# Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Implement a multiprocess PPOCritic """ import itertools import logging import os import torch import torch.distributed from flash_attn.bert_padding import index_first_axis, pad_input, rearrange, unpad_input from torch import nn, optim from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from verl import DataProto from verl.trainer.ppo import core_algos from verl.utils.debug import GPUMemoryLogger from verl.utils.fsdp_utils import FSDPModule, fsdp2_clip_grad_norm_ from verl.utils.py_functional import append_to_dict from verl.utils.seqlen_balancing import get_reverse_idx, rearrange_micro_batches from verl.utils.torch_functional import masked_mean from verl.utils.ulysses import gather_outpus_and_unpad, ulysses_pad_and_slice_inputs from verl.workers.critic import BasePPOCritic __all__ = ["DataParallelPPOCritic"] logger = logging.getLogger(__file__) logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN")) class DataParallelPPOCritic(BasePPOCritic): def __init__(self, config, critic_module: nn.Module, critic_optimizer: optim.Optimizer): super().__init__(config=config) self.critic_module = critic_module self.critic_optimizer = critic_optimizer self.use_remove_padding = self.config.model.get("use_remove_padding", False) print(f"Critic use_remove_padding={self.use_remove_padding}") self.ulysses_sequence_parallel_size = self.config.get("ulysses_sequence_parallel_size", 1) def _forward_micro_batch(self, micro_batch): response_length = micro_batch["responses"].size(-1) multi_modal_inputs = {} if "multi_modal_inputs" in micro_batch: for key in micro_batch["multi_modal_inputs"][0].keys(): multi_modal_inputs[key] = torch.cat([inputs[key] for inputs in micro_batch["multi_modal_inputs"]], dim=0) with torch.autocast(device_type="cuda", dtype=torch.bfloat16): input_ids = micro_batch["input_ids"] batch, seqlen = input_ids.shape attention_mask = micro_batch["attention_mask"] position_ids = micro_batch["position_ids"] if position_ids.dim() == 3: # qwen2vl mrope position_ids = position_ids.transpose(0, 1) if self.use_remove_padding: input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary if position_ids.dim() == 3: position_ids_rmpad = index_first_axis(rearrange(position_ids, "c b s ... -> (b s) c ..."), indices).transpose(0, 1).unsqueeze(1) # (3, bsz, seqlen) -> (3, 1, bsz * seqlen) else: position_ids_rmpad = index_first_axis(rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices).transpose(0, 1) # pad and slice the inputs if sp > 1 if self.ulysses_sequence_parallel_size > 1: input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad_and_slice_inputs(input_ids_rmpad, position_ids_rmpad, sp_size=self.ulysses_sequence_parallel_size) # only pass input_ids and position_ids to enable flash_attn_varlen output = self.critic_module( input_ids=input_ids_rmpad, attention_mask=None, position_ids=position_ids_rmpad, **multi_modal_inputs, use_cache=False, ) # prevent model thinks we are generating values_rmpad = output.logits values_rmpad = values_rmpad.squeeze(0) # (total_nnz) # gather output if sp > 1 if self.ulysses_sequence_parallel_size > 1: values_rmpad = gather_outpus_and_unpad(values_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size) # pad it back values = pad_input(values_rmpad, indices=indices, batch=batch, seqlen=seqlen).squeeze(-1) values = values[:, -response_length - 1 : -1] else: output = self.critic_module( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, **multi_modal_inputs, use_cache=False, ) # prevent model thinks we are generating values = output.logits values = values[:, -response_length - 1 : -1].squeeze(-1) return values def _optimizer_step(self): assert self.config.grad_clip is not None if isinstance(self.critic_module, FSDP): grad_norm = self.critic_module.clip_grad_norm_(self.config.grad_clip) elif isinstance(self.critic_module, FSDPModule): grad_norm = fsdp2_clip_grad_norm_(self.critic_module.parameters(), max_norm=self.config.grad_clip) else: grad_norm = torch.nn.utils.clip_grad_norm_(self.critic_module.parameters(), max_norm=self.config.grad_clip) # if grad_norm is not finite, skip the update if not torch.isfinite(grad_norm): print(f"WARN: grad_norm is not finite: {grad_norm}") self.critic_optimizer.zero_grad() else: self.critic_optimizer.step() return grad_norm @GPUMemoryLogger(role="dp critic", logger=logger) def compute_values(self, data: DataProto) -> torch.Tensor: self.critic_module.eval() micro_batch_size = data.meta_info["micro_batch_size"] select_keys = ["responses", "input_ids", "attention_mask", "position_ids"] batch = data.select(batch_keys=select_keys).batch use_dynamic_bsz = data.meta_info["use_dynamic_bsz"] has_multi_modal_inputs = "multi_modal_inputs" in data.non_tensor_batch.keys() if has_multi_modal_inputs: num_micro_batches = data.batch.batch_size[0] // micro_batch_size non_tensor_select_keys = ["multi_modal_inputs"] micro_batches = data.select(select_keys, non_tensor_select_keys).chunk(num_micro_batches) elif use_dynamic_bsz: # split using dynamic bsz max_token_len = data.meta_info["max_token_len"] * self.ulysses_sequence_parallel_size micro_batches, indices = rearrange_micro_batches(batch=batch, max_token_len=max_token_len) else: micro_batches = batch.split(micro_batch_size) values_lst = [] for micro_batch in micro_batches: if isinstance(micro_batch, DataProto): micro_batch = {**micro_batch.batch, **micro_batch.non_tensor_batch} with torch.no_grad(): values = self._forward_micro_batch(micro_batch) values_lst.append(values) values = torch.concat(values_lst, dim=0) responses = data.batch["responses"] attention_mask = data.batch["attention_mask"] response_length = responses.size(1) if use_dynamic_bsz: indices = list(itertools.chain.from_iterable(indices)) assert len(indices) == values.size(0), f"{len(indices)} vs. {values.size()}" revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long) values = values[revert_indices] values = values * attention_mask[:, -response_length - 1 : -1] return values @GPUMemoryLogger(role="dp critic", logger=logger) def update_critic(self, data: DataProto): # make sure we are in training mode self.critic_module.train() metrics = {} select_keys = ["input_ids", "responses", "attention_mask", "position_ids", "values", "returns"] batch = data.select(batch_keys=select_keys).batch has_multi_modal_inputs = "multi_modal_inputs" in data.non_tensor_batch.keys() # Split to make minibatch iterator for updating the actor # See PPO paper for details. https://arxiv.org/abs/1707.06347 if has_multi_modal_inputs: num_mini_batches = data.batch.batch_size[0] // self.config.ppo_mini_batch_size non_tensor_select_keys = ["multi_modal_inputs"] dataloader = data.select(select_keys, non_tensor_select_keys).chunk(num_mini_batches) else: dataloader = batch.split(self.config.ppo_mini_batch_size) for epoch in range(self.config.ppo_epochs): for batch_idx, data in enumerate(dataloader): # split batch into micro_batches mini_batch = data if has_multi_modal_inputs: num_micro_batches = mini_batch.batch.batch_size[0] // self.config.ppo_micro_batch_size_per_gpu micro_batches = data.select(select_keys, non_tensor_select_keys).chunk(num_micro_batches) elif self.config.use_dynamic_bsz: max_token_len = self.config.ppo_max_token_len_per_gpu * self.ulysses_sequence_parallel_size micro_batches, _ = rearrange_micro_batches(batch=mini_batch, max_token_len=max_token_len) else: micro_batches = mini_batch.split(self.config.ppo_micro_batch_size_per_gpu) self.gradient_accumulation = self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu self.critic_optimizer.zero_grad() for data in micro_batches: # Support all devices if isinstance(data, DataProto): data = {**data.batch.to(torch.cuda.current_device()), **data.non_tensor_batch} else: data = data.to(torch.cuda.current_device()) # critic device is cpu when using offload responses = data["responses"] attention_mask = data["attention_mask"] values = data["values"] returns = data["returns"] response_length = responses.size(1) response_mask = attention_mask[:, -response_length - 1 : -1] vpreds = self._forward_micro_batch(data) # assert not torch.any(torch.isnan(vpreds)).item() vf_loss, vf_clipfrac = core_algos.compute_value_loss( vpreds=vpreds, values=values, returns=returns, response_mask=response_mask, cliprange_value=self.config.cliprange_value, ) if self.config.use_dynamic_bsz: # relative to the dynamic bsz loss = vf_loss * (len(data) / self.config.ppo_mini_batch_size) else: loss = vf_loss / self.gradient_accumulation loss.backward() data = { "critic/vf_loss": vf_loss.detach().item(), "critic/vf_clipfrac": vf_clipfrac.detach().item(), "critic/vpred_mean": masked_mean(vpreds, response_mask).detach().item(), } append_to_dict(metrics, data) grad_norm = self._optimizer_step() data = {"critic/grad_norm": grad_norm.detach().item()} append_to_dict(metrics, data) self.critic_optimizer.zero_grad() return metrics