From a3ea2cb4c69cfd111495f249007e2fff043993f3 Mon Sep 17 00:00:00 2001 From: XupuWang <806218468@qq.com> Date: Thu, 20 Jun 2019 15:13:29 +0800 Subject: [PATCH 01/34] fix negative time number in local mode when trial time is short --- src/nni_manager/training_service/local/localTrainingService.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/nni_manager/training_service/local/localTrainingService.ts b/src/nni_manager/training_service/local/localTrainingService.ts index 2ab316a8b3..45543f117f 100644 --- a/src/nni_manager/training_service/local/localTrainingService.ts +++ b/src/nni_manager/training_service/local/localTrainingService.ts @@ -510,7 +510,7 @@ class LocalTrainingService implements TrainingService { else{ script.push( `eval ${localTrailConfig.command} 2>${path.join(workingDirectory, 'stderr')}`, - `echo $? \`date +%s000\` >${path.join(workingDirectory, '.nni', 'state')}`); + `echo $? \`date +%s%3N\` >${path.join(workingDirectory, '.nni', 'state')}`); } return script; } From 01c60bde63ca7a92b8f822fe23582ee879aea678 Mon Sep 17 00:00:00 2001 From: v-liguo Date: Fri, 21 Jun 2019 13:40:25 +0800 Subject: [PATCH 02/34] fix bug of duration<0 --- src/webui/src/components/overview/SuccessTable.tsx | 11 +++++++++-- src/webui/src/components/trial-detail/TableList.tsx | 3 ++- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/src/webui/src/components/overview/SuccessTable.tsx b/src/webui/src/components/overview/SuccessTable.tsx index 97108e11b3..18d7ee55a6 100644 --- a/src/webui/src/components/overview/SuccessTable.tsx +++ b/src/webui/src/components/overview/SuccessTable.tsx @@ -72,8 +72,15 @@ class SuccessTable extends React.Component { sorter: (a: TableObj, b: TableObj) => (a.duration as number) - (b.duration as number), render: (text: string, record: TableObj) => { let duration; - if (record.duration) { - duration = convertDuration(record.duration); + if (record.duration !== undefined) { + // duration is nagative number(-1) & 0-1 + if (record.duration > 0 && record.duration < 1 || record.duration < 0) { + duration = `${record.duration}s`; + } else { + duration = convertDuration(record.duration); + } + } else { + duration = 0; } return (
{duration}
diff --git a/src/webui/src/components/trial-detail/TableList.tsx b/src/webui/src/components/trial-detail/TableList.tsx index 954a1cd1b5..448c237eb2 100644 --- a/src/webui/src/components/trial-detail/TableList.tsx +++ b/src/webui/src/components/trial-detail/TableList.tsx @@ -264,7 +264,8 @@ class TableList extends React.Component { render: (text: string, record: TableObj) => { let duration; if (record.duration !== undefined) { - if (record.duration > 0 && record.duration < 1) { + // duration is nagative number(-1) & 0-1 + if (record.duration > 0 && record.duration < 1 || record.duration < 0) { duration = `${record.duration}s`; } else { duration = convertDuration(record.duration); From ace513255bb4ef96e18d5487969d96303324e534 Mon Sep 17 00:00:00 2001 From: XupuWang <806218468@qq.com> Date: Fri, 21 Jun 2019 14:28:56 +0800 Subject: [PATCH 03/34] fix windows version and readme --- docs/en_US/Trials.md | 2 +- docs/zh_CN/Trials.md | 4 ++-- .../training_service/local/localTrainingService.ts | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/en_US/Trials.md b/docs/en_US/Trials.md index 382ef65024..544f657a5f 100644 --- a/docs/en_US/Trials.md +++ b/docs/en_US/Trials.md @@ -144,7 +144,7 @@ export NNI_TRIAL_SEQ_ID=1 export MULTI_PHASE=false export CUDA_VISIBLE_DEVICES= eval python3 mnist.py 2>/home/user_name/nni/experiments/$experiment_id$/trials/$trial_id$/stderr -echo $? `date +%s000` >/home/user_name/nni/experiments/$experiment_id$/trials/$trial_id$/.nni/state +echo $? `date +%s%3N` >/home/user_name/nni/experiments/$experiment_id$/trials/$trial_id$/.nni/state ``` ### Other Modes diff --git a/docs/zh_CN/Trials.md b/docs/zh_CN/Trials.md index 993952c72a..475438c55a 100644 --- a/docs/zh_CN/Trials.md +++ b/docs/zh_CN/Trials.md @@ -149,7 +149,7 @@ export NNI_TRIAL_SEQ_ID=1 export MULTI_PHASE=false export CUDA_VISIBLE_DEVICES= eval python3 mnist.py 2>/home/user_name/nni/experiments/$experiment_id$/trials/$trial_id$/stderr -echo $? `date +%s000` >/home/user_name/nni/experiments/$experiment_id$/trials/$trial_id$/.nni/state +echo $? `date +%s%3N` >/home/user_name/nni/experiments/$experiment_id$/trials/$trial_id$/.nni/state ``` ### 其它模式 @@ -166,4 +166,4 @@ echo $? `date +%s000` >/home/user_name/nni/experiments/$experiment_id$/trials/$t * [为 CIFAR 10 分类找到最佳的 optimizer](Cifar10Examples.md) * [如何在 NNI 调优 SciKit-learn 的参数](SklearnExamples.md) * [在阅读理解上使用自动模型架构搜索。](SquadEvolutionExamples.md) -* [如何在 NNI 上调优 GBDT](GbdtExample.md) \ No newline at end of file +* [如何在 NNI 上调优 GBDT](GbdtExample.md) diff --git a/src/nni_manager/training_service/local/localTrainingService.ts b/src/nni_manager/training_service/local/localTrainingService.ts index 45543f117f..8875817a3a 100644 --- a/src/nni_manager/training_service/local/localTrainingService.ts +++ b/src/nni_manager/training_service/local/localTrainingService.ts @@ -504,7 +504,7 @@ class LocalTrainingService implements TrainingService { script.push( `cmd /c ${localTrailConfig.command} 2>${path.join(workingDirectory, 'stderr')}`, `$NOW_DATE = [int64](([datetime]::UtcNow)-(get-date "1/1/1970")).TotalSeconds`, - `$NOW_DATE = "$NOW_DATE" + "000"`, + `$NOW_DATE = "$NOW_DATE" + (Get-Date -Format fff).ToString()`, `Write $LASTEXITCODE " " $NOW_DATE | Out-File ${path.join(workingDirectory, '.nni', 'state')} -NoNewline -encoding utf8`); } else{ From 48d3e4da0e214df85b42839e94c3acbf8fdf3c5c Mon Sep 17 00:00:00 2001 From: XupuWang <806218468@qq.com> Date: Fri, 21 Jun 2019 14:33:13 +0800 Subject: [PATCH 04/34] change tab --- src/nni_manager/training_service/local/localTrainingService.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/nni_manager/training_service/local/localTrainingService.ts b/src/nni_manager/training_service/local/localTrainingService.ts index 8875817a3a..d9c3cdedeb 100644 --- a/src/nni_manager/training_service/local/localTrainingService.ts +++ b/src/nni_manager/training_service/local/localTrainingService.ts @@ -504,7 +504,7 @@ class LocalTrainingService implements TrainingService { script.push( `cmd /c ${localTrailConfig.command} 2>${path.join(workingDirectory, 'stderr')}`, `$NOW_DATE = [int64](([datetime]::UtcNow)-(get-date "1/1/1970")).TotalSeconds`, - `$NOW_DATE = "$NOW_DATE" + (Get-Date -Format fff).ToString()`, + `$NOW_DATE = "$NOW_DATE" + (Get-Date -Format fff).ToString()`, `Write $LASTEXITCODE " " $NOW_DATE | Out-File ${path.join(workingDirectory, '.nni', 'state')} -NoNewline -encoding utf8`); } else{ From 61dbdca2a9125da5da1977354a84190b19e14732 Mon Sep 17 00:00:00 2001 From: root Date: Fri, 21 Jun 2019 14:48:05 +0800 Subject: [PATCH 05/34] change line --- src/nni_manager/training_service/local/localTrainingService.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/nni_manager/training_service/local/localTrainingService.ts b/src/nni_manager/training_service/local/localTrainingService.ts index d9c3cdedeb..3f65ad635c 100644 --- a/src/nni_manager/training_service/local/localTrainingService.ts +++ b/src/nni_manager/training_service/local/localTrainingService.ts @@ -504,7 +504,7 @@ class LocalTrainingService implements TrainingService { script.push( `cmd /c ${localTrailConfig.command} 2>${path.join(workingDirectory, 'stderr')}`, `$NOW_DATE = [int64](([datetime]::UtcNow)-(get-date "1/1/1970")).TotalSeconds`, - `$NOW_DATE = "$NOW_DATE" + (Get-Date -Format fff).ToString()`, + `$NOW_DATE = "$NOW_DATE" + (Get-Date -Format fff).ToString()`, `Write $LASTEXITCODE " " $NOW_DATE | Out-File ${path.join(workingDirectory, '.nni', 'state')} -NoNewline -encoding utf8`); } else{ From f2e6d16ad16a3d15497b362c4adb5b9a0d421411 Mon Sep 17 00:00:00 2001 From: leonardo <806218468@qq.com> Date: Thu, 1 Aug 2019 15:29:29 +0800 Subject: [PATCH 06/34] add compressor --- src/sdk/pynni/nni/compressors/AGPruner.py | 138 ++++++++++++++++++ src/sdk/pynni/nni/compressors/QATquantizer.py | 89 +++++++++++ src/sdk/pynni/nni/compressors/_nnimc_tf.py | 115 +++++++++++++++ src/sdk/pynni/nni/compressors/_nnimc_torch.py | 127 ++++++++++++++++ src/sdk/pynni/nni/compressors/level_pruner.py | 38 +++++ src/sdk/pynni/nni/compressors/main_tf.py | 120 +++++++++++++++ src/sdk/pynni/nni/compressors/main_torch.py | 89 +++++++++++ .../pynni/nni/compressors/naive_quantizer.py | 39 +++++ src/sdk/pynni/nni/compressors/nnimc.py | 24 +++ 9 files changed, 779 insertions(+) create mode 100644 src/sdk/pynni/nni/compressors/AGPruner.py create mode 100644 src/sdk/pynni/nni/compressors/QATquantizer.py create mode 100644 src/sdk/pynni/nni/compressors/_nnimc_tf.py create mode 100644 src/sdk/pynni/nni/compressors/_nnimc_torch.py create mode 100644 src/sdk/pynni/nni/compressors/level_pruner.py create mode 100644 src/sdk/pynni/nni/compressors/main_tf.py create mode 100644 src/sdk/pynni/nni/compressors/main_torch.py create mode 100644 src/sdk/pynni/nni/compressors/naive_quantizer.py create mode 100644 src/sdk/pynni/nni/compressors/nnimc.py diff --git a/src/sdk/pynni/nni/compressors/AGPruner.py b/src/sdk/pynni/nni/compressors/AGPruner.py new file mode 100644 index 0000000000..9b085cd79e --- /dev/null +++ b/src/sdk/pynni/nni/compressors/AGPruner.py @@ -0,0 +1,138 @@ +try: + import torch + from nnimc import TorchPruner + + class TorchAGPruner(TorchPruner): + def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): + """ + """ + super().__init__() + self.initial_sparsity = initial_sparsity + self.final_sparsity = final_sparsity + self.start_epoch = start_epoch + self.end_epoch = end_epoch + self.now_epoch = start_epoch + self.freq = frequency + self.mask_list = {} + + def compute_target_sparsity(self, now_epoch): + if self.end_epoch <= self.start_epoch or self.end_epoch <= self.now_epoch: + return self.final_sparsity + span = ((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq + assert span>0 + target_sparsity = (self.final_sparsity + + (self.initial_sparsity - self.final_sparsity)* + (1.0 - ((now_epoch - self.start_epoch)/span))**3 ) + return target_sparsity + + def calc_mask(self, layer_info, weight): + now_epoch = self.now_epoch + mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) + target_sparsity = self.compute_target_sparsity(now_epoch) + k = int(weight.numel() * target_sparsity) + if k == 0: + return mask + + w_abs = weight.abs()*mask + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + new_mask = torch.gt(w_abs, threshold).type(weight.type()) + self.mask_list[layer_info.name] = new_mask + return new_mask + + def update_epoch(self, epoch): + if epoch <= 0: + return + self.now_epoch = epoch + + + class TorchSensitivityPruner(TorchPruner): + def __init__(self, sparsity): + super().__init__() + self.sparsity = sparsity + self.mask_list = {} + + def calc_mask(self, layer_info, weight): + mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) + weight = weight*mask + target_sparsity = self.sparsity * torch.std(weight).item() + k = int(weight.numel() * target_sparsity) + if k == 0: + return mask + + w_abs = weight.abs() + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + new_mask = torch.gt(w_abs, threshold).type(weight.type()) + self.mask_list[layer_info.name] = new_mask + return new_mask + +except ModuleNotFoundError: + pass + + +try: + import tensorflow as tf + from nnimc import TfPruner + + class TfAGPruner(TfPruner): + def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): + super().__init__() + self.initial_sparsity = initial_sparsity + self.final_sparsity = final_sparsity + self.start_epoch = start_epoch + self.end_epoch = end_epoch + self.freq = frequency + self.now_epoch = tf.Variable(0) + self.assign_handler = [] + + def compute_target_sparsity(self): + + if self.end_epoch <= self.start_epoch : + return self.final_sparsity + + now_epoch = tf.minimum(self.now_epoch, tf.constant(self.end_epoch)) + span = int(((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq) + assert span>0 + base = tf.cast(now_epoch - self.initial_sparsity, tf.float32) / span + target_sparsity = (self.final_sparsity + + (self.initial_sparsity - self.final_sparsity)* + (tf.pow(1.0 - base,3))) + return target_sparsity + + def calc_mask(self, layer, weight): + + target_sparsity = self.compute_target_sparsity() + threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) + self.assign_handler.append(tf.assign(weight, weight*mask)) + return mask + + def update_epoch(self, sess, epoch): + sess.run(self.assign_handler) + sess.run(tf.assign(self.now_epoch, int(epoch))) + + + class TfSensitivityPruner(TfPruner): + def __init__(self, sparsity): + super().__init__() + self.sparsity = sparsity + self.layer_mask = {} + self.assign_handler = [] + + def calc_mask(self, layer_info, weight): + target_sparsity = self.sparsity * tf.math.reduce_std(weight) + mask = tf.get_variable(layer_info.name+'_mask',initializer=tf.ones(weight.shape), trainable=False) + self.layer_mask[layer_info.name] = mask + + weight_assign_handler = tf.assign(weight, mask*weight) + with tf.control_dependencies([weight_assign_handler]): + threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + new_mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) + mask_update_handler = tf.assign(mask, new_mask) + self.assign_handler.append(mask_update_handler) + return mask + + def update_graph(self, sess): + sess.run(self.assign_handler) + +except ModuleNotFoundError: + pass diff --git a/src/sdk/pynni/nni/compressors/QATquantizer.py b/src/sdk/pynni/nni/compressors/QATquantizer.py new file mode 100644 index 0000000000..26ded2b345 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/QATquantizer.py @@ -0,0 +1,89 @@ +try: + import torch + from nnimc import TorchQuantizer + + class TorchDoReFaQuantizer(TorchQuantizer): + def __init__(self, q_bits): + super().__init__() + self.q_bits = q_bits + + def quantize_weight(self, layer_info, weight): + out = weight.tanh() + out = out /( 2 * out.abs().max()) + 0.5 + out = self.quantize(out, self.q_bits) + out = 2 * out -1 + #print(weight,out) + return out + + def quantize(self, input_ri, q_bits): + scale = pow(2, q_bits)-1 + output = torch.round(input_ri*scale)/scale + #input_ri.mul_(scale).round_() + #input_ri.div_(scale) + return output + + class TorchQATquantizer(TorchQuantizer): + def __init__(self, q_bits): + super().__init__() + self.q_bits = q_bits + + def quantize_weight(self, layer_info, weight): + if self.q_bits <= 1: + return weight + a = torch.min(weight) + b = torch.max(weight) + n = pow(2,self.q_bits) + scale = (b-a)/(n-1) + zero_point = a + #print(a,b,scale) + out = torch.round((weight - zero_point)/scale) + out = out*scale + zero_point + orig_type = weight.dtype + #print(weight,out) + return out.type(orig_type) + +except ModuleNotFoundError: + pass + + + +try: + import tensorflow as tf + from nnimc import TfQuantizer + from tensorflow.python.framework import ops as tf_ops + + class TfQATquantizer(TfQuantizer): + def __init__(self, q_bits): + super().__init__() + self.q_bits = q_bits + + def quantize_weight(self, layer_info, weight): + a = tf.stop_gradient(tf.reduce_min(weight)) + b = tf.stop_gradient(tf.reduce_max(weight)) + n = tf.cast(2 ** self.q_bits, tf.float32) + scale = b-a/(n-1) + + with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): + qw = tf.round((weight-a)/scale)*scale +a + + return qw + + class TfDoReFaQuantizer(TfQuantizer): + def __init__(self, q_bits): + super().__init__() + self.q_bits = q_bits + + def quantize_weight(self, layer_info, weight): + a = tf.math.tanh(weight) + b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5 + + scale = pow(2, self.q_bits-1) + with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): + qw = tf.round(b*scale)/scale + r_qw = 2*qw - 1 + return r_qw + + + +except ModuleNotFoundError: + pass \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/_nnimc_tf.py new file mode 100644 index 0000000000..ae6c58a3f3 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/_nnimc_tf.py @@ -0,0 +1,115 @@ +import tensorflow as tf +from tensorflow import Graph, Operation, Tensor + +from typing import List + +__all__ = [ + 'TfCompressor', + 'TfPruner', + 'TfQuantizer', + '_tf_detect_prunable_layers' +] + + +class TfCompressor: + """TODO""" + + def __init__(self) -> None: + self._bound_model: Optional[Graph] = None + + + def compress(self, model: Graph) -> None: + """ + Compress given graph with algorithm implemented by subclass. + This will edit the graph. + """ + assert self._bound_model is None, "Each NNI compressor instance can only compress one model" + self._bound_model = model + self.bind_model(model) + + def compress_default_graph(self) -> None: + """ + Compress the default graph with algorithm implemented by subclass. + This will edit the graph. + """ + self.compress(tf.get_default_graph()) + + + def bind_model(self, model: Graph) -> None: + """ + This method is called when a model is bound to the compressor. + Users can optionally overload this method to do model-specific initialization. + It is guaranteed that only one model will be bound to each compressor instance. + """ + pass + + +class TfLayerInfo: + def __init__(self, layer: Operation): + self.name: str = layer.name + self.layer: Operation = layer + self.weight_index: int + + if layer.type == 'Conv2D': + self.weight_index = 1 + else: + raise ValueError('Unsupported layer') + + +def _tf_detect_prunable_layers(model: Graph) -> List[TfLayerInfo]: + # search for Conv2D layers + # TODO: whitelist + return [ TfLayerInfo(op) for op in model.get_operations() if op.type == 'Conv2D' ] + + +class TfPruner(TfCompressor): + """TODO""" + + def __init__(self) -> None: + super().__init__() + + def calc_mask(self, layer_info: TfLayerInfo, weight: Tensor) -> Tensor: + """ + Pruners should overload this method to provide mask for weight tensors. + The mask must have the same shape and type comparing to the weight. + It will be applied with `multiply()` operation. + This method works as a subgraph which will be inserted into the bound model. + """ + raise NotImplementedError("Pruners must overload calc_mask()") + + + def compress(self, model: Graph) -> None: + super().compress(model) + # TODO: configurable whitelist + for layer_info in _tf_detect_prunable_layers(model): + self._instrument_layer(layer_info) + + def _instrument_layer(self, layer_info): + # it seems the graph editor can only swap edges of nodes or remove all edges from a node + # it cannot remove one edge from a node, nor can it assign a new edge to a node + # we assume there is a proxy operation between the weight and the Conv2D layer + # this is true as long as the weight is `tf.Value` + # not sure what will happen if the weight is calculated from other operations + weight_op = layer_info.layer.inputs[layer_info.weight_index].op + weight = weight_op.inputs[0] + mask = self.calc_mask(layer_info, weight) + new_weight = weight * mask + tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) + + +class TfQuantizer(TfCompressor): + def __init__(self) -> None: + super().__init__() + + def quantize_weight(self, layer_info: TfLayerInfo, weight: Tensor) -> Tensor: + raise NotImplementedError() + + + def compress(self, model: Graph) -> None: + for layer_info in _tf_detect_prunable_layers(model): + self._instrument_layer(layer_info) + + def _instrument_layer(self, layer_info): + weight_op = layer_info.layer.inputs[layer_info.weight_index].op + new_weight = self.quantize_weight(layer_info, weight_op.inputs[0]) + tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) diff --git a/src/sdk/pynni/nni/compressors/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/_nnimc_torch.py new file mode 100644 index 0000000000..b713599a01 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/_nnimc_torch.py @@ -0,0 +1,127 @@ +from torch import Tensor +from torch.nn import Module, Parameter + +from typing import List + +__all__ = [ + 'TorchCompressor', + 'TorchPruner', + 'TorchQuantizer', + '_torch_detect_prunable_layers' +] + + +class TorchCompressor: + """TODO""" + + def __init__(self) -> None: + self._bound_model: Optional[Module] = None + + + def compress(self, model: Module) -> None: + """ + Compress the model with algorithm implemented by subclass. + The model will be instrumented and user should never edit it after calling this method. + """ + assert self._bound_model is None, "Each NNI compressor instance can only compress one model" + self._bound_model = model + self.bind_model(model) + + + def bind_model(self, model: Module) -> None: + """ + This method is called when a model is bound to the compressor. + Users can optionally overload this method to do model-specific initialization. + It is guaranteed that only one model will be bound to each compressor instance. + """ + pass + + +class TorchLayerInfo: + def __init__(self, name: str, layer: Module): + self.name: str = name + self.layer: Module = layer + + self._forward: Optional[Function] = None + + +def _torch_detect_prunable_layers(model: Module) -> List[TorchLayerInfo]: + # search for all layers which have parameter "weight" + ret = [ ] + for name, layer in model.named_modules(): + try: + if isinstance(layer.weight, Parameter) and isinstance(layer.weight.data, Tensor): + ret.append(TorchLayerInfo(name, layer)) + except AttributeError: + pass + return ret + + +class TorchPruner(TorchCompressor): + """TODO""" + + def __init__(self) -> None: + super().__init__() + + def calc_mask(self, layer_info: TorchLayerInfo, weight: Tensor) -> Tensor: + """ + Pruners should overload this method to provide mask for weight tensors. + The mask must have the same shape and type comparing to the weight. + It will be applied with `mul()` operation. + This method is effectively hooked to `forward()` method of the model. + """ + raise NotImplementedError("Pruners must overload calc_mask()") + + + def compress(self, model: Module) -> None: + super().compress(model) + # TODO: configurable whitelist + for layer_info in _torch_detect_prunable_layers(model): + self._instrument_layer(layer_info) + + def _instrument_layer(self, layer_info: TorchLayerInfo): + # TODO: bind additional properties to layer_info instead of layer + # create a wrapper forward function to replace the original one + assert layer_info._forward is None, 'Each model can only be compressed once' + layer_info._forward = layer_info.layer.forward + + def new_forward(*input): + # apply mask to weight + mask = self.calc_mask(layer_info, layer_info.layer.weight.data) + layer_info._backup_weight = layer_info.layer.weight.data + layer_info.layer.weight.data = layer_info.layer.weight.data.mul(mask) + # calculate forward + ret = layer_info._forward(*input) + # recover original weight + layer_info.layer.weight.data = layer_info._backup_weight + return ret + + layer_info.layer.forward = new_forward + + +class TorchQuantizer(TorchCompressor): + def __init__(self) -> None: + super().__init__() + + def quantize_weight(self, layer_info: TorchLayerInfo, weight: Tensor) -> Tensor: + # FIXME: where dequantize goes? + raise NotImplementedError() + + def compress(self, model: Module) -> None: + super().compress(model) + count = 0 + for layer_info in _torch_detect_prunable_layers(model): + if count == 0: + count = count +1 + continue + self._instrument_layer(layer_info) + + def _instrument_layer(self, layer_info: TorchLayerInfo): + assert layer_info._forward is None + layer_info._forward = layer_info.layer.forward + + def new_forward(*input): + layer_info.layer.weight.data = self.quantize_weight(layer_info, layer_info.layer.weight.data) + return layer_info._forward(*input) + + layer_info.layer.forward = new_forward diff --git a/src/sdk/pynni/nni/compressors/level_pruner.py b/src/sdk/pynni/nni/compressors/level_pruner.py new file mode 100644 index 0000000000..3e9398b34f --- /dev/null +++ b/src/sdk/pynni/nni/compressors/level_pruner.py @@ -0,0 +1,38 @@ +try: + import torch + from nnimc import TorchPruner + + class TorchLevelPruner(TorchPruner): + def __init__(self, sparsity = 0, layer_sparsity = { }): + super().__init__() + self.default_sparsity = sparsity + self.layer_sparsity = layer_sparsity + + def calc_mask(self, layer_info, weight): + sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) + w_abs = weight.abs() + k = int(weight.numel() * sparsity) + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + return torch.gt(w_abs, threshold).type(weight.type()) + +except ModuleNotFoundError: + pass + + +try: + import tensorflow as tf + from nnimc import TfPruner + + class TfLevelPruner(TfPruner): + def __init__(self, sparsity = 0, layer_sparsity = { }): + super().__init__() + self.default_sparsity = sparsity + self.layer_sparsity = layer_sparsity + + def calc_mask(self, layer_info, weight): + sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) + threshold = tf.contrib.distributions.percentile(weight.abs(), sparsity * 100) + return tf.cast(tf.math.greater(weight.abs(), threshold), weight.dtype) + +except ModuleNotFoundError: + pass diff --git a/src/sdk/pynni/nni/compressors/main_tf.py b/src/sdk/pynni/nni/compressors/main_tf.py new file mode 100644 index 0000000000..a7710bd6f5 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/main_tf.py @@ -0,0 +1,120 @@ +from level_pruner import TfLevelPruner +from naive_quantizer import TfNaiveQuantizer +from AGPruner import TfAGPruner,TfSensitivityPruner +from QATquantizer import TfDeReFaQuantizer,TfQATquantizer +import tensorflow as tf +from tensorflow.examples.tutorials.mnist import input_data + + +def weight_variable(shape): + return tf.Variable(tf.truncated_normal(shape, stddev = 0.1)) + +def bias_variable(shape): + return tf.Variable(tf.constant(0.1, shape = shape)) + +def conv2d(x_input, w_matrix): + return tf.nn.conv2d(x_input, w_matrix, strides = [ 1, 1, 1, 1 ], padding = 'SAME') + +def max_pool(x_input, pool_size): + size = [ 1, pool_size, pool_size, 1 ] + return tf.nn.max_pool(x_input, ksize = size, strides = size, padding = 'SAME') + + +class Mnist: + def __init__(self): + images = tf.placeholder(tf.float32, [ None, 784 ], name = 'input_x') + labels = tf.placeholder(tf.float32, [ None, 10 ], name = 'input_y') + keep_prob = tf.placeholder(tf.float32, name='keep_prob') + + self.images = images + self.labels = labels + self.keep_prob = keep_prob + + self.train_step = None + self.accuracy = None + + self.w1 = None + self.b1 = None + self.fcw1 = None + self.cross = None + with tf.name_scope('reshape'): + x_image = tf.reshape(images, [ -1, 28, 28, 1 ]) + with tf.name_scope('conv1'): + w_conv1 = weight_variable([ 5, 5, 1, 32 ]) + self.w1 = w_conv1 + b_conv1 = bias_variable([ 32 ]) + self.b1 = b_conv1 + h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) + with tf.name_scope('pool1'): + h_pool1 = max_pool(h_conv1, 2) + with tf.name_scope('conv2'): + w_conv2 = weight_variable([ 5, 5, 32, 64 ]) + b_conv2 = bias_variable([ 64 ]) + h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2) + with tf.name_scope('pool2'): + h_pool2 = max_pool(h_conv2, 2) + with tf.name_scope('fc1'): + w_fc1 = weight_variable([ 7 * 7 * 64, 1024 ]) + self.fcw1 = w_fc1 + b_fc1 = bias_variable([ 1024 ]) + h_pool2_flat = tf.reshape(h_pool2, [ -1, 7 * 7 * 64 ]) + h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1) + with tf.name_scope('dropout'): + h_fc1_drop = tf.nn.dropout(h_fc1, 0.5) + with tf.name_scope('fc2'): + w_fc2 = weight_variable([ 1024, 10 ]) + b_fc2 = bias_variable([ 10 ]) + y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2 + with tf.name_scope('loss'): + cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = labels, logits = y_conv)) + self.cross = cross_entropy + with tf.name_scope('adam_optimizer'): + self.train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy) + with tf.name_scope('accuracy'): + correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(labels, 1)) + self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + + +def main(): + tf.set_random_seed(0) + + data = input_data.read_data_sets('data', one_hot = True) + + model = Mnist() + + #TfNaiveQuantizer().compress_default_graph() + TfDeReFaQuantizer(q_bits = 8).compress_default_graph() + #TfAGP = TfAGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) + #TfPruner = TfSensitivityPruner(sparsity = 0.8) + #TfPruner.compress_default_graph() + + writer = tf.summary.FileWriter("log/simple_example.log", tf.get_default_graph()) + print('writer done') + writer.close() + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for batch_idx in range(2000): + batch = data.train.next_batch(2000) + model.train_step.run(feed_dict = { + model.images: batch[0], + model.labels: batch[1], + model.keep_prob: 0.5 + }) + if batch_idx % 10 == 0: + test_acc = model.accuracy.eval(feed_dict = { + model.images: data.test.images, + model.labels: data.test.labels, + model.keep_prob: 1.0 + }) + #TfPruner.update_graph(sess) + print('test accuracy', test_acc) + #print(tf.gradients(model.cross,[model.w1, model.fcw1])) + + test_acc = model.accuracy.eval(feed_dict = { + model.images: test.images, + model.labels: test.labels, + model.keep_prob: 1.0 + }) + print('final result is', test_acc) + +main() diff --git a/src/sdk/pynni/nni/compressors/main_torch.py b/src/sdk/pynni/nni/compressors/main_torch.py new file mode 100644 index 0000000000..478c30f49b --- /dev/null +++ b/src/sdk/pynni/nni/compressors/main_torch.py @@ -0,0 +1,89 @@ +from level_pruner import TorchLevelPruner +from naive_quantizer import TorchNaiveQuantizer +from AGPruner import TorchAGPruner,TorchSensitivityPruner +from QATquantizer import TorchDoReFaQuantizer,TorchQATquantizer +import torch +import torch.nn.functional as F +from torchvision import datasets, transforms + + +class Mnist(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv1 = torch.nn.Conv2d(1, 20, 5, 1) + self.conv2 = torch.nn.Conv2d(20, 50, 5, 1) + self.fc1 = torch.nn.Linear(4 * 4 * 50, 500) + self.fc2 = torch.nn.Linear(500, 10) + + def forward(self, x): + x = F.relu(self.conv1(x)) + x = F.max_pool2d(x, 2, 2) + x = F.relu(self.conv2(x)) + x = F.max_pool2d(x, 2, 2) + x = x.view(-1, 4 * 4 * 50) + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return F.log_softmax(x, dim = 1) + + +def train(model, device, train_loader, optimizer): + model.train() + for batch_idx, (data, target) in enumerate(train_loader): + data, target = data.to(device), target.to(device) + optimizer.zero_grad() + output = model(data) + loss = F.nll_loss(output, target) + loss.backward() + optimizer.step() + if batch_idx % 100 == 0: + print('{:2.0f}% Loss {}'.format(100 * batch_idx / len(train_loader), loss.item())) + +def test(model, device, test_loader): + model.eval() + test_loss = 0 + correct = 0 + with torch.no_grad(): + for data, target in test_loader: + data, target = data.to(device), target.to(device) + output = model(data) + test_loss += F.nll_loss(output, target, reduction = 'sum').item() + pred = output.argmax(dim = 1, keepdim = True) + correct += pred.eq(target.view_as(pred)).sum().item() + test_loss /= len(test_loader.dataset) + + print('Loss: {} Accuracy: {}%)\n'.format( + test_loss, 100 * correct / len(test_loader.dataset))) + +def main(): + torch.manual_seed(0) + device = torch.device('cpu') + + trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) + train_loader = torch.utils.data.DataLoader( + datasets.MNIST('data', train = True, download = True, transform = trans), + batch_size = 64, shuffle = True) + test_loader = torch.utils.data.DataLoader( + datasets.MNIST('data', train = False, transform = trans), + batch_size = 1000, shuffle = True) + + model = Mnist() + #TorchLevelPruner(0.5).compress(model) + #TorchNaiveQuantizer().compress(model) + + #TorchQATquantizer(q_bits = 32).compress(model) + #model = compressor.compress(Mnist()).to(device) + #pruner = TorchAGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) + #pruner = TorchSensitivityPruner(sparsity = 0.8) + #pruner.compress(model) + optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum = 0.5) + for epoch in range(10): + print('# Epoch {} #'.format(epoch)) + if epoch == 2: + TorchDoReFaQuantizer(q_bits = 32).compress(model) + train(model, device, train_loader, optimizer) + test(model, device, test_loader) + #pruner.update_epoch(epoch) + + #print(epoch, pruner.compute_target_sparsity(epoch)) + +main() diff --git a/src/sdk/pynni/nni/compressors/naive_quantizer.py b/src/sdk/pynni/nni/compressors/naive_quantizer.py new file mode 100644 index 0000000000..36019c7b1f --- /dev/null +++ b/src/sdk/pynni/nni/compressors/naive_quantizer.py @@ -0,0 +1,39 @@ +try: + import torch + from nnimc import TorchQuantizer + + class TorchNaiveQuantizer(TorchQuantizer): + def __init__(self): + super().__init__() + self.layer_scale = { } + + def quantize_weight(self, layer_info, weight): + new_scale = weight.abs().max() / 127 + # TODO: use int id + scale = max(self.layer_scale.get(layer_info.name, 0), new_scale) + self.layer_scale[layer_info.name] = scale + orig_type = weight.type() # TODO: user layer_info + return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) + +except ModuleNotFoundError: + pass + + +try: + import tensorflow as tf + from nnimc import TfQuantizer + + class TfNaiveQuantizer(TfQuantizer): + def __init__(self): + super().__init__() + self.layer_scale = { } + + def quantize_weight(self, layer_info, weight): + new_scale = tf.reduce_max(tf.abs(weight)) / 127 + scale = tf.maximum(self.layer_scale.get(layer_info.name, tf.constant(0.0)), new_scale) + self.layer_scale[layer_info.name] = scale + orig_type = weight.dtype + return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale + +except ModuleNotFoundError: + pass diff --git a/src/sdk/pynni/nni/compressors/nnimc.py b/src/sdk/pynni/nni/compressors/nnimc.py new file mode 100644 index 0000000000..c3e789e82c --- /dev/null +++ b/src/sdk/pynni/nni/compressors/nnimc.py @@ -0,0 +1,24 @@ +try: + import torch + from _nnimc_torch import * + _torch_available = True +except ModuleNotFoundError: + _torch_available = False + +try: + import tensorflow as tf + from _nnimc_tf import * + _tf_available = True +except ModuleNotFoundError: + _tf_available = False + + +def detect_prunable_layers(model): + """ + Search for all prunable layers in the model. + This can be useful to create search space. + """ + if _torch_available and isinstance(model, torch.nn.Model): + return _torch_detect_prunable_layers(model) + if _tf_available and isinstance(model, tf.Graph): + return _tf_detect_prunable_layers(model) From c6e6b75d6a43d1138c0db5ebb9497bbf1f7aaf87 Mon Sep 17 00:00:00 2001 From: leonardo <806218468@qq.com> Date: Thu, 1 Aug 2019 16:11:26 +0800 Subject: [PATCH 07/34] add __init__ --- src/sdk/pynni/nni/compressors/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 src/sdk/pynni/nni/compressors/__init__.py diff --git a/src/sdk/pynni/nni/compressors/__init__.py b/src/sdk/pynni/nni/compressors/__init__.py new file mode 100644 index 0000000000..e69de29bb2 From 2979de3e9d3d08d9984d71ce2968d98717179acb Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Fri, 2 Aug 2019 10:27:34 +0800 Subject: [PATCH 08/34] compressor --- docs/zh_CN/Trials.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/zh_CN/Trials.md b/docs/zh_CN/Trials.md index 13c23f3337..720981c323 100644 --- a/docs/zh_CN/Trials.md +++ b/docs/zh_CN/Trials.md @@ -168,4 +168,4 @@ echo $? `date +%s%3N` >/home/user_name/nni/experiments/$experiment_id$/trials/$t * [为 CIFAR 10 分类找到最佳的 optimizer](Cifar10Examples.md) * [如何在 NNI 调优 SciKit-learn 的参数](SklearnExamples.md) * [在阅读理解上使用自动模型架构搜索。](SquadEvolutionExamples.md) -* [如何在 NNI 上调优 GBDT](GbdtExample.md) +* [如何在 NNI 上调优 GBDT](GbdtExample.md) \ No newline at end of file From 365585f939b028fda02fc10386ec20aaa4197966 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Tue, 13 Aug 2019 15:23:23 +0800 Subject: [PATCH 09/34] new framework --- .../pynni/nni/compressors/example/__init__.py | 0 .../nni/compressors/{ => example}/main_tf.py | 13 +-- .../compressors/{ => example}/main_torch.py | 0 src/sdk/pynni/nni/compressors/nnimc.py | 4 +- .../nni/compressors/tfCompressor/__init__.py | 2 + .../{ => tfCompressor}/_nnimc_tf.py | 26 +++--- .../nni/compressors/tfCompressor/pruner.py | 78 +++++++++++++++++ .../nni/compressors/tfCompressor/quantizer.py | 49 +++++++++++ .../compressors/torchCompressor/__init__.py | 2 + .../{ => torchCompressor}/_nnimc_torch.py | 30 +++---- .../nni/compressors/torchCompressor/pruner.py | 83 +++++++++++++++++++ .../compressors/torchCompressor/quantizer.py | 54 ++++++++++++ 12 files changed, 305 insertions(+), 36 deletions(-) create mode 100644 src/sdk/pynni/nni/compressors/example/__init__.py rename src/sdk/pynni/nni/compressors/{ => example}/main_tf.py (92%) rename src/sdk/pynni/nni/compressors/{ => example}/main_torch.py (100%) create mode 100644 src/sdk/pynni/nni/compressors/tfCompressor/__init__.py rename src/sdk/pynni/nni/compressors/{ => tfCompressor}/_nnimc_tf.py (83%) create mode 100644 src/sdk/pynni/nni/compressors/tfCompressor/pruner.py create mode 100644 src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py create mode 100644 src/sdk/pynni/nni/compressors/torchCompressor/__init__.py rename src/sdk/pynni/nni/compressors/{ => torchCompressor}/_nnimc_torch.py (81%) create mode 100644 src/sdk/pynni/nni/compressors/torchCompressor/pruner.py create mode 100644 src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py diff --git a/src/sdk/pynni/nni/compressors/example/__init__.py b/src/sdk/pynni/nni/compressors/example/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/sdk/pynni/nni/compressors/main_tf.py b/src/sdk/pynni/nni/compressors/example/main_tf.py similarity index 92% rename from src/sdk/pynni/nni/compressors/main_tf.py rename to src/sdk/pynni/nni/compressors/example/main_tf.py index a7710bd6f5..f803dc25a3 100644 --- a/src/sdk/pynni/nni/compressors/main_tf.py +++ b/src/sdk/pynni/nni/compressors/example/main_tf.py @@ -1,7 +1,8 @@ -from level_pruner import TfLevelPruner -from naive_quantizer import TfNaiveQuantizer -from AGPruner import TfAGPruner,TfSensitivityPruner -from QATquantizer import TfDeReFaQuantizer,TfQATquantizer +#from level_pruner import TfLevelPruner +#from naive_quantizer import TfNaiveQuantizer +#from AGPruner import TfAGPruner,TfSensitivityPruner +#from QATquantizer import TfDeReFaQuantizer,TfQATquantizer +from nni.compressors.tfCompressor import TfLevelPruner import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data @@ -83,11 +84,11 @@ def main(): model = Mnist() #TfNaiveQuantizer().compress_default_graph() - TfDeReFaQuantizer(q_bits = 8).compress_default_graph() + #TfDeReFaQuantizer(q_bits = 8).compress_default_graph() #TfAGP = TfAGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) #TfPruner = TfSensitivityPruner(sparsity = 0.8) #TfPruner.compress_default_graph() - + TfLevelPruner(0.5).compress_default_graph() writer = tf.summary.FileWriter("log/simple_example.log", tf.get_default_graph()) print('writer done') writer.close() diff --git a/src/sdk/pynni/nni/compressors/main_torch.py b/src/sdk/pynni/nni/compressors/example/main_torch.py similarity index 100% rename from src/sdk/pynni/nni/compressors/main_torch.py rename to src/sdk/pynni/nni/compressors/example/main_torch.py diff --git a/src/sdk/pynni/nni/compressors/nnimc.py b/src/sdk/pynni/nni/compressors/nnimc.py index c3e789e82c..b1f83d4ffa 100644 --- a/src/sdk/pynni/nni/compressors/nnimc.py +++ b/src/sdk/pynni/nni/compressors/nnimc.py @@ -1,13 +1,13 @@ try: import torch - from _nnimc_torch import * + from nni.compressors.torchCompressor._nnimc_torch import * _torch_available = True except ModuleNotFoundError: _torch_available = False try: import tensorflow as tf - from _nnimc_tf import * + from nni.compressors.tfCompressor._nnimc_tf import * _tf_available = True except ModuleNotFoundError: _tf_available = False diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py b/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py new file mode 100644 index 0000000000..10002a9e78 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py @@ -0,0 +1,2 @@ +from .pruner import TfLevelPruner, TfAGPruner, TfSensitivityPruner +from .quantizer import TfNaiveQuantizer, TfQATquantizer, TfDoReFaQuantizer \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py similarity index 83% rename from src/sdk/pynni/nni/compressors/_nnimc_tf.py rename to src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py index ae6c58a3f3..200cac5401 100644 --- a/src/sdk/pynni/nni/compressors/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py @@ -14,11 +14,11 @@ class TfCompressor: """TODO""" - def __init__(self) -> None: - self._bound_model: Optional[Graph] = None + def __init__(self): + self._bound_model = None - def compress(self, model: Graph) -> None: + def compress(self, model): """ Compress given graph with algorithm implemented by subclass. This will edit the graph. @@ -27,7 +27,7 @@ def compress(self, model: Graph) -> None: self._bound_model = model self.bind_model(model) - def compress_default_graph(self) -> None: + def compress_default_graph(self): """ Compress the default graph with algorithm implemented by subclass. This will edit the graph. @@ -35,7 +35,7 @@ def compress_default_graph(self) -> None: self.compress(tf.get_default_graph()) - def bind_model(self, model: Graph) -> None: + def bind_model(self, model): """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. @@ -45,7 +45,7 @@ def bind_model(self, model: Graph) -> None: class TfLayerInfo: - def __init__(self, layer: Operation): + def __init__(self, layer): self.name: str = layer.name self.layer: Operation = layer self.weight_index: int @@ -56,7 +56,7 @@ def __init__(self, layer: Operation): raise ValueError('Unsupported layer') -def _tf_detect_prunable_layers(model: Graph) -> List[TfLayerInfo]: +def _tf_detect_prunable_layers(model): # search for Conv2D layers # TODO: whitelist return [ TfLayerInfo(op) for op in model.get_operations() if op.type == 'Conv2D' ] @@ -65,10 +65,10 @@ def _tf_detect_prunable_layers(model: Graph) -> List[TfLayerInfo]: class TfPruner(TfCompressor): """TODO""" - def __init__(self) -> None: + def __init__(self): super().__init__() - def calc_mask(self, layer_info: TfLayerInfo, weight: Tensor) -> Tensor: + def calc_mask(self, layer_info, weight): """ Pruners should overload this method to provide mask for weight tensors. The mask must have the same shape and type comparing to the weight. @@ -78,7 +78,7 @@ def calc_mask(self, layer_info: TfLayerInfo, weight: Tensor) -> Tensor: raise NotImplementedError("Pruners must overload calc_mask()") - def compress(self, model: Graph) -> None: + def compress(self, model): super().compress(model) # TODO: configurable whitelist for layer_info in _tf_detect_prunable_layers(model): @@ -98,14 +98,14 @@ def _instrument_layer(self, layer_info): class TfQuantizer(TfCompressor): - def __init__(self) -> None: + def __init__(self): super().__init__() - def quantize_weight(self, layer_info: TfLayerInfo, weight: Tensor) -> Tensor: + def quantize_weight(self, layer_info, weight) -> Tensor: raise NotImplementedError() - def compress(self, model: Graph) -> None: + def compress(self, model): for layer_info in _tf_detect_prunable_layers(model): self._instrument_layer(layer_info) diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py new file mode 100644 index 0000000000..20e47a87d0 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py @@ -0,0 +1,78 @@ + +try: + import tensorflow as tf + from nni.compressors.nnimc import TfPruner + class TfLevelPruner(TfPruner): + def __init__(self, sparsity = 0, layer_sparsity = {}): + super().__init__() + self.default_sparsity = sparsity + self.layer_sparsity = layer_sparsity + + def calc_mask(self, layer_info, weight): + sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) + threshold = tf.contrib.distributions.percentile(weight.abs(), sparsity * 100) + return tf.cast(tf.math.greater(weight.abs(), threshold), weight.dtype) + + class TfAGPruner(TfPruner): + def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): + super().__init__() + self.initial_sparsity = initial_sparsity + self.final_sparsity = final_sparsity + self.start_epoch = start_epoch + self.end_epoch = end_epoch + self.freq = frequency + self.now_epoch = tf.Variable(0) + self.assign_handler = [] + + def compute_target_sparsity(self): + + if self.end_epoch <= self.start_epoch : + return self.final_sparsity + + now_epoch = tf.minimum(self.now_epoch, tf.constant(self.end_epoch)) + span = int(((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq) + assert span>0 + base = tf.cast(now_epoch - self.initial_sparsity, tf.float32) / span + target_sparsity = (self.final_sparsity + + (self.initial_sparsity - self.final_sparsity)* + (tf.pow(1.0 - base,3))) + return target_sparsity + + def calc_mask(self, layer, weight): + + target_sparsity = self.compute_target_sparsity() + threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) + self.assign_handler.append(tf.assign(weight, weight*mask)) + return mask + + def update_epoch(self, sess, epoch): + sess.run(self.assign_handler) + sess.run(tf.assign(self.now_epoch, int(epoch))) + + + class TfSensitivityPruner(TfPruner): + def __init__(self, sparsity): + super().__init__() + self.sparsity = sparsity + self.layer_mask = {} + self.assign_handler = [] + + def calc_mask(self, layer_info, weight): + target_sparsity = self.sparsity * tf.math.reduce_std(weight) + mask = tf.get_variable(layer_info.name+'_mask',initializer=tf.ones(weight.shape), trainable=False) + self.layer_mask[layer_info.name] = mask + + weight_assign_handler = tf.assign(weight, mask*weight) + with tf.control_dependencies([weight_assign_handler]): + threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + new_mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) + mask_update_handler = tf.assign(mask, new_mask) + self.assign_handler.append(mask_update_handler) + return mask + + def update_graph(self, sess): + sess.run(self.assign_handler) + +except ModuleNotFoundError: + pass diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py new file mode 100644 index 0000000000..d98a487978 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py @@ -0,0 +1,49 @@ +try: + import tensorflow as tf + from nni.compressors.nnimc import TfQuantizer + + class TfNaiveQuantizer(TfQuantizer): + def __init__(self): + super().__init__() + self.layer_scale = { } + + def quantize_weight(self, layer_info, weight): + new_scale = tf.reduce_max(tf.abs(weight)) / 127 + scale = tf.maximum(self.layer_scale.get(layer_info.name, tf.constant(0.0)), new_scale) + self.layer_scale[layer_info.name] = scale + orig_type = weight.dtype + return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale + + class TfQATquantizer(TfQuantizer): + def __init__(self, q_bits): + super().__init__() + self.q_bits = q_bits + + def quantize_weight(self, layer_info, weight): + a = tf.stop_gradient(tf.reduce_min(weight)) + b = tf.stop_gradient(tf.reduce_max(weight)) + n = tf.cast(2 ** self.q_bits, tf.float32) + scale = b-a/(n-1) + + with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): + qw = tf.round((weight-a)/scale)*scale +a + + return qw + + class TfDoReFaQuantizer(TfQuantizer): + def __init__(self, q_bits): + super().__init__() + self.q_bits = q_bits + + def quantize_weight(self, layer_info, weight): + a = tf.math.tanh(weight) + b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5 + + scale = pow(2, self.q_bits-1) + with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): + qw = tf.round(b*scale)/scale + r_qw = 2*qw - 1 + return r_qw + +except ModuleNotFoundError: + pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py b/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py new file mode 100644 index 0000000000..f3fdb8ba81 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py @@ -0,0 +1,2 @@ +from .pruner import TorchLevelPruner, TorchAGPruner, TorchSensitivityPruner +from .quantizer import TorchNaiveQuantizer, TorchDoReFaQuantizer, TorchQATquantizer diff --git a/src/sdk/pynni/nni/compressors/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py similarity index 81% rename from src/sdk/pynni/nni/compressors/_nnimc_torch.py rename to src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py index b713599a01..d467b52d65 100644 --- a/src/sdk/pynni/nni/compressors/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py @@ -14,11 +14,11 @@ class TorchCompressor: """TODO""" - def __init__(self) -> None: - self._bound_model: Optional[Module] = None + def __init__(self): + self._bound_model = None - def compress(self, model: Module) -> None: + def compress(self, model): """ Compress the model with algorithm implemented by subclass. The model will be instrumented and user should never edit it after calling this method. @@ -28,7 +28,7 @@ def compress(self, model: Module) -> None: self.bind_model(model) - def bind_model(self, model: Module) -> None: + def bind_model(self, model) -> None: """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. @@ -38,14 +38,14 @@ def bind_model(self, model: Module) -> None: class TorchLayerInfo: - def __init__(self, name: str, layer: Module): + def __init__(self, name, layer): self.name: str = name self.layer: Module = layer - self._forward: Optional[Function] = None + self._forward = None -def _torch_detect_prunable_layers(model: Module) -> List[TorchLayerInfo]: +def _torch_detect_prunable_layers(model): # search for all layers which have parameter "weight" ret = [ ] for name, layer in model.named_modules(): @@ -60,10 +60,10 @@ def _torch_detect_prunable_layers(model: Module) -> List[TorchLayerInfo]: class TorchPruner(TorchCompressor): """TODO""" - def __init__(self) -> None: + def __init__(self): super().__init__() - def calc_mask(self, layer_info: TorchLayerInfo, weight: Tensor) -> Tensor: + def calc_mask(self, layer_info, weight): """ Pruners should overload this method to provide mask for weight tensors. The mask must have the same shape and type comparing to the weight. @@ -73,13 +73,13 @@ def calc_mask(self, layer_info: TorchLayerInfo, weight: Tensor) -> Tensor: raise NotImplementedError("Pruners must overload calc_mask()") - def compress(self, model: Module) -> None: + def compress(self, model): super().compress(model) # TODO: configurable whitelist for layer_info in _torch_detect_prunable_layers(model): self._instrument_layer(layer_info) - def _instrument_layer(self, layer_info: TorchLayerInfo): + def _instrument_layer(self, layer_info): # TODO: bind additional properties to layer_info instead of layer # create a wrapper forward function to replace the original one assert layer_info._forward is None, 'Each model can only be compressed once' @@ -100,14 +100,14 @@ def new_forward(*input): class TorchQuantizer(TorchCompressor): - def __init__(self) -> None: + def __init__(self): super().__init__() - def quantize_weight(self, layer_info: TorchLayerInfo, weight: Tensor) -> Tensor: + def quantize_weight(self, layer_info, weight): # FIXME: where dequantize goes? raise NotImplementedError() - def compress(self, model: Module) -> None: + def compress(self, model): super().compress(model) count = 0 for layer_info in _torch_detect_prunable_layers(model): @@ -116,7 +116,7 @@ def compress(self, model: Module) -> None: continue self._instrument_layer(layer_info) - def _instrument_layer(self, layer_info: TorchLayerInfo): + def _instrument_layer(self, layer_info): assert layer_info._forward is None layer_info._forward = layer_info.layer.forward diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py new file mode 100644 index 0000000000..42d3c7e5ab --- /dev/null +++ b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py @@ -0,0 +1,83 @@ +try: + import torch + from nni.compressors.nnimc import TorchPruner + class TorchLevelPruner(TorchPruner): + def __init__(self, sparsity = 0, layer_sparsity = { }): + super().__init__() + self.default_sparsity = sparsity + self.layer_sparsity = layer_sparsity + + def calc_mask(self, layer_info, weight): + sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) + w_abs = weight.abs() + k = int(weight.numel() * sparsity) + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + return torch.gt(w_abs, threshold).type(weight.type()) + + class TorchAGPruner(TorchPruner): + def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): + """ + """ + super().__init__() + self.initial_sparsity = initial_sparsity + self.final_sparsity = final_sparsity + self.start_epoch = start_epoch + self.end_epoch = end_epoch + self.now_epoch = start_epoch + self.freq = frequency + self.mask_list = {} + + def compute_target_sparsity(self, now_epoch): + if self.end_epoch <= self.start_epoch or self.end_epoch <= self.now_epoch: + return self.final_sparsity + span = ((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq + assert span>0 + target_sparsity = (self.final_sparsity + + (self.initial_sparsity - self.final_sparsity)* + (1.0 - ((now_epoch - self.start_epoch)/span))**3 ) + return target_sparsity + + def calc_mask(self, layer_info, weight): + now_epoch = self.now_epoch + mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) + target_sparsity = self.compute_target_sparsity(now_epoch) + k = int(weight.numel() * target_sparsity) + if k == 0: + return mask + + w_abs = weight.abs()*mask + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + new_mask = torch.gt(w_abs, threshold).type(weight.type()) + self.mask_list[layer_info.name] = new_mask + return new_mask + + def update_epoch(self, epoch): + if epoch <= 0: + return + self.now_epoch = epoch + + + class TorchSensitivityPruner(TorchPruner): + def __init__(self, sparsity): + super().__init__() + self.sparsity = sparsity + self.mask_list = {} + + def calc_mask(self, layer_info, weight): + mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) + weight = weight*mask + target_sparsity = self.sparsity * torch.std(weight).item() + k = int(weight.numel() * target_sparsity) + if k == 0: + return mask + + w_abs = weight.abs() + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + new_mask = torch.gt(w_abs, threshold).type(weight.type()) + self.mask_list[layer_info.name] = new_mask + return new_mask + +except ModuleNotFoundError: + pass + + diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py new file mode 100644 index 0000000000..e5d879d1d5 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py @@ -0,0 +1,54 @@ +try: + import torch + from nni.compressors.nnimc import TorchQuantizer + + class TorchNaiveQuantizer(TorchQuantizer): + def __init__(self): + super().__init__() + self.layer_scale = { } + + def quantize_weight(self, layer_info, weight): + new_scale = weight.abs().max() / 127 + # TODO: use int id + scale = max(self.layer_scale.get(layer_info.name, 0), new_scale) + self.layer_scale[layer_info.name] = scale + orig_type = weight.type() # TODO: user layer_info + return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) + + class TorchDoReFaQuantizer(TorchQuantizer): + def __init__(self, q_bits): + super().__init__() + self.q_bits = q_bits + + def quantize_weight(self, layer_info, weight): + out = weight.tanh() + out = out /( 2 * out.abs().max()) + 0.5 + out = self.quantize(out, self.q_bits) + out = 2 * out -1 + return out + + def quantize(self, input_ri, q_bits): + scale = pow(2, q_bits)-1 + output = torch.round(input_ri*scale)/scale + return output + + class TorchQATquantizer(TorchQuantizer): + def __init__(self, q_bits): + super().__init__() + self.q_bits = q_bits + + def quantize_weight(self, layer_info, weight): + if self.q_bits <= 1: + return weight + a = torch.min(weight) + b = torch.max(weight) + n = pow(2,self.q_bits) + scale = (b-a)/(n-1) + zero_point = a + out = torch.round((weight - zero_point)/scale) + out = out*scale + zero_point + orig_type = weight.dtype + return out.type(orig_type) + +except ModuleNotFoundError: + pass From 1f92408f6aa68c91b99ac04a1f2d362925d62502 Mon Sep 17 00:00:00 2001 From: XupuWang <806218468@qq.com> Date: Tue, 13 Aug 2019 16:34:16 +0800 Subject: [PATCH 10/34] change import --- src/sdk/pynni/nni/compressors/nnimc.py | 4 ++-- src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py | 6 +++--- src/sdk/pynni/nni/compressors/tfCompressor/pruner.py | 2 +- src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py | 2 +- .../pynni/nni/compressors/torchCompressor/_nnimc_torch.py | 4 ++-- src/sdk/pynni/nni/compressors/torchCompressor/pruner.py | 2 +- src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py | 2 +- 7 files changed, 11 insertions(+), 11 deletions(-) diff --git a/src/sdk/pynni/nni/compressors/nnimc.py b/src/sdk/pynni/nni/compressors/nnimc.py index b1f83d4ffa..2959d38a4b 100644 --- a/src/sdk/pynni/nni/compressors/nnimc.py +++ b/src/sdk/pynni/nni/compressors/nnimc.py @@ -1,13 +1,13 @@ try: import torch - from nni.compressors.torchCompressor._nnimc_torch import * + from .torchCompressor._nnimc_torch import * _torch_available = True except ModuleNotFoundError: _torch_available = False try: import tensorflow as tf - from nni.compressors.tfCompressor._nnimc_tf import * + from .tfCompressor._nnimc_tf import * _tf_available = True except ModuleNotFoundError: _tf_available = False diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py index 200cac5401..22b7a3d3c3 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py @@ -46,9 +46,9 @@ def bind_model(self, model): class TfLayerInfo: def __init__(self, layer): - self.name: str = layer.name - self.layer: Operation = layer - self.weight_index: int + self.name = layer.name + self.layer = layer + self.weight_index = None if layer.type == 'Conv2D': self.weight_index = 1 diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py index 20e47a87d0..a5f734e1f7 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py @@ -1,7 +1,7 @@ try: import tensorflow as tf - from nni.compressors.nnimc import TfPruner + from ._nnimc_tf import TfPruner class TfLevelPruner(TfPruner): def __init__(self, sparsity = 0, layer_sparsity = {}): super().__init__() diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py index d98a487978..b8043e19e0 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py @@ -1,6 +1,6 @@ try: import tensorflow as tf - from nni.compressors.nnimc import TfQuantizer + from ._nnimc_tf import TfQuantizer class TfNaiveQuantizer(TfQuantizer): def __init__(self): diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py index d467b52d65..481b2f8dac 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py @@ -39,8 +39,8 @@ def bind_model(self, model) -> None: class TorchLayerInfo: def __init__(self, name, layer): - self.name: str = name - self.layer: Module = layer + self.name = name + self.layer = layer self._forward = None diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py index 42d3c7e5ab..cf15bb10bf 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py @@ -1,6 +1,6 @@ try: import torch - from nni.compressors.nnimc import TorchPruner + from ._nnimc_torch import TorchPruner class TorchLevelPruner(TorchPruner): def __init__(self, sparsity = 0, layer_sparsity = { }): super().__init__() diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py index e5d879d1d5..67fe0e583b 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py @@ -1,6 +1,6 @@ try: import torch - from nni.compressors.nnimc import TorchQuantizer + from ._nnimc_torch import TorchQuantizer class TorchNaiveQuantizer(TorchQuantizer): def __init__(self): From f9250751dce46519c2b9a5b849555d45c1d263bc Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Tue, 13 Aug 2019 17:17:30 +0800 Subject: [PATCH 11/34] change import error --- setup.py | 2 +- src/sdk/pynni/nni/compressors/AGPruner.py | 4 +- src/sdk/pynni/nni/compressors/QATquantizer.py | 2 +- .../pynni/nni/compressors/example/main_tf.py | 4 +- src/sdk/pynni/nni/compressors/level_pruner.py | 4 +- .../pynni/nni/compressors/naive_quantizer.py | 4 +- src/sdk/pynni/nni/compressors/nnimc.py | 4 +- .../nni/compressors/tfCompressor/pruner.py | 6 +- .../nni/compressors/tfCompressor/quantizer.py | 2 +- .../nni/compressors/torchCompressor/pruner.py | 2 +- .../compressors/torchCompressor/quantizer.py | 2 +- src/webui/yarn.lock | 651 +++++++----------- 12 files changed, 266 insertions(+), 421 deletions(-) diff --git a/setup.py b/setup.py index 296b2a8f22..70411453d4 100644 --- a/setup.py +++ b/setup.py @@ -27,7 +27,7 @@ def read(fname): setup( name = 'nni', - version = '999.0.0-developing', + version = 'v0.8-263-g1f92408', author = 'Microsoft NNI Team', author_email = 'nni@microsoft.com', description = 'Neural Network Intelligence project', diff --git a/src/sdk/pynni/nni/compressors/AGPruner.py b/src/sdk/pynni/nni/compressors/AGPruner.py index 9b085cd79e..4f8b052d21 100644 --- a/src/sdk/pynni/nni/compressors/AGPruner.py +++ b/src/sdk/pynni/nni/compressors/AGPruner.py @@ -65,7 +65,7 @@ def calc_mask(self, layer_info, weight): self.mask_list[layer_info.name] = new_mask return new_mask -except ModuleNotFoundError: +except ImportError: pass @@ -134,5 +134,5 @@ def calc_mask(self, layer_info, weight): def update_graph(self, sess): sess.run(self.assign_handler) -except ModuleNotFoundError: +except ImportError: pass diff --git a/src/sdk/pynni/nni/compressors/QATquantizer.py b/src/sdk/pynni/nni/compressors/QATquantizer.py index 26ded2b345..513b2c8fb9 100644 --- a/src/sdk/pynni/nni/compressors/QATquantizer.py +++ b/src/sdk/pynni/nni/compressors/QATquantizer.py @@ -42,7 +42,7 @@ def quantize_weight(self, layer_info, weight): #print(weight,out) return out.type(orig_type) -except ModuleNotFoundError: +except ImportError: pass diff --git a/src/sdk/pynni/nni/compressors/example/main_tf.py b/src/sdk/pynni/nni/compressors/example/main_tf.py index f803dc25a3..cf5e177140 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf.py +++ b/src/sdk/pynni/nni/compressors/example/main_tf.py @@ -112,8 +112,8 @@ def main(): #print(tf.gradients(model.cross,[model.w1, model.fcw1])) test_acc = model.accuracy.eval(feed_dict = { - model.images: test.images, - model.labels: test.labels, + model.images: data.test.images, + model.labels: data.test.labels, model.keep_prob: 1.0 }) print('final result is', test_acc) diff --git a/src/sdk/pynni/nni/compressors/level_pruner.py b/src/sdk/pynni/nni/compressors/level_pruner.py index 3e9398b34f..b5ecc041fc 100644 --- a/src/sdk/pynni/nni/compressors/level_pruner.py +++ b/src/sdk/pynni/nni/compressors/level_pruner.py @@ -15,7 +15,7 @@ def calc_mask(self, layer_info, weight): threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() return torch.gt(w_abs, threshold).type(weight.type()) -except ModuleNotFoundError: +except ImportError: pass @@ -34,5 +34,5 @@ def calc_mask(self, layer_info, weight): threshold = tf.contrib.distributions.percentile(weight.abs(), sparsity * 100) return tf.cast(tf.math.greater(weight.abs(), threshold), weight.dtype) -except ModuleNotFoundError: +except ImportError: pass diff --git a/src/sdk/pynni/nni/compressors/naive_quantizer.py b/src/sdk/pynni/nni/compressors/naive_quantizer.py index 36019c7b1f..2dcece43b1 100644 --- a/src/sdk/pynni/nni/compressors/naive_quantizer.py +++ b/src/sdk/pynni/nni/compressors/naive_quantizer.py @@ -15,7 +15,7 @@ def quantize_weight(self, layer_info, weight): orig_type = weight.type() # TODO: user layer_info return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) -except ModuleNotFoundError: +except ImportError: pass @@ -35,5 +35,5 @@ def quantize_weight(self, layer_info, weight): orig_type = weight.dtype return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale -except ModuleNotFoundError: +except ImportError: pass diff --git a/src/sdk/pynni/nni/compressors/nnimc.py b/src/sdk/pynni/nni/compressors/nnimc.py index 2959d38a4b..97f69900dd 100644 --- a/src/sdk/pynni/nni/compressors/nnimc.py +++ b/src/sdk/pynni/nni/compressors/nnimc.py @@ -2,14 +2,14 @@ import torch from .torchCompressor._nnimc_torch import * _torch_available = True -except ModuleNotFoundError: +except ImportError: _torch_available = False try: import tensorflow as tf from .tfCompressor._nnimc_tf import * _tf_available = True -except ModuleNotFoundError: +except ImportError: _tf_available = False diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py index a5f734e1f7..33c175694f 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py @@ -10,8 +10,8 @@ def __init__(self, sparsity = 0, layer_sparsity = {}): def calc_mask(self, layer_info, weight): sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) - threshold = tf.contrib.distributions.percentile(weight.abs(), sparsity * 100) - return tf.cast(tf.math.greater(weight.abs(), threshold), weight.dtype) + threshold = tf.contrib.distributions.percentile(tf.abs(weight), sparsity * 100) + return tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype) class TfAGPruner(TfPruner): def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): @@ -74,5 +74,5 @@ def calc_mask(self, layer_info, weight): def update_graph(self, sess): sess.run(self.assign_handler) -except ModuleNotFoundError: +except ImportError: pass diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py index b8043e19e0..84ca614ba5 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py @@ -45,5 +45,5 @@ def quantize_weight(self, layer_info, weight): r_qw = 2*qw - 1 return r_qw -except ModuleNotFoundError: +except ImportError: pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py index cf15bb10bf..6d90c6c1b0 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py @@ -77,7 +77,7 @@ def calc_mask(self, layer_info, weight): self.mask_list[layer_info.name] = new_mask return new_mask -except ModuleNotFoundError: +except ImportError: pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py index 67fe0e583b..c8ce7030b1 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py @@ -50,5 +50,5 @@ def quantize_weight(self, layer_info, weight): orig_type = weight.dtype return out.type(orig_type) -except ModuleNotFoundError: +except ImportError: pass diff --git a/src/webui/yarn.lock b/src/webui/yarn.lock index 8d896151a1..a451ba093b 100644 --- a/src/webui/yarn.lock +++ b/src/webui/yarn.lock @@ -62,18 +62,6 @@ version "1.0.0" resolved "https://registry.yarnpkg.com/@csstools/sass-import-resolve/-/sass-import-resolve-1.0.0.tgz#32c3cdb2f7af3cd8f0dca357b592e7271f3831b5" -"@types/events@*": - version "3.0.0" - resolved "https://registry.yarnpkg.com/@types/events/-/events-3.0.0.tgz#2862f3f58a9a7f7c3e78d79f130dd4d71c25c2a7" - -"@types/glob@^7.1.1": - version "7.1.1" - resolved "https://registry.yarnpkg.com/@types/glob/-/glob-7.1.1.tgz#aa59a1c6e3fbc421e07ccd31a944c30eba521575" - dependencies: - "@types/events" "*" - "@types/minimatch" "*" - "@types/node" "*" - "@types/history@^3": version "3.2.2" resolved "https://registry.yarnpkg.com/@types/history/-/history-3.2.2.tgz#b6affa240cb10b5f841c6443d8a24d7f3fc8bb0c" @@ -86,10 +74,6 @@ version "0.0.29" resolved "http://registry.npmjs.org/@types/json5/-/json5-0.0.29.tgz#ee28707ae94e11d2b827bcbe5270bcea7f3e71ee" -"@types/minimatch@*": - version "3.0.3" - resolved "https://registry.yarnpkg.com/@types/minimatch/-/minimatch-3.0.3.tgz#3dca0e3f33b200fc7d1139c0cd96c1268cadfd9d" - "@types/node@*", "@types/node@^10.7.0": version "10.12.0" resolved "https://registry.yarnpkg.com/@types/node/-/node-10.12.0.tgz#ea6dcbddbc5b584c83f06c60e82736d8fbb0c235" @@ -192,10 +176,6 @@ address@1.0.3, address@^1.0.1: version "1.0.3" resolved "https://registry.yarnpkg.com/address/-/address-1.0.3.tgz#b5f50631f8d6cec8bd20c963963afb55e06cbce9" -ajv-errors@^1.0.0: - version "1.0.1" - resolved "https://registry.yarnpkg.com/ajv-errors/-/ajv-errors-1.0.1.tgz#f35986aceb91afadec4102fbd85014950cefa64d" - ajv-keywords@^2.0.0: version "2.1.1" resolved "https://registry.yarnpkg.com/ajv-keywords/-/ajv-keywords-2.1.1.tgz#617997fc5f60576894c435f940d819e135b80762" @@ -247,10 +227,6 @@ ansi-align@^2.0.0: dependencies: string-width "^2.0.0" -ansi-colors@^3.0.0: - version "3.2.4" - resolved "https://registry.yarnpkg.com/ansi-colors/-/ansi-colors-3.2.4.tgz#e3a3da4bfbae6c86a9c285625de124a234026fbf" - ansi-escapes@^3.0.0: version "3.1.0" resolved "https://registry.yarnpkg.com/ansi-escapes/-/ansi-escapes-3.1.0.tgz#f73207bb81207d75fd6c83f125af26eea378ca30" @@ -413,6 +389,14 @@ array-flatten@^2.1.0: version "2.1.1" resolved "https://registry.yarnpkg.com/array-flatten/-/array-flatten-2.1.1.tgz#426bb9da84090c1838d812c8150af20a8331e296" +array-includes@^3.0.3: + version "3.0.3" + resolved "https://registry.yarnpkg.com/array-includes/-/array-includes-3.0.3.tgz#184b48f62d92d7452bb31b323165c7f8bd02266d" + integrity sha1-GEtI9i2S10UrsxsyMWXH+L0CJm0= + dependencies: + define-properties "^1.1.2" + es-abstract "^1.7.0" + array-map@~0.0.0: version "0.0.0" resolved "https://registry.yarnpkg.com/array-map/-/array-map-0.0.0.tgz#88a2bab73d1cf7bcd5c1b118a003f66f665fa662" @@ -1540,10 +1524,6 @@ camelcase@^4.0.0, camelcase@^4.1.0: version "4.1.0" resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-4.1.0.tgz#d545635be1e33c542649c69173e5de6acfae34dd" -camelcase@^5.0.0: - version "5.3.1" - resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-5.3.1.tgz#e3c9b31569e106811df242f715725a1f4c494320" - caniuse-api@^1.5.2: version "1.6.1" resolved "https://registry.yarnpkg.com/caniuse-api/-/caniuse-api-1.6.1.tgz#b534e7c734c4f81ec5fbe8aca2ad24354b962c6c" @@ -1604,6 +1584,15 @@ chalk@^2.0.0, chalk@^2.0.1, chalk@^2.3.0, chalk@^2.4.1: escape-string-regexp "^1.0.5" supports-color "^5.3.0" +chalk@^2.1.0: + version "2.4.2" + resolved "https://registry.yarnpkg.com/chalk/-/chalk-2.4.2.tgz#cd42541677a54333cf541a49108c1432b44c9424" + integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ== + dependencies: + ansi-styles "^3.2.1" + escape-string-regexp "^1.0.5" + supports-color "^5.3.0" + chardet@^0.4.0: version "0.4.2" resolved "https://registry.yarnpkg.com/chardet/-/chardet-0.4.2.tgz#b5473b33dc97c424e5d98dc87d55d4d8a29c8bf2" @@ -1623,6 +1612,25 @@ chokidar@^1.6.0, chokidar@^1.7.0: optionalDependencies: fsevents "^1.0.0" +chokidar@^2.0.0: + version "2.1.6" + resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-2.1.6.tgz#b6cad653a929e244ce8a834244164d241fa954c5" + integrity sha512-V2jUo67OKkc6ySiRpJrjlpJKl9kDuG+Xb8VgsGzb+aEouhgS1D0weyPU4lEzdAcsCAvrih2J2BqyXqHWvVLw5g== + dependencies: + anymatch "^2.0.0" + async-each "^1.0.1" + braces "^2.3.2" + glob-parent "^3.1.0" + inherits "^2.0.3" + is-binary-path "^1.0.0" + is-glob "^4.0.0" + normalize-path "^3.0.0" + path-is-absolute "^1.0.0" + readdirp "^2.2.1" + upath "^1.1.1" + optionalDependencies: + fsevents "^1.2.7" + chokidar@^2.0.2: version "2.0.4" resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-2.0.4.tgz#356ff4e2b0e8e43e322d18a372460bbcf3accd26" @@ -1642,24 +1650,6 @@ chokidar@^2.0.2: optionalDependencies: fsevents "^1.2.2" -chokidar@^2.1.6: - version "2.1.6" - resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-2.1.6.tgz#b6cad653a929e244ce8a834244164d241fa954c5" - dependencies: - anymatch "^2.0.0" - async-each "^1.0.1" - braces "^2.3.2" - glob-parent "^3.1.0" - inherits "^2.0.3" - is-binary-path "^1.0.0" - is-glob "^4.0.0" - normalize-path "^3.0.0" - path-is-absolute "^1.0.0" - readdirp "^2.2.1" - upath "^1.1.1" - optionalDependencies: - fsevents "^1.2.7" - chownr@^1.0.1: version "1.1.1" resolved "https://registry.yarnpkg.com/chownr/-/chownr-1.1.1.tgz#54726b8b8fff4df053c42187e801fb4412df1494" @@ -1869,9 +1859,10 @@ compressible@~2.0.16: dependencies: mime-db ">= 1.40.0 < 2" -compression@^1.7.4: +compression@^1.5.2: version "1.7.4" resolved "https://registry.yarnpkg.com/compression/-/compression-1.7.4.tgz#95523eff170ca57c29a0ca41e6fe131f41e5bb8f" + integrity sha512-jaSIDzP9pZVS4ZfQ+TzvtiWhdpFhE2RDHz8QJkpX9SIpLq88VueF5jJw6t+6CUQcAoA6t+x89MLrWAqpfDE8iQ== dependencies: accepts "~1.3.5" bytes "3.0.0" @@ -1905,9 +1896,10 @@ configstore@^3.0.0: write-file-atomic "^2.0.0" xdg-basedir "^3.0.0" -connect-history-api-fallback@^1.6.0: +connect-history-api-fallback@^1.3.0: version "1.6.0" resolved "https://registry.yarnpkg.com/connect-history-api-fallback/-/connect-history-api-fallback-1.6.0.tgz#8b32089359308d111115d81cad3fceab888f97bc" + integrity sha512-e54B99q/OUoH64zYYRf3HBP5z24G38h5D3qXu23JGRoigpX5Ss4r9ZnDk3g0Z8uQC2x2lPaJ+UlWBc1ZWBWdLg== console-browserify@^1.1.0: version "1.1.0" @@ -2065,16 +2057,6 @@ cross-spawn@5.1.0, cross-spawn@^5.0.1, cross-spawn@^5.1.0: shebang-command "^1.2.0" which "^1.2.9" -cross-spawn@^6.0.0: - version "6.0.5" - resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-6.0.5.tgz#4a5ec7c64dfae22c3a14124dbacdee846d80cbc4" - dependencies: - nice-try "^1.0.4" - path-key "^2.0.1" - semver "^5.5.0" - shebang-command "^1.2.0" - which "^1.2.9" - cryptiles@2.x.x, cryptiles@^4.1.2: version "4.1.3" resolved "https://registry.yarnpkg.com/cryptiles/-/cryptiles-4.1.3.tgz#2461d3390ea0b82c643a6ba79f0ed491b0934c25" @@ -2268,19 +2250,13 @@ debug@=3.1.0: dependencies: ms "2.0.0" -debug@^3.1.0, debug@^3.2.5: +debug@^3.1.0: version "3.2.6" resolved "https://registry.yarnpkg.com/debug/-/debug-3.2.6.tgz#e83d17de16d8a7efb7717edbe5fb10135eee629b" dependencies: ms "^2.1.1" -debug@^4.1.0, debug@^4.1.1: - version "4.1.1" - resolved "https://registry.yarnpkg.com/debug/-/debug-4.1.1.tgz#3b72260255109c6b589cee050f1d516139664791" - dependencies: - ms "^2.1.1" - -decamelize@^1.0.0, decamelize@^1.1.1, decamelize@^1.1.2, decamelize@^1.2.0: +decamelize@^1.0.0, decamelize@^1.1.1, decamelize@^1.1.2: version "1.2.0" resolved "https://registry.yarnpkg.com/decamelize/-/decamelize-1.2.0.tgz#f6534d15148269b20352e7bee26f501f9a191290" @@ -2304,13 +2280,6 @@ deepmerge@^2.0.1: version "2.2.1" resolved "https://registry.yarnpkg.com/deepmerge/-/deepmerge-2.2.1.tgz#5d3ff22a01c00f645405a2fbc17d0778a1801170" -default-gateway@^4.2.0: - version "4.2.0" - resolved "https://registry.yarnpkg.com/default-gateway/-/default-gateway-4.2.0.tgz#167104c7500c2115f6dd69b0a536bb8ed720552b" - dependencies: - execa "^1.0.0" - ip-regex "^2.1.0" - default-require-extensions@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/default-require-extensions/-/default-require-extensions-1.0.0.tgz#f37ea15d3e13ffd9b437d33e1a75b5fb97874cb8" @@ -2358,17 +2327,17 @@ del@^2.2.2: pinkie-promise "^2.0.0" rimraf "^2.2.8" -del@^4.1.1: - version "4.1.1" - resolved "https://registry.yarnpkg.com/del/-/del-4.1.1.tgz#9e8f117222ea44a31ff3a156c049b99052a9f0b4" +del@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/del/-/del-3.0.0.tgz#53ecf699ffcbcb39637691ab13baf160819766e5" + integrity sha1-U+z2mf/LyzljdpGrE7rxYIGXZuU= dependencies: - "@types/glob" "^7.1.1" globby "^6.1.0" - is-path-cwd "^2.0.0" - is-path-in-cwd "^2.0.0" - p-map "^2.0.0" - pify "^4.0.1" - rimraf "^2.6.3" + is-path-cwd "^1.0.0" + is-path-in-cwd "^1.0.0" + p-map "^1.1.1" + pify "^3.0.0" + rimraf "^2.2.8" delayed-stream@~1.0.0: version "1.0.0" @@ -2407,9 +2376,10 @@ detect-newline@^2.1.0: version "2.1.0" resolved "https://registry.yarnpkg.com/detect-newline/-/detect-newline-2.1.0.tgz#f41f1c10be4b00e87b5f13da680759f2c5bfd3e2" -detect-node@^2.0.4: +detect-node@^2.0.3: version "2.0.4" resolved "https://registry.yarnpkg.com/detect-node/-/detect-node-2.0.4.tgz#014ee8f8f669c5c58023da64b8179c083a28c46c" + integrity sha512-ZIzRpLJrOj7jjP2miAtgqIfmzbxa4ZOr5jJc601zklsfEx9oTzmmj2nVpIPRpNlRTIh8lc1kyViIY7BWSGNmKw== detect-port-alt@1.1.6: version "1.1.6" @@ -2659,9 +2629,22 @@ es-abstract@^1.5.1: is-callable "^1.1.3" is-regex "^1.0.4" -es-to-primitive@^1.1.1: +es-abstract@^1.7.0: + version "1.13.0" + resolved "https://registry.yarnpkg.com/es-abstract/-/es-abstract-1.13.0.tgz#ac86145fdd5099d8dd49558ccba2eaf9b88e24e9" + integrity sha512-vDZfg/ykNxQVwup/8E1BZhVzFfBxs9NqMzGcvIJrqg5k2/5Za2bWo40dK2J1pgLngZ7c+Shh8lwYtLGyrwPutg== + dependencies: + es-to-primitive "^1.2.0" + function-bind "^1.1.1" + has "^1.0.3" + is-callable "^1.1.4" + is-regex "^1.0.4" + object-keys "^1.0.12" + +es-to-primitive@^1.1.1, es-to-primitive@^1.2.0: version "1.2.0" resolved "https://registry.yarnpkg.com/es-to-primitive/-/es-to-primitive-1.2.0.tgz#edf72478033456e8dda8ef09e00ad9650707f377" + integrity sha512-qZryBOJjV//LaxLTV6UC//WewneB3LcXOL9NP++ozKVXsIIIpm/2c13UDiD9Jp2eThsecw9m3jPqDwTyobcdbg== dependencies: is-callable "^1.1.4" is-date-object "^1.0.1" @@ -2803,12 +2786,6 @@ eventsource@0.1.6: dependencies: original ">=0.0.5" -eventsource@^1.0.7: - version "1.0.7" - resolved "https://registry.yarnpkg.com/eventsource/-/eventsource-1.0.7.tgz#8fbc72c93fcd34088090bc0a4e64f4b5cee6d8d0" - dependencies: - original "^1.0.0" - evp_bytestokey@^1.0.0, evp_bytestokey@^1.0.3: version "1.0.3" resolved "https://registry.yarnpkg.com/evp_bytestokey/-/evp_bytestokey-1.0.3.tgz#7fcbdb198dc71959432efe13842684e0525acb02" @@ -2834,18 +2811,6 @@ execa@^0.7.0: signal-exit "^3.0.0" strip-eof "^1.0.0" -execa@^1.0.0: - version "1.0.0" - resolved "https://registry.yarnpkg.com/execa/-/execa-1.0.0.tgz#c6236a5bb4df6d6f15e88e7f017798216749ddd8" - dependencies: - cross-spawn "^6.0.0" - get-stream "^4.0.0" - is-stream "^1.1.0" - npm-run-path "^2.0.0" - p-finally "^1.0.0" - signal-exit "^3.0.0" - strip-eof "^1.0.0" - exit@^0.1.2: version "0.1.2" resolved "https://registry.yarnpkg.com/exit/-/exit-0.1.2.tgz#0632638f8d877cc82107d30a0fff1a17cba1cd0c" @@ -2891,9 +2856,10 @@ expect@^22.4.0: jest-message-util "^22.4.3" jest-regex-util "^22.4.3" -express@^4.17.1: +express@^4.16.2: version "4.17.1" resolved "https://registry.yarnpkg.com/express/-/express-4.17.1.tgz#4491fc38605cf51f8629d39c2b5d026f98a4c134" + integrity sha512-mHJ9O79RqluphRrcw2X/GTh3k9tVv8YcoyY4Kkh4WDMUYKRZUq0h1o0w2rrrxBqM7VoeUVqgb27xlEMXTnYt4g== dependencies: accepts "~1.3.7" array-flatten "1.1.1" @@ -3019,12 +2985,6 @@ faye-websocket@~0.11.0: dependencies: websocket-driver ">=0.5.1" -faye-websocket@~0.11.1: - version "0.11.3" - resolved "https://registry.yarnpkg.com/faye-websocket/-/faye-websocket-0.11.3.tgz#5c0e9a8968e8912c286639fde977a8b209f2508e" - dependencies: - websocket-driver ">=0.5.1" - fb-watchman@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/fb-watchman/-/fb-watchman-2.0.0.tgz#54e9abf7dfa2f26cd9b1636c588c1afc05de5d58" @@ -3126,12 +3086,6 @@ find-up@^2.0.0, find-up@^2.1.0: dependencies: locate-path "^2.0.0" -find-up@^3.0.0: - version "3.0.0" - resolved "https://registry.yarnpkg.com/find-up/-/find-up-3.0.0.tgz#49169f1d7993430646da61ecc5ae355c21c97b73" - dependencies: - locate-path "^3.0.0" - flatten@^1.0.2: version "1.0.2" resolved "https://registry.yarnpkg.com/flatten/-/flatten-1.0.2.tgz#dae46a9d78fbe25292258cc1e780a41d95c03782" @@ -3307,12 +3261,6 @@ get-stream@^3.0.0: version "3.0.0" resolved "http://registry.npmjs.org/get-stream/-/get-stream-3.0.0.tgz#8e943d1358dc37555054ecbe2edb05aa174ede14" -get-stream@^4.0.0: - version "4.1.0" - resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-4.1.0.tgz#c1b255575f3dc21d59bfc79cd3d2b46b1c3a54b5" - dependencies: - pump "^3.0.0" - get-value@^2.0.3, get-value@^2.0.6: version "2.0.6" resolved "https://registry.yarnpkg.com/get-value/-/get-value-2.0.6.tgz#dc15ca1c672387ca76bd37ac0a395ba2042a2c28" @@ -3360,17 +3308,6 @@ glob@^7.0.3, glob@^7.0.5, glob@^7.1.1, glob@^7.1.2: once "^1.3.0" path-is-absolute "^1.0.0" -glob@^7.1.3: - version "7.1.4" - resolved "https://registry.yarnpkg.com/glob/-/glob-7.1.4.tgz#aa608a2f6c577ad357e1ae5a5c26d9a8d1969255" - dependencies: - fs.realpath "^1.0.0" - inflight "^1.0.4" - inherits "2" - minimatch "^3.0.4" - once "^1.3.0" - path-is-absolute "^1.0.0" - global-dirs@^0.1.0: version "0.1.1" resolved "https://registry.yarnpkg.com/global-dirs/-/global-dirs-0.1.1.tgz#b319c0dd4607f353f3be9cca4c72fc148c49f445" @@ -3458,9 +3395,10 @@ hammerjs@^2.0.8: version "2.0.8" resolved "https://registry.yarnpkg.com/hammerjs/-/hammerjs-2.0.8.tgz#04ef77862cff2bb79d30f7692095930222bf60f1" -handle-thing@^2.0.0: - version "2.0.0" - resolved "https://registry.yarnpkg.com/handle-thing/-/handle-thing-2.0.0.tgz#0e039695ff50c93fc288557d696f3c1dc6776754" +handle-thing@^1.2.5: + version "1.2.5" + resolved "https://registry.yarnpkg.com/handle-thing/-/handle-thing-1.2.5.tgz#fd7aad726bf1a5fd16dfc29b2f7a6601d27139c4" + integrity sha1-/Xqtcmvxpf0W38KbL3pmAdJxOcQ= handlebars@^4.0.3, handlebars@^4.1.0: version "4.1.2" @@ -3547,7 +3485,7 @@ has-values@^1.0.0: is-number "^3.0.0" kind-of "^4.0.0" -has@^1.0.1: +has@^1.0.1, has@^1.0.3: version "1.0.3" resolved "https://registry.yarnpkg.com/has/-/has-1.0.3.tgz#722d7cbfc1f6aa8241f16dd814e011e1f41e8796" dependencies: @@ -3641,9 +3579,10 @@ html-encoding-sniffer@^1.0.2: dependencies: whatwg-encoding "^1.0.1" -html-entities@^1.2.1: +html-entities@^1.2.0: version "1.2.1" resolved "https://registry.yarnpkg.com/html-entities/-/html-entities-1.2.1.tgz#0df29351f0721163515dfb9e5543e5f6eed5162f" + integrity sha1-DfKTUfByEWNRXfueVUPl9u7VFi8= html-minifier@^3.2.3: version "3.5.20" @@ -3714,18 +3653,20 @@ http-parser-js@>=0.4.0: version "0.4.13" resolved "https://registry.yarnpkg.com/http-parser-js/-/http-parser-js-0.4.13.tgz#3bd6d6fde6e3172c9334c3b33b6c193d80fe1137" -http-proxy-middleware@^0.19.1: - version "0.19.1" - resolved "https://registry.yarnpkg.com/http-proxy-middleware/-/http-proxy-middleware-0.19.1.tgz#183c7dc4aa1479150306498c210cdaf96080a43a" +http-proxy-middleware@~0.17.4: + version "0.17.4" + resolved "https://registry.yarnpkg.com/http-proxy-middleware/-/http-proxy-middleware-0.17.4.tgz#642e8848851d66f09d4f124912846dbaeb41b833" + integrity sha1-ZC6ISIUdZvCdTxJJEoRtuutBuDM= dependencies: - http-proxy "^1.17.0" - is-glob "^4.0.0" - lodash "^4.17.11" - micromatch "^3.1.10" + http-proxy "^1.16.2" + is-glob "^3.1.0" + lodash "^4.17.2" + micromatch "^2.3.11" -http-proxy@^1.17.0: +http-proxy@^1.16.2: version "1.17.0" resolved "https://registry.yarnpkg.com/http-proxy/-/http-proxy-1.17.0.tgz#7ad38494658f84605e2f6db4436df410f4e5be9a" + integrity sha512-Taqn+3nNvYRfJ3bGvKfBSRwy1v6eePlm3oc/aWVxZp57DQr5Eq3xhKJi7Z4hZpS8PC3H4qI+Yly5EmFacGuA/g== dependencies: eventemitter3 "^3.0.0" follow-redirects "^1.0.0" @@ -3808,13 +3749,6 @@ import-local@^1.0.0: pkg-dir "^2.0.0" resolve-cwd "^2.0.0" -import-local@^2.0.0: - version "2.0.0" - resolved "https://registry.yarnpkg.com/import-local/-/import-local-2.0.0.tgz#55070be38a5993cf18ef6db7e961f5bee5c5a09d" - dependencies: - pkg-dir "^3.0.0" - resolve-cwd "^2.0.0" - imurmurhash@^0.1.4: version "0.1.4" resolved "https://registry.yarnpkg.com/imurmurhash/-/imurmurhash-0.1.4.tgz#9218b9b2b928a238b13dc4fb6b6d576f231453ea" @@ -3875,12 +3809,12 @@ inquirer@3.3.0: strip-ansi "^4.0.0" through "^2.3.6" -internal-ip@^4.3.0: - version "4.3.0" - resolved "https://registry.yarnpkg.com/internal-ip/-/internal-ip-4.3.0.tgz#845452baad9d2ca3b69c635a137acb9a0dad0907" +internal-ip@1.2.0: + version "1.2.0" + resolved "https://registry.yarnpkg.com/internal-ip/-/internal-ip-1.2.0.tgz#ae9fbf93b984878785d50a8de1b356956058cf5c" + integrity sha1-rp+/k7mEh4eF1QqN4bNWlWBYz1w= dependencies: - default-gateway "^4.2.0" - ipaddr.js "^1.9.0" + meow "^3.3.0" interpret@^1.0.0: version "1.1.0" @@ -3900,14 +3834,6 @@ invert-kv@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/invert-kv/-/invert-kv-1.0.0.tgz#104a8e4aaca6d3d8cd157a8ef8bfab2d7a3ffdb6" -invert-kv@^2.0.0: - version "2.0.0" - resolved "https://registry.yarnpkg.com/invert-kv/-/invert-kv-2.0.0.tgz#7393f5afa59ec9ff5f67a27620d11c226e3eec02" - -ip-regex@^2.1.0: - version "2.1.0" - resolved "https://registry.yarnpkg.com/ip-regex/-/ip-regex-2.1.0.tgz#fa78bf5d2e6913c911ce9f819ee5146bb6d844e9" - ip@^1.1.0, ip@^1.1.5: version "1.1.5" resolved "https://registry.yarnpkg.com/ip/-/ip-1.1.5.tgz#bdded70114290828c0a039e72ef25f5aaec4354a" @@ -3916,10 +3842,6 @@ ipaddr.js@1.9.0: version "1.9.0" resolved "https://registry.yarnpkg.com/ipaddr.js/-/ipaddr.js-1.9.0.tgz#37df74e430a0e47550fe54a2defe30d8acd95f65" -ipaddr.js@^1.9.0: - version "1.9.1" - resolved "https://registry.yarnpkg.com/ipaddr.js/-/ipaddr.js-1.9.1.tgz#bff38543eeb8984825079ff3a2a8e6cbd46781b3" - is-absolute-url@^2.0.0: version "2.1.0" resolved "https://registry.yarnpkg.com/is-absolute-url/-/is-absolute-url-2.1.0.tgz#50530dfb84fcc9aa7dbe7852e83a37b93b9f2aa6" @@ -4115,34 +4037,18 @@ is-path-cwd@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/is-path-cwd/-/is-path-cwd-1.0.0.tgz#d225ec23132e89edd38fda767472e62e65f1106d" -is-path-cwd@^2.0.0: - version "2.2.0" - resolved "https://registry.yarnpkg.com/is-path-cwd/-/is-path-cwd-2.2.0.tgz#67d43b82664a7b5191fd9119127eb300048a9fdb" - is-path-in-cwd@^1.0.0: version "1.0.1" resolved "https://registry.yarnpkg.com/is-path-in-cwd/-/is-path-in-cwd-1.0.1.tgz#5ac48b345ef675339bd6c7a48a912110b241cf52" dependencies: is-path-inside "^1.0.0" -is-path-in-cwd@^2.0.0: - version "2.1.0" - resolved "https://registry.yarnpkg.com/is-path-in-cwd/-/is-path-in-cwd-2.1.0.tgz#bfe2dca26c69f397265a4009963602935a053acb" - dependencies: - is-path-inside "^2.1.0" - is-path-inside@^1.0.0: version "1.0.1" resolved "https://registry.yarnpkg.com/is-path-inside/-/is-path-inside-1.0.1.tgz#8ef5b7de50437a3fdca6b4e865ef7aa55cb48036" dependencies: path-is-inside "^1.0.1" -is-path-inside@^2.1.0: - version "2.1.0" - resolved "https://registry.yarnpkg.com/is-path-inside/-/is-path-inside-2.1.0.tgz#7c9810587d659a40d27bcdb4d5616eab059494b2" - dependencies: - path-is-inside "^1.0.2" - is-plain-obj@^1.0.0: version "1.1.0" resolved "https://registry.yarnpkg.com/is-plain-obj/-/is-plain-obj-1.1.0.tgz#71a50c8429dfca773c92a390a4a03b39fcd51d3e" @@ -4709,9 +4615,10 @@ jsprim@^1.2.2: json-schema "0.2.3" verror "1.10.0" -killable@^1.0.1: +killable@^1.0.0: version "1.0.1" resolved "https://registry.yarnpkg.com/killable/-/killable-1.0.1.tgz#4c8ce441187a061c7474fb87ca08e2a638194892" + integrity sha512-LzqtLKlUwirEUyl/nicirVmNiPvYs7l5n8wOPP7fyJVpUPkvCnW/vuiXGpylGUlnPDnB7311rARzAt3Mhswpjg== kind-of@^3.0.2, kind-of@^3.0.3, kind-of@^3.2.0: version "3.2.2" @@ -4755,12 +4662,6 @@ lcid@^1.0.0: dependencies: invert-kv "^1.0.0" -lcid@^2.0.0: - version "2.0.0" - resolved "https://registry.yarnpkg.com/lcid/-/lcid-2.0.0.tgz#6ef5d2df60e52f82eb228a4c373e8d1f397253cf" - dependencies: - invert-kv "^2.0.0" - left-pad@^1.3.0: version "1.3.0" resolved "https://registry.yarnpkg.com/left-pad/-/left-pad-1.3.0.tgz#5b8a3a7765dfe001261dde915589e782f8c94d1e" @@ -4844,13 +4745,6 @@ locate-path@^2.0.0: p-locate "^2.0.0" path-exists "^3.0.0" -locate-path@^3.0.0: - version "3.0.0" - resolved "https://registry.yarnpkg.com/locate-path/-/locate-path-3.0.0.tgz#dbec3b3ab759758071b58fe59fc41871af21400e" - dependencies: - p-locate "^3.0.0" - path-exists "^3.0.0" - lodash._getnative@^3.0.0: version "3.9.1" resolved "https://registry.yarnpkg.com/lodash._getnative/-/lodash._getnative-3.9.1.tgz#570bc7dede46d61cdcde687d65d3eecbaa3aaff5" @@ -4940,13 +4834,29 @@ lodash.uniq@^4.5.0: version "4.5.0" resolved "https://registry.yarnpkg.com/lodash.uniq/-/lodash.uniq-4.5.0.tgz#d0225373aeb652adc1bc82e4945339a842754773" -"lodash@>=3.5 <5", lodash@^4.13.1, lodash@^4.16.5, lodash@^4.17.10, lodash@^4.17.11, lodash@^4.17.13, lodash@^4.17.3, lodash@^4.17.4, lodash@^4.17.5, lodash@^4.3.0: +"lodash@>=3.5 <5", lodash@^4.13.1, lodash@^4.16.5, lodash@^4.17.10, lodash@^4.17.11, lodash@^4.17.13, lodash@^4.17.2, lodash@^4.17.3, lodash@^4.17.4, lodash@^4.17.5, lodash@^4.3.0: version "4.17.14" resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.14.tgz#9ce487ae66c96254fe20b599f21b6816028078ba" -loglevel@^1.6.3: +log-symbols@^2.1.0: + version "2.2.0" + resolved "https://registry.yarnpkg.com/log-symbols/-/log-symbols-2.2.0.tgz#5740e1c5d6f0dfda4ad9323b5332107ef6b4c40a" + integrity sha512-VeIAFslyIerEJLXHziedo2basKbMKtTw3vfn5IzG0XTjhAVEJyNHnL2p7vc+wBDSdQuUpNw3M2u6xb9QsAY5Eg== + dependencies: + chalk "^2.0.1" + +loglevel@^1.4.1: version "1.6.3" resolved "https://registry.yarnpkg.com/loglevel/-/loglevel-1.6.3.tgz#77f2eb64be55a404c9fd04ad16d57c1d6d6b1280" + integrity sha512-LoEDv5pgpvWgPF4kNYuIp0qqSJVWak/dML0RY74xlzMZiT9w77teNAwKYKWBTYjlokMirg+o3jBwp+vlLrcfAA== + +loglevelnext@^1.0.1: + version "1.0.5" + resolved "https://registry.yarnpkg.com/loglevelnext/-/loglevelnext-1.0.5.tgz#36fc4f5996d6640f539ff203ba819641680d75a2" + integrity sha512-V/73qkPuJmx4BcBF19xPBr+0ZRVBhc4POxvZTZdMeXpJ4NItXSJ/MSwuFT0kQJlCbXvdlZoQQ/418bS1y9Jh6A== + dependencies: + es6-symbol "^3.1.1" + object.assign "^4.1.0" longest@^1.0.1: version "1.0.1" @@ -4958,7 +4868,7 @@ loose-envify@^1.0.0, loose-envify@^1.1.0, loose-envify@^1.2.0, loose-envify@^1.3 dependencies: js-tokens "^3.0.0 || ^4.0.0" -loud-rejection@^1.0.0: +loud-rejection@^1.0.0, loud-rejection@^1.6.0: version "1.6.0" resolved "https://registry.yarnpkg.com/loud-rejection/-/loud-rejection-1.6.0.tgz#5b46f80147edee578870f086d04821cf998e551f" dependencies: @@ -5046,16 +4956,17 @@ mem@^1.1.0, mem@^4.0.0: mimic-fn "^2.0.0" p-is-promise "^2.0.0" -memory-fs@^0.4.0, memory-fs@^0.4.1, memory-fs@~0.4.1: +memory-fs@^0.4.0, memory-fs@~0.4.1: version "0.4.1" resolved "https://registry.yarnpkg.com/memory-fs/-/memory-fs-0.4.1.tgz#3a9a20b8462523e447cfbc7e8bb80ed667bfc552" dependencies: errno "^0.1.3" readable-stream "^2.0.1" -meow@^3.7.0: +meow@^3.3.0, meow@^3.7.0: version "3.7.0" resolved "https://registry.yarnpkg.com/meow/-/meow-3.7.0.tgz#72cb668b425228290abbfa856892587308a801fb" + integrity sha1-cstmi0JSKCkKu/qFaJJYcwioAfs= dependencies: camelcase-keys "^2.0.0" decamelize "^1.1.2" @@ -5153,9 +5064,10 @@ mime@1.6.0, mime@^1.2.11, mime@^1.4.1: version "1.6.0" resolved "https://registry.yarnpkg.com/mime/-/mime-1.6.0.tgz#32cd9e5c64553bd58d19a568af452acff04981b1" -mime@^2.4.2: +mime@^2.1.0: version "2.4.4" resolved "https://registry.yarnpkg.com/mime/-/mime-2.4.4.tgz#bd7b91135fc6b01cde3e9bae33d659b63d8857e5" + integrity sha512-LRxmNwziLPT828z+4YkNzloCFC2YM4wrB99k+AV5ZbEyfGNWfG8SO1FUXLmLDBSo89NrJZ4DIWeLjy1CHGhMGA== mimic-fn@^1.0.0: version "1.2.0" @@ -5349,10 +5261,6 @@ next-tick@1: version "1.0.0" resolved "https://registry.yarnpkg.com/next-tick/-/next-tick-1.0.0.tgz#ca86d1fe8828169b0120208e3dc8424b9db8342c" -nice-try@^1.0.4: - version "1.0.5" - resolved "https://registry.yarnpkg.com/nice-try/-/nice-try-1.0.5.tgz#a3378a7696ce7d223e88fc9b764bd7ef1089e366" - no-case@^2.2.0: version "2.3.2" resolved "https://registry.yarnpkg.com/no-case/-/no-case-2.3.2.tgz#60b813396be39b3f1288a4c1ed5d1e7d28b464ac" @@ -5544,6 +5452,11 @@ object-copy@^0.1.0: define-property "^0.2.5" kind-of "^3.0.3" +object-keys@^1.0.11: + version "1.1.1" + resolved "https://registry.yarnpkg.com/object-keys/-/object-keys-1.1.1.tgz#1c47f272df277f3b1daf061677d9c82e2322c60e" + integrity sha512-NuAESUOUMrlIXOfHKzD6bpPu3tYt3xvjNdRIQ+FeT0lNb4K8WR70CaDxhuNguS2XG+GjkyMwOzsN5ZktImfhLA== + object-keys@^1.0.12: version "1.0.12" resolved "https://registry.yarnpkg.com/object-keys/-/object-keys-1.0.12.tgz#09c53855377575310cca62f55bb334abff7b3ed2" @@ -5554,6 +5467,16 @@ object-visit@^1.0.0: dependencies: isobject "^3.0.0" +object.assign@^4.1.0: + version "4.1.0" + resolved "https://registry.yarnpkg.com/object.assign/-/object.assign-4.1.0.tgz#968bf1100d7956bb3ca086f006f846b3bc4008da" + integrity sha512-exHJeq6kBKj58mqGyTQ9DFvrZC/eR6OwxzoM9YRoGBqrXYonaFyGiFMuc9VZrXf7DarreEwMpurG3dd+CNyW5w== + dependencies: + define-properties "^1.1.2" + function-bind "^1.1.1" + has-symbols "^1.0.0" + object-keys "^1.0.11" + object.getownpropertydescriptors@^2.0.3: version "2.0.3" resolved "https://registry.yarnpkg.com/object.getownpropertydescriptors/-/object.getownpropertydescriptors-2.0.3.tgz#8758c846f5b407adab0f236e0986f14b051caa16" @@ -5574,7 +5497,7 @@ object.pick@^1.3.0: dependencies: isobject "^3.0.1" -obuf@^1.0.0, obuf@^1.1.2: +obuf@^1.0.0, obuf@^1.1.1: version "1.1.2" resolved "https://registry.yarnpkg.com/obuf/-/obuf-1.1.2.tgz#09bea3343d41859ebd446292d11c9d4db619084e" @@ -5612,9 +5535,10 @@ opn@5.2.0: dependencies: is-wsl "^1.1.0" -opn@^5.5.0: +opn@^5.1.0: version "5.5.0" resolved "https://registry.yarnpkg.com/opn/-/opn-5.5.0.tgz#fc7164fab56d235904c51c3b27da6758ca3b9bfc" + integrity sha512-PqHpggC9bLV0VeWcdKhkpxY+3JTzetLSqTCWL/z/tFIbI6G8JCjondXklT1JinczLz2Xib62sSp0T/gKT4KksA== dependencies: is-wsl "^1.1.0" @@ -5636,7 +5560,7 @@ optionator@^0.8.1: type-check "~0.3.2" wordwrap "~1.0.0" -original@>=0.0.5, original@^1.0.0: +original@>=0.0.5: version "1.0.2" resolved "https://registry.yarnpkg.com/original/-/original-1.0.2.tgz#e442a61cffe1c5fd20a65f3261c26663b303f25f" dependencies: @@ -5658,14 +5582,6 @@ os-locale@^2.0.0: lcid "^1.0.0" mem "^1.1.0" -os-locale@^3.0.0: - version "3.1.0" - resolved "https://registry.yarnpkg.com/os-locale/-/os-locale-3.1.0.tgz#a802a6ee17f24c10483ab9935719cef4ed16bf1a" - dependencies: - execa "^1.0.0" - lcid "^2.0.0" - mem "^4.0.0" - os-tmpdir@^1.0.0, os-tmpdir@^1.0.1, os-tmpdir@~1.0.2: version "1.0.2" resolved "https://registry.yarnpkg.com/os-tmpdir/-/os-tmpdir-1.0.2.tgz#bbe67406c79aa85c5cfec766fe5734555dfa1274" @@ -5695,42 +5611,21 @@ p-limit@^1.1.0: dependencies: p-try "^1.0.0" -p-limit@^2.0.0: - version "2.2.0" - resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-2.2.0.tgz#417c9941e6027a9abcba5092dd2904e255b5fbc2" - dependencies: - p-try "^2.0.0" - p-locate@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/p-locate/-/p-locate-2.0.0.tgz#20a0103b222a70c8fd39cc2e580680f3dde5ec43" dependencies: p-limit "^1.1.0" -p-locate@^3.0.0: - version "3.0.0" - resolved "https://registry.yarnpkg.com/p-locate/-/p-locate-3.0.0.tgz#322d69a05c0264b25997d9f40cd8a891ab0064a4" - dependencies: - p-limit "^2.0.0" - -p-map@^2.0.0: - version "2.1.0" - resolved "https://registry.yarnpkg.com/p-map/-/p-map-2.1.0.tgz#310928feef9c9ecc65b68b17693018a665cea175" - -p-retry@^3.0.1: - version "3.0.1" - resolved "https://registry.yarnpkg.com/p-retry/-/p-retry-3.0.1.tgz#316b4c8893e2c8dc1cfa891f406c4b422bebf328" - dependencies: - retry "^0.12.0" +p-map@^1.1.1: + version "1.2.0" + resolved "https://registry.yarnpkg.com/p-map/-/p-map-1.2.0.tgz#e4e94f311eabbc8633a1e79908165fca26241b6b" + integrity sha512-r6zKACMNhjPJMTl8KcFH4li//gkrXWfbD6feV8l6doRHlzljFWGJ2AP6iKaCJXyZmAUMOPtvbW7EXkbWO/pLEA== p-try@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/p-try/-/p-try-1.0.0.tgz#cbc79cdbaf8fd4228e13f621f2b1a237c1b207b3" -p-try@^2.0.0: - version "2.2.0" - resolved "https://registry.yarnpkg.com/p-try/-/p-try-2.2.0.tgz#cb2868540e313d61de58fafbe35ce9004d5540e6" - package-json@^4.0.0: version "4.0.1" resolved "https://registry.yarnpkg.com/package-json/-/package-json-4.0.1.tgz#8869a0401253661c4c4ca3da6c2121ed555f5eed" @@ -5825,11 +5720,11 @@ path-is-absolute@^1.0.0, path-is-absolute@^1.0.1: version "1.0.1" resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f" -path-is-inside@^1.0.1, path-is-inside@^1.0.2: +path-is-inside@^1.0.1: version "1.0.2" resolved "https://registry.yarnpkg.com/path-is-inside/-/path-is-inside-1.0.2.tgz#365417dede44430d1c11af61027facf074bdfc53" -path-key@^2.0.0, path-key@^2.0.1: +path-key@^2.0.0: version "2.0.1" resolved "https://registry.yarnpkg.com/path-key/-/path-key-2.0.1.tgz#411cadb574c5a140d3a4b1910d40d80cc9f40b40" @@ -5887,10 +5782,6 @@ pify@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/pify/-/pify-3.0.0.tgz#e5a4acd2c101fdf3d9a4d07f0dbc4db49dd28176" -pify@^4.0.1: - version "4.0.1" - resolved "https://registry.yarnpkg.com/pify/-/pify-4.0.1.tgz#4b2cd25c50d598735c50292224fd8c6df41e3231" - pinkie-promise@^2.0.0: version "2.0.1" resolved "https://registry.yarnpkg.com/pinkie-promise/-/pinkie-promise-2.0.1.tgz#2135d6dfa7a358c069ac9b178776288228450ffa" @@ -5907,19 +5798,14 @@ pkg-dir@^2.0.0: dependencies: find-up "^2.1.0" -pkg-dir@^3.0.0: - version "3.0.0" - resolved "https://registry.yarnpkg.com/pkg-dir/-/pkg-dir-3.0.0.tgz#2749020f239ed990881b1f71210d51eb6523bea3" - dependencies: - find-up "^3.0.0" - pn@^1.1.0: version "1.1.0" resolved "https://registry.yarnpkg.com/pn/-/pn-1.1.0.tgz#e2f4cef0e219f463c179ab37463e4e1ecdccbafb" -portfinder@^1.0.20: +portfinder@^1.0.9: version "1.0.21" resolved "https://registry.yarnpkg.com/portfinder/-/portfinder-1.0.21.tgz#60e1397b95ac170749db70034ece306b9a27e324" + integrity sha512-ESabpDCzmBS3ekHbmpAIiESq3udRsCBGiBZLsC+HgBKv2ezb0R4oG+7RnYEVZ/ZCfhel5Tx3UzdNWA0Lox2QCA== dependencies: async "^1.5.2" debug "^2.2.0" @@ -6571,13 +6457,6 @@ pump@^2.0.0, pump@^2.0.1: end-of-stream "^1.1.0" once "^1.3.1" -pump@^3.0.0: - version "3.0.0" - resolved "https://registry.yarnpkg.com/pump/-/pump-3.0.0.tgz#b4a2116815bde2f4e1ea602354e8c75565107a64" - dependencies: - end-of-stream "^1.1.0" - once "^1.3.1" - pumpify@^1.3.3: version "1.5.1" resolved "https://registry.yarnpkg.com/pumpify/-/pumpify-1.5.1.tgz#36513be246ab27570b1a374a5ce278bfd74370ce" @@ -6664,7 +6543,7 @@ randomfill@^1.0.3: randombytes "^2.0.5" safe-buffer "^5.1.0" -range-parser@^1.2.1, range-parser@~1.2.1: +range-parser@^1.0.3, range-parser@~1.2.1: version "1.2.1" resolved "https://registry.yarnpkg.com/range-parser/-/range-parser-1.2.1.tgz#3cf37023d199e1c24d1a55b84800c2f3e6468031" @@ -7254,7 +7133,7 @@ read-pkg@^2.0.0: normalize-package-data "^2.3.2" path-type "^2.0.0" -"readable-stream@1 || 2", readable-stream@^2.0.0, readable-stream@^2.0.1, readable-stream@^2.0.2, readable-stream@^2.0.4, readable-stream@^2.0.6, readable-stream@^2.1.5, readable-stream@^2.2.2, readable-stream@^2.3.3, readable-stream@^2.3.6: +"readable-stream@1 || 2", readable-stream@^2.0.0, readable-stream@^2.0.1, readable-stream@^2.0.2, readable-stream@^2.0.4, readable-stream@^2.0.6, readable-stream@^2.1.5, readable-stream@^2.2.2, readable-stream@^2.2.9, readable-stream@^2.3.3, readable-stream@^2.3.6: version "2.3.6" resolved "http://registry.npmjs.org/readable-stream/-/readable-stream-2.3.6.tgz#b11c27d88b8ff1fbe070643cf94b0c79ae1b0aaf" dependencies: @@ -7275,14 +7154,6 @@ readable-stream@1.0: isarray "0.0.1" string_decoder "~0.10.x" -readable-stream@^3.0.6: - version "3.4.0" - resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-3.4.0.tgz#a51c26754658e0a3c21dbf59163bd45ba6f447fc" - dependencies: - inherits "^2.0.3" - string_decoder "^1.1.1" - util-deprecate "^1.0.1" - readdirp@^2.0.0, readdirp@^2.2.1: version "2.2.1" resolved "https://registry.yarnpkg.com/readdirp/-/readdirp-2.2.1.tgz#0e87622a3325aa33e892285caf8b4e846529a525" @@ -7566,10 +7437,6 @@ ret@~0.1.10: version "0.1.15" resolved "https://registry.yarnpkg.com/ret/-/ret-0.1.15.tgz#b8a4825d5bdb1fc3f6f53c2bc33f81388681c7bc" -retry@^0.12.0: - version "0.12.0" - resolved "https://registry.yarnpkg.com/retry/-/retry-0.12.0.tgz#1b42a6266a21f07421d1b0b54b7dc167b01c013b" - right-align@^0.1.1: version "0.1.3" resolved "https://registry.yarnpkg.com/right-align/-/right-align-0.1.3.tgz#61339b722fe6a3515689210d24e14c96148613ef" @@ -7582,12 +7449,6 @@ rimraf@^2.2.8, rimraf@^2.5.4, rimraf@^2.6.1, rimraf@^2.6.2: dependencies: glob "^7.0.5" -rimraf@^2.6.3: - version "2.6.3" - resolved "https://registry.yarnpkg.com/rimraf/-/rimraf-2.6.3.tgz#b2d104fe0d8fb27cf9e0a1cda8262dd3833c6cab" - dependencies: - glob "^7.1.3" - ripemd160@^2.0.0, ripemd160@^2.0.1: version "2.0.2" resolved "https://registry.yarnpkg.com/ripemd160/-/ripemd160-2.0.2.tgz#a1c1a6f624751577ba5d07914cbc92850585890c" @@ -7681,21 +7542,14 @@ schema-utils@^0.4.5: ajv "^6.1.0" ajv-keywords "^3.1.0" -schema-utils@^1.0.0: - version "1.0.0" - resolved "https://registry.yarnpkg.com/schema-utils/-/schema-utils-1.0.0.tgz#0b79a93204d7b600d4b2850d1f66c2a34951c770" - dependencies: - ajv "^6.1.0" - ajv-errors "^1.0.0" - ajv-keywords "^3.1.0" - select-hose@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/select-hose/-/select-hose-2.0.0.tgz#625d8658f865af43ec962bfc376a37359a4994ca" -selfsigned@^1.10.4: +selfsigned@^1.9.1: version "1.10.4" resolved "https://registry.yarnpkg.com/selfsigned/-/selfsigned-1.10.4.tgz#cdd7eccfca4ed7635d47a08bf2d5d3074092e2cd" + integrity sha512-9AukTiDmHXGXWtWjembZ5NDmVvP2695EtpgbCsxCa68w3c88B+alqbmZ4O3hZ4VWGXeGWzEVdvqgAJD8DQPCDw== dependencies: node-forge "0.7.5" @@ -7709,14 +7563,6 @@ semver-diff@^2.0.0: version "5.6.0" resolved "https://registry.yarnpkg.com/semver/-/semver-5.6.0.tgz#7e74256fbaa49c75aa7c7a205cc22799cac80004" -semver@^5.5.0: - version "5.7.0" - resolved "https://registry.yarnpkg.com/semver/-/semver-5.7.0.tgz#790a7cf6fea5459bac96110b29b60412dc8ff96b" - -semver@^6.1.1: - version "6.2.0" - resolved "https://registry.yarnpkg.com/semver/-/semver-6.2.0.tgz#4d813d9590aaf8a9192693d6c85b9344de5901db" - send@0.17.1: version "0.17.1" resolved "https://registry.yarnpkg.com/send/-/send-0.17.1.tgz#c1d8b059f7900f7466dd4938bdc44e11ddb376c8" @@ -7739,9 +7585,10 @@ serialize-javascript@^1.4.0: version "1.5.0" resolved "https://registry.yarnpkg.com/serialize-javascript/-/serialize-javascript-1.5.0.tgz#1aa336162c88a890ddad5384baebc93a655161fe" -serve-index@^1.9.1: +serve-index@^1.7.2: version "1.9.1" resolved "https://registry.yarnpkg.com/serve-index/-/serve-index-1.9.1.tgz#d3768d69b1e7d82e5ce050fff5b453bea12a9239" + integrity sha1-03aNabHn2C5c4FD/9bRTvqEqkjk= dependencies: accepts "~1.3.4" batch "0.6.1" @@ -7893,9 +7740,10 @@ sntp@1.x.x: dependencies: hoek "2.x.x" -sockjs-client@1.1.5: - version "1.1.5" - resolved "https://registry.yarnpkg.com/sockjs-client/-/sockjs-client-1.1.5.tgz#1bb7c0f7222c40f42adf14f4442cbd1269771a83" +sockjs-client@1.1.4: + version "1.1.4" + resolved "https://registry.yarnpkg.com/sockjs-client/-/sockjs-client-1.1.4.tgz#5babe386b775e4cf14e7520911452654016c8b12" + integrity sha1-W6vjhrd15M8U51IJEUUmVAFsixI= dependencies: debug "^2.6.6" eventsource "0.1.6" @@ -7904,16 +7752,16 @@ sockjs-client@1.1.5: json3 "^3.3.2" url-parse "^1.1.8" -sockjs-client@1.3.0: - version "1.3.0" - resolved "https://registry.yarnpkg.com/sockjs-client/-/sockjs-client-1.3.0.tgz#12fc9d6cb663da5739d3dc5fb6e8687da95cb177" +sockjs-client@1.1.5: + version "1.1.5" + resolved "https://registry.yarnpkg.com/sockjs-client/-/sockjs-client-1.1.5.tgz#1bb7c0f7222c40f42adf14f4442cbd1269771a83" dependencies: - debug "^3.2.5" - eventsource "^1.0.7" - faye-websocket "~0.11.1" - inherits "^2.0.3" + debug "^2.6.6" + eventsource "0.1.6" + faye-websocket "~0.11.0" + inherits "^2.0.1" json3 "^3.3.2" - url-parse "^1.4.3" + url-parse "^1.1.8" sockjs@0.3.19: version "0.3.19" @@ -7996,26 +7844,30 @@ spdx-license-ids@^3.0.0: version "3.0.1" resolved "https://registry.yarnpkg.com/spdx-license-ids/-/spdx-license-ids-3.0.1.tgz#e2a303236cac54b04031fa7a5a79c7e701df852f" -spdy-transport@^3.0.0: - version "3.0.0" - resolved "https://registry.yarnpkg.com/spdy-transport/-/spdy-transport-3.0.0.tgz#00d4863a6400ad75df93361a1608605e5dcdcf31" +spdy-transport@^2.0.18: + version "2.1.1" + resolved "https://registry.yarnpkg.com/spdy-transport/-/spdy-transport-2.1.1.tgz#c54815d73858aadd06ce63001e7d25fa6441623b" + integrity sha512-q7D8c148escoB3Z7ySCASadkegMmUZW8Wb/Q1u0/XBgDKMO880rLQDj8Twiew/tYi7ghemKUi/whSYOwE17f5Q== dependencies: - debug "^4.1.0" - detect-node "^2.0.4" + debug "^2.6.8" + detect-node "^2.0.3" hpack.js "^2.1.6" - obuf "^1.1.2" - readable-stream "^3.0.6" - wbuf "^1.7.3" + obuf "^1.1.1" + readable-stream "^2.2.9" + safe-buffer "^5.0.1" + wbuf "^1.7.2" -spdy@^4.0.0: - version "4.0.0" - resolved "https://registry.yarnpkg.com/spdy/-/spdy-4.0.0.tgz#81f222b5a743a329aa12cea6a390e60e9b613c52" +spdy@^3.4.1: + version "3.4.7" + resolved "https://registry.yarnpkg.com/spdy/-/spdy-3.4.7.tgz#42ff41ece5cc0f99a3a6c28aabb73f5c3b03acbc" + integrity sha1-Qv9B7OXMD5mjpsKKq7c/XDsDrLw= dependencies: - debug "^4.1.0" - handle-thing "^2.0.0" + debug "^2.6.8" + handle-thing "^1.2.5" http-deceiver "^1.2.7" + safe-buffer "^5.0.1" select-hose "^2.0.0" - spdy-transport "^3.0.0" + spdy-transport "^2.0.18" split-string@^3.0.1, split-string@^3.0.2: version "3.1.0" @@ -8130,12 +7982,6 @@ string_decoder@^1.0.0, string_decoder@~1.1.1: dependencies: safe-buffer "~5.1.0" -string_decoder@^1.1.1: - version "1.2.0" - resolved "https://registry.yarnpkg.com/string_decoder/-/string_decoder-1.2.0.tgz#fe86e738b19544afe70469243b2a1ee9240eae8d" - dependencies: - safe-buffer "~5.1.0" - string_decoder@~0.10.x: version "0.10.31" resolved "https://registry.yarnpkg.com/string_decoder/-/string_decoder-0.10.31.tgz#62e203bc41766c6c28c9fc84301dab1c5310fa94" @@ -8209,15 +8055,10 @@ supports-color@^4.2.1: dependencies: has-flag "^2.0.0" -supports-color@^5.3.0, supports-color@^5.4.0: +supports-color@^5.1.0, supports-color@^5.3.0, supports-color@^5.4.0: version "5.5.0" resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.5.0.tgz#e2e69a44ac8772f78a1ec0b35b689df6530efc8f" - dependencies: - has-flag "^3.0.0" - -supports-color@^6.1.0: - version "6.1.0" - resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-6.1.0.tgz#0764abc69c63d5ac842dd4867e8d025e880df8f3" + integrity sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow== dependencies: has-flag "^3.0.0" @@ -8689,6 +8530,11 @@ urix@^0.1.0: version "0.1.0" resolved "https://registry.yarnpkg.com/urix/-/urix-0.1.0.tgz#da937f7a62e21fec1fd18d49b35c2935067a6c72" +url-join@^2.0.2: + version "2.0.5" + resolved "https://registry.yarnpkg.com/url-join/-/url-join-2.0.5.tgz#5af22f18c052a000a48d7b82c5e9c2e2feeda728" + integrity sha1-WvIvGMBSoACkjXuCxenC4v7tpyg= + url-loader@0.6.2: version "0.6.2" resolved "https://registry.yarnpkg.com/url-loader/-/url-loader-0.6.2.tgz#a007a7109620e9d988d14bce677a1decb9a993f7" @@ -8721,7 +8567,7 @@ use@^3.1.0: version "3.1.1" resolved "https://registry.yarnpkg.com/use/-/use-3.1.1.tgz#d50c8cac79a19fbc20f2911f56eb973f4e10070f" -util-deprecate@^1.0.1, util-deprecate@~1.0.1: +util-deprecate@~1.0.1: version "1.0.2" resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf" @@ -8752,7 +8598,7 @@ utils-merge@1.0.1: version "1.0.1" resolved "https://registry.yarnpkg.com/utils-merge/-/utils-merge-1.0.1.tgz#9f95710f50a267947b2ccc124741c1028427e713" -uuid@^3.0.0, uuid@^3.0.1, uuid@^3.3.2: +uuid@^3.0.0, uuid@^3.0.1, uuid@^3.1.0, uuid@^3.3.2: version "3.3.2" resolved "https://registry.yarnpkg.com/uuid/-/uuid-3.3.2.tgz#1b4af4955eb3077c501c23872fc6513811587131" @@ -8830,7 +8676,7 @@ watchpack@^1.4.0: graceful-fs "^4.1.2" neo-async "^2.5.0" -wbuf@^1.1.0, wbuf@^1.7.3: +wbuf@^1.1.0, wbuf@^1.7.2: version "1.7.3" resolved "https://registry.yarnpkg.com/wbuf/-/wbuf-1.7.3.tgz#c1d8d149316d3ea852848895cb6a0bfe887b87df" dependencies: @@ -8840,57 +8686,61 @@ webidl-conversions@^4.0.2: version "4.0.2" resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-4.0.2.tgz#a855980b1f0b6b359ba1d5d9fb39ae941faa63ad" -webpack-dev-middleware@^3.7.0: - version "3.7.0" - resolved "https://registry.yarnpkg.com/webpack-dev-middleware/-/webpack-dev-middleware-3.7.0.tgz#ef751d25f4e9a5c8a35da600c5fda3582b5c6cff" +webpack-dev-middleware@2.0.6: + version "2.0.6" + resolved "https://registry.yarnpkg.com/webpack-dev-middleware/-/webpack-dev-middleware-2.0.6.tgz#a51692801e8310844ef3e3790e1eacfe52326fd4" + integrity sha512-tj5LLD9r4tDuRIDa5Mu9lnY2qBBehAITv6A9irqXhw/HQquZgTx3BCd57zYbU2gMDnncA49ufK2qVQSbaKJwOw== dependencies: - memory-fs "^0.4.1" - mime "^2.4.2" - range-parser "^1.2.1" - webpack-log "^2.0.0" + loud-rejection "^1.6.0" + memory-fs "~0.4.1" + mime "^2.1.0" + path-is-absolute "^1.0.0" + range-parser "^1.0.3" + url-join "^2.0.2" + webpack-log "^1.0.1" -webpack-dev-server@2.9.4, webpack-dev-server@^3.1.11: - version "3.7.2" - resolved "https://registry.yarnpkg.com/webpack-dev-server/-/webpack-dev-server-3.7.2.tgz#f79caa5974b7f8b63268ef5421222a8486d792f5" +webpack-dev-server@2.9.4, webpack-dev-server@3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/webpack-dev-server/-/webpack-dev-server-3.0.0.tgz#0ca2d293dc7a7b1a94fc5fd62cfca2a9fa61bcf7" + integrity sha512-oqGjPBE4XKmo2VPDrBcFaU4PzXuhEkpmt7p01tAHfDV5OHv/NGJHem0shd20/3IuTG/H70KgwGPLkZkeP9151w== dependencies: ansi-html "0.0.7" + array-includes "^3.0.3" bonjour "^3.5.0" - chokidar "^2.1.6" - compression "^1.7.4" - connect-history-api-fallback "^1.6.0" - debug "^4.1.1" - del "^4.1.1" - express "^4.17.1" - html-entities "^1.2.1" - http-proxy-middleware "^0.19.1" - import-local "^2.0.0" - internal-ip "^4.3.0" + chokidar "^2.0.0" + compression "^1.5.2" + connect-history-api-fallback "^1.3.0" + debug "^3.1.0" + del "^3.0.0" + express "^4.16.2" + html-entities "^1.2.0" + http-proxy-middleware "~0.17.4" + import-local "^1.0.0" + internal-ip "1.2.0" ip "^1.1.5" - killable "^1.0.1" - loglevel "^1.6.3" - opn "^5.5.0" - p-retry "^3.0.1" - portfinder "^1.0.20" - schema-utils "^1.0.0" - selfsigned "^1.10.4" - semver "^6.1.1" - serve-index "^1.9.1" + killable "^1.0.0" + loglevel "^1.4.1" + opn "^5.1.0" + portfinder "^1.0.9" + selfsigned "^1.9.1" + serve-index "^1.7.2" sockjs "0.3.19" - sockjs-client "1.3.0" - spdy "^4.0.0" - strip-ansi "^3.0.1" - supports-color "^6.1.0" - url "^0.11.0" - webpack-dev-middleware "^3.7.0" - webpack-log "^2.0.0" - yargs "12.0.5" + sockjs-client "1.1.4" + spdy "^3.4.1" + strip-ansi "^3.0.0" + supports-color "^5.1.0" + webpack-dev-middleware "2.0.6" + yargs "9.0.1" -webpack-log@^2.0.0: - version "2.0.0" - resolved "https://registry.yarnpkg.com/webpack-log/-/webpack-log-2.0.0.tgz#5b7928e0637593f119d32f6227c1e0ac31e1b47f" +webpack-log@^1.0.1: + version "1.2.0" + resolved "https://registry.yarnpkg.com/webpack-log/-/webpack-log-1.2.0.tgz#a4b34cda6b22b518dbb0ab32e567962d5c72a43d" + integrity sha512-U9AnICnu50HXtiqiDxuli5gLB5PGBo7VvcHx36jRZHwK4vzOYLbImqT4lwWwoMHdQWwEKw736fCHEekokTEKHA== dependencies: - ansi-colors "^3.0.0" - uuid "^3.3.2" + chalk "^2.1.0" + log-symbols "^2.1.0" + loglevelnext "^1.0.1" + uuid "^3.1.0" webpack-manifest-plugin@1.3.2: version "1.3.2" @@ -9067,7 +8917,7 @@ y18n@^3.2.1: version "3.2.1" resolved "https://registry.yarnpkg.com/y18n/-/y18n-3.2.1.tgz#6d15fba884c08679c0d77e88e7759e811e07fa41" -"y18n@^3.2.1 || ^4.0.0", y18n@^4.0.0: +y18n@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/y18n/-/y18n-4.0.0.tgz#95ef94f85ecc81d007c264e190a120f0a3c8566b" @@ -9079,13 +8929,6 @@ yallist@^3.0.0, yallist@^3.0.2: version "3.0.2" resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.0.2.tgz#8452b4bb7e83c7c188d8041c1a837c773d6d8bb9" -yargs-parser@^11.1.1: - version "11.1.1" - resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-11.1.1.tgz#879a0865973bca9f6bab5cbdf3b1c67ec7d3bcf4" - dependencies: - camelcase "^5.0.0" - decamelize "^1.2.0" - yargs-parser@^7.0.0: version "7.0.0" resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-7.0.0.tgz#8d0ac42f16ea55debd332caf4c4038b3e3f5dfd9" @@ -9098,22 +8941,24 @@ yargs-parser@^8.1.0: dependencies: camelcase "^4.1.0" -yargs@12.0.5: - version "12.0.5" - resolved "https://registry.yarnpkg.com/yargs/-/yargs-12.0.5.tgz#05f5997b609647b64f66b81e3b4b10a368e7ad13" +yargs@9.0.1: + version "9.0.1" + resolved "https://registry.yarnpkg.com/yargs/-/yargs-9.0.1.tgz#52acc23feecac34042078ee78c0c007f5085db4c" + integrity sha1-UqzCP+7Kw0BCB47njAwAf1CF20w= dependencies: - cliui "^4.0.0" - decamelize "^1.2.0" - find-up "^3.0.0" + camelcase "^4.1.0" + cliui "^3.2.0" + decamelize "^1.1.1" get-caller-file "^1.0.1" - os-locale "^3.0.0" + os-locale "^2.0.0" + read-pkg-up "^2.0.0" require-directory "^2.1.1" require-main-filename "^1.0.1" set-blocking "^2.0.0" string-width "^2.0.0" which-module "^2.0.0" - y18n "^3.2.1 || ^4.0.0" - yargs-parser "^11.1.1" + y18n "^3.2.1" + yargs-parser "^7.0.0" yargs@^10.0.3: version "10.1.2" From 7672887f416125ec38be8abbe994595861fd9fd4 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Wed, 14 Aug 2019 18:07:05 +0800 Subject: [PATCH 12/34] add doc and change files --- docs/en_US/Compressor/Overview.md | 39 +++++ docs/en_US/Compressor/Pruner.md | 73 +++++++++ docs/en_US/Compressor/Quantizer.md | 46 ++++++ docs/img/AGPruner.PNG | Bin 0 -> 8576 bytes src/sdk/pynni/nni/compressors/AGPruner.py | 138 ------------------ src/sdk/pynni/nni/compressors/QATquantizer.py | 89 ----------- .../example/{main_tf.py => main_tf_pruner.py} | 26 ++-- .../compressors/example/main_tf_quantizer.py | 113 ++++++++++++++ .../{main_torch.py => main_torch_pruner.py} | 25 ++-- .../example/main_torch_quantizer.py | 83 +++++++++++ src/sdk/pynni/nni/compressors/level_pruner.py | 38 ----- .../pynni/nni/compressors/naive_quantizer.py | 39 ----- src/sdk/pynni/nni/compressors/nnimc.py | 24 --- .../nni/compressors/tfCompressor/__init__.py | 4 +- .../nni/compressors/tfCompressor/pruner.py | 12 +- .../nni/compressors/tfCompressor/quantizer.py | 6 +- .../compressors/torchCompressor/__init__.py | 4 +- .../nni/compressors/torchCompressor/pruner.py | 9 +- .../compressors/torchCompressor/quantizer.py | 6 +- 19 files changed, 396 insertions(+), 378 deletions(-) create mode 100644 docs/en_US/Compressor/Overview.md create mode 100644 docs/en_US/Compressor/Pruner.md create mode 100644 docs/en_US/Compressor/Quantizer.md create mode 100644 docs/img/AGPruner.PNG delete mode 100644 src/sdk/pynni/nni/compressors/AGPruner.py delete mode 100644 src/sdk/pynni/nni/compressors/QATquantizer.py rename src/sdk/pynni/nni/compressors/example/{main_tf.py => main_tf_pruner.py} (82%) create mode 100644 src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py rename src/sdk/pynni/nni/compressors/example/{main_torch.py => main_torch_pruner.py} (76%) create mode 100644 src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py delete mode 100644 src/sdk/pynni/nni/compressors/level_pruner.py delete mode 100644 src/sdk/pynni/nni/compressors/naive_quantizer.py delete mode 100644 src/sdk/pynni/nni/compressors/nnimc.py diff --git a/docs/en_US/Compressor/Overview.md b/docs/en_US/Compressor/Overview.md new file mode 100644 index 0000000000..8e1b2d2a19 --- /dev/null +++ b/docs/en_US/Compressor/Overview.md @@ -0,0 +1,39 @@ +# Compressor +NNI provides easy-to-use toolkit to help user design and use compression algorithm. + +## Framework +We use the instrumentation method to insert a node or function after the corresponding position in the model. +
+When compression algorithm designer implements one prune algorithm, he only need to pay attention to the generation method of mask, without caring about applying the mask to the garph. +## algorithm +We now provide some naive compression algorithm and four popular compress agorithms for users, including two pruning algorithm and two quantization algorithm. +Below is a list of model compression algorithms supported in our compressor +| Name | Paper | +| ---------- | ----------| +| AGPruner | [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878)| +| SensitivityPruner |[Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626)| +| QATquantizer |[Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf)| +| DoReFaQuantizer |[DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://arxiv.org/abs/1606.06160)| + +## Usage + +Take naive level pruner as an example + +If you want to prune all weight to 80% sparsity, you can add code below into your code before your training code. + +Tensorflow code +``` +nni.compressors.tfCompressor.LevelPruner(sparsity=0.8).compress(model_graph) +``` + +Pytorch code +``` +nni.compressors.torchCompressor.LevelPruner(sparsity=0.8).compress(model) +``` + +Our compressor will automatically insert mask into your model, and you can train your model with masks without changing your training code. You will get a compressed model when you finish your training. + +You can get more information in Algorithm details + + + diff --git a/docs/en_US/Compressor/Pruner.md b/docs/en_US/Compressor/Pruner.md new file mode 100644 index 0000000000..4482b71465 --- /dev/null +++ b/docs/en_US/Compressor/Pruner.md @@ -0,0 +1,73 @@ +Pruner on NNI Compressor +=== + +## AGPruner +In [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878), authors Michael Zhu and Suyog Gupta provide an algorithm to prune the weight gradually. + +>We introduce a new automated gradual pruning algorithm in which the sparsity is increased from an initial sparsity value si (usually 0) to a final sparsity value sf over a span of n pruning steps, starting at training step t0 and with pruning frequency ∆t: +![](../../img/AGPruner.PNG) +>The binary weight masks are updated every ∆t steps as the network is trained to gradually increase the sparsity of the network while allowing the network training steps to recover from any pruning-induced loss in accuracy. In our experience, varying the pruning frequency ∆t between 100 and 1000 training steps had a negligible impact on the final model quality. Once the model achieves the target sparsity sf , the weight masks are no longer updated. The intuition behind this sparsity function in equation + +### Usage +You can prune all weight from %0 to 80% sparsity in 10 epoch with the code below. + +First, you should import pruner and add mask to model. + +Tensorflow code +``` +from nni.compressors.tfCompressor import AGPruner +pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1).compress(tf.get_default_graph()) +``` +Pytorch code +``` +from nni.compressors.torchCompressor import AGPruner +pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1).compress(model) +``` + +Second, you should add code below to update epoch number when you finish one epoch in your training code. + +Tensorflow code +``` +pruner.update_epoch(epoch, sess) +``` +Pytorch code +``` +pruner.update_epoch(epoch) +``` +You can view example for more information +*** + +## SensitivityPruner +In [Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626), author Song Han and provide an algorithm to find the sensitivity of each layer and set the pruning threshold to each layer. + +>We used the sensitivity results to find each layer’s threshold: for example, the smallest threshold was applied to the most sensitive layer, which is the first convolutional layer... The pruning threshold is chosen as a quality parameter multiplied by the standard deviation of a layer’s weights + +### Usage +You can prune weight step by step and reach one target sparsity by SensitivityPruner with the code below. + +Tensorflow code +``` +from nni.compressors.tfCompressor import SensitivityPruner + +pruner = SensitivityPruner(sparsity = 0.8) +pruner.compress(tf.get_default_graph()) +``` +Pytorch code +``` +from nni.compressors.torchCompressor import SensitivityPruner + +pruner = SensitivityPruner(sparsity = 0.8) +pruner.compress(model) +``` +Like AGPruner, you should update mask information every epoch by adding code below + +Tensorflow code +``` +pruner.update_epoch(epoch, sess) +``` +Pytorch code +``` +pruner.update_epoch(epoch) +``` +You can view example for more information +*** diff --git a/docs/en_US/Compressor/Quantizer.md b/docs/en_US/Compressor/Quantizer.md new file mode 100644 index 0000000000..c440da8e69 --- /dev/null +++ b/docs/en_US/Compressor/Quantizer.md @@ -0,0 +1,46 @@ +Quantizer on NNI Compressor +=== +## QATquantizer +In [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf), authors Benoit Jacob and Skirmantas Kligys provide an algorithm to quantize the model with training. + +>We propose an approach that simulates quantization effects in the forward pass of training. Backpropagation still happens as usual, and all weights and biases are stored in floating point so that they can be easily nudged by small amounts. The forward propagation pass however simulates quantized inference as it will happen in the inference engine, by implementing in floating-point arithmetic the rounding behavior of the quantization scheme +>* Weights are quantized before they are convolved with the input. If batch normalization (see [17]) is used for the layer, the batch normalization parameters are “folded into” the weights before quantization. +>* Activations are quantized at points where they would be during inference, e.g. after the activation function is applied to a convolutional or fully connected layer’s output, or after a bypass connection adds or concatenates the outputs of several layers together such as in ResNets. + + + +### Usage +You can quantize your model to 8 bits with the code below before your training code. + +Tensorflow code +``` +from nni.compressors.tfCompressor import QATquantizer +QATquantizer(q_bits = 8).compress(tf.get_default_graph()) +``` +Pytorch code +``` +from nni.compressors.torchCompressor import QATquantizer +QATquantizer(q_bits = 8).compress(model) +``` + +You can view example for more information + +*** +## DoReFaQuantizer +In [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://arxiv.org/abs/1606.06160), authors Shuchang Zhou and Yuxin Wu provide an algorithm named DoReFa to quantize the weight, activation and gradients with training. + +### Usage +To implement DoReFaQuantizer, you can add code below before your training code + +Tensorflow code +``` +from nni.compressors.tfCompressor import DoReFaQuantizer +DoReFaQuantizer(q_bits = 8).compress(tf.get_default_graph()) +``` +Pytorch code +``` +from nni.compressors.torchCompressor import DoReFaQuantizer +DoReFaQuantizer(q_bits = 8).compress(model) +``` + +You can view example for more information diff --git a/docs/img/AGPruner.PNG b/docs/img/AGPruner.PNG new file mode 100644 index 0000000000000000000000000000000000000000..889f42e7647e705f773b58141b2ef6c3067d3e95 GIT binary patch literal 8576 zcmd6tRa6{7x2Pe(onV7|kl^kFC%C%}9w0D4@WEvugF}EMI3xsz;LZ#|0s%sR!EFc< z++FVEKWD9bU+(K!=b^iIt*)-#yK2`i`6@wAM~x7V1`iDljqsJavH=>}lW3G|f`f&+ zH$KUgMm^Ad4b&9TYR2gPq7oP`3fc;2X!R-hcR);38W*H)?u&*-)c5Z~AN2a_h=#^Y 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zAOYDiGdkE)KwJ)q7L2kVx$RLrfEIKm39_{%@KBMMRzCmUBAH!(uYz>_4n0^)GQvX+ z3daf0t)e!nf0MCQBJ`v=X#qb=xsP9>{9@b(DSrVgZGrS|q!8WZqY$c;+wAkU{pryX*ii;JQm9G#wUkso{1p=LSXU_v=&c~7!PBw7os8% zj$u1Ki}M0cvOX!2BG=-`el#7%EJy`)3uxW^zoh3x+t38^;{=x3U9x(Sf|WR{gQDmx zc+tw5Ml26y{B~}k&xW^|D*Zhq^S3%Fg+m5uJJofSY}(S5`2I5(sDIM5<__Zv%X5h$ z{q(rg+EkAsee5Xmy03qZ*1;PG-!v*Qtyado8p5p~P+Jc`%CVz({6yeCpbBJQge{`r zH2-@W>k17N^D+zVCgI5E23paekUh5=Fx8cslHFU{PI8Pi?Ru(GVeaaBt9}?PC3EO^ z=S`17yp|}iYN1Yo^LX<88H8zKT$w9}fX?3x>+pLV%nL>RUFGniQ4|h`*&-3e%TZE1 wC92B)9S11xN9p+QV1TRO|5O&<)kpLZM<2qs&_mQ|2{g10 - target_sparsity = (self.final_sparsity + - (self.initial_sparsity - self.final_sparsity)* - (1.0 - ((now_epoch - self.start_epoch)/span))**3 ) - return target_sparsity - - def calc_mask(self, layer_info, weight): - now_epoch = self.now_epoch - mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) - target_sparsity = self.compute_target_sparsity(now_epoch) - k = int(weight.numel() * target_sparsity) - if k == 0: - return mask - - w_abs = weight.abs()*mask - threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() - new_mask = torch.gt(w_abs, threshold).type(weight.type()) - self.mask_list[layer_info.name] = new_mask - return new_mask - - def update_epoch(self, epoch): - if epoch <= 0: - return - self.now_epoch = epoch - - - class TorchSensitivityPruner(TorchPruner): - def __init__(self, sparsity): - super().__init__() - self.sparsity = sparsity - self.mask_list = {} - - def calc_mask(self, layer_info, weight): - mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) - weight = weight*mask - target_sparsity = self.sparsity * torch.std(weight).item() - k = int(weight.numel() * target_sparsity) - if k == 0: - return mask - - w_abs = weight.abs() - threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() - new_mask = torch.gt(w_abs, threshold).type(weight.type()) - self.mask_list[layer_info.name] = new_mask - return new_mask - -except ImportError: - pass - - -try: - import tensorflow as tf - from nnimc import TfPruner - - class TfAGPruner(TfPruner): - def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): - super().__init__() - self.initial_sparsity = initial_sparsity - self.final_sparsity = final_sparsity - self.start_epoch = start_epoch - self.end_epoch = end_epoch - self.freq = frequency - self.now_epoch = tf.Variable(0) - self.assign_handler = [] - - def compute_target_sparsity(self): - - if self.end_epoch <= self.start_epoch : - return self.final_sparsity - - now_epoch = tf.minimum(self.now_epoch, tf.constant(self.end_epoch)) - span = int(((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq) - assert span>0 - base = tf.cast(now_epoch - self.initial_sparsity, tf.float32) / span - target_sparsity = (self.final_sparsity + - (self.initial_sparsity - self.final_sparsity)* - (tf.pow(1.0 - base,3))) - return target_sparsity - - def calc_mask(self, layer, weight): - - target_sparsity = self.compute_target_sparsity() - threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) - mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) - self.assign_handler.append(tf.assign(weight, weight*mask)) - return mask - - def update_epoch(self, sess, epoch): - sess.run(self.assign_handler) - sess.run(tf.assign(self.now_epoch, int(epoch))) - - - class TfSensitivityPruner(TfPruner): - def __init__(self, sparsity): - super().__init__() - self.sparsity = sparsity - self.layer_mask = {} - self.assign_handler = [] - - def calc_mask(self, layer_info, weight): - target_sparsity = self.sparsity * tf.math.reduce_std(weight) - mask = tf.get_variable(layer_info.name+'_mask',initializer=tf.ones(weight.shape), trainable=False) - self.layer_mask[layer_info.name] = mask - - weight_assign_handler = tf.assign(weight, mask*weight) - with tf.control_dependencies([weight_assign_handler]): - threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) - new_mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) - mask_update_handler = tf.assign(mask, new_mask) - self.assign_handler.append(mask_update_handler) - return mask - - def update_graph(self, sess): - sess.run(self.assign_handler) - -except ImportError: - pass diff --git a/src/sdk/pynni/nni/compressors/QATquantizer.py b/src/sdk/pynni/nni/compressors/QATquantizer.py deleted file mode 100644 index 513b2c8fb9..0000000000 --- a/src/sdk/pynni/nni/compressors/QATquantizer.py +++ /dev/null @@ -1,89 +0,0 @@ -try: - import torch - from nnimc import TorchQuantizer - - class TorchDoReFaQuantizer(TorchQuantizer): - def __init__(self, q_bits): - super().__init__() - self.q_bits = q_bits - - def quantize_weight(self, layer_info, weight): - out = weight.tanh() - out = out /( 2 * out.abs().max()) + 0.5 - out = self.quantize(out, self.q_bits) - out = 2 * out -1 - #print(weight,out) - return out - - def quantize(self, input_ri, q_bits): - scale = pow(2, q_bits)-1 - output = torch.round(input_ri*scale)/scale - #input_ri.mul_(scale).round_() - #input_ri.div_(scale) - return output - - class TorchQATquantizer(TorchQuantizer): - def __init__(self, q_bits): - super().__init__() - self.q_bits = q_bits - - def quantize_weight(self, layer_info, weight): - if self.q_bits <= 1: - return weight - a = torch.min(weight) - b = torch.max(weight) - n = pow(2,self.q_bits) - scale = (b-a)/(n-1) - zero_point = a - #print(a,b,scale) - out = torch.round((weight - zero_point)/scale) - out = out*scale + zero_point - orig_type = weight.dtype - #print(weight,out) - return out.type(orig_type) - -except ImportError: - pass - - - -try: - import tensorflow as tf - from nnimc import TfQuantizer - from tensorflow.python.framework import ops as tf_ops - - class TfQATquantizer(TfQuantizer): - def __init__(self, q_bits): - super().__init__() - self.q_bits = q_bits - - def quantize_weight(self, layer_info, weight): - a = tf.stop_gradient(tf.reduce_min(weight)) - b = tf.stop_gradient(tf.reduce_max(weight)) - n = tf.cast(2 ** self.q_bits, tf.float32) - scale = b-a/(n-1) - - with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): - qw = tf.round((weight-a)/scale)*scale +a - - return qw - - class TfDoReFaQuantizer(TfQuantizer): - def __init__(self, q_bits): - super().__init__() - self.q_bits = q_bits - - def quantize_weight(self, layer_info, weight): - a = tf.math.tanh(weight) - b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5 - - scale = pow(2, self.q_bits-1) - with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): - qw = tf.round(b*scale)/scale - r_qw = 2*qw - 1 - return r_qw - - - -except ModuleNotFoundError: - pass \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/example/main_tf.py b/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py similarity index 82% rename from src/sdk/pynni/nni/compressors/example/main_tf.py rename to src/sdk/pynni/nni/compressors/example/main_tf_pruner.py index cf5e177140..29dda59f06 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf.py +++ b/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py @@ -1,8 +1,4 @@ -#from level_pruner import TfLevelPruner -#from naive_quantizer import TfNaiveQuantizer -#from AGPruner import TfAGPruner,TfSensitivityPruner -#from QATquantizer import TfDeReFaQuantizer,TfQATquantizer -from nni.compressors.tfCompressor import TfLevelPruner +from nni.compressors.tfCompressor import AGPruner import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data @@ -83,15 +79,13 @@ def main(): model = Mnist() - #TfNaiveQuantizer().compress_default_graph() - #TfDeReFaQuantizer(q_bits = 8).compress_default_graph() - #TfAGP = TfAGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) - #TfPruner = TfSensitivityPruner(sparsity = 0.8) - #TfPruner.compress_default_graph() - TfLevelPruner(0.5).compress_default_graph() - writer = tf.summary.FileWriter("log/simple_example.log", tf.get_default_graph()) - print('writer done') - writer.close() + '''you can change this to SensitivityPruner to implement it + pruner = SensitivityPruner(sparsity = 0.8) + ''' + pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) + pruner.compress(tf.get_default_graph()) + + with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for batch_idx in range(2000): @@ -107,9 +101,9 @@ def main(): model.labels: data.test.labels, model.keep_prob: 1.0 }) - #TfPruner.update_graph(sess) + pruner.update_epoch(batch_idx / 10,sess) print('test accuracy', test_acc) - #print(tf.gradients(model.cross,[model.w1, model.fcw1])) + test_acc = model.accuracy.eval(feed_dict = { model.images: data.test.images, diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py new file mode 100644 index 0000000000..a2b4cb1066 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py @@ -0,0 +1,113 @@ +from nni.compressors.tfCompressor import QATquantizer +import tensorflow as tf +from tensorflow.examples.tutorials.mnist import input_data + + +def weight_variable(shape): + return tf.Variable(tf.truncated_normal(shape, stddev = 0.1)) + +def bias_variable(shape): + return tf.Variable(tf.constant(0.1, shape = shape)) + +def conv2d(x_input, w_matrix): + return tf.nn.conv2d(x_input, w_matrix, strides = [ 1, 1, 1, 1 ], padding = 'SAME') + +def max_pool(x_input, pool_size): + size = [ 1, pool_size, pool_size, 1 ] + return tf.nn.max_pool(x_input, ksize = size, strides = size, padding = 'SAME') + + +class Mnist: + def __init__(self): + images = tf.placeholder(tf.float32, [ None, 784 ], name = 'input_x') + labels = tf.placeholder(tf.float32, [ None, 10 ], name = 'input_y') + keep_prob = tf.placeholder(tf.float32, name='keep_prob') + + self.images = images + self.labels = labels + self.keep_prob = keep_prob + + self.train_step = None + self.accuracy = None + + self.w1 = None + self.b1 = None + self.fcw1 = None + self.cross = None + with tf.name_scope('reshape'): + x_image = tf.reshape(images, [ -1, 28, 28, 1 ]) + with tf.name_scope('conv1'): + w_conv1 = weight_variable([ 5, 5, 1, 32 ]) + self.w1 = w_conv1 + b_conv1 = bias_variable([ 32 ]) + self.b1 = b_conv1 + h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) + with tf.name_scope('pool1'): + h_pool1 = max_pool(h_conv1, 2) + with tf.name_scope('conv2'): + w_conv2 = weight_variable([ 5, 5, 32, 64 ]) + b_conv2 = bias_variable([ 64 ]) + h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2) + with tf.name_scope('pool2'): + h_pool2 = max_pool(h_conv2, 2) + with tf.name_scope('fc1'): + w_fc1 = weight_variable([ 7 * 7 * 64, 1024 ]) + self.fcw1 = w_fc1 + b_fc1 = bias_variable([ 1024 ]) + h_pool2_flat = tf.reshape(h_pool2, [ -1, 7 * 7 * 64 ]) + h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1) + with tf.name_scope('dropout'): + h_fc1_drop = tf.nn.dropout(h_fc1, 0.5) + with tf.name_scope('fc2'): + w_fc2 = weight_variable([ 1024, 10 ]) + b_fc2 = bias_variable([ 10 ]) + y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2 + with tf.name_scope('loss'): + cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = labels, logits = y_conv)) + self.cross = cross_entropy + with tf.name_scope('adam_optimizer'): + self.train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy) + with tf.name_scope('accuracy'): + correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(labels, 1)) + self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + + +def main(): + tf.set_random_seed(0) + + data = input_data.read_data_sets('data', one_hot = True) + + model = Mnist() + + '''you can change this to DoReFaQuantizer to implement it + DoReFaQuantizer(q_bits = 0.8).compress(tf.get_default_graph()) + ''' + QATquantizer(q_bits = 8).compress(tf.get_default_graph()) + + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for batch_idx in range(2000): + batch = data.train.next_batch(2000) + model.train_step.run(feed_dict = { + model.images: batch[0], + model.labels: batch[1], + model.keep_prob: 0.5 + }) + if batch_idx % 10 == 0: + test_acc = model.accuracy.eval(feed_dict = { + model.images: data.test.images, + model.labels: data.test.labels, + model.keep_prob: 1.0 + }) + print('test accuracy', test_acc) + + + test_acc = model.accuracy.eval(feed_dict = { + model.images: data.test.images, + model.labels: data.test.labels, + model.keep_prob: 1.0 + }) + print('final result is', test_acc) + +main() diff --git a/src/sdk/pynni/nni/compressors/example/main_torch.py b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py similarity index 76% rename from src/sdk/pynni/nni/compressors/example/main_torch.py rename to src/sdk/pynni/nni/compressors/example/main_torch_pruner.py index 478c30f49b..2b26555b52 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py @@ -1,7 +1,4 @@ -from level_pruner import TorchLevelPruner -from naive_quantizer import TorchNaiveQuantizer -from AGPruner import TorchAGPruner,TorchSensitivityPruner -from QATquantizer import TorchDoReFaQuantizer,TorchQATquantizer +from nni.compressors.torchCompressor import AGPruner import torch import torch.nn.functional as F from torchvision import datasets, transforms @@ -67,23 +64,21 @@ def main(): batch_size = 1000, shuffle = True) model = Mnist() - #TorchLevelPruner(0.5).compress(model) - #TorchNaiveQuantizer().compress(model) - #TorchQATquantizer(q_bits = 32).compress(model) - #model = compressor.compress(Mnist()).to(device) - #pruner = TorchAGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) - #pruner = TorchSensitivityPruner(sparsity = 0.8) - #pruner.compress(model) + '''you can change this to SensitivityPruner to implement it + pruner = SensitivityPruner(sparsity = 0.8) + ''' + pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) + pruner.compress(model) + optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum = 0.5) for epoch in range(10): print('# Epoch {} #'.format(epoch)) - if epoch == 2: - TorchDoReFaQuantizer(q_bits = 32).compress(model) train(model, device, train_loader, optimizer) test(model, device, test_loader) - #pruner.update_epoch(epoch) - #print(epoch, pruner.compute_target_sparsity(epoch)) + pruner.update_epoch(epoch) + + main() diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py new file mode 100644 index 0000000000..45392cb847 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py @@ -0,0 +1,83 @@ +from nni.compressors.torchCompressor import QATquantizer +import torch +import torch.nn.functional as F +from torchvision import datasets, transforms + + +class Mnist(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv1 = torch.nn.Conv2d(1, 20, 5, 1) + self.conv2 = torch.nn.Conv2d(20, 50, 5, 1) + self.fc1 = torch.nn.Linear(4 * 4 * 50, 500) + self.fc2 = torch.nn.Linear(500, 10) + + def forward(self, x): + x = F.relu(self.conv1(x)) + x = F.max_pool2d(x, 2, 2) + x = F.relu(self.conv2(x)) + x = F.max_pool2d(x, 2, 2) + x = x.view(-1, 4 * 4 * 50) + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return F.log_softmax(x, dim = 1) + + +def train(model, device, train_loader, optimizer): + model.train() + for batch_idx, (data, target) in enumerate(train_loader): + data, target = data.to(device), target.to(device) + optimizer.zero_grad() + output = model(data) + loss = F.nll_loss(output, target) + loss.backward() + optimizer.step() + if batch_idx % 100 == 0: + print('{:2.0f}% Loss {}'.format(100 * batch_idx / len(train_loader), loss.item())) + +def test(model, device, test_loader): + model.eval() + test_loss = 0 + correct = 0 + with torch.no_grad(): + for data, target in test_loader: + data, target = data.to(device), target.to(device) + output = model(data) + test_loss += F.nll_loss(output, target, reduction = 'sum').item() + pred = output.argmax(dim = 1, keepdim = True) + correct += pred.eq(target.view_as(pred)).sum().item() + test_loss /= len(test_loader.dataset) + + print('Loss: {} Accuracy: {}%)\n'.format( + test_loss, 100 * correct / len(test_loader.dataset))) + +def main(): + torch.manual_seed(0) + device = torch.device('cpu') + + trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) + train_loader = torch.utils.data.DataLoader( + datasets.MNIST('data', train = True, download = True, transform = trans), + batch_size = 64, shuffle = True) + test_loader = torch.utils.data.DataLoader( + datasets.MNIST('data', train = False, transform = trans), + batch_size = 1000, shuffle = True) + + model = Mnist() + + '''you can change this to DoReFaQuantizer to implement it + DoReFaQuantizer(q_bits = 8).compress(model) + ''' + QATquantizer(q_bits = 8).compress(model) + + + optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum = 0.5) + for epoch in range(10): + print('# Epoch {} #'.format(epoch)) + train(model, device, train_loader, optimizer) + test(model, device, test_loader) + + + + +main() diff --git a/src/sdk/pynni/nni/compressors/level_pruner.py b/src/sdk/pynni/nni/compressors/level_pruner.py deleted file mode 100644 index b5ecc041fc..0000000000 --- a/src/sdk/pynni/nni/compressors/level_pruner.py +++ /dev/null @@ -1,38 +0,0 @@ -try: - import torch - from nnimc import TorchPruner - - class TorchLevelPruner(TorchPruner): - def __init__(self, sparsity = 0, layer_sparsity = { }): - super().__init__() - self.default_sparsity = sparsity - self.layer_sparsity = layer_sparsity - - def calc_mask(self, layer_info, weight): - sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) - w_abs = weight.abs() - k = int(weight.numel() * sparsity) - threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() - return torch.gt(w_abs, threshold).type(weight.type()) - -except ImportError: - pass - - -try: - import tensorflow as tf - from nnimc import TfPruner - - class TfLevelPruner(TfPruner): - def __init__(self, sparsity = 0, layer_sparsity = { }): - super().__init__() - self.default_sparsity = sparsity - self.layer_sparsity = layer_sparsity - - def calc_mask(self, layer_info, weight): - sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) - threshold = tf.contrib.distributions.percentile(weight.abs(), sparsity * 100) - return tf.cast(tf.math.greater(weight.abs(), threshold), weight.dtype) - -except ImportError: - pass diff --git a/src/sdk/pynni/nni/compressors/naive_quantizer.py b/src/sdk/pynni/nni/compressors/naive_quantizer.py deleted file mode 100644 index 2dcece43b1..0000000000 --- a/src/sdk/pynni/nni/compressors/naive_quantizer.py +++ /dev/null @@ -1,39 +0,0 @@ -try: - import torch - from nnimc import TorchQuantizer - - class TorchNaiveQuantizer(TorchQuantizer): - def __init__(self): - super().__init__() - self.layer_scale = { } - - def quantize_weight(self, layer_info, weight): - new_scale = weight.abs().max() / 127 - # TODO: use int id - scale = max(self.layer_scale.get(layer_info.name, 0), new_scale) - self.layer_scale[layer_info.name] = scale - orig_type = weight.type() # TODO: user layer_info - return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) - -except ImportError: - pass - - -try: - import tensorflow as tf - from nnimc import TfQuantizer - - class TfNaiveQuantizer(TfQuantizer): - def __init__(self): - super().__init__() - self.layer_scale = { } - - def quantize_weight(self, layer_info, weight): - new_scale = tf.reduce_max(tf.abs(weight)) / 127 - scale = tf.maximum(self.layer_scale.get(layer_info.name, tf.constant(0.0)), new_scale) - self.layer_scale[layer_info.name] = scale - orig_type = weight.dtype - return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale - -except ImportError: - pass diff --git a/src/sdk/pynni/nni/compressors/nnimc.py b/src/sdk/pynni/nni/compressors/nnimc.py deleted file mode 100644 index 97f69900dd..0000000000 --- a/src/sdk/pynni/nni/compressors/nnimc.py +++ /dev/null @@ -1,24 +0,0 @@ -try: - import torch - from .torchCompressor._nnimc_torch import * - _torch_available = True -except ImportError: - _torch_available = False - -try: - import tensorflow as tf - from .tfCompressor._nnimc_tf import * - _tf_available = True -except ImportError: - _tf_available = False - - -def detect_prunable_layers(model): - """ - Search for all prunable layers in the model. - This can be useful to create search space. - """ - if _torch_available and isinstance(model, torch.nn.Model): - return _torch_detect_prunable_layers(model) - if _tf_available and isinstance(model, tf.Graph): - return _tf_detect_prunable_layers(model) diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py b/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py index 10002a9e78..99fd4056b6 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py @@ -1,2 +1,2 @@ -from .pruner import TfLevelPruner, TfAGPruner, TfSensitivityPruner -from .quantizer import TfNaiveQuantizer, TfQATquantizer, TfDoReFaQuantizer \ No newline at end of file +from .pruner import LevelPruner, AGPruner, SensitivityPruner +from .quantizer import QATquantizer, DoReFaQuantizer \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py index 33c175694f..5c7acdf95e 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py @@ -2,7 +2,7 @@ try: import tensorflow as tf from ._nnimc_tf import TfPruner - class TfLevelPruner(TfPruner): + class LevelPruner(TfPruner): def __init__(self, sparsity = 0, layer_sparsity = {}): super().__init__() self.default_sparsity = sparsity @@ -13,7 +13,7 @@ def calc_mask(self, layer_info, weight): threshold = tf.contrib.distributions.percentile(tf.abs(weight), sparsity * 100) return tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype) - class TfAGPruner(TfPruner): + class AGPruner(TfPruner): def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): super().__init__() self.initial_sparsity = initial_sparsity @@ -38,7 +38,7 @@ def compute_target_sparsity(self): (tf.pow(1.0 - base,3))) return target_sparsity - def calc_mask(self, layer, weight): + def calc_mask(self, layer_info, weight): target_sparsity = self.compute_target_sparsity() threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) @@ -46,12 +46,12 @@ def calc_mask(self, layer, weight): self.assign_handler.append(tf.assign(weight, weight*mask)) return mask - def update_epoch(self, sess, epoch): + def update_epoch(self, epoch, sess): sess.run(self.assign_handler) sess.run(tf.assign(self.now_epoch, int(epoch))) - class TfSensitivityPruner(TfPruner): + class SensitivityPruner(TfPruner): def __init__(self, sparsity): super().__init__() self.sparsity = sparsity @@ -71,7 +71,7 @@ def calc_mask(self, layer_info, weight): self.assign_handler.append(mask_update_handler) return mask - def update_graph(self, sess): + def update_epoch(self, epoch, sess): sess.run(self.assign_handler) except ImportError: diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py index 84ca614ba5..1bbaa97c19 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py @@ -2,7 +2,7 @@ import tensorflow as tf from ._nnimc_tf import TfQuantizer - class TfNaiveQuantizer(TfQuantizer): + class NaiveQuantizer(TfQuantizer): def __init__(self): super().__init__() self.layer_scale = { } @@ -14,7 +14,7 @@ def quantize_weight(self, layer_info, weight): orig_type = weight.dtype return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale - class TfQATquantizer(TfQuantizer): + class QATquantizer(TfQuantizer): def __init__(self, q_bits): super().__init__() self.q_bits = q_bits @@ -30,7 +30,7 @@ def quantize_weight(self, layer_info, weight): return qw - class TfDoReFaQuantizer(TfQuantizer): + class DoReFaQuantizer(TfQuantizer): def __init__(self, q_bits): super().__init__() self.q_bits = q_bits diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py b/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py index f3fdb8ba81..b85722d9cc 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py @@ -1,2 +1,2 @@ -from .pruner import TorchLevelPruner, TorchAGPruner, TorchSensitivityPruner -from .quantizer import TorchNaiveQuantizer, TorchDoReFaQuantizer, TorchQATquantizer +from .pruner import LevelPruner, AGPruner, SensitivityPruner +from .quantizer import DoReFaQuantizer, QATquantizer diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py index 6d90c6c1b0..1a9235c9f8 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py @@ -1,7 +1,7 @@ try: import torch from ._nnimc_torch import TorchPruner - class TorchLevelPruner(TorchPruner): + class LevelPruner(TorchPruner): def __init__(self, sparsity = 0, layer_sparsity = { }): super().__init__() self.default_sparsity = sparsity @@ -14,7 +14,7 @@ def calc_mask(self, layer_info, weight): threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() return torch.gt(w_abs, threshold).type(weight.type()) - class TorchAGPruner(TorchPruner): + class AGPruner(TorchPruner): def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): """ """ @@ -57,7 +57,7 @@ def update_epoch(self, epoch): self.now_epoch = epoch - class TorchSensitivityPruner(TorchPruner): + class SensitivityPruner(TorchPruner): def __init__(self, sparsity): super().__init__() self.sparsity = sparsity @@ -76,6 +76,9 @@ def calc_mask(self, layer_info, weight): new_mask = torch.gt(w_abs, threshold).type(weight.type()) self.mask_list[layer_info.name] = new_mask return new_mask + + def update_epoch(self, epoch): + pass except ImportError: pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py index c8ce7030b1..b1d6f6d791 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py @@ -2,7 +2,7 @@ import torch from ._nnimc_torch import TorchQuantizer - class TorchNaiveQuantizer(TorchQuantizer): + class NaiveQuantizer(TorchQuantizer): def __init__(self): super().__init__() self.layer_scale = { } @@ -15,7 +15,7 @@ def quantize_weight(self, layer_info, weight): orig_type = weight.type() # TODO: user layer_info return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) - class TorchDoReFaQuantizer(TorchQuantizer): + class DoReFaQuantizer(TorchQuantizer): def __init__(self, q_bits): super().__init__() self.q_bits = q_bits @@ -32,7 +32,7 @@ def quantize(self, input_ri, q_bits): output = torch.round(input_ri*scale)/scale return output - class TorchQATquantizer(TorchQuantizer): + class QATquantizer(TorchQuantizer): def __init__(self, q_bits): super().__init__() self.q_bits = q_bits From 17a20fc4515bef8fb3fd8384231550961972c6a7 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Thu, 15 Aug 2019 11:42:39 +0800 Subject: [PATCH 13/34] add test function in pynni/test --- .../compressors/example/main_torch_pruner.py | 1 - .../example/main_torch_quantizer.py | 1 - .../nni/compressors/tfCompressor/__init__.py | 2 +- .../compressors/torchCompressor/__init__.py | 2 +- src/sdk/pynni/tests/test_compressor.py | 112 ++++++++++++++++++ 5 files changed, 114 insertions(+), 4 deletions(-) create mode 100644 src/sdk/pynni/tests/test_compressor.py diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py index 2b26555b52..4a0d355128 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py @@ -1,6 +1,5 @@ from nni.compressors.torchCompressor import AGPruner import torch -import torch.nn.functional as F from torchvision import datasets, transforms diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py index 45392cb847..789053835c 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py @@ -1,6 +1,5 @@ from nni.compressors.torchCompressor import QATquantizer import torch -import torch.nn.functional as F from torchvision import datasets, transforms diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py b/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py index 99fd4056b6..feaa9110e5 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py @@ -1,2 +1,2 @@ from .pruner import LevelPruner, AGPruner, SensitivityPruner -from .quantizer import QATquantizer, DoReFaQuantizer \ No newline at end of file +from .quantizer import NaiveQuantizer, QATquantizer, DoReFaQuantizer \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py b/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py index b85722d9cc..3e197df317 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py @@ -1,2 +1,2 @@ from .pruner import LevelPruner, AGPruner, SensitivityPruner -from .quantizer import DoReFaQuantizer, QATquantizer +from .quantizer import NaiveQuantizer, DoReFaQuantizer, QATquantizer diff --git a/src/sdk/pynni/tests/test_compressor.py b/src/sdk/pynni/tests/test_compressor.py new file mode 100644 index 0000000000..5f0142aabf --- /dev/null +++ b/src/sdk/pynni/tests/test_compressor.py @@ -0,0 +1,112 @@ +from unittest import TestCase, main +from nni.compressors import tfCompressor,torchCompressor +import torch +import tensorflow as tf + +def weight_variable(shape): + return tf.Variable(tf.truncated_normal(shape, stddev = 0.1)) + +def bias_variable(shape): + return tf.Variable(tf.constant(0.1, shape = shape)) + +def conv2d(x_input, w_matrix): + return tf.nn.conv2d(x_input, w_matrix, strides = [ 1, 1, 1, 1 ], padding = 'SAME') + +def max_pool(x_input, pool_size): + size = [ 1, pool_size, pool_size, 1 ] + return tf.nn.max_pool(x_input, ksize = size, strides = size, padding = 'SAME') + + +class TfMnist: + def __init__(self): + images = tf.placeholder(tf.float32, [ None, 784 ], name = 'input_x') + labels = tf.placeholder(tf.float32, [ None, 10 ], name = 'input_y') + keep_prob = tf.placeholder(tf.float32, name='keep_prob') + + self.images = images + self.labels = labels + self.keep_prob = keep_prob + + self.train_step = None + self.accuracy = None + + self.w1 = None + self.b1 = None + self.fcw1 = None + self.cross = None + with tf.name_scope('reshape'): + x_image = tf.reshape(images, [ -1, 28, 28, 1 ]) + with tf.name_scope('conv1'): + w_conv1 = weight_variable([ 5, 5, 1, 32 ]) + self.w1 = w_conv1 + b_conv1 = bias_variable([ 32 ]) + self.b1 = b_conv1 + h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) + with tf.name_scope('pool1'): + h_pool1 = max_pool(h_conv1, 2) + with tf.name_scope('conv2'): + w_conv2 = weight_variable([ 5, 5, 32, 64 ]) + b_conv2 = bias_variable([ 64 ]) + h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2) + with tf.name_scope('pool2'): + h_pool2 = max_pool(h_conv2, 2) + with tf.name_scope('fc1'): + w_fc1 = weight_variable([ 7 * 7 * 64, 1024 ]) + self.fcw1 = w_fc1 + b_fc1 = bias_variable([ 1024 ]) + h_pool2_flat = tf.reshape(h_pool2, [ -1, 7 * 7 * 64 ]) + h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1) + with tf.name_scope('dropout'): + h_fc1_drop = tf.nn.dropout(h_fc1, 0.5) + with tf.name_scope('fc2'): + w_fc2 = weight_variable([ 1024, 10 ]) + b_fc2 = bias_variable([ 10 ]) + y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2 + with tf.name_scope('loss'): + cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = labels, logits = y_conv)) + self.cross = cross_entropy + with tf.name_scope('adam_optimizer'): + self.train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy) + with tf.name_scope('accuracy'): + correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(labels, 1)) + self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + +class TorchMnist(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv1 = torch.nn.Conv2d(1, 20, 5, 1) + self.conv2 = torch.nn.Conv2d(20, 50, 5, 1) + self.fc1 = torch.nn.Linear(4 * 4 * 50, 500) + self.fc2 = torch.nn.Linear(500, 10) + + def forward(self, x): + x = F.relu(self.conv1(x)) + x = F.max_pool2d(x, 2, 2) + x = F.relu(self.conv2(x)) + x = F.max_pool2d(x, 2, 2) + x = x.view(-1, 4 * 4 * 50) + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return F.log_softmax(x, dim = 1) + +class CompressorTestCase(TestCase): + def test_tf_pruner(self): + model = TfMnist() + tfCompressor.LevelPruner(sparsity = 0.8).compress_default_graph() + + + def test_tf_quantizer(self): + model = TfMnist() + tfCompressor.NaiveQuantizer().compress_default_graph() + + def test_torch_pruner(self): + model = TorchMnist() + torchCompressor.LevelPruner(sparsity = 0.8).compress(model) + + def test_torch_quantizer(self): + model = TorchMnist() + torchCompressor.NaiveQuantizer().compress(model) + + +if __name__ == '__main__': + main() From 9ab12f30038e09b09f2fa687c458f92e99e6a746 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Thu, 15 Aug 2019 14:16:48 +0800 Subject: [PATCH 14/34] fix bug F --- src/sdk/pynni/nni/compressors/example/main_torch_pruner.py | 1 + src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py | 1 + src/sdk/pynni/tests/test_compressor.py | 1 + 3 files changed, 3 insertions(+) diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py index 4a0d355128..2b26555b52 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py @@ -1,5 +1,6 @@ from nni.compressors.torchCompressor import AGPruner import torch +import torch.nn.functional as F from torchvision import datasets, transforms diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py index 789053835c..45392cb847 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py @@ -1,5 +1,6 @@ from nni.compressors.torchCompressor import QATquantizer import torch +import torch.nn.functional as F from torchvision import datasets, transforms diff --git a/src/sdk/pynni/tests/test_compressor.py b/src/sdk/pynni/tests/test_compressor.py index 5f0142aabf..5d5c955ee5 100644 --- a/src/sdk/pynni/tests/test_compressor.py +++ b/src/sdk/pynni/tests/test_compressor.py @@ -1,6 +1,7 @@ from unittest import TestCase, main from nni.compressors import tfCompressor,torchCompressor import torch +import torch.nn.functional as F import tensorflow as tf def weight_variable(shape): From 491ef33908938d3025455150c91418594cdde5d2 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Thu, 15 Aug 2019 15:19:57 +0800 Subject: [PATCH 15/34] test --- .../nni/compressors/tfCompressor/pruner.py | 136 +++++++++--------- 1 file changed, 67 insertions(+), 69 deletions(-) diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py index 5c7acdf95e..e93f11cd92 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py @@ -1,78 +1,76 @@ -try: - import tensorflow as tf - from ._nnimc_tf import TfPruner - class LevelPruner(TfPruner): - def __init__(self, sparsity = 0, layer_sparsity = {}): - super().__init__() - self.default_sparsity = sparsity - self.layer_sparsity = layer_sparsity - def calc_mask(self, layer_info, weight): - sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) - threshold = tf.contrib.distributions.percentile(tf.abs(weight), sparsity * 100) - return tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype) +import tensorflow as tf +from ._nnimc_tf import TfPruner +class LevelPruner(TfPruner): + def __init__(self, sparsity = 0, layer_sparsity = {}): + super().__init__() + self.default_sparsity = sparsity + self.layer_sparsity = layer_sparsity + + def calc_mask(self, layer_info, weight): + sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) + threshold = tf.contrib.distributions.percentile(tf.abs(weight), sparsity * 100) + return tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype) + +class AGPruner(TfPruner): + def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): + super().__init__() + self.initial_sparsity = initial_sparsity + self.final_sparsity = final_sparsity + self.start_epoch = start_epoch + self.end_epoch = end_epoch + self.freq = frequency + self.now_epoch = tf.Variable(0) + self.assign_handler = [] - class AGPruner(TfPruner): - def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): - super().__init__() - self.initial_sparsity = initial_sparsity - self.final_sparsity = final_sparsity - self.start_epoch = start_epoch - self.end_epoch = end_epoch - self.freq = frequency - self.now_epoch = tf.Variable(0) - self.assign_handler = [] + def compute_target_sparsity(self): - def compute_target_sparsity(self): - - if self.end_epoch <= self.start_epoch : - return self.final_sparsity - - now_epoch = tf.minimum(self.now_epoch, tf.constant(self.end_epoch)) - span = int(((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq) - assert span>0 - base = tf.cast(now_epoch - self.initial_sparsity, tf.float32) / span - target_sparsity = (self.final_sparsity + - (self.initial_sparsity - self.final_sparsity)* - (tf.pow(1.0 - base,3))) - return target_sparsity - - def calc_mask(self, layer_info, weight): - - target_sparsity = self.compute_target_sparsity() - threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) - mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) - self.assign_handler.append(tf.assign(weight, weight*mask)) - return mask - - def update_epoch(self, epoch, sess): - sess.run(self.assign_handler) - sess.run(tf.assign(self.now_epoch, int(epoch))) + if self.end_epoch <= self.start_epoch : + return self.final_sparsity + + now_epoch = tf.minimum(self.now_epoch, tf.constant(self.end_epoch)) + span = int(((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq) + assert span>0 + base = tf.cast(now_epoch - self.initial_sparsity, tf.float32) / span + target_sparsity = (self.final_sparsity + + (self.initial_sparsity - self.final_sparsity)* + (tf.pow(1.0 - base,3))) + return target_sparsity + + def calc_mask(self, layer_info, weight): + target_sparsity = self.compute_target_sparsity() + threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) + self.assign_handler.append(tf.assign(weight, weight*mask)) + return mask + + def update_epoch(self, epoch, sess): + sess.run(self.assign_handler) + sess.run(tf.assign(self.now_epoch, int(epoch))) - class SensitivityPruner(TfPruner): - def __init__(self, sparsity): - super().__init__() - self.sparsity = sparsity - self.layer_mask = {} - self.assign_handler = [] - def calc_mask(self, layer_info, weight): - target_sparsity = self.sparsity * tf.math.reduce_std(weight) - mask = tf.get_variable(layer_info.name+'_mask',initializer=tf.ones(weight.shape), trainable=False) - self.layer_mask[layer_info.name] = mask - - weight_assign_handler = tf.assign(weight, mask*weight) - with tf.control_dependencies([weight_assign_handler]): - threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) - new_mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) - mask_update_handler = tf.assign(mask, new_mask) - self.assign_handler.append(mask_update_handler) - return mask +class SensitivityPruner(TfPruner): + def __init__(self, sparsity): + super().__init__() + self.sparsity = sparsity + self.layer_mask = {} + self.assign_handler = [] + + def calc_mask(self, layer_info, weight): + target_sparsity = self.sparsity * tf.math.reduce_std(weight) + mask = tf.get_variable(layer_info.name+'_mask',initializer=tf.ones(weight.shape), trainable=False) + self.layer_mask[layer_info.name] = mask + + weight_assign_handler = tf.assign(weight, mask*weight) + with tf.control_dependencies([weight_assign_handler]): + threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + new_mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) + mask_update_handler = tf.assign(mask, new_mask) + self.assign_handler.append(mask_update_handler) + return mask - def update_epoch(self, epoch, sess): - sess.run(self.assign_handler) + def update_epoch(self, epoch, sess): + sess.run(self.assign_handler) -except ImportError: - pass From 02877edb73b54095b0a4c287c1f00e019c9abdfe Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Thu, 15 Aug 2019 15:40:40 +0800 Subject: [PATCH 16/34] add requirment --- src/sdk/pynni/requirements.txt | 4 ++++ src/sdk/pynni/setup.py | 4 +++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/src/sdk/pynni/requirements.txt b/src/sdk/pynni/requirements.txt index 3adfb06cf5..93f4c05277 100644 --- a/src/sdk/pynni/requirements.txt +++ b/src/sdk/pynni/requirements.txt @@ -8,3 +8,7 @@ hyperopt # metis tuner sklearn + +#compressor +tensorflow +torch diff --git a/src/sdk/pynni/setup.py b/src/sdk/pynni/setup.py index e7ba29dad2..beeedd099d 100644 --- a/src/sdk/pynni/setup.py +++ b/src/sdk/pynni/setup.py @@ -36,7 +36,9 @@ def read(fname): 'json_tricks', 'numpy', 'scipy', - 'coverage' + 'coverage', + 'tensorflow', + 'torch' ], package_data = {'nni': ['**/requirements.txt']}, From fe46e9f956a848a6e40ed1070c9f60f76c7d4df5 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Thu, 15 Aug 2019 16:00:27 +0800 Subject: [PATCH 17/34] change setup --- setup.py | 4 +++- src/sdk/pynni/requirements.txt | 4 ---- src/sdk/pynni/setup.py | 4 +--- 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/setup.py b/setup.py index 70411453d4..c391ea3ef4 100644 --- a/setup.py +++ b/setup.py @@ -58,7 +58,9 @@ def read(fname): 'schema', 'PythonWebHDFS', 'colorama', - 'sklearn' + 'sklearn', + 'tensorflow', + 'torch' ], entry_points = { diff --git a/src/sdk/pynni/requirements.txt b/src/sdk/pynni/requirements.txt index 93f4c05277..3adfb06cf5 100644 --- a/src/sdk/pynni/requirements.txt +++ b/src/sdk/pynni/requirements.txt @@ -8,7 +8,3 @@ hyperopt # metis tuner sklearn - -#compressor -tensorflow -torch diff --git a/src/sdk/pynni/setup.py b/src/sdk/pynni/setup.py index beeedd099d..e7ba29dad2 100644 --- a/src/sdk/pynni/setup.py +++ b/src/sdk/pynni/setup.py @@ -36,9 +36,7 @@ def read(fname): 'json_tricks', 'numpy', 'scipy', - 'coverage', - 'tensorflow', - 'torch' + 'coverage' ], package_data = {'nni': ['**/requirements.txt']}, From 2b5114b8b5440de5bd9b62897a4f4362959b2804 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Thu, 15 Aug 2019 16:17:48 +0800 Subject: [PATCH 18/34] change dependencies to zaure-piplines.yml --- azure-pipelines.yml | 5 +++++ setup.py | 4 +--- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index f142de7bd3..1fb6b0c686 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -10,6 +10,11 @@ jobs: steps: - script: python3 -m pip install --upgrade pip setuptools --user displayName: 'Install python tools' + - script: | + python3 -m pip install torch==0.4.1 --user + python3 -m pip install torchvision==0.2.1 --user + python3 -m pip install tensorflow==1.12.0 --user + displayname: 'Install dependencies for integration' - script: | source install.sh displayName: 'Install nni toolkit via source code' diff --git a/setup.py b/setup.py index c391ea3ef4..70411453d4 100644 --- a/setup.py +++ b/setup.py @@ -58,9 +58,7 @@ def read(fname): 'schema', 'PythonWebHDFS', 'colorama', - 'sklearn', - 'tensorflow', - 'torch' + 'sklearn' ], entry_points = { From a68d911e64dc491f6e223a896380bea899dfae81 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Thu, 15 Aug 2019 16:19:29 +0800 Subject: [PATCH 19/34] change --- azure-pipelines.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 1fb6b0c686..44564a408e 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -14,7 +14,7 @@ jobs: python3 -m pip install torch==0.4.1 --user python3 -m pip install torchvision==0.2.1 --user python3 -m pip install tensorflow==1.12.0 --user - displayname: 'Install dependencies for integration' + displayName: 'Install dependencies for integration' - script: | source install.sh displayName: 'Install nni toolkit via source code' From 555cbb829f768a42b64cf7f1e86663fad6d37a5f Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Thu, 15 Aug 2019 16:28:29 +0800 Subject: [PATCH 20/34] add mac --- azure-pipelines.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 44564a408e..43b0298e4e 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -55,6 +55,11 @@ jobs: steps: - script: python3 -m pip install --upgrade pip setuptools displayName: 'Install python tools' + - script: | + python3 -m pip install torch==0.4.1 --user + python3 -m pip install torchvision==0.2.1 --user + python3 -m pip install tensorflow==1.12.0 --user + displayName: 'Install dependencies for integration' - script: | source install.sh displayName: 'Install nni toolkit via source code' From 6badf9ec9c9d1201926aea613b8990e8a4fd26a9 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Thu, 15 Aug 2019 16:34:28 +0800 Subject: [PATCH 21/34] add macos --- azure-pipelines.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 43b0298e4e..af6bb741f3 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -58,7 +58,7 @@ jobs: - script: | python3 -m pip install torch==0.4.1 --user python3 -m pip install torchvision==0.2.1 --user - python3 -m pip install tensorflow==1.12.0 --user + python3 -m pip install tensorflow --user displayName: 'Install dependencies for integration' - script: | source install.sh From 6f3f1ffbdf6bb3939e693ec426ad8834c1dc8c96 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Mon, 19 Aug 2019 17:59:52 +0800 Subject: [PATCH 22/34] change compressor with __call__(), add frame method --- docs/en_US/Compressor/Overview.md | 96 +++++++++++++++++-- docs/en_US/Compressor/Pruner.md | 10 +- docs/en_US/Compressor/Quantizer.md | 12 ++- .../nni/compressors/example/main_tf_pruner.py | 4 +- .../compressors/example/main_tf_quantizer.py | 5 +- .../compressors/example/main_torch_pruner.py | 4 +- .../example/main_torch_quantizer.py | 5 +- .../nni/compressors/tfCompressor/__init__.py | 3 +- .../nni/compressors/tfCompressor/_nnimc_tf.py | 30 +++++- .../compressors/torchCompressor/__init__.py | 1 + .../torchCompressor/_nnimc_torch.py | 22 ++++- .../nni/compressors/torchCompressor/pruner.py | 2 - 12 files changed, 166 insertions(+), 28 deletions(-) diff --git a/docs/en_US/Compressor/Overview.md b/docs/en_US/Compressor/Overview.md index 8e1b2d2a19..b81b7b6059 100644 --- a/docs/en_US/Compressor/Overview.md +++ b/docs/en_US/Compressor/Overview.md @@ -3,19 +3,21 @@ NNI provides easy-to-use toolkit to help user design and use compression algori ## Framework We use the instrumentation method to insert a node or function after the corresponding position in the model. -
+ When compression algorithm designer implements one prune algorithm, he only need to pay attention to the generation method of mask, without caring about applying the mask to the garph. -## algorithm +## Algorithm We now provide some naive compression algorithm and four popular compress agorithms for users, including two pruning algorithm and two quantization algorithm. Below is a list of model compression algorithms supported in our compressor -| Name | Paper | -| ---------- | ----------| -| AGPruner | [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878)| + +|Name|Paper| +|---|---| +| AGPruner| [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878)| | SensitivityPruner |[Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626)| | QATquantizer |[Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf)| | DoReFaQuantizer |[DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://arxiv.org/abs/1606.06160)| ## Usage +### For compression algorithm user Take naive level pruner as an example @@ -23,17 +25,97 @@ If you want to prune all weight to 80% sparsity, you can add code below into you Tensorflow code ``` -nni.compressors.tfCompressor.LevelPruner(sparsity=0.8).compress(model_graph) +pruner = nni.compressors.tfCompressor.LevelPruner(sparsity=0.8) +pruner(model_graph) ``` Pytorch code ``` -nni.compressors.torchCompressor.LevelPruner(sparsity=0.8).compress(model) +pruner = nni.compressors.torchCompressor.LevelPruner(sparsity=0.8) +pruner(model) ``` Our compressor will automatically insert mask into your model, and you can train your model with masks without changing your training code. You will get a compressed model when you finish your training. You can get more information in Algorithm details +### For compression algorithm designer +We use the instrumentation method to insert a node or function after the corresponding position in the model. And we provide interface for designer to design compression algorithm easily. + +If you want to use mask to prune a model, you can use Pruner as base class. And design your mask in calc_mask() method. + +Tensorflow code +``` +class YourPruner(nni.compressors.tfCompressor.TfPruner): + def __init__(self, your_input): + pass + + # don not change calc_mask() input + def calc_mask(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your mask and return your mask + return your_mask + + # you can also design your method help to generate mask + def your_method(self, your_input): + #your code + +``` +Pytorch code +``` +class YourPruner(nni.compressors.torchCompressor.TorchPruner): + def __init__(self, your_input): + pass + + # don not change calc_mask() input + def calc_mask(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your mask and return your mask + return your_mask + + # you can also design your method help to generate mask + def your_method(self, your_input): + #your code +``` + +if you want to generate new weight and replace the old one, you are suggested to use Quantizer as base class. And you can manage the weight in quantize_weight() method. + +Tensorflow code +``` +class YourPruner(nni.compressors.tfCompressor.TfQuantizer): + def __init__(self, your_input): + pass + + # don not change quantize_weight() input + def quantize_weight(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your quantizer and return new weight + return new_weight + + # you can also design your method + def your_method(self, your_input): + #your code + +``` +Pytorch code +``` +class YourPruner(nni.compressors.torchCompressor.TorchQuantizer): + def __init__(self, your_input): + pass + + # don not change quantize_weight() input + def quantize_weight(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your quantizer and return new weight + return new_weight + + # you can also design your method + def your_method(self, your_input): + #your code +``` diff --git a/docs/en_US/Compressor/Pruner.md b/docs/en_US/Compressor/Pruner.md index 4482b71465..683391f0f3 100644 --- a/docs/en_US/Compressor/Pruner.md +++ b/docs/en_US/Compressor/Pruner.md @@ -16,12 +16,14 @@ First, you should import pruner and add mask to model. Tensorflow code ``` from nni.compressors.tfCompressor import AGPruner -pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1).compress(tf.get_default_graph()) +pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) +pruner(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import AGPruner -pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1).compress(model) +pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) +pruner(model) ``` Second, you should add code below to update epoch number when you finish one epoch in your training code. @@ -50,14 +52,14 @@ Tensorflow code from nni.compressors.tfCompressor import SensitivityPruner pruner = SensitivityPruner(sparsity = 0.8) -pruner.compress(tf.get_default_graph()) +pruner(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import SensitivityPruner pruner = SensitivityPruner(sparsity = 0.8) -pruner.compress(model) +pruner(model) ``` Like AGPruner, you should update mask information every epoch by adding code below diff --git a/docs/en_US/Compressor/Quantizer.md b/docs/en_US/Compressor/Quantizer.md index c440da8e69..1c673f1159 100644 --- a/docs/en_US/Compressor/Quantizer.md +++ b/docs/en_US/Compressor/Quantizer.md @@ -15,12 +15,14 @@ You can quantize your model to 8 bits with the code below before your training c Tensorflow code ``` from nni.compressors.tfCompressor import QATquantizer -QATquantizer(q_bits = 8).compress(tf.get_default_graph()) +quantizer = QATquantizer(q_bits = 8) +quantizer(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import QATquantizer -QATquantizer(q_bits = 8).compress(model) +quantizer = QATquantizer(q_bits = 8) +quantizer(model) ``` You can view example for more information @@ -35,12 +37,14 @@ To implement DoReFaQuantizer, you can add code below before your training code Tensorflow code ``` from nni.compressors.tfCompressor import DoReFaQuantizer -DoReFaQuantizer(q_bits = 8).compress(tf.get_default_graph()) +quantizer = DoReFaQuantizer(q_bits = 8) +quantizer(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import DoReFaQuantizer -DoReFaQuantizer(q_bits = 8).compress(model) +quantizer = DoReFaQuantizer(q_bits = 8) +quantizer(model) ``` You can view example for more information diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py b/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py index 29dda59f06..328b8e2fd3 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py +++ b/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py @@ -83,7 +83,9 @@ def main(): pruner = SensitivityPruner(sparsity = 0.8) ''' pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) - pruner.compress(tf.get_default_graph()) + pruner(tf.get_default_graph()) + # you can also use compress(model) or compress_default_graph() for tensorflow compressor + # pruner.compress(tf.get_default_graph()) with tf.Session() as sess: diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py index a2b4cb1066..4efdb25a8f 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py +++ b/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py @@ -82,7 +82,10 @@ def main(): '''you can change this to DoReFaQuantizer to implement it DoReFaQuantizer(q_bits = 0.8).compress(tf.get_default_graph()) ''' - QATquantizer(q_bits = 8).compress(tf.get_default_graph()) + quantizer = QATquantizer(q_bits = 8) + quantizer(tf.get_default_graph()) + # you can also use compress(model) or compress_default_graph() + # method like QATquantizer(q_bits = 8).compress_default_graph() with tf.Session() as sess: diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py index 2b26555b52..35f64d0511 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py @@ -69,7 +69,9 @@ def main(): pruner = SensitivityPruner(sparsity = 0.8) ''' pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) - pruner.compress(model) + pruner(model) + # you can also use compress(model) method + # like that pruner.compress(model) optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum = 0.5) for epoch in range(10): diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py index 45392cb847..6d03db0df4 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py @@ -68,7 +68,10 @@ def main(): '''you can change this to DoReFaQuantizer to implement it DoReFaQuantizer(q_bits = 8).compress(model) ''' - QATquantizer(q_bits = 8).compress(model) + quantizer = QATquantizer(q_bits = 8) + quantizer(model) + # you can also use compress(model) method + # like thaht quantizer.compress(model) optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum = 0.5) diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py b/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py index feaa9110e5..5ec47f9c51 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py @@ -1,2 +1,3 @@ from .pruner import LevelPruner, AGPruner, SensitivityPruner -from .quantizer import NaiveQuantizer, QATquantizer, DoReFaQuantizer \ No newline at end of file +from .quantizer import NaiveQuantizer, QATquantizer, DoReFaQuantizer +from ._nnimc_tf import TfPruner, TfQuantizer \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py index 22b7a3d3c3..61bb1eb409 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py @@ -49,8 +49,8 @@ def __init__(self, layer): self.name = layer.name self.layer = layer self.weight_index = None - - if layer.type == 'Conv2D': + self.support_type = ['Conv2D', 'MatMul', 'DepthwiseConv2dNative'] + if layer.type in self.support_types: self.weight_index = 1 else: raise ValueError('Unsupported layer') @@ -59,7 +59,8 @@ def __init__(self, layer): def _tf_detect_prunable_layers(model): # search for Conv2D layers # TODO: whitelist - return [ TfLayerInfo(op) for op in model.get_operations() if op.type == 'Conv2D' ] + whiltlist = ['Conv2D', 'MatMul', 'DepthwiseConv2dNative'] + return [ TfLayerInfo(op) for op in model.get_operations() if op.type in whiltlist ] class TfPruner(TfCompressor): @@ -68,6 +69,10 @@ class TfPruner(TfCompressor): def __init__(self): super().__init__() + def __call__(self, model): + self.compress(model) + return model + def calc_mask(self, layer_info, weight): """ Pruners should overload this method to provide mask for weight tensors. @@ -77,7 +82,6 @@ def calc_mask(self, layer_info, weight): """ raise NotImplementedError("Pruners must overload calc_mask()") - def compress(self, model): super().compress(model) # TODO: configurable whitelist @@ -95,13 +99,23 @@ def _instrument_layer(self, layer_info): mask = self.calc_mask(layer_info, weight) new_weight = weight * mask tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) + + def update_epoch(self, epoch, sess): + pass + + def step(self, sess): + pass class TfQuantizer(TfCompressor): def __init__(self): super().__init__() - def quantize_weight(self, layer_info, weight) -> Tensor: + def __call__(self, model): + self.compress(model) + return model + + def quantize_weight(self, layer_info, weight): raise NotImplementedError() @@ -113,3 +127,9 @@ def _instrument_layer(self, layer_info): weight_op = layer_info.layer.inputs[layer_info.weight_index].op new_weight = self.quantize_weight(layer_info, weight_op.inputs[0]) tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) + + def update_epoch(self, epoch, sess): + pass + + def step(self, sess): + pass \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py b/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py index 3e197df317..6e6dc56cc4 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py @@ -1,2 +1,3 @@ from .pruner import LevelPruner, AGPruner, SensitivityPruner from .quantizer import NaiveQuantizer, DoReFaQuantizer, QATquantizer +from ._nnimc_torch import TorchPruner, TorchQuantizer \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py index 481b2f8dac..c332f3b8ff 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py @@ -28,7 +28,7 @@ def compress(self, model): self.bind_model(model) - def bind_model(self, model) -> None: + def bind_model(self, model): """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. @@ -63,6 +63,10 @@ class TorchPruner(TorchCompressor): def __init__(self): super().__init__() + def __call__(self, model): + self.compress(model) + return model + def calc_mask(self, layer_info, weight): """ Pruners should overload this method to provide mask for weight tensors. @@ -97,12 +101,22 @@ def new_forward(*input): return ret layer_info.layer.forward = new_forward + + def update_epoch(self, epoch): + pass + + def step(self): + pass class TorchQuantizer(TorchCompressor): def __init__(self): super().__init__() + def __call__(self, model): + self.compress(model) + return model + def quantize_weight(self, layer_info, weight): # FIXME: where dequantize goes? raise NotImplementedError() @@ -125,3 +139,9 @@ def new_forward(*input): return layer_info._forward(*input) layer_info.layer.forward = new_forward + + def update_epoch(self, epoch): + pass + + def step(self): + pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py index 1a9235c9f8..9a6b0fae75 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py @@ -77,8 +77,6 @@ def calc_mask(self, layer_info, weight): self.mask_list[layer_info.name] = new_mask return new_mask - def update_epoch(self, epoch): - pass except ImportError: pass From 41751de70a0c026adb1afd05f185fcaddebad8f2 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Tue, 20 Aug 2019 18:11:41 +0800 Subject: [PATCH 23/34] add configure without doc --- .../nni/compressors/example/main_tf_pruner.py | 12 +- .../compressors/example/main_tf_quantizer.py | 5 +- .../compressors/example/main_torch_pruner.py | 13 +- .../example/main_torch_quantizer.py | 5 +- .../nni/compressors/tfCompressor/_nnimc_tf.py | 29 ++- .../nni/compressors/tfCompressor/pruner.py | 114 +++++++-- .../nni/compressors/tfCompressor/quantizer.py | 54 ++++- .../torchCompressor/_nnimc_torch.py | 31 ++- .../nni/compressors/torchCompressor/pruner.py | 228 ++++++++++++------ .../compressors/torchCompressor/quantizer.py | 55 ++++- 10 files changed, 411 insertions(+), 135 deletions(-) diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py b/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py index 328b8e2fd3..9947d7e530 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py +++ b/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py @@ -80,9 +80,17 @@ def main(): model = Mnist() '''you can change this to SensitivityPruner to implement it - pruner = SensitivityPruner(sparsity = 0.8) + pruner = SensitivityPruner(configure_list) ''' - pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) + configure_list = [{ + 'initial_sparsity': 0, + 'final_sparsity': 0.8, + 'start_epoch': 1, + 'end_epoch': 10, + 'frequency': 1, + 'support_type': 'default' + }] + pruner = AGPruner(configure_list) pruner(tf.get_default_graph()) # you can also use compress(model) or compress_default_graph() for tensorflow compressor # pruner.compress(tf.get_default_graph()) diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py index 4efdb25a8f..fb04063f68 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py +++ b/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py @@ -80,9 +80,10 @@ def main(): model = Mnist() '''you can change this to DoReFaQuantizer to implement it - DoReFaQuantizer(q_bits = 0.8).compress(tf.get_default_graph()) + DoReFaQuantizer(configure_list).compress(tf.get_default_graph()) ''' - quantizer = QATquantizer(q_bits = 8) + configure_list = [{'q_bits':8, 'support_type':'default'}] + quantizer = QATquantizer(configure_list) quantizer(tf.get_default_graph()) # you can also use compress(model) or compress_default_graph() # method like QATquantizer(q_bits = 8).compress_default_graph() diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py index 35f64d0511..d140a37cce 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py @@ -66,9 +66,18 @@ def main(): model = Mnist() '''you can change this to SensitivityPruner to implement it - pruner = SensitivityPruner(sparsity = 0.8) + pruner = SensitivityPruner(configure_list) ''' - pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) + configure_list = [{ + 'initial_sparsity': 0, + 'final_sparsity': 0.8, + 'start_epoch': 1, + 'end_epoch': 10, + 'frequency': 1, + 'support_type': 'default' + }] + + pruner = AGPruner(configure_list) pruner(model) # you can also use compress(model) method # like that pruner.compress(model) diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py index 6d03db0df4..0c7fdb3d3e 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py @@ -66,9 +66,10 @@ def main(): model = Mnist() '''you can change this to DoReFaQuantizer to implement it - DoReFaQuantizer(q_bits = 8).compress(model) + DoReFaQuantizer(configure_list).compress(model) ''' - quantizer = QATquantizer(q_bits = 8) + configure_list = [{'q_bits':8, 'support_type':'default'}] + quantizer = QATquantizer(configure_list) quantizer(model) # you can also use compress(model) method # like thaht quantizer.compress(model) diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py index 61bb1eb409..8226c9d903 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py @@ -1,13 +1,15 @@ import tensorflow as tf from tensorflow import Graph, Operation, Tensor - +import yaml from typing import List __all__ = [ 'TfCompressor', 'TfPruner', 'TfQuantizer', - '_tf_detect_prunable_layers' + '_tf_detect_prunable_layers', + '_tf_default_get_configure', + '_tf_default_load_configure_file' ] @@ -25,7 +27,7 @@ def compress(self, model): """ assert self._bound_model is None, "Each NNI compressor instance can only compress one model" self._bound_model = model - self.bind_model(model) + self.preprocess_model(model) def compress_default_graph(self): """ @@ -35,7 +37,7 @@ def compress_default_graph(self): self.compress(tf.get_default_graph()) - def bind_model(self, model): + def preprocess_model(self, model): """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. @@ -50,7 +52,7 @@ def __init__(self, layer): self.layer = layer self.weight_index = None self.support_type = ['Conv2D', 'MatMul', 'DepthwiseConv2dNative'] - if layer.type in self.support_types: + if layer.type in self.support_type: self.weight_index = 1 else: raise ValueError('Unsupported layer') @@ -62,6 +64,23 @@ def _tf_detect_prunable_layers(model): whiltlist = ['Conv2D', 'MatMul', 'DepthwiseConv2dNative'] return [ TfLayerInfo(op) for op in model.get_operations() if op.type in whiltlist ] +def _tf_default_get_configure(configure_list, layer_info): + configure = {} + for config in configure_list: + if config.get('support_type', '') == 'default': + configure = config + elif layer_info.layer.type in config.get('support_type', []): + configure = config + elif layer_info.name in config.get('support_op', []): + configure = config + + return configure + +def _tf_default_load_configure_file(config_path, class_name): + assert config_path is not None and config_path.endswith('yaml') + file = open(config_path, 'r') + yaml_text = yaml.load(file.read()) + return yaml_text.get(class_name, {}) class TfPruner(TfCompressor): """TODO""" diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py index e93f11cd92..1c49c7a264 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py @@ -2,45 +2,86 @@ import tensorflow as tf from ._nnimc_tf import TfPruner +from ._nnimc_tf import _tf_default_get_configure, _tf_default_load_configure_file + class LevelPruner(TfPruner): - def __init__(self, sparsity = 0, layer_sparsity = {}): + def __init__(self, configure_list): + """ + Configure Args: + sparsity + """ super().__init__() - self.default_sparsity = sparsity - self.layer_sparsity = layer_sparsity + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + def load_configure(self, config_path): + config_list = _tf_default_load_configure_file(config_path, 'LevelPruner') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_sparsity(self, configure={}): + sparsity = configure.get('sparsity', 0) + return sparsity + def calc_mask(self, layer_info, weight): - sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) + sparsity = self.get_sparsity(_tf_default_get_configure(self.configure_list, layer_info)) + threshold = tf.contrib.distributions.percentile(tf.abs(weight), sparsity * 100) return tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype) class AGPruner(TfPruner): - def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): + def __init__(self, configure_list): + """ + Configure Args + initial_sparsity + final_sparsity + start_epoch + end_epoch + frequency + """ super().__init__() - self.initial_sparsity = initial_sparsity - self.final_sparsity = final_sparsity - self.start_epoch = start_epoch - self.end_epoch = end_epoch - self.freq = frequency + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + self.now_epoch = tf.Variable(0) self.assign_handler = [] - def compute_target_sparsity(self): - - if self.end_epoch <= self.start_epoch : - return self.final_sparsity + def compute_target_sparsity(self, layer_info): + configure = _tf_default_get_configure(self.configure_list, layer_info) + end_epoch = configure.get('end_epoch', 1) + start_epoch = configure.get('start_epoch', 0) + freq = configure.get('frequency', 1) + final_sparsity = configure.get('final_sparsity', 0) + initial_sparsity = configure.get('initial_sparsity', 0) + + if end_epoch <= start_epoch: + return final_sparsity - now_epoch = tf.minimum(self.now_epoch, tf.constant(self.end_epoch)) - span = int(((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq) - assert span>0 - base = tf.cast(now_epoch - self.initial_sparsity, tf.float32) / span - target_sparsity = (self.final_sparsity + - (self.initial_sparsity - self.final_sparsity)* - (tf.pow(1.0 - base,3))) + now_epoch = tf.minimum(self.now_epoch, tf.constant(end_epoch)) + span = int(((end_epoch - start_epoch-1)//freq)*freq) + assert span > 0 + base = tf.cast(now_epoch - initial_sparsity, tf.float32) / span + target_sparsity = (final_sparsity + + (initial_sparsity - final_sparsity)* + (tf.pow(1.0 - base, 3))) return target_sparsity - + + def load_configure(self, config_path): + config_list = _tf_default_load_configure_file(config_path, 'AGPruner') + for config in config_list.get('config', []): + self.configure_list.append(config) + def calc_mask(self, layer_info, weight): - target_sparsity = self.compute_target_sparsity() + target_sparsity = self.compute_target_sparsity(layer_info) threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) self.assign_handler.append(tf.assign(weight, weight*mask)) @@ -52,14 +93,35 @@ def update_epoch(self, epoch, sess): class SensitivityPruner(TfPruner): - def __init__(self, sparsity): + def __init__(self, configure_list): + """ + Configure Args: + sparsity + """ super().__init__() - self.sparsity = sparsity + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + self.layer_mask = {} self.assign_handler = [] + def load_configure(self, config_path): + config_list = _tf_default_load_configure_file(config_path, 'SensitivityPruner') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_sparsity(self, configure={}): + sparsity = configure.get('sparsity', 0) + return sparsity + def calc_mask(self, layer_info, weight): - target_sparsity = self.sparsity * tf.math.reduce_std(weight) + sparsity = self.get_sparsity(_tf_default_get_configure(self.configure_list, layer_info)) + + target_sparsity = sparsity * tf.math.reduce_std(weight) mask = tf.get_variable(layer_info.name+'_mask',initializer=tf.ones(weight.shape), trainable=False) self.layer_mask[layer_info.name] = mask diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py index 1bbaa97c19..8cca3170b7 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py @@ -1,11 +1,12 @@ try: import tensorflow as tf from ._nnimc_tf import TfQuantizer + from ._nnimc_tf import _tf_default_get_configure, _tf_default_load_configure_file class NaiveQuantizer(TfQuantizer): def __init__(self): super().__init__() - self.layer_scale = { } + self.layer_scale = {} def quantize_weight(self, layer_info, weight): new_scale = tf.reduce_max(tf.abs(weight)) / 127 @@ -15,14 +16,34 @@ def quantize_weight(self, layer_info, weight): return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale class QATquantizer(TfQuantizer): - def __init__(self, q_bits): + def __init__(self, configure_list): + """ + Configure Args: + q_bits + """ super().__init__() - self.q_bits = q_bits + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + def load_configure(self, config_path): + config_list = _tf_default_load_configure_file(config_path, 'QATquantizer') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_qbits(self, configure={}): + sparsity = configure.get('q_bits',0) + return sparsity def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) + a = tf.stop_gradient(tf.reduce_min(weight)) b = tf.stop_gradient(tf.reduce_max(weight)) - n = tf.cast(2 ** self.q_bits, tf.float32) + n = tf.cast(2 ** q_bits, tf.float32) scale = b-a/(n-1) with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): @@ -31,15 +52,34 @@ def quantize_weight(self, layer_info, weight): return qw class DoReFaQuantizer(TfQuantizer): - def __init__(self, q_bits): + def __init__(self, configure_list): + """ + Configure Args: + q_bits + """ super().__init__() - self.q_bits = q_bits + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + def load_configure(self, config_path): + config_list = _tf_default_load_configure_file(config_path, 'DoReFaQuantizer') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_qbits(self, configure={}): + sparsity = configure.get('q_bits',0) + return sparsity def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) a = tf.math.tanh(weight) b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5 - scale = pow(2, self.q_bits-1) + scale = pow(2, q_bits-1) with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): qw = tf.round(b*scale)/scale r_qw = 2*qw - 1 diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py index c332f3b8ff..00a53b0065 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py @@ -1,13 +1,15 @@ from torch import Tensor from torch.nn import Module, Parameter - +import yaml from typing import List __all__ = [ 'TorchCompressor', 'TorchPruner', 'TorchQuantizer', - '_torch_detect_prunable_layers' + '_torch_detect_prunable_layers', + '_torch_default_get_configure', + '_torch_default_load_configure_file' ] @@ -25,10 +27,10 @@ def compress(self, model): """ assert self._bound_model is None, "Each NNI compressor instance can only compress one model" self._bound_model = model - self.bind_model(model) + self.preprocess_model(model) - def bind_model(self, model): + def preprocess_model(self, model): """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. @@ -56,6 +58,23 @@ def _torch_detect_prunable_layers(model): pass return ret +def _torch_default_get_configure(configure_list, layer_info): + configure = {} + for config in configure_list: + if config.get('support_type', '') == 'default': + configure = config + elif layer_info.layer.type() in config.get('support_type', []): + configure = config + elif layer_info.name in config.get('support_op', []): + configure = config + + return configure + +def _torch_default_load_configure_file(config_path, class_name): + assert config_path is not None and config_path.endswith('yaml') + file = open(config_path, 'r') + yaml_text = yaml.load(file.read()) + return yaml_text.get(class_name, {}) class TorchPruner(TorchCompressor): """TODO""" @@ -102,10 +121,10 @@ def new_forward(*input): layer_info.layer.forward = new_forward - def update_epoch(self, epoch): + def update_epoch(self, epoch, **kwargs): pass - def step(self): + def step(self, **kwargs): pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py index 9a6b0fae75..8178a8c22c 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py @@ -1,84 +1,160 @@ -try: - import torch - from ._nnimc_torch import TorchPruner - class LevelPruner(TorchPruner): - def __init__(self, sparsity = 0, layer_sparsity = { }): - super().__init__() - self.default_sparsity = sparsity - self.layer_sparsity = layer_sparsity + +import torch +from ._nnimc_torch import TorchPruner +from ._nnimc_torch import _torch_default_get_configure, _torch_default_load_configure_file + + + +class LevelPruner(TorchPruner): + """Prune to an exact pruning level specification + + """ + def __init__(self, configure_list): + """ + we suggest user to use json configure list, like [{},{}...], to set configure + format : + [ + { + 'sparsity': 0, + 'support_type': 'default' + }, + { + 'sparsity': 50, + 'support_op': conv1 + } + ] + if you want input multiple configure from file, you'd better use load_configure_file(path) to load + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + + def load_configure(self, config_path): + config_list = _torch_default_load_configure_file(config_path, 'LevelPruner') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_sparsity(self, configure={}): + sparsity = configure.get('sparsity',0) + return sparsity + + def calc_mask(self, layer_info, weight): + sparsity = self.get_sparsity(_torch_default_get_configure(self.configure_list, layer_info)) + w_abs = weight.abs() + k = int(weight.numel() * sparsity) + if k == 0: + return torch.ones(weight.shape) + + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + return torch.gt(w_abs, threshold).type(weight.type()) + +class AGPruner(TorchPruner): + def __init__(self, configure_list): + """ + Configure Args + initial_sparsity + final_sparsity + start_epoch + end_epoch + frequency + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + self.mask_list = {} + + def load_configure(self, config_path): + config_list = _torch_default_load_configure_file(config_path, 'AGPruner') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def compute_target_sparsity(self, now_epoch, layer_info): + configure = _torch_default_get_configure(self.configure_list, layer_info) + end_epoch = configure.get('end_epoch', 1) + start_epoch = configure.get('start_epoch', 0) + freq = configure.get('frequency', 1) + final_sparsity = configure.get('final_sparsity', 0) + initial_sparsity = configure.get('initial_sparsity', 0) + + if end_epoch <= start_epoch or end_epoch <= now_epoch or initial_sparsity >= final_sparsity: + return final_sparsity + span = ((end_epoch - start_epoch-1)//freq)*freq + assert span > 0 + target_sparsity = (final_sparsity + + (initial_sparsity - final_sparsity)* + (1.0 - ((now_epoch - start_epoch)/span))**3) + return target_sparsity + + def calc_mask(self, layer_info, weight): + now_epoch = self.now_epoch + mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) + target_sparsity = self.compute_target_sparsity(now_epoch, layer_info) + k = int(weight.numel() * target_sparsity) + if k == 0: + return mask + + w_abs = weight.abs()*mask + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + new_mask = torch.gt(w_abs, threshold).type(weight.type()) + self.mask_list[layer_info.name] = new_mask + return new_mask - def calc_mask(self, layer_info, weight): - sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) - w_abs = weight.abs() - k = int(weight.numel() * sparsity) - threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() - return torch.gt(w_abs, threshold).type(weight.type()) + def update_epoch(self, epoch): + if epoch <= 0: + return + self.now_epoch = epoch - class AGPruner(TorchPruner): - def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): - """ - """ - super().__init__() - self.initial_sparsity = initial_sparsity - self.final_sparsity = final_sparsity - self.start_epoch = start_epoch - self.end_epoch = end_epoch - self.now_epoch = start_epoch - self.freq = frequency - self.mask_list = {} - - def compute_target_sparsity(self, now_epoch): - if self.end_epoch <= self.start_epoch or self.end_epoch <= self.now_epoch: - return self.final_sparsity - span = ((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq - assert span>0 - target_sparsity = (self.final_sparsity + - (self.initial_sparsity - self.final_sparsity)* - (1.0 - ((now_epoch - self.start_epoch)/span))**3 ) - return target_sparsity - - def calc_mask(self, layer_info, weight): - now_epoch = self.now_epoch - mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) - target_sparsity = self.compute_target_sparsity(now_epoch) - k = int(weight.numel() * target_sparsity) - if k == 0: - return mask - - w_abs = weight.abs()*mask - threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() - new_mask = torch.gt(w_abs, threshold).type(weight.type()) - self.mask_list[layer_info.name] = new_mask - return new_mask - - def update_epoch(self, epoch): - if epoch <= 0: - return - self.now_epoch = epoch + +class SensitivityPruner(TorchPruner): + def __init__(self, configure_list): + """ + configure Args: + sparsity + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + self.mask_list = {} + + def load_configure(self, config_path): + config_list = _torch_default_load_configure_file(config_path, 'SensitivityPruner') + for config in config_list.get('config', []): + self.configure_list.append(config) + def get_sparsity(self, configure={}): + sparsity = configure.get('sparsity',0) + return sparsity + + def calc_mask(self, layer_info, weight): + mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) + weight = weight*mask + sparsity = self.get_sparsity(_torch_default_get_configure(self.configure_list, layer_info)) + target_sparsity = sparsity * torch.std(weight).item() + k = int(weight.numel() * target_sparsity) + if k == 0: + return mask - class SensitivityPruner(TorchPruner): - def __init__(self, sparsity): - super().__init__() - self.sparsity = sparsity - self.mask_list = {} - - def calc_mask(self, layer_info, weight): - mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) - weight = weight*mask - target_sparsity = self.sparsity * torch.std(weight).item() - k = int(weight.numel() * target_sparsity) - if k == 0: - return mask - - w_abs = weight.abs() - threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() - new_mask = torch.gt(w_abs, threshold).type(weight.type()) - self.mask_list[layer_info.name] = new_mask - return new_mask + w_abs = weight.abs() + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + new_mask = torch.gt(w_abs, threshold).type(weight.type()) + self.mask_list[layer_info.name] = new_mask + return new_mask -except ImportError: - pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py index b1d6f6d791..c4db1d089a 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py @@ -1,11 +1,12 @@ try: import torch from ._nnimc_torch import TorchQuantizer - + from ._nnimc_torch import _torch_default_get_configure, _torch_default_load_configure_file + class NaiveQuantizer(TorchQuantizer): def __init__(self): super().__init__() - self.layer_scale = { } + self.layer_scale = {} def quantize_weight(self, layer_info, weight): new_scale = weight.abs().max() / 127 @@ -16,14 +17,34 @@ def quantize_weight(self, layer_info, weight): return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) class DoReFaQuantizer(TorchQuantizer): - def __init__(self, q_bits): + def __init__(self, configure_list): + """ + configure Args: + q_bits + """ super().__init__() - self.q_bits = q_bits + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + def load_configure(self, config_path): + config_list = _torch_default_load_configure_file(config_path, 'DoReFaQuantizer') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_qbits(self, configure={}): + sparsity = configure.get('q_bits',0) + return sparsity + def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) + out = weight.tanh() out = out /( 2 * out.abs().max()) + 0.5 - out = self.quantize(out, self.q_bits) + out = self.quantize(out, q_bits) out = 2 * out -1 return out @@ -34,11 +55,31 @@ def quantize(self, input_ri, q_bits): class QATquantizer(TorchQuantizer): def __init__(self, q_bits): + """ + Configure Args: + q_bits + """ super().__init__() - self.q_bits = q_bits + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + def load_configure(self, config_path): + config_list = _torch_default_load_configure_file(config_path, 'QATquantizer') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_qbits(self, configure={}): + sparsity = configure.get('q_bits',0) + return sparsity + def quantize_weight(self, layer_info, weight): - if self.q_bits <= 1: + q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) + + if q_bits <= 1: return weight a = torch.min(weight) b = torch.max(weight) From baffb8d9d640b6c77f86fb4d0ded9c345aec0c45 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Wed, 21 Aug 2019 14:32:39 +0800 Subject: [PATCH 24/34] update doc and test unit --- docs/en_US/Compressor/Overview.md | 65 ++++++++++++++++++- .../torchCompressor/_nnimc_torch.py | 2 +- src/sdk/pynni/tests/test_compressor.py | 6 +- 3 files changed, 68 insertions(+), 5 deletions(-) diff --git a/docs/en_US/Compressor/Overview.md b/docs/en_US/Compressor/Overview.md index b81b7b6059..50a128b859 100644 --- a/docs/en_US/Compressor/Overview.md +++ b/docs/en_US/Compressor/Overview.md @@ -25,13 +25,13 @@ If you want to prune all weight to 80% sparsity, you can add code below into you Tensorflow code ``` -pruner = nni.compressors.tfCompressor.LevelPruner(sparsity=0.8) +pruner = nni.compressors.tfCompressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) pruner(model_graph) ``` Pytorch code ``` -pruner = nni.compressors.torchCompressor.LevelPruner(sparsity=0.8) +pruner = nni.compressors.torchCompressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) pruner(model) ``` @@ -39,6 +39,42 @@ Our compressor will automatically insert mask into your model, and you can train You can get more information in Algorithm details +#### Configuration +We now provide an default format for our build-in algorithm, algorithm designer can follow this format and use our default configure parser + +Following our default format, user can set configure in his code or a yaml file. And pass configure to compressor by init() or load_configure() + + +Code +``` +[{ + 'sparsity':0.8, + 'support_layer':'default' + 'support_op':['op_name1','op_name2'] +}] +``` + +file +``` +tfAGPruner: + config: + - + start_epoch: 0 + end_epoch: 16 + frequency: 2 + initial_sparsity: 0.05 + final_sparsity: 0.60 + support_type: default + - + prune: False + start_epoch: 0 + end_epoch: 20 + frequency: 2 + initial_sparsity: 0.05 + final_sparsity: 0.60 + support_type: [Linear] + support_op: [conv1, conv2] +``` ### For compression algorithm designer We use the instrumentation method to insert a node or function after the corresponding position in the model. And we provide interface for designer to design compression algorithm easily. @@ -48,6 +84,7 @@ Tensorflow code ``` class YourPruner(nni.compressors.tfCompressor.TfPruner): def __init__(self, your_input): + # defaultly, we suggest you to use configure_list as input pass # don not change calc_mask() input @@ -118,4 +155,28 @@ class YourPruner(nni.compressors.torchCompressor.TorchQuantizer): #your code ``` +#### Preprocess Model +Sometimes, designer wants to preprocess model before compress, designer can overload preprocess_model() method + +``` +class YourPruner(nni.compressors.torchCompressor.TorchQuantizer): + def __init__(self, your_input): + pass + + # don not change quantize_weight() input + def quantize_weight(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your quantizer and return new weight + return new_weight + + # you can also design your method + def your_method(self, your_input): + #your code + + def preprocess_model(self, model): + #preprocess model +``` +#### Step and Epoch +if an designer wants to update mask every step, designer can implement step() or update_epoch() method in his code, and tell user to call when use it diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py index 00a53b0065..59ec58ede8 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py @@ -63,7 +63,7 @@ def _torch_default_get_configure(configure_list, layer_info): for config in configure_list: if config.get('support_type', '') == 'default': configure = config - elif layer_info.layer.type() in config.get('support_type', []): + elif type(layer_info.layer).__name__ in config.get('support_type', []): configure = config elif layer_info.name in config.get('support_op', []): configure = config diff --git a/src/sdk/pynni/tests/test_compressor.py b/src/sdk/pynni/tests/test_compressor.py index 5d5c955ee5..5c698ad80f 100644 --- a/src/sdk/pynni/tests/test_compressor.py +++ b/src/sdk/pynni/tests/test_compressor.py @@ -93,7 +93,8 @@ def forward(self, x): class CompressorTestCase(TestCase): def test_tf_pruner(self): model = TfMnist() - tfCompressor.LevelPruner(sparsity = 0.8).compress_default_graph() + configure_list = [{'sparsity':0.8, 'support_type':'default'}] + tfCompressor.LevelPruner(configure_list).compress_default_graph() def test_tf_quantizer(self): @@ -102,7 +103,8 @@ def test_tf_quantizer(self): def test_torch_pruner(self): model = TorchMnist() - torchCompressor.LevelPruner(sparsity = 0.8).compress(model) + configure_list = [{'sparsity':0.8, 'support_type':'default'}] + torchCompressor.LevelPruner(configure_list).compress(model) def test_torch_quantizer(self): model = TorchMnist() From 2213dfaa357ee24fb96a65105faffd9789e42676 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Fri, 23 Aug 2019 13:06:58 +0800 Subject: [PATCH 25/34] add configure parser change doc and test --- docs/en_US/Compressor/Overview.md | 20 ++-- setup.py | 5 +- .../nni/compressors/example/main_tf_pruner.py | 2 +- .../compressors/example/main_tf_quantizer.py | 2 +- .../compressors/example/main_torch_pruner.py | 2 +- .../example/main_torch_quantizer.py | 2 +- .../nni/compressors/tfCompressor/quantizer.py | 89 ----------------- .../__init__.py | 0 .../_nnimc_tf.py | 4 +- .../{tfCompressor => tf_compressor}/pruner.py | 4 +- .../compressors/tf_compressor/quantizer.py | 85 +++++++++++++++++ .../compressors/torchCompressor/quantizer.py | 95 ------------------- .../__init__.py | 0 .../_nnimc_torch.py | 4 +- .../pruner.py | 12 +-- .../compressors/torch_compressor/quantizer.py | 91 ++++++++++++++++++ src/sdk/pynni/requirements.txt | 3 + src/sdk/pynni/setup.py | 3 +- src/sdk/pynni/tests/test_compressor.py | 10 +- 19 files changed, 212 insertions(+), 221 deletions(-) delete mode 100644 src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py rename src/sdk/pynni/nni/compressors/{tfCompressor => tf_compressor}/__init__.py (100%) rename src/sdk/pynni/nni/compressors/{tfCompressor => tf_compressor}/_nnimc_tf.py (97%) rename src/sdk/pynni/nni/compressors/{tfCompressor => tf_compressor}/pruner.py (99%) create mode 100644 src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py delete mode 100644 src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py rename src/sdk/pynni/nni/compressors/{torchCompressor => torch_compressor}/__init__.py (100%) rename src/sdk/pynni/nni/compressors/{torchCompressor => torch_compressor}/_nnimc_torch.py (98%) rename src/sdk/pynni/nni/compressors/{torchCompressor => torch_compressor}/pruner.py (97%) create mode 100644 src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py diff --git a/docs/en_US/Compressor/Overview.md b/docs/en_US/Compressor/Overview.md index 50a128b859..b33c588def 100644 --- a/docs/en_US/Compressor/Overview.md +++ b/docs/en_US/Compressor/Overview.md @@ -4,7 +4,7 @@ NNI provides easy-to-use toolkit to help user design and use compression algori ## Framework We use the instrumentation method to insert a node or function after the corresponding position in the model. -When compression algorithm designer implements one prune algorithm, he only need to pay attention to the generation method of mask, without caring about applying the mask to the garph. +When compression algorithm designer implements one prune algorithm, designer only need to pay attention to the generation method of mask, without caring about applying the mask to the graph. ## Algorithm We now provide some naive compression algorithm and four popular compress agorithms for users, including two pruning algorithm and two quantization algorithm. Below is a list of model compression algorithms supported in our compressor @@ -25,13 +25,13 @@ If you want to prune all weight to 80% sparsity, you can add code below into you Tensorflow code ``` -pruner = nni.compressors.tfCompressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +pruner = nni.compressors.tf_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) pruner(model_graph) ``` Pytorch code ``` -pruner = nni.compressors.torchCompressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +pruner = nni.compressors.torch_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) pruner(model) ``` @@ -56,7 +56,7 @@ Code file ``` -tfAGPruner: +AGPruner: config: - start_epoch: 0 @@ -82,7 +82,7 @@ If you want to use mask to prune a model, you can use Pruner as base class. And Tensorflow code ``` -class YourPruner(nni.compressors.tfCompressor.TfPruner): +class YourPruner(nni.compressors.tf_compressor.TfPruner): def __init__(self, your_input): # defaultly, we suggest you to use configure_list as input pass @@ -101,7 +101,7 @@ class YourPruner(nni.compressors.tfCompressor.TfPruner): ``` Pytorch code ``` -class YourPruner(nni.compressors.torchCompressor.TorchPruner): +class YourPruner(nni.compressors.torch_compressor.TorchPruner): def __init__(self, your_input): pass @@ -121,7 +121,7 @@ if you want to generate new weight and replace the old one, you are suggested to Tensorflow code ``` -class YourPruner(nni.compressors.tfCompressor.TfQuantizer): +class YourPruner(nni.compressors.tf_compressor.TfQuantizer): def __init__(self, your_input): pass @@ -139,7 +139,7 @@ class YourPruner(nni.compressors.tfCompressor.TfQuantizer): ``` Pytorch code ``` -class YourPruner(nni.compressors.torchCompressor.TorchQuantizer): +class YourPruner(nni.compressors.torch_compressor.TorchQuantizer): def __init__(self, your_input): pass @@ -159,7 +159,7 @@ class YourPruner(nni.compressors.torchCompressor.TorchQuantizer): Sometimes, designer wants to preprocess model before compress, designer can overload preprocess_model() method ``` -class YourPruner(nni.compressors.torchCompressor.TorchQuantizer): +class YourPruner(nni.compressors.torch_compressor.TorchQuantizer): def __init__(self, your_input): pass @@ -178,5 +178,5 @@ class YourPruner(nni.compressors.torchCompressor.TorchQuantizer): #preprocess model ``` #### Step and Epoch -if an designer wants to update mask every step, designer can implement step() or update_epoch() method in his code, and tell user to call when use it +if an designer wants to update mask every step, designer can implement step() or update_epoch() method in his code, and tell user to call when use it. diff --git a/setup.py b/setup.py index 70411453d4..f470dedff1 100644 --- a/setup.py +++ b/setup.py @@ -27,7 +27,7 @@ def read(fname): setup( name = 'nni', - version = 'v0.8-263-g1f92408', + version = '999.0.0-developing', author = 'Microsoft NNI Team', author_email = 'nni@microsoft.com', description = 'Neural Network Intelligence project', @@ -58,7 +58,8 @@ def read(fname): 'schema', 'PythonWebHDFS', 'colorama', - 'sklearn' + 'sklearn', + 'pyyaml' ], entry_points = { diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py b/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py index 9947d7e530..5a3912562f 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py +++ b/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py @@ -1,4 +1,4 @@ -from nni.compressors.tfCompressor import AGPruner +from nni.compressors.tf_compressor import AGPruner import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py index fb04063f68..3159e143aa 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py +++ b/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py @@ -1,4 +1,4 @@ -from nni.compressors.tfCompressor import QATquantizer +from nni.compressors.tf_compressor import QATquantizer import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py index d140a37cce..8a9d8cbe6a 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py @@ -1,4 +1,4 @@ -from nni.compressors.torchCompressor import AGPruner +from nni.compressors.torch_compressor import AGPruner import torch import torch.nn.functional as F from torchvision import datasets, transforms diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py index 0c7fdb3d3e..a5823921c4 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py +++ b/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py @@ -1,4 +1,4 @@ -from nni.compressors.torchCompressor import QATquantizer +from nni.compressors.torch_compressor import QATquantizer import torch import torch.nn.functional as F from torchvision import datasets, transforms diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py deleted file mode 100644 index 8cca3170b7..0000000000 --- a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py +++ /dev/null @@ -1,89 +0,0 @@ -try: - import tensorflow as tf - from ._nnimc_tf import TfQuantizer - from ._nnimc_tf import _tf_default_get_configure, _tf_default_load_configure_file - - class NaiveQuantizer(TfQuantizer): - def __init__(self): - super().__init__() - self.layer_scale = {} - - def quantize_weight(self, layer_info, weight): - new_scale = tf.reduce_max(tf.abs(weight)) / 127 - scale = tf.maximum(self.layer_scale.get(layer_info.name, tf.constant(0.0)), new_scale) - self.layer_scale[layer_info.name] = scale - orig_type = weight.dtype - return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale - - class QATquantizer(TfQuantizer): - def __init__(self, configure_list): - """ - Configure Args: - q_bits - """ - super().__init__() - self.configure_list = [] - if isinstance(configure_list, list): - for configure in configure_list: - self.configure_list.append(configure) - else: - raise ValueError('please init with configure list') - - def load_configure(self, config_path): - config_list = _tf_default_load_configure_file(config_path, 'QATquantizer') - for config in config_list.get('config', []): - self.configure_list.append(config) - - def get_qbits(self, configure={}): - sparsity = configure.get('q_bits',0) - return sparsity - - def quantize_weight(self, layer_info, weight): - q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) - - a = tf.stop_gradient(tf.reduce_min(weight)) - b = tf.stop_gradient(tf.reduce_max(weight)) - n = tf.cast(2 ** q_bits, tf.float32) - scale = b-a/(n-1) - - with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): - qw = tf.round((weight-a)/scale)*scale +a - - return qw - - class DoReFaQuantizer(TfQuantizer): - def __init__(self, configure_list): - """ - Configure Args: - q_bits - """ - super().__init__() - self.configure_list = [] - if isinstance(configure_list, list): - for configure in configure_list: - self.configure_list.append(configure) - else: - raise ValueError('please init with configure list') - - def load_configure(self, config_path): - config_list = _tf_default_load_configure_file(config_path, 'DoReFaQuantizer') - for config in config_list.get('config', []): - self.configure_list.append(config) - - def get_qbits(self, configure={}): - sparsity = configure.get('q_bits',0) - return sparsity - - def quantize_weight(self, layer_info, weight): - q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) - a = tf.math.tanh(weight) - b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5 - - scale = pow(2, q_bits-1) - with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): - qw = tf.round(b*scale)/scale - r_qw = 2*qw - 1 - return r_qw - -except ImportError: - pass diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py b/src/sdk/pynni/nni/compressors/tf_compressor/__init__.py similarity index 100% rename from src/sdk/pynni/nni/compressors/tfCompressor/__init__.py rename to src/sdk/pynni/nni/compressors/tf_compressor/__init__.py diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py similarity index 97% rename from src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py rename to src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py index 8226c9d903..35751a1943 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py @@ -51,7 +51,7 @@ def __init__(self, layer): self.name = layer.name self.layer = layer self.weight_index = None - self.support_type = ['Conv2D', 'MatMul', 'DepthwiseConv2dNative'] + self.support_type = ['Conv2D', 'DepthwiseConv2dNative'] if layer.type in self.support_type: self.weight_index = 1 else: @@ -61,7 +61,7 @@ def __init__(self, layer): def _tf_detect_prunable_layers(model): # search for Conv2D layers # TODO: whitelist - whiltlist = ['Conv2D', 'MatMul', 'DepthwiseConv2dNative'] + whiltlist = ['Conv2D', 'DepthwiseConv2dNative'] return [ TfLayerInfo(op) for op in model.get_operations() if op.type in whiltlist ] def _tf_default_get_configure(configure_list, layer_info): diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py similarity index 99% rename from src/sdk/pynni/nni/compressors/tfCompressor/pruner.py rename to src/sdk/pynni/nni/compressors/tf_compressor/pruner.py index 1c49c7a264..3b889b2374 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py @@ -1,5 +1,3 @@ - - import tensorflow as tf from ._nnimc_tf import TfPruner from ._nnimc_tf import _tf_default_get_configure, _tf_default_load_configure_file @@ -84,6 +82,7 @@ def calc_mask(self, layer_info, weight): target_sparsity = self.compute_target_sparsity(layer_info) threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) + print('tensor weight', weight) self.assign_handler.append(tf.assign(weight, weight*mask)) return mask @@ -135,4 +134,3 @@ def calc_mask(self, layer_info, weight): def update_epoch(self, epoch, sess): sess.run(self.assign_handler) - diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py b/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py new file mode 100644 index 0000000000..2c09a01f32 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py @@ -0,0 +1,85 @@ +import tensorflow as tf +from ._nnimc_tf import TfQuantizer +from ._nnimc_tf import _tf_default_get_configure, _tf_default_load_configure_file + +class NaiveQuantizer(TfQuantizer): + def __init__(self): + super().__init__() + self.layer_scale = {} + + def quantize_weight(self, layer_info, weight): + new_scale = tf.reduce_max(tf.abs(weight)) / 127 + scale = tf.maximum(self.layer_scale.get(layer_info.name, tf.constant(0.0)), new_scale) + self.layer_scale[layer_info.name] = scale + orig_type = weight.dtype + return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale + +class QATquantizer(TfQuantizer): + def __init__(self, configure_list): + """ + Configure Args: + q_bits + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + def load_configure(self, config_path): + config_list = _tf_default_load_configure_file(config_path, 'QATquantizer') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_qbits(self, configure={}): + sparsity = configure.get('q_bits',0) + return sparsity + + def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) + + a = tf.stop_gradient(tf.reduce_min(weight)) + b = tf.stop_gradient(tf.reduce_max(weight)) + n = tf.cast(2 ** q_bits, tf.float32) + scale = b-a/(n-1) + + with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): + qw = tf.round((weight-a)/scale)*scale +a + + return qw + +class DoReFaQuantizer(TfQuantizer): + def __init__(self, configure_list): + """ + Configure Args: + q_bits + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + def load_configure(self, config_path): + config_list = _tf_default_load_configure_file(config_path, 'DoReFaQuantizer') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_qbits(self, configure={}): + sparsity = configure.get('q_bits',0) + return sparsity + + def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) + a = tf.math.tanh(weight) + b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5 + + scale = pow(2, q_bits-1) + with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): + qw = tf.round(b*scale)/scale + r_qw = 2*qw - 1 + return r_qw diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py deleted file mode 100644 index c4db1d089a..0000000000 --- a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py +++ /dev/null @@ -1,95 +0,0 @@ -try: - import torch - from ._nnimc_torch import TorchQuantizer - from ._nnimc_torch import _torch_default_get_configure, _torch_default_load_configure_file - - class NaiveQuantizer(TorchQuantizer): - def __init__(self): - super().__init__() - self.layer_scale = {} - - def quantize_weight(self, layer_info, weight): - new_scale = weight.abs().max() / 127 - # TODO: use int id - scale = max(self.layer_scale.get(layer_info.name, 0), new_scale) - self.layer_scale[layer_info.name] = scale - orig_type = weight.type() # TODO: user layer_info - return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) - - class DoReFaQuantizer(TorchQuantizer): - def __init__(self, configure_list): - """ - configure Args: - q_bits - """ - super().__init__() - self.configure_list = [] - if isinstance(configure_list, list): - for configure in configure_list: - self.configure_list.append(configure) - else: - raise ValueError('please init with configure list') - - def load_configure(self, config_path): - config_list = _torch_default_load_configure_file(config_path, 'DoReFaQuantizer') - for config in config_list.get('config', []): - self.configure_list.append(config) - - def get_qbits(self, configure={}): - sparsity = configure.get('q_bits',0) - return sparsity - - def quantize_weight(self, layer_info, weight): - q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) - - out = weight.tanh() - out = out /( 2 * out.abs().max()) + 0.5 - out = self.quantize(out, q_bits) - out = 2 * out -1 - return out - - def quantize(self, input_ri, q_bits): - scale = pow(2, q_bits)-1 - output = torch.round(input_ri*scale)/scale - return output - - class QATquantizer(TorchQuantizer): - def __init__(self, q_bits): - """ - Configure Args: - q_bits - """ - super().__init__() - self.configure_list = [] - if isinstance(configure_list, list): - for configure in configure_list: - self.configure_list.append(configure) - else: - raise ValueError('please init with configure list') - - def load_configure(self, config_path): - config_list = _torch_default_load_configure_file(config_path, 'QATquantizer') - for config in config_list.get('config', []): - self.configure_list.append(config) - - def get_qbits(self, configure={}): - sparsity = configure.get('q_bits',0) - return sparsity - - def quantize_weight(self, layer_info, weight): - q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) - - if q_bits <= 1: - return weight - a = torch.min(weight) - b = torch.max(weight) - n = pow(2,self.q_bits) - scale = (b-a)/(n-1) - zero_point = a - out = torch.round((weight - zero_point)/scale) - out = out*scale + zero_point - orig_type = weight.dtype - return out.type(orig_type) - -except ImportError: - pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py b/src/sdk/pynni/nni/compressors/torch_compressor/__init__.py similarity index 100% rename from src/sdk/pynni/nni/compressors/torchCompressor/__init__.py rename to src/sdk/pynni/nni/compressors/torch_compressor/__init__.py diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py similarity index 98% rename from src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py rename to src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py index 59ec58ede8..d502f72a4d 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py @@ -121,10 +121,10 @@ def new_forward(*input): layer_info.layer.forward = new_forward - def update_epoch(self, epoch, **kwargs): + def update_epoch(self, epoch): pass - def step(self, **kwargs): + def step(self): pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py similarity index 97% rename from src/sdk/pynni/nni/compressors/torchCompressor/pruner.py rename to src/sdk/pynni/nni/compressors/torch_compressor/pruner.py index 8178a8c22c..04fa6b56c4 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py @@ -1,4 +1,3 @@ - import torch from ._nnimc_torch import TorchPruner from ._nnimc_torch import _torch_default_get_configure, _torch_default_load_configure_file @@ -72,6 +71,7 @@ def __init__(self, configure_list): raise ValueError('please init with configure list') self.mask_list = {} + self.now_epoch = 1 def load_configure(self, config_path): config_list = _torch_default_load_configure_file(config_path, 'AGPruner') @@ -81,11 +81,10 @@ def load_configure(self, config_path): def compute_target_sparsity(self, now_epoch, layer_info): configure = _torch_default_get_configure(self.configure_list, layer_info) end_epoch = configure.get('end_epoch', 1) - start_epoch = configure.get('start_epoch', 0) + start_epoch = configure.get('start_epoch', 1) freq = configure.get('frequency', 1) final_sparsity = configure.get('final_sparsity', 0) initial_sparsity = configure.get('initial_sparsity', 0) - if end_epoch <= start_epoch or end_epoch <= now_epoch or initial_sparsity >= final_sparsity: return final_sparsity span = ((end_epoch - start_epoch-1)//freq)*freq @@ -100,7 +99,7 @@ def calc_mask(self, layer_info, weight): mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) target_sparsity = self.compute_target_sparsity(now_epoch, layer_info) k = int(weight.numel() * target_sparsity) - if k == 0: + if k == 0 or target_sparsity>=1 or target_sparsity<=0: return mask w_abs = weight.abs()*mask @@ -154,7 +153,4 @@ def calc_mask(self, layer_info, weight): new_mask = torch.gt(w_abs, threshold).type(weight.type()) self.mask_list[layer_info.name] = new_mask return new_mask - - - - + \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py b/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py new file mode 100644 index 0000000000..d25feee8b8 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py @@ -0,0 +1,91 @@ +import torch +from ._nnimc_torch import TorchQuantizer +from ._nnimc_torch import _torch_default_get_configure, _torch_default_load_configure_file + +class NaiveQuantizer(TorchQuantizer): + def __init__(self): + super().__init__() + self.layer_scale = {} + + def quantize_weight(self, layer_info, weight): + new_scale = weight.abs().max() / 127 + # TODO: use int id + scale = max(self.layer_scale.get(layer_info.name, 0), new_scale) + self.layer_scale[layer_info.name] = scale + orig_type = weight.type() # TODO: user layer_info + return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) + +class DoReFaQuantizer(TorchQuantizer): + def __init__(self, configure_list): + """ + configure Args: + q_bits + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + def load_configure(self, config_path): + config_list = _torch_default_load_configure_file(config_path, 'DoReFaQuantizer') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_qbits(self, configure={}): + sparsity = configure.get('q_bits',0) + return sparsity + + def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) + + out = weight.tanh() + out = out /( 2 * out.abs().max()) + 0.5 + out = self.quantize(out, q_bits) + out = 2 * out -1 + return out + + def quantize(self, input_ri, q_bits): + scale = pow(2, q_bits)-1 + output = torch.round(input_ri*scale)/scale + return output + +class QATquantizer(TorchQuantizer): + def __init__(self, configure_list): + """ + Configure Args: + q_bits + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + def load_configure(self, config_path): + config_list = _torch_default_load_configure_file(config_path, 'QATquantizer') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_qbits(self, configure={}): + sparsity = configure.get('q_bits',0) + return sparsity + + def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) + + if q_bits <= 1: + return weight + a = torch.min(weight) + b = torch.max(weight) + n = pow(2, q_bits) + scale = (b-a)/(n-1) + zero_point = a + out = torch.round((weight - zero_point)/scale) + out = out*scale + zero_point + orig_type = weight.dtype + return out.type(orig_type) diff --git a/src/sdk/pynni/requirements.txt b/src/sdk/pynni/requirements.txt index 3adfb06cf5..a9db2a090d 100644 --- a/src/sdk/pynni/requirements.txt +++ b/src/sdk/pynni/requirements.txt @@ -8,3 +8,6 @@ hyperopt # metis tuner sklearn + +# compresser +pyyaml diff --git a/src/sdk/pynni/setup.py b/src/sdk/pynni/setup.py index e7ba29dad2..2375510dd3 100644 --- a/src/sdk/pynni/setup.py +++ b/src/sdk/pynni/setup.py @@ -36,7 +36,8 @@ def read(fname): 'json_tricks', 'numpy', 'scipy', - 'coverage' + 'coverage', + 'pyyaml' ], package_data = {'nni': ['**/requirements.txt']}, diff --git a/src/sdk/pynni/tests/test_compressor.py b/src/sdk/pynni/tests/test_compressor.py index 5c698ad80f..c952170f31 100644 --- a/src/sdk/pynni/tests/test_compressor.py +++ b/src/sdk/pynni/tests/test_compressor.py @@ -1,5 +1,5 @@ from unittest import TestCase, main -from nni.compressors import tfCompressor,torchCompressor +from nni.compressors import tf_compressor,torch_compressor import torch import torch.nn.functional as F import tensorflow as tf @@ -94,21 +94,21 @@ class CompressorTestCase(TestCase): def test_tf_pruner(self): model = TfMnist() configure_list = [{'sparsity':0.8, 'support_type':'default'}] - tfCompressor.LevelPruner(configure_list).compress_default_graph() + tf_compressor.LevelPruner(configure_list).compress_default_graph() def test_tf_quantizer(self): model = TfMnist() - tfCompressor.NaiveQuantizer().compress_default_graph() + tf_compressor.NaiveQuantizer().compress_default_graph() def test_torch_pruner(self): model = TorchMnist() configure_list = [{'sparsity':0.8, 'support_type':'default'}] - torchCompressor.LevelPruner(configure_list).compress(model) + torch_compressor.LevelPruner(configure_list).compress(model) def test_torch_quantizer(self): model = TorchMnist() - torchCompressor.NaiveQuantizer().compress(model) + torch_compressor.NaiveQuantizer().compress(model) if __name__ == '__main__': From 0538d303d10ae62defb39d570c63c2d3cff6c1a7 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Fri, 23 Aug 2019 14:16:43 +0800 Subject: [PATCH 26/34] test commit --- src/sdk/pynni/nni/compressors/torch_compressor/pruner.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py index 04fa6b56c4..cb9affbf90 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py @@ -6,7 +6,6 @@ class LevelPruner(TorchPruner): """Prune to an exact pruning level specification - """ def __init__(self, configure_list): """ From 3b02ef699301d3b1e80d095e552531e4c956c13c Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Mon, 26 Aug 2019 15:08:08 +0800 Subject: [PATCH 27/34] move example --- examples/compressor/configure_example.yaml | 9 +++++++++ .../compressor}/main_tf_pruner.py | 1 + .../compressor}/main_tf_quantizer.py | 0 .../compressor}/main_torch_pruner.py | 1 + .../compressor}/main_torch_quantizer.py | 0 setup.py | 5 ++--- src/sdk/pynni/nni/compressors/example/__init__.py | 0 .../pynni/nni/compressors/tf_compressor/_nnimc_tf.py | 11 ++++++++--- .../pynni/nni/compressors/tf_compressor/pruner.py | 2 +- .../nni/compressors/torch_compressor/_nnimc_torch.py | 12 +++++++++--- .../pynni/nni/compressors/torch_compressor/pruner.py | 2 +- src/sdk/pynni/requirements.txt | 3 --- src/sdk/pynni/setup.py | 3 +-- 13 files changed, 33 insertions(+), 16 deletions(-) create mode 100644 examples/compressor/configure_example.yaml rename {src/sdk/pynni/nni/compressors/example => examples/compressor}/main_tf_pruner.py (98%) rename {src/sdk/pynni/nni/compressors/example => examples/compressor}/main_tf_quantizer.py (100%) rename {src/sdk/pynni/nni/compressors/example => examples/compressor}/main_torch_pruner.py (98%) rename {src/sdk/pynni/nni/compressors/example => examples/compressor}/main_torch_quantizer.py (100%) delete mode 100644 src/sdk/pynni/nni/compressors/example/__init__.py diff --git a/examples/compressor/configure_example.yaml b/examples/compressor/configure_example.yaml new file mode 100644 index 0000000000..3e43f2d7c0 --- /dev/null +++ b/examples/compressor/configure_example.yaml @@ -0,0 +1,9 @@ +AGPruner: + config: + - + start_epoch: 1 + end_epoch: 10 + frequency: 1 + initial_sparsity: 0.05 + final_sparsity: 0.8 + support_type: 'default' diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py b/examples/compressor/main_tf_pruner.py similarity index 98% rename from src/sdk/pynni/nni/compressors/example/main_tf_pruner.py rename to examples/compressor/main_tf_pruner.py index 5a3912562f..2a8eed226e 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py +++ b/examples/compressor/main_tf_pruner.py @@ -91,6 +91,7 @@ def main(): 'support_type': 'default' }] pruner = AGPruner(configure_list) + pruner.load_configure('configure_example.yaml') pruner(tf.get_default_graph()) # you can also use compress(model) or compress_default_graph() for tensorflow compressor # pruner.compress(tf.get_default_graph()) diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py b/examples/compressor/main_tf_quantizer.py similarity index 100% rename from src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py rename to examples/compressor/main_tf_quantizer.py diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py b/examples/compressor/main_torch_pruner.py similarity index 98% rename from src/sdk/pynni/nni/compressors/example/main_torch_pruner.py rename to examples/compressor/main_torch_pruner.py index 8a9d8cbe6a..965b695349 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py +++ b/examples/compressor/main_torch_pruner.py @@ -78,6 +78,7 @@ def main(): }] pruner = AGPruner(configure_list) + pruner.load_configure('configure_example.yaml') pruner(model) # you can also use compress(model) method # like that pruner.compress(model) diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py b/examples/compressor/main_torch_quantizer.py similarity index 100% rename from src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py rename to examples/compressor/main_torch_quantizer.py diff --git a/setup.py b/setup.py index f470dedff1..a1239b2de7 100644 --- a/setup.py +++ b/setup.py @@ -27,7 +27,7 @@ def read(fname): setup( name = 'nni', - version = '999.0.0-developing', + version = 'v0.8-347-g0538d30', author = 'Microsoft NNI Team', author_email = 'nni@microsoft.com', description = 'Neural Network Intelligence project', @@ -58,8 +58,7 @@ def read(fname): 'schema', 'PythonWebHDFS', 'colorama', - 'sklearn', - 'pyyaml' + 'sklearn' ], entry_points = { diff --git a/src/sdk/pynni/nni/compressors/example/__init__.py b/src/sdk/pynni/nni/compressors/example/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py index 35751a1943..d0566967ac 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py @@ -1,6 +1,6 @@ import tensorflow as tf from tensorflow import Graph, Operation, Tensor -import yaml +from ruamel.yaml import YAML from typing import List __all__ = [ @@ -77,10 +77,15 @@ def _tf_default_get_configure(configure_list, layer_info): return configure def _tf_default_load_configure_file(config_path, class_name): + print('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) assert config_path is not None and config_path.endswith('yaml') file = open(config_path, 'r') + yaml = YAML(typ='safe') yaml_text = yaml.load(file.read()) - return yaml_text.get(class_name, {}) + configure_file = yaml_text.get(class_name, {}) + if not configure_file: + print('WARNING: load Nothing from configure file, Default { }') + return configure_file class TfPruner(TfCompressor): """TODO""" @@ -135,7 +140,7 @@ def __call__(self, model): return model def quantize_weight(self, layer_info, weight): - raise NotImplementedError() + raise NotImplementedError("Quantizer must overload quantize_weight()") def compress(self, model): diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py index 3b889b2374..266e6c7784 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py @@ -66,7 +66,7 @@ def compute_target_sparsity(self, layer_info): now_epoch = tf.minimum(self.now_epoch, tf.constant(end_epoch)) span = int(((end_epoch - start_epoch-1)//freq)*freq) assert span > 0 - base = tf.cast(now_epoch - initial_sparsity, tf.float32) / span + base = tf.cast(now_epoch - start_epoch, tf.float32) / span target_sparsity = (final_sparsity + (initial_sparsity - final_sparsity)* (tf.pow(1.0 - base, 3))) diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py index d502f72a4d..8584ea195a 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py @@ -1,6 +1,6 @@ from torch import Tensor from torch.nn import Module, Parameter -import yaml +from ruamel.yaml import YAML from typing import List __all__ = [ @@ -71,10 +71,16 @@ def _torch_default_get_configure(configure_list, layer_info): return configure def _torch_default_load_configure_file(config_path, class_name): + print('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) assert config_path is not None and config_path.endswith('yaml') file = open(config_path, 'r') + yaml = YAML(typ='safe') yaml_text = yaml.load(file.read()) - return yaml_text.get(class_name, {}) + configure_file = yaml_text.get(class_name, {}) + if not configure_file: + print('WARNING: load Nothing from configure file, Default { }') + return configure_file + class TorchPruner(TorchCompressor): """TODO""" @@ -138,7 +144,7 @@ def __call__(self, model): def quantize_weight(self, layer_info, weight): # FIXME: where dequantize goes? - raise NotImplementedError() + raise NotImplementedError("Quantizer must overload quantize_weight()") def compress(self, model): super().compress(model) diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py index cb9affbf90..c8c2a8874d 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py @@ -98,7 +98,7 @@ def calc_mask(self, layer_info, weight): mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) target_sparsity = self.compute_target_sparsity(now_epoch, layer_info) k = int(weight.numel() * target_sparsity) - if k == 0 or target_sparsity>=1 or target_sparsity<=0: + if k == 0 or target_sparsity >= 1 or target_sparsity <= 0: return mask w_abs = weight.abs()*mask diff --git a/src/sdk/pynni/requirements.txt b/src/sdk/pynni/requirements.txt index a9db2a090d..3adfb06cf5 100644 --- a/src/sdk/pynni/requirements.txt +++ b/src/sdk/pynni/requirements.txt @@ -8,6 +8,3 @@ hyperopt # metis tuner sklearn - -# compresser -pyyaml diff --git a/src/sdk/pynni/setup.py b/src/sdk/pynni/setup.py index 2375510dd3..e7ba29dad2 100644 --- a/src/sdk/pynni/setup.py +++ b/src/sdk/pynni/setup.py @@ -36,8 +36,7 @@ def read(fname): 'json_tricks', 'numpy', 'scipy', - 'coverage', - 'pyyaml' + 'coverage' ], package_data = {'nni': ['**/requirements.txt']}, From 3a94357175efcae2437d96fd2083478ab7faa782 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Mon, 26 Aug 2019 16:43:24 +0800 Subject: [PATCH 28/34] add user hint --- .../compressors/tf_compressor/_nnimc_tf.py | 3 +- .../nni/compressors/tf_compressor/pruner.py | 2 +- .../compressors/tf_compressor/quantizer.py | 22 +- .../torch_compressor/_nnimc_torch.py | 2 +- .../compressors/torch_compressor/pruner.py | 2 +- .../compressors/torch_compressor/quantizer.py | 22 +- src/webui/yarn.lock | 5957 +++++++---------- 7 files changed, 2420 insertions(+), 3590 deletions(-) diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py index d0566967ac..6f49c95004 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py @@ -61,6 +61,7 @@ def __init__(self, layer): def _tf_detect_prunable_layers(model): # search for Conv2D layers # TODO: whitelist + # please make sure op not from optimizer whiltlist = ['Conv2D', 'DepthwiseConv2dNative'] return [ TfLayerInfo(op) for op in model.get_operations() if op.type in whiltlist ] @@ -143,7 +144,7 @@ def quantize_weight(self, layer_info, weight): raise NotImplementedError("Quantizer must overload quantize_weight()") - def compress(self, model): + def compress(self, model): for layer_info in _tf_detect_prunable_layers(model): self._instrument_layer(layer_info) diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py index 266e6c7784..0aa23c4763 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py @@ -60,7 +60,7 @@ def compute_target_sparsity(self, layer_info): final_sparsity = configure.get('final_sparsity', 0) initial_sparsity = configure.get('initial_sparsity', 0) - if end_epoch <= start_epoch: + if end_epoch <= start_epoch or initial_sparsity >= final_sparsity: return final_sparsity now_epoch = tf.minimum(self.now_epoch, tf.constant(end_epoch)) diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py b/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py index 2c09a01f32..be614a29ac 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py @@ -33,9 +33,14 @@ def load_configure(self, config_path): for config in config_list.get('config', []): self.configure_list.append(config) - def get_qbits(self, configure={}): - sparsity = configure.get('q_bits',0) - return sparsity + def get_qbits(self, configure): + if not isinstance(configure, dict): + print('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + configure = {} + qbits = configure.get('q_bits', 32) + if qbits == 0: + print('WARNING: you can not set q_bits ZERO!') + return qbits def quantize_weight(self, layer_info, weight): q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) @@ -69,9 +74,14 @@ def load_configure(self, config_path): for config in config_list.get('config', []): self.configure_list.append(config) - def get_qbits(self, configure={}): - sparsity = configure.get('q_bits',0) - return sparsity + def get_qbits(self, configure): + if not isinstance(configure, dict): + print('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + configure = {} + qbits = configure.get('q_bits', 32) + if qbits == 0: + print('WARNING: you can not set q_bits ZERO!') + return qbits def quantize_weight(self, layer_info, weight): q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py index 8584ea195a..9d46a1fac1 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py @@ -49,7 +49,7 @@ def __init__(self, name, layer): def _torch_detect_prunable_layers(model): # search for all layers which have parameter "weight" - ret = [ ] + ret = [] for name, layer in model.named_modules(): try: if isinstance(layer.weight, Parameter) and isinstance(layer.weight.data, Tensor): diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py index c8c2a8874d..3c9b3f78df 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py @@ -135,7 +135,7 @@ def load_configure(self, config_path): self.configure_list.append(config) def get_sparsity(self, configure={}): - sparsity = configure.get('sparsity',0) + sparsity = configure.get('sparsity', 0) return sparsity def calc_mask(self, layer_info, weight): diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py b/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py index d25feee8b8..fcc5bfab12 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py @@ -34,9 +34,14 @@ def load_configure(self, config_path): for config in config_list.get('config', []): self.configure_list.append(config) - def get_qbits(self, configure={}): - sparsity = configure.get('q_bits',0) - return sparsity + def get_qbits(self, configure): + if not isinstance(configure, dict): + print('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + configure = {} + qbits = configure.get('q_bits', 32) + if qbits == 0: + print('WARNING: you can not set q_bits ZERO!') + return qbits def quantize_weight(self, layer_info, weight): q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) @@ -71,9 +76,14 @@ def load_configure(self, config_path): for config in config_list.get('config', []): self.configure_list.append(config) - def get_qbits(self, configure={}): - sparsity = configure.get('q_bits',0) - return sparsity + def get_qbits(self, configure): + if not isinstance(configure, dict): + print('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + configure = {} + qbits = configure.get('q_bits', 32) + if qbits == 0: + print('WARNING: you can not set q_bits ZERO!') + return qbits def quantize_weight(self, layer_info, weight): q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) diff --git a/src/webui/yarn.lock b/src/webui/yarn.lock index 8d896151a1..009489ef1f 100644 --- a/src/webui/yarn.lock +++ b/src/webui/yarn.lock @@ -3,72 +3,73 @@ "@ant-design/icons-react@~1.1.2": - 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path-is-absolute "^1.0.0" - readdirp "^2.0.0" - upath "^1.0.5" - optionalDependencies: - fsevents "^1.2.2" +chardet@^0.7.0: + version "0.7.0" + resolved "https://registry.yarnpkg.com/chardet/-/chardet-0.7.0.tgz#90094849f0937f2eedc2425d0d28a9e5f0cbad9e" + integrity sha512-mT8iDcrh03qDGRRmoA2hmBJnxpllMR+0/0qlzjqZES6NdiWDcZkCNAk4rPFZ9Q85r27unkiNNg8ZOiwZXBHwcA== -chokidar@^2.1.6: +chokidar@^2.0.2, chokidar@^2.0.4, chokidar@^2.1.6: version "2.1.6" resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-2.1.6.tgz#b6cad653a929e244ce8a834244164d241fa954c5" + integrity sha512-V2jUo67OKkc6ySiRpJrjlpJKl9kDuG+Xb8VgsGzb+aEouhgS1D0weyPU4lEzdAcsCAvrih2J2BqyXqHWvVLw5g== dependencies: anymatch "^2.0.0" async-each "^1.0.1" @@ -1660,17 +1137,27 @@ chokidar@^2.1.6: optionalDependencies: fsevents "^1.2.7" -chownr@^1.0.1: - version "1.1.1" - resolved "https://registry.yarnpkg.com/chownr/-/chownr-1.1.1.tgz#54726b8b8fff4df053c42187e801fb4412df1494" +chownr@^1.1.1: + version "1.1.2" + resolved "https://registry.yarnpkg.com/chownr/-/chownr-1.1.2.tgz#a18f1e0b269c8a6a5d3c86eb298beb14c3dd7bf6" + integrity sha512-GkfeAQh+QNy3wquu9oIZr6SS5x7wGdSgNQvD10X3r+AZr1Oys22HW8kAmDMvNg2+Dm0TeGaEuO8gFwdBXxwO8A== + +chrome-trace-event@^1.0.2: + version "1.0.2" + resolved "https://registry.yarnpkg.com/chrome-trace-event/-/chrome-trace-event-1.0.2.tgz#234090ee97c7d4ad1a2c4beae27505deffc608a4" + integrity sha512-9e/zx1jw7B4CO+c/RXoCsfg/x1AfUBioy4owYH0bJprEYAx5hRFLRhWBqHAG57D0ZM4H7vxbP7bPe0VwhQRYDQ== + dependencies: + tslib "^1.9.0" ci-info@^1.5.0: version "1.6.0" resolved "https://registry.yarnpkg.com/ci-info/-/ci-info-1.6.0.tgz#2ca20dbb9ceb32d4524a683303313f0304b1e497" + integrity sha512-vsGdkwSCDpWmP80ncATX7iea5DWQemg1UgCW5J8tqjU3lYw4FBYuj89J0CTVomA7BEfvSZd84GmHko+MxFQU2A== cipher-base@^1.0.0, cipher-base@^1.0.1, cipher-base@^1.0.3: version "1.0.4" resolved "https://registry.yarnpkg.com/cipher-base/-/cipher-base-1.0.4.tgz#8760e4ecc272f4c363532f926d874aae2c1397de" + integrity sha512-Kkht5ye6ZGmwv40uUDZztayT2ThLQGfnj/T71N/XzeZeo3nf8foyW7zGTsPYkEya3m5f3cAypH+qe7YOrM1U2Q== dependencies: inherits "^2.0.1" safe-buffer "^5.0.1" @@ -1684,6 +1171,7 @@ clap@^1.0.9: class-utils@^0.3.5: version "0.3.6" resolved "https://registry.yarnpkg.com/class-utils/-/class-utils-0.3.6.tgz#f93369ae8b9a7ce02fd41faad0ca83033190c463" + integrity sha512-qOhPa/Fj7s6TY8H8esGu5QNpMMQxz79h+urzrNYN6mn+9BnxlDGf5QZ+XeCDsxSjPqsSR56XOZOJmpeurnLMeg== dependencies: arr-union "^3.1.0" define-property "^0.2.5" @@ -1694,42 +1182,30 @@ classnames@2.x, classnames@^2.2.0, classnames@^2.2.1, classnames@^2.2.3, classna version "2.2.6" resolved "https://registry.yarnpkg.com/classnames/-/classnames-2.2.6.tgz#43935bffdd291f326dad0a205309b38d00f650ce" -clean-css@4.2.x: +clean-css@^4.2.1: version "4.2.1" resolved "https://registry.yarnpkg.com/clean-css/-/clean-css-4.2.1.tgz#2d411ef76b8569b6d0c84068dabe85b0aa5e5c17" + integrity sha512-4ZxI6dy4lrY6FHzfiy1aEOXgu4LIsW2MhwG0VBKdcoGoH/XLFgaHSdLTGr4O8Be6A8r3MOphEiI8Gc1n0ecf3g== dependencies: source-map "~0.6.0" cli-boxes@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/cli-boxes/-/cli-boxes-1.0.0.tgz#4fa917c3e59c94a004cd61f8ee509da651687143" + integrity sha1-T6kXw+WclKAEzWH47lCdplFocUM= cli-cursor@^2.1.0: version "2.1.0" resolved "https://registry.yarnpkg.com/cli-cursor/-/cli-cursor-2.1.0.tgz#b35dac376479facc3e94747d41d0d0f5238ffcb5" + integrity sha1-s12sN2R5+sw+lHR9QdDQ9SOP/LU= dependencies: restore-cursor "^2.0.0" -cli-width@^2.0.0: - 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version "1.5.3" - resolved "https://registry.yarnpkg.com/color-string/-/color-string-1.5.3.tgz#c9bbc5f01b58b5492f3d6857459cb6590ce204cc" - dependencies: - color-name "^1.0.0" - simple-swizzle "^0.2.2" - color@^0.11.0: version "0.11.4" resolved "http://registry.npmjs.org/color/-/color-0.11.4.tgz#6d7b5c74fb65e841cd48792ad1ed5e07b904d764" @@ -1802,13 +1272,6 @@ color@^0.11.0: color-convert "^1.3.0" color-string "^0.3.0" -color@^1.0.3: - version "1.0.3" - resolved "http://registry.npmjs.org/color/-/color-1.0.3.tgz#e48e832d85f14ef694fb468811c2d5cfe729b55d" - dependencies: - color-convert "^1.8.2" - color-string "^1.4.0" - colormin@^1.0.5: version "1.1.2" resolved "https://registry.yarnpkg.com/colormin/-/colormin-1.1.2.tgz#ea2f7420a72b96881a38aae59ec124a6f7298133" @@ -1821,33 +1284,27 @@ colors@~1.1.2: version "1.1.2" resolved "https://registry.yarnpkg.com/colors/-/colors-1.1.2.tgz#168a4701756b6a7f51a12ce0c97bfa28c084ed63" -combined-stream@1.0.6: - version "1.0.6" - resolved "http://registry.npmjs.org/combined-stream/-/combined-stream-1.0.6.tgz#723e7df6e801ac5613113a7e445a9b69cb632818" - 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version "1.6.0" - resolved "https://registry.yarnpkg.com/convert-source-map/-/convert-source-map-1.6.0.tgz#51b537a8c43e0f04dec1993bffcdd504e758ac20" - dependencies: - safe-buffer "~5.1.1" - cookie-signature@1.0.6: version "1.0.6" resolved "https://registry.yarnpkg.com/cookie-signature/-/cookie-signature-1.0.6.tgz#e303a882b342cc3ee8ca513a79999734dab3ae2c" @@ -1950,6 +1410,7 @@ cookie@0.4.0: copy-concurrently@^1.0.0: version "1.0.5" resolved "https://registry.yarnpkg.com/copy-concurrently/-/copy-concurrently-1.0.5.tgz#92297398cae34937fcafd6ec8139c18051f0b5e0" + integrity sha512-f2domd9fsVDFtaFcbaRZuYXwtdmnzqbADSwhSWYxYB/Q8zsdUUFMXVRwXGDMWmbEzAn1kdRrtI1T/KTFOL4X2A== dependencies: aproba "^1.1.1" fs-write-stream-atomic "^1.0.8" @@ -1961,10 +1422,12 @@ copy-concurrently@^1.0.0: copy-descriptor@^0.1.0: version "0.1.1" resolved "https://registry.yarnpkg.com/copy-descriptor/-/copy-descriptor-0.1.1.tgz#676f6eb3c39997c2ee1ac3a924fd6124748f578d" + integrity sha1-Z29us8OZl8LuGsOpJP1hJHSPV40= copy-to-clipboard@^3.0.8: version "3.2.0" resolved "https://registry.yarnpkg.com/copy-to-clipboard/-/copy-to-clipboard-3.2.0.tgz#d2724a3ccbfed89706fac8a894872c979ac74467" + integrity sha512-eOZERzvCmxS8HWzugj4Uxl8OJxa7T2k1Gi0X5qavwydHIfuSHq2dTD09LOg/XyGq4Zpb5IsR/2OJ5lbOegz78w== dependencies: toggle-selection "^1.0.6" @@ -1973,44 +1436,29 @@ core-js@^1.0.0: resolved "https://registry.yarnpkg.com/core-js/-/core-js-1.2.7.tgz#652294c14651db28fa93bd2d5ff2983a4f08c636" core-js@^2.4.0, core-js@^2.5.0: - 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version "6.4.1" - resolved "https://registry.yarnpkg.com/elliptic/-/elliptic-6.4.1.tgz#c2d0b7776911b86722c632c3c06c60f2f819939a" + version "6.5.0" + resolved "https://registry.yarnpkg.com/elliptic/-/elliptic-6.5.0.tgz#2b8ed4c891b7de3200e14412a5b8248c7af505ca" + integrity sha512-eFOJTMyCYb7xtE/caJ6JJu+bhi67WCYNbkGSknu20pmM8Ke/bqOfdnZWxyoGN26JgfxTbXrsCkEw4KheCT/KGg== dependencies: bn.js "^4.4.0" brorand "^1.0.1" @@ -2603,6 +2094,7 @@ elliptic@^6.0.0: emojis-list@^2.0.0: version "2.1.0" resolved "https://registry.yarnpkg.com/emojis-list/-/emojis-list-2.1.0.tgz#4daa4d9db00f9819880c79fa457ae5b09a1fd389" + integrity sha1-TapNnbAPmBmIDHn6RXrlsJof04k= encodeurl@~1.0.2: version "1.0.2" @@ -2617,17 +2109,18 @@ encoding@^0.1.11: end-of-stream@^1.0.0, end-of-stream@^1.1.0: version "1.4.1" resolved "https://registry.yarnpkg.com/end-of-stream/-/end-of-stream-1.4.1.tgz#ed29634d19baba463b6ce6b80a37213eab71ec43" + integrity sha512-1MkrZNvWTKCaigbn+W15elq2BB/L22nqrSY5DKlo3X6+vclJm8Bb5djXJBmEX6fS3+zCh/F4VBK5Z2KxJt4s2Q== dependencies: once "^1.4.0" -enhanced-resolve@^3.0.0, enhanced-resolve@^3.4.0: - 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-lodash.endswith@^4.2.1: - version "4.2.1" - resolved "https://registry.yarnpkg.com/lodash.endswith/-/lodash.endswith-4.2.1.tgz#fed59ac1738ed3e236edd7064ec456448b37bc09" + integrity sha1-0JF4cW/+pN3p5ft7N/bwgCJ0WAw= lodash.flow@^3.3.0: version "3.5.0" resolved "https://registry.yarnpkg.com/lodash.flow/-/lodash.flow-3.5.0.tgz#87bf40292b8cf83e4e8ce1a3ae4209e20071675a" + integrity sha1-h79AKSuM+D5OjOGjrkIJ4gBxZ1o= lodash.isarguments@^3.0.0: version "3.1.0" @@ -4891,14 +3851,6 @@ lodash.isarray@^3.0.0: version "3.0.4" resolved "https://registry.yarnpkg.com/lodash.isarray/-/lodash.isarray-3.0.4.tgz#79e4eb88c36a8122af86f844aa9bcd851b5fbb55" -lodash.isfunction@^3.0.8: - version "3.0.9" - resolved "https://registry.yarnpkg.com/lodash.isfunction/-/lodash.isfunction-3.0.9.tgz#06de25df4db327ac931981d1bdb067e5af68d051" - -lodash.isstring@^4.0.1: - version "4.0.1" - resolved "https://registry.yarnpkg.com/lodash.isstring/-/lodash.isstring-4.0.1.tgz#d527dfb5456eca7cc9bb95d5daeaf88ba54a5451" - lodash.keys@^3.1.2: version "3.1.2" resolved "https://registry.yarnpkg.com/lodash.keys/-/lodash.keys-3.1.2.tgz#4dbc0472b156be50a0b286855d1bd0b0c656098a" @@ -4911,15 +3863,7 @@ lodash.memoize@^4.1.2: version "4.1.2" resolved "https://registry.yarnpkg.com/lodash.memoize/-/lodash.memoize-4.1.2.tgz#bcc6c49a42a2840ed997f323eada5ecd182e0bfe" -lodash.sortby@^4.7.0: - version "4.7.0" - resolved "https://registry.yarnpkg.com/lodash.sortby/-/lodash.sortby-4.7.0.tgz#edd14c824e2cc9c1e0b0a1b42bb5210516a42438" - -lodash.startswith@^4.2.1: - version "4.2.1" - resolved "https://registry.yarnpkg.com/lodash.startswith/-/lodash.startswith-4.2.1.tgz#c598c4adce188a27e53145731cdc6c0e7177600c" - -lodash.template@^4.2.4, lodash.template@^4.4.0, lodash.template@^4.5.0: +lodash.template@^4.4.0, lodash.template@^4.5.0: version "4.5.0" resolved "https://registry.yarnpkg.com/lodash.template/-/lodash.template-4.5.0.tgz#f976195cf3f347d0d5f52483569fe8031ccce8ab" dependencies: @@ -4927,10 +3871,11 @@ lodash.template@^4.2.4, lodash.template@^4.4.0, lodash.template@^4.5.0: lodash.templatesettings "^4.0.0" lodash.templatesettings@^4.0.0: - version "4.1.0" - resolved "https://registry.yarnpkg.com/lodash.templatesettings/-/lodash.templatesettings-4.1.0.tgz#2b4d4e95ba440d915ff08bc899e4553666713316" + version "4.2.0" + resolved "https://registry.yarnpkg.com/lodash.templatesettings/-/lodash.templatesettings-4.2.0.tgz#e481310f049d3cf6d47e912ad09313b154f0fb33" + integrity sha512-stgLz+i3Aa9mZgnjr/O+v9ruKZsPsndy7qPZOchbqk2cnTU1ZaldKK+v7m54WoKIyxiuMZTKT2H81F8BeAc3ZQ== dependencies: - lodash._reinterpolate "~3.0.0" + lodash._reinterpolate "^3.0.0" lodash.throttle@^4.0.0: version "4.1.1" @@ -4940,17 +3885,15 @@ lodash.uniq@^4.5.0: version "4.5.0" resolved "https://registry.yarnpkg.com/lodash.uniq/-/lodash.uniq-4.5.0.tgz#d0225373aeb652adc1bc82e4945339a842754773" -"lodash@>=3.5 <5", lodash@^4.13.1, lodash@^4.16.5, lodash@^4.17.10, lodash@^4.17.11, lodash@^4.17.13, lodash@^4.17.3, lodash@^4.17.4, lodash@^4.17.5, lodash@^4.3.0: - version "4.17.14" - resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.14.tgz#9ce487ae66c96254fe20b599f21b6816028078ba" +"lodash@>=3.5 <5", lodash@^4.16.5, lodash@^4.17.11, lodash@^4.17.14, lodash@^4.17.4, lodash@^4.17.5: + version "4.17.15" + resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.15.tgz#b447f6670a0455bbfeedd11392eff330ea097548" + integrity sha512-8xOcRHvCjnocdS5cpwXQXVzmmh5e5+saE2QGoeQmbKmRS6J3VQppPOIt0MnmE+4xlZoumy0GPG0D0MVIQbNA1A== loglevel@^1.6.3: version "1.6.3" resolved "https://registry.yarnpkg.com/loglevel/-/loglevel-1.6.3.tgz#77f2eb64be55a404c9fd04ad16d57c1d6d6b1280" - -longest@^1.0.1: - version "1.0.1" - resolved "https://registry.yarnpkg.com/longest/-/longest-1.0.1.tgz#30a0b2da38f73770e8294a0d22e6625ed77d0097" + integrity sha512-LoEDv5pgpvWgPF4kNYuIp0qqSJVWak/dML0RY74xlzMZiT9w77teNAwKYKWBTYjlokMirg+o3jBwp+vlLrcfAA== loose-envify@^1.0.0, loose-envify@^1.1.0, loose-envify@^1.2.0, loose-envify@^1.3.1, loose-envify@^1.4.0: version "1.4.0" @@ -4961,6 +3904,7 @@ loose-envify@^1.0.0, loose-envify@^1.1.0, loose-envify@^1.2.0, loose-envify@^1.3 loud-rejection@^1.0.0: version "1.6.0" resolved "https://registry.yarnpkg.com/loud-rejection/-/loud-rejection-1.6.0.tgz#5b46f80147edee578870f086d04821cf998e551f" + integrity sha1-W0b4AUft7leIcPCG0Eghz5mOVR8= dependencies: currently-unhandled "^0.4.1" signal-exit "^3.0.0" @@ -4972,49 +3916,71 @@ lower-case@^1.1.1: lowercase-keys@^1.0.0: version "1.0.1" resolved "https://registry.yarnpkg.com/lowercase-keys/-/lowercase-keys-1.0.1.tgz#6f9e30b47084d971a7c820ff15a6c5167b74c26f" + integrity sha512-G2Lj61tXDnVFFOi8VZds+SoQjtQC3dgokKdDG2mTm1tx4m50NUHBOZSBwQQHyy0V12A0JTG4icfZQH+xPyh8VA== -lru-cache@^4.0.1, lru-cache@^4.1.1: - version "4.1.3" - resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-4.1.3.tgz#a1175cf3496dfc8436c156c334b4955992bce69c" +lru-cache@^4.0.1: + version "4.1.5" + resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-4.1.5.tgz#8bbe50ea85bed59bc9e33dcab8235ee9bcf443cd" + integrity sha512-sWZlbEP2OsHNkXrMl5GYk/jKk70MBng6UU4YI/qGDYbgf6YbP4EvmqISbXCoJiRKs+1bSpFHVgQxvJ17F2li5g== dependencies: pseudomap "^1.0.2" yallist "^2.1.2" +lru-cache@^5.1.1: + version "5.1.1" + resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-5.1.1.tgz#1da27e6710271947695daf6848e847f01d84b920" + integrity sha512-KpNARQA3Iwv+jTA0utUVVbrh+Jlrr1Fv0e56GGzAFOXN7dk/FviaDW8LHmK52DlcH4WP2n6gI8vN1aesBFgo9w== + dependencies: + yallist "^3.0.2" + make-dir@^1.0.0: version "1.3.0" resolved "https://registry.yarnpkg.com/make-dir/-/make-dir-1.3.0.tgz#79c1033b80515bd6d24ec9933e860ca75ee27f0c" + integrity sha512-2w31R7SJtieJJnQtGc7RVL2StM2vGYVfqUOvUDxH6bC6aJTxPxTF0GnIgCyu7tjockiUWAYQRbxa7vKn34s5sQ== dependencies: pify "^3.0.0" -makeerror@1.0.x: - version "1.0.11" - resolved "https://registry.yarnpkg.com/makeerror/-/makeerror-1.0.11.tgz#e01a5c9109f2af79660e4e8b9587790184f5a96c" +make-dir@^2.0.0: + version "2.1.0" + resolved "https://registry.yarnpkg.com/make-dir/-/make-dir-2.1.0.tgz#5f0310e18b8be898cc07009295a30ae41e91e6f5" + integrity sha512-LS9X+dc8KLxXCb8dni79fLIIUA5VyZoyjSMCwTluaXA0o27cCK0bhXkpgw+sTXVpPy/lSO57ilRixqk0vDmtRA== dependencies: - tmpl "1.0.x" + pify "^4.0.1" + semver "^5.6.0" + +mamacro@^0.0.3: + version "0.0.3" + resolved "https://registry.yarnpkg.com/mamacro/-/mamacro-0.0.3.tgz#ad2c9576197c9f1abf308d0787865bd975a3f3e4" + integrity sha512-qMEwh+UujcQ+kbz3T6V+wAmO2U8veoq2w+3wY8MquqwVA3jChfwY+Tk52GZKDfACEPjuZ7r2oJLejwpt8jtwTA== map-age-cleaner@^0.1.1: version "0.1.3" resolved "https://registry.yarnpkg.com/map-age-cleaner/-/map-age-cleaner-0.1.3.tgz#7d583a7306434c055fe474b0f45078e6e1b4b92a" + integrity sha512-bJzx6nMoP6PDLPBFmg7+xRKeFZvFboMrGlxmNj9ClvX53KrmvM5bXFXEWjbz4cz1AFn+jWJ9z/DJSz7hrs0w3w== dependencies: p-defer "^1.0.0" map-cache@^0.2.2: version "0.2.2" resolved "https://registry.yarnpkg.com/map-cache/-/map-cache-0.2.2.tgz#c32abd0bd6525d9b051645bb4f26ac5dc98a0dbf" + integrity sha1-wyq9C9ZSXZsFFkW7TyasXcmKDb8= map-obj@^1.0.0, map-obj@^1.0.1: version "1.0.1" resolved "https://registry.yarnpkg.com/map-obj/-/map-obj-1.0.1.tgz#d933ceb9205d82bdcf4886f6742bdc2b4dea146d" + integrity sha1-2TPOuSBdgr3PSIb2dCvcK03qFG0= map-visit@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/map-visit/-/map-visit-1.0.0.tgz#ecdca8f13144e660f1b5bd41f12f3479d98dfb8f" + integrity sha1-7Nyo8TFE5mDxtb1B8S80edmN+48= dependencies: object-visit "^1.0.0" matchmediaquery@^0.3.0: version "0.3.0" resolved "https://registry.yarnpkg.com/matchmediaquery/-/matchmediaquery-0.3.0.tgz#6f672bcdbc44de16825c6917fbcdcfb9b82607b1" + integrity sha512-u0dlv+VENJ+3YepvwSPBieuvnA6DWfaYa/ctwysAR13y4XLJNyt7bEVKzNj/Nvjo+50d88Pj+xL9xaSo6JmX/w== dependencies: css-mediaquery "^0.1.2" @@ -5022,13 +3988,10 @@ math-expression-evaluator@^1.2.14: version "1.2.17" resolved "https://registry.yarnpkg.com/math-expression-evaluator/-/math-expression-evaluator-1.2.17.tgz#de819fdbcd84dccd8fae59c6aeb79615b9d266ac" -math-random@^1.0.1: - version "1.0.1" - resolved "https://registry.yarnpkg.com/math-random/-/math-random-1.0.1.tgz#8b3aac588b8a66e4975e3cdea67f7bb329601fac" - md5.js@^1.3.4: version "1.3.5" resolved "https://registry.yarnpkg.com/md5.js/-/md5.js-1.3.5.tgz#b5d07b8e3216e3e27cd728d72f70d1e6a342005f" + integrity sha512-xitP+WxNPcTTOgnTJcrhM0xvdPepipPSf3I8EIpGKeFLjt3PlJLIDG3u8EX53ZIubkb+5U2+3rELYpEhHhzdkg== dependencies: hash-base "^3.0.0" inherits "^2.0.1" @@ -5038,7 +4001,7 @@ media-typer@0.3.0: version "0.3.0" resolved "https://registry.yarnpkg.com/media-typer/-/media-typer-0.3.0.tgz#8710d7af0aa626f8fffa1ce00168545263255748" -mem@^1.1.0, mem@^4.0.0: +mem@^4.0.0: version "4.3.0" resolved "https://registry.yarnpkg.com/mem/-/mem-4.3.0.tgz#461af497bc4ae09608cdb2e60eefb69bff744178" dependencies: @@ -5046,9 +4009,10 @@ mem@^1.1.0, mem@^4.0.0: mimic-fn "^2.0.0" p-is-promise "^2.0.0" -memory-fs@^0.4.0, memory-fs@^0.4.1, memory-fs@~0.4.1: +memory-fs@^0.4.0, memory-fs@^0.4.1: version "0.4.1" resolved "https://registry.yarnpkg.com/memory-fs/-/memory-fs-0.4.1.tgz#3a9a20b8462523e447cfbc7e8bb80ed667bfc552" + integrity sha1-OpoguEYlI+RHz7x+i7gO1me/xVI= dependencies: errno "^0.1.3" readable-stream "^2.0.1" @@ -5056,6 +4020,7 @@ memory-fs@^0.4.0, memory-fs@^0.4.1, memory-fs@~0.4.1: meow@^3.7.0: version "3.7.0" resolved "https://registry.yarnpkg.com/meow/-/meow-3.7.0.tgz#72cb668b425228290abbfa856892587308a801fb" + integrity sha1-cstmi0JSKCkKu/qFaJJYcwioAfs= dependencies: camelcase-keys "^2.0.0" decamelize "^1.1.2" @@ -5072,41 +4037,24 @@ merge-descriptors@1.0.1: version "1.0.1" resolved "https://registry.yarnpkg.com/merge-descriptors/-/merge-descriptors-1.0.1.tgz#b00aaa556dd8b44568150ec9d1b953f3f90cbb61" -merge-stream@^1.0.1: - 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version "1.0.5" - resolved "https://registry.yarnpkg.com/npm-bundled/-/npm-bundled-1.0.5.tgz#3c1732b7ba936b3a10325aef616467c0ccbcc979" + version "1.0.6" + resolved "https://registry.yarnpkg.com/npm-bundled/-/npm-bundled-1.0.6.tgz#e7ba9aadcef962bb61248f91721cd932b3fe6bdd" + integrity sha512-8/JCaftHwbd//k6y2rEWp6k1wxVfpFzB6t1p825+cUb7Ym2XQfhwIC5KwhrvzZRJu+LtDE585zVaS32+CGtf0g== npm-packlist@^1.1.6: - version "1.1.12" - resolved "https://registry.yarnpkg.com/npm-packlist/-/npm-packlist-1.1.12.tgz#22bde2ebc12e72ca482abd67afc51eb49377243a" + version "1.4.4" + resolved "https://registry.yarnpkg.com/npm-packlist/-/npm-packlist-1.4.4.tgz#866224233850ac534b63d1a6e76050092b5d2f44" + integrity sha512-zTLo8UcVYtDU3gdeaFu2Xu0n0EvelfHDGuqtNIn5RO7yQj4H1TqNdBc/yZjxnWA0PVB8D3Woyp0i5B43JwQ6Vw== dependencies: ignore-walk "^3.0.1" npm-bundled "^1.0.1" @@ -5494,12 +4437,14 @@ npm-packlist@^1.1.6: npm-run-path@^2.0.0: version "2.0.2" resolved "https://registry.yarnpkg.com/npm-run-path/-/npm-run-path-2.0.2.tgz#35a9232dfa35d7067b4cb2ddf2357b1871536c5f" + integrity sha1-NakjLfo11wZ7TLLd8jV7GHFTbF8= dependencies: path-key "^2.0.0" npmlog@^4.0.2: version "4.1.2" resolved "https://registry.yarnpkg.com/npmlog/-/npmlog-4.1.2.tgz#08a7f2a8bf734604779a9efa4ad5cc717abb954b" + integrity sha512-2uUqazuKlTaSI/dC8AzicUck7+IrEaOnN/e0jd3Xtt1KcGpwx30v50mL7oPyr/h9bL3E4aZccVwpwP+5W9Vjkg== dependencies: are-we-there-yet "~1.1.2" console-control-strings "~1.1.0" @@ -5519,26 +4464,22 @@ num2fraction@^1.2.2: number-is-nan@^1.0.0: version "1.0.1" resolved "https://registry.yarnpkg.com/number-is-nan/-/number-is-nan-1.0.1.tgz#097b602b53422a522c1afb8790318336941a011d" - 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version "1.1.29" - resolved "https://registry.yarnpkg.com/psl/-/psl-1.1.29.tgz#60f580d360170bb722a797cc704411e6da850c67" + version "1.3.0" + resolved "https://registry.yarnpkg.com/psl/-/psl-1.3.0.tgz#e1ebf6a3b5564fa8376f3da2275da76d875ca1bd" + integrity sha512-avHdspHO+9rQTLbv1RO+MPYeP/SzsCoxofjVnHanETfQhTJrmB0HlDoW+EiN/R+C0BZ+gERab9NY0lPN2TxNag== public-encrypt@^4.0.0: version "4.0.3" resolved "https://registry.yarnpkg.com/public-encrypt/-/public-encrypt-4.0.3.tgz#4fcc9d77a07e48ba7527e7cbe0de33d0701331e0" + integrity sha512-zVpa8oKZSz5bTMTFClc1fQOnyyEzpl5ozpi1B5YcvBrdohMjH2rfsBtyXcuNuwjsDIXmBYlF2N5FlJYhR29t8Q== dependencies: bn.js "^4.1.0" browserify-rsa "^4.0.0" @@ -6564,9 +5616,10 @@ public-encrypt@^4.0.0: randombytes "^2.0.1" safe-buffer "^5.1.2" -pump@^2.0.0, pump@^2.0.1: +pump@^2.0.0: version "2.0.1" resolved "https://registry.yarnpkg.com/pump/-/pump-2.0.1.tgz#12399add6e4cf7526d973cbc8b5ce2e2908b3909" + integrity sha512-ruPMNRkN3MHP1cWJc9OWr+T/xDP0jhXYCLfJcBuX54hhfIBnaQmAUMfDcG4DM5UMWByBbJY69QSphm3jtDKIkA== dependencies: end-of-stream "^1.1.0" once "^1.3.1" @@ -6574,6 +5627,7 @@ pump@^2.0.0, pump@^2.0.1: pump@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/pump/-/pump-3.0.0.tgz#b4a2116815bde2f4e1ea602354e8c75565107a64" + integrity sha512-LwZy+p3SFs1Pytd/jYct4wpv49HiYCqd9Rlc5ZVdk0V+8Yzv6jR5Blk3TRmPL1ft69TxP0IMZGJ+WPFU2BFhww== dependencies: end-of-stream "^1.1.0" once "^1.3.1" @@ -6581,6 +5635,7 @@ pump@^3.0.0: pumpify@^1.3.3: version "1.5.1" resolved "https://registry.yarnpkg.com/pumpify/-/pumpify-1.5.1.tgz#36513be246ab27570b1a374a5ce278bfd74370ce" + integrity sha512-oClZI37HvuUJJxSKKrC17bZ9Cu0ZYhEAGPsPUy9KlMUmv9dKX2o77RUmq7f3XjIxbwyGwYzbzQ1L2Ks8sIradQ== dependencies: duplexify "^3.6.0" inherits "^2.0.3" @@ -6589,6 +5644,7 @@ pumpify@^1.3.3: punycode@1.3.2: version "1.3.2" resolved "https://registry.yarnpkg.com/punycode/-/punycode-1.3.2.tgz#9653a036fb7c1ee42342f2325cceefea3926c48d" + integrity sha1-llOgNvt8HuQjQvIyXM7v6jkmxI0= punycode@^1.2.4, punycode@^1.4.1: version "1.4.1" @@ -6597,10 +5653,12 @@ punycode@^1.2.4, punycode@^1.4.1: punycode@^2.1.0: version "2.1.1" resolved "https://registry.yarnpkg.com/punycode/-/punycode-2.1.1.tgz#b58b010ac40c22c5657616c8d2c2c02c7bf479ec" + integrity sha512-XRsRjdf+j5ml+y/6GKHPZbrF/8p2Yga0JPtdqTIY2Xe5ohJPD9saDJJLPvp9+NSBprVvevdXZybnj2cv8OEd0A== pure-color@^1.2.0: version "1.3.0" resolved "https://registry.yarnpkg.com/pure-color/-/pure-color-1.3.0.tgz#1fe064fb0ac851f0de61320a8bf796836422f33e" + integrity sha1-H+Bk+wrIUfDeYTIKi/eWg2Qi8z4= q@^1.1.2: version "1.5.1" @@ -6610,17 +5668,15 @@ qs@6.7.0: version "6.7.0" resolved "https://registry.yarnpkg.com/qs/-/qs-6.7.0.tgz#41dc1a015e3d581f1621776be31afb2876a9b1bc" -qs@~6.4.0: - version "6.4.0" - resolved "https://registry.yarnpkg.com/qs/-/qs-6.4.0.tgz#13e26d28ad6b0ffaa91312cd3bf708ed351e7233" - qs@~6.5.2: version "6.5.2" resolved "https://registry.yarnpkg.com/qs/-/qs-6.5.2.tgz#cb3ae806e8740444584ef154ce8ee98d403f3e36" + integrity sha512-N5ZAX4/LxJmF+7wN74pUD6qAh9/wnvdQcjq9TZjevvXzSUo7bfmw91saqMjzGS2xq91/odN2dW/WOl7qQHNDGA== query-string@^4.1.0, query-string@^4.2.2: version "4.3.4" resolved "https://registry.yarnpkg.com/query-string/-/query-string-4.3.4.tgz#bbb693b9ca915c232515b228b1a02b609043dbeb" + integrity sha1-u7aTucqRXCMlFbIosaArYJBD2+s= dependencies: object-assign "^4.1.0" strict-uri-encode "^1.0.0" @@ -6628,38 +5684,36 @@ query-string@^4.1.0, query-string@^4.2.2: querystring-es3@^0.2.0: version "0.2.1" resolved "https://registry.yarnpkg.com/querystring-es3/-/querystring-es3-0.2.1.tgz#9ec61f79049875707d69414596fd907a4d711e73" + integrity sha1-nsYfeQSYdXB9aUFFlv2Qek1xHnM= querystring@0.2.0: version "0.2.0" resolved "https://registry.yarnpkg.com/querystring/-/querystring-0.2.0.tgz#b209849203bb25df820da756e747005878521620" + integrity sha1-sgmEkgO7Jd+CDadW50cAWHhSFiA= -querystringify@^2.0.0: - 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dependencies: - is-number "^4.0.0" - kind-of "^6.0.0" - math-random "^1.0.1" - randombytes@^2.0.0, randombytes@^2.0.1, randombytes@^2.0.5: - version "2.0.6" - resolved "https://registry.yarnpkg.com/randombytes/-/randombytes-2.0.6.tgz#d302c522948588848a8d300c932b44c24231da80" + version "2.1.0" + resolved "https://registry.yarnpkg.com/randombytes/-/randombytes-2.1.0.tgz#df6f84372f0270dc65cdf6291349ab7a473d4f2a" + integrity sha512-vYl3iOX+4CKUWuxGi9Ukhie6fsqXqS9FE2Zaic4tNFD2N2QQaXOMFbuKK4QmDHC0JO6B1Zp41J0LpT0oR68amQ== dependencies: safe-buffer "^5.1.0" randomfill@^1.0.3: version "1.0.4" resolved "https://registry.yarnpkg.com/randomfill/-/randomfill-1.0.4.tgz#c92196fc86ab42be983f1bf31778224931d61458" + integrity sha512-87lcbR8+MhcWcUiQ+9e+Rwx8MyR2P7qnt15ynUlbm3TU/fjbgz4GsvfSUDTemtCCtVCqb4ZcEFlyPNTh9bBTLw== dependencies: randombytes "^2.0.5" safe-buffer "^5.1.0" @@ -6687,14 +5741,16 @@ rc-align@^2.4.0, rc-align@^2.4.1: rc-util "^4.0.4" rc-animate@2.x, rc-animate@^2.3.0, rc-animate@^2.5.4: - version "2.5.4" - resolved "https://registry.yarnpkg.com/rc-animate/-/rc-animate-2.5.4.tgz#3b308c42e137a2e3fb578650fdb145c2100fcc35" + version "2.9.2" + resolved "https://registry.yarnpkg.com/rc-animate/-/rc-animate-2.9.2.tgz#5964767805c886f1bdc7563d3935a74912a0b78f" + integrity sha512-rkJjeJgfbDqVjVX1/QTRfS7PiCq3AnmeYo840cVcuC4pXq4k4yAQMsC2v5BPXXdawC04vnyO4/qHQdbx9ANaiw== dependencies: babel-runtime "6.x" classnames "^2.2.6" css-animation "^1.3.2" prop-types "15.x" raf "^3.4.0" + rc-util "^4.8.0" react-lifecycles-compat "^3.0.4" rc-animate@^3.0.0-rc.1, rc-animate@^3.0.0-rc.4, rc-animate@^3.0.0-rc.5: @@ -6711,8 +5767,9 @@ rc-animate@^3.0.0-rc.1, rc-animate@^3.0.0-rc.4, rc-animate@^3.0.0-rc.5: react-lifecycles-compat "^3.0.4" rc-calendar@~9.7.9: - version "9.7.10" - resolved "https://registry.yarnpkg.com/rc-calendar/-/rc-calendar-9.7.10.tgz#269393d9b5d4b5091cff65096bac7900ac809373" + version "9.7.11" + resolved "https://registry.yarnpkg.com/rc-calendar/-/rc-calendar-9.7.11.tgz#fa27a6e47018eb71eb6d1857cdcbd161c266dbe0" + integrity sha512-zMvrqz60O+rgLaGBh5Ds5vT69DfxQPiD/eU+D/O0vx0KMooUa3EKTzQvPcAl4fuOdGM0k4ux9z0MIM/I2El0+A== dependencies: babel-runtime "6.x" classnames "2.x" @@ -6754,14 +5811,16 @@ rc-collapse@~1.10.0: rc-dialog@~7.2.1: version "7.2.1" resolved "https://registry.yarnpkg.com/rc-dialog/-/rc-dialog-7.2.1.tgz#ac92fcffdf2a0eaa64b77f829336653d911a57be" + integrity sha512-UgKNFq6ekC7XZUnXYss/9/HSDajwy04qJ++8Iuecvfq48FbMf1k9UtTW0QGcbKIfLHWUJV13/DsTBJki2H6vWw== dependencies: babel-runtime "6.x" rc-animate "2.x" rc-util "^4.4.0" rc-drawer@~1.7.6: - version "1.7.6" - resolved "https://registry.yarnpkg.com/rc-drawer/-/rc-drawer-1.7.6.tgz#925ce0768cf81ef5fa83eb22d90c603422b1b1b1" + version "1.7.8" + resolved "https://registry.yarnpkg.com/rc-drawer/-/rc-drawer-1.7.8.tgz#e4d0659dc203909e5ffacbac8a68926e0c222fb5" + integrity sha512-gshd9fG12iSFDHicKzZQf8YbrTPyXA+ubkmaIwrudreI6+ipDUpXByflhEgiaRBhkZyv4L0EkW1srjetyzkfwA== dependencies: babel-runtime "6.x" classnames "^2.2.5" @@ -6771,6 +5830,7 @@ rc-drawer@~1.7.6: rc-dropdown@~2.2.1: version "2.2.1" resolved "https://registry.yarnpkg.com/rc-dropdown/-/rc-dropdown-2.2.1.tgz#172b6e87f0909fe8ab983e375f62e2866f3250c3" + integrity sha512-eyTYEkdyhQGHcNc+YDH6MrExX783zPAxQCnQ0+ArIchXmKmDw6MRV88kbRT69jfmqEkH6n8kOKt65EFCNWzk6g== dependencies: babel-runtime "^6.26.0" prop-types "^15.5.8" @@ -6790,28 +5850,31 @@ rc-editor-core@~0.8.3: setimmediate "^1.0.5" rc-editor-mention@^1.1.7: - version "1.1.7" - resolved "https://registry.yarnpkg.com/rc-editor-mention/-/rc-editor-mention-1.1.7.tgz#c72d181859beda96669f4b43e19a941e68fa985b" + version "1.1.13" + resolved "https://registry.yarnpkg.com/rc-editor-mention/-/rc-editor-mention-1.1.13.tgz#9f1cab1065f86b01523840321790c2ab12ac5e8b" + integrity sha512-3AOmGir91Fi2ogfRRaXLtqlNuIwQpvla7oUnGHS1+3eo7b+fUp5IlKcagqtwUBB5oDNofoySXkLBxzWvSYNp/Q== dependencies: babel-runtime "^6.23.0" classnames "^2.2.5" dom-scroll-into-view "^1.2.0" draft-js "~0.10.0" + immutable "~3.7.4" prop-types "^15.5.8" rc-animate "^2.3.0" rc-editor-core "~0.8.3" rc-form@^2.2.3: - version "2.2.5" - resolved "https://registry.yarnpkg.com/rc-form/-/rc-form-2.2.5.tgz#a7cdf21bdec31ee667f3ceb8acca68484e35c69a" + version "2.4.8" + resolved "https://registry.yarnpkg.com/rc-form/-/rc-form-2.4.8.tgz#79a1f124d4fa81dff2af2992e94aa3e58cad683c" + integrity sha512-hlHajcYg51pFQf+B6neAbhy2ZA+8DmxnDxiOYZRAXCLhPN788ZnrtZq5/iADDWcZqjHFnXiThoZE/Fu8syciDQ== dependencies: - async-validator "~1.8.5" + async-validator "~1.11.3" babel-runtime "6.x" create-react-class "^15.5.3" dom-scroll-into-view "1.x" - hoist-non-react-statics "^2.3.1" + hoist-non-react-statics "^3.3.0" lodash "^4.17.4" - warning "^3.0.0" + warning "^4.0.3" rc-hammerjs@~0.6.0: version "0.6.9" @@ -6821,9 +5884,10 @@ rc-hammerjs@~0.6.0: hammerjs "^2.0.8" prop-types "^15.5.9" -rc-input-number@~4.1.0: - version "4.1.0" - resolved "https://registry.yarnpkg.com/rc-input-number/-/rc-input-number-4.1.0.tgz#1f96aa586f6e83cb797f3d904adac6af42f4c559" +rc-input-number@~4.3.0: + version "4.3.9" + resolved "https://registry.yarnpkg.com/rc-input-number/-/rc-input-number-4.3.9.tgz#35994556ebda84b7e6b1fb479784844b6b4a738a" + integrity sha512-u2ioYqAwHIbDpp6sUEIOv8kqQw8e7VfTR6Kn+wJYff7TBA3Zw2Ql9I4DQniuLXglCJzDiHqG1OR5v3kN3GrD4w== dependencies: babel-runtime "6.x" classnames "^2.2.0" @@ -6833,12 +5897,14 @@ rc-input-number@~4.1.0: rmc-feedback "^2.0.0" rc-menu@^7.3.0, rc-menu@~7.4.12: - version "7.4.14" - resolved "https://registry.yarnpkg.com/rc-menu/-/rc-menu-7.4.14.tgz#28de792ce7c7dc0dec8ec5f72c9a6b367132815b" + version "7.4.23" + resolved "https://registry.yarnpkg.com/rc-menu/-/rc-menu-7.4.23.tgz#e07d497864274076299d7d8a84d14fc86b6bd30d" + integrity sha512-d0pUMN0Zr3GCFxNpas8p7AUTeX8viItUOQXku4AsyX82ZzUz79HgGul2Nk17BIFTtLzqdB7/NT6WVb5PAOOILw== dependencies: babel-runtime "6.x" classnames "2.x" dom-scroll-into-view "1.x" + ismobilejs "^0.5.1" mini-store "^2.0.0" mutationobserver-shim "^0.3.2" prop-types "^15.5.6" @@ -6850,6 +5916,7 @@ rc-menu@^7.3.0, rc-menu@~7.4.12: rc-notification@~3.2.0: version "3.2.0" resolved "https://registry.yarnpkg.com/rc-notification/-/rc-notification-3.2.0.tgz#bbfb6a92c4e54c9eeb7ac51a7e8c64011ea12ab1" + integrity sha512-UUr9WDjZ/4dHNGSNDMfpt8ut1w/Ed7ZaXAR/yVyFjPo/RxFE22alGWDfCD4W/kcGx+H5sI2wY5w7pTWToSdK6A== dependencies: babel-runtime "6.x" classnames "2.x" @@ -6858,31 +5925,37 @@ rc-notification@~3.2.0: rc-util "^4.0.4" rc-pagination@~1.17.3: - 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warning "^3.0.0" + warning "^4.0.3" rc-steps@~3.3.0: version "3.3.0" @@ -6921,6 +5995,7 @@ rc-steps@~3.3.0: rc-switch@~1.8.0: version "1.8.0" resolved "https://registry.yarnpkg.com/rc-switch/-/rc-switch-1.8.0.tgz#cff32fd04c406d8c0c0397e69bc36350a333e236" + integrity sha512-n4H+K2XJCqGwVQKwWOjbxl1kpdov0PVE9DGhzs/S20gk65s/nAOkpdO9tBD7IM/20KRNTBh0fEWkEedByrqh6w== dependencies: babel-runtime "^6.23.0" classnames "^2.2.1" @@ -6929,6 +6004,7 @@ rc-switch@~1.8.0: rc-table@~6.3.4: version "6.3.7" resolved "https://registry.yarnpkg.com/rc-table/-/rc-table-6.3.7.tgz#0de4f71415abe3a8d9f9937ab7ddd142ab6143b7" + integrity sha512-L8giFL1TJsbJYvN27UrUp68GWZMqD34XC/7BPFGI5JdPsv9296JpBmPHfO2ac0sQk423H+Mi5Z0cJKKDZWGu8w== dependencies: babel-runtime "6.x" classnames "^2.2.5" @@ -6941,14 +6017,17 @@ rc-table@~6.3.4: shallowequal "^1.0.2" warning "^3.0.0" -rc-tabs@~9.4.6: - version "9.4.6" - resolved "https://registry.yarnpkg.com/rc-tabs/-/rc-tabs-9.4.6.tgz#54dd60033d3dd86ba0cb3e4f09e9836379f5ac7a" +rc-tabs@~9.5.1: + version "9.5.8" + resolved "https://registry.yarnpkg.com/rc-tabs/-/rc-tabs-9.5.8.tgz#c097e48999f704b5710307ca95db8bdfdf53c6a7" + integrity sha512-fvkM5FLa0Kq9jz7YNE72T9WeMEbF264FIhqRnKyvmKtaam2lI81qxbofMEfBGhNxcv1whWlZStHj9b1Wi3Q24w== dependencies: babel-runtime "6.x" classnames "2.x" + create-react-context "0.2.2" lodash "^4.17.5" prop-types "15.x" + raf "^3.4.1" rc-hammerjs "~0.6.0" rc-util "^4.0.4" warning "^3.0.0" @@ -6966,14 +6045,16 @@ rc-time-picker@~3.4.0: rc-tooltip@^3.7.0, rc-tooltip@~3.7.3: version "3.7.3" resolved "https://registry.yarnpkg.com/rc-tooltip/-/rc-tooltip-3.7.3.tgz#280aec6afcaa44e8dff0480fbaff9e87fc00aecc" + integrity sha512-dE2ibukxxkrde7wH9W8ozHKUO4aQnPZ6qBHtrTH9LoO836PjDdiaWO73fgPB05VfJs9FbZdmGPVEbXCeOP99Ww== dependencies: babel-runtime "6.x" prop-types "^15.5.8" rc-trigger "^2.2.2" -rc-tree-select@~2.3.1: - 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version "1.5.0" - resolved "https://registry.yarnpkg.com/serialize-javascript/-/serialize-javascript-1.5.0.tgz#1aa336162c88a890ddad5384baebc93a655161fe" +serialize-javascript@^1.7.0: + version "1.7.0" + resolved "https://registry.yarnpkg.com/serialize-javascript/-/serialize-javascript-1.7.0.tgz#d6e0dfb2a3832a8c94468e6eb1db97e55a192a65" + integrity sha512-ke8UG8ulpFOxO8f8gRYabHQe/ZntKlcig2Mp+8+URDP1D8vJZ0KUt7LYo07q25Z/+JVSgpr/cui9PIp5H6/+nA== serve-index@^1.9.1: version "1.9.1" resolved "https://registry.yarnpkg.com/serve-index/-/serve-index-1.9.1.tgz#d3768d69b1e7d82e5ce050fff5b453bea12a9239" + integrity sha1-03aNabHn2C5c4FD/9bRTvqEqkjk= dependencies: accepts "~1.3.4" batch "0.6.1" @@ -7763,23 +6708,17 @@ serve-static@1.14.1: serviceworker-cache-polyfill@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/serviceworker-cache-polyfill/-/serviceworker-cache-polyfill-4.0.0.tgz#de19ee73bef21ab3c0740a37b33db62464babdeb" + integrity sha1-3hnuc77yGrPAdAo3sz22JGS6ves= set-blocking@^2.0.0, set-blocking@~2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/set-blocking/-/set-blocking-2.0.0.tgz#045f9782d011ae9a6803ddd382b24392b3d890f7" + integrity sha1-BF+XgtARrppoA93TgrJDkrPYkPc= -set-value@^0.4.3: - 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version "1.0.9" - resolved "https://registry.yarnpkg.com/sntp/-/sntp-1.0.9.tgz#6541184cc90aeea6c6e7b35e2659082443c66198" - dependencies: - hoek "2.x.x" - -sockjs-client@1.1.5: - version "1.1.5" - resolved "https://registry.yarnpkg.com/sockjs-client/-/sockjs-client-1.1.5.tgz#1bb7c0f7222c40f42adf14f4442cbd1269771a83" - dependencies: - debug "^2.6.6" - eventsource "0.1.6" - faye-websocket "~0.11.0" - inherits "^2.0.1" - json3 "^3.3.2" - url-parse "^1.1.8" - sockjs-client@1.3.0: version "1.3.0" resolved "https://registry.yarnpkg.com/sockjs-client/-/sockjs-client-1.3.0.tgz#12fc9d6cb663da5739d3dc5fb6e8687da95cb177" + integrity sha512-R9jxEzhnnrdxLCNln0xg5uGHqMnkhPSTzUZH2eXcR03S/On9Yvoq2wyUZILRUhZCNVu2PmwWVoyuiPz8th8zbg== dependencies: debug "^3.2.5" eventsource "^1.0.7" @@ -7925,16 +6848,19 @@ sockjs@0.3.19: sort-keys@^1.0.0: version "1.1.2" resolved "https://registry.yarnpkg.com/sort-keys/-/sort-keys-1.1.2.tgz#441b6d4d346798f1b4e49e8920adfba0e543f9ad" + integrity sha1-RBttTTRnmPG05J6JIK37oOVD+a0= dependencies: is-plain-obj "^1.0.0" source-list-map@^2.0.0: version "2.0.1" resolved "https://registry.yarnpkg.com/source-list-map/-/source-list-map-2.0.1.tgz#3993bd873bfc48479cca9ea3a547835c7c154b34" + integrity sha512-qnQ7gVMxGNxsiL4lEuJwe/To8UnK7fAnmbGEEH8RpLouuKbeEm0lhbQVFIrNSuB+G7tVrAlVsZgETT5nljf+Iw== source-map-loader@^0.2.1: version "0.2.4" resolved "https://registry.yarnpkg.com/source-map-loader/-/source-map-loader-0.2.4.tgz#c18b0dc6e23bf66f6792437557c569a11e072271" + integrity sha512-OU6UJUty+i2JDpTItnizPrlpOIBLmQbWMuBg9q5bVtnHACqw1tn9nNwqJLbv0/00JjnJb/Ee5g5WS5vrRv7zIQ== dependencies: async "^2.5.0" loader-utils "^1.1.0" @@ -7942,6 +6868,7 @@ source-map-loader@^0.2.1: source-map-resolve@^0.5.0: version "0.5.2" resolved "https://registry.yarnpkg.com/source-map-resolve/-/source-map-resolve-0.5.2.tgz#72e2cc34095543e43b2c62b2c4c10d4a9054f259" + integrity sha512-MjqsvNwyz1s0k81Goz/9vRBe9SZdB09Bdw+/zYyO+3CuPk6fouTaxscHkgtE8jKvf01kVfl8riHzERQ/kefaSA== dependencies: atob "^2.1.1" decode-uri-component "^0.2.0" @@ -7949,15 +6876,10 @@ source-map-resolve@^0.5.0: source-map-url "^0.4.0" urix "^0.1.0" -source-map-support@^0.4.15: - version "0.4.18" - resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.4.18.tgz#0286a6de8be42641338594e97ccea75f0a2c585f" - dependencies: - source-map "^0.5.6" - -source-map-support@^0.5.0: - version "0.5.9" - resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.9.tgz#41bc953b2534267ea2d605bccfa7bfa3111ced5f" +source-map-support@~0.5.12: + version "0.5.13" + resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.13.tgz#31b24a9c2e73c2de85066c0feb7d44767ed52932" + integrity sha512-SHSKFHadjVA5oR4PPqhtAVdcBWwRYVd6g6cAXnIbRiIwc2EhPrTuKUBdSLvlEKyIP3GCf89fltvcZiP9MMFA1w== dependencies: buffer-from "^1.0.0" source-map "^0.6.0" @@ -7965,18 +6887,22 @@ source-map-support@^0.5.0: source-map-url@^0.4.0: version "0.4.0" resolved "https://registry.yarnpkg.com/source-map-url/-/source-map-url-0.4.0.tgz#3e935d7ddd73631b97659956d55128e87b5084a3" + integrity sha1-PpNdfd1zYxuXZZlW1VEo6HtQhKM= -source-map@^0.5.3, source-map@^0.5.6, source-map@^0.5.7, source-map@~0.5.1: +source-map@^0.5.3, source-map@^0.5.6: version "0.5.7" resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.5.7.tgz#8a039d2d1021d22d1ea14c80d8ea468ba2ef3fcc" + integrity sha1-igOdLRAh0i0eoUyA2OpGi6LvP8w= source-map@^0.6.0, source-map@^0.6.1, source-map@~0.6.0, source-map@~0.6.1: version "0.6.1" resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263" + integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g== spdx-correct@^3.0.0: - version "3.0.2" - resolved "https://registry.yarnpkg.com/spdx-correct/-/spdx-correct-3.0.2.tgz#19bb409e91b47b1ad54159243f7312a858db3c2e" + version "3.1.0" + resolved "https://registry.yarnpkg.com/spdx-correct/-/spdx-correct-3.1.0.tgz#fb83e504445268f154b074e218c87c003cd31df4" + integrity sha512-lr2EZCctC2BNR7j7WzJ2FpDznxky1sjfxvvYEyzxNyb6lZXHODmEoJeFu4JupYlkfha1KZpJyoqiJ7pgA1qq8Q== dependencies: spdx-expression-parse "^3.0.0" spdx-license-ids "^3.0.0" @@ -7984,21 +6910,25 @@ spdx-correct@^3.0.0: spdx-exceptions@^2.1.0: version "2.2.0" resolved "https://registry.yarnpkg.com/spdx-exceptions/-/spdx-exceptions-2.2.0.tgz#2ea450aee74f2a89bfb94519c07fcd6f41322977" + integrity sha512-2XQACfElKi9SlVb1CYadKDXvoajPgBVPn/gOQLrTvHdElaVhr7ZEbqJaRnJLVNeaI4cMEAgVCeBMKF6MWRDCRA== spdx-expression-parse@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/spdx-expression-parse/-/spdx-expression-parse-3.0.0.tgz#99e119b7a5da00e05491c9fa338b7904823b41d0" + integrity sha512-Yg6D3XpRD4kkOmTpdgbUiEJFKghJH03fiC1OPll5h/0sO6neh2jqRDVHOQ4o/LMea0tgCkbMgea5ip/e+MkWyg== dependencies: spdx-exceptions "^2.1.0" spdx-license-ids "^3.0.0" spdx-license-ids@^3.0.0: - version "3.0.1" - resolved "https://registry.yarnpkg.com/spdx-license-ids/-/spdx-license-ids-3.0.1.tgz#e2a303236cac54b04031fa7a5a79c7e701df852f" + version "3.0.5" + resolved "https://registry.yarnpkg.com/spdx-license-ids/-/spdx-license-ids-3.0.5.tgz#3694b5804567a458d3c8045842a6358632f62654" + integrity sha512-J+FWzZoynJEXGphVIS+XEh3kFSjZX/1i9gFBaWQcB+/tmpe2qUsSBABpcxqxnAxFdiUFEgAX1bjYGQvIZmoz9Q== spdy-transport@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/spdy-transport/-/spdy-transport-3.0.0.tgz#00d4863a6400ad75df93361a1608605e5dcdcf31" + integrity sha512-hsLVFE5SjA6TCisWeJXFKniGGOpBgMLmerfO2aCyCU5s7nJ/rpAepqmFifv/GCbSbueEeAJJnmSQ2rKC/g8Fcw== dependencies: debug "^4.1.0" detect-node "^2.0.4" @@ -8008,8 +6938,9 @@ spdy-transport@^3.0.0: wbuf "^1.7.3" spdy@^4.0.0: - version "4.0.0" - resolved "https://registry.yarnpkg.com/spdy/-/spdy-4.0.0.tgz#81f222b5a743a329aa12cea6a390e60e9b613c52" + version "4.0.1" + resolved "https://registry.yarnpkg.com/spdy/-/spdy-4.0.1.tgz#6f12ed1c5db7ea4f24ebb8b89ba58c87c08257f2" + integrity sha512-HeZS3PBdMA+sZSu0qwpCxl3DeALD5ASx8pAX0jZdKXSpPWbQ6SYGnlg3BBmYLx5LtiZrmkAZfErCm2oECBcioA== dependencies: debug "^4.1.0" handle-thing "^2.0.0" @@ -8020,16 +6951,19 @@ spdy@^4.0.0: split-string@^3.0.1, split-string@^3.0.2: version "3.1.0" resolved "https://registry.yarnpkg.com/split-string/-/split-string-3.1.0.tgz#7cb09dda3a86585705c64b39a6466038682e8fe2" + integrity sha512-NzNVhJDYpwceVVii8/Hu6DKfD2G+NrQHlS/V/qgv763EYudVwEcMQNxd2lh+0VrUByXN/oJkl5grOhYWvQUYiw== dependencies: extend-shallow "^3.0.0" sprintf-js@~1.0.2: version "1.0.3" resolved "https://registry.yarnpkg.com/sprintf-js/-/sprintf-js-1.0.3.tgz#04e6926f662895354f3dd015203633b857297e2c" + integrity sha1-BOaSb2YolTVPPdAVIDYzuFcpfiw= sshpk@^1.7.0: - version "1.15.1" - resolved "https://registry.yarnpkg.com/sshpk/-/sshpk-1.15.1.tgz#b79a089a732e346c6e0714830f36285cd38191a2" + version "1.16.1" + resolved "https://registry.yarnpkg.com/sshpk/-/sshpk-1.16.1.tgz#fb661c0bef29b39db40769ee39fa70093d6f6877" + integrity sha512-HXXqVUq7+pcKeLqqZj6mHFUMvXtOJt1uoUx09pFW6011inTMxqI8BA8PM95myrIyyKwdnzjdFjLiE6KBPVtJIg== dependencies: asn1 "~0.2.3" assert-plus "^1.0.0" @@ -8041,19 +6975,17 @@ sshpk@^1.7.0: safer-buffer "^2.0.2" tweetnacl "~0.14.0" -ssri@^5.2.4: - version "5.3.0" - resolved "https://registry.yarnpkg.com/ssri/-/ssri-5.3.0.tgz#ba3872c9c6d33a0704a7d71ff045e5ec48999d06" +ssri@^6.0.1: + version "6.0.1" + resolved "https://registry.yarnpkg.com/ssri/-/ssri-6.0.1.tgz#2a3c41b28dd45b62b63676ecb74001265ae9edd8" + integrity sha512-3Wge10hNcT1Kur4PDFwEieXSCMCJs/7WvSACcrMYrNp+b8kDL1/0wJch5Ni2WrtwEa2IO8OsVfeKIciKCDx/QA== dependencies: - safe-buffer "^5.1.1" - -stack-utils@^1.0.1: - version "1.0.1" - resolved "https://registry.yarnpkg.com/stack-utils/-/stack-utils-1.0.1.tgz#d4f33ab54e8e38778b0ca5cfd3b3afb12db68620" + figgy-pudding "^3.5.1" static-extend@^0.1.1: version "0.1.2" resolved "https://registry.yarnpkg.com/static-extend/-/static-extend-0.1.2.tgz#60809c39cbff55337226fd5e0b520f341f1fb5c6" + integrity sha1-YICcOcv/VTNyJv1eC1IPNB8ftcY= dependencies: define-property "^0.2.5" object-copy "^0.1.0" @@ -8062,13 +6994,10 @@ static-extend@^0.1.1: version "1.5.0" resolved "https://registry.yarnpkg.com/statuses/-/statuses-1.5.0.tgz#161c7dac177659fd9811f43771fa99381478628c" -stealthy-require@^1.1.0: - 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If you want to prune all weight to 80% sparsity, you can add code below into your code before your training code. diff --git a/docs/en_US/Compressor/Pruner.md b/docs/en_US/Compressor/Pruner.md index 683391f0f3..1b00332270 100644 --- a/docs/en_US/Compressor/Pruner.md +++ b/docs/en_US/Compressor/Pruner.md @@ -1,5 +1,26 @@ Pruner on NNI Compressor === +## LevelPruner + +This is one basic pruner: you can set a target sparsity level(expressed as a fraction, 0.6 means we will prune 60%). + +We first sort the weights in the specified layer by their absolute values. And then mask to zero the smallest magnitude weights until the desired sparsity level is reached. + +### Usage + +Tensorflow code +``` +pruner = nni.compressors.tf_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +pruner(model_graph) +``` + +Pytorch code +``` +pruner = nni.compressors.torch_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +pruner(model) +``` + +*** ## AGPruner In [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878), authors Michael Zhu and Suyog Gupta provide an algorithm to prune the weight gradually. diff --git a/docs/en_US/Compressor/Quantizer.md b/docs/en_US/Compressor/Quantizer.md index 1c673f1159..bbffe8e069 100644 --- a/docs/en_US/Compressor/Quantizer.md +++ b/docs/en_US/Compressor/Quantizer.md @@ -1,5 +1,19 @@ Quantizer on NNI Compressor === +## NaiveQuantizer + +We provide NaiveQuantizer to quantizer weight to default 8 bits, you can use it to test quantize algorithm. + +### Usage +tensorflow +``` +nni.compressors.tf_compressor.NaiveQuantizer()(model_graph) +``` +pytorch +``` +nni.compressors.torch_compressor.NaiveQuantizer()(model) +``` +*** ## QATquantizer In [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf), authors Benoit Jacob and Skirmantas Kligys provide an algorithm to quantize the model with training. diff --git a/examples/compressor/configure_example.yaml b/examples/model_compress/configure_example.yaml similarity index 100% rename from examples/compressor/configure_example.yaml rename to examples/model_compress/configure_example.yaml diff --git a/examples/compressor/main_tf_pruner.py b/examples/model_compress/main_tf_pruner.py similarity index 100% rename from examples/compressor/main_tf_pruner.py rename to examples/model_compress/main_tf_pruner.py diff --git a/examples/compressor/main_tf_quantizer.py b/examples/model_compress/main_tf_quantizer.py similarity index 100% rename from examples/compressor/main_tf_quantizer.py rename to examples/model_compress/main_tf_quantizer.py diff --git a/examples/compressor/main_torch_pruner.py b/examples/model_compress/main_torch_pruner.py similarity index 100% rename from examples/compressor/main_torch_pruner.py rename to examples/model_compress/main_torch_pruner.py diff --git a/examples/compressor/main_torch_quantizer.py b/examples/model_compress/main_torch_quantizer.py similarity index 100% rename from examples/compressor/main_torch_quantizer.py rename to examples/model_compress/main_torch_quantizer.py diff --git a/setup.py b/setup.py index a1239b2de7..296b2a8f22 100644 --- a/setup.py +++ b/setup.py @@ -27,7 +27,7 @@ def read(fname): setup( name = 'nni', - version = 'v0.8-347-g0538d30', + version = '999.0.0-developing', author = 'Microsoft NNI Team', author_email = 'nni@microsoft.com', description = 'Neural Network Intelligence project', From b8c674d33fd610fdde9054e0d04487bc678296c1 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Tue, 27 Aug 2019 14:13:52 +0800 Subject: [PATCH 30/34] change for PR --- examples/model_compress/main_tf_pruner.py | 6 +- .../compressors/tf_compressor/_nnimc_tf.py | 68 +++++++++++++------ .../nni/compressors/tf_compressor/pruner.py | 48 +++++++------ .../compressors/tf_compressor/quantizer.py | 36 ++++++---- .../torch_compressor/_nnimc_torch.py | 56 +++++++++++---- .../compressors/torch_compressor/pruner.py | 52 +++++++++----- .../compressors/torch_compressor/quantizer.py | 35 ++++++---- 7 files changed, 203 insertions(+), 98 deletions(-) diff --git a/examples/model_compress/main_tf_pruner.py b/examples/model_compress/main_tf_pruner.py index 2a8eed226e..4fa29a8098 100644 --- a/examples/model_compress/main_tf_pruner.py +++ b/examples/model_compress/main_tf_pruner.py @@ -91,7 +91,11 @@ def main(): 'support_type': 'default' }] pruner = AGPruner(configure_list) - pruner.load_configure('configure_example.yaml') + # if you want to load from yaml file + # configure_file = nni.compressors.tf_compressor._nnimc_tf._tf_default_load_configure_file('configure_example.yaml','AGPruner') + # configure_list = configure_file.get('config',[]) + # pruner.load_configure(configure_list) + # you can also handle it yourself and input an configure list in json pruner(tf.get_default_graph()) # you can also use compress(model) or compress_default_graph() for tensorflow compressor # pruner.compress(tf.get_default_graph()) diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py index 6f49c95004..85a73443ef 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py @@ -2,6 +2,9 @@ from tensorflow import Graph, Operation, Tensor from ruamel.yaml import YAML from typing import List +import logging + +logger = logging.getLogger('tf_compressor') __all__ = [ 'TfCompressor', @@ -14,7 +17,9 @@ class TfCompressor: - """TODO""" + """ + Base compressor for tensorflow + """ def __init__(self): self._bound_model = None @@ -44,9 +49,25 @@ def preprocess_model(self, model): It is guaranteed that only one model will be bound to each compressor instance. """ pass + + def update_epoch(self, epoch, sess): + """ + if user want to update mask every epoch, user can override this method + """ + pass + + def step(self, sess): + """ + if user want to update mask every step, user can override this method + """ + pass class TfLayerInfo: + """ + layer info for tensorflow + TODO: support type + """ def __init__(self, layer): self.name = layer.name self.layer = layer @@ -66,6 +87,13 @@ def _tf_detect_prunable_layers(model): return [ TfLayerInfo(op) for op in model.get_operations() if op.type in whiltlist ] def _tf_default_get_configure(configure_list, layer_info): + """ + Get configure for input layer + defaultly the later config will cover front config + WARNING: please mask sure default configure is the first in the list + """ + if not configure_list: + logger.warning('WARNING: configure list is None') configure = {} for config in configure_list: if config.get('support_type', '') == 'default': @@ -75,21 +103,28 @@ def _tf_default_get_configure(configure_list, layer_info): elif layer_info.name in config.get('support_op', []): configure = config + if not configure: + logger.warning('WARNING: can not get configure, default NONE!!!') return configure def _tf_default_load_configure_file(config_path, class_name): - print('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) + """ + Load configure yaml file and reture cofigure in json + """ + logger.info('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) assert config_path is not None and config_path.endswith('yaml') file = open(config_path, 'r') yaml = YAML(typ='safe') yaml_text = yaml.load(file.read()) configure_file = yaml_text.get(class_name, {}) if not configure_file: - print('WARNING: load Nothing from configure file, Default { }') + logger.warning('WARNING: load Nothing from configure file, Default { }') return configure_file class TfPruner(TfCompressor): - """TODO""" + """ + Base pruner for tensorflow pruner + """ def __init__(self): super().__init__() @@ -114,25 +149,26 @@ def compress(self, model): self._instrument_layer(layer_info) def _instrument_layer(self, layer_info): - # it seems the graph editor can only swap edges of nodes or remove all edges from a node - # it cannot remove one edge from a node, nor can it assign a new edge to a node - # we assume there is a proxy operation between the weight and the Conv2D layer - # this is true as long as the weight is `tf.Value` - # not sure what will happen if the weight is calculated from other operations + """ + it seems the graph editor can only swap edges of nodes or remove all edges from a node + it cannot remove one edge from a node, nor can it assign a new edge to a node + we assume there is a proxy operation between the weight and the Conv2D layer + this is true as long as the weight is `tf.Value` + not sure what will happen if the weight is calculated from other operations + """ weight_op = layer_info.layer.inputs[layer_info.weight_index].op weight = weight_op.inputs[0] mask = self.calc_mask(layer_info, weight) new_weight = weight * mask tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) - def update_epoch(self, epoch, sess): - pass - def step(self, sess): - pass class TfQuantizer(TfCompressor): + """ + Base quantizer for tensorflow quantizer + """ def __init__(self): super().__init__() @@ -152,9 +188,3 @@ def _instrument_layer(self, layer_info): weight_op = layer_info.layer.inputs[layer_info.weight_index].op new_weight = self.quantize_weight(layer_info, weight_op.inputs[0]) tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) - - def update_epoch(self, epoch, sess): - pass - - def step(self, sess): - pass \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py index 0aa23c4763..6eb17e33a4 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py @@ -2,6 +2,9 @@ from ._nnimc_tf import TfPruner from ._nnimc_tf import _tf_default_get_configure, _tf_default_load_configure_file +import logging +logger = logging.getLogger('tensorflow pruner') + class LevelPruner(TfPruner): def __init__(self, configure_list): """ @@ -16,10 +19,6 @@ def __init__(self, configure_list): else: raise ValueError('please init with configure list') - def load_configure(self, config_path): - config_list = _tf_default_load_configure_file(config_path, 'LevelPruner') - for config in config_list.get('config', []): - self.configure_list.append(config) def get_sparsity(self, configure={}): sparsity = configure.get('sparsity', 0) @@ -32,14 +31,23 @@ def calc_mask(self, layer_info, weight): return tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype) class AGPruner(TfPruner): + """ + An automated gradual pruning algorithm that prunes the smallest magnitude + weights to achieve a preset level of network sparsity. + + Michael Zhu and Suyog Gupta, "To prune, or not to prune: exploring the + efficacy of pruning for model compression", 2017 NIPS Workshop on Machine + Learning of Phones and other Consumer Devices, + https://arxiv.org/pdf/1710.01878.pdf + """ def __init__(self, configure_list): """ Configure Args - initial_sparsity - final_sparsity - start_epoch - end_epoch - frequency + initial_sparsity: + final_sparsity: you should make sure initial_sparsity <= final_sparsity + start_epoch: start epoch numer begin update mask + end_epoch: end epoch number stop update mask + frequency: if you want update every 2 epoch, you can set it 2 """ super().__init__() self.configure_list = [] @@ -61,6 +69,7 @@ def compute_target_sparsity(self, layer_info): initial_sparsity = configure.get('initial_sparsity', 0) if end_epoch <= start_epoch or initial_sparsity >= final_sparsity: + logger.warning('your end epoch <= start epoch or initial_sparsity >= final_sparsity') return final_sparsity now_epoch = tf.minimum(self.now_epoch, tf.constant(end_epoch)) @@ -72,17 +81,13 @@ def compute_target_sparsity(self, layer_info): (tf.pow(1.0 - base, 3))) return target_sparsity - def load_configure(self, config_path): - config_list = _tf_default_load_configure_file(config_path, 'AGPruner') - for config in config_list.get('config', []): - self.configure_list.append(config) def calc_mask(self, layer_info, weight): target_sparsity = self.compute_target_sparsity(layer_info) threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + # stop gradient in case gradient change the mask mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) - print('tensor weight', weight) self.assign_handler.append(tf.assign(weight, weight*mask)) return mask @@ -92,10 +97,17 @@ def update_epoch(self, epoch, sess): class SensitivityPruner(TfPruner): + """ + Use algorithm from "Learning both Weights and Connections for Efficient Neural Networks" + https://arxiv.org/pdf/1506.02626v3.pdf + + I.e.: "The pruning threshold is chosen as a quality parameter multiplied + by the standard deviation of a layers weights." + """ def __init__(self, configure_list): """ Configure Args: - sparsity + sparsity: chosen pruning sparsity """ super().__init__() self.configure_list = [] @@ -108,10 +120,6 @@ def __init__(self, configure_list): self.layer_mask = {} self.assign_handler = [] - def load_configure(self, config_path): - config_list = _tf_default_load_configure_file(config_path, 'SensitivityPruner') - for config in config_list.get('config', []): - self.configure_list.append(config) def get_sparsity(self, configure={}): sparsity = configure.get('sparsity', 0) @@ -125,8 +133,10 @@ def calc_mask(self, layer_info, weight): self.layer_mask[layer_info.name] = mask weight_assign_handler = tf.assign(weight, mask*weight) + # use control_dependencies so that weight_assign_handler will be executed before mask_update_handler with tf.control_dependencies([weight_assign_handler]): threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + # stop gradient in case gradient change the mask new_mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) mask_update_handler = tf.assign(mask, new_mask) self.assign_handler.append(mask_update_handler) diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py b/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py index be614a29ac..6f9abde84d 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py @@ -2,7 +2,13 @@ from ._nnimc_tf import TfQuantizer from ._nnimc_tf import _tf_default_get_configure, _tf_default_load_configure_file +import logging +logger = logging.getLogger('tensorflow quantizer') + class NaiveQuantizer(TfQuantizer): + """ + quantize weight to 8 bits + """ def __init__(self): super().__init__() self.layer_scale = {} @@ -15,6 +21,11 @@ def quantize_weight(self, layer_info, weight): return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale class QATquantizer(TfQuantizer): + """ + Quantizer using the DoReFa scheme, as defined in: + Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference + http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf + """ def __init__(self, configure_list): """ Configure Args: @@ -28,18 +39,15 @@ def __init__(self, configure_list): else: raise ValueError('please init with configure list') - def load_configure(self, config_path): - config_list = _tf_default_load_configure_file(config_path, 'QATquantizer') - for config in config_list.get('config', []): - self.configure_list.append(config) def get_qbits(self, configure): if not isinstance(configure, dict): - print('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + logger.warning('WARNING: you should input a dict to get_qbits, set DEFAULT { }') configure = {} qbits = configure.get('q_bits', 32) if qbits == 0: - print('WARNING: you can not set q_bits ZERO!') + logger.warning('WARNING: you can not set q_bits ZERO!') + qbits = 32 return qbits def quantize_weight(self, layer_info, weight): @@ -50,12 +58,18 @@ def quantize_weight(self, layer_info, weight): n = tf.cast(2 ** q_bits, tf.float32) scale = b-a/(n-1) + # use gradient_override_map to change round to idetity for gradient with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): qw = tf.round((weight-a)/scale)*scale +a return qw class DoReFaQuantizer(TfQuantizer): + """ + Quantizer using the DoReFa scheme, as defined in: + Zhou et al., DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients + (https://arxiv.org/abs/1606.06160) + """ def __init__(self, configure_list): """ Configure Args: @@ -69,18 +83,15 @@ def __init__(self, configure_list): else: raise ValueError('please init with configure list') - def load_configure(self, config_path): - config_list = _tf_default_load_configure_file(config_path, 'DoReFaQuantizer') - for config in config_list.get('config', []): - self.configure_list.append(config) def get_qbits(self, configure): if not isinstance(configure, dict): - print('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + logger.warning('WARNING: you should input a dict to get_qbits, set DEFAULT { }') configure = {} qbits = configure.get('q_bits', 32) if qbits == 0: - print('WARNING: you can not set q_bits ZERO!') + logger.warning('WARNING: you can not set q_bits ZERO!') + qbits = 32 return qbits def quantize_weight(self, layer_info, weight): @@ -89,6 +100,7 @@ def quantize_weight(self, layer_info, weight): b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5 scale = pow(2, q_bits-1) + # use gradient_override_map to change round to idetity for gradient with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): qw = tf.round(b*scale)/scale r_qw = 2*qw - 1 diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py index 9d46a1fac1..0311194957 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py @@ -2,7 +2,9 @@ from torch.nn import Module, Parameter from ruamel.yaml import YAML from typing import List +import logging +logger = logging.getLogger('torch_compressor') __all__ = [ 'TorchCompressor', 'TorchPruner', @@ -14,7 +16,9 @@ class TorchCompressor: - """TODO""" + """ + Base compressor for pytorch + """ def __init__(self): self._bound_model = None @@ -37,9 +41,24 @@ def preprocess_model(self, model): It is guaranteed that only one model will be bound to each compressor instance. """ pass + + def update_epoch(self, epoch): + """ + if user want to update model every epoch, user can override this method + """ + pass + + def step(self): + """ + if user want to update model every step, user can override this method + """ + pass class TorchLayerInfo: + """ + layer info for pytorch + """ def __init__(self, name, layer): self.name = name self.layer = layer @@ -59,6 +78,13 @@ def _torch_detect_prunable_layers(model): return ret def _torch_default_get_configure(configure_list, layer_info): + """ + Get configure for input layer + defaultly the later config will cover front config + WARNING: please mask sure default configure is the first in the list + """ + if not configure_list: + logger.warning('WARNING: configure list is None') configure = {} for config in configure_list: if config.get('support_type', '') == 'default': @@ -67,23 +93,26 @@ def _torch_default_get_configure(configure_list, layer_info): configure = config elif layer_info.name in config.get('support_op', []): configure = config - + if not configure: + logger.warning('WARNING: can not get configure, default NONE!!!') return configure def _torch_default_load_configure_file(config_path, class_name): - print('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) + logger.info('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) assert config_path is not None and config_path.endswith('yaml') file = open(config_path, 'r') yaml = YAML(typ='safe') yaml_text = yaml.load(file.read()) configure_file = yaml_text.get(class_name, {}) if not configure_file: - print('WARNING: load Nothing from configure file, Default { }') + logger.warning('WARNING: load Nothing from configure file, Default { }') return configure_file class TorchPruner(TorchCompressor): - """TODO""" + """ + Base pruner for pytorch pruner + """ def __init__(self): super().__init__() @@ -127,14 +156,12 @@ def new_forward(*input): layer_info.layer.forward = new_forward - def update_epoch(self, epoch): - pass - - def step(self): - pass class TorchQuantizer(TorchCompressor): + """ + Base quantizer for pytorch quantizer + """ def __init__(self): super().__init__() @@ -143,7 +170,10 @@ def __call__(self, model): return model def quantize_weight(self, layer_info, weight): - # FIXME: where dequantize goes? + """ + user should know where dequantize goes and implement it in quantize method + we now do not provide dequantize method + """ raise NotImplementedError("Quantizer must overload quantize_weight()") def compress(self, model): @@ -165,8 +195,4 @@ def new_forward(*input): layer_info.layer.forward = new_forward - def update_epoch(self, epoch): - pass - def step(self): - pass diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py index 3c9b3f78df..cb1ecb6924 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py @@ -2,7 +2,8 @@ from ._nnimc_torch import TorchPruner from ._nnimc_torch import _torch_default_get_configure, _torch_default_load_configure_file - +import logging +logger = logging.getLogger('torch pruner') class LevelPruner(TorchPruner): """Prune to an exact pruning level specification @@ -31,11 +32,6 @@ def __init__(self, configure_list): else: raise ValueError('please init with configure list') - - def load_configure(self, config_path): - config_list = _torch_default_load_configure_file(config_path, 'LevelPruner') - for config in config_list.get('config', []): - self.configure_list.append(config) def get_sparsity(self, configure={}): sparsity = configure.get('sparsity',0) @@ -52,14 +48,23 @@ def calc_mask(self, layer_info, weight): return torch.gt(w_abs, threshold).type(weight.type()) class AGPruner(TorchPruner): + """ + An automated gradual pruning algorithm that prunes the smallest magnitude + weights to achieve a preset level of network sparsity. + + Michael Zhu and Suyog Gupta, "To prune, or not to prune: exploring the + efficacy of pruning for model compression", 2017 NIPS Workshop on Machine + Learning of Phones and other Consumer Devices, + https://arxiv.org/pdf/1710.01878.pdf + """ def __init__(self, configure_list): """ Configure Args initial_sparsity - final_sparsity - start_epoch - end_epoch - frequency + final_sparsity: you should make sure initial_sparsity <= final_sparsity + start_epoch: start epoch numer begin update mask + end_epoch: end epoch number stop update mask, you should make sure start_epoch <= end_epoch + frequency: if you want update every 2 epoch, you can set it 2 """ super().__init__() self.configure_list = [] @@ -72,11 +77,6 @@ def __init__(self, configure_list): self.mask_list = {} self.now_epoch = 1 - def load_configure(self, config_path): - config_list = _torch_default_load_configure_file(config_path, 'AGPruner') - for config in config_list.get('config', []): - self.configure_list.append(config) - def compute_target_sparsity(self, now_epoch, layer_info): configure = _torch_default_get_configure(self.configure_list, layer_info) end_epoch = configure.get('end_epoch', 1) @@ -84,8 +84,13 @@ def compute_target_sparsity(self, now_epoch, layer_info): freq = configure.get('frequency', 1) final_sparsity = configure.get('final_sparsity', 0) initial_sparsity = configure.get('initial_sparsity', 0) - if end_epoch <= start_epoch or end_epoch <= now_epoch or initial_sparsity >= final_sparsity: + if end_epoch <= start_epoch or initial_sparsity >= final_sparsity: + logger.warning('your end epoch <= start epoch or initial_sparsity >= final_sparsity') + return final_sparsity + + if end_epoch <= now_epoch: return final_sparsity + span = ((end_epoch - start_epoch-1)//freq)*freq assert span > 0 target_sparsity = (final_sparsity + @@ -100,7 +105,7 @@ def calc_mask(self, layer_info, weight): k = int(weight.numel() * target_sparsity) if k == 0 or target_sparsity >= 1 or target_sparsity <= 0: return mask - + # if we want to generate new mask, we should update weigth first w_abs = weight.abs()*mask threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() new_mask = torch.gt(w_abs, threshold).type(weight.type()) @@ -114,10 +119,17 @@ def update_epoch(self, epoch): class SensitivityPruner(TorchPruner): + """ + Use algorithm from "Learning both Weights and Connections for Efficient Neural Networks" + https://arxiv.org/pdf/1506.02626v3.pdf + + I.e.: "The pruning threshold is chosen as a quality parameter multiplied + by the standard deviation of a layers weights." + """ def __init__(self, configure_list): """ configure Args: - sparsity + sparsity: chosen pruning sparsity """ super().__init__() self.configure_list = [] @@ -130,6 +142,9 @@ def __init__(self, configure_list): self.mask_list = {} def load_configure(self, config_path): + """ + if you want load configure from yaml file, you can use it and input config_path + """ config_list = _torch_default_load_configure_file(config_path, 'SensitivityPruner') for config in config_list.get('config', []): self.configure_list.append(config) @@ -140,6 +155,7 @@ def get_sparsity(self, configure={}): def calc_mask(self, layer_info, weight): mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) + # if we want to generate new mask, we should update weigth first weight = weight*mask sparsity = self.get_sparsity(_torch_default_get_configure(self.configure_list, layer_info)) target_sparsity = sparsity * torch.std(weight).item() diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py b/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py index fcc5bfab12..ac92472535 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py @@ -1,21 +1,29 @@ import torch from ._nnimc_torch import TorchQuantizer from ._nnimc_torch import _torch_default_get_configure, _torch_default_load_configure_file - +import logging +logger = logging.getLogger('torch quantizer') class NaiveQuantizer(TorchQuantizer): + """ + quantize weight to 8 bits + """ def __init__(self): super().__init__() self.layer_scale = {} def quantize_weight(self, layer_info, weight): new_scale = weight.abs().max() / 127 - # TODO: use int id scale = max(self.layer_scale.get(layer_info.name, 0), new_scale) self.layer_scale[layer_info.name] = scale orig_type = weight.type() # TODO: user layer_info return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) class DoReFaQuantizer(TorchQuantizer): + """ + Quantizer using the DoReFa scheme, as defined in: + Zhou et al., DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients + (https://arxiv.org/abs/1606.06160) + """ def __init__(self, configure_list): """ configure Args: @@ -29,18 +37,15 @@ def __init__(self, configure_list): else: raise ValueError('please init with configure list') - def load_configure(self, config_path): - config_list = _torch_default_load_configure_file(config_path, 'DoReFaQuantizer') - for config in config_list.get('config', []): - self.configure_list.append(config) def get_qbits(self, configure): if not isinstance(configure, dict): - print('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + logger.warning('WARNING: you should input a dict to get_qbits, set DEFAULT { }') configure = {} qbits = configure.get('q_bits', 32) if qbits == 0: - print('WARNING: you can not set q_bits ZERO!') + logger.warning('WARNING: you can not set q_bits ZERO!') + qbits = 32 return qbits def quantize_weight(self, layer_info, weight): @@ -58,6 +63,11 @@ def quantize(self, input_ri, q_bits): return output class QATquantizer(TorchQuantizer): + """ + Quantizer using the DoReFa scheme, as defined in: + Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference + http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf + """ def __init__(self, configure_list): """ Configure Args: @@ -71,18 +81,15 @@ def __init__(self, configure_list): else: raise ValueError('please init with configure list') - def load_configure(self, config_path): - config_list = _torch_default_load_configure_file(config_path, 'QATquantizer') - for config in config_list.get('config', []): - self.configure_list.append(config) def get_qbits(self, configure): if not isinstance(configure, dict): - print('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + logger.warning('WARNING: you should input a dict to get_qbits, set DEFAULT { }') configure = {} qbits = configure.get('q_bits', 32) if qbits == 0: - print('WARNING: you can not set q_bits ZERO!') + logger.warning('WARNING: you can not set q_bits ZERO!') + qbits = 32 return qbits def quantize_weight(self, layer_info, weight): From 1cbab8288222ee5f2642a10f4b49d2b3d51cc8b2 Mon Sep 17 00:00:00 2001 From: "Xupu Wang (FA Talent)" Date: Mon, 19 Aug 2019 17:59:52 +0800 Subject: [PATCH 31/34] This is a combination of 9 commits change compressor with __call__(), add frame method add configure without doc update doc and test unit add configure parser change doc and test test commit move example add user hint add doc and change example name change for PR --- docs/en_US/Compressor/Overview.md | 161 +- docs/en_US/Compressor/Pruner.md | 31 +- docs/en_US/Compressor/Quantizer.md | 26 +- .../model_compress/configure_example.yaml | 9 + .../model_compress}/main_tf_pruner.py | 23 +- .../model_compress}/main_tf_quantizer.py | 10 +- .../model_compress}/main_torch_pruner.py | 20 +- .../model_compress}/main_torch_quantizer.py | 10 +- setup.py | 2 +- .../pynni/nni/compressors/example/__init__.py | 0 .../nni/compressors/tfCompressor/_nnimc_tf.py | 115 - .../nni/compressors/tfCompressor/pruner.py | 76 - .../nni/compressors/tfCompressor/quantizer.py | 49 - .../__init__.py | 3 +- .../compressors/tf_compressor/_nnimc_tf.py | 190 + .../nni/compressors/tf_compressor/pruner.py | 146 + .../compressors/tf_compressor/quantizer.py | 107 + .../nni/compressors/torchCompressor/pruner.py | 86 - .../compressors/torchCompressor/quantizer.py | 54 - .../__init__.py | 1 + .../_nnimc_torch.py | 89 +- .../compressors/torch_compressor/pruner.py | 171 + .../compressors/torch_compressor/quantizer.py | 108 + src/sdk/pynni/tests/test_compressor.py | 12 +- src/webui/yarn.lock | 5957 +++++++---------- 25 files changed, 3455 insertions(+), 4001 deletions(-) create mode 100644 examples/model_compress/configure_example.yaml rename {src/sdk/pynni/nni/compressors/example => examples/model_compress}/main_tf_pruner.py (80%) rename {src/sdk/pynni/nni/compressors/example => examples/model_compress}/main_tf_quantizer.py (90%) rename {src/sdk/pynni/nni/compressors/example => examples/model_compress}/main_torch_pruner.py (81%) rename {src/sdk/pynni/nni/compressors/example => examples/model_compress}/main_torch_quantizer.py (88%) delete mode 100644 src/sdk/pynni/nni/compressors/example/__init__.py delete mode 100644 src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py delete mode 100644 src/sdk/pynni/nni/compressors/tfCompressor/pruner.py delete mode 100644 src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py rename src/sdk/pynni/nni/compressors/{tfCompressor => tf_compressor}/__init__.py (71%) create mode 100644 src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py create mode 100644 src/sdk/pynni/nni/compressors/tf_compressor/pruner.py create mode 100644 src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py delete mode 100644 src/sdk/pynni/nni/compressors/torchCompressor/pruner.py delete mode 100644 src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py rename src/sdk/pynni/nni/compressors/{torchCompressor => torch_compressor}/__init__.py (71%) rename src/sdk/pynni/nni/compressors/{torchCompressor => torch_compressor}/_nnimc_torch.py (60%) create mode 100644 src/sdk/pynni/nni/compressors/torch_compressor/pruner.py create mode 100644 src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py diff --git a/docs/en_US/Compressor/Overview.md b/docs/en_US/Compressor/Overview.md index 8e1b2d2a19..eac1013439 100644 --- a/docs/en_US/Compressor/Overview.md +++ b/docs/en_US/Compressor/Overview.md @@ -3,37 +3,180 @@ NNI provides easy-to-use toolkit to help user design and use compression algori ## Framework We use the instrumentation method to insert a node or function after the corresponding position in the model. -
-When compression algorithm designer implements one prune algorithm, he only need to pay attention to the generation method of mask, without caring about applying the mask to the garph. -## algorithm + +When compression algorithm designer implements one prune algorithm, designer only need to pay attention to the generation method of mask, without caring about applying the mask to the graph. +## Algorithm We now provide some naive compression algorithm and four popular compress agorithms for users, including two pruning algorithm and two quantization algorithm. Below is a list of model compression algorithms supported in our compressor -| Name | Paper | -| ---------- | ----------| -| AGPruner | [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878)| + +|Name|Paper| +|---|---| +| AGPruner| [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878)| | SensitivityPruner |[Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626)| | QATquantizer |[Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf)| | DoReFaQuantizer |[DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://arxiv.org/abs/1606.06160)| ## Usage +### For compression algorithm user -Take naive level pruner as an example +Take naive level pruner as an example, you can get detailed information in Pruner details. If you want to prune all weight to 80% sparsity, you can add code below into your code before your training code. Tensorflow code ``` -nni.compressors.tfCompressor.LevelPruner(sparsity=0.8).compress(model_graph) +pruner = nni.compressors.tf_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +pruner(model_graph) ``` Pytorch code ``` -nni.compressors.torchCompressor.LevelPruner(sparsity=0.8).compress(model) +pruner = nni.compressors.torch_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +pruner(model) ``` Our compressor will automatically insert mask into your model, and you can train your model with masks without changing your training code. You will get a compressed model when you finish your training. You can get more information in Algorithm details +#### Configuration +We now provide an default format for our build-in algorithm, algorithm designer can follow this format and use our default configure parser + +Following our default format, user can set configure in his code or a yaml file. And pass configure to compressor by init() or load_configure() + + +Code +``` +[{ + 'sparsity':0.8, + 'support_layer':'default' + 'support_op':['op_name1','op_name2'] +}] +``` + +file +``` +AGPruner: + config: + - + start_epoch: 0 + end_epoch: 16 + frequency: 2 + initial_sparsity: 0.05 + final_sparsity: 0.60 + support_type: default + - + prune: False + start_epoch: 0 + end_epoch: 20 + frequency: 2 + initial_sparsity: 0.05 + final_sparsity: 0.60 + support_type: [Linear] + support_op: [conv1, conv2] +``` +### For compression algorithm designer +We use the instrumentation method to insert a node or function after the corresponding position in the model. And we provide interface for designer to design compression algorithm easily. + +If you want to use mask to prune a model, you can use Pruner as base class. And design your mask in calc_mask() method. + +Tensorflow code +``` +class YourPruner(nni.compressors.tf_compressor.TfPruner): + def __init__(self, your_input): + # defaultly, we suggest you to use configure_list as input + pass + + # don not change calc_mask() input + def calc_mask(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your mask and return your mask + return your_mask + + # you can also design your method help to generate mask + def your_method(self, your_input): + #your code +``` +Pytorch code +``` +class YourPruner(nni.compressors.torch_compressor.TorchPruner): + def __init__(self, your_input): + pass + + # don not change calc_mask() input + def calc_mask(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your mask and return your mask + return your_mask + + # you can also design your method help to generate mask + def your_method(self, your_input): + #your code +``` + +if you want to generate new weight and replace the old one, you are suggested to use Quantizer as base class. And you can manage the weight in quantize_weight() method. + +Tensorflow code +``` +class YourPruner(nni.compressors.tf_compressor.TfQuantizer): + def __init__(self, your_input): + pass + + # don not change quantize_weight() input + def quantize_weight(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your quantizer and return new weight + return new_weight + + # you can also design your method + def your_method(self, your_input): + #your code + +``` +Pytorch code +``` +class YourPruner(nni.compressors.torch_compressor.TorchQuantizer): + def __init__(self, your_input): + pass + + # don not change quantize_weight() input + def quantize_weight(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your quantizer and return new weight + return new_weight + + # you can also design your method + def your_method(self, your_input): + #your code +``` + +#### Preprocess Model +Sometimes, designer wants to preprocess model before compress, designer can overload preprocess_model() method + +``` +class YourPruner(nni.compressors.torch_compressor.TorchQuantizer): + def __init__(self, your_input): + pass + + # don not change quantize_weight() input + def quantize_weight(self, layer_info, weight): + # you can get layer name in layer_info.name + # you can get weight data in weight + # design your quantizer and return new weight + return new_weight + + # you can also design your method + def your_method(self, your_input): + #your code + + def preprocess_model(self, model): + #preprocess model +``` +#### Step and Epoch +if an designer wants to update mask every step, designer can implement step() or update_epoch() method in his code, and tell user to call when use it. diff --git a/docs/en_US/Compressor/Pruner.md b/docs/en_US/Compressor/Pruner.md index 4482b71465..1b00332270 100644 --- a/docs/en_US/Compressor/Pruner.md +++ b/docs/en_US/Compressor/Pruner.md @@ -1,5 +1,26 @@ Pruner on NNI Compressor === +## LevelPruner + +This is one basic pruner: you can set a target sparsity level(expressed as a fraction, 0.6 means we will prune 60%). + +We first sort the weights in the specified layer by their absolute values. And then mask to zero the smallest magnitude weights until the desired sparsity level is reached. + +### Usage + +Tensorflow code +``` +pruner = nni.compressors.tf_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +pruner(model_graph) +``` + +Pytorch code +``` +pruner = nni.compressors.torch_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +pruner(model) +``` + +*** ## AGPruner In [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878), authors Michael Zhu and Suyog Gupta provide an algorithm to prune the weight gradually. @@ -16,12 +37,14 @@ First, you should import pruner and add mask to model. Tensorflow code ``` from nni.compressors.tfCompressor import AGPruner -pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1).compress(tf.get_default_graph()) +pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) +pruner(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import AGPruner -pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1).compress(model) +pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) +pruner(model) ``` Second, you should add code below to update epoch number when you finish one epoch in your training code. @@ -50,14 +73,14 @@ Tensorflow code from nni.compressors.tfCompressor import SensitivityPruner pruner = SensitivityPruner(sparsity = 0.8) -pruner.compress(tf.get_default_graph()) +pruner(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import SensitivityPruner pruner = SensitivityPruner(sparsity = 0.8) -pruner.compress(model) +pruner(model) ``` Like AGPruner, you should update mask information every epoch by adding code below diff --git a/docs/en_US/Compressor/Quantizer.md b/docs/en_US/Compressor/Quantizer.md index c440da8e69..bbffe8e069 100644 --- a/docs/en_US/Compressor/Quantizer.md +++ b/docs/en_US/Compressor/Quantizer.md @@ -1,5 +1,19 @@ Quantizer on NNI Compressor === +## NaiveQuantizer + +We provide NaiveQuantizer to quantizer weight to default 8 bits, you can use it to test quantize algorithm. + +### Usage +tensorflow +``` +nni.compressors.tf_compressor.NaiveQuantizer()(model_graph) +``` +pytorch +``` +nni.compressors.torch_compressor.NaiveQuantizer()(model) +``` +*** ## QATquantizer In [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf), authors Benoit Jacob and Skirmantas Kligys provide an algorithm to quantize the model with training. @@ -15,12 +29,14 @@ You can quantize your model to 8 bits with the code below before your training c Tensorflow code ``` from nni.compressors.tfCompressor import QATquantizer -QATquantizer(q_bits = 8).compress(tf.get_default_graph()) +quantizer = QATquantizer(q_bits = 8) +quantizer(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import QATquantizer -QATquantizer(q_bits = 8).compress(model) +quantizer = QATquantizer(q_bits = 8) +quantizer(model) ``` You can view example for more information @@ -35,12 +51,14 @@ To implement DoReFaQuantizer, you can add code below before your training code Tensorflow code ``` from nni.compressors.tfCompressor import DoReFaQuantizer -DoReFaQuantizer(q_bits = 8).compress(tf.get_default_graph()) +quantizer = DoReFaQuantizer(q_bits = 8) +quantizer(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import DoReFaQuantizer -DoReFaQuantizer(q_bits = 8).compress(model) +quantizer = DoReFaQuantizer(q_bits = 8) +quantizer(model) ``` You can view example for more information diff --git a/examples/model_compress/configure_example.yaml b/examples/model_compress/configure_example.yaml new file mode 100644 index 0000000000..3e43f2d7c0 --- /dev/null +++ b/examples/model_compress/configure_example.yaml @@ -0,0 +1,9 @@ +AGPruner: + config: + - + start_epoch: 1 + end_epoch: 10 + frequency: 1 + initial_sparsity: 0.05 + final_sparsity: 0.8 + support_type: 'default' diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py b/examples/model_compress/main_tf_pruner.py similarity index 80% rename from src/sdk/pynni/nni/compressors/example/main_tf_pruner.py rename to examples/model_compress/main_tf_pruner.py index 29dda59f06..4fa29a8098 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf_pruner.py +++ b/examples/model_compress/main_tf_pruner.py @@ -1,4 +1,4 @@ -from nni.compressors.tfCompressor import AGPruner +from nni.compressors.tf_compressor import AGPruner import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data @@ -80,10 +80,25 @@ def main(): model = Mnist() '''you can change this to SensitivityPruner to implement it - pruner = SensitivityPruner(sparsity = 0.8) + pruner = SensitivityPruner(configure_list) ''' - pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) - pruner.compress(tf.get_default_graph()) + configure_list = [{ + 'initial_sparsity': 0, + 'final_sparsity': 0.8, + 'start_epoch': 1, + 'end_epoch': 10, + 'frequency': 1, + 'support_type': 'default' + }] + pruner = AGPruner(configure_list) + # if you want to load from yaml file + # configure_file = nni.compressors.tf_compressor._nnimc_tf._tf_default_load_configure_file('configure_example.yaml','AGPruner') + # configure_list = configure_file.get('config',[]) + # pruner.load_configure(configure_list) + # you can also handle it yourself and input an configure list in json + pruner(tf.get_default_graph()) + # you can also use compress(model) or compress_default_graph() for tensorflow compressor + # pruner.compress(tf.get_default_graph()) with tf.Session() as sess: diff --git a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py b/examples/model_compress/main_tf_quantizer.py similarity index 90% rename from src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py rename to examples/model_compress/main_tf_quantizer.py index a2b4cb1066..3159e143aa 100644 --- a/src/sdk/pynni/nni/compressors/example/main_tf_quantizer.py +++ b/examples/model_compress/main_tf_quantizer.py @@ -1,4 +1,4 @@ -from nni.compressors.tfCompressor import QATquantizer +from nni.compressors.tf_compressor import QATquantizer import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data @@ -80,9 +80,13 @@ def main(): model = Mnist() '''you can change this to DoReFaQuantizer to implement it - DoReFaQuantizer(q_bits = 0.8).compress(tf.get_default_graph()) + DoReFaQuantizer(configure_list).compress(tf.get_default_graph()) ''' - QATquantizer(q_bits = 8).compress(tf.get_default_graph()) + configure_list = [{'q_bits':8, 'support_type':'default'}] + quantizer = QATquantizer(configure_list) + quantizer(tf.get_default_graph()) + # you can also use compress(model) or compress_default_graph() + # method like QATquantizer(q_bits = 8).compress_default_graph() with tf.Session() as sess: diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py b/examples/model_compress/main_torch_pruner.py similarity index 81% rename from src/sdk/pynni/nni/compressors/example/main_torch_pruner.py rename to examples/model_compress/main_torch_pruner.py index 2b26555b52..965b695349 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_pruner.py +++ b/examples/model_compress/main_torch_pruner.py @@ -1,4 +1,4 @@ -from nni.compressors.torchCompressor import AGPruner +from nni.compressors.torch_compressor import AGPruner import torch import torch.nn.functional as F from torchvision import datasets, transforms @@ -66,10 +66,22 @@ def main(): model = Mnist() '''you can change this to SensitivityPruner to implement it - pruner = SensitivityPruner(sparsity = 0.8) + pruner = SensitivityPruner(configure_list) ''' - pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) - pruner.compress(model) + configure_list = [{ + 'initial_sparsity': 0, + 'final_sparsity': 0.8, + 'start_epoch': 1, + 'end_epoch': 10, + 'frequency': 1, + 'support_type': 'default' + }] + + pruner = AGPruner(configure_list) + pruner.load_configure('configure_example.yaml') + pruner(model) + # you can also use compress(model) method + # like that pruner.compress(model) optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum = 0.5) for epoch in range(10): diff --git a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py b/examples/model_compress/main_torch_quantizer.py similarity index 88% rename from src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py rename to examples/model_compress/main_torch_quantizer.py index 45392cb847..a5823921c4 100644 --- a/src/sdk/pynni/nni/compressors/example/main_torch_quantizer.py +++ b/examples/model_compress/main_torch_quantizer.py @@ -1,4 +1,4 @@ -from nni.compressors.torchCompressor import QATquantizer +from nni.compressors.torch_compressor import QATquantizer import torch import torch.nn.functional as F from torchvision import datasets, transforms @@ -66,9 +66,13 @@ def main(): model = Mnist() '''you can change this to DoReFaQuantizer to implement it - DoReFaQuantizer(q_bits = 8).compress(model) + DoReFaQuantizer(configure_list).compress(model) ''' - QATquantizer(q_bits = 8).compress(model) + configure_list = [{'q_bits':8, 'support_type':'default'}] + quantizer = QATquantizer(configure_list) + quantizer(model) + # you can also use compress(model) method + # like thaht quantizer.compress(model) optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum = 0.5) diff --git a/setup.py b/setup.py index 70411453d4..296b2a8f22 100644 --- a/setup.py +++ b/setup.py @@ -27,7 +27,7 @@ def read(fname): setup( name = 'nni', - version = 'v0.8-263-g1f92408', + version = '999.0.0-developing', author = 'Microsoft NNI Team', author_email = 'nni@microsoft.com', description = 'Neural Network Intelligence project', diff --git a/src/sdk/pynni/nni/compressors/example/__init__.py b/src/sdk/pynni/nni/compressors/example/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py deleted file mode 100644 index 22b7a3d3c3..0000000000 --- a/src/sdk/pynni/nni/compressors/tfCompressor/_nnimc_tf.py +++ /dev/null @@ -1,115 +0,0 @@ -import tensorflow as tf -from tensorflow import Graph, Operation, Tensor - -from typing import List - -__all__ = [ - 'TfCompressor', - 'TfPruner', - 'TfQuantizer', - '_tf_detect_prunable_layers' -] - - -class TfCompressor: - """TODO""" - - def __init__(self): - self._bound_model = None - - - def compress(self, model): - """ - Compress given graph with algorithm implemented by subclass. - This will edit the graph. - """ - assert self._bound_model is None, "Each NNI compressor instance can only compress one model" - self._bound_model = model - self.bind_model(model) - - def compress_default_graph(self): - """ - Compress the default graph with algorithm implemented by subclass. - This will edit the graph. - """ - self.compress(tf.get_default_graph()) - - - def bind_model(self, model): - """ - This method is called when a model is bound to the compressor. - Users can optionally overload this method to do model-specific initialization. - It is guaranteed that only one model will be bound to each compressor instance. - """ - pass - - -class TfLayerInfo: - def __init__(self, layer): - self.name = layer.name - self.layer = layer - self.weight_index = None - - if layer.type == 'Conv2D': - self.weight_index = 1 - else: - raise ValueError('Unsupported layer') - - -def _tf_detect_prunable_layers(model): - # search for Conv2D layers - # TODO: whitelist - return [ TfLayerInfo(op) for op in model.get_operations() if op.type == 'Conv2D' ] - - -class TfPruner(TfCompressor): - """TODO""" - - def __init__(self): - super().__init__() - - def calc_mask(self, layer_info, weight): - """ - Pruners should overload this method to provide mask for weight tensors. - The mask must have the same shape and type comparing to the weight. - It will be applied with `multiply()` operation. - This method works as a subgraph which will be inserted into the bound model. - """ - raise NotImplementedError("Pruners must overload calc_mask()") - - - def compress(self, model): - super().compress(model) - # TODO: configurable whitelist - for layer_info in _tf_detect_prunable_layers(model): - self._instrument_layer(layer_info) - - def _instrument_layer(self, layer_info): - # it seems the graph editor can only swap edges of nodes or remove all edges from a node - # it cannot remove one edge from a node, nor can it assign a new edge to a node - # we assume there is a proxy operation between the weight and the Conv2D layer - # this is true as long as the weight is `tf.Value` - # not sure what will happen if the weight is calculated from other operations - weight_op = layer_info.layer.inputs[layer_info.weight_index].op - weight = weight_op.inputs[0] - mask = self.calc_mask(layer_info, weight) - new_weight = weight * mask - tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) - - -class TfQuantizer(TfCompressor): - def __init__(self): - super().__init__() - - def quantize_weight(self, layer_info, weight) -> Tensor: - raise NotImplementedError() - - - def compress(self, model): - for layer_info in _tf_detect_prunable_layers(model): - self._instrument_layer(layer_info) - - def _instrument_layer(self, layer_info): - weight_op = layer_info.layer.inputs[layer_info.weight_index].op - new_weight = self.quantize_weight(layer_info, weight_op.inputs[0]) - tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py b/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py deleted file mode 100644 index e93f11cd92..0000000000 --- a/src/sdk/pynni/nni/compressors/tfCompressor/pruner.py +++ /dev/null @@ -1,76 +0,0 @@ - - -import tensorflow as tf -from ._nnimc_tf import TfPruner -class LevelPruner(TfPruner): - def __init__(self, sparsity = 0, layer_sparsity = {}): - super().__init__() - self.default_sparsity = sparsity - self.layer_sparsity = layer_sparsity - - def calc_mask(self, layer_info, weight): - sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) - threshold = tf.contrib.distributions.percentile(tf.abs(weight), sparsity * 100) - return tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype) - -class AGPruner(TfPruner): - def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): - super().__init__() - self.initial_sparsity = initial_sparsity - self.final_sparsity = final_sparsity - self.start_epoch = start_epoch - self.end_epoch = end_epoch - self.freq = frequency - self.now_epoch = tf.Variable(0) - self.assign_handler = [] - - def compute_target_sparsity(self): - - if self.end_epoch <= self.start_epoch : - return self.final_sparsity - - now_epoch = tf.minimum(self.now_epoch, tf.constant(self.end_epoch)) - span = int(((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq) - assert span>0 - base = tf.cast(now_epoch - self.initial_sparsity, tf.float32) / span - target_sparsity = (self.final_sparsity + - (self.initial_sparsity - self.final_sparsity)* - (tf.pow(1.0 - base,3))) - return target_sparsity - - def calc_mask(self, layer_info, weight): - - target_sparsity = self.compute_target_sparsity() - threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) - mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) - self.assign_handler.append(tf.assign(weight, weight*mask)) - return mask - - def update_epoch(self, epoch, sess): - sess.run(self.assign_handler) - sess.run(tf.assign(self.now_epoch, int(epoch))) - - -class SensitivityPruner(TfPruner): - def __init__(self, sparsity): - super().__init__() - self.sparsity = sparsity - self.layer_mask = {} - self.assign_handler = [] - - def calc_mask(self, layer_info, weight): - target_sparsity = self.sparsity * tf.math.reduce_std(weight) - mask = tf.get_variable(layer_info.name+'_mask',initializer=tf.ones(weight.shape), trainable=False) - self.layer_mask[layer_info.name] = mask - - weight_assign_handler = tf.assign(weight, mask*weight) - with tf.control_dependencies([weight_assign_handler]): - threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) - new_mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) - mask_update_handler = tf.assign(mask, new_mask) - self.assign_handler.append(mask_update_handler) - return mask - - def update_epoch(self, epoch, sess): - sess.run(self.assign_handler) - diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py deleted file mode 100644 index 1bbaa97c19..0000000000 --- a/src/sdk/pynni/nni/compressors/tfCompressor/quantizer.py +++ /dev/null @@ -1,49 +0,0 @@ -try: - import tensorflow as tf - from ._nnimc_tf import TfQuantizer - - class NaiveQuantizer(TfQuantizer): - def __init__(self): - super().__init__() - self.layer_scale = { } - - def quantize_weight(self, layer_info, weight): - new_scale = tf.reduce_max(tf.abs(weight)) / 127 - scale = tf.maximum(self.layer_scale.get(layer_info.name, tf.constant(0.0)), new_scale) - self.layer_scale[layer_info.name] = scale - orig_type = weight.dtype - return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale - - class QATquantizer(TfQuantizer): - def __init__(self, q_bits): - super().__init__() - self.q_bits = q_bits - - def quantize_weight(self, layer_info, weight): - a = tf.stop_gradient(tf.reduce_min(weight)) - b = tf.stop_gradient(tf.reduce_max(weight)) - n = tf.cast(2 ** self.q_bits, tf.float32) - scale = b-a/(n-1) - - with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): - qw = tf.round((weight-a)/scale)*scale +a - - return qw - - class DoReFaQuantizer(TfQuantizer): - def __init__(self, q_bits): - super().__init__() - self.q_bits = q_bits - - def quantize_weight(self, layer_info, weight): - a = tf.math.tanh(weight) - b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5 - - scale = pow(2, self.q_bits-1) - with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): - qw = tf.round(b*scale)/scale - r_qw = 2*qw - 1 - return r_qw - -except ImportError: - pass diff --git a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py b/src/sdk/pynni/nni/compressors/tf_compressor/__init__.py similarity index 71% rename from src/sdk/pynni/nni/compressors/tfCompressor/__init__.py rename to src/sdk/pynni/nni/compressors/tf_compressor/__init__.py index feaa9110e5..5ec47f9c51 100644 --- a/src/sdk/pynni/nni/compressors/tfCompressor/__init__.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/__init__.py @@ -1,2 +1,3 @@ from .pruner import LevelPruner, AGPruner, SensitivityPruner -from .quantizer import NaiveQuantizer, QATquantizer, DoReFaQuantizer \ No newline at end of file +from .quantizer import NaiveQuantizer, QATquantizer, DoReFaQuantizer +from ._nnimc_tf import TfPruner, TfQuantizer \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py new file mode 100644 index 0000000000..85a73443ef --- /dev/null +++ b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py @@ -0,0 +1,190 @@ +import tensorflow as tf +from tensorflow import Graph, Operation, Tensor +from ruamel.yaml import YAML +from typing import List +import logging + +logger = logging.getLogger('tf_compressor') + +__all__ = [ + 'TfCompressor', + 'TfPruner', + 'TfQuantizer', + '_tf_detect_prunable_layers', + '_tf_default_get_configure', + '_tf_default_load_configure_file' +] + + +class TfCompressor: + """ + Base compressor for tensorflow + """ + + def __init__(self): + self._bound_model = None + + + def compress(self, model): + """ + Compress given graph with algorithm implemented by subclass. + This will edit the graph. + """ + assert self._bound_model is None, "Each NNI compressor instance can only compress one model" + self._bound_model = model + self.preprocess_model(model) + + def compress_default_graph(self): + """ + Compress the default graph with algorithm implemented by subclass. + This will edit the graph. + """ + self.compress(tf.get_default_graph()) + + + def preprocess_model(self, model): + """ + This method is called when a model is bound to the compressor. + Users can optionally overload this method to do model-specific initialization. + It is guaranteed that only one model will be bound to each compressor instance. + """ + pass + + def update_epoch(self, epoch, sess): + """ + if user want to update mask every epoch, user can override this method + """ + pass + + def step(self, sess): + """ + if user want to update mask every step, user can override this method + """ + pass + + +class TfLayerInfo: + """ + layer info for tensorflow + TODO: support type + """ + def __init__(self, layer): + self.name = layer.name + self.layer = layer + self.weight_index = None + self.support_type = ['Conv2D', 'DepthwiseConv2dNative'] + if layer.type in self.support_type: + self.weight_index = 1 + else: + raise ValueError('Unsupported layer') + + +def _tf_detect_prunable_layers(model): + # search for Conv2D layers + # TODO: whitelist + # please make sure op not from optimizer + whiltlist = ['Conv2D', 'DepthwiseConv2dNative'] + return [ TfLayerInfo(op) for op in model.get_operations() if op.type in whiltlist ] + +def _tf_default_get_configure(configure_list, layer_info): + """ + Get configure for input layer + defaultly the later config will cover front config + WARNING: please mask sure default configure is the first in the list + """ + if not configure_list: + logger.warning('WARNING: configure list is None') + configure = {} + for config in configure_list: + if config.get('support_type', '') == 'default': + configure = config + elif layer_info.layer.type in config.get('support_type', []): + configure = config + elif layer_info.name in config.get('support_op', []): + configure = config + + if not configure: + logger.warning('WARNING: can not get configure, default NONE!!!') + return configure + +def _tf_default_load_configure_file(config_path, class_name): + """ + Load configure yaml file and reture cofigure in json + """ + logger.info('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) + assert config_path is not None and config_path.endswith('yaml') + file = open(config_path, 'r') + yaml = YAML(typ='safe') + yaml_text = yaml.load(file.read()) + configure_file = yaml_text.get(class_name, {}) + if not configure_file: + logger.warning('WARNING: load Nothing from configure file, Default { }') + return configure_file + +class TfPruner(TfCompressor): + """ + Base pruner for tensorflow pruner + """ + + def __init__(self): + super().__init__() + + def __call__(self, model): + self.compress(model) + return model + + def calc_mask(self, layer_info, weight): + """ + Pruners should overload this method to provide mask for weight tensors. + The mask must have the same shape and type comparing to the weight. + It will be applied with `multiply()` operation. + This method works as a subgraph which will be inserted into the bound model. + """ + raise NotImplementedError("Pruners must overload calc_mask()") + + def compress(self, model): + super().compress(model) + # TODO: configurable whitelist + for layer_info in _tf_detect_prunable_layers(model): + self._instrument_layer(layer_info) + + def _instrument_layer(self, layer_info): + """ + it seems the graph editor can only swap edges of nodes or remove all edges from a node + it cannot remove one edge from a node, nor can it assign a new edge to a node + we assume there is a proxy operation between the weight and the Conv2D layer + this is true as long as the weight is `tf.Value` + not sure what will happen if the weight is calculated from other operations + """ + weight_op = layer_info.layer.inputs[layer_info.weight_index].op + weight = weight_op.inputs[0] + mask = self.calc_mask(layer_info, weight) + new_weight = weight * mask + tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) + + + + +class TfQuantizer(TfCompressor): + """ + Base quantizer for tensorflow quantizer + """ + def __init__(self): + super().__init__() + + def __call__(self, model): + self.compress(model) + return model + + def quantize_weight(self, layer_info, weight): + raise NotImplementedError("Quantizer must overload quantize_weight()") + + + def compress(self, model): + for layer_info in _tf_detect_prunable_layers(model): + self._instrument_layer(layer_info) + + def _instrument_layer(self, layer_info): + weight_op = layer_info.layer.inputs[layer_info.weight_index].op + new_weight = self.quantize_weight(layer_info, weight_op.inputs[0]) + tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op) diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py new file mode 100644 index 0000000000..6eb17e33a4 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/tf_compressor/pruner.py @@ -0,0 +1,146 @@ +import tensorflow as tf +from ._nnimc_tf import TfPruner +from ._nnimc_tf import _tf_default_get_configure, _tf_default_load_configure_file + +import logging +logger = logging.getLogger('tensorflow pruner') + +class LevelPruner(TfPruner): + def __init__(self, configure_list): + """ + Configure Args: + sparsity + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + + def get_sparsity(self, configure={}): + sparsity = configure.get('sparsity', 0) + return sparsity + + def calc_mask(self, layer_info, weight): + sparsity = self.get_sparsity(_tf_default_get_configure(self.configure_list, layer_info)) + + threshold = tf.contrib.distributions.percentile(tf.abs(weight), sparsity * 100) + return tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype) + +class AGPruner(TfPruner): + """ + An automated gradual pruning algorithm that prunes the smallest magnitude + weights to achieve a preset level of network sparsity. + + Michael Zhu and Suyog Gupta, "To prune, or not to prune: exploring the + efficacy of pruning for model compression", 2017 NIPS Workshop on Machine + Learning of Phones and other Consumer Devices, + https://arxiv.org/pdf/1710.01878.pdf + """ + def __init__(self, configure_list): + """ + Configure Args + initial_sparsity: + final_sparsity: you should make sure initial_sparsity <= final_sparsity + start_epoch: start epoch numer begin update mask + end_epoch: end epoch number stop update mask + frequency: if you want update every 2 epoch, you can set it 2 + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + self.now_epoch = tf.Variable(0) + self.assign_handler = [] + + def compute_target_sparsity(self, layer_info): + configure = _tf_default_get_configure(self.configure_list, layer_info) + end_epoch = configure.get('end_epoch', 1) + start_epoch = configure.get('start_epoch', 0) + freq = configure.get('frequency', 1) + final_sparsity = configure.get('final_sparsity', 0) + initial_sparsity = configure.get('initial_sparsity', 0) + + if end_epoch <= start_epoch or initial_sparsity >= final_sparsity: + logger.warning('your end epoch <= start epoch or initial_sparsity >= final_sparsity') + return final_sparsity + + now_epoch = tf.minimum(self.now_epoch, tf.constant(end_epoch)) + span = int(((end_epoch - start_epoch-1)//freq)*freq) + assert span > 0 + base = tf.cast(now_epoch - start_epoch, tf.float32) / span + target_sparsity = (final_sparsity + + (initial_sparsity - final_sparsity)* + (tf.pow(1.0 - base, 3))) + return target_sparsity + + + def calc_mask(self, layer_info, weight): + + target_sparsity = self.compute_target_sparsity(layer_info) + threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + # stop gradient in case gradient change the mask + mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) + self.assign_handler.append(tf.assign(weight, weight*mask)) + return mask + + def update_epoch(self, epoch, sess): + sess.run(self.assign_handler) + sess.run(tf.assign(self.now_epoch, int(epoch))) + + +class SensitivityPruner(TfPruner): + """ + Use algorithm from "Learning both Weights and Connections for Efficient Neural Networks" + https://arxiv.org/pdf/1506.02626v3.pdf + + I.e.: "The pruning threshold is chosen as a quality parameter multiplied + by the standard deviation of a layers weights." + """ + def __init__(self, configure_list): + """ + Configure Args: + sparsity: chosen pruning sparsity + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + self.layer_mask = {} + self.assign_handler = [] + + + def get_sparsity(self, configure={}): + sparsity = configure.get('sparsity', 0) + return sparsity + + def calc_mask(self, layer_info, weight): + sparsity = self.get_sparsity(_tf_default_get_configure(self.configure_list, layer_info)) + + target_sparsity = sparsity * tf.math.reduce_std(weight) + mask = tf.get_variable(layer_info.name+'_mask',initializer=tf.ones(weight.shape), trainable=False) + self.layer_mask[layer_info.name] = mask + + weight_assign_handler = tf.assign(weight, mask*weight) + # use control_dependencies so that weight_assign_handler will be executed before mask_update_handler + with tf.control_dependencies([weight_assign_handler]): + threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100) + # stop gradient in case gradient change the mask + new_mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype)) + mask_update_handler = tf.assign(mask, new_mask) + self.assign_handler.append(mask_update_handler) + return mask + + def update_epoch(self, epoch, sess): + sess.run(self.assign_handler) diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py b/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py new file mode 100644 index 0000000000..6f9abde84d --- /dev/null +++ b/src/sdk/pynni/nni/compressors/tf_compressor/quantizer.py @@ -0,0 +1,107 @@ +import tensorflow as tf +from ._nnimc_tf import TfQuantizer +from ._nnimc_tf import _tf_default_get_configure, _tf_default_load_configure_file + +import logging +logger = logging.getLogger('tensorflow quantizer') + +class NaiveQuantizer(TfQuantizer): + """ + quantize weight to 8 bits + """ + def __init__(self): + super().__init__() + self.layer_scale = {} + + def quantize_weight(self, layer_info, weight): + new_scale = tf.reduce_max(tf.abs(weight)) / 127 + scale = tf.maximum(self.layer_scale.get(layer_info.name, tf.constant(0.0)), new_scale) + self.layer_scale[layer_info.name] = scale + orig_type = weight.dtype + return tf.cast(tf.cast(weight / scale, tf.int8), orig_type) * scale + +class QATquantizer(TfQuantizer): + """ + Quantizer using the DoReFa scheme, as defined in: + Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference + http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf + """ + def __init__(self, configure_list): + """ + Configure Args: + q_bits + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + + def get_qbits(self, configure): + if not isinstance(configure, dict): + logger.warning('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + configure = {} + qbits = configure.get('q_bits', 32) + if qbits == 0: + logger.warning('WARNING: you can not set q_bits ZERO!') + qbits = 32 + return qbits + + def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) + + a = tf.stop_gradient(tf.reduce_min(weight)) + b = tf.stop_gradient(tf.reduce_max(weight)) + n = tf.cast(2 ** q_bits, tf.float32) + scale = b-a/(n-1) + + # use gradient_override_map to change round to idetity for gradient + with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): + qw = tf.round((weight-a)/scale)*scale +a + + return qw + +class DoReFaQuantizer(TfQuantizer): + """ + Quantizer using the DoReFa scheme, as defined in: + Zhou et al., DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients + (https://arxiv.org/abs/1606.06160) + """ + def __init__(self, configure_list): + """ + Configure Args: + q_bits + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + + def get_qbits(self, configure): + if not isinstance(configure, dict): + logger.warning('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + configure = {} + qbits = configure.get('q_bits', 32) + if qbits == 0: + logger.warning('WARNING: you can not set q_bits ZERO!') + qbits = 32 + return qbits + + def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_tf_default_get_configure(self.configure_list, layer_info)) + a = tf.math.tanh(weight) + b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5 + + scale = pow(2, q_bits-1) + # use gradient_override_map to change round to idetity for gradient + with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}): + qw = tf.round(b*scale)/scale + r_qw = 2*qw - 1 + return r_qw diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py b/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py deleted file mode 100644 index 1a9235c9f8..0000000000 --- a/src/sdk/pynni/nni/compressors/torchCompressor/pruner.py +++ /dev/null @@ -1,86 +0,0 @@ -try: - import torch - from ._nnimc_torch import TorchPruner - class LevelPruner(TorchPruner): - def __init__(self, sparsity = 0, layer_sparsity = { }): - super().__init__() - self.default_sparsity = sparsity - self.layer_sparsity = layer_sparsity - - def calc_mask(self, layer_info, weight): - sparsity = self.layer_sparsity.get(layer_info.name, self.default_sparsity) - w_abs = weight.abs() - k = int(weight.numel() * sparsity) - threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() - return torch.gt(w_abs, threshold).type(weight.type()) - - class AGPruner(TorchPruner): - def __init__(self, initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=1, frequency=1): - """ - """ - super().__init__() - self.initial_sparsity = initial_sparsity - self.final_sparsity = final_sparsity - self.start_epoch = start_epoch - self.end_epoch = end_epoch - self.now_epoch = start_epoch - self.freq = frequency - self.mask_list = {} - - def compute_target_sparsity(self, now_epoch): - if self.end_epoch <= self.start_epoch or self.end_epoch <= self.now_epoch: - return self.final_sparsity - span = ((self.end_epoch - self.start_epoch-1)//self.freq)*self.freq - assert span>0 - target_sparsity = (self.final_sparsity + - (self.initial_sparsity - self.final_sparsity)* - (1.0 - ((now_epoch - self.start_epoch)/span))**3 ) - return target_sparsity - - def calc_mask(self, layer_info, weight): - now_epoch = self.now_epoch - mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) - target_sparsity = self.compute_target_sparsity(now_epoch) - k = int(weight.numel() * target_sparsity) - if k == 0: - return mask - - w_abs = weight.abs()*mask - threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() - new_mask = torch.gt(w_abs, threshold).type(weight.type()) - self.mask_list[layer_info.name] = new_mask - return new_mask - - def update_epoch(self, epoch): - if epoch <= 0: - return - self.now_epoch = epoch - - - class SensitivityPruner(TorchPruner): - def __init__(self, sparsity): - super().__init__() - self.sparsity = sparsity - self.mask_list = {} - - def calc_mask(self, layer_info, weight): - mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) - weight = weight*mask - target_sparsity = self.sparsity * torch.std(weight).item() - k = int(weight.numel() * target_sparsity) - if k == 0: - return mask - - w_abs = weight.abs() - threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() - new_mask = torch.gt(w_abs, threshold).type(weight.type()) - self.mask_list[layer_info.name] = new_mask - return new_mask - - def update_epoch(self, epoch): - pass - -except ImportError: - pass - - diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py b/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py deleted file mode 100644 index b1d6f6d791..0000000000 --- a/src/sdk/pynni/nni/compressors/torchCompressor/quantizer.py +++ /dev/null @@ -1,54 +0,0 @@ -try: - import torch - from ._nnimc_torch import TorchQuantizer - - class NaiveQuantizer(TorchQuantizer): - def __init__(self): - super().__init__() - self.layer_scale = { } - - def quantize_weight(self, layer_info, weight): - new_scale = weight.abs().max() / 127 - # TODO: use int id - scale = max(self.layer_scale.get(layer_info.name, 0), new_scale) - self.layer_scale[layer_info.name] = scale - orig_type = weight.type() # TODO: user layer_info - return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) - - class DoReFaQuantizer(TorchQuantizer): - def __init__(self, q_bits): - super().__init__() - self.q_bits = q_bits - - def quantize_weight(self, layer_info, weight): - out = weight.tanh() - out = out /( 2 * out.abs().max()) + 0.5 - out = self.quantize(out, self.q_bits) - out = 2 * out -1 - return out - - def quantize(self, input_ri, q_bits): - scale = pow(2, q_bits)-1 - output = torch.round(input_ri*scale)/scale - return output - - class QATquantizer(TorchQuantizer): - def __init__(self, q_bits): - super().__init__() - self.q_bits = q_bits - - def quantize_weight(self, layer_info, weight): - if self.q_bits <= 1: - return weight - a = torch.min(weight) - b = torch.max(weight) - n = pow(2,self.q_bits) - scale = (b-a)/(n-1) - zero_point = a - out = torch.round((weight - zero_point)/scale) - out = out*scale + zero_point - orig_type = weight.dtype - return out.type(orig_type) - -except ImportError: - pass diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py b/src/sdk/pynni/nni/compressors/torch_compressor/__init__.py similarity index 71% rename from src/sdk/pynni/nni/compressors/torchCompressor/__init__.py rename to src/sdk/pynni/nni/compressors/torch_compressor/__init__.py index 3e197df317..6e6dc56cc4 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/__init__.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/__init__.py @@ -1,2 +1,3 @@ from .pruner import LevelPruner, AGPruner, SensitivityPruner from .quantizer import NaiveQuantizer, DoReFaQuantizer, QATquantizer +from ._nnimc_torch import TorchPruner, TorchQuantizer \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py similarity index 60% rename from src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py rename to src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py index 481b2f8dac..0311194957 100644 --- a/src/sdk/pynni/nni/compressors/torchCompressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py @@ -1,18 +1,24 @@ from torch import Tensor from torch.nn import Module, Parameter - +from ruamel.yaml import YAML from typing import List +import logging +logger = logging.getLogger('torch_compressor') __all__ = [ 'TorchCompressor', 'TorchPruner', 'TorchQuantizer', - '_torch_detect_prunable_layers' + '_torch_detect_prunable_layers', + '_torch_default_get_configure', + '_torch_default_load_configure_file' ] class TorchCompressor: - """TODO""" + """ + Base compressor for pytorch + """ def __init__(self): self._bound_model = None @@ -25,19 +31,34 @@ def compress(self, model): """ assert self._bound_model is None, "Each NNI compressor instance can only compress one model" self._bound_model = model - self.bind_model(model) + self.preprocess_model(model) - def bind_model(self, model) -> None: + def preprocess_model(self, model): """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. It is guaranteed that only one model will be bound to each compressor instance. """ pass + + def update_epoch(self, epoch): + """ + if user want to update model every epoch, user can override this method + """ + pass + + def step(self): + """ + if user want to update model every step, user can override this method + """ + pass class TorchLayerInfo: + """ + layer info for pytorch + """ def __init__(self, name, layer): self.name = name self.layer = layer @@ -47,7 +68,7 @@ def __init__(self, name, layer): def _torch_detect_prunable_layers(model): # search for all layers which have parameter "weight" - ret = [ ] + ret = [] for name, layer in model.named_modules(): try: if isinstance(layer.weight, Parameter) and isinstance(layer.weight.data, Tensor): @@ -56,13 +77,50 @@ def _torch_detect_prunable_layers(model): pass return ret +def _torch_default_get_configure(configure_list, layer_info): + """ + Get configure for input layer + defaultly the later config will cover front config + WARNING: please mask sure default configure is the first in the list + """ + if not configure_list: + logger.warning('WARNING: configure list is None') + configure = {} + for config in configure_list: + if config.get('support_type', '') == 'default': + configure = config + elif type(layer_info.layer).__name__ in config.get('support_type', []): + configure = config + elif layer_info.name in config.get('support_op', []): + configure = config + if not configure: + logger.warning('WARNING: can not get configure, default NONE!!!') + return configure + +def _torch_default_load_configure_file(config_path, class_name): + logger.info('load CLASS:{0} from PATH:{1}'.format(class_name, config_path)) + assert config_path is not None and config_path.endswith('yaml') + file = open(config_path, 'r') + yaml = YAML(typ='safe') + yaml_text = yaml.load(file.read()) + configure_file = yaml_text.get(class_name, {}) + if not configure_file: + logger.warning('WARNING: load Nothing from configure file, Default { }') + return configure_file + class TorchPruner(TorchCompressor): - """TODO""" + """ + Base pruner for pytorch pruner + """ def __init__(self): super().__init__() + def __call__(self, model): + self.compress(model) + return model + def calc_mask(self, layer_info, weight): """ Pruners should overload this method to provide mask for weight tensors. @@ -97,15 +155,26 @@ def new_forward(*input): return ret layer_info.layer.forward = new_forward + class TorchQuantizer(TorchCompressor): + """ + Base quantizer for pytorch quantizer + """ def __init__(self): super().__init__() + def __call__(self, model): + self.compress(model) + return model + def quantize_weight(self, layer_info, weight): - # FIXME: where dequantize goes? - raise NotImplementedError() + """ + user should know where dequantize goes and implement it in quantize method + we now do not provide dequantize method + """ + raise NotImplementedError("Quantizer must overload quantize_weight()") def compress(self, model): super().compress(model) @@ -125,3 +194,5 @@ def new_forward(*input): return layer_info._forward(*input) layer_info.layer.forward = new_forward + + diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py new file mode 100644 index 0000000000..cb1ecb6924 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/torch_compressor/pruner.py @@ -0,0 +1,171 @@ +import torch +from ._nnimc_torch import TorchPruner +from ._nnimc_torch import _torch_default_get_configure, _torch_default_load_configure_file + +import logging +logger = logging.getLogger('torch pruner') + +class LevelPruner(TorchPruner): + """Prune to an exact pruning level specification + """ + def __init__(self, configure_list): + """ + we suggest user to use json configure list, like [{},{}...], to set configure + format : + [ + { + 'sparsity': 0, + 'support_type': 'default' + }, + { + 'sparsity': 50, + 'support_op': conv1 + } + ] + if you want input multiple configure from file, you'd better use load_configure_file(path) to load + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + + def get_sparsity(self, configure={}): + sparsity = configure.get('sparsity',0) + return sparsity + + def calc_mask(self, layer_info, weight): + sparsity = self.get_sparsity(_torch_default_get_configure(self.configure_list, layer_info)) + w_abs = weight.abs() + k = int(weight.numel() * sparsity) + if k == 0: + return torch.ones(weight.shape) + + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + return torch.gt(w_abs, threshold).type(weight.type()) + +class AGPruner(TorchPruner): + """ + An automated gradual pruning algorithm that prunes the smallest magnitude + weights to achieve a preset level of network sparsity. + + Michael Zhu and Suyog Gupta, "To prune, or not to prune: exploring the + efficacy of pruning for model compression", 2017 NIPS Workshop on Machine + Learning of Phones and other Consumer Devices, + https://arxiv.org/pdf/1710.01878.pdf + """ + def __init__(self, configure_list): + """ + Configure Args + initial_sparsity + final_sparsity: you should make sure initial_sparsity <= final_sparsity + start_epoch: start epoch numer begin update mask + end_epoch: end epoch number stop update mask, you should make sure start_epoch <= end_epoch + frequency: if you want update every 2 epoch, you can set it 2 + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + self.mask_list = {} + self.now_epoch = 1 + + def compute_target_sparsity(self, now_epoch, layer_info): + configure = _torch_default_get_configure(self.configure_list, layer_info) + end_epoch = configure.get('end_epoch', 1) + start_epoch = configure.get('start_epoch', 1) + freq = configure.get('frequency', 1) + final_sparsity = configure.get('final_sparsity', 0) + initial_sparsity = configure.get('initial_sparsity', 0) + if end_epoch <= start_epoch or initial_sparsity >= final_sparsity: + logger.warning('your end epoch <= start epoch or initial_sparsity >= final_sparsity') + return final_sparsity + + if end_epoch <= now_epoch: + return final_sparsity + + span = ((end_epoch - start_epoch-1)//freq)*freq + assert span > 0 + target_sparsity = (final_sparsity + + (initial_sparsity - final_sparsity)* + (1.0 - ((now_epoch - start_epoch)/span))**3) + return target_sparsity + + def calc_mask(self, layer_info, weight): + now_epoch = self.now_epoch + mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) + target_sparsity = self.compute_target_sparsity(now_epoch, layer_info) + k = int(weight.numel() * target_sparsity) + if k == 0 or target_sparsity >= 1 or target_sparsity <= 0: + return mask + # if we want to generate new mask, we should update weigth first + w_abs = weight.abs()*mask + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + new_mask = torch.gt(w_abs, threshold).type(weight.type()) + self.mask_list[layer_info.name] = new_mask + return new_mask + + def update_epoch(self, epoch): + if epoch <= 0: + return + self.now_epoch = epoch + + +class SensitivityPruner(TorchPruner): + """ + Use algorithm from "Learning both Weights and Connections for Efficient Neural Networks" + https://arxiv.org/pdf/1506.02626v3.pdf + + I.e.: "The pruning threshold is chosen as a quality parameter multiplied + by the standard deviation of a layers weights." + """ + def __init__(self, configure_list): + """ + configure Args: + sparsity: chosen pruning sparsity + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + self.mask_list = {} + + def load_configure(self, config_path): + """ + if you want load configure from yaml file, you can use it and input config_path + """ + config_list = _torch_default_load_configure_file(config_path, 'SensitivityPruner') + for config in config_list.get('config', []): + self.configure_list.append(config) + + def get_sparsity(self, configure={}): + sparsity = configure.get('sparsity', 0) + return sparsity + + def calc_mask(self, layer_info, weight): + mask = self.mask_list.get(layer_info.name, torch.ones(weight.shape)) + # if we want to generate new mask, we should update weigth first + weight = weight*mask + sparsity = self.get_sparsity(_torch_default_get_configure(self.configure_list, layer_info)) + target_sparsity = sparsity * torch.std(weight).item() + k = int(weight.numel() * target_sparsity) + if k == 0: + return mask + + w_abs = weight.abs() + threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max() + new_mask = torch.gt(w_abs, threshold).type(weight.type()) + self.mask_list[layer_info.name] = new_mask + return new_mask + \ No newline at end of file diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py b/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py new file mode 100644 index 0000000000..ac92472535 --- /dev/null +++ b/src/sdk/pynni/nni/compressors/torch_compressor/quantizer.py @@ -0,0 +1,108 @@ +import torch +from ._nnimc_torch import TorchQuantizer +from ._nnimc_torch import _torch_default_get_configure, _torch_default_load_configure_file +import logging +logger = logging.getLogger('torch quantizer') +class NaiveQuantizer(TorchQuantizer): + """ + quantize weight to 8 bits + """ + def __init__(self): + super().__init__() + self.layer_scale = {} + + def quantize_weight(self, layer_info, weight): + new_scale = weight.abs().max() / 127 + scale = max(self.layer_scale.get(layer_info.name, 0), new_scale) + self.layer_scale[layer_info.name] = scale + orig_type = weight.type() # TODO: user layer_info + return weight.div(scale).type(torch.int8).type(orig_type).mul(scale) + +class DoReFaQuantizer(TorchQuantizer): + """ + Quantizer using the DoReFa scheme, as defined in: + Zhou et al., DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients + (https://arxiv.org/abs/1606.06160) + """ + def __init__(self, configure_list): + """ + configure Args: + q_bits + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + + def get_qbits(self, configure): + if not isinstance(configure, dict): + logger.warning('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + configure = {} + qbits = configure.get('q_bits', 32) + if qbits == 0: + logger.warning('WARNING: you can not set q_bits ZERO!') + qbits = 32 + return qbits + + def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) + + out = weight.tanh() + out = out /( 2 * out.abs().max()) + 0.5 + out = self.quantize(out, q_bits) + out = 2 * out -1 + return out + + def quantize(self, input_ri, q_bits): + scale = pow(2, q_bits)-1 + output = torch.round(input_ri*scale)/scale + return output + +class QATquantizer(TorchQuantizer): + """ + Quantizer using the DoReFa scheme, as defined in: + Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference + http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf + """ + def __init__(self, configure_list): + """ + Configure Args: + q_bits + """ + super().__init__() + self.configure_list = [] + if isinstance(configure_list, list): + for configure in configure_list: + self.configure_list.append(configure) + else: + raise ValueError('please init with configure list') + + + def get_qbits(self, configure): + if not isinstance(configure, dict): + logger.warning('WARNING: you should input a dict to get_qbits, set DEFAULT { }') + configure = {} + qbits = configure.get('q_bits', 32) + if qbits == 0: + logger.warning('WARNING: you can not set q_bits ZERO!') + qbits = 32 + return qbits + + def quantize_weight(self, layer_info, weight): + q_bits = self.get_qbits(_torch_default_get_configure(self.configure_list, layer_info)) + + if q_bits <= 1: + return weight + a = torch.min(weight) + b = torch.max(weight) + n = pow(2, q_bits) + scale = (b-a)/(n-1) + zero_point = a + out = torch.round((weight - zero_point)/scale) + out = out*scale + zero_point + orig_type = weight.dtype + return out.type(orig_type) diff --git a/src/sdk/pynni/tests/test_compressor.py b/src/sdk/pynni/tests/test_compressor.py index 5d5c955ee5..c952170f31 100644 --- a/src/sdk/pynni/tests/test_compressor.py +++ b/src/sdk/pynni/tests/test_compressor.py @@ -1,5 +1,5 @@ from unittest import TestCase, main -from nni.compressors import tfCompressor,torchCompressor +from nni.compressors import tf_compressor,torch_compressor import torch import torch.nn.functional as F import tensorflow as tf @@ -93,20 +93,22 @@ def forward(self, x): class CompressorTestCase(TestCase): def test_tf_pruner(self): model = TfMnist() - tfCompressor.LevelPruner(sparsity = 0.8).compress_default_graph() + configure_list = [{'sparsity':0.8, 'support_type':'default'}] + tf_compressor.LevelPruner(configure_list).compress_default_graph() def test_tf_quantizer(self): model = TfMnist() - tfCompressor.NaiveQuantizer().compress_default_graph() + tf_compressor.NaiveQuantizer().compress_default_graph() def test_torch_pruner(self): model = TorchMnist() - torchCompressor.LevelPruner(sparsity = 0.8).compress(model) + configure_list = [{'sparsity':0.8, 'support_type':'default'}] + torch_compressor.LevelPruner(configure_list).compress(model) def test_torch_quantizer(self): model = TorchMnist() - torchCompressor.NaiveQuantizer().compress(model) + torch_compressor.NaiveQuantizer().compress(model) if __name__ == '__main__': diff --git a/src/webui/yarn.lock b/src/webui/yarn.lock index 8d896151a1..009489ef1f 100644 --- a/src/webui/yarn.lock +++ b/src/webui/yarn.lock @@ -3,72 +3,73 @@ "@ant-design/icons-react@~1.1.2": - version "1.1.2" - resolved "https://registry.yarnpkg.com/@ant-design/icons-react/-/icons-react-1.1.2.tgz#df25c4560864f8a3b687b305c3238daff048ed72" + version "1.1.5" + resolved "https://registry.yarnpkg.com/@ant-design/icons-react/-/icons-react-1.1.5.tgz#1b03da8dcced2a4bb982ef7b25c1d24014c35a68" + integrity sha512-p2ybKfO/r2lC1RZu4rDY2VBDZq2zqAaJzf/B1HrKTxGo8/mM1zOOEoob/LRXZphJ9jD5wCcTdcmcB9YMaAWW4Q== dependencies: ant-design-palettes "^1.1.3" babel-runtime "^6.26.0" "@ant-design/icons@~1.1.15": - version "1.1.15" - resolved "https://registry.yarnpkg.com/@ant-design/icons/-/icons-1.1.15.tgz#2ff689b87bb160c246a07adaa99cdb1c8dfd4412" + version "1.1.16" + resolved 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integrity sha1-Ro+DBE42mrIBD6xfBs7uFbsssfQ= + dependencies: + caller-callsite "^2.0.0" + callsites@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/callsites/-/callsites-2.0.0.tgz#06eb84f00eea413da86affefacbffb36093b3c50" + integrity sha1-BuuE8A7qQT2oav/vrL/7Ngk7PFA= -camel-case@3.0.x: +camel-case@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/camel-case/-/camel-case-3.0.0.tgz#ca3c3688a4e9cf3a4cda777dc4dcbc713249cf73" + integrity sha1-yjw2iKTpzzpM2nd9xNy8cTJJz3M= dependencies: no-case "^2.2.0" upper-case "^1.1.1" @@ -1524,25 +1046,25 @@ camel-case@3.0.x: camelcase-keys@^2.0.0: version "2.1.0" resolved "https://registry.yarnpkg.com/camelcase-keys/-/camelcase-keys-2.1.0.tgz#308beeaffdf28119051efa1d932213c91b8f92e7" + integrity sha1-MIvur/3ygRkFHvodkyITyRuPkuc= dependencies: camelcase "^2.0.0" map-obj "^1.0.0" -camelcase@^1.0.2: - version "1.2.1" - resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-1.2.1.tgz#9bb5304d2e0b56698b2c758b08a3eaa9daa58a39" 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+ resolved "https://registry.yarnpkg.com/duplexify/-/duplexify-3.7.1.tgz#2a4df5317f6ccfd91f86d6fd25d8d8a103b88309" + integrity sha512-07z8uv2wMyS51kKhD1KsdXJg5WQ6t93RneqRxUHnskXVtlYYkLqM0gqStQZ3pj073g687jPCHrqNfCzawLYh5g== dependencies: end-of-stream "^1.0.0" inherits "^2.0.1" @@ -2563,6 +2045,7 @@ duplexify@^3.4.2, duplexify@^3.6.0: ecc-jsbn@~0.1.1: version "0.1.2" resolved "https://registry.yarnpkg.com/ecc-jsbn/-/ecc-jsbn-0.1.2.tgz#3a83a904e54353287874c564b7549386849a98c9" + integrity sha1-OoOpBOVDUyh4dMVkt1SThoSamMk= dependencies: jsbn "~0.1.0" safer-buffer "^2.1.0" @@ -2570,27 +2053,35 @@ ecc-jsbn@~0.1.1: echarts-for-react@^2.0.14: version "2.0.14" resolved "https://registry.yarnpkg.com/echarts-for-react/-/echarts-for-react-2.0.14.tgz#6df7a31eea43ef35321e20a5854109cbc52c9df3" + integrity sha512-W56Cr3KP5KatVlGPKNZmmZT1IsozehT03sin7ABhzsV98jJ3rDfgKedIvrn/mlp22u/Wneddd0P7rqxLq2lO8g== dependencies: fast-deep-equal "^2.0.1" size-sensor "^0.2.0" echarts@^4.1.0: - version "4.1.0" - 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dependencies: websocket-driver ">=0.5.1" -fb-watchman@^2.0.0: - version "2.0.0" - resolved "https://registry.yarnpkg.com/fb-watchman/-/fb-watchman-2.0.0.tgz#54e9abf7dfa2f26cd9b1636c588c1afc05de5d58" - dependencies: - bser "^2.0.0" - fbjs@^0.8.0, fbjs@^0.8.15, fbjs@^0.8.16, fbjs@^0.8.9: version "0.8.17" resolved "https://registry.yarnpkg.com/fbjs/-/fbjs-0.8.17.tgz#c4d598ead6949112653d6588b01a5cdcd9f90fdd" @@ -3043,52 +2417,48 @@ fbjs@^0.8.0, fbjs@^0.8.15, fbjs@^0.8.16, fbjs@^0.8.9: setimmediate "^1.0.5" ua-parser-js "^0.7.18" +figgy-pudding@^3.5.1: + version "3.5.1" + resolved "https://registry.yarnpkg.com/figgy-pudding/-/figgy-pudding-3.5.1.tgz#862470112901c727a0e495a80744bd5baa1d6790" + integrity sha512-vNKxJHTEKNThjfrdJwHc7brvM6eVevuO5nTj6ez8ZQ1qbXTvGthucRF7S4vf2cr71QVnT70V34v0S1DyQsti0w== + figures@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/figures/-/figures-2.0.0.tgz#3ab1a2d2a62c8bfb431a0c94cb797a2fce27c962" + integrity sha1-OrGi0qYsi/tDGgyUy3l6L84nyWI= 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dependencies: - inherits "^2.0.1" - readable-stream "^2.0.4" + inherits "^2.0.3" + readable-stream "^2.3.6" follow-redirects@1.5.10: version "1.5.10" resolved "https://registry.yarnpkg.com/follow-redirects/-/follow-redirects-1.5.10.tgz#7b7a9f9aea2fdff36786a94ff643ed07f4ff5e2a" + integrity sha512-0V5l4Cizzvqt5D44aTXbFZz+FtyXV1vrDN6qrelxtfYQKW0KO0W2T/hkE8xvGa/540LkZlkaUjO4ailYTFtHVQ== dependencies: debug "=3.1.0" @@ -3155,47 +2528,51 @@ follow-redirects@^1.0.0: dependencies: debug "=3.1.0" -for-in@^1.0.1, for-in@^1.0.2: +for-in@^1.0.2: version "1.0.2" resolved "https://registry.yarnpkg.com/for-in/-/for-in-1.0.2.tgz#81068d295a8142ec0ac726c6e2200c30fb6d5e80" - -for-own@^0.1.4: - version "0.1.5" - resolved "https://registry.yarnpkg.com/for-own/-/for-own-0.1.5.tgz#5265c681a4f294dabbf17c9509b6763aa84510ce" - dependencies: - for-in "^1.0.1" + integrity sha1-gQaNKVqBQuwKxybG4iAMMPttXoA= forever-agent@~0.6.1: version "0.6.1" resolved 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dependencies: - big.js "^3.1.3" + big.js "^5.2.2" emojis-list "^2.0.0" - json5 "^0.5.0" + json5 "^1.0.1" locate-path@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/locate-path/-/locate-path-2.0.0.tgz#2b568b265eec944c6d9c0de9c3dbbbca0354cd8e" + integrity sha1-K1aLJl7slExtnA3pw9u7ygNUzY4= dependencies: p-locate "^2.0.0" path-exists "^3.0.0" @@ -4847,6 +3806,7 @@ locate-path@^2.0.0: locate-path@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/locate-path/-/locate-path-3.0.0.tgz#dbec3b3ab759758071b58fe59fc41871af21400e" + integrity sha512-7AO748wWnIhNqAuaty2ZWHkQHRSNfPVIsPIfwEOWO22AmaoVrWavlOcMR5nzTLNYvp36X220/maaRsrec1G65A== dependencies: p-locate "^3.0.0" path-exists "^3.0.0" @@ -4855,9 +3815,10 @@ lodash._getnative@^3.0.0: version "3.9.1" resolved "https://registry.yarnpkg.com/lodash._getnative/-/lodash._getnative-3.9.1.tgz#570bc7dede46d61cdcde687d65d3eecbaa3aaff5" -lodash._reinterpolate@^3.0.0, lodash._reinterpolate@~3.0.0: +lodash._reinterpolate@^3.0.0: 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sha1-0JF4cW/+pN3p5ft7N/bwgCJ0WAw= lodash.flow@^3.3.0: version "3.5.0" resolved "https://registry.yarnpkg.com/lodash.flow/-/lodash.flow-3.5.0.tgz#87bf40292b8cf83e4e8ce1a3ae4209e20071675a" + integrity sha1-h79AKSuM+D5OjOGjrkIJ4gBxZ1o= lodash.isarguments@^3.0.0: version "3.1.0" @@ -4891,14 +3851,6 @@ lodash.isarray@^3.0.0: version "3.0.4" resolved "https://registry.yarnpkg.com/lodash.isarray/-/lodash.isarray-3.0.4.tgz#79e4eb88c36a8122af86f844aa9bcd851b5fbb55" -lodash.isfunction@^3.0.8: - version "3.0.9" - resolved "https://registry.yarnpkg.com/lodash.isfunction/-/lodash.isfunction-3.0.9.tgz#06de25df4db327ac931981d1bdb067e5af68d051" - -lodash.isstring@^4.0.1: - version "4.0.1" - resolved "https://registry.yarnpkg.com/lodash.isstring/-/lodash.isstring-4.0.1.tgz#d527dfb5456eca7cc9bb95d5daeaf88ba54a5451" - lodash.keys@^3.1.2: version "3.1.2" resolved "https://registry.yarnpkg.com/lodash.keys/-/lodash.keys-3.1.2.tgz#4dbc0472b156be50a0b286855d1bd0b0c656098a" @@ -4911,15 +3863,7 @@ lodash.memoize@^4.1.2: version "4.1.2" resolved "https://registry.yarnpkg.com/lodash.memoize/-/lodash.memoize-4.1.2.tgz#bcc6c49a42a2840ed997f323eada5ecd182e0bfe" -lodash.sortby@^4.7.0: - version "4.7.0" - resolved "https://registry.yarnpkg.com/lodash.sortby/-/lodash.sortby-4.7.0.tgz#edd14c824e2cc9c1e0b0a1b42bb5210516a42438" - -lodash.startswith@^4.2.1: - version "4.2.1" - resolved "https://registry.yarnpkg.com/lodash.startswith/-/lodash.startswith-4.2.1.tgz#c598c4adce188a27e53145731cdc6c0e7177600c" - -lodash.template@^4.2.4, lodash.template@^4.4.0, lodash.template@^4.5.0: +lodash.template@^4.4.0, lodash.template@^4.5.0: version "4.5.0" resolved "https://registry.yarnpkg.com/lodash.template/-/lodash.template-4.5.0.tgz#f976195cf3f347d0d5f52483569fe8031ccce8ab" dependencies: @@ -4927,10 +3871,11 @@ lodash.template@^4.2.4, lodash.template@^4.4.0, lodash.template@^4.5.0: lodash.templatesettings "^4.0.0" lodash.templatesettings@^4.0.0: - version "4.1.0" - resolved "https://registry.yarnpkg.com/lodash.templatesettings/-/lodash.templatesettings-4.1.0.tgz#2b4d4e95ba440d915ff08bc899e4553666713316" + version "4.2.0" + resolved "https://registry.yarnpkg.com/lodash.templatesettings/-/lodash.templatesettings-4.2.0.tgz#e481310f049d3cf6d47e912ad09313b154f0fb33" + integrity sha512-stgLz+i3Aa9mZgnjr/O+v9ruKZsPsndy7qPZOchbqk2cnTU1ZaldKK+v7m54WoKIyxiuMZTKT2H81F8BeAc3ZQ== dependencies: - lodash._reinterpolate "~3.0.0" + lodash._reinterpolate "^3.0.0" lodash.throttle@^4.0.0: version "4.1.1" @@ -4940,17 +3885,15 @@ lodash.uniq@^4.5.0: version "4.5.0" resolved "https://registry.yarnpkg.com/lodash.uniq/-/lodash.uniq-4.5.0.tgz#d0225373aeb652adc1bc82e4945339a842754773" -"lodash@>=3.5 <5", lodash@^4.13.1, lodash@^4.16.5, lodash@^4.17.10, lodash@^4.17.11, lodash@^4.17.13, lodash@^4.17.3, lodash@^4.17.4, lodash@^4.17.5, lodash@^4.3.0: - version "4.17.14" - resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.14.tgz#9ce487ae66c96254fe20b599f21b6816028078ba" +"lodash@>=3.5 <5", lodash@^4.16.5, lodash@^4.17.11, lodash@^4.17.14, lodash@^4.17.4, lodash@^4.17.5: + version "4.17.15" + resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.15.tgz#b447f6670a0455bbfeedd11392eff330ea097548" + integrity sha512-8xOcRHvCjnocdS5cpwXQXVzmmh5e5+saE2QGoeQmbKmRS6J3VQppPOIt0MnmE+4xlZoumy0GPG0D0MVIQbNA1A== loglevel@^1.6.3: version "1.6.3" resolved "https://registry.yarnpkg.com/loglevel/-/loglevel-1.6.3.tgz#77f2eb64be55a404c9fd04ad16d57c1d6d6b1280" - -longest@^1.0.1: - version "1.0.1" - resolved "https://registry.yarnpkg.com/longest/-/longest-1.0.1.tgz#30a0b2da38f73770e8294a0d22e6625ed77d0097" + integrity sha512-LoEDv5pgpvWgPF4kNYuIp0qqSJVWak/dML0RY74xlzMZiT9w77teNAwKYKWBTYjlokMirg+o3jBwp+vlLrcfAA== loose-envify@^1.0.0, loose-envify@^1.1.0, loose-envify@^1.2.0, loose-envify@^1.3.1, loose-envify@^1.4.0: version "1.4.0" @@ -4961,6 +3904,7 @@ loose-envify@^1.0.0, loose-envify@^1.1.0, loose-envify@^1.2.0, loose-envify@^1.3 loud-rejection@^1.0.0: version 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"https://registry.yarnpkg.com/map-obj/-/map-obj-1.0.1.tgz#d933ceb9205d82bdcf4886f6742bdc2b4dea146d" + integrity sha1-2TPOuSBdgr3PSIb2dCvcK03qFG0= map-visit@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/map-visit/-/map-visit-1.0.0.tgz#ecdca8f13144e660f1b5bd41f12f3479d98dfb8f" + integrity sha1-7Nyo8TFE5mDxtb1B8S80edmN+48= dependencies: object-visit "^1.0.0" matchmediaquery@^0.3.0: version "0.3.0" resolved "https://registry.yarnpkg.com/matchmediaquery/-/matchmediaquery-0.3.0.tgz#6f672bcdbc44de16825c6917fbcdcfb9b82607b1" + integrity sha512-u0dlv+VENJ+3YepvwSPBieuvnA6DWfaYa/ctwysAR13y4XLJNyt7bEVKzNj/Nvjo+50d88Pj+xL9xaSo6JmX/w== dependencies: css-mediaquery "^0.1.2" @@ -5022,13 +3988,10 @@ math-expression-evaluator@^1.2.14: version "1.2.17" resolved "https://registry.yarnpkg.com/math-expression-evaluator/-/math-expression-evaluator-1.2.17.tgz#de819fdbcd84dccd8fae59c6aeb79615b9d266ac" -math-random@^1.0.1: - version "1.0.1" - resolved "https://registry.yarnpkg.com/math-random/-/math-random-1.0.1.tgz#8b3aac588b8a66e4975e3cdea67f7bb329601fac" - md5.js@^1.3.4: version "1.3.5" resolved "https://registry.yarnpkg.com/md5.js/-/md5.js-1.3.5.tgz#b5d07b8e3216e3e27cd728d72f70d1e6a342005f" + integrity sha512-xitP+WxNPcTTOgnTJcrhM0xvdPepipPSf3I8EIpGKeFLjt3PlJLIDG3u8EX53ZIubkb+5U2+3rELYpEhHhzdkg== dependencies: hash-base "^3.0.0" inherits "^2.0.1" @@ -5038,7 +4001,7 @@ media-typer@0.3.0: version "0.3.0" resolved "https://registry.yarnpkg.com/media-typer/-/media-typer-0.3.0.tgz#8710d7af0aa626f8fffa1ce00168545263255748" -mem@^1.1.0, mem@^4.0.0: +mem@^4.0.0: version "4.3.0" resolved "https://registry.yarnpkg.com/mem/-/mem-4.3.0.tgz#461af497bc4ae09608cdb2e60eefb69bff744178" dependencies: @@ -5046,9 +4009,10 @@ mem@^1.1.0, mem@^4.0.0: mimic-fn "^2.0.0" p-is-promise "^2.0.0" -memory-fs@^0.4.0, memory-fs@^0.4.1, memory-fs@~0.4.1: +memory-fs@^0.4.0, memory-fs@^0.4.1: version "0.4.1" resolved "https://registry.yarnpkg.com/memory-fs/-/memory-fs-0.4.1.tgz#3a9a20b8462523e447cfbc7e8bb80ed667bfc552" + integrity sha1-OpoguEYlI+RHz7x+i7gO1me/xVI= dependencies: errno "^0.1.3" readable-stream "^2.0.1" @@ -5056,6 +4020,7 @@ memory-fs@^0.4.0, memory-fs@^0.4.1, memory-fs@~0.4.1: meow@^3.7.0: version "3.7.0" resolved "https://registry.yarnpkg.com/meow/-/meow-3.7.0.tgz#72cb668b425228290abbfa856892587308a801fb" + integrity sha1-cstmi0JSKCkKu/qFaJJYcwioAfs= dependencies: camelcase-keys "^2.0.0" decamelize "^1.1.2" @@ -5072,41 +4037,24 @@ merge-descriptors@1.0.1: version "1.0.1" resolved "https://registry.yarnpkg.com/merge-descriptors/-/merge-descriptors-1.0.1.tgz#b00aaa556dd8b44568150ec9d1b953f3f90cbb61" -merge-stream@^1.0.1: - version "1.0.1" - resolved "https://registry.yarnpkg.com/merge-stream/-/merge-stream-1.0.1.tgz#4041202d508a342ba00174008df0c251b8c135e1" - dependencies: - readable-stream "^2.0.1" - -merge@^1.2.0, merge@^1.2.1: - version "1.2.1" - resolved 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version "0.1.1" + resolved "https://registry.yarnpkg.com/microevent.ts/-/microevent.ts-0.1.1.tgz#70b09b83f43df5172d0205a63025bce0f7357fa0" + integrity sha512-jo1OfR4TaEwd5HOrt5+tAZ9mqT4jmpNAusXtyfNzqVm9uiSYFZlKM1wYL4oU7azZW/PxQW53wM0S6OR1JHNa2g== micromatch@^3.1.10, micromatch@^3.1.4: version "3.1.10" resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-3.1.10.tgz#70859bc95c9840952f359a068a3fc49f9ecfac23" + integrity sha512-MWikgl9n9M3w+bpsY3He8L+w9eF9338xRl8IAO5viDizwSzziFEyUzo2xrrloB64ADbTf8uA8vRqqttDTOmccg== dependencies: arr-diff "^4.0.0" array-unique "^0.3.2" @@ -5122,9 +4070,18 @@ micromatch@^3.1.10, micromatch@^3.1.4: snapdragon "^0.8.1" to-regex "^3.0.2" +micromatch@^4.0.0: + version "4.0.2" + resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-4.0.2.tgz#4fcb0999bf9fbc2fcbdd212f6d629b9a56c39259" + integrity sha512-y7FpHSbMUMoyPbYUSzO6PaZ6FyRnQOpHuKwbo1G+Knck95XVU4QAiKdGEnj5wwoS7PlOgthX/09u5iFJ+aYf5Q== + dependencies: + braces "^3.0.1" + picomatch "^2.0.5" + 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dot-prop "^4.1.1" + cssesc "^2.0.0" indexes-of "^1.0.1" uniq "^1.0.1" @@ -6416,9 +5460,15 @@ postcss-value-parser@^3.0.1, postcss-value-parser@^3.0.2, postcss-value-parser@^ version "3.3.1" resolved "https://registry.yarnpkg.com/postcss-value-parser/-/postcss-value-parser-3.3.1.tgz#9ff822547e2893213cf1c30efa51ac5fd1ba8281" -postcss-values-parser@^1.5.0: - version "1.5.0" - resolved "http://registry.npmjs.org/postcss-values-parser/-/postcss-values-parser-1.5.0.tgz#5d9fa63e2bcb0179ce48f3235303765eb89f3047" +postcss-value-parser@^4.0.0: + version "4.0.0" + resolved "https://registry.yarnpkg.com/postcss-value-parser/-/postcss-value-parser-4.0.0.tgz#99a983d365f7b2ad8d0f9b8c3094926eab4b936d" + integrity sha512-ESPktioptiSUchCKgggAkzdmkgzKfmp0EU8jXH+5kbIUB+unr0Y4CY9SRMvibuvYUBjNh1ACLbxqYNpdTQOteQ== + +postcss-values-parser@^2.0.0, postcss-values-parser@^2.0.1: + version "2.0.1" + resolved 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dependencies: end-of-stream "^1.1.0" once "^1.3.1" @@ -6574,6 +5627,7 @@ pump@^2.0.0, pump@^2.0.1: pump@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/pump/-/pump-3.0.0.tgz#b4a2116815bde2f4e1ea602354e8c75565107a64" + integrity sha512-LwZy+p3SFs1Pytd/jYct4wpv49HiYCqd9Rlc5ZVdk0V+8Yzv6jR5Blk3TRmPL1ft69TxP0IMZGJ+WPFU2BFhww== dependencies: end-of-stream "^1.1.0" once "^1.3.1" @@ -6581,6 +5635,7 @@ pump@^3.0.0: pumpify@^1.3.3: version "1.5.1" resolved "https://registry.yarnpkg.com/pumpify/-/pumpify-1.5.1.tgz#36513be246ab27570b1a374a5ce278bfd74370ce" + integrity sha512-oClZI37HvuUJJxSKKrC17bZ9Cu0ZYhEAGPsPUy9KlMUmv9dKX2o77RUmq7f3XjIxbwyGwYzbzQ1L2Ks8sIradQ== dependencies: duplexify "^3.6.0" inherits "^2.0.3" @@ -6589,6 +5644,7 @@ pumpify@^1.3.3: punycode@1.3.2: version "1.3.2" resolved "https://registry.yarnpkg.com/punycode/-/punycode-1.3.2.tgz#9653a036fb7c1ee42342f2325cceefea3926c48d" + integrity sha1-llOgNvt8HuQjQvIyXM7v6jkmxI0= punycode@^1.2.4, punycode@^1.4.1: version 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is-number "^4.0.0" - kind-of "^6.0.0" - math-random "^1.0.1" - randombytes@^2.0.0, randombytes@^2.0.1, randombytes@^2.0.5: - version "2.0.6" - resolved "https://registry.yarnpkg.com/randombytes/-/randombytes-2.0.6.tgz#d302c522948588848a8d300c932b44c24231da80" + version "2.1.0" + resolved "https://registry.yarnpkg.com/randombytes/-/randombytes-2.1.0.tgz#df6f84372f0270dc65cdf6291349ab7a473d4f2a" + integrity sha512-vYl3iOX+4CKUWuxGi9Ukhie6fsqXqS9FE2Zaic4tNFD2N2QQaXOMFbuKK4QmDHC0JO6B1Zp41J0LpT0oR68amQ== dependencies: safe-buffer "^5.1.0" randomfill@^1.0.3: version "1.0.4" resolved "https://registry.yarnpkg.com/randomfill/-/randomfill-1.0.4.tgz#c92196fc86ab42be983f1bf31778224931d61458" + integrity sha512-87lcbR8+MhcWcUiQ+9e+Rwx8MyR2P7qnt15ynUlbm3TU/fjbgz4GsvfSUDTemtCCtVCqb4ZcEFlyPNTh9bBTLw== dependencies: randombytes "^2.0.5" safe-buffer "^5.1.0" @@ -6687,14 +5741,16 @@ rc-align@^2.4.0, rc-align@^2.4.1: rc-util "^4.0.4" rc-animate@2.x, rc-animate@^2.3.0, rc-animate@^2.5.4: - version "2.5.4" - resolved "https://registry.yarnpkg.com/rc-animate/-/rc-animate-2.5.4.tgz#3b308c42e137a2e3fb578650fdb145c2100fcc35" + version "2.9.2" + resolved "https://registry.yarnpkg.com/rc-animate/-/rc-animate-2.9.2.tgz#5964767805c886f1bdc7563d3935a74912a0b78f" + integrity sha512-rkJjeJgfbDqVjVX1/QTRfS7PiCq3AnmeYo840cVcuC4pXq4k4yAQMsC2v5BPXXdawC04vnyO4/qHQdbx9ANaiw== dependencies: babel-runtime "6.x" classnames "^2.2.6" css-animation "^1.3.2" prop-types "15.x" raf "^3.4.0" + rc-util "^4.8.0" react-lifecycles-compat "^3.0.4" rc-animate@^3.0.0-rc.1, rc-animate@^3.0.0-rc.4, rc-animate@^3.0.0-rc.5: @@ -6711,8 +5767,9 @@ rc-animate@^3.0.0-rc.1, rc-animate@^3.0.0-rc.4, rc-animate@^3.0.0-rc.5: react-lifecycles-compat "^3.0.4" rc-calendar@~9.7.9: - version "9.7.10" - resolved "https://registry.yarnpkg.com/rc-calendar/-/rc-calendar-9.7.10.tgz#269393d9b5d4b5091cff65096bac7900ac809373" + version "9.7.11" + resolved "https://registry.yarnpkg.com/rc-calendar/-/rc-calendar-9.7.11.tgz#fa27a6e47018eb71eb6d1857cdcbd161c266dbe0" + integrity sha512-zMvrqz60O+rgLaGBh5Ds5vT69DfxQPiD/eU+D/O0vx0KMooUa3EKTzQvPcAl4fuOdGM0k4ux9z0MIM/I2El0+A== dependencies: babel-runtime "6.x" classnames "2.x" @@ -6754,14 +5811,16 @@ rc-collapse@~1.10.0: rc-dialog@~7.2.1: version "7.2.1" resolved "https://registry.yarnpkg.com/rc-dialog/-/rc-dialog-7.2.1.tgz#ac92fcffdf2a0eaa64b77f829336653d911a57be" + integrity sha512-UgKNFq6ekC7XZUnXYss/9/HSDajwy04qJ++8Iuecvfq48FbMf1k9UtTW0QGcbKIfLHWUJV13/DsTBJki2H6vWw== dependencies: babel-runtime "6.x" rc-animate "2.x" rc-util "^4.4.0" rc-drawer@~1.7.6: - version "1.7.6" - resolved "https://registry.yarnpkg.com/rc-drawer/-/rc-drawer-1.7.6.tgz#925ce0768cf81ef5fa83eb22d90c603422b1b1b1" + version "1.7.8" + resolved "https://registry.yarnpkg.com/rc-drawer/-/rc-drawer-1.7.8.tgz#e4d0659dc203909e5ffacbac8a68926e0c222fb5" + integrity 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rc-hammerjs@~0.6.0: hammerjs "^2.0.8" prop-types "^15.5.9" -rc-input-number@~4.1.0: - version "4.1.0" - resolved "https://registry.yarnpkg.com/rc-input-number/-/rc-input-number-4.1.0.tgz#1f96aa586f6e83cb797f3d904adac6af42f4c559" +rc-input-number@~4.3.0: + version "4.3.9" + resolved "https://registry.yarnpkg.com/rc-input-number/-/rc-input-number-4.3.9.tgz#35994556ebda84b7e6b1fb479784844b6b4a738a" + integrity sha512-u2ioYqAwHIbDpp6sUEIOv8kqQw8e7VfTR6Kn+wJYff7TBA3Zw2Ql9I4DQniuLXglCJzDiHqG1OR5v3kN3GrD4w== dependencies: babel-runtime "6.x" classnames "^2.2.0" @@ -6833,12 +5897,14 @@ rc-input-number@~4.1.0: rmc-feedback "^2.0.0" rc-menu@^7.3.0, rc-menu@~7.4.12: - version "7.4.14" - resolved "https://registry.yarnpkg.com/rc-menu/-/rc-menu-7.4.14.tgz#28de792ce7c7dc0dec8ec5f72c9a6b367132815b" + version "7.4.23" + resolved "https://registry.yarnpkg.com/rc-menu/-/rc-menu-7.4.23.tgz#e07d497864274076299d7d8a84d14fc86b6bd30d" + integrity 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version "8.6.13" + resolved "https://registry.yarnpkg.com/rc-slider/-/rc-slider-8.6.13.tgz#88a8150c2dda6709f3f119135de11fba80af765b" + integrity sha512-fCUe8pPn8n9pq1ARX44nN2nzJoATtna4x/PdskUrxIvZXN8ja7HuceN/hq6kokZjo3FBD2B1yMZvZh6oi68l6Q== dependencies: babel-runtime "6.x" classnames "^2.2.5" @@ -6907,7 +5981,7 @@ rc-slider@~8.6.3: rc-tooltip "^3.7.0" rc-util "^4.0.4" shallowequal "^1.0.1" - warning "^3.0.0" + warning "^4.0.3" rc-steps@~3.3.0: version "3.3.0" @@ -6921,6 +5995,7 @@ rc-steps@~3.3.0: rc-switch@~1.8.0: version "1.8.0" resolved "https://registry.yarnpkg.com/rc-switch/-/rc-switch-1.8.0.tgz#cff32fd04c406d8c0c0397e69bc36350a333e236" + integrity sha512-n4H+K2XJCqGwVQKwWOjbxl1kpdov0PVE9DGhzs/S20gk65s/nAOkpdO9tBD7IM/20KRNTBh0fEWkEedByrqh6w== dependencies: babel-runtime "^6.23.0" classnames "^2.2.1" @@ -6929,6 +6004,7 @@ rc-switch@~1.8.0: rc-table@~6.3.4: version "6.3.7" resolved "https://registry.yarnpkg.com/rc-table/-/rc-table-6.3.7.tgz#0de4f71415abe3a8d9f9937ab7ddd142ab6143b7" 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dependencies: - camelcase "^1.0.2" - cliui "^2.1.0" - decamelize "^1.0.0" - window-size "0.1.0" - -zrender@4.0.4: - version "4.0.4" - resolved "https://registry.yarnpkg.com/zrender/-/zrender-4.0.4.tgz#910e60d888f00c9599073f23758dd23345fe48fd" +zrender@4.0.7: + version "4.0.7" + resolved "https://registry.yarnpkg.com/zrender/-/zrender-4.0.7.tgz#15ae960822f5efed410995d37e5107fe3de10e6d" + integrity sha512-TNloHe0ums6zxbHfnaCryM61J4IWDajZwNq6dHk9vfWhhysO/OeFvvR0drBs/nbXha2YxSzfQj2FiCd6RVBe+Q== From 3c7d6bf73e8774efb9b244eec026399d912da8b0 Mon Sep 17 00:00:00 2001 From: leonardo <806218468@qq.com> Date: Fri, 30 Aug 2019 15:54:31 +0800 Subject: [PATCH 32/34] change doc and bind_model() --- docs/en_US/Compressor/Pruner.md | 50 ++++++++++++++++--- docs/en_US/Compressor/Quantizer.md | 22 ++++++-- .../compressors/tf_compressor/_nnimc_tf.py | 2 +- .../torch_compressor/_nnimc_torch.py | 2 +- 4 files changed, 61 insertions(+), 15 deletions(-) diff --git a/docs/en_US/Compressor/Pruner.md b/docs/en_US/Compressor/Pruner.md index 1b00332270..c52184a21e 100644 --- a/docs/en_US/Compressor/Pruner.md +++ b/docs/en_US/Compressor/Pruner.md @@ -10,16 +10,22 @@ We first sort the weights in the specified layer by their absolute values. And t Tensorflow code ``` -pruner = nni.compressors.tf_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +configure_list = [{'sparsity':0.8,'support_type': 'default'}] +pruner = nni.compressors.tf_compressor.LevelPruner(configure_list) pruner(model_graph) ``` Pytorch code ``` -pruner = nni.compressors.torch_compressor.LevelPruner([{'sparsity':0.8,'support_type': 'default'}]) +configure_list = [{'sparsity':0.8,'support_type': 'default'}] +pruner = nni.compressors.torch_compressor.LevelPruner(configure_list) pruner(model) ``` +#### User configuration for LevelPruner +* **sparsity:** This is to specify the sparsity operations to be compressed to + + *** ## AGPruner @@ -37,13 +43,29 @@ First, you should import pruner and add mask to model. Tensorflow code ``` from nni.compressors.tfCompressor import AGPruner -pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) +configure_list = [{ + 'initial_sparsity': 0, + 'final_sparsity': 0.8, + 'start_epoch': 1, + 'end_epoch': 10, + 'frequency': 1, + 'support_type': 'default' + }] +pruner = AGPruner(configure_list) pruner(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import AGPruner -pruner = AGPruner(initial_sparsity=0, final_sparsity=0.8, start_epoch=1, end_epoch=10, frequency=1) +configure_list = [{ + 'initial_sparsity': 0, + 'final_sparsity': 0.8, + 'start_epoch': 1, + 'end_epoch': 10, + 'frequency': 1, + 'support_type': 'default' + }] +pruner = AGPruner(configure_list) pruner(model) ``` @@ -58,6 +80,14 @@ Pytorch code pruner.update_epoch(epoch) ``` You can view example for more information + +#### User configuration for AGPruner +* **initial_sparsity:** This is to specify the sparsity when compressor starts to compress +* **final_sparsity:** This is to specify the sparsity when compressor finishes to compress +* **start_epoch:** This is to specify the epoch number when compressor starts to compress +* **end_epoch:** This is to specify the epoch number when compressor finishes to compress +* **frequency:** This is to specify every *frequency* number epochs compressor compress once + *** ## SensitivityPruner @@ -71,15 +101,15 @@ You can prune weight step by step and reach one target sparsity by SensitivityPr Tensorflow code ``` from nni.compressors.tfCompressor import SensitivityPruner - -pruner = SensitivityPruner(sparsity = 0.8) +configure_list = [{'sparsity':0.8,'support_type': 'default'}] +pruner = SensitivityPruner(configure_list) pruner(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import SensitivityPruner - -pruner = SensitivityPruner(sparsity = 0.8) +configure_list = [{'sparsity':0.8,'support_type': 'default'}] +pruner = SensitivityPruner(configure_list) pruner(model) ``` Like AGPruner, you should update mask information every epoch by adding code below @@ -93,4 +123,8 @@ Pytorch code pruner.update_epoch(epoch) ``` You can view example for more information + +#### User configuration for SensitivityPruner +* **sparsity:** This is to specify the sparsity operations to be compressed to + *** diff --git a/docs/en_US/Compressor/Quantizer.md b/docs/en_US/Compressor/Quantizer.md index bbffe8e069..05898df10a 100644 --- a/docs/en_US/Compressor/Quantizer.md +++ b/docs/en_US/Compressor/Quantizer.md @@ -2,7 +2,7 @@ Quantizer on NNI Compressor === ## NaiveQuantizer -We provide NaiveQuantizer to quantizer weight to default 8 bits, you can use it to test quantize algorithm. +We provide NaiveQuantizer to quantizer weight to default 8 bits, you can use it to test quantize algorithm without any configure. ### Usage tensorflow @@ -13,6 +13,7 @@ pytorch ``` nni.compressors.torch_compressor.NaiveQuantizer()(model) ``` + *** ## QATquantizer In [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf), authors Benoit Jacob and Skirmantas Kligys provide an algorithm to quantize the model with training. @@ -29,18 +30,24 @@ You can quantize your model to 8 bits with the code below before your training c Tensorflow code ``` from nni.compressors.tfCompressor import QATquantizer -quantizer = QATquantizer(q_bits = 8) +configure_list = [{'q_bits':8, 'support_type':'default'}] +quantizer = QATquantizer(configure_list) quantizer(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import QATquantizer -quantizer = QATquantizer(q_bits = 8) +configure_list = [{'q_bits':8, 'support_type':'default'}] +quantizer = QATquantizer(configure_list) quantizer(model) ``` You can view example for more information +#### User configuration for QATquantizer +* **q_bits:** This is to specify the q_bits operations to be quantized to + + *** ## DoReFaQuantizer In [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://arxiv.org/abs/1606.06160), authors Shuchang Zhou and Yuxin Wu provide an algorithm named DoReFa to quantize the weight, activation and gradients with training. @@ -51,14 +58,19 @@ To implement DoReFaQuantizer, you can add code below before your training code Tensorflow code ``` from nni.compressors.tfCompressor import DoReFaQuantizer -quantizer = DoReFaQuantizer(q_bits = 8) +configure_list = [{'q_bits':8, 'support_type':'default'}] +quantizer = DoReFaQuantizer(configure_list) quantizer(tf.get_default_graph()) ``` Pytorch code ``` from nni.compressors.torchCompressor import DoReFaQuantizer -quantizer = DoReFaQuantizer(q_bits = 8) +configure_list = [{'q_bits':8, 'support_type':'default'}] +quantizer = DoReFaQuantizer(configure_list) quantizer(model) ``` You can view example for more information + +#### User configuration for QATquantizer +* **q_bits:** This is to specify the q_bits operations to be quantized to diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py index 85a73443ef..d421d37a57 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py @@ -42,7 +42,7 @@ def compress_default_graph(self): self.compress(tf.get_default_graph()) - def preprocess_model(self, model): + def bind_model(self, model): """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py index 0311194957..33253af650 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py @@ -34,7 +34,7 @@ def compress(self, model): self.preprocess_model(model) - def preprocess_model(self, model): + def bind_model(self, model): """ This method is called when a model is bound to the compressor. Users can optionally overload this method to do model-specific initialization. From 2e7de798baf7042a865485be075394b71b1c5606 Mon Sep 17 00:00:00 2001 From: leonardo <806218468@qq.com> Date: Fri, 30 Aug 2019 16:28:51 +0800 Subject: [PATCH 33/34] change prepeocess_model() to bind_model() --- docs/en_US/Compressor/Overview.md | 4 ++-- src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py | 2 +- .../pynni/nni/compressors/torch_compressor/_nnimc_torch.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/en_US/Compressor/Overview.md b/docs/en_US/Compressor/Overview.md index eac1013439..ff9644de1a 100644 --- a/docs/en_US/Compressor/Overview.md +++ b/docs/en_US/Compressor/Overview.md @@ -156,7 +156,7 @@ class YourPruner(nni.compressors.torch_compressor.TorchQuantizer): ``` #### Preprocess Model -Sometimes, designer wants to preprocess model before compress, designer can overload preprocess_model() method +Sometimes, designer wants to preprocess model before compress, designer can overload bind_model() method ``` class YourPruner(nni.compressors.torch_compressor.TorchQuantizer): @@ -174,7 +174,7 @@ class YourPruner(nni.compressors.torch_compressor.TorchQuantizer): def your_method(self, your_input): #your code - def preprocess_model(self, model): + def bind_model(self, model): #preprocess model ``` #### Step and Epoch diff --git a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py index d421d37a57..715f3a07cd 100644 --- a/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py +++ b/src/sdk/pynni/nni/compressors/tf_compressor/_nnimc_tf.py @@ -32,7 +32,7 @@ def compress(self, model): """ assert self._bound_model is None, "Each NNI compressor instance can only compress one model" self._bound_model = model - self.preprocess_model(model) + self.bind_model(model) def compress_default_graph(self): """ diff --git a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py index 33253af650..7b484dccbb 100644 --- a/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py +++ b/src/sdk/pynni/nni/compressors/torch_compressor/_nnimc_torch.py @@ -31,7 +31,7 @@ def compress(self, model): """ assert self._bound_model is None, "Each NNI compressor instance can only compress one model" self._bound_model = model - self.preprocess_model(model) + self.bind_model(model) def bind_model(self, model): From 420294b0ecfc3f8d64174c1a18d3120c02fe6040 Mon Sep 17 00:00:00 2001 From: leonardo <806218468@qq.com> Date: Thu, 5 Sep 2019 20:28:38 +0800 Subject: [PATCH 34/34] move function --- docs/en_US/Compressor/Overview.md | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/docs/en_US/Compressor/Overview.md b/docs/en_US/Compressor/Overview.md index c6742b8502..47aaec1e06 100644 --- a/docs/en_US/Compressor/Overview.md +++ b/docs/en_US/Compressor/Overview.md @@ -118,7 +118,8 @@ class YourPruner(nni.compressors.tf_compressor.TfPruner): def update_epoch(self, epoch_num, sess): pass - def step(self): + # note for pytorch version, there is no sess in input arguments + def step(self, sess): # can do some processing based on the model or weights binded # in the func bind_model pass @@ -159,18 +160,12 @@ class YourPruner(nni.compressors.tf_compressor.TfQuantizer): def update_epoch(self, epoch_num, sess): pass - def step(self): + # note for pytorch version, there is no sess in input arguments + def step(self, sess): # can do some processing based on the model or weights binded # in the func bind_model pass - # don not change quantize_weight() input - def quantize_weight(self, layer_info, weight): - # you can get layer name in layer_info.name - # you can get weight data in weight - # design your quantizer and return new weight - return new_weight - # you can also design your method def your_method(self, your_input): #your code