Update sequoia mode with transposed value cache and 4:508 input:cache length

No change in output but should be faster (primarily due to the transposed value cache).

sequoia/Llama-2-7b-hf_chunk1.mlmodelc/analytics/coremldata.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e729e06a5dac91d54425432e10c01d40645eefd035e7d3569e6aaf5acc4a1493
+oid sha256:e8372d12aa224d728fc434e91b2c1432b7ef69216416bb047c5f7ae2707e4120
 size 243

sequoia/Llama-2-7b-hf_chunk1.mlmodelc/coremldata.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1a55bcffcb4e191cd6358ad92d705948cd757010e873528f66b6e21943904acd
+oid sha256:9d888daf26172f67d0fb48d9f30faca6f62b348e0e571de6855c2a60530aa2bb
 size 485

sequoia/Llama-2-7b-hf_chunk1.mlmodelc/metadata.json

@@ -138,9 +138,9 @@
             "hasShapeFlexibility" : "0",
             "isOptional" : "0",
             "dataType" : "Int32",
-            "formattedType" : "MultiArray (Int32 1 × 1)",
+            "formattedType" : "MultiArray (Int32 1 × 4)",
             "shortDescription" : "",
-            "shape" : "[1, 1]",
+            "shape" : "[1, 4]",
             "name" : "input_ids",
             "type" : "MultiArray"
           },
@@ -165,9 +165,9 @@
             "hasShapeFlexibility" : "0",
             "isOptional" : "0",
             "dataType" : "Float16",
-            "formattedType" : "MultiArray (Float16 1 × 4096 × 1 × 1)",
+            "formattedType" : "MultiArray (Float16 1 × 4096 × 1 × 4)",
             "shortDescription" : "",
-            "shape" : "[1, 4096, 1, 1]",
+            "shape" : "[1, 4096, 1, 4]",
             "name" : "x",
             "type" : "MultiArray"
           },
@@ -175,9 +175,9 @@
             "hasShapeFlexibility" : "0",
             "isOptional" : "0",
             "dataType" : "Float16",
-            "formattedType" : "MultiArray (Float16 128 × 1)",
+            "formattedType" : "MultiArray (Float16 128 × 4)",
             "shortDescription" : "",
-            "shape" : "[128, 1]",
+            "shape" : "[128, 4]",
             "name" : "cos",
             "type" : "MultiArray"
           },
@@ -185,9 +185,9 @@
             "hasShapeFlexibility" : "0",
             "isOptional" : "0",
             "dataType" : "Float16",
-            "formattedType" : "MultiArray (Float16 128 × 1)",
+            "formattedType" : "MultiArray (Float16 128 × 4)",
             "shortDescription" : "",
-            "shape" : "[128, 1]",
+            "shape" : "[128, 4]",
             "name" : "sin",
             "type" : "MultiArray"
           },
@@ -195,23 +195,24 @@
             "hasShapeFlexibility" : "0",
             "isOptional" : "0",
             "dataType" : "Float16",
-            "formattedType" : "MultiArray (Float16 1 × 1 × 1 × 512)",
+            "formattedType" : "MultiArray (Float16 1 × 1 × 4 × 512)",
             "shortDescription" : "",
-            "shape" : "[1, 1, 1, 512]",
+            "shape" : "[1, 1, 4, 512]",
             "name" : "mask",
             "type" : "MultiArray"
           }
         ],
         "name" : "input_1_context_512",
         "mlProgramOperationTypeHistogram" : {
-          "Select" : 1,
+          "Select" : 2,
           "Ios18.maximum" : 1,
           "Ios18.gather" : 3,
           "Ios18.sub" : 3,
           "Ios18.transpose" : 1,
-          "Ios18.less" : 1,
+          "Ios18.less" : 2,
           "Ios18.cast" : 2,
-          "Ios18.expandDims" : 4
+          "Ios18.expandDims" : 4,
+          "Tile" : 2
         }
       }
     ],
@@ -265,7 +266,7 @@
       }
     ],
     "defaultFunctionName" : "input_512_context_512",
-    "generatedClassName" : "Llama_2_7b_hf_2024_07_02_20_36_17_merged_chunk1",
+    "generatedClassName" : "Llama_2_7b_hf_2024_07_17_19_34_17_merged_chunk1",
     "userDefinedMetadata" : {
 
     },

sequoia/Llama-2-7b-hf_chunk1.mlmodelc/model.mil

@@ -1,49 +1,56 @@
 program(1.3)
-[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3400.34.1"}, {"coremlc-version", "3400.42.1"}})]
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3400.42.1"}, {"coremlc-version", "3400.51.1"}})]
 {
-    func input_1_context_512<ios18>(tensor<int32, [1]> full_sequence_length, tensor<int32, [1, 1]> input_ids) {
-            tensor<int32, [1]> T = const()[name = string("T"), val = tensor<int32, [1]>([1])];
+    func input_1_context_512<ios18>(tensor<int32, [1]> full_sequence_length, tensor<int32, [1, 4]> input_ids) {
+            tensor<int32, [1]> T = const()[name = string("T"), val = tensor<int32, [1]>([4])];
             int32 x_axis_0 = const()[name = string("x_axis_0"), val = int32(0)];
             int32 x_batch_dims_0 = const()[name = string("x_batch_dims_0"), val = int32(0)];
             bool x_validate_indices_0 = const()[name = string("x_validate_indices_0"), val = bool(false)];
             tensor<fp16, [32000, 4096]> wte_weight_to_fp16 = const()[name = string("wte_weight_to_fp16"), val = tensor<fp16, [32000, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
             string input_ids_to_int16_dtype_0 = const()[name = string("input_ids_to_int16_dtype_0"), val = string("int16")];
-            tensor<int16, [1, 1]> input_ids_to_int16 = cast(dtype = input_ids_to_int16_dtype_0, x = input_ids)[name = string("cast_6")];
-            tensor<fp16, [1, 1, 4096]> x_cast_fp16_cast_uint16 = gather(axis = x_axis_0, batch_dims = x_batch_dims_0, indices = input_ids_to_int16, validate_indices = x_validate_indices_0, x = wte_weight_to_fp16)[name = string("x_cast_fp16_cast_uint16")];
+            tensor<int16, [1, 4]> input_ids_to_int16 = cast(dtype = input_ids_to_int16_dtype_0, x = input_ids)[name = string("cast_6")];
+            tensor<fp16, [1, 4, 4096]> x_cast_fp16_cast_uint16 = gather(axis = x_axis_0, batch_dims = x_batch_dims_0, indices = input_ids_to_int16, validate_indices = x_validate_indices_0, x = wte_weight_to_fp16)[name = string("x_cast_fp16_cast_uint16")];
             tensor<int32, [3]> var_16_perm_0 = const()[name = string("op_16_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
             tensor<int32, [1]> var_18_axes_0 = const()[name = string("op_18_axes_0"), val = tensor<int32, [1]>([2])];
-            tensor<fp16, [1, 4096, 1]> var_16_cast_fp16 = transpose(perm = var_16_perm_0, x = x_cast_fp16_cast_uint16)[name = string("transpose_0")];
-            tensor<fp16, [1, 4096, 1, 1]> x = expand_dims(axes = var_18_axes_0, x = var_16_cast_fp16)[name = string("op_18_cast_fp16")];
+            tensor<fp16, [1, 4096, 4]> var_16_cast_fp16 = transpose(perm = var_16_perm_0, x = x_cast_fp16_cast_uint16)[name = string("transpose_0")];
+            tensor<fp16, [1, 4096, 1, 4]> x = expand_dims(axes = var_18_axes_0, x = var_16_cast_fp16)[name = string("op_18_cast_fp16")];
             tensor<int32, [1]> pos_offset = sub(x = T, y = full_sequence_length)[name = string("pos_offset")];
-            tensor<int32, [1]> var_26 = const()[name = string("op_26"), val = tensor<int32, [1]>([0])];
-            tensor<int32, [1]> input_pos_1 = sub(x = var_26, y = pos_offset)[name = string("input_pos_1")];
-            tensor<int32, [1]> var_34 = const()[name = string("op_34"), val = tensor<int32, [1]>([0])];
-            tensor<int32, [1]> input_pos = maximum(x = input_pos_1, y = var_34)[name = string("input_pos")];
+            tensor<int32, [4]> var_26 = const()[name = string("op_26"), val = tensor<int32, [4]>([0, 1, 2, 3])];
+            tensor<int32, [4]> input_pos_1 = sub(x = var_26, y = pos_offset)[name = string("input_pos_1")];
+            tensor<int32, [4]> var_34 = const()[name = string("op_34"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> input_pos = maximum(x = input_pos_1, y = var_34)[name = string("input_pos")];
             int32 var_45 = const()[name = string("op_45"), val = int32(1)];
             int32 var_46_batch_dims_0 = const()[name = string("op_46_batch_dims_0"), val = int32(0)];
             bool var_46_validate_indices_0 = const()[name = string("op_46_validate_indices_0"), val = bool(false)];
             tensor<fp16, [128, 512]> var_44_to_fp16 = const()[name = string("op_44_to_fp16"), val = tensor<fp16, [128, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262144128)))];
             string input_pos_to_uint16_dtype_0 = const()[name = string("input_pos_to_uint16_dtype_0"), val = string("uint16")];
-            tensor<uint16, [1]> input_pos_to_uint16 = cast(dtype = input_pos_to_uint16_dtype_0, x = input_pos)[name = string("cast_5")];
-            tensor<fp16, [128, 1]> cos = gather(axis = var_45, batch_dims = var_46_batch_dims_0, indices = input_pos_to_uint16, validate_indices = var_46_validate_indices_0, x = var_44_to_fp16)[name = string("op_46_cast_fp16_cast_uint16")];
+            tensor<uint16, [4]> input_pos_to_uint16 = cast(dtype = input_pos_to_uint16_dtype_0, x = input_pos)[name = string("cast_5")];
+            tensor<fp16, [128, 4]> cos = gather(axis = var_45, batch_dims = var_46_batch_dims_0, indices = input_pos_to_uint16, validate_indices = var_46_validate_indices_0, x = var_44_to_fp16)[name = string("op_46_cast_fp16_cast_uint16")];
             int32 var_56 = const()[name = string("op_56"), val = int32(1)];
             int32 var_57_batch_dims_0 = const()[name = string("op_57_batch_dims_0"), val = int32(0)];
             bool var_57_validate_indices_0 = const()[name = string("op_57_validate_indices_0"), val = bool(false)];
             tensor<fp16, [128, 512]> var_55_to_fp16 = const()[name = string("op_55_to_fp16"), val = tensor<fp16, [128, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262275264)))];
-            tensor<fp16, [128, 1]> sin = gather(axis = var_56, batch_dims = var_57_batch_dims_0, indices = input_pos_to_uint16, validate_indices = var_57_validate_indices_0, x = var_55_to_fp16)[name = string("op_57_cast_fp16_cast_uint16")];
-            tensor<int32, [512]> var_104 = const()[name = string("op_104"), val = tensor<int32, [512]>([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511])];
-            int32 var_105 = const()[name = string("op_105"), val = int32(512)];
-            tensor<int32, [1]> var_107 = sub(x = var_105, y = full_sequence_length)[name = string("op_107")];
-            tensor<bool, [512]> var_108 = less(x = var_104, y = var_107)[name = string("op_108")];
+            tensor<fp16, [128, 4]> sin = gather(axis = var_56, batch_dims = var_57_batch_dims_0, indices = input_pos_to_uint16, validate_indices = var_57_validate_indices_0, x = var_55_to_fp16)[name = string("op_57_cast_fp16_cast_uint16")];
+            tensor<int32, [4, 1]> var_92 = const()[name = string("op_92"), val = tensor<int32, [4, 1]>([[0], [1], [2], [3]])];
+            tensor<bool, [4, 1]> var_95 = less(x = var_92, y = pos_offset)[name = string("op_95")];
+            tensor<int32, [2]> var_95_after_broadcast_reps_0 = const()[name = string("op_95_after_broadcast_reps_0"), val = tensor<int32, [2]>([1, 512])];
+            tensor<bool, [4, 512]> var_95_after_broadcast = tile(reps = var_95_after_broadcast_reps_0, x = var_95)[name = string("op_95_after_broadcast")];
+            tensor<fp16, [4, 512]> all_mask_to_fp16 = const()[name = string("all_mask_to_fp16"), val = tensor<fp16, [4, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263455104)))];
+            tensor<fp16, [4, 512]> m_1_to_fp16 = const()[name = string("m_1_to_fp16"), val = tensor<fp16, [4, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263459264)))];
+            tensor<fp16, [4, 512]> m_3_cast_fp16 = select(a = all_mask_to_fp16, b = m_1_to_fp16, cond = var_95_after_broadcast)[name = string("m_3_cast_fp16")];
+            tensor<int32, [512]> var_105 = const()[name = string("op_105"), val = tensor<int32, [512]>([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511])];
+            int32 var_106 = const()[name = string("op_106"), val = int32(512)];
+            tensor<int32, [1]> var_108 = sub(x = var_106, y = full_sequence_length)[name = string("op_108")];
+            tensor<bool, [512]> var_109 = less(x = var_105, y = var_108)[name = string("op_109")];
             tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([0])];
-            tensor<bool, [1, 512]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = var_108)[name = string("expand_dims_0")];
-            tensor<fp16, [1, 512]> all_mask_to_fp16 = const()[name = string("all_mask_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263455104)))];
-            tensor<fp16, [1, 512]> m_1_to_fp16 = const()[name = string("m_1_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263456192)))];
-            tensor<fp16, [1, 512]> m_cast_fp16 = select(a = all_mask_to_fp16, b = m_1_to_fp16, cond = expand_dims_0)[name = string("m_cast_fp16")];
-            tensor<int32, [1]> var_111_axes_0 = const()[name = string("op_111_axes_0"), val = tensor<int32, [1]>([0])];
-            tensor<fp16, [1, 1, 512]> var_111_cast_fp16 = expand_dims(axes = var_111_axes_0, x = m_cast_fp16)[name = string("op_111_cast_fp16")];
-            tensor<int32, [1]> var_113_axes_0 = const()[name = string("op_113_axes_0"), val = tensor<int32, [1]>([0])];
-            tensor<fp16, [1, 1, 1, 512]> mask = expand_dims(axes = var_113_axes_0, x = var_111_cast_fp16)[name = string("op_113_cast_fp16")];
+            tensor<bool, [1, 512]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = var_109)[name = string("expand_dims_0")];
+            tensor<int32, [2]> var_109_after_broadcast_reps_0 = const()[name = string("op_109_after_broadcast_reps_0"), val = tensor<int32, [2]>([4, 1])];
+            tensor<bool, [4, 512]> var_109_after_broadcast = tile(reps = var_109_after_broadcast_reps_0, x = expand_dims_0)[name = string("op_109_after_broadcast")];
+            tensor<fp16, [4, 512]> m_cast_fp16 = select(a = all_mask_to_fp16, b = m_3_cast_fp16, cond = var_109_after_broadcast)[name = string("m_cast_fp16")];
+            tensor<int32, [1]> var_112_axes_0 = const()[name = string("op_112_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 4, 512]> var_112_cast_fp16 = expand_dims(axes = var_112_axes_0, x = m_cast_fp16)[name = string("op_112_cast_fp16")];
+            tensor<int32, [1]> var_114_axes_0 = const()[name = string("op_114_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 1, 4, 512]> mask = expand_dims(axes = var_114_axes_0, x = var_112_cast_fp16)[name = string("op_114_cast_fp16")];
         } -> (x, cos, sin, mask);
     func input_512_context_512<ios18>(tensor<int32, [1]> full_sequence_length, tensor<int32, [1, 512]> input_ids) {
             tensor<int32, [1]> T = const()[name = string("T"), val = tensor<int32, [1]>([512])];

sequoia/Llama-2-7b-hf_chunk1.mlmodelc/weights/weight.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:63ea75c6154c60560d9edb4d2e2f028afa38a3927bb7277b7d01558bc198e965
-size 263457280
+oid sha256:a66aa1771f06ceee6e578b7f93444d38b2cb55120a2a84494e7649b4e424a176
+size 263463424

sequoia/Llama-2-7b-hf_chunk10.mlmodelc/analytics/coremldata.bin

@@ -1,3 +1,3 @@
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           "Ios18.conv" : 21,
-          "Ios18.matmul" : 6,
           "Ios18.expandDims" : 6,
+          "Ios18.matmul" : 6,
           "Ios18.concat" : 18,
           "Ios18.add" : 15,
           "Ios18.realDiv" : 6,
           "Ios18.silu" : 3,
           "Ios18.softmax" : 3,
           "Ios18.sliceByIndex" : 18,
+          "Ios18.transpose" : 6,
           "Ios16.reduceL2Norm" : 6,
           "Ios18.squeeze" : 6,
           "Ios18.reshape" : 12,
@@ -419,14 +421,15 @@
     "mlProgramOperationTypeHistogram" : {
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       "Ios18.conv" : 21,
-      "Ios18.matmul" : 6,
       "Ios18.expandDims" : 6,
-      "Ios18.concat" : 18,
+      "Ios18.matmul" : 6,
+      "Ios18.concat" : 12,
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       "Ios18.realDiv" : 6,
       "Ios18.silu" : 3,
       "Ios18.softmax" : 3,
       "Ios18.sliceByIndex" : 18,
+      "Ios18.transpose" : 6,
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@@ -491,7 +494,7 @@
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     "defaultFunctionName" : "input_512_context_512",
-    "generatedClassName" : "Llama_2_7b_hf_2024_07_02_20_36_17_merged_chunk9",
+    "generatedClassName" : "Llama_2_7b_hf_2024_07_17_19_34_17_merged_chunk9",
     "userDefinedMetadata" : {
 
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@@ -1,3 +1,3 @@
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