[numpy] Refactor np modules (apache#14989)

* Refactor

* Initial refactoring

* Fix notebook

* Move numpy op check from backend to frontend

* Add homogeneous ndarray check

* Fix grouping inhomogeneous types of symbols

* Improve error handling of different types of symbols as outputs

* Fix test

* Fix numpy test

* Fix ci

* Try to fix gpu ci failure

example/numpy/demo.ipynb

@@ -6,21 +6,21 @@
    "source": [
     "# Fundamentals of MXNet Numpy Module\n",
     "\n",
-    "## Operator Namespaces for Imperative Programming\n",
+    "## Namespaces for Imperative Programming\n",
     "- `mxnet.numpy`: Regular NumPy operators\n",
     "- `mxnet.numpy.random`: NumPy random operators\n",
     "- `mxnet.numpy.linalg`: NumPy linear algebra operators\n",
-    "- `mxnet.numpy.ext`: Operators implemented in MXNet that do not exist in official NumPy\n",
+    "- `mxnet.numpy_extension`: Operators implemented in MXNet that do not exist in the official NumPy\n",
     "\n",
     "## Operator Namespaces for Gluon\n",
-    "`F` can be either `mxnet.ndarray` or `mxnet.symbol`.\n",
+    "`F` can be either `mxnet.ndarray` or `mxnet.symbol`. Note that `np` and `npe` are aliases of `numpy` and `numpy_extension`, respectively.\n",
     "- `F.np`: Regular NumPy operators\n",
     "- `F.np.random`: NumPy random operators\n",
     "- `F.np.linalg`: NumPy linear algebra operators\n",
-    "- `F.np.ext`: Operators implemented in MXNet that do not exist in official NumPy\n",
+    "- `F.npe`: Operators implemented in MXNet that do not exist in official NumPy\n",
     "\n",
     "## New `ndarray` and `symbol`\n",
-    "`mxnet.numpy.ndarray` and `mxnet.symbol.numpy._NumpySymbol` (not visible to users)\n",
+    "`mxnet.numpy.ndarray` (visible to users) and `mxnet.symbol.numpy._Symbol` (not visible to users)\n",
     "- Same name as in the official NumPy package\n",
     "- Dispatch convience fluent method calls to MXNet Numpy operators\n",
     "- Override many convenience fluent methods that do not exist in the official NumPy ndarray\n",
@@ -46,7 +46,7 @@
     "\n",
     "# create a scalar tensor\n",
     "x = np.array(3.14)\n",
-    "print(x)"
+    "print(x)  # x is actually an ndarray, but a scalar value will be printed"
    ]
   },
   {
@@ -170,13 +170,15 @@
     "from mxnet import gluon\n",
     "class TestBinaryBroadcast(gluon.HybridBlock):\n",
     "    def hybrid_forward(self, F, x1, x2):\n",
-    "        print(\"x1 type:\", str(type(x1)))\n",
-    "        print(\"x2 type:\", str(type(x2)))\n",
+    "        print(\"x1 type in hybrid_forward:\", str(type(x1)))\n",
+    "        print(\"x2 type in hybrid_forward:\", str(type(x2)))\n",
     "        return x1 + x2\n",
     "\n",
     "net = TestBinaryBroadcast()\n",
     "x1 = mx.nd.ones((2, 1))\n",
     "x2 = mx.nd.ones((1, 3))\n",
+    "print('x1 input tensor type: ', str(type(x1)))\n",
+    "print('x2 input tensor type: ', str(type(x2)))\n",
     "out = net(x1, x2)  # ok: imperative execution supports broadcasting\n",
     "print(out)"
    ]
@@ -203,13 +205,15 @@
    "source": [
     "class TestBinaryBroadcast2(gluon.HybridBlock):\n",
     "    def hybrid_forward(self, F, x1, x2):\n",
-    "        print(\"x1 type:\", str(type(x1)))\n",
-    "        print(\"x2 type:\", str(type(x2)))\n",
+    "        print(\"x1 type in hybrid_forward:\", str(type(x1)))\n",
+    "        print(\"x2 type in hybrid_forward:\", str(type(x2)))\n",
     "        return x1.as_np_ndarray() + x2  # convert x1 to new numpy ndarray/symbol\n",
     "\n",
     "net2 = TestBinaryBroadcast2()\n",
     "net2.hybridize()\n",
     "\n",
+    "print('x1 input tensor type: ', str(type(x1)))\n",
+    "print('x2 input tensor type: ', str(type(x2)))\n",
     "out =net2(x1, x2)\n",
     "print(out)"
    ]
@@ -224,7 +228,9 @@
     "net.hybridize()  # mark the block for execution using a computational graph\n",
     "\n",
     "x1 = x1.as_np_ndarray()  # convert x1 to np.ndarray so that _NumpySymbol will be used in graph construction\n",
+    "print('x1 input tensor type: ', str(type(x1)))\n",
     "x2 = x2.as_np_ndarray()  # convert x2 to np.ndarray so that _NumpySymbol will be used in graph construction\n",
+    "print('x2 input tensor type: ', str(type(x2)))\n",
     "out = net(x1, x2)  # ok: `+` operation supports broadcasting for _NumpySymbol\n",
     "print(out)  # mxnet.numpy.ndarray type, because it's from a np operator"
    ]
@@ -245,7 +251,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## MXNet Numpy Operators in Imperative Programming"
+    "### MXNet Numpy Operators in Imperative Programming"
    ]
   },
   {
@@ -255,15 +261,9 @@
    "outputs": [],
    "source": [
     "import mxnet as mx\n",
-    "from mxnet import numpy as np\n",
+    "from mxnet import numpy as np, numpy_extension as npe\n",
     "from mxnet import autograd\n",
-    "try:\n",
-    "    from mxboard import SummaryWriter\n",
-    "except ImportError:\n",
-    "    SummaryWriter = None\n",
     "\n",
-    "# create a summary writer for visualization\n",
-    "sw = SummaryWriter(logdir='./logs', flush_secs=2) if SummaryWriter is not None else None\n",
     "\n",
     "# Use numpy-compatible semantics to support scalar tensors\n",
     "mx.set_np_compat(True)\n",
@@ -285,11 +285,11 @@
     "learning_rate = 1e-6\n",
     "\n",
     "\n",
-    "for t in range(1000):\n",
+    "for t in range(50):\n",
     "    with autograd.record():\n",
     "        # Forward pass: compute predicted y\n",
     "        h = x.dot(w1)  # equivalent to np.dot(x, w1)\n",
-    "        h_relu = np.ext.relu(h)  # equivalent to mx.nd.relu(h)\n",
+    "        h_relu = npe.relu(h)  # equivalent to mx.nd.relu(h)\n",
     "        y_pred = h_relu.dot(w2)  # equivalent to np.dot(h_relu, w2)\n",
     "\n",
     "        # Compute loss\n",
@@ -302,23 +302,14 @@
     "\n",
     "    # Update weights\n",
     "    w1 -= learning_rate * w1.grad\n",
-    "    w2 -= learning_rate * w2.grad\n",
-    "\n",
-    "    if sw is not None:\n",
-    "        sw.add_scalar('loss', loss.item(), global_step=t)  # loss.item() copies the tensor element to a python scalar\n",
-    "        if t % 50 == 0:\n",
-    "            sw.add_histogram(tag='w1', values=w1, global_step=t)\n",
-    "            sw.add_histogram(tag='w2', values=w2, global_step=t)\n",
-    "\n",
-    "if sw is not None:\n",
-    "    sw.close()"
+    "    w2 -= learning_rate * w2.grad"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## MXNet Numpy Operators in Gluon `HybridBlock`"
+    "### MXNet Numpy Operators in Gluon `HybridBlock`"
    ]
   },
   {
@@ -329,13 +320,7 @@
    "source": [
     "import mxnet as mx\n",
     "from mxnet import gluon, autograd\n",
-    "try:\n",
-    "    from mxboard import SummaryWriter\n",
-    "except ImportError:\n",
-    "    SummaryWriter = None\n",
     "\n",
-    "# create a summary writer for visualization\n",
-    "sw = SummaryWriter(logdir='./logs', flush_secs=2) if SummaryWriter is not None else None\n",
     "\n",
     "# Use numpy-compatible semantics to support scalar tensors\n",
     "mx.set_np_compat(True)\n",
@@ -352,7 +337,7 @@
     "\n",
     "    def hybrid_forward(self, F, x, w1, w2):\n",
     "        h = x.dot(w1)  # equivalent to F.np.dot(x, w1)\n",
-    "        h_relu = F.np.ext.relu(h)  # equivalent to F.relu(h)\n",
+    "        h_relu = F.npe.relu(h)  # equivalent to F.relu(h)\n",
     "        y_pred = h_relu.dot(w2)  # equivalent to F.np.dot(h_relu, w2)\n",
     "        return y_pred\n",
     "\n",
@@ -373,21 +358,13 @@
     "total_loss = TotalLoss()\n",
     "trainer = gluon.Trainer(regressor.collect_params(), 'sgd', {'learning_rate': 1e-3, 'momentum': 0.9})\n",
     "\n",
-    "for t in range(1000):\n",
+    "for t in range(50):\n",
     "    with autograd.record():\n",
     "        output = regressor(x)  # output is a type of np.ndarray because np.dot is the last op in the network\n",
     "        loss = total_loss(output, y)  # loss is a scalar np.ndarray\n",
     "    loss.backward()\n",
     "    print(t, loss)  # note that loss.asnumpy() is called\n",
-    "    trainer.step(1)\n",
-    "    if sw is not None:\n",
-    "        sw.add_scalar('loss', loss.item(), global_step=t)  # loss.item() copies the tensor element to a python scalar\n",
-    "        if t % 50 == 0:\n",
-    "            for k, v in regressor.collect_params().items():\n",
-    "                sw.add_histogram(tag=k, values=v.data(), global_step=t)\n",
-    "\n",
-    "if sw is not None:\n",
-    "    sw.close()"
+    "    trainer.step(1)"
    ]
   }
  ],

include/mxnet/c_api.h

@@ -2902,14 +2902,6 @@ MXNET_DLL int MXEnginePushSync(EngineSyncFunc sync_func, void* func_param,
                                EngineVarHandle mutable_vars_handle, int num_mutable_vars,
                                EngineFnPropertyHandle prop_handle DEFAULT(NULL),
                                int priority DEFAULT(0), const char* opr_name DEFAULT(NULL));
-/*!
-  * \brief Determines if an op is a Numpy op by its name prefix.
-  * Every Numpy op starts with a prefix string "_numpy_".
-  * \param creator Operator handle
-  * \param is_np_op Indicator of whether creator is a numpy op handle
-  */
-MXNET_DLL int MXIsNumpyCompatOp(AtomicSymbolCreator creator,
-                                int* is_np_op);
 /*!
  * \brief Create an NDArray from source sharing the same data chunk.
  * \param src source NDArray
@@ -2922,15 +2914,6 @@ MXNET_DLL int MXShallowCopyNDArray(NDArrayHandle src, NDArrayHandle* out);
  * \param out new Symbol sharing the same graph structure with src
  */
 MXNET_DLL int MXShallowCopySymbol(SymbolHandle src, SymbolHandle * out);
-/*!
- * \brief Checks if an output of CachedOp is from a numpy op.
- * \param handle CachedOp shared ptr
- * \param output_idx index of the output of the CachedOp
- * \param is_from_np_op indicator of whether the output is from a numpy op
- */
-MXNET_DLL int MXIsCachedOpOutputFromNumpyCompatOp(CachedOpHandle handle,
-                                                  int output_idx,
-                                                  int* is_from_np_op);
 
 /*!
   * \brief Push an asynchronous operation to the engine.

include/mxnet/op_attr_types.h

@@ -319,15 +319,6 @@ using FNeedRequantize = std::function<bool (const NodeAttrs& attrs)>;
 using FAvoidQuantizeInput = std::function<bool (const NodeAttrs& attrs,
                                                 size_t index)>;
 
-/*!
- * \brief Indicates whether this operator is NumPy compatible.
- * It is for distinguishing the operator from classic MXNet operators
- * which do not support zero-dim and zero-size tensors.
- * In Python, it is used to determine whether to output numpy ndarrays
- * or symbols that are NumPy compatible.
- */
-using TIsNumpyCompatible = bool;
-
 }  // namespace mxnet
 
 #endif  // MXNET_OP_ATTR_TYPES_H_

python/mxnet/__init__.py

@@ -30,6 +30,9 @@
 from . import ndarray
 from . import ndarray as nd
 from . import numpy
+from . import numpy_extension
+from . import numpy as np
+from . import numpy_extension as npe
 from . import name
 # use mx.sym as short for symbol
 from . import symbol as sym

python/mxnet/_ctypes/ndarray.py

@@ -26,7 +26,7 @@
 from ..base import _LIB
 from ..base import c_str_array, c_handle_array
 from ..base import NDArrayHandle, CachedOpHandle
-from ..base import check_call, _is_np_compat_op
+from ..base import check_call
 
 
 class NDArrayBase(object):
@@ -70,7 +70,7 @@ def _set_np_ndarray_class(cls):
     _np_ndarray_cls = cls
 
 
-def _imperative_invoke(handle, ndargs, keys, vals, out):
+def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op):
     """ctypes implementation of imperative invoke wrapper"""
     if out is not None:
         original_output = out
@@ -99,9 +99,9 @@ def _imperative_invoke(handle, ndargs, keys, vals, out):
         c_str_array([str(s) for s in vals]),
         ctypes.byref(out_stypes)))
 
+    create_ndarray_fn = _np_ndarray_cls if is_np_op else _ndarray_cls
     if original_output is not None:
         return original_output
-    create_ndarray_fn = _np_ndarray_cls if _is_np_compat_op(handle) else _ndarray_cls
     if num_output.value == 1:
         return create_ndarray_fn(ctypes.cast(output_vars[0], NDArrayHandle),
                                  stype=out_stypes[0])
@@ -112,11 +112,14 @@ def _imperative_invoke(handle, ndargs, keys, vals, out):
 
 class CachedOp(object):
     """Cached operator handle."""
-    __slots__ = ["handle"]
+    __slots__ = ["handle", "is_np_sym"]
 
     def __init__(self, sym, flags=()):
         self.handle = CachedOpHandle()
 
+        from ..symbol.numpy._symbol import _Symbol
+        self.is_np_sym = True if isinstance(sym, _Symbol) else False
+
         check_call(_LIB.MXCreateCachedOpEx(
             sym.handle,
             len(flags),
@@ -167,12 +170,10 @@ def __call__(self, *args, **kwargs):
 
         if original_output is not None:
             return original_output
+        create_ndarray_fn = _np_ndarray_cls if self.is_np_sym else _ndarray_cls
         if num_output.value == 1:
-            create_ndarray_fn = _np_ndarray_cls if self._is_from_np_compat_op(0) else _ndarray_cls
             return create_ndarray_fn(ctypes.cast(output_vars[0], NDArrayHandle),
                                      stype=out_stypes[0])
         else:
-            return [_np_ndarray_cls(ctypes.cast(output_vars[i], NDArrayHandle), stype=out_stypes[i])
-                    if self._is_from_np_compat_op(i) else
-                    _ndarray_cls(ctypes.cast(output_vars[i], NDArrayHandle), stype=out_stypes[i])
-                    for i in range(num_output.value)]
+            return [create_ndarray_fn(ctypes.cast(output_vars[i], NDArrayHandle),
+                                      stype=out_stypes[i]) for i in range(num_output.value)]

python/mxnet/_ctypes/symbol.py

@@ -22,7 +22,7 @@
 
 import ctypes
 from ..base import _LIB
-from ..base import c_str_array, c_handle_array, c_str, mx_uint, _is_np_compat_op
+from ..base import c_str_array, c_handle_array, c_str, mx_uint
 from ..base import SymbolHandle
 from ..base import check_call
 
@@ -122,7 +122,7 @@ def _set_np_symbol_class(cls):
     _np_symbol_cls = cls
 
 
-def _symbol_creator(handle, args, kwargs, keys, vals, name):
+def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op):
     sym_handle = SymbolHandle()
     check_call(_LIB.MXSymbolCreateAtomicSymbol(
         ctypes.c_void_p(handle),
@@ -135,10 +135,8 @@ def _symbol_creator(handle, args, kwargs, keys, vals, name):
         raise TypeError(
             'Operators with variable length input can only accept input'
             'Symbols either as positional or keyword arguments, not both')
-    if _is_np_compat_op(handle):
-        s = _np_symbol_cls(sym_handle)
-    else:
-        s = _symbol_cls(sym_handle)
+    create_symbol_fn = _np_symbol_cls if is_np_op else _symbol_cls
+    s = create_symbol_fn(sym_handle)
     if args:
         s._compose(*args, name=name)
     elif kwargs: