{ "graphs": [ { "name": "", "inputs": [ { "name": "input:0", "arguments": [ { "name": "input:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "input:1", "arguments": [ { "name": "input:1", "type": { "string": "int32['1', '128']", "shape": { "dimensions": [ "1", "128" ] }, "dataType": "int32" } } ], "visible": true } ], "outputs": [ { "name": "output:0", "arguments": [ { "name": "23_Rearrange:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "nodes": [ { "name": "0_Matmul", "type": { "name": "Matmul", "category": "layer" }, "inputs": [ { "name": "0", "arguments": [ { "name": "input:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "const:0", "type": { "string": "float32['768', '768']", "shape": { "dimensions": [ "768", "768" ] }, "dataType": "float32" }, "initializer": { "kind": "Initializer", "value": "<>" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "0_Matmul:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "require_prologue", "type": "bool", "value": "False", "visible": true, "description": "" }, { "name": "transpose_b", "type": "bool", "value": "False", "visible": true, "description": "" } ], "description": [ "Task(", " name: matmul", " parameters: ", " a: tensor(float32, [1, 128, 768])", " b: tensor(float32, [768, 768])", " c: tensor(float32, [1, 128, 768])", " inputs: [a, b]", " outputs: [c]", " computations: ", " c: float32[1, 128, 768] where c[v, v_1, v_2] = reduce([768], (v_3) => (a[0, v_1, v_3] * b[v_3, v_2]), sum)", " attributes: {}", ")" ] }, { "name": "1_Add", "type": { "name": "Add", "category": "transform" }, "inputs": [ { "name": "0", "arguments": [ { "name": "0_Matmul:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "const:1", "type": { "string": "float32['768']", "shape": { "dimensions": [ "768" ] }, "dataType": "float32" }, "initializer": { "kind": "Initializer", "value": "<>" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "1_Add:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [], "description": [ "Task(", " name: add", " parameters: ", " x: tensor(float32, [1, 128, 768])", " y: tensor(float32, [768])", " z: tensor(float32, [1, 128, 768])", " inputs: [x, y]", " outputs: [z]", " computations: ", " z: float32[1, 128, 768] where z[v, v_1, v_2] = (x[0, v_1, v_2] + y[v_2])", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5] => [v_3, v_4, v_5])", ")" ] }, { "name": "2_Reshape", "type": { "name": "Reshape", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "1_Add:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "2_Reshape:0", "type": { "string": "float32['1', '128', '12', '64']", "shape": { "dimensions": [ "1", "128", "12", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "shape", "type": "Sequence[int]", "value": "[1, 128, 12, 64]", "visible": true, "description": "" } ], "description": [ "Task(", " name: reshape", " parameters: ", " x: tensor(float32, [1, 128, 768])", " y: tensor(float32, [1, 128, 12, 64])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[1, 128, 12, 64] where y[v, v_1, v_2, v_3] = x[((((v_3 + (v_2 * 64)) + (v_1 * 768)) + (v * 98304)) / 98304), (((((v_3 + (v_2 * 64)) + (v_1 * 768)) + (v * 98304)) / 768) % 128), ((((v_3 + (v_2 * 64)) + (v_1 * 768)) + (v * 98304)) % 768)]", " attributes: {shape: [1, 128, 12, 64]}", " inverse_map:", " x: InverseMap([v_4, v_5, v_6] => [(((v_6 + (v_5 * 768)) + (v_4 * 98304)) / 98304), ((((v_6 + (v_5 * 768)) + (v_4 * 98304)) / 768) % 128), ((((v_6 + (v_5 * 768)) + (v_4 * 98304)) / 64) % 12), (((v_6 + (v_5 * 768)) + (v_4 * 98304)) % 64)])", ")" ] }, { "name": "3_Rearrange", "type": { "name": "Rearrange", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "2_Reshape:0", "type": { "string": "float32['1', '128', '12', '64']", "shape": { "dimensions": [ "1", "128", "12", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "3_Rearrange:0", "type": { "string": "float32['12', '128', '64']", "shape": { "dimensions": [ "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "plan", "type": "Sequence[list]", "value": "[[0, 2], [1], [3]]", "visible": true, "description": "" } ], "description": [ "Task(", " name: rearrange", " parameters: ", " x: tensor(float32, [1, 128, 12, 64])", " y: tensor(float32, [12, 128, 64])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[12, 128, 64] where y[v, v_1, v_2] = x[0, (v_1 % 128), (v % 12), (v_2 % 64)]", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5, v_6] => [((v_3 * 12) + v_5), v_4, v_6])", ")" ] }, { "name": "4_Matmul", "type": { "name": "Matmul", "category": "layer" }, "inputs": [ { "name": "0", "arguments": [ { "name": "input:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "const:2", "type": { "string": "float32['768', '768']", "shape": { "dimensions": [ "768", "768" ] }, "dataType": "float32" }, "initializer": { "kind": "Initializer", "value": "<>" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "4_Matmul:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "require_prologue", "type": "bool", "value": "False", "visible": true, "description": "" }, { "name": "transpose_b", "type": "bool", "value": "False", "visible": true, "description": "" } ], "description": [ "Task(", " name: matmul", " parameters: ", " a: tensor(float32, [1, 128, 768])", " b: tensor(float32, [768, 768])", " c: tensor(float32, [1, 128, 768])", " inputs: [a, b]", " outputs: [c]", " computations: ", " c: float32[1, 128, 768] where c[v, v_1, v_2] = reduce([768], (v_3) => (a[0, v_1, v_3] * b[v_3, v_2]), sum)", " attributes: {}", ")" ] }, { "name": "5_Add", "type": { "name": "Add", "category": "transform" }, "inputs": [ { "name": "0", "arguments": [ { "name": "4_Matmul:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "const:3", "type": { "string": "float32['768']", "shape": { "dimensions": [ "768" ] }, "dataType": "float32" }, "initializer": { "kind": "Initializer", "value": "<>" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "5_Add:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [], "description": [ "Task(", " name: add", " parameters: ", " x: tensor(float32, [1, 128, 768])", " y: tensor(float32, [768])", " z: tensor(float32, [1, 128, 768])", " inputs: [x, y]", " outputs: [z]", " computations: ", " z: float32[1, 128, 768] where z[v, v_1, v_2] = (x[0, v_1, v_2] + y[v_2])", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5] => [v_3, v_4, v_5])", ")" ] }, { "name": "6_Reshape", "type": { "name": "Reshape", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "5_Add:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "6_Reshape:0", "type": { "string": "float32['1', '128', '12', '64']", "shape": { "dimensions": [ "1", "128", "12", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "shape", "type": "Sequence[int]", "value": "[1, 128, 12, 64]", "visible": true, "description": "" } ], "description": [ "Task(", " name: reshape", " parameters: ", " x: tensor(float32, [1, 128, 768])", " y: tensor(float32, [1, 128, 12, 64])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[1, 128, 12, 64] where y[v, v_1, v_2, v_3] = x[((((v_3 + (v_2 * 64)) + (v_1 * 768)) + (v * 98304)) / 98304), (((((v_3 + (v_2 * 64)) + (v_1 * 768)) + (v * 98304)) / 768) % 128), ((((v_3 + (v_2 * 64)) + (v_1 * 768)) + (v * 98304)) % 768)]", " attributes: {shape: [1, 128, 12, 64]}", " inverse_map:", " x: InverseMap([v_4, v_5, v_6] => [(((v_6 + (v_5 * 768)) + (v_4 * 98304)) / 98304), ((((v_6 + (v_5 * 768)) + (v_4 * 98304)) / 768) % 128), ((((v_6 + (v_5 * 768)) + (v_4 * 98304)) / 64) % 12), (((v_6 + (v_5 * 768)) + (v_4 * 98304)) % 64)])", ")" ] }, { "name": "7_Rearrange", "type": { "name": "Rearrange", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "6_Reshape:0", "type": { "string": "float32['1', '128', '12', '64']", "shape": { "dimensions": [ "1", "128", "12", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "7_Rearrange:0", "type": { "string": "float32['12', '128', '64']", "shape": { "dimensions": [ "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "plan", "type": "Sequence[list]", "value": "[[0, 2], [1], [3]]", "visible": true, "description": "" } ], "description": [ "Task(", " name: rearrange", " parameters: ", " x: tensor(float32, [1, 128, 12, 64])", " y: tensor(float32, [12, 128, 64])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[12, 128, 64] where y[v, v_1, v_2] = x[0, (v_1 % 128), (v % 12), (v_2 % 64)]", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5, v_6] => [((v_3 * 12) + v_5), v_4, v_6])", ")" ] }, { "name": "8_PermuteDims", "type": { "name": "PermuteDims", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "7_Rearrange:0", "type": { "string": "float32['12', '128', '64']", "shape": { "dimensions": [ "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "8_PermuteDims:0", "type": { "string": "float32['12', '64', '128']", "shape": { "dimensions": [ "12", "64", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "axes", "type": "Sequence[int]", "value": "[0, 2, 1]", "visible": true, "description": "" } ], "description": [ "Task(", " name: rearrange", " parameters: ", " x: tensor(float32, [12, 128, 64])", " y: tensor(float32, [12, 64, 128])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[12, 64, 128] where y[v, v_1, v_2] = x[(v % 12), (v_2 % 128), (v_1 % 64)]", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5] => [v_3, v_5, v_4])", ")" ] }, { "name": "9_Matmul", "type": { "name": "Matmul", "category": "layer" }, "inputs": [ { "name": "0", "arguments": [ { "name": "3_Rearrange:0", "type": { "string": "float32['12', '128', '64']", "shape": { "dimensions": [ "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "8_PermuteDims:0", "type": { "string": "float32['12', '64', '128']", "shape": { "dimensions": [ "12", "64", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "9_Matmul:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "require_prologue", "type": "bool", "value": "False", "visible": true, "description": "" }, { "name": "transpose_b", "type": "bool", "value": "False", "visible": true, "description": "" } ], "description": [ "Task(", " name: matmul", " parameters: ", " a: tensor(float32, [12, 128, 64])", " b: tensor(float32, [12, 64, 128])", " c: tensor(float32, [12, 128, 128])", " inputs: [a, b]", " outputs: [c]", " computations: ", " c: float32[12, 128, 128] where c[v, v_1, v_2] = reduce([64], (v_3) => (a[v, v_1, v_3] * b[v, v_3, v_2]), sum)", " attributes: {}", ")" ] }, { "name": "10_DivideScalar", "type": { "name": "DivideScalar", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "9_Matmul:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "10_DivideScalar:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "scalar", "type": "Constant", "value": "8.0f", "visible": true, "description": "" } ], "description": [ "Task(", " name: divs", " parameters: ", " x: tensor(float32, [12, 128, 128])", " y: tensor(float32, [12, 128, 128])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[12, 128, 128] where y[v, v_1, v_2] = (x[v, v_1, v_2] / 8.0f)", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5] => [v_3, v_4, v_5])", ")" ] }, { "name": "11_Add", "type": { "name": "Add", "category": "transform" }, "inputs": [ { "name": "0", "arguments": [ { "name": "10_DivideScalar:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "input:1", "type": { "string": "int32['1', '128']", "shape": { "dimensions": [ "1", "128" ] }, "dataType": "int32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "11_Add:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [], "description": [ "Task(", " name: add", " parameters: ", " x: tensor(float32, [12, 128, 128])", " y: tensor(int32, [1, 128])", " z: tensor(float32, [12, 128, 128])", " inputs: [x, y]", " outputs: [z]", " computations: ", " z: float32[12, 128, 128] where z[v, v_1, v_2] = (x[v, v_1, v_2] + y[0, v_2])", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5] => [v_3, v_4, v_5])", ")" ] }, { "name": "12_ReduceMax", "type": { "name": "ReduceMax", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "11_Add:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "12_ReduceMax:0", "type": { "string": "float32['12', '128', '1']", "shape": { "dimensions": [ "12", "128", "1" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "dims", "type": "Sequence[int]", "value": "[2]", "visible": true, "description": "" }, { "name": "keepdims", "type": "bool", "value": "True", "visible": true, "description": "" } ], "description": [ "Task(", " name: reduce_max", " parameters: ", " x: tensor(float32, [12, 128, 128])", " y: tensor(float32, [12, 128, 1])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[12, 128, 1] where y[v, v_1, v_2] = reduce([128], (v_3) => x[v, v_1, v_3], max)", " attributes: {dims: [2], keep_dim: True, reduce_type: max, accumulate_dtype: float32}", ")" ] }, { "name": "13_Subtract", "type": { "name": "Subtract", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "11_Add:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "12_ReduceMax:0", "type": { "string": "float32['12', '128', '1']", "shape": { "dimensions": [ "12", "128", "1" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "13_Subtract:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [], "description": [ "Task(", " name: subtract", " parameters: ", " x: tensor(float32, [12, 128, 128])", " y: tensor(float32, [12, 128, 1])", " z: tensor(float32, [12, 128, 128])", " inputs: [x, y]", " outputs: [z]", " computations: ", " z: float32[12, 128, 128] where z[v, v_1, v_2] = (x[v, v_1, v_2] - y[v, v_1, 0])", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5] => [v_3, v_4, v_5])", ")" ] }, { "name": "14_Exp", "type": { "name": "Exp", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "13_Subtract:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "14_Exp:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [], "description": [ "Task(", " name: exp", " parameters: ", " x: tensor(float32, [12, 128, 128])", " y: tensor(float32, [12, 128, 128])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[12, 128, 128] where y[v, v_1, v_2] = generic_exp(x[v, v_1, v_2])", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5] => [v_3, v_4, v_5])", ")" ] }, { "name": "15_ReduceSum", "type": { "name": "ReduceSum", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "14_Exp:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "15_ReduceSum:0", "type": { "string": "float32['12', '128', '1']", "shape": { "dimensions": [ "12", "128", "1" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "dims", "type": "Sequence[int]", "value": "[2]", "visible": true, "description": "" }, { "name": "keepdims", "type": "bool", "value": "True", "visible": true, "description": "" } ], "description": [ "Task(", " name: reduce_sum", " parameters: ", " x: tensor(float32, [12, 128, 128])", " y: tensor(float32, [12, 128, 1])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[12, 128, 1] where y[v, v_1, v_2] = reduce([128], (v_3) => x[v, v_1, v_3], sum)", " attributes: {dims: [2], keep_dim: True, reduce_type: sum, accumulate_dtype: float32}", ")" ] }, { "name": "16_Divide", "type": { "name": "Divide", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "14_Exp:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "15_ReduceSum:0", "type": { "string": "float32['12', '128', '1']", "shape": { "dimensions": [ "12", "128", "1" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "16_Divide:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [], "description": [ "Task(", " name: div", " parameters: ", " x: tensor(float32, [12, 128, 128])", " y: tensor(float32, [12, 128, 1])", " z: tensor(float32, [12, 128, 128])", " inputs: [x, y]", " outputs: [z]", " computations: ", " z: float32[12, 128, 128] where z[v, v_1, v_2] = (x[v, v_1, v_2] / y[v, v_1, 0])", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5] => [v_3, v_4, v_5])", ")" ] }, { "name": "17_Matmul", "type": { "name": "Matmul", "category": "layer" }, "inputs": [ { "name": "0", "arguments": [ { "name": "input:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "const:4", "type": { "string": "float32['768', '768']", "shape": { "dimensions": [ "768", "768" ] }, "dataType": "float32" }, "initializer": { "kind": "Initializer", "value": "<>" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "17_Matmul:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "require_prologue", "type": "bool", "value": "False", "visible": true, "description": "" }, { "name": "transpose_b", "type": "bool", "value": "False", "visible": true, "description": "" } ], "description": [ "Task(", " name: matmul", " parameters: ", " a: tensor(float32, [1, 128, 768])", " b: tensor(float32, [768, 768])", " c: tensor(float32, [1, 128, 768])", " inputs: [a, b]", " outputs: [c]", " computations: ", " c: float32[1, 128, 768] where c[v, v_1, v_2] = reduce([768], (v_3) => (a[0, v_1, v_3] * b[v_3, v_2]), sum)", " attributes: {}", ")" ] }, { "name": "18_Add", "type": { "name": "Add", "category": "transform" }, "inputs": [ { "name": "0", "arguments": [ { "name": "17_Matmul:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "const:5", "type": { "string": "float32['768']", "shape": { "dimensions": [ "768" ] }, "dataType": "float32" }, "initializer": { "kind": "Initializer", "value": "<>" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "18_Add:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [], "description": [ "Task(", " name: add", " parameters: ", " x: tensor(float32, [1, 128, 768])", " y: tensor(float32, [768])", " z: tensor(float32, [1, 128, 768])", " inputs: [x, y]", " outputs: [z]", " computations: ", " z: float32[1, 128, 768] where z[v, v_1, v_2] = (x[0, v_1, v_2] + y[v_2])", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5] => [v_3, v_4, v_5])", ")" ] }, { "name": "19_Reshape", "type": { "name": "Reshape", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "18_Add:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "19_Reshape:0", "type": { "string": "float32['1', '128', '12', '64']", "shape": { "dimensions": [ "1", "128", "12", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "shape", "type": "Sequence[int]", "value": "[1, 128, 12, 64]", "visible": true, "description": "" } ], "description": [ "Task(", " name: reshape", " parameters: ", " x: tensor(float32, [1, 128, 768])", " y: tensor(float32, [1, 128, 12, 64])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[1, 128, 12, 64] where y[v, v_1, v_2, v_3] = x[((((v_3 + (v_2 * 64)) + (v_1 * 768)) + (v * 98304)) / 98304), (((((v_3 + (v_2 * 64)) + (v_1 * 768)) + (v * 98304)) / 768) % 128), ((((v_3 + (v_2 * 64)) + (v_1 * 768)) + (v * 98304)) % 768)]", " attributes: {shape: [1, 128, 12, 64]}", " inverse_map:", " x: InverseMap([v_4, v_5, v_6] => [(((v_6 + (v_5 * 768)) + (v_4 * 98304)) / 98304), ((((v_6 + (v_5 * 768)) + (v_4 * 98304)) / 768) % 128), ((((v_6 + (v_5 * 768)) + (v_4 * 98304)) / 64) % 12), (((v_6 + (v_5 * 768)) + (v_4 * 98304)) % 64)])", ")" ] }, { "name": "20_Rearrange", "type": { "name": "Rearrange", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "19_Reshape:0", "type": { "string": "float32['1', '128', '12', '64']", "shape": { "dimensions": [ "1", "128", "12", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "20_Rearrange:0", "type": { "string": "float32['12', '128', '64']", "shape": { "dimensions": [ "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "plan", "type": "Sequence[list]", "value": "[[0, 2], [1], [3]]", "visible": true, "description": "" } ], "description": [ "Task(", " name: rearrange", " parameters: ", " x: tensor(float32, [1, 128, 12, 64])", " y: tensor(float32, [12, 128, 64])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[12, 128, 64] where y[v, v_1, v_2] = x[0, (v_1 % 128), (v % 12), (v_2 % 64)]", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5, v_6] => [((v_3 * 12) + v_5), v_4, v_6])", ")" ] }, { "name": "21_Matmul", "type": { "name": "Matmul", "category": "layer" }, "inputs": [ { "name": "0", "arguments": [ { "name": "16_Divide:0", "type": { "string": "float32['12', '128', '128']", "shape": { "dimensions": [ "12", "128", "128" ] }, "dataType": "float32" } } ], "visible": true }, { "name": "1", "arguments": [ { "name": "20_Rearrange:0", "type": { "string": "float32['12', '128', '64']", "shape": { "dimensions": [ "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "21_Matmul:0", "type": { "string": "float32['12', '128', '64']", "shape": { "dimensions": [ "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "require_prologue", "type": "bool", "value": "False", "visible": true, "description": "" }, { "name": "transpose_b", "type": "bool", "value": "False", "visible": true, "description": "" } ], "description": [ "Task(", " name: matmul", " parameters: ", " a: tensor(float32, [12, 128, 128])", " b: tensor(float32, [12, 128, 64])", " c: tensor(float32, [12, 128, 64])", " inputs: [a, b]", " outputs: [c]", " computations: ", " c: float32[12, 128, 64] where c[v, v_1, v_2] = reduce([128], (v_3) => (a[v, v_1, v_3] * b[v, v_3, v_2]), sum)", " attributes: {}", ")" ] }, { "name": "22_Reshape", "type": { "name": "Reshape", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "21_Matmul:0", "type": { "string": "float32['12', '128', '64']", "shape": { "dimensions": [ "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "22_Reshape:0", "type": { "string": "float32['1', '12', '128', '64']", "shape": { "dimensions": [ "1", "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "shape", "type": "Sequence[int]", "value": "[1, 12, 128, 64]", "visible": true, "description": "" } ], "description": [ "Task(", " name: reshape", " parameters: ", " x: tensor(float32, [12, 128, 64])", " y: tensor(float32, [1, 12, 128, 64])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[1, 12, 128, 64] where y[v, v_1, v_2, v_3] = x[((((v_3 + (v_2 * 64)) + (v_1 * 8192)) + (v * 98304)) / 8192), (((((v_3 + (v_2 * 64)) + (v_1 * 8192)) + (v * 98304)) / 64) % 128), ((((v_3 + (v_2 * 64)) + (v_1 * 8192)) + (v * 98304)) % 64)]", " attributes: {shape: [1, 12, 128, 64]}", " inverse_map:", " x: InverseMap([v_4, v_5, v_6] => [(((v_6 + (v_5 * 64)) + (v_4 * 8192)) / 98304), ((((v_6 + (v_5 * 64)) + (v_4 * 8192)) / 8192) % 12), ((((v_6 + (v_5 * 64)) + (v_4 * 8192)) / 64) % 128), (((v_6 + (v_5 * 64)) + (v_4 * 8192)) % 64)])", ")" ] }, { "name": "23_Rearrange", "type": { "name": "Rearrange", "category": null }, "inputs": [ { "name": "0", "arguments": [ { "name": "22_Reshape:0", "type": { "string": "float32['1', '12', '128', '64']", "shape": { "dimensions": [ "1", "12", "128", "64" ] }, "dataType": "float32" } } ], "visible": true } ], "outputs": [ { "name": "0", "arguments": [ { "name": "23_Rearrange:0", "type": { "string": "float32['1', '128', '768']", "shape": { "dimensions": [ "1", "128", "768" ] }, "dataType": "float32" } } ], "visible": true } ], "attributes": [ { "name": "plan", "type": "Sequence[list]", "value": "[[0], [2], [1, 3]]", "visible": true, "description": "" } ], "description": [ "Task(", " name: rearrange", " parameters: ", " x: tensor(float32, [1, 12, 128, 64])", " y: tensor(float32, [1, 128, 768])", " inputs: [x]", " outputs: [y]", " computations: ", " y: float32[1, 128, 768] where y[v, v_1, v_2] = x[0, ((v_2 / 64) % 12), (v_1 % 128), (v_2 % 64)]", " attributes: {}", " inverse_map:", " x: InverseMap([v_3, v_4, v_5, v_6] => [v_3, v_5, ((v_4 * 64) + v_6)])", ")" ] } ] } ], "description": "Converted from FlowGraph", "author": "", "company": "", "license": "", "domain": "", "source": "Hidet", "format": "netron" }