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zPsb@U3)DLOs9lz)V$vve88vY3}$|MJ%ZdedeYC}r`J^gWGXyugXQVm2eUj~{~(u?Cu<29di&?m zIkP}^7f-T(oE3ln-yqFCc`aF(o?cT}fKY4hqhleH>uL71pIJap_DBF0@YD5*g#}6| zf7Ax+)Afpl6{>@JRLA!8It@AWNk6j!pjP<5kA>htpJu?PYc?A*)Ry_^Sm-^fM;YW2 z^|)Vvkm&I_16i1!j29pq)4z{NI~#2X>emJyy1SM$cF~7!P>C31EiEB*Vd$8IvitvG zo&)}|H$xu@KmgyI2D(hTtZa;I%#1n)dSG2WHbxMDNe_ZD*V8cofLVdu@c(nk|Gsl+ YYX{kr|Fb&=vN5v&;K|4YrG?=C7q_CA!~g&Q literal 0 HcmV?d00001 From 9d5949f04b1f8b76184818abbd99938c2020803f Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 23 Nov 2023 12:34:20 +0100 Subject: [PATCH 2/6] examples : fix typo in parallel example doc comment (#4181) Signed-off-by: Daniel Bevenius --- examples/parallel/parallel.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index a78df305f..9b89bdfec 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -1,5 +1,5 @@ // A basic application simulating a server with multiple clients. -// The clients submite requests to the server and they are processed in parallel. +// The clients submit requests to the server and they are processed in parallel. #include "common.h" #include "llama.h" From d103d935c0e75769a6a597f7a64cab72c6cc3e79 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 23 Nov 2023 13:51:22 +0200 Subject: [PATCH 3/6] readme : update hot topics --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e14886737..276461f81 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- *No hot topics atm. Open to suggestions about what is hot today* +- Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167 ---- From 6b0a7420d03b9d13cb0e9439a01ce8476d8bf093 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 23 Nov 2023 19:07:56 +0200 Subject: [PATCH 4/6] llama : KV cache view API + better KV cache management (#4170) * llama : keep track of used KV cells + better KV cache management * llama : zero KV cache used upon clear ggml-ci * llama : allow exporting a view of the KV cache (#4180) * Allow exporting a view of the KV cache * Allow dumping the sequences per cell in common * Track max contiguous cells value and position as well * Fix max contiguous empty cells index calculation Make dump functions deal with lengths or sequences counts > 10 better * Fix off by one error in dump_kv_cache_view * Add doc comments for KV cache view functions Eliminate cell sequence struct; use llama_seq_id directly Minor cleanups * common : add -dkvc arg for enabling kv cache dumps --------- Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> --- common/common.cpp | 79 ++++++++++++++++++++ common/common.h | 11 +++ examples/parallel/parallel.cpp | 9 +++ llama.cpp | 128 +++++++++++++++++++++++++++++++-- llama.h | 57 ++++++++++++++- 5 files changed, 277 insertions(+), 7 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index eec704b99..1dcc235ea 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -12,6 +12,7 @@ #include #include #include +#include #include #include #include @@ -495,6 +496,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.chatml = true; } else if (arg == "--infill") { params.infill = true; + } else if (arg == "-dkvc" || arg == "--dump-kv-cache") { + params.dump_kv_cache = true; } else if (arg == "--multiline-input") { params.multiline_input = true; } else if (arg == "--simple-io") { @@ -835,6 +838,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { #endif // GGML_USE_CUBLAS #endif printf(" --verbose-prompt print prompt before generation\n"); + printf(" -dkvc, --dump-kv-cache\n"); + printf(" verbose print of the KV cache\n"); printf(" --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n"); printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n"); @@ -1386,3 +1391,77 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); } + +// +// KV cache utils +// + +void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { + static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+"; + + printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d", + view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); + + llama_kv_cache_view_cell * c_curr = view.cells; + llama_seq_id * cs_curr = view.cells_sequences; + + for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) { + if (i % row_size == 0) { + printf("\n%5d: ", i); + } + int seq_count = 0; + for (int j = 0; j < view.n_max_seq; j++) { + if (cs_curr[j] >= 0) { seq_count++; } + } + putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]); + } + + printf("\n=== Done dumping\n"); +} + +void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { + static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; + + printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n", + view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); + + std::unordered_map seqs; + llama_kv_cache_view_cell * c_curr = view.cells; + llama_seq_id * cs_curr = view.cells_sequences; + + for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) { + for (int j = 0; j < view.n_max_seq; j++) { + if (cs_curr[j] < 0) { continue; } + if (seqs.find(cs_curr[j]) == seqs.end()) { + if (seqs.size() + 1 >= sizeof(slot_chars)) { break; } + seqs[cs_curr[j]] = seqs.size(); + } + } + if (seqs.size() + 1 >= sizeof(slot_chars)) { break; } + } + + printf("=== Sequence legend: "); + for (const auto & it : seqs) { + printf("%zu=%d, ", it.second, it.first); + } + printf("'+'=other sequence ids"); + + c_curr = view.cells; + cs_curr = view.cells_sequences; + for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) { + if (i % row_size == 0) { + printf("\n%5d: ", i); + } + for (int j = 0; j < view.n_max_seq; j++) { + if (cs_curr[j] >= 0) { + const auto & it = seqs.find(cs_curr[j]); + putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+'); + } else { + putchar('.'); + } + } + putchar(' '); + } + + printf("\n=== Done dumping\n"); +} diff --git a/common/common.h b/common/common.h index 88fa13fc0..2f6fe48ab 100644 --- a/common/common.h +++ b/common/common.h @@ -122,6 +122,7 @@ struct gpt_params { bool numa = false; // attempt optimizations that help on some NUMA systems bool verbose_prompt = false; // print prompt tokens before generation bool infill = false; // use infill mode + bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes // multimodal models (see examples/llava) std::string mmproj = ""; // path to multimodal projector @@ -218,3 +219,13 @@ std::string get_sortable_timestamp(); void dump_non_result_info_yaml( FILE * stream, const gpt_params & params, const llama_context * lctx, const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); + +// +// KV cache utils +// + +// Dump the KV cache view with the number of sequences per cell. +void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80); + +// Dump the KV cache view showing individual sequences in each cell (long output). +void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40); diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 9b89bdfec..d2e074d9e 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -113,6 +113,8 @@ int main(int argc, char ** argv) { // insert new requests as soon as the previous one is done const bool cont_batching = params.cont_batching; + const bool dump_kv_cache = params.dump_kv_cache; + #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("parallel", "log")); LOG_TEE("Log start\n"); @@ -172,6 +174,8 @@ int main(int argc, char ** argv) { int32_t n_total_gen = 0; int32_t n_cache_miss = 0; + struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, n_clients); + const auto t_main_start = ggml_time_us(); LOG_TEE("%s: Simulating parallel requests from clients:\n", __func__); @@ -201,6 +205,11 @@ int main(int argc, char ** argv) { LOG_TEE("Processing requests ...\n\n"); while (true) { + if (dump_kv_cache) { + llama_kv_cache_view_update(ctx, &kvc_view); + dump_kv_cache_view_seqs(kvc_view, 40); + } + llama_batch_clear(batch); // decode any currently ongoing sequences diff --git a/llama.cpp b/llama.cpp index c2ad04869..9fb7244b4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1280,6 +1280,7 @@ struct llama_kv_cache { // cannot be freely changed after a slot has been allocated. uint32_t head = 0; uint32_t size = 0; + uint32_t used = 0; // used cells (i.e. at least one seq_id) // computed before each graph build uint32_t n = 0; @@ -1504,6 +1505,7 @@ static bool llama_kv_cache_init( cache.head = 0; cache.size = n_ctx; + cache.used = 0; cache.cells.clear(); cache.cells.resize(n_ctx); @@ -1605,6 +1607,8 @@ static bool llama_kv_cache_find_slot( } } + cache.used += n_tokens; + return true; } @@ -1625,6 +1629,7 @@ static void llama_kv_cache_clear(struct llama_kv_cache & cache) { cache.cells[i].seq_id.clear(); } cache.head = 0; + cache.used = 0; } static void llama_kv_cache_seq_rm( @@ -1647,6 +1652,9 @@ static void llama_kv_cache_seq_rm( continue; } if (cache.cells[i].seq_id.empty()) { + // keep count of the number of used cells + if (cache.cells[i].pos >= 0) cache.used--; + cache.cells[i].pos = -1; if (new_head == cache.size) new_head = i; } @@ -1654,7 +1662,7 @@ static void llama_kv_cache_seq_rm( } // If we freed up a slot, set head to it so searching can start there. - if (new_head != cache.size) cache.head = new_head; + if (new_head != cache.size && new_head < cache.head) cache.head = new_head; } static void llama_kv_cache_seq_cp( @@ -1680,6 +1688,7 @@ static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id for (uint32_t i = 0; i < cache.size; ++i) { if (!cache.cells[i].has_seq_id(seq_id)) { + if (cache.cells[i].pos >= 0) cache.used--; cache.cells[i].pos = -1; cache.cells[i].seq_id.clear(); if (new_head == cache.size) new_head = i; @@ -1690,7 +1699,7 @@ static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id } // If we freed up a slot, set head to it so searching can start there. - if (new_head != cache.size) cache.head = new_head; + if (new_head != cache.size && new_head < cache.head) cache.head = new_head; } static void llama_kv_cache_seq_shift( @@ -1711,6 +1720,7 @@ static void llama_kv_cache_seq_shift( cache.cells[i].delta += delta; if (cache.cells[i].pos < 0) { + if (!cache.cells[i].seq_id.empty()) cache.used--; cache.cells[i].pos = -1; cache.cells[i].seq_id.clear(); if (new_head == cache.size) new_head = i; @@ -5469,6 +5479,12 @@ static int llama_decode_internal( batch.seq_id = seq_id_arr.data(); } + // if we have enough unused cells before the current head -> + // better to start searching from the beginning of the cache, hoping to fill it + if (kv_self.head > kv_self.used + 2*n_tokens) { + kv_self.head = 0; + } + if (!llama_kv_cache_find_slot(kv_self, batch)) { return 1; } @@ -5479,7 +5495,7 @@ static int llama_decode_internal( //kv_self.n = std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)); // TODO: this might be better for CUDA? kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, llama_kv_cache_cell_max(kv_self))); - //printf("kv_self.n = %d\n", kv_self.n); + //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); ggml_allocr_reset(lctx.alloc); @@ -8789,8 +8805,107 @@ int llama_model_apply_lora_from_file(const struct llama_model * model, const cha } } +struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq) { + struct llama_kv_cache_view result = { + /*.n_cells = */ 0, + /*.n_max_seq = */ n_max_seq, + /*.token_count = */ 0, + /*.used_cells = */ llama_get_kv_cache_used_cells(ctx), + /*.max_contiguous = */ 0, + /*.max_contiguous_idx = */ -1, + /*.cells = */ nullptr, + /*.cells_sequences = */ nullptr, + }; + return result; +} + +void llama_kv_cache_view_free(struct llama_kv_cache_view * view) { + if (view->cells != nullptr) { + free(view->cells); + view->cells = nullptr; + } + if (view->cells_sequences != nullptr) { + free(view->cells_sequences); + view->cells_sequences = nullptr; + } +} + +void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view) { + if (uint32_t(view->n_cells) < ctx->kv_self.size || view->cells == nullptr) { + view->n_cells = int32_t(ctx->kv_self.size); + void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells); + GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells"); + view->cells = (struct llama_kv_cache_view_cell *)p; + p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_max_seq * view->n_cells); + GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences"); + view->cells_sequences = (llama_seq_id *)p; + } + + const std::vector & kv_cells = ctx->kv_self.cells; + llama_kv_cache_view_cell * c_curr = view->cells; + llama_seq_id * cs_curr = view->cells_sequences; + int32_t used_cells = 0; + int32_t token_count = 0; + int32_t curr_contig_idx = -1; + uint32_t max_contig = 0; + int32_t max_contig_idx = -1; + + for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_max_seq) { + const size_t curr_size = kv_cells[i].seq_id.size(); + token_count += curr_size; + c_curr->pos = kv_cells[i].pos + kv_cells[i].delta; + + if (curr_size > 0) { + if (curr_contig_idx >= 0 && uint32_t(i - curr_contig_idx) > max_contig) { + max_contig = i - curr_contig_idx; + max_contig_idx = curr_contig_idx; + } + curr_contig_idx = -1; + } else if (curr_contig_idx < 0) { + curr_contig_idx = i; + } + + int seq_idx = 0; + for (const llama_seq_id it : kv_cells[i].seq_id) { + if (seq_idx >= view->n_max_seq) { + break; + } + cs_curr[seq_idx] = it; + seq_idx++; + } + if (seq_idx != 0) { + used_cells++; + } + for (; seq_idx < view->n_max_seq; seq_idx++) { + cs_curr[seq_idx] = -1; + } + } + if (curr_contig_idx >= 0 && kv_cells.size() - curr_contig_idx > max_contig) { + max_contig_idx = curr_contig_idx; + max_contig = kv_cells.size() - curr_contig_idx; + } + view->max_contiguous = max_contig; + view->max_contiguous_idx = max_contig_idx; + view->token_count = token_count; + view->used_cells = used_cells; + if (uint32_t(used_cells) != ctx->kv_self.used) { + LLAMA_LOG_ERROR("%s: used cells mismatch. kv_cache says %d but we calculated %d\n", + __func__, ctx->kv_self.used, used_cells); + } +} + int llama_get_kv_cache_token_count(const struct llama_context * ctx) { - return ctx->kv_self.head; + int result = 0; + + for (uint32_t i = 0; i < ctx->kv_self.size; i++) { + result += ctx->kv_self.cells[i].seq_id.size(); + } + + return result; +} + +int llama_get_kv_cache_used_cells(const struct llama_context * ctx) { + return ctx->kv_self.used; } void llama_kv_cache_clear(struct llama_context * ctx) { @@ -8960,10 +9075,12 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat const size_t kv_buf_size = kv_self.buf.size; const uint32_t kv_head = kv_self.head; const uint32_t kv_size = kv_self.size; + const uint32_t kv_used = kv_self.used; data_ctx->write(&kv_buf_size, sizeof(kv_buf_size)); data_ctx->write(&kv_head, sizeof(kv_head)); data_ctx->write(&kv_size, sizeof(kv_size)); + data_ctx->write(&kv_used, sizeof(kv_used)); if (kv_buf_size) { const size_t elt_size = ggml_element_size(kv_self.k); @@ -9086,10 +9203,12 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { size_t kv_buf_size; uint32_t kv_head; uint32_t kv_size; + uint32_t kv_used; memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size); memcpy(&kv_head, inp, sizeof(kv_head)); inp += sizeof(kv_head); memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size); + memcpy(&kv_used, inp, sizeof(kv_used)); inp += sizeof(kv_used); if (kv_buf_size) { GGML_ASSERT(kv_self.buf.size == kv_buf_size); @@ -9124,6 +9243,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { ctx->kv_self.head = kv_head; ctx->kv_self.size = kv_size; + ctx->kv_self.used = kv_used; ctx->kv_self.cells.resize(kv_size); diff --git a/llama.h b/llama.h index 70e8fda4b..1a62058d1 100644 --- a/llama.h +++ b/llama.h @@ -361,9 +361,60 @@ extern "C" { // KV cache // - // Returns the number of tokens in the KV cache - LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx), - "avoid using this, it will be removed in the future, instead - count the tokens in user code"); + // Information associated with an individual cell in the KV cache view. + struct llama_kv_cache_view_cell { + // The position for this cell. Takes KV cache shifts into account. + // May be negative if the cell is not populated. + llama_pos pos; + }; + + // An updateable view of the KV cache. + struct llama_kv_cache_view { + // Number of KV cache cells. This will be the same as the context size. + int32_t n_cells; + + // Maximum number of sequences that can exist in a cell. It's not an error + // if there are more sequences in a cell than this value, however they will + // not be visible in the view cells_sequences. + int32_t n_max_seq; + + // Number of tokens in the cache. For example, if there are two populated + // cells, the first with 1 sequence id in it and the second with 2 sequence + // ids then you'll have 3 tokens. + int32_t token_count; + + // Number of populated cache cells. + int32_t used_cells; + + // Maximum contiguous empty slots in the cache. + int32_t max_contiguous; + + // Index to the start of the max_contiguous slot range. Can be negative + // when cache is full. + int32_t max_contiguous_idx; + + // Information for an individual cell. + struct llama_kv_cache_view_cell * cells; + + // The sequences for each cell. There will be n_max_seq items per cell. + llama_seq_id * cells_sequences; + }; + + // Create an empty KV cache view. (use only for debugging purposes) + LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq); + + // Free a KV cache view. (use only for debugging purposes) + LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view); + + // Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes) + LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view); + + // Returns the number of tokens in the KV cache (slow, use only for debug) + // If a KV cell has multiple sequences assigned to it, it will be counted multiple times + LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx); + + // Returns the number of used KV cells (i.e. have at least one sequence assigned to them) + LLAMA_API int llama_get_kv_cache_used_cells(const struct llama_context * ctx); // Clear the KV cache LLAMA_API void llama_kv_cache_clear( From 55978ce09b69d3987d17d08d92d8cc27193e0773 Mon Sep 17 00:00:00 2001 From: Haohui Mai Date: Thu, 23 Nov 2023 13:56:53 -0800 Subject: [PATCH 5/6] Fix incorrect format strings and uninitialized variables. (#4133) * Fix incorrect format strings and uninitialized variables. * Address comments * Add the missing include statement --- examples/server/server.cpp | 2 ++ ggml-cuda.cu | 3 ++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1f2c55f2d..be23ad169 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1095,6 +1095,7 @@ struct llama_server_context std::lock_guard lock(mutex_results); task_result res; res.id = id; + res.stop = false; res.error = true; res.result_json = { { "content", error } }; queue_results.push_back(res); @@ -1255,6 +1256,7 @@ struct llama_server_context std::lock_guard lock(mutex_tasks); task_server task; task.id = id_gen++; + task.target_id = 0; task.data = data; task.infill_mode = infill; task.embedding_mode = embedding; diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 50e03de50..f0db7ae35 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1,4 +1,5 @@ #include +#include #include #include #include @@ -8057,7 +8058,7 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ if (tensor->op == GGML_OP_MUL_MAT) { if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { #ifndef NDEBUG - fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %d, src1->ne[3] = %d - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); + fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = " PRId64 ", src1->ne[3] = " PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); #endif return false; } From b35f3d0def3efde92ed465d92a267430d957e87d Mon Sep 17 00:00:00 2001 From: Aaryaman Vasishta Date: Fri, 24 Nov 2023 16:52:39 +0900 Subject: [PATCH 6/6] readme : use PATH for Windows ROCm (#4195) * Update README.md to use PATH for Windows ROCm * Update README.md * Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 276461f81..5189e1255 100644 --- a/README.md +++ b/README.md @@ -422,8 +422,9 @@ Building the program with BLAS support may lead to some performance improvements CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake .. -DLLAMA_HIPBLAS=ON cmake --build . ``` - - Using `CMake` for Windows: + - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS): ```bash + set PATH=%HIP_PATH%\bin;%PATH% mkdir build cd build cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..

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