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imatrix : fix 3d activations when model tensor is 2d

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This commit is contained in:
Francis Couture-Harpin
2025-07-31 11:20:58 -04:00
parent 73439beb1b
commit d4f36e5e2b
1 changed files with 17 additions and 18 deletions
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35
tools/imatrix/imatrix.cpp
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@@ -127,7 +127,6 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
GGML_ASSERT(ids->ne[1] == src1->ne[2]);
// TODO: 4d? (is that even used in practice?)
// the extra dimension would need to be stored somewhere to be reflected in the imatrix file
if (ggml_nrows(src1) != src1->ne[1] * src1->ne[2]) {
LOG_ERR("%s: tensor has more than 3 dimensions: %s", __func__, wname.c_str());
@@ -197,7 +196,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
}
} else {
auto & e = m_stats[wname];
const int64_t n_mat = src1->ne[2] * src1->ne[3];
const int64_t n_mat = src0->ne[2] * src0->ne[3];
// use a single count per dense tensor
if ((int64_t) e.counts.size() == n_mat) {
@@ -220,19 +219,16 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)(src1->ne[0] * n_mat));
exit(1); //GGML_ABORT("fatal error");
}
else if (e.counts.size() != 1) {
LOG_ERR("%s: inconsistent matrix count for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.counts.size(), 1);
exit(1); //GGML_ABORT("fatal error");
}
LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d x %5d, %d\n", __func__, m_last_chunk, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->ne[2], (int)src1->type);
for (int64_t i3 = 0; i3 < src1->ne[3]; ++i3) {
for (int64_t i2 = 0; i2 < src1->ne[2]; ++i2) {
const int64_t mat_id = i3 * src1->ne[2] + i2;
// handle 3D+ tensors, but flatten 3D+ activations when model tensor is 2D
const int64_t mat_id = (i3 % src0->ne[3]) * src0->ne[2] + (i2 % src0->ne[2]);
const int64_t mat_start = mat_id * src1->ne[0];
for (int64_t row = 0; row < src1->ne[1]; ++row) {
const float * x = (const float *) (data + row * src1->nb[1] + i2 * src1->nb[2] + i3 * src1->ne[3]);
const float * x = (const float *) (data + row * src1->nb[1] + i2 * src1->nb[2] + i3 * src1->nb[3]);
for (int64_t j = 0; j < src1->ne[0]; ++j) {
e.values[mat_start + j] += x[j] * x[j];
if (!std::isfinite((float)e.values[j])) {
@@ -243,16 +239,19 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
}
}
}
e.counts[0] += src1->ne[1];
const int32_t n_chunk = e.counts[0] / chunk_size;
if (n_chunk > m_last_chunk) {
const int32_t chunk_step = n_chunk - m_last_chunk;
m_last_chunk = n_chunk;
if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
save_imatrix();
}
if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
save_imatrix(m_last_chunk);
// only 1 count in practice, except when a tensor is used for both MUL_MAT_ID and MUL_MAT
for (size_t i = 0; i < e.counts.size(); ++i) {
e.counts[i] += ggml_nrows(src1);
const int32_t n_chunk = e.counts[i] / chunk_size;
if (n_chunk > m_last_chunk) {
const int32_t chunk_step = n_chunk - m_last_chunk;
m_last_chunk = n_chunk;
if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
save_imatrix();
}
if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
save_imatrix(m_last_chunk);
}
}
}
}