neuralmagic / sparseml Goto Github PK

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

License: Apache License 2.0

Makefile 0.05% Python 84.86% Jupyter Notebook 14.86% Shell 0.14% Dockerfile 0.09%

pytorch keras sparsification-recipes tensorflow smaller-models deep-learning-library deep-learning-algorithms deep-learning-models automl pruning-algorithms

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sparseml's Issues

[transformers] Incorrect steps_per_epoch calculation in DDP run

Describe the bug
Using self.args._n_gpu to infer the total number of GPUs doesn't work for DDP runs. In a DDP run, each process is responsible only for one GPU, i.e. self.args._n_gpu=1 always, making the manager_steps_per_epoch calculation wrong for DDP runs on multiple GPUs.

Do we currently support TensorFlow v2, and if not, are there any plans to support it?

pruned yolov5 unchanged parameter count

Hi, I tried the yolov5 tutorials with --recipe = yolov5s.pruned_quantized.md
The QAT works as I can see fake quantized modules in netron but the number of parameters remain unchanged.
Are there any additional steps to take to remove the pruned parameters?
Thanks!

Jetson boards support

Hello. Thank you for your valuable solutions. I am interested to know the optimized yolov5 models can be deployed on Jetson boards? Are they compatible with Jetson CPU architecture?

Pruning a sparse model

Is your feature request related to a problem? Please describe.
As far as I understand, pruning a sparse model is not supported. For example, a common use-case is to load a pruned model, with (for example) 70% sparsity, and to prune it additionally for 20% with (for example) GMPruningModifier. Currently, there is no way to initialize module masks (within the GMPruningModifier) from this sparse checkpoint.

Describe the solution you'd like
I think that having a possibility to specify in your recipe that you want to load sparsity from the checkpoint would be really useful. For example:

  - !GMPruningModifier
    params: __ALL__
    init_sparsity: 0.05
    final_sparsity: 0.8
    start_epoch: 0.0
    end_epoch: 36.0
    update_frequency: 1.0
    load_sparsity: True

Describe alternatives you've considered

alternative: Currently, it's possible to define a ConstantPruningModifier that would hold loaded module sparsities. This is beneficial only when additional finetuning of a sparse model is needed. Sparsity information contained in the ConstantPruningModifier is not accessible in other pruners (for example GMPruningModifier) and can't be used to prune an already sparse model.
alternative: One could try to bypass this issue when initializing module masks by doing something like this:

self._param_mask[self._param == 0.0] = 0.0

in the __init__ of ModuleParamPruningMask. Unfortunately, one has to modify the sparseml source code to achieve this and to add the explicit trigger for this in his code, for example command-line flag --initialize-masks-from-ckpt.

Solving this issue in the YAML recipe has the additional benefit for users to easily reproduce their experiments by having all necessary information stored in the recipe.

Can't get attribute 'SPPF'

Describe the bug
I get Can't get attribute 'SPPF' on <module 'models.common' from 'PATH_TO_DIR/sparseml/integrations/ultralytics-yolov5/yolov5/models/common.py'> when I try to train using train.py from the yolov5 within sparseml

Expected behavior
train.py should train my model

Environment

OS [e.g. Ubuntu 18.04]: Windows 10
Python version [e.g. 3.7]: 3.7 (Conda)
SparseML version or commit hash [e.g. 0.1.0, f7245c8]: 0.9.0
ML framework version(s) [e.g. torch 1.7.1]: torch 1.7.0
Other Python package versions [e.g. SparseZoo, DeepSparse, numpy, ONNX]:
Other relevant environment information [e.g. hardware, CUDA version]:
IDE: PyCharm 2021.3
GPU: NVIDIA GeForce RTX 2080 Founders Edition
CPU: AMD Ryzen 5 3600
CUDA:

To Reproduce
(anaconda prompt)
Clone sparseml repository
cd to integrations/ultralytics-yolov5
run setup_integration.sh
cd to yolov5
python train.py --cfg ../models/yolov5s.yaml --weights yolov5s.pt --data D:/Projects/Python/ML/Aimbot/yolov5/data/12-22_V1.yaml --hyp data/hyp.scratch.yaml

Errors

Additional context
I was able to train the model with the exact same command (file paths adjusted accordingly) with standalone yolov5 on a standalone local directory, however this issue only arises when I try to train using the yolov5 within sparseml/integrations/ultralytics-yolov5.

Same Input node hits assert error when attempting to re-quantize bias.

Describe the bug
Nodes in a hierarchical structure from a keras model to ONNX model, hits an assert error when trying to quantize input bias.

Expected behavior
ONNX quantizer doesn't re-use layers to quantize models in the structure. Layers are quantized one at a time.

Environment
Include all relevant environment information:

OS [e.g. Ubuntu 18.04]: Ubuntu 20.04
Python version [e.g. 3.7]: 3.6
SparseML version or commit hash [e.g. 0.1.0, f7245c8]: 0.7.0 -- vixadd@c5b4e37
ML framework version(s) [e.g. torch 1.7.1]: Tensorflow 2.3.0 -- Keras Model
Other Python package versions [e.g. SparseZoo, DeepSparse, numpy, ONNX]: ONNX 1.10.1
Other relevant environment information [e.g. hardware, CUDA version]:

To Reproduce
-- Run a keras model that reuses multiple heirarchical convolution layers. Convert to ONNX and run that through the quantizer function in the onnx.optim library.

Errors
Assert Error: -- (In my code posted above.)

WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_3_1/init/conv2d_3_1/Conv2D_bias_fused_bn already has a quantization value.
WARNING: wrapped_model/call/encoder_1/init/conv2d_init/init/conv2d_init/Conv2D_bias_fused_bn already has a quantization value.

Additional context
I have already tried renaming my layers to unique identifiers. It doesn't work. The layers, while unique, get reused when quantizing bias. I am confused as to why the quantizer re-uses conv layers, and then complains about the fact that layers are being re-used. Please tell me if I'm missing something. -- Thanks.

Optimizers not using specified arguments in the Transformers integration

Describe the bug
Optimizers in integrations/transformers are always initialized with default arguments. Any user-defined arguments (for example via command line and argparser) are completely discarded.

Expected behavior
To be able to set optimizer's arguments, for example learning rate.

To Reproduce
Run any integrations/transformers example python script and compare specified optimizer's params and the actual optimizer's params (for example --learning_rate X).

PyTorch->ONNX supported versions

Hi,

When converting my own PT model to ONNX for input by sparsify, which onnx version, IR and opset versions are supported?
Thanks.

update docs and code on https://github.com/onnx/keras-onnx no more supported

Describe the bug
fail to convert model to ONNX

Expected behavior
model is converted to ONNX

Environment
Include all relevant environment information:

OS [e.g. Ubuntu 18.04]: fedora 33
Python version [e.g. 3.7]: 3.9
SparseML version or commit hash [e.g. 0.1.0, f7245c8]: 0.7.0
ML framework version(s) [e.g. torch 1.7.1]: tensorflow 2.6.0
Other Python package versions [e.g. SparseZoo, DeepSparse, numpy, ONNX]:

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            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
            "version": "==4.62.2"
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        "traitlets": {
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}

Other relevant environment information [e.g. hardware, CUDA version]:

      # Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz
      cpu_family: 6
      cpu_model: 142

To Reproduce

pip install micropipenv
python3 -m venv venv/ && . venv/bin/activate
Create Pipfile and Pipfile.lock files using data above
micropipenv install
python3 scrip.py with the content provided below

import tensorflow as tf

# Importing the required Keras modules containing model and layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (
    Dense,
    Conv2D,
    Dropout,
    Flatten,
    MaxPooling2D,
)

# MNIST dataset parameters.
num_classes = 10  # total classes (0-9 digits).

# Training parameters.
batch_size = 128
display_step = 10

# Network parameters.
conv1_filters = 32  # number of filters for 1st conv layer.
conv2_filters = 64  # number of filters for 2nd conv layer.
fc1_units = 1024  # number of neurons for 1st fully-connected layer.

input_shape = (28, 28, 1)

# Creating a Sequential Model and adding the layers
conv_net = Sequential()
conv_net.add(
    Conv2D(
        conv1_filters,
        kernel_size=(5, 5),
        padding="same",
        activation="relu",
        input_shape=input_shape,
    )
)
conv_net.add(MaxPooling2D(pool_size=(2, 2)))
conv_net.add(
    Conv2D(
        conv2_filters, kernel_size=(3, 3), padding="same", activation="relu"
    )
)
conv_net.add(MaxPooling2D(pool_size=(2, 2)))
conv_net.add(Flatten())  # Flattening the 2D arrays for fully connected layers
conv_net.add(Dense(fc1_units, activation=tf.nn.relu))
conv_net.add(Dropout(0.5))
conv_net.add(Dense(num_classes, activation=tf.nn.softmax))

conv_net.compile(
    optimizer="adam",
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy"],
)

import os
from sparseml.keras.utils import ModelExporter

model = conv_net  # fill in with your model
exporter = ModelExporter(model, output_dir=os.path.join(".", "onnx-export"))
exporter.export_onnx()

Errors

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_1459245/4273403008.py in <module>
      4 model = conv_net  # fill in with your model
      5 exporter = ModelExporter(model, output_dir=os.path.join(".", "onnx-export"))
----> 6 exporter.export_onnx()

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/sparseml/keras/utils/exporter.py in export_onnx(self, name, opset, doc_string, debug_mode, raise_on_tf_support, **kwargs)
     74         """
     75         if keras2onnx_import_error is not None:
---> 76             raise keras2onnx_import_error
     77 
     78         if raise_on_tf_support:

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/sparseml/keras/utils/exporter.py in <module>
     25 
     26 try:
---> 27     import keras2onnx
     28 
     29     keras2onnx_import_error = None

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/__init__.py in <module>
     32 from .funcbook import set_converter, set_converters
     33 
---> 34 from .main import convert_keras
     35 from .main import export_tf_frozen_graph
     36 from .main import build_io_names_tf2onnx

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/main.py in <module>
     11 from .proto import onnx
     12 from .topology import convert_topology
---> 13 from .ke2onnx import static_set_ke2onnx_converters
     14 from .parser import parse_graph, parse_graph_modeless
     15 from .topology import Topology

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/ke2onnx/__init__.py in <module>
      4 # license information.
      5 ###############################################################################
----> 6 from .main import keras_layer_to_operator
      7 from .main import static_set_ke2onnx_converters
      8 from .layer_spec import keras_layer_spec

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/ke2onnx/main.py in <module>
     11 from ..common.onnx_ops import apply_reshape, apply_concat, apply_transpose, apply_flatten, OnnxOperatorBuilder
     12 
---> 13 from .activation import convert_keras_activation
     14 from .adv_activation import convert_keras_advanced_activation
     15 from .batch_norm import convert_keras_batch_normalization

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/ke2onnx/activation.py in <module>
     19     except ImportError:
     20         pass
---> 21 if not relu6 and hasattr(keras.applications.mobilenet, 'relu6'):
     22     relu6 = keras.applications.mobilenet.relu6
     23 

AttributeError: module 'tensorflow.python.keras' has no attribute 'applications'

Additional context
Following doc from: https://docs.neuralmagic.com/sparseml/source/onnx_export#exporting-keras-to-onnx

Deploying with Tensor RT

Would you like to give the tutorial of deplopying with Tensor RT for sparsed yolov5l moldel ?

ModuleNotFoundError: No module named 'sparseml.pytorch.utils.quantization'

Describe the bug
in running train.py for yolov5 integration in sparseml, I get error that no module sparseml , (i report it in another issue but no answer i have seen yet) so I manualyy copy sparseml from src to yolo module, but now it get another error that:

sparseml/integrations/ultralytics-yolov5/yolov5/models/export.py", line 21,

from sparseml.pytorch.utils.quantization import skip_onnx_input_quantize
ModuleNotFoundError: No module named 'sparseml.pytorch.utils.quantization'

Expected behavior
I Expect importing properly! but when I check adress that export.py try to import, I find out that he/she is wright! there is no quantization module in sparseml.pytorch.utils.

Environment
Include all relevant environment information:

OS : Ubuntu 18.04
Python version :3.7

To Reproduce
Exact steps to reproduce the behavior:
git clone sparseml, pip install sparseml,
go to integration of yolov5 and bash setup
copy sparseml from src to yolo root (you didn't say that but if we don't copy, it cant recognize sparse module -I report this issue too but no answer received-)
go to yolo root
then run :

python3.7 train.py --data voc.yaml --cfg ../models/yolov5l.yaml --weights zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned_quant-aggressive_95?recipe_type=transfer --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5.transfer_learn_pruned_quantized.md

Errors
Traceback (most recent call last):
File "train.py", line 23, in
import test # import test.py to get mAP after each epoch
File "/home/fteam/Desktop/naser/R.Zamani/sparseml/integratio ns/ultralytics-yolov5/yolov5/test.py", line 12, in
from models.export import load_checkpoint
File "/home/fteam/Desktop/naser/R.Zamani/sparseml/integratio ns/ultralytics-yolov5/yolov5/models/export.py", line 21, in
from sparseml.pytorch.utils.quantization import skip_onnx_ input_quantize
ModuleNotFoundError: No module named 'sparseml.pytorch.utils.q uantization'

Additional context
Add any other context about the problem here. Also include any relevant files.

INT8 Performance without Pruning

It is really nice to have comparison of INT8 Performance - without Pruning vs with Pruning
Can that be included in the Blog Page ? ( Pruning = ZERO but with INT8 Quantization )
And also, it is good to mention how much Pruning has been applied. ( Say at 50% Pruning, achieved 99% of FP32 accuracy )

Can we specify the constrain on Pruning ratio. ( Say Pruning 50% or 65% etc. )
This is essential when the accuracy within certain limits doesn't matter much, but Throughput matters.

Mismatched lib versions

Describe the bug
Because of the sparsezoo~=_VERSION_MAJOR_MINOR pip installation picks the latest minor version, instead of the latest patch version. This causes some incompatibilities between installed versions of sparseml and sparsezoo.

Expected behavior
When __version__ = "0.2.0" is specified, to get sparsezoo==0.2.0 and not sparsezoo==0.3.1.

When using Sparsify UI, is anything uploaded elsewhere?

Hi,
When running the Sparsify UI which uses SparseML and other repositories, is any model data or other information sent to a remote Neural Magic or other server? or is everything done locally on my own machine?
I couldn't find an answer in the docs.
Thanks.

Question : Is latest yolov5 version compatible with sparsification ?

More precisely, the version number 6.
Thnx in advance if you've already try !

Have a library to be used in jupyter notebooks

Is your feature request related to a problem? Please describe.
As SparseML user,

I would like to have a way to start training of a model using the library, not only CLI.

Reference: os-climate/aicoe-osc-demo#91

Describe the solution you'd like
Have methods in library that can be imported and used from a jupyter notebook.

Describe alternatives you've considered

Additional context

from sparseml.transformers import question_answering

question_answering(
    output_dir="dense_bert-question_answering_squad",
    model_name_or_path="zoo:nlp/masked_language_modeling/bert-base/pytorch/huggingface/wikipedia_bookcorpus/base-none",
    recipe="zoo:nlp/masked_language_modeling/bert-base/pytorch/huggingface/wikipedia_bookcorpus/base-none?recipe_type=transfer-question_answering",
    dataset_name="squad",
    per_device_train_batch_size=16,
    per_device_eval_batch_size=24,
    preprocessing_num_workers=6,
    do_train=True,
    do_eval=True,
    evaluation_strateg="epoch",
    fp16=True,
    seed=42,
    save_strategy="epoch",
    save_total_limit=1
)

Consider not installing sparseml from setup_integration scripts

Describe the bug
Installing sparseml while setting up integrations overrules existing sparseml installation. For example, if I have sparseml installed in editable mode (pip install -e) and I want to setup integration with huggingface-transformers, this line

sparseml/integrations/huggingface-transformers/setup_integration.sh

Line 12 in 4fe5e44

pip install sparseml[torch] datasets seqeval

would make my current sparseml installation obsolete. I think the same would happen if I had sparseml installed in the normal way (non editable), but with a version that is not the latest published at pip.

Expected behavior
To still have/use the same sparseml version I had before running setup_integration.sh scripts.

Potential fix
I think a reasonable assumption would be that sparseml is already installed before trying to set any additional integration, so pip install sparseml could be removed from setup_integration.sh scripts.

"Error(s) in loading state_dict for Model:" when I try to export a model edited with a recipe

Hi, I have trained a yolov5s model for several epochs, and I followed the tutorial to apply a recipe to it. At the export step, I have this error:

RuntimeError: Error(s) in loading state_dict for Model:
Missing key(s) in state_dict: "model.0.conv.conv.quant.activation_post_process.scale", "model.0.conv.conv.quant.activation_post_process.zero_point", ...

Unexpected key(s) in state_dict: "model.0.conv.conv.weight", "model.0.conv.conv.bias", "model.1.conv.weight", "model.1.conv.bias", ...

Someone can help me?

ALL_TOKEN but only for prunable layers (ConvNd and Linear)

Is your feature request related to a problem? Please describe.
When one wants to prune all prunable layers of a model, it is necessary to specify all layer names manually in the YAML recipe. This is not convenient, especially when starting to work with new models, one has to make a dummy run to extract layer names and to use them later on to create a YAML config. As far as I understand, there is ALL_TOKEN = '__ALL__' which adds all layers (including BatchNorm, biases and so on). Most of the time, we don't want to prune all of these.

Describe the solution you'd like
To be able to create a YAML file with: params: __ALL_PRUNABLE__

Describe alternatives you've considered
My current solution is to do something like this in the GMPruningModifier class:

if '__ALL_PRUNABLE__' in self._params:
            self._params = [layer_name + ".weight" for (layer_name, _) in get_prunable_layers(module)]

inference with gpu

Hi, how can I run the sparsified yolov5l model with gpu ? I don't find the commands.

Error: No module named 'sparseml'

Hi, im using sparseml yolov5 integration. as you said in totorial for transfer learning I run this code:

python train.py --data voc.yaml --cfg ../models/yolov5s.yaml --weights zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned_quant-aggressive_94?recipe_type=transfer --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov5.transfer_learn_pruned_quantized.md

(I run this bash script in train.py folder i.e: sparseml/integrations/ultralytics-yolov5/yolov5)

but I get this error:
.../models/export.py", line 20, in
from sparseml.pytorch.utils import ModuleExporter
ModuleNotFoundError: No module named 'sparseml'

it seems that it cant impoort from sparseml module
is it a bug? or I am doing sth wrong?
thanks!

DimensionSparsityMaskCreator creates unexpected mask_type

Describe the bug
When using a recipe, using the parameter mask_type: unstructured works as expected:

    - !GMPruningModifier
        mask_type: unstructured

But according to the doc, we can choose to use "channel" or "filter" for structured pruning, and here is the problem.
When using either of those, we have an error saying we can't iterate on a string at the line reduced_dims = [idx for idx in range(n_dims) if idx not in self._dim]

self._dim is defined in the __init__ of DimensionSparsityMaskCreator.
But, it is overriden:

...
        if isinstance(dim, str):
            valid_dim_names = ["channel", "filter"]
            if dim in valid_dim_names:
                self._dim_name = dim
                self._dim = [1] if dim == "channel" else [0, 1]
...
        self._dim = dim  # List[int]

But dim is either "channel" or "filter", meaning that we can't define reduced_dims.

ALSO, something strange is that when using mask_type: 1 (I expected to trigger the following code)

        if isinstance(dim, int):
            dim = [dim]

I get the error telling me:

File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/mask_creator_pruning.py", line 619, in load_mask_creator
    if ("[" in obj and "]" in obj) or ("(" in obj and ")" in obj):
TypeError: argument of type 'int' is not iterable

Expected behavior

As indicated in the docstrings, mask_type should accept channel, filter, int or List[int], in addition to "unstructured"

Environment
Include all relevant environment information:

OS [e.g. Ubuntu 18.04]: RedHat
Python version [e.g. 3.7]: 3.8
SparseML version or commit hash [e.g. 0.1.0, f7245c8]: 0.8.0
ML framework version(s) [e.g. torch 1.7.1]: Torch 1.9.0
Other Python package versions [e.g. SparseZoo, DeepSparse, numpy, ONNX]:
Other relevant environment information [e.g. hardware, CUDA version]:

To Reproduce

Exact steps to reproduce the behavior:

Define a model,
Try to train it with a recipe GMPruningModifier, mask_type: channel

Errors
If applicable, add a full print-out of any errors or exceptions that are raised or include screenshots to help explain your problem.

mask_type: channel

File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/torch/cuda/amp/grad_scaler.py", line 285, in _maybe_opt_step
    retval = optimizer.step(*args, **kwargs)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/manager.py", line 160, in step
    return self._perform_wrapped_step(*args, **kwargs)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/manager.py", line 198, in _perform_wrapped_step
    self._wrapped_manager.update(
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/manager.py", line 448, in update
    mod.scheduled_update(module, optimizer, epoch, steps_per_epoch)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/modifier.py", line 519, in scheduled_update
    self.update(module, optimizer, epoch, steps_per_epoch)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/modifier_pruning.py", line 277, in update
    self._check_mask_update(module, epoch, steps_per_epoch)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/modifier_pruning.py", line 761, in _check_mask_update
    self._module_masks.set_param_masks_from_sparsity(self._applied_sparsity)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/mask_pruning.py", line 383, in set_param_masks_from_sparsity
    masks = self._mask_creator.create_sparsity_masks(
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/mask_creator_pruning.py", line 443, in create_sparsity_masks
    return super().create_sparsity_masks(tensors, sparsity, global_sparsity=False)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/mask_creator_pruning.py", line 373, in create_sparsity_masks
    grouped_tensors = [self.group_tensor(tensor) for tensor in tensors]
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/mask_creator_pruning.py", line 373, in <listcomp>
    grouped_tensors = [self.group_tensor(tensor) for tensor in tensors]
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/mask_creator_pruning.py", line 456, in group_tensor
    reduced_dims = [idx for idx in range(n_dims) if idx not in self._dim]
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/mask_creator_pruning.py", line 456, in <listcomp>
    reduced_dims = [idx for idx in range(n_dims) if idx not in self._dim]
TypeError: 'in <string>' requires string as left operand, not int

mask_type: 1:

Traceback (most recent call last):
  File "experiment_sparseml.py", line 176, in <module>
    main()
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/main.py", line 48, in decorated_main
    _run_hydra(
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
    run_and_report(
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
    raise ex
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
    return func()
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
    lambda: hydra.run(
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
    _ = ret.return_value
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
    raise self._return_value
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
    ret.return_value = task_function(task_cfg)
  File "experiment_sparseml.py", line 112, in main
    callbacks=[SparseMLCallback(recipe_path='/gpfswork/rech/dod/uuj72kc/bagginghsf/sparsify_70_qat_channel.yaml')],
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/pl_bolts/callbacks/sparseml.py", line 42, in __init__
    self.manager = ScheduledModifierManager.from_yaml(recipe_path)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/manager.py", line 269, in from_yaml
    modifiers = Modifier.load_list(yaml_str)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/modifier.py", line 90, in load_list
    return Modifier.load_framework_list(yaml_str, PYTORCH_FRAMEWORK)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/optim/modifier.py", line 294, in load_framework_list
    container = yaml.safe_load(yaml_str)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/yaml/__init__.py", line 125, in safe_load
    return load(stream, SafeLoader)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/yaml/__init__.py", line 81, in load
    return loader.get_single_data()
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/yaml/constructor.py", line 51, in get_single_data
    return self.construct_document(node)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/yaml/constructor.py", line 60, in construct_document
    for dummy in generator:
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/optim/modifier.py", line 760, in constructor
    instance.__init__(**state)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/modifier_pruning.py", line 545, in __init__
    else load_mask_creator(mask_type)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/optim/mask_creator_pruning.py", line 619, in load_mask_creator
    if ("[" in obj and "]" in obj) or ("(" in obj and ")" in obj):
TypeError: argument of type 'int' is not iterable

Additional context
Add any other context about the problem here. Also include any relevant files.

YOLOv3-tiny Pruning Recipe

Hi, is there any recipe provided for pruning the YOLOv3-tiny model ? I could see the recipe for YOLOv3-SPP model but couldn't fine one for the tiny model.
Can you please share a recipe for that or provide a guide for making one for the tiny model?

Thanks.

Save model function doesn't save the manager_state_dict from wrapped optimiser.

There is a utility function in the pytorch.utils:
https://github.com/neuralmagic/sparseml/blob/main/src/sparseml/pytorch/utils/model.py
And there is a line that is expected to save manager, if the optimizer is wrapped by ScheduledModifierManager:

    if include_modifiers and optimizer and hasattr(optimizer, "manager_state_dict"):
        save_dict["manager"] = optimizer.manager_state_dict()

However RecipeManagerStepWrapper doesn't have this property.
I think what was expected is something like this:

    if include_modifiers and optimizer and hasattr(optimizer, "wrapped_manager"):
        save_dict["manager"] = optimizer.manager.state_dict()

And then one can add load_manager in addition to the existing load_model, load_optimizer, load_epoch.

Version

I am using the latest nightly release '0.12.0.20220316'

No matching distribution found for sparsezoo~=0.2

Describe the bug
Following the DEVELOPING.md guidelines results with the following errors after cloning the repo and running python3 -m pip install -e ./[dev,torch] in the dedicated conda environment.

ERROR: Could not find a version that satisfies the requirement sparsezoo~=0.2 (from sparseml[dev,torch])
ERROR: No matching distribution found for sparsezoo~=0.2

Expected behavior
Project installed in the editable mode.

Environment
Include all relevant environment information:

OS: Debian 4.19.171-2 (2021-01-30) x86_64
Python version: 3.{6,7,8,9}
SparseML version or commit hash: latest at the main branch (10c112135466176fac4a3b713fd354d50ba53ddc)

To Reproduce
Exact steps to reproduce the behavior:

git clone https://github.com/neuralmagic/sparseml.git
cd sparseml
python3 -m pip install -e ./[dev,torch]

[M-FAC integration] DDP processes with different seeds might end up with different masks

Describe the bug
I think there might be some corner cases where DDP processes will end up with different masks. Since many DDP experiments tend to have different seeds for each process, the case with "too many zeros" will use random.shuffle()(

sparseml/src/sparseml/pytorch/optim/mask_creator_pruning.py

Line 171 in 4fe5e44

random.shuffle(rand_indices)

) and create different masks on each process.

Expected behavior
To have exactly the same masks on each DDP process.

To Reproduce
I think it's not trivial to come up with an example where the program execution will reach this point:

sparseml/src/sparseml/pytorch/optim/mask_creator_pruning.py

Line 167 in 4fe5e44

# too many zeros so will go over the already given sparsity

, but a straight-forward way to see this issue would be to generate a random number somewhere at the start of this loop and see that it's different in each DDP process (when seeds are not the same for each process).

Memory leak when using debug-steps

Describe the bug
There is a memory leak when using --debug-steps. RAM fills up quickly, especially when using larger number of workers (for example 20).

Expected behavior
Having a run without out of memory issues.

Environment
Include all relevant environment information:

OS: Debian 4.19.171-2 (2021-01-30) x86_64
Python version: 3.8
SparseML version or commit hash: b82577
ML framework version(s): torch 1.7.1

To Reproduce
Run:

CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/pytorch_vision.py train --recipe-path ./dummy.yaml --arch-key resnet50 --dataset imagenet --dataset-path ~/ILSVRC --train-batch-size 256 --test-batch-size 512 --loader-num-workers 20 --debug-steps 1

where dummy.yaml is:

modifiers:
  - !EpochRangeModifier
    start_epoch: 0.0
    end_epoch: 90.0

Errors
RuntimeError: DataLoader worker (pid <worker_pid>) is killed by signal: Killed.

Directory with recipes

Hi!
I've been trying to run some sparseml examples from the /scripts/pytorch_vision.py but I can't find the directory with recipes. For example: ~/sparseml_recipes/pruning_resnet50.yaml, ~/sparseml_recipes/pruning_mobilenet.yaml. Looks like the sparseml_recipes directory doesn't exist at all.

【question】Why my yolov5s model is 80MB after pruned？

I followed this：
https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/tutorials/sparsifying_yolov5_using_recipes.md
and I get a big model more than 80M size.

`quantize_torch_qat_export` can't load model: Error parsing message with type 'onnx.ModelProto'

Describe the bug

I am training a segmentation model with pruning and quantization. While exporting the model, I pass convert_qat=True because I assume it is not optimal to bring a fake quantized model into production.

The strange thing is that the model I get in my output folder seems correctly converted...

As soon as it tries to execute quantize_torch_qat_export, I get the following error:

Error executing job with overrides: ['datasets.main_path=/gpfsscratch/rech/dod/uuj72kc/datasets/', 'datasets.num_workers=1', 'lightning.default_root_dir=/gpfswork/rech/dod/uuj72kc/bagginghsf', 'lightning.stochastic_weight_avg=False']
Traceback (most recent call last):
  File "/gpfsdswork/projects/rech/dod/uuj72kc/bagginghsf/experiment_sparseml.py", line 176, in <module>
    main()
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/main.py", line 48, in decorated_main
    _run_hydra(
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/utils.py", line 377, in _run_hydra
    run_and_report(
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/utils.py", line 214, in run_and_report
    raise ex
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
    return func()
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/utils.py", line 378, in <lambda>
    lambda: hydra.run(
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 111, in run
    _ = ret.return_value
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
    raise self._return_value
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
    ret.return_value = task_function(task_cfg)
  File "/gpfsdswork/projects/rech/dod/uuj72kc/bagginghsf/experiment_sparseml.py", line 150, in main
    SparseMLCallback.export_to_sparse_onnx(model,
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/pl_bolts/callbacks/sparseml.py", line 94, in export_to_sparse_onnx
    exporter.export_onnx(sample_batch=sample_batch, **export_kwargs)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/utils/exporter.py", line 192, in export_onnx
    export_onnx(
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/utils/exporter.py", line 468, in export_onnx
    quantize_torch_qat_export(model=file_path, output_file_path=file_path)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/sparseml/pytorch/utils/quantization/quantize_qat_export.py", line 1081, in quantize_torch_qat_export
    model = onnx.load(model)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/onnx/__init__.py", line 121, in load_model
    model = load_model_from_string(s, format=format)
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/onnx/__init__.py", line 158, in load_model_from_string
    return _deserialize(s, ModelProto())
  File "/gpfswork/rech/dod/uuj72kc/anaconda3/envs/SparseML/lib/python3.8/site-packages/onnx/__init__.py", line 99, in _deserialize
    decoded = cast(Optional[int], proto.ParseFromString(s))
google.protobuf.message.DecodeError: Error parsing message with type 'onnx.ModelProto'

Full log if needed: http://pastebin.fr/97220 (sorry, it's quite long and verbose)

If it can help, here is the model I am left with, I assume it is the one that is saved right before the call of quantize_torch_cat_export: https://filebin.net/wqm5kv6ohorsmc2q (will be deleted automatically in 6 days, if needed I can reupload it)

Expected behavior

Well, convert a fake quantized model to a fully quantized model I think :)

Environment
Include all relevant environment information:

OS [e.g. Ubuntu 18.04]: RedHat
Python version [e.g. 3.7]: 3.8
SparseML version or commit hash [e.g. 0.1.0, f7245c8]: 0.8.0
ML framework version(s) [e.g. torch 1.7.1]: Torch 1.9.0
Other Python package versions [e.g. SparseZoo, DeepSparse, numpy, ONNX]:
Other relevant environment information [e.g. hardware, CUDA version]: Distributed Data Parallel with PyTorch Lightning on 8 NVidia A100 GPUs

To Reproduce
Exact steps to reproduce the behavior:

Train a model with a QAT recipe,
export using convert_qat=True

Errors
If applicable, add a full print-out of any errors or exceptions that are raised or include screenshots to help explain your problem.

Full log if needed: http://pastebin.fr/97220 (sorry, it's quite long and verbose)

4-core machine benchmarking results

Hi,

I am testing ResNet-50 benchmark on a 4-core c5.2xlarge instance. I could not find the results on the web-page.

Are the results on attached files are correct?

If this is correct, does this mean that the effect of sparse software is better on more core CPU machines (ex., 24 cores) than in small CPU core machines?

Thank you in advance.

Batch 1

Batch 64

what is recipe?

thanks for the great work, but I was wondering that what is the recipe that is one of the arguments?
how to write recipe according to different tasks, such as:detection, segmentation,
there are recipes in your examples, which is hard for understanding, could you give tutorials ?
thanks

Checkpoint path for integrations/timm not parsed

Describe the bug
Checkpoint path returned by zoo_model.download_framework_files(extensions=[".pth"]) is of List[str] type.

sparseml/integrations/timm/train.py

Line 392 in 0c8b1d4

 args.initial_checkpoint = zoo_model.download_framework_files(extensions=[".pth"]) 

create_model(...) from the timm repo expects a str type (only one path)

sparseml/integrations/timm/train.py

Line 397 in 0c8b1d4

model = create_model(

Expected behavior
To have: args.initial_checkpoint = "model.pth" instead of args.initial_checkpoint = ["model.pth"].

To Reproduce
Exact steps to reproduce the behavior: use a local recipe and a SparseZoo checkpoint.

python integrations/timm/train.py \
  /PATH/TO/DATASET/imagenet/ \
  --sparseml-recipe /PATH/TO/RECIPE/recipe.yaml \
  --initial-checkpoint zoo:model/stub/path \
  --dataset imagenet \
  --batch-size 64 \
  --remode pixel --reprob 0.6 --smoothing 0.1 \
  --output models/optimized \
  --model resnet50 \
  --workers 8 \

Errors
stat: path should be string, bytes, os.PathLike or integer, not list

Permanently Removing Pruned Weights

Hi, thanks for the well-organized repository.

I've been following the classification tutorial that prunes and finetunes ResNet50 for Imagenette. The pruning seemed to have worked and both PTH and ONNX files were saved. I noticed that the weight size doesn't actual decrease from the original unpruned model. I'm assuming the pruned weights are zeroed out? Is there a utility to actually remove the neurons with zeroed weights in PyTorch?

ONNx model error

after export the model in windows system getting this error

ERROR: Invalid requirement: "'torch~=1.7.0'"
ERROR: Invalid requirement: "'torchvision"
YOLOv5 v5.0-148-g63b9a73 torch 1.7.0 CPU

Traceback (most recent call last):
File "detect.py", line 184, in
detect(opt=opt)
File "detect.py", line 33, in detect
model, extras = load_checkpoint('ensemble', weights, device) # load FP32 model
File "D:\yolo_pruning\From_gc\Yolo_pruning\sparseml\integrations\ultralytics-yolov5\yolov5\models\export.py", line 57, in load_checkpoint
else weights, map_location=device) # load checkpoint
File "C:\Users\AppsTekO22\miniconda3\envs\yolo_inf_py3.7.0\lib\site-packages\torch\serialization.py", line 595, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "C:\Users\AppsTekO22\miniconda3\envs\yolo_inf_py3.7.0\lib\site-packages\torch\serialization.py", line 764, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, '\x08'.

ERROR: Invalid requirement: "'torch~=1.7.0'"
ERROR: Invalid requirement: "'torchvision"
YOLOv5 v5.0-148-g63b9a73 torch 1.7.0 CPU

getting error for convverted .pt file

C:\Users\AppsTekO22\miniconda3\envs\yolo_inf_py3.7.0\lib\site-packages\torch\serialization.py:591: UserWarning: 'torch.load' received a zip file that looks like a TorchScript archive dispatching to 'torch.jit.load' (call 'torch.jit.load' directly to silence this warning)
" silence this warning)", UserWarning)
Traceback (most recent call last):
File "detect.py", line 184, in
detect(opt=opt)
File "detect.py", line 33, in detect
model, extras = load_checkpoint('ensemble', weights, device) # load FP32 model
File "D:\yolo_pruning\From_gc\Yolo_pruning\sparseml\integrations\ultralytics-yolov5\yolov5\models\export.py", line 58, in load_checkpoint
start_epoch = ckpt['epoch'] + 1 if 'epoch' in ckpt else 0
File "C:\Users\AppsTekO22\miniconda3\envs\yolo_inf_py3.7.0\lib\site-packages\torch\jit_script.py", line 624, in contains
return self.forward_magic_method("contains", key)
File "C:\Users\AppsTekO22\miniconda3\envs\yolo_inf_py3.7.0\lib\site-packages\torch\jit_script.py", line 611, in forward_magic_method
raise NotImplementedError()
NotImplementedError

Please help me , Thank you

Baseline Dense FP32 Performance

Hi,

I would like to know whether you applied any Neural Magic technique to get the baseline Dense FP32 model performance?

Thanks.

Training yolo-v3 models sometimes results in checkpoints that cannot be loaded correctly

Hi SparseML team.

Describe the bug
When I train models, in some cases I cannot load them into the test.py tool. Specifically I get an error message at this line that tells me I cannot change requires_grad flag of non-leaf variables.

Interestingly, this seems to happen only when training on coco, but not voc or coco128.

Expected behavior
A clear and concise description of what you expected to happen.

Environment
Include all relevant environment information:

OS [e.g. Ubuntu 18.04]:
Python version [e.g. 3.7]:
SparseML version or commit hash [e.g. 0.1.0, f7245c8]:
ML framework version(s) [e.g. torch 1.7.1]:
Other Python package versions [e.g. SparseZoo, DeepSparse, numpy, ONNX]:
Other relevant environment information [e.g. hardware, CUDA version]:

I am using the sparseml/integrations/ultralytics-yolov3 sparseml integration as described in that readme. My HEADs are set to:
sparseml repo: 5713665 (note: I don't use the pip version of sparse ml, I source the code in src/ directly)
yolov3 repo: e3bbbad5431f4a3c600547c79f968be540886310
OS: Ubuntu 18
Torch: 1.7.1
Cuda 11.2

To Reproduce
Exact steps to reproduce the behavior:
Train model:
CUDA_VISIBLE_DEVICES=0,5,6,7 python -m torch.distributed.launch --master_port=654327 --nproc_per_node 4 train.py --workers 16 --batch 32 --img 512 --weights PRETRAINED_WEIGHTS --data data/coco128.yaml --hyp ../data/hyp.pruned.yaml --recipe ../recipes/short-yolov3-gmp.85.acdc.md --cfg models/yolov3-spp.yaml --disable-ema

(note that the exact recipe doesn't seem to matter, I just made a really short one)

Test model:
CUDA_VISIBLE_DEVICES=1 python test.py --weights runs/train/exp81/weights/last.pt

train.py and test.py are located in sparseml/integrations/ultralytics-yolov3/yolov3

Errors
If applicable, add a full print-out of any errors or exceptions that are raised or include screenshots to help explain your problem.

Namespace(augment=False, batch_size=32, conf_thres=0.001, data='data/coco128.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.6, name='exp', project='runs/test', save_conf=False, save_hybrid=False, save_json=False, save_txt=False, single_cls=False, task='val', verbose=False, weights=['runs/train/exp81/weights/last.pt'])
YOLOv3 🚀 v9.5.0-33-g62c06bb torch 1.7.1 CUDA:0 (GeForce RTX 2080 Ti, 11019.4375MB)

Fusing layers...
Model Summary: 269 layers, 62971933 parameters, 62971933 gradients, 157.1 GFLOPS
Traceback (most recent call last):
File "test.py", line 328, in
save_conf=opt.save_conf,
File "test.py", line 59, in test
model, extras = load_checkpoint('ensemble', weights, device) # load FP32 model
File "/nfs/scistore14/alistgrp/eiofinov/sparseml/integrations/ultralytics-yolov3/yolov3/models/export.py", line 70, in load_checkpoint
p.requires_grad = True
RuntimeError: you can only change requires_grad flags of leaf variables.

NOTE: wrapping the offending line in a try statement or just saying if p.is_leaf fixes the problem and leads to test output. But this is probably not ideal :)

Additional context
Add any other context about the problem here. Also include any relevant files.

from sparseml.keras.optim import ScheduledModifierManager fails

Describe the bug
from sparseml.keras.optim import ScheduledModifierManager fails

Expected behavior
Code run without problems.

Environment
Include all relevant environment information:

OS [e.g. Ubuntu 18.04]: fedora 33
Python version [e.g. 3.7]: 3.9
SparseML version or commit hash [e.g. 0.1.0, f7245c8]: 0.7.0
ML framework version(s) [e.g. torch 1.7.1]: tensorflow 2.6.0
Other Python package versions [e.g. SparseZoo, DeepSparse, numpy, ONNX]:

[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"

[packages]
sparseml = "*"
tensorflow = "*"
numpy = "*"

[dev-packages]

[requires]
python_version = "3.8"

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            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
            "version": "==0.37.0"
        },
        "widgetsnbextension": {
            "hashes": [
                "sha256:079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7",
                "sha256:bd314f8ceb488571a5ffea6cc5b9fc6cba0adaf88a9d2386b93a489751938bcd"
            ],
            "version": "==3.5.1"
        },
        "wrapt": {
            "hashes": [
                "sha256:b62ffa81fb85f4332a4f609cab4ac40709470da05643a082ec1eb88e6d9b97d7"
            ],
            "version": "==1.12.1"
        }
    },
    "develop": {}
}

Other relevant environment information [e.g. hardware, CUDA version]:

      # Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz
      cpu_family: 6
      cpu_model: 142

To Reproduce

pip install micropipenv
python3 -m venv venv/ && . venv/bin/activate
Create Pipfile and Pipfile.lock files using data above
micropipenv install
python3 scrip.py with the content provided below

from sparseml.keras.optim import ScheduledModifierManager

model = None  # your model definition
optimizer = None  # your optimizer definition
num_train_batches = len(train_data) / batch_size  # your number of batches per training epoch

manager = ScheduledModifierManager.from_yaml("recipe.yaml")
model, optimizer, callbacks = manager.modify(
    model, optimizer, steps_per_epoch=num_train_batches
)

# Keras compilation and training code...
# Be sure to compile model after calling modify and pass the callbacks into the fit or fit_generator function.
# Note, if you are using train_on_batch, then you will need to invoke the callbacks after every step.
model.compile(...)
model.fit(..., callbacks=callbacks)

Errors

2021-09-14 11:44:45.584942: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-09-14 11:44:45.584969: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
  File "sprsify.py", line 1, in <module>
    from sparseml.keras.optim import ScheduledModifierManager
  File "/home/fmurdaca/.local/share/virtualenvs/sparsify-6AJ6jKNw/lib/python3.8/site-packages/sparseml/keras/optim/__init__.py", line 28, in <module>
    from .modifier_lr import *
  File "/home/fmurdaca/.local/share/virtualenvs/sparsify-6AJ6jKNw/lib/python3.8/site-packages/sparseml/keras/optim/modifier_lr.py", line 36, in <module>
    class LRModifierCallback(keras.callbacks.Callback):
  File "/home/fmurdaca/.local/share/virtualenvs/sparsify-6AJ6jKNw/lib/python3.8/site-packages/sparseml/keras/optim/modifier_lr.py", line 51, in LRModifierCallback
    learning_rate: Union[float, keras.optimizers.schedules.LearningRateSchedule],
AttributeError: module 'keras.optimizers' has no attribute 'schedules'

Additional context
Followed doc from https://docs.neuralmagic.com/sparseml/source/code#keras-sparsification

Update model weights in tutorial docs.

What is the URL, file, or UI containing proposed doc change
https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/tutorials/sparsifying_yolov5_using_recipes.md

Where does one find the original content or where would this change go?
https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/tutorials/sparsifying_yolov5_using_recipes.md#applying-a-recipe

What is the current content or situation in question
The current doc contains incorrect weights sizes.

What is the proposed change
Updated the weights, PR - 2454c0b

Additional context
Old -

New-

Unified naming convention

Is your feature request related to a problem? Please describe.
It might be worth considering a unified naming convention for these attributes:

        # initialize masks to all ones
        self._param_masks = [torch.ones(param.shape) for param in self._params]
        self._params_init = [None] * len(self._layers)  # type: List[Tensor]
        self._params_unmasked = [None] * len(self._layers)  # type: List[Tensor]
        self._params_grad = [None] * len(self._layers)  # type: List[Tensor]
        self._params_movement = [None] * len(self._layers)  # type: List[Tensor]

Describe the solution you'd like
Either all with self._param_* or all with self._params_*. Then we wouldn't need to check __init__ to see if an attribute starts with param or params. This would probably make these things easier to identify: #424

Number of steps per epoch in Transformers integration miscalculated

Describe the bug
steps_per_epoch calculated w.r.t the wrong train dataset object, resulting with the wrong number of train data samples

sparseml/integrations/transformers/run_qa.py

Line 719 in c47bfb8

 steps_per_epoch = math.ceil(len(datasets["train"]) / (training_args.per_device_train_batch_size*training_args._n_gpu)) 

Expected behavior
To be calculated w.r.t to the train_dataset object, which is used by QuestionAnsweringTrainer.

Additional context
In the integrations/transformers example:len(datasets["train"]) = 87 599 and len(train_dataset) = 88 524. The consequence of this is that pruners are not triggered exactly when specified with start_epoch and update_frequency.

[M-FAC integration] num_grads correction in DDP mode breaks

Describe the bug
When num_grads is given as an integer (and not as a dict), DDP-correction of it (dividing with world_size) raises an exception.

Expected behavior
To properly divide num_grads by world_size.

To Reproduce
Run any M-FAC recipe with num_grads is not a dict.

'LeakyReLU' object has no attribute 'activation_post_process'

When trying to run a quantization recipe on a model with Leaky ReLUs, I get this error, raised by return self.activation_post_process(output).

Thanks.

AttributeError: 'GMPruningModifier' object has no attribute '_module_masks'

Hello! I want to use sparseml to sparsify my network. But now I encountering a problem that when I set the 'lr_class' param of the LearningRateModifier as 'StepLR', an error like this will emerge:
AttributeError: 'GMPruningModifier' object has no attribute '_module_masks'
But it works when I set the param as 'MultiStepLR' and the rest keep unchanged.

Running out of CUDA memory in quantisation step

Describe the question
I recently discovered your work on speeding up YoloV5 on CPU and so I wanted to give it a try on a private dataset. I tried to run the yolov5.transfer_learn_pruned_quantized recipie and everything works fine until the quantisation epochs start. I am afraid my NVIDIA GeForce GTX 1080 Ti may not have enough memory capacity (around 10 GB).

I am wondering if you have any tips for me? Is there for example an option to adjust batch size during the quantisation epochs, or would it be better to adjust the batch size for the whole training run? Any suggestions would be much appreciated!

Environment

OS: 5.13.7-gentoo
Python version: python 3.7
SparseML version or commit hash: b176894
ML framework version(s) : torch==1.7.1
Other Python package versions: sparsezoo==0.6.0, sparseml==0.6.0, numpy==1.21.2, onnx==1.7.0
Other relevant environment information: NVIDIA GeForce GTX 1080 Ti, driver version 470.57.02, CUDA 11.4

To Reproduce
Follow this tutorial exactly Sparse Transfer Learning With YOLOv5 on model YOLOv5s Pruned Quantized. Due to my GPU limitations I get the following:

When I start the training I get this warning, which I am unsure if really matters, but to give complete context:

/home/stian/miniconda3/envs/yolov5/lib/python3.7/site-packages/sparsezoo/requests/base.py:80: UserWarning: Invalid query string for stub cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned_quant-aggress
ive_94 valid params include [], given ['recipe_type']                                              
  f"Invalid query string for stub {stub} valid params include {valid_params},"

Then after epoch 50 when quantisation epochs kick in:

Traceback (most recent call last):                                                                                                                                                                          
  File "train.py", line 564, in <module>                                                                                                                                                                    
    train(hyp, opt, device, tb_writer)                                                                                                                                                                      
  File "train.py", line 316, in train                                                                                                                                                                       
    pred = model(imgs)  # forward                                                                     
  File "/home/stian/miniconda3/envs/yolov5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl                                                                                 
    result = self.forward(*input, **kwargs)                                                                                                                                                                 
  File "/home/stian/Projects/sparseml/integrations/ultralytics-yolov5/yolov5/models/yolo.py", line 119, in forward                                                                                          
    return self.forward_once(x, profile)  # single-scale inference, train                                                                                                                                   
  File "/home/stian/Projects/sparseml/integrations/ultralytics-yolov5/yolov5/models/yolo.py", line 150, in forward_once                                                                                     
    x = m(x)  # run                                                                                   
  File "/home/stian/miniconda3/envs/yolov5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl                                                                                 
    result = self.forward(*input, **kwargs)                                                           
  File "/home/stian/Projects/sparseml/integrations/ultralytics-yolov5/yolov5/models/common.py", line 138, in forward                                                                                        
    return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))                                                                                                                                   
  File "/home/stian/miniconda3/envs/yolov5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl                                                                                 
    result = self.forward(*input, **kwargs)                                                           
  File "/home/stian/Projects/sparseml/integrations/ultralytics-yolov5/yolov5/models/common.py", line 42, in forward                                                                                         
    return self.act(self.bn(self.conv(x)))                                                            
  File "/home/stian/miniconda3/envs/yolov5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl                                                                                 
    result = self.forward(*input, **kwargs)                                                           
  File "/home/stian/Projects/sparseml/integrations/ultralytics-yolov5/yolov5/utils/activations.py", line 38, in forward                                                                                     
    return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX                     
  File "/home/stian/miniconda3/envs/yolov5/lib/python3.7/site-packages/torch/nn/functional.py", line 1188, in hardtanh                                                                                      
    result = torch._C._nn.hardtanh(input, min_val, max_val)                                           
RuntimeError: CUDA out of memory. Tried to allocate 14.00 MiB (GPU 0; 10.92 GiB total capacity; 9.99 GiB already allocated; 12.69 MiB free; 10.22 GiB reserved in total by PyTorch)

Epoch progress bars not displayed during torch DDP pruning using scripts/pytorch_vision.py

Describe the bug
After training begins using torch.distributed's DistributedDataParallel with scripts/pytorch_vision.py, console output stops while parallel training occurs in background. Tensorboard logging also does not write any updates. This is likely due to the logic for which node logs updates.
Training is still running as the gpu memory usage is listed under nvidia-smi

Expected behavior
Epoch and modifier progress should be logged to the terminal as well as tensorboard.

Environment
Include all relevant environment information:

OS [e.g. Ubuntu 18.04]: ubuntu 16.04
Python version [e.g. 3.7]: 3.6
SparseML version or commit hash [e.g. 0.1.0, f7245c8]: 6f77f0a
ML framework version(s) [e.g. torch 1.7.1]: torch 1.7.1
Other Python package versions [e.g. SparseZoo, DeepSparse, numpy, ONNX]: n/a
Other relevant environment information [e.g. hardware, CUDA version]: 4x NVIDIA GPUs

To Reproduce
Exact steps to reproduce the behavior:

python -m torch.distributed.launch \
  --nproc_per_node 4 \
  scripts/pytorch_vision.py train \
    --use-mixed-precision \
    --recipe-path pruning_resnet50_imagenet.yaml \
    --arch-key resnet50 \
   ...
    --save-dir sparisfy_test \
    --logs-dir sparisfy_test \
    --model-tag resnet50-imagenet \
    --pretrained True \
    --save-best-after 34 \

Errors
No errors, just no logging.

Additional context

Pruning Implementation

Hi,

I was wondering, how did you implement the unstructured pruning method?
Did you use a mask matrix to set the parameters to zero? And how did you prevent the update of those parameters set to zero?
And in general, how do you get speed from compression and evaluate on CPU as fast as on GPU?
Thank you in advance!

Help with implementing YoloV5 and SparseML

Hello,
I have been trying to implement SparseML on YoloV5, and the Readme says there is a tutorial coming soon. Would you be able to give me some tips or a small tutorial on how to implement SparseML on YoloV5?

does sparseml support calculation of memory consumption per sample?

Hi, I wonder if sparseml provides tools to calculate the memory consumption per sample? I mean the feature map size in every layer. For the standard feature map size calculation I know there are some tools. I don't know if sparseml has some unstructural pruning techniques which could enable less feature map memory consumption. If sparseml has such kind of pruning techniques I don't know some tools like torchstat could still calculate the correct feature map memory.

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