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rongzha1TaoLv
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TaoLv
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graph: backend: dnnl: fix bug for pattern lnorm + tc + mul + q
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+275
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‎src/graph/backend/dnnl/kernels/layernorm.hpp‎

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@@ -91,6 +91,11 @@ struct layernorm_fwd_t : public kernel_base_t {
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BACKEND_DNNL_ADD_PASS(pipeline, fuse_post_typecast_to_predecessor);
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BACKEND_DNNL_ADD_PASS(pipeline, remove_quant_data_with_no_effect);
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BACKEND_DNNL_ADD_PASS(pipeline, replace_quant_data_with_binary_post_op);
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// broadcast and swap should be before fuse_post_ops
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BACKEND_DNNL_ADD_PASS(pipeline, binary_canonicalization);
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BACKEND_DNNL_ADD_PASS(pipeline, binary_broadcast_swap);
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BACKEND_DNNL_ADD_PASS(pipeline, fuse_post_ops);
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BACKEND_DNNL_ADD_PASS(pipeline, convert_to_runtime_dst_scales);
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BACKEND_DNNL_ADD_PASS(pipeline, fuse_dst_scales);

‎tests/benchdnn/inputs/graph/pattern/harness_int8_all‎

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@@ -117,5 +117,8 @@
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# layernorm with zp != 0
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--reset --op-attrs=2:zps:1 --case=pattern/int8/int8_lnorm_gelu_quantize.json
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--reset --case=pattern/int8/int8_lnorm_multiply_quantize.json
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--reset --case=pattern/int8/int8_lnorm_tc_multiply_quantize.json
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# layernorm with zp != 0 and broadcast binary
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--reset --op-attrs=3:zps:1 --in-shapes=5:512 --case=pattern/int8/int8_lnorm_tc_multiply_quantize.json
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#softmax
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--reset --case=pattern/int8/int8_softmax_add.json

‎tests/benchdnn/inputs/graph/pattern/int8/int8_lnorm_tc_multiply_quantize.json‎

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{
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"version": "3.5.0",
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"engine_kind": "cpu",
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"fpmath_mode": "strict",
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"input_ports": [
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0,
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1,
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2,
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5
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],
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"output_ports": [
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7
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],
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"graph": [
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{
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"id": 0,
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"name": "layernorm",
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"kind": "LayerNorm",
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"attrs": {
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"begin_norm_axis": {
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"type": "s64",
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"value": -1
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},
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"use_affine": {
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"type": "bool",
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"value": 1
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},
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"keep_stats": {
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"type": "bool",
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"value": 0
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},
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"epsilon": {
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"type": "f32",
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"value": 0.0625
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}
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},
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"inputs": [
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{
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"id": 0,
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"dtype": "bf16",
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"shape": [
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1,
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128,
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512
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],
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"stride": [
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65536,
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512,
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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},
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{
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"id": 1,
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"dtype": "f32",
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"shape": [
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512
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],
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"stride": [
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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},
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{
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"id": 2,
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"dtype": "f32",
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"shape": [
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512
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],
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"stride": [
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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}
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],
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"outputs": [
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{
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"id": 3,
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"dtype": "bf16",
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"shape": [
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1,
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128,
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512
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],
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"stride": [
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65536,
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512,
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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}
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]
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},
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{
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"id": 1,
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"name": "typecast",
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"kind": "TypeCast",
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"attrs": {},
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"inputs": [
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{
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"id": 3,
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"dtype": "bf16",
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"shape": [
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1,
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128,
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512
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],
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"stride": [
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65536,
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512,
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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}
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],
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"outputs": [
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{
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"id": 4,
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"dtype": "f32",
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"shape": [
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1,
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128,
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512
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],
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"stride": [
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65536,
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512,
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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}
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]
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},
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{
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"id": 2,
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"name": "multiply",
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"kind": "Multiply",
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"attrs": {
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"auto_broadcast": {
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"type": "string",
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"value": "numpy"
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}
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},
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"inputs": [
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{
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"id": 4,
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"dtype": "f32",
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"shape": [
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1,
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128,
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512
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],
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"stride": [
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512,
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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},
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{
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"id": 5,
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"dtype": "f32",
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"shape": [
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1,
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128,
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512
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],
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"stride": [
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65536,
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512,
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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}
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],
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"outputs": [
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{
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"id": 6,
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"dtype": "f32",
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"shape": [
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1,
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128,
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512
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],
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"stride": [
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65536,
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512,
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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}
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]
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},
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{
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"id": 3,
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"name": "quantize",
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"kind": "Quantize",
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"attrs": {
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"axis": {
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"type": "s64",
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"value": 0
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},
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"qtype": {
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"type": "string",
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"value": "per_tensor"
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},
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"scales": {
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"type": "f32[]",
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"value": [
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0.5
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]
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},
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"zps": {
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"type": "s64[]",
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"value": [
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0
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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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"id": 6,
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"dtype": "f32",
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"shape": [
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],
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"stride": [
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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}
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],
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"outputs": [
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{
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"id": 7,
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"dtype": "s8",
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"shape": [
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1,
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128,
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512
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],
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"stride": [
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65536,
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512,
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1
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],
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"layout_type": "strided",
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"property_type": "undef"
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}
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]
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}
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]
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}

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