Merge remote-tracking branch 'upstream/main'

.github/workflows/test-ivy-cron-gpu.yml

@@ -1,8 +1,8 @@
 name: test-ivy-cron-gpu
 on:
   workflow_dispatch:
-  schedule:
-    - cron: '25 * * * *'
+  # schedule:
+  #   - cron: '25 * * * *'
 permissions:
   actions: read
 concurrency:

.vaunt/config.yaml

@@ -29,7 +29,7 @@ achievements:
         - trigger:
             actor: author
             action: pull_request
-            condition: count(merged = true & labels in {'Priority PR'} & created_at >= "2023-12-26") >= 1
+            condition: count(merged = true & labels in {'Priority'} & created_at >= "2023-12-26") >= 1
 
   - achievement:
       name: Ivy Inspectors
@@ -69,7 +69,7 @@ achievements:
         - trigger:
             actor: author
             action: pull_request
-            condition: count(merged = true & labels in {'Priority PR'} & created_at >= "2023-12-26") >= 5
+            condition: count(merged = true & labels in {'Priority'} & created_at >= "2023-12-26") >= 5
 
   - achievement:
       name: Ivy Inspectors Bronze
@@ -109,7 +109,7 @@ achievements:
         - trigger:
             actor: author
             action: pull_request
-            condition: count(merged = true & labels in {'Priority PR'} & created_at >= "2023-12-26") >= 15
+            condition: count(merged = true & labels in {'Priority'} & created_at >= "2023-12-26") >= 15
 
   - achievement:
       name: Ivy Inspectors Silver
@@ -149,7 +149,7 @@ achievements:
         - trigger:
             actor: author
             action: pull_request
-            condition: count(merged = true & labels in {'Priority PR'} & created_at >= "2023-12-26") >= 30
+            condition: count(merged = true & labels in {'Priority'} & created_at >= "2023-12-26") >= 30
 
   - achievement:
       name: Ivy Inspectors Gold

docker/gpu_framework_directory.py

@@ -43,6 +43,13 @@ def install_pkg(path, pkg, base="fw/"):
             "  --no-cache-dir",
             shell=True,
         )
+        subprocess.run(
+            f"yes |pip3 install --upgrade torchvision --index-url"
+            f" https://download.pytorch.org/whl/cu121 --target"
+            f" {path} --default-timeout=100 --extra-index-url"
+            " --no-cache-dir",
+            shell=True,
+        )
     elif pkg.split("==")[0] if "==" in pkg else pkg == "jax":
         subprocess.run(
             f"yes |pip install --upgrade --target {path} 'jax[cuda12_pip]' -f"

docker/requirement_mappings_gpu.json

@@ -2,6 +2,6 @@
   "jax": ["dm-haiku", "flax"],
   "numpy": ["numpy"],
   "mxnet": ["mxnet"],
-  "torch": ["torch-scatter", "torchvision"],
+  "torch": ["torchvision", "torch-scatter"],
   "tensorflow": ["tensorflow-probability"]
 }

docs/overview/volunteer_ranks.rst

@@ -1,7 +1,7 @@
 Contributor Leaderboard
 =======================
 
-This page lists all of our amazing Contributors who have contributed to the project! We are grateful for your contributions and we hope to see you grow with the project! The ranks listed here are based on the `level of contribution <contributing/contributor_rewards.rst>`_\.
+This page lists all of our amazing Contributors who have contributed to the project! We are grateful for your contributions and we hope to see you grow with the project! The ranks listed here are based on the `level of contribution <https://unify.ai/docs/ivy/overview/contributing/volunteer_workflow.html>`_\.
 
 Top Contributors
 ----------------
@@ -15,15 +15,18 @@ Top Contributors
    * - V\. Sai Suraj
      - `Sai-Suraj-27 <https://github.com/Sai-Suraj-27>`_
      - Merging Master Gold, Debugging Dynamo Silver
-Rising Contributors
--------------------
+   * - samunder singh
+     - `samthakur587 <https://github.com/samthakur587>`_
+     - Merging Master Gold, Debugging Dynamo Silver
+Core Contributors
+-----------------
 .. list-table::
    :widths: 50 50 50
    :header-rows: 1
 
    * - Name
      - Github ID
      - Badges
-   * - samunder singh
-     - `samthakur587 <https://github.com/samthakur587>`_
-     - Merging Master Silver, Debugging Dynamo Silver
+   * - Muhammad ishaque
+     - `MuhammadNizamani <https://github.com/MuhammadNizamani>`_
+     - Merging Master Bronze

ivy/_version.py

@@ -1 +1 @@
-__version__ = "0.0.6.2"
+__version__ = "0.0.7.0"

ivy/data_classes/array/experimental/activations.py

@@ -325,7 +325,7 @@ def hardtanh(
         >>> x = ivy.array([-1., .2, 1.])
         >>> y = x.hardtanh()
         >>> print(y)
-        ivy.array([-1., 1., 1.])
+        ivy.array([-1. ,  0.2,  1. ])
         """
         return ivy.hardtanh(self._data, min_val=min_val, max_val=max_val, out=out)
 

ivy/data_classes/array/experimental/elementwise.py

@@ -1191,3 +1191,34 @@ def erfc(
         ivy.array([1.00000000e+00, 1.84270084e+00, 2.80259693e-45])
         """
         return ivy.erfc(self._data, out=out)
+
+    def erfinv(
+        self: ivy.Array,
+        /,
+        *,
+        out: Optional[ivy.Array] = None,
+    ) -> ivy.Array:
+        """ivy.Array instance method variant of ivy.erfinv. This method simply
+        wraps the function, and so the docstring for ivy.erfinv also applies to
+        this method with minimal changes.
+
+        Parameters
+        ----------
+        self
+            Input array with real or complex valued argument.
+        out
+            Alternate output array in which to place the result.
+            The default is None.
+
+        Returns
+        -------
+        ret
+            Values of the inverse error function.
+
+        Examples
+        --------
+        >>> x = ivy.array([0, -1., 10.])
+        >>> x.erfinv()
+        ivy.array([1.00000000e+00, 1.84270084e+00, 2.80259693e-45])
+        """
+        return ivy.erfinv(self._data, out=out)

ivy/data_classes/array/experimental/linear_algebra.py

@@ -832,7 +832,7 @@ def general_inner_product(
 
         >>> a = ivy.array([1, 2, 3])
         >>> b = ivy.array([4, 5, 6])
-        >>> result = a.general_inner_product(b, n_modes=1)
+        >>> result = a.general_inner_product(b, 1)
         >>> print(result)
         ivy.array(32)
 
@@ -844,7 +844,7 @@ def general_inner_product(
 
         >>> a = ivy.array([[1, 1], [1, 1]])
         >>> b = ivy.array([[1, 2, 3, 4],[1, 1, 1, 1]])
-        >>> result = a.general_inner_product(b, n_modes=1)
+        >>> result = a.general_inner_product(b, 1)
         >>> print(result)
         ivy.array([[2, 3, 4, 5],
             [2, 3, 4, 5]])

ivy/data_classes/array/experimental/losses.py

@@ -98,7 +98,7 @@ def log_poisson_loss(
         ivy.array([1.28402555, 1.28402555, 1.03402555, 1.28402555])
 
         >>> z = ivy.array([0.1, 0.1, 0.7, 0.1])
-        >>> loss = x.x.log_poisson_loss(z, reduction='mean')
+        >>> loss = x.log_poisson_loss(z, reduction='mean')
         >>> print(loss)
         ivy.array(1.1573164)
         """
@@ -353,9 +353,9 @@ def poisson_nll_loss(
         --------
         >>> input_tensor = ivy.array([1, 2, 3, 4], dtype=ivy.float64)
         >>> target_tensor = ivy.array([2, 2, 2, 2], dtype=ivy.float64)
-        >>> loss = poisson_nll_loss(input_tensor, target_tensor, log_input=True)
+        >>> loss = input_tensor.poisson_nll_loss(target_tensor, log_input=True)
         >>> print(loss)
-        ivy.array(16.1978)
+        ivy.array(16.1977562)
         """
         return ivy.poisson_nll_loss(
             self._data,

ivy/data_classes/array/experimental/manipulation.py

@@ -1134,6 +1134,64 @@ def take(
             self, indices, axis=axis, mode=mode, fill_value=fill_value, out=out
         )
 
+    def unflatten(
+        self: ivy.Array,
+        /,
+        shape: Union[Tuple[int], ivy.Array, ivy.NativeArray],
+        dim: Optional[int] = 0,
+        *,
+        out: Optional[ivy.Array] = None,
+    ) -> ivy.Array:
+        """ivy.Array instance method variant of ivy.unflatten. This method
+        simply wraps the function, and so the docstring for ivy.unflatten also
+        applies to this method with minimal changes.
+
+        Parameters
+        ----------
+        self
+            input array
+        shape
+            array indices. Must have an integer data type.
+        dim
+            axis over which to unflatten. If `axis` is negative,
+            the function must determine the axis along which to select values
+            by counting from the last dimension.
+            By default, the flattened input array is used.
+        out
+            optional output array, for writing the result to. It must
+            have a shape that the inputs broadcast to.
+
+        Returns
+        -------
+            ret
+                an array having the same data type as `x`.
+                The output array must have the same rank
+                (i.e., number of dimensions) as `x` and
+                must have the same shape as `x`,
+                except for the axis specified by `dim`
+                which is replaced with a tuple specified in `shape`.
+
+
+        Examples
+        --------
+        With 'ivy.Array' input:
+
+        >>> x = ivy.array([[1.2, 2.3, 3.4, 4.5],
+        ...               [5.6, 6.7, 7.8, 8.9]])
+        >>> dim = 1
+        >>> shape = (2, 2)
+        >>> y = ivy.zeros([2, 2, 2])
+        >>> x.unflatten(shape=shape, dim=dim, out=y)
+        >>> print(y)
+        ivy.array([[[1.2, 2.3], [3.4, 4.5]], [[5.6, 6.7], [7.8, 8.9]]])
+        """
+        return ivy.unflatten(
+            self._data,
+            shape=shape,
+            dim=dim,
+            out=out,
+        )
+
     def trim_zeros(
         self: ivy.Array,
         /,

ivy/data_classes/array/losses.py

@@ -50,7 +50,7 @@ def cross_entropy(
         >>> y = ivy.array([0.25, 0.25, 0.25, 0.25])
         >>> z = x.cross_entropy(y)
         >>> print(z)
-        ivy.array(1.3862944)
+        ivy.array(0.34657359)
         """
         return ivy.cross_entropy(
             self._data, pred, axis=axis, epsilon=epsilon, reduction=reduction, out=out
@@ -110,7 +110,7 @@ def binary_cross_entropy(
         >>> y = ivy.array([0.7, 0.8, 0.2])
         >>> z = x.binary_cross_entropy(y)
         >>> print(z)
-        ivy.array([0.357, 0.223, 0.223])
+        ivy.array(0.26765382)
         """
         return ivy.binary_cross_entropy(
             self._data,
@@ -170,7 +170,7 @@ def sparse_cross_entropy(
         >>> y = ivy.array([0.7, 0.8, 0.2])
         >>> z = x.sparse_cross_entropy(y)
         >>> print(z)
-        ivy.array([0.223, 0.223, 0.357])
+        ivy.array([0.07438118, 0.07438118, 0.11889165])
         """
         return ivy.sparse_cross_entropy(
             self._data, pred, axis=axis, epsilon=epsilon, reduction=reduction, out=out

ivy/data_classes/container/experimental/activations.py

@@ -968,10 +968,10 @@ def _static_hardtanh(
         Examples
         --------
         >>> x = x = ivy.Container(a=ivy.array([0.39, -2.0]), b=ivy.array([2., -0.2]))
-        >>> y = ivy.Container.static_hardtanh(x)
+        >>> y = ivy.Container._static_hardtanh(x)
         >>> print(y)
         {
-            a: ivy.array([0.39, -1.]),
+            a: ivy.array([0.3899, -1.]),
             b: ivy.array([1., -0.2])
         }
         """
@@ -1033,11 +1033,11 @@ def hardtanh(
 
         Examples
         --------
-        >>> x = x = ivy.Container(a=ivy.array([0.39, -2.0]), b=ivy.array([2., -0.2]))
-        >>> y = ivy.Container.static_hardtanh(x)
+        >>> x = ivy.Container(a=ivy.array([0.39, -2.0]), b=ivy.array([2., -0.2]))
+        >>> y = ivy.Container.hardtanh(x)
         >>> print(y)
         {
-            a: ivy.array([0.39, -1.]),
+            a: ivy.array([0.389999, -1.]),
             b: ivy.array([1., -0.2])
         }
         """
@@ -1820,8 +1820,7 @@ def hardshrink(
 
         Examples
         --------
-        >>> import ivy.numpy as np
-        >>> x = ivy.Container(a=np.array([1., -2.]), b=np.array([0.4, -0.2]))
+        >>> x = ivy.Container(a=ivy.array([1., -2.]), b=ivy.array([0.4, -0.2]))
         >>> y = ivy.Container.hardshrink(x)
         >>> print(y)
         {