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Remove MmaOpDetails::input_layout and getInputLayout #3322

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Oct 31, 2024
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Remove MmaOpDetails::input_layout and getInputLayout #3322

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58 changes: 0 additions & 58 deletions csrc/ir/utils.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1202,55 +1202,6 @@ TensorViewDetails getDetailsFor(const std::vector<IterDomain*>& dims) {
return details;
}

MmaLayout getInputLayout(
const TensorViewDetails& in_a,
const TensorViewDetails& in_b,
const MmaOp::AxesData& m_axes,
const MmaOp::AxesData& n_axes,
const MmaOp::AxesData& k_axes) {
// TT layout (b - broadcast, r - reduction):
// A = [M, K, b]
// B = [b, K, N]
// C = [M, r, N] (root domain)
if ((m_axes.front() < in_a.bcasts.front()) &&
(k_axes.front() < in_a.bcasts.front()) &&
(in_b.bcasts.front() < k_axes.front()) &&
(in_b.bcasts.front() < n_axes.front())) {
return MmaLayout::TT;
}
// TN layout (b - broadcast, r - reduction):
// A = [M, b, K]
// B = [b, N, K]
// C = [M, N, r] (root domain)
if ((m_axes.front() < in_a.bcasts.front()) &&
(in_a.bcasts.front() < k_axes.front()) &&
(in_b.bcasts.front() < n_axes.front()) &&
(in_b.bcasts.front() < k_axes.front())) {
return MmaLayout::TN;
}
// NT layout (b - broadcast, r - reduction):
// A = [K, M, b]
// B = [K, b, N]
// C = [r, M, N] (root domain)
if ((k_axes.front() < in_a.bcasts.front()) &&
(m_axes.front() < in_a.bcasts.front()) &&
(k_axes.front() < in_b.bcasts.front()) &&
(in_b.bcasts.front() < n_axes.front())) {
return MmaLayout::NT;
}
// NN layout (b - broadcast, r - reduction):
// A = [b, K, M]
// B = [N, K, b]
// C = [N, r, M] (root domain)
if ((in_a.bcasts.front() < k_axes.front()) &&
(k_axes.front() < m_axes.front()) && (n_axes.front() < k_axes.front()) &&
(k_axes.front() < in_b.bcasts.front())) {
return MmaLayout::NN;
}

NVF_THROW("Unsupported input layout");
}

MmaOpDetails getMmaOpDetails(
TensorView* out,
TensorView* in_a,
Expand Down Expand Up @@ -1383,15 +1334,6 @@ MmaOpDetails getMmaOpDetails(
!details.k_axes.empty(),
"MmaOp inputs must define at least a single K dimension");

// TODO: for tensor contraction / split-k uses of MmaOp different input layout
// rules may be needed
details.input_layout = getInputLayout(
in_a_details,
in_b_details,
details.m_axes,
details.n_axes,
details.k_axes);

return details;
}

Expand Down
2 changes: 0 additions & 2 deletions csrc/ir/utils.h
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Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,6 @@ struct MmaOpDetails {
// Concrete or broadcast axes that are present in all inputs
// and output
AxesData batch_axes;
// A placeholder for mma input layout
std::optional<MmaLayout> input_layout = std::nullopt;
};

// A helper structure with pieces of information about TensorView
Expand Down
63 changes: 63 additions & 0 deletions tests/cpp/test_matmul.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -140,6 +140,69 @@ TEST_P(MatmulTestWithLayout, AmpereMatmul) {
NVF_CHECK(cg_outputs[0].allclose(tref, 0.0001, 0.0001));
}

// Single batch dimension which is broadcast
TEST_P(MatmulTestWithLayout, AmpereMatmulBroadcastBatch) {
// Keep multiples of 8 to keep vectorizable.
int M = 504, N = 136, K = 248;

Fusion fusion;
FusionGuard fg(&fusion);

auto shapes = matmulAtInputShape3DTuring(-1, -1, -1, layout);

auto tv0 = makeContigConcreteTensor(shapes.first, DataType::Half);
auto tv1 = makeContigConcreteTensor(shapes.second, DataType::Half);

fusion.addInput(tv0);
fusion.addInput(tv1);

tv0 = canonicalizeInputToBMNK(tv0, layout, MmaOperand::A);
tv1 = canonicalizeInputToBMNK(tv1, layout, MmaOperand::B);
// Broadcast inputs to 1, M, 1, K and 1, 1, N, K
tv0 = broadcast(tv0, {true, false, false, false});
tv1 = broadcast(tv1, {true, false, false, false});
auto tv2 = fusedMultiplySum(tv0, tv1, {-1});

fusion.addOutput(tv2);

MatMulTileOptions gemm_tile;
gemm_tile.cta_tile = GemmTile(128, 128, 32);
gemm_tile.warp_tile = GemmTile(64, 64, 32);
gemm_tile.instruction_tile = GemmTile(16, 8, 16);

MatmulParams mparams;
mparams.supported_vec_size = {8, 8, 4};
mparams.mma_macro = MmaMacro::Ampere_16_8_16;
mparams.tile_sizes = gemm_tile;
mparams.async_gmem_load_operands = true;
mparams.circular_buffer_options.circular_buffer_smem_write = true;
mparams.circular_buffer_options.circular_buffer_smem_read = true;
mparams.circular_buffer_options.smem_circular_buffer_stage = 4;
SchedulerEntry::makeSchedulerInstance(SchedulerType::Matmul)
->schedule(&fusion, &mparams);

auto inputs = matmulAtInput3DTuring(M, N, K, layout);

FusionExecutor fe;
NVFUSER_TEST_CUDA_ARCH_COMPILE_CHECK(
8,
0,
fe.compileFusion(
&fusion,
{inputs.first, inputs.second},
LaunchParams(),
matmul_cparams));
ASSERT_TRUE(getBankConflictInfo(fe.kernel()).empty());
ASSERT_FALSE(
PredicatedChecker::isCpAsyncMmaPredicatedByIfThenElse(fe.kernel()));
auto cg_outputs = fe.runFusion({inputs.first, inputs.second});
auto tref =
atMatmul(
inputs.first.to(at::kFloat), inputs.second.to(at::kFloat), layout)
.unsqueeze(0);
NVF_CHECK(cg_outputs[0].allclose(tref, 0.0001, 0.0001));
}

TEST_P(MatmulTestWithLayout, AmperePrologueFusionBroadcast) {
// Keep multiples of 8 to keep vectorizable.
int M = 504, N = 136, K = 248;
Expand Down
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