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[Doc] update results (#5105)

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J-shang 2022-09-06 15:16:15 +08:00 коммит произвёл GitHub
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3
docs/source/conf.py
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@ -111,7 +111,8 @@ linkcheck_ignore = [
r'https://1drv\.ms/', # OneDrive (shortcut)
r'https://onedrive\.live\.com/', # OneDrive
r'https://www\.openml\.org/', # OpenML
r'https://ml\.informatik\.uni-freiburg\.de/'
r'https://ml\.informatik\.uni-freiburg\.de/',
r'https://docs\.nvidia\.com/deeplearning/',
]
# Ignore all links located in release.rst

2
docs/source/tutorials/pruning_bert_glue.ipynb сгенерированный
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@ -177,7 +177,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Result\nThe speedup is test on the entire validation dataset with batch size 32 on A100.\nWe test under two pytorch version and found the latency varying widely.\n\nSetting 1: pytorch 1.12.1\n\nSetting 2: pytorch 1.10.0\n\n.. list-table:: Prune Bert-base-uncased on MNLI\n :header-rows: 1\n :widths: auto\n\n * - Attention Pruning Method\n - FFN Pruning Method\n - Total Sparsity\n - Accuracy\n - Acc. Drop\n - Speedup (S1)\n - Speedup (S2)\n * -\n -\n - 0%\n - 84.73 / 84.63\n - +0.0 / +0.0\n - 12.56s (x1.00)\n - 4.05s (x1.00)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=5)\n - `taylor-fo-weight-pruner`\n - 51.39%\n - 84.25 / 84.96\n - -0.48 / +0.33\n - 6.85s (x1.83)\n - 2.7s (x1.50)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=10)\n - `taylor-fo-weight-pruner`\n - 66.67%\n - 83.98 / 83.75\n - -0.75 / -0.88\n - 4.73s (x2.66)\n - 2.16s (x1.86)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=20)\n - `taylor-fo-weight-pruner`\n - 77.78%\n - 83.02 / 83.06\n - -1.71 / -1.57\n - 3.35s (x3.75)\n - 1.72s (x2.35)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=30)\n - `taylor-fo-weight-pruner`\n - 87.04%\n - 81.24 / 80.99\n - -3.49 / -3.64\n - 2.19s (x5.74)\n - 1.31s (x3.09)\n\n"
"## Result\nThe speedup is test on the entire validation dataset with batch size 128 on A100.\nWe test under two pytorch version and found the latency varying widely.\n\nSetting 1: pytorch 1.12.1\n\nSetting 2: pytorch 1.10.0\n\n.. list-table:: Prune Bert-base-uncased on MNLI\n :header-rows: 1\n :widths: auto\n\n * - Attention Pruning Method\n - FFN Pruning Method\n - Total Sparsity\n - Accuracy\n - Acc. Drop\n - Speedup (S1)\n - Speedup (S2)\n * -\n -\n - 85.1M (-0.0%)\n - 84.85 / 85.28\n - +0.0 / +0.0\n - 25.60s (x1.00)\n - 8.10s (x1.00)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=1)\n - `taylor-fo-weight-pruner`\n - 54.1M (-36.43%)\n - 85.38 / 85.41\n - +0.53 / +0.13\n - 17.93s (x1.43)\n - 7.22s (x1.12)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=5)\n - `taylor-fo-weight-pruner`\n - 37.1M (-56.40%)\n - 84.73 / 85.12\n - -0.12 / -0.16\n - 12.83s (x2.00)\n - 5.61s (x1.44)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=10)\n - `taylor-fo-weight-pruner`\n - 24.1M (-71.68%)\n - 84.14 / 84.78\n - -0.71 / -0.50\n - 8.93s (x2.87)\n - 4.55s (x1.78)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=20)\n - `taylor-fo-weight-pruner`\n - 14.3M (-83.20%)\n - 83.26 / 82.96\n - -1.59 / -2.32\n - 5.98s (x4.28)\n - 3.56s (x2.28)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=30)\n - `taylor-fo-weight-pruner`\n - 9.9M (-88.37%)\n - 82.22 / 82.19\n - -2.63 / -3.09\n - 4.36s (x5.88)\n - 3.12s (x2.60)\n * - `movement-pruner` (soft, sparsity=0.1, regular_scale=40)\n - `taylor-fo-weight-pruner`\n - 8.8M (-89.66%)\n - 81.64 / 82.39\n - -3.21 / -2.89\n - 3.88s (x6.60)\n - 2.81s (x2.88)\n\n"
]
}
],

64
docs/source/tutorials/pruning_bert_glue.py сгенерированный
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@ -537,7 +537,7 @@ for current_epoch in range(total_epochs):
# %%
# Result
# ------
# The speedup is test on the entire validation dataset with batch size 32 on A100.
# The speedup is test on the entire validation dataset with batch size 128 on A100.
# We test under two pytorch version and found the latency varying widely.
#
# Setting 1: pytorch 1.12.1
@ -557,36 +557,50 @@ for current_epoch in range(total_epochs):
# - Speedup (S2)
# * -
# -
# - 0%
# - 84.73 / 84.63
# - 85.1M (-0.0%)
# - 84.85 / 85.28
# - +0.0 / +0.0
# - 12.56s (x1.00)
# - 4.05s (x1.00)
# - 25.60s (x1.00)
# - 8.10s (x1.00)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=1)
# - :ref:`taylor-fo-weight-pruner`
# - 54.1M (-36.43%)
# - 85.38 / 85.41
# - +0.53 / +0.13
# - 17.93s (x1.43)
# - 7.22s (x1.12)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=5)
# - :ref:`taylor-fo-weight-pruner`
# - 51.39%
# - 84.25 / 84.96
# - -0.48 / +0.33
# - 6.85s (x1.83)
# - 2.7s (x1.50)
# - 37.1M (-56.40%)
# - 84.73 / 85.12
# - -0.12 / -0.16
# - 12.83s (x2.00)
# - 5.61s (x1.44)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=10)
# - :ref:`taylor-fo-weight-pruner`
# - 66.67%
# - 83.98 / 83.75
# - -0.75 / -0.88
# - 4.73s (x2.66)
# - 2.16s (x1.86)
# - 24.1M (-71.68%)
# - 84.14 / 84.78
# - -0.71 / -0.50
# - 8.93s (x2.87)
# - 4.55s (x1.78)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=20)
# - :ref:`taylor-fo-weight-pruner`
# - 77.78%
# - 83.02 / 83.06
# - -1.71 / -1.57
# - 3.35s (x3.75)
# - 1.72s (x2.35)
# - 14.3M (-83.20%)
# - 83.26 / 82.96
# - -1.59 / -2.32
# - 5.98s (x4.28)
# - 3.56s (x2.28)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=30)
# - :ref:`taylor-fo-weight-pruner`
# - 87.04%
# - 81.24 / 80.99
# - -3.49 / -3.64
# - 2.19s (x5.74)
# - 1.31s (x3.09)
# - 9.9M (-88.37%)
# - 82.22 / 82.19
# - -2.63 / -3.09
# - 4.36s (x5.88)
# - 3.12s (x2.60)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=40)
# - :ref:`taylor-fo-weight-pruner`
# - 8.8M (-89.66%)
# - 81.64 / 82.39
# - -3.21 / -2.89
# - 3.88s (x6.60)
# - 2.81s (x2.88)

2
docs/source/tutorials/pruning_bert_glue.py.md5 сгенерированный
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@ -1 +1 @@
4935f5727dd073c91bcfab8b9f0676d7
d3191675dd9427c6906f2bd3929ee382

68
docs/source/tutorials/pruning_bert_glue.rst сгенерированный
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@ -643,11 +643,11 @@ NNI will support per-step-pruning-schedule in the future, then can use an pruner
.. GENERATED FROM PYTHON SOURCE LINES 538-593
.. GENERATED FROM PYTHON SOURCE LINES 538-607
Result
------
The speedup is test on the entire validation dataset with batch size 32 on A100.
The speedup is test on the entire validation dataset with batch size 128 on A100.
We test under two pytorch version and found the latency varying widely.
Setting 1: pytorch 1.12.1
@ -667,44 +667,58 @@ Setting 2: pytorch 1.10.0
- Speedup (S2)
* -
-
- 0%
- 84.73 / 84.63
- 85.1M (-0.0%)
- 84.85 / 85.28
- +0.0 / +0.0
- 12.56s (x1.00)
- 4.05s (x1.00)
- 25.60s (x1.00)
- 8.10s (x1.00)
* - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=1)
- :ref:`taylor-fo-weight-pruner`
- 54.1M (-36.43%)
- 85.38 / 85.41
- +0.53 / +0.13
- 17.93s (x1.43)
- 7.22s (x1.12)
* - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=5)
- :ref:`taylor-fo-weight-pruner`
- 51.39%
- 84.25 / 84.96
- -0.48 / +0.33
- 6.85s (x1.83)
- 2.7s (x1.50)
- 37.1M (-56.40%)
- 84.73 / 85.12
- -0.12 / -0.16
- 12.83s (x2.00)
- 5.61s (x1.44)
* - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=10)
- :ref:`taylor-fo-weight-pruner`
- 66.67%
- 83.98 / 83.75
- -0.75 / -0.88
- 4.73s (x2.66)
- 2.16s (x1.86)
- 24.1M (-71.68%)
- 84.14 / 84.78
- -0.71 / -0.50
- 8.93s (x2.87)
- 4.55s (x1.78)
* - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=20)
- :ref:`taylor-fo-weight-pruner`
- 77.78%
- 83.02 / 83.06
- -1.71 / -1.57
- 3.35s (x3.75)
- 1.72s (x2.35)
- 14.3M (-83.20%)
- 83.26 / 82.96
- -1.59 / -2.32
- 5.98s (x4.28)
- 3.56s (x2.28)
* - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=30)
- :ref:`taylor-fo-weight-pruner`
- 87.04%
- 81.24 / 80.99
- -3.49 / -3.64
- 2.19s (x5.74)
- 1.31s (x3.09)
- 9.9M (-88.37%)
- 82.22 / 82.19
- -2.63 / -3.09
- 4.36s (x5.88)
- 3.12s (x2.60)
* - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=40)
- :ref:`taylor-fo-weight-pruner`
- 8.8M (-89.66%)
- 81.64 / 82.39
- -3.21 / -2.89
- 3.88s (x6.60)
- 2.81s (x2.88)
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 41.637 seconds)
**Total running time of the script:** ( 0 minutes 20.822 seconds)
.. _sphx_glr_download_tutorials_pruning_bert_glue.py:

Двоичные данные
docs/source/tutorials/pruning_bert_glue_codeobj.pickle сгенерированный
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Двоичный файл не отображается.

4
docs/source/tutorials/sg_execution_times.rst сгенерированный
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@ -5,10 +5,10 @@
Computation times
=================
**01:51.710** total execution time for **tutorials** files:
**00:20.822** total execution time for **tutorials** files:
+-----------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorials_pruning_bert_glue.py` (``pruning_bert_glue.py``) | 00:00.000 | 0.0 MB |
| :ref:`sphx_glr_tutorials_pruning_bert_glue.py` (``pruning_bert_glue.py``) | 00:20.822 | 0.0 MB |
+-----------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_tutorials_darts.py` (``darts.py``) | 01:51.710 | 0.0 MB |
+-----------------------------------------------------------------------------------------------------+-----------+--------+

64
examples/tutorials/pruning_bert_glue.py
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@ -537,7 +537,7 @@ for current_epoch in range(total_epochs):
# %%
# Result
# ------
# The speedup is test on the entire validation dataset with batch size 32 on A100.
# The speedup is test on the entire validation dataset with batch size 128 on A100.
# We test under two pytorch version and found the latency varying widely.
#
# Setting 1: pytorch 1.12.1
@ -557,36 +557,50 @@ for current_epoch in range(total_epochs):
# - Speedup (S2)
# * -
# -
# - 0%
# - 84.73 / 84.63
# - 85.1M (-0.0%)
# - 84.85 / 85.28
# - +0.0 / +0.0
# - 12.56s (x1.00)
# - 4.05s (x1.00)
# - 25.60s (x1.00)
# - 8.10s (x1.00)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=1)
# - :ref:`taylor-fo-weight-pruner`
# - 54.1M (-36.43%)
# - 85.38 / 85.41
# - +0.53 / +0.13
# - 17.93s (x1.43)
# - 7.22s (x1.12)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=5)
# - :ref:`taylor-fo-weight-pruner`
# - 51.39%
# - 84.25 / 84.96
# - -0.48 / +0.33
# - 6.85s (x1.83)
# - 2.7s (x1.50)
# - 37.1M (-56.40%)
# - 84.73 / 85.12
# - -0.12 / -0.16
# - 12.83s (x2.00)
# - 5.61s (x1.44)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=10)
# - :ref:`taylor-fo-weight-pruner`
# - 66.67%
# - 83.98 / 83.75
# - -0.75 / -0.88
# - 4.73s (x2.66)
# - 2.16s (x1.86)
# - 24.1M (-71.68%)
# - 84.14 / 84.78
# - -0.71 / -0.50
# - 8.93s (x2.87)
# - 4.55s (x1.78)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=20)
# - :ref:`taylor-fo-weight-pruner`
# - 77.78%
# - 83.02 / 83.06
# - -1.71 / -1.57
# - 3.35s (x3.75)
# - 1.72s (x2.35)
# - 14.3M (-83.20%)
# - 83.26 / 82.96
# - -1.59 / -2.32
# - 5.98s (x4.28)
# - 3.56s (x2.28)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=30)
# - :ref:`taylor-fo-weight-pruner`
# - 87.04%
# - 81.24 / 80.99
# - -3.49 / -3.64
# - 2.19s (x5.74)
# - 1.31s (x3.09)
# - 9.9M (-88.37%)
# - 82.22 / 82.19
# - -2.63 / -3.09
# - 4.36s (x5.88)
# - 3.12s (x2.60)
# * - :ref:`movement-pruner` (soft, sparsity=0.1, regular_scale=40)
# - :ref:`taylor-fo-weight-pruner`
# - 8.8M (-89.66%)
# - 81.64 / 82.39
# - -3.21 / -2.89
# - 3.88s (x6.60)
# - 2.81s (x2.88)