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projections_1_t.mlmodelc/analytics/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aa1a49ec2de2cb0407730bab7315db0e5069e4d66be1785fe07743551d868b75
+size 243

projections_1_t.mlmodelc/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf22a6914f59bf27c845e5646604b0c8d010a2d48fbb318f33786b46b055ff39
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projections_1_t.mlmodelc/model.mil

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+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3405.2.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios18>(tensor<fp16, [1, 1024, 1, ?]> lm_hidden, tensor<fp16, [1, 1024, 1, ?]> residual_hidden, tensor<fp16, [?]> t) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"lm_hidden", [1, 1024, 1, 1]}, {"residual_hidden", [1, 1024, 1, 1]}, {"t", [1]}}), ("EnumeratedShapes", {{"165c41fc", {{"lm_hidden", [1, 1024, 1, 128]}, {"residual_hidden", [1, 1024, 1, 128]}, {"t", [128]}}}, {"b30ebd7c", {{"lm_hidden", [1, 1024, 1, 1]}, {"residual_hidden", [1, 1024, 1, 1]}, {"t", [1]}}}})))] {
+            string dit_hidden_1_pad_type_0 = const()[name = string("dit_hidden_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> dit_hidden_1_strides_0 = const()[name = string("dit_hidden_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> dit_hidden_1_pad_0 = const()[name = string("dit_hidden_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> dit_hidden_1_dilations_0 = const()[name = string("dit_hidden_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 dit_hidden_1_groups_0 = const()[name = string("dit_hidden_1_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1, 1]> var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = tensor<fp16, [1024, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [1024]> lm_to_dit_proj_bias_to_fp16 = const()[name = string("lm_to_dit_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2097280)))];
+            tensor<fp16, [1, 1024, 1, ?]> dit_hidden_1_cast_fp16 = conv(bias = lm_to_dit_proj_bias_to_fp16, dilations = dit_hidden_1_dilations_0, groups = dit_hidden_1_groups_0, pad = dit_hidden_1_pad_0, pad_type = dit_hidden_1_pad_type_0, strides = dit_hidden_1_strides_0, weight = var_11_to_fp16, x = lm_hidden)[name = string("dit_hidden_1_cast_fp16")];
+            string dit_hidden_2_pad_type_0 = const()[name = string("dit_hidden_2_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> dit_hidden_2_strides_0 = const()[name = string("dit_hidden_2_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> dit_hidden_2_pad_0 = const()[name = string("dit_hidden_2_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> dit_hidden_2_dilations_0 = const()[name = string("dit_hidden_2_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 dit_hidden_2_groups_0 = const()[name = string("dit_hidden_2_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1, 1]> var_22_to_fp16 = const()[name = string("op_22_to_fp16"), val = tensor<fp16, [1024, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2099392)))];
+            tensor<fp16, [1024]> res_to_dit_proj_bias_to_fp16 = const()[name = string("res_to_dit_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4196608)))];
+            tensor<fp16, [1, 1024, 1, ?]> dit_hidden_2_cast_fp16 = conv(bias = res_to_dit_proj_bias_to_fp16, dilations = dit_hidden_2_dilations_0, groups = dit_hidden_2_groups_0, pad = dit_hidden_2_pad_0, pad_type = dit_hidden_2_pad_type_0, strides = dit_hidden_2_strides_0, weight = var_22_to_fp16, x = residual_hidden)[name = string("dit_hidden_2_cast_fp16")];
+            tensor<fp16, [1, 1024, 1, ?]> dit_hidden = add(x = dit_hidden_1_cast_fp16, y = dit_hidden_2_cast_fp16)[name = string("op_30_cast_fp16")];
+            string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_dilations_0 = const()[name = string("input_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_groups_0 = const()[name = string("input_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1, 1]> var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = tensor<fp16, [1024, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4198720)))];
+            tensor<fp16, [1024]> stop_proj_bias_to_fp16 = const()[name = string("stop_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6295936)))];
+            tensor<fp16, [1, 1024, 1, ?]> input_cast_fp16 = conv(bias = stop_proj_bias_to_fp16, dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = var_35_to_fp16, x = lm_hidden)[name = string("input_cast_fp16")];
+            tensor<fp16, [1, 1024, 1, ?]> x_cast_fp16 = silu(x = input_cast_fp16)[name = string("x_cast_fp16")];
+            string var_53_pad_type_0 = const()[name = string("op_53_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_53_strides_0 = const()[name = string("op_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_53_pad_0 = const()[name = string("op_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_53_dilations_0 = const()[name = string("op_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_53_groups_0 = const()[name = string("op_53_groups_0"), val = int32(1)];
+            tensor<fp16, [2, 1024, 1, 1]> var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = tensor<fp16, [2, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6298048)))];
+            tensor<fp16, [1, 2, 1, ?]> var_53_cast_fp16 = conv(dilations = var_53_dilations_0, groups = var_53_groups_0, pad = var_53_pad_0, pad_type = var_53_pad_type_0, strides = var_53_strides_0, weight = var_48_to_fp16, x = x_cast_fp16)[name = string("op_53_cast_fp16")];
+            int32 var_54 = const()[name = string("op_54"), val = int32(1)];
+            tensor<fp16, [1, 2, 1, ?]> stop_logit_cast_fp16 = softmax(axis = var_54, x = var_53_cast_fp16)[name = string("stop_logit_cast_fp16")];
+            tensor<int32, [4]> var_64_begin_0 = const()[name = string("op_64_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
+            tensor<int32, [4]> var_64_end_0 = const()[name = string("op_64_end_0"), val = tensor<int32, [4]>([1, 2, 1, 0])];
+            tensor<bool, [4]> var_64_end_mask_0 = const()[name = string("op_64_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
+            tensor<bool, [4]> var_64_squeeze_mask_0 = const()[name = string("op_64_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
+            tensor<fp16, [1, 1, ?]> var_64_cast_fp16 = slice_by_index(begin = var_64_begin_0, end = var_64_end_0, end_mask = var_64_end_mask_0, squeeze_mask = var_64_squeeze_mask_0, x = stop_logit_cast_fp16)[name = string("op_64_cast_fp16")];
+            tensor<bool, [1, 1, ?]> var_65_cast_fp16 = greater(x = var_64_cast_fp16, y = t)[name = string("op_65_cast_fp16")];
+            string var_70_dtype_0 = const()[name = string("op_70_dtype_0"), val = string("int32")];
+            tensor<int32, [1, 1, ?]> stop_flag = cast(dtype = var_70_dtype_0, x = var_65_cast_fp16)[name = string("cast_4")];
+        } -> (dit_hidden, stop_flag);
+}

projections_1_t.mlmodelc/weights/weight.bin

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