # For FLOPS, we assume bf16 and ignore sparsity. GPU_SPECS = { @@ -383,7 +384,7 @@ def run_benchmark( ] submission_start_time = time.time() for batch_idx in range(num_batches): - param_prompt_Q.put(vllm_utils3.PromptRequest(prompts=all_prompts[batch_idx], dataset_index=batch_idx)) + param_prompt_Q.put(PromptRequest(prompts=all_prompts[batch_idx], dataset_index=batch_idx)) submission_time = time.time() - submission_start_time logger.info(f"All batches submitted in {submission_time:.2f}s") diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 5edc5d374..e91891881 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -128,14 +128,8 @@ repeat_each, sync_gs_bucket, ) -from open_instruct.vllm_utils3 import ( - GenerationResult, - LLMRayActor, - PromptRequest, - RequestInfo, - create_vllm_engines, - init_process_group, -) +from open_instruct.vllm_utils3 import LLMRayActor, create_vllm_engines, init_process_group +from open_instruct.queue_types import GenerationResult, PromptRequest, RequestInfo # Setup logging with filename and line number format logging.basicConfig( @@ -1230,12 +1224,18 @@ def accumulate_inference_batches( all_ground_truths = [] all_datasets = [] - for i in tqdm( - range(args.vllm_num_engines), - total=args.vllm_num_engines, - desc=f"Accumulating results from {args.vllm_num_engines} engines ({timeout=})", - bar_format="{l_bar}{bar}{r_bar}\n", - ): + + if args.verbose: + engine_iter = tqdm( + range(args.vllm_num_engines), + total=args.vllm_num_engines, + desc=f"Accumulating results from {args.vllm_num_engines} engines ({timeout=})", + bar_format="{l_bar}{bar}{r_bar}\n", + ) + else: + engine_iter = range(args.vllm_num_engines) + + for i in engine_iter: result = inference_results_Q.get(timeout=timeout) dataset_indices = result.dataset_index @@ -1913,22 +1913,29 @@ def generate_thread(vllm_engines, local_eval_every, num_training_steps, resume_t logger.info("[Generate Thread] 🚀 Starting generation thread") while not stop_event.is_set(): - with Timer("🔥 Generation time"): + with Timer("🔥 Generation time") as _gen_timer: engine_refs = [ engine.process_from_queue.remote(num_training_steps, resume_training_step, timeout=0.1) for engine in vllm_engines ] engine_futures = [ref.future() for ref in engine_refs] processed_results = [] - with tqdm( - total=len(vllm_engines), - desc="[Generate Thread] Waiting for vLLM engines to return", - bar_format="{l_bar}{bar}{r_bar}\n", - ) as pbar: + if args.verbose: + with tqdm( + total=len(vllm_engines), + desc="[Generate Thread] Waiting for vLLM engines to return", + bar_format="{l_bar}{bar}{r_bar}\n", + ) as pbar: + for future in futures.as_completed(engine_futures): + processed_results.append(future.result()) + pbar.update(1) + else: for future in futures.as_completed(engine_futures): processed_results.append(future.result()) - pbar.update(1) num_processed = sum(int(result) for result in processed_results) + # Suppress timing output if nothing was processed + if num_processed == 0: + _gen_timer.noop = 1 if num_processed == 0: # If no batches were processed, sleep for a short time to avoid busy waiting time.sleep(1) diff --git a/open_instruct/queue_types.py b/open_instruct/queue_types.py new file mode 100644 index 000000000..2d1a92ead --- /dev/null +++ b/open_instruct/queue_types.py @@ -0,0 +1,44 @@ +from dataclasses import dataclass +from typing import Any, List, Optional + + +@dataclass +class RequestInfo: + """Container for tool usage information used in queue payloads.""" + + num_calls: List[int] + timeouts: List[int] + tool_errors: List[str] + tool_outputs: List[str] + tool_runtimes: List[float] + tool_calleds: List[bool] + + +@dataclass +class GenerationResult: + """Container for generation results returned via Ray queues.""" + + responses: List[List[int]] + finish_reasons: List[str] + masks: List[List[int]] + request_info: RequestInfo + dataset_index: Optional[List[int]] = None + training_step: Optional[int] = None + + +@dataclass +class PromptRequest: + """Container for prompt requests sent via Ray queues. + + Note: We intentionally type `generation_config` as `Any` to avoid importing + heavy dependencies (e.g., vLLM) at import time in deserializers like Ray's + `_QueueActor`. + """ + + prompts: List[List[int]] + generation_config: Any + training_step: Optional[int] = None + dataset_index: Optional[List[int]] = None + is_eval: bool = False + + diff --git a/open_instruct/rl_utils2.py b/open_instruct/rl_utils2.py index 7e99c17b4..0cf417053 100644 --- a/open_instruct/rl_utils2.py +++ b/open_instruct/rl_utils2.py @@ -19,8 +19,6 @@ def __init__(self, description: str, noop: int = 0): self.noop = noop def __enter__(self): - if self.noop: - return self.start_time = time.perf_counter() return self diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index 085d246c1..c3df6c0ff 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -11,7 +11,8 @@ from vllm import SamplingParams from open_instruct import grpo_fast, model_utils, utils -from open_instruct.vllm_utils3 import GenerationResult, PromptRequest, RequestInfo, create_vllm_engines +from open_instruct.vllm_utils3 import create_vllm_engines +from open_instruct.queue_types import GenerationResult, PromptRequest, RequestInfo class TestGrpoFastBase(unittest.TestCase): diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index c425d7671..0bb5b0ec6 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -15,7 +15,6 @@ """This file is copied from https://github.com/OpenRLHF/OpenRLHF""" -import dataclasses import logging import os import queue @@ -39,44 +38,11 @@ ) from open_instruct.utils import ray_get_with_progress +from open_instruct.queue_types import GenerationResult, PromptRequest, RequestInfo logger = logging.getLogger(__name__) -@dataclasses.dataclass -class RequestInfo: - """Container for tool usage information.""" - - num_calls: List[int] - timeouts: List[int] - tool_errors: List[str] - tool_outputs: List[str] - tool_runtimes: List[float] - tool_calleds: List[bool] - - -@dataclasses.dataclass -class GenerationResult: - """Container for generation results from vLLM.""" - - responses: List[List[int]] - finish_reasons: List[str] - masks: List[List[int]] - request_info: RequestInfo - dataset_index: Optional[List[int]] = None - training_step: Optional[int] = None - - -@dataclasses.dataclass -class PromptRequest: - """Container for prompt requests to vLLM.""" - - prompts: List[List[int]] - generation_config: vllm.SamplingParams - training_step: Optional[int] = None - dataset_index: Optional[List[int]] = None - is_eval: bool = False - def ray_noset_visible_devices(env_vars=os.environ): # Refer to From 95c98602e1a54598c129f17531a1af7f336e056c Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Fri, 8 Aug 2025 13:16:00 -0700 Subject: [PATCH 46/64] lint --- open_instruct/grpo_fast.py | 1 - open_instruct/queue_types.py | 2 -- open_instruct/vllm_utils3.py | 1 - 3 files changed, 4 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index e91891881..fe9b54079 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1224,7 +1224,6 @@ def accumulate_inference_batches( all_ground_truths = [] all_datasets = [] - if args.verbose: engine_iter = tqdm( range(args.vllm_num_engines), diff --git a/open_instruct/queue_types.py b/open_instruct/queue_types.py index 2d1a92ead..ae27a2e38 100644 --- a/open_instruct/queue_types.py +++ b/open_instruct/queue_types.py @@ -40,5 +40,3 @@ class PromptRequest: training_step: Optional[int] = None dataset_index: Optional[List[int]] = None is_eval: bool = False - - diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index 0bb5b0ec6..594f40a57 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -43,7 +43,6 @@ logger = logging.getLogger(__name__) - def ray_noset_visible_devices(env_vars=os.environ): # Refer to # https://github.com/ray-project/ray/blob/161849364a784442cc659fb9780f1a6adee85fce/python/ray/_private/accelerators/nvidia_gpu.py#L95-L96 From 09b47c25ac5fc90ae560a7b901a2929c003d24e6 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Fri, 8 Aug 2025 13:18:27 -0700 Subject: [PATCH 47/64] lint --- open_instruct/grpo_fast.py | 4 ++-- open_instruct/test_grpo_fast.py | 2 +- open_instruct/vllm_utils3.py | 3 +-- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index fe9b54079..184c657bf 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -66,7 +66,6 @@ import torch import torch.utils import torch.utils.data -import wandb from huggingface_hub import HfApi from peft import PeftModel, get_peft_model_state_dict from ray.util import queue as ray_queue @@ -79,6 +78,7 @@ from transformers.integrations import HfDeepSpeedConfig from vllm import SamplingParams +import wandb from open_instruct.dataset_transformation import ( GROUND_TRUTHS_KEY, INPUT_IDS_PROMPT_KEY, @@ -104,6 +104,7 @@ print_rich_table, push_folder_to_hub, ) +from open_instruct.queue_types import GenerationResult, PromptRequest, RequestInfo from open_instruct.rl_utils2 import Timer, pack_sequences from open_instruct.utils import ( ArgumentParserPlus, @@ -129,7 +130,6 @@ sync_gs_bucket, ) from open_instruct.vllm_utils3 import LLMRayActor, create_vllm_engines, init_process_group -from open_instruct.queue_types import GenerationResult, PromptRequest, RequestInfo # Setup logging with filename and line number format logging.basicConfig( diff --git a/open_instruct/test_grpo_fast.py b/open_instruct/test_grpo_fast.py index c3df6c0ff..345191888 100644 --- a/open_instruct/test_grpo_fast.py +++ b/open_instruct/test_grpo_fast.py @@ -11,8 +11,8 @@ from vllm import SamplingParams from open_instruct import grpo_fast, model_utils, utils -from open_instruct.vllm_utils3 import create_vllm_engines from open_instruct.queue_types import GenerationResult, PromptRequest, RequestInfo +from open_instruct.vllm_utils3 import create_vllm_engines class TestGrpoFastBase(unittest.TestCase): diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index 594f40a57..e9d0e8c53 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -37,8 +37,8 @@ rendezvous, ) +from open_instruct.queue_types import GenerationResult, RequestInfo from open_instruct.utils import ray_get_with_progress -from open_instruct.queue_types import GenerationResult, PromptRequest, RequestInfo logger = logging.getLogger(__name__) @@ -307,7 +307,6 @@ def create_vllm_engines( results_queue=None, eval_results_queue=None, ) -> list[LLMRayActor]: - import vllm assert vllm.__version__ >= "0.8.1", "OpenRLHF only supports vllm >= 0.8.1" From e66f434fe92e23b1c75ae9a42c4038bc496987b0 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Fri, 8 Aug 2025 13:20:58 -0700 Subject: [PATCH 48/64] lint again --- open_instruct/vllm_utils3.py | 1 - 1 file changed, 1 deletion(-) diff --git a/open_instruct/vllm_utils3.py b/open_instruct/vllm_utils3.py index e9d0e8c53..f8a208f9f 100644 --- a/open_instruct/vllm_utils3.py +++ b/open_instruct/vllm_utils3.py @@ -307,7 +307,6 @@ def create_vllm_engines( results_queue=None, eval_results_queue=None, ) -> list[LLMRayActor]: - assert vllm.__version__ >= "0.8.1", "OpenRLHF only supports vllm >= 0.8.1" # Convert max_tool_calls to a dict mapping tool end strings to their limits From e08874a54a02e7a46232f484ffd03f5aaf5027df Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Fri, 8 Aug 2025 13:27:18 -0700 Subject: [PATCH 49/64] fix lint i hope --- open_instruct/grpo_fast.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 184c657bf..2ea6334a0 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -66,6 +66,7 @@ import torch import torch.utils import torch.utils.data +import wandb from huggingface_hub import HfApi from peft import PeftModel, get_peft_model_state_dict from ray.util import queue as ray_queue @@ -78,7 +79,6 @@ from transformers.integrations import HfDeepSpeedConfig from vllm import SamplingParams -import wandb from open_instruct.dataset_transformation import ( GROUND_TRUTHS_KEY, INPUT_IDS_PROMPT_KEY, From bd4b8397abd865115880e87ec399e8107be6c7ec Mon Sep 17 00:00:00 2001 From: root Date: Fri, 15 Aug 2025 05:24:01 +0000 Subject: [PATCH 50/64] adding current working debug state --- open_instruct/grpo_fast.py | 15 ++++++++----- open_instruct/utils.py | 44 +++++++++++++++++++++++++++++--------- 2 files changed, 44 insertions(+), 15 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index b63d27423..8f7788356 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -589,7 +589,7 @@ def load(self, path: str, map_location=None): if args.sequence_parallel_size > 1: self.mpu = UlyssesSPAttentionHF.register_with_transformers( model_name_or_path=model_config.model_name_or_path, - core_attn_implementation="flash_attention_2", + core_attn_implementation="sdpa", sequence_parallel_size=args.sequence_parallel_size, max_length=args.max_token_length, micro_batch_size=args.per_device_train_batch_size, @@ -600,7 +600,7 @@ def load(self, path: str, map_location=None): model_config.model_name_or_path, revision=model_config.model_revision, torch_dtype=torch.bfloat16, - attn_implementation="flash_attention_2", + attn_implementation="sdpa", use_cache=False, ) disable_dropout_in_model(self.policy) @@ -628,7 +628,7 @@ def load(self, path: str, map_location=None): optimizer=self.optimizer, config=ds_config, lr_scheduler=scheduler, - dist_init_required=True, + dist_init_required=False, mpu=self.mpu, ) optimization_steps_done = 0 @@ -674,7 +674,7 @@ def load(self, path: str, map_location=None): model_config.model_name_or_path, revision=model_config.model_revision, torch_dtype=torch.bfloat16, - attn_implementation="flash_attention_2", + attn_implementation="sdpa", use_cache=False, ) disable_dropout_in_model(self.ref_policy) @@ -688,11 +688,13 @@ def load(self, path: str, map_location=None): self.sp_rank = groups._get_sequence_parallel_rank() print(f"SETTING UP: {self.sp_rank=} {self.sp_world_size=}") self.splitter = UlyssesSPSplitter( - sp_rank=self.sp_rank, sp_group=self.sp_group, sp_world_size=self.sp_world_size, device=self.device + sp_rank=self.sp_rank, sp_group=self.sp_group, sp_world_size=self.sp_world_size, device=self.device, pad_token_id=self.tokenizer.pad_token_id ) else: self.splitter = None + print("WE SETUP !!") + return optimization_steps_done def forward( @@ -709,6 +711,7 @@ def forward( padding_mask = query_response != pad_token_id input_ids = torch.masked_fill(query_response, ~padding_mask, 0) # NOTE: the [:-1] and [1:] are because the logits and generated tokens are off by 1 in index + print("pre forward") output = model( input_ids=input_ids[:, :-1], # @vwxyzjn: without clamp, we get index out of bounds errors; TODO: investigate @@ -716,6 +719,7 @@ def forward( position_ids=position_ids[:, :-1], return_dict=True, ) + print("post forward") logits = output.logits logits /= temperature + 1e-7 logprob = log_softmax_and_gather(logits, input_ids[:, 1:]) @@ -918,6 +922,7 @@ def train( attention_mask = collated_attention_masks[i] position_id = collated_position_ids[i] response_mask = collated_response_masks[i] + print(f"Calling forward! {groups._get_sequence_parallel_rank()=}") ref_logprob, _ = self.forward( self.ref_policy, query_response, diff --git a/open_instruct/utils.py b/open_instruct/utils.py index 44967916f..e340c92fe 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -1608,9 +1608,8 @@ def check_runtime_leaks( return report - class UlyssesSPSplitter: - def __init__(self, sp_rank: int, sp_group, sp_world_size, device): + def __init__(self, sp_rank: int, sp_group, sp_world_size, device, pad_token_id): """ Adapted from the UlyssesSPDataLoaderAdapter (https://github.com/deepspeedai/DeepSpeed/blob/master/deepspeed/runtime/sequence_parallel/ulysses_sp.py#L427) @@ -1620,26 +1619,49 @@ def __init__(self, sp_rank: int, sp_group, sp_world_size, device): self.sp_group = sp_group self.sp_world_size = sp_world_size self.device = device + self.pad_token_id = pad_token_id def split_batch(self, batch): micro_batches = defaultdict(dict) - # we have batches of variable seqlen so in order to do all_gather on batches - we need to know the exact length of each tensor on each rank - seqlen = torch.tensor(len(batch["input_ids"][0][0]), dtype=torch.int64, device=self.device) + # Get all sequence lengths and find the maximum + # batch[k] is a list of tensors, so we need to get the first item's sequence length + local_seqlen = torch.tensor([len(batch["input_ids"][0])], dtype=torch.int64, device=self.device) seqlens = [torch.zeros(1, dtype=torch.int64, device=self.device) for _ in range(self.sp_world_size)] - dist.all_gather(seqlens, seqlen, group=self.sp_group) + dist.all_gather(seqlens, local_seqlen, group=self.sp_group) seqlens = [x[0].item() for x in seqlens] + # Use the maximum sequence length for padding + max_seqlen = max(seqlens) + + # Ensure max_seqlen is divisible by sp_world_size + if max_seqlen % self.sp_world_size != 0: + max_seqlen = ((max_seqlen + self.sp_world_size - 1) // self.sp_world_size) * self.sp_world_size + for k in batch.keys(): if torch.is_tensor(batch[k]): + # batch[k] is a single tensor, move to device and pad batch[k] = batch[k].to(self.device) + + # Pad the tensor to max_seqlen if needed + if batch[k].shape[1] < max_seqlen: + pad_len = max_seqlen - batch[k].shape[1] + if k == "input_ids": + batch[k] = F.pad(batch[k], (0, pad_len), value=self.pad_token_id) + elif k == "attention_mask": + batch[k] = F.pad(batch[k], (0, pad_len), value=0) + else: + batch[k] = F.pad(batch[k], (0, pad_len), value=-100 if k == "labels" else 0) + with torch.no_grad(): + # Now all tensors have the same shape (batch_size, max_seqlen) tensor_list = [ - torch.zeros((batch[k].shape[0], seqlens[i]), dtype=batch[k].dtype, device=batch[k].device) - for i in range(self.sp_world_size) + torch.zeros((batch[k].shape[0], max_seqlen), dtype=batch[k].dtype, device=batch[k].device) + for _ in range(self.sp_world_size) ] dist.all_gather(tensor_list, batch[k], group=self.sp_group) else: + # batch[k] is a list or other non-tensor tensor_list = [None for _ in range(self.sp_world_size)] dist.all_gather_object(tensor_list, batch[k], group=self.sp_group) @@ -1651,7 +1673,8 @@ def split_batch(self, batch): batch_shards = [] for batch in micro_batches.values(): - seq_length = len(batch["input_ids"][0][0]) + # After all_gather, batch[k] should be tensors again + seq_length = len(batch["input_ids"][0][0]) if isinstance(batch["input_ids"], list) else batch["input_ids"].shape[1] if seq_length % self.sp_world_size != 0: raise ValueError(f"batch's seqlen={seq_length} isn't divisible by sp-size={self.sp_world_size}") @@ -1672,8 +1695,9 @@ def split_batch(self, batch): # leave non-tensors alone if not torch.is_tensor(batch[k]): continue - # at seqlen>10M and 32+ gpus this can take GBs of memory so keep the prefill buffer on cpu - batch[k] = batch[k][:, chunk_len * self.sp_rank : chunk_len * (self.sp_rank + 1)].cpu() + for idx in range(len(batch[k])): + # at seqlen>10M and 32+ gpus this can take GBs of memory so keep the prefill buffer on cpu + batch[k][idx] = batch[k][idx][:, chunk_len * self.sp_rank : chunk_len * (self.sp_rank + 1)].cpu() batch_shards.append(batch) From 8b459cd918a7e3fc18f054ed0dc28856c617980a Mon Sep 17 00:00:00 2001 From: root Date: Fri, 15 Aug 2025 05:58:56 +0000 Subject: [PATCH 51/64] some fixes --- open_instruct/grpo_fast.py | 27 ++++----- open_instruct/utils.py | 112 ++++++++++++++++++++++++++++--------- 2 files changed, 98 insertions(+), 41 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 8f7788356..53db0dbc6 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -922,7 +922,7 @@ def train( attention_mask = collated_attention_masks[i] position_id = collated_position_ids[i] response_mask = collated_response_masks[i] - print(f"Calling forward! {groups._get_sequence_parallel_rank()=}") + print(f"Calling forward! {groups._get_sequence_parallel_rank()=} {query_response.shape=}") ref_logprob, _ = self.forward( self.ref_policy, query_response, @@ -1048,20 +1048,17 @@ def train( pg_loss_max + (args.beta * kl), mb_response_masks_bool, args.masked_mean_axis ) else: - # if SP, compute per-token loss, then we need to gather across SP ranks - per_tok_loss = pg_loss_max + (args.beta * kl) * mb_response_masks_bool.float() - losses_per_rank = torch.distributed.nn.functional.all_gather(per_tok_loss, group=self.sp_group) - non_masked_tokens = mb_response_masks_bool.sum() - non_masked_tokens_per_rank = torch.distributed.nn.functional.all_gather( - non_masked_tokens, group=self.sp_group - ) - total_loss = sum( - losses_per_rank[rank] * non_masked_tokens_per_rank[rank] - for rank in range(self.sp_world_size) - ) - total_non_masked_tokens = sum(non_masked_tokens_per_rank) - loss = (total_loss / total_non_masked_tokens).sum() - # TODO: do we still need grad acc...? I think so. + # SP: gather loss sums from all ranks, divide by total valid tokens + local_loss_sum = ((pg_loss_max + args.beta * kl) * mb_response_masks_bool.float()).sum() + good_tokens = mb_response_masks_bool.sum() + + loss_sums_per_rank = torch.distributed.nn.functional.all_gather(local_loss_sum, group=self.sp_group) + good_tokens_per_rank = torch.distributed.nn.functional.all_gather(good_tokens, group=self.sp_group) + + total_loss_sum = sum(loss_sums_per_rank) + total_good_tokens = sum(good_tokens_per_rank) + + loss = total_loss_sum / total_good_tokens if total_good_tokens > 0 else torch.tensor(0.0, device=local_loss_sum.device) loss = loss / accumulation_steps self.model.backward(loss) if (local_step + 1) % accumulation_steps == 0: diff --git a/open_instruct/utils.py b/open_instruct/utils.py index e340c92fe..34e93391b 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -54,6 +54,7 @@ import ray import requests import torch +import torch.nn.functional as F from accelerate.logging import get_logger from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk from datasets.builder import DatasetGenerationError @@ -1624,9 +1625,20 @@ def __init__(self, sp_rank: int, sp_group, sp_world_size, device, pad_token_id): def split_batch(self, batch): micro_batches = defaultdict(dict) - # Get all sequence lengths and find the maximum - # batch[k] is a list of tensors, so we need to get the first item's sequence length - local_seqlen = torch.tensor([len(batch["input_ids"][0])], dtype=torch.int64, device=self.device) + # First, find the maximum sequence length across all ranks + # Handle both tensor and list cases for getting local sequence length + if torch.is_tensor(batch["input_ids"]): + # batch["input_ids"] is a tensor of shape [batch_size, seq_len] + local_seqlen = torch.tensor([batch["input_ids"].shape[1]], dtype=torch.int64, device=self.device) + else: + # batch["input_ids"] is a list of tensors - find max length in this rank's data + max_local_len = 0 + for item in batch["input_ids"]: + if torch.is_tensor(item): + max_local_len = max(max_local_len, item.shape[-1]) + local_seqlen = torch.tensor([max_local_len], dtype=torch.int64, device=self.device) + + # Gather sequence lengths from all ranks seqlens = [torch.zeros(1, dtype=torch.int64, device=self.device) for _ in range(self.sp_world_size)] dist.all_gather(seqlens, local_seqlen, group=self.sp_group) seqlens = [x[0].item() for x in seqlens] @@ -1638,9 +1650,10 @@ def split_batch(self, batch): if max_seqlen % self.sp_world_size != 0: max_seqlen = ((max_seqlen + self.sp_world_size - 1) // self.sp_world_size) * self.sp_world_size + # Now pad everything to max_seqlen BEFORE any collective operations for k in batch.keys(): if torch.is_tensor(batch[k]): - # batch[k] is a single tensor, move to device and pad + # Single tensor case batch[k] = batch[k].to(self.device) # Pad the tensor to max_seqlen if needed @@ -1652,16 +1665,32 @@ def split_batch(self, batch): batch[k] = F.pad(batch[k], (0, pad_len), value=0) else: batch[k] = F.pad(batch[k], (0, pad_len), value=-100 if k == "labels" else 0) - + else: + # List case - pad each tensor in the list to max_seqlen + for i, item in enumerate(batch[k]): + if torch.is_tensor(item): + item = item.to(self.device) + if item.shape[-1] < max_seqlen: + pad_len = max_seqlen - item.shape[-1] + if k == "input_ids": + item = F.pad(item, (0, pad_len), value=self.pad_token_id) + elif k == "attention_mask": + item = F.pad(item, (0, pad_len), value=0) + else: + item = F.pad(item, (0, pad_len), value=-100 if k == "labels" else 0) + batch[k][i] = item # Update the list in place + + # Now that everything is padded to the same length, do the collective operations + for k in batch.keys(): + if torch.is_tensor(batch[k]): with torch.no_grad(): - # Now all tensors have the same shape (batch_size, max_seqlen) tensor_list = [ torch.zeros((batch[k].shape[0], max_seqlen), dtype=batch[k].dtype, device=batch[k].device) for _ in range(self.sp_world_size) ] dist.all_gather(tensor_list, batch[k], group=self.sp_group) else: - # batch[k] is a list or other non-tensor + # Now all tensors in the list should have the same shape tensor_list = [None for _ in range(self.sp_world_size)] dist.all_gather_object(tensor_list, batch[k], group=self.sp_group) @@ -1672,33 +1701,64 @@ def split_batch(self, batch): del batch batch_shards = [] - for batch in micro_batches.values(): - # After all_gather, batch[k] should be tensors again - seq_length = len(batch["input_ids"][0][0]) if isinstance(batch["input_ids"], list) else batch["input_ids"].shape[1] + for batch_data in micro_batches.values(): + # Determine sequence length based on the structure + if torch.is_tensor(batch_data["input_ids"]): + seq_length = batch_data["input_ids"].shape[1] + batch_size = batch_data["input_ids"].shape[0] + else: + # List of tensors + seq_length = batch_data["input_ids"][0].shape[-1] + batch_size = len(batch_data["input_ids"]) + if seq_length % self.sp_world_size != 0: raise ValueError(f"batch's seqlen={seq_length} isn't divisible by sp-size={self.sp_world_size}") chunk_len = seq_length // self.sp_world_size - # for rl training, we don't have labels, so we don't need to do this - if "labels" in batch: - # because we have to gather logits from all sp ranks we have to do the loss function ourselves - # therefore remove labels to avoid an attempt to calculate loss by transformers - labels = batch.pop("labels") - labels = torch.nn.functional.pad(labels, (0, 1), value=-100) - batch["shift_labels"] = labels[..., 1:].contiguous() - # free up temp memory + # Handle labels if present + if "labels" in batch_data: + labels = batch_data.pop("labels") + if torch.is_tensor(labels): + labels = F.pad(labels, (0, 1), value=-100) + batch_data["shift_labels"] = labels[..., 1:].contiguous() + else: + # Handle list case + shift_labels = [] + for label in labels: + padded_label = F.pad(label, (0, 1), value=-100) + shift_labels.append(padded_label[..., 1:].contiguous()) + batch_data["shift_labels"] = shift_labels del labels - # batch sharding - for k in batch.keys(): - # leave non-tensors alone - if not torch.is_tensor(batch[k]): + # Batch sharding + for k in batch_data.keys(): + if not torch.is_tensor(batch_data[k]) and not isinstance(batch_data[k], list): continue - for idx in range(len(batch[k])): - # at seqlen>10M and 32+ gpus this can take GBs of memory so keep the prefill buffer on cpu - batch[k][idx] = batch[k][idx][:, chunk_len * self.sp_rank : chunk_len * (self.sp_rank + 1)].cpu() - batch_shards.append(batch) + if torch.is_tensor(batch_data[k]): + # Tensor case: shard each item in batch dimension + sharded_items = [] + for idx in range(batch_size): + item = batch_data[k][idx] + sharded_item = item[chunk_len * self.sp_rank : chunk_len * (self.sp_rank + 1)].cpu() + sharded_items.append(sharded_item) + batch_data[k] = sharded_items + else: + # List case: shard each tensor in the list + sharded_items = [] + for item in batch_data[k]: + if torch.is_tensor(item): + # Handle different tensor dimensions appropriately + if item.dim() == 1: + sharded_item = item[chunk_len * self.sp_rank : chunk_len * (self.sp_rank + 1)].cpu() + else: + sharded_item = item[:, chunk_len * self.sp_rank : chunk_len * (self.sp_rank + 1)].cpu() + sharded_items.append(sharded_item) + else: + sharded_items.append(item) + batch_data[k] = sharded_items + + batch_shards.append(batch_data) return batch_shards From 69c24083809fa48b9f0529ce219c3d87b93cb3a4 Mon Sep 17 00:00:00 2001 From: root Date: Fri, 15 Aug 2025 05:59:29 +0000 Subject: [PATCH 52/64] fix some deps --- pyproject.toml | 4 +-- uv.lock | 75 +++++++++++++++++++------------------------------- 2 files changed, 31 insertions(+), 48 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index d55bc14a3..88b044e9a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ dependencies = [ "bitsandbytes>=0.44.1; platform_system != 'Darwin'", "datasets>=3.1.0", "debugpy>=1.8.13", - "deepspeed==0.17.2", + "deepspeed==0.17.4", "hf-transfer>=0.1.8", "litellm>=1.72.0", "matplotlib>=3.9.3", @@ -24,7 +24,7 @@ dependencies = [ "packaging>=24.2", "peft>=0.13.2", "tqdm", - "ray[default]>=2.44.1", + "ray[default]>=2.44.1,<2.47", "setuptools>=75.6.0,<80.0.0", "tensorboard>=2.18.0", "torch==2.7.0", diff --git a/uv.lock b/uv.lock index 50a93e348..9f1771ddc 100644 --- a/uv.lock +++ b/uv.lock @@ -697,7 +697,7 @@ wheels = [ [[package]] name = "deepspeed" -version = "0.17.2" +version = "0.17.4" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "einops" }, @@ -713,7 +713,7 @@ dependencies = [ { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform != 'darwin'" }, { name = "tqdm" }, ] -sdist = { url = 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"https://files.pythonhosted.org/packages/bf/f7/dd2269cce89fd1221947dd7cc3a60707ffe721ef55c1803ac3b1a1f7ae5c/xformers-0.0.30.tar.gz", hash = "sha256:a12bf3eb39e294cdbe8a7253ac9b665f41bac61d6d98df174e34ef7bdb6f2fc4", size = 10214139, upload-time = "2025-04-28T20:51:02.045Z" } wheels = [ From e22747cc320f02b77b3cf34186a69f1f6cf5a7ae Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Sun, 17 Aug 2025 11:04:37 -0700 Subject: [PATCH 53/64] cleaning --- open_instruct/grpo_fast.py | 97 +++++++++++++++----------------------- 1 file changed, 39 insertions(+), 58 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 53db0dbc6..a5ec5f392 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -686,15 +686,12 @@ def load(self, path: str, map_location=None): self.sp_group = groups._get_sequence_parallel_group() self.sp_world_size = groups._get_sequence_parallel_world_size() self.sp_rank = groups._get_sequence_parallel_rank() - print(f"SETTING UP: {self.sp_rank=} {self.sp_world_size=}") self.splitter = UlyssesSPSplitter( sp_rank=self.sp_rank, sp_group=self.sp_group, sp_world_size=self.sp_world_size, device=self.device, pad_token_id=self.tokenizer.pad_token_id ) else: self.splitter = None - print("WE SETUP !!") - return optimization_steps_done def forward( @@ -711,7 +708,6 @@ def forward( padding_mask = query_response != pad_token_id input_ids = torch.masked_fill(query_response, ~padding_mask, 0) # NOTE: the [:-1] and [1:] are because the logits and generated tokens are off by 1 in index - print("pre forward") output = model( input_ids=input_ids[:, :-1], # @vwxyzjn: without clamp, we get index out of bounds errors; TODO: investigate @@ -719,7 +715,6 @@ def forward( position_ids=position_ids[:, :-1], return_dict=True, ) - print("post forward") logits = output.logits logits /= temperature + 1e-7 logprob = log_softmax_and_gather(logits, input_ids[:, 1:]) @@ -859,58 +854,45 @@ def train( original_length = len(collated_query_responses) if self.splitter is not None: - batch = { - "input_ids": collated_query_responses, - "attention_mask": collated_attention_masks, - "position_ids": collated_position_ids, - "response_masks": collated_response_masks, - "tool_masks": collated_tool_masks, - "advantages": collated_advantages, - } - # Pad the items in the batch so they are divisible by sp_world_size - # where attention mask is 0, we dont end up using anyway. - for i in range(len(batch["input_ids"])): - for k in batch.keys(): - if torch.is_tensor(batch[k][i]): - seq_length = batch[k][i].shape[1] - if seq_length % self.sp_world_size != 0: - padding_length = self.sp_world_size - (seq_length % self.sp_world_size) - padding = torch.zeros( - (batch[k][i].shape[0], padding_length), - dtype=batch[k][i].dtype, - device=batch[k][i].device, - ) - batch[k][i] = torch.cat((batch[k][i], padding), dim=1) - sharded_batches = self.splitter.split_batch(batch) - - # we need to flatten out the sharded batches so its like: - # b0 sp0, b0 sp1, ..., b1 sp0, b1 sp1, ..., b2 sp0, b2 sp1, ... etc. - # right now, it comes out a list of sp shards, and inside is (bsz, seq_len) - collated_query_responses = [] - collated_attention_masks = [] - collated_position_ids = [] - collated_response_masks = [] - collated_tool_masks = [] - collated_advantages = [] - - for batch_idx in range(original_length): - for sp_idx in range(len(sharded_batches)): - collated_query_responses.append(sharded_batches[sp_idx]["input_ids"][batch_idx]) - collated_attention_masks.append(sharded_batches[sp_idx]["attention_mask"][batch_idx]) - collated_position_ids.append(sharded_batches[sp_idx]["position_ids"][batch_idx]) - collated_response_masks.append(sharded_batches[sp_idx]["response_masks"][batch_idx]) - collated_tool_masks.append(sharded_batches[sp_idx]["tool_masks"][batch_idx]) - collated_advantages.append(sharded_batches[sp_idx]["advantages"][batch_idx]) - - collated_query_responses = move_to_device(collated_query_responses, self.ref_policy.device) - collated_attention_masks = move_to_device(collated_attention_masks, self.ref_policy.device) - collated_position_ids = move_to_device(collated_position_ids, self.ref_policy.device) - collated_response_masks = move_to_device(collated_response_masks, self.ref_policy.device) - collated_tool_masks = move_to_device(collated_tool_masks, self.ref_policy.device) - collated_advantages = move_to_device(collated_advantages, self.ref_policy.device) - - # adjust accumulation steps for sp (do we need to do this?) - # accumulation_steps = accumulation_steps * self.sp_world_size + with Timer("Splitting batch for SP", noop=self.rank != 0): + batch = { + "input_ids": collated_query_responses, + "attention_mask": collated_attention_masks, + "position_ids": collated_position_ids, + "response_masks": collated_response_masks, + "tool_masks": collated_tool_masks, + "advantages": collated_advantages, + } + # Pad the items in the batch so they are divisible by sp_world_size + # where attention mask is 0, we dont end up using anyway. + for i in range(len(batch["input_ids"])): + for k in batch.keys(): + if torch.is_tensor(batch[k][i]): + seq_length = batch[k][i].shape[1] + if seq_length % self.sp_world_size != 0: + padding_length = self.sp_world_size - (seq_length % self.sp_world_size) + padding = torch.zeros( + (batch[k][i].shape[0], padding_length), + dtype=batch[k][i].dtype, + device=batch[k][i].device, + ) + batch[k][i] = torch.cat((batch[k][i], padding), dim=1) + sharded_batches = self.splitter.split_batch(batch) + + # take just the sp_rank item. + collated_query_responses = sharded_batches[self.sp_rank]["input_ids"] + collated_attention_masks = sharded_batches[self.sp_rank]["attention_mask"] + collated_position_ids = sharded_batches[self.sp_rank]["position_ids"] + collated_response_masks = sharded_batches[self.sp_rank]["response_masks"] + collated_tool_masks = sharded_batches[self.sp_rank]["tool_masks"] + collated_advantages = sharded_batches[self.sp_rank]["advantages"] + + collated_query_responses = move_to_device(collated_query_responses, self.ref_policy.device) + collated_attention_masks = move_to_device(collated_attention_masks, self.ref_policy.device) + collated_position_ids = move_to_device(collated_position_ids, self.ref_policy.device) + collated_response_masks = move_to_device(collated_response_masks, self.ref_policy.device) + collated_tool_masks = move_to_device(collated_tool_masks, self.ref_policy.device) + collated_advantages = move_to_device(collated_advantages, self.ref_policy.device) # Calculate the logprob of the reference policy collated_ref_logprobs = [] @@ -922,7 +904,6 @@ def train( attention_mask = collated_attention_masks[i] position_id = collated_position_ids[i] response_mask = collated_response_masks[i] - print(f"Calling forward! {groups._get_sequence_parallel_rank()=} {query_response.shape=}") ref_logprob, _ = self.forward( self.ref_policy, query_response, From e7109702025475f04e509a6ef94d7427a0de1784 Mon Sep 17 00:00:00 2001 From: root Date: Sun, 17 Aug 2025 19:14:42 +0000 Subject: [PATCH 54/64] clean up code --- open_instruct/grpo_fast.py | 67 +++++++++++++++++++++++++++----------- 1 file changed, 48 insertions(+), 19 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index a5ec5f392..3a281f4e6 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -854,7 +854,7 @@ def train( original_length = len(collated_query_responses) if self.splitter is not None: - with Timer("Splitting batch for SP", noop=self.rank != 0): + with Timer("✂️ Splitting batch for SP", noop=self.rank != 0): batch = { "input_ids": collated_query_responses, "attention_mask": collated_attention_masks, @@ -1032,10 +1032,8 @@ def train( # SP: gather loss sums from all ranks, divide by total valid tokens local_loss_sum = ((pg_loss_max + args.beta * kl) * mb_response_masks_bool.float()).sum() good_tokens = mb_response_masks_bool.sum() - loss_sums_per_rank = torch.distributed.nn.functional.all_gather(local_loss_sum, group=self.sp_group) good_tokens_per_rank = torch.distributed.nn.functional.all_gather(good_tokens, group=self.sp_group) - total_loss_sum = sum(loss_sums_per_rank) total_good_tokens = sum(good_tokens_per_rank) @@ -1046,11 +1044,53 @@ def train( self.model.step() local_step += 1 with torch.no_grad(): - # NOTE: in packed implementation, kl calculation are averages over response tokens - kl1_stats[i] = masked_mean(kl1, mb_response_masks_bool, args.masked_mean_axis).float() - kl2_stats[i] = masked_mean(kl2, mb_response_masks_bool, args.masked_mean_axis).float() - kl3_stats[i] = masked_mean(kl3, mb_response_masks_bool, args.masked_mean_axis).float() - kl4_stats[i] = masked_mean(kl4, mb_response_masks_bool, args.masked_mean_axis).float() + if args.sequence_parallel_size == 1: + # NOTE: in packed implementation, kl calculation are averages over response tokens + kl1_stats[i] = masked_mean(kl1, mb_response_masks_bool, args.masked_mean_axis).float() + kl2_stats[i] = masked_mean(kl2, mb_response_masks_bool, args.masked_mean_axis).float() + kl3_stats[i] = masked_mean(kl3, mb_response_masks_bool, args.masked_mean_axis).float() + kl4_stats[i] = masked_mean(kl4, mb_response_masks_bool, args.masked_mean_axis).float() + pg_clipfrac_stats[i] = masked_mean( + (pg_losses2 > pg_losses).float(), mb_response_masks_bool, args.masked_mean_axis + ) + pg_loss_stats[i] = masked_mean(pg_loss_max, mb_response_masks_bool, args.masked_mean_axis) + loss_stats[i] = loss + ratio_stats[i] = masked_mean(ratio, mb_response_masks_bool, args.masked_mean_axis) + if args.record_entropy: + # Calculate entropy statistics + entropy_stats[i] = masked_mean( + mb_entropy, mb_response_masks_bool, args.masked_mean_axis + ).float() + else: + # do the rank gather thing like for the main loss. + # this is because we have to pad out to the max length + # for the whole minibatch to get ulysses to work, so + # sometimes in a microbatch we end up with all padding + # on one rank. + def gather_mean_stats(stats_tensor, mask_tensor): + local_stats_sum = (stats_tensor * mask_tensor.float()).sum() + good_tokens = mask_tensor.sum() + loss_sums_per_rank = torch.distributed.nn.functional.all_gather(local_stats_sum, group=self.sp_group) + good_tokens_per_rank = torch.distributed.nn.functional.all_gather(good_tokens, group=self.sp_group) + total_stats_sum = sum(loss_sums_per_rank) + total_good_tokens = sum(good_tokens_per_rank) + if total_good_tokens > 0: + return total_stats_sum / total_good_tokens + else: + return torch.tensor(0.0, device=local_stats_sum.device) + kl1_stats[i] = gather_mean_stats(kl1, mb_response_masks_bool) + kl2_stats[i] = gather_mean_stats(kl2, mb_response_masks_bool) + kl3_stats[i] = gather_mean_stats(kl3, mb_response_masks_bool) + kl4_stats[i] = gather_mean_stats(kl4, mb_response_masks_bool) + pg_clipfrac_stats[i] = gather_mean_stats( + (pg_losses2 > pg_losses).float(), mb_response_masks_bool + ) + pg_loss_stats[i] = gather_mean_stats(pg_loss_max, mb_response_masks_bool) + loss_stats[i] = gather_mean_stats(loss, mb_response_masks_bool) + ratio_stats[i] = gather_mean_stats(ratio, mb_response_masks_bool) + if args.record_entropy: + entropy_stats[i] = gather_mean_stats(mb_entropy, mb_response_masks_bool) + # multiply by beta if args.kl_estimator == "kl1": kl_loss_stats[i] = kl1_stats[i] * args.beta elif args.kl_estimator == "kl2": @@ -1059,17 +1099,6 @@ def train( kl_loss_stats[i] = kl3_stats[i] * args.beta elif args.kl_estimator == "kl4": kl_loss_stats[i] = kl4_stats[i] * args.beta - pg_clipfrac_stats[i] = masked_mean( - (pg_losses2 > pg_losses).float(), mb_response_masks_bool, args.masked_mean_axis - ) - pg_loss_stats[i] = masked_mean(pg_loss_max, mb_response_masks_bool, args.masked_mean_axis) - loss_stats[i] = loss - ratio_stats[i] = masked_mean(ratio, mb_response_masks_bool, args.masked_mean_axis) - if args.record_entropy: - # Calculate entropy statistics - entropy_stats[i] = masked_mean( - mb_entropy, mb_response_masks_bool, args.masked_mean_axis - ).float() with torch.no_grad(): self.local_metrics.add("objective/kl_avg", kl1_stats.mean()) From 326f0df41057f9e09b4ce74eb3d219daef553c24 Mon Sep 17 00:00:00 2001 From: root Date: Sun, 17 Aug 2025 19:15:07 +0000 Subject: [PATCH 55/64] lint --- open_instruct/grpo_fast.py | 37 +++++++++++++++++++++++++++---------- open_instruct/utils.py | 1 + 2 files changed, 28 insertions(+), 10 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 3a281f4e6..1e86500b2 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -687,7 +687,11 @@ def load(self, path: str, map_location=None): self.sp_world_size = groups._get_sequence_parallel_world_size() self.sp_rank = groups._get_sequence_parallel_rank() self.splitter = UlyssesSPSplitter( - sp_rank=self.sp_rank, sp_group=self.sp_group, sp_world_size=self.sp_world_size, device=self.device, pad_token_id=self.tokenizer.pad_token_id + sp_rank=self.sp_rank, + sp_group=self.sp_group, + sp_world_size=self.sp_world_size, + device=self.device, + pad_token_id=self.tokenizer.pad_token_id, ) else: self.splitter = None @@ -885,7 +889,7 @@ def train( collated_position_ids = sharded_batches[self.sp_rank]["position_ids"] collated_response_masks = sharded_batches[self.sp_rank]["response_masks"] collated_tool_masks = sharded_batches[self.sp_rank]["tool_masks"] - collated_advantages = sharded_batches[self.sp_rank]["advantages"] + collated_advantages = sharded_batches[self.sp_rank]["advantages"] collated_query_responses = move_to_device(collated_query_responses, self.ref_policy.device) collated_attention_masks = move_to_device(collated_attention_masks, self.ref_policy.device) @@ -1032,12 +1036,20 @@ def train( # SP: gather loss sums from all ranks, divide by total valid tokens local_loss_sum = ((pg_loss_max + args.beta * kl) * mb_response_masks_bool.float()).sum() good_tokens = mb_response_masks_bool.sum() - loss_sums_per_rank = torch.distributed.nn.functional.all_gather(local_loss_sum, group=self.sp_group) - good_tokens_per_rank = torch.distributed.nn.functional.all_gather(good_tokens, group=self.sp_group) + loss_sums_per_rank = torch.distributed.nn.functional.all_gather( + local_loss_sum, group=self.sp_group + ) + good_tokens_per_rank = torch.distributed.nn.functional.all_gather( + good_tokens, group=self.sp_group + ) total_loss_sum = sum(loss_sums_per_rank) total_good_tokens = sum(good_tokens_per_rank) - - loss = total_loss_sum / total_good_tokens if total_good_tokens > 0 else torch.tensor(0.0, device=local_loss_sum.device) + + loss = ( + total_loss_sum / total_good_tokens + if total_good_tokens > 0 + else torch.tensor(0.0, device=local_loss_sum.device) + ) loss = loss / accumulation_steps self.model.backward(loss) if (local_step + 1) % accumulation_steps == 0: @@ -1064,20 +1076,25 @@ def train( else: # do the rank gather thing like for the main loss. # this is because we have to pad out to the max length - # for the whole minibatch to get ulysses to work, so - # sometimes in a microbatch we end up with all padding + # for the whole minibatch to get ulysses to work, so + # sometimes in a microbatch we end up with all padding # on one rank. def gather_mean_stats(stats_tensor, mask_tensor): local_stats_sum = (stats_tensor * mask_tensor.float()).sum() good_tokens = mask_tensor.sum() - loss_sums_per_rank = torch.distributed.nn.functional.all_gather(local_stats_sum, group=self.sp_group) - good_tokens_per_rank = torch.distributed.nn.functional.all_gather(good_tokens, group=self.sp_group) + loss_sums_per_rank = torch.distributed.nn.functional.all_gather( + local_stats_sum, group=self.sp_group + ) + good_tokens_per_rank = torch.distributed.nn.functional.all_gather( + good_tokens, group=self.sp_group + ) total_stats_sum = sum(loss_sums_per_rank) total_good_tokens = sum(good_tokens_per_rank) if total_good_tokens > 0: return total_stats_sum / total_good_tokens else: return torch.tensor(0.0, device=local_stats_sum.device) + kl1_stats[i] = gather_mean_stats(kl1, mb_response_masks_bool) kl2_stats[i] = gather_mean_stats(kl2, mb_response_masks_bool) kl3_stats[i] = gather_mean_stats(kl3, mb_response_masks_bool) diff --git a/open_instruct/utils.py b/open_instruct/utils.py index 34e93391b..0770e130b 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -1609,6 +1609,7 @@ def check_runtime_leaks( return report + class UlyssesSPSplitter: def __init__(self, sp_rank: int, sp_group, sp_world_size, device, pad_token_id): """ From 9a9be7a1c23f73d4fe3e2f5173091cf64302d75f Mon Sep 17 00:00:00 2001 From: root Date: Sun, 17 Aug 2025 19:16:48 +0000 Subject: [PATCH 56/64] updated reqs --- requirements.txt | 71 ++++++++++++++---------------------------------- 1 file changed, 20 insertions(+), 51 deletions(-) diff --git a/requirements.txt b/requirements.txt index c723e82f9..9bb3f21db 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,7 +19,7 @@ aiohttp-cors==0.8.1 # via ray aiosignal==1.4.0 # via aiohttp -airportsdata==20250706 +airportsdata==20250811 # via outlines annotated-types==0.7.0 # via pydantic @@ -46,7 +46,7 @@ babel==2.17.0 backrefs==5.9 # via mkdocs-material beaker-py==1.36.4 -bitsandbytes==0.46.1 ; sys_platform != 'darwin' +bitsandbytes==0.47.0 ; sys_platform != 'darwin' # via open-instruct black==25.1.0 blake3==1.0.5 @@ -63,7 +63,7 @@ certifi==2025.8.3 # sentry-sdk cffi==1.17.1 ; implementation_name == 'pypy' # via pyzmq -charset-normalizer==3.4.2 +charset-normalizer==3.4.3 # via requests click==8.2.1 # via @@ -98,12 +98,6 @@ contourpy==1.3.2 ; python_full_version < '3.11' # via matplotlib contourpy==1.3.3 ; python_full_version >= '3.11' # via matplotlib -cuda-bindings==13.0.0 - # via cuda-python -cuda-pathfinder==1.1.0 - # via cuda-bindings -cuda-python==13.0.0 - # via flashinfer-python cupy-cuda12x==13.5.1 ; sys_platform != 'darwin' # via ray cycler==0.12.1 @@ -112,7 +106,7 @@ datasets==4.0.0 # via open-instruct debugpy==1.8.16 # via open-instruct -deepspeed==0.17.2 +deepspeed==0.17.4 # via open-instruct depyf==0.18.0 # via vllm @@ -137,7 +131,6 @@ einops==0.8.1 # via # deepspeed # flash-attn - # flashinfer-python # vllm email-validator==2.2.0 # via @@ -169,9 +162,7 @@ filelock==3.18.0 # virtualenv # vllm flake8==7.3.0 -flash-attn==2.8.2 - # via open-instruct -flashinfer-python==0.2.8 +flash-attn==2.8.2 ; sys_platform != 'darwin' # via open-instruct fonttools==4.59.0 # via matplotlib @@ -227,7 +218,7 @@ httpx==0.28.1 # fastapi-cloud-cli # litellm # openai -huggingface-hub==0.34.3 +huggingface-hub==0.34.4 # via # accelerate # datasets @@ -277,7 +268,7 @@ jsonschema==4.25.0 # ray jsonschema-specifications==2025.4.1 # via jsonschema -kiwisolver==1.4.8 +kiwisolver==1.4.9 # via matplotlib langdetect==1.0.9 # via open-instruct @@ -285,9 +276,9 @@ lark==1.2.2 # via # outlines # vllm -liger-kernel==0.6.1 +liger-kernel==0.6.1 ; sys_platform != 'darwin' # via open-instruct -litellm==1.75.0 +litellm==1.75.5.post1 # via open-instruct llguidance==0.7.30 ; platform_machine == 'aarch64' or platform_machine == 'arm64' or platform_machine == 'x86_64' # via vllm @@ -303,7 +294,7 @@ markdown==3.8.2 # pymdown-extensions # tensorboard markdown-include==0.8.1 -markdown-it-py==3.0.0 +markdown-it-py==4.0.0 # via rich markupsafe==3.0.2 # via @@ -337,7 +328,7 @@ msgpack==1.1.1 # ray msgspec==0.19.0 # via vllm -multidict==6.6.3 +multidict==6.6.4 # via # aiohttp # yarl @@ -351,10 +342,9 @@ networkx==3.4.2 ; python_full_version < '3.11' # via torch networkx==3.5 ; python_full_version >= '3.11' # via torch -ninja==1.11.1.4 +ninja==1.13.0 # via # deepspeed - # flashinfer-python # vllm # xgrammar nltk==3.9.1 @@ -369,7 +359,6 @@ numpy==1.26.4 # cupy-cuda12x # datasets # deepspeed - # flashinfer-python # gguf # matplotlib # mistral-common @@ -413,9 +402,7 @@ nvidia-cusparse-cu12==12.5.7.53 ; platform_machine == 'x86_64' and sys_platform nvidia-cusparselt-cu12==0.6.3 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch nvidia-ml-py==12.575.51 - # via - # nvitop - # pynvml + # via nvitop nvidia-nccl-cu12==2.26.2 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch nvidia-nvjitlink-cu12==12.8.61 ; platform_machine == 'x86_64' and sys_platform == 'linux' @@ -424,13 +411,11 @@ nvidia-nvjitlink-cu12==12.8.61 ; platform_machine == 'x86_64' and sys_platform = # nvidia-cusolver-cu12 # nvidia-cusparse-cu12 # torch -nvidia-nvshmem-cu12==3.3.20 - # via flashinfer-python nvidia-nvtx-cu12==12.8.55 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch nvitop==1.5.2 # via open-instruct -openai==1.99.2 +openai==1.99.8 # via # litellm # vllm @@ -446,7 +431,6 @@ opentelemetry-api==1.36.0 # via # opentelemetry-exporter-otlp-proto-grpc # opentelemetry-exporter-otlp-proto-http - # opentelemetry-exporter-prometheus # opentelemetry-sdk # opentelemetry-semantic-conventions # vllm @@ -460,20 +444,15 @@ opentelemetry-exporter-otlp-proto-grpc==1.36.0 # via opentelemetry-exporter-otlp opentelemetry-exporter-otlp-proto-http==1.36.0 # via opentelemetry-exporter-otlp -opentelemetry-exporter-prometheus==0.57b0 - # via ray opentelemetry-proto==1.36.0 # via # opentelemetry-exporter-otlp-proto-common # opentelemetry-exporter-otlp-proto-grpc # opentelemetry-exporter-otlp-proto-http - # ray opentelemetry-sdk==1.36.0 # via # opentelemetry-exporter-otlp-proto-grpc # opentelemetry-exporter-otlp-proto-http - # opentelemetry-exporter-prometheus - # ray # vllm opentelemetry-semantic-conventions==0.57b0 # via opentelemetry-sdk @@ -530,7 +509,6 @@ pluggy==1.6.0 # via pytest prometheus-client==0.22.1 # via - # opentelemetry-exporter-prometheus # prometheus-fastapi-instrumentator # ray # vllm @@ -612,8 +590,6 @@ pygments==2.19.2 # rich pymdown-extensions==10.16.1 # via mkdocs-material -pynvml==12.0.0 - # via flashinfer-python pyparsing==3.2.3 # via matplotlib pytest==8.4.1 @@ -638,9 +614,7 @@ pytz==2025.2 # google-api-core # pandas pywin32==311 ; sys_platform == 'win32' - # via - # cuda-bindings - # docker + # via docker pyyaml==6.0.2 # via # accelerate @@ -663,7 +637,7 @@ pyyaml-env-tag==1.1 # via mkdocs pyzmq==27.0.1 # via vllm -ray==2.48.0 +ray==2.46.0 # via # open-instruct # vllm @@ -683,7 +657,6 @@ requests==2.32.4 # beaker-py # datasets # docker - # flashinfer-python # google-api-core # huggingface-hub # mistral-common @@ -701,7 +674,7 @@ rich==13.9.4 # beaker-py # rich-toolkit # typer -rich-toolkit==0.14.9 +rich-toolkit==0.15.0 # via # fastapi-cli # fastapi-cloud-cli @@ -714,7 +687,7 @@ rpds-py==0.27.0 rsa==4.9.1 # via google-auth ruff==0.12.8 -safetensors==0.6.1 +safetensors==0.6.2 # via # accelerate # peft @@ -772,7 +745,7 @@ tensorboard==2.20.0 # via open-instruct tensorboard-data-server==0.7.2 # via tensorboard -tiktoken==0.10.0 +tiktoken==0.11.0 # via # litellm # mistral-common @@ -793,9 +766,6 @@ torch==2.7.0 ; sys_platform == 'darwin' # accelerate # compressed-tensors # deepspeed - # flash-attn - # flashinfer-python - # liger-kernel # open-instruct # outlines # peft @@ -810,7 +780,6 @@ torch==2.7.0+cu128 ; sys_platform != 'darwin' # compressed-tensors # deepspeed # flash-attn - # flashinfer-python # liger-kernel # open-instruct # outlines @@ -844,7 +813,7 @@ transformers==4.53.3 # peft # vllm # xgrammar -triton==3.3.0 +triton==3.3.0 ; sys_platform != 'darwin' # via # liger-kernel # torch From d8836a4299357141d248fa7393ac0b60fe0b8d98 Mon Sep 17 00:00:00 2001 From: root Date: Sun, 17 Aug 2025 19:31:33 +0000 Subject: [PATCH 57/64] minor fix --- open_instruct/grpo_fast.py | 1 + 1 file changed, 1 insertion(+) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index f3cdc1399..cde7ed857 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -2060,6 +2060,7 @@ def generate_thread(vllm_engines, local_eval_every, num_training_steps, resume_t ) as pbar: for future in futures.as_completed(engine_futures): processed_results.append(future.result()) + pbar.update(1) num_processed = sum(int(result) for result in processed_results) # Suppress timing output if nothing was processed if num_processed == 0: From 72a6951cb2eb0a5ad186b5467d75901f812cc2a0 Mon Sep 17 00:00:00 2001 From: root Date: Sun, 17 Aug 2025 19:34:19 +0000 Subject: [PATCH 58/64] fix uv lock --- uv.lock | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/uv.lock b/uv.lock index 88c529d9d..5fc17a7b0 100644 --- a/uv.lock +++ b/uv.lock @@ -858,7 +858,7 @@ name = "exceptiongroup" version = "1.3.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "typing-extensions", marker = "python_full_version < '3.12'" }, + { name = "typing-extensions", marker = "python_full_version < '3.11'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" } wheels = [ @@ -2285,7 +2285,7 @@ name = "nvidia-cudnn-cu12" version = "9.7.1.26" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12", marker = "sys_platform != 'darwin'" }, + { name = "nvidia-cublas-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/25/dc/dc825c4b1c83b538e207e34f48f86063c88deaa35d46c651c7c181364ba2/nvidia_cudnn_cu12-9.7.1.26-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:6d011159a158f3cfc47bf851aea79e31bcff60d530b70ef70474c84cac484d07", size = 726851421, upload-time = "2025-02-06T22:18:29.812Z" }, @@ -2296,7 +2296,7 @@ name = "nvidia-cufft-cu12" version = "11.3.3.41" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/ac/26/b53c493c38dccb1f1a42e1a21dc12cba2a77fbe36c652f7726d9ec4aba28/nvidia_cufft_cu12-11.3.3.41-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:da650080ab79fcdf7a4b06aa1b460e99860646b176a43f6208099bdc17836b6a", size = 193118795, upload-time = "2025-01-23T17:56:30.536Z" }, @@ -2323,9 +2323,9 @@ name = "nvidia-cusolver-cu12" version = "11.7.2.55" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12", marker = "sys_platform != 'darwin'" }, - { name = "nvidia-cusparse-cu12", marker = "sys_platform != 'darwin'" }, - { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" }, + { name = "nvidia-cublas-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparse-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/c2/08/953675873a136d96bb12f93b49ba045d1107bc94d2551c52b12fa6c7dec3/nvidia_cusolver_cu12-11.7.2.55-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4d1354102f1e922cee9db51920dba9e2559877cf6ff5ad03a00d853adafb191b", size = 260373342, upload-time = "2025-01-23T17:58:56.406Z" }, @@ -2336,7 +2336,7 @@ name = "nvidia-cusparse-cu12" version = "12.5.7.53" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/c2/ab/31e8149c66213b846c082a3b41b1365b831f41191f9f40c6ddbc8a7d550e/nvidia_cusparse_cu12-12.5.7.53-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3c1b61eb8c85257ea07e9354606b26397612627fdcd327bfd91ccf6155e7c86d", size = 292064180, upload-time = "2025-01-23T18:00:23.233Z" }, @@ -2493,8 +2493,8 @@ requires-dist = [ { name = "requests", marker = "extra == 'code'", specifier = ">=2.28.0" }, { name = "setuptools", specifier = ">=75.6.0,<80.0.0" }, { name = "tensorboard", specifier = ">=2.18.0" }, - { name = "torch", marker = "sys_platform != 'darwin'", specifier = "==2.7.0", index = "https://download.pytorch.org/whl/cu128" }, - { name = "torch", marker = "sys_platform == 'darwin'", specifier = "==2.7.0" }, + { name = "torch", marker = "sys_platform != 'darwin'", specifier = ">=2.7.0,<2.8", index = "https://download.pytorch.org/whl/cu128" }, + { name = "torch", marker = "sys_platform == 'darwin'", specifier = ">=2.7.0,<2.8" }, { name = "tqdm" }, { name = "transformers", specifier = ">=4.52.4,<4.54.0" }, { name = "uvicorn", marker = "extra == 'code'", specifier = ">=0.20.0" }, @@ -4822,8 +4822,8 @@ name = "xformers" version = "0.0.30" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy", marker = "sys_platform != 'darwin'" }, - { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform != 'darwin'" }, + { name = "numpy", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, + { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/bf/f7/dd2269cce89fd1221947dd7cc3a60707ffe721ef55c1803ac3b1a1f7ae5c/xformers-0.0.30.tar.gz", hash = "sha256:a12bf3eb39e294cdbe8a7253ac9b665f41bac61d6d98df174e34ef7bdb6f2fc4", size = 10214139, upload-time = "2025-04-28T20:51:02.045Z" } wheels = [ From 690e5c366834814b6286ec5c4086151e78607dcc Mon Sep 17 00:00:00 2001 From: root Date: Sun, 17 Aug 2025 19:35:33 +0000 Subject: [PATCH 59/64] lint --- open_instruct/grpo_fast.py | 4 ++-- open_instruct/utils.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 9e5856f36..99925039c 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -70,7 +70,6 @@ import torch import torch.utils import torch.utils.data -import wandb from huggingface_hub import HfApi from peft import PeftModel, get_peft_model_state_dict from ray.util import queue as ray_queue @@ -82,6 +81,7 @@ from transformers.integrations import HfDeepSpeedConfig from vllm import SamplingParams +import wandb from open_instruct.dataset_transformation import ( GROUND_TRUTHS_KEY, INPUT_IDS_PROMPT_KEY, @@ -113,6 +113,7 @@ ArgumentParserPlus, BeakerRuntimeConfig, RayProcess, + UlyssesSPSplitter, _z3_params_to_fetch, calibrate_checkpoint_state_dir, clean_last_n_checkpoints_deepspeed, @@ -132,7 +133,6 @@ ray_get_with_progress, repeat_each, sync_gs_bucket, - UlyssesSPSplitter, ) from open_instruct.vllm_utils3 import LLMRayActor, create_vllm_engines, init_process_group diff --git a/open_instruct/utils.py b/open_instruct/utils.py index f9090d469..c5ce01cd0 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -39,11 +39,11 @@ import re import shutil import socket -from collections import defaultdict import subprocess import sys import threading import time +from collections import defaultdict from concurrent import futures from ctypes import CDLL, POINTER, Structure, c_char_p, c_int, c_ulong, c_void_p from dataclasses import dataclass, field From 3f21465609dce86b68fc33f2bc45be9879ee96ff Mon Sep 17 00:00:00 2001 From: root Date: Sun, 17 Aug 2025 19:38:19 +0000 Subject: [PATCH 60/64] lint2 --- open_instruct/grpo_fast.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 99925039c..e815819fc 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -70,6 +70,7 @@ import torch import torch.utils import torch.utils.data +import wandb from huggingface_hub import HfApi from peft import PeftModel, get_peft_model_state_dict from ray.util import queue as ray_queue @@ -81,7 +82,6 @@ from transformers.integrations import HfDeepSpeedConfig from vllm import SamplingParams -import wandb from open_instruct.dataset_transformation import ( GROUND_TRUTHS_KEY, INPUT_IDS_PROMPT_KEY, @@ -863,7 +863,6 @@ def train( # recalculate the "real" number of mini-batches num_mini_batches = len(collated_query_responses) // accumulation_steps - original_length = len(collated_query_responses) if self.splitter is not None: with Timer("✂️ Splitting batch for SP", noop=self.rank != 0): From 2860ddf175d93c8185d03fd9b29c612af33208d6 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Tue, 19 Aug 2025 11:20:33 -0700 Subject: [PATCH 61/64] first pass at fixing checkpoint saving --- open_instruct/grpo_fast.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index e815819fc..aa106a142 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -1142,6 +1142,11 @@ def gather_mean_stats(stats_tensor, mask_tensor): def save_checkpoint_state(self, checkpoint_state_dir: str, client_state: Dict[str, str]) -> None: args = self.args + # mpu is just used for sequence parallel, so we remove it for saving, and then re-add it after. + old_mpu = None + if self.model.mpu is not None: + old_mpu = self.mpu + self.model.mpu = None self.model.save_checkpoint(checkpoint_state_dir, client_state=client_state) # `save_checkpoint` needs to be called on all ranks, only rank 0 will have all the states if self.rank == 0: @@ -1152,6 +1157,9 @@ def save_checkpoint_state(self, checkpoint_state_dir: str, client_state: Dict[st ray.remote(sync_gs_bucket).options(num_cpus=1).remote( checkpoint_state_dir, args.gs_checkpoint_state_dir ) + # add back the mpu + if old_mpu is not None: + self.model.mpu = old_mpu def save_model(self, output_dir: str, chat_template_name: str, tokenizer: PreTrainedTokenizer) -> None: model_to_save = self.model From 1c7e6bc916ec05ffcd67f9b048aab6f43e9f4140 Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Tue, 19 Aug 2025 11:34:07 -0700 Subject: [PATCH 62/64] loading also needs to remove the mpu --- open_instruct/grpo_fast.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index ae1f93b99..f80ab18a6 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -653,6 +653,10 @@ def load(self, path: str, map_location=None): f"Skipping loading checkpoint state from {args.checkpoint_state_dir} because it does not exist!" ) else: + old_mpu = None + if self.mpu is not None: + old_mpu = self.mpu + self.model.mpu = None path, states = self.model.load_checkpoint( args.checkpoint_state_dir, load_module_strict=True, @@ -660,6 +664,8 @@ def load(self, path: str, map_location=None): load_lr_scheduler_states=True, load_module_only=False, ) + if old_mpu is not None: + self.model.mpu = old_mpu if path is None: raise ValueError(f"Failed to load checkpoint from {args.checkpoint_state_dir}") optimization_steps_done = states["training_step"] From 65b7c4a06c70f98f917c6a14a48de9834823412a Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Tue, 19 Aug 2025 11:39:04 -0700 Subject: [PATCH 63/64] some merge fixes --- open_instruct/grpo_fast.py | 1 - open_instruct/utils.py | 6 ------ 2 files changed, 7 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index f80ab18a6..0d22cf881 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -62,7 +62,6 @@ from argparse import Namespace from collections import defaultdict from dataclasses import asdict, dataclass, field -from datetime import timedelta from queue import Empty, Full, Queue from typing import Any, Callable, Dict, Iterator, List, Literal, Optional diff --git a/open_instruct/utils.py b/open_instruct/utils.py index ae64e7f5e..5c4b6e6ea 100644 --- a/open_instruct/utils.py +++ b/open_instruct/utils.py @@ -1541,15 +1541,9 @@ def is_allowed_thread(t): if _rt and hasattr(_rt, "_resource_tracker"): cache = getattr(_rt._resource_tracker, "_cache", {}) for name, (count, rtype) in cache.items(): - if count > 0 and rtype == "semaphore": - report.leaked_semaphores.append(name) - if count > 0 and rtype == "shared_memory": - report.leaked_shm.append(name) if count > 0: leak_logger.warning(f"Leaked {rtype} resources: {count}") - return report - class UlyssesSPSplitter: def __init__(self, sp_rank: int, sp_group, sp_world_size, device, pad_token_id): From d024f8635160d6cef20fc08083ecf391ac58330d Mon Sep 17 00:00:00 2001 From: Hamish Ivison Date: Tue, 19 Aug 2025 11:40:28 -0700 Subject: [PATCH 64/64] back to fa2 --- open_instruct/grpo_fast.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py index 0d22cf881..5035d9f91 100644 --- a/open_instruct/grpo_fast.py +++ b/open_instruct/grpo_fast.py @@ -602,7 +602,7 @@ def load(self, path: str, map_location=None): if args.sequence_parallel_size > 1: self.mpu = UlyssesSPAttentionHF.register_with_transformers( model_name_or_path=model_config.model_name_or_path, - core_attn_implementation="sdpa", + core_attn_implementation="flash_attention_2", sequence_parallel_size=args.sequence_parallel_size, max_length=args.max_token_length, micro_batch_size=args.per_device_train_batch_size, @@ -613,7 +613,7 @@ def load(self, path: str, map_location=None): model_config.model_name_or_path, revision=model_config.model_revision, torch_dtype=torch.bfloat16, - attn_implementation="sdpa", + attn_implementation="flash_attention_2", use_cache=False, ) disable_dropout_in_model(self.policy) @@ -693,7 +693,7 @@ def load(self, path: str, map_location=None): model_config.model_name_or_path, revision=model_config.model_revision, torch_dtype=torch.bfloat16, - attn_implementation="sdpa", + attn_implementation="flash_attention_2", use_cache=False, ) disable_dropout_in_model(self.ref_policy)