Update notebooks.

Quansight-Labs · Apr 23, 2019 · e430f7e · e430f7e
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Showing 2 changed files with 38 additions and 39 deletions.
diff --git a/notebooks/01_user_facing.ipynb b/notebooks/01_user_facing.ipynb
@@ -22,13 +22,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "from unumpy.xnd_backend import XndBackend\n",
     "from unumpy.numpy_backend import NumpyBackend\n",
-    "from unumpy.pytorch_backend import TorchBackend"
+    "from unumpy.torch_backend import TorchBackend"
    ]
   },
   {
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -118,12 +118,27 @@
      "text": [
       "Using the TorchBackend with coerce=True on a NumPy array: <class 'torch.Tensor'>\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hameerabbasi/Quansight/uarray/unumpy/torch_backend.py:80: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  ret = torch.tensor(a, dtype=dtype)\n"
+     ]
     }
    ],
    "source": [
     "with ua.set_backend(TorchBackend, coerce=True):\n",
     "    print('Using the TorchBackend with coerce=True on a NumPy array: {}'.format(type(np.sum(z))))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/notebooks/02_basic_dev_tutorial.ipynb b/notebooks/02_basic_dev_tutorial.ipynb
@@ -17,7 +17,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from uarray import argument_extractor"
+    "from uarray import create_multimethod, all_of_type\n",
+    "import unumpy as unp"
    ]
   },
   {
@@ -30,7 +31,8 @@
     "    out_args = arrays + args[2:]\n",
     "    return out_args, kwargs\n",
     "\n",
-    "@argument_extractor(solve_argreplacer)\n",
+    "@create_multimethod(solve_argreplacer)\n",
+    "@all_of_type(unp.ndarray)\n",
     "def solve(a, b, sym_pos=False, lower=False, overwrite_a=False, overwrite_b=False, debug=None, check_finite=True, assume_a='gen', transposed=False):\n",
     "    return (a, b)"
    ]
@@ -49,8 +51,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from uarray import multimethod\n",
-    "from unumpy.numpy_backend import NumpyBackend\n",
+    "from uarray import register_implementation\n",
+    "from unumpy.numpy_backend import NumpyBackend, compat_check as np_cc\n",
     "import scipy.linalg as linalg"
    ]
   },
@@ -71,7 +73,7 @@
     }
    ],
    "source": [
-    "multimethod(NumpyBackend, solve)(linalg.solve)"
+    "register_implementation(solve, NumpyBackend, compat_check=np_cc)(linalg.solve)"
    ]
   },
   {
@@ -88,8 +90,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from uarray import multimethod\n",
-    "from unumpy.pytorch_backend import TorchBackend\n",
+    "from uarray import register_implementation\n",
+    "from unumpy.torch_backend import TorchBackend, compat_check as torch_cc\n",
     "import torch"
    ]
   },
@@ -120,32 +122,7 @@
     }
    ],
    "source": [
-    "multimethod(TorchBackend, solve)(solve_impl)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backend Code\n",
-    "## For NumPy\n",
-    "```python\n",
-    "from uarray.backend import TypeCheckBackend, register_backend\n",
-    "import numpy as np\n",
-    "\n",
-    "NumpyBackend = TypeCheckBackend((np.ndarray, np.generic), convertor=np.array,\n",
-    "                                fallback_types=(tuple, list, int, float, bool))\n",
-    "register_backend(NumpyBackend)\n",
-    "```\n",
-    "\n",
-    "## For PyTorch\n",
-    "```python\n",
-    "import torch\n",
-    "from uarray.backend import TypeCheckBackend, register_backend\n",
-    "\n",
-    "TorchBackend = TypeCheckBackend((torch.Tensor,), convertor=torch.Tensor)\n",
-    "register_backend(TorchBackend)\n",
-    "```"
+    "register_implementation(solve, TorchBackend, compat_check=torch_cc)(solve_impl)"
    ]
   },
   {
@@ -256,12 +233,19 @@
    ],
    "source": [
     "import uarray as ua\n",
-    "with ua.set_backend(NumpyBackend, coerce=None):\n",
+    "with ua.set_backend(NumpyBackend, coerce=True):\n",
     "    print(type(solve(a, b)))\n",
     "        \n",
     "with ua.set_backend(TorchBackend, coerce=True):\n",
     "    print(type(solve(a, b)))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {