332 lines
13 KiB
Python
332 lines
13 KiB
Python
"""1D OPT model compatible with HuggingFace weights."""
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import os
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import glob
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import filelock
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from tqdm import tqdm
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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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 huggingface_hub import snapshot_download
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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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from cacheflow.parallel_utils.parallel_state import (
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get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
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from cacheflow.parallel_utils.tensor_parallel import (VocabParallelEmbedding,
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ColumnParallelLinear,
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RowParallelLinear)
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from cacheflow.sequence import SequenceOutputs
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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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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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total_num_heads = num_heads
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assert num_heads % tensor_model_parallel_world_size == 0
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self.num_heads = total_num_heads // tensor_model_parallel_world_size
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self.head_dim = embed_dim // total_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 = ColumnParallelLinear(embed_dim, embed_dim, bias=bias,
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gather_output=False,
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perform_initialization=False)
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self.v_proj = ColumnParallelLinear(embed_dim, embed_dim, bias=bias,
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gather_output=False,
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perform_initialization=False)
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self.q_proj = ColumnParallelLinear(embed_dim, embed_dim, bias=bias,
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gather_output=False,
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perform_initialization=False)
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self.out_proj = RowParallelLinear(embed_dim, embed_dim, bias=bias,
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input_is_parallel=True,
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perform_initialization=False)
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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.config = config
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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 = ColumnParallelLinear(self.embed_dim, config.ffn_dim,
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bias=config.enable_bias,
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gather_output=False,
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perform_initialization=False)
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self.fc2 = RowParallelLinear(config.ffn_dim, self.embed_dim,
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bias=config.enable_bias,
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input_is_parallel=True,
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perform_initialization=False)
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self.final_layer_norm = nn.LayerNorm(
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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 OPTDecoder(nn.Module):
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def __init__(self, config: OPTConfig):
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super().__init__()
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self.config = 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 = VocabParallelEmbedding(config.vocab_size,
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config.word_embed_proj_dim,
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perform_initialization=False)
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# Positional embeddings are replicated (not sharded).
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self.embed_positions = OPTLearnedPositionalEmbedding(
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config.max_position_embeddings, config.hidden_size)
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# Project out & in will be replicated if they exist.
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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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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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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(nn.Module):
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def __init__(self, config: OPTConfig):
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super().__init__()
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self.decoder = OPTDecoder(config)
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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(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.model = OPTModel(config)
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# TODO(zhuohan): create a new weight after implementing pipeline
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# parallelism
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self.lm_head_weight = self.model.decoder.embed_tokens.weight
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self.sampler = Sampler()
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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, SequenceOutputs]:
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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(
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self.lm_head_weight, hidden_states, input_metadata)
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return next_tokens
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_column_parallel_weights = ["embed_tokens.weight",
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"q_proj.weight", "k_proj.weight",
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"v_proj.weight", "fc1.weight"]
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_column_parallel_biases = ["q_proj.bias", "k_proj.bias",
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"v_proj.bias", "fc1.bias"]
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_row_parallel_weights = ["out_proj.weight", "fc2.weight"]
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def load_weights(self, weights_path: str):
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tensor_model_parallel_rank = get_tensor_model_parallel_rank()
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state_dict = self.state_dict()
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for name, param in state_dict.items():
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if "lm_head_weight" in name:
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continue
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loaded_weight = torch.from_numpy(np.load(os.path.join(weights_path,
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name)))
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for p in (self._column_parallel_weights
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+ self._column_parallel_biases):
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if p in name:
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shard_size = param.shape[0]
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loaded_weight = loaded_weight[
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shard_size * tensor_model_parallel_rank
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:shard_size * (tensor_model_parallel_rank + 1)]
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break
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for p in self._row_parallel_weights:
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if p in name:
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shard_size = param.shape[1]
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loaded_weight = loaded_weight[
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:,
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shard_size * tensor_model_parallel_rank
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:shard_size * (tensor_model_parallel_rank + 1)]
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break
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assert param.shape == loaded_weight.shape
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param.data.copy_(loaded_weight)
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@staticmethod
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def download_weights(model_name: str, path: str):
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path = os.path.join(path, f"{model_name}-np")
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path = os.path.abspath(os.path.expanduser(path))
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os.makedirs(path, exist_ok=True)
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lock_path = os.path.join(path, "file_lock")
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lock = filelock.FileLock(lock_path)
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with lock:
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test_weight_path = os.path.join(
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path, "model.decoder.embed_positions.weight")
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if os.path.exists(test_weight_path):
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return path
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folder = snapshot_download(model_name, allow_patterns="*.bin",
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cache_dir=os.path.join(path, "cache"))
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bin_files = glob.glob(os.path.join(folder, "*.bin"))
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if "/" in model_name:
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model_name = model_name.split("/")[1].lower()
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for bin_file in tqdm(bin_files, desc="Convert format"):
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state = torch.load(bin_file)
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for name, param in tqdm(state.items(), leave=False):
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if name.startswith("decoder."):
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name = "model." + name
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param_path = os.path.join(path, name)
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with open(param_path, "wb") as f:
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np.save(f, param.cpu().detach().numpy())
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return path
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