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+ [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.2.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.0b2"}})]
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+ {
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+ func main<ios18>(tensor<int32, [1, ?]> input_ids) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"input_ids", [1, 1]}}), ("EnumeratedShapes", {{"79ae981e", {{"input_ids", [1, 1]}}}, {"c09fdef5", {{"input_ids", [1, 256]}}}})))] {
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+ bool hidden_states_validate_indices_0 = const()[name = string("hidden_states_validate_indices_0"), val = bool(false)];
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+ tensor<fp16, [128256, 4096]> embed_tokens_weight_to_fp16 = const()[name = string("embed_tokens_weight_to_fp16"), val = tensor<fp16, [128256, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
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+ tensor<fp16, [1, ?, 4096]> hidden_states = gather(axis = hidden_states_axis_0, batch_dims = hidden_states_batch_dims_0, indices = input_ids, validate_indices = hidden_states_validate_indices_0, x = embed_tokens_weight_to_fp16)[name = string("hidden_states_cast_fp16")];
10
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DeepSeek_lm_head_lut6.mlmodelc/model.mil ADDED
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+ program(1.3)
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+ [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.5.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.2"}})]
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+ {
4
+ func main<ios18>(tensor<fp16, [1, 1, 4096]> hidden_states) {
5
+ tensor<int32, [3]> var_5 = const()[name = string("op_5"), val = tensor<int32, [3]>([0, 2, 1])];
6
+ tensor<int32, [1]> input_axes_0 = const()[name = string("input_axes_0"), val = tensor<int32, [1]>([2])];
7
+ tensor<fp16, [1, 4096, 1]> var_6_cast_fp16 = transpose(perm = var_5, x = hidden_states)[name = string("transpose_8")];
8
+ tensor<fp16, [1, 4096, 1, 1]> input_cast_fp16 = expand_dims(axes = input_axes_0, x = var_6_cast_fp16)[name = string("input_cast_fp16")];
9
+ string var_29_pad_type_0 = const()[name = string("op_29_pad_type_0"), val = string("valid")];
10
+ tensor<int32, [2]> var_29_strides_0 = const()[name = string("op_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
11
+ tensor<int32, [4]> var_29_pad_0 = const()[name = string("op_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
12
+ tensor<int32, [2]> var_29_dilations_0 = const()[name = string("op_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
13
+ int32 var_29_groups_0 = const()[name = string("op_29_groups_0"), val = int32(1)];
14
+ tensor<fp16, [16032, 4096, 1, 1]> var_9_promoted_to_fp16 = const()[name = string("op_9_promoted_to_fp16"), val = tensor<fp16, [16032, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
15
+ tensor<fp16, [1, 16032, 1, 1]> var_29_cast_fp16 = conv(dilations = var_29_dilations_0, groups = var_29_groups_0, pad = var_29_pad_0, pad_type = var_29_pad_type_0, strides = var_29_strides_0, weight = var_9_promoted_to_fp16, x = input_cast_fp16)[name = string("op_29_cast_fp16")];
16
+ tensor<int32, [1]> var_31_axes_0 = const()[name = string("op_31_axes_0"), val = tensor<int32, [1]>([2])];
17
+ tensor<fp16, [1, 16032, 1]> var_31_cast_fp16 = squeeze(axes = var_31_axes_0, x = var_29_cast_fp16)[name = string("op_31_cast_fp16")];
18
+ tensor<int32, [3]> var_34_perm_0 = const()[name = string("op_34_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
19
+ string var_55_pad_type_0 = const()[name = string("op_55_pad_type_0"), val = string("valid")];
20
+ tensor<int32, [2]> var_55_strides_0 = const()[name = string("op_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
21
+ tensor<int32, [4]> var_55_pad_0 = const()[name = string("op_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
22
+ tensor<int32, [2]> var_55_dilations_0 = const()[name = string("op_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
23
+ int32 var_55_groups_0 = const()[name = string("op_55_groups_0"), val = int32(1)];
24
+ tensor<fp16, [16032, 4096, 1, 1]> var_35_promoted_to_fp16 = const()[name = string("op_35_promoted_to_fp16"), val = tensor<fp16, [16032, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131334272)))];
25
+ tensor<fp16, [1, 16032, 1, 1]> var_55_cast_fp16 = conv(dilations = var_55_dilations_0, groups = var_55_groups_0, pad = var_55_pad_0, pad_type = var_55_pad_type_0, strides = var_55_strides_0, weight = var_35_promoted_to_fp16, x = input_cast_fp16)[name = string("op_55_cast_fp16")];
26
+ tensor<int32, [1]> var_57_axes_0 = const()[name = string("op_57_axes_0"), val = tensor<int32, [1]>([2])];
27
+ tensor<fp16, [1, 16032, 1]> var_57_cast_fp16 = squeeze(axes = var_57_axes_0, x = var_55_cast_fp16)[name = string("op_57_cast_fp16")];
28
+ tensor<int32, [3]> var_60_perm_0 = const()[name = string("op_60_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
29
+ string var_81_pad_type_0 = const()[name = string("op_81_pad_type_0"), val = string("valid")];
30
+ tensor<int32, [2]> var_81_strides_0 = const()[name = string("op_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
31
+ tensor<int32, [4]> var_81_pad_0 = const()[name = string("op_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
32
+ tensor<int32, [2]> var_81_dilations_0 = const()[name = string("op_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
33
+ int32 var_81_groups_0 = const()[name = string("op_81_groups_0"), val = int32(1)];
34
+ tensor<fp16, [16032, 4096, 1, 1]> var_61_promoted_to_fp16 = const()[name = string("op_61_promoted_to_fp16"), val = tensor<fp16, [16032, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262668480)))];
35
+ tensor<fp16, [1, 16032, 1, 1]> var_81_cast_fp16 = conv(dilations = var_81_dilations_0, groups = var_81_groups_0, pad = var_81_pad_0, pad_type = var_81_pad_type_0, strides = var_81_strides_0, weight = var_61_promoted_to_fp16, x = input_cast_fp16)[name = string("op_81_cast_fp16")];
36
+ tensor<int32, [1]> var_83_axes_0 = const()[name = string("op_83_axes_0"), val = tensor<int32, [1]>([2])];
37
+ tensor<fp16, [1, 16032, 1]> var_83_cast_fp16 = squeeze(axes = var_83_axes_0, x = var_81_cast_fp16)[name = string("op_83_cast_fp16")];
38
+ tensor<int32, [3]> var_86_perm_0 = const()[name = string("op_86_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
39
+ string var_107_pad_type_0 = const()[name = string("op_107_pad_type_0"), val = string("valid")];
40
+ tensor<int32, [2]> var_107_strides_0 = const()[name = string("op_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
41
+ tensor<int32, [4]> var_107_pad_0 = const()[name = string("op_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
42
+ tensor<int32, [2]> var_107_dilations_0 = const()[name = string("op_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
43
+ int32 var_107_groups_0 = const()[name = string("op_107_groups_0"), val = int32(1)];
44
+ tensor<fp16, [16032, 4096, 1, 1]> var_87_promoted_to_fp16 = const()[name = string("op_87_promoted_to_fp16"), val = tensor<fp16, [16032, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394002688)))];
45
+ tensor<fp16, [1, 16032, 1, 1]> var_107_cast_fp16 = conv(dilations = var_107_dilations_0, groups = var_107_groups_0, pad = var_107_pad_0, pad_type = var_107_pad_type_0, strides = var_107_strides_0, weight = var_87_promoted_to_fp16, x = input_cast_fp16)[name = string("op_107_cast_fp16")];
46
+ tensor<int32, [1]> var_109_axes_0 = const()[name = string("op_109_axes_0"), val = tensor<int32, [1]>([2])];
47
+ tensor<fp16, [1, 16032, 1]> var_109_cast_fp16 = squeeze(axes = var_109_axes_0, x = var_107_cast_fp16)[name = string("op_109_cast_fp16")];
48
+ tensor<int32, [3]> var_112_perm_0 = const()[name = string("op_112_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
49
+ string var_133_pad_type_0 = const()[name = string("op_133_pad_type_0"), val = string("valid")];
50
+ tensor<int32, [2]> var_133_strides_0 = const()[name = string("op_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
51
+ tensor<int32, [4]> var_133_pad_0 = const()[name = string("op_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
52
+ tensor<int32, [2]> var_133_dilations_0 = const()[name = string("op_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
53
+ int32 var_133_groups_0 = const()[name = string("op_133_groups_0"), val = int32(1)];
54
+ tensor<fp16, [16032, 4096, 1, 1]> var_113_promoted_to_fp16 = const()[name = string("op_113_promoted_to_fp16"), val = tensor<fp16, [16032, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525336896)))];
55
+ tensor<fp16, [1, 16032, 1, 1]> var_133_cast_fp16 = conv(dilations = var_133_dilations_0, groups = var_133_groups_0, pad = var_133_pad_0, pad_type = var_133_pad_type_0, strides = var_133_strides_0, weight = var_113_promoted_to_fp16, x = input_cast_fp16)[name = string("op_133_cast_fp16")];
56
+ tensor<int32, [1]> var_135_axes_0 = const()[name = string("op_135_axes_0"), val = tensor<int32, [1]>([2])];
57
+ tensor<fp16, [1, 16032, 1]> var_135_cast_fp16 = squeeze(axes = var_135_axes_0, x = var_133_cast_fp16)[name = string("op_135_cast_fp16")];
58
+ tensor<int32, [3]> var_138_perm_0 = const()[name = string("op_138_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
59
+ string var_159_pad_type_0 = const()[name = string("op_159_pad_type_0"), val = string("valid")];
60
+ tensor<int32, [2]> var_159_strides_0 = const()[name = string("op_159_strides_0"), val = tensor<int32, [2]>([1, 1])];
61
+ tensor<int32, [4]> var_159_pad_0 = const()[name = string("op_159_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
62
+ tensor<int32, [2]> var_159_dilations_0 = const()[name = string("op_159_dilations_0"), val = tensor<int32, [2]>([1, 1])];
63
+ int32 var_159_groups_0 = const()[name = string("op_159_groups_0"), val = int32(1)];
64
+ tensor<fp16, [16032, 4096, 1, 1]> var_139_promoted_to_fp16 = const()[name = string("op_139_promoted_to_fp16"), val = tensor<fp16, [16032, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656671104)))];
65
+ tensor<fp16, [1, 16032, 1, 1]> var_159_cast_fp16 = conv(dilations = var_159_dilations_0, groups = var_159_groups_0, pad = var_159_pad_0, pad_type = var_159_pad_type_0, strides = var_159_strides_0, weight = var_139_promoted_to_fp16, x = input_cast_fp16)[name = string("op_159_cast_fp16")];
66
+ tensor<int32, [1]> var_161_axes_0 = const()[name = string("op_161_axes_0"), val = tensor<int32, [1]>([2])];
67
+ tensor<fp16, [1, 16032, 1]> var_161_cast_fp16 = squeeze(axes = var_161_axes_0, x = var_159_cast_fp16)[name = string("op_161_cast_fp16")];
68
+ tensor<int32, [3]> var_164_perm_0 = const()[name = string("op_164_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
69
+ string var_185_pad_type_0 = const()[name = string("op_185_pad_type_0"), val = string("valid")];
70
+ tensor<int32, [2]> var_185_strides_0 = const()[name = string("op_185_strides_0"), val = tensor<int32, [2]>([1, 1])];
71
+ tensor<int32, [4]> var_185_pad_0 = const()[name = string("op_185_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
72
+ tensor<int32, [2]> var_185_dilations_0 = const()[name = string("op_185_dilations_0"), val = tensor<int32, [2]>([1, 1])];
73
+ int32 var_185_groups_0 = const()[name = string("op_185_groups_0"), val = int32(1)];
74
+ tensor<fp16, [16032, 4096, 1, 1]> var_165_promoted_to_fp16 = const()[name = string("op_165_promoted_to_fp16"), val = tensor<fp16, [16032, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(788005312)))];
75
+ tensor<fp16, [1, 16032, 1, 1]> var_185_cast_fp16 = conv(dilations = var_185_dilations_0, groups = var_185_groups_0, pad = var_185_pad_0, pad_type = var_185_pad_type_0, strides = var_185_strides_0, weight = var_165_promoted_to_fp16, x = input_cast_fp16)[name = string("op_185_cast_fp16")];
76
+ tensor<int32, [1]> var_187_axes_0 = const()[name = string("op_187_axes_0"), val = tensor<int32, [1]>([2])];
77
+ tensor<fp16, [1, 16032, 1]> var_187_cast_fp16 = squeeze(axes = var_187_axes_0, x = var_185_cast_fp16)[name = string("op_187_cast_fp16")];
78
+ tensor<int32, [3]> var_190_perm_0 = const()[name = string("op_190_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
79
+ string var_211_pad_type_0 = const()[name = string("op_211_pad_type_0"), val = string("valid")];
80
+ tensor<int32, [2]> var_211_strides_0 = const()[name = string("op_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
81
+ tensor<int32, [4]> var_211_pad_0 = const()[name = string("op_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
82
+ tensor<int32, [2]> var_211_dilations_0 = const()[name = string("op_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
83
+ int32 var_211_groups_0 = const()[name = string("op_211_groups_0"), val = int32(1)];
84
+ tensor<fp16, [16032, 4096, 1, 1]> var_191_promoted_to_fp16 = const()[name = string("op_191_promoted_to_fp16"), val = tensor<fp16, [16032, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(919339520)))];
85
+ tensor<fp16, [1, 16032, 1, 1]> var_211_cast_fp16 = conv(dilations = var_211_dilations_0, groups = var_211_groups_0, pad = var_211_pad_0, pad_type = var_211_pad_type_0, strides = var_211_strides_0, weight = var_191_promoted_to_fp16, x = input_cast_fp16)[name = string("op_211_cast_fp16")];
86
+ tensor<int32, [1]> var_213_axes_0 = const()[name = string("op_213_axes_0"), val = tensor<int32, [1]>([2])];
87
+ tensor<fp16, [1, 16032, 1]> var_213_cast_fp16 = squeeze(axes = var_213_axes_0, x = var_211_cast_fp16)[name = string("op_213_cast_fp16")];
88
+ tensor<int32, [3]> var_216_perm_0 = const()[name = string("op_216_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
89
+ tensor<fp16, [1, 1, 16032]> logits8 = transpose(perm = var_216_perm_0, x = var_213_cast_fp16)[name = string("transpose_0")];
90
+ tensor<fp16, [1, 1, 16032]> logits7 = transpose(perm = var_190_perm_0, x = var_187_cast_fp16)[name = string("transpose_1")];
91
+ tensor<fp16, [1, 1, 16032]> logits6 = transpose(perm = var_164_perm_0, x = var_161_cast_fp16)[name = string("transpose_2")];
92
+ tensor<fp16, [1, 1, 16032]> logits5 = transpose(perm = var_138_perm_0, x = var_135_cast_fp16)[name = string("transpose_3")];
93
+ tensor<fp16, [1, 1, 16032]> logits4 = transpose(perm = var_112_perm_0, x = var_109_cast_fp16)[name = string("transpose_4")];
94
+ tensor<fp16, [1, 1, 16032]> logits3 = transpose(perm = var_86_perm_0, x = var_83_cast_fp16)[name = string("transpose_5")];
95
+ tensor<fp16, [1, 1, 16032]> logits2 = transpose(perm = var_60_perm_0, x = var_57_cast_fp16)[name = string("transpose_6")];
96
+ tensor<fp16, [1, 1, 16032]> logits1 = transpose(perm = var_34_perm_0, x = var_31_cast_fp16)[name = string("transpose_7")];
97
+ } -> (logits1, logits2, logits3, logits4, logits5, logits6, logits7, logits8);
98
+ }