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fffefb1c22
### Description Changes in this PR: 1) use the optimized version `makeMatMulPacked[Vec4]Source` to support matmul. 2) enable the conv2dByMatMul path. 3) support broadcast 4) use IndicesHelper. MatMul with M = 512, K = 512, N = 512 becomes 2ms from 15ms when enabling profilingMode on my ADL.
319 lines
13 KiB
Text
319 lines
13 KiB
Text
[
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{
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"name": "matmul tests with no attributes",
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"operator": "MatMul",
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"attributes": [],
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"cases": [
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{
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"name": "multiplies 2D tensors [2,4]x[4,2]",
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"inputs": [
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{
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"data": [1, 2, 1, 3, 2, 3, 1, 2],
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"dims": [2, 4],
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"type": "float32"
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"data": [2, 1, 1, 2, 2, 3, 0, 4],
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"dims": [4, 2],
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"type": "float32"
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}
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],
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"outputs": [
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{
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"data": [6, 20, 9, 19],
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"dims": [2, 2],
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"type": "float32"
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}
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]
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},
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{
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"name": "multiplies 2D tensors [2,2]x[2,2]",
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"inputs": [
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{
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"data": [1, 2, 3, 4],
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"dims": [2, 2],
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"type": "float32"
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"outputs": [
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"dims": [2, 2],
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"type": "float32"
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}
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]
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},
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{
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"name": "multiplies 2D tensors [2,3]x[3,2]",
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"inputs": [
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{
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"data": [1, 2, 3, 4, 5, 6],
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"dims": [2, 3],
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"type": "float32"
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{
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"data": [1, 2, 3, 4, 5, 6],
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"dims": [3, 2],
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"type": "float32"
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"outputs": [
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{
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"data": [22, 28, 49, 64],
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"dims": [2, 2],
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"type": "float32"
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}
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]
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},
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{
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"name": "multiplies 3D tensors",
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"inputs": [
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{
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"data": [1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24, 9, 10, 11, 12, 13, 14, 15, 16],
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"outputs": [
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{
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"type": "float32"
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]
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},
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{
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"name": "multiplies 4D tensors",
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{
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"data": [
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"type": "float32"
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}
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],
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"outputs": [
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{
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"data": [
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"type": "float32"
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}
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]
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},
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{
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"name": "multiplies 2D broadcasted to 4D tensors",
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"inputs": [
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{
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"data": [1, 2, 1, 3, 2, 3, 1, 2],
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"type": "float32"
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}
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],
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"outputs": [
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{
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"data": [
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"type": "float32"
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}
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]
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},
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{
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"name": "multiplies 4D broadcasted to 5D tensors",
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"inputs": [
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{
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"type": "float32"
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"outputs": [
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{
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"data": [
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{
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},
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{
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"inputs": [
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{
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{
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"type": "float32"
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]
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},
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{
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"name": "multiplies 5D with 3D tensors vec4",
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{
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