Set warmup and tuning iterations through env variable for hipBLASLt

[ahsan-ca]

This is a draft PR with changes to test different number of warmup and tuning iterations for hipBLASLt.

[ahsan-ca]

@causten In case you'd like to change the iterations for the models you are trying this out with:
cold_calls alters warmup iterations.
hot_calls alters tuning iterations.

[causten]

Your testing will take a while if you hardcode the values. You'll need to rebuild the code each time. If you allow env variables then you would cut down on recompiling the various scenarios

[ahsan-ca]

Added the env variables.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 92.29%. Comparing base (5dc0199) to head (50ec9a0).

Additional details and impacted files

@@           Coverage Diff            @@
##           develop    #3792   +/-   ##
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  Coverage    92.29%   92.29%           
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  Files          519      519           
  Lines        22233    22233           
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  Hits         20520    20520           
  Misses        1713     1713           

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[migraphx-bot]

Test	Batch	Rate new 50ec9a	Rate old 5dc019	Diff	Compare
torchvision-resnet50	64	3,233.07	3,229.26	0.12%	✅
torchvision-resnet50_fp16	64	6,864.21	6,862.73	0.02%	✅
torchvision-densenet121	32	2,430.89	2,429.91	0.04%	✅
torchvision-densenet121_fp16	32	4,167.69	4,173.61	-0.14%	✅
torchvision-inceptionv3	32	1,612.25	1,611.02	0.08%	✅
torchvision-inceptionv3_fp16	32	2,683.80	2,683.26	0.02%	✅
cadene-inceptionv4	16	749.16	749.06	0.01%	✅
cadene-resnext64x4	16	808.50	808.64	-0.02%	✅
slim-mobilenet	64	6,659.37	6,657.58	0.03%	✅
slim-nasnetalarge	64	198.93	198.90	0.01%	✅
slim-resnet50v2	64	3,429.01	3,424.77	0.12%	✅
bert-mrpc-onnx	8	1,139.23	1,138.15	0.09%	✅
bert-mrpc-tf	1	470.41	470.57	-0.04%	✅
pytorch-examples-wlang-gru	1	426.61	425.82	0.18%	✅
pytorch-examples-wlang-lstm	1	460.12	397.58	15.73%	🔆
torchvision-resnet50_1	1	784.25	789.46	-0.66%	✅
cadene-dpn92_1	1	416.68	415.00	0.41%	✅
cadene-resnext101_1	1	389.67	389.35	0.08%	✅
onnx-taau-downsample	1	373.16	372.43	0.20%	✅
dlrm-criteoterabyte	1	30.55	30.53	0.05%	✅
dlrm-criteoterabyte_fp16	1	49.15	49.09	0.12%	✅
agentmodel	1	7,745.75	7,273.63	6.49%	🔆
unet_fp16	2	57.74	57.82	-0.15%	✅
resnet50v1_fp16	1	957.53	978.89	-2.18%	✅
resnet50v1_int8	1	787.43	781.27	0.79%	✅
bert_base_cased_fp16	64	1,172.27	1,171.32	0.08%	✅
bert_large_uncased_fp16	32	362.45	362.15	0.08%	✅
bert_large_fp16	1	199.68	198.55	0.57%	✅
distilgpt2_fp16	16	2,214.44	2,214.29	0.01%	✅
yolov5s	1	527.42	515.26	2.36%	✅
tinyllama	1	43.46	43.42	0.08%	✅
vicuna-fastchat	1	43.75	43.81	-0.14%	✅
whisper-tiny-encoder	1	410.78	410.65	0.03%	✅
whisper-tiny-decoder	1	407.57	406.71	0.21%	✅

Check results before merge 🔆

[migraphx-bot]

     ✅ bert-mrpc-onnx: PASSED: MIGraphX meets tolerance

     ✅ bert-mrpc-tf: PASSED: MIGraphX meets tolerance

     ✅ pytorch-examples-wlang-gru: PASSED: MIGraphX meets tolerance

     ✅ pytorch-examples-wlang-lstm: PASSED: MIGraphX meets tolerance

     ✅ torchvision-resnet50_1: PASSED: MIGraphX meets tolerance

     ✅ cadene-dpn92_1: PASSED: MIGraphX meets tolerance

     ✅ cadene-resnext101_1: PASSED: MIGraphX meets tolerance

     ✅ dlrm-criteoterabyte: PASSED: MIGraphX meets tolerance

     ✅ agentmodel: PASSED: MIGraphX meets tolerance

     ✅ unet: PASSED: MIGraphX meets tolerance

     ✅ resnet50v1: PASSED: MIGraphX meets tolerance

     ✅ bert_base_cased_fp16: PASSED: MIGraphX meets tolerance

🔴bert_large_uncased_fp16: FAILED: MIGraphX is not within tolerance - check verbose output

     ✅ bert_large: PASSED: MIGraphX meets tolerance

     ✅ yolov5s: PASSED: MIGraphX meets tolerance

     ✅ tinyllama: PASSED: MIGraphX meets tolerance

     ✅ vicuna-fastchat: PASSED: MIGraphX meets tolerance

     ✅ whisper-tiny-encoder: PASSED: MIGraphX meets tolerance

     ✅ whisper-tiny-decoder: PASSED: MIGraphX meets tolerance

     ✅ distilgpt2_fp16: PASSED: MIGraphX meets tolerance