443 lines
17 KiB
Python
443 lines
17 KiB
Python
# Copyright 2024 Cohere 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.
|
|
|
|
# This file is based on the LLama model definition file in transformers
|
|
"""PyTorch Cohere model."""
|
|
from typing import Iterable, List, Optional, Set, Tuple, Union
|
|
|
|
import torch
|
|
import torch.utils.checkpoint
|
|
from torch import nn
|
|
from transformers import CohereConfig
|
|
|
|
from vllm.attention import Attention, AttentionMetadata
|
|
from vllm.compilation.decorators import support_torch_compile
|
|
from vllm.config import CacheConfig, LoRAConfig
|
|
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
|
|
from vllm.model_executor.layers.activation import SiluAndMul
|
|
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
|
|
QKVParallelLinear,
|
|
RowParallelLinear)
|
|
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
|
from vllm.model_executor.layers.quantization import QuantizationConfig
|
|
from vllm.model_executor.layers.rotary_embedding import get_rope
|
|
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
|
|
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
|
VocabParallelEmbedding)
|
|
from vllm.model_executor.model_loader.weight_utils import (
|
|
default_weight_loader, maybe_remap_kv_scale_name,
|
|
row_parallel_weight_loader)
|
|
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
|
from vllm.model_executor.utils import set_weight_attrs
|
|
from vllm.sequence import IntermediateTensors
|
|
|
|
from .interfaces import SupportsLoRA, SupportsPP
|
|
from .utils import (is_pp_missing_parameter,
|
|
make_empty_intermediate_tensors_factory, make_layers)
|
|
|
|
|
|
@torch.compile
|
|
def layer_norm_func(hidden_states, weight, variance_epsilon):
|
|
input_dtype = hidden_states.dtype
|
|
hidden_states = hidden_states.to(torch.float32)
|
|
mean = hidden_states.mean(-1, keepdim=True)
|
|
variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
|
|
hidden_states = (hidden_states - mean) * torch.rsqrt(variance +
|
|
variance_epsilon)
|
|
hidden_states = weight.to(torch.float32) * hidden_states
|
|
return hidden_states.to(input_dtype)
|
|
|
|
|
|
class LayerNorm(nn.Module):
|
|
|
|
def __init__(self, param_shape=None, eps=1e-5):
|
|
super().__init__()
|
|
self.weight = nn.Parameter(torch.ones(param_shape))
|
|
self.variance_epsilon = eps
|
|
set_weight_attrs(self.weight,
|
|
{"weight_loader": row_parallel_weight_loader})
|
|
|
|
def forward(self, hidden_states, residuals=None):
|
|
hidden_states = layer_norm_func(hidden_states, self.weight,
|
|
self.variance_epsilon)
|
|
return hidden_states, residuals
|
|
|
|
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
|
|
class CohereMLP(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: CohereConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
):
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = config.intermediate_size
|
|
self.gate_up_proj = MergedColumnParallelLinear(
|
|
self.hidden_size,
|
|
[self.intermediate_size] * 2,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
self.down_proj = RowParallelLinear(
|
|
self.intermediate_size,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
self.act_fn = SiluAndMul()
|
|
|
|
def forward(self, x):
|
|
gate_up, _ = self.gate_up_proj(x)
|
|
x = self.act_fn(gate_up)
|
|
x, _ = self.down_proj(x)
|
|
return x
|
|
|
|
|
|
class CohereAttention(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: CohereConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
):
|
|
super().__init__()
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
self.config = config
|
|
self.attention_dropout = config.attention_dropout
|
|
self.hidden_size = config.hidden_size
|
|
self.total_num_heads = config.num_attention_heads
|
|
self.num_heads = self.total_num_heads // tp_size
|
|
self.head_dim = self.hidden_size // self.total_num_heads
|
|
self.total_num_kv_heads = config.num_key_value_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.q_size = self.num_heads * self.head_dim
|
|
self.kv_size = self.num_kv_heads * self.head_dim
|
|
self.scaling = self.head_dim**-0.5
|
|
self.max_position_embeddings = getattr(
|
|
config, "model_max_length", None) or getattr(
|
|
config, "max_position_embeddings", 8192)
|
|
self.rope_theta = config.rope_theta
|
|
self.rope_scaling = getattr(config, "rope_scaling", None)
|
|
self.use_qk_norm = getattr(config, "use_qk_norm", False)
|
|
self.qkv_proj = QKVParallelLinear(
|
|
self.hidden_size,
|
|
self.head_dim,
|
|
self.total_num_heads,
|
|
self.total_num_kv_heads,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
self.o_proj = RowParallelLinear(
|
|
self.total_num_heads * self.head_dim,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
self.rotary_emb = get_rope(
|
|
self.head_dim,
|
|
rotary_dim=self.head_dim,
|
|
max_position=self.max_position_embeddings,
|
|
base=self.rope_theta,
|
|
rope_scaling=self.rope_scaling,
|
|
is_neox_style=False,
|
|
)
|
|
self.attn = Attention(self.num_heads,
|
|
self.head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_kv_heads,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config)
|
|
if self.use_qk_norm:
|
|
self.q_norm = LayerNorm(param_shape=(self.num_heads,
|
|
self.head_dim),
|
|
eps=config.layer_norm_eps)
|
|
self.k_norm = LayerNorm(param_shape=(self.num_kv_heads,
|
|
self.head_dim),
|
|
eps=config.layer_norm_eps)
|
|
|
|
def _apply_qk_norm(self, q, k):
|
|
q = q.view(*q.shape[:-1], -1, self.head_dim)
|
|
k = k.view(*k.shape[:-1], -1, self.head_dim)
|
|
q, _ = self.q_norm(q)
|
|
k, _ = self.k_norm(k)
|
|
q = q.view(*q.shape[:-2], -1)
|
|
k = k.view(*k.shape[:-2], -1)
|
|
return q, k
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
) -> torch.Tensor:
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
|
if self.use_qk_norm:
|
|
q, k = self._apply_qk_norm(q, k)
|
|
q, k = self.rotary_emb(positions, q, k)
|
|
attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
|
|
class CohereDecoderLayer(nn.Module):
|
|
|
|
def __init__(self,
|
|
config: CohereConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
|
|
self.self_attn = CohereAttention(config,
|
|
cache_config,
|
|
quant_config=quant_config)
|
|
|
|
self.mlp = CohereMLP(config, quant_config=quant_config)
|
|
self.input_layernorm = LayerNorm(param_shape=(config.hidden_size),
|
|
eps=config.layer_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
residual: Optional[torch.Tensor],
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
# Self Attention
|
|
residual = hidden_states
|
|
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
|
hidden_states_attention = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
)
|
|
hidden_states_mlp = self.mlp(hidden_states)
|
|
# Add everything together
|
|
hidden_states = residual + hidden_states_attention + hidden_states_mlp
|
|
|
|
return hidden_states, residual
|
|
|
|
|
|
@support_torch_compile
|
|
class CohereModel(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: CohereConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
lora_config: Optional[LoRAConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
self.config = config
|
|
lora_vocab = (lora_config.lora_extra_vocab_size *
|
|
(lora_config.max_loras or 1)) if lora_config else 0
|
|
self.vocab_size = config.vocab_size + lora_vocab
|
|
self.org_vocab_size = config.vocab_size
|
|
self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
|
|
config.hidden_size)
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda prefix: CohereDecoderLayer(config, cache_config,
|
|
quant_config),
|
|
prefix=f"{prefix}.layers")
|
|
self.norm = LayerNorm(param_shape=(config.hidden_size),
|
|
eps=config.layer_norm_eps)
|
|
self.make_empty_intermediate_tensors = (
|
|
make_empty_intermediate_tensors_factory(
|
|
["hidden_states", "residual"], config.hidden_size))
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors],
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
if get_pp_group().is_first_rank:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
for i in range(self.start_layer, self.end_layer):
|
|
layer = self.layers[i]
|
|
hidden_states, residual = layer(
|
|
positions,
|
|
hidden_states,
|
|
kv_caches[i - self.start_layer],
|
|
attn_metadata,
|
|
residual,
|
|
)
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors({
|
|
"hidden_states": hidden_states,
|
|
"residual": residual
|
|
})
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
return hidden_states
|
|
|
|
|
|
class CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"gate_up_proj": [
|
|
"gate_proj",
|
|
"up_proj",
|
|
],
|
|
}
|
|
# LoRA specific attributes
|
|
supported_lora_modules = [
|
|
"qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens"
|
|
]
|
|
embedding_modules = {"embed_tokens": "input_embeddings"}
|
|
embedding_padding_modules = []
|
|
|
|
def __init__(
|
|
self,
|
|
config: CohereConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
lora_config: Optional[LoRAConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
# currently all existing command R models have `tie_word_embeddings`
|
|
# enabled
|
|
assert config.tie_word_embeddings
|
|
self.unpadded_vocab_size = config.vocab_size
|
|
if lora_config:
|
|
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
|
self.quant_config = quant_config
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
config.vocab_size,
|
|
scale=config.logit_scale)
|
|
self.model = CohereModel(config,
|
|
cache_config,
|
|
quant_config,
|
|
lora_config=lora_config)
|
|
self.sampler = get_sampler()
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
hidden_states = self.model(input_ids, positions, kv_caches,
|
|
attn_metadata, intermediate_tensors)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
is_not_lora = hasattr(self.model.embed_tokens, 'weight')
|
|
if is_not_lora:
|
|
logits = self.logits_processor(self.model.embed_tokens,
|
|
hidden_states, sampling_metadata)
|
|
else:
|
|
logits = self.logits_processor(self.model.embed_tokens.base_layer,
|
|
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]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: Set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
for param_name, shard_name, shard_id in stacked_params_mapping:
|
|
if shard_name not in name:
|
|
continue
|
|
name = name.replace(shard_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# lm_head is not used in vllm as it is tied with embed_token.
|
|
# To prevent errors, skip loading lm_head.weight.
|
|
if "lm_head.weight" in name:
|
|
continue
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|