{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8cb2d883-6db2-4a1f-b699-ff8343df0fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging, torch, torchvision, torch.nn.functional as F, torchvision.transforms.functional as TF, matplotlib as mpl\n",
    "import fastcore.all as fc\n",
    "from matplotlib import pyplot as plt\n",
    "from functools import partial\n",
    "from torch import tensor,nn,optim, einsum\n",
    "from torch.utils.data import DataLoader, default_collate\n",
    "from torchvision.utils import make_grid\n",
    "from datasets import load_dataset,load_dataset_builder\n",
    "from miniai.datasets import *\n",
    "from miniai.learner import *\n",
    "from miniai.conv import *\n",
    "from fastcore.all import *\n",
    "from fastprogress import progress_bar\n",
    "from einops import rearrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8273fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['image.cmap'] = 'gray_r'\n",
    "logging.disable(logging.WARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33e945bc-26a4-4194-ba12-4cbb7b79e49d",
   "metadata": {},
   "source": [
    "Load a dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99edd708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b08231c14e84b5daa7000741e36d79d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = 'image','label'\n",
    "name = \"fashion_mnist\"\n",
    "dsd = load_dataset(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3c14d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "@inplace\n",
    "def transformi(b): b[x] = [TF.resize(TF.to_tensor(o), (32,32)) for o in b[x]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ee14c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([256, 1, 32, 32]), tensor([9, 0, 0, 3, 0, 2, 7, 2, 5, 5]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs = 256\n",
    "tds = dsd.with_transform(transformi)\n",
    "dls = DataLoaders.from_dd(tds, bs, num_workers=8)\n",
    "dt = dls.train\n",
    "xb,yb = next(iter(dt))\n",
    "xb.shape,yb[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cff4bef-3f20-4002-8040-eb014bfbe27b",
   "metadata": {},
   "source": [
    "Define a model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8db368e6-7702-4799-b39b-ed44f53f6ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exists(x):\n",
    "    return x is not None\n",
    "\n",
    "def default(val, d):\n",
    "    if exists(val):\n",
    "        return val\n",
    "    return d() if callable(d) else d\n",
    "\n",
    "def identity(t, *args, **kwargs):\n",
    "    return t\n",
    "\n",
    "class Residual(nn.Module):\n",
    "    def __init__(self, fn):\n",
    "        super().__init__()\n",
    "        self.fn = fn\n",
    "\n",
    "    def forward(self, x, *args, **kwargs):\n",
    "        return self.fn(x, *args, **kwargs) + x\n",
    "\n",
    "def Upsample(dim, dim_out = None):\n",
    "    return nn.Sequential(\n",
    "        nn.Upsample(scale_factor = 2, mode = 'nearest'),\n",
    "        nn.Conv2d(dim, default(dim_out, dim), 3, padding = 1)\n",
    "    )\n",
    "\n",
    "def Downsample(dim, dim_out = None):\n",
    "    return nn.Conv2d(dim, default(dim_out, dim), 4, 2, 1)\n",
    "\n",
    "class SinusoidalPositionEmbeddings(nn.Module):\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "\n",
    "    def forward(self, time):\n",
    "        device = time.device\n",
    "        half_dim = self.dim // 2\n",
    "        embeddings = math.log(10000) / (half_dim - 1)\n",
    "        embeddings = torch.exp(torch.arange(half_dim, device=device) * -embeddings)\n",
    "        embeddings = time[:, None] * embeddings[None, :]\n",
    "        embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)\n",
    "        return embeddings\n",
    "\n",
    "class Block(nn.Module):\n",
    "    def __init__(self, dim, dim_out, groups = 8):\n",
    "        super().__init__()\n",
    "        self.proj = nn.Conv2d(dim, dim_out, 3, padding = 1)\n",
    "        self.norm = nn.GroupNorm(groups, dim_out)\n",
    "        self.act = nn.SiLU()\n",
    "\n",
    "    def forward(self, x, scale_shift = None):\n",
    "        x = self.proj(x)\n",
    "        x = self.norm(x)\n",
    "\n",
    "        if exists(scale_shift):\n",
    "            scale, shift = scale_shift\n",
    "            x = x * (scale + 1) + shift\n",
    "\n",
    "        x = self.act(x)\n",
    "        return x\n",
    "\n",
    "class ResnetBlock(nn.Module):\n",
    "    \"\"\"https://arxiv.org/abs/1512.03385\"\"\"\n",
    "    \n",
    "    def __init__(self, dim, dim_out, *, time_emb_dim=None, groups=8):\n",
    "        super().__init__()\n",
    "        self.mlp = (\n",
    "            nn.Sequential(nn.SiLU(), nn.Linear(time_emb_dim, dim_out))\n",
    "            if exists(time_emb_dim)\n",
    "            else None\n",
    "        )\n",
    "\n",
    "        self.block1 = Block(dim, dim_out, groups=groups)\n",
    "        self.block2 = Block(dim_out, dim_out, groups=groups)\n",
    "        self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()\n",
    "\n",
    "    def forward(self, x, time_emb=None):\n",
    "        h = self.block1(x)\n",
    "\n",
    "        if exists(self.mlp) and exists(time_emb):\n",
    "            time_emb = self.mlp(time_emb)\n",
    "            h = rearrange(time_emb, \"b c -> b c 1 1\") + h\n",
    "\n",
    "        h = self.block2(h)\n",
    "        return h + self.res_conv(x)\n",
    "\n",
    "class Attention(nn.Module):\n",
    "    def __init__(self, dim, heads=4, dim_head=32):\n",
    "        super().__init__()\n",
    "        self.scale = dim_head**-0.5\n",
    "        self.heads = heads\n",
    "        hidden_dim = dim_head * heads\n",
    "        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)\n",
    "        self.to_out = nn.Conv2d(hidden_dim, dim, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        b, c, h, w = x.shape\n",
    "        qkv = self.to_qkv(x).chunk(3, dim=1)\n",
    "        q, k, v = map(\n",
    "            lambda t: rearrange(t, \"b (h c) x y -> b h c (x y)\", h=self.heads), qkv\n",
    "        )\n",
    "        q = q * self.scale\n",
    "\n",
    "        sim = einsum(\"b h d i, b h d j -> b h i j\", q, k)\n",
    "        sim = sim - sim.amax(dim=-1, keepdim=True).detach()\n",
    "        attn = sim.softmax(dim=-1)\n",
    "\n",
    "        out = einsum(\"b h i j, b h d j -> b h i d\", attn, v)\n",
    "        out = rearrange(out, \"b h (x y) d -> b (h d) x y\", x=h, y=w)\n",
    "        return self.to_out(out)\n",
    "\n",
    "class PreNorm(nn.Module):\n",
    "    def __init__(self, dim, fn):\n",
    "        super().__init__()\n",
    "        self.fn = fn\n",
    "        self.norm = nn.GroupNorm(1, dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.norm(x)\n",
    "        return self.fn(x)\n",
    "\n",
    "class Unet(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        dim,\n",
    "        init_dim = None,\n",
    "        out_dim = None,\n",
    "        dim_mults=(1, 2, 4, 8),\n",
    "        channels = 3,\n",
    "        resnet_block_groups = 8\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        # determine dimensions\n",
    "\n",
    "        self.channels = channels\n",
    "\n",
    "        init_dim = default(init_dim, dim)\n",
    "        self.init_conv = nn.Conv2d(channels, init_dim, 7, padding = 3)\n",
    "\n",
    "        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]\n",
    "        in_out = list(zip(dims[:-1], dims[1:]))\n",
    "\n",
    "        block_klass = partial(ResnetBlock, groups = resnet_block_groups)\n",
    "\n",
    "        # time embeddings\n",
    "\n",
    "        time_dim = dim * 4\n",
    "\n",
    "        self.time_mlp = nn.Sequential(\n",
    "            SinusoidalPositionEmbeddings(dim),\n",
    "            nn.Linear(dim, time_dim),\n",
    "            nn.GELU(),\n",
    "            nn.Linear(time_dim, time_dim)\n",
    "        )\n",
    "\n",
    "        # layers\n",
    "\n",
    "        self.downs = nn.ModuleList([])\n",
    "        self.ups = nn.ModuleList([])\n",
    "        num_resolutions = len(in_out)\n",
    "\n",
    "        for ind, (dim_in, dim_out) in enumerate(in_out):\n",
    "            is_last = ind >= (num_resolutions - 1)\n",
    "\n",
    "            self.downs.append(nn.ModuleList([\n",
    "                block_klass(dim_in, dim_in, time_emb_dim = time_dim),\n",
    "                block_klass(dim_in, dim_in, time_emb_dim = time_dim),\n",
    "                Residual(PreNorm(dim_in, Attention(dim_in))),\n",
    "                Downsample(dim_in, dim_out) if not is_last else nn.Conv2d(dim_in, dim_out, 3, padding = 1)\n",
    "            ]))\n",
    "\n",
    "        mid_dim = dims[-1]\n",
    "        self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim)\n",
    "        self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))\n",
    "        self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim)\n",
    "\n",
    "        for ind, (dim_in, dim_out) in enumerate(reversed(in_out)):\n",
    "            is_last = ind == (len(in_out) - 1)\n",
    "\n",
    "            self.ups.append(nn.ModuleList([\n",
    "                block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim),\n",
    "                block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim),\n",
    "                Residual(PreNorm(dim_out, Attention(dim_out))),\n",
    "                Upsample(dim_out, dim_in) if not is_last else  nn.Conv2d(dim_out, dim_in, 3, padding = 1)\n",
    "            ]))\n",
    "\n",
    "        default_out_dim = channels \n",
    "        self.out_dim = default(out_dim, default_out_dim)\n",
    "\n",
    "        self.final_res_block = block_klass(dim * 2, dim, time_emb_dim = time_dim)\n",
    "        self.final_conv = nn.Conv2d(dim, self.out_dim, 1)\n",
    "\n",
    "    def forward(self, x, time, x_self_cond = None):\n",
    "        x = self.init_conv(x)\n",
    "        r = x.clone()\n",
    "\n",
    "        t = self.time_mlp(time)\n",
    "\n",
    "        h = []\n",
    "\n",
    "        for block1, block2, attn, downsample in self.downs:\n",
    "            x = block1(x, t)\n",
    "            h.append(x)\n",
    "\n",
    "            x = block2(x, t)\n",
    "            x = attn(x)\n",
    "            h.append(x)\n",
    "\n",
    "            x = downsample(x)\n",
    "\n",
    "        x = self.mid_block1(x, t)\n",
    "        x = self.mid_attn(x)\n",
    "        x = self.mid_block2(x, t)\n",
    "\n",
    "        for block1, block2, attn, upsample in self.ups:\n",
    "            x = torch.cat((x, h.pop()), dim = 1)\n",
    "            x = block1(x, t)\n",
    "\n",
    "            x = torch.cat((x, h.pop()), dim = 1)\n",
    "            x = block2(x, t)\n",
    "            x = attn(x)\n",
    "\n",
    "            x = upsample(x)\n",
    "\n",
    "        x = torch.cat((x, r), dim = 1)\n",
    "\n",
    "        x = self.final_res_block(x, t)\n",
    "        return self.final_conv(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa916302-00c5-4ec0-ac69-de4dccce755f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DDPMCB(Callback):\n",
    "    order = DeviceCB.order+1\n",
    "    def __init__(self, n_steps, beta_min, beta_max):\n",
    "        store_attr()\n",
    "        try: self.device = L(self.learn.cbs).filter(f=fc.risinstance(DeviceCB))[0].device\n",
    "        except: self.device=def_device\n",
    "        self.beta = torch.linspace(self.beta_min, self.beta_max, self.n_steps).to(self.device) # variance schedule, linearly increased with timestep\n",
    "        self.alpha = 1. - self.beta \n",
    "        self.alpha_bar = torch.cumprod(self.alpha, dim=0)\n",
    "        self.sigma = torch.sqrt(self.beta)\n",
    "\n",
    "    def before_batch(self):\n",
    "        eps = torch.randn(self.learn.batch[0].shape, device=self.learn.batch[0].device)  # noise, x_T\n",
    "        x0 = self.learn.batch[0] # original images, x_0\n",
    "        batch_size = x0.shape[0]\n",
    "        t = torch.randint(0, self.n_steps, (batch_size,), device=x0.device, dtype=torch.long) # select random timesteps\n",
    "        alpha_bar_t = self.alpha_bar[t].reshape(-1, 1, 1, 1)\n",
    "        \n",
    "        xt =  torch.sqrt(alpha_bar_t)*x0 + torch.sqrt(1-alpha_bar_t)*eps #noisify the image\n",
    "        self.learn.batch = (xt, t, eps) # input to our model is noisy image and timestep, ground truth is the noise \n",
    "    \n",
    "    @torch.no_grad()\n",
    "    def sample(self, image_size, batch_size=16, channels=3):\n",
    "        shape = (batch_size, channels, image_size, image_size)\n",
    "        self.learn.model.to(self.device)\n",
    "        xt = torch.randn(shape, device=self.device)\n",
    "        with torch.profiler.profile(\n",
    "            schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),\n",
    "            on_trace_ready=torch.profiler.tensorboard_trace_handler('./log/ddpm_sampling_wo_no_grad'),\n",
    "            record_shapes=True,\n",
    "            profile_memory=True,\n",
    "            with_stack=True\n",
    "        ) as prof:\n",
    "            for t in reversed(range(self.n_steps)):\n",
    "                t_batch = torch.full((xt.shape[0],), t, device=xt.device, dtype=torch.long)\n",
    "                z = torch.randn(xt.shape, device=xt.device) if t > 0 else torch.zeros(xt.shape, device=xt.device)\n",
    "                alpha_t = self.alpha[t] # get noise level at current timestep\n",
    "                alpha_bar_t = self.alpha_bar[t]\n",
    "                sigma_t = self.sigma[t]\n",
    "                alpha_bar_t_1 = self.alpha_bar[t-1]  if t > 0 else torch.tensor(1, device=xt.device)\n",
    "                beta_bar_t = 1 - alpha_bar_t\n",
    "                beta_bar_t_1 = 1 - alpha_bar_t_1\n",
    "                x0hat = (xt - torch.sqrt(beta_bar_t) * self.learn.model(xt, t_batch))/torch.sqrt(alpha_bar_t)\n",
    "                x0hat = torch.clamp(x0hat, -1, 1)\n",
    "                xt = x0hat * torch.sqrt(alpha_bar_t_1)*(1-alpha_t)/beta_bar_t + xt * torch.sqrt(alpha_t)*beta_bar_t_1/beta_bar_t + sigma_t*z    \n",
    "                #xt = 1/torch.sqrt(alpha_t) * (xt - (1-alpha_t)/torch.sqrt(1-alpha_bar_t) * self.model(xt, t_batch))  + sigma_t*z # predict x_(t-1) in accordance to Algorithm 2 in paper\n",
    "                prof.step()\n",
    "        return xt\n",
    "        \n",
    "    def predict(self): self.learn.preds = self.learn.model(self.learn.batch[0],self.learn.batch[1])\n",
    "    def get_loss(self): self.learn.loss = self.learn.loss_func(self.learn.preds, self.learn.batch[2])\n",
    "    def backward(self): self.learn.loss.backward()\n",
    "    def step(self): self.learn.opt.step()\n",
    "    def zero_grad(self): self.learn.opt.zero_grad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc78b703-9e50-452b-903c-218b08af2391",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DDPMMetricsCB(MetricsCB):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "    def after_batch(self): self.loss.update(to_cpu(self.learn.loss), weight=len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30733743",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ProfilerCB(Callback):\n",
    "    order = 30\n",
    "    def __init__(self, **kwargs): self.prof = torch.profiler.profile(**kwargs)\n",
    "    def before_fit(self): self.prof.start()\n",
    "    def after_batch(self): self.prof.step()\n",
    "    def after_fit(self): self.prof.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "07704f2c-2c5e-4422-9134-d81b9016c1a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Unet(dim=32, channels=1, dim_mults=(1,2,4,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e64d43f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "profiler_args = {'schedule': torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),\n",
    "                'on_trace_ready': torch.profiler.tensorboard_trace_handler('./log/ddpm_training'),\n",
    "                'record_shapes': True,\n",
    "                'profile_memory': True,\n",
    "                'with_stack': True\n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b78c80e8-1bb5-4591-9021-40b2f41468be",
   "metadata": {},
   "outputs": [],
   "source": [
    "cbs = [DDPMCB(n_steps=1000, beta_min=0.0001, beta_max=0.02), DeviceCB(), ProgressCB(),DDPMMetricsCB(), ProfilerCB(**profiler_args)]\n",
    "learn = Learner(model, dls, nn.MSELoss(), lr=1e-3, cbs=cbs, opt_func=optim.Adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1fbe1213-6e5f-4879-8414-574a2d393914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "{'loss': '0.055', 'epoch': 0, 'train': True}<p>{'loss': '0.027', 'epoch': 0, 'train': False}<p>{'loss': '0.024', 'epoch': 1, 'train': True}<p>{'loss': '0.022', 'epoch': 1, 'train': False}<p>{'loss': '0.021', 'epoch': 2, 'train': True}<p>{'loss': '0.021', 'epoch': 2, 'train': False}<p>{'loss': '0.019', 'epoch': 3, 'train': True}<p>{'loss': '0.019', 'epoch': 3, 'train': False}<p>{'loss': '0.018', 'epoch': 4, 'train': True}<p>{'loss': '0.018', 'epoch': 4, 'train': False}"
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  },
  {
   "cell_type": "markdown",
   "id": "3a335290-ad68-4c18-8bc5-85b4753dceda",
   "metadata": {},
   "source": [
    "Viewing the predictions on images with increasing noise levels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e98b94f-38c5-4474-9e49-721201f2a188",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 79.35 GiB total capacity; 49.87 GiB already allocated; 197.69 MiB free; 50.09 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [12], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m batch_size \u001b[39m=\u001b[39m \u001b[39m16\u001b[39m\n\u001b[0;32m----> 2\u001b[0m samples \u001b[39m=\u001b[39m learn\u001b[39m.\u001b[39;49mcbs[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49msample(\u001b[39m32\u001b[39;49m, batch_size\u001b[39m=\u001b[39;49mbatch_size,channels\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)\n",
      "Cell \u001b[0;32mIn [8], line 43\u001b[0m, in \u001b[0;36mDDPMCB.sample\u001b[0;34m(self, image_size, batch_size, channels)\u001b[0m\n\u001b[1;32m     41\u001b[0m beta_bar_t \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m \u001b[39m-\u001b[39m alpha_bar_t\n\u001b[1;32m     42\u001b[0m beta_bar_t_1 \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m \u001b[39m-\u001b[39m alpha_bar_t_1\n\u001b[0;32m---> 43\u001b[0m x0hat \u001b[39m=\u001b[39m (xt \u001b[39m-\u001b[39m torch\u001b[39m.\u001b[39msqrt(beta_bar_t) \u001b[39m*\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlearn\u001b[39m.\u001b[39;49mmodel(xt, t_batch))\u001b[39m/\u001b[39mtorch\u001b[39m.\u001b[39msqrt(alpha_bar_t)\n\u001b[1;32m     44\u001b[0m x0hat \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mclamp(x0hat, \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[1;32m     45\u001b[0m xt \u001b[39m=\u001b[39m x0hat \u001b[39m*\u001b[39m torch\u001b[39m.\u001b[39msqrt(alpha_bar_t_1)\u001b[39m*\u001b[39m(\u001b[39m1\u001b[39m\u001b[39m-\u001b[39malpha_t)\u001b[39m/\u001b[39mbeta_bar_t \u001b[39m+\u001b[39m xt \u001b[39m*\u001b[39m torch\u001b[39m.\u001b[39msqrt(alpha_t)\u001b[39m*\u001b[39mbeta_bar_t_1\u001b[39m/\u001b[39mbeta_bar_t \u001b[39m+\u001b[39m sigma_t\u001b[39m*\u001b[39mz    \n",
      "File \u001b[0;32m~/anaconda3/envs/course22p2/lib/python3.10/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "Cell \u001b[0;32mIn [6], line 206\u001b[0m, in \u001b[0;36mUnet.forward\u001b[0;34m(self, x, time, x_self_cond)\u001b[0m\n\u001b[1;32m    203\u001b[0m h\u001b[39m.\u001b[39mappend(x)\n\u001b[1;32m    205\u001b[0m x \u001b[39m=\u001b[39m block2(x, t)\n\u001b[0;32m--> 206\u001b[0m x \u001b[39m=\u001b[39m attn(x)\n\u001b[1;32m    207\u001b[0m h\u001b[39m.\u001b[39mappend(x)\n\u001b[1;32m    209\u001b[0m x \u001b[39m=\u001b[39m downsample(x)\n",
      "File \u001b[0;32m~/anaconda3/envs/course22p2/lib/python3.10/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "Cell \u001b[0;32mIn [6], line 18\u001b[0m, in \u001b[0;36mResidual.forward\u001b[0;34m(self, x, *args, **kwargs)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 18\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfn(x, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs) \u001b[39m+\u001b[39m x\n",
      "File \u001b[0;32m~/anaconda3/envs/course22p2/lib/python3.10/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "Cell \u001b[0;32mIn [6], line 119\u001b[0m, in \u001b[0;36mPreNorm.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m    118\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnorm(x)\n\u001b[0;32m--> 119\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfn(x)\n",
      "File \u001b[0;32m~/anaconda3/envs/course22p2/lib/python3.10/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "Cell \u001b[0;32mIn [6], line 103\u001b[0m, in \u001b[0;36mAttention.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     98\u001b[0m q, k, v \u001b[39m=\u001b[39m \u001b[39mmap\u001b[39m(\n\u001b[1;32m     99\u001b[0m     \u001b[39mlambda\u001b[39;00m t: rearrange(t, \u001b[39m\"\u001b[39m\u001b[39mb (h c) x y -> b h c (x y)\u001b[39m\u001b[39m\"\u001b[39m, h\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mheads), qkv\n\u001b[1;32m    100\u001b[0m )\n\u001b[1;32m    101\u001b[0m q \u001b[39m=\u001b[39m q \u001b[39m*\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscale\n\u001b[0;32m--> 103\u001b[0m sim \u001b[39m=\u001b[39m einsum(\u001b[39m\"\u001b[39;49m\u001b[39mb h d i, b h d j -> b h i j\u001b[39;49m\u001b[39m\"\u001b[39;49m, q, k)\n\u001b[1;32m    104\u001b[0m sim \u001b[39m=\u001b[39m sim \u001b[39m-\u001b[39m sim\u001b[39m.\u001b[39mamax(dim\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, keepdim\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\u001b[39m.\u001b[39mdetach()\n\u001b[1;32m    105\u001b[0m attn \u001b[39m=\u001b[39m sim\u001b[39m.\u001b[39msoftmax(dim\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/course22p2/lib/python3.10/site-packages/torch/functional.py:360\u001b[0m, in \u001b[0;36meinsum\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    356\u001b[0m     \u001b[39m# recurse incase operands contains value that has torch function\u001b[39;00m\n\u001b[1;32m    357\u001b[0m     \u001b[39m# in the original implementation this line is omitted\u001b[39;00m\n\u001b[1;32m    358\u001b[0m     \u001b[39mreturn\u001b[39;00m einsum(equation, \u001b[39m*\u001b[39m_operands)\n\u001b[0;32m--> 360\u001b[0m \u001b[39mreturn\u001b[39;00m _VF\u001b[39m.\u001b[39;49meinsum(equation, operands)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 79.35 GiB total capacity; 49.87 GiB already allocated; 197.69 MiB free; 50.09 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
     ]
    }
   ],
   "source": [
    "batch_size = 16\n",
    "samples = learn.cbs[0].sample(32, batch_size=batch_size,channels=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "680058ba-9cc8-4327-b2de-c5a96f058c48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 1, 32, 32])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c18b3adb-cd33-42f0-b058-5496fb5e3508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_images(-1*samples, figsize=(5,5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "course22p2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "0652906208a1dcd94e9ea7623081d93dd4d2f6cda070da042189d63fdc8dadfe"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}