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from typing import Optional
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.nn.functional as F
import process_group_manager as pgm
### begin TP communications
def split_tensor_along_last_dim(tensor, num_partitions):
"""Split a tensor along its last dimension into num_partitions chunks."""
last_dim = tensor.dim() - 1
assert tensor.size()[last_dim] % num_partitions == 0, f"{tensor.size()[last_dim]} is not divisible by {num_partitions}"
last_dim_size = tensor.size()[last_dim] // num_partitions
return torch.split(tensor, last_dim_size, dim=last_dim)
class Reduce(torch.autograd.Function):
"""All-reduce in forward pass, identity in backward pass."""
@staticmethod
def forward(ctx, input):
if pgm.process_group_manager.tp_world_size == 1:
return input
dist.all_reduce(input, op=dist.ReduceOp.SUM, group=pgm.process_group_manager.tp_group)
return input
@staticmethod
def backward(ctx, grad_output):
return grad_output
class Gather(torch.autograd.Function):
"""Gather in forward pass, split in backward pass."""
@staticmethod
def forward(ctx, input):
if pgm.process_group_manager.tp_world_size == 1:
return input
last_dim = input.dim() - 1
# Need contiguous tensors for collectives -> https://github.com/pytorch/pytorch/blob/main/torch/distributed/nn/functional.py#L321
input = input.contiguous()
tensor_list = [torch.empty_like(input) for _ in range(pgm.process_group_manager.tp_world_size)]
tensor_list[pgm.process_group_manager.tp_rank] = input
dist.all_gather(tensor_list, input, group=pgm.process_group_manager.tp_group)
output = torch.cat(tensor_list, dim=last_dim).contiguous()
return output
@staticmethod
def backward(ctx, grad_output):
if pgm.process_group_manager.tp_world_size == 1:
return grad_output
# Split gradient according to TP size
chunks = split_tensor_along_last_dim(grad_output, pgm.process_group_manager.tp_world_size)
return chunks[pgm.process_group_manager.tp_rank].contiguous()
class Copy(torch.autograd.Function):
"""Identity in forward pass, all-reduce in backward pass."""
@staticmethod
def forward(ctx, input):
return input
@staticmethod
def backward(ctx, grad_output):
if pgm.process_group_manager.tp_world_size == 1:
return grad_output
dist.all_reduce(grad_output, op=dist.ReduceOp.SUM, group=pgm.process_group_manager.tp_group)
return grad_output
### end TP communications
def apply_tensor_parallel(model):
def _replace_module(_module, _linear_proj_name, _style, args={}):
assert _style in ["column", "row", 'vocab']
linear_layer = getattr(_module, _linear_proj_name)
if _style == "column":
new_linear_layer = ColumnParallelLinear(
in_features=linear_layer.in_features,
out_features=linear_layer.out_features,
bias=linear_layer.bias is not None,
gather_output=args.get("gather_output", False)
)
elif _style == "row":
new_linear_layer = RowParallelLinear(
in_features=linear_layer.in_features,
out_features=linear_layer.out_features,
bias=linear_layer.bias is not None,
)
else:
new_linear_layer = VocabParallelEmbedding(
num_embeddings=linear_layer.num_embeddings,
embedding_dim=linear_layer.embedding_dim,
)
setattr(_module, _linear_proj_name, new_linear_layer)
module_linear_name_stype_mapping_list = [
("attention", "q_proj", "column"),
("attention", "k_proj", "column"),
("attention", "v_proj", "column"),
("attention", "out_proj", "row"),
("mlp", "up_proj", "column"),
("mlp", "gate_proj", "column"),
("mlp", "down_proj", "row"),
]
for layer in model.decoder_layers:
for module_name, linear_proj_name, style in module_linear_name_stype_mapping_list:
_replace_module(getattr(layer, module_name), linear_proj_name, style)
_replace_module(model, "embedding", "vocab")
_replace_module(model, "final_proj", "column", args={"gather_output": True})
return model
class ColumnParallelLinear(nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool, gather_output: bool = False):
super(ColumnParallelLinear, self).__init__()
self.tp_world_size = pgm.process_group_manager.tp_world_size
self.tp_rank = pgm.process_group_manager.tp_rank
self.in_features = in_features
self.out_features = out_features
assert out_features % self.tp_world_size == 0, "Hidden dimension must be divisible by the tensor parallel world size"
self.output_size_per_partition = out_features // self.tp_world_size
self.gather_output = gather_output
# Note: torch.nn.functional.linear performs XW^T + b so we exchange the order of dimensions
self.weight = nn.Parameter(torch.Tensor(self.output_size_per_partition, self.in_features)) # W_i
if bias:
self.bias = nn.Parameter(torch.Tensor(self.output_size_per_partition))
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter("bias", None)
self.reset_parameters()
def reset_parameters(self):
# Initialize weight tensor with the default initialization method used for nn.Linear in PyTorch
if self.tp_world_size == 1:
# U(-sqrt(k), sqrt(k))
k = 1 / self.weight.size(1)
bound = math.sqrt(k)
torch.nn.init.uniform_(self.weight, -bound, bound)
return
# When TP > 1, Initialize master weight
master_weight = torch.empty(self.out_features, self.in_features, dtype=self.weight.dtype, requires_grad=False)
# Calculate bound based on master weight's input dimension. U(-sqrt(k), sqrt(k))
k = 1 / master_weight.size(1)
bound = math.sqrt(k)
torch.nn.init.uniform_(master_weight, -bound, bound)
# Split the model into size of self.output_size_per_partitio and take the corresponding partition
weight_list = torch.split(master_weight, self.output_size_per_partition, dim=0)
self.weight.data = weight_list[self.tp_rank].contiguous()
def forward(self, input):
input_parallel = Copy.apply(input)
# XW_i^T + b, output is Y_i
output = F.linear(input_parallel, self.weight, self.bias)
if self.gather_output:
output = Gather.apply(output)
return output
class RowParallelLinear(nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool):
super(RowParallelLinear, self).__init__()
self.tp_world_size = pgm.process_group_manager.tp_world_size
self.tp_rank = pgm.process_group_manager.tp_rank
self.in_features = in_features
self.out_features = out_features
assert in_features % self.tp_world_size == 0, "Hidden dimension must be divisible by the tensor parallel world size"
self.input_size_per_partition = in_features // self.tp_world_size
self.weight = nn.Parameter(torch.Tensor(self.out_features, self.input_size_per_partition))
if bias:
self.bias = nn.Parameter(torch.Tensor(self.out_features))
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter("bias", None)
self.reset_parameters()
def reset_parameters(self):
# Initialize weight tensor with the default initialization method used for nn.Linear in PyTorch
if self.tp_world_size == 1:
# U(-sqrt(k), sqrt(k))
k = 1 / self.weight.size(1)
bound = math.sqrt(k)
torch.nn.init.uniform_(self.weight, -bound, bound)
return
# When TP > 1, Initialize master weight
master_weight = torch.empty(self.out_features, self.in_features, dtype=self.weight.dtype, requires_grad=False)
# Calculate bound based on master weight's input dimension. U(-sqrt(k), sqrt(k))
k = 1 / master_weight.size(1)
bound = math.sqrt(k)
torch.nn.init.uniform_(master_weight, -bound, bound)
# Split the model into size of self.input_size_per_partition and take the corresponding partition
weight_list = torch.split(master_weight, self.input_size_per_partition, dim=1)
self.weight.data = weight_list[self.tp_rank].contiguous()
def forward(self, input):
# X_i * W_i^T + b
output_parallel = F.linear(input, self.weight)
# All-reduce across all the partitions.
output = Reduce.apply(output_parallel)
return output if self.bias is None else output + self.bias
class VocabParallelEmbedding(nn.Module):
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: Optional[int] = None,
max_norm: Optional[float] = None,
norm_type: float = 2.0,
scale_grad_by_freq: bool = False,
sparse: bool = False
):
super(VocabParallelEmbedding, self).__init__()
self.tp_world_size = pgm.process_group_manager.tp_world_size
self.tp_rank = pgm.process_group_manager.tp_rank
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = norm_type
self.scale_grad_by_freq = scale_grad_by_freq
self.sparse = sparse
# Divide the weight matrix along the vocaburaly dimension.
self.vocab_start_index, self.vocab_end_index = self._vocab_range_from_global_vocab_size(
self.num_embeddings, pgm.process_group_manager.tp_rank, pgm.process_group_manager.tp_world_size
)
self.num_embeddings_per_partition = self.vocab_end_index - self.vocab_start_index
self.weight = nn.Parameter(torch.Tensor(self.num_embeddings_per_partition, self.embedding_dim))
self.reset_parameters()
def _vocab_range_from_global_vocab_size(self, global_vocab_size: int, rank: int, world_size: int):
assert global_vocab_size % world_size == 0, f"{global_vocab_size} is not divisible by {world_size}"
per_partition_vocab_size = global_vocab_size // world_size
# vocab_range_from_per_partition_vocab_size
index_f = rank * per_partition_vocab_size
index_l = index_f + per_partition_vocab_size
return index_f, index_l
def reset_parameters(self):
if self.tp_world_size == 1:
# Initialize Vocab embedding with N(0, 1)
torch.nn.init.normal_(self.weight, mean=0.0, std=1.0)
return
# When TP > 1, Initialize master weight
master_weight = torch.empty(self.num_embeddings, self.embedding_dim, dtype=self.weight.dtype, requires_grad=False)
torch.nn.init.normal_(master_weight, mean=0.0, std=1.0)
# Split the model into size of self.num_embeddings_per_partition and take the corresponding partition
weight_list = torch.split(master_weight, self.num_embeddings_per_partition, dim=0)
self.weight.data = weight_list[self.tp_rank].contiguous()
def forward(self, input):
"""
Performs an embedding lookup for input tokens in the parallelized embedding layer
1. Masks tokens that fall outside the specified vocabulary range and adjusts the input
2. Performs embedding lookups for valid tokens, setting embeddings of out-of-vocabulary tokens to zero
3. Reduces the embeddings across model parallel GPUs using all-reduce for synchronization
"""
# Build the mask for out-of-vocabulary tokens.
input_mask = (input < self.vocab_start_index) | (input >= self.vocab_end_index)
# Mask the input.
masked_input = input.clone() - self.vocab_start_index
masked_input[input_mask] = 0
# Get the embeddings for the valid tokens.
output_parallel = F.embedding(
masked_input,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
# Embedding of out-of-vocabulary tokens is set to 0.
output_parallel[input_mask, :] = 0.0
output = Reduce.apply(output_parallel)
return output |