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refiners/notebooks/basics.ipynb

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add basics notebook
2023-12-21 08:43:37 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Refiners Demo\n",
"\n",
"This notebook aims to demonstrate the basics of using the [Refiners](https://github.com/finegrain-ai/refiners) micro-framework.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# to run you need to have `Refiners` installed (uncomment the line below)\n",
"# %pip install git+https://github.com/finegrain-ai/refiners.git"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from refiners.fluxion import layers as fl, manual_seed\n",
"from torch import nn\n",
"\n",
"torch.set_grad_enabled(mode=False)\n",
"manual_seed(82570858)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Basics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The core idea of Refiners is to improve on the `Sequential` API of PyTorch.\n",
"\n",
"A `Sequential` is defined by:\n",
"\n",
"`Sequential([layer1, layer2, layer3])(x) = layer3(layer2(layer1(x)))`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sequential(\n",
" (0): Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
" (1): ReLU()\n",
" (2): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
" (3): ReLU()\n",
" (4): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
" (5): ReLU()\n",
" (6): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
")"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Native PyTorch sequential\n",
"sequential = nn.Sequential(\n",
" fl.Conv2d(3, 32, 3, padding=1),\n",
" nn.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" nn.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" nn.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
")\n",
"\n",
"sequential"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(CHAIN)\n",
" ├── Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #1\n",
" ├── ReLU() #1\n",
" ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #2\n",
" ├── ReLU() #2\n",
" ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #3\n",
" ├── ReLU() #3\n",
" └── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #4"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Same as above, but with a Fluxion Chain\n",
"chain = fl.Chain(\n",
" fl.Conv2d(3, 32, 3, padding=1),\n",
" fl.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" fl.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" fl.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
")\n",
"\n",
"chain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note here that the keys of the Chain are the names of the layers, whereas in PyTorch Sequential API, the keys are the indices of the layers.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sequential keys:\n",
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"\n",
"Chain keys:\n",
"Conv2d_1\n",
"ReLU_1\n",
"Conv2d_2\n",
"ReLU_2\n",
"Conv2d_3\n",
"ReLU_3\n",
"Conv2d_4\n"
]
}
],
"source": [
"print(\"Sequential keys:\")\n",
"for key, _ in sequential.named_children():\n",
" print(key)\n",
"\n",
"print(\"\\nChain keys:\")\n",
"for key, _ in chain.named_children():\n",
" print(key)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This choice is made because when a model is simple, it is easy to remember the indices of the layers, but when a model is complex, it is hard to remember the indices of the layers.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also improved on the Errors to showcase exactly where the error is coming from.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = torch.randn(1, 4, 32, 32)\n",
"# uncomment to run\n",
"# sequential(x)"
]
},
{
"attachments": {
"image.png": {
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}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"![image.png](attachment:image.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# uncomment to run\n",
"# chain(x)"
]
},
{
"attachments": {
"image.png": {
"image/png": "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
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"![image.png](attachment:image.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sequential is excellent for building basic and straightforward models, but most models don't have such a simple linear structure. \n",
"\n",
"Let's say you want to add a skip connection to the ConvNet."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 32, 32, 32])\n"
]
},
{
"data": {
"text/plain": [
"ConvNet(\n",
" (sequential): Sequential(\n",
" (0): Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
" (1): ReLU()\n",
" (2): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
" (3): ReLU()\n",
" (4): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
" (5): ReLU()\n",
" (6): Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
" )\n",
" (skip): Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1))\n",
")"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ConvNet with a residual connection in PyTorch\n",
"\n",
"\n",
"class ConvNet(nn.Module):\n",
" def __init__(self) -> None:\n",
" super().__init__()\n",
" self.sequential = nn.Sequential(\n",
" fl.Conv2d(3, 32, 3, padding=1),\n",
" nn.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" nn.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" nn.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" )\n",
" self.skip = fl.Conv2d(3, 32, 3, padding=1)\n",
"\n",
" def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
" return self.sequential(x) + self.skip(x)\n",
"\n",
"\n",
"convnet = ConvNet()\n",
"x = torch.randn(1, 3, 32, 32)\n",
"print(convnet(x).shape)\n",
"convnet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `repr` of this PyTorch is not declarative anymore: you cannot know how the model works.\n",
"\n",
"You can use Refiners' predefined `Chain` subclasses to handle such cases and build more complex models. \n",
"\n",
"Let's start with the `Sum` class.\n",
"\n",
"`fl.Sum([layer1, layer2, layer3])(x) = layer1(x) + layer2(x) + layer3(x)`"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 32, 32, 32])\n"
]
},
{
"data": {
"text/plain": [
"(SUM)\n",
" ├── (CHAIN)\n",
" │ ├── Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #1\n",
" │ ├── ReLU() #1\n",
" │ ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #2\n",
" │ ├── ReLU() #2\n",
" │ ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #3\n",
" │ ├── ReLU() #3\n",
" │ └── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #4\n",
" └── Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ConvNet with a residual connection in Refiners\n",
"convnet = fl.Sum(\n",
" fl.Chain(\n",
" fl.Conv2d(3, 32, 3, padding=1),\n",
" fl.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" fl.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" fl.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" ),\n",
" fl.Conv2d(3, 32, 3, padding=1),\n",
")\n",
"\n",
"x = torch.randn(1, 3, 32, 32)\n",
"print(convnet(x).shape)\n",
"convnet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can subclass the basics `Chain` to give a name to improve declarativity. The `repr` will still tell you which kind of `Chain` it is and the name of the `Chain`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(SUM) ResidualNet()\n",
" ├── (CHAIN) ConvNet()\n",
" │ ├── Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #1\n",
" │ ├── ReLU() #1\n",
" │ ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #2\n",
" │ ├── ReLU() #2\n",
" │ ├── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #3\n",
" │ ├── ReLU() #3\n",
" │ └── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1)) #4\n",
" └── Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), padding=(1, 1))"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class ConvNet(fl.Chain):\n",
" def __init__(self) -> None:\n",
" super().__init__(\n",
" fl.Conv2d(3, 32, 3, padding=1),\n",
" fl.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" fl.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" fl.ReLU(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" )\n",
"\n",
"\n",
"class ResidualNet(fl.Sum):\n",
" def __init__(self) -> None:\n",
" super().__init__(\n",
" ConvNet(),\n",
" fl.Conv2d(32, 32, 3, padding=1),\n",
" )\n",
"\n",
"\n",
"ResidualNet()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are some examples of `Chain` subclasses:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor([[-0.4723, -0.2809]]), tensor([[-0.5384, 0.6123, 0.2659, 0.0916]]))"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run layers in parallel to output a tuple\n",
"par = fl.Parallel(\n",
" fl.Linear(2, 2),\n",
" fl.Linear(2, 4),\n",
")\n",
"\n",
"x = torch.randn(1, 2)\n",
"par(x) # (Linear_1(x), Linear_2(x))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[-1.0949, 0.0749, 0.2607, 0.4013]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run layers in parallel and then concatenate the outputs\n",
"cat = fl.Concatenate(\n",
" fl.Linear(2, 2),\n",
" fl.Linear(2, 2),\n",
" dim=-1,\n",
")\n",
"\n",
"x = torch.randn(1, 2)\n",
"cat(x) # Concatenate((Linear_1(x), Linear_2(x)), dim=-1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[-0.1487, -1.1180]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run sequentially layers and then add the input\n",
"residual = fl.Residual(\n",
" fl.Linear(2, 2),\n",
" fl.Linear(2, 2),\n",
")\n",
"\n",
"x = torch.randn(1, 2)\n",
"residual(x) # Linear_2(Linear_1(x)) + x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now build something more complex such as a Vision Transformer. \n",
"\n",
"Let's start with the heart of a transformer layer: the Multi-Head Attention."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 197, 128])\n"
]
},
{
"data": {
"text/plain": [
"(RES) Attention()\n",
" ├── (PAR)\n",
" │ └── Linear(in_features=128, out_features=128) (x3)\n",
" ├── ScaledDotProductAttention(num_heads=8)\n",
" └── Linear(in_features=128, out_features=128)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from refiners.fluxion.layers.attentions import ScaledDotProductAttention\n",
"\n",
"\n",
"class Attention(fl.Residual):\n",
" def __init__(self, dim: int = 128, num_heads: int = 8) -> None:\n",
" self.dim = dim\n",
" self.num_heads = num_heads\n",
" super().__init__(\n",
" fl.Parallel(\n",
" fl.Linear(dim, dim),\n",
" fl.Linear(dim, dim),\n",
" fl.Linear(dim, dim),\n",
" ),\n",
" ScaledDotProductAttention(num_heads=num_heads),\n",
" fl.Linear(dim, dim),\n",
" )\n",
"\n",
"\n",
"x = torch.randn(1, 197, 128)\n",
"attention = Attention()\n",
"print(attention(x).shape)\n",
"attention"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(RES) FeedForward()\n",
" ├── Linear(in_features=128, out_features=512) #1\n",
" ├── SiLU()\n",
" └── Linear(in_features=512, out_features=128) #2"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class FeedForward(fl.Residual):\n",
" def __init__(self, dim: int = 128, inner_dim: int = 512) -> None:\n",
" self.dim = dim\n",
" self.inner_dim = inner_dim\n",
" super().__init__(\n",
" fl.Linear(dim, inner_dim),\n",
" fl.SiLU(),\n",
" fl.Linear(inner_dim, dim),\n",
" )\n",
"\n",
"\n",
"FeedForward()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(CHAIN) TranformerLayer()\n",
" ├── LayerNorm(normalized_shape=(128,)) #1\n",
" ├── (RES) Attention()\n",
" │ ├── (PAR)\n",
" │ │ └── Linear(in_features=128, out_features=128) (x3)\n",
" │ ├── ScaledDotProductAttention(num_heads=8)\n",
" │ └── Linear(in_features=128, out_features=128)\n",
" ├── LayerNorm(normalized_shape=(128,)) #2\n",
" └── (RES) FeedForward()\n",
" ├── Linear(in_features=128, out_features=512) #1\n",
" ├── SiLU()\n",
" └── Linear(in_features=512, out_features=128) #2"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class TranformerLayer(fl.Chain):\n",
" def __init__(\n",
" self, dim: int = 128, num_heads: int = 8, inner_dim: int = 512\n",
" ) -> None:\n",
" self.dim = dim\n",
" self.num_heads = num_heads\n",
" self.inner_dim = inner_dim\n",
" super().__init__(\n",
" fl.LayerNorm(dim),\n",
" Attention(dim, num_heads),\n",
" fl.LayerNorm(dim),\n",
" FeedForward(dim, inner_dim),\n",
" )\n",
"\n",
"\n",
"TranformerLayer()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 196, 128])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class PatchEncoder(fl.Chain):\n",
" def __init__(\n",
" self, in_channels: int = 3, dim: int = 128, patch_size: int = 16\n",
" ) -> None:\n",
" self.in_channels = in_channels\n",
" self.dim = dim\n",
" self.patch_size = patch_size\n",
" super().__init__(\n",
" fl.Conv2d(\n",
" in_channels=in_channels,\n",
" out_channels=dim,\n",
" kernel_size=patch_size,\n",
" stride=patch_size,\n",
" ),\n",
" fl.Reshape(-1, dim), # Reshape always preserves the batch dimension\n",
" )\n",
"\n",
"\n",
"x = torch.randn(1, 3, 224, 224)\n",
"PatchEncoder()(x).shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"class PositionalToken(fl.Residual):\n",
" def __init__(self, num_patches: int = 196) -> None:\n",
" self.num_patches = num_patches\n",
" super().__init__(fl.Parameter(num_patches, 128))\n",
"\n",
"\n",
"class ClassToken(fl.Chain):\n",
" def __init__(self, dim: int = 128) -> None:\n",
" self.dim = dim\n",
" super().__init__(fl.Parameter(1, dim))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have every bit to build a full Vision Transformer."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(CHAIN) ViT()\n",
" ├── (CAT)\n",
" │ ├── (CHAIN) PatchEncoder()\n",
" │ │ ├── Conv2d(in_channels=3, out_channels=128, kernel_size=(16, 16), stride=(16, 16))\n",
" │ │ └── Reshape(shape=(-1, 128))\n",
" │ └── (CHAIN) ClassToken()\n",
" │ └── Parameter(dims=(1, 128))\n",
" ├── (RES) PositionalToken(num_patches=197)\n",
" │ └── Parameter(dims=(197, 128))\n",
" └── (CHAIN) Transformer()\n",
" └── (CHAIN) TranformerLayer() (x4)\n",
" ├── LayerNorm(normalized_shape=(128,)) #1\n",
" ├── (RES) Attention()\n",
" │ ├── (PAR)\n",
" │ │ └── Linear(in_features=128, out_features=128) (x3)\n",
" │ ├── ScaledDotProductAttention(num_heads=8)\n",
" │ └── Linear(in_features=128, out_features=128)\n",
" ├── LayerNorm(normalized_shape=(128,)) #2\n",
" └── (RES) FeedForward()\n",
" ├── Linear(in_features=128, out_features=512) #1\n",
" ├── SiLU()\n",
" └── Linear(in_features=512, out_features=128) #2\n",
"torch.Size([1, 197, 128])\n"
]
}
],
"source": [
"class Transformer(fl.Chain):\n",
" pass\n",
"\n",
"\n",
"class ViT(fl.Chain):\n",
" def __init__(\n",
" self,\n",
" dim: int = 128,\n",
" patch_size: int = 16,\n",
" image_size: int = 224,\n",
" num_layers: int = 4,\n",
" ) -> None:\n",
" self.dim = dim\n",
" self.patch_size = patch_size\n",
" self.image_size = image_size\n",
" self.num_layers = num_layers\n",
" self.num_patches = (image_size // patch_size) ** 2 + 1\n",
" super().__init__(\n",
" fl.Concatenate(\n",
" PatchEncoder(in_channels=3, dim=dim, patch_size=patch_size),\n",
" ClassToken(dim=dim),\n",
" dim=1,\n",
" ),\n",
" PositionalToken(num_patches=self.num_patches),\n",
" Transformer(TranformerLayer(dim=dim) for _ in range(num_layers)),\n",
" )\n",
"\n",
"\n",
"x = torch.randn(1, 3, 224, 224)\n",
"vit = ViT()\n",
"print(repr(vit))\n",
"print(vit(x).shape)"
]
},
{
"attachments": {
"image.png": {
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}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Advanced - Context API\n",
"\n",
"This ViT is still rudimentary and linear: we have one input in and one output out. But often, you want to use multiple inputs/modalities in the flow of your model.\n",
"\n",
"Let's take, for example, the `MaskDecoder` of the Segment Anything model by Meta; it's a Transformer that takes as input an image and a prompt and outputs a segmentation mask. You can prompt it with points to guide the segmentation. [Here is a link](https://github.com/finegrain-ai/refiners/blob/main/src/refiners/foundationals/segment_anything/mask_decoder.py) to the complete implementation in Refiners\n",
"\n",
"So the inputs are:\n",
"\n",
" - an image of shape (3, 224, 224)\n",
" - several points of shape (N, 2) \n",
"\n",
"One way to consider the points is to add a `CrossAttention` layer that will attend to the points from the image. Cross attention is a standard `Attention` layer, but the key and value come from a source different from the query. In our case, the query is the image, the key and value are the points embeddings.\n",
"\n",
"![image.png](attachment:image.png)\n",
"\n",
"\n",
"Let's start by building a point encoder (to simplify that all points have the same \"meaning\")."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 5, 128])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class PointEncoder(fl.Chain):\n",
" def __init__(self, dim: int = 128) -> None:\n",
" self.dim = dim\n",
" super().__init__(\n",
" fl.Linear(2, dim),\n",
" fl.SiLU(),\n",
" fl.Linear(dim, dim),\n",
" fl.SiLU(),\n",
" fl.Linear(dim, dim),\n",
" fl.Unsqueeze(0),\n",
" )\n",
"\n",
"\n",
"points = torch.randn(5, 2)\n",
"PointEncoder()(points).shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 197, 128])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Chains can handle multiple inputs\n",
"class CrossAttention(fl.Chain):\n",
" def __init__(self, dim: int = 128, num_heads: int = 8) -> None:\n",
" self.dim = dim\n",
" self.num_heads = num_heads\n",
" super().__init__(\n",
" fl.Parallel(\n",
" fl.GetArg(0),\n",
" fl.GetArg(1),\n",
" fl.GetArg(1),\n",
" ),\n",
" fl.Distribute(\n",
" fl.Linear(dim, dim),\n",
" fl.Linear(dim, dim),\n",
" fl.Linear(dim, dim),\n",
" ),\n",
" ScaledDotProductAttention(num_heads=num_heads),\n",
" fl.Linear(dim, dim),\n",
" )\n",
"\n",
"\n",
"points_embedding = torch.randn(1, 5, 128)\n",
"patch_embedding = torch.randn(1, 197, 128)\n",
"CrossAttention()(patch_embedding, points_embedding).shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now ideally, I would like to insert this `CrossAttention` layer in the middle of the `Transformer` like this:\n",
"\n",
"```python\n",
"class TranformerLayer(fl.Chain):\n",
" def __init__(\n",
" self, dim: int = 128, num_heads: int = 8, inner_dim: int = 512\n",
" ) -> None:\n",
" self.dim = dim\n",
" self.num_heads = num_heads\n",
" self.inner_dim = inner_dim\n",
" super().__init__(\n",
" fl.LayerNorm(dim),\n",
" Attention(dim, num_heads),\n",
" fl.LayerNorm(dim),\n",
" CrossAttention(dim, num_heads),\n",
" fl.LayerNorm(dim),\n",
" FeedForward(dim, inner_dim),\n",
" )\n",
"\n",
"```\n",
"\n",
"But how do the `point_embedding` get into the `CrossAttention` layer? That's where the `Context` API comes into play."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chain = fl.Chain(\n",
" fl.Linear(2, 2),\n",
" fl.Concatenate(\n",
" fl.Linear(2, 2),\n",
" fl.UseContext(\"embedding\", \"value\"),\n",
" dim=-1,\n",
" ),\n",
" fl.Linear(5, 2),\n",
")\n",
"\n",
"chain.set_context(\"embedding\", {\"value\": torch.randn(1, 3)})\n",
"print(f\"Current embedding context: {chain.use_context('embedding')}\")\n",
"\n",
"x = torch.randn(1, 2)\n",
"chain(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the context is recursive, so you can access the context of an outer `Chain` from an inner `Chain`.\n",
"\n",
"We can rewrite the `CrossAttention` layer using context instead of passing the `point_embedding` as an argument."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 197, 128])\n"
]
},
{
"data": {
"text/plain": [
"(CHAIN) PointsCrossAttention()\n",
" ├── (PAR)\n",
" │ ├── Identity()\n",
" │ └── UseContext(context=vit, key=points_embedding) (x2)\n",
" ├── (DISTR)\n",
" │ └── Linear(in_features=128, out_features=128) (x3)\n",
" ├── ScaledDotProductAttention(num_heads=8)\n",
" └── Linear(in_features=128, out_features=128)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class PointsCrossAttention(fl.Chain):\n",
" def __init__(self, dim: int = 128, num_heads: int = 8) -> None:\n",
" self.dim = dim\n",
" self.num_heads = num_heads\n",
" super().__init__(\n",
" fl.Parallel(\n",
" fl.Identity(),\n",
" fl.UseContext(\"vit\", \"points_embedding\"),\n",
" fl.UseContext(\"vit\", \"points_embedding\"),\n",
" ),\n",
" fl.Distribute(\n",
" fl.Linear(dim, dim),\n",
" fl.Linear(dim, dim),\n",
" fl.Linear(dim, dim),\n",
" ),\n",
" ScaledDotProductAttention(num_heads=num_heads),\n",
" fl.Linear(dim, dim),\n",
" )\n",
"\n",
"\n",
"points_cross_attention = PointsCrossAttention()\n",
"\n",
"# If the context is not set, the layer will raise an error\n",
"points_embedding = torch.randn(1, 5, 128)\n",
"points_cross_attention.set_context(\"vit\", {\"points_embedding\": points_embedding})\n",
"\n",
"x = torch.randn(1, 197, 128)\n",
"\n",
"print(points_cross_attention(x).shape)\n",
"points_cross_attention"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's rewrite the `TransformerLayer` using the `PointsCrossAttention` layer.\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 197, 128])\n"
]
}
],
"source": [
"class TranformerLayer(fl.Chain):\n",
" def __init__(\n",
" self, dim: int = 128, num_heads: int = 8, inner_dim: int = 512\n",
" ) -> None:\n",
" self.dim = dim\n",
" self.num_heads = num_heads\n",
" self.inner_dim = inner_dim\n",
" super().__init__(\n",
" fl.LayerNorm(dim),\n",
" Attention(dim, num_heads),\n",
" fl.LayerNorm(dim),\n",
" PointsCrossAttention(dim, num_heads),\n",
" fl.LayerNorm(dim),\n",
" FeedForward(dim, inner_dim),\n",
" )\n",
"\n",
"\n",
"layer = TranformerLayer()\n",
"x = torch.randn(1, 197, 128)\n",
"points_embedding = torch.randn(1, 5, 128)\n",
"layer.set_context(\"vit\", {\"points_embedding\": points_embedding})\n",
"print(layer(x).shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ViT is still valid as is, but we might want to add the `PointEncoder` directly into the model to not have to deal with multiple models separately. To do that, we can wrap the `PointEncoder` into a `Passthrough` layer that will let the main arguments pass through, but will also add the `point_embedding` to the context using a `SetContext` layer."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 197, 128])\n"
]
},
{
"data": {
"text/plain": [
"(CHAIN) ViT()\n",
" ├── (PASS) PointEncoder()\n",
" │ ├── UseContext(context=vit, key=points_tensor)\n",
" │ ├── Linear(in_features=2, out_features=128) #1\n",
" │ ├── SiLU() #1\n",
" │ ├── Linear(in_features=128, out_features=128) #2\n",
" │ ├── SiLU() #2\n",
" │ ├── Linear(in_features=128, out_features=128) #3\n",
" │ ├── Unsqueeze(dim=0)\n",
" │ └── SetContext(context=vit, key=points_embedding)\n",
" ├── (CAT)\n",
" │ ├── (CHAIN) PatchEncoder()\n",
" │ │ ├── Conv2d(in_channels=3, out_channels=128, kernel_size=(16, 16), stride=(16, 16))\n",
" │ │ └── Reshape(shape=(-1, 128))\n",
" │ └── (CHAIN) ClassToken()\n",
" │ └── Parameter(dims=(1, 128))\n",
" ├── (RES) PositionalToken(num_patches=197)\n",
" │ └── Parameter(dims=(197, 128))\n",
" └── (CHAIN) Transformer()\n",
" └── (CHAIN) TranformerLayer() (x4)\n",
" ├── LayerNorm(normalized_shape=(128,)) #1\n",
" ├── (RES) Attention()\n",
" │ ├── (PAR)\n",
" │ │ └── Linear(in_features=128, out_features=128) (x3)\n",
" │ ├── ScaledDotProductAttention(num_heads=8)\n",
" │ └── Linear(in_features=128, out_features=128)\n",
" ├── LayerNorm(normalized_shape=(128,)) #2\n",
" ├── (CHAIN) PointsCrossAttention()\n",
" │ ├── (PAR)\n",
" │ │ ├── Identity()\n",
" │ │ └── UseContext(context=vit, key=points_embedding) (x2)\n",
" │ ├── (DISTR)\n",
" │ │ └── Linear(in_features=128, out_features=128) (x3)\n",
" │ ├── ScaledDotProductAttention(num_heads=8)\n",
" │ └── Linear(in_features=128, out_features=128)\n",
" ├── LayerNorm(normalized_shape=(128,)) #3\n",
" └── (RES) FeedForward()\n",
" ├── Linear(in_features=128, out_features=512) #1\n",
" ├── SiLU()\n",
" └── Linear(in_features=512, out_features=128) #2"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class PointEncoder(fl.Passthrough):\n",
" def __init__(self, dim: int = 128) -> None:\n",
" self.dim = dim\n",
" super().__init__(\n",
" fl.UseContext(\"vit\", \"points_tensor\"),\n",
" fl.Linear(2, dim),\n",
" fl.SiLU(),\n",
" fl.Linear(dim, dim),\n",
" fl.SiLU(),\n",
" fl.Linear(dim, dim),\n",
" fl.Unsqueeze(0),\n",
" fl.SetContext(\"vit\", \"points_embedding\"),\n",
" )\n",
"\n",
"\n",
"class ViT(fl.Chain):\n",
" def __init__(\n",
" self,\n",
" dim: int = 128,\n",
" patch_size: int = 16,\n",
" image_size: int = 224,\n",
" num_layers: int = 4,\n",
" ) -> None:\n",
" self.dim = dim\n",
" self.patch_size = patch_size\n",
" self.image_size = image_size\n",
" self.num_layers = num_layers\n",
" self.num_patches = (image_size // patch_size) ** 2 + 1\n",
" super().__init__(\n",
" PointEncoder(dim=dim),\n",
" fl.Concatenate(\n",
" PatchEncoder(in_channels=3, dim=dim, patch_size=patch_size),\n",
" ClassToken(dim=dim),\n",
" dim=1,\n",
" ),\n",
" PositionalToken(num_patches=self.num_patches),\n",
" Transformer(TranformerLayer(dim=dim) for _ in range(num_layers)),\n",
" )\n",
"\n",
"\n",
"vit = ViT()\n",
"x = torch.randn(1, 3, 224, 224)\n",
"points = torch.randn(5, 2)\n",
"vit.set_context(\"vit\", {\"points_tensor\": points})\n",
"print(vit(x).shape)\n",
"vit"
]
},
{
"attachments": {
"image.png": {
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}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adaptation\n",
"\n",
"I think to have a very explicit and declarative model like we showcased here is an interesting property, but the place where it shines the most is when you want to adapt a model to a new task.\n",
"\n",
"Let's demonstrate on a simple example how to create a LoRA adaptation on the ViT without having to rewrite the whole model.\n",
"\n",
"The low-rank adaptation technique adds lighter new layers on top of the model. The rank is the inner_dim of the new layers. The outer layer is zero-initialized, so the model's output is the same before training.\n",
"![image.png](attachment:image.png)\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 1, 128])\n"
]
},
{
"data": {
"text/plain": [
"(CHAIN) Lora(in_features=128, out_features=128)\n",
" ├── Linear(in_features=128, out_features=16) #1\n",
" └── Linear(in_features=16, out_features=128) #2"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class Lora(fl.Chain):\n",
" def __init__(\n",
" self,\n",
" in_features: int,\n",
" out_features: int,\n",
" rank: int = 16,\n",
" ) -> None:\n",
" self.in_features = in_features\n",
" self.out_features = out_features\n",
" self.rank = rank\n",
" self.scale: float = 1.0\n",
"\n",
" super().__init__(\n",
" fl.Linear(in_features=in_features, out_features=rank, bias=False),\n",
" fl.Linear(in_features=rank, out_features=out_features),\n",
" )\n",
"\n",
" nn.init.normal_(tensor=self.Linear_1.weight, std=1 / self.rank)\n",
" nn.init.zeros_(tensor=self.Linear_2.weight)\n",
"\n",
"\n",
"lora = Lora(128, 128)\n",
"x = torch.randn(1, 1, 128)\n",
"print(lora(x).shape)\n",
"lora"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we want to be able to insert this into any `Linear` layer of the Model. To do that, we can use the `Adapter` class."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Adapter:\n",
"(SUM) LoraAdapter()\n",
" ├── Linear(in_features=128, out_features=128)\n",
" └── (CHAIN) Lora(in_features=128, out_features=128)\n",
" ├── Linear(in_features=128, out_features=16) #1\n",
" └── Linear(in_features=16, out_features=128) #2\n",
"\n",
"Note that the original attention is not modified:\n",
"(RES) Attention()\n",
" ├── (PAR)\n",
" │ └── Linear(in_features=128, out_features=128) (x3)\n",
" ├── ScaledDotProductAttention(num_heads=8)\n",
" └── Linear(in_features=128, out_features=128) \n",
"\n"
]
}
],
"source": [
"from refiners.fluxion.adapters import Adapter\n",
"\n",
"\n",
"class LoraAdapter(fl.Sum, Adapter[fl.Linear]):\n",
" def __init__(\n",
" self,\n",
" target: fl.Linear,\n",
" rank: int = 16,\n",
" ) -> None:\n",
" self.in_features = target.in_features\n",
" self.out_features = target.out_features\n",
" self.rank = rank\n",
" # the setup_adapter method is used to remove boilerplate code\n",
" with self.setup_adapter(target):\n",
" super().__init__(\n",
" target,\n",
" Lora(\n",
" in_features=target.in_features,\n",
" out_features=target.out_features,\n",
" rank=rank,\n",
" ),\n",
" )\n",
"\n",
"\n",
"attention = Attention()\n",
"linear = attention.ensure_find(fl.Linear)\n",
"adapter = LoraAdapter(linear)\n",
"print(\n",
" f\"\"\"\n",
"Adapter:\n",
"{repr(adapter)}\n",
"\n",
"Note that the original attention is not modified:\n",
"{repr(attention)} \n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now `inject` the `Adapter` into the `FeedForward` layer of the `TransformerLayer`. One subtlety is that the `Linear` layer is considered a `WeightedModule` and as such can belong to multiple `Chain` at the same time. So we need to specify which `Chain` we want to inject the `Adapter` into."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(RES) Attention()\n",
" ├── (PAR)\n",
" │ ├── (SUM) LoraAdapter()\n",
" │ │ ├── Linear(in_features=128, out_features=128)\n",
" │ │ └── (CHAIN) Lora(in_features=128, out_features=128)\n",
" │ │ ├── Linear(in_features=128, out_features=16) #1\n",
" │ │ └── Linear(in_features=16, out_features=128) #2\n",
" │ └── Linear(in_features=128, out_features=128) (x2)\n",
" ├── ScaledDotProductAttention(num_heads=8)\n",
" └── Linear(in_features=128, out_features=128)\n",
"(RES) Attention()\n",
" ├── (PAR)\n",
" │ └── Linear(in_features=128, out_features=128) (x3)\n",
" ├── ScaledDotProductAttention(num_heads=8)\n",
" └── Linear(in_features=128, out_features=128)\n"
]
}
],
"source": [
"adapter.inject(parent=attention.Parallel)\n",
"print(repr(attention))\n",
"\n",
"# we can also `eject` the adapter to get back to normal\n",
"adapter.eject()\n",
"print(repr(attention))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally let's write a top-level adapter that will inject the `Adapter` into all the `Linear` layers of the `ViT`."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(CHAIN) ViT()\n",
" ├── (PASS) PointEncoder()\n",
" │ ├── UseContext(context=vit, key=points_tensor)\n",
" │ ├── (SUM) LoraAdapter() #1\n",
" │ │ ├── Linear(in_features=2, out_features=128)\n",
" │ │ └── (CHAIN) Lora(in_features=2, out_features=128)\n",
" │ │ ├── Linear(in_features=2, out_features=16) #1\n",
" │ │ └── Linear(in_features=16, out_features=128) #2\n",
" │ ├── SiLU() #1\n",
" │ ├── (SUM) LoraAdapter() #2\n",
" │ │ ├── Linear(in_features=128, out_features=128)\n",
" │ │ └── (CHAIN) Lora(in_features=128, out_features=128)\n",
" │ │ ├── Linear(in_features=128, out_features=16) #1\n",
" │ │ └── Linear(in_features=16, out_features=128) #2\n",
" │ ├── SiLU() #2\n",
" │ ├── (SUM) LoraAdapter() #3\n",
" │ │ ├── Linear(in_features=128, out_features=128)\n",
" │ │ └── (CHAIN) Lora(in_features=128, out_features=128)\n",
" │ │ ├── Linear(in_features=128, out_features=16) #1\n",
" │ │ └── Linear(in_features=16, out_features=128) #2\n",
" │ ├── Unsqueeze(dim=0)\n",
" │ └── SetContext(context=vit, key=points_embedding)\n",
" ├── (CAT)\n",
" │ ├── (CHAIN) PatchEncoder()\n",
" │ │ ├── Conv2d(in_channels=3, out_channels=128, kernel_size=(16, 16), stride=(16, 16))\n",
" │ │ └── Reshape(shape=(-1, 128))\n",
" │ └── (CHAIN) ClassToken()\n",
" │ └── Parameter(dims=(1, 128))\n",
" ├── (RES) PositionalToken(num_patches=197)\n",
" │ └── Parameter(dims=(197, 128))\n",
" └── (CHAIN) Transformer()\n",
" └── (CHAIN) TranformerLayer() (x4)\n",
" ├── LayerNorm(normalized_shape=(128,)) #1\n",
" ├── (RES) Attention()\n",
" │ ├── (PAR)\n",
" │ │ └── (SUM) LoraAdapter() (x3)\n",
" │ │ ├── Linear(in_features=128, out_features=128)\n",
" │ │ └── (CHAIN) Lora(in_features=128, out_features=128)\n",
" │ │ ├── Linear(in_features=128, out_features=16) #1\n",
" │ │ └── Linear(in_features=16, out_features=128) #2\n",
" │ ├── ScaledDotProductAttention(num_heads=8)\n",
" │ └── (SUM) LoraAdapter()\n",
" │ ├── Linear(in_features=128, out_features=128)\n",
" │ └── (CHAIN) Lora(in_features=128, out_features=128)\n",
" │ ├── Linear(in_features=128, out_features=16) #1\n",
" │ └── Linear(in_features=16, out_features=128) #2\n",
" ├── LayerNorm(normalized_shape=(128,)) #2\n",
" ├── (CHAIN) PointsCrossAttention()\n",
" │ ├── (PAR)\n",
" │ │ ├── Identity()\n",
" │ │ └── UseContext(context=vit, key=points_embedding) (x2)\n",
" │ ├── (DISTR)\n",
" │ │ └── (SUM) LoraAdapter() (x3)\n",
" │ │ ├── Linear(in_features=128, out_features=128)\n",
" │ │ └── (CHAIN) Lora(in_features=128, out_features=128)\n",
" │ │ ├── Linear(in_features=128, out_features=16) #1\n",
" │ │ └── Linear(in_features=16, out_features=128) #2\n",
" │ ├── ScaledDotProductAttention(num_heads=8)\n",
" │ └── (SUM) LoraAdapter()\n",
" │ ├── Linear(in_features=128, out_features=128)\n",
" │ └── (CHAIN) Lora(in_features=128, out_features=128)\n",
" │ ├── Linear(in_features=128, out_features=16) #1\n",
" │ └── Linear(in_features=16, out_features=128) #2\n",
" ├── LayerNorm(normalized_shape=(128,)) #3\n",
" └── (RES) FeedForward()\n",
" ├── (SUM) LoraAdapter() #1\n",
" │ ├── Linear(in_features=128, out_features=512)\n",
" │ └── (CHAIN) Lora(in_features=128, out_features=512)\n",
" │ ├── Linear(in_features=128, out_features=16) #1\n",
" │ └── Linear(in_features=16, out_features=512) #2\n",
" ├── SiLU()\n",
" └── (SUM) LoraAdapter() #2\n",
" ├── Linear(in_features=512, out_features=128)\n",
" └── (CHAIN) Lora(in_features=512, out_features=128)\n",
" ├── Linear(in_features=512, out_features=16) #1\n",
" └── Linear(in_features=16, out_features=128) #2\n",
"torch.Size([1, 197, 128])\n",
"(CHAIN) ViT()\n",
" ├── (PASS) PointEncoder()\n",
" │ ├── UseContext(context=vit, key=points_tensor)\n",
" │ ├── Linear(in_features=2, out_features=128) #1\n",
" │ ├── SiLU() #1\n",
" │ ├── Linear(in_features=128, out_features=128) #2\n",
" │ ├── SiLU() #2\n",
" │ ├── Linear(in_features=128, out_features=128) #3\n",
" │ ├── Unsqueeze(dim=0)\n",
" │ └── SetContext(context=vit, key=points_embedding)\n",
" ├── (CAT)\n",
" │ ├── (CHAIN) PatchEncoder()\n",
" │ │ ├── Conv2d(in_channels=3, out_channels=128, kernel_size=(16, 16), stride=(16, 16))\n",
" │ │ └── Reshape(shape=(-1, 128))\n",
" │ └── (CHAIN) ClassToken()\n",
" │ └── Parameter(dims=(1, 128))\n",
" ├── (RES) PositionalToken(num_patches=197)\n",
" │ └── Parameter(dims=(197, 128))\n",
" └── (CHAIN) Transformer()\n",
" └── (CHAIN) TranformerLayer() (x4)\n",
" ├── LayerNorm(normalized_shape=(128,)) #1\n",
" ├── (RES) Attention()\n",
" │ ├── (PAR)\n",
" │ │ └── Linear(in_features=128, out_features=128) (x3)\n",
" │ ├── ScaledDotProductAttention(num_heads=8)\n",
" │ └── Linear(in_features=128, out_features=128)\n",
" ├── LayerNorm(normalized_shape=(128,)) #2\n",
" ├── (CHAIN) PointsCrossAttention()\n",
" │ ├── (PAR)\n",
" │ │ ├── Identity()\n",
" │ │ └── UseContext(context=vit, key=points_embedding) (x2)\n",
" │ ├── (DISTR)\n",
" │ │ └── Linear(in_features=128, out_features=128) (x3)\n",
" │ ├── ScaledDotProductAttention(num_heads=8)\n",
" │ └── Linear(in_features=128, out_features=128)\n",
" ├── LayerNorm(normalized_shape=(128,)) #3\n",
" └── (RES) FeedForward()\n",
" ├── Linear(in_features=128, out_features=512) #1\n",
" ├── SiLU()\n",
" └── Linear(in_features=512, out_features=128) #2\n"
]
}
],
"source": [
"from typing import Self\n",
"\n",
"\n",
"class ViTLoraAdapter(fl.Chain, Adapter[ViT]):\n",
" def __init__(\n",
" self,\n",
" target: ViT,\n",
" rank: int = 16,\n",
" ) -> None:\n",
" self.rank = rank\n",
" with self.setup_adapter(target):\n",
" super().__init__(target)\n",
"\n",
" # Let's wrap all the Linear layers in the ViT model into LoraAdapters\n",
" self.sub_adapters: list[tuple[LoraAdapter, fl.Chain]] = []\n",
" for linear, parent in self.target.walk(fl.Linear):\n",
" self.sub_adapters.append((LoraAdapter(target=linear, rank=rank), parent))\n",
"\n",
" def inject(self, parent: fl.Chain | None = None) -> Self:\n",
" for adapter, adapter_parent in self.sub_adapters:\n",
" adapter.inject(adapter_parent)\n",
" return super().inject(parent)\n",
"\n",
" def eject(self) -> None:\n",
" for adapter, _ in self.sub_adapters:\n",
" adapter.eject()\n",
" super().eject()\n",
"\n",
"\n",
"vit = ViT()\n",
"x = torch.randn(1, 3, 224, 224)\n",
"points = torch.randn(5, 2)\n",
"vit.set_context(\"vit\", {\"points_tensor\": points})\n",
"adapter = ViTLoraAdapter(vit)\n",
"adapter.inject() # since `ViT` has no parent, no need to pass it to `inject`\n",
"print(repr(vit))\n",
"print(vit(x).shape)\n",
"\n",
"# we can also `eject` the adapter to get back to normal\n",
"adapter.eject()\n",
"print(repr(vit))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in a new issue Copy permalink