Developer guide for Tensor Parallelism — AWS Neuron Documentation (original) (raw)

Training

This document is relevant for: Inf2, Trn1, Trn2

Developer guide for Tensor Parallelism#

Training#

For training models with tensor-parallelism, one would have to make few changes to their model/training script. Below we walk through the different changes one would have to make to shard the models across devices.

Creating DataLoader:#

When we shard the model across devices using tensor parallelism, all the tensor parallel workers are operating on the same batch of data. Hence, to ensure that each tensor parallel worker is getting the same data, we make use of DistributedSampler as shown in the snippet below

def create_pretraining_dataset( input_file, max_pred_length, mini_batch_size, worker_init ): train_data = pretraining_dataset( input_file=input_file, max_pred_length=max_pred_length ) # To distribute the data across different workers in the world, # we use the DistributedSampler. The num_replicas should be equal # to the data_parallel_world_size. Note: data_parallel_rank=0 can have # multiple tensor parallel ranks and each of these should get the same # data. train_sampler = DistributedSampler( train_data, num_replicas=parallel_state.get_data_parallel_world_size(), rank=parallel_state.get_data_parallel_rank(), ) train_dataloader = DataLoader( train_data, sampler=train_sampler, batch_size=mini_batch_size, num_workers=0, worker_init_fn=worker_init, drop_last=True, pin_memory=True, ) return train_dataloader

Creating Model:#

One can create models by replacing the large linear layers withColumnParallel and RowParallel Linear layers. In case of transformers, we have a good structure where the Attention block usually have linear projections for QKV and this is followed by a fully connected layer. Let’s take a look at the example for the BERT model. We make the attention module of BERT model to use tensor parallel layers, thereby adding the ability to shard the model across devices.

class ParallelSelfAttention(transformers.models.bert.modeling_bert.BertSelfAttention): def init(self, config, position_embedding_type=None): super().init(config, position_embedding_type)

    self.query = ColumnParallelLinear(config.hidden_size,
                                      self.all_head_size,
                                      gather_output=False)
    self.key = ColumnParallelLinear(config.hidden_size,
                                    self.all_head_size,
                                    gather_output=False)
    self.value = ColumnParallelLinear(config.hidden_size,
                                      self.all_head_size,
                                      gather_output=False)
    # Since we shard the number of attention heads across tensor parallel
    # ranks, each rank would have a subset of heads, hence, we update
    # the num_attention_heads here.
    tp_size = parallel_state.get_tensor_parallel_size()
    self.num_attention_heads = self.num_attention_heads // tp_size
    self.all_head_size = self.all_head_size // tp_size

As seen we just had to swap out the linear layers with ColumnParallel Linear layers and the rest of the forward method of the attention layer can work as is. Note: In the above ColumnParallelLinear layer we are not gathering output from each rank, in other words, each ranks is working on its own shard. We can make gather_output=True and that would gather output and you would get a full dim output. However, gathering output from all ranks would introduce an all-gather operation which can be expensive depending on the size of the tensor. In the case of attention module, we know that the SelfAttention block is followed by MLP block. Hence, we replace the linear layer there with a RowParallelLinear as shown below:

class ParallelSelfOutput(transformers.models.bert.modeling_bert.BertSelfOutput): def init(self, config): super().init(config) self.dense = RowParallelLinear(config.hidden_size, config.hidden_size, input_is_parallel=True)

As seen we just had to replace the dense layer here, and pass theinput_is_parallel argument. This way, the RowParallelLinearshould operator on partitions and get a collective result.

Making just the above two changes can help you partition good chunk of your model across multiple workers, thereby allowing models of larger size to be trained on a single instance. Note: Majority of the parameters of a transformer model are in these linear layers and hence partitioning these layers can help you scale.

Final Training script:#

Once the dataloader and model changes are done, we are ready to build the training script. Good news, you can use the same training loop as before for data-parallel training, and would need just the minor tweaks to get it all started.

from neuronx_distributed.parallel_layers import parallel_state, clip_grad_norm

neuronx_distributed.parallel_state.initialize_model_parallel(tensor_model_parallel_size=2) dataloader = create_pretraining_dataset( input_file, max_pred_length, mini_batch_size, worker_init)

model = YourNewlyBuiltParallelModel(config)

We have to move the model to device using this API, because when

we move model to device using .to(device), the model parameter's

attributes aren't preserved. This causes some of the tensor parallel

attributes to be lost. Hence, this API takes care of preserving the

tensor parallel attributes.

parallel_layers.move_model_to_device(model, device)

for inputs, labels in dataloader: output = model(*inputs) loss = loss_fn(output, labels) loss.backward() # Here we use clip_grad_norm from neuronx_distributed as that # can handle tensor parallel ranks clip_grad_norm(model.parameters(), max_norm) # For the optimzer step, we have to pass the data_parallel group xm.optimizer_step( optimzer, groups=parallel_state.get_data_parallel_group(as_list=True) ) optimizer.zero_grad() scheduler.step()

Few things to take note of in the above code snippet: 1. We are initializing the model parallel with tensor parallel size of 2. This will shard the model across 2 devices. 2. We use themove_model_to_device API to move model to device. This is equivalent to doing model.to(device). We need to explicity call this API since some of the tensor-parallel attributes do not get copied over when we move the model to device using model.to(device). 3. We are calling the clip_grad_norm from parallel_layers. This clip_grad_norm should take care of accumulating the max_norm from the tensor_parallel ranks and producing the correct output. 4. We pass thedata_parallel_group to the optimizer_step. If we don’t pass the group, default would be all the workers in the world.

Saving Model:#

Once training is done, we want to save the model. This can be done easily by calling the save api fromneuronx_distributed.parallel_layers . Here is an example:

neuronx_distributed.parallel_layers.save({ 'epoch': epoch, 'model': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'loss': loss, ... }, PATH)

Note the model key used here, we need to provide the same key during model load.

This document is relevant for: Inf2, Trn1, Trn2

Developer guide for Tensor Parallelism — AWS Neuron Documentation (original) (raw)

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