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verl/workers/reward_model/megatron/reward_model.py Normal file
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# 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.
"""
Megatron Reward Model.
"""
from tensordict import TensorDict
from functools import partial
from verl import DataProto
from verl.utils.torch_functional import logprobs_from_logits
import torch
import torch
import torch.distributed
from verl.utils.torch_functional import get_eos_mask, pad_sequence_to_length
from verl.utils.megatron.pipeline_parallel import (compute_transformers_input_shapes, make_batch_generator)
from verl import DataProto
from verl.utils.torch_functional import logprobs_from_logits, broadcast_dict_tensor, split_dict_tensor_into_batches
from verl.utils.torch_dtypes import PrecisionType
from verl.workers.reward_model.base import BasePPORewardModel
from verl.utils.megatron import sequence_parallel as sp_utils
from megatron.core import parallel_state as mpu
from megatron.core.pipeline_parallel import get_forward_backward_func
class MegatronRewardModel(BasePPORewardModel):
def __init__(self,
config,
model_config,
reward_model_module: torch.nn.ModuleList,
megatron_config,
sft_tokenizer=None,
rm_tokenizer=None):
self.config = config
self.reward_model_module = reward_model_module
self.megatron_config = megatron_config
self.model_config = model_config
self.device = 'cuda'
self.sft_tokenizer = sft_tokenizer
self.rm_tokenizer = rm_tokenizer
self.use_different_tokenizer = rm_tokenizer is not None
if self.config.param_offload:
self.offload_params_to_cpu()
def re_encode_by_rm_tokenizer(self, data: DataProto) -> DataProto:
assert self.use_different_tokenizer, 're-encode need rm tokenizer not be None!'
# need to use rm tokenizer to re-generate input_ids, attention_mask and position_ids
# 1. remove pad for each sequence
# 2. decode by sft_tokenizer, remove sft system prompts
# 3. encode by rm_tokenizer with rm system prompts, get rm_input_ids
# 4. generate attention_mask and position_ids
input_ids = data.batch['input_ids'] # (bs, seq_len)
attention_mask = data.batch['attention_mask']
position_ids = data.batch['position_ids']
ori_values = {'input_ids': input_ids, 'attention_mask': attention_mask, 'position_ids': position_ids}
ori_bs, ori_seqlen = input_ids.size(0), input_ids.size(1)
input_ids_for_rm = []
attention_mask_for_rm = []
position_ids_for_rm = []
print_decode = True
ori_seqlen = ori_seqlen + 128
for id, mask in zip(input_ids, attention_mask):
# 1. remove pad for each sequence
non_zero_indices = torch.nonzero(mask).view(-1)
begin_pos, end_pos = non_zero_indices[0].item(), non_zero_indices[-1].item()
valid_id = id[begin_pos:end_pos + 1]
# 2. decode by sft_tokenizer, remove sft system prompts
decode_result = self.sft_tokenizer.decode(valid_id)
# workaround
decode_with_rm_chat = decode_result.replace("<|user|>\n", "[INST] ").replace(
"</s>\n<|assistant|>\n", " [/INST]").replace("</s> \n<|assistant|>\n", " [/INST]") + "</s>"
print(f"decode_with_rm_chat: {decode_with_rm_chat}")
if print_decode and torch.distributed.get_rank() == 0:
# only print first decode result
print(f'device {torch.cuda.current_device()}: sft decode result:\n{decode_result}\n \
\ndevice {torch.cuda.current_device()}: sft decode result with rm chat template:\n{decode_with_rm_chat}\n\n'
)
print_decode = False
# 3. encode by rm_tokenizer
rm_input_ids = self.rm_tokenizer(decode_with_rm_chat,
return_tensors='pt')['input_ids'][0].to(input_ids.device)
# 4. generate attention_mask and position_ids
rm_attention_mask = torch.ones_like(rm_input_ids, device=input_ids.device)
cur_seqlen = rm_input_ids.shape[-1]
# NOTE(gh): the later reward compute will process the shape (bs, seqlen_pad_128)
if cur_seqlen > ori_seqlen:
print(f'warninig: rm encode seqlen {cur_seqlen} > sft encode seqlen {ori_seqlen}')
rm_input_ids = rm_input_ids[:ori_seqlen]
rm_attention_mask = rm_attention_mask[:ori_seqlen]
else:
# right padding
rm_input_ids = pad_sequence_to_length(rm_input_ids, ori_seqlen, self.rm_tokenizer.pad_token_id)
rm_attention_mask = pad_sequence_to_length(rm_attention_mask, ori_seqlen, 0)
rm_position_ids = torch.arange(0, ori_seqlen, device=input_ids.device)
input_ids_for_rm.append(torch.unsqueeze(rm_input_ids, dim=0))
attention_mask_for_rm.append(torch.unsqueeze(rm_attention_mask, dim=0))
position_ids_for_rm.append(torch.unsqueeze(rm_position_ids, dim=0))
input_ids_for_rm = torch.cat(input_ids_for_rm, dim=0)
attention_mask_for_rm = torch.cat(attention_mask_for_rm, dim=0)
position_ids_for_rm = torch.cat(position_ids_for_rm, dim=0)
# (bs, seqlen) will not change, but input_ids, attention_mask and position_ids will change
# NOTE(gh): need to replace into origin values after compute reward!
data.batch['input_ids'] = input_ids_for_rm
data.batch['attention_mask'] = attention_mask_for_rm
data.batch['position_ids'] = position_ids_for_rm
return data, ori_values
@torch.no_grad()
def compute_reward(self, data: DataProto) -> DataProto:
if self.config.param_offload:
self.load_params_to_cuda()
if self.use_different_tokenizer:
data, ori_values = self.re_encode_by_rm_tokenizer(data)
input_ids = data.batch['input_ids'] # (bs, seq_len')
attention_mask = data.batch['attention_mask']
position_ids = data.batch['position_ids']
responses = data.batch['responses']
batch_size = responses.size(0)
response_length = responses.size(1)
with torch.no_grad():
output = self.forward_batch(data)
if mpu.is_pipeline_last_stage(ignore_virtual=True):
logits = torch.cat([o['logits'] for o in output], dim=0)
else:
logits = torch.empty(
(input_ids.shape[0], input_ids.shape[1]),
dtype=torch.bfloat16, # TODO(sgm): check why is bfloat16
device=input_ids.device)
# broadcast across pp ranks
torch.distributed.broadcast(tensor=logits,
src=mpu.get_pipeline_model_parallel_last_rank(),
group=mpu.get_pipeline_model_parallel_group(),
async_op=False)
# (bs, seqlen', hidden_size) -> (bs, seqlen', 1) -> (bs, seqlen')
token_level_rewards = logits
# find the last token reward
ends = attention_mask.cumsum(dim=-1).argmax(dim=-1).view(-1, 1) # (bs, 1)
rewards = torch.gather(token_level_rewards, dim=1, index=ends) # (bs, 1)
if self.use_different_tokenizer:
data.batch.update(ori_values)
input_ids = ori_values['input_ids']
attention_mask = ori_values['attention_mask']
position_ids = ori_values['position_ids']
token_level_rewards = rewards.expand(attention_mask.shape[0], attention_mask.shape[1]) # (bs, ori_seqlen)
# assign last valid token reward to ori position
eos_mask_idx = torch.argmax(position_ids * attention_mask, dim=-1) # (bs,)
eos_mask = torch.zeros_like(attention_mask)
eos_mask[torch.arange(batch_size), eos_mask_idx] = 1.
token_level_rewards = token_level_rewards * eos_mask
token_level_rewards = token_level_rewards[:, -response_length:]
if self.config.param_offload:
self.offload_params_to_cpu()
else:
# add empty cache after each compute
torch.cuda.empty_cache()
batch = TensorDict({'rm_scores': token_level_rewards}, batch_size=input_ids.shape[0])
return DataProto(batch=batch)
def forward_batch(self, data: DataProto):
"""
We assume:
- The model takes input: (input_ids, attention_mask, position_ids). No rmpad for the input
- The communication shape is (total_nnz_pad_to_sp // tp_size, 1, hidden_size) if sequence parallel is enabled
"""
# broadcast from last pp rank to all other pp ranks
# TODO: actually, we just need to control the sampling order.
data.batch = data.batch.contiguous()
broadcast_dict_tensor(data.batch,
src=mpu.get_pipeline_model_parallel_last_rank(),
group=mpu.get_pipeline_model_parallel_group())
# split into micro-batches
if self.config is not None and 'ppo_micro_batch_size' in self.config:
infer_batch_size = self.config.ppo_micro_batch_size
else:
infer_batch_size = data.batch.batch_size[0]
data.batch['attention_mask'] = data.batch['attention_mask'].to(bool)
batches = split_dict_tensor_into_batches(data.batch, batch_size=infer_batch_size)
n_micro_batch = len(batches)
seq_len = batches[0]['input_ids'].shape[1]
# compute input shapes for pp stages
input_shapes = compute_transformers_input_shapes(
batches,
meta_info={
'sequence_parallel': self.megatron_config.sequence_parallel,
'hidden_size': self.model_config.hidden_size
})
# compute input shapes for pp stages
forward_backward_func = get_forward_backward_func()
def loss_func(output):
return 1., {'logits': output.logits}
def forward_step(batch_iter, model):
batch = next(batch_iter)
input_ids = batch['input_ids']
attention_mask = batch['attention_mask']
position_ids = batch['position_ids']
output = model(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids)
return output, loss_func
# batch should be a list of batches inside micro-batches
batch_generator = make_batch_generator(batches, vpp_size=len(self.reward_model_module))
# TODO: we may use the new schedule instead
# for flash-attn: (seq_len, batch_size, hidden_size) = (mbs*seq_len, 1, hidden_size)
if mpu.get_pipeline_model_parallel_world_size() > 1:
losses_reduced = forward_backward_func(
forward_step_func=forward_step,
data_iterator=batch_generator,
model=self.reward_model_module,
num_microbatches=n_micro_batch,
input_shapes=input_shapes, # must set for flash-attn sequence packing
seq_length=infer_batch_size * seq_len, # no use when input_shapes was set
hidden_size=self.model_config.hidden_size, # no use when input_shapes was set
micro_batch_size=1, # no use when input_shapes was set
forward_only=True,
)
else:
losses_reduced = forward_backward_func(
forward_step_func=forward_step,
data_iterator=batch_generator,
model=self.reward_model_module,
num_microbatches=n_micro_batch,
seq_length=infer_batch_size * seq_len, # in use for pp = 1
hidden_size=self.model_config.hidden_size, # in use for pp = 1
micro_batch_size=1, # in use for pp = 1
forward_only=True,
)
# loss_reduces contains the stats returned from loss_func
return losses_reduced
def offload_params_to_cpu(self):
if self.device == 'cuda':
for reward_model_module in self.reward_model_module:
for name, param in reward_model_module.named_parameters():
param.data = param.data.to('cpu', non_blocking=True)
self.device = 'cpu'
torch.cuda.empty_cache()
def load_params_to_cuda(self):
if self.device == 'cpu':
for reward_model_module in self.reward_model_module:
for name, param in reward_model_module.named_parameters():
param.data = param.data.to(torch.cuda.current_device(), non_blocking=True)
self.device = 'cuda'