227 lines
10 KiB
Python
227 lines
10 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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The vllm_rollout that can be applied in different backend
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When working with FSDP:
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- Use DTensor weight loader (recommended) or HF weight loader
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- Utilize state_dict from the FSDP to synchronize the weights among tp ranks in vLLM
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When working with Megatron:
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- Use Megatron weight loader
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- During training, only the current pp stage holds the parameters
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- Before inference, broadcast the parameters of the current pp rank to all other pp ranks (all pp ranks holds all the parameters)
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- Bind the parameters to the inference engine
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- Do inference in tp. pp is treated as additional dp
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- After inference, all the parameters that doesn't belong to this pp rank is freed.
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"""
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from typing import List
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from contextlib import contextmanager
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from omegaconf import DictConfig
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import torch
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import torch.distributed
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from tensordict import TensorDict
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from torch import nn
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from verl import DataProto
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from verl.utils.torch_functional import get_eos_mask, pad_sequence_to_length
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from verl.workers.rollout.base import BaseRollout
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from verl.third_party.vllm import LLM, vllm_version
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from verl.third_party.vllm import parallel_state as vllm_ps
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from vllm import SamplingParams
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# TODO
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# 1. support pp in vllm
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# 2. passing tokenizer is not necessary? no encoding/decoding is happending here
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# 3. simplify init logics
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# NOTE(sgm): add for verl. We can optimize it by making the dataloader yield List[int] without padding.
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def _pre_process_inputs(pad_token_id, prompt_token_ids: torch.Tensor) -> List[int]:
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# remove the left padding in the prompt token_id
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# pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id
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non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0]
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token_ids = prompt_token_ids[non_pad_index:].tolist()
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return token_ids
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class vLLMRollout(BaseRollout):
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def __init__(self, actor_module: nn.Module, config: DictConfig, tokenizer, model_hf_config, **kwargs):
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"""A vLLM rollout. It requires the module is supported by the vllm.
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Args:
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module: module here follows huggingface APIs
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config: DictConfig
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tokenizer: the task/model tokenizer
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model_hf_config: the huggingface config to initiallize the generating model in vllm
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**kwargs: train_tp, for Megatron Backend to initialize hybrid engine (zero redundancy) process group
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"""
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super().__init__()
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self.config = config
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assert not (not config.enforce_eager and config.free_cache_engine), \
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"disable CUDA graph (enforce_eager = False) if free cache engine"
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tensor_parallel_size = self.config.get('tensor_model_parallel_size', 1)
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assert tensor_parallel_size <= torch.distributed.get_world_size(), \
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"tensor parallel size should be less than or equal to the world size"
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if kwargs.get('train_tp', None) is not None:
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# deployed with megatron
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import os
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os.environ['CUDA_TIMER_STREAM_KAFKA_ENABLE'] = '0'
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os.environ['MEGATRON_IMPORT_TIMERS'] = '0'
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train_tp = kwargs.get('train_tp', None)
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num_tp_per_train_tp = train_tp // tensor_parallel_size
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if vllm_version in ('0.4.2', '0.5.4', '0.6.3'):
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vllm_ps.initialize_parallel_state(tensor_model_parallel_size=tensor_parallel_size,
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num_tp_per_train_tp=num_tp_per_train_tp)
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assert model_hf_config.max_position_embeddings >= config.prompt_length + config.response_length, \
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"model context length should be greater than total sequence length"
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self.inference_engine = LLM(actor_module,
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tokenizer=tokenizer,
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model_hf_config=model_hf_config,
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tensor_parallel_size=tensor_parallel_size,
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dtype=config.dtype,
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enforce_eager=config.enforce_eager,
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gpu_memory_utilization=config.gpu_memory_utilization,
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skip_tokenizer_init=False,
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max_model_len=config.prompt_length + config.response_length,
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load_format=config.load_format)
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# Offload vllm model to reduce peak memory usage
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self.inference_engine.offload_model_weights()
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kwargs = dict(
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n=1,
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logprobs=1, # can be set to 0 and let actor to recompute
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max_tokens=config.response_length,
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)
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# we may detokenize the result all together later
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if vllm_version in ('0.4.2', '0.5.4', '0.6.3'):
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kwargs['detokenize'] = False
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# supporting adding any sampling params from the config file
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for k in config.keys():
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if hasattr(SamplingParams(), str(k)):
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kwargs[k] = config.get(k)
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print(f"kwargs: {kwargs}")
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self.sampling_params = SamplingParams(**kwargs)
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self.pad_token_id = tokenizer.pad_token_id
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@contextmanager
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def update_sampling_params(self, **kwargs):
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# update sampling params
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old_sampling_params_args = {}
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if kwargs:
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for key, value in kwargs.items():
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if hasattr(self.sampling_params, key):
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old_value = getattr(self.sampling_params, key)
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old_sampling_params_args[key] = old_value
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setattr(self.sampling_params, key, value)
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yield
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# roll back to previous sampling params
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# if len(old_sampling_params_args):
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for key, value in old_sampling_params_args.items():
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setattr(self.sampling_params, key, value)
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@torch.no_grad()
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def generate_sequences(self, prompts: DataProto, **kwargs) -> DataProto:
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# rebuild vllm cache engine
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if self.config.free_cache_engine:
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self.inference_engine.init_cache_engine()
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idx = prompts.batch['input_ids'] # (bs, prompt_length)
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# left-padded attention_mask
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attention_mask = prompts.batch['attention_mask']
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position_ids = prompts.batch['position_ids']
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# used to construct attention_mask
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eos_token_id = prompts.meta_info['eos_token_id']
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batch_size = idx.size(0)
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idx_list = []
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# parse idx from torch.Tensor to List[List[str]]
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for i in range(batch_size):
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idx_list.append(_pre_process_inputs(self.pad_token_id, idx[i]))
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do_sample = prompts.meta_info.get('do_sample', True)
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if not do_sample:
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kwargs = {
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'best_of': 1,
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'top_p': 1.0,
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'top_k': -1,
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'min_p': 0.0,
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'temperature': 0,
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'n': 1 # if greedy, only 1 response
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}
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# users can customize different sampling_params at different run
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with self.update_sampling_params(**kwargs):
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output = self.inference_engine.generate(
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prompts=None, # because we have already convert it to prompt token id
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sampling_params=self.sampling_params,
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prompt_token_ids=idx_list,
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use_tqdm=False)
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# TODO(sgm): disable logprob when recompute_log_prob is enable
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# if n = 1: (bs, response_length) ; if n > 1: (bs * n, response_length)
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response = output[0].to(idx.device)
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log_probs = output[1].to(idx.device)
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if response.shape[1] < self.config.response_length:
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response = pad_sequence_to_length(response, self.config.response_length, self.pad_token_id)
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log_probs = pad_sequence_to_length(log_probs, self.config.response_length, self.pad_token_id)
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if self.config.n > 1 and do_sample:
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idx = idx.repeat_interleave(self.config.n, dim=0)
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attention_mask = attention_mask.repeat_interleave(self.config.n, dim=0)
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position_ids = position_ids.repeat_interleave(self.config.n, dim=0)
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batch_size = batch_size * self.config.n
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seq = torch.cat([idx, response], dim=-1)
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response_length = response.size(1)
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delta_position_id = torch.arange(1, response_length + 1, device=position_ids.device)
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delta_position_id = delta_position_id.unsqueeze(0).repeat(batch_size, 1)
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# TODO(sgm): fix position_ids on right_pad
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# prompt: left pad + response: right pad
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# attention_mask: [0,0,0,0,1,1,1,1, | 1,1,1,0,0,0,0,0]
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# position_ids: [0,0,0,0,0,1,2,3, | 4,5,6,7,8,9,10,11]
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response_position_ids = position_ids[:, -1:] + delta_position_id
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position_ids = torch.cat([position_ids, response_position_ids], dim=-1)
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response_attention_mask = get_eos_mask(response_id=response, eos_token=eos_token_id, dtype=attention_mask.dtype)
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attention_mask = torch.cat((attention_mask, response_attention_mask), dim=-1)
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# all the tp ranks should contain the same data here. data in all ranks are valid
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batch = TensorDict(
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{
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'prompts': idx,
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'responses': response,
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'input_ids': seq, # here input_ids become the whole sentences
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# 'old_log_probs': log_probs, # we will recompute old log prob with actor
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'attention_mask': attention_mask,
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'position_ids': position_ids
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},
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batch_size=batch_size)
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# free vllm cache engine
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if self.config.free_cache_engine:
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self.inference_engine.free_cache_engine()
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return DataProto(batch=batch)
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