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remove redundant cast in a test #3852

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merged 5 commits into from
Feb 10, 2025
Merged

remove redundant cast in a test #3852

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1d25565
remove redundant cast
liqiangxl Feb 7, 2025
b80bbab
Update tests/cpp/test_persistent_buffer.cpp
liqiangxl Feb 9, 2025
5554160
Merge branch 'main' into llu/clean_test
liqiangxl Feb 9, 2025
b073f4c
unorder
liqiangxl Feb 9, 2025
b909282
maybecast
liqiangxl Feb 9, 2025
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38 changes: 15 additions & 23 deletions tests/cpp/test_persistent_buffer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1472,18 +1472,19 @@ TEST_P(LayerNormSharedMemoryTest, FusionLayerNormSharedMemoryBuffer_CUDA) {
constexpr int64_t dim0 = 2048;
std::vector<int64_t> input_shape{dim0, hidden_size};
std::vector<int64_t> norm_shape{hidden_size};
auto input_half = makeContigTensor(2, dtype);
auto weight_half = makeContigTensor(1, dtype);
auto bias_half = makeContigTensor(1, dtype);
fusion.addInput(input_half);
fusion.addInput(weight_half);
fusion.addInput(bias_half);
auto input = castOp(DataType::Float, input_half);
auto weight = castOp(DataType::Float, weight_half);
auto bias = castOp(DataType::Float, bias_half);

auto input = makeContigTensor(2, dtype);
auto weight = makeContigTensor(1, dtype);
auto bias = makeContigTensor(1, dtype);
fusion.addInput(input);
fusion.addInput(weight);
fusion.addInput(bias);
input = maybeCastOp(DataType::Float, input);
weight = maybeCastOp(DataType::Float, weight);
bias = maybeCastOp(DataType::Float, bias);
auto result = layer_norm(input, norm_shape, weight, bias, eps_ptr);
auto result_output = castOp(dtype, result.output);
fusion.addOutput(result_output);
result.output = maybeCastOp(dtype, result.output);
fusion.addOutput(result.output);
fusion.addOutput(result.mean);
fusion.addOutput(result.invstd);

Expand Down Expand Up @@ -1534,18 +1535,9 @@ TEST_P(LayerNormSharedMemoryTest, FusionLayerNormSharedMemoryBuffer_CUDA) {
auto cg_outputs = executor_cache.runFusionWithInputs(aten_inputs);
auto runtime = executor_cache.getMostRecentKernelRuntime();
if (has_enough_regs_smem) {
// For dtype float, no op scheduler is also used.
if (dtype == DataType::Float) {
EXPECT_THAT(
runtime->fusionSegments()->groups(),
UnorderedElementsAre(
HeuristicIs(SchedulerType::NoOp),
HeuristicIs(SchedulerType::InnerPersistent)));
} else {
EXPECT_THAT(
runtime->fusionSegments()->groups(),
UnorderedElementsAre(HeuristicIs(SchedulerType::InnerPersistent)));
}
EXPECT_THAT(
runtime->fusionSegments()->groups(),
UnorderedElementsAre(HeuristicIs(SchedulerType::InnerPersistent)));
Fusion* scheduled_fusion = runtime->executors()
.back()
->as<KernelExecutor>()
Expand Down