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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.github/workflows/check-vendor.yml
#	.github/workflows/close-issue.yml
#	.github/workflows/editorconfig.yml
#	.github/workflows/gguf-publish.yml
#	.github/workflows/labeler.yml
#	.github/workflows/pre-tokenizer-hashes.yml
#	.github/workflows/python-check-requirements.yml
#	.github/workflows/python-lint.yml
#	.github/workflows/python-type-check.yml
#	.github/workflows/server.yml
#	.github/workflows/update-ops-docs.yml
#	README.md
#	docs/build.md
#	examples/model-conversion/scripts/utils/perplexity-gen.sh
#	examples/model-conversion/scripts/utils/perplexity-run-simple.sh
#	examples/model-conversion/scripts/utils/perplexity-run.sh
#	examples/model-conversion/scripts/utils/quantize.sh
#	examples/model-conversion/scripts/utils/run-embedding-server.sh
#	ggml/src/ggml-cpu/ggml-cpu.c
#	ggml/src/ggml-hexagon/htp/flash-attn-ops.c
#	ggml/src/ggml-opencl/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-opencl/kernels/cvt.cl
#	ggml/src/ggml-opencl/kernels/mul_mv_q6_k_f32.cl
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	scripts/compare-llama-bench.py
#	tests/test-backend-ops.cpp
#	tests/test-gguf.cpp
#	tools/cli/README.md
#	tools/completion/README.md
#	tools/server/README.md
This commit is contained in:
Concedo
2026-01-27 23:06:13 +08:00
parent 3a84623ea5 68ac3acb43
commit f6ece6fd37
40 changed files with 3108 additions and 477 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/llama-kv-cache.cpp
+28 -8
View File
@@ -97,6 +97,8 @@ llama_kv_cache::llama_kv_cache(
__func__, hparams.n_embd_v_gqa_max());
}
const bool is_mla = hparams.is_mla();
for (uint32_t il = 0; il < hparams.n_layer; il++) {
if (!hparams.has_kv(il)) {
LLAMA_LOG_DEBUG("%s: layer %3d: does not have KV cache\n", __func__, il);
@@ -130,18 +132,21 @@ llama_kv_cache::llama_kv_cache(
throw std::runtime_error("failed to create ggml context for kv cache");
}
ggml_tensor * k = ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream);
ggml_tensor * v = ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream);
const bool has_k = true;
const bool has_v = !is_mla;
ggml_format_name(k, "cache_k_l%d", il);
ggml_format_name(v, "cache_v_l%d", il);
ggml_tensor * k = has_k ? ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream) : nullptr;
ggml_tensor * v = has_v ? ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream) : nullptr;
has_k && ggml_format_name(k, "cache_k_l%d", il);
has_v && ggml_format_name(v, "cache_v_l%d", il);
std::vector<ggml_tensor *> k_stream;
std::vector<ggml_tensor *> v_stream;
for (uint32_t s = 0; s < n_stream; ++s) {
k_stream.push_back(ggml_view_2d(ctx, k, n_embd_k_gqa, kv_size, k->nb[1], s*k->nb[2]));
v_stream.push_back(ggml_view_2d(ctx, v, n_embd_v_gqa, kv_size, v->nb[1], s*v->nb[2]));
k_stream.push_back(has_k ? ggml_view_2d(ctx, k, n_embd_k_gqa, kv_size, k->nb[1], s*k->nb[2]) : nullptr);
v_stream.push_back(has_v ? ggml_view_2d(ctx, v, n_embd_v_gqa, kv_size, v->nb[1], s*v->nb[2]) : nullptr);
}
map_layer_ids[il] = layers.size();
@@ -647,7 +652,10 @@ bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const stream_co
const auto & layer = layers[il];
ggml_backend_tensor_copy(layer.k_stream[ssrc], layer.k_stream[sdst]);
ggml_backend_tensor_copy(layer.v_stream[ssrc], layer.v_stream[sdst]);
if (layer.v_stream[ssrc]) {
ggml_backend_tensor_copy(layer.v_stream[ssrc], layer.v_stream[sdst]);
}
}
}
}
@@ -1516,7 +1524,7 @@ size_t llama_kv_cache::size_v_bytes() const {
size_t size_v_bytes = 0;
for (const auto & layer : layers) {
size_v_bytes += ggml_nbytes(layer.v);
size_v_bytes += layer.v ? ggml_nbytes(layer.v) : 0;
}
return size_v_bytes;
@@ -1798,6 +1806,9 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
auto * v = layer.v_stream[cr.strm];
if (!v) {
continue;
}
// Write value type
const int32_t v_type_i = (int32_t) v->type;
@@ -1824,6 +1835,9 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
auto * v = layer.v_stream[cr.strm];
if (!v) {
continue;
}
// Write value type
const int32_t v_type_i = (int32_t) v->type;
@@ -2027,6 +2041,9 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
auto * v = layer.v_stream[strm];
if (!v) {
continue;
}
// Read type of value
int32_t v_type_i_ref;
@@ -2068,6 +2085,9 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
auto * v = layer.v_stream[strm];
if (!v) {
continue;
}
// Read type of value
int32_t v_type_i_ref;