adopt the fix proposed by @johahi for fixing the small numerical disc…

…repancy in the pretrained model between tensorflow and pytorch #31

MANIFEST.in

@@ -1 +1 @@
-recursive-include enformer_pytorch *.yml
+include enformer_pytorch/precomputed/tf_gammas.pt

README.md

@@ -119,16 +119,18 @@ Deepmind has released the weights for their tensorflow sonnet Enformer model! I
 
 Update: <a href="https://github.com/jstjohn">John St. John</a> did some work and found that the `enformer-official-rough` model hits the reported marks in the paper - human pearson R of `0.625` for validation, and `0.65` for test.
 
+Update: As of version 0.8.0, if one were to use the `from_pretrained` function to load the pretrained model, it should automatically use precomputed gamma positions to address a difference between tensorflow and pytorch `xlogy`. This should resolve the numerical discrepancy above. If you were to further finetune and not be using the `from_pretrained` function, please make sure to set `use_tf_gamma = True` when using `.from_hparams` to instantiate the `Enformer`
+
 ```bash
 $ pip install enformer-pytorch>=0.5
 ````
 
 Loading the model
 
 ```python
-from enformer_pytorch import Enformer
+from enformer_pytorch import from_pretrained
 
-enformer = Enformer.from_pretrained('EleutherAI/enformer-official-rough')
+enformer = from_pretrained('EleutherAI/enformer-official-rough')
 ```
 
 Quick sanity check on a single human validation point
@@ -143,19 +145,19 @@ This is all made possible thanks to HuggingFace's [custom model](https://hugging
 You can also load, with overriding of the `target_length` parameter, if you are working with shorter sequence lengths
 
 ```python
-from enformer_pytorch import Enformer
+from enformer_pytorch import from_pretrained
 
-model = Enformer.from_pretrained('EleutherAI/enformer-official-rough', target_length = 128, dropout_rate = 0.1)
+model = from_pretrained('EleutherAI/enformer-official-rough', target_length = 128, dropout_rate = 0.1)
 
 # do your fine-tuning
 ```
 
 To save on memory during fine-tuning a large Enformer model
 
 ```python
-from enformer_pytorch import Enformer
+from enformer_pytorch import from_pretrained
 
-enformer = Enformer.from_pretrained('EleutherAI/enformer-official-rough', use_checkpointing = True)
+enformer = from_pretrained('EleutherAI/enformer-official-rough', use_checkpointing = True)
 
 # finetune enformer on a limited budget
 ```
@@ -168,10 +170,10 @@ Fine-tuning on new tracks
 
 ```python
 import torch
-from enformer_pytorch import Enformer
+from enformer_pytorch import from_pretrained
 from enformer_pytorch.finetune import HeadAdapterWrapper
 
-enformer = Enformer.from_pretrained('EleutherAI/enformer-official-rough')
+enformer = from_pretrained('EleutherAI/enformer-official-rough')
 
 model = HeadAdapterWrapper(
     enformer = enformer,
@@ -190,10 +192,10 @@ Finetuning on contextual data (cell type, transcription factor, etc)
 
 ```python
 import torch
-from enformer_pytorch import Enformer
+from enformer_pytorch import from_pretrained
 from enformer_pytorch.finetune import ContextAdapterWrapper
 
-enformer = Enformer.from_pretrained('EleutherAI/enformer-official-rough')
+enformer = from_pretrained('EleutherAI/enformer-official-rough')
 
 model = ContextAdapterWrapper(
     enformer = enformer,
@@ -218,10 +220,10 @@ Finally, there is also a way to use attention aggregation from a set of context
 
 ```python
 import torch
-from enformer_pytorch import Enformer
+from enformer_pytorch import from_pretrained
 from enformer_pytorch.finetune import ContextAttentionAdapterWrapper
 
-enformer = Enformer.from_pretrained('EleutherAI/enformer-official-rough')
+enformer = from_pretrained('EleutherAI/enformer-official-rough')
 
 model = ContextAttentionAdapterWrapper(
     enformer = enformer,
@@ -315,6 +317,8 @@ seq, rand_shift_val, rc_bool = ds[0] # (196608,), (1,), (1,)
 
 Special thanks goes out to <a href="https://www.eleuther.ai/">EleutherAI</a> for providing the resources to retrain the model, during a time when the official model from Deepmind had not been released yet.
 
+Thanks also goes out to <a href="johahi">@johahi</a> for finding out that there are numerical differences between the torch and tensorflow implementations of `xlogy`. He provided a fix for this difference, which is adopted in this repository in `v0.8.0`
+
 ## Todo
 
 - [x] script to load weights from trained tensorflow enformer model to pytorch model

enformer_pytorch/__init__.py

@@ -1,3 +1,3 @@
 from enformer_pytorch.config_enformer import EnformerConfig
-from enformer_pytorch.modeling_enformer import Enformer, SEQUENCE_LENGTH, AttentionPool
+from enformer_pytorch.modeling_enformer import Enformer, from_pretrained, SEQUENCE_LENGTH, AttentionPool
 from enformer_pytorch.data import seq_indices_to_one_hot, str_to_one_hot, GenomeIntervalDataset, FastaInterval

enformer_pytorch/config_enformer.py

@@ -18,6 +18,7 @@ def __init__(
         use_convnext = False,
         num_downsamples = 7,    # genetic sequence is downsampled 2 ** 7 == 128x in default Enformer - can be changed for higher resolution
         dim_divisible_by = 128,
+        use_tf_gamma = False,
         **kwargs,
     ):
         self.dim = dim
@@ -32,5 +33,6 @@ def __init__(
         self.use_checkpointing = use_checkpointing
         self.num_downsamples = num_downsamples
         self.dim_divisible_by = dim_divisible_by
-
+        self.use_tf_gamma = use_tf_gamma
+
         super().__init__(**kwargs)

enformer_pytorch/modeling_enformer.py

@@ -1,4 +1,6 @@
 import math
+from pathlib import Path
+
 import torch
 from torch import nn, einsum
 import torch.nn.functional as F
@@ -18,6 +20,13 @@
 SEQUENCE_LENGTH = 196_608
 TARGET_LENGTH = 896
 
+# gamma positions from tensorflow
+# addressing a difference between xlogy results from tensorflow and pytorch
+# solution came from @johahi
+
+DIR = Path(__file__).parents[0]
+TF_GAMMAS = torch.load(str(DIR / "precomputed"/ "tf_gammas.pt"))
+
 # helpers
 
 def exists(val):
@@ -26,6 +35,11 @@ def exists(val):
 def default(val, d):
     return val if exists(val) else d
 
+def always(val):
+    def inner(*args, **kwargs):
+        return val
+    return inner
+
 def map_values(fn, d):
     return {key: fn(values) for key, values in d.items()}
 
@@ -75,30 +89,24 @@ def get_positional_features_gamma(positions, features, seq_len, stddev = None, s
     if not exists(start_mean):
         start_mean = seq_len / features
 
-    # turns out xlogy between tensorflow and torch differs because of the log - thanks to phd student @johahi for finding this!
-    # do everything in float64 here for precision
-
-    dtype = positions.dtype
-    positions = positions.double()
-    mean = torch.linspace(start_mean, seq_len, features, device = positions.device, dtype = torch.float64)
+    mean = torch.linspace(start_mean, seq_len, features, device = positions.device)
 
     mean = mean[None, ...]
     concentration = (mean / stddev) ** 2
     rate = mean / stddev ** 2
 
-    probabilities = gamma_pdf(positions.abs()[..., None], concentration, rate)
+    probabilities = gamma_pdf(positions.float().abs()[..., None], concentration, rate)
     probabilities = probabilities + eps
     outputs = probabilities / torch.amax(probabilities, dim = -1, keepdim = True)
+    return outputs
 
-    return outputs.to(dtype)
-
-def get_positional_embed(seq_len, feature_size, device):
+def get_positional_embed(seq_len, feature_size, device, use_tf_gamma):
     distances = torch.arange(-seq_len + 1, seq_len, device = device)
 
     feature_functions = [
         get_positional_features_exponential,
         get_positional_features_central_mask,
-        get_positional_features_gamma
+        get_positional_features_gamma if not use_tf_gamma else always(TF_GAMMAS.to(device))
     ]
 
     num_components = len(feature_functions) * 2
@@ -213,7 +221,8 @@ def __init__(
         dim_key = 64,
         dim_value = 64,
         dropout = 0.,
-        pos_dropout = 0.
+        pos_dropout = 0.,
+        use_tf_gamma = False
     ):
         super().__init__()
         self.scale = dim_key ** -0.5
@@ -240,6 +249,10 @@ def __init__(
         self.pos_dropout = nn.Dropout(pos_dropout)
         self.attn_dropout = nn.Dropout(dropout)
 
+        # whether to use tf gamma
+
+        self.use_tf_gamma = use_tf_gamma
+
     def forward(self, x):
         n, h, device = x.shape[-2], self.heads, x.device
 
@@ -253,7 +266,7 @@ def forward(self, x):
 
         content_logits = einsum('b h i d, b h j d -> b h i j', q + self.rel_content_bias, k)
 
-        positions = get_positional_embed(n, self.num_rel_pos_features, device)
+        positions = get_positional_embed(n, self.num_rel_pos_features, device, use_tf_gamma = self.use_tf_gamma)
         positions = self.pos_dropout(positions)
         rel_k = self.to_rel_k(positions)
 
@@ -308,6 +321,11 @@ def __init__(self, config):
 
         self.conv_tower = nn.Sequential(*conv_layers)
 
+        # whether to use tensorflow gamma positions
+
+        use_tf_gamma = config.use_tf_gamma
+        self.use_tf_gamma = use_tf_gamma
+
         # transformer
 
         transformer = []
@@ -322,7 +340,8 @@ def __init__(self, config):
                         dim_value = config.dim // config.heads,
                         dropout = config.attn_dropout,
                         pos_dropout = config.pos_dropout,
-                        num_rel_pos_features = config.dim // config.heads
+                        num_rel_pos_features = config.dim // config.heads,
+                        use_tf_gamma = use_tf_gamma
                     ),
                     nn.Dropout(config.dropout_rate)
                 )),
@@ -454,3 +473,13 @@ def forward(
             return out, x
 
         return out
+
+# from pretrained function
+
+def from_pretrained(name, use_tf_gamma = None, **kwargs):
+    enformer = Enformer.from_pretrained(name, **kwargs)
+
+    if name == 'EleutherAI/enformer-official-rough':
+        enformer.use_tf_gamma = default(use_tf_gamma, True)
+
+    return enformer

setup.py

@@ -4,7 +4,7 @@
   name = 'enformer-pytorch',
   packages = find_packages(exclude=[]),
   include_package_data = True,
-  version = '0.7.7',
+  version = '0.8.2',
   license='MIT',
   description = 'Enformer - Pytorch',
   author = 'Phil Wang',

test_pretrained.py

@@ -1,7 +1,7 @@
 import torch
-from enformer_pytorch import Enformer
+from enformer_pytorch import from_pretrained
 
-enformer = Enformer.from_pretrained('EleutherAI/enformer-official-rough').cuda()
+enformer = from_pretrained('EleutherAI/enformer-official-rough').cuda()
 enformer.eval()
 
 data = torch.load('./data/test-sample.pt')