Add register_op_hook for gluon (apache#15839)

* Set monitor callback basic support

* Trigger CI

* Add base.pyi and ndarray.pyx

* Change not supported to experimental and check for both static_shape and static_alloc

include/mxnet/c_api.h

@@ -111,6 +111,11 @@ typedef void (*EngineFuncParamDeleter)(void*);
 typedef void (*ExecutorMonitorCallback)(const char*,
                                         NDArrayHandle,
                                         void*);
+/*! \brief Monitor callback called at operator level for cached op */
+typedef void (*CachedOpMonitorCallback)(const char*,
+                                        const char*,
+                                        NDArrayHandle);
+
 
 struct NativeOpInfo {
   void (*forward)(int, float**, int*, unsigned**, int*, void*);
@@ -1284,6 +1289,13 @@ MXNET_DLL int MXInvokeCachedOpEx(CachedOpHandle handle,
                                  NDArrayHandle **outputs,
                                  const int** out_stypes);
 
+/*!
+ * \brief cached op set monitor callback
+ */
+MXNET_DLL int MXCachedOpRegisterOpHook(NDArrayHandle handle,
+                                       CachedOpMonitorCallback callback,
+                                       bool monitor_all);
+
 //--------------------------------------------
 // Part 3: symbolic configuration generation
 //--------------------------------------------

python/mxnet/_ctypes/ndarray.py

@@ -29,6 +29,13 @@
 from ..base import check_call
 
 
+def _monitor_callback_wrapper(callback):
+    """A wrapper for the user-defined handle."""
+    def callback_handle(name, opr_name, array, _):
+        """ ctypes function """
+        callback(name, opr_name, array)
+    return callback_handle
+
 class NDArrayBase(object):
     """Base data structure for ndarray"""
     __slots__ = ["handle", "writable"]
@@ -112,10 +119,11 @@ def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op):
 
 class CachedOp(object):
     """Cached operator handle."""
-    __slots__ = ["handle", "is_np_sym"]
+    __slots__ = ["handle", "is_np_sym", "_monitor_callback"]
 
     def __init__(self, sym, flags=()):
         self.handle = CachedOpHandle()
+        self._monitor_callback = None
 
         from ..symbol.numpy._symbol import _Symbol
         self.is_np_sym = bool(isinstance(sym, _Symbol))
@@ -170,3 +178,21 @@ def __call__(self, *args, **kwargs):
         else:
             return [create_ndarray_fn(ctypes.cast(output_vars[i], NDArrayHandle),
                                       stype=out_stypes[i]) for i in range(num_output.value)]
+
+    def _register_op_hook(self, callback, monitor_all=False):
+        """Install callback for monitor.
+
+        Parameters
+        ----------
+        callback : function
+            Takes a string for node_name, string for op_name and a NDArrayHandle.
+        monitor_all : bool, default False
+            If true, monitor both input _imperative_invoked output, otherwise monitor output only.
+        """
+        cb_type = ctypes.CFUNCTYPE(None, ctypes.c_char_p, ctypes.c_char_p, NDArrayHandle, ctypes.c_void_p)
+        if callback:
+            self._monitor_callback = cb_type(_monitor_callback_wrapper(callback))
+        check_call(_LIB.MXCachedOpRegisterOpHook(
+            self.handle,
+            self._monitor_callback,
+            ctypes.c_int(monitor_all)))

python/mxnet/cython/base.pyi

@@ -2,13 +2,18 @@ from ..base import MXNetError
 
 from libcpp.vector cimport vector
 from libcpp.string cimport string
+from libcpp cimport bool as _bool
 from cpython.version cimport PY_MAJOR_VERSION
 
 ctypedef void* SymbolHandle
 ctypedef void* NDArrayHandle
 ctypedef void* OpHandle
 ctypedef void* CachedOpHandle
+ctypedef void* MonitorCallbackHandle
 ctypedef unsigned nn_uint
+ctypedef void (*CachedOpMonitorCallback)(const char*,
+                                         const char*,
+                                         NDArrayHandle)
 
 cdef py_str(const char* x):
     if PY_MAJOR_VERSION < 3:
@@ -112,3 +117,6 @@ cdef extern from "mxnet/c_api.h":
                            int *num_outputs,
                            NDArrayHandle **outputs,
                            const int **out_stypes);
+    int MXCachedOpRegisterOpHook(NDArrayHandle handle,
+                                 CachedOpMonitorCallback callback,
+                                 _bool monitor_all);

python/mxnet/cython/ndarray.pyx

@@ -22,6 +22,7 @@ import ctypes as _ctypes
 import numpy as np
 from ..ndarray_doc import _build_doc
 from libc.stdint cimport uint32_t, int64_t
+from ..base import _LIB
 
 include "./base.pyi"
 
@@ -47,7 +48,6 @@ cdef class NDArrayBase:
                 return _ctypes.cast(<unsigned long long>self.chandle, _ctypes.c_void_p)
         def __set__(self, value):
             self._set_handle(value)
-
     property writable:
         def __get__(self):
             return bool(self.cwritable)
@@ -75,6 +75,10 @@ def _set_np_ndarray_class(cls):
     global _np_ndarray_cls
     _np_ndarray_cls = cls
 
+def _monitor_callback_wrapper(callback):
+    def callback_handle(name, opr_name, arr, _):
+        callback(name, opr_name, arr)
+    return callback_handle
 
 cdef NewArray(NDArrayHandle handle, int stype=-1, int is_np_array=0):
     """Create a new array given handle"""
@@ -103,6 +107,7 @@ cdef class CachedOp:
             self._set_handle(value)
 
     cdef int is_np_sym
+    cdef readonly object mhandle
 
     def __init__(self, sym, flags=()):
         cdef vector[string] s_flag_keys
@@ -169,6 +174,15 @@ cdef class CachedOp:
         else:
             return [NewArray(p_output_vars[i], p_output_stypes[i], self.is_np_sym) for i in range(num_output)]
 
+    def _register_op_hook(self, callback, monitor_all=False):
+        cb_type = _ctypes.CFUNCTYPE(None, _ctypes.c_char_p, _ctypes.c_char_p, _ctypes.c_void_p, _ctypes.c_void_p)
+        if callback:
+            self.mhandle = cb_type(_monitor_callback_wrapper(callback))
+        chandle = _ctypes.cast(<unsigned long long>self.chandle, _ctypes.c_void_p)
+        CALL(_LIB.MXCachedOpRegisterOpHook(chandle,
+                                           self.mhandle,
+                                           _ctypes.c_int(monitor_all)))
+
 
 def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op=0):
     """cython implementation of imperative invoke wrapper"""

python/mxnet/gluon/block.py

@@ -590,6 +590,19 @@ def forward(self, *args):
         # pylint: disable= invalid-name
         raise NotImplementedError
 
+    def register_op_hook(self, callback, monitor_all=False):
+        """Install callback monitor.
+
+        Parameters
+        ----------
+        callback : function
+            Takes a string and a NDArrayHandle.
+        monitor_all : bool, default False
+            If true, monitor both input and output, otherwise monitor output only.
+        """
+        for cld in self._children.values():
+            cld.register_op_hook(callback, monitor_all)
+
     def summary(self, *inputs):
         """Print the summary of the model's output and parameters.
 
@@ -754,6 +767,8 @@ def __init__(self, prefix=None, params=None):
         self._in_format = None
         self._active = False
         self._flags = []
+        self._callback = None
+        self._monitor_all = False
 
     def __setattr__(self, name, value):
         """Registers parameters."""
@@ -833,6 +848,12 @@ def _deferred_infer_shape(self, *args):
     def _call_cached_op(self, *args):
         if self._cached_op is None:
             self._build_cache(*args)
+        assert self._cached_op, "cached op is not None"
+        if self._callback:
+            self._cached_op._register_op_hook(self._callback, self._monitor_all)
+            if len(self._flags) >= 2 and (self._flags[1] or self._flags[0]):
+                warnings.warn("register_op_hook is experimental when static_alloc=True / static_shape=True "
+                              " and may not work correctly")
 
         args, fmt = _flatten(args, "input")
         assert fmt == self._in_format, "Invalid input format"
@@ -938,6 +959,22 @@ def export(self, path, epoch=0, remove_amp_cast=True):
         save_fn = _mx_npx.save if is_np_array() else ndarray.save
         save_fn('%s-%04d.params'%(path, epoch), arg_dict)
 
+    def register_op_hook(self, callback, monitor_all=False):
+        """Install op hook for block recursively.
+
+        Parameters
+        ----------
+        callback : function
+            Takes a string and a NDArrayHandle.
+        monitor_all : bool, default False
+            If true, monitor both input and output, otherwise monitor output only.
+        """
+        self._callback = callback
+        self._monitor_all = monitor_all
+        for cld in self._children.values():
+            cld._callback = callback
+            cld._monitor_all = monitor_all
+
     def forward(self, x, *args):
         """Defines the forward computation. Arguments can be either
         :py:class:`NDArray` or :py:class:`Symbol`."""

src/c_api/c_api_ndarray.cc

@@ -378,3 +378,23 @@ int MXAutogradGetSymbol(NDArrayHandle handle, SymbolHandle *out) {
   *out = reinterpret_cast<SymbolHandle>(sym);
   API_END();
 }
+
+int MXCachedOpRegisterOpHook(NDArrayHandle handle,
+                             CachedOpMonitorCallback callback,
+                             bool monitor_all) {
+  API_BEGIN();
+  CachedOpMonitorCallback callback_temp = nullptr;
+  std::function<void(const char *, const char *, void*)> clbk;
+  if (callback) {
+    callback_temp = callback;
+    clbk = [callback_temp](const char *name, const char *opr_name,
+                           void *handle) {
+      callback_temp(name, opr_name, handle);
+    };
+  } else {
+      clbk = nullptr;
+  }
+  CachedOpPtr op = *static_cast<CachedOpPtr *>(handle);
+  op->RegisterOpHook(clbk, monitor_all);
+  API_END();
+}

src/common/utils.cc

@@ -51,5 +51,62 @@ void CastStorageDispatch<cpu>(const OpContext& ctx,
   mxnet::op::CastStorageComputeImpl<cpu>(ctx, input, output);
 }
 
+void ExecuteMonInputCallback(
+    const nnvm::IndexedGraph &idx, const std::vector<NDArray *> &state_arrays,
+    size_t nid, const std::function<void(const char *, const char *, void *)>
+                    &monitor_callback) {
+  static const auto &flist_inputs =
+      nnvm::Op::GetAttr<nnvm::FListInputNames>("FListInputNames");
+  std::vector<std::string> input_names;
+  const nnvm::IndexedGraph::Node &inode = idx[nid];
+  const nnvm::Node *node = inode.source;
+  if (flist_inputs.count(node->op())) {
+    input_names = flist_inputs[node->op()](node->attrs);
+  } else {
+    for (size_t i = 0; i < node->num_inputs(); ++i) {
+      input_names.emplace_back("input" + std::to_string(i));
+    }
+  }
+
+  for (size_t i = 0; i < node->num_inputs(); ++i) {
+    const nnvm::NodeEntry &input = node->inputs[i];
+    if (state_arrays[idx.entry_id(input)]->is_none()) {
+      continue;
+    }
+    NDArray *cpy = new NDArray(*state_arrays[idx.entry_id(input)]);
+    std::string name = inode.source->attrs.name + "_" + input_names[i];
+    monitor_callback(name.c_str(), inode.source->op()->name.c_str(),
+                     reinterpret_cast<void *>(cpy));
+  }
+}
+
+void ExecuteMonOutputCallback(
+    const nnvm::IndexedGraph &idx, const std::vector<NDArray *> &state_arrays,
+    size_t nid, const std::function<void(const char *, const char *, void *)>
+                    &monitor_callback) {
+  static const auto &flist_outputs =
+      nnvm::Op::GetAttr<nnvm::FListOutputNames>("FListOutputNames");
+  std::vector<std::string> output_names;
+  const nnvm::IndexedGraph::Node &inode = idx[nid];
+  const nnvm::Node *node = inode.source;
+  if (flist_outputs.count(node->op())) {
+    output_names = flist_outputs[node->op()](node->attrs);
+  } else {
+    for (size_t i = 0; i < node->num_outputs(); ++i) {
+      output_names.emplace_back(std::to_string(i));
+    }
+  }
+
+  for (size_t i = 0; i < node->num_outputs(); ++i) {
+    if (state_arrays[idx.entry_id(nid, i)]->is_none()) {
+      continue;
+    }
+    NDArray *cpy = new NDArray(*state_arrays[idx.entry_id(nid, i)]);
+    std::string name = inode.source->attrs.name + "_" + output_names[i];
+    monitor_callback(name.c_str(), inode.source->op()->name.c_str(),
+                     reinterpret_cast<void *>(cpy));
+  }
+}
+
 }  // namespace common
 }  // namespace mxnet

src/common/utils.h

@@ -804,6 +804,15 @@ inline void ConvertToLegacyShape(mxnet::ShapeVector* shapes) {
     ConvertToLegacyShape(&(shapes->at(i)));
   }
 }
+void ExecuteMonInputCallback(
+    const nnvm::IndexedGraph &idx, const std::vector<NDArray *> &state_arrays,
+    size_t nid, const std::function<void(const char *, const char *, void *)>
+                    &monitor_callback);
+
+void ExecuteMonOutputCallback(
+    const nnvm::IndexedGraph &idx, const std::vector<NDArray *> &state_arrays,
+    size_t nid, const std::function<void(const char *, const char *, void *)>
+                    &monitor_callback);
 
 /*!
  * \brief This is function can return the output names of a NodeEntry.

src/imperative/cached_op.cc

@@ -697,6 +697,9 @@ void CachedOp::StaticRunOps(
         ndinputs.emplace_back(state_arrays[idx.entry_id(j)]);
         CHECK(!ndinputs.back()->is_none());
       }
+      if (monitor_callback_ && monitor_all_) {
+          mxnet::common::ExecuteMonInputCallback(idx, state_arrays, i, monitor_callback_);
+      }
       ndoutputs.clear();
       ndoutputs.reserve(num_outputs);
       req.clear();
@@ -708,6 +711,7 @@ void CachedOp::StaticRunOps(
         CHECK(req.back() == kNullOp || !ndoutputs.back()->is_none());
       }
       const DispatchMode dispatch_mode = dispatch_modes[i];
+
       if (createop.count(node.source->op())) {
         arg_shapes.clear();
         arg_dtypes.clear();
@@ -735,6 +739,9 @@ void CachedOp::StaticRunOps(
             default_ctx, node.source->attrs, ndinputs, ndoutputs, req,
             dispatch_mode);
       }
+      if (monitor_callback_) {
+          mxnet::common::ExecuteMonOutputCallback(idx, state_arrays, i, monitor_callback_);
+      }
     }
   }
 }
@@ -883,12 +890,12 @@ OpStatePtr CachedOp::DynamicForward(
     // So if it's not the inline mode, we disable recording.
     RunGraph(false, idx, arrays, 0, idx.num_nodes(), std::move(array_reqs),
             std::move(ref_count), &states, dispatch_modes,
-            recording && inlining_);
+            recording && inlining_, nullptr, monitor_callback_, monitor_all_);
   } else {
     mxnet::ShapeVector shapes = g.GetAttr<mxnet::ShapeVector>("shape");
     NaiveRunGraph(false, default_ctx, idx, arrays, 0, idx.num_nodes(),
                   std::move(array_reqs), std::move(ref_count), &states,
-                  dispatch_modes, recording && inlining_, &shapes);
+                  dispatch_modes, recording && inlining_, &shapes, monitor_callback_, monitor_all_);
     {
       auto state_ptr = GetCachedOpState(default_ctx);
       auto& state = state_ptr.get_state<CachedOpState>();
@@ -1028,7 +1035,7 @@ void CachedOp::DynamicBackward(
 
   RunGraph(retain_graph, idx, arrays, num_forward_nodes, idx.num_nodes(),
            std::move(array_reqs), std::move(ref_count), &states, dispatch_modes,
-           Imperative::Get()->is_recording());
+           Imperative::Get()->is_recording(), nullptr, monitor_callback_);
 
   if (retain_graph) {
     buff.resize(num_forward_entries);
@@ -1295,6 +1302,16 @@ void CachedOpBackward(const OpStatePtr& state_ptr,
       CopyFromTo(out_bufs[i], outputs[i]);
 }
 
+/*
+ * Register the callback to be called when the operator is executed
+ */
+void CachedOp::RegisterOpHook(const CachedOp::CachedOpMonCallback& callback,
+                              bool monitor_all) {
+    CHECK(callback) << "invalid callback";
+    monitor_callback_ = callback;
+    monitor_all_ = monitor_all;
+}
+
 OpStatePtr CreateCachedOpState(const NodeAttrs& attrs,
                                Context ctx,
                                const mxnet::ShapeVector& in_shapes,