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[Unity] Fix FewShotTuning Failure When Missing Global Symbol #15097

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Merged
masahi merged 1 commit into from
Jun 13, 2023
Merged

[Unity] Fix FewShotTuning Failure When Missing Global Symbol #15097

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3 changes: 2 additions & 1 deletion src/relax/transform/few_shot_tuning.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,8 @@ tir::PrimFunc FewShotTunePrimFunc(const tir::PrimFunc& prim_func, const Target&
ICHECK(runner.defined()) << "ValueError: The local runner is not defined!";
}
// create an IRModule
IRModule mod = IRModule(Map<GlobalVar, BaseFunc>({{GlobalVar("main"), prim_func}}));
IRModule mod = IRModule(Map<GlobalVar, BaseFunc>(
{{GlobalVar("main"), WithAttr(prim_func, tvm::attr::kGlobalSymbol, String("main"))}}));
// fetch the number of physical cores
static const auto* f_cpu_count = runtime::Registry::Get("meta_schedule.cpu_count");
ICHECK(f_cpu_count) << "ValueError: Cannot find the packed function \"meta_schedule._cpu_count\"";
Expand Down
14 changes: 8 additions & 6 deletions tests/python/relax/test_transform_few_shot_tuning.py
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Expand Up @@ -39,7 +39,7 @@ def matmul(
B: T.Buffer((32, 32), "float16"),
C: T.Buffer((32, 32), "float16"),
):
T.func_attr({"global_symbol": "main", "tir.noalias": True})
T.func_attr({"tir.noalias": True})
# with T.block("root"):
for i, j, k in T.grid(32, 32, 32):
with T.block("C"):
Expand All @@ -54,7 +54,7 @@ def matmul(
class Softmax:
@T.prim_func
def softmax(rxplaceholder: T.Buffer((T.int64(8), T.int64(3456), T.int64(3456)), "float32"), T_softmax_norm: T.Buffer((T.int64(8), T.int64(3456), T.int64(3456)), "float32")):
T.func_attr({"global_symbol": "main", "op_pattern": 4, "tir.noalias": True})
T.func_attr({"op_pattern": 4, "tir.noalias": True})
# with T.block("root"):
T_softmax_maxelem = T.alloc_buffer((T.int64(8), T.int64(3456)), "float32")
T_softmax_exp = T.alloc_buffer((T.int64(8), T.int64(3456), T.int64(3456)), "float32")
Expand Down Expand Up @@ -93,7 +93,7 @@ def softmax(rxplaceholder: T.Buffer((T.int64(8), T.int64(3456), T.int64(3456)),
class Fused_Variance_Cast1:
@T.prim_func
def main(lv3: T.Buffer((T.int64(1), T.int64(32), T.int64(34560)), "float32"), compute: T.Buffer((T.int64(1), T.int64(32), T.int64(1)), "float16")):
T.func_attr({"tir.noalias": True, "global_symbol": "main"})
T.func_attr({"tir.noalias": True})
# with T.block("root"):
rxplaceholder_red = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1)))
T_divide = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1)))
Expand Down Expand Up @@ -152,7 +152,7 @@ def main(lv3: T.Buffer((T.int64(1), T.int64(32), T.int64(34560)), "float32"), co
class Fuse_Mean_Cast1:
@T.prim_func
def main(lv: T.Buffer((T.int64(1), T.int64(32), T.int64(34560)), "float32"), compute: T.Buffer((T.int64(1), T.int64(32), T.int64(1)), "float16")):
T.func_attr({"tir.noalias": True, "global_symbol": "main"})
T.func_attr({"tir.noalias": True})
# with T.block("root"):
rxplaceholder_red = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1)))
T_divide = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1)))
Expand Down Expand Up @@ -181,7 +181,7 @@ def main(lv: T.Buffer((T.int64(1), T.int64(32), T.int64(34560)), "float32"), com
class Module:
@T.prim_func
def main(lv26: T.Buffer((T.int64(1), T.int64(3456), T.int64(2560)), "float16"), T_multiply: T.Buffer((T.int64(1), T.int64(3456), T.int64(1280)), "float16")):
T.func_attr({"tir.noalias": True, "global_symbol": "main"})
T.func_attr({"tir.noalias": True})
# with T.block("root"):
T_strided_slice_with_axes = T.alloc_buffer((T.int64(1), T.int64(3456), T.int64(1280)), "float16")
T_divide = T.alloc_buffer((T.int64(1), T.int64(3456), T.int64(1280)), "float16")
Expand Down Expand Up @@ -339,7 +339,9 @@ def _get_input_output_info(func: tvm.tir.PrimFunc) -> Tuple[List[np.ndarray], Tu
def _expected_results(
mod: tvm.ir.IRModule, inputs: List[np.ndarray], output_shape: Tuple, output_dtype: str
) -> np.ndarray:
rt_mod = tvm.build(mod, target="llvm")
func = _get_single_prim_func(mod)
func = func.with_attr("global_symbol", "main")
rt_mod = tvm.build(func, target="llvm")
data = [
tvm.nd.array(x)
for x in [
Expand Down