[Doc] update results (#5105)

docs/source/conf.py

@@ -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

docs/source/tutorials/pruning_bert_glue.ipynb

@@ -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"
       ]
     }
   ],

docs/source/tutorials/pruning_bert_glue.py

@@ -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)

docs/source/tutorials/pruning_bert_glue.py.md5

-4935f5727dd073c91bcfab8b9f0676d7
\ No newline at end of file
+d3191675dd9427c6906f2bd3929ee382
\ No newline at end of file

docs/source/tutorials/pruning_bert_glue.rst

@@ -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:

docs/source/tutorials/sg_execution_times.rst

@@ -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 |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+

examples/tutorials/pruning_bert_glue.py

@@ -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)