Building a Parallel Large Language Model Network

In recent years, with the rapid development of deep learning technologies, especially the emergence of large-scale pre-trained models (such as ChatGPT, LLaMA, and Pangu), the AI field has made significant progress. However, as model sizes continue to expand, the computing resources required by these large models, particularly GPU memory, are growing exponentially. For example, the Pangu model with 71 billion parameters requires approximately 142 GB of GPU memory at half-precision (FP16). In addition, the increasing sequence length of large models places immense pressure on GPU memory. The constraints of GPU memory not only affect model loading but also limit batch sizes. Smaller batch sizes may lead to decreased inference efficiency, consequently impacting the overall throughput of the system.

The pressure on GPU memory makes it challenging for a single device to complete inference tasks within a reasonable time frame, and parallel computing has become a key strategy to address this challenge.

Model Parallelism

When the number of model parameters is too large to fit into the GPU memory capacity of a single device, model parallelism can distribute different parts of the model across multiple devices. This approach effectively reduces the GPU memory requirements of a single device and enables inference of larger-scale models.

Basic MatMul Module

In large model computations, matrix multiplication (MatMul) accounts for a significant portion of both weight and computation workload. MatMul exhibits both column-wise parallelism and row-wise parallelism.

Starting with the original implementation of nn.Dense in MindSpore, we can build implementations for both column-wise and row-wise MatMul.

1. Creation and management of communication domains and management of large model configurations

   Builds the CommunicationHelper class to manage the model parallel domain.

   from mindspore.communication import create_group, get_group_size
   

   class CommunicationHelper:
       def __init__(self, group_name, size):
           self.group_name = group_name
           self.size = size
           self.rank_list = [i for i in range(size)]
   
       def create_tensor_model_parallel_group(self):
           create_group(group=self.group_name, rank_ids=self.rank_list)
   
       def get_tensor_model_parallel_group_size(self):
           return get_group_size(group=self.group_name)
   
       def get_tensor_model_parallel_group(self):
           return self.group_name
   

   Build ConfigHelper to manage and configure large model parameters.

   class ConfigHelper:
       def __init__(self,
                    vocab_size,
                    hidden_size,
                    ffn_hidden_size,
                    num_layers,
                    batch_size,
                    seq_length, dtype,
                    num_heads,
                    has_bias=False):
           self.vocab_size = vocab_size
           self.hidden_size = hidden_size
           self.ffn_hidden_size = ffn_hidden_size
           self.num_layers = num_layers
           self.batch_size = batch_size
           self.seq_length = seq_length
           self.dtype = dtype
           self.num_heads = num_heads
           self.has_bias = has_bias
   

2. Column-wise MatMul

   ColumnParallelLinear class calculates and initializes the sharded weights' shape based on the number of devices used for model parallelism. Column-wise means to shard out_channels. During the model's forward pass, MatMul is called to compute the parallel results. Finally, an AllGather operation can be optionally performed on the parallel results to obtain the complete output.

   The MindSpore training and inference integrated framework supports enabling infer_boost. This parameter activates the high-performance self-developed operator library within the MindSpore framework. To enable this mode, you need to:

   • Set variables.

   from mindspore import set_context
   set_context(jit_config={"jit_level": 'O0', "infer_boost": 'on'})
   

   • Set system environment variables.

   export ASCEND_HOME_PATH={$ascend_custom_path}
   

   For example, if there are 2 devices for model parallelism, set environment variables, initialize the communication group, and configure the model parameter config as follows:

   from mindspore import nn, Parameter, ops, Tensor
   from mindspore.common import dtype as mstype
   from mindspore.communication import init
   from mindspore.common.initializer import initializer
   import numpy as np
   
   from mindspore import set_context
   set_context(jit_config={"jit_level": 'O0', "infer_boost": 'on'})
   
   TP_GROUP_NAME='tp'
   TP_SIZE = 2
   COMMUN_HELPER = CommunicationHelper(group_name=TP_GROUP_NAME, size=TP_SIZE)
   
   init()
   COMMUN_HELPER.create_tensor_model_parallel_group()
   
   config = ConfigHelper(batch_size=64,
                         vocab_size=32000,
                         num_layers=4,
                         seq_length=2048,
                         hidden_size=1024,
                         ffn_hidden_size=4096,
                         dtype=mstype.float16,
                         num_heads=8,
                         has_bias=False)
   

   Column-wise MatMul module

   class ColumnParallelLinear(nn.Cell):
       def __init__(self,
                    in_channels,
                    out_channels,
                    weight_init=None,
                    bias_init=None,
                    has_bias=True,
                    dtype=mstype.float32):
           super().__init__()
           self.in_channels = in_channels
           self.out_channels = out_channels
           self.has_bias = has_bias
           self.tensor_parallel_group_size = COMMUN_HELPER.get_tensor_model_parallel_group_size()
           self.out_channels_per_partition = out_channels // self.tensor_parallel_group_size
           self.dtype = dtype
           weight_shape = (self.out_channels_per_partition, self.in_channels)
           self.weight = Parameter(initializer(weight_init, weight_shape, self.dtype), name="weight")
           if self.has_bias:
               self.bias = Parameter(initializer(bias_init, (self.out_channels_per_partition), self.dtype), name="bias")
               self.bias_add = ops.Add()
           self.matmul = ops.BatchMatMul(transpose_b=True)
           self.cast = ops.Cast()
   
       def construct(self, x):
           origin_dtype = x.dtype
           x = self.cast(x, self.dtype)
           out = self.matmul(x, self.weight)
           if self.has_bias:
               out = self.bias_add(
                   out, self.cast(self.bias, self.dtype)
               )
           out = self.cast(out, origin_dtype)
           return out
   

   The output of column-wise MatMul is parallel. To obtain a complete output, use GatherLastDim.

   class GatherLastDim(nn.Cell):
       def __init__(self):
           super().__init__()
           self.all_gather = ops.AllGather(group=COMMUN_HELPER.get_tensor_model_parallel_group())
           self.world_size = COMMUN_HELPER.get_tensor_model_parallel_group_size()
           self.split = ops.Split(axis=0, output_num=self.world_size)
   
       def construct(self, input_):
           output = self.all_gather(input_)
           tensor_list = self.split(output)
           output = ops.cat(tensor_list, axis=-1)
           return output
   

   Inference of column-wise MatMul:

   column_parallel_linear = ColumnParallelLinear(in_channels=config.hidden_size,
                                                 out_channels=config.hidden_size,
                                                 weight_init='normal',
                                                 dtype=config.dtype,
                                                 has_bias=False)
   input_x = Tensor(np.random.randn(config.batch_size, config.seq_length, config.hidden_size).astype(np.float32))
   out_parallel = column_parallel_linear(input_x)
   print(out_parallel.shape)
   
   gather_last_dim = GatherLastDim()
   out = gather_last_dim(out_parallel)
   print(out.shape)
   

3. Row-wise MatMul

   Similar to column-wise MatMul, RowParallelLinear shards the weight based on the size of the model parallel domain. During initialization, the sharding direction is row, that is, sharding in_channels before initialization. During the model's forward pass, after the MatMul is performed on the input and weight, the AllReduce operation needs to be performed on all device results.

   The row-wise MatMul module is implemented as follows:

   class RowParallelLinear(nn.Cell):
       def __init__(self,
                    in_channels,
                    out_channels,
                    weight_init='normal',
                    bias_init=None,
                    has_bias=True,
                    dtype=mstype.float32):
           super().__init__()
           self.in_channels = in_channels
           self.out_channels = out_channels
           self.has_bias = has_bias
           self.tensor_parallel_group_size = COMMUN_HELPER.get_tensor_model_parallel_group_size()
           self.in_channels_per_partition = in_channels // self.tensor_parallel_group_size
           self.dtype = dtype
           weight_shape = (self.out_channels, self.in_channels_per_partition)
           self.weight = Parameter(initializer(weight_init, weight_shape, self.dtype), name="weight")
           if self.has_bias:
               self.bias = Parameter(initializer(bias_init, (self.in_channels_per_partition), self.dtype), name="bias")
               self.bias_add = ops.Add()
           self.bmm = ops.BatchMatMul(transpose_b=True)
           self.all_reduce = ops.AllReduce(group=COMMUN_HELPER.get_tensor_model_parallel_group())
           self.cast = ops.Cast()
   
       def construct(self, x):
           origin_dtype = x.dtype
           x = self.cast(x, self.dtype)
           out = self.bmm(x, self.weight)
           if self.has_bias:
               output_parallel = self.bias_add(output_parallel, self.cast(self.bias, self.dtype))
           out = self.all_reduce(out)
           out = self.cast(out, origin_dtype)
           return out
   

   Inference of row-wise MatMul:

   row_parallel_linear = RowParallelLinear(in_channels=config.hidden_size,
                                           out_channels=config.hidden_size,
                                           weight_init='normal',
                                           dtype=config.dtype,
                                           has_bias=False)
   out = row_parallel_linear(out_parallel)
   print(out.shape)
   

4. Embedding

   In addition to MatMul, the Embedding layer can also be parallelized. The Embedding weights can be sharded across multiple devices, with each device responsible for mapping a different range of token IDs.

   

   Specifically:

   

   Based on nn.Embedding, build an Embedding layer for model parallelism.

    class VocabParallelEmbedding(nn.Cell):
        def __init__(self,
                     num_embeddings,
                     embedding_dim,
                     init_method="normal",
                     init_type=mstype.float32):
            super().__init__()
            self.num_embeddings = num_embeddings
            self.embedding_dim = embedding_dim
            self.tensor_model_parallel_size = COMMUN_HELPER.get_tensor_model_parallel_group_size()
            per_partition_vocab_size = self.num_embeddings // self.tensor_model_parallel_size
            self.vocab_start_index = COMMUN_HELPER.get_tensor_model_parallel_group_rank() * per_partition_vocab_size
            self.vocab_end_index = self.vocab_start_index + per_partition_vocab_size
            self.num_embeddings_per_partition = (
                self.vocab_end_index - self.vocab_start_index
            )
            self.embedding_weight = Parameter(
                initializer(
                    init=init_method,
                    shape=(self.num_embeddings_per_partition, self.embedding_dim),
                    dtype=init_type,
                ),
                name="embedding_weight",
            )
            self.all_reduce = ops.AllReduce(group=COMMUN_HELPER.get_tensor_model_parallel_group())
            self.max_index_per_partition = Tensor(self.num_embeddings_per_partition - 1, dtype=mstype.int32)
            self.expand_dims = ops.ExpandDims()
            self.gather = ops.Gather()
            self.sub = ops.Sub()
            self.relu = ops.ReLU()
            self.minimum = ops.Minimum()
            self.eq = ops.Equal()
            self.mul = ops.Mul()
   
        def construct(self, x):
            displaced_x = self.sub(x, self.vocab_start_index)
            down_truncated_x = self.relu(displaced_x)
            truncated_x = self.minimum(down_truncated_x, self.max_index_per_partition)
            input_mask = self.eq(displaced_x, truncated_x)
            input_mask = self.expand_dims(input_mask, -1)
            output_parallel = self.gather(self.embedding_weight, truncated_x, 0)
            output_parallel = self.mul(output_parallel, input_mask)
            output = self.all_reduce(output_parallel)
            return output
   

   Inference of parallel Embedding:

   input_ids = np.random.randint(0, config.vocab_size, size=(config.batch_size, config.seq_length), dtype=np.int32)
   input_ids = Tensor(input_ids)
   
   embedding_output = vocab_parallel_embedding(input_ids)
   print(embedding_output.shape)
   

TransformerModel Parallel Adaptation

It can be seen that the tensor is processed sequentially. First, it passes through the ColumnParallelLinear column-wise MatMul to obtain the parallel results. Then, it is input to the RowParallelLinear row-wise MatMul, resulting in the complete output of the two MatMul operations.

Based on the preceding analysis, you can change the TransformerModel built in Building a Large Language Model Inference Network from Scratch to a model structure that supports parallelism.

1. Attention

   Take multi-head attention (MHA) as an example. The typical attention module in a Transformer is multi-headed, with each attention head operating independently. Therefore, when a single attention head is complete, the activation value can be sharded along the hidden_size dimension. For example, assume that the number of MHA headers (num_heads) is 16, the dimension (head_dim) of each header is 256, then the hidden_size is 4096, and the linears of Q/K/V have in/out dimensions of 4096. When the model parallelism is set to tensor_model_parallel=4, these linears are sharded into four devices. Each device(4096,1024) means that each device computes 4 heads.

   

   The following is an example of the Attention module code:

   class ParallelAttention(nn.Cell):
       def __init__(self, config):
           super().__init__()
           self.tensor_model_parallel_size = COMMUN_HELPER.get_tensor_model_parallel_group_size()
           self.num_heads_per_partition = config.num_heads // self.tensor_model_parallel_size
           self.head_dim = config.hidden_size // config.num_heads
           self.norm_factor = math.sqrt(self.head_dim)
           self.q = ColumnParallelLinear(in_channels=config.hidden_size,
                                         out_channels=config.hidden_size,
                                         weight_init='normal',
                                         has_bias=config.has_bias)
           self.k = ColumnParallelLinear(in_channels=config.hidden_size,
                                         out_channels=config.hidden_size,
                                         weight_init='normal',
                                         dtype=config.dtype,
                                         has_bias=config.has_bias)
           self.v = ColumnParallelLinear(in_channels=config.hidden_size,
                                         out_channels=config.hidden_size,
                                         weight_init='normal',
                                         dtype=config.dtype,
                                         has_bias=config.has_bias)
           self.flash_attention = ops.operations.nn_ops.FlashAttentionScore(head_num=self.num_heads_per_partition,
                                                                           scale_value=1.0/self.norm_factor,
                                                                           next_tokens=0)
           self.out = RowParallelLinear(in_channels=config.hidden_size,
                                        out_channels=config.hidden_size,
                                        weight_init='normal',
                                        dtype=config.dtype,
                                        has_bias=config.has_bias)
   
       def construct(self, x, mask):
           query = self.q(x)
           key = self.k(x)
           value = self.v(x)
           _, _, _, context_layer = self.flash_attention(query, key, value, attn_mask=mask)
           output = self.out(context_layer)
           return output
   

2. MLP

   The MLP module is actually two fully-connected layers, which can also be processed by parallel MatMul. The code is as follows:

   class ParallelMLP(nn.Cell):
       def __init__(self, config):
           super().__init__()
           self.w1 = ColumnParallelLinear(in_channels=config.hidden_size,
                                          out_channels=config.ffn_hidden_size,
                                          weight_init='normal',
                                          dtype=config.dtype,
                                          has_bias=config.has_bias)
           self.w2 = RowParallelLinear(in_channels=config.ffn_hidden_size,
                                       out_channels=config.hidden_size,
                                       weight_init='normal',
                                       dtype=config.dtype,
                                       has_bias=config.has_bias)
           self.act_func = nn.SiLU()
           self.mul = ops.Mul()
   
       def construct(self, x):
           x = self.w1(x)
           x = self.act_func(x)
           output = self.w2(x)
           return output
   

3. TransformerLayer

   TransformerLayer consists of Attention and MLP. Since there are no single operators that can be parallelized, you only need to pass the parallel parameters to Attention and MLP.

   class ParallelTransformerLayer(nn.Cell):
       def __init__(self, config):
           super().__init__()
           self.attention = ParallelAttention(config=config)
           self.feed_forward = ParallelMLP(config=config)
           self.attention_norm = RMSNorm(dim=config.hidden_size, dtype=config.dtype)
           self.ffn_norm = RMSNorm(dim=config.hidden_size, dtype=config.dtype)
           self.add = ops.Add()
   
       def construct(self, x, mask):
           norm_output = self.attention_norm(x)
           attention_output = self.attention(norm_output, mask)
           norm_input = self.add(x, attention_output)
           norm_output = self.ffn_norm(norm_input)
           mlp_output = self.feed_forward(norm_output)
           output = self.add(norm_input, mlp_output)
           return output
   

4. TransformerModel

   class ParallelTransformer(nn.Cell):
       def __init__(self, config):
           super().__init__()
           self.embedding = VocabParallelEmbedding(num_embeddings=config.vocab_size,
                                                   embedding_dim=config.hidden_size,
                                                   init_method='normal',
                                                   init_type=config.dtype)
           self.layers = nn.CellList()
           self.num_layers = config.num_layers
           for _ in range(config.num_layers):
               layer = ParallelTransformerLayer(config=config)
               self.layers.append(layer)
           self.norm_out = RMSNorm(dim=config.hidden_size, dtype=config.dtype)
   
       def construct(self, x, mask):
           hidden_state = self.embedding(x)
           for i in range(self.num_layers):
               hidden_state = self.layers[i](hidden_state, mask)
           hidden_state = self.norm_out(hidden_state)
           return hidden_state
   

For details about the end-to-end large language model code project, see model_dev.py script. Run the following command to verify the code:

msrun --worker_num 2 --local_worker_num 2 --master_port 8124 --log_dir msrun_log --join True --cluster_time_out 300 model_dev.py