20220911-h13m58s51_conll2003_distilbert_quantization

fxmarty/20220911-h13m58s51_conll2003_distilbert_quantization

20220911-h13m58s51_conll2003_distilbert_quantization is a token classification model from fxmarty. Use it when you need labels on individual words, such as names.

task: token-classification Backend: sagemaker-training Backend args: {'instancetype': 'ml.m5.2xlarge', 'supportedinstructions': 'avx512'} Number of evaluation samples: All dataset

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Updated Sep 11, 2022

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Task: Token Classification
Access: Public
Created: Sep 11, 2022
Updated: Sep 11, 2022
SHA: 7b390521

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Datasets

conll2003

Model card

From the Hugging Face model README

task: token-classification
Backend: sagemaker-training
Backend args: {'instance_type': 'ml.m5.2xlarge', 'supported_instructions': 'avx512'}
Number of evaluation samples: All dataset

Fixed parameters:

dataset: [{'path': 'conll2003', 'eval_split': 'validation', 'data_keys': {'primary': 'tokens'}, 'ref_keys': ['ner_tags'], 'name': None, 'calibration_split': 'train'}]
name_or_path: elastic/distilbert-base-uncased-finetuned-conll03-english
from_transformers: True
operators_to_quantize: ['Add', 'MatMul']
calibration:
- method: percentile
- num_calibration_samples: 128
- calibration_histogram_percentile: 99.999

Benchmarked parameters:

framework: onnxruntime, pytorch
quantization_approach: dynamic, static
node_exclusion: [], ['layernorm', 'gelu', 'residual', 'gather', 'softmax']
per_channel: False, True
framework_args: {'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}, {}
reduce_range: True, False
apply_quantization: True, False

Evaluation

Non-time metrics

framework	quantization_approach	node_exclusion	per_channel	framework_args	reduce_range	apply_quantization		overall_precision		overall_recall		overall_f1		overall_accuracy
`onnxruntime`	`None`	`None`	`None`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`None`	`False`	\|	0.936	\|	0.944	\|	0.940	\|	0.988
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.935	\|	0.943	\|	0.939	\|	0.988
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	0.926	\|	0.931	\|	0.929	\|	0.987
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.000	\|	0.000	\|	0.000	\|	0.833
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	0.934	\|	0.944	\|	0.939	\|	0.988
`onnxruntime`	`dynamic`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.935	\|	0.943	\|	0.939	\|	0.988
`onnxruntime`	`dynamic`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	0.926	\|	0.931	\|	0.929	\|	0.987
`onnxruntime`	`dynamic`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.000	\|	0.000	\|	0.000	\|	0.833
`onnxruntime`	`dynamic`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	0.934	\|	0.944	\|	0.939	\|	0.988
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.913	\|	0.792	\|	0.848	\|	0.969
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	0.913	\|	0.792	\|	0.848	\|	0.969
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.000	\|	0.000	\|	0.000	\|	0.833
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	0.896	\|	0.783	\|	0.836	\|	0.968
`onnxruntime`	`static`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.925	\|	0.844	\|	0.883	\|	0.975
`onnxruntime`	`static`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	0.925	\|	0.844	\|	0.883	\|	0.975
`onnxruntime`	`static`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.045	\|	0.004	\|	0.008	\|	0.825
`onnxruntime`	`static`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	0.922	\|	0.839	\|	0.879	\|	0.975
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	0.936	\|	0.944	\|	0.940	\|	0.988

Time metrics

Time benchmarks were run for 15 seconds per config.

Below, time metrics for batch size = 1, input length = 32.

framework	quantization_approach	node_exclusion	per_channel	framework_args	reduce_range	apply_quantization		latency_mean (ms)		throughput (/s)
`onnxruntime`	`None`	`None`	`None`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`None`	`False`	\|	14.22	\|	70.33
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.22	\|	97.87
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.16	\|	98.47
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.52	\|	95.07
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.70	\|	93.47
`onnxruntime`	`dynamic`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.22	\|	97.87
`onnxruntime`	`dynamic`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.24	\|	97.67
`onnxruntime`	`dynamic`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.36	\|	96.53
`onnxruntime`	`dynamic`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.50	\|	95.27
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.98	\|	91.07
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	11.31	\|	88.47
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	11.23	\|	89.07
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	11.48	\|	87.20
`onnxruntime`	`static`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	13.54	\|	73.87
`onnxruntime`	`static`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	13.74	\|	72.80
`onnxruntime`	`static`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	13.80	\|	72.53
`onnxruntime`	`static`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	14.08	\|	71.07
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	31.23	\|	32.07

Below, time metrics for batch size = 1, input length = 64.

framework	quantization_approach	node_exclusion	per_channel	framework_args	reduce_range	apply_quantization		latency_mean (ms)		throughput (/s)
`onnxruntime`	`None`	`None`	`None`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`None`	`False`	\|	24.52	\|	40.80
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	18.47	\|	54.20
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	18.53	\|	54.00
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	18.85	\|	53.07
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	19.14	\|	52.27
`onnxruntime`	`dynamic`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	18.50	\|	54.07
`onnxruntime`	`dynamic`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	18.50	\|	54.07
`onnxruntime`	`dynamic`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	18.69	\|	53.53
`onnxruntime`	`dynamic`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	19.46	\|	51.40
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	20.42	\|	49.00
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	19.91	\|	50.27
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	20.20	\|	49.53
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	20.74	\|	48.27
`onnxruntime`	`static`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	24.91	\|	40.20
`onnxruntime`	`static`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	24.35	\|	41.13
`onnxruntime`	`static`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	24.99	\|	40.07
`onnxruntime`	`static`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	24.95	\|	40.13
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	41.31	\|	24.27

Below, time metrics for batch size = 1, input length = 128.

framework	quantization_approach	node_exclusion	per_channel	framework_args	reduce_range	apply_quantization		latency_mean (ms)		throughput (/s)
`onnxruntime`	`None`	`None`	`None`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`None`	`False`	\|	46.79	\|	21.40
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	35.84	\|	27.93
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.07	\|	28.53
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	35.71	\|	28.00
`onnxruntime`	`dynamic`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.91	\|	27.87
`onnxruntime`	`dynamic`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	35.42	\|	28.27
`onnxruntime`	`dynamic`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.22	\|	28.40
`onnxruntime`	`dynamic`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	35.51	\|	28.20
`onnxruntime`	`dynamic`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.90	\|	27.87
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	39.88	\|	25.13
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	39.27	\|	25.47
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	39.37	\|	25.40
`onnxruntime`	`static`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	39.16	\|	25.60
`onnxruntime`	`static`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	44.43	\|	22.53
`onnxruntime`	`static`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	46.13	\|	21.73
`onnxruntime`	`static`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	45.48	\|	22.00
`onnxruntime`	`static`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	45.82	\|	21.87
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	53.93	\|	18.60

Task: Token Classification
Created: Sep 11, 2022
Updated: Sep 11, 2022