From 23a8e388a7f78ab993b45e8c039fbacb8932a33d Mon Sep 17 00:00:00 2001 From: Robert Crowe Date: Wed, 15 Jan 2025 13:04:24 -0800 Subject: [PATCH 1/4] Updates in Colab (small tweaks) --- docs/source/JAX_porting_PyTorch_model.ipynb | 4800 ++++++++++--------- 1 file changed, 2556 insertions(+), 2244 deletions(-) diff --git a/docs/source/JAX_porting_PyTorch_model.ipynb b/docs/source/JAX_porting_PyTorch_model.ipynb index e1b1456..3080781 100644 --- a/docs/source/JAX_porting_PyTorch_model.ipynb +++ b/docs/source/JAX_porting_PyTorch_model.ipynb @@ -1,2247 +1,2559 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "b69996dc-49af-4a0e-a4e6-36d81b51f2b4", - "metadata": {}, - "source": [ - "# Porting a PyTorch model to JAX\n", - "\n", - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/JAX_porting_PyTorch_model.ipynb)\n", - "\n", - "In this tutorial we will learn how to port a PyTorch model to JAX and [Flax](https://flax.readthedocs.io/en/latest/nnx_basics.html). Flax provides an API very similar to the PyTorch `torch.nn` module and porting PyTorch models is rather straightforward. To install Flax, we can simply execute the following command: `pip install -U flax treescope`.\n", - "\n", - "Say we have a trained PyTorch computer-vision model to classify images that we would like to port to JAX. We will use [`TorchVision`](https://pytorch.org/vision/stable/index.html) to provide a [MaxVit](https://pytorch.org/vision/stable/models/maxvit.html) model trained on ImageNet (MaxViT: Multi-Axis Vision Transformer, https://arxiv.org/abs/2204.01697).\n", - "\n", - "First, we set up the model using TorchVision and explore briefly the model's architecture and the blocks we need to port. Next, we define equivalent blocks and the whole model using Flax. After that, we port the weights. Finally, we run some tests to ensure the correctness of the ported model." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "38504f77-4150-47bd-9cf9-3116fe370746", - "metadata": {}, - "outputs": [], - "source": [ - "import jax\n", - "import jax.numpy as jnp\n", - "from flax import nnx" - ] - }, - { - "cell_type": "markdown", - "id": "95a364c2-d34e-4820-8a86-f43f59c911bf", - "metadata": {}, - "source": [ - "## MaxViT PyTorch model setup\n", - "\n", - "### Model's architecture\n", - "\n", - "The MaxVit model is [implemented in TorchVision](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568). If we inspect the [forward pass](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L707-L712) of the model, we can see that it contains three high-level parts:\n", - "- [stem](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L641-L655): a few convolutions, batchnorms, GELU activations.\n", - "- [blocks](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L672-L692): list of MaxViT blocks\n", - "- [classifier](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L696-L703): adaptive average pooling, few linear layers and Tanh activation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.11/site-packages/torch/functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1720538435607/work/aten/src/ATen/native/TensorShape.cpp:3609.)\n", - " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" - ] - } - ], - "source": [ - "from torchvision.models import maxvit_t, MaxVit_T_Weights\n", - "\n", - "torch_model = maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1)" - ] - }, - { - "cell_type": "markdown", - "id": "45635b2d-a77a-4368-9ecb-dbb440e647ee", - "metadata": {}, - "source": [ - "We can use `flax.nnx.display` to display the model's architecture:\n", - "```python\n", - "nnx.display(torch_model)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "0a36676a-1561-4de0-8e25-38bab90581d0", - "metadata": {}, - "source": [ - "We can see that there are four MaxViT blocks in the model and each block contains:\n", - "- MaxViT layers: two layers for blocks 0, 1, 3 and five layers for the block 4" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0d5bf6aa-c720-4400-a276-602fff53b413", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4, [2, 2, 5, 2])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(torch_model.blocks), [len(b.layers) for b in torch_model.blocks]" - ] - }, - { - "cell_type": "markdown", - "id": "a1d55688-5999-41de-a915-eae8b281eb18", - "metadata": {}, - "source": [ - "A [MaxViT layer](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L386) is composed of: [`MBConv`](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L53), `window_attention` as [`PartitionAttentionLayer`](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L282) and `grid_attention` as `PartitionAttentionLayer`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer']]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[[mod.__class__.__name__ for mod in maxvit_layer.layers] for b in torch_model.blocks for maxvit_layer in b.layers]" - ] - }, - { - "cell_type": "markdown", - "id": "d57f8545-43a4-423d-b701-c2e2ca0ebfc1", - "metadata": {}, - "source": [ - "### Inference on data\n", - "\n", - "Let's check the model on dummy input and on a real image" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([2, 1000])\n" - ] - } - ], - "source": [ - "import torch\n", - "\n", - "\n", - "torch_model.eval()\n", - "with torch.inference_mode():\n", - " x = torch.rand(2, 3, 224, 224)\n", - " output = torch_model(x)\n", - "\n", - "print(output.shape) # (2, 1000)" - ] - }, - { - "cell_type": "markdown", - "id": "133bcf21-8a9c-4c27-b551-39b7dfdcfe1c", - "metadata": {}, - "source": [ - "We can download an image of a Pembroke Corgy dog from [TorchVision's gallery](https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true) together with [ImageNet classes dictionary](https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json):\n", - "\n", - "```bash\n", - "wget \"https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\" -O dog1.jpg\n", - "wget \"https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\" -O imagenet_class_index.json\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "82be8baf-1292-4766-be34-28c510563d71", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction for the Dog: ['n02113023', 'Pembroke'], score: 0.7800846695899963\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "import json\n", - "from torchvision.io import read_image\n", - "\n", - "\n", - "preprocess = MaxVit_T_Weights.IMAGENET1K_V1.transforms()\n", - "\n", - "with open(\"imagenet_class_index.json\") as labels_file:\n", - " labels = json.load(labels_file)\n", - "\n", - "\n", - "dog1 = read_image(\"dog1.jpg\")\n", - "tensor = preprocess(dog1)\n", - "\n", - "torch_model.eval()\n", - "with torch.inference_mode():\n", - " output = torch_model(tensor.unsqueeze(dim=0))\n", - "\n", - "class_id = output.argmax(dim=1).item()\n", - "\n", - "print(f\"Prediction for the Dog: {labels[str(class_id)]}, score: {output.softmax(dim=-1)[0, class_id]}\")\n", - "\n", - "plt.title(f\"{labels[str(class_id)]}\\nScore: {output.softmax(dim=-1)[0, class_id]}\")\n", - "plt.imshow(dog1.permute(1, 2, 0))" - ] - }, - { - "cell_type": "markdown", - "id": "8cbe4ccc-224b-4e8a-a2a9-e2c756c9b207", - "metadata": {}, - "source": [ - "## Port MaxViT model to JAX\n", - "\n", - "To port the [PyTorch implementation of the MaxVit model](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568) in JAX using the Flax module, we will implement the following required modules:\n", - "\n", - "- `MaxViT`\n", - " - `MaxVitBlock`\n", - " - `MaxVitLayer`\n", - " - `MBConv`\n", - " - `Conv2dNormActivation`\n", - " - `SqueezeExcitation`\n", - " - `PartitionAttentionLayer`\n", - " - `RelativePositionalMultiHeadAttention`\n", - " - `WindowDepartition`\n", - " - `WindowPartition`\n", - " - `SwapAxes`\n", - " - `StochasticDepth`\n", - "\n", - "The Flax NNX module is very similar to PyTorch `torch.nn` module and we can map the following modules between PyTorch and Flax:\n", - "- `nn.Sequential` and `nn.ModuleList` -> `nnx.Sequential`\n", - "- `nn.Linear` -> `nnx.Linear`\n", - "- `nn.Conv2d` -> `nnx.Conv`\n", - "- `nn.BatchNorm2d` -> `nnx.BatchNorm`\n", - "- Activations like `nn.ReLU` -> `nnx.relu`\n", - "- Pooling layers like `nn.AvgPool2d(...)` -> `lambda x: nnx.avg_pool(x, ...)`\n", - "- `nn.AdaptiveAvgPool2d(1)` -> `lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2]))`, x is in NHWC format\n", - "- `nn.Flatten()` -> `lambda x: x.reshape(x.shape[0], -1)`\n", - "\n", - "\n", - "If the PyTorch model defines a learnable parameter and a buffer:\n", - "```python\n", - "class Model(nn.Module):\n", - " def __init__(self, ...):\n", - " ...\n", - " self.p = nn.Parameter(torch.ones(10))\n", - " self.register_buffer(\"b\", torch.ones(5))\n", - "```\n", - "an equivalent code in Flax would be\n", - "```python\n", - "class Buffer(nnx.Variable):\n", - " pass\n", - "\n", - "\n", - "class Model(nnx.Module):\n", - " def __init__(self, ...):\n", - " ...\n", - " self.p = nnx.Param(jnp.ones((10,)))\n", - " self.b = Buffer(jnp.ones(5))\n", - "```\n", - "\n", - "To inspect NNX module's learnable parameters and buffers, we can use `nnx.state`:\n", - "```python\n", - "nnx_module = ...\n", - "for k, v in nnx.state(nnx_module, nnx.Param).flat_state().items():\n", - " print(\n", - " k,\n", - " v.value.mean() if v.value is not None else None\n", - " )\n", - "\n", - "for k, v in nnx.state(nnx_module, (nnx.BatchStat, Buffer)).flat_state().items():\n", - " print(\n", - " k,\n", - " v.value.mean() if v.value.dtype == \"float32\" else v.value.sum()\n", - " )\n", - "```\n", - "The equivalent PyTorch code is:\n", - "```python\n", - "torch_module = ...\n", - "\n", - "for m, p in torch_module.named_parameters():\n", - " print(m, p.detach().mean())\n", - "\n", - "for m, b in torch_module.named_buffers():\n", - " print(\n", - " m,\n", - " b.mean() if b.dtype == torch.float32 else b.sum()\n", - " )\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "305ac55b-62ed-4f4d-902c-c6f3082afb02", - "metadata": {}, - "source": [ - "Please note some differences between `torch.nn` and Flax when porting models:\n", - "- We should pass `rngs` to all NNX modules with parameters: e.g. `nnx.Linear(..., rngs=nnx.Rngs(0))`\n", - "- For a 2D convolution:\n", - " - In Flax, we need to explicitly define `kernel_size`, `strides` as two ints tuples, e.g. `(3, 3)`\n", - " - If PyTorch code defines `padding` as integer, e.g. 2, in Flax it should be explicitly defined as a tuple of two ints per dimension, i.e. `((2, 2), (2, 2))`.\n", - "- For a batch normalization: `momentum` value in `torch.nn` should be defined as `1.0 - momentum` in Flax.\n", - "- 4D input arrays in Flax should be in NHWC format, i.e. of shape (N, H, W, C) compared to NCHW format (or (N, C, H, W) shape) in PyTorch." - ] - }, - { - "cell_type": "markdown", - "id": "8d7e3479-bffe-4cb6-81e1-ed8f972c5bf0", - "metadata": {}, - "source": [ - "Below we implement one by one all the modules from the above list and add simple forward pass checks.\n", - "Let's first implement equivalent of `nn.Identity`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "54ece7f1-14c1-41ef-980a-fc279d1702f2", - "metadata": {}, - "outputs": [], - "source": [ - "class Identity(nnx.Module):\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " return x" - ] - }, - { - "cell_type": "markdown", - "id": "dd87b2aa-0285-4995-a9aa-ebd58ae00de6", - "metadata": {}, - "source": [ - "### `Conv2dNormActivation` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/misc.py#L125)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "69d71163-676e-4ad3-8d8c-45efaafd76e7", - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Callable, List, Optional, Tuple\n", - "from flax import nnx\n", - "\n", - "\n", - "class Conv2dNormActivation(nnx.Sequential):\n", - " def __init__(\n", - " self,\n", - " in_channels: int,\n", - " out_channels: int,\n", - " kernel_size: int = 3,\n", - " stride: int = 1,\n", - " padding: Optional[int] = None,\n", - " groups: int = 1,\n", - " norm_layer: Callable[..., nnx.Module] = nnx.BatchNorm,\n", - " activation_layer: Callable = nnx.relu,\n", - " dilation: int = 1,\n", - " bias: Optional[bool] = None,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " self.out_channels = out_channels\n", - "\n", - " if padding is None:\n", - " padding = (kernel_size - 1) // 2 * dilation\n", - " if bias is None:\n", - " bias = norm_layer is None\n", - "\n", - " # sequence integer pairs that give the padding to apply before\n", - " # and after each spatial dimension\n", - " padding = ((padding, padding), (padding, padding))\n", - "\n", - " layers = [\n", - " nnx.Conv(\n", - " in_channels,\n", - " out_channels,\n", - " kernel_size=(kernel_size, kernel_size),\n", - " strides=(stride, stride),\n", - " padding=padding,\n", - " kernel_dilation=(dilation, dilation),\n", - " feature_group_count=groups,\n", - " use_bias=bias,\n", - " rngs=rngs,\n", - " )\n", - " ]\n", - "\n", - " if norm_layer is not None:\n", - " layers.append(norm_layer(out_channels, rngs=rngs))\n", - "\n", - " if activation_layer is not None:\n", - " layers.append(activation_layer)\n", - "\n", - " super().__init__(*layers)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4, 14, 14, 64)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = jnp.ones((4, 28, 28, 32))\n", - "mod = Conv2dNormActivation(32, 64, 3, 2, 1)\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "2d0cd827-ad40-4cd3-9560-6565a3df10bc", - "metadata": {}, - "source": [ - "### `SqueezeExcitation` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/misc.py#L224)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4232689e-e6cc-4ffd-8a2a-41fbc34e57c2", - "metadata": {}, - "outputs": [], - "source": [ - "class SqueezeExcitation(nnx.Module):\n", - " def __init__(\n", - " self,\n", - " input_channels: int,\n", - " squeeze_channels: int,\n", - " activation: Callable = nnx.relu,\n", - " scale_activation: Callable = nnx.sigmoid,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " self.avgpool = nnx.avg_pool\n", - " self.fc1 = nnx.Conv(input_channels, squeeze_channels, (1, 1), rngs=rngs)\n", - " self.fc2 = nnx.Conv(squeeze_channels, input_channels, (1, 1), rngs=rngs)\n", - " self.activation = activation\n", - " self.scale_activation = scale_activation\n", - "\n", - " def _scale(self, x: jax.Array) -> jax.Array:\n", - " scale = self.avgpool(x, (x.shape[1], x.shape[2]))\n", - " scale = self.fc1(scale)\n", - " scale = self.activation(scale)\n", - " scale = self.fc2(scale)\n", - " return self.scale_activation(scale)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " scale = self._scale(x)\n", - " return scale * x" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "83c55286-b92e-49aa-bd5f-c2448a787673", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4, 28, 28, 32)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = jnp.ones((4, 28, 28, 32))\n", - "mod = SqueezeExcitation(32, 4)\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "7935790a-4cb1-46dc-ab73-12d3cb8fc636", - "metadata": {}, - "source": [ - "### `StochasticDepth` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/stochastic_depth.py#L50)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "96834419-eec1-4690-8bb0-447524f6bdde", - "metadata": {}, - "outputs": [], - "source": [ - "def stochastic_depth(\n", - " x: jax.Array,\n", - " p: float,\n", - " mode: str,\n", - " deterministic: bool = False,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - ") -> jax.Array:\n", - " if p < 0.0 or p > 1.0:\n", - " raise ValueError(f\"drop probability has to be between 0 and 1, but got {p}\")\n", - " if mode not in [\"batch\", \"row\"]:\n", - " raise ValueError(f\"mode has to be either 'batch' or 'row', but got {mode}\")\n", - " if deterministic or p == 0.0:\n", - " return x\n", - "\n", - " survival_rate = 1.0 - p\n", - " if mode == \"row\":\n", - " size = [x.shape[0]] + [1] * (x.ndim - 1)\n", - " else:\n", - " size = [1] * x.ndim\n", - "\n", - " noise = jax.random.bernoulli(\n", - " rngs.dropout(), p=survival_rate, shape=size\n", - " ).astype(dtype=x.dtype)\n", - "\n", - " if survival_rate > 0.0:\n", - " noise = noise / survival_rate\n", - "\n", - " return x * noise\n", - "\n", - "\n", - "class StochasticDepth(nnx.Module):\n", - " def __init__(\n", - " self,\n", - " p: float,\n", - " mode: str,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " self.p = p\n", - " self.mode = mode\n", - " self.deterministic = False\n", - " self.rngs = rngs\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " return stochastic_depth(\n", - " x, self.p, self.mode, self.deterministic, rngs=self.rngs\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "fd95babb-95b4-4015-957d-11b9c7b9957d", - "metadata": {}, - "outputs": [], - "source": [ - "x = jnp.ones((4, 28, 28, 32))\n", - "mod = StochasticDepth(0.5, \"row\")\n", - "\n", - "mod.eval()\n", - "y = mod(x)\n", - "assert (y == x).all()\n", - "\n", - "mod.train()\n", - "y = mod(x)\n", - "assert (y != x).any()" - ] - }, - { - "cell_type": "markdown", - "id": "0ce251eb-a8dc-4415-9856-d16421c1d646", - "metadata": {}, - "source": [ - "### `MBConv` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L53)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "636c713c-4a21-439a-b220-2b9407a06dfc", - "metadata": {}, - "outputs": [], - "source": [ - "class MBConv(nnx.Module):\n", - " def __init__(\n", - " self,\n", - " in_channels: int,\n", - " out_channels: int,\n", - " expansion_ratio: float,\n", - " squeeze_ratio: float,\n", - " stride: int,\n", - " activation_layer: Callable,\n", - " norm_layer: Callable[..., nnx.Module],\n", - " p_stochastic_dropout: float = 0.0,\n", - " rngs = nnx.Rngs(0),\n", - " ):\n", - " should_proj = stride != 1 or in_channels != out_channels\n", - " if should_proj:\n", - " proj = [nnx.Conv(\n", - " in_channels, out_channels, kernel_size=(1, 1), strides=(1, 1), use_bias=True, rngs=rngs\n", - " )]\n", - " if stride == 2:\n", - " padding = ((1, 1), (1, 1))\n", - " proj = [\n", - " lambda x: nnx.avg_pool(\n", - " x, window_shape=(3, 3), strides=(stride, stride), padding=padding\n", - " )\n", - " ] + proj\n", - " self.proj = nnx.Sequential(*proj)\n", - " else:\n", - " self.proj = Identity()\n", - "\n", - " mid_channels = int(out_channels * expansion_ratio)\n", - " sqz_channels = int(out_channels * squeeze_ratio)\n", - "\n", - " if p_stochastic_dropout:\n", - " self.stochastic_depth = StochasticDepth(p_stochastic_dropout, mode=\"row\", rngs=rngs)\n", - " else:\n", - " self.stochastic_depth = Identity()\n", - "\n", - " _layers = [\n", - " norm_layer(in_channels, rngs=rngs), # pre_norm\n", - " Conv2dNormActivation( # conv_a\n", - " in_channels,\n", - " mid_channels,\n", - " kernel_size=1,\n", - " stride=1,\n", - " padding=0,\n", - " activation_layer=activation_layer,\n", - " norm_layer=norm_layer,\n", - " rngs=rngs,\n", - " ),\n", - " Conv2dNormActivation( # conv_b\n", - " mid_channels,\n", - " mid_channels,\n", - " kernel_size=3,\n", - " stride=stride,\n", - " padding=1,\n", - " activation_layer=activation_layer,\n", - " norm_layer=norm_layer,\n", - " groups=mid_channels,\n", - " rngs=rngs,\n", - " ),\n", - " SqueezeExcitation( # squeeze_excitation\n", - " mid_channels, sqz_channels, activation=nnx.silu, rngs=rngs\n", - " ),\n", - " nnx.Conv( # conv_c\n", - " mid_channels, out_channels, kernel_size=(1, 1), use_bias=True, rngs=rngs\n", - " )\n", - " ]\n", - " self.layers = nnx.Sequential(*_layers)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " res = self.proj(x)\n", - " x = self.stochastic_depth(self.layers(x))\n", - " return res + x" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4, 14, 14, 64)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from functools import partial\n", - "\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "x = jnp.ones((4, 28, 28, 32))\n", - "mod = MBConv(32, 64, 4, 0.25, 2, activation_layer=nnx.gelu, norm_layer=norm_layer)\n", - "y = mod(x)\n", - "y.shape" - ] - }, - { - "cell_type": "markdown", - "id": "3a8d9cb4-795b-4cb2-a014-bb440acc800b", - "metadata": {}, - "source": [ - "### `RelativePositionalMultiHeadAttention` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L140). First we reimplement a helper function `_get_relative_position_index`:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "df647057-8c6f-4c6b-84f9-d6f78e649343", - "metadata": {}, - "outputs": [], - "source": [ - "def _get_relative_position_index(height: int, width: int) -> jax.Array:\n", - " # PyTorch code:\n", - " # coords = torch.stack(torch.meshgrid([torch.arange(height), torch.arange(width)]))\n", - "\n", - " coords = jnp.stack(\n", - " jnp.meshgrid(*[jnp.arange(height), jnp.arange(width)], indexing=\"ij\")\n", - " )\n", - " # PyTorch code: coords_flat = torch.flatten(coords, 1)\n", - " coords_flat = coords.reshape(coords.shape[0], -1)\n", - "\n", - " relative_coords = coords_flat[:, :, None] - coords_flat[:, None, :]\n", - " relative_coords = jnp.permute_dims(relative_coords, (1, 2, 0))\n", - "\n", - " # PyTorch code:\n", - " # relative_coords[:, :, 0] += height - 1\n", - " # relative_coords[:, :, 1] += width - 1\n", - " # relative_coords[:, :, 0] *= 2 * width - 1\n", - " relative_coords = relative_coords + jnp.array((height - 1, width - 1))\n", - " relative_coords = relative_coords * jnp.array((2 * width - 1, 1))\n", - "\n", - " return relative_coords.sum(-1)" - ] - }, - { - "cell_type": "markdown", - "id": "2670d86b", - "metadata": {}, - "source": [ - "Let us check our implementation against PyTorch implementation:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "5ce55b8b-5305-4a57-a413-8df43392ec3a", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import _get_relative_position_index as pytorch_get_relative_position_index\n", - "\n", - "\n", - "output = _get_relative_position_index(13, 12)\n", - "expected = pytorch_get_relative_position_index(13, 12)\n", - "assert (output == jnp.asarray(expected)).all()" - ] - }, - { - "cell_type": "markdown", - "id": "5518bfc4", - "metadata": {}, - "source": [ - "Next, we can port `RelativePositionalMultiHeadAttention` module which a learnable parameter and a buffer:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1f46b3e4-fd69-42c2-8ca7-d242a20d13de", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4, 32, 49, 64)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import math\n", - "\n", - "\n", - "class Buffer(nnx.Variable):\n", - " pass\n", - "\n", - "\n", - "class RelativePositionalMultiHeadAttention(nnx.Module):\n", - " def __init__(\n", - " self,\n", - " feat_dim: int,\n", - " head_dim: int,\n", - " max_seq_len: int,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " if feat_dim % head_dim != 0:\n", - " raise ValueError(f\"feat_dim: {feat_dim} must be divisible by head_dim: {head_dim}\")\n", - "\n", - " self.n_heads = feat_dim // head_dim\n", - " self.head_dim = head_dim\n", - " self.size = int(math.sqrt(max_seq_len))\n", - " self.max_seq_len = max_seq_len\n", - "\n", - " self.to_qkv = nnx.Linear(feat_dim, self.n_heads * self.head_dim * 3, rngs=rngs)\n", - " self.scale_factor = feat_dim**-0.5\n", - "\n", - " self.merge = nnx.Linear(self.head_dim * self.n_heads, feat_dim, rngs=rngs)\n", - "\n", - " self.relative_position_index = Buffer(_get_relative_position_index(self.size, self.size))\n", - "\n", - " # initialize with truncated normal bias\n", - " initializer = jax.nn.initializers.truncated_normal(stddev=0.02)\n", - " shape = ((2 * self.size - 1) * (2 * self.size - 1), self.n_heads)\n", - " self.relative_position_bias_table = nnx.Param(initializer(rngs.params(), shape, jnp.float32))\n", - "\n", - " def get_relative_positional_bias(self) -> jax.Array:\n", - " bias_index = self.relative_position_index.value.ravel()\n", - " relative_bias = self.relative_position_bias_table[bias_index].reshape((self.max_seq_len, self.max_seq_len, -1))\n", - " relative_bias = jnp.permute_dims(relative_bias, (2, 0, 1))\n", - " return jnp.expand_dims(relative_bias, axis=0)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " B, G, P, D = x.shape\n", - " H, DH = self.n_heads, self.head_dim\n", - "\n", - " qkv = self.to_qkv(x)\n", - "\n", - " q, k, v = jnp.split(qkv, 3, axis=-1)\n", - " q = jnp.permute_dims(q.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", - " k = jnp.permute_dims(k.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", - " v = jnp.permute_dims(v.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", - "\n", - " k = k * self.scale_factor\n", - "\n", - " dot_prod = jnp.einsum(\"B G H I D, B G H J D -> B G H I J\", q, k)\n", - " pos_bias = self.get_relative_positional_bias()\n", - "\n", - " dot_prod = jax.nn.softmax(dot_prod + pos_bias, axis=-1)\n", - "\n", - " out = jnp.einsum(\"B G H I J, B G H J D -> B G H I D\", dot_prod, v)\n", - " out = jnp.permute_dims(out, (0, 1, 3, 2, 4)).reshape((B, G, P, D))\n", - "\n", - " out = self.merge(out)\n", - " return out" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18d0c993", - "metadata": {}, - "outputs": [], - "source": [ - "x = jnp.ones((4, 32, 49, 64))\n", - "\n", - "mod = RelativePositionalMultiHeadAttention(64, 16, 49)\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "875aba65-53d0-4241-bdd7-36384054ca59", - "metadata": {}, - "source": [ - "### `SwapAxes`, `WindowPartition`, `WindowDepartition` implementations\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L213)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d8a19362-733a-4359-9658-53dcffa25220", - "metadata": {}, - "outputs": [], - "source": [ - "class SwapAxes(nnx.Module):\n", - " def __init__(self, a: int, b: int):\n", - " self.a = a\n", - " self.b = b\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " res = jnp.swapaxes(x, self.a, self.b)\n", - " return res\n", - "\n", - "\n", - "class WindowPartition(nnx.Module):\n", - " def __call__(self, x: jax.Array, p: int) -> jax.Array:\n", - " # Output array with expected layout of [B, H/P, W/P, P*P, C].\n", - " B, H, W, C = x.shape\n", - " P = p\n", - " # chunk up H and W dimensions\n", - " x = x.reshape((B, H // P, P, W // P, P, C))\n", - " x = jnp.permute_dims(x, (0, 1, 3, 2, 4, 5))\n", - " # colapse P * P dimension\n", - " x = x.reshape((B, (H // P) * (W // P), P * P, C))\n", - " return x\n", - "\n", - "\n", - "class WindowDepartition(nnx.Module):\n", - " def __call__(self, x: jax.Array, p: int, h_partitions: int, w_partitions: int) -> jax.Array:\n", - " # Output array with expected layout of [B, H, W, C].\n", - " B, G, PP, C = x.shape\n", - " P = p\n", - " HP, WP = h_partitions, w_partitions\n", - " # split P * P dimension into 2 P tile dimensions\n", - " x = x.reshape((B, HP, WP, P, P, C))\n", - " # permute into B, HP, P, WP, P, C\n", - " x = jnp.permute_dims(x, (0, 1, 3, 2, 4, 5))\n", - " # reshape into B, H, W, C\n", - " x = x.reshape((B, HP * P, WP * P, C))\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "daee5b6b-595f-4344-af93-6e4bd44c217f", - "metadata": {}, - "outputs": [], - "source": [ - "x = jnp.ones((3, 4, 5, 6))\n", - "mod = SwapAxes(1, 2)\n", - "y = mod(x)\n", - "assert y.shape == (3, 5, 4, 6)\n", - "\n", - "x = jnp.ones((4, 128, 128, 3))\n", - "mod = WindowPartition()\n", - "y = mod(x, p=16)\n", - "assert y.shape == (4, (128 // 16) * (128 // 16), 16 * 16, 3)\n", - "\n", - "x = jnp.ones((4, (128 // 16) * (128 // 16), 16 * 16, 3))\n", - "mod = WindowDepartition()\n", - "y = mod(x, p=16, h_partitions=128 // 16, w_partitions=128 // 16)\n", - "assert y.shape == (4, 128, 128, 3)" - ] - }, - { - "cell_type": "markdown", - "id": "fe9643dd-b328-43c9-a82f-7180ee2b9a00", - "metadata": {}, - "source": [ - "### `PartitionAttentionLayer` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L282)." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "dfb3c640-4b51-4ca5-a7ba-2ad5f9907c57", - "metadata": {}, - "outputs": [], - "source": [ - "class PartitionAttentionLayer(nnx.Module):\n", - " \"\"\"\n", - " Layer for partitioning the input tensor into non-overlapping windows and\n", - " applying attention to each window.\n", - " \"\"\"\n", - " def __init__(\n", - " self,\n", - " in_channels: int,\n", - " head_dim: int,\n", - " # partitioning parameters\n", - " partition_size: int,\n", - " partition_type: str,\n", - " # grid size needs to be known at initialization time\n", - " # because we need to know hamy relative offsets there are in the grid\n", - " grid_size: Tuple[int, int],\n", - " mlp_ratio: int,\n", - " activation_layer: Callable,\n", - " norm_layer: Callable[..., nnx.Module],\n", - " attention_dropout: float,\n", - " mlp_dropout: float,\n", - " p_stochastic_dropout: float,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " self.n_heads = in_channels // head_dim\n", - " self.head_dim = head_dim\n", - " self.n_partitions = grid_size[0] // partition_size\n", - " self.partition_type = partition_type\n", - " self.grid_size = grid_size\n", - "\n", - " if partition_type not in [\"grid\", \"window\"]:\n", - " raise ValueError(\"partition_type must be either 'grid' or 'window'\")\n", - "\n", - " if partition_type == \"window\":\n", - " self.p, self.g = partition_size, self.n_partitions\n", - " else:\n", - " self.p, self.g = self.n_partitions, partition_size\n", - "\n", - " self.partition_op = WindowPartition()\n", - " self.departition_op = WindowDepartition()\n", - " self.partition_swap = SwapAxes(-2, -3) if partition_type == \"grid\" else Identity()\n", - " self.departition_swap = SwapAxes(-2, -3) if partition_type == \"grid\" else Identity()\n", - "\n", - " self.attn_layer = nnx.Sequential(\n", - " norm_layer(in_channels, rngs=rngs),\n", - " # it's always going to be partition_size ** 2 because\n", - " # of the axis swap in the case of grid partitioning\n", - " RelativePositionalMultiHeadAttention(\n", - " in_channels, head_dim, partition_size**2, rngs=rngs\n", - " ),\n", - " nnx.Dropout(attention_dropout, rngs=rngs),\n", - " )\n", - "\n", - " # pre-normalization similar to transformer layers\n", - " self.mlp_layer = nnx.Sequential(\n", - " nnx.LayerNorm(in_channels, rngs=rngs),\n", - " nnx.Linear(in_channels, in_channels * mlp_ratio, rngs=rngs),\n", - " activation_layer,\n", - " nnx.Linear(in_channels * mlp_ratio, in_channels, rngs=rngs),\n", - " nnx.Dropout(mlp_dropout, rngs=rngs),\n", - " )\n", - "\n", - " # layer scale factors\n", - " self.stochastic_dropout = StochasticDepth(p_stochastic_dropout, mode=\"row\", rngs=rngs)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " # Undefined behavior if H or W are not divisible by p\n", - " # https://github.com/google-research/maxvit/blob/da76cf0d8a6ec668cc31b399c4126186da7da944/maxvit/models/maxvit.py#L766\n", - " gh, gw = self.grid_size[0] // self.p, self.grid_size[1] // self.p\n", - " torch._assert(\n", - " self.grid_size[0] % self.p == 0 and self.grid_size[1] % self.p == 0,\n", - " \"Grid size must be divisible by partition size. Got grid size of {} and partition size of {}\".format(\n", - " self.grid_size, self.p\n", - " ),\n", - " )\n", - " x = self.partition_op(x, self.p) # (B, H, W, C) -> (B, H/P, W/P, P*P, C)\n", - " x = self.partition_swap(x) # -> grid: (B, H/P, P*P, W/P, C)\n", - " x = x + self.stochastic_dropout(self.attn_layer(x))\n", - " x = x + self.stochastic_dropout(self.mlp_layer(x))\n", - " x = self.departition_swap(x) # grid: (B, H/P, P*P, W/P, C) -> (B, H/P, W/P, P*P, C)\n", - " x = self.departition_op(x, self.p, gh, gw) # -> (B, H, W, C)\n", - "\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(4, 224, 224, 36)\n", - "(4, 224, 224, 36)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 224, 224, 36))\n", - "\n", - "grid_size = (224, 224)\n", - "mod = PartitionAttentionLayer(\n", - " 36, 6, 7, \"window\", grid_size=grid_size, mlp_ratio=4,\n", - " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", - " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)\n", - "\n", - "mod = PartitionAttentionLayer(\n", - " 36, 6, 7, \"grid\", grid_size=grid_size, mlp_ratio=4,\n", - " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", - " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "b89b4ca6-c17a-4c0f-859a-de7134348818", - "metadata": {}, - "source": [ - "### `MaxVitLayer` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L386)." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "45b3199e-711d-4125-86b9-22e90fafa28c", - "metadata": {}, - "outputs": [], - "source": [ - "class MaxVitLayer(nnx.Module):\n", - " \"\"\"\n", - " MaxVit layer consisting of a MBConv layer followed by a PartitionAttentionLayer with `window`\n", - " and a PartitionAttentionLayer with `grid`.\n", - " \"\"\"\n", - " def __init__(\n", - " self,\n", - " # conv parameters\n", - " in_channels: int,\n", - " out_channels: int,\n", - " squeeze_ratio: float,\n", - " expansion_ratio: float,\n", - " stride: int,\n", - " # conv + transformer parameters\n", - " norm_layer: Callable[..., nnx.Module],\n", - " activation_layer: Callable,\n", - " # transformer parameters\n", - " head_dim: int,\n", - " mlp_ratio: int,\n", - " mlp_dropout: float,\n", - " attention_dropout: float,\n", - " p_stochastic_dropout: float,\n", - " # partitioning parameters\n", - " partition_size: int,\n", - " grid_size: Tuple[int, int],\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " layers = [\n", - " # convolutional layer\n", - " MBConv(\n", - " in_channels=in_channels,\n", - " out_channels=out_channels,\n", - " expansion_ratio=expansion_ratio,\n", - " squeeze_ratio=squeeze_ratio,\n", - " stride=stride,\n", - " activation_layer=activation_layer,\n", - " norm_layer=norm_layer,\n", - " p_stochastic_dropout=p_stochastic_dropout,\n", - " rngs=rngs,\n", - " ),\n", - " # window_attention\n", - " PartitionAttentionLayer(\n", - " in_channels=out_channels,\n", - " head_dim=head_dim,\n", - " partition_size=partition_size,\n", - " partition_type=\"window\",\n", - " grid_size=grid_size,\n", - " mlp_ratio=mlp_ratio,\n", - " activation_layer=activation_layer,\n", - " norm_layer=nnx.LayerNorm,\n", - " attention_dropout=attention_dropout,\n", - " mlp_dropout=mlp_dropout,\n", - " p_stochastic_dropout=p_stochastic_dropout,\n", - " rngs=rngs,\n", - " ),\n", - " # grid_attention\n", - " PartitionAttentionLayer(\n", - " in_channels=out_channels,\n", - " head_dim=head_dim,\n", - " partition_size=partition_size,\n", - " partition_type=\"grid\",\n", - " grid_size=grid_size,\n", - " mlp_ratio=mlp_ratio,\n", - " activation_layer=activation_layer,\n", - " norm_layer=nnx.LayerNorm,\n", - " attention_dropout=attention_dropout,\n", - " mlp_dropout=mlp_dropout,\n", - " p_stochastic_dropout=p_stochastic_dropout,\n", - " rngs=rngs,\n", - " )\n", - " ]\n", - " self.layers = nnx.Sequential(*layers)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " return self.layers(x)\n", - "\n", - "\n", - "def _get_conv_output_shape(\n", - " input_size: Tuple[int, int], kernel_size: int, stride: int, padding: int\n", - ") -> Tuple[int, int]:\n", - " return (\n", - " (input_size[0] - kernel_size + 2 * padding) // stride + 1,\n", - " (input_size[1] - kernel_size + 2 * padding) // stride + 1,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(4, 112, 112, 36)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 224, 224, 3))\n", - "\n", - "grid_size = _get_conv_output_shape((224, 224), kernel_size=3, stride=2, padding=1)\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "\n", - "mod = MaxVitLayer(\n", - " 3, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", - " stride=2, norm_layer=norm_layer, activation_layer=nnx.gelu,\n", - " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", - " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", - " partition_size=7, grid_size=grid_size,\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "21460039-0ed8-4c37-8382-7d91655f1086", - "metadata": {}, - "source": [ - "### `MaxVitBlock` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L483)." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "e4fd31d2-4354-4694-87b4-3d0644388d3d", - "metadata": {}, - "outputs": [], - "source": [ - "class MaxVitBlock(nnx.Module):\n", - " \"\"\"\n", - " A MaxVit block consisting of `n_layers` MaxVit layers.\n", - " \"\"\"\n", - " def __init__(\n", - " self,\n", - " # conv parameters\n", - " in_channels: int,\n", - " out_channels: int,\n", - " squeeze_ratio: float,\n", - " expansion_ratio: float,\n", - " # conv + transformer parameters\n", - " norm_layer: Callable[..., nnx.Module],\n", - " activation_layer: Callable,\n", - " # transformer parameters\n", - " head_dim: int,\n", - " mlp_ratio: int,\n", - " mlp_dropout: float,\n", - " attention_dropout: float,\n", - " # partitioning parameters\n", - " partition_size: int,\n", - " input_grid_size: Tuple[int, int],\n", - " # number of layers\n", - " n_layers: int,\n", - " p_stochastic: List[float],\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " if not len(p_stochastic) == n_layers:\n", - " raise ValueError(f\"p_stochastic must have length n_layers={n_layers}, got p_stochastic={p_stochastic}.\")\n", - "\n", - " # account for the first stride of the first layer\n", - " self.grid_size = _get_conv_output_shape(input_grid_size, kernel_size=3, stride=2, padding=1)\n", - "\n", - " layers = []\n", - " for idx, p in enumerate(p_stochastic):\n", - " stride = 2 if idx == 0 else 1\n", - " layers.append(\n", - " MaxVitLayer(\n", - " in_channels=in_channels if idx == 0 else out_channels,\n", - " out_channels=out_channels,\n", - " squeeze_ratio=squeeze_ratio,\n", - " expansion_ratio=expansion_ratio,\n", - " stride=stride,\n", - " norm_layer=norm_layer,\n", - " activation_layer=activation_layer,\n", - " head_dim=head_dim,\n", - " mlp_ratio=mlp_ratio,\n", - " mlp_dropout=mlp_dropout,\n", - " attention_dropout=attention_dropout,\n", - " partition_size=partition_size,\n", - " grid_size=self.grid_size,\n", - " p_stochastic_dropout=p,\n", - " ),\n", - " )\n", - " self.layers = nnx.Sequential(*layers)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " return self.layers(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e168c27f-98db-4831-9723-dffac88f3226", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(4, 112, 112, 36)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 224, 224, 3))\n", - "\n", - "input_grid_size = (224, 224)\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "\n", - "mod = MaxVitBlock(\n", - " 3, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", - " norm_layer=norm_layer, activation_layer=nnx.gelu,\n", - " head_dim=6, mlp_ratio=4, mlp_dropout=0.5, attention_dropout=0.4,\n", - " partition_size=7, input_grid_size=input_grid_size,\n", - " n_layers=2,\n", - " p_stochastic=[0.0, 0.2],\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "cef5687d-e390-438b-95b3-e66406e2c000", - "metadata": {}, - "source": [ - "### `MaxVit` implementation\n", - "\n", - "Finally, we can assemble everything together and define the MaxVit model.\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568)." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "0e874e63-0eb7-40ea-82f3-bf10ac33d7a6", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "\n", - "def _make_block_input_shapes(input_size: Tuple[int, int], n_blocks: int) -> List[Tuple[int, int]]:\n", - " \"\"\"Util function to check that the input size is correct for a MaxVit configuration.\"\"\"\n", - " shapes = []\n", - " block_input_shape = _get_conv_output_shape(input_size, 3, 2, 1)\n", - " for _ in range(n_blocks):\n", - " block_input_shape = _get_conv_output_shape(block_input_shape, 3, 2, 1)\n", - " shapes.append(block_input_shape)\n", - " return shapes\n", - "\n", - "\n", - "class MaxVit(nnx.Module):\n", - " \"\"\"\n", - " Implements MaxVit Transformer from the \"MaxViT: Multi-Axis Vision Transformer\" paper.\n", - " \"\"\"\n", - " def __init__(\n", - " self,\n", - " # input size parameters\n", - " input_size: Tuple[int, int],\n", - " # stem and task parameters\n", - " stem_channels: int,\n", - " # partitioning parameters\n", - " partition_size: int,\n", - " # block parameters\n", - " block_channels: List[int],\n", - " block_layers: List[int],\n", - " # attention head dimensions\n", - " head_dim: int,\n", - " stochastic_depth_prob: float,\n", - " # conv + transformer parameters\n", - " # norm_layer is applied only to the conv layers\n", - " # activation_layer is applied both to conv and transformer layers\n", - " norm_layer: Optional[Callable[..., nnx.Module]] = None,\n", - " activation_layer: Callable = nnx.gelu,\n", - " # conv parameters\n", - " squeeze_ratio: float = 0.25,\n", - " expansion_ratio: float = 4,\n", - " # transformer parameters\n", - " mlp_ratio: int = 4,\n", - " mlp_dropout: float = 0.0,\n", - " attention_dropout: float = 0.0,\n", - " # task parameters\n", - " num_classes: int = 1000,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " input_channels = 3\n", - "\n", - " if norm_layer is None:\n", - " norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "\n", - " # Make sure input size will be divisible by the partition size in all blocks\n", - " # Undefined behavior if H or W are not divisible by p\n", - " block_input_sizes = _make_block_input_shapes(input_size, len(block_channels))\n", - " for idx, block_input_size in enumerate(block_input_sizes):\n", - " if block_input_size[0] % partition_size != 0 or block_input_size[1] % partition_size != 0:\n", - " raise ValueError(\n", - " f\"Input size {block_input_size} of block {idx} is not divisible by partition size {partition_size}. \"\n", - " f\"Consider changing the partition size or the input size.\\n\"\n", - " f\"Current configuration yields the following block input sizes: {block_input_sizes}.\"\n", - " )\n", - "\n", - " # stem\n", - " self.stem = nnx.Sequential(\n", - " Conv2dNormActivation(\n", - " input_channels,\n", - " stem_channels,\n", - " 3,\n", - " stride=2,\n", - " norm_layer=norm_layer,\n", - " activation_layer=activation_layer,\n", - " bias=False,\n", - " rngs=rngs,\n", - " ),\n", - " Conv2dNormActivation(\n", - " stem_channels,\n", - " stem_channels,\n", - " 3,\n", - " stride=1,\n", - " norm_layer=None,\n", - " activation_layer=None,\n", - " bias=True,\n", - " rngs=rngs,\n", - " ),\n", - " )\n", - "\n", - " # account for stem stride\n", - " input_size = _get_conv_output_shape(input_size, kernel_size=3, stride=2, padding=1)\n", - " self.partition_size = partition_size\n", - "\n", - " # blocks\n", - " blocks = []\n", - " in_channels = [stem_channels] + block_channels[:-1]\n", - " out_channels = block_channels\n", - "\n", - " # precompute the stochastic depth probabilities from 0 to stochastic_depth_prob\n", - " # since we have N blocks with L layers, we will have N * L probabilities uniformly distributed\n", - " # over the range [0, stochastic_depth_prob]\n", - " p_stochastic = np.linspace(0, stochastic_depth_prob, sum(block_layers)).tolist()\n", - "\n", - " p_idx = 0\n", - " for in_channel, out_channel, num_layers in zip(in_channels, out_channels, block_layers):\n", - " blocks.append(\n", - " MaxVitBlock(\n", - " in_channels=in_channel,\n", - " out_channels=out_channel,\n", - " squeeze_ratio=squeeze_ratio,\n", - " expansion_ratio=expansion_ratio,\n", - " norm_layer=norm_layer,\n", - " activation_layer=activation_layer,\n", - " head_dim=head_dim,\n", - " mlp_ratio=mlp_ratio,\n", - " mlp_dropout=mlp_dropout,\n", - " attention_dropout=attention_dropout,\n", - " partition_size=partition_size,\n", - " input_grid_size=input_size,\n", - " n_layers=num_layers,\n", - " p_stochastic=p_stochastic[p_idx : p_idx + num_layers],\n", - " ),\n", - " )\n", - " input_size = blocks[-1].grid_size\n", - " p_idx += num_layers\n", - " self.blocks = nnx.Sequential(*blocks)\n", - "\n", - " self.classifier = nnx.Sequential(\n", - " lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2])), # nn.AdaptiveAvgPool2d(1)\n", - " lambda x: x.reshape(x.shape[0], -1), # nn.Flatten()\n", - " nnx.LayerNorm(block_channels[-1], rngs=rngs),\n", - " nnx.Linear(block_channels[-1], block_channels[-1], rngs=rngs),\n", - " nnx.tanh,\n", - " nnx.Linear(block_channels[-1], num_classes, use_bias=False, rngs=rngs),\n", - " )\n", - "\n", - " self._init_weights(rngs)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " x = self.stem(x)\n", - " x = self.blocks(x)\n", - " x = self.classifier(x)\n", - " return x\n", - "\n", - " def _init_weights(self, rngs):\n", - " normal_initializer = nnx.initializers.normal(stddev=0.02)\n", - " for name, module in self.iter_modules():\n", - " if isinstance(module, (nnx.Conv, nnx.Linear)):\n", - " module.kernel.value = normal_initializer(\n", - " rngs(), module.kernel.value.shape, module.kernel.value.dtype\n", - " )\n", - " if module.bias.value is not None:\n", - " module.bias.value = jnp.zeros(\n", - " module.bias.value.shape, dtype=module.bias.value.dtype\n", - " )\n", - " elif isinstance(module, nnx.BatchNorm):\n", - " module.scale.value = jnp.ones(module.scale.value.shape, module.scale.value.dtype)\n", - " module.bias.value = jnp.zeros(module.bias.value.shape, module.bias.value.dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(4, 1000)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 224, 224, 3))\n", - "\n", - "mod = MaxVit(\n", - " input_size=(224, 224),\n", - " stem_channels=64,\n", - " block_channels=[64, 128, 256, 512],\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=32,\n", - " stochastic_depth_prob=0.2,\n", - " partition_size=7,\n", - " num_classes=1000,\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "fa2a4a47-b6c9-43ba-822b-002e0c03e85c", - "metadata": {}, - "outputs": [], - "source": [ - "def maxvit_t(\n", - " input_size=(224, 224),\n", - " stem_channels=64,\n", - " block_channels=[64, 128, 256, 512],\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=32,\n", - " stochastic_depth_prob=0.2,\n", - " partition_size=7,\n", - " num_classes=1000,\n", - "):\n", - " model = MaxVit(\n", - " input_size=input_size,\n", - " stem_channels=stem_channels,\n", - " block_channels=block_channels,\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=head_dim,\n", - " stochastic_depth_prob=stochastic_depth_prob,\n", - " partition_size=partition_size,\n", - " num_classes=num_classes,\n", - " )\n", - " return model" - ] - }, - { - "cell_type": "markdown", - "id": "25ff32f7-e4a1-4029-b114-8ecafb4378fd", - "metadata": {}, - "source": [ - "### Test JAX implementation" - ] - }, - { - "cell_type": "markdown", - "id": "b3e02373-c3b6-4ffd-a98e-e425824f2f88", - "metadata": {}, - "source": [ - "Let us import equivalent PyTorch modules and check our implementations against PyTorch. Please note that\n", - "PyTorch modules will contain random parameters and buffers that we need to set into our Flax implementations.\n", - "\n", - "Below we define a helper class `Torch2Flax` to copy parameters and buffers from a PyTorch module into equivalent Flax module." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "22f49ecd-4999-4c1c-b1d8-d16faeb60389", - "metadata": {}, - "outputs": [], - "source": [ - "import torch.nn as nn\n", - "\n", - "\n", - "class Torch2Flax:\n", - " @staticmethod\n", - " def conv_params_permute(name, torch_param):\n", - " if name == \"weight\":\n", - " return torch_param.permute(2, 3, 1, 0)\n", - " return torch_param\n", - "\n", - " @staticmethod\n", - " def linear_params_permute(name, torch_param):\n", - " if name == \"weight\":\n", - " return torch_param.permute(1, 0)\n", - " return torch_param\n", - "\n", - " @staticmethod\n", - " def default_params_transform(name, param):\n", - " return param\n", - "\n", - " modules_mapping_info = {\n", - " nn.Conv2d: {\n", - " \"type\": nnx.Conv,\n", - " \"params_mapping\": {\n", - " \"weight\": \"kernel\",\n", - " \"bias\": \"bias\",\n", - " },\n", - " \"params_transform\": conv_params_permute,\n", - " },\n", - " nn.BatchNorm2d: {\n", - " \"type\": nnx.BatchNorm,\n", - " \"params_mapping\": {\n", - " \"weight\": \"scale\",\n", - " \"bias\": \"bias\",\n", - " \"running_mean\": \"mean\",\n", - " \"running_var\": \"var\",\n", - " },\n", - " },\n", - " nn.Linear: {\n", - " \"type\": nnx.Linear,\n", - " \"params_mapping\": {\n", - " \"weight\": \"kernel\",\n", - " \"bias\": \"bias\",\n", - " },\n", - " \"params_transform\": linear_params_permute,\n", - " },\n", - " nn.LayerNorm: {\n", - " \"type\": nnx.LayerNorm,\n", - " \"params_mapping\": {\n", - " \"weight\": \"scale\",\n", - " \"bias\": \"bias\",\n", - " },\n", - " }\n", - " } | {\n", - " torch_mod: {\n", - " \"type\": nnx_fn_type,\n", - " \"params_mapping\": {},\n", - " } for torch_mod, nnx_fn_type in [\n", - " (nn.Identity, Identity),\n", - " (nn.Flatten, type(lambda x: x)),\n", - " (nn.ReLU, type(nnx.relu)),\n", - " (nn.GELU, type(nnx.gelu)),\n", - " (nn.SELU, type(nnx.selu)),\n", - " (nn.SiLU, type(nnx.silu)),\n", - " (nn.Tanh, type(nnx.tanh)),\n", - " (nn.Dropout, nnx.Dropout),\n", - " (nn.Sigmoid, type(nnx.sigmoid)),\n", - " (nn.AvgPool2d, type(lambda x: nnx.avg_pool(x, (2, 2)))),\n", - " (nn.AdaptiveAvgPool2d, type(lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2])))),\n", - " ]\n", - " }\n", - "\n", - " def _copy_params_buffers(self, torch_nn_module, nnx_module):\n", - " torch_module_type = type(torch_nn_module)\n", - " assert torch_module_type in self.modules_mapping_info, torch_module_type\n", - " module_mapping_info = self.modules_mapping_info[torch_module_type]\n", - " assert isinstance(nnx_module, module_mapping_info[\"type\"]), (\n", - " nnx_module, type(nnx_module), module_mapping_info[\"type\"]\n", - " )\n", - "\n", - " for torch_key, nnx_key in module_mapping_info[\"params_mapping\"].items():\n", - "\n", - " torch_value = getattr(torch_nn_module, torch_key)\n", - " nnx_param = getattr(nnx_module, nnx_key)\n", - " assert nnx_param is not None, (torch_key, nnx_key, nnx_module)\n", - "\n", - " if torch_value is None:\n", - " assert nnx_param.value is None, nnx_param\n", - " continue\n", - "\n", - " params_transform = module_mapping_info.get(\"params_transform\", Torch2Flax.default_params_transform)\n", - " torch_value = params_transform(torch_key, torch_value)\n", - "\n", - " assert nnx_param.value.shape == torch_value.data.shape, (\n", - " nnx_key, nnx_param.value.shape, torch_key, torch_value.data.shape\n", - " )\n", - " nnx_param.value = jnp.asarray(torch_value.data)\n", - "\n", - " def _copy_sequential(self, torch_nn_seq, nnx_seq, skip_modules=None):\n", - " assert isinstance(torch_nn_seq, (nn.Sequential, nn.ModuleList)), type(torch_nn_seq)\n", - " assert isinstance(nnx_seq, nnx.Sequential), type(nnx_seq)\n", - " for i, index in enumerate(torch_nn_seq):\n", - " torch_module = torch_nn_seq[i]\n", - " nnx_module = nnx_seq.layers[i]\n", - " self.copy_module(torch_module, nnx_module, skip_modules=skip_modules)\n", - "\n", - " def copy_module(self, torch_module, nnx_module, skip_modules=None):\n", - " if skip_modules is None:\n", - " skip_modules = []\n", - "\n", - " if isinstance(torch_module, (nn.Sequential, nn.ModuleList)):\n", - " self._copy_sequential(torch_module, nnx_module, skip_modules=skip_modules)\n", - " elif type(torch_module) in self.modules_mapping_info:\n", - " self._copy_params_buffers(torch_module, nnx_module)\n", - " else:\n", - " if skip_modules is not None:\n", - " if torch_module.__class__.__name__ in skip_modules:\n", - " return\n", - "\n", - " named_children = list(torch_module.named_children())\n", - " assert len(named_children) > 0, type(torch_module)\n", - " for name, torch_child in named_children:\n", - " nnx_child = getattr(nnx_module, name, None)\n", - " assert nnx_child is not None, (name, nnx_module)\n", - " self.copy_module(torch_child, nnx_child, skip_modules=skip_modules)\n", - " # Copy buffers and params of the module itself (not its children)\n", - " for name, torch_buffer in torch_module.named_buffers():\n", - " if \".\" in name:\n", - " # This is child's buffer\n", - " continue\n", - " nnx_buffer = getattr(nnx_module, name)\n", - " assert isinstance(nnx_buffer, nnx.Variable), (name, nnx_buffer, nnx_module)\n", - "\n", - " assert nnx_buffer.value.shape == torch_buffer.shape, (\n", - " name, nnx_buffer.value.shape, torch_buffer.shape\n", - " )\n", - " nnx_buffer.value = jnp.asarray(torch_buffer)\n", - "\n", - " for name, torch_param in torch_module.named_parameters():\n", - " if \".\" in name:\n", - " # This is child's parameter\n", - " continue\n", - " nnx_param = getattr(nnx_module, name)\n", - " assert isinstance(nnx_param, nnx.Param), (name, nnx_param, nnx_module)\n", - "\n", - " assert nnx_param.value.shape == torch_param.data.shape, (\n", - " name, nnx_param.value.shape, torch_param.data.shape\n", - " )\n", - " nnx_param.value = jnp.asarray(torch_param.data)\n", - "\n", - "\n", - "def test_modules(\n", - " nnx_module, torch_module, torch_input, atol=1e-3, mode=\"eval\", permute_torch_input=True, device=\"cuda\"\n", - "):\n", - " assert torch_input.ndim == 4\n", - " assert mode in (\"eval\", \"train\")\n", - "\n", - " torch_input = torch_input.to(device)\n", - " torch_module = torch_module.to(device)\n", - "\n", - " if mode == \"eval\":\n", - " torch_module.eval()\n", - " nnx_module.eval()\n", - " else:\n", - " torch_module.train()\n", - " nnx_module.train()\n", - "\n", - " with torch.inference_mode(mode=mode==\"eval\"):\n", - " torch_output = torch_module(torch_input)\n", - "\n", - " if permute_torch_input:\n", - " torch_input = torch_input.permute(0, 2, 3, 1)\n", - "\n", - " jax_input = jnp.asarray(torch_input, device=jax.devices(device)[0])\n", - " jax_output = nnx_module(jax_input)\n", - " assert jax_output.device == jax.devices(device)[0]\n", - "\n", - " torch_output = torch_output.detach()\n", - " if permute_torch_input and torch_output.ndim == 4:\n", - " torch_output = torch_output.permute(0, 2, 3, 1)\n", - " jax_expected = jnp.asarray(torch_output)\n", - "\n", - " assert jnp.allclose(jax_output, jax_expected, atol=atol), (\n", - " jnp.abs(jax_output - jax_expected).max(),\n", - " jnp.abs(jax_output - jax_expected).mean(),\n", - " )\n", - "\n", - "\n", - "t2f = Torch2Flax()" - ] - }, - { - "cell_type": "markdown", - "id": "a323a19e-fc64-4f8d-8be2-c7886b6191b9", - "metadata": {}, - "source": [ - "Let us now test our JAX modules. We only test the result of the forward pass in the inference mode such that we avoid discrepancies related to random layers like `Dropout`, `StochasticDepth`, etc.\n", - "By default, we use absolute error tolerence `1e-3` when comparing the JAX output against expected PyTorch result.\n", - "For larger modules we set the device to CPU for the JAX model to execute on in order to reduce the errors between CPU and CUDA." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "2e10fb43-6ae6-47c7-81fe-5027b115b25f", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.ops.misc import Conv2dNormActivation as PyTorchConv2dNormActivation\n", - "\n", - "\n", - "torch_module = PyTorchConv2dNormActivation(32, 64, 3, 2, 1)\n", - "nnx_module = Conv2dNormActivation(32, 64, 3, 2, 1)\n", - "\n", - "t2f.copy_module(torch_module, nnx_module)\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "7ac4b49a-712f-4725-a8d8-33e72b8d0b66", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.ops.misc import SqueezeExcitation as PyTorchSqueezeExcitation\n", - "\n", - "\n", - "torch_module = PyTorchSqueezeExcitation(32, 4)\n", - "nnx_module = SqueezeExcitation(32, 4)\n", - "\n", - "t2f.copy_module(torch_module, nnx_module)\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "746c8882-0001-4c97-b5cf-576dc5c87c02", - "metadata": {}, - "outputs": [], - "source": [ - "import torch.nn as nn\n", - "from functools import partial\n", - "from torchvision.models.maxvit import MBConv as PyTorchMBConv\n", - "\n", - "\n", - "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", - "torch_module = PyTorchMBConv(32, 64, 4, 0.25, 2, activation_layer=nn.GELU, norm_layer=norm_layer)\n", - "\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "nnx_module = MBConv(32, 64, 4, 0.25, 2, activation_layer=nnx.gelu, norm_layer=norm_layer)\n", - "\n", - "\n", - "t2f.copy_module(torch_module, nnx_module)\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "249f6d28-57b6-4d36-9079-cd60964e6afc", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import RelativePositionalMultiHeadAttention as PyTorchRelativePositionalMultiHeadAttention\n", - "\n", - "\n", - "torch_module = PyTorchRelativePositionalMultiHeadAttention(64, 16, 49)\n", - "nnx_module = RelativePositionalMultiHeadAttention(64, 16, 49)\n", - "\n", - "t2f.copy_module(torch_module, nnx_module)\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 32, 49, 64), permute_torch_input=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "f48fc475-c556-4101-ad2b-19480a73c6ba", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import PartitionAttentionLayer as PyTorchPartitionAttentionLayer\n", - "\n", - "\n", - "grid_size = (224, 224)\n", - "for partition_type in [\"window\", \"grid\"]:\n", - "\n", - " torch_module = PyTorchPartitionAttentionLayer(\n", - " 36, 6, 7, partition_type, grid_size=grid_size, mlp_ratio=4,\n", - " activation_layer=nn.GELU, norm_layer=nn.LayerNorm,\n", - " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", - " )\n", - "\n", - " nnx_module = PartitionAttentionLayer(\n", - " 36, 6, 7, partition_type, grid_size=grid_size, mlp_ratio=4,\n", - " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", - " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", - " )\n", - "\n", - " t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - " ])\n", - "\n", - " test_modules(nnx_module, torch_module, torch.randn(4, 36, 224, 224))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "7ab2de6a-9790-444b-b175-535cdb05f5d8", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import MaxVitLayer as PyTorchMaxVitLayer\n", - "\n", - "\n", - "stride = 2\n", - "\n", - "grid_size = _get_conv_output_shape((224, 224), kernel_size=3, stride=2, padding=1)\n", - "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", - "\n", - "torch_module = PyTorchMaxVitLayer(\n", - " 36, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", - " stride=stride, norm_layer=norm_layer, activation_layer=nn.GELU,\n", - " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", - " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", - " partition_size=7, grid_size=grid_size,\n", - ")\n", - "\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "nnx_module = MaxVitLayer(\n", - " 36, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", - " stride=stride, norm_layer=norm_layer, activation_layer=nnx.gelu,\n", - " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", - " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", - " partition_size=7, grid_size=grid_size,\n", - ")\n", - "\n", - "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - "])\n", - "\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 36, 224, 224), device=\"cpu\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "e8e8f997-0184-4af2-82b0-ceaa580645c8", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import MaxVitBlock as PyTorchMaxVitBlock\n", - "\n", - "\n", - "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", - "torch_module = PyTorchMaxVitBlock(\n", - " 64, 128, squeeze_ratio=0.25, expansion_ratio=4,\n", - " norm_layer=norm_layer, activation_layer=nn.GELU,\n", - " head_dim=32, mlp_ratio=4, mlp_dropout=0.0, attention_dropout=0.0,\n", - " partition_size=7, input_grid_size=(56, 56),\n", - " n_layers=2,\n", - " p_stochastic=[0.13333333333333333, 0.2],\n", - ")\n", - "\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "nnx_module = MaxVitBlock(\n", - " 64, 128, squeeze_ratio=0.25, expansion_ratio=4,\n", - " norm_layer=norm_layer, activation_layer=nnx.gelu,\n", - " head_dim=32, mlp_ratio=4, mlp_dropout=0.0, attention_dropout=0.0,\n", - " partition_size=7, input_grid_size=(56, 56),\n", - " n_layers=2,\n", - " p_stochastic=[0.13333333333333333, 0.2],\n", - ")\n", - "\n", - "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - "])\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 64, 56, 56), device=\"cpu\")" - ] - }, - { - "cell_type": "markdown", - "id": "e313819a-e93a-4201-806d-783bd1336c78", - "metadata": {}, - "source": [ - "Finally, we can check the MaxVit implementation. Note that we raised the absolute tolerence to `1e-1` when comparing JAX output logits against PyTorch expected logits." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "e2af63a4-b16b-40a3-ac00-bfc23d532c82", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import MaxVit as PyTorchMaxVit\n", - "\n", - "\n", - "torch.manual_seed(77)\n", - "\n", - "\n", - "torch_module = PyTorchMaxVit(\n", - " input_size=(224, 224),\n", - " stem_channels=64,\n", - " block_channels=[64, 128, 256, 512],\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=32,\n", - " stochastic_depth_prob=0.2,\n", - " partition_size=7,\n", - " num_classes=1000,\n", - ")\n", - "\n", - "nnx_module = MaxVit(\n", - " input_size=(224, 224),\n", - " stem_channels=64,\n", - " block_channels=[64, 128, 256, 512],\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=32,\n", - " stochastic_depth_prob=0.2,\n", - " partition_size=7,\n", - " num_classes=1000,\n", - ")\n", - "\n", - "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - "])\n", - "\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 3, 224, 224), device=\"cpu\", atol=1e-1)" - ] - }, - { - "cell_type": "markdown", - "id": "0d3c4f4d-2a50-46f4-814c-42c1a423cfd0", - "metadata": {}, - "source": [ - "### Check Flax model\n", - "Let us now reuse trained weights from TorchVision's MaxViT model to check output logits and the predictions on our example image:" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "7975f311-7a02-4c82-99db-b0b50fb37528", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.models import maxvit_t as pytorch_maxvit_t, MaxVit_T_Weights\n", - "\n", - "torch_model = pytorch_maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1)\n", - "flax_model = maxvit_t()\n", - "\n", - "t2f = Torch2Flax()\n", - "t2f.copy_module(torch_model, flax_model, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction for the Dog:\n", - "- PyTorch model result: ['n02113023', 'Pembroke'], score: 0.7800846695899963\n", - "- Flax model result: ['n02113023', 'Pembroke'], score: 0.7799879908561707\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import json\n", - "from torchvision.io import read_image\n", - "\n", - "\n", - "preprocess = MaxVit_T_Weights.IMAGENET1K_V1.transforms()\n", - "\n", - "with open(\"imagenet_class_index.json\") as labels_file:\n", - " labels = json.load(labels_file)\n", - "\n", - "\n", - "dog1 = read_image(\"dog1.jpg\")\n", - "tensor = preprocess(dog1).unsqueeze(dim=0)\n", - "\n", - "torch_model.eval()\n", - "with torch.inference_mode():\n", - " torch_output = torch_model(tensor)\n", - "\n", - "torch_class_id = torch_output.argmax(dim=1).item()\n", - "\n", - "jax_array = jnp.asarray(tensor.permute(0, 2, 3, 1), device=jax.devices(\"cpu\")[0])\n", - "flax_model.eval()\n", - "flax_output = flax_model(jax_array)\n", - "\n", - "flax_class_id = torch_output.argmax(axis=1).item()\n", - "\n", - "print(\"Prediction for the Dog:\")\n", - "print(f\"- PyTorch model result: {labels[str(torch_class_id)]}, score: {torch_output.softmax(axis=1)[0, torch_class_id]}\")\n", - "print(f\"- Flax model result: {labels[str(flax_class_id)]}, score: {jax.nn.softmax(flax_output, axis=1)[0, flax_class_id]}\")\n", - "\n", - "\n", - "plt.subplot(121)\n", - "plt.title(f\"{labels[str(torch_class_id)]}\\nScore: {torch_output.softmax(dim=-1)[0, class_id]:.4f}\")\n", - "plt.imshow(dog1.permute(1, 2, 0))\n", - "\n", - "plt.subplot(122)\n", - "plt.title(f\"{labels[str(flax_class_id)]}\\nScore: {jax.nn.softmax(flax_output, axis=1)[0, flax_class_id]:.4f}\")\n", - "plt.imshow(dog1.permute(1, 2, 0))" - ] - }, - { - "cell_type": "markdown", - "id": "c77f3244", - "metadata": {}, - "source": [ - "Let's compute cosine distance between the logits:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array(0.99999857, dtype=float32)" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "cells": [ + { + "cell_type": "markdown", + "id": "b69996dc-49af-4a0e-a4e6-36d81b51f2b4", + "metadata": { + "id": "b69996dc-49af-4a0e-a4e6-36d81b51f2b4" + }, + "source": [ + "# Porting a PyTorch model to JAX\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/JAX_porting_PyTorch_model.ipynb)\n", + "\n", + "**Note: On Colab we recommend running this on a T4 GPU instance. On Kaggle we recommend a T4x2 or P100 instance.**\n", + "\n", + "In this tutorial we will learn how to port a PyTorch model to JAX and [Flax](https://flax.readthedocs.io/en/latest/nnx_basics.html). Flax provides an API very similar to the PyTorch `torch.nn` module and porting PyTorch models is rather straightforward. To install Flax, we can simply execute the following command: `pip install -U flax treescope`." + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install -Uq flax treescope" + ], + "metadata": { + "id": "NHqB3sNbrygd", + "outputId": "7e0f46f0-a30b-4e49-a995-6b91452bd521", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "NHqB3sNbrygd", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/424.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m419.8/424.2 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m424.2/424.2 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/175.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.6/175.6 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Say we have a trained PyTorch computer-vision model to classify images that we would like to port to JAX. We will use [`TorchVision`](https://pytorch.org/vision/stable/index.html) to provide a [MaxVit](https://pytorch.org/vision/stable/models/maxvit.html) model trained on ImageNet (MaxViT: Multi-Axis Vision Transformer, https://arxiv.org/abs/2204.01697).\n", + "\n", + "First, we set up the model using TorchVision and explore briefly the model's architecture and the blocks we need to port. Next, we define equivalent blocks and the whole model using Flax. After that, we port the weights. Finally, we run some tests to ensure the correctness of the ported model.\n", + "\n", + "**On Colab, you should restart the runtime after doing `pip install`. Runtime > Restart session**" + ], + "metadata": { + "id": "ABCg5TvPr1pm" + }, + "id": "ABCg5TvPr1pm" + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "38504f77-4150-47bd-9cf9-3116fe370746", + "metadata": { + "id": "38504f77-4150-47bd-9cf9-3116fe370746" + }, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "from flax import nnx" + ] + }, + { + "cell_type": "markdown", + "id": "95a364c2-d34e-4820-8a86-f43f59c911bf", + "metadata": { + "id": "95a364c2-d34e-4820-8a86-f43f59c911bf" + }, + "source": [ + "## MaxViT PyTorch model setup\n", + "\n", + "### Model's architecture\n", + "\n", + "The MaxVit model is [implemented in TorchVision](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568). If we inspect the [forward pass](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L707-L712) of the model, we can see that it contains three high-level parts:\n", + "- [stem](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L641-L655): a few convolutions, batchnorms, GELU activations.\n", + "- [blocks](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L672-L692): list of MaxViT blocks\n", + "- [classifier](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L696-L703): adaptive average pooling, few linear layers and Tanh activation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", + "metadata": { + "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", + "outputId": "43752a13-637a-4dc4-95f2-98f0ccfd17a5", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torch/functional.py:534: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3595.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n", + "Downloading: \"https://download.pytorch.org/models/maxvit_t-bc5ab103.pth\" to /root/.cache/torch/hub/checkpoints/maxvit_t-bc5ab103.pth\n", + "100%|██████████| 119M/119M [00:01<00:00, 99.7MB/s]\n" + ] + } + ], + "source": [ + "from torchvision.models import maxvit_t, MaxVit_T_Weights\n", + "\n", + "torch_model = maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1)" + ] + }, + { + "cell_type": "markdown", + "id": "45635b2d-a77a-4368-9ecb-dbb440e647ee", + "metadata": { + "id": "45635b2d-a77a-4368-9ecb-dbb440e647ee" + }, + "source": [ + "We can use `flax.nnx.display` to display the model's architecture:" + ] + }, + { + "cell_type": "code", + "source": [ + "# nnx.display(torch_model)" + ], + "metadata": { + "id": "sZ9x7NpHtBcx" + }, + "id": "sZ9x7NpHtBcx", + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "id": "0a36676a-1561-4de0-8e25-38bab90581d0", + "metadata": { + "id": "0a36676a-1561-4de0-8e25-38bab90581d0" + }, + "source": [ + "We can see that there are four MaxViT blocks in the model and each block contains:\n", + "- MaxViT layers: two layers for blocks 0, 1, 3 and five layers for the block 4" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0d5bf6aa-c720-4400-a276-602fff53b413", + "metadata": { + "id": "0d5bf6aa-c720-4400-a276-602fff53b413", + "outputId": "a7bf9edf-8b72-4f19-a776-9d57ab99f8c3", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(4, [2, 2, 5, 2])" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "len(torch_model.blocks), [len(b.layers) for b in torch_model.blocks]" + ] + }, + { + "cell_type": "markdown", + "id": "a1d55688-5999-41de-a915-eae8b281eb18", + "metadata": { + "id": "a1d55688-5999-41de-a915-eae8b281eb18" + }, + "source": [ + "A [MaxViT layer](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L386) is composed of: [`MBConv`](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L53), `window_attention` as [`PartitionAttentionLayer`](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L282) and `grid_attention` as `PartitionAttentionLayer`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", + "metadata": { + "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", + "outputId": "bdc1e7a2-0be7-4657-ca17-94338338ded0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer']]" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "[[mod.__class__.__name__ for mod in maxvit_layer.layers] for b in torch_model.blocks for maxvit_layer in b.layers]" + ] + }, + { + "cell_type": "markdown", + "id": "d57f8545-43a4-423d-b701-c2e2ca0ebfc1", + "metadata": { + "id": "d57f8545-43a4-423d-b701-c2e2ca0ebfc1" + }, + "source": [ + "### Inference on data\n", + "\n", + "Let's check the model on dummy input and on a real image" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", + "metadata": { + "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", + "outputId": "407caaa9-0101-40bf-c60b-be023d009c0a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.Size([2, 1000])\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "torch_model.eval()\n", + "with torch.inference_mode():\n", + " x = torch.rand(2, 3, 224, 224)\n", + " output = torch_model(x)\n", + "\n", + "print(output.shape) # (2, 1000)" + ] + }, + { + "cell_type": "markdown", + "id": "133bcf21-8a9c-4c27-b551-39b7dfdcfe1c", + "metadata": { + "id": "133bcf21-8a9c-4c27-b551-39b7dfdcfe1c" + }, + "source": [ + "We can download an image of a Pembroke Corgy dog from [TorchVision's gallery](https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true) together with [ImageNet classes dictionary](https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json):\n", + "\n", + "```bash\n", + "wget \"https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\" -O dog1.jpg\n", + "wget \"https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\" -O imagenet_class_index.json\n", + "```" + ] + }, + { + "cell_type": "code", + "source": [ + "%%bash\n", + "if [ -f \"dog1.jpg\" ]; then\n", + " echo \"dog1.jpg already exists.\"\n", + "else\n", + " wget \"https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\" -O dog1.jpg\n", + "fi\n", + "if [ -f \"imagenet_class_index.json\" ]; then\n", + " echo \"imagenet_class_index.json already exists.\"\n", + "else\n", + " wget \"https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\" -O imagenet_class_index.json\n", + "fi" + ], + "metadata": { + "id": "qC9hpYfNtOEF", + "outputId": "7d5b91fa-6d25-4404-f1be-78efef163d10", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "qC9hpYfNtOEF", + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "--2025-01-15 20:57:39-- https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\n", + "Resolving github.com (github.com)... 140.82.112.4\n", + "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://github.com/pytorch/vision/raw/refs/heads/main/gallery/assets/dog1.jpg [following]\n", + "--2025-01-15 20:57:40-- https://github.com/pytorch/vision/raw/refs/heads/main/gallery/assets/dog1.jpg\n", + "Reusing existing connection to github.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/dog1.jpg [following]\n", + "--2025-01-15 20:57:40-- https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/dog1.jpg\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 97422 (95K) [image/jpeg]\n", + "Saving to: ‘dog1.jpg’\n", + "\n", + " 0K .......... .......... .......... .......... .......... 52% 3.20M 0s\n", + " 50K .......... .......... .......... .......... ..... 100% 19.6M=0.02s\n", + "\n", + "2025-01-15 20:57:40 (5.31 MB/s) - ‘dog1.jpg’ saved [97422/97422]\n", + "\n", + "--2025-01-15 20:57:40-- https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 35364 (35K) [text/plain]\n", + "Saving to: ‘imagenet_class_index.json’\n", + "\n", + " 0K .......... .......... .......... .... 100% 4.74M=0.007s\n", + "\n", + "2025-01-15 20:57:41 (4.74 MB/s) - ‘imagenet_class_index.json’ saved [35364/35364]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "82be8baf-1292-4766-be34-28c510563d71", + "metadata": { + "id": "82be8baf-1292-4766-be34-28c510563d71", + "outputId": "981e2912-9d20-4297-8ab6-0493dada77f7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Prediction for the Dog: ['n02113023', 'Pembroke'], score: 0.7800844311714172\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 9 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "import json\n", + "from torchvision.io import read_image\n", + "\n", + "\n", + "preprocess = MaxVit_T_Weights.IMAGENET1K_V1.transforms()\n", + "\n", + "with open(\"imagenet_class_index.json\") as labels_file:\n", + " labels = json.load(labels_file)\n", + "\n", + "\n", + "dog1 = read_image(\"dog1.jpg\")\n", + "tensor = preprocess(dog1)\n", + "\n", + "torch_model.eval()\n", + "with torch.inference_mode():\n", + " output = torch_model(tensor.unsqueeze(dim=0))\n", + "\n", + "class_id = output.argmax(dim=1).item()\n", + "\n", + "print(f\"Prediction for the Dog: {labels[str(class_id)]}, score: {output.softmax(dim=-1)[0, class_id]}\")\n", + "\n", + "plt.title(f\"{labels[str(class_id)]}\\nScore: {output.softmax(dim=-1)[0, class_id]}\")\n", + "plt.imshow(dog1.permute(1, 2, 0))" + ] + }, + { + "cell_type": "markdown", + "id": "8cbe4ccc-224b-4e8a-a2a9-e2c756c9b207", + "metadata": { + "id": "8cbe4ccc-224b-4e8a-a2a9-e2c756c9b207" + }, + "source": [ + "## Port MaxViT model to JAX\n", + "\n", + "To port the [PyTorch implementation of the MaxVit model](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568) in JAX using the Flax module, we will implement the following required modules:\n", + "\n", + "- `MaxViT`\n", + " - `MaxVitBlock`\n", + " - `MaxVitLayer`\n", + " - `MBConv`\n", + " - `Conv2dNormActivation`\n", + " - `SqueezeExcitation`\n", + " - `PartitionAttentionLayer`\n", + " - `RelativePositionalMultiHeadAttention`\n", + " - `WindowDepartition`\n", + " - `WindowPartition`\n", + " - `SwapAxes`\n", + " - `StochasticDepth`\n", + "\n", + "The Flax NNX module is very similar to PyTorch `torch.nn` module and we can map the following modules between PyTorch and Flax:\n", + "- `nn.Sequential` and `nn.ModuleList` -> `nnx.Sequential`\n", + "- `nn.Linear` -> `nnx.Linear`\n", + "- `nn.Conv2d` -> `nnx.Conv`\n", + "- `nn.BatchNorm2d` -> `nnx.BatchNorm`\n", + "- Activations like `nn.ReLU` -> `nnx.relu`\n", + "- Pooling layers like `nn.AvgPool2d(...)` -> `lambda x: nnx.avg_pool(x, ...)`\n", + "- `nn.AdaptiveAvgPool2d(1)` -> `lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2]))`, x is in NHWC format\n", + "- `nn.Flatten()` -> `lambda x: x.reshape(x.shape[0], -1)`\n", + "\n", + "\n", + "If the PyTorch model defines a learnable parameter and a buffer:\n", + "```python\n", + "class Model(nn.Module):\n", + " def __init__(self, ...):\n", + " ...\n", + " self.p = nn.Parameter(torch.ones(10))\n", + " self.register_buffer(\"b\", torch.ones(5))\n", + "```\n", + "an equivalent code in Flax would be\n", + "```python\n", + "class Buffer(nnx.Variable):\n", + " pass\n", + "\n", + "\n", + "class Model(nnx.Module):\n", + " def __init__(self, ...):\n", + " ...\n", + " self.p = nnx.Param(jnp.ones((10,)))\n", + " self.b = Buffer(jnp.ones(5))\n", + "```\n", + "\n", + "To inspect NNX module's learnable parameters and buffers, we can use `nnx.state`:\n", + "```python\n", + "nnx_module = ...\n", + "for k, v in nnx.state(nnx_module, nnx.Param).flat_state().items():\n", + " print(\n", + " k,\n", + " v.value.mean() if v.value is not None else None\n", + " )\n", + "\n", + "for k, v in nnx.state(nnx_module, (nnx.BatchStat, Buffer)).flat_state().items():\n", + " print(\n", + " k,\n", + " v.value.mean() if v.value.dtype == \"float32\" else v.value.sum()\n", + " )\n", + "```\n", + "The equivalent PyTorch code is:\n", + "```python\n", + "torch_module = ...\n", + "\n", + "for m, p in torch_module.named_parameters():\n", + " print(m, p.detach().mean())\n", + "\n", + "for m, b in torch_module.named_buffers():\n", + " print(\n", + " m,\n", + " b.mean() if b.dtype == torch.float32 else b.sum()\n", + " )\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "305ac55b-62ed-4f4d-902c-c6f3082afb02", + "metadata": { + "id": "305ac55b-62ed-4f4d-902c-c6f3082afb02" + }, + "source": [ + "Please note some differences between `torch.nn` and Flax when porting models:\n", + "- We should pass `rngs` to all NNX modules with parameters: e.g. `nnx.Linear(..., rngs=nnx.Rngs(0))`\n", + "- For a 2D convolution:\n", + " - In Flax, we need to explicitly define `kernel_size`, `strides` as two ints tuples, e.g. `(3, 3)`\n", + " - If PyTorch code defines `padding` as integer, e.g. 2, in Flax it should be explicitly defined as a tuple of two ints per dimension, i.e. `((2, 2), (2, 2))`.\n", + "- For a batch normalization: `momentum` value in `torch.nn` should be defined as `1.0 - momentum` in Flax.\n", + "- 4D input arrays in Flax should be in NHWC format, i.e. of shape (N, H, W, C) compared to NCHW format (or (N, C, H, W) shape) in PyTorch." + ] + }, + { + "cell_type": "markdown", + "id": "8d7e3479-bffe-4cb6-81e1-ed8f972c5bf0", + "metadata": { + "id": "8d7e3479-bffe-4cb6-81e1-ed8f972c5bf0" + }, + "source": [ + "Below we implement one by one all the modules from the above list and add simple forward pass checks.\n", + "Let's first implement equivalent of `nn.Identity`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "54ece7f1-14c1-41ef-980a-fc279d1702f2", + "metadata": { + "id": "54ece7f1-14c1-41ef-980a-fc279d1702f2" + }, + "outputs": [], + "source": [ + "class Identity(nnx.Module):\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "id": "dd87b2aa-0285-4995-a9aa-ebd58ae00de6", + "metadata": { + "id": "dd87b2aa-0285-4995-a9aa-ebd58ae00de6" + }, + "source": [ + "### `Conv2dNormActivation` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/misc.py#L125)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "69d71163-676e-4ad3-8d8c-45efaafd76e7", + "metadata": { + "id": "69d71163-676e-4ad3-8d8c-45efaafd76e7" + }, + "outputs": [], + "source": [ + "from typing import Callable, List, Optional, Tuple\n", + "from flax import nnx\n", + "\n", + "\n", + "class Conv2dNormActivation(nnx.Sequential):\n", + " def __init__(\n", + " self,\n", + " in_channels: int,\n", + " out_channels: int,\n", + " kernel_size: int = 3,\n", + " stride: int = 1,\n", + " padding: Optional[int] = None,\n", + " groups: int = 1,\n", + " norm_layer: Callable[..., nnx.Module] = nnx.BatchNorm,\n", + " activation_layer: Callable = nnx.relu,\n", + " dilation: int = 1,\n", + " bias: Optional[bool] = None,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " self.out_channels = out_channels\n", + "\n", + " if padding is None:\n", + " padding = (kernel_size - 1) // 2 * dilation\n", + " if bias is None:\n", + " bias = norm_layer is None\n", + "\n", + " # sequence integer pairs that give the padding to apply before\n", + " # and after each spatial dimension\n", + " padding = ((padding, padding), (padding, padding))\n", + "\n", + " layers = [\n", + " nnx.Conv(\n", + " in_channels,\n", + " out_channels,\n", + " kernel_size=(kernel_size, kernel_size),\n", + " strides=(stride, stride),\n", + " padding=padding,\n", + " kernel_dilation=(dilation, dilation),\n", + " feature_group_count=groups,\n", + " use_bias=bias,\n", + " rngs=rngs,\n", + " )\n", + " ]\n", + "\n", + " if norm_layer is not None:\n", + " layers.append(norm_layer(out_channels, rngs=rngs))\n", + "\n", + " if activation_layer is not None:\n", + " layers.append(activation_layer)\n", + "\n", + " super().__init__(*layers)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", + "metadata": { + "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", + "outputId": "fcce4d81-0ab8-48b7-da59-d234cbec4bb9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4, 14, 14, 64)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 28, 28, 32))\n", + "mod = Conv2dNormActivation(32, 64, 3, 2, 1)\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "2d0cd827-ad40-4cd3-9560-6565a3df10bc", + "metadata": { + "id": "2d0cd827-ad40-4cd3-9560-6565a3df10bc" + }, + "source": [ + "### `SqueezeExcitation` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/misc.py#L224)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4232689e-e6cc-4ffd-8a2a-41fbc34e57c2", + "metadata": { + "id": "4232689e-e6cc-4ffd-8a2a-41fbc34e57c2" + }, + "outputs": [], + "source": [ + "class SqueezeExcitation(nnx.Module):\n", + " def __init__(\n", + " self,\n", + " input_channels: int,\n", + " squeeze_channels: int,\n", + " activation: Callable = nnx.relu,\n", + " scale_activation: Callable = nnx.sigmoid,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " self.avgpool = nnx.avg_pool\n", + " self.fc1 = nnx.Conv(input_channels, squeeze_channels, (1, 1), rngs=rngs)\n", + " self.fc2 = nnx.Conv(squeeze_channels, input_channels, (1, 1), rngs=rngs)\n", + " self.activation = activation\n", + " self.scale_activation = scale_activation\n", + "\n", + " def _scale(self, x: jax.Array) -> jax.Array:\n", + " scale = self.avgpool(x, (x.shape[1], x.shape[2]))\n", + " scale = self.fc1(scale)\n", + " scale = self.activation(scale)\n", + " scale = self.fc2(scale)\n", + " return self.scale_activation(scale)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " scale = self._scale(x)\n", + " return scale * x" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "83c55286-b92e-49aa-bd5f-c2448a787673", + "metadata": { + "id": "83c55286-b92e-49aa-bd5f-c2448a787673", + "outputId": "f30455f1-3d80-4708-d8fc-c308c9555718", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4, 28, 28, 32)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 28, 28, 32))\n", + "mod = SqueezeExcitation(32, 4)\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "7935790a-4cb1-46dc-ab73-12d3cb8fc636", + "metadata": { + "id": "7935790a-4cb1-46dc-ab73-12d3cb8fc636" + }, + "source": [ + "### `StochasticDepth` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/stochastic_depth.py#L50)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "96834419-eec1-4690-8bb0-447524f6bdde", + "metadata": { + "id": "96834419-eec1-4690-8bb0-447524f6bdde" + }, + "outputs": [], + "source": [ + "def stochastic_depth(\n", + " x: jax.Array,\n", + " p: float,\n", + " mode: str,\n", + " deterministic: bool = False,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + ") -> jax.Array:\n", + " if p < 0.0 or p > 1.0:\n", + " raise ValueError(f\"drop probability has to be between 0 and 1, but got {p}\")\n", + " if mode not in [\"batch\", \"row\"]:\n", + " raise ValueError(f\"mode has to be either 'batch' or 'row', but got {mode}\")\n", + " if deterministic or p == 0.0:\n", + " return x\n", + "\n", + " survival_rate = 1.0 - p\n", + " if mode == \"row\":\n", + " size = [x.shape[0]] + [1] * (x.ndim - 1)\n", + " else:\n", + " size = [1] * x.ndim\n", + "\n", + " noise = jax.random.bernoulli(\n", + " rngs.dropout(), p=survival_rate, shape=size\n", + " ).astype(dtype=x.dtype)\n", + "\n", + " if survival_rate > 0.0:\n", + " noise = noise / survival_rate\n", + "\n", + " return x * noise\n", + "\n", + "\n", + "class StochasticDepth(nnx.Module):\n", + " def __init__(\n", + " self,\n", + " p: float,\n", + " mode: str,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " self.p = p\n", + " self.mode = mode\n", + " self.deterministic = False\n", + " self.rngs = rngs\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " return stochastic_depth(\n", + " x, self.p, self.mode, self.deterministic, rngs=self.rngs\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd95babb-95b4-4015-957d-11b9c7b9957d", + "metadata": { + "id": "fd95babb-95b4-4015-957d-11b9c7b9957d" + }, + "outputs": [], + "source": [ + "x = jnp.ones((4, 28, 28, 32))\n", + "mod = StochasticDepth(0.5, \"row\")\n", + "\n", + "mod.eval()\n", + "y = mod(x)\n", + "assert (y == x).all()\n", + "\n", + "mod.train()\n", + "y = mod(x)\n", + "assert (y != x).any()" + ] + }, + { + "cell_type": "markdown", + "id": "0ce251eb-a8dc-4415-9856-d16421c1d646", + "metadata": { + "id": "0ce251eb-a8dc-4415-9856-d16421c1d646" + }, + "source": [ + "### `MBConv` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L53)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "636c713c-4a21-439a-b220-2b9407a06dfc", + "metadata": { + "id": "636c713c-4a21-439a-b220-2b9407a06dfc" + }, + "outputs": [], + "source": [ + "class MBConv(nnx.Module):\n", + " def __init__(\n", + " self,\n", + " in_channels: int,\n", + " out_channels: int,\n", + " expansion_ratio: float,\n", + " squeeze_ratio: float,\n", + " stride: int,\n", + " activation_layer: Callable,\n", + " norm_layer: Callable[..., nnx.Module],\n", + " p_stochastic_dropout: float = 0.0,\n", + " rngs = nnx.Rngs(0),\n", + " ):\n", + " should_proj = stride != 1 or in_channels != out_channels\n", + " if should_proj:\n", + " proj = [nnx.Conv(\n", + " in_channels, out_channels, kernel_size=(1, 1), strides=(1, 1), use_bias=True, rngs=rngs\n", + " )]\n", + " if stride == 2:\n", + " padding = ((1, 1), (1, 1))\n", + " proj = [\n", + " lambda x: nnx.avg_pool(\n", + " x, window_shape=(3, 3), strides=(stride, stride), padding=padding\n", + " )\n", + " ] + proj\n", + " self.proj = nnx.Sequential(*proj)\n", + " else:\n", + " self.proj = Identity()\n", + "\n", + " mid_channels = int(out_channels * expansion_ratio)\n", + " sqz_channels = int(out_channels * squeeze_ratio)\n", + "\n", + " if p_stochastic_dropout:\n", + " self.stochastic_depth = StochasticDepth(p_stochastic_dropout, mode=\"row\", rngs=rngs)\n", + " else:\n", + " self.stochastic_depth = Identity()\n", + "\n", + " _layers = [\n", + " norm_layer(in_channels, rngs=rngs), # pre_norm\n", + " Conv2dNormActivation( # conv_a\n", + " in_channels,\n", + " mid_channels,\n", + " kernel_size=1,\n", + " stride=1,\n", + " padding=0,\n", + " activation_layer=activation_layer,\n", + " norm_layer=norm_layer,\n", + " rngs=rngs,\n", + " ),\n", + " Conv2dNormActivation( # conv_b\n", + " mid_channels,\n", + " mid_channels,\n", + " kernel_size=3,\n", + " stride=stride,\n", + " padding=1,\n", + " activation_layer=activation_layer,\n", + " norm_layer=norm_layer,\n", + " groups=mid_channels,\n", + " rngs=rngs,\n", + " ),\n", + " SqueezeExcitation( # squeeze_excitation\n", + " mid_channels, sqz_channels, activation=nnx.silu, rngs=rngs\n", + " ),\n", + " nnx.Conv( # conv_c\n", + " mid_channels, out_channels, kernel_size=(1, 1), use_bias=True, rngs=rngs\n", + " )\n", + " ]\n", + " self.layers = nnx.Sequential(*_layers)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " res = self.proj(x)\n", + " x = self.stochastic_depth(self.layers(x))\n", + " return res + x" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", + "metadata": { + "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", + "outputId": "84332ba0-44c3-4416-c8e3-df8584f6eec2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(4, 14, 14, 64)" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "source": [ + "from functools import partial\n", + "\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "x = jnp.ones((4, 28, 28, 32))\n", + "mod = MBConv(32, 64, 4, 0.25, 2, activation_layer=nnx.gelu, norm_layer=norm_layer)\n", + "y = mod(x)\n", + "y.shape" + ] + }, + { + "cell_type": "markdown", + "id": "3a8d9cb4-795b-4cb2-a014-bb440acc800b", + "metadata": { + "id": "3a8d9cb4-795b-4cb2-a014-bb440acc800b" + }, + "source": [ + "### `RelativePositionalMultiHeadAttention` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L140). First we reimplement a helper function `_get_relative_position_index`:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "df647057-8c6f-4c6b-84f9-d6f78e649343", + "metadata": { + "id": "df647057-8c6f-4c6b-84f9-d6f78e649343" + }, + "outputs": [], + "source": [ + "def _get_relative_position_index(height: int, width: int) -> jax.Array:\n", + " # PyTorch code:\n", + " # coords = torch.stack(torch.meshgrid([torch.arange(height), torch.arange(width)]))\n", + "\n", + " coords = jnp.stack(\n", + " jnp.meshgrid(*[jnp.arange(height), jnp.arange(width)], indexing=\"ij\")\n", + " )\n", + " # PyTorch code: coords_flat = torch.flatten(coords, 1)\n", + " coords_flat = coords.reshape(coords.shape[0], -1)\n", + "\n", + " relative_coords = coords_flat[:, :, None] - coords_flat[:, None, :]\n", + " relative_coords = jnp.permute_dims(relative_coords, (1, 2, 0))\n", + "\n", + " # PyTorch code:\n", + " # relative_coords[:, :, 0] += height - 1\n", + " # relative_coords[:, :, 1] += width - 1\n", + " # relative_coords[:, :, 0] *= 2 * width - 1\n", + " relative_coords = relative_coords + jnp.array((height - 1, width - 1))\n", + " relative_coords = relative_coords * jnp.array((2 * width - 1, 1))\n", + "\n", + " return relative_coords.sum(-1)" + ] + }, + { + "cell_type": "markdown", + "id": "2670d86b", + "metadata": { + "id": "2670d86b" + }, + "source": [ + "Let us check our implementation against PyTorch implementation:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5ce55b8b-5305-4a57-a413-8df43392ec3a", + "metadata": { + "id": "5ce55b8b-5305-4a57-a413-8df43392ec3a" + }, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import _get_relative_position_index as pytorch_get_relative_position_index\n", + "\n", + "\n", + "output = _get_relative_position_index(13, 12)\n", + "expected = pytorch_get_relative_position_index(13, 12)\n", + "assert (output == jnp.asarray(expected)).all()" + ] + }, + { + "cell_type": "markdown", + "id": "5518bfc4", + "metadata": { + "id": "5518bfc4" + }, + "source": [ + "Next, we can port `RelativePositionalMultiHeadAttention` module which a learnable parameter and a buffer:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1f46b3e4-fd69-42c2-8ca7-d242a20d13de", + "metadata": { + "id": "1f46b3e4-fd69-42c2-8ca7-d242a20d13de" + }, + "outputs": [], + "source": [ + "import math\n", + "\n", + "\n", + "class Buffer(nnx.Variable):\n", + " pass\n", + "\n", + "\n", + "class RelativePositionalMultiHeadAttention(nnx.Module):\n", + " def __init__(\n", + " self,\n", + " feat_dim: int,\n", + " head_dim: int,\n", + " max_seq_len: int,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " if feat_dim % head_dim != 0:\n", + " raise ValueError(f\"feat_dim: {feat_dim} must be divisible by head_dim: {head_dim}\")\n", + "\n", + " self.n_heads = feat_dim // head_dim\n", + " self.head_dim = head_dim\n", + " self.size = int(math.sqrt(max_seq_len))\n", + " self.max_seq_len = max_seq_len\n", + "\n", + " self.to_qkv = nnx.Linear(feat_dim, self.n_heads * self.head_dim * 3, rngs=rngs)\n", + " self.scale_factor = feat_dim**-0.5\n", + "\n", + " self.merge = nnx.Linear(self.head_dim * self.n_heads, feat_dim, rngs=rngs)\n", + "\n", + " self.relative_position_index = Buffer(_get_relative_position_index(self.size, self.size))\n", + "\n", + " # initialize with truncated normal bias\n", + " initializer = jax.nn.initializers.truncated_normal(stddev=0.02)\n", + " shape = ((2 * self.size - 1) * (2 * self.size - 1), self.n_heads)\n", + " self.relative_position_bias_table = nnx.Param(initializer(rngs.params(), shape, jnp.float32))\n", + "\n", + " def get_relative_positional_bias(self) -> jax.Array:\n", + " bias_index = self.relative_position_index.value.ravel()\n", + " relative_bias = self.relative_position_bias_table[bias_index].reshape((self.max_seq_len, self.max_seq_len, -1))\n", + " relative_bias = jnp.permute_dims(relative_bias, (2, 0, 1))\n", + " return jnp.expand_dims(relative_bias, axis=0)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " B, G, P, D = x.shape\n", + " H, DH = self.n_heads, self.head_dim\n", + "\n", + " qkv = self.to_qkv(x)\n", + "\n", + " q, k, v = jnp.split(qkv, 3, axis=-1)\n", + " q = jnp.permute_dims(q.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", + " k = jnp.permute_dims(k.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", + " v = jnp.permute_dims(v.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", + "\n", + " k = k * self.scale_factor\n", + "\n", + " dot_prod = jnp.einsum(\"B G H I D, B G H J D -> B G H I J\", q, k)\n", + " pos_bias = self.get_relative_positional_bias()\n", + "\n", + " dot_prod = jax.nn.softmax(dot_prod + pos_bias, axis=-1)\n", + "\n", + " out = jnp.einsum(\"B G H I J, B G H J D -> B G H I D\", dot_prod, v)\n", + " out = jnp.permute_dims(out, (0, 1, 3, 2, 4)).reshape((B, G, P, D))\n", + "\n", + " out = self.merge(out)\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "18d0c993", + "metadata": { + "id": "18d0c993", + "outputId": "77a3c978-aa0d-4f8e-9efd-642b79ed40c7", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4, 32, 49, 64)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 32, 49, 64))\n", + "\n", + "mod = RelativePositionalMultiHeadAttention(64, 16, 49)\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "875aba65-53d0-4241-bdd7-36384054ca59", + "metadata": { + "id": "875aba65-53d0-4241-bdd7-36384054ca59" + }, + "source": [ + "### `SwapAxes`, `WindowPartition`, `WindowDepartition` implementations\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L213)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d8a19362-733a-4359-9658-53dcffa25220", + "metadata": { + "id": "d8a19362-733a-4359-9658-53dcffa25220" + }, + "outputs": [], + "source": [ + "class SwapAxes(nnx.Module):\n", + " def __init__(self, a: int, b: int):\n", + " self.a = a\n", + " self.b = b\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " res = jnp.swapaxes(x, self.a, self.b)\n", + " return res\n", + "\n", + "\n", + "class WindowPartition(nnx.Module):\n", + " def __call__(self, x: jax.Array, p: int) -> jax.Array:\n", + " # Output array with expected layout of [B, H/P, W/P, P*P, C].\n", + " B, H, W, C = x.shape\n", + " P = p\n", + " # chunk up H and W dimensions\n", + " x = x.reshape((B, H // P, P, W // P, P, C))\n", + " x = jnp.permute_dims(x, (0, 1, 3, 2, 4, 5))\n", + " # colapse P * P dimension\n", + " x = x.reshape((B, (H // P) * (W // P), P * P, C))\n", + " return x\n", + "\n", + "\n", + "class WindowDepartition(nnx.Module):\n", + " def __call__(self, x: jax.Array, p: int, h_partitions: int, w_partitions: int) -> jax.Array:\n", + " # Output array with expected layout of [B, H, W, C].\n", + " B, G, PP, C = x.shape\n", + " P = p\n", + " HP, WP = h_partitions, w_partitions\n", + " # split P * P dimension into 2 P tile dimensions\n", + " x = x.reshape((B, HP, WP, P, P, C))\n", + " # permute into B, HP, P, WP, P, C\n", + " x = jnp.permute_dims(x, (0, 1, 3, 2, 4, 5))\n", + " # reshape into B, H, W, C\n", + " x = x.reshape((B, HP * P, WP * P, C))\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "daee5b6b-595f-4344-af93-6e4bd44c217f", + "metadata": { + "id": "daee5b6b-595f-4344-af93-6e4bd44c217f" + }, + "outputs": [], + "source": [ + "x = jnp.ones((3, 4, 5, 6))\n", + "mod = SwapAxes(1, 2)\n", + "y = mod(x)\n", + "assert y.shape == (3, 5, 4, 6)\n", + "\n", + "x = jnp.ones((4, 128, 128, 3))\n", + "mod = WindowPartition()\n", + "y = mod(x, p=16)\n", + "assert y.shape == (4, (128 // 16) * (128 // 16), 16 * 16, 3)\n", + "\n", + "x = jnp.ones((4, (128 // 16) * (128 // 16), 16 * 16, 3))\n", + "mod = WindowDepartition()\n", + "y = mod(x, p=16, h_partitions=128 // 16, w_partitions=128 // 16)\n", + "assert y.shape == (4, 128, 128, 3)" + ] + }, + { + "cell_type": "markdown", + "id": "fe9643dd-b328-43c9-a82f-7180ee2b9a00", + "metadata": { + "id": "fe9643dd-b328-43c9-a82f-7180ee2b9a00" + }, + "source": [ + "### `PartitionAttentionLayer` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L282)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "dfb3c640-4b51-4ca5-a7ba-2ad5f9907c57", + "metadata": { + "id": "dfb3c640-4b51-4ca5-a7ba-2ad5f9907c57" + }, + "outputs": [], + "source": [ + "class PartitionAttentionLayer(nnx.Module):\n", + " \"\"\"\n", + " Layer for partitioning the input tensor into non-overlapping windows and\n", + " applying attention to each window.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " in_channels: int,\n", + " head_dim: int,\n", + " # partitioning parameters\n", + " partition_size: int,\n", + " partition_type: str,\n", + " # grid size needs to be known at initialization time\n", + " # because we need to know hamy relative offsets there are in the grid\n", + " grid_size: Tuple[int, int],\n", + " mlp_ratio: int,\n", + " activation_layer: Callable,\n", + " norm_layer: Callable[..., nnx.Module],\n", + " attention_dropout: float,\n", + " mlp_dropout: float,\n", + " p_stochastic_dropout: float,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " self.n_heads = in_channels // head_dim\n", + " self.head_dim = head_dim\n", + " self.n_partitions = grid_size[0] // partition_size\n", + " self.partition_type = partition_type\n", + " self.grid_size = grid_size\n", + "\n", + " if partition_type not in [\"grid\", \"window\"]:\n", + " raise ValueError(\"partition_type must be either 'grid' or 'window'\")\n", + "\n", + " if partition_type == \"window\":\n", + " self.p, self.g = partition_size, self.n_partitions\n", + " else:\n", + " self.p, self.g = self.n_partitions, partition_size\n", + "\n", + " self.partition_op = WindowPartition()\n", + " self.departition_op = WindowDepartition()\n", + " self.partition_swap = SwapAxes(-2, -3) if partition_type == \"grid\" else Identity()\n", + " self.departition_swap = SwapAxes(-2, -3) if partition_type == \"grid\" else Identity()\n", + "\n", + " self.attn_layer = nnx.Sequential(\n", + " norm_layer(in_channels, rngs=rngs),\n", + " # it's always going to be partition_size ** 2 because\n", + " # of the axis swap in the case of grid partitioning\n", + " RelativePositionalMultiHeadAttention(\n", + " in_channels, head_dim, partition_size**2, rngs=rngs\n", + " ),\n", + " nnx.Dropout(attention_dropout, rngs=rngs),\n", + " )\n", + "\n", + " # pre-normalization similar to transformer layers\n", + " self.mlp_layer = nnx.Sequential(\n", + " nnx.LayerNorm(in_channels, rngs=rngs),\n", + " nnx.Linear(in_channels, in_channels * mlp_ratio, rngs=rngs),\n", + " activation_layer,\n", + " nnx.Linear(in_channels * mlp_ratio, in_channels, rngs=rngs),\n", + " nnx.Dropout(mlp_dropout, rngs=rngs),\n", + " )\n", + "\n", + " # layer scale factors\n", + " self.stochastic_dropout = StochasticDepth(p_stochastic_dropout, mode=\"row\", rngs=rngs)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " # Undefined behavior if H or W are not divisible by p\n", + " # https://github.com/google-research/maxvit/blob/da76cf0d8a6ec668cc31b399c4126186da7da944/maxvit/models/maxvit.py#L766\n", + " gh, gw = self.grid_size[0] // self.p, self.grid_size[1] // self.p\n", + " torch._assert(\n", + " self.grid_size[0] % self.p == 0 and self.grid_size[1] % self.p == 0,\n", + " \"Grid size must be divisible by partition size. Got grid size of {} and partition size of {}\".format(\n", + " self.grid_size, self.p\n", + " ),\n", + " )\n", + " x = self.partition_op(x, self.p) # (B, H, W, C) -> (B, H/P, W/P, P*P, C)\n", + " x = self.partition_swap(x) # -> grid: (B, H/P, P*P, W/P, C)\n", + " x = x + self.stochastic_dropout(self.attn_layer(x))\n", + " x = x + self.stochastic_dropout(self.mlp_layer(x))\n", + " x = self.departition_swap(x) # grid: (B, H/P, P*P, W/P, C) -> (B, H/P, W/P, P*P, C)\n", + " x = self.departition_op(x, self.p, gh, gw) # -> (B, H, W, C)\n", + "\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", + "metadata": { + "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", + "outputId": "f177ec1c-44b4-4600-fc94-c0416647f25a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4, 224, 224, 36)\n", + "(4, 224, 224, 36)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 224, 224, 36))\n", + "\n", + "grid_size = (224, 224)\n", + "mod = PartitionAttentionLayer(\n", + " 36, 6, 7, \"window\", grid_size=grid_size, mlp_ratio=4,\n", + " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", + " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)\n", + "\n", + "mod = PartitionAttentionLayer(\n", + " 36, 6, 7, \"grid\", grid_size=grid_size, mlp_ratio=4,\n", + " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", + " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "b89b4ca6-c17a-4c0f-859a-de7134348818", + "metadata": { + "id": "b89b4ca6-c17a-4c0f-859a-de7134348818" + }, + "source": [ + "### `MaxVitLayer` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L386)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "45b3199e-711d-4125-86b9-22e90fafa28c", + "metadata": { + "id": "45b3199e-711d-4125-86b9-22e90fafa28c" + }, + "outputs": [], + "source": [ + "class MaxVitLayer(nnx.Module):\n", + " \"\"\"\n", + " MaxVit layer consisting of a MBConv layer followed by a PartitionAttentionLayer with `window`\n", + " and a PartitionAttentionLayer with `grid`.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " # conv parameters\n", + " in_channels: int,\n", + " out_channels: int,\n", + " squeeze_ratio: float,\n", + " expansion_ratio: float,\n", + " stride: int,\n", + " # conv + transformer parameters\n", + " norm_layer: Callable[..., nnx.Module],\n", + " activation_layer: Callable,\n", + " # transformer parameters\n", + " head_dim: int,\n", + " mlp_ratio: int,\n", + " mlp_dropout: float,\n", + " attention_dropout: float,\n", + " p_stochastic_dropout: float,\n", + " # partitioning parameters\n", + " partition_size: int,\n", + " grid_size: Tuple[int, int],\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " layers = [\n", + " # convolutional layer\n", + " MBConv(\n", + " in_channels=in_channels,\n", + " out_channels=out_channels,\n", + " expansion_ratio=expansion_ratio,\n", + " squeeze_ratio=squeeze_ratio,\n", + " stride=stride,\n", + " activation_layer=activation_layer,\n", + " norm_layer=norm_layer,\n", + " p_stochastic_dropout=p_stochastic_dropout,\n", + " rngs=rngs,\n", + " ),\n", + " # window_attention\n", + " PartitionAttentionLayer(\n", + " in_channels=out_channels,\n", + " head_dim=head_dim,\n", + " partition_size=partition_size,\n", + " partition_type=\"window\",\n", + " grid_size=grid_size,\n", + " mlp_ratio=mlp_ratio,\n", + " activation_layer=activation_layer,\n", + " norm_layer=nnx.LayerNorm,\n", + " attention_dropout=attention_dropout,\n", + " mlp_dropout=mlp_dropout,\n", + " p_stochastic_dropout=p_stochastic_dropout,\n", + " rngs=rngs,\n", + " ),\n", + " # grid_attention\n", + " PartitionAttentionLayer(\n", + " in_channels=out_channels,\n", + " head_dim=head_dim,\n", + " partition_size=partition_size,\n", + " partition_type=\"grid\",\n", + " grid_size=grid_size,\n", + " mlp_ratio=mlp_ratio,\n", + " activation_layer=activation_layer,\n", + " norm_layer=nnx.LayerNorm,\n", + " attention_dropout=attention_dropout,\n", + " mlp_dropout=mlp_dropout,\n", + " p_stochastic_dropout=p_stochastic_dropout,\n", + " rngs=rngs,\n", + " )\n", + " ]\n", + " self.layers = nnx.Sequential(*layers)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " return self.layers(x)\n", + "\n", + "\n", + "def _get_conv_output_shape(\n", + " input_size: Tuple[int, int], kernel_size: int, stride: int, padding: int\n", + ") -> Tuple[int, int]:\n", + " return (\n", + " (input_size[0] - kernel_size + 2 * padding) // stride + 1,\n", + " (input_size[1] - kernel_size + 2 * padding) // stride + 1,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", + "metadata": { + "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", + "outputId": "b226b757-db6a-4b1b-bb2d-de3ef3176480", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4, 112, 112, 36)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 224, 224, 3))\n", + "\n", + "grid_size = _get_conv_output_shape((224, 224), kernel_size=3, stride=2, padding=1)\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "\n", + "mod = MaxVitLayer(\n", + " 3, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", + " stride=2, norm_layer=norm_layer, activation_layer=nnx.gelu,\n", + " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", + " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", + " partition_size=7, grid_size=grid_size,\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "21460039-0ed8-4c37-8382-7d91655f1086", + "metadata": { + "id": "21460039-0ed8-4c37-8382-7d91655f1086" + }, + "source": [ + "### `MaxVitBlock` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L483)." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "e4fd31d2-4354-4694-87b4-3d0644388d3d", + "metadata": { + "id": "e4fd31d2-4354-4694-87b4-3d0644388d3d" + }, + "outputs": [], + "source": [ + "class MaxVitBlock(nnx.Module):\n", + " \"\"\"\n", + " A MaxVit block consisting of `n_layers` MaxVit layers.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " # conv parameters\n", + " in_channels: int,\n", + " out_channels: int,\n", + " squeeze_ratio: float,\n", + " expansion_ratio: float,\n", + " # conv + transformer parameters\n", + " norm_layer: Callable[..., nnx.Module],\n", + " activation_layer: Callable,\n", + " # transformer parameters\n", + " head_dim: int,\n", + " mlp_ratio: int,\n", + " mlp_dropout: float,\n", + " attention_dropout: float,\n", + " # partitioning parameters\n", + " partition_size: int,\n", + " input_grid_size: Tuple[int, int],\n", + " # number of layers\n", + " n_layers: int,\n", + " p_stochastic: List[float],\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " if not len(p_stochastic) == n_layers:\n", + " raise ValueError(f\"p_stochastic must have length n_layers={n_layers}, got p_stochastic={p_stochastic}.\")\n", + "\n", + " # account for the first stride of the first layer\n", + " self.grid_size = _get_conv_output_shape(input_grid_size, kernel_size=3, stride=2, padding=1)\n", + "\n", + " layers = []\n", + " for idx, p in enumerate(p_stochastic):\n", + " stride = 2 if idx == 0 else 1\n", + " layers.append(\n", + " MaxVitLayer(\n", + " in_channels=in_channels if idx == 0 else out_channels,\n", + " out_channels=out_channels,\n", + " squeeze_ratio=squeeze_ratio,\n", + " expansion_ratio=expansion_ratio,\n", + " stride=stride,\n", + " norm_layer=norm_layer,\n", + " activation_layer=activation_layer,\n", + " head_dim=head_dim,\n", + " mlp_ratio=mlp_ratio,\n", + " mlp_dropout=mlp_dropout,\n", + " attention_dropout=attention_dropout,\n", + " partition_size=partition_size,\n", + " grid_size=self.grid_size,\n", + " p_stochastic_dropout=p,\n", + " ),\n", + " )\n", + " self.layers = nnx.Sequential(*layers)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " return self.layers(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "e168c27f-98db-4831-9723-dffac88f3226", + "metadata": { + "id": "e168c27f-98db-4831-9723-dffac88f3226", + "outputId": "79ca004c-aaf3-4eb5-9413-35e55b267965", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4, 112, 112, 36)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 224, 224, 3))\n", + "\n", + "input_grid_size = (224, 224)\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "\n", + "mod = MaxVitBlock(\n", + " 3, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", + " norm_layer=norm_layer, activation_layer=nnx.gelu,\n", + " head_dim=6, mlp_ratio=4, mlp_dropout=0.5, attention_dropout=0.4,\n", + " partition_size=7, input_grid_size=input_grid_size,\n", + " n_layers=2,\n", + " p_stochastic=[0.0, 0.2],\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "cef5687d-e390-438b-95b3-e66406e2c000", + "metadata": { + "id": "cef5687d-e390-438b-95b3-e66406e2c000" + }, + "source": [ + "### `MaxVit` implementation\n", + "\n", + "Finally, we can assemble everything together and define the MaxVit model.\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568)." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "0e874e63-0eb7-40ea-82f3-bf10ac33d7a6", + "metadata": { + "id": "0e874e63-0eb7-40ea-82f3-bf10ac33d7a6" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def _make_block_input_shapes(input_size: Tuple[int, int], n_blocks: int) -> List[Tuple[int, int]]:\n", + " \"\"\"Util function to check that the input size is correct for a MaxVit configuration.\"\"\"\n", + " shapes = []\n", + " block_input_shape = _get_conv_output_shape(input_size, 3, 2, 1)\n", + " for _ in range(n_blocks):\n", + " block_input_shape = _get_conv_output_shape(block_input_shape, 3, 2, 1)\n", + " shapes.append(block_input_shape)\n", + " return shapes\n", + "\n", + "\n", + "class MaxVit(nnx.Module):\n", + " \"\"\"\n", + " Implements MaxVit Transformer from the \"MaxViT: Multi-Axis Vision Transformer\" paper.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " # input size parameters\n", + " input_size: Tuple[int, int],\n", + " # stem and task parameters\n", + " stem_channels: int,\n", + " # partitioning parameters\n", + " partition_size: int,\n", + " # block parameters\n", + " block_channels: List[int],\n", + " block_layers: List[int],\n", + " # attention head dimensions\n", + " head_dim: int,\n", + " stochastic_depth_prob: float,\n", + " # conv + transformer parameters\n", + " # norm_layer is applied only to the conv layers\n", + " # activation_layer is applied both to conv and transformer layers\n", + " norm_layer: Optional[Callable[..., nnx.Module]] = None,\n", + " activation_layer: Callable = nnx.gelu,\n", + " # conv parameters\n", + " squeeze_ratio: float = 0.25,\n", + " expansion_ratio: float = 4,\n", + " # transformer parameters\n", + " mlp_ratio: int = 4,\n", + " mlp_dropout: float = 0.0,\n", + " attention_dropout: float = 0.0,\n", + " # task parameters\n", + " num_classes: int = 1000,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " input_channels = 3\n", + "\n", + " if norm_layer is None:\n", + " norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "\n", + " # Make sure input size will be divisible by the partition size in all blocks\n", + " # Undefined behavior if H or W are not divisible by p\n", + " block_input_sizes = _make_block_input_shapes(input_size, len(block_channels))\n", + " for idx, block_input_size in enumerate(block_input_sizes):\n", + " if block_input_size[0] % partition_size != 0 or block_input_size[1] % partition_size != 0:\n", + " raise ValueError(\n", + " f\"Input size {block_input_size} of block {idx} is not divisible by partition size {partition_size}. \"\n", + " f\"Consider changing the partition size or the input size.\\n\"\n", + " f\"Current configuration yields the following block input sizes: {block_input_sizes}.\"\n", + " )\n", + "\n", + " # stem\n", + " self.stem = nnx.Sequential(\n", + " Conv2dNormActivation(\n", + " input_channels,\n", + " stem_channels,\n", + " 3,\n", + " stride=2,\n", + " norm_layer=norm_layer,\n", + " activation_layer=activation_layer,\n", + " bias=False,\n", + " rngs=rngs,\n", + " ),\n", + " Conv2dNormActivation(\n", + " stem_channels,\n", + " stem_channels,\n", + " 3,\n", + " stride=1,\n", + " norm_layer=None,\n", + " activation_layer=None,\n", + " bias=True,\n", + " rngs=rngs,\n", + " ),\n", + " )\n", + "\n", + " # account for stem stride\n", + " input_size = _get_conv_output_shape(input_size, kernel_size=3, stride=2, padding=1)\n", + " self.partition_size = partition_size\n", + "\n", + " # blocks\n", + " blocks = []\n", + " in_channels = [stem_channels] + block_channels[:-1]\n", + " out_channels = block_channels\n", + "\n", + " # precompute the stochastic depth probabilities from 0 to stochastic_depth_prob\n", + " # since we have N blocks with L layers, we will have N * L probabilities uniformly distributed\n", + " # over the range [0, stochastic_depth_prob]\n", + " p_stochastic = np.linspace(0, stochastic_depth_prob, sum(block_layers)).tolist()\n", + "\n", + " p_idx = 0\n", + " for in_channel, out_channel, num_layers in zip(in_channels, out_channels, block_layers):\n", + " blocks.append(\n", + " MaxVitBlock(\n", + " in_channels=in_channel,\n", + " out_channels=out_channel,\n", + " squeeze_ratio=squeeze_ratio,\n", + " expansion_ratio=expansion_ratio,\n", + " norm_layer=norm_layer,\n", + " activation_layer=activation_layer,\n", + " head_dim=head_dim,\n", + " mlp_ratio=mlp_ratio,\n", + " mlp_dropout=mlp_dropout,\n", + " attention_dropout=attention_dropout,\n", + " partition_size=partition_size,\n", + " input_grid_size=input_size,\n", + " n_layers=num_layers,\n", + " p_stochastic=p_stochastic[p_idx : p_idx + num_layers],\n", + " ),\n", + " )\n", + " input_size = blocks[-1].grid_size\n", + " p_idx += num_layers\n", + " self.blocks = nnx.Sequential(*blocks)\n", + "\n", + " self.classifier = nnx.Sequential(\n", + " lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2])), # nn.AdaptiveAvgPool2d(1)\n", + " lambda x: x.reshape(x.shape[0], -1), # nn.Flatten()\n", + " nnx.LayerNorm(block_channels[-1], rngs=rngs),\n", + " nnx.Linear(block_channels[-1], block_channels[-1], rngs=rngs),\n", + " nnx.tanh,\n", + " nnx.Linear(block_channels[-1], num_classes, use_bias=False, rngs=rngs),\n", + " )\n", + "\n", + " self._init_weights(rngs)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " x = self.stem(x)\n", + " x = self.blocks(x)\n", + " x = self.classifier(x)\n", + " return x\n", + "\n", + " def _init_weights(self, rngs):\n", + " normal_initializer = nnx.initializers.normal(stddev=0.02)\n", + " for name, module in self.iter_modules():\n", + " if isinstance(module, (nnx.Conv, nnx.Linear)):\n", + " module.kernel.value = normal_initializer(\n", + " rngs(), module.kernel.value.shape, module.kernel.value.dtype\n", + " )\n", + " if module.bias.value is not None:\n", + " module.bias.value = jnp.zeros(\n", + " module.bias.value.shape, dtype=module.bias.value.dtype\n", + " )\n", + " elif isinstance(module, nnx.BatchNorm):\n", + " module.scale.value = jnp.ones(module.scale.value.shape, module.scale.value.dtype)\n", + " module.bias.value = jnp.zeros(module.bias.value.shape, module.bias.value.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", + "metadata": { + "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", + "outputId": "7e3fb6a7-d058-41a2-aac1-ef3cdda3d486", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(4, 1000)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 224, 224, 3))\n", + "\n", + "mod = MaxVit(\n", + " input_size=(224, 224),\n", + " stem_channels=64,\n", + " block_channels=[64, 128, 256, 512],\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=32,\n", + " stochastic_depth_prob=0.2,\n", + " partition_size=7,\n", + " num_classes=1000,\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "fa2a4a47-b6c9-43ba-822b-002e0c03e85c", + "metadata": { + "id": "fa2a4a47-b6c9-43ba-822b-002e0c03e85c" + }, + "outputs": [], + "source": [ + "def maxvit_t(\n", + " input_size=(224, 224),\n", + " stem_channels=64,\n", + " block_channels=[64, 128, 256, 512],\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=32,\n", + " stochastic_depth_prob=0.2,\n", + " partition_size=7,\n", + " num_classes=1000,\n", + "):\n", + " model = MaxVit(\n", + " input_size=input_size,\n", + " stem_channels=stem_channels,\n", + " block_channels=block_channels,\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=head_dim,\n", + " stochastic_depth_prob=stochastic_depth_prob,\n", + " partition_size=partition_size,\n", + " num_classes=num_classes,\n", + " )\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "id": "25ff32f7-e4a1-4029-b114-8ecafb4378fd", + "metadata": { + "id": "25ff32f7-e4a1-4029-b114-8ecafb4378fd" + }, + "source": [ + "### Test JAX implementation" + ] + }, + { + "cell_type": "markdown", + "id": "b3e02373-c3b6-4ffd-a98e-e425824f2f88", + "metadata": { + "id": "b3e02373-c3b6-4ffd-a98e-e425824f2f88" + }, + "source": [ + "Let us import equivalent PyTorch modules and check our implementations against PyTorch. Please note that\n", + "PyTorch modules will contain random parameters and buffers that we need to set into our Flax implementations.\n", + "\n", + "Below we define a helper class `Torch2Flax` to copy parameters and buffers from a PyTorch module into equivalent Flax module." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "22f49ecd-4999-4c1c-b1d8-d16faeb60389", + "metadata": { + "id": "22f49ecd-4999-4c1c-b1d8-d16faeb60389" + }, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "\n", + "\n", + "class Torch2Flax:\n", + " @staticmethod\n", + " def conv_params_permute(name, torch_param):\n", + " if name == \"weight\":\n", + " return torch_param.permute(2, 3, 1, 0)\n", + " return torch_param\n", + "\n", + " @staticmethod\n", + " def linear_params_permute(name, torch_param):\n", + " if name == \"weight\":\n", + " return torch_param.permute(1, 0)\n", + " return torch_param\n", + "\n", + " @staticmethod\n", + " def default_params_transform(name, param):\n", + " return param\n", + "\n", + " modules_mapping_info = {\n", + " nn.Conv2d: {\n", + " \"type\": nnx.Conv,\n", + " \"params_mapping\": {\n", + " \"weight\": \"kernel\",\n", + " \"bias\": \"bias\",\n", + " },\n", + " \"params_transform\": conv_params_permute,\n", + " },\n", + " nn.BatchNorm2d: {\n", + " \"type\": nnx.BatchNorm,\n", + " \"params_mapping\": {\n", + " \"weight\": \"scale\",\n", + " \"bias\": \"bias\",\n", + " \"running_mean\": \"mean\",\n", + " \"running_var\": \"var\",\n", + " },\n", + " },\n", + " nn.Linear: {\n", + " \"type\": nnx.Linear,\n", + " \"params_mapping\": {\n", + " \"weight\": \"kernel\",\n", + " \"bias\": \"bias\",\n", + " },\n", + " \"params_transform\": linear_params_permute,\n", + " },\n", + " nn.LayerNorm: {\n", + " \"type\": nnx.LayerNorm,\n", + " \"params_mapping\": {\n", + " \"weight\": \"scale\",\n", + " \"bias\": \"bias\",\n", + " },\n", + " }\n", + " } | {\n", + " torch_mod: {\n", + " \"type\": nnx_fn_type,\n", + " \"params_mapping\": {},\n", + " } for torch_mod, nnx_fn_type in [\n", + " (nn.Identity, Identity),\n", + " (nn.Flatten, type(lambda x: x)),\n", + " (nn.ReLU, type(nnx.relu)),\n", + " (nn.GELU, type(nnx.gelu)),\n", + " (nn.SELU, type(nnx.selu)),\n", + " (nn.SiLU, type(nnx.silu)),\n", + " (nn.Tanh, type(nnx.tanh)),\n", + " (nn.Dropout, nnx.Dropout),\n", + " (nn.Sigmoid, type(nnx.sigmoid)),\n", + " (nn.AvgPool2d, type(lambda x: nnx.avg_pool(x, (2, 2)))),\n", + " (nn.AdaptiveAvgPool2d, type(lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2])))),\n", + " ]\n", + " }\n", + "\n", + " def _copy_params_buffers(self, torch_nn_module, nnx_module):\n", + " torch_module_type = type(torch_nn_module)\n", + " assert torch_module_type in self.modules_mapping_info, torch_module_type\n", + " module_mapping_info = self.modules_mapping_info[torch_module_type]\n", + " assert isinstance(nnx_module, module_mapping_info[\"type\"]), (\n", + " nnx_module, type(nnx_module), module_mapping_info[\"type\"]\n", + " )\n", + "\n", + " for torch_key, nnx_key in module_mapping_info[\"params_mapping\"].items():\n", + "\n", + " torch_value = getattr(torch_nn_module, torch_key)\n", + " nnx_param = getattr(nnx_module, nnx_key)\n", + " assert nnx_param is not None, (torch_key, nnx_key, nnx_module)\n", + "\n", + " if torch_value is None:\n", + " assert nnx_param.value is None, nnx_param\n", + " continue\n", + "\n", + " params_transform = module_mapping_info.get(\"params_transform\", Torch2Flax.default_params_transform)\n", + " torch_value = params_transform(torch_key, torch_value)\n", + "\n", + " assert nnx_param.value.shape == torch_value.data.shape, (\n", + " nnx_key, nnx_param.value.shape, torch_key, torch_value.data.shape\n", + " )\n", + " nnx_param.value = jnp.asarray(torch_value.data)\n", + "\n", + " def _copy_sequential(self, torch_nn_seq, nnx_seq, skip_modules=None):\n", + " assert isinstance(torch_nn_seq, (nn.Sequential, nn.ModuleList)), type(torch_nn_seq)\n", + " assert isinstance(nnx_seq, nnx.Sequential), type(nnx_seq)\n", + " for i, index in enumerate(torch_nn_seq):\n", + " torch_module = torch_nn_seq[i]\n", + " nnx_module = nnx_seq.layers[i]\n", + " self.copy_module(torch_module, nnx_module, skip_modules=skip_modules)\n", + "\n", + " def copy_module(self, torch_module, nnx_module, skip_modules=None):\n", + " if skip_modules is None:\n", + " skip_modules = []\n", + "\n", + " if isinstance(torch_module, (nn.Sequential, nn.ModuleList)):\n", + " self._copy_sequential(torch_module, nnx_module, skip_modules=skip_modules)\n", + " elif type(torch_module) in self.modules_mapping_info:\n", + " self._copy_params_buffers(torch_module, nnx_module)\n", + " else:\n", + " if skip_modules is not None:\n", + " if torch_module.__class__.__name__ in skip_modules:\n", + " return\n", + "\n", + " named_children = list(torch_module.named_children())\n", + " assert len(named_children) > 0, type(torch_module)\n", + " for name, torch_child in named_children:\n", + " nnx_child = getattr(nnx_module, name, None)\n", + " assert nnx_child is not None, (name, nnx_module)\n", + " self.copy_module(torch_child, nnx_child, skip_modules=skip_modules)\n", + " # Copy buffers and params of the module itself (not its children)\n", + " for name, torch_buffer in torch_module.named_buffers():\n", + " if \".\" in name:\n", + " # This is child's buffer\n", + " continue\n", + " nnx_buffer = getattr(nnx_module, name)\n", + " assert isinstance(nnx_buffer, nnx.Variable), (name, nnx_buffer, nnx_module)\n", + "\n", + " assert nnx_buffer.value.shape == torch_buffer.shape, (\n", + " name, nnx_buffer.value.shape, torch_buffer.shape\n", + " )\n", + " nnx_buffer.value = jnp.asarray(torch_buffer)\n", + "\n", + " for name, torch_param in torch_module.named_parameters():\n", + " if \".\" in name:\n", + " # This is child's parameter\n", + " continue\n", + " nnx_param = getattr(nnx_module, name)\n", + " assert isinstance(nnx_param, nnx.Param), (name, nnx_param, nnx_module)\n", + "\n", + " assert nnx_param.value.shape == torch_param.data.shape, (\n", + " name, nnx_param.value.shape, torch_param.data.shape\n", + " )\n", + " nnx_param.value = jnp.asarray(torch_param.data)\n", + "\n", + "\n", + "def test_modules(\n", + " nnx_module, torch_module, torch_input, atol=1e-3, mode=\"eval\", permute_torch_input=True, device=\"cuda\"\n", + "):\n", + " assert torch_input.ndim == 4\n", + " assert mode in (\"eval\", \"train\")\n", + "\n", + " torch_input = torch_input.to(device)\n", + " torch_module = torch_module.to(device)\n", + "\n", + " if mode == \"eval\":\n", + " torch_module.eval()\n", + " nnx_module.eval()\n", + " else:\n", + " torch_module.train()\n", + " nnx_module.train()\n", + "\n", + " with torch.inference_mode(mode=mode==\"eval\"):\n", + " torch_output = torch_module(torch_input)\n", + "\n", + " if permute_torch_input:\n", + " torch_input = torch_input.permute(0, 2, 3, 1)\n", + "\n", + " jax_input = jnp.asarray(torch_input, device=jax.devices(device)[0])\n", + " jax_output = nnx_module(jax_input)\n", + " assert jax_output.device == jax.devices(device)[0]\n", + "\n", + " torch_output = torch_output.detach()\n", + " if permute_torch_input and torch_output.ndim == 4:\n", + " torch_output = torch_output.permute(0, 2, 3, 1)\n", + " jax_expected = jnp.asarray(torch_output)\n", + "\n", + " assert jnp.allclose(jax_output, jax_expected, atol=atol), (\n", + " jnp.abs(jax_output - jax_expected).max(),\n", + " jnp.abs(jax_output - jax_expected).mean(),\n", + " )\n", + "\n", + "\n", + "t2f = Torch2Flax()" + ] + }, + { + "cell_type": "markdown", + "id": "a323a19e-fc64-4f8d-8be2-c7886b6191b9", + "metadata": { + "id": "a323a19e-fc64-4f8d-8be2-c7886b6191b9" + }, + "source": [ + "Let us now test our JAX modules. We only test the result of the forward pass in the inference mode such that we avoid discrepancies related to random layers like `Dropout`, `StochasticDepth`, etc.\n", + "By default, we use absolute error tolerence `1e-3` when comparing the JAX output against expected PyTorch result.\n", + "For larger modules we set the device to CPU for the JAX model to execute on in order to reduce the errors between CPU and CUDA." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2e10fb43-6ae6-47c7-81fe-5027b115b25f", + "metadata": { + "id": "2e10fb43-6ae6-47c7-81fe-5027b115b25f" + }, + "outputs": [], + "source": [ + "from torchvision.ops.misc import Conv2dNormActivation as PyTorchConv2dNormActivation\n", + "\n", + "\n", + "torch_module = PyTorchConv2dNormActivation(32, 64, 3, 2, 1)\n", + "nnx_module = Conv2dNormActivation(32, 64, 3, 2, 1)\n", + "\n", + "t2f.copy_module(torch_module, nnx_module)\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7ac4b49a-712f-4725-a8d8-33e72b8d0b66", + "metadata": { + "id": "7ac4b49a-712f-4725-a8d8-33e72b8d0b66" + }, + "outputs": [], + "source": [ + "from torchvision.ops.misc import SqueezeExcitation as PyTorchSqueezeExcitation\n", + "\n", + "\n", + "torch_module = PyTorchSqueezeExcitation(32, 4)\n", + "nnx_module = SqueezeExcitation(32, 4)\n", + "\n", + "t2f.copy_module(torch_module, nnx_module)\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "746c8882-0001-4c97-b5cf-576dc5c87c02", + "metadata": { + "id": "746c8882-0001-4c97-b5cf-576dc5c87c02" + }, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "from functools import partial\n", + "from torchvision.models.maxvit import MBConv as PyTorchMBConv\n", + "\n", + "\n", + "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", + "torch_module = PyTorchMBConv(32, 64, 4, 0.25, 2, activation_layer=nn.GELU, norm_layer=norm_layer)\n", + "\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "nnx_module = MBConv(32, 64, 4, 0.25, 2, activation_layer=nnx.gelu, norm_layer=norm_layer)\n", + "\n", + "\n", + "t2f.copy_module(torch_module, nnx_module)\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "249f6d28-57b6-4d36-9079-cd60964e6afc", + "metadata": { + "id": "249f6d28-57b6-4d36-9079-cd60964e6afc" + }, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import RelativePositionalMultiHeadAttention as PyTorchRelativePositionalMultiHeadAttention\n", + "\n", + "\n", + "torch_module = PyTorchRelativePositionalMultiHeadAttention(64, 16, 49)\n", + "nnx_module = RelativePositionalMultiHeadAttention(64, 16, 49)\n", + "\n", + "t2f.copy_module(torch_module, nnx_module)\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 32, 49, 64), permute_torch_input=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f48fc475-c556-4101-ad2b-19480a73c6ba", + "metadata": { + "id": "f48fc475-c556-4101-ad2b-19480a73c6ba" + }, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import PartitionAttentionLayer as PyTorchPartitionAttentionLayer\n", + "\n", + "\n", + "grid_size = (224, 224)\n", + "for partition_type in [\"window\", \"grid\"]:\n", + "\n", + " torch_module = PyTorchPartitionAttentionLayer(\n", + " 36, 6, 7, partition_type, grid_size=grid_size, mlp_ratio=4,\n", + " activation_layer=nn.GELU, norm_layer=nn.LayerNorm,\n", + " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", + " )\n", + "\n", + " nnx_module = PartitionAttentionLayer(\n", + " 36, 6, 7, partition_type, grid_size=grid_size, mlp_ratio=4,\n", + " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", + " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", + " )\n", + "\n", + " t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + " ])\n", + "\n", + " test_modules(nnx_module, torch_module, torch.randn(4, 36, 224, 224))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7ab2de6a-9790-444b-b175-535cdb05f5d8", + "metadata": { + "id": "7ab2de6a-9790-444b-b175-535cdb05f5d8" + }, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import MaxVitLayer as PyTorchMaxVitLayer\n", + "\n", + "\n", + "stride = 2\n", + "\n", + "grid_size = _get_conv_output_shape((224, 224), kernel_size=3, stride=2, padding=1)\n", + "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", + "\n", + "torch_module = PyTorchMaxVitLayer(\n", + " 36, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", + " stride=stride, norm_layer=norm_layer, activation_layer=nn.GELU,\n", + " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", + " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", + " partition_size=7, grid_size=grid_size,\n", + ")\n", + "\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "nnx_module = MaxVitLayer(\n", + " 36, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", + " stride=stride, norm_layer=norm_layer, activation_layer=nnx.gelu,\n", + " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", + " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", + " partition_size=7, grid_size=grid_size,\n", + ")\n", + "\n", + "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + "])\n", + "\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 36, 224, 224), device=\"cpu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "e8e8f997-0184-4af2-82b0-ceaa580645c8", + "metadata": { + "id": "e8e8f997-0184-4af2-82b0-ceaa580645c8" + }, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import MaxVitBlock as PyTorchMaxVitBlock\n", + "\n", + "\n", + "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", + "torch_module = PyTorchMaxVitBlock(\n", + " 64, 128, squeeze_ratio=0.25, expansion_ratio=4,\n", + " norm_layer=norm_layer, activation_layer=nn.GELU,\n", + " head_dim=32, mlp_ratio=4, mlp_dropout=0.0, attention_dropout=0.0,\n", + " partition_size=7, input_grid_size=(56, 56),\n", + " n_layers=2,\n", + " p_stochastic=[0.13333333333333333, 0.2],\n", + ")\n", + "\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "nnx_module = MaxVitBlock(\n", + " 64, 128, squeeze_ratio=0.25, expansion_ratio=4,\n", + " norm_layer=norm_layer, activation_layer=nnx.gelu,\n", + " head_dim=32, mlp_ratio=4, mlp_dropout=0.0, attention_dropout=0.0,\n", + " partition_size=7, input_grid_size=(56, 56),\n", + " n_layers=2,\n", + " p_stochastic=[0.13333333333333333, 0.2],\n", + ")\n", + "\n", + "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + "])\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 64, 56, 56), device=\"cpu\")" + ] + }, + { + "cell_type": "markdown", + "id": "e313819a-e93a-4201-806d-783bd1336c78", + "metadata": { + "id": "e313819a-e93a-4201-806d-783bd1336c78" + }, + "source": [ + "Finally, we can check the MaxVit implementation. Note that we raised the absolute tolerence to `1e-1` when comparing JAX output logits against PyTorch expected logits." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e2af63a4-b16b-40a3-ac00-bfc23d532c82", + "metadata": { + "id": "e2af63a4-b16b-40a3-ac00-bfc23d532c82" + }, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import MaxVit as PyTorchMaxVit\n", + "\n", + "\n", + "torch.manual_seed(77)\n", + "\n", + "\n", + "torch_module = PyTorchMaxVit(\n", + " input_size=(224, 224),\n", + " stem_channels=64,\n", + " block_channels=[64, 128, 256, 512],\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=32,\n", + " stochastic_depth_prob=0.2,\n", + " partition_size=7,\n", + " num_classes=1000,\n", + ")\n", + "\n", + "nnx_module = MaxVit(\n", + " input_size=(224, 224),\n", + " stem_channels=64,\n", + " block_channels=[64, 128, 256, 512],\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=32,\n", + " stochastic_depth_prob=0.2,\n", + " partition_size=7,\n", + " num_classes=1000,\n", + ")\n", + "\n", + "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + "])\n", + "\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 3, 224, 224), device=\"cpu\", atol=1e-1)" + ] + }, + { + "cell_type": "markdown", + "id": "0d3c4f4d-2a50-46f4-814c-42c1a423cfd0", + "metadata": { + "id": "0d3c4f4d-2a50-46f4-814c-42c1a423cfd0" + }, + "source": [ + "### Check Flax model\n", + "Let us now reuse trained weights from TorchVision's MaxViT model to check output logits and the predictions on our example image:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "7975f311-7a02-4c82-99db-b0b50fb37528", + "metadata": { + "id": "7975f311-7a02-4c82-99db-b0b50fb37528" + }, + "outputs": [], + "source": [ + "from torchvision.models import maxvit_t as pytorch_maxvit_t, MaxVit_T_Weights\n", + "\n", + "torch_model = pytorch_maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1)\n", + "flax_model = maxvit_t()\n", + "\n", + "t2f = Torch2Flax()\n", + "t2f.copy_module(torch_model, flax_model, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", + "metadata": { + "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", + "outputId": "8cb2a997-a6c8-452d-eb61-3a103278a569", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 398 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Prediction for the Dog:\n", + "- PyTorch model result: ['n02113023', 'Pembroke'], score: 0.7800844311714172\n", + "- Flax model result: ['n02113023', 'Pembroke'], score: 0.7799879908561707\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 44 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "import json\n", + "from torchvision.io import read_image\n", + "\n", + "\n", + "preprocess = MaxVit_T_Weights.IMAGENET1K_V1.transforms()\n", + "\n", + "with open(\"imagenet_class_index.json\") as labels_file:\n", + " labels = json.load(labels_file)\n", + "\n", + "\n", + "dog1 = read_image(\"dog1.jpg\")\n", + "tensor = preprocess(dog1).unsqueeze(dim=0)\n", + "\n", + "torch_model.eval()\n", + "with torch.inference_mode():\n", + " torch_output = torch_model(tensor)\n", + "\n", + "torch_class_id = torch_output.argmax(dim=1).item()\n", + "\n", + "jax_array = jnp.asarray(tensor.permute(0, 2, 3, 1), device=jax.devices(\"cpu\")[0])\n", + "flax_model.eval()\n", + "flax_output = flax_model(jax_array)\n", + "\n", + "flax_class_id = torch_output.argmax(axis=1).item()\n", + "\n", + "print(\"Prediction for the Dog:\")\n", + "print(f\"- PyTorch model result: {labels[str(torch_class_id)]}, score: {torch_output.softmax(axis=1)[0, torch_class_id]}\")\n", + "print(f\"- Flax model result: {labels[str(flax_class_id)]}, score: {jax.nn.softmax(flax_output, axis=1)[0, flax_class_id]}\")\n", + "\n", + "\n", + "plt.subplot(121)\n", + "plt.title(f\"{labels[str(torch_class_id)]}\\nScore: {torch_output.softmax(dim=-1)[0, class_id]:.4f}\")\n", + "plt.imshow(dog1.permute(1, 2, 0))\n", + "\n", + "plt.subplot(122)\n", + "plt.title(f\"{labels[str(flax_class_id)]}\\nScore: {jax.nn.softmax(flax_output, axis=1)[0, flax_class_id]:.4f}\")\n", + "plt.imshow(dog1.permute(1, 2, 0))" + ] + }, + { + "cell_type": "markdown", + "id": "c77f3244", + "metadata": { + "id": "c77f3244" + }, + "source": [ + "Let's compute cosine distance between the logits:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", + "metadata": { + "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", + "outputId": "b77515f7-7ed5-41b0-cff5-c4aba353dc7b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Array(0.99999845, dtype=float32)" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ], + "source": [ + "expected = jnp.asarray(torch_output)\n", + "\n", + "cosine_dist = (expected * flax_output).sum() / (jnp.linalg.norm(flax_output) * jnp.linalg.norm(expected))\n", + "cosine_dist" + ] + }, + { + "cell_type": "markdown", + "id": "65e57aa6-1572-4805-9207-bc8a5f9f3ab1", + "metadata": { + "id": "65e57aa6-1572-4805-9207-bc8a5f9f3ab1" + }, + "source": [ + "## Further reading\n", + "\n", + "- [Flax documentation: Core Exampels](https://flax.readthedocs.io/en/latest/examples/core_examples.html)\n", + "- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)" + ] } - ], - "source": [ - "expected = jnp.asarray(torch_output)\n", - "\n", - "cosine_dist = (expected * flax_output).sum() / (jnp.linalg.norm(flax_output) * jnp.linalg.norm(expected))\n", - "cosine_dist" - ] - }, - { - "cell_type": "markdown", - "id": "65e57aa6-1572-4805-9207-bc8a5f9f3ab1", - "metadata": {}, - "source": [ - "## Further reading\n", - "\n", - "- [Flax documentation: Core Exampels](https://flax.readthedocs.io/en/latest/examples/core_examples.html)\n", - "- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3", + "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": "3.11.9" + }, + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "accelerator": "GPU" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From ba190384af7c261ac0c60fb5958cc88f11e644fd Mon Sep 17 00:00:00 2001 From: Robert Crowe Date: Wed, 15 Jan 2025 13:07:42 -0800 Subject: [PATCH 2/4] More tweaks --- docs/source/JAX_porting_PyTorch_model.ipynb | 113 ++++++++++---------- 1 file changed, 54 insertions(+), 59 deletions(-) diff --git a/docs/source/JAX_porting_PyTorch_model.ipynb b/docs/source/JAX_porting_PyTorch_model.ipynb index 3080781..5d14b65 100644 --- a/docs/source/JAX_porting_PyTorch_model.ipynb +++ b/docs/source/JAX_porting_PyTorch_model.ipynb @@ -22,11 +22,11 @@ "!pip install -Uq flax treescope" ], "metadata": { - "id": "NHqB3sNbrygd", - "outputId": "7e0f46f0-a30b-4e49-a995-6b91452bd521", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "NHqB3sNbrygd", + "outputId": "7e0f46f0-a30b-4e49-a995-6b91452bd521" }, "id": "NHqB3sNbrygd", "execution_count": 1, @@ -92,11 +92,11 @@ "execution_count": 3, "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", "metadata": { - "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", - "outputId": "43752a13-637a-4dc4-95f2-98f0ccfd17a5", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", + "outputId": "43752a13-637a-4dc4-95f2-98f0ccfd17a5" }, "outputs": [ { @@ -154,11 +154,11 @@ "execution_count": 5, "id": "0d5bf6aa-c720-4400-a276-602fff53b413", "metadata": { - "id": "0d5bf6aa-c720-4400-a276-602fff53b413", - "outputId": "a7bf9edf-8b72-4f19-a776-9d57ab99f8c3", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "0d5bf6aa-c720-4400-a276-602fff53b413", + "outputId": "a7bf9edf-8b72-4f19-a776-9d57ab99f8c3" }, "outputs": [ { @@ -191,11 +191,11 @@ "execution_count": 6, "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", "metadata": { - "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", - "outputId": "bdc1e7a2-0be7-4657-ca17-94338338ded0", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", + "outputId": "bdc1e7a2-0be7-4657-ca17-94338338ded0" }, "outputs": [ { @@ -240,11 +240,11 @@ "execution_count": 7, "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", "metadata": { - "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", - "outputId": "407caaa9-0101-40bf-c60b-be023d009c0a", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", + "outputId": "407caaa9-0101-40bf-c60b-be023d009c0a" }, "outputs": [ { @@ -273,12 +273,7 @@ "id": "133bcf21-8a9c-4c27-b551-39b7dfdcfe1c" }, "source": [ - "We can download an image of a Pembroke Corgy dog from [TorchVision's gallery](https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true) together with [ImageNet classes dictionary](https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json):\n", - "\n", - "```bash\n", - "wget \"https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\" -O dog1.jpg\n", - "wget \"https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\" -O imagenet_class_index.json\n", - "```" + "We can download an image of a Pembroke Corgy dog from [TorchVision's gallery](https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true) together with [ImageNet classes dictionary](https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json):" ] }, { @@ -288,20 +283,20 @@ "if [ -f \"dog1.jpg\" ]; then\n", " echo \"dog1.jpg already exists.\"\n", "else\n", - " wget \"https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\" -O dog1.jpg\n", + " wget -nv \"https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\" -O dog1.jpg\n", "fi\n", "if [ -f \"imagenet_class_index.json\" ]; then\n", " echo \"imagenet_class_index.json already exists.\"\n", "else\n", - " wget \"https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\" -O imagenet_class_index.json\n", + " wget -nv \"https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\" -O imagenet_class_index.json\n", "fi" ], "metadata": { - "id": "qC9hpYfNtOEF", - "outputId": "7d5b91fa-6d25-4404-f1be-78efef163d10", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "qC9hpYfNtOEF", + "outputId": "7d5b91fa-6d25-4404-f1be-78efef163d10" }, "id": "qC9hpYfNtOEF", "execution_count": 8, @@ -351,12 +346,12 @@ "execution_count": 9, "id": "82be8baf-1292-4766-be34-28c510563d71", "metadata": { - "id": "82be8baf-1292-4766-be34-28c510563d71", - "outputId": "981e2912-9d20-4297-8ab6-0493dada77f7", "colab": { "base_uri": "https://localhost:8080/", "height": 508 - } + }, + "id": "82be8baf-1292-4766-be34-28c510563d71", + "outputId": "981e2912-9d20-4297-8ab6-0493dada77f7" }, "outputs": [ { @@ -621,11 +616,11 @@ "execution_count": 12, "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", "metadata": { - "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", - "outputId": "fcce4d81-0ab8-48b7-da59-d234cbec4bb9", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", + "outputId": "fcce4d81-0ab8-48b7-da59-d234cbec4bb9" }, "outputs": [ { @@ -696,11 +691,11 @@ "execution_count": 14, "id": "83c55286-b92e-49aa-bd5f-c2448a787673", "metadata": { - "id": "83c55286-b92e-49aa-bd5f-c2448a787673", - "outputId": "f30455f1-3d80-4708-d8fc-c308c9555718", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "83c55286-b92e-49aa-bd5f-c2448a787673", + "outputId": "f30455f1-3d80-4708-d8fc-c308c9555718" }, "outputs": [ { @@ -909,11 +904,11 @@ "execution_count": 18, "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", "metadata": { - "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", - "outputId": "84332ba0-44c3-4416-c8e3-df8584f6eec2", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", + "outputId": "84332ba0-44c3-4416-c8e3-df8584f6eec2" }, "outputs": [ { @@ -1098,11 +1093,11 @@ "execution_count": 22, "id": "18d0c993", "metadata": { - "id": "18d0c993", - "outputId": "77a3c978-aa0d-4f8e-9efd-642b79ed40c7", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "18d0c993", + "outputId": "77a3c978-aa0d-4f8e-9efd-642b79ed40c7" }, "outputs": [ { @@ -1315,11 +1310,11 @@ "execution_count": 26, "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", "metadata": { - "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", - "outputId": "f177ec1c-44b4-4600-fc94-c0416647f25a", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", + "outputId": "f177ec1c-44b4-4600-fc94-c0416647f25a" }, "outputs": [ { @@ -1466,11 +1461,11 @@ "execution_count": 28, "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", "metadata": { - "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", - "outputId": "b226b757-db6a-4b1b-bb2d-de3ef3176480", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", + "outputId": "b226b757-db6a-4b1b-bb2d-de3ef3176480" }, "outputs": [ { @@ -1585,11 +1580,11 @@ "execution_count": 30, "id": "e168c27f-98db-4831-9723-dffac88f3226", "metadata": { - "id": "e168c27f-98db-4831-9723-dffac88f3226", - "outputId": "79ca004c-aaf3-4eb5-9413-35e55b267965", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "e168c27f-98db-4831-9723-dffac88f3226", + "outputId": "79ca004c-aaf3-4eb5-9413-35e55b267965" }, "outputs": [ { @@ -1804,11 +1799,11 @@ "execution_count": 32, "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", "metadata": { - "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", - "outputId": "7e3fb6a7-d058-41a2-aac1-ef3cdda3d486", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", + "outputId": "7e3fb6a7-d058-41a2-aac1-ef3cdda3d486" }, "outputs": [ { @@ -2396,12 +2391,12 @@ "execution_count": 44, "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", "metadata": { - "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", - "outputId": "8cb2a997-a6c8-452d-eb61-3a103278a569", "colab": { "base_uri": "https://localhost:8080/", "height": 398 - } + }, + "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", + "outputId": "8cb2a997-a6c8-452d-eb61-3a103278a569" }, "outputs": [ { @@ -2489,11 +2484,11 @@ "execution_count": 45, "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", "metadata": { - "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", - "outputId": "b77515f7-7ed5-41b0-cff5-c4aba353dc7b", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", + "outputId": "b77515f7-7ed5-41b0-cff5-c4aba353dc7b" }, "outputs": [ { From bb81654e9ddd2a77340999c20457d2031da837ad Mon Sep 17 00:00:00 2001 From: Robert Crowe Date: Wed, 15 Jan 2025 13:13:45 -0800 Subject: [PATCH 3/4] Oops, removing second Colab button. --- docs/source/JAX_porting_PyTorch_model.ipynb | 90 +++++++-------------- 1 file changed, 29 insertions(+), 61 deletions(-) diff --git a/docs/source/JAX_porting_PyTorch_model.ipynb b/docs/source/JAX_porting_PyTorch_model.ipynb index 5d14b65..d1f9755 100644 --- a/docs/source/JAX_porting_PyTorch_model.ipynb +++ b/docs/source/JAX_porting_PyTorch_model.ipynb @@ -26,7 +26,7 @@ "base_uri": "https://localhost:8080/" }, "id": "NHqB3sNbrygd", - "outputId": "7e0f46f0-a30b-4e49-a995-6b91452bd521" + "outputId": "128df299-9dc3-45c1-ee61-8a38d3556be7" }, "id": "NHqB3sNbrygd", "execution_count": 1, @@ -35,8 +35,8 @@ "output_type": "stream", "name": "stdout", "text": [ - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/424.2 kB\u001b[0m \u001b[31m?\u001b[0m eta 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We will use [`TorchVision`](https://pytorch.org/vision/stable/index.html) to provide a [MaxVit](https://pytorch.org/vision/stable/models/maxvit.html) model trained on ImageNet (MaxViT: Multi-Axis Vision Transformer, https://arxiv.org/abs/2204.01697).\n", "\n", - "First, we set up the model using TorchVision and explore briefly the model's architecture and the blocks we need to port. Next, we define equivalent blocks and the whole model using Flax. After that, we port the weights. Finally, we run some tests to ensure the correctness of the ported model.\n", - "\n", - "**On Colab, you should restart the runtime after doing `pip install`. Runtime > Restart session**" + "First, we set up the model using TorchVision and explore briefly the model's architecture and the blocks we need to port. Next, we define equivalent blocks and the whole model using Flax. After that, we port the weights. Finally, we run some tests to ensure the correctness of the ported model." ], "metadata": { "id": "ABCg5TvPr1pm" @@ -96,7 +94,7 @@ "base_uri": "https://localhost:8080/" }, "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", - "outputId": "43752a13-637a-4dc4-95f2-98f0ccfd17a5" + "outputId": "a2a332f1-37dd-4a43-d2f6-c273865814db" }, "outputs": [ { @@ -106,7 +104,7 @@ "/usr/local/lib/python3.10/dist-packages/torch/functional.py:534: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3595.)\n", " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n", "Downloading: \"https://download.pytorch.org/models/maxvit_t-bc5ab103.pth\" to /root/.cache/torch/hub/checkpoints/maxvit_t-bc5ab103.pth\n", - "100%|██████████| 119M/119M [00:01<00:00, 99.7MB/s]\n" + "100%|██████████| 119M/119M [00:02<00:00, 53.9MB/s]\n" ] } ], @@ -158,7 +156,7 @@ "base_uri": "https://localhost:8080/" }, "id": "0d5bf6aa-c720-4400-a276-602fff53b413", - "outputId": "a7bf9edf-8b72-4f19-a776-9d57ab99f8c3" + "outputId": "6c0368fe-2d3c-47e2-92ea-5cd7bbe0ed65" }, "outputs": [ { @@ -195,7 +193,7 @@ "base_uri": "https://localhost:8080/" }, "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", - "outputId": "bdc1e7a2-0be7-4657-ca17-94338338ded0" + "outputId": "b5b5af72-7992-4d26-eb14-c1f471173a7d" }, "outputs": [ { @@ -244,7 +242,7 @@ "base_uri": "https://localhost:8080/" }, "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", - "outputId": "407caaa9-0101-40bf-c60b-be023d009c0a" + "outputId": "0b74d247-5458-429d-e310-fe42579ce15c" }, "outputs": [ { @@ -296,7 +294,7 @@ "base_uri": "https://localhost:8080/" }, "id": "qC9hpYfNtOEF", - "outputId": "7d5b91fa-6d25-4404-f1be-78efef163d10" + "outputId": "82a2c276-5ef8-44ee-cf12-fede419b7f90" }, "id": "qC9hpYfNtOEF", "execution_count": 8, @@ -305,38 +303,8 @@ "output_type": "stream", "name": "stderr", "text": [ - "--2025-01-15 20:57:39-- https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\n", - "Resolving github.com (github.com)... 140.82.112.4\n", - "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://github.com/pytorch/vision/raw/refs/heads/main/gallery/assets/dog1.jpg [following]\n", - "--2025-01-15 20:57:40-- https://github.com/pytorch/vision/raw/refs/heads/main/gallery/assets/dog1.jpg\n", - "Reusing existing connection to github.com:443.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/dog1.jpg [following]\n", - "--2025-01-15 20:57:40-- https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/dog1.jpg\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 97422 (95K) [image/jpeg]\n", - "Saving to: ‘dog1.jpg’\n", - "\n", - " 0K .......... .......... .......... .......... .......... 52% 3.20M 0s\n", - " 50K .......... .......... .......... .......... ..... 100% 19.6M=0.02s\n", - "\n", - "2025-01-15 20:57:40 (5.31 MB/s) - ‘dog1.jpg’ saved [97422/97422]\n", - "\n", - "--2025-01-15 20:57:40-- https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 35364 (35K) [text/plain]\n", - "Saving to: ‘imagenet_class_index.json’\n", - "\n", - " 0K .......... .......... .......... .... 100% 4.74M=0.007s\n", - "\n", - "2025-01-15 20:57:41 (4.74 MB/s) - ‘imagenet_class_index.json’ saved [35364/35364]\n", - "\n" + "2025-01-15 21:10:00 URL:https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/dog1.jpg [97422/97422] -> \"dog1.jpg\" [1]\n", + "2025-01-15 21:10:01 URL:https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json [35364/35364] -> \"imagenet_class_index.json\" [1]\n" ] } ] @@ -351,21 +319,21 @@ "height": 508 }, "id": "82be8baf-1292-4766-be34-28c510563d71", - "outputId": "981e2912-9d20-4297-8ab6-0493dada77f7" + "outputId": "e952fefe-fd5b-4d91-af86-e23e414589fa" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Prediction for the Dog: ['n02113023', 'Pembroke'], score: 0.7800844311714172\n" + "Prediction for the Dog: ['n02113023', 'Pembroke'], score: 0.7800846099853516\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -377,7 +345,7 @@ "text/plain": [ "
" ], - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {} } @@ -620,7 +588,7 @@ "base_uri": "https://localhost:8080/" }, "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", - "outputId": "fcce4d81-0ab8-48b7-da59-d234cbec4bb9" + "outputId": "fd9c4225-728d-4fad-fe47-c3095aa11c7f" }, "outputs": [ { @@ -695,7 +663,7 @@ "base_uri": "https://localhost:8080/" }, "id": "83c55286-b92e-49aa-bd5f-c2448a787673", - "outputId": "f30455f1-3d80-4708-d8fc-c308c9555718" + "outputId": "2a9cb19c-715e-40f5-ff83-a6097d80c5d2" }, "outputs": [ { @@ -908,7 +876,7 @@ "base_uri": "https://localhost:8080/" }, "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", - "outputId": "84332ba0-44c3-4416-c8e3-df8584f6eec2" + "outputId": "f2fb2da2-7f63-4b77-d6ce-7266b0348ec3" }, "outputs": [ { @@ -1097,7 +1065,7 @@ "base_uri": "https://localhost:8080/" }, "id": "18d0c993", - "outputId": "77a3c978-aa0d-4f8e-9efd-642b79ed40c7" + "outputId": "da8d58ad-d6b3-431d-d890-002beee81dec" }, "outputs": [ { @@ -1314,7 +1282,7 @@ "base_uri": "https://localhost:8080/" }, "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", - "outputId": "f177ec1c-44b4-4600-fc94-c0416647f25a" + "outputId": "af0e7cce-85d6-4646-d8b7-9153d54bb8c0" }, "outputs": [ { @@ -1465,7 +1433,7 @@ "base_uri": "https://localhost:8080/" }, "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", - "outputId": "b226b757-db6a-4b1b-bb2d-de3ef3176480" + "outputId": "034c847f-9346-4953-99f2-5d59be74f9b0" }, "outputs": [ { @@ -1584,7 +1552,7 @@ "base_uri": "https://localhost:8080/" }, "id": "e168c27f-98db-4831-9723-dffac88f3226", - "outputId": "79ca004c-aaf3-4eb5-9413-35e55b267965" + "outputId": "2bcd5022-1c2c-4ae0-f696-47717eaf4bd4" }, "outputs": [ { @@ -1803,7 +1771,7 @@ "base_uri": "https://localhost:8080/" }, "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", - "outputId": "7e3fb6a7-d058-41a2-aac1-ef3cdda3d486" + "outputId": "70162701-605c-4d0e-f215-21864071375f" }, "outputs": [ { @@ -2396,7 +2364,7 @@ "height": 398 }, "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", - "outputId": "8cb2a997-a6c8-452d-eb61-3a103278a569" + "outputId": "9f5673f5-61a8-4bc0-af96-51d3a4f982de" }, "outputs": [ { @@ -2404,7 +2372,7 @@ "name": "stdout", "text": [ "Prediction for the Dog:\n", - "- PyTorch model result: ['n02113023', 'Pembroke'], score: 0.7800844311714172\n", + "- PyTorch model result: ['n02113023', 'Pembroke'], score: 0.7800846099853516\n", "- Flax model result: ['n02113023', 'Pembroke'], score: 0.7799879908561707\n" ] }, @@ -2412,7 +2380,7 @@ "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2488,14 +2456,14 @@ "base_uri": "https://localhost:8080/" }, "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", - "outputId": "b77515f7-7ed5-41b0-cff5-c4aba353dc7b" + "outputId": "b03dadcc-07f5-42b5-a2d5-86eac15d7f02" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "Array(0.99999845, dtype=float32)" + "Array(0.99999857, dtype=float32)" ] }, "metadata": {}, From 11c803d8f276f538f4d1f738005d4b3196835202 Mon Sep 17 00:00:00 2001 From: rcrowe-google Date: Fri, 17 Jan 2025 00:14:42 +0000 Subject: [PATCH 4/4] Following the instructions for once! --- docs/source/JAX_porting_PyTorch_model.ipynb | 4823 +++++++++---------- docs/source/JAX_porting_PyTorch_model.md | 30 +- 2 files changed, 2326 insertions(+), 2527 deletions(-) diff --git a/docs/source/JAX_porting_PyTorch_model.ipynb b/docs/source/JAX_porting_PyTorch_model.ipynb index d1f9755..da41dbb 100644 --- a/docs/source/JAX_porting_PyTorch_model.ipynb +++ b/docs/source/JAX_porting_PyTorch_model.ipynb @@ -1,2522 +1,2307 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "b69996dc-49af-4a0e-a4e6-36d81b51f2b4", - "metadata": { - "id": "b69996dc-49af-4a0e-a4e6-36d81b51f2b4" - }, - "source": [ - "# Porting a PyTorch model to JAX\n", - "\n", - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/JAX_porting_PyTorch_model.ipynb)\n", - "\n", - "**Note: On Colab we recommend running this on a T4 GPU instance. On Kaggle we recommend a T4x2 or P100 instance.**\n", - "\n", - "In this tutorial we will learn how to port a PyTorch model to JAX and [Flax](https://flax.readthedocs.io/en/latest/nnx_basics.html). Flax provides an API very similar to the PyTorch `torch.nn` module and porting PyTorch models is rather straightforward. To install Flax, we can simply execute the following command: `pip install -U flax treescope`." - ] - }, - { - "cell_type": "code", - "source": [ - "!pip install -Uq flax treescope" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NHqB3sNbrygd", - "outputId": "128df299-9dc3-45c1-ee61-8a38d3556be7" - }, - "id": "NHqB3sNbrygd", - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/424.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m419.8/424.2 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m424.2/424.2 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/175.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.6/175.6 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Say we have a trained PyTorch computer-vision model to classify images that we would like to port to JAX. We will use [`TorchVision`](https://pytorch.org/vision/stable/index.html) to provide a [MaxVit](https://pytorch.org/vision/stable/models/maxvit.html) model trained on ImageNet (MaxViT: Multi-Axis Vision Transformer, https://arxiv.org/abs/2204.01697).\n", - "\n", - "First, we set up the model using TorchVision and explore briefly the model's architecture and the blocks we need to port. Next, we define equivalent blocks and the whole model using Flax. After that, we port the weights. Finally, we run some tests to ensure the correctness of the ported model." - ], - "metadata": { - "id": "ABCg5TvPr1pm" - }, - "id": "ABCg5TvPr1pm" - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "38504f77-4150-47bd-9cf9-3116fe370746", - "metadata": { - "id": "38504f77-4150-47bd-9cf9-3116fe370746" - }, - "outputs": [], - "source": [ - "import jax\n", - "import jax.numpy as jnp\n", - "from flax import nnx" - ] - }, - { - "cell_type": "markdown", - "id": "95a364c2-d34e-4820-8a86-f43f59c911bf", - "metadata": { - "id": "95a364c2-d34e-4820-8a86-f43f59c911bf" - }, - "source": [ - "## MaxViT PyTorch model setup\n", - "\n", - "### Model's architecture\n", - "\n", - "The MaxVit model is [implemented in TorchVision](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568). If we inspect the [forward pass](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L707-L712) of the model, we can see that it contains three high-level parts:\n", - "- [stem](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L641-L655): a few convolutions, batchnorms, GELU activations.\n", - "- [blocks](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L672-L692): list of MaxViT blocks\n", - "- [classifier](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L696-L703): adaptive average pooling, few linear layers and Tanh activation." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", - "outputId": "a2a332f1-37dd-4a43-d2f6-c273865814db" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/torch/functional.py:534: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3595.)\n", - " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n", - "Downloading: \"https://download.pytorch.org/models/maxvit_t-bc5ab103.pth\" to /root/.cache/torch/hub/checkpoints/maxvit_t-bc5ab103.pth\n", - "100%|██████████| 119M/119M [00:02<00:00, 53.9MB/s]\n" - ] - } - ], - "source": [ - "from torchvision.models import maxvit_t, MaxVit_T_Weights\n", - "\n", - "torch_model = maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1)" - ] - }, - { - "cell_type": "markdown", - "id": "45635b2d-a77a-4368-9ecb-dbb440e647ee", - "metadata": { - "id": "45635b2d-a77a-4368-9ecb-dbb440e647ee" - }, - "source": [ - "We can use `flax.nnx.display` to display the model's architecture:" - ] - }, - { - "cell_type": "code", - "source": [ - "# nnx.display(torch_model)" - ], - "metadata": { - "id": "sZ9x7NpHtBcx" - }, - "id": "sZ9x7NpHtBcx", - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "markdown", - "id": "0a36676a-1561-4de0-8e25-38bab90581d0", - "metadata": { - "id": "0a36676a-1561-4de0-8e25-38bab90581d0" - }, - "source": [ - "We can see that there are four MaxViT blocks in the model and each block contains:\n", - "- MaxViT layers: two layers for blocks 0, 1, 3 and five layers for the block 4" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0d5bf6aa-c720-4400-a276-602fff53b413", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0d5bf6aa-c720-4400-a276-602fff53b413", - "outputId": "6c0368fe-2d3c-47e2-92ea-5cd7bbe0ed65" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(4, [2, 2, 5, 2])" - ] - }, - "metadata": {}, - "execution_count": 5 - } - ], - "source": [ - "len(torch_model.blocks), [len(b.layers) for b in torch_model.blocks]" - ] - }, - { - "cell_type": "markdown", - "id": "a1d55688-5999-41de-a915-eae8b281eb18", - "metadata": { - "id": "a1d55688-5999-41de-a915-eae8b281eb18" - }, - "source": [ - "A [MaxViT layer](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L386) is composed of: [`MBConv`](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L53), `window_attention` as [`PartitionAttentionLayer`](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L282) and `grid_attention` as `PartitionAttentionLayer`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", - "outputId": "b5b5af72-7992-4d26-eb14-c1f471173a7d" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", - " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer']]" - ] - }, - "metadata": {}, - "execution_count": 6 - } - ], - "source": [ - "[[mod.__class__.__name__ for mod in maxvit_layer.layers] for b in torch_model.blocks for maxvit_layer in b.layers]" - ] - }, - { - "cell_type": "markdown", - "id": "d57f8545-43a4-423d-b701-c2e2ca0ebfc1", - "metadata": { - "id": "d57f8545-43a4-423d-b701-c2e2ca0ebfc1" - }, - "source": [ - "### Inference on data\n", - "\n", - "Let's check the model on dummy input and on a real image" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", - "outputId": "0b74d247-5458-429d-e310-fe42579ce15c" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "torch.Size([2, 1000])\n" - ] - } - ], - "source": [ - "import torch\n", - "\n", - "torch_model.eval()\n", - "with torch.inference_mode():\n", - " x = torch.rand(2, 3, 224, 224)\n", - " output = torch_model(x)\n", - "\n", - "print(output.shape) # (2, 1000)" - ] - }, - { - "cell_type": "markdown", - "id": "133bcf21-8a9c-4c27-b551-39b7dfdcfe1c", - "metadata": { - "id": "133bcf21-8a9c-4c27-b551-39b7dfdcfe1c" - }, - "source": [ - "We can download an image of a Pembroke Corgy dog from [TorchVision's gallery](https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true) together with [ImageNet classes dictionary](https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json):" - ] - }, - { - "cell_type": "code", - "source": [ - "%%bash\n", - "if [ -f \"dog1.jpg\" ]; then\n", - " echo \"dog1.jpg already exists.\"\n", - "else\n", - " wget -nv \"https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\" -O dog1.jpg\n", - "fi\n", - "if [ -f \"imagenet_class_index.json\" ]; then\n", - " echo \"imagenet_class_index.json already exists.\"\n", - "else\n", - " wget -nv \"https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\" -O imagenet_class_index.json\n", - "fi" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qC9hpYfNtOEF", - "outputId": "82a2c276-5ef8-44ee-cf12-fede419b7f90" - }, - "id": "qC9hpYfNtOEF", - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "2025-01-15 21:10:00 URL:https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/dog1.jpg [97422/97422] -> \"dog1.jpg\" [1]\n", - "2025-01-15 21:10:01 URL:https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json [35364/35364] -> \"imagenet_class_index.json\" [1]\n" - ] - } - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "82be8baf-1292-4766-be34-28c510563d71", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 508 - }, - "id": "82be8baf-1292-4766-be34-28c510563d71", - "outputId": "e952fefe-fd5b-4d91-af86-e23e414589fa" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Prediction for the Dog: ['n02113023', 'Pembroke'], score: 0.7800846099853516\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 9 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "import json\n", - "from torchvision.io import read_image\n", - "\n", - "\n", - "preprocess = MaxVit_T_Weights.IMAGENET1K_V1.transforms()\n", - "\n", - "with open(\"imagenet_class_index.json\") as labels_file:\n", - " labels = json.load(labels_file)\n", - "\n", - "\n", - "dog1 = read_image(\"dog1.jpg\")\n", - "tensor = preprocess(dog1)\n", - "\n", - "torch_model.eval()\n", - "with torch.inference_mode():\n", - " output = torch_model(tensor.unsqueeze(dim=0))\n", - "\n", - "class_id = output.argmax(dim=1).item()\n", - "\n", - "print(f\"Prediction for the Dog: {labels[str(class_id)]}, score: {output.softmax(dim=-1)[0, class_id]}\")\n", - "\n", - "plt.title(f\"{labels[str(class_id)]}\\nScore: {output.softmax(dim=-1)[0, class_id]}\")\n", - "plt.imshow(dog1.permute(1, 2, 0))" - ] - }, - { - "cell_type": "markdown", - "id": "8cbe4ccc-224b-4e8a-a2a9-e2c756c9b207", - "metadata": { - "id": "8cbe4ccc-224b-4e8a-a2a9-e2c756c9b207" - }, - "source": [ - "## Port MaxViT model to JAX\n", - "\n", - "To port the [PyTorch implementation of the MaxVit model](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568) in JAX using the Flax module, we will implement the following required modules:\n", - "\n", - "- `MaxViT`\n", - " - `MaxVitBlock`\n", - " - `MaxVitLayer`\n", - " - `MBConv`\n", - " - `Conv2dNormActivation`\n", - " - `SqueezeExcitation`\n", - " - `PartitionAttentionLayer`\n", - " - `RelativePositionalMultiHeadAttention`\n", - " - `WindowDepartition`\n", - " - `WindowPartition`\n", - " - `SwapAxes`\n", - " - `StochasticDepth`\n", - "\n", - "The Flax NNX module is very similar to PyTorch `torch.nn` module and we can map the following modules between PyTorch and Flax:\n", - "- `nn.Sequential` and `nn.ModuleList` -> `nnx.Sequential`\n", - "- `nn.Linear` -> `nnx.Linear`\n", - "- `nn.Conv2d` -> `nnx.Conv`\n", - "- `nn.BatchNorm2d` -> `nnx.BatchNorm`\n", - "- Activations like `nn.ReLU` -> `nnx.relu`\n", - "- Pooling layers like `nn.AvgPool2d(...)` -> `lambda x: nnx.avg_pool(x, ...)`\n", - "- `nn.AdaptiveAvgPool2d(1)` -> `lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2]))`, x is in NHWC format\n", - "- `nn.Flatten()` -> `lambda x: x.reshape(x.shape[0], -1)`\n", - "\n", - "\n", - "If the PyTorch model defines a learnable parameter and a buffer:\n", - "```python\n", - "class Model(nn.Module):\n", - " def __init__(self, ...):\n", - " ...\n", - " self.p = nn.Parameter(torch.ones(10))\n", - " self.register_buffer(\"b\", torch.ones(5))\n", - "```\n", - "an equivalent code in Flax would be\n", - "```python\n", - "class Buffer(nnx.Variable):\n", - " pass\n", - "\n", - "\n", - "class Model(nnx.Module):\n", - " def __init__(self, ...):\n", - " ...\n", - " self.p = nnx.Param(jnp.ones((10,)))\n", - " self.b = Buffer(jnp.ones(5))\n", - "```\n", - "\n", - "To inspect NNX module's learnable parameters and buffers, we can use `nnx.state`:\n", - "```python\n", - "nnx_module = ...\n", - "for k, v in nnx.state(nnx_module, nnx.Param).flat_state().items():\n", - " print(\n", - " k,\n", - " v.value.mean() if v.value is not None else None\n", - " )\n", - "\n", - "for k, v in nnx.state(nnx_module, (nnx.BatchStat, Buffer)).flat_state().items():\n", - " print(\n", - " k,\n", - " v.value.mean() if v.value.dtype == \"float32\" else v.value.sum()\n", - " )\n", - "```\n", - "The equivalent PyTorch code is:\n", - "```python\n", - "torch_module = ...\n", - "\n", - "for m, p in torch_module.named_parameters():\n", - " print(m, p.detach().mean())\n", - "\n", - "for m, b in torch_module.named_buffers():\n", - " print(\n", - " m,\n", - " b.mean() if b.dtype == torch.float32 else b.sum()\n", - " )\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "305ac55b-62ed-4f4d-902c-c6f3082afb02", - "metadata": { - "id": "305ac55b-62ed-4f4d-902c-c6f3082afb02" - }, - "source": [ - "Please note some differences between `torch.nn` and Flax when porting models:\n", - "- We should pass `rngs` to all NNX modules with parameters: e.g. `nnx.Linear(..., rngs=nnx.Rngs(0))`\n", - "- For a 2D convolution:\n", - " - In Flax, we need to explicitly define `kernel_size`, `strides` as two ints tuples, e.g. `(3, 3)`\n", - " - If PyTorch code defines `padding` as integer, e.g. 2, in Flax it should be explicitly defined as a tuple of two ints per dimension, i.e. `((2, 2), (2, 2))`.\n", - "- For a batch normalization: `momentum` value in `torch.nn` should be defined as `1.0 - momentum` in Flax.\n", - "- 4D input arrays in Flax should be in NHWC format, i.e. of shape (N, H, W, C) compared to NCHW format (or (N, C, H, W) shape) in PyTorch." - ] - }, - { - "cell_type": "markdown", - "id": "8d7e3479-bffe-4cb6-81e1-ed8f972c5bf0", - "metadata": { - "id": "8d7e3479-bffe-4cb6-81e1-ed8f972c5bf0" - }, - "source": [ - "Below we implement one by one all the modules from the above list and add simple forward pass checks.\n", - "Let's first implement equivalent of `nn.Identity`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "54ece7f1-14c1-41ef-980a-fc279d1702f2", - "metadata": { - "id": "54ece7f1-14c1-41ef-980a-fc279d1702f2" - }, - "outputs": [], - "source": [ - "class Identity(nnx.Module):\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " return x" - ] - }, - { - "cell_type": "markdown", - "id": "dd87b2aa-0285-4995-a9aa-ebd58ae00de6", - "metadata": { - "id": "dd87b2aa-0285-4995-a9aa-ebd58ae00de6" - }, - "source": [ - "### `Conv2dNormActivation` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/misc.py#L125)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "69d71163-676e-4ad3-8d8c-45efaafd76e7", - "metadata": { - "id": "69d71163-676e-4ad3-8d8c-45efaafd76e7" - }, - "outputs": [], - "source": [ - "from typing import Callable, List, Optional, Tuple\n", - "from flax import nnx\n", - "\n", - "\n", - "class Conv2dNormActivation(nnx.Sequential):\n", - " def __init__(\n", - " self,\n", - " in_channels: int,\n", - " out_channels: int,\n", - " kernel_size: int = 3,\n", - " stride: int = 1,\n", - " padding: Optional[int] = None,\n", - " groups: int = 1,\n", - " norm_layer: Callable[..., nnx.Module] = nnx.BatchNorm,\n", - " activation_layer: Callable = nnx.relu,\n", - " dilation: int = 1,\n", - " bias: Optional[bool] = None,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " self.out_channels = out_channels\n", - "\n", - " if padding is None:\n", - " padding = (kernel_size - 1) // 2 * dilation\n", - " if bias is None:\n", - " bias = norm_layer is None\n", - "\n", - " # sequence integer pairs that give the padding to apply before\n", - " # and after each spatial dimension\n", - " padding = ((padding, padding), (padding, padding))\n", - "\n", - " layers = [\n", - " nnx.Conv(\n", - " in_channels,\n", - " out_channels,\n", - " kernel_size=(kernel_size, kernel_size),\n", - " strides=(stride, stride),\n", - " padding=padding,\n", - " kernel_dilation=(dilation, dilation),\n", - " feature_group_count=groups,\n", - " use_bias=bias,\n", - " rngs=rngs,\n", - " )\n", - " ]\n", - "\n", - " if norm_layer is not None:\n", - " layers.append(norm_layer(out_channels, rngs=rngs))\n", - "\n", - " if activation_layer is not None:\n", - " layers.append(activation_layer)\n", - "\n", - " super().__init__(*layers)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", - "outputId": "fd9c4225-728d-4fad-fe47-c3095aa11c7f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(4, 14, 14, 64)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 28, 28, 32))\n", - "mod = Conv2dNormActivation(32, 64, 3, 2, 1)\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "2d0cd827-ad40-4cd3-9560-6565a3df10bc", - "metadata": { - "id": "2d0cd827-ad40-4cd3-9560-6565a3df10bc" - }, - "source": [ - "### `SqueezeExcitation` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/misc.py#L224)." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4232689e-e6cc-4ffd-8a2a-41fbc34e57c2", - "metadata": { - "id": "4232689e-e6cc-4ffd-8a2a-41fbc34e57c2" - }, - "outputs": [], - "source": [ - "class SqueezeExcitation(nnx.Module):\n", - " def __init__(\n", - " self,\n", - " input_channels: int,\n", - " squeeze_channels: int,\n", - " activation: Callable = nnx.relu,\n", - " scale_activation: Callable = nnx.sigmoid,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " self.avgpool = nnx.avg_pool\n", - " self.fc1 = nnx.Conv(input_channels, squeeze_channels, (1, 1), rngs=rngs)\n", - " self.fc2 = nnx.Conv(squeeze_channels, input_channels, (1, 1), rngs=rngs)\n", - " self.activation = activation\n", - " self.scale_activation = scale_activation\n", - "\n", - " def _scale(self, x: jax.Array) -> jax.Array:\n", - " scale = self.avgpool(x, (x.shape[1], x.shape[2]))\n", - " scale = self.fc1(scale)\n", - " scale = self.activation(scale)\n", - " scale = self.fc2(scale)\n", - " return self.scale_activation(scale)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " scale = self._scale(x)\n", - " return scale * x" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "83c55286-b92e-49aa-bd5f-c2448a787673", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "83c55286-b92e-49aa-bd5f-c2448a787673", - "outputId": "2a9cb19c-715e-40f5-ff83-a6097d80c5d2" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(4, 28, 28, 32)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 28, 28, 32))\n", - "mod = SqueezeExcitation(32, 4)\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "7935790a-4cb1-46dc-ab73-12d3cb8fc636", - "metadata": { - "id": "7935790a-4cb1-46dc-ab73-12d3cb8fc636" - }, - "source": [ - "### `StochasticDepth` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/stochastic_depth.py#L50)." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "96834419-eec1-4690-8bb0-447524f6bdde", - "metadata": { - "id": "96834419-eec1-4690-8bb0-447524f6bdde" - }, - "outputs": [], - "source": [ - "def stochastic_depth(\n", - " x: jax.Array,\n", - " p: float,\n", - " mode: str,\n", - " deterministic: bool = False,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - ") -> jax.Array:\n", - " if p < 0.0 or p > 1.0:\n", - " raise ValueError(f\"drop probability has to be between 0 and 1, but got {p}\")\n", - " if mode not in [\"batch\", \"row\"]:\n", - " raise ValueError(f\"mode has to be either 'batch' or 'row', but got {mode}\")\n", - " if deterministic or p == 0.0:\n", - " return x\n", - "\n", - " survival_rate = 1.0 - p\n", - " if mode == \"row\":\n", - " size = [x.shape[0]] + [1] * (x.ndim - 1)\n", - " else:\n", - " size = [1] * x.ndim\n", - "\n", - " noise = jax.random.bernoulli(\n", - " rngs.dropout(), p=survival_rate, shape=size\n", - " ).astype(dtype=x.dtype)\n", - "\n", - " if survival_rate > 0.0:\n", - " noise = noise / survival_rate\n", - "\n", - " return x * noise\n", - "\n", - "\n", - "class StochasticDepth(nnx.Module):\n", - " def __init__(\n", - " self,\n", - " p: float,\n", - " mode: str,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " self.p = p\n", - " self.mode = mode\n", - " self.deterministic = False\n", - " self.rngs = rngs\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " return stochastic_depth(\n", - " x, self.p, self.mode, self.deterministic, rngs=self.rngs\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "fd95babb-95b4-4015-957d-11b9c7b9957d", - "metadata": { - "id": "fd95babb-95b4-4015-957d-11b9c7b9957d" - }, - "outputs": [], - "source": [ - "x = jnp.ones((4, 28, 28, 32))\n", - "mod = StochasticDepth(0.5, \"row\")\n", - "\n", - "mod.eval()\n", - "y = mod(x)\n", - "assert (y == x).all()\n", - "\n", - "mod.train()\n", - "y = mod(x)\n", - "assert (y != x).any()" - ] - }, - { - "cell_type": "markdown", - "id": "0ce251eb-a8dc-4415-9856-d16421c1d646", - "metadata": { - "id": "0ce251eb-a8dc-4415-9856-d16421c1d646" - }, - "source": [ - "### `MBConv` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L53)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "636c713c-4a21-439a-b220-2b9407a06dfc", - "metadata": { - "id": "636c713c-4a21-439a-b220-2b9407a06dfc" - }, - "outputs": [], - "source": [ - "class MBConv(nnx.Module):\n", - " def __init__(\n", - " self,\n", - " in_channels: int,\n", - " out_channels: int,\n", - " expansion_ratio: float,\n", - " squeeze_ratio: float,\n", - " stride: int,\n", - " activation_layer: Callable,\n", - " norm_layer: Callable[..., nnx.Module],\n", - " p_stochastic_dropout: float = 0.0,\n", - " rngs = nnx.Rngs(0),\n", - " ):\n", - " should_proj = stride != 1 or in_channels != out_channels\n", - " if should_proj:\n", - " proj = [nnx.Conv(\n", - " in_channels, out_channels, kernel_size=(1, 1), strides=(1, 1), use_bias=True, rngs=rngs\n", - " )]\n", - " if stride == 2:\n", - " padding = ((1, 1), (1, 1))\n", - " proj = [\n", - " lambda x: nnx.avg_pool(\n", - " x, window_shape=(3, 3), strides=(stride, stride), padding=padding\n", - " )\n", - " ] + proj\n", - " self.proj = nnx.Sequential(*proj)\n", - " else:\n", - " self.proj = Identity()\n", - "\n", - " mid_channels = int(out_channels * expansion_ratio)\n", - " sqz_channels = int(out_channels * squeeze_ratio)\n", - "\n", - " if p_stochastic_dropout:\n", - " self.stochastic_depth = StochasticDepth(p_stochastic_dropout, mode=\"row\", rngs=rngs)\n", - " else:\n", - " self.stochastic_depth = Identity()\n", - "\n", - " _layers = [\n", - " norm_layer(in_channels, rngs=rngs), # pre_norm\n", - " Conv2dNormActivation( # conv_a\n", - " in_channels,\n", - " mid_channels,\n", - " kernel_size=1,\n", - " stride=1,\n", - " padding=0,\n", - " activation_layer=activation_layer,\n", - " norm_layer=norm_layer,\n", - " rngs=rngs,\n", - " ),\n", - " Conv2dNormActivation( # conv_b\n", - " mid_channels,\n", - " mid_channels,\n", - " kernel_size=3,\n", - " stride=stride,\n", - " padding=1,\n", - " activation_layer=activation_layer,\n", - " norm_layer=norm_layer,\n", - " groups=mid_channels,\n", - " rngs=rngs,\n", - " ),\n", - " SqueezeExcitation( # squeeze_excitation\n", - " mid_channels, sqz_channels, activation=nnx.silu, rngs=rngs\n", - " ),\n", - " nnx.Conv( # conv_c\n", - " mid_channels, out_channels, kernel_size=(1, 1), use_bias=True, rngs=rngs\n", - " )\n", - " ]\n", - " self.layers = nnx.Sequential(*_layers)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " res = self.proj(x)\n", - " x = self.stochastic_depth(self.layers(x))\n", - " return res + x" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", - "outputId": "f2fb2da2-7f63-4b77-d6ce-7266b0348ec3" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(4, 14, 14, 64)" - ] - }, - "metadata": {}, - "execution_count": 18 - } - ], - "source": [ - "from functools import partial\n", - "\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "x = jnp.ones((4, 28, 28, 32))\n", - "mod = MBConv(32, 64, 4, 0.25, 2, activation_layer=nnx.gelu, norm_layer=norm_layer)\n", - "y = mod(x)\n", - "y.shape" - ] - }, - { - "cell_type": "markdown", - "id": "3a8d9cb4-795b-4cb2-a014-bb440acc800b", - "metadata": { - "id": "3a8d9cb4-795b-4cb2-a014-bb440acc800b" - }, - "source": [ - "### `RelativePositionalMultiHeadAttention` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L140). First we reimplement a helper function `_get_relative_position_index`:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "df647057-8c6f-4c6b-84f9-d6f78e649343", - "metadata": { - "id": "df647057-8c6f-4c6b-84f9-d6f78e649343" - }, - "outputs": [], - "source": [ - "def _get_relative_position_index(height: int, width: int) -> jax.Array:\n", - " # PyTorch code:\n", - " # coords = torch.stack(torch.meshgrid([torch.arange(height), torch.arange(width)]))\n", - "\n", - " coords = jnp.stack(\n", - " jnp.meshgrid(*[jnp.arange(height), jnp.arange(width)], indexing=\"ij\")\n", - " )\n", - " # PyTorch code: coords_flat = torch.flatten(coords, 1)\n", - " coords_flat = coords.reshape(coords.shape[0], -1)\n", - "\n", - " relative_coords = coords_flat[:, :, None] - coords_flat[:, None, :]\n", - " relative_coords = jnp.permute_dims(relative_coords, (1, 2, 0))\n", - "\n", - " # PyTorch code:\n", - " # relative_coords[:, :, 0] += height - 1\n", - " # relative_coords[:, :, 1] += width - 1\n", - " # relative_coords[:, :, 0] *= 2 * width - 1\n", - " relative_coords = relative_coords + jnp.array((height - 1, width - 1))\n", - " relative_coords = relative_coords * jnp.array((2 * width - 1, 1))\n", - "\n", - " return relative_coords.sum(-1)" - ] - }, - { - "cell_type": "markdown", - "id": "2670d86b", - "metadata": { - "id": "2670d86b" - }, - "source": [ - "Let us check our implementation against PyTorch implementation:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "5ce55b8b-5305-4a57-a413-8df43392ec3a", - "metadata": { - "id": "5ce55b8b-5305-4a57-a413-8df43392ec3a" - }, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import _get_relative_position_index as pytorch_get_relative_position_index\n", - "\n", - "\n", - "output = _get_relative_position_index(13, 12)\n", - "expected = pytorch_get_relative_position_index(13, 12)\n", - "assert (output == jnp.asarray(expected)).all()" - ] - }, - { - "cell_type": "markdown", - "id": "5518bfc4", - "metadata": { - "id": "5518bfc4" - }, - "source": [ - "Next, we can port `RelativePositionalMultiHeadAttention` module which a learnable parameter and a buffer:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "1f46b3e4-fd69-42c2-8ca7-d242a20d13de", - "metadata": { - "id": "1f46b3e4-fd69-42c2-8ca7-d242a20d13de" - }, - "outputs": [], - "source": [ - "import math\n", - "\n", - "\n", - "class Buffer(nnx.Variable):\n", - " pass\n", - "\n", - "\n", - "class RelativePositionalMultiHeadAttention(nnx.Module):\n", - " def __init__(\n", - " self,\n", - " feat_dim: int,\n", - " head_dim: int,\n", - " max_seq_len: int,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " if feat_dim % head_dim != 0:\n", - " raise ValueError(f\"feat_dim: {feat_dim} must be divisible by head_dim: {head_dim}\")\n", - "\n", - " self.n_heads = feat_dim // head_dim\n", - " self.head_dim = head_dim\n", - " self.size = int(math.sqrt(max_seq_len))\n", - " self.max_seq_len = max_seq_len\n", - "\n", - " self.to_qkv = nnx.Linear(feat_dim, self.n_heads * self.head_dim * 3, rngs=rngs)\n", - " self.scale_factor = feat_dim**-0.5\n", - "\n", - " self.merge = nnx.Linear(self.head_dim * self.n_heads, feat_dim, rngs=rngs)\n", - "\n", - " self.relative_position_index = Buffer(_get_relative_position_index(self.size, self.size))\n", - "\n", - " # initialize with truncated normal bias\n", - " initializer = jax.nn.initializers.truncated_normal(stddev=0.02)\n", - " shape = ((2 * self.size - 1) * (2 * self.size - 1), self.n_heads)\n", - " self.relative_position_bias_table = nnx.Param(initializer(rngs.params(), shape, jnp.float32))\n", - "\n", - " def get_relative_positional_bias(self) -> jax.Array:\n", - " bias_index = self.relative_position_index.value.ravel()\n", - " relative_bias = self.relative_position_bias_table[bias_index].reshape((self.max_seq_len, self.max_seq_len, -1))\n", - " relative_bias = jnp.permute_dims(relative_bias, (2, 0, 1))\n", - " return jnp.expand_dims(relative_bias, axis=0)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " B, G, P, D = x.shape\n", - " H, DH = self.n_heads, self.head_dim\n", - "\n", - " qkv = self.to_qkv(x)\n", - "\n", - " q, k, v = jnp.split(qkv, 3, axis=-1)\n", - " q = jnp.permute_dims(q.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", - " k = jnp.permute_dims(k.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", - " v = jnp.permute_dims(v.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", - "\n", - " k = k * self.scale_factor\n", - "\n", - " dot_prod = jnp.einsum(\"B G H I D, B G H J D -> B G H I J\", q, k)\n", - " pos_bias = self.get_relative_positional_bias()\n", - "\n", - " dot_prod = jax.nn.softmax(dot_prod + pos_bias, axis=-1)\n", - "\n", - " out = jnp.einsum(\"B G H I J, B G H J D -> B G H I D\", dot_prod, v)\n", - " out = jnp.permute_dims(out, (0, 1, 3, 2, 4)).reshape((B, G, P, D))\n", - "\n", - " out = self.merge(out)\n", - " return out" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "18d0c993", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "18d0c993", - "outputId": "da8d58ad-d6b3-431d-d890-002beee81dec" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(4, 32, 49, 64)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 32, 49, 64))\n", - "\n", - "mod = RelativePositionalMultiHeadAttention(64, 16, 49)\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "875aba65-53d0-4241-bdd7-36384054ca59", - "metadata": { - "id": "875aba65-53d0-4241-bdd7-36384054ca59" - }, - "source": [ - "### `SwapAxes`, `WindowPartition`, `WindowDepartition` implementations\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L213)." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "d8a19362-733a-4359-9658-53dcffa25220", - "metadata": { - "id": "d8a19362-733a-4359-9658-53dcffa25220" - }, - "outputs": [], - "source": [ - "class SwapAxes(nnx.Module):\n", - " def __init__(self, a: int, b: int):\n", - " self.a = a\n", - " self.b = b\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " res = jnp.swapaxes(x, self.a, self.b)\n", - " return res\n", - "\n", - "\n", - "class WindowPartition(nnx.Module):\n", - " def __call__(self, x: jax.Array, p: int) -> jax.Array:\n", - " # Output array with expected layout of [B, H/P, W/P, P*P, C].\n", - " B, H, W, C = x.shape\n", - " P = p\n", - " # chunk up H and W dimensions\n", - " x = x.reshape((B, H // P, P, W // P, P, C))\n", - " x = jnp.permute_dims(x, (0, 1, 3, 2, 4, 5))\n", - " # colapse P * P dimension\n", - " x = x.reshape((B, (H // P) * (W // P), P * P, C))\n", - " return x\n", - "\n", - "\n", - "class WindowDepartition(nnx.Module):\n", - " def __call__(self, x: jax.Array, p: int, h_partitions: int, w_partitions: int) -> jax.Array:\n", - " # Output array with expected layout of [B, H, W, C].\n", - " B, G, PP, C = x.shape\n", - " P = p\n", - " HP, WP = h_partitions, w_partitions\n", - " # split P * P dimension into 2 P tile dimensions\n", - " x = x.reshape((B, HP, WP, P, P, C))\n", - " # permute into B, HP, P, WP, P, C\n", - " x = jnp.permute_dims(x, (0, 1, 3, 2, 4, 5))\n", - " # reshape into B, H, W, C\n", - " x = x.reshape((B, HP * P, WP * P, C))\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "daee5b6b-595f-4344-af93-6e4bd44c217f", - "metadata": { - "id": "daee5b6b-595f-4344-af93-6e4bd44c217f" - }, - "outputs": [], - "source": [ - "x = jnp.ones((3, 4, 5, 6))\n", - "mod = SwapAxes(1, 2)\n", - "y = mod(x)\n", - "assert y.shape == (3, 5, 4, 6)\n", - "\n", - "x = jnp.ones((4, 128, 128, 3))\n", - "mod = WindowPartition()\n", - "y = mod(x, p=16)\n", - "assert y.shape == (4, (128 // 16) * (128 // 16), 16 * 16, 3)\n", - "\n", - "x = jnp.ones((4, (128 // 16) * (128 // 16), 16 * 16, 3))\n", - "mod = WindowDepartition()\n", - "y = mod(x, p=16, h_partitions=128 // 16, w_partitions=128 // 16)\n", - "assert y.shape == (4, 128, 128, 3)" - ] - }, - { - "cell_type": "markdown", - "id": "fe9643dd-b328-43c9-a82f-7180ee2b9a00", - "metadata": { - "id": "fe9643dd-b328-43c9-a82f-7180ee2b9a00" - }, - "source": [ - "### `PartitionAttentionLayer` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L282)." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "dfb3c640-4b51-4ca5-a7ba-2ad5f9907c57", - "metadata": { - "id": "dfb3c640-4b51-4ca5-a7ba-2ad5f9907c57" - }, - "outputs": [], - "source": [ - "class PartitionAttentionLayer(nnx.Module):\n", - " \"\"\"\n", - " Layer for partitioning the input tensor into non-overlapping windows and\n", - " applying attention to each window.\n", - " \"\"\"\n", - " def __init__(\n", - " self,\n", - " in_channels: int,\n", - " head_dim: int,\n", - " # partitioning parameters\n", - " partition_size: int,\n", - " partition_type: str,\n", - " # grid size needs to be known at initialization time\n", - " # because we need to know hamy relative offsets there are in the grid\n", - " grid_size: Tuple[int, int],\n", - " mlp_ratio: int,\n", - " activation_layer: Callable,\n", - " norm_layer: Callable[..., nnx.Module],\n", - " attention_dropout: float,\n", - " mlp_dropout: float,\n", - " p_stochastic_dropout: float,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " self.n_heads = in_channels // head_dim\n", - " self.head_dim = head_dim\n", - " self.n_partitions = grid_size[0] // partition_size\n", - " self.partition_type = partition_type\n", - " self.grid_size = grid_size\n", - "\n", - " if partition_type not in [\"grid\", \"window\"]:\n", - " raise ValueError(\"partition_type must be either 'grid' or 'window'\")\n", - "\n", - " if partition_type == \"window\":\n", - " self.p, self.g = partition_size, self.n_partitions\n", - " else:\n", - " self.p, self.g = self.n_partitions, partition_size\n", - "\n", - " self.partition_op = WindowPartition()\n", - " self.departition_op = WindowDepartition()\n", - " self.partition_swap = SwapAxes(-2, -3) if partition_type == \"grid\" else Identity()\n", - " self.departition_swap = SwapAxes(-2, -3) if partition_type == \"grid\" else Identity()\n", - "\n", - " self.attn_layer = nnx.Sequential(\n", - " norm_layer(in_channels, rngs=rngs),\n", - " # it's always going to be partition_size ** 2 because\n", - " # of the axis swap in the case of grid partitioning\n", - " RelativePositionalMultiHeadAttention(\n", - " in_channels, head_dim, partition_size**2, rngs=rngs\n", - " ),\n", - " nnx.Dropout(attention_dropout, rngs=rngs),\n", - " )\n", - "\n", - " # pre-normalization similar to transformer layers\n", - " self.mlp_layer = nnx.Sequential(\n", - " nnx.LayerNorm(in_channels, rngs=rngs),\n", - " nnx.Linear(in_channels, in_channels * mlp_ratio, rngs=rngs),\n", - " activation_layer,\n", - " nnx.Linear(in_channels * mlp_ratio, in_channels, rngs=rngs),\n", - " nnx.Dropout(mlp_dropout, rngs=rngs),\n", - " )\n", - "\n", - " # layer scale factors\n", - " self.stochastic_dropout = StochasticDepth(p_stochastic_dropout, mode=\"row\", rngs=rngs)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " # Undefined behavior if H or W are not divisible by p\n", - " # https://github.com/google-research/maxvit/blob/da76cf0d8a6ec668cc31b399c4126186da7da944/maxvit/models/maxvit.py#L766\n", - " gh, gw = self.grid_size[0] // self.p, self.grid_size[1] // self.p\n", - " torch._assert(\n", - " self.grid_size[0] % self.p == 0 and self.grid_size[1] % self.p == 0,\n", - " \"Grid size must be divisible by partition size. Got grid size of {} and partition size of {}\".format(\n", - " self.grid_size, self.p\n", - " ),\n", - " )\n", - " x = self.partition_op(x, self.p) # (B, H, W, C) -> (B, H/P, W/P, P*P, C)\n", - " x = self.partition_swap(x) # -> grid: (B, H/P, P*P, W/P, C)\n", - " x = x + self.stochastic_dropout(self.attn_layer(x))\n", - " x = x + self.stochastic_dropout(self.mlp_layer(x))\n", - " x = self.departition_swap(x) # grid: (B, H/P, P*P, W/P, C) -> (B, H/P, W/P, P*P, C)\n", - " x = self.departition_op(x, self.p, gh, gw) # -> (B, H, W, C)\n", - "\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", - "outputId": "af0e7cce-85d6-4646-d8b7-9153d54bb8c0" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(4, 224, 224, 36)\n", - "(4, 224, 224, 36)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 224, 224, 36))\n", - "\n", - "grid_size = (224, 224)\n", - "mod = PartitionAttentionLayer(\n", - " 36, 6, 7, \"window\", grid_size=grid_size, mlp_ratio=4,\n", - " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", - " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)\n", - "\n", - "mod = PartitionAttentionLayer(\n", - " 36, 6, 7, \"grid\", grid_size=grid_size, mlp_ratio=4,\n", - " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", - " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "b89b4ca6-c17a-4c0f-859a-de7134348818", - "metadata": { - "id": "b89b4ca6-c17a-4c0f-859a-de7134348818" - }, - "source": [ - "### `MaxVitLayer` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L386)." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "45b3199e-711d-4125-86b9-22e90fafa28c", - "metadata": { - "id": "45b3199e-711d-4125-86b9-22e90fafa28c" - }, - "outputs": [], - "source": [ - "class MaxVitLayer(nnx.Module):\n", - " \"\"\"\n", - " MaxVit layer consisting of a MBConv layer followed by a PartitionAttentionLayer with `window`\n", - " and a PartitionAttentionLayer with `grid`.\n", - " \"\"\"\n", - " def __init__(\n", - " self,\n", - " # conv parameters\n", - " in_channels: int,\n", - " out_channels: int,\n", - " squeeze_ratio: float,\n", - " expansion_ratio: float,\n", - " stride: int,\n", - " # conv + transformer parameters\n", - " norm_layer: Callable[..., nnx.Module],\n", - " activation_layer: Callable,\n", - " # transformer parameters\n", - " head_dim: int,\n", - " mlp_ratio: int,\n", - " mlp_dropout: float,\n", - " attention_dropout: float,\n", - " p_stochastic_dropout: float,\n", - " # partitioning parameters\n", - " partition_size: int,\n", - " grid_size: Tuple[int, int],\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " layers = [\n", - " # convolutional layer\n", - " MBConv(\n", - " in_channels=in_channels,\n", - " out_channels=out_channels,\n", - " expansion_ratio=expansion_ratio,\n", - " squeeze_ratio=squeeze_ratio,\n", - " stride=stride,\n", - " activation_layer=activation_layer,\n", - " norm_layer=norm_layer,\n", - " p_stochastic_dropout=p_stochastic_dropout,\n", - " rngs=rngs,\n", - " ),\n", - " # window_attention\n", - " PartitionAttentionLayer(\n", - " in_channels=out_channels,\n", - " head_dim=head_dim,\n", - " partition_size=partition_size,\n", - " partition_type=\"window\",\n", - " grid_size=grid_size,\n", - " mlp_ratio=mlp_ratio,\n", - " activation_layer=activation_layer,\n", - " norm_layer=nnx.LayerNorm,\n", - " attention_dropout=attention_dropout,\n", - " mlp_dropout=mlp_dropout,\n", - " p_stochastic_dropout=p_stochastic_dropout,\n", - " rngs=rngs,\n", - " ),\n", - " # grid_attention\n", - " PartitionAttentionLayer(\n", - " in_channels=out_channels,\n", - " head_dim=head_dim,\n", - " partition_size=partition_size,\n", - " partition_type=\"grid\",\n", - " grid_size=grid_size,\n", - " mlp_ratio=mlp_ratio,\n", - " activation_layer=activation_layer,\n", - " norm_layer=nnx.LayerNorm,\n", - " attention_dropout=attention_dropout,\n", - " mlp_dropout=mlp_dropout,\n", - " p_stochastic_dropout=p_stochastic_dropout,\n", - " rngs=rngs,\n", - " )\n", - " ]\n", - " self.layers = nnx.Sequential(*layers)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " return self.layers(x)\n", - "\n", - "\n", - "def _get_conv_output_shape(\n", - " input_size: Tuple[int, int], kernel_size: int, stride: int, padding: int\n", - ") -> Tuple[int, int]:\n", - " return (\n", - " (input_size[0] - kernel_size + 2 * padding) // stride + 1,\n", - " (input_size[1] - kernel_size + 2 * padding) // stride + 1,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", - "outputId": "034c847f-9346-4953-99f2-5d59be74f9b0" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(4, 112, 112, 36)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 224, 224, 3))\n", - "\n", - "grid_size = _get_conv_output_shape((224, 224), kernel_size=3, stride=2, padding=1)\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "\n", - "mod = MaxVitLayer(\n", - " 3, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", - " stride=2, norm_layer=norm_layer, activation_layer=nnx.gelu,\n", - " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", - " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", - " partition_size=7, grid_size=grid_size,\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "21460039-0ed8-4c37-8382-7d91655f1086", - "metadata": { - "id": "21460039-0ed8-4c37-8382-7d91655f1086" - }, - "source": [ - "### `MaxVitBlock` implementation\n", - "\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L483)." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "e4fd31d2-4354-4694-87b4-3d0644388d3d", - "metadata": { - "id": "e4fd31d2-4354-4694-87b4-3d0644388d3d" - }, - "outputs": [], - "source": [ - "class MaxVitBlock(nnx.Module):\n", - " \"\"\"\n", - " A MaxVit block consisting of `n_layers` MaxVit layers.\n", - " \"\"\"\n", - " def __init__(\n", - " self,\n", - " # conv parameters\n", - " in_channels: int,\n", - " out_channels: int,\n", - " squeeze_ratio: float,\n", - " expansion_ratio: float,\n", - " # conv + transformer parameters\n", - " norm_layer: Callable[..., nnx.Module],\n", - " activation_layer: Callable,\n", - " # transformer parameters\n", - " head_dim: int,\n", - " mlp_ratio: int,\n", - " mlp_dropout: float,\n", - " attention_dropout: float,\n", - " # partitioning parameters\n", - " partition_size: int,\n", - " input_grid_size: Tuple[int, int],\n", - " # number of layers\n", - " n_layers: int,\n", - " p_stochastic: List[float],\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " if not len(p_stochastic) == n_layers:\n", - " raise ValueError(f\"p_stochastic must have length n_layers={n_layers}, got p_stochastic={p_stochastic}.\")\n", - "\n", - " # account for the first stride of the first layer\n", - " self.grid_size = _get_conv_output_shape(input_grid_size, kernel_size=3, stride=2, padding=1)\n", - "\n", - " layers = []\n", - " for idx, p in enumerate(p_stochastic):\n", - " stride = 2 if idx == 0 else 1\n", - " layers.append(\n", - " MaxVitLayer(\n", - " in_channels=in_channels if idx == 0 else out_channels,\n", - " out_channels=out_channels,\n", - " squeeze_ratio=squeeze_ratio,\n", - " expansion_ratio=expansion_ratio,\n", - " stride=stride,\n", - " norm_layer=norm_layer,\n", - " activation_layer=activation_layer,\n", - " head_dim=head_dim,\n", - " mlp_ratio=mlp_ratio,\n", - " mlp_dropout=mlp_dropout,\n", - " attention_dropout=attention_dropout,\n", - " partition_size=partition_size,\n", - " grid_size=self.grid_size,\n", - " p_stochastic_dropout=p,\n", - " ),\n", - " )\n", - " self.layers = nnx.Sequential(*layers)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " return self.layers(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "e168c27f-98db-4831-9723-dffac88f3226", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "e168c27f-98db-4831-9723-dffac88f3226", - "outputId": "2bcd5022-1c2c-4ae0-f696-47717eaf4bd4" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(4, 112, 112, 36)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 224, 224, 3))\n", - "\n", - "input_grid_size = (224, 224)\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "\n", - "mod = MaxVitBlock(\n", - " 3, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", - " norm_layer=norm_layer, activation_layer=nnx.gelu,\n", - " head_dim=6, mlp_ratio=4, mlp_dropout=0.5, attention_dropout=0.4,\n", - " partition_size=7, input_grid_size=input_grid_size,\n", - " n_layers=2,\n", - " p_stochastic=[0.0, 0.2],\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "cef5687d-e390-438b-95b3-e66406e2c000", - "metadata": { - "id": "cef5687d-e390-438b-95b3-e66406e2c000" - }, - "source": [ - "### `MaxVit` implementation\n", - "\n", - "Finally, we can assemble everything together and define the MaxVit model.\n", - "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568)." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "0e874e63-0eb7-40ea-82f3-bf10ac33d7a6", - "metadata": { - "id": "0e874e63-0eb7-40ea-82f3-bf10ac33d7a6" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "\n", - "def _make_block_input_shapes(input_size: Tuple[int, int], n_blocks: int) -> List[Tuple[int, int]]:\n", - " \"\"\"Util function to check that the input size is correct for a MaxVit configuration.\"\"\"\n", - " shapes = []\n", - " block_input_shape = _get_conv_output_shape(input_size, 3, 2, 1)\n", - " for _ in range(n_blocks):\n", - " block_input_shape = _get_conv_output_shape(block_input_shape, 3, 2, 1)\n", - " shapes.append(block_input_shape)\n", - " return shapes\n", - "\n", - "\n", - "class MaxVit(nnx.Module):\n", - " \"\"\"\n", - " Implements MaxVit Transformer from the \"MaxViT: Multi-Axis Vision Transformer\" paper.\n", - " \"\"\"\n", - " def __init__(\n", - " self,\n", - " # input size parameters\n", - " input_size: Tuple[int, int],\n", - " # stem and task parameters\n", - " stem_channels: int,\n", - " # partitioning parameters\n", - " partition_size: int,\n", - " # block parameters\n", - " block_channels: List[int],\n", - " block_layers: List[int],\n", - " # attention head dimensions\n", - " head_dim: int,\n", - " stochastic_depth_prob: float,\n", - " # conv + transformer parameters\n", - " # norm_layer is applied only to the conv layers\n", - " # activation_layer is applied both to conv and transformer layers\n", - " norm_layer: Optional[Callable[..., nnx.Module]] = None,\n", - " activation_layer: Callable = nnx.gelu,\n", - " # conv parameters\n", - " squeeze_ratio: float = 0.25,\n", - " expansion_ratio: float = 4,\n", - " # transformer parameters\n", - " mlp_ratio: int = 4,\n", - " mlp_dropout: float = 0.0,\n", - " attention_dropout: float = 0.0,\n", - " # task parameters\n", - " num_classes: int = 1000,\n", - " rngs: nnx.Rngs = nnx.Rngs(0),\n", - " ):\n", - " input_channels = 3\n", - "\n", - " if norm_layer is None:\n", - " norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "\n", - " # Make sure input size will be divisible by the partition size in all blocks\n", - " # Undefined behavior if H or W are not divisible by p\n", - " block_input_sizes = _make_block_input_shapes(input_size, len(block_channels))\n", - " for idx, block_input_size in enumerate(block_input_sizes):\n", - " if block_input_size[0] % partition_size != 0 or block_input_size[1] % partition_size != 0:\n", - " raise ValueError(\n", - " f\"Input size {block_input_size} of block {idx} is not divisible by partition size {partition_size}. \"\n", - " f\"Consider changing the partition size or the input size.\\n\"\n", - " f\"Current configuration yields the following block input sizes: {block_input_sizes}.\"\n", - " )\n", - "\n", - " # stem\n", - " self.stem = nnx.Sequential(\n", - " Conv2dNormActivation(\n", - " input_channels,\n", - " stem_channels,\n", - " 3,\n", - " stride=2,\n", - " norm_layer=norm_layer,\n", - " activation_layer=activation_layer,\n", - " bias=False,\n", - " rngs=rngs,\n", - " ),\n", - " Conv2dNormActivation(\n", - " stem_channels,\n", - " stem_channels,\n", - " 3,\n", - " stride=1,\n", - " norm_layer=None,\n", - " activation_layer=None,\n", - " bias=True,\n", - " rngs=rngs,\n", - " ),\n", - " )\n", - "\n", - " # account for stem stride\n", - " input_size = _get_conv_output_shape(input_size, kernel_size=3, stride=2, padding=1)\n", - " self.partition_size = partition_size\n", - "\n", - " # blocks\n", - " blocks = []\n", - " in_channels = [stem_channels] + block_channels[:-1]\n", - " out_channels = block_channels\n", - "\n", - " # precompute the stochastic depth probabilities from 0 to stochastic_depth_prob\n", - " # since we have N blocks with L layers, we will have N * L probabilities uniformly distributed\n", - " # over the range [0, stochastic_depth_prob]\n", - " p_stochastic = np.linspace(0, stochastic_depth_prob, sum(block_layers)).tolist()\n", - "\n", - " p_idx = 0\n", - " for in_channel, out_channel, num_layers in zip(in_channels, out_channels, block_layers):\n", - " blocks.append(\n", - " MaxVitBlock(\n", - " in_channels=in_channel,\n", - " out_channels=out_channel,\n", - " squeeze_ratio=squeeze_ratio,\n", - " expansion_ratio=expansion_ratio,\n", - " norm_layer=norm_layer,\n", - " activation_layer=activation_layer,\n", - " head_dim=head_dim,\n", - " mlp_ratio=mlp_ratio,\n", - " mlp_dropout=mlp_dropout,\n", - " attention_dropout=attention_dropout,\n", - " partition_size=partition_size,\n", - " input_grid_size=input_size,\n", - " n_layers=num_layers,\n", - " p_stochastic=p_stochastic[p_idx : p_idx + num_layers],\n", - " ),\n", - " )\n", - " input_size = blocks[-1].grid_size\n", - " p_idx += num_layers\n", - " self.blocks = nnx.Sequential(*blocks)\n", - "\n", - " self.classifier = nnx.Sequential(\n", - " lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2])), # nn.AdaptiveAvgPool2d(1)\n", - " lambda x: x.reshape(x.shape[0], -1), # nn.Flatten()\n", - " nnx.LayerNorm(block_channels[-1], rngs=rngs),\n", - " nnx.Linear(block_channels[-1], block_channels[-1], rngs=rngs),\n", - " nnx.tanh,\n", - " nnx.Linear(block_channels[-1], num_classes, use_bias=False, rngs=rngs),\n", - " )\n", - "\n", - " self._init_weights(rngs)\n", - "\n", - " def __call__(self, x: jax.Array) -> jax.Array:\n", - " x = self.stem(x)\n", - " x = self.blocks(x)\n", - " x = self.classifier(x)\n", - " return x\n", - "\n", - " def _init_weights(self, rngs):\n", - " normal_initializer = nnx.initializers.normal(stddev=0.02)\n", - " for name, module in self.iter_modules():\n", - " if isinstance(module, (nnx.Conv, nnx.Linear)):\n", - " module.kernel.value = normal_initializer(\n", - " rngs(), module.kernel.value.shape, module.kernel.value.dtype\n", - " )\n", - " if module.bias.value is not None:\n", - " module.bias.value = jnp.zeros(\n", - " module.bias.value.shape, dtype=module.bias.value.dtype\n", - " )\n", - " elif isinstance(module, nnx.BatchNorm):\n", - " module.scale.value = jnp.ones(module.scale.value.shape, module.scale.value.dtype)\n", - " module.bias.value = jnp.zeros(module.bias.value.shape, module.bias.value.dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", - "outputId": "70162701-605c-4d0e-f215-21864071375f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(4, 1000)\n" - ] - } - ], - "source": [ - "x = jnp.ones((4, 224, 224, 3))\n", - "\n", - "mod = MaxVit(\n", - " input_size=(224, 224),\n", - " stem_channels=64,\n", - " block_channels=[64, 128, 256, 512],\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=32,\n", - " stochastic_depth_prob=0.2,\n", - " partition_size=7,\n", - " num_classes=1000,\n", - ")\n", - "\n", - "y = mod(x)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "fa2a4a47-b6c9-43ba-822b-002e0c03e85c", - "metadata": { - "id": "fa2a4a47-b6c9-43ba-822b-002e0c03e85c" - }, - "outputs": [], - "source": [ - "def maxvit_t(\n", - " input_size=(224, 224),\n", - " stem_channels=64,\n", - " block_channels=[64, 128, 256, 512],\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=32,\n", - " stochastic_depth_prob=0.2,\n", - " partition_size=7,\n", - " num_classes=1000,\n", - "):\n", - " model = MaxVit(\n", - " input_size=input_size,\n", - " stem_channels=stem_channels,\n", - " block_channels=block_channels,\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=head_dim,\n", - " stochastic_depth_prob=stochastic_depth_prob,\n", - " partition_size=partition_size,\n", - " num_classes=num_classes,\n", - " )\n", - " return model" - ] - }, - { - "cell_type": "markdown", - "id": "25ff32f7-e4a1-4029-b114-8ecafb4378fd", - "metadata": { - "id": "25ff32f7-e4a1-4029-b114-8ecafb4378fd" - }, - "source": [ - "### Test JAX implementation" - ] - }, - { - "cell_type": "markdown", - "id": "b3e02373-c3b6-4ffd-a98e-e425824f2f88", - "metadata": { - "id": "b3e02373-c3b6-4ffd-a98e-e425824f2f88" - }, - "source": [ - "Let us import equivalent PyTorch modules and check our implementations against PyTorch. Please note that\n", - "PyTorch modules will contain random parameters and buffers that we need to set into our Flax implementations.\n", - "\n", - "Below we define a helper class `Torch2Flax` to copy parameters and buffers from a PyTorch module into equivalent Flax module." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "22f49ecd-4999-4c1c-b1d8-d16faeb60389", - "metadata": { - "id": "22f49ecd-4999-4c1c-b1d8-d16faeb60389" - }, - "outputs": [], - "source": [ - "import torch.nn as nn\n", - "\n", - "\n", - "class Torch2Flax:\n", - " @staticmethod\n", - " def conv_params_permute(name, torch_param):\n", - " if name == \"weight\":\n", - " return torch_param.permute(2, 3, 1, 0)\n", - " return torch_param\n", - "\n", - " @staticmethod\n", - " def linear_params_permute(name, torch_param):\n", - " if name == \"weight\":\n", - " return torch_param.permute(1, 0)\n", - " return torch_param\n", - "\n", - " @staticmethod\n", - " def default_params_transform(name, param):\n", - " return param\n", - "\n", - " modules_mapping_info = {\n", - " nn.Conv2d: {\n", - " \"type\": nnx.Conv,\n", - " \"params_mapping\": {\n", - " \"weight\": \"kernel\",\n", - " \"bias\": \"bias\",\n", - " },\n", - " \"params_transform\": conv_params_permute,\n", - " },\n", - " nn.BatchNorm2d: {\n", - " \"type\": nnx.BatchNorm,\n", - " \"params_mapping\": {\n", - " \"weight\": \"scale\",\n", - " \"bias\": \"bias\",\n", - " \"running_mean\": \"mean\",\n", - " \"running_var\": \"var\",\n", - " },\n", - " },\n", - " nn.Linear: {\n", - " \"type\": nnx.Linear,\n", - " \"params_mapping\": {\n", - " \"weight\": \"kernel\",\n", - " \"bias\": \"bias\",\n", - " },\n", - " \"params_transform\": linear_params_permute,\n", - " },\n", - " nn.LayerNorm: {\n", - " \"type\": nnx.LayerNorm,\n", - " \"params_mapping\": {\n", - " \"weight\": \"scale\",\n", - " \"bias\": \"bias\",\n", - " },\n", - " }\n", - " } | {\n", - " torch_mod: {\n", - " \"type\": nnx_fn_type,\n", - " \"params_mapping\": {},\n", - " } for torch_mod, nnx_fn_type in [\n", - " (nn.Identity, Identity),\n", - " (nn.Flatten, type(lambda x: x)),\n", - " (nn.ReLU, type(nnx.relu)),\n", - " (nn.GELU, type(nnx.gelu)),\n", - " (nn.SELU, type(nnx.selu)),\n", - " (nn.SiLU, type(nnx.silu)),\n", - " (nn.Tanh, type(nnx.tanh)),\n", - " (nn.Dropout, nnx.Dropout),\n", - " (nn.Sigmoid, type(nnx.sigmoid)),\n", - " (nn.AvgPool2d, type(lambda x: nnx.avg_pool(x, (2, 2)))),\n", - " (nn.AdaptiveAvgPool2d, type(lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2])))),\n", - " ]\n", - " }\n", - "\n", - " def _copy_params_buffers(self, torch_nn_module, nnx_module):\n", - " torch_module_type = type(torch_nn_module)\n", - " assert torch_module_type in self.modules_mapping_info, torch_module_type\n", - " module_mapping_info = self.modules_mapping_info[torch_module_type]\n", - " assert isinstance(nnx_module, module_mapping_info[\"type\"]), (\n", - " nnx_module, type(nnx_module), module_mapping_info[\"type\"]\n", - " )\n", - "\n", - " for torch_key, nnx_key in module_mapping_info[\"params_mapping\"].items():\n", - "\n", - " torch_value = getattr(torch_nn_module, torch_key)\n", - " nnx_param = getattr(nnx_module, nnx_key)\n", - " assert nnx_param is not None, (torch_key, nnx_key, nnx_module)\n", - "\n", - " if torch_value is None:\n", - " assert nnx_param.value is None, nnx_param\n", - " continue\n", - "\n", - " params_transform = module_mapping_info.get(\"params_transform\", Torch2Flax.default_params_transform)\n", - " torch_value = params_transform(torch_key, torch_value)\n", - "\n", - " assert nnx_param.value.shape == torch_value.data.shape, (\n", - " nnx_key, nnx_param.value.shape, torch_key, torch_value.data.shape\n", - " )\n", - " nnx_param.value = jnp.asarray(torch_value.data)\n", - "\n", - " def _copy_sequential(self, torch_nn_seq, nnx_seq, skip_modules=None):\n", - " assert isinstance(torch_nn_seq, (nn.Sequential, nn.ModuleList)), type(torch_nn_seq)\n", - " assert isinstance(nnx_seq, nnx.Sequential), type(nnx_seq)\n", - " for i, index in enumerate(torch_nn_seq):\n", - " torch_module = torch_nn_seq[i]\n", - " nnx_module = nnx_seq.layers[i]\n", - " self.copy_module(torch_module, nnx_module, skip_modules=skip_modules)\n", - "\n", - " def copy_module(self, torch_module, nnx_module, skip_modules=None):\n", - " if skip_modules is None:\n", - " skip_modules = []\n", - "\n", - " if isinstance(torch_module, (nn.Sequential, nn.ModuleList)):\n", - " self._copy_sequential(torch_module, nnx_module, skip_modules=skip_modules)\n", - " elif type(torch_module) in self.modules_mapping_info:\n", - " self._copy_params_buffers(torch_module, nnx_module)\n", - " else:\n", - " if skip_modules is not None:\n", - " if torch_module.__class__.__name__ in skip_modules:\n", - " return\n", - "\n", - " named_children = list(torch_module.named_children())\n", - " assert len(named_children) > 0, type(torch_module)\n", - " for name, torch_child in named_children:\n", - " nnx_child = getattr(nnx_module, name, None)\n", - " assert nnx_child is not None, (name, nnx_module)\n", - " self.copy_module(torch_child, nnx_child, skip_modules=skip_modules)\n", - " # Copy buffers and params of the module itself (not its children)\n", - " for name, torch_buffer in torch_module.named_buffers():\n", - " if \".\" in name:\n", - " # This is child's buffer\n", - " continue\n", - " nnx_buffer = getattr(nnx_module, name)\n", - " assert isinstance(nnx_buffer, nnx.Variable), (name, nnx_buffer, nnx_module)\n", - "\n", - " assert nnx_buffer.value.shape == torch_buffer.shape, (\n", - " name, nnx_buffer.value.shape, torch_buffer.shape\n", - " )\n", - " nnx_buffer.value = jnp.asarray(torch_buffer)\n", - "\n", - " for name, torch_param in torch_module.named_parameters():\n", - " if \".\" in name:\n", - " # This is child's parameter\n", - " continue\n", - " nnx_param = getattr(nnx_module, name)\n", - " assert isinstance(nnx_param, nnx.Param), (name, nnx_param, nnx_module)\n", - "\n", - " assert nnx_param.value.shape == torch_param.data.shape, (\n", - " name, nnx_param.value.shape, torch_param.data.shape\n", - " )\n", - " nnx_param.value = jnp.asarray(torch_param.data)\n", - "\n", - "\n", - "def test_modules(\n", - " nnx_module, torch_module, torch_input, atol=1e-3, mode=\"eval\", permute_torch_input=True, device=\"cuda\"\n", - "):\n", - " assert torch_input.ndim == 4\n", - " assert mode in (\"eval\", \"train\")\n", - "\n", - " torch_input = torch_input.to(device)\n", - " torch_module = torch_module.to(device)\n", - "\n", - " if mode == \"eval\":\n", - " torch_module.eval()\n", - " nnx_module.eval()\n", - " else:\n", - " torch_module.train()\n", - " nnx_module.train()\n", - "\n", - " with torch.inference_mode(mode=mode==\"eval\"):\n", - " torch_output = torch_module(torch_input)\n", - "\n", - " if permute_torch_input:\n", - " torch_input = torch_input.permute(0, 2, 3, 1)\n", - "\n", - " jax_input = jnp.asarray(torch_input, device=jax.devices(device)[0])\n", - " jax_output = nnx_module(jax_input)\n", - " assert jax_output.device == jax.devices(device)[0]\n", - "\n", - " torch_output = torch_output.detach()\n", - " if permute_torch_input and torch_output.ndim == 4:\n", - " torch_output = torch_output.permute(0, 2, 3, 1)\n", - " jax_expected = jnp.asarray(torch_output)\n", - "\n", - " assert jnp.allclose(jax_output, jax_expected, atol=atol), (\n", - " jnp.abs(jax_output - jax_expected).max(),\n", - " jnp.abs(jax_output - jax_expected).mean(),\n", - " )\n", - "\n", - "\n", - "t2f = Torch2Flax()" - ] - }, - { - "cell_type": "markdown", - "id": "a323a19e-fc64-4f8d-8be2-c7886b6191b9", - "metadata": { - "id": "a323a19e-fc64-4f8d-8be2-c7886b6191b9" - }, - "source": [ - "Let us now test our JAX modules. We only test the result of the forward pass in the inference mode such that we avoid discrepancies related to random layers like `Dropout`, `StochasticDepth`, etc.\n", - "By default, we use absolute error tolerence `1e-3` when comparing the JAX output against expected PyTorch result.\n", - "For larger modules we set the device to CPU for the JAX model to execute on in order to reduce the errors between CPU and CUDA." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "2e10fb43-6ae6-47c7-81fe-5027b115b25f", - "metadata": { - "id": "2e10fb43-6ae6-47c7-81fe-5027b115b25f" - }, - "outputs": [], - "source": [ - "from torchvision.ops.misc import Conv2dNormActivation as PyTorchConv2dNormActivation\n", - "\n", - "\n", - "torch_module = PyTorchConv2dNormActivation(32, 64, 3, 2, 1)\n", - "nnx_module = Conv2dNormActivation(32, 64, 3, 2, 1)\n", - "\n", - "t2f.copy_module(torch_module, nnx_module)\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "7ac4b49a-712f-4725-a8d8-33e72b8d0b66", - "metadata": { - "id": "7ac4b49a-712f-4725-a8d8-33e72b8d0b66" - }, - "outputs": [], - "source": [ - "from torchvision.ops.misc import SqueezeExcitation as PyTorchSqueezeExcitation\n", - "\n", - "\n", - "torch_module = PyTorchSqueezeExcitation(32, 4)\n", - "nnx_module = SqueezeExcitation(32, 4)\n", - "\n", - "t2f.copy_module(torch_module, nnx_module)\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "746c8882-0001-4c97-b5cf-576dc5c87c02", - "metadata": { - "id": "746c8882-0001-4c97-b5cf-576dc5c87c02" - }, - "outputs": [], - "source": [ - "import torch.nn as nn\n", - "from functools import partial\n", - "from torchvision.models.maxvit import MBConv as PyTorchMBConv\n", - "\n", - "\n", - "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", - "torch_module = PyTorchMBConv(32, 64, 4, 0.25, 2, activation_layer=nn.GELU, norm_layer=norm_layer)\n", - "\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "nnx_module = MBConv(32, 64, 4, 0.25, 2, activation_layer=nnx.gelu, norm_layer=norm_layer)\n", - "\n", - "\n", - "t2f.copy_module(torch_module, nnx_module)\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "249f6d28-57b6-4d36-9079-cd60964e6afc", - "metadata": { - "id": "249f6d28-57b6-4d36-9079-cd60964e6afc" - }, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import RelativePositionalMultiHeadAttention as PyTorchRelativePositionalMultiHeadAttention\n", - "\n", - "\n", - "torch_module = PyTorchRelativePositionalMultiHeadAttention(64, 16, 49)\n", - "nnx_module = RelativePositionalMultiHeadAttention(64, 16, 49)\n", - "\n", - "t2f.copy_module(torch_module, nnx_module)\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 32, 49, 64), permute_torch_input=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "f48fc475-c556-4101-ad2b-19480a73c6ba", - "metadata": { - "id": "f48fc475-c556-4101-ad2b-19480a73c6ba" - }, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import PartitionAttentionLayer as PyTorchPartitionAttentionLayer\n", - "\n", - "\n", - "grid_size = (224, 224)\n", - "for partition_type in [\"window\", \"grid\"]:\n", - "\n", - " torch_module = PyTorchPartitionAttentionLayer(\n", - " 36, 6, 7, partition_type, grid_size=grid_size, mlp_ratio=4,\n", - " activation_layer=nn.GELU, norm_layer=nn.LayerNorm,\n", - " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", - " )\n", - "\n", - " nnx_module = PartitionAttentionLayer(\n", - " 36, 6, 7, partition_type, grid_size=grid_size, mlp_ratio=4,\n", - " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", - " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", - " )\n", - "\n", - " t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - " ])\n", - "\n", - " test_modules(nnx_module, torch_module, torch.randn(4, 36, 224, 224))" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "7ab2de6a-9790-444b-b175-535cdb05f5d8", - "metadata": { - "id": "7ab2de6a-9790-444b-b175-535cdb05f5d8" - }, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import MaxVitLayer as PyTorchMaxVitLayer\n", - "\n", - "\n", - "stride = 2\n", - "\n", - "grid_size = _get_conv_output_shape((224, 224), kernel_size=3, stride=2, padding=1)\n", - "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", - "\n", - "torch_module = PyTorchMaxVitLayer(\n", - " 36, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", - " stride=stride, norm_layer=norm_layer, activation_layer=nn.GELU,\n", - " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", - " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", - " partition_size=7, grid_size=grid_size,\n", - ")\n", - "\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "nnx_module = MaxVitLayer(\n", - " 36, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", - " stride=stride, norm_layer=norm_layer, activation_layer=nnx.gelu,\n", - " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", - " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", - " partition_size=7, grid_size=grid_size,\n", - ")\n", - "\n", - "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - "])\n", - "\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 36, 224, 224), device=\"cpu\")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "e8e8f997-0184-4af2-82b0-ceaa580645c8", - "metadata": { - "id": "e8e8f997-0184-4af2-82b0-ceaa580645c8" - }, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import MaxVitBlock as PyTorchMaxVitBlock\n", - "\n", - "\n", - "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", - "torch_module = PyTorchMaxVitBlock(\n", - " 64, 128, squeeze_ratio=0.25, expansion_ratio=4,\n", - " norm_layer=norm_layer, activation_layer=nn.GELU,\n", - " head_dim=32, mlp_ratio=4, mlp_dropout=0.0, attention_dropout=0.0,\n", - " partition_size=7, input_grid_size=(56, 56),\n", - " n_layers=2,\n", - " p_stochastic=[0.13333333333333333, 0.2],\n", - ")\n", - "\n", - "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", - "nnx_module = MaxVitBlock(\n", - " 64, 128, squeeze_ratio=0.25, expansion_ratio=4,\n", - " norm_layer=norm_layer, activation_layer=nnx.gelu,\n", - " head_dim=32, mlp_ratio=4, mlp_dropout=0.0, attention_dropout=0.0,\n", - " partition_size=7, input_grid_size=(56, 56),\n", - " n_layers=2,\n", - " p_stochastic=[0.13333333333333333, 0.2],\n", - ")\n", - "\n", - "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - "])\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 64, 56, 56), device=\"cpu\")" - ] - }, - { - "cell_type": "markdown", - "id": "e313819a-e93a-4201-806d-783bd1336c78", - "metadata": { - "id": "e313819a-e93a-4201-806d-783bd1336c78" - }, - "source": [ - "Finally, we can check the MaxVit implementation. Note that we raised the absolute tolerence to `1e-1` when comparing JAX output logits against PyTorch expected logits." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "e2af63a4-b16b-40a3-ac00-bfc23d532c82", - "metadata": { - "id": "e2af63a4-b16b-40a3-ac00-bfc23d532c82" - }, - "outputs": [], - "source": [ - "from torchvision.models.maxvit import MaxVit as PyTorchMaxVit\n", - "\n", - "\n", - "torch.manual_seed(77)\n", - "\n", - "\n", - "torch_module = PyTorchMaxVit(\n", - " input_size=(224, 224),\n", - " stem_channels=64,\n", - " block_channels=[64, 128, 256, 512],\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=32,\n", - " stochastic_depth_prob=0.2,\n", - " partition_size=7,\n", - " num_classes=1000,\n", - ")\n", - "\n", - "nnx_module = MaxVit(\n", - " input_size=(224, 224),\n", - " stem_channels=64,\n", - " block_channels=[64, 128, 256, 512],\n", - " block_layers=[2, 2, 5, 2],\n", - " head_dim=32,\n", - " stochastic_depth_prob=0.2,\n", - " partition_size=7,\n", - " num_classes=1000,\n", - ")\n", - "\n", - "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - "])\n", - "\n", - "\n", - "test_modules(nnx_module, torch_module, torch.randn(4, 3, 224, 224), device=\"cpu\", atol=1e-1)" - ] - }, - { - "cell_type": "markdown", - "id": "0d3c4f4d-2a50-46f4-814c-42c1a423cfd0", - "metadata": { - "id": "0d3c4f4d-2a50-46f4-814c-42c1a423cfd0" - }, - "source": [ - "### Check Flax model\n", - "Let us now reuse trained weights from TorchVision's MaxViT model to check output logits and the predictions on our example image:" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "7975f311-7a02-4c82-99db-b0b50fb37528", - "metadata": { - "id": "7975f311-7a02-4c82-99db-b0b50fb37528" - }, - "outputs": [], - "source": [ - "from torchvision.models import maxvit_t as pytorch_maxvit_t, MaxVit_T_Weights\n", - "\n", - "torch_model = pytorch_maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1)\n", - "flax_model = maxvit_t()\n", - "\n", - "t2f = Torch2Flax()\n", - "t2f.copy_module(torch_model, flax_model, skip_modules=[\n", - " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 398 - }, - "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", - "outputId": "9f5673f5-61a8-4bc0-af96-51d3a4f982de" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Prediction for the Dog:\n", - "- PyTorch model result: ['n02113023', 'Pembroke'], score: 0.7800846099853516\n", - "- Flax model result: ['n02113023', 'Pembroke'], score: 0.7799879908561707\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 44 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "import json\n", - "from torchvision.io import read_image\n", - "\n", - "\n", - "preprocess = MaxVit_T_Weights.IMAGENET1K_V1.transforms()\n", - "\n", - "with open(\"imagenet_class_index.json\") as labels_file:\n", - " labels = json.load(labels_file)\n", - "\n", - "\n", - "dog1 = read_image(\"dog1.jpg\")\n", - "tensor = preprocess(dog1).unsqueeze(dim=0)\n", - "\n", - "torch_model.eval()\n", - "with torch.inference_mode():\n", - " torch_output = torch_model(tensor)\n", - "\n", - "torch_class_id = torch_output.argmax(dim=1).item()\n", - "\n", - "jax_array = jnp.asarray(tensor.permute(0, 2, 3, 1), device=jax.devices(\"cpu\")[0])\n", - "flax_model.eval()\n", - "flax_output = flax_model(jax_array)\n", - "\n", - "flax_class_id = torch_output.argmax(axis=1).item()\n", - "\n", - "print(\"Prediction for the Dog:\")\n", - "print(f\"- PyTorch model result: {labels[str(torch_class_id)]}, score: {torch_output.softmax(axis=1)[0, torch_class_id]}\")\n", - "print(f\"- Flax model result: {labels[str(flax_class_id)]}, score: {jax.nn.softmax(flax_output, axis=1)[0, flax_class_id]}\")\n", - "\n", - "\n", - "plt.subplot(121)\n", - "plt.title(f\"{labels[str(torch_class_id)]}\\nScore: {torch_output.softmax(dim=-1)[0, class_id]:.4f}\")\n", - "plt.imshow(dog1.permute(1, 2, 0))\n", - "\n", - "plt.subplot(122)\n", - "plt.title(f\"{labels[str(flax_class_id)]}\\nScore: {jax.nn.softmax(flax_output, axis=1)[0, flax_class_id]:.4f}\")\n", - "plt.imshow(dog1.permute(1, 2, 0))" - ] - }, - { - "cell_type": "markdown", - "id": "c77f3244", - "metadata": { - "id": "c77f3244" - }, - "source": [ - "Let's compute cosine distance between the logits:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", - "outputId": "b03dadcc-07f5-42b5-a2d5-86eac15d7f02" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Array(0.99999857, dtype=float32)" - ] - }, - "metadata": {}, - "execution_count": 45 - } - ], - "source": [ - "expected = jnp.asarray(torch_output)\n", - "\n", - "cosine_dist = (expected * flax_output).sum() / (jnp.linalg.norm(flax_output) * jnp.linalg.norm(expected))\n", - "cosine_dist" - ] - }, - { - "cell_type": "markdown", - "id": "65e57aa6-1572-4805-9207-bc8a5f9f3ab1", - "metadata": { - "id": "65e57aa6-1572-4805-9207-bc8a5f9f3ab1" - }, - "source": [ - "## Further reading\n", - "\n", - "- [Flax documentation: Core Exampels](https://flax.readthedocs.io/en/latest/examples/core_examples.html)\n", - "- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)" - ] + "cells": [ + { + "cell_type": "markdown", + "id": "b69996dc-49af-4a0e-a4e6-36d81b51f2b4", + "metadata": {}, + "source": [ + "# Porting a PyTorch model to JAX\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/JAX_porting_PyTorch_model.ipynb)\n", + "\n", + "**Note: On Colab we recommend running this on a T4 GPU instance. On Kaggle we recommend a T4x2 or P100 instance.**\n", + "\n", + "In this tutorial we will learn how to port a PyTorch model to JAX and [Flax](https://flax.readthedocs.io/en/latest/nnx_basics.html). Flax provides an API very similar to the PyTorch `torch.nn` module and porting PyTorch models is rather straightforward. To install Flax, we can simply execute the following command: `pip install -U flax treescope`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "NHqB3sNbrygd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/424.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r", + "\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m419.8/424.2 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m424.2/424.2 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/175.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.6/175.6 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.11.9" - }, - "colab": { - "provenance": [], - "gpuType": "T4" - }, - "accelerator": "GPU" + ], + "source": [ + "!pip install -Uq flax treescope" + ] + }, + { + "cell_type": "markdown", + "id": "ABCg5TvPr1pm", + "metadata": {}, + "source": [ + "Say we have a trained PyTorch computer-vision model to classify images that we would like to port to JAX. We will use [`TorchVision`](https://pytorch.org/vision/stable/index.html) to provide a [MaxVit](https://pytorch.org/vision/stable/models/maxvit.html) model trained on ImageNet (MaxViT: Multi-Axis Vision Transformer, https://arxiv.org/abs/2204.01697).\n", + "\n", + "First, we set up the model using TorchVision and explore briefly the model's architecture and the blocks we need to port. Next, we define equivalent blocks and the whole model using Flax. After that, we port the weights. Finally, we run some tests to ensure the correctness of the ported model." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "38504f77-4150-47bd-9cf9-3116fe370746", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "from flax import nnx" + ] + }, + { + "cell_type": "markdown", + "id": "95a364c2-d34e-4820-8a86-f43f59c911bf", + "metadata": {}, + "source": [ + "## MaxViT PyTorch model setup\n", + "\n", + "### Model's architecture\n", + "\n", + "The MaxVit model is [implemented in TorchVision](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568). If we inspect the [forward pass](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L707-L712) of the model, we can see that it contains three high-level parts:\n", + "- [stem](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L641-L655): a few convolutions, batchnorms, GELU activations.\n", + "- [blocks](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L672-L692): list of MaxViT blocks\n", + "- [classifier](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L696-L703): adaptive average pooling, few linear layers and Tanh activation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9b1be406-d21c-410d-a2ac-9bd690e5ad60", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torch/functional.py:534: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3595.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n", + "Downloading: \"https://download.pytorch.org/models/maxvit_t-bc5ab103.pth\" to /root/.cache/torch/hub/checkpoints/maxvit_t-bc5ab103.pth\n", + "100%|██████████| 119M/119M [00:02<00:00, 53.9MB/s]\n" + ] + } + ], + "source": [ + "from torchvision.models import maxvit_t, MaxVit_T_Weights\n", + "\n", + "torch_model = maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1)" + ] + }, + { + "cell_type": "markdown", + "id": "45635b2d-a77a-4368-9ecb-dbb440e647ee", + "metadata": {}, + "source": [ + "We can use `flax.nnx.display` to display the model's architecture:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "sZ9x7NpHtBcx", + "metadata": {}, + "outputs": [], + "source": [ + "# nnx.display(torch_model)" + ] + }, + { + "cell_type": "markdown", + "id": "0a36676a-1561-4de0-8e25-38bab90581d0", + "metadata": {}, + "source": [ + "We can see that there are four MaxViT blocks in the model and each block contains:\n", + "- MaxViT layers: two layers for blocks 0, 1, 3 and five layers for the block 4" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0d5bf6aa-c720-4400-a276-602fff53b413", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, [2, 2, 5, 2])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(torch_model.blocks), [len(b.layers) for b in torch_model.blocks]" + ] + }, + { + "cell_type": "markdown", + "id": "a1d55688-5999-41de-a915-eae8b281eb18", + "metadata": {}, + "source": [ + "A [MaxViT layer](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L386) is composed of: [`MBConv`](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L53), `window_attention` as [`PartitionAttentionLayer`](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L282) and `grid_attention` as `PartitionAttentionLayer`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "03ce0555-888a-4086-bb6c-64c36ae60b14", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer'],\n", + " ['MBConv', 'PartitionAttentionLayer', 'PartitionAttentionLayer']]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[[mod.__class__.__name__ for mod in maxvit_layer.layers] for b in torch_model.blocks for maxvit_layer in b.layers]" + ] + }, + { + "cell_type": "markdown", + "id": "d57f8545-43a4-423d-b701-c2e2ca0ebfc1", + "metadata": {}, + "source": [ + "### Inference on data\n", + "\n", + "Let's check the model on dummy input and on a real image" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d6c95620-bf50-47e4-b8d6-3a85262941ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 1000])\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "\n", + "torch_model.eval()\n", + "with torch.inference_mode():\n", + " x = torch.rand(2, 3, 224, 224)\n", + " output = torch_model(x)\n", + "\n", + "print(output.shape) # (2, 1000)" + ] + }, + { + "cell_type": "markdown", + "id": "133bcf21-8a9c-4c27-b551-39b7dfdcfe1c", + "metadata": {}, + "source": [ + "We can download an image of a Pembroke Corgy dog from [TorchVision's gallery](https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true) together with [ImageNet classes dictionary](https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json):" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "qC9hpYfNtOEF", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-15 21:10:00 URL:https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/dog1.jpg [97422/97422] -> \"dog1.jpg\" [1]\n", + "2025-01-15 21:10:01 URL:https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json [35364/35364] -> \"imagenet_class_index.json\" [1]\n" + ] + } + ], + "source": [ + "%%bash\n", + "if [ -f \"dog1.jpg\" ]; then\n", + " echo \"dog1.jpg already exists.\"\n", + "else\n", + " wget -nv \"https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true\" -O dog1.jpg\n", + "fi\n", + "if [ -f \"imagenet_class_index.json\" ]; then\n", + " echo \"imagenet_class_index.json already exists.\"\n", + "else\n", + " wget -nv \"https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json\" -O imagenet_class_index.json\n", + "fi" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "82be8baf-1292-4766-be34-28c510563d71", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction for the Dog: ['n02113023', 'Pembroke'], score: 0.7800846099853516\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "import json\n", + "from torchvision.io import read_image\n", + "\n", + "\n", + "preprocess = MaxVit_T_Weights.IMAGENET1K_V1.transforms()\n", + "\n", + "with open(\"imagenet_class_index.json\") as labels_file:\n", + " labels = json.load(labels_file)\n", + "\n", + "\n", + "dog1 = read_image(\"dog1.jpg\")\n", + "tensor = preprocess(dog1)\n", + "\n", + "torch_model.eval()\n", + "with torch.inference_mode():\n", + " output = torch_model(tensor.unsqueeze(dim=0))\n", + "\n", + "class_id = output.argmax(dim=1).item()\n", + "\n", + "print(f\"Prediction for the Dog: {labels[str(class_id)]}, score: {output.softmax(dim=-1)[0, class_id]}\")\n", + "\n", + "plt.title(f\"{labels[str(class_id)]}\\nScore: {output.softmax(dim=-1)[0, class_id]}\")\n", + "plt.imshow(dog1.permute(1, 2, 0))" + ] + }, + { + "cell_type": "markdown", + "id": "8cbe4ccc-224b-4e8a-a2a9-e2c756c9b207", + "metadata": {}, + "source": [ + "## Port MaxViT model to JAX\n", + "\n", + "To port the [PyTorch implementation of the MaxVit model](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568) in JAX using the Flax module, we will implement the following required modules:\n", + "\n", + "- `MaxViT`\n", + " - `MaxVitBlock`\n", + " - `MaxVitLayer`\n", + " - `MBConv`\n", + " - `Conv2dNormActivation`\n", + " - `SqueezeExcitation`\n", + " - `PartitionAttentionLayer`\n", + " - `RelativePositionalMultiHeadAttention`\n", + " - `WindowDepartition`\n", + " - `WindowPartition`\n", + " - `SwapAxes`\n", + " - `StochasticDepth`\n", + "\n", + "The Flax NNX module is very similar to PyTorch `torch.nn` module and we can map the following modules between PyTorch and Flax:\n", + "- `nn.Sequential` and `nn.ModuleList` -> `nnx.Sequential`\n", + "- `nn.Linear` -> `nnx.Linear`\n", + "- `nn.Conv2d` -> `nnx.Conv`\n", + "- `nn.BatchNorm2d` -> `nnx.BatchNorm`\n", + "- Activations like `nn.ReLU` -> `nnx.relu`\n", + "- Pooling layers like `nn.AvgPool2d(...)` -> `lambda x: nnx.avg_pool(x, ...)`\n", + "- `nn.AdaptiveAvgPool2d(1)` -> `lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2]))`, x is in NHWC format\n", + "- `nn.Flatten()` -> `lambda x: x.reshape(x.shape[0], -1)`\n", + "\n", + "\n", + "If the PyTorch model defines a learnable parameter and a buffer:\n", + "```python\n", + "class Model(nn.Module):\n", + " def __init__(self, ...):\n", + " ...\n", + " self.p = nn.Parameter(torch.ones(10))\n", + " self.register_buffer(\"b\", torch.ones(5))\n", + "```\n", + "an equivalent code in Flax would be\n", + "```python\n", + "class Buffer(nnx.Variable):\n", + " pass\n", + "\n", + "\n", + "class Model(nnx.Module):\n", + " def __init__(self, ...):\n", + " ...\n", + " self.p = nnx.Param(jnp.ones((10,)))\n", + " self.b = Buffer(jnp.ones(5))\n", + "```\n", + "\n", + "To inspect NNX module's learnable parameters and buffers, we can use `nnx.state`:\n", + "```python\n", + "nnx_module = ...\n", + "for k, v in nnx.state(nnx_module, nnx.Param).flat_state().items():\n", + " print(\n", + " k,\n", + " v.value.mean() if v.value is not None else None\n", + " )\n", + "\n", + "for k, v in nnx.state(nnx_module, (nnx.BatchStat, Buffer)).flat_state().items():\n", + " print(\n", + " k,\n", + " v.value.mean() if v.value.dtype == \"float32\" else v.value.sum()\n", + " )\n", + "```\n", + "The equivalent PyTorch code is:\n", + "```python\n", + "torch_module = ...\n", + "\n", + "for m, p in torch_module.named_parameters():\n", + " print(m, p.detach().mean())\n", + "\n", + "for m, b in torch_module.named_buffers():\n", + " print(\n", + " m,\n", + " b.mean() if b.dtype == torch.float32 else b.sum()\n", + " )\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "305ac55b-62ed-4f4d-902c-c6f3082afb02", + "metadata": {}, + "source": [ + "Please note some differences between `torch.nn` and Flax when porting models:\n", + "- We should pass `rngs` to all NNX modules with parameters: e.g. `nnx.Linear(..., rngs=nnx.Rngs(0))`\n", + "- For a 2D convolution:\n", + " - In Flax, we need to explicitly define `kernel_size`, `strides` as two ints tuples, e.g. `(3, 3)`\n", + " - If PyTorch code defines `padding` as integer, e.g. 2, in Flax it should be explicitly defined as a tuple of two ints per dimension, i.e. `((2, 2), (2, 2))`.\n", + "- For a batch normalization: `momentum` value in `torch.nn` should be defined as `1.0 - momentum` in Flax.\n", + "- 4D input arrays in Flax should be in NHWC format, i.e. of shape (N, H, W, C) compared to NCHW format (or (N, C, H, W) shape) in PyTorch." + ] + }, + { + "cell_type": "markdown", + "id": "8d7e3479-bffe-4cb6-81e1-ed8f972c5bf0", + "metadata": {}, + "source": [ + "Below we implement one by one all the modules from the above list and add simple forward pass checks.\n", + "Let's first implement equivalent of `nn.Identity`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "54ece7f1-14c1-41ef-980a-fc279d1702f2", + "metadata": {}, + "outputs": [], + "source": [ + "class Identity(nnx.Module):\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "id": "dd87b2aa-0285-4995-a9aa-ebd58ae00de6", + "metadata": {}, + "source": [ + "### `Conv2dNormActivation` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/misc.py#L125)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "69d71163-676e-4ad3-8d8c-45efaafd76e7", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Callable, List, Optional, Tuple\n", + "from flax import nnx\n", + "\n", + "\n", + "class Conv2dNormActivation(nnx.Sequential):\n", + " def __init__(\n", + " self,\n", + " in_channels: int,\n", + " out_channels: int,\n", + " kernel_size: int = 3,\n", + " stride: int = 1,\n", + " padding: Optional[int] = None,\n", + " groups: int = 1,\n", + " norm_layer: Callable[..., nnx.Module] = nnx.BatchNorm,\n", + " activation_layer: Callable = nnx.relu,\n", + " dilation: int = 1,\n", + " bias: Optional[bool] = None,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " self.out_channels = out_channels\n", + "\n", + " if padding is None:\n", + " padding = (kernel_size - 1) // 2 * dilation\n", + " if bias is None:\n", + " bias = norm_layer is None\n", + "\n", + " # sequence integer pairs that give the padding to apply before\n", + " # and after each spatial dimension\n", + " padding = ((padding, padding), (padding, padding))\n", + "\n", + " layers = [\n", + " nnx.Conv(\n", + " in_channels,\n", + " out_channels,\n", + " kernel_size=(kernel_size, kernel_size),\n", + " strides=(stride, stride),\n", + " padding=padding,\n", + " kernel_dilation=(dilation, dilation),\n", + " feature_group_count=groups,\n", + " use_bias=bias,\n", + " rngs=rngs,\n", + " )\n", + " ]\n", + "\n", + " if norm_layer is not None:\n", + " layers.append(norm_layer(out_channels, rngs=rngs))\n", + "\n", + " if activation_layer is not None:\n", + " layers.append(activation_layer)\n", + "\n", + " super().__init__(*layers)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e5269a0a-f43f-4fdf-9955-aa3fcde60c01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 14, 14, 64)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 28, 28, 32))\n", + "mod = Conv2dNormActivation(32, 64, 3, 2, 1)\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "2d0cd827-ad40-4cd3-9560-6565a3df10bc", + "metadata": {}, + "source": [ + "### `SqueezeExcitation` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/misc.py#L224)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4232689e-e6cc-4ffd-8a2a-41fbc34e57c2", + "metadata": {}, + "outputs": [], + "source": [ + "class SqueezeExcitation(nnx.Module):\n", + " def __init__(\n", + " self,\n", + " input_channels: int,\n", + " squeeze_channels: int,\n", + " activation: Callable = nnx.relu,\n", + " scale_activation: Callable = nnx.sigmoid,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " self.avgpool = nnx.avg_pool\n", + " self.fc1 = nnx.Conv(input_channels, squeeze_channels, (1, 1), rngs=rngs)\n", + " self.fc2 = nnx.Conv(squeeze_channels, input_channels, (1, 1), rngs=rngs)\n", + " self.activation = activation\n", + " self.scale_activation = scale_activation\n", + "\n", + " def _scale(self, x: jax.Array) -> jax.Array:\n", + " scale = self.avgpool(x, (x.shape[1], x.shape[2]))\n", + " scale = self.fc1(scale)\n", + " scale = self.activation(scale)\n", + " scale = self.fc2(scale)\n", + " return self.scale_activation(scale)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " scale = self._scale(x)\n", + " return scale * x" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "83c55286-b92e-49aa-bd5f-c2448a787673", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 28, 28, 32)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 28, 28, 32))\n", + "mod = SqueezeExcitation(32, 4)\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "7935790a-4cb1-46dc-ab73-12d3cb8fc636", + "metadata": {}, + "source": [ + "### `StochasticDepth` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/ops/stochastic_depth.py#L50)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "96834419-eec1-4690-8bb0-447524f6bdde", + "metadata": {}, + "outputs": [], + "source": [ + "def stochastic_depth(\n", + " x: jax.Array,\n", + " p: float,\n", + " mode: str,\n", + " deterministic: bool = False,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + ") -> jax.Array:\n", + " if p < 0.0 or p > 1.0:\n", + " raise ValueError(f\"drop probability has to be between 0 and 1, but got {p}\")\n", + " if mode not in [\"batch\", \"row\"]:\n", + " raise ValueError(f\"mode has to be either 'batch' or 'row', but got {mode}\")\n", + " if deterministic or p == 0.0:\n", + " return x\n", + "\n", + " survival_rate = 1.0 - p\n", + " if mode == \"row\":\n", + " size = [x.shape[0]] + [1] * (x.ndim - 1)\n", + " else:\n", + " size = [1] * x.ndim\n", + "\n", + " noise = jax.random.bernoulli(\n", + " rngs.dropout(), p=survival_rate, shape=size\n", + " ).astype(dtype=x.dtype)\n", + "\n", + " if survival_rate > 0.0:\n", + " noise = noise / survival_rate\n", + "\n", + " return x * noise\n", + "\n", + "\n", + "class StochasticDepth(nnx.Module):\n", + " def __init__(\n", + " self,\n", + " p: float,\n", + " mode: str,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " self.p = p\n", + " self.mode = mode\n", + " self.deterministic = False\n", + " self.rngs = rngs\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " return stochastic_depth(\n", + " x, self.p, self.mode, self.deterministic, rngs=self.rngs\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd95babb-95b4-4015-957d-11b9c7b9957d", + "metadata": {}, + "outputs": [], + "source": [ + "x = jnp.ones((4, 28, 28, 32))\n", + "mod = StochasticDepth(0.5, \"row\")\n", + "\n", + "mod.eval()\n", + "y = mod(x)\n", + "assert (y == x).all()\n", + "\n", + "mod.train()\n", + "y = mod(x)\n", + "assert (y != x).any()" + ] + }, + { + "cell_type": "markdown", + "id": "0ce251eb-a8dc-4415-9856-d16421c1d646", + "metadata": {}, + "source": [ + "### `MBConv` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L53)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "636c713c-4a21-439a-b220-2b9407a06dfc", + "metadata": {}, + "outputs": [], + "source": [ + "class MBConv(nnx.Module):\n", + " def __init__(\n", + " self,\n", + " in_channels: int,\n", + " out_channels: int,\n", + " expansion_ratio: float,\n", + " squeeze_ratio: float,\n", + " stride: int,\n", + " activation_layer: Callable,\n", + " norm_layer: Callable[..., nnx.Module],\n", + " p_stochastic_dropout: float = 0.0,\n", + " rngs = nnx.Rngs(0),\n", + " ):\n", + " should_proj = stride != 1 or in_channels != out_channels\n", + " if should_proj:\n", + " proj = [nnx.Conv(\n", + " in_channels, out_channels, kernel_size=(1, 1), strides=(1, 1), use_bias=True, rngs=rngs\n", + " )]\n", + " if stride == 2:\n", + " padding = ((1, 1), (1, 1))\n", + " proj = [\n", + " lambda x: nnx.avg_pool(\n", + " x, window_shape=(3, 3), strides=(stride, stride), padding=padding\n", + " )\n", + " ] + proj\n", + " self.proj = nnx.Sequential(*proj)\n", + " else:\n", + " self.proj = Identity()\n", + "\n", + " mid_channels = int(out_channels * expansion_ratio)\n", + " sqz_channels = int(out_channels * squeeze_ratio)\n", + "\n", + " if p_stochastic_dropout:\n", + " self.stochastic_depth = StochasticDepth(p_stochastic_dropout, mode=\"row\", rngs=rngs)\n", + " else:\n", + " self.stochastic_depth = Identity()\n", + "\n", + " _layers = [\n", + " norm_layer(in_channels, rngs=rngs), # pre_norm\n", + " Conv2dNormActivation( # conv_a\n", + " in_channels,\n", + " mid_channels,\n", + " kernel_size=1,\n", + " stride=1,\n", + " padding=0,\n", + " activation_layer=activation_layer,\n", + " norm_layer=norm_layer,\n", + " rngs=rngs,\n", + " ),\n", + " Conv2dNormActivation( # conv_b\n", + " mid_channels,\n", + " mid_channels,\n", + " kernel_size=3,\n", + " stride=stride,\n", + " padding=1,\n", + " activation_layer=activation_layer,\n", + " norm_layer=norm_layer,\n", + " groups=mid_channels,\n", + " rngs=rngs,\n", + " ),\n", + " SqueezeExcitation( # squeeze_excitation\n", + " mid_channels, sqz_channels, activation=nnx.silu, rngs=rngs\n", + " ),\n", + " nnx.Conv( # conv_c\n", + " mid_channels, out_channels, kernel_size=(1, 1), use_bias=True, rngs=rngs\n", + " )\n", + " ]\n", + " self.layers = nnx.Sequential(*_layers)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " res = self.proj(x)\n", + " x = self.stochastic_depth(self.layers(x))\n", + " return res + x" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "5cd24b07-f160-422c-bea3-2baf5ebca5b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 14, 14, 64)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from functools import partial\n", + "\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "x = jnp.ones((4, 28, 28, 32))\n", + "mod = MBConv(32, 64, 4, 0.25, 2, activation_layer=nnx.gelu, norm_layer=norm_layer)\n", + "y = mod(x)\n", + "y.shape" + ] + }, + { + "cell_type": "markdown", + "id": "3a8d9cb4-795b-4cb2-a014-bb440acc800b", + "metadata": {}, + "source": [ + "### `RelativePositionalMultiHeadAttention` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L140). First we reimplement a helper function `_get_relative_position_index`:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "df647057-8c6f-4c6b-84f9-d6f78e649343", + "metadata": {}, + "outputs": [], + "source": [ + "def _get_relative_position_index(height: int, width: int) -> jax.Array:\n", + " # PyTorch code:\n", + " # coords = torch.stack(torch.meshgrid([torch.arange(height), torch.arange(width)]))\n", + "\n", + " coords = jnp.stack(\n", + " jnp.meshgrid(*[jnp.arange(height), jnp.arange(width)], indexing=\"ij\")\n", + " )\n", + " # PyTorch code: coords_flat = torch.flatten(coords, 1)\n", + " coords_flat = coords.reshape(coords.shape[0], -1)\n", + "\n", + " relative_coords = coords_flat[:, :, None] - coords_flat[:, None, :]\n", + " relative_coords = jnp.permute_dims(relative_coords, (1, 2, 0))\n", + "\n", + " # PyTorch code:\n", + " # relative_coords[:, :, 0] += height - 1\n", + " # relative_coords[:, :, 1] += width - 1\n", + " # relative_coords[:, :, 0] *= 2 * width - 1\n", + " relative_coords = relative_coords + jnp.array((height - 1, width - 1))\n", + " relative_coords = relative_coords * jnp.array((2 * width - 1, 1))\n", + "\n", + " return relative_coords.sum(-1)" + ] + }, + { + "cell_type": "markdown", + "id": "2670d86b", + "metadata": {}, + "source": [ + "Let us check our implementation against PyTorch implementation:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5ce55b8b-5305-4a57-a413-8df43392ec3a", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import _get_relative_position_index as pytorch_get_relative_position_index\n", + "\n", + "\n", + "output = _get_relative_position_index(13, 12)\n", + "expected = pytorch_get_relative_position_index(13, 12)\n", + "assert (output == jnp.asarray(expected)).all()" + ] + }, + { + "cell_type": "markdown", + "id": "5518bfc4", + "metadata": {}, + "source": [ + "Next, we can port `RelativePositionalMultiHeadAttention` module which a learnable parameter and a buffer:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1f46b3e4-fd69-42c2-8ca7-d242a20d13de", + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "\n", + "class Buffer(nnx.Variable):\n", + " pass\n", + "\n", + "\n", + "class RelativePositionalMultiHeadAttention(nnx.Module):\n", + " def __init__(\n", + " self,\n", + " feat_dim: int,\n", + " head_dim: int,\n", + " max_seq_len: int,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " if feat_dim % head_dim != 0:\n", + " raise ValueError(f\"feat_dim: {feat_dim} must be divisible by head_dim: {head_dim}\")\n", + "\n", + " self.n_heads = feat_dim // head_dim\n", + " self.head_dim = head_dim\n", + " self.size = int(math.sqrt(max_seq_len))\n", + " self.max_seq_len = max_seq_len\n", + "\n", + " self.to_qkv = nnx.Linear(feat_dim, self.n_heads * self.head_dim * 3, rngs=rngs)\n", + " self.scale_factor = feat_dim**-0.5\n", + "\n", + " self.merge = nnx.Linear(self.head_dim * self.n_heads, feat_dim, rngs=rngs)\n", + "\n", + " self.relative_position_index = Buffer(_get_relative_position_index(self.size, self.size))\n", + "\n", + " # initialize with truncated normal bias\n", + " initializer = jax.nn.initializers.truncated_normal(stddev=0.02)\n", + " shape = ((2 * self.size - 1) * (2 * self.size - 1), self.n_heads)\n", + " self.relative_position_bias_table = nnx.Param(initializer(rngs.params(), shape, jnp.float32))\n", + "\n", + " def get_relative_positional_bias(self) -> jax.Array:\n", + " bias_index = self.relative_position_index.value.ravel()\n", + " relative_bias = self.relative_position_bias_table[bias_index].reshape((self.max_seq_len, self.max_seq_len, -1))\n", + " relative_bias = jnp.permute_dims(relative_bias, (2, 0, 1))\n", + " return jnp.expand_dims(relative_bias, axis=0)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " B, G, P, D = x.shape\n", + " H, DH = self.n_heads, self.head_dim\n", + "\n", + " qkv = self.to_qkv(x)\n", + "\n", + " q, k, v = jnp.split(qkv, 3, axis=-1)\n", + " q = jnp.permute_dims(q.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", + " k = jnp.permute_dims(k.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", + " v = jnp.permute_dims(v.reshape((B, G, P, H, DH)), (0, 1, 3, 2, 4))\n", + "\n", + " k = k * self.scale_factor\n", + "\n", + " dot_prod = jnp.einsum(\"B G H I D, B G H J D -> B G H I J\", q, k)\n", + " pos_bias = self.get_relative_positional_bias()\n", + "\n", + " dot_prod = jax.nn.softmax(dot_prod + pos_bias, axis=-1)\n", + "\n", + " out = jnp.einsum(\"B G H I J, B G H J D -> B G H I D\", dot_prod, v)\n", + " out = jnp.permute_dims(out, (0, 1, 3, 2, 4)).reshape((B, G, P, D))\n", + "\n", + " out = self.merge(out)\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "18d0c993", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 32, 49, 64)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 32, 49, 64))\n", + "\n", + "mod = RelativePositionalMultiHeadAttention(64, 16, 49)\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "875aba65-53d0-4241-bdd7-36384054ca59", + "metadata": {}, + "source": [ + "### `SwapAxes`, `WindowPartition`, `WindowDepartition` implementations\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L213)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d8a19362-733a-4359-9658-53dcffa25220", + "metadata": {}, + "outputs": [], + "source": [ + "class SwapAxes(nnx.Module):\n", + " def __init__(self, a: int, b: int):\n", + " self.a = a\n", + " self.b = b\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " res = jnp.swapaxes(x, self.a, self.b)\n", + " return res\n", + "\n", + "\n", + "class WindowPartition(nnx.Module):\n", + " def __call__(self, x: jax.Array, p: int) -> jax.Array:\n", + " # Output array with expected layout of [B, H/P, W/P, P*P, C].\n", + " B, H, W, C = x.shape\n", + " P = p\n", + " # chunk up H and W dimensions\n", + " x = x.reshape((B, H // P, P, W // P, P, C))\n", + " x = jnp.permute_dims(x, (0, 1, 3, 2, 4, 5))\n", + " # colapse P * P dimension\n", + " x = x.reshape((B, (H // P) * (W // P), P * P, C))\n", + " return x\n", + "\n", + "\n", + "class WindowDepartition(nnx.Module):\n", + " def __call__(self, x: jax.Array, p: int, h_partitions: int, w_partitions: int) -> jax.Array:\n", + " # Output array with expected layout of [B, H, W, C].\n", + " B, G, PP, C = x.shape\n", + " P = p\n", + " HP, WP = h_partitions, w_partitions\n", + " # split P * P dimension into 2 P tile dimensions\n", + " x = x.reshape((B, HP, WP, P, P, C))\n", + " # permute into B, HP, P, WP, P, C\n", + " x = jnp.permute_dims(x, (0, 1, 3, 2, 4, 5))\n", + " # reshape into B, H, W, C\n", + " x = x.reshape((B, HP * P, WP * P, C))\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "daee5b6b-595f-4344-af93-6e4bd44c217f", + "metadata": {}, + "outputs": [], + "source": [ + "x = jnp.ones((3, 4, 5, 6))\n", + "mod = SwapAxes(1, 2)\n", + "y = mod(x)\n", + "assert y.shape == (3, 5, 4, 6)\n", + "\n", + "x = jnp.ones((4, 128, 128, 3))\n", + "mod = WindowPartition()\n", + "y = mod(x, p=16)\n", + "assert y.shape == (4, (128 // 16) * (128 // 16), 16 * 16, 3)\n", + "\n", + "x = jnp.ones((4, (128 // 16) * (128 // 16), 16 * 16, 3))\n", + "mod = WindowDepartition()\n", + "y = mod(x, p=16, h_partitions=128 // 16, w_partitions=128 // 16)\n", + "assert y.shape == (4, 128, 128, 3)" + ] + }, + { + "cell_type": "markdown", + "id": "fe9643dd-b328-43c9-a82f-7180ee2b9a00", + "metadata": {}, + "source": [ + "### `PartitionAttentionLayer` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L282)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "dfb3c640-4b51-4ca5-a7ba-2ad5f9907c57", + "metadata": {}, + "outputs": [], + "source": [ + "class PartitionAttentionLayer(nnx.Module):\n", + " \"\"\"\n", + " Layer for partitioning the input tensor into non-overlapping windows and\n", + " applying attention to each window.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " in_channels: int,\n", + " head_dim: int,\n", + " # partitioning parameters\n", + " partition_size: int,\n", + " partition_type: str,\n", + " # grid size needs to be known at initialization time\n", + " # because we need to know hamy relative offsets there are in the grid\n", + " grid_size: Tuple[int, int],\n", + " mlp_ratio: int,\n", + " activation_layer: Callable,\n", + " norm_layer: Callable[..., nnx.Module],\n", + " attention_dropout: float,\n", + " mlp_dropout: float,\n", + " p_stochastic_dropout: float,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " self.n_heads = in_channels // head_dim\n", + " self.head_dim = head_dim\n", + " self.n_partitions = grid_size[0] // partition_size\n", + " self.partition_type = partition_type\n", + " self.grid_size = grid_size\n", + "\n", + " if partition_type not in [\"grid\", \"window\"]:\n", + " raise ValueError(\"partition_type must be either 'grid' or 'window'\")\n", + "\n", + " if partition_type == \"window\":\n", + " self.p, self.g = partition_size, self.n_partitions\n", + " else:\n", + " self.p, self.g = self.n_partitions, partition_size\n", + "\n", + " self.partition_op = WindowPartition()\n", + " self.departition_op = WindowDepartition()\n", + " self.partition_swap = SwapAxes(-2, -3) if partition_type == \"grid\" else Identity()\n", + " self.departition_swap = SwapAxes(-2, -3) if partition_type == \"grid\" else Identity()\n", + "\n", + " self.attn_layer = nnx.Sequential(\n", + " norm_layer(in_channels, rngs=rngs),\n", + " # it's always going to be partition_size ** 2 because\n", + " # of the axis swap in the case of grid partitioning\n", + " RelativePositionalMultiHeadAttention(\n", + " in_channels, head_dim, partition_size**2, rngs=rngs\n", + " ),\n", + " nnx.Dropout(attention_dropout, rngs=rngs),\n", + " )\n", + "\n", + " # pre-normalization similar to transformer layers\n", + " self.mlp_layer = nnx.Sequential(\n", + " nnx.LayerNorm(in_channels, rngs=rngs),\n", + " nnx.Linear(in_channels, in_channels * mlp_ratio, rngs=rngs),\n", + " activation_layer,\n", + " nnx.Linear(in_channels * mlp_ratio, in_channels, rngs=rngs),\n", + " nnx.Dropout(mlp_dropout, rngs=rngs),\n", + " )\n", + "\n", + " # layer scale factors\n", + " self.stochastic_dropout = StochasticDepth(p_stochastic_dropout, mode=\"row\", rngs=rngs)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " # Undefined behavior if H or W are not divisible by p\n", + " # https://github.com/google-research/maxvit/blob/da76cf0d8a6ec668cc31b399c4126186da7da944/maxvit/models/maxvit.py#L766\n", + " gh, gw = self.grid_size[0] // self.p, self.grid_size[1] // self.p\n", + " torch._assert(\n", + " self.grid_size[0] % self.p == 0 and self.grid_size[1] % self.p == 0,\n", + " \"Grid size must be divisible by partition size. Got grid size of {} and partition size of {}\".format(\n", + " self.grid_size, self.p\n", + " ),\n", + " )\n", + " x = self.partition_op(x, self.p) # (B, H, W, C) -> (B, H/P, W/P, P*P, C)\n", + " x = self.partition_swap(x) # -> grid: (B, H/P, P*P, W/P, C)\n", + " x = x + self.stochastic_dropout(self.attn_layer(x))\n", + " x = x + self.stochastic_dropout(self.mlp_layer(x))\n", + " x = self.departition_swap(x) # grid: (B, H/P, P*P, W/P, C) -> (B, H/P, W/P, P*P, C)\n", + " x = self.departition_op(x, self.p, gh, gw) # -> (B, H, W, C)\n", + "\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d6feac34-35be-420b-a7cb-78995aed4c7a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 224, 224, 36)\n", + "(4, 224, 224, 36)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 224, 224, 36))\n", + "\n", + "grid_size = (224, 224)\n", + "mod = PartitionAttentionLayer(\n", + " 36, 6, 7, \"window\", grid_size=grid_size, mlp_ratio=4,\n", + " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", + " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)\n", + "\n", + "mod = PartitionAttentionLayer(\n", + " 36, 6, 7, \"grid\", grid_size=grid_size, mlp_ratio=4,\n", + " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", + " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "b89b4ca6-c17a-4c0f-859a-de7134348818", + "metadata": {}, + "source": [ + "### `MaxVitLayer` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L386)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "45b3199e-711d-4125-86b9-22e90fafa28c", + "metadata": {}, + "outputs": [], + "source": [ + "class MaxVitLayer(nnx.Module):\n", + " \"\"\"\n", + " MaxVit layer consisting of a MBConv layer followed by a PartitionAttentionLayer with `window`\n", + " and a PartitionAttentionLayer with `grid`.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " # conv parameters\n", + " in_channels: int,\n", + " out_channels: int,\n", + " squeeze_ratio: float,\n", + " expansion_ratio: float,\n", + " stride: int,\n", + " # conv + transformer parameters\n", + " norm_layer: Callable[..., nnx.Module],\n", + " activation_layer: Callable,\n", + " # transformer parameters\n", + " head_dim: int,\n", + " mlp_ratio: int,\n", + " mlp_dropout: float,\n", + " attention_dropout: float,\n", + " p_stochastic_dropout: float,\n", + " # partitioning parameters\n", + " partition_size: int,\n", + " grid_size: Tuple[int, int],\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " layers = [\n", + " # convolutional layer\n", + " MBConv(\n", + " in_channels=in_channels,\n", + " out_channels=out_channels,\n", + " expansion_ratio=expansion_ratio,\n", + " squeeze_ratio=squeeze_ratio,\n", + " stride=stride,\n", + " activation_layer=activation_layer,\n", + " norm_layer=norm_layer,\n", + " p_stochastic_dropout=p_stochastic_dropout,\n", + " rngs=rngs,\n", + " ),\n", + " # window_attention\n", + " PartitionAttentionLayer(\n", + " in_channels=out_channels,\n", + " head_dim=head_dim,\n", + " partition_size=partition_size,\n", + " partition_type=\"window\",\n", + " grid_size=grid_size,\n", + " mlp_ratio=mlp_ratio,\n", + " activation_layer=activation_layer,\n", + " norm_layer=nnx.LayerNorm,\n", + " attention_dropout=attention_dropout,\n", + " mlp_dropout=mlp_dropout,\n", + " p_stochastic_dropout=p_stochastic_dropout,\n", + " rngs=rngs,\n", + " ),\n", + " # grid_attention\n", + " PartitionAttentionLayer(\n", + " in_channels=out_channels,\n", + " head_dim=head_dim,\n", + " partition_size=partition_size,\n", + " partition_type=\"grid\",\n", + " grid_size=grid_size,\n", + " mlp_ratio=mlp_ratio,\n", + " activation_layer=activation_layer,\n", + " norm_layer=nnx.LayerNorm,\n", + " attention_dropout=attention_dropout,\n", + " mlp_dropout=mlp_dropout,\n", + " p_stochastic_dropout=p_stochastic_dropout,\n", + " rngs=rngs,\n", + " )\n", + " ]\n", + " self.layers = nnx.Sequential(*layers)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " return self.layers(x)\n", + "\n", + "\n", + "def _get_conv_output_shape(\n", + " input_size: Tuple[int, int], kernel_size: int, stride: int, padding: int\n", + ") -> Tuple[int, int]:\n", + " return (\n", + " (input_size[0] - kernel_size + 2 * padding) // stride + 1,\n", + " (input_size[1] - kernel_size + 2 * padding) // stride + 1,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6a130b58-95cf-4ad7-8a42-5044a37c7c09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 112, 112, 36)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 224, 224, 3))\n", + "\n", + "grid_size = _get_conv_output_shape((224, 224), kernel_size=3, stride=2, padding=1)\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "\n", + "mod = MaxVitLayer(\n", + " 3, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", + " stride=2, norm_layer=norm_layer, activation_layer=nnx.gelu,\n", + " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", + " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", + " partition_size=7, grid_size=grid_size,\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "21460039-0ed8-4c37-8382-7d91655f1086", + "metadata": {}, + "source": [ + "### `MaxVitBlock` implementation\n", + "\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L483)." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "e4fd31d2-4354-4694-87b4-3d0644388d3d", + "metadata": {}, + "outputs": [], + "source": [ + "class MaxVitBlock(nnx.Module):\n", + " \"\"\"\n", + " A MaxVit block consisting of `n_layers` MaxVit layers.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " # conv parameters\n", + " in_channels: int,\n", + " out_channels: int,\n", + " squeeze_ratio: float,\n", + " expansion_ratio: float,\n", + " # conv + transformer parameters\n", + " norm_layer: Callable[..., nnx.Module],\n", + " activation_layer: Callable,\n", + " # transformer parameters\n", + " head_dim: int,\n", + " mlp_ratio: int,\n", + " mlp_dropout: float,\n", + " attention_dropout: float,\n", + " # partitioning parameters\n", + " partition_size: int,\n", + " input_grid_size: Tuple[int, int],\n", + " # number of layers\n", + " n_layers: int,\n", + " p_stochastic: List[float],\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " if not len(p_stochastic) == n_layers:\n", + " raise ValueError(f\"p_stochastic must have length n_layers={n_layers}, got p_stochastic={p_stochastic}.\")\n", + "\n", + " # account for the first stride of the first layer\n", + " self.grid_size = _get_conv_output_shape(input_grid_size, kernel_size=3, stride=2, padding=1)\n", + "\n", + " layers = []\n", + " for idx, p in enumerate(p_stochastic):\n", + " stride = 2 if idx == 0 else 1\n", + " layers.append(\n", + " MaxVitLayer(\n", + " in_channels=in_channels if idx == 0 else out_channels,\n", + " out_channels=out_channels,\n", + " squeeze_ratio=squeeze_ratio,\n", + " expansion_ratio=expansion_ratio,\n", + " stride=stride,\n", + " norm_layer=norm_layer,\n", + " activation_layer=activation_layer,\n", + " head_dim=head_dim,\n", + " mlp_ratio=mlp_ratio,\n", + " mlp_dropout=mlp_dropout,\n", + " attention_dropout=attention_dropout,\n", + " partition_size=partition_size,\n", + " grid_size=self.grid_size,\n", + " p_stochastic_dropout=p,\n", + " ),\n", + " )\n", + " self.layers = nnx.Sequential(*layers)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " return self.layers(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "e168c27f-98db-4831-9723-dffac88f3226", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 112, 112, 36)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 224, 224, 3))\n", + "\n", + "input_grid_size = (224, 224)\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "\n", + "mod = MaxVitBlock(\n", + " 3, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", + " norm_layer=norm_layer, activation_layer=nnx.gelu,\n", + " head_dim=6, mlp_ratio=4, mlp_dropout=0.5, attention_dropout=0.4,\n", + " partition_size=7, input_grid_size=input_grid_size,\n", + " n_layers=2,\n", + " p_stochastic=[0.0, 0.2],\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "cef5687d-e390-438b-95b3-e66406e2c000", + "metadata": {}, + "source": [ + "### `MaxVit` implementation\n", + "\n", + "Finally, we can assemble everything together and define the MaxVit model.\n", + "[PyTorch source implementation](https://github.com/pytorch/vision/blob/945bdad7523806b15d3740ce6ace2fced9ef9d3b/torchvision/models/maxvit.py#L568)." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "0e874e63-0eb7-40ea-82f3-bf10ac33d7a6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def _make_block_input_shapes(input_size: Tuple[int, int], n_blocks: int) -> List[Tuple[int, int]]:\n", + " \"\"\"Util function to check that the input size is correct for a MaxVit configuration.\"\"\"\n", + " shapes = []\n", + " block_input_shape = _get_conv_output_shape(input_size, 3, 2, 1)\n", + " for _ in range(n_blocks):\n", + " block_input_shape = _get_conv_output_shape(block_input_shape, 3, 2, 1)\n", + " shapes.append(block_input_shape)\n", + " return shapes\n", + "\n", + "\n", + "class MaxVit(nnx.Module):\n", + " \"\"\"\n", + " Implements MaxVit Transformer from the \"MaxViT: Multi-Axis Vision Transformer\" paper.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " # input size parameters\n", + " input_size: Tuple[int, int],\n", + " # stem and task parameters\n", + " stem_channels: int,\n", + " # partitioning parameters\n", + " partition_size: int,\n", + " # block parameters\n", + " block_channels: List[int],\n", + " block_layers: List[int],\n", + " # attention head dimensions\n", + " head_dim: int,\n", + " stochastic_depth_prob: float,\n", + " # conv + transformer parameters\n", + " # norm_layer is applied only to the conv layers\n", + " # activation_layer is applied both to conv and transformer layers\n", + " norm_layer: Optional[Callable[..., nnx.Module]] = None,\n", + " activation_layer: Callable = nnx.gelu,\n", + " # conv parameters\n", + " squeeze_ratio: float = 0.25,\n", + " expansion_ratio: float = 4,\n", + " # transformer parameters\n", + " mlp_ratio: int = 4,\n", + " mlp_dropout: float = 0.0,\n", + " attention_dropout: float = 0.0,\n", + " # task parameters\n", + " num_classes: int = 1000,\n", + " rngs: nnx.Rngs = nnx.Rngs(0),\n", + " ):\n", + " input_channels = 3\n", + "\n", + " if norm_layer is None:\n", + " norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "\n", + " # Make sure input size will be divisible by the partition size in all blocks\n", + " # Undefined behavior if H or W are not divisible by p\n", + " block_input_sizes = _make_block_input_shapes(input_size, len(block_channels))\n", + " for idx, block_input_size in enumerate(block_input_sizes):\n", + " if block_input_size[0] % partition_size != 0 or block_input_size[1] % partition_size != 0:\n", + " raise ValueError(\n", + " f\"Input size {block_input_size} of block {idx} is not divisible by partition size {partition_size}. \"\n", + " f\"Consider changing the partition size or the input size.\\n\"\n", + " f\"Current configuration yields the following block input sizes: {block_input_sizes}.\"\n", + " )\n", + "\n", + " # stem\n", + " self.stem = nnx.Sequential(\n", + " Conv2dNormActivation(\n", + " input_channels,\n", + " stem_channels,\n", + " 3,\n", + " stride=2,\n", + " norm_layer=norm_layer,\n", + " activation_layer=activation_layer,\n", + " bias=False,\n", + " rngs=rngs,\n", + " ),\n", + " Conv2dNormActivation(\n", + " stem_channels,\n", + " stem_channels,\n", + " 3,\n", + " stride=1,\n", + " norm_layer=None,\n", + " activation_layer=None,\n", + " bias=True,\n", + " rngs=rngs,\n", + " ),\n", + " )\n", + "\n", + " # account for stem stride\n", + " input_size = _get_conv_output_shape(input_size, kernel_size=3, stride=2, padding=1)\n", + " self.partition_size = partition_size\n", + "\n", + " # blocks\n", + " blocks = []\n", + " in_channels = [stem_channels] + block_channels[:-1]\n", + " out_channels = block_channels\n", + "\n", + " # precompute the stochastic depth probabilities from 0 to stochastic_depth_prob\n", + " # since we have N blocks with L layers, we will have N * L probabilities uniformly distributed\n", + " # over the range [0, stochastic_depth_prob]\n", + " p_stochastic = np.linspace(0, stochastic_depth_prob, sum(block_layers)).tolist()\n", + "\n", + " p_idx = 0\n", + " for in_channel, out_channel, num_layers in zip(in_channels, out_channels, block_layers):\n", + " blocks.append(\n", + " MaxVitBlock(\n", + " in_channels=in_channel,\n", + " out_channels=out_channel,\n", + " squeeze_ratio=squeeze_ratio,\n", + " expansion_ratio=expansion_ratio,\n", + " norm_layer=norm_layer,\n", + " activation_layer=activation_layer,\n", + " head_dim=head_dim,\n", + " mlp_ratio=mlp_ratio,\n", + " mlp_dropout=mlp_dropout,\n", + " attention_dropout=attention_dropout,\n", + " partition_size=partition_size,\n", + " input_grid_size=input_size,\n", + " n_layers=num_layers,\n", + " p_stochastic=p_stochastic[p_idx : p_idx + num_layers],\n", + " ),\n", + " )\n", + " input_size = blocks[-1].grid_size\n", + " p_idx += num_layers\n", + " self.blocks = nnx.Sequential(*blocks)\n", + "\n", + " self.classifier = nnx.Sequential(\n", + " lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2])), # nn.AdaptiveAvgPool2d(1)\n", + " lambda x: x.reshape(x.shape[0], -1), # nn.Flatten()\n", + " nnx.LayerNorm(block_channels[-1], rngs=rngs),\n", + " nnx.Linear(block_channels[-1], block_channels[-1], rngs=rngs),\n", + " nnx.tanh,\n", + " nnx.Linear(block_channels[-1], num_classes, use_bias=False, rngs=rngs),\n", + " )\n", + "\n", + " self._init_weights(rngs)\n", + "\n", + " def __call__(self, x: jax.Array) -> jax.Array:\n", + " x = self.stem(x)\n", + " x = self.blocks(x)\n", + " x = self.classifier(x)\n", + " return x\n", + "\n", + " def _init_weights(self, rngs):\n", + " normal_initializer = nnx.initializers.normal(stddev=0.02)\n", + " for name, module in self.iter_modules():\n", + " if isinstance(module, (nnx.Conv, nnx.Linear)):\n", + " module.kernel.value = normal_initializer(\n", + " rngs(), module.kernel.value.shape, module.kernel.value.dtype\n", + " )\n", + " if module.bias.value is not None:\n", + " module.bias.value = jnp.zeros(\n", + " module.bias.value.shape, dtype=module.bias.value.dtype\n", + " )\n", + " elif isinstance(module, nnx.BatchNorm):\n", + " module.scale.value = jnp.ones(module.scale.value.shape, module.scale.value.dtype)\n", + " module.bias.value = jnp.zeros(module.bias.value.shape, module.bias.value.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "7e0f08b8-03a8-4941-8ca3-10d960783486", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 1000)\n" + ] + } + ], + "source": [ + "x = jnp.ones((4, 224, 224, 3))\n", + "\n", + "mod = MaxVit(\n", + " input_size=(224, 224),\n", + " stem_channels=64,\n", + " block_channels=[64, 128, 256, 512],\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=32,\n", + " stochastic_depth_prob=0.2,\n", + " partition_size=7,\n", + " num_classes=1000,\n", + ")\n", + "\n", + "y = mod(x)\n", + "print(y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "fa2a4a47-b6c9-43ba-822b-002e0c03e85c", + "metadata": {}, + "outputs": [], + "source": [ + "def maxvit_t(\n", + " input_size=(224, 224),\n", + " stem_channels=64,\n", + " block_channels=[64, 128, 256, 512],\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=32,\n", + " stochastic_depth_prob=0.2,\n", + " partition_size=7,\n", + " num_classes=1000,\n", + "):\n", + " model = MaxVit(\n", + " input_size=input_size,\n", + " stem_channels=stem_channels,\n", + " block_channels=block_channels,\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=head_dim,\n", + " stochastic_depth_prob=stochastic_depth_prob,\n", + " partition_size=partition_size,\n", + " num_classes=num_classes,\n", + " )\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "id": "25ff32f7-e4a1-4029-b114-8ecafb4378fd", + "metadata": {}, + "source": [ + "### Test JAX implementation" + ] + }, + { + "cell_type": "markdown", + "id": "b3e02373-c3b6-4ffd-a98e-e425824f2f88", + "metadata": {}, + "source": [ + "Let us import equivalent PyTorch modules and check our implementations against PyTorch. Please note that\n", + "PyTorch modules will contain random parameters and buffers that we need to set into our Flax implementations.\n", + "\n", + "Below we define a helper class `Torch2Flax` to copy parameters and buffers from a PyTorch module into equivalent Flax module." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "22f49ecd-4999-4c1c-b1d8-d16faeb60389", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "\n", + "\n", + "class Torch2Flax:\n", + " @staticmethod\n", + " def conv_params_permute(name, torch_param):\n", + " if name == \"weight\":\n", + " return torch_param.permute(2, 3, 1, 0)\n", + " return torch_param\n", + "\n", + " @staticmethod\n", + " def linear_params_permute(name, torch_param):\n", + " if name == \"weight\":\n", + " return torch_param.permute(1, 0)\n", + " return torch_param\n", + "\n", + " @staticmethod\n", + " def default_params_transform(name, param):\n", + " return param\n", + "\n", + " modules_mapping_info = {\n", + " nn.Conv2d: {\n", + " \"type\": nnx.Conv,\n", + " \"params_mapping\": {\n", + " \"weight\": \"kernel\",\n", + " \"bias\": \"bias\",\n", + " },\n", + " \"params_transform\": conv_params_permute,\n", + " },\n", + " nn.BatchNorm2d: {\n", + " \"type\": nnx.BatchNorm,\n", + " \"params_mapping\": {\n", + " \"weight\": \"scale\",\n", + " \"bias\": \"bias\",\n", + " \"running_mean\": \"mean\",\n", + " \"running_var\": \"var\",\n", + " },\n", + " },\n", + " nn.Linear: {\n", + " \"type\": nnx.Linear,\n", + " \"params_mapping\": {\n", + " \"weight\": \"kernel\",\n", + " \"bias\": \"bias\",\n", + " },\n", + " \"params_transform\": linear_params_permute,\n", + " },\n", + " nn.LayerNorm: {\n", + " \"type\": nnx.LayerNorm,\n", + " \"params_mapping\": {\n", + " \"weight\": \"scale\",\n", + " \"bias\": \"bias\",\n", + " },\n", + " }\n", + " } | {\n", + " torch_mod: {\n", + " \"type\": nnx_fn_type,\n", + " \"params_mapping\": {},\n", + " } for torch_mod, nnx_fn_type in [\n", + " (nn.Identity, Identity),\n", + " (nn.Flatten, type(lambda x: x)),\n", + " (nn.ReLU, type(nnx.relu)),\n", + " (nn.GELU, type(nnx.gelu)),\n", + " (nn.SELU, type(nnx.selu)),\n", + " (nn.SiLU, type(nnx.silu)),\n", + " (nn.Tanh, type(nnx.tanh)),\n", + " (nn.Dropout, nnx.Dropout),\n", + " (nn.Sigmoid, type(nnx.sigmoid)),\n", + " (nn.AvgPool2d, type(lambda x: nnx.avg_pool(x, (2, 2)))),\n", + " (nn.AdaptiveAvgPool2d, type(lambda x: nnx.avg_pool(x, (x.shape[1], x.shape[2])))),\n", + " ]\n", + " }\n", + "\n", + " def _copy_params_buffers(self, torch_nn_module, nnx_module):\n", + " torch_module_type = type(torch_nn_module)\n", + " assert torch_module_type in self.modules_mapping_info, torch_module_type\n", + " module_mapping_info = self.modules_mapping_info[torch_module_type]\n", + " assert isinstance(nnx_module, module_mapping_info[\"type\"]), (\n", + " nnx_module, type(nnx_module), module_mapping_info[\"type\"]\n", + " )\n", + "\n", + " for torch_key, nnx_key in module_mapping_info[\"params_mapping\"].items():\n", + "\n", + " torch_value = getattr(torch_nn_module, torch_key)\n", + " nnx_param = getattr(nnx_module, nnx_key)\n", + " assert nnx_param is not None, (torch_key, nnx_key, nnx_module)\n", + "\n", + " if torch_value is None:\n", + " assert nnx_param.value is None, nnx_param\n", + " continue\n", + "\n", + " params_transform = module_mapping_info.get(\"params_transform\", Torch2Flax.default_params_transform)\n", + " torch_value = params_transform(torch_key, torch_value)\n", + "\n", + " assert nnx_param.value.shape == torch_value.data.shape, (\n", + " nnx_key, nnx_param.value.shape, torch_key, torch_value.data.shape\n", + " )\n", + " nnx_param.value = jnp.asarray(torch_value.data)\n", + "\n", + " def _copy_sequential(self, torch_nn_seq, nnx_seq, skip_modules=None):\n", + " assert isinstance(torch_nn_seq, (nn.Sequential, nn.ModuleList)), type(torch_nn_seq)\n", + " assert isinstance(nnx_seq, nnx.Sequential), type(nnx_seq)\n", + " for i, index in enumerate(torch_nn_seq):\n", + " torch_module = torch_nn_seq[i]\n", + " nnx_module = nnx_seq.layers[i]\n", + " self.copy_module(torch_module, nnx_module, skip_modules=skip_modules)\n", + "\n", + " def copy_module(self, torch_module, nnx_module, skip_modules=None):\n", + " if skip_modules is None:\n", + " skip_modules = []\n", + "\n", + " if isinstance(torch_module, (nn.Sequential, nn.ModuleList)):\n", + " self._copy_sequential(torch_module, nnx_module, skip_modules=skip_modules)\n", + " elif type(torch_module) in self.modules_mapping_info:\n", + " self._copy_params_buffers(torch_module, nnx_module)\n", + " else:\n", + " if skip_modules is not None:\n", + " if torch_module.__class__.__name__ in skip_modules:\n", + " return\n", + "\n", + " named_children = list(torch_module.named_children())\n", + " assert len(named_children) > 0, type(torch_module)\n", + " for name, torch_child in named_children:\n", + " nnx_child = getattr(nnx_module, name, None)\n", + " assert nnx_child is not None, (name, nnx_module)\n", + " self.copy_module(torch_child, nnx_child, skip_modules=skip_modules)\n", + " # Copy buffers and params of the module itself (not its children)\n", + " for name, torch_buffer in torch_module.named_buffers():\n", + " if \".\" in name:\n", + " # This is child's buffer\n", + " continue\n", + " nnx_buffer = getattr(nnx_module, name)\n", + " assert isinstance(nnx_buffer, nnx.Variable), (name, nnx_buffer, nnx_module)\n", + "\n", + " assert nnx_buffer.value.shape == torch_buffer.shape, (\n", + " name, nnx_buffer.value.shape, torch_buffer.shape\n", + " )\n", + " nnx_buffer.value = jnp.asarray(torch_buffer)\n", + "\n", + " for name, torch_param in torch_module.named_parameters():\n", + " if \".\" in name:\n", + " # This is child's parameter\n", + " continue\n", + " nnx_param = getattr(nnx_module, name)\n", + " assert isinstance(nnx_param, nnx.Param), (name, nnx_param, nnx_module)\n", + "\n", + " assert nnx_param.value.shape == torch_param.data.shape, (\n", + " name, nnx_param.value.shape, torch_param.data.shape\n", + " )\n", + " nnx_param.value = jnp.asarray(torch_param.data)\n", + "\n", + "\n", + "def test_modules(\n", + " nnx_module, torch_module, torch_input, atol=1e-3, mode=\"eval\", permute_torch_input=True, device=\"cuda\"\n", + "):\n", + " assert torch_input.ndim == 4\n", + " assert mode in (\"eval\", \"train\")\n", + "\n", + " torch_input = torch_input.to(device)\n", + " torch_module = torch_module.to(device)\n", + "\n", + " if mode == \"eval\":\n", + " torch_module.eval()\n", + " nnx_module.eval()\n", + " else:\n", + " torch_module.train()\n", + " nnx_module.train()\n", + "\n", + " with torch.inference_mode(mode=mode==\"eval\"):\n", + " torch_output = torch_module(torch_input)\n", + "\n", + " if permute_torch_input:\n", + " torch_input = torch_input.permute(0, 2, 3, 1)\n", + "\n", + " jax_input = jnp.asarray(torch_input, device=jax.devices(device)[0])\n", + " jax_output = nnx_module(jax_input)\n", + " assert jax_output.device == jax.devices(device)[0]\n", + "\n", + " torch_output = torch_output.detach()\n", + " if permute_torch_input and torch_output.ndim == 4:\n", + " torch_output = torch_output.permute(0, 2, 3, 1)\n", + " jax_expected = jnp.asarray(torch_output)\n", + "\n", + " assert jnp.allclose(jax_output, jax_expected, atol=atol), (\n", + " jnp.abs(jax_output - jax_expected).max(),\n", + " jnp.abs(jax_output - jax_expected).mean(),\n", + " )\n", + "\n", + "\n", + "t2f = Torch2Flax()" + ] + }, + { + "cell_type": "markdown", + "id": "a323a19e-fc64-4f8d-8be2-c7886b6191b9", + "metadata": {}, + "source": [ + "Let us now test our JAX modules. We only test the result of the forward pass in the inference mode such that we avoid discrepancies related to random layers like `Dropout`, `StochasticDepth`, etc.\n", + "By default, we use absolute error tolerence `1e-3` when comparing the JAX output against expected PyTorch result.\n", + "For larger modules we set the device to CPU for the JAX model to execute on in order to reduce the errors between CPU and CUDA." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2e10fb43-6ae6-47c7-81fe-5027b115b25f", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.ops.misc import Conv2dNormActivation as PyTorchConv2dNormActivation\n", + "\n", + "\n", + "torch_module = PyTorchConv2dNormActivation(32, 64, 3, 2, 1)\n", + "nnx_module = Conv2dNormActivation(32, 64, 3, 2, 1)\n", + "\n", + "t2f.copy_module(torch_module, nnx_module)\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7ac4b49a-712f-4725-a8d8-33e72b8d0b66", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.ops.misc import SqueezeExcitation as PyTorchSqueezeExcitation\n", + "\n", + "\n", + "torch_module = PyTorchSqueezeExcitation(32, 4)\n", + "nnx_module = SqueezeExcitation(32, 4)\n", + "\n", + "t2f.copy_module(torch_module, nnx_module)\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "746c8882-0001-4c97-b5cf-576dc5c87c02", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "from functools import partial\n", + "from torchvision.models.maxvit import MBConv as PyTorchMBConv\n", + "\n", + "\n", + "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", + "torch_module = PyTorchMBConv(32, 64, 4, 0.25, 2, activation_layer=nn.GELU, norm_layer=norm_layer)\n", + "\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "nnx_module = MBConv(32, 64, 4, 0.25, 2, activation_layer=nnx.gelu, norm_layer=norm_layer)\n", + "\n", + "\n", + "t2f.copy_module(torch_module, nnx_module)\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 32, 46, 46))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "249f6d28-57b6-4d36-9079-cd60964e6afc", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import RelativePositionalMultiHeadAttention as PyTorchRelativePositionalMultiHeadAttention\n", + "\n", + "\n", + "torch_module = PyTorchRelativePositionalMultiHeadAttention(64, 16, 49)\n", + "nnx_module = RelativePositionalMultiHeadAttention(64, 16, 49)\n", + "\n", + "t2f.copy_module(torch_module, nnx_module)\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 32, 49, 64), permute_torch_input=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f48fc475-c556-4101-ad2b-19480a73c6ba", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import PartitionAttentionLayer as PyTorchPartitionAttentionLayer\n", + "\n", + "\n", + "grid_size = (224, 224)\n", + "for partition_type in [\"window\", \"grid\"]:\n", + "\n", + " torch_module = PyTorchPartitionAttentionLayer(\n", + " 36, 6, 7, partition_type, grid_size=grid_size, mlp_ratio=4,\n", + " activation_layer=nn.GELU, norm_layer=nn.LayerNorm,\n", + " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", + " )\n", + "\n", + " nnx_module = PartitionAttentionLayer(\n", + " 36, 6, 7, partition_type, grid_size=grid_size, mlp_ratio=4,\n", + " activation_layer=nnx.gelu, norm_layer=nnx.LayerNorm,\n", + " attention_dropout=0.4, mlp_dropout=0.3, p_stochastic_dropout=0.2,\n", + " )\n", + "\n", + " t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + " ])\n", + "\n", + " test_modules(nnx_module, torch_module, torch.randn(4, 36, 224, 224))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7ab2de6a-9790-444b-b175-535cdb05f5d8", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import MaxVitLayer as PyTorchMaxVitLayer\n", + "\n", + "\n", + "stride = 2\n", + "\n", + "grid_size = _get_conv_output_shape((224, 224), kernel_size=3, stride=2, padding=1)\n", + "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", + "\n", + "torch_module = PyTorchMaxVitLayer(\n", + " 36, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", + " stride=stride, norm_layer=norm_layer, activation_layer=nn.GELU,\n", + " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", + " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", + " partition_size=7, grid_size=grid_size,\n", + ")\n", + "\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "nnx_module = MaxVitLayer(\n", + " 36, 36, squeeze_ratio=0.25, expansion_ratio=4,\n", + " stride=stride, norm_layer=norm_layer, activation_layer=nnx.gelu,\n", + " head_dim=6, mlp_ratio=4, mlp_dropout=0.5,\n", + " attention_dropout=0.4, p_stochastic_dropout=0.3,\n", + " partition_size=7, grid_size=grid_size,\n", + ")\n", + "\n", + "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + "])\n", + "\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 36, 224, 224), device=\"cpu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "e8e8f997-0184-4af2-82b0-ceaa580645c8", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import MaxVitBlock as PyTorchMaxVitBlock\n", + "\n", + "\n", + "norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)\n", + "torch_module = PyTorchMaxVitBlock(\n", + " 64, 128, squeeze_ratio=0.25, expansion_ratio=4,\n", + " norm_layer=norm_layer, activation_layer=nn.GELU,\n", + " head_dim=32, mlp_ratio=4, mlp_dropout=0.0, attention_dropout=0.0,\n", + " partition_size=7, input_grid_size=(56, 56),\n", + " n_layers=2,\n", + " p_stochastic=[0.13333333333333333, 0.2],\n", + ")\n", + "\n", + "norm_layer = partial(nnx.BatchNorm, epsilon=1e-3, momentum=0.99)\n", + "nnx_module = MaxVitBlock(\n", + " 64, 128, squeeze_ratio=0.25, expansion_ratio=4,\n", + " norm_layer=norm_layer, activation_layer=nnx.gelu,\n", + " head_dim=32, mlp_ratio=4, mlp_dropout=0.0, attention_dropout=0.0,\n", + " partition_size=7, input_grid_size=(56, 56),\n", + " n_layers=2,\n", + " p_stochastic=[0.13333333333333333, 0.2],\n", + ")\n", + "\n", + "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + "])\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 64, 56, 56), device=\"cpu\")" + ] + }, + { + "cell_type": "markdown", + "id": "e313819a-e93a-4201-806d-783bd1336c78", + "metadata": {}, + "source": [ + "Finally, we can check the MaxVit implementation. Note that we raised the absolute tolerence to `1e-1` when comparing JAX output logits against PyTorch expected logits." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e2af63a4-b16b-40a3-ac00-bfc23d532c82", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.models.maxvit import MaxVit as PyTorchMaxVit\n", + "\n", + "\n", + "torch.manual_seed(77)\n", + "\n", + "\n", + "torch_module = PyTorchMaxVit(\n", + " input_size=(224, 224),\n", + " stem_channels=64,\n", + " block_channels=[64, 128, 256, 512],\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=32,\n", + " stochastic_depth_prob=0.2,\n", + " partition_size=7,\n", + " num_classes=1000,\n", + ")\n", + "\n", + "nnx_module = MaxVit(\n", + " input_size=(224, 224),\n", + " stem_channels=64,\n", + " block_channels=[64, 128, 256, 512],\n", + " block_layers=[2, 2, 5, 2],\n", + " head_dim=32,\n", + " stochastic_depth_prob=0.2,\n", + " partition_size=7,\n", + " num_classes=1000,\n", + ")\n", + "\n", + "t2f.copy_module(torch_module, nnx_module, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + "])\n", + "\n", + "\n", + "test_modules(nnx_module, torch_module, torch.randn(4, 3, 224, 224), device=\"cpu\", atol=1e-1)" + ] + }, + { + "cell_type": "markdown", + "id": "0d3c4f4d-2a50-46f4-814c-42c1a423cfd0", + "metadata": {}, + "source": [ + "### Check Flax model\n", + "Let us now reuse trained weights from TorchVision's MaxViT model to check output logits and the predictions on our example image:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "7975f311-7a02-4c82-99db-b0b50fb37528", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision.models import maxvit_t as pytorch_maxvit_t, MaxVit_T_Weights\n", + "\n", + "torch_model = pytorch_maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1)\n", + "flax_model = maxvit_t()\n", + "\n", + "t2f = Torch2Flax()\n", + "t2f.copy_module(torch_model, flax_model, skip_modules=[\n", + " \"WindowPartition\", \"WindowDepartition\", \"SwapAxes\", \"StochasticDepth\",\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "922cc4b5-f181-4865-9043-fd3b56bafe43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction for the Dog:\n", + "- PyTorch model result: ['n02113023', 'Pembroke'], score: 0.7800846099853516\n", + "- Flax model result: ['n02113023', 'Pembroke'], score: 0.7799879908561707\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "from torchvision.io import read_image\n", + "\n", + "\n", + "preprocess = MaxVit_T_Weights.IMAGENET1K_V1.transforms()\n", + "\n", + "with open(\"imagenet_class_index.json\") as labels_file:\n", + " labels = json.load(labels_file)\n", + "\n", + "\n", + "dog1 = read_image(\"dog1.jpg\")\n", + "tensor = preprocess(dog1).unsqueeze(dim=0)\n", + "\n", + "torch_model.eval()\n", + "with torch.inference_mode():\n", + " torch_output = torch_model(tensor)\n", + "\n", + "torch_class_id = torch_output.argmax(dim=1).item()\n", + "\n", + "jax_array = jnp.asarray(tensor.permute(0, 2, 3, 1), device=jax.devices(\"cpu\")[0])\n", + "flax_model.eval()\n", + "flax_output = flax_model(jax_array)\n", + "\n", + "flax_class_id = torch_output.argmax(axis=1).item()\n", + "\n", + "print(\"Prediction for the Dog:\")\n", + "print(f\"- PyTorch model result: {labels[str(torch_class_id)]}, score: {torch_output.softmax(axis=1)[0, torch_class_id]}\")\n", + "print(f\"- Flax model result: {labels[str(flax_class_id)]}, score: {jax.nn.softmax(flax_output, axis=1)[0, flax_class_id]}\")\n", + "\n", + "\n", + "plt.subplot(121)\n", + "plt.title(f\"{labels[str(torch_class_id)]}\\nScore: {torch_output.softmax(dim=-1)[0, class_id]:.4f}\")\n", + "plt.imshow(dog1.permute(1, 2, 0))\n", + "\n", + "plt.subplot(122)\n", + "plt.title(f\"{labels[str(flax_class_id)]}\\nScore: {jax.nn.softmax(flax_output, axis=1)[0, flax_class_id]:.4f}\")\n", + "plt.imshow(dog1.permute(1, 2, 0))" + ] + }, + { + "cell_type": "markdown", + "id": "c77f3244", + "metadata": {}, + "source": [ + "Let's compute cosine distance between the logits:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "36801241-11cc-4850-8ea2-f0a306eaba2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array(0.99999857, dtype=float32)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expected = jnp.asarray(torch_output)\n", + "\n", + "cosine_dist = (expected * flax_output).sum() / (jnp.linalg.norm(flax_output) * jnp.linalg.norm(expected))\n", + "cosine_dist" + ] + }, + { + "cell_type": "markdown", + "id": "65e57aa6-1572-4805-9207-bc8a5f9f3ab1", + "metadata": {}, + "source": [ + "## Further reading\n", + "\n", + "- [Flax documentation: Core Examples](https://flax.readthedocs.io/en/latest/examples/core_examples.html)\n", + "- [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/JAX_porting_PyTorch_model.md b/docs/source/JAX_porting_PyTorch_model.md index f043aac..cbd0af4 100644 --- a/docs/source/JAX_porting_PyTorch_model.md +++ b/docs/source/JAX_porting_PyTorch_model.md @@ -16,8 +16,14 @@ kernelspec: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/JAX_porting_PyTorch_model.ipynb) +**Note: On Colab we recommend running this on a T4 GPU instance. On Kaggle we recommend a T4x2 or P100 instance.** + In this tutorial we will learn how to port a PyTorch model to JAX and [Flax](https://flax.readthedocs.io/en/latest/nnx_basics.html). Flax provides an API very similar to the PyTorch `torch.nn` module and porting PyTorch models is rather straightforward. To install Flax, we can simply execute the following command: `pip install -U flax treescope`. +```{code-cell} ipython3 +!pip install -Uq flax treescope +``` + Say we have a trained PyTorch computer-vision model to classify images that we would like to port to JAX. We will use [`TorchVision`](https://pytorch.org/vision/stable/index.html) to provide a [MaxVit](https://pytorch.org/vision/stable/models/maxvit.html) model trained on ImageNet (MaxViT: Multi-Axis Vision Transformer, https://arxiv.org/abs/2204.01697). First, we set up the model using TorchVision and explore briefly the model's architecture and the blocks we need to port. Next, we define equivalent blocks and the whole model using Flax. After that, we port the weights. Finally, we run some tests to ensure the correctness of the ported model. @@ -44,11 +50,10 @@ torch_model = maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1) ``` We can use `flax.nnx.display` to display the model's architecture: -```python -nnx.display(torch_model) -``` -+++ +```{code-cell} ipython3 +# nnx.display(torch_model) +``` We can see that there are four MaxViT blocks in the model and each block contains: - MaxViT layers: two layers for blocks 0, 1, 3 and five layers for the block 4 @@ -81,9 +86,18 @@ print(output.shape) # (2, 1000) We can download an image of a Pembroke Corgy dog from [TorchVision's gallery](https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true) together with [ImageNet classes dictionary](https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json): -```bash -wget "https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true" -O dog1.jpg -wget "https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json" -O imagenet_class_index.json +```{code-cell} ipython3 +%%bash +if [ -f "dog1.jpg" ]; then + echo "dog1.jpg already exists." +else + wget -nv "https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true" -O dog1.jpg +fi +if [ -f "imagenet_class_index.json" ]; then + echo "imagenet_class_index.json already exists." +else + wget -nv "https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json" -O imagenet_class_index.json +fi ``` ```{code-cell} ipython3 @@ -1615,5 +1629,5 @@ cosine_dist ## Further reading -- [Flax documentation: Core Exampels](https://flax.readthedocs.io/en/latest/examples/core_examples.html) +- [Flax documentation: Core Examples](https://flax.readthedocs.io/en/latest/examples/core_examples.html) - [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)