def _getitem(arr, key): if not isinstance(arr, chainerx.ndarray): return arr[key] try: return arr[key] except (IndexError, chainerx.DimensionError): pass is_backprop_required = arr.is_backprop_required() arr = backend.from_chainerx(arr) if isinstance(key, chainerx.ndarray): key = backend.from_chainerx(key) if isinstance(arr, cuda.ndarray): with arr.device: ret = arr[key] else: ret = arr[key] # Doing this check after the fallback __getitem__ because the error which # caused the fallback might not be due to advanced indexing. In such # case the fallback __getitem__ should also raise the error. if is_backprop_required: raise RuntimeError( 'ChainerX getitem fallback for advanced indexing is not supported ' 'for arrays that are connected to a graph.') return backend.to_chainerx(ret)
def update_core_chainerx(self, param): """Updates the ChainerX parameter. This method can be overridden to implement custom update logic. The default implementation is to convert the parameter to a memory-shared NumPy/CuPy parameter and call the corresponding update method. See :meth:`update_core` for details. Args: param (~chainer.Variable): Variable to be updated. """ grad_array = param.grad backend_name = param.array.device.backend.name if backend_name == 'native': update_core = self.update_core_cpu elif backend_name == 'cuda': update_core = self.update_core_gpu else: raise RuntimeError( 'Default implementation of Optimizer.update_core_chainerx is ' 'only provided for native or cuda backends (actual: {}). ' 'Override Optimizer.update_core_chainerx() to implement ' 'custom update logic.'.format(backend_name)) # Convert state arrays to NumPy/CuPy chainerx_state_arrays = {} for state_name, st in self.state.items(): st = self.state[state_name] if isinstance(st, chainerx.ndarray): self.state[state_name] = backend.from_chainerx(st) chainerx_state_arrays[state_name] = st # Create a temporary parameter with memory-shared NumPy/CuPy array # If the ChainerX parameter has a cached NumPy/CuPy copy, use the # cache and avoid redundant conversion. Else, create the cache here # and use it. if param._chainerx_fallback_array is None: param._chainerx_fallback_array = backend.from_chainerx(param.array) temp_param = variable.Variable._init_unchecked( param._chainerx_fallback_array, is_chainerx_array=False) if grad_array is not None: temp_param._set_grad_without_check( backend.from_chainerx(grad_array)) # Update update_core(temp_param) # Restore state arrays for state_name, arr in chainerx_state_arrays.items(): cur_arr = self.state[state_name] if cur_arr is not arr: arr = backend.to_chainerx(cur_arr) self.state[state_name] = arr
def update_core_chainerx(self, param): """Updates the ChainerX parameter. This method can be overridden to implement custom update logic. The default implementation is to convert the parameter to a memory-shared NumPy/CuPy parameter and call the corresponding update method. See :meth:`update_core` for details. Args: param (~chainer.Variable): Variable to be updated. """ grad_array = param.grad backend_name = param.array.device.backend.name if backend_name == 'native': update_core = self.update_core_cpu elif backend_name == 'cuda': update_core = self.update_core_gpu else: raise RuntimeError( 'Default implementation of Optimizer.update_core_chainerx is ' 'only provided for native or cuda backends (actual: {}). ' 'Override Optimizer.update_core_chainerx() to implement ' 'custom update logic.'.format(backend_name)) # Convert state arrays to NumPy/CuPy chainerx_state_arrays = {} for state_name, st in self.state.items(): st = self.state[state_name] if isinstance(st, chainerx.ndarray): self.state[state_name] = backend.from_chainerx(st) chainerx_state_arrays[state_name] = st # Create a temporary parameter with memory-shared NumPy/CuPy array # If the ChainerX parameter has a cached NumPy/CuPy copy, use the # cache and avoid redundant conversion. Else, create the cache here # and use it. if param._chainerx_fallback_array is None: param._chainerx_fallback_array = backend.from_chainerx( param.array) temp_param = variable.Variable(param._chainerx_fallback_array) if grad_array is not None: temp_param._set_grad_without_check( backend.from_chainerx(grad_array)) # Update update_core(temp_param) # Restore state arrays for state_name, arr in chainerx_state_arrays.items(): self.state[state_name] = arr
def _setitem(arr, key, value): """Sets arr[key] to value by falling back to a non-ChainerX arrays. Supports both basic and advanced indexing. Note: With the ``cuda`` backend, the behavior differs from NumPy when integer arrays in ``slices`` reference the same location multiple times. In that case, the value that is actually stored is undefined. >>> import chainerx >>> chainerx.set_default_device('cuda:0') >>> a = chainerx.zeros((2,), dtype=chainerx.float) >>> i = chainerx.array([0, 1, 0, 1, 0, 1]) >>> v = chainerx.arange(6).astype(chainerx.float) >>> a[i] = v >>> a # doctest: +SKIP array([2., 3.], shape=(2,), dtype=float64, device='cuda:0') On the other hand, NumPy and ``native`` backend store the value corresponding to the last index among the indices referencing duplicate locations. >>> import numpy >>> a_cpu = numpy.zeros((2,), dtype=numpy.float) >>> i_cpu = numpy.array([0, 1, 0, 1, 0, 1]) >>> v_cpu = numpy.arange(6).astype(numpy.float) >>> a_cpu[i_cpu] = v_cpu >>> a_cpu array([4., 5.]) """ if not isinstance(arr, chainerx.ndarray): arr[key] = value return if arr.is_backprop_required(): raise RuntimeError( 'ChainerX setitem fallback for advanced indexing is not supported ' 'for arrays that are connected to a graph.') arr = backend.from_chainerx(arr) if isinstance(key, chainerx.ndarray): key = backend.from_chainerx(key) if isinstance(value, chainerx.ndarray): value = backend.from_chainerx(value) if isinstance(arr, cuda.ndarray): with arr.device: arr[key] = value else: arr[key] = value
def backward(self, target_input_indexes, grad_outputs): retained_inputs = self.get_retained_inputs() inputs = [None] * len(self.inputs) in_data = [None] * len(self.inputs) for retained, i_in in six.moves.zip(retained_inputs, self._input_indexes_to_retain): inputs[i_in] = retained in_data[i_in] = None if retained is None else retained.array in_data = tuple(in_data) grad_out_data = tuple( [None if grad is None else grad.data for grad in grad_outputs]) is_chainerx_fallback_mode = self._is_chainerx_fallback_mode if is_chainerx_fallback_mode: # Convert input and output gradients to numpy/cupy in_data = backend.from_chainerx(in_data) grad_out_data = backend.from_chainerx(grad_out_data) # Call Function.backward with cuda.get_device_from_array(*(in_data + grad_out_data)): if is_chainerx_fallback_mode: # Enable attribute fallback with function_node._chainerx_attribute_fallback( self._function, self.chainerx_device): gxs = self._function.backward(in_data, grad_out_data) else: gxs = self._function.backward(in_data, grad_out_data) # Check gradients for x, gx in six.moves.zip(self.inputs, gxs): if gx is not None: variable._check_grad_type(self, x, True, gx) # Convert input gradients back to ChainerX if is_chainerx_fallback_mode: gxs = backend.to_chainerx(gxs) ret = [] for i in target_input_indexes: if gxs[i] is None: g = None else: # Intentionally not passing requires_grad=False so that # backprop routines can raise an error when a further backprop # is attempted against this gradient variable. g = variable.Variable(gxs[i]) if g.xp is not chainerx: g.node._old_style_grad_generator = self._function.label ret.append(g) return tuple(ret)
def backward(self, target_input_indexes, grad_outputs): retained_inputs = self.get_retained_inputs() inputs = [None] * len(self.inputs) in_data = [None] * len(self.inputs) for retained, i_in in six.moves.zip( retained_inputs, self._input_indexes_to_retain): inputs[i_in] = retained in_data[i_in] = retained.array in_data = tuple(in_data) grad_out_data = tuple([None if grad is None else grad.data for grad in grad_outputs]) is_chainex_fallback_mode = self._is_chainex_fallback_mode if is_chainex_fallback_mode: # Convert input and output gradients to numpy/cupy in_data = backend.from_chainerx(in_data) grad_out_data = backend.from_chainerx(grad_out_data) # Call Function.backward with cuda.get_device_from_array(*(in_data + grad_out_data)): if is_chainex_fallback_mode: # Enable attribute fallback with function_node._chainerx_attribute_fallback( self._function, self.chainerx_device): gxs = self._function.backward(in_data, grad_out_data) else: gxs = self._function.backward(in_data, grad_out_data) for x, gx in six.moves.zip(self.inputs, gxs): variable._check_grad_type(self, x, True, gx, False) # Convert input gradients back to ChainerX if is_chainex_fallback_mode: gxs = backend.to_chainerx(gxs) ret = [] for i in target_input_indexes: if gxs[i] is None: g = None else: # Intentionally not passing requires_grad=False so that # backprop routines can raise an error when a further backprop # is attempted against this gradient variable. g = variable.Variable(gxs[i]) if g.xp is not chainerx: g.node._old_style_grad_generator = self._function.label ret.append(g) return tuple(ret)
def _chainerx_apply_fallback_preprocess(self, in_data, inputs): chainerx_in_data = in_data in_data = [] device = None for data, x in six.moves.zip(chainerx_in_data, inputs): if data is None: fallback_data = None else: # Use the cached fallback arrays as inputs if they exist. x_is_variable = isinstance(x, variable.Variable) if x_is_variable and x._chainerx_fallback_array is not None: fallback_data = x._chainerx_fallback_array if device is None: device = x.device else: fallback_data = backend.from_chainerx(data) if device is None: device = backend.ChainerxDevice(data.device) # Update the fallback cache if possible. if x_is_variable: x._chainerx_fallback_array = fallback_data in_data.append(fallback_data) in_data = tuple(in_data) return chainerx_in_data, in_data, device
def forward_cpu(self, inputs): class_weight = backend.from_chainerx(self.class_weight) self.retain_inputs((0, 1)) x, t = inputs if chainer.is_debug(): _check_input_values(x, t, self.ignore_label) log_y = log_softmax._log_softmax(x) if self.cache_score: self.y = numpy.exp(log_y) if class_weight is not None: shape = [1 if d != 1 else -1 for d in six.moves.range(x.ndim)] log_y *= _broadcast_to(class_weight.reshape(shape), x.shape) log_yd = numpy.rollaxis(log_y, 1) log_yd = log_yd.reshape(len(log_yd), -1) t_valid = t != self.ignore_label t = t * t_valid log_p = log_yd[t.ravel(), numpy.arange(t.size)] log_p *= t_valid.ravel() if self.reduce == 'mean': # deal with the case where the SoftmaxCrossEntropy is # unpickled from the old version if self.normalize: count = t_valid.sum() else: count = len(x) self._coeff = 1.0 / max(count, 1) y = log_p.sum(keepdims=True) * (-self._coeff) return y.reshape(()), else: return -log_p.reshape(t.shape),
def forward_cpu(self, inputs): class_weight = backend.from_chainerx(self.class_weight) self.retain_inputs((0, 1)) x, t = inputs if chainer.is_debug(): _check_input_values(x, t, self.ignore_label) log_y = log_softmax._log_softmax(x) if self.cache_score: self.y = numpy.exp(log_y) if class_weight is not None: shape = [1 if d != 1 else -1 for d in six.moves.range(x.ndim)] log_y *= _broadcast_to(class_weight.reshape(shape), x.shape) log_yd = numpy.rollaxis(log_y, 1) log_yd = log_yd.reshape(len(log_yd), -1) t_valid = t != self.ignore_label t = t * t_valid log_p = log_yd[t.ravel(), numpy.arange(t.size)] log_p *= t_valid.ravel() if self.reduce == 'mean': # deal with the case where the SoftmaxCrossEntropy is # unpickled from the old version if self.normalize: count = t_valid.sum() else: count = len(x) self._coeff = 1.0 / max(count, 1) y = log_p.sum(keepdims=True) * (-self._coeff) return y.reshape(()), else: return -log_p.reshape(t.shape),
def _make_samples(self, t): size = int(t.shape[0]) # first one is the positive, and others are sampled negatives samples = self.sampler((size, self.sample_size + 1)) samples = backend.from_chainerx(samples) samples[:, 0] = t return samples
def forward_gpu(self, inputs): t = backend.from_chainerx(self.t) # Workaround for ChainerX. gx = cuda.cupy.zeros(self.shape, self.dtype) gx = cuda.elementwise('S t, T gloss', 'raw T gx', 'int ind[] = {i, t}; gx[ind] = gloss;', 'getitem_bwd')(t, inputs[0], gx) return gx,
def getattribute(self, name): value = sup.__getattribute__(name) if isinstance(value, chainerx.ndarray): fallback_arr = fallback_array_cache.get(name) if fallback_arr is None: fallback_arr = backend.from_chainerx(value) fallback_array_cache[name] = fallback_arr return fallback_arr return value
def getattribute(self, name): value = sup.__getattribute__(name) if isinstance(value, chainerx.ndarray): fallback_arr = fallback_array_cache.get(name) if fallback_arr is None: fallback_arr = backend.from_chainerx(value) fallback_array_cache[name] = fallback_arr return fallback_arr return value
def output_data(self): """A tuple of the retained output arrays. It has the same length as the :attr:`outputs`. Elements that are not retained are set to ``None``. """ if self.node._is_chainerx_fallback_mode: return backend.from_chainerx(self.node.output_data) return self.node.output_data
def output_data(self): """A tuple of the retained output arrays. It has the same length as the :attr:`outputs`. Elements that are not retained are set to ``None``. """ if self.node._is_chainerx: return backend.from_chainerx(self.node.output_data) return self.node.output_data
def forward_gpu(self, inputs): t = backend.from_chainerx(self.t) # Workaround for ChainerX. gx = cuda.cupy.zeros(self.shape, self.dtype) gx = cuda.elementwise( 'S t, T gloss', 'raw T gx', 'int ind[] = {i, t}; gx[ind] = gloss;', 'getitem_bwd' )(t, inputs[0], gx) return gx,
def _getitem(arr, key): try: return arr[key] except (IndexError, chainerx.DimensionError): pass if isinstance(arr, chainerx.ndarray): arr = backend.from_chainerx(arr) is_arr_chainerx = True else: is_arr_chainerx = False if isinstance(key, chainerx.ndarray): key = backend.from_chainerx(key) if isinstance(arr, cuda.ndarray): with arr.device: ret = arr[key] else: ret = arr[key] if is_arr_chainerx: ret = backend.to_chainerx(ret) return ret
def forward_gpu(self, inputs): class_weight = backend.from_chainerx(self.class_weight) self.retain_inputs((0, 1)) cupy = cuda.cupy x, t = inputs if chainer.is_debug(): _check_input_values(x, t, self.ignore_label) if x.size == 0: y = cupy.zeros(t.shape, dtype=x.dtype) if self.cache_score: self.y = y if self.reduce == 'mean': return y.sum(), else: return y, log_y = log_softmax._log_softmax(x) if self.cache_score: self.y = cupy.exp(log_y) if class_weight is not None: shape = [1 if d != 1 else -1 for d in six.moves.range(x.ndim)] log_y *= cupy.broadcast_to(class_weight.reshape(shape), x.shape) if self.normalize: coeff = cupy.maximum(1, (t != self.ignore_label).sum()) else: coeff = max(1, len(t)) self._coeff = cupy.divide(1.0, coeff, dtype=x.dtype) log_y = cupy.rollaxis(log_y, 1, log_y.ndim) if self.reduce == 'mean': ret = cuda.reduce( 'S t, raw T log_y, int32 n_channel, raw T coeff, ' 'S ignore_label', 'T out', 't == ignore_label ? T(0) : log_y[_j * n_channel + t]', 'a + b', 'out = a * -coeff[0]', '0', 'crossent_fwd' )(t, log_y.reduced_view(), log_y.shape[-1], self._coeff, self.ignore_label) else: ret = cuda.elementwise( 'S t, raw T log_y, int32 n_channel, T ignore', 'T out', ''' if (t == ignore) { out = 0; } else { out = -log_y[i * n_channel + t]; } ''', 'softmax_crossent_no_reduce_fwd' )(t, log_y.reduced_view(), log_y.shape[-1], self.ignore_label) ret = ret.reshape(t.shape) return ret,
def forward_gpu(self, inputs): class_weight = backend.from_chainerx(self.class_weight) self.retain_inputs((0, 1)) cupy = cuda.cupy x, t = inputs if chainer.is_debug(): _check_input_values(x, t, self.ignore_label) if x.size == 0: y = cupy.zeros(t.shape, dtype=x.dtype) if self.cache_score: self.y = y if self.reduce == 'mean': return y.sum(), else: return y, log_y = log_softmax._log_softmax(x) if self.cache_score: self.y = cupy.exp(log_y) if class_weight is not None: shape = [1 if d != 1 else -1 for d in six.moves.range(x.ndim)] log_y *= cupy.broadcast_to(class_weight.reshape(shape), x.shape) if self.normalize: coeff = cupy.maximum(1, (t != self.ignore_label).sum()) else: coeff = max(1, len(t)) self._coeff = cupy.divide(1.0, coeff, dtype=x.dtype) log_y = cupy.rollaxis(log_y, 1, log_y.ndim) if self.reduce == 'mean': ret = cuda.reduce( 'S t, raw T log_y, int32 n_channel, raw T coeff, ' 'S ignore_label', 'T out', 't == ignore_label ? T(0) : log_y[_j * n_channel + t]', 'a + b', 'out = a * -coeff[0]', '0', 'crossent_fwd')(t, log_y.reduced_view(), log_y.shape[-1], self._coeff, self.ignore_label) else: ret = cuda.elementwise( 'S t, raw T log_y, int32 n_channel, T ignore', 'T out', ''' if (t == ignore) { out = 0; } else { out = -log_y[i * n_channel + t]; } ''', 'softmax_crossent_no_reduce_fwd')(t, log_y.reduced_view(), log_y.shape[-1], self.ignore_label) ret = ret.reshape(t.shape) return ret,
def from_chainerx(self): """Converts parameter variables and persistent values from ChainerX \ to NumPy/CuPy devices without any copy.""" d = self.__dict__ for name in self._params: d[name].from_chainerx() for name in self._persistent: if not numpy.isscalar(d[name]): d[name] = backend.from_chainerx(d[name]) if isinstance(self._device, backend.ChainerxDevice): self._device = self._device.fallback_device return self
def test_from_chainerx(self, backend_config): arr = backend_config.get_array(numpy.ones((2, 3), numpy.float32)) arr_converted = backend.from_chainerx(arr) src_device = backend_config.device if src_device.xp is chainerx: dst_xp = src_device.fallback_device.xp assert isinstance(arr_converted, dst_xp.ndarray) if dst_xp is cuda.cupy: assert arr_converted.device.id == src_device.device.index else: assert arr is arr_converted with backend_config: self.check_equal_memory_shared(arr, arr_converted)
def test_from_chainerx(self, backend_config): arr = backend_config.get_array(numpy.ones((2, 3), numpy.float32)) arr_converted = backend.from_chainerx(arr) src_device = backend_config.device if src_device.xp is chainerx: dst_xp = src_device.fallback_device.xp assert isinstance(arr_converted, dst_xp.ndarray) if dst_xp is cuda.cupy: assert arr_converted.device.id == src_device.device.index else: assert arr is arr_converted with backend_config: self.check_equal_memory_shared(arr, arr_converted)
def forward(self, xs): a = xs[0] b = xs[1] y = a.copy() xp = backend.get_array_module(a) slices = tuple([ backend.from_chainerx(s) if isinstance(s, chainerx.ndarray) else s for s in self.slices ]) if y[slices].shape != b.shape: raise ValueError('Chainer does not support automatic broadcasting ' 'of variables.') if xp is numpy: numpy.add.at(y, slices, b), else: cuda.cupyx.scatter_add(y, slices, b), return y,
def _chainerx_apply_fallback_preprocess(self, in_data, inputs): chainerx_in_data = in_data in_data = [] for i in six.moves.range(len(inputs)): # Use the cached fallback arrays as inputs if they exist. x = inputs[i] x_is_variable = isinstance(x, variable.Variable) if x_is_variable and x._chainerx_fallback_array is not None: x_data = x._chainerx_fallback_array else: x_data = backend.from_chainerx(chainerx_in_data[i]) # Update the fallback cache if possible. if x_is_variable: x._chainerx_fallback_array = x_data in_data.append(x_data) in_data = tuple(in_data) return chainerx_in_data, in_data
def _chainerx_apply_fallback_preprocess(self, in_data, inputs): chainerx_in_data = in_data in_data = [] device = None for data, x in six.moves.zip(chainerx_in_data, inputs): # Use the cached fallback arrays as inputs if they exist. x_is_variable = isinstance(x, variable.Variable) if x_is_variable and x._chainerx_fallback_array is not None: fallback_data = x._chainerx_fallback_array if device is None: device = x.device else: fallback_data = backend.from_chainerx(data) if device is None: device = backend.ChainerxDevice(data.device) # Update the fallback cache if possible. if x_is_variable: x._chainerx_fallback_array = fallback_data in_data.append(fallback_data) in_data = tuple(in_data) return chainerx_in_data, in_data, device
def forward(self, inputs): self.retain_inputs((0, 1)) x, gamma, beta = inputs xp = backend.get_array_module(x) if self.running_mean is None: self.running_mean = xp.zeros_like(gamma) self.running_var = xp.zeros_like(gamma) self.axis = _compute_axis(x.ndim, gamma.ndim, self.axis) self.key_axis = _compute_key_axis(x.ndim, gamma.ndim, self.axis) if all(x.shape[i] == 1 for i in self.axis): if 0 in self.axis: warnings.warn( 'A batch with no more than one sample has been given' ' to F.batch_normalization. F.batch_normalization' ' will always output a zero tensor for such batches.' ' This could be caused by incorrect configuration in' ' your code (such as running evaluation while' ' chainer.config.train=True),' ' but could also happen in the last batch of training' ' if non-repeating iterator is used.', UserWarning) else: warnings.warn( 'F.batch_normalization received a batch with single' ' dimensions along all axes that are used for aggregating' ' statistics. F.batch_normalization' ' will always output a zero tensor for such batches.', UserWarning) # TODO(niboshi): Refactor calculation of expander and axis into a # function and call it just before they are used. # expander inserts singleton dimensions to gamma and beta so that they # can be broadcasted with x. expander = [None for _ in range(x.ndim)] for i in self.key_axis: expander[i] = slice(None) expander = tuple(expander) self.expander = expander self.mode = _BNMode(x, gamma, self.key_axis) self.use_cudnn = self.mode.can_use_cudnn(xp) self.use_ideep = self.mode.can_use_ideep() if self.use_ideep: # TODO(niboshi): Refactor iDeep part into a separate method expand_dim = False if x.ndim == 2: expand_dim = True x = x[:, :, None, None] y, self.mean, self.var, self.inv_std = ( intel64.ideep.batchNormalization.Forward( intel64.ideep.array(x), intel64.ideep.array(gamma), intel64.ideep.array(beta), None, None, self.eps )) m = x.size // gamma.size adjust = m / max(m - 1., 1.) # Update running_mean if isinstance(self.running_mean, intel64.ideep.mdarray): self.running_mean.inplace_axpby( self.decay, (1 - self.decay), self.mean) else: self.running_mean *= self.decay self.running_mean += self.mean * (1 - self.decay) # Update running_var if isinstance(self.running_var, intel64.ideep.mdarray): self.running_var.inplace_axpby( self.decay, (1 - self.decay), self.var * adjust) else: self.running_var *= self.decay self.running_var += self.var * adjust * (1 - self.decay) if expand_dim: y = numpy.squeeze(y, axis=(2, 3)) elif self.use_cudnn: if self.mean is None: # Output cache to speed up backward pass. self.mean = xp.empty_like(gamma) # Output cache to speed up backward pass. self.inv_std = xp.empty_like(gamma) y = cudnn.batch_normalization_forward_training( x, gamma, beta, self.running_mean, self.running_var, self.mean, self.inv_std, self.eps, self.decay, self.mode.is_for_conv2d, self.mode.get_cudnn_mode(), configuration.config.debug) else: # Generic CPU and GPU implementation gamma = gamma[expander] beta = beta[expander] self.mean = x.mean(axis=self.axis) var = x.var(axis=self.axis) if xp is numpy: self.inv_std = numpy.reciprocal(numpy.sqrt( var + self.eps, dtype=x.dtype)) else: self.inv_std = cuda.cupyx.rsqrt(var + self.eps) y = _apply_bn_fwd(xp, x, self.mean[expander], self.inv_std[expander], gamma, beta) # Update running statistics m = x.size // gamma.size adjust = m / max(m - 1., 1.) # unbiased estimation xp = backend.get_array_module(self.running_mean, self.running_var) if xp is chainerx: self.running_mean, self.running_var = backend.from_chainerx( (self.running_mean, self.running_var)) self.running_mean *= self.decay self.running_mean += (1 - self.decay) * self.mean self.running_var *= self.decay self.running_var += (1 - self.decay) * adjust * var if xp is chainerx: self.running_mean = backend.to_chainerx(self.running_mean) self.running_var = backend.to_chainerx(self.running_var) return y,
def forward(self, inputs): self.retain_inputs((0, 1)) x, gamma, beta = inputs xp = backend.get_array_module(x) if self.running_mean is None: self.running_mean = xp.zeros_like(gamma) self.running_var = xp.zeros_like(gamma) self.axis = _compute_axis(x.ndim, gamma.ndim, self.axis) self.key_axis = _compute_key_axis(x.ndim, gamma.ndim, self.axis) if all(x.shape[i] == 1 for i in self.axis): if 0 in self.axis: warnings.warn( 'A batch with no more than one sample has been given' ' to F.batch_normalization. F.batch_normalization' ' will always output a zero tensor for such batches.' ' This could be caused by incorrect configuration in' ' your code (such as running evaluation while' ' chainer.config.train=True),' ' but could also happen in the last batch of training' ' if non-repeating iterator is used.', UserWarning) else: warnings.warn( 'F.batch_normalization received a batch with single' ' dimensions along all axes that are used for aggregating' ' statistics. F.batch_normalization' ' will always output a zero tensor for such batches.', UserWarning) # TODO(niboshi): Refactor calculation of expander and axis into a # function and call it just before they are used. # expander inserts singleton dimensions to gamma and beta so that they # can be broadcasted with x. expander = [None for _ in range(x.ndim)] for i in self.key_axis: expander[i] = slice(None) expander = tuple(expander) self.expander = expander self.mode = _BNMode(x, gamma, self.key_axis) self.use_cudnn = self.mode.can_use_cudnn(xp) self.use_ideep = self.mode.can_use_ideep() if self.use_ideep: # TODO(niboshi): Refactor iDeep part into a separate method expand_dim = False if x.ndim == 2: expand_dim = True x = x[:, :, None, None] y, self.mean, self.var, self.inv_std = ( intel64.ideep.batchNormalization.Forward( intel64.ideep.array(x), intel64.ideep.array(gamma), intel64.ideep.array(beta), None, None, self.eps)) m = x.size // gamma.size adjust = m / max(m - 1., 1.) # Update running_mean if isinstance(self.running_mean, intel64.ideep.mdarray): self.running_mean.inplace_axpby(self.decay, (1 - self.decay), self.mean) else: self.running_mean *= self.decay self.running_mean += self.mean * (1 - self.decay) # Update running_var if isinstance(self.running_var, intel64.ideep.mdarray): self.running_var.inplace_axpby(self.decay, (1 - self.decay), self.var * adjust) else: self.running_var *= self.decay self.running_var += self.var * adjust * (1 - self.decay) if expand_dim: y = numpy.squeeze(y, axis=(2, 3)) elif self.use_cudnn: if self.mean is None: # Output cache to speed up backward pass. self.mean = xp.empty_like(gamma) # Output cache to speed up backward pass. self.inv_std = xp.empty_like(gamma) y = cudnn.batch_normalization_forward_training( x, gamma, beta, self.running_mean, self.running_var, self.mean, self.inv_std, self.eps, self.decay, self.mode.is_for_conv2d, self.mode.get_cudnn_mode(), chainer.is_debug()) else: # Generic CPU and GPU implementation gamma = gamma[expander] beta = beta[expander] self.mean = x.mean(axis=self.axis) var = x.var(axis=self.axis) if xp is numpy: self.inv_std = numpy.reciprocal( numpy.sqrt(var + self.eps, dtype=x.dtype)) else: self.inv_std = cuda.cupyx.rsqrt(var + self.eps) y = _apply_bn_fwd(xp, x, self.mean[expander], self.inv_std[expander], gamma, beta) # Update running statistics m = x.size // gamma.size adjust = m / max(m - 1., 1.) # unbiased estimation xp = backend.get_array_module(self.running_mean, self.running_var) if xp is chainerx: self.running_mean, self.running_var = backend.from_chainerx( (self.running_mean, self.running_var)) self.running_mean *= self.decay self.running_mean += (1 - self.decay) * self.mean self.running_var *= self.decay self.running_var += (1 - self.decay) * adjust * var if xp is chainerx: self.running_mean = backend.to_chainerx(self.running_mean) self.running_var = backend.to_chainerx(self.running_var) return y,
def forward(self, inputs): self.retain_inputs((0, 1)) x, gamma, beta = inputs xp = backend.get_array_module(x) if self.running_mean is None: self.running_mean = xp.zeros_like(gamma) self.running_var = xp.zeros_like(gamma) self.axis = _compute_axis(x.ndim, gamma.ndim, self.axis) self.key_axis = _compute_key_axis(x.ndim, gamma.ndim, self.axis) if all(x.shape[i] == 1 for i in self.axis): if 0 in self.axis: warnings.warn( 'A batch with no more than one sample has been given' ' to F.batch_normalization. F.batch_normalization' ' will always output a zero tensor for such batches.' ' This could be caused by incorrect configuration in' ' your code (such as running evaluation while' ' chainer.config.train=True),' ' but could also happen in the last batch of training' ' if non-repeating iterator is used.', UserWarning) else: warnings.warn( 'F.batch_normalization received a batch with single' ' dimensions along all axes that are used for aggregating' ' statistics. F.batch_normalization' ' will always output a zero tensor for such batches.', UserWarning) # TODO(niboshi): Refactor calculation of expander and axis into a # function and call it just before they are used. # expander inserts singleton dimensions to gamma and beta so that they # can be broadcasted with x. expander = [None for _ in range(x.ndim)] for i in self.key_axis: expander[i] = slice(None) expander = tuple(expander) self.expander = expander self.mode = _BNMode(x, gamma, self.key_axis) self.use_cudnn = self.mode.can_use_cudnn(xp) self.use_ideep = self.mode.can_use_ideep() if self.use_ideep: # TODO(niboshi): Refactor iDeep part into a separate method expand_dim = False if x.ndim == 2: expand_dim = True x = x[:, :, None, None] y, self.mean, self.var, self.inv_std = ( intel64.ideep.batchNormalization.Forward( intel64.ideep.array(x), intel64.ideep.array(gamma), intel64.ideep.array(beta), None, None, self.eps )) m = x.size // gamma.size adjust = m / max(m - 1., 1.) # Update running_mean if isinstance(self.running_mean, intel64.ideep.mdarray): self.running_mean.inplace_axpby( self.decay, (1 - self.decay), self.mean) else: self.running_mean *= self.decay self.running_mean += self.mean * (1 - self.decay) # Update running_var if isinstance(self.running_var, intel64.ideep.mdarray): self.running_var.inplace_axpby( self.decay, (1 - self.decay), self.var * adjust) else: self.running_var *= self.decay self.running_var += self.var * adjust * (1 - self.decay) if expand_dim: y = numpy.squeeze(y, axis=(2, 3)) elif self.use_cudnn: # TODO(niboshi): Refactor cuDNN part into a separate method x = cuda.cupy.ascontiguousarray(x) gamma = cuda.cupy.ascontiguousarray(gamma) beta = cuda.cupy.ascontiguousarray(beta) dtype = x.dtype handle = cudnn.get_handle() x_desc = cudnn.create_tensor_descriptor( _as4darray(x, self.mode)) cudnn_mode = self.mode.get_cudnn_mode() derivedBnDesc = cudnn.create_uninitialized_tensor_descriptor() libcudnn.deriveBNTensorDescriptor(derivedBnDesc.value, x_desc.value, cudnn_mode) dtype_param = _get_dtype_of_tensor_descriptor(derivedBnDesc) if dtype_param is not dtype: gamma = gamma.astype(dtype_param) beta = beta.astype(dtype_param) running_mean = self.running_mean.astype(dtype_param) running_var = self.running_var.astype(dtype_param) else: running_mean = self.running_mean running_var = self.running_var oz_dtype = ( numpy.float64 if x.dtype == numpy.float64 else numpy.float32) one = numpy.array(1, dtype=oz_dtype).ctypes zero = numpy.array(0, dtype=oz_dtype).ctypes y = cuda.cupy.empty_like(x) # Factor used in the moving average factor = 1 - self.decay if self.mean is None: # Output cache to speed up backward pass. self.mean = xp.empty_like(gamma) # Output cache to speed up backward pass. self.inv_std = xp.empty_like(gamma) # Note: cuDNN computes the mini-batch mean and variance # internally. We can simply (optionally) pass # it the running-average mean and variance arrays. # Note: This API seems to set the inverse of the standard deviation # (instead of variance) to resultSaveInvVariance argument. The # current implementation of our BN depends on this behavior so that # we can reduce the number of reduction kernels. libcudnn.batchNormalizationForwardTraining( handle, cudnn_mode, one.data, zero.data, x_desc.value, x.data.ptr, x_desc.value, y.data.ptr, derivedBnDesc.value, gamma.data.ptr, beta.data.ptr, factor, running_mean.data.ptr, running_var.data.ptr, self.eps, self.mean.data.ptr, self.inv_std.data.ptr) # Note: When the CUDNN_BATCHNORM_SPATIAL_PERSISTENT mode is used, # there is a possibility of numerical overflow. You can use # queryRuntimeError() to make sure whether the overflow actually # occured or not during the batch normalization. if (cudnn_mode is libcudnn.CUDNN_BATCHNORM_SPATIAL_PERSISTENT and configuration.config.debug): query_mode = libcudnn.CUDNN_ERRQUERY_BLOCKING rstatus = libcudnn.queryRuntimeError(handle, query_mode) if rstatus is not libcudnn.CUDNN_STATUS_SUCCESS: warnings.warn( 'A numerical overflow might have happend in cuDNN' 'batch normalization (status:{})'.format(rstatus)) if dtype_param is not dtype: # When data type of prameters is converted, say, from fp16 # to fp32, the values of fp32 arrays of running_mean and # running_var updated by batchNormalizationForwardTraining # must be explicitly written back to their original fp16 # arrays. running_mean = running_mean.astype(dtype) running_var = running_var.astype(dtype) self.running_mean.data.copy_from(running_mean.data, running_mean.nbytes) self.running_var.data.copy_from(running_var.data, running_var.nbytes) else: # Generic CPU and GPU implementation gamma = gamma[expander] beta = beta[expander] self.mean = x.mean(axis=self.axis) var = x.var(axis=self.axis) if xp is numpy: self.inv_std = numpy.reciprocal(numpy.sqrt( var + self.eps, dtype=x.dtype)) else: self.inv_std = cuda.cupyx.rsqrt(var + self.eps) y = _apply_bn_fwd(xp, x, self.mean[expander], self.inv_std[expander], gamma, beta) # Update running statistics m = x.size // gamma.size adjust = m / max(m - 1., 1.) # unbiased estimation xp = backend.get_array_module(self.running_mean, self.running_var) if xp is chainerx: self.running_mean, self.running_var = backend.from_chainerx( (self.running_mean, self.running_var)) self.running_mean *= self.decay self.running_mean += (1 - self.decay) * self.mean self.running_var *= self.decay self.running_var += (1 - self.decay) * adjust * var if xp is chainerx: self.running_mean = backend.to_chainerx(self.running_mean) self.running_var = backend.to_chainerx(self.running_var) return y,
def forward_cpu(self, inputs): t = backend.from_chainerx(self.t) # Workaround for ChainerX. gx = numpy.zeros(self.shape, self.dtype) gx[six.moves.range(self.t.size), t] = inputs[0] return gx,
def forward_cpu(self, inputs): t = backend.from_chainerx(self.t) # Workaround for ChainerX. gx = numpy.zeros(self.shape, self.dtype) gx[six.moves.range(self.t.size), t] = inputs[0] return gx,
def forward_cpu(self, inputs): b = backend.from_chainerx(self.b) # Workaround for ChainerX y = (b > 0) * inputs[0] return utils.force_array(y, dtype=y.dtype),
def forward(self, xs): slices = tuple([ backend.from_chainerx(s) if isinstance(s, chainerx.ndarray) else s for s in self.slices ]) return utils.force_array(xs[0][slices]),
def forward_cpu(self, inputs): b = backend.from_chainerx(self.b) # Workaround for ChainerX y = (b > 0) * inputs[0] return utils.force_array(y, dtype=y.dtype),
def forward_gpu(self, inputs): b = backend.from_chainerx(self.b) # Workaround for ChainerX gx = _relu_grad2_kernel(b, inputs[0]) return gx,
def forward_gpu(self, inputs): b = backend.from_chainerx(self.b) # Workaround for ChainerX gx = _relu_grad2_kernel(b, inputs[0]) return gx,