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