141 lines
5.7 KiB
Python
141 lines
5.7 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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Rollout with huggingface models.
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TODO: refactor this class. Currently, it will hang when using FSDP HybridShard. We should actually create a single GPU model.
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Then, get full state_dict and bind the state_dict to the single GPU model. Then, use the single GPU model to perform generation.
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"""
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import contextlib
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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 torch.distributed.fsdp import FullyShardedDataParallel as FSDP
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from verl import DataProto
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from verl.utils.torch_functional import get_eos_mask
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from .base import BaseRollout
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from transformers import GenerationConfig
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__all__ = ['HFRollout']
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class HFRollout(BaseRollout):
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def __init__(self, module: nn.Module, config):
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super().__init__()
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self.config = config
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self.module = module
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def generate_sequences(self, prompts: DataProto) -> DataProto:
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batch_size = prompts.batch.batch_size[0]
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num_chunks = max(batch_size // self.config.get('micro_batch_size', batch_size), 1)
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batch_prompts = prompts.chunk(chunks=num_chunks)
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output = [self._generate_minibatch(p) for p in batch_prompts]
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output = DataProto.concat(output)
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return output
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@torch.no_grad()
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def _generate_minibatch(self, prompts: DataProto) -> DataProto:
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idx = prompts.batch['input_ids'] # (bs, prompt_length)
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attention_mask = prompts.batch['attention_mask'] # left-padded 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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pad_token_id = prompts.meta_info['pad_token_id']
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batch_size = idx.size(0)
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prompt_length = idx.size(1)
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self.module.eval()
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param_ctx = contextlib.nullcontext()
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# make sampling args can be overriden by inputs
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do_sample = prompts.meta_info.get('do_sample', self.config.do_sample)
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response_length = prompts.meta_info.get('response_length', self.config.response_length)
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top_p = prompts.meta_info.get('top_p', self.config.get('top_p', 1.0))
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top_k = prompts.meta_info.get('top_k', self.config.get('top_k', 0))
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if top_k is None:
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top_k = 0
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top_k = max(0, top_k) # to be compatible with vllm
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temperature = prompts.meta_info.get('temperature', self.config.temperature)
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generation_config = GenerationConfig(temperature=temperature, top_p=top_p, top_k=top_k)
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if isinstance(self.module, FSDP):
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# recurse need to set to False according to https://github.com/pytorch/pytorch/issues/100069
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param_ctx = FSDP.summon_full_params(self.module, writeback=False, recurse=False)
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with param_ctx:
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with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
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output = self.module.generate(
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input_ids=idx,
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attention_mask=attention_mask,
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do_sample=do_sample,
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max_new_tokens=response_length,
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# max_length=max_length,
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eos_token_id=eos_token_id,
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pad_token_id=pad_token_id,
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generation_config=generation_config,
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# renormalize_logits=True,
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output_scores=False, # this is potentially very large
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return_dict_in_generate=True,
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use_cache=True)
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# TODO: filter out the seq with no answers like ds-chat
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seq = output.sequences
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# huggingface generate will stop generating when all the batch reaches [EOS].
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# We have to pad to response_length
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sequence_length = prompt_length + self.config.response_length
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delta_length = sequence_length - seq.shape[1]
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if delta_length > 0:
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delta_tokens = torch.ones(size=(batch_size, delta_length), device=seq.device, dtype=seq.dtype)
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delta_tokens = pad_token_id * delta_tokens
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seq = torch.cat((seq, delta_tokens), dim=1)
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assert seq.shape[1] == sequence_length
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prompt = seq[:, :prompt_length] # (bs, prompt_length)
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response = seq[:, prompt_length:] # (bs, response_length)
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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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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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batch = TensorDict(
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{
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'prompts': prompt,
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'responses': response,
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'input_ids': seq,
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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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# empty cache before compute old_log_prob
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torch.cuda.empty_cache()
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self.module.train()
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return DataProto(batch=batch)
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