Update release

README.md

@@ -173,6 +173,8 @@ A lot of our models have been published by talend authors developing these excit
 - [GenePT](https://github.com/yiqunchen/GenePT)
 - [Caduceus](https://github.com/kuleshov-group/caduceus)
 - [Evo2](https://github.com/ArcInstitute/evo2)
+- [torch](https://github.com/pytorch/pytorch/blob/main/LICENSE)
+- [torchvision](https://github.com/pytorch/vision/blob/release/0.21/LICENSE)
 
 ### Licenses
 

helical/models/hyena_dna/standalone_hyenadna.py

@@ -23,7 +23,7 @@
 from typing import Optional
 from functools import partial
 from torch import Tensor
-from torchvision.ops import StochasticDepth
+from helical.models.hyena_dna.stochastic_depth import StochasticDepth
 from collections import namedtuple
 import numpy as np
 import os

helical/models/hyena_dna/stochastic_depth.py

@@ -0,0 +1,128 @@
+"""
+
+BSD 3-Clause License
+
+Copyright (c) Soumith Chintala 2016, 
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Copied from torchvision to remove that dependency
+https://github.com/pytorch/vision/blob/release/0.21/torchvision/utils.py
+https://github.com/pytorch/vision/blob/release/0.21/torchvision/ops/stochastic_depth.py
+"""
+
+import torch
+import torch.fx
+from torch import nn, Tensor
+from typing import Any
+from types import FunctionType
+
+def _log_api_usage_once(obj: Any) -> None:
+
+    """
+    Logs API usage(module and name) within an organization.
+    In a large ecosystem, it's often useful to track the PyTorch and
+    TorchVision APIs usage. This API provides the similar functionality to the
+    logging module in the Python stdlib. It can be used for debugging purpose
+    to log which methods are used and by default it is inactive, unless the user
+    manually subscribes a logger via the `SetAPIUsageLogger method <https://github.com/pytorch/pytorch/blob/eb3b9fe719b21fae13c7a7cf3253f970290a573e/c10/util/Logging.cpp#L114>`_.
+    Please note it is triggered only once for the same API call within a process.
+    It does not collect any data from open-source users since it is no-op by default.
+    For more information, please refer to
+    * PyTorch note: https://pytorch.org/docs/stable/notes/large_scale_deployments.html#api-usage-logging;
+    * Logging policy: https://github.com/pytorch/vision/issues/5052;
+
+    Args:
+        obj (class instance or method): an object to extract info from.
+    """
+    module = obj.__module__
+    if not module.startswith("torchvision"):
+        module = f"torchvision.internal.{module}"
+    name = obj.__class__.__name__
+    if isinstance(obj, FunctionType):
+        name = obj.__name__
+    torch._C._log_api_usage_once(f"{module}.{name}")
+
+def stochastic_depth(input: Tensor, p: float, mode: str, training: bool = True) -> Tensor:
+    """
+    Implements the Stochastic Depth from `"Deep Networks with Stochastic Depth"
+    <https://arxiv.org/abs/1603.09382>`_ used for randomly dropping residual
+    branches of residual architectures.
+
+    Args:
+        input (Tensor[N, ...]): The input tensor or arbitrary dimensions with the first one
+                    being its batch i.e. a batch with ``N`` rows.
+        p (float): probability of the input to be zeroed.
+        mode (str): ``"batch"`` or ``"row"``.
+                    ``"batch"`` randomly zeroes the entire input, ``"row"`` zeroes
+                    randomly selected rows from the batch.
+        training: apply stochastic depth if is ``True``. Default: ``True``
+
+    Returns:
+        Tensor[N, ...]: The randomly zeroed tensor.
+    """
+    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
+        _log_api_usage_once(stochastic_depth)
+    if p < 0.0 or p > 1.0:
+        raise ValueError(f"drop probability has to be between 0 and 1, but got {p}")
+    if mode not in ["batch", "row"]:
+        raise ValueError(f"mode has to be either 'batch' or 'row', but got {mode}")
+    if not training or p == 0.0:
+        return input
+
+    survival_rate = 1.0 - p
+    if mode == "row":
+        size = [input.shape[0]] + [1] * (input.ndim - 1)
+    else:
+        size = [1] * input.ndim
+    noise = torch.empty(size, dtype=input.dtype, device=input.device)
+    noise = noise.bernoulli_(survival_rate)
+    if survival_rate > 0.0:
+        noise.div_(survival_rate)
+    return input * noise
+
+
+torch.fx.wrap("stochastic_depth")
+
+
+class StochasticDepth(nn.Module):
+    """
+    See :func:`stochastic_depth`.
+    """
+
+    def __init__(self, p: float, mode: str) -> None:
+        super().__init__()
+        _log_api_usage_once(self)
+        self.p = p
+        self.mode = mode
+
+    def forward(self, input: Tensor) -> Tensor:
+        return stochastic_depth(input, self.p, self.mode, self.training)
+
+    def __repr__(self) -> str:
+        s = f"{self.__class__.__name__}(p={self.p}, mode={self.mode})"
+        return s

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "helical"
-version = "1.4.1"
+version = "1.4.2"
 authors = [
   { name="Helical Team", email="support@helical-ai.com" },
 ]
@@ -28,15 +28,11 @@ dependencies = [
     'scipy==1.13.1',
     'gitpython==3.1.43',
     'torch==2.6.0',
-    'torchvision==0.21.0',
     'accelerate==1.4.0',
     'transformers==4.49.0',
     'loompy==3.0.7',
     'scib==1.1.5',
     'scikit-misc==0.3.1',
-    'azure-identity==1.16.1',
-    'azure-storage-blob==12.19.1',
-    'azure-core==1.30.1',
     'einops==0.8.0',
     'omegaconf==2.3.0',
     'hydra-core==1.3.2',