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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "07b2bef9-bbaf-41b8-b960-7ac373ff3e8d",
"metadata": {},
"outputs": [],
"source": [
"!pip install diffusers==0.14.0 transformers==4.26.1 accelerate==0.16.0 safetensors==0.3.1 matplotlib"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ebecb44-f796-4c76-8385-888a2f46fd6a",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"NEURON_FUSE_SOFTMAX\"] = \"1\"\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch_neuronx\n",
"import numpy as np\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import image as mpimg\n",
"import time\n",
"import copy\n",
"\n",
"from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler\n",
"from diffusers.models.unet_2d_condition import UNet2DConditionOutput\n",
"from diffusers.models.cross_attention import CrossAttention\n",
"\n",
"# Define datatype\n",
"DTYPE = torch.float32"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9950025f-877a-4c11-b30e-9c32f0825e94",
"metadata": {},
"outputs": [],
"source": [
"class UNetWrap(nn.Module):\n",
" def __init__(self, unet):\n",
" super().__init__()\n",
" self.unet = unet\n",
"\n",
" def forward(self, sample, timestep, encoder_hidden_states, cross_attention_kwargs=None):\n",
" out_tuple = self.unet(sample, timestep, encoder_hidden_states, return_dict=False)\n",
" return out_tuple\n",
"\n",
"class NeuronUNet(nn.Module):\n",
" def __init__(self, unetwrap):\n",
" super().__init__()\n",
" self.unetwrap = unetwrap\n",
" self.config = unetwrap.unet.config\n",
" self.in_channels = unetwrap.unet.in_channels\n",
" self.device = unetwrap.unet.device\n",
"\n",
" def forward(self, sample, timestep, encoder_hidden_states, cross_attention_kwargs=None):\n",
" sample = self.unetwrap(sample, timestep.to(dtype=DTYPE).expand((sample.shape[0],)), encoder_hidden_states)[0]\n",
" return UNet2DConditionOutput(sample=sample)\n",
"\n",
"class NeuronTextEncoder(nn.Module):\n",
" def __init__(self, text_encoder):\n",
" super().__init__()\n",
" self.neuron_text_encoder = text_encoder\n",
" self.config = text_encoder.config\n",
" self.dtype = text_encoder.dtype\n",
" self.device = text_encoder.device\n",
"\n",
" def forward(self, emb, attention_mask = None):\n",
" return [self.neuron_text_encoder(emb)['last_hidden_state']]\n",
"# Optimized attention\n",
"def get_attention_scores(self, query, key, attn_mask): \n",
" dtype = query.dtype\n",
"\n",
" if self.upcast_attention:\n",
" query = query.float()\n",
" key = key.float()\n",
"\n",
" # Check for square matmuls\n",
" if(query.size() == key.size()):\n",
" attention_scores = custom_badbmm(\n",
" key,\n",
" query.transpose(-1, -2)\n",
" )\n",
"\n",
" if self.upcast_softmax:\n",
" attention_scores = attention_scores.float()\n",
"\n",
" attention_probs = attention_scores.softmax(dim=1).permute(0,2,1)\n",
" attention_probs = attention_probs.to(dtype)\n",
"\n",
" else:\n",
" attention_scores = custom_badbmm(\n",
" query,\n",
" key.transpose(-1, -2)\n",
" )\n",
"\n",
" if self.upcast_softmax:\n",
" attention_scores = attention_scores.float()\n",
"\n",
" attention_probs = attention_scores.softmax(dim=-1)\n",
" attention_probs = attention_probs.to(dtype)\n",
" \n",
" return attention_probs\n",
"\n",
"# In the original badbmm the bias is all zeros, so only apply scale\n",
"def custom_badbmm(a, b):\n",
" bmm = torch.bmm(a, b)\n",
" scaled = bmm * 0.125\n",
" return scaled"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ffc64d14-f48c-488c-b60a-36e3ebfdab83",
"metadata": {},
"outputs": [],
"source": [
"model_id = \"stabilityai/stable-diffusion-2-1\"\n",
"text_encoder_filename = 'text_encoder.pt'\n",
"decoder_filename = 'vae_decoder.pt'\n",
"unet_filename = 'unet.pt'\n",
"post_quant_conv_filename = 'vae_post_quant_conv.pt'\n",
"\n",
"pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=DTYPE)\n",
"pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n",
"\n",
"# Load the compiled UNet onto two neuron cores.\n",
"pipe.unet = NeuronUNet(UNetWrap(pipe.unet))\n",
"device_ids = [0,1]\n",
"pipe.unet.unetwrap = torch_neuronx.DataParallel(torch.jit.load(unet_filename), device_ids, set_dynamic_batching=False)\n",
"\n",
"# Load other compiled models onto a single neuron core.\n",
"pipe.text_encoder = NeuronTextEncoder(pipe.text_encoder)\n",
"pipe.text_encoder.neuron_text_encoder = torch.jit.load(text_encoder_filename)\n",
"pipe.vae.decoder = torch.jit.load(decoder_filename)\n",
"pipe.vae.post_quant_conv = torch.jit.load(post_quant_conv_filename)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (torch-neuronx)",
"language": "python",
"name": "aws_neuron_venv_pytorch"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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