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# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/a5cc30d72ae2dc19af534e4b35c986cc28db1275/src/transformers/models/falcon/modeling_falcon.py
# Copyright 2023 The vLLM team.
# Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights
# reserved.
#
# 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.
"""PyTorch Falcon model."""
import math
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import LayerNorm
from transformers import FalconConfig as HF_FalconConfig
from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.attention import PagedAttention
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
LinearMethodBase,
QKVParallelLinear,
RowParallelLinear)
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 (
VocabParallelEmbedding, ParallelLMHead)
from vllm.model_executor.parallel_utils.communication_op import (
tensor_model_parallel_all_reduce)
from vllm.model_executor.parallel_utils.parallel_state import (
get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.weight_utils import (default_weight_loader,
hf_model_weights_iterator)
from vllm.sequence import SamplerOutput
from vllm.transformers_utils.configs import RWConfig
KVCache = Tuple[torch.Tensor, torch.Tensor]
FalconConfig = Union[HF_FalconConfig, RWConfig]
def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
base = torch.tensor(2**(-(2**-(math.log2(closest_power_of_2) - 3))),
dtype=torch.float32)
powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
slopes = torch.pow(base, powers)
if closest_power_of_2 != total_num_heads:
extra_base = torch.tensor(
2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
dtype=torch.float32)
num_remaining_heads = min(closest_power_of_2,
total_num_heads - closest_power_of_2)
extra_powers = torch.arange(1,
1 + 2 * num_remaining_heads,
2,
dtype=torch.int32)
slopes = torch.cat(
[slopes, torch.pow(extra_base, extra_powers)], dim=0)
return slopes
class FalconAttention(nn.Module):
def __init__(
self,
config: FalconConfig,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.hidden_size = config.hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = config.num_attention_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.head_dim = self.hidden_size // self.total_num_heads
assert self.head_dim * self.total_num_heads == self.hidden_size
self.new_decoder_architecture = config.new_decoder_architecture
self.multi_query = config.multi_query
if self.new_decoder_architecture:
self.total_num_kv_heads = config.num_kv_heads
elif self.multi_query:
self.total_num_kv_heads = 1
else:
self.total_num_kv_heads = self.total_num_heads
if self.total_num_kv_heads >= tp_size:
# Number of KV heads is greater than TP size, so we partition
# the KV heads across multiple tensor parallel GPUs.
assert self.total_num_kv_heads % tp_size == 0
else:
# Number of KV heads is less than TP size, so we replicate
# the KV heads across multiple tensor parallel GPUs.
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.query_key_value = QKVParallelLinear(
self.hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=config.bias,
skip_bias_add=True,
linear_method=linear_method,
)
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
# Layer-wise attention scaling
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
self.reduce_row_parallel_results = not (config.new_decoder_architecture
or config.parallel_attn)
self.dense = RowParallelLinear(
self.hidden_size,
self.hidden_size,
bias=config.bias,
skip_bias_add=True,
linear_method=linear_method,
reduce_results=self.reduce_row_parallel_results)
self.use_rotary = config.rotary
self.use_alibi = config.alibi
assert not (self.use_rotary and self.use_alibi), (
"Rotary and alibi are mutually exclusive.")
if self.use_rotary:
rope_theta = getattr(config, "rope_theta", 10000)
max_position_embeddings = getattr(config,
"max_position_embeddings", 8192)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=max_position_embeddings,
base=rope_theta,
)
self.attn = PagedAttention(self.num_heads,
self.head_dim,
self.inv_norm_factor,
num_kv_heads=self.num_kv_heads)
elif self.use_alibi:
tp_rank = get_tensor_model_parallel_rank()
head_start = tp_rank * self.num_heads
head_end = (tp_rank + 1) * self.num_heads
alibi_slopes = (_get_alibi_slopes(self.total_num_heads) *
self.inv_norm_factor)
alibi_slopes = alibi_slopes[head_start:head_end].tolist()
self.attn = PagedAttention(self.num_heads,
self.head_dim,
self.inv_norm_factor,
num_kv_heads=self.num_kv_heads,
alibi_slopes=alibi_slopes)
else:
self.attn = PagedAttention(self.num_heads,
self.head_dim,
scale=self.inv_norm_factor,
num_kv_heads=self.num_kv_heads)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
) -> torch.Tensor:
qkv, bias = self.query_key_value(hidden_states)
if bias is not None:
qkv += bias
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
if self.use_rotary:
q, k = self.rotary_emb(positions, q, k)
k_cache, v_cache = kv_cache
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
attn_output, bias = self.dense(attn_output)
return attn_output, bias
class FalconMLP(nn.Module):
def __init__(
self,
config: FalconConfig,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
hidden_size = config.hidden_size
self.dense_h_to_4h = ColumnParallelLinear(hidden_size,
4 * hidden_size,
bias=config.bias,
skip_bias_add=True,
linear_method=linear_method)
quant_config = getattr(linear_method, "quant_config", None)
self.act = get_act_fn("gelu", quant_config, 4 * hidden_size)
self.reduce_row_parallel_results = not (config.new_decoder_architecture
or config.parallel_attn)
self.dense_4h_to_h = RowParallelLinear(
4 * hidden_size,
hidden_size,
bias=config.bias,
skip_bias_add=True,
reduce_results=self.reduce_row_parallel_results,
linear_method=linear_method)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# NOTE(zhuohan): Following huggingface, we do not fuse bias add here.
x, bias = self.dense_h_to_4h(x)
if bias is not None:
x += bias
x = self.act(x)
x, bias = self.dense_4h_to_h(x)
return x, bias
class FalconDecoderLayer(nn.Module):
def __init__(
self,
config: FalconConfig,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.self_attention = FalconAttention(config, linear_method)
self.mlp = FalconMLP(config, linear_method)
self.config = config
if config.new_decoder_architecture:
# The layer norm before self-attention
self.ln_attn = LayerNorm(hidden_size,
eps=config.layer_norm_epsilon)
# The layer norm before the MLP
self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
else:
self.input_layernorm = LayerNorm(hidden_size,
eps=config.layer_norm_epsilon)
if not config.parallel_attn:
self.post_attention_layernorm = LayerNorm(
hidden_size, eps=config.layer_norm_epsilon)
self.reduce_row_parallel_results = not (config.new_decoder_architecture
or config.parallel_attn)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
) -> torch.Tensor:
residual = hidden_states
if self.config.new_decoder_architecture:
attention_layernorm_out = self.ln_attn(hidden_states)
mlp_layernorm_out = self.ln_mlp(hidden_states)
else:
attention_layernorm_out = self.input_layernorm(hidden_states)
# Self attention.
attention_output, attention_bias = self.self_attention(
positions=positions,
hidden_states=attention_layernorm_out,
kv_cache=kv_cache,
input_metadata=input_metadata,
)
if self.reduce_row_parallel_results and attention_bias is not None:
attention_output += attention_bias
if not self.config.new_decoder_architecture:
if self.config.parallel_attn:
mlp_layernorm_out = attention_layernorm_out
else:
residual += attention_output
mlp_layernorm_out = self.post_attention_layernorm(residual)
# MLP.
mlp_output, mlp_bias = self.mlp(mlp_layernorm_out)
if self.reduce_row_parallel_results and mlp_bias is not None:
mlp_output += mlp_bias
if not self.reduce_row_parallel_results:
# When MLP and Attention layers are parallel, we can use
# only one all-reduce operator to reduce the results from
# both MLP and Attention layers.
mlp_output += attention_output
mlp_output = tensor_model_parallel_all_reduce(mlp_output)
if attention_bias is not None:
mlp_output += attention_bias
if mlp_bias is not None:
mlp_output += mlp_bias
output = mlp_output + residual
return output
class FalconModel(nn.Module):
def __init__(
self,
config: FalconConfig,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.use_alibi = config.alibi
# Embedding + LN Embedding
self.word_embeddings = VocabParallelEmbedding(
config.vocab_size,
self.embed_dim,
)
# Transformer blocks
self.h = nn.ModuleList([
FalconDecoderLayer(config, linear_method)
for _ in range(config.num_hidden_layers)
])
# Final Layer Norm
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
def forward(
self,
input_ids: torch.LongTensor,
positions: torch.Tensor,
kv_caches: List[KVCache],
input_metadata: InputMetadata,
) -> torch.Tensor:
hidden_states = self.word_embeddings(input_ids)
for i in range(len(self.h)):
layer = self.h[i]
hidden_states = layer(
positions,
hidden_states,
kv_caches[i],
input_metadata,
)
hidden_states = self.ln_f(hidden_states)
return hidden_states
class FalconForCausalLM(nn.Module):
def __init__(
self,
config: FalconConfig,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.config = config
self.linear_method = linear_method
self.transformer = FalconModel(config, linear_method)
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
)
self.sampler = Sampler(config.vocab_size)
def forward(
self,
input_ids: torch.LongTensor,
positions: torch.Tensor,
kv_caches: List[KVCache],
input_metadata: InputMetadata,
) -> torch.Tensor:
hidden_states = self.transformer(
input_ids,
positions,
kv_caches,
input_metadata,
)
return hidden_states
def sample(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(self.lm_head.weight, hidden_states,
sampling_metadata)
return next_tokens
def load_weights(self,
model_name_or_path: str,
cache_dir: Optional[str] = None,
load_format: str = "auto",
revision: Optional[str] = None):
total_num_heads = self.config.num_attention_heads
if self.config.new_decoder_architecture:
total_num_kv_heads = self.config.num_kv_heads
elif self.config.multi_query:
total_num_kv_heads = 1
else:
total_num_kv_heads = total_num_heads
num_query_heads_per_kv_head = total_num_heads // total_num_kv_heads
params_dict = dict(self.named_parameters())
for name, loaded_weight in hf_model_weights_iterator(
model_name_or_path, cache_dir, load_format, revision):
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
if "query_key_value" in name:
output_dim = getattr(param, "output_dim", None)
loaded_weight_shape = loaded_weight.shape
if output_dim is not None:
loaded_weight = loaded_weight.view(
loaded_weight_shape[:output_dim] +
(total_num_kv_heads, num_query_heads_per_kv_head + 2,
-1) + loaded_weight_shape[output_dim + 1:])
wq = loaded_weight.narrow(
output_dim + 1, 0,
num_query_heads_per_kv_head).reshape(
*loaded_weight_shape[:output_dim], -1,
*loaded_weight_shape[output_dim + 1:])
wk = loaded_weight.narrow(
output_dim + 1, num_query_heads_per_kv_head,
1).reshape(*loaded_weight_shape[:output_dim], -1,
*loaded_weight_shape[output_dim + 1:])
wv = loaded_weight.narrow(
output_dim + 1, num_query_heads_per_kv_head + 1,
1).reshape(*loaded_weight_shape[:output_dim], -1,
*loaded_weight_shape[output_dim + 1:])
loaded_weight = torch.cat([wq, wk, wv], dim=output_dim)
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
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