334 lines
13 KiB
Python
334 lines
13 KiB
Python
# coding=utf-8
|
|
# adapted from https://github.com/huggingface/transformers/blob/v4.39.3/src/transformers/models/persimmon/modeling_persimmon.py
|
|
# Copyright 2023 The vLLM team.
|
|
# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
|
# and OPT implementations in this library. It has been modified from its
|
|
# original forms to accommodate minor architectural differences compared
|
|
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Inference-only persimmon model compatible with HuggingFace weights."""
|
|
from typing import Iterable, List, Optional, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
from transformers import PersimmonConfig
|
|
from transformers.activations import ReLUSquaredActivation
|
|
|
|
from vllm.attention import Attention, AttentionMetadata
|
|
from vllm.config import CacheConfig
|
|
from vllm.distributed import get_tensor_model_parallel_world_size
|
|
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
|
|
QKVParallelLinear,
|
|
RowParallelLinear)
|
|
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
|
from vllm.model_executor.layers.quantization.base_config import (
|
|
QuantizationConfig)
|
|
from vllm.model_executor.layers.rotary_embedding import get_rope
|
|
from vllm.model_executor.layers.sampler import Sampler
|
|
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
|
ParallelLMHead, VocabParallelEmbedding)
|
|
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
|
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
|
from vllm.sequence import IntermediateTensors, SamplerOutput
|
|
|
|
|
|
class PersimmonMLP(nn.Module):
|
|
|
|
def __init__(self,
|
|
config: PersimmonConfig,
|
|
quant_config: Optional[QuantizationConfig] = None):
|
|
super().__init__()
|
|
self.dense_h_to_4h = ColumnParallelLinear(config.hidden_size,
|
|
config.intermediate_size,
|
|
quant_config=quant_config)
|
|
self.dense_4h_to_h = RowParallelLinear(config.intermediate_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config)
|
|
self.act = ReLUSquaredActivation()
|
|
|
|
def forward(self, hidden_states) -> torch.Tensor:
|
|
hidden_states, _ = self.dense_h_to_4h(hidden_states)
|
|
hidden_states = self.act(hidden_states)
|
|
hidden_states, _ = self.dense_4h_to_h(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class PersimmonAttention(nn.Module):
|
|
|
|
def __init__(self,
|
|
config: PersimmonConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None):
|
|
super().__init__()
|
|
self.config = config
|
|
tensor_parallel_world_size = get_tensor_model_parallel_world_size()
|
|
|
|
self.hidden_size = config.hidden_size
|
|
self.total_num_heads = config.num_attention_heads
|
|
self.num_heads = self.total_num_heads // tensor_parallel_world_size
|
|
self.head_dim = self.hidden_size // self.total_num_heads
|
|
self.max_position_embeddings = config.max_position_embeddings
|
|
self.rope_theta = config.rope_theta
|
|
self.partial_rotary_factor = config.partial_rotary_factor
|
|
self.is_causal = True
|
|
|
|
assert (self.head_dim * self.total_num_heads) == self.hidden_size
|
|
assert self.total_num_heads % tensor_parallel_world_size == 0
|
|
|
|
self.query_key_value = QKVParallelLinear(
|
|
self.hidden_size,
|
|
self.head_dim,
|
|
self.total_num_heads,
|
|
bias=True,
|
|
quant_config=quant_config,
|
|
)
|
|
self.dense = RowParallelLinear(
|
|
self.num_heads * self.head_dim,
|
|
self.hidden_size,
|
|
bias=True,
|
|
quant_config=quant_config,
|
|
)
|
|
self.is_qk_layernorm = config.qk_layernorm
|
|
|
|
if self.is_qk_layernorm:
|
|
self.q_layernorm = nn.LayerNorm(self.head_dim)
|
|
self.k_layernorm = nn.LayerNorm(self.head_dim)
|
|
|
|
self.rotary_emb = get_rope(
|
|
self.head_dim,
|
|
rotary_dim=int(self.partial_rotary_factor * self.head_dim),
|
|
max_position=self.max_position_embeddings,
|
|
base=self.rope_theta,
|
|
)
|
|
self.scaling = self.head_dim**-0.5
|
|
self.attn = Attention(self.num_heads,
|
|
self.head_dim,
|
|
scale=self.scaling,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config)
|
|
|
|
def _split_heads(self, x: torch.Tensor) -> torch.Tensor:
|
|
# [seq_length, hidden_size] -> [seq_length, num_heads, head_dim]
|
|
seq_length = x.shape[0]
|
|
return x.view(seq_length, self.num_heads, self.head_dim)
|
|
|
|
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
|
|
# [seq_length, num_heads, head_dim] -> [seq_length, hidden_size]
|
|
seq_length = x.shape[0]
|
|
return x.view(seq_length, self.num_heads * self.head_dim)
|
|
|
|
def forward(
|
|
self,
|
|
position_ids: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
) -> torch.Tensor:
|
|
# [seq_length, 3 x hidden_size]
|
|
qkv, _ = self.query_key_value(hidden_states)
|
|
q, k, v = qkv.chunk(chunks=3, dim=-1)
|
|
|
|
if self.is_qk_layernorm:
|
|
# [seq_length, num_heads, head_dim]
|
|
q = self._split_heads(q)
|
|
k = self._split_heads(k)
|
|
|
|
q = self.q_layernorm(q)
|
|
k = self.k_layernorm(k)
|
|
|
|
q = self._merge_heads(q)
|
|
k = self._merge_heads(k)
|
|
|
|
q, k = self.rotary_emb(position_ids, q, k)
|
|
attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
|
|
output, _ = self.dense(attn_output)
|
|
return output
|
|
|
|
|
|
class PersimmonDecoderLayer(nn.Module):
|
|
|
|
def __init__(self,
|
|
config: PersimmonConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.self_attn = PersimmonAttention(config=config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config)
|
|
self.mlp = PersimmonMLP(config, quant_config=quant_config)
|
|
self.input_layernorm = nn.LayerNorm(config.hidden_size,
|
|
eps=config.layer_norm_eps)
|
|
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
|
|
eps=config.layer_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
position_ids: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
) -> torch.Tensor:
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# Self Attention
|
|
hidden_states = self.self_attn(
|
|
position_ids=position_ids,
|
|
hidden_states=hidden_states,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
|
|
hidden_states = hidden_states + residual
|
|
|
|
outputs = hidden_states
|
|
return outputs
|
|
|
|
|
|
class PersimmonModel(nn.Module):
|
|
|
|
def __init__(self,
|
|
config: PersimmonConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None):
|
|
super().__init__()
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
|
|
config.hidden_size)
|
|
self.layers = nn.ModuleList([
|
|
PersimmonDecoderLayer(config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config)
|
|
for _ in range(config.num_hidden_layers)
|
|
])
|
|
self.final_layernorm = nn.LayerNorm(config.hidden_size,
|
|
eps=config.layer_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
for i in range(len(self.layers)):
|
|
hidden_states = self.layers[i](
|
|
positions,
|
|
hidden_states,
|
|
kv_caches[i],
|
|
attn_metadata,
|
|
)
|
|
hidden_states = self.final_layernorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class PersimmonForCausalLM(nn.Module):
|
|
|
|
def __init__(self,
|
|
config,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None):
|
|
super().__init__()
|
|
self.config = config
|
|
self.vocab_size = config.vocab_size
|
|
self.model = PersimmonModel(config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config)
|
|
self.lm_head = ParallelLMHead(config.vocab_size,
|
|
config.hidden_size,
|
|
bias=False)
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.sampler = Sampler()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
):
|
|
hidden_states = self.model(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
kv_caches=kv_caches,
|
|
attn_metadata=attn_metadata,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if ("rotary_emb.cos_cached" in name
|
|
or "rotary_emb.sin_cached" in name):
|
|
# Models trained using ColossalAI may include these tensors in
|
|
# the checkpoint. Skip them.
|
|
continue
|
|
param = params_dict[name]
|
|
|
|
if "query_key_value" in name:
|
|
# copy from vllm/model_executor/models/bloom.py
|
|
# NOTE: Persimmon's fused QKV's output_dim has the shape of
|
|
# (num_heads * 3 * head_size), while the
|
|
# required shape is (3 * num_heads * head_size).
|
|
# Thus, we need weight conversion.
|
|
output_dim = getattr(param, "output_dim", None)
|
|
num_heads = self.config.num_attention_heads
|
|
if output_dim is not None:
|
|
loaded_weight_shape = loaded_weight.shape
|
|
loaded_weight = loaded_weight.view(
|
|
loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
|
|
loaded_weight_shape[output_dim + 1:])
|
|
loaded_weight = loaded_weight.transpose(
|
|
output_dim, output_dim + 1)
|
|
loaded_weight = loaded_weight.reshape(loaded_weight_shape)
|
|
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|