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hrx: keep routed-expert activations inside the f16 range, so a large SwiGLU output is not inf (engine #1539) #284

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hrx: keep routed-expert activations inside the f16 range, so a large SwiGLU output is not inf (engine #1539) #284

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fddb191
hrx: keep routed-expert activations inside the f16 range, so a large …
Nov 10, 2026
6a74ee8
tests: pin the lower edge of the routed-matmul f16 window too (review…
Nov 10, 2026
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25 changes: 20 additions & 5 deletions ggml/src/ggml-hrx/kernel-corpus/kernels/loom-libs/motifs/mul_mat_id_f32_f32_wmma_core.loom
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Original file line number Diff line number Diff line change
Expand Up @@ -76,6 +76,16 @@ template.def<@ggml.mul_mat_id_f32_f32_wmma.core> device @ggml_mul_mat_id_f32_f32
%c0_i32 = scalar.constant 0 : i32
%cn1_i32 = scalar.constant -1 : i32
%c0_f16x4 = vector.constant 0.0 : vector<4xf16>
// engine #1539: the activations go through f16 for the matrix multiply, and f16 ends at 65504. An expert's
// down-projection reads the SwiGLU output, which is not normalised: flint's layer 46 reaches +-9.1e4 on an ordinary
// prompt, the narrowing made that inf, the route sum inf - inf = NaN, and the NaN was stored as that token's keys and
// values. So the activations are narrowed at 2^-6 of their value and the f32 accumulator is scaled back by 2^6 when a
// tile is published. Both factors are powers of two, so nothing is rounded differently while the scaled value is a
// normal f16: the representable window moves from [6.1e-5, 6.55e4] to [3.9e-3, 4.19e6]. Below 3.9e-3 an element is
// rounded to a multiple of 3.8e-6 instead of keeping 11 bits, which is far under the rounding already applied to the
// order-1 elements it is summed with (4.9e-4 at 1.0).
%activation_prescale = vector.constant 0.015625 : vector<4xf32>
%result_unscale = vector.constant 64.0 : vector<4xf32>
%zero_accumulator = vector.constant 0.0 : vector<4xf32>
%m = index.constant 16 : index
%n = index.constant 16 : index
Expand Down Expand Up @@ -209,7 +219,8 @@ template.def<@ggml.mul_mat_id_f32_f32_wmma.core> device @ggml_mul_mat_id_f32_f32
%input_k = index.add %k_origin, %load_k : index
%mask = vector.mask.range [%input_k to %input_size step %c1] : index -> vector<4xi1>
%loaded = vector.load.mask %input_view[%token, %input_route, %input_k], %mask, %c0_f32x4 : view<[%bounded_token_count]x[%bounded_input_route_count]x[%input_size]xf32>, vector<4xi1>, vector<4xf32>
%converted = vector.fptrunc %loaded : vector<4xf32> to vector<4xf16>
%prescaled = vector.mulf %loaded, %activation_prescale : vector<4xf32>
%converted = vector.fptrunc %prescaled : vector<4xf32> to vector<4xf16>
scf.yield %converted : vector<4xf16>
} else {
scf.yield %c0_f16x4 : vector<4xf16>
Expand Down Expand Up @@ -277,28 +288,32 @@ template.def<@ggml.mul_mat_id_f32_f32_wmma.core> device @ggml_mul_mat_id_f32_f32
vector.fragment.store<result> %result00, %result_fragment_view[%c0, %c0] shape [%m, %n] : vector<4xf32>, view<16x16xf32, %result_fragment_layout>
kernel.barrier<workgroup> scope(subgroup) ordering(acq_rel)
scf.if %writes00 {
%values = vector.load %result_physical_view[%publish_route, %publish_channel_add] : view<16x16xf32> -> vector<4xf32>
%scaled_values = vector.load %result_physical_view[%publish_route, %publish_channel_add] : view<16x16xf32> -> vector<4xf32>
%values = vector.mulf %scaled_values, %result_unscale : vector<4xf32>
template.apply<@ggml.mul_mat_id_f32_f32_wmma.publish_vector4>(%writes00, %bounded_assignment0, %assignment_token0, %assignment_route0, %channel0, %bounded_token_count, %bounded_route_count, %bounded_output_size, %values, %bias, %output, %residual_input, %residual_output, %norm_weight, %normalized_output, %completion_counters) : (i1, index, index, index, index, index, index, index, vector<4xf32>, buffer, buffer, buffer, buffer, buffer, buffer, buffer)
}
kernel.barrier<workgroup> scope(subgroup) ordering(acq_rel)
vector.fragment.store<result> %result01, %result_fragment_view[%c0, %c0] shape [%m, %n] : vector<4xf32>, view<16x16xf32, %result_fragment_layout>
kernel.barrier<workgroup> scope(subgroup) ordering(acq_rel)
scf.if %writes01 {
%values = vector.load %result_physical_view[%publish_route, %publish_channel_add] : view<16x16xf32> -> vector<4xf32>
%scaled_values = vector.load %result_physical_view[%publish_route, %publish_channel_add] : view<16x16xf32> -> vector<4xf32>
%values = vector.mulf %scaled_values, %result_unscale : vector<4xf32>
template.apply<@ggml.mul_mat_id_f32_f32_wmma.publish_vector4>(%writes01, %bounded_assignment1, %assignment_token1, %assignment_route1, %channel0, %bounded_token_count, %bounded_route_count, %bounded_output_size, %values, %bias, %output, %residual_input, %residual_output, %norm_weight, %normalized_output, %completion_counters) : (i1, index, index, index, index, index, index, index, vector<4xf32>, buffer, buffer, buffer, buffer, buffer, buffer, buffer)
}
kernel.barrier<workgroup> scope(subgroup) ordering(acq_rel)
vector.fragment.store<result> %result10, %result_fragment_view[%c0, %c0] shape [%m, %n] : vector<4xf32>, view<16x16xf32, %result_fragment_layout>
kernel.barrier<workgroup> scope(subgroup) ordering(acq_rel)
scf.if %writes10 {
%values = vector.load %result_physical_view[%publish_route, %publish_channel_add] : view<16x16xf32> -> vector<4xf32>
%scaled_values = vector.load %result_physical_view[%publish_route, %publish_channel_add] : view<16x16xf32> -> vector<4xf32>
%values = vector.mulf %scaled_values, %result_unscale : vector<4xf32>
template.apply<@ggml.mul_mat_id_f32_f32_wmma.publish_vector4>(%writes10, %bounded_assignment0, %assignment_token0, %assignment_route0, %channel1, %bounded_token_count, %bounded_route_count, %bounded_output_size, %values, %bias, %output, %residual_input, %residual_output, %norm_weight, %normalized_output, %completion_counters) : (i1, index, index, index, index, index, index, index, vector<4xf32>, buffer, buffer, buffer, buffer, buffer, buffer, buffer)
}
kernel.barrier<workgroup> scope(subgroup) ordering(acq_rel)
vector.fragment.store<result> %result11, %result_fragment_view[%c0, %c0] shape [%m, %n] : vector<4xf32>, view<16x16xf32, %result_fragment_layout>
kernel.barrier<workgroup> scope(subgroup) ordering(acq_rel)
scf.if %writes11 {
%values = vector.load %result_physical_view[%publish_route, %publish_channel_add] : view<16x16xf32> -> vector<4xf32>
%scaled_values = vector.load %result_physical_view[%publish_route, %publish_channel_add] : view<16x16xf32> -> vector<4xf32>
%values = vector.mulf %scaled_values, %result_unscale : vector<4xf32>
template.apply<@ggml.mul_mat_id_f32_f32_wmma.publish_vector4>(%writes11, %bounded_assignment1, %assignment_token1, %assignment_route1, %channel1, %bounded_token_count, %bounded_route_count, %bounded_output_size, %values, %bias, %output, %residual_input, %residual_output, %norm_weight, %normalized_output, %completion_counters) : (i1, index, index, index, index, index, index, index, vector<4xf32>, buffer, buffer, buffer, buffer, buffer, buffer, buffer)
}
kernel.barrier<workgroup> scope(workgroup) ordering(acq_rel)
Expand Down
51 changes: 51 additions & 0 deletions tests/test-backend-ops.cpp
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Expand Up @@ -4592,6 +4592,51 @@ struct test_mul_mat_id : public test_case {
}
};

// GGML_OP_MUL_MAT_ID whose activations exceed the f16 range (engine #1539).
// An expert's down-projection reads the SwiGLU output, which nothing normalises: flint-40b's layer 46 reaches +-9.1e4 on
// an ordinary 22-token prompt. A backend that narrows the activations to f16 for the multiply turns anything above 65504
// into inf, and the sum over the routes into NaN - finite on the CPU, non-finite on the device, and invisible to every
// other MUL_MAT_ID case here because init_tensor_uniform() stays inside [-1, 1]. One activation in 64 of every fourth
// token is set to +-magnitude; the rest keep their order-1 values, as in the model.
// A magnitude below 1 is the other end of the same window: every activation is scaled by it, so a narrowing that
// trades the top of the f16 range for the bottom has a case pulling back. 3.2e-2 is the smallest column maximum
// measured on flint's expert down-projections (37278 columns over 141 layer executions; median 4.1).
struct test_mul_mat_id_large_activation : public test_mul_mat_id {
const float magnitude;

std::string vars() override {
return test_mul_mat_id::vars() + ",magnitude=" + var_to_str(magnitude);
}

test_mul_mat_id_large_activation(ggml_type type_a, int n_mats, int n_used, int64_t m, int64_t n, int64_t k, float magnitude)
: test_mul_mat_id(type_a, GGML_TYPE_F32, n_mats, n_used, false, m, n, k), magnitude(magnitude) {}

void initialize_tensors(ggml_context * ctx) override {
init_mul_mat_id_tensors(ctx, n_mats);
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (strcmp(t->name, "b") != 0) {
continue;
}
std::vector<float> data(ggml_nelements(t));
ggml_backend_tensor_get(t, data.data(), 0, ggml_nbytes(t));
const int64_t per_token = t->ne[0] * t->ne[1];
if (magnitude < 1.0f) {
for (float & value : data) {
value *= magnitude;
}
ggml_backend_tensor_set(t, data.data(), 0, ggml_nbytes(t));
continue;
}
for (int64_t token = 0; token < t->ne[2]; token += 4) {
for (int64_t i = 0; i < per_token; i += 64) {
data[token * per_token + i] = ((i / 64 + token / 4) % 2 == 0) ? magnitude : -magnitude;
}
}
ggml_backend_tensor_set(t, data.data(), 0, ggml_nbytes(t));
}
}
};

// GGML_OP_MUL_MAT_ID + GGML_OP_ADD or GGML_OP_MUL
struct test_mul_mat_id_fusion : public test_case {
const ggml_type type_a;
Expand Down Expand Up @@ -9321,6 +9366,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_mul_mat_row_padded_activation(GGML_TYPE_BF16, GGML_TYPE_F32, 3712, 512, 232, 264));
test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_BF16, GGML_TYPE_F32, 8, 2, false, 384, 8, 1480));

// engine #1539: flint-40b's expert down-projection (256 experts, 6 used, 512 -> 2048, q5_K) at the 22-token batch
// that produced inf, with activations of the measured magnitude; then one decode-sized batch of the same contraction.
test_cases.emplace_back(new test_mul_mat_id_large_activation(GGML_TYPE_Q5_K, 256, 6, 2048, 22, 512, 9.1e4f));
test_cases.emplace_back(new test_mul_mat_id_large_activation(GGML_TYPE_Q5_K, 256, 6, 2048, 1, 512, 9.1e4f));
test_cases.emplace_back(new test_mul_mat_id_large_activation(GGML_TYPE_Q5_K, 256, 6, 2048, 22, 512, 3.2e-2f));

test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0, GGML_TYPE_F32, 576, 512, 576, {1,1}, {1,1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0, GGML_TYPE_F32, 1, 2048, 8192, {1, 1}, {1, 1}));
for (ggml_type type_a : all_types) {
Expand Down