Skip to content

[CUDA] Port ShardedMoE to the new MoE GEMM backend #31191

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
tianleiwu wants to merge 2 commits into
base: main
Choose a base branch
from
Draft

[CUDA] Port ShardedMoE to the new MoE GEMM backend #31191

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
3912854
fix sharded moe
tianleiwu Jul 30, 2026
0ad62e3
fix plugin ep build
tianleiwu Jul 30, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
58 changes: 32 additions & 26 deletions onnxruntime/contrib_ops/cpu/moe/moe_helper.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -131,22 +131,26 @@ Status CheckInputs(MoEParameters& parameters,
const int64_t fc1_inter_size = is_fused_swiglu ? (inter_size + inter_size) : inter_size;
const int64_t zp_pack_size = pack_size; // Zero points packing (1 for 8-bit, 2 for 4-bit)

// Weights, and every tensor indexed by the weights' expert dimension, only cover the experts that
// are local to this rank. They are identical to num_experts unless expert parallelism is used
// (ShardedMoE), where each rank only owns a slice of the experts.
if (legacy_shape) {
// legacy shape does not match column major memory layout. This is for backward compatibility.
CHECK_SHAPE(fc1_experts_weights_shape, "fc1_experts_weights", num_experts, hidden_size, fc1_inter_size / pack_size);
CHECK_SHAPE(fc2_experts_weights_shape, "fc2_experts_weights", num_experts, inter_size, hidden_size / pack_size);
CHECK_SHAPE(fc3_experts_weights_shape, "fc3_experts_weights", num_experts, hidden_size, inter_size / pack_size);
CHECK_SHAPE(fc1_experts_weights_shape, "fc1_experts_weights", local_num_experts, hidden_size, fc1_inter_size / pack_size);
CHECK_SHAPE(fc2_experts_weights_shape, "fc2_experts_weights", local_num_experts, inter_size, hidden_size / pack_size);
CHECK_SHAPE(fc3_experts_weights_shape, "fc3_experts_weights", local_num_experts, hidden_size, inter_size / pack_size);
} else {
CHECK_SHAPE(fc1_experts_weights_shape, "fc1_experts_weights", num_experts, fc1_inter_size, hidden_size / pack_size);
CHECK_SHAPE(fc2_experts_weights_shape, "fc2_experts_weights", num_experts, hidden_size, inter_size / pack_size);
CHECK_SHAPE(fc3_experts_weights_shape, "fc3_experts_weights", num_experts, inter_size, hidden_size / pack_size);
CHECK_SHAPE(fc1_experts_weights_shape, "fc1_experts_weights", local_num_experts, fc1_inter_size, hidden_size / pack_size);
CHECK_SHAPE(fc2_experts_weights_shape, "fc2_experts_weights", local_num_experts, hidden_size, inter_size / pack_size);
CHECK_SHAPE(fc3_experts_weights_shape, "fc3_experts_weights", local_num_experts, inter_size, hidden_size / pack_size);
}

CHECK_TENSOR_SHAPE(router_probs, num_rows, num_experts);

CHECK_TENSOR_SHAPE(fc1_experts_bias, num_experts, fc1_inter_size);
CHECK_TENSOR_SHAPE(fc1_experts_bias, local_num_experts, fc1_inter_size);
// fc2_experts_bias is applied after the results of all ranks are combined, so it covers all experts.
CHECK_TENSOR_SHAPE(fc2_experts_bias, num_experts, hidden_size);
CHECK_TENSOR_SHAPE(fc3_experts_bias, num_experts, inter_size);
CHECK_TENSOR_SHAPE(fc3_experts_bias, local_num_experts, inter_size);

// Validate scale tensors: Handle both row-wise and block-wise quantization flexibly
// First, detect the actual quantization method from the tensor shapes
Expand All @@ -166,29 +170,29 @@ Status CheckInputs(MoEParameters& parameters,
const int64_t fc2_blocks_per_row = (inter_size + block_size - 1) / block_size;
const int64_t fc3_blocks_per_row = (hidden_size + block_size - 1) / block_size;

CHECK_TENSOR_SHAPE(fc1_experts_scales, num_experts, fc1_inter_size, fc1_blocks_per_row);
CHECK_TENSOR_SHAPE(fc2_experts_scales, num_experts, hidden_size, fc2_blocks_per_row);
CHECK_TENSOR_SHAPE(fc3_experts_scales, num_experts, inter_size, fc3_blocks_per_row);
CHECK_TENSOR_SHAPE(fc1_experts_scales, local_num_experts, fc1_inter_size, fc1_blocks_per_row);
CHECK_TENSOR_SHAPE(fc2_experts_scales, local_num_experts, hidden_size, fc2_blocks_per_row);
CHECK_TENSOR_SHAPE(fc3_experts_scales, local_num_experts, inter_size, fc3_blocks_per_row);

// Validate zero-point tensors (block-wise)
const int64_t fc1_zp_blocks = (fc1_blocks_per_row + zp_pack_size - 1) / zp_pack_size;
const int64_t fc2_zp_blocks = (fc2_blocks_per_row + zp_pack_size - 1) / zp_pack_size;
const int64_t fc3_zp_blocks = (fc3_blocks_per_row + zp_pack_size - 1) / zp_pack_size;

CHECK_TENSOR_SHAPE(fc1_zero_points, num_experts, fc1_inter_size, fc1_zp_blocks);
CHECK_TENSOR_SHAPE(fc2_zero_points, num_experts, hidden_size, fc2_zp_blocks);
CHECK_TENSOR_SHAPE(fc3_zero_points, num_experts, inter_size, fc3_zp_blocks);
CHECK_TENSOR_SHAPE(fc1_zero_points, local_num_experts, fc1_inter_size, fc1_zp_blocks);
CHECK_TENSOR_SHAPE(fc2_zero_points, local_num_experts, hidden_size, fc2_zp_blocks);
CHECK_TENSOR_SHAPE(fc3_zero_points, local_num_experts, inter_size, fc3_zp_blocks);
} else {
// Row-wise quantization: 2D scale tensors or 3D with last dimension = 1
// Handle both {num_experts, features} and {num_experts, features, 1} shapes
if (fc1_experts_scales != nullptr) {
const auto& fc1_scales_dims = fc1_experts_scales->Shape().GetDims();
if (fc1_scales_dims.size() == 2) {
CHECK_TENSOR_SHAPE(fc1_experts_scales, num_experts, fc1_inter_size);
CHECK_TENSOR_SHAPE(fc1_zero_points, num_experts, (fc1_inter_size + zp_pack_size - 1) / zp_pack_size);
CHECK_TENSOR_SHAPE(fc1_experts_scales, local_num_experts, fc1_inter_size);
CHECK_TENSOR_SHAPE(fc1_zero_points, local_num_experts, (fc1_inter_size + zp_pack_size - 1) / zp_pack_size);
} else if (fc1_scales_dims.size() == 3) {
CHECK_TENSOR_SHAPE(fc1_experts_scales, num_experts, fc1_inter_size, 1);
CHECK_TENSOR_SHAPE(fc1_zero_points, num_experts, (fc1_inter_size + zp_pack_size - 1) / zp_pack_size);
CHECK_TENSOR_SHAPE(fc1_experts_scales, local_num_experts, fc1_inter_size, 1);
CHECK_TENSOR_SHAPE(fc1_zero_points, local_num_experts, (fc1_inter_size + zp_pack_size - 1) / zp_pack_size);
} else {
ORT_THROW("fc1_experts_scales must be 2D or 3D tensor");
}
Expand All @@ -197,11 +201,11 @@ Status CheckInputs(MoEParameters& parameters,
if (fc2_experts_scales != nullptr) {
const auto& fc2_scales_dims = fc2_experts_scales->Shape().GetDims();
if (fc2_scales_dims.size() == 2) {
CHECK_TENSOR_SHAPE(fc2_experts_scales, num_experts, hidden_size);
CHECK_TENSOR_SHAPE(fc2_zero_points, num_experts, (hidden_size + zp_pack_size - 1) / zp_pack_size);
CHECK_TENSOR_SHAPE(fc2_experts_scales, local_num_experts, hidden_size);
CHECK_TENSOR_SHAPE(fc2_zero_points, local_num_experts, (hidden_size + zp_pack_size - 1) / zp_pack_size);
} else if (fc2_scales_dims.size() == 3) {
CHECK_TENSOR_SHAPE(fc2_experts_scales, num_experts, hidden_size, 1);
CHECK_TENSOR_SHAPE(fc2_zero_points, num_experts, (hidden_size + zp_pack_size - 1) / zp_pack_size);
CHECK_TENSOR_SHAPE(fc2_experts_scales, local_num_experts, hidden_size, 1);
CHECK_TENSOR_SHAPE(fc2_zero_points, local_num_experts, (hidden_size + zp_pack_size - 1) / zp_pack_size);
} else {
ORT_THROW("fc2_experts_scales must be 2D or 3D tensor");
}
Expand All @@ -210,11 +214,11 @@ Status CheckInputs(MoEParameters& parameters,
if (fc3_experts_scales != nullptr) {
const auto& fc3_scales_dims = fc3_experts_scales->Shape().GetDims();
if (fc3_scales_dims.size() == 2) {
CHECK_TENSOR_SHAPE(fc3_experts_scales, num_experts, inter_size);
CHECK_TENSOR_SHAPE(fc3_zero_points, num_experts, (inter_size + zp_pack_size - 1) / zp_pack_size);
CHECK_TENSOR_SHAPE(fc3_experts_scales, local_num_experts, inter_size);
CHECK_TENSOR_SHAPE(fc3_zero_points, local_num_experts, (inter_size + zp_pack_size - 1) / zp_pack_size);
} else if (fc3_scales_dims.size() == 3) {
CHECK_TENSOR_SHAPE(fc3_experts_scales, num_experts, inter_size, 1);
CHECK_TENSOR_SHAPE(fc3_zero_points, num_experts, (inter_size + zp_pack_size - 1) / zp_pack_size);
CHECK_TENSOR_SHAPE(fc3_experts_scales, local_num_experts, inter_size, 1);
CHECK_TENSOR_SHAPE(fc3_zero_points, local_num_experts, (inter_size + zp_pack_size - 1) / zp_pack_size);
} else {
ORT_THROW("fc3_experts_scales must be 2D or 3D tensor");
}
Expand All @@ -241,6 +245,8 @@ Status CheckInputs(MoEParameters& parameters,
parameters.parallel_type = MoEParallelType::TP;
}
} else if (num_experts > local_num_experts) {
ORT_RETURN_IF(local_num_experts <= 0 || num_experts % local_num_experts != 0,
"num_experts (", num_experts, ") must be divisible by local_num_experts (", local_num_experts, ")");
if (parameters.tensor_shards == 1) {
parameters.parallel_type = MoEParallelType::EP;
} else {
Expand Down
Loading
Loading