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Fix qwen36 moe #301

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Fix qwen36 moe #301

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7 changes: 2 additions & 5 deletions ggml/src/ggml-openvino/ggml-openvino.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1223,11 +1223,8 @@ static ggml_openvino_op_support is_op_supported_case(const ggml_tensor * op) {
if (ggml_openvino_get_device_name() == "GPU" && op->src[0] != nullptr && op->src[0]->type == GGML_TYPE_BF16) {
return {false, "MUL_MAT_ID with BF16 weights on GPU is not supported"};
}
// GPU MUL_MAT_ID uses a Gather+MatMul fallback because the GPU plugin rejects internal
// GatherMatmul for these test shapes. Skip cases that would materialize a large selected
// expert-weight temporary.
if (ggml_openvino_get_device_name() == "GPU" && mul_mat_id_requires_large_tmp(op)) {
return {false, "MUL_MAT_ID requires large temporary on GPU"};
if (op->src[0] != nullptr && op->src[0]->type == GGML_TYPE_MXFP4 && mul_mat_id_requires_large_tmp(op)) {
return {false, "MUL_MAT_ID with MXFP4 weights requires large temporary"};
}
break;
}
Expand Down
60 changes: 3 additions & 57 deletions ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
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Expand Up @@ -56,54 +56,6 @@ ov::Output<ov::Node> static_shape_dims_or_shapeof(const ov::Output<ov::Node> & i
return get_dimensions(shape, dims);
}

ov::Output<ov::Node> translate_mul_mat_id_gather_matmul_fallback(const NodeContext & context,
ov::Output<ov::Node> expert_weights,
ov::Output<ov::Node> activations,
ov::Output<ov::Node> ids) {
auto gather_axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {0});
ov::Output<ov::Node> selected_weights = std::make_shared<ov::op::v8::Gather>(expert_weights, ids, gather_axis);

const auto output_type = context.get_output_type();
if (selected_weights.get_element_type() != ov::element::f32) {
selected_weights = std::make_shared<ov::op::v0::Convert>(selected_weights, ov::element::f32);
}
if (activations.get_element_type() != ov::element::f32) {
activations = std::make_shared<ov::op::v0::Convert>(activations, ov::element::f32);
}

auto activations_shape = std::make_shared<ov::op::v3::ShapeOf>(activations, ov::element::i64);
auto ids_shape = std::make_shared<ov::op::v3::ShapeOf>(ids, ov::element::i64);
ov::Output<ov::Node> acts_target_dims = std::make_shared<ov::op::v0::Concat>(
ov::OutputVector{
get_dimensions(activations_shape, {0}),
get_dimensions(ids_shape, {1}),
get_dimensions(activations_shape, {2}),
},
0);
ov::Output<ov::Node> acts_broadcasted =
std::make_shared<ov::op::v3::Broadcast>(activations, acts_target_dims, ov::op::BroadcastType::BIDIRECTIONAL);

auto activations_expanded = std::make_shared<ov::op::v0::Unsqueeze>(acts_broadcasted, const_i64({2}));
ov::Output<ov::Node> result =
std::make_shared<ov::op::v0::MatMul>(activations_expanded, selected_weights, false, true);

auto output_shape = context.get_output_shape();
FRONT_END_OP_CONVERSION_CHECK(output_shape.rank().is_static() && output_shape.rank().get_length() == 4,
"Unexpected MUL_MAT_ID output rank");
FRONT_END_OP_CONVERSION_CHECK(output_shape[3].is_static(), "Expected static row dimension for MUL_MAT_ID output");

auto batch_dim = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
auto row_dim = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[3].get_length()});
auto result_target_dims = std::make_shared<ov::op::v0::Concat>(
ov::OutputVector{batch_dim, get_dimensions(ids_shape, {0, 1}), row_dim}, 0);
result = std::make_shared<ov::op::v1::Reshape>(result, result_target_dims, false);

if (result.get_element_type() != output_type) {
result = std::make_shared<ov::op::v0::Convert>(result, output_type);
}
return result;
}

ov::Output<ov::Node> translate_mul_mat_id_mxfp4_packed(const NodeContext & context,
ov::Output<ov::Node> expert_weights,
ov::Output<ov::Node> activations,
Expand Down Expand Up @@ -229,7 +181,6 @@ OutputVector translate_mul_mat_id(const NodeContext & context) {
auto expert_weights_rank = expert_weights.get_partial_shape().rank();
FRONT_END_OP_CONVERSION_CHECK(expert_weights_rank.is_static(),
"Expected static rank for MUL_MAT_ID expert weights");
const bool use_gpu_fallback = ggml_openvino_get_device_name() == "GPU";
if (expert_weights_rank.get_length() == 4) {
auto expert_weights_shape_3d = static_shape_dims_or_shapeof(expert_weights, {1, 2, 3});
expert_weights = std::make_shared<ov::op::v1::Reshape>(expert_weights, expert_weights_shape_3d, false);
Expand All @@ -246,14 +197,9 @@ OutputVector translate_mul_mat_id(const NodeContext & context) {
}

const auto output_type = context.get_output_type();
if (activations.get_element_type() != ov::element::f32) {
activations = std::make_shared<ov::op::v0::Convert>(activations, ov::element::f32);
}

if (use_gpu_fallback || !expert_weights.get_partial_shape().is_static() || !activations.get_partial_shape().is_static() ||
!ids.get_partial_shape().is_static()) {
return rename_outputs_with_suffix({translate_mul_mat_id_gather_matmul_fallback(context, expert_weights, activations, ids)},
context.get_name());
const auto activations_type = ggml_openvino_get_device_name() == "GPU" ? ov::element::f16 : ov::element::f32;
if (activations.get_element_type() != activations_type) {
activations = std::make_shared<ov::op::v0::Convert>(activations, activations_type);
}

// GatherMatmul's A input is [n_used_or_1, n_tokens, k]; activations_3d is
Expand Down
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