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Unverified Commit bce5e224 authored by Nicolas Patry's avatar Nicolas Patry Committed by GitHub
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Adding Idefics multi modal model. (#842) 


Co-Authored-By: default avatarVictor Sanh <victorsanh@gmail.com>


# What does this PR do?

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Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
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---------
Co-authored-by: default avatarVictor Sanh <victorsanh@gmail.com>
parent b9e33c49
Show whitespace changes
Inline Side-by-side
Showing with 5227 additions and 331 deletions
integration-tests/models/__snapshots__/test_idefics/test_idefics.json 0 → 100644
View file @ bce5e224
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integration-tests/models/__snapshots__/test_idefics/test_idefics_load.json 0 → 100644
View file @ bce5e224
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"generated_text": "\nAssistant: A chicken is"
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]
integration-tests/models/test_idefics.py 0 → 100644
View file @ bce5e224
import pytest
@pytest.fixture(scope="module")
def idefics_handle(launcher):
with launcher(
"HuggingFaceM4/idefics-9b-instruct", num_shard=2
) as handle:
yield handle
@pytest.fixture(scope="module")
async def idefics(idefics_handle):
await idefics_handle.health(300)
return idefics_handle.client
@pytest.mark.asyncio
async def test_idefics(idefics, response_snapshot):
response = await idefics.generate(
"User:![](https://temp-5681.s3.us-west-2.amazonaws.com/chicken_on_money.png)Can you tell me a very short story based on the image?",
max_new_tokens=10,
decoder_input_details=True,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_idefics_load(idefics, generate_load, response_snapshot):
responses = await generate_load(
idefics,
"User:![](https://temp-5681.s3.us-west-2.amazonaws.com/chicken_on_money.png)Can you tell me a very short story based on the image?",
max_new_tokens=10,
n=4,
)
generated_texts = [r.generated_text for r in responses]
assert len(generated_texts) == 4
assert generated_texts, all(
[text == generated_texts[0] for text in generated_texts]
)
assert responses == response_snapshot
server/poetry.lock
View file @ bce5e224
......@@ -152,13 +152,13 @@ frozenlist = ">=1.1.0"
[[package]]
name = "async-timeout"
version = "4.0.2"
version = "4.0.3"
description = "Timeout context manager for asyncio programs"
optional = true
python-versions = ">=3.6"
python-versions = ">=3.7"
files = [
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{file = "async_timeout-4.0.2-py3-none-any.whl", hash = "sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c"},
{file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
{file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
]
[[package]]
......@@ -310,35 +310,6 @@ files = [
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
[[package]]
name = "cmake"
version = "3.27.0"
description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
optional = false
python-versions = "*"
files = [
{file = "cmake-3.27.0-py2.py3-none-macosx_10_10_universal2.macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:9ccab4cd93578d3c2df32e66b44b313b75a7484032645040431dc06a583ca4aa"},
{file = "cmake-3.27.0-py2.py3-none-manylinux2010_i686.manylinux_2_12_i686.whl", hash = "sha256:199bfaefb752e82d8067aeee5d6a6e0414fe0d60e9a3fd08e95d537a97e0db16"},
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]
[package.dependencies]
cmake = "*"
filelock = "*"
lit = "*"
torch = "*"
[package.extras]
tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
tutorials = ["matplotlib", "pandas", "tabulate"]
[[package]]
name = "typer"
version = "0.6.1"
......@@ -2448,4 +2465,4 @@ quantize = ["accelerate", "datasets", "texttable"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
content-hash = "6b6dbd9e11bc2f71cd88e2ab50adce249a722fea02e7cdcf5fe1b94c7b997a57"
content-hash = "91a848038c08a44c67acfb4257781440ccc1e74a4b82f09513e75588fa33f72b"
server/pyproject.toml
View file @ bce5e224
......@@ -31,8 +31,9 @@ einops = "^0.6.1"
texttable = { version = "^1.6.7", optional = true }
datasets = { version = "^2.14.0", optional = true }
peft = "^0.4.0"
torch = {version = "^2.0.1+cu118", source = "pytorch-gpu-src"}
torch = { version = "^2.0.1" }
scipy = "^1.11.1"
pillow = "^10.0.0"
[tool.poetry.extras]
accelerate = ["accelerate"]
......
server/requirements.txt
View file @ bce5e224
--extra-index-url https://download.pytorch.org/whl/cu118
accelerate==0.20.3 ; python_version >= "3.9" and python_version < "3.13"
backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
certifi==2023.7.22 ; python_version >= "3.9" and python_version < "3.13"
charset-normalizer==3.2.0 ; python_version >= "3.9" and python_version < "3.13"
click==8.1.6 ; python_version >= "3.9" and python_version < "3.13"
cmake==3.27.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.13"
colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
......@@ -13,14 +10,13 @@ filelock==3.12.2 ; python_version >= "3.9" and python_version < "3.13"
fsspec==2023.6.0 ; python_version >= "3.9" and python_version < "3.13"
googleapis-common-protos==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
grpc-interceptor==0.15.2 ; python_version >= "3.9" and python_version < "3.13"
grpcio-reflection==1.56.2 ; python_version >= "3.9" and python_version < "3.13"
grpcio-status==1.56.2 ; python_version >= "3.9" and python_version < "3.13"
grpcio==1.56.2 ; python_version >= "3.9" and python_version < "3.13"
grpcio-reflection==1.57.0 ; python_version >= "3.9" and python_version < "3.13"
grpcio-status==1.57.0 ; python_version >= "3.9" and python_version < "3.13"
grpcio==1.57.0 ; python_version >= "3.9" and python_version < "3.13"
hf-transfer==0.1.3 ; python_version >= "3.9" and python_version < "3.13"
huggingface-hub==0.14.1 ; python_version >= "3.9" and python_version < "3.13"
idna==3.4 ; python_version >= "3.9" and python_version < "3.13"
jinja2==3.1.2 ; python_version >= "3.9" and python_version < "3.13"
lit==16.0.6 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.13"
loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
markupsafe==2.1.3 ; python_version >= "3.9" and python_version < "3.13"
mpmath==1.3.0 ; python_version >= "3.9" and python_version < "3.13"
......@@ -37,10 +33,11 @@ opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
packaging==23.1 ; python_version >= "3.9" and python_version < "3.13"
peft==0.4.0 ; python_version >= "3.9" and python_version < "3.13"
protobuf==4.23.4 ; python_version >= "3.9" and python_version < "3.13"
pillow==10.0.0 ; python_version >= "3.9" and python_version < "3.13"
protobuf==4.24.0 ; python_version >= "3.9" and python_version < "3.13"
psutil==5.9.5 ; python_version >= "3.9" and python_version < "3.13"
pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
regex==2023.6.3 ; python_version >= "3.9" and python_version < "3.13"
regex==2023.8.8 ; python_version >= "3.9" and python_version < "3.13"
requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
safetensors==0.3.2 ; python_version >= "3.9" and python_version < "3.13"
scipy==1.11.1 ; python_version >= "3.9" and python_version < "3.13"
......@@ -48,10 +45,9 @@ sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
setuptools==68.0.0 ; python_version >= "3.9" and python_version < "3.13"
sympy==1.12 ; python_version >= "3.9" and python_version < "3.13"
tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "3.13"
torch==2.0.1+cu118 ; python_version >= "3.9" and python_version < "3.13"
tqdm==4.65.0 ; python_version >= "3.9" and python_version < "3.13"
torch==2.0.1 ; python_version >= "3.9" and python_version < "3.13"
tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.31.0 ; python_version >= "3.9" and python_version < "3.13"
triton==2.0.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.9" and python_version < "3.13"
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
typing-extensions==4.7.1 ; python_version >= "3.9" and python_version < "3.13"
urllib3==2.0.4 ; python_version >= "3.9" and python_version < "3.13"
......
server/text_generation_server/models/__init__.py
View file @ bce5e224
......@@ -54,6 +54,7 @@ try:
from text_generation_server.models.flash_santacoder import (
FlashSantacoderSharded,
)
from text_generation_server.models.idefics import IDEFICSSharded
except ImportError as e:
logger.warning(f"Could not import Flash Attention enabled models: {e}")
......@@ -64,6 +65,7 @@ if FLASH_ATTENTION:
__all__.append(FlashRWSharded)
__all__.append(FlashSantacoderSharded)
__all__.append(FlashLlama)
__all__.append(IDEFICSSharded)
def get_model(
......@@ -248,6 +250,17 @@ def get_model(
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "idefics":
if FLASH_ATTENTION:
return IDEFICSSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if sharded:
raise ValueError("sharded is not supported for AutoModel")
......
server/text_generation_server/models/custom_modeling/idefics_config.py 0 → 100644
View file @ bce5e224
# coding=utf-8
# Copyright 2022 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.
""" Idefics model configuration"""
import copy
from transformers import PretrainedConfig
IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"HuggingFaceM4/idefics-9b": "https://huggingface.co/HuggingFaceM4/idefics-9b/blob/main/config.json",
"HuggingFaceM4/idefics-80b": "https://huggingface.co/HuggingFaceM4/idefics-80b/blob/main/config.json",
}
class IdeficsVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics 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 Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `hidden_size`)
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
intermediate_size (`int`, *optional*, defaults to 5120):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
image_num_channels (`int`, *optional*, defaults to `3`):
Number of image channels.
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
testing).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
"""
model_type = "idefics"
attribute_map = {
"hidden_size": "embed_dim",
}
def __init__(
self,
embed_dim=768,
image_size=224,
intermediate_size=5120,
patch_size=14,
num_hidden_layers=32,
num_attention_heads=16,
num_channels=3,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
**kwargs,
):
self.embed_dim = embed_dim
self.image_size = image_size
self.intermediate_size = intermediate_size
self.patch_size = patch_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.layer_norm_eps = layer_norm_eps
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.hidden_act = hidden_act
super().__init__(**kwargs)
class IdeficsPerceiverConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics 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 Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_resampler (`bool`, *optional*, defaults to `False`):
Whether or not to use the resampler
resampler_n_latents (`int`, *optional*, defaults to ):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
resampler_depth (`int`, *optional*, defaults to 6):
Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
resampler_n_heads (`int`, *optional*, defaults to 16):
Number of heads in each Transformer block (for multi-headed self-attention).
resampler_head_dim (`int`, *optional*, defaults to 96):
Dimensionality of each head projection in the Transformer block.
qk_layer_norms_perceiver (`bool`, *optional*, defaults to `False`):
Whether or not to use qk layer norms in perceiver
"""
model_type = "idefics"
def __init__(
self,
use_resampler=False,
resampler_n_latents=64,
resampler_depth=6,
resampler_n_heads=16,
resampler_head_dim=96,
qk_layer_norms_perceiver=False,
**kwargs,
):
self.use_resampler = use_resampler
self.resampler_n_latents = resampler_n_latents
self.resampler_depth = resampler_depth
self.resampler_n_heads = resampler_n_heads
self.resampler_head_dim = resampler_head_dim
self.qk_layer_norms_perceiver = qk_layer_norms_perceiver
super().__init__(**kwargs)
class IdeficsConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics 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 Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
additional_vocab_size (`int`, *optional`, defaults to 0):
Additional vocabulary size of the model, typically for the special "<img>" token. Additional vocab tokens
are always trainable whereas regular vocab tokens can be frozen or not.
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Idefics model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~IdeficsModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
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.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
alpha_initializer (`str`, *optional*, defaults to `"zeros"`):
Initialization type for the alphas.
alphas_initializer_range (`float`, *optional*, defaults to 0.0):
The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross
Attention.
alpha_type (`str`, *optional*, defaults to `"float"`):
Whether the gating alphas should be vectors or single floats.
rms_norm_eps (`float`, *optional*, defaults to 1e-6):
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*, defaults to 0)
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1)
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2)
End of stream token id.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
cross_layer_interval (`int`, *optional*, default to 1)
Interval for cross attention (from text to image) layers.
qk_layer_norms (`bool`, *optional*, defaults to `False`): Whether to add layer norm after q and k
freeze_text_layers (`bool`, *optional*, defaults to `True`): Whether to freeze text layers
freeze_text_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing text layers when `freeze_text_layers` is `True`
freeze_lm_head (`bool`, *optional*, defaults to `False`): Whether to freeze lm head
freeze_vision_layers (`bool`, *optional*, defaults to `True`): Whether to freeze vision layers
freeze_vision_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing vision layers when `freeze_vision_layers` is `True`
use_resampler (`bool`, *optional*, defaults to `False`): Whether to use the Resampler
vision_config (`IdeficsVisionConfig`, *optional*): Custom vision config or dict
perceiver_config (`IdeficsPerceiverConfig`, *optional*): Custom perceiver config or dict
Example:
```python
>>> from transformers import IdeficsModel, IdeficsConfig
>>> # Initializing a Idefics idefics-9b style configuration
>>> configuration = IdeficsConfig()
>>> # Initializing a model from the idefics-9b style configuration
>>> model = IdeficsModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "idefics"
is_composition = True
def __init__(
self,
vocab_size=32000,
additional_vocab_size=0,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
dropout=0.0,
hidden_act="silu",
initializer_range=0.02,
alpha_initializer="zeros",
alphas_initializer_range=0.0,
alpha_type="float",
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
cross_layer_interval=1,
qk_layer_norms=False,
freeze_text_layers=True,
freeze_text_module_exceptions=[],
freeze_lm_head=False,
freeze_vision_layers=True,
freeze_vision_module_exceptions=[],
use_resampler=False,
vision_config=None,
perceiver_config=None,
**kwargs,
):
self.vocab_size = vocab_size
self.additional_vocab_size = additional_vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.dropout = dropout
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.alpha_initializer = alpha_initializer
self.alphas_initializer_range = alphas_initializer_range
self.alpha_type = alpha_type
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.cross_layer_interval = cross_layer_interval
self.qk_layer_norms = qk_layer_norms
self.freeze_vision_layers = freeze_vision_layers
self.freeze_text_layers = freeze_text_layers
self.freeze_text_module_exceptions = freeze_text_module_exceptions
self.freeze_vision_module_exceptions = freeze_vision_module_exceptions
self.freeze_lm_head = freeze_lm_head
self.use_resampler = use_resampler
if perceiver_config is None:
self.perceiver_config = IdeficsPerceiverConfig()
elif isinstance(perceiver_config, dict):
self.perceiver_config = IdeficsPerceiverConfig(**perceiver_config)
elif isinstance(perceiver_config, IdeficsPerceiverConfig):
self.perceiver_config = perceiver_config
if vision_config is None:
self.vision_config = IdeficsVisionConfig()
elif isinstance(vision_config, dict):
self.vision_config = IdeficsVisionConfig(**vision_config)
elif isinstance(vision_config, IdeficsVisionConfig):
self.vision_config = vision_config
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,
)
# IMPORTANT: Do not do any __init__ args-based checks in the constructor, since
# PretrainedConfig.from_dict first instantiates the class with the config dict and only then
# updates the config object with `kwargs` from from_pretrained, so during the instantiation
# of this object many attributes have default values and haven't yet been overridden.
# Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run.
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
output["vision_config"] = self.vision_config.to_dict()
output["perceiver_config"] = self.perceiver_config.to_dict()
output["model_type"] = self.__class__.model_type
return output
server/text_generation_server/models/custom_modeling/idefics_image_processing.py 0 → 100644
View file @ bce5e224
# coding=utf-8
# Copyright 2022 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.
"""Image processor class for Idefics."""
from typing import Callable, Dict, List, Optional, Union, Iterable
import numpy as np
from PIL import Image
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_transforms import resize, to_channel_dimension_format, rescale, normalize
from transformers.image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from io import BytesIO
import requests
from transformers import TensorType, is_torch_available
IDEFICS_STANDARD_MEAN = [0.48145466, 0.4578275, 0.40821073]
IDEFICS_STANDARD_STD = [0.26862954, 0.26130258, 0.27577711]
def convert_to_rgb(image):
# `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
# for transparent images. The call to `alpha_composite` handles this case
if image.mode == "RGB":
return image
image_rgba = image.convert("RGBA")
background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image_rgba)
alpha_composite = alpha_composite.convert("RGB")
return alpha_composite
class IdeficsImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics image processor.
Args:
image_size (`int`, *optional*, defaults to `224`):
Resize to image size
image_num_channels (`int`, *optional*, defaults to `3`):
Number of image channels.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
image_size: int = 224,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
image_num_channels: Optional[int] = 3,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.image_size = image_size
self.image_num_channels = image_num_channels
self.image_mean = image_mean
self.image_std = image_std
def preprocess(
self,
images: ImageInput,
image_num_channels: Optional[int] = 3,
image_size: Optional[Dict[str, int]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
transform: Callable = None,
**kwargs,
) -> TensorType.PYTORCH:
"""
Preprocess a batch of images.
Args:
images (`ImageInput`):
A list of images to preprocess.
image_size (`int`, *optional*, defaults to `self.image_size`):
Resize to image size
image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`):
Number of image channels.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can
be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess`
method. Can be overridden by the `image_std` parameter in the `preprocess` method.
transform (`Callable`, *optional*, defaults to `None`):
A custom transform function that accepts a single image can be passed for training. For example,
`torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is
assumed - and then a preset of inference-specific transforms will be applied to the images
Returns:
a PyTorch tensor of the processed images
"""
image_size = image_size if image_size is not None else self.image_size
image_num_channels = image_num_channels if image_num_channels is not None else self.image_num_channels
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
size = (image_size, image_size)
if len(images) == 0:
return []
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
# For training a user needs to pass their own set of transforms as a Callable.
# For reference this is what was used in the original IDEFICS training:
# transform = transforms.Compose([
# convert_to_rgb,
# transforms.RandomResizedCrop((size, size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),
# transforms.ToTensor(),
# transforms.Normalize(mean=image_mean, std=image_std),
# ])
if transform is not None:
if not is_torch_available():
raise ImportError("To pass in `transform` torch must be installed")
import torch
images = [transform(x) for x in images]
return torch.stack(images)
# for inference we do the exact transforms that were used to train IDEFICS
images = [convert_to_rgb(x) for x in images]
# further transforms expect numpy arrays
images = [to_numpy_array(x) for x in images]
images = [resize(x, size, resample=PILImageResampling.BICUBIC) for x in images]
images = [self.rescale(image=image, scale=1 / 255) for image in images]
images = [self.normalize(x, mean=image_mean, std=image_std) for x in images]
images = [to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images]
# TODO: this converts to torch tensors - switch to convert_to_tensors once it becomes available
images = BatchFeature(data={"pixel_values": images}, tensor_type=TensorType.PYTORCH)["pixel_values"]
return images
def fetch_images(self, image_url_or_urls: Union[str, List[str]]):
"""
Convert a single or a list of urls into the corresponding `PIL.Image` objects.
If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
returned.
"""
headers = {
"User-Agent": (
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0"
" Safari/537.36"
)
}
if isinstance(image_url_or_urls, list):
return [self.fetch_images(x) for x in image_url_or_urls]
elif isinstance(image_url_or_urls, str):
response = requests.get(image_url_or_urls, stream=True, headers=headers)
response.raise_for_status()
return Image.open(BytesIO(response.content))
else:
raise ValueError(f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}")
def rescale(
self,
image: np.ndarray,
scale: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Rescale an image by a scale factor. image = image * scale.
Args:
image (`np.ndarray`):
Image to rescale.
scale (`float`):
The scaling factor to rescale pixel values by.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The rescaled image.
"""
# return rescale(image, scale=scale, data_format=data_format, input_data_format=input_data_format, **kwargs)
# requires 4.32
return rescale(image, scale=scale, data_format=data_format, **kwargs)
def normalize(
self,
image: np.ndarray,
mean: Union[float, Iterable[float]],
std: Union[float, Iterable[float]],
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Normalize an image. image = (image - image_mean) / image_std.
Args:
image (`np.ndarray`):
Image to normalize.
mean (`float` or `Iterable[float]`):
Image mean to use for normalization.
std (`float` or `Iterable[float]`):
Image standard deviation to use for normalization.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The normalized image.
"""
# TODO 4.32
return normalize(
image, mean=mean, std=std, data_format=data_format, **kwargs
)
import transformers
transformers.IdeficsImageProcessor = IdeficsImageProcessor
server/text_generation_server/models/custom_modeling/idefics_modeling.py 0 → 100644
View file @ bce5e224
# coding=utf-8
# Copyright 2022 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.
""" PyTorch Idefics model."""
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, dataclass
from transformers.modeling_utils import PretrainedConfig
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
from text_generation_server.models.custom_modeling.idefics_vision import IdeficsVisionTransformer
from text_generation_server.models.custom_modeling.idefics_perceiver import IdeficsPerceiverResampler
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
TensorParallelHead,
PositionRotaryEmbedding,
FastLinear,
)
import dropout_layer_norm
@dataclass
class BaseModelOutputWithPastImage(BaseModelOutputWithPast):
image_hidden_states: Optional[torch.FloatTensor] = None
@dataclass
class CausalLMOutputWithPastImage(CausalLMOutputWithPast):
image_hidden_states: Optional[torch.FloatTensor] = None
# logger = logging.get_logger(__name__)
# _CONFIG_FOR_DOC = "IdeficsConfig"
# IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST = [
# "HuggingFaceM4/idefics-9b",
# "HuggingFaceM4/idefics-80b",
# # See all Idefics models at https://huggingface.co/models?filter=idefics
# ]
def expand_inputs_for_generation(
input_ids,
expand_size=1,
is_encoder_decoder=False,
attention_mask=None,
encoder_outputs=None,
**model_kwargs,
):
expanded_return_idx = (
torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device)
)
input_ids = input_ids.index_select(0, expanded_return_idx)
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = token_type_ids.index_select(0, expanded_return_idx)
if attention_mask is not None:
model_kwargs["attention_mask"] = attention_mask.index_select(0, expanded_return_idx)
model_kwargs["image_attention_mask"] = model_kwargs["image_attention_mask"].index_select(
0, expanded_return_idx
)
model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(0, expanded_return_idx)
if is_encoder_decoder:
if encoder_outputs is None:
raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
0, expanded_return_idx.to(encoder_outputs.last_hidden_state.device)
)
model_kwargs["encoder_outputs"] = encoder_outputs
return input_ids, model_kwargs
def update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False):
# must have this key set to at least None
model_kwargs["past_key_values"] = model_kwargs.get("past_key_values", None)
# update past
if "past_key_values" in outputs:
model_kwargs["past"] = outputs.past_key_values
elif "mems" in outputs:
model_kwargs["past"] = outputs.mems
elif "past_buckets_states" in outputs:
model_kwargs["past"] = outputs.past_buckets_states
else:
model_kwargs["past"] = None
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
# update attention masks
if not is_encoder_decoder:
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
)
if "image_attention_mask" in model_kwargs:
image_attention_mask = model_kwargs["image_attention_mask"]
last_mask = image_attention_mask[:, -1, :].unsqueeze(1)
model_kwargs["image_attention_mask"] = last_mask
return model_kwargs
def prepare_inputs_for_generation(input_ids, past=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past:
input_ids = input_ids[:, -1].unsqueeze(-1)
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past:
position_ids = position_ids[:, -1].unsqueeze(-1)
pixel_values = kwargs.get("pixel_values", None)
image_attention_mask = kwargs.get("image_attention_mask", None)
# if pixel_values is None or image_attention_mask is None:
# raise ValueError("pixel values and image attention mask cannot be None")
return {
"input_ids": input_ids,
"past_key_values": past,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"pixel_values": pixel_values,
"image_attention_mask": image_attention_mask,
}
def freeze_model(model, module_exceptions=[]):
mapping = {
"LayerNorm": nn.LayerNorm,
"Linear": nn.Linear,
"Embedding": nn.Embedding,
}
module_exceptions_mapped = [mapping[m] for m in module_exceptions]
for module in model.modules():
if module_exceptions and any([isinstance(module, t) for t in module_exceptions_mapped]):
module.requires_grad_(True) # Explicitely setting it to true to avoid any mistakes
else:
module.requires_grad_(False)
return model
class IdeficsDecoupledPartialTPEmbedding(nn.Module):
def __init__(
self,
config,
weights,
):
super().__init__()
self.num_embeddings = config.vocab_size
self.weight = TensorParallelEmbedding(prefix="model.embed_tokens", weights=weights)
self.additional_weight = nn.Parameter(weights.get_tensor(f"model.embed_tokens.additional_embedding.weight"))
def forward(self, input_ids):
# Clone so that we don't modify the original input_ids later on
input_ids = input_ids.clone()
additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
input_ids_additional_vocab = input_ids[additional_vocab_indices]
additional_embeddings = torch.nn.functional.embedding(input_ids_additional_vocab - self.num_embeddings, self.additional_weight)
# for successful lookup replace input_ids with 0, the results of these will be discarded anyway
input_ids[additional_vocab_indices] = 0
full_vector = self.weight(input_ids)
# overwrite the records with high indices
full_vector[additional_vocab_indices] = additional_embeddings
return full_vector
class IdeficsDecoupledTensorParallelLinear(nn.Module):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0,
then it will create `out_additional_features * in_features` additional parameters that are always trained. If
`out_additional_features=0`, then the module defaults back to the regular behavior of `nn.Linear`.
"""
def __init__(
self,
config,
weights,
) -> None:
super().__init__()
self.fc = TensorParallelHead.load(
config=config, prefix="lm_head", weights=weights
)
self.additional_fc = FastLinear.load(
config=config, prefix="lm_head.additional_fc", weights=weights, bias=False,
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
output = self.fc(input)
additional_features = self.additional_fc(input)
output = torch.cat((output, additional_features), -1)
return output
def extra_repr(self) -> str:
"""Overwriting `nn.Linear.extra_repr` to include new parameters."""
return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format(
self.in_features,
self.out_features,
self.out_additional_features,
self.bias is not None,
self.partially_freeze,
)
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class IdeficsRMSNorm(nn.Module):
def __init__(self, prefix, weights, eps=1e-6):
"""
LlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
weight = weights.get_tensor(f"{prefix}.weight")
self.weight = nn.Parameter(weight)
self.variance_epsilon = eps
def forward(self, hidden_states, residual=None):
if hidden_states.shape[-1] > 8192:
if residual is not None:
hidden_states += residual
residual = hidden_states
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(
variance + self.variance_epsilon
)
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
else:
# faster post attention rms norm
unwrap = False
if len(hidden_states.shape) > 2:
unwrap = True
shape = hidden_states.shape
hidden_states = hidden_states.reshape(-1, shape[-1])
normed_hidden_states, res, *rest = dropout_layer_norm.dropout_add_ln_fwd(
hidden_states,
residual,
self.weight,
None,
None,
None,
None,
None,
0.0,
self.variance_epsilon,
1.0,
0,
None,
False,
True, # Activate RMSNorm
)
if res is None:
res = hidden_states
if unwrap:
normed_hidden_states = normed_hidden_states.view(*shape)
return normed_hidden_states
# this was adapted from LlamaMLP
class IdeficsMLP(nn.Module):
def __init__(
self,
config,
prefix,
weights,
):
super().__init__()
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.down_proj", weights=weights, bias=False,
)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
gate_up_states = self.gate_up_proj(hidden_states)
shape = gate_up_states.shape
gate_up_states = gate_up_states.view(*shape[:-1], 2, shape[-1] // 2)
return self.down_proj(self.act_fn(gate_up_states[:, :, 0]) * gate_up_states[:, :, 1])
# this was adapted from LlamaAttention
class IdeficsAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config,
prefix,
weights,
qk_layer_norms: bool = False,
is_cross_attention: bool = False,
):
super().__init__()
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.dropout = config.dropout
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.is_cross_attention = is_cross_attention
# if not hasattr(nn.functional, "scaled_dot_product_attention"):
# raise ValueError("this model requires pytorch 2.0 or higher")
process_group = weights.process_group
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads //= weights.process_group.size()
if self.is_cross_attention:
# kv_input_dim = (
# self.hidden_size if not hasattr(config.vision_config, "embed_dim") else config.vision_config.embed_dim
# )
self.q_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.q_proj", weights=weights, bias=False
)
self.k_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.k_proj", weights=weights, bias=False
)
self.v_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.v_proj", weights=weights, bias=False
)
else:
self.qkv = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
self.o_proj = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.o_proj", weights=weights, bias=False
)
self.rotary_emb = PositionRotaryEmbedding.static(
config=config, dim=self.head_dim, base=10000.0, device=weights.device
)
self.qk_layer_norms = qk_layer_norms
if self.qk_layer_norms:
self.q_layer_norm = IdeficsRMSNorm(
prefix=f"{prefix}.q_layer_norm", weights=weights, eps=config.rms_norm_eps
)
self.k_layer_norm = IdeficsRMSNorm(
prefix=f"{prefix}.q_layer_norm", weights=weights, eps=config.rms_norm_eps
)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# if key_value_states are provided this layer is used as a cross-attention layer
is_cross_attention = self.is_cross_attention or key_value_states is not None
bsz, q_len, _ = hidden_states.size()
if is_cross_attention:
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)# .transpose(1, 2)
query_states = query_states.transpose(1, 2)
_, kv_len, _ = key_value_states.size() # Note that, in this case, `kv_len` == `kv_seq_len`
key_states = self.k_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = (
self.v_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
)
else:
qkv = self.qkv(hidden_states)
query_states, key_states, value_states = qkv.split(self.num_heads * self.head_dim, dim=2)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)# .transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim)# . transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim)# .transpose(1, 2)
kv_seq_len = q_len
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
max_s = max(kv_seq_len, q_len)
cos, sin = self.rotary_emb.get_cos_sin(
position_ids.view(-1), max_s, hidden_states.dtype
)
shape = query_states.shape
query_states = self.rotary_emb(query_states.view(-1, *shape[2:]), cos, sin).view(shape)
shape = key_states.shape
key_states = self.rotary_emb(key_states.reshape(-1, *shape[2:]), cos, sin).view(shape)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
if self.qk_layer_norms:
query_states = self.q_layer_norm(query_states)
key_states = self.k_layer_norm(key_states)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_output = nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.dropout,
)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, -1)
attn_output = self.o_proj(attn_output)
attn_weights = None
if output_attentions:
logger.warning_once(
"attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead"
)
return attn_output, attn_weights, past_key_value
# this was adapted from LlamaDecoderLayer
class IdeficsDecoderLayer(nn.Module):
def __init__(self, layer_id: int, config: IdeficsConfig, weights):
super().__init__()
self.process_group = weights.process_group
self.hidden_size = config.hidden_size
prefix = f"model.layers.{layer_id}"
self.self_attn = IdeficsAttention(
config=config,
prefix=f"{prefix}.self_attn",
weights=weights,
qk_layer_norms=False,
is_cross_attention=False,
)
self.mlp = IdeficsMLP(
config=config,
prefix=f"{prefix}.mlp",
weights=weights,
)
self.input_layernorm = IdeficsRMSNorm(prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps)
self.post_attention_layernorm = IdeficsRMSNorm(prefix=f"{prefix}.post_attention_layernorm", weights=weights, eps=config.rms_norm_eps)
self.dropout = config.dropout
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
# hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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 = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class IdeficsGatedCrossAttentionLayer(nn.Module):
def __init__(self, layer_id, config: IdeficsConfig, weights):
super().__init__()
self.process_group = weights.process_group
self.hidden_size = config.hidden_size
prefix = f"model.gated_cross_attn_layers.{layer_id}"
self.cross_attn = IdeficsAttention(
config=config,
prefix=f"{prefix}.cross_attn",
weights=weights,
qk_layer_norms=True,
is_cross_attention=True,
)
self.mlp = IdeficsMLP(
config=config,
prefix=f"{prefix}.mlp",
weights=weights,
)
self.input_layernorm = IdeficsRMSNorm(prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps)
self.post_attention_layernorm = IdeficsRMSNorm(prefix=f"{prefix}.post_attention_layernorm", weights=weights, eps=config.rms_norm_eps)
self.config = config.dropout
self.act_cross_attn = nn.Tanh()
self.act_dense = nn.Tanh()
self.alpha_cross_attn = nn.Parameter(weights.get_tensor(f"{prefix}.alpha_cross_attn"))
self.alpha_dense = nn.Parameter(weights.get_tensor(f"{prefix}.alpha_dense"))
if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")):
raise ValueError("Alpha parameters not initialized correctly!")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_hidden_states: Optional[torch.Tensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
no_images: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
no_images (`bool`, *optional*, defaults to `False`): If `True` the vision part is ignored
"""
if image_hidden_states is None:
raise ValueError(
"`image_hidden_states` is required for Idefics cross attention module which are visual features to be"
" conditioned on."
)
if past_key_value is not None:
raise NotImplementedError("Past key value states are not implemented for Idefics cross attention module.")
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.cross_attn(
hidden_states=hidden_states,
key_value_states=image_hidden_states,
attention_mask=image_attention_mask,
output_attentions=output_attentions,
)
# hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
# when there are no images the model is used in pure language mode
gate = 0 if no_images else 1
hidden_states = residual + gate * self.act_cross_attn(self.alpha_cross_attn) * hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
# hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
LLAMA_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`IdeficsConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
# @add_start_docstrings(
# "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
# LLAMA_START_DOCSTRING,
# )
class IdeficsPreTrainedModel(PreTrainedModel):
config_class = IdeficsConfig
# base_model_prefix = "model"
# supports_gradient_checkpointing = True
# _no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"]
# def _init_weights(self, module):
# # important: this ported version of Idefics isn't meant for training from scratch - only
# # inference and fine-tuning - so the proper init weights code has been removed - the m4 code
# # base should be used for training from scratch and it contains the correct code.
# std = self.config.initializer_range
# if isinstance(module, nn.Linear):
# module.weight.data.normal_(mean=0.0, std=std)
# if module.bias is not None:
# module.bias.data.zero_()
# elif isinstance(module, nn.Embedding):
# module.weight.data.normal_(mean=0.0, std=std)
# if module.padding_idx is not None:
# module.weight.data[module.padding_idx].zero_()
# def _set_gradient_checkpointing(self, module, value=False):
# if isinstance(module, IdeficsModel):
# module.gradient_checkpointing = value
# LLAMA_INPUTS_DOCSTRING = r"""
# Args:
# input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
# Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
# it.
# Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
# [`PreTrainedTokenizer.__call__`] for details.
# [What are input IDs?](../glossary#input-ids)
# attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
# Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
# - 1 for tokens that are **not masked**,
# - 0 for tokens that are **masked**.
# [What are attention masks?](../glossary#attention-mask)
# Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
# [`PreTrainedTokenizer.__call__`] for details.
# If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
# `past_key_values`).
# If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
# and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
# information on the default strategy.
# - 1 indicates the head is **not masked**,
# - 0 indicates the head is **masked**.
# position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
# Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
# config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
# past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
# Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
# `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
# `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
# Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
# blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
# If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
# don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
# `decoder_input_ids` of shape `(batch_size, sequence_length)`.
# inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
# Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
# is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
# model's internal embedding lookup matrix.
# use_cache (`bool`, *optional*):
# If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
# `past_key_values`).
# output_attentions (`bool`, *optional*):
# Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
# tensors for more detail.
# output_hidden_states (`bool`, *optional*):
# Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
# more detail.
# return_dict (`bool`, *optional*):
# Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
# """
# @add_start_docstrings(
# "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
# LLAMA_START_DOCSTRING,
# )
class IdeficsModel(IdeficsPreTrainedModel):
# """
# Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`]
# Args:
# config: IdeficsConfig
# """
def __init__(self, config: IdeficsConfig, weights):
super().__init__(config)
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = IdeficsDecoupledPartialTPEmbedding(
config=config,
weights=weights,
)
self.image_size = config.vision_config.image_size
self.vision_config = config.vision_config
self.vision_model = IdeficsVisionTransformer(
prefix="model.vision_model",
config=config.vision_config,
weights=weights,
)
# Perceiver Resampler
if config.use_resampler:
perceiver_config = config.perceiver_config
self.perceiver_resampler = IdeficsPerceiverResampler(
prefix=f"model.perceiver_resampler",
config=config,
embed_dim=config.vision_config.embed_dim,
depth=perceiver_config.resampler_depth,
n_heads=perceiver_config.resampler_n_heads,
head_dim=perceiver_config.resampler_head_dim,
n_latents=perceiver_config.resampler_n_latents,
weights=weights,
)
self.layers = nn.ModuleList(
[
IdeficsDecoderLayer(layer_id, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
self.cross_layer_interval = config.cross_layer_interval
num_cross_layers = config.num_hidden_layers // self.cross_layer_interval
self.gated_cross_attn_layers = nn.ModuleList(
[
IdeficsGatedCrossAttentionLayer(layer_id, config, weights)
for layer_id in range(num_cross_layers)]
)
# self.gradient_checkpointing = False
self.norm = IdeficsRMSNorm(prefix=f"model.norm", weights=weights, eps=config.rms_norm_eps)
# self.gradient_checkpointing = False
# Initialize weights and apply final processing
# self.post_init()
# self.freeze_relevant_params(config)
# def freeze_relevant_params(self, config=None):
# if config is None:
# config = self.config
# if config.freeze_text_layers:
# self.freeze_text_layers(config.freeze_text_module_exceptions)
# if config.freeze_vision_layers:
# freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions)
# def freeze_text_layers(self, module_exceptions=[]):
# for module in [self.layers, self.norm]:
# freeze_model(module, module_exceptions=module_exceptions)
# def freeze_vision_layers(self, module_exceptions=[]):
# freeze_model(self.vision_model, module_exceptions=module_exceptions)
# def get_input_embeddings(self):
# return self.embed_tokens
# def set_input_embeddings(self, value):
# self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
# @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
image_embeddings: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastImage]:
device = input_ids.device if input_ids is not None else inputs_embeds.device
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
elif position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
no_images = False
if image_hidden_states is None:
if pixel_values is None and image_embeddings is None:
raise ValueError("Either pixel_values and image_embeddings have to be not-None.")
elif pixel_values is not None and image_embeddings is not None:
raise ValueError("You cannot specify both pixel_values and image_embeddings at the same time")
elif pixel_values is not None:
no_images = len(torch.nonzero(pixel_values)) == 0
pixel_values = pixel_values.to(dtype=self.dtype, device=device) # fp16 compatibility
batch_size, num_images = pixel_values.shape[:2]
pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:])
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(pixel_values=pixel_values).last_hidden_state
elif image_embeddings is not None:
batch_size, num_images, image_seq_len, image_hidden_size = image_embeddings.size()
image_hidden_states = image_embeddings.to(dtype=self.dtype, device=input_ids.device)
image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size)
if self.config.use_resampler:
image_hidden_states = self.perceiver_resampler(image_hidden_states)
image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2)
image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size)
else:
no_images = False
num_images = pixel_values.shape[1]
image_seq_len = image_hidden_states.shape[1] // num_images
# # Hack to use the model in full language modeling mode
# image_attention_mask = torch.zeros(batch_size, seq_length, 1, dtype=torch.long, device=image_hidden_states.device)
# Make image_attention_mask compatible with hidden states
text_seq_len = image_attention_mask.size(1)
image_attention_mask = image_attention_mask.unsqueeze(-1)
image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len)
image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len)
image_batch_size, image_sequence_length, _ = image_hidden_states.size()
image_hidden_shape = (image_batch_size, image_sequence_length)
if image_attention_mask is None:
image_attention_mask = torch.ones(image_hidden_shape, device=device)
image_attention_mask = self.invert_attention_mask(image_attention_mask)
# if list(image_attention_mask.shape) != [4, 1, 1024, 64]:
# raise ValueError(f"Image hidden_states {image_hidden_states.shape} - mask {image_attention_mask.shape} {num_images} {image_seq_len} {text_seq_len}")
# if image_hidden_states is not None:
# else:
# image_attention_mask = None
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
hidden_states = inputs_embeds
# if self.gradient_checkpointing and self.training:
# if use_cache:
# logger.warning_once(
# "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
# )
# use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
def vblock(
main_block,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
output_attentions,
use_cache,
no_images,
layer_idx,
cross_layer_interval,
gated_cross_attn_layers,
):
# TODO(ls): Add cross attention values to respective lists
if layer_idx % cross_layer_interval == 0:
xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
outputs = xblock(
hidden_states,
attention_mask=attention_mask,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
output_attentions=output_attentions,
use_cache=use_cache,
past_key_value=None, # not implemented
no_images=no_images,
)
hidden_states = outputs[0]
layer_outputs = main_block(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
return layer_outputs
# if self.gradient_checkpointing and self.training:
# past_key_value = None
# if use_cache:
# logger.warning_once(
# "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
# )
# use_cache = False
# layer_outputs = torch.utils.checkpoint.checkpoint(
# vblock,
# decoder_layer,
# hidden_states,
# attention_mask,
# position_ids,
# past_key_value,
# image_hidden_states,
# image_attention_mask,
# output_attentions,
# use_cache,
# no_images,
# idx,
# self.cross_layer_interval,
# self.gated_cross_attn_layers,
# )
# else:
layer_outputs = vblock(
decoder_layer,
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
output_attentions=output_attentions,
use_cache=use_cache,
no_images=no_images,
layer_idx=idx,
cross_layer_interval=self.cross_layer_interval,
gated_cross_attn_layers=self.gated_cross_attn_layers,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPastImage(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
image_hidden_states=image_hidden_states,
)
class IdeficsForVisionText2Text(IdeficsPreTrainedModel):
def __init__(
self,
config,
weights,
):
super().__init__(config)
self.model = IdeficsModel(
config=config,
weights=weights,
)
self.lm_head = IdeficsDecoupledTensorParallelLinear(
config=config,
weights=weights,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_embeddings: Optional[torch.FloatTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPastImage]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, LlamaForCausalLM
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
image_embeddings=image_embeddings,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
return CausalLMOutputWithPastImage(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=outputs.image_hidden_states
)
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs)
unwanted_kwargs = ["token_type_ids"]
for kwarg in unwanted_kwargs:
inputs.pop(kwarg, None)
return inputs
@staticmethod
def _expand_inputs_for_generation(
*args,
**model_kwargs,
):
return expand_inputs_for_generation(*args, **model_kwargs)
@staticmethod
def _update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False):
return update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
server/text_generation_server/models/custom_modeling/idefics_perceiver.py 0 → 100644
View file @ bce5e224
# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License.
#
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Generic interface to various configurations of the Perceiver Resampler, that simply takes in a series of (potentially
time-indexed) contextual embeddings, and "resamples" (compresses) them down to a pre-specified number of latents! Note
that the Perceiver in general resamples based solely off the *long-range* context; there's a nice opportunity here to
prime the Perceiver Resampler with say a single layer's worth of language embeddings (the target domain), and use that
to softly "retrieve & compress" what we need --> this would be a novel contribution we should explore.
References:
- DeepMind's Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
- Code borrowed w/ love from: https://github.com/lucidrains/flamingo-pytorch
"""
from typing import Optional, Tuple
import torch
import torch.nn as nn
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelRowLinear,
)
EPS=1e-5
class IdeficsPerceiverResampler(nn.Module):
def __init__(
self,
prefix,
config,
embed_dim: int,
depth: int,
n_heads: int,
head_dim: int,
n_latents: int,
weights,
) -> None:
"""
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or
MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then
returns a Tensor of shape [bsz, n_latents, embed_dim]. :param embed_dim: Dimensionality of embeddings being fed
to the Perceiver Resampler (also dimensionality of latent embeddings *returned* by the Perceiver Resampler.
Could be e.g., VIT embed_dim, ResNet pool dim, and so on.
Args:
config (`IdeficsConfig`): config object
embed_dim (`int`): The size of each embedding vector
depth (`int`): Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
n_heads (`int`): Number of heads in each Transformer block (for multi-headed self-attention).
head_dim (`int`): Dimensionality of each head projection in the Transformer block.
n_latents (`int`):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim, self.n_latents = embed_dim, n_heads, head_dim, n_latents
self.qk_layer_norms = config.perceiver_config.qk_layer_norms_perceiver
# Create Latents for Perceiver
self.latents = nn.Parameter(weights.get_tensor(f"{prefix}.latents"))
self.intermediate_dim = (
self.embed_dim * 4
if not hasattr(config.vision_config, "embed_dim")
else config.vision_config.embed_dim * 4
)
# Create Transformer Blocks
self.blocks = nn.ModuleList(
[
nn.ModuleList(
[
IdeficsPerceiverAttention(
prefix=f"{prefix}.blocks.{layer_id}.0",
config=config,
embed_dim=self.embed_dim,
n_heads=self.n_heads,
head_dim=self.head_dim,
qk_layer_norms=self.qk_layer_norms,
weights=weights,
),
IdeficsMLP(
prefix=f"{prefix}.blocks.{layer_id}.1",
intermediate_size=self.intermediate_dim,
config=config,
weights=weights
),
]
)
for layer_id in range(depth)
]
)
self.layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.layer_norm", weights=weights, eps=EPS)
def forward(self, context: torch.Tensor) -> torch.Tensor:
"""Resample arbitrary length context & *compress* down to self.n_latents latent embeddings"""
# einsum.repeat(self.latents, "seq embed -> bsz seq embed", bsz=context.shape[0])
latents = self.latents.repeat(context.shape[0], 1, 1)
# Feed through Perceiver Attention blocks...
for attn, ff in self.blocks:
latents = attn(context, latents) + latents
latents = ff(latents) + latents
return self.layer_norm(latents)
class IdeficsPerceiverAttention(nn.Module):
def __init__(self,
prefix,
config,
embed_dim: int,
n_heads: int,
head_dim: int,
qk_layer_norms: bool,
weights
) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim = embed_dim, n_heads, head_dim
self.qk_layer_norms = qk_layer_norms
# Normalization & Scaling
self.context_layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.context_layer_norm", weights=weights, eps=EPS)
self.latents_layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.latents_layer_norm", weights=weights, eps=EPS)
if self.qk_layer_norms:
self.q_layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.q_layer_norm", weights=weights, eps=EPS)
self.k_layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.k_layer_norm", weights=weights, eps=EPS)
self.qk_scale = self.head_dim**-0.5
process_group = weights.process_group
if n_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {n_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.n_heads //= weights.process_group.size()
# Q, K, V Projection (no bias -- detail from Perceiver/Flamingo Papers).
self.q_proj = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.q_proj", weights=weights, bias=False
)
self.k_proj = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.k_proj", weights=weights, bias=False
)
self.v_proj = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.v_proj", weights=weights, bias=False
)
self.output_proj = TensorParallelRowLinear.load(
config=config, prefix=f"{prefix}.output_proj", weights=weights, bias=False
)
def forward(self, context: torch.Tensor, latents: torch.Tensor) -> torch.Tensor:
"""
Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension!
Args:
context (`torch.Tensor`):
Tensor of shape `[bsz, seq, embed_dim]` representing long-form context to resample.
latents (`torch.Tensor`):
Tensor of shape `[bsz, n_latents, embed_dim]` representing fixed length latents to compress to.
Returns:
`torch.Tensor`: Tensor of shape `[bsz, n_latents, embed_dim]` representing attention over latents w/ cross
from context.
"""
context = self.context_layer_norm(context)
latents = self.latents_layer_norm(latents)
batch_size, seq_length, embed_dim = context.shape[:3]
# Query, Key, Value Projections --> Note that in Flamingo, latents are *concatenated* with context prior to attn!
# Note: This results in queries w/ `seq = n_latents`, and keys, values with `seq = len(context) + n_latents`
q = self.q_proj(latents)
k = self.k_proj(torch.cat([context, latents], dim=-2))
v = self.v_proj(torch.cat([context, latents], dim=-2))
# Multiheaded Self-Attention w/ stable softmax (subtract per-row max -- `amax` -- before softmax call)
# =>> `attn` should be a 2D matrix of shape [n_latents x (context + n_latents)]
# einsum.rearrange(x, "bsz seq (heads embed) -> bsz heads seq embed", heads=self.n_heads)
q, k, v = [x.reshape(batch_size, x.shape[1], self.n_heads, self.head_dim).transpose(1, 2) for x in (q, k, v)]
if self.qk_layer_norms:
q = self.q_layer_norm(q)
k = self.k_layer_norm(k)
scores = torch.einsum("... i d, ... j d -> ... i j", q * self.qk_scale, k)
stabilized_scores = scores - (scores.amax(dim=-1, keepdim=True).detach())
attn = stabilized_scores.softmax(dim=-1)
# Attend & project back to output...
resampled = torch.einsum("... i j, ... j d -> ... i d", attn, v)
# einsum.rearrange(resampled, "bsz heads seq embed -> bsz seq (heads embed)", heads=self.n_heads)
return self.output_proj(resampled.transpose(1, 2).flatten(-2))
class IdeficsMLP(nn.Module):
def __init__(self,
prefix,
intermediate_size,
config,
weights,
):
"""Simple MLP block with intermediate_size and embedding size"""
super().__init__()
self.embed_dim = config.vision_config.embed_dim
self.ln = nn.LayerNorm.load(prefix=f"{prefix}.ln", weights=weights, eps=EPS)
self.fc = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.fc", weights=weights, bias=False,
)
self.act = nn.ReLU()
self.c_proj = TensorParallelRowLinear.load(
config=config, prefix=f"{prefix}.c_proj", weights=weights, bias=False,
)
def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
hidden_states = self.ln(hidden_states)
hidden_states = self.fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
return hidden_states
server/text_generation_server/models/custom_modeling/idefics_processing.py 0 → 100644
View file @ bce5e224
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""
Processor class for IDEFICS.
"""
from typing import Callable, List, Optional, Union
from urllib.parse import urlparse
from transformers.feature_extraction_utils import BatchFeature
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
from transformers.utils import TensorType, is_torch_available
from text_generation_server.models.custom_modeling.idefics_image_processing import IdeficsImageProcessor
if is_torch_available():
import torch
IMAGE_TOKEN = "<image>"
# copied from m4.training.packing
def incremental_to_binary_attention_mask(incremental_mask, num_classes=-1):
# This function converts: [-1, 0, 1] => [[0, 0], [1, 0], [0, 1]]
# If any of images index are more than num_classes, set them to -1.
# Words after the max number of images allowed have been seen don't attend on anything
if num_classes != -1:
incremental_mask[incremental_mask >= num_classes] = -1
negatives = incremental_mask == -1
incremental_mask[negatives] = 0
attn_mask = torch.nn.functional.one_hot(incremental_mask, num_classes=num_classes)
attn_mask[negatives, :] = 0
return attn_mask
# copied from m4.training.packing
def image_attention_mask_for_packed_input_ids(input_ids, tokenizer):
image_attention_mask = torch.full_like(input_ids, fill_value=-1)
next_image_attention_mask = torch.full_like(input_ids, fill_value=-1)
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
eod_token_id = tokenizer.eos_token_id
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx, token_id in enumerate(input_ids[batch_idx]):
if token_id == image_token_id:
count += 1
image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
image_attention_mask[batch_idx][idx] = count
if seen_eod:
image_attention_mask[batch_idx][idx] = -1
if token_id == eod_token_id:
seen_eod = True
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx in range(input_ids[batch_idx].size(0) - 1, -1, -1):
token_id = input_ids[batch_idx][idx]
if token_id == image_token_id:
count += 1
next_image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
next_image_attention_mask[batch_idx][idx] = count
if token_id == eod_token_id:
seen_eod = True
if seen_eod:
next_image_attention_mask[batch_idx][idx] = -1
non_negative_indices = next_image_attention_mask[batch_idx] != -1
next_image_attention_mask[batch_idx][non_negative_indices] -= count
next_image_attention_mask[batch_idx][non_negative_indices] *= -1
return image_attention_mask, next_image_attention_mask
def is_url(string):
"""Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
invalidated the url"""
if " " in string:
return False
result = urlparse(string)
return all([result.scheme, result.netloc])
class IdeficsProcessor(ProcessorMixin):
r"""
Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
[`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See
the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
Args:
image_processor (`IdeficsImageProcessor`):
An instance of [`IdeficsImageProcessor`]. The image processor is a required input.
tokenizer (`LlamaTokenizerFast`):
An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image)
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "IdeficsImageProcessor"
tokenizer_class = "LlamaTokenizerFast"
def __init__(self, image_processor, tokenizer=None, image_size=224, add_end_of_utterance_token=None, **kwargs):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(image_processor, tokenizer)
self.current_processor = self.image_processor
self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
self.default_image_dims = (
self.image_processor.image_num_channels,
self.image_processor.image_size,
self.image_processor.image_size,
)
self.tokenizer_was_trained_with_end_of_utterance_token = (
True
if "<end_of_utterance>" in self.tokenizer.special_tokens_map.get("additional_special_tokens", [])
else False
)
def __call__(
self,
prompts: Union[List[TextInput], List[List[TextInput]]],
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
transform: Callable = None,
add_eos_token=False,
add_end_of_utterance_token=None,
debug=False,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchEncoding:
"""This method takes batched or non-batched prompts made of text and images and converts them into prompts that
the model was trained on and prepares the image pixel values for the model to process.
Args:
prompts (`Union[List[TextInput], [List[List[TextInput]]]]`):
either a single prompt or a batched list of prompts - see the detailed description immediately after
the end of the arguments doc section.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
transform (`Callable`, *optional*):
A custom transform function that accepts a single image can be passed for training. For example,
`torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific
set of transforms will be applied to the images
add_eos_token (`bool`, *optional*, defaults to `False`):
Adds `eos_token` at the end of the final prompt if True`
add_end_of_utterance_token (`bool`, *optional*)
Whether to automatically add `<end_of_utterance>` after each prompt's text input (unless followed by an
image). If `None` the tokenizer will be checked instead and if this token is found in
`additional_special_tokens` then the value will be `True`.
debug (`bool`, *optional*, defaults to `False`):
`True` value will help debug prompt generation by dumping useful information
return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
The type of tensors to return. Can be one of:
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
Returns:
a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
directly passed to `model.generate`
Detailed explanation:
Each entry in `prompts` is either a text to be passed as is or an image that will be processed.
An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.
When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
entry into the prompt.
Example:
```python
checkpoint = "HuggingFaceM4/idefics-9b"
processor = AutoProcessor.from_pretrained(checkpoint)
url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
img = processor.image_processor.fetch_images([url])[0]
prompts = [
"User:",
img,
"Describe this image.\nAssistant: An image of two kittens in grass.\n",
"User:",
"https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
"Describe this image.\nAssistant:",
]
inputs = processor(prompts, return_tensors="pt")
generated_ids = model.generate(**inputs, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
In this example the `prompts` will be converted into:
```
<s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant: An image of two kittens in grass.
User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant:'
```
and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the
`pixel_values` dict entry of the return value.
This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
first image is passed as object and the second one as a url.
To do training do:
```python
image_transform = transforms.Compose(
[
transforms.RandomResizedCrop(
(w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
),
transforms.ToTensor(),
transforms.Normalize(mean=self.image_mean, std=self.image_std),
]
)
inputs = processor(prompts, transform=image_transform, return_tensors="pt")
```
In order to help debug prompt generation enable `debug=True` which will show you what's happening.
"""
# if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it
if add_end_of_utterance_token is None:
add_end_of_utterance_token = self.tokenizer_was_trained_with_end_of_utterance_token
# turn non-batched prompts into batched
if not any(isinstance(i, list) for i in prompts):
prompts = [prompts]
fake_token = "<fake_token_around_image>"
image_token = "<image>"
end_of_utterance_token = "<end_of_utterance>"
def image_tokens(last_was_image):
if last_was_image:
return image_token + fake_token
else:
return fake_token + image_token + fake_token
all_texts = []
all_images = []
for sample in prompts:
# the model was trained on samples starting with <s>
full_text = f"{self.tokenizer.bos_token}"
# an image can either be an image object in the item or the url, everything else is a verbatim prompt text
image_objects = []
last_was_image = False
last_was_text = False
for i, item in enumerate(sample):
if i > 0:
last_was_text = True if not last_was_image else False
if isinstance(item, str):
item = item.strip(" ")
if is_url(item):
image = self.image_processor.fetch_images(item)
full_text += image_tokens(last_was_image)
image_objects.append(image)
last_was_image = True
else:
# we add end_of_utterance_token between each subsequent text prompts (but not at the last one!)
if add_end_of_utterance_token and last_was_text:
full_text += end_of_utterance_token
full_text += item
last_was_image = False
else:
# must be an image obj
full_text += image_tokens(last_was_image)
image_objects.append(item)
last_was_image = True
if add_eos_token:
full_text += self.tokenizer.eos_token
if debug is True:
print(f"{full_text=}")
image_objects = self.image_processor(image_objects, transform=transform)
text_encoding = self.tokenizer(
text=full_text,
add_special_tokens=False,
padding=padding,
truncation=truncation,
max_length=max_length,
)
all_texts.append(text_encoding["input_ids"])
all_images.append(image_objects)
max_seq_len = max(len(x) for x in all_texts)
# max_num_images has to be at least 1 even when there are no images
max_num_images = max(len(x) for x in all_images)
max_num_images = max(1, max_num_images)
at_least_one_image = sum(len(x) for x in all_images) > 0
output_input_ids = []
output_images = []
output_attention_masks = []
for text, images in zip(all_texts, all_images):
padded_input_ids = [self.tokenizer.pad_token_id] * max_seq_len
unpadded_seq_len = len(text)
start = max_seq_len - unpadded_seq_len
padded_input_ids[start:] = text[:max_seq_len]
attention_mask = torch.zeros((max_seq_len,), dtype=torch.long)
attention_mask[start:] = 1
image_count = padded_input_ids.count(self.image_token_id)
local_max_num_images = min(image_count, max_num_images)
current_images = images[:local_max_num_images]
if len(current_images) > 0:
padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:])
padded_image_tensor[: current_images.size(0)] = current_images
else:
padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims)
output_images.append(padded_image_tensor)
output_input_ids.append(torch.tensor(padded_input_ids))
output_attention_masks.append(attention_mask)
output_input_ids = torch.stack(output_input_ids)
output_images = torch.stack(output_images)
output_attention_masks = torch.stack(output_attention_masks)
if at_least_one_image:
image_attention_mask, _ = image_attention_mask_for_packed_input_ids(output_input_ids, self.tokenizer)
image_attention_mask = incremental_to_binary_attention_mask(
image_attention_mask, num_classes=max_num_images
)
else:
# in full language mode we set the image mask to all-0s
image_attention_mask = torch.zeros(
output_input_ids.shape[0], output_input_ids.shape[1], 1, dtype=torch.bool
)
return BatchFeature(
data={
"input_ids": output_input_ids,
"attention_mask": output_attention_masks,
"pixel_values": output_images,
"image_attention_mask": image_attention_mask,
}
)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
server/text_generation_server/models/custom_modeling/idefics_vision.py 0 → 100644
View file @ bce5e224
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and The HuggingFace 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 IdeficsVision model: a copy of CLIPVisionModel using a simpler config object"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from transformers.utils import (
ModelOutput,
logging,
)
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelRowLinear,
TensorParallelEmbedding,
)
logger = logging.get_logger(__name__)
@dataclass
class IdeficsVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
The image embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
image_embeds: Optional[torch.FloatTensor] = None
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Idefics
class IdeficsVisionEmbeddings(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(weights.get_tensor(f"{prefix}.class_embedding"))
self.patch_embedding = nn.Conv2d.load_no_bias(
prefix=f"{prefix}.patch_embedding",
weights=weights,
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
# self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.position_embedding = TensorParallelEmbedding(
prefix="model.vision_model.embeddings.position_embedding", weights=weights
)
# self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
self.position_ids = weights.get_tensor(f"{prefix}.position_ids")
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
batch_size = pixel_values.shape[0]
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->IdeficsVision
class IdeficsVisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
process_group = weights.process_group
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.embed_dim = self.embed_dim // weights.process_group.size()
self.k_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.k_proj", weights=weights, bias=True
)
self.v_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.v_proj", weights=weights, bias=True
)
self.q_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.q_proj", weights=weights, bias=True
)
self.out_proj = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.out_proj", weights=weights, bias=True
)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->IdeficsVision
class IdeficsVisionMLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.fc1", weights=weights, bias=True
)
self.fc2 = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.fc2", weights=weights, bias=True
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->IdeficsVision
class IdeficsVisionEncoderLayer(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = IdeficsVisionAttention(prefix=f"{prefix}.self_attn", config=config, weights=weights)
self.layer_norm1 = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm1", weights=weights, eps=config.layer_norm_eps
)
self.mlp = IdeficsVisionMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.layer_norm2 = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm2", weights=weights, eps=config.layer_norm_eps
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->IdeficsVision
class IdeficsVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`IdeficsVisionEncoderLayer`].
Args:
config: IdeficsVisionConfig
"""
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.layers = nn.ModuleList(
[
IdeficsVisionEncoderLayer(prefix=f"{prefix}.encoder.layers.{layer_id}", config=config, weights=weights)
for layer_id in range(config.num_hidden_layers)
]
)
# self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# if self.gradient_checkpointing and self.training:
# def create_custom_forward(module):
# def custom_forward(*inputs):
# return module(*inputs, output_attentions)
# return custom_forward
# layer_outputs = torch.utils.checkpoint.checkpoint(
# create_custom_forward(encoder_layer),
# hidden_states,
# attention_mask,
# causal_attention_mask,
# )
# else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer
class IdeficsVisionTransformer(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = IdeficsVisionEmbeddings(prefix=f"{prefix}.embeddings", config=config, weights=weights)
self.pre_layrnorm = nn.LayerNorm.load(
prefix=f"{prefix}.pre_layrnorm", weights=weights, eps=config.layer_norm_eps
)
self.encoder = IdeficsVisionEncoder(prefix=prefix, config=config, weights=weights)
self.post_layernorm = nn.LayerNorm.load(
prefix=f"{prefix}.post_layernorm", weights=weights, eps=config.layer_norm_eps
)
# copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
server/text_generation_server/models/idefics.py 0 → 100644
View file @ bce5e224
import torch
import torch.distributed
from typing import List, Optional, Tuple
from transformers import (
AutoTokenizer,
AutoConfig,
AutoProcessor,
)
from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
from text_generation_server.models.custom_modeling.idefics_processing import (
IdeficsProcessor,
)
from transformers import LlamaTokenizerFast
from text_generation_server.models.custom_modeling.idefics_modeling import (
IdeficsForVisionText2Text,
)
from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
class IDEFICSSharded(IdeficsCausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
# 9b seems to work correctly enough in float16, but 80b seems
# to be really saturating for f16.
dtype = torch.bfloat16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32
self.device, self.dtype = device, dtype
config = IdeficsConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
config.vision_config.quantize = quantize
tokenizer = LlamaTokenizerFast.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
self.processor = IdeficsProcessor.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device=device,
dtype=dtype,
process_group=self.process_group,
)
model = IdeficsForVisionText2Text(config, weights)
torch.distributed.barrier(group=self.process_group)
super(IdeficsCausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
server/text_generation_server/models/idefics_causal_lm.py 0 → 100644
View file @ bce5e224
import torch
import inspect
import re
from io import BytesIO
import base64
from PIL import Image
import re
from dataclasses import dataclass
from opentelemetry import trace
from transformers import AutoProcessor, AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase, ProcessorMixin
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model
from text_generation_server.models.types import (
Batch,
PrefillTokens,
Generation,
GeneratedText,
)
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
import re
IMAGES = re.compile(r'!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)')
def split(string):
parts = []
cursor = 0
for pattern in IMAGES.finditer(string):
start = pattern.start()
if start != cursor:
parts.append(string[cursor:start])
parts.append(pattern.group(1))
cursor = pattern.end()
if cursor != len(string):
parts.append(string[cursor:])
return parts
tracer = trace.get_tracer(__name__)
@dataclass
class IdeficsCausalLMBatch(Batch):
batch_id: int
requests: List[generate_pb2.Request]
requests_idx_mapping: Dict[int, int]
# Decoder values
input_ids: torch.Tensor
attention_mask: torch.Tensor
position_ids: torch.Tensor
pixel_values: Optional[torch.Tensor]
image_hidden_states: Optional[torch.Tensor]
image_attention_mask: Optional[torch.Tensor]
past_key_values: Optional[List[Tuple]]
# All tokens
all_input_ids: List[torch.Tensor]
# Lengths of all generations present in the batch
input_lengths: List[int]
prefix_offsets: List[int]
read_offsets: List[int]
# Generation helpers
next_token_choosers: List[NextTokenChooser]
stopping_criterias: List[StoppingCriteria]
# Metadata used for padding
max_input_length: int
padding_right_offset: int
# Maximum number of tokens this batch will grow to
max_tokens: int
# Past metadata
keys_head_dim_last: bool = True
def to_pb(self) -> generate_pb2.CachedBatch:
return generate_pb2.CachedBatch(
id=self.batch_id,
request_ids=[r.id for r in self.requests],
size=len(self),
max_tokens=self.max_tokens,
)
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
processor: ProcessorMixin, # Hack
dtype: torch.dtype,
device: torch.device,
) -> "IdeficsCausalLMBatch":
inputs = []
next_token_choosers = []
stopping_criterias = []
prefix_offsets = []
read_offsets = []
requests_idx_mapping = {}
# Parse batch
max_truncation = 0
padding_right_offset = 0
max_decode_tokens = 0
for i, r in enumerate(pb.requests):
requests_idx_mapping[r.id] = i
inputs.append(r.inputs)
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
stopping_criterias.append(stopping_criteria)
max_truncation = max(max_truncation, r.truncate)
max_decode_tokens += stopping_criteria.max_new_tokens
padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens
)
prompts = []
for inp in inputs:
# Each input is encoded into a list, where each element of this input list is either a string or a URL
prompts.append(split(inp))
# The processor replaces the call to tokenizer, and
# a/ takes care of fetching images from the URL
# b/ generate the correct input_ids, attention_mask, pixel_values, image_attention_mask to feed to the model
tokenized_inputs = processor(
prompts,
return_tensors="pt",
padding=True,
truncation=True,
max_length=max_truncation,
add_end_of_utterance_token=False, # Already taken care of inside the prompts, so bypassing the processor's handling of this token
).to(device)
for _ in pb.requests:
input_len = tokenized_inputs["input_ids"].shape[1]
prefix_offsets.append(input_len - 5) # To decode without potential fallbacks errors
read_offsets.append(input_len) # To decode without potential fallbacks errors
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
input_ids = tokenized_inputs["input_ids"]
pixel_values = tokenized_inputs["pixel_values"]
image_hidden_states = None
# Allocate maximum attention_mask
attention_mask = input_ids.new_zeros(
(pb.size, max_input_length + padding_right_offset)
)
# Copy tokenizer attention_mask into fully allocated attention_mask
attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
# Do the same for image_attention_mask
image_attention_mask = input_ids.new_zeros(
(pb.size, max_input_length + padding_right_offset, tokenized_inputs["pixel_values"].size(1))
)
image_attention_mask[:, :max_input_length, :] = tokenized_inputs["image_attention_mask"]
position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1) # It's input_ids but splitted into a tuple of tensors where each tensor is (seq_len, 1) size. It is then transformed into a list
max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
return cls(
batch_id=pb.id,
requests=pb.requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
pixel_values=pixel_values,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
past_key_values=None,
all_input_ids=list(all_input_ids),
input_lengths=input_lengths.tolist(),
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length.item(),
padding_right_offset=padding_right_offset,
max_tokens=max_tokens,
)
@tracer.start_as_current_span("filter")
def filter(self, request_ids: List[int]) -> Optional["IdeficsCausalLMBatch"]:
# It deletes requests from the batch. For instance when client lost connection
if len(request_ids) == 0:
raise ValueError("Batch must have at least one request")
if len(request_ids) == len(self):
return self
keep_indices = []
# New values after filtering
requests_idx_mapping = {}
requests = []
input_lengths = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
max_input_length = 0
next_token_choosers = []
stopping_criterias = []
total_remaining_decode_tokens = 0
new_padding_right_offset = 0
for i, request_id in enumerate(request_ids):
idx = self.requests_idx_mapping[request_id]
requests_idx_mapping[request_id] = i
keep_indices.append(idx)
requests.append(self.requests[idx])
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx])
all_input_ids.append(self.all_input_ids[idx])
request_input_length = self.input_lengths[idx]
input_lengths.append(request_input_length)
max_input_length = max(max_input_length, request_input_length)
next_token_choosers.append(self.next_token_choosers[idx])
stopping_criteria = self.stopping_criterias[idx]
stopping_criterias.append(stopping_criteria)
remaining_decode_tokens = (
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
)
total_remaining_decode_tokens += remaining_decode_tokens
new_padding_right_offset = max(
new_padding_right_offset, remaining_decode_tokens
)
# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
input_ids = self.input_ids[keep_indices]
position_ids = self.position_ids[keep_indices]
self.attention_mask = self.attention_mask[
keep_indices,
-(self.padding_right_offset + max_input_length) : (
self.attention_mask.shape[1] - self.padding_right_offset
)
+ new_padding_right_offset,
]
# Do the same for pixel_values and image_attention_mask
pixel_values = self.pixel_values[keep_indices]
self.image_attention_mask = self.image_attention_mask[
keep_indices,
-(self.padding_right_offset + max_input_length) : (
self.image_attention_mask.shape[1] - self.padding_right_offset
)
+ new_padding_right_offset,
:
]
if self.image_hidden_states is None:
image_hidden_states = None
else:
image_hidden_states = self.image_hidden_states[keep_indices]
# Ensure that past_key_values tensors can be updated in-place
if type(self.past_key_values[0]) == tuple:
self.past_key_values = [list(layer) for layer in self.past_key_values]
# Update tensors in-place to allow incremental garbage collection
past_kv_length = max_input_length - 1
for layer in self.past_key_values:
past_keys, past_values = layer
if len(past_keys.shape) == 3:
# Force past to be of dim [self_size, num_heads, ...] for easy indexing
past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
if self.keys_head_dim_last:
layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
else:
layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
del past_keys
layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
del past_values
max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens
self.requests = requests
self.requests_idx_mapping = requests_idx_mapping
self.input_ids = input_ids
self.pixel_values = pixel_values
self.image_hidden_states = image_hidden_states
self.position_ids = position_ids
self.all_input_ids = all_input_ids
self.input_lengths = input_lengths
self.prefix_offsets = prefix_offsets
self.read_offsets = read_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.max_input_length = max_input_length
self.padding_right_offset = new_padding_right_offset
self.max_tokens = max_tokens
return self
@classmethod
@tracer.start_as_current_span("concatenate")
def concatenate(cls, batches: List["IdeficsCausalLMBatch"]) -> "IdeficsCausalLMBatch":
# It adds new requests to the batch
# Used for padding
total_batch_size = 0
max_input_length = 0
max_num_images = 0
padding_right_offset = 0
for batch in batches:
total_batch_size += len(batch)
max_input_length = max(max_input_length, batch.max_input_length)
max_num_images = max(max_num_images, batch.pixel_values.size(1))
padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
# Batch attributes
requests = []
requests_idx_mapping = {}
input_lengths = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
next_token_choosers = []
stopping_criterias = []
max_tokens = 0
# Batch tensors
input_ids = None
attention_mask = None
position_ids = None
pixel_values = None
image_hidden_states = None
image_attention_mask = None
past_key_values = []
# Used for slicing correctly inside the tensors
# Equivalent to a cumsum on batch sizes
start_index = 0
for i, batch in enumerate(batches):
requests.extend(batch.requests)
input_lengths.extend(batch.input_lengths)
prefix_offsets.extend(batch.prefix_offsets)
read_offsets.extend(batch.read_offsets)
all_input_ids.extend(batch.all_input_ids)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
if i == 0:
requests_idx_mapping = batch.requests_idx_mapping
else:
# We need to offset the mapping for each batch by the cumulative batch size
for k, v in batch.requests_idx_mapping.items():
requests_idx_mapping[k] = v + start_index
# Slicing end index for this batch
end_index = start_index + len(batch)
# We only concatenate batches that did at least one step
if batch.past_key_values is None:
raise ValueError("only concatenate prefilled batches")
# Create empty tensor
# input_ids is always of shape [batch_size, 1]
# We do not need to pad it
if input_ids is None:
input_ids = batch.input_ids.new_empty((total_batch_size, 1))
# Copy to correct indices
input_ids[start_index:end_index] = batch.input_ids
# Create padded tensor
if attention_mask is None:
attention_mask = batch.attention_mask.new_zeros(
(total_batch_size, max_input_length + padding_right_offset),
)
curr_batch_max_num_images = batch.pixel_values.size(1)
if pixel_values is None:
pixel_values = batch.pixel_values.new_zeros((total_batch_size, max_num_images, 3, 224, 224))
pixel_values[start_index:end_index, :curr_batch_max_num_images] = batch.pixel_values
if image_attention_mask is None:
image_attention_mask = batch.image_attention_mask.new_zeros(
(total_batch_size, max_input_length + padding_right_offset, max_num_images)
)
# We need to slice the attention mask to remove padding from previous steps
# and to remove unused allocated space
left_offset = max_input_length - batch.max_input_length
batch_left_offset = (
batch.attention_mask.shape[1]
- batch.max_input_length
- batch.padding_right_offset
)
attention_mask[
start_index:end_index,
left_offset:-padding_right_offset,
] = batch.attention_mask[
:,
batch_left_offset : -batch.padding_right_offset,
]
image_attention_mask[
start_index:end_index,
left_offset:-padding_right_offset,
:curr_batch_max_num_images
] = batch.image_attention_mask[
:,
batch_left_offset : - batch.padding_right_offset,
:
]
# Create empty tensor
# position_ids is always of shape [batch_size, 1]
if position_ids is None:
position_ids = batch.position_ids.new_empty((total_batch_size, 1))
position_ids[start_index:end_index] = batch.position_ids
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
# And ensure that we can update tensors in-place
if type(batch.past_key_values[0]) == tuple:
batch.past_key_values = [
[t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
for layer in batch.past_key_values
]
elif len(batch.past_key_values[0][0].shape) == 3:
for layer in batch.past_key_values:
for k, t in enumerate(layer):
layer[k] = t.view(len(batch), -1, *t.shape[-2:])
# Add eventual padding tokens that were added while concatenating
max_tokens += batch.max_tokens + (
max_input_length - batch.max_input_length
) * len(batch)
start_index = end_index
first_past_kvs = batches[0].past_key_values
_, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
padded_past_values_shape = (
total_batch_size,
num_heads,
max_input_length - 1,
head_dim,
)
if batches[0].keys_head_dim_last:
padded_past_keys_shape = padded_past_values_shape
else:
# seq_length is last for BLOOM
padded_past_keys_shape = (
total_batch_size,
num_heads,
head_dim,
max_input_length - 1,
)
# Iterate over attention layers
# Concatenate past key values layer by layer to allow incremental garbage collection
for j in range(len(first_past_kvs)):
padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
start_index = 0
for batch in batches:
past_keys = batch.past_key_values[j][0]
# Clear reference to the original tensor
batch.past_key_values[j][0] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the keys to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
if batch.keys_head_dim_last:
padded_past_keys[
start_index:end_index, :, -past_seq_len:, :
] = past_keys[:, :, -past_seq_len:, :]
else:
# BLOOM case
padded_past_keys[
start_index:end_index, :, :, -past_seq_len:
] = past_keys[:, :, :, -past_seq_len:]
del past_keys
start_index = end_index
padded_past_values = first_past_kvs[j][1].new_zeros(
padded_past_values_shape
)
start_index = 0
for batch in batches:
past_values = batch.past_key_values[j][1]
# Clear reference to the original tensor
batch.past_key_values[j][1] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the past values to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
padded_past_values[
start_index:end_index, :, -past_seq_len:, :
] = past_values[:, :, -past_seq_len:, :]
del past_values
# Update values
start_index = end_index
past_key_values.append([padded_past_keys, padded_past_values])
return cls(
batch_id=batches[0].batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
pixel_values=pixel_values,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
past_key_values=past_key_values,
all_input_ids=all_input_ids,
input_lengths=input_lengths,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length,
padding_right_offset=padding_right_offset,
keys_head_dim_last=batches[0].keys_head_dim_last,
max_tokens=max_tokens,
)
def __len__(self):
return len(self.requests)
class IdeficsCausalLM(Model):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
from text_generation_server.models.custom_modeling.idefics_modeling import IdeficsForVisionText2Text
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.float16 if dtype is None else dtype
else:
if quantize:
raise ValueError("quantization is not available on CPU")
device = torch.device("cpu")
dtype = torch.float32
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
self.processor = AutoProcessor.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
model = IdeficsForVisionText2Text.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
device_map="auto"
if torch.cuda.is_available() and torch.cuda.device_count() > 1
else None,
load_in_8bit=quantize == "bitsandbytes",
trust_remote_code=trust_remote_code,
)
if torch.cuda.is_available() and torch.cuda.device_count() == 1:
model = model.cuda()
if tokenizer.pad_token_id is None:
if model.config.pad_token_id is not None:
tokenizer.pad_token_id = model.config.pad_token_id
elif model.config.eos_token_id is not None:
tokenizer.pad_token_id = model.config.eos_token_id
elif tokenizer.eos_token_id is not None:
tokenizer.pad_token_id = tokenizer.eos_token_id
else:
tokenizer.add_special_tokens({"pad_token": "<unk>"})
super(IdeficsCausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
)
@property
def batch_type(self) -> Type[IdeficsCausalLMBatch]:
return IdeficsCausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
return self.tokenizer.decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
def forward(
self,
input_ids,
attention_mask,
position_ids,
pixel_values,
image_hidden_states,
image_attention_mask,
past_key_values: Optional = None,
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
# Model Forward
kwargs = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"pixel_values": pixel_values,
"image_hidden_states": image_hidden_states,
"image_attention_mask": image_attention_mask,
"past_key_values": past_key_values,
"use_cache": True,
"return_dict": True,
}
if self.has_position_ids:
kwargs["position_ids"] = position_ids
outputs = self.model.forward(**kwargs)
return outputs.logits, outputs.past_key_values, outputs.image_hidden_states
@tracer.start_as_current_span("generate_token")
def generate_token(
self, batch: IdeficsCausalLMBatch
) -> Tuple[List[Generation], Optional[IdeficsCausalLMBatch]]:
# slice the attention mask to the correct shape
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
if batch.input_ids.size(1) == 1:
# THIS is a hack: when calling idefics.generate, the first time, we need the whole image_attention_mask (size bs x max_seq_len x max_num_images),
# but the subsequent times, we only need the last attention mask along the `max_seq_len` dimension
# this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated
# token need to attend to the encoder hidden states (i.e. the vision encoder)
# Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic
image_attention_mask = batch.image_attention_mask[:, -(batch.padding_right_offset+1)].unsqueeze(1)
else:
image_attention_mask = batch.image_attention_mask[:, : -batch.padding_right_offset]
logits, past, image_hidden_states = self.forward(
input_ids=batch.input_ids,
attention_mask=attention_mask,
position_ids=batch.position_ids,
pixel_values=batch.pixel_values,
image_hidden_states=batch.image_hidden_states,
image_attention_mask=image_attention_mask,
past_key_values=batch.past_key_values,
)
# Hardcoded remove image tokens
logits[:, 32000:32001] = torch.finfo(logits.dtype).min
# Results
generations: List[Generation] = []
stopped = True
# Zipped iterator
iterator = zip(
batch.requests,
batch.input_lengths,
batch.prefix_offsets,
batch.read_offsets,
logits,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_input_ids,
)
# For each member of the batch
for i, (
request,
input_length,
prefix_offset,
read_offset,
logits,
next_token_chooser,
stopping_criteria,
all_input_ids,
) in enumerate(iterator):
# Select next token
next_token_id, logprobs = next_token_chooser(
all_input_ids.view(1, -1), logits[-1:, :]
)
# Append next token to all tokens
all_input_ids = torch.cat([all_input_ids, next_token_id])
new_input_length = input_length + 1
# Generated token
next_token_logprob = logprobs[-1, next_token_id]
next_token_id_squeezed = next_token_id.squeeze()
next_token_text, prefix_offset, read_offset = self.decode_token(
all_input_ids[:, 0], prefix_offset, read_offset
)
# Evaluate stopping criteria
stop, reason = stopping_criteria(
next_token_id_squeezed,
next_token_text,
)
if not stop:
stopped = False
# Shard generations
# All generations will be appended in the rust sharded client
if i % self.world_size == self.rank:
if stop:
# Decode generated tokens
output_text = self.decode(
all_input_ids[-stopping_criteria.current_tokens :, 0]
)
# Get seed
if isinstance(next_token_chooser.choice, Sampling):
seed = next_token_chooser.choice.seed
else:
seed = None
generated_text = GeneratedText(
output_text, stopping_criteria.current_tokens, reason, seed
)
else:
generated_text = None
# Prefill
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
# Remove generated token to only have prefill and add nan for first prompt token
prefill_logprobs = [float("nan")] + torch.log_softmax(
logits, -1
).gather(1, all_input_ids[1:]).squeeze(1)[
-new_input_length:-1
].tolist()
prefill_token_ids = all_input_ids[-new_input_length:-1]
prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
prefill_tokens = PrefillTokens(
prefill_token_ids, prefill_logprobs, prefill_texts
)
else:
prefill_tokens = None
generation = Generation(
request.id,
prefill_tokens,
next_token_id_squeezed,
next_token_logprob,
next_token_text,
next_token_id_squeezed.item() in self.all_special_ids,
generated_text,
)
generations.append(generation)
# Update values
batch.input_ids[i, 0] = next_token_id
batch.all_input_ids[i] = all_input_ids
batch.input_lengths[i] = new_input_length
batch.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_offset
batch.max_input_length = max(batch.max_input_length, new_input_length)
# We finished all generations in the batch; there is no next batch
if stopped:
return generations, None
# Slice unused values from prefill
batch.input_ids = batch.input_ids[:, :1]
# Update attention_mask as we added a new token to input_ids
batch.attention_mask[:, -batch.padding_right_offset] = 1
batch.image_attention_mask[:, -batch.padding_right_offset, :] = batch.image_attention_mask[:, -(batch.padding_right_offset+1), :]
# Decrease right offset
batch.padding_right_offset -= 1
# Update position_ids
batch.position_ids = batch.position_ids[:, -1:] + 1
# Update past key values
batch.past_key_values = past
batch.image_hidden_states = image_hidden_states
return generations, batch
server/text_generation_server/server.py
View file @ bce5e224
......@@ -14,7 +14,7 @@ from text_generation_server.interceptor import ExceptionInterceptor
from text_generation_server.models import Model, get_model
from text_generation_server.pb import generate_pb2_grpc, generate_pb2
from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
def __init__(self, model: Model, cache: Cache, server_urls: List[str]):
......@@ -26,6 +26,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
# Force inference mode for the lifetime of TextGenerationService
self._inference_mode_raii_guard = torch._C._InferenceMode(True)
async def Info(self, request, context):
return self.model.info
......@@ -54,6 +55,11 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
async def Warmup(self, request, context):
if self.model.batch_type == IdeficsCausalLMBatch: #Hack, i would rather use kwargs in the `from_pb` call
batch = self.model.batch_type.from_pb(
request.batch, self.model.tokenizer, self.model.processor, self.model.dtype, self.model.device
)
else:
batch = self.model.batch_type.from_pb(
request.batch, self.model.tokenizer, self.model.dtype, self.model.device
)
......@@ -64,6 +70,11 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
)
async def Prefill(self, request, context):
if self.model.batch_type == IdeficsCausalLMBatch: #Hack, i would rather use kwargs in the `from_pb` call
batch = self.model.batch_type.from_pb(
request.batch, self.model.tokenizer, self.model.processor, self.model.dtype, self.model.device
)
else:
batch = self.model.batch_type.from_pb(
request.batch, self.model.tokenizer, self.model.dtype, self.model.device
)
......
server/text_generation_server/utils/layers.py
View file @ bce5e224
......@@ -51,7 +51,31 @@ def load_layer_norm_no_bias(cls, prefix, weights, eps):
ln.bias = None
return ln
@classmethod
def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, stride):
weight = weights.get_tensor(f"{prefix}.weight")
bias = weights.get_tensor(f"{prefix}.bias")
with init_empty_weights():
conv2d = cls(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride)
conv2d.weight = nn.Parameter(weight)
conv2d.bias = nn.Parameter(bias)
return conv2d
@classmethod
def load_conv2d_no_bias(cls, prefix, weights, in_channels, out_channels, kernel_size, stride):
weight = weights.get_tensor(f"{prefix}.weight")
with init_empty_weights():
conv2d = cls(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride)
conv2d.weight = nn.Parameter(weight)
conv2d.bias = None
return conv2d
torch.nn.Conv2d.load = load_conv2d
torch.nn.Conv2d.load_no_bias = load_conv2d_no_bias
torch.nn.LayerNorm.load = load_layer_norm
torch.nn.LayerNorm.load_no_bias = load_layer_norm_no_bias
......
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