FredJia-intellif commented on code in PR #18027:
URL: https://github.com/apache/tvm/pull/18027#discussion_r2227231750
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src/tir/transforms/dtype_conversion.h:
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@@ -121,12 +122,43 @@ class FloatConfig {
// NVIDIA/Arm/Intel's FP8 formats for Deep Learning
// Reference: https://arxiv.org/abs/2209.05433
switch (dtype.code()) {
+ case DataType::kFloat8_e3m4:
+ // E3M4 format, not consistent with IEEE-754
+ return FloatConfig(3, 4, 3, InftyStyle::kNone, NaNStyle::kAllOnes);
+ case DataType::kFloat8_e4m3:
+ // E4M3 format, not consistent with IEEE-754
+ return FloatConfig(4, 3, 7, InftyStyle::kNone, NaNStyle::kAllOnes);
+ case DataType::kFloat8_e4m3b11fnuz:
+ // E4M3 variant with b11 encoding, not consistent with IEEE-754
+ return FloatConfig(4, 3, 7, InftyStyle::kNone, NaNStyle::kAllOnes);
case DataType::kFloat8_e4m3fn:
// E4M3 format, not consistent with IEEE-754
return FloatConfig(4, 3, 7, InftyStyle::kNone, NaNStyle::kAllOnes);
- default:
- // E5M2 format, consistent with IEEE-754
+ case DataType::kFloat8_e4m3fnuz:
+ // UE4M3 format, not consistent with IEEE-754
+ return FloatConfig(4, 3, 7, InftyStyle::kNone, NaNStyle::kAllOnes);
Review Comment:
@Kathryn-cat , hi, why are the FloatConfig of e4m3, e4m3b11fnuz, e4m3fn and
e4m3fnuz exactly the same?
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