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CLIP (ViT) (#315)

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* probably approximatelly correct CLIPTextEncoder

* implemented CLIPEncoderLayer as built-in nn.TransformerEncoderLayer

* replaced embedding layer with simple matrix

* implemented ViT

* added ViT tests

* fixed tests

* added pooler_output for text

* implemented complete CLIPModel

* implemented init

* implemented convert.py and from_pretrained

* fixed some minor bugs and added the README.md

* removed tokenizer unused comments

* removed unused deps

* updated ACKNOWLEDGEMENTS.md

* Feat: Image Processor for CLIP (#1)

@nkasmanoff:
* clip image processor
* added example usage

* refactored image preprocessing

* deleted unused image_config.py

* removed preprocessing port

* added dependency to mlx-data

* fixed attribution and moved photos to assets

* implemented a simple port of CLIPImageProcessor

* review changes

* PR review changes

* renamed too verbose arg

* updated README.md

* nits in readme / conversion

* simplify some stuff, remove unneeded inits

* remove more init stuff

* more simplify

* make test a unit test

* update main readme

* readme nits

---------

Co-authored-by: Noah Kasmanoff <nkasmanoff@gmail.com>
Co-authored-by: Awni Hannun <awni@apple.com>
This commit is contained in:
Gabrijel Boduljak
2024-01-31 23:19:53 +01:00
committed by GitHub
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commit 94358219cf
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# Copyright © 2023-2024 Apple Inc.
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from mlx.core import linalg as LA
from mlx.nn.losses import cross_entropy
from mlx.utils import tree_flatten
@dataclass
class CLIPVisionOutput:
pooler_output: mx.array
last_hidden_state: mx.array
@dataclass
class CLIPTextOutput:
pooler_output: mx.array
last_hidden_state: mx.array
@dataclass
class CLIPModelOutput:
loss: Optional[mx.array]
text_embeds: Optional[mx.array]
image_embeds: Optional[mx.array]
text_model_output: CLIPTextOutput
vision_model_output: CLIPVisionOutput
@dataclass
class CLIPTextConfig:
num_hidden_layers: int
hidden_size: int
intermediate_size: int
num_attention_heads: int
max_position_embeddings: int
vocab_size: int
@dataclass
class CLIPVisionConfig:
num_hidden_layers: int
hidden_size: int
intermediate_size: int
num_attention_heads: int
num_channels: int
image_size: int
patch_size: int
@dataclass
class CLIPConfig:
text_config: CLIPTextConfig
vision_config: CLIPVisionConfig
projection_dim: int
def quick_gelu(x: mx.array) -> mx.array:
"""
A fast GELU approximation https://github.com/hendrycks/GELUs
"""
return x * mx.sigmoid(1.702 * x)
def clip_loss(logits: mx.array) -> mx.array:
N, M = logits.shape
caption_loss = cross_entropy(logits, mx.arange(N), reduction="mean")
image_loss = cross_entropy(logits.T, mx.arange(M), reduction="mean")
return (caption_loss + image_loss) / 2.0
class CLIPEncoderLayer(nn.TransformerEncoderLayer):
"""The transformer encoder layer from CLIP."""
def __init__(self, hidden_dim: int, intermediate_dim: int, num_heads: int):
super().__init__(
dims=hidden_dim,
mlp_dims=intermediate_dim,
num_heads=num_heads,
activation=quick_gelu,
norm_first=True,
)
# Add biases to the attention projections
self.attention = nn.MultiHeadAttention(hidden_dim, num_heads, bias=True)
class CLIPTextModel(nn.Module):
"""Implements the text encoder transformer from CLIP."""
def __init__(self, config: CLIPTextConfig):
super().__init__()
self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.position_embedding = mx.zeros(
(config.max_position_embeddings, config.hidden_size)
)
self.layers = [
CLIPEncoderLayer(
config.hidden_size, config.intermediate_size, config.num_attention_heads
)
for _ in range(config.num_hidden_layers)
]
self.final_layer_norm = nn.LayerNorm(config.hidden_size)
def _embed(self, x: mx.array) -> mx.array:
embeddings = self.token_embedding(x)
embeddings += self.position_embedding[: x.shape[1]]
return embeddings
def __call__(self, x: mx.array) -> CLIPTextOutput:
B, N = x.shape
eot_tokens = mx.argmax(x, axis=-1)
x = self._embed(x)
mask = nn.MultiHeadAttention.create_additive_causal_mask(N, x.dtype)
for l in self.layers:
x = l(x, mask)
last_hidden_state = self.final_layer_norm(x)
pooler_output = last_hidden_state[mx.arange(B), eot_tokens]
return CLIPTextOutput(
pooler_output=pooler_output, last_hidden_state=last_hidden_state
)
class CLIPVisionModel(nn.Module):
"""Implements the vision encoder transformer from CLIP."""
def __init__(self, config: CLIPVisionConfig):
super().__init__()
self.class_embedding = mx.zeros((config.hidden_size,))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=config.hidden_size,
kernel_size=config.patch_size,
stride=config.patch_size,
bias=False,
)
num_patches = (config.image_size // config.patch_size) ** 2
num_positions = num_patches + 1
self.position_embedding = mx.zeros((num_positions, config.hidden_size))
self.pre_layernorm = nn.LayerNorm(config.hidden_size)
self.layers = [
CLIPEncoderLayer(
config.hidden_size, config.intermediate_size, config.num_attention_heads
)
for _ in range(config.num_hidden_layers)
]
self.post_layernorm = nn.LayerNorm(config.hidden_size)
def _embed(self, x: mx.array) -> mx.array:
batch_size = x.shape[0]
# Patchify using conv:
# [batch_size, sqrt(num_patches), sqrt(num_patches), embed_dim]
patch_embeddings = self.patch_embedding(x)
# [batch_size, num_patches, embed_dim]
patch_embeddings = mx.flatten(patch_embeddings, start_axis=1, end_axis=2)
embed_dim = patch_embeddings.shape[-1]
# Prepend <CLS> embeddings
# [batch_size, 1, embed_dim]
cls_embeddings = mx.broadcast_to(
self.class_embedding, (batch_size, 1, embed_dim)
)
# [batch_size, num_patches + 1, embed_dim]
embeddings = mx.concatenate((cls_embeddings, patch_embeddings), axis=1)
# Add positional encoding
embeddings += self.position_embedding
return embeddings
def __call__(self, x: mx.array) -> CLIPVisionOutput:
x = self._embed(x)
x = self.pre_layernorm(x)
for l in self.layers:
x = l(x, mask=None)
# Extract <CLS> token embedding
pooler_output = self.post_layernorm(x[:, 0, :])
return CLIPVisionOutput(pooler_output=pooler_output, last_hidden_state=x)
class CLIPModel(nn.Module):
def __init__(self, config: CLIPConfig):
self.text_model = CLIPTextModel(config.text_config)
self.vision_model = CLIPVisionModel(config.vision_config)
text_embed_dim = config.text_config.hidden_size
vision_embed_dim = config.vision_config.hidden_size
projection_dim = config.projection_dim
self.visual_projection = nn.Linear(vision_embed_dim, projection_dim, bias=False)
self.text_projection = nn.Linear(text_embed_dim, projection_dim, bias=False)
self.logit_scale = mx.array(0.0)
def get_text_features(self, x: mx.array) -> mx.array:
return self.text_projection(self.text_model(x).pooler_output)
def get_image_features(self, x: mx.array) -> mx.array:
return self.visual_projection(self.vision_model(x).pooler_output)
def __call__(
self,
input_ids: Optional[mx.array] = None,
pixel_values: Optional[mx.array] = None,
return_loss=False,
) -> CLIPModelOutput:
if input_ids is not None:
text_model_output = self.text_model(input_ids)
text_embeds = self.text_projection(text_model_output.pooler_output)
text_embeds = text_embeds / LA.norm(text_embeds, axis=-1, keepdims=True)
else:
text_embeds = None
text_model_output = None
if pixel_values is not None:
vision_model_output = self.vision_model(pixel_values)
image_embeds = self.visual_projection(vision_model_output.pooler_output)
image_embeds = image_embeds / LA.norm(image_embeds, axis=-1, keepdims=True)
else:
image_embeds = None
vision_model_output = None
if return_loss and (input_ids is None or pixel_values is None):
raise ValueError("Must provide text and image inputs to compute loss.")
if return_loss:
logit_scale = mx.exp(self.logit_scale)
logits = (text_embeds @ image_embeds.T) * logit_scale
loss = clip_loss(logits)
else:
loss = None
return CLIPModelOutput(
loss=loss,
text_embeds=text_embeds,
image_embeds=image_embeds,
vision_model_output=vision_model_output,
text_model_output=text_model_output,
)
@staticmethod
def from_pretrained(path: str):
path = Path(path)
with open(path / "config.json", "r") as fid:
config = json.load(fid)
text_config = config["text_config"]
text_config = CLIPTextConfig(
num_hidden_layers=text_config["num_hidden_layers"],
hidden_size=text_config["hidden_size"],
intermediate_size=text_config["intermediate_size"],
num_attention_heads=text_config["num_attention_heads"],
max_position_embeddings=text_config["max_position_embeddings"],
vocab_size=text_config["vocab_size"],
)
vision_config = config["vision_config"]
vision_config = CLIPVisionConfig(
num_hidden_layers=vision_config["num_hidden_layers"],
hidden_size=vision_config["hidden_size"],
intermediate_size=vision_config["intermediate_size"],
num_attention_heads=vision_config["num_attention_heads"],
num_channels=3,
image_size=vision_config["image_size"],
patch_size=vision_config["patch_size"],
)
config = CLIPConfig(
text_config=text_config,
vision_config=vision_config,
projection_dim=config["projection_dim"],
)
model = CLIPModel(config)
model.load_weights(str(path / "weights.npz"))
return model