| |
|
|
| import torch |
| import torch.nn as nn |
| from einops import rearrange |
| from torch import Tensor |
|
|
| from flash_attn.utils.distributed import all_reduce, reduce_scatter |
|
|
|
|
| class GPT2Embeddings(nn.Module): |
| def __init__( |
| self, |
| embed_dim, |
| vocab_size, |
| max_position_embeddings, |
| padding_idx=None, |
| word_embed_proj_dim=None, |
| device=None, |
| dtype=None, |
| ): |
| """ |
| If max_position_embeddings <= 0, there's no position embeddings |
| If word_embe_proj_dim is not None (e.g., OPT-350m), we embed to that dimension |
| the project up to embed_dim |
| """ |
| factory_kwargs = {"device": device, "dtype": dtype} |
| super().__init__() |
| if word_embed_proj_dim is None: |
| self.word_embeddings = nn.Embedding( |
| vocab_size, embed_dim, padding_idx=padding_idx, **factory_kwargs |
| ) |
| self.project_in = None |
| else: |
| self.word_embeddings = nn.Embedding( |
| vocab_size, word_embed_proj_dim, padding_idx=padding_idx, **factory_kwargs |
| ) |
| self.project_in = nn.Linear( |
| word_embed_proj_dim, embed_dim, bias=False, **factory_kwargs |
| ) |
| self.max_position_embeddings = max_position_embeddings |
| if self.max_position_embeddings > 0: |
| self.position_embeddings = nn.Embedding( |
| max_position_embeddings, embed_dim, **factory_kwargs |
| ) |
|
|
| def forward(self, input_ids, position_ids=None): |
| """ |
| input_ids: (batch, seqlen) |
| position_ids: (batch, seqlen) |
| """ |
| batch_size, seqlen = input_ids.shape |
| embeddings = self.word_embeddings(input_ids) |
| if self.project_in is not None: |
| embeddings = self.project_in(embeddings) |
| if self.max_position_embeddings > 0: |
| if position_ids is None: |
| position_ids = torch.arange(seqlen, dtype=torch.long, device=input_ids.device) |
| position_embeddings = self.position_embeddings(position_ids) |
| embeddings = embeddings + position_embeddings |
| return embeddings |
|
|
|
|
| class BertEmbeddings(nn.Module): |
| def __init__( |
| self, |
| embed_dim, |
| vocab_size, |
| max_position_embeddings, |
| type_vocab_size, |
| padding_idx=None, |
| device=None, |
| dtype=None, |
| ): |
| """ |
| If max_position_embeddings <= 0, there's no position embeddings |
| If type_vocab_size <= 0, there's no token type embeddings |
| """ |
| factory_kwargs = {"device": device, "dtype": dtype} |
| super().__init__() |
| self.word_embeddings = nn.Embedding( |
| vocab_size, embed_dim, padding_idx=padding_idx, **factory_kwargs |
| ) |
| self.max_position_embeddings = max_position_embeddings |
| self.type_vocab_size = type_vocab_size |
| if self.max_position_embeddings > 0: |
| self.position_embeddings = nn.Embedding( |
| max_position_embeddings, embed_dim, **factory_kwargs |
| ) |
| if self.type_vocab_size > 0: |
| self.token_type_embeddings = nn.Embedding(type_vocab_size, embed_dim, **factory_kwargs) |
|
|
| def forward(self, input_ids, position_ids=None, token_type_ids=None): |
| """ |
| input_ids: (batch, seqlen) |
| position_ids: (batch, seqlen) |
| token_type_ids: (batch, seqlen) |
| """ |
| batch_size, seqlen = input_ids.shape |
| embeddings = self.word_embeddings(input_ids) |
| if self.max_position_embeddings > 0: |
| if position_ids is None: |
| position_ids = torch.arange(seqlen, dtype=torch.long, device=input_ids.device) |
| position_embeddings = self.position_embeddings(position_ids) |
| embeddings = embeddings + position_embeddings |
| if self.type_vocab_size > 0: |
| if token_type_ids is None: |
| token_type_ids = torch.zeros(seqlen, dtype=torch.long, device=input_ids.device) |
| token_type_embeddings = self.token_type_embeddings(token_type_ids) |
| embeddings = embeddings + token_type_embeddings |
| return embeddings |
|
|
|
|
| class VocabParallelEmbedding(nn.Embedding): |
| def __init__(self, num_embeddings, *args, process_group=None, padding_idx=None, **kwargs): |
| self.process_group = process_group |
| if process_group is not None: |
| world_size = torch.distributed.get_world_size(process_group) |
| if num_embeddings % world_size != 0: |
| raise ValueError( |
| f"num_embeddings ({num_embeddings}) must be divisible by " |
| f"world_size ({world_size})" |
| ) |
| if world_size > 1 and padding_idx is not None: |
| raise RuntimeError("ParallelEmbedding does not support padding_idx") |
| else: |
| world_size = 1 |
| super().__init__(num_embeddings // world_size, *args, padding_idx=padding_idx, **kwargs) |
|
|
| def forward(self, input: Tensor) -> Tensor: |
| if self.process_group is None: |
| return super().forward(input) |
| else: |
| rank = torch.distributed.get_rank(self.process_group) |
| vocab_size = self.num_embeddings |
| vocab_start_index, vocab_end_index = rank * vocab_size, (rank + 1) * vocab_size |
| |
| input_ids_mask = (input < vocab_start_index) | (input >= vocab_end_index) |
| input = input - vocab_start_index |
| input[input_ids_mask] = 0 |
| embeddings = super().forward(input) |
| embeddings[input_ids_mask] = 0.0 |
| return embeddings |
|
|
|
|
| class ColumnParallelEmbedding(nn.Embedding): |
| def __init__(self, num_embeddings, embedding_dim, *args, process_group=None, **kwargs): |
| self.process_group = process_group |
| if process_group is not None: |
| world_size = torch.distributed.get_world_size(process_group) |
| if embedding_dim % world_size != 0: |
| raise ValueError( |
| f"embedding_dim ({embedding_dim}) must be divisible by " |
| f"world_size ({world_size})" |
| ) |
| else: |
| world_size = 1 |
| super().__init__(num_embeddings, embedding_dim // world_size, *args, **kwargs) |
|
|
|
|
| class ParallelGPT2Embeddings(nn.Module): |
| def __init__( |
| self, |
| embed_dim, |
| vocab_size, |
| max_position_embeddings, |
| process_group, |
| padding_idx=None, |
| sequence_parallel=True, |
| device=None, |
| dtype=None, |
| ): |
| """ |
| If max_position_embeddings <= 0, there's no position embeddings |
| """ |
| factory_kwargs = {"device": device, "dtype": dtype} |
| super().__init__() |
| self.process_group = process_group |
| self.sequence_parallel = sequence_parallel |
| self.word_embeddings = VocabParallelEmbedding( |
| vocab_size, |
| embed_dim, |
| padding_idx=padding_idx, |
| process_group=process_group, |
| **factory_kwargs, |
| ) |
| self.max_position_embeddings = max_position_embeddings |
| if self.max_position_embeddings > 0: |
| self.position_embeddings = ColumnParallelEmbedding( |
| max_position_embeddings, embed_dim, process_group=process_group, **factory_kwargs |
| ) |
|
|
| def forward(self, input_ids, position_ids=None, combine_batch_seqlen_dim=False): |
| """ |
| input_ids: (batch, seqlen) |
| position_ids: (batch, seqlen) |
| """ |
| batch_size, seqlen = input_ids.shape |
| world_size = torch.distributed.get_world_size(self.process_group) |
| embeddings = self.word_embeddings(input_ids) |
| if self.max_position_embeddings > 0: |
| if position_ids is None: |
| position_ids = torch.arange(seqlen, dtype=torch.long, device=input_ids.device) |
| position_embeddings = self.position_embeddings(position_ids) |
| if world_size <= 1: |
| embeddings = embeddings + position_embeddings |
| else: |
| partition_dim = self.position_embeddings.embedding_dim |
| rank = torch.distributed.get_rank(self.process_group) |
| embeddings[ |
| ..., rank * partition_dim : (rank + 1) * partition_dim |
| ] += position_embeddings |
| if combine_batch_seqlen_dim: |
| embeddings = rearrange(embeddings, "b s d -> (b s) d") |
| reduce_fn = reduce_scatter if self.sequence_parallel else all_reduce |
| return embeddings if world_size <= 1 else reduce_fn(embeddings, self.process_group) |
|
|
