Do not run hip gemm pointwise fusion for quant_dot

[ahsan-ca]

This PR makes change to ensure that hip gemm pointwise fusion
is performed for dot, and not for quant_dot.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 92.23%. Comparing base (2e59073) to head (567c4a6).

Additional details and impacted files

@@           Coverage Diff            @@
##           develop    #3702   +/-   ##
========================================
  Coverage    92.23%   92.23%           
========================================
  Files          514      514           
  Lines        21746    21746           
========================================
  Hits         20057    20057           
  Misses        1689     1689           

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[migraphx-bot]

Test	Batch	Rate new 567c4a	Rate old 64fe0c	Diff	Compare
torchvision-resnet50	64	3,256.04	3,254.94	0.03%	✅
torchvision-resnet50_fp16	64	6,989.29	6,977.96	0.16%	✅
torchvision-densenet121	32	2,435.79	2,436.55	-0.03%	✅
torchvision-densenet121_fp16	32	4,044.49	4,076.26	-0.78%	✅
torchvision-inceptionv3	32	1,627.34	1,627.46	-0.01%	✅
torchvision-inceptionv3_fp16	32	2,743.15	2,741.58	0.06%	✅
cadene-inceptionv4	16	764.85	764.31	0.07%	✅
cadene-resnext64x4	16	813.71	813.14	0.07%	✅
slim-mobilenet	64	7,470.82	7,466.83	0.05%	✅
slim-nasnetalarge	64	209.08	209.03	0.03%	✅
slim-resnet50v2	64	3,440.88	3,443.32	-0.07%	✅
bert-mrpc-onnx	8	1,147.99	1,144.17	0.33%	✅
bert-mrpc-tf	1	484.60	474.21	2.19%	✅
pytorch-examples-wlang-gru	1	421.57	416.53	1.21%	✅
pytorch-examples-wlang-lstm	1	403.21	384.23	4.94%	🔆
torchvision-resnet50_1	1	768.62	783.29	-1.87%	✅
cadene-dpn92_1	1	400.45	398.94	0.38%	✅
cadene-resnext101_1	1	382.85	383.46	-0.16%	✅
onnx-taau-downsample	1	345.62	345.52	0.03%	✅
dlrm-criteoterabyte	1	33.33	33.33	0.01%	✅
dlrm-criteoterabyte_fp16	1	52.73	52.73	-0.01%	✅
agentmodel	1	8,141.53	8,127.83	0.17%	✅
unet_fp16	2	58.76	58.89	-0.21%	✅
resnet50v1_fp16	1	1,058.42	938.63	12.76%	🔆
resnet50v1_int8	1	1,009.30	984.73	2.50%	✅
bert_base_cased_fp16	64	1,168.81	1,170.23	-0.12%	✅
bert_large_uncased_fp16	32	363.00	362.94	0.02%	✅
bert_large_fp16	1	200.22	200.28	-0.03%	✅
distilgpt2_fp16	16	2,199.43	2,198.50	0.04%	✅
yolov5s	1	531.57	531.33	0.05%	✅
tinyllama	1	43.64	43.34	0.68%	✅
vicuna-fastchat	1	174.38	172.03	1.37%	✅
whisper-tiny-encoder	1	417.58	418.00	-0.10%	✅
whisper-tiny-decoder	1	429.18	428.83	0.08%	✅

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