pre converting params to torch allows mega to run in standard colab runtime

README.md

@@ -5,7 +5,7 @@
 
 This is a minimal implementation of Boris Dayma's [DALL·E Mini](https://github.com/borisdayma/dalle-mini).  It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch.  To run the torch model, the only third party dependencies are numpy and torch.  Flax is used to convert the weights (which can be saved with `torch.save` once the model is loaded), and wandb is only used to download the models.
 
-It currently takes **7.3 seconds** to generate an avocado armchair with DALL·E Mega in PyTorch on Colab (with nonexpendable model in high RAM runtime)
+It currently takes **7.3 seconds** to generate an avocado armchair with DALL·E Mega in PyTorch on Colab
 
 ### Setup
 

min_dalle/load_params.py

@@ -1,6 +1,5 @@
 import os
 import numpy
-from copy import deepcopy
 from typing import Dict
 from flax.traverse_util import flatten_dict
 from flax.serialization import msgpack_restore
@@ -105,4 +104,29 @@ def convert_dalle_bart_torch_from_flax_params(
 
     P['embed_tokens.weight'] = P.pop('embed_tokens.embedding')
     P['embed_positions.weight'] = P.pop('embed_positions.embedding')
-    return P
+    return P
+
+
+def convert_and_save_mega_torch_params(is_mega: bool, model_path: str):
+    print("converting params to torch")
+    layer_count = 24 if is_mega else 12
+    flax_params = load_dalle_bart_flax_params(model_path)
+    encoder_params = convert_dalle_bart_torch_from_flax_params(
+        flax_params['encoder'],
+        layer_count=layer_count,
+        is_encoder=True
+    )
+    decoder_params = convert_dalle_bart_torch_from_flax_params(
+        flax_params['decoder'],
+        layer_count=layer_count,
+        is_encoder=False
+    )
+
+    for i in decoder_params:
+        decoder_params[i] = decoder_params[i].to(torch.float16)
+    
+    for i in encoder_params:
+        encoder_params[i] = encoder_params[i].to(torch.float16)
+
+    torch.save(encoder_params, os.path.join(model_path, 'encoder.pt'))
+    torch.save(decoder_params, os.path.join(model_path, 'decoder.pt'))

min_dalle/min_dalle_base.py

@@ -10,12 +10,12 @@ class MinDalleBase:
     def __init__(self, is_mega: bool):
         self.is_mega = is_mega
         model_name = 'dalle_bart_{}'.format('mega' if is_mega else 'mini')
-        model_path = os.path.join('pretrained', model_name)
+        self.model_path = os.path.join('pretrained', model_name)
 
-        print("reading files from {}".format(model_path))
-        config_path = os.path.join(model_path, 'config.json')
-        vocab_path = os.path.join(model_path, 'vocab.json')
-        merges_path = os.path.join(model_path, 'merges.txt')
+        print("reading files from {}".format(self.model_path))
+        config_path = os.path.join(self.model_path, 'config.json')
+        vocab_path = os.path.join(self.model_path, 'vocab.json')
+        merges_path = os.path.join(self.model_path, 'merges.txt')
 
         with open(config_path, 'r', encoding='utf8') as f: 
             self.config = json.load(f)
@@ -24,7 +24,6 @@ class MinDalleBase:
         with open(merges_path, 'r', encoding='utf8') as f:
             merges = f.read().split("\n")[1:-1]
             
-        self.model_params = load_dalle_bart_flax_params(model_path)
         self.tokenizer = TextTokenizer(vocab, merges)
 
 

min_dalle/min_dalle_flax.py

@@ -7,12 +7,15 @@ from .min_dalle_base import MinDalleBase
 from .models.dalle_bart_encoder_flax import DalleBartEncoderFlax
 from .models.dalle_bart_decoder_flax import DalleBartDecoderFlax
 
+from .load_params import load_dalle_bart_flax_params
+
 
 class MinDalleFlax(MinDalleBase):
     def __init__(self, is_mega: bool, is_reusable: bool = True):
         super().__init__(is_mega)
         self.is_reusable = is_reusable
         print("initializing MinDalleFlax")
+        self.model_params = load_dalle_bart_flax_params(self.model_path)
         if is_reusable:
             self.init_encoder()
             self.init_decoder()

min_dalle/min_dalle_torch.py

@@ -6,7 +6,10 @@ import torch
 torch.set_grad_enabled(False)
 torch.set_num_threads(os.cpu_count())
 
-from .load_params import convert_dalle_bart_torch_from_flax_params
+from .load_params import (
+    convert_and_save_mega_torch_params,
+    load_dalle_bart_flax_params
+)
 from .min_dalle_base import MinDalleBase
 from .models.dalle_bart_encoder_torch import DalleBartEncoderTorch
 from .models.dalle_bart_decoder_torch import DalleBartDecoderTorch
@@ -19,10 +22,22 @@ class MinDalleTorch(MinDalleBase):
         is_reusable: bool = True,
         token_count: int = 256
     ):
+        print("initializing MinDalleTorch")
         super().__init__(is_mega)
         self.is_reusable = is_reusable
         self.token_count = token_count
-        print("initializing MinDalleTorch")
+
+        if not is_mega:
+            self.model_params = load_dalle_bart_flax_params(self.model_path)
+    
+        self.encoder_params_path = os.path.join(self.model_path, 'encoder.pt')
+        self.decoder_params_path = os.path.join(self.model_path, 'decoder.pt')
+
+        is_converted = os.path.exists(self.encoder_params_path)
+        is_converted &= os.path.exists(self.decoder_params_path)
+        if not is_converted:
+            convert_and_save_mega_torch_params(is_mega, self.model_path)
+
         if is_reusable:
             self.init_encoder()
             self.init_decoder()
@@ -39,11 +54,7 @@ class MinDalleTorch(MinDalleBase):
             text_token_count = self.config['max_text_length'],
             glu_embed_count = self.config['encoder_ffn_dim']
         )
-        params = convert_dalle_bart_torch_from_flax_params(
-            self.model_params.pop('encoder'), 
-            layer_count=self.config['encoder_layers'], 
-            is_encoder=True
-        )
+        params = torch.load(self.encoder_params_path)
         self.encoder.load_state_dict(params, strict=False)
         del params
         if torch.cuda.is_available(): self.encoder = self.encoder.cuda()
@@ -63,11 +74,7 @@ class MinDalleTorch(MinDalleBase):
             start_token = self.config['decoder_start_token_id'],
             is_verbose = True
         )
-        params = convert_dalle_bart_torch_from_flax_params(
-            self.model_params.pop('decoder'), 
-            layer_count=self.config['decoder_layers'],
-            is_encoder=False
-        )
+        params = torch.load(self.decoder_params_path)
         self.decoder.load_state_dict(params, strict=False)
         del params
         if torch.cuda.is_available(): self.decoder = self.decoder.cuda()