diff --git a/README.md b/README.md
index 67eb9a5..8d4a545 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,9 @@ PyTorch implementation of L2L execution algorithm from paper [Training Large Neu
You need to define a torch model where all layers are specified in ModuleList.
-for example
+See [examples folder](examples)
+
+### Basic usage
```python
import torch
@@ -55,6 +57,8 @@ class M(nn.Module):
return x
+
+model = M(depth=5, dim=40).train() # on CPU
```
Then, you can use the L2L wrapper over this model.
@@ -62,8 +66,6 @@ Then, you can use the L2L wrapper over this model.
```python
from layer_to_layer_pytorch.l2l import Layer2Layer
-model = M(depth=5, dim=40).train() # on CPU
-
l2l_model = Layer2Layer(
model,
layers_attr="layers", # attribute with ModuleList
@@ -81,23 +83,46 @@ x = torch.rand(1_000, 40) # on CPU
y = torch.rand(1_000, 40) # on CPU
losses = []
-loss_fn = nn.MSELoss(reduction="sum") # since L2L calcs average losses itself, we just need to save them
+criterion = nn.MSELoss()
-optimizer = optim.AdamW(l2l_model.main_model.parameters(), lr=0.001) # optimizer works with the main model on CPU
+optimizer = optim.AdamW(l2l_model.main_params) # optimizer works with the main model on CPU
-for i in trange(5000):
+for i in trange(2000):
l2l_model.zero_grad()
- l2l_model.forward(x)
+ _ = l2l_model.forward(x)
- loss_value = l2l_model.backward(x, y, loss_fn)
+ loss_value: float = l2l_model.compute_loss(y, criterion)
if i % 50 == 0:
- tqdm.write(f"[{i}] loss = {loss_value.item()}")
- losses.append(loss_value.item())
+ tqdm.write(f"[{i}] loss = {loss_value}")
+ losses.append(loss_value)
+
+ l2l_model.backward()
optimizer.step()
+ l2l_model.update_main_model_params() # Sync params with CPU
+```
+
+### FP-16 usage
+
+Cross-mixes-precision available in init params
+
+```python
+from layer_to_layer_pytorch.l2l import Layer2Layer
+
+l2l_model = Layer2Layer(
+ model,
+ layers_attr="layers",
+ microbatch_size=100,
+
+ # fp-16
+ mixed_precision=True,
+ loss_scale = 128.0
+)
```
+And then train the same way 😉
+
## Installation
```bash
diff --git a/examples/train.ipynb b/examples/train.ipynb
new file mode 100644
index 0000000..0071ac0
--- /dev/null
+++ b/examples/train.ipynb
@@ -0,0 +1,8139 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# !pip install layer-to-layer-pytorch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Optional\n",
+ "\n",
+ "import torch\n",
+ "from torch import nn, optim\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "from tqdm.auto import tqdm, trange\n",
+ "from tqdm.contrib import tenumerate, tzip\n",
+ "\n",
+ "from layer_to_layer_pytorch import Layer2Layer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "class M(nn.Module):\n",
+ " def __init__(self, depth: int, dim: int, hidden_dim: Optional[int] = None):\n",
+ " super().__init__()\n",
+ " hidden_dim = hidden_dim or dim\n",
+ " self.layers = nn.ModuleList(\n",
+ " [\n",
+ " nn.Sequential(\n",
+ " nn.Linear(dim, hidden_dim),\n",
+ " nn.BatchNorm1d(hidden_dim),\n",
+ " nn.LeakyReLU(),\n",
+ " )\n",
+ " ]\n",
+ " + [\n",
+ " nn.Sequential(\n",
+ " nn.Linear(hidden_dim, hidden_dim),\n",
+ " nn.BatchNorm1d(hidden_dim),\n",
+ " nn.LeakyReLU(),\n",
+ " )\n",
+ " for i in range(depth)\n",
+ " ]\n",
+ " + [nn.Linear(hidden_dim, dim), nn.Sigmoid()]\n",
+ " )\n",
+ " \n",
+ " def __len__(self) -> int:\n",
+ " return len(self.layers)\n",
+ "\n",
+ " def forward(self, batch: torch.Tensor) -> torch.Tensor:\n",
+ " x = batch\n",
+ " for l in self.layers:\n",
+ " x = l(x)\n",
+ "\n",
+ " return x\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = M(depth=5, dim=40, hidden_dim=100)\n",
+ "\n",
+ "l2l_model = Layer2Layer(model, layers_attr=\"layers\", microbatch_size=100, verbose=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = torch.rand(1_000, 40)\n",
+ "y = torch.rand(1_000, 40)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[layers.0.0.weight]: True\n",
+ "[layers.0.0.bias]: True\n",
+ "[layers.0.1.weight]: True\n",
+ "[layers.0.1.bias]: True\n",
+ "[layers.1.0.weight]: True\n",
+ "[layers.1.0.bias]: True\n",
+ "[layers.1.1.weight]: True\n",
+ "[layers.1.1.bias]: True\n",
+ "[layers.2.0.weight]: True\n",
+ "[layers.2.0.bias]: True\n",
+ "[layers.2.1.weight]: True\n",
+ "[layers.2.1.bias]: True\n",
+ "[layers.3.0.weight]: True\n",
+ "[layers.3.0.bias]: True\n",
+ "[layers.3.1.weight]: True\n",
+ "[layers.3.1.bias]: True\n",
+ "[layers.4.0.weight]: True\n",
+ "[layers.4.0.bias]: True\n",
+ "[layers.4.1.weight]: True\n",
+ "[layers.4.1.bias]: True\n",
+ "[layers.5.0.weight]: True\n",
+ "[layers.5.0.bias]: True\n",
+ "[layers.5.1.weight]: True\n",
+ "[layers.5.1.bias]: True\n",
+ "[layers.6.weight]: True\n",
+ "[layers.6.bias]: True\n"
+ ]
+ }
+ ],
+ "source": [
+ "for name, param in l2l_model.model.named_parameters():\n",
+ " print(f\"[{name}]: {param.requires_grad}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8427a73755ee488a9eabea522dd89327",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=2000.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0] loss = 0.09251299779862165\n",
+ "[50] loss = 0.04679687600582838\n",
+ "[100] loss = 0.024244420928880572\n",
+ "[150] loss = 0.012858417234383523\n",
+ "[200] loss = 0.007469074393156916\n",
+ "[250] loss = 0.004840212088311091\n",
+ "[300] loss = 0.0034365870524197817\n",
+ "[350] loss = 0.0025724523584358394\n",
+ "[400] loss = 0.0020228293433319777\n",
+ "[450] loss = 0.001649867874220945\n",
+ "[500] loss = 0.001370955680613406\n",
+ "[550] loss = 0.001156660437118262\n",
+ "[600] loss = 0.0010158663790207356\n",
+ "[650] loss = 0.0008867938231560402\n",
+ "[700] loss = 0.0008164640748873353\n",
+ "[750] loss = 0.0007635831498191692\n",
+ "[800] loss = 0.0006904192341607995\n",
+ "[850] loss = 0.0006020103282935452\n",
+ "[900] loss = 0.000585917350690579\n",
+ "[950] loss = 0.0005711435842385981\n",
+ "[1000] loss = 0.0005517072750080843\n",
+ "[1050] loss = 0.00042251174090779386\n",
+ "[1100] loss = 0.00043898035073652864\n",
+ "[1150] loss = 0.000410193380957935\n",
+ "[1200] loss = 0.00040243057810585015\n",
+ "[1250] loss = 0.00041606493323342875\n",
+ "[1300] loss = 0.0003635622069850797\n",
+ "[1350] loss = 0.0003410119952604873\n",
+ "[1400] loss = 0.000347757559211459\n",
+ "[1450] loss = 0.00033284839992120396\n",
+ "[1500] loss = 0.00028941587333974894\n",
+ "[1550] loss = 0.00031473712442675605\n",
+ "[1600] loss = 0.00026775159130920656\n",
+ "[1650] loss = 0.0002649552479851991\n",
+ "[1700] loss = 0.00023840018729970325\n",
+ "[1750] loss = 0.000247815372858895\n",
+ "[1800] loss = 0.0002410621236776933\n",
+ "[1850] loss = 0.0002461158837832045\n",
+ "[1900] loss = 0.00026135327789234\n",
+ "[1950] loss = 0.00024941059928096365\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "losses = []\n",
+ "criterion = nn.MSELoss()\n",
+ "optimizer = optim.AdamW(l2l_model.main_params)\n",
+ "\n",
+ "for i in trange(2000):\n",
+ " l2l_model.zero_grad()\n",
+ " _ = l2l_model.forward(x)\n",
+ " loss_value = l2l_model.compute_loss(y, criterion)\n",
+ " \n",
+ " if i % 50 == 0:\n",
+ " tqdm.write(f\"[{i}] loss = {loss_value}\")\n",
+ " losses.append(loss_value)\n",
+ "\n",
+ " l2l_model.backward()\n",
+ " optimizer.step()\n",
+ " l2l_model.update_main_model_params() # Sync params with CPU\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "type": "scatter",
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