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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.github/workflows/build.yml
#	Makefile
#	flake.lock
#	ggml-cuda.cu
#	ggml-cuda.h
This commit is contained in:
Concedo 2024-03-19 18:57:22 +08:00
commit a3fa919c67
33 changed files with 1777 additions and 1484 deletions
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32
examples/imatrix/imatrix.cpp
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@ -57,13 +57,31 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
const struct ggml_tensor * src0 = t->src[0];
const struct ggml_tensor * src1 = t->src[1];
std::string wname;
{
// remove any prefix and suffixes from the name
// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
const char * p = strchr(src0->name, '#');
if (p != NULL) {
p = p + 1;
const char * q = strchr(p, '#');
if (q != NULL) {
wname = std::string(p, q - p);
} else {
wname = p;
}
} else {
wname = src0->name;
}
}
// when ask is true, the scheduler wants to know if we are interested in data from this tensor
// if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
if (ask) {
if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
if (t->op != GGML_OP_MUL_MAT) return false;
if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return false;
if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false;
return true;
}
@ -95,12 +113,12 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
// this is necessary to guarantee equal number of "ncall" for each tensor
for (int ex = 0; ex < n_as; ++ex) {
src0 = t->src[2 + ex];
auto& e = m_stats[src0->name];
auto& e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
exit(1); //GGML_ASSERT(false);
}
// NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
@ -108,7 +126,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
//if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
++e.ncall;
if (m_params.verbosity > 1) {
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
}
for (int row = 0; row < (int)src1->ne[1]; ++row) {
const int excur = m_ids[row*n_as + idx];
@ -130,17 +148,17 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
}
}
} else {
auto& e = m_stats[src0->name];
auto& e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
exit(1); //GGML_ASSERT(false);
}
++e.ncall;
if (m_params.verbosity > 1) {
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
}
for (int row = 0; row < (int)src1->ne[1]; ++row) {
const float * x = data + row * src1->ne[0];