diff --git a/include/mxnet/base.h b/include/mxnet/base.h index c1e2da7a9db3..02dd204fc77d 100644 --- a/include/mxnet/base.h +++ b/include/mxnet/base.h @@ -451,7 +451,9 @@ inline int32_t Context::GetGPUCount() { } int32_t count; cudaError_t e = cudaGetDeviceCount(&count); - if (e == cudaErrorNoDevice) { + // TODO(junwu): Remove e == cudaErrorInsufficientDriver + // This is skipped for working around wheel build system with older CUDA driver. + if (e == cudaErrorNoDevice || e == cudaErrorInsufficientDriver) { return 0; } CHECK_EQ(e, cudaSuccess) << " CUDA: " << cudaGetErrorString(e); diff --git a/include/mxnet/c_api.h b/include/mxnet/c_api.h index 376c7e9d1063..9d647c3b69d7 100644 --- a/include/mxnet/c_api.h +++ b/include/mxnet/c_api.h @@ -2909,6 +2909,18 @@ 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 Create an NDArray from source sharing the same data chunk. + * \param src source NDArray + * \param out new NDArray sharing the same data chunck with src + */ +MXNET_DLL int MXShallowCopyNDArray(NDArrayHandle src, NDArrayHandle* out); +/*! + * \brief Create an Symbol from source sharing the same graph structure. + * \param src source Symbol + * \param out new Symbol sharing the same graph structure with src + */ +MXNET_DLL int MXShallowCopySymbol(SymbolHandle src, SymbolHandle * out); /*! * \brief Push an asynchronous operation to the engine. diff --git a/include/mxnet/tuple.h b/include/mxnet/tuple.h index bc630f153744..f018c8faabea 100644 --- a/include/mxnet/tuple.h +++ b/include/mxnet/tuple.h @@ -272,6 +272,14 @@ class Tuple { is.get(); if (ch == '(' || ch == '[') break; if (!isspace(ch)) { + if (ch == 'N') { + std::string tmp_val; + is >> tmp_val; + if (tmp_val == "one") { // is stores "None" + t.SetDim(-1); + return is; + } + } is.setstate(std::ios::failbit); return is; } @@ -653,6 +661,13 @@ inline bool shape_is_known(const TShape& x) { return true; } +inline bool shape_is_known(const std::vector& shapes) { + for (const TShape& shape : shapes) { + if (!shape_is_known(shape)) return false; + } + return true; +} + /*! \brief helper function to cast type of container elements */ template inline DstIter ShapeTypeCast(const SrcIter begin, diff --git a/python/mxnet/__init__.py b/python/mxnet/__init__.py index 233bb2a1f57e..e9c1229d7f2f 100644 --- a/python/mxnet/__init__.py +++ b/python/mxnet/__init__.py @@ -25,11 +25,16 @@ from . import engine from .base import MXNetError from .util import is_np_shape, set_np_shape, np_shape, use_np_shape +from .util import is_np_array, np_array, use_np_array, use_np from . import base from . import library from . import contrib 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 npx from . import name # use mx.sym as short for symbol from . import symbol as sym diff --git a/python/mxnet/_ctypes/ndarray.py b/python/mxnet/_ctypes/ndarray.py index f324545a2352..b1a38c1d2621 100644 --- a/python/mxnet/_ctypes/ndarray.py +++ b/python/mxnet/_ctypes/ndarray.py @@ -55,6 +55,8 @@ def __reduce__(self): _ndarray_cls = None +_np_ndarray_cls = None + def _set_ndarray_class(cls): """Set the symbolic class to be cls""" @@ -62,7 +64,13 @@ def _set_ndarray_class(cls): _ndarray_cls = cls -def _imperative_invoke(handle, ndargs, keys, vals, out): +def _set_np_ndarray_class(cls): + """Set the symbolic class to be cls""" + global _np_ndarray_cls + _np_ndarray_cls = cls + + +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 @@ -91,23 +99,27 @@ 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 if num_output.value == 1: - return _ndarray_cls(ctypes.cast(output_vars[0], NDArrayHandle), - stype=out_stypes[0]) + return create_ndarray_fn(ctypes.cast(output_vars[0], NDArrayHandle), + stype=out_stypes[0]) else: - return [_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)] 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 = bool(isinstance(sym, _Symbol)) + check_call(_LIB.MXCreateCachedOpEx( sym.handle, len(flags), @@ -151,10 +163,10 @@ 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: - return _ndarray_cls(ctypes.cast(output_vars[0], NDArrayHandle), - stype=out_stypes[0]) + return create_ndarray_fn(ctypes.cast(output_vars[0], NDArrayHandle), + stype=out_stypes[0]) else: - return [_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)] diff --git a/python/mxnet/_ctypes/symbol.py b/python/mxnet/_ctypes/symbol.py index fe4cb950ed14..01ba18b38963 100644 --- a/python/mxnet/_ctypes/symbol.py +++ b/python/mxnet/_ctypes/symbol.py @@ -26,7 +26,9 @@ from ..base import SymbolHandle from ..base import check_call +# The symbol class to be used (Cython or Ctypes) _symbol_cls = None +_np_symbol_cls = None class SymbolBase(object): """Symbol is symbolic graph.""" @@ -115,7 +117,13 @@ def _set_symbol_class(cls): _symbol_cls = cls -def _symbol_creator(handle, args, kwargs, keys, vals, name): +def _set_np_symbol_class(cls): + """Set the numpy-compatible symbolic class to be cls""" + global _np_symbol_cls + _np_symbol_cls = cls + + +def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op): sym_handle = SymbolHandle() check_call(_LIB.MXSymbolCreateAtomicSymbol( ctypes.c_void_p(handle), @@ -128,7 +136,8 @@ 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') - 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: diff --git a/python/mxnet/_numpy_op_doc.py b/python/mxnet/_numpy_op_doc.py new file mode 100644 index 000000000000..5543ebc8e8c9 --- /dev/null +++ b/python/mxnet/_numpy_op_doc.py @@ -0,0 +1,54 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# pylint: skip-file + +"""Doc placeholder for numpy ops with prefix _np.""" + + +def _np_ones_like(a): + """Return an array of ones with the same shape and type as a given array. + + Parameters + ---------- + a : ndarray + The shape and data-type of `a` define these same attributes of + the returned array. + + Returns + ------- + out : ndarray + Array of ones with the same shape and type as `a`. + """ + pass + + +def _np_zeros_like(a): + """Return an array of zeros with the same shape and type as a given array. + + Parameters + ---------- + a : ndarray + The shape and data-type of `a` define these same attributes of + the returned array. + + Returns + ------- + out : ndarray + Array of zeros with the same shape and type as `a`. + """ + pass diff --git a/python/mxnet/base.py b/python/mxnet/base.py index 17819bde28b2..848c36fb4d89 100644 --- a/python/mxnet/base.py +++ b/python/mxnet/base.py @@ -16,7 +16,7 @@ # under the License. # coding: utf-8 -# pylint: disable=invalid-name, no-member, trailing-comma-tuple, bad-mcs-classmethod-argument, unnecessary-pass, wrong-import-position +# pylint: disable=invalid-name, no-member, trailing-comma-tuple, bad-mcs-classmethod-argument, unnecessary-pass, too-many-lines, wrong-import-position """ctypes library of mxnet and helper functions.""" from __future__ import absolute_import @@ -598,7 +598,9 @@ def _init_op_module(root_namespace, module_name, make_op_func): ctypes.byref(plist))) op_names = [] for i in range(size.value): - op_names.append(py_str(plist[i])) + op_name = py_str(plist[i]) + if not _is_np_op(op_name): + op_names.append(op_name) module_op = sys.modules["%s.%s.op" % (root_namespace, module_name)] module_internal = sys.modules["%s.%s._internal" % (root_namespace, module_name)] @@ -692,7 +694,9 @@ def write_all_str(module_file, module_all_list): ctypes.byref(plist))) op_names = [] for i in range(size.value): - op_names.append(py_str(plist[i])) + op_name = py_str(plist[i]) + if not _is_np_op(op_name): + op_names.append(op_name) module_op_file = get_module_file("%s.%s.op" % (root_namespace, module_name)) module_op_all = [] @@ -735,7 +739,115 @@ def write_all_str(module_file, module_all_list): ctypes.pythonapi.PyCapsule_New.restype = ctypes.py_object ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p + from .runtime import Features if Features().is_enabled("TVM_OP"): _LIB_TVM_OP = libinfo.find_lib_path("libtvmop") check_call(_LIB.MXLoadTVMOp(c_str(_LIB_TVM_OP[0]))) + + +_NP_OP_PREFIX = '_np_' +_NP_OP_SUBMODULE_LIST = ['_random_', '_linalg_'] + +_NP_EXT_OP_PREFIX = '_npx_' +_NP_EXT_OP_SUBMODULE_LIST = ['_image_'] + +_NP_INTERNAL_OP_PREFIX = '_npi_' + + +def _is_np_op(op_name): + return op_name.startswith(_NP_OP_PREFIX) or op_name.startswith(_NP_EXT_OP_PREFIX)\ + or op_name.startswith(_NP_INTERNAL_OP_PREFIX) + + +def _get_op_submodule_name(op_name, op_name_prefix, submodule_name_list): + """Get the submodule name of a specific op""" + assert op_name.startswith(op_name_prefix) + for submodule_name in submodule_name_list: + if op_name[len(op_name_prefix):].startswith(submodule_name): + return submodule_name + return "" + + +def _init_np_op_module(root_module_name, np_module_name, mx_module_name, make_op_func): + """ + Register numpy operators in namespaces `mxnet.numpy`, `mxnet.ndarray.numpy` + and `mxnet.symbol.numpy`. They are used in imperative mode, Gluon APIs w/o hybridization, + and Gluon APIs w/ hybridization, respectively. Essentially, operators with the same name + registered in three namespaces, respectively share the same functionality in C++ backend. + Different namespaces are needed for dispatching operator calls in Gluon's `HybridBlock` by `F`. + + Parameters + ---------- + root_module_name : str + Top level module name, `mxnet` in the current cases. + np_module_name : str + Second level module name, `numpy` or `numpy_extension` in the current case. + make_op_func : function + Function for creating op functions. + """ + from . import _numpy_op_doc as _np_op_doc + if np_module_name == 'numpy': + op_name_prefix = _NP_OP_PREFIX + submodule_name_list = _NP_OP_SUBMODULE_LIST + elif np_module_name == 'numpy_extension': + op_name_prefix = _NP_EXT_OP_PREFIX + submodule_name_list = _NP_EXT_OP_SUBMODULE_LIST + elif np_module_name == 'numpy._internal': + op_name_prefix = _NP_INTERNAL_OP_PREFIX + submodule_name_list = [] + else: + raise ValueError('unsupported np module name {}'.format(np_module_name)) + + plist = ctypes.POINTER(ctypes.c_char_p)() + size = ctypes.c_uint() + check_call(_LIB.MXListAllOpNames(ctypes.byref(size), ctypes.byref(plist))) + op_names = [] + for i in range(size.value): + name = py_str(plist[i]) + if name.startswith(op_name_prefix): + op_names.append(name) + + if mx_module_name is None: + # register np/npx ops for imperative programming + op_module_name = "%s.%s._op" % (root_module_name, np_module_name) # e.g. mxnet.numpy._op + op_submodule_name = "%s.%s" % (root_module_name, np_module_name) # e.g. mxnet.numpy.random + elif mx_module_name in ('ndarray', 'symbol'): + # register numpy internal ops and np/npx ops for use in Gluon + # np internal ops are registered in mxnet.ndarray/symbol.numpy._internal + # np ops are registered in mxnet.ndarray/symbol.numpy._op + # npx ops are registered in mxnet.ndarray/symbol.numpy_extension._op + op_module_name = "%s.%s.%s" % (root_module_name, mx_module_name, np_module_name) + if op_name_prefix != _NP_INTERNAL_OP_PREFIX: + op_module_name += '._op' + # e.g. mxnet.symbol.numpy.random + op_submodule_name = "%s.%s.%s" % (root_module_name, mx_module_name, np_module_name) + else: + raise ValueError('unsupported mxnet module {}'.format(mx_module_name)) + op_submodule_name += '.%s' + + op_module = sys.modules[op_module_name] + submodule_dict = {} + for submodule_name in submodule_name_list: + submodule_dict[submodule_name] = sys.modules[op_submodule_name % submodule_name[1:-1]] + for name in op_names: + hdl = OpHandle() + check_call(_LIB.NNGetOpHandle(c_str(name), ctypes.byref(hdl))) + submodule_name = _get_op_submodule_name(name, op_name_prefix, submodule_name_list) + if len(submodule_name) > 0: + func_name = name[(len(op_name_prefix) + len(submodule_name)):] + cur_module = submodule_dict[submodule_name] + module_name_local = op_submodule_name % submodule_name[1:-1] + else: + func_name = name[len(op_name_prefix):] + cur_module = op_module + module_name_local =\ + op_module_name[:-len('._op')] if op_module_name.endswith('._op') else op_module_name + + function = make_op_func(hdl, name, func_name) + function.__module__ = module_name_local + setattr(cur_module, function.__name__, function) + cur_module.__all__.append(function.__name__) + + if hasattr(_np_op_doc, name): + function.__doc__ = getattr(_np_op_doc, name).__doc__ diff --git a/python/mxnet/cython/ndarray.pyx b/python/mxnet/cython/ndarray.pyx index f9279889b504..50791e9b9a86 100644 --- a/python/mxnet/cython/ndarray.pyx +++ b/python/mxnet/cython/ndarray.pyx @@ -64,21 +64,27 @@ cdef class NDArrayBase: _ndarray_cls = None +_np_ndarray_cls = None def _set_ndarray_class(cls): global _ndarray_cls _ndarray_cls = cls -cdef NewArray(NDArrayHandle handle, int stype=-1): +def _set_np_ndarray_class(cls): + global _np_ndarray_cls + _np_ndarray_cls = cls + + +cdef NewArray(NDArrayHandle handle, int stype=-1, int is_np_array=0): """Create a new array given handle""" - return _ndarray_cls(_ctypes.cast(handle, _ctypes.c_void_p), stype=stype) + create_array_fn = _np_ndarray_cls if is_np_array else _ndarray_cls + return create_array_fn(_ctypes.cast(handle, _ctypes.c_void_p), stype=stype) cdef class CachedOp: """Cached operator handle.""" cdef CachedOpHandle chandle - cdef _set_handle(self, handle): cdef unsigned long long ptr if handle is None: @@ -96,6 +102,8 @@ cdef class CachedOp: def __set__(self, value): self._set_handle(value) + cdef int is_np_sym + def __init__(self, sym, flags=()): cdef vector[string] s_flag_keys cdef vector[string] s_flag_vals @@ -106,6 +114,9 @@ cdef class CachedOp: cdef vector[const char*] c_flag_keys = SVec2Ptr(s_flag_keys) cdef vector[const char*] c_flag_vals = SVec2Ptr(s_flag_vals) + from ..symbol.numpy._symbol import _Symbol + self.is_np_sym = bool(isinstance(sym, _Symbol)) + CALL(MXCreateCachedOpEx( (sym.handle.value), len(flags), @@ -154,12 +165,12 @@ cdef class CachedOp: if original_output is not None: return original_output if num_output == 1: - return NewArray(p_output_vars[0], p_output_stypes[0]) + return NewArray(p_output_vars[0], p_output_stypes[0], self.is_np_sym) else: - return [NewArray(p_output_vars[i], p_output_stypes[i]) for i in range(num_output)] + return [NewArray(p_output_vars[i], p_output_stypes[i], self.is_np_sym) for i in range(num_output)] -def _imperative_invoke(handle, ndargs, keys, vals, out): +def _imperative_invoke(handle, ndargs, keys, vals, out, is_np_op=0): """cython implementation of imperative invoke wrapper""" cdef unsigned long long ihandle = handle cdef OpHandle chandle = ihandle @@ -211,6 +222,6 @@ def _imperative_invoke(handle, ndargs, keys, vals, out): if original_output is not None: return original_output if num_output == 1: - return NewArray(p_output_vars[0], p_output_stypes[0]) + return NewArray(p_output_vars[0], p_output_stypes[0], is_np_op) else: - return [NewArray(p_output_vars[i], p_output_stypes[i]) for i in range(num_output)] + return [NewArray(p_output_vars[i], p_output_stypes[i], is_np_op) for i in range(num_output)] diff --git a/python/mxnet/cython/symbol.pyx b/python/mxnet/cython/symbol.pyx index 1bdea6c6c547..86fe8ae6db4f 100644 --- a/python/mxnet/cython/symbol.pyx +++ b/python/mxnet/cython/symbol.pyx @@ -84,19 +84,27 @@ cdef SymbolSetAttr(SymbolHandle handle, dict kwargs): _symbol_cls = SymbolBase +_np_symbol_cls = None def _set_symbol_class(cls): global _symbol_cls _symbol_cls = cls -cdef NewSymbol(SymbolHandle handle): + +def _set_np_symbol_class(cls): + global _np_symbol_cls + _np_symbol_cls = cls + + +cdef NewSymbol(SymbolHandle handle, int is_np_sym=0): """Create a new symbol given handle""" - sym = _symbol_cls(None) + create_symbol_fn = _np_symbol_cls if is_np_sym else _symbol_cls + sym = create_symbol_fn(None) (sym).chandle = handle return sym -def _symbol_creator(handle, args, kwargs, keys, vals, name): +def _symbol_creator(handle, args, kwargs, keys, vals, name, is_np_op=0): cdef unsigned long long ihandle = handle cdef OpHandle chandle = ihandle cdef vector[string] ckeys @@ -143,4 +151,4 @@ def _symbol_creator(handle, args, kwargs, keys, vals, name): &csym_keys[0] if csym_keys.size() != 0 else NULL, &sym_args[0] if sym_args.size() != 0 else NULL)) - return NewSymbol(ret_handle) + return NewSymbol(ret_handle, is_np_op) diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py index bd22cf85d561..97e6e8b68453 100644 --- a/python/mxnet/gluon/block.py +++ b/python/mxnet/gluon/block.py @@ -26,7 +26,6 @@ import re from collections import OrderedDict - from ..base import mx_real_t, MXNetError from .. import symbol, ndarray, initializer from ..symbol import Symbol @@ -34,6 +33,10 @@ from .. import name as _name from .parameter import Parameter, ParameterDict, DeferredInitializationError from .utils import _indent, _brief_print_list, HookHandle +from .utils import _check_same_symbol_type, _check_all_np_ndarrays +from .. import numpy_extension as _mx_npx +from .. import numpy as _mx_np +from .. util import is_np_array, np_shape, np_array class _BlockScope(object): @@ -332,7 +335,8 @@ def save_parameters(self, filename): """ params = self._collect_params_with_prefix() arg_dict = {key : val._reduce() for key, val in params.items()} - ndarray.save(filename, arg_dict) + save_fn = _mx_npx.save if is_np_array() else ndarray.save + save_fn(filename, arg_dict) def save_params(self, filename): """[Deprecated] Please use save_parameters. Note that if you want load @@ -381,7 +385,28 @@ def load_parameters(self, filename, ctx=None, allow_missing=False, `Saving and Loading Gluon Models \ `_ """ - loaded = ndarray.load(filename) + if is_np_array(): + # failure may happen when loading parameters saved as NDArrays within + # NumPy semantics. Check the failure type and recover from it if it happens. + try: + loaded = _mx_npx.load(filename) + except MXNetError as e: + err_msg = str(e) + if 'is_np_shape' in err_msg: + # Loading failure due to parameters saved without numpy semantics. + # Temporarily disable numpy semantics and load parameters. After it's + # done, resume the numpy semantics. This is fine because the cases + # numpy ndarray covers is a superset of the legacy ndarray's. + with np_array(False): + with np_shape(False): + loaded_nds = ndarray.load(filename) + assert isinstance(loaded_nds, dict),\ + 'expecting a dict type, got {}'.format(str(type(loaded_nds))) + loaded = {k: loaded_nds[k].as_np_ndarray() for k in loaded_nds} + else: + raise ValueError(err_msg) + else: + loaded = ndarray.load(filename) params = self._collect_params_with_prefix() if not loaded and not params: return @@ -549,7 +574,8 @@ def __call__(self, *args): for hook in self._forward_hooks.values(): hook(self, args, out) - + if _mx_npx.is_np_array(): + _check_all_np_ndarrays(out) return out def forward(self, *args): @@ -739,9 +765,13 @@ def _get_graph(self, *args): if not self._cached_graph: args, self._in_format = _flatten(args, "input") if len(args) > 1: - inputs = [symbol.var('data%d'%i) for i in range(len(args))] + inputs = [symbol.var('data%d' % i).as_np_ndarray() + if isinstance(args[i], _mx_np.ndarray) + else symbol.var('data%d' % i) for i in range(len(args))] else: - inputs = [symbol.var('data')] + inputs = [symbol.var('data').as_np_ndarray() + if isinstance(args[0], _mx_np.ndarray) + else symbol.var('data')] grouped_inputs = _regroup(inputs, self._in_format)[0] params = {i: j.var() for i, j in self._reg_params.items()} @@ -749,7 +779,7 @@ def _get_graph(self, *args): out = self.hybrid_forward(symbol, *grouped_inputs, **params) # pylint: disable=no-value-for-parameter out, self._out_format = _flatten(out, "output") - self._cached_graph = inputs, symbol.Group(out) + self._cached_graph = inputs, symbol.Group(out, _check_same_symbol_type(out)) return self._cached_graph @@ -905,7 +935,8 @@ def export(self, path, epoch=0, remove_amp_cast=True): else: assert name in aux_names arg_dict['aux:%s'%name] = param._reduce() - ndarray.save('%s-%04d.params'%(path, epoch), arg_dict) + save_fn = _mx_npx.save if is_np_array() else ndarray.save + save_fn('%s-%04d.params'%(path, epoch), arg_dict) def forward(self, x, *args): """Defines the forward computation. Arguments can be either @@ -1058,7 +1089,7 @@ def __init__(self, outputs, inputs, params=None): syms, self._in_format = _flatten(inputs, "input") out, self._out_format = _flatten(outputs, "output") - out = symbol.Group(out) + out = symbol.Group(out, _check_same_symbol_type(out)) input_names = set() for i in syms: diff --git a/python/mxnet/gluon/data/dataloader.py b/python/mxnet/gluon/data/dataloader.py index 65fd7d84b5bb..4dfa94f72c50 100644 --- a/python/mxnet/gluon/data/dataloader.py +++ b/python/mxnet/gluon/data/dataloader.py @@ -124,6 +124,7 @@ def __init__(self, *args, **kwargs): self._send = self._writer.send self._recv = self._reader.recv + def default_batchify_fn(data): """Collate data into batch.""" if isinstance(data[0], nd.NDArray): @@ -382,6 +383,7 @@ def same_process_iter(): def __len__(self): return len(self._batch_sampler) + _worker_dataset = None def _worker_initializer(dataset): """Initialier for processing pool.""" diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py index a174d82341af..89a3c338173c 100644 --- a/python/mxnet/gluon/parameter.py +++ b/python/mxnet/gluon/parameter.py @@ -18,6 +18,8 @@ # coding: utf-8 # pylint: disable=unnecessary-pass, too-many-lines """Neural network parameter.""" +from __future__ import absolute_import + __all__ = ['DeferredInitializationError', 'Parameter', 'Constant', 'ParameterDict', 'tensor_types'] @@ -31,7 +33,8 @@ from ..context import Context, cpu from .. import autograd from .utils import _indent, _brief_print_list, shape_is_known -from .. import is_np_shape +from ..util import is_np_shape, is_np_array +from .. import numpy as _mx_np # pylint: disable=reimported # pylint: disable= invalid-name tensor_types = (symbol.Symbol, ndarray.NDArray) @@ -131,7 +134,6 @@ def __init__(self, name, grad_req='write', shape=None, dtype=mx_real_t, self._grad_stype = grad_stype self._stype = stype - def __repr__(self): s = 'Parameter {name} (shape={shape}, dtype={dtype})' return s.format(name=self.name, shape=self.shape, dtype=self.dtype) @@ -191,9 +193,9 @@ def shape(self, new_shape): return assert len(self._shape) == len(new_shape) and \ - all(j in (0, i) for i, j in zip(new_shape, self._shape)), \ + all(j in (-1, 0, i) for i, j in zip(new_shape, self._shape)), \ "Expected shape %s is incompatible with given shape %s."%( - str(new_shape), str(self._shape)) + str(new_shape), str(self._shape)) # -1 means unknown dim size in np_shape mode self._shape = new_shape @@ -272,12 +274,14 @@ def _load_init(self, data, ctx, cast_dtype=False, dtype_source='current'): if cast_dtype: assert dtype_source in ['current', 'saved'] if self.shape: + unknown_dim_size = -1 if is_np_shape() else 0 for self_dim, data_dim in zip(self.shape, data.shape): - assert self_dim in (0, data_dim), \ + assert self_dim in (unknown_dim_size, data_dim), \ "Failed loading Parameter '%s' from saved params: " \ "shape incompatible expected %s vs saved %s"%( self.name, str(self.shape), str(data.shape)) - self.shape = tuple(i if i != 0 else j for i, j in zip(self.shape, data.shape)) + self.shape = tuple(i if i != unknown_dim_size else j + for i, j in zip(self.shape, data.shape)) if self.dtype: if cast_dtype and np.dtype(self.dtype).type != data.dtype: if dtype_source == 'current': @@ -318,6 +322,7 @@ def _finish_deferred_init(self): return init, ctx, default_init, data = self._deferred_init self._deferred_init = () + assert shape_is_known(self.shape), \ "Cannot initialize Parameter '%s' because it has " \ "invalid shape: %s. Please specify in_units, " \ @@ -326,8 +331,16 @@ def _finish_deferred_init(self): with autograd.pause(): if data is None: - data = ndarray.zeros(shape=self.shape, dtype=self.dtype, - ctx=context.cpu(), stype=self._stype) + kwargs = {'shape': self.shape, 'dtype': self.dtype, 'ctx': context.cpu()} + if is_np_array(): + if self._stype != 'default': + raise ValueError("mxnet.numpy.zeros does not support stype = {}" + .format(self._stype)) + zeros_fn = _mx_np.zeros + else: + kwargs['stype'] = self._stype + zeros_fn = ndarray.zeros + data = zeros_fn(**kwargs) initializer.create(default_init)( initializer.InitDesc(self.name, {'__init__': init}), data) @@ -352,8 +365,15 @@ def _init_grad(self): self._grad = None return - self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context, - stype=self._grad_stype) for i in self._data] + if is_np_array(): + if self._grad_stype != 'default': + raise ValueError("mxnet.numpy.zeros does not support stype = {}" + .format(self._grad_stype)) + self._grad = [_mx_np.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context) + for i in self._data] + else: + self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context, + stype=self._grad_stype) for i in self._data] autograd.mark_variables(self._check_and_get(self._data, list), self._grad, self.grad_req) @@ -363,7 +383,10 @@ def _reduce(self): ctx = context.cpu() if self._stype == 'default': block = self.list_data() - data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block) + if is_np_array(): + data = sum([w.copyto(ctx) for w in block]) / len(block) + else: + data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block) else: # fetch all rows for 'row_sparse' param all_row_ids = ndarray.arange(0, self.shape[0], dtype='int64', ctx=ctx) @@ -463,7 +486,6 @@ def reset_ctx(self, ctx): raise ValueError("Cannot reset context for Parameter '%s' because it " "has not been initialized."%self.name) - def set_data(self, data): """Sets this parameter's value on all contexts.""" self.shape = data.shape @@ -602,6 +624,8 @@ def var(self): self._var = symbol.var(self.name, shape=self.shape, dtype=self.dtype, lr_mult=self.lr_mult, wd_mult=self.wd_mult, init=self.init, stype=self._stype) + if is_np_array(): + self._var = self._var.as_np_ndarray() return self._var def cast(self, dtype): @@ -763,12 +787,12 @@ def get(self, name, **kwargs): inferred_shape = [] matched = True for dim1, dim2 in zip(v, existing): - if dim1 != dim2 and dim1 * dim2 != 0: + if dim1 != dim2 and dim1 > 0 and dim2 > 0: matched = False break elif dim1 == dim2: inferred_shape.append(dim1) - elif dim1 == 0: + elif dim1 in (0, -1): # -1 means unknown dim size in np_shape mode inferred_shape.append(dim2) else: inferred_shape.append(dim1) diff --git a/python/mxnet/gluon/rnn/rnn_layer.py b/python/mxnet/gluon/rnn/rnn_layer.py index b3cc596282a7..2a9cd88bb214 100644 --- a/python/mxnet/gluon/rnn/rnn_layer.py +++ b/python/mxnet/gluon/rnn/rnn_layer.py @@ -236,7 +236,6 @@ def __call__(self, inputs, states=None, sequence_length=None, **kwargs): else: return super(_RNNLayer, self).__call__(inputs, states, **kwargs) - def hybrid_forward(self, F, inputs, states, sequence_length=None, **kwargs): if F is ndarray: batch_size = inputs.shape[self._layout.find('N')] @@ -285,7 +284,6 @@ def _forward_kernel(self, F, inputs, states, sequence_length, **kwargs): lstm_state_clip_max=self._lstm_state_clip_max, lstm_state_clip_nan=self._lstm_state_clip_nan) - if self._mode == 'lstm': outputs, states = rnn[0], [rnn[1], rnn[2]] else: diff --git a/python/mxnet/gluon/utils.py b/python/mxnet/gluon/utils.py index 2060f61a0212..f12f15e7d41a 100644 --- a/python/mxnet/gluon/utils.py +++ b/python/mxnet/gluon/utils.py @@ -40,6 +40,7 @@ class requests_failed_to_import(object): from .. import ndarray from ..util import is_np_shape + def split_data(data, num_slice, batch_axis=0, even_split=True): """Splits an NDArray into `num_slice` slices along `batch_axis`. Usually used for data parallelism where each slices is sent @@ -108,7 +109,7 @@ def split_and_load(data, ctx_list, batch_axis=0, even_split=True): Returns ------- - list of NDArray + list of NDArrays Each corresponds to a context in `ctx_list`. """ if not isinstance(data, ndarray.NDArray): @@ -414,6 +415,7 @@ def __enter__(self): def __exit__(self, ptype, value, trace): self.detach() + def shape_is_known(shape): """Check whether a shape is completely known with or without np semantics. @@ -430,3 +432,44 @@ def shape_is_known(shape): assert dim_size > unknown_dim_size, "shape dimension size cannot be less than {}, while " \ "received {}".format(unknown_dim_size, dim_size) return True + + +def _check_same_symbol_type(symbols): + """Check whether all the symbols in the list are of the same type. + Raise type error if the types are different. Return the class of + the symbols.""" + from ..symbol.numpy import _Symbol as np_symbol + from ..symbol import Symbol as nd_symbol + is_np_sym = isinstance(symbols[0], np_symbol) + for s in symbols[1:]: + if is_np_sym != isinstance(s, np_symbol): + raise TypeError('Found both classic symbol (mx.sym.Symbol) and numpy symbol ' + '(mx.sym.np._Symbol) in outputs. This will prevent you from building ' + 'a computation graph by grouping them since different types of symbols ' + 'are not allowed to be grouped in Gluon to form a computation graph. ' + 'You will need to convert them to the same type of symbols, either ' + 'classic or numpy following this rule: if you want numpy ndarray ' + 'output(s) from the computation graph, please convert all the classic ' + 'symbols in the list to numpy symbols by calling `as_np_ndarray()` ' + 'on each of them; if you want classic ndarray output(s) from the ' + 'computation graph, please convert all the numpy symbols in the list ' + 'to classic symbols by calling `as_nd_ndarray()` on each of them.') + return np_symbol if is_np_sym else nd_symbol + + +def _check_all_np_ndarrays(out): + """Check if ndarrays/symbols in out are all np.ndarray/np._Symbol.""" + from ..numpy import ndarray as np_ndarray + from ..symbol.numpy import _Symbol as np_symbol + from ..symbol import Symbol as nd_symbol + from ..ndarray import NDArray as nd_ndarray + + # pylint: disable=no-else-raise + if isinstance(out, (nd_ndarray, nd_symbol)) and not isinstance(out, (np_ndarray, np_symbol)): + raise TypeError("Block's output ndarrays/symbols must be of type `mxnet.numpy.ndarray`" + " or `mxnet.symbol.numpy._Symbol`, while got output type {}" + .format(str(type(out)))) + elif isinstance(out, (list, tuple)): + for i in out: + _check_all_np_ndarrays(i) + # pylint: enable=no-else-raise diff --git a/python/mxnet/image/image.py b/python/mxnet/image/image.py index bffd286cb538..5c3f02cac1ab 100644 --- a/python/mxnet/image/image.py +++ b/python/mxnet/image/image.py @@ -428,7 +428,7 @@ def fixed_crop(src, x0, y0, w, h, size=None, interp=2): NDArray An `NDArray` containing the cropped image. """ - out = nd.slice(src, begin=(y0, x0, 0), end=(y0 + h, x0 + w, int(src.shape[2]))) + out = src[y0:y0+h, x0:x0+w] if size is not None and (w, h) != size: sizes = (h, w, size[1], size[0]) out = imresize(out, *size, interp=_get_interp_method(interp, sizes)) diff --git a/python/mxnet/ndarray/__init__.py b/python/mxnet/ndarray/__init__.py index f09908e894d5..f6b8712a2513 100644 --- a/python/mxnet/ndarray/__init__.py +++ b/python/mxnet/ndarray/__init__.py @@ -17,7 +17,7 @@ """NDArray API of MXNet.""" -from . import _internal, contrib, linalg, op, random, sparse, utils, image, ndarray +from . import _internal, contrib, linalg, op, random, sparse, utils, image, ndarray, numpy # pylint: disable=wildcard-import, redefined-builtin try: from .gen_op import * # pylint: disable=unused-wildcard-import @@ -30,6 +30,8 @@ from .utils import load, load_frombuffer, save, zeros, empty, array from .sparse import _ndarray_cls from .ndarray import _GRAD_REQ_MAP, _DTYPE_MX_TO_NP, _DTYPE_NP_TO_MX, _new_empty_handle +from . import numpy as np +from . import numpy_extension as npx __all__ = op.__all__ + ndarray.__all__ + utils.__all__ + \ - ['contrib', 'linalg', 'random', 'sparse', 'image'] + ['contrib', 'linalg', 'random', 'sparse', 'image', 'numpy', 'numpy_extension'] diff --git a/python/mxnet/ndarray/_internal.py b/python/mxnet/ndarray/_internal.py index 8045d9bd2b14..d48255647939 100644 --- a/python/mxnet/ndarray/_internal.py +++ b/python/mxnet/ndarray/_internal.py @@ -23,18 +23,18 @@ try: if int(_os.environ.get("MXNET_ENABLE_CYTHON", True)) == 0: from .._ctypes.ndarray import NDArrayBase, CachedOp - from .._ctypes.ndarray import _set_ndarray_class, _imperative_invoke + from .._ctypes.ndarray import _set_ndarray_class, _imperative_invoke, _set_np_ndarray_class elif _sys.version_info >= (3, 0): from .._cy3.ndarray import NDArrayBase, CachedOp - from .._cy3.ndarray import _set_ndarray_class, _imperative_invoke + from .._cy3.ndarray import _set_ndarray_class, _imperative_invoke, _set_np_ndarray_class else: from .._cy2.ndarray import NDArrayBase, CachedOp - from .._cy2.ndarray import _set_ndarray_class, _imperative_invoke + from .._cy2.ndarray import _set_ndarray_class, _imperative_invoke, _set_np_ndarray_class except ImportError: if int(_os.environ.get("MXNET_ENFORCE_CYTHON", False)) != 0: raise ImportError("Cython Module cannot be loaded but MXNET_ENFORCE_CYTHON=1") from .._ctypes.ndarray import NDArrayBase, CachedOp - from .._ctypes.ndarray import _set_ndarray_class, _imperative_invoke + from .._ctypes.ndarray import _set_ndarray_class, _imperative_invoke, _set_np_ndarray_class from ..base import _Null try: @@ -42,4 +42,5 @@ except ImportError: pass -__all__ = ['NDArrayBase', 'CachedOp', '_imperative_invoke', '_set_ndarray_class'] +__all__ = ['NDArrayBase', 'CachedOp', '_imperative_invoke', '_set_ndarray_class', + '_set_np_ndarray_class'] diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py index 0b7dca4ebba4..c1c45f04ead0 100644 --- a/python/mxnet/ndarray/ndarray.py +++ b/python/mxnet/ndarray/ndarray.py @@ -184,6 +184,24 @@ class NDArray(NDArrayBase): # See C++ side of definition(kTVMNDArrayTypeCode) at include/mxmet/tensor_blob.h _tvm_tcode = 19 # pylint: disable= no-member, undefined-variable + + def as_np_ndarray(self): + """Convert mxnet.ndarray.NDArray to mxnet.numpy.ndarray.""" + storage_type = self.stype + if storage_type != 'default': + raise ValueError('cannot convert ndarray of stype {} to numpy ndarray' + .format(str(type(storage_type)))) + from ..numpy import ndarray + hdl = NDArrayHandle() + check_call(_LIB.MXShallowCopyNDArray(self.handle, ctypes.byref(hdl))) + return ndarray(handle=hdl, writable=self.writable) + + def as_nd_ndarray(self): + """A convenience function for creating a classic ndarray from the current + ndarray with zero copy. For this class, it just returns itself since it is + already a classic ndarray.""" + return self + @property def _tvm_handle(self): return self.handle.value @@ -912,7 +930,7 @@ def _slice(self, start, stop): check_call(_LIB.MXNDArraySlice( self.handle, mx_uint(start), mx_uint(stop), ctypes.byref(handle))) - return NDArray(handle=handle, writable=self.writable) + return self.__class__(handle=handle, writable=self.writable) def _at(self, idx): """Returns a view of the array sliced at `idx` in the first dim. @@ -946,7 +964,7 @@ def _at(self, idx): % (idx-length, length)) check_call(_LIB.MXNDArrayAt( self.handle, mx_uint(idx), ctypes.byref(handle))) - return NDArray(handle=handle, writable=self.writable) + return self.__class__(handle=handle, writable=self.writable) def reshape(self, *shape, **kwargs): """Returns a **view** of this array with a new shape without altering any data. @@ -1069,7 +1087,7 @@ def reshape(self, *shape, **kwargs): c_array(ctypes.c_int64, shape), reverse, ctypes.byref(handle))) - return NDArray(handle=handle, writable=self.writable) + return self.__class__(handle=handle, writable=self.writable) def reshape_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`reshape_like`. @@ -2490,7 +2508,7 @@ def _get_broadcast_shape(shape1, shape2): for a, b in zip(shape1[::-1], shape2[::-1]): if a != 1 and b != 1 and a != b: raise ValueError('shape1=%s is not broadcastable to shape2=%s' % (shape1, shape2)) - shape[i] = max(a, b) + shape[i] = b if a == 1 else a i -= 1 return tuple(shape) diff --git a/python/mxnet/ndarray/numpy/__init__.py b/python/mxnet/ndarray/numpy/__init__.py new file mode 100644 index 000000000000..7eb478f792f5 --- /dev/null +++ b/python/mxnet/ndarray/numpy/__init__.py @@ -0,0 +1,26 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Module for numpy ops under mxnet.ndarray.""" + +from . import random +from . import linalg +from . import _op, _internal +from . import _register +from ._op import * # pylint: disable=wildcard-import + +__all__ = _op.__all__ diff --git a/python/mxnet/ndarray/numpy/_internal.py b/python/mxnet/ndarray/numpy/_internal.py new file mode 100644 index 000000000000..c5f292842b3b --- /dev/null +++ b/python/mxnet/ndarray/numpy/_internal.py @@ -0,0 +1,20 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for numpy internal ops.""" + +__all__ = [] diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py new file mode 100644 index 000000000000..d7c06e76c182 --- /dev/null +++ b/python/mxnet/ndarray/numpy/_op.py @@ -0,0 +1,295 @@ +# pylint: disable=C0302 +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for numpy operators used in Gluon dispatched by F=ndarray.""" + +from __future__ import absolute_import +import numpy as _np +from ...base import numeric_types +from ...util import set_module +from ...context import current_context +from . import _internal as _npi + +__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power'] + + +@set_module('mxnet.ndarray.numpy') +def zeros(shape, dtype=_np.float32, order='C', ctx=None): + """Return a new array of given shape and type, filled with zeros. + This function currently only supports storing multi-dimensional data + in row-major (C-style). + + Parameters + ---------- + shape : int or tuple of int + The shape of the empty array. + dtype : str or numpy.dtype, optional + An optional value type. Default is `numpy.float32`. Note that this + behavior is different from NumPy's `ones` function where `float64` + is the default value, because `float32` is considered as the default + data type in deep learning. + order : {'C'}, optional, default: 'C' + How to store multi-dimensional data in memory, currently only row-major + (C-style) is supported. + ctx : Context, optional + An optional device context (default is the current default context). + + Returns + ------- + out : ndarray + Array of zeros with the given shape, dtype, and ctx. + """ + if order != 'C': + raise NotImplementedError + if ctx is None: + ctx = current_context() + dtype = _np.float32 if dtype is None else dtype + return _npi.zeros(shape=shape, ctx=ctx, dtype=dtype) + + +@set_module('mxnet.ndarray.numpy') +def ones(shape, dtype=_np.float32, order='C', ctx=None): + """Return a new array of given shape and type, filled with ones. + This function currently only supports storing multi-dimensional data + in row-major (C-style). + + Parameters + ---------- + shape : int or tuple of int + The shape of the empty array. + dtype : str or numpy.dtype, optional + An optional value type. Default is `numpy.float32`. Note that this + behavior is different from NumPy's `ones` function where `float64` + is the default value, because `float32` is considered as the default + data type in deep learning. + order : {'C'}, optional, default: 'C' + How to store multi-dimensional data in memory, currently only row-major + (C-style) is supported. + ctx : Context, optional + An optional device context (default is the current default context). + + Returns + ------- + out : ndarray + Array of zeros with the given shape, dtype, and ctx. + """ + if order != 'C': + raise NotImplementedError + if ctx is None: + ctx = current_context() + dtype = _np.float32 if dtype is None else dtype + return _npi.ones(shape=shape, ctx=ctx, dtype=dtype) + + +#pylint: disable= too-many-arguments, no-member, protected-access +def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, out=None): + """ Helper function for element-wise operation. + The function will perform numpy-like broadcasting if needed and call different functions. + + Parameters + -------- + lhs : ndarray or numeric value + Left-hand side operand. + + rhs : ndarray or numeric value + Right-hand operand, + + fn_array : function + Function to be called if both lhs and rhs are of ``ndarray`` type. + + fn_scalar : function + Function to be called if both lhs and rhs are numeric values. + + lfn_scalar : function + Function to be called if lhs is ``ndarray`` while rhs is numeric value + + rfn_scalar : function + Function to be called if lhs is numeric value while rhs is ``ndarray``; + if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar + + Returns + -------- + mxnet.numpy.ndarray or scalar + result array or scalar + """ + from ...numpy import ndarray + if isinstance(lhs, numeric_types): + if isinstance(rhs, numeric_types): + return fn_scalar(lhs, rhs, out=out) + else: + if rfn_scalar is None: + # commutative function + return lfn_scalar(rhs, float(lhs), out=out) + else: + return rfn_scalar(rhs, float(lhs), out=out) + elif isinstance(rhs, numeric_types): + return lfn_scalar(lhs, float(rhs), out=out) + elif isinstance(rhs, ndarray): + return fn_array(lhs, rhs, out=out) + else: + raise TypeError('type {} not supported'.format(str(type(rhs)))) +#pylint: enable= too-many-arguments, no-member, protected-access + + +@set_module('mxnet.ndarray.numpy') +def add(x1, x2, out=None): + """Add arguments element-wise. + + Parameters + ---------- + x1, x2 : ndarrays or scalar values + The arrays to be added. If x1.shape != x2.shape, they must be broadcastable to + a common shape (which may be the shape of one or the other). + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + add : ndarray or scalar + The sum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars. + """ + return _ufunc_helper(x1, x2, _npi.add, _np.add, _npi.add_scalar, None, out) + + +@set_module('mxnet.ndarray.numpy') +def subtract(x1, x2, out=None): + """Subtract arguments element-wise. + + Parameters + ---------- + x1, x2 : ndarrays or scalar values + The arrays to be subtracted from each other. If x1.shape != x2.shape, + they must be broadcastable to a common shape (which may be the shape + of one or the other). + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + subtract : ndarray or scalar + The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars. + """ + return _ufunc_helper(x1, x2, _npi.subtract, _np.subtract, _npi.subtract_scalar, + _npi.rsubtract_scalar, out) + + +@set_module('mxnet.ndarray.numpy') +def multiply(x1, x2, out=None): + """Multiply arguments element-wise. + + Parameters + ---------- + x1, x2 : ndarrays or scalar values + The arrays to be multiplied. If x1.shape != x2.shape, they must be broadcastable to + a common shape (which may be the shape of one or the other). + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + out : ndarray or scalar + The multiplication of x1 and x2, element-wise. This is a scalar if both x1 and x2 + are scalars. + """ + return _ufunc_helper(x1, x2, _npi.multiply, _np.multiply, _npi.multiply_scalar, None, out) + + +@set_module('mxnet.ndarray.numpy') +def divide(x1, x2, out=None): + """Returns a true division of the inputs, element-wise. + + Parameters + ---------- + x1 : ndarray or scalar + Dividend array. + + x2 : ndarray or scalar + Divisor array. + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + out : ndarray or scalar + This is a scalar if both x1 and x2 are scalars. + """ + return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar, + _npi.rtrue_divide_scalar, out) + + +@set_module('mxnet.ndarray.numpy') +def mod(x1, x2, out=None): + """Return element-wise remainder of division. + + Parameters + ---------- + x1 : ndarray or scalar + Dividend array. + + x2 : ndarray or scalar + Divisor array. + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + out : ndarray or scalar + This is a scalar if both x1 and x2 are scalars. + """ + return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out) + + +@set_module('mxnet.ndarray.numpy') +def power(x1, x2, out=None): + """First array elements raised to powers from second array, element-wise. + + Parameters + ---------- + x1 : ndarray or scalar + The bases. + + x2 : ndarray or scalar + The exponent. + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + out : ndarray or scalar + The bases in x1 raised to the exponents in x2. + This is a scalar if both x1 and x2 are scalars. + """ + return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out) diff --git a/python/mxnet/ndarray/numpy/_register.py b/python/mxnet/ndarray/numpy/_register.py new file mode 100644 index 000000000000..3ac464e24217 --- /dev/null +++ b/python/mxnet/ndarray/numpy/_register.py @@ -0,0 +1,28 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Registering numpy ops.""" + +from ...base import _init_np_op_module +from ..register import _make_ndarray_function + + +_init_np_op_module(root_module_name='mxnet', np_module_name='numpy', + mx_module_name='ndarray', make_op_func=_make_ndarray_function) + +_init_np_op_module(root_module_name='mxnet', np_module_name='numpy._internal', + mx_module_name='ndarray', make_op_func=_make_ndarray_function) diff --git a/python/mxnet/ndarray/numpy/linalg.py b/python/mxnet/ndarray/numpy/linalg.py new file mode 100644 index 000000000000..0222bb45d148 --- /dev/null +++ b/python/mxnet/ndarray/numpy/linalg.py @@ -0,0 +1,22 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for operators used in Gluon dispatched by F=ndarray.""" + +from __future__ import absolute_import + +__all__ = [] diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py new file mode 100644 index 000000000000..339fb1e77920 --- /dev/null +++ b/python/mxnet/ndarray/numpy/random.py @@ -0,0 +1,21 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for operators used in Gluon dispatched by F=ndarray.""" +from __future__ import absolute_import + +__all__ = [] diff --git a/python/mxnet/ndarray/numpy_extension/__init__.py b/python/mxnet/ndarray/numpy_extension/__init__.py new file mode 100644 index 000000000000..5be34ac9b3d5 --- /dev/null +++ b/python/mxnet/ndarray/numpy_extension/__init__.py @@ -0,0 +1,25 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Module for the ops not belonging to the official numpy package.""" + +from . import _op +from . import image +from . import _register +from ._op import * # pylint: disable=wildcard-import + +__all__ = _op.__all__ diff --git a/python/mxnet/ndarray/numpy_extension/_op.py b/python/mxnet/ndarray/numpy_extension/_op.py new file mode 100644 index 000000000000..22738a0f1950 --- /dev/null +++ b/python/mxnet/ndarray/numpy_extension/_op.py @@ -0,0 +1,21 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for the operators not belonging to the official numpy package +used in Gluon dispatched by F=ndarray module.""" + +__all__ = [] diff --git a/python/mxnet/ndarray/numpy_extension/_register.py b/python/mxnet/ndarray/numpy_extension/_register.py new file mode 100644 index 000000000000..32cd0686551c --- /dev/null +++ b/python/mxnet/ndarray/numpy_extension/_register.py @@ -0,0 +1,25 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Registering numpy_extension ops.""" + +from ...base import _init_np_op_module +from ..register import _make_ndarray_function + + +_init_np_op_module(root_module_name='mxnet', np_module_name='numpy_extension', + mx_module_name='ndarray', make_op_func=_make_ndarray_function) diff --git a/python/mxnet/ndarray/numpy_extension/image.py b/python/mxnet/ndarray/numpy_extension/image.py new file mode 100644 index 000000000000..b3bd27fc503c --- /dev/null +++ b/python/mxnet/ndarray/numpy_extension/image.py @@ -0,0 +1,20 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Image pre-processing operators.""" + +__all__ = [] diff --git a/python/mxnet/ndarray/register.py b/python/mxnet/ndarray/register.py index 1ccf228698ba..bdbfa1584ca6 100644 --- a/python/mxnet/ndarray/register.py +++ b/python/mxnet/ndarray/register.py @@ -24,12 +24,97 @@ from ._internal import NDArrayBase, _imperative_invoke # pylint: disable=unused-import from ..ndarray_doc import _build_doc -from ..base import mx_uint, check_call, _LIB, py_str, _init_op_module, _Null # pylint: disable=unused-import +from ..base import mx_uint, check_call, _LIB, py_str, _init_op_module, _Null, _is_np_op # pylint: disable=unused-import +from ..util import use_np_shape # pylint: disable=unused-import + + +def _verify_all_np_ndarrays(op_name, func_name, args, out): + """Verify if all the arrays are numpy ndarrays. + + Parameters + ---------- + op_name : str + Operator full name registered in backend. + func_name : str + Operator name exposed to users. This is usually the name by stripping off + the prefix of the full operator names registered in backend. + args : list of arrays + Input ndarray arguments to be checked. + out : ndarray or None or list of ndarrays + User-provided output ndarrays. + """ + from ..numpy import ndarray as np_ndarray + for arr in args: + if (arr is not None) and (not isinstance(arr, np_ndarray)): + raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. ' + 'This is a numpy operator which can only accept ' + 'MXNet numpy ndarrays, while received a legacy ndarray. ' + 'Please ensure that you have activated numpy semantics by calling ' + '`npx.set_np()` in your code. If you still see this error with numpy ' + 'semantics activated, please call `as_np_ndarray()` upon the legacy ' + 'ndarray to convert it to an MXNet numpy ndarray, and then feed the ' + 'converted array to this operator.' + .format(op_name, func_name)) + if out is None: + return + if not isinstance(out, (list, tuple)): + out = [out] + for arr in out: + if (arr is not None) and (not isinstance(arr, np_ndarray)): + raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. ' + 'This is a numpy operator which can only accept ' + 'MXNet numpy ndarrays, while received a legacy ndarray. ' + 'Please ensure that you have activated numpy semantics by calling ' + '`npx.set_np()` in your code. If you still see this error with numpy ' + 'semantics activated, please call `as_np_ndarray()` upon the legacy ' + 'ndarray to convert it to an MXNet numpy ndarray, and then feed the ' + 'converted array to this operator.' + .format(op_name, func_name)) + + +def _verify_all_legacy_ndarrays(op_name, func_name, args, out): + """Verify if all the arrays are legacy ndarrays. + + Parameters + ---------- + op_name : str + Operator full name registered in backend. + func_name : str + Operator name exposed to users. This is usually the name by stripping off + the prefix of the full operator names registered in backend. + args : list of arrays + Input ndarray arguments to be checked. + out : ndarray or None or list of ndarrays + User-provided output ndarrays. + """ + from ..numpy import ndarray as np_ndarray + for arr in args: + if (arr is not None) and (isinstance(arr, np_ndarray)): + raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. ' + 'This is a legacy operator which can only accept ' + 'legacy ndarrays, while received an MXNet numpy ndarray. ' + 'Please call `as_nd_ndarray()` upon the numpy ndarray to ' + 'convert it to a legacy ndarray, and then feed the converted ' + 'array to this operator.' + .format(op_name, func_name)) + if out is None: + return + if not isinstance(out, (list, tuple)): + out = [out] + for arr in out: + if (arr is not None) and (isinstance(arr, np_ndarray)): + raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. ' + 'This is a legacy operator which can only write to ' + 'legacy ndarrays, while received an MXNet numpy ndarray. ' + 'Please call `as_nd_ndarray()` upon the numpy ndarray to ' + 'convert it to a legacy ndarray, and then feed the converted ' + 'array to this operator.' + .format(op_name, func_name)) # pylint: disable=too-many-locals -def _generate_ndarray_function_code(handle, name, func_name, signature_only=False): - """Generate function for ndarray op by handle and function name.""" +def _generate_ndarray_function_code(handle, op_name, func_name, signature_only=False): + """Generate function for ndarray op by handle and function op_name.""" real_name = ctypes.c_char_p() desc = ctypes.c_char_p() num_args = mx_uint() @@ -52,7 +137,7 @@ def _generate_ndarray_function_code(handle, name, func_name, signature_only=Fals arg_types = [py_str(arg_types[i]) for i in range(narg)] key_var_num_args = py_str(key_var_num_args.value) ret_type = py_str(ret_type.value) if ret_type.value is not None else '' - doc_str = _build_doc(name, + doc_str = _build_doc(op_name, py_str(desc.value), arg_names, arg_types, @@ -90,6 +175,10 @@ def _generate_ndarray_function_code(handle, name, func_name, signature_only=Fals signature = ndsignature + signature code = [] + is_np_op = _is_np_op(op_name) + doc_str_idx = 1 + if is_np_op: + doc_str_idx = 2 if arr_name: code.append(""" def %s(*%s, **kwargs):"""%(func_name, arr_name)) @@ -134,15 +223,26 @@ def %s(%s):"""%(func_name, ', '.join(signature))) vals.append(%s)"""%(name, name, name)) # dtype if dtype_name is not None: - code.append(""" + if is_np_op: + code.append(""" + if %s is not _Null and %s is not None: + keys.append('%s') + vals.append(_np.dtype(%s).name)"""%(dtype_name, dtype_name, dtype_name, dtype_name)) + else: + code.append(""" if %s is not _Null: keys.append('%s') vals.append(_np.dtype(%s).name)"""%(dtype_name, dtype_name, dtype_name)) + verify_ndarrays_fn =\ + _verify_all_np_ndarrays.__name__ if is_np_op else _verify_all_legacy_ndarrays.__name__ if not signature_only: code.append(""" - return _imperative_invoke(%d, ndargs, keys, vals, out)"""%( - handle.value)) + {verify_fn}("{op_name}", "{func_name}", ndargs, out) + """.format(verify_fn=verify_ndarrays_fn, op_name=op_name, func_name=func_name)) + code.append(""" + return _imperative_invoke(%d, ndargs, keys, vals, out, %s)"""%( + handle.value, str(is_np_op))) else: code.append(""" return (0,)""") @@ -150,7 +250,7 @@ def %s(%s):"""%(func_name, ', '.join(signature))) doc_str_lines = _os.linesep+''.join([' '+s if s.strip() else s for s in 'r"""{doc_str}"""'.format(doc_str=doc_str) .splitlines(True)]) - code.insert(1, doc_str_lines) + code.insert(doc_str_idx, doc_str_lines) return ''.join(code), doc_str diff --git a/python/mxnet/ndarray/utils.py b/python/mxnet/ndarray/utils.py index ff93d0be6d73..730f2172c4f4 100644 --- a/python/mxnet/ndarray/utils.py +++ b/python/mxnet/ndarray/utils.py @@ -248,6 +248,7 @@ def save(fname, data): >>> mx.nd.load('my_dict') {'y': , 'x': } """ + from ..numpy import ndarray as np_ndarray if isinstance(data, NDArray): data = [data] handles = c_array(NDArrayHandle, []) @@ -257,11 +258,17 @@ def save(fname, data): if any(not isinstance(k, string_types) for k in str_keys) or \ any(not isinstance(v, NDArray) for v in nd_vals): raise TypeError('save only accept dict str->NDArray or list of NDArray') + if any(isinstance(v, np_ndarray) for v in nd_vals): + raise TypeError('cannot save mxnet.numpy.ndarray using mxnet.ndarray.save;' + ' use mxnet.numpy.save instead.') keys = c_str_array(str_keys) handles = c_handle_array(nd_vals) elif isinstance(data, list): if any(not isinstance(v, NDArray) for v in data): raise TypeError('save only accept dict str->NDArray or list of NDArray') + if any(isinstance(v, np_ndarray) for v in data): + raise TypeError('cannot save mxnet.numpy.ndarray using mxnet.ndarray.save;' + ' use mxnet.numpy.save instead.') keys = None handles = c_handle_array(data) else: diff --git a/python/mxnet/numpy/__init__.py b/python/mxnet/numpy/__init__.py new file mode 100644 index 000000000000..979e8d61a5ab --- /dev/null +++ b/python/mxnet/numpy/__init__.py @@ -0,0 +1,30 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""MXNet NumPy module.""" + +from __future__ import division, absolute_import, print_function + +from . import random +from . import linalg +from .multiarray import * # pylint: disable=wildcard-import +from . import _op +from . import _register +from ._op import * # pylint: disable=wildcard-import +from .utils import * # pylint: disable=wildcard-import + +__all__ = [] diff --git a/python/mxnet/numpy/_op.py b/python/mxnet/numpy/_op.py new file mode 100644 index 000000000000..8f6f9cc053e4 --- /dev/null +++ b/python/mxnet/numpy/_op.py @@ -0,0 +1,20 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for registering numpy ops for imperative programming.""" + +__all__ = [] diff --git a/python/mxnet/numpy/_register.py b/python/mxnet/numpy/_register.py new file mode 100644 index 000000000000..8a2d2ea61c24 --- /dev/null +++ b/python/mxnet/numpy/_register.py @@ -0,0 +1,27 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Registering ops in mxnet.numpy for imperative programming.""" + +from __future__ import absolute_import + +from ..base import _init_np_op_module +from ..ndarray.register import _make_ndarray_function + + +_init_np_op_module(root_module_name='mxnet', np_module_name='numpy', + mx_module_name=None, make_op_func=_make_ndarray_function) diff --git a/python/mxnet/numpy/linalg.py b/python/mxnet/numpy/linalg.py new file mode 100644 index 000000000000..c4109378e146 --- /dev/null +++ b/python/mxnet/numpy/linalg.py @@ -0,0 +1,22 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for ops used in imperative programming.""" + +from __future__ import absolute_import + +__all__ = [] diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py new file mode 100644 index 000000000000..54b1069b1029 --- /dev/null +++ b/python/mxnet/numpy/multiarray.py @@ -0,0 +1,1551 @@ +#!/usr/bin/env python + +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# pylint: disable=too-many-lines +"""numpy ndarray and util functions.""" + +from __future__ import absolute_import +from __future__ import division + +try: + from __builtin__ import slice as py_slice +except ImportError: + from builtins import slice as py_slice + +from array import array as native_array +import sys +import ctypes +import warnings +import numpy as _np +from ..ndarray import NDArray, _DTYPE_NP_TO_MX, _GRAD_REQ_MAP +from ..ndarray._internal import _set_np_ndarray_class +from . import _op as _mx_np_op +from ..base import check_call, _LIB, NDArrayHandle +from ..base import mx_real_t, c_array_buf, mx_uint, numeric_types, integer_types +from ..util import _sanity_check_params, set_module +from ..context import current_context +from ..ndarray import numpy as _mx_nd_np +from ..ndarray.numpy import _internal as _npi + +__all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', + 'mod', 'power'] + + +# This function is copied from ndarray.py since pylint +# keeps giving false alarm error of undefined-all-variable +def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t): + """Return a new handle with specified shape and context. + + Empty handle is only used to hold results. + + Returns + ------- + handle + A new empty `ndarray` handle. + """ + hdl = NDArrayHandle() + check_call(_LIB.MXNDArrayCreateEx( + c_array_buf(mx_uint, native_array('I', shape)), + mx_uint(len(shape)), + ctypes.c_int(ctx.device_typeid), + ctypes.c_int(ctx.device_id), + ctypes.c_int(int(delay_alloc)), + ctypes.c_int(int(_DTYPE_NP_TO_MX[_np.dtype(dtype).type])), + ctypes.byref(hdl))) + return hdl + + +# Have to use 0 as default value for stype since pylint does not allow +# importing _STORAGE_TYPE_DEFAULT from ndarray.py. +def _np_ndarray_cls(handle, writable=True, stype=0): + if stype != 0: + raise ValueError('_np_ndarray_cls currently only supports default storage ' + 'type, while received stype = {}'.format(stype)) + return ndarray(handle, writable=writable) + + +_set_np_ndarray_class(_np_ndarray_cls) + + +def _get_index(idx): + if isinstance(idx, NDArray) and not isinstance(idx, ndarray): + raise TypeError('Cannot have mx.nd.NDArray as index') + if isinstance(idx, ndarray): + return idx.as_nd_ndarray() + elif sys.version_info[0] > 2 and isinstance(idx, range): + return array(_np.arange(idx.start, idx.stop, idx.step, dtype=_np.int32)).as_nd_ndarray() + else: + return idx + + +@set_module('mxnet.numpy') # pylint: disable=invalid-name +class ndarray(NDArray): + """An array object represents a multidimensional, homogeneous array of fixed-size items. + An associated data-type object describes the format of each element in the array + (its byte-order, how many bytes it occupies in memory, whether it is an integer, a + floating point number, or something else, etc.). Arrays should be constructed using + `array`, `zeros` or `empty`. Currently, only c-contiguous arrays are supported.""" + + # pylint: disable=too-many-return-statements + def __getitem__(self, key): + # TODO(junwu): calling base class __getitem__ is a temp solution + ndim = self.ndim + shape = self.shape + if ndim == 0: + if key != (): + raise IndexError('scalar tensor can only accept `()` as index') + if isinstance(key, tuple) and len(key) == 0: + return self + elif isinstance(key, tuple) and len(key) == ndim\ + and all(isinstance(idx, integer_types) for idx in key): + out = self + for idx in key: + out = out[idx] + return out + elif isinstance(key, integer_types): + if key > shape[0] - 1: + raise IndexError( + 'index {} is out of bounds for axis 0 with size {}'.format( + key, shape[0])) + return self._at(key) + elif isinstance(key, py_slice): + if key.step is not None and key.step != 1: + if key.step == 0: + raise ValueError("slice step cannot be zero") + return self.as_nd_ndarray()._get_nd_basic_indexing(key).as_np_ndarray() + elif key.start is not None or key.stop is not None: + return self._slice(key.start, key.stop) + else: + return self + + if isinstance(key, ndarray): + key = key.as_nd_ndarray() + elif isinstance(key, tuple): + key = [_get_index(idx) for idx in key] + key = tuple(key) + elif isinstance(key, list): + key = [_get_index(idx) for idx in key] + elif sys.version_info[0] > 2 and isinstance(key, range): + key = _get_index(key) + return self.as_nd_ndarray().__getitem__(key).as_np_ndarray() + # pylint: enable=too-many-return-statements + + def __setitem__(self, key, value): + # TODO(junwu): calling base class __setitem__ is a temp solution + if isinstance(value, NDArray) and not isinstance(value, ndarray): + raise TypeError('Cannot assign mx.nd.NDArray to mxnet.numpy.ndarray') + if self.ndim == 0: + if not isinstance(key, tuple) or len(key) != 0: + raise IndexError('scalar tensor can only accept `()` as index') + if isinstance(value, ndarray): + value = value.as_nd_ndarray() + # TODO(junwu): Better handling of this situation + if isinstance(key, tuple) and len(key) == 0: + self.as_nd_ndarray().__setitem__(slice(None), value) + return + + if isinstance(key, ndarray): + key = key.as_nd_ndarray() + elif isinstance(key, tuple): + key = [_get_index(idx) for idx in key] + key = tuple(key) + elif isinstance(key, list): + key = [_get_index(idx) for idx in key] + elif sys.version_info[0] > 2 and isinstance(key, range): + key = _get_index(key) + self.as_nd_ndarray().__setitem__(key, value) + + def __add__(self, other): + """x.__add__(y) <=> x + y""" + return add(self, other) + + def __iadd__(self, other): + """x.__iadd__(y) <=> x += y""" + if not self.writable: + raise ValueError('trying to add to a readonly ndarray') + return add(self, other, out=self) + + def __sub__(self, other): + """x.__sub__(y) <=> x - y""" + return subtract(self, other) + + def __isub__(self, other): + """x.__isub__(y) <=> x -= y""" + if not self.writable: + raise ValueError('trying to subtract from a readonly ndarray') + return subtract(self, other, out=self) + + def __rsub__(self, other): + """x.__rsub__(y) <=> y - x""" + return subtract(other, self) + + def __mul__(self, other): + """x.__mul__(y) <=> x * y""" + return multiply(self, other) + + def __neg__(self): + return self.__mul__(-1.0) + + def __imul__(self, other): + """x.__imul__(y) <=> x *= y""" + if not self.writable: + raise ValueError('trying to add to a readonly ndarray') + return multiply(self, other, out=self) + + def __rmul__(self, other): + """x.__rmul__(y) <=> y * x""" + return self.__mul__(other) + + def __div__(self, other): + raise AttributeError('ndarray.__div__ is replaced by __truediv__. If you are using' + ' Python2, please use the statement from __future__ import division' + ' to change the / operator to mean true division throughout the' + ' module. If you are using Python3, this error should not have' + ' been encountered.') + + def __rdiv__(self, other): + raise AttributeError('ndarray.__rdiv__ is replaced by __rtruediv__. If you are using' + ' Python2, please use the statement from __future__ import division' + ' to change the / operator to mean true division throughout the' + ' module. If you are using Python3, this error should not have' + ' been encountered.') + + def __idiv__(self, other): + raise AttributeError('ndarray.__idiv__ is replaced by __irtruediv__. If you are using' + ' Python2, please use the statement from __future__ import division' + ' to change the / operator to mean true division throughout the' + ' module. If you are using Python3, this error should not have' + ' been encountered.') + + def __truediv__(self, other): + """x.__truediv__(y) <=> x / y""" + return divide(self, other) + + def __rtruediv__(self, other): + """x.__rtruediv__(y) <=> y / x""" + return divide(other, self) + + def __itruediv__(self, other): + return divide(self, other, out=self) + + def __mod__(self, other): + """x.__mod__(y) <=> x % y""" + return mod(self, other) + + def __rmod__(self, other): + """x.__rmod__(y) <=> y % x""" + return mod(other, self) + + def __imod__(self, other): + """x.__imod__(y) <=> x %= y""" + return mod(self, other, out=self) + + def __pow__(self, other): + """x.__pow__(y) <=> x ** y""" + return power(self, other) + + def __rpow__(self, other): + """x.__rpow__(y) <=> y ** x""" + return power(other, self) + + def __eq__(self, other): + """x.__eq__(y) <=> x == y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, ndarray): + return _npi.equal(self, other) + elif isinstance(other, numeric_types): + return _npi.equal_scalar(self, float(other)) + else: + raise TypeError("ndarray does not support type {} as operand".format(str(type(other)))) + + def __hash__(self): + raise NotImplementedError + + def __ne__(self, other): + """x.__ne__(y) <=> x != y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, ndarray): + return _npi.not_equal(self, other) + elif isinstance(other, numeric_types): + return _npi.not_equal_scalar(self, float(other)) + else: + raise TypeError("ndarray does not support type {} as operand".format(str(type(other)))) + + def __gt__(self, other): + """x.__gt__(y) <=> x > y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, ndarray): + return _npi.greater(self, other) + elif isinstance(other, numeric_types): + return _npi.greater_scalar(self, float(other)) + else: + raise TypeError("ndarray does not support type {} as operand".format(str(type(other)))) + + def __ge__(self, other): + """x.__ge__(y) <=> x >= y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, ndarray): + return _npi.greater_equal(self, other) + elif isinstance(other, numeric_types): + return _npi.greater_equal_scalar(self, float(other)) + else: + raise TypeError("ndarray does not support type {} as operand".format(str(type(other)))) + + def __lt__(self, other): + """x.__lt__(y) <=> x < y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, ndarray): + return _npi.less(self, other) + elif isinstance(other, numeric_types): + return _npi.less_scalar(self, float(other)) + else: + raise TypeError("ndarray does not support type {} as operand".format(str(type(other)))) + + def __le__(self, other): + """x.__le__(y) <=> x <= y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, ndarray): + return _npi.less_equal(self, other) + elif isinstance(other, numeric_types): + return _npi.less_equal_scalar(self, float(other)) + else: + raise TypeError("ndarray does not support type {} as operand".format(str(type(other)))) + + def __bool__(self): + num_elements = self.size + if num_elements == 0: + warnings.simplefilter('default') + warnings.warn('The truth value of an empty array is ambiguous. Returning False, but in' + ' future this will result in an error.', DeprecationWarning) + return False + elif num_elements == 1: + return bool(self.item()) + else: + raise ValueError("The truth value of an ndarray with multiple elements is ambiguous.") + + __nonzero__ = __bool__ + + def __float__(self): + num_elements = self.size + if num_elements != 1: + raise TypeError('only size-1 arrays can be converted to Python scalars') + return float(self.item()) + + def __int__(self): + num_elements = self.size + if num_elements != 1: + raise TypeError('only size-1 arrays can be converted to Python scalars') + return int(self.item()) + + def __len__(self): + """Number of elements along the first axis.""" + shape = self.shape + if len(shape) == 0: + raise TypeError('len() of unsized object') + return self.shape[0] + + def __reduce__(self): + return ndarray, (None,), self.__getstate__() + + def item(self, *args): + """Copy an element of an array to a standard Python scalar and return it. + + Parameters + ---------- + *args : Arguments (variable number and type) + none: in this case, the method only works for arrays with one element (a.size == 1), + which element is copied into a standard Python scalar object and returned. + + int_type: this argument is interpreted as a flat index into the array, specifying which + element to copy and return. + + tuple of int_types: functions as does a single int_type argument, except that the + argument is interpreted as an nd-index into the array. + + Returns + ------- + z : Standard Python scalar object + A copy of the specified element of the array as a suitable Python scalar. + """ + # TODO(junwu): no need to call asnumpy() on the whole array. + return self.asnumpy().item(*args) + + @property + # pylint: disable= invalid-name, undefined-variable + def T(self): + """Same as self.transpose(). This always returns a copy of self.""" + return self.transpose() + # pylint: enable= invalid-name, undefined-variable + + def all(self, axis=None, out=None, keepdims=False): + raise NotImplementedError + + def any(self, axis=None, out=None, keepdims=False): + raise NotImplementedError + + def as_nd_ndarray(self): + """Convert mxnet.numpy.ndarray to mxnet.ndarray.NDArray to use its fluent methods.""" + hdl = NDArrayHandle() + check_call(_LIB.MXShallowCopyNDArray(self.handle, ctypes.byref(hdl))) + return NDArray(handle=hdl, writable=self.writable) + + def as_np_ndarray(self): + """A convenience function for creating a numpy ndarray from the current ndarray + with zero copy. For this class, it just returns itself since it's already a + numpy ndarray.""" + return self + + def __repr__(self): + """ + Returns a string representation of the array. The dtype of the ndarray will not + be appended to the string if it is `float32`. The context of the ndarray will + be appended for devices other than CPU. + + Examples + -------- + >>> from mxnet import np, npx + >>> a = np.random.uniform(size=(2, 3)) + >>> a + array([[0.5488135 , 0.5928446 , 0.71518934], + [0.84426576, 0.60276335, 0.8579456 ]]) + >>> print(a) + [[0.5488135 0.5928446 0.71518934] + [0.84426576 0.60276335 0.8579456 ]] + >>> a.dtype + + >>> b = a.astype(np.float64) + >>> b + array([[0.54881352, 0.59284461, 0.71518934], + [0.84426576, 0.60276335, 0.85794562]], dtype=float64) + >>> print(b) + [[0.54881352 0.59284461 0.71518934] + [0.84426576 0.60276335 0.85794562]] + >>> b.dtype + + >>> c = a.copyto(npx.gpu(0)) + >>> c + array([[0.5488135 , 0.5928446 , 0.71518934], + [0.84426576, 0.60276335, 0.8579456 ]], ctx=gpu(0)) + >>> print(c) + [[0.5488135 0.5928446 0.71518934] + [0.84426576 0.60276335 0.8579456 ]] @gpu(0) + >>> d = b.copyto(npx.gpu(0)) + >>> d + array([[0.54881352, 0.59284461, 0.71518934], + [0.84426576, 0.60276335, 0.85794562]], dtype=float64, ctx=gpu(0)) + >>> print(d) + [[0.54881352 0.59284461 0.71518934] + [0.84426576 0.60276335 0.85794562]] @gpu(0) + """ + array_str = self.asnumpy().__repr__() + dtype = self.dtype + if 'dtype=' in array_str: + if dtype == _np.float32: + array_str = array_str[:array_str.rindex(',')] + ')' + elif dtype != _np.float32: + array_str = array_str[:-1] + ', dtype={})'.format(dtype.__name__) + + context = self.context + if context.device_type == 'cpu': + return array_str + return array_str[:-1] + ', ctx={})'.format(str(context)) + + def __str__(self): + """Returns a string representation of the array.""" + array_str = self.asnumpy().__str__() + context = self.context + if context.device_type == 'cpu' or self.ndim == 0: + return array_str + return '{array} @{ctx}'.format(array=array_str, ctx=context) + + def attach_grad(self, grad_req='write'): # pylint: disable=arguments-differ + """Attach a gradient buffer to this ndarray, so that `backward` + can compute gradient with respect to it. + + Parameters + ---------- + grad_req : {'write', 'add', 'null'} + How gradient will be accumulated. + - 'write': gradient will be overwritten on every backward. + - 'add': gradient will be added to existing value on every backward. + - 'null': do not compute gradient for this NDArray. + """ + grad = _mx_np_op.zeros_like(self) # pylint: disable=undefined-variable + grad_req = _GRAD_REQ_MAP[grad_req] + check_call(_LIB.MXAutogradMarkVariables( + 1, ctypes.pointer(self.handle), + ctypes.pointer(mx_uint(grad_req)), + ctypes.pointer(grad.handle))) + + @property + def grad(self): + """Returns gradient buffer attached to this ndarray.""" + hdl = NDArrayHandle() + check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl))) + if hdl.value is None: + return None + return _np_ndarray_cls(hdl) + + def detach(self): + """Returns a new ndarray, detached from the current graph.""" + hdl = NDArrayHandle() + check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl))) + return _np_ndarray_cls(hdl) + + def astype(self, dtype, *args, **kwargs): # pylint: disable=arguments-differ,unused-argument + """ + Copy of the array, cast to a specified type. + + Parameters + ---------- + dtype : str or dtype + Typecode or data-type to which the array is cast. + copy : bool, optional + Default `True`. By default, astype always returns a newly + allocated ndarray on the same context. If this is set to + `False`, and the dtype requested is the same as the ndarray's + dtype, the ndarray is returned instead of a copy. + + Returns + ------- + arr_t : ndarray + Unless `copy` is False and the other conditions for returning the input + array are satisfied (see description for `copy` input parameter), `arr_t` + is a new array of the same shape as the input array with `dtype`. + """ + _sanity_check_params('astype', ['order', 'casting', 'subok'], kwargs) + copy = kwargs.get('copy', True) + if not copy and _np.dtype(dtype) == self.dtype: + return self + + res = empty(self.shape, dtype=dtype, ctx=self.context) + self.copyto(res) + return res + + def copyto(self, other): + """Copies the value of this array to another array. + + If ``other`` is a ``ndarray`` object, then ``other.shape`` and + ``self.shape`` should be the same. This function copies the value from + ``self`` to ``other``. + + If ``other`` is a context, a new ``NDArray`` will be first created on + the target context, and the value of ``self`` is copied. + + Parameters + ---------- + other : ndarray or Context + The destination array or context. + + Returns + ------- + ndarray + The copied array. If ``other`` is an ``ndarray``, then the return value + and ``other`` will point to the same ``ndarray``. + + Examples + -------- + >>> x = np.ones((2,3)) + >>> y = np.zeros((2,3), mx.gpu(0)) + >>> z = x.copyto(y) + >>> z is y + True + >>> y.asnumpy() + array([[ 1., 1., 1.], + [ 1., 1., 1.]], dtype=float32) + """ + if isinstance(other, ndarray): + other = other.as_nd_ndarray() + return self.as_nd_ndarray().copyto(other).as_np_ndarray() + + def asscalar(self): + raise AttributeError('mxnet.numpy.ndarray object has no attribute asscalar') + + def argmax(self, axis=None, out=None): # pylint: disable=arguments-differ + raise NotImplementedError + + def as_in_context(self, context): + """Returns an array on the target device with the same value as this array. + + If the target context is the same as ``self.context``, then ``self`` is + returned. Otherwise, a copy is made. + + Parameters + ---------- + context : Context + The target context. + + Returns + ------- + ndarray + The target array. + """ + if self.context == context: + return self + return self.copyto(context) + + def copy(self, order='C'): # pylint: disable=arguments-differ + if order != 'C': + raise NotImplementedError('ndarray.copy only supports order=\'C\', while ' + 'received {}'.format(str(order))) + return super(ndarray, self).copy().as_np_ndarray() + + def dot(self, b, out=None): + raise NotImplementedError + + def reshape(self, *args, **kwargs): # pylint: disable=arguments-differ + """Returns an array containing the same data with a new shape. + + Notes + ----- + Unlike the free function `numpy.reshape`, this method on `ndarray` allows + the elements of the shape parameter to be passed in as separate arguments. + For example, ``a.reshape(10, 11)`` is equivalent to + ``a.reshape((10, 11))``. + """ + order = 'C' + if len(kwargs) > 1: + raise TypeError('function takes at most 1 keyword argument') + if len(kwargs) == 1: + if 'order' not in kwargs: + raise TypeError('{} is an invalid keyword argument for this function' + .format(kwargs.keys()[0])) + order = kwargs.pop('order', 'C') + if order != 'C': + raise NotImplementedError('only supports C-order,' + ' while received {}'.format(order)) + if len(args) == 0: + raise TypeError('reshape() takes exactly 1 argument (0 given)') + raise NotImplementedError + + def reshape_like(self, *args, **kwargs): + """Convenience fluent method for :py:func:`reshape_like`. + + The arguments are the same as for :py:func:`reshape_like`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute reshape_like') + + def zeros_like(self, *args, **kwargs): + """Convenience fluent method for :py:func:`zeros_like`. + + The arguments are the same as for :py:func:`zeros_like`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute zeros_like') + + def ones_like(self, *args, **kwargs): + """Convenience fluent method for :py:func:`ones_like`. + + The arguments are the same as for :py:func:`ones_like`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute ones_like') + + def broadcast_axes(self, *args, **kwargs): + """Convenience fluent method for :py:func:`broadcast_axes`. + + The arguments are the same as for :py:func:`broadcast_axes`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_like') + + def repeat(self, repeats, axis=None): # pylint: disable=arguments-differ + """Repeat elements of an array.""" + raise NotImplementedError + + def pad(self, *args, **kwargs): + """Convenience fluent method for :py:func:`pad`. + + The arguments are the same as for :py:func:`pad`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute pad') + + def swapaxes(self, axis1, axis2): # pylint: disable=arguments-differ + """Return a copy of the array with axis1 and axis2 interchanged. + Refer to `mxnet.numpy.swapaxes` for full documentation. + """ + raise NotImplementedError + + def split(self, *args, **kwargs): + """Convenience fluent method for :py:func:`split`. + + The arguments are the same as for :py:func:`split`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute split') + + def split_v2(self, *args, **kwargs): + """Convenience fluent method for :py:func:`split_v2`. + + The arguments are the same as for :py:func:`split_v2`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute split_v2') + + def slice(self, *args, **kwargs): + """Convenience fluent method for :py:func:`slice`. + + The arguments are the same as for :py:func:`slice`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute slice') + + def slice_axis(self, *args, **kwargs): + """Convenience fluent method for :py:func:`slice_axis`. + + The arguments are the same as for :py:func:`slice_axis`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute slice_axis') + + def slice_like(self, *args, **kwargs): + """Convenience fluent method for :py:func:`slice_like`. + + The arguments are the same as for :py:func:`slice_like`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute slice_like') + + def take(self, *args, **kwargs): + """Convenience fluent method for :py:func:`take`. + + The arguments are the same as for :py:func:`take`, with + this array as data. + """ + raise NotImplementedError + + def one_hot(self, *args, **kwargs): + """Convenience fluent method for :py:func:`one_hot`. + + The arguments are the same as for :py:func:`one_hot`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute one_hot') + + def pick(self, *args, **kwargs): + """Convenience fluent method for :py:func:`pick`. + + The arguments are the same as for :py:func:`pick`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute pick') + + def sort(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sort`. + + The arguments are the same as for :py:func:`sort`, with + this array as data. + """ + raise NotImplementedError + + def topk(self, *args, **kwargs): + """Convenience fluent method for :py:func:`topk`. + + The arguments are the same as for :py:func:`topk`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute topk') + + def argsort(self, *args, **kwargs): + """Convenience fluent method for :py:func:`argsort`. + + The arguments are the same as for :py:func:`argsort`, with + this array as data. + """ + raise NotImplementedError + + def argmax_channel(self, *args, **kwargs): + """Convenience fluent method for :py:func:`argmax_channel`. + + The arguments are the same as for :py:func:`argmax_channel`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute argmax_channel') + + def argmin(self, *args, **kwargs): + """Convenience fluent method for :py:func:`argmin`. + + The arguments are the same as for :py:func:`argmin`, with + this array as data. + """ + raise NotImplementedError + + def clip(self, min=None, max=None, out=None): # pylint: disable=arguments-differ + """Return an array whose values are limited to [min, max]. + One of max or min must be given. + """ + raise NotImplementedError + + def abs(self, *args, **kwargs): + """Convenience fluent method for :py:func:`abs`. + + The arguments are the same as for :py:func:`abs`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute abs') + + def sign(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sign`. + + The arguments are the same as for :py:func:`sign`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute abs') + + def flatten(self, order='C'): # pylint: disable=arguments-differ + """Return a copy of the array collapsed into one dimension.""" + raise NotImplementedError + + def shape_array(self, *args, **kwargs): + """Convenience fluent method for :py:func:`shape_array`. + + The arguments are the same as for :py:func:`shape_array`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute shape_array') + + def size_array(self, *args, **kwargs): + """Convenience fluent method for :py:func:`size_array`. + + The arguments are the same as for :py:func:`size_array`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute size_array') + + def expand_dims(self, *args, **kwargs): # pylint: disable=arguments-differ + """Convenience fluent method for :py:func:`expand_dims`. + + The arguments are the same as for :py:func:`expand_dims`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute expand_dims') + + def tile(self, *args, **kwargs): + """Convenience fluent method for :py:func:`tile`. + + The arguments are the same as for :py:func:`tile`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute tile') + + def transpose(self, *axes): # pylint: disable=arguments-differ + """Permute the dimensions of an array.""" + raise NotImplementedError + + def flip(self, *args, **kwargs): + """Convenience fluent method for :py:func:`flip`. + + The arguments are the same as for :py:func:`flip`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute flip') + + def depth_to_space(self, *args, **kwargs): + """Convenience fluent method for :py:func:`depth_to_space`. + + The arguments are the same as for :py:func:`depth_to_space`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute depth_to_space') + + def space_to_depth(self, *args, **kwargs): + """Convenience fluent method for :py:func:`space_to_depth`. + + The arguments are the same as for :py:func:`space_to_depth`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute space_to_depth') + + def diag(self, k=0, **kwargs): + """Convenience fluent method for :py:func:`diag`. + + The arguments are the same as for :py:func:`diag`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute diag') + + def sum(self, axis=None, dtype=None, out=None, keepdims=False): # pylint: disable=arguments-differ + """Convenience fluent method for :py:func:`sum`. + + The arguments are the same as for :py:func:`sum`, with + this array as data. + """ + raise NotImplementedError + + def nansum(self, *args, **kwargs): + """Convenience fluent method for :py:func:`nansum`. + + The arguments are the same as for :py:func:`nansum`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute nansum') + + def prod(self, axis=None, dtype=None, out=None, keepdims=False): # pylint: disable=arguments-differ + """Return the product of the array elements over the given axis.""" + raise NotImplementedError + + def nanprod(self, *args, **kwargs): + """Convenience fluent method for :py:func:`nanprod`. + + The arguments are the same as for :py:func:`nanprod`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute nanprod') + + def mean(self, axis=None, dtype=None, out=None, keepdims=False): # pylint: disable=arguments-differ + """Returns the average of the array elements along given axis.""" + raise NotImplementedError + + # TODO(junwu): Use mxnet std op instead of onp.std + def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False): # pylint: disable=arguments-differ + """Returns the standard deviation of the array elements along given axis.""" + ret_np = self.asnumpy().std(axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims) + return array(ret_np, dtype=ret_np.dtype, ctx=self.context) + + def cumsum(self, axis=None, dtype=None, out=None): + """Return the cumulative sum of the elements along the given axis.""" + raise NotImplementedError + + def tolist(self): + return self.asnumpy().tolist() + + def max(self, axis=None, out=None, keepdims=False): # pylint: disable=arguments-differ + """Return the maximum along a given axis.""" + raise NotImplementedError + + def min(self, *args, **kwargs): + """Convenience fluent method for :py:func:`min`. + + The arguments are the same as for :py:func:`min`, with + this array as data. + """ + raise NotImplementedError + + def norm(self, *args, **kwargs): + """Convenience fluent method for :py:func:`norm`. + + The arguments are the same as for :py:func:`norm`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute norm') + + def round(self, *args, **kwargs): + """Convenience fluent method for :py:func:`round`. + + The arguments are the same as for :py:func:`round`, with + this array as data. + """ + raise NotImplementedError + + def rint(self, *args, **kwargs): + """Convenience fluent method for :py:func:`rint`. + + The arguments are the same as for :py:func:`rint`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute rint') + + def fix(self, *args, **kwargs): + """Convenience fluent method for :py:func:`fix`. + + The arguments are the same as for :py:func:`fix`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute fix') + + def floor(self, *args, **kwargs): + """Convenience fluent method for :py:func:`floor`. + + The arguments are the same as for :py:func:`floor`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute floor') + + def ceil(self, *args, **kwargs): + """Convenience fluent method for :py:func:`ceil`. + + The arguments are the same as for :py:func:`ceil`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute ceil') + + def trunc(self, *args, **kwargs): + """Convenience fluent method for :py:func:`trunc`. + + The arguments are the same as for :py:func:`trunc`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute trunc') + + def sin(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sin`. + + The arguments are the same as for :py:func:`sin`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute sin') + + def cos(self, *args, **kwargs): + """Convenience fluent method for :py:func:`cos`. + + The arguments are the same as for :py:func:`cos`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute cos') + + def tan(self, *args, **kwargs): + """Convenience fluent method for :py:func:`tan`. + + The arguments are the same as for :py:func:`tan`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute tan') + + def arcsin(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arcsin`. + + The arguments are the same as for :py:func:`arcsin`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute arcsin') + + def arccos(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arccos`. + + The arguments are the same as for :py:func:`arccos`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute arccos') + + def arctan(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arctan`. + + The arguments are the same as for :py:func:`arctan`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute arctan') + + def degrees(self, *args, **kwargs): + """Convenience fluent method for :py:func:`degrees`. + + The arguments are the same as for :py:func:`degrees`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute degrees') + + def radians(self, *args, **kwargs): + """Convenience fluent method for :py:func:`radians`. + + The arguments are the same as for :py:func:`radians`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute radians') + + def sinh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sinh`. + + The arguments are the same as for :py:func:`sinh`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute sinh') + + def cosh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`cosh`. + + The arguments are the same as for :py:func:`cosh`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute cosh') + + def tanh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`tanh`. + + The arguments are the same as for :py:func:`tanh`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute tanh') + + def arcsinh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arcsinh`. + + The arguments are the same as for :py:func:`arcsinh`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute arcsinh') + + def arccosh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arccosh`. + + The arguments are the same as for :py:func:`arccosh`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute arccosh') + + def arctanh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arctanh`. + + The arguments are the same as for :py:func:`arctanh`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute arctanh') + + def exp(self, *args, **kwargs): + """Convenience fluent method for :py:func:`exp`. + + The arguments are the same as for :py:func:`exp`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute exp') + + def expm1(self, *args, **kwargs): + """Convenience fluent method for :py:func:`expm1`. + + The arguments are the same as for :py:func:`expm1`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute expm1') + + def log(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log`. + + The arguments are the same as for :py:func:`log`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute log') + + def log10(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log10`. + + The arguments are the same as for :py:func:`log10`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute log10') + + def log2(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log2`. + + The arguments are the same as for :py:func:`log2`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute log2') + + def log1p(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log1p`. + + The arguments are the same as for :py:func:`log1p`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute log1p') + + def sqrt(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sqrt`. + + The arguments are the same as for :py:func:`sqrt`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute sqrt') + + def rsqrt(self, *args, **kwargs): + """Convenience fluent method for :py:func:`rsqrt`. + + The arguments are the same as for :py:func:`rsqrt`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute rsqrt') + + def cbrt(self, *args, **kwargs): + """Convenience fluent method for :py:func:`cbrt`. + + The arguments are the same as for :py:func:`cbrt`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute cqrt') + + def rcbrt(self, *args, **kwargs): + """Convenience fluent method for :py:func:`rcbrt`. + + The arguments are the same as for :py:func:`rcbrt`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute rcqrt') + + def square(self, *args, **kwargs): + """Convenience fluent method for :py:func:`square`. + + The arguments are the same as for :py:func:`square`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute square') + + def reciprocal(self, *args, **kwargs): + """Convenience fluent method for :py:func:`reciprocal`. + + The arguments are the same as for :py:func:`reciprocal`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute reciprocal') + + def relu(self, *args, **kwargs): + """Convenience fluent method for :py:func:`relu`. + + The arguments are the same as for :py:func:`relu`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute relu') + + def sigmoid(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sigmoid`. + + The arguments are the same as for :py:func:`sigmoid`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute sigmoid') + + def softmax(self, *args, **kwargs): + """Convenience fluent method for :py:func:`softmax`. + + The arguments are the same as for :py:func:`softmax`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute softmax') + + def log_softmax(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log_softmax`. + + The arguments are the same as for :py:func:`log_softmax`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute log_softmax') + + def softmin(self, *args, **kwargs): + """Convenience fluent method for :py:func:`softmin`. + + The arguments are the same as for :py:func:`softmin`, with + this array as data. + """ + raise AttributeError('mxnet.numpy.ndarray object has no attribute softmin') + + def squeeze(self, axis=None): # pylint: disable=arguments-differ + """Remove single-dimensional entries from the shape of a. + """ + raise NotImplementedError + + def broadcast_to(self, shape): + raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_to') + + def broadcast_like(self, other): + raise AttributeError('mxnet.numpy.ndarray object has no attribute broadcast_like') + + @property + def shape(self): + return super(ndarray, self).shape + + @property + def ndim(self): + """Number of array dimensions.""" + return len(self.shape) + + @property + def size(self): + """Number of elements in the array.""" + return super(ndarray, self).size + + def tostype(self, stype): + raise AttributeError('mxnet.numpy.ndarray object has no attribute tostype') + + +@set_module('mxnet.numpy') +def empty(shape, dtype=float, order='C', ctx=None): + """Return a new array of given shape and type, without initializing entries. + + Parameters + ---------- + shape : int or tuple of int Shape of the empty array, e.g., ``(2, 3)`` or ``2``. + dtype : data-type, optional + Desired output data-type for the array, e.g, `numpy.int8`. Default is + `numpy.float32`. Note that this behavior is different from NumPy's `empty` + function where `float64` is the default value, because `float32` is + considered as the default data type in deep learning. + order : {'C'}, optional, default: 'C' + How to store multi-dimensional data in memory, currently only row-major + (C-style) is supported. + ctx : device context, optional + Device context on which the memory is allocated. Default is + `mxnet.context.current_context()`. + + Returns + ------- + out : ndarray + Array of uninitialized (arbitrary) data of the given shape, dtype, and order. + """ + if order != 'C': + raise NotImplementedError + if ctx is None: + ctx = current_context() + if dtype is None: + dtype = _np.float32 + if isinstance(shape, int): + shape = (shape,) + return ndarray(handle=_new_alloc_handle(shape, ctx, False, dtype)) + + +@set_module('mxnet.numpy') +def array(object, dtype=None, ctx=None): + """ + Create an array. + + Parameters + ---------- + object : array_like or `numpy.ndarray` or `mxnet.numpy.ndarray` + An array, any object exposing the array interface, an object whose + __array__ method returns an array, or any (nested) sequence. + dtype : data-type, optional + The desired data-type for the array. Default is `float32`. + ctx : device context, optional + Device context on which the memory is allocated. Default is + `mxnet.context.current_context()`. + + Returns + ------- + out : ndarray + An array object satisfying the specified requirements. + """ + if ctx is None: + ctx = current_context() + if isinstance(object, ndarray): + dtype = object.dtype if dtype is None else dtype + else: + dtype = mx_real_t if dtype is None else dtype + if not isinstance(object, (ndarray, _np.ndarray)): + try: + object = _np.array(object, dtype=dtype) + except Exception as e: + raise TypeError('{}'.format(str(e))) + ret = empty(object.shape, dtype=dtype, ctx=ctx) + if len(object.shape) == 0: + ret[()] = object + else: + ret[:] = object + return ret + + +@set_module('mxnet.numpy') +def zeros(shape, dtype=_np.float32, order='C', ctx=None): + """Return a new array of given shape and type, filled with zeros. + This function currently only supports storing multi-dimensional data + in row-major (C-style). + + Parameters + ---------- + shape : int or tuple of int + The shape of the empty array. + dtype : str or numpy.dtype, optional + An optional value type (default is `numpy.float32`). Note that this + behavior is different from NumPy's `ones` function where `float64` + is the default value, because `float32` is considered as the default + data type in deep learning. + order : {'C'}, optional, default: 'C' + How to store multi-dimensional data in memory, currently only row-major + (C-style) is supported. + ctx : Context, optional + An optional device context (default is the current default context). + + Returns + ------- + out : ndarray + Array of zeros with the given shape, dtype, and ctx. + """ + return _mx_nd_np.zeros(shape, dtype, order, ctx) + + +@set_module('mxnet.numpy') +def ones(shape, dtype=_np.float32, order='C', ctx=None): + """Return a new array of given shape and type, filled with zeros. + This function currently only supports storing multi-dimensional data + in row-major (C-style). + + Parameters + ---------- + shape : int or tuple of int + The shape of the empty array. + dtype : str or numpy.dtype, optional + An optional value type. Default is `numpy.float32`. Note that this + behavior is different from NumPy's `ones` function where `float64` + is the default value, because `float32` is considered as the default + data type in deep learning. + order : {'C'}, optional, default: 'C' + How to store multi-dimensional data in memory, currently only row-major + (C-style) is supported. + ctx : Context, optional + An optional device context (default is the current default context). + + Returns + ------- + out : ndarray + Array of zeros with the given shape, dtype, and ctx. + """ + return _mx_nd_np.ones(shape, dtype, order, ctx) + + +@set_module('mxnet.numpy') +def add(x1, x2, out=None): + """Add arguments element-wise. + + Parameters + ---------- + x1, x2 : ndarrays or scalar values + The arrays to be added. If x1.shape != x2.shape, they must be broadcastable to + a common shape (which may be the shape of one or the other). + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + add : ndarray or scalar + The sum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars. + """ + return _mx_nd_np.add(x1, x2, out) + + +@set_module('mxnet.numpy') +def subtract(x1, x2, out=None): + """Subtract arguments element-wise. + + Parameters + ---------- + x1, x2 : ndarrays or scalar values + The arrays to be subtracted from each other. If x1.shape != x2.shape, + they must be broadcastable to a common shape (which may be the shape + of one or the other). + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + subtract : ndarray or scalar + The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars. + """ + return _mx_nd_np.subtract(x1, x2, out) + + +@set_module('mxnet.numpy') +def multiply(x1, x2, out=None): + """Multiply arguments element-wise. + + Parameters + ---------- + x1, x2 : ndarrays or scalar values + The arrays to be multiplied. If x1.shape != x2.shape, they must be broadcastable to + a common shape (which may be the shape of one or the other). + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + out : ndarray or scalar + The difference of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars. + """ + return _mx_nd_np.multiply(x1, x2, out) + + +@set_module('mxnet.numpy') +def divide(x1, x2, out=None): + """Returns a true division of the inputs, element-wise. + + Parameters + ---------- + x1 : ndarray or scalar + Dividend array. + + x2 : ndarray or scalar + Divisor array. + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + out : ndarray or scalar + This is a scalar if both x1 and x2 are scalars. + """ + return _mx_nd_np.divide(x1, x2, out=out) + + +@set_module('mxnet.numpy') +def mod(x1, x2, out=None): + """Return element-wise remainder of division. + + Parameters + ---------- + x1 : ndarray or scalar + Dividend array. + + x2 : ndarray or scalar + Divisor array. + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + out : ndarray or scalar + This is a scalar if both x1 and x2 are scalars. + """ + return _mx_nd_np.mod(x1, x2, out=out) + + +@set_module('mxnet.numpy') +def power(x1, x2, out=None): + """First array elements raised to powers from second array, element-wise. + + Parameters + ---------- + x1 : ndarray or scalar + The bases. + + x2 : ndarray or scalar + The exponent. + + out : ndarray + A location into which the result is stored. If provided, it must have a shape + that the inputs broadcast to. If not provided or None, a freshly-allocated array + is returned. + + Returns + ------- + out : ndarray or scalar + The bases in x1 raised to the exponents in x2. + This is a scalar if both x1 and x2 are scalars. + """ + return _mx_nd_np.power(x1, x2, out=out) diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py new file mode 100644 index 000000000000..c4109378e146 --- /dev/null +++ b/python/mxnet/numpy/random.py @@ -0,0 +1,22 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for ops used in imperative programming.""" + +from __future__ import absolute_import + +__all__ = [] diff --git a/python/mxnet/numpy/utils.py b/python/mxnet/numpy/utils.py new file mode 100644 index 000000000000..920897efc80b --- /dev/null +++ b/python/mxnet/numpy/utils.py @@ -0,0 +1,35 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Util functions for the numpy module.""" + + +from __future__ import absolute_import + +import numpy as onp + +__all__ = ['float16', 'float32', 'float64', 'uint8', 'int32', 'int8', 'int64', 'pi'] + +float16 = onp.float16 +float32 = onp.float32 +float64 = onp.float64 +uint8 = onp.uint8 +int32 = onp.int32 +int8 = onp.int8 +int64 = onp.int64 + +pi = onp.pi diff --git a/python/mxnet/numpy_extension/__init__.py b/python/mxnet/numpy_extension/__init__.py new file mode 100644 index 000000000000..d71d65f08de2 --- /dev/null +++ b/python/mxnet/numpy_extension/__init__.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python + +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Module for ops not belonging to the official numpy package for imperative programming.""" + +from __future__ import absolute_import +from . import _op +from . import image +from . import _register +from ._op import * # pylint: disable=wildcard-import +from ..context import * # pylint: disable=wildcard-import +from ..util import is_np_shape, is_np_array, set_np, reset_np +from ..ndarray import waitall +from .utils import * # pylint: disable=wildcard-import + +__all__ = [] diff --git a/python/mxnet/numpy_extension/_op.py b/python/mxnet/numpy_extension/_op.py new file mode 100644 index 000000000000..a995e480221a --- /dev/null +++ b/python/mxnet/numpy_extension/_op.py @@ -0,0 +1,20 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for registering numpy_extension ops for imperative programming.""" + +__all__ = [] diff --git a/python/mxnet/numpy_extension/_register.py b/python/mxnet/numpy_extension/_register.py new file mode 100644 index 000000000000..8abb7254057c --- /dev/null +++ b/python/mxnet/numpy_extension/_register.py @@ -0,0 +1,27 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Registering ops in mxnet.numpy_extension for imperative programming.""" + +from __future__ import absolute_import + +from ..base import _init_np_op_module +from ..ndarray.register import _make_ndarray_function + + +_init_np_op_module(root_module_name='mxnet', np_module_name='numpy_extension', + mx_module_name=None, make_op_func=_make_ndarray_function) diff --git a/python/mxnet/numpy_extension/image.py b/python/mxnet/numpy_extension/image.py new file mode 100644 index 000000000000..00a028b3c18f --- /dev/null +++ b/python/mxnet/numpy_extension/image.py @@ -0,0 +1,22 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Image pre-processing operators.""" + +from ..image import * # pylint: disable=wildcard-import, unused-wildcard-import + +__all__ = [] diff --git a/python/mxnet/numpy_extension/utils.py b/python/mxnet/numpy_extension/utils.py new file mode 100644 index 000000000000..0aa89badbb58 --- /dev/null +++ b/python/mxnet/numpy_extension/utils.py @@ -0,0 +1,122 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Util functions for the numpy module.""" + + +from __future__ import absolute_import + +import ctypes +from .. util import is_np_array, is_np_shape +from .. base import _LIB, check_call, string_types, c_str_array +from .. base import c_handle_array, c_str, mx_uint, NDArrayHandle, py_str +from ..numpy import ndarray + +__all__ = ['save', 'load'] + + +def save(file, arr): + """Saves a list of `ndarray`s or a dict of `str`->`ndarray` to file. + + Examples of filenames: + + - ``/path/to/file`` + - ``s3://my-bucket/path/to/file`` (if compiled with AWS S3 supports) + - ``hdfs://path/to/file`` (if compiled with HDFS supports) + + Parameters + ---------- + file : str + Filename to which the data is saved. + arr : `ndarray` or list of `ndarray`s or dict of `str` to `ndarray` + The data to be saved. + + Notes + ----- + This function can only be called within numpy semantics, i.e., `npx.is_np_shape()` + and `npx.is_np_array()` must both return true. + """ + if not (is_np_shape() and is_np_array()): + raise ValueError('Cannot save `mxnet.numpy.ndarray` in legacy mode. Please activate' + ' numpy semantics by calling `npx.set_np()` in the global scope' + ' before calling this function.') + if isinstance(arr, ndarray): + arr = [arr] + if isinstance(arr, dict): + str_keys = arr.keys() + nd_vals = arr.values() + if any(not isinstance(k, string_types) for k in str_keys) or \ + any(not isinstance(v, ndarray) for v in nd_vals): + raise TypeError('Only accepts dict str->ndarray or list of ndarrays') + keys = c_str_array(str_keys) + handles = c_handle_array(nd_vals) + elif isinstance(arr, list): + if any(not isinstance(v, ndarray) for v in arr): + raise TypeError('Only accepts dict str->ndarray or list of ndarrays') + keys = None + handles = c_handle_array(arr) + else: + raise ValueError("data needs to either be a ndarray, dict of (str, ndarray) pairs " + "or a list of ndarrays.") + check_call(_LIB.MXNDArraySave(c_str(file), + mx_uint(len(handles)), + handles, + keys)) + + +def load(file): + """Loads an array from file. + + See more details in ``save``. + + Parameters + ---------- + file : str + The filename. + + Returns + ------- + result : list of ndarrays or dict of str -> ndarray + Data stored in the file. + + Notes + ----- + This function can only be called within numpy semantics, i.e., `npx.is_np_shape()` + and `npx.is_np_array()` must both return true. + """ + if not (is_np_shape() and is_np_array()): + raise ValueError('Cannot load `mxnet.numpy.ndarray` in legacy mode. Please activate' + ' numpy semantics by calling `npx.set_np()` in the global scope' + ' before calling this function.') + if not isinstance(file, string_types): + raise TypeError('file required to be a string') + out_size = mx_uint() + out_name_size = mx_uint() + handles = ctypes.POINTER(NDArrayHandle)() + names = ctypes.POINTER(ctypes.c_char_p)() + check_call(_LIB.MXNDArrayLoad(c_str(file), + ctypes.byref(out_size), + ctypes.byref(handles), + ctypes.byref(out_name_size), + ctypes.byref(names))) + if out_name_size.value == 0: + return [ndarray(NDArrayHandle(handles[i])) for i in range(out_size.value)] + else: + assert out_name_size.value == out_size.value + return dict( + (py_str(names[i]), ndarray(NDArrayHandle(handles[i]))) + for i in range(out_size.value)) diff --git a/python/mxnet/optimizer/optimizer.py b/python/mxnet/optimizer/optimizer.py index c2c1aa6a76f4..d953e9247900 100644 --- a/python/mxnet/optimizer/optimizer.py +++ b/python/mxnet/optimizer/optimizer.py @@ -18,6 +18,7 @@ # pylint: disable=too-many-lines """Weight updating functions.""" +from __future__ import absolute_import import logging import math import pickle @@ -33,6 +34,7 @@ multi_mp_sgd_mom_update) from ..ndarray import sparse from ..random import normal +from ..util import is_np_array __all__ = [ 'AdaDelta', 'AdaGrad', 'Adam', 'Adamax', 'DCASGD', 'FTML', 'Ftrl', 'LBSGD', @@ -119,6 +121,7 @@ def __init__(self, rescale_grad=1., param_idx2name=None, wd=0., self.idx2name = param_idx2name.copy() self.sym_info = (sym.attr_dict(), sym.list_arguments()) if sym is not None else () self.param_dict = param_dict if param_dict else {} + self.allow_np_array = is_np_array() self.set_lr_mult({}) self.set_wd_mult({}) @@ -1644,6 +1647,28 @@ def update(self, index, weight, grad, state): # backward compatibility wrapper for Optimizer.CreateOptimizer create = Optimizer.create_optimizer # pylint: disable=invalid-name + +def _as_classic(a, allow_np): + # TODO(junwu): This is a temp solution for allowing converting + # np.ndarray to mx.nd.NDArray to be fed into the optimizer since + # users may have custom optimizers implemented using mx.nd.NDArray ops. + from ..numpy import ndarray as np_ndarray + if isinstance(a, (tuple, list)): + if any(isinstance(x, np_ndarray) for x in a): + if allow_np: + return [x.as_nd_ndarray() for x in a] + else: + raise ValueError('Converting np.ndarray to mx.nd.NDArray is not allowed') + else: + if isinstance(a, np_ndarray): + if allow_np: + return a.as_nd_ndarray() + else: + raise ValueError('Converting np.ndarray to mx.nd.NDArray is not allowed') + return a + + + class Updater(object): """Updater for kvstore.""" def __init__(self, optimizer): @@ -1654,14 +1679,15 @@ def __init__(self, optimizer): def __call__(self, index, grad, weight): """Updates weight given gradient and index.""" + allow_np = self.optimizer.allow_np_array if not isinstance(index, (list, tuple)): indices = [index] - grads = [grad] - weights = [weight] + grads = [_as_classic(grad, allow_np)] + weights = [_as_classic(weight, allow_np)] else: indices = index - grads = grad - weights = weight + grads = _as_classic(grad, allow_np) + weights = _as_classic(weight, allow_np) if weights: self.optimizer._set_current_context(weights[0].context.device_id) for i, idx in enumerate(indices): diff --git a/python/mxnet/symbol/__init__.py b/python/mxnet/symbol/__init__.py index f438e4954aa9..2ce395bdd279 100644 --- a/python/mxnet/symbol/__init__.py +++ b/python/mxnet/symbol/__init__.py @@ -17,7 +17,7 @@ """Symbol API of MXNet.""" -from . import _internal, contrib, linalg, op, random, sparse, image, symbol +from . import _internal, contrib, linalg, op, random, sparse, image, symbol, numpy # pylint: disable=wildcard-import, redefined-builtin try: from .gen_op import * # pylint: disable=unused-wildcard-import @@ -27,5 +27,8 @@ from .op import * from .symbol import * # pylint: enable=wildcard-import +from . import numpy as np +from . import numpy_extension as npx -__all__ = op.__all__ + symbol.__all__ + ['contrib', 'linalg', 'random', 'sparse', 'image'] +__all__ = op.__all__ + symbol.__all__\ + + ['contrib', 'linalg', 'random', 'sparse', 'image', 'numpy', 'numpy_extension'] diff --git a/python/mxnet/symbol/_internal.py b/python/mxnet/symbol/_internal.py index 7e9787e32b1c..d46c0e64e6f1 100644 --- a/python/mxnet/symbol/_internal.py +++ b/python/mxnet/symbol/_internal.py @@ -24,18 +24,18 @@ try: if int(_os.environ.get("MXNET_ENABLE_CYTHON", True)) == 0: - from .._ctypes.symbol import SymbolBase, _set_symbol_class + from .._ctypes.symbol import SymbolBase, _set_symbol_class, _set_np_symbol_class from .._ctypes.symbol import _symbol_creator elif _sys.version_info >= (3, 0): - from .._cy3.symbol import SymbolBase, _set_symbol_class + from .._cy3.symbol import SymbolBase, _set_symbol_class, _set_np_symbol_class from .._cy3.symbol import _symbol_creator else: - from .._cy2.symbol import SymbolBase, _set_symbol_class + from .._cy2.symbol import SymbolBase, _set_symbol_class, _set_np_symbol_class from .._cy2.symbol import _symbol_creator except ImportError: if int(_os.environ.get("MXNET_ENFORCE_CYTHON", False)) != 0: raise ImportError("Cython Module cannot be loaded but MXNET_ENFORCE_CYTHON=1") - from .._ctypes.symbol import SymbolBase, _set_symbol_class + from .._ctypes.symbol import SymbolBase, _set_symbol_class, _set_np_symbol_class from .._ctypes.symbol import _symbol_creator from ..attribute import AttrScope from ..base import _Null @@ -45,4 +45,4 @@ except ImportError: pass -__all__ = ['SymbolBase', '_set_symbol_class', '_symbol_creator'] +__all__ = ['SymbolBase', '_set_symbol_class', '_symbol_creator', '_set_np_symbol_class'] diff --git a/python/mxnet/symbol/numpy/__init__.py b/python/mxnet/symbol/numpy/__init__.py new file mode 100644 index 000000000000..857849c4ae62 --- /dev/null +++ b/python/mxnet/symbol/numpy/__init__.py @@ -0,0 +1,28 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Module for numpy ops under mxnet.symbol.""" + +from . import random +from . import linalg +from . import _op, _symbol, _internal +from ._symbol import _Symbol +from . import _register +from ._op import * # pylint: disable=wildcard-import +from ._symbol import * # pylint: disable=wildcard-import + +__all__ = _op.__all__ + _symbol.__all__ diff --git a/python/mxnet/symbol/numpy/_internal.py b/python/mxnet/symbol/numpy/_internal.py new file mode 100644 index 000000000000..c5f292842b3b --- /dev/null +++ b/python/mxnet/symbol/numpy/_internal.py @@ -0,0 +1,20 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for numpy internal ops.""" + +__all__ = [] diff --git a/python/mxnet/symbol/numpy/_op.py b/python/mxnet/symbol/numpy/_op.py new file mode 100644 index 000000000000..a4a979f30b18 --- /dev/null +++ b/python/mxnet/symbol/numpy/_op.py @@ -0,0 +1,20 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for operators used in Gluon dispatched by F=symbol module.""" + +__all__ = [] diff --git a/python/mxnet/symbol/numpy/_register.py b/python/mxnet/symbol/numpy/_register.py new file mode 100644 index 000000000000..3245c8d6d638 --- /dev/null +++ b/python/mxnet/symbol/numpy/_register.py @@ -0,0 +1,28 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Registering numpy ops.""" + +from ...base import _init_np_op_module +from ..register import _make_symbol_function + +_init_np_op_module(root_module_name='mxnet', np_module_name='numpy', + mx_module_name='symbol', make_op_func=_make_symbol_function) + + +_init_np_op_module(root_module_name='mxnet', np_module_name='numpy._internal', + mx_module_name='symbol', make_op_func=_make_symbol_function) diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py new file mode 100644 index 000000000000..616f3066d98d --- /dev/null +++ b/python/mxnet/symbol/numpy/_symbol.py @@ -0,0 +1,1013 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# pylint: disable=too-many-lines +"""numpy namespace for operators used in Gluon APIs dispatched by F=symbol module.""" + +from __future__ import absolute_import +import ctypes +import numpy as _np +from ...base import _LIB, SymbolHandle, numeric_types, mx_uint +from ...util import check_call, set_module +from ...context import current_context +from ..symbol import Symbol +from .._internal import _set_np_symbol_class +from . import _internal as _npi + +__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power'] + + +def _num_outputs(sym): + return len(sym.as_nd_ndarray()) + + +@set_module('mxnet.symbol.numpy') +class _Symbol(Symbol): + def __getitem__(self, key): + num_outputs = _num_outputs(self) + if num_outputs == 1: + raise NotImplementedError + if not isinstance(key, int): + raise NotImplementedError + if key >= num_outputs: + # Important, python determines the end by this exception + raise IndexError + handle = SymbolHandle() + check_call(_LIB.MXSymbolGetOutput( + self.handle, mx_uint(key), ctypes.byref(handle))) + return _Symbol(handle=handle) + + def __setitem__(self, key, value): + raise NotImplementedError + + def __iter__(self): + raise AttributeError('_Symbol object has no attribute __iter__') + + def __add__(self, other): + """x.__add__(y) <=> x + y""" + return add(self, other) + + def __sub__(self, other): + """x.__sub__(y) <=> x - y""" + return subtract(self, other) + + def __rsub__(self, other): + """x.__rsub__(y) <=> y - x""" + return subtract(other, self) + + def __mul__(self, other): + """x.__mul__(y) <=> x * y""" + return multiply(self, other) + + def __rmul__(self, other): + """x.__rmul__(y) <=> y * x""" + return multiply(other, self) + + def __div__(self, other): + raise AttributeError('_Symbol.__div__ is replaced by __truediv__. If you are using' + ' Python2, please use the statement from __future__ import division' + ' to change the / operator to mean true division throughout the' + ' module. If you are using Python3, this error should not have' + ' been encountered.') + + def __rdiv__(self, other): + raise AttributeError('_Symbol.__rdiv__ is replaced by __rtruediv__. If you are using' + ' Python2, please use the statement from __future__ import division' + ' to change the / operator to mean true division throughout the' + ' module. If you are using Python3, this error should not have' + ' been encountered.') + + def __mod__(self, other): + """x.__mod__(y) <=> x % y""" + return mod(self, other) + + def __rmod__(self, other): + """x.__rmod__(y) <=> y % x""" + return mod(other, self) + + def __idiv__(self, other): + raise NotImplementedError + + def __truediv__(self, other): + """x.__truediv__(y) <=> x / y""" + return divide(self, other) + + def __rtruediv__(self, other): + """x.__rtruediv__(y) <=> y / x""" + return divide(other, self) + + def __itruediv__(self, other): + raise NotImplementedError + + def __pow__(self, other): + """x.__pow__(y) <=> x ** y""" + return power(self, other) + + def __rpow__(self, other): + return power(other, self) + + def __neg__(self): + """x.__neg__() <=> - x""" + return self.__mul__(-1.0) + + def __deepcopy__(self, _): + return super(_Symbol, self).as_np_ndarray() + + def __eq__(self, other): + """x.__eq__(y) <=> x == y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, _Symbol): + return _npi.equal(self, other) + elif isinstance(other, numeric_types): + return _npi.equal_scalar(self, float(other)) + else: + raise TypeError("_Symbol does not support type {} as operand".format(str(type(other)))) + + def __ne__(self, other): + """x.__ne__(y) <=> x != y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, _Symbol): + return _npi.not_equal(self, other) + elif isinstance(other, numeric_types): + return _npi.not_equal_scalar(self, float(other)) + else: + raise TypeError("_Symbol does not support type {} as operand".format(str(type(other)))) + + def __gt__(self, other): + """x.__gt__(y) <=> x > y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, _Symbol): + return _npi.greater(self, other) + elif isinstance(other, numeric_types): + return _npi.greater_scalar(self, float(other)) + else: + raise TypeError("_Symbol does not support type {} as operand".format(str(type(other)))) + + def __ge__(self, other): + """x.__ge__(y) <=> x >= y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, _Symbol): + return _npi.greater_equal(self, other) + elif isinstance(other, numeric_types): + return _npi.greater_equal_scalar(self, float(other)) + else: + raise TypeError("_Symbol does not support type {} as operand".format(str(type(other)))) + + def __lt__(self, other): + """x.__lt__(y) <=> x < y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, _Symbol): + return _npi.less(self, other) + elif isinstance(other, numeric_types): + return _npi.less_scalar(self, float(other)) + else: + raise TypeError("_Symbol does not support type {} as operand".format(str(type(other)))) + + def __le__(self, other): + """x.__le__(y) <=> x <= y""" + # TODO(junwu): Return boolean ndarray when dtype=bool_ is supported + if isinstance(other, _Symbol): + return _npi.less_equal(self, other) + elif isinstance(other, numeric_types): + return _npi.less_equal_scalar(self, float(other)) + else: + raise TypeError("_Symbol does not support type {} as operand".format(str(type(other)))) + + def __len__(self): + raise NotImplementedError + + def as_nd_ndarray(self): + """Convert _Symbol to mxnet.symbol.Symbol to use its convenience fluent methods.""" + hdl = SymbolHandle() + check_call(_LIB.MXShallowCopySymbol(self.handle, ctypes.byref(hdl))) + return Symbol(handle=hdl) + + def as_np_ndarray(self): + """For the convenience of conversion between legacy and np symbols.""" + return self + + @property + # pylint: disable= invalid-name, undefined-variable + def T(self): + """Same as self.transpose().""" + return self.transpose() + # pylint: enable= invalid-name, undefined-variable + + def astype(self, dtype, **kwargs): # pylint: disable=arguments-differ + raise NotImplementedError + + def dot(self, b, out=None): + raise NotImplementedError + + def reshape(self, *args, **kwargs): # pylint: disable=arguments-differ + """Returns an array containing the same data with a new shape. + + Notes + ----- + Unlike the free function `numpy.reshape`, this method on `ndarray` allows + the elements of the shape parameter to be passed in as separate arguments. + For example, ``a.reshape(10, 11)`` is equivalent to + ``a.reshape((10, 11))``. + """ + order = 'C' + if len(kwargs) > 1: + raise TypeError('function takes at most 1 keyword argument') + if len(kwargs) == 1: + if 'order' not in kwargs: + raise TypeError('{} is an invalid keyword argument for this function' + .format(kwargs.keys()[0])) + order = kwargs.pop('order', 'C') + if order != 'C': + raise NotImplementedError('only supports C-order,' + ' while received {}'.format(order)) + if len(args) == 0: + raise TypeError('reshape() takes exactly 1 argument (0 given)') + raise NotImplementedError + + def argmax(self, axis=None, out=None): # pylint: disable=arguments-differ + raise NotImplementedError + + def reshape_like(self, *args, **kwargs): + """Convenience fluent method for :py:func:`reshape_like`. + + The arguments are the same as for :py:func:`reshape_like`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute reshape_like') + + def zeros_like(self, *args, **kwargs): + """Convenience fluent method for :py:func:`zeros_like`. + + The arguments are the same as for :py:func:`zeros_like`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute zeros_like') + + def ones_like(self, *args, **kwargs): + """Convenience fluent method for :py:func:`ones_like`. + + The arguments are the same as for :py:func:`ones_like`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute ones_like') + + def broadcast_axes(self, *args, **kwargs): + """Convenience fluent method for :py:func:`broadcast_axes`. + + The arguments are the same as for :py:func:`broadcast_axes`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute broadcast_like') + + def repeat(self, repeats, axis=None): # pylint: disable=arguments-differ + """Repeat elements of an array.""" + raise NotImplementedError + + def pad(self, *args, **kwargs): + """Convenience fluent method for :py:func:`pad`. + + The arguments are the same as for :py:func:`pad`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute pad') + + def swapaxes(self, axis1, axis2): # pylint: disable=arguments-differ + """Return a copy of the array with axis1 and axis2 interchanged. + Refer to `mxnet.numpy.swapaxes` for full documentation. + """ + raise NotImplementedError + + def split(self, *args, **kwargs): + """Convenience fluent method for :py:func:`split`. + + The arguments are the same as for :py:func:`split`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute split') + + def split_v2(self, *args, **kwargs): + """Convenience fluent method for :py:func:`split_v2`. + + The arguments are the same as for :py:func:`split_v2`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute split_v2') + + def slice(self, *args, **kwargs): + """Convenience fluent method for :py:func:`slice`. + + The arguments are the same as for :py:func:`slice`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute slice') + + def slice_axis(self, *args, **kwargs): + """Convenience fluent method for :py:func:`slice_axis`. + + The arguments are the same as for :py:func:`slice_axis`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute slice_axis') + + def slice_like(self, *args, **kwargs): + """Convenience fluent method for :py:func:`slice_like`. + + The arguments are the same as for :py:func:`slice_like`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute slice_like') + + def take(self, *args, **kwargs): + """Convenience fluent method for :py:func:`take`. + + The arguments are the same as for :py:func:`take`, with + this array as data. + """ + raise NotImplementedError + + def one_hot(self, *args, **kwargs): + """Convenience fluent method for :py:func:`one_hot`. + + The arguments are the same as for :py:func:`one_hot`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute one_hot') + + def pick(self, *args, **kwargs): + """Convenience fluent method for :py:func:`pick`. + + The arguments are the same as for :py:func:`pick`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute pick') + + def sort(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sort`. + + The arguments are the same as for :py:func:`sort`, with + this array as data. + """ + raise NotImplementedError + + def topk(self, *args, **kwargs): + """Convenience fluent method for :py:func:`topk`. + + The arguments are the same as for :py:func:`topk`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute topk') + + def argsort(self, *args, **kwargs): + """Convenience fluent method for :py:func:`argsort`. + + The arguments are the same as for :py:func:`argsort`, with + this array as data. + """ + raise NotImplementedError + + def argmax_channel(self, *args, **kwargs): + """Convenience fluent method for :py:func:`argmax_channel`. + + The arguments are the same as for :py:func:`argmax_channel`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute argmax_channel') + + def argmin(self, *args, **kwargs): + """Convenience fluent method for :py:func:`argmin`. + + The arguments are the same as for :py:func:`argmin`, with + this array as data. + """ + raise NotImplementedError + + def clip(self, min=None, max=None, out=None): # pylint: disable=arguments-differ + """Return an array whose values are limited to [min, max]. + One of max or min must be given. + """ + raise NotImplementedError + + def abs(self, *args, **kwargs): + """Convenience fluent method for :py:func:`abs`. + + The arguments are the same as for :py:func:`abs`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute abs') + + def sign(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sign`. + + The arguments are the same as for :py:func:`sign`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute abs') + + def flatten(self, order='C'): # pylint: disable=arguments-differ + """Return a copy of the array collapsed into one dimension.""" + return self.reshape(-1, order=order) + + def shape_array(self, *args, **kwargs): + """Convenience fluent method for :py:func:`shape_array`. + + The arguments are the same as for :py:func:`shape_array`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute shape_array') + + def size_array(self, *args, **kwargs): + """Convenience fluent method for :py:func:`size_array`. + + The arguments are the same as for :py:func:`size_array`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute size_array') + + def expand_dims(self, *args, **kwargs): # pylint: disable=arguments-differ + """Convenience fluent method for :py:func:`expand_dims`. + + The arguments are the same as for :py:func:`expand_dims`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute expand_dims') + + def tile(self, *args, **kwargs): + """Convenience fluent method for :py:func:`tile`. + + The arguments are the same as for :py:func:`tile`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute tile') + + def transpose(self, *axes): # pylint: disable=arguments-differ + """Convenience fluent method for :py:func:`transpose`. + + The arguments are the same as for :py:func:`transpose`, with + this array as data. + """ + raise NotImplementedError + + def flip(self, *args, **kwargs): + """Convenience fluent method for :py:func:`flip`. + + The arguments are the same as for :py:func:`flip`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute flip') + + def depth_to_space(self, *args, **kwargs): + """Convenience fluent method for :py:func:`depth_to_space`. + + The arguments are the same as for :py:func:`depth_to_space`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute depth_to_space') + + def space_to_depth(self, *args, **kwargs): + """Convenience fluent method for :py:func:`space_to_depth`. + + The arguments are the same as for :py:func:`space_to_depth`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute space_to_depth') + + def diag(self, k=0, **kwargs): + """Convenience fluent method for :py:func:`diag`. + + The arguments are the same as for :py:func:`diag`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute diag') + + def sum(self, axis=None, dtype=None, out=None, keepdims=False): # pylint: disable=arguments-differ + """Convenience fluent method for :py:func:`sum`. + + The arguments are the same as for :py:func:`sum`, with + this array as data. + """ + raise NotImplementedError + + def nansum(self, *args, **kwargs): + """Convenience fluent method for :py:func:`nansum`. + + The arguments are the same as for :py:func:`nansum`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute nansum') + + def prod(self, axis=None, dtype=None, out=None, keepdims=False): # pylint: disable=arguments-differ + """Return the product of the array elements over the given axis.""" + raise NotImplementedError + + def nanprod(self, *args, **kwargs): + """Convenience fluent method for :py:func:`nanprod`. + + The arguments are the same as for :py:func:`nanprod`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute nanprod') + + def mean(self, axis=None, dtype=None, out=None, keepdims=False): # pylint: disable=arguments-differ + """Convenience fluent method for :py:func:`mean`. + + The arguments are the same as for :py:func:`mean`, with + this array as data. + """ + raise NotImplementedError + + def cumsum(self, axis=None, dtype=None, out=None): + """Return the cumulative sum of the elements along the given axis.""" + raise NotImplementedError + + def max(self, axis=None, out=None, keepdims=False): # pylint: disable=arguments-differ + """Return the maximum along a given axis.""" + raise NotImplementedError + + def min(self, *args, **kwargs): + """Convenience fluent method for :py:func:`min`. + + The arguments are the same as for :py:func:`min`, with + this array as data. + """ + raise NotImplementedError + + def norm(self, *args, **kwargs): + """Convenience fluent method for :py:func:`norm`. + + The arguments are the same as for :py:func:`norm`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute norm') + + def round(self, *args, **kwargs): + """Convenience fluent method for :py:func:`round`. + + The arguments are the same as for :py:func:`round`, with + this array as data. + """ + raise NotImplementedError + + def rint(self, *args, **kwargs): + """Convenience fluent method for :py:func:`rint`. + + The arguments are the same as for :py:func:`rint`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute rint') + + def fix(self, *args, **kwargs): + """Convenience fluent method for :py:func:`fix`. + + The arguments are the same as for :py:func:`fix`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute fix') + + def floor(self, *args, **kwargs): + """Convenience fluent method for :py:func:`floor`. + + The arguments are the same as for :py:func:`floor`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute floor') + + def ceil(self, *args, **kwargs): + """Convenience fluent method for :py:func:`ceil`. + + The arguments are the same as for :py:func:`ceil`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute ceil') + + def trunc(self, *args, **kwargs): + """Convenience fluent method for :py:func:`trunc`. + + The arguments are the same as for :py:func:`trunc`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute trunc') + + def sin(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sin`. + + The arguments are the same as for :py:func:`sin`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute sin') + + def cos(self, *args, **kwargs): + """Convenience fluent method for :py:func:`cos`. + + The arguments are the same as for :py:func:`cos`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute cos') + + def tan(self, *args, **kwargs): + """Convenience fluent method for :py:func:`tan`. + + The arguments are the same as for :py:func:`tan`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute tan') + + def arcsin(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arcsin`. + + The arguments are the same as for :py:func:`arcsin`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute arcsin') + + def arccos(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arccos`. + + The arguments are the same as for :py:func:`arccos`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute arccos') + + def arctan(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arctan`. + + The arguments are the same as for :py:func:`arctan`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute arctan') + + def degrees(self, *args, **kwargs): + """Convenience fluent method for :py:func:`degrees`. + + The arguments are the same as for :py:func:`degrees`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute degrees') + + def radians(self, *args, **kwargs): + """Convenience fluent method for :py:func:`radians`. + + The arguments are the same as for :py:func:`radians`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute radians') + + def sinh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sinh`. + + The arguments are the same as for :py:func:`sinh`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute sinh') + + def cosh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`cosh`. + + The arguments are the same as for :py:func:`cosh`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute cosh') + + def tanh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`tanh`. + + The arguments are the same as for :py:func:`tanh`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute tanh') + + def arcsinh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arcsinh`. + + The arguments are the same as for :py:func:`arcsinh`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute arcsinh') + + def arccosh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arccosh`. + + The arguments are the same as for :py:func:`arccosh`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute arccosh') + + def arctanh(self, *args, **kwargs): + """Convenience fluent method for :py:func:`arctanh`. + + The arguments are the same as for :py:func:`arctanh`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute arctanh') + + def exp(self, *args, **kwargs): + """Convenience fluent method for :py:func:`exp`. + + The arguments are the same as for :py:func:`exp`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute exp') + + def expm1(self, *args, **kwargs): + """Convenience fluent method for :py:func:`expm1`. + + The arguments are the same as for :py:func:`expm1`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute expm1') + + def log(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log`. + + The arguments are the same as for :py:func:`log`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute log') + + def log10(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log10`. + + The arguments are the same as for :py:func:`log10`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute log10') + + def log2(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log2`. + + The arguments are the same as for :py:func:`log2`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute log2') + + def log1p(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log1p`. + + The arguments are the same as for :py:func:`log1p`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute log1p') + + def sqrt(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sqrt`. + + The arguments are the same as for :py:func:`sqrt`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute sqrt') + + def rsqrt(self, *args, **kwargs): + """Convenience fluent method for :py:func:`rsqrt`. + + The arguments are the same as for :py:func:`rsqrt`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute rsqrt') + + def cbrt(self, *args, **kwargs): + """Convenience fluent method for :py:func:`cbrt`. + + The arguments are the same as for :py:func:`cbrt`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute cqrt') + + def rcbrt(self, *args, **kwargs): + """Convenience fluent method for :py:func:`rcbrt`. + + The arguments are the same as for :py:func:`rcbrt`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute rcqrt') + + def square(self, *args, **kwargs): + """Convenience fluent method for :py:func:`square`. + + The arguments are the same as for :py:func:`square`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute square') + + def reciprocal(self, *args, **kwargs): + """Convenience fluent method for :py:func:`reciprocal`. + + The arguments are the same as for :py:func:`reciprocal`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute reciprocal') + + def relu(self, *args, **kwargs): + """Convenience fluent method for :py:func:`relu`. + + The arguments are the same as for :py:func:`relu`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute relu') + + def sigmoid(self, *args, **kwargs): + """Convenience fluent method for :py:func:`sigmoid`. + + The arguments are the same as for :py:func:`sigmoid`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute sigmoid') + + def softmax(self, *args, **kwargs): + """Convenience fluent method for :py:func:`softmax`. + + The arguments are the same as for :py:func:`softmax`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute softmax') + + def log_softmax(self, *args, **kwargs): + """Convenience fluent method for :py:func:`log_softmax`. + + The arguments are the same as for :py:func:`log_softmax`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute log_softmax') + + def softmin(self, *args, **kwargs): + """Convenience fluent method for :py:func:`softmin`. + + The arguments are the same as for :py:func:`softmin`, with + this array as data. + """ + raise AttributeError('_Symbol object has no attribute softmin') + + def squeeze(self, axis=None): # pylint: disable=arguments-differ + """Remove single-dimensional entries from the shape of a. + """ + raise NotImplementedError + + def broadcast_to(self, *args, **kwargs): + raise AttributeError('_Symbol object has no attribute broadcast_to') + + def broadcast_like(self, *args, **kwargs): + raise AttributeError('_Symbol object has no attribute broadcast_like') + + +@set_module('mxnet.symbol.numpy') +def zeros(shape, dtype=_np.float32, order='C', ctx=None): + """Return a new array of given shape and type, filled with zeros. + This function currently only supports storing multi-dimensional data + in row-major (C-style). + + Parameters + ---------- + shape : int or tuple of int + The shape of the empty array. + dtype : str or numpy.dtype, optional + An optional value type. Default is `numpy.float32`. Note that this + behavior is different from NumPy's `zeros` function where `float64` + is the default value, because `float32` is considered as the default + data type in deep learning. + order : {'C'}, optional, default: 'C' + How to store multi-dimensional data in memory, currently only row-major + (C-style) is supported. + ctx : Context, optional + An optional device context (default is the current default context). + + Returns + ------- + out : Symbol + Array of zeros with the given shape, dtype, and ctx. + """ + if order != 'C': + raise NotImplementedError + if ctx is None: + ctx = current_context() + dtype = _np.float32 if dtype is None else dtype + return _npi.zeros(shape=shape, ctx=ctx, dtype=dtype) + + +@set_module('mxnet.symbol.numpy') +def ones(shape, dtype=_np.float32, order='C', ctx=None): + """Return a new array of given shape and type, filled with zeros. + This function currently only supports storing multi-dimensional data + in row-major (C-style). + + Parameters + ---------- + shape : int or tuple of int + The shape of the empty array. + dtype : str or numpy.dtype, optional + An optional value type. Default is `numpy.float32`. Note that this + behavior is different from NumPy's `ones` function where `float64` + is the default value, because `float32` is considered as the default + data type in deep learning. + order : {'C'}, optional, default: 'C' + How to store multi-dimensional data in memory, currently only row-major + (C-style) is supported. + ctx : Context, optional + An optional device context (default is the current default context). + + Returns + ------- + out : ndarray + Array of zeros with the given shape, dtype, and ctx. + """ + if order != 'C': + raise NotImplementedError + if ctx is None: + ctx = current_context() + dtype = _np.float32 if dtype is None else dtype + return _npi.ones(shape=shape, ctx=ctx, dtype=dtype) + + +#pylint: disable= too-many-arguments, no-member, protected-access +def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None, out=None): + """ Helper function for element-wise operation. + The function will perform numpy-like broadcasting if needed and call different functions. + + Parameters + -------- + lhs : Symbol or numeric value + Left-hand side operand. + + rhs : Symbol or numeric value + Right-hand operand, + + fn_array : function + Function to be called if both lhs and rhs are of ``Symbol`` type. + + fn_scalar : function + Function to be called if both lhs and rhs are numeric values. + + lfn_scalar : function + Function to be called if lhs is ``Symbol`` while rhs is numeric value + + rfn_scalar : function + Function to be called if lhs is numeric value while rhs is ``Symbol``; + if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar + + Returns + -------- + mxnet.numpy.ndarray + result array + """ + if isinstance(lhs, numeric_types): + if isinstance(rhs, numeric_types): + return fn_scalar(lhs, rhs, out=out) + else: + if rfn_scalar is None: + # commutative function + return lfn_scalar(rhs, float(lhs), out=out) + else: + return rfn_scalar(rhs, float(lhs), out=out) + elif isinstance(rhs, numeric_types): + return lfn_scalar(lhs, float(rhs), out=out) + elif isinstance(rhs, Symbol): + return fn_array(lhs, rhs, out=out) + else: + raise TypeError('type %s not supported' % str(type(rhs))) +#pylint: enable= too-many-arguments, no-member, protected-access + + +@set_module('mxnet.symbol.numpy') +def add(x1, x2, out=None): + return _ufunc_helper(x1, x2, _npi.add, _np.add, _npi.add_scalar, None, out) + + +@set_module('mxnet.symbol.numpy') +def subtract(x1, x2, out=None): + return _ufunc_helper(x1, x2, _npi.subtract, _np.subtract, _npi.subtract_scalar, + _npi.rsubtract_scalar, out) + + +@set_module('mxnet.symbol.numpy') +def multiply(x1, x2, out=None): + return _ufunc_helper(x1, x2, _npi.multiply, _np.multiply, _npi.multiply_scalar, None, out) + + +@set_module('mxnet.symbol.numpy') +def divide(x1, x2, out=None): + return _ufunc_helper(x1, x2, _npi.true_divide, _np.divide, _npi.true_divide_scalar, + _npi.rtrue_divide_scalar, out) + + +@set_module('mxnet.symbol.numpy') +def mod(x1, x2, out=None): + return _ufunc_helper(x1, x2, _npi.mod, _np.mod, _npi.mod_scalar, _npi.rmod_scalar, out) + + +@set_module('mxnet.symbol.numpy') +def power(x1, x2, out=None): + return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out) + + +_set_np_symbol_class(_Symbol) diff --git a/python/mxnet/symbol/numpy/linalg.py b/python/mxnet/symbol/numpy/linalg.py new file mode 100644 index 000000000000..28cfd0f3806a --- /dev/null +++ b/python/mxnet/symbol/numpy/linalg.py @@ -0,0 +1,22 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for operators used in Gluon dispatched by F=symbol.""" + +from __future__ import absolute_import + +__all__ = [] diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py new file mode 100644 index 000000000000..28cfd0f3806a --- /dev/null +++ b/python/mxnet/symbol/numpy/random.py @@ -0,0 +1,22 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for operators used in Gluon dispatched by F=symbol.""" + +from __future__ import absolute_import + +__all__ = [] diff --git a/python/mxnet/symbol/numpy_extension/__init__.py b/python/mxnet/symbol/numpy_extension/__init__.py new file mode 100644 index 000000000000..5be34ac9b3d5 --- /dev/null +++ b/python/mxnet/symbol/numpy_extension/__init__.py @@ -0,0 +1,25 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Module for the ops not belonging to the official numpy package.""" + +from . import _op +from . import image +from . import _register +from ._op import * # pylint: disable=wildcard-import + +__all__ = _op.__all__ diff --git a/python/mxnet/symbol/numpy_extension/_op.py b/python/mxnet/symbol/numpy_extension/_op.py new file mode 100644 index 000000000000..82eaa8e6ec9f --- /dev/null +++ b/python/mxnet/symbol/numpy_extension/_op.py @@ -0,0 +1,21 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Namespace for operators not belonging to the official numpy package +used in Gluon APIs dispatched by F=symbol module.""" + +__all__ = [] diff --git a/python/mxnet/symbol/numpy_extension/_register.py b/python/mxnet/symbol/numpy_extension/_register.py new file mode 100644 index 000000000000..b118987b1fd3 --- /dev/null +++ b/python/mxnet/symbol/numpy_extension/_register.py @@ -0,0 +1,24 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Registering numpy_extension ops.""" + +from ...base import _init_np_op_module +from ..register import _make_symbol_function + +_init_np_op_module(root_module_name='mxnet', np_module_name='numpy_extension', + mx_module_name='symbol', make_op_func=_make_symbol_function) diff --git a/python/mxnet/symbol/numpy_extension/image.py b/python/mxnet/symbol/numpy_extension/image.py new file mode 100644 index 000000000000..b3bd27fc503c --- /dev/null +++ b/python/mxnet/symbol/numpy_extension/image.py @@ -0,0 +1,20 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Image pre-processing operators.""" + +__all__ = [] diff --git a/python/mxnet/symbol/register.py b/python/mxnet/symbol/register.py index ac59f8b97f15..a17dd79048d4 100644 --- a/python/mxnet/symbol/register.py +++ b/python/mxnet/symbol/register.py @@ -27,12 +27,60 @@ from ..attribute import AttrScope from ..base import mx_uint, check_call, _LIB, py_str from ..symbol_doc import _build_doc -from ..base import _Null, _init_op_module +from ..base import _Null, _init_op_module, _is_np_op from ..name import NameManager # pylint: enable=unused-import -def _generate_symbol_function_code(handle, name, func_name, signature_only=False): +def _verify_np_symbol(op_name, func_name, sym): + """Verify if the sym is a numpy symbol. + + Parameters + ---------- + op_name : str + Operator full name registered in backend. + func_name : str + Operator name exposed to users. This is usually the name by stripping off + the prefix of the full operator names registered in backend. + sym : symbol to be verified + """ + from .numpy._symbol import _Symbol as np_symbol + if not isinstance(sym, np_symbol): + raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. ' + 'This is a numpy operator which can only accept ' + 'MXNet numpy ndarrays, while received a legacy ndarray. ' + 'Please ensure that you have activated numpy semantics by calling ' + '`npx.set_np()` in your code. If you still see this error with numpy ' + 'semantics activated, please call `as_np_ndarray()` upon the legacy ' + 'ndarray to convert it to an MXNet numpy ndarray, and then feed the ' + 'converted array to this operator.' + .format(op_name, func_name)) + + +def _verify_legacy_symbol(op_name, func_name, sym): + """Verify if the sym is a legacy symbol. + + Parameters + ---------- + op_name : str + Operator full name registered in backend. + func_name : str + Operator name exposed to users. This is usually the name by stripping off + the prefix of the full operator names registered in backend. + sym : symbol to be verified + """ + from .numpy._symbol import _Symbol as np_symbol + if isinstance(sym, np_symbol): + raise TypeError('Operator `{}` registered in backend is known as `{}` in Python. ' + 'This is a legacy operator which can only accept ' + 'legacy ndarrays, while received an MXNet numpy ndarray. ' + 'Please call `as_nd_ndarray()` upon the numpy ndarray to ' + 'convert it to a legacy ndarray, and then feed the converted ' + 'array to this operator.' + .format(op_name, func_name)) + + +def _generate_symbol_function_code(handle, op_name, func_name, signature_only=False): """Generate function for symbol op by handle and function name.""" real_name = ctypes.c_char_p() desc = ctypes.c_char_p() @@ -56,7 +104,7 @@ def _generate_symbol_function_code(handle, name, func_name, signature_only=False arg_types = [py_str(arg_types[i]) for i in range(narg)] key_var_num_args = py_str(key_var_num_args.value) ret_type = py_str(ret_type.value) if ret_type.value is not None else '' - doc_str = _build_doc(name, + doc_str = _build_doc(op_name, py_str(desc.value), arg_names, arg_types, @@ -95,6 +143,8 @@ def _generate_symbol_function_code(handle, name, func_name, signature_only=False signature.append('**kwargs') signature = ndsignature + signature + is_np_op = _is_np_op(op_name) + verify_symbol_fn = _verify_np_symbol.__name__ if is_np_op else _verify_legacy_symbol.__name__ code = [] if arr_name: code.append(""" @@ -106,7 +156,8 @@ def %s(*%s, **kwargs):"""%(func_name, arr_name)) assert isinstance(i, SymbolBase), \\ "Positional arguments must be Symbol instances, " \\ "but got %s"%str(i) - sym_args.append(i)""".format(arr_name)) + {}('{}', '{}', i) + sym_args.append(i)""".format(arr_name, verify_symbol_fn, op_name, func_name)) if dtype_name is not None: code.append(""" if '%s' in kwargs: @@ -128,9 +179,10 @@ def %s(*%s, **kwargs):"""%(func_name, arr_name)) for k, v in kwargs.items(): if isinstance(v, SymbolBase): sym_kwargs[k] = v + %s('%s', '%s', v) else: keys.append(k) - vals.append(v)"""%(func_name.lower())) + vals.append(v)"""%(func_name.lower(), verify_symbol_fn, op_name, func_name)) if key_var_num_args: # pylint: disable=using-constant-test code.append(""" if '%s' not in kwargs: @@ -139,8 +191,8 @@ def %s(*%s, **kwargs):"""%(func_name, arr_name)) key_var_num_args, key_var_num_args)) code.append(""" - return _symbol_creator(%d, sym_args, sym_kwargs, keys, vals, name)"""%( - handle.value)) + return _symbol_creator(%d, sym_args, sym_kwargs, keys, vals, name, %s)"""%( + handle.value, str(is_np_op))) else: code.append(""" def %s(%s):"""%(func_name, ', '.join(signature))) @@ -155,9 +207,10 @@ def %s(%s):"""%(func_name, ', '.join(signature))) for _k, _v in kwargs.items(): if isinstance(_v, SymbolBase): sym_kwargs[_k] = _v + {}('{}', '{}', _v) else: _keys.append(_k) - _vals.append(_v)""") + _vals.append(_v)""".format(verify_symbol_fn, op_name, func_name)) # NDArray args for name in ndarg_names: # pylint: disable=redefined-argument-from-local code.append(""" @@ -165,6 +218,9 @@ def %s(%s):"""%(func_name, ', '.join(signature))) assert isinstance({name}, SymbolBase), \\ "Argument {name} must be Symbol instances, but got %s"%str({name}) sym_kwargs['{name}'] = {name}""".format(name=name)) + code.append(""" + {}('{}', '{}', {name}) + """.format(verify_symbol_fn, op_name, func_name, name=name)) # kwargs for name in kwarg_names: # pylint: disable=redefined-argument-from-local code.append(""" @@ -173,7 +229,13 @@ def %s(%s):"""%(func_name, ', '.join(signature))) _vals.append(%s)"""%(name, name, name)) # dtype if dtype_name is not None: - code.append(""" + if is_np_op: + code.append(""" + if %s is not _Null and %s is not None: + _keys.append('%s') + _vals.append(_np.dtype(%s).name)"""%(dtype_name, dtype_name, dtype_name, dtype_name)) + else: + code.append(""" if %s is not _Null: _keys.append('%s') _vals.append(_np.dtype(%s).name)"""%(dtype_name, dtype_name, dtype_name)) @@ -182,8 +244,8 @@ def %s(%s):"""%(func_name, ', '.join(signature))) if not hasattr(NameManager._current, "value"): NameManager._current.value = NameManager() name = NameManager._current.value.get(name, '%s') - return _symbol_creator(%d, None, sym_kwargs, _keys, _vals, name)"""%( - func_name.lower(), handle.value)) + return _symbol_creator(%d, None, sym_kwargs, _keys, _vals, name, %s)"""%( + func_name.lower(), handle.value, str(is_np_op))) if signature_only: code.append(""" diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py index 68322297c8bb..542b3796a9fe 100644 --- a/python/mxnet/symbol/symbol.py +++ b/python/mxnet/symbol/symbol.py @@ -30,7 +30,7 @@ import warnings from numbers import Number -import numpy as _numpy +import numpy as _numpy # pylint: disable=relative-import from ..attribute import AttrScope from ..base import _LIB, numeric_types, c_array, c_array_buf, c_str, c_str_array, c_handle_array @@ -61,6 +61,17 @@ class Symbol(SymbolBase): # Make numpy functions return Symbol instead of numpy object array __array_priority__ = 1000.0 + def as_np_ndarray(self): + """Convert mx.sym.Symbol to mx.sym.np._Symbol.""" + from .numpy import _Symbol + hdl = SymbolHandle() + check_call(_LIB.MXShallowCopySymbol(self.handle, ctypes.byref(hdl))) + return _Symbol(hdl) + + def as_nd_ndarray(self): + """Returns self. For the convenience of conversion between legacy and np symbols.""" + return self + def __repr__(self): """Gets a string representation of the symbol.""" name = self.name @@ -148,6 +159,8 @@ def __rsub__(self, other): array([[-2., -2., -2.], [-2., -2., -2.]], dtype=float32) """ + if isinstance(other, Symbol): + return other.__sub__(self) if isinstance(other, Number): return _internal._RMinusScalar(self, scalar=other) else: @@ -196,6 +209,8 @@ def __rdiv__(self, other): array([[ 0.33333334, 0.33333334, 0.33333334], [ 0.33333334, 0.33333334, 0.33333334]], dtype=float32) """ + if isinstance(other, Symbol): + return other.__truediv__(self) if isinstance(other, Number): return _internal._RDivScalar(self, scalar=other) else: @@ -226,6 +241,8 @@ def __rmod__(self, other): array([[ 1., 1., 1., [ 1., 1., 1., dtype=float32) """ + if isinstance(other, Symbol): + return other.__mod__(self) if isinstance(other, Number): return _internal._RModScalar(self, scalar=other) else: @@ -256,7 +273,13 @@ def __pow__(self, other): raise TypeError('type %s not supported' % str(type(other))) def __rpow__(self, other): - raise NotImplementedForSymbol(self.__rpow__, 'y**x', other) + """x.__rpow__(y) <=> y ** x""" + if isinstance(other, Symbol): + return other.__pow__(self) + elif isinstance(other, Number): + return _internal._rpower_scalar(self, scalar=other) + else: + raise TypeError('type %s not supported' % str(type(other))) def __neg__(self): """x.__neg__() <=> -x @@ -2671,8 +2694,12 @@ def var(name, attr=None, shape=None, lr_mult=None, wd_mult=None, dtype=None, Variable = var -def Group(symbols): +def Group(symbols, create_fn=Symbol): """Creates a symbol that contains a collection of other symbols, grouped together. + A classic symbol (`mx.sym.Symbol`) will be returned if all the symbols in the list + are of that type; a numpy symbol (`mx.sym.np._Symbol`) will be returned if all the + symbols in the list are of that type. A type error will be raised if a list of mixed + classic and numpy symbols are provided. Example ------- @@ -2686,6 +2713,9 @@ def Group(symbols): symbols : list List of symbols to be grouped. + create_fn : mx.sym.Symbol or mx.sym.np._Symbol + Symbol class for creating the grouped symbol. + Returns ------- sym : Symbol @@ -2697,7 +2727,7 @@ def Group(symbols): check_call(_LIB.MXSymbolCreateGroup( mx_uint(len(symbols)), c_handle_array(symbols), ctypes.byref(handle))) - return Symbol(handle) + return create_fn(handle) def load(fname): diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py index 0e260ceb7676..2275f4d5da0f 100644 --- a/python/mxnet/test_utils.py +++ b/python/mxnet/test_utils.py @@ -48,6 +48,8 @@ from .ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID from .ndarray import array from .symbol import Symbol +from .symbol.numpy import _Symbol as np_symbol +from .util import use_np # pylint: disable=unused-import def default_context(): @@ -89,7 +91,8 @@ def get_etol(etol=None): def random_arrays(*shapes): """Generate some random numpy arrays.""" - arrays = [np.random.randn(*s).astype(default_dtype()) + arrays = [np.array(np.random.randn(), dtype=default_dtype()) + if len(s) == 0 else np.random.randn(*s).astype(default_dtype()) for s in shapes] if len(arrays) == 1: return arrays[0] @@ -408,16 +411,20 @@ def create_sparse_array_zd(shape, stype, density, data_init=None, density=density, shuffle_csr_indices=shuffle_csr_indices) -def rand_shape_2d(dim0=10, dim1=10): - return rnd.randint(1, dim0 + 1), rnd.randint(1, dim1 + 1) +def rand_shape_2d(dim0=10, dim1=10, allow_zero_size=False): + low = 0 if allow_zero_size else 1 + return rnd.randint(low, dim0 + 1), rnd.randint(low, dim1 + 1) -def rand_shape_3d(dim0=10, dim1=10, dim2=10): - return rnd.randint(1, dim0 + 1), rnd.randint(1, dim1 + 1), rnd.randint(1, dim2 + 1) +def rand_shape_3d(dim0=10, dim1=10, dim2=10, allow_zero_size=False): + low = 0 if allow_zero_size else 1 + return rnd.randint(low, dim0 + 1), rnd.randint(low, dim1 + 1), rnd.randint(low, dim2 + 1) -def rand_shape_nd(num_dim, dim=10): - return tuple(rnd.randint(1, dim+1, size=num_dim)) + +def rand_shape_nd(num_dim, dim=10, allow_zero_size=False): + low = 0 if allow_zero_size else 1 + return tuple(rnd.randint(low, dim+1, size=num_dim)) def rand_coord_2d(x_low, x_high, y_low, y_high): @@ -829,7 +836,7 @@ def as_stype(var, stype, dtype): continue stype = executor.arg_dict[k].stype old_value = v.copy() - for i in range(np.prod(v.shape)): + for i in range(int(np.prod(v.shape))): # inplace update v.ravel()[i] += eps/2.0 executor.arg_dict[k][:] = as_stype(v, stype, dtype=dtype) @@ -941,7 +948,12 @@ def random_projection(shape): input_shape = {k: v.shape for k, v in location.items()} _, out_shape, _ = sym.infer_shape(**input_shape) proj = mx.sym.Variable("__random_proj") + is_np_sym = bool(isinstance(sym, np_symbol)) + if is_np_sym: # convert to np symbol for using element-wise multiplication + proj = proj.as_np_ndarray() out = sym * proj + if is_np_sym: # convert to classic symbol so that make_loss can be used + out = out.as_nd_ndarray() out = mx.sym.make_loss(out) location = dict(list(location.items()) + diff --git a/python/mxnet/util.py b/python/mxnet/util.py index 5bc1dc809c88..d4e95e0c0c9c 100644 --- a/python/mxnet/util.py +++ b/python/mxnet/util.py @@ -20,6 +20,9 @@ import os import sys import functools +import itertools +import inspect +import threading from .base import _LIB, check_call @@ -76,14 +79,24 @@ def set_np_shape(active): >>> print(mx.is_np_shape()) True """ + if active: + import logging + logging.info('NumPy-shape semantics has been activated in your code. ' + 'This is required for creating and manipulating scalar and zero-size ' + 'tensors, which were not supported in MXNet before, as in the official ' + 'NumPy library. Please DO NOT manually deactivate this semantics while ' + 'using `mxnet.numpy` and `mxnet.numpy_extension` modules.') + elif is_np_array(): + raise ValueError('Deactivating NumPy shape semantics while NumPy array semantics is still' + ' active is not allowed. Please consider calling `npx.reset_np()` to' + ' deactivate both of them.') prev = ctypes.c_int() check_call(_LIB.MXSetIsNumpyShape(ctypes.c_int(active), ctypes.byref(prev))) return bool(prev.value) def is_np_shape(): - """ - Checks whether the NumPy shape semantics is currently turned on. + """Checks whether the NumPy shape semantics is currently turned on. In NumPy shape semantics, `()` represents the shape of scalar tensors, and tuples with `0` elements, for example, `(0,)`, `(1, 0, 2)`, represent the shapes of zero-size tensors. This is turned off by default for keeping @@ -213,39 +226,379 @@ def np_shape(active=True): return _NumpyShapeScope(active) +def wraps_safely(wrapped, assigned=functools.WRAPPER_ASSIGNMENTS): + """This function is safe version of `functools.wraps` in Python2 which skips wrapping functions + for the attributes that do not exist.""" + if sys.version_info[0] > 2: + return functools.wraps(wrapped) + else: + return functools.wraps(wrapped, + assigned=itertools.ifilter( + functools.partial(hasattr, wrapped), assigned)) + + def use_np_shape(func): - """Wraps a function with an activated NumPy-shape scope. This ensures - that the execution of the function is guaranteed with the support of - scalar and zero-size tensors as in NumPy. + """A decorator wrapping a function or class with activated NumPy-shape semantics. + When `func` is a function, this ensures that the execution of the function is scoped with NumPy + shape semantics, such as the support for zero-dim and zero size tensors. When + `func` is a class, it ensures that all the methods, static functions, and properties + of the class are executed with the NumPy shape semantics. + + Example:: + import mxnet as mx + @mx.use_np_shape + def scalar_one(): + return mx.nd.ones(()) + print(scalar_one()) + + @np.use_np_shape + class ScalarTensor(object): + def __init__(self, val=None): + if val is None: + val = ScalarTensor.random().value + self._scalar = mx.nd.ones(()) * val + + def __repr__(self): + print("Is __repr__ in np_shape semantics? {}!".format(str(np.is_np_shape()))) + return str(self._scalar.asnumpy()) + + @staticmethod + def random(): + val = mx.nd.random.uniform().asnumpy().item() + return ScalarTensor(val) + + @property + def value(self): + print("Is value property in np_shape semantics? {}!".format(str(np.is_np_shape()))) + return self._scalar.asnumpy().item() + + + print("Is global scope of np_shape activated? {}!".format(str(np.is_np_shape()))) + scalar_tensor = ScalarTensor() + print(scalar_tensor) + + Parameters + ---------- + func : a user-provided callable function or class to be scoped by the NumPy-shape semantics. + + Returns + ------- + Function or class + A function or class wrapped in the NumPy-shape scope. + """ + + if inspect.isclass(func): + for name, method in inspect.getmembers( + func, + predicate= + lambda f: inspect.isfunction(f) or inspect.ismethod(f) or isinstance(f, property)): + if isinstance(method, property): + setattr(func, name, property(use_np_shape(method.__get__), + method.__set__, + method.__delattr__, + method.__doc__)) + else: + setattr(func, name, use_np_shape(method)) + return func + elif callable(func): + @wraps_safely(func) + def _with_np_shape(*args, **kwargs): + with np_shape(active=True): + return func(*args, **kwargs) + return _with_np_shape + else: + raise TypeError('use_np_shape can only decorate classes and callable objects, ' + 'while received a {}'.format(str(type(func)))) + + +def _sanity_check_params(func_name, unsupported_params, param_dict): + for param_name in unsupported_params: + if param_name in param_dict: + raise NotImplementedError("function {} does not support parameter {}" + .format(func_name, param_name)) + + +def set_module(module): + """Decorator for overriding __module__ on a function or class. + + Example usage:: + + @set_module('mxnet.numpy') + def example(): + pass + + assert example.__module__ == 'numpy' + """ + def decorator(func): + if module is not None: + func.__module__ = module + return func + return decorator + + +class _NumpyArrayScope(object): + """Scope for managing NumPy array creation. This is often used + with `is_np_array=True` in initializer to enforce array creation + as type `mxnet.numpy.ndarray`, instead of `mx.nd.NDArray` in Gluon. + + Do not use this class directly. Use `np_array(active)` instead. + """ + _current = threading.local() + + def __init__(self, is_np_array): # pylint: disable=redefined-outer-name + self._old_scope = None + self._is_np_array = is_np_array + + def __enter__(self): + if not hasattr(_NumpyArrayScope._current, "value"): + _NumpyArrayScope._current.value = _NumpyArrayScope(False) + self._old_scope = _NumpyArrayScope._current.value + _NumpyArrayScope._current.value = self + return self + + def __exit__(self, ptype, value, trace): + assert self._old_scope + _NumpyArrayScope._current.value = self._old_scope + + +def np_array(active=True): + """Returns an activated/deactivated NumPy-array scope to be used in 'with' statement + and captures code that needs the NumPy-array semantics. + + Currently, this is used in Gluon to enforce array creation in `Block`s as type + `mxnet.numpy.ndarray`, instead of `mx.nd.NDArray`. + + It is recommended to use the decorator `use_np_array` to decorate the classes + that need this semantics, instead of using this function in a `with` statement + unless you know exactly what has been scoped by this semantics. Please note that this is designed as an infrastructure for the incoming MXNet-NumPy operators. Legacy operators registered in the modules `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts in NumPy even within this scope. + Parameters + ---------- + active : bool + Indicates whether to activate NumPy-array semantics. + + Returns + ------- + _NumpyShapeScope + A scope object for wrapping the code w/ or w/o NumPy-shape semantics. + """ + return _NumpyArrayScope(active) + + +def is_np_array(): + """Checks whether the NumPy-array semantics is currently turned on. + This is currently used in Gluon for checking whether an array of type `mxnet.numpy.ndarray` + or `mx.nd.NDArray` should be created. For example, at the time when a parameter + is created in a `Block`, an `mxnet.numpy.ndarray` is created if this returns true; else + an `mx.nd.NDArray` is created. + + Normally, users are not recommended to use this API directly unless you known exactly + what is going on under the hood. + + Please note that this is designed as an infrastructure for the incoming + MXNet-NumPy operators. Legacy operators registered in the modules + `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts + in NumPy within this semantics. + + Returns + ------- + A bool value indicating whether the NumPy-array semantics is currently on. + """ + return _NumpyArrayScope._current.value._is_np_array if hasattr( + _NumpyArrayScope._current, "value") else False + + +def use_np_array(func): + """A decorator wrapping Gluon `Block`s and all its methods, properties, and static functions + with the semantics of NumPy-array, which means that where ndarrays are created, + `mxnet.numpy.ndarray`s should be created, instead of legacy ndarrays of type `mx.nd.NDArray`. + For example, at the time when a parameter is created in a `Block`, an `mxnet.numpy.ndarray` + is created if it's decorated with this decorator. + + Example:: + import mxnet as mx + from mxnet import gluon, np + + + class TestHybridBlock1(gluon.HybridBlock): + def __init__(self): + super(TestHybridBlock1, self).__init__() + self.w = self.params.get('w', shape=(2, 2)) + + def hybrid_forward(self, F, x, w): + return F.dot(x, w) + + + x = mx.nd.ones((2, 2)) + net1 = TestHybridBlock1() + net1.initialize() + out = net1.forward(x) + for _, v in net1.collect_params().items(): + assert type(v.data()) is mx.nd.NDArray + assert type(out) is mx.nd.NDArray + + + @np.use_np_array + class TestHybridBlock2(gluon.HybridBlock): + def __init__(self): + super(TestHybridBlock2, self).__init__() + self.w = self.params.get('w', shape=(2, 2)) + + def hybrid_forward(self, F, x, w): + return F.np.dot(x, w) + + + x = np.ones((2, 2)) + net2 = TestHybridBlock2() + net2.initialize() + out = net2.forward(x) + for _, v in net2.collect_params().items(): + print(type(v.data())) + assert type(v.data()) is np.ndarray + assert type(out) is np.ndarray Parameters ---------- - func : a user-provided callable function to be scoped by the NumPy-shape semantics. + func : a user-provided callable function or class to be scoped by the NumPy-array semantics. Returns ------- - Function - A function for wrapping the user functions in the NumPy-shape semantics. + Function or class + A function or class wrapped in the NumPy-array scope. + """ + if inspect.isclass(func): + for name, method in inspect.getmembers( + func, + predicate= + lambda f: inspect.isfunction(f) or inspect.ismethod(f) or isinstance(f, property)): + if isinstance(method, property): + setattr(func, name, property(use_np_array(method.__get__), + method.__set__, + method.__delattr__, + method.__doc__)) + else: + setattr(func, name, use_np_array(method)) + return func + elif callable(func): + @wraps_safely(func) + def _with_np_array(*args, **kwargs): + with np_array(active=True): + return func(*args, **kwargs) + return _with_np_array + else: + raise TypeError('use_np_array can only decorate classes and callable objects, ' + 'while received a {}'.format(str(type(func)))) - Examples - -------- - >>> import mxnet as mx - >>> @mx.use_np_shape - ... def scalar_one(): - ... return mx.nd.ones(()) - ... - >>> print(scalar_one()) +def use_np(func): + """A convenience decorator for wrapping user provided functions and classes in the scope of + both NumPy-shape and NumPy-array semantics, which means that (1) empty tuples `()` and tuples + with zeros, such as `(0, 1)`, `(1, 0, 2)`, will be treated as scalar tensors' shapes and + zero-size tensors' shapes in shape inference functions of operators, instead of as unknown + in legacy mode; (2) ndarrays of type `mxnet.numpy.ndarray` should be created instead of + `mx.nd.NDArray`. + + Example:: + import mxnet as mx + from mxnet import gluon, np + + + class TestHybridBlock1(gluon.HybridBlock): + def __init__(self): + super(TestHybridBlock1, self).__init__() + self.w = self.params.get('w', shape=(2, 2)) + + def hybrid_forward(self, F, x, w): + return F.dot(x, w) + F.ones((1,)) + + + x = mx.nd.ones((2, 2)) + net1 = TestHybridBlock1() + net1.initialize() + out = net1.forward(x) + for _, v in net1.collect_params().items(): + assert type(v.data()) is mx.nd.NDArray + assert type(out) is mx.nd.NDArray + + + @np.use_np + class TestHybridBlock2(gluon.HybridBlock): + def __init__(self): + super(TestHybridBlock2, self).__init__() + self.w = self.params.get('w', shape=(2, 2)) + + def hybrid_forward(self, F, x, w): + return F.np.dot(x, w) + F.np.ones(()) + + + x = np.ones((2, 2)) + net2 = TestHybridBlock2() + net2.initialize() + out = net2.forward(x) + for _, v in net2.collect_params().items(): + print(type(v.data())) + assert type(v.data()) is np.ndarray + assert type(out) is np.ndarray + + Parameters + ---------- + func : a user-provided callable function or class to be scoped by the + NumPy-shape and NumPy-array semantics. + + Returns + ------- + Function or class + A function or class wrapped in the Numpy-shape and NumPy-array scope. """ - @functools.wraps(func) - def _with_np_shape(*args, **kwargs): - with np_shape(active=True): - return func(*args, **kwargs) + return use_np_shape(use_np_array(func)) + + +def _set_np_array(active): + """Turns on/off NumPy array semantics for the current thread in which `mxnet.numpy.ndarray` + is expected to be created, instead of the legacy `mx.nd.NDArray`. + + Parameters + --------- + active : bool + A boolean value indicating whether the NumPy-array semantics should be turned on or off. + + Returns + ------- + A bool value indicating the previous state of NumPy array semantics. + """ + if active: + import logging + logging.info('NumPy array semantics has been activated in your code. This allows you' + ' to use operators from MXNet NumPy and NumPy Extension modules as well' + ' as MXNet NumPy `ndarray`s.') + cur_state = is_np_array() + _NumpyArrayScope._current.value = _NumpyArrayScope(active) + return cur_state + + +def set_np(shape=True, array=True): + """Setting NumPy shape and array semantics at the same time. + It is required to keep NumPy shape semantics active when activating NumPy array semantics. + Deactivating NumPy shape semantics while NumPy array semantics is still active is not allowed. + + Parameters + ---------- + shape : bool + A boolean value indicating whether the NumPy-shape semantics should be turned on or off. + array : bool + A boolean value indicating whether the NumPy-array semantics should be turned on or off. + """ + if not shape and array: + raise ValueError('NumPy Shape semantics is required in using NumPy array semantics.') + _set_np_array(array) + set_np_shape(shape) + - return _with_np_shape +def reset_np(): + """Deactivate NumPy shape and array semantics at the same time.""" + set_np(shape=False, array=False) diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc index 5b23a8c694f6..dfb01dca94d9 100644 --- a/src/c_api/c_api.cc +++ b/src/c_api/c_api.cc @@ -1604,3 +1604,12 @@ int MXStorageEmptyCache(int dev_type, int dev_id) { Storage::Get()->ReleaseAll(ctx); API_END(); } + +int MXShallowCopyNDArray(NDArrayHandle src_handle, NDArrayHandle* out) { + NDArray* ret = nullptr; + API_BEGIN(); + NDArray* src_array = static_cast(src_handle); + ret = new NDArray(*src_array); + *out = ret; + API_END_HANDLE_ERROR(delete ret); +} diff --git a/src/c_api/c_api_common.h b/src/c_api/c_api_common.h index 013ecab93da8..233acc85f36b 100644 --- a/src/c_api/c_api_common.h +++ b/src/c_api/c_api_common.h @@ -31,6 +31,7 @@ #include #include #include +#include #include #include #include diff --git a/src/c_api/c_api_symbolic.cc b/src/c_api/c_api_symbolic.cc index 627acf46cc6b..020c0d17f0d1 100644 --- a/src/c_api/c_api_symbolic.cc +++ b/src/c_api/c_api_symbolic.cc @@ -1061,11 +1061,20 @@ int MXGenAtomicSymbolFromSymbol(SymbolHandle sym_handle, SymbolHandle *ret_sym_h API_BEGIN(); nnvm::Symbol *source = static_cast(sym_handle); CHECK_EQ(source->outputs.size(), 1U) - << "Generating atomic symbol from other symbol only works for nongrouped symbol."; - const auto& node = source->outputs[0]; + << "Generating atomic symbol from other symbol only works for nongrouped symbol."; + const auto &node = source->outputs[0]; const auto *op = node.node->op(); const auto attrs = source->ListAttrs(nnvm::Symbol::ListAttrOption::kShallow); *s = nnvm::Symbol::CreateFunctor(op, attrs); *ret_sym_handle = s; API_END_HANDLE_ERROR(delete s); } + +int MXShallowCopySymbol(SymbolHandle src, SymbolHandle* out) { + nnvm::Symbol* out_sym = new nnvm::Symbol; + API_BEGIN(); + nnvm::Symbol* src_sym = static_cast(src); + *out_sym = *src_sym; + *out = out_sym; + API_END_HANDLE_ERROR(delete out_sym); +} diff --git a/src/imperative/imperative_utils.h b/src/imperative/imperative_utils.h index 07fe04782bd0..477139fd84b8 100644 --- a/src/imperative/imperative_utils.h +++ b/src/imperative/imperative_utils.h @@ -884,7 +884,6 @@ inline std::multimap AllocateMemory( } CHECK_EQ(stypes[i], kDefaultStorage); if (mem_plan[i].root == i) { - CHECK_GT(mem_plan[i].size, 0); auto iter = pool.lower_bound(mem_plan[i].size); if (iter != pool.end()) { *arrays[i] = iter->second.AsArray(shapes[i], dtypes[i]); diff --git a/src/io/image_io.cc b/src/io/image_io.cc index c0357998f31c..db9ac7682287 100644 --- a/src/io/image_io.cc +++ b/src/io/image_io.cc @@ -357,6 +357,7 @@ inline void copyMakeBorder(const nnvm::NodeAttrs& attrs, } NNVM_REGISTER_OP(_cvimdecode) +.add_alias("_npi_cvimdecode") .describe("Decode image with OpenCV. \n" "Note: return image in RGB by default, " "instead of OpenCV's default BGR.") @@ -368,6 +369,7 @@ NNVM_REGISTER_OP(_cvimdecode) .add_arguments(ImdecodeParam::__FIELDS__()); NNVM_REGISTER_OP(_cvimread) +.add_alias("_npi_cvimread") .describe("Read and decode image with OpenCV. \n" "Note: return image in RGB by default, " "instead of OpenCV's default BGR.") @@ -378,6 +380,7 @@ NNVM_REGISTER_OP(_cvimread) .add_arguments(ImreadParam::__FIELDS__()); NNVM_REGISTER_OP(_cvimresize) +.add_alias("_npi_cvimresize") .describe("Resize image with OpenCV. \n") .set_num_inputs(1) .set_num_outputs(1) diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc index 37c32c09cebb..7fca6aa3f733 100644 --- a/src/ndarray/ndarray.cc +++ b/src/ndarray/ndarray.cc @@ -319,7 +319,7 @@ NDArray NDArray::AtWithRecord(index_t idx) { CHECK(storage_type() == kDefaultStorage) << "Storage type " << storage_type() << " doesn't support At()"; NDArray ret = this->SliceWithRecord(idx, idx+1); - if (shape_.ndim() > 1) { + if (shape_.ndim() > 1 || Imperative::Get()->is_np_shape()) { return ret.ReshapeWithRecord(mxnet::TShape(shape_.data()+1, shape_.data()+shape_.ndim())); } else { return ret; @@ -1212,7 +1212,10 @@ void CopyFromTo(const NDArray& from, const NDArray& to, int priority, bool is_op << "from.shape = " << from.shape() << " to.shape=" << to.shape(); CHECK(!mxnet::op::shape_is_none(from.shape())) << "source operands have undefined shape"; - if (from.shape().Size() == 0U) return; + // zero-size array, no need to copy + if (from.shape().Size() == 0U) { + return; + } // important: callback must always capture by value const Context from_ctx = from.ctx(); const int a = from_ctx.dev_mask(); @@ -1732,7 +1735,7 @@ bool NDArray::Load(dmlc::Stream *strm) { CHECK(!Imperative::Get()->is_np_shape()) << "ndarray was not saved in np shape semantics, but being loaded in np shape semantics." " Please turn off np shape semantics in Python using `with np_shape(False)`" - " to scope of the code of loading the ndarray."; + " to scope the code of loading the ndarray."; } if (magic != NDARRAY_V2_MAGIC && magic != NDARRAY_V3_MAGIC) { return LegacyLoad(strm, magic); @@ -1872,6 +1875,10 @@ void NDArray::SyncCopyFromCPU(const void *data, size_t size) const { mxnet::TShape dshape = this->shape(); CHECK_EQ(dshape.Size(), size) << "Memory size do not match"; + // zero-size array, no need to copy + if (size == 0U) { + return; + } TBlob src((void*)data, dshape, cpu::kDevMask, this->dtype_, 0); // NOLINT(*) if (this->ctx().dev_mask() == cpu::kDevMask) { @@ -2003,6 +2010,10 @@ void NDArray::SyncCopyToCPU(void *data, size_t size) const { mxnet::TShape dshape = this->shape(); CHECK_EQ(dshape.Size(), size) << "Memory size do not match"; + // zero-size array, no need to copy + if (size == 0U) { + return; + } TBlob dst(data, dshape, cpu::kDevMask, this->dtype_, 0); // NOLINT(*) if (this->ctx().dev_mask() == cpu::kDevMask) { diff --git a/src/operator/contrib/multibox_detection.cc b/src/operator/contrib/multibox_detection.cc index 37bb5a500d71..cb2dfe34bfc3 100644 --- a/src/operator/contrib/multibox_detection.cc +++ b/src/operator/contrib/multibox_detection.cc @@ -220,5 +220,9 @@ MXNET_REGISTER_OP_PROPERTY(_contrib_MultiBoxDetection, MultiBoxDetectionProp) .add_argument("loc_pred", "NDArray-or-Symbol", "Location regression predictions.") .add_argument("anchor", "NDArray-or-Symbol", "Multibox prior anchor boxes") .add_arguments(MultiBoxDetectionParam::__FIELDS__()); + +NNVM_REGISTER_OP(_contrib_MultiBoxDetection) +.add_alias("_npx_multibox_detection"); + } // namespace op } // namespace mxnet diff --git a/src/operator/contrib/multibox_prior.cc b/src/operator/contrib/multibox_prior.cc index 2ad173a2dd93..66fd2c11517a 100644 --- a/src/operator/contrib/multibox_prior.cc +++ b/src/operator/contrib/multibox_prior.cc @@ -100,5 +100,8 @@ MXNET_REGISTER_OP_PROPERTY(_contrib_MultiBoxPrior, MultiBoxPriorProp) .add_arguments(MultiBoxPriorParam::__FIELDS__()) .describe("Generate prior(anchor) boxes from data, sizes and ratios."); +NNVM_REGISTER_OP(_contrib_MultiBoxPrior) +.add_alias("_npx_multibox_prior"); + } // namespace op } // namespace mxnet diff --git a/src/operator/contrib/multibox_target.cc b/src/operator/contrib/multibox_target.cc index a1808c5a7c81..feab3977f82c 100644 --- a/src/operator/contrib/multibox_target.cc +++ b/src/operator/contrib/multibox_target.cc @@ -307,5 +307,9 @@ MXNET_REGISTER_OP_PROPERTY(_contrib_MultiBoxTarget, MultiBoxTargetProp) .add_argument("label", "NDArray-or-Symbol", "Object detection labels.") .add_argument("cls_pred", "NDArray-or-Symbol", "Class predictions.") .add_arguments(MultiBoxTargetParam::__FIELDS__()); + +NNVM_REGISTER_OP(_contrib_MultiBoxTarget) +.add_alias("_npx_multibox_target"); + } // namespace op } // namespace mxnet diff --git a/src/operator/image/crop.cc b/src/operator/image/crop.cc index 52d2f11a464b..6067f89d7033 100644 --- a/src/operator/image/crop.cc +++ b/src/operator/image/crop.cc @@ -35,6 +35,7 @@ namespace image { DMLC_REGISTER_PARAMETER(CropParam); NNVM_REGISTER_OP(_image_crop) +.add_alias("_npx__image_crop") .describe(R"code(Crop an image NDArray of shape (H x W x C) or (N x H x W x C) to the given size. Example: diff --git a/src/operator/image/image_random.cc b/src/operator/image/image_random.cc index 34f4cb4d395c..0c4603ecc475 100644 --- a/src/operator/image/image_random.cc +++ b/src/operator/image/image_random.cc @@ -39,6 +39,7 @@ DMLC_REGISTER_PARAMETER(RandomLightingParam); DMLC_REGISTER_PARAMETER(RandomColorJitterParam); NNVM_REGISTER_OP(_image_to_tensor) +.add_alias("_npx__image_to_tensor") .describe(R"code(Converts an image NDArray of shape (H x W x C) or (N x H x W x C) with values in the range [0, 255] to a tensor NDArray of shape (C x H x W) or (N x C x H x W) with values in the range [0, 1] @@ -102,6 +103,7 @@ with values in the range [0, 1] .add_argument("data", "NDArray-or-Symbol", "Input ndarray"); NNVM_REGISTER_OP(_image_normalize) +.add_alias("_npx__image_normalize") .describe(R"code(Normalize an tensor of shape (C x H x W) or (N x C x H x W) with mean and standard deviation. @@ -189,28 +191,34 @@ NNVM_REGISTER_OP(_backward_image_normalize) .set_attr("FCompute", NormalizeOpBackward); MXNET_REGISTER_IMAGE_AUG_OP(_image_flip_left_right) +.add_alias("_npx__image_flip_left_right") .describe(R"code()code" ADD_FILELINE) .set_attr("FCompute", FlipLeftRight); MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_flip_left_right) +.add_alias("_npx__image_random_flip_left_right") .describe(R"code()code" ADD_FILELINE) .set_attr("FCompute", RandomFlipLeftRight); MXNET_REGISTER_IMAGE_AUG_OP(_image_flip_top_bottom) +.add_alias("_npx__image_flip_top_bottom") .describe(R"code()code" ADD_FILELINE) .set_attr("FCompute", FlipTopBottom); MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_flip_top_bottom) +.add_alias("_npx__image_random_flip_top_bottom") .describe(R"code()code" ADD_FILELINE) .set_attr("FCompute", RandomFlipTopBottom); MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_brightness) +.add_alias("_npx__image_random_brightness") .describe(R"code()code" ADD_FILELINE) .set_attr_parser(ParamParser) .set_attr("FCompute", RandomBrightness) .add_arguments(RandomEnhanceParam::__FIELDS__()); MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_contrast) +.add_alias("_npx__image_random_contrast") .describe(R"code()code" ADD_FILELINE) .set_attr_parser(ParamParser) .set_attr("FCompute", RandomContrast) @@ -218,6 +226,7 @@ MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_contrast) MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_saturation) +.add_alias("_npx__image_random_saturation") .describe(R"code()code" ADD_FILELINE) .set_attr_parser(ParamParser) .set_attr("FCompute", RandomSaturation) @@ -225,6 +234,7 @@ MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_saturation) MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_hue) +.add_alias("_npx__image_random_hue") .describe(R"code()code" ADD_FILELINE) .set_attr_parser(ParamParser) .set_attr("FCompute", RandomHue) @@ -232,6 +242,7 @@ MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_hue) MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_color_jitter) +.add_alias("_npx__image_random_color_jitter") .describe(R"code()code" ADD_FILELINE) .set_attr_parser(ParamParser) .set_attr("FCompute", RandomColorJitter) @@ -239,6 +250,7 @@ MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_color_jitter) MXNET_REGISTER_IMAGE_AUG_OP(_image_adjust_lighting) +.add_alias("_npx__image_adjust_lighting") .describe(R"code(Adjust the lighting level of the input. Follow the AlexNet style.)code" ADD_FILELINE) .set_attr_parser(ParamParser) .set_attr("FCompute", AdjustLighting) @@ -246,6 +258,7 @@ MXNET_REGISTER_IMAGE_AUG_OP(_image_adjust_lighting) MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_lighting) +.add_alias("_npx__image_random_lighting") .describe(R"code(Randomly add PCA noise. Follow the AlexNet style.)code" ADD_FILELINE) .set_attr_parser(ParamParser) .set_attr("FCompute", RandomLighting) diff --git a/src/operator/image/resize.cc b/src/operator/image/resize.cc index d93769faa8b3..d2397ea72685 100644 --- a/src/operator/image/resize.cc +++ b/src/operator/image/resize.cc @@ -34,6 +34,7 @@ namespace image { DMLC_REGISTER_PARAMETER(ResizeParam); NNVM_REGISTER_OP(_image_resize) +.add_alias("_npx__image_resize") .describe(R"code(Resize an image NDArray of shape (H x W x C) or (N x H x W x C) to the given size Example: diff --git a/src/operator/leaky_relu.cc b/src/operator/leaky_relu.cc index 214e41a84611..c25833b799d0 100644 --- a/src/operator/leaky_relu.cc +++ b/src/operator/leaky_relu.cc @@ -71,6 +71,7 @@ The following modified ReLU Activation functions are supported: .add_arguments(LeakyReLUParam::__FIELDS__()); NNVM_REGISTER_OP(LeakyReLU) +.add_alias("_npx_leaky_relu") .set_attr("FSetInputVarAttrOnCompose", [](const nnvm::NodeAttrs& attrs, nnvm::NodePtr var, const int index) { if (index == 1 && var->attrs.dict.find("__init__") == var->attrs.dict.end()) { diff --git a/src/operator/nn/activation.cc b/src/operator/nn/activation.cc index 5b6cece4a92e..5abb6670c9b0 100644 --- a/src/operator/nn/activation.cc +++ b/src/operator/nn/activation.cc @@ -154,6 +154,7 @@ inline static bool BackwardActStorageType(const nnvm::NodeAttrs& attrs, MXNET_OPERATOR_REGISTER_UNARY(Activation) +.add_alias("_npx_activation") .describe(R"code(Applies an activation function element-wise to the input. The following activation functions are supported: diff --git a/src/operator/nn/batch_norm.cc b/src/operator/nn/batch_norm.cc index 2564609c6b90..6382d46d272d 100644 --- a/src/operator/nn/batch_norm.cc +++ b/src/operator/nn/batch_norm.cc @@ -520,6 +520,7 @@ std::vector BatchNormGrad(const nnvm::NodePtr& n, } NNVM_REGISTER_OP(BatchNorm) +.add_alias("_npx_batch_norm") .describe(R"code(Batch normalization. Normalizes a data batch by mean and variance, and applies a scale ``gamma`` as diff --git a/src/operator/nn/concat.cc b/src/operator/nn/concat.cc index 8fb229889332..80469b5385eb 100644 --- a/src/operator/nn/concat.cc +++ b/src/operator/nn/concat.cc @@ -32,9 +32,9 @@ namespace mxnet { namespace op { -static bool ConcatShape(const nnvm::NodeAttrs& attrs, - mxnet::ShapeVector *in_shape, - mxnet::ShapeVector *out_shape) { +bool ConcatShape(const nnvm::NodeAttrs& attrs, + mxnet::ShapeVector *in_shape, + mxnet::ShapeVector *out_shape) { using namespace mshadow; const ConcatParam& param_ = nnvm::get(attrs.parsed); CHECK_EQ(in_shape->size(), static_cast(param_.num_args)); @@ -138,9 +138,9 @@ static bool RNNParamConcatShape(const nnvm::NodeAttrs& attrs, return shape_is_known(dshape); } -static bool ConcatType(const nnvm::NodeAttrs& attrs, - std::vector *in_type, - std::vector *out_type) { +bool ConcatType(const nnvm::NodeAttrs& attrs, + std::vector *in_type, + std::vector *out_type) { const ConcatParam& param_ = nnvm::get(attrs.parsed); int dtype = -1; @@ -403,6 +403,7 @@ NNVM_REGISTER_OP(_backward_Concat) // which handles the case where the first one or two inputs may have // unknown shape that can be inferred from output shape. NNVM_REGISTER_OP(_rnn_param_concat) +.add_alias("_npi_rnn_param_concat") #if MXNET_USE_MKLDNN == 1 .set_attr("FResourceRequest", [](const NodeAttrs& n) { return std::vector{ResourceRequest::kTempSpace}; diff --git a/src/operator/nn/convolution.cc b/src/operator/nn/convolution.cc index 536e9a731171..32ed93e4a463 100644 --- a/src/operator/nn/convolution.cc +++ b/src/operator/nn/convolution.cc @@ -397,6 +397,7 @@ struct ConvolutionGrad { }; NNVM_REGISTER_OP(Convolution) +.add_alias("_npx_convolution") .describe(R"code(Compute *N*-D convolution on *(N+2)*-D input. In the 2-D convolution, given input data with shape *(batch_size, diff --git a/src/operator/nn/deconvolution.cc b/src/operator/nn/deconvolution.cc index 09b255d009e0..9f461f4e9de3 100644 --- a/src/operator/nn/deconvolution.cc +++ b/src/operator/nn/deconvolution.cc @@ -408,6 +408,7 @@ struct DeconvolutionGrad { DMLC_REGISTER_PARAMETER(DeconvolutionParam); NNVM_REGISTER_OP(Deconvolution) +.add_alias("_npx_deconvolution") .describe("Computes 1D or 2D transposed convolution (aka fractionally strided convolution) of the " "input tensor. This operation can be seen as the gradient of Convolution operation with " "respect to its input. Convolution usually reduces the size of the input. Transposed " diff --git a/src/operator/nn/dropout.cc b/src/operator/nn/dropout.cc index d2fe3a5651a4..c4ad4250853e 100644 --- a/src/operator/nn/dropout.cc +++ b/src/operator/nn/dropout.cc @@ -63,6 +63,7 @@ struct DropoutGrad { DMLC_REGISTER_PARAMETER(DropoutParam); NNVM_REGISTER_OP(Dropout) +.add_alias("_npx_dropout") .describe(R"(Applies dropout operation to input array. - During training, each element of the input is set to zero with probability p. diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc index 27f6595aee9e..06ad6d034398 100644 --- a/src/operator/nn/fully_connected.cc +++ b/src/operator/nn/fully_connected.cc @@ -244,6 +244,7 @@ DMLC_REGISTER_PARAMETER(FullyConnectedParam); NNVM_REGISTER_OP(FullyConnected) MXNET_ADD_SPARSE_OP_ALIAS(FullyConnected) +.add_alias("_npx_fully_connected") .describe(R"code(Applies a linear transformation: :math:`Y = XW^T + b`. If ``flatten`` is set to be true, then the shapes are: diff --git a/src/operator/nn/layer_norm.cc b/src/operator/nn/layer_norm.cc index e95f47255d7a..0b53d5091194 100644 --- a/src/operator/nn/layer_norm.cc +++ b/src/operator/nn/layer_norm.cc @@ -127,6 +127,7 @@ void LayerNormGradCompute(const nnvm::NodeAttrs& attrs, } NNVM_REGISTER_OP(LayerNorm) +.add_alias("_npx_layer_norm") .describe(R"code(Layer normalization. Normalizes the channels of the input tensor by mean and variance, and applies a scale ``gamma`` as diff --git a/src/operator/nn/pooling.cc b/src/operator/nn/pooling.cc index 41a486e141d1..485fc1345dfd 100644 --- a/src/operator/nn/pooling.cc +++ b/src/operator/nn/pooling.cc @@ -364,7 +364,8 @@ inline static bool BackwardPoolingStorageType(const nnvm::NodeAttrs &attrs, DMLC_REGISTER_PARAMETER(PoolingParam); NNVM_REGISTER_OP(Pooling) - .describe(R"code(Performs pooling on the input. +.add_alias("_npx_pooling") +.describe(R"code(Performs pooling on the input. The shapes for 1-D pooling are diff --git a/src/operator/nn/softmax.cc b/src/operator/nn/softmax.cc index 5a581e4ea5ef..b9a31ccc9313 100644 --- a/src/operator/nn/softmax.cc +++ b/src/operator/nn/softmax.cc @@ -77,6 +77,7 @@ inline static bool SoftmaxStorageType(const nnvm::NodeAttrs& attrs, NNVM_REGISTER_OP(softmax) +.add_alias("_npx_softmax") .describe(R"code(Applies the softmax function. The resulting array contains elements in the range (0,1) and the elements along the given axis sum up to 1. @@ -205,6 +206,7 @@ NNVM_REGISTER_OP(_backward_softmin) mxnet_op::softmax_bwd, true>); NNVM_REGISTER_OP(log_softmax) +.add_alias("_npx_log_softmax") .describe(R"code(Computes the log softmax of the input. This is equivalent to computing softmax followed by log. diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc new file mode 100644 index 000000000000..c36423dff9fd --- /dev/null +++ b/src/operator/numpy/np_elemwise_broadcast_op.cc @@ -0,0 +1,186 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +/*! + * Copyright (c) 2019 by Contributors + * \file np_elemwise_binary_op.cc + * \brief CPU Implementation of basic functions for elementwise numpy binary broadcast operator. + */ + +#include "../tensor/elemwise_binary_broadcast_op.h" +#include "../tensor/elemwise_binary_scalar_op.h" + +namespace mxnet { +namespace op { + +bool NumpyBinaryScalarType(const nnvm::NodeAttrs& attrs, + std::vector* in_attrs, + std::vector* out_attrs) { + CHECK_EQ(in_attrs->size(), 1U); + CHECK_EQ(out_attrs->size(), 1U); + TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0)); + TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0)); + return in_attrs->at(0) != -1; +} + +#define MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(name) \ + NNVM_REGISTER_OP(name) \ + .set_num_inputs(1) \ + .set_num_outputs(1) \ + .set_attr_parser([](NodeAttrs* attrs) { \ + attrs->parsed = std::stod(attrs->dict["scalar"]); \ + }) \ + .set_attr("FInferShape", ElemwiseShape<1, 1>) \ + .set_attr("FInferType", NumpyBinaryScalarType) \ + .set_attr("FInplaceOption", \ + [](const NodeAttrs& attrs){ \ + return std::vector >{{0, 0}}; \ + }) \ + .add_argument("data", "NDArray-or-Symbol", "source input") \ + .add_argument("scalar", "float", "scalar input") + + +MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_add) +.describe(R"code(Add arguments element-wise with broadcasting if necessary. + +Example:: + + x = [[ 1., 1., 1.], + [ 1., 1., 1.]] + + y = [[ 0.], + [ 1.]] + + add(x, y) = [[ 1., 1., 1.], + [ 2., 2., 2.]] + +)code" ADD_FILELINE) +.set_attr("FCompute", BinaryBroadcastCompute) +.set_attr("FGradient", ElemwiseGradUseNone{"_backward_broadcast_add"}); + +MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_subtract) +.describe(R"code(Subtract arguments element-wise with broadcasting if necessary. + +Example:: + + x = [[ 1., 1., 1.], + [ 1., 1., 1.]] + + y = [[ 0.], + [ 1.]] + + subtract(x, y) = [[ 1., 1., 1.], + [ 0., 0., 0.]] + +)code" ADD_FILELINE) +.set_attr("FCompute", BinaryBroadcastCompute) +.set_attr("FGradient", ElemwiseGradUseNone{"_backward_broadcast_sub"}); + +MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_multiply) +.describe(R"code(Multiply arguments with broadcasting if necessary. + +Example:: + + x = [[ 1., 1., 1.], + [ 1., 1., 1.]] + + y = [[ 0.], + [ 1.]] + + multiply(x, y) = [[ 0., 0., 0.], + [ 1., 1., 1.]] + +)code" ADD_FILELINE) +.set_attr("FCompute", BinaryBroadcastCompute) +.set_attr("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mul"}); + +MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_mod) +.describe(R"code(Return element-wise remainder of division. +It is equivalent to the Python modulus operator``x1 % x2`` and has the same sign as the divisor x2. + +Example:: + + x = [[ 8., 8., 8.], + [ 8., 8., 8.]] + + y = [[ 2.], + [ 3.]] + + mod(x, y) = [[ 0., 0., 0.], + [ 2., 2., 2.]] + +)code" ADD_FILELINE) +.set_attr("FCompute", BinaryBroadcastCompute) +.set_attr("FGradient", ElemwiseGradUseIn{"_backward_broadcast_mod"}); + +MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(_npi_power) +.describe(R"code(First array elements raised to powers from second array, element-wise. + +Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be +broadcastable to the same shape. + +Example:: + + x = [[ 1., 1., 1.], + [ 1., 1., 1.]] + + y = [[ 0.], + [ 1.]] + + power(x, y) = [[ 2., 2., 2.], + [ 4., 4., 4.]] + +)code" ADD_FILELINE) +.set_attr("FCompute", BinaryBroadcastCompute) +.set_attr("FGradient", ElemwiseGradUseIn{"_backward_broadcast_power"}); + +MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_add_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseNone{"_copy"}); + +MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_subtract_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseNone{"_copy"}); + +MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rsubtract_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseNone{"negative"}); + +MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_multiply_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseNone{"_backward_mul_scalar"}); + +MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_mod_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseIn{"_backward_mod_scalar"}); + +MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rmod_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseIn{"_backward_rmod_scalar"}); + +MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_power_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseIn{"_backward_power_scalar"}); + +MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rpower_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseOut{"_backward_rpower_scalar"}); + +} // namespace op +} // namespace mxnet diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cu b/src/operator/numpy/np_elemwise_broadcast_op.cu new file mode 100644 index 000000000000..c858b3a4987a --- /dev/null +++ b/src/operator/numpy/np_elemwise_broadcast_op.cu @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +/*! + * Copyright (c) 2019 by Contributors + * \file np_elemwise_broadcast_op.cu + * \brief GPU Implementation of basic functions for elementwise binary broadcast operator. + */ +#include "../tensor/elemwise_binary_broadcast_op.h" +#include "../tensor/elemwise_binary_scalar_op.h" + +namespace mxnet { +namespace op { +NNVM_REGISTER_OP(_npi_add) +.set_attr("FCompute", BinaryBroadcastCompute); + +NNVM_REGISTER_OP(_npi_subtract) +.set_attr("FCompute", BinaryBroadcastCompute); + +NNVM_REGISTER_OP(_npi_multiply) +.set_attr("FCompute", BinaryBroadcastCompute); + +NNVM_REGISTER_OP(_npi_mod) +.set_attr("FCompute", BinaryBroadcastCompute); + +NNVM_REGISTER_OP(_npi_power) +.set_attr("FCompute", BinaryBroadcastCompute); + +NNVM_REGISTER_OP(_npi_maximum) +.set_attr("FCompute", BinaryBroadcastCompute); + +NNVM_REGISTER_OP(_npi_minimum) +.set_attr("FCompute", BinaryBroadcastCompute); + +NNVM_REGISTER_OP(_npi_add_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_subtract_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_rsubtract_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_multiply_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_mod_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_rmod_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_power_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_rpower_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_maximum_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_minimum_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +} // namespace op +} // namespace mxnet diff --git a/src/operator/numpy/np_init_op.cc b/src/operator/numpy/np_init_op.cc new file mode 100644 index 000000000000..83a44c8ae280 --- /dev/null +++ b/src/operator/numpy/np_init_op.cc @@ -0,0 +1,111 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +/*! + * Copyright (c) 2019 by Contributors + * \file np_init_op.cc + * \brief CPU Implementation of numpy init op + */ +#include "../tensor/init_op.h" +#include "../tensor/elemwise_unary_op.h" + +namespace mxnet { +namespace op { + +NNVM_REGISTER_OP(_npi_zeros) +.describe("Return a new array of given shape, type, and context, filled with zeros.") +.set_num_inputs(0) +.set_num_outputs(1) +.set_attr_parser(ParamParser) +.set_attr("FInferShape", InitShape) +.set_attr("FInferType", InitType) +.set_attr("FInferStorageType", InitStorageType) +.set_attr("FCompute", FillCompute) +.add_arguments(InitOpParam::__FIELDS__()); + +NNVM_REGISTER_OP(_npi_ones) +.describe("Return a new array of given shape, type, and context, filled with ones.") +.set_num_inputs(0) +.set_num_outputs(1) +.set_attr_parser(ParamParser) +.set_attr("FInferShape", InitShape) +.set_attr("FInferType", InitType) +.set_attr("FCompute", FillCompute) +.add_arguments(InitOpParam::__FIELDS__()); + +NNVM_REGISTER_OP(_np_zeros_like) +.describe(R"code(Return an array of zeros with the same shape and type as a given array. + +Examples:: + + x = [[ 1., 1., 1.], + [ 1., 1., 1.]] + + zeros_like(x) = [[ 0., 0., 0.], + [ 0., 0., 0.]] + +)code") +.set_num_inputs(1) +.set_num_outputs(1) +.set_attr("FInferShape", ElemwiseShape<1, 1>) +.set_attr("FInferType", ElemwiseType<1, 1>) +.set_attr("FIgnoreInputs", + [](const NodeAttrs& attrs) { + return std::vector(1, 0); + }) +.set_attr("FListInputNames", + [](const NodeAttrs& attrs) { + return std::vector{"a"}; + }) +.set_attr("FCompute", FillCompute) +.set_attr("FGradient", MakeZeroGradNodes) +.add_argument("a", "NDArray-or-Symbol", + "The shape and data-type of a define these same attributes of the returned array."); + +NNVM_REGISTER_OP(_np_ones_like) +.describe(R"code(Return an array of ones with the same shape and type as a given array. + +Examples:: + + x = [[ 0., 0., 0.], + [ 0., 0., 0.]] + + ones_like(x) = [[ 1., 1., 1.], + [ 1., 1., 1.]] + +)code") +.set_num_inputs(1) +.set_num_outputs(1) +.set_attr("FInferShape", ElemwiseShape<1, 1>) +.set_attr("FInferType", ElemwiseType<1, 1>) +.set_attr("FIgnoreInputs", + [](const NodeAttrs& attrs) { + return std::vector(1, 0); + }) +.set_attr("FListInputNames", + [](const NodeAttrs& attrs) { + return std::vector{"a"}; + }) +.set_attr("FCompute", FillCompute) +.set_attr("FGradient", MakeZeroGradNodes) +.add_argument("a", "NDArray-or-Symbol", + "The shape and data-type of a define these same attributes of the returned array."); + +} // namespace op +} // namespace mxnet diff --git a/src/operator/numpy/np_init_op.cu b/src/operator/numpy/np_init_op.cu new file mode 100644 index 000000000000..2eb8ed6d83b7 --- /dev/null +++ b/src/operator/numpy/np_init_op.cu @@ -0,0 +1,44 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +/*! + * Copyright (c) 2019 by Contributors + * \file np_init_op.cu + * \brief GPU Implementation of numpy init op + */ + +#include "../tensor/init_op.h" + +namespace mxnet { +namespace op { + +NNVM_REGISTER_OP(_npi_zeros) +.set_attr("FCompute", FillCompute); + +NNVM_REGISTER_OP(_npi_ones) +.set_attr("FCompute", FillCompute); + +NNVM_REGISTER_OP(_np_zeros_like) +.set_attr("FCompute", FillCompute); + +NNVM_REGISTER_OP(_np_ones_like) +.set_attr("FCompute", FillCompute); + +} // namespace op +} // namespace mxnet diff --git a/src/operator/numpy/np_true_divide.cc b/src/operator/numpy/np_true_divide.cc new file mode 100644 index 000000000000..429762778700 --- /dev/null +++ b/src/operator/numpy/np_true_divide.cc @@ -0,0 +1,127 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +/*! + * Copyright (c) 2019 by Contributors + * \file np_true_divide.cc + * \brief CPU Implementation of true_divide operator. + */ +#include "../tensor/elemwise_binary_broadcast_op.h" +#include "../tensor/elemwise_binary_scalar_op.h" + +namespace mxnet { +namespace op { + +template +bool TrueDivideType(const nnvm::NodeAttrs& attrs, + std::vector* in_attrs, + std::vector* out_attrs) { + CHECK_EQ(in_attrs->size(), static_cast(num_inputs)); + CHECK_EQ(out_attrs->size(), 1U); + for (const int dtype : *in_attrs) { + if (dtype == -1) return false; + } + if (num_inputs == 2) { + const int lhs_dtype = in_attrs->at(0); + const int rhs_dtype = in_attrs->at(1); + CHECK_EQ(lhs_dtype, rhs_dtype) + << "_true_divide currently only supports same dtype for dividend and divisor"; + } + auto is_float = [](const int dtype) { + return dtype == mshadow::kFloat32 || dtype == mshadow::kFloat64 || dtype == mshadow::kFloat16; + }; + + for (const int dtype : *in_attrs) { + CHECK(is_float(dtype)) << "_true_divide currently only supports float dtype"; + } + TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0)); + return true; +} + +NNVM_REGISTER_OP(_npi_true_divide) +.describe(R"code( +Returns a true division of the inputs, element-wise. + +It currently only supports dtype float16, float32, and float64. + +Example:: + + x = [[ 6., 6., 6.], + [ 6., 6., 6.]] + + y = [[ 2.], + [ 3.]] + + _true_divide(x, y) = [[ 3., 3., 3.], + [ 2., 2., 2.]] + +)code" ADD_FILELINE) +.set_num_inputs(2) +.set_num_outputs(1) +.set_attr("FListInputNames", + [](const NodeAttrs& attrs) { + return std::vector{"lhs", "rhs"}; + }) +.set_attr("FInferShape", BinaryBroadcastShape) +.set_attr("FInferType", TrueDivideType<2>) +.set_attr("FInplaceOption", + [](const NodeAttrs& attrs){ + return std::vector >{{0, 0}, {1, 0}}; + }) +.set_attr("FCompute", BinaryBroadcastCompute) +.set_attr("FGradient", ElemwiseGradUseIn{"_backward_broadcast_div"}) +.add_argument("lhs", "NDArray-or-Symbol", "Dividend array") +.add_argument("rhs", "NDArray-or-Symbol", "Divisor array"); + +NNVM_REGISTER_OP(_npi_true_divide_scalar) +.set_num_inputs(1) +.set_num_outputs(1) +.set_attr_parser([](NodeAttrs* attrs) { + attrs->parsed = std::stod(attrs->dict["scalar"]); + }) +.set_attr("FInferShape", ElemwiseShape<1, 1>) +.set_attr("FInferType", TrueDivideType<1>) +.set_attr("FInplaceOption", + [](const NodeAttrs& attrs) { + return std::vector >{{0, 0}}; + }) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseNone{"_backward_div_scalar"}) +.add_argument("data", "NDArray-or-Symbol", "source input") +.add_argument("scalar", "float", "scalar input"); + +NNVM_REGISTER_OP(_npi_rtrue_divide_scalar) +.set_num_inputs(1) +.set_num_outputs(1) +.set_attr_parser([](NodeAttrs* attrs) { + attrs->parsed = std::stod(attrs->dict["scalar"]); + }) +.set_attr("FInferShape", ElemwiseShape<1, 1>) +.set_attr("FInferType", TrueDivideType<1>) +.set_attr("FInplaceOption", + [](const NodeAttrs& attrs) { + return std::vector >{{0, 0}}; + }) +.set_attr("FCompute", BinaryScalarOp::Compute) +.set_attr("FGradient", ElemwiseGradUseNone{"_backward_rdiv_scalar"}) +.add_argument("data", "NDArray-or-Symbol", "source input") +.add_argument("scalar", "float", "scalar input"); + +} // namespace op +} // namespace mxnet diff --git a/src/operator/numpy/np_true_divide.cu b/src/operator/numpy/np_true_divide.cu new file mode 100644 index 000000000000..be10c44f92a1 --- /dev/null +++ b/src/operator/numpy/np_true_divide.cu @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +/*! + * Copyright (c) 2019 by Contributors + * \file np_true_divide.cu + * \brief GPU Implementation of true_divide operator. + */ +#include "../tensor/elemwise_binary_broadcast_op.h" +#include "../tensor/elemwise_binary_scalar_op.h" + +namespace mxnet { +namespace op { + +NNVM_REGISTER_OP(_npi_true_divide) +.set_attr("FCompute", BinaryBroadcastCompute); + +NNVM_REGISTER_OP(_npi_true_divide_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +NNVM_REGISTER_OP(_npi_rtrue_divide_scalar) +.set_attr("FCompute", BinaryScalarOp::Compute); + +} // namespace op +} // namespace mxnet diff --git a/src/operator/quantization/quantized_concat.cc b/src/operator/quantization/quantized_concat.cc index f7a810b1e404..5835701497d9 100644 --- a/src/operator/quantization/quantized_concat.cc +++ b/src/operator/quantization/quantized_concat.cc @@ -28,8 +28,8 @@ namespace mxnet { namespace op { -static bool ConcatShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector* in_shape, - mxnet::ShapeVector* out_shape) { +static bool QuantizedConcatShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector* in_shape, + mxnet::ShapeVector* out_shape) { const ConcatParam& param_ = nnvm::get(attrs.parsed); CHECK_EQ(in_shape->size(), static_cast(param_.num_args * 3)); CHECK_EQ(out_shape->size(), 3U); @@ -74,8 +74,8 @@ static bool ConcatShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector* in_sha return shape_is_known(dshape); } -static bool ConcatType(const nnvm::NodeAttrs& attrs, std::vector* in_type, - std::vector* out_type) { +static bool QuantizedConcatType(const nnvm::NodeAttrs& attrs, std::vector* in_type, + std::vector* out_type) { const ConcatParam& param_ = nnvm::get(attrs.parsed); CHECK_EQ(in_type->size(), static_cast(param_.num_args * 3)); CHECK_EQ(out_type->size(), 3U); @@ -130,8 +130,8 @@ If any input holds int8, then the output will be int8. Otherwise output will be // TODO(Xinyu): a temp solution to enable GluonCV INT8 flow, // will be reverted after the improvement of CachedOP is done. .set_attr("FGradient", MakeZeroGradNodes) -.set_attr("FInferType", ConcatType) -.set_attr("FInferShape", ConcatShape) +.set_attr("FInferType", QuantizedConcatType) +.set_attr("FInferShape", QuantizedConcatShape) .set_attr("key_var_num_args", "num_args") .add_argument("data", "NDArray-or-Symbol[]", "List of arrays to concatenate") .add_arguments(ConcatParam::__FIELDS__()); diff --git a/src/operator/random/sample_op.cc b/src/operator/random/sample_op.cc index 56a162be5da4..543146257ddf 100644 --- a/src/operator/random/sample_op.cc +++ b/src/operator/random/sample_op.cc @@ -81,6 +81,7 @@ DMLC_REGISTER_PARAMETER(SampleGenNegBinomialLikeParam); MXNET_OPERATOR_REGISTER_SAMPLE(_random_uniform, SampleUniformParam) .add_alias("uniform") .add_alias("random_uniform") +.add_alias("_npi_random_uniform") .describe(R"code(Draw random samples from a uniform distribution. .. note:: The existing alias ``uniform`` is deprecated. @@ -99,6 +100,7 @@ Example:: MXNET_OPERATOR_REGISTER_SAMPLE(_random_normal, SampleNormalParam) .add_alias("normal") .add_alias("random_normal") +.add_alias("_npi_random_normal") .describe(R"code(Draw random samples from a normal (Gaussian) distribution. .. note:: The existing alias ``normal`` is deprecated. diff --git a/src/operator/random/shuffle_op.cc b/src/operator/random/shuffle_op.cc index 70315716dea2..86797c136bab 100644 --- a/src/operator/random/shuffle_op.cc +++ b/src/operator/random/shuffle_op.cc @@ -122,6 +122,7 @@ void ShuffleForwardCPU(const nnvm::NodeAttrs& attrs, NNVM_REGISTER_OP(_shuffle) .add_alias("shuffle") +.add_alias("_np__random_shuffle") .describe(R"code(Randomly shuffle the elements. This shuffles the array along the first axis. diff --git a/src/operator/rnn.cc b/src/operator/rnn.cc index 6a0dbd7a4e23..244e39335a91 100644 --- a/src/operator/rnn.cc +++ b/src/operator/rnn.cc @@ -634,6 +634,7 @@ static void RNNStatefulComputeCPU(const OpStatePtr& state_ptr, #endif NNVM_REGISTER_OP(RNN) +.add_alias("_npx_rnn") .describe(R"code(Applies recurrent layers to input data. Currently, vanilla RNN, LSTM and GRU are implemented, with both multi-layer and bidirectional support. diff --git a/src/operator/roi_pooling.cc b/src/operator/roi_pooling.cc index bba3bea5ce6a..56c872500822 100644 --- a/src/operator/roi_pooling.cc +++ b/src/operator/roi_pooling.cc @@ -230,5 +230,9 @@ Example:: "corners of designated region of interest. `batch_index` indicates the index of corresponding " "image in the input array") .add_arguments(ROIPoolingParam::__FIELDS__()); + +NNVM_REGISTER_OP(ROIPooling) +.add_alias("_npx_roi_pooling"); + } // namespace op } // namespace mxnet diff --git a/src/operator/sequence_mask.cc b/src/operator/sequence_mask.cc index f4f81a801e70..d7731026ce21 100644 --- a/src/operator/sequence_mask.cc +++ b/src/operator/sequence_mask.cc @@ -191,5 +191,8 @@ Example:: "vector of sequence lengths of the form [batch_size]") .add_arguments(SequenceMaskParam::__FIELDS__()); +NNVM_REGISTER_OP(SequenceMask) +.add_alias("_npx_sequence_mask"); + } // namespace op } // namespace mxnet diff --git a/src/operator/swapaxis-inl.h b/src/operator/swapaxis-inl.h index b17a81f75bc6..fd9872db6ec8 100644 --- a/src/operator/swapaxis-inl.h +++ b/src/operator/swapaxis-inl.h @@ -47,7 +47,7 @@ enum SwapAxisOpOutputs {kOut}; struct SwapAxisParam : public dmlc::Parameter { // use int for enumeration - uint32_t dim1, dim2; + int dim1, dim2; DMLC_DECLARE_PARAMETER(SwapAxisParam) { DMLC_DECLARE_FIELD(dim1) .set_default(0) @@ -106,8 +106,6 @@ class SwapAxisOp : public Operator { const std::vector &req) { using namespace mshadow; using namespace mshadow::expr; - int dim1 = param_.dim1; - int dim2 = param_.dim2; TBlob data_in = in_data[swapaxisenum::kData]; TBlob data_out = out_data[swapaxisenum::kData]; @@ -115,10 +113,27 @@ class SwapAxisOp : public Operator { mxnet::TShape shape_in = data_in.shape_; mxnet::TShape shape_out = data_out.shape_; + int axis1 = param_.dim1; + if (axis1 < 0) { + axis1 += shape_in.ndim(); + } + CHECK(axis1 >= 0 && axis1 < shape_in.ndim()) + << "axis1: axis " << param_.dim1 << " is out of bounds for array of ndim " + << shape_in.ndim(); + + int axis2 = param_.dim2; + if (axis2 < 0) { + axis2 += shape_in.ndim(); + } + CHECK(axis2 >= 0 && axis2 < shape_in.ndim()) + << "axis2: axis " << param_.dim2 << " is out of bounds for array of ndim " + << shape_in.ndim(); + + if (shape_in.Size() == 0U) return; Shape<5> inter_shape; - Reshape2Five(&inter_shape, shape_in, dim1, dim2); + Reshape2Five(&inter_shape, shape_in, axis1, axis2); Tensor inter_data_in = data_in.get_with_shape(inter_shape, s); @@ -187,13 +202,28 @@ class SwapAxisProp : public OperatorProperty { CHECK_EQ(in_shape->size(), 1U); mxnet::TShape &shape0 = (*in_shape)[swapaxisenum::kData]; + if (!ndim_is_known(shape0)) return false; + int axis1 = param_.dim1; + if (axis1 < 0) { + axis1 += shape0.ndim(); + } + CHECK(axis1 >= 0 && axis1 < shape0.ndim()) + << "axis1: axis " << param_.dim1 << " is out of bounds for array of ndim " << shape0.ndim(); + + int axis2 = param_.dim2; + if (axis2 < 0) { + axis2 += shape0.ndim(); + } + CHECK(axis2 >= 0 && axis2 < shape0.ndim()) + << "axis2: axis " << param_.dim2 << " is out of bounds for array of ndim " << shape0.ndim(); + out_shape->clear(); out_shape->push_back(shape0); mxnet::TShape &shape1 = (*out_shape)[swapaxisenum::kOut]; - std::swap(shape1[param_.dim1], shape1[param_.dim2]); + std::swap(shape1[axis1], shape1[axis2]); - return true; + return shape_is_known(*out_shape); } bool InferType(std::vector *in_type, diff --git a/src/operator/swapaxis.cc b/src/operator/swapaxis.cc index 45bcca4db9ae..32b26cc14f0c 100644 --- a/src/operator/swapaxis.cc +++ b/src/operator/swapaxis.cc @@ -69,6 +69,6 @@ Examples:: [ 3, 7]]] )code" ADD_FILELINE); -NNVM_REGISTER_OP(SwapAxis).add_alias("swapaxes"); +NNVM_REGISTER_OP(SwapAxis).add_alias("swapaxes").add_alias("_npi_swapaxes"); } // namespace op } // namespace mxnet diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h index c7c49937730c..27edf0195a1c 100644 --- a/src/operator/tensor/broadcast_reduce_op.h +++ b/src/operator/tensor/broadcast_reduce_op.h @@ -168,15 +168,27 @@ struct BroadcastLikeParam : public dmlc::Parameter { } }; -inline int CheckAxis(int axis, int ndim) { - CHECK(axis < ndim && axis >= -ndim) - << "axis " << axis << " exceeds the input dimension of " << ndim; - return (axis + ndim)%ndim; +/* + * Check whether the axis is within the legal range. + */ +inline int CheckAxis(const int axis, const int ndim) { + if (ndim == 0) { + CHECK(axis == 0 || axis == -1) << "axis " << axis + << " is out of bounds for 0-dimension tensor"; + return 0; + } else { + CHECK(axis < ndim && axis >= -ndim) + << "axis " << axis << " exceeds the input dimension of " << ndim; + return (axis + ndim) % ndim; + } } inline mxnet::TShape AxisShapeCompact(mxnet::TShape shape, int *axis, bool allow_2d) { int ndim = shape.ndim(); - index_t leading = 1, trailing = 1, M = shape[*axis]; + index_t leading = 1, trailing = 1, M = 1; + if (shape.ndim() > *axis) { + M = shape[*axis]; + } for (int i = 0; i < *axis; ++i) leading *= shape[i]; for (int i = *axis + 1; i < ndim; ++i) trailing *= shape[i]; if (allow_2d && trailing == 1) { @@ -553,14 +565,37 @@ void SearchAxisCompute(const nnvm::NodeAttrs& attrs, using namespace mshadow::expr; const ReduceAxisParam& param = nnvm::get(attrs.parsed); Stream *s = ctx.get_stream(); - if (!param.axis) LOG(FATAL) << "Global reduction not supported yet"; + int axis = inputs[0].ndim(); + TBlob input = inputs[0]; + if (param.axis.has_value()) { + axis = param.axis.value(); + } else { + // If global reduction, reshape the input tensor into 2D shape (1, inputs[0].shape_.Size()) + // and search on axis = 1. + mxnet::TShape shape_2d(2, 1); + shape_2d[1] = input.shape_.Size(); + input = TBlob(input.dptr_, shape_2d, input.dev_mask(), input.type_flag_, input.dev_id()); + axis = 1; + } - int axis = CheckAxis(param.axis.value(), inputs[0].shape_.ndim()); - mxnet::TShape shape = AxisShapeCompact(inputs[0].shape_, &axis, false); + axis = CheckAxis(axis, input.shape_.ndim()); + if (inputs[0].shape_.ndim() != 0) { + if (param.axis.has_value()) { + // cannot do argmax in an empty dimension + CHECK_NE(inputs[0].shape_[axis], 0) + << "searching input tensor of shape " << inputs[0].shape_ + << " along axis = " << axis << " of zero dim-size is not allowed"; + } else { + // cannot do argmax on an empty array + CHECK_NE(inputs[0].shape_.Size(), 0U) << "attempt to search an empty sequence"; + } + } + if (input.shape_.Size() == 0U) return; // zero-size tensor + mxnet::TShape shape = AxisShapeCompact(input.shape_, &axis, false); MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, { Tensor out = outputs[0].get_with_shape( Shape2(shape[0], shape[2]), s); - Tensor in = inputs[0].get_with_shape( + Tensor in = input.get_with_shape( shape.get<3>(), s); CHECK(req[0] != kAddTo) << "AddTo is not supported"; ASSIGN_DISPATCH(out, req[0], (reduce_with_axis(in, 1))); @@ -865,8 +900,8 @@ struct reduce_axes_backward_broadcast { OType *out, DType *igrad, OType *ograd, - mshadow::Shape<5> in_shape, - mshadow::Shape<5> out_shape, + mshadow::Shape in_shape, + mshadow::Shape out_shape, const uint32_t ndim) { size_t in_stride = 1; size_t out_stride = 1; @@ -902,9 +937,9 @@ void ReduceAxesBackwardUseInOutImpl(const OpContext& ctx, MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, { MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, { - mshadow::Shape<5> in_shape; - mshadow::Shape<5> out_shape; - for (int i = 0; i < 5; ++i) { + mshadow::Shape in_shape; + mshadow::Shape out_shape; + for (int i = 0; i < MXNET_SPECIAL_MAX_NDIM; ++i) { if (i < dst_shape.ndim()) { in_shape[i] = src_shape[i]; out_shape[i] = dst_shape[i]; @@ -914,36 +949,36 @@ void ReduceAxesBackwardUseInOutImpl(const OpContext& ctx, } } if (dst_shape.ndim() == 2) { - Tensor igrad = - outputs[0].get_with_shape(src_shape.get<2>(), s); - Tensor ograd = - inputs[0].get_with_shape(dst_shape.get<2>(), s); - Tensor data = - inputs[1].get_with_shape(src_shape.get<2>(), s); - Tensor out = - inputs[2].get_with_shape(dst_shape.get<2>(), s); + Tensor igrad = + outputs[0].get_with_shape(src_shape.get<2>(), s); + Tensor ograd = + inputs[0].get_with_shape(dst_shape.get<2>(), s); + Tensor data = + inputs[1].get_with_shape(src_shape.get<2>(), s); + Tensor out = + inputs[2].get_with_shape(dst_shape.get<2>(), s); MXNET_REQ_TYPE_SWITCH(req[0], Req, { Kernel, xpu>::Launch( s, outputs[0].shape_.Size(), data.dptr_, out.dptr_, igrad.dptr_, ograd.dptr_, in_shape, out_shape, src_shape.ndim()); }); - if (normalize) igrad /= scalar(src_shape.Size()/dst_shape.Size()); + if (normalize) igrad /= scalar(src_shape.Size()/dst_shape.Size()); } else { const int ndim = MXNET_SPECIAL_MAX_NDIM; - Tensor igrad = - outputs[0].get_with_shape(src_shape.get(), s); - Tensor ograd = - inputs[0].get_with_shape(dst_shape.get(), s); - Tensor data = - inputs[1].get_with_shape(src_shape.get(), s); - Tensor out = - inputs[2].get_with_shape(dst_shape.get(), s); + Tensor igrad = + outputs[0].get_with_shape(src_shape.get(), s); + Tensor ograd = + inputs[0].get_with_shape(dst_shape.get(), s); + Tensor data = + inputs[1].get_with_shape(src_shape.get(), s); + Tensor out = + inputs[2].get_with_shape(dst_shape.get(), s); MXNET_REQ_TYPE_SWITCH(req[0], Req, { Kernel, xpu>::Launch( s, outputs[0].shape_.Size(), data.dptr_, out.dptr_, igrad.dptr_, ograd.dptr_, in_shape, out_shape, src_shape.ndim()); }); - if (normalize) igrad /= scalar(src_shape.Size()/dst_shape.Size()); + if (normalize) igrad /= scalar(src_shape.Size()/dst_shape.Size()); } }); }); @@ -968,6 +1003,34 @@ void ReduceAxesBackwardUseInOut(const nnvm::NodeAttrs& attrs, ReduceAxesBackwardUseInOutImpl(ctx, small, inputs, req, outputs); } +template +struct broadcast_kernel { + template + MSHADOW_XINLINE static void Map(index_t i, + IType *input, + OType *output, + mshadow::Shape in_shape, + mshadow::Shape out_shape, + const OpReqType req, + const uint32_t ndim) { + size_t in_stride = 1; + size_t out_stride = 1; + index_t idx = i; + index_t in_idx = i; + for (int iter = ndim - 1; iter >= 0; --iter) { + size_t dim_idx = idx % out_shape[iter]; + in_idx -= dim_idx * out_stride; + if (in_shape[iter] != 1) { + in_idx += dim_idx * in_stride; + } + idx /= out_shape[iter]; + in_stride *= in_shape[iter]; + out_stride *= out_shape[iter]; + } + KERNEL_ASSIGN(output[i], req, OP::Map(input[in_idx])); + } +}; + template inline void BroadcastComputeImpl(const nnvm::NodeAttrs& attrs, const OpContext& ctx, @@ -977,24 +1040,40 @@ inline void BroadcastComputeImpl(const nnvm::NodeAttrs& attrs, const mxnet::TShape& small) { using namespace mshadow; using namespace mshadow::expr; + using namespace mxnet_op; mxnet::TShape src_shape, dst_shape; BroadcastReduceShapeCompact(outputs[0].shape_, small, &dst_shape, &src_shape); Stream *s = ctx.get_stream(); - MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, { - if (dst_shape.ndim() == 2) { - Tensor out = - outputs[0].get_with_shape(dst_shape.get<2>(), s); - Tensor data = - inputs[0].get_with_shape(src_shape.get<2>(), s); - ASSIGN_DISPATCH(out, req[0], broadcast_to(data, dst_shape)); - } else { - const int ndim = MXNET_SPECIAL_MAX_NDIM; - Tensor out = - outputs[0].get_with_shape(dst_shape.get(), s); - Tensor data = - inputs[0].get_with_shape(src_shape.get(), s); - ASSIGN_DISPATCH(out, req[0], broadcast_to(data, dst_shape)); - } + MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { + MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, { + mshadow::Shape in_shape; + mshadow::Shape out_shape; + for (int i = 0; i < MXNET_SPECIAL_MAX_NDIM; ++i) { + if (i < dst_shape.ndim()) { + in_shape[i] = src_shape[i]; + out_shape[i] = dst_shape[i]; + } else { + in_shape[i] = 1; + out_shape[i] = 1; + } + } + if (dst_shape.ndim() == 2) { + Tensor out = + outputs[0].get_with_shape(dst_shape.get<2>(), s); + Tensor data = + inputs[0].get_with_shape(src_shape.get<2>(), s); + Kernel, xpu>::Launch( + s, out.shape_.Size(), data.dptr_, out.dptr_, in_shape, out_shape, req[0], 2); + } else { + const int ndim = MXNET_SPECIAL_MAX_NDIM; + Tensor out = + outputs[0].get_with_shape(dst_shape.get(), s); + Tensor data = + inputs[0].get_with_shape(src_shape.get(), s); + Kernel, xpu>::Launch( + s, out.shape_.Size(), data.dptr_, out.dptr_, in_shape, out_shape, req[0], ndim); + } + }); }); } @@ -1215,8 +1294,8 @@ struct norm_backward_broadcast { DType *igrad, OType *ograd, DType *data, - mshadow::Shape<5> in_shape, - mshadow::Shape<5> out_shape, + mshadow::Shape in_shape, + mshadow::Shape out_shape, const uint32_t ndim) { size_t in_stride = 1; size_t out_stride = 1; @@ -1258,9 +1337,9 @@ void LpNormGradCompute(const nnvm::NodeAttrs& attrs, mxnet::TShape src_shape, dst_shape; BroadcastReduceShapeCompact(outputs[0].shape_, small, &src_shape, &dst_shape); Stream *s = ctx.get_stream(); - mshadow::Shape<5> in_shape; - mshadow::Shape<5> out_shape; - for (int i = 0; i < 5; ++i) { + mshadow::Shape in_shape; + mshadow::Shape out_shape; + for (int i = 0; i < MXNET_SPECIAL_MAX_NDIM; ++i) { if (i < dst_shape.ndim()) { in_shape[i] = src_shape[i]; out_shape[i] = dst_shape[i]; diff --git a/src/operator/tensor/broadcast_reduce_op_index.cc b/src/operator/tensor/broadcast_reduce_op_index.cc index 56af3887c763..52082f759e7a 100644 --- a/src/operator/tensor/broadcast_reduce_op_index.cc +++ b/src/operator/tensor/broadcast_reduce_op_index.cc @@ -110,6 +110,7 @@ Examples:: NNVM_REGISTER_OP(pick) .add_alias("choose_element_0index") +.add_alias("_npx_pick") .describe(R"code(Picks elements from an input array according to the input indices along the given axis. Given an input array of shape ``(d0, d1)`` and indices of shape ``(i0,)``, the result will be diff --git a/src/operator/tensor/dot.cc b/src/operator/tensor/dot.cc index 7d7b6c06c846..11a056146e1d 100644 --- a/src/operator/tensor/dot.cc +++ b/src/operator/tensor/dot.cc @@ -111,6 +111,7 @@ NNVM_REGISTER_OP(_backward_dot) .add_arguments(DotParam::__FIELDS__()); NNVM_REGISTER_OP(batch_dot) +.add_alias("_npx_batch_dot") .describe(R"doc(Batchwise dot product. ``batch_dot`` is used to compute dot product of ``x`` and ``y`` when ``x`` and diff --git a/src/operator/tensor/elemwise_binary_broadcast_op.h b/src/operator/tensor/elemwise_binary_broadcast_op.h index f84767dd4b2f..8a81bbc1c475 100644 --- a/src/operator/tensor/elemwise_binary_broadcast_op.h +++ b/src/operator/tensor/elemwise_binary_broadcast_op.h @@ -292,6 +292,7 @@ void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs, const std::vector& inputs, const std::vector& req, const std::vector& outputs) { + if (outputs[0].shape_.Size() == 0U) return; mxnet::TShape new_lshape, new_rshape, new_oshape; int ndim = BinaryBroadcastShapeCompact(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_, &new_lshape, &new_rshape, &new_oshape); diff --git a/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc b/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc index cd433e00a770..e3c2e0e898d9 100644 --- a/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc +++ b/src/operator/tensor/elemwise_binary_broadcast_op_logic.cc @@ -30,6 +30,7 @@ namespace mxnet { namespace op { MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_equal) +.add_alias("_npi_equal") .describe(R"code(Returns the result of element-wise **equal to** (==) comparison operation with broadcasting. Example:: @@ -48,6 +49,7 @@ Example:: .set_attr("FGradient", MakeZeroGradNodes); MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_not_equal) +.add_alias("_npi_not_equal") .describe(R"code(Returns the result of element-wise **not equal to** (!=) comparison operation with broadcasting. Example:: @@ -66,6 +68,7 @@ Example:: .set_attr("FGradient", MakeZeroGradNodes); MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_greater) +.add_alias("_npi_greater") .describe(R"code(Returns the result of element-wise **greater than** (>) comparison operation with broadcasting. Example:: @@ -84,6 +87,7 @@ Example:: .set_attr("FGradient", MakeZeroGradNodes); MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_greater_equal) +.add_alias("_npi_greater_equal") .describe(R"code(Returns the result of element-wise **greater than or equal to** (>=) comparison operation with broadcasting. Example:: @@ -102,6 +106,7 @@ Example:: .set_attr("FGradient", MakeZeroGradNodes); MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_lesser) +.add_alias("_npi_less") .describe(R"code(Returns the result of element-wise **lesser than** (<) comparison operation with broadcasting. Example:: @@ -120,6 +125,7 @@ Example:: .set_attr("FGradient", MakeZeroGradNodes); MXNET_OPERATOR_REGISTER_BINARY_BROADCAST(broadcast_lesser_equal) +.add_alias("_npi_less_equal") .describe(R"code(Returns the result of element-wise **lesser than or equal to** (<=) comparison operation with broadcasting. Example:: diff --git a/src/operator/tensor/elemwise_binary_op.h b/src/operator/tensor/elemwise_binary_op.h index 2fe3fd9919cf..9c1d8b17fdea 100644 --- a/src/operator/tensor/elemwise_binary_op.h +++ b/src/operator/tensor/elemwise_binary_op.h @@ -487,9 +487,11 @@ class ElemwiseBinaryOp : public OpBase { MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, { const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size()) + DataType::kLanes - 1) / DataType::kLanes; - Kernel, xpu>::Launch(s, size, - outputs[0].dptr(), - inputs[0].dptr(), inputs[1].dptr()); + if (size != 0) { + Kernel, xpu>::Launch(s, size, + outputs[0].dptr(), + inputs[0].dptr(), inputs[1].dptr()); + } }); }); } @@ -510,9 +512,11 @@ class ElemwiseBinaryOp : public OpBase { MSHADOW_TYPE_SWITCH_WITH_HALF2(outputs[0].type_flag_, DType, { const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(), inputs[1].Size()) + DataType::kLanes - 1) / DataType::kLanes; - Kernel, xpu>::Launch(s, size, - outputs[0].dptr(), - inputs[0].dptr(), inputs[1].dptr()); + if (size != 0) { + Kernel, xpu>::Launch(s, size, + outputs[0].dptr(), + inputs[0].dptr(), inputs[1].dptr()); + } }); }); } diff --git a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc index f027665a549b..3a687c2aa062 100644 --- a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc +++ b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc @@ -84,7 +84,8 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_hypot_scalar) cpu, mshadow_op::hypot_grad_left>); NNVM_REGISTER_OP(smooth_l1) - .describe(R"code(Calculate Smooth L1 Loss(lhs, scalar) by summing +.add_alias("_npx_smooth_l1") +.describe(R"code(Calculate Smooth L1 Loss(lhs, scalar) by summing .. math:: diff --git a/src/operator/tensor/elemwise_binary_scalar_op_logic.cc b/src/operator/tensor/elemwise_binary_scalar_op_logic.cc index 17e76153ebb2..87ba394c99b2 100644 --- a/src/operator/tensor/elemwise_binary_scalar_op_logic.cc +++ b/src/operator/tensor/elemwise_binary_scalar_op_logic.cc @@ -71,26 +71,32 @@ static bool BinaryScalarLogicStorageType(const nnvm::NodeAttrs& attrs, MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_equal_scalar, mshadow_op::eq) +.add_alias("_npi_equal_scalar") .set_attr("FGradient", MakeZeroGradNodes) .add_alias("_EqualScalar"); MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_not_equal_scalar, mshadow_op::ne) +.add_alias("_npi_not_equal_scalar") .set_attr("FGradient", MakeZeroGradNodes) .add_alias("_NotEqualScalar"); MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_greater_scalar, mshadow_op::gt) +.add_alias("_npi_greater_scalar") .set_attr("FGradient", MakeZeroGradNodes) .add_alias("_GreaterScalar"); MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_greater_equal_scalar, mshadow_op::ge) +.add_alias("_npi_greater_equal_scalar") .set_attr("FGradient", MakeZeroGradNodes) .add_alias("_GreaterEqualScalar"); MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_lesser_scalar, mshadow_op::lt) +.add_alias("_npi_less_scalar") .set_attr("FGradient", MakeZeroGradNodes) .add_alias("_LesserScalar"); MXNET_OPERATOR_REGISTER_BINARY_SCALAR_LOGIC(_lesser_equal_scalar, mshadow_op::le) +.add_alias("_npi_less_equal_scalar") .set_attr("FGradient", MakeZeroGradNodes) .add_alias("_LesserEqualScalar"); diff --git a/src/operator/tensor/elemwise_unary_op.h b/src/operator/tensor/elemwise_unary_op.h index 458106e02671..87964ac246f0 100644 --- a/src/operator/tensor/elemwise_unary_op.h +++ b/src/operator/tensor/elemwise_unary_op.h @@ -243,8 +243,10 @@ class UnaryOp : public OpBase { mshadow::Stream *s = ctx.get_stream(); MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, { MXNET_ASSIGN_REQ_SWITCH(req[0], Req, { - mxnet_op::Kernel, xpu>::Launch( - s, inputs[0].Size(), outputs[0].dptr(), inputs[0].dptr()); + if (inputs[0].Size() != 0) { + mxnet_op::Kernel, xpu>::Launch( + s, inputs[0].Size(), outputs[0].dptr(), inputs[0].dptr()); + } }); }); } diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc index 3caf840f1ebf..c293ee6e22bd 100644 --- a/src/operator/tensor/elemwise_unary_op_basic.cc +++ b/src/operator/tensor/elemwise_unary_op_basic.cc @@ -511,6 +511,7 @@ Negative indices are supported, and `None` can be used for either `lhs_end` or ` - lhs shape = (30, 12), rhs shape = (4, 2, 2, 3), lhs_begin=-1, lhs_end=None, rhs_begin=1, rhs_end=None, output shape = (30, 2, 2, 3) )code" ADD_FILELINE) +.add_alias("_npx_reshape_like") .set_num_inputs(2) .set_attr_parser(ParamParser) .set_attr("FListInputNames", @@ -649,6 +650,7 @@ Example:: DMLC_REGISTER_PARAMETER(CastParam); NNVM_REGISTER_OP(Cast) .add_alias("cast") +.add_alias("_npx_cast") .describe(R"code(Casts all elements of the input to a new type. .. note:: ``Cast`` is deprecated. Use ``cast`` instead. @@ -1289,6 +1291,7 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_expm1, unary_bwd *s = ctx.get_stream(); MSHADOW_TYPE_SWITCH(ret.type_flag_, DType, { switch (axes.ndim()) { - case 0: + case 0: { + Tensor in = src.get_with_shape(mshadow::Shape1(1), s); + Tensor out = ret.get_with_shape(mshadow::Shape1(1), s); + Copy(out, in, s); break; + } case 1: { Tensor in = src.get(s); Tensor out = ret.get(s); @@ -1796,9 +1802,6 @@ inline bool TileOpShape(const nnvm::NodeAttrs& attrs, SHAPE_ASSIGN_CHECK(*out_attrs, 0, ishape); return true; } - for (int i = 0; i < reps.ndim(); ++i) { - CHECK_GT(reps[i], 0) << "invalid reps=" << i << ", dim size must be greater than zero"; - } mxnet::TShape oshape(std::max(ishape.ndim(), reps.ndim()), -1); int i1 = ishape.ndim() - 1; int i2 = reps.ndim() - 1; @@ -1811,6 +1814,11 @@ inline bool TileOpShape(const nnvm::NodeAttrs& attrs, oshape[i] = reps[i2--]; } } + // If reps contains 0s, oshape is a zero-size shape. + // Need to distinguish between np_shape mode and legacy mode. + if (!Imperative::Get()->is_np_shape()) { + common::ConvertToNumpyShape(&oshape); + } SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape); return shape_is_known(oshape); } @@ -1829,7 +1837,7 @@ inline bool TileOpType(const nnvm::NodeAttrs& attrs, /*! * \brief Reshape the input and output tensors for - * using broadcast_to to achieve the funcitonality + * using broadcast_to to achieve the functionality * of operator tile. * \return a pair of mxnet::TShape's, first is the reshaped * input shape, second is the reshaped output shape. @@ -1837,7 +1845,7 @@ inline bool TileOpType(const nnvm::NodeAttrs& attrs, inline std::pair ReshapeInputOutputForTileOp( const mxnet::TShape& ishape, const mxnet::Tuple& reps) { - if (ishape.ndim() == 0 || reps.ndim() == 0) { + if (reps.ndim() == 0) { return std::make_pair(ishape, ishape); } @@ -2192,7 +2200,7 @@ inline size_t SqueezeShapeHelper(mxnet::TShape* shape) { CHECK(shape != nullptr); size_t count = 0; for (int i = 0; i < shape->ndim(); ++i) { - if ((*shape)[i] == 0) { + if ((*shape)[i] == -1) { ++count; } else { std::swap((*shape)[i], (*shape)[i-count]); @@ -2225,12 +2233,12 @@ inline bool SqueezeShape(const nnvm::NodeAttrs& attrs, CHECK_EQ(dshape[axes[i]], 1) << "cannot select an axis to squeeze out which has size=" << dshape[axes[i]] << " not equal to one"; - CHECK_NE(oshape[axes[i]], 0) << "duplicate value in axis"; - oshape[axes[i]] = 0; + CHECK_NE(oshape[axes[i]], -1) << "duplicate value in axis"; + oshape[axes[i]] = -1; } } else { for (int i = 0; i < oshape.ndim(); ++i) { - if (oshape[i] == 1) oshape[i] = 0; + if (oshape[i] == 1) oshape[i] = -1; } } size_t oshape_size = SqueezeShapeHelper(&oshape); @@ -2646,10 +2654,14 @@ inline bool SplitOpShape(const nnvm::NodeAttrs& attrs, for (int i = 0; i < num_outputs; ++i) { int start = indices[i]; int end = (i < num_outputs - 1) ? indices[i + 1] : ishape[real_axis]; - CHECK(start < end) - << "start " << start << " is not less than end " << end << "for subarray " << i; - CHECK(end <= ishape[real_axis]) - << "end " << end << " is no less than the size of the axis " << ishape[real_axis]; + if (ishape[real_axis] == 0U) { + end = start; + } else { + CHECK(start < end) + << "start " << start << " is not less than end " << end << "for subarray " << i; + CHECK(end <= ishape[real_axis]) + << "end " << end << " is no less than the size of the axis " << ishape[real_axis]; + } dshape[real_axis] = (end - start); if (param.squeeze_axis) { CHECK_EQ(end - start, 1U) << "expected axis size of 1 but got " << end - start; diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc index e78050aec009..59e8386d6679 100644 --- a/src/operator/tensor/matrix_op.cc +++ b/src/operator/tensor/matrix_op.cc @@ -263,6 +263,7 @@ static inline bool FlattenStorageType(const nnvm::NodeAttrs& attrs, NNVM_REGISTER_OP(Flatten) .add_alias("flatten") +.add_alias("_npx_batch_flatten") .describe(R"code(Flattens the input array into a 2-D array by collapsing the higher dimensions. .. note:: `Flatten` is deprecated. Use `flatten` instead. @@ -408,6 +409,7 @@ Examples:: NNVM_REGISTER_OP(expand_dims) +.add_alias("_npi_expand_dims") .describe(R"code(Inserts a new axis of size 1 into the array shape For example, given ``x`` with shape ``(2,3,4)``, then ``expand_dims(x, axis=1)`` @@ -696,6 +698,7 @@ NNVM_REGISTER_OP(_backward_slice_like) NNVM_REGISTER_OP(clip) MXNET_ADD_SPARSE_OP_ALIAS(clip) +.add_alias("_npi_clip") .describe(R"code(Clips (limits) the values in an array. Given an interval, values outside the interval are clipped to the interval edges. @@ -770,6 +773,7 @@ NNVM_REGISTER_OP(_backward_clip) .set_attr("FCompute", ClipGrad_); NNVM_REGISTER_OP(repeat) +.add_alias("_np_repeat") .describe(R"code(Repeats elements of an array. By default, ``repeat`` flattens the input array into 1-D and then repeats the @@ -820,6 +824,7 @@ NNVM_REGISTER_OP(_backward_repeat) }); NNVM_REGISTER_OP(tile) +.add_alias("_npi_tile") .describe(R"code(Repeats the whole array multiple times. If ``reps`` has length *d*, and input array has dimension of *n*. There are @@ -1121,6 +1126,7 @@ Example:: .add_arguments(DepthToSpaceParam::__FIELDS__()); NNVM_REGISTER_OP(_split_v2) +.add_alias("_npi_split") .describe(R"code(Splits an array along a particular axis into multiple sub-arrays. Example:: diff --git a/src/operator/tensor/ordering_op.cc b/src/operator/tensor/ordering_op.cc index b0ade2076a2c..58c98f3a380c 100644 --- a/src/operator/tensor/ordering_op.cc +++ b/src/operator/tensor/ordering_op.cc @@ -34,6 +34,7 @@ DMLC_REGISTER_PARAMETER(SortParam); DMLC_REGISTER_PARAMETER(ArgSortParam); NNVM_REGISTER_OP(topk) +.add_alias("_npx_topk") .describe(R"code(Returns the top *k* elements in an input array along the given axis. The returned elements will be sorted. diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py index a8aa1ff853f7..0b5142dd8c8b 100644 --- a/tests/python/gpu/test_operator_gpu.py +++ b/tests/python/gpu/test_operator_gpu.py @@ -35,6 +35,7 @@ from common import setup_module, with_seed, teardown, assert_raises_cudnn_not_satisfied from common import run_in_spawned_process from test_operator import * +from test_numpy_ndarray import * from test_optimizer import * from test_random import * from test_exc_handling import * diff --git a/tests/python/unittest/test_numpy_ndarray.py b/tests/python/unittest/test_numpy_ndarray.py new file mode 100644 index 000000000000..6024ac9b4acd --- /dev/null +++ b/tests/python/unittest/test_numpy_ndarray.py @@ -0,0 +1,672 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# pylint: skip-file +from __future__ import absolute_import +from __future__ import division +import os +import numpy as _np +import mxnet as mx +from mxnet import np, npx, autograd +from mxnet.gluon import HybridBlock +from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, assert_exception, use_np +from common import with_seed, TemporaryDirectory + + +@with_seed() +@use_np +def test_array_creation(): + dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None] + objects = [ + [], + (), + [[1, 2], [3, 4]], + _np.random.uniform(size=rand_shape_nd(3)), + _np.random.uniform(size=(3, 0, 4)) + ] + for dtype in dtypes: + for src in objects: + mx_arr = np.array(src, dtype=dtype) + assert mx_arr.context == mx.current_context() + if isinstance(src, mx.nd.NDArray): + np_arr = _np.array(src.asnumpy(), dtype=dtype if dtype is not None else _np.float32) + else: + np_arr = _np.array(src, dtype=dtype if dtype is not None else _np.float32) + assert mx_arr.dtype == np_arr.dtype + assert same(mx_arr.asnumpy(), np_arr) + + +@with_seed() +@use_np +def test_zeros(): + # test np.zeros in Gluon + class TestZeros(HybridBlock): + def __init__(self, shape, dtype=None): + super(TestZeros, self).__init__() + self._shape = shape + self._dtype = dtype + + def hybrid_forward(self, F, x, *args, **kwargs): + return x + F.np.zeros(shape, dtype) + + class TestZerosOutputType(HybridBlock): + def hybrid_forward(self, F, x, *args, **kwargs): + return x, F.np.zeros(shape=()) + + # test np.zeros in imperative + def check_zero_array_creation(shape, dtype): + np_out = _np.zeros(shape=shape, dtype=dtype) + mx_out = np.zeros(shape=shape, dtype=dtype) + assert same(mx_out.asnumpy(), np_out) + if dtype is None: + assert mx_out.dtype == _np.float32 + assert np_out.dtype == _np.float64 + + shapes = [(0,), (2, 0, 2), (0, 0, 0, 0), ()] + shapes += [rand_shape_nd(ndim, allow_zero_size=True) for ndim in range(5)] + dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None] + for shape in shapes: + for dtype in dtypes: + check_zero_array_creation(shape, dtype) + x = np.array(_np.random.uniform(size=shape), dtype=dtype) + if dtype is None: + x = x.astype('float32') + for hybridize in [True, False]: + test_zeros = TestZeros(shape, dtype) + test_zeros_output_type = TestZerosOutputType() + if hybridize: + test_zeros.hybridize() + test_zeros_output_type.hybridize() + y = test_zeros(x) + assert type(y) == np.ndarray + assert same(x.asnumpy(), y.asnumpy()) + y = test_zeros_output_type(x) + assert type(y[1]) == np.ndarray + + +@with_seed() +@use_np +def test_ones(): + # test np.ones in Gluon + class TestOnes(HybridBlock): + def __init__(self, shape, dtype=None): + super(TestOnes, self).__init__() + self._shape = shape + self._dtype = dtype + + def hybrid_forward(self, F, x, *args, **kwargs): + return x * F.np.ones(shape, dtype) + + class TestOnesOutputType(HybridBlock): + def hybrid_forward(self, F, x, *args, **kwargs): + return x, F.np.ones(shape=()) + + # test np.ones in imperative + def check_ones_array_creation(shape, dtype): + np_out = _np.ones(shape=shape, dtype=dtype) + mx_out = np.ones(shape=shape, dtype=dtype) + assert same(mx_out.asnumpy(), np_out) + if dtype is None: + assert mx_out.dtype == _np.float32 + assert np_out.dtype == _np.float64 + + shapes = [(0,), (2, 0, 2), (0, 0, 0, 0), ()] + shapes += [rand_shape_nd(ndim, allow_zero_size=True) for ndim in range(5)] + dtypes = [_np.int8, _np.int32, _np.float16, _np.float32, _np.float64, None] + for shape in shapes: + for dtype in dtypes: + check_ones_array_creation(shape, dtype) + x = mx.nd.array(_np.random.uniform(size=shape), dtype=dtype).as_np_ndarray() + if dtype is None: + x = x.astype('float32') + for hybridize in [True, False]: + test_ones = TestOnes(shape, dtype) + test_ones_output_type = TestOnesOutputType() + if hybridize: + test_ones.hybridize() + test_ones_output_type.hybridize() + y = test_ones(x) + assert type(y) == np.ndarray + assert same(x.asnumpy(), y.asnumpy()) + y = test_ones_output_type(x) + assert type(y[1]) == np.ndarray + + +@with_seed() +def test_ndarray_binary_element_wise_ops(): + np_op_map = { + '+': _np.add, + '*': _np.multiply, + '-': _np.subtract, + '/': _np.divide, + 'mod': _np.mod, + 'pow': _np.power, + '==': _np.equal, + '>': _np.greater, + '>=': _np.greater_equal, + '<': _np.less, + '<=': _np.less_equal + } + + def get_np_ret(x1, x2, op): + return np_op_map[op](x1, x2) + + @use_np + class TestBinaryElementWiseOp(HybridBlock): + def __init__(self, op, scalar=None, reverse=False): + super(TestBinaryElementWiseOp, self).__init__() + self._op = op + self._scalar = scalar + self._reverse = reverse # if false, scalar is the right operand. + + def hybrid_forward(self, F, x, *args): + if self._op == '+': + if self._scalar is not None: + return x + self._scalar if not self._reverse else self._scalar + x + else: + return x + args[0] if not self._reverse else args[0] + x + elif self._op == '*': + if self._scalar is not None: + return x * self._scalar if not self._reverse else self._scalar * x + else: + return x * args[0] if not self._reverse else args[0] * x + elif self._op == '-': + if self._scalar is not None: + return x - self._scalar if not self._reverse else self._scalar - x + else: + return x - args[0] if not self._reverse else args[0] - x + elif self._op == '/': + if self._scalar is not None: + return x / self._scalar if not self._reverse else self._scalar / x + else: + return x / args[0] if not self._reverse else args[0] / x + elif self._op == 'mod': + if self._scalar is not None: + return x % self._scalar if not self._reverse else self._scalar % x + else: + return x % args[0] if not self._reverse else args[0] % x + elif self._op == 'pow': + if self._scalar is not None: + return x ** self._scalar if not self._reverse else self._scalar ** x + else: + return x ** args[0] if not self._reverse else args[0] ** x + elif self._op == '>': + if self._scalar is not None: + return x > self._scalar if not self._reverse else self._scalar > x + else: + return x > args[0] + elif self._op == '>=': + if self._scalar is not None: + return x >= self._scalar if not self._reverse else self._scalar >= x + else: + return x >= args[0] + elif self._op == '<': + if self._scalar is not None: + return x < self._scalar if not self._reverse else self._scalar < x + else: + return x < args[0] + elif self._op == '<=': + if self._scalar is not None: + return x <= self._scalar if not self._reverse else self._scalar <= x + else: + return x <= args[0] + elif self._op == '==': + if self._scalar is not None: + return x == self._scalar if not self._reverse else self._scalar == x + else: + return x == args[0] + else: + print(self._op) + assert False + + @use_np + def check_binary_op_result(shape1, shape2, op, dtype=None): + if shape1 is None: + mx_input1 = abs(_np.random.uniform()) + 1 + np_input1 = mx_input1 + else: + mx_input1 = rand_ndarray(shape1, dtype=dtype).abs() + 1 + np_input1 = mx_input1.asnumpy() + if shape2 is None: + mx_input2 = abs(_np.random.uniform()) + 1 + np_input2 = mx_input2 + else: + mx_input2 = rand_ndarray(shape2, dtype=dtype).abs() + 1 + np_input2 = mx_input2.asnumpy() + + scalar = None + reverse = False + if isinstance(mx_input1, mx.nd.NDArray) and not isinstance(mx_input2, mx.nd.NDArray): + scalar = mx_input2 + reverse = False + elif isinstance(mx_input2, mx.nd.NDArray) and not isinstance(mx_input1, mx.nd.NDArray): + scalar = mx_input1 + reverse = True + + np_out = get_np_ret(np_input1, np_input2, op) + for hybridize in [True, False]: + if scalar is None: + get_mx_ret_np = TestBinaryElementWiseOp(op) + get_mx_ret_classic = TestBinaryElementWiseOp(op) + if hybridize: + get_mx_ret_np.hybridize() + get_mx_ret_classic.hybridize() + mx_out = get_mx_ret_np(mx_input1.as_np_ndarray(), mx_input2.as_np_ndarray()) + assert type(mx_out) == np.ndarray + assert np_out.shape == mx_out.shape + assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5) + else: + get_mx_ret = TestBinaryElementWiseOp(op, scalar=scalar, reverse=reverse) + if hybridize: + get_mx_ret.hybridize() + if reverse: + mx_out = get_mx_ret(mx_input2.as_np_ndarray()) + assert type(mx_out) == np.ndarray + else: + mx_out = get_mx_ret(mx_input1.as_np_ndarray()) + assert type(mx_out) == np.ndarray + assert np_out.shape == mx_out.shape + assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5) + + dtypes = [_np.float32, _np.float64, None] + ops = np_op_map.keys() + for dtype in dtypes: + for op in ops: + check_binary_op_result((3, 4), (3, 4), op, dtype) + check_binary_op_result(None, (3, 4), op, dtype) + check_binary_op_result((3, 4), None, op, dtype) + check_binary_op_result((1, 4), (3, 1), op, dtype) + check_binary_op_result(None, (3, 1), op, dtype) + check_binary_op_result((1, 4), None, op, dtype) + check_binary_op_result((1, 4), (3, 5, 4), op, dtype) + check_binary_op_result((), (3, 5, 4), op, dtype) + check_binary_op_result((), None, op, dtype) + check_binary_op_result(None, (), op, dtype) + check_binary_op_result((0, 2), (1, 1), op, dtype) + check_binary_op_result((0, 2), None, op, dtype) + check_binary_op_result(None, (0, 2), op, dtype) + + +@with_seed() +def test_hybrid_block_multiple_outputs(): + @use_np + class TestAllNumpyOutputs(HybridBlock): + def hybrid_forward(self, F, x, *args, **kwargs): + return F.np.add(x, x), F.np.multiply(x, x) + + class TestAllClassicOutputs(HybridBlock): + def hybrid_forward(self, F, x, *args, **kwargs): + return x.as_nd_ndarray() + x.as_nd_ndarray(), x.as_nd_ndarray() * x.as_nd_ndarray() + + data_np = np.ones((2, 3)) + for block, expected_out_type in [(TestAllClassicOutputs, mx.nd.NDArray), + (TestAllNumpyOutputs, np.ndarray)]: + net = block() + for hybridize in [True, False]: + if hybridize: + net.hybridize() + out1, out2 = net(data_np) + assert type(out1) is expected_out_type + assert type(out2) is expected_out_type + + @use_np + class TestMixedTypeOutputsFailure(HybridBlock): + def hybrid_forward(self, F, x, *args, **kwargs): + return x.as_nd_ndarray() + x.as_nd_ndarray(), F.np.multiply(x, x) + + net = TestMixedTypeOutputsFailure() + assert_exception(net, TypeError, data_np) + net.hybridize() + assert_exception(net, TypeError, data_np) + + +@with_seed() +@use_np +def test_grad_ndarray_type(): + data = np.array(2, dtype=_np.float32) + data.attach_grad() + assert type(data.grad) == np.ndarray + assert type(data.detach()) == np.ndarray + + +@with_seed() +def test_np_ndarray_astype(): + mx_data = np.array([2, 3, 4, 5], dtype=_np.int32) + np_data = mx_data.asnumpy() + + def check_astype_equal(dtype, copy, expect_zero_copy=False): + mx_ret = mx_data.astype(dtype=dtype, copy=copy) + assert type(mx_ret) is np.ndarray + np_ret = np_data.astype(dtype=dtype, copy=copy) + assert mx_ret.dtype == np_ret.dtype + assert same(mx_ret.asnumpy(), np_ret) + if expect_zero_copy: + assert id(mx_ret) == id(mx_data) + assert id(np_ret) == id(np_data) + + for dtype in [_np.int8, _np.uint8, _np.int32, _np.float16, _np.float32, _np.float64]: + for copy in [True, False]: + check_astype_equal(dtype, copy, copy is False and mx_data.dtype == dtype) + + +@with_seed() +def test_np_ndarray_copy(): + mx_data = np.array([2, 3, 4, 5], dtype=_np.int32) + assert_exception(mx_data.copy, NotImplementedError, order='F') + mx_ret = mx_data.copy() + np_ret = mx_data.asnumpy().copy() + assert same(mx_ret.asnumpy(), np_ret) + + +@with_seed() +@use_np +def test_np_ndarray_indexing(): + def test_getitem(np_array, index): + """`is_scalar` indicates whether we should expect a scalar for the result. + If so, the indexed array of NDArray should call asscalar to compare + with numpy's indexed array.""" + np_index = index + if isinstance(index, np.ndarray): + np_index = index.asnumpy() + if isinstance(index, tuple): + np_index = [] + for idx in index: + if isinstance(idx, np.ndarray): + np_index.append(idx.asnumpy()) + else: + np_index.append(idx) + np_index = tuple(np_index) + + np_indexed_array = np_array[np_index] + mx_array = np.array(np_array, dtype=np_array.dtype) + mx_indexed_array = mx_array[index].asnumpy() + assert same(np_indexed_array, mx_indexed_array), 'Failed with index=%s' % str(index) + + def test_setitem(np_array, index): + def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None): + if np_value is not None: + np_array[np_index] = np_value + elif isinstance(mx_value, np.ndarray): + np_array[np_index] = mx_value.asnumpy() + else: + np_array[np_index] = mx_value + mx_array[mx_index] = mx_value + assert same(np_array, mx_array.asnumpy()) + + np_index = index + if isinstance(index, np.ndarray): + np_index = index.asnumpy() + if isinstance(index, tuple): + np_index = [] + for idx in index: + if isinstance(idx, np.ndarray): + np_index.append(idx.asnumpy()) + else: + np_index.append(idx) + np_index = tuple(np_index) + + mx_array = np.array(np_array, dtype=np_array.dtype) + np_array = mx_array.asnumpy() + indexed_array_shape = np_array[np_index].shape + np_indexed_array = _np.random.randint(low=-10000, high=0, size=indexed_array_shape) + # test value is a numpy array without broadcast + assert_same(np_array, np_index, mx_array, index, np_indexed_array) + # test value is an numeric_type + assert_same(np_array, np_index, mx_array, index, _np.random.randint(low=-10000, high=0)) + if len(indexed_array_shape) > 1: + # test ndarray with broadcast + assert_same(np_array, np_index, mx_array, index, + _np.random.uniform(low=-10000, high=0, size=(indexed_array_shape[-1],))) + # test numpy array with broadcast + assert_same(np_array, np_index, mx_array, index, + _np.random.randint(low=-10000, high=0, size=(indexed_array_shape[-1],))) + # test list with broadcast + assert_same(np_array, np_index, mx_array, index, + [_np.random.randint(low=-10000, high=0)] * indexed_array_shape[-1]) + + def test_getitem_autograd(np_array, index): + x = np.array(np_array, dtype=np_array.dtype) + x.attach_grad() + with autograd.record(): + y = x[index] + y.backward() + value = np.ones_like(y) + x_grad = np.zeros_like(x) + x_grad[index] = value + assert same(x_grad.asnumpy(), x.grad.asnumpy()) + + def test_setitem_autograd(np_array, index): + x = np.array(np_array, dtype=np_array.dtype) + out_shape = x[index].shape + y = np.array(_np.random.uniform(size=out_shape)) + y.attach_grad() + try: + with autograd.record(): + x[index] = y + assert False # should not reach here + except mx.base.MXNetError as err: + assert str(err).find('Inplace operations (+=, -=, x[:]=, etc) are not supported when recording with') != -1 + + def np_int(index, int_type=_np.int32): + def convert(num): + if num is None: + return num + else: + return int_type(num) + + if isinstance(index, slice): + return slice(convert(index.start), convert(index.stop), convert(index.step)) + elif isinstance(index, tuple): # tuple of slices and integers + ret = [] + for elem in index: + if isinstance(elem, slice): + ret.append(slice(convert(elem.start), convert(elem.stop), convert(elem.step))) + else: + ret.append(convert(elem)) + return tuple(ret) + else: + assert False + + shape = (8, 16, 9, 9) + np_array = _np.arange(_np.prod(shape), dtype='int32').reshape(shape) + index_list = [ + (), + 0, + _np.int32(0), + _np.int64(0), + 5, + _np.int32(5), + _np.int64(5), + -1, + _np.int32(-1), + _np.int64(-1), + slice(5), + np_int(slice(5), _np.int32), + np_int(slice(5), _np.int64), + slice(1, 5), + np_int(slice(1, 5), _np.int32), + np_int(slice(1, 5), _np.int64), + slice(1, 5, 2), + np_int(slice(1, 5, 2), _np.int32), + np_int(slice(1, 5, 2), _np.int64), + slice(7, 0, -1), + np_int(slice(7, 0, -1)), + np_int(slice(7, 0, -1), _np.int64), + slice(None, 6), + np_int(slice(None, 6)), + np_int(slice(None, 6), _np.int64), + slice(None, 6, 3), + np_int(slice(None, 6, 3)), + np_int(slice(None, 6, 3), _np.int64), + slice(1, None), + np_int(slice(1, None)), + np_int(slice(1, None), _np.int64), + slice(1, None, 3), + np_int(slice(1, None, 3)), + np_int(slice(1, None, 3), _np.int64), + slice(None, None, 2), + np_int(slice(None, None, 2)), + np_int(slice(None, None, 2), _np.int64), + slice(None, None, -1), + np_int(slice(None, None, -1)), + np_int(slice(None, None, -1), _np.int64), + slice(None, None, -2), + np_int(slice(None, None, -2), _np.int32), + np_int(slice(None, None, -2), _np.int64), + (slice(None), slice(None), 1, 8), + (slice(None), slice(None), -1, 8), + (slice(None), slice(None), 1, -8), + (slice(None), slice(None), -1, -8), + np_int((slice(None), slice(None), 1, 8)), + np_int((slice(None), slice(None), 1, 8), _np.int64), + (slice(None), slice(None), 1, 8), + np_int((slice(None), slice(None), -1, -8)), + np_int((slice(None), slice(None), -1, -8), _np.int64), + (slice(None), 2, slice(1, 5), 1), + np_int((slice(None), 2, slice(1, 5), 1)), + np_int((slice(None), 2, slice(1, 5), 1), _np.int64), + (1, 2, 3), + np_int((1, 2, 3)), + np_int((1, 2, 3), _np.int64), + (-1, -2, -3), + np_int((-1, -2, -3)), + np_int((-1, -2, -3), _np.int64), + (1, 2, 3, 4), + np_int((1, 2, 3, 4)), + np_int((1, 2, 3, 4), _np.int64), + (-4, -3, -2, -1), + np_int((-4, -3, -2, -1)), + np_int((-4, -3, -2, -1), _np.int64), + (slice(None, None, -1), 2, slice(1, 5), 1), + np_int((slice(None, None, -1), 2, slice(1, 5), 1)), + np_int((slice(None, None, -1), 2, slice(1, 5), 1), _np.int64), + (slice(None, None, -1), 2, slice(1, 7, 2), 1), + np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)), + np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1), _np.int64), + (slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), + np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3))), + np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), _np.int64), + (slice(1, 8, 2), 1, slice(3, 8), 2), + np_int((slice(1, 8, 2), 1, slice(3, 8), 2)), + np_int((slice(1, 8, 2), 1, slice(3, 8), 2), _np.int64), + [1], + [1, 2], + [2, 1, 3], + [7, 5, 0, 3, 6, 2, 1], + _np.array([6, 3], dtype=_np.int32), + _np.array([[3, 4], [0, 6]], dtype=_np.int32), + _np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int32), + _np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int64), + _np.array([[2], [0], [1]], dtype=_np.int32), + _np.array([[2], [0], [1]], dtype=_np.int64), + np.array([4, 7], dtype=_np.int32), + np.array([4, 7], dtype=_np.int64), + np.array([[3, 6], [2, 1]], dtype=_np.int32), + np.array([[3, 6], [2, 1]], dtype=_np.int64), + np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int32), + np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int64), + (1, [2, 3]), + (1, [2, 3], _np.array([[3], [0]], dtype=_np.int32)), + (1, [2, 3]), + (1, [2, 3], _np.array([[3], [0]], dtype=_np.int64)), + (1, [2], _np.array([[5], [3]], dtype=_np.int32), slice(None)), + (1, [2], _np.array([[5], [3]], dtype=_np.int64), slice(None)), + (1, [2, 3], _np.array([[6], [0]], dtype=_np.int32), slice(2, 5)), + (1, [2, 3], _np.array([[6], [0]], dtype=_np.int64), slice(2, 5)), + (1, [2, 3], _np.array([[4], [7]], dtype=_np.int32), slice(2, 5, 2)), + (1, [2, 3], _np.array([[4], [7]], dtype=_np.int64), slice(2, 5, 2)), + (1, [2], _np.array([[3]], dtype=_np.int32), slice(None, None, -1)), + (1, [2], _np.array([[3]], dtype=_np.int64), slice(None, None, -1)), + (1, [2], _np.array([[3]], dtype=_np.int32), np.array([[5, 7], [2, 4]], dtype=_np.int64)), + (1, [2], np.array([[4]], dtype=_np.int32), np.array([[1, 3], [5, 7]], dtype='int64')), + [0], + [0, 1], + [1, 2, 3], + [2, 0, 5, 6], + ([1, 1], [2, 3]), + ([1], [4], [5]), + ([1], [4], [5], [6]), + ([[1]], [[2]]), + ([[1]], [[2]], [[3]], [[4]]), + (slice(0, 2), [[1], [6]], slice(0, 2), slice(0, 5, 2)), + ([[[[1]]]], [[1]], slice(0, 3), [1, 5]), + ([[[[1]]]], 3, slice(0, 3), [1, 3]), + ([[[[1]]]], 3, slice(0, 3), 0), + ([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)), + ([1, 2], slice(3, 5), [2, 3], [3, 4]), + ([1, 2], slice(3, 5), (2, 3), [3, 4]), + range(4), + range(3, 0, -1), + (range(4,), [1]), + # slice(0, 0) does not support output zero-size tensor yet + ] + for index in index_list: + test_getitem(np_array, index) + test_setitem(np_array, index) + test_getitem_autograd(np_array, index) + if not isinstance(index, tuple) or len(index) != 0: + # When index = (), this is same a[()] = b is equivalent to b.copyto(a) + # which should have no problem to do autograd + test_setitem_autograd(np_array, index) + + +@with_seed() +@use_np +def test_np_save_load_ndarrays(): + shapes = [(2, 0, 1), (0,), (), (), (0, 4), (), (3, 0, 0, 0), (2, 1), (0, 5, 0), (4, 5, 6), (0, 0, 0)] + array_list = [_np.random.randint(0, 10, size=shape) for shape in shapes] + array_list = [np.array(arr, dtype=arr.dtype) for arr in array_list] + # test save/load single ndarray + for i, arr in enumerate(array_list): + with TemporaryDirectory() as work_dir: + fname = os.path.join(work_dir, 'dataset.npy') + npx.save(fname, arr) + arr_loaded = npx.load(fname) + assert isinstance(arr_loaded, list) + assert len(arr_loaded) == 1 + assert _np.array_equal(arr_loaded[0].asnumpy(), array_list[i].asnumpy()) + + # test save/load a list of ndarrays + with TemporaryDirectory() as work_dir: + fname = os.path.join(work_dir, 'dataset.npy') + npx.save(fname, array_list) + array_list_loaded = mx.nd.load(fname) + assert isinstance(arr_loaded, list) + assert len(array_list) == len(array_list_loaded) + assert all(isinstance(arr, np.ndarray) for arr in arr_loaded) + for a1, a2 in zip(array_list, array_list_loaded): + assert _np.array_equal(a1.asnumpy(), a2.asnumpy()) + + # test save/load a dict of str->ndarray + arr_dict = {} + keys = [str(i) for i in range(len(array_list))] + for k, v in zip(keys, array_list): + arr_dict[k] = v + with TemporaryDirectory() as work_dir: + fname = os.path.join(work_dir, 'dataset.npy') + npx.save(fname, arr_dict) + arr_dict_loaded = npx.load(fname) + assert isinstance(arr_dict_loaded, dict) + assert len(arr_dict_loaded) == len(arr_dict) + for k, v in arr_dict_loaded.items(): + assert k in arr_dict + assert _np.array_equal(v.asnumpy(), arr_dict[k].asnumpy()) + + +if __name__ == '__main__': + import nose + nose.runmodule() diff --git a/tests/python/unittest/test_thread_local.py b/tests/python/unittest/test_thread_local.py index b553299ab4d7..ee56ba780a95 100644 --- a/tests/python/unittest/test_thread_local.py +++ b/tests/python/unittest/test_thread_local.py @@ -23,6 +23,7 @@ from mxnet.attribute import AttrScope from mxnet.name import NameManager from mxnet.test_utils import set_default_context +from mxnet.util import _NumpyArrayScope def test_context(): ctx_list = [] @@ -163,6 +164,41 @@ def f(): thread.join() assert status[0], "Failed to execute a symbolic graph within a thread" + +def test_np_array_scope(): + np_array_scope_list = [] + _NumpyArrayScope._current = _NumpyArrayScope(False) + np_array_scope_list.append(_NumpyArrayScope._current) + + def f(): + _NumpyArrayScope._current = _NumpyArrayScope(True) + np_array_scope_list.append(_NumpyArrayScope._current) + + thread = threading.Thread(target=f) + thread.start() + thread.join() + assert len(np_array_scope_list) == 2 + assert not np_array_scope_list[0]._is_np_array + assert np_array_scope_list[1]._is_np_array + + event = threading.Event() + status = [False] + + def g(): + with mx.np_array(False): + event.wait() + if not mx.is_np_array(): + status[0] = True + + thread = threading.Thread(target=g) + thread.start() + _NumpyArrayScope._current = _NumpyArrayScope(True) + event.set() + thread.join() + event.clear() + assert status[0], "Spawned thread didn't set status correctly" + + if __name__ == '__main__': import nose nose.runmodule()