PyTorch 2.0 TTNN Compiler

This project allows to run PyTorch code on Tenstorrent hardware.

Supported Models

The table below summarizes the results of running various ML models through our TTNN compiler. For each model, we track whether the run was successful, the number of operations before and after conversion, the number of to_device and from_device operations, performance metrics, and accuracy.

Model	Run Success	Torch Ops Before (Unique Ops)	Torch Ops Remain (Unique Ops)	To/From Device Ops	Original Run Time (ms)	Compiled Run Time (ms)	Accuracy (%)
Autoencoder (conv)	🚧	9 (3)	6 (2)	3	1337	314.93	100.0
Autoencoder (conv)-train	🚧	24 (7)	21 (6)	3	1988.33	1321.0	100.0
Autoencoder (linear)	🚧	22 (3)	1 (1)	1	1325.56	812.88	100.0
Autoencoder (linear)-train	🚧	104 (8)	31 (4)	40	1798.33	6112.67	100.0
BERT	✅	1393 (21)	0 (0)	28	71575.9	49045.6	98.88
Bloom	🚧	1407 (29)	105 (9)	78	78687.4	58629.53	41.09
CLIP	🚧	1396 (30)	632 (20)	357	4798.48	33033.74	94.11
CLIP-train	❌	3943 (44)	N/A	N/A	25963.6	N/A	N/A
DETR	❌	1668 (41)	N/A	N/A	89729	N/A	N/A
DPR	❌	720 (22)	N/A	N/A	5577.48	N/A	N/A
FLAN-T5	❌	20106 (38)	N/A	N/A	12582.4	N/A	N/A
Falcon	🚧	2694 (29)	583 (17)	576	133002	191097.31	95.02
GLPN-KITTI	❌	3074 (30)	N/A	N/A	126610	N/A	N/A
GPT-2	❌	748 (31)	N/A	N/A	7089.46	N/A	N/A
GPTNeo	❌	2761 (36)	N/A	N/A	14691.9	N/A	N/A
Hand Landmark	❌	N/A	N/A	N/A	6115.49	N/A	N/A
HardNet	🚧	245 (10)	240 (5)	1	4511.35	12918.2	99.99
HardNet-train	🚧	867 (21)	650 (15)	71	12115.8	45096.38	100.0
Llama	🚧	104 (5)	33 (2)	35	242747	167580.53	100.0
MLPMixer	❌	253 (11)	N/A	N/A	5441.92	N/A	N/A
MLPMixer-train	❌	616 (19)	N/A	N/A	17382.3	N/A	N/A
Mnist	🚧	14 (8)	3 (2)	3	3711.39	4745.71	98.88
Mnist-train	🚧	46 (15)	20 (8)	14	3501.9	6950.09	100.0
MobileNetSSD	❌	575 (34)	N/A	N/A	1142.43	N/A	N/A
MobileNetV2	🚧	154 (9)	139 (3)	11	843.1	11656.39	99.98
OPT	❌	4073 (32)	N/A	N/A	31291.9	N/A	N/A
OpenPose V2	🚧	155 (7)	98 (4)	50	2817.68	7916.12	93.11
OpenPose V2-train	🚧	523 (14)	450 (12)	69	9860.72	27751.17	100.0
Perceiver IO	🚧	1532 (21)	4 (3)	30	66782	64844.16	99.94
ResNet18	🚧	70 (9)	41 (3)	17	2184.85	5046.11	99.99
ResNet18-train	🚧	241 (19)	197 (14)	31	6564.42	19230.47	100.0
ResNet50	🚧	176 (9)	107 (3)	49	4149.54	17436.06	99.98
ResNet50-train	🚧	616 (19)	524 (14)	71	16070.8	42933.82	100.0
RoBERTa	❌	719 (21)	N/A	N/A	17300.3	N/A	N/A
SegFormer	❌	768 (27)	N/A	N/A	26060.7	N/A	N/A
SegFormer-train	❌	1872 (40)	N/A	N/A	54648.9	N/A	N/A
SqueezeBERT	🚧	16 (9)	4 (2)	4	7448.21	5310.57	100.0
Stable Diffusion V2	❌	1883 (32)	N/A	N/A	2.09198e+06	N/A	N/A
U-Net	🚧	68 (6)	49 (4)	19	40201.9	41711.71	100.0
U-Net-train	🚧	236 (15)	205 (12)	27	78688.1	90950.49	100.0
Unet-brain	🚧	68 (6)	49 (4)	19	40387.7	42396.33	N/A
Unet-brain-train	🚧	236 (15)	205 (12)	27	79632.8	85364.46	100.0
Unet-carvana	🚧	67 (5)	49 (4)	18	62921.6	64848.64	100.0
Unet-carvana-train	🚧	232 (13)	202 (11)	26	142637	152138.14	100.0
ViLT	🚧	55 (18)	31 (14)	8	16538.5	19866.4	88.03
Whisper	❌	4294 (19)	N/A	N/A	243685	N/A	N/A
XGLM	🚧	1459 (30)	94 (18)	78	45533	59245.47	87.99
YOLOS	🚧	966 (28)	50 (8)	32	20144.5	48544.56	42.43
YOLOv3	❌	268 (10)	N/A	N/A	214728	N/A	N/A
YOLOv5	🚧	3 (3)	2 (2)	1	31609.8	29510.87	100.0
albert/albert-base-v2	🚧	791 (21)	41 (5)	62	3401.37	32049.73	66.33
albert/albert-base-v2-classification	🚧	779 (21)	28 (3)	63	3249.45	17310.19	-99.53
albert/albert-large-v2	🚧	1547 (21)	101 (5)	170	5327.23	39768.73	17.62
albert/albert-xlarge-v2	🚧	1547 (21)	77 (5)	122	16889.7	42096.27	51.12
albert/albert-xxlarge-v2	🚧	791 (21)	65 (6)	39	42072	38159.16	15.46
codegen	❌	9237 (37)	N/A	N/A	11740.1	N/A	N/A
densenet121	🚧	432 (10)	307 (5)	121	2823.78	20091.98	99.99
densenet161	🚧	572 (10)	407 (5)	161	7373.44	38499.01	99.99
densenet169	🚧	600 (10)	427 (5)	169	3310.82	26175.12	99.99
densenet201	🚧	712 (10)	507 (5)	201	4847.3	55142.83	99.99
distilbert-base-uncased	❌	367 (17)	N/A	N/A	8361.94	N/A	N/A
dla34.in1k	🚧	135 (9)	85 (4)	34	6069.93	8262.23	100.0
dla34.in1k-train	🚧	469 (18)	378 (13)	57	12031.7	32889.43	100.0
ese_vovnet19b_dw.ra_in1k	🚧	111 (12)	75 (5)	27	2065.5	16223.18	99.98
ese_vovnet19b_dw.ra_in1k-train	🚧	360 (25)	277 (16)	60	5283.96	25003.03	100.0
facebook/deit-base-patch16-224	🚧	686 (18)	257 (6)	195	20954	21980.32	97.18
facebook/deit-base-patch16-224-train	🚧	1856 (28)	684 (15)	658	72669.1	50721.55	100.0
ghostnet_100.in1k	🚧	515 (14)	262 (5)	72	752.2	23403.22	99.97
ghostnet_100.in1k-train	🚧	1468 (33)	1044 (24)	190	1627.17	62035.8	100.0
ghostnetv2_100.in1k	🚧	809 (20)	394 (12)	95	1615.6	37644.62	99.93
ghostnetv2_100.in1k-train	❌	2126 (41)	N/A	N/A	2579.98	N/A	N/A
googlenet	🚧	214 (15)	140 (5)	61	1773.25	16773.12	99.97
hrnet_w18.ms_aug_in1k	🚧	1488 (14)	705 (8)	281	5566.64	62911.17	99.94
hrnet_w18.ms_aug_in1k-train	❌	4277 (24)	N/A	N/A	16857.6	N/A	N/A
inception_v4.tf_in1k	🚧	495 (11)	341 (5)	150	13749.2	33889.79	99.98
inception_v4.tf_in1k-train	🚧	1702 (24)	1406 (16)	236	37924.7	122028.81	100.0
microsoft/beit-base-patch16-224	🚧	793 (21)	292 (6)	195	15748.7	20194.94	98.95
microsoft/beit-base-patch16-224-train	🚧	2229 (34)	768 (19)	809	76034.4	64984.99	100.0
microsoft/beit-large-patch16-224	🚧	1573 (21)	580 (6)	387	47573.4	44856.53	99.47
microsoft/beit-large-patch16-224-train	🚧	4437 (34)	1524 (19)	1613	491441	128378.66	100.0
mixer_b16_224.goog_in21k	🚧	356 (11)	27 (4)	27	17721.9	13685.3	58.85
mixer_b16_224.goog_in21k-train	🚧	959 (18)	155 (11)	357	56765.7	38446.16	100.0
mobilenet_v2	🚧	154 (9)	139 (3)	11	824.02	10407.55	99.98
mobilenet_v3_large	🚧	188 (11)	137 (4)	44	761.33	14060.74	99.94
mobilenet_v3_small	🚧	158 (11)	114 (4)	39	505.8	16522.52	99.93
mobilenetv1_100.ra4_e3600_r224_in1k	🚧	85 (7)	81 (3)	1	1312.87	6179.8	99.19
mobilenetv1_100.ra4_e3600_r224_in1k-train	🚧	231 (15)	220 (11)	6	4085.98	15887.8	100.0
regnet_x_16gf	🚧	235 (8)	142 (2)	67	17821.5	34501.8	99.96
regnet_x_1_6gf	🚧	195 (8)	118 (2)	55	1742.44	12344.93	99.97
regnet_x_32gf	🚧	245 (8)	148 (2)	70	35194.5	51785.7	99.97
regnet_x_3_2gf	🚧	265 (8)	160 (2)	76	2976.18	14834.75	99.95
regnet_x_400mf	🚧	235 (8)	142 (2)	67	872.66	12990.26	99.96
regnet_x_800mf	🚧	175 (8)	106 (2)	49	1195.19	13594.22	99.96
regnet_x_8gf	🚧	245 (8)	148 (2)	70	7216.56	18505.15	99.95
regnet_y_128gf	🚧	447 (10)	226 (2)	136	516541	549899.82	99.86
regnet_y_16gf	🚧	303 (10)	154 (2)	91	14877.1	44061.93	99.94
regnet_y_1_6gf	🚧	447 (10)	226 (2)	136	2381.61	26895.32	99.9
regnet_y_32gf	🚧	335 (10)	170 (2)	101	32296	57080.89	99.93
regnet_y_3_2gf	🚧	351 (10)	178 (2)	106	3985.46	26390.28	99.93
regnet_y_400mf	🚧	271 (10)	138 (2)	81	867.4	25439.88	99.77
regnet_y_800mf	🚧	239 (10)	122 (2)	71	1425.48	27316.8	99.88
regnet_y_8gf	🚧	287 (10)	146 (2)	86	9803.33	29674.72	99.96
resnet101	🚧	346 (9)	209 (3)	100	7526.94	19056.74	99.96
resnet152	🚧	516 (9)	311 (3)	151	10215.1	36087.0	99.95
resnet18	🚧	70 (9)	41 (3)	17	2087.8	10660.24	99.99
resnet34	🚧	126 (9)	73 (3)	33	3927.67	7546.99	99.99
resnet50	🚧	176 (9)	107 (3)	49	4071.94	12739.83	99.98
resnext101_32x8d	🚧	346 (9)	209 (3)	100	14908.7	27322.34	99.97
resnext101_64x4d	🚧	346 (9)	209 (3)	100	14004.3	26953.24	99.97
resnext50_32x4d	🚧	176 (9)	107 (3)	49	4060.45	9983.94	99.98
retinanet_resnet50_fpn	❌	1107 (32)	N/A	N/A	2609.91	N/A	N/A
retinanet_resnet50_fpn_v2	❌	617 (33)	N/A	N/A	2187.05	N/A	N/A
speecht5-tts	🚧	862 (21)	51 (9)	66	53375.6	94771.96	-0.99
ssd300_vgg16	❌	387 (32)	N/A	N/A	3246.89	N/A	N/A
ssdlite320_mobilenet_v3_large	❌	575 (34)	N/A	N/A	581.4	N/A	N/A
swin_b	🚧	1898 (30)	336 (16)	192	14116.5	87463.01	4.7
swin_s	🚧	1898 (30)	338 (16)	194	10363.3	45186.43	12.53
swin_t	🚧	968 (30)	176 (16)	98	5503.04	57937.4	15.54
swin_v2_b	🚧	2474 (37)	475 (18)	243	17847.8	73365.17	-0.22
swin_v2_s	🚧	2474 (37)	477 (18)	245	10426.6	54625.29	1.73
swin_v2_t	🚧	1256 (37)	249 (18)	119	5733.39	68914.5	10.99
t5-base	❌	14731 (38)	N/A	N/A	9095.16	N/A	N/A
t5-large	❌	22738 (38)	N/A	N/A	78308.8	N/A	N/A
t5-small	❌	6160 (38)	N/A	N/A	8292.52	N/A	N/A
textattack/albert-base-v2-imdb	🚧	782 (22)	42 (6)	63	3066.9	17579.66	100.0
tf_efficientnet_lite0.in1k	🚧	149 (9)	136 (4)	10	1765.16	10359.33	99.98
tf_efficientnet_lite0.in1k-train	🚧	403 (17)	374 (12)	24	2768.62	22706.99	100.0
tf_efficientnet_lite1.in1k	🚧	194 (9)	176 (4)	15	2020.45	13381.87	99.98
tf_efficientnet_lite1.in1k-train	🚧	523 (17)	484 (12)	34	3276.86	24412.53	100.0
tf_efficientnet_lite2.in1k	🚧	194 (9)	176 (4)	15	2060.03	12891.94	99.98
tf_efficientnet_lite2.in1k-train	🚧	523 (17)	484 (12)	34	4588.13	25859.37	100.0
tf_efficientnet_lite3.in1k	🚧	221 (9)	200 (4)	18	2463.76	11809.62	99.98
tf_efficientnet_lite3.in1k-train	🚧	595 (17)	550 (12)	40	7127.07	30883.27	100.0
tf_efficientnet_lite4.in1k	🚧	275 (9)	248 (4)	24	4759.59	16560.78	99.98
tf_efficientnet_lite4.in1k-train	🚧	739 (17)	682 (12)	52	16333.4	59532.86	100.0
twmkn9/albert-base-v2-squad2	🚧	783 (23)	28 (3)	65	3510.35	26854.39	97.58
vgg11	🚧	33 (8)	15 (4)	11	11615.5	20310.32	99.97
vgg11_bn	🚧	41 (9)	23 (5)	11	11707.1	15362.81	99.98
vgg13	🚧	37 (8)	17 (4)	13	19567.2	21567.41	99.98
vgg13_bn	🚧	47 (9)	27 (5)	13	18124.7	22348.68	99.98
vgg16	🚧	43 (8)	20 (4)	16	25346.9	35202.22	99.98
vgg16_bn	🚧	56 (9)	33 (5)	16	23502	27642.26	99.98
vgg19	🚧	49 (8)	23 (4)	19	31330.9	32977.1	99.98
vgg19_bn	🚧	65 (9)	39 (5)	19	31725	36309.45	99.97
vit_b_16	🚧	552 (17)	148 (6)	111	12258.5	29569.52	99.12
vit_b_32	🚧	552 (17)	149 (7)	110	4265.64	15591.78	98.08
vit_h_14	🚧	1452 (17)	164 (6)	163	763516	821420.18	99.29
vit_l_16	🚧	1092 (17)	292 (6)	219	42210.7	59423.71	99.62
vit_l_32	🚧	1092 (17)	124 (6)	123	13802.6	34313.69	98.86
wide_resnet101_2	🚧	346 (9)	209 (3)	100	22240.9	36777.9	99.97
wide_resnet50_2	🚧	176 (9)	107 (3)	49	12236.3	23852.43	99.98
xception71.tf_in1k	🚧	393 (9)	292 (2)	80	18169	46931.77	99.98
xception71.tf_in1k-train	🚧	1370 (18)	1240 (12)	108	61299	134629.6	100.0

Explanation of Metrics

Model: Name of the model.
Run Success: Indicates whether the model runs successfully after conversion.
Torch Ops Before (Unique Ops): The total number of operations used by the model in the original Torch implementation. The number in parenthesis represents the total unique ops.
Torch Ops Remain (Unique Ops): The total number of operations used after conversion to TTNN. The number in parenthesis represents the total unique ops.
To/From Device Ops: The number of to/from_device operations (data transfer to/from the device).
Original Run Time (ms): Execution time (in seconds) of the model before conversion.
Compiled Run Time (ms): Execution time (in seconds) of the model after conversion.
Accuracy (%): Model accuracy on a predefined test dataset after conversion.

---

Quickstart

The torch_ttnn module has a backend function, which can be used with the torch.compile().

import torch
import torch_ttnn

# A torch Module
class FooModule(torch.nn.Module):
    ...
# Create a module
module = FooModule()

# Compile the module, with ttnn backend
device = ttnn.open_device(device_id=0)
option = torch_ttnn.TorchTtnnOption(device=self.device)
ttnn_module = torch.compile(module, backend=torch_ttnn.backend, options=option)

# Running inference / training
ttnn_module(input_data)

Tracer

The tracer dump the information of fx graph such as node's op_name and shape.

For example, you can run this script to parse the information

PYTHONPATH=$(pwd) python3 tools/stat_models.py --trace_orig --backward --profile
ls stat/raw

By default, the raw result will be stored at stat/raw, and you can run this script to generate the report

python3 tools/generate_report.py
ls stat/

Now the stat/ folder have these report

• fw_node_count.csv
• bw_node_count.csv
• fw_total_input_size_dist/
• bw_total_input_size_dist/
• fw_total_output_size_dist/
• bw_total_output_size_dist/
• profile/

The node_count.csv show the node with op_type appear in the fx graph. This report can help analyze the frequency of op type appear in the graph.

The *_total_*_size_dist/ statistics the op_type's input/output_size distribution from all fx graph recored in stat/raw. This report can help analyze the memory footprint durning the calculation of op_type.

• Notice: the default input_shapes in tools/stat_torchvision.py is [1,3,224,224], which has dependency with *_total_*_size_dist/ report.

• Notice: the aten ir interface is in there

The profile/ is the tools provided by pytorch, you can open it by the url: chrome://tracing

For developers

Install torch-ttnn with editable mode

During development, you may want to use the torch-ttnn package for testing. In order to do that, you can install the torch-ttnn package in "editable" mode with

pip install -e .

Now, you can utilize torch_ttnn in your Python code. Any modifications you make to the torch_ttnn package will take effect immediately, eliminating the need for constant reinstallation via pip.

Build wheel file

For developers want to deploy the wheel, you can build the wheel file with

python -m build

Then you can upload the .whl file to the PyPI (Python Package Index).

Run transformer models

To run transformer model with ttnn backend, run:

PYTHONPATH="$TT_METAL_HOME:$(pwd)" python3 tools/run_transformers.py --model "phiyodr/bert-large-finetuned-squad2" --backend torch_ttnn

You can also substitute the backend with torch_stat to run a reference comparison.

Add a model test

If you want to record run time metrics for a model or test, include a Pytest fixture named record_property as a parameter and set the "model_name" key.
If you also want to compile the model with torch_ttnn backend, set the "torch_ttnn" key to a tuple in this order (model, test_inputs, outputs). "model_name" still needs to be set. See the example code snippet below. torch.nn.Module models with generate method is supported.

def Model(torch.nn.Module):
    def forward(self, x):
        # ...
        return outputs

# Add compilation_xfail marker if torch/CPU runs, but compiled version is xfail
@pytest.mark.compilation_xfail
# Add "record_property" parameter
def test_model_name(record_property):
    # Should be set as early as possible
    record_property("model_name", "Model Name")

    model = Model()
    # ...
    outputs = model(test_input)
    # outputs = model(**test_inputs) # dictionary inputs are also supported
    # ...

    # Can be set once all three objects for the tuple are defined
    record_property("torch_ttnn", (model, test_input(s), outputs))

If model.generate(inputs) is used, pass in model.generate instead of model to record_property.