[PTQ][OV] BF16 support

[KodiaqQ]

Changes

• Added BF16 type support
• Added FQ parameters generation based on type
• Extended the list of the supported types for OpenVINO input data with ov.Tensor

Reason for changes

• BF16 support

Related tickets

• 126782

Tests

• Updated existing tests with BF16.
• Edit: not updated due to lack of precision support in the OpenVINO

[codecov[bot]]

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 91.20%. Comparing base (f4bd077) to head (9471ac8).

Additional details and impacted files

@@           Coverage Diff            @@
##           develop    #2307   +/-   ##
========================================
  Coverage    91.19%   91.20%           
========================================
  Files          483      483           
  Lines        46443    46435    -8     
========================================
- Hits         42355    42351    -4     
+ Misses        4088     4084    -4     

Files	Coverage Δ
nncf/openvino/engine.py	96.55% <ø> (ø)
nncf/openvino/graph/model_transformer.py	94.84% <100.00%> (+0.82%)	:arrow_up:
nncf/openvino/graph/node_utils.py	98.80% <100.00%> (-0.02%)	:arrow_down:
nncf/openvino/graph/transformations/commands.py	97.67% <100.00%> (+0.11%)	:arrow_up:
nncf/openvino/quantization/quantize_ifmodel.py	100.00% <100.00%> (ø)
.../algorithms/weight_compression/openvino_backend.py	98.84% <100.00%> (-0.01%)	:arrow_down:

Flag	Coverage Δ
COMMON	41.93% <0.00%> (+<0.01%)	:arrow_up:
ONNX	34.19% <0.00%> (+<0.01%)	:arrow_up:
OPENVINO	40.98% <100.00%> (-0.01%)	:arrow_down:
TENSORFLOW	29.39% <0.00%> (+<0.01%)	:arrow_up:
TORCH	65.11% <7.54%> (+0.01%)	:arrow_up:

Flags with carried forward coverage won't be shown. Click here to find out more.

Components	Coverage Δ
common	93.54% <ø> (ø)
torch	93.65% <ø> (ø)
tensorflow	93.26% <ø> (ø)
onnx	93.06% <ø> (ø)
openvino	94.62% <100.00%> (+0.10%)	:arrow_up:
ptq	90.50% <100.00%> (-0.01%)	:arrow_down:

[KodiaqQ]

@alexsu52, @l-bat, please, review.

[KodiaqQ]

@l-bat, @alexsu52, @andrey-churkin, review, please.

[KodiaqQ]

Compression time comparison. Numbers were collected on local i9-10980XE.

Model	Backend	Compr. Time (develop)	Compr. Time (bf16 branch)
hf/bert-base-uncased	OV	00:00:15	00:00:15
timm/crossvit_9_240	OV	00:00:20	00:00:20
timm/darknet53	OV	00:00:44	00:00:32
timm/deit3_small_patch16_224	OV	00:00:25	00:00:25
timm/dla34	OV	00:00:15	00:00:15
timm/dpn68	OV	00:00:16	00:00:16
timm/efficientnet_b0_BC	OV	00:00:20	00:00:20
timm/efficientnet_b0	OV	00:00:12	00:00:12
timm/efficientnet_lite0	OV	00:00:11	00:00:11
timm/hrnet_w18	OV	00:01:20	00:01:11
timm/inception_resnet_v2	OV	00:01:23	00:01:13
timm/levit_128	OV	00:00:18	00:00:18
timm/mobilenetv2_050_BC	OV	00:00:14	00:00:14
timm/mobilenetv2_050	OV	00:00:09	00:00:10
timm/mobilenetv3_small_050_BC	OV	00:00:09	00:00:09
timm/mobilenetv3_small_050	OV	00:00:07	00:00:06
timm/regnetx_002	OV	00:00:09	00:00:08
timm/resnest14d	OV	00:00:16	00:00:15
timm/resnet18	OV	00:00:10	00:00:10
timm/swin_base_patch4_window7_224	OV	00:01:18	00:01:18
timm/tf_inception_v3	OV	00:00:31	00:00:31
timm/vgg11	OV	00:00:31	00:00:30
timm/visformer_small	OV	00:00:18	00:00:17
timm/wide_resnet50_2	OV	00:00:39	00:00:39

No degradations were observed.

[KodiaqQ]

I would suggest to check accuracy and performance of weight compression algorithms for FP32 and FP16 precision.

Here are the results of weight compression validation on the local machine - i9-10980XE:

Model	Backend	Metric name	Metric value (develop)	Metric value (bf16 branch)	Compr. Time (develop)	Compr. Time (bf16 branch)
tinyllama_data_aware	OV	Similarity	0,83853	0,83853	00:01:26	00:01:26
tinyllama_data_free	OV	Similarity	0,72057	0,72057	00:00:44	00:00:44

Also, here are the numbers from the examples/llm_compression/openvino/tiny_llama. Develop: BF16 branch:

There were no degradations observed. @alexsu52, how did you reproduce the issue?

[alexsu52]

I would suggest to check accuracy and performance of weight compression algorithms for FP32 and FP16 precision.

Here are the results of weight compression validation on the local machine - i9-10980XE: Model Backend Metric name Metric value (develop) Metric value (bf16 branch) Compr. Time (develop) Compr. Time (bf16 branch) tinyllama_data_aware OV Similarity 0,83853 0,83853 00:01:26 00:01:26 tinyllama_data_free OV Similarity 0,72057 0,72057 00:00:44 00:00:44

Also, here are the numbers from the examples/llm_compression/openvino/tiny_llama. Develop: BF16 branch:

There were no degradations observed. @alexsu52, how did you reproduce the issue?

I used model with FP16 weights.

[KodiaqQ]

@andrey-churkin, @l-bat, @kshpv, @alexsu52, @daniil-lyakhov, @andreyanufr, review, please.

[KodiaqQ]

FP16 model as input (bloomz-560m). Develop: Branch: