248 lines
9.0 KiB
Python
248 lines
9.0 KiB
Python
"""1D OPT model compatible with HuggingFace weights."""
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from typing import Dict, List, Optional, Tuple
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import torch
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from torch import nn
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from transformers import OPTConfig
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from transformers import PreTrainedModel
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from cacheflow.models import InputMetadata
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from cacheflow.models.attention import OPTCacheFlowAttention
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from cacheflow.models.sample import Sampler
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class OPTLearnedPositionalEmbedding(nn.Embedding):
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def __init__(self, num_embeddings: int, embedding_dim: int):
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# OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
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# and adjust num_embeddings appropriately. Other models don't have this hack
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self.offset = 2
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super().__init__(num_embeddings + self.offset, embedding_dim)
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def forward(self, positions: torch.LongTensor):
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return super().forward(positions + self.offset)
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class OPTAttention(nn.Module):
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def __init__(
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self,
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embed_dim: int,
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num_heads: int,
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bias: bool = True,
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) -> None:
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super().__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.head_dim = embed_dim // num_heads
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self.scaling = self.head_dim**-0.5
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# TODO(woosuk): Fuse the three linear layers into one QKV linear layer.
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self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.attn = OPTCacheFlowAttention(scale=self.scaling)
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def forward(
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self,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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q = self.q_proj(hidden_states)
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k = self.k_proj(hidden_states)
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v = self.v_proj(hidden_states)
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key_cache, value_cache = kv_cache
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attn_output = self.attn(
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q, k, v, key_cache, value_cache, input_metadata, cache_event)
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output = self.out_proj(attn_output)
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return output
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class OPTDecoderLayer(nn.Module):
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def __init__(self, config: OPTConfig):
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super().__init__()
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self.embed_dim = config.hidden_size
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self.self_attn = OPTAttention(
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embed_dim=self.embed_dim,
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num_heads=config.num_attention_heads,
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bias=config.enable_bias,
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)
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self.do_layer_norm_before = config.do_layer_norm_before
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assert config.activation_function == 'relu'
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self.activation_fn = nn.ReLU()
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self.self_attn_layer_norm = nn.LayerNorm(
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self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
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self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=config.enable_bias)
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self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=config.enable_bias)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
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def forward(
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self,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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# Self Attention
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residual = hidden_states
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# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
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if self.do_layer_norm_before:
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hidden_states = self.self_attn_layer_norm(hidden_states)
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hidden_states = self.self_attn(
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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input_metadata=input_metadata,
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cache_event=cache_event)
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hidden_states = residual + hidden_states
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# 350m applies layer norm AFTER attention
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if not self.do_layer_norm_before:
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hidden_states = self.self_attn_layer_norm(hidden_states)
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# Fully Connected
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residual = hidden_states
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# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
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if self.do_layer_norm_before:
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hidden_states = self.final_layer_norm(hidden_states)
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hidden_states = self.fc1(hidden_states)
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hidden_states = self.activation_fn(hidden_states)
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hidden_states = self.fc2(hidden_states)
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hidden_states = residual + hidden_states
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# 350m applies layer norm AFTER attention
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if not self.do_layer_norm_before:
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hidden_states = self.final_layer_norm(hidden_states)
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return hidden_states
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class OPTPreTrainedModel(PreTrainedModel):
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config_class = OPTConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["OPTDecoderLayer"]
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_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
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def _init_weights(self, module) -> None:
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del module # unused
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return
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class OPTDecoder(OPTPreTrainedModel):
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def __init__(self, config: OPTConfig):
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super().__init__(config)
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self.padding_idx = config.pad_token_id
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self.max_target_positions = config.max_position_embeddings
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx)
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self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size)
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if config.word_embed_proj_dim != config.hidden_size:
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self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
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else:
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self.project_out = None
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if config.word_embed_proj_dim != config.hidden_size:
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self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False)
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else:
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self.project_in = None
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# Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
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# with checkpoints that have been fine-tuned before transformers v4.20.1
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# see https://github.com/facebookresearch/metaseq/pull/164
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if config.do_layer_norm_before and not config._remove_final_layer_norm:
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self.final_layer_norm = nn.LayerNorm(
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config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine
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)
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else:
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self.final_layer_norm = None
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self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids: torch.LongTensor,
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positions: torch.LongTensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> torch.Tensor:
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inputs_embeds = self.embed_tokens(input_ids)
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pos_embeds = self.embed_positions(positions)
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pos_embeds = None
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if self.project_in is not None:
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inputs_embeds = self.project_in(inputs_embeds)
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hidden_states = inputs_embeds + pos_embeds
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for i in range(len(self.layers)):
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if cache_events is None:
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cache_event = None
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else:
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cache_event = cache_events[i]
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layer = self.layers[i]
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hidden_states = layer(
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hidden_states, kv_caches[i], input_metadata, cache_event)
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if self.final_layer_norm is not None:
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hidden_states = self.final_layer_norm(hidden_states)
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if self.project_out is not None:
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hidden_states = self.project_out(hidden_states)
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return hidden_states
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class OPTModel(OPTPreTrainedModel):
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def __init__(self, config: OPTConfig):
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super().__init__(config)
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self.decoder = OPTDecoder(config)
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids: torch.LongTensor,
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positions: torch.LongTensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> torch.Tensor:
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return self.decoder(
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input_ids, positions, kv_caches, input_metadata, cache_events)
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class OPTForCausalLM(OPTPreTrainedModel):
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_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
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def __init__(self, config):
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super().__init__(config)
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self.model = OPTModel(config)
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# the lm_head weight is automatically tied to the embed tokens weight
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self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
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self.sampler = Sampler(embedding=self.lm_head.weight)
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids: torch.LongTensor,
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positions: torch.LongTensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> Dict[int, Tuple[int, int]]:
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hidden_states = self.model(
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input_ids, positions, kv_caches, input_metadata, cache_events)
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next_tokens = self.sampler(hidden_states, input_metadata)
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return next_tokens
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