#!/usr/bin/env python # -*- encoding: utf-8 -*- import math from typing import Callable, List, Optional, Tuple, Union import torch import torch.distributed as dist import torch.nn as nn from torch import Tensor from torch.distributed import ProcessGroup from torch.nn.parameter import Parameter from colossalai.lazy import LazyInitContext from colossalai.nn import init as init from colossalai.nn.layer.utils import divide from colossalai.tensor.d_tensor.api import ( customized_distributed_tensor_to_existing_param, distribute_tensor_with_customization, is_customized_distributed_tensor, is_distributed_tensor, shard_rowwise, sharded_tensor_to_existing_param, ) from ._operation import ( gather_forward_split_backward, linear_with_async_comm, matmul_with_async_comm, reduce_backward, reduce_forward, split_forward_gather_backward, ) from .parallel_module import ParallelModule from .utils import create_randomizer_with_offset __all__ = ['FusedLinear1D_Col', 'FusedLinear1D_Row', 'GPT2FusedLinearConv1D_Col', 'GPT2FusedLinearConv1D_Row'] # ==================================== # For GPT Only # ==================================== def split_fused_qkv_in_gpt2_style(qkv: torch.Tensor, n_fused: int, process_group: ProcessGroup, is_transposed: bool = False): """ The fused qkv tensor looks like [Q1, Q2, K1, K2, V1, V2], this function will split them into [Q1, K1, V1] and [Q2, K2, V2]. Args: qkv (torch.Tensor): The fused qkv tensor. n_fused (int): The number items fused together, defaults to 3 (query, key and value). process_group (ProcessGroup): The process group for distributed communication. is_transposed (bool): generally the tensor is the shape of (out_features, in_features). Set this to True if the tensor is in the shape (in_features, out_features). """ # get the number of slice for the fused qkv rank = dist.get_rank(group=process_group) world_size = dist.get_world_size(group=process_group) order = torch.arange(world_size * n_fused) # split the fused qkv # from # [Q, K, V] # to # [Q1, Q2, K1, K2, V1, V2] if is_transposed: weight_chunks = torch.chunk(qkv, world_size * n_fused, dim=-1) else: weight_chunks = torch.chunk(qkv, world_size * n_fused, dim=0) # rearrange the slice into the final order # from # [Q1, Q2, K1, K2, V1, V2] # to # [Q1, K1, V1], [Q2, K2, V2] weight_chunks_of_current_rank = [weight_chunks[i] for i in order[rank::world_size]] if is_transposed: weight_of_current_rank = torch.cat(weight_chunks_of_current_rank, dim=-1) else: weight_of_current_rank = torch.cat(weight_chunks_of_current_rank, dim=0) return weight_of_current_rank def gather_fused_qkv_in_gpt2_style(qkv: torch.Tensor, n_fused: int, process_group: ProcessGroup, is_transposed: bool = False): """ The splitted qkv tensor looks like [Q1, K1, V1] and [Q2, K2, V2], this function will gather them into [Q1, Q2, K1, K2, V1, V2]. Args: qkv (torch.Tensor): The fused qkv tensor. n_fused (int): The number items fused together, defaults to 3 (query, key and value). process_group (ProcessGroup): The process group for distributed communication. is_transposed (bool): generally the tensor is the shape of (out_features, in_features). Set this to True if the tensor is in the shape (in_features, out_features). """ world_size = dist.get_world_size(group=process_group) # gather the tensors # from # [Q1, K1, V1], [Q2, K2, V2] # to # [Q1, K1, V1, Q2, K2, V2] origin_device = qkv.device qkv = qkv.cuda() gather_list = [torch.zeros_like(qkv) for _ in range(world_size)] dist.all_gather(gather_list, qkv, group=process_group) if is_transposed: gather_weight = torch.cat(gather_list, dim=-1) else: gather_weight = torch.cat(gather_list, dim=0) gather_weight = gather_weight.to(origin_device) qkv = qkv.to(origin_device) # rearrange the tensor slices # from # [Q1, K1, V1, Q2, K2, V2] # to # [Q1, Q2, K1, K2, V1, V2] if is_transposed: weight_chunks = torch.chunk(gather_weight, world_size * n_fused, dim=-1) else: weight_chunks = torch.chunk(gather_weight, world_size * n_fused, dim=0) reordered_chunk_list = [] for i in range(n_fused): reordered_chunk_list.extend(weight_chunks[i::n_fused]) if is_transposed: reordered_gather_weight = torch.cat(reordered_chunk_list, dim=-1) else: reordered_gather_weight = torch.cat(reordered_chunk_list, dim=0) return reordered_gather_weight class GPT2FusedLinearConv1D_Col(ParallelModule): r"""Linear layer with column parallelism. The linear layer is defined as :math:`Y = XA + b`. A is parallelized along its second dimension as :math:`A = [A_1, ..., A_p]`. This layer is used to fit `Conv1D` layer (Fused QKV) in gpt2 of huggingface. Args: in_features (int): size of each input sample. out_features (int): size of each output sample. bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. dtype (`torch.dtype`): The dtype of parameters, defaults to None. device (`torch.device`): The device of parameters, defaults to None. n_fused (int): The number items fused, defaults to 3 (QKV). process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None. gather_output (bool, optional): If true, call all-gather on output and make Y available to all GPUs, otherwise, every GPU will have its output which is :math:`Y_i = XA_i`, defaults to False skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion, defaults to False weight_initializer (`typing.Callable`): The initializer of weight, defaults to kaiming uniform initializer. bias_initializer (`typing.Callable`): The initializer of bias, defaults to xavier uniform initializer. More details about ``initializer`` please refer to `init `_. """ def __init__(self, in_features: int, out_features: int, bias: bool = True, dtype: torch.dtype = None, device: torch.device = None, process_group: ProcessGroup = None, async_communication: bool = False, gather_output: bool = False, skip_bias_add: bool = False, n_fused: int = 3, weight: Optional[Parameter] = None, bias_: Optional[Parameter] = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): super().__init__() # Keep input parameters self.in_features = in_features self.out_features = out_features self.gather_output = gather_output self.skip_bias_add = skip_bias_add self.device = device self.n_fused = n_fused self.process_group = process_group self.async_communication = async_communication if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') # offset the seed with randomizer index and rank seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) # sanity check if weight is not None: assert not bias or bias_ is not None, 'bias_ must be provided if bias is True when weight is not None' else: assert bias_ is None, 'bias_ must be None if weight is None' # Parameters. if weight is None: # Initialize weight. factory_kwargs = {'device': device, 'dtype': dtype} self.weight = Parameter(torch.empty(self.in_features, self.out_features, **factory_kwargs)) else: weight.data = weight.data.to(device=device, dtype=dtype) self.weight = weight def shard_fn(tensor): return split_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, True) def gather_fn(tensor): return gather_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, True) if not is_customized_distributed_tensor(self.weight): with torch.no_grad(): sharded_weight = distribute_tensor_with_customization(self.weight.data, shard_fn, gather_fn) customized_distributed_tensor_to_existing_param(sharded_weight, self.weight) if bias: if bias_ is None: self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) else: bias_.data = bias_.data.to(device=device, dtype=dtype) self.bias = bias_ if not is_customized_distributed_tensor(self.bias): with torch.no_grad(): sharded_bias = distribute_tensor_with_customization(self.bias.data, shard_fn, gather_fn) customized_distributed_tensor_to_existing_param(sharded_bias, self.bias) else: self.bias = None if weight is None: # init weights self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs) -> ParallelModule: r""" Convert a huggingface layer `Conv1D` in gpt2 to a parallelized linear layer. Args: module (`nn.Linear`): The module to be converted. process_group (`Union[ProcessGroup, List[ProcessGroup]]`): The process group to be used for weight sharding and communication. n_fused (int): The number of layers to be fused. In GPT2, Q,K,V are fused in one weight. """ LazyInitContext.materialize(module) # get the attributes in_features = module.weight.shape[0] out_features = module.weight.shape[1] bias = module.bias is not None device = module.weight.device # ensure only one process group is passed if isinstance(process_group, (list, tuple)): assert len(process_group) == 1, \ f'Expected only one process group, got {len(process_group)}.' process_group = process_group[0] tp_size = dist.get_world_size(process_group) if out_features < tp_size: return module if out_features % tp_size != 0: raise ValueError( f"The size of out_features:{out_features} is not integer multiples of tensor parallel size: {tp_size}!") linear_1d = GPT2FusedLinearConv1D_Col(in_features=in_features, out_features=out_features, bias=bias, device=device, process_group=process_group, weight=module.weight, bias_=module.bias, *args, **kwargs) return linear_1d def reset_parameters(self, weight_initializer, bias_initializer) -> None: with self.randomizer.fork_rng(enable_cpu=True): fan_in, fan_out = self.in_features, self.out_features weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) if self.bias is not None: bias_initializer(self.bias, fan_in=fan_in) def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: assert input_.shape[-1] == self.weight.shape[0], \ 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( input_.shape, self.weight.shape, self.weight.shape[-1]) # Set up backprop all-reduce. input_parallel = reduce_backward(input_, self.process_group) # input_parallel = input_ # Matrix multiply. bias = self.bias if not self.skip_bias_add else None output_parallel = matmul_with_async_comm(input_parallel, self.weight, bias, self.process_group, self.async_communication) if self.gather_output: # All-gather across the partitions. output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) else: output = output_parallel if self.skip_bias_add: return output, self.bias else: return output class GPT2FusedLinearConv1D_Row(ParallelModule): r""" Linear layer with row parallelism. This layer is used to fit `Conv1D` layer (Fused QKV) in gpt2 of huggingface. Args: in_features (int): size of each input sample. out_features (int): size of each output sample. bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. dtype (`torch.dtype`): The dtype of parameters, defaults to None. parallel_input (bool): If set to ``True``, it's assumed that the input is split, defaults to False. skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion, defaults to False weight_initializer (:class:`typing.Callable`, optional): The initializer of weight, defaults to kaiming uniform initializer. bias_initializer (:class:`typing.Callable`, optional): The initializer of bias, defaults to xavier uniform initializer. More details about ``initializer`` please refer to `init `_. """ def __init__(self, in_features: int, out_features: int, bias: bool = True, dtype: torch.dtype = None, device: torch.device = None, process_group: ProcessGroup = None, parallel_input: bool = True, skip_bias_add: bool = False, weight: Optional[Parameter] = None, bias_: Optional[Parameter] = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), stream_chunk_num: int = 1): super().__init__() self.stream_chunk_num = stream_chunk_num # Keep input parameters self.in_features = in_features self.out_features = out_features self.parallel_input = parallel_input self.skip_bias_add = skip_bias_add self.process_group = process_group self.num_partitions = dist.get_world_size(self.process_group) if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') # offset the seed with randomizer index and rank seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) # Divide the weight matrix along the last dimension. self.input_size_per_partition = divide(in_features, self.num_partitions) # sanity check if weight is not None: assert not bias or bias_ is not None, 'bias_ must be provided if bias is True when weight is not None' else: assert bias_ is None, 'bias_ must be None if weight is None' # Parameters. if weight is None: # Initialize weight. factory_kwargs = {'device': device, 'dtype': dtype} self.weight = Parameter(torch.empty(self.in_features, self.out_features, **factory_kwargs)) else: weight.data = weight.data.to(device=device, dtype=dtype) self.weight = weight if not is_distributed_tensor(self.weight): sharded_weight = shard_rowwise(self.weight.data, self.process_group) sharded_tensor_to_existing_param(sharded_weight, self.weight) if self.stream_chunk_num > 1: # TODO() work for inference only self.chunk_weight() if bias: if bias_ is None: self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) else: bias_.data = bias_.data.to(device=device, dtype=dtype) self.bias = bias_ else: self.bias = None if weight is None: # init weights self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs) -> ParallelModule: r""" Convert a native PyTorch linear layer to a parallelized linear layer. """ LazyInitContext.materialize(module) # get the attributes in_features = module.weight.shape[0] out_features = module.weight.shape[1] bias = module.bias is not None device = module.weight.device # ensure only one process group is passed if isinstance(process_group, (list, tuple)): assert len(process_group) == 1, \ f'Expected only one process group, got {len(process_group)}.' process_group = process_group[0] tp_size = dist.get_world_size(process_group) if in_features < tp_size: return module if in_features % tp_size != 0: raise ValueError( f"The size of in_features:{in_features} is not integer multiples of tensor parallel size: {tp_size}!") linear_1d = GPT2FusedLinearConv1D_Row(in_features=in_features, out_features=out_features, bias=bias, device=device, process_group=process_group, weight=module.weight, bias_=module.bias, *args, **kwargs) return linear_1d def chunk_weight(self): self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) def reset_parameters(self, weight_initializer, bias_initializer) -> None: with self.randomizer.fork_rng(enable_cpu=True): fan_in, fan_out = self.in_features, self.out_features weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) if self.bias is not None: bias_initializer(self.bias, fan_in=fan_in) if self.process_group is None: src_rank = 0 else: src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) origin_device = self.bias.device self.bias.data = self.bias.cuda() dist.broadcast(self.bias, src=src_rank, group=self.process_group) self.bias.data = self.bias.to(origin_device) def forward(self, input_: Tensor) -> Tensor: # Set up backprop all-reduce. if self.parallel_input: assert input_.shape[-1] == self.weight.shape[0], \ 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( input_.shape, self.weight.shape, self.weight.shape[0]) input_ = input_ else: assert divide(input_.shape[-1], self.num_partitions) == self.weight.shape[0], \ 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( input_.shape, self.weight.shape, self.weight.shape[0] * self.num_partitions) input_ = split_forward_gather_backward(input_, dim=-1, process_group=self.process_group) if self.stream_chunk_num > 1: if self.training: raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!") with torch.no_grad(): output_parallel_list = [None for i in range(self.stream_chunk_num)] handle_list = [] for i in range(self.stream_chunk_num): output_parallel_list[i] = torch.matmul(input_, self.weight_list[i]) handle = torch.distributed.all_reduce(output_parallel_list[i], group=self.process_group, async_op=True) handle_list.append(handle) # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D) for handle in handle_list: handle.wait() output = torch.cat(output_parallel_list, dim=-1) else: output_parallel = torch.matmul(input_, self.weight) output = reduce_forward(output_parallel, self.process_group) if not self.skip_bias_add: if self.bias is not None: output = output + self.bias return output else: return output, self.bias # ==================================== # For Fused torch.nn.Linear # ==================================== class FusedLinear1D_Col(ParallelModule): r"""Fused Linear layer with column parallelism. The linear layer is defined as :math:`Y = XA + b`. A is parallelized along its second dimension as :math:`A = [A_1, ..., A_p]`. This layer is used to fit `torch.nn.Linear` layer (Fused QKV) in normal torch layer of huggingface, like SAM. Args: in_features (int): size of each input sample. out_features (int): size of each output sample. bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. dtype (`torch.dtype`): The dtype of parameters, defaults to None. device (`torch.device`): The device of parameters, defaults to None. n_fused (int): The number items fused, defaults to 3 (QKV). process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None. gather_output (bool, optional): If true, call all-gather on output and make Y available to all GPUs, otherwise, every GPU will have its output which is :math:`Y_i = XA_i`, defaults to False skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion, defaults to False weight_initializer (`typing.Callable`): The initializer of weight, defaults to kaiming uniform initializer. bias_initializer (`typing.Callable`): The initializer of bias, defaults to xavier uniform initializer. More details about ``initializer`` please refer to `init `_. """ def __init__(self, in_features: int, out_features: int, bias: bool = True, dtype: torch.dtype = None, device: torch.device = None, process_group: ProcessGroup = None, async_communication: bool = False, gather_output: bool = False, skip_bias_add: bool = False, n_fused: int = 3, weight: Optional[Parameter] = None, bias_: Optional[Parameter] = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): super().__init__() # Keep input parameters self.in_features = in_features self.out_features = out_features self.gather_output = gather_output self.skip_bias_add = skip_bias_add self.device = device self.n_fused = n_fused self.process_group = process_group self.async_communication = async_communication if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') # offset the seed with randomizer index and rank seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) # sanity check if weight is not None: assert not bias or bias_ is not None, 'bias_ must be provided if bias is True when weight is not None' else: assert bias_ is None, 'bias_ must be None if weight is None' # Parameters. if weight is None: # Initialize weight. factory_kwargs = {'device': device, 'dtype': dtype} self.weight = Parameter(torch.empty(self.out_features, self.in_features, **factory_kwargs)) else: weight.data = weight.data.to(device=device, dtype=dtype) self.weight = weight def shard_fn(tensor): return split_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, False) def gather_fn(tensor): return gather_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, False) if not is_customized_distributed_tensor(self.weight): with torch.no_grad(): sharded_weight = distribute_tensor_with_customization(self.weight.data, shard_fn, gather_fn) customized_distributed_tensor_to_existing_param(sharded_weight, self.weight) if bias: if bias_ is None: self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) else: bias_.data = bias_.data.to(device=device, dtype=dtype) self.bias = bias_ if not is_customized_distributed_tensor(self.bias): with torch.no_grad(): sharded_bias = distribute_tensor_with_customization(self.bias.data, shard_fn, gather_fn) customized_distributed_tensor_to_existing_param(sharded_bias, self.bias) else: self.bias = None if weight is None: # init weights self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], n_fused: int, *args, **kwargs) -> ParallelModule: r""" Convert a fused `torch.nn.linear` layer to a parallelized linear layer. Args: module (`nn.Linear`): The module to be converted. process_group (`Union[ProcessGroup, List[ProcessGroup]]`): The process group to be used for weight sharding and communication. n_fused (int): The number of layers to be fused. In common, Q,K,V are fused in one weight. """ # get the attributes in_features = module.in_features out_features = module.out_features bias = module.bias is not None device = module.weight.device # ensure only one process group is passed if isinstance(process_group, (list, tuple)): assert len(process_group) == 1, \ f'Expected only one process group, got {len(process_group)}.' process_group = process_group[0] linear_1d = FusedLinear1D_Col(in_features=in_features, out_features=out_features, bias=bias, device=device, process_group=process_group, weight=module.weight, bias_=module.bias, *args, **kwargs) # # TODO: copy the sharded weights # with torch.no_grad(): # sharded_weight = split_fused_qkv_in_gpt2_style(module.weight.data, # n_fused=n_fused, # process_group=process_group, # is_transposed=False) # linear_1d.weight.data.copy_(sharded_weight.data) # if bias: # sharded_bias = split_fused_qkv_in_gpt2_style(module.bias.data, # n_fused=n_fused, # process_group=process_group, # is_transposed=False) # linear_1d.bias.data.copy_(sharded_bias.data) print(linear_1d.weight.shape) return linear_1d def reset_parameters(self, weight_initializer, bias_initializer) -> None: with self.randomizer.fork_rng(enable_cpu=True): fan_in, fan_out = self.in_features, self.out_features weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) if self.bias is not None: bias_initializer(self.bias, fan_in=fan_in) def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: assert input_.shape[-1] == self.weight.shape[-1], \ 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( input_.shape, self.weight.shape, self.weight.shape[-1]) # Set up backprop all-reduce. # input_parallel = reduce_backward(input_, self.process_group) input_parallel = input_ # Matrix multiply. bias = self.bias if not self.skip_bias_add else None output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True) if self.gather_output: # All-gather across the partitions. output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) else: output = output_parallel if self.skip_bias_add: return output, self.bias else: return output