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[Codegen, CUDA] Add handling of fp8 broadcast / const #16865

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Merged
merged 6 commits into from
Apr 10, 2024
Merged

[Codegen, CUDA] Add handling of fp8 broadcast / const #16865

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72b4ae7
[Codegen, CUDA] Add handling of fp8 broadcast / const
vinx13 Apr 9, 2024
87dfbfb
test
vinx13 Apr 9, 2024
9dbc763
Update src/target/source/codegen_cuda.cc
vinx13 Apr 9, 2024
9e40692
Update src/target/source/codegen_cuda.cc
csullivan Apr 9, 2024
59fd1c4
lint
vinx13 Apr 9, 2024
aef7683
add check to skip test
vinx13 Apr 10, 2024
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21 changes: 21 additions & 0 deletions src/target/source/codegen_cuda.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1260,6 +1260,21 @@ void CodeGenCUDA::VisitExpr_(const BroadcastNode* op, std::ostream& os) { // NO
return;
}

if (op->dtype.is_float8()) {
int lanes = op->dtype.lanes();
ICHECK(lanes == 1 || lanes == 2 || lanes == 4);
std::string v = PrintExpr(op->value);
// Implicit conversion from float back to fp8
PrintType(op->dtype, os);
os << "(make_float" << lanes << "(";
for (int i = 0; i < lanes; ++i) {
if (i != 0) os << ", ";
os << "static_cast<float>(" << v << ")";
}
os << "))";
return;
}

if ((op->dtype.is_int() || op->dtype.is_uint()) && op->dtype.bits() == 4) {
bool fail = false;
const int64_t* p = as_const_int(op->value);
Expand Down Expand Up @@ -1359,6 +1374,12 @@ inline void PrintConst(const FloatImmNode* op, std::ostream& os, CodeGenCUDA* p)
os << '(' << std::scientific << op->value << 'f' << ')';
return;
}
// Type code is kE5M2Float or kE4M4Float
if (op->dtype.is_float8()) {
p->PrintType(op->dtype, os);
os << '(' << std::scientific << op->value << 'f' << ')';
return;
}
// Type code is kFloat
switch (op->dtype.bits()) {
case 64:
Expand Down
15 changes: 15 additions & 0 deletions tests/python/codegen/test_target_codegen_cuda_fp8.py
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Expand Up @@ -799,5 +799,20 @@ def test_main(self, weight_shape, model_dtype, target_str, compiled_functions):
tvm.testing.assert_allclose(weight_np, dequant_weight_np, atol=10, rtol=5e-2)


@tvm.testing.requires_cuda_compute_version(9)
@pytest.mark.parametrize("dtype", ["e5m2_float8", "e4m3_float8"])
def test_const(dtype):
@T.prim_func
def func(A: T.Buffer((4,), dtype)) -> None:
A_local = T.alloc_buffer((4,), dtype=dtype, scope="local")
for tx in T.thread_binding(0, 4, "threadIdx.x"):
for i in T.vectorized(4):
A_local[i] = T.float32(1.0).astype(dtype)
A[tx] = A_local[tx]

mod = tvm.IRModule({"main": func})
tvm.build(mod, target="cuda")


if __name__ == "__main__":
tvm.testing.main()