allow for final l2norm clamping of the sampled image embed

* Added autoresume after crash functionality to the trackers

* Updated documentation

* Clarified what goes in the autorestart object

* Fixed style issues

Unraveled conditional block

Chnaged to using helper function to get step count

.github/workflows/ci.yml

@@ -0,0 +1,33 @@
+name: Continuous integration
+
+on:
+  push:
+    branches:
+    - main
+  pull_request:
+    branches:
+    - main
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.8]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install
+      run: |
+        python3 -m venv .env
+        source .env/bin/activate
+        make install
+    - name: Tests
+      run: |
+        source .env/bin/activate
+        make test
+

.gitignore

@@ -136,3 +136,5 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+.tracker_data
+*.pth

Makefile

@@ -0,0 +1,6 @@
+install:
+	pip install -U pip
+	pip install -e .
+
+test:
+	CUDA_VISIBLE_DEVICES= python train_decoder.py --config_file configs/train_decoder_config.test.json

README.md

@@ -44,6 +44,7 @@ This library would not have gotten to this working state without the help of
 - <a href="https://github.com/krish240574">Kumar</a> for working on the initial diffusion training script
 - <a href="https://github.com/rom1504">Romain</a> for the pull request reviews and project management
 - <a href="https://github.com/Ciaohe">He Cao</a> and <a href="https://github.com/xiankgx">xiankgx</a> for the Q&A and for identifying of critical bugs
+- <a href="https://github.com/marunine">Marunine</a> for identifying issues with resizing of the low resolution conditioner, when training the upsampler, in addition to various other bug fixes
 - <a href="https://github.com/crowsonkb">Katherine</a> for her advice
 - <a href="https://stability.ai/">Stability AI</a> for the generous sponsorship
 - <a href="https://huggingface.co">🤗 Huggingface</a> and in particular <a href="https://github.com/sgugger">Sylvain</a> for the <a href="https://github.com/huggingface/accelerate">Accelerate</a> library
@@ -354,7 +355,8 @@ prior_network = DiffusionPriorNetwork(
 diffusion_prior = DiffusionPrior(
     net = prior_network,
     clip = clip,
-    timesteps = 100,
+    timesteps = 1000,
+    sample_timesteps = 64,
     cond_drop_prob = 0.2
 ).cuda()
 
@@ -581,7 +583,9 @@ unet1 = Unet(
     image_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
-    dim_mults=(1, 2, 4, 8)
+    dim_mults=(1, 2, 4, 8),
+    text_embed_dim = 512,
+    cond_on_text_encodings = True  # set to True for any unets that need to be conditioned on text encodings (ex. first unet in cascade)
 ).cuda()
 
 unet2 = Unet(
@@ -596,14 +600,14 @@ decoder = Decoder(
     unet = (unet1, unet2),
     image_sizes = (128, 256),
     clip = clip,
-    timesteps = 100,
+    timesteps = 1000,
+    sample_timesteps = (250, 27),
     image_cond_drop_prob = 0.1,
-    text_cond_drop_prob = 0.5,
-    condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 for unet_number in (1, 2):
-    loss = decoder(images, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
+    loss = decoder(images, text = text, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
     loss.backward()
 
 # do above for many steps

configs/README.md

@@ -30,6 +30,7 @@ Defines the configuration options for the decoder model. The unets defined above
 | `loss_type` | No | `l2` | The loss function. Options are `l1`, `huber`, or `l2`. |
 | `beta_schedule` | No | `cosine` | The noising schedule. Options are `cosine`, `linear`, `quadratic`, `jsd`, or `sigmoid`. |
 | `learned_variance` | No | `True` | Whether to learn the variance. |
+| `clip` | No | `None` | The clip model to use if embeddings are being generated on the fly. Takes keys `make` and `model` with defaults `openai` and `ViT-L/14`. |
 
 Any parameter from the `Decoder` constructor can also be given here.
 
@@ -39,7 +40,8 @@ Settings for creation of the dataloaders.
 | Option | Required | Default | Description |
 | ------ | -------- | ------- | ----------- |
 | `webdataset_base_url` | Yes | N/A | The url of a shard in the webdataset with the shard replaced with `{}`[^1]. |
-| `embeddings_url` | No | N/A | The url of the folder containing embeddings shards. Not required if embeddings are in webdataset. |
+| `img_embeddings_url` | No | `None` | The url of the folder containing image embeddings shards. Not required if embeddings are in webdataset or clip is being used. |
+| `text_embeddings_url` | No | `None` | The url of the folder containing text embeddings shards. Not required if embeddings are in webdataset or clip is being used. |
 | `num_workers` | No | `4` | The number of workers used in the dataloader. |
 | `batch_size` | No | `64` | The batch size. |
 | `start_shard` | No | `0` | Defines the start of the shard range the dataset will recall. |
@@ -106,6 +108,13 @@ Tracking is split up into three sections:
 
 **Logging:**
 
+All loggers have the following keys:
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `log_type` | Yes | N/A | The type of logger class to use. |
+| `resume` | No | `False` | For loggers that have the option to resume an old run, resume it using maually input parameters. |
+| `auto_resume` | No | `False` | If true, the logger will attempt to resume an old run using parameters from that previous run. |
+
 If using `console` there is no further configuration than setting `log_type` to `console`.
 | Option | Required | Default | Description |
 | ------ | -------- | ------- | ----------- |
@@ -119,10 +128,15 @@ If using `wandb`
 | `wandb_project` | Yes | N/A | The wandb project save the run to. |
 | `wandb_run_name` | No | `None` | The wandb run name. |
 | `wandb_run_id` | No | `None` | The wandb run id. Used if resuming an old run. |
-| `wandb_resume` | No | `False` | Whether to resume an old run. |
 
 **Loading:**
 
+All loaders have the following keys:
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `load_from` | Yes | N/A | The type of loader class to use. |
+| `only_auto_resume` | No | `False` | If true, the loader will only load the model if the run is being auto resumed. |
+
 If using `local`
 | Option | Required | Default | Description |
 | ------ | -------- | ------- | ----------- |

configs/train_decoder_config.example.json

@@ -20,7 +20,7 @@
     },
     "data": {
         "webdataset_base_url": "pipe:s3cmd get s3://bucket/path/{}.tar -",
-        "embeddings_url": "s3://bucket/embeddings/path/",
+        "img_embeddings_url": "s3://bucket/img_embeddings/path/",
         "num_workers": 4,
         "batch_size": 64,
         "start_shard": 0,

configs/train_decoder_config.test.json

@@ -0,0 +1,102 @@
+{
+    "decoder": {
+        "unets": [
+            {
+                "dim": 16,
+                "image_embed_dim": 768,
+                "cond_dim": 16,
+                "channels": 3,
+                "dim_mults": [1, 2, 4, 8],
+                "attn_dim_head": 16,
+                "attn_heads": 4,
+		"self_attn": [false, true, true, true]
+            }
+        ],
+        "clip": {
+            "make": "openai",
+            "model": "ViT-L/14"
+        },
+
+	"timesteps": 10,
+        "image_sizes": [64],
+        "channels": 3,
+        "loss_type": "l2",
+        "beta_schedule": ["cosine"],
+        "learned_variance": true
+    },
+    "data": {
+        "webdataset_base_url": "test_data/{}.tar",
+        "num_workers": 4,
+        "batch_size": 4,
+        "start_shard": 0,
+        "end_shard": 9,
+        "shard_width": 1,
+        "index_width": 1,
+        "splits": {
+            "train": 0.75,
+            "val": 0.15,
+            "test": 0.1
+        },
+        "shuffle_train": false,
+        "resample_train": true,
+        "preprocessing": {
+            "RandomResizedCrop": {
+                "size": [224, 224],
+                "scale": [0.75, 1.0],
+                "ratio": [1.0, 1.0]
+            },
+            "ToTensor": true
+        }
+    },
+    "train": {
+        "epochs": 1,
+        "lr": 1e-16,
+        "wd": 0.01,
+        "max_grad_norm": 0.5,
+        "save_every_n_samples": 100,
+        "n_sample_images": 1,
+        "device": "cpu",
+        "epoch_samples": 50,
+        "validation_samples": 5,
+        "use_ema": true,
+        "ema_beta": 0.99,
+        "amp": false,
+        "save_all": false,
+        "save_latest": true,
+        "save_best": true,
+        "unet_training_mask": [true]
+    },
+    "evaluate": {
+        "n_evaluation_samples": 2,
+        "FID": {
+            "feature": 64
+        },
+        "IS": {
+            "feature": 64,
+            "splits": 10
+        },
+        "KID": {
+            "feature": 64,
+            "subset_size": 2
+        },
+        "LPIPS": {
+            "net_type": "vgg",
+            "reduction": "mean"
+        }
+    },
+    "tracker": {
+        "overwrite_data_path": true,
+
+	"log": {
+            "log_type": "console"
+	},
+
+        "load": {
+            "load_from": null
+        },
+
+       "save": [{
+            "save_to": "local"
+        }]
+    }
+}

dalle2_pytorch/dalle2_pytorch.py

@@ -125,14 +125,28 @@ def log(t, eps = 1e-12):
 def l2norm(t):
     return F.normalize(t, dim = -1)
 
-def resize_image_to(image, target_image_size):
+def resize_image_to(
+    image,
+    target_image_size,
+    clamp_range = None,
+    nearest = False,
+    **kwargs
+):
     orig_image_size = image.shape[-1]
 
     if orig_image_size == target_image_size:
         return image
 
-    scale_factors = target_image_size / orig_image_size
-    return resize(image, scale_factors = scale_factors)
+    if not nearest:
+        scale_factors = target_image_size / orig_image_size
+        out = resize(image, scale_factors = scale_factors, **kwargs)
+    else:
+        out = F.interpolate(image, target_image_size, mode = 'nearest', align_corners = False)
+
+    if exists(clamp_range):
+        out = out.clamp(*clamp_range)
+
+    return out
 
 # image normalization functions
 # ddpms expect images to be in the range of -1 to 1
@@ -155,6 +169,11 @@ class BaseClipAdapter(nn.Module):
         self.clip = clip
         self.overrides = kwargs
 
+    def validate_and_resize_image(self, image):
+        image_size = image.shape[-1]
+        assert image_size >= self.image_size, f'you are passing in an image of size {image_size} but CLIP requires the image size to be at least {self.image_size}'
+        return resize_image_to(image, self.image_size)
+
     @property
     def dim_latent(self):
         raise NotImplementedError
@@ -205,7 +224,7 @@ class XClipAdapter(BaseClipAdapter):
 
     @torch.no_grad()
     def embed_image(self, image):
-        image = resize_image_to(image, self.image_size)
+        image = self.validate_and_resize_image(image)
         encoder_output = self.clip.visual_transformer(image)
         image_cls, image_encodings = encoder_output[:, 0], encoder_output[:, 1:]
         image_embed = self.clip.to_visual_latent(image_cls)
@@ -240,7 +259,7 @@ class CoCaAdapter(BaseClipAdapter):
 
     @torch.no_grad()
     def embed_image(self, image):
-        image = resize_image_to(image, self.image_size)
+        image = self.validate_and_resize_image(image)
         image_embed, image_encodings = self.clip.embed_image(image)
         return EmbeddedImage(image_embed, image_encodings)
 
@@ -301,7 +320,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
     @torch.no_grad()
     def embed_image(self, image):
         assert not self.cleared
-        image = resize_image_to(image, self.image_size)
+        image = self.validate_and_resize_image(image)
         image = self.clip_normalize(image)
         image_embed = self.clip.encode_image(image)
         return EmbeddedImage(l2norm(image_embed.float()), None)
@@ -486,6 +505,12 @@ class NoiseScheduler(nn.Module):
             extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
         )
 
+    def predict_noise_from_start(self, x_t, t, x0):
+        return (
+            (x0 - extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t) / \
+            extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+        )
+
     def p2_reweigh_loss(self, loss, times):
         if not self.has_p2_loss_reweighting:
             return loss
@@ -892,19 +917,23 @@ class DiffusionPrior(nn.Module):
         image_size = None,
         image_channels = 3,
         timesteps = 1000,
+        sample_timesteps = None,
         cond_drop_prob = 0.,
         loss_type = "l2",
         predict_x_start = True,
         beta_schedule = "cosine",
-        condition_on_text_encodings = True, # the paper suggests this is needed, but you can turn it off for your CLIP preprocessed text embed -> image embed training
-        sampling_clamp_l2norm = False,
+        condition_on_text_encodings = True,  # the paper suggests this is needed, but you can turn it off for your CLIP preprocessed text embed -> image embed training
+        sampling_clamp_l2norm = False,       # whether to l2norm clamp the image embed at each denoising iteration (analogous to -1 to 1 clipping for usual DDPMs)
+        sampling_final_clamp_l2norm = False, # whether to l2norm the final image embedding output (this is also done for images in ddpm)
         training_clamp_l2norm = False,
         init_image_embed_l2norm = False,
-        image_embed_scale = None,           # this is for scaling the l2-normed image embedding, so it is more suitable for gaussian diffusion, as outlined by Katherine (@crowsonkb) https://github.com/lucidrains/DALLE2-pytorch/issues/60#issue-1226116132
+        image_embed_scale = None,            # this is for scaling the l2-normed image embedding, so it is more suitable for gaussian diffusion, as outlined by Katherine (@crowsonkb) https://github.com/lucidrains/DALLE2-pytorch/issues/60#issue-1226116132
         clip_adapter_overrides = dict()
     ):
         super().__init__()
 
+        self.sample_timesteps = sample_timesteps
+
         self.noise_scheduler = NoiseScheduler(
             beta_schedule = beta_schedule,
             timesteps = timesteps,
@@ -935,23 +964,32 @@ class DiffusionPrior(nn.Module):
         self.condition_on_text_encodings = condition_on_text_encodings
 
         # in paper, they do not predict the noise, but predict x0 directly for image embedding, claiming empirically better results. I'll just offer both.
+
         self.predict_x_start = predict_x_start
 
         # @crowsonkb 's suggestion - https://github.com/lucidrains/DALLE2-pytorch/issues/60#issue-1226116132
+
         self.image_embed_scale = default(image_embed_scale, self.image_embed_dim ** 0.5)
 
         # whether to force an l2norm, similar to clipping denoised, when sampling
+
         self.sampling_clamp_l2norm = sampling_clamp_l2norm
+        self.sampling_final_clamp_l2norm = sampling_final_clamp_l2norm
+
         self.training_clamp_l2norm = training_clamp_l2norm
         self.init_image_embed_l2norm = init_image_embed_l2norm
 
         # device tracker
+
         self.register_buffer('_dummy', torch.tensor([True]), persistent = False)
 
     @property
     def device(self):
         return self._dummy.device
 
+    def l2norm_clamp_embed(self, image_embed):
+        return l2norm(image_embed) * self.image_embed_scale
+
     def p_mean_variance(self, x, t, text_cond, clip_denoised = False, cond_scale = 1.):
         assert not (cond_scale != 1. and not self.can_classifier_guidance), 'the model was not trained with conditional dropout, and thus one cannot use classifier free guidance (cond_scale anything other than 1)'
 
@@ -959,8 +997,6 @@ class DiffusionPrior(nn.Module):
 
         if self.predict_x_start:
             x_recon = pred
-            # not 100% sure of this above line - for any spectators, let me know in the github issues (or through a pull request) if you know how to correctly do this
-            # i'll be rereading https://arxiv.org/abs/2111.14822, where i think a similar approach is taken
         else:
             x_recon = self.noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
 
@@ -983,21 +1019,81 @@ class DiffusionPrior(nn.Module):
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
 
     @torch.no_grad()
-    def p_sample_loop(self, shape, text_cond, cond_scale = 1.):
-        device = self.device
-
-        b = shape[0]
-        image_embed = torch.randn(shape, device=device)
+    def p_sample_loop_ddpm(self, shape, text_cond, cond_scale = 1.):
+        batch, device = shape[0], self.device
+        image_embed = torch.randn(shape, device = device)
 
         if self.init_image_embed_l2norm:
             image_embed = l2norm(image_embed) * self.image_embed_scale
 
         for i in tqdm(reversed(range(0, self.noise_scheduler.num_timesteps)), desc='sampling loop time step', total=self.noise_scheduler.num_timesteps):
-            times = torch.full((b,), i, device = device, dtype = torch.long)
+            times = torch.full((batch,), i, device = device, dtype = torch.long)
             image_embed = self.p_sample(image_embed, times, text_cond = text_cond, cond_scale = cond_scale)
 
+        if self.sampling_final_clamp_l2norm and self.predict_x_start:
+            image_embed = self.l2norm_clamp_embed(image_embed)
+
         return image_embed
 
+    @torch.no_grad()
+    def p_sample_loop_ddim(self, shape, text_cond, *, timesteps, eta = 1., cond_scale = 1.):
+        batch, device, alphas, total_timesteps = shape[0], self.device, self.noise_scheduler.alphas_cumprod_prev, self.noise_scheduler.num_timesteps
+
+        times = torch.linspace(0., total_timesteps, steps = timesteps + 2)[:-1]
+
+        times = list(reversed(times.int().tolist()))
+        time_pairs = list(zip(times[:-1], times[1:]))
+
+        image_embed = torch.randn(shape, device = device)
+
+        if self.init_image_embed_l2norm:
+            image_embed = l2norm(image_embed) * self.image_embed_scale
+
+        for time, time_next in tqdm(time_pairs, desc = 'sampling loop time step'):
+            alpha = alphas[time]
+            alpha_next = alphas[time_next]
+
+            time_cond = torch.full((batch,), time, device = device, dtype = torch.long)
+
+            pred = self.net.forward_with_cond_scale(image_embed, time_cond, cond_scale = cond_scale, **text_cond)
+
+            if self.predict_x_start:
+                x_start = pred
+                pred_noise = self.noise_scheduler.predict_noise_from_start(image_embed, t = time_cond, x0 = pred)
+            else:
+                x_start = self.noise_scheduler.predict_start_from_noise(image_embed, t = time_cond, noise = pred)
+                pred_noise = pred
+
+            if not self.predict_x_start:
+                x_start.clamp_(-1., 1.)
+
+            if self.predict_x_start and self.sampling_clamp_l2norm:
+                x_start = self.l2norm_clamp_embed(x_start)
+
+            c1 = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
+            c2 = ((1 - alpha_next) - torch.square(c1)).sqrt()
+            noise = torch.randn_like(image_embed) if time_next > 0 else 0.
+
+            image_embed = x_start * alpha_next.sqrt() + \
+                          c1 * noise + \
+                          c2 * pred_noise
+
+        if self.predict_x_start and self.sampling_final_clamp_l2norm:
+            image_embed = self.l2norm_clamp_embed(image_embed)
+
+        return image_embed
+
+    @torch.no_grad()
+    def p_sample_loop(self, *args, timesteps = None, **kwargs):
+        timesteps = default(timesteps, self.noise_scheduler.num_timesteps)
+        assert timesteps <= self.noise_scheduler.num_timesteps
+        is_ddim = timesteps < self.noise_scheduler.num_timesteps
+
+        if not is_ddim:
+            return self.p_sample_loop_ddpm(*args, **kwargs)
+
+        return self.p_sample_loop_ddim(*args, **kwargs, timesteps = timesteps)
+
     def p_losses(self, image_embed, times, text_cond, noise = None):
         noise = default(noise, lambda: torch.randn_like(image_embed))
 
@@ -1011,7 +1107,7 @@ class DiffusionPrior(nn.Module):
         )
 
         if self.predict_x_start and self.training_clamp_l2norm:
-            pred = l2norm(pred) * self.image_embed_scale
+            pred = self.l2norm_clamp_embed(pred)
 
         target = noise if not self.predict_x_start else image_embed
 
@@ -1032,7 +1128,15 @@ class DiffusionPrior(nn.Module):
 
     @torch.no_grad()
     @eval_decorator
-    def sample(self, text, num_samples_per_batch = 2, cond_scale = 1.):
+    def sample(
+        self,
+        text,
+        num_samples_per_batch = 2,
+        cond_scale = 1.,
+        timesteps = None
+    ):
+        timesteps = default(timesteps, self.sample_timesteps)
+
         # in the paper, what they did was
         # sample 2 image embeddings, choose the top 1 similarity, as judged by CLIP
         text = repeat(text, 'b ... -> (b r) ...', r = num_samples_per_batch)
@@ -1047,7 +1151,7 @@ class DiffusionPrior(nn.Module):
         if self.condition_on_text_encodings:
             text_cond = {**text_cond, 'text_encodings': text_encodings, 'mask': text_mask}
 
-        image_embeds = self.p_sample_loop((batch_size, image_embed_dim), text_cond = text_cond, cond_scale = cond_scale)
+        image_embeds = self.p_sample_loop((batch_size, image_embed_dim), text_cond = text_cond, cond_scale = cond_scale, timesteps = timesteps)
 
         # retrieve original unscaled image embed
 
@@ -1449,10 +1553,12 @@ class Unet(nn.Module):
         # text encoding conditioning (optional)
 
         self.text_to_cond = None
+        self.text_embed_dim = None
 
         if cond_on_text_encodings:
             assert exists(text_embed_dim), 'text_embed_dim must be given to the unet if cond_on_text_encodings is True'
             self.text_to_cond = nn.Linear(text_embed_dim, cond_dim)
+            self.text_embed_dim = text_embed_dim
 
         # finer control over whether to condition on image embeddings and text encodings
         # so one can have the latter unets in the cascading DDPMs only focus on super-resoluting
@@ -1681,6 +1787,8 @@ class Unet(nn.Module):
         text_tokens = None
 
         if exists(text_encodings) and self.cond_on_text_encodings:
+            assert self.text_embed_dim == text_encodings.shape[-1], f'the text encodings you are passing in have a dimension of {text_encodings.shape[-1]}, but the unet was created with text_embed_dim of {self.text_embed_dim}.'
+
             text_tokens = self.text_to_cond(text_encodings)
             text_tokens = text_tokens[:, :self.max_text_len]
 
@@ -1776,11 +1884,17 @@ class LowresConditioner(nn.Module):
     def __init__(
         self,
         downsample_first = True,
+        downsample_mode_nearest = False,
         blur_sigma = 0.6,
         blur_kernel_size = 3,
+        input_image_range = None
     ):
         super().__init__()
         self.downsample_first = downsample_first
+        self.downsample_mode_nearest = downsample_mode_nearest
+
+        self.input_image_range = input_image_range
+
         self.blur_sigma = blur_sigma
         self.blur_kernel_size = blur_kernel_size
 
@@ -1794,7 +1908,7 @@ class LowresConditioner(nn.Module):
         blur_kernel_size = None
     ):
         if self.training and self.downsample_first and exists(downsample_image_size):
-            cond_fmap = resize_image_to(cond_fmap, downsample_image_size)
+            cond_fmap = resize_image_to(cond_fmap, downsample_image_size, clamp_range = self.input_image_range, nearest = self.downsample_mode_nearest)
 
         if self.training:
             # when training, blur the low resolution conditional image
@@ -1814,7 +1928,7 @@ class LowresConditioner(nn.Module):
 
             cond_fmap = gaussian_blur2d(cond_fmap, cast_tuple(blur_kernel_size, 2), cast_tuple(blur_sigma, 2))
 
-        cond_fmap = resize_image_to(cond_fmap, target_image_size)
+        cond_fmap = resize_image_to(cond_fmap, target_image_size, clamp_range = self.input_image_range)
 
         return cond_fmap
 
@@ -1828,6 +1942,7 @@ class Decoder(nn.Module):
         channels = 3,
         vae = tuple(),
         timesteps = 1000,
+        sample_timesteps = None,
         image_cond_drop_prob = 0.1,
         text_cond_drop_prob = 0.5,
         loss_type = 'l2',
@@ -1837,6 +1952,7 @@ class Decoder(nn.Module):
         image_sizes = None,                         # for cascading ddpm, image size at each stage
         random_crop_sizes = None,                   # whether to random crop the image at that stage in the cascade (super resoluting convolutions at the end may be able to generalize on smaller crops)
         lowres_downsample_first = True,             # cascading ddpm - resizes to lower resolution, then to next conditional resolution + blur
+        lowres_downsample_mode_nearest = False,     # cascading ddpm - whether to use nearest mode downsampling for lower resolution
         blur_sigma = 0.6,                           # cascading ddpm - blur sigma
         blur_kernel_size = 3,                       # cascading ddpm - blur kernel size
         clip_denoised = True,
@@ -1850,7 +1966,8 @@ class Decoder(nn.Module):
         use_dynamic_thres = False,                  # from the Imagen paper
         dynamic_thres_percentile = 0.9,
         p2_loss_weight_gamma = 0.,                  # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time - 1. is recommended
-        p2_loss_weight_k = 1
+        p2_loss_weight_k = 1,
+        ddim_sampling_eta = 1.                      # can be set to 0. for deterministic sampling afaict
     ):
         super().__init__()
 
@@ -1930,9 +2047,10 @@ class Decoder(nn.Module):
             self.unets.append(one_unet)
             self.vaes.append(one_vae.copy_for_eval())
 
-        # determine from unets whether conditioning on text encoding is needed
+        # sampling timesteps, defaults to non-ddim with full timesteps sampling
 
-        self.condition_on_text_encodings = any([unet.cond_on_text_encodings for unet in self.unets])
+        self.sample_timesteps = cast_tuple(sample_timesteps, num_unets)
+        self.ddim_sampling_eta = ddim_sampling_eta
 
         # create noise schedulers per unet
 
@@ -1944,7 +2062,9 @@ class Decoder(nn.Module):
 
         self.noise_schedulers = nn.ModuleList([])
 
-        for unet_beta_schedule, unet_p2_loss_weight_gamma in zip(beta_schedule, p2_loss_weight_gamma):
+        for ind, (unet_beta_schedule, unet_p2_loss_weight_gamma, sample_timesteps) in enumerate(zip(beta_schedule, p2_loss_weight_gamma, self.sample_timesteps)):
+            assert not exists(sample_timesteps) or sample_timesteps <= timesteps, f'sampling timesteps {sample_timesteps} must be less than or equal to the number of training timesteps {timesteps} for unet {ind + 1}'
+
             noise_scheduler = NoiseScheduler(
                 beta_schedule = unet_beta_schedule,
                 timesteps = timesteps,
@@ -1972,6 +2092,10 @@ class Decoder(nn.Module):
 
         self.predict_x_start = cast_tuple(predict_x_start, len(unets)) if not predict_x_start_for_latent_diffusion else tuple(map(lambda t: isinstance(t, VQGanVAE), self.vaes))
 
+        # input image range
+
+        self.input_image_range = (-1. if not auto_normalize_img else 0., 1.)
+
         # cascading ddpm related stuff
 
         lowres_conditions = tuple(map(lambda t: t.lowres_cond, self.unets))
@@ -1979,8 +2103,10 @@ class Decoder(nn.Module):
 
         self.to_lowres_cond = LowresConditioner(
             downsample_first = lowres_downsample_first,
+            downsample_mode_nearest = lowres_downsample_mode_nearest,
             blur_sigma = blur_sigma,
             blur_kernel_size = blur_kernel_size,
+            input_image_range = self.input_image_range
         )
 
         # classifier free guidance
@@ -2012,6 +2138,10 @@ class Decoder(nn.Module):
     def device(self):
         return self._dummy.device
 
+    @property
+    def condition_on_text_encodings(self):
+        return any([unet.cond_on_text_encodings for unet in self.unets])
+
     def get_unet(self, unet_number):
         assert 0 < unet_number <= len(self.unets)
         index = unet_number - 1
@@ -2035,6 +2165,26 @@ class Decoder(nn.Module):
         for unet, device in zip(self.unets, devices):
             unet.to(device)
 
+    def dynamic_threshold(self, x):
+        """ proposed in https://arxiv.org/abs/2205.11487 as an improved clamping in the setting of classifier free guidance """
+        
+        # s is the threshold amount
+        # static thresholding would just be s = 1
+        s = 1.
+        if self.use_dynamic_thres:
+            s = torch.quantile(
+                rearrange(x, 'b ... -> b (...)').abs(),
+                self.dynamic_thres_percentile,
+                dim = -1
+            )
+
+            s.clamp_(min = 1.)
+            s = s.view(-1, *((1,) * (x.ndim - 1)))
+
+        # clip by threshold, depending on whether static or dynamic
+        x = x.clamp(-s, s) / s
+        return x
+
     def p_mean_variance(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, text_mask = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = False, learned_variance = False, cond_scale = 1., model_output = None):
         assert not (cond_scale != 1. and not self.can_classifier_guidance), 'the decoder was not trained with conditional dropout, and thus one cannot use classifier free guidance (cond_scale anything other than 1)'
 
@@ -2049,21 +2199,7 @@ class Decoder(nn.Module):
             x_recon = noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
 
         if clip_denoised:
-            # s is the threshold amount
-            # static thresholding would just be s = 1
-            s = 1.
-            if self.use_dynamic_thres:
-                s = torch.quantile(
-                    rearrange(x_recon, 'b ... -> b (...)').abs(),
-                    self.dynamic_thres_percentile,
-                    dim = -1
-                )
-
-                s.clamp_(min = 1.)
-                s = s.view(-1, *((1,) * (x_recon.ndim - 1)))
-
-            # clip by threshold, depending on whether static or dynamic
-            x_recon = x_recon.clamp(-s, s) / s
+            x_recon = self.dynamic_threshold(x_recon)
 
         model_mean, posterior_variance, posterior_log_variance = noise_scheduler.q_posterior(x_start=x_recon, x_t=x, t=t)
 
@@ -2093,7 +2229,7 @@ class Decoder(nn.Module):
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
 
     @torch.no_grad()
-    def p_sample_loop(self, unet, shape, image_embed, noise_scheduler, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, text_mask = None, cond_scale = 1, is_latent_diffusion = False):
+    def p_sample_loop_ddpm(self, unet, shape, image_embed, noise_scheduler, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, text_mask = None, cond_scale = 1, is_latent_diffusion = False):
         device = self.device
 
         b = shape[0]
@@ -2121,6 +2257,62 @@ class Decoder(nn.Module):
         unnormalize_img = self.unnormalize_img(img)
         return unnormalize_img
 
+    @torch.no_grad()
+    def p_sample_loop_ddim(self, unet, shape, image_embed, noise_scheduler, timesteps, eta = 1., predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, text_mask = None, cond_scale = 1, is_latent_diffusion = False):
+        batch, device, total_timesteps, alphas, eta = shape[0], self.device, noise_scheduler.num_timesteps, noise_scheduler.alphas_cumprod_prev, self.ddim_sampling_eta
+
+        times = torch.linspace(0., total_timesteps, steps = timesteps + 2)[:-1]
+
+        times = list(reversed(times.int().tolist()))
+        time_pairs = list(zip(times[:-1], times[1:]))
+
+        img = torch.randn(shape, device = device)
+
+        for time, time_next in tqdm(time_pairs, desc = 'sampling loop time step'):
+            alpha = alphas[time]
+            alpha_next = alphas[time_next]
+
+            time_cond = torch.full((batch,), time, device = device, dtype = torch.long)
+
+            pred = unet.forward_with_cond_scale(img, time_cond, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img)
+
+            if learned_variance:
+                pred, _ = pred.chunk(2, dim = 1)
+
+            if predict_x_start:
+                x_start = pred
+                pred_noise = noise_scheduler.predict_noise_from_start(img, t = time_cond, x0 = pred)
+            else:
+                x_start = noise_scheduler.predict_start_from_noise(img, t = time_cond, noise = pred)
+                pred_noise = pred
+
+            if clip_denoised:
+                x_start = self.dynamic_threshold(x_start)
+
+            c1 = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
+            c2 = ((1 - alpha_next) - torch.square(c1)).sqrt()
+            noise = torch.randn_like(img) if time_next > 0 else 0.
+
+            img = x_start * alpha_next.sqrt() + \
+                  c1 * noise + \
+                  c2 * pred_noise
+
+        img = self.unnormalize_img(img)
+        return img
+
+    @torch.no_grad()
+    def p_sample_loop(self, *args, noise_scheduler, timesteps = None, **kwargs):
+        num_timesteps = noise_scheduler.num_timesteps
+
+        timesteps = default(timesteps, num_timesteps)
+        assert timesteps <= num_timesteps
+        is_ddim = timesteps < num_timesteps
+
+        if not is_ddim:
+            return self.p_sample_loop_ddpm(*args, noise_scheduler = noise_scheduler, **kwargs)
+
+        return self.p_sample_loop_ddim(*args, noise_scheduler = noise_scheduler, timesteps = timesteps, **kwargs)
+
     def p_losses(self, unet, x_start, times, *, image_embed, noise_scheduler, lowres_cond_img = None, text_encodings = None, text_mask = None, predict_x_start = False, noise = None, learned_variance = False, clip_denoised = False, is_latent_diffusion = False):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
@@ -2221,7 +2413,7 @@ class Decoder(nn.Module):
         img = None
         is_cuda = next(self.parameters()).is_cuda
 
-        for unet_number, unet, vae, channel, image_size, predict_x_start, learned_variance, noise_scheduler in tqdm(zip(range(1, len(self.unets) + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start, self.learned_variance, self.noise_schedulers)):
+        for unet_number, unet, vae, channel, image_size, predict_x_start, learned_variance, noise_scheduler, sample_timesteps in tqdm(zip(range(1, len(self.unets) + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start, self.learned_variance, self.noise_schedulers, self.sample_timesteps)):
 
             context = self.one_unet_in_gpu(unet = unet) if is_cuda and not distributed else null_context()
 
@@ -2250,7 +2442,8 @@ class Decoder(nn.Module):
                     clip_denoised = not is_latent_diffusion,
                     lowres_cond_img = lowres_cond_img,
                     is_latent_diffusion = is_latent_diffusion,
-                    noise_scheduler = noise_scheduler
+                    noise_scheduler = noise_scheduler,
+                    timesteps = sample_timesteps
                 )
 
                 img = vae.decode(img)

dalle2_pytorch/dataloaders/decoder_loader.py

@@ -1,6 +1,7 @@
 import os
 import webdataset as wds
 import torch
+from torch.utils.data import DataLoader
 import numpy as np
 import fsspec
 import shutil
@@ -255,7 +256,7 @@ def create_image_embedding_dataloader(
     )
     if shuffle_num is not None and shuffle_num > 0:
         ds.shuffle(1000)
-    return wds.WebLoader(
+    return DataLoader(
         ds,
         num_workers=num_workers,
         batch_size=batch_size,

dalle2_pytorch/trackers.py

@@ -1,5 +1,6 @@
 import urllib.request
 import os
+import json
 from pathlib import Path
 import shutil
 from itertools import zip_longest
@@ -37,14 +38,17 @@ class BaseLogger:
         data_path (str): A file path for storing temporary data.
         verbose (bool): Whether of not to always print logs to the console.
     """
-    def __init__(self, data_path: str, verbose: bool = False, **kwargs):
+    def __init__(self, data_path: str, resume: bool = False, auto_resume: bool = False, verbose: bool = False, **kwargs):
         self.data_path = Path(data_path)
+        self.resume = resume
+        self.auto_resume = auto_resume
         self.verbose = verbose
 
     def init(self, full_config: BaseModel, extra_config: dict, **kwargs) -> None:
         """
         Initializes the logger.
         Errors if the logger is invalid.
+        full_config is the config file dict while extra_config is anything else from the script that is not defined the config file.
         """
         raise NotImplementedError
 
@@ -60,6 +64,14 @@ class BaseLogger:
     def log_error(self, error_string, **kwargs) -> None:
         raise NotImplementedError
 
+    def get_resume_data(self, **kwargs) -> dict:
+        """
+        Sets tracker attributes that along with { "resume": True } will be used to resume training.
+        It is assumed that after init is called this data will be complete.
+        If the logger does not have any resume functionality, it should return an empty dict.
+        """
+        raise NotImplementedError
+
 class ConsoleLogger(BaseLogger):
     def init(self, full_config: BaseModel, extra_config: dict, **kwargs) -> None:
         print("Logging to console")
@@ -76,6 +88,9 @@ class ConsoleLogger(BaseLogger):
     def log_error(self, error_string, **kwargs) -> None:
         print(error_string)
 
+    def get_resume_data(self, **kwargs) -> dict:
+        return {}
+
 class WandbLogger(BaseLogger):
     """
     Logs to a wandb run.
@@ -85,7 +100,6 @@ class WandbLogger(BaseLogger):
         wandb_project (str): The wandb project to log to.
         wandb_run_id (str): The wandb run id to resume.
         wandb_run_name (str): The wandb run name to use.
-        wandb_resume (bool): Whether to resume a wandb run.
     """
     def __init__(self,
         data_path: str,
@@ -93,7 +107,6 @@ class WandbLogger(BaseLogger):
         wandb_project: str,
         wandb_run_id: Optional[str] = None,
         wandb_run_name: Optional[str] = None,
-        wandb_resume: bool = False,
         **kwargs
     ):
         super().__init__(data_path, **kwargs)
@@ -101,7 +114,6 @@ class WandbLogger(BaseLogger):
         self.project = wandb_project
         self.run_id = wandb_run_id
         self.run_name = wandb_run_name
-        self.resume = wandb_resume
 
     def init(self, full_config: BaseModel, extra_config: dict, **kwargs) -> None:
         assert self.entity is not None, "wandb_entity must be specified for wandb logger"
@@ -149,6 +161,14 @@ class WandbLogger(BaseLogger):
             print(error_string)
         self.wandb.log({"error": error_string, **kwargs}, step=step)
 
+    def get_resume_data(self, **kwargs) -> dict:
+        # In order to resume, we need wandb_entity, wandb_project, and wandb_run_id
+        return {
+            "entity": self.entity,
+            "project": self.project,
+            "run_id": self.wandb.run.id
+        }
+
 logger_type_map = {
     'console': ConsoleLogger,
     'wandb': WandbLogger,
@@ -168,8 +188,9 @@ class BaseLoader:
     Parameters:
         data_path (str): A file path for storing temporary data.
     """
-    def __init__(self, data_path: str, **kwargs):
+    def __init__(self, data_path: str, only_auto_resume: bool = False, **kwargs):
         self.data_path = Path(data_path)
+        self.only_auto_resume = only_auto_resume
 
     def init(self, logger: BaseLogger, **kwargs) -> None:
         raise NotImplementedError
@@ -304,6 +325,10 @@ class LocalSaver(BaseSaver):
     def save_file(self, local_path: str, save_path: str, **kwargs) -> None:
         # Copy the file to save_path
         save_path_file_name = Path(save_path).name
+        # Make sure parent directory exists
+        save_path_parent = Path(save_path).parent
+        if not save_path_parent.exists():
+            save_path_parent.mkdir(parents=True)
         print(f"Saving {save_path_file_name} {self.save_type} to local path {save_path}")
         shutil.copy(local_path, save_path)
 
@@ -385,11 +410,7 @@ class Tracker:
     def __init__(self, data_path: Optional[str] = DEFAULT_DATA_PATH, overwrite_data_path: bool = False, dummy_mode: bool = False):
         self.data_path = Path(data_path)
         if not dummy_mode:
-            if overwrite_data_path:
-                if self.data_path.exists():
-                    shutil.rmtree(self.data_path)
-                self.data_path.mkdir(parents=True)
-            else:
+            if not overwrite_data_path:
                 assert not self.data_path.exists(), f'Data path {self.data_path} already exists. Set overwrite_data_path to True to overwrite.'
                 if not self.data_path.exists():
                     self.data_path.mkdir(parents=True)
@@ -398,7 +419,46 @@ class Tracker:
         self.savers: List[BaseSaver]= []
         self.dummy_mode = dummy_mode
 
+    def _load_auto_resume(self) -> bool:
+        # If the file does not exist, we return False. If autoresume is enabled we print a warning so that the user can know that this is the first run.
+        if not self.auto_resume_path.exists():
+            if self.logger.auto_resume:
+                print("Auto_resume is enabled but no auto_resume.json file exists. Assuming this is the first run.")
+            return False
+
+        # Now we know that the autoresume file exists, but if we are not auto resuming we should remove it so that we don't accidentally load it next time
+        if not self.logger.auto_resume:
+            print(f'Removing auto_resume.json because auto_resume is not enabled in the config')
+            self.auto_resume_path.unlink()
+            return False
+
+        # Otherwise we read the json into a dictionary will will override parts of logger.__dict__
+        with open(self.auto_resume_path, 'r') as f:
+            auto_resume_dict = json.load(f)
+        # Check if the logger is of the same type as the autoresume save
+        if auto_resume_dict["logger_type"] != self.logger.__class__.__name__:
+            raise Exception(f'The logger type in the auto_resume file is {auto_resume_dict["logger_type"]} but the current logger is {self.logger.__class__.__name__}. Either use the original logger type, set `auto_resume` to `False`, or delete your existing tracker-data folder.')
+        # Then we are ready to override the logger with the autoresume save
+        self.logger.__dict__["resume"] = True
+        print(f"Updating {self.logger.__dict__} with {auto_resume_dict}")
+        self.logger.__dict__.update(auto_resume_dict)
+        return True
+
+    def _save_auto_resume(self):
+        # Gets the autoresume dict from the logger and adds "logger_type" to it then saves it to the auto_resume file
+        auto_resume_dict = self.logger.get_resume_data()
+        auto_resume_dict['logger_type'] = self.logger.__class__.__name__
+        with open(self.auto_resume_path, 'w') as f:
+            json.dump(auto_resume_dict, f)
+
     def init(self, full_config: BaseModel, extra_config: dict):
+        self.auto_resume_path = self.data_path / 'auto_resume.json'
+        # Check for resuming the run
+        self.did_auto_resume = self._load_auto_resume()
+        if self.did_auto_resume:
+            print(f'\n\nWARNING: RUN HAS BEEN AUTO-RESUMED WITH THE LOGGER TYPE {self.logger.__class__.__name__}.\nIf this was not your intention, stop this run and set `auto_resume` to `False` in the config.\n\n')
+            print(f"New logger config: {self.logger.__dict__}")
+        
         assert self.logger is not None, '`logger` must be set before `init` is called'
         if self.dummy_mode:
             # The only thing we need is a loader
@@ -406,12 +466,17 @@ class Tracker:
                 self.loader.init(self.logger)
             return
         assert len(self.savers) > 0, '`savers` must be set before `init` is called'
+
         self.logger.init(full_config, extra_config)
         if self.loader is not None:
             self.loader.init(self.logger)
         for saver in self.savers:
             saver.init(self.logger)
 
+        if self.logger.auto_resume:
+            # Then we need to save the autoresume file. It is assumed after logger.init is called that the logger is ready to be saved.
+            self._save_auto_resume()
+
     def add_logger(self, logger: BaseLogger):
         self.logger = logger
 
@@ -503,11 +568,16 @@ class Tracker:
                     self.logger.log_error(f'Error saving checkpoint: {e}', **kwargs)
                     print(f'Error saving checkpoint: {e}')
     
+    @property
+    def can_recall(self):
+        # Defines whether a recall can be performed.
+        return self.loader is not None and (not self.loader.only_auto_resume or self.did_auto_resume)
+    
     def recall(self):
-        if self.loader is not None:
+        if self.can_recall:
             return self.loader.recall()
         else:
-            raise ValueError('No loader specified')
+            raise ValueError('Tried to recall, but no loader was set or auto-resume was not performed.')
 
 
     

dalle2_pytorch/train_configs.py

@@ -47,6 +47,8 @@ class TrainSplitConfig(BaseModel):
 
 class TrackerLogConfig(BaseModel):
     log_type: str = 'console'
+    resume: bool = False  # For logs that are saved to unique locations, resume a previous run
+    auto_resume: bool = False  # If the process crashes and restarts, resume from the run that crashed
     verbose: bool = False
 
     class Config:
@@ -59,6 +61,7 @@ class TrackerLogConfig(BaseModel):
 
 class TrackerLoadConfig(BaseModel):
     load_from: Optional[str] = None
+    only_auto_resume: bool = False  # Only attempt to load if the logger is auto-resuming
 
     class Config:
         extra = "allow"
@@ -151,6 +154,7 @@ class DiffusionPriorConfig(BaseModel):
     image_size: int
     image_channels: int = 3
     timesteps: int = 1000
+    sample_timesteps: Optional[int] = None
     cond_drop_prob: float = 0.
     loss_type: str = 'l2'
     predict_x_start: bool = True
@@ -230,6 +234,7 @@ class DecoderConfig(BaseModel):
     clip: Optional[AdapterConfig]   # The clip model to use if embeddings are not provided
     channels: int = 3
     timesteps: int = 1000
+    sample_timesteps: Optional[SingularOrIterable(int)] = None
     loss_type: str = 'l2'
     beta_schedule: ListOrTuple(str) = 'cosine'
     learned_variance: bool = True

dalle2_pytorch/trainer.py

@@ -21,7 +21,7 @@ import pytorch_warmup as warmup
 
 from ema_pytorch import EMA
 
-from accelerate import Accelerator
+from accelerate import Accelerator, DistributedType
 
 import numpy as np
 
@@ -76,6 +76,7 @@ def cast_torch_tensor(fn):
     def inner(model, *args, **kwargs):
         device = kwargs.pop('_device', next(model.parameters()).device)
         cast_device = kwargs.pop('_cast_device', True)
+        cast_deepspeed_precision = kwargs.pop('_cast_deepspeed_precision', True)
 
         kwargs_keys = kwargs.keys()
         all_args = (*args, *kwargs.values())
@@ -85,6 +86,21 @@ def cast_torch_tensor(fn):
         if cast_device:
             all_args = tuple(map(lambda t: t.to(device) if exists(t) and isinstance(t, torch.Tensor) else t, all_args))
 
+        if cast_deepspeed_precision:
+            try:
+                accelerator = model.accelerator
+                if accelerator is not None and accelerator.distributed_type == DistributedType.DEEPSPEED:
+                    cast_type_map = {
+                        "fp16": torch.half,
+                        "bf16": torch.bfloat16,
+                        "no": torch.float
+                    }
+                    precision_type = cast_type_map[accelerator.mixed_precision]
+                    all_args = tuple(map(lambda t: t.to(precision_type) if exists(t) and isinstance(t, torch.Tensor) else t, all_args))
+            except AttributeError:
+                # Then this model doesn't have an accelerator
+                pass
+
         args, kwargs_values = all_args[:split_kwargs_index], all_args[split_kwargs_index:]
         kwargs = dict(tuple(zip(kwargs_keys, kwargs_values)))
 
@@ -192,6 +208,7 @@ class DiffusionPriorTrainer(nn.Module):
             self.device = diffusion_prior_device
         else:
             self.device = accelerator.device if exists(accelerator) else device
+            diffusion_prior.to(self.device)
 
         # save model
 
@@ -445,6 +462,7 @@ class DecoderTrainer(nn.Module):
         self,
         decoder,
         accelerator = None,
+        dataloaders = None,
         use_ema = True,
         lr = 1e-4,
         wd = 1e-2,
@@ -507,11 +525,31 @@ class DecoderTrainer(nn.Module):
 
         self.register_buffer('steps', torch.tensor([0] * self.num_unets))
 
+        if self.accelerator.distributed_type == DistributedType.DEEPSPEED and decoder.clip is not None:
+            # Then we need to make sure clip is using the correct precision or else deepspeed will error
+            cast_type_map = {
+                "fp16": torch.half,
+                "bf16": torch.bfloat16,
+                "no": torch.float
+            }
+            precision_type = cast_type_map[accelerator.mixed_precision]
+            assert precision_type == torch.float, "DeepSpeed currently only supports float32 precision when using on the fly embedding generation from clip"
+            clip = decoder.clip
+            clip.to(precision_type)
+
         decoder, *optimizers = list(self.accelerator.prepare(decoder, *optimizers))
-        schedulers = list(self.accelerator.prepare(*schedulers))
 
         self.decoder = decoder
 
+        # prepare dataloaders
+
+        train_loader = val_loader = None
+        if exists(dataloaders):
+            train_loader, val_loader = self.accelerator.prepare(dataloaders["train"], dataloaders["val"])
+
+        self.train_loader = train_loader
+        self.val_loader = val_loader
+
         # store optimizers
 
         for opt_ind, optimizer in zip(range(len(optimizers)), optimizers):
@@ -526,6 +564,17 @@ class DecoderTrainer(nn.Module):
 
         self.warmup_schedulers = warmup_schedulers
 
+    def validate_and_return_unet_number(self, unet_number = None):
+        if self.num_unets == 1:
+            unet_number = default(unet_number, 1)
+
+        assert exists(unet_number) and 1 <= unet_number <= self.num_unets
+        return unet_number
+
+    def num_steps_taken(self, unet_number = None):
+        unet_number = self.validate_and_return_unet_number(unet_number)
+        return self.steps[unet_number - 1].item()
+
     def save(self, path, overwrite = True, **kwargs):
         path = Path(path)
         assert not (path.exists() and not overwrite)
@@ -594,10 +643,7 @@ class DecoderTrainer(nn.Module):
         self.steps += F.one_hot(unet_index_tensor, num_classes = len(self.steps))
 
     def update(self, unet_number = None):
-        if self.num_unets == 1:
-            unet_number = default(unet_number, 1)
-
-        assert exists(unet_number) and 1 <= unet_number <= self.num_unets
+        unet_number = self.validate_and_return_unet_number(unet_number)
         index = unet_number - 1
 
         optimizer = getattr(self, f'optim{index}')
@@ -663,11 +709,13 @@ class DecoderTrainer(nn.Module):
         max_batch_size = None,
         **kwargs
     ):
-        if self.num_unets == 1:
-            unet_number = default(unet_number, 1)
+        unet_number = self.validate_and_return_unet_number(unet_number)
 
         total_loss = 0.
 
+        
+        using_amp = self.accelerator.mixed_precision != 'no'
+
         for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
             with self.accelerator.autocast():
                 loss = self.decoder(*chunked_args, unet_number = unet_number, **chunked_kwargs)

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.16.14'
+__version__ = '0.19.6'

train_decoder.py

@@ -132,7 +132,7 @@ def get_example_data(dataloader, device, n=5):
             break
     return list(zip(images[:n], img_embeddings[:n], text_embeddings[:n], captions[:n]))
 
-def generate_samples(trainer, example_data, condition_on_text_encodings=False, text_prepend=""):
+def generate_samples(trainer, example_data, condition_on_text_encodings=False, text_prepend="", match_image_size=True):
     """
     Takes example data and generates images from the embeddings
     Returns three lists: real images, generated images, and captions
@@ -160,6 +160,9 @@ def generate_samples(trainer, example_data, condition_on_text_encodings=False, t
     samples = trainer.sample(**sample_params)
     generated_images = list(samples)
     captions = [text_prepend + txt for txt in txts]
+    if match_image_size:
+        generated_image_size = generated_images[0].shape[-1]
+        real_images = [resize_image_to(image, generated_image_size, clamp_range=(0, 1)) for image in real_images]
     return real_images, generated_images, captions
 
 def generate_grid_samples(trainer, examples, condition_on_text_encodings=False, text_prepend=""):
@@ -167,14 +170,6 @@ def generate_grid_samples(trainer, examples, condition_on_text_encodings=False,
     Generates samples and uses torchvision to put them in a side by side grid for easy viewing
     """
     real_images, generated_images, captions = generate_samples(trainer, examples, condition_on_text_encodings, text_prepend)
-
-    real_image_size = real_images[0].shape[-1]
-    generated_image_size = generated_images[0].shape[-1]
-
-    # training images may be larger than the generated one
-    if real_image_size > generated_image_size:
-        real_images = [resize_image_to(image, generated_image_size) for image in real_images]
-
     grid_images = [torchvision.utils.make_grid([original_image, generated_image]) for original_image, generated_image in zip(real_images, generated_images)]
     return grid_images, captions
                     
@@ -279,6 +274,7 @@ def train(
     trainer = DecoderTrainer(
         decoder=decoder,
         accelerator=accelerator,
+        dataloaders=dataloaders,
         **kwargs
     )
 
@@ -289,9 +285,8 @@ def train(
     sample = 0
     samples_seen = 0
     val_sample = 0
-    step = lambda: int(trainer.step.item())
 
-    if tracker.loader is not None:
+    if tracker.can_recall:
         start_epoch, validation_losses, next_task, recalled_sample, samples_seen = recall_trainer(tracker, trainer)
         if next_task == 'train':
             sample = recalled_sample
@@ -304,6 +299,8 @@ def train(
     if not exists(unet_training_mask):
         # Then the unet mask should be true for all unets in the decoder
         unet_training_mask = [True] * trainer.num_unets
+    first_training_unet = min(index for index, mask in enumerate(unet_training_mask) if mask)
+    step = lambda: int(trainer.num_steps_taken(unet_number=first_training_unet+1))
     assert len(unet_training_mask) == trainer.num_unets, f"The unet training mask should be the same length as the number of unets in the decoder. Got {len(unet_training_mask)} and {trainer.num_unets}"
 
     accelerator.print(print_ribbon("Generating Example Data", repeat=40))
@@ -326,7 +323,7 @@ def train(
         last_snapshot = sample
 
         if next_task == 'train':
-            for i, (img, emb, txt) in enumerate(dataloaders["train"]):
+            for i, (img, emb, txt) in enumerate(trainer.train_loader):
                 # We want to count the total number of samples across all processes
                 sample_length_tensor[0] = len(img)
                 all_samples = accelerator.gather(sample_length_tensor)  # TODO: accelerator.reduce is broken when this was written. If it is fixed replace this.
@@ -419,7 +416,7 @@ def train(
             timer = Timer()
             accelerator.wait_for_everyone()
             i = 0
-            for i, (img, emb, txt) in enumerate(dataloaders["val"]):
+            for i, (img, emb, txt) in enumerate(trainer.val_loader):  # Use the accelerate prepared loader
                 val_sample_length_tensor[0] = len(img)
                 all_samples = accelerator.gather(val_sample_length_tensor)
                 total_samples = all_samples.sum().item()
@@ -524,6 +521,20 @@ def initialize_training(config: TrainDecoderConfig, config_path):
     # Set up accelerator for configurable distributed training
     ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=config.train.find_unused_parameters)
     accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
+
+    if accelerator.num_processes > 1:
+        # We are using distributed training and want to immediately ensure all can connect
+        accelerator.print("Waiting for all processes to connect...")
+        accelerator.wait_for_everyone()
+        accelerator.print("All processes online and connected")
+
+    # If we are in deepspeed fp16 mode, we must ensure learned variance is off
+    if accelerator.mixed_precision == "fp16" and accelerator.distributed_type == accelerate_dataclasses.DistributedType.DEEPSPEED and config.decoder.learned_variance:
+        raise ValueError("DeepSpeed fp16 mode does not support learned variance")
+
+    if accelerator.process_index != accelerator.local_process_index and accelerator.distributed_type == accelerate_dataclasses.DistributedType.DEEPSPEED:
+        # This is an invalid configuration until we figure out how to handle this
+        raise ValueError("DeepSpeed does not support multi-node distributed training")
     
     # Set up data
     all_shards = list(range(config.data.start_shard, config.data.end_shard + 1))