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Merge commit '00131d6eaf4df029e1ec84de868c2c5957503007' into concedo_experimental

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# Conflicts:
#	docs/ops.md
#	examples/save-load-state/save-load-state.cpp
#	ggml/CMakeLists.txt
#	ggml/src/ggml-cann/aclnn_ops.cpp
#	ggml/src/ggml-cann/aclnn_ops.h
#	ggml/src/ggml-cann/ggml-cann.cpp
#	ggml/src/ggml-hip/CMakeLists.txt
#	ggml/src/ggml-sycl/cpy.cpp
#	ggml/src/ggml-sycl/cpy.hpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-sycl/set_rows.cpp
#	scripts/server-bench.py
#	tests/CMakeLists.txt
#	tests/test-backend-ops.cpp
#	tests/test-thread-safety.cpp
#	tools/llama-bench/llama-bench.cpp
This commit is contained in:
Concedo
2025-08-02 10:15:39 +08:00
parent cd0dc0abec 00131d6eaf
commit b04362f831
34 changed files with 17144 additions and 4130 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/llama-batch.cpp
+1 -1
View File
@@ -59,7 +59,7 @@ bool llama_batch_allocr::init(
for (int32_t i = 0; i < batch.n_tokens; ++i) {
for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) {
if (batch.seq_id && (batch.seq_id[i][s] < 0 || batch.seq_id[i][s] >= (llama_seq_id) n_seq_max)) {
LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d > %d\n", __func__, i, s, batch.seq_id[i][s], (llama_seq_id) n_seq_max);
LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d >= %d\n", __func__, i, s, batch.seq_id[i][s], (llama_seq_id) n_seq_max);
return false;
}
}
src/llama-graph.cpp
+16 -27
View File
@@ -188,38 +188,23 @@ void llm_graph_input_mean::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs_unq = ubatch->n_seqs_unq;
if (cparams.embeddings && (
cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
cparams.pooling_type == LLAMA_POOLING_TYPE_RANK
)) {
cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
cparams.pooling_type == LLAMA_POOLING_TYPE_RANK ||
cparams.pooling_type == LLAMA_POOLING_TYPE_LAST
)) {
GGML_ASSERT(cls);
GGML_ASSERT(ggml_backend_buffer_is_host(cls->buffer));
uint32_t * data = (uint32_t *) cls->data;
memset(cls->data, 0, n_seqs_unq*ggml_element_size(cls));
for (int i = 0; i < n_tokens; i += n_seq_tokens) {
for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[i][s];
const int32_t seq_idx = ubatch->seq_idx[seq_id];
std::vector<int> target_pos(n_seqs_unq, -1);
std::vector<int> target_row(n_seqs_unq, -1);
data[seq_idx] = i;
}
}
}
if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
GGML_ASSERT(cls);
GGML_ASSERT(ggml_backend_buffer_is_host(cls->buffer));
uint32_t * data = (uint32_t *) cls->data;
memset(cls->data, 0, n_seqs_unq*ggml_element_size(cls));
std::vector<int> last_pos(n_seqs_unq, -1);
std::vector<int> last_row(n_seqs_unq, -1);
bool last = cparams.pooling_type == LLAMA_POOLING_TYPE_LAST;
for (int i = 0; i < n_tokens; ++i) {
const llama_pos pos = ubatch->pos[i];
@@ -228,16 +213,20 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
const llama_seq_id seq_id = ubatch->seq_id[i][s];
const int32_t seq_idx = ubatch->seq_idx[seq_id];
if (pos >= last_pos[seq_idx]) {
last_pos[seq_idx] = pos;
last_row[seq_idx] = i;
if (
(target_pos[seq_idx] == -1) ||
( last && pos >= target_pos[seq_idx]) ||
(!last && pos < target_pos[seq_idx])
) {
target_pos[seq_idx] = pos;
target_row[seq_idx] = i;
}
}
}
for (int s = 0; s < n_seqs_unq; ++s) {
if (last_row[s] >= 0) {
data[s] = last_row[s];
if (target_row[s] >= 0) {
data[s] = target_row[s];
}
}
}
src/llama-graph.h
+15 -12
View File
@@ -144,7 +144,7 @@ public:
ggml_tensor * pos_bucket = nullptr; // I32 [n_batch, n_batch]
const llama_hparams & hparams;
const llama_hparams hparams;
};
class llm_graph_input_pos_bucket_kv : public llm_graph_input_i {
@@ -158,7 +158,7 @@ public:
ggml_tensor * pos_bucket = nullptr; // I32 [n_kv, n_batch]
const llama_hparams & hparams;
const llama_hparams hparams;
const llama_kv_cache_unified_context * mctx;
};
@@ -177,8 +177,8 @@ public:
ggml_tensor * out_ids; // I32 [n_outputs]
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_hparams hparams;
const llama_cparams cparams;
const uint32_t n_outputs;
};
@@ -192,7 +192,7 @@ public:
ggml_tensor * mean; // F32 [n_batch, n_batch]
const llama_cparams & cparams;
const llama_cparams cparams;
};
class llm_graph_input_cls : public llm_graph_input_i {
@@ -204,7 +204,7 @@ public:
ggml_tensor * cls; // I32 [n_batch]
const llama_cparams & cparams;
const llama_cparams cparams;
};
class llm_graph_input_rs : public llm_graph_input_i {
@@ -247,8 +247,8 @@ public:
ggml_tensor * kq_mask = nullptr; // F32 [n_tokens, n_batch, 1, 1]
ggml_tensor * kq_mask_cnv = nullptr; // [n_tokens, n_batch, 1, 1]
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_hparams hparams;
const llama_cparams cparams;
};
class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
@@ -278,8 +278,11 @@ public:
ggml_tensor * self_kq_mask = nullptr; // F32 [n_kv, n_batch/n_stream, 1, n_stream]
ggml_tensor * self_kq_mask_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream]
const llama_hparams & hparams;
const llama_cparams & cparams;
// note: these have to be copies because in order to be able to reuse a graph, its inputs
// need to carry these parameters with them. otherwise, they can point to freed
// llm_graph_params from a previous batch, causing stack-use-after-return
const llama_hparams hparams;
const llama_cparams cparams;
const llama_kv_cache_unified_context * mctx;
};
@@ -318,8 +321,8 @@ public:
ggml_tensor * self_kq_mask_swa = nullptr; // F32 [n_kv, n_batch/n_stream, 1, n_stream]
ggml_tensor * self_kq_mask_swa_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream]
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_hparams hparams;
const llama_cparams cparams;
const llama_kv_cache_unified_iswa_context * mctx;
};