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Integrate Triton up to 8d445186 #33799

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4 changes: 2 additions & 2 deletions jaxlib/gpu/triton.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -45,8 +45,8 @@ namespace jax::JAX_GPU_NAMESPACE {

NB_MODULE(_triton, m) {
nb::class_<Kernel>(m, "TritonKernel")
.def(nb::init<std::string, uint32_t, uint32_t, std::string, std::string,
int, uint32_t, uint32_t, uint32_t>());
.def(nb::init<std::string, uint32_t, uint32_t, uint32_t, std::string,
std::string, int>());

nb::class_<KernelCall::Parameter>(m, "TritonParameter");

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6 changes: 3 additions & 3 deletions jaxlib/gpu/triton.proto
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Expand Up @@ -5,13 +5,13 @@ package jax_triton;
message TritonKernel {
string kernel_name = 1; // Kernel function name within module.
uint32 num_warps = 2;
optional uint32 num_ctas = 10;
uint32 shared_mem_bytes = 3;
string ptx = 4;
string ttir = 5;
uint32 compute_capability = 6;
uint32 cluster_dim_0 = 7;
uint32 cluster_dim_1 = 8;
uint32 cluster_dim_2 = 9;

reserved 7, 8, 9; // cluster_dim_0, cluster_dim_1, cluster_dim_2
}

message TritonKernelCall {
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37 changes: 17 additions & 20 deletions jaxlib/gpu/triton_kernels.cc
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Expand Up @@ -315,17 +315,16 @@ class ModuleImage {
ABSL_GUARDED_BY(mutex_);
};

Kernel::Kernel(std::string kernel_name, uint32_t num_warps,
Kernel::Kernel(std::string kernel_name, uint32_t num_warps, uint32_t num_ctas,
uint32_t shared_mem_bytes, std::string ptx, std::string ttir,
int compute_capability, uint32_t cluster_dim_0,
uint32_t cluster_dim_1, uint32_t cluster_dim_2)
int compute_capability)
: kernel_name_(std::move(kernel_name)),
block_dim_x_(num_warps * kNumThreadsPerWarp),
num_ctas_(num_ctas),
shared_mem_bytes_(shared_mem_bytes),
ptx_(std::move(ptx)),
ttir_(std::move(ttir)),
compute_capability_(compute_capability),
cluster_dims_{cluster_dim_0, cluster_dim_1, cluster_dim_2} {}
compute_capability_(compute_capability) {}

absl::Status Kernel::Launch(gpuStream_t stream, uint32_t grid[3],
void** params) {
Expand Down Expand Up @@ -362,26 +361,24 @@ absl::Status Kernel::Launch(gpuStream_t stream, uint32_t grid[3],

JAX_ASSIGN_OR_RETURN(gpuFunction_t kernel,
module_image_->GetFunctionForContext(context));
const uint32_t cluster_size =
cluster_dims_[0] * cluster_dims_[1] * cluster_dims_[2];
if (cluster_size <= 1) {
if (num_ctas_ == 1) {
return JAX_AS_STATUS(gpuLaunchKernel(
kernel, grid[0], grid[1], grid[2], block_dim_x_,
/*blockDimY=*/1, /*blockDimZ=*/1, shared_mem_bytes_, stream, params,
/*extra=*/nullptr));
}
CUlaunchAttribute launch_attrs[2];
launch_attrs[0].id = CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION;
launch_attrs[0].value.clusterDim.x = cluster_dims_[0];
launch_attrs[0].value.clusterDim.y = cluster_dims_[1];
launch_attrs[0].value.clusterDim.z = cluster_dims_[2];
launch_attrs[0].value.clusterDim.x = num_ctas_;
launch_attrs[0].value.clusterDim.y = 1;
launch_attrs[0].value.clusterDim.z = 1;
launch_attrs[1].id = CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE;
launch_attrs[1].value.clusterSchedulingPolicyPreference =
CU_CLUSTER_SCHEDULING_POLICY_SPREAD;
CUlaunchConfig launch_config = {
/*gridDimX=*/grid[0] * cluster_dims_[0],
/*gridDimY=*/grid[1] * cluster_dims_[1],
/*gridDimZ=*/grid[2] * cluster_dims_[2],
/*gridDimX=*/grid[0] * num_ctas_,
/*gridDimY=*/grid[1],
/*gridDimZ=*/grid[2],
/*blockDimX=*/block_dim_x_,
/*blockDimY=*/1,
/*blockDimZ=*/1,
Expand All @@ -396,23 +393,23 @@ absl::Status Kernel::Launch(gpuStream_t stream, uint32_t grid[3],
}

/*static*/ Kernel Kernel::FromProto(const jax_triton::TritonKernel& proto) {
return Kernel(proto.kernel_name(), proto.num_warps(),
// Use 1 as default value if not specified in already serialized kernels.
int num_ctas = proto.has_num_ctas() ? proto.num_ctas() : 1;

return Kernel(proto.kernel_name(), proto.num_warps(), num_ctas,
proto.shared_mem_bytes(), proto.ptx(), proto.ttir(),
proto.compute_capability(), proto.cluster_dim_0(),
proto.cluster_dim_1(), proto.cluster_dim_2());
proto.compute_capability());
}

jax_triton::TritonKernel Kernel::ToProto() const {
jax_triton::TritonKernel proto;
proto.set_kernel_name(kernel_name_);
proto.set_num_warps(block_dim_x_ / kNumThreadsPerWarp);
proto.set_num_ctas(num_ctas_);
proto.set_shared_mem_bytes(shared_mem_bytes_);
proto.set_ptx(ptx_);
proto.set_ttir(ttir_);
proto.set_compute_capability(compute_capability_);
proto.set_cluster_dim_0(cluster_dims_[0]);
proto.set_cluster_dim_1(cluster_dims_[1]);
proto.set_cluster_dim_2(cluster_dims_[2]);
return proto;
}

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12 changes: 5 additions & 7 deletions jaxlib/gpu/triton_kernels.h
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Original file line number Diff line number Diff line change
Expand Up @@ -38,10 +38,9 @@ class ModuleImage;

class Kernel {
public:
Kernel(std::string kernel_name, uint32_t num_warps, uint32_t shared_mem_bytes,
std::string ptx, std::string ttir, int compute_capability,
uint32_t cluster_dim_0, uint32_t cluster_dim_1,
uint32_t cluster_dim_2);
Kernel(std::string kernel_name, uint32_t num_warps, uint32_t num_ctas,
uint32_t shared_mem_bytes, std::string ptx, std::string ttir,
int compute_capability);

absl::Status Launch(gpuStream_t stream, uint32_t grid[3], void** params);

Expand All @@ -54,11 +53,11 @@ class Kernel {
private:
std::string kernel_name_;
uint32_t block_dim_x_;
uint32_t num_ctas_;
uint32_t shared_mem_bytes_;
std::string ptx_;
std::string ttir_;
int compute_capability_;
uint32_t cluster_dims_[3];

ModuleImage* module_image_ = nullptr;
};
Expand Down Expand Up @@ -107,8 +106,7 @@ class AutotunedKernelCall {

AutotunedKernelCall(
std::string name, std::vector<Config> configs,
std::vector<std::tuple<size_t,
size_t, size_t>> input_output_aliases);
std::vector<std::tuple<size_t, size_t, size_t>> input_output_aliases);

static absl::StatusOr<KernelCall> Autotune(AutotunedKernelCall kernel_call,
gpuStream_t stream,
Expand Down
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