20220911-h13m58s53_squad_qa_distilbert_dynamic

fxmarty/20220911-h13m58s53_squad_qa_distilbert_dynamic

20220911-h13m58s53_squad_qa_distilbert_dynamic is a question answering model from fxmarty. Use it when the input is a question plus a passage.

task: question-answering 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: Question Answering
Access: Public
Created: Sep 11, 2022
Updated: Sep 11, 2022
SHA: 16f4a289

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Datasets

squad

Model card

From the Hugging Face model README

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

Fixed parameters:

dataset: [{'path': 'squad', 'eval_split': 'validation', 'data_keys': {'question': 'question', 'context': 'context'}, 'ref_keys': ['answers'], 'name': None, 'calibration_split': None}]
name_or_path: distilbert-base-uncased-distilled-squad
from_transformers: True
quantization_approach: dynamic

Benchmarked parameters:

framework: onnxruntime, pytorch
operators_to_quantize: ['Add', 'MatMul'], ['Add']
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	operators_to_quantize	node_exclusion	per_channel	framework_args	reduce_range	apply_quantization		exact_match		f1
`onnxruntime`	`None`	`None`	`None`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`None`	`False`	\|	78.884	\|	86.690
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	76.764	\|	85.053
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	69.622	\|	79.914
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.435	\|	5.887
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.165	\|	85.973
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	76.764	\|	85.053
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	69.622	\|	79.914
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.435	\|	5.887
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.165	\|	85.973
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.884	\|	86.690
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	78.884	\|	86.690

Time metrics

Time benchmarks were run for 15 seconds per config.

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

framework	operators_to_quantize	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`	\|	14.26	\|	70.13
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.08	\|	99.20
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.60	\|	94.33
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.88	\|	91.93
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.84	\|	92.27
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.34	\|	96.73
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.41	\|	96.07
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.96	\|	91.27
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.69	\|	93.53
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	14.43	\|	69.33
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	14.52	\|	68.87
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	14.35	\|	69.73
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	14.50	\|	69.00
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	14.20	\|	70.47
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	14.24	\|	70.27
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	14.58	\|	68.67
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	14.73	\|	67.87
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	31.49	\|	31.80

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

framework	operators_to_quantize	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`	\|	24.83	\|	40.33
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	18.49	\|	54.13
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	18.87	\|	53.00
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	19.17	\|	52.20
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	18.92	\|	52.87
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	19.13	\|	52.33
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	18.95	\|	52.80
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	19.08	\|	52.47
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	19.14	\|	52.27
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	24.83	\|	40.33
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	24.84	\|	40.27
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	24.66	\|	40.60
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	24.76	\|	40.40
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	25.07	\|	39.93
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	25.27	\|	39.60
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	24.76	\|	40.40
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	24.70	\|	40.53
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	41.26	\|	24.27

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

framework	operators_to_quantize	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`	\|	46.89	\|	21.33
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	34.84	\|	28.73
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.88	\|	27.93
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	36.92	\|	27.13
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	36.25	\|	27.60
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	36.17	\|	27.67
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.59	\|	28.13
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	37.36	\|	26.80
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.97	\|	27.87
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	46.94	\|	21.33
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	47.19	\|	21.20
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	47.05	\|	21.27
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	46.79	\|	21.40
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	46.87	\|	21.40
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	47.04	\|	21.27
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	47.08	\|	21.27
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	47.05	\|	21.27
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	54.61	\|	18.33

Task: Question Answering
Created: Sep 11, 2022
Updated: Sep 11, 2022