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Deprecate JAISLMHeadModel (#43784)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2026-05-28 19:29:12 +01:00
committed by GitHub
parent 9006204e90
commit 085ac221a3
8 changed files with 1 additions and 651 deletions
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docs/models/supported_models.md
-1
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@@ -428,7 +428,6 @@ th {
| `InternLM3ForCausalLM` | InternLM3 | `internlm/internlm3-8b-instruct`, etc. | ✅︎ | ✅︎ |
| `IQuestCoderForCausalLM` | IQuestCoderV1 | `IQuestLab/IQuest-Coder-V1-40B-Instruct`, etc. | | |
| `IQuestLoopCoderForCausalLM` | IQuestLoopCoderV1 | `IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct`, etc. | | |
| `JAISLMHeadModel` | Jais | `inceptionai/jais-13b`, `inceptionai/jais-13b-chat`, `inceptionai/jais-30b-v3`, `inceptionai/jais-30b-chat-v3`, etc. | | ✅︎ |
| `Jais2ForCausalLM` | Jais2 | `inceptionai/Jais-2-8B-Chat`, `inceptionai/Jais-2-70B-Chat`, etc. | | ✅︎ |
| `JambaForCausalLM` | Jamba | `ai21labs/AI21-Jamba-1.5-Large`, `ai21labs/AI21-Jamba-1.5-Mini`, `ai21labs/Jamba-v0.1`, etc. | ✅︎ | ✅︎ |
| `KimiLinearForCausalLM` | Kimi-Linear-48B-A3B-Base, Kimi-Linear-48B-A3B-Instruct | `moonshotai/Kimi-Linear-48B-A3B-Base`, `moonshotai/Kimi-Linear-48B-A3B-Instruct` | | ✅︎ |
tests/distributed/test_pipeline_parallel.py
-1
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@@ -127,7 +127,6 @@ TEXT_GENERATION_MODELS = {
# Uses Llama
# "internlm/internlm-chat-7b": PPTestSettings.fast(),
"internlm/internlm2-chat-7b": PPTestSettings.fast(),
"inceptionai/jais-13b-chat": PPTestSettings.fast(),
"ai21labs/Jamba-tiny-dev": PPTestSettings.fast(),
"pfnet/plamo-2-1b": PPTestSettings.fast(),
"pfnet/plamo-3-nict-2b-base": PPTestSettings.fast(),
tests/models/registry.py
-1
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@@ -363,7 +363,6 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
"IQuestLoopCoderForCausalLM": _HfExamplesInfo(
"IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct", trust_remote_code=True
),
"JAISLMHeadModel": _HfExamplesInfo("inceptionai/jais-13b-chat"),
"Jais2ForCausalLM": _HfExamplesInfo(
"inceptionai/Jais-2-8B-Chat", min_transformers_version="4.58"
),
vllm/model_executor/models/jais.py
-401
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@@ -1,401 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from
# https://huggingface.co/inceptionai/jais-30b-chat-v3/blob/main/modeling_jais.py
# Copyright 2023 The vLLM team.
# Copyright 2023 the Jais authors and HuggingFace Inc. team. All rights
# reserved.
# Copyright 2023 Cerebras Systems.
#
# 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 Jais model compatible with HuggingFace weights."""
import math
from collections.abc import Iterable
from itertools import islice
import torch
from torch import nn
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (
get_pp_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
)
from vllm.model_executor.layers.attention import Attention
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 import QuantizationConfig
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.sequence import IntermediateTensors
from vllm.transformers_utils.configs.jais import JAISConfig
from .interfaces import SupportsPP
from .utils import (
AutoWeightsLoader,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
make_layers,
maybe_prefix,
)
class SwiGLUActivation(nn.Module):
def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
return x1 * nn.functional.silu(x2)
def _get_alibi_slopes(n):
def get_slopes_power_of_2(n):
start = 2 ** (-(2 ** -(math.log2(n) - 3)))
ratio = start
return [start * ratio**i for i in range(n)]
if math.log2(n).is_integer():
return get_slopes_power_of_2(n)
else:
closest_power_of_2 = 2 ** math.floor(math.log2(n))
return (
get_slopes_power_of_2(closest_power_of_2)
+ _get_alibi_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
)
class JAISAttention(nn.Module):
def __init__(
self,
config: JAISConfig,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
self.hidden_size = config.hidden_size
total_num_heads = config.num_attention_heads
tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
assert total_num_heads % tensor_model_parallel_world_size == 0
self.num_heads = total_num_heads // tensor_model_parallel_world_size
self.head_dim = self.hidden_size // total_num_heads
if hasattr(config, "scale_qk_dot_by_d"):
config.mup_scale_qk_dot_by_d = config.scale_qk_dot_by_d
self.attn_scale_power = 1.0 if config.mup_scale_qk_dot_by_d else 0.5
self.scale = self.head_dim**-self.attn_scale_power
self.c_attn = QKVParallelLinear(
self.hidden_size,
self.head_dim,
total_num_heads,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.c_attn",
)
self.c_proj = RowParallelLinear(
self.hidden_size,
self.hidden_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.c_proj",
)
self.use_alibi = config.position_embedding_type == "alibi"
alibi_slopes = None
if 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(total_num_heads)
alibi_slopes = alibi_slopes[head_start:head_end]
self.attn = Attention(
self.num_heads,
self.head_dim,
scale=self.scale,
alibi_slopes=alibi_slopes,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
def forward(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
qkv, _ = self.c_attn(hidden_states)
q, k, v = qkv.chunk(chunks=3, dim=-1)
attn_output = self.attn(q, k, v)
attn_output, _ = self.c_proj(attn_output)
return attn_output
class JAISMLP(nn.Module):
def __init__(
self,
intermediate_size: int,
config: JAISConfig,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
hidden_size = config.hidden_size
self.swiglu = config.activation_function == "swiglu"
self.c_fc = ColumnParallelLinear(
hidden_size,
intermediate_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.c_fc",
)
self.c_fc2 = (
ColumnParallelLinear(
hidden_size,
intermediate_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.c_fc2",
)
if self.swiglu
else None
)
self.c_proj = RowParallelLinear(
intermediate_size,
hidden_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.c_proj",
)
self.act = SwiGLUActivation()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if self.swiglu:
hidden_states2, _ = self.c_fc2(hidden_states)
hidden_states, _ = self.c_fc(hidden_states)
hidden_states = (
self.act(hidden_states, hidden_states2)
if self.swiglu
else self.act(hidden_states)
)
hidden_states, _ = self.c_proj(hidden_states)
return hidden_states
class JAISBlock(nn.Module):
def __init__(
self,
config: JAISConfig,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = JAISAttention(
config, cache_config, quant_config, prefix=f"{prefix}.attn"
)
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = JAISMLP(inner_dim, config, quant_config, prefix=f"{prefix}.mlp")
def forward(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_output = self.attn(
hidden_states=hidden_states,
)
# residual connection
hidden_states = attn_output + residual
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
return hidden_states
@support_torch_compile
class JAISModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
assert not config.scale_attn_by_inverse_layer_idx
assert not config.reorder_and_upcast_attn
self.embed_dim = config.hidden_size
self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
self.wpe = (
nn.Embedding(config.max_position_embeddings, self.embed_dim)
if config.position_embedding_type != "alibi"
else None
)
if hasattr(config, "embeddings_scale"):
self.embeddings_scale = config.embeddings_scale
else:
self.embeddings_scale = config.mup_embeddings_scale
self.start_layer, self.end_layer, self.h = make_layers(
config.num_hidden_layers,
lambda prefix: JAISBlock(
config=config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix,
),
prefix=f"{prefix}.h",
)
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
["hidden_states"], config.n_embd
)
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.wte(input_ids)
def forward(
self,
input_ids: torch.Tensor | None,
position_ids: torch.Tensor,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
) -> IntermediateTensors | torch.Tensor:
if get_pp_group().is_first_rank:
if inputs_embeds is None:
inputs_embeds = self.embed_input_ids(input_ids)
if self.wpe is not None:
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
else:
hidden_states = inputs_embeds
hidden_states *= torch.tensor(
float(self.embeddings_scale), dtype=hidden_states.dtype
)
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
for layer in islice(self.h, self.start_layer, self.end_layer):
hidden_states = layer(hidden_states)
if not get_pp_group().is_last_rank:
return IntermediateTensors({"hidden_states": hidden_states})
hidden_states = self.ln_f(hidden_states)
return hidden_states
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if ".attn.bias" in name or ".attn.masked_bias" in name:
# Skip attention mask.
# NOTE: "c_attn.bias" should not be skipped.
continue
if "relative_pe" in name:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
# The HF's GPT-2 implementation uses Conv1D instead of Linear.
# Because of this, we need to transpose the weights.
# Note(zhuohan): the logic below might break quantized models.
for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
if conv1d_weight_name not in name:
continue
if not name.endswith(".weight"):
continue
loaded_weight = loaded_weight.t()
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
class JAISLMHeadModel(nn.Module, SupportsPP):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
self.transformer = JAISModel(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
)
if self.config.tie_word_embeddings:
self.lm_head = self.transformer.wte
else:
self.lm_head = ParallelLMHead(
self.config.vocab_size,
self.config.hidden_size,
prefix=maybe_prefix(prefix, "lm_head"),
)
if hasattr(config, "width_scale"):
self.output_logits_scale = config.width_scale
else:
self.output_logits_scale = config.mup_output_alpha * config.mup_width_scale
self.logits_processor = LogitsProcessor(
vocab_size=config.vocab_size, scale=self.output_logits_scale
)
self.make_empty_intermediate_tensors = (
self.transformer.make_empty_intermediate_tensors
)
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.transformer.embed_input_ids(input_ids)
def forward(
self,
input_ids: torch.Tensor | None,
positions: torch.Tensor,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
) -> IntermediateTensors | torch.Tensor:
hidden_states = self.transformer(
input_ids, positions, intermediate_tensors, inputs_embeds
)
return hidden_states
def compute_logits(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor | None:
logits = self.logits_processor(self.lm_head, hidden_states)
return logits
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(
self,
skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
)
return loader.load_weights(weights)
vllm/model_executor/models/registry.py
+1 -1
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@@ -146,7 +146,6 @@ _TEXT_GENERATION_MODELS = {
"InternLM3ForCausalLM": ("llama", "LlamaForCausalLM"),
"IQuestCoderForCausalLM": ("llama", "LlamaForCausalLM"),
"IQuestLoopCoderForCausalLM": ("iquest_loopcoder", "IQuestLoopCoderForCausalLM"),
"JAISLMHeadModel": ("jais", "JAISLMHeadModel"),
"Jais2ForCausalLM": ("jais2", "Jais2ForCausalLM"),
"JambaForCausalLM": ("jamba", "JambaForCausalLM"),
"KimiLinearForCausalLM": ("kimi_linear", "KimiLinearForCausalLM"),
@@ -705,6 +704,7 @@ _PREVIOUSLY_SUPPORTED_MODELS = {
"Phi3SmallForCausalLM": "0.9.2",
"Phi4FlashForCausalLM": "0.10.2",
"Phi4MultimodalForCausalLM": "0.12.0",
"JAISLMHeadModel": "0.22.0",
# encoder-decoder models except whisper
# have been removed for V0 deprecation.
"DonutForConditionalGeneration": "0.10.2",
vllm/transformers_utils/config.py
-1
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@@ -113,7 +113,6 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
kimi_k25="KimiK25Config",
RefinedWeb="RWConfig", # For tiiuae/falcon-40b(-instruct)
RefinedWebModel="RWConfig", # For tiiuae/falcon-7b(-instruct)
jais="JAISConfig",
mlp_speculator="MLPSpeculatorConfig",
medusa="MedusaConfig",
midashenglm="MiDashengLMConfig",
vllm/transformers_utils/configs/__init__.py
-2
View File
@@ -46,7 +46,6 @@ _CLASS_TO_MODULE: dict[str, str] = {
# tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
# `FalconConfig` class from the official HuggingFace transformers library.
"RWConfig": "vllm.transformers_utils.configs.falcon",
"JAISConfig": "vllm.transformers_utils.configs.jais",
"LagunaConfig": "vllm.transformers_utils.configs.laguna",
"Lfm2MoeConfig": "vllm.transformers_utils.configs.lfm2_moe",
"MedusaConfig": "vllm.transformers_utils.configs.medusa",
@@ -115,7 +114,6 @@ __all__ = [
"HyperCLOVAXConfig",
"IsaacConfig",
"RWConfig",
"JAISConfig",
"LagunaConfig",
"Lfm2MoeConfig",
"MedusaConfig",
vllm/transformers_utils/configs/jais.py
-243
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@@ -1,243 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Copyright 2023 Cerebras Systems.
#
# 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.
"""JAIS configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class JAISConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a
[`JAISModel`]. It is used to instantiate a JAIS model according to the
specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used
to control the model outputs. Read the documentation from
[`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50257):
Vocabulary size of the JAIS model. Defines the number of different
tokens that can be represented by the
`inputs_ids` passed when calling [`JAISModel`].
n_positions (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used
with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 768):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the
Transformer encoder.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set
it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"gelu"`):
Activation function, to be selected in the list
`["relu", "silu", "gelu", "tanh", "gelu_new", "swiglu"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in
the embeddings, encoder, and pooler.
embd_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for
initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values
attentions (not used by all models).
scale_attn_by_inverse_layer_idx (`bool`, *optional*, default `True`):
Whether to additionally scale attention weights
by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention
(dot-product)
and upcast attention dot-product/softmax to float() when training
with mixed precision.
position_embedding_type (`str`, *optional*, defaults to `"learned"`):
Positional embedding can be either `"alibi"` or `"learned"`.
mup_width_scale (`float`, *optional*, defaults to 1.0):
muP parameter to scale learning rate and initializers. Calculated
as (`d_model,0 / d_model`), where
`d_model` is the model's width and `d_model,0` is the proxy
model's width.
mup_embeddings_scale (`float`, *optional*, defaults to 1.0):
muP parameter to scale token and position embeddings.
mup_output_alpha (`float`, *optional*, defaults to 1.0):
muP parameter to scale output logits
(`output_logits_scale = mup_output_alpha * mup_width_scale`).
mup_scale_qk_dot_by_d (`bool`, *optional*, defaults to `False`):
Scale attention weights by dividing by hidden_size instead of
sqrt(hidden_size). Need to set scale_attn_weights to `True` as
well.
alibi_scaling (`dict`, *optional*):
Dictionary containing the scaling configuration for ALiBi
embeddings. Currently only supports linear
scaling strategy. Can specify either the scaling `factor` (must be
a float greater than 1) for fixed scaling
or `train_seq_len` for dynamic scaling on input samples with
sequence length > `train_seq_len`. The expected
formats are `{"type": strategy name, "factor": scaling factor}` or
`{"type": strategy name,
"train_seq_len": training sequence length}`.
architectures (`list`, *optional*, defaults to ['JAISLMHeadModel']):
architecture names for Jais.
Example:
```python
>>> from transformers import JAISConfig, JAISModel
>>> # Initializing a JAIS configuration
>>> configuration = JAISConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = JAISModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "jais"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=50257,
n_positions=1024,
n_embd=768,
n_layer=12,
n_head=12,
n_inner=None,
activation_function="gelu_new",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
position_embedding_type="learned",
mup_width_scale=1.0,
mup_embeddings_scale=1.0,
mup_output_alpha=1.0,
mup_scale_qk_dot_by_d=False,
alibi_scaling=None,
architectures=None,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.position_embedding_type = position_embedding_type
self.mup_width_scale = mup_width_scale
self.mup_embeddings_scale = mup_embeddings_scale
self.mup_output_alpha = mup_output_alpha
self.mup_scale_qk_dot_by_d = mup_scale_qk_dot_by_d
self.alibi_scaling = alibi_scaling
self._alibi_scaling_validation()
if architectures is None:
architectures = ["JAISLMHeadModel"]
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
architectures=architectures,
**kwargs,
)
def _alibi_scaling_validation(self):
"""
Validate the `alibi_scaling` configuration.
"""
if self.alibi_scaling is None:
return
if not isinstance(self.alibi_scaling, dict) or len(self.alibi_scaling) != 2:
raise ValueError(
"`alibi_scaling` must be a dictionary with two fields, "
"`type` and `factor` or `type` and `train_seq_len`, "
f"got {self.alibi_scaling}"
)
alibi_scaling_type = self.alibi_scaling.get("type", None)
alibi_scaling_factor = self.alibi_scaling.get("factor", None)
alibi_dynamic_scaling = self.alibi_scaling.get("train_seq_len", None)
if alibi_scaling_type is None or alibi_scaling_type != "linear":
raise ValueError(
f"`alibi_scaling`'s type field must be 'linear', "
f"got {alibi_scaling_type}"
)
if (
alibi_scaling_factor is not None
and not isinstance(alibi_scaling_factor, float)
or (alibi_scaling_factor is not None and alibi_scaling_factor <= 1.0)
):
raise ValueError(
f"`alibi_scaling`'s factor field must be a float > 1.0, "
f"got {alibi_scaling_factor}"
)
if (
alibi_dynamic_scaling is not None
and not isinstance(alibi_dynamic_scaling, int)
or (alibi_dynamic_scaling is not None and alibi_dynamic_scaling <= 1)
):
raise ValueError(
f"`alibi_scaling`'s `train_seq_len` field must be an "
f"integer > 1, got {alibi_dynamic_scaling}"
)