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https://github.com/immich-app/ml-models.git
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Mert
df0ea33c8a
* feat: torch-free export core, shared graph/IR helpers, and CLI scaffolding * feat: fused InsightFace face exporter * feat: RKNN export path with per-SoC compilation * feat: single-partition CoreML CLIP export and runtime rewrite API * feat: refactor to passes, ep-specific fixes and optimizations * feat: check command diffing committed graph renderings, wired into CI * chore: graph and rewrite-plan renderings for the catalog * use pokedex large
1286 lines
81 KiB
Plaintext
1286 lines
81 KiB
Plaintext
<
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ir_version: 10,
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opset_import: ["" : 23],
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producer_name: "pytorch"
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>
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main_graph (uint8[batch,378,378,3] image) => (float[batch,1152] image_embedding)
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<
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float[batch,729,1152] add_1007
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float[batch,729,1152] add_1068
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float[batch,729,1152] add_107
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float[batch,729,1152] add_1097
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float[batch,729,1152] add_1158
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float[batch,729,1152] add_1187
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float[batch,729,1152] add_1248
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float[batch,729,1152] add_1277
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float[batch,729,1152] add_13
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float[batch,729,1152] add_1338
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float[batch,729,1152] add_1367
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float[batch,729,1152] add_1428
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float[batch,729,1152] add_1457
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float[batch,729,1152] add_1518
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float[batch,729,1152] add_1547
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float[batch,729,1152] add_1608
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float[batch,729,1152] add_1637
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float[batch,729,1152] add_168
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float[batch,729,1152] add_1698
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float[batch,729,1152] add_1727
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float[batch,729,1152] add_1788
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float[batch,729,1152] add_1817
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float[batch,729,1152] add_1878
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float[batch,729,1152] add_1907
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float[batch,729,1152] add_1968
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float[batch,729,1152] add_197
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float[batch,729,1152] add_1997
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float[batch,729,1152] add_2058
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float[batch,729,1152] add_2087
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float[batch,729,1152] add_2148
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float[batch,729,1152] add_2177
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float[batch,729,1152] add_2238
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float[batch,729,1152] add_2267
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float[batch,729,1152] add_2328
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float[batch,729,1152] add_2357
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float[batch,729,1152] add_2418
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float[batch,729,1152] add_2447
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float[batch,1,1152] add_2545
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float[batch,729,1152] add_258
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float[batch,729,1152] add_287
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float[batch,729,1152] add_348
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float[batch,729,1152] add_377
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float[batch,729,1152] add_438
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float[batch,729,1152] add_467
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float[batch,729,1152] add_528
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float[batch,729,1152] add_557
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float[batch,729,1152] add_618
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float[batch,729,1152] add_647
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float[batch,729,1152] add_708
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float[batch,729,1152] add_737
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float[batch,729,1152] add_78
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float[batch,729,1152] add_798
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float[batch,729,1152] add_827
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float[batch,729,1152] add_888
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float[batch,729,1152] add_917
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float[batch,729,1152] add_978
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float[batch,1] clamp_min
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float[batch,1152,27,27] conv2d
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float[batch,729,4304] gelu
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float[batch,729,4304] gelu_1
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float[batch,729,4304] gelu_10
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float[batch,729,4304] gelu_11
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float[batch,729,4304] gelu_12
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float[batch,729,4304] gelu_13
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float[batch,729,4304] gelu_14
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float[batch,729,4304] gelu_15
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float[batch,729,4304] gelu_16
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float[batch,729,4304] gelu_17
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float[batch,729,4304] gelu_18
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float[batch,729,4304] gelu_19
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float[batch,729,4304] gelu_2
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float[batch,729,4304] gelu_20
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float[batch,729,4304] gelu_21
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float[batch,729,4304] gelu_22
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float[batch,729,4304] gelu_23
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float[batch,729,4304] gelu_24
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float[batch,729,4304] gelu_25
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float[batch,729,4304] gelu_26
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float[batch,1,4304] gelu_27
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float[batch,729,4304] gelu_3
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float[batch,729,4304] gelu_4
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float[batch,729,4304] gelu_5
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float[batch,729,4304] gelu_6
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float[batch,729,4304] gelu_7
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float[batch,729,4304] gelu_8
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float[batch,729,4304] gelu_9
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float[batch,3,378,378] image_chw
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float[batch] image_ez
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float[batch] image_ez_r
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float[batch,1,1,1] image_ez_s
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float[batch,378,378,3] image_f32
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float[batch,729,1152] layer_norm
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float[batch,729,1152] layer_norm_1
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float[batch,729,1152] layer_norm_10
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float[batch,729,1152] layer_norm_11
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float[batch,729,1152] layer_norm_12
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float[batch,729,1152] layer_norm_13
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float[batch,729,1152] layer_norm_14
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float[batch,729,1152] layer_norm_15
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float[batch,729,1152] layer_norm_16
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float[batch,729,1152] layer_norm_17
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float[batch,729,1152] layer_norm_18
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float[batch,729,1152] layer_norm_19
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float[batch,729,1152] layer_norm_2
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float[batch,729,1152] layer_norm_20
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float[batch,729,1152] layer_norm_21
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float[batch,729,1152] layer_norm_22
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float[batch,729,1152] layer_norm_23
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float[batch,729,1152] layer_norm_24
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float[batch,729,1152] layer_norm_25
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float[batch,729,1152] layer_norm_26
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float[batch,729,1152] layer_norm_27
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float[batch,729,1152] layer_norm_28
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float[batch,729,1152] layer_norm_29
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float[batch,729,1152] layer_norm_3
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float[batch,729,1152] layer_norm_30
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float[batch,729,1152] layer_norm_31
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float[batch,729,1152] layer_norm_32
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float[batch,729,1152] layer_norm_33
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float[batch,729,1152] layer_norm_34
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float[batch,729,1152] layer_norm_35
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float[batch,729,1152] layer_norm_36
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float[batch,729,1152] layer_norm_37
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float[batch,729,1152] layer_norm_38
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float[batch,729,1152] layer_norm_39
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float[batch,729,1152] layer_norm_4
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float[batch,729,1152] layer_norm_40
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float[batch,729,1152] layer_norm_41
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float[batch,729,1152] layer_norm_42
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float[batch,729,1152] layer_norm_43
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float[batch,729,1152] layer_norm_44
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float[batch,729,1152] layer_norm_45
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float[batch,729,1152] layer_norm_46
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float[batch,729,1152] layer_norm_47
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float[batch,729,1152] layer_norm_48
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float[batch,729,1152] layer_norm_49
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float[batch,729,1152] layer_norm_5
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float[batch,729,1152] layer_norm_50
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float[batch,729,1152] layer_norm_51
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float[batch,729,1152] layer_norm_52
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float[batch,729,1152] layer_norm_53
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float[batch,729,1152] layer_norm_54
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float[batch,1,1152] layer_norm_55
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float[batch,729,1152] layer_norm_6
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float[batch,729,1152] layer_norm_7
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float[batch,729,1152] layer_norm_8
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float[batch,729,1152] layer_norm_9
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float[batch,1] linalg_vector_norm
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float[batch,729,3456] linear
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float[batch,729,1152] linear_1
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float[batch,729,4304] linear_10
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float[batch,729,3456] linear_100
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float[batch,729,1152] linear_101
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float[batch,729,4304] linear_102
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float[batch,729,1152] linear_103
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float[batch,729,3456] linear_104
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float[batch,729,1152] linear_105
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float[batch,729,4304] linear_106
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float[batch,729,1152] linear_107
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float[batch,729,2304] linear_109
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float[batch,729,1152] linear_11
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float[batch,1,1152] linear_110
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float[batch,1,4304] linear_111
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float[batch,1,1152] linear_112
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float[batch,729,3456] linear_12
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float[batch,729,1152] linear_13
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float[batch,729,4304] linear_14
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float[batch,729,1152] linear_15
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float[batch,729,3456] linear_16
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float[batch,729,1152] linear_17
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float[batch,729,4304] linear_18
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float[batch,729,1152] linear_19
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float[batch,729,4304] linear_2
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float[batch,729,3456] linear_20
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float[batch,729,1152] linear_21
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float[batch,729,4304] linear_22
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float[batch,729,1152] linear_23
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float[batch,729,3456] linear_24
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float[batch,729,1152] linear_25
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float[batch,729,4304] linear_26
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float[batch,729,1152] linear_27
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float[batch,729,3456] linear_28
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float[batch,729,1152] linear_29
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float[batch,729,1152] linear_3
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float[batch,729,4304] linear_30
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float[batch,729,1152] linear_31
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float[batch,729,3456] linear_32
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float[batch,729,1152] linear_33
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float[batch,729,4304] linear_34
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float[batch,729,1152] linear_35
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float[batch,729,3456] linear_36
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float[batch,729,1152] linear_37
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float[batch,729,4304] linear_38
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float[batch,729,1152] linear_39
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float[batch,729,3456] linear_4
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float[batch,729,3456] linear_40
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float[batch,729,1152] linear_41
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float[batch,729,4304] linear_42
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float[batch,729,1152] linear_43
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float[batch,729,3456] linear_44
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float[batch,729,1152] linear_45
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float[batch,729,4304] linear_46
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float[batch,729,1152] linear_47
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float[batch,729,3456] linear_48
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float[batch,729,1152] linear_49
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float[batch,729,1152] linear_5
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float[batch,729,4304] linear_50
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float[batch,729,1152] linear_51
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float[batch,729,3456] linear_52
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float[batch,729,1152] linear_53
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float[batch,729,4304] linear_54
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float[batch,729,1152] linear_55
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float[batch,729,3456] linear_56
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float[batch,729,1152] linear_57
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float[batch,729,4304] linear_58
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float[batch,729,1152] linear_59
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float[batch,729,4304] linear_6
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float[batch,729,3456] linear_60
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float[batch,729,1152] linear_61
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float[batch,729,4304] linear_62
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float[batch,729,1152] linear_63
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float[batch,729,3456] linear_64
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float[batch,729,1152] linear_65
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float[batch,729,4304] linear_66
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float[batch,729,1152] linear_67
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float[batch,729,3456] linear_68
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float[batch,729,1152] linear_69
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float[batch,729,1152] linear_7
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float[batch,729,4304] linear_70
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float[batch,729,1152] linear_71
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float[batch,729,3456] linear_72
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float[batch,729,1152] linear_73
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float[batch,729,4304] linear_74
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float[batch,729,1152] linear_75
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float[batch,729,3456] linear_76
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float[batch,729,1152] linear_77
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float[batch,729,4304] linear_78
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float[batch,729,1152] linear_79
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float[batch,729,3456] linear_8
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float[batch,729,3456] linear_80
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float[batch,729,1152] linear_81
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float[batch,729,4304] linear_82
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float[batch,729,1152] linear_83
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float[batch,729,3456] linear_84
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float[batch,729,1152] linear_85
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float[batch,729,4304] linear_86
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float[batch,729,1152] linear_87
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float[batch,729,3456] linear_88
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float[batch,729,1152] linear_89
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float[batch,729,1152] linear_9
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float[batch,729,4304] linear_90
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float[batch,729,1152] linear_91
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float[batch,729,3456] linear_92
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float[batch,729,1152] linear_93
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float[batch,729,4304] linear_94
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float[batch,729,1152] linear_95
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float[batch,729,3456] linear_96
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float[batch,729,1152] linear_97
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float[batch,729,4304] linear_98
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float[batch,729,1152] linear_99
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float[batch,729,1152] node_scaled_dot_product_attention_10_k
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float[batch,729,1152] node_scaled_dot_product_attention_10_q
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float[batch,729,1152] node_scaled_dot_product_attention_10_v
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float[batch,729,1152] node_scaled_dot_product_attention_11_k
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float[batch,729,1152] node_scaled_dot_product_attention_11_q
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float[batch,729,1152] node_scaled_dot_product_attention_11_v
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float[batch,729,1152] node_scaled_dot_product_attention_12_k
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float[batch,729,1152] node_scaled_dot_product_attention_12_q
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float[batch,729,1152] node_scaled_dot_product_attention_12_v
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float[batch,729,1152] node_scaled_dot_product_attention_13_k
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float[batch,729,1152] node_scaled_dot_product_attention_13_q
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float[batch,729,1152] node_scaled_dot_product_attention_13_v
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float[batch,729,1152] node_scaled_dot_product_attention_14_k
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float[batch,729,1152] node_scaled_dot_product_attention_14_q
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float[batch,729,1152] node_scaled_dot_product_attention_14_v
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float[batch,729,1152] node_scaled_dot_product_attention_15_k
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float[batch,729,1152] node_scaled_dot_product_attention_15_q
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float[batch,729,1152] node_scaled_dot_product_attention_15_v
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float[batch,729,1152] node_scaled_dot_product_attention_16_k
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float[batch,729,1152] node_scaled_dot_product_attention_16_q
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float[batch,729,1152] node_scaled_dot_product_attention_16_v
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float[batch,729,1152] node_scaled_dot_product_attention_17_k
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float[batch,729,1152] node_scaled_dot_product_attention_17_q
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float[batch,729,1152] node_scaled_dot_product_attention_17_v
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float[batch,729,1152] node_scaled_dot_product_attention_18_k
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float[batch,729,1152] node_scaled_dot_product_attention_18_q
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float[batch,729,1152] node_scaled_dot_product_attention_18_v
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float[batch,729,1152] node_scaled_dot_product_attention_19_k
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float[batch,729,1152] node_scaled_dot_product_attention_19_q
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float[batch,729,1152] node_scaled_dot_product_attention_19_v
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float[batch,729,1152] node_scaled_dot_product_attention_1_k
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float[batch,729,1152] node_scaled_dot_product_attention_1_q
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float[batch,729,1152] node_scaled_dot_product_attention_1_v
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float[batch,729,1152] node_scaled_dot_product_attention_20_k
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float[batch,729,1152] node_scaled_dot_product_attention_20_q
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float[batch,729,1152] node_scaled_dot_product_attention_20_v
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float[batch,729,1152] node_scaled_dot_product_attention_21_k
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float[batch,729,1152] node_scaled_dot_product_attention_21_q
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float[batch,729,1152] node_scaled_dot_product_attention_21_v
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float[batch,729,1152] node_scaled_dot_product_attention_22_k
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float[batch,729,1152] node_scaled_dot_product_attention_22_q
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float[batch,729,1152] node_scaled_dot_product_attention_22_v
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float[batch,729,1152] node_scaled_dot_product_attention_23_k
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float[batch,729,1152] node_scaled_dot_product_attention_23_q
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float[batch,729,1152] node_scaled_dot_product_attention_23_v
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float[batch,729,1152] node_scaled_dot_product_attention_24_k
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float[batch,729,1152] node_scaled_dot_product_attention_24_q
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float[batch,729,1152] node_scaled_dot_product_attention_24_v
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float[batch,729,1152] node_scaled_dot_product_attention_25_k
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float[batch,729,1152] node_scaled_dot_product_attention_25_q
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float[batch,729,1152] node_scaled_dot_product_attention_25_v
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float[batch,729,1152] node_scaled_dot_product_attention_26_k
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float[batch,729,1152] node_scaled_dot_product_attention_26_q
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float[batch,729,1152] node_scaled_dot_product_attention_26_v
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float[batch,729,1152] node_scaled_dot_product_attention_27_k
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float[batch,1,1152] node_scaled_dot_product_attention_27_q
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float[batch,1,1] node_scaled_dot_product_attention_27_q_col_out
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float[batch,729,1152] node_scaled_dot_product_attention_27_v
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float[batch,729,1152] node_scaled_dot_product_attention_2_k
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float[batch,729,1152] node_scaled_dot_product_attention_2_q
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float[batch,729,1152] node_scaled_dot_product_attention_2_v
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float[batch,729,1152] node_scaled_dot_product_attention_3_k
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float[batch,729,1152] node_scaled_dot_product_attention_3_q
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float[batch,729,1152] node_scaled_dot_product_attention_3_v
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float[batch,729,1152] node_scaled_dot_product_attention_4_k
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float[batch,729,1152] node_scaled_dot_product_attention_4_q
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float[batch,729,1152] node_scaled_dot_product_attention_4_v
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float[batch,729,1152] node_scaled_dot_product_attention_5_k
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float[batch,729,1152] node_scaled_dot_product_attention_5_q
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float[batch,729,1152] node_scaled_dot_product_attention_5_v
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float[batch,729,1152] node_scaled_dot_product_attention_6_k
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float[batch,729,1152] node_scaled_dot_product_attention_6_q
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float[batch,729,1152] node_scaled_dot_product_attention_6_v
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float[batch,729,1152] node_scaled_dot_product_attention_7_k
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float[batch,729,1152] node_scaled_dot_product_attention_7_q
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float[batch,729,1152] node_scaled_dot_product_attention_7_v
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float[batch,729,1152] node_scaled_dot_product_attention_8_k
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float[batch,729,1152] node_scaled_dot_product_attention_8_q
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float[batch,729,1152] node_scaled_dot_product_attention_8_v
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float[batch,729,1152] node_scaled_dot_product_attention_9_k
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float[batch,729,1152] node_scaled_dot_product_attention_9_q
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|
float[batch,729,1152] node_scaled_dot_product_attention_9_v
|
|
float[batch,729,1152] node_scaled_dot_product_attention_k
|
|
float[batch,729,1152] node_scaled_dot_product_attention_q
|
|
float[batch,729,1152] node_scaled_dot_product_attention_v
|
|
float[batch,729,1152] scaled_dot_product_attention
|
|
float[batch,729,1152] scaled_dot_product_attention_1
|
|
float[batch,729,1152] scaled_dot_product_attention_10
|
|
float[batch,729,1152] scaled_dot_product_attention_11
|
|
float[batch,729,1152] scaled_dot_product_attention_12
|
|
float[batch,729,1152] scaled_dot_product_attention_13
|
|
float[batch,729,1152] scaled_dot_product_attention_14
|
|
float[batch,729,1152] scaled_dot_product_attention_15
|
|
float[batch,729,1152] scaled_dot_product_attention_16
|
|
float[batch,729,1152] scaled_dot_product_attention_17
|
|
float[batch,729,1152] scaled_dot_product_attention_18
|
|
float[batch,729,1152] scaled_dot_product_attention_19
|
|
float[batch,729,1152] scaled_dot_product_attention_2
|
|
float[batch,729,1152] scaled_dot_product_attention_20
|
|
float[batch,729,1152] scaled_dot_product_attention_21
|
|
float[batch,729,1152] scaled_dot_product_attention_22
|
|
float[batch,729,1152] scaled_dot_product_attention_23
|
|
float[batch,729,1152] scaled_dot_product_attention_24
|
|
float[batch,729,1152] scaled_dot_product_attention_25
|
|
float[batch,729,1152] scaled_dot_product_attention_26
|
|
float[batch,1,1152] scaled_dot_product_attention_27
|
|
float[batch,729,1152] scaled_dot_product_attention_3
|
|
float[batch,729,1152] scaled_dot_product_attention_4
|
|
float[batch,729,1152] scaled_dot_product_attention_5
|
|
float[batch,729,1152] scaled_dot_product_attention_6
|
|
float[batch,729,1152] scaled_dot_product_attention_7
|
|
float[batch,729,1152] scaled_dot_product_attention_8
|
|
float[batch,729,1152] scaled_dot_product_attention_9
|
|
float[batch,1152] select
|
|
float[batch,729,1152] transpose
|
|
float[batch,729,3456] val_115
|
|
float[batch,729,1152] val_116
|
|
float[batch,729,4304] val_117
|
|
float[batch,729,1152] val_118
|
|
float[batch,729,3456] val_119
|
|
float[batch,729,1152] val_120
|
|
float[batch,729,4304] val_121
|
|
float[batch,729,1152] val_122
|
|
float[batch,729,3456] val_123
|
|
float[batch,729,1152] val_124
|
|
float[batch,729,4304] val_125
|
|
float[batch,729,1152] val_126
|
|
float[batch,729,3456] val_127
|
|
float[batch,729,1152] val_128
|
|
float[batch,729,4304] val_129
|
|
float[batch,729,1152] val_130
|
|
float[batch,729,3456] val_131
|
|
float[batch,729,1152] val_132
|
|
float[batch,729,4304] val_133
|
|
float[batch,729,1152] val_134
|
|
float[batch,729,3456] val_135
|
|
float[batch,729,1152] val_136
|
|
float[batch,729,4304] val_137
|
|
float[batch,729,1152] val_138
|
|
float[batch,729,3456] val_139
|
|
float[batch,729,1152] val_140
|
|
float[batch,729,4304] val_141
|
|
float[batch,729,1152] val_142
|
|
float[batch,729,3456] val_143
|
|
float[batch,729,1152] val_144
|
|
float[batch,729,4304] val_145
|
|
float[batch,729,1152] val_146
|
|
float[batch,729,3456] val_147
|
|
float[batch,729,1152] val_148
|
|
float[batch,729,4304] val_149
|
|
float[batch,729,1152] val_150
|
|
float[batch,729,3456] val_151
|
|
float[batch,729,1152] val_152
|
|
float[batch,729,4304] val_153
|
|
float[batch,729,1152] val_154
|
|
float[batch,729,3456] val_155
|
|
float[batch,729,1152] val_156
|
|
float[batch,729,4304] val_157
|
|
float[batch,729,1152] val_158
|
|
float[batch,729,3456] val_159
|
|
float[batch,729,1152] val_160
|
|
float[batch,729,4304] val_161
|
|
float[batch,729,1152] val_162
|
|
float[batch,729,3456] val_163
|
|
float[batch,729,1152] val_164
|
|
float[batch,729,4304] val_165
|
|
float[batch,729,1152] val_166
|
|
float[batch,729,3456] val_167
|
|
float[batch,729,1152] val_168
|
|
float[batch,729,4304] val_169
|
|
float[batch,729,1152] val_170
|
|
float[batch,729,3456] val_171
|
|
float[batch,729,1152] val_172
|
|
float[batch,729,4304] val_173
|
|
float[batch,729,1152] val_174
|
|
float[batch,729,3456] val_175
|
|
float[batch,729,1152] val_176
|
|
float[batch,729,4304] val_177
|
|
float[batch,729,1152] val_178
|
|
float[batch,729,3456] val_179
|
|
float[batch,729,1152] val_180
|
|
float[batch,729,4304] val_181
|
|
float[batch,729,1152] val_182
|
|
float[batch,729,3456] val_183
|
|
float[batch,729,1152] val_184
|
|
float[batch,729,4304] val_185
|
|
float[batch,729,1152] val_186
|
|
float[batch,729,3456] val_187
|
|
float[batch,729,1152] val_188
|
|
float[batch,729,4304] val_189
|
|
float[batch,729,1152] val_190
|
|
float[batch,729,3456] val_191
|
|
float[batch,729,1152] val_192
|
|
float[batch,729,4304] val_193
|
|
float[batch,729,1152] val_194
|
|
float[batch,729,3456] val_195
|
|
float[batch,729,1152] val_196
|
|
float[batch,729,4304] val_197
|
|
float[batch,729,1152] val_198
|
|
float[batch,729,3456] val_199
|
|
float[batch,729,1152] val_200
|
|
float[batch,729,4304] val_201
|
|
float[batch,729,1152] val_202
|
|
float[batch,729,3456] val_203
|
|
float[batch,729,1152] val_204
|
|
float[batch,729,4304] val_205
|
|
float[batch,729,1152] val_206
|
|
float[batch,729,3456] val_207
|
|
float[batch,729,1152] val_208
|
|
float[batch,729,4304] val_209
|
|
float[batch,729,1152] val_210
|
|
float[batch,729,3456] val_211
|
|
float[batch,729,1152] val_212
|
|
float[batch,729,4304] val_213
|
|
float[batch,729,1152] val_214
|
|
float[batch,729,3456] val_215
|
|
float[batch,729,1152] val_216
|
|
float[batch,729,4304] val_217
|
|
float[batch,729,1152] val_218
|
|
float[batch,729,3456] val_219
|
|
float[batch,729,1152] val_220
|
|
float[batch,729,4304] val_221
|
|
float[batch,729,1152] val_222
|
|
float[batch,729,2304] val_223
|
|
float[batch,1,1152] val_224
|
|
float[batch,1,4304] val_225
|
|
float[batch,1,1152] val_226
|
|
float[batch,1152,729] view
|
|
>
|
|
{
|
|
[pre_cast] image_f32 = Cast <to: int = 1> (image)
|
|
[pre_nhwc_to_nchw] image_chw = Transpose <perm: ints = [0, 3, 1, 2]> (image_f32)
|
|
[node_conv2d] conv2d = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [14, 14]> (image_chw, "visual.trunk.patch_embed.proj.weight", "visual.trunk.patch_embed.proj.bias")
|
|
view = Reshape <allowzero: int = 1> (conv2d, view_target)
|
|
[node_transpose] transpose = Transpose <perm: ints = [0, 2, 1]> (view)
|
|
add_13 = Add (transpose, "visual.trunk.pos_embed")
|
|
[node_layer_norm] layer_norm = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_13, "visual.trunk.blocks.0.norm1.weight", "visual.trunk.blocks.0.norm1.bias")
|
|
val_115 = MatMul (layer_norm, val_3)
|
|
[node_linear] linear = Add (val_115, "visual.trunk.blocks.0.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_qkv_split] node_scaled_dot_product_attention_q, node_scaled_dot_product_attention_k, node_scaled_dot_product_attention_v = Split <axis: int = -1> (linear, attn3d_split_3x1152)
|
|
[node_scaled_dot_product_attention] scaled_dot_product_attention = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_q, node_scaled_dot_product_attention_k, node_scaled_dot_product_attention_v)
|
|
val_116 = MatMul (scaled_dot_product_attention, val_4)
|
|
linear_1 = Add (val_116, "visual.trunk.blocks.0.attn.proj.bias")
|
|
add_78 = Add (add_13, linear_1)
|
|
layer_norm_1 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_78, "visual.trunk.blocks.0.norm2.weight", "visual.trunk.blocks.0.norm2.bias")
|
|
val_117 = MatMul (layer_norm_1, val_5)
|
|
linear_2 = Add (val_117, "visual.trunk.blocks.0.mlp.fc1.bias")
|
|
[node_gelu] gelu = Gelu <approximate: string = "none"> (linear_2)
|
|
val_118 = MatMul (gelu, val_6)
|
|
linear_3 = Add (val_118, "visual.trunk.blocks.0.mlp.fc2.bias")
|
|
add_107 = Add (add_78, linear_3)
|
|
layer_norm_2 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_107, "visual.trunk.blocks.1.norm1.weight", "visual.trunk.blocks.1.norm1.bias")
|
|
val_119 = MatMul (layer_norm_2, val_7)
|
|
linear_4 = Add (val_119, "visual.trunk.blocks.1.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_1_qkv_split] node_scaled_dot_product_attention_1_q, node_scaled_dot_product_attention_1_k, node_scaled_dot_product_attention_1_v = Split <axis: int = -1> (linear_4, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_1 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_1_q, node_scaled_dot_product_attention_1_k, node_scaled_dot_product_attention_1_v)
|
|
val_120 = MatMul (scaled_dot_product_attention_1, val_8)
|
|
linear_5 = Add (val_120, "visual.trunk.blocks.1.attn.proj.bias")
|
|
add_168 = Add (add_107, linear_5)
|
|
layer_norm_3 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_168, "visual.trunk.blocks.1.norm2.weight", "visual.trunk.blocks.1.norm2.bias")
|
|
val_121 = MatMul (layer_norm_3, val_9)
|
|
linear_6 = Add (val_121, "visual.trunk.blocks.1.mlp.fc1.bias")
|
|
gelu_1 = Gelu <approximate: string = "none"> (linear_6)
|
|
val_122 = MatMul (gelu_1, val_10)
|
|
linear_7 = Add (val_122, "visual.trunk.blocks.1.mlp.fc2.bias")
|
|
add_197 = Add (add_168, linear_7)
|
|
layer_norm_4 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_197, "visual.trunk.blocks.2.norm1.weight", "visual.trunk.blocks.2.norm1.bias")
|
|
val_123 = MatMul (layer_norm_4, val_11)
|
|
linear_8 = Add (val_123, "visual.trunk.blocks.2.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_2_qkv_split] node_scaled_dot_product_attention_2_q, node_scaled_dot_product_attention_2_k, node_scaled_dot_product_attention_2_v = Split <axis: int = -1> (linear_8, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_2 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_2_q, node_scaled_dot_product_attention_2_k, node_scaled_dot_product_attention_2_v)
|
|
val_124 = MatMul (scaled_dot_product_attention_2, val_12)
|
|
linear_9 = Add (val_124, "visual.trunk.blocks.2.attn.proj.bias")
|
|
add_258 = Add (add_197, linear_9)
|
|
layer_norm_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_258, "visual.trunk.blocks.2.norm2.weight", "visual.trunk.blocks.2.norm2.bias")
|
|
val_125 = MatMul (layer_norm_5, val_13)
|
|
linear_10 = Add (val_125, "visual.trunk.blocks.2.mlp.fc1.bias")
|
|
gelu_2 = Gelu <approximate: string = "none"> (linear_10)
|
|
val_126 = MatMul (gelu_2, val_14)
|
|
linear_11 = Add (val_126, "visual.trunk.blocks.2.mlp.fc2.bias")
|
|
add_287 = Add (add_258, linear_11)
|
|
layer_norm_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_287, "visual.trunk.blocks.3.norm1.weight", "visual.trunk.blocks.3.norm1.bias")
|
|
val_127 = MatMul (layer_norm_6, val_15)
|
|
linear_12 = Add (val_127, "visual.trunk.blocks.3.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_3_qkv_split] node_scaled_dot_product_attention_3_q, node_scaled_dot_product_attention_3_k, node_scaled_dot_product_attention_3_v = Split <axis: int = -1> (linear_12, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_3 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_3_q, node_scaled_dot_product_attention_3_k, node_scaled_dot_product_attention_3_v)
|
|
val_128 = MatMul (scaled_dot_product_attention_3, val_16)
|
|
linear_13 = Add (val_128, "visual.trunk.blocks.3.attn.proj.bias")
|
|
add_348 = Add (add_287, linear_13)
|
|
layer_norm_7 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_348, "visual.trunk.blocks.3.norm2.weight", "visual.trunk.blocks.3.norm2.bias")
|
|
val_129 = MatMul (layer_norm_7, val_17)
|
|
linear_14 = Add (val_129, "visual.trunk.blocks.3.mlp.fc1.bias")
|
|
gelu_3 = Gelu <approximate: string = "none"> (linear_14)
|
|
val_130 = MatMul (gelu_3, val_18)
|
|
linear_15 = Add (val_130, "visual.trunk.blocks.3.mlp.fc2.bias")
|
|
add_377 = Add (add_348, linear_15)
|
|
layer_norm_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_377, "visual.trunk.blocks.4.norm1.weight", "visual.trunk.blocks.4.norm1.bias")
|
|
val_131 = MatMul (layer_norm_8, val_19)
|
|
linear_16 = Add (val_131, "visual.trunk.blocks.4.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_4_qkv_split] node_scaled_dot_product_attention_4_q, node_scaled_dot_product_attention_4_k, node_scaled_dot_product_attention_4_v = Split <axis: int = -1> (linear_16, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_4 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_4_q, node_scaled_dot_product_attention_4_k, node_scaled_dot_product_attention_4_v)
|
|
val_132 = MatMul (scaled_dot_product_attention_4, val_20)
|
|
linear_17 = Add (val_132, "visual.trunk.blocks.4.attn.proj.bias")
|
|
add_438 = Add (add_377, linear_17)
|
|
layer_norm_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_438, "visual.trunk.blocks.4.norm2.weight", "visual.trunk.blocks.4.norm2.bias")
|
|
val_133 = MatMul (layer_norm_9, val_21)
|
|
linear_18 = Add (val_133, "visual.trunk.blocks.4.mlp.fc1.bias")
|
|
gelu_4 = Gelu <approximate: string = "none"> (linear_18)
|
|
val_134 = MatMul (gelu_4, val_22)
|
|
linear_19 = Add (val_134, "visual.trunk.blocks.4.mlp.fc2.bias")
|
|
add_467 = Add (add_438, linear_19)
|
|
layer_norm_10 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_467, "visual.trunk.blocks.5.norm1.weight", "visual.trunk.blocks.5.norm1.bias")
|
|
val_135 = MatMul (layer_norm_10, val_23)
|
|
linear_20 = Add (val_135, "visual.trunk.blocks.5.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_5_qkv_split] node_scaled_dot_product_attention_5_q, node_scaled_dot_product_attention_5_k, node_scaled_dot_product_attention_5_v = Split <axis: int = -1> (linear_20, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_5 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_5_q, node_scaled_dot_product_attention_5_k, node_scaled_dot_product_attention_5_v)
|
|
val_136 = MatMul (scaled_dot_product_attention_5, val_24)
|
|
linear_21 = Add (val_136, "visual.trunk.blocks.5.attn.proj.bias")
|
|
add_528 = Add (add_467, linear_21)
|
|
layer_norm_11 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_528, "visual.trunk.blocks.5.norm2.weight", "visual.trunk.blocks.5.norm2.bias")
|
|
val_137 = MatMul (layer_norm_11, val_25)
|
|
linear_22 = Add (val_137, "visual.trunk.blocks.5.mlp.fc1.bias")
|
|
gelu_5 = Gelu <approximate: string = "none"> (linear_22)
|
|
val_138 = MatMul (gelu_5, val_26)
|
|
linear_23 = Add (val_138, "visual.trunk.blocks.5.mlp.fc2.bias")
|
|
add_557 = Add (add_528, linear_23)
|
|
layer_norm_12 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_557, "visual.trunk.blocks.6.norm1.weight", "visual.trunk.blocks.6.norm1.bias")
|
|
val_139 = MatMul (layer_norm_12, val_27)
|
|
linear_24 = Add (val_139, "visual.trunk.blocks.6.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_6_qkv_split] node_scaled_dot_product_attention_6_q, node_scaled_dot_product_attention_6_k, node_scaled_dot_product_attention_6_v = Split <axis: int = -1> (linear_24, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_6 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_6_q, node_scaled_dot_product_attention_6_k, node_scaled_dot_product_attention_6_v)
|
|
val_140 = MatMul (scaled_dot_product_attention_6, val_28)
|
|
linear_25 = Add (val_140, "visual.trunk.blocks.6.attn.proj.bias")
|
|
add_618 = Add (add_557, linear_25)
|
|
layer_norm_13 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_618, "visual.trunk.blocks.6.norm2.weight", "visual.trunk.blocks.6.norm2.bias")
|
|
val_141 = MatMul (layer_norm_13, val_29)
|
|
linear_26 = Add (val_141, "visual.trunk.blocks.6.mlp.fc1.bias")
|
|
gelu_6 = Gelu <approximate: string = "none"> (linear_26)
|
|
val_142 = MatMul (gelu_6, val_30)
|
|
linear_27 = Add (val_142, "visual.trunk.blocks.6.mlp.fc2.bias")
|
|
add_647 = Add (add_618, linear_27)
|
|
layer_norm_14 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_647, "visual.trunk.blocks.7.norm1.weight", "visual.trunk.blocks.7.norm1.bias")
|
|
val_143 = MatMul (layer_norm_14, val_31)
|
|
linear_28 = Add (val_143, "visual.trunk.blocks.7.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_7_qkv_split] node_scaled_dot_product_attention_7_q, node_scaled_dot_product_attention_7_k, node_scaled_dot_product_attention_7_v = Split <axis: int = -1> (linear_28, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_7 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_7_q, node_scaled_dot_product_attention_7_k, node_scaled_dot_product_attention_7_v)
|
|
val_144 = MatMul (scaled_dot_product_attention_7, val_32)
|
|
linear_29 = Add (val_144, "visual.trunk.blocks.7.attn.proj.bias")
|
|
add_708 = Add (add_647, linear_29)
|
|
layer_norm_15 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_708, "visual.trunk.blocks.7.norm2.weight", "visual.trunk.blocks.7.norm2.bias")
|
|
val_145 = MatMul (layer_norm_15, val_33)
|
|
linear_30 = Add (val_145, "visual.trunk.blocks.7.mlp.fc1.bias")
|
|
gelu_7 = Gelu <approximate: string = "none"> (linear_30)
|
|
val_146 = MatMul (gelu_7, val_34)
|
|
linear_31 = Add (val_146, "visual.trunk.blocks.7.mlp.fc2.bias")
|
|
add_737 = Add (add_708, linear_31)
|
|
layer_norm_16 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_737, "visual.trunk.blocks.8.norm1.weight", "visual.trunk.blocks.8.norm1.bias")
|
|
val_147 = MatMul (layer_norm_16, val_35)
|
|
linear_32 = Add (val_147, "visual.trunk.blocks.8.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_8_qkv_split] node_scaled_dot_product_attention_8_q, node_scaled_dot_product_attention_8_k, node_scaled_dot_product_attention_8_v = Split <axis: int = -1> (linear_32, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_8 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_8_q, node_scaled_dot_product_attention_8_k, node_scaled_dot_product_attention_8_v)
|
|
val_148 = MatMul (scaled_dot_product_attention_8, val_36)
|
|
linear_33 = Add (val_148, "visual.trunk.blocks.8.attn.proj.bias")
|
|
add_798 = Add (add_737, linear_33)
|
|
layer_norm_17 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_798, "visual.trunk.blocks.8.norm2.weight", "visual.trunk.blocks.8.norm2.bias")
|
|
val_149 = MatMul (layer_norm_17, val_37)
|
|
linear_34 = Add (val_149, "visual.trunk.blocks.8.mlp.fc1.bias")
|
|
gelu_8 = Gelu <approximate: string = "none"> (linear_34)
|
|
val_150 = MatMul (gelu_8, val_38)
|
|
linear_35 = Add (val_150, "visual.trunk.blocks.8.mlp.fc2.bias")
|
|
add_827 = Add (add_798, linear_35)
|
|
layer_norm_18 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_827, "visual.trunk.blocks.9.norm1.weight", "visual.trunk.blocks.9.norm1.bias")
|
|
val_151 = MatMul (layer_norm_18, val_39)
|
|
linear_36 = Add (val_151, "visual.trunk.blocks.9.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_9_qkv_split] node_scaled_dot_product_attention_9_q, node_scaled_dot_product_attention_9_k, node_scaled_dot_product_attention_9_v = Split <axis: int = -1> (linear_36, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_9 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_9_q, node_scaled_dot_product_attention_9_k, node_scaled_dot_product_attention_9_v)
|
|
val_152 = MatMul (scaled_dot_product_attention_9, val_40)
|
|
linear_37 = Add (val_152, "visual.trunk.blocks.9.attn.proj.bias")
|
|
add_888 = Add (add_827, linear_37)
|
|
layer_norm_19 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_888, "visual.trunk.blocks.9.norm2.weight", "visual.trunk.blocks.9.norm2.bias")
|
|
val_153 = MatMul (layer_norm_19, val_41)
|
|
linear_38 = Add (val_153, "visual.trunk.blocks.9.mlp.fc1.bias")
|
|
gelu_9 = Gelu <approximate: string = "none"> (linear_38)
|
|
val_154 = MatMul (gelu_9, val_42)
|
|
linear_39 = Add (val_154, "visual.trunk.blocks.9.mlp.fc2.bias")
|
|
add_917 = Add (add_888, linear_39)
|
|
layer_norm_20 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_917, "visual.trunk.blocks.10.norm1.weight", "visual.trunk.blocks.10.norm1.bias")
|
|
val_155 = MatMul (layer_norm_20, val_43)
|
|
linear_40 = Add (val_155, "visual.trunk.blocks.10.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_10_qkv_split] node_scaled_dot_product_attention_10_q, node_scaled_dot_product_attention_10_k, node_scaled_dot_product_attention_10_v = Split <axis: int = -1> (linear_40, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_10 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_10_q, node_scaled_dot_product_attention_10_k, node_scaled_dot_product_attention_10_v)
|
|
val_156 = MatMul (scaled_dot_product_attention_10, val_44)
|
|
linear_41 = Add (val_156, "visual.trunk.blocks.10.attn.proj.bias")
|
|
add_978 = Add (add_917, linear_41)
|
|
layer_norm_21 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_978, "visual.trunk.blocks.10.norm2.weight", "visual.trunk.blocks.10.norm2.bias")
|
|
val_157 = MatMul (layer_norm_21, val_45)
|
|
linear_42 = Add (val_157, "visual.trunk.blocks.10.mlp.fc1.bias")
|
|
gelu_10 = Gelu <approximate: string = "none"> (linear_42)
|
|
val_158 = MatMul (gelu_10, val_46)
|
|
linear_43 = Add (val_158, "visual.trunk.blocks.10.mlp.fc2.bias")
|
|
add_1007 = Add (add_978, linear_43)
|
|
layer_norm_22 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1007, "visual.trunk.blocks.11.norm1.weight", "visual.trunk.blocks.11.norm1.bias")
|
|
val_159 = MatMul (layer_norm_22, val_47)
|
|
linear_44 = Add (val_159, "visual.trunk.blocks.11.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_11_qkv_split] node_scaled_dot_product_attention_11_q, node_scaled_dot_product_attention_11_k, node_scaled_dot_product_attention_11_v = Split <axis: int = -1> (linear_44, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_11 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_11_q, node_scaled_dot_product_attention_11_k, node_scaled_dot_product_attention_11_v)
|
|
val_160 = MatMul (scaled_dot_product_attention_11, val_48)
|
|
linear_45 = Add (val_160, "visual.trunk.blocks.11.attn.proj.bias")
|
|
add_1068 = Add (add_1007, linear_45)
|
|
layer_norm_23 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1068, "visual.trunk.blocks.11.norm2.weight", "visual.trunk.blocks.11.norm2.bias")
|
|
val_161 = MatMul (layer_norm_23, val_49)
|
|
linear_46 = Add (val_161, "visual.trunk.blocks.11.mlp.fc1.bias")
|
|
gelu_11 = Gelu <approximate: string = "none"> (linear_46)
|
|
val_162 = MatMul (gelu_11, val_50)
|
|
linear_47 = Add (val_162, "visual.trunk.blocks.11.mlp.fc2.bias")
|
|
add_1097 = Add (add_1068, linear_47)
|
|
layer_norm_24 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1097, "visual.trunk.blocks.12.norm1.weight", "visual.trunk.blocks.12.norm1.bias")
|
|
val_163 = MatMul (layer_norm_24, val_51)
|
|
linear_48 = Add (val_163, "visual.trunk.blocks.12.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_12_qkv_split] node_scaled_dot_product_attention_12_q, node_scaled_dot_product_attention_12_k, node_scaled_dot_product_attention_12_v = Split <axis: int = -1> (linear_48, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_12 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_12_q, node_scaled_dot_product_attention_12_k, node_scaled_dot_product_attention_12_v)
|
|
val_164 = MatMul (scaled_dot_product_attention_12, val_52)
|
|
linear_49 = Add (val_164, "visual.trunk.blocks.12.attn.proj.bias")
|
|
add_1158 = Add (add_1097, linear_49)
|
|
layer_norm_25 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1158, "visual.trunk.blocks.12.norm2.weight", "visual.trunk.blocks.12.norm2.bias")
|
|
val_165 = MatMul (layer_norm_25, val_53)
|
|
linear_50 = Add (val_165, "visual.trunk.blocks.12.mlp.fc1.bias")
|
|
gelu_12 = Gelu <approximate: string = "none"> (linear_50)
|
|
val_166 = MatMul (gelu_12, val_54)
|
|
linear_51 = Add (val_166, "visual.trunk.blocks.12.mlp.fc2.bias")
|
|
add_1187 = Add (add_1158, linear_51)
|
|
layer_norm_26 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1187, "visual.trunk.blocks.13.norm1.weight", "visual.trunk.blocks.13.norm1.bias")
|
|
val_167 = MatMul (layer_norm_26, val_55)
|
|
linear_52 = Add (val_167, "visual.trunk.blocks.13.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_13_qkv_split] node_scaled_dot_product_attention_13_q, node_scaled_dot_product_attention_13_k, node_scaled_dot_product_attention_13_v = Split <axis: int = -1> (linear_52, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_13 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_13_q, node_scaled_dot_product_attention_13_k, node_scaled_dot_product_attention_13_v)
|
|
val_168 = MatMul (scaled_dot_product_attention_13, val_56)
|
|
linear_53 = Add (val_168, "visual.trunk.blocks.13.attn.proj.bias")
|
|
add_1248 = Add (add_1187, linear_53)
|
|
layer_norm_27 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1248, "visual.trunk.blocks.13.norm2.weight", "visual.trunk.blocks.13.norm2.bias")
|
|
val_169 = MatMul (layer_norm_27, val_57)
|
|
linear_54 = Add (val_169, "visual.trunk.blocks.13.mlp.fc1.bias")
|
|
gelu_13 = Gelu <approximate: string = "none"> (linear_54)
|
|
val_170 = MatMul (gelu_13, val_58)
|
|
linear_55 = Add (val_170, "visual.trunk.blocks.13.mlp.fc2.bias")
|
|
add_1277 = Add (add_1248, linear_55)
|
|
layer_norm_28 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1277, "visual.trunk.blocks.14.norm1.weight", "visual.trunk.blocks.14.norm1.bias")
|
|
val_171 = MatMul (layer_norm_28, val_59)
|
|
linear_56 = Add (val_171, "visual.trunk.blocks.14.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_14_qkv_split] node_scaled_dot_product_attention_14_q, node_scaled_dot_product_attention_14_k, node_scaled_dot_product_attention_14_v = Split <axis: int = -1> (linear_56, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_14 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_14_q, node_scaled_dot_product_attention_14_k, node_scaled_dot_product_attention_14_v)
|
|
val_172 = MatMul (scaled_dot_product_attention_14, val_60)
|
|
linear_57 = Add (val_172, "visual.trunk.blocks.14.attn.proj.bias")
|
|
add_1338 = Add (add_1277, linear_57)
|
|
layer_norm_29 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1338, "visual.trunk.blocks.14.norm2.weight", "visual.trunk.blocks.14.norm2.bias")
|
|
val_173 = MatMul (layer_norm_29, val_61)
|
|
linear_58 = Add (val_173, "visual.trunk.blocks.14.mlp.fc1.bias")
|
|
gelu_14 = Gelu <approximate: string = "none"> (linear_58)
|
|
val_174 = MatMul (gelu_14, val_62)
|
|
linear_59 = Add (val_174, "visual.trunk.blocks.14.mlp.fc2.bias")
|
|
add_1367 = Add (add_1338, linear_59)
|
|
layer_norm_30 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1367, "visual.trunk.blocks.15.norm1.weight", "visual.trunk.blocks.15.norm1.bias")
|
|
val_175 = MatMul (layer_norm_30, val_63)
|
|
linear_60 = Add (val_175, "visual.trunk.blocks.15.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_15_qkv_split] node_scaled_dot_product_attention_15_q, node_scaled_dot_product_attention_15_k, node_scaled_dot_product_attention_15_v = Split <axis: int = -1> (linear_60, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_15 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_15_q, node_scaled_dot_product_attention_15_k, node_scaled_dot_product_attention_15_v)
|
|
val_176 = MatMul (scaled_dot_product_attention_15, val_64)
|
|
linear_61 = Add (val_176, "visual.trunk.blocks.15.attn.proj.bias")
|
|
add_1428 = Add (add_1367, linear_61)
|
|
layer_norm_31 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1428, "visual.trunk.blocks.15.norm2.weight", "visual.trunk.blocks.15.norm2.bias")
|
|
val_177 = MatMul (layer_norm_31, val_65)
|
|
linear_62 = Add (val_177, "visual.trunk.blocks.15.mlp.fc1.bias")
|
|
gelu_15 = Gelu <approximate: string = "none"> (linear_62)
|
|
val_178 = MatMul (gelu_15, val_66)
|
|
linear_63 = Add (val_178, "visual.trunk.blocks.15.mlp.fc2.bias")
|
|
add_1457 = Add (add_1428, linear_63)
|
|
layer_norm_32 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1457, "visual.trunk.blocks.16.norm1.weight", "visual.trunk.blocks.16.norm1.bias")
|
|
val_179 = MatMul (layer_norm_32, val_67)
|
|
linear_64 = Add (val_179, "visual.trunk.blocks.16.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_16_qkv_split] node_scaled_dot_product_attention_16_q, node_scaled_dot_product_attention_16_k, node_scaled_dot_product_attention_16_v = Split <axis: int = -1> (linear_64, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_16 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_16_q, node_scaled_dot_product_attention_16_k, node_scaled_dot_product_attention_16_v)
|
|
val_180 = MatMul (scaled_dot_product_attention_16, val_68)
|
|
linear_65 = Add (val_180, "visual.trunk.blocks.16.attn.proj.bias")
|
|
add_1518 = Add (add_1457, linear_65)
|
|
layer_norm_33 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1518, "visual.trunk.blocks.16.norm2.weight", "visual.trunk.blocks.16.norm2.bias")
|
|
val_181 = MatMul (layer_norm_33, val_69)
|
|
linear_66 = Add (val_181, "visual.trunk.blocks.16.mlp.fc1.bias")
|
|
gelu_16 = Gelu <approximate: string = "none"> (linear_66)
|
|
val_182 = MatMul (gelu_16, val_70)
|
|
linear_67 = Add (val_182, "visual.trunk.blocks.16.mlp.fc2.bias")
|
|
add_1547 = Add (add_1518, linear_67)
|
|
layer_norm_34 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1547, "visual.trunk.blocks.17.norm1.weight", "visual.trunk.blocks.17.norm1.bias")
|
|
val_183 = MatMul (layer_norm_34, val_71)
|
|
linear_68 = Add (val_183, "visual.trunk.blocks.17.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_17_qkv_split] node_scaled_dot_product_attention_17_q, node_scaled_dot_product_attention_17_k, node_scaled_dot_product_attention_17_v = Split <axis: int = -1> (linear_68, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_17 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_17_q, node_scaled_dot_product_attention_17_k, node_scaled_dot_product_attention_17_v)
|
|
val_184 = MatMul (scaled_dot_product_attention_17, val_72)
|
|
linear_69 = Add (val_184, "visual.trunk.blocks.17.attn.proj.bias")
|
|
add_1608 = Add (add_1547, linear_69)
|
|
layer_norm_35 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1608, "visual.trunk.blocks.17.norm2.weight", "visual.trunk.blocks.17.norm2.bias")
|
|
val_185 = MatMul (layer_norm_35, val_73)
|
|
linear_70 = Add (val_185, "visual.trunk.blocks.17.mlp.fc1.bias")
|
|
gelu_17 = Gelu <approximate: string = "none"> (linear_70)
|
|
val_186 = MatMul (gelu_17, val_74)
|
|
linear_71 = Add (val_186, "visual.trunk.blocks.17.mlp.fc2.bias")
|
|
add_1637 = Add (add_1608, linear_71)
|
|
layer_norm_36 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1637, "visual.trunk.blocks.18.norm1.weight", "visual.trunk.blocks.18.norm1.bias")
|
|
val_187 = MatMul (layer_norm_36, val_75)
|
|
linear_72 = Add (val_187, "visual.trunk.blocks.18.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_18_qkv_split] node_scaled_dot_product_attention_18_q, node_scaled_dot_product_attention_18_k, node_scaled_dot_product_attention_18_v = Split <axis: int = -1> (linear_72, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_18 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_18_q, node_scaled_dot_product_attention_18_k, node_scaled_dot_product_attention_18_v)
|
|
val_188 = MatMul (scaled_dot_product_attention_18, val_76)
|
|
linear_73 = Add (val_188, "visual.trunk.blocks.18.attn.proj.bias")
|
|
add_1698 = Add (add_1637, linear_73)
|
|
layer_norm_37 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1698, "visual.trunk.blocks.18.norm2.weight", "visual.trunk.blocks.18.norm2.bias")
|
|
val_189 = MatMul (layer_norm_37, val_77)
|
|
linear_74 = Add (val_189, "visual.trunk.blocks.18.mlp.fc1.bias")
|
|
gelu_18 = Gelu <approximate: string = "none"> (linear_74)
|
|
val_190 = MatMul (gelu_18, val_78)
|
|
linear_75 = Add (val_190, "visual.trunk.blocks.18.mlp.fc2.bias")
|
|
add_1727 = Add (add_1698, linear_75)
|
|
layer_norm_38 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1727, "visual.trunk.blocks.19.norm1.weight", "visual.trunk.blocks.19.norm1.bias")
|
|
val_191 = MatMul (layer_norm_38, val_79)
|
|
linear_76 = Add (val_191, "visual.trunk.blocks.19.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_19_qkv_split] node_scaled_dot_product_attention_19_q, node_scaled_dot_product_attention_19_k, node_scaled_dot_product_attention_19_v = Split <axis: int = -1> (linear_76, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_19 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_19_q, node_scaled_dot_product_attention_19_k, node_scaled_dot_product_attention_19_v)
|
|
val_192 = MatMul (scaled_dot_product_attention_19, val_80)
|
|
linear_77 = Add (val_192, "visual.trunk.blocks.19.attn.proj.bias")
|
|
add_1788 = Add (add_1727, linear_77)
|
|
layer_norm_39 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1788, "visual.trunk.blocks.19.norm2.weight", "visual.trunk.blocks.19.norm2.bias")
|
|
val_193 = MatMul (layer_norm_39, val_81)
|
|
linear_78 = Add (val_193, "visual.trunk.blocks.19.mlp.fc1.bias")
|
|
gelu_19 = Gelu <approximate: string = "none"> (linear_78)
|
|
val_194 = MatMul (gelu_19, val_82)
|
|
linear_79 = Add (val_194, "visual.trunk.blocks.19.mlp.fc2.bias")
|
|
add_1817 = Add (add_1788, linear_79)
|
|
layer_norm_40 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1817, "visual.trunk.blocks.20.norm1.weight", "visual.trunk.blocks.20.norm1.bias")
|
|
val_195 = MatMul (layer_norm_40, val_83)
|
|
linear_80 = Add (val_195, "visual.trunk.blocks.20.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_20_qkv_split] node_scaled_dot_product_attention_20_q, node_scaled_dot_product_attention_20_k, node_scaled_dot_product_attention_20_v = Split <axis: int = -1> (linear_80, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_20 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_20_q, node_scaled_dot_product_attention_20_k, node_scaled_dot_product_attention_20_v)
|
|
val_196 = MatMul (scaled_dot_product_attention_20, val_84)
|
|
linear_81 = Add (val_196, "visual.trunk.blocks.20.attn.proj.bias")
|
|
add_1878 = Add (add_1817, linear_81)
|
|
layer_norm_41 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1878, "visual.trunk.blocks.20.norm2.weight", "visual.trunk.blocks.20.norm2.bias")
|
|
val_197 = MatMul (layer_norm_41, val_85)
|
|
linear_82 = Add (val_197, "visual.trunk.blocks.20.mlp.fc1.bias")
|
|
gelu_20 = Gelu <approximate: string = "none"> (linear_82)
|
|
val_198 = MatMul (gelu_20, val_86)
|
|
linear_83 = Add (val_198, "visual.trunk.blocks.20.mlp.fc2.bias")
|
|
add_1907 = Add (add_1878, linear_83)
|
|
layer_norm_42 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1907, "visual.trunk.blocks.21.norm1.weight", "visual.trunk.blocks.21.norm1.bias")
|
|
val_199 = MatMul (layer_norm_42, val_87)
|
|
linear_84 = Add (val_199, "visual.trunk.blocks.21.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_21_qkv_split] node_scaled_dot_product_attention_21_q, node_scaled_dot_product_attention_21_k, node_scaled_dot_product_attention_21_v = Split <axis: int = -1> (linear_84, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_21 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_21_q, node_scaled_dot_product_attention_21_k, node_scaled_dot_product_attention_21_v)
|
|
val_200 = MatMul (scaled_dot_product_attention_21, val_88)
|
|
linear_85 = Add (val_200, "visual.trunk.blocks.21.attn.proj.bias")
|
|
add_1968 = Add (add_1907, linear_85)
|
|
layer_norm_43 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1968, "visual.trunk.blocks.21.norm2.weight", "visual.trunk.blocks.21.norm2.bias")
|
|
val_201 = MatMul (layer_norm_43, val_89)
|
|
linear_86 = Add (val_201, "visual.trunk.blocks.21.mlp.fc1.bias")
|
|
gelu_21 = Gelu <approximate: string = "none"> (linear_86)
|
|
val_202 = MatMul (gelu_21, val_90)
|
|
linear_87 = Add (val_202, "visual.trunk.blocks.21.mlp.fc2.bias")
|
|
add_1997 = Add (add_1968, linear_87)
|
|
layer_norm_44 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1997, "visual.trunk.blocks.22.norm1.weight", "visual.trunk.blocks.22.norm1.bias")
|
|
val_203 = MatMul (layer_norm_44, val_91)
|
|
linear_88 = Add (val_203, "visual.trunk.blocks.22.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_22_qkv_split] node_scaled_dot_product_attention_22_q, node_scaled_dot_product_attention_22_k, node_scaled_dot_product_attention_22_v = Split <axis: int = -1> (linear_88, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_22 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_22_q, node_scaled_dot_product_attention_22_k, node_scaled_dot_product_attention_22_v)
|
|
val_204 = MatMul (scaled_dot_product_attention_22, val_92)
|
|
linear_89 = Add (val_204, "visual.trunk.blocks.22.attn.proj.bias")
|
|
add_2058 = Add (add_1997, linear_89)
|
|
layer_norm_45 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2058, "visual.trunk.blocks.22.norm2.weight", "visual.trunk.blocks.22.norm2.bias")
|
|
val_205 = MatMul (layer_norm_45, val_93)
|
|
linear_90 = Add (val_205, "visual.trunk.blocks.22.mlp.fc1.bias")
|
|
gelu_22 = Gelu <approximate: string = "none"> (linear_90)
|
|
val_206 = MatMul (gelu_22, val_94)
|
|
linear_91 = Add (val_206, "visual.trunk.blocks.22.mlp.fc2.bias")
|
|
add_2087 = Add (add_2058, linear_91)
|
|
layer_norm_46 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2087, "visual.trunk.blocks.23.norm1.weight", "visual.trunk.blocks.23.norm1.bias")
|
|
val_207 = MatMul (layer_norm_46, val_95)
|
|
linear_92 = Add (val_207, "visual.trunk.blocks.23.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_23_qkv_split] node_scaled_dot_product_attention_23_q, node_scaled_dot_product_attention_23_k, node_scaled_dot_product_attention_23_v = Split <axis: int = -1> (linear_92, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_23 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_23_q, node_scaled_dot_product_attention_23_k, node_scaled_dot_product_attention_23_v)
|
|
val_208 = MatMul (scaled_dot_product_attention_23, val_96)
|
|
linear_93 = Add (val_208, "visual.trunk.blocks.23.attn.proj.bias")
|
|
add_2148 = Add (add_2087, linear_93)
|
|
layer_norm_47 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2148, "visual.trunk.blocks.23.norm2.weight", "visual.trunk.blocks.23.norm2.bias")
|
|
val_209 = MatMul (layer_norm_47, val_97)
|
|
linear_94 = Add (val_209, "visual.trunk.blocks.23.mlp.fc1.bias")
|
|
gelu_23 = Gelu <approximate: string = "none"> (linear_94)
|
|
val_210 = MatMul (gelu_23, val_98)
|
|
linear_95 = Add (val_210, "visual.trunk.blocks.23.mlp.fc2.bias")
|
|
add_2177 = Add (add_2148, linear_95)
|
|
layer_norm_48 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2177, "visual.trunk.blocks.24.norm1.weight", "visual.trunk.blocks.24.norm1.bias")
|
|
val_211 = MatMul (layer_norm_48, val_99)
|
|
linear_96 = Add (val_211, "visual.trunk.blocks.24.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_24_qkv_split] node_scaled_dot_product_attention_24_q, node_scaled_dot_product_attention_24_k, node_scaled_dot_product_attention_24_v = Split <axis: int = -1> (linear_96, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_24 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_24_q, node_scaled_dot_product_attention_24_k, node_scaled_dot_product_attention_24_v)
|
|
val_212 = MatMul (scaled_dot_product_attention_24, val_100)
|
|
linear_97 = Add (val_212, "visual.trunk.blocks.24.attn.proj.bias")
|
|
add_2238 = Add (add_2177, linear_97)
|
|
layer_norm_49 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2238, "visual.trunk.blocks.24.norm2.weight", "visual.trunk.blocks.24.norm2.bias")
|
|
val_213 = MatMul (layer_norm_49, val_101)
|
|
linear_98 = Add (val_213, "visual.trunk.blocks.24.mlp.fc1.bias")
|
|
gelu_24 = Gelu <approximate: string = "none"> (linear_98)
|
|
val_214 = MatMul (gelu_24, val_102)
|
|
linear_99 = Add (val_214, "visual.trunk.blocks.24.mlp.fc2.bias")
|
|
add_2267 = Add (add_2238, linear_99)
|
|
layer_norm_50 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2267, "visual.trunk.blocks.25.norm1.weight", "visual.trunk.blocks.25.norm1.bias")
|
|
val_215 = MatMul (layer_norm_50, val_103)
|
|
linear_100 = Add (val_215, "visual.trunk.blocks.25.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_25_qkv_split] node_scaled_dot_product_attention_25_q, node_scaled_dot_product_attention_25_k, node_scaled_dot_product_attention_25_v = Split <axis: int = -1> (linear_100, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_25 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_25_q, node_scaled_dot_product_attention_25_k, node_scaled_dot_product_attention_25_v)
|
|
val_216 = MatMul (scaled_dot_product_attention_25, val_104)
|
|
linear_101 = Add (val_216, "visual.trunk.blocks.25.attn.proj.bias")
|
|
add_2328 = Add (add_2267, linear_101)
|
|
layer_norm_51 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2328, "visual.trunk.blocks.25.norm2.weight", "visual.trunk.blocks.25.norm2.bias")
|
|
val_217 = MatMul (layer_norm_51, val_105)
|
|
linear_102 = Add (val_217, "visual.trunk.blocks.25.mlp.fc1.bias")
|
|
gelu_25 = Gelu <approximate: string = "none"> (linear_102)
|
|
val_218 = MatMul (gelu_25, val_106)
|
|
linear_103 = Add (val_218, "visual.trunk.blocks.25.mlp.fc2.bias")
|
|
add_2357 = Add (add_2328, linear_103)
|
|
layer_norm_52 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2357, "visual.trunk.blocks.26.norm1.weight", "visual.trunk.blocks.26.norm1.bias")
|
|
val_219 = MatMul (layer_norm_52, val_107)
|
|
linear_104 = Add (val_219, "visual.trunk.blocks.26.attn.qkv.bias")
|
|
[node_scaled_dot_product_attention_26_qkv_split] node_scaled_dot_product_attention_26_q, node_scaled_dot_product_attention_26_k, node_scaled_dot_product_attention_26_v = Split <axis: int = -1> (linear_104, attn3d_split_3x1152)
|
|
scaled_dot_product_attention_26 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_26_q, node_scaled_dot_product_attention_26_k, node_scaled_dot_product_attention_26_v)
|
|
val_220 = MatMul (scaled_dot_product_attention_26, val_108)
|
|
linear_105 = Add (val_220, "visual.trunk.blocks.26.attn.proj.bias")
|
|
add_2418 = Add (add_2357, linear_105)
|
|
layer_norm_53 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2418, "visual.trunk.blocks.26.norm2.weight", "visual.trunk.blocks.26.norm2.bias")
|
|
val_221 = MatMul (layer_norm_53, val_109)
|
|
linear_106 = Add (val_221, "visual.trunk.blocks.26.mlp.fc1.bias")
|
|
gelu_26 = Gelu <approximate: string = "none"> (linear_106)
|
|
val_222 = MatMul (gelu_26, val_110)
|
|
linear_107 = Add (val_222, "visual.trunk.blocks.26.mlp.fc2.bias")
|
|
add_2447 = Add (add_2418, linear_107)
|
|
layer_norm_54 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2447, "visual.trunk.norm.weight", "visual.trunk.norm.bias")
|
|
[ez_slice] image_ez_s = Slice (image_f32, image_ez_starts, image_ez_ends, image_ez_axes)
|
|
[ez_flatten] image_ez_r = Reshape (image_ez_s, val_0)
|
|
[ez_mul] image_ez = Mul (image_ez_r, image_ez_zero)
|
|
val_223 = MatMul (layer_norm_54, val_111)
|
|
linear_109 = Add (val_223, "visual.trunk.attn_pool.kv.bias")
|
|
[node_scaled_dot_product_attention_27_qkv_split] node_scaled_dot_product_attention_27_k, node_scaled_dot_product_attention_27_v = Split <axis: int = -1> (linear_109, attn3d_split_2x1152)
|
|
[node_scaled_dot_product_attention_27_q_col] node_scaled_dot_product_attention_27_q_col_out = Unsqueeze (image_ez, node_scaled_dot_product_attention_27_q_col_axes)
|
|
[node_scaled_dot_product_attention_27_q_bcast] node_scaled_dot_product_attention_27_q = Add (node_scaled_dot_product_attention_27_q3, node_scaled_dot_product_attention_27_q_col_out)
|
|
scaled_dot_product_attention_27 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_27_q, node_scaled_dot_product_attention_27_k, node_scaled_dot_product_attention_27_v)
|
|
val_224 = MatMul (scaled_dot_product_attention_27, val_112)
|
|
linear_110 = Add (val_224, "visual.trunk.attn_pool.proj.bias")
|
|
layer_norm_55 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (linear_110, "visual.trunk.attn_pool.norm.weight", "visual.trunk.attn_pool.norm.bias")
|
|
val_225 = MatMul (layer_norm_55, val_113)
|
|
linear_111 = Add (val_225, "visual.trunk.attn_pool.mlp.fc1.bias")
|
|
gelu_27 = Gelu <approximate: string = "none"> (linear_111)
|
|
val_226 = MatMul (gelu_27, val_114)
|
|
linear_112 = Add (val_226, "visual.trunk.attn_pool.mlp.fc2.bias")
|
|
add_2545 = Add (linear_110, linear_112)
|
|
select = Squeeze (add_2545, val_2)
|
|
[node_linalg_vector_norm] linalg_vector_norm = ReduceL2 <keepdims: int = 1, noop_with_empty_axes: int = 0> (select, val_0)
|
|
[node_clamp_min] clamp_min = Clip (linalg_vector_norm, val_1)
|
|
[node_div] image_embedding = Div (select, clamp_min)
|
|
}
|
|
|
|
weights:
|
|
attn3d_split_2x1152 INT64[2] 414304bef26c
|
|
attn3d_split_3x1152 INT64[3] 125b254aeb28
|
|
image_ez_axes INT64[3] e2e2033ae7e1
|
|
image_ez_ends INT64[3] 605390e5a369
|
|
image_ez_starts INT64[3] 9d908ecfb6b2
|
|
image_ez_zero FLOAT[] df3f619804a9
|
|
node_scaled_dot_product_attention_27_q3 FLOAT[1,1,1152] 123062ca2341
|
|
node_scaled_dot_product_attention_27_q_col_axes INT64[2] 0c730b69905c
|
|
val_0 INT64[1] 12a3ae445661
|
|
val_1 FLOAT[] 6708d9be4956
|
|
val_10 FLOAT[4304,1152] c5dd20b2aab2
|
|
val_100 FLOAT[1152,1152] 9ffb60acbc85
|
|
val_101 FLOAT[1152,4304] 8cc3edad872c
|
|
val_102 FLOAT[4304,1152] d693bb03ea0c
|
|
val_103 FLOAT[1152,3456] 89f53f80324b
|
|
val_104 FLOAT[1152,1152] 57d39b4a124e
|
|
val_105 FLOAT[1152,4304] 4c1db412e132
|
|
val_106 FLOAT[4304,1152] d31c45e9dbd4
|
|
val_107 FLOAT[1152,3456] 2a19416ef19f
|
|
val_108 FLOAT[1152,1152] bfb8bd1ef09f
|
|
val_109 FLOAT[1152,4304] 8ca8ab39fd36
|
|
val_11 FLOAT[1152,3456] 501a692ff72c
|
|
val_110 FLOAT[4304,1152] 888c2580d3d7
|
|
val_111 FLOAT[1152,2304] bc6c5234f0ab
|
|
val_112 FLOAT[1152,1152] 1908eeddb1fd
|
|
val_113 FLOAT[1152,4304] 2b67b1111bf8
|
|
val_114 FLOAT[4304,1152] 169f514660df
|
|
val_12 FLOAT[1152,1152] da88c88fd6c8
|
|
val_13 FLOAT[1152,4304] 692235ab9081
|
|
val_14 FLOAT[4304,1152] b2d5d6d751a6
|
|
val_15 FLOAT[1152,3456] e5d894566782
|
|
val_16 FLOAT[1152,1152] 150d8f31bbee
|
|
val_17 FLOAT[1152,4304] 33546044c78f
|
|
val_18 FLOAT[4304,1152] 4845ce7b8bc1
|
|
val_19 FLOAT[1152,3456] 7f1073b04ebf
|
|
val_2 INT64[1] 7c9fa136d441
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val_20 FLOAT[1152,1152] c9a36f5370f7
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val_21 FLOAT[1152,4304] ca098fbeef2d
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val_22 FLOAT[4304,1152] 953cb977769b
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val_23 FLOAT[1152,3456] f7f4186a2274
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val_24 FLOAT[1152,1152] 9634ff3fe925
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val_25 FLOAT[1152,4304] 4a42b402e67c
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val_26 FLOAT[4304,1152] 69abcf79a53d
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val_27 FLOAT[1152,3456] 2bd3b2168adf
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val_28 FLOAT[1152,1152] ea35ed2639fe
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val_29 FLOAT[1152,4304] 58ec918e559e
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val_3 FLOAT[1152,3456] b7d39dc8d977
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val_30 FLOAT[4304,1152] 30006defbf5a
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val_31 FLOAT[1152,3456] cbf97cf8122b
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val_32 FLOAT[1152,1152] 93e082e93ef3
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val_33 FLOAT[1152,4304] e3ed5ca2c8c9
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val_34 FLOAT[4304,1152] 7aaee19e6bb8
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val_35 FLOAT[1152,3456] 13cf872a4d8f
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val_36 FLOAT[1152,1152] 12980e9c66ca
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val_37 FLOAT[1152,4304] 5b1b53a16731
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val_38 FLOAT[4304,1152] 2e260e680231
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val_39 FLOAT[1152,3456] 8ea16bda590c
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val_4 FLOAT[1152,1152] de397b78d005
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val_40 FLOAT[1152,1152] 9911d2fb8445
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val_41 FLOAT[1152,4304] 19f343bffe4e
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val_42 FLOAT[4304,1152] 89dff656c2d2
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val_43 FLOAT[1152,3456] 2d2cc8f22e06
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val_44 FLOAT[1152,1152] 977ab901ed1f
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val_45 FLOAT[1152,4304] 153c7be12f9a
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val_46 FLOAT[4304,1152] 88234f7bc426
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val_47 FLOAT[1152,3456] 257247285928
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val_48 FLOAT[1152,1152] 4c8f6e378291
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val_49 FLOAT[1152,4304] af5bc04ee774
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val_5 FLOAT[1152,4304] bd915405c4b7
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val_50 FLOAT[4304,1152] cefd08dacbd6
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val_51 FLOAT[1152,3456] 4d10814e88ef
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val_52 FLOAT[1152,1152] b5a298bc45b4
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val_53 FLOAT[1152,4304] f831afea0cb2
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val_54 FLOAT[4304,1152] ad2e50f7af26
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val_55 FLOAT[1152,3456] c4ab079947f2
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val_56 FLOAT[1152,1152] 3dc267c255c7
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val_57 FLOAT[1152,4304] 54a0c21f7331
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val_58 FLOAT[4304,1152] 464547f5fd13
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val_59 FLOAT[1152,3456] 1559df2ce94b
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val_6 FLOAT[4304,1152] 63f01f0e861d
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val_60 FLOAT[1152,1152] 25d30aea8031
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val_61 FLOAT[1152,4304] ead421030117
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val_62 FLOAT[4304,1152] 744c02a1eb39
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val_63 FLOAT[1152,3456] 16e39d2c89d6
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val_64 FLOAT[1152,1152] 84e0280e9563
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val_65 FLOAT[1152,4304] c9b01f06581b
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val_66 FLOAT[4304,1152] 0171dd258db8
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val_67 FLOAT[1152,3456] 00d4d590a602
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val_68 FLOAT[1152,1152] 0bb6606d4aa0
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val_69 FLOAT[1152,4304] a0873a8eb499
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val_7 FLOAT[1152,3456] 647389de34d0
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val_70 FLOAT[4304,1152] 0b7e36f0bdda
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val_71 FLOAT[1152,3456] 62b7d7d97c6b
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val_72 FLOAT[1152,1152] a0bddc7d0abd
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val_73 FLOAT[1152,4304] aa5aecdcdb24
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val_74 FLOAT[4304,1152] 1ac3530bb0e1
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val_75 FLOAT[1152,3456] 927ad89d1706
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val_76 FLOAT[1152,1152] 1538de08f8dc
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val_77 FLOAT[1152,4304] 1b8cfdf9f0cc
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val_78 FLOAT[4304,1152] 51031b33d5bd
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val_79 FLOAT[1152,3456] 3ad7dc15cf14
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val_8 FLOAT[1152,1152] 7d1b19767a5b
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val_80 FLOAT[1152,1152] 0122ffa9c767
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val_81 FLOAT[1152,4304] e42c5a3b2734
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val_82 FLOAT[4304,1152] a47d5c50294a
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val_83 FLOAT[1152,3456] 410e59c45170
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val_84 FLOAT[1152,1152] f6d140782e55
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val_85 FLOAT[1152,4304] 6aa77198633a
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val_86 FLOAT[4304,1152] 36e34177fe9e
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val_87 FLOAT[1152,3456] 264038960ea8
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val_88 FLOAT[1152,1152] 76e610e692e9
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val_89 FLOAT[1152,4304] d1cc2f687cdf
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val_9 FLOAT[1152,4304] 16027b6ea2d4
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val_90 FLOAT[4304,1152] 29ec1d7db771
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val_91 FLOAT[1152,3456] 39f39ae8ea46
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val_92 FLOAT[1152,1152] 0564399602cc
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val_93 FLOAT[1152,4304] 4f179782513c
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val_94 FLOAT[4304,1152] 5c28da3af947
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val_95 FLOAT[1152,3456] 7bd982a8988d
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val_96 FLOAT[1152,1152] e4f8a7b9e680
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val_97 FLOAT[1152,4304] bb934e2b12b9
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val_98 FLOAT[4304,1152] deb43349ee3d
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val_99 FLOAT[1152,3456] 0fbb4aec691a
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|
view_target INT64[3] 8af5235ecd35
|
|
visual.trunk.attn_pool.kv.bias FLOAT[2304] 898fb8861cb4
|
|
visual.trunk.attn_pool.mlp.fc1.bias FLOAT[4304] 607cec8b0ea0
|
|
visual.trunk.attn_pool.mlp.fc2.bias FLOAT[1152] 1e5cc82cd601
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|
visual.trunk.attn_pool.norm.bias FLOAT[1152] a85bf0fed840
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|
visual.trunk.attn_pool.norm.weight FLOAT[1152] cb889056baf7
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|
visual.trunk.attn_pool.proj.bias FLOAT[1152] 8bef7a6bcf15
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|
visual.trunk.blocks.0.attn.proj.bias FLOAT[1152] 3623ee5138d1
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|
visual.trunk.blocks.0.attn.qkv.bias FLOAT[3456] 306356851fa8
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|
visual.trunk.blocks.0.mlp.fc1.bias FLOAT[4304] cd419483bdab
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|
visual.trunk.blocks.0.mlp.fc2.bias FLOAT[1152] ce03e0a93c52
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visual.trunk.blocks.0.norm1.bias FLOAT[1152] 6a7e5ac5474b
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|
visual.trunk.blocks.0.norm1.weight FLOAT[1152] e0f66e57f0ac
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visual.trunk.blocks.0.norm2.bias FLOAT[1152] f13b58cad35a
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visual.trunk.blocks.0.norm2.weight FLOAT[1152] 1db9b2b83190
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|
visual.trunk.blocks.1.attn.proj.bias FLOAT[1152] f2693457f792
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|
visual.trunk.blocks.1.attn.qkv.bias FLOAT[3456] 8f5cfc935bf5
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|
visual.trunk.blocks.1.mlp.fc1.bias FLOAT[4304] 55a636add87b
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|
visual.trunk.blocks.1.mlp.fc2.bias FLOAT[1152] 6f5654e787a3
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|
visual.trunk.blocks.1.norm1.bias FLOAT[1152] ab1e6e9735ff
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visual.trunk.blocks.1.norm1.weight FLOAT[1152] 9d965e20b0fa
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visual.trunk.blocks.1.norm2.bias FLOAT[1152] 281826b662c3
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|
visual.trunk.blocks.1.norm2.weight FLOAT[1152] ea726dcea5db
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|
visual.trunk.blocks.10.attn.proj.bias FLOAT[1152] 41d0728f60bf
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|
visual.trunk.blocks.10.attn.qkv.bias FLOAT[3456] f3c519288f1f
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|
visual.trunk.blocks.10.mlp.fc1.bias FLOAT[4304] 0175b2797ac8
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|
visual.trunk.blocks.10.mlp.fc2.bias FLOAT[1152] 41557e321a8f
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visual.trunk.blocks.10.norm1.bias FLOAT[1152] 106eec4c1e7e
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visual.trunk.blocks.10.norm1.weight FLOAT[1152] 5a876ca81551
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visual.trunk.blocks.10.norm2.bias FLOAT[1152] f5dd80ef823c
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visual.trunk.blocks.10.norm2.weight FLOAT[1152] cc79fc4bed96
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visual.trunk.blocks.11.attn.proj.bias FLOAT[1152] b67beac85914
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visual.trunk.blocks.11.attn.qkv.bias FLOAT[3456] ffbdd8fc5295
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visual.trunk.blocks.11.mlp.fc1.bias FLOAT[4304] 11f1c862df94
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visual.trunk.blocks.11.mlp.fc2.bias FLOAT[1152] 91beff5f6ae7
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visual.trunk.blocks.11.norm1.bias FLOAT[1152] c3b6318fa8b1
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visual.trunk.blocks.11.norm1.weight FLOAT[1152] a5d82dc5d8ba
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visual.trunk.blocks.11.norm2.bias FLOAT[1152] 80aa98958f1a
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visual.trunk.blocks.11.norm2.weight FLOAT[1152] 95cf987db374
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visual.trunk.blocks.12.attn.proj.bias FLOAT[1152] 7cd3cf9ab30b
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|
visual.trunk.blocks.12.attn.qkv.bias FLOAT[3456] 89cdc7290ee6
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|
visual.trunk.blocks.12.mlp.fc1.bias FLOAT[4304] 90eb1fe73bef
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visual.trunk.blocks.12.mlp.fc2.bias FLOAT[1152] 2ebb99842e0b
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visual.trunk.blocks.12.norm1.bias FLOAT[1152] dd2d49ca36ad
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visual.trunk.blocks.12.norm1.weight FLOAT[1152] 458ddc156f26
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visual.trunk.blocks.12.norm2.bias FLOAT[1152] 25557f48fd0e
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visual.trunk.blocks.12.norm2.weight FLOAT[1152] 6341a0ed712d
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visual.trunk.blocks.13.attn.proj.bias FLOAT[1152] ab2dca0fa6bd
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visual.trunk.blocks.13.attn.qkv.bias FLOAT[3456] 8d1ca41c5575
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visual.trunk.blocks.13.mlp.fc1.bias FLOAT[4304] 2877d90928ef
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visual.trunk.blocks.13.mlp.fc2.bias FLOAT[1152] 4f48e5b9661c
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visual.trunk.blocks.13.norm1.bias FLOAT[1152] d868cdc38017
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visual.trunk.blocks.13.norm1.weight FLOAT[1152] e145409fcc05
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visual.trunk.blocks.13.norm2.bias FLOAT[1152] e0a7effc34c9
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visual.trunk.blocks.13.norm2.weight FLOAT[1152] f8e951f804c2
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visual.trunk.blocks.14.attn.proj.bias FLOAT[1152] 524ae3aed39d
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|
visual.trunk.blocks.14.attn.qkv.bias FLOAT[3456] 5510d2bda238
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visual.trunk.blocks.14.mlp.fc1.bias FLOAT[4304] fd91960ba169
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visual.trunk.blocks.14.mlp.fc2.bias FLOAT[1152] 3f62c0e13c34
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visual.trunk.blocks.14.norm1.bias FLOAT[1152] a46016024e9b
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visual.trunk.blocks.14.norm1.weight FLOAT[1152] a22cb2985a8b
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visual.trunk.blocks.14.norm2.bias FLOAT[1152] f8551642c638
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visual.trunk.blocks.14.norm2.weight FLOAT[1152] 25732666765a
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visual.trunk.blocks.15.attn.proj.bias FLOAT[1152] d9df66a91117
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visual.trunk.blocks.15.attn.qkv.bias FLOAT[3456] 70fa165a31f9
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visual.trunk.blocks.15.mlp.fc1.bias FLOAT[4304] 0431bc85ca7b
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visual.trunk.blocks.15.mlp.fc2.bias FLOAT[1152] ee8e52237db5
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visual.trunk.blocks.15.norm1.bias FLOAT[1152] 6717c608bafd
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visual.trunk.blocks.15.norm1.weight FLOAT[1152] a29bbc7caa1f
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visual.trunk.blocks.15.norm2.bias FLOAT[1152] 8073552abf08
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visual.trunk.blocks.15.norm2.weight FLOAT[1152] 97685ae3b863
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visual.trunk.blocks.16.attn.proj.bias FLOAT[1152] 1d2d9be32e8b
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visual.trunk.blocks.16.attn.qkv.bias FLOAT[3456] bf7ed2802073
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visual.trunk.blocks.16.mlp.fc1.bias FLOAT[4304] 4bae0e367756
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visual.trunk.blocks.16.mlp.fc2.bias FLOAT[1152] db3cc42740f6
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visual.trunk.blocks.16.norm1.bias FLOAT[1152] e6cda96d5307
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visual.trunk.blocks.16.norm1.weight FLOAT[1152] c613ee8990d1
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visual.trunk.blocks.16.norm2.bias FLOAT[1152] 3c2293838fdf
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visual.trunk.blocks.16.norm2.weight FLOAT[1152] 843cff81e0ed
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visual.trunk.blocks.17.attn.proj.bias FLOAT[1152] cee2117752eb
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visual.trunk.blocks.17.attn.qkv.bias FLOAT[3456] 82351435565b
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visual.trunk.blocks.17.mlp.fc1.bias FLOAT[4304] 2aa2f4edd20c
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visual.trunk.blocks.17.mlp.fc2.bias FLOAT[1152] dedc44186f67
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visual.trunk.blocks.17.norm1.bias FLOAT[1152] 51607c62f290
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visual.trunk.blocks.17.norm1.weight FLOAT[1152] 0d49b75a2c8f
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visual.trunk.blocks.17.norm2.bias FLOAT[1152] a0fd8a784737
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visual.trunk.blocks.17.norm2.weight FLOAT[1152] 9a821a6d747f
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visual.trunk.blocks.18.attn.proj.bias FLOAT[1152] b530b6137369
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visual.trunk.blocks.18.attn.qkv.bias FLOAT[3456] 4b5675864d26
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visual.trunk.blocks.18.mlp.fc1.bias FLOAT[4304] 11915526a623
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visual.trunk.blocks.18.mlp.fc2.bias FLOAT[1152] 95192de6b718
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visual.trunk.blocks.18.norm1.bias FLOAT[1152] 0013d2546bcd
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visual.trunk.blocks.18.norm1.weight FLOAT[1152] 76c9f9debeb9
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visual.trunk.blocks.18.norm2.bias FLOAT[1152] c72f1d582e05
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visual.trunk.blocks.18.norm2.weight FLOAT[1152] f79fba11d575
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visual.trunk.blocks.19.attn.proj.bias FLOAT[1152] 77757ef34306
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visual.trunk.blocks.19.attn.qkv.bias FLOAT[3456] fcaffeb2e7fa
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visual.trunk.blocks.19.mlp.fc1.bias FLOAT[4304] 3aa8502ed416
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visual.trunk.blocks.19.mlp.fc2.bias FLOAT[1152] 30a85ccbfbac
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visual.trunk.blocks.19.norm1.bias FLOAT[1152] 76cbc2ae47d1
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visual.trunk.blocks.19.norm1.weight FLOAT[1152] 20d6b65cd5e6
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visual.trunk.blocks.19.norm2.bias FLOAT[1152] 048d41f8dc26
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visual.trunk.blocks.19.norm2.weight FLOAT[1152] 827df7c19b8d
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visual.trunk.blocks.2.attn.proj.bias FLOAT[1152] aa96bd218dec
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visual.trunk.blocks.2.attn.qkv.bias FLOAT[3456] ae2acbcb61d8
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visual.trunk.blocks.2.mlp.fc1.bias FLOAT[4304] d2547ce6fd61
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visual.trunk.blocks.2.mlp.fc2.bias FLOAT[1152] edd51791293b
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visual.trunk.blocks.2.norm1.bias FLOAT[1152] e05ebfc78533
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visual.trunk.blocks.2.norm1.weight FLOAT[1152] cd25249aa377
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visual.trunk.blocks.2.norm2.bias FLOAT[1152] 9d62a363e515
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visual.trunk.blocks.2.norm2.weight FLOAT[1152] dd8ca9b03290
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visual.trunk.blocks.20.attn.proj.bias FLOAT[1152] a2f94376f04c
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visual.trunk.blocks.20.attn.qkv.bias FLOAT[3456] 27dc767e927b
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visual.trunk.blocks.20.mlp.fc1.bias FLOAT[4304] 9fd785ab2519
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visual.trunk.blocks.20.mlp.fc2.bias FLOAT[1152] 9fa25cee854a
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visual.trunk.blocks.20.norm1.bias FLOAT[1152] 1653d00da6ec
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visual.trunk.blocks.20.norm1.weight FLOAT[1152] c3ac68dd6623
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visual.trunk.blocks.20.norm2.bias FLOAT[1152] 9394637de81d
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visual.trunk.blocks.20.norm2.weight FLOAT[1152] e72b7b08c057
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visual.trunk.blocks.21.attn.proj.bias FLOAT[1152] 267ed35ad51f
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visual.trunk.blocks.21.attn.qkv.bias FLOAT[3456] fb7d540a1444
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visual.trunk.blocks.21.mlp.fc1.bias FLOAT[4304] 94735636ec12
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visual.trunk.blocks.21.norm1.bias FLOAT[1152] 59cec192c34a
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visual.trunk.blocks.21.norm1.weight FLOAT[1152] 32751434e370
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visual.trunk.blocks.22.attn.proj.bias FLOAT[1152] 0e9021d3adbb
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visual.trunk.blocks.22.attn.qkv.bias FLOAT[3456] 63afb9830e06
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visual.trunk.blocks.22.mlp.fc1.bias FLOAT[4304] b70b45b2d221
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