Add from_pt argument in .from_pretrained

src/diffusers/modeling_flax_pytorch_utils.py

@@ -0,0 +1,117 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch - Flax general utilities."""
+import re
+
+import jax.numpy as jnp
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax.random import PRNGKey
+
+from .utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+def rename_key(key):
+    regex = r"\w+[.]\d+"
+    pats = re.findall(regex, key)
+    for pat in pats:
+        key = key.replace(pat, "_".join(pat.split(".")))
+    return key
+
+
+#####################
+# PyTorch => Flax #
+#####################
+
+# Adapted from https://github.com/huggingface/transformers/blob/c603c80f46881ae18b2ca50770ef65fa4033eacd/src/transformers/modeling_flax_pytorch_utils.py#L69
+# and https://github.com/patil-suraj/stable-diffusion-jax/blob/main/stable_diffusion_jax/convert_diffusers_to_jax.py
+def rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict):
+    """Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary"""
+
+    # conv norm or layer norm
+    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
+    if (
+        any("norm" in str_ for str_ in pt_tuple_key)
+        and (pt_tuple_key[-1] == "bias")
+        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
+        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
+    ):
+        renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
+        return renamed_pt_tuple_key, pt_tensor
+    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
+        renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
+        return renamed_pt_tuple_key, pt_tensor
+
+    # embedding
+    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
+        pt_tuple_key = pt_tuple_key[:-1] + ("embedding",)
+        return renamed_pt_tuple_key, pt_tensor
+
+    # conv layer
+    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
+    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
+        pt_tensor = pt_tensor.transpose(2, 3, 1, 0)
+        return renamed_pt_tuple_key, pt_tensor
+
+    # linear layer
+    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
+    if pt_tuple_key[-1] == "weight":
+        pt_tensor = pt_tensor.T
+        return renamed_pt_tuple_key, pt_tensor
+
+    # old PyTorch layer norm weight
+    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
+    if pt_tuple_key[-1] == "gamma":
+        return renamed_pt_tuple_key, pt_tensor
+
+    # old PyTorch layer norm bias
+    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
+    if pt_tuple_key[-1] == "beta":
+        return renamed_pt_tuple_key, pt_tensor
+
+    return pt_tuple_key, pt_tensor
+
+
+def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model, init_key=42):
+    # Step 1: Convert pytorch tensor to numpy
+    pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
+
+    # Step 2: Since the model is stateless, get random Flax params
+    random_flax_params = flax_model.init_weights(PRNGKey(init_key))
+
+    random_flax_state_dict = flatten_dict(random_flax_params)
+    flax_state_dict = {}
+
+    # Need to change some parameters name to match Flax names
+    for pt_key, pt_tensor in pt_state_dict.items():
+        renamed_pt_key = rename_key(pt_key)
+        pt_tuple_key = tuple(renamed_pt_key.split("."))
+
+        # Correctly rename weight parameters
+        flax_key, flax_tensor = rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict)
+
+        if flax_key in random_flax_state_dict:
+            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
+                raise ValueError(
+                    f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
+                    f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
+                )
+
+        # also add unexpected weight so that warning is thrown
+        flax_state_dict[flax_key] = jnp.asarray(flax_tensor)
+
+    return unflatten_dict(flax_state_dict)

src/diffusers/modeling_flax_utils.py

@@ -27,7 +27,8 @@
 from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from requests import HTTPError
 
-from .modeling_utils import WEIGHTS_NAME
+from .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax
+from .modeling_utils import WEIGHTS_NAME, load_state_dict
 from .utils import CONFIG_NAME, DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, logging
 
 
@@ -245,6 +246,8 @@ def from_pretrained(
                 The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                 git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                 identifier allowed by git.
+            from_pt (`bool`, *optional*, defaults to `False`):
+                Load the model weights from a PyTorch checkpoint save file.
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                 `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
@@ -272,6 +275,7 @@ def from_pretrained(
         config = kwargs.pop("config", None)
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
         force_download = kwargs.pop("force_download", False)
+        from_pt = kwargs.pop("from_pt", False)
         resume_download = kwargs.pop("resume_download", False)
         proxies = kwargs.pop("proxies", None)
         local_files_only = kwargs.pop("local_files_only", False)
@@ -306,10 +310,16 @@ def from_pretrained(
                 # Load from a Flax checkpoint
                 model_file = os.path.join(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME)
             # At this stage we don't have a weight file so we will raise an error.
+            elif from_pt:
+                if not os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME):
+                    raise EnvironmentError(
+                        f"Error no file named {WEIGHTS_NAME} found in directory {pretrained_model_name_or_path} "
+                    )
+                model_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
             elif os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME):
                 raise EnvironmentError(
-                    f"Error no file named {FLAX_WEIGHTS_NAME} found in directory {pretrained_model_name_or_path} "
-                    "but there is a file for PyTorch weights."
+                    f"{WEIGHTS_NAME} file found in directory {pretrained_model_name_or_path}. Please load the model"
+                    " using  `from_pt=True`."
                 )
             else:
                 raise EnvironmentError(
@@ -320,7 +330,7 @@ def from_pretrained(
             try:
                 model_file = hf_hub_download(
                     pretrained_model_name_or_path,
-                    filename=FLAX_WEIGHTS_NAME,
+                    filename=FLAX_WEIGHTS_NAME if not from_pt else WEIGHTS_NAME,
                     cache_dir=cache_dir,
                     force_download=force_download,
                     proxies=proxies,
@@ -370,25 +380,32 @@ def from_pretrained(
                     f"containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}."
                 )
 
-        try:
-            with open(model_file, "rb") as state_f:
-                state = from_bytes(cls, state_f.read())
-        except (UnpicklingError, msgpack.exceptions.ExtraData) as e:
+        if from_pt:
+            # Step 1: Get the pytorch file
+            pytorch_model_file = load_state_dict(model_file)
+
+            # Step 2: Convert the weights
+            state = convert_pytorch_state_dict_to_flax(pytorch_model_file, model)
+        else:
             try:
-                with open(model_file) as f:
-                    if f.read().startswith("version"):
-                        raise OSError(
-                            "You seem to have cloned a repository without having git-lfs installed. Please"
-                            " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
-                            " folder you cloned."
-                        )
-                    else:
-                        raise ValueError from e
-            except (UnicodeDecodeError, ValueError):
-                raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ")
-        # make sure all arrays are stored as jnp.ndarray
-        # NOTE: This is to prevent a bug this will be fixed in Flax >= v0.3.4:
-        # https://github.com/google/flax/issues/1261
+                with open(model_file, "rb") as state_f:
+                    state = from_bytes(cls, state_f.read())
+            except (UnpicklingError, msgpack.exceptions.ExtraData) as e:
+                try:
+                    with open(model_file) as f:
+                        if f.read().startswith("version"):
+                            raise OSError(
+                                "You seem to have cloned a repository without having git-lfs installed. Please"
+                                " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
+                                " folder you cloned."
+                            )
+                        else:
+                            raise ValueError from e
+                except (UnicodeDecodeError, ValueError):
+                    raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ")
+            # make sure all arrays are stored as jnp.ndarray
+            # NOTE: This is to prevent a bug this will be fixed in Flax >= v0.3.4:
+            # https://github.com/google/flax/issues/1261
         state = jax.tree_util.tree_map(lambda x: jax.device_put(x, jax.devices("cpu")[0]), state)
 
         # flatten dicts

src/diffusers/models/attention_flax.py

@@ -32,7 +32,7 @@ def setup(self):
         self.key = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_k")
         self.value = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_v")
 
-        self.proj_attn = nn.Dense(self.query_dim, dtype=self.dtype, name="to_out")
+        self.proj_attn = nn.Dense(self.query_dim, dtype=self.dtype, name="to_out_0")
 
     def reshape_heads_to_batch_dim(self, tensor):
         batch_size, seq_len, dim = tensor.shape
@@ -82,9 +82,9 @@ class FlaxBasicTransformerBlock(nn.Module):
 
     def setup(self):
         # self attention
-        self.self_attn = FlaxAttentionBlock(self.dim, self.n_heads, self.d_head, self.dropout, dtype=self.dtype)
+        self.attn1 = FlaxAttentionBlock(self.dim, self.n_heads, self.d_head, self.dropout, dtype=self.dtype)
         # cross attention
-        self.cross_attn = FlaxAttentionBlock(self.dim, self.n_heads, self.d_head, self.dropout, dtype=self.dtype)
+        self.attn2 = FlaxAttentionBlock(self.dim, self.n_heads, self.d_head, self.dropout, dtype=self.dtype)
         self.ff = FlaxGluFeedForward(dim=self.dim, dropout=self.dropout, dtype=self.dtype)
         self.norm1 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
         self.norm2 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
@@ -93,12 +93,12 @@ def setup(self):
     def __call__(self, hidden_states, context, deterministic=True):
         # self attention
         residual = hidden_states
-        hidden_states = self.self_attn(self.norm1(hidden_states), deterministic=deterministic)
+        hidden_states = self.attn1(self.norm1(hidden_states), deterministic=deterministic)
         hidden_states = hidden_states + residual
 
         # cross attention
         residual = hidden_states
-        hidden_states = self.cross_attn(self.norm2(hidden_states), context, deterministic=deterministic)
+        hidden_states = self.attn2(self.norm2(hidden_states), context, deterministic=deterministic)
         hidden_states = hidden_states + residual
 
         # feed forward
@@ -167,14 +167,28 @@ class FlaxGluFeedForward(nn.Module):
     dropout: float = 0.0
     dtype: jnp.dtype = jnp.float32
 
+    def setup(self):
+        # The second linear layer needs to be called
+        # net_2 for now to match the index of the Sequential layer
+        self.net_0 = FlaxGEGLU(self.dim, self.dropout, self.dtype)
+        self.net_2 = nn.Dense(self.dim, dtype=self.dtype)
+
+    def __call__(self, hidden_states, deterministic=True):
+        hidden_states = self.net_0(hidden_states)
+        hidden_states = self.net_2(hidden_states)
+        return hidden_states
+
+
+class FlaxGEGLU(nn.Module):
+    dim: int
+    dropout: float = 0.0
+    dtype: jnp.dtype = jnp.float32
+
     def setup(self):
         inner_dim = self.dim * 4
-        self.dense1 = nn.Dense(inner_dim * 2, dtype=self.dtype)
-        self.dense2 = nn.Dense(self.dim, dtype=self.dtype)
+        self.proj = nn.Dense(inner_dim * 2, dtype=self.dtype)
 
     def __call__(self, hidden_states, deterministic=True):
-        hidden_states = self.dense1(hidden_states)
+        hidden_states = self.proj(hidden_states)
         hidden_linear, hidden_gelu = jnp.split(hidden_states, 2, axis=2)
-        hidden_states = hidden_linear * nn.gelu(hidden_gelu)
-        hidden_states = self.dense2(hidden_states)
-        return hidden_states
+        return hidden_linear * nn.gelu(hidden_gelu)

src/diffusers/models/unet_2d_condition_flax.py

@@ -76,7 +76,7 @@ class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
 
     def init_weights(self, rng: jax.random.PRNGKey) -> FrozenDict:
         # init input tensors
-        sample_shape = (1, self.sample_size, self.sample_size, self.in_channels)
+        sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
         sample = jnp.zeros(sample_shape, dtype=jnp.float32)
         timesteps = jnp.ones((1,), dtype=jnp.int32)
         encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32)
@@ -214,10 +214,17 @@ def __call__(
             When returning a tuple, the first element is the sample tensor.
         """
         # 1. time
+        if not isinstance(timesteps, jnp.ndarray):
+            timesteps = jnp.array([timesteps], dtype=jnp.int32)
+        elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0:
+            timesteps = timesteps.astype(dtype=jnp.float32)
+            timesteps = jnp.expand_dims(timesteps, 0)
+
         t_emb = self.time_proj(timesteps)
         t_emb = self.time_embedding(t_emb)
 
         # 2. pre-process
+        sample = jnp.transpose(sample, (0, 2, 3, 1))
         sample = self.conv_in(sample)
 
         # 3. down
@@ -251,6 +258,7 @@ def __call__(
         sample = self.conv_norm_out(sample)
         sample = nn.silu(sample)
         sample = self.conv_out(sample)
+        sample = jnp.transpose(sample, (0, 3, 1, 2))
 
         if not return_dict:
             return (sample,)