350 lines
13 KiB
Python
350 lines
13 KiB
Python
# coding=utf-8
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights
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# reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only OPT model compatible with HuggingFace weights."""
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from typing import Iterable, 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 vllm.attention import Attention, AttentionMetadata
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import SamplerOutput
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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
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# embedding ids by 2 and adjust num_embeddings appropriately. Other
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# 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.Tensor):
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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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quant_config: Optional[QuantizationConfig] = None,
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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 = (
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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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self.qkv_proj = QKVParallelLinear(
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embed_dim,
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self.head_dim,
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total_num_heads,
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bias=bias,
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quant_config=quant_config,
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)
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self.out_proj = RowParallelLinear(
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embed_dim,
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embed_dim,
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bias=bias,
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quant_config=quant_config,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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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: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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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__(
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self,
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config: OPTConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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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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quant_config=quant_config,
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)
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self.do_layer_norm_before = config.do_layer_norm_before
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self.self_attn_layer_norm = nn.LayerNorm(
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self.embed_dim,
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elementwise_affine=config.layer_norm_elementwise_affine)
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self.fc1 = ColumnParallelLinear(
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self.embed_dim,
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config.ffn_dim,
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bias=config.enable_bias,
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quant_config=quant_config,
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)
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self.activation_fn = get_act_fn(config.activation_function,
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quant_config, config.ffn_dim)
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self.fc2 = RowParallelLinear(
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config.ffn_dim,
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self.embed_dim,
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bias=config.enable_bias,
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quant_config=quant_config,
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)
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self.final_layer_norm = nn.LayerNorm(
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self.embed_dim,
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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: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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(hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata)
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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__(
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self,
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config: OPTConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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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(
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config.vocab_size,
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config.word_embed_proj_dim,
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)
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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 = ReplicatedLinear(config.hidden_size,
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config.word_embed_proj_dim,
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bias=False,
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quant_config=quant_config)
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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 = ReplicatedLinear(config.word_embed_proj_dim,
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config.hidden_size,
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bias=False,
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quant_config=quant_config)
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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
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# keep backward compatibility with checkpoints that have been fine-tuned
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# 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,
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elementwise_affine=config.layer_norm_elementwise_affine)
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else:
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self.final_layer_norm = None
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self.layers = nn.ModuleList([
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OPTDecoderLayer(config, quant_config)
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for _ in range(config.num_hidden_layers)
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])
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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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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layer = self.layers[i]
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hidden_states = layer(hidden_states, kv_caches[i], attn_metadata)
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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__(
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self,
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config: OPTConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.decoder = OPTDecoder(config, quant_config)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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return self.decoder(input_ids, positions, kv_caches, attn_metadata)
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class OPTForCausalLM(nn.Module):
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def __init__(
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self,
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config,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.model = OPTModel(config, quant_config)
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self.lm_head_weight = self.model.decoder.embed_tokens.weight
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self.logits_processor = LogitsProcessor(config.vocab_size)
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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.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, kv_caches,
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attn_metadata)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head_weight, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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]
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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for name, loaded_weight in weights:
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if "lm_head.weight" in name:
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continue
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if name.startswith("decoder."):
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name = "model." + name
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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