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fsq_layer.mlmodelc/analytics/coremldata.bin

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+oid sha256:0c681f1760e3627e783d507ac461c12ba200de1eb8f2684006d04733594749b3
+size 243

fsq_layer.mlmodelc/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:91a8c6ee76db8383776831e6d600571905d5642461ab3b9ba51fc0a25b36fa58
+size 207

fsq_layer.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"}})]
+{
+    func main<ios18>(tensor<fp16, [1, 1024, 1, ?]> x) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"x", [1, 1024, 1, 32]}}), ("EnumeratedShapes", {{"2a8dd4d2", {{"x", [1, 1024, 1, 64]}}}, {"3bf9b6d7", {{"x", [1, 1024, 1, 128]}}}, {"44109b23", {{"x", [1, 1024, 1, 1]}}}, {"759fca31", {{"x", [1, 1024, 1, 8]}}}, {"d30434c5", {{"x", [1, 1024, 1, 16]}}}, {"e483fcaa", {{"x", [1, 1024, 1, 4]}}}, {"fabb4dad", {{"x", [1, 1024, 1, 32]}}}})))] {
+            string hidden_3_pad_type_0 = const()[name = string("hidden_3_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> hidden_3_strides_0 = const()[name = string("hidden_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> hidden_3_pad_0 = const()[name = string("hidden_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> hidden_3_dilations_0 = const()[name = string("hidden_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 hidden_3_groups_0 = const()[name = string("hidden_3_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 1024, 1, 1]> var_8_to_fp16 = const()[name = string("op_8_to_fp16"), val = tensor<fp16, [256, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [256]> in_proj_bias_to_fp16 = const()[name = string("in_proj_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416)))];
+            tensor<fp16, [1, 256, 1, ?]> hidden_3_cast_fp16 = conv(bias = in_proj_bias_to_fp16, dilations = hidden_3_dilations_0, groups = hidden_3_groups_0, pad = hidden_3_pad_0, pad_type = hidden_3_pad_type_0, strides = hidden_3_strides_0, weight = var_8_to_fp16, x = x)[name = string("hidden_3_cast_fp16")];
+            tensor<fp16, [1, 256, 1, ?]> hidden_cast_fp16 = tanh(x = hidden_3_cast_fp16)[name = string("hidden_cast_fp16")];
+            fp16 var_16_promoted_to_fp16 = const()[name = string("op_16_promoted_to_fp16"), val = fp16(0x1.2p+3)];
+            tensor<fp16, [1, 256, 1, ?]> var_17_cast_fp16 = mul(x = hidden_cast_fp16, y = var_16_promoted_to_fp16)[name = string("op_17_cast_fp16")];
+            tensor<fp16, [1, 256, 1, ?]> var_18_cast_fp16 = round(x = var_17_cast_fp16)[name = string("op_18_cast_fp16")];
+            fp16 _inversed_x_y_0_to_fp16 = const()[name = string("_inversed_x_y_0_to_fp16"), val = fp16(0x1.c7p-4)];
+            tensor<fp16, [1, 256, 1, ?]> _inversed_x_cast_fp16 = mul(x = var_18_cast_fp16, y = _inversed_x_y_0_to_fp16)[name = string("_inversed_x_cast_fp16")];
+            string var_31_pad_type_0 = const()[name = string("op_31_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_31_strides_0 = const()[name = string("op_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_31_pad_0 = const()[name = string("op_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_31_dilations_0 = const()[name = string("op_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_31_groups_0 = const()[name = string("op_31_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 256, 1, 1]> var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = tensor<fp16, [1024, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992)))];
+            tensor<fp16, [1024]> out_proj_bias_to_fp16 = const()[name = string("out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1049344)))];
+            tensor<fp16, [1, 1024, 1, ?]> output = conv(bias = out_proj_bias_to_fp16, dilations = var_31_dilations_0, groups = var_31_groups_0, pad = var_31_pad_0, pad_type = var_31_pad_type_0, strides = var_31_strides_0, weight = var_25_to_fp16, x = _inversed_x_cast_fp16)[name = string("op_31_cast_fp16")];
+        } -> (output);
+}

fsq_layer.mlmodelc/weights/weight.bin

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