added support of b2b bmm (#849)
* added support of b2b bmm * fixed arguments and params structures * added batch_count argument * removed SplitKSerial and added new test case with b2b bmm * fixed support of Kbatched and added new test case with batch stride * added batch support for bias and scale * make test * small changes --------- Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
This commit is contained in:
Aleksandr Pivovar
co-authored by
Haicheng Wu
Haicheng Wu
parent
d572cc1aab
commit
4a68cf748e
@@ -119,8 +119,6 @@ template <
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int AlignmentB =
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DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
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ElementC_, ElementAccumulator_>::kAlignmentB,
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/// If true, kernel supports split-K with serial reduction
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bool SplitKSerial = false,
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/// Operation performed by GEMM
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typename Operator_ = typename DefaultGemmConfiguration<
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OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
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@@ -154,7 +152,6 @@ class B2bGemm {
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static int const kAlignmentA = AlignmentA;
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static int const kAlignmentB = AlignmentB;
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static int const kAlignmentC = EpilogueOutputOp1::kCount;
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static bool const kSplitKSerial = SplitKSerial;
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static ComplexTransform const kTransformA = ComplexTransform::kNone;
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static ComplexTransform const kTransformB = ComplexTransform::kNone;
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@@ -184,7 +181,6 @@ class B2bGemm {
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EpilogueOutputOp1,
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ThreadblockSwizzle,
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kStages,
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kSplitKSerial,
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Operator,
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SmemAccumulator
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>::B2bGemmKernel;
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@@ -196,6 +192,7 @@ class B2bGemm {
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// Data members
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//
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GemmUniversalMode mode;
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GemmCoord problem_size_0;
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GemmCoord problem_size_1;
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TensorRef<ElementA const, LayoutA> ref_A0;
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@@ -206,9 +203,16 @@ class B2bGemm {
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TensorRef<ElementB const, LayoutB> ref_B1;
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TensorRef<ElementC const, LayoutC> ref_C1;
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TensorRef<ElementC, LayoutC> ref_D1;
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int64_t batch_stride_A0;
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int64_t batch_stride_B0;
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int64_t batch_stride_B1;
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int64_t batch_stride_C1;
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int64_t batch_stride_D1;
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int64_t batch_stride_Bias0;
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int64_t batch_stride_Scale0;
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typename EpilogueOutputOp0::Params epilogue0;
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typename EpilogueOutputOp1::Params epilogue1;
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int split_k_slices;
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int batch_count;
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//
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// Methods
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@@ -216,13 +220,14 @@ class B2bGemm {
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/// Default ctor
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CUTLASS_HOST_DEVICE
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Arguments(): problem_size_0(0, 0, 0), problem_size_1(0, 0, 0), split_k_slices(1) {
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Arguments(): mode(mode), problem_size_0(0, 0, 0), problem_size_1(0, 0, 0), batch_count(1) {
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}
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/// Constructs an Arguments structure
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/// Constructs an Arguments structure
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CUTLASS_HOST_DEVICE
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Arguments(
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GemmUniversalMode mode_,
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GemmCoord problem_size_0_,
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GemmCoord problem_size_1_,
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TensorRef<ElementA const, LayoutA> ref_A0_,
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@@ -233,12 +238,20 @@ class B2bGemm {
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TensorRef<ElementB const, LayoutB> ref_B1_,
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TensorRef<ElementC const, LayoutC> ref_C1_,
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TensorRef<ElementC, LayoutC> ref_D1_,
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typename EpilogueOutputOp0::Params epilogue0_ =
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int64_t batch_stride_A0_,
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int64_t batch_stride_B0_,
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int64_t batch_stride_B1_,
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int64_t batch_stride_C1_,
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int64_t batch_stride_D1_,
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int64_t batch_stride_Bias0_,
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int64_t batch_stride_Scale0_,
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typename EpilogueOutputOp0::Params epilogue0_ =
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typename EpilogueOutputOp0::Params(),
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typename EpilogueOutputOp1::Params epilogue1_ =
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typename EpilogueOutputOp1::Params epilogue1_ =
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typename EpilogueOutputOp1::Params(),
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int split_k_slices_ = 1
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int batch_count_ = 1
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):
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mode(mode_),
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problem_size_0(problem_size_0_),
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problem_size_1(problem_size_1_),
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ref_A0(ref_A0_),
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@@ -249,9 +262,16 @@ class B2bGemm {
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ref_B1(ref_B1_),
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ref_C1(ref_C1_),
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ref_D1(ref_D1_),
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batch_stride_A0(batch_stride_A0_),
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batch_stride_B0(batch_stride_B0_),
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batch_stride_B1(batch_stride_B1_),
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batch_stride_C1(batch_stride_C1_),
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batch_stride_D1(batch_stride_D1_),
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batch_stride_Bias0(batch_stride_Bias0_),
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batch_stride_Scale0(batch_stride_Scale0_),
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epilogue0(epilogue0_),
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epilogue1(epilogue1_),
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split_k_slices(split_k_slices_) {
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batch_count(batch_count_) {
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}
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};
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@@ -269,10 +289,6 @@ public:
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/// Determines whether the GEMM can execute the given problem.
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static Status can_implement(Arguments const &args) {
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if (!kSplitKSerial && args.split_k_slices > 1) {
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return Status::kErrorInvalidProblem;
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}
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Status status = B2bGemmKernel::can_implement(
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args.problem_size_0,
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args.problem_size_1,
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@@ -295,20 +311,14 @@ public:
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static size_t get_workspace_size(Arguments const &args) {
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size_t bytes = 0;
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// Determine grid shape
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape(
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args.problem_size_0,
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args.problem_size_0,
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{ThreadblockShape0::kM, ThreadblockShape0::kN, ThreadblockShape0::kK},
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args.split_k_slices);
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if (kSplitKSerial && args.split_k_slices > 1) {
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bytes += sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
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}
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args.batch_count);
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return bytes;
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}
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@@ -320,38 +330,17 @@ public:
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ThreadblockSwizzle threadblock_swizzle;
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cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
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args.problem_size_0,
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args.problem_size_0,
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{ThreadblockShape0::kM, ThreadblockShape0::kN, ThreadblockShape0::kK},
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args.split_k_slices);
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args.batch_count);
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// cutlass::gemm::GemmCoord grid_shape_1 = threadblock_swizzle.get_tiled_shape(
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// args.problem_size_1,
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// args.problem_size_1,
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// {ThreadblockShape1::kM, ThreadblockShape1::kN, ThreadblockShape1::kK},
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// args.split_k_slices);
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if (kSplitKSerial) {
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if (args.split_k_slices > 1) {
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if (!workspace) {
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return Status::kErrorWorkspaceNull;
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}
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size_t bytes = get_workspace_size(args);
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cudaError_t result = cudaMemsetAsync(workspace, 0, bytes, stream);
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if (result != cudaSuccess) {
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return Status::kErrorInternal;
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}
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}
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}
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else {
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if (args.split_k_slices > 1) {
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return Status::kErrorInvalidProblem;
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}
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}
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// args.batch_count);
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// Initialize the Params structure
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params_ = typename B2bGemmKernel::Params{
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args.mode,
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args.problem_size_0,
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args.problem_size_1,
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grid_shape,
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@@ -363,6 +352,13 @@ public:
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args.ref_B1.non_const_ref(),
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args.ref_C1.non_const_ref(),
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args.ref_D1,
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args.batch_stride_A0,
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args.batch_stride_B0,
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args.batch_stride_B1,
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args.batch_stride_C1,
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args.batch_stride_D1,
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args.batch_stride_Bias0,
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args.batch_stride_Scale0,
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args.epilogue0,
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args.epilogue1,
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static_cast<int *>(workspace),
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@@ -373,12 +369,6 @@ public:
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/// Lightweight update given a subset of arguments
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Status update(Arguments const &args, void *workspace = nullptr) {
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if (kSplitKSerial && args.split_k_slices > 1) {
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if (!workspace) {
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return Status::kErrorWorkspaceNull;
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}
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}
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params_.ref_A0.reset(args.ref_A0.non_const_ref().data());
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params_.ref_B0.reset(args.ref_B0.non_const_ref().data());
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@@ -430,12 +420,12 @@ public:
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/// Runs the kernel using initialized state.
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Status operator()(
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Arguments const &args,
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void *workspace = nullptr,
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Arguments const &args,
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void *workspace = nullptr,
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cudaStream_t stream = nullptr) {
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Status status = initialize(args, workspace, stream);
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if (status == Status::kSuccess) {
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status = run(stream);
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}
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