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feat: IGL save resume #4554

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merged 12 commits into from Apr 14, 2023
Merged

feat: IGL save resume #4554

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f0f9ef2
Save resume
cheng-tan Mar 29, 2023
7c01149
Fix linter
cheng-tan Apr 5, 2023
84e53fd
Move read/write ftrl model field to ftrl files
cheng-tan Apr 7, 2023
753b6cf
Add prediction printing to IGL reduction
cheng-tan Apr 7, 2023
22c7b1a
Add tests for igl save load
cheng-tan Apr 10, 2023
0410716
linter
cheng-tan Apr 10, 2023
02b756c
linter
cheng-tan Apr 10, 2023
9e35fff
Disable IGL dsjson test in runtests
cheng-tan Apr 10, 2023
9bc0314
Disable flatbuffer test
cheng-tan Apr 10, 2023
563d047
Fix run_tests.py
cheng-tan Apr 10, 2023
0816837
Fix comment
cheng-tan Apr 11, 2023
7cbe31c
Merge branch 'master' into igl-save-resume
cheng-tan Apr 13, 2023
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2 changes: 1 addition & 1 deletion cs/unittest/RunTests.tt
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Expand Up @@ -40,7 +40,7 @@ var skipList = new[] { 13, 32, 39, 258, 40, 259, 41, 260, 59, 60, 61, 66, 68, 90
256, 299, 300, 306, 310, 311, 327, 328, 329, 330, 331, 367, 368, 396, 397, 398, // DSJSON not supported
383, 389, 390, 391, 392, 393, // no data file
400, 404, // positional args
405, 406, 407, 411, 415, 417 // DSJSON not supported
405, 406, 407, 411, 415, 417, 456, 457 // DSJSON not supported
};

var outputModels = new Dictionary<string, TestCase>();
Expand Down
33 changes: 32 additions & 1 deletion test/core.vwtest.json
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Expand Up @@ -5891,10 +5891,41 @@
"desc": "large action spaces with cb_explore_adf epsilon greedy and ips",
"vw_command": "--cb_explore_adf -d train-sets/las_100_actions.txt --noconstant --large_action_space --cb_type ips",
"diff_files": {
"stderr": "train-sets/ref/las_egreedy_ips.stderr"
"stderr": "train-sets/ref/las_egreedy_ips.stderr"
},
"input_files": [
"train-sets/las_100_actions.txt"
]
},
{
"id": 456,
"desc": "Igl save/load #1",
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"vw_command": "--dsjson --cb_explore_adf --epsilon 0.2 -q UA --coin --experimental_igl -d train-sets/igl_3states_1.dsjson -f igl_1.vw -p igl_1.pred",
"diff_files": {
"stderr": "train-sets/ref/igl_3states_1.stderr",
"stdout": "train-sets/ref/igl_result.stdout",
"igl_1.pred": "pred-sets/ref/igl_1.pred"
},
"input_files": [
"train-sets/igl_3states_1.dsjson"
]
},
{
"id": 457,
"desc": "Igl save/load #2",
"vw_command": "--dsjson --cb_explore_adf --epsilon 0.2 -q UA --coin --experimental_igl -d train-sets/igl_3states_2.dsjson -i igl_1.vw -p igl_2.pred --readable_model igl_2.readable",
"diff_files": {
"stderr": "train-sets/ref/igl_3states_2.stderr",
"stdout": "train-sets/ref/igl_save_load.stdout",
"igl_2.pred": "pred-sets/ref/igl_2.pred",
"igl_2.readable": "train-sets/ref/igl_2.readable"
},
"input_files": [
"train-sets/igl_3states_2.dsjson",
"igl_1.vw"
],
"depends_on": [
456
]
}
]
20 changes: 20 additions & 0 deletions test/pred-sets/ref/igl_1.pred
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@@ -0,0 +1,20 @@
0:0.25,1:0.25,2:0.25,3:0.25

0:0.25,1:0.25,2:0.25,3:0.25

0:0.25,1:0.25,2:0.25,3:0.25

0:0.25,1:0.25,2:0.25,3:0.25

0:0.25,1:0.25,2:0.25,3:0.25

0:0.25,1:0.25,2:0.25,3:0.25

0:0.25,1:0.25,2:0.25,3:0.25

0:0.25,1:0.25,2:0.25,3:0.25

2:0.85,0:0.05,1:0.05,3:0.05

2:0.85,0:0.05,1:0.05,3:0.05

20 changes: 20 additions & 0 deletions test/pred-sets/ref/igl_2.pred
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@@ -0,0 +1,20 @@
0:0.316667,1:0.316667,3:0.316667,2:0.05

0:0.85,1:0.05,3:0.05,2:0.05

0:0.85,1:0.05,3:0.05,2:0.05

0:0.85,2:0.05,1:0.05,3:0.05

0:0.85,2:0.05,1:0.05,3:0.05

0:0.85,1:0.05,3:0.05,2:0.05

0:0.85,1:0.05,2:0.05,3:0.05

0:0.85,1:0.05,3:0.05,2:0.05

0:0.85,1:0.05,3:0.05,2:0.05

0:0.85,1:0.05,3:0.05,2:0.05

4 changes: 3 additions & 1 deletion test/run_tests.py
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Expand Up @@ -770,7 +770,7 @@ def convert_tests_for_flatbuffers(
)
continue
# todo: 300 understand why is it failing
# test 189, 312, 316, 318, 351, 438, 394 and 319 depend on dsjson parser behaviour
# test 189, 312, 316, 318, 319, 351, 367, 368, 394, 438, 456, 457 depend on dsjson parser behaviour
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# they can be enabled if we ignore diffing the --extra_metrics
# (324-326) deals with corrupted data, so cannot be translated to fb
# pdrop is not supported in fb, so 327-331 are excluded
Expand Down Expand Up @@ -813,6 +813,8 @@ def convert_tests_for_flatbuffers(
"444",
"450",
"452",
"456",
"457",
):
test.skip = True
test.skip_reason = "test skipped for automatic converted flatbuffer tests for unknown reason"
Expand Down
10 changes: 10 additions & 0 deletions test/train-sets/igl_3states_1.dsjson
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@@ -0,0 +1,10 @@
{"_label_cost": 0, "_label_probability": 0.25, "_label_Action": 4, "_labelIndex": 3, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=afternoon": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.25, 0.25, 0.25, 0.25], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.25, "_label_Action": 2, "_labelIndex": 1, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Tom": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.25, 0.25, 0.25, 0.25], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.25, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=click": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Tom": 1, "time_of_day=afternoon": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.25, 0.25, 0.25, 0.25], "_original_label_cost": 0}
{"_label_cost": -1.0, "_label_probability": 0.25, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=like": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Tom": 1, "time_of_day=afternoon": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.25, 0.25, 0.25, 0.25], "_original_label_cost": -0.5}
{"_label_cost": 0, "_label_probability": 0.25, "_label_Action": 2, "_labelIndex": 1, "o": [{"v": {"v=skip": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Tom": 1, "time_of_day=afternoon": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.25, 0.25, 0.25, 0.25], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.25, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.25, 0.25, 0.25, 0.25], "_original_label_cost": 0}
{"_label_cost": -1.0, "_label_probability": 0.8500000016763806, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=click": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Tom": 1, "time_of_day=afternoon": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.04999999944120647, 0.04999999944120647, 0.8500000016763806, 0.04999999944120647], "_original_label_cost": -0.5}
{"_label_cost": 0, "_label_probability": 0.8500000016763806, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.04999999944120647, 0.04999999944120647, 0.8500000016763806, 0.04999999944120647], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.3166666677221655, "_label_Action": 1, "_labelIndex": 0, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.3166666677221655, 0.3166666677221655, 0.04999999683350349, 0.3166666677221655], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.8500000016763806, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=afternoon": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.04999999944120647, 0.04999999944120647, 0.8500000016763806, 0.04999999944120647], "_original_label_cost": 0}
10 changes: 10 additions & 0 deletions test/train-sets/igl_3states_2.dsjson
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{"_label_cost": 0, "_label_probability": 0.3166666677221655, "_label_Action": 1, "_labelIndex": 0, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.3166666677221655, 0.3166666677221655, 0.04999999683350349, 0.3166666677221655], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.3166666677221655, "_label_Action": 2, "_labelIndex": 1, "o": [{"v": {"v=click": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.3166666677221655, 0.3166666677221655, 0.04999999683350349, 0.3166666677221655], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.45000000111758703, "_label_Action": 4, "_labelIndex": 3, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.45000000111758703, 0.04999999888241295, 0.04999999888241295, 0.45000000111758703], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.04999999944120647, "_label_Action": 4, "_labelIndex": 3, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Tom": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.04999999944120647, 0.04999999944120647, 0.8500000016763806, 0.04999999944120647], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.8500000016763806, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=afternoon": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.04999999944120647, 0.04999999944120647, 0.8500000016763806, 0.04999999944120647], "_original_label_cost": 0}
{"_label_cost": -1.0, "_label_probability": 0.04999999944120647, "_label_Action": 4, "_labelIndex": 3, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Tom": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.04999999944120647, 0.04999999944120647, 0.8500000016763806, 0.04999999944120647], "_original_label_cost": -0.5}
{"_label_cost": 0, "_label_probability": 0.8500000016763806, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Tom": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.04999999944120647, 0.04999999944120647, 0.8500000016763806, 0.04999999944120647], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.8500000016763806, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=afternoon": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.04999999944120647, 0.04999999944120647, 0.8500000016763806, 0.04999999944120647], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.8500000016763806, "_label_Action": 3, "_labelIndex": 2, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=afternoon": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.04999999944120647, 0.04999999944120647, 0.8500000016763806, 0.04999999944120647], "_original_label_cost": 0}
{"_label_cost": 0, "_label_probability": 0.45000000111758703, "_label_Action": 1, "_labelIndex": 0, "o": [{"v": {"v=none": 1}, "_definitely_bad": false}], "a": [0, 1, 2, 3], "c": {"User": {"user=Anna": 1, "time_of_day=morning": 1}, "_multi": [{"Action": {"action=politics": 1}}, {"Action": {"action=sports": 1}}, {"Action": {"action=music": 1}}, {"Action": {"action=food": 1}}]}, "p": [0.45000000111758703, 0.04999999888241295, 0.04999999888241295, 0.45000000111758703], "_original_label_cost": 0}
134 changes: 134 additions & 0 deletions test/train-sets/ref/igl_2.readable
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@@ -0,0 +1,134 @@
Version 9.8.0
Id
Min label:-1
Max label:1
bits:18
lda:0
0 ngram:
0 skip:
options: --cb_adf --cb_explore_adf --cb_type mtr --coin --csoaa_ldf multiline --csoaa_rank --epsilon 0.200000002980232 --experimental_igl --quadratic UA
Checksum: 3121739552
igl.pi.gd_per_model_states.size() = 1
igl.pi.gd_per_model_states[0]_normalized_sum_norm_x = 129.23013424873352
igl.pi.gd_per_model_states[0]_total_weight = 21.538355708122253
event_sum 0
action_sum 0
:1
initial_t 0
norm normalizer 1307.79
t 20
sum_loss -2.11765
sum_loss_since_last_dump -0.0588235
dump_interval 16
min_label -1
max_label 1
weighted_labeled_examples 10
weighted_labels 0
weighted_unlabeled_examples 0
example_number 10
total_features 280
total_weight 145.31
sd::oec.weighted_labeled_examples 8
current_pass 1
l1_state 0
l2_state 1
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159367:-0.374912 -0.0736163 2.29584 1 -1.65589 1.11111
162636:-0.0191232 -0.467518 1.74034 1 -0.704933 1
169194:0 -0.273395 0.273395 1 0 1
174124:0 -0.220204 0.220204 1 0 1
175906:-0.65242 -6.16794 6.16794 1 17.5315 1.76627
177472:-0.0525927 1.84721 4.16414 1 -0.308794 3.00568
194190:0.00109881 1.15039 54.4154 1 -2.42034 4.36713
195062:0 -2.53536 2.53536 1 0 2.53536
196264:0.0222109 3.85825 13.3002 1 -1.57445 3.30251
198403:-0.0181454 -0.293359 1.61321 1 -0.574762 1
198522:-0.0450209 -0.929767 3.55759 1 -0.965221 1
201038:0.0853859 7.02709 12.7687 1 2.898 4.24321
208092:0 -0.378745 0.378745 1 0 1
209060:0 -0.342453 0.342453 1 0 1
212146:0 -0.363413 0.363413 1 0 1
212266:-0.0151965 -3.24764 13.038 1 -1.00013 4.36713
216590:0.0767658 4.67911 6.63029 1 0.0649194 3.41527
225028:0.00100277 0.801102 45.569 1 -2.43254 4.36713
225029:-0.00862278 -11.2789 21.5085 1 -1.7962 15.209
226546:0.0126401 0.639065 6.36243 1 0.750951 3.50075
226948:0.0271454 6.57775 20.2441 1 -1.42637 3.30251
226949:-0.104827 -0.796456 2.28658 1 -0.533525 1
228076:-0.145777 -0.717649 0.717649 1 0.301177 1
229630:0 -0.230076 0.230076 1 0 1
230046:0.0191037 3.47951 13.679 1 -1.6483 3.30251
232120:0.000216762 -0.120813 67.6952 1 -2.09091 4.36713
232121:-0.00898405 -12.451 23.4195 1 -1.79197 15.209
242032:-0.00605899 -0.433612 1.63343 1 -0.672706 1
247227:-0.246914 -0.503097 0.673373 1 -0.12613 1
247283:0.00683661 -12.4037 18.0143 1 -0.617725 15.209
248182:0.00130444 3.09824 6.56511 1 -1.46193 3.00568
248502:-0.108249 -0.466135 0.466135 1 0.140439 1
250644:-0.32426 -5.19834 5.19834 1 7.15901 1.76627
258750:0 0.599911 0.599911 1 0 1
31 changes: 31 additions & 0 deletions test/train-sets/ref/igl_3states_1.stderr
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creating quadratic features for pairs: UA
final_regressor = igl_1.vw
predictions = igl_1.pred
Enabling FTRL based optimization
Algorithm used: Coin Betting
ftrl_alpha = 4
ftrl_beta = 1
using no cache
Reading datafile = train-sets/igl_3states_1.dsjson
num sources = 1
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
cb_type = mtr
Enabled learners: ftrl-Coin Betting, scorer-identity, csoaa_ldf-rank, cb_adf, cb_explore_adf_greedy, experimental_igl, shared_feature_merger
Input label = CB_WITH_OBSERVATIONS
Output pred = ACTION_PROBS
average since example example current current current
loss last counter weight label predict features
0.000000 0.000000 1 1.0 3:0:0.25 0:0.25 21
0.000000 0.000000 2 2.0 1:0:0.25 0:0.25 21
0.000000 0.000000 4 4.0 2:0:0.25 0:0.25 21
-0.03676 -0.07352 8 8.0 2:0:0.85 0:0.25 21

finished run
number of examples = 10
weighted example sum = 10.000000
weighted label sum = 0.000000
average loss = -0.029412
total feature number = 280