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Mixtral multipack (#928)

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* mixtral multipack

* use mixtral model

* sample yml

* calculate cu_seqlens properly

* use updated flash ettention setting

* attn var checks

* force use of flash attention 2 for packing

* lint

* disable future fix for now

* update support table
This commit is contained in:
Wing Lian
2023-12-09 21:26:30 -05:00
committed by GitHub
parent 03c6318ba3
commit 68b227a7d8
8 changed files with 1793 additions and 25 deletions
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3
.mypy.ini
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@@ -8,6 +8,9 @@ ignore_missing_imports = True
[mypy-axolotl.monkeypatch.*]
ignore_errors = True
[mypy-axolotl.models.mixtral.*]
ignore_errors = True
[mypy-axolotl.models.phi.*]
ignore_errors = True

28
README.md
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@@ -65,19 +65,21 @@ Features:
## Axolotl supports
| | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
|----------|:----------|:-----|-------|------|-------------------|------------|--------------|
| llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Pythia | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| cerebras | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| btlm | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| mpt | ✅ | ❌ | ❓ | ❌ | ❌ | ❌ | ❓ |
| falcon | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| gpt-j | ✅ | ✅ | ✅ | ❌ | ❌ | ❓ | ❓ |
| XGen | ✅ | ❓ | ✅ | ❓ | ❓ | ❓ | ✅ |
| phi | ✅ | ✅ | ✅ | ❓ | ❓ | ❓ | ❓ |
| RWKV | ✅ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ |
| Qwen | ✅ | ✅ | ✅ | ❓ | ❓ | ❓ | ❓ |
| | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
|-------------|:----------|:-----|-------|------|-------------------|------------|--------------|
| llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Mistral | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Mixtral-MoE | ✅ | ✅ | ✅ | ❓ | ❓ | ❓ | ❓ |
| Pythia | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| cerebras | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| btlm | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| mpt | ✅ | ❌ | ❓ | ❌ | ❌ | ❌ | ❓ |
| falcon | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| gpt-j | ✅ | ✅ | ✅ | ❌ | ❌ | ❓ | ❓ |
| XGen | ✅ | ❓ | ✅ | ❓ | ❓ | ❓ | ✅ |
| phi | ✅ | ✅ | ✅ | ❓ | ❓ | ❓ | ❓ |
| RWKV | ✅ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ |
| Qwen | ✅ | ✅ | ✅ | ❓ | ❓ | ❓ | ❓ |
## Quickstart ⚡

1
docker/Dockerfile-runpod
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@@ -4,6 +4,7 @@ FROM winglian/axolotl:$BASE_TAG
ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
ENV HF_HOME="/workspace/data/huggingface-cache/hub"
COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh

78
examples/mistral/mixtral.yml Normal file
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@@ -0,0 +1,78 @@
base_model: DiscoResearch/mixtral-7b-8expert
model_type: MixtralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: tatsu-lab/alpaca
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
#lora_target_modules:
# - gate
# - q_proj
# - k_proj
# - v_proj
# - o_proj
# - w1
# - w2
# - w3
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
eval_steps:
eval_table_size:
eval_table_max_new_tokens: 128
save_steps:
debug:
deepspeed: deepspeed/zero2.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

6
src/axolotl/models/mixtral/__init__.py Normal file
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@@ -0,0 +1,6 @@
"""
Custom modeling code for mixtral
"""
from .configuration_moe_mistral import MixtralConfig # noqa
from .modeling_moe_mistral import MixtralForCausalLM # noqa

154
src/axolotl/models/mixtral/configuration_moe_mistral.py Normal file
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@@ -0,0 +1,154 @@
# coding=utf-8
# Copyright 2023 Mistral AI and the 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.
""" Mistral model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
"mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
}
class MixtralConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
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 32000):
Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MistralModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 14336):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
allows sequence of up to 4096*32 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention window size. If not specified, will default to `4096`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import MistralModel, MistralConfig
>>> # Initializing a Mistral 7B style configuration
>>> configuration = MixtralConfig()
>>> # Initializing a model from the Mistral 7B style configuration
>>> model = MixtralModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mistral"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=4096 * 32,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=10000.0,
attention_dropout=0.0,
num_experts_per_token=2,
num_experts=8,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.num_experts = num_experts
self.num_experts_per_token = num_experts_per_token
# pylint: disable=duplicate-code
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)

1506
src/axolotl/models/mixtral/modeling_moe_mistral.py Normal file
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42
src/axolotl/utils/models.py
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@@ -54,18 +54,25 @@ def check_model_config(cfg: DictDefault, model_config: AutoConfig):
def load_model_config(cfg):
model_config_name = cfg.base_model_config or cfg.base_model
trust_remote_code = cfg.trust_remote_code is True
try:
model_config = AutoConfig.from_pretrained(
model_config_name, trust_remote_code=trust_remote_code
)
except ValueError as err:
if "mamba" in model_config_name:
return addict.Dict(
{
"model_type": "mamba",
}
model_type = cfg.model_type
if model_type == "MixtralForCausalLM":
from axolotl.models.mixtral.configuration_moe_mistral import MixtralConfig
model_config = MixtralConfig.from_pretrained(model_config_name)
else:
try:
model_config = AutoConfig.from_pretrained(
model_config_name, trust_remote_code=trust_remote_code
)
raise err
except ValueError as err:
if "mamba" in model_config_name:
return addict.Dict(
{
"model_type": "mamba",
}
)
raise err
if cfg.model_config:
for key, val in cfg.model_config.items():
@@ -301,7 +308,9 @@ def load_model(
or cfg.is_falcon_derived_model
or cfg.is_mistral_derived_model
):
model_kwargs["use_flash_attention_2"] = True
# TODO enable once properly supported in transformers
# model_kwargs["attn_implementation"] = "flash_attention_2"
model_kwargs["use_flash_attention_2"] = True # legacy, to be deprecated
try:
if cfg.is_llama_derived_model and not cfg.trust_remote_code and not cfg.gptq:
@@ -363,6 +372,15 @@ def load_model(
load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
**model_kwargs,
)
elif model_type == "MixtralForCausalLM":
from axolotl.models.mixtral import MixtralForCausalLM
model = MixtralForCausalLM.from_pretrained(
base_model,
load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
**model_kwargs,
)
elif model_type == "MambaLMHeadModel":
# FIXME this is janky at best and hacked together to make it work
MambaLMHeadModel = fix_mamba_attn_for_loss() # pylint: disable=invalid-name